04_geocentric_models-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import arviz as az\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow_probability.substrates import jax as tfp\n",
    "tfd = tfp.distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a function to plot the density using arviz plot_kde function\n",
    "def plot_density(positions, x_label=\"Position\", y_label=\"Density\"):\n",
    "    az.plot_kde(positions)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [6, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate a key\n",
    "# jax.random.key (new version of jax.random.PRNGKey) \n",
    "#   not working with tfp.substrates.jax.mcmc\n",
    "def a_key():\n",
    "    # return jax.random.key(random.randint(0, 1000_000))\n",
    "    return jax.random.PRNGKey(random.randint(0, 1000_000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jnp array replaces tf array\n",
    "# but jnp has yet no equivalant to tf.Variable() \n",
    "from tensorflow_probability.python.internal.backend import jax as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.Variable(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Why normal distributions are normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.1 Normal by addition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.1\n",
    "\n",
    "This snippet is showing the notion of \"Normal distribution by Addition\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = tfd.Uniform(low=-1.0, high=1.0)\n",
    "pos = jnp.sum(u1.sample(sample_shape=(16, 1000), seed=a_key()), axis=0)\n",
    "plot_density(pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.2 Normal by multiplication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.2 & 4.3\n",
    "\n",
    "This snippet is showing the notion of \"Normal distribution by Multiplication\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u2 = tfd.Uniform(low=1.0, high=1.1)\n",
    "pos = jnp.prod(u2.sample(sample_shape=(12, 10000), seed=a_key()), axis=0)\n",
    "plot_density(pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.4\n",
    "\n",
    "Continuation of the notion of \"Normal distribution by Multiplication\"\n",
    "\n",
    "Author explains that - \"The smaller the effect of a variable (locus in the example), the better the additive approximation will be\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "big = jnp.prod(tfd.Uniform(low=1.0, high=1.5).sample(sample_shape=(12, 10000), \n",
    "                                                     seed=a_key()), axis=0)\n",
    "small = jnp.prod(tfd.Uniform(low=1.0, high=1.01).sample(sample_shape=(12, 10000), \n",
    "                                                        seed=a_key()), axis=0)\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "az.plot_kde(big, ax=ax[0])\n",
    "az.plot_kde(small, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.3 Normal by log-multiplication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.5\n",
    "\n",
    "This snippet is showing the notion of \"Normal distribution by log-multiplication\"\n",
    "\n",
    "Author explains - \"Large deviates that are multi- plied together do not produce Gaussian distributions, but they do tend to produce Gaussian distributions on the log scale. So even multiplicative interactions of large deviations can produce Gaussian distributions, once we measure the outcomes on the log scale.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_big = jnp.log(\n",
    "  jnp.prod(\n",
    "    tfd.Uniform(low=1.0, high=1.5).sample(sample_shape=(12, 10000), \n",
    "                                          seed=a_key()), axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "az.plot_kde(big, ax=ax[0])\n",
    "az.plot_kde(log_big, ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 A language for describing models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.6\n",
    "\n",
    "Here the globe tossing model is described again.\n",
    "\n",
    "$w$ = Observed count of water\n",
    "\n",
    "$n$ = Total number of tosses\n",
    "\n",
    "$p$ = Proportion of water on the globel\n",
    "\n",
    "\n",
    "This is the first introduction/usage of Bayes' Theorem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w, n = 6.0, 9.0\n",
    "\n",
    "p_grid = jnp.linspace(0.0, 1.0, 100)\n",
    "\n",
    "b1_dist = tfd.Binomial(total_count=n, probs=p_grid)\n",
    "u3_dist = tfd.Uniform(low=0.0, high=1.0)\n",
    "\n",
    "posterior = b1_dist.prob(w) * u3_dist.prob(p_grid)\n",
    "posterior = posterior / jnp.sum(posterior)\n",
    "\n",
    "plt.plot(p_grid, posterior)\n",
    "plt.xlabel(\"p\")\n",
    "plt.ylabel(\"Density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Gaussian model of height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.1 The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.7, 4.8 \n",
    "\n",
    "\n",
    "We are now building the Gaussian model of height\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.summary(d.to_dict(orient=\"list\"), kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.10\n",
    "\n",
    "For now we are going to work only with the height columns so let's look at it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.height.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.11\n",
    "\n",
    "We are only interested in the heights of adults only (for the time being; we will incorporate the whole dataset later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2 = d[d.age >= 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.2 The model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.12 & 4.13\n",
    "\n",
    "Author explains that whatever is your prior, it's a very good idea to always plot it. This way we will have a better\n",
    "sense of our model.\n",
    "\n",
    "Here we have assumed certain values for our priors and as per the instruction plotting them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "x = jnp.linspace(100.0, 250.0, 100)\n",
    "mu_prior = tfd.Normal(loc=178.0, scale=20.0)\n",
    "ax1.set_title(\"Prior for mu\")\n",
    "ax1.plot(x, mu_prior.prob(x))\n",
    "\n",
    "x = jnp.linspace(-10.0, 60.0, 100)\n",
    "scale_prior = tfd.Uniform(low=0.0, high=50.0)\n",
    "ax2.set_title(\"Prior for scale/sigma\")\n",
    "ax2.plot(x, scale_prior.prob(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.14\n",
    "\n",
    "In the above section we have chosen priors for $h$, $\\mu$, $\\sigma$ and even plotted them. It is also important to now see what kind of distribution these assumptions generate. This is called **PRIOR PREDICTIVE simulation** and is important part of modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the joint distribution of h, mu and sigma\n",
    "n_samples = 1000\n",
    "\n",
    "sample_mu = tfd.Normal(loc=178.0, scale=20.0).sample(n_samples, seed=a_key())\n",
    "sample_sigma = tfd.Uniform(low=0.0, high=50.0).sample(n_samples, seed=a_key())\n",
    "prior_h = tfd.Normal(loc=sample_mu, scale=sample_sigma).sample(seed=a_key())\n",
    "\n",
    "az.plot_kde(prior_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.15\n",
    "\n",
    "As mentioned in above cell the prior predictive simulation is helpful in assigning sensible priors. Here we see if we change the scale for *sample_mu* to 100 (instead of using 20 as in the above cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the joint distribution of h, mu and sigma\n",
    "n_samples = 1000\n",
    "\n",
    "sample_mu = tfd.Normal(loc=178.0, scale=100.0).sample(n_samples, seed=a_key())\n",
    "sample_sigma = tfd.Uniform(low=0.0, high=50.0).sample(n_samples, seed=a_key())\n",
    "prior_h = tfd.Normal(loc=sample_mu, scale=sample_sigma).sample(seed=a_key())\n",
    "\n",
    "az.plot_kde(prior_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, what the above plot is showing that there are about 4% of people that have a **negative height** and about 18% on the right side have height not common at all for humans to attain ! So clearly this choice of scale (i.e. 100) is not a good one as the prior. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.3 Grid approximation of the posterior distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.16\n",
    "\n",
    "Here we are doing **Grid approximation of the posterior distribution**. A very crude technique that may work for small/simple models. It is customary to do it to make a point but later we will use well established inference methods based on MCMC and VI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_likelihood(mu, sigma, sample_data):\n",
    "    def _compute(mu, sigma, sample_data):\n",
    "        norm_dist = tfd.Normal(loc=mu, scale=sigma)\n",
    "        return jnp.sum(norm_dist.log_prob(sample_data))\n",
    "\n",
    "    return jax.vmap(_compute, in_axes=(0, 0, None))(mu, sigma, sample_data)\n",
    "\n",
    "\n",
    "def grid_approximation(sample_data, mu_start=150.0, mu_stop=160.0, \n",
    "                       sigma_start=7.0, sigma_stop=9.0):\n",
    "    mu_list = jnp.linspace(start=mu_start, stop=mu_stop, num=200)\n",
    "    sigma_list = jnp.linspace(start=sigma_start, stop=sigma_stop, num=200)\n",
    "\n",
    "    mesh = jnp.meshgrid(mu_list, sigma_list)\n",
    "    mu = jnp.reshape(mesh[0], -1)\n",
    "    sigma = jnp.reshape(mesh[1], -1)\n",
    "\n",
    "    log_likelihood = compute_likelihood(mu, sigma, sample_data)\n",
    "\n",
    "    logprob_mu = tfd.Normal(178.0, 20.0).log_prob(mu)\n",
    "    logprob_sigma = tfd.Uniform(low=0.0, high=50.0).log_prob(sigma)\n",
    "\n",
    "    log_joint_prod = log_likelihood + logprob_mu + logprob_sigma\n",
    "    joint_prob = jnp.exp(log_joint_prod - jnp.max(log_joint_prod))\n",
    "\n",
    "    return mesh, joint_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh, joint_prob = grid_approximation(d2.height.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.17\n",
    "\n",
    "Make a contour plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_joint_prob = jnp.reshape(joint_prob, (200, 200))\n",
    "plt.contour(*mesh, reshaped_joint_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.18\n",
    "\n",
    "Here we are plotting a heatmap instead of contour plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(reshaped_joint_prob, origin=\"lower\", extent=(150, 160, 7, 9), aspect=\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.4 Sampling from the posterior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.19 & 4.20\n",
    "\n",
    "We are at point of now **Sampling from the Posterior**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joint_prob.shape, joint_prob[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a trick to sample row numbers randomly with the help of Categorical distribution\n",
    "sample_rows = tfd.Categorical(probs=(joint_prob / jnp.sum(joint_prob))).sample(\n",
    "  100_00, seed=a_key())\n",
    "sample_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = mesh[0].reshape(-1)\n",
    "sigma = mesh[1].reshape(-1)\n",
    "\n",
    "# We are sampling 2 parameters here from the selected rows\n",
    "sample_mu = mu[sample_rows]\n",
    "sample_sigma = sigma[sample_rows]\n",
    "\n",
    "plt.scatter(sample_mu, sample_sigma, s=64, alpha=0.1, edgecolor=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.21\n",
    "\n",
    "Time to summarize/describe the distribution of confidence in each combination of $\\mu$ & $\\sigma$ using the samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "az.plot_kde(sample_mu, ax=ax[0])\n",
    "ax[0].set_xlabel(\"sample_mu\")\n",
    "ax[0].set_yticks([])\n",
    "az.plot_kde(sample_sigma, ax=ax[1])\n",
    "ax[1].set_xlabel(\"sample_sigma\")\n",
    "ax[1].set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As should be clear from the plot, the densities here are very close *normal distribution*. As sample size increases, posterior densities show this behavior. \n",
    "\n",
    "Pay attention to sample_sigma, density of it has a **longer right-hand tail**. Author mentions here that this condition is very common for standard deviation parameters. No explanation about it at this point of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.22\n",
    "\n",
    "Now we want to summarize the widths of these densities with **posterior compatibility intervals**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(sample_mu), type(sample_mu.__array__()), type(jax.device_get(sample_mu))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.5 Finding the posterior distribution using quap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.5 Finding the posterior distribution using quap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"sample_mu -\", az.hdi(sample_mu.__array__()))\n",
    "print(\"sample_sigma -\", az.hdi(jax.device_get(sample_sigma)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.23 & Code 4.24\n",
    "\n",
    "Author now wants to use Quadratic approximation but he wants to make a point about using it and impact of $\\sigma$ on this approximation.\n",
    "\n",
    "Here the above analysis of the height data is repeated but with only a fraction of original data. The reasoning behind is to demonstrate that, in principle, the posterior is not always Gaussian in shape.\n",
    "\n",
    "Author explains that for a Guassian $\\mu$ & Gaussian likelihood the shape is always Gaussian irrespective of the sample size but it is the $\\sigma$ that causes problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We just get 20 samples for now and repeat our exercise\n",
    "d3 = d2.height.sample(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh2, joint_prob2 = grid_approximation(\n",
    "    d3.values,\n",
    "    mu_start=150.0,\n",
    "    mu_stop=170.0,\n",
    "    sigma_start=4.0,\n",
    "    sigma_stop=20.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu2 = mesh2[0].reshape(-1)\n",
    "sigma2 = mesh2[1].reshape(-1)\n",
    "\n",
    "# We are sampling 2 parameters here from the selected rows\n",
    "sample_mu2 = mu2[sample_rows]\n",
    "sample_sigma2 = sigma2[sample_rows]\n",
    "\n",
    "plt.scatter(sample_mu2, sample_sigma2, s=64, alpha=0.1, edgecolor=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "az.plot_kde(sample_mu2, ax=ax[0])\n",
    "ax[0].set_xlabel(\"sample_mu2\")\n",
    "ax[0].set_yticks([])\n",
    "az.plot_kde(sample_sigma2, ax=ax[1])\n",
    "ax[1].set_xlabel(\"sample_sigma2\")\n",
    "ax[1].set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.plot_kde(sample_sigma2)\n",
    "x = jnp.sort(sample_sigma2)\n",
    "plt.plot(x, tfd.Normal(loc=jnp.mean(x), scale=jnp.std(x)).prob(x), \"--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.5 Finding the posterior distribution using quap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.26\n",
    "\n",
    "We will reload our data frame and start to use quadratic approximiation now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
    "d2 = d[d.age > 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.27\n",
    "\n",
    "We are now ready to define our model using tensorflow probability.\n",
    "\n",
    "\n",
    "There are few ways to define a model using tfp, I am going to use a variant\n",
    "of JointSequential that uses Coroutines (generator) based syntax. However, you can always\n",
    "define as a plain function and return log probs yourself. An example of plain function that would be \n",
    "\n",
    "```python\n",
    "def joint_log_prob_model(mu, sigma, data):\n",
    "  mu_dist = tfd.Normal(loc=178., scale=20.)\n",
    "  sigma_dist = tfd.Uniform(low=0., high=50.)\n",
    "  height_dist = tfd.Normal(loc=mu, scale=sigma)\n",
    "  \n",
    "  return (\n",
    "      mu_dist.log_prob(mu) +\n",
    "      sigma_dist.log_prob(sigma) +           \n",
    "      tf.reduce_sum(height_dist.log_prob(data)) \n",
    "  )\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sample = d2.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### using JointDistributionNamedAutoBatched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m41 = dict(\n",
    "    mu = tfd.Normal(loc=178.0, scale=20.0),\n",
    "    sigma = tfd.Uniform(low=0.0, high=50.0),\n",
    "    height = lambda mu, sigma: tfd.Sample(tfd.Normal(loc=mu, scale=sigma), n_sample)\n",
    ")\n",
    "\n",
    "jd_4_1_N = tfd.JointDistributionNamedAutoBatched(m41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_4_1_N.sample(seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample of 'Named': dictionary\n",
    "type(jd_4_1_N.sample(seed=a_key()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mcmc.sample_chain(current_state): Tensor or Python list of Tensor\n",
    "#  - error when dict input \n",
    "# a list of two jnp.array\n",
    "init_state = [jd_4_1_N.sample(seed=a_key())[s] for s in [\"mu\", \"sigma\"]]\n",
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Match the function args with the init_state\n",
    "# init_state(a list of two jnp.array) be interpreted as \"mu\" and \"sigma\"\n",
    "def target_log_prob(mu, sigma):\n",
    "    # sample of 'Named': dictionary\n",
    "    samples = {\"mu\": mu, \"sigma\": sigma, \"height\": jnp.array(d2[\"height\"])}\n",
    "    return jd_4_1_N.log_prob(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob,\n",
    "    step_size=0.2, \n",
    "    num_leapfrog_steps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = tfp.mcmc.sample_chain(\n",
    "    1000,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel,\n",
    "    num_burnin_steps=200,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_dict = dict(zip([\"mu\", \"sigma\"], states))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.summary(states_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shape of mcmc.sample_chain output depends on shape of init_state\n",
    "init_state = jnp.concatenate(\n",
    "  [jnp.expand_dims(s, axis=0) for s in init_state if jnp.ndim(s)==0])\n",
    "# an jnp.array of two elements\n",
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def target_log_prob(mu, sigma):\n",
    "def target_log_prob(states):\n",
    "    mu, sigma = states\n",
    "    states = {\"mu\": mu, \"sigma\": sigma, \"height\": jnp.array(d2[\"height\"])}\n",
    "    return jd_4_1_N.log_prob(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob,\n",
    "    step_size=0.2, \n",
    "    num_leapfrog_steps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = tfp.mcmc.sample_chain(\n",
    "    1000,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel,\n",
    "    num_burnin_steps=200,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### using JointDistributionCoroutineAutoBatched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def m41c():\n",
    "    mu = yield tfd.Normal(loc=178.0, scale=20.0, name=\"mu\")\n",
    "    sigma = yield tfd.Uniform(low=0.0, high=50.0, name=\"sigma\")\n",
    "    height = yield tfd.Sample(tfd.Normal(loc=mu, scale=sigma), n_sample, name=\"height\")\n",
    "\n",
    "jd_4_1_C = tfd.JointDistributionCoroutineAutoBatched(m41c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Couroutine' sample: StructTuple\n",
    "init2 = jd_4_1_C.sample(seed=a_key())\n",
    "init2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(init2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seemingly a NamedTuple\n",
    "init2.mu, init2.sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init2[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a NamedTuple for Coroutine.log_prob()\n",
    "StateTuple = namedtuple(\"StateTuple\", [\"mu\", \"sigma\", \"height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mcmc.sample_chain() can interpret NamedTuple, so two args needed\n",
    "def target_log_prob(mu, sigma):\n",
    "    params = {\"mu\": mu, \"sigma\": sigma, \"height\": jnp.array(d2[\"height\"])}\n",
    "    return jd_4_1_C.log_prob(StateTuple(**params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob,\n",
    "    step_size=0.2, \n",
    "    num_leapfrog_steps=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init2 = init2[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states2 = tfp.mcmc.sample_chain(\n",
    "    1000,\n",
    "    current_state=init2,\n",
    "    kernel=kernel,\n",
    "    num_burnin_steps=200,\n",
    "    trace_fn=None,\n",
    "    seed=a_key()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.summary(states2._asdict(), round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers shown above provide Gaussian approximations for each parameter’s marginal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a 'jit' version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def run_chain(init_state, key):\n",
    "#     target_log_prob = target_log_prob(...)\n",
    "#     kernel = tfp.mcmc.HamiltonianMonteCarlo(...)\n",
    "#     return tfp.mcmc.sample_chain(...)\n",
    "\n",
    "# states = jax.jit(run_chain)(init_state, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfd.Sample(tfd.Normal(loc=0., scale=1.), (2,)).sample(seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jnp.array([[3,2]]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.30\n",
    "\n",
    "What should be the starting values for the sampling ?\n",
    "\n",
    "Author recommends that we could use the mean from the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_4_2 = dict(\n",
    "    # mu = tfd.Normal(loc=178.0, scale=20.0),\n",
    "    mu = tfd.Normal(loc=178.0, scale=0.1),\n",
    "    sigma = tfd.Uniform(low=0.0, high=50.0),\n",
    "    height = lambda mu, sigma: tfd.Sample(tfd.Normal(loc=mu, scale=sigma), n_sample)\n",
    ")\n",
    "\n",
    "jdc_4_2 = tfd.JointDistributionNamedAutoBatched(model_4_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 0\n",
    "num_results = 1000\n",
    "step_size = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_state = [jdc_4_2.sample(seed=a_key())[s] for s in [\"mu\", \"sigma\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_log_prob(mu, sigma):\n",
    "    samples = {\"mu\": mu, \"sigma\": sigma, \"height\": d2.height.values}\n",
    "    return jdc_4_2.log_prob(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob,\n",
    "    step_size=step_size, \n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states, [step_size, log_accept_ratio] = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=lambda _, pkr: [pkr.inner_results.accepted_results.step_size,\n",
    "                             pkr.inner_results.log_accept_ratio],\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jnp.unique(step_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jnp.exp(tfp.math.reduce_logmeanexp(jnp.minimum(log_accept_ratio, 0.)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_dict = dict(zip([\"mu\", \"sigma\"], [jax.device_get(s) for s in states]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.summary(states_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taken verbatim from the book :\n",
    "\n",
    "Notice that the estimate for μ has hardly moved off the prior. The prior was very concentrated around 178. So this is not surprising. But also notice that the estimate for σ has changed quite a lot, even though we didn’t change its prior at all. Once the golem is certain that the mean is near 178—as the prior insists—then the golem has to estimate σ conditional on that fact. This results in a different posterior for σ, even though all we changed is prior information about the other parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
    "d2 = d[d.age > 18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sample = d2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m41 = dict(\n",
    "    mu = tfd.Normal(loc=178.0, scale=20.0),\n",
    "    sigma = tfd.Uniform(low=0.0, high=50.0),\n",
    "    height = lambda mu, sigma: tfd.Sample(tfd.Normal(loc=mu, scale=sigma), n_sample)\n",
    ")\n",
    "\n",
    "jd_4_1 = tfd.JointDistributionNamedAutoBatched(m41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = \"mu\", \"sigma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_log_prob_41(mu, sigma):\n",
    "    sample = dict(zip(state_name, (mu, sigma)), height = d2.height.values)\n",
    "    return jd_4_1.log_prob(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 500\n",
    "num_results = 500\n",
    "step_size = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_41 = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob_41,\n",
    "    step_size=step_size,\n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel_41 = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel_41, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - init_state = [jnp.zeros(()), jnp.zeros(())]\n",
    "#   : stay at zero, bad initial\n",
    "# - init_state = [jd_4_1.sample((), seed=a_key())[s] for s in state_name]\n",
    "#   : a safe way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of chains: 3\n",
    "init_state = [jd_4_1.sample((3,), seed=a_key())[s] for s in state_name]\n",
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_41 = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel_41,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())\n",
    "\n",
    "states_41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tfp.mcmc.sample_chain output shape: (sample, batch, event)\n",
    "#  == in terms of arviz, (draws, chains, *shape)\n",
    "# arvis input shape: (chains, draws, *shape)\n",
    "states_41 = [jnp.moveaxis(s, 0, 1) for s in states_41]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Linear prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_41_dict = dict(zip(state_name, states_41))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stats for all chains\n",
    "az.summary(states_41_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stats for the first chains\n",
    "az.summary(dict(mu=states_41_dict['mu'][0], sigma=states_41_dict['sigma'][0]), \n",
    "           round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.plot_trace(states_41_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.32\n",
    "\n",
    "\n",
    "Now this approximation of posterior is multi-dimension where $\\mu$ and $\\sigma$ are contributing a dimension each. In other words, we have a multi-dimensional gaussian distribution\n",
    "\n",
    "Since it is multi-dimensional, let's see the covariances between the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.plot_pair(states_41_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_mu, sample_sigma = states_41_dict['mu'][0], states_41_dict['sigma'][0]\n",
    "cov_41 = tfp.stats.covariance(jnp.stack([sample_mu, sample_sigma], axis=1))\n",
    "cov_41"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfp.stats.correlation(jnp.stack([sample_mu, sample_sigma], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.34\n",
    "\n",
    "We did not use the quadratic approximation, instead we use a MCMC method to sample from the posterior. Thus, we already have samples. We can do something like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jnp.mean(sample_mu), jnp.mean(sample_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mvn = tfd.MultivariateNormalTriL(loc=[jnp.mean(sample_mu), jnp.mean(sample_sigma)], \n",
    "                                 scale_tril=jnp.linalg.cholesky(cov_41))\n",
    "mvn.sample(100, seed=a_key())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Linear prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.37\n",
    "\n",
    "So now let’s look at how height in these Kalahari foragers (the outcome variable) covaries with weight (the predictor variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(d2.height, d2.weight, \".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.1 The linear model strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.38, & 4.39 \n",
    "\n",
    "In brief, we have now started to model the relation between height & weight formally and the strategy is formally Linear Model and process is called Linear Regression. \n",
    "\n",
    "Here is the description of the model -\n",
    "\n",
    "$h_i \\sim Normal(\\mu_i,\\sigma)$    \n",
    "\n",
    "$\\mu_i = \\alpha + \\beta(x_i - \\bar{x})$   \n",
    "\n",
    "$\\alpha \\sim Normal(178,20)$       \n",
    "\n",
    "$\\beta \\sim Normal(0,10)$          \n",
    "\n",
    "$\\sigma \\sim Uniform(0,50)$        \n",
    "\n",
    "\n",
    "where, $h_i$ is the likelhood, $\\mu_i$ is the linear model and rest are the priors\n",
    "\n",
    "Important thing to note is that $h_i$ has a subscript $i$, which means that *the mean depends upon the row*\n",
    "\n",
    "$\\mu_i$ is also called a **Deterministic Variable** as given the values of other **Stochastic Variables** we are certain about the values it would take !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "\n",
    "a = tfd.Normal(loc=178.0, scale=20.0).sample(N, seed=a_key())\n",
    "b = tfd.Normal(loc=0.0, scale=10.0).sample(N, seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.shape, b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(\n",
    "    xlim=(d2.weight.min(), d2.weight.max()),\n",
    "    ylim=(-100, 400),\n",
    "    xlabel=\"weight\",\n",
    "    ylabel=\"height\",\n",
    ")\n",
    "plt.axhline(y=0, c=\"k\", ls=\"--\")\n",
    "plt.axhline(y=272, c=\"k\", ls=\"-\", lw=0.5)\n",
    "plt.title(\"b ~ Normal(0, 10)\")\n",
    "xbar = d2.weight.mean()\n",
    "x = np.linspace(d2.weight.min(), d2.weight.max(), 101)\n",
    "for i in range(N):\n",
    "    plt.plot(x, a[i] + b[i] * (x - xbar), \"k\", alpha=0.2)\n",
    "\n",
    "plt.text(x=35, y=282, s=\"World's tallest person (272cm)\")\n",
    "plt.text(x=35, y=-25, s=\"Embryo (0cm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'---' represents a zero - no one is shorter than this & also is shown the line for world's tallest person.\n",
    "\n",
    "Clearly, the lines are showing unnatural relationships therefore befoe we have even see the data, this is a bad model. Again prior predictive checks are coming in handy !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.40\n",
    "\n",
    "We use some common sense here, we know that average height increases with average weight (to some extent and to a certain point) therefore we must restrict it to positive values only. \n",
    "\n",
    "Earlier we used Normal distribution for beta but a better one is LogNormal\n",
    "\n",
    "$\\beta \\sim LogNormal(0,1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = tfd.LogNormal(loc=0.0, scale=1.0).sample(1000, seed=a_key())\n",
    "az.plot_kde(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.41\n",
    "\n",
    "Doing the prior predictive check again where $\\beta$ is LogNormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tfd.Normal(loc=178.0, scale=20.0).sample(100, seed=a_key())\n",
    "b = tfd.LogNormal(loc=0.0, scale=1.0).sample(100, seed=a_key())\n",
    "\n",
    "plt.subplot(\n",
    "    xlim=(d2.weight.min(), d2.weight.max()),\n",
    "    ylim=(-100, 400),\n",
    "    xlabel=\"weight\",\n",
    "    ylabel=\"height\",\n",
    ")\n",
    "plt.axhline(y=0, c=\"k\", ls=\"--\")\n",
    "plt.axhline(y=272, c=\"k\", ls=\"-\", lw=0.5)\n",
    "plt.title(\"b ~ LogNormal(0, 1)\")\n",
    "xbar = d2.weight.mean()\n",
    "x = np.linspace(d2.weight.min(), d2.weight.max(), 101)\n",
    "for i in range(N):\n",
    "    plt.plot(x, a[i] + b[i] * (x - xbar), \"k\", alpha=0.2)\n",
    "\n",
    "plt.text(x=35, y=282, s=\"World's tallest person (272cm)\")\n",
    "plt.text(x=35, y=-25, s=\"Embryo (0cm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this is much sensible plot; nearly all lines in the joint prior for $\\alpha$ & $\\beta$ are now in human reason."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.2 Finding the posterior distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.42\n",
    "\n",
    "Time to build the model again with all the new things we have discovered and updates discussed above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
    "d2 = d[d.age > 18]\n",
    "x_bar = d2.weight.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict elements do not recognize other elements\n",
    "# e.g., mu = alpha + beta*(d2.weight.values - x_bar)\n",
    "#       -> NameError: name 'alpha' is not defined\n",
    "m43 = dict(\n",
    "    alpha = tfd.Normal(loc=178.0, scale=20.0),\n",
    "    beta = tfd.LogNormal(loc=0.0, scale=1.0),\n",
    "    sigma = tfd.Uniform(low=0.0, high=50.0),\n",
    "    # mu = alpha + beta*(d2.weight.values - x_bar),\n",
    "    mu = lambda alpha, beta: tfd.Deterministic(alpha + beta*(d2.weight.values - x_bar)),\n",
    "    height = lambda mu, sigma: tfd.Normal(loc=mu, scale=sigma)\n",
    ")\n",
    "\n",
    "jd_43 = tfd.JointDistributionNamedAutoBatched(m43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([]),\n",
       " {'beta': TensorShape([]),\n",
       "  'alpha': TensorShape([]),\n",
       "  'mu': TensorShape([346]),\n",
       "  'sigma': TensorShape([]),\n",
       "  'height': TensorShape([346])})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd_43.batch_shape, jd_43.event_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'beta': Array(0.9115048, dtype=float32),\n",
       " 'alpha': Array(206.24863, dtype=float32),\n",
       " 'mu': Array([208.78267, 198.44638, 194.23434, 213.53735, 202.81346, 222.60745,\n",
       "        200.04851, 215.75966, 196.97346, 214.85524, 210.66904, 202.76178,\n",
       "        198.03293, 208.66818, 196.0432 , 209.45453, 203.76958, 200.15187,\n",
       "        209.84213, 204.13135, 197.54196, 199.73842, 197.51611, 201.93488,\n",
       "        215.42374, 199.37665, 200.28108, 207.67152, 200.59117, 197.61948,\n",
       "        208.39505, 208.83435, 210.22975, 210.2039 , 216.74161, 200.84958,\n",
       "        210.66904, 202.78763, 200.33276, 203.8372 , 218.60214, 207.77489,\n",
       "        205.5009 , 211.58499, 214.7002 , 207.0255 , 208.96355, 212.78798,\n",
       "        208.60178, 206.79294, 203.84709, 198.44638, 216.17311, 199.0924 ,\n",
       "        209.22195, 199.2216 , 208.18834, 208.29169, 217.64603, 199.76427,\n",
       "        208.21417, 204.57063, 211.00497, 201.08214, 211.39267, 201.13382,\n",
       "        198.2655 , 207.36143, 203.7179 , 208.60178, 215.96638, 209.45453,\n",
       "        204.20886, 197.15436, 216.92249, 211.9094 , 208.24002, 202.50337,\n",
       "        195.00957, 217.20674, 204.3639 , 201.59895, 207.07718, 202.65842,\n",
       "        208.57594, 199.37665, 206.22444, 203.7179 , 215.19116, 213.97665,\n",
       "        212.76213, 203.84709, 210.3848 , 203.22691, 216.25063, 203.61453,\n",
       "        213.58904, 210.97913, 203.95045, 209.9455 , 200.28108, 210.59152,\n",
       "        219.6616 , 208.88603, 201.00461, 198.80815, 203.58868, 205.81099,\n",
       "        208.8085 , 215.50125, 195.26797, 202.45169, 211.85844, 209.89381,\n",
       "        213.89912, 214.49347, 215.6563 , 203.69205, 202.50337, 210.48816,\n",
       "        205.60426, 207.07718, 203.43364, 212.11612, 205.39754, 194.54443,\n",
       "        216.92249, 213.20143, 198.44638, 209.71294, 217.10338, 203.56285,\n",
       "        210.92744, 207.61984, 221.52214, 209.84213, 193.92426, 197.36107,\n",
       "        206.79294, 205.19081, 206.71541, 202.7101 , 206.43117, 214.08002,\n",
       "        212.8655 , 200.82373, 206.84462, 202.0124 , 212.11612, 200.43613,\n",
       "        200.95294, 205.60426, 201.21133, 207.82657, 202.21913, 210.92744,\n",
       "        202.81346, 214.95757, 206.1986 , 197.8262 , 206.40532, 209.04108,\n",
       "        206.81877, 209.32532, 202.73595, 200.20355, 200.04851, 209.2478 ,\n",
       "        200.66869, 201.67647, 210.9533 , 214.67435, 206.75401, 201.13382,\n",
       "        204.57063, 194.23434, 206.61205, 199.86763, 198.08461, 202.45169,\n",
       "        208.86018, 208.6793 , 207.4648 , 202.7101 , 198.54974, 209.06691,\n",
       "        198.6531 , 216.74161, 200.46196, 208.47258, 198.18797, 202.81346,\n",
       "        205.99187, 211.16002, 215.57877, 199.76427, 218.47293, 205.68178,\n",
       "        205.66022, 205.81099, 205.86267, 202.08992, 205.37169, 196.56001,\n",
       "        195.06125, 197.95541, 210.35893, 213.66656, 198.98904, 202.11577,\n",
       "        211.31506, 205.26833, 210.66437, 195.81064, 205.16496, 209.06691,\n",
       "        204.15718, 196.09488, 201.39223, 197.95541, 211.5993 , 206.8963 ,\n",
       "        218.80887, 204.80319, 212.5554 , 211.39258, 196.40497, 201.08214,\n",
       "        219.48071, 212.7363 , 201.62479, 205.2425 , 214.9586 , 206.27612,\n",
       "        202.86514, 203.38196, 203.95023, 204.51895, 202.40001, 204.26054,\n",
       "        207.30975, 208.8085 , 203.95045, 209.40285, 206.99966, 213.40816,\n",
       "        208.5501 , 204.88072, 204.77736, 213.6924 , 205.91435, 208.21417,\n",
       "        194.05347, 216.63824, 216.0439 , 208.47258, 206.35364, 206.68958,\n",
       "        209.73877, 207.64568, 205.19081, 216.27647, 201.72815, 211.49594,\n",
       "        207.39207, 210.17807, 205.39754, 211.70267, 202.34833, 199.42833,\n",
       "        212.21948, 204.75151, 207.77489, 201.23718, 196.89595, 201.00461,\n",
       "        199.32497, 209.55789, 197.64532, 205.03577, 209.11859, 211.65099,\n",
       "        205.29417, 209.11859, 207.74904, 216.6124 , 209.48036, 209.14444,\n",
       "        208.47258, 197.59364, 198.55048, 209.8163 , 211.9094 , 207.594  ,\n",
       "        198.88568, 209.53204, 199.86763, 208.29169, 215.68213, 214.59683,\n",
       "        209.377  , 204.44144, 196.35329, 210.72072, 205.60426, 195.68143,\n",
       "        208.29169, 207.23222, 208.39505, 202.42586, 211.08249, 206.3278 ,\n",
       "        203.69205, 203.1494 , 206.40532, 211.9094 , 210.10054, 211.85771,\n",
       "        205.16496, 197.5678 , 204.27966, 207.82657, 203.35612, 200.00888,\n",
       "        204.65756, 214.08002, 204.3639 , 201.44391, 204.69983, 213.20143,\n",
       "        200.2294 , 204.20886, 197.90373, 205.5009 , 199.89346, 205.39754,\n",
       "        205.32   , 208.83435, 201.10797, 202.60674, 202.40001, 208.05913,\n",
       "        196.40497, 212.7363 , 214.46762, 213.07222], dtype=float32),\n",
       " 'sigma': Array(18.331284, dtype=float32),\n",
       " 'height': Array([182.50829, 187.08391, 182.28978, 208.4179 , 219.18452, 221.07317,\n",
       "        230.44122, 244.9325 , 215.20558, 198.1828 , 192.0441 , 203.11739,\n",
       "        204.3037 , 200.43152, 205.01445, 229.18605, 225.88367, 212.15096,\n",
       "        233.07925, 187.83733, 215.36067, 200.62038, 209.34996, 194.82164,\n",
       "        205.41759, 199.33315, 190.15091, 211.55203, 184.40074, 173.25504,\n",
       "        224.36935, 207.82756, 202.22743, 205.10774, 192.23549, 199.18877,\n",
       "        215.33331, 213.44844, 188.42517, 206.64403, 217.39526, 174.14981,\n",
       "        201.15034, 228.09818, 235.67975, 209.9302 , 223.60918, 191.2955 ,\n",
       "        188.35896, 204.32896, 200.43362, 204.44215, 198.11842, 207.36864,\n",
       "        219.93713, 195.81677, 189.73012, 186.09462, 210.7548 , 233.16594,\n",
       "        202.58043, 233.36038, 223.68362, 145.32011, 195.79504, 196.50943,\n",
       "        178.1377 , 176.76741, 184.9305 , 232.44202, 220.49983, 213.62292,\n",
       "        234.7704 , 187.70792, 191.29971, 193.0773 , 198.03352, 210.62593,\n",
       "        180.50598, 200.69499, 196.91093, 180.31358, 234.69246, 220.19534,\n",
       "        184.54593, 196.51236, 218.39914, 172.44754, 169.68073, 225.2623 ,\n",
       "        211.5755 , 212.1597 , 184.22832, 196.85406, 187.06126, 207.11151,\n",
       "        221.63226, 227.83487, 219.5154 , 189.52301, 179.17932, 193.26343,\n",
       "        218.44272, 186.16264, 209.46326, 232.57407, 192.38208, 188.37318,\n",
       "        217.77487, 259.63876, 213.53107, 200.63113, 228.49844, 186.13715,\n",
       "        196.56583, 217.52757, 212.21028, 196.87723, 218.36557, 222.89963,\n",
       "        190.06575, 183.78204, 216.0022 , 194.56287, 187.43187, 189.80998,\n",
       "        221.11093, 195.21747, 164.8575 , 205.75177, 232.32806, 221.4769 ,\n",
       "        257.93945, 209.91113, 231.32935, 212.98326, 173.04263, 159.39912,\n",
       "        204.16434, 230.35321, 218.79327, 177.12593, 240.25705, 194.59648,\n",
       "        218.617  , 203.67665, 226.92964, 199.02849, 225.43127, 201.44287,\n",
       "        211.9573 , 188.41803, 208.81252, 190.91365, 213.44252, 208.47388,\n",
       "        210.9378 , 237.3129 , 205.23112, 215.02264, 164.98904, 190.58383,\n",
       "        199.21025, 214.13728, 198.55203, 187.72382, 210.95132, 228.2118 ,\n",
       "        210.4664 , 211.70317, 222.25266, 215.1644 , 215.29568, 185.24876,\n",
       "        210.08963, 215.75266, 166.76518, 198.44994, 200.2621 , 200.68204,\n",
       "        206.57501, 205.72676, 172.77864, 247.72662, 176.80649, 229.19846,\n",
       "        200.04068, 201.67018, 185.99086, 216.72977, 158.45451, 234.61935,\n",
       "        212.16243, 226.36058, 214.73232, 211.12993, 179.27263, 200.30374,\n",
       "        206.74672, 199.36269, 226.92198, 171.84042, 221.17842, 188.89142,\n",
       "        233.18436, 192.2368 , 203.49252, 209.8763 , 192.93648, 220.58235,\n",
       "        219.33826, 200.26044, 203.61858, 191.60153, 209.1063 , 202.11977,\n",
       "        205.66087, 181.66927, 179.36009, 187.45699, 207.6234 , 189.42212,\n",
       "        229.0126 , 240.61696, 211.36528, 224.55624, 196.05421, 196.62071,\n",
       "        222.2091 , 227.57841, 213.61641, 225.51129, 219.11378, 199.08629,\n",
       "        226.79828, 216.38452, 193.03767, 229.4104 , 203.60191, 190.12201,\n",
       "        207.60434, 192.69489, 195.8931 , 201.65593, 237.80087, 239.94821,\n",
       "        241.10388, 199.37526, 218.54428, 238.89024, 210.69353, 205.35971,\n",
       "        195.88388, 181.53839, 220.84671, 213.2271 , 180.11882, 191.25418,\n",
       "        210.3258 , 204.29076, 234.6958 , 169.88434, 182.30164, 214.88727,\n",
       "        182.88518, 190.7012 , 199.25769, 228.61389, 194.15956, 193.07178,\n",
       "        196.13358, 210.37036, 177.31055, 216.92473, 216.70718, 216.8688 ,\n",
       "        196.6717 , 203.84195, 208.69022, 219.5744 , 210.05618, 220.6938 ,\n",
       "        219.36215, 198.4036 , 218.01164, 205.91199, 216.4751 , 224.41763,\n",
       "        238.79448, 180.57462, 230.58267, 178.0812 , 192.35974, 209.26192,\n",
       "        168.4035 , 216.53583, 222.87163, 221.13583, 217.62012, 180.70457,\n",
       "        171.2274 , 211.23085, 180.39307, 203.84244, 192.87299, 175.75302,\n",
       "        211.69897, 185.80016, 201.24286, 179.34116, 213.10736, 242.93007,\n",
       "        182.37369, 236.86584, 186.8878 , 195.1775 , 191.66241, 222.0962 ,\n",
       "        210.58463, 196.07751, 210.13309, 205.99306, 208.72476, 203.09293,\n",
       "        189.45798, 221.51703, 231.89694, 213.36774, 203.38539, 228.98027,\n",
       "        183.76765, 179.49985, 184.10239, 207.70557, 219.04907, 209.87369,\n",
       "        208.97604, 202.41382, 204.4969 , 208.25151, 211.20969, 172.8662 ,\n",
       "        213.25174, 230.70663, 195.61673, 193.27667], dtype=float32)}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd_43.sample(seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-824.8803, dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd_43.log_prob(jd_43.sample(seed=a_key()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = \"alpha\", \"beta\", \"sigma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Array(185.3092, dtype=float32),\n",
       " Array(0.71245664, dtype=float32),\n",
       " Array(41.973328, dtype=float32)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state = [jd_43.sample(seed=a_key())[s] for s in state_name]\n",
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"mu\" is deterministic, but must be included\n",
    "def target_log_prob_43(alpha, beta, sigma):\n",
    "    sample = dict(zip(state_name, (alpha, beta, sigma)),\n",
    "                  mu = alpha + beta*(d2.weight.values - x_bar),\n",
    "                  height = d2.height.values)\n",
    "    return jd_43.log_prob(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-1714.5527, dtype=float32)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_log_prob_43(init_state[0], init_state[1], init_state[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-1675.2456, dtype=float32)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_log_prob_43(jnp.array(180.), jnp.array(0.8), jnp.array(40.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 400\n",
    "num_results = 500\n",
    "step_size = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_43 = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob_43,\n",
    "    step_size=step_size,\n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel_43 = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel_43, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_43 = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel_43,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_43_dict = dict(zip(state_name, states_43))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>154.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>154.21</td>\n",
       "      <td>155.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>5.17</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean    sd  hdi_5.5%  hdi_94.5%\n",
       "alpha  154.64  0.29    154.21     155.13\n",
       "beta     0.90  0.04      0.83       0.97\n",
       "sigma    5.17  0.21      4.80       5.48"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats for all chains\n",
    "az.summary(states_43_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'alpha'}>,\n",
       "        <Axes: title={'center': 'alpha'}>],\n",
       "       [<Axes: title={'center': 'beta'}>,\n",
       "        <Axes: title={'center': 'beta'}>],\n",
       "       [<Axes: title={'center': 'sigma'}>,\n",
       "        <Axes: title={'center': 'sigma'}>]], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(states_43_dict);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JointDistributionCoroutineAutoBatched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the intermediate value 'mu' can be defined without tfd.Deterministic\n",
    "# when samples of 'mu' are needed, use tfd.Deterministic\n",
    "# don't forget to use 'name=' arg\n",
    "def m43():\n",
    "    alpha = yield tfd.Normal(loc=178.0, scale=20.0, name=\"alpha\")\n",
    "    beta = yield tfd.LogNormal(loc=0.0, scale=1.0, name=\"beta\")\n",
    "    sigma = yield tfd.Uniform(low=0.0, high=50.0, name=\"sigma\")\n",
    "    mu = alpha + beta*(d2.weight.values - x_bar)\n",
    "    # mu = lambda alpha, beta: tfd.Deterministic(alpha + beta*(d2.weight.values - x_bar))\n",
    "    height = yield tfd.Normal(loc=mu, scale=sigma, name=\"height\")\n",
    "\n",
    "jd_43 = tfd.JointDistributionCoroutineAutoBatched(m43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([]),\n",
       " StructTuple(\n",
       "   alpha=TensorShape([]),\n",
       "   beta=TensorShape([]),\n",
       "   sigma=TensorShape([]),\n",
       "   height=TensorShape([346])\n",
       " ))"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd_43.batch_shape, jd_43.event_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-1170.6125, dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd_43.log_prob(jd_43.sample(seed=a_key()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = \"alpha\", \"beta\", \"sigma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StructTuple(\n",
       "  alpha=Array(172.12474, dtype=float32),\n",
       "  beta=Array(1.0588244, dtype=float32),\n",
       "  sigma=Array(35.67117, dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state = jd_43.sample(seed=a_key())[:-1]\n",
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up a namedtupe is combersome\n",
    "# getting inclined to Couroutine then Named as model becomes complex\n",
    "StateTuple = namedtuple(\"StateTuple\", state_name + (\"height\",))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StateTuple(alpha=10.0, beta=1.0, sigma=2.0, height=3.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StateTuple(**{\"alpha\": 10., \"beta\": 1., \"sigma\": 2., \"height\": 3.})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_log_prob_43(alpha, beta, sigma):\n",
    "    state = {\"alpha\": alpha, \"beta\": beta, \"sigma\": sigma, \"height\": d2.height.values}\n",
    "    return jd_43.log_prob(StateTuple(**state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-1746.1475, dtype=float32)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_log_prob_43(init_state[0], init_state[1], init_state[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 400\n",
    "num_results = 500\n",
    "step_size = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_43 = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob_43,\n",
    "    step_size=step_size,\n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel_43 = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel_43, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMMENT:\n",
    "# zeros, ones for init_state as in official documents\n",
    "#   usually do not work, unless they are in 'nice' region\n",
    "# jd.sample() is better option \n",
    "states_43 = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel_43,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_43_dict = states_43._asdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>154.57</td>\n",
       "      <td>0.24</td>\n",
       "      <td>154.17</td>\n",
       "      <td>154.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>5.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.84</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean    sd  hdi_5.5%  hdi_94.5%\n",
       "alpha  154.57  0.24    154.17     154.91\n",
       "beta     0.90  0.05      0.82       0.97\n",
       "sigma    5.16  0.19      4.84       5.44"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats for all chains\n",
    "az.summary(states_43_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(states_43_dict);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.43 \n",
    "\n",
    "Here the author talks about using log($\\beta$) instead of just $\\beta$ and says that the results will be same. \n",
    "\n",
    "But I did not find/understand the reason for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def m43b():\n",
    "    alpha = yield tfd.Normal(loc=178.0, scale=20.0, name=\"alpha\")\n",
    "    beta = yield tfd.LogNormal(loc=0.0, scale=1.0, name=\"beta\")\n",
    "    sigma = yield tfd.Uniform(low=0.0, high=50.0, name=\"sigma\")\n",
    "    exp_value = jnp.exp(beta)\n",
    "    mu = alpha + exp_value*(d2.weight.values - x_bar)\n",
    "    height = yield tfd.Normal(loc=mu, scale=sigma, name=\"height\")\n",
    "\n",
    "jd_43b = tfd.JointDistributionCoroutineAutoBatched(m43b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = \"alpha\", \"beta\", \"sigma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_state = jd_43b.sample(seed=a_key())[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StructTuple(\n",
       "  alpha=Array(175.5064, dtype=float32),\n",
       "  beta=Array(0.6140351, dtype=float32),\n",
       "  sigma=Array(39.706802, dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "StateTuple = namedtuple(\"StateTuple\", state_name + (\"height\",))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_log_prob_43b(alpha, beta, sigma):\n",
    "    state = {\"alpha\": alpha, \"beta\": beta, \"sigma\": sigma, \"height\": d2.height.values}\n",
    "    return jd_43b.log_prob(StateTuple(**state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 400\n",
    "num_results = 500\n",
    "step_size = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_43b = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob_43b,\n",
    "    step_size=step_size,\n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel_43b = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel_43b, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_43b = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel_43b,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StructTuple(\n",
       "  alpha=Array([155.10489, 155.10489, 155.10489, 155.10489, 155.10489, 155.09988,\n",
       "           155.09988, 154.97894, 154.97894, 154.97894, 154.97894, 154.97894,\n",
       "           154.97894, 154.97894, 154.97894, 154.7985 , 154.7985 , 154.7985 ,\n",
       "           154.7985 , 154.7985 , 154.7985 , 154.7985 , 154.7985 , 154.85637,\n",
       "           154.85637, 154.88728, 154.88728, 154.88728, 154.88728, 154.85538,\n",
       "           154.85538, 154.85538, 154.85538, 154.85538, 154.85538, 154.85538,\n",
       "           154.85538, 154.85538, 155.04242, 155.04242, 155.04672, 155.04672,\n",
       "           155.02876, 155.02876, 155.02876, 154.9176 , 154.9176 , 154.90218,\n",
       "           154.90218, 154.90218, 154.9445 , 154.9445 , 154.9445 , 154.9445 ,\n",
       "           154.92627, 154.92627, 154.92627, 154.92627, 154.92627, 154.92627,\n",
       "           154.92627, 154.92627, 154.92627, 154.92627, 154.92627, 154.92627,\n",
       "           154.92627, 154.92627, 154.92627, 154.93916, 154.93916, 154.93916,\n",
       "           154.93916, 154.93916, 154.93916, 154.93916, 154.93916, 154.93916,\n",
       "           154.94109, 154.99818, 154.99818, 154.99818, 154.99818, 154.99818,\n",
       "           154.99818, 154.99818, 154.99818, 154.99818, 154.99818, 154.99818,\n",
       "           154.99818, 154.99818, 154.99818, 154.99818, 154.99818, 154.99818,\n",
       "           154.99818, 154.99818, 154.99818, 154.99818, 154.99818, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 154.96826, 154.96826,\n",
       "           154.96826, 154.96826, 154.96826, 154.96826, 155.0565 , 155.0565 ,\n",
       "           155.0565 , 155.0565 , 155.0565 , 155.0565 , 154.84315, 154.84315,\n",
       "           154.84315, 154.84315, 154.84315, 154.84315, 154.84315, 154.84315,\n",
       "           154.84315, 154.84315, 154.84315, 154.84315, 154.84315, 154.69022,\n",
       "           154.69022, 154.69022, 154.69022, 154.69022, 154.69022, 154.69022,\n",
       "           154.69022, 154.69022, 154.69022, 154.69022, 154.69022, 154.69022,\n",
       "           154.69022, 154.69022, 154.80081, 154.80081, 154.80081, 154.84917,\n",
       "           154.84917, 154.84917, 154.84917, 154.84917, 154.84917, 154.84917,\n",
       "           154.84917, 154.84917, 154.80685, 154.80685, 154.80685, 154.80685,\n",
       "           154.80685, 154.80685, 154.80685, 154.80685, 154.80685, 154.80685,\n",
       "           154.80685, 154.80685, 154.80685, 154.80685, 154.80685, 154.80685,\n",
       "           154.80685, 154.80685, 154.80685, 154.80685, 154.80685, 154.80685,\n",
       "           154.76062, 154.76062, 154.76062, 154.76062, 154.76062, 154.76062,\n",
       "           154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 ,\n",
       "           154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 ,\n",
       "           154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 ,\n",
       "           154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 , 154.7112 ,\n",
       "           154.7112 , 154.7112 , 154.54286, 154.54286, 154.54286, 154.54286,\n",
       "           154.54286, 154.54286, 154.54286, 154.54286, 154.54008, 154.54008,\n",
       "           154.49539, 154.49539, 154.49539, 154.49539, 154.49539, 154.49539,\n",
       "           154.60095, 154.632  , 154.632  , 154.632  , 154.61948, 154.61948,\n",
       "           154.61948, 154.61948, 154.61948, 154.61948, 154.61948, 154.61948,\n",
       "           154.61948, 154.61948, 154.61948, 154.61948, 154.61948, 154.61948,\n",
       "           154.61948, 154.61948, 154.61948, 154.61948, 154.61948, 154.61948,\n",
       "           154.5224 , 154.5224 , 154.43909, 154.43909, 154.43909, 154.43909,\n",
       "           154.47125, 154.47125, 154.47125, 154.47125, 154.47125, 154.47125,\n",
       "           154.47125, 154.47125, 154.47125, 154.47125, 154.47125, 154.47125,\n",
       "           154.47125, 154.47125, 154.47125, 154.5805 , 154.5805 , 154.5805 ,\n",
       "           154.5805 , 154.5805 , 154.5805 , 154.5805 , 154.5805 , 154.5805 ,\n",
       "           154.5805 , 154.56859, 154.56859, 154.56859, 154.56859, 154.56859,\n",
       "           154.56859, 154.56859, 154.56859, 154.58792, 154.54843, 154.54843,\n",
       "           154.5844 , 154.53946, 154.53946, 154.53946, 154.53946, 154.64743,\n",
       "           154.64743, 154.72404, 154.72404, 154.61966, 154.60744, 154.60744,\n",
       "           154.53871, 154.60254, 154.60254, 154.68816, 154.68816, 154.68425,\n",
       "           154.68425, 154.68425, 154.68425, 154.68425, 154.75642, 154.76219,\n",
       "           154.76219, 154.76219, 154.76219, 154.76219, 154.91821, 154.91821,\n",
       "           154.91821, 154.91821, 154.91821, 154.91821, 154.91821, 154.91821,\n",
       "           154.89053, 154.89053, 154.77544, 154.79993, 154.79993, 154.79993,\n",
       "           154.79993, 154.90637, 154.90637, 154.76506, 154.76506, 154.8299 ,\n",
       "           154.8299 , 154.8299 , 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.89081, 154.89081, 154.89081, 154.89081, 154.89081, 154.89081,\n",
       "           154.86977, 154.86977, 154.86977, 154.86977, 154.86977, 154.86977,\n",
       "           154.86977, 154.86977, 154.86977, 154.87233, 154.87233, 154.87233,\n",
       "           154.9285 , 154.9285 , 154.9285 , 154.9285 , 154.74362, 154.74362,\n",
       "           154.74883, 154.74883, 154.87587, 154.87587, 154.87587, 154.87587,\n",
       "           154.87587, 154.87587, 154.87587, 154.87587, 154.87587, 154.87587,\n",
       "           154.87587, 154.87587, 154.87587, 154.87587, 154.87587, 154.87587,\n",
       "           154.87587, 154.87587, 154.87587, 154.87587, 154.8398 , 154.8398 ,\n",
       "           154.71901, 154.71901, 154.71901, 154.68481, 154.68481, 154.65137,\n",
       "           154.65137, 154.6312 , 154.6312 , 154.6312 , 154.6312 , 154.6312 ,\n",
       "           154.6312 , 154.6312 , 154.6312 , 154.6312 , 154.6312 , 154.6312 ,\n",
       "           154.6312 , 154.6312 ], dtype=float32),\n",
       "  beta=Array([0.02982619, 0.02982619, 0.02982619, 0.02982619, 0.02982619,\n",
       "           0.02963988, 0.02963988, 0.05784466, 0.05784466, 0.05784466,\n",
       "           0.05784466, 0.05784466, 0.05784466, 0.05784466, 0.05784466,\n",
       "           0.03227408, 0.03227408, 0.03227408, 0.03227408, 0.03227408,\n",
       "           0.03227408, 0.03227408, 0.03227408, 0.02780995, 0.02780995,\n",
       "           0.02112957, 0.02112957, 0.02112957, 0.02112957, 0.06125259,\n",
       "           0.06125259, 0.06125259, 0.06125259, 0.06125259, 0.06125259,\n",
       "           0.06125259, 0.06125259, 0.06125259, 0.02839672, 0.02839672,\n",
       "           0.02928066, 0.02928066, 0.02153433, 0.02153433, 0.02153433,\n",
       "           0.01198236, 0.01198236, 0.02256684, 0.02256684, 0.02256684,\n",
       "           0.01522336, 0.01522336, 0.01522336, 0.01522336, 0.02218448,\n",
       "           0.02218448, 0.02218448, 0.02218448, 0.02218448, 0.02218448,\n",
       "           0.02218448, 0.02218448, 0.02218448, 0.02218448, 0.02218448,\n",
       "           0.02218448, 0.02218448, 0.02218448, 0.02218448, 0.01441955,\n",
       "           0.01441955, 0.01441955, 0.01441955, 0.01441955, 0.01441955,\n",
       "           0.01441955, 0.01441955, 0.01441955, 0.02030281, 0.04134024,\n",
       "           0.04134024, 0.04134024, 0.04134024, 0.04134024, 0.04134024,\n",
       "           0.04134024, 0.04134024, 0.04134024, 0.04134024, 0.04134024,\n",
       "           0.04134024, 0.04134024, 0.04134024, 0.04134024, 0.04134024,\n",
       "           0.04134024, 0.04134024, 0.04134024, 0.04134024, 0.04134024,\n",
       "           0.04134024, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04040869, 0.04040869, 0.04040869, 0.04040869,\n",
       "           0.04040869, 0.04911112, 0.04911112, 0.04911112, 0.04911112,\n",
       "           0.04911112, 0.04911112, 0.02888792, 0.02888792, 0.02888792,\n",
       "           0.02888792, 0.02888792, 0.02888792, 0.02888792, 0.02888792,\n",
       "           0.02888792, 0.02888792, 0.02888792, 0.02888792, 0.02888792,\n",
       "           0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 ,\n",
       "           0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 ,\n",
       "           0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 , 0.0466752 ,\n",
       "           0.02737654, 0.02737654, 0.02737654, 0.01502651, 0.01502651,\n",
       "           0.01502651, 0.01502651, 0.01502651, 0.01502651, 0.01502651,\n",
       "           0.01502651, 0.01502651, 0.02504511, 0.02504511, 0.02504511,\n",
       "           0.02504511, 0.02504511, 0.02504511, 0.02504511, 0.02504511,\n",
       "           0.02504511, 0.02504511, 0.02504511, 0.02504511, 0.02504511,\n",
       "           0.02504511, 0.02504511, 0.02504511, 0.02504511, 0.02504511,\n",
       "           0.02504511, 0.02504511, 0.02504511, 0.02504511, 0.03883662,\n",
       "           0.03883662, 0.03883662, 0.03883662, 0.03883662, 0.03883662,\n",
       "           0.04667564, 0.04667564, 0.04667564, 0.04667564, 0.04667564,\n",
       "           0.04667564, 0.04667564, 0.04667564, 0.04667564, 0.04667564,\n",
       "           0.04667564, 0.04667564, 0.04667564, 0.04667564, 0.04667564,\n",
       "           0.04667564, 0.04667564, 0.04667564, 0.04667564, 0.04667564,\n",
       "           0.04667564, 0.04667564, 0.04667564, 0.04667564, 0.04667564,\n",
       "           0.04667564, 0.022812  , 0.022812  , 0.022812  , 0.022812  ,\n",
       "           0.022812  , 0.022812  , 0.022812  , 0.022812  , 0.01941044,\n",
       "           0.01941044, 0.01773695, 0.01773695, 0.01773695, 0.01773695,\n",
       "           0.01773695, 0.01773695, 0.01896216, 0.0348485 , 0.0348485 ,\n",
       "           0.0348485 , 0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 ,\n",
       "           0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 ,\n",
       "           0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 ,\n",
       "           0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 , 0.0294483 ,\n",
       "           0.0294483 , 0.02309185, 0.02309185, 0.0581045 , 0.0581045 ,\n",
       "           0.0581045 , 0.0581045 , 0.0448209 , 0.0448209 , 0.0448209 ,\n",
       "           0.0448209 , 0.0448209 , 0.0448209 , 0.0448209 , 0.0448209 ,\n",
       "           0.0448209 , 0.0448209 , 0.0448209 , 0.0448209 , 0.0448209 ,\n",
       "           0.0448209 , 0.0448209 , 0.07355282, 0.07355282, 0.07355282,\n",
       "           0.07355282, 0.07355282, 0.07355282, 0.07355282, 0.07355282,\n",
       "           0.07355282, 0.07355282, 0.06590367, 0.06590367, 0.06590367,\n",
       "           0.06590367, 0.06590367, 0.06590367, 0.06590367, 0.06590367,\n",
       "           0.02187338, 0.02905266, 0.02905266, 0.05446517, 0.0426929 ,\n",
       "           0.0426929 , 0.0426929 , 0.0426929 , 0.02745154, 0.02745154,\n",
       "           0.03986135, 0.03986135, 0.02803136, 0.03262526, 0.03262526,\n",
       "           0.02687379, 0.03590105, 0.03590105, 0.02358837, 0.02358837,\n",
       "           0.03269978, 0.03269978, 0.03269978, 0.03269978, 0.03269978,\n",
       "           0.02250459, 0.0408877 , 0.0408877 , 0.0408877 , 0.0408877 ,\n",
       "           0.0408877 , 0.04546464, 0.04546464, 0.04546464, 0.04546464,\n",
       "           0.04546464, 0.04546464, 0.04546464, 0.04546464, 0.03244095,\n",
       "           0.03244095, 0.02069944, 0.03506428, 0.03506428, 0.03506428,\n",
       "           0.03506428, 0.02057559, 0.02057559, 0.03289845, 0.03289845,\n",
       "           0.04423603, 0.04423603, 0.04423603, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.06403665, 0.06403665,\n",
       "           0.06403665, 0.06403665, 0.06403665, 0.0875374 , 0.0875374 ,\n",
       "           0.0875374 , 0.0875374 , 0.0875374 , 0.0875374 , 0.0875374 ,\n",
       "           0.0875374 , 0.0875374 , 0.0298339 , 0.0298339 , 0.0298339 ,\n",
       "           0.03387418, 0.03387418, 0.03387418, 0.03387418, 0.09493995,\n",
       "           0.09493995, 0.04853506, 0.04853506, 0.05407115, 0.05407115,\n",
       "           0.05407115, 0.05407115, 0.05407115, 0.05407115, 0.05407115,\n",
       "           0.05407115, 0.05407115, 0.05407115, 0.05407115, 0.05407115,\n",
       "           0.05407115, 0.05407115, 0.05407115, 0.05407115, 0.05407115,\n",
       "           0.05407115, 0.05407115, 0.05407115, 0.02216144, 0.02216144,\n",
       "           0.03286765, 0.03286765, 0.03286765, 0.0184856 , 0.0184856 ,\n",
       "           0.04382872, 0.04382872, 0.06002248, 0.06002248, 0.06002248,\n",
       "           0.06002248, 0.06002248, 0.06002248, 0.06002248, 0.06002248,\n",
       "           0.06002248, 0.06002248, 0.06002248, 0.06002248, 0.06002248],      dtype=float32),\n",
       "  sigma=Array([5.549618 , 5.549618 , 5.549618 , 5.549618 , 5.549618 , 5.5127087,\n",
       "           5.5127087, 5.443937 , 5.443937 , 5.443937 , 5.443937 , 5.443937 ,\n",
       "           5.443937 , 5.443937 , 5.443937 , 5.4043417, 5.4043417, 5.4043417,\n",
       "           5.4043417, 5.4043417, 5.4043417, 5.4043417, 5.4043417, 5.394472 ,\n",
       "           5.394472 , 5.4627213, 5.4627213, 5.4627213, 5.4627213, 5.3724294,\n",
       "           5.3724294, 5.3724294, 5.3724294, 5.3724294, 5.3724294, 5.3724294,\n",
       "           5.3724294, 5.3724294, 5.4099565, 5.4099565, 5.2589993, 5.2589993,\n",
       "           5.1552715, 5.1552715, 5.1552715, 5.0516   , 5.0516   , 5.1127324,\n",
       "           5.1127324, 5.1127324, 5.1286235, 5.1286235, 5.1286235, 5.1286235,\n",
       "           5.1438365, 5.1438365, 5.1438365, 5.1438365, 5.1438365, 5.1438365,\n",
       "           5.1438365, 5.1438365, 5.1438365, 5.1438365, 5.1438365, 5.1438365,\n",
       "           5.1438365, 5.1438365, 5.1438365, 5.040039 , 5.040039 , 5.040039 ,\n",
       "           5.040039 , 5.040039 , 5.040039 , 5.040039 , 5.040039 , 5.040039 ,\n",
       "           5.0232716, 4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474,\n",
       "           4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474,\n",
       "           4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474,\n",
       "           4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.9998474, 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.963007 ,\n",
       "           4.963007 , 4.963007 , 4.963007 , 4.963007 , 4.958418 , 4.958418 ,\n",
       "           4.958418 , 4.958418 , 4.958418 , 4.958418 , 5.1187973, 5.1187973,\n",
       "           5.1187973, 5.1187973, 5.1187973, 5.1187973, 5.1187973, 5.1187973,\n",
       "           5.1187973, 5.1187973, 5.1187973, 5.1187973, 5.1187973, 5.1188745,\n",
       "           5.1188745, 5.1188745, 5.1188745, 5.1188745, 5.1188745, 5.1188745,\n",
       "           5.1188745, 5.1188745, 5.1188745, 5.1188745, 5.1188745, 5.1188745,\n",
       "           5.1188745, 5.1188745, 5.186497 , 5.186497 , 5.186497 , 5.23413  ,\n",
       "           5.23413  , 5.23413  , 5.23413  , 5.23413  , 5.23413  , 5.23413  ,\n",
       "           5.23413  , 5.23413  , 5.2470446, 5.2470446, 5.2470446, 5.2470446,\n",
       "           5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446,\n",
       "           5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446,\n",
       "           5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446, 5.2470446,\n",
       "           5.13235  , 5.13235  , 5.13235  , 5.13235  , 5.13235  , 5.13235  ,\n",
       "           5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285,\n",
       "           5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285,\n",
       "           5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285,\n",
       "           5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285, 5.1867285,\n",
       "           5.1867285, 5.1867285, 5.302004 , 5.302004 , 5.302004 , 5.302004 ,\n",
       "           5.302004 , 5.302004 , 5.302004 , 5.302004 , 5.283596 , 5.283596 ,\n",
       "           5.3437023, 5.3437023, 5.3437023, 5.3437023, 5.3437023, 5.3437023,\n",
       "           5.2712197, 5.400936 , 5.400936 , 5.400936 , 5.3886466, 5.3886466,\n",
       "           5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466,\n",
       "           5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466,\n",
       "           5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466, 5.3886466,\n",
       "           5.4216356, 5.4216356, 5.5567894, 5.5567894, 5.5567894, 5.5567894,\n",
       "           5.7040977, 5.7040977, 5.7040977, 5.7040977, 5.7040977, 5.7040977,\n",
       "           5.7040977, 5.7040977, 5.7040977, 5.7040977, 5.7040977, 5.7040977,\n",
       "           5.7040977, 5.7040977, 5.7040977, 5.699623 , 5.699623 , 5.699623 ,\n",
       "           5.699623 , 5.699623 , 5.699623 , 5.699623 , 5.699623 , 5.699623 ,\n",
       "           5.699623 , 5.757947 , 5.757947 , 5.757947 , 5.757947 , 5.757947 ,\n",
       "           5.757947 , 5.757947 , 5.757947 , 5.6889873, 5.64466  , 5.64466  ,\n",
       "           5.6239467, 5.6072373, 5.6072373, 5.6072373, 5.6072373, 5.770073 ,\n",
       "           5.770073 , 5.8949904, 5.8949904, 5.798443 , 5.8378468, 5.8378468,\n",
       "           5.8149047, 5.653862 , 5.653862 , 5.709038 , 5.709038 , 5.7710156,\n",
       "           5.7710156, 5.7710156, 5.7710156, 5.7710156, 5.7886324, 5.689559 ,\n",
       "           5.689559 , 5.689559 , 5.689559 , 5.689559 , 5.663725 , 5.663725 ,\n",
       "           5.663725 , 5.663725 , 5.663725 , 5.663725 , 5.663725 , 5.663725 ,\n",
       "           5.6302805, 5.6302805, 5.6180687, 5.6439924, 5.6439924, 5.6439924,\n",
       "           5.6439924, 5.7684493, 5.7684493, 5.816962 , 5.816962 , 5.6699133,\n",
       "           5.6699133, 5.6699133, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686, 5.7078686,\n",
       "           5.6661243, 5.6661243, 5.6661243, 5.6661243, 5.6661243, 5.6661243,\n",
       "           5.6661243, 5.6661243, 5.6661243, 5.6270733, 5.6270733, 5.6270733,\n",
       "           5.7081494, 5.7081494, 5.7081494, 5.7081494, 5.78778  , 5.78778  ,\n",
       "           5.7884865, 5.7884865, 5.7971663, 5.7971663, 5.7971663, 5.7971663,\n",
       "           5.7971663, 5.7971663, 5.7971663, 5.7971663, 5.7971663, 5.7971663,\n",
       "           5.7971663, 5.7971663, 5.7971663, 5.7971663, 5.7971663, 5.7971663,\n",
       "           5.7971663, 5.7971663, 5.7971663, 5.7971663, 5.8051386, 5.8051386,\n",
       "           5.755088 , 5.755088 , 5.755088 , 5.6062126, 5.6062126, 5.7328076,\n",
       "           5.7328076, 5.7840815, 5.7840815, 5.7840815, 5.7840815, 5.7840815,\n",
       "           5.7840815, 5.7840815, 5.7840815, 5.7840815, 5.7840815, 5.7840815,\n",
       "           5.7840815, 5.7840815], dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_43b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_43b_dict = states_43b._asdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>154.81</td>\n",
       "      <td>0.16</td>\n",
       "      <td>154.54</td>\n",
       "      <td>155.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>5.38</td>\n",
       "      <td>0.30</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean    sd  hdi_5.5%  hdi_94.5%\n",
       "alpha  154.81  0.16    154.54     155.00\n",
       "beta     0.04  0.02      0.02       0.06\n",
       "sigma    5.38  0.30      4.96       5.77"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats for all chains\n",
    "az.summary(states_43b_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X7OPzLfkwyjKu6cQUP9YIgoCJmdH44+ApGCXZJUE/+aYZM2Zg7ty5SE1NRW5uLh577DFMmTIF27dvh0bTXFH86quvRlpaGuLi4rBv3z48/PDD2L17N1auXGl1m2VlZTAajYiNjbW4PzY2FsXFtouYabVaaLWn0+VraqxX8hZFATqD5OihOswUPItC29RWdwe29VoDTlQ2OmVb0cEaRASqnbItb2IuHtVqjK3pf4xricgTGNgSUac06Y34dscJzBgUh8igrk/xY0togApXjU7BR3/lYcH5mUgKD3Dq9nUGCZ9tzse5/WKQEum8bZ+TGY1vtp/AwaIaZCW2n0ZKPcfll19u/n9WVhZGjBiB1NRU/Pzzz+b05ZtvvtlimT59+mDEiBHYsWMHhg0bZnPbZ44Nl2W53fHizz//PJ566qkO26xwU/EoU+wqtroQJAhC8/7dHNjmltUju7TOKdsqqVVjQh/nD9XwNL2xJRW5dVXklvcbA1si8gQGtkTUKb8dOIXqRj0uH5ns8n3ddHY6Pt6Qhw/W5eLJiwc6ddvL9xahtFaL67owxY81Y9MjAQAbsssY2JJN8fHxSE1NxdGjR20uM2zYMKhUKhw9etRqYBsVFQWFQtGmd7akpKRNL25rDz/8MBYsWGD+u6amBsnJbT/PCjemIotWAnF3zaPbmtZgRLCfElP62X7+7LHnRBVOOqnn19uYXhOlxYWI5n+ZikxEnsAxtkTUKV9tLUBKRADGpEW6fF9xoX6YMzQJS7cWOHU+S1mW8f66HGTGBmFCnyinbRdoTj/MjA3Chuxyp26Xupfy8nIUFBQgPj7e5jL79++HXq+3uYxarcbw4cPbpCqvXLkS48aNs7ldjUaDkJAQi5s1CkFwS/EmW4GtQhRhkFyfCt2a1iBBrej6ePsgjRI6owStoftNV6Q3Nr9erXvYTf9nYEtEnsDAlogcVlDRgL+yy/C3EUkWJzWudMvEdDQZjPhkQ57Ttrkxpxz7C2sw/+z0Lk/xY824jChsya0wp+xR91dXV4ddu3Zh165dAIDc3Fzs2rUL+fn5qKurw/3334+NGzciLy8Pa9aswcyZMxEVFYXZs2cDALKzs/H0009j27ZtyMvLw/LlyzF37lwMHToU48ePN+/n3HPPtZhSaMGCBfjggw/w4Ycf4uDBg7j33nuRn5+P2267rcvHpBAFNOmN+HVfUZvb9uPOm4pLlk+PqbXcv/vH2GoNEtTKrp8iBfk1J8bVNRm6vC1vY5Rki/G1AKf7ISLPYioyETnsm+0nIAC4dLjrikadKSM6CNMGxOGTjXm45Zx0BGq6/vX1wbpcRAVpMGtoghNa2NbYjEh8vCEPe05UY3hquEv2Qd5l27ZtmDx5svlvU6rvvHnzsGjRIuzduxdLlixBVVUV4uPjMXnyZCxduhTBwcEAmntf//jjD7z++uuoq6tDcnIyLrzwQjzxxBNQtOpBzM7ORllZmfnvyy+/HOXl5Xj66adRVFSErKwsLF++HKmpXS+IZhp7fmYnXFmdFmV1zsugkGyMCVaIotsDW51BQniAEwLblu+pOq3BpbUIPEEvSRbja4HWY2wZ2RKR+zGwJSKHSJKMb7afwDmZ0YgP9Xfrvm+fnIFf9xdj8fpc3HVuny5t61hJLVYdKsF952dCo+x6yqE1Y9IiIQjAxuwyBrY9xKRJk9o9qV+xYkW76ycnJ2Pt2rUd7icvL6/Nfbfffjtuv/32Dtd1VIifyuo48b0nqnGyqsFp+zFKstUeW0+MsdUZJKgVXQ9s/VUKKEQBe05UY3+h9arTvsogyQj2szyNFNhjS0QexMCWiBzyV3YZTlY14tEL+7t934OTwnDhoHj8Z202rhqdgqgu9IC8/scx+KsUuHqMc6b4sSY0QIWshFBsyC7HHVO6FogTeRtBcG4AI8mwOjWWKAhuTefXGyVIsuyUVGRBEHBWchiqGvROaJn3iQm2/A7mPLZE5EkMbInIIUu3FiAiUI3z+netWmhnPTCtL1bsL8ZrK4/g2dmDOrWN7ccr8ePuQtxzXh+Xzy85LiMSH23IQ5PeCD+Va3qGiTxBFJzbkyrbqoqsENBkcF+gZJqzV+OEwBYAksIDkNRDEjYY2BKRJ7F4FBHZrapBh9/2n8IlZyU6pTejM3pFBeL6cb3w+ZZ8bMtzvHCNJMn4108HEBfih1vOSXdBCy2NzYiEziBhR36ly/dF5E4KUXBqACPJp1NZz9yP0ei7gW1PYu5wZ1xLRB7Ab20istsPO09CZ5TcMndtexZMzURSuD8e/HYPmvSOTaPx455C7Cqowv9N74sAteuTVkb2ioBCFLAl13nVY4m8gbkCrpN6bZvH2Fqfx/bM6YaOldThQGENDhTW4ESl88b5AoCuJe3ZUxfvfJmp+JeRPbZE5AFMRSYiu3217QSGJIehb1ywR9sRoFbihTmDcfUHm/HWqmO4f1pfu9Yrr9PiXz8dxJCkUFxyVqKLW9ksUKPEgPgQbMtjjy11L0KrtFMRXZ8uS5Zlm2NsW/cM12kN2F9YbREEx4f6W13XltyyetRrrU/BU9PUPB6Wga3jTC8B41oi8gQGtkRkl30nq3GgqAbPzs7ydFMAAON7R+GKkcl4Z80xjOgVjkl9Y9pdXpZlPPTdXtQ26fHy3NFum38XAEb0CsfSrQUwGCUonVBplcgbOHvOUkkGRCsfD6XCsse2UdecpTEuIxJGScbGnHJU1OsQHWxfMbnqRj32nKiCQhSs9hCLApAeFQR/jol3mMAxtkTkQQxsicguX20rgJ9KxMwhrpnztTOemDkQ+wtrcOfnO/HN38fZ7EmWJBn/+vkAVh44hSdmDkCfWPf2OI/qFYGP/srDgaIaDE4Kc+u+iVzF1EPqrCBGkmUohbaRrUIQIMsyJEmGKArm4Qd+KgU0ShGCIKC4ugn+avsC0fzyBgiCgPMHxLpsqq+eSmGex9bDDSGiDhklGUdO1ZqLAMYEaxAT4ufhVnUNA1si6lCT3ogfdp7EBVnxCPFTebo5Zv5qBd6/bgRmv/MXrnx/E/570ygMTLCcb9NglPDgt3vx7Y4TuOWcdFw/rpfb2zmiVwQAYGteJQNb6jacXQFXslEV2RRAGyQZalFAY0tg669SQBQFRASokVNWh5yyOrv3FRfix6DWBU7PY8vIlsjblddpceRULVQKEQZJRmWDjoEtEXV/y/cWoabJ4PGiUdbEhfph6S1jcdUHm3DZoo245Zx0XDc2FZFBGhRWNeKJZfux8sApPDCtL26flGFOlXOn6GAN0qICsTW3Ajedneb2/RO5guDkVGRZblVVtxVlS35yTlkd+sWFoElvhEYpmocTDEsNQ6WD88RGuniar57K2e8JInc4VlJrda5pQQB6xwQj1N97Lug7k7alAvzEzGjsL6xBg8563QFfwsCWiDr02eZ89I4Jwqi0CE83xaqUyAB8+/dxePbng3j9j6N4/Y+j8Fcp0Kg3QhSAf12ShWvHpHq0jSNSw7H6cAlkWfZIcE3kbK7osbX22Qjxbz5VOVxci16RgWjUWc4JHaBWuqXCOXWM89iSLzpUXAulKLTJ4qjVGqBRKhCaGGpjTd+mbTW1mSh0jyEE/CUgonYdLKrB9uOVeGLmAK8OyGJD/PDGlUNxx5TeWHO4BOV1OsSG+OG8/rFIiQzwdPMwMi0CX28/gdyyeqRHB3m6OURdZkoRliXnbM8oWa+KHBagxpR+MVh1qATF1U1o1BtZ2MlLieYxtt3gDJl6DEkGekUFol9ciMX9a4+UorapbS+mJMnIr2iAQhSQHOH584vO0hkkKEQBSkVzrYLucEGKgS0RtevzzfnwU4mYMzTJ002xS2ZsMDLdXBzKHiNbxtluy6tkYEvdguk6l7PmLJVspCIDQLCfCoFqJY6W1EFrMCI8gKnE3sjZlbKJXE2W5eapxqxcuA/2U6KsVtvm/soGHXafqALQPNTIz0cvtOmMEtQtMzUoRMFcRMqXcd4JIrKpXmvA9ztPYubgBIQGdM8xJu7SKzIAUUEabMmr8HRTiJzC2Wmnso3iUSZ9YoOgUggI0igRH+rbBU66K0EQuk3PD/UMpmDOWkZasEaJRr0ReqNlWkrr6cd8ORjU6o3m+bpFoXtckGKPLRHZ9MOuk6jTGnDV6BRPN8XnCYKAkb3CsY2BLXUTzh9je7oX2JrUyECkRgY6ZV/kOgIAyUnp6USuZso4sTYMIrhlFog9J6owLCXcHPxaBLatvv90BglHS2o9/v5XiAL6xgVbPabWdEapVWArdIshBAxsicgqWZbx0V95GJIUirOSwzzdnG5hRK8I/LKvGCW1TYgJZo8T+TZT6l57J3G1TXpszC43B78pEYEYkBBidVlb0/2QbxHZY0s+xPRWtRYDhrVkqp2obESf2GDzdIdG4+n3t9QqyC2t0+JYSR3UCrHdi3SuJMvNAWt4oArxof7tLqszSAhrGdYhsMeWiLqzP4+W4VhJHV6/4iyvLhrlS0a1Gmd7waB4D7eGqGuElsFM7QUx5XU6NOqN6BUZiPJ6LYprGq0GtrIsM7DtJrpLdVXqGUypxNa+e/xUCozNiMTG7HIYWgWzhlZX81qnIutaqgxP7hfjsXG3kiTj571FqKzXdxjYag0SNK16bLvDBSkGtkRk1YfrcxEbomEA5kT944MRqFZgS24Fn1fyefakItdpDVCKIoYkh+FAYQ2yS+usTnll2oSClT98niAIkOH7J8jUM7SXigyczkwx2hhXe2YqMgBzsOgJoigg1F+FqgadzWUMRsk8dlh9RmBr+n4ur9Nif2FNlz7JGqWIUb0izHOOuwMDWyJq41hJLdYeKcUD0/pCxTNNp1EqRAxLDce24xxnS77PnlTkeq0BgZrmnosgjRKSLKNRb2wz76zp5JDZIb6vuxShoZ7BNF2ZrWwRpdh8DmQrmG39/advqTLs6e+x8AA1jlfUY6uNmh5ltVroWgpi+bXM3Ws61ZNbah2U1mlR2aDrsNfXFq3BiFM1TajVGhDq777iowxsiaiNxevzoFGKuGoUi0Y524jUCLz+xxHUNunNhSmIfJFgntql/R5b00mNKcCt0xraBLambTAV2fd1l5RG6hlMQapo4xq+6f7WwWzrtOTW92sNkld0BiSG+6O8Xos6bds5eAEgKliDuBA/KEQBsSHN9T5MwbhRliGieeofpShiVFpEp9pQ3aDHmiMlaNAxsCUiDyqsasQ32wtw5agUhAdyrkhnG5UWAUkGduRXYWJmtKebQ9RpHaUiN+mNqNMakBDWfMU/UNN8ylGvNQJnTDXdXgEX8i2iiG5RXZV6BtP3l7V5bIFWPbZG66nI0hljbNUeTEM2iQhUY1LfGIfWOfP7XJK6NjTEX918IbNBZ+z8RjqBgS0RWfjP2mwAwG0TMzzcku5paEoYVAoBW3LLGdiSTxPNPbZtH5NlGSsPnAIABPs1n2r4qRRQKUTsOVGFPSeqbGyTka2va57H1tOtILKP1E7xKMBGj21Lb6ZBkiwu7OmN3tFj2xmm73PT4Ri7WMxPrRShUohoZGBLRJ5SUtOEL7YW4LLhyeZeFnIuP5UCg5PCsCWX42zJt5mKrVjrsdUbm6scx4b4IaHVGK1hKeGobtRb3Z4owJwWR75LFASLXixXKalpwr7CapuPJ4YFoG9csM3HiYDWqcgdjbE9PZhWkmWolSIMOqlNVWRTZoqvObPH1ihJHc6D2xF/tYI9tkTkOf/5MweSJOP2SeytdaVRaRFYvC4XTXqjx6YEIOoqoZ1UZFNhkqRwf4sTxrhQP8SFMnjtzkQBqGzQYUN2WYfLRgZqOh18ltRqUa81IiGs7fupol6PwqpGBrbUIamDYRCm4M7YqkiUwdgc2Dbo0KbH1pMVkbtCFC2rPxsl28G+vQLVSlQ26CBJstsqI/vms09ETneisgGfbjqOOcMSkRwR4OnmdGuj0iKgM0rYVVDl6aYQdYlCFKxWRTZNe+EN483IvRLD/BGkUUGS0O6ttsmA3LL6Tu+nSW9EoEaB4akRbW7xoX5o1Lu3p4h8kym7oL3eSYUotJm7VqWwDHhlWYbO6B1jbDvjzKElzcWjuhaMBqgVaNIbsdmNGWrssSUiAMDzyw9BIQpYcH5fTzel2xueGg5RALbkVmBMeqSnm0PUabYq4Jrnc1QwI6GnSY8OQnp0UIfL7TtZjYKKhk7vR2uQoFFaf3/5qRTQGyWfHvNI7mHsYIwtACjPuIBnkCQEKJVQiM3Vgxt1RlS2zBvrq+830/GbCr9JsmyzoJa9+sQGIaesHo1669WZXcE3n30icqqN2eX4eW8R/jG5N9ME3SDET4UBCSEcZ0s+r3nOUtuBra/2XpDriYJgUZDHUc1DOay/v0z3N7HXljpgz1RjipZCUSZGSYZCFKBoubC3KbfcPGdsgNo3L+YJZ/TYGpyQPqxRKpAc7m+Rxu1q/MUh6uEMRglP/bgfyRH+uOnsNE83p8cY1SsS249XQu/Ob3wiJ7NVAVdnbA4oGNiSLabers5qr8fWv6V2AdORqSPm6X7aTUW2nOLH0JKmK7a8h7V6IxLD/DEpM8ZnC+Ap2kz3I3e5eBTQ9c+5o/iLQ9TDvbMmG4eKa/HPCwewkJEbjUqLQKPeiH0nbVf1JN/z559/YubMmUhISIAgCPjhhx8sHr/++ushCILFbcyYMVa3JcsyZsyYYXU7ZzIYDPjnP/+JtLQ0+Pv7Iz09HU8//TQkawNgnUhhIxVZa2iuqOmMEyPqnsxj+jpx0muUZOiNks0eW9Mcmlo9LxxS+zoqHgU099ieOXetqcfWKMvQGWUE+6kQGqBycWtdp21V5K5N99N6u7bmOncFjrEl6sF25Ffi9T+O4tJhSZg2MM7TzelRRvYKB9A8znZoSriHW0POUl9fjyFDhuCGG27ApZdeanWZ6dOn46OPPjL/rVarrS63cOFCc+Xhjrz44ot499138cknn2DgwIHYtm0bbrjhBoSGhuLuu+92/EDsJIqnx2S1pmunN40IaFWFVZYhwrETaFOKsc0xtkr22JJ9TAFce9+1CkFo02OrEAWIYvPFE1k+XUzKV5kD25ZrQUa568WjAECpEGBwY48tA1uiHqqouhF//3Q7EsP88eTFAzzdnB4nMkiDPjFB2JJbgVsncnql7mLGjBmYMWNGu8toNBrExbV/IWn37t149dVXsXXrVsTHx3e4340bN2LWrFm48MILAQC9evXCF198gW3bttnf+E4QBMHq+CmdwXerg5J7mFIfmyvMOrau1lSczEaPrSgK0CgVOFhUg4NFNV1qpygIGJ0egZhg30wxpfbZ0zPZuiqyJDXP0a0UBYiCgCZD88UTXy0aZSK0NN/pqciCAFmW3TblDwNboh6oqkGHmz7ehgatEf+9aTSC/Xw3fcaXjUqLwI+7C906xxt53po1axATE4OwsDBMnDgRzz77LGJiYsyPNzQ04Morr8Rbb73VYQBscvbZZ+Pdd9/FkSNHkJmZid27d2P9+vVYuHChzXW0Wi20Wq3575oaxwMAURBQrzUgu7TO4v7aJgMCNTzFINtOV2G1fx1ZlrExuxw1Tc1VVtvLChiaEoaqBn2X2ggAh4prUNWgZ2DbTcly8xja9igVAkpqddiUU24xJlchNn//AfDZ+WtNzkxFNjgpFVnRhcyMzuCvDlEPc6KyAdd/tBX5FQ34cN5IZMZyAntPGZUWgc825+PwqVr0jw/xdHPIDWbMmIG5c+ciNTUVubm5eOyxxzBlyhRs374dGo0GAHDvvfdi3LhxmDVrlt3bffDBB1FdXY1+/fpBoVDAaDTi2WefxZVXXmlzneeffx5PPfVUl44nSKPEicoGq2PF48MYCJBtYksc4EhlZL1RRmmdFhGBaiRH+CPEz/ZpbGyIn1MK+eSW1bG6cjdmlOUOh3yYAryKeh2CNEpEBmoQGaRBcXWTOXvA13tsFa1SkWW5uVfaWcWjgM5lZnQGA1vqNo6X12PJxuPYkV8JoyRjYEIorh6dgqzEUE83zStIkoz31+XgjT+OQiEK+Gz+aIzsFeHpZvVoo9Kan/8tuRUMbHuIyy+/3Pz/rKwsjBgxAqmpqfj5558xZ84cLFu2DKtWrcLOnTsd2u7SpUvx6aef4vPPP8fAgQOxa9cu3HPPPUhISMC8efOsrvPwww9jwYIF5r9ramqQnJzs0H6Hp4ZjSJL171ilj5/okWu1PuG1l2nZ1IhApEQGuKRdZ9KoFObghbofo9TxfK2m92pCmD/OSg4z398600rl4z22p6f7kc0FtZwR2IqC45/zrmBgSz5PlmUsXp+LF389BEEQMLJXOJSiiJ92F+LLrfm4cXwaHpjWt0dW/JUkGSerGrG/sAbf7zyBFftPYeqAWDx20QAkR7jnpIBsiw/1R0pEALbkVmDeuF6ebg55QHx8PFJTU3H06FEAwKpVq5CdnY2wsDCL5S699FJMmDABa9assbqdBx54AA899BCuuOIKAMCgQYNw/PhxPP/88zYDW41GY+4l7goGsNQZCnMqsv0nvKZxjgo3FurRKEX22HZjsh09k4aWQgKh/pbDtlqv122KR8ny6c+Zk4pHAY5lZnRpf27ZC5GL6I0SHv/fPnyxpQAXDIrDUxdnITq4+UStTmvAyysOY/H6XBwqrsEH1400TwHQnRglGRuyy7DywCnkVzSgsKoR1Y16NGiNqNcZzFfeAtQK/N/0vrh9Um/PNpgsjEqLwOpDJRxn20OVl5ejoKDAXCDqoYcewvz58y2WGTRoEF577TXMnDnT5nYaGhogipYBpkKhcPl0P0SdJXSiJ8f0du6oh82Z/FQKlNfp3LY/ci+jJKOjt1NdyzjaM1PfW/9kq338Ap95+i3ZuZ8zc5E4IwNbog49uWw/vthSgHvO64O7z+1jMU4iSKPEkxcPxNCUMNy7dBduXrINH90w0ufHQZjoDBI+2ZCH99floKRWi2CNEukxQciIDkJYgBpBGgUC1EokhvkjMy4YAxNCus2xdyfje0fim+0nOM62m6irq8OxY8fMf+fm5mLXrl2IiIhAREQEnnzySVx66aWIj49HXl4eHnnkEURFRWH27NkAgLi4OKsFo1JSUpCWlmb++9xzz8Xs2bNxxx13AABmzpyJZ599FikpKRg4cCB27tyJV199FTfeeKOLj5ioc1oXlbGXM3uS7OWnVLDHthuT5I7fT8F+KlQ36hHSpse2+ZxKrRDtnprNW4mtMihMn0nRCaeMYic+513BwJZ81ldbC/DZ5nz8Y3IG7jkv0+Zys85KhMEo476vd+PFXw7hnxf5/tQ2fx4pxeP/24e88gZMzIzGvy5JwaS+0Zw30geNz4gCAPx1rIyBbTewbds2TJ482fy3aQzrvHnzsGjRIuzduxdLlixBVVUV4uPjMXnyZCxduhTBwY4VccvOzkZZWZn57zfffBOPPfYYbr/9dpSUlCAhIQG33norHn/8ceccGJGTKTpRFdnUu+uM+TXt5acSIckyp7DqpiS54zG2Q5JC0Sc2qE3ngOlt2B06DUwBqCSf/pw5JRXZtF2OsSWyLbesHo/9bx8mZkZjwfl9O1z+0uFJ2F9Ygw/W52JEr3BMz+p4XkhvJMsy3lp1DK/+fgR9YoLw35tGYUKfaE83i7ogJsQPmbFBWH+sDPMnpHu6OdRFkyZNanfM4IoVKxzeprXt5eXlWfwdHByMhQsXtju9D5E3Mc2b6VDxKNNUK24cz2iqz5FTVme+eCygueo3Lyb7Hq3BiH0na6BvGTdb06hHWIC63XWUChEhVoJX04WO7lLDRRQEGCXZqYGtKWA2MLAldyupbcLX205gx/FKaFQiUiMDMSMrDoMSQ70qxcIoyXjg693wVyvw0tzBdn/wHr6gH7bnV+LR7/dhZK8IRAZ1vWiKO8myjCeW7ceSjcdx6bAkPDs7q9t8mfZ043tH4cstBewRIKIeQ9GJMbaGlnF67hxjG+ynhCAIOFxca3G/1hCCvnGcLs/XbD9eiYp6HUL8mtOKAzVKJHRyarI+McGICtIgqJvM2a0QBUhyq8DWmWNsGdiSuzTpjXh79TG8uzYbBklGv7gQGCUJvx8owaI12Tivfwxenjukwyta7vLFlnxsO16J1684y6EJ01UKES9fNhgXvrEeTyzbj7euGubCVjrf878cwpKNx3HbxAw8OL2vV11soK45u3cUPvorDzvzKzE6PdLTzSEicjmxC6nI7hxjG+ynwgVZcWh9Xr7qUAnH3fogvVFCaa0W/eKcc1FCIQqI8rFOkvaIQvPnsbpRD8A5n7POTOvVFQxse7ji6ibc8PFWHCyqwdzhSbhjSm+kRgYCAGqa9PhqawH+/ethzHxrPZbeMhYJYf4ebW9Nkx6vrTyCcRmRuHhIgsPr94kNxt3n9cFLKw7j8pGlPpPG+9W2Arz3Zw5uGN+LQW03NDo9EgpRwF/HyhjYElGPYCpM40hRGXMqspsryJ85pZWfSuTctj7I1OPvz2w3GwTklNWZ/3JGBllnisR1BXPeerDcsnrMfucvnKhowCc3jsJLc4eYg1oACPFTYf6EdHx921hUN+hxzeLNKKvTerDFwDurs1HRoMOjF/bvdHB384R0pEcF4oll+6HzgR+mnfmV+Of3+zChTxT+eeEABrXdUJBGiaHJYVh/rKzjhYmIuoHOpCh6oniUNRqlAloDe2x9ja5lXK1KyfMoazSq5rAwLsQPkzJjEKDuev+nu1ORGdj2UKW1Wlz34WbojRK++fs4TMy03XM5JDkMH90wCkVVTbjz851ue3OeqaCiAR/+lYvLhiVhYEJop7ejVop48uKByCmtx+L1uU5sofNVNejw9093ID7MD29dOcztV6nJfcb3jsLuE9WoadJ7uilERC7XenoRexmMMhSi4PELvBqViCa9918YJ0umglHdoYqxK4xNj8Q5faIxKi0CoQGqjlewgygK5qJU7sBXtgeq0xpww8dbUF6nw4fXj7RrnMHw1HA8NycLG3PK8fbqYx0u7wqv/HYYCkHA/dM6roLckXMyozF9YBzeXHUUxdVNTmidazyxbD/K67VYdPVwp33JkHc6u08UjJKMzTkVnm4KEZHLiS0BqkOpyFLHU7O4gx97bH0SA9v2+akUCA9UO/3CkUJkYEsuojdKuP2zHThUVIt3rh6GwUlhdq87e2gSLhuehIW/H8GBwhrXNdKKI6dq8b/dhbjx7F6IDelc9boz/fOi/jBKMv796yGnbM/Zlu8twv92FeKe8zIxIIHzm3Z3ZyWHIVCtwF9MRyaiHkIUHJ/uxxsylzQqEUZJNgdK5BlF1Y3Ykd9c5dgepjG2KjdOF0Vgjy25hizLePDbPfjzSCleuHQwJvWNcXgbj100ABGBajz+v30OpQ911WsrjyBIo8QtEzKcts2k8ADcck46vtt5ErsKqpy2XWcordXi0e/34qzkMNx6Duc27QlUChGj0yPx59FSTzeFiMgtFILgYFVkCUovCEo0LUV1WEDKs7JL6lFQ0YDj5fV2La9jj61HKEUBp2qasDWvAo0612Y68JXtQV7+7TC+23ESD0zri8uGJ3VqG6H+Kjw0oz+2Ha/EdztOOrmF1u07WY1f9hXj5gnpTk/HvW1iBmKCNXj6x/1uDdTbI8syHv5uLxr1RrzytyFtqjFS9zW5bzRySuuRW2bfjzQRkS8THUxRNBhl89hcT9Iom6vqltQ0oaxO67RbeZ0WkofqmPgivdQcqNr7HjIYZQiC4PHiYz1NYrg/1EoRhVWNOFXj2uF/nO6nh/jvxjy8vTob145Jxe2TutbrOWdoIr7Yko/nfzmE8wfGmie5dpXXVh5BeIAKN4zv5fRtB2qUeHB6P9z39W4s212IWWclOn0fjvpux0n8fvAUnpg5ABnRQZ5uDrnRuf1j8dj/9uOPg6cwfwJ76omoexMdHWMry1CKnr/YG6hpDmz3nqx2+rYHxIegT2zX51jtCfQtPeYGOwNbvVGCyguKj/U0/eND0D8+BD/tKUSd1uDSfTGw7QF+3VeMx5ftx7SBsXjy4oFd/kCLooCnZw3EzDfX47WVR/DEzIFOamlbO/Ir8cehEjw8ox+CXRRAzx6aiCUb8/DiL4cwdUAc/NWem9+suLoJT/64H2PSIzBvbC+PtYM8IyHMHwPiQ/A7A1si6gEUomNVkY2SdwS2AWolJvWNcfoY20055eZ0WeqYKaC1t8dWZ5SYhuxBgWolGvWuTUVmYNvNbcguw11f7sTwlHC8fsVQpxVdGJgQimvGpGLJxuO4alSKy64uvvrbEUQFaXCdC4M8URTw+MwBuHTRRrz3Zw7uPq+Py/bVHlmW8cj3e2GUZLx02RCITJXpkc4bEIu3Vx9DVYMOYQFqTzeHiMhlmovK2L+8QZLhp/KO38ZQf+dfbFcIAiTGtXaR5dPFu+ztsTUYZa8Yo91TBagVqHdxjy0vW3RjO/IrMf+TbUiPCsQH80bAT+XcnsgF52ciSKPEs8sPOnW7JptyyrH+WBn+MTnD5b2ow1MjMHNIAt5dm42i6kaX7suW73eexKpDJXhwej8kRwR4pA3keef1j4FRkrHmMItIEVH3JgoC6nUGFFQ02HXT6iWvGGPrKqIoQPKSeh/ernUwa7TzaoDeKEHNHluPCdQo0eDi4lHsse2mdhdU4foPtyA2xA9Lbhrlkp6fsAA17j63D57+6QDWHinFxMxop21blmW8+tsRxIf64cpRKU7bbnsemtEPv+0vxr9/PYzXLj/LLfs0KaxqxJPL9mNUWgSuHZPq1n2Td8lKCEVsiAa/HzyFS4Z6fsw3EZGr+KsVKKxqnrLF7nWcfJHem4gCA1t7mabuEQTB/P/W1h0tRVWDHpmxwYgMaj4HbtIbEeKCnnayT4BaAb1RQlF1I+JD/V2yDwa23dDqQyW4/bMdiA7W4NP5oxET7Jx5X625Zkwq/rvpOJ79+QDGZ0xwWgXfP4+WYUteBZ6dneX0nmZbEsP8ces56Xhj1TFcNzYVQ1PC3bJfoyTjnqW7IMvAK3OZgtzTiaKAKf1i8dPuQugMEtRKXl0mou5peEo4BsQ7Nk97gAfrYLiaQgQDWzuZxiL7KcU2Y2xlWTbPbXuouMbiMVeeE1P7TBcVtuRW4IJB8S4Z78wzpm5ElmV8siEP85dsQ5/YIHx3+zgkhrnmioiJWinikQv648ipOny5tcAp25QkGf/+9RBSIwPwtxHJTtmmvW6blIHYEA2e/umA20ruv7P6GLbkVuCZ2VlMQSYAwLSBsajVGrCOc9oSUTcmigICNUqHbt25oq0gCOBsP/YxmAJblaLNGFvT3wMTQjExMxrje0eZb/3jWXHaU6KCNBicFAYAaNC6JiWZgW03cbKqEbf8dzueWLYfU/rF4IubxyAqSOOWfZ/XPwZj0yPx2sojqGnSd3l7y/cVYX9hDRacn+n26nUBaiUemtEPO/Or8MnGPJfvb8OxMiz84yjmDEv0iqmGyDuc3TsKkYFq/LCr0NNNISIiN2kupsXI1h76lvRjf7UCkixbVNc2PYcqhYCwADWigjTmm7MyC6lzIgKb08LrdK4pIsVX18dV1uvwym+HMeXlNVh/tAxPzByA964djkCN+7LMBUHAPy/qj4oGHd5efaxL29IbJbzy2xH0jw/BzMEJTmqhYy45KxHnD4jF878cwsGimo5X6KTDxbW49b/b0ScmCE/PynLZfsj3KBUiLhocj5UHil0+5xsREXkHBcfY2k1vTkVuTk1v3WtreoxBrPcJbBlK0OCicxu+4j6oUWfEmsMlWLB0F0Y//wfeXHUMFw6Kx6r7J+KG8WkeSdMZmBCKucOT8NH6PBRUNHR6O19uLUBuWT0emJbpsbGmgiDgxUsHI8xfhbu+2IkmF8y5VVzdhOs/2oIgPyU+vmEUgtx4IYJ8w8VnJaJJL2HlgWJPN4WIiNxAFMDpfuxkCmT9VM2hTOueblMxKRVrlngdpUKERqlAvYuqI/Ns2stVNehwtKQOh4prcaioBoeLa7HnRDV0RgnBGiWuGpWCq0e7bh5ZR9w/tS9+3lOEJ5btx+J5IxwOsMvrtHh5xWGM7x2JyX1jXNRK+0QEqvHa5WfhmsWb8ej3+/Dy3MFOu2BQUtOEq97fhLomA77++1jEhbKQAbU1LCUMKREB+GFnIWYPTfJ0c4iIyMUEQYDEyNYu+lZjbIEzemwl9th6syCNEuV1WhglGQonX3xgYOslmvRG5JbVY39hDfaeqMLB4lpkl9ShvKWqGwCEBajQLy4YN56dhnP6RGFYarjbKgbbIybED/dP64unfjyAZbsLHR4z+uKvh9CgM+Cpi7O8ojjE+N5RWHBeJl5ZeQQDEkJw09lpXd5mQUUD5n24BSW1Wnxy4yj0i3OsGiT1HIIgYNZZCXhnTTbK6rRuGzNPRESeoRAFNBmYitwRnUFCblk9REEw12IxGtv22CoVnj+XpLaC/JQoL9diW14FRqdHOnXbDGw9xGCUsPZIKf48Uoq/ssuRU1pnroQXrFGif3wIpmXFISM6CL1jgtAvLhgxwRqvCPjac93YXvhxdyGeXLYfY9IjERtiX2/k6kMl+GrbCdw+KQO9Y4Jc3Er73TGlNw4V1+KZnw8gMlDdpXlFt+RW4O+fbocky/jkxlEYnuqe6YTId806KxFvrjqGH3aexPwJ6Z5uDhERuZAowKIIEll3sqqxeU5aP5W5x8/QqqfblJasZCqyV+oXF4yKei3K63WQZdmpsQ0DWzerrNfhh10n8fnmfBwtqUOgWoEx6ZG4cFA8MmOD0S8+GGmRgT47l6lCFPDS3CGY+eZ63PXFTnw2f3SHqSDldVo88M0e9I8Pwd3n9XFTS+0jCAJe+dsQVNTrcM/SXdh+vBKPXtjfoZ5yrcGIt1dn461VR9ErKhCL541EWlSgC1tN3UXvmCCM7BWOzzfn46azPTN+noiI3EMUBY6xtYMpDfmczGhUNzbPxmGUrRSPEpmK7I38VApkxgZj+/FK1DQZENoyv60zMLB1k1M1TXj/zxx8tjkfjXojBiWGYtHVw3Bu/1iold3rg5cRHYTnZg/CPUt34dnlB/H4RQNsnpA36Y245b/bUdukx2fzR0Oj9J7UahM/lQJLbhqFV347gnfXZuOv7DI8N3sQxnSQPiFJMlbsL8a/VxxGblk95g5PwpMXD3RrxWryfdeMScXdX+7ChuxyjO8d5enmEBGRi4iCYBGgOaK0VotTNU0AgLSowG59rmEwyhAFAQpRMPfYWhSPajXdD3kn07Q/OaV1CA9Qt7tsbW293dvtvu96L2GUZHy8IQ+v/HYYOoOES4Ym4pZz0pHpBcWeXOmSoYk4UFSD9/7MgUapwIPT+7YJbuu1Btz1xU7syK/EoquHo2+c9z4nKoWIh2b0wzmZUfi/b/bgivc2YWSvcMwdkYxxGZGIC/GDUiFCkmQcK63D7wdP4dvtJ5BdWo8+MUH49KbROLsPgxJy3PSsOEQGqvHppuMMbImIujGFIHQ6FflgUQ2qWnov9UYJQ1O673AnoySb04xN/xrOGGOrEAVmOXmxALUSwX5K5Fc0IL+D2VQa6mrt3i4DWxc6Xl6PO7/YiT0nqnFe/1g8MXMAkiMCPN0st3l4Rj806Y14d202DhTV4LnZWUgKbz7+3QVVeOCb3ThWUofnZg/C9Kw4D7fWPuMyovD7gon4alsBPvorD//3zR7zY6Yrg6ZJw0f2Csc952XigkHxTq/6Rj2HRqnAlaNS8M6aYzheXo/USKaxExF1R4IASJ0cYluvNSAtMhBGWUZhVSOS67To7JmHQhQQ6q/y2sDQKJ+upmu9x1ZiGrIPmJQZA52x49z7mhr7YycGti7yx8FTuGfpLihFAYuuHobpWXFe+wXhKoIg4KmLB6J/fAie/vEAzvn3aqRFBUJrkHCishFRQRp8On80xmX4Vi+Un0qB68b2wrVjUrG/sAYHCmtQXNMEvVGCUZLROyYIYzMiER/q7+mmUjcxb1wvvL8uB+/9mYNnZw/ydHOoHX/++SdeeuklbN++HUVFRfj+++9xySWXmB+//vrr8cknn1isM3r0aGzatKnNtmRZxgUXXIBff/21zXasOXnyJB588EH88ssvaGxsRGZmJhYvXozhw4c749CIyMUUomARoNlLazBCZ5QQqFEg1F+F4+X1+OtYWZfaMjw13NwZ4W2MkmSueKxSiBAEAQ2t5kXVG2WmIfsAURTgJ3Y8BFHnQF0bBrZOJkkyFv5xFG/8cRSDk0LxztXDvPaLwR0EQcCVo1IwuW8MvtlegINFtVAqBNw6MQNzhib69BgQQRCQlRiKrMRQTzeFurnoYA3+NiIZS7cV4O5z+yDGzmrj5H719fUYMmQIbrjhBlx66aVWl5k+fTo++ugj899qtfXxRQsXLrT7gmhlZSXGjx+PyZMn45dffkFMTAyys7MRFhbm8DEQkWeIggCpE6nI9drmoC5Io0RkkAYTM6PN2WOOkmQZm3LKoTV4bxWr5lTj5h5ZhSggOkiDk1WN6B8fDEEQYDBKzJTroXw3qvBCjToj7vh8B/44VILLRyTjqVkDvWqeWU+KC/XDHVO8q+IxkS+55Zx0fLElH2+vPoanZmV5ujlkw4wZMzBjxox2l9FoNIiLa3/4xe7du/Hqq69i69atiI+P73C/L774IpKTky0C5l69etnVZiLyDqZYTJJkh2bHqNcaAMDcWRDWQTGe9pjG+Bo6GRi7g1GSoWh10S8lMgDb8irw054iTBsYhwa9EeoOZuSg7omvupNUN+px7eLNWH24BM9ckoUXLxvMoJaInCY5IgBXjU7BZ5vzkVtmf4VA8j5r1qxBTEwMMjMzcfPNN6OkpMTi8YaGBlx55ZV46623OgyATZYtW4YRI0Zg7ty5iImJwdChQ/H++++3u45Wq0VNTY3FjYg8xxTMOloZuU5rgCgICFB3/bxTEAQoRdFiXlhv03qMLQAkhPohOlgDSZaxNa8CNY36bjfjCNnHp1718jot1h8tw9fbCvDZ5uP4amsBduZXdmo8gjOV1mpxxXubsOdENd65ejiuGZPq0fYQUfd017l94KdS4MVfDnm6KdRJM2bMwGeffYZVq1bhlVdewdatWzFlyhRotVrzMvfeey/GjRuHWbNm2b3dnJwcLFq0CH369MGKFStw22234a677sKSJUtsrvP8888jNDTUfEtOTu7SsRFR14gtvZCOpiNrDRLUStFptVw6O9bXXQySbB5jCzQH431bZhupqNcBAPrHh3ikbeRZXp2KLEnNV15+3FOIv46V2+ylUCtFpEcFYlRaBOYMS8KQpFC3FWoqqGjAtYs3o6RWi49uGMnpOIjIZaKCNLh9cgb+/eth/H7gFM4bEOvpJpGDLr/8cvP/s7KyMGLECKSmpuLnn3/GnDlzsGzZMqxatQo7d+50aLuSJGHEiBF47rnnAABDhw7F/v37sWjRIlx33XVW13n44YexYMEC8981NTUMbok8yJRe62hnqVGSzNPeOINSFMxzwXpSaa0WUUHqNuf0RqPcZgytqqWHVpJlxIb4IciHa7hQ53ndqy7LMvacqMaPuwvx054iFNc0IUCtwIQ+UbhqVAoGJ4UiIcwf/moFtAYJO/MrcaCwBoeKa/H1thNYsvE4RqSG444pvTExM9qlAe7RU7W4dvEWNBmM+PzmMTgrOcxl+yIiAoCbJ6Tjx91FePSHvRiZFoFQf5Wnm0RdEB8fj9TUVBw9ehQAsGrVKqtFny699FJMmDABa9assbmdAQMGWNzXv39/fPvttzb3rdFooNFoutR+InIe0ymroz22BiuBXlcoFJ7vsT1Z1YhteRUYmhwOlVJAk15CWlTzdHeGVvPYmrQeU6vi+NoeyysCW1mWceRUHX7aU4gfdxcir7wBaqWIyX2j8diQAZjSLwb+NsYNJIb546LBCQCaxxh8v/Mk/rM2G9d/tBWj0iLwr1lZ6BsX7PQ2b8urwE2fbIOfSsRXt45FZqzz90FEdCaVQsRLlw3GrLf/wkPf7sE7Vw/rcVOJdSfl5eUoKCgwF4h66KGHMH/+fItlBg0ahNdeew0zZ860uZ3x48fj8OHDFvcdOXIEqakcGkPkKxSdHGNrlGSnztuqEkXo7ZhftCNF1Y3YmldpLkhlTUZ0kNXZJSrqmlOKtQYjTlRpUa81mgNbSZbNadvmNrcKZjUcX9tjuT2wzS2rx+HiGpTV6VBU3Yi9J2uw50QVqhr0UIgCxveOwh1T+mDqwFiE+DnWExGkUeLaMam4YmQyvtl+Ai+tOIwL3liH68f1wj3n9UGwg9uzZcX+Ytz1xU4khftjyU2jkRjG+UqJyH2yEkPxyAX98a+fDuA/f+bgtokZnm4Stairq8OxY8fMf+fm5mLXrl2IiIhAREQEnnzySVx66aWIj49HXl4eHnnkEURFRWH27NkAgLi4OKsFo1JSUpCWlmb++9xzz8Xs2bNxxx13ADg9Lve5557D3/72N2zZsgXvvfce3nvvPRcfMRE5iylYkx1NRZadG9gqRMEpxaNqGg2QZRn94qyPdy2r0+J4eQP6xgW36WWtbdIDaJ6TtkFrRJP+9Dy1Z46xNbXZNF0Se2x7LrcHtku3FuDdtdnNOxcF9I0LxoyseAxNDsO5/WMQGdT1tCiVQsSVo1IwIysOr/x2BB/+lYsfdxfi8ZkDcOGg+E73bsiyjA/W5eL5Xw7irOQwLJ43EuGBnS+pTkTUWTeO74XdBVV44ZdDiArS4LLhSZ5uEgHYtm0bJk+ebP7bNIZ13rx5WLRoEfbu3YslS5agqqoK8fHxmDx5MpYuXYrgYMeyfrKzs1FWVmb+e+TIkfj+++/x8MMP4+mnn0ZaWhoWLlyIq6++2jkHRkQuZ4pNHe2xNUgy/FROHGOrENBk6HoqslFqTpG2lTkZU6/Bn0dLse5oqXleWpOaxubAtkFnQKPeCEmWoTUYoRRFyHLbVGSg+fxfazBCpWAWU0/l9sD22rGpmHVWAqKCNAgPUEHpwqsqYQFq/OuSLFw+MhmPfL8Xd3y+E19lnsC/Zg1EamSgQ9uqadLjga93Y8X+U7hwcDxevmyIzfRoIiJXEwQBL80djOpGPf7vm90wGCVcMSrF083q8SZNmtRu2t2KFSsc3qa17eXl5bW576KLLsJFF13k8PaJyDt0tiqytWJKXaEQBRidMI+t0cpY2NbCA9XIiA4yz8Pbmn+IHyrqtahs0Jufjya9BP+WAP7MQBgA1EoBWgM41U8P5vbANjHM3+2pu1mJofj+9vH4fPNx/PvXwzj/tT9x3ZhU3DYpA1Ed9BBLkozvdp7Ei78eQlWDDk/OHIB543pxTBsReZxGqcC71wzH7Z9tx0Pf7UVBZQMemNbP080iIqJO6Gxga62YUlc4qyqyQZLajIU9k7XxtSY78yuRX9Fg/lurN5qLRCmsbNeUgqxmKnKP5RXFo9xBIQq4dmwvTMuKwysrjuCjDXn4bHM+Lh2eiNlDE9E/PgQB6tNPR5PeiI055Vj4+1HsLqjC8NRwfHT9yHY/gERE7uavVuCDeSPx2P/24e3V2UiPCsKcYYled/FNkmScqGxEaV0T+sQGO1xDgYiou+v8dD9tiyl1hVIUnTLG1mhlLKwjzsyMbNJL8Fc3t0thZbvmwJY9tj1WjwlsTWKC/fDiZYPx90kZeGfNMXyz/QQ+3ZQPoLk3OSHMDzWNBuSU1UFvlBEX4ofXrzgLFw9J8LoTRSIioPnC3ZMzB2LfyWrc9/VuvLs2G9eMScV5A2I9WtyupLYJP+8pwor9xdh/sga1Lelmn88fjXGc85uIyILQaoyttSEIts5DDZLUpQDyTMqW6X5kWe7SuW/zGNvOB5kBKsswpclghFFqvs/WGNvW/1LP0+MCW5NeUYH492VD8M+LBmBjdjmOldThWEkdTtU0IS0qEOcPiMWIXuEYlxHFKz9E5PXUShHf/n0clu8twuL1uXhi2X48sWw/+sYGY3K/GEzpF4NhKWEurWsANI8H3Xa8Eu//mYPfD56CJAMDE0Jw6fAkDEgIQUKoPwYkWK+QSUTUk5mCtW15FW0eEwUBSeH+iA7WICk8wHy/ab5ZZ6Yim8brGiS5S4WYOhpj25GEMD8Y5TAEqBXYmV+FslotDC1jf62NKVYzsO3xemxgaxLip8K0gXGYNtDTLSEi6hqVQsSssxIx66xE5JbVY9WhEqw+VILF63Pw7tpshPgpcU5mNCa23GJC/Jy2b4NRwvJ9xVi8Lge7T1QjIlCN2yf1xuxhiciIDnLafoiIuqsAtRJnJYdBZ2ibBlyvM+J4eT3yKxqQEOoP0Rx8tqTmOnG6H1MwapRkqLpQJ7U5MO58u5QK0Tx3bYifEqV1WpTWaSEIAvytNMxfrYBaIbIqcg/W4wNbIqLuKC0qEDednYabzk5DndaA9UfLsPpQCdYcKcFPe4oAAP3jQ3BOZhQmZkZjRGpEp7JTmvRGfLP9BN77Mwf5FQ3IiA7E83MGYfbQRPh15YyIiKgHam/WjogANXYWVEJrkMzjT13RY2vK7OlqASmjJMNf7Zx2jUmPhM7YHMSLgmD19yo9KhBJ4f4cOtiDMbAlIurmgjRKTM+Kw/SsOMiyjCOn6rD2SAnWHinFR+vz8J+1OQhUKzA2IwqT+0Vj9tBEi2J6Z2rSG7H9eCV+2lOIn3YXoVZrwPDUcDx+0QBM6Rdj7kkgIiLn8VM1B3NNeqM5sDUFn86c7scUJFc36hGk6XyoYJrH1hlEUYCf2P7FUnuWoe6NgS0RUQ8iCAL6xgWjb1wwbjknAw06AzbllOPPI2VYe6QUvx88had+PIDUiACkRgYiPtTPfGW8pFaLk5UN2HeyBjqjhEC1AhcOjsffRiRjeGo4r5ITEbmQpiULplFvRHjLfZILemw1Ld/52/IqEJ0V3+laM86ehoioIwxsiYh6sAC1ElP6xWJKv1gAwI78SqzYX4z88gbklTdgZ34ldEYJsgzEBGsQH+aHG89Ow5j0CIxOi2wzHQMREblG6x5bE31LMSVnZsqEBaiRHhWEnLI66IxSpwNbZ09DRNQRBrZERGQ2LCUcw1LCO16QiIjcSqNUQBQENOlPF5dyxRhbAIgJ0SCnrA4GY+fnszXKXZvHlshRrIdNREREROQD/FQKNBlO99gaZeePsQVOB8qmHmFHmebBZSoyuRN7bImIiIiIfICfSkS91oCqBh0AoLZJDwBQOnG6H6B1ZeTO9di6Yhoioo4wsCUiIiIi8gEBaiVOVDZg7ZFS832CIDg95dc0F6yhkz22pnhYwTG25EYMbImIiIiIfEBWYgiSw/0t7tOoFFApnNxjK3ZtLltzjy3H2JIbMbAlIiIiIvIBGqUCMSGur0ZvGhvb2eJRripqRdQeJr4TEREREZGZKApQiEKni0eZenqdXdSKqD3ssSUiIiIiIgtKUTSnFBsl2TyvuT10hpZUZI6xJTdiYEtERERERBZUitM9tlUNOpysakSIvwoqOyodK0URcSF+CPJjqEHuw3cbERERERFZUCpE8xjbem3z3Lmj0yIQoGb4QN6JY2yJiIiIiMiCShTMY2XrtAaIggB/lesLVxF1FgNbIiIiIiKyoFSI0Lf02DboDAjUKCBwzCx5MQa2RERERERkQSEK5ml76rQGBDIFmbwcA1siIiIiIrKgUgho0Bmx/mgZapsMCNQwsCXvxncoERERERFZSAjzR53WAACICtIgMczfwy0iah8DWyIiIiIishAVpEFUkMbTzSCyG1ORiYiIiIiIyKcxsCUiIiIiIiKfxsCWiIiIiIiIfJpdY2xlubnUd01NjUsbQ0REZC/Tb5LpN4q6hr/1RETkbRz5rbcrsK2trQUAJCcnd6FZREREzldbW4vQ0FBPN8PnlZeXA+BvPREReR97fusF2Y7wV5IkFBYWIjg4GIIgOK2BnlZTU4Pk5GQUFBQgJCTE081xGR5n99ITjrMnHCPA4+wqWZZRW1uLhIQEiCJH1nRVVVUVwsPDkZ+fzwsFTtJTPuPuxOfUufh8Oh+fU+dy5Lferh5bURSRlJTklMZ5o5CQkB7xxuNxdi894Th7wjECPM6uYADmPKYThtDQ0B7xfnSnnvIZdyc+p87F59P5+Jw6j72/9bzETURERERERD6NgS0RERERERH5tB4d2Go0GjzxxBPQaDSebopL8Ti7l55wnD3hGAEeJ3kXvk7Ox+fU+ficOhefT+fjc+o5dhWPIiIiIiIiIvJWPbrHlqgznnzySadVB//hhx+wcOFCp2yLiIiInIO/9US+h4EtkQfxx46IiKh74289kXswsCUiIiIiIiKfxsCWqJP27duHCRMmwN/fHykpKXj11VctHs/NzcVVV12F6Oho+Pn5YdiwYVi2bJn58euvvx6ffPIJjh8/DkEQIAgCJk2aBAAoLS3FLbfcgt69e8Pf3x+9evXCzTffjIqKCnceIhERUY/G33oi3+Gzge2ff/6JmTNnIiEhAYIg4IcffrB4/Prrrzd/gZhuY8aMsbotWZYxY8YMq9s5k2nMRetbXFyck46qLU8dJwCcPHkS11xzDSIjIxEQEICzzjoL27dvd8JRWfLUMfbq1avNdgVBwD/+8Q+72n3JJZfg4osvxg8//IDp06fjvvvuw+LFiwEABQUFGD16NA4cOIDXX38d//vf/xATE4NZs2YhIiICgiBg9OjRuOCCCxAXF4eNGzfiggsuwNq1ayEIAmJiYvD+++9DFEWsWLECL7zwAnbt2oXp06c7fJwGgwH//Oc/kZaWBn9/f6Snp+Ppp5+GJEl2HaejPPV61tbW4p577kFqair8/f0xbtw4bN261UlH1ZYzjnPSpEltlrniiis63Pc777yDtLQ0+Pn5Yfjw4Vi3bp0zD82Cp46zo/2S87nzfeXLOnpvyrKMJ598EgkJCfD398ekSZOwf/9+i2W0Wi3uvPNOREVFITAwEBdffDFOnDjhxqPwHs8//zxGjhyJ4OBgxMTE4JJLLsHhw4ctlvHkc+rob/2gQYNwySWX4OeffwYAPPbYYxa/9Rs3bsQ777wDAKioqIC/vz9efPFFq7/1nbVo0SIMHjwYISEhCAkJwdixY/HLL7+YH+d7tOuef/55CIKAe+65x3wfn1fP89nAtr6+HkOGDMFbb71lc5np06ejqKjIfFu+fLnV5RYuXOhQgYCBAwdabHfv3r0Ot99enjrOyspKjB8/HiqVCr/88gsOHDiAV155BWFhYZ05jHZ56hi3bt1qsc2VK1cCAObOnWvX+v/4xz/wwAMPYNq0aXjvvfdw4YUX4qmnnoIkSXjyySchiiLWrFmDq666CtOmTcPdd9+NXr16ITw8HAAQHx+P6OhoaDQajBkzBtHR0W2Oc9OmTTjnnHMwd+5cfPfdd9i6dSt27Njh0HG++OKLePfdd/HWW2/h4MGD+Pe//42XXnoJb775pl3rO8pTr+f8+fOxcuVK/Pe//8XevXsxdepUnHfeeTh58mSnjqMjzjrOm2++2WKZ//znP+3ud+nSpbjnnnvw6KOPYufOnZgwYQJmzJiB/Pz8Lh+TNZ46Tnv2S87j7veVL+vovfnvf/8br776Kt566y1s3boVcXFxOP/881FbW2te5p577sH333+PL7/8EuvXr0ddXR0uuugiGI1Gdx2G11i7di3+8Y9/YNOmTVi5ciUMBgOmTp2K+vp68zKefE4d/a3/5JNPMHnyZDz++OMAgIyMDIvf+jFjxmDAgAEAgL59++L111/HpZdeavW3vrOSkpLwwgsvYNu2bdi2bRumTJmCWbNmmYMsvke7ZuvWrXjvvfcwePBgi/v5vHoBuRsAIH///fcW982bN0+eNWtWh+vu2rVLTkpKkouKiqxu50xPPPGEPGTIkE63tSvceZwPPvigfPbZZ3e+sZ3kzmM809133y1nZGTIkiS1u9wTTzwhA5Dz8/Mt7v/kk0/M9yckJMg33nijrNfrLW4vvfSSLAiCuX3z5s2TU1NT2xynJEnym2++KQ8aNEgODAyUAZhvL7zwgkPHeeGFF8o33nijxX1z5syRr7nmGoeen85w1+vZ0NAgKxQK+aeffrK4f8iQIfKjjz7aiZY7prPHOXHiRPnuu+92aF+jRo2Sb7vtNov7+vXrJz/00EMObacz3HmcHe2XnMuT7ytfduZ7U5IkOS4uTn7hhRfM9zU1NcmhoaHyu+++K8uyLFdVVckqlUr+8ssvzcucPHlSFkVR/vXXX93Wdm9VUlIiA5DXrl0ry7LnnlNn/NbX1tbKsixb/Na31t5v/RdffNGpdtsSHh4uf/DBB3yPdlFtba3cp08feeXKlRa/bXxevYPP9tjaY82aNYiJiUFmZiZuvvlmlJSUWDze0NCAK6+8Em+99ZZD6cRHjx5FQkIC0tLScMUVVyAnJ8fZTXeIK45z2bJlGDFiBObOnYuYmBgMHToU77//viuabxdXvZYmOp0On376KW688Ua7ewhjYmIs/o6NjQUAFBYWoqSkBB9++CFUKpXF7YEHHoDcztTRpuOMiYnBnXfeifHjx+O7777Dli1bsGnTJgDA66+/7tBxnn322fjjjz9w5MgRAMDu3buxfv16XHDBBXat7wrOfj0NBgOMRiP8/Pws7vf398f69eud2nZHdHScAPDZZ58hKioKAwcOxP33329xZfdMOp0O27dvx9SpUy3unzp1KjZs2OD09tvL2cdJ7uWt7ytflJubi+LiYovnUqPRYOLEiebncvv27dDr9RbLJCQkICsri883gOrqagBAREQEAM8/p135re9orOwbb7yBO++8E+eff36b3/qmpqYutdvEaDTiyy+/RH19PcaOHevx59PX/eMf/8CFF16I8847z+J+Pq/eQenpBrjKjBkzMHfuXKSmpiI3NxePPfYYpkyZgu3bt0Oj0QAA7r33XowbNw6zZs2ye7ujR4/GkiVLkJmZiVOnTuGZZ57BuHHjsH//fkRGRrrqcGxy1XHm5ORg0aJFWLBgAR555BFs2bIFd911FzQaDa677jpXHY5VrjrG1n744QdUVVXh+uuvt3udkpISJCcnm/8+deoUgOYvqcjISEyaNAn333+/1XVHjhzZ5r7Wxzl37lw0NTVh3bp1WLhwITQaDXJzcwEAvXv3dug4H3zwQVRXV6Nfv35QKBQwGo149tlnceWVV9q9DWdyxesZHByMsWPH4l//+hf69++P2NhYfPHFF9i8eTP69OnjysOxyZ7jvPrqq5GWloa4uDjs27cPDz/8MHbv3m1Oiz9TWVkZjEaj+cTKJDY2FsXFxS4/JmtccZzkXt74vvJVpufL2nN5/Phx8zJqtdo8LKX1Mj39+ZZlGQsWLMDZZ5+NrKwsAJ5/TrvyW9/RhdmvvvoK5557Ll555RXzfabf+q7au3cvxo4di6amJgQFBeH777/HgAEDzAEU36OO+/LLL7Fjxw6r9Ts8/T6lZt02sL388svN/8/KysKIESOQmpqKn3/+GXPmzMGyZcuwatUq7Ny506Htzpgxw/z/QYMGYezYscjIyMAnn3yCBQsWOK399nLVcUqShBEjRuC5554DAAwdOhT79+/HokWL3B7YuuoYW1u8eDFmzJiBhIQEu9f55ptvcO+995r//uqrr5CcnIzExERMnz4dmzdvRlZWVptexNY0Go35qmzr4/Tz88PIkSOxfv1683E++uijAICrrrrKoWNbunQpPv30U3z++ecYOHAgdu3ahXvuuQcJCQmYN2+eQ9tyBle9nv/9739x4403IjExEQqFAsOGDcNVV13VpXFKXdHRcQLN405bL9OnTx+MGDECO3bswLBhw2xu+8ysAlmWHaoT4EyuPE5yL296X/m6zjyXfL6BO+64A3v27LGaaeOp59TZv/WtNTQ0ICQkxOK+JUuWdKm9Jn379sWuXbtQVVWFb7/9FvPmzcPatWvNj/M96piCggLcfffd+O2339p9rfm8ela3TkVuLT4+HqmpqTh69CgAYNWqVcjOzkZYWBiUSiWUyuYY/9JLLzWXYbdHYGAgBg0aZN6upznrOOPj483FDUz69+/vFYVEnP1aHj9+HL///jvmz5/vUDveeecdvPTSS/jtt99w66234ueff8YTTzwBURTx9NNPo7KyEhMnTsSSJUuwdu1a/PDDD/jXv/5lcZLfv39/nDp1CosXL8bWrVvNlSCnT5+ONWvWIDg4GMuXL8ejjz5qrmh4++23O3ScDzzwAB566CFcccUVGDRoEK699lrce++9eP755x06Xldx1uuZkZGBtWvXoq6uDgUFBdiyZQv0ej3S0tLccRgdOvM4rRk2bBhUKpXNZaKioqBQKNpc2S0pKWlzldhTnHGc5F6+8L7yFaYeuvaey7i4OOh0OlRWVtpcpie68847sWzZMqxevRpJSUnm+z39nLr6t37FihV47rnn8Pvvv+PRRx/F559/3qX2mqjVavTu3RsjRozA888/jyFDhuD111/3+PPpq7Zv346SkhIMHz7cfG6ydu1avPHGG1Aqlebnhc+rZ/WYwLa8vBwFBQWIj48HADz00EPYs2cPdu3aZb4BwGuvvYaPPvrI7u1qtVocPHjQvF1Pc9Zxjh8/vk25/SNHjiA1NdVlbbeXs1/Ljz76CDExMbjwwgsdascPP/yAZcuWYdasWfj555/xyiuv4KabbgIApKSkYNu2bRg0aBAefvhhnH/++fj73/+OdevWYfLkyeZt3HTTTbjsssuwYMECjBo1CrfeeisA4PHHH8e8efNQUVGBzz77DLt378aKFSsAAE8//bRDx9nQ0ABRtPyoKxQKl0334yhnv56BgYGIj49HZWUlVqxY0en0dGc78zit2b9/P/R6vc1l1Go1hg8f3iaFd+XKlRg3bpxT29tZzjhOci9feF/5ClPKfevnUqfTYe3atebncvjw4VCpVBbLFBUVYd++fT3y+ZZlGXfccQe+++47rFq1qs3FSE8/p67+rb/11lvx2muvYfbs2di9ezd+/fXXLrXXFlmWodVqPf58+qpzzz0Xe/futTg3GTFiBK6++mrs2rUL6enpfF69gQcKVjlFbW2tvHPnTnnnzp0yAPnVV1+Vd+7cKR8/flyura2V77vvPnnDhg1ybm6uvHr1anns2LFyYmKiXFNTY3ObsFJ5c8qUKfKbb75p/vu+++6T16xZI+fk5MibNm2SL7roIjk4OFjOy8vrVse5ZcsWWalUys8++6x89OhR+bPPPpMDAgLkTz/9tNscoyzLstFolFNSUuQHH3zQ6cd1Jk8d57x58+TExET5p59+knNzc+XvvvtOjoqKkv/v//6vWx3nr7/+Kv/yyy9yTk6O/Ntvv8lDhgyRR40aJet0Oq88zmPHjslPPfWUvHXrVjk3N1f++eef5X79+slDhw6VDQaDzeP88ssvZZVKJS9evFg+cOCAfM8998iBgYFe+x3U2eNsb7/kfO5+X/myjt6bL7zwghwaGip/99138t69e+Urr7xSjo+Pt/iOu+222+SkpCT5999/l3fs2CFPmTJFHjJkiMVnoqf4+9//LoeGhspr1qyRi4qKzLeGhgbzMnxOHfPwww/Lf/75p5ybmyvv2bNHfuSRR2RRFOXffvtNlmU+n85yZsV/Pq+e57OB7erVqy3Koptu8+bNkxsaGuSpU6fK0dHRskqlklNSUuR58+a1Kdl+Jmsnz6mpqfITTzxh/vvyyy+X4+PjZZVKJSckJMhz5syR9+/f74IjbOap45RlWf7xxx/lrKwsWaPRyP369ZPfe+89Jx9dM08e44oVK2QA8uHDh518VG156jhramrku+++W05JSZH9/Pzk9PR0+dFHH5W1Wq0LjtJzx7l06VI5PT1dVqvVclxcnPyPf/xDrqqqcsERNuvqcebn58vnnHOOHBERIavVajkjI0O+66675PLy8naPU5Zl+e2335ZTU1NltVotDxs2zDwtRnc6zvb2S67hzveVL+vovSlJkvzEE0/IcXFxskajkc855xx57969FttobGyU77jjDjkiIkL29/eXL7roog6/B7sra88lAPmjjz4yL8Pn1DE33nij+bMcHR0tn3vuueagVpb5fDrLmYEtn1fPE2S5nblHiIiIiIiIiLxcjxljS0RERERERN0TA1siIiIiIiLyaQxsiYiIiIiIyKcxsCUiIiIiIiKfprRnIUmSUFhYiODgYAiC4Oo2ERERdUiWZdTW1iIhIaHNXM3kOP7WExGRt3Hkt96uwLawsBDJyclOaRwREZEzFRQUICkpydPN8Hn8rSciIm9lz2+9XYFtcHCweYMhISFdbxkREVEX1dTUIDk52fwbRV3D33oiIvI2jvzW2xXYmlKSQkJC+GNHRERehWmzzsHfeiIi8lb2/NZzUBIRERERERH5NAa2RERERERE5NMY2JJXKK3V4lRNk6ebQURERETU4zTpjaio10GSZE83pdPsGmNL5Er/23US9y7dBQBYcH4m7pjSx7MNIiIiIiLqQTZml6OmSY/BSWFIiwr0dHM6hT225FHVDXo89eMB9IsLwYyseLz82xGsPlzi6WYREREREfUYOqMEANC3/OuL2GNLHvXa70dQ3ajHZ/NHo3dMEA4W1+CF5YcwsU80RJGVTomIiIiI3EX23Uxk9tiS51TU6/DZ5uO4clQy+seHQKUQce95mTh8qhZrj5Z6unlERERERD2KDN+NbBnYksd8v/Mk9EYZ88b2Mt83PSsOMcEafLrxuOcaRkRERETUg5h6atljS9QJP+4uxJCkUPSJDTbfp1KImDMsCWuPlKK6Ue/B1hERERER9RTNES0DWyIHldZqsftEFaYOjGvz2PSsOBgkGasPsYgUEREREZGrmQJayYcjWwa25BFrDpdAloFz+8e0eWxwYijiQ/3w675iD7SMiIiIiIh8DQNb8oiNOeWICtKgb6s0ZBNRFDBtYBzWHilFk97ogdYREREREfUcpn5aH+6wZWBLnrE5pwKj0yMgCNan9Jk6MBaNeiPWHS1zc8uIiIiIiHoWc/EoVkUmst/JqkacrGrEmLQIm8uM7BWBQLUCfx1jYEtEREREZA9ZllHdoEdVg87qTWuwng0pd4PiUUpPN4B6nj0FVQCAoSnhNpdRKUSMSotgYEtEREREZKcTlY3YkV9p8/GwADUmZka3uf90j63vYmBLbrf3ZDXUChGZVsbXtja+dxSe+fkgSmqaEBPi56bWERERERH5Jq1BAgCMSouA4owhf8dK6tBoo37N6TG2vhvaMhWZ3G5fYQ36xgVDrWz/7Tc2IxIAsCG73B3NIiIiIiLyaabANDbYDzEhljc/tcJmqrFpPd8NaxnYkpvJsox9J6uRlRjS4bL940IQEahmOjIRERERkR2klshUFNsWaBXQceDqwx22DGzJvQqrm1BRr0NWYmiHy4qigLHpkdiQXe7TaRFERERERO4gyTJEG7OOCIJg9Zza8j7fPedmYEtute9kNQBgkB2BLdCcjnyyqhEFFY2ubBYRERERkc8zSjIUVnprAds9tq3jWl/uS2JgS26172Q1lKLQYeEoE9M42405TEcmIiIiImqPLAM24lrz4+2u79zmuBUDW3KrA4U16B0TBD+Vwq7l06MCEROswUYWkCIiIiIiapckyxBspiJbX8ciEdmHI1sGtuRWOWX16B0TZPfygiBgbEYkNuZwnC0RERERUXvaHWNrIxm59Tm25MPn2wxsyW10Bgn5FQ3IiLY/sAWAsemROFWjRW5ZvYtaRkRERETk+6R2UpEFwXqPLHtsiRyUX1EPoyQjPTrQofVOj7NlOjIRERERkS1yOz22gB3Fo3x4lC0DW3Kb7NLmHldHe2xTIgKQEOrHcbZERERERO2QZNtjaW332FpEtj6LgS25TU5LYJsW5ViPrSAIGJMRiU05FRxnS0RERERkQ/vT/QhWe2Tl7hHXMrAl98kprUNciB8CNUqH1x2bHomyOi2OldS5oGVERERERL6vvVRkWz22luu7oFFuwsCW3CanrN7h8bUmHGdLRERERNS+DlORO1ifY2yJ7JBTWtfpwDYpPAApEQEcZ0tEPuGdd95BWloa/Pz8MHz4cKxbt67d5deuXYvhw4fDz88P6enpePfddy0enzRpEgRBaHO78MILzcs8+eSTbR6Pi4tzyfEREZF3am+6HwBWh/W1vkvy3biWgS25R2W9DpUNeqRHOVY4qrWx6ZHYlFMOyZc/cUTU7S1duhT33HMPHn30UezcuRMTJkzAjBkzkJ+fb3X53NxcXHDBBZgwYQJ27tyJRx55BHfddRe+/fZb8zLfffcdioqKzLd9+/ZBoVBg7ty5FtsaOHCgxXJ79+516bESEZF36Xge27Za99L6cj0bBrbkFjllzWNjO9tjCzSnI1c26HH4VK2zmkVE5HSvvvoqbrrpJsyfPx/9+/fHwoULkZycjEWLFlld/t1330VKSgoWLlyI/v37Y/78+bjxxhvx8ssvm5eJiIhAXFyc+bZy5UoEBAS0CWyVSqXFctHR0S49ViIi8i5yB/PYNi9jGbyyeBSRAzo71U9r5nG2TEcmIi+l0+mwfft2TJ061eL+qVOnYsOGDVbX2bhxY5vlp02bhm3btkGv11tdZ/HixbjiiisQGGh5sfDo0aNISEhAWloarrjiCuTk5Nhsq1arRU1NjcWNiIh8myTLEGz22DY7s1NWtvmHb2FgS26RU1oPtVJEQph/p7cRG+KH3jFBWHOk1IktIyJynrKyMhiNRsTGxlrcHxsbi+LiYqvrFBcXW13eYDCgrKyszfJbtmzBvn37MH/+fIv7R48ejSVLlmDFihV4//33UVxcjHHjxqG83PrFwOeffx6hoaHmW3JysiOHSkREXqjd6X5MPbbtrO/LxaMcn3eFqBNySuuQFhlo84Nmr6kDYvH+uhxUN+oR6q9yUuuIiJzrzKvlcjtX0G0tb+1+oLm3NisrC6NGjbK4f8aMGeb/Dxo0CGPHjkVGRgY++eQTLFiwoM12Hn74YYv7a2pqGNwSkdPojRKKq5vanT4mKliNADXDEWeS2klFhq0xti0vkigIPj3dD99J5BY5ZfXoE9P5NGSTaQPj8M6abKw+VIJLhiY6oWVERM4TFRUFhULRpne2pKSkTa+sSVxcnNXllUolIiMjLe5vaGjAl19+iaeffrrDtgQGBmLQoEE4evSo1cc1Gg00Gk2H2yEi6oz8igbsO1nd7jJJ4f4Ynhrhphb1DB3NYws0pysrWgW5pljW1wNbpiKTyxmMEo6Xd34O29YGJ4UiPtQPK/ZbT+kjIvIktVqN4cOHY+XKlRb3r1y5EuPGjbO6ztixY9ss/9tvv2HEiBFQqSwzU7766itotVpcc801HbZFq9Xi4MGDiI+Pd/AoiIi6ztgyi8XUAXGYNrDtLSJQjSa95OFWdj/tzmNrYx1TMCvaMc+tN2NgSy53orIReqPcpal+TARBwNQBsVhzuBSNOqMTWkdE5FwLFizABx98gA8//BAHDx7Evffei/z8fNx2220AmlOAr7vuOvPyt912G44fP44FCxbg4MGD+PDDD7F48WLcf//9bba9ePFiXHLJJW16cgHg/vvvx9q1a5Gbm4vNmzfjsssuQ01NDebNm+e6gyUiskFqiZb81Qr4qazfdEYGts4mybbH2Jq06ZVt+VsQBJ+e7oepyORyzpjqp7WLz0rAJxuP48fdhfjbSI4HIyLvcvnll6O8vBxPP/00ioqKkJWVheXLlyM1NRUAUFRUZDGnbVpaGpYvX457770Xb7/9NhISEvDGG2/g0ksvtdjukSNHsH79evz2229W93vixAlceeWVKCsrQ3R0NMaMGYNNmzaZ90tE5E7N087YDrBUChE6AwNbZ5Jluf15bFvut1Ugytd7bBnYksvltEz1k96FqX5aG5YSjn5xwfh083EGtkTklW6//XbcfvvtVh/7+OOP29w3ceJE7Nixo91tZmZmtnsl/csvv3SojURErtRegAUAaga2Ttc6pdga29P9nC4eZZB8N7RlKjK5XHZpPaKC1E6rYiwIAq4ek4o9J6qx50SVU7ZJRERERM4jt1udF1ArRUiyDAPTkZ3G2E5F/eb7ra9nDohFwJf7bBnYksvllNY5ZXxta7OHJiJQrcAH63Kdul0iIiIi6jpJlm0GUkBzjy0A6I2+G0h5G6nVtD3WCC19tm17bE8/7sNDbBnYkuvllDmnInJrQRolbhifhmW7C9lrS0RERORlZNl2zyHQ3GMLgOnITtRhKrLNHlvZ/LgPx7UMbMm1apv0KK3VOj2wBYBbJ6YjLsQP9321G7VN+jaPy7KMYyV1WHXoFPadrPbpKm9EREREvqSjMbYqRfNjWiNnuXCWjnpsz1zOxPSXQhTaPOZLWDyKXMpcOMrJqcgAEOynwquXD8G8D7fgmsVb8OwlWciIDsKeE1X47cAprDxwCvkVDebl+8UF47XLz0L/+BCnt4WIiIiITpPsGGMLMBXZmSTzWFnHxtiaH/fxVGQGtuRSzp7q50zjMqKw6OrhuO/r3bjozfXm+zVKERP6ROH2SRkYkBCCo6fq8NKKw/jbfzbiy1vGYGBCqEvaQ0RERETNmXPtVkVmKrLTne6xtf746el+LLVOYfbhuJaBLblWTmk9lKKA5IgAl+3jvAGxWPvAJPx+sARldVqkRQViQp8oBKhPv70HJ4VhXO9IXLZoI/7+6Q78es8Ei8eJiIiIyHlk4PT8MlaoFSIEQcCeE1Vt6qWIgoCRvSIQF+rnyiZ2O3LLNYKOUpHPHJ5nmu5HEASfHrrHM3tyqZzSeqREBkClcO1w7rAANS4bntTuMvGh/nh57hBc+f4m/G9XIa4cleLSNhERERH1VJLUfo+tIAgYnhqOuiaD5XqyjCOnalGnNdhYk2wxtioCZY15HtszHzij6JQsy+0W/vJWDGzJpbJdMNVPV4xJj8DAhBB8siEPl49ItjkGgYiIiIg6r6MxtgCQGObf5j6j1BzY+mJSbHWjHpLkuXbXNDYXU7U53Y85cLW83/Sn6by4uaK1K1roWgxsyWUkSUZuWT0mZkZ7uilmgiDglnPScfeXu/DT3iJcPCTB000iIiIi6nY6GmNri7lX0cfi2tJaLTZkl3m6GQBgM1PSNI/tmdcMzpwmyMeeejMGtj6mulGPYyV1iA3RICncdeNWneFkVSO0BgkZ0d7TYwsAMwcnYNGabLy96hhmDo73yVQLIiIiIm9mT4+tNYKPBlc6Y/MA16HJ4fBTe25GVaUoItRfZfWx08+t7TG2gGkMru+dHzOw9RF6o4TXfz+K99flQNtSPW5S32i8MGew1w6szylrmerHRRWRO0sUBdw6MR33Lt2N9cfKMKGP9/QoExEREXUHMmQIguMBnim48rX5VE0pyFHBaq8tUNpRqCraqJrsKzx3OYHsZjBKuP2zHXhr9TFcNDgBH98wEg/N6IetuRWY/c5fKGg1V6s3ySk1TfXjXT22AHDhoAREBKrxzfYTnm4KEVGPIUkydAbJ6s1g5JQfRN1JZ3tsAVN1Xue2x9VOT7XjxT2dLU07cxhwm1RkH3vuTbzzcgJZeOOPo1h54BT+dUkWrh2TCgCY1DcGk/pG48r3NuHW/27Hd7ePg59K4eGWWsoprUdYgAoRgWpPN6UNtVLEhYPi8c32E6jXGhCo4UeBiMjV1h0rQ1WDzubj4zKiEB2scWOLiMhVJFnudJFOAb4XXBkl7w9sBRt9tubiUeYeWx978luwx9bL/XWsDG+uPoYrRiabg1qTfnEhePXys3CgqAbP/HzAQy20LaesDulR3pWG3NolQxPRqDfitwPFnm4KEVGP0KgzICpIg8FJYRa3/vEhAIAmvdHDLSQiZ5FludOjNAXB94IrUy+owgdm3Ggzj22bv13fhkadEeV12g5vFXVau7fJbiovVlanxd1f7kJmTDCemDnQ6jKT+8bgtokZeHdtNi45KxEjekW4uZW25ZTWY3zvKE83w6ZhKWFIiQjA9zsLMXto+3PgEhFR18kyEOqvQtoZFz2b9EYcLKrxuTF1RGRb85QxnQvyRJ9ORfZwQ9phqzCX7IGg/K9jZajXdTxXcUNdrd3bZGDrxV757QhqGvX44ubR8FfbTjO+57w++HF3IR7/3378eOfZXnGlqEFnQFF1k9cVjmpNEARcPCQBi9Zmo7pBj9AA6xXkiIjIOWydp9qaW5GIfFeXxtg6tyluIckyBEHw6tk2OppKyZ3fxVqDhMQwf/TqILuztsb+IY1MRfZSx0rq8NW2Alw7NhV9YoPbXdZPpcBjF/XHgaIafLWtwE0tbF9OaUtF5CjvKxzV2tSBsTBKMlYfLvF0U4iIuj3JxryWpnFf7LEl8h5NeiPyyxuQX96AgooG6AyOFXiz9Xm3hyD43oUuoyRD4cVBLdBqOh8blxlFN1akNsoygvyUiArStHuLDLK/7gJ7bL3Uv389hAC1AndM7m3X8tMGxmFkr3C8+cdRzBmWCI3Ss4WkTFP9ZHhxjy0ADEoMRVyIH34/eAqXDE30dHOIiLzO8fJ67D5R3WYMFgDEBPthbEak3dtqTk1se79oIz2OiDwnp7QeR0tOp4EOiA/psLOlNVmWrX7e7SP43IUuSQIUXt5laH45bFZFds90P5IkQ5adfyHAy5/+nmlbXgV+O3AKf5+UgXA7KwoLgoB7z89EYXUTlm71fK9tTmkdRAFIiQzwdFPaJQgCzu4ThY3Z5VZP2oiIerraJgNEoblgYetbWIAaNU16h7Ylw1Zg23Iyxe9hIq9hlGSoFSKmDoiDQhSgNzr2+ZTlzlcIbi4e5VtMqcjezFbzTD24p1ORXfvsG03jkZ08fJKBrZeRZRnPLT+I+FA/3Dg+zaF1x2VEYUx6BN5efczjlSVzSuuRHBHg8Z5je4zLiER5vQ6HT9k/OJ2IqKeQZBlKUUDfuGCLW0SA2uGTn+YqqVZSkW3MrUhEnmMK1PzVCihFwTydjf3r2w6kOtI83Y9vfSEYXdAD6Wynh31Y3u/uHlvTe4k9tl6iqkGHH3cXYvneIuSXNzjtw7difzF25Ffh3vMzOzUv7b3nZeJUjRafb853Sns661hJHTKivXt8rcm4jObKzRuOlXu4JURE3keyUdnU0TFwpt9JaxfozeO+fOs8lqhbk3H68yoKAgyS+8bYioLgcz22sixD9PbIymaPbTPzsBAXP/mmNHNnF7zlGFsH1TTp8cxPB/D9zpMWKRlDkkLx7OxByEoM7fS2tQYjnlt+CP3ignHpsM5NPzM6PRLjMiLx3p85uGZMKtRK93/CJElGTlkdJvTx3ql+WosL9UN6dCA2ZJfjxrMd6yUnIuruZBsnp6IgmNPJ7GHqIbB1nisKvjemjqg7a/3ZVyo602Pb04pHdT712t06Kh7l6qsKpveSs1ORGdg6oLpRj+sWb8aBohpcO6YXLhmaAKUoYtvxCryzOhuz3/kLj17QH9c7mEJssmTDceRXNODTm0Z36QrGbRMzcN2HW/Dj7kJcOtz987OerGpEk15CRoxv9NgCzenI/9tZCINRgtLbR/4TEbmRrSk7RNGx1OHTmU3Wf9988USWqDtrnUqsEEUYHPjAmz7vnU5FFgBTdLXnRBVqGg0428s7TIxS5wN5d7E1nc+Zr5fWaERBhb5L38l+KhExIX5WHzN1/js7FZmBrZ10Bgk3frwVB4tq8d61IzC5X4z5sQEJIbh4SAIe/HYPnvzxACrqdbj3/EyHBpCX12nxxqqjOLdfTJc/uBP6RKF/fAjeX5eDOcMS3T6Q3VRBr7cPBbbjM6Lw6aZ87CuswVnJYZ5uDhGR12ivx9aRYThnprpZ2x57bIm8yemqxo6OsTUt2vl5bAVzUJXbMtOGt5Nl2emptc5mnsf2jPtPfz83L5FbWo/imqYu72/awDirQytNae1MRfaQhb8fwfbjlVh09TCLoNYkLECNRVcPxz//tw9vrDqGBp0Rj17Y3+6g8qUVh9GoM+KRC/t3ua2CIOCWc9Jw79LdWH24BFP6xXZ5m444VlIHwLcC2zHpzdNVbMguY2Brw6maJry84jBWHSqBSiHimjEpuG1iBnu4ibo5SYbVTlbTXZIk25VOJpl7BGz02HayfUTkGq3H1ytEAVoH5rHt6PPeIcH3iskZfSGwtVGB/sziUQ06IzRKBSZmRndqP4XVjdh3strmxUqji8bY8ozUDtmldXh/XQ6uGJmMGYPibS4nigKevSQLN52dhg/W5+KfP+yDZMencvXhEny5tQC3nJPutIJLMwcnIDUyAK//ftTtVeWOnqpDbIgGIX4qt+63K8ID1RgQH8ICUjYcOVWLC99Yhx/3FGJyvxhkJYbi5d+O4LZPt0NvdKyYBFFP8M477yAtLQ1+fn4YPnw41q1b1+7ya9euxfDhw+Hn54f09HS8++67Fo9//PHHEAShza2pyfKKuqP7tYetcXKmYNbeXlbTYrZOYwT22BJ5FVk+/XlVioJd57Qmps9yp8fYwveqIkuy81Nrnc3cY9vmqbUs7teoN8Jfrej0TdNS48fWW8ZVqcgMbDsgyzKe+vEA/FUKPDCtb4fLC4KAf17YH3dM7o3PNufjgW/2wNDOiX9JbRMe+nYP+sUF4+7z+jit3UqFiH9M7o3dJ6qx5kip07Zrj2OldT7VW2syvncktuZVQGvw7FRJ3uZEZQOuXbwZSlHEz3dNwMtzh+CDeSPw7Ows/H6wBM/8dMDTTSTyKkuXLsU999yDRx99FDt37sSECRMwY8YM5Odbr1afm5uLCy64ABMmTMDOnTvxyCOP4K677sK3335rsVxISAiKioosbn5+p8cvObpfezUHtm3vN52wOtqrYus8hmNsibxL63lZFaLg4Bjb5n87G7ZYq4rs7YGuJPlCj631+82vV8sCeqMEvy4UoO1obvLT89h2ehfW9+vczXU/fx0rx59HSrHg/ExEBmnsWkcQBNw/rS8emNYX3+44gbu/3IVGXdtgqV5rwI0fb0VdkwELrzjL6XO+zh6aiOQIf7f22sqyjGMldegTE+yW/TnTuIwoaA0SduZXebopXsMoybjj851o0ktYctMoi4yCq0en4uYJafhk43FszGZPN5HJq6++iptuugnz589H//79sXDhQiQnJ2PRokVWl3/33XeRkpKChQsXon///pg/fz5uvPFGvPzyyxbLCYKAuLg4i1tX9ms32XqvizkV2eEeW+tnVhxjS+Rd5FaF4xSiAKMD0/2cmdrqKGsXuhwJrD2h9YUAb2X6/m1TPKrl39ZxeWemHTXvp4O5yU29/0xFdrM3Vx1FfKgfrhqd6vC6/5jcG49fNADL9xXhwjfWYe2RUvMLeaykFpe/txEHi2rxzjXD0S8uxNlNh0oh4o7JvbGroApr3dRrW1KrRW2TwacqIpuMTIuAUhSw4ViZp5viNT7ddBy7CqrwzCVZyIxte7Hivql9kRzhj2eXH3AoRYmou9LpdNi+fTumTp1qcf/UqVOxYcMGq+ts3LixzfLTpk3Dtm3boNfrzffV1dUhNTUVSUlJuOiii7Bz584u7Ver1aKmpsbiZk3ryqitnb4ib3U1K9tpv0qq6INj6oi6s9aF4xztse3o894RAUKbKWkcnW7I3Yw+1GNra7qf1oG5v7rzgW1H0waZp/thKrL7bMmtwObcCtw2MaPT88HeeHYavrh5DHRGCfM+3IJxL6zC9IV/YtrCdSiubsL71w3v9MBse8wZloSkcH+8/od7em0PFzdXRO7jg4FtkEaJIclh2MDeRwBAcXUTXlpxGJP6RuOiwdbHlvupFLjv/L7Yd7IGa46UuLmFRN6nrKwMRqMRsbGWRftiY2NRXFxsdZ3i4mKryxsMBpSVNV9o69evHz7++GMsW7YMX3zxBfz8/DB+/HgcPXq00/t9/vnnERoaar4lJydbXU6SZau9rKYUMrt7bFv+bS8V2eWTJxKR3VrXjVOKIoySbPe5pHmMbScDPUFAm68DX+ix9fK41qbTPeyn7/PrQibp6R5b9xaPYlXkdry7NhtRQRpcPtL6j729xqRHYtV9k7DywCmsPFCMep0RUwfG4bqxqYiyM725s0y9tg99txfrjpbhHBcG0QBwsKj5in9/F/RAu8O4jEgsWpONeq0BgZqe/fF4Ytk+GCUZ/5qV1W5qzUWD4/HvXw/ho7/y3F6Bm8hbnfmZkTtIUbO2fOv7x4wZgzFjxpgfHz9+PIYNG4Y333wTb7zxRqf2+/DDD2PBggXmv2tqaqwGt7ZO1kzbtT8VuYOqyILAHlsfsyW3os2UINFBGozNiPRQi8iZZBkQWi5gmQIQoyRDqeg4GGl/1uqOWYlr261Z4w0kG8M2vFHbVOTm34rWFzH9VJ3v/zRtx2Zg2/Jlr2Qqsnvklzdg9eESXDMmpUs55iZqpYgLB8dj4RVD8f51I7Dg/EyXB7Umc4YlITHMPb22B4tqkBjmj9AA36mI3NrYjEgYJBlb8io83RSPWrG/GCv2n8KC8zORHBHQ7rJKhYjrxvXCuqNlOHKq1k0tJPJOUVFRUCgUbXpJS0pK2vSmmsTFxVldXqlUIjLSeoAgiiJGjhxp7rHtzH41Gg1CQkIsbtbIsvVeF9F8Rd7qam23c8Z6ZxLA4lG+prJBh1B/FTJjg5AZG4RQfxVqmvQdr0g+oXVFdFMAYm+vqdwSg3Y60BPaBkXe3mNrlKxXkPcmtjKETRWwFa0uWnQtFdn6fkyMkmyu7u9MDGxt+GzzcSgEAVeNSvF0U7pMrWyukLz9eCX+cvF0NgeLatE/3vcKR5kMSwmHRin26GJItU16PPG//RiYEIIbxveya50rRibDX6XAko15Lm0bkbdTq9UYPnw4Vq5caXH/ypUrMW7cOKvrjB07ts3yv/32G0aMGAGVyvpFQlmWsWvXLsTHx3d6v/aSYb3HVmEeY+vYiS6LR3UfeqOE6CAN+sWFoF9cCCIC1Ky30I00D0No1rrH1t51AdsXsjoiQGhzocvbx9hKvjCPLax/b5v+DNIoMSY9EmPSIxHchWk7O8roMUqy03trAaYiW9WkN2LptgJMy4pDTIhfxyv4gMuGJ+Ht1cfw6srDGN870iVV27QGI7JL6zB1oO+mo/qpFBjRKxx/9eACUq/8dgQltU1477rhUCrsu/YVFqDGjEFx+GlPER6/aGCnx6QTdQcLFizAtddeixEjRmDs2LF47733kJ+fj9tuuw1AcwrwyZMnsWTJEgDAbbfdhrfeegsLFizAzTffjI0bN2Lx4sX44osvzNt86qmnMGbMGPTp0wc1NTV44403sGvXLrz99tt277ezJMl6+vDpExf7tmMqVsLpfroHoyTDKMlQterhEcXTY+fsUac1oLJe59B+o4M1Tsmk66pTNU3QGZyfGhsb4uc1v6EyTmdrmANbB8fYdvZ8UxTa9vbpvTgVWZK6Fsi7S3s1nUyPxToh9jE/D7aqItuYH72rGNha8dOeIlQ16HHdGMcrIXsrtVLE3ef2wf99uwe/HyzB+QOcH3wePVUHgySjf7xvjq81GZcRhZd/O4zKeh3CA9Webo5b7Sqowicb83D9uF4YnBTm0LqzzkrEdztO4s8jpTjPBe8vIl9x+eWXo7y8HE8//TSKioqQlZWF5cuXIzW1+TelqKjIYm7ZtLQ0LF++HPfeey/efvttJCQk4I033sCll15qXqaqqgq33HILiouLERoaiqFDh+LPP//EqFGj7N5vZ9k6AemoOMiZTs+TaP1xURC8fp5KOs0UZKhaXQBViI6Nk95zogqltVqH9pseFYRBSaEOreNsdVoDNuW4JrOrX1wI+sZ5Seab3Lp4VEtga3SsWFyne2ytfB94c4/t6XlZvTuyNceb1sbYdnpEtJX9dHDh01UVpBnYWvHfjXnIjA3CqLQITzfFqeYMS8R//szGSysOYUq/GKe/ocyFo3w8sB2bEQlZBjbnVmB6VlzHK3QTeqOEh77dg7gQP9w3ta/D64/PiERUkBo/7DrJwJZ6vNtvvx2333671cc+/vjjNvdNnDgRO3bssLm91157Da+99lqX9ttZkmy9AIzoYPEocw+OjZMna8ViyHuZA9tWvYumixMdFUs7vQ0Z0UEanJUSZtc+/zpWDr0Dc6m6SpPeCAAYnhqOCCdeAF91qAQGLzg+k9bzsiocHGPb1R5bwUqPravG2OaXN9gcG54WFWhXMVFT0K3w+jG21tsn25jWrfP7af7X1u9Dc9q28/ZnwsD2DLsLqrD7RDX+dUn7lWB9kVIh4oFpfXHbpzvw/c6TuGx4klO3f7CoFgFqBVI7KDbk7QYlhiJArcCmnPIeFdh+sC4Xh4pr8f51IxDUiYrQSoWIiwYn4Mut+ajTGjq1DSLyRtZ7bE+PsbV3K81spyILMHrRST21T9/Sc2eRityql8aOwrmQZBkqpQIBavt+L5Si4BVjeE1BfbCfyu6220MhtB1X6kmtgx1ly/xex0rqcLKqscN1G3XNwX/nx9jC/KVh6r012Nlb7AhZlrH7RBUEoW1QqjNKUCtFZMZ23IN+eroc744dTvfYtn0undly8zznNh43Sq55rnjmeYYlG48jSKPE7KGJnm6KS0wbGIchyWF4beURzBwSD00X5qg60/7CavSLC/b6NIyOqBQiRvSKcFmakTc6VlKL134/ggsGxXUpTf3CwfH4eEMe1h0pxYxB1ue+JSLfItm6kt/ZVGQbj4uC/eN1yfNMwZ1a0brHtvlfe9MMJQeryApe8h4xja1VO7nLSfCyAmqthyEEaBQI8VehtkmPWjsrX4f4qTpdWbe5x7b5uTAFYa7ozW7SS5BkGUOTwpESadkx89OeQruC6WMltSiqbp72SvSO4dE22fq4Ob3HtuVfaxeiZFlGvdZg17RRjmJg20plvQ4/7inElSOTu21vkyAIeHB6X1z1/mZ8tikfN56d5pTtGowS9pyoxlWjfb+KNACMSY/Av3/tGeNsDUYJ9321G8EaJf41K6tL2xqW0pyWtfLgKQa2RN2ErTG2jk/30/E8thxj6ztMwd2ZY2wB+y92ODrOTiEKXjHO0hzUO7nIk7dd3JFxOkBRKURM7hvjxr0LkCTLwMgVr32DzgDA+tQ2Sjvfbzml9ZDk5sJm4QG+cc7YZrofi1e768R2MnoOFdeipkmPmGDnF+j18usK7vXVtgLoDBKuHdt9ikZZMy4jCudkRuOt1cfsvurWkUPFtWjUGzHUznEy3m5sevPckZtzu/98tv/5Mwe7T1TjmUuyENnFuZUVooAp/WKw+lCJ10+kTkT2kWTrvRDmtFMHJ7K1FceIrIrsU6wVj+rMuGtHxiQqvKRHU2doDsidXatEFLwjcDdpHivtmX2bqiK3fr2rGvTIKa1DfnmD0y6CNbSkTAdYCWwVomhXL7FRkpEY5o9xGVF2jcf1JMHBISSd30/zv7KVZOTGljHqg11QBI6BbQujJOPTzccxLiMSvWO8pBqdC/3ftL6oqNfhg3W5TtnezvxKAM09dt1BVmIoAlvG2XZn2/IqWtLSE5zWw3pe/1hUNuixI7/KKdsjIs+SZevVMtu7Im+N6XzdZvEowfpJEHknq2NsHZzr1Cg5lv7oLam6OqNkEdA7iyjaPy+0O8iy58aMmjI4Wr+VKht02HuyGjsLKlHd6JyOGVNg629lCil7e2wNkuyStFpXEQShzXet64pHtX1MlmUE+yldchHAuy8ruNGfR0pRUNGIR2b093RT3CIrMRQXDY7HB+tycN3Y1C731O3Ir0JciB8Swvyd1ELP6gnjbEtqmvD3z3YgJSIAz83uWgpyaxP6REGtFPH7wVPdrrI4UU/T3tyMDk/3Y64EY2uJ5tRDb9CkN5rHzHVVVJAawX4qp2zLm+hbgrvWqeWOpqcbZccCAoUoQGvwfOCnbykq5GzNgbvTN9tpnmyKqUq66ftlcFIYksL9Udmgw8bscvOFla5q0Bngr1JYrQ+jEIUOKzFLkgxJls3TIfkCAdam+3Fu8SjTBUxrF2qa6za45vliYNtiycY8xIX4uWR+V29139S++GVfMd5enY3HZw7o0rZ25ld2mzRkkzHpkXjx10OoqNc5tZy/N9AZJPzj8x2o1xrw+fzRTj3pCtQoMT4jEr8fOIVHLugZF4qIuqv2puxwNO30dNVQ64+bUg+9QXZpHY6V1DllWxqlApP6Rrukh8+TrPVaKhxITzdNC+RIj6BCtP/95ko6g+T0wlGA96Ram9gaX+8OQsvQBNPToRAEqBQiNAqFuW2dYTBK2JFfZR4uVdOkR5DG+jmQPT22pumnlD72+W4T2No5RZe92rvI5WjROEcwsAWQW1aP1YdLseD8TJ97Y3ZFWlQg/jYiGZ9uOo6bJqQhsZO9reV1WuSVN+Dq0d1rbPKY9Obexi255Zie1X0KIUmSjPu+3o2teZV45+ph6GNHGXtHnTcgFo9+vw/ZpXXIiA5y+vaJyD2kdoJRR3vnOprXUvSik3qjJEOjFDGlX9cudtdpDVh/rAwr9hc7qWXeJdTfMiAQHLjYYTRnAzhSFdk7evV1RsklRUa96TMAOD891RFCS5/t6e+N5vtN4/2Nkgy9UUJdkwEh/iq7xzvXaQ0oqm5EiL8KaoWIYD8VUmxMU6kQBTQZ2n/Dmaom+1SPrZWmOrvH9vRQFes9tq56uhjYAvhkQx7USrHbVPR1xN3n9sF3O07g9d+P4N+XDenUNkwFlob36h7ja01Oj7Ot6DaBrSzLePqnA/hxdyH+eWF/XOCiysXn9ovFo9iH3w+cQsZEBrZEvsp0Umm9KrLtExdrOprux9RD4w2klrGFXU03jVCqMTY9EtWNOie1zLtEBFoOYzIFF0Z7AtuWZRyqiiwIdm3b1XQGCaoAF4yx9aLPAOD5HltJbvUd1PI+UbQax70zvwpF1Y3IiA5CVqJ9hYhMF1SyEkIRHdz+MDylQoBR10Fg27I9nxpji7YXUFw1xtba29nRTA1H9PjAtrpRj6+2FWDWkAREdXGcqS+KC/XD9eN64f11ObjlnPROFc5ac7gEYQEqDEkKc34DPai7jbPVGSQ89eN+fLY5H7dOTMf8Ceku21dcqB8GJoRg9eES3Doxw2X7ISL3sHYOYjrRdHRMoK0TGtGLpvtxdBqa9kQHazo8ge4uTE+ZPS+jqefVkarIoiDYX4XbSfQt0xm2TknVuigVWRAEGL2hSxqnL1h5rMe2Jcg/M2ukdWDb1FJdV2sw2r3d9rJQzqQQxQ7nsTWlNDu7QrYrWX9Nnfu5EgTBZrE3W5X2naHn5N3a8PW2AjTojLhhvHPmc/VFt03MQKBaiVd+O+LwurIsY+2RUkzoE+1TH2p7jc2IxKHiWpTXaT3dlC5p0htx26fb8dnmfNw+KQMPTe/n8n1O7huDbXmVqHHSlFJE5H7t9dia7ndkaheg/RNl7whrXduj0J05UhXZaO6Jc2T79vUGO1NVgx4nKhtQp9WjUW9Eo96IMH8VYkKcf7HCm+ax7WhMvKsJaK7cK5/xHWS6EGKUZfP7rKPgszXJgUwBhR3TL5keV7kqUnOB9rJmnL0fax9XV2YC+M6r4AJGScbHG/IwNj0SAxJCPN0cjwkPVOPWien4ZV8xdhdUObTuoeJanKrRYmJmtGsa52HjMprns/0r23d7beu1Btz48VasPlyCF+YMwv9N7+eyanStTe4XA4MkY/3RMpfvi4hcwzxFj82CT/YHth0t5k3jC42S5+bv9GWOFBRzJMAwUYjurxpsClyGp0RgYmY0JmZG45zMaJdk+Ymi+3ukbTn9Gnq2eNSZ30Gte2xNacAdVS5uzTy2257A1o6qyKbHFb6Uimwl5V2WbU/F1lm2vtMZ2LrIT3sKcaKyETee3XN7a01uGJ+GqCA1XvjlkEOTg689UgoAOCczylVN86ishFBEBKqx9nCpp5vSKTVNelz34RZszq3AwsvPwhWj3DeO/KzkMIQHqLDqUInb9klEztVxj639YwLNp8k2zmcEL+qtkmTfSi30FqerIne8rCmAczQV2VRN2V0MLQfjjsClucfWOz4EplZ4rMdWsJzux/QdJAgCxJaeVFPatiOBrfmCih3vO6WiOTBr7/1m6i32pR5bwMo8ts13O3cvNn4fZBcWj/KlV8GpjJKMN/44iv7xITi3X4ynm+NxgRolHpjWFxtzyvHY//bZvd7vB04hKzEEMcF+Lmyd54iigHP6RGHtkVKvuYpqr4p6Ha56fxP2nqjGO1cPw6yzEt26f4UoYGJmNNYcLvG5546Imp1OR7R+FmJrDJX1bbWkIts4e/KmMbaeLJrjyxyZ29h0Ed2RDCLTa+LIBfiuMgXp7qh6K3rRPLYdVTF3NQHN3wfWLq6ZpuExBbStxyU3z0HdaB5/eybz2G47e2yB9gNn84UPH7oQZrvH1rlsXfiUnDy1kMU+XbJVH/Dj7kJkl9bjnvP62JWO0BNcPjIFt05Mx+eb87Ehu+P00YKKBmw7XomLBie4oXWeM7FvNMrqtDhYXOPpptitpLYJV7y3EUdP1eH9eSMwbWCcR9oxuV8Myup02FdY7ZH9E1HXnB7fZv1xR8YEdlS0xVxF0wuC2+YTL0+3wve4vCqyA9t3FlPg4o4LHd6Ujt9RFXNXM18kaQlEW79NxJYUYdMFDn2rMbYHimqwJbcC+05aP+8wmgP2jtugPGPMuM4gQWeQoDeeDqRNQa/Kl1KR4a56Bu0Uj2KPrfM06ox4acVhZCWGYOqArs1R193ce14mekUG4J/f7+uwyty3O05AEICLh3TvwPacPtEQBGCNj6Qjn6xqxN/e3YiTlY345MZRHh3/PDEzGqIApiMT+Shz0OqEMbamUynb89iesU8PMkqyQymy1Mw8xtaOF7FzVZGb/3Vn7GcKatzVY9vZCzvHy+vx17EybDhWhqqGrk8v1VG2hquZ9mq00nOsEAToWuaXFc4o8GTqqdXZmH/W6EAKfOse24NFNfhlXxF+2VeE5XuLUFzd1PyYsbmCuqd6tjvrzPeZ7IJeVFGwHkBzjK2TvbPmGE5WNeLJmQN97o3oan4qBZ6dPQg5ZfV44ZdDNpfTGoz4dFM+zu0Xg4Qwfze20P0igzQYlBjqE8FZblk9/vbuRlTU6/DZzWMwJj3So+0JC1BjWEo4VvvIRQEistThGFvRmfPYCi3LeT6ylWX7isuQJVMgYM/FiU5VRfZAKrKx5YTfHe+HrowzP1HZiJpGPcrqdShqCbq6Qob9PZuuIJxxkaT1069QCObOF41StEgVNo15tZU+7EjRMmXLm9NolFGvNcBPpTDPl1uvM7TsR3LLRQ9nav19XlqrRXF1E5oMkgtSka1f+HRl1XmfCGz3nazGJxvy8PKKw/hqawH2nKjq9Jdablk9/rM2B5cNT8KIXhFObmn3ML53FOafnYaP/srDygOnrC7z1dYClNVpe0zhrWkD47D9eCVKarr+Y+Eqh4prMPfdjdAajPjylrE4KznM000C0JyOvOdEFcp8fMokop6oo8BWcGBMYIfFo8z7tL99rsIxtp1nby++uXiUt6ciG2W3BS5dSUU2SjLCA9XwU4o2x5c6oqOK6K5m2q+5irGNHluNUoQst05LlizWO5NpfKc9HVune2wlGCUZfioF0iIDm7fTqiKz0qcKRzWTZKCsTosN2WXYnFuOqgYdVE6em9lW8ShJdt37SumazTrHuqOl+Pevh7G3JU++9VielIgA3DC+Fy4cHG934SJJkvHYD/ugUYl4aIbr5/H0Zf83vR+25FXg/q9346c7z0ZyRID5sfI6LV7/4yjGpkdirId7BN1lelYcXlpxGCsOnMK1Y1I93Zw2dhVUYd6HWxCoVuDT+WOQHh3k6SaZTekXg5dWHMaaw6W4bHiSp5tDRI74//buOzyKav0D+Hdme8puei8kARJ6SSihN0EE7L1hQUVBUX52r1f0onjVq14LcEVEUQSVYkUp0nuvoQQIJCEJIXU3Zeuc3x+bXbJkE7LJ1uT9PM8+wOzs7tnDJDPvvOe85xrzYnmu+cuTXKsYjeXC9epqnZ5gEpjHqsH6OhF/7bU/AfsBy7VYj5FmVF12FvOx4KbAlm/5jR0TMw+JVUhFqHVCYMuu8fPqalcPRbYJbHkO1TrzQSCXiFBZa8C5y1WQS0TWwNYyBze7WGOzzq1aa2j28HfLDY1qnQkmwXyDg+fNVZmNAkNhZS3yymqgVEha+3XdyvL1NVpz1nlQShikIh5+MpFTP8cytP5EoRqX1FqIeA59EoJdeuPQKwPbWr0Jr606ipUHLyIpzB9v39Id13WNRJi/DPnltTiYV47FOy/gzd+y8O6fJzF5UAdMHZ6CEH9po+8pCAxv/Hoc286U4N1be7hk/bG2RCrm8ek9fTDx022Y9Nk2zL65Oyb2jEGVzohp3x+ARmvErBvbz1DulPAAdIoIwF/HCr0usL1QWo0HFu5GqL8USx4biFgvGxqeFhWIaJUcG08VU2BLiI+5krVpfF5ssUaHv44VNniuY0QgOkZcuclmXuKh8XPGleJRLW+vs9ByPy3X3CVrrjUawO571yWU3Fs8yr0ZW8tyRo5eXwl1AbhcIkJVXcDSGp5f7sd22Hn97uA5DnrTlYwtYC4aVZ9JEFBapcOpIg3EPA+Ou5LNtbzmWiR1+x3MK4dcIoJSbg5gLVWZSzTmucwdvSiZ0BzmvjQPrxbzPMIDXRQT1SUk88trYRIYdEYTyqv15uJRLkpye11ge7GiFo8v3ocThWo8N6Yzpo5Ihkx85Q5CQqgfEkL9cFPvWJwprsL/Np/Fl1vP4fvduZgyNAkPD06C6qo7J1U6I15ecQS/HynEkyNS3LqWpy9LDPXHr9OH4P9+PITp3x/EZxvOoKCiFtV6Ez66qzdSowI93US3Gt89Cp9vOouyan2TN1EcxRhDlc4IiYiHXOLY3bKt2Zfx4vIjEPGcVwa1gPnkNCI1Ar8fKYDBJDh9qAshxHWuZFntP58aGYhL6obTDAora3FJrW0Q2DZ1rW7NxnlBYOvKOWBtXXOHpzsy19HCWpzKjQeJwJhb1rAF6n8/wNGPtATgPMejxND6qT+WrHhjy3O5mjVjayezL67XOfaumywZVUvwOzw1HAEyMTadKkZlraHZNw0CZGJ0ighEdrEGWoPJeu3H1wW2AmPwk4ptRjX6AvNSSkC1zgh/J2dp67PcqDGaBEQozclJo8Bc+vvVqwLbPTllePK7/dCbBHz1UD+MSG16fdmOEQF4/45eeGJ4Cj5adxofr8/GvE1ncV3XSPSOD4JMIkL2JQ1+P1KI8ho9/jGhC6YMTXbTt2kbksL88dPUQVi0PQdbskvQOz4I9w1IRI84laeb5nYTesbgkw1n8NvhAkwe1KHV77c1+zK+3XkBW7IvQ2sQwHFAXLAC6QnBGJAcigFJIUgK87f7CzinpBr/WXsKvx8pRKeIAHw5OcMrg1qLUWkRWLonF/svlHu8oBUhpPmulVWLUMoRoWw4HcggCCitsq3MKjDWrItkb1juxESBbYuJuOYORa7b35E5tg5UXXYW986xNf8pMAaRgwGlIJiHIsvEPPQm85zQ1ow6sEwJ8FzG1vznlcD2ynP1fzbtZV/lEhFq9EbrPFzLUjyWG+uOVOKOUsmRXayx+Vwxz8FUN6/X1wpHAQDqqhVX640IlLtuGLVlCqlBYNYkpSVr7qpu85rAdsnuC3jjl+NIDPXDggczHJoj2DEiAJ/f1xdPF6nx/e5crM+6hN+PmIdFySU8xnaNwqNDktDLS4rp+BoRz2HK0OR2f1MgNSoQveJU+GFvXqsC2xq9EbN+PY4f9+UjWiXH3f0SEBesQK3ehFOXNNh5rhQ/HyoAAEQEytA/KQTpicHwk4pQUKHF7pxS7DpXBoVEhOfGdMYTw5MdzvS626CUUEhFPDaeKqbAlhAvd1mjhZYzZyYsF4aOXoT4SUXIN5gaDKls6n0sVWe9IbB15VC5tk7EN2/JmpYEXpZjxJ1DkQXmvqWf6ld9dvS0brkZY7keqDWYECBr+WX+tZb6crWrs/M2Gdt6x43MTkcp6gJbraEusK37Yba8riUFy+q/3pKxNZqYTfbYV3AwL4tUrTMhSum6pAgHcz8xxqw3ICyBraumMno8sK3SGfH6z8ew6uBFjE6LwMd3927x3YO0KCXeuqk73rqpe90YbgalQkJDH4nT3NkvHq+tOoZjFyutJd8dcUmtxZRv9uF4QSWeGdUR00d1gvSqu42MMVworcHunFLsPleG3Tll1hs1Ip5Dp4gAvDAuFXdmxLtuXoST+cvEGJAcgo0ni/HK+C6ebg4hpAl7csrgF2Cw2eZo9tJPIgZjDDV6E/zrLq6vNRTZ8lSN3oRLai3CAmQI8nPetI/mssxxpIxtyzR3yZqWBIyeWOvYKDDImzkns7VaMxzfcqPAEtgezC1vcbVenjNnKuu3yVOqdeZCWPWXW6ofbNr7v1FIeaDaHNyL+CtLNYnr4gFHlm4S2/lcMc/BaGI+u961VMTjct1KFUqF60JBngO0dcsySUS8eYi4yfG59Y7wWGBbozfi98OF+Gj9aRRrdPi/6zpj2siOTlsnLNiJcyAJsZjUKwb/+j0LS/fk4u1bejj02mKNFnf+bydKNDosfKgfRjYy1J7jOHQI80eHMH/c1c88H7ykSgejiUGlkEAh9e7sbGNGpUXgzd+ykF9eg7hg35qPQkh7MiglDEqlEibGUF6th1Ts+Px/S3VNm8AWDE2lfywXOrvOlQIAQv1lGNIprAXfoHVaUq2XXCHiORRrtI0uF2ihNwoOZ2yt6+S6cx1bQYBY5J7zLmcN3B37fpZjVsQDQX4SRATKYRQEGAXHy0czBpTXXJlG4KmfAktfFGu0DbJ7NkOR7fxusgx71RpMNskty5BkR4LR+hlZy/EnqluWySgI8JN6PEfosIwOIajRG8FxHJRyF7afA/RG87EpFnEQ81zbG4r85dZzWLT9PAoqa8EY0DchCHPv64s+CcHubgohDlPKJbilTxx+2p+PZ0Z3QqSduWX2XKyoxaNf70WJRofvHxvo8LD4tlDFe2SqObDdeOqy11WWJoRcEewvhbLu5nBLf/f41d2Ayy+vQViA1FpQqKnryfpBjkTEQ29q/ZIlLWGJmXxx6pw36BgRgKLK5q357mghxvpDdd3FaHLncj8tG45vCWB5joNExCMzpXVTfn4/UmBdMshjy/3U+9wBSSE2z1mCTY7jrMGqmOet/WBJAGgNJkjrBbaWDLYjP9v1s971hzJrja2fx+wpUjEPqdj1CUCeuxLIinkePH+lmrWrfqbcPkY3PFCGAckheG5MZyx7fCBWPDmIglriU6aNTIEgMMzbdLZZ+x/Jr8D1H21BblkNFjyY0W7nencI80dyuP817+IT0hbMnTsXSUlJkMvlSE9Px9atW5vcf/PmzUhPT4dcLkdycjLmz59v8/yCBQswdOhQBAcHIzg4GGPGjMGePXts9pk1axY4jrN5REVFOf27NYdCIgLHccgtq0FhXZDDGGsy+xMWIMWApFAM7RSOuGCFdX6vu7WkWi+5IlqlQJ+E4GY9EkP9HXpvy8VwblkNjl2sbNZc3tYSGGvxkF5HtXSotdCCQlxNkYp46/xUjxWPqvf3q5MI9YMiqYhHiL8UGR2uxBKKevOM62dcLUGwI2loEc9Zg2y+XmArCMytS0H5Ir4usw1cydhahiK76n6J2wPbm3rH4sM7e+OZ0Z0wMDm03ayDStqOuGA/3JERj+/35OJiRW2T+54q0uDBr/YgJECKtc8Nw6CO7h9W503Gd4/CjjMlKK/WX3tnQnzUDz/8gGeffRavvfYaDh48iKFDh2L8+PHIzc21u39OTg5uuOEGDB06FAcPHsSrr76KZ555BitWrLDus2nTJtxzzz3YuHEjdu7ciYSEBIwdOxYXL160ea9u3bqhsLDQ+jh69KhLv2tjzMt8hQOANfPDcK11bDlEqeQI8ZdCKuahN3mmiFRL1lcl7iERcQgPkKFGb8LZy1Wo0rV+vdZrMboxK2cZIutowG5y8s0YmUQEXd3cSI8t99PEx4YHyhAeKEPH8ABwHIehncIRqZRbCxRZpk6Yi3DVy7jW/d3R+yHienNrAXM/mwTmsxlbd6n/fyjheYh4zmZ0gStQVSVCWuDpUR0h5jm8/vOxRk9AZ4qr8MDC3ZCLRfju0QE0rxTAxJ4xMAoMa44XebophLjMhx9+iEcffRRTpkxBly5d8PHHHyM+Ph7z5s2zu//8+fORkJCAjz/+GF26dMGUKVPwyCOP4IMPPrDus2TJEjz11FPo3bs30tLSsGDBAgiCgL///tvmvcRiMaKioqyP8PBwl37XpijlEvAcZ828miskN++1EhEPxph1GJs7WYa5UlzrfTiOw6COYeibEATA9UOSGXNv8GJJ9jj6ta7MsXVOO+sXZOI8FCk0FfioFBIMSglD1xilzfYhncLRKy7oSmYWsPm7uIVDvS39ammTOUCry9j6YFVkd6l/OIpEHEQ8Z51zS4EtIV4kJkiBl8enYcPJYny07nSD4Hb/hXLcMX8HGIDvpgzwucW7XSUtKhDJ4f7WKs+EtDV6vR779+/H2LFjbbaPHTsWO3bssPuanTt3Nth/3Lhx2LdvHwwGg93X1NTUwGAwICTEdu5ZdnY2YmJikJSUhLvvvhvnzp1rxbdpPZmYh84a2DY/WLTMi/PEcGRLUEGZGO9l+b8xNhEBGkwCNFpDqx7qWqPN57la/XVsHWEppuWsCr31CzJ5rHhU3Z+ODAMPkInRIczf5jW2xaPMf3f0hoh1Hq/oSoCrNwlgjIYiN832poKoLRaPIqSteGBgIrIK1PhkwxmU1xgwY0wn6IwCfj54ER+tO434ED8sfqQ/BbX1cByHiT2i8dnGM7is0fnMckWENFdJSQlMJhMiIyNttkdGRqKoyP5IhaKiIrv7G41GlJSUIDo6usFrXn75ZcTGxmLMmDHWbQMGDMDixYvRuXNnXLp0CbNnz8agQYNw/PhxhIY2LCaj0+mg0+ms/1ar1Q591+aQinno6g1Fbu70I+lVax66E6OhyF7PErgYmxiuvu1MCdS19m8MOUrqpmUjr167tbksQ5GdtbKIrF7G1mM/B3UfK2lBRvTqQnQWlsDU0aHIorrjzXLjQCy6slaziBa8blT9w1Ei4q3VpIE2vI4tIb6K4zjMubUHgv2lmLfpLL7ddcH63KReMZh9U3eo/Fq2JnNbdlOfWHyy4Qx+PngRjw1L9nRzCHGJq0/a5mG4Tc8vvXp/e9sB4L333sPSpUuxadMmyOVXiqqMHz/e+vcePXogMzMTKSkp+OabbzBz5swG7zNnzhy8+eabzftCLSQTi6xVMBlrfvbHkxlbWu7H+4lEloxt48eHVm9CpFLe6pvLPAdEBDZvBYTWsga2Dh72lmPWWdnD+oGt5+Ja8wdLWrCGsNgmsK3397og1NEbB/Xn1gK2mXHK2DbOcjxznDlbW3/YNmVsCfFCHMfhpevTMLFnNHafK4NcIkKPWBV6xKk83TSvlRIegH4dgrFsby6mDE2iAnKkTQkLC4NIJGqQnS0uLm6QlbWIioqyu79YLG6Qaf3ggw/wzjvvYP369ejZs2eTbfH390ePHj2QnZ1t9/lXXnnFJuBVq9WIj49v8j0dJRXzqK42D+e8VnBfn+ViVu+BjC0t9+P9LMFEU0NKDQJDkJ8EsUEKdzWr1SzzWVu6jq3TMrb1hiJ76gaP5WNbki3nr5GxdXQO85XiUXWZ23rvT3NsG2f5P5RcNUf56r87EwW2hDhBtxgVusVQMNtcd/VLwPM/HcaB3HKkJ4Zc+wWE+AipVIr09HSsW7cOt9xyi3X7unXrcNNNN9l9TWZmJn777TebbWvXrkVGRgYkkiujPt5//33Mnj0ba9asQUZGxjXbotPpcOLECQwdOtTu8zKZDDKZa6cD1J9jK7DmB4v2MrYarQGX1FoAHGKDFNa1Kp2NlvvxfteaY2u0zn/0rWGilov9YxcrcapIY/sczyEjMRj+soaX7iYnz7H1qxfYeurnwDLMXNLCYeAZHUJQrTMiWnXlxoaoGTdE7LHOreVt3+fqvxNbvHXotrnj6v88UmBLCGkzbugRhVm/HseS3bkU2JI2Z+bMmXjggQeQkZGBzMxMfPHFF8jNzcXUqVMBmDOlFy9exOLFiwEAU6dOxWeffYaZM2fisccew86dO7Fw4UIsXbrU+p7vvfceXn/9dXz//ffo0KGDNcMbEBCAgIAAAMDzzz+PSZMmISEhAcXFxZg9ezbUajUmT57s5h64Qi7hYRQEmAQGBtbspUMkIvPakfUztqeKNNYl1nRGk/Vm4qkiDc6XVjvUrtTIQHQIs7+GqqvngJHWu1bG1hLwtmR+pif5S0VICvNvMARfYEBhZS1KqnR2A1tn34wJ9pdiROcI8HzLA8vWssyvb+n/ob1MveWGWYcwx4anW+bR2s3Y+tjNE0+4eig34Lpq2xTYEkLczk8qxh0Zcfhu1wW8OC4NUSr3zF8ixB3uuusulJaW4q233kJhYSG6d++O1atXIzExEQBQWFhos6ZtUlISVq9ejeeeew6ff/45YmJi8Mknn+C2226z7jN37lzo9XrcfvvtNp/1xhtvYNasWQCA/Px83HPPPSgpKUF4eDgGDhyIXbt2WT/XE6Qic+Ynr6wGOoNgLQp1LRzHQSrikH2pCrmlNchMCYXeJCDEXwqtQbC58L+k1oLnOEQ0sxhdYWUtLlfpmghszX9SIsZ7WebsNVY8yhIUiT0UlLUUx3HoGRdk97nVRwuh0dpft9cV88I9XSMkqO7znblUIs9zuLFXjMM3rSyBmSWGrR/MUsa2cfHBfjCYBGuh0Pp9RRlbQkib8uiQJCzeeQGLduTglfFdPN0cQpzqqaeewlNPPWX3ua+//rrBtuHDh+PAgQONvt/58+ev+ZnLli1rbvPcxl9mDmwP51cAMC+V1lzdY1Uo1uiQV1YDtdYIk2AeWioV2w5R1hsFhAZI0Ss+qFnvW6UzNlmUSnDymqDENcQ813jG1jKMtQ39HwbIxFBr7Vd5dnbxKG8QKJfgpt6xTn/flozEsAxFtgS09ZO0banPnU3lJ0GfhGDrv20DW9d8JgW2hBCPiAv2w6Se0fh+Vy6eGtERKoVz7w4zxrA7pwybTl3GySI1qnVGhAfK0C1GhQk9ohvN1hBCnCc0QIbrukZah4b6S5t/2REX7IewABnyympgNAkwmhgUEg4AbzNEWWcUHCowIxXzqNLZz3wBVBXZV4h4vtGqyAbBNzO2TQmUi1Gs0dl9ztnL/RBbEYFyaA2CNTDzl4rNVX55zqaCNGkaZWwJIW3akyM64pfDBZi76YzTsraMMazNuoSP1p3GySINpCIeadGBCJSLcapIgz+PFeH9NacwOi0CL1yfirQopVM+1xsxxnCySIO958tQUKGF0SQgLliBPgnB6BmnojmExC38HAhmr2aZ32cwMRhMAsQiHhzHUFNjDkxNAoNREBy6uJSI+CYztpag2V1rl5KWaSpja6jL2LalirWBcglyy2qw/0JZg9oUJoHRCAMXCvGXIsRfav23v0yMiT1jPNgi31S/uJmrLj8osCWEeExqVCDuyojHom3ncf+AxFavN1is0eKVFUfx98lidI4MwId39sL47tE21VMvqbVYvj8fX2w5hwmfbMO0kR3x9KiOHiuQ4QoGk4Af9ubh6x3ncaa4CoC5Oq2I51CjNwEwF9Z4ZEgS7ukf36rAgxBXEvHmIlIGkwCjwCCuy5JYAlPLnzJx8yskS0W8dQ6mPVqDCTIxT9kvLyfiOWsAezVjG7w5Ea2S49zlKuSX1yIlXI8gvyuBltYgUBEj4vVEIsrYEkLauJljO+PXwwWY9etxfDk5o8VZxI0ni/H8T4dRozfhnxO74sHMRLvD0CKVckwb2RH3D0zE7N+z8Mnf2dh4shj/vbs3ksMDWvt1PG77mRL84+djyCmpRp+EILx3W08M6RSGmCAFGGMordZja/Zl/LQvH//6PQsLtpzDqxO6YFLPaMrgEq8kFZmLBBlMAiQiHhxnzqoyxqyBbXOLUpn3NWf6Gsty6YyCzTqexDuJea7R9V4tAW9bymL6y8QYkRqBNceLcPZyNeJD6pbREswF0eKdWGSJEFewLCPFc5zTlqa6GgW2hBCPigiU46Xr0/DGr8fx3e5cPDDQsQquWoMJ7/55El/vOI+u0Up8ck8fdIy4doCqUkjw/h29cF3XSLy88igmfboN797WE5N6+ebwohq9Ee/+eRKLd15Ax4gAfPNIfwzrFGYTrHIch7AAGW7pE4db+sRh/4UyvPVbFp5ZehDf776AObf2RBLNPSZeRszz0BrNIw0s89oAcwCqq9vuUGBbV6lZbxTsroVrydgS7yYW8ajR258rbZl725ZG4gDm4zxKJUd+eQ3yy2us2zmOQ0ozznuEeFKwvxTju0eD51w3H5wCW0KIxz2YmYjNpy9j9u9Z6BqtRHpi8LVfBOD0JQ2eWXoQJ4s0mDIkCS9cn+rQkEQAGNstCj3jgvDM0oN4eulB7DpXitcndoXchzI2+y+U4f9+PIwLZTV4fFgyZl7XuVntT08MwaqnBuPHfXmY8+dJXP/xFvzf2M54dEhym8p0EN8mFnGorRtCLxFx1pEYepMAXQsythIxZ329AvYCW3OVZeLdRNeoitzWglqL3vFBSLlqdJFUzCPAzvq2hHgbR35Xt0Tb/KknhPgUjuPw/u09ERuswENf7cGB3PIm9zcJDF9sOYuJn25DSZUOXz/cD/+Y2NXhoNYiSiXH948NwFMjUrBkdy5unbsD50uqW/Re7qQzmrPVt8/fCQbgxycy8eoNXRwKynmew939E7Bu5jCMTI3AO6tP4ta523GqSOO6hhPiAKmIt84NF4t467xJvVGwFnpyJMNa//X26IwmyFv4u4S4j5jnrNW2r2YwCW12GRaJiLcWM7I8KKglxIwCW0KIVwgNkOH7KQMRGiDFXf/bic82ZKPazpIc+y+U4bZ5O/DO6pMY0yUCa58bjhGpEa3+fLGIx4vXp+Hrh/uhsLIWEz/dht+PFLT6fV3lRKEaN322HfM3n8V9AxKw+pmh6Nch5NovbEREoBzzH0jH3Pv64mJFLSZ+uhUfrz9tzZS1lCAwXCitxprjRVi4LQfvrzmJvLKaa7+QkDpiEW8dcizhOesd/5ySahRWaMFznEPZOYn4Ssb3anqjAJPAIJPQ5ZG3E/HmudelVboGj1q9qc1mbAkhjaNbPIQQrxGlkuOXaUPwz1+P4YO1pzF301lkdAhBWIAUEp5HTmk19uSUIUopxyf39HFJwaMRqRFYPWMonv7+IKZ/fxA7zpbipevTnL7ObktptAZ8tuEMvtqeg2A/Kb5+uJ9TAnuLG3pEIzM5FP/6PQsfr8/Gd7su4LGhybi7XwJUftfuA43WgEN5Fdh/oRz7L5TjUG4FNPVuUEhFPAanhLW6AjZpP+ov2SLiOfhJRfCTinFJrQUAh4cNWzK2JXbWBNUZzAG0L01FaK/kEvM6ttvOlNh9PjxQ5uYWEUI8jWOskZJy9ajVaqhUKlRWVkKpbLtrPhJCvMehvAr8tC8PxwvUKK3WoaLGgBB/KR4YmIj7BiTaLfriTAaTgA/Xncb/Np+FSiHBs2M6465+8R674K3RG/HD3jzM3XQWJVU63N0vHi9dn2az5IOzHcgtxyd/Z2PTqcuQingM6RSG/kkhSA7zrwsmOJRU6VBYUYuTRRocyC1HdnEVGAN4DugSrUTfhGD0iFOhS5QSCaF+UMrFTrsZQecm5/LW/jyaX4lzJeZlq0akRrT6JpMgMPx1vKjJJX9GdI5o1o0c4jkmgaG8Ro/GrmID5WK6QUFIG+DIuYkCW0KIT2CMeWQ5mhOFarz9xwlsO1OCEH8p7u4Xj1v7xiIlPMDl7WGM4ejFSvx+pBA/7ctDeY0BA5ND8NoNXdEjTuXSz64vq0CNVQfzseFkMc5etj/3ONhPgr4JweiTEIS+CcHoFR8EfxfP+6Jzk3N5a3+eLFJb53xf1zXSKesu64ymRufYinne5TfOCCGENA8FtoQQ4kSMMew6V4bFO89jbdYlmASGhBA/DO4Yiq4xKqRFBSJKKUekUt7iin86own55bXILatBzuVqHMyrwL7zZSis1EIq4jG6SwQeH5aMPgnNqxjtKpU1BuSV16C8Rg8ACPaTIjZIgSA/idtvPNC5ybm8tT/PFFfheEElAGB892iXV9UkhBDiPRw5N9EcW0IIuQaO45CZEorMlFAUq7X4+2QxNpwsxprjl7B0T57NvkF+EvhJRFDUzQNUSEVQSEQQGIPRxGAUBBjq/qzRm6DRGqHRGqA12GaPYoMU6NchBMM6h+O6rpFeM8dX5SeBys992WJCJPXm2Nb/OyGEEFIfBbaEEOKACKUc9/RPwD39E8AYwyW1DtnFGhRValGs0eGyxlyRs8ZgQq3eiBq9CWqtASKOg4jnIBOL4C/jIOY5KKRiBMrND6VcgrhgBeJD/JAQ4oewACp8QggAhPhLEeovg79M5JHpCIQQQnwDBbaEENJCHMchSiVHlEru6aYQ0mYFyiUY0inM080ghBDi5WiiCiGEEEIIIYQQn0aBLSGEEEIIIYQQn0aBLSGEEEIIIYQQn9asObaWFYHUarVLG0MIIYQ0l+Wc1IxV60gz0LmeEEKIt3HkXN+swFajMS+MHh8f34pmEUIIIc6n0WigUtESRK1VWloKgM71hBBCvE9zzvUca0b4KwgCCgoKEBgY2CZL7avVasTHxyMvL8+rFqVvy6jP3Y/63P2oz12LMQaNRoOYmBjwPM2saa2KigoEBwcjNzeXbhQ4Cf0OcD7qU+ei/nQ+6lPncuRc36yMLc/ziIuLc0rjvJlSqaQD0M2oz92P+tz9qM9dhwIw57FcMKhUKjpenYx+Bzgf9alzUX86H/Wp8zT3XE+3uAkhhBBCCCGE+DQKbAkhhBBCCCGE+DQKbAHIZDK88cYbkMlknm5Ku0F97n7U5+5HfU58CR2vzkd96nzUp85F/el81Kee06ziUYQQQgghhBBCiLeijC0hLbBp0yZwHIdNmzZ5uimEEEIIcQE61xPiWyhjS0gLqNVqZGVloWvXrlTxjhBCCGmD6FxPiG+hwJYQQgghhBBCiE+jociENOL06dO45ZZbEBERAblcjoSEBNxxxx0wGo12hycZDAY8//zziIiIQEBAAG688UZs374dHMfh66+/tu730EMPoUOHDti5cycGDBgAhUKB1NRUrF69GgDw/vvvIzExESqVCrfeeivKysps2vXpp59i4MCBCA4ORkhICIYPH45t27a5o0sIIYSQNoXO9YS0HW0ysJ07dy6SkpIgl8uRnp6OrVu3Nrn/5s2bkZ6eDrlcjuTkZMyfP9/m+QULFmDo0KEIDg5GcHAwxowZgz179rjyK/gcZ/d5fcuWLQPHcbj55pud3OqmTZgwARcvXsS8efOwZs0avPvuu5DJZBAEwe7+r776Kj766CM8/vjjWLlyJXr27In777/f7r7l5eWYMmUKnnzySaxatQrh4eG4/fbbMXPmTGzfvh1z587Fhx9+iPXr1+OZZ56xee2FCxfw2GOP4YEHHoBUKsW2bdswbNgwLFq0qMnv05w+r6iowLRp0xAdHQ25XI4uXbpYT8LENcf5xx9/jNTUVCgUCsTHx+O5556DVqt11VcgpFGOHt/t1ZYtWzBp0iTExMSA4zj8/PPPNs8zxjBr1izExMRAoVBgxIgROH78uM0+Op0OTz/9NMLCwuDv748bb7wR+fn5bvwW3mPOnDno168fAgMDERERgZtvvhmnTp2y2ceVfert5/rly5dj6dKl6NGjB0aNGoXDhw9f8zvNmzcPPXv2hFKphFKpRGZmJv7880/r83SMtt6cOXPAcRyeffZZ6zbqVy/A2phly5YxiUTCFixYwLKystiMGTOYv78/u3Dhgt39z507x/z8/NiMGTNYVlYWW7BgAZNIJGz58uXWfe699172+eefs4MHD7ITJ06whx9+mKlUKpafn++ur+XVXNHnFufPn2exsbFs6NCh7KabbnLxN7ni8uXLDAD75Zdf7D6/ceNGBoBt3LiRMcZYaWkpUygU7Nlnn7XZb8aMGQwAW7RokXXb5MmTGQC2c+dO67bDhw8zAKxr167MZDJZtz/33HNMKpXabGPsSp//73//Y0eOHGEJCQlMLBa3qs91Oh3LyMhgN9xwA9u2bRs7f/4827p1Kzt06FCz+qytc8Vx/t133zGZTMaWLFnCcnJy2Jo1a1h0dHSD44gQV3P0+G7PVq9ezV577TW2YsUKBoCtWrXK5vl3332XBQYGshUrVrCjR4+yu+66i0VHRzO1Wm3dZ+rUqSw2NpatW7eOHThwgI0cOZL16tWLGY1GN38bzxs3bhxbtGgRO3bsGDt06BCbMGECS0hIYFVVVdZ9XNWn3n6utzCZTMxgMLDrrruOPf30041+H4tff/2V/fHHH+zUqVPs1KlT7NVXX2USiYQdO3aMMUbHaGvt2bOHdejQgfXs2ZPNmDHDup361fPaXGDbv39/NnXqVJttaWlp7OWXX7a7/4svvsjS0tJstj3xxBNs4MCBjX6G0WhkgYGB7Jtvvml9g9sAV/W50WhkgwcPZl9++SWbPHmyWwNbQRBYcnIy69KlC1uwYAE7e/aszfNXn+w2bdrEALAtW7bY7Ldlyxa7JzulUmmzn06nYwDYM888Y7P9f//7HwPACgsLrdt2797NVCoVUygUjOM4BoABYP7+/q3q83nz5rHk5GSm1+ub7px2yhXH+bRp09ioUaNs9pk5cyYbMmSIk1pNSPM4enwTs6sDW0EQWFRUFHv33Xet27RaLVOpVGz+/PmMMcYqKiqYRCJhy5Yts+5z8eJFxvM8++uvv9zWdm9VXFzMALDNmzczxlzbp95+rh8/fjyLiIiwOdePGzeu0e/TlODgYPbll1/SMdpKGo2GderUia1bt44NHz7cGthSv3qHNjUUWa/XY//+/Rg7dqzN9rFjx2LHjh12X7Nz584G+48bNw779u2DwWCw+5qamhoYDAaEhIQ4p+E+zJV9/tZbbyE8PByPPvqo8xt+DRzHYd26dUhPT8eLL76IlJQUdOzYEQsWLLC7f2FhIQAgIiLCZvvV/7YIDg62+bdUKm1yu2Voam5uLq677jpUVlbiqaeewo4dO7B3715cf/31CAoKalWf//rrr8jMzMS0adMQGRmJ7t2745133oHJZLL7nu2Jq47zIUOGYP/+/dapDefOncPq1asxYcIEF3wLQuxryfFN7MvJyUFRUZFNX8pkMgwfPtzal/v374fBYLDZJyYmBt27d6f+BlBZWQkA1mssV/apt5/rKyoq8Omnn9qc6x2dqmIymbBs2TJUV1cjMzOTjtFWmjZtGiZMmIAxY8bYbKd+9Q5iTzfAmUpKSmAymRAZGWmzPTIyEkVFRXZfU1RUZHd/o9GIkpISREdHN3jNyy+/jNjY2AYHdXvkqj7fvn07Fi5ciEOHDrmq6deUnJyMb7/9FowxHDlyBB9//DEef/xxdOjQARKJxGZfy3FSXFyM1NRU6/bi4mKntmnNmjVQq9UAgFtvvRUDBw4EYL7ZIpVKW9Xn586dw4YNG3Dfffdh9erVyM7OxrRp02A0GvHPf/7Tqd/D17jqOL/77rtx+fJlDBkyBIwxGI1GPPnkk3j55Zdd9l0IuVpLjm9in6W/7PXlhQsXrPtIpdIGwQ31t3mO4syZMzFkyBB0794dgOv71JvP9T/++CPi4uKs22tqasBxXLPe4+jRo8jMzIRWq0VAQABWrVqFrl27WgMoOkYdt2zZMhw4cAB79+5t8Bz97HuHNpWxtbj6h54x1uQvAnv729sOAO+99x6WLl2KlStXQi6XO6G1bYMz+1yj0eD+++/HggULEBYW5vzGOojjOPTq1Qv//e9/AQDHjh1rsE+PHj2gUCiwcuVKm+0rVqxwaltqamps2gUAZ86cwY4dO1p9nAuCgIiICHzxxRdIT0/H3Xffjddeew3z5s1z6nfwZc7+3bJp0ya8/fbbmDt3Lg4cOICVK1fi999/x7/+9S8nt5yQa3P0+CaNa0lfUn8D06dPx5EjR7B06dIGz7m6T73xXF8/sLac65srNTUVhw4dwq5du/Dkk09i8uTJyMrKsj5Px6hj8vLyMGPGDHz33XdNXv9Tv3pWm8rYhoWFQSQSNbjrUVxc3OAOikVUVJTd/cViMUJDQ222f/DBB3jnnXewfv169OzZ07mN91Gu6PPjx4/j/PnzmDRpkvV5S3VCsViMU6dOISUlxcnfxNaRI0cwY8YM3HXXXejYsSOMRiO++uorSCQSjBw5EhUVFTb7h4SEYPr06fjPf/6DgIAADBs2DFu2bMEvv/wCAOB559xDGjNmDMRiMYxGI/766y9kZ2dj1qxZiI+Ph16vR0JCgt3XNec4j46OhkQigUgksu7TpUsXFBUVQa/XW4dKtUeu+t3y+uuv44EHHsCUKVMAmC+aqqur8fjjj+O1115z2nFDSFNacnwT+6KiogCYMzP1R3zV78uoqCjo9XqUl5fbZG6Ki4sxaNAg9zbYizz99NP49ddfsWXLFpsspSv71NvP9Q8++CD+7//+D0VFRdZzfXNJpVJ07NgRAJCRkYG9e/fiv//9L1566SUAdIw6av/+/SguLkZ6erp1m8lkwpYtW/DZZ59ZK3lTv3pWm7pqkkqlSE9Px7p162y2r1u3rtEDJjMzs8H+a9euRUZGhs2dsvfffx//+te/8NdffyEjI8P5jfdRrujztLQ0HD16FIcOHbI+brzxRowcORKHDh1y6Bd7S0VFRSEhIQEffvghbrzxRtx77724dOkS/vjjD/Tu3dvua95++20899xzmD9/Pm6++WYcOHAAn376KQBAqVQ6pV3dunXDkiVLIJfL8fbbb+Pf//433nnnHQwbNgzl5eWtOs4HDx6MM2fO2CxxcPr0aURHR7froBZw3e+WmpqaBhdCIpEIzFzYz4nfgJDGteT4JvYlJSUhKirKpi/1ej02b95s7cv09HRIJBKbfQoLC3Hs2LF22d+MMUyfPh0rV67Ehg0bkJSUZPO8K/vU28/1Fy5cwI033mhzrm8pxhh0Oh0doy00evToBtemGRkZuO+++3Do0CEkJydTv3oDt5WpchPLkgULFy5kWVlZ7Nlnn2X+/v7s/PnzjDHGXn75ZfbAAw9Y97csyfHcc8+xrKwstnDhwgZLcvz73/9mUqmULV++nBUWFlofGo3G7d/PG7miz6/m7qrIzvL+++8zANa+cBZX9Hlubi4LCAhg06dPZ6dOnWK///47i4iIYLNnz3Zq232VK/r8jTfeYIGBgWzp0qXs3LlzbO3atSwlJYXdeeedbv9+pH271vFNrtBoNOzgwYPs4MGDDAD78MMP2cGDB61LI7377rtMpVKxlStXsqNHj7J77rnH7pIfcXFxbP369ezAgQNs1KhR7XbJjyeffJKpVCq2adMmm2usmpoa6z7e3qeuOte31CuvvMK2bNnCcnJy2JEjR9irr77KeJ5na9euZYx5f3/6ivpVkRmjfvUGbS6wZYyxzz//nCUmJjKpVMr69u1rLRnPmDlAGj58uM3+mzZtYn369GFSqZR16NCBzZs3z+b5xMREa5n1+o833njDDd/GNzi7z6/mC4Htjh072FtvvcX+/PNPtnbtWvb6668zuVzObr/9dpd8niv6fMeOHWzAgAFMJpOx5ORk9vbbb9Mv23qc3ecGg4HNmjWLpaSkMLlczuLj49lTTz3FysvL3fBtCLHV1PFNrrAsAXP1Y/LkyYwx87Ifb7zxBouKimIymYwNGzaMHT161OY9amtr2fTp01lISAhTKBRs4sSJLDc31wPfxvPs9SWuWjrHm/rU3ef6lnjkkUesP8vh4eFs9OjR1qCWMe/qT192dWBL/ep5HGM03o0QZzhy5AimT5+Oo0ePoqqqCtHR0bjjjjswe/ZsKBQKTzePEEIIIa1E53pCvBcFtoQQQgghhBBCfFqbKh5FCCGEEEIIIaT9ocCWEEIIIYQQQohPo8CWEEIIIYQQQohPEzdnJ0EQUFBQgMDAQHAc5+o2EUIIIdfEGINGo0FMTEyD9YCJ4+hcTwghxNs4cq5vVmBbUFCA+Ph4pzSOEEIIcaa8vDzExcV5uhk+j871hBBCvFVzzvXNCmwDAwOtb6hUKlvfMkIIIaSV1Go14uPjreco0jp0rieEEOJtHDnXNyuwtQxJUiqVdLIjhBDiVWjYrHPQuZ4QQoi3as65niYlEUIIIYQQQgjxaRTYEkIIIYQQQgjxaW4PbAsra5FXVuPujyWEEEIIIYS0ESaBQa01QBCYp5tCvITbA9uvt5/H0Pc24ubPt+PYxUp3fzwhhBBCCCHExx3ILcfGk8XIKlR7uinES7g9sL1vQCLemNQVl9Ra3DZvB7acvuzuJhBCCCGEEEJ8WI3eBACoNZg83BLiLdwe2CaE+uHhwUn445mh6BgRgKnf7Uf2JY27m0EIIYQQQgjxUSZBAAAYjIKHW0K8hceKR4X4S7Ho4X7wl4kxY9khmGh8PCGEEEIIIaQZDCZz7KA3UWBLzDxaFTkiUI5/3dQdWYVqLNl9wZNNIYQQQgghhPgIS1LMaKLkGDHz+HI/47pFYnjncLy/5hRKq3Sebg4hhBBCCCHEyxnrAlsDZWxJHY8HthzH4Z+TuqJaZ8RX23M83RxCCCGEEEKIFzOaBDDGIBHx0Nf9nRCPB7YAkBIegBt6RGPxzgvQaA2ebg4hhBBCCCHES1mytQqJCIB56Z9jFytpTdt2zisCWwB4ckQKNFojvtuV6+mmEEIIIYQQQryUNbCVmgPb/PJanL1chRKa1tiueU1g2y1GhWGdw/HtzvNUIZkQQgghhBBil8lkm7G1KNZQYNueeU1gCwD3DUhAQaUWW05f9nRTCCGEEEIIIV7IULeGrZ/0SmAbqZSjWKN1WxsuVtRi7fEiHLtY6bbPJE3zqsB2VFoEwgNl+H4PDUcmhBBCCCGENGRZ4kdeL2Mb7CeFRmt0WyGpokotag0mXKYssV0Gk4DCylpU64xu+0yx2z6pGSQiHndmxGH+5nMoVmsRoZR7ukmEEEIIIYQQL2K8KmPLcRxEPAfAvL6tWMS5vA21epP189oaQWAordaDoeXf7WShBuU1egT5STG8c7gTW9c4rwpsAeCO9Hh8vvEsfj1cgClDkz3dHEIIIYQQQogXsWRs/aTmUKZbjBIiri6wZazRAMdoEiAWOWfAarXenIk0tpHAljEGo8BgEhiO5FeisLK21e8pE/Oo0rbTjC0AdAjzR684FQW2hBBCCCGEkAYswaRUzOOm3rEAgLyyGgCNZ1B1RhP+OlaEbjEqdIwIaNXnmwQGraFtZWx3ni3F5XpVpdOilAgPlLX4/WRiHpfUWhy9WAmd0QSZ2LbQV355DSpqrr3Ma5VG3ezP9LrAFgBu7B2Lf/2ehZySaiSF+Xu6OYQQQgghhBAvYRQEm+HHAKx/byyDWlhhLixVUqVrdWBrydYGysXQuDEj6UqVtQaEB8gQE6SAQipCpBOmhFoy6rX6hoHt4bxKCIzZ/B/aU1PV/MyxVwa2E3tGY/YfWfjtcAGeGd3J080hhBBCCCGEeAGN1oBTRRqIedshxeK6AEmoC2xr9Sbsu1BmDXS1dXNi61dSbglBYDh+0ZxFVMol0GiNMAnXDtC8mSAw6E0CwgJl6ODEpKKfzNzX1XoTgvyubNcZTTAKAnrEqpAc3vRNBrW6+e3xqqrIFpFKOQYkhWD10UJPN4UQQgghhBDiApYhvVc/dEZTo6+5pDYPl40LVths56/K2JbX6FFWrYdcLEKATIywumG1BpPQqjaX1eitywoF+UnqPrN17+lp+ro+kTpp/rGFX13V6lq9bVa71nqTwbk5Vq/M2ALAuG5RePO3LOSW1iAh1O/aLyCEEEIIIYT4jPUnLlnnql4tPTEYccENYwDLnNaecSqb7Q0ytnXvm54YDKnYHLBtzb4MvbF1c2IthatGdI6AWmuwbpN5bVR1bTpjXWArdm5gKxbxkIl55JTUoKzagO6xSvhJxaixBLay1mXPr+aVGVsAuK5rJABgbVaRh1tCCCGEEEIIcSad0ZydjQv2Q+/4IJsHz3GNzl21DPvlONuhv1dnbLUGE0Q8ZxOsiXm+1Rlby+vFonpLDLlp7VxX0dcFtjInB7YAkBQWAKmYR2FlLUqr9ABwJbCVtJPANi7YD12ilViXdcnTTSGEEEIIIYQ4kVZvDqZigxRIDPW3ecjEvDWLeDWTwKzZ2frE9daxBQCtQYD8qoJFUjHX6mHDlvcX8dyVzzT5eGBrck3GFgBSowIxICkEwJWbDjV6I2Ri3mlLL1l4bWALAGO7RmLv+TKUV+s93RRCCCGEEEKIk1iGCivsZO1kElGjQ5RNjIHnGga2lm2WwFNnMEFxVaEoiYhv/VDkusBYIuKvWYnZV+hdNBTZov5Nh4KKWuSUVEPh5Pm1gJcHttd1jYTAgA0niz3dFEIIIYQQQoiTWAJbmaRhOCJvMmMrQCxqImPLrsyxlV/13hKRM4YiM+tSQ5bKzL6+lq3OaALHcU4vHmUhqhfYFlaaC28lu2BJV68ObLvFKBEbpKDhyIQQQgghhLQhtXoTeI6zO6+zyYytALsZW5GdochXr50qEfEQGGtVIGoSGCR1nyUSWTK2Pl4V2ShAKmo4b9lZLDcCjIIAk8CgUkgQH+L84sBeXb+L4ziM6RKBH/flQ2swQe7kCcaEEEIIIYQQ96s1mKCQiOwGU5Y5toyxBs8bBaHBGrbAleDJJDCcKFTDKAgNYgdJXSBqMAkQ8S2LK8yvNb9P/SG2RpOA86XVaGVCuNXEIg5Jof7WYlqNMQkMZy9XwWhiKK3Su2wYsrVddf83RpP9/z+nfIZL3tWJRqZF4JudF7DvfDmGdArzdHMIIYQQQghpV3RGEzacKLYWGWoOiYhHcrh/gwJOFupaQ6NJK7lEBMYY9KaGWVdBABqLi0QcB4NJQE5JNQAgPEDWoE2AuVhSSxNmJoFZ36f+HNvCSi2OF6hb9J7OppRLEB4oa3Kf0iodThSqwXMcOA6It7O0kjOJeB5GgcEoMNfN5XXJuzrRwORQyMQ8Np0qpsCWEEIIIYQQN6vVm6A3CYgL9kNAMxdsLa/R41SRpsl9ksMC7G63DE/WGRsGtkZBgJ/YfhtEPIcqnXmZoL4JwVD5SWyetwSktXqT9TOkIt6hIbgGE7PO8a2fsdUYjOA5DhN7RrtsSO+1aA0mrDlehCqd8ZqBrWUO86i0CPi7YRFea8ZWEODXwmz5NT/DJe/qRHKJCAOTQ7H59GX8w9ONIYQQQgghpJ2xVP1NCPG7ZsBUn94ogKHx+axXB61XtpuDTq3BBKXcNjgVGENjNY5EPIequvVv7WVkLZnCXedKrdviQ/zQNyG48S9xFaNwZSgyx3HgOXPAptYaECAXeyyoBczfWSrirX3QFFcu8WOPiOdgNDEYTczpy/xYeHXxKIvhncORXVyFixW1nm4KIYQQQggh7YpQF9hK7FQjbopUzEMmFjX6aIwlg1ija1hAymhiEDUyFlnEc1eWEZI2fH+VQoL0xGD0jAtCz7gghAfIUKzWgrHmF5My1huKDJgzkUYTg0ZrbBCEe0KAXAyNznDN/fRGATzH2XwXVxLzHARmHopsbx1iZ/CJwHZEajgAYPOpyx5uCSGEEEIIIe2LJWN7rYJEziKXiGyGFdcnMAZRI1nR+kWJ7K2PCwBxwX5ICvNHUpg/4kP8oDMKUDcjw2lhNNkGZiKeQ2m1DjV6I5Ryzw+G9ZeJm5exNQpuy9YCdRnbujm29pZrcgbP934zmA88BTafLsa9AxI83RxCCCGEEELaDcvyOK7KtNnjLxOjRm8nYysw61Dgq1niWplY1Og+9YXVFZc6nFfRIMPLAegUGQiVwjYLe3VVX5VCgiK1FjzHITSg+cO0XSVQJkZeWQ1+PVxg9/nIQBkGJIdCb3JvYCsWcdBrzZWu221VZMA8fn1453D8fLAABpPgtpQ5IYQQQggh7Z0lY9ucYNFZAuxkHlndGrSNtcMSMNkbhmyPQipCXLAf1FpDg+xwldYImViEHnEqm+1XZxwHJIc267PcJT7EDwzmzPbVSqv0KNbowBiDziBA5saYSsTz1rWJXXWDxCcCWwAY0TkC3+3Kxf4L5RjoZQcQIYQQQgghbZVJMBcaclWmzR5/qRgFFbXQGU3W+bh18XWjga1l+LEjQ4LTE+0XjtqWXYLK2itzVfVGAQJjEJjr5og6g1wiQufIQLvP5ZXVoKRKh2q9CXpTw8JcriTiOGuw3a6HIgNAZkooJCIOm09fpsCWEEIIIYQQN/FExjawLjhdl3UJ47tH183RFJpsR/dYJVIi/BudX+uIID8JLpTWgDGG4wVqnL1cZX3OV0ePWvq0Smv0yBxbC1fdIPGZ/xV/mRj9OoRQASlCCCGEEELcyFwwyb1hQ2yQAinhATAJDJc1OgBAXVzbaMaU4zj4SZ2z5I5KIYFREHCxohaX1FoE+UnRI1aFXnFBiA1WtPr9PcGyBrFaa4DexJqsTO1s9bO0rsrY+kxgC5iX/ckqVKNYrfV0UwghhBBCCGkXmprX6io8z6FrtBISEY+iumv/a2VsnSnIzzxMd/+FclTpjIgLViA5PAAdwvx9NmMrFvHwk4pxprgKjDGHl29qDduMLQW2GJEaAQDYfJqytoQQQgghhLiDK9cebQrPc4hUylFUWQtBYNbqzO4IbAPlEozoHGH9d5i/5yseO0NaVCAilTLEh/ghSiV32+fWP37ELrox4FOBbefIAEQp5dhEgS0hhBBCCCFuITAGkRuze/XFhyigMwo4V1KNihpzMSd3ZY9VfhJkJociWqWAUuEzpYmaFB/ih/TEEPRNCIaf1H3fyR0ZW5/6H7Is+7Mmq8gjQyIIIYQQQghpbwwmwWOVgMMDZPCXinG8oNK6zZ1DgSOUckQo3ZfZbKvq/5+56v/PpwJbABjaOQw/7MvDsYuV6BUf5OnmEEIIIYQQ0qYJgnsrItfHcRwGdwyDRmfO1kp4HiqF+5apIc4RHiBD34RgyMS8y44lnwtsB6eEgeOALacvU2BLCCGEEEKIixkFAX5iz4UNCqkICqn7KvgS5+N5DvEhfq79DJe+uwsE+0vRMy4IW7NLPN0UQgghhBBC2jyaAkh8gc8FtgAwrFMYDuSWQ6M1eLophBBCCCGEtGmeqopMiCN8MrAd2ikcRoFh59lSTzeFEEIIIYSQNo0ytsQX+GRg2ychCAEyMQ1HJoQQQgghxAXUWgOOXazE0fxKGCmwJT7A54pHAeYS0ZkpodiaTevZEkIIIc50OK8cAYEmcByHzpEBbl3nkBDiPXJLa3D2chVkYh5SEY9gP6mnm0RIk3z2bDWsUxjWZV1CbmkNEkJdW2GLEEIIaS/Kqg3QcXpU643wk4rQOTLQ000ihHiAwSTATyrGdV0jPd0UQprFZwPboZ3CAQBbsi/j/tBED7eGENIe5JbW4I+jhThZpEZJlQ5+UjE6RgRgUEooBqWE0TAt0iaMTIuAUqnE+qxLUNdSkUZC2isTFYwiPsYn59gCQIcwfySE+NFwZEKIy10orcbUb/dj2Psb8e+/TuJQXgUMRob88los3JqDBxbuwdB/b8Ci7TnQGwVPN5f4uFmzZoHjOJtHVFRUo/tv2rSpwf4cx+HkyZOtaodSIYFGa2zVexBCfJdRYBCLKLAlvsNnM7YAMLRTGH49VACjSYBY5LMxOiHEi/24Lw+v/3wMEhGPmdd1xh0ZcYhWKazPaw0mbD59GYt3nsebv2Xh250X8J87e6FPQrAHW018Xbdu3bB+/Xrrv0Ui0TVfc+rUKSiVSuu/w8PDW9UGpUKMIrUWgsDAU9aGkHbHaKKCUcS3+HhgG44lu3NxOL8C6Ykhnm4OIaQNYYxh9h8nsHBbDoZ2CsN/7uyFiEB5g/3kEhHGdYvCuG5R2Hz6Mv7x81HcPn8nnhnVCdNHdaSLAtIiYrG4ySytPREREQgKCnJaGwLlEjDGsP1sCQanhFFwS0g7YxQE+Il9OlQg7YxPpzkHdQyFiOew+TQt+0MIcR7GGF5ddQwLt+Xg0SFJ+Prh/naD2qsN7xyO1c8MxU29Y/DR+tN49Ju90GhpjiJxXHZ2NmJiYpCUlIS7774b586du+Zr+vTpg+joaIwePRobN25sdRvCAqSQiHiUVetRYzC1+v0IIb6F1q4lvsanA1ulXII+8UE0z5YQ4lSf/H0GS/fk4ulRHfGPCV0cOrEHyiX48M7e+PdtPbAtuwS3zt2BvLIaF7aWtDUDBgzA4sWLsWbNGixYsABFRUUYNGgQSktL7e4fHR2NL774AitWrMDKlSuRmpqK0aNHY8uWLU1+jk6ng1qttnnUJxOL0K+DeTSUlgJbQtodIxWPIj7G58cXDO0Ujv/+fRqVNQao/CSebg4hxMf9cugiPlp/GndlxGPmdZ3BcS07qd/VLwGJof6Y+t1+3DpvBxY/0h9dopXXfiFp98aPH2/9e48ePZCZmYmUlBR88803mDlzZoP9U1NTkZqaav13ZmYm8vLy8MEHH2DYsGGNfs6cOXPw5ptvNtkWucR8/1tHRdEIaXdMVDyK+BifztgCwNDOYRAYsP0sDUcmhLTO0fxKvPDTEQxKCcW/bu7e4qDWYmByKJZPHQQJz+HO/+3E3vNlTmopaU/8/f3Ro0cPZGdnN/s1AwcOvOb+r7zyCiorK62PvLy8BvvIxOaiVTrK2BLS7hhMAsS8z4cKpB3x+aO1V1wQlHIxDUcmhLRKjd6IGcsOIjxQhnn3pUMqds6vx44RAVj+5CBEBMrwwMLd2HDyklPel7QfOp0OJ06cQHR0dLNfc/DgwWvuL5PJoFQqbR5Xk4p58BwHrYEytoS0J0aT+Wee5tgSX+Lzga2I5zCkUxi2nC4BY8zTzSGE+KjZf5xATmk1Pryzl9OnNcQEKfDT1EFIjQzEY4v3Y9XBfKe+P2lbnn/+eWzevBk5OTnYvXs3br/9dqjVakyePBmAOdP64IMPWvf/+OOP8fPPPyM7OxvHjx/HK6+8ghUrVmD69OlOaY9MzENnbHnGtkZvhEZrgEZrgEmg8zQhvsBY97NKc2yJL/H5ObZAXSXSo0U4U1yFTpGBnm4OIcTH/H3iEr7fnYunRqRgQHKoSz4jxF+KJY8NxNRv9+O5Hw6josaAhwcnueSziG/Lz8/HPffcg5KSEoSHh2PgwIHYtWsXEhMTAQCFhYXIzc217q/X6/H888/j4sWLUCgU6NatG/744w/ccMMNTmmPTCJq8Rzb0iodtp25MlUoJkhhLUhFCPFelptQNMeW+JI2EdiOTI0AAPx9spgCW0KIQ6p1Rrz+8zF0iVbi2TGdXfpZATIxFj6Uged+OIQ3f8tCWbW+VQWqSNu0bNmyJp//+uuvbf794osv4sUXX3RZe+RiHrUtnGNbrTO/rldcEHLLalCtMzqzaYQQF7mSsfX5wZ2kHWkTgW2EUo6ecSr8feISpg5P8XRzCCE+5JO/s1Go1uLz+/o6bV5tU2RiET69py+C/I7h0w1nUFqtx1s3doNYRBcPxDvJJCJcrtJhT07D4mch/lJ0jAho9LXauiHMCSF+KK/Ro6RK77J2EkKch+bYEl/UZq6kRqdFYv+FcpRX00mTENI8J4vU+HJbDu7pn4A+CcFu+1wRz+Htm7vj6VEd8f3uXExZvA8arcFtn0+II6JVcgTIxKjRG20eZdV6nCrSNFnfQmcQIBPz4HkOEhEPg4mKUBHiCyxDkSU0FJn4kLYT2HaJgMCATaeLPd0UQogPEASGf6w6hiCFBC+NS3P753Mch/8bm4p/39YD27JLcNu8Hcgrq3F7Owi5lkilHCNSIxo8ukQHwigITVZM1hpN1iWDpGJzYCtQASlCvJ5lKDJlbIkvaTOBbbcYJSKVMvx9ggJbQsi1rTiQj30XyvHahC5Or4LsiLv6JeDbRwegWKPDzZ9vxz5a65b4iEC5+eemqdEGlowtAEjrhtvrKWtLiFfTGU3Yd6EcAM2xJb6lzRytHMdhVFokNp++TEOdCCFNqtEb8f6aU8hIDMYtfWI93RxkpoTi56cGQ+UnwT0LduHr7Tm0fBnxegEyc5kOTRMFoXRGE2QSc8ZWUhfgNnWOVmsNqNW3fGkhQkjrFat1YIwhLEAGuaTNhAqkHWhTR+votAhotEbspYwHIaQJC7bkoFijw2sTunhNReIOYf74edpgjEqLwKzfsjD9+4M075Z4NamYh0wsQkFFLc4Uaxo8qnRGaO1kbA0m+zdtSqp02HiyGGuzimi9W0JagDHmlJuilbUGiHgOg1JCveYcSUhztImqyBaDO4ZBJuax4UQxBqWEebo5hBAvVKzW4n9bzmJSrxi3FoxqDqVcgvn3p2PhthzM+fMkThSqMff+vkiLUnq6aYTYFR4oRX55LcrsFG7MvlQFoyBAXpextQ5FbmRN3IqaK+9hMAkQ8SIXtJiQtulIfgVySqoBAL3jg5AY6t/i96qsNUClkFBQS3xOmwpsFVIRBncMw98ni/GPiV093RxCiBf6aP1pGE0ML45L9XRT7OI4DlOGJqNXfBCmf38AN3++HbNv7oHb0+M83TRCGkhPDEHv+IYZospaA7ZmXwZgPjcDgERsvkhubCiyWntlSLORMraEOKSy1oAAmRhiEY8ThZpWv1d8sJ+TWkaI+7SpwBYAxnSJxKurjuL0JQ06RwZ6ujmEEC9yqkiDH/bmYcrQZMSHePdJu1+HEPzxzFA8u+wQnv/pMPadL8OsG7tZs1+EeAt7VVND/KUYkRqBWr0JEYEyAIDkGsWj1LVXht7TUGRCHGMUGJQKCTpFBGBLdgkO5VW06v1CA6TOaRghbtTmAttx3SLx+i/H8PvhAswc650ZGUKIZ8z58wSUCgmmjejo6aY0S1iADN880h//XX8an2w4g6MXKzHvvnQkhHp3UE4IAKgUEqgUVyqOS0Q8OI5DZa0BJVU6m30ZA6p0RigVEqhrDRTYEuIgk4lBzHMI8pPi+m5REFox15bjYF2mixBf0uYC29AAGQalhOL3o4V47rrOND+AEAIA2Jp9GZtOXcY/J3b16PI+jhLxHGaOTUWfxGA898MhTPh0Kz66szfGdI30dNMIcZifRIS8sppG12wO85dBXWuAUaDVDQhxhFEQrEvzSMVtqjYsIc3W5gJbAJjQIxovrzyKk0UadImmoiuEtHcmgeHtP04gMdQP9w9M9HRzWmRkagT+eGYonlpyAI99uw+v3dAFjw5Jopt3xKcM7hiGGr395YF4jgPPcThXUkUZW0IcZDAxu9MCCGlP2uQtnXHdoiDmOfx+pMDTTWk1QWCorDWgWmekdS0JaaGVB/JxskiDl69P8+k72bFBCvzw+EDc0CMas/84gTd/y6IAgPgUhVSE0ACZ3UewvxQikfnCnI5rQppPEBgExiARUWBL2rc2mbEN9pdicMcw/HGkEM+PTfWpjIbRJGBL9mWsOXYJB3LLca6k2nqCl4p4RKnk6BUfhL4JQRjTJdLrC+AQ4mm1ehM+WHsK6YnBuL57lKeb02pyiQif3t0H8cF+mL/5LAora/HZvX2thXkI8WVingJbQpqi1hpQrNbabBPVDUGmjC1p79pkYAsAE3pG48XlR3Dsoho94lSebs41GUwCluy6gAVbc3CxohZKuRgDk0NxffcohPhLYRIYymv0uFBag4O55fjtcAHe/C0L3WOVuLd/Im7tG0vVUgmx48ut53BJrcO8+9N96iZXU3iew8vj0xAbJMfrvxzHsz8cwid396GLGuLzLMcwLfdDiH0nCzUorKy1+5xlji0h7VWbDWzHdYvCP34+hhUH8r0+sN1/oRwvLj+Ms5erMTA5BG/e2A3DU8ObzMDkl9fgr2NF+PVwAV5ddRQfrjuNR4Z0wH0DEm2qUBLSnhVrtJi3+Swm9IxG34RgTzfH6R7I7ACDieGt37MgF4vw/u09wVNwS3wYZWwJaVqtwYSIQDkGJIUAAArVWuw7XwYAENNQZNLOtdnAVqWQYFy3KPxy6CJevaGLV86rEwSGeZvP4sN1pxEbpMCih/thZGpEs14bF+yHKUOT8eiQJOzOKcP8zWfx3l+nMHfjWdw3IAGPDElCpFLu4m9AiHf7aF02DCYBL41L83RTXOaRIUmoNZjw/ppT8JOK8NZN3dpMZpq0P1xdASmjiQJbQuyp1ZsQpJJYb2LK613fUmBL2rs2G9gCwB3pcfjtcAE2nLyE67tHe7o5Nmr0Rjz9/UH8fbIYN/WOweybuyNQ7nimleM4DEwOxcDkUJwoVON/m8/iy205+Gp7Dm7pE4snhqcgJTzABd+AEO92+pIGP+zNxSODk9r8uq/TRnZEjd6IzzeeRaBcjBevb7uBPGn7xDxHGVtC7BAEBp3RZDP1TFbv7zQUmbR3bTqwHdwxDNEqOZbvz/eqwLZYo8WjX+9DVqEab9/SHff2T3BKhqVLtBIf390Hz49LxZdbc/DD3jz8tD8fN/SIxvSRHWnpI9KuvLP6BALlEkwf1dHTTXGL58emQl1rxNxNZ9EhzB93ZsR7ukmEtIiI52CiVQAIaUBnNK/vrKgf2NbL2FKdBdLetenAVsRzuLVvLOZvPodijRYRgZ4fmltUqcVdX+xEiUaHLydnNHvosSPigv0w68ZueGZ0J3y9PQeLdpzHH0cKMaZLJJ4e1RG94oOc/pmEeJONJ4ux6dRl/HNiVwT5ST3dHLfgOA5vTOqK3LIavLryKOKD/ZCZEurpZhHiMLGIg0kQPN0MQtzOYBKw8WQx9KaGx7+I45BWl6CQS68Es/XrsdByP6S9a/NjFm7rGweTwLDqwEVPNwXFai3uXbALZdV6LH18oEuC2vpC/KWYOTYV218ehefHdsb+C2W46fPtePCrPdiTU0br4pI2yWAS8K8/spAS7o8HMhM93Ry3Eot4fHpvHySH++PJJftxscJ+5UxCvBnNsSXtVa3BhFqDCZFKOZLDAmweJsZwqkgDAI2ugkEZW9LetfnANjk8AAOTQ/Dd7gsenbNTUaPHvV/uxiW1Ft880h8944Lc9tlKuQTTR3XCtpdG4bUbuiCrQI07/7cTkz7bhp/25UFrMLmtLYS42uKdF3DucjVen9i1Xa7tqpRLsODBDJhMDNO/PwC9kTJfxLeIeZ7m2JJ2yVD3+7pDqD+6xihtHtEqOXRG8/WaopHAVkJzbEk71y5+Ah4alIS8slpsOFnskc/XGwU8+d0B5JbVYNHD/T227Ii/TIzHhiVj20sj8e/besBoYnhh+REMencD3l9zEvnlNR5pFyHOUlqlw8frT2NUWgRGuHhEhDdLDPXH+3f0xMHcCvz7r5Oebg4hDqE5tqS9stzQsTekODHUHxIRjyA/aaM3bWm5N9Letek5thZjukQgNkiBb3acx3VdI9362YwxvP7zMew8V4r/3t0b/evWHfMkuUSEu/ol4M6MeOzOKcM3O85j3qazmLvpLAanhOGOjDiM6xbV6FAXQrzVf9adRq3ehH9M6OLppnjc9d2j8cjgJCzcloN+HUJwffcoTzeJkGYRizhojRTYkvbHUBfYiu0ErmEBMtzQw3sKoRLijdpFYCsW8XgwMxFz/jyJYxcr0T1W5bbP/nJrDn7Yl4cZozvhpt6xbvvc5qi/VFBBRS1WHsjHj/vyMWPZIQTKxbipdwxuT49HrzgVrYtJvF5WgRrL9piX90mmJa4AAC+PT8PBvHK8sPwwesapEBOk8HSTCLkmnuOgrjVAazDRDVbSrliGIosdzLyOSI1Alc7oiiYR4lPaxVBkALh3QAKUcjHmbjrjts9cl3UJ7/x5ApN6xeDZMZ3c9rktEROkwPRRnbDp+RFY9vhAXNc1Esv35+Pmz7dj9H8249O/s2moMvFagsDw+i/HEOwnxdOjvftnzZ2kYh6f3N0HgsDw4vIjEGjeIvEBcon50uRAbrmHW0KIexmtQ5EduzxXKSSIpRuXhLSPjC0ABMoleGhwEj7dkI0zxRp0jAh06ecdL6jEjGUH0SsuCO/f3tNnMp48fyWL++aN3fDnsSKsOnARH64/jQ/Xn8aIzuG4b0AiRqZFtNvqe4wxlFbroTcKUEhECPKT+Mz/b1u1bG8e9l8ox0d39YJKIfF0c7xKfIgf/jmpK15acRSLd57HQ4OTPN0kQprUJUqJokqtdc1OQlriVJEGZdX6Vr8PzwFdY5QIlLv+3GIUBHAc126vrwhprXYT2ALAw4M6YOHWc/hofTY+v7evyz6nWK3FlG/2IdhPii8eTPfZoVSBcgnuzIjHnRnxKKioxY/78rBsTx6mLN6HaJUcD2Qm4r4Bie0ikDhTrMGa45ew5fRlHMmvRG29StIBMjF6xwdhaKcw3NQ7FlEqz6+X3J5c1ujw7p8nMLhjKG72suH+3uLOjHisy7qEOX+exJBO4egYQUO1iffieQ4h/lJc1ug83RTi5RpbtrCy1oCTRWoEysWtqo4vMPOqFuGBMvcEtiYGCQW1hLRYuwpsg/2leHxYCj5afxqPDC5HeqLzqxPX6k14bPE+qGsNWP7kIEQEto0gJyZIgWfHdMb0kR2x4WQxvt11Ae/9dQpzN57FI0OSMG1kCmRi3wzgm7L3fBk+WncaO86WAgB6xKpw34AEJIb6QS4RoVpnxPnSGuw9X4Y5f57Ev/86iUm9YvDcmM7oEObv4da3D7P/yILWKGD2zT0oc94IjuMw59aeGPfxFvzfj4ew4slBdouTEOItJCLeOiyTEHvyymqaHK4uE/MY1im8Vb/rGGP49XCB245Fg0mg382EtEK7CmwB4LFhSfh+zwW8/UcWVjw5yKkXwoLA8PxPh3H0YiW+nJyBLtFKp723txCLeIztFoWx3aJwolCNuZvO4pO/s7Fifz6eHtURt6XHtYm1Qy+UVmPO6pP463gRolVyvDI+DbelxyEsQNboa3JLa7B0by6+2XEevx8pxAMDE/HCuFT4y9rdj5nbbM2+jF8OFWDmdZ2RRDcSmhQeKMM7t3TH1O8OYMHWHDw5IsXTTSKkURIRD4NJAGOMblgRu0qqdJCKeKQ0MgIl1F/a6iCR4ziIed5t64EbBQaxnaV+CCHN0+6uuP2kYjw/NhUvLD+C1UeLMKGn80qnv7/2FP44Woh/TuyKUWnuXVbIE7pEK/HpPX1wT/94fLDmFF5eeRRzN53FM6M74ZY+sT45R0RnNOGTv7OxYEsOpGIeL4xLxaNDkpo1nDwh1A8vXZ+GR4ck4dO/s/HNzvP4++QlvH97LwxMDnVD69uXyhoDXlx+BB0jAvDE8GRPN8cnXN89GhN6RuOj9adxXddIGpJMvJZlHU+DiUEq9r1zCXG9Gr0JgXIJOke6tmaKWMRZ15d1NYNJgIT3/eQAIZ7SLn96bu0bh67RSsz+IwuVNQanvOfSPbmYt+ksJmcm4uHBHZzynr5iUEoYVjw5CIse7ocgPwme/+kwbp+/AyeL1J5umkPOFFfh5s934PONZ3FT7xhseH44po3s6PAc6bAAGd68qTuWT82EhOdxz4Jd+HzjmUbnApGW+eevx3BZo8PHd/Vuk8PgXeWtG7shQCbGC8sPu+1ijRBHWUb+GAUqIEXsq9YZ4S9z/e9+iYhz23FoNFHGlpDWaJeBrYjn8N7tPVFSpcMrq460OuDYcvoy/vHzMYxKi8DrE7u2y2FTHMdhZGoEfpk2GP+9uzfyymow8ZNteO+vk9DWK7Tkrf44UoibPtuGyxotvn64H96/o1er50enJ4bgj2eG4pbesXh/zSlM/W4/qmmdOaf49XABfjlUgOeu6+zWdanbgtAAGd68sRsO5lZg0fYcTzeHELssF/cGI918IQ0JAkOtweSWqT7mocjuOQ6NgmAdrUAIcVy7DGwBoHusCi+MS8Xqo0VYuK3lF3fzNp3FI1/vRWpkID69p0+7n/TPcRxu6h2L9TOH4/b0OMzddBbjPt6C7WdKPN00uwwmAf/6PQvTvj+AbjEqrH5mKEakRjjt/RVSEf5zZy+8eWM3rD9RjLu/2EWVPlupsLIW/1h1FBmJwZg6nOaJtsTEntEY1y0S7685hZySak83h5AGLBlbA2VsiR3VevNNYj+p6zO27h2KzCCmociEtFi7m2Nb35QhyTicX4m3V59AtErh0Hxbo0nA7D9O4Osd53Fjrxi8dVM3KhJUT5CfFO/e1hO39InFq6uO4r4vd+PufvF4dUIXKN1QMr85itVaTPv+APaeL8ejQ5Lw8vg0lxS+4jgOkwd1QEKoH6YtOYDb5u3AN4/0p2JHLWAwCZix9BBMAsOHd/b2yXnc3oDjOPzr5u4Y+9EWvLT8CJY9PhA89SXxItbA1kSBLbF1OK8CFbXmaWT+Utdfd0lEPKqMtqOtTALD/gvlTj8+dUaBhiIT0grt+rYQz3P4zx290K9DCJ5ZdhA/7M1t1uvKqvV45Jt9+HrHeTw2NAkf39UbQX5SF7fWNw1IDsXqGUMxbWQKftqfj7EfbsGGk5c83SzsySnDhE+34XiBGp/d2wevT+zq8mrOI1MjsPSxgajWGXHbvB04kl/h0s9ri/7950nsOV+Gf9/eEwmhfp5ujk+LCJTjjUldsed8GRbvPO/p5hBiwzIc02iiocjkCq3BhPOl1TCaBESrFFAqXH+jXMxzMFxVFbmy1oDCyloYBQaOg9MeYQFSRCnbxjKRhHhCu08xyiUiLHqoH6Z/fwAvrTiKfefL8dqELnYD1Qul1fjP2tP47UgBxHXzdO/MiPdAq32LTCzCC+PSML57NJ7/6TAe+Xofbu0Ti39O6ur2GwKMMXy1/TzeWX0CHUL98P2UAejk4oqK9fWKD8LKpwbhgYV7cM8Xu7DgwQwM6hjmts/3ZauPFuLLbTl4ZHASJvaM8XRz2oSbe8fit8OFeG/NKYzpGom4YLpZQLyD5UajnjK2pB6dwXw8dI1RIlqlcMtnSkR8g6HINXVDofsmBCHQS0ahEUIosAUA+MvEWPBgBv77dzbmbTqLP48V4abeMeibEIyyaj0KK7U4VlCJPTllUEhEeHxYMm7tE4fUKPcFRG1B91gVfp0+BPM2ncVnG7OxJbsEs2/ujuu7R7nl86t1Rry04gh+P1KIG3pE4b3beyHAA8PHE0P9sXxqJh78ag8eWrQXn9zTx2194KuO5lfi/348jPTEYLxyQ5qnm9NmcByH2XVDkl9bdQxfP9yvXRa/I95HzFPGljSkM5qLUcrdWAlfLOJguCqwtRTFVDi4agIhxLUosK0jFvH4v7GpmNQrBgu2nMOKA/lYsts8NDlQJkZCqB9eGJeKOzLiWl0ttz2TinnMGNMJ47pH4sXlRzD1u/24rmskXr2hi0vnnB7Jr8BzPxzC+dIavHZDF0wZmuTRC/gIpRw/PJGJR7/ei6eW7Me7t/bEnf0o+29PQUUtHv1mL0IDpJh/f7rLh4y3NzFBCrw0Pg2v/3wMPx+6iFv6xHm6SYSA4zhIRTwKKmuhNwpIjQqEVEw/++2dti5j6+gyfK0h5nkwxmA0CdYCoTV6E2Rivt0XDCXE21Bge5XOkYF4/45e+NfN3VFQUYvwQBkNM3GBtCglVj45CIu2n8cnf2fjug8348HMDpgxuhNUfs7rb6NJwNxNZ/HJ39mICJRhyZQBGJgc6rT3bw2VQoJvHx2Ap5bsx4srjqCiVo/Hh1GV3/oqaw145Ou9qDWYsGTKAIQHyjzdpDbpvv4J+O1QAd78LQtDO4UjLID6mXhehFKOyxodzpVUISxQ2ujQU8YYdEbHhiy7MzAizmPJ2MrceJPDOt9bYLAkimv1JjqGCPFCFNg2Qi4RITk8wNPNaNPEIh6PDUvGLX1j8dG60/h6Rw5WHszHjNGdcP/AxFZn5vaeL8Nbv2Xh6MVK3No3FrNu7OY1FZktFFIRvngwA8//dBjvrD6JsmoDXro+lYaDAqjSGfHQoj04d7kaix7u59a50O0Nz3OYc1sPjP/vVry4/Ai+fDCDqiQTj0tPDEat3oS1WUVNrme7O6cMl9Rah967U0QgusYoW9tE4mZagwCpiHfr7ydxvQrdlmC2xmByS0VmQohj6KeSeFxYgAxv39IDkwd1wOw/TuDN37Lw1XZzkaA7MuIdngd7OK8C8zadxV/HixAbpMD8+/vi+u7NX8rJ3SQiHh/d2RtBCgnmbz6Liho93r6lR7teyqZWb8KjX+/F0fxKzL8/HYOpwJbLpYQH4I1JXfHaqmNYsPUcnqA1gokXsCx9cvV6tgaTAJPAUKM34ZJai/gQP4T6N68Y4akijXUdVOJbdEb3Z0oldefik0Uaa6a4WmdEmD+NbCHE21BgS7xG58hALH6kPzadKsbcjWfx5m9Z+GDNKYzuEonx3aOQmRLaaBXlC6XV2HiyGL8eLsCB3AoEysV4YVwqHh2S5BPDhXiew6wbuyHYX4qP12ejosaA/97TGzI3FsjwFpU1BjzyzV4czC3HJ/f0wZiukZ5uUrtxb/8E7DxbivfWnEJGh2CkJ4Z4ukmknRPzHDiOs1kvtEZvxIaTxdZKtTzHoVuMstm/L/PLaxss30J8g9YguHUYMgAEyiUIkIlRWqW3bpOIeJoaQ4gXosCWeJ0RqREYkRqBI/kVWLY3D2uPF+HXwwUAgI4RAegdHwR/qQgmxlBapcfJIg1ySqoBAJ0jA/DWTd1wW984+Hug4nFrcByHZ8d0RpBCglm/ZeGRr/fifw9keKRys6dcUmvx4MI9yCmtxrz70zGuG1WLdieO4zDn1h44drES078/iNXPDEVwM7NghLgCx3GQ8JxNdeQLpTUQGNAzLgg8BwTIxA7dBBTzHGrrqtoS5ztTXIX88hqXvHeVzoholXsLeCqkIozuQjdYCfEF7eeKmficnnFB6BkXhH/d1B2H8sqx93w59p0vx9bsyzCaGDiOQ4i/BJ0iAvDI4A4YkRqB+BDfX4fzocFJCPKT4vmfDuO+Bbuw6OH+CGkHwcWB3HJM/XY/avQmfPNwf2SmeEeRr/YmUC7BZ/f2xa3zdmD60gP4+uH+VImaeJRExMNgEnCySI1TRRoAQLRK0eJK+hIxD42WhiK7ysWKWmgNgkvOW35SMRJCXLeCAiHEt1FgS7yeiOeQnhhiHhY53NOtcY+b+8RCpZDgySX7ccf8Hfj20QGICXLPYvTuxhjD0j15mPXrccQEyfHdlAHoTIWiPKp7rArv3toDM388jH/9noW3buru6SaRdkws4mAwMdRW6eEvFSM+xA9xwS3/fSjheRgFGorsKoLAEBYgRUYHmspACHEvug1PiJcamRaBbx8dgGKNDrfP24Gzl6s83SSnK63S4Ylv9+PVVUcxqGMofpk2hIJaL3Fr3zhMHZ6CxTsv4LtdFzzdHNKOSesytrUGE4L9JUiNCmzVVBOJmIPe1HiVZdI6JoG16+KHhBDPocCWEC/Wr0MIfnwiE3oTw+3zdmBr9mVPN8kpGGNYsT8f4z7egk2nL+OfE7viq8n9nLqGMWm9F8alYkyXCMz69Ti2ZZd4ujmknRKLeOhNQl3hoNYX1BPzPBhjMJooa+sKRgpsCSEeQoEtIV6uS7QSK58chEilHA9+tQefbciGIPhutiG3tAZ3/m8n/u+nw0gM9cfvTw/BI0OSaN1ULyTiOXx8dx90igzE49/uw/4L5Z5uEmmHJCIOWr0JRkGAQtr6wFZqXZfUd3+PejOBMfC0FjshxAMosCXEBySE+mHVU4NxS59YfLD2NB75Zi8uqbWebpZDKmsN+PTvbEz8dCuyi6vw/u098dMTmTT02MsFyMRY/Eh/RKnkeGjRHhy7WOnpJpF2RlKXsQXglOXbGlsblziHUWDWPiaEEHeiwJYQH6GQivCfO3rhnVt6YNe5Uoz5cDN+2JsLxrw763C+pBr//uskhry7Af9Zdxr9k0Lx67QhuCMjnrK0PiI8UIYlUwZApZDgvi9340AuZW6J+9Svyi13whqmlvejtWydTxAYGGMQ0+92QogHUFVkQnwIx3G4d0AChnQMw8srj+ClFUfx88ECvD6xK7rGKD3dPKsLpdVYl3UJq48W4kBuBXgOmNAzBtNGpiAtynvaSZovWqXAD09k4oEvd+O+BbvxxYPpGNop3NPNIu1A/eyfM4ciG314Soe3svQpDUUmhHgCBbaE+KCEUD8smTIAP+zNw7//OokJn27FrX3iMG1kCpLDA9zenlq9CfsvlGP72RKsz7qE7GJzBeduMUr8Y0IX3NgrBhFKudvbRZwrNkiBH6dmYvJXe/Dwor149YYueHhwB3B0EUtcSGqTsXXeUGQ9ZWydTqgbQSTmaUAgIcT9KLAlxEdxHIe7+yfghp7RmL/pLBZuy8GKA/kYlRaB+wcmYGincJshfM5UozfiUG4Fdp4rxa5zpTiUVwGDiUEi4jAgKRT3D0zEmK6RiG2ja++2Z2EBMvzwRCZe+Okw3vo9Cwdyy/HOrT2glFNFa+IaEUoZOkUEwk8qcsr0BUtgW6zRwmQnayviOcQFK+iGTQtYM7YU1xJCPIBjzZigp1aroVKpUFlZCaWShhES4o1Kq3T4fncuvtl5ASVVOoT6S3F99ygM6xyOzJTQFgceBpOAs5erkFWgxpH8ShzILcfxAjVMgnkeVc84FQYmhyIzJRTpicHwk9L9svaAMYYvtpzDe2tOISJQhn9O7Iqx3aLcuswHnZucq730J2MMa45fgs5oanSfvgnBiA/xc2Or2obKGgM2nS7GgKRQRKlolA4hpPUcOTdRYEtIG2MwCdiafRkrD1zE5lOXodEZIeI5pEYGIjncH4mhfgj1lyHYXwKpSASxiINExMEkAEVqLQoralFYqUVBRS2K1OY/Lcti+ElF6JMQhPSEYKR3CEFGYjD8ZRTItmdH8ivwwk9HcOqSBnHBCkzO7IA7+8VDpXB9BretnptmzZqFN99802ZbZGQkioqKGn3N5s2bMXPmTBw/fhwxMTF48cUXMXXqVIc+t632pz0mgcHYSFXkbdklkEtEGNwxzM2t8n1l1Xpszb6MQSlhCA+Uebo5hJA2wJFzE12REtLGSEQ8RqVFYlRaJAwmAYfzKrAluwTHLlbieIEaa44XNbl+o1TEI0olR5RKjj7xQZjYMxpdopXoGq1EYqi/WzNyxPv1jAvC6hlDsf7EJSzanoO3V5/AR+tP47a+cbinfwLSogKp+nULdOvWDevXr7f+WyRqfG5pTk4ObrjhBjz22GP47rvvsH37djz11FMIDw/Hbbfd5o7m+hwRz0HE2+/ThBA/ZBWqUas3OaVYVXtiuVlA5wlCiCdQYEtIGyYR8cjoEIKMDiHWbYwxaHRGVFQboDeZYDAxGE0MHAdEKuUI9ZdSIEIcIuI5jOsWhXHdonCiUI2vt5/HD/vy8O2uC/CXitAtVoUuUYEID5QhLECGkWkRiKRiYk0Si8WIiopq1r7z589HQkICPv74YwBAly5dsG/fPnzwwQcU2LZApEqOrEI1Lmt0SAil4ciOsMxZpsCWEOIJFNgS0s5wHAelXELFfohLdIlW4t+398RL49Ow8WQxjl6sxLGLlfj5UAEqaw0AgGWPD6TA9hqys7MRExMDmUyGAQMG4J133kFycrLdfXfu3ImxY8fabBs3bhwWLlwIg8EAiYR+1h2hlEsgl4hQpNYiQtn64bQyMd9uClFRYEsI8SQKbAkhhDhdiL8Ut6XH4bb0OOs2vVFAeY3eLfNvfdmAAQOwePFidO7cGZcuXcLs2bMxaNAgHD9+HKGhoQ32LyoqQmRkpM22yMhIGI1GlJSUIDo62u7n6HQ66HQ667/VarVzv4gPi1TKcaG0GoWVta1+r9gghc2ombbMEtiKKbAlhHgABbaEEELcQirmKVPbDOPHj7f+vUePHsjMzERKSgq++eYbzJw50+5rrs4IWupCNpUpnDNnToMiVcQsLSoQIX7SVr9PXnkNSqv1TmiRb7AEtnw7yVATQrwLBbaEEEKIF/P390ePHj2QnZ1t9/moqKgGFZOLi4shFovtZngtXnnlFZtAWa1WIz4+3jmN9nFyicgp82v1JgElBZXQGwVIxW1/cVfK2BJCPKnt/5YlhBBCfJhOp8OJEycaHVKcmZmJdevW2Wxbu3YtMjIympxfK5PJoFQqbR7EuZQKc/5ArTV4uCXuYWIMHMdRAUJCiEdQxpYQQgjxIs8//zwmTZqEhIQEFBcXY/bs2VCr1Zg8eTIAc6b14sWLWLx4MQBg6tSp+OyzzzBz5kw89thj2LlzJxYuXIilS5d68msQwFqkr6hS6+GWuEaATAy55MqSSEYTo2wtIcRjKLAlhBBCvEh+fj7uuecelJSUIDw8HAMHDsSuXbuQmJgIACgsLERubq51/6SkJKxevRrPPfccPv/8c8TExOCTTz6hpX68gFwiglwiwtnLVTh7ucrTzXE6Ec8hWnVl3nxFjYHm1xJCPIZjlgoTTVCr1VCpVKisrKShSoQQQrwCnZuci/rTNWr1JtTojZ5uhtMJDDhfWg3NVcOswwJk6BkX5JlGEULaHEfOTZSxJYQQQghxEYVUBIVUdO0dfVB4YOvX+SWEEGeh4lGEEEIIIYQQQnwaBbaEEEIIIYQQQnwaBbaEEEIIIYQQQnxas+bYWupLqdVqlzaGEEIIaS7LOakZNRBJM9C5nhBCiLdx5FzfrMBWo9EAAOLj41vRLEIIIcT5NBoNVCqVp5vh80pLSwHQuZ4QQoj3ac65vlnLMoLZjQAADH5JREFU/QiCgIKCAgQGBoLzgvXJ1Go14uPjkZeXR0sStAL1o3NQPzoP9aVztJd+ZIxBo9EgJiYGPE8za1qroqICwcHByM3NpRsFTtJefhbdifrUuag/nY/61LkcOdc3K2PL8zzi4uKc0jhnUiqVdMA4AfWjc1A/Og/1pXO0h36kAMx5LBcMKpWqzR837tYefhbdjfrUuag/nY/61Hmae66nW9yEEEIIIYQQQnwaBbaEEEIIIYQQQnyaTwa2MpkMb7zxBmQymaeb4tOoH52D+tF5qC+dg/qRtAQdN85Hfep81KfORf3pfNSnntOs4lGEEEIIIYQQQoi38smMLSGEEEIIIYQQYkGBLSGEEEIIIYQQn0aBLSGEEEIIIYQQn0aBLSGEEEIIIYQQn+Z1ge2sWbPAcZzNIyoqqsnXbN68Genp6ZDL5UhOTsb8+fPd1Frv5Wg/rly5Etdddx3Cw8OhVCqRmZmJNWvWuLHF3qklx6PF9u3bIRaL0bt3b9c20ke0pC91Oh1ee+01JCYmQiaTISUlBV999ZWbWuydWtKPS5YsQa9eveDn54fo6Gg8/PDDKC0tdVOLia+YO3cukpKSIJfLkZ6ejq1bt3q6SV5py5YtmDRpEmJiYsBxHH7++Web5xljmDVrFmJiYqBQKDBixAgcP37cZh+dToenn34aYWFh8Pf3x4033oj8/Hw3fgvvMWfOHPTr1w+BgYGIiIjAzTffjFOnTtnsQ33qmHnz5qFnz55QKpXWa7o///zT+jz1Z+vNmTMHHMfh2WeftW6jfvU8rwtsAaBbt24oLCy0Po4ePdrovjk5ObjhhhswdOhQHDx4EK+++iqeeeYZrFixwo0t9k6O9OOWLVtw3XXXYfXq1di/fz9GjhyJSZMm4eDBg25ssXdypB8tKisr8eCDD2L06NFuaKHvcLQv77zzTvz9999YuHAhTp06haVLlyItLc1NrfVejvTjtm3b8OCDD+LRRx/F8ePH8dNPP2Hv3r2YMmWKG1tMvN0PP/yAZ599Fq+99hoOHjyIoUOHYvz48cjNzfV007xOdXU1evXqhc8++8zu8++99x4+/PBDfPbZZ9i7dy+ioqJw3XXXQaPRWPd59tlnsWrVKixbtgzbtm1DVVUVJk6cCJPJ5K6v4TU2b96MadOmYdeuXVi3bh2MRiPGjh2L6upq6z7Up46Ji4vDu+++i3379mHfvn0YNWoUbrrpJmuQRf3ZOnv37sUXX3yBnj172mynfvUCzMu88cYbrFevXs3e/8UXX2RpaWk225544gk2cOBAJ7fMtzjaj/Z07dqVvfnmm85pkI9qaT/edddd7B//+IdT/h/aCkf74s8//2QqlYqVlpa6rlE+yNF+fP/991lycrLNtk8++YTFxcU5uWXEl/Xv359NnTrVZltaWhp7+eWXPdQi3wCArVq1yvpvQRBYVFQUe/fdd63btFotU6lUbP78+YwxxioqKphEImHLli2z7nPx4kXG8zz766+/3NZ2b1VcXMwAsM2bNzPGqE+dJTg4mH355ZfUn62k0WhYp06d2Lp169jw4cPZjBkzGGN0nHoLr8zYZmdnIyYmBklJSbj77rtx7ty5RvfduXMnxo4da7Nt3Lhx2LdvHwwGg6ub6tUc6cerCYIAjUaDkJAQF7bQNzjaj4sWLcLZs2fxxhtvuKmFvsORvvz111+RkZGB9957D7GxsejcuTOef/551NbWurHF3smRfhw0aBDy8/OxevVqMMZw6dIlLF++HBMmTHBji4k30+v12L9/f4Nz6dixY7Fjxw4Ptco35eTkoKioyKYvZTIZhg8fbu3L/fv3w2Aw2OwTExOD7t27U3/DPOIJgPX6g/q0dUwmE5YtW4bq6mpkZmZSf7bStGnTMGHCBIwZM8ZmO/Wrd/C6wHbAgAFYvHgx1qxZgwULFqCoqAiDBg1qdD5YUVERIiMjbbZFRkbCaDSipKTEHU32So7249X+85//oLq6GnfeeaeLW+rdHO3H7OxsvPzyy1iyZAnEYrGbW+vdHO3Lc+fOYdu2bTh27BhWrVqFjz/+GMuXL8e0adPc3HLv4mg/Dho0CEuWLMFdd90FqVSKqKgoBAUF4dNPP3Vzy4m3KikpgclksnsuLSoq8lCrfJOlv5rqy6KiIkilUgQHBze6T3vFGMPMmTMxZMgQdO/eHQD1aUsdPXoUAQEBkMlkmDp1KlatWoWuXbtSf7bCsmXLcODAAcyZM6fBc9Sv3sHrrrzHjx9v/XuPHj2QmZmJlJQUfPPNN5g5c6bd13AcZ/Nvxpjd7e1JS/rRYunSpZg1axZ++eUXREREuLqpXs2RfjSZTLj33nvx5ptvonPnzu5uqtdz9JgUBAEcx2HJkiVQqVQAgA8//BC33347Pv/8cygUCre13Zs42o9ZWVl45pln8M9//hPjxo1DYWEhXnjhBUydOhULFy50Z9OJl7N3Lm3P59HWaElfUn8D06dPx5EjR7Bt27YGz1GfOiY1NRWHDh1CRUUFVqxYgcmTJ2Pz5s3W56k/HZOXl4cZM2Zg7dq1kMvlje5H/epZXpexvZq/vz969OiB7Oxsu89HRUU1uMtRXFwMsViM0NBQdzTRJ1yrHy1++OEHPProo/jxxx8bDLMgTfejRqPBvn37MH36dIjFYojFYrz11ls4fPgwxGIxNmzY4IEWe69rHZPR0dGIjY21BrUA0KVLFzDGqIJgPdfqxzlz5mDw4MF44YUX0LNnT4wbNw5z587FV199hcLCQje3lnijsLAwiEQiu+fSq7MPpGmWCuVN9WVUVBT0ej3Ky8sb3ac9evrpp/Hrr79i48aNiIuLs26nPm0ZqVSKjh07IiMjA3PmzEGvXr3w3//+l/qzhfbv34/i4mKkp6dbr/E2b96MTz75BGKx2Nov1K+e5fWBrU6nw4kTJxAdHW33+czMTKxbt85m29q1a5GRkQGJROKOJvqEa/UjYM7UPvTQQ/j+++9p/l0jmupHpVKJo0eP4tChQ9bH1KlTrXdNBwwY4IEWe69rHZODBw9GQUEBqqqqrNtOnz4NnudtLnrau2v1Y01NDXje9le9SCQCcGV0C2nfpFIp0tPTG5xL161bh0GDBnmoVb4pKSkJUVFRNn2p1+uxefNma1+mp6dDIpHY7FNYWIhjx461y/5mjGH69OlYuXIlNmzYgKSkJJvnqU+dgzEGnU5H/dlCo0ePbnCNl5GRgfvuuw+HDh1CcnIy9as3cH+9qqb93//9H9u0aRM7d+4c27VrF5s4cSILDAxk58+fZ4wx9vLLL7MHHnjAuv+5c+eYn58fe+6551hWVhZbuHAhk0gkbPny5Z76Cl7B0X78/vvvmVgsZp9//jkrLCy0PioqKjz1FbyCo/14NaqKfIWjfanRaFhcXBy7/fbb2fHjx9nmzZtZp06d2JQpUzz1FbyCo/24aNEiJhaL2dy5c9nZs2fZtm3bWEZGBuvfv7+nvgLxQsuWLWMSiYQtXLiQZWVlsWeffZb5+/tbjytyhUajYQcPHmQHDx5kANiHH37IDh48yC5cuMAYY+zdd99lKpWKrVy5kh09epTdc889LDo6mqnVaut7TJ06lcXFxbH169ezAwcOsFGjRrFevXoxo9Hoqa/lMU8++SRTqVRs06ZNNtcfNTU11n2oTx3zyiuvsC1btrCcnBx25MgR9uqrrzKe59natWsZY9SfzlK/KjJj1K/ewOsC27vuuotFR0cziUTCYmJi2K233sqOHz9ufX7y5Mls+PDhNq/ZtGkT69OnD5NKpaxDhw5s3rx5bm6193G0H4cPH84ANHhMnjzZ/Y33Ii05HuujwPaKlvTliRMn2JgxY5hCoWBxcXFs5syZNhc77VFL+vGTTz5hXbt2ZQqFgkVHR7P77ruP5efnu7nlxNt9/vnnLDExkUmlUta3b1/rcivE1saNG5s8XwqCwN544w0WFRXFZDIZGzZsGDt69KjNe9TW1rLp06ezkJAQplAo2MSJE1lubq4Hvo3n2etLAGzRokXWfahPHfPII49Yf5bDw8PZ6NGjrUEtY9SfznJ1YEv96nkcYzQWjRBCCCGEEEKI7/L6ObaEEEIIIYQQQkhTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLTKLAlhBBCCCGEEOLT/h8eNohU4S5XAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bad posterior and trace\n",
    "# need more refinement or wrong\n",
    "az.plot_trace(states_43b_dict);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ca/r4Ms2tKTf9dZwgmSXRo0cgZTp1LlP1zW16qnG7VoXV9sokyTdzZVMoEbV0yhizmgl4Bv0yAABAehxdA+nNN9/UlVdeqX79+ql37946//zz9cQTT6S0j48++kg333yzBg8erF69emngwIGaOnWqtm/fHvPxQ4YMUV5eXsx/t9xyixmHBYTEKixtNqtrVDnJnOrhem72eMOEkJl1lzIVHB22KqowfCZTz5ZOGaPGH08keQQAAADAVI6dwrZmzRpVV1erV69euvbaa+Xz+fTss89qypQp2rp1q+68886E+3j//fc1btw47d69WxMnTtQ111yjzZs367HHHtOf/vQnrV+/XmeccUa35/l8Pt16663dbq+oqDDj0ICQWIWlzcYUwO7MqruUqXiJIi8n+QAAAAC4kyOnsHV1demss87SRx99pNdee02jR4+WJB04cEAXXHCBWlpa1NzcrDPPPNNwP//0T/+k2tpaPfTQQ/r+978fuv3pp5/W1Vdfrerqar344osRzxkyZIgkaevWraYcC0PlYSQ4Aimo4rR+lkxjywazCnXnkvD6VJJ0WXmJrq4o5f0DLES/DAAAkB5HJpBWrVql6upqTZ06Vb/+9a8j7nvyySd17bXX6o477tC9994bdx+fffaZ+vTpo6KiIu3atUt5eXkR948ePVpvvfWW3n//fZ1++umh20kgIdusroGUDdGJkEwLdecSEm9AdtEvAwAApMeRU9jWrFkjSbrsssu63Re8be3atYb72Lt3r7q6unTaaad1Sx5JUllZmd566y2tXr06IoEkSZ2dnXrssce0Y8cO9evXT+PGjdO5556b5tHklmSKGCOSW5NG4WItH08yJDlWrmAIAAAAAGZxZAJp8+bNkhRzilq/fv1UXFwcekw8/fr1U48ePbRt2zYFAoFuSaTW1mOrUm3atKnbcz/++GPdeOONEbddfvnlevzxx1VcXGz4up2dners7Axt+/1+g0d7S/jqVsHl1Eki5YaxZUURK725rYaP0SigdEcIMbIIAAAAgJc4chW2jo5jhYR9Pl/M+wsLC0OPiefEE09UZWWl2tra9POf/zzivmeffVZvvfWWJGn//v0R9910001as2aN9uzZI7/fr9dff11XXHGFXnzxRV111VVKNOPvvvvuk8/nC/0rLS01fLyXrN2023Ab3hUs1D19fJljp6/VN7dpUW2z6qNWPAtOv1ve0KppKxoj7je6z8jMlU1pPQ8AAAAAnMqRCSSzPPjggzrppJM0a9YsXX755frRj36kb33rW/r2t7+tc845R5LUo0ePiOfcfffdqqysVHFxsfr06aOxY8fq+eef14UXXqjXXntNf/rTnwxf84477lBHR0fo3/bt2y07PqepHNbfcBveVlVeork15Y5NHsVL6MSafpfMffEsrmtR7YZdKT/PSLzkFwAAAABkiyMTSMGRR/FGGQULYCZy7rnn6s0339TVV1+tpqYmPfTQQ2ppadEvf/lLXX/99ZKkU045JeF+8vPzNXXqVEnSq6++avjYgoICFRYWRvzLFXOqh2vWhKEaOahQsyYMZfqaiUYvqNOQf6/V6AV1djfFlYwSQdHT7cK3je6LJ9bIu0ym9KU7CgoAAAAAzOTIGkjB2kebN2/WeeedF3Hfvn371N7ernHjkis8fNZZZ+nJJ5/sdnuwxlFFRUVS+wnWPjp06FBSj89Vc6qHkzgy2egFddr3aZckad+nXRq9oE5/nVdtc6vcxahGU3D6Xax6RUb3xVM5rH+oBpgk1YwckNGoLAqUAwAAAHACRyaQKisrdd9992nVqlW69tprI+5btWpV6DHpOnDggJ577jmdfPLJmjhxYlLPeeONNyRJQ4YMSft1gXQEk0fxtpFYokSQ0Upoqa6SFkygmrUaodsLlAMAAADwhrxAoqrQNujq6tLw4cO1Y8cOvf766xo1apSkY4mfCy64QC0tLdq4caOGDRsmSWpvb1d7e7uKi4sjVkn79NNPddxxx6lnzy/yZJ2dnbr++uv19NNP66GHHtL3v//90H3Nzc0aOHCg+vbtG9GedevWaeLEiQoEAtq0aZMGDx6c9LEEp9t1dHTk1HQ2JG/myqZQYmPplDHd7g8fgSRJ/U7oyQgkF0p0no2wohtgHvplAACA9DgygSRJq1evVnV1tQoKCvSd73xHhYWFevbZZ9Xa2qp77rlHc+fODT12/vz5WrBggebNm6f58+eHbl+3bp2+9a1vaeLEiSotLZXf71dtba0+/PBDTZ8+Xb/85S+Vl5cXsZ/7779fl156qYYMGaKCggK98847WrVqlfLz8/WLX/xC06ZNS+k4CFRhZObKpoiCyzUjBxgmkUgepSeTBIwZyZtkz3M6Ji9br407OzSo7wmacFZ/kkxAAvTLAAAA6XHkFDZJmjBhgtatW6d58+bpqaee0uHDhzVixAgtXLhQU6ZMSWofgwcP1sUXX6yGhga1tbXpxBNP1JgxY/Tggw/qn//5n2O+5rvvvqumpiatXbtWn332mUpKSnTNNdfotttu0/nnn2/2YSLHJbvKF0mj9AWLUEvS8oZWPXJDRdIJlkyeGy6d1dySMXnZejVu2ydJ2rLnoLbsac2onQAAAAAQj2MTSJJ0/vnn64UXXkj4uPnz50eMPAoaPHiwnnrqqaRfr7KyMqPaSkCqxpYVRYxM8VJ9m8V1LabVAcpEJkWozSpgbdV53rgz9kqVFNoGAAAAYLZ8uxsA5LKlU8aoZuQAFZ/Uy9RpTXZbXNeiJau3aMMOv5as3qLFdS22tSU6WZNK8iaT54az6jyPGOiLebuXEpEAAAAAnMGxNZC8gloLsILTiypPerghYin7kYMK9dzs8d0el61RSnbXQLJSJjWQnDJKDMgm+mUAAID0OHoKG4DuzKrLY6XKYf0jEkiVw/p3e0xwlJKk0GMTJTHSTeZUlZek/R5l8lzJ+gTUMzPGpfW8dN5/AAAAALmLKWyAy1hVkNlMc6qHa9aEoRo5qFCzJgyNmZhYu2m34Xa0YOJseUOrpq1oVH1zW8T9F9xbr7J/r9UF99YnbF99c5sW1TZ320c6Fte1aNLDDTGn6SVqs51Sff8BAAAA5DYSSIDLmFWXx2pzqofrudnj445qiR6VFGuUUjijxNkF99Zrl79TAUm7/J2GSSQzkzqJaj0lSvYZJZ+slur7DwAAACC3MYUNWeH0OjJuEaxZUzNygAb2Pd7V72cwsZRsDZ6xZUVa3tAasR30sb8z4rHR2+HMWllNij2KJ/w4YrU5+Lewc/9noZXZ7JhClur7DwAAACC3kUCC5dxQs8dMM1c26Y3Wvdp/6LC6jkr9Tuipv86rzni/0TVrZk0Y6vr3cU718KQTF1XlJXrkhgo91bi9232nFhZoV1jS6NTCgrj7MUpEpSpRradgm4PJU0mhv4Vo0cknqwU/p2PLikgeAQAAAEiIBBIsZ+aID6ebubIpNKokaN+nXRq9oC7jJFKi0S5ulsoItVX/mHK2qrktlIx87c4qXXBvvT72d+rUwgK9dmdV3OdHJ3Uy+SymOorHqF5VNqaQBVdsO75nvvZ92iVJxz6vK5u0dMoYy18fAAAAgHuRQILlzBzx4RTxEh7xEgTBH+uZSGZlMzdKZYSaUTLSKGkUvVx9piurhTMaRRV9bLMmDI24v2bkAH3494NZmUI2edl6NW7bJ0n69POjEfc5sRA7AAAAAGchgQTLmTniwwmMEh5jy4q6jUCSjk1jy5TXatYER8MUHn9cxO1GI9TSSUbauVx9dGKms+uIbX8LG3d2xL0v1aRu+PQ3Ri4BAAAAuYEEErLCzBEfdjMaBbN0yhjJohpIUmo1g5wscjRMZMFro2RGOslIO6f+xUp42fW3MGKgL/SeS8c+lz165KecBAqfpsn0NwAAACB3kEACUpRoFAw/phOLHg3Tq0ee/n/jhiSVFEo1AWPn1D8njb57Zsa40KivEQN9embGuLT2EyuBisywSiUAAADcgAQSkCInJAWia/qYIZs/YqNHw5zzpb6aW1Oe9v6M3g+7p/7FSnjZNQUs3aRRuOhpml6oaWanXFulEgAAAO6VFwgEAnY3wsv8fr98Pp86OjpUWFhod3PgAeE1fSRp1oShGSdFwn/ESsrKj1gzRsNI1rwfVopeqa9m5ADXjVqjBpJ5FtU2R4xonD6+LKNkKhKjXwYAAEgPI5CQU5wyVSSTH+BW1PQxqutkFTNGw0j21jhKVnCE1OCTe+uV99oi7nPjFDCSRokl+13jxVUqAQAA4E35djcAyJbgKJvlDa2atqJR9c1tiZ9kgeAIlPZPDqt2wy7NXNmU0vOja/ikWtNncV2LJj3coMV1LaHbon+0uulHbKbvh9WCI6Q27PCrdsMuffr50Yj73fReIzmpfNcEp8ROH1/G9DUAAAA4GiOQkDPsGGWTbDtSkUlNn3hL2qdb1ymVEV1Wjf6yu8ZRItEjpIJOOC5fl5xVYuloHqaa2SPV7xovrVIJAAAA72IEEnKGU0bZmNGOOdXD9dzs8SknS2JN9wqqKi/R3JrylJJHyY6ysHr0V7rvRzbEGxH18HfGRCR1Yo0My0SmI92QPqd81wAAAABmYgQScoYTVk+T/lE/xqaRIdFL2hf07KFJDzekNXInlVEWThn9ZYfwEVKDT+6tgX2P7/b5izcyLBnxRhllOtIN6XPKdw0AAABgJhJILuWUYtBu45SpInZNJwpPZhT07KHGbfskpZ60kFIr/pvrhYLnVA83fG/TLQQevqJb7YZd0sqm0GdrbFlRxGpv4e95sKh3tqf82fW6dnDKdw0AAABgFhJILhS+5PryhlYKr+aodJOIwWTGpIcbIm5PdfWyVEZZMCLDWPTIsGQLgRuNMoo30i2T0U6ZsOt1AQAAAJiDBJIL5fJ0IBwTnUScNWGoOruOpJScSSZpkWjESCqjLBiREV+6hcCNRhlJsUe6pTvaKVN2vS4AAAAAc5BAcqHo6UA7939mY2uQiXRHEUUnEYMjO8wckealESNumDqVaJpbLEb1tOLVRkp3tFOm7HpdAAAAAObIeBW2Q4cO6Z577tFXvvIV9e3bVz169Ij5r2dPclVmqSovUc3IAaHt2g27TFu5CdmTycpkRjWEki2WbLQiWzLbbhFMhG3Y4deS1Vs897eydMoYNf54YrfkUbwV2OZUD9esCUM1clChZk0YmrWEWiqva/aKdAAAAAAyl1FWp6OjQ+PHj9fGjRvVo0cP9erVS4FAQAMGDNDHH3+sQCAgSTrttNNMaSy+8OHfD0ZsMx3EfTKZihheU6igZ4/QSCEp+QLViUaEeGXEiJVTp+KN8smWeCPYEq3Als5oJzMk87peGvkGAAAAeElGI5B+8pOf6J133tH3vvc9+f1+TZ48WXl5edqxY4cOHjyoRx99VKeeeqrGjh2rDz74wKw2Q7F/7MNdohM9qa5MVlVeork15ZpTPVyP3FCh6ePLUpq+lmhESPj9NSMHqLPrSEqjpJzCqr8Vo1E+2WA0gi3Tz5advDLyDQAAAPCajEYg/eEPf9DAgQP1s5/9TMcdd5zy8vJC9x1//PG64YYbVFFRodGjR2vx4sX64Q9/mHGDcUy6RXcRyewRJKnUNDJzZbJUC1SHt9PoszOnerhGlfZ19ap/Vv2tJBrlE1T1wBpt3XtQQ4p6q/72i0157XivHzwvRrWRnM4rI98AAAAAr8kLBOeZpeHEE09UVVWV/vjHP0qSvvvd7+rRRx/VZ599puOOOy70uMsuu0w7d+7UO++8k3mLXcbv98vn86mjo0OFhYV2NydnJJMYCo4gCaoZOSCjH9rhK6NJcmyiJdV2LqptjijaPn18mebWlFvaRjdI5vNT9cAabdnzxXTToaeYl0RK5Ty6oYh4OLe1F+5CvwwAAJCejKawHX/88SooKAhtBwOxjz/+OOJxJ598slpbWwVvq29u06LaZtunOSU7tSjZESTJHley+7Nbqu1083QoKy2dMkY1Iweo+KRecZOPW/ceNNzORHAEW6Kpi24sIj6neriemz2e5BEAAADgIBklkEpLS7Vt27bQ9llnnSVJWrt2bei2rq4uvfnmmyoq4kenl2WyopjZkk2QJJMYSeW43JJoSbWdySYqclGsFdDCDSnqbbid6WpjwTpYRueEmkLIlFMuDgAAAMBeGSWQxo8fr3feeUcdHR2SpEmTJum4447T97//fS1btkzPPfecJk+erK1bt6qystKUBsOZnDT6JtkESTIjSFI5LqckWhL92EunnckkKtBd/e0Xa+gpvdUzv/v0tWyNDKLgPjLhpIsDAAAAsFdGRbSvvfZa/eUvf9Frr72myy+/XAMHDtS9996rOXPmaNasWZKkQCCgU089Vf/5n/9pSoPhTGPLiiLq5Ng5+iaVAsKJah6lelypFrM2W3hdHKOC13a300msrrcTr+ZRrJFBVrw+BfeRCaNi7QAAAMgtGRXRjueNN97Q73//e+3bt0/Dhg3T1KlTdfLJJ5v9Mq6QS8U6U1mBzE3cdFwUvE5NcBRQ0KwJQ7OWYLHztYFkuWVxgFTkUr8MAABgJksSSPgCgSrMZrTCnBd/7Flp0sMNEUvGjxxUqOdmj8/a69u92pibkqOwj9c+J/TLAAAA6ckogXTTTTfpwgsv1E033WT4uEcffVR//vOf9etf/zrdl3ItAlWYKZml4732Y8/KJEsujwIi2YhcRb8MAACQnoxqID366KOSlDCB9Oqrr+qxxx7LyQQSYKZ4Rb2jk0bJJgKcnmwKT/AERwqZmeDJ5fpA1LYBAAAAkIqMVmFL1uHDh9WjR49svBTgabFWmIteJanqgTVJ7ctodSWnLNudjSXo51QP13Ozx+dU8khKfrVCAAAAAJCykEAKBAJqamrSKaecYvVLAZ63dMoY1YwcoOKTeoWmr0WPJNmy56DG3lufMPljNJrJKct2swS9darKS/TIDRWaPr6M6WsAAAAAEkp5Ctsll1wSsf3iiy92uy2oq6tL77//vj7++GNdf/316bUQQEh9c5sG9j1eP/nWOaEf/GPLiiJWXpOkNn+npq1oNEwMRD8vOALFSVObcnmKWTakMt0RAAAAQG5LuYh2fv4Xg5by8vKU6OnHHXecLr/8cv3qV79ScXFxeq10MYp1wixGRY+rHlijLXsOdnvO9PFlmltTbrjP6BpIFFcG4GX0ywAAAOlJeQRSa+uxEQuBQECnn366Jk+erP/6r/+K+dhevXqpuLhYxx13XGatBGA4Mqj+9os1edl6vf3Rfh0+8kVSN1Fdm/ARKOHJpEduqHB0cW0AAAAAQHalnEA67bTTQv8/b948jR49OuI2ANaIN+Us6JkZ4zRzZZNqN+wK3fb7v+5IKgEUPupoeUOrHrmhwnDkUjZVPbBGW/ce1JCi3qq//eKEj19c15LWlDenr0gHAAAAAHZKOYEUbt68eWa1Azlo8rL12rizQyMG+vTMjHGWvEa6yQQnChY9NkpyxCuMnYgVdY9G3P2CDh4+qt698rXxP65Iax/hU/O27DmoqgfWGCaRFte1aMnqLZKkDTv8kpTUeY+VQCOJBAAAAABfMGUVtnfeeUe33nqrvva1r2n48OH60Y9+FLrv1Vdf1c9+9jP9/e9/N+Ol4BGTl61X47Z9+vTzo2rctk+Tl603/TWCyYQNO/xasnqLFte1mP4a2VZVXqK5NeWGhbGNtqNNXrZeZ9/1gla/tzul5yUSTB5J0sHDRzXi7hfS2s/WvQcNt6Ot3bTbcDuedBNvuWzmyiZV3POSZq5ssrspAAAAALIg4wTS/fffrzFjxuhnP/uZXnvtNW3ZskXt7e2h+w8dOqTbbrtNTz/9dMr7fvPNN3XllVeqX79+6t27t84//3w98cQTKe3jo48+0s0336zBgwerV69eGjhwoKZOnart27db+rowtnFnh+G2GdJNJrjZ0iljVDNygIpP6qWakQO0dMqYuI8NT+Jt2XNQQ0/pbdqS7sHkUfh2fXOb4XPqm9u0qLY54nFDinpHPCZ6O1rlsP6G2/GkmniLJ9YxeFFwqmT7J4dVu2EXSSQAAAAgB2Q0he1///d/9e///u8644wz9MADD+hrX/uaTjnllIjHVFVVqbi4WH/4wx908803J73vNWvWqLq6Wr169dK1114rn8+nZ599VlOmTNHWrVt15513JtzH+++/r3Hjxmn37t2aOHGirrnmGm3evFmPPfaY/vSnP2n9+vU644wzTH9dJDZioE+N2/ZFbJutclj/0DSm4HYuMEoahYtO2u3Y/6lpdY9698rvlkSatqIxbnIqegrZrAlD1dl1RP9+xdn6yQvvJl0DKThdLdVpi8lMD0wk+hgqTuunrXsPamxZUdLnxC0YsQUAAADknrxAIBBI/LDYLr74YjU1Nentt9/WkCFDJEn5+fm68cYb9etf/zr0uIsuukg7duzQ+++/n9R+u7q6dNZZZ+mjjz7Sa6+9ptGjR0uSDhw4oAsuuEAtLS1qbm7WmWeeabiff/qnf1Jtba0eeughff/73w/d/vTTT+vqq69WdXW1XnzxRdNfNxzLBccXXgOppPD40I93M39se6kGktmCI5CCKk7rZ2otqvBpbEHTx5fFTFItqm2OKBAeLhv1iMwooG10DIlGg7lNdLF2rx0fvI1+GQAAID0ZTWH761//qgsuuCCUPIpn0KBB2rVrl+Fjwr3yyit6//33dd1114WSOJLUp08f3XXXXerq6tJvfvMbw3189tlnqqurU0lJiWbPnh1x37e//W2NGjVKdXV1+uCDD0x9XSTvmRnj9O7CK1RSeLxl02HmVA/Xc7PHOzZ5ZOeUp2dmjFPFaf10wnH5GSWP4h3Dxv+4Qo/cUBFxW7ypYUZTxqwe3bK4rkXTVjRqeUOrpq1oTPtc2HkM2ZbKVEkAAAAA3pDRFLauri6deOKJCR+3Z88e9erVK+n9rlmzRpJ02WWXdbsveNvatWsN97F37151dXXptNNOU15eXrf7y8rK9NZbb2n16tU6/fTTTXtdpM7J02GsXNrdCSt/ZTriKNExJDs1LPxxBT17hFZSkzIv6J2o/eGvJaW/Al34Mfz1w/0Ro7usPAa7kDQCAAAAcktGCaQzzjhDf/nLX3TkyBH16NEj5mMOHjyot956S+XlyddW2bx5syTFnCrWr18/FRcXhx4TT79+/dSjRw9t27ZNgUCgWxKptfXYVJNNmzaZ+rqdnZ3q7OwMbfv9foNHQzr24zp8OkwmP7ZnrmwybSqc1QmeWIkzty0dn8wxVJWXJHVc4Y8bVdrXssRduFjJykw+f+HHYOZnMR12vz4AAAAAb8loCtvkyZP10Ucf6a677or7mLvuukv79u3TNddck/R+OzqOFff1+WIXVi4sLAw9Jp4TTzxRlZWVamtr089//vOI+5599lm99dZbkqT9+/eb+rr33XeffD5f6F9paanh42HedBizV4YyY2SU0RQ1s1b+Mks6y7JbdQxV5SWaW1NuSfJocV2LJj3coMV1Ld3aO2vCUNNec+mUMWr88UTbkkeskma/ycvW6+y7XtDkZevtbgoAAACQsYxGIN1+++168skn9Z//+Z9at26drrrqKknSBx98oCVLlugPf/iDXnnlFZ177rm65ZZbTGlwKh588EFdeOGFmjVrlp577jmdc8452rJli/73f/9X55xzjt5+++24I6fSdccdd+gHP/hBaNvv95NESoIZP7LNngo3tqwooihyqsmRTKd3ZbMAeHhR5NoNu9R0b71eu7Mq4fPMWL0smxbXtYSmrG3Y4desCUNd1f5kOXlaaK4IL1LfuG2fJi9bb2qRegAAACDbMkog9e7dW6tXr9aNN96oF198Ua+++qok6c9//rMaGhoUCAR06aWXauXKlSooKEh6v8ERQPFG+wRXUEnk3HPP1Ztvvql58+Zp9erVWr16tYYOHapf/vKX2r9/v374wx/qlFNOMfV1CwoKUjpWmMfMqXBS5smRTKZ3RSc6JFmaRIpu6y5/p2aubEoqsZfsFDUnWLtpd7ftOdXDXdP+ZJn9t4DUbdzZEXfbytpqAAAAgFUySiBJUv/+/fWnP/1Jb731ll566SVt27ZNR44c0Ze+9CVVVVVp7NixKe8zWINo8+bNOu+88yLu27dvn9rb2zVuXHJXcs866yw9+eST3W6/8cYbJUkVFV+sEmXm6yL7lk4ZI5lc9yWT5EgmI5jiJTqsEp1wkJwzasXMkViVw/qHEnLBbS+y4m8BqRkx0BdRSH3EwGMXH5xQPB8AAABIR8YJpKBRo0Zp1KhROnDggCSpT58+ae+rsrJS9913n1atWqVrr7024r5Vq1aFHpOuAwcO6LnnntPJJ5+siRMnZu11YT0n/VDOZARTthMdS6eMUdO99drl/6IAvBNGrZg9Eiv43GxNDbSTk/4WctEzM8Zp8rL12rizQyMG+kLT17xQPB8AAAC5KS8QCAQy3cnzzz+vpUuXav369frkk08kHZve9rWvfU3/5//8H02aNCml/XV1dWn48OHasWOHXn/9dY0aNUrSscTPBRdcoJaWFm3cuFHDhg2TJLW3t6u9vV3FxcUqLi4O7efTTz/Vcccdp549v8iTdXZ26vrrr9fTTz+thx56SN///vfTft1kBKe9dXR0qLCwMKX3AbktmzWQgpy2ctekhxsiEmkjBxXqudnjTds/U4mQbeEjkCQxAskG9MsAAADpySiBFAgENG3aND366KMK7qZv374KBAKhOkJ5eXm6/vrr9Zvf/EZ5eXlJ73v16tWqrq5WQUGBvvOd76iwsFDPPvusWltbdc8992ju3Lmhx86fP18LFizQvHnzNH/+/NDt69at07e+9S1NnDhRpaWl8vv9qq2t1Ycffqjp06frl7/8Zbc2pfK6yXB7oMoPbNgpfASSdGyVNLOSafyQh134XrWX2/tlAAAAu2Q0he2hhx7Sb37zGw0cOFB33XWXrrvuutDUtQMHDuiJJ57QwoUL9fjjj2vUqFG69dZbk973hAkTtG7dOs2bN09PPfWUDh8+rBEjRmjhwoWaMmVKUvsYPHiwLr74YjU0NKitrU0nnniixowZowcffFD//M//bNnregW1OmA3K6ecxZtKxI97WM1NhecBAACAoIxGIJWXl+vDDz/Uhg0bVFZWFvMxra2tGjlypAYPHqzm5ua0G+pWbr7Suai2OaII9PTxZZpbU25ji6zh5IRBJm2LVX/FTla8z5lMuYs1AkkSo5IAj3NzvwwAAGCn/Eye3NraqksvvTRu8kiSysrKdOmll6q1tTXuY+BM0UWUnVBU2WzBJMLyhlZNW9Go+uY2u5sUEq9t9c1tWlTbbNjWycvWq3HbPn36+VE1btunycvWm962RG2IfrzZ7/PMlU2q3bBL7Z8cVu2GXZq5siml5weLnE8fXxZKFD3VuD3iMU5ZiQ4AAAAA7JbRFLZTTjlFvXr1Svi4Xr16RRS3hjtksoqYWzh5RaRYbZOU1LTCjTs7DLczkc7URive53jvTyrCpxLVN7dpVVRiy6lJU6eNLgMAAADgfRmNQPrmN7+pV155Rfv27Yv7mL///e965ZVX9I1vfCOTl4JNqspLNLem3DFJFbM5eZRVrLYlmzQZMdBnuJ2JdBI3VrzPZu8z+jgui6pTk+qoK6tYPboMAAAAAGLJKIF0zz336PTTT9cll1yiV155pdv9r7zyiiZOnKjTTz9d9957byYvBVgi1jQmp4jVtmSTJs/MGKeK0/rphOPyVXFaP1NHqaSTuLHifV46ZYxqRg5Q8Um9VDNyQMo1kKJFH8fVFaWh/3fSVEezR5c5JTEGAAAAwNlSKqJ9ySWXdLuts7NTr732mvLy8nTyySfrtNNOkyR9+OGH2rv32BX9r371qzr++OP18ssvm9Rs96BYJ8zmhKLfTmiDFaKPa3Fdi9Zu2q0e+fl6a/v+0OPsLCgfHIEUlEmCMFYhcS+dTyfLpAA8MkO/DAAAkJ6UEkj5+ekPWMrLy9ORI0fSfr5bEagC7rS4rkVLVm+JeZ/diRazaiDlykqLThMsAB9kxgg6JI9+GQAAID0pFdFmJTUATmTFiKi1m3ZHbJ928om6bESJbaOuwo/RrCmJY8uKIhJITqoB5mVmFIAHAAAAsi2lBFJwehqQiFenOMF50lkVLhmVw/prww5/aHvSuQM1p3p4xvtNh1XH6KaVFr005WtsWVHECCQSdwAAAHCDlBJIQDKs+rGL1OVCIi/WaA4zjjWYLFq7abcqh/XvljxK9b3N5FxYdYzSsSSS0z8b4VO+ajfsklY2uTqJtHTKGMlDCTEAAADkBhJIMF2mP3adkPSwsg3ZOr5MEnnB4tGxEidO89cP9xtuZ2JO9fCYx5/qe5tpUjXTqWZO+JvKhBenfJE0AgAAgNuQQILpMvmx64TRS2a3IfzHu6SsHV+6ibzw4tHBKVxOTiJt3XvQcNsKqb63mSZVU51qZtdnzipM+QIAAADsl/6yakAcwR+708eXpfxj1QkjDcxsQzAZtbyhVdNWNOqpxu2m7TuR6B/Zyf7oji4eHb2dqsV1LZr0cIMW17VktJ940j3ObL6mGW2sKi/R3JrypJJHdn3mrLJ0yhjVjByg4pN6sWIZAAAAYBNGIMES6dZVccKqUGa2IdGPdSuPL90CydHFoyuH9U+7DemOZkqlYLId9WTSeW8v+8djrq4otXQEUPRnbvPuTyK23Tp6h6QRAAAAYK+8QCAQsLsRXub3++Xz+dTR0aHCwkK7m+MKTqjXYlYbwqfDSdIjN1RIkuXHl2n7zaqBNOnhhohk1MhBhXpu9njD54QXTJbk+hEnsT4D2ap9Fc3t7yVgBvplAACA9DACCY7jhFWhzGpDvJEqTi2eHRSveHSq0hnN5IRpjGaycgW1WMI/c6s2tmnb3w+F7vvw79bXhwIAAADgTSSQAItlmoyavGy9Nu7s0IiBPj0zY1zCx6easLByxbXg/lLZv9cKJtsxLTP4mSvo2SM0hVDKbDoikG2pTGUFAACA9ZjCZjGGyruPk360TF62Xo3b9oW2K07rlzCJlMqUqfAaRZI0a8JQR6y45qRzYIbglMKd+z/Th38/aEmyLh4rE4SAVaycykq/DAAAkB5GIAFhwn+01G7YJa1ssjWBsXFnh+F2LKkUeI614poTkgxeSBqFqyov0Vvb94c+W6kUFM+UWdMRgWzy2lRWAAAAL8i3uwGAk1j9o8VoSfv65jYtqm1WfXNb6LYRA30Rj4nejifZJd+jpzQxxck6sZJ1cL9Yf7fIXPRUT7dPZQUAAPACRiABYaysv2O0pH28wtfPzBiXcg2kWOKtypZOjSKkJ52C4nA2MwrWI7alU8ZIHpvKCgAA4HYkkIAw6fxoiZeciWY0Xcyo8HW6SaPw9hn9yGWKU3ZkM1mX7GcSmcn2Cnu5hqQRAACAs5BAAqKk8qMllREIRiNQrFypix+5zpGNZB2jYrLHjhX2AAAAALuQQAIyEJ2cmfv7DZr9P00xp5sZjUBJpfB1qpz4I3f0gjrt+7RL/U7oqb/Oq7a7OZ5CwjB7rPy7BQAAAJwmLxAIBOxuhJfl6nLBubJ0ePhoj2gVp/XLePqZWZw0pSmYPAoiiWSu6M8kI5CASLnaLwMAAGSKEUhIKNVkkFGxaK8JH4Hw2PqtOnzki3zsxp0dNrYsUlV5iWOSCOHJo1jbyAyjYgAAAABYgQQSDKWTDDIqFu1WRiN4gsmZv364X43b9oVuz8vL0+K6Ftcfu9n6ndCz2wgkI7kymi3IjNFiTkoYAgAAAPCGfLsbAGeLlQxKJHp58vDtxXUtmvRwgxbXtZjTwCwITgla3tCqaSsaVd/cFvNxz8wYp4rT+qnnP/6qDh0+oiWrt1h6rJOXrdfZd72gycvWW/YaZpvy1SHqkXfs/xNNXwsmMDfs8Fv+XjpBsp81AAAAAMg2EkgwZJQMimdO9XDNmjBUIwcVataEoaFRI25NBsQqShzPMzPG6ewBkTU1kkm6pWPysvVq3LZPn35+VI3b9rkiiRT8DARn+k356hDDx6eTwHSzVD5rAAAAAJBNJJBgKF4yKJnnPTd7fMTj3ZoMiF61LNEqZukk3dIRXWPJSTWX4kn1M5Dpe1nf3KZFtc2uGcmT6mcNAAAAALKFGkhIaE71cFNqz1QO6x+qoxTcdoNUixIH3yur6/aMGOiLqLk0YqDPkteJJ51aPal+BjJ5L8NXI1ve0BpajcyumkrJvF8UwAYAAADgVHmBQCCQ+GFIF8sFR8q1gshWm7xsvTbu7NCIgT49M2Nc1l43k6Xis/UZWFTbrOUNraHt6ePLVNCzR6govKSYo+qsaF8m7xcAc9EvAwAApIcRSMgqs0Yz4ZhsJo3CxarVk2xCZFRpX3V2HdGo0r4WtOwLY8uKIhJIY8uK9NDLmyIeE71CYDqrDiYjk/cLAAAAAJyAGkgu5sYVzeA+seoIpVurJ5urjAWng00fXxYa8ZOoppJVdbqobQQAAADA7RiB5FJWjZQAwsWrI5RurZ5sj8QJtjUoUU0lq+p0UdsIAAAAgNuRQHKpWCMlSCDBbEYJn+jkTDJiTSvLNqNplFYWQI9+v9IpQg4AAAAAdiGB5FJuXdHMjXK58PdfP9wfsZ1pwscNI3GyUacr3sguAAAAAHAqEkgula2l4nNdLk8VnLmySY3b9oW2K07rZ0qSI52RS3ZLNFoomdFE4YnIzq4jEfdRVBsAAACA05FAcjFWNLNeLk8VjJ6+tnXvwYz36cZpW4lGCyUzmig6EVkzckDE/RTVBgAAAOB0rMIGGEi0alemnLySntkrh2VzBTYzxaoDJX2xOt1TjdsNHy9JT775YcT2/791b7cV4gAAAADAyRiBBNs5eVSKlVMFnT49bumUMdLKptC5WTplTEb7y/YKbGaJVfg7fNRRrMd3k5fXbduNU/lyiZO/lwAAAAA7kECCrdxQTNiqqYJumB6XadIonBNWYEtHrMLfi2qbIx5zWXmJTis6MW6y4ZqK0lCyMLgN53LD9xIAAACQbSSQYCu3jkoxQ66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",
      "text/plain": [
       "<Figure size 1380x920 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_pair(states_43_dict);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ 0.096,  0.   , -0.005],\n",
       "       [ 0.   ,  0.002, -0.   ],\n",
       "       [-0.005, -0.   ,  0.037]], dtype=float32)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "jnp.round(tfp.stats.covariance(pandas.DataFrame(states_43_dict).to_numpy()), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see very little covariance among the parameters in this case. This lack of covariance among the parameters results from something called **Centering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4.3.2 Plotting posterior inference against the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.46\n",
    "\n",
    "This snippet appears in the section titled **Plotting posterior inference against the data**. \n",
    "\n",
    "Author examplins that plotting is extremely useful in see when key observations or patterns in the plotted data are not close to the model's pediction.\n",
    "\n",
    "In this snippet, we start with the simple version of the task (i.e. plotting) by superimposing the posterior mean values over the height & weight data. Later more and more information to prediction plots will be added until the entire posterior distribution is covered.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
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       "\n",
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       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
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       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\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",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\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",
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       "\n",
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       "\n",
       ".xr-var-dtype {\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",
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       "\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",
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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; Size: 10kB\n",
       "Dimensions:  (chain: 1, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 8B 0\n",
       "  * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    alpha    (chain, draw) float32 2kB 154.8 154.4 154.2 ... 154.7 154.7 154.4\n",
       "    beta     (chain, draw) float32 2kB 0.8884 0.8795 0.8769 ... 0.9305 0.8752\n",
       "    sigma    (chain, draw) float32 2kB 5.489 5.434 5.248 ... 5.396 5.396 5.681\n",
       "Attributes:\n",
       "    created_at:     2024-06-14T17:55:28.572625+00:00\n",
       "    arviz_version:  0.18.0</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-8f91dc98-86c9-4791-aa20-5117773e581e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f91dc98-86c9-4791-aa20-5117773e581e' 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>: 1</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-9b2932ff-6363-4a58-918e-852054638667' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b2932ff-6363-4a58-918e-852054638667' 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</div><input id='attrs-f929bf84-bc5a-4f82-a78a-4925aef58e8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f929bf84-bc5a-4f82-a78a-4925aef58e8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87fc96fb-16a9-49a7-baee-bb8e01ec4703' class='xr-var-data-in' type='checkbox'><label for='data-87fc96fb-16a9-49a7-baee-bb8e01ec4703' 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])</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-c6e34a08-bac4-4881-aaa7-d31ada2d952a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6e34a08-bac4-4881-aaa7-d31ada2d952a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1cd6630-6d1c-485a-8585-8d5972452099' class='xr-var-data-in' type='checkbox'><label for='data-e1cd6630-6d1c-485a-8585-8d5972452099' 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-57998925-3286-4c47-a44e-87751868733c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57998925-3286-4c47-a44e-87751868733c' 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)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>154.8 154.4 154.2 ... 154.7 154.4</div><input id='attrs-05655b1c-59f9-4f8a-869f-7f2a0e9abec7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05655b1c-59f9-4f8a-869f-7f2a0e9abec7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f5eee66-a56b-4eaf-9d5a-ab5932e228f4' class='xr-var-data-in' type='checkbox'><label for='data-5f5eee66-a56b-4eaf-9d5a-ab5932e228f4' 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([[154.78323, 154.44318, 154.24622, 154.24622, 154.24622, 154.38783,\n",
       "        154.38783, 154.28853, 154.08472, 154.08472, 154.12096, 154.06313,\n",
       "        154.02658, 154.02184, 154.06036, 154.04402, 154.05283, 154.05283,\n",
       "        154.31284, 154.31284, 154.31284, 154.41263, 154.41263, 154.43864,\n",
       "        154.43864, 154.43864, 154.71935, 154.68544, 154.51869, 154.56718,\n",
       "        154.5972 , 154.7252 , 154.7252 , 154.49869, 154.49869, 154.49869,\n",
       "        154.72675, 154.72675, 154.99698, 154.80891, 154.99481, 154.97101,\n",
       "        154.97101, 154.8759 , 154.79434, 154.85812, 154.90588, 154.70706,\n",
       "        154.60353, 154.60353, 154.60353, 154.60353, 154.60353, 154.60353,\n",
       "        154.60353, 154.74716, 154.58894, 154.49359, 154.53473, 154.29482,\n",
       "        154.00807, 154.00807, 154.00586, 154.19676, 154.19676, 154.17892,\n",
       "        154.17892, 154.17892, 154.01616, 154.11035, 154.11035, 154.11035,\n",
       "        154.25856, 154.18916, 154.18916, 154.18916, 154.3277 , 154.286  ,\n",
       "        154.38565, 154.21289, 154.2267 , 154.2267 , 154.2267 , 154.10587,\n",
       "        154.40315, 154.40315, 154.55132, 154.41917, 154.41917, 154.41917,\n",
       "        154.41917, 154.79167, 154.84865, 154.84865, 154.44238, 154.44238,\n",
       "        154.41202, 154.41202, 154.49449, 154.49449, 154.49449, 154.60931,\n",
       "        154.60931, 154.3846 , 154.3846 , 154.54771, 154.75172, 154.9931 ,\n",
       "        154.9931 , 154.78857, 154.78857, 154.83142, 154.83142, 154.83142,\n",
       "        154.83525, 154.83913, 154.78746, 154.96115, 154.96115, 154.96115,\n",
       "...\n",
       "        154.57341, 154.57341, 154.57341, 154.8568 , 154.8568 , 154.8568 ,\n",
       "        154.81087, 154.81087, 154.81087, 154.81087, 154.81087, 154.79704,\n",
       "        154.53413, 154.53413, 154.62283, 154.83896, 154.47206, 154.47206,\n",
       "        154.47206, 154.56906, 154.41342, 154.70987, 154.70987, 154.8266 ,\n",
       "        154.7991 , 154.68341, 154.68341, 154.68341, 154.90366, 154.94017,\n",
       "        154.84424, 154.84424, 154.84424, 154.50679, 154.66263, 154.54619,\n",
       "        154.72864, 154.86044, 154.95607, 154.95607, 154.95607, 155.38454,\n",
       "        155.66124, 155.66124, 155.66124, 155.37274, 155.37274, 155.37274,\n",
       "        155.54393, 155.3394 , 155.27771, 155.36732, 155.3156 , 155.37794,\n",
       "        155.37794, 155.34499, 155.34499, 155.34499, 155.34499, 154.96898,\n",
       "        154.91997, 154.91997, 154.91997, 154.91997, 154.95195, 154.99637,\n",
       "        154.98723, 154.98723, 154.98723, 154.84464, 154.75392, 154.8655 ,\n",
       "        154.8655 , 154.80222, 154.64499, 154.64499, 154.64499, 154.64499,\n",
       "        154.64499, 154.78555, 154.60474, 154.60474, 154.60474, 154.60474,\n",
       "        154.35895, 154.35895, 154.35895, 154.35895, 154.09505, 154.09505,\n",
       "        154.09505, 154.09505, 153.9056 , 153.9056 , 153.90057, 153.90598,\n",
       "        153.90598, 154.06148, 154.06148, 154.10416, 154.04865, 154.23209,\n",
       "        154.14023, 154.25334, 154.59962, 154.59962, 154.7128 , 154.59624,\n",
       "        154.63576, 154.45213, 154.40797, 154.48483, 154.58414, 154.6828 ,\n",
       "        154.6828 , 154.36967]], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.8884 0.8795 ... 0.9305 0.8752</div><input id='attrs-79150ff2-dc26-4b8c-adb6-707eeba71659' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79150ff2-dc26-4b8c-adb6-707eeba71659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e3e7514-291d-4be1-b9a0-e0bf9a3ffe59' class='xr-var-data-in' type='checkbox'><label for='data-4e3e7514-291d-4be1-b9a0-e0bf9a3ffe59' 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([[0.88835245, 0.87951946, 0.87691385, 0.87691385, 0.87691385,\n",
       "        1.0320106 , 1.0320106 , 0.97838265, 0.8682179 , 0.8682179 ,\n",
       "        0.8822836 , 0.9770582 , 0.8389799 , 0.97880274, 0.8844427 ,\n",
       "        0.9592255 , 0.95025176, 0.95025176, 0.93549   , 0.93549   ,\n",
       "        0.93549   , 0.930065  , 0.930065  , 0.90648097, 0.90648097,\n",
       "        0.90648097, 0.9480491 , 0.9925823 , 0.8631931 , 0.82522833,\n",
       "        0.8636692 , 0.86515707, 0.86515707, 0.8974556 , 0.8974556 ,\n",
       "        0.8974556 , 0.8979371 , 0.8979371 , 0.8874408 , 0.85915256,\n",
       "        0.91227686, 0.9179241 , 0.9179241 , 0.9553023 , 0.99101657,\n",
       "        0.9845071 , 1.013456  , 0.8667169 , 0.87136185, 0.87136185,\n",
       "        0.87136185, 0.87136185, 0.87136185, 0.87136185, 0.87136185,\n",
       "        0.901704  , 0.8911858 , 0.93012756, 0.95950913, 0.87318754,\n",
       "        0.8696916 , 0.8696916 , 0.8929399 , 0.90514296, 0.90514296,\n",
       "        0.89885056, 0.89885056, 0.89885056, 0.9592142 , 0.90162134,\n",
       "        0.90162134, 0.90162134, 0.89985716, 0.91721046, 0.91721046,\n",
       "        0.91721046, 0.88203114, 0.9338581 , 0.89080864, 0.9290657 ,\n",
       "        0.91815454, 0.91815454, 0.91815454, 0.85683775, 0.9363118 ,\n",
       "        0.9363118 , 0.9006432 , 0.8680003 , 0.8680003 , 0.8680003 ,\n",
       "        0.8680003 , 0.8556569 , 0.87700915, 0.87700915, 0.7902995 ,\n",
       "        0.7902995 , 0.82994485, 0.82994485, 0.89804846, 0.89804846,\n",
       "...\n",
       "        0.9907718 , 0.9907718 , 0.9803401 , 0.8230663 , 0.88310033,\n",
       "        0.88310033, 0.88310033, 0.947829  , 0.9436132 , 0.9442628 ,\n",
       "        0.9442628 , 0.9442628 , 0.909809  , 0.9354473 , 0.8514858 ,\n",
       "        0.9377754 , 0.86133474, 0.92167884, 0.92167884, 0.92167884,\n",
       "        0.9308434 , 0.8774499 , 0.8774499 , 0.8774499 , 0.8870641 ,\n",
       "        0.8870641 , 0.8870641 , 0.9160451 , 0.92910326, 0.85718054,\n",
       "        0.89631784, 0.87519264, 0.9635694 , 0.9635694 , 0.8979237 ,\n",
       "        0.8979237 , 0.8979237 , 0.8979237 , 0.8823349 , 0.8895985 ,\n",
       "        0.8895985 , 0.8895985 , 0.8895985 , 0.87350965, 0.82785034,\n",
       "        0.9355435 , 0.9355435 , 0.9355435 , 0.88356626, 0.89052427,\n",
       "        0.9369172 , 0.9369172 , 0.96415365, 0.905657  , 0.905657  ,\n",
       "        0.905657  , 0.905657  , 0.905657  , 0.84515035, 0.9332872 ,\n",
       "        0.9332872 , 0.9332872 , 0.9332872 , 0.95427924, 0.95427924,\n",
       "        0.95427924, 0.95427924, 0.9045238 , 0.9045238 , 0.9045238 ,\n",
       "        0.9045238 , 0.8632744 , 0.8632744 , 0.92457044, 0.92173046,\n",
       "        0.92173046, 0.8706452 , 0.8706452 , 0.84745556, 0.916372  ,\n",
       "        0.8909022 , 0.95848775, 0.88290304, 0.86791146, 0.86791146,\n",
       "        0.9429655 , 0.85833806, 0.90222913, 0.8834628 , 0.8900062 ,\n",
       "        0.91061795, 0.90307504, 0.93046284, 0.93046284, 0.87518775]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.489 5.434 5.248 ... 5.396 5.681</div><input id='attrs-4b2a6c3e-09f9-488b-8301-d3ef3a3be67f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b2a6c3e-09f9-488b-8301-d3ef3a3be67f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b031bb-c48d-450d-b945-47048cafeb72' class='xr-var-data-in' type='checkbox'><label for='data-d8b031bb-c48d-450d-b945-47048cafeb72' 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([[5.4886556, 5.4336057, 5.2482977, 5.2482977, 5.2482977, 5.166994 ,\n",
       "        5.166994 , 5.1790915, 4.964711 , 4.964711 , 5.2282495, 5.230567 ,\n",
       "        4.977498 , 5.0523987, 5.1195045, 5.0648227, 5.0174084, 5.0174084,\n",
       "        4.982948 , 4.982948 , 4.982948 , 5.045315 , 5.045315 , 5.173838 ,\n",
       "        5.173838 , 5.173838 , 5.3097982, 5.2409296, 5.3277497, 5.418646 ,\n",
       "        5.479681 , 5.1864066, 5.1864066, 5.148522 , 5.148522 , 5.148522 ,\n",
       "        5.1463866, 5.1463866, 4.942483 , 5.0594726, 4.862282 , 5.116731 ,\n",
       "        5.116731 , 5.07964  , 5.1763783, 5.0735326, 5.2163467, 5.352212 ,\n",
       "        5.3194127, 5.3194127, 5.3194127, 5.3194127, 5.3194127, 5.3194127,\n",
       "        5.3194127, 5.4983044, 5.6468186, 5.4984407, 5.30166  , 5.171583 ,\n",
       "        5.350964 , 5.350964 , 5.2852197, 5.361173 , 5.361173 , 5.4968767,\n",
       "        5.4968767, 5.4968767, 5.3789606, 5.427329 , 5.427329 , 5.427329 ,\n",
       "        5.2258415, 5.266969 , 5.266969 , 5.266969 , 5.2514668, 5.002309 ,\n",
       "        5.1216106, 5.100286 , 5.114059 , 5.114059 , 5.114059 , 5.2647734,\n",
       "        4.9736557, 4.9736557, 4.821779 , 4.9380064, 4.9380064, 4.9380064,\n",
       "        4.9380064, 4.7152953, 4.849909 , 4.849909 , 4.9891644, 4.9891644,\n",
       "        5.3190074, 5.3190074, 5.179937 , 5.179937 , 5.179937 , 5.164987 ,\n",
       "        5.164987 , 5.0735397, 5.0735397, 5.0929117, 5.0267158, 5.1361256,\n",
       "        5.1361256, 5.170941 , 5.170941 , 4.984734 , 4.984734 , 4.984734 ,\n",
       "        4.911158 , 5.114317 , 4.986334 , 5.019104 , 5.019104 , 5.019104 ,\n",
       "...\n",
       "        4.9161673, 4.9161673, 4.9161673, 5.02189  , 5.02189  , 5.02189  ,\n",
       "        4.887861 , 4.887861 , 4.887861 , 4.887861 , 4.887861 , 5.055221 ,\n",
       "        5.237322 , 5.237322 , 5.243101 , 5.2919574, 5.3701353, 5.3701353,\n",
       "        5.3701353, 5.411032 , 5.442354 , 5.076676 , 5.076676 , 5.2230716,\n",
       "        5.34413  , 5.4759088, 5.4759088, 5.4759088, 5.3241544, 5.181965 ,\n",
       "        5.3231025, 5.3231025, 5.3231025, 5.359424 , 5.386728 , 5.4915237,\n",
       "        5.5031037, 5.4464707, 5.3727117, 5.3727117, 5.3727117, 5.389615 ,\n",
       "        5.349943 , 5.349943 , 5.349943 , 5.448411 , 5.448411 , 5.448411 ,\n",
       "        5.4480205, 5.1379714, 5.215138 , 5.2128944, 4.9986625, 5.0772066,\n",
       "        5.0772066, 5.1282773, 5.1282773, 5.1282773, 5.1282773, 4.9618263,\n",
       "        4.9744015, 4.9744015, 4.9744015, 4.9744015, 5.08608  , 5.179642 ,\n",
       "        4.9218373, 4.9218373, 4.9218373, 4.9627247, 4.9939885, 4.9341245,\n",
       "        4.9341245, 4.8022375, 4.7919574, 4.7919574, 4.7919574, 4.7919574,\n",
       "        4.7919574, 4.946193 , 4.8965635, 4.8965635, 4.8965635, 4.8965635,\n",
       "        4.8679585, 4.8679585, 4.8679585, 4.8679585, 5.030734 , 5.030734 ,\n",
       "        5.030734 , 5.030734 , 5.172426 , 5.172426 , 5.09061  , 5.1974626,\n",
       "        5.1974626, 5.2682095, 5.2682095, 5.401724 , 5.3572593, 5.204229 ,\n",
       "        5.169112 , 5.131115 , 5.1744885, 5.1744885, 5.137738 , 5.453305 ,\n",
       "        5.419089 , 5.390717 , 5.296079 , 5.31772  , 5.5153265, 5.3961897,\n",
       "        5.3961897, 5.6811056]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae06e8fe-9393-48f9-a497-8f3e5537c6ab' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae06e8fe-9393-48f9-a497-8f3e5537c6ab' 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-c50ffdd0-0bca-4cb1-85ae-8e35c7720065' class='xr-index-data-in' type='checkbox'/><label for='index-c50ffdd0-0bca-4cb1-85ae-8e35c7720065' 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], 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-7b5d8752-be30-44da-8b06-f6f6403bcd80' class='xr-index-data-in' type='checkbox'/><label for='index-7b5d8752-be30-44da-8b06-f6f6403bcd80' 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-05b44ea1-8497-4bf3-a0ea-4e1d13c2665e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05b44ea1-8497-4bf3-a0ea-4e1d13c2665e' class='xr-section-summary' >Attributes: <span>(2)</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>2024-06-14T17:55:28.572625+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.18.0</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",
       ":root {\n",
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       "  --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",
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       "\n",
       "html[theme=dark],\n",
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       "}\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"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_43 = az.from_dict(states_43_dict)\n",
    "idata_43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "y = xr.DataArray(d2.height.values)\n",
    "x = xr.DataArray(d2.weight.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "idata_43.posterior[\"y_model\"] = idata_43.posterior[\"alpha\"] + \\\n",
    "    idata_43.posterior[\"beta\"]*(x - x_bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_lm(idata=idata_43, y=y, x=x, y_model=\"y_model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Curves from lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5.1 Polynomial regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.64\n",
    "\n",
    "Here we start to look at polynomial regression and splines.\n",
    "\n",
    "Polynomial Regression is the first one. It helps to build curved associations. This time we will use full data instead of just adults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>544.000000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>544.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>138.263596</td>\n",
       "      <td>35.610618</td>\n",
       "      <td>29.344393</td>\n",
       "      <td>0.472426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.602448</td>\n",
       "      <td>14.719178</td>\n",
       "      <td>20.746888</td>\n",
       "      <td>0.499699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>53.975000</td>\n",
       "      <td>4.252425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.095000</td>\n",
       "      <td>22.007717</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.590000</td>\n",
       "      <td>40.057844</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.480000</td>\n",
       "      <td>47.209005</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.070000</td>\n",
       "      <td>62.992589</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           height      weight         age        male\n",
       "count  544.000000  544.000000  544.000000  544.000000\n",
       "mean   138.263596   35.610618   29.344393    0.472426\n",
       "std     27.602448   14.719178   20.746888    0.499699\n",
       "min     53.975000    4.252425    0.000000    0.000000\n",
       "25%    125.095000   22.007717   12.000000    0.000000\n",
       "50%    148.590000   40.057844   27.000000    0.000000\n",
       "75%    157.480000   47.209005   43.000000    1.000000\n",
       "max    179.070000   62.992589   88.000000    1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
    "d.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 4.65\n",
    "\n",
    "Most common polynomial regression is a parabolic (degree 2) model of the mean.\n",
    "\n",
    "$\\mu_i \\sim \\alpha + \\beta_1x_i + \\beta_2x_i^2$\n",
    "\n",
    "\n",
    "**Standardization** is important in general but it becomes even more important for polynomials. Think what happens when you square or cube big numbers. They will create large variations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[\"weight_std\"] = (d.weight - d.weight.mean()) / d.weight.std()\n",
    "d[\"weight_std2\"] = d.weight_std ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def m45():\n",
    "    alpha = yield tfd.Normal(loc=178.0, scale=20.0, name=\"alpha\")\n",
    "    beta1 = yield tfd.LogNormal(loc=0.0, scale=1.0, name=\"beta1\")\n",
    "    beta2 = yield tfd.Normal(loc=0.0, scale=1.0, name=\"beta2\")\n",
    "    sigma = yield tfd.Uniform(low=0.0, high=50.0, name=\"sigma\")\n",
    "\n",
    "    mu = alpha + beta1*d.weight_std.values + beta2*d.weight_std2.values\n",
    "\n",
    "    height = yield tfd.Normal(loc=mu, scale=sigma, name=\"height\")\n",
    "\n",
    "jd_45 = tfd.JointDistributionCoroutineAutoBatched(m45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = \"alpha\", \"beta1\", \"beta2\", \"sigma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('alpha', 'beta1', 'beta2', 'sigma')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state = jd_45.sample(seed=a_key())\n",
    "names = init_state._fields\n",
    "init_state = init_state[:-1]\n",
    "\n",
    "StateTuple = namedtuple(\"StateTuple\", names)\n",
    "state_name = names[:-1]\n",
    "state_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_log_prob_45(alpha, beta1, beta2, sigma):\n",
    "    state = {\"alpha\": alpha, \"beta1\": beta1, \"beta2\": beta2, \"sigma\": sigma, \"height\": d.height.values}\n",
    "    return jd_45.log_prob(StateTuple(**state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_burnin_steps = 400\n",
    "num_results = 500\n",
    "step_size = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_45 = tfp.mcmc.HamiltonianMonteCarlo(\n",
    "    target_log_prob_fn=target_log_prob_45,\n",
    "    step_size=step_size,\n",
    "    num_leapfrog_steps=2)\n",
    "\n",
    "kernel_45 = tfp.mcmc.SimpleStepSizeAdaptation(\n",
    "    inner_kernel=kernel_45, num_adaptation_steps=int(num_burnin_steps * 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_45 = tfp.mcmc.sample_chain(\n",
    "    num_results=num_results,\n",
    "    current_state=init_state,\n",
    "    kernel=kernel_45,\n",
    "    num_burnin_steps=num_burnin_steps,\n",
    "    trace_fn=None,\n",
    "    seed=a_key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_45_dict = states_45._asdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>146.03</td>\n",
       "      <td>0.40</td>\n",
       "      <td>145.40</td>\n",
       "      <td>146.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta1</th>\n",
       "      <td>21.76</td>\n",
       "      <td>0.28</td>\n",
       "      <td>21.25</td>\n",
       "      <td>22.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta2</th>\n",
       "      <td>-7.77</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-8.24</td>\n",
       "      <td>-7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>5.81</td>\n",
       "      <td>0.18</td>\n",
       "      <td>5.50</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean    sd  hdi_5.5%  hdi_94.5%\n",
       "alpha  146.03  0.40    145.40     146.66\n",
       "beta1   21.76  0.28     21.25      22.13\n",
       "beta2   -7.77  0.29     -8.24      -7.33\n",
       "sigma    5.81  0.18      5.50       6.04"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats for all chains\n",
    "az.summary(states_45_dict, round_to=2, kind=\"stats\", hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(states_43_dict);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tfpjax",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}