{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fce7707e",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "\n",
    "This is the first notebook for a tutorial on \"Practical Bayesian Modeling with PyMC\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6e9b43",
   "metadata": {},
   "source": [
    "**Abstract**\n",
    "\n",
    "In this tutorial, we explore Bayesian regression using PyMC -- the primary library for Bayesian sampling in Python -- focusing on survey data and other datasets with categorical outcomes. Starting with logistic regression, we’ll build up to categorical and ordered logistic regression, showcasing how Bayesian approaches provide versatile tools for developing and evaluating complex models. Participants will leave with practical skills for implementing Bayesian regression models in PyMC, along with a deeper appreciation for the power of Bayesian inference in real-world data analysis. Participants should be familiar with Python, the SciPy ecosystem, and basic statistics, but no experience with Bayesian methods is required."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89c73067",
   "metadata": {},
   "source": [
    "**Description**\n",
    "\n",
    "In this hands-on tutorial, we will dive into the world of Bayesian regression using PyMC, with a particular focus on datasets that feature categorical outcomes. PyMC provides versatile tools for iterative development of powerful models. This tutorial guides participants through the fundamentals of Bayesian regression, starting from logistic regression and extending to categorical and ordered logistic regression. It also introduces the PyMC package, its syntax and capabilities.\n",
    "\n",
    "Outline:\n",
    "\n",
    "* Logistic Regression with PyMC\n",
    "\n",
    "  - Overview of Bayesian inference\n",
    "  - Modeling binary outcomes with logistic regression\n",
    "  - Introduction to PyMC and its capabilities\n",
    "  - Hands-on example: Happiness data in the General Social Survey\n",
    "\n",
    "* Categorical Regression\n",
    "\n",
    "  - Extending logistic regression to multi-class outcomes\n",
    "  - Differences between Bayesian models and GLM\n",
    "  - Hands-on example: Political alignment and party affiliation (?)\n",
    "\n",
    "* Ordered Logistic Regression\n",
    "  - Introduction to ordinal outcomes and their challenges\n",
    "  - Implementing ordered logistic regression in PyMC\n",
    "  - Hands-on example: Political alignment and party affiliation (?)\n",
    "\n",
    "* Conclusion and Q&A\n",
    "  - Recap of key concepts\n",
    "  - Resources for further learning\n",
    "  - Open discussion and questions\n",
    "\n",
    "This tutorial is aimed at data scientists who are comfortable with Python, the SciPy ecosystem, and basic statistics but are new to Bayesian statistics and/or PyMC. By the end of the session, participants will have hands-on experience with Bayesian regression models and a solid understanding of how to apply these techniques to real-world data analysis. \n",
    "\n",
    "Notes:\n",
    "The material for the tutorial is in this repository:\n",
    "https://github.com/AllenDowney/SurveyDataPyMC/tree/main/notebooks\n",
    "\n",
    "The dataset from the General Social Survey (GSS) can be found here:\n",
    "https://gssdataexplorer.norc.org/\n",
    "\n",
    "\n",
    "[The slides for the tutorial are here](COMING SOON)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c2f26e",
   "metadata": {},
   "source": [
    "[Click here to run this notebook on Colab](https://colab.research.google.com/github/AllenDowney/SurveyDataPyMC/blob/main/notebooks/01_logistic_regression.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0a5bca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fce89fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get utils.py\n",
    "\n",
    "from os.path import basename, exists\n",
    "\n",
    "\n",
    "def download(url):\n",
    "    filename = basename(url)\n",
    "    if not exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print(\"Downloaded \" + local)\n",
    "\n",
    "\n",
    "download(\"https://github.com/AllenDowney/SurveyDataPyMC/raw/main/notebooks/utils.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f24d9f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "\n",
    "from utils import value_counts, decorate, load_idata_or_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "860f08d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the figures smaller to save some screen real estate\n",
    "plt.rcParams[\"figure.dpi\"] = 75\n",
    "plt.rcParams[\"figure.figsize\"] = [6, 3.5]\n",
    "plt.rcParams[\"axes.titlelocation\"] = \"left\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d12ba9",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The dataset we'll use is an extract from the General Social Survey.\n",
    "The following cell downloads it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ad453fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dataset is prepared in GssExtract/notebooks/02_make_extract-2022_4.ipynb\n",
    "# It has been resampled to correct for stratified sampling\n",
    "\n",
    "DATA_PATH = \"https://github.com/AllenDowney/GssExtract/raw/main/data/interim/\"\n",
    "filename = \"gss_extract_2022_4.hdf\"\n",
    "download(DATA_PATH + filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2794fd",
   "metadata": {},
   "source": [
    "The file is in HDF format, which we can read using Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b91bdaed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72390, 60)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss = pd.read_hdf(filename, \"gss\")\n",
    "gss.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "250d175e",
   "metadata": {},
   "source": [
    "The dataset includes one row for each respondent and one column for each variable.\n",
    "\n",
    "To demonstrate logistic regression, we'll use responses to [this question](https://gssdataexplorer.norc.org/variables/441/vshow)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10535f31",
   "metadata": {},
   "source": [
    "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?\n",
    "\t\n",
    "```\n",
    "1\tMost people can be trusted\n",
    "2\tCan't be too careful\n",
    "3\tDepends\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b924e423",
   "metadata": {},
   "source": [
    "Here are the counts of the responses -- there are a large number of NaN values because not every respondent was asked the question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13d77833",
   "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>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>15783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>24890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>29751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        counts\n",
       "values        \n",
       "1.0      15783\n",
       "2.0      24890\n",
       "3.0       1966\n",
       "NaN      29751"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts(gss[\"trust\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3515c58c",
   "metadata": {},
   "source": [
    "Although there are three possible responses, we'll treat this as a binary variable.\n",
    "So we'll recode the responses so `1` means \"most people can be trusted\" and `0` means either of the other responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbb2d5e8",
   "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>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>26856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>15783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>29751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        counts\n",
       "values        \n",
       "0.0      26856\n",
       "1.0      15783\n",
       "NaN      29751"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss[\"y\"] = gss[\"trust\"].replace({1: 1, 2: 0, 3: 0})\n",
    "value_counts(gss[\"y\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cf376a",
   "metadata": {},
   "source": [
    "Here's what the percentage of affirmative responses looks like over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ca78bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_series = gss.groupby(\"year\")[\"y\"].mean() * 100\n",
    "time_series.plot(style=\"o\")\n",
    "decorate(ylabel=\"percent\", title=\"Can people be trusted?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2506ffe4",
   "metadata": {},
   "source": [
    "Sadly, levels of trust have been declining in the US for decades."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05bc2da",
   "metadata": {},
   "source": [
    "## Who is most trusting?\n",
    "\n",
    "Who do you think is most trusting, Democrats, Republicans, or independents?\n",
    "To find out, we'll use another [GSS variable](https://gssdataexplorer.norc.org/variables/141/vshow), which contains responses to this question:\n",
    "\n",
    "> Generally speaking, do you usually think of yourself as a Republican, Democrat, Independent, or what?\n",
    "\n",
    "With these options:\n",
    "\n",
    "```\n",
    "0\tStrong democrat\n",
    "1\tNot very strong democrat\t\n",
    "2\tIndependent, close to democrat\t\n",
    "3\tIndependent (neither, no response)\t\n",
    "4\tIndependent, close to republican\t\n",
    "5\tNot very strong republican\t\n",
    "6\tStrong republican\n",
    "7\tOther party\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12935ee",
   "metadata": {},
   "source": [
    "To simplify the analysis, we'll consider just three groups, Democrats (strong or not), Independent (leaning either way) and Republican (strong or not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c65cd07",
   "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>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>25439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>26705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>18338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        counts\n",
       "values        \n",
       "0.0      25439\n",
       "1.0      26705\n",
       "2.0      18338\n",
       "NaN       1908"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_map = {\n",
    "    0: 0,\n",
    "    1: 0,\n",
    "    2: 1,\n",
    "    3: 1,\n",
    "    4: 1,\n",
    "    5: 2,\n",
    "    6: 2,\n",
    "    7: np.nan,\n",
    "}\n",
    "\n",
    "gss[\"partyid3\"] = gss[\"partyid\"].replace(party_map)\n",
    "value_counts(gss[\"partyid3\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b6f15e",
   "metadata": {},
   "source": [
    "Well use this dictionary to map from codes to names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07ea06a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "party_names = {\n",
    "    0: \"Democrat\",\n",
    "    1: \"Independent\",\n",
    "    2: \"Republican\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3459c29a",
   "metadata": {},
   "source": [
    "The following table shows how the percentages in each group have changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5f1c7df",
   "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>partyid3</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Independent</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>41.055341</td>\n",
       "      <td>48.325359</td>\n",
       "      <td>59.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>43.001686</td>\n",
       "      <td>49.295775</td>\n",
       "      <td>59.292035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>33.571429</td>\n",
       "      <td>40.525328</td>\n",
       "      <td>44.623656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>41.401274</td>\n",
       "      <td>44.383057</td>\n",
       "      <td>55.974843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>34.542587</td>\n",
       "      <td>44.649446</td>\n",
       "      <td>41.087613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>44.144144</td>\n",
       "      <td>44.878957</td>\n",
       "      <td>49.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>41.403509</td>\n",
       "      <td>45.679012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>42.407407</td>\n",
       "      <td>48.098859</td>\n",
       "      <td>52.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>32.342657</td>\n",
       "      <td>37.894737</td>\n",
       "      <td>47.783251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>33.455882</td>\n",
       "      <td>38.336347</td>\n",
       "      <td>49.403341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "partyid3   Democrat  Independent  Republican\n",
       "year                                        \n",
       "1972      41.055341    48.325359   59.146341\n",
       "1973      43.001686    49.295775   59.292035\n",
       "1975      33.571429    40.525328   44.623656\n",
       "1976      41.401274    44.383057   55.974843\n",
       "1978      34.542587    44.649446   41.087613\n",
       "1980      44.144144    44.878957   49.722222\n",
       "1983      30.000000    41.403509   45.679012\n",
       "1984      42.407407    48.098859   52.162162\n",
       "1986      32.342657    37.894737   47.783251\n",
       "1987      33.455882    38.336347   49.403341"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = (\n",
    "    gss.pivot_table(index=\"year\", columns=\"partyid3\", values=\"y\", aggfunc=\"mean\") * 100\n",
    ").rename(columns=party_names)\n",
    "\n",
    "table.iloc[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a64e2f",
   "metadata": {},
   "source": [
    "Here's what the columns look like as time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca8751b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"C0\", \"C4\", \"C3\"]\n",
    "table.plot(color=colors)\n",
    "decorate(ylabel=\"percent\", title=\"Percent saying people can be trusted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c06d2d",
   "metadata": {},
   "source": [
    "It looks like Republicans were the most trusting group, historically, but that might be changing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebc180b",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "We'll use logistic regression to model changes in these responses over time and differences between groups.\n",
    "\n",
    "The fundamental idea of logistic regression is that each observation unit -- like a survey respondent -- has some latent propensity to choose one of two options, and we assume:\n",
    "\n",
    "* The latent propensities, `z[i]`, are a linear function of explanatory variables.\n",
    "\n",
    "* The probability a respondent chooses a particular option is `expit(z[i])`.\n",
    "\n",
    "Where `expit` is the function that maps from log-odds to probability (defined in `scipy.special`, also available from PyMC as `pm.math.sigmoid`).\n",
    "\n",
    "As a first example, we'll make a model with `year` as an explanatory variable, so we'll assume\n",
    "\n",
    "```\n",
    "z = alpha + beta * year\n",
    "```\n",
    "\n",
    "In a minute we'll see how to represent this model in PyMC, but first let's prepare the data.\n",
    "We'll select the rows with valid data for the response variable and `year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c8e6e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42639, 62)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gss.dropna(subset=[\"y\", \"year\"])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab91000",
   "metadata": {},
   "source": [
    "And we'll extract the values as NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1cb2fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"y\"].to_numpy()\n",
    "year = data[\"year\"].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc91d9d",
   "metadata": {},
   "source": [
    "We'll shift `year` so it's centered at its mean (and we'll see why later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca9e87a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "year_shift = data[\"year\"].mean()\n",
    "year = year - year_shift"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be70c97",
   "metadata": {},
   "source": [
    "Now here's the key idea of PyMC (and MCMC in general): if you can describe the data-generating process, the sampler can generate a sample from the posterior distribution of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43eb6e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as logistic_model:\n",
    "\n",
    "    # Priors for intercept and slope\n",
    "    alpha = pm.Normal(\"alpha\", 0, 1)\n",
    "    beta = pm.Normal(\"beta\", 0, 1)\n",
    "\n",
    "    # Linear predictor (log-odds)\n",
    "    z = alpha + beta * year\n",
    "\n",
    "    # The inverse logit function is called sigma\n",
    "    p = pm.math.sigmoid(z)\n",
    "\n",
    "    # Bernoulli likelihood with logit link\n",
    "    y_obs = pm.Bernoulli(\"y_obs\", p=p, observed=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a59f2a",
   "metadata": {},
   "source": [
    "Objects created in the context manager are registered as elements of the model.\n",
    "Here's a graphical representation of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29062a7f",
   "metadata": {},
   "outputs": [
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   "source": [
    "pm.model_to_graphviz(logistic_model)"
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  {
   "cell_type": "markdown",
   "id": "ce41d872",
   "metadata": {},
   "source": [
    "## The posterior distribution\n",
    "\n",
    "The function that samples from the posterior distribution is called `sample`.\n",
    "\n",
    "```\n",
    "with logistic_model:\n",
    "    idata = pm.sample(draws=500, tune=500)\n",
    "```\n",
    "\n",
    "`draw` indicates the number of samples we want from each chain.\n",
    "`tune` is the number of samples used to tune the chains before we start saving them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d27bd8",
   "metadata": {},
   "source": [
    "For the workshop, we'll use the following function, which loads the results if they are already saved, or runs the sampler otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4bb6a14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded idata from logistic_model_idata.nc\n"
     ]
    }
   ],
   "source": [
    "filename = \"logistic_model_idata.nc\"\n",
    "idata = load_idata_or_sample(logistic_model, filename, draws=500, tune=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b8c8c0",
   "metadata": {},
   "source": [
    "The result is an [ArViz `InferenceData` object](https://python.arviz.org/en/stable/api/inference_data.html), which contains several groups of data stored as [xarray `DataSet` objects](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b56d08e",
   "metadata": {},
   "outputs": [
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       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 4, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    alpha    (chain, draw) float64 ...\n",
       "    beta     (chain, draw) float64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T15:35:52.348659+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1\n",
       "    sampling_time:              25.825820922851562\n",
       "    tuning_steps:               500</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-519dd9a8-a77e-4235-bf02-d5b47a2ee219' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-519dd9a8-a77e-4235-bf02-d5b47a2ee219' 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>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f2257e8d-cff4-4049-b578-4ce63199b32b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2257e8d-cff4-4049-b578-4ce63199b32b' class='xr-section-summary' >Coordinates: <span>(2)</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-8b04162c-229a-424d-b62d-1cb351e0b221' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b04162c-229a-424d-b62d-1cb351e0b221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e869537-f066-481c-8598-c16bfacbafee' class='xr-var-data-in' type='checkbox'><label for='data-8e869537-f066-481c-8598-c16bfacbafee' 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 ... 495 496 497 498 499</div><input id='attrs-23d09415-88c5-4640-8ab6-eae42d06ff80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23d09415-88c5-4640-8ab6-eae42d06ff80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af06f91d-1374-46dd-bca6-137cc7663a63' class='xr-var-data-in' type='checkbox'><label for='data-af06f91d-1374-46dd-bca6-137cc7663a63' 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, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5491352-b840-4339-b73c-84e99a979561' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5491352-b840-4339-b73c-84e99a979561' class='xr-section-summary' >Data variables: <span>(2)</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>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'>...</div><input id='attrs-fa70a956-896f-448c-8b6b-eb95683d9bbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa70a956-896f-448c-8b6b-eb95683d9bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bb10bba-316b-41a2-b87c-d6d9c8ed13a9' class='xr-var-data-in' type='checkbox'><label for='data-0bb10bba-316b-41a2-b87c-d6d9c8ed13a9' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a66631fd-ba22-49f3-aa5a-ae746fb46494' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a66631fd-ba22-49f3-aa5a-ae746fb46494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d94b5d1-325d-4288-b80c-932e9779dd61' class='xr-var-data-in' type='checkbox'><label for='data-8d94b5d1-325d-4288-b80c-932e9779dd61' 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>[2000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70765d8b-855c-48f9-92d4-a7f553111ee9' class='xr-section-summary-in' type='checkbox'  ><label for='section-70765d8b-855c-48f9-92d4-a7f553111ee9' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e5e6073-cd68-4a35-b4d7-0a71fee1eda1' class='xr-index-data-in' type='checkbox'/><label for='index-8e5e6073-cd68-4a35-b4d7-0a71fee1eda1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6f3350f-9cb1-497a-a625-30308408f43d' class='xr-index-data-in' type='checkbox'/><label for='index-d6f3350f-9cb1-497a-a625-30308408f43d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db1abf32-9c92-4cd8-b597-a067e746b3eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db1abf32-9c92-4cd8-b597-a067e746b3eb' 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>2025-04-02T15:35:52.348659+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd><dt><span>sampling_time :</span></dt><dd>25.825820922851562</dd><dt><span>tuning_steps :</span></dt><dd>500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsd4739d82-709f-4e2a-9db3-c416ccffd897\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsd4739d82-709f-4e2a-9db3-c416ccffd897\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 500)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
       "Data variables: (12/17)\n",
       "    max_energy_error       (chain, draw) float64 ...\n",
       "    tree_depth             (chain, draw) int64 ...\n",
       "    reached_max_treedepth  (chain, draw) bool ...\n",
       "    perf_counter_diff      (chain, draw) float64 ...\n",
       "    index_in_trajectory    (chain, draw) int64 ...\n",
       "    largest_eigval         (chain, draw) float64 ...\n",
       "    ...                     ...\n",
       "    process_time_diff      (chain, draw) float64 ...\n",
       "    diverging              (chain, draw) bool ...\n",
       "    energy                 (chain, draw) float64 ...\n",
       "    smallest_eigval        (chain, draw) float64 ...\n",
       "    lp                     (chain, draw) float64 ...\n",
       "    acceptance_rate        (chain, draw) float64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T15:35:52.362736+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1\n",
       "    sampling_time:              25.825820922851562\n",
       "    tuning_steps:               500</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-2a490357-08aa-4455-9332-fd1252665137' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a490357-08aa-4455-9332-fd1252665137' 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>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-27dbdf73-53e8-48b9-b43d-c041c9ff1468' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27dbdf73-53e8-48b9-b43d-c041c9ff1468' class='xr-section-summary' >Coordinates: <span>(2)</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-f4348ab1-a659-4f15-917c-3e9581dd42e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4348ab1-a659-4f15-917c-3e9581dd42e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-761f0ede-7964-489d-ae9f-41d0796c62bc' class='xr-var-data-in' type='checkbox'><label for='data-761f0ede-7964-489d-ae9f-41d0796c62bc' 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 ... 495 496 497 498 499</div><input id='attrs-9e8dd0f5-5b46-446d-8acb-d0c24615b2ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e8dd0f5-5b46-446d-8acb-d0c24615b2ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd7f439a-ae24-4bc5-814d-06e9bc73e1ab' class='xr-var-data-in' type='checkbox'><label for='data-fd7f439a-ae24-4bc5-814d-06e9bc73e1ab' 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, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b096b3ec-3b99-48c5-958d-1c53b3262449' class='xr-section-summary-in' type='checkbox'  ><label for='section-b096b3ec-3b99-48c5-958d-1c53b3262449' class='xr-section-summary' >Data variables: <span>(17)</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>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7c4213f-9de9-47fd-a846-360d914302d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7c4213f-9de9-47fd-a846-360d914302d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02116b1b-0146-42cb-a750-da3f9ab1697f' class='xr-var-data-in' type='checkbox'><label for='data-02116b1b-0146-42cb-a750-da3f9ab1697f' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42ece1ce-3eeb-445b-a085-309cb5240ae3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42ece1ce-3eeb-445b-a085-309cb5240ae3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9df076-03cb-4ae7-b2e4-d2d0bec2a644' class='xr-var-data-in' type='checkbox'><label for='data-dd9df076-03cb-4ae7-b2e4-d2d0bec2a644' 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>[2000 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d71ae37-08bd-45f1-83e9-63a2bd84ea68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d71ae37-08bd-45f1-83e9-63a2bd84ea68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4069630-9827-435c-a4a7-114447f759fb' class='xr-var-data-in' type='checkbox'><label for='data-b4069630-9827-435c-a4a7-114447f759fb' 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>[2000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ac00231-335a-4e8e-907b-cf2c216a09d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ac00231-335a-4e8e-907b-cf2c216a09d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae6108d1-2ff5-4d64-a40a-2731005503f8' class='xr-var-data-in' type='checkbox'><label for='data-ae6108d1-2ff5-4d64-a40a-2731005503f8' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff10d164-f4c2-49ef-be2d-2d2e02b21319' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff10d164-f4c2-49ef-be2d-2d2e02b21319' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fdd432-5770-44f4-999b-5eb2c55eb182' class='xr-var-data-in' type='checkbox'><label for='data-46fdd432-5770-44f4-999b-5eb2c55eb182' 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>[2000 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-deaa69cc-ec11-4617-8d91-c7a8a4d41039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-deaa69cc-ec11-4617-8d91-c7a8a4d41039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f612169c-5188-4a88-b306-d7c4ed347f11' class='xr-var-data-in' type='checkbox'><label for='data-f612169c-5188-4a88-b306-d7c4ed347f11' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-67793b11-53c1-43f3-81cb-72d25dbcd0b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67793b11-53c1-43f3-81cb-72d25dbcd0b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-892c76c7-e4ed-46a1-9535-e3af6f5cedc9' class='xr-var-data-in' type='checkbox'><label for='data-892c76c7-e4ed-46a1-9535-e3af6f5cedc9' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eddd5e3d-e2b9-47dc-982d-59d6b292122e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eddd5e3d-e2b9-47dc-982d-59d6b292122e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c929dfad-311c-4862-b192-08fdbcc13776' class='xr-var-data-in' type='checkbox'><label for='data-c929dfad-311c-4862-b192-08fdbcc13776' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be879a42-2c69-4c91-8dd7-fdda6400faf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be879a42-2c69-4c91-8dd7-fdda6400faf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d7b22b9-49ca-455d-ac78-02e9671a1d54' class='xr-var-data-in' type='checkbox'><label for='data-1d7b22b9-49ca-455d-ac78-02e9671a1d54' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4639410-f6d2-40a3-a633-13d0e1975179' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4639410-f6d2-40a3-a633-13d0e1975179' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38548be7-cca6-446d-b430-ca8c67af6881' class='xr-var-data-in' type='checkbox'><label for='data-38548be7-cca6-446d-b430-ca8c67af6881' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4b8aa85-3567-4c29-a14b-c743f2f53f4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4b8aa85-3567-4c29-a14b-c743f2f53f4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c229608-d675-41d1-9a13-a736663e9eca' class='xr-var-data-in' type='checkbox'><label for='data-4c229608-d675-41d1-9a13-a736663e9eca' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6429c4eb-d6c8-4e36-a42e-d5cf10fb5636' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6429c4eb-d6c8-4e36-a42e-d5cf10fb5636' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cb77032-faa3-466f-9cbc-b390d8670f4a' class='xr-var-data-in' type='checkbox'><label for='data-1cb77032-faa3-466f-9cbc-b390d8670f4a' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-570d138b-2604-49ae-9706-5f8fa9f8429d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-570d138b-2604-49ae-9706-5f8fa9f8429d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99dbd139-26c8-4f0a-b9b1-27c279cf0e34' class='xr-var-data-in' type='checkbox'><label for='data-99dbd139-26c8-4f0a-b9b1-27c279cf0e34' 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>[2000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5673d311-73a9-4e2f-9e71-7f1f5cc114a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5673d311-73a9-4e2f-9e71-7f1f5cc114a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa1824bf-d375-4dec-b75b-47dfa900c9d3' class='xr-var-data-in' type='checkbox'><label for='data-fa1824bf-d375-4dec-b75b-47dfa900c9d3' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d118fe7-f9dc-445a-9207-49f646d2d390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d118fe7-f9dc-445a-9207-49f646d2d390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b640035c-6c81-48d8-b479-40de5f1cf98f' class='xr-var-data-in' type='checkbox'><label for='data-b640035c-6c81-48d8-b479-40de5f1cf98f' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-614328d4-bc42-48b5-873f-834df33d1542' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-614328d4-bc42-48b5-873f-834df33d1542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd6575b-34dc-4c73-9c73-1bbf1ccfe195' class='xr-var-data-in' type='checkbox'><label for='data-afd6575b-34dc-4c73-9c73-1bbf1ccfe195' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c1ad16c-c67d-4653-b8a9-cb54f4262228' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c1ad16c-c67d-4653-b8a9-cb54f4262228' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e815e834-e43f-4f13-826d-aa730f4f4a9e' class='xr-var-data-in' type='checkbox'><label for='data-e815e834-e43f-4f13-826d-aa730f4f4a9e' 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>[2000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7bd373c7-f7a7-4297-87b3-8cd6afbcd186' class='xr-section-summary-in' type='checkbox'  ><label for='section-7bd373c7-f7a7-4297-87b3-8cd6afbcd186' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e3df987-f1ff-4c70-8e16-ff6eb8a1aee5' class='xr-index-data-in' type='checkbox'/><label for='index-7e3df987-f1ff-4c70-8e16-ff6eb8a1aee5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-beccf473-dff7-422a-814c-da86d143a3e3' class='xr-index-data-in' type='checkbox'/><label for='index-beccf473-dff7-422a-814c-da86d143a3e3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbf652af-eb5b-4a7b-84df-52b9eb6da8c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbf652af-eb5b-4a7b-84df-52b9eb6da8c2' 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>2025-04-02T15:35:52.362736+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd><dt><span>sampling_time :</span></dt><dd>25.825820922851562</dd><dt><span>tuning_steps :</span></dt><dd>500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_dataa1f23d51-51f9-4517-a355-a0e1ec8914a4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_dataa1f23d51-51f9-4517-a355-a0e1ec8914a4\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (y_obs_dim_0: 42639)\n",
       "Coordinates:\n",
       "  * y_obs_dim_0  (y_obs_dim_0) int64 0 1 2 3 4 ... 42634 42635 42636 42637 42638\n",
       "Data variables:\n",
       "    y_obs        (y_obs_dim_0) int64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T15:35:52.369617+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1</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-ff301873-dcfd-405f-a766-e8d197bb0ad6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff301873-dcfd-405f-a766-e8d197bb0ad6' 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'>y_obs_dim_0</span>: 42639</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3fd95149-41a4-4a15-a179-5545b961e60f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3fd95149-41a4-4a15-a179-5545b961e60f' class='xr-section-summary' >Coordinates: <span>(1)</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'>y_obs_dim_0</span></div><div class='xr-var-dims'>(y_obs_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 42635 42636 42637 42638</div><input id='attrs-345ae7d6-3321-4931-b523-4389efe63532' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-345ae7d6-3321-4931-b523-4389efe63532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4ab20be-095f-4e87-97ed-9667ded5d233' class='xr-var-data-in' type='checkbox'><label for='data-f4ab20be-095f-4e87-97ed-9667ded5d233' 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, ..., 42636, 42637, 42638])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46b493f4-e80d-4b6f-acd7-10c75017ed35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46b493f4-e80d-4b6f-acd7-10c75017ed35' class='xr-section-summary' >Data variables: <span>(1)</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>y_obs</span></div><div class='xr-var-dims'>(y_obs_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-035994bb-80b9-44fd-b2e8-2316d119d639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-035994bb-80b9-44fd-b2e8-2316d119d639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f174fb-2e2b-41b6-a229-5644fe96ec37' class='xr-var-data-in' type='checkbox'><label for='data-a4f174fb-2e2b-41b6-a229-5644fe96ec37' 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>[42639 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5edeaac8-68b5-4d4a-8c13-474970630cc8' class='xr-section-summary-in' type='checkbox'  ><label for='section-5edeaac8-68b5-4d4a-8c13-474970630cc8' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>y_obs_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-90605bed-4110-421f-8119-23d243a58f81' class='xr-index-data-in' type='checkbox'/><label for='index-90605bed-4110-421f-8119-23d243a58f81' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "       ...\n",
       "       42629, 42630, 42631, 42632, 42633, 42634, 42635, 42636, 42637, 42638],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y_obs_dim_0&#x27;, length=42639))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da3b9da6-183a-437d-be39-ed516a86e56d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da3b9da6-183a-437d-be39-ed516a86e56d' class='xr-section-summary' >Attributes: <span>(4)</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>2025-04-02T15:35:52.369617+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd></dl></div></li></ul></div></div><br></div>\n",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata"
   ]
  },
  {
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   "source": [
    "ArViz provides a variety of functions for processing and visualizing the results.\n",
    "In this example, the primary thing we're interested in is the posterior distributions of the coefficients `alpha` and `beta`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2731f31a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1035x301.875 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(idata)\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38600bd5",
   "metadata": {},
   "source": [
    "### Comparing Prior with Posterior\n",
    "\n",
    "To see what we learned from the data, we can compare the prior and posterior distributions of the coefficents.\n",
    "`sample_prior_predictive` runs the model forward to generate samples from the prior distributions (and the prior predictive, which we'll talk about later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8dbb064e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta, y_obs]\n"
     ]
    }
   ],
   "source": [
    "with logistic_model:\n",
    "    idata_prior = pm.sample_prior_predictive(draws=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2137685a",
   "metadata": {},
   "source": [
    "The result is another `InferenceData` object.\n",
    "It will be convenient to put all of the samples in one object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4043aeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "idata.extend(idata_prior)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4522662",
   "metadata": {},
   "source": [
    "Now we can compare the prior and posterior distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca849b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1035x603.75 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_dist_comparison(idata, var_names=[\"alpha\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2a7e48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1035x603.75 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_dist_comparison(idata, var_names=[\"beta\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500bb996",
   "metadata": {},
   "source": [
    "Looking at the posterior distributions of `alpha` and `beta`, we can see that they fall comfortably within the prior distributions of these parameters, which means that the priors didn't have much effect on the results.\n",
    "\n",
    "As an experiment, try increasing or decreasing `sigma_prior` and see what effect it has on the posterior distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1af0d8",
   "metadata": {},
   "source": [
    "## Sampling diagnostics\n",
    "\n",
    "After sampling, we want to check that the process worked well — meaning:\n",
    "\n",
    "* All chains have effectively explored the posterior distribution, and\n",
    "\n",
    "* Successive draws within each chain are only weakly correlated (each draw should provide mostly new information).\n",
    "\n",
    "We can check these properties visually using `plot_trace`:\n",
    "\n",
    "*    On the left, the distribution from each chain should look similar — this is evidence that the chains all converged to the same region of the posterior.\n",
    "\n",
    "*    On the right, the sequence of draws within each chain should look like uncorrelated noise (sometimes called \"hairy caterpillars\"). It shouldn't look like a random walk and there shouldn't be flat spots, which would suggest the chain got stuck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "40880262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(idata, var_names=[\"alpha\", \"beta\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59ff135",
   "metadata": {},
   "source": [
    "To check these properties numerically, we can look at two key summary statistics: ESS and r-hat.\n",
    "\n",
    "*    r-hat quantifies the difference between chains. If all chains converged to the same posterior distribution, r-hat should be close to 1. If the chains are exploring different regions, r-hat will be larger than 1, indicating failure to converge.\n",
    "\n",
    "*    Effective Sample Size (ESS) tells us how much independent information the chains actually contribute. If successive values within a chain are highly correlated, the chain isn’t exploring the posterior efficiently, and ESS will be smaller than the total number of draws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab460206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>-0.538</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.558</td>\n",
       "      <td>-0.520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1506.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha -0.538  0.010  -0.558   -0.520        0.0      0.0    1986.0    1506.0   \n",
       "beta  -0.015  0.001  -0.016   -0.014        0.0      0.0    1854.0    1213.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  \n",
       "beta     1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bc8cf7",
   "metadata": {},
   "source": [
    "## Generating predictions\n",
    "\n",
    "[Note: I cut some content here that I think would work well in the slides]\n",
    "\n",
    "There are two ways to generate predictions:\n",
    "\n",
    "* We can use the results from PyMC to compute our own predictions.\n",
    "\n",
    "* We can use the PyMC model.\n",
    "\n",
    "With this example, we'll demonstrate the first way.\n",
    "With the next example, we'll demonstrate the second.\n",
    "\n",
    "First, we'll extract the samples of the coefficients from `idata` and convert them to NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d272e139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect posterior draws of alpha and beta\n",
    "samples = az.extract(idata, var_names=[\"alpha\", \"beta\"], num_samples=100)\n",
    "\n",
    "alphas = samples[\"alpha\"].to_numpy()\n",
    "betas = samples[\"beta\"].to_numpy()\n",
    "betas.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a478af",
   "metadata": {},
   "source": [
    "Here's the range of years we'll predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c403beca",
   "metadata": {},
   "outputs": [],
   "source": [
    "year_range = np.arange(1970, 2031)\n",
    "year_centered = year_range - year_shift"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4071586c",
   "metadata": {},
   "source": [
    "To generate predictions, we pretty much reimplement the model in NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6cbc192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.special import expit\n",
    "\n",
    "for alpha, beta in zip(alphas, betas):\n",
    "    zs = alpha + beta * year_centered\n",
    "    probs = expit(zs) * 100\n",
    "    plt.plot(year_range, probs, color=\"C0\", alpha=0.02)\n",
    "\n",
    "time_series.plot(style=\"o\", label=\"observed\")\n",
    "decorate(ylabel=\"percent\", title=\"Percent saying people can be trusted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d13821",
   "metadata": {},
   "source": [
    "It looks like the model fits the data well, and makes a plausible projection for the future."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05abf2a4",
   "metadata": {},
   "source": [
    "## Centering\n",
    "\n",
    "You might remember that we mean-centered the predictor, `years`.\n",
    "Now here's why: because we centered `years`, the sampled slopes and intercepts are uncorrelated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2659c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plot_pair(idata, var_names=[\"alpha\", \"beta\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbc7230",
   "metadata": {},
   "source": [
    "As an experiment, try:\n",
    "\n",
    "* Set `year_shift=1970` and run the model again. You might get some warnings from the sampler, and `plot_pair` will show that the alphas and betas are correlated. The not-quite-centered predictor stretches the shape of the joint posterior distribution and makes it harder to sample efficiently.\n",
    "\n",
    "* Set `year_shift=0` and run the model again. With the predictor completely uncentered, the joint posterior distribution is so stretched that the sampler diverges frequently -- and basically fails.\n",
    "\n",
    "Without centering, the intercept represents the log-odds of the outcome when `year=0`, which is way outside the range of the data.\n",
    "This forces the intercept and slope to compensate for each other — if the slope increases, the intercept must decrease to hit the same observed points.\n",
    "\n",
    "As a general rule, it's a good idea to center continuous predictors in Bayesian regression models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc3812e",
   "metadata": {},
   "source": [
    "## Categorical predictors\n",
    "\n",
    "Now let's add political party as a predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6258988e",
   "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>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>25439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>26705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>18338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        counts\n",
       "values        \n",
       "0.0      25439\n",
       "1.0      26705\n",
       "2.0      18338\n",
       "NaN       1908"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts(gss[\"partyid3\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f84b7c",
   "metadata": {},
   "source": [
    "To prepare the data, we'll select observations where all the variables in the model are valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "633c1c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41605, 62)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gss.dropna(subset=[\"y\", \"year\", \"partyid3\"])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48456bf",
   "metadata": {},
   "source": [
    "And again we'll center the years and convert all data to NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8d7c517",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"y\"].to_numpy()\n",
    "\n",
    "year = data[\"year\"].to_numpy()\n",
    "year_shift = data[\"year\"].mean()\n",
    "year = year - year_shift\n",
    "\n",
    "partyid3 = data[\"partyid3\"].astype(int).to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015a5c10",
   "metadata": {},
   "source": [
    "This version of the model add a separate intercept for each group, and demonstrates two additional features:\n",
    "\n",
    "* Data: making the input data part of the model so we can modify it to make out-of-sample predictions\n",
    "\n",
    "* Deterministic: saving the result of intermediate calculations as part of the `InferenceData`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c29ec21",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as logistic_model2:\n",
    "    \n",
    "    # add the predictors to the model as mutable Data\n",
    "    year_pt = pm.Data(\"year\", year)\n",
    "    party3_pt = pm.Data(\"party3\", partyid3)\n",
    "\n",
    "    # Party-specific intercepts (one for each group)\n",
    "    alpha = pm.Normal(\"alpha\", 0, 1, shape=3)\n",
    "\n",
    "    # Shared slope for year (assuming effect of year is the same across parties)\n",
    "    beta = pm.Normal(\"beta\", 0, 1)\n",
    "\n",
    "    # Linear predictor (log-odds)\n",
    "    z = alpha[party3_pt] + beta * year_pt\n",
    "\n",
    "    # Compute and save the probabilities\n",
    "    p = pm.Deterministic(\"p\", pm.math.sigmoid(z))\n",
    "\n",
    "    # Bernoulli likelihood\n",
    "    y_obs = pm.Bernoulli(\"y_obs\", p=p, observed=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8815bed",
   "metadata": {},
   "source": [
    "Here's the graph representation of the model."
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  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "edd6dcdb",
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     "text": [
      "Loaded idata from logistic_model2_idata.nc\n"
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   "source": [
    "filename = 'logistic_model2_idata.nc'\n",
    "idata2 = load_idata_or_sample(logistic_model2, filename, draws=500, tune=500)"
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   "cell_type": "markdown",
   "id": "0c6ac023",
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   "source": [
    "Everything marked as `Deterministic` gets saved in the `idata`."
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   "cell_type": "code",
   "execution_count": 38,
   "id": "01e677e8",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (chain: 4, draw: 500, alpha_dim_0: 3, p_dim_0: 41605)\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 0 1 2 3\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "  * alpha_dim_0  (alpha_dim_0) int64 0 1 2\n",
       "  * p_dim_0      (p_dim_0) int64 0 1 2 3 4 5 ... 41600 41601 41602 41603 41604\n",
       "Data variables:\n",
       "    alpha        (chain, draw, alpha_dim_0) float64 ...\n",
       "    beta         (chain, draw) float64 ...\n",
       "    p            (chain, draw, p_dim_0) float64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T18:38:36.706173+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1\n",
       "    sampling_time:              51.27971315383911\n",
       "    tuning_steps:               500</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-268264e5-08a9-4361-b899-cb8701955ee5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-268264e5-08a9-4361-b899-cb8701955ee5' 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>: 500</li><li><span class='xr-has-index'>alpha_dim_0</span>: 3</li><li><span class='xr-has-index'>p_dim_0</span>: 41605</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6d323c39-97da-4657-8a36-176ce4d6a870' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d323c39-97da-4657-8a36-176ce4d6a870' class='xr-section-summary' >Coordinates: <span>(4)</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-451177bb-0828-4554-a008-6ee1e9bb12bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-451177bb-0828-4554-a008-6ee1e9bb12bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-643f9fe0-da9c-4317-8156-a88df4799dac' class='xr-var-data-in' type='checkbox'><label for='data-643f9fe0-da9c-4317-8156-a88df4799dac' 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 ... 495 496 497 498 499</div><input id='attrs-a5951138-8504-4e46-9cd6-d7d123eeb8b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5951138-8504-4e46-9cd6-d7d123eeb8b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd795af2-551f-4629-9970-f04c6974b673' class='xr-var-data-in' type='checkbox'><label for='data-fd795af2-551f-4629-9970-f04c6974b673' 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, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alpha_dim_0</span></div><div class='xr-var-dims'>(alpha_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-2af9b980-a9cf-45c5-83ed-f553f5cf4cc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2af9b980-a9cf-45c5-83ed-f553f5cf4cc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11b2313-6282-411f-901f-b4cac388b4a5' class='xr-var-data-in' type='checkbox'><label for='data-d11b2313-6282-411f-901f-b4cac388b4a5' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p_dim_0</span></div><div class='xr-var-dims'>(p_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 41601 41602 41603 41604</div><input id='attrs-ef7ebe39-cfa2-4276-81bc-ae8acc3e9b23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef7ebe39-cfa2-4276-81bc-ae8acc3e9b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73461ae7-8522-406e-be87-ce7c25b58acf' class='xr-var-data-in' type='checkbox'><label for='data-73461ae7-8522-406e-be87-ce7c25b58acf' 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, ..., 41602, 41603, 41604])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c7944ae-6b18-409f-ac7d-96fcb08e89df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c7944ae-6b18-409f-ac7d-96fcb08e89df' class='xr-section-summary' >Data variables: <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>alpha</span></div><div class='xr-var-dims'>(chain, draw, alpha_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-54a4c865-0734-437f-b0b1-be93f27f0d9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54a4c865-0734-437f-b0b1-be93f27f0d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20896b1f-ae3d-4e88-bc6d-8b81869684c8' class='xr-var-data-in' type='checkbox'><label for='data-20896b1f-ae3d-4e88-bc6d-8b81869684c8' 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>[6000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-253d8b03-3ebf-47b6-a2c5-5b2948329bae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-253d8b03-3ebf-47b6-a2c5-5b2948329bae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c20a08e1-b0c8-4629-9d93-46c3a6d2cf7d' class='xr-var-data-in' type='checkbox'><label for='data-c20a08e1-b0c8-4629-9d93-46c3a6d2cf7d' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, p_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78220b03-1f11-4aeb-8dd2-cb828b39ecce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78220b03-1f11-4aeb-8dd2-cb828b39ecce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aef8af15-9897-4a0b-bcc5-216c41bc5163' class='xr-var-data-in' type='checkbox'><label for='data-aef8af15-9897-4a0b-bcc5-216c41bc5163' 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>[83210000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac15b1ed-6dec-439c-8d7a-91fc4c5f0bc2' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac15b1ed-6dec-439c-8d7a-91fc4c5f0bc2' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-367ed9c7-c60c-4264-bc6f-b25ebe37b2d6' class='xr-index-data-in' type='checkbox'/><label for='index-367ed9c7-c60c-4264-bc6f-b25ebe37b2d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db07b470-b146-4f07-96a7-f46c629aa58b' class='xr-index-data-in' type='checkbox'/><label for='index-db07b470-b146-4f07-96a7-f46c629aa58b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alpha_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28f1978f-ed17-4318-a325-66c0a7749592' class='xr-index-data-in' type='checkbox'/><label for='index-28f1978f-ed17-4318-a325-66c0a7749592' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;alpha_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-100e316a-c70a-4d47-975f-69f785895e4d' class='xr-index-data-in' type='checkbox'/><label for='index-100e316a-c70a-4d47-975f-69f785895e4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "       ...\n",
       "       41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;p_dim_0&#x27;, length=41605))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-742cfb0f-54fc-4b8c-9c33-74ac5b4dc94b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-742cfb0f-54fc-4b8c-9c33-74ac5b4dc94b' 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>2025-04-02T18:38:36.706173+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd><dt><span>sampling_time :</span></dt><dd>51.27971315383911</dd><dt><span>tuning_steps :</span></dt><dd>500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsdca427a8-3dc2-4b38-b4ff-23b24c1ccf9b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsdca427a8-3dc2-4b38-b4ff-23b24c1ccf9b\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 500)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
       "Data variables: (12/17)\n",
       "    max_energy_error       (chain, draw) float64 ...\n",
       "    tree_depth             (chain, draw) int64 ...\n",
       "    reached_max_treedepth  (chain, draw) bool ...\n",
       "    perf_counter_diff      (chain, draw) float64 ...\n",
       "    index_in_trajectory    (chain, draw) int64 ...\n",
       "    largest_eigval         (chain, draw) float64 ...\n",
       "    ...                     ...\n",
       "    process_time_diff      (chain, draw) float64 ...\n",
       "    diverging              (chain, draw) bool ...\n",
       "    energy                 (chain, draw) float64 ...\n",
       "    smallest_eigval        (chain, draw) float64 ...\n",
       "    lp                     (chain, draw) float64 ...\n",
       "    acceptance_rate        (chain, draw) float64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T18:38:36.721731+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1\n",
       "    sampling_time:              51.27971315383911\n",
       "    tuning_steps:               500</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-580744e3-7883-449f-ba88-830cfa19d75a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-580744e3-7883-449f-ba88-830cfa19d75a' 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>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b868549d-42cf-4cda-a11b-48a509fc78a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b868549d-42cf-4cda-a11b-48a509fc78a6' class='xr-section-summary' >Coordinates: <span>(2)</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-8ef8e8bf-bc88-4139-b019-ad15ffecb452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ef8e8bf-bc88-4139-b019-ad15ffecb452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab96f06-1627-4fbb-8a11-27fdedd2f4c8' class='xr-var-data-in' type='checkbox'><label for='data-0ab96f06-1627-4fbb-8a11-27fdedd2f4c8' 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 ... 495 496 497 498 499</div><input id='attrs-76e151b2-baef-4981-9cdb-ba191b39233e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76e151b2-baef-4981-9cdb-ba191b39233e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd1babe1-77f6-4944-b634-eb7f28e595c4' class='xr-var-data-in' type='checkbox'><label for='data-cd1babe1-77f6-4944-b634-eb7f28e595c4' 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, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff24fff1-4e4a-449b-9961-0baa371ea6e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff24fff1-4e4a-449b-9961-0baa371ea6e7' class='xr-section-summary' >Data variables: <span>(17)</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>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc2c8ab3-e7a0-4b4d-9917-fda7e1c6c198' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc2c8ab3-e7a0-4b4d-9917-fda7e1c6c198' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021c3a9d-49a0-4782-8113-87a69b443a42' class='xr-var-data-in' type='checkbox'><label for='data-021c3a9d-49a0-4782-8113-87a69b443a42' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0ddd120-b734-4876-ba3c-ef6bfdbe45be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0ddd120-b734-4876-ba3c-ef6bfdbe45be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b252633-d4c9-46df-8ad0-272f7fcda737' class='xr-var-data-in' type='checkbox'><label for='data-5b252633-d4c9-46df-8ad0-272f7fcda737' 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>[2000 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63516401-8b59-4621-9f16-49f1c5d5c7d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63516401-8b59-4621-9f16-49f1c5d5c7d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96bbbc81-38fa-49d1-b9c1-b73ee0b3e57d' class='xr-var-data-in' type='checkbox'><label for='data-96bbbc81-38fa-49d1-b9c1-b73ee0b3e57d' 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>[2000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73da72bd-cef2-4650-b70a-dcbefb0ee9b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73da72bd-cef2-4650-b70a-dcbefb0ee9b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ca18406-7a09-491b-8a9c-d5cee6d4be34' class='xr-var-data-in' type='checkbox'><label for='data-0ca18406-7a09-491b-8a9c-d5cee6d4be34' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b926bf6-0461-42be-90cf-de9b9ebac840' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b926bf6-0461-42be-90cf-de9b9ebac840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8311fde-fbf4-4f07-a369-479baf97f0fd' class='xr-var-data-in' type='checkbox'><label for='data-e8311fde-fbf4-4f07-a369-479baf97f0fd' 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>[2000 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cb006c6-7f59-4665-8206-49e7428d2536' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cb006c6-7f59-4665-8206-49e7428d2536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11f03887-e50e-48db-9c09-3f0eb7b60d06' class='xr-var-data-in' type='checkbox'><label for='data-11f03887-e50e-48db-9c09-3f0eb7b60d06' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e8dee1a-46e9-4b9f-9778-0a0a45b0c53a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e8dee1a-46e9-4b9f-9778-0a0a45b0c53a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f682eedc-223c-49f6-95cd-94a84a3751f2' class='xr-var-data-in' type='checkbox'><label for='data-f682eedc-223c-49f6-95cd-94a84a3751f2' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f620a4e7-5763-4a46-a04e-4764e5cd9fd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f620a4e7-5763-4a46-a04e-4764e5cd9fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60130359-263a-43f1-8a61-02f770565206' class='xr-var-data-in' type='checkbox'><label for='data-60130359-263a-43f1-8a61-02f770565206' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cc5da92-2717-4c67-9cfb-4e6b263a37a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9cc5da92-2717-4c67-9cfb-4e6b263a37a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b49d977-5f08-44a8-8ff5-0abfabe20b2b' class='xr-var-data-in' type='checkbox'><label for='data-1b49d977-5f08-44a8-8ff5-0abfabe20b2b' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0148487-6d33-446a-9794-4be3bf486882' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0148487-6d33-446a-9794-4be3bf486882' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0454581-7dd4-4129-b1ad-1c1ecb3ec933' class='xr-var-data-in' type='checkbox'><label for='data-d0454581-7dd4-4129-b1ad-1c1ecb3ec933' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59f3e5f5-6fd6-4317-ae7e-9d329683a7d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59f3e5f5-6fd6-4317-ae7e-9d329683a7d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc0bbcd8-1ed2-4f32-b3ac-3ad0509d2abb' class='xr-var-data-in' type='checkbox'><label for='data-fc0bbcd8-1ed2-4f32-b3ac-3ad0509d2abb' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c30d5f0e-a069-4466-809f-b8eafe7184db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c30d5f0e-a069-4466-809f-b8eafe7184db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7925fb-b2ea-45c4-97bb-ee9b581615f9' class='xr-var-data-in' type='checkbox'><label for='data-2e7925fb-b2ea-45c4-97bb-ee9b581615f9' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-168bdb9e-49a5-4829-9662-ccacb0dc64e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168bdb9e-49a5-4829-9662-ccacb0dc64e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd6b8f5d-73cc-4dd5-80e5-a1cab2dd0f9c' class='xr-var-data-in' type='checkbox'><label for='data-bd6b8f5d-73cc-4dd5-80e5-a1cab2dd0f9c' 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>[2000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbb5aef3-91f5-49e2-9c46-b086b3156f2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbb5aef3-91f5-49e2-9c46-b086b3156f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e5d88e-31fe-4979-af71-e5d4aa996169' class='xr-var-data-in' type='checkbox'><label for='data-35e5d88e-31fe-4979-af71-e5d4aa996169' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da3bd0de-f0d8-402e-88f3-4e1ccae84619' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da3bd0de-f0d8-402e-88f3-4e1ccae84619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e784505-f7ec-4dae-a2b4-e9f1c8f069d3' class='xr-var-data-in' type='checkbox'><label for='data-3e784505-f7ec-4dae-a2b4-e9f1c8f069d3' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a51f1c18-3faf-49c0-a5d6-bf25b89d0ca5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a51f1c18-3faf-49c0-a5d6-bf25b89d0ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00821dc1-9ed7-4ef8-8014-6e3f1d8db9c1' class='xr-var-data-in' type='checkbox'><label for='data-00821dc1-9ed7-4ef8-8014-6e3f1d8db9c1' 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>[2000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e848c6f2-1988-4fdf-9f1b-435fa44f0dc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e848c6f2-1988-4fdf-9f1b-435fa44f0dc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b24c1b2d-be6e-4a38-a080-9ddc8d0ba652' class='xr-var-data-in' type='checkbox'><label for='data-b24c1b2d-be6e-4a38-a080-9ddc8d0ba652' 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>[2000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2287680-3e62-4205-ba4d-eaf8acf5698b' class='xr-section-summary-in' type='checkbox'  ><label for='section-c2287680-3e62-4205-ba4d-eaf8acf5698b' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67c3a899-edaf-47e0-8387-5962858d95fe' class='xr-index-data-in' type='checkbox'/><label for='index-67c3a899-edaf-47e0-8387-5962858d95fe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eae98f16-9fb4-42d1-b9db-4e10f003dbb4' class='xr-index-data-in' type='checkbox'/><label for='index-eae98f16-9fb4-42d1-b9db-4e10f003dbb4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35f445e8-b09e-46ab-8621-118f72c1367f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35f445e8-b09e-46ab-8621-118f72c1367f' 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>2025-04-02T18:38:36.721731+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd><dt><span>sampling_time :</span></dt><dd>51.27971315383911</dd><dt><span>tuning_steps :</span></dt><dd>500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data8c09288a-d200-46d5-820b-a3e72fea719f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data8c09288a-d200-46d5-820b-a3e72fea719f\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (y_obs_dim_0: 41605)\n",
       "Coordinates:\n",
       "  * y_obs_dim_0  (y_obs_dim_0) int64 0 1 2 3 4 ... 41600 41601 41602 41603 41604\n",
       "Data variables:\n",
       "    y_obs        (y_obs_dim_0) int64 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T18:38:36.727821+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1</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-07fd597a-2609-4f64-b41b-41aa0211b309' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07fd597a-2609-4f64-b41b-41aa0211b309' 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'>y_obs_dim_0</span>: 41605</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-92701d4d-6496-4464-be66-da1973d7674d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92701d4d-6496-4464-be66-da1973d7674d' class='xr-section-summary' >Coordinates: <span>(1)</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'>y_obs_dim_0</span></div><div class='xr-var-dims'>(y_obs_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 41601 41602 41603 41604</div><input id='attrs-538a12fa-762f-4c17-9293-500d0fd2bc26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-538a12fa-762f-4c17-9293-500d0fd2bc26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-061d1fa9-6b17-47f7-9fb2-2b3638291fbc' class='xr-var-data-in' type='checkbox'><label for='data-061d1fa9-6b17-47f7-9fb2-2b3638291fbc' 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, ..., 41602, 41603, 41604])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee7ed268-1e3b-46c8-97e6-33cb09e9a274' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee7ed268-1e3b-46c8-97e6-33cb09e9a274' class='xr-section-summary' >Data variables: <span>(1)</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>y_obs</span></div><div class='xr-var-dims'>(y_obs_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8eb21fd0-b70a-4081-b8b7-82a219c364f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8eb21fd0-b70a-4081-b8b7-82a219c364f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b07f6c20-1f2c-488a-a9e1-cbab9dda190a' class='xr-var-data-in' type='checkbox'><label for='data-b07f6c20-1f2c-488a-a9e1-cbab9dda190a' 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>[41605 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f20fe87-2894-4392-92a3-a7528ae29bab' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f20fe87-2894-4392-92a3-a7528ae29bab' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>y_obs_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7adc0c5d-ae97-4487-8071-45aaaa3bc93b' class='xr-index-data-in' type='checkbox'/><label for='index-7adc0c5d-ae97-4487-8071-45aaaa3bc93b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "       ...\n",
       "       41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y_obs_dim_0&#x27;, length=41605))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6a26a8f-ad2f-4c5e-b849-1b3dbd5cc97a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6a26a8f-ad2f-4c5e-b849-1b3dbd5cc97a' class='xr-section-summary' >Attributes: <span>(4)</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>2025-04-02T18:38:36.727821+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_constant_datadbde9421-62f8-4b2f-bc78-dfcbfbd8317c\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_constant_datadbde9421-62f8-4b2f-bc78-dfcbfbd8317c\" class = \"xr-section-summary\">constant_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
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       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (year_dim_0: 41605, party3_dim_0: 41605)\n",
       "Coordinates:\n",
       "  * year_dim_0    (year_dim_0) int64 0 1 2 3 4 ... 41600 41601 41602 41603 41604\n",
       "  * party3_dim_0  (party3_dim_0) int64 0 1 2 3 4 ... 41601 41602 41603 41604\n",
       "Data variables:\n",
       "    year          (year_dim_0) float64 ...\n",
       "    party3        (party3_dim_0) int32 ...\n",
       "Attributes:\n",
       "    created_at:                 2025-04-02T18:38:36.729538+00:00\n",
       "    arviz_version:              0.21.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.21.1</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-a18bf619-52aa-4fd7-a3f6-9e6fcbdad046' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a18bf619-52aa-4fd7-a3f6-9e6fcbdad046' 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'>year_dim_0</span>: 41605</li><li><span class='xr-has-index'>party3_dim_0</span>: 41605</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-55c7d408-bc18-4d34-ade5-15b5548baff0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55c7d408-bc18-4d34-ade5-15b5548baff0' class='xr-section-summary' >Coordinates: <span>(2)</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'>year_dim_0</span></div><div class='xr-var-dims'>(year_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 41601 41602 41603 41604</div><input id='attrs-bfb39f7c-1f3d-4c9b-91e0-16300042c0ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfb39f7c-1f3d-4c9b-91e0-16300042c0ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7ad4202-5a28-449e-a093-9a6ac3e9d20a' class='xr-var-data-in' type='checkbox'><label for='data-c7ad4202-5a28-449e-a093-9a6ac3e9d20a' 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, ..., 41602, 41603, 41604])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>party3_dim_0</span></div><div class='xr-var-dims'>(party3_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 41601 41602 41603 41604</div><input id='attrs-533f2540-8d51-4181-92bd-6f956415a4fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-533f2540-8d51-4181-92bd-6f956415a4fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36a9fa03-161a-49b0-9fc5-924978df9afc' class='xr-var-data-in' type='checkbox'><label for='data-36a9fa03-161a-49b0-9fc5-924978df9afc' 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, ..., 41602, 41603, 41604])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c71eeaf1-07f8-42c5-887b-87779c9b1d6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c71eeaf1-07f8-42c5-887b-87779c9b1d6b' class='xr-section-summary' >Data variables: <span>(2)</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>year</span></div><div class='xr-var-dims'>(year_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc3d6eef-e318-481d-b729-cf9546dbb0dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc3d6eef-e318-481d-b729-cf9546dbb0dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49548988-9485-43e8-b53e-f6d548be24c5' class='xr-var-data-in' type='checkbox'><label for='data-49548988-9485-43e8-b53e-f6d548be24c5' 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>[41605 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>party3</span></div><div class='xr-var-dims'>(party3_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7ab708fb-ef16-49a9-8b6d-d939ef75cd9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ab708fb-ef16-49a9-8b6d-d939ef75cd9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7d0da66-3c80-477a-b3f8-64070c486220' class='xr-var-data-in' type='checkbox'><label for='data-f7d0da66-3c80-477a-b3f8-64070c486220' 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>[41605 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61e92ea0-575b-415d-b2e5-5571ac163267' class='xr-section-summary-in' type='checkbox'  ><label for='section-61e92ea0-575b-415d-b2e5-5571ac163267' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>year_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee8f8a72-5cf0-4feb-b481-feaa96c18e01' class='xr-index-data-in' type='checkbox'/><label for='index-ee8f8a72-5cf0-4feb-b481-feaa96c18e01' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "       ...\n",
       "       41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year_dim_0&#x27;, length=41605))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>party3_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93a46e5a-5219-4957-927b-1199cb58a96b' class='xr-index-data-in' type='checkbox'/><label for='index-93a46e5a-5219-4957-927b-1199cb58a96b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "       ...\n",
       "       41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;party3_dim_0&#x27;, length=41605))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7a8f796-df83-4778-8f4b-b96a3048f1e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7a8f796-df83-4778-8f4b-b96a3048f1e8' class='xr-section-summary' >Attributes: <span>(4)</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>2025-04-02T18:38:36.729538+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.21.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
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       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "  margin-top: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
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       "\n",
       ".xr-obj-type {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-item input {\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data\n",
       "\t> constant_data"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ea925f",
   "metadata": {},
   "source": [
    "So when we plot the posteriors, we'll use `var_names` to show only the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "df6af7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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lVZCku3/HzsIEfk6WaO5lj3YBjmgf4ARTpUJMMBEREem89IJynE4pQGx2MWKzS3C9oBzZRZWoUmv/9THO1qbwcbRAcy97RHrf/uNoZVqP1URERFSfJEnC6ZQC7Ll2EyeS8nC9oBzl1Zq7fsfKVIlmXnZo5eOALsEuaOxhw4kDRHpIJkl/P91IRIYuJa8Mi3bFYM+1mwCAMHcbNPOyg4edBZRyGVLzy3A04fYAwNpMicldA/B8B1+uMkBERFTHtFoJO6/ewNcHkxB9oxgA4GlvjjB3G3jYWaCBrRmUChkkCSgsr0ZKfjkSbpYg7mYJJOn2KgM9G7lhaKQn2vg58EM6ERGRkZMkCWfTbmHLhUwcSchFekHFnfu8HS3g42iJBrZmcLE2haWpEmYmtycclldrUFGtRkmVGtlFlYi/WYKk3LI7j/V3tsSAcHcMjfSEp71Fvb8vIiIiqn1arYTfL2Xh28NJiM0ugUwGNPW0Q6CLFbwcLGBnYQIAyC+tRkpeGc6kFuBGUSUAINDFCkOae+Lplg3hwAsPifQGJwoQGZGKag0+3RuHlSdTAQCjWntjTDsf+DpZ/uN3JUlCVEoBFu+OxfnrhWjmZYcfx7TkQZ6IiKiOHIrLwaJdsYjNLkEDWzOMbOWFJyM84OV4/5PvheXVOByfi22XbuBIfC6qNVqEuFnjje5B6BXmygkDRERERqa8Wo01Udex8mQq0gsqoJDL0MLbHu0DnNDW3xGhDWxgZfpwC43eKqvGubRbOHf9Fo7E5+JaVjHkMqBfU3e8+kQAglyt6+jdEBERUV07k1qAD7ZeQ8yNYnjYmWNsOx8MauYBZ+t/X0VIkiSkF1Rg+5UsbDqficScUpgq5RjS3APPt/dFIMcGRDqPEwWIjERiTikmrT6PuJsl6BXmiml9QuFzjwkCfydJEn45lYY526Lh52SJVeNbw9XGrB6KiYiIjENReQ1mb7uGzRcy4WxtisldA/BMq4aPvIVAYXk11p1Nx0/HUpFdXIkW3vaY0S8Uzbzsa7mciIiIdE1ljQY/HU/B90eScau8BiFu1hjeoiEGhLv/54n+RxFzoxgrjqdi84VMqLVajG7jjbd6BcPGzKRWX4eIiIjqTmWNBkv2xeP7o8lwsFDhje6BeKaV10OvLixJEs5fv4Ufj6Vg99VsaCWgU5AzXu7sh7Z+jryAgUhHcaIAkRE4FJeDyWsuQJIkLHyqKQaGuz/0c+y+mo1X156Hl4MFNk5sBzsLrixARET0uBJzSvDCyrNIyy/Hc2298W7vEFg+5NV9/6ayRoMfj6Xgm0NJKKtWY0xbH7zdK7jWnp+IiIh0y4mkPLy/+SqS88rQzMsOrz4RgK7BLnV+Yj6zsAIf7YrF75ey4GFnjs+ejkArX4c6fU0iIiJ6fOkF5Ri/8izibpagbxM3zB/UpFZWFE4vKMfKE6n47Uw6SqrUiPS2x+QnAtAlyJkTBoh0DCcKEBm4X06lYfbv1+BhZ46fxrZAgMujL/ez51o2Jq46h0hve/zyQus7excSERHRwzsYl4PX1lwAAHw+ohm6hrjUyevklVZh3vZobL14++T9p8PD0cbPsU5ei4iIiOpfZY0G87ZHY3XUdThZqfDBgDAMaNqg3k/EH4rLwdsbLqOgrBof9G+E59p688sAIiIiHXU5oxDPrziL0qoaLBrSFE9GuNf6cbuooga/nEzFj8dScKu8Bo09bDC5ayB6NnKFXM4xApEu4EQBIgO2bH8CPtsfj0hve3z3bCQcrR5/mcFfTqZi5tZrGNHKCwuHNKmFSiIiIuOz88oNvLr2AjztzfHDcy3qZd++AzE3MW3TFeSVVuHtXiGY0MmPH8yJiIj0XHJuKSatuYCYG8V4qrknPujfCLYW4pb+zy+twsRV53E6tQBj2/ngg/6NON4gIiLSMQfjcvDKqvOwUCnw49iWiGhoV6evV1alxpqo61h+JBl5pVUIcrXCpK4B6NekAZQPucUBEdUuThQgMlDLDydh4a5YdA91wZcjm9fq1f/vbriM386m46uRzdGvaYNae14iIiJjsC/6JiauOocAFyusebFNrSzr96DySqvw+q8XcDwxHz0auWLp0xHcioCIiEhPHU/Mw4RfzkGjlfDh4MYY0txTdBIAoFqtxXubLmPT+Uw83aIhFgxpAgUnCxAREemEP7cY9rAzx8rnW8Hb0bLeXruyRoN1Z9Px7aEkZBVVwsfRAm/2CMKApu6cWEgkCCcKEBmgTeczMGXdJXQMdML3z7Wo9S0CyqvVGPDFMeSUVGHfm53hZmtWq89PRERkqI7E52L8yrNo6GCOX19qC2frx1/t52FptBKW7o/HF38kIszdBj+NbQlXGx7LiYiI9Mnuqzfw2tqLcLY2xcrnWz7WNoN1QauV8P7Wq1gTdR0jW3vhw0GNuQ0BERGRYFsvZmLKukvwc7LE6vGt4SLoXEC1WovNFzLwxR+JyLhVgdAGNninVzC6BDtzvEBUzzhRgMjAnEktwKjvoxDkZoV1E9rCQlU3VwlezSzCk18dR/dQFyx/tkWdvAYREZEhicsuwVPfnICDpQrrX24r/Mv5zRcy8M6Gy3C2MsWK51shqB62PyAiIqLHt/5sOt7deBkBLlb43/OtdXbyviRJeG/jFfx2Nh1TegThtW6BopOIiIiM1u6r2Xhl9TmEuNnglxda1co2xY+rWq3F2tPX8cUfCcgrrUZLH3u80zsELX0cRKcRGQ1OFCAyIGn5ZRj01XGolHJsndShzk8WLNwZg+VHkvHt6Ej0buxWp69FRESkz/JLq/DkV8dRVFGDLZPaw9/ZSnQSAOBkUj4m/HIWCrkMq8e3QSN3G9FJRERE9B92X72BV1afRxMPW6x8vhXsLOpvC6NHodZoMeGXczgQm4PPRzTDwHB30UlERERG50xqAUb9EAUvBwtseLmtzo0fyqrU+Pl4CpYfTkZJlRpPhLjg7V7BCG3AcxREdY0TBYgMREW1BoO+Oo7rBeVY/3JbNPawrZfX7Ln0MNQaCQff6lLrWxwQEREZAq1WwtgVZ3AsIRcrn2+FjoHOopPucjWzCKN/jAIArHqhdb2MIYiIiOjhnUjKw9ifzsDb0QLrdfAk/78pr1ZjyNcnkJZfjs2T2iHEjSf9iYiI6kvCzdurG1qolNj4Sjt42JmLTvpXt8qq8e3hJKw4kYoajRYvdvLDm92D+L0DUR2Siw4gotoxZ9s1xN0swaKnmtTbCX5zlQIz+obiRlElfjqeUi+vSUREpG+WH0nGkfhcvNk9SOcmCQBAYw9brBnfBnKZDCO/P4VL6YWik4iIiOhvYrOL8dL/zsHZ2hT/e0H3VxL4KwuVEsufjYSJQoaJq86jrEotOomIiMgo3CiqwJifTkMCsOL5ljo9SQAA7C1VmNY3FIfe7oIejVyx/HAy+iw7inNpt0SnERksThQgMgBbL2bi1zPpGNGqIZ6M8KjX1+4V5oYW3vb45mAS8kur6vW1iYiIdN3Z1AJ8sjcO7QMc8UrXANE5/6qRuw3WvtgGKqUco3+MQnRWsegkIiIi+j+F5dV48X9nIZcB/3uhFRrY6vZJ/nvxdrTEx8PCkZJXhnnbo0XnEBERGbyiihqM/ekM8kqr8d2zLfRqRZ8Gtub4dnQkvhrZHMUVNXh6+Ul8fyQZXCCdqPZxogCRnkvNK8P0TVcQ4maNWQPC6v31ZTIZpvcLRUmVGt8cSqr31yciItJVJZU1eP3Xi7C3UOGzpyOgkMtEJ/2nYDdrrHmxDRRyGZ776TRS88pEJxERERk9tUaLV9deQOatCnwxsjn8na1EJz2yXmFuGNGqIX49k44917JF5xARERmsKrUGE345i/icEnz2dATa+juKTnpoMpkM/Zo2wK7XO6KFjz0+3BmDF/93FqVcmYioVnGiAJEe02olvLPxMtRaCV+ObC5sr57mXvboFuKC1VHXuaoAERHR/1m8Ow6ZhRX4eGhTuFibic55IEGu1lgxrhXKq9UY/WMUbhZXik4iIiIyah/vicPRhDy80zsEnYN0bwujhzWzfyN4O1rgg61XUVJZIzqHiIjI4Gi1Eqasu4RTyQX4oH8j9GvaQHTSY3GxMcPq8W3wWrdA7I/JwfBvTyKH5yqIag0nChDpsVVRaTidUoCpPYMQ4CL2qoJJTwSgokaDn46nCO0gIiLSBVHJ+fjlVBqGNPNA1xAX0TkPJaKhHb57tgVyiqvw7I9RKCyvFp1ERERklA7F5WD5kWT0a9IAEzr5ic6pFRYqJeYPaoybxVX4dG+86BwiIiKDIkkS5u2Ixo7LNzChsx/GtfcVnVQrFHIZpvQIwrJnIpCQU4LBX59AYk6p6Cwig8CJAkR6Kr2gHIt2xSK8oR1e6CD+hEFzL3t0CHDCyhNpKCrnVQFERGS8Kms0eG/TFThZqTCzfyPROY+kQ6ATlj0TgcScUkz45Ryq1VrRSUREREYlt6QKb62/BE97cyx8qglkMt3ewuhhdAx0xsBwd6w8mYpL6YWic4iIiAzGd0eS8fPxVAxu5oF3e4WIzql1T0Z4YMW4ViiuqMEz351CCrdMJHpsnChApIckScK0TVeg1kj4eGhTndnzePITASitUmPlyVTRKURERMJ88UcCUvLKMPfJxrC3VInOeWR9mjTAzP6NEJVSgPc2XoYkSaKTiIiIjIJWK2Hq+ku4VV6DZc80g42ZieikWvd+/1BYmSoxffMVaLQcYxARET2uzRcysHBXLDoGOuGjp5pCriPfGdS29gFOWPlCK1RUqzHq+1NILygXnUSk1zhRgEgP/XYmHccS8/BatwAEuVqLzrmjta8DWvrY48djKSirUovOISIiqncpeWX4/kgKugY7o28T/d4HEADGtffF2HY+2HQhE58fSBSdQ0REZBRWRaXhSHwu3uweiEhve9E5dcLF2gzv9A7BtaxibDyXITqHiIhIrx1LyMPb6y+jsYcNvhkdCZXSsL/6a+5ljx/HtkRBeTVG/RCFnJJK0UlEesuw/7UgMkC3yqqxaHcsQhvYYEJnf9E5d5HJZJjYxR9FFTXYeJ4f9ImIyLhIkoQ5264BAGYNCBNcU3tm9m+E7qEu+Gx/PDZf4PGdiIioLmUWVuCjXbGIaGiHiV0CROfUqREtG8Lf2RJL9sWjolojOoeIiEgvxd8swcRV5+Bma4afxraElalSdFK9aOPniO+ebYGswgpM+OUcKms4liB6FJwoQKRnluyLR2F5DeY+GQYThe79Fe4S5AI/Z0v8fDwVWi4fSERERmR/TA4OxeViQmc/+DhZis6pNQq5DMueaYYwdxu8s+EyopLzRScREREZJEmS8P7mK6hSa/HRU7qzzWBdUSrkeKd3CLKLK7HiRKroHCIiIr2TU1KJcT+fAWTAinEt4WJtJjqpXnUKcsa8QY1x4Xoh3uWWiUSPRPe+ZSSifxWdVYzVUWkYFOGOlj4OonMAAOfPn0eXLl1gYWEBV1dXTJnyJkZFNkBKXhn+iM35z8fW1NRg0aJFCA4OhqmpKVxdXTFgwACo1be3LdBoNPj444/RpUsXuLi4wNbWFq1bt8aGDRvq460RERE9sMoaDeZuvwYPO3O8oudX//392P7GG29Arq3BT2NbwsnKFC/9cg5JuaX3fOz9ju0AsGTJEkRGRsLe3h7m5uYICQnBvHnzUFFRUV9vkYiISCf9fikLB+Ny8UoXfwS76c42g391r3HC/Y7h//XZvmcjV0R62+PrQ4m4VVYNAPjiiy8QFBQEU1NT+Pn54aOPPoJWq62Pt0dERKQ3Kqo1eHHlWdwsrsTy0ZEIcNHNscNfPco44k/79u1Dy5YtYW5uDi8vL8ydOxcajQYjWnnh+fa+2HoxC1O/+A1jxoxBaGgo5HI5unTpcs/nmj17NmQy2T/+vPXWW7X4bon0g3GsQUJkACRJwuzfr8HMRIH3+oSKzgEApKWl4YknnkCrVq2wdetWZGZmYurUqXgi6wbsGr+AH4+loHsj13s+VpIkDB06FCdPnsT777+P8PBw5OfnY+/evXdOAFRUVGDBggUYM2YM3nrrLZiammLdunUYNmwYvvzyS0yaNKk+3y4REdG/Wn44GekFFfhmVHOYqxSicx7Zvx3bs7Oz8euvv+LHMS0x7NsTGPfzGWx+pR0crUzvPPZBju0AUFhYiOHDhyMsLAwWFhY4efIk5s2bh8uXL2P9+vUi3jYREZFwRRU1mLstGv7Olpj0hG5OOrzfOOHf3O+z/Xv9RmDYtyfx9aFESBc2YdasWXj33XfRtWtXHD58GDNmzEBpaSnmzZtXj++WiIhId2m1Et747QIuZRRh8dCmaBfgJDrpvh51HAEAZ86cQb9+/TB06FB89NFHuHr1Kt59912Ul5dj0aJFmNEvFEm5pfj557WwSj+BTu3boqqq6j+fU6FQ4NixY3fd5u7u/tjvk0jfyCSuxUGkF36/lIXX1l7AO72DdeZKxUmTJmHDhg1ITk6GpeXtJZbXrFmDUaNG4Y2vt2BzmhI7XuuAMHfbfzx25cqVeOGFF3D27FlERETc8/k1Gg2Ki4thb29/1+19+vRBbGwsUlJSav09ERERPaybxZXo/PFBRHrbY9ULrSGT6e8ywf91bL906RKaNm2KQ3E5eGHlWUQ0tMPq8a1hZnJ7YsSDHNv/zfTp07Fo0SIUFRXB2lr3r4IgIiKqbfO3R+OHYylYM761zp7sf5Bxwr08yGf751ecwbHYTGR+ORrPPfssvvnmmzu/N3nyZPzwww9IT0+Hs7Nz3b1BIiIiPbFgZwy+O5KMyV0D8FavYNE5D+RRxxEA0K9fP6Snp+PixYuQy28vlL5gwQLMmTMHmZmZcHJyQkFZNfp8dhhamQw7X+uIEYP7Qa1W49ChQ/94vtmzZ2P+/Pl3rX5IZKy49QBRLRg7diwCAgJw5MgRNG/eHObm5oiIiMDp06dRXV2NKVOmwNnZGa6urnjnnXeg0WjuPDYxMRHDhw+Hg4MDzM3N0a5du3/MZNuybQeeGzYIWV89i3f6RyAiIgIrVqy463cOHToEmUyGXbt24cUXX4S9vT3c3NwwefJkVFZW1sn73rFjBwYOHHjnwA4ATz31FExNTWF24xKUchl+Pp56z8cuX74cXbp0+c8vEhQKxT9OJABA8+bNkZWV9bj5RERE/+phju1PPjcRldU1mNG3EWQy2QMd23fv3o0+ffrAzc0NVlZWenFs3759OwCgS7AL5gwMw7m0W5i6/hK02tvzjh/k2P5vHB0dIZPJoFRywTMiItJ9tX0OYN22vVhxIhU9G7miXYCTXo8T7uVBPtu/0T0QJTdSUV5Wht69e9/1e71790ZVVRX27NlTS++EiIhInMcdR7Tu1h8zn2qFjCVPYdOccQZ1vuFeqqursX//fjz99NN3JgkAwKhRo1BdXY29e/cCABwsVfhiVCQKyqoxZd3FOnkPRIaIEwWIakl+fj4mTZqEqVOnYsOGDVCr1Rg0aBDGjx+PyspKrF69GhMmTMDHH39858CclpaGNm3aICkpCd988w02bdoEZ2dndO/eHRcvXrzz3Cv3REHeMBxzl3yFrVu3YsiQIXjppZfw7bff/qNj0qRJsLOzw7p16/D666/jm2++wUcffXTX76jV6vv+ud/+f+Xl5UhLS0NYWNhdt5uamsLf3x8ZKQno3dgN2y5loai85q7fqampwdmzZxEaGoopU6bA3t4epqam6Nq1Ky5cuHDf/9ZHjx5FaKhubL9ARESG60GO7cNGj0XU1hUILDyHRu42D3xsT05ORs+ePbFixQq9ObbHxMTcuW10G2+81MkPOy7fwMd74x7p2K5Wq1FWVobDhw9j6dKlePnll2Fubn6//7cQERHphNo8BzByyADU5CRjRr/bn3P1fZzwoP762b6ppx1a+99eSaH8bxf3mZre3uro2rVrD/0aREREuuhRxxGRLVvjYnQsWox8Gxs3bjDI8w1/l5SUhOrq6n881tvbGxYWFnc9tpWvA6b0CMLRhDxkFlb8Z5NGo4GbmxuUSiUCAgLw0Ucf3fd9EBkkiYge25gxYyQA0rlz5+7ctm3bNgmANGDAgLt+t3nz5lKvXr0kSZKkcePGSe7u7lJRUdGd+9VqtRQSEiINGzZMkiRJSs0rlQKn75TG/hQlSZIkaTQaqaamRpo4caLUtGnTO487ePCgBEB6+eWX73q9/v37S6GhoXd+TklJkQDc98+YMWP+8z1nZmZKAKSff/75H/e1b99e6tu3r3QiMU/yfne79MPR5Lvuv3HjhgRAsra2liIjI6Xff/9d2rp1qxQRESE5ODhI+fn5//q6q1atkgBIq1at+s8+IiKix/Ggx/bxK89Ipm4BUucnukuS9GDH9r/Tp2P73d1a6eVfzkre726Xvt555qGO7X+OBf7aplar/7OPiIhIV9TmOYA/om9ISgdPqXGHXvd8LX0dJ9zPvT7bn4xJlyCTS11GvHLX786dO1cCIL300ksP9RpERES66FHHEYOfHi0prR2ltnO2SXkllZIkGe75hr86fvy4BEA6ePDgP+7z8PCQXnnl7nGDRqOVhn1zQrLwiZBatetwz+f85ZdfpMWLF0t79+6Vdu3aJU2YMEGSyWTSxIkT//N9EBkiru1JVEscHBzQvHnzOz8HBQUBAHr06HHX7wUHB+Py5csAgD179qB///6wsLC4az+c7t27Y+PGjQCAedtjUFOci5K9q9Hwg8PIysq6M7Ptz1n1f9W3b9+7fm7cuDEOHjx452d3d3ecOXPmvu/Hyen/74n49716lEolJOn2MsP32of5z/va+DnAz9kSq6PS8Hx7nzu/++dySTU1Ndi+fTvc3NwAAJGRkfD398dXX32FmTNn/uN5o6Ki8NJLL2HkyJEYNWrUfd8DERHR47jfsf1sagH2Rd9EUFAQ8m6mAXiwYzsAZGRkYObMmdi/f79eHdv/Si6X4bOnI5BdfAqLdkYDePBju5OTE86cOYPy8nJERUXhww8/hFqtxqpVq+77PoiIiHRBbZwDUGu0mL/9GuwDI5GXePrOYwxhnPBf/u2zfZsQTwR16Icjm1ZgxdrOGNSnJ44ePYply5YBwF3LDRMREemzhx1H5BRXYvuuXbAJao2fX+wAWzPFneO1IZ5vuNf9D3OuYvHQptjxEZCUWwqNVoJCfvdjR48efdfPvXv3hqWlJZYuXYr33nsPXl5e/9lEZEg4UYColvx9vz2VSvWvt/+5z09OTg6+++47fPfdd/94PpVKhUNxOdgXfQM1OxchqroE06dPR0hICKytrfHdd9/h+++/v2+HqanpXfsKqVSqB9o7+K8fwE1MTO66LyUlBS4uLgCAW7du/eOxhYWF8PPzg0wmw6jW3pi3PRqnkgvQ1t8RwO2BkEwmQ1hY2J0vEgDAw8MDISEhuHr16j+eMzo6Gv369UPbtm3x888/37efiIjocf3XsV2SJCzcFQtrMyUaN3TE6cw4APc/tgOAVqvFgAEDkJeXp3fH9r8zM1Hgh+daYOCyElyHDP5BIQ90bFcqlWjRogUAoFOnTnB3d8fo0aPx+uuvo2XLlvd9L0RERKLVxTkAwLDGCfdyv8/2P37zBXo+OQzjRj6NcQAsLS2xePFiTJo0CQ0aNHig1yAiItJ1DzOOqKisxIv/O4ua0kIUnNuJEPe7f+evjzfEcYSDg8N/PvbP+//Kx8kSPo4WSMopxg9HkzGhs/9938MzzzyDJUuW4Ny5c5woQEaFEwWIBHJ0dES3bt3w5ptv/uM+tUbC9G3RsK3Ox5XkGKxduxbPPPPMnfvvtafQg0hNTYWvr+99f2/MmDF39j/6+6xBd3d3qFQqeHt7Izo6+q77qqqqkJSUdOeqgKeae2Dx7lisikq7M1HA3Nwcvr6+95zxp9Vq7xqM/Nnco0cP+Pr6YvPmzXcGPkRERKLsjb6Jc2m38G7vEESl/v8PxP91bP9z9ntSUhIuXryot8f2v3O0MsX/XuqARp+6ITWvDNlFlXCzNbtz/72O7X8XGRkJ4PZ/G04UICIiQ/XXcUJppRov/XIWztamWDI8AkrF7fGEoY0T/t5yv8/2HcK8MfCdZbiccB0/PxOEJqHBdyYctm/f/r6vQUREZGjySqtwKaMItnYO6Ne7h1GdbwAAf39/qFQqREdHY9CgQXduT0tLQ3l5OUJDQ+/5uAZ25sgtrsCSffHoFeYGHyfL/3wP/7VyAZEh40QBIoF69eqF8+fPo2nTpv/4gLz8cBKS83LwWht3TAXuur+kpARbtmx5pNd8lOWC/rzi7+/69++PDRs2YOnSpbCwsAAAbN68GVVVVejfvz8AwM5ChQHh7thyIRO5JVVwtr69xNGTTz6Jr7/+GllZWXB3dwcApKenIy4uDsOHD7/zGjdv3kSPHj1gY2ODXbt2wdra+pHeNxERUW3RaLVYvDsWbjZmGNfeB1Er/v99/3Vs/1N5eTkA/T2234u/sxWGDH4Sv638ESOX7cLv7w6Alanynsf2ezly5Mjt5/G//yx/IiIiffXXccKn+5NQbe+LT19qg9Z+jnd+xxDHCcDDfbaf0Mkf45ILcK3CDq0tLPD5558jKCgIXbt2ve/7ICIiMiTRWcUor9Lg7Q6+SLjRxyjPN6hUKvTo0QO//fYb3nvvvTurF6xduxYqlQo9e/b818f6OVuiAMCMLVew6oXW/zkJYO3atZDL5XcuZCAyFpwoQCTQ3Llz0bJlS3Tt2hWvvPIKPD09kZeXh8MnTmHN6Ux0GTkZkwZFYmnDhpg2bRrkcjm0Wi0WLVoEW1tb5OfnP/RrqlSqfz1YP6y3334bq1atwpAhQzB16lRkZWVh6tSpePrpp9G0adM7v1dyYg2Sli7GN00O4oMRXe56bL9+/fDBBx9AkiTMmTMHjo6OePnllwEAFRUV6N27NzIzM7Fy5UokJiYiMTHxzvM2a9bsnnsrERER1aWo5AIkVTpi8VNNYWaiuOu+fzu2nzt3DgqFAvPmzUNoaCga6vmxffbs2ZgzZw5SUlLg4+MDAFgy/wPs2rwex756CwNyEvBKFz/MnzfvrmN7UVERevfujdGjRyMwMBBarRbHjh3DZ599hr59+3I1ASIiMmh/jhPad+yM9AYd0TzEH5kXDmH6D4Y9TniYz/Zr165FUVERnIor8dHXUdi06DwOHjyI/fv337WsMRERkaHbcfkGom8Uw1ylwLS+ochoYrznGz744AN06NABzz33HF544QVcvXoVc+fOxZtvvnnXJIXc3FwcPnwYwO0tnzQaDToqEvH79iwsdwNe7t8GwO3JDGPHjkVQUBCqq6uxdetW/Pjjj3j11VfRsGHDWnm/RPqCEwWIBPL29saZM2fwwQcfYOrUqSgoKICLiwtMXAOgDOyGuU82hqmpKbZs2YLJkydj5MiRcHZ2xuTJk1FVVYWZM2cK7z9w4ACmTJmCgQMHwtraGqNGjcLChQvv+j03m9vLD2+9mIUZT0tQyGVo0KABDh06hClTpuDZZ5+FXC7HE088gY0bN945uN+8eRMXL14EgHteifjXwQIREVF92X0tG0HtmuGpSM9/3Pdvx/bIyEhMmjQJwO0P2vp+bL+XBg0a4MSxIxg69mUc/m4WTvykQJ+e3e86tpuZmSE0NBRLly5FZmYmTE1N4efnh4ULF2LChAl1/faIiIiE+nOc0G/MZOTu/x77d5bhshGMEx72s/0XX3yBhKQkaKCEbet2OHny5F1fIBARERm6KxlFmLr+ImzNTWBqZQqFXGbU5xtatWqFHTt2YNq0aejTpw+cnJzw7rvv4v3337/r965du4Zhw4bddVv03NcAAAtkNRjetTkcLFXw9/fHkiVLkJ2dDUmSEBISgi+++AKvvPJK7b1hIj0hk+61STgRCXM8MQ+jfojC2HY+mD0wTHROrVkdlYYZm69ixbiW6BLsIjqHiIjokSw/nISFu2Lx3bOR6BnmJjpHJ0mShOmbr2Lt6euY0iMIr3ULFJ1ERESkM+JvlqD30iMY0twTnwwLF52js6rVWnRc/AecrEyx/dUO3C+YiIiMRn5pFQZ8cQyVai22TmqPhg4WopP03rm0Ajz1zUkMae6BJcMjROcQ6RSu2UWkQ6rVWsz6/RqcrEzxZo8g0Tm1amC4OyxUCqw9fV10ChER0SMpqazBt4eTEN7QDj0auYrO0VkymQzzngxDtxAXLNkXj3Vn00UnERER6YzFu+OgVMgN7jN/bVMp5RjX3hfXsopxIunhl0omIiLSR2qNFpPXXEB2cSW+HNGMkwRqSaS3A0a38cKm85k4kZgnOodIp3CiAJEO+fl4ChJzSjGtTwhszU1E59QqazMTDAx3x/6YHNwsrhSdQ0RE9NB+OpaKW+U1eKtnEK9quw+lQo4vRjZDeEM7TNt0BYfjc0UnERERCXcmtQD7Y25iTFtveNiZi87ReSNbe8HKVInvjyaLTiEiIqoXi3bF4mRyPqb1CUW7ACfROQbl7V4hcLJSYdbv11Cj0YrOIdIZnChApCOu55dj6f4EtPC2x5DmHqJz6sTI1l7QaCWs55WFRESkZwrLq/HD0WS08nVAB35YfyAWKiV+HNMCnvbmmLjqHK5mFolOIiIiEkaSJCzaFQtrMyVe6RIgOkcv2JiZYGikJw7F5SIlr0x0DhERUZ36/VIWfjiWggHh7hjf0Vd0jsGxNTfBu71DkJBTipUnUkXnEOkMThQg0gFarYR3N16GRpKw6KkmBnuVYhMPW4S522Dt6XRotZLoHCIioge2/EgySqrUeLtXsMEep+uCk5UpVo5rBTMTBcb+fAbpBeWik4iIiITYH5ODc2m38HJnf9hbqkTn6I0x7XwAgCf0iYjIoCXlluK9jZcR4maNjwz4+wHRnmruieZedli6PwE5XPWYCAAnChDphDWnr+Nkcj7e6B6IABdr0Tl1RiaTYUQrL2QWVuAo9wIiIiI9kVNSiRXHU9E5yBktfRxE5+gdHydL/DimBUqrajDm59O4VVYtOomIiKheabQSFu+OhYu1KZ5vzysEH4avkyW6Bjtjw7kMlFapRecQERHVusoaDSatPg8A+GpUc1iolIKLDJdcLsPcJxujrFqNRbtiRecQ6QROFCASLONWORbujEFTT1u81NFPdE6dezLCHRYqBdZEpYlOISIieiBfH0xCRY0GU3sGiU7RW8287PHliOZIzSvDS7+cRZVaIzqJiIio3mw8n4GEnFK80T0I5iqF6By9M7a9L0qr1Nh4LkN0ChERUa37cEcMYrNLMO/JxvB3thKdY/Aae9hiZCsvbLqQiTOpBaJziITjRAEigdQaLd787SKqNVosHtoUSoXh/5W0NjPBwHB37I/J4fI+RESk87IKK7Am6jp6hbmiqaed6By91r2RK+YMDMOZ1FuYueUqJInbEBERkeGrrNHgs33x8HOyxPAWnqJz9FLHACf4OVti5YlUbmNIREQGZc+1bPxyKg1DmnvgqUiOE+rLWz2DYWdhgg+2XoOGYwsycob/rSSRDvv8QALOpN7CtD6hCHGzEZ1Tb0a08oJGK2E9rwYgIiId98UfCajRajG1Z7DoFIPwbFsfjGrthXVnM/DT8VTROURERHXufydTcaOoEm/3CjaKiwPqglwuw5i2PkjOK8ORhFzROURERLUir7QK0zddgbejBeY92Vh0jlGxt1Th7V7BiLlRzJWPyejxEwqRIIficvDFwUR0C3HBuPY+onPqVVNPWzRqYIO1p6/zagAiItJZqXllWHc2A0+GuyPI1Vp0jsGYPTAMbfwc8OGOaByO58l+IiIyXEUVNfjqYBLCG9qhd2M30Tl67alIT1iZKrHyRKroFCIioscmSRJmbL6CgvJqfDosHJamStFJRueZll4Ic7fBkn3xKCqvEZ1DJAwnChAJkJJXhtfWXoCnvTk+GRYOmUwmOqleyWQyjGjthYxbFTiamCc6h4iI6J6W7o8HALzRPUhwiWExUcjx9ahIeNibY/Ka80jKLRWdREREVCeWH05CUUUN3u0dbHSf+2ublakSw1p44mBcLlLzykTnEBERPZYtFzOx59pNvNTJDy18HETnGCWFXIaZ/RvhVnkNvvgjQXQOkTCcKEBUz26VVWP8yjOo0Uj4/rkWsLdUiU4S4skId5ibKLA26rroFCIion+Iv1mCrZeyMCzSEz5OlqJzDI6DpQo/jmkJrVbCiyvPoriSs/eJiMiw3CyuxE/HU9A5yBnt/J1E5xiE0W28AQCruUQwERHpsZySSszaeg3BrtaY0oMXJojUxs8RvcJcsfJkKlI4EZGMFCcKENWj8mo1nl95Bil5ZVj2TARC3GxEJwljY2aCgeHu2B9zEznFlaJziIiI7rJkbzxM5HK82i1QdIrBCnK1xtJnmiE5rwzvbrgMSeJ2REREZDiW7k9AZY0W7/QOFp1iMPydrdAhwAnrzmagskYjOoeIiOiRzNseg9IqNRYPbQpTpUJ0jtGb3jcUALBgZ4zgEiIxOFGAqJ7UaLSYvOYCLlwvxIeDm6BnGPcnHNHaC2qthPXnMkSnEBER3XElowi7r2VjZGsveNiZi84xaD0aueLlzv7YdTUbPx1PFZ1DRERUK+KyS/DbmesY3MwDYe62onMMyrNtvVFUUYPfL2WJTiEiInpoh+JysO1SFp5r64PwhnaicwiAt6MlxrX3xb7omziRxG2SyfhwogBRPVBrtHjjt4v4IzYHU3sEYUQrL9FJOiHc0xahDWyw9vR1aLS8ipCIiHTDp/viYGYixytd/UWnGIW3egahla8DFu6Mwbm0W6JziIiIHtvCXTEwUcjxVi+uJlDbuoW4oIGtGVad4vYDRESkXyqqNXh/y1W42ZhxjKBjJj8RAAdLFeZtj+H3FGR0OFGAqI7VaLR4/deL2HH5BiZ08sPkJwJEJ+kMmUyG0W28kHGrAgdjc0TnEBER4WxqAQ7F5WJsO1+4WJuJzjEKSoUcX45oBjsLFSavOY+CsmrRSURERI/saEIuDsXl4oUOvlyZqA4oFXKMbOWFyxlFuJReKDqHiIjogS07kICMWxWY82QYrEyVonPoL2zMTDClRxBibhRj/dl00TlE9YoTBYjqUJVag9fWXsCOKzfwcmd/vNcnBDKZTHSWThnczAM2ZkqsOJEqOoWIiIycJEn4eE8crE2VmNDJT3SOUXGxMcPnIyJws7gSr/96gTP4iYhIL2m0Ej7cEQMnKxUmduHKRHXl6VYNYaKQ4ReuKkBERHoiNrsY3x9NRs9GrujFLYl10jMtGyLI1Qqf7I1HaZVadA5RveFEAaI6UlalxgsrzmLX1WxM6uqPd3sHc5LAPViolHimlReOJeYh4WaJ6BwiIjJixxLzEJVSgBc6+sLeUiU6x+i083fC1J7BOJqQh++PJovOISIiemgbz2UgNrsEb3QPgrWZiegcg+VibYbejRtg26Us3OJKREREpOMkScKc36OhUsgxe2CY6Bz6F0qFHO/3a4S80ip8fTBRdA5RveFEAaI6UFBWjZHfn8KxxDxM7xuCt3txJYH/8mwbb8hlwM9cVYCIiATRaiUs3BkLB0sVXujgKzrHaE3s7I82fg5YsjceMTeKRecQERE9sLIqNT7ZG4cAFys807Kh6ByD92wbb1SptVh/jssDExGRbtsbfRMnk/MxsYs/3LktkU7rFOSMrsHO+OFYCtILykXnENULThQgqmVZhRUY9u0JXM0qxsdDm+KlTlxu8H4aOligRyNXbDqfgaLyGtE5RERkhLZeykT0jWK89kQArwAUSC6X4ZNh4VAp5Xjzt4uoUmtEJxERET2Q744kI6ekCtP7hkCp4Om2utbSxx7BrtZYdeo6tNyyiIiIdFSVWoMFO2PgbmuGFztyi0N9MKNfI2i0EhbtjhWdQlQv+MmFqBal5pVh2LcnkX6rAt+OjsSwFryK4EGNa++Lyhot1p65LjqFiIiMTGWNBp/siYe3owVGtvYWnWP0PO0tMGtAI8Rml2DZ/gTROURERPeVWViB744ko32AI7oGu4jOMQoymQyj23rjekE5jiTkis4hIiK6pxXHU5GWX473+obCXKUQnUMPIMDFCs+28caOyzdwNrVAdA5RneNEAaJaEpddgmHLT+JWeTVWjG2JHo1cRSfplda+DmjsYYMfj6WgsoZXDxIRUf355WQaMgsr8E6vEKiUHB7rgqGRnujRyBXfHk7CuTR+MCciIt22YEcMqjVafNA/jNsO1qPBzTxgZarELyfTRKcQERH9w62yanz5RyKae9lhQNMGonPoIbzeLRC25iaYuz2aKxeRweOZUKJacDmjEE9/dxJVNRqsGt8a7QKcRCfpHZlMhkldApBbUoUN5zJE5xARkZEoKq/BlwcTEd7QDn2buInOof8jk8mwcEgT2Fuo8Pb6y9yCgIiIdNaxhDzsuHIDY9r6INjNWnSOUbEyVWJIcw/8EZfDfYSJiEjnfHM4CSVVakzvG8qJhHrG3lKF17oF4nJGEX6/lCU6h6hOcaIA0WO6llWE0T9EQSGTYe1LbdDcy150kt7qFeYGf2dLfHs4CTUaregcIiIyAl8fSkRRRQ2m9QnhB3cd42Rlig8GNEJyXhm+PpgkOoeIiOgfqtVazPr9KpysTPFGj0DROUZpdBtvSBKw5jS3MSQiIt1xo6gCK06koluIC1r4OIjOoUfwbBtveDta4OM9cVwBmQwaJwoQPYaEmyV49sfTUMhlWPNiG4S524pO0mtyuQyvdAlAxq0KbONMPSIiqmOZhRX4+f8+uLfxcxSdQ/cwMNwdnYKc8c2hJCTmlIrOISIiustPx1OQlFuGaX1CYGNmIjrHKAW5WqO1rwN+O5POFYiIiEhnfH4gATUaLd7qFSw6hR6RSinHO71CkFlYgf+dTBWdQ1RnOFGA6BGlF5Rj1A9RqNFo8csLrbnEYC0ZGOEOT3tzfH0oifv/EBFRnfp0bxzUGi3e7RMiOoX+hUwmw/wnG0MuB6ZvvgJJ4tiAiIh0w/X8cizdH49WPg4Y3MxDdI5Re7atNwrKqrHzyg3RKUREREjOLcW6sxkYFOGB0AY2onPoMfRt4oaIhnb48o9EFJZXi84hqhOcKED0CIoqajD259MorqzBinGt0NiDKwnUFhOFHC939kdiTim2XMwUnUNERAYqOqsYmy9kYniLhghy5WQ/XeblaIHXuwXhdEoB1p/NEJ1DREQESZIwY8sVaLXAgiGNIZdz+yKReoW5wdnaFL+cTBOdQkREhGUHEiAD8Gb3INEp9JhkMhlm9AtFcaUaX/6RKDqHqE5wogDRQ6rRaPHK6nNIzivDsmeaIdLbXnSSwXm6ZUP4OlniE+7/Q0REdWTR7liYKuV4swc/uOuD8R19EeJmjQ93xqCgjLP4iYhIrK0Xs3A0IQ+vdPVHgAsnHIpmopBjRCsvnL9eiKuZRaJziIjIiCXllmLbpSwMjfSEl6OF6ByqBS19HNCzkSv+dzIN6QXlonOIah0nChA9pA+2XsPxxHxM7xOKXmFuonMMkolCjnd7ByOrqBIrTqSKziEiIgNzJD4XR+JzMb6DH1xtzETn0AMwUcjx4eDGKKqowZJ9caJziIjIiN0qq8bc7dHwd7bExC7+onPo/4xo1RAKuQyro7iqABERifPlH4mQyWR4pUuA6BSqRe/2CYFGkvDxHp6PIMPDiQJED2H92XSsPX0dz7RsiPEdfUXnGLReYW6I9LbHVwcTcYtXDhIRUS1Ra7SYtz0aTlYqTOjsJzqHHkKktwMGRbhjTdR1xNwoFp1DRERGav6O26vbLBzSFKZKhegc+j8NbM3RI9QVWy5koaiiRnQOEREZoZS8Mmy9mIkhzTy4moCB8Xe2wohWDfH7pSxczigUnUNUqzhRgOgBxWYXY+bWq2jsYYPZA8Mgk3EPwrokk8kwvW8ISirVWLo/XnQOEREZiNVR15GQU4p3eoXA2sxEdA49pHf7hMBUqcDcbdGQJEl0DhERGZl90Tex8XwGRrb2QitfB9E59DfPtfVGRY0GG89liE4hIiIj9OdqApOf4GoChuj1bkGwVCnw4Y4Yno8gg8KJAkQPoLRKjVdWnYeJQo6vR0bCzIRXDdSHSG8H9G/aAL+cSuNMPSIiemy3yqqxZF88GnvYYGikp+gcegQNbM3xShd/nEzOx55r2aJziIjIiOSXVmHapsvwcrDA9L6honPoHtr6O8LP2RKrTqXxBD4REdWrtPwybLmYiUERHvB2tBSdQ3XA2doUEzr7IyqlAH/E5ojOIao1nChAdB+SJOG9jZeRnFeGT4aFc9mgevbBgEawNFXivY1XoNZoRecQEZEeW7o/HkUVNZg1IAxyOVcG0lcvdvKDh5055u+IQWWNRnQOEREZAUmSMG3TFRSUVWPJ8HBYmSpFJ9E9yGQyPNvGG8l5ZTiRlC86h4iIjMiXfyRCkiSuJmDgxnf0hYu1KRbuiuV3FWQwOFGA6D7WnU3H9ss38GJHX/QKcxOdY3RcrM0wrU8oom8U4+tDSaJziIhIT8XfLMGqqOvo37QBWvpwqWB9ZmaiwIx+oci4VYEfj6WIziEiIiOw/lwG9kbfxITO/mjBcYROG9LcE+YmCvzvZKroFCIiMhI3iiqw+UImBoa7w9eJqwkYMguVElN7BiExpxTrznKrIzIMnChA9B/SC8oxd1s0GnvY4J3eIaJzjNYzLRuiU5Azlh1IwLm0AtE5RESkZyRJwrzt0VDKZZjGpYINQp/GbmjpY49vDiUhv7RKdA4RERmwP88LhDawwZvdg0Tn0H3YmptgUDMP7Iu+iRtFFaJziIjICKw4kQq1VsJLnfxFp1A9GBrZEEGuVvhsfzzKqtSic4geGycKEP0LrVbC1PWXUKOVsGR4BEwU/Osiilwuw6fDwmFvYYLX1l7ErbJq0UlERKRH9sfk4GhCHiZ09oeHnbnoHKoFMtntSR+lVWp88Uei6BwiIjJQmv87L1Ct1mLp0xFQKXleQB+MbuMFrQSsjbouOoWIiAxcaZUaa6Kuo0OAExq524jOoXqgkMswrU8ockuq8P3RZNE5RI+Nn3CI/sVPx1NwOqUAb/cMRpCrtegco+dsbYolwyNwo6gCE1efQw33ACIiogdQpdbgwx3RaGBrhpc7+4nOoVrU3MsefZu4YdWpNKTmlYnOISIiA/TjseTb5wV6BSPYjecF9EWYuy0ive2x9kw6qtU8d0BERHXntzPpKKlU48VOPN9gTLoEO6OdvyO+O5KMnJJK0TlEj4UTBYjuITWvDB/viUMrHwc838FXdA79n05BzpjeNxSnkgvw/uarkCRJdBIREem4n46lIjW/HO/1CYGFSik6h2rZ271ubw318d44wSVERGRoYrOL8cmeeLT2dcALPC+gd55t443ckirsjc4WnUJERAZKrdHip2MpCHa1RqdAJ9E5VI9ksturCpRXa7B0f4LoHKLHwokCRH8jSRJmbLkCCcCip5pAIZeJTqK/eKGDL0a08sJvZ9PxwdZrnCxARET/KquwAl/8kYCWPvYYGO4uOofqgK+TJUa19sKOyzdw4fot0TlERGQgqtQavPnbJaiUcnw6PBxynhfQO32auMHRUoX/nUwTnUJERAZq19VsZBZW4IWOvpDJOFYwNk08bTEw3B2/nUlHClc5JD3GiQJEf7PhXAaOJ+bjtScC4OdsJTqH/kYmk+HDQY0xNNITv5xKwzsbLnMbAiIiuqcPd8SgSq3F3Ccb80O7AXu1WyCsTJVYuDOWEwiJiKhWLNkbj5gbxZg9MAye9haic+gRmCoVeLplQ5xOKUBcdonoHCIiMjCSJOGHo8lwtjbFkxG8MMFYTekRBBmAJfviRacQPTJOFCD6i7zSKny4MwbBrtZ4qZO/6Bz6F3K5DB891RSj23hh/bkMjPv5DPJLq0RnERGRDjmakIsdV25gTFsfhDawEZ1DdcjJyhQTOvnhdGoBDsXlis4hIiI9dyIpD98dTUbfJm54qrmH6Bx6DCNbe0EmA1ad4qoCRERUu06nFOBSRhHGtvOBqVIhOocE8XGyxNMtG2LbpSxcyyoSnUP0SDhRgOgv5m2PRlFFDRY+1QQqJf966DKFXIZ5TzbG9L4hOJGUh15Lj2DPtWxeSUhERKhSazBr6zU4W5vijR6BonOoHjzfwRdOVios3hMHrZZjASIiejRFFTV4a90luFib4sNBTbgikZ7ztLdAtxAXbDqfgdIqtegcIiIyIN8fTYG5iQKjWnuJTiHBXusWCDMTOT7ZEyc6heiR8JtQov9zMC4HWy9m4bk23mjuZS86hx6ATCbDS538sW5CW1iolJjwyzk899NpLitIRGTkfjyWguS8MszoGwobMxPROVQPLE2VmNQ1ADE3irH9yg3ROUREpKc+2HoVWUWV+GRYOOwtVaJzqBY829YHZdUabD6fITqFiIgMRFJuKfbH3MTwFp6ws+B4wdi52phhTDsfHIzLxZnUAtE5RA+NEwWIAJRXq/H+5qtoYGuGt3uHiM6hh9TCxwG73+iI17oF4kxqAfosO4JXVp/D8cQ8rjBARGRkMgsr8MWBRLTydeA+gUZmZGsveNiZY8neONRotKJziIhIz2y9mImtF7PwfHtfdAx0Fp1DtaRjgBO8HS3wy6k0nh8gIqJa8eOxFMhlt1e2IwKAiZ39YW2mxOLdsRxvkN7hRAEiAN8eSkJmYQVmDwyDlalSdA49AguVElN6BOGPqV0wvEVD/BGbg1E/RKHLJ4ewaFcsLqUX8iBNRGQE5m+PRrVGi3lPNuZywUbGVKnAG90DkZpfjvVnedUgERE9uMzCCry/5SqCXK3wTu9g0TlUi+RyGUa39kb8zVKcTuFVfkRE9HjyS6uw8VwGeoW5wdvRUnQO6Qg7CxUmdPLDmdRbOBSXKzqH6KFwogAZvfSCcnx7JBkdA53Qs5Gr6Bx6TO525lj0VFNETe+OWQMawc7cBN8eTsKTXx1H+0V/YPbv13AqOR8a7l9MRGRwDsfnYtfVbIxr54NgN2vROSTAkOaeCHCxwucHElBZoxGdQ0REekCjlTDlt4uoqtFi6dPNYGaiEJ1EtWxopCdMlXL8cipNdAoREem5X06loUqtxYud/ESnkI4Z194XTlYqLN4TBy2/eyA9wokCZPQ+3BEDrVbCrAGNeOWhAbE1N8G49r7YOrkDjr/3BD7o3wieDhZYeTIVz3x3Cq0+3I9pmy7jUFwOqtVcnpiISN9VqTWY/fs1uFib4vXugaJzSBCFXIa3egYhu7gSv5zklwFERHR/PxxNRlRKAd7qFYRG7jaic6gO2FuqMCDcHbuvZiOnuFJ0DhER6anKGg1+OZmGSG97NPeyF51DOsbSVInJXQMQc6MY26/cEJ1D9MA4UYCM2vHEPOy+lo0x7XwQ4MIrDw2Vh505nu/gi3UT2uL09O5YMLgJwjxssf5sBsb+fAaR8/dh9u/XkJxbKjqViIge0fLDyUjJK8OMfqGwNjMRnUMC9QpzQ7inLb4+lIiSyhrROUREpMOis4rxyd44tPFzwPgOvDLQkD3X1htqrYRVUddFpxARkZ7adD4T+WXVeLGjr+gU0lEjWnvBw84cS/bGoUbDixNJP3CiABkttUaLOduuwclKxSsPjYiztSlGtvbC/55vhXPv98BnT4cj0tseK06k4olPD2P8yrNIuFkiOpOIiB5Ccm4pvjyYiPYBjhgY7i46hwSTyWR4p3cIbpXX4PujKaJziIhIR1WrtZiy7iLMTBT4dHgE5HKuMGjImnraoaWPPVafSuP2RERE9NC0Wgk/HEuGt6MFejRyE51DOspUqcCbPYKQml+O9WczROcQPRBOFCCjtepUGuJvluLtXsGw4ZWHRsnWwgSDm3lixbhWOPhWF4xt54MjCbnovewo3t9yBbfKqkUnEhHRfUiShOmbr0AG4MNBTbiNEAEA2gc4oX2AI348moz80irROUREpIO+PJiI2OwSfNC/ETzszEXnUD14oYMf8suqsflCpugUIiLSM3/E5iA5twwvdPCFgpML6T8MbuaBQBcrfH4ggZMTSS9wogAZpfzSKizZF4+mnrYYFtlQdA7pAF8nS8weGIYDUzqjX5MGWHXqOvosO4oTiXmi04iI6D+sP5eBU8kFeK1bIHycLEXnkA55u1cIyqo1+OpgkugUIiLSMdeyivD1wUR0CXbG0EhP0TlUT3o0coWXgwV+PJYCrVYSnUNERHrk+6PJsLMw4biB7kshl2FqzyBkF1di1ak00TlE98WJAmSUPt0Xj+JKNWYNCOPygnSXhg4W+HxEM6x5sTUAYNSPUfjhaDIkiScRiIh0TX5pFRbsjEGwqzVe6sR9heluEQ3t0CvMFatOpSGzsEJ0DhER6YgajRZvr78MMxMFFgzmakTGRCGX4fn2PkjMKcXhhFzROUREpCcuZxQiKqUAo1t7w0KlFJ1DeqBXmBuaeNji60NJKK1Si84h+k+cKEBG52pmEdaevo4hzTwQ6W0vOod0VDt/J+x6vSPa+Dpi/o4YzNkWzckCREQ6Zv6OGBRV1GDBkCYwUXBYS//0Vs9gqLVaLNsfLzqFiIh0xLeHkhB9oxgz+oXCnVsOGJ1hLRrC2kyJH4+miE4hIiI98f3RFKgUcjzXzlt0CukJmUyGt3oFo6CsGj8f45iDdBvPqJJRkSQJc7Zdg7mJAu/2CRGdQzrO3lKFlc+3wpBmHlhxIhXztsdwsgARkY44mpCLzRcyMaq1Fyf+0b8KdLXG4Gae2HAuA4k5paJziIhIsLjsEnz+RwI6BDjhmZbchtAYWZoqMbK1F44l5iHmRrHoHCIi0nEZt8qx88oNDGrmDhdrM9E5pEc6BTqhlY8DvjuSjMLyatE5RP+KEwXIqOy+mo0zqbcw+YkAuNrwwE73p1LK8fGwcAyKcMdPx1Ow7ECC6CQiIqNXWaPB+1uuwsXaFO/05sQ/+m9vdA+EUi7Hkn1xolOIiEggtUaLdzZcgolCjoVDuOWAMRvbzgdKuQw/8go/IiK6j5+Pp0KjlTC+I7c7pIfz56oCJVVqLD+SLDqH6F9xogAZjWq1Fh/tjoW7rRmeb+8rOof0iEIuwyfDwtEtxAVL9ydgx+UbopOIiIzasgMJSMsvx5yBYbAxMxGdQzquoYMFRrb2ws4r2bicUSg6h4iIBPnhWAouZRRhWp8QNHSwEJ1DAjWwNUf/pg2w9WImcoorRecQEZGOKqqowa+nr6NLsDOCXK1F55AeauXrgM5BzlhxPBU5JRxzkG7iRAEyGmtPX0dqfjne6hUMMxOF6BzSM0qFHMtGNEOQqxWmrr/IJQqJiASJzirGd0eS0T3UFb0bu4nOIT0xqWsALFQKfLyHqwoQERmjxJxSLNkXjzZ+DhjVmvsLE/BCBz/UaCT8cipNdAoREemoX09fR1m1Bi9xNQF6DG/1DEZFjQZfH0wSnUJ0T5woQEahuLIGyw4kIMzdBoMiPETnkJ6yMlXih+dawkQhx6trL6CiWiM6iYjIqKg1Wry36TLMTRSYNyiMSwbTA3O2NsULHXxxNCEPJ5LyROcQEVE90molTNt0GQqZDB891RRyOccPBDTxtEVrXwesOpXGz/ZERPQPNRotVpxIRaMGNmjr7yg6h/RYE09b9A5zw5qo68gsrBCdQ/QPnChARuHbQ0koKKvG9L6hPClAj8XL0QILBjdBYk4p5u2IFp1DRGRUVpxIxeWMIrzbJwQNbM1F55CeebGTH+wsTLB4dxwkSRKdQ0RE9WTDuQycSb2FN3sEwtvRUnQO6ZDxHf1wq7wGG85niE4hIiIds/1yFm4UVeLFTr68SIEe29SeQajRavH5/gTRKUT/wIkCZPBuFFXgx2Mp6BzkjPYBTqJzyAAMCHfHsEhPrIm6juOJvCqRiKg+pBeU49O98WjhbY9RrbxE55AesjEzwcTO/riYXoi90TdF5xARUT0oKKvGgl0xCHGzxrj2vqJzSMd0C3GBn7MlvjuSBLVGKzqHiIh0hCRJWH44GQ1szdC/qbvoHDIAga7WGBzhgQ3nM5CcWyo6h+gunChABu/TvfGo0WgxrW+I6BQyIDMHNIKLtSmmb76CyhouU0hEVJckScL0zVeg0UpYOKQJVweiRzamnQ9cbUzxyZ44aLRcVYCIyNAt3BmDwvIafDi4MUwUPAVGd5PLZXi5sz/SCyqw48oN0TlERKQjjiXmITa7BM+39+X4gWrNG92DIAPwGVcVIB3Df+XIoMVmF2Pj+QwMjfREiJuN6BwyIDZmJpj7ZBjS8sux7AAP7kREdWnLxUwcTcjDK139EehqLTqH9JiZiQKvdwtCQk4ptlzIFJ1DRER1KCo5H+vPZWBEq4aI9HYQnUM6alCEBxrYmuGbQ0ncmoiIiAAA3x1JhrWpEs+0aig6hQyIl6MFnm7ZENsuZSE6q1h0DtEdnChABu3TvfEwUcgxpUew6BQyQL0bN0D3UFf8cDQZKXllonOIiAxSfmkV5m6LRqCLFSZ28RedQwZgWAtP+DhaYMm+eFSpuSoQEZEhqlZrMWPLVThaqvBub64uSP9OpZRjfEc/xGaX4GBcjugcIiISLDqrGEcT8jCytReszUxE55CBefWJQJgq5ViyL050CtEdnChAButyRiH2Rd/E6NbecLM1E51DBmpm/1DIIMP87dGiU4iIDNK87dEorKjBoqeawFSpEJ1DBsBEIcfUnsHILKzA2qjronOIiKgOfH80GYk5pZjRLxR2FirROaTjRrRqCHsLE3x9MEl0ChERCfbD0WQo5TKMbe8jOoUMkJutGZ5t4439MTk4f/2W6BwiAJwoQAbss33xMDdR8OpDqlPejpZ4oaMvDsTm4HB8rugcIiKDciguB1suZuHZNt5cMphqVb8mDRDmboMv/khESWWN6BwiIqpF1/PL8fmBBLTxc8DgZh6ic0gPWKiUGNvOF2fTbuFMaoHoHCIiEuRGUQV+v5SFgRHuaGBrLjqHDNTELv6wVCnwyR6uKkC6gRMFyCCdS7uFg3G5eK6dN5ytTUXnkIGb1DUAjpYqfLwnlnsaEhHVkrIqNWZsvooGtmZ4uxe3EKLaJZfLMK1PKPLLqvHtYV49SERkKCRJwge/X4VWkjB/UBPIZDLRSaQnxrTzhoVKga8PJopOISIiQX4+ngq1VsKLHf1Ep5ABc7QyxQsdfHEiKR8nEvNE5xBxogAZps/2xcNSpcCETlxNgOqelakSE7v442pmMXZfzRadQ0RkEJbsi0dmYQXmPdmY+wJSnegQ6ITOQc744WgKbhRViM4hIqJasPtqNg7F5eLlzv4IcLESnUN6xM5ChZGtvHAwLhfRWcWic4iIqJ4VV9ZgTdR1dAx0QmgDG9E5ZODGd/KDrbkJPt4bxwsPSThOFCCDE5Wcj2OJeRjX3hcOltyLkOrH6DbeaGBrhk/3xUOj5cGdiOhxXEovxM/HU9CvaQN0b+QqOocM2LS+IajRaPHp3njRKURE9JhKq9SYve0avBwsMKlrgOgc0kPjO/rBRCHjakNEREbot9PpKK1S46VOXE2A6p6NmQle7uyPC9cLcSAmR3QOGTlOFCCDIkkSPt0XD2szJZcIonplZqLAq08EIjGnFFsuZIrOISLSWzUaLd7deBlWpkrMGtBIdA4ZuBA3GwyN9MTG8xm8epCISM99ujcON4urMG9QY5iZKETnkB5yszXDkGae2H45C2n5ZaJziIiontRotPjpeApCG9igQ4CT6BwyEmPaecPJyhSf7I2DlhcekkCcKEAG5XhiPk6nFGB8Bz/YWnCZYqpfw1p4wtvRAksPxKNGoxWdQ0Skl747kozY7BK8368RXKzNROeQEZjSIximSjkW7ooRnUJERI/oamYRVp5IRf+mDdA5yFl0DumxCZ39IAFYfiRZdAoREdWTzRcycaOoEhM6+UEmk4nOISNhoVJicld/xGaXYMeVG6JzyIhxogAZjNurCcTBzsIEz3fwEZ1DRshEIcfkrgFIL6jAjss8uBMRPazk3FIsO5CAdv6OGNbCU3QOGQk3WzOM7+CHowl5OBKfKzqHiIgekkYrYcbmK7BUKTGzP1cjosfj52yFvo0bYMPZDOQUV4rOISKiOqbRSvj2UBIaOpijf9MGonPIyIxo7QUPO3N8ti8eal54SIJwogAZjEPxubhwvRAvdfKDtRlXEyAxnozwgLutGb45lARJ4pJBREQPSpIkzNh8FTIACwY34Sx+qlcTOvvB0VKFBTtjoOGSf0REemXVqTRcyijC1J5BcLXhakT0+CZ28Ue1Rosfj6eITiEiojq26+oNJOeV4eXO/lAq+HUZ1S9TpQKvdwtEcl4ZNp7PEJ1DRor/8pFBkCQJy/YnwN7CBGPa+ojOISOmUsoxvqMf4m6W4GBcjugcIiK9seViJk4m5+O1boHwcbIUnUNGxtrMBG90D0Rsdgk28cM5EZHeyC6qxMd74hDe0A7P8lwA1ZLGHrboGOiE1aeuo6i8RnQOERHVEUmS8NXBJLhYm2JoJFc1JDGGNPeAn7MlluyLR0W1RnQOGSFOFCCDcCIpHxfTCzG+ox8sTZWic8jIPdOqIewsTPDNoSTRKUREeqGovAYf7oiBv7MlXuzoJzqHjNQzrbzg52SJT/bGoaxKLTqHiIgewKzfr6KiRoOFg5tAIedqRFR7JnUNQGmVGitOpIpOISKiOnIoLhcxN4rxUic/mCoVonPISCkVckzrE4qbxVX4/miy6BwyQpwoQAbh8wMJsDFT4tm23qJTiGChUmJsOx+cSb2FM6kFonOIiHTex3tjkVdajXmDGkOl5PCUxDBRyDGj3+0P518fShSdQ0RE97H3Wjb2XLuJ8R180cjdRnQOGZjWvg5o5eOAH48lo7iSqwoQERkaSZLw5cFE2FuYYEQrL9E5ZOS6h7qgta8Dvj2chJySStE5ZGR4Jpb03umUAkSlFGBse1/YmJmIziECAIxp6wMLlYKrChAR3cfF9EKsjrqOwc080M7fSXQOGbknQlzQJdgZ3x9NwfX8ctE5RET0L0qr1Jj1+zV42pvj9e6BonPIAMlkMrzePRDFlWr8j6sKEBEZnKiUApxLu4Vx7X25QjEJJ5PJ8H6/Riiv1uCzffGic8jIcKIA6b0vDybCUqXA8+19RKcQ3WFvqcKIVl74IzYHMTeKRecQEekktUaLGZuvwNpUiel9Q0XnEEEmk2Fm/0bQaiXM3xEtOoeIiP7FJ3vicKOoEvMHNYaFiif3qW6083dEpLc9fjiWglJuS0REZFCW7o+HtakSY9r6iE4hAgA08bTF4GYe+O1MOuKyS0TnkBHhRAHSaxfTC3EkPhfPtvWBnYVKdA7RXV7o4AuFXIafj6eITiEi0km/nErDtaxivNM7BM7WpqJziAAA/s5WGNfeB3ujb+JoQq7oHCIi+ptL6YVYeTIVA8Ld0SXYRXQOGTCZTIbXuwWisLwGK7mqABGRwTiRlIdTyQV4voMvbC24QjHpjrd6BcNEIceCnTGiU8iIcKIA6bUv/0iEmYkc4zv6ik4h+gd3O3P0buyGLRezkF9aJTqHiEin3CyuxKd74xHe0A4juR8g6ZhXuwXCyUqFOduiUaPRis4hIqL/o9ZoMW3T7dWIPujfSHQOGYGOgU5o5mWHH44mo4yrChAR6T1JkvDZvnjYmCnxfAd+p0C6xcPOHC908MXh+FwcieeFC1Q/OFGA9FZ0VjH2x9zEyFbecLLiVYikm55v74NqtRa/nkkXnUJEpFPmbY9GebUaHw5qDLlcJjqH6C42ZiZ4p1cIEnNK8cvJNNE5RET0f346noLoG8WY1jeUqxFRvZDJZHitWyBuldfgl1McExAR6btjiXk4k3oLL3b0g605VxMg3TOxiz8cLVX4cEcM1LxwgeoBJwqQ3vrqYCJUCjle6uQnOoXoXzX3skcTD1v8cjKNVyQSEf2f0ykF2H75Bp5t443GHraic4juaWikJ5p62uKz/fHILeHKQEREoqUXlOOzfQlo6WOPp1s0FJ1DRqRLkDPCPW3x/ZFklFdzVQEiIn0lSRKW7IuHnYUJxrb3EZ1DdE/WZiZ4q1cw4m6WcJIi1QtOFCC9lJhTgp1Xb2BYC0+42ZqJziH6VzKZDOPa+yC7uBK7r2aLziEiEk6rlTB3+zXYmpvgzR5BonOI/pVcLsOcgWEorVJjIfcHJCISSpIkzNx6FWqtFguHNOFqRFSvZDIZXu8eiPyyaq40RESkxw7F5eLC9UK81MkP1mZcTYB01/AWDdHU0xZL9vLCBap7nChAeumrg0lQyGR4ubO/6BSi++rXtAGcrEzx8/EU0SlERMJtOJeBq5nFeLN7IOwsVKJziP5TMy97jGjlhU0XMnEqOV90DhGR0dp++QYOxeViYmd/BLhYi84hI9Q12AXhnrb49nASiitrROcQEdFD+nM1AQdLFca09RGdQ/SfFHIZ5j7ZGKXVany0O1Z0Dhk4ThQgvZOWX4atFzMxuJkHGjpYiM4hui9TpQKjWnvh/PVCXEovFJ1DRCRMaZUai/fEIcDFCqPaeIvOIXog7/YKgaOlCu9vuYpqNbcRIiKqb0XlNZizLRp+TpZ4pWuA6BwyUjKZDO/0DsGt8hr8cCRZdA4RET2k/TE5uJJZhJc7+8HSVCk6h+i+Ihra4ekWDbHhXAbOpd0SnUMGjBMFSO98fTAJAHiCgPTKqDZeMFHIsOJEqugUIiJhvjqYiLzSKszs3wgmCg5DST/YWphget9QJOaU4odj/GKAiKi+Ldodi7zSKswf3BhmJgrROWTE2gc4oX2AI344loK8Ui4DTESkLzRaCZ/ujYOTlSmebeMjOofogb3TOwS25ib4YOtVaLSS6BwyUDxDS3olvaAcG89nYGC4O3ydLEXnED0wF2sz9G/qju2Xs5BTUik6h4io3l3PL8ePR1PwRIgLOgc5i84heihDmnugta8DPj+QgPSCctE5RERG40xqAdaevo6hkZ5o5+8kOocIb/cKQXm1Bl8dTBSdQkRED2jLhUzEZpfg9W4BMFdx0iHpDwdLFd7qFYxrWcVYyQsQqY5wogDplW8PJ0EjSZj8BFcTIP0zrr0PajQSVp+6LjqFiKjeLdwVA60kYUa/UNEpRA9NJpNh/qDG0GglzNx6FZLEmfxERHWtWq3F9E1X4GCpwoy+HD+QbohoaIdeYa5Yfeo6Mm5x8iARka6rrNFgyb54+Dha4JlWXqJziB7ayFZeaO5lh4/3xPHCBaoTnChAeuNGUQXWn81A3yYNEOBiLTqH6KE19bRDpLc9VkddR5VaIzqHiKjenErOx66r2XiurQ/8na1E5xA9kkBXa0zsEoBDcbnYcjFTdA4RkcFbfjgJCTmleL9fKOwtVaJziO54q2cw1Fotlu1PEJ1CRET38b+TqcgsrMDbvUK4BSLpJYVcho+eagqNVsL0zVd44QLVOv7LSHpj+eFkVGu0eJWrCZAeG9vOB3mlVdh55YboFCKieqHRSpizLRr2FiZ4vVug6ByixzKpqz8CXKwwd1s09yYmIqpDybml+OJgItoHOGJwMw/ROUR3CXS1xuBmnth4PgOJOSWic4iI6F8Uldfgq4NJCG9oh75N3ETnED2yQFdrTOoagKMJedh4nhcuUO3iRAHSCznFlVhz+jp6hbkixM1GdA7RI+vd2A2uNqb4+XgqZ/8RkVFYdzYdMTeKMaVHEGwtTETnED0WU6UCHz3VFIUVNZizLVp0DhGRQZIkCTM2X4UMwIeDmkAmk4lOIvqHN7oHQiGX4dO98aJTiIjoX3x9KBFFFTWY1ieE4wnSexO7+CPY1Rrztkcjt4QXLlDt4UQB0gvfHUlGtVqLV5/glYik30wUcjzbxhuXM4pwIb1QdA4RUZ0qrqzBJ3viEOxqjRHcC5AMRKS3Pca09cG2S1nYH31TdA4RkcHZeD4TJ5Pz8Vq3QPg4WYrOIbqnhg4WGNXaG7uuZuMSP9sTEemcjFvl+PlEKp4IcUEbP0fROUSPTaWU46OhTVFSWYPZ266JziEDwokCpPPySquwKioN3UJc0NjDVnQO0WMb0coLKqUcK46nik4hIqpTX/2RiPyyarzfPxRK7gVIBuTtXsHwsDPH+1uuoriyRnQOEZHBKCirxoc7ohHkaoUXO/qJziH6T5O6BsBCpcCiXbFcMZCISMcs2BkDjVbCtD4holOIak1EQzuMa++LHZdvYO+1bNE5ZCB4xpZ03g9HU1BZo8Wr3NeYDISjlSkGhrtj55UbuFlcKTqHiKhOpOaV4afjKege6oKOgc6ic4hqlaWpEguHNEF2cSUW7YoVnUNEZDDm74jGrfIaLBzSBColT1mRbnO2NsWETv44mZyPfVxliIhIZ5xIysPOK9l4rq03Al2tRecQ1aqpPYPQ0MEcM7deRVEFL1ygx8dPXaTTbpVV438nU9EpyBkRDe1E5xDVmrHtfKDWSlh9Kk10ChFRnViwMwYAMKNfI8ElRHWjU5AznmruiTVR13EqOV90DhGR3juemIdN5zMxqrUXIr0dROcQPZCXOvmhga0ZFuyMQbVaKzqHiMjoqTVazPk9Gg6WKrzRPUh0DlGts1ApsWhIU9wsrsK87dGic8gAcKIA6bQfjiWjvFqD17sFiE4hqlWNPWzR0sceq6Ouo7JGIzqHiKhWnUjMw97omxjbzge+3FuYDNjM/qFwsjLF2xsuobRKLTqHiEhvVdZoMGPzFThbm+Kd3lwimPSHuUqBd3uHIDW/HP87mSo6h4jI6K05fR1xN0vwTq9g2JqbiM4hqhPtA5wwuo0XNpzLwB5uQUCPiRMFSGfllVbh5+Op6BjoZBBXE+zfvx9t2rSBmZkZXFxc8NJLL6GwsPBff1+tVqNp06aQyWRYtWrVXfclJSWhc+fOsLa2RteuXZGSknLX/dXV1QgODsbWrVsfqG3s2LEICLj3ZIz58+dDJpPddZtMJrvzR6VSwcXFBV26dMHHH3+MoqKifzyHTCbD/PnzH6jFmIxt54v8smpsv3xDdAoRUa1Ra7SYu/327P3JTxjHtkE8xhvvMd7OQoVFQ5ogvaACH+7gTH4iokf11cFEpOaXY/aAMIM+qc8xg2GOGQaGuyO8oR2WHUhAQVm16BwiIqNVUFaNT/fGo7GHDYa1aCg6RyiOOQxzzPFX0/uGwtvRAtM3XUFeaZXoHNJjnChAOuvrg0kor9Zgas9g0SmP7fDhw+jduzc8PDywefNmzJ8/Hxs3bsTAgQMhSdI9H7Ns2TLk5ube877nnnsOZmZm2LhxI+RyOcaMGXPX/Z9++in8/f3x5JNP1vp7+dOECRNw8uRJHDx4EN999x0iIyMxf/58NG3aFLGx3Kv3QfQMc0UDWzP8fDzlX/93QESkb347m47Y7BJM7Rlk0Cf6/8RjPHVv5IqnWzTE2tPpOBDD/YmJiB5Wws0SfHs4CU+EuKBvEzfROXWGYwbDJZfL8EH/RiipVGPp/njROURERuvTvXEoqqjB7AFhUMhl93+AgeKYwzhYqJRYMjwCt8qrMW3TFX6/QI9MKTqA6F5uFFVgVVQaejZyRURDO9E5j23u3LkIDg7G+vXrIZffnp/j4OCAYcOGYdu2bRg4cOBdv5+ZmYk5c+bgyy+//MeBt6ysDCdOnMDp06fRsmVL2Nraok2bNigvL4eFhQXS09PxySef4NSpU3X6njw9PdGmTZs7Pw8aNAivvPIK2rZti6FDh+Ly5ct33ivdm4lCjtFtvPHxnjicS7uFFj76v3IGERm3oooafLo3HiFu1nimpZfonHrBYzwBwMwBjXA8KQ/vbryCPW/YwdHKVHQSEZFe0GolTNt0BUq5HHMGhv3jCjJDwjGDYYv0tseAcHesjrqOEa28ENrARnQSEZFRuZZVhLWnr2NwMw+jP8fKMYfxiPS2x8Qu/vjqYBI2nMsw+pU06NEY398c0gufH0hEjUZrEKsJAEBUVBR69ux518GqT58+AIDff//9H7//+uuvY+DAgejUqdM/7ququr2MjLm5OQDA0tLyrtunTJmCl19+GYGB9b/cs7+/P2bOnIlr167hjz/+qPfX10cjWnlBpZTj5xOpolOIiB7bF/+33OoHAxoZzex9HuMJAKxMb8/kzy+rwozNVzmTn4joAf16Jh1n025hSo8gNHSwEJ1TpzhmMHzT+4bAVCnHzC1XodVyLEBEVF8kScKc36NhZqLAe31CROcIxzGHcXm9WxBCG9hgzrZoZNwqF51DeogTBUjnpOWXYf3ZdAwMd0ewm7XonFqhUCigUqnuuk2pVEImk+HatWt33b57927s3bsXH3/88T2fy8HBAf7+/li+fDlu3bqFr7/+GoGBgbC3t8eBAwdw+vRpzJgx45E61Wr1P/5otdqHeo4/Bx3Hjx9/pAZj42CpwqAId+y+mo0bRRWic4iIHllSbilWnEhFz0auaOfvJDqn3vAYT39q5euAlzr6Yfe1bGy+kCk6h4hI5+WUVGLhrhiEudtgXHsf0Tl1jmMGw9fA1hyvdwvE2bRb2Hg+Q3QOEZHR2HoxC6dTCzD5iQC42piJzhGOYw7jolLK8dnT4ahWa/HW+kucrEgPjVsPkM5Zuj8BEoA3uweJTqk1QUFBiIqKuuu206dPQ5IkFBQU3LmtsrISkydPxqxZs9CgQQOkpqbe8/mWL1+OoUOH4ssvv4S9vT02bdqEmpoavPrqq1iyZAksLB7+SoykpCSYmDz+XtING95e3iY7O/uxn8tYjG3ni3VnM7DqVBre7sVZr0Sknz7cEQO5TIYZ/UJFp9QrHuPpr97sEYRDcbmYtfUaWng7wMvRsK+OJSJ6HHO3RaOsSo2FQ5pAqTD861g4ZjAOz3fwxYZzGVi4KxY9GrnCzkJ1/wcREdEjKyyvxrzt0fB3tsQLHXxF5+gEjjmMT4ibDab2DMLCXbH46XgKxnf0E51EesTwP4mRXrmaWYQtFzMxLNITPk6WonNqzWuvvYbDhw9j4cKFyMvLw8WLF/HKK69AoVDctQTQggULoFKp8Nprr/3n83Xr1g3Z2dmIiYnBjRs30KVLFyxduhQeHh546qmncOrUKTRr1gzW1tbo27fvAx0oPT09cebMmX/8mThx4kO91z+X2jXkvSVrWyN3G7T2dcCaqOuorNGIziEiemiH4nLwR2wOXujoC29Hwzl+Pwge4+mvzEwU+HxEM1RrtHjt1wuo0Tzc1RBERMbiYFwOtl++gefa+qCpp53onHrBMYNxMFHIMW9QYxSUVePjPXGic4iIDN6iXbHIL6vGgsFNYKpUiM7RCRxzGKfxHf3QytcBi3fH4VpWkegc0iOcKEA6Q5IkzN8RDXMTBab0MJzVBABg9OjRePfddzF79mw4OzsjMjISnTp1QkREBBo0aAAASEtLw+LFizFv3jyUlZWhsLAQxcXFAIDy8nIUFd39j7upqSlCQkJgamqKrKwsLFq0CF988QWqq6sxdOhQjBgxAhkZGVAoFPc92P/5fC1atPjHH3d394d6r+np6QAANze3h3qcsRvX3ge3ymvw+6Us0SlERA+lRqPFvO3RcLY2xaSuAaJz6h2P8fR3wW7WeL9/I1xML8Rn++JF5xAR6ZzKGg1mbb0GNxszvNUrWHROveGYwXi08XPEkGYeWHP6Oi6mF4rOISIyWGdSC/DrmXQMb+GJ1n6OonN0Bsccxkkhl+GzpyNgaiLH679eREU1L0ikB8OJAqQz9kXfxKnkAkzs7A8XA9tLSCaTYdGiRcjLy8OlS5dw8+ZNLFu2DAkJCWjfvj0AICUlBVVVVRg6dCjs7e1hb2+P8PBwAMCECRPg6Pjvg5233noL48ePR0hICGJjY5GZmYlXXnkFtra2mDBhAg4cOFAv7xMAdu3aBQB33hc9mO6hrvCwM8fPx1PvzIQkItIHv5xMQ1JuGd7tHQIrU+Pb1YrHeLqX0a290LORK745nITjiXmic4iIdMpPx1NwvaAcM/qFGtXYgWMG4zKtbyisTZV4d8NlVKl5op6IqLZVq7WYvukKHCxVmNbHuLZAvB+OOYyXh505FgxugsScUizcFSM6h/SE8XwiI51WrdZi4a5YNLA1M+j9U6ytrdG0aVMAwPfff4/KykqMGzcOABAREYGDBw/e9fvZ2dkYMWIEpk+fjp49e97zOY8cOYIjR44gNjb2rtvLy8thZWWFsrKyOngn95aUlIT58+ejcePGeOKJJ+rtdQ2BUiHHs229sWhXLE6nFHAWLBHphfzSKny2Px7hnrYY0sxDdI5QPMbTX8lkMiwe2hR9lh3FG79dxO7XO8LRylR0FhGRcDeLK/HlH4lo5eOA/k0biM4RgmMG4+BsbYpZA8Iwdf0lfPlHIqb2NJ7VM4iI6sP3R5ORkFOKJcPDYW+pEp2jkzjmME4Dwt1xMC4H/zuZhs5BzugW6io6iXQcJwqQTvjfyVSk5JXhs6fDYa4yvL2Ezp07h3379qFZs2ZQq9XYv38/Pv/8c3z22Wfw87s9McLOzg5dunS563GpqakAgNDQUHTu3Pkfz6tWqzF58mR88sknsLKyAgAEBwfD3d0dU6dOxbPPPouFCxeiW7dutf6eMjIycOrUKWg0GuTl5eHo0aP44YcfYGNjg/Xr13NfoEfwTMuGWLo/HitOpHKiABHphSX74lFSqcYHA8Iglxvnv/s8xtO/sbNQYenTERjx/Sm8tf4SfhzT0mj/nhAR/emj3bGoqNHggwGNjO54wjGD8RnS3AM7rtzA14eS0CvMDY09bEUnEREZhLT8Mnx+IAHt/B0x2MgvWrgXjjlozsAwnEktwDsbLmP3G53gbM0LF+jfcesBEu5mcSWW7k9AREM7PBlumAd2lUqF33//HcOGDcOwYcNw8uRJrF+//oH26/kvX375JRwcHPDMM8/cuc3U1BTr16/H5cuX8dRTT8Hd3R2ff/75476Ff1i+fDnatm2LLl26YPz48Thz5gxmzJiBK1euICQkpNZfzxjYWagwuJkH9lzLRmZhhegcIqL/FHOjGGtPX8egCHdEetuLzhGGx3j6L639HPHqE4E4GJeL5UeSRecQEQl14fotbDqfiWdaNjTKL0w5ZjA+MpkMCwY3gYVKgbfWX0K1Wis6iYhI72m1Et7beAUSgPmDGvML4nvgmIOszUyw9OlmKKyowdsbLnGrY/pPMon/CyHBXl17ATsuZ+H3yR2M8mQB0V/FZhej99KjeLmzP97rw0EOEekmSZIw8vsoXEwvxB9vdUYDW3PRSUQ6S6OV8NxPUTiZlI/V49ugrT9XDSIi46PVShjyzQkk5Zbi0FtduB0LGZV1Z9PxzobLeL1bIN7sESQ6h4hIr606lYb3t1zFu71DMLGLv+gcIp22dH88lu5PwJyBYRjTzkd0DukorihAQh1PzMO2S1l4to03JwkQAQhxs0FbP0esPX0d5dVq0TlERPe07fINnEzOxytd/DlJgOg+FHIZlj3TDC7WZnh17QXkFFeKTiIiqnebL2TiYnohXu8WyEkCZHSGRXqic5AzvjqYiGtZRaJziIj0VsatcizcGYNwT1u82NFXdA6RzpvcNQCR3vb4cGcM4rJLROeQjuJEARKmskaDmVuvwsnKFFN6BovOIdIZ4zv6oqiiBr+eThedQkT0D8WVNZi3PRq+TpZ4qbOf6BwiveBkZYqvRjVDYXk1Jq+5ALWGSw8TkfEorVLjo92x8HO2xHNtfUTnENU7mUyGhUOawNxEganrLqFKrRGdRESkdyRJwrRNV1Ct0WLx0HAoFfxqi+h+lAo5lj4dAZVCjtd/vYDKGo5B6J/4rykJs+xAApJzyzCzfyhszU1E5xDpjK7BLghytcKPx1JQwy8SiEjHfLYvHrklVZj7ZBhMlQrROUR6I9LbAdP6huJ0agE+3hMnOoeIqN58fTAROSVVmNm/EVRKnoYi4+RuZ45ZA8MQm12CTzgOICJ6aL+dScfRhDy89kQggt2sRecQ6Y2GDhaYN+j2GGTxbo5B6J/4CY2EuJheiOWHk9CjkSsGhruLziHSKXK5DBM6+SOzsALbLmWJziEiuuNaVhFWnkhF/6YN0DHQWXQOkd55vr0P+jVpgOVHkrHryg3ROUREde56fjl+OJqCJ0Jc0DXYRXQOkVBPNfdAvyYN8P3RFBxPzBOdQ0SkN7IKK/DhjhiEudvg5S7+onOI9M6gCA8MDHfHT8dTcDg+V3QO6RhOFKB6V1mjwdvrL8HazAQfDm4MmUwmOolI5wyMcIe7rRmWH06GJEmic4iIoNVKeH/LVViolJjZv5HoHCK9JJPJ8NHQpghwscLU9ZcQm10sOomIqE7N3xENrSTh/X6holOIhJPJZPhwcGO42Zhh6rpLKCyvFp1ERKTztFoJ7226gooaDT4eGg4TbjlA9NBkMhnmDWoMDztzvLX+EvJLq0QnkQ7hv6pU7z7bF4+EnFLMGRgGF2sz0TlEOslEIcf4jn6Iu1mCg3E5onOIiLD2zHVcuF6IN3sEwdWGx2+iR2VlqsT3z7WAUi7Di/87i1tl/JKAiAzTsYQ87I2+iXHtfeDnbCU6h0gn2Fmo8OnwcGQXV2LG5qu8MICI6D5+PpGKI/G5eK1bIBq524jOIdJbtuYm+OzpCOSXVuHdjVc4BqE7OFGA6tXh+FwsP5KMPo3d8GQEtxwg+i/PtGoIOwsTfHsoWXQKERm5jFvlWLAjBk08bDGmrbfoHCK95+tkiS9GNkfmrQpMWnMeao1WdBIRUa1Sa7SYu/0anKxUeLVboOgcIp3SPsAJL3b0xY4rN7DpfKboHCIinRWdVYyPdsWipY89JnUNEJ1DpPda+TrglS4B2B9zE2tOXxedQzqCEwWo3uSUVGLquovwsDPHoiFNueUA0X1YqJR4rq0PTqcW4GxqgegcIjJSkiRh2qYrqNZo8cmwcCi5zB9Rregc5IxpfUJxIikfH+6MEZ1DRFSrVkddR/zNUrzdKxg2Ziaic4h0zlu9ghHawAYfbL2KtPwy0TlERDqnolqD1369AFMTOT57OgIKOb9LIKoNr3cPRHhDO8zbHo3EnFLROaQDeKaX6oVGK2Hquku4VV6Dz0c0g60FTxQQPYix7XxgoVJg2YEE0SlEZKR+O5OOowl5eL1bIILdrEXnEBmU8R19MbiZB34+nop1Z9JF5xAR1YpbZdVYsi8ejT1sMDSyoegcIp1kqlTg82cioJEkvLr2AqrVXF2IiOiv5u+4/SXmwiFN4GlvITqHyGCYKORY9nQE5DIZXv/1AqrUGtFJJBgnClC9WLIvDkcT8jClRxAive1F5xDpDQdLFca198HRhDycTuGqAkRUvzILKzB/Rwwae9hgQmd/0TlEBkcmk2HhkCYI97TF9M1XcCQ+V3QSEdFj+2x/PIoqajBrQBiv/iP6D4Gu1pg1IAyXM4rwyd440TlERDpj77VsrI66jqGRnujflNsXE9U2HydLzB4YhmtZxViyN150DgnGiQJU57ZdysJXB5PQK8wVE/klA9FDe7GjH6xNlViyjycOiKj+aLUS3tt4GVVqDT4eGg4TbjlAVCfMTBT4YUxLuNuZY+Kqc7iaWSQ6iYjokcVmF2PVqTQMDHdHSx8H0TlEOu+Zlg3Rr0kDfHckGYfickTnEBEJl1VYgXc3XoaPowVmDwwTnUNksIZFeqJvEzcsP5KM44l5onNIIJ7xpTp1LasIb2+4hGBXa3w6PAJyXk1A9NDsLFR4voMvTiUX4AQP2kRUT346nnJny4HQBjaic4gMmrO1KVaMawmVUo5xK84gvaBcdBIR0UOTJAlzfo+GSinHe31CROcQ6QWZTIYFQ5rAw84cb62/hJySStFJRETCVKu1mLTmPMqqNPh8RDNYmSpFJxEZLJlMhgWDm8DNxuz2tuFl1aKTSBBOFKA6k19ahZf+dw5mJgp8/1wLHtiJHsPzHXxhY6bEkn3xkCRJdA4RGbhrWUVYvDsOrXwdMLFLgOgcIqPg52yFH8e2REllDcb+fBqF5fyQTkT6Zc+1mziZnI+JnQPgbmcuOodIb9iam+DzEc1wq7wGU367BK2Wn/mJyDgt2BmDC9cL8cGARmjqaSc6h8jg2VmosOTpcNwsqcT0zVf4vYOR4kQBqhPl1Wq8sPIsbhRV4MsRzeHlaCE6iUiv2Zqb4KVOfjibdgtHEriqABHVnYpqDV5bewFmJnJ89nQE9xYmqkfNvezxxYjmSMkrwwsrz6K8Wi06iYjogVT+v/buO7yp8u0D+Ddpmu69d0uZLWWUUTYoMgRBHIiC4gARByLuAYLr97plqIgCiopMQRAQGQKyRwstLbu0hS6690qT5/0jNFIo0JH0JM33c125oOk56XPfbe7zJLnPc1RqfLTlFPycbfDMwFZSD4fI5HQLcsHLQ9pi34UcLPr3otTDISJqdpvi0vHTgWTc19UPE6ICpR4OkdnoE+qOKQNa4a/4TKw5lir1cEgCbBQgvVOpNXh+eQxOXC7A/+6LQL827lIPiahFeKJvCFxsLfH532d5hgERGYQQAu/8cRKJ2aX4v/s7wY9nAxI1uyFhXvhwTASiU/Lx9M/HUKFSSz0kIqLbWrIvCZfzyvH2iA6wtrSQejhEJmnqwFD0CXXDF9vO4vilfKmHQ0TUbC5kleCNtXFo62WPj+7rCJmMJywQNadXhrRDRz9HzPkzAUk5pVIPh5oZGwVIr4QQeOP3OOw6m41Xh7bFwz3Z/UekL/ZWCrxwZxucTCvEhtg0qYdDRC3Qr4dSsC4mDY/3DsLITj5SD4fIbI2PCsTMkR2w/0Iunlseg6pqjdRDIiK6qczCCnyz6wJ6hrhiRIS31MMhMlkWchm+GtcFjjaWeHHlcRRVqKQeEhGRwRWUVeHpn48BAL6d0A22Sl6+mKi5KRVyzHu4KzRC4KWVx6FS8z0Ic8JGAdKrj7ee0X3A8PwdvKYxkb491isIIe52+HTrWZRX8QxDItKf6JR8vL/pFLoFueCdkWFSD4fI7E3u3wqvDGmLf85k4aVVx1HNF+pEZKQ+3XoGFSo1Zo8K4xmARE3k5WiNL8Z2xuW8cryzPp7XCiaiFk2l1uDZX2OQkluKBeO7orWnvdRDIjJboR72ePeecMSmFmLujnNSD4eaERsFSG9++PciFu25iJGdfPDuqHC+QUBkAEqFHG/d3R4ZhRX4ntctJCI9ySquwHPLo+Fsq8S3EyKhVHCKSGQMXrizNZ4dFIotJzPx2to4qHnpISIyMjGX8rHueBoe7hmIcF8nqYdD1CLc0d4Tk/qF4M/YdF4rmIhaLCEE3t0Qj4MXc/H2iA64s72X1EMiMnuP9AzAkDAvfLs7EQcTc6UeDjUTvgtMevHzwWR8tOU0+rZ2w5cPdYaFnE0CRIYyJMwLvVu54bs9icgsrJB6OERk4ipUajy/PAa5JVX4ZnwkvBytpR4SEV0lk8nw+rB2eKJPMNYfT8OMVSe4BCARGQ21RmD2hgQ4WivwypC2Ug+HqEV5fbj2WsGzNybgQlax1MMhItK7JfuSsOLIZTzcIwCT+oVIPRwigvY9iE8e6AQvB2u8uPI4sor52YM5YKMANdmvh1Lw7oYE9AxxxQ8Tu8NKYSH1kIhaNJlMhpn3dEBltRofbTkt9XCIyIRpNAKvrY3D0eR8zB4Vhp4hrlIPiYiuI5PJMHtUGCb3C8HG2HQ8tzwGFSpefoiIpLfq6GWcTCvEK0Pbwc3eSurhELUoVgoLzH+4K2Qy4IXfjvPYT0QtyoYTafhoy2n0auWK9+/tyJWJiYyIq50SX4/virzSKkxfcYKXQTQDbBSgJll55BJm/hGP7kEu+PGJHrBVKqQeEpFZCPd1wsTewfgzNh37zudIPRwiMlFfbD+LP2PT8XT/EDzWO1jq4RDRTchkMrwzsgOmD26D7aeuYPKyYyirqpZ6WERkxgrKqvDZ32fQ3tsBE6ICpR4OUYvUysMeH9zbEWcyi/F/PEmAiFqIPeey8crqWLT3dsT3E7vz0odERqh7sCveHN4eBy/m4qsd56QeDhkYqzA12upjl/HW+pOIDHTGT0/1hJ0VmwSImtPLQ9vCw8EKszbE8+wCImqwlUcu4ZtdiRge7o237u4g9XCI6DZkMhlmDGmLt0e0x74LOZi45AiKKlRSD4uIzNQX284hv0yF9+/tCIUF31oiMpQHuvnjvq5+WHYwBdsSMqUeDhFRkxy/lI+pv0TDz8UGy57qAUdrS6mHREQ3Mbl/CIaEeeGbXYnYdSZL6uGQAfHVHDXKuphUvPF7HDr5a5sE7NkkQNTsHK0tMWdUOJJySvH532elHg4RmZAdp67gnT/i0TnAGV+N6wK5nMv8EZmKKQNC8cGYjjiWko+xCw8ivaBc6iERkZlJSC/E8sMpuLeLLy9bRNQMPhjTEcFutnj99zhkFPK4T0Sm6WxmMZ786SjsrRX45akoeDpYSz0kIroFmUyGz8d2RoCrDWasPoE0vvfQYrFRgBps9dHLeGVNLDr6OuHnp3qy849IQiM7+WBUZ18s2Z+EQxdzpR4OEZmAfedz8NzyGAS52WLJ491ho7SQekhE1ECP9QrC1+O7Iim3FPd9ux+n0oukHhIRmQkhBGZvSICNpQXeHsEViYiag72VAgseiURpZTVeWnkCao2QekhERA1yNrMYj/xwCEIAy57siUA3W6mHRET14GRjiW/Hd0NZpRpTf4lGeRVXNW6J2ChADfLLwWS8/nscOvs749fJUXCyYZMAkdQ+uDccHvZWeHVNLIq5BDER3cLR5Dw8/fMxeDlZ4bfJveBubyX1kIioke7p5Ivlk6NQWa3BQ4sOYs+5bKmHRERmYP3xNBxLyceLg9vAy5FnAhI1lwh/J7wxvD0OJ+Xh638uSD0cIqJ6O3elGON/OIRqtQbLJ0chzNdR6iERUQNE+Dvho/s64mRaIV7/PQ5CsGGxpWGjANXb4r0XMWtDAnoGu7JJgMiIONsq8cmDnZCaX45Zf8TzYE1EdYq9XICnfjwKJxtL/Da5F7yd+OY+kanrEeyK35/tAxc7Szz54xEs3nuR8wAiMpj80ip8tPk0Qj3s8GTfEKmHQ2R2nuobgoFtPTBv5zkcScqTejhERLd1/mqTgEqtwfLJvdDRz0nqIRFRI4ztHoDJ/ULwZ2w6vtnFhsWWho0CVC9f/3MeH24+jX6t3fHTUz1gb6WQekhEdI072nniyb7B+ONEOn46kCz1cIjIyBxJysOExYdhZSnH8qejEODKZf6IWopQD3v88Vxf9AxxxYebT+OV1bGoUHE5QCLSvw83n0ZuaRU+fqATlAq+nUTU3ORyGb54qDPc7K0wfeVxFJRVST0kIqKbik7Jx9hFB1FZrcGvk6MQ4c8mASJT9taIDhjY1gOfbzuHvxMypR4O6RFf2dEtCSHwxbaz+HzbOdzRzgOLH+8OWyWbBIiM0dsjOug+JDh0MVfq4RCRkdhzLhsTlx6Gg7UCq57pjVAPe6mHRER65mZvhV8mReGJPsFYdzwNDy06iNT8MqmHRUQtyN7z2fg9JhUTogLRI9hV6uEQmS13eyt89VAXZBZV4OXVsdBouJIQERmfnaevYMLiQ1BayLFqSm908neWekhE1EQWchkWjO+KUA87zFh1AnGpBVIPifSEjQJ0U0IIfLT5NBb8cwFDw7zw3WPdYG1pIfWwiOgmLC3k+GZ8JDzsrfD88hhcyuUHBETmbmt8BiYvOwovR2usZpMAUYtmaSHHnNHh+PTBTjiTWYyR8/dhG7v8iUgPyqqq8fb6k/BytMIbd7eXejhEZq9fG3e8eGcb/HMmi8v/EpHRWX30Mqb8Eg1fJxv8/mwfhPk6Sj0kItITR2tLLH2iB2wsLfDUT0eRklsq9ZBID9goQHVSqTV4dU0cFu9LwqjOvvhmQiSsFGwSIDJ2Hg5WWPRYN5RWVWPi0sPIKamUekhEJJG10al4bnkMQtztsOaZ3rzcAJGZeKh7ANY/1weudkpM+SUaH2w6hapqjdTDIiIT9n9bzuByXjnev7cjHK0tpR4OEQGYPrgNBrb1wJc7zuHfc9lSD4eICEIILNh5Hq//HocIPyesfbYP34cgaoGC3Oyw9IkeKK1UY+LSI/z8oQVgowDdoLxKjWd+icbvMamY2DsIc8d1gaUF/1SITEXnAGcsnNANl/PL8cSPR1BSWS31kIioGQkh8NX2c3h1TSw6+jlh1ZTe8HS0lnpYRNSMwn2d8Oe0fhjd2RdL9iVh7KKDXGmIiBpl99ks/HIoBfdH+mFYuLfUwyGiq+RyGeaO6wJfJxtMX3mclxwiIkmp1BrM/CMeX2zXXr74t6ej4GqnlHpYRGQgnQOc8e2jkUjNL8ekn46ilJ8/mDSZEIIXsyKdwjIVnlp2FNEp+ZhxV1u8OLg1ZDKZ1MMiokZYF5OKl1fHomeIK5Y83h0OPPuHqMWrUKnx+to4bIxNx10dvDDv4S6ws1JIPSwikogQAiuPXsacjQlQyGWYdU8YxvUI4PyeiOolv7QKw+b+C0sLOf56qT9XEyAyQidTC/HAdwfQ3tsBq6b0ho2Sq4ESUfMqKKvCc8tjcCAxFw9198dH90XwpEMiM7Hm2GW8tjYOfULdsPSJHrx0uYliowDpZBZWYOLSwzifVYIP7u2IR3sFST0kImqinw8m490NCejs74RlT/WEsy27eYlaqtySSkz5JRrRKfmY3C8Eb43oAAs5PwwkIuD8lWLMWH0C8WlFuLO9Jz5+IAKeDlxphIhuTgiB55bHYGtCJn6b3Au9Q92kHhIR3cTa6FS8uiYWIyK88fUjkZDzNQARNZMLWSWYvOwoUvLK8PbdHTC5fwibkonMzJJ9Sfhg0ykMaOuB7x/rxmYBE8RGAQIAJGaXYOKSI8gursS8h7vg7ggfqYdERHqy+thlvPl7HNp4OmDZUz3h7cQPBohamvi0Qjy7PBrpBRV4/95wTIhisx8R1VZVrcHX/5zHN7sT4WitwAdjOmJkhA/fyCOiOv3w70V8tOU0pg4MxZt3t5d6OER0Gx//dQbf7UnE83eE4rVhfM4SkeHtPpuFaSuOQwhgwSNdcUd7T6mHREQS+W5PIj7+6wwGt/fEwke7QangqiKmhI0ChMMXc/HMr9GoVgt8P7Eb+oS6Sz0kItKzzXEZmLHqBJxtLfH9xO7oEuAs9ZCISA+EEFhx5DLm/JkAW6UFFjzSFf3beEg9LCIyYscv5ePl1bFIyinF4PaeeH9MR/g520g9LCIyIgcTc/HoksPoGeyKXyb1hILLBxMZPY3mv1VAPh/bGQ9285d6SETUQgkh8OP+ZHy4+RT8XWyx5PHuaOPlIPWwiEhi83eex5fbz2FImBcWPNKVKwuYEDYKmLnfo1Px5ro4uNlZYckT3RHu6yT1kIjIQI4l52Hqr9EoqqjGJw9E4L6ufOOAyJSVVVVj5vp4rDuehs4Bzvh2QiQ/7COieqlQqbHgn/NYtOcilAo5Xh3aDo/3CeblSogI6QXlGP31PlhayPHntH5wt7eSekhEVE/lVWo8tOggzmQW4acne6Jva54IRET6VVWtwbsb4rHy6GVEhbhi4aPd4GrHy5wSkdaX289h/s7z6BPqhu8ndoe9lULqIVE9sFHATGk0Al9uP4evd11AhJ8TFj/eHV6OXI6cqKVLzS/D0z9H43RGER7pGYDZo8LZ3Udkgo4l5+HVNbFIzi3DE32C8faIDlzWi4ga7ExmEd5adxLHLxUgws8JH4zpyFWHiMxYYbkKY787gOTcMqya0gtdA12kHhIRNdCVogrc/+0B5JdVYfnkKD6PiUhvsosr8dzyaBxNzscjPQPx3uhwvg9BRDdYvPciPtx8Gp38nfDjEz3gxsZjo8dKbgRWrFiBjh07wtraGm3atMG3335b732zsrIwdepU+Pr6wsrKCiEhIZg5c2atbQYNGgSZTFbrZmEhx6c//IZh4V5Y9UwvNgkQmYm9Wzfg0vfPIvXL+/H55OGIfORVnM0svu1+ddURmUyGTZs21dqutLQU06ZNg7e3N6ytrdGtWzds3LjRUOEQScKQx+3du3fX+VyruSVdSsVHm09h7KKDKKmsxqLHumEOX5wTUQPV1LEuwZ6I/+oJDKyOQXJuKcZ8sx9vrI1DbknlTfetz+sPItKfxs47nnjiCbRr1w729vZwcnJCz549sXz58ltu5+Hqgj2fPY0x9hf54SKRifJytMbyyVGwVSrwxI9H8cXCpQ2uIRkZGXjzzTcRGRkJR0dHeHl5YeTIkYiJialze84NiKTXlPcp5s2bh9DQUFhbWyMiIgJr1qy5YZv3v/wW7aIGY/3ro5HyyT1Qxq696fsQrAlE5m1y/1b4YmxnJKQXYcDUD9G+Q7jBatNPP/2E++67D/7+/pDJZJgzZ85tH3fVqlWQyWRo3bp1Q8Jq0bjug8T++OMPjB8/HtOnT8eCBQvw77//Ytq0aZDL5Zg6deot983OzkafPn3g4OCAzz//HH5+fkhJScHFixdv2LZHjx6YP38+knNK8cnWM8gsqsCL9w/EG6O7Qc4lRonMwrX15uuvF+D71ZuwctFc9C9VYc4bL2FK/1a3vP5oTR25Vvv27Wt9PWbMGERHR+N///sfgoODsXTpUtx3333466+/MHToUIPERdScDH3cjoyMxMGDB2/Y97777oOLhzeeXpuIC1klGBHhjQ/u7ciuXCJqsLrq2Pvvz8EnX85Dll9/rDp2GX/FZ+DFwW3wWO8gWCn+W3moIa8/iKjpmjLvqKysxIwZMxAaGgq1Wo01a9bg0UcfRUFBAZ5//vla2704fTq2XhI4cjEXbcvi8dlbLyDIAbW2IyLTEexuh18m9cTwGV/g1Tnv47HJzzaohkRHR2PNmjWYNGkSoqKiUFJSgi+++AJ9+vTBgQMHEBkZqduWcwMi6TVlvjB37ly8+uqrmDNnDvr27Yt169Zh3LhxsLe3x9133w0AWH3sMj75ejFERTFG33MPNqz6BTJZ3Z8nsCYQEQA80M0fJw/swHv/9yG8+9yPhe9/gkunovVamwDgt99+Q25uLu655x788MMPtx1XcXExXn75ZXh7ezc5xpaElx6QWHh4OIKDg7F582bdfVOmTMGGDRuQlpYGheLmvRxPPvkkdu3ahfj4eNjb2990u0GDBkGhUOCFz5bhzXVxUFrIMe/hrrijvadeYyEi41ZXvZnw+FP4ff0f8Jr6E7oGueHzsZ3R2vPGelJTR3bs2HHTx9+7dy8GDBiAlStXYty4cQAAIQQ6d+4MhUJx07MPiExJcxy3r7fj34MYMrAPXAY/jcABD+L9eztiVCefm74wJyK6ldvVsbj0Yry3MQGxqYXwd7HBa8PaYVQnX8jlskbXMSJqnKbMO+rSp08fqFQqHD16VHdfVbUG01bE4O+EK5g6MBRv3t2+zu2IyPSEtu2AbDgiZMIH+GVST3Tyd65XDSkoKIC9vX2t75eUlCAkJAT33HMPfvzxR939nBsQSa+x84Wqqip4e3vjkUcewTfffKO7f+jQocjNzcWhI0fxwaZT+PlgCiJ8HbBoYg/4OttAoVBg5syZdZ65y5pARDXCw8Ph7OmLkkGvQaXW4MuHumDd/HebXJuio6N192k0Gsjl2hMfb1Wbarz00ks4efIkAgICsG/fPly4cEE/wZo4rlMroeTkZJw6dQrjx4+vdf+ECROQlZWFI0eO3HTf4uJirFixApMnT77tQVcjBC5kleClVScQ4m6PTdP6s0mAyMzcrN5MeepxVBbn4/HWKpzNLMaI+Xvxw78XodY0vIfs8OHDAIDhw4fr7pPJZBg6dCiOHz+Oy5cvNy0IIok113G7RlW1Bj/8exEPv/YJILfA+EcewY6XB2J0Z182CRBRo9SnjkUGuuCP5/tiwSNdIZMB01eewJhv92NHbHKD6xgRNV5T5h034+bmVusNuQqVGs/8cgx/J1zBtDtb443h7ercjohMT3JyMi6eP4OZ05+GpYUM4384jAMXcupVQ5ydnW+oAfb29mjbti3S09N19zXmNQ4R6VdT5gsHDhxAfn5+nfvGxMTg/s824eeDKXgg0h9rnu0LX2ebW46FNYGIatTUpucmP4ENz/eFj5M1pv4aDev2A5tcm9LS0nT31TQJ1EdsbCwWLVqEBQsWNDygFo6NAhI6ffo0AG1nzbXCwsJqfb8uMTExqKyshIeHB8aMGQNra2s4OTnh0UcfRU5Ojm672MsFiEstxKUzsUif9xB2vjYYD949CBs2bDBARERkrG5Xb4IsCrD1pf7o4u+Mj7acxrhFB5GUU1pr20OHDsHBwQFKpRI9e/a8oY5YWGiXJlYqlbXut7LSLo1+6tQp/QVEJIHmOG4DgFoj8MfxNAyb+y8+3BSPktP/ov8dg/H1pDvgzksNEFET1LeOyWQyjOrsix0vD8Sse8JwKa8Mj36yEpWVlai2tL9tHSOipmvKvKOGEALV1dXIz8/HTz/9hG3btmH69OkAgMzCCoxbdBC7zmbjlSFtMP3OUBQUFNywHRGZppoaMbRvd6yc0htONpZ4bOkRxJXY1/p+fRUVFSEuLg4dOnTQ3deQ1zhEZBhNmS/cbF+FWwAAIDouHrNHheHzsZ1gbWlxw/7XY00gohrX1pdgdzusf74vBrXzwPpk7YlPsScT6rXvtWrq2pkzZxo8HiEEnn32WTz//PO6x6H/sFFAQvn5+QC0nbrXcnFxAQDk5eXddN+MjAwAwGuvvQY7Ozts2rQJX375Jf7++288+OCDqKrWYN6O87h/4QFYB4TjxXc+xI6//8LKlSvh4OCAMWPGYNWqVYYJjIiMTn3qTZCbHVZO6YVZ94ThZFoh7p73L3749yKq1RoMGDAAc+fOxebNm29aR9q2bQvgv5UFahw6dEj3M4hMmSGP28B/DQJDvtqDl1adQFW1Bo/65aOyOB8vTJlkgIiIyNw0tI5ZKSwwqV8I9rx2B+4ItAQAvP/u20jIqsQ3P626oY4Rkf40Zd5RY9WqVbC0tISrqyumTJmCefPm4eGHH0bMpXyM+nofTmUU4eP7I+CRHVPndkRkuq6tIa097bHhhb7oFuiCz/doz8K7ktWwD+1eeeUVVFdXY9q0abr76vMah4gMqynzhZp9nZycAGg/SPtpfxLe3nwRAPB8b2882Tek3isasiYQUY3ra5OjtSWWPN4DT9/VEQAwd3MMErNLbrlvTW2q0ZDXQddbvHgxkpOTMXv27Abvaw64llwzEEJArVbrvpbJZLCwsIAQQvd1Q9U8XmhoKH799VfdYzg5OWHs2LHo89I3yLFvhQFtPfDZm0vg5Wit2/fee+9Fnz598M477+iuI05ELUNT641cLsOkfiEY1M4Db/4eh4+2nMbG2HR8PPUVhPv+d3Cuq44MGzYM7dq1w/PPP49ffvkFQUFBWLp0Kfbs2XP1sdmbRqZBiuP2u4vWYl+xOy7mlMLfxQYf3x+B+yP9MfHR8XBycsLo0aP1EBkRmQt91zEnG0vcE+GNlQC8/IKAO6bhgxMCw8O74J3/fYEZUx7H3r170b9/f32GQWQWDDHvqDFs2DAcPXoUeXl52Lx5M1544QXsPpuNYzbd4GyrxMopvdAtyBX5bRxu2E4ul2PKlClNjo+IDKu+NcTd3gq/To7Cu3/E4hMAS/cnYXRaITr6OdX1sLV8/fXXWLx4Mb7//nuEhobq7r/daxzODYj0xxDzBSGEbr/iChXe/P0kNp/MQAcfB1wC0NbboUGPx5pAZH4aUpss5DK8PSIMswEUlKkwesE+fPxAJ4zq7HvDY9ZV02oes6FycnLw5ptvYv78+XBwaFhdMxf81KYZ7NmzB5aWlrpbzaTa1dUVwH8dMjVqvq75fl3c3NwAAIMHD9Y9aQrKqnCgwgcAcCX5HL4Y2xnLnuxRq0kA0C4P/uCDDyIxMfGGn01Epk1f9SbUwx6rpvTGh2M6IimnFKO/3o+P/zqDCpX2wF9XHVEoFFizZg0AoFu3bnB3d8c333yDd999FwDg4+NjoKiJ9Ku5jttXiipwShYIAPhm3S5UawQ+vj8C/7wyCA/3DERFWQk2btyIBx98ENbW1jd9bCKi6xmyjk24fyT2vn4nnugTjH/OZuHLBO0lUXbsP6r3OIjMgSGerzVcXFzQvXt3DB06FNPe+RD+USOw+tuPERXijE3T+qFbkOsN282bNw9PPvkkXnvttVpv+hGRcWpIDVEq5HhloD8AoNLCFvd9ux9fbT+ne51flxUrVmD69OmYNWsWnn766Vrfq+s1DgDcddddAID4+Hg9REhEgGHmC66urhBC4PCZyxi1YB82n8zAMwNbYfbQ4NvuWxfWBCLz09ja9NLIrvBzscG0FcfxzvqTKKuq1m1TU5sKCwtr7VtQUFDrsetr1qxZCA0NxciRI1FQUICCggJUVVVBo9GgoKAAlZWVDXq8logrCjSDbt264ejR/944q7led811vU6dOoUuXbrovl9zHe9rr/t1vZrrcwghUFWtwS+HUjB/53ndshtT+wfigW7+N91fH2cnEJHx0We9kctleLRXEO7q4IVZG+Lx3Z5EbI3PwP/uj0CfUPc660hERAQSEhJw8eJFVFZWom3btvjiiy9gbW2NyMhIvcdLZAiGPm6fTC3E0v1J2BSXjoqSIgDA2C6e+ObVQbCQ//d8Wrt2LcrLyzFx4kT9BEZEZsPQdczbyRpzRodj6sBQfPlnND4DsGD7aaT5HMWzg1qjW5CLniMiarkM8Xy9VmG5CvN2nMfPB5NR4RQIUVWGz+8JgbfTzZsQu3XrhsWLFyM7Oxve3t4NDYmImlFja8g3z4/C2lQ7zNt5Hn+cSMOcUeEY1M6j1uv7LVu2YOLEiZgyZQref//9G372tXODa2k0GgBARUWFHiIkIsAw84X27dsDAB76ZA2cgzvih4ndMSTMC8uWLbvtvnVhTSAyP42tTQOjuuKl7lF4d0M8lh++hH0XcvDF2M7oHuxaa98+ffrcsG9Da9Pp06dx9OhR3aULruXi4oIPPvgAM2fObNBjtjQy0dj1GkgvOnbsiJCQEPz555+6+6ZOnYr169cjLS0NCsXNezkiIyNRUFqJwMlfIzm3DJ38ndALZ/HOtMnYu3cv+vXrV+d+arUavXr1QmFhIc6dO6f3mIjIODWl3gghsDU+E+9uTEB2cSUe7OqDfz6dgpLiolvWkdLSUkRGRmLAgAH44Ycf9BoPkRQa+zwqr1IjvFMX5JaUw3nCXCgVcozq5AvfvON47bmn6jxu33HHHUhOTsbFixfZ2EdEetPU1x9qtRonTpzQ1aVVq1bh4YcfxpMf/4wDJe6oUGkQFeKK5+5ojQFt3Fm/iJqgKc/X4goVfj6YgiX7kpBXWoW7O3oj449PsGv738jNzb3lvuPHj8fmzZtvux0RGbfb1RALCwtsOZmJ9zcl4EpRJXoEu2DGkLboE+qOffv2YejQoRg1ahRWrFhx00sJ3mpucKv3JolIfxozX8gqqsArq6Lx24vDEdRzKPas/wUBrrYAgOHDhyM7OxvR0dF1/jyFQoGZM2dizpw5N3yPNYGIatS3Nv2dkIl31p9EbmkVpgxohecHBCM4wA8TJkzAggULdPs2tjadOHFCtxpBjY8//hgnT57E8uXLERISgqCgIP0EbaoESWrdunUCgJgxY4bYtWuXeO+994RcLhcLFy6std3AgQNFUFCQEEKIarVGbI5LF92e+VxAJhduEYPEu1//KhYu/E64urqKu+66S7ffv//+K0aPHi2WLVsm/vnnH7Fq1SoxcOBAIZPJxNq1a5szVCKSWGPqjRC168jGLdvE6Jc/FVYBHQVkMvHWFz8IjUaj2/ajjz4Sv/32m9i1a5dYsmSJ6NChg2jTpo3IyclprjCJDKqhz6PzV4rEnI3xImL2VuH50PsCMrnoMnCEWPH7RrFo0aIbjts1UlJShEwmEzNnzmyu0IjITDR2PiCEEH///beQy+Vi3LhxYuvWrTfUsdySSvHF32dExOytIuiNTWLk/H/Fpth0Ua3WCCJquMY8X3OKK8TkDxcJpw79hNvIGaLvi/PF/327TDz22GMCgPj00091+23atEk88MADYtmyZWLXrl1i/fr1dW5HRKapvjWkX/8BwtXLT3f8HjxrubCzdxStW7cWe/bsEQcPHtTdYmJiau17u7kBERleQ+cLW+LSRZf3/hat3tosxjz7lrCwsBAffvih2L17t5g2bZqQyWRiy5YttfZNSEgQa9asEWvWrBFyuVyMHTtW93VpaaluO9YEIqrRkNoUEBgopv5yTAS9sUnc9cVuMX3mh3qtTdd7/PHHRWhoqEHiNkVsFDACy5cvF2FhYUKpVIrQ0FDx9ddf37DNwIEDRWBgkFiy96Lo/8k/IuiNTaLnR9vF9I+/F126dBVKpVJ4enqKF154QZSUlOj2O3/+vBg+fLjw8fERlpaWwtHRUdx5551i27ZtzRkiERmJ+tabaz8YqKuOdOvdX3R6+jMR9MYmMemnoyK9oEwIIcQbb7whAgMDhVKpFD4+PmLq1KkiOzu7ucIjaha3ex5VqtSiY7fewtbNWwS9sUkEvbFJPLbksNganyHWrV8vuna9+XG7xv/+9z8BQJw5c6a5wiIiM9KY+UCNDRs23LaOFVeoxKI9F0SPD7eLoDc2iTs+2yVWHkkRlSq1oUIiarHq+3z19gsQzy2PFq3f3ix8Jy8Uvp0HCA8vH6FUKoW3t7e44447xJ9//llrv9OnT4sxY8YIPz+/W25HRKarIcf8grIqMW/HORF036sCQJ23xs4NiMiw6vNc79d/gHD08BFBb2wSgz7bJY5fyhdCCDF37lwREhIilEqlCA8PF6tXr75h39mzZ9+0LiQlJdXaljWBiGo0ZB6i0WjEH8dTReT720TQG5vEgImvisCgYL3VpmuxUaA2XnrAyAkhEJtaiN+jU7H+eBpKKqvR2tMeT/YNxgOR/rC2tJB6iERkpipUaszfeR7f/3sR1pYWeGN4O0yICoJcziWGyTyl5JZi5dHLWH30MnJLq+Bqp8RD3QPwSM8ABLnZST08IqJmV6FSY11MGhb9m4iU3DJ4OFjhsV5BGB8VCHd7K6mHR2TShBA4e6UYf53MxPrjabiUVwZrSzlGRvhifFQAugW5Sj1EIjJRldVqbI7LwK+HUhBzqQAA0DnAGaM6+WBomDcC3WylHSARNYgQAn/GZeCDTaeQXVyJx3oF4a0R7WGr5OWFiMj4FJap8MX2s/j1UApslQq8dFcbTOwdDKWi7ssgUdOxUcBIpReUY/3xNKyLSUVidinkMmBgWw882TcE/XmtTyIyIqfSi/DmujjEpRYiMtAZs0eFo3OAs9TDImoWpZXV2HIyA2uiU3EkKQ8AEBXiigm9gjAs3AtWCjb0ERFVqzX4Kz4TS/Yl4cTlAigVctzb2RdP9g1BmK+j1MMjMhnVag1iUwux7VQm/o7PRHJuGQAgzMcRD/cMwL1d/OBkYynxKImoJbmYXXL1/ck0pBWUAwDaeztgSJgX7mzviQg/Jygs+MY9kbG6mF2CdzckYN+FHAS72eKDMR3Rv42H1MMiIrqt+LRCzNoQj+OXChDoaotXhrbFqE6+PEnRANgoYETKqqqxNT4Tv8ek4kBiLoQA2nrZ44FIf4zp6gcvR2uph0hEVCe1RuDH/UmYu+M8SiqrcX9XP7w2vB18nGykHhqR3gkhcDQ5H2uOXcbmkxkoq1LDw8EK93f1w9ju/mjt6SD1EImIjFbMpXz8uD8Zf53MQLVGICrEFU/2DcGQMC9Y8AU/US0ajcD5rBIcSMzB/gu5OHwxF8WV1QC0Z/cOD/fGsHAvtPKwl3ikRNTSCSGQkF6EbQmZ2HbqCs5kFgMAHKwViApxQ9/WbugT6o62XvY8uYnICOSUVOLrfy5g+eEUyGQyPD+oNZ4Z2IqrExORSdFoBDbGpuOL7WdxOa8cHXwcMeOuNrirgxcbBvSIjQIS02gEDiXl4vfoNPwVr/2wwc1OidFdfPFApD/CfR05wSYik5FdXIkvt5/DqqOXoFTI8cyAUDw9oBXsrbicGZm+81eKsSkuAxtj05GUUwqFXIa7OnhhbHd/DGzrwTNpiIgaIKOwHL8cTMFvRy6hoEwFHydrjO0egHE9AuDnzEZDMj+lldW4lFeGC1klOJlWiNjLBYhPK0RplRoA4GJriT6h7ujT2g13tPOEL58nRCShS7ll+Pd8Ng4k5uBgYi7yy1QAAHd7K/QJdUPPEFd0DXRGOy8Hvk4iakbFFSr8uD8Zi/YkorRKjRER3nhjeHteDpGITFpVtQYrj17C/J3nkVNShVbudniqXwgeiPSHjZINUE3FRgEJ1HThbj6ZgY0n0pFWUA6lhRyDO3jigUh/DGznAUtOoonIhJ3OKMKHm09h/4VcONta4un+rfB4n2A2DJDJuZhdgk1xGdgcl4GzV7RnzYT5OOKBbv4Y08UXbrzGNhFRk5RXqbExNg2/HbmM2MsFkMmAAW088EjPAAzu4MXXRdRiCCGQXVyJlLwyXMotu/pvKS7lleFSXhlySqpqbd/K3Q4R/k7o5O+MXq1c0cHbkWfNEJFR0mgETmcW4cCFXBxIzMGRpDxdk5Ot0gKd/J3QNdAFXQOc0TXQBR4OfA1FpG/ZxZX4cX8SfjmUguKKakSFuOLNu9uja6CL1EMjItKb8io11sakYum+JCTllMLF1hITooIwrkcAAlxtpR6eyWKjQDMRQuB0RjE2n0zH5rgM3bUEuwQ444Fu/hjVyQfOtkqJR0lEpD9CCPx7Pgdzd5zD8UsFcLa1xOO9gzGhVyA8HXgpFTJOKrUGx5LzsftsFv45k4XzWSUAtJcCuqeTL0Z28kEol/clIjKI0xlFWHnkEtYfT0NRRTXc7a0wurMvRnX2QZcAZ660RkavslqN1Pxy7Yf/uWVIyS272gigbQioUGlqbW9vpUCgqy2C3GwR6GaLQFdbhLjbIdzXCU42lhJFQUTUNCq1Bmczi3H8Uj6OXypAzKV83fugABDgaoNwHyeE+Tqig48jwnwd4etkzeM8UQMJIRCbWogVhy9h/Yk0VFVr0CfUDVMHhqJ/G3c+p4ioxdJoBHacvoLFe5NwJDkPANCvtTse6hGAoWFevMxKA7FRwICEEDh7pRibr56JeDGnFADQyd8JIyN8MCLCh10uRNTiCSGw93wO5u08j+iUfFhayDAywgcT+wSjK9/0J4mp1BrEpRbicFIuDl/MQ3RKPkquXvs3ws8Jd7T3xD2dfNDWy0HikRIRmY8KlRpbTmZg1dHLOJKcByEAfxcbjOzkg7s7+qCTnxPPrCbJlFVV42K29oN/bSNAKVKuNgWkF5bj+ndYfJysEeBqiyBXbSNAoJstgtzsEOhqCxdbS86Ficgs5JVW4cRlbePAicsFSEgvQl7pfyupONlYooOPA8J8nLT/+jqijacDlAquLER0vZySSmyOy8CKI5dwJrMYFnIZhoV74ZkBoegc4Cz18IiImtXZzGKsOnoZ64+nIr9MBScbSwwL98KICB/0be3OVQrrgY0CeqZSaxCdko9dZ7Kw4/QVJGZrmwM6+jliZIQvRkb4INCNzQFEZJ5iLxdg2YFk/BmXDpVaIMTdDqM7++LuCG+083LgG6VkUOVVaiRmlyA+rRAJ6UWITy/E6Ywi3dl9Xo5WiApxQ7/W7hjUzgOejlz5gohIapmFFdhyMgN/xqXj+KUCAIC7vRID23rijvYe6N/Gg2dek0GUV6lxIasE564U41xWMc5fKcH5rGKk5tduBlBayOHvaoMg1/8aAILctDd/F1uezUJEVAchBLKKK3EqvQinMrS30xlFSMop1dVYpYUcbbzsEe7riHBfJ3T0c0R7b0fY8ZKGZIYyCsux49QVbDmZicNJudAIINDVFuN6BGBsN3++f0FEZq+yWo3tp65gXUwa9p7Phkot4GRjiTvaeWBAW+17B7z8Ud3YKNBEQgik5Jbh0MVc7L2Qg3/PZaO4QnsmYkc/R9zdUbtyQIi7ncQjbZiKigpUVFRIPQwiugVra2tYW5vmC4Gs4gr8cTwNfxxPx6mMIgCAt6M1Brb1QL827ugS4Ax/Fxs2DlCDlFepcaWoAplFFbhy9XY5rxxJOaW4mF2C9ML/jmsKuQxtvBzQ0dcR3YNdEBXihiA3W5P9m+Nxm8h8mfJ8oKFS88vwzxntpWEOJuaisloDuQwI83VEz2A39AxxRc8QV7ja8ZJuVH8lldW4kFWiu52/2hhwfUOAj5M12ng5oI2nPVp72l9tBrCDt6M1LMxshQvOO4ikYQ7H/LKqapzJLMap9CIkpBfhVHohTmcWo6pa29wtk0F3iZYIP0dE+Dmjo58jHKzZNEgtS2G5Cocv5mL/hRzsu5CjOxnR08EKIyJ8cHdHb/QIdm0xq2xxbkFk/ExpHlJYrsKOU1fwV3wG9l/IRblKDQAI83HEgLYe6NvaDV0DXWDP5kMAbBRosAqVGqczihCfXoTo5DwcupiHzCLtQcxOaYF+bdxxZ3tPDGrnCS8T7uSbM2cO3nvvPamHQUS3MHv2bMyZM0fqYTTZhaxi/HMmC3vOZeNIUh5Uau1hydVOiQg/J7T2tEewux1C3OwQ7G4Lb0drKLhkkNlIzS/Dt7sTUVWtgUqt0f1bWa1BSWU1iiuqUVyhQnFFNcqq1DfsbyGXIejq9X5D3O0Q6qk9I6Wtl0OLOsOPx20i89VS5gMNVV6lxsGLOdh7PgdHkvJwKqNI96Fua097dPTVXvM43NcJHXwc2Txg5sqr1EjNL0Nqfjku55chKadU1xiQUVj7TemahoC2nvZo42Wvaw7gh1D/4byDSBrmesyvVmuQmF2KhHTtynA1/9acqAUArTzsEOHnhAg/J3Tyd0a4L1ceINMghEBuaRXOXylBQnoh4lILcTKtEElXL2EslwGdA5zRv7U7BrT1QGSgS4tpDrgW5xZExs9U5yGV1WocS87Hv+eysedcNs5kFgPQ1tf23toTyLoFuaB7sCv8nG0kHq00OGO6CY1G4EpxBZKyS3H2SjHi07QT0fNZJVBrtO9A2VhaoHuwCx7rHYRerdzQyd+J17sgImqg1p4OaO3pgCkDQlFaWY3jlwoQm1qAk1dfHO05l11re5kMcLFVws1OCXd7K7jZX/3XTgkXOyWcbS3hZGMJZ5ur/7e1hIOVwmTPFDd3ReXV+D06FUoLOSwVclhayKBUyGFpIYe9lQJejlZX37xXwMVOCW9Ha3g7WsPT0RreTtbwdLDisZmIqAWyUVrgzvZeuLO9FwDtGQPRKXk4fDEP0Sn52H7qCv44ka7b3tvRGiHu2qbDYDc7BLvbIfjq2eCONpwnmBKNRiAurRAqtQaqag2qrjYSllZVI69UhfzSKuSVVSGvpApXirWrC+WUVNZ6DJlMu1xvuK8jRnfxRRtPB7T2tEeohx0bAoiIjIzCQo523g5o5+2A+yO192k0ApfyyhCXVoiTqQWISy3EztNZ2HD12C+TAa097BHhX9M84IQwHyfYKFtOsziZBpVag4IyFbKLK5FRWI70gnKkF1YgvaAcl/PKkJhdisJylW57BysFOvo5YUiYF7oFuaB3qBscOTchImo0K4UF+rZ2R9/W7nhrRAdcKarAkSTt+wbRKflYfvgSfj6YAkB7mcO2Xto5R7ur/4Z62rf4Omy2jQLVag2yiiuRWVSBzMKrt6IKXMotQ3JuKZJzS3XXLAa0TQFhvo54NCoQ4X5O6OjrhDZe9vzwgYhIj+ysFOjXxh392rjr7qtQqZGSqz3zKzm3FFeKKpBbUoWckkrkllThfFYx8kqroLnF+jgWctnV5gFt44CzjbaZwNrSAlYKOZQKOawUFlBe/b9GCGg0AtUaAfU1t8pr3ozW3a75urJardtmxl1tMaqzbzNkrWUL83XE2Q/vlnoYRERk5JxsLGs1Dmg0Apfzy3TXPj6dUYzk3FJEp+SjSq2pta+VQg5PRyt4OmgbzJxtlXCysYSjjQKO1pZwtLGEraXF1flC7XmDQi6DXCaDTKb9UEIuu8nXAKwtLfgBhR6oNBqM+Wb/LbdRWsjhYmcJDwcr9Ah2gb+LDQJcbRHgYosAVxv4u9i2qJWFiIjMjVwu0zb9udth9NXX3RqNQFJuKU6m1pyVXYCt8ZlYF5Om3UcGtPVy0K484O+EYDc7+Dpbw8fJpsmrDwghUFal1q16V1JZjZKKapRUale/K628el+lGlXVGlRrNFCpBVq52+HpAa2anA+6PSEENEL7b/XV93uq1dr3b6rVAtVqof2/Rvu1Sq39HVWrNVBpBFTV2pUNy6qqUaFSo6xKjXLV1VvV1ZtKjeKKahSUVSG/TIX8sqpaK19cy8nGEv4uNhjQ1gOhHnYI9dCufhjsZtciVwwgIjIWXo7WGNXZV/e+fWllNWIvF+BYSj5OpRfh7JViHLyYW+sydC62lgh0tUWAqy38XWzh4WAFDwcruNsr4elgBTc7K9hZKaBUmObnxUZ16YFT6UXYcjIDACAgdL8IAUAI7X01d2jv034txH/bXL9vZbUaReXVKKpQoahchaKKahSVaw/UdX2o5G6v1J5p4maHEA/tUtetPe3RysPerK47yOsCERk/U7oukKGpNQL5ZVUoKFOhsFz7b0GZCgXlKhSWVaGgvO6vK1RqVKk1aMiRUGkh1zUU3Oz/Vgo5nugTjMEdvAwXNNE1eNwmMl+cDzSMWiOQXlCO5NxSpOSWIauoAleKKpFVXIGs4kpkF1eioFyluxayPj3RJxhzRofr/XHNjRACG2PTYWkhv3rTrjZkq1ToVpiyU1pwlQgD4ryDSBo85jecWiNwMbtEt5x7XGoBEtKLUHndcd7RWgF3e+2b/HZWFrC3UsDa0gK6twoEoBEC5TUfEFepUVpVjfKrzQGlldW3PHnhenKZdqWEPqFu+OnJnnqL15zdPW8vUnJLtSd+XG0IEFd/bw353TSGXAbYKrV/M/ZWFnC2VcL16oqXLrZKuNhawt3eCj7ONvDTU3NKS8O5BZHxM6d5SHmVGheySnAmswhJOaW4lFeGy3lluJRXhvwy1U33U1rIYWtlATuldj5ho1TAUi6DhVwGhYUMCrn2ZIOar2VXTyqoee0qA+BgrcBH90U0T6BXGVWjwPrjqZixKlavj6lUyLVng1gr4GhjqTsjxNXWEj7ONtolip2sdf/yrAIiIvNS001eec0qAXKZdhWCmptCLodcrj3Y801nIiKilq9CpUZRuQqF5SoUVahQXqVBlVqNSpX2zLNKlQaVag00GnHTN6RrGti12wCdA5wwqJ2n1KERERGZtWq1BonZpbicV6ZdCr6wAhkF5cgrU6G08r+z/ytUagDaVYIA7YfBNpbaN/1tlRawVVrAxtIC9tYKOFgpYG+tgL2V5X9f6+6r/X+lhZxnjBvAJ1vPIK+kCnI5AMgg163spP0ApmbFJ7lMuyKFpVzbcKiwkMHy6oc3lgo5LOUyKK42IlpaaD/QqdnOxtJCt0JUze/fRmnB94qIiMxIaWU1ckoqkVOiPdEgu6QKuSWVupWFyq6uIFRWVY2yKjXUGu0qNTUrFlfr/tVAu9jhfyfEA4CrnRLbXx7YrDEZVaOASq3RdXRquygAbT8FdJMy3b9XJ2o1h+D/Oi/++5qIiIiIiIiIiIiIiIiIiIhqM6pGASIiIiIiIiIiIiIiIiIiIjIsudQDICIiIiIiIiIiIiIiIiIioubDRgEiIiIiIiIiIiIiIiIiIiIzwkYBIiIiIiIiIiIiIiIiIiIiM8JGASIiIiIiIiIiIiIiIiIiIjPCRgEiIiIiIiIiIiIiIiIiIiIzwkYBIiIiIiIiIiIiIiIiIiIiM8JGASIiIiIiIiIiIiIiIiIiIjPCRgEiIiIiIiIiIiIiIiIiIiIzwkYBIiIiIiIiIiIiIiIiIiIiM6Joys5Lly5FeXl5o/YtKyuDra1tU358i8cc3R5zdGvMz+0xR7fHHN1ec+XIxsYGTz31lMF/TlM0ZW5gKPwbbhrmr2mYv6ZjDpuG+WsaU8ifsc8PGjs3MIXcGxLjZ/yMn/GbK8bf9PiNfW6wZMkS5OXlmfXv2dDM/XlkaMyv4TC3hsX8GpYx5/eWcwMhgerqajFnzhxRXV0txY83CczR7TFHt8b83B5zdHvM0e0xR8aNv5+mYf6ahvlrOuawaZi/pmH+pGPuuWf8jJ/xM37Gz/hbMnOJUyrMr2Exv4bD3BoW82tYppxfXnqAiIiIiIiIiIiIiIiIiIjIjEjSKCCTyTBw4EDIZDIpfrxJYI5ujzm6Nebn9pij22OObo85Mm78/TQN89c0zF/TMYdNw/w1DfMnHXPPPeNn/Iyf8TN+xt+SmUucUmF+DYv5NRzm1rCYX8My5fzKhBBC6kEQERERERERERERERERERFR8+ClB4iIiIiIiIiIiIiIiIiIiMwIGwWIiIiIiIiIiIiIiIiIiIjMCBsFiIiIiIiIiIiIiIiIiIiIzIhBGgUuXLiAkSNHwt7eHs7OzpgwYQKys7Nvu19ZWRnefPNNBAcHQ6lUws/PD7Nnz661ze7duzFw4EA4OzvDyckJ/fv3x86dOw0RhkEZMkcAsHjxYnTu3BnW1tZwdXXFiBEjDBGGQRk6RwBQWlqKsLAwyGQy7N69W88RGJ6hcnTy5Ek89thjCA4OhrW1NUJCQvDSSy+hoKDAgNEYhiH/jrKysjB+/Hg4OzvD3t4e99xzDy5evGioUAyiMfn56aefIJPJbrhd/xwy53pd3xwBLaNeS4lzjqbhfKTpOF9pGs5lmobznKbhPEg6jcn9nj170K9fP3h4eMDKygpBQUGYNm1anc9rYz/+GDp+wLiPHYaK31RqvyF//6ZQuxsT/4YNGzBixAh4e3vD1tYW4eHhmDdvHoQQtbYzhdpryPiBlln/GhI/0PLqX33il7r+NXZOeuzYMQwYMAA2Njbw8PDAc889h7KyMt33Kyoq8OSTT6Jr165QKpUIDg6u83GSkpIwbty4Wjn65ptv9BWepKTMbXJycp3zXplMhpEjR+ozTMlI/bcLAHv37sWdd94JBwcH2Nvbo0ePHkhMTNRHeJKTOr/BwcE3/O0OGjRIT9FJT+r8Xuvxxx+HTCbDnDlzmhCRcZE6v1OmTEGbNm1gZ2cHZ2dn9O3bF5s3b9ZXeLXovVGgtLQUgwcPRmVlJfbs2YPNmzcjPj4e9957700ncACgVqsxcuRIbN++Hd999x3Onj2LjRs3onfv3rptUlJSMGLECHTo0AGHDh3C4cOHER4ejpEjRyIpKUnfoRiMIXMEALNmzcI777yDl19+GSdPnsS+ffvw2GOPGTosvTJ0jmpMnToVrVu3NlQYBmXIHMXExMDe3h6LFy/GqVOn8N133+HPP//EI4880hyh6Y0hc6TRaHDPPfcgISEBmzdvxp49e1BRUYG77rqrVuE3Zo3NT42MjIxatz59+ui+Z+71usatcgS0jHotJc45mobzkabjfKVpOJdpGs5zmobzIOk0Nvf29vZ49tlnsXPnTpw7dw7ff/89/v77b0ycOLHWdsZ+/DF0/DWM9dhhyPhNofYbMn5TqN2NjX/37t3o3bs31q1bh/j4eLzyyit488038emnn+q2MYXaa8j4gZZb/+obf42WVv/qE7+U9a+xcaWlpWHw4MHw9/fHkSNHsHz5cvz555946qmndNuo1WoolUpMmTIFDz/88E0fa+TIkUhPT8fGjRuRkJCAF154AdOnT8dvv/2m11ibm9S5DQgIuGHOW9N8M378eL3GKgWp8wsAW7duxfDhwzFo0CDs378fsbGxePfdd2Fra6vXWKVgDPkFgFdeeaXW3/C6dev0FqOUjCW/gLZJMSEhAb6+vnqJzRgYQ367du2KH374AfHx8Th48CD69++Pe++9F0ePHtVrrAAAoWfff/+9sLS0FDk5Obr7YmNjBQCxc+fOm+63dOlS4eDgIK5cuXLTbdauXSsAiKKiIt19hYWFAoBYu3atfgJoBobM0YULF4RcLhdbt27V65ibmyFzVGPRokWia9eu4syZMwKA2LVrlz6G3myaI0fXWrVqlZDJZKKwsLDRY25uhszRtm3bBAARGxuruy8nJ0dYWlqKJUuW6CcAA2tsfn788Udxu8OHudfr+uSopdRrKXHO0TScjzQd5ytNw7lM03Ce0zScB0mnsbmvy9y5c4WDg4Pua1M4/hgy/hrGfOxojvivZWy135Dxm0Lt1mf8zz77rIiMjNR9bQq115Dxm1v9uz7+GuZS/24W/7Waq/41Nq63335beHp6CpVKpbtvw4YNAoC4cOHCDdvPnj1bBAUF3XB/Tk6OACA2btxY6/6uXbuKF154oRERGQ+pc1uX6dOnC3d3d1FRUVH/QIyU1PlVq9UiJCREvPnmm00LxEhJnV8hhAgKChKzZ89udAzGzBjyK4QQJ06cEJ6enuLChQstKt/Gkt/rOTs7i88//7ze29eX3lcU2L9/P6KiouDm5qa7r1OnTvD398fevXtvut/vv/+Onj17Yv78+QgMDESrVq0wadIk5OTk6Lbp3r07rK2t8e2336KqqgpVVVVYuHAhnJ2dbzhL05gZMkfr16+HpaUlcnJy0LFjR/j4+ODuu+9GXFycQWPSN0PmCABOnDiBmTNnYtWqVbCysjJYHIZk6Bxdr6CgADY2NibV0WjIHO3fvx/+/v7o1KmT7j43NzdERUXd8rGNSWPzU6NVq1bw8fHBoEGDsGnTplrfM/d6XeNWOWop9VpKnHM0DecjTcf5StNwLtM0nOc0DedB0mlq7mtcunQJa9eurbV8qCkcfwwZP2D8xw5Dx389Y6v9hozfFGq3vuIHtL9bd3d33demUHsNGb851T/gxvgB86l/QN3x17VNc9S/xsa1f/9+DB06FAqFQnff8OHDIZfLsW/fvnr/fFdXV4SHh2PZsmXIy8uDEALbt2/H2bNncffddzcuKCMhdW6vV15ejmXLluGpp54yyudYQ0md35iYGCQlJcHf3x8DBgyAh4cHoqKi8PvvvzcuICMjdX5rLFy4EG5ubggLC8O0adOQm5vb4McwRsaQ3+LiYowdOxbz5s1DaGhow4MwYsaQ32upVCr8/PPPKC4uxoABAxr9ODejuP0mQF5eHvLy8m65jaOjIzw9PZGRkQEfH58bvu/t7Y309PSb7p+YmIikpCQoFAqsXr0apaWlmDFjBkaNGoUDBw5AJpMhKCgIu3fvxkMPPYS3335b97g7duyo82c2J2PJUWJiIjQaDd577z3MnTsXHh4e+OyzzzBgwACcOXMG3t7eTY61sYwlR0VFRXjwwQfx1VdfoU2bNkhOTm5qaHpjLDm6XkZGBt577z1MnTq1VpGTgrHkqLGPbWjNkZ927dph2bJl6NSpEyoqKrB69WqMGjUKixcvxqRJkwDA7Ot1fXJkzPVaSsbyHDfmv+FbMZb8mfLft7Hk0JjnK7diLPm7njHNZW7FWPJnrPOc2zGWY7ypHkOaojlyXyM8PByJiYmorKzE6NGjsWLFCt33pDr+GEv8Uh07jCX+6zVX7TeW+KWq3c0Zf41du3Zh9erVtZYPlqr2Gkv85lD/atQVvznUvxp1xX+9pta/5ogrIyMDPXv2rHWfUqmEq6trg/Ihk8mwc+dOjBs3Dm5ublAoFLCwsMB3332HESNG1Ptxmosp5fZ6K1asQGFhIZ555plGP4ahmVJ+ExMTAWgvG/Ppp5+ie/fu2LRpEx588EFs2bLFKBtdTCm/APDiiy+ia9eu8PDwwOnTpzFz5kxs27YNJ06cgI2NTYMeqzmYWn4nTZqEQYMG1evyBMbA1PILAMuXL8czzzyD8vJyODk54Y8//kCPHj0a/Di3U6+Zwvz58/Hee+/dcpvHH38cP/300y2vz1DXm3Y11Go1hBBYuXIlnJ2dAQBLly5Fjx49cPToUfTs2RPZ2dmYNGkShg0bhqeffhoAsGjRIowaNQqHDh1CYGBgfcIxCGPJkVqthkqlwty5c3WToZ9//hn+/v74+eef8frrrzc8OD0xlhxNnjwZAwcOxIQJExoVhyEZS46ulZWVhWHDhiEiIgL/93//V/9gDMRYctTYxza05shP7969a13HuFevXsjOzsYnn3yie4Pc3Ot1fXJkzPVaSsbyHDfmv+FbMZb8mfLft7Hk0JjnK7diLPm7lrHNZW7FWPJnrPOc2zGWY7ypHkOaojlyX2PLli0oLS1FfHw83n77bTz77LNYtmwZAOnmV8YSv1THDmOJ/1rNWfuNJX6pandzxg8Ahw4dwn333YdZs2Zh9OjRuvulqr3GEr851D/g5vGbQ/0Dbh7/tfRR/5ojLn3VLCEEpk+fDrVajZ07d8Ld3R07d+7E888/Dw8PD4wcObLej9UcTCm311u4cCGGDRuGVq1aNfoxDM2U8qtWqwFo69fkyZMBAF26dMGhQ4ewYMECo2wUMKX8AsDLL7+s+3/Hjh0RGRmJ1q1bY/369Rg/fnyDHqs5mFJ+v/32W5w+fRpHjhyp9z5SM6X81hg9ejSioqKQm5uLX3/9FRMnTsSOHTsQGRnZ4Me6lXpdeuDdd9+FSqW65W3p0qUAAF9fX2RkZNzwGJmZmbfs4PX19YWPj4/uzSpA2ykNACkpKQCABQsWoLKyEt9//z169OiBHj164IcffoCtrS0WLlxY76ANwVhy5OvrCwAICwvTbWNtbY3Q0FDdNlIxlhxt27YNy5Ytg0KhgEKhQOvWrQEAgwcPvu2yhYZmLDmqkZqaioEDByIgIAB//PEHlEqlHqJsGmPJUWMf29CaIz91iYqKqtWxb+71ui7X58iY67WUjOU5bsx/w7diLPkz5b9vY8mhMc9XbsVY8lfDGOcyt2Is+TPWec7tGMsx3lSPIU3RnLkPCgpCWFgYHnroISxZsgQ///wzzpw5o3tsoPmPP8YSv1THDmOJv0Zz135jiV+q2t2c8e/evRtDhgzByy+/jNmzZ9f6nlS111jiN4f6d6v4zaH+3Sr+Gvqqf801J71+P5VKhby8vAbVrF27dmHVqlVYsWIF7rzzTnTq1AkzZszAww8/jA8//LDej9NcTCm31zp27BiOHTuGZ599tlH7NxdTym9ddRvQvjYz1vdNTCm/dQkNDYWbm5vRrthoSvndtm0bTp06BQcHB92xNyUlBe+//z4UCoWuEcaYmFJ+azg4OKB169aIiorCggUL0LlzZ3z66acNfpzbqVejgFwu1/2yb3aTy7UP1bdvXxw+fLjWEg7x8fFITU1F//79b/oz+vfvj8zMTBQVFenuO3v2LAAgODgYAFBWVqb7OTVkMhnkcjk0Gk39IjYQY8lRzf419wNAVVUVkpKSdNtIxVhydPDgQZw4cUJ327JlCwBg8eLF+PHHH/UddoMYS44A7fJH/fv3R1hYGNavXw9ra2s9R9s4xpKjvn37IjU1FfHx8bpt8vLycPjw4Vs+tqE1R37qEhMTg4CAAN3X5l6v63J9joy5XkvJWJ7jxvw3fCvGkj9T/vs2lhwa83zlVowlf4DxzmVuxVjyZ6zznNsxlmO8qR5DmkKq3NeorKwEIN3xx1jil+rYYSzxA9LUfmOJX6ra3Vzxb9q0CSNGjMDs2bPx7rvv3vB9qWqvscTf0uvf7eJv6fXvdvED+q1/zRFX3759sX379lofJm3duhUajQb9+vWr91jLysoAABYWFrXut7CwMMp5lynl9loLFy5EQECA0a3QcD1Tym/37t1hY2NTq24D2jpurO+bmFJ+65KSkoLc3Nxar92MiSnld/78+YiNja117PX19cXUqVNx4sSJG2qyMTCl/N7Kta999EboWUlJiQgMDBRDhgwR0dHRYv/+/aJz586iV69eQqPR6LZr166dWLBgge7r9PR04ezsLMaMGSNOnjwpDh8+LLp37y769u2r22/Pnj1CLpeLl156SZw+fVqcOnVKvPjii8LCwkIcOHBA36EYjCFzpNFoRK9evUSHDh3Enj17xOnTp8Xjjz8uXFxcRGZmZrPH2liGzNH1kpKSBACxa9cuQ4elV4bMUUJCgvD29hbDhg0Tly9fFhkZGbpbZWVls8faWIbMkVqtFj169BBdunQR+/fvF9HR0eKuu+4SISEhorS0tNljbYzG5mfOnDliy5Yt4vz58yI+Pl7MmjVLyGQy8fXXX+u2Mfd6XZ8ctZR6LSXOOZqG85Gm43ylaTiXaRrOc5qG8yDpNDb38+bNExs2bBBnzpwRiYmJYv369aJt27YiMjLSpI4/hoz/esZ47DBk/KZQ+w0ZvynU7sbGv2rVKmFpaSlmzZpV6/eakZGh28YUaq8h42/J9a8+8V+vJdW/+sQvZf1rbFypqanC0dFRTJgwQZw8eVJs375dBAYGinHjxtV6/ISEBHH8+HHxzDPPCB8fH3H8+HFx/PhxUVxcLIQQIjc3V3h6eoohQ4aIw4cPi8TERPH9998LpVIpPv30U4PGbmhS57ZGfn6+sLW1FR988IFhA25mxpDf119/XTg4OIgVK1aICxcuiK+++krI5XLx999/Gz4BBiZ1fg8cOCA+//xzER0dLZKSksSWLVtERESECA4OvuFv3BRJnd+6BAUFidmzZ+s9VilInd/z58+Ljz76SBw5ckSkpKSImJgY8fLLLwuZTCbWr1+v93j13igghDaIESNGCFtbW+Ho6CgeeeQRkZWVVfsHAzf80cTExIhBgwYJGxsb4ePjI5588kmRk5NTa5v169eLXr16CScnJ+Hk5CR69+4tNm7caIgwDMqQOcrKyhKPPvqocHZ2Fs7OzmLo0KEiLi7O0CHpnSFzdC1jfPFQX4bK0ezZswWAOm+mlidD/h1lZmaKhx9+WDg6OgpbW1sxYsQIceHCBUOHpFeNyc+MGTNEcHCwsLa2Fi4uLqJ3795i7dq1Nzy2Odfr+uaopdRrKXHO0TScjzQd5ytNw7lM03Ce0zScB0mnMbmfO3eu6Nixo7CzsxN2dnaiffv24q233hK5ubm19jOF448h47+WsR47DBW/qdR+Q/7+TaF2Nyb+gQMH3vR3ey1TqL2GjL+l1r/6xn+tllT/6hO/1PWvsXPSo0ePin79+glra2vh5uYmpk6dKkpKSmptExQUdNu4YmNjxahRo4Snp6ewsbERHTp0EJ999plQq9WGCrnZSJ1bIbTHIIVCIdLT0w0RoqSkzm91dbWYNWuW8PPzEzY2NqJr165i3bp1hgq32UmZ3+joaNG7d2/h4uIilEqlaNWqlZg6deotm8xMjdR/v9drSY0CQkib38uXL4sRI0YILy8vYWlpKby9vcXQoUMN1kQkuxoMERERERERERERERERERERmQH57TchIiIiIiIiIiIiIiIiIiKiloKNAkRERERERERERERERERERGaEjQJERERERERERERERERERERmhI0CREREREREREREREREREREZoSNAkRERERERERERERERERERGaEjQJERERERERERERERERERERmhI0CREREREREREREREREREREZoSNAkRERERERERERERERERERGaEjQJERERERERERERERERERERm5P8B0S+TzcsYvmAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2070x301.875 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(idata2, var_names=[\"alpha\", \"beta\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a8a06a",
   "metadata": {},
   "source": [
    "Here are the trace plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e95e7378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(idata2, compact=False, var_names=[\"alpha\", \"beta\"])\n",
    "decorate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734febbe",
   "metadata": {},
   "source": [
    "And the diagnostic statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "96846604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.618</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.651</td>\n",
       "      <td>-0.587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-0.633</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.666</td>\n",
       "      <td>-0.602</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2904.0</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-0.293</td>\n",
       "      <td>0.019</td>\n",
       "      <td>-0.328</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2472.0</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0] -0.618  0.017  -0.651   -0.587        0.0      0.0    3296.0   \n",
       "alpha[1] -0.633  0.017  -0.666   -0.602        0.0      0.0    2904.0   \n",
       "alpha[2] -0.293  0.019  -0.328   -0.258        0.0      0.0    3104.0   \n",
       "beta     -0.015  0.001  -0.016   -0.014        0.0      0.0    2472.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    1691.0    1.0  \n",
       "alpha[1]    1370.0    1.0  \n",
       "alpha[2]    1554.0    1.0  \n",
       "beta        1772.0    1.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(idata2, var_names=[\"alpha\", \"beta\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de123b1",
   "metadata": {},
   "source": [
    "Now to generate predictions, we can use the PyMC model directly -- we don't have to reimplement it by hand.\n",
    "\n",
    "We'll use `compute_deterministics` to compute the values of `p`, conditioned on the posterior distributions of `alpha` and `beta`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "96bc203c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e2cdb0507fe4c408227fec858ebad7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab5675270efa42cf81264bd2128f28df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f82eb42769524672813f7970784cfb53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities = {}\n",
    "\n",
    "with logistic_model2:\n",
    "    for key, party_name in party_names.items():\n",
    "        # Assign covariates to compute the posterior distributions of\n",
    "        pm.set_data(\n",
    "            {\n",
    "                \"party3\": np.tile(key, len(year_centered)),\n",
    "                \"year\": year_centered,\n",
    "            }\n",
    "        )\n",
    "\n",
    "        # Compute the posterior predictive\n",
    "        idata = pm.compute_deterministics(idata2['posterior'])\n",
    "\n",
    "        probabilities[party_name] = az.extract(idata, num_samples=100)[\"p\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1aa599a",
   "metadata": {},
   "source": [
    "Here's what the results look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a9c861e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x262.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original data\n",
    "table.plot(color=colors)\n",
    "\n",
    "# Plot posterior draws for each party affiliation\n",
    "for key, party_name in party_names.items():\n",
    "    ps = probabilities[party_name].to_numpy() * 100\n",
    "    plt.plot(year_range, ps, color=colors[key], alpha=0.02)\n",
    "\n",
    "decorate(ylabel=\"percent\", title=\"Percent saying people can be trusted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdade23",
   "metadata": {},
   "source": [
    "The posterior distributions of `p` overlap for the Democrat and Independent groups, but the distribution for Republicans is notably different.\n",
    "\n",
    "Based on these results, we can say with some confidence that Republicans are more likely to say people can be trusted -- at least under the assumption that the changes over time are well modeled by the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d70d1249",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}