{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the sampler\n",
    "\n",
    "`spvcm` is a generic gibbs sampling framework for spatially-correlated variance components models. The current supported models are:\n",
    "\n",
    "- `spvcm.both` contains specifications with correlated errors in both levels, with the first statement `se/sma` describing the lower level and the second statement `se/sma` describing the upper level. In addition, `MVCM`, the multilevel variance components model with no spatial correlation, is in the `both` namespace. \n",
    "- `spvcm.lower` contains two specifications, `se/sma`, that can be used for a variance components model with correlated lower-level errors.\n",
    "- `spvcm.upper` contains two specifications, `se/sma` that can be used for a variance components model with correlated upper-level errors. \n",
    "\n",
    "### Specification\n",
    "\n",
    "These derive from a variance components specification: \n",
    "\n",
    "$$ Y \\sim \\mathcal{N}(X\\beta, \\Psi_1(\\lambda, \\sigma^2) + \\Delta\\Psi_2(\\rho, \\tau^2)\\Delta') $$\n",
    "\n",
    "Where:\n",
    "1. $\\beta$, called `Betas` in code, is the marginal effect parameter. In this implementation, any region-level covariates $Z$ get appended to the end of $X$. So, if $X$ is $n \\times p$ ($n$ observations of $p$ covariates)  and $Z$ is $J \\times p'$ ($p'$ covariates observed for $J$ regions), then the model's $X$ matrix is $n \\times (p + p')$ and $\\beta$ is $p + p' \\times 1$. \n",
    "2. $\\Psi_1$ is the covariance function for the response-level model. In the software, a separable covariance is assumed, so that $\\Psi_1(\\rho, \\sigma^2) = \\Psi_1(\\rho) * I \\sigma^2)$, where $I$ is the $n \\times n$ covariance matrix. Thus, $\\rho$ is the spatial autoregressive parameter and $\\sigma^2$ is the variance parameter. In the software, $\\Psi_1$ takes any of the following forms:\n",
    "    - Spatial Error (`SE`): $\\Psi_1(\\rho) = [(I - \\rho \\mathbf{W})'(I - \\rho \\mathbf{W})]^{-1} \\sigma^2$\n",
    "    - Spatial Moving Average (`SMA`): $\\Psi_1(\\rho) = (I + \\rho \\mathbf{W})(I + \\lambda \\mathbf{W})'$\n",
    "    - Identity: $\\Psi_1(\\rho) = I$\n",
    "2. $\\Psi_2$ is the region-level covariance function, with region-level autoregressive parameter $\\lambda$ and region-level variance $\\tau^2$. It has the same potential forms as $\\Psi_1$. \n",
    "3. $\\alpha$, called `Alphas` in code, is the region-level random effect. In a variance components model, this is interpreted as a random effect for the upper-level. For a Varying-intercept format, this random component should be added to a region-level fixed effect to provide the varying intercept. This may also make it more difficult to identify the spatial parameter. \n",
    "\n",
    "## Softare implementation\n",
    "\n",
    "All of the possible combinations of Spatial Moving Average and Spatial Error processes are contained in the following classes. I will walk through estimating one below, and talk about the various features of the package. \n",
    "\n",
    "First, the API of the package is defined by the `spvcm.api` submodule. To load it, use `import spvcm.api as spvcm`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spvcm.lower_level.sma.model.Lower_SMA"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spvcm.api as spvcm #package API\n",
    "spvcm.both.Generic # abstract customizable class, ignores rho/lambda, equivalent to MVCM\n",
    "spvcm.both.MVCM # no spatial effect\n",
    "spvcm.both.SESE #  both spatial error (SE)\n",
    "spvcm.both.SESMA # response-level SE, region-level spatial moving average\n",
    "spvcm.both.SMASE # response-level SMA, region-level SE\n",
    "spvcm.both.SMASMA # both levels SMA\n",
    "spvcm.upper.SE # response-level uncorrelated, region-level SE\n",
    "spvcm.upper.SMA # response-level uncorrelated, region-level SMA\n",
    "spvcm.lower.SE # response-level SE, region-level uncorrelated\n",
    "spvcm.lower.SMA # response-level SMA, region-level uncorrelated "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the structure of the model, you need at least:\n",
    "- `X`, data at the response (lower) level\n",
    "- `Y`, system response in the lower level\n",
    "- `membership` or `Delta`, the membership vector relating each observation to its group or the \"dummy variable\" matrix encoding the same information. \n",
    "\n",
    "Then, if spatial correlation is desired, `M` is the \"upper-level\" weights matrix and `W` the lower-level weights matrix. \n",
    "\n",
    "Any upper-level data should be passed in $Z$, and have $J$ rows. To fit a varying-intercept model, include an identity matrix in $Z$. You can include state-level and response-level intercept terms simultaneously. \n",
    "\n",
    "Finally, there are many configuration and tuning options that can be passed in at the start, or assigned after the model is initialized. \n",
    "\n",
    "First, though, let's set up some data for a model on southern counties predicting `HR90`, the Homicide Rate in the US South in 1990, using the the percent of the labor force that is unemployed (`UE90`), a principal component expressing the population structure (`PS90`), and a principal component expressing resource deprivation. \n",
    "\n",
    "We will also use the state-level average percentage of families below the poverty line and the average Gini coefficient at the state level for a $Z$ variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#seaborn is required for the traceplots\n",
    "import pysal as ps\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading in the data, we'll extract these values we need from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = ps.pdio.read_files(ps.examples.get_path('south.shp'))\n",
    "data = data[data.STATE_NAME != 'District of Columbia']\n",
    "X = data[['UE90', 'PS90', 'RD90']].values\n",
    "N = X.shape[0]\n",
    "Z = data.groupby('STATE_NAME')[['FP89', 'GI89']].mean().values\n",
    "J = Z.shape[0]\n",
    "\n",
    "Y = data.HR90.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll construct some queen contiguity weights from the files to show how to run a model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "W2 = ps.queen_from_shapefile(ps.examples.get_path('us48.shp'), \n",
    "                             idVariable='STATE_NAME')\n",
    "W2 = ps.w_subset(W2, ids=data.STATE_NAME.unique().tolist()) #only keep what's in the data\n",
    "W1 = ps.queen_from_shapefile(ps.examples.get_path('south.shp'),\n",
    "                             idVariable='FIPS')\n",
    "W1 = ps.w_subset(W1, ids=data.FIPS.tolist()) #again, only keep what's in the data\n",
    "\n",
    "W1.transform = 'r'\n",
    "W2.transform = 'r'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data, upper-level weights, and lower-level weights, we can construct a membership vector *or* a dummy data matrix. For now, I'll create the membership vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "membership = data.STATE_NAME.apply(lambda x: W2.id_order.index(x)).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, we could also build the dummy variable matrix using `pandas`, if we have a suitable categorical variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Delta_frame = pd.get_dummies(data.STATE_NAME)\n",
    "Delta = Delta_frame.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every call to the sampler is of the following form:\n",
    "\n",
    "`sampler(Y, X, W, M, Z, membership, Delta, n_samples, **configuration)`\n",
    "\n",
    "Where `W`, `M` are passed if appropriate, `Z` is passed if used, and only one of `membership` or `Delta` is required. In the end, `Z` is appended to `X`, so the effects pertaining to the upper level will be at the tail end of the $\\beta$ effects vector. If both `Delta` and `membership` are supplied, they're verified against each other to ensure that they agree before they are used in the model. \n",
    "\n",
    "For all models, the membership vector or an equivalent dummy variable matrix is required. For models with correlation in the upper level, only the upper-level weights matrix $\\mathbf{M}$ is needed. For lower level models, the lower-level weights matrix $\\mathbf{W}$ is required. For models with correlation in both levels, both $\\mathbf{W}$ and $\\mathbf{M}$ are required. \n",
    "\n",
    "Every sampler uses, either in whole or in part, `spvcm.both.generic`, which implements the full generic sampler discussed in the working paper. For efficiency, the upper-level samplers modify this runtime to avoid processing the full lower-level covariance matrix. \n",
    "\n",
    "Like many of the `R` packages dedicated to bayesian models, configuration occurs by passing the correct dictionary to the model call. In addition, you can \"setup\" the model, configure it, and then run samples in separate steps. \n",
    "\n",
    "The most common way to call the sampler is something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsma = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                      n_samples=5000,\n",
    "                      configs=dict(tuning=1000, adapt_step=1.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This models, `spvcm.upper.SMA`, is a variance components/varying intercept model with a state-level SMA-correlated error. \n",
    "\n",
    "Thus, there are only five parameters in this model, since $\\rho$, the lower-level autoregressive parameter, is constrained to zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lambda', 'Betas', 'Sigma2', 'Alphas', 'Tau2']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace.varnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results and state of the sampler are stored within the `vcsma` object. I'll step through the most important parts of this object. \n",
    "\n",
    "# `trace`\n",
    "\n",
    "The quickest way to get information out of the model is via the trace object. This is where the results of the tracked parameters are stored each iteration. Any variable in the sampler state can be added to the tracked params. Trace objects are essentially dictionaries with the keys being the name of the tracked parameter and the values being a list of each iteration's sampler output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lambda', 'Betas', 'Sigma2', 'Alphas', 'Tau2']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace.varnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, `Lambda` is the upper-level moving average parameter, `Alphas` is the vector of correlated group-level random effects, `Tau2` is the upper-level variance, `Betas` are the marginal effects, and `Sigma2` is the lower-level error variance.\n",
    "\n",
    "I've written two helper functions for working with traces. First is to just dump all the output into a pandas dataframe, which makes it super easy to do work on the samples, or write them out to `csv` and assess convergence in R's `coda` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trace_dataframe = vcsma.trace.to_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the dataframe will have columns containing the elements of the parameters and each row is a single iteration of the sampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lambda</th>\n",
       "      <th>Sigma2</th>\n",
       "      <th>Tau2</th>\n",
       "      <th>Betas_0</th>\n",
       "      <th>Betas_1</th>\n",
       "      <th>Betas_2</th>\n",
       "      <th>Betas_3</th>\n",
       "      <th>Betas_4</th>\n",
       "      <th>Betas_5</th>\n",
       "      <th>Alphas_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Alphas_6</th>\n",
       "      <th>Alphas_7</th>\n",
       "      <th>Alphas_8</th>\n",
       "      <th>Alphas_9</th>\n",
       "      <th>Alphas_10</th>\n",
       "      <th>Alphas_11</th>\n",
       "      <th>Alphas_12</th>\n",
       "      <th>Alphas_13</th>\n",
       "      <th>Alphas_14</th>\n",
       "      <th>Alphas_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.940050</td>\n",
       "      <td>34.046017</td>\n",
       "      <td>1.334209</td>\n",
       "      <td>7.032362</td>\n",
       "      <td>-0.502780</td>\n",
       "      <td>2.010436</td>\n",
       "      <td>4.469775</td>\n",
       "      <td>-0.028225</td>\n",
       "      <td>9.839930</td>\n",
       "      <td>-1.288980</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.583933</td>\n",
       "      <td>0.924241</td>\n",
       "      <td>0.087917</td>\n",
       "      <td>0.051871</td>\n",
       "      <td>0.856965</td>\n",
       "      <td>-0.090386</td>\n",
       "      <td>0.495369</td>\n",
       "      <td>1.762157</td>\n",
       "      <td>-0.796564</td>\n",
       "      <td>2.272783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.940050</td>\n",
       "      <td>34.215037</td>\n",
       "      <td>1.587374</td>\n",
       "      <td>5.277195</td>\n",
       "      <td>-0.286292</td>\n",
       "      <td>1.834136</td>\n",
       "      <td>3.738277</td>\n",
       "      <td>-0.100795</td>\n",
       "      <td>14.294029</td>\n",
       "      <td>-1.221123</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.449357</td>\n",
       "      <td>1.559842</td>\n",
       "      <td>-0.018327</td>\n",
       "      <td>0.043533</td>\n",
       "      <td>1.671848</td>\n",
       "      <td>-0.134705</td>\n",
       "      <td>0.897505</td>\n",
       "      <td>1.570762</td>\n",
       "      <td>-0.016576</td>\n",
       "      <td>1.841339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.940050</td>\n",
       "      <td>33.171990</td>\n",
       "      <td>2.780147</td>\n",
       "      <td>6.501418</td>\n",
       "      <td>-0.196233</td>\n",
       "      <td>2.006755</td>\n",
       "      <td>4.027759</td>\n",
       "      <td>-0.005111</td>\n",
       "      <td>5.549450</td>\n",
       "      <td>-2.063556</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.361113</td>\n",
       "      <td>2.283653</td>\n",
       "      <td>-0.092895</td>\n",
       "      <td>-0.374784</td>\n",
       "      <td>1.027415</td>\n",
       "      <td>0.093002</td>\n",
       "      <td>-0.463750</td>\n",
       "      <td>1.537222</td>\n",
       "      <td>0.463958</td>\n",
       "      <td>1.777161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.853473</td>\n",
       "      <td>31.246997</td>\n",
       "      <td>1.680753</td>\n",
       "      <td>13.216350</td>\n",
       "      <td>-0.251791</td>\n",
       "      <td>1.859299</td>\n",
       "      <td>3.977210</td>\n",
       "      <td>0.119079</td>\n",
       "      <td>-15.384568</td>\n",
       "      <td>-3.300783</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.440598</td>\n",
       "      <td>1.070437</td>\n",
       "      <td>-1.102695</td>\n",
       "      <td>-1.098938</td>\n",
       "      <td>0.954793</td>\n",
       "      <td>-1.346576</td>\n",
       "      <td>-1.292709</td>\n",
       "      <td>1.483239</td>\n",
       "      <td>-0.950301</td>\n",
       "      <td>1.109188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.853473</td>\n",
       "      <td>32.761820</td>\n",
       "      <td>3.508263</td>\n",
       "      <td>4.432656</td>\n",
       "      <td>-0.215052</td>\n",
       "      <td>1.626291</td>\n",
       "      <td>4.020410</td>\n",
       "      <td>-0.020324</td>\n",
       "      <td>13.994845</td>\n",
       "      <td>-2.571416</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.216185</td>\n",
       "      <td>1.413811</td>\n",
       "      <td>-1.069306</td>\n",
       "      <td>-1.187765</td>\n",
       "      <td>-0.131962</td>\n",
       "      <td>-0.378420</td>\n",
       "      <td>-1.337283</td>\n",
       "      <td>1.369119</td>\n",
       "      <td>-2.404278</td>\n",
       "      <td>2.538615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lambda     Sigma2      Tau2    Betas_0   Betas_1   Betas_2   Betas_3  \\\n",
       "0  0.940050  34.046017  1.334209   7.032362 -0.502780  2.010436  4.469775   \n",
       "1  0.940050  34.215037  1.587374   5.277195 -0.286292  1.834136  3.738277   \n",
       "2  0.940050  33.171990  2.780147   6.501418 -0.196233  2.006755  4.027759   \n",
       "3  0.853473  31.246997  1.680753  13.216350 -0.251791  1.859299  3.977210   \n",
       "4  0.853473  32.761820  3.508263   4.432656 -0.215052  1.626291  4.020410   \n",
       "\n",
       "    Betas_4    Betas_5  Alphas_0    ...      Alphas_6  Alphas_7  Alphas_8  \\\n",
       "0 -0.028225   9.839930 -1.288980    ...     -0.583933  0.924241  0.087917   \n",
       "1 -0.100795  14.294029 -1.221123    ...     -1.449357  1.559842 -0.018327   \n",
       "2 -0.005111   5.549450 -2.063556    ...     -1.361113  2.283653 -0.092895   \n",
       "3  0.119079 -15.384568 -3.300783    ...     -1.440598  1.070437 -1.102695   \n",
       "4 -0.020324  13.994845 -2.571416    ...     -1.216185  1.413811 -1.069306   \n",
       "\n",
       "   Alphas_9  Alphas_10  Alphas_11  Alphas_12  Alphas_13  Alphas_14  Alphas_15  \n",
       "0  0.051871   0.856965  -0.090386   0.495369   1.762157  -0.796564   2.272783  \n",
       "1  0.043533   1.671848  -0.134705   0.897505   1.570762  -0.016576   1.841339  \n",
       "2 -0.374784   1.027415   0.093002  -0.463750   1.537222   0.463958   1.777161  \n",
       "3 -1.098938   0.954793  -1.346576  -1.292709   1.483239  -0.950301   1.109188  \n",
       "4 -1.187765  -0.131962  -0.378420  -1.337283   1.369119  -2.404278   2.538615  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can write this out to a csv or analyze it in memory like a typical pandas dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lambda        0.608686\n",
       "Sigma2       32.265190\n",
       "Tau2          1.828138\n",
       "Betas_0       8.156127\n",
       "Betas_1      -0.258890\n",
       "Betas_2       1.796985\n",
       "Betas_3       3.973051\n",
       "Betas_4      -0.003564\n",
       "Betas_5       2.108436\n",
       "Alphas_0     -1.588250\n",
       "Alphas_1     -0.124204\n",
       "Alphas_2     -0.086578\n",
       "Alphas_3      0.059299\n",
       "Alphas_4     -2.261698\n",
       "Alphas_5     -0.931411\n",
       "Alphas_6     -0.400545\n",
       "Alphas_7      1.117568\n",
       "Alphas_8      0.102000\n",
       "Alphas_9     -0.169745\n",
       "Alphas_10     1.024000\n",
       "Alphas_11     0.308998\n",
       "Alphas_12     0.189192\n",
       "Alphas_13     1.873476\n",
       "Alphas_14    -0.617676\n",
       "Alphas_15     2.084861\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_dataframe.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second is a method to plot the traces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljw/anaconda3/envs/py3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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jZVnyCPY337clIDT7GRjN4x0rFuHXj27Hc5u78cL2PtvvXoIENpYoOHGrywPP\n7cP2A8O440EmgKmKhLJZOYg2dLtlOa40nMdeOMjeSMBN92zCf9y2WrgelufYAMKmg71jWPnSIc8u\nSZCLKmVNEt1SfvSHjd59FqooFdWLuhVKBuZ21gcEzP938yqPQuc/n/4R9twpsoztB4fxTz94Gut2\n9ruCsXNeXgGbE/Cjh+v+9PiLB3H7gzpaG2LMvc/mxl+9yI71aRWxiIKt+4bwk7s3sf32sPqBYew6\nPOJ5Xp9cf9i+LsFrlSuUccC2fsiyhBXLmdvWhl0D9iQte+7hlwUADvWN4bM/edb5/8hg1klHvOPg\nCCKRYExF92AWl589y9FLwiyI+3vT2LjLtp7VZFQSLBUSs1D4sXzP3Dg6xYQQFaazFlU3a7+0ox93\nrtzp/O+cvmU5MTgAU2DFz+z1Nz7ubc8PE5Teh9ccwBdueR49g7UvvBAEQRBBLIslydl1aARrtvXi\n4TUHcOfKnfjfezfjO3etx60PbMUfntyNlS8dwrqd/djXk8aob0FWAtCUUnHKzDq8+eK5+J+Pvwof\nuvr08AGnMCek+1NrYxy5EJeC//z5morHcCG2Wo5jLvSIq/NhvtaOm5QdUwEL+PHdm/Dxt52Oc5a4\nmqdfGGDHVhzeYXZHHeoTUazazARLw3YJ8ru3AF43iz+v2uekHA2zVDjH+ObEr8jn3ns2Wn31AlJx\nFTnb7Wv11l74sXyCoki+aKB/OIe2pgQWz2rEg6v329vLeHL9YUchkwVBMNxPnOG3FLlBziH3COx+\n9w/n0FQfC9TbqE9GKyqY1awcuQJzX5IlCYWSgSVzmrBmW68jzPktMIBX4Qr0bVlOLAS3kLz5kgVY\ns7UHl58zGwCcmBFZClmdBpy85hbcKt/fu2sDPvHX7AvrVw9vx2MvHuTDBbhgaSd2HRp1xuBNuGD7\n1V++iO//46VVvfULJRNDaXdFJhlz61fM76pHa0Mch/sznmvww99vQHtTArPaU4Hr5J6biWhEYTFP\nrl0rdA6WT+GupCk4SkVIO/68jttJBVzL2/h+SIf7M9hzZNRxXxOfnR/9cRNu/dzlkCQJSgXFOWCo\nYOsmuPGXLyBTKGNWW6r2zFMEQRAEAPZdfKg/g52HRtAzmEPfcA7ZQuXELyKNKRVtDTF0tSQxs70e\nM9rq0NmcQFtjomrR2unCCalURBQZpy1owaYQX/qJIvq7l8vsV/pRwX86bLGf/5ZzFw9RGBDqv9lx\nDSYA1zfZfzshAAAgAElEQVR+Ij78bvYclvOfBzOLyoXHzUZ4H1Fl7Do8grNPCZrXKgWEnjKnaUIZ\naSRBAA2zfqza0o3nNnXju598NVRVRksDC5LdtHsQuw+PIqq62ZcqXhdh82kLWnDmwlb85rEdzq6K\nFg5bwvqXm57D5957NrS53loK/KhaU7n6pgJJZivrUd+XhGkG0+NWc39ilgrAhOs6l4ipoUUHq8WI\njIwVsGZrL861swGxGCG2jysUAMuQ5EfsVpbdyfPjc4Vy6OfAex7ecxStRHWJCLpakk4sCO9qNFtC\nRFUwq72ObQ9VKgwcGcjiczc9i8EqZuRS2XQ+G5z6kCxwnrlKwv/2pp/cswlfvO583xES5nbWOQUn\nqzGRz7dhmp5c6M6h9uuOgyNYMqeJuWyGWeN819yy3BiZukQEzfWx45anniAI4mRg/a5+5oZaxX20\nKaWirTGOGa0pzGirQ1dLCp0tJ47iUI0TUqmQFWncyrP7e9LIFw3M6ahjQaYVflv5yj7gxlJ4VplD\nfpRf0NmKuckDtYW2opArC5mWnP6O4jeeKy+8NoMnpkLoUBTKFs1qrJgO1rLgyQLFpxwms/K0mxXl\n2SpKQdmwMGbHRFhC/ANX5Jg7j1eAtWB57okoOEUUGfOFIF0ejB12llxYB+BUsA5rNMH4cmc+zBUN\niNh1NPg1NPlysYB4z8OUIFn2WioUWUL/SN6p2j67PYWDfZnAsc59kyT08/tkb6xUx+O3j+8MbPM8\ns2ISgIkEsdtNH3x+P5JxFX0jObQ3uiv+shCDYnrcwdz7dKBnDPO7GtwuLQt52wpzrtaBh9cwZT/M\nPe2/b1/LgsPHyX4kKluO25Xl3teAIcl+XTavJTTOJdi/+36dL+kB4K1ZIabd5XMTx7zxV6x+yliu\n6BTsqzSWd7b2502WsX7nAM46tWvceRMEQZzs7O9J46HV7qKyqkiY257EglmNOH1xJ1rrI+hsShz3\nmkrTiRNSZZIgBfzV/PzsT9vwF9u/DRB8+P0IP8w/vX9LYHdfSDAl92HfsncIUVXxrLbLHgFNwppt\nvc4+/vrFW553XIpqwXF/QlDJ8SgVwntVkTy1NjzxDz5hhhMm8P7Vq+YDYAX1OPc+s8fti7uChQig\nfN58f1RVkC8a6LYD7BVJcgRu09UqMCy40fi7VQRTEM8c5Z/16q096B3MOucTKhzbwjO/Lg8+vx8P\nPr/f6bcab7lkPrMahVgquAuO/3r7GRzNs7gHuDEVJcPE3/7VUmhzm3DpmTOcZ6TNFs5luUoKXHuH\n+PzVqhKIt120kIiXoX8kx2qkjMP6nf3YtGcQM1q8RQAlScI2W1lcLcSLyMwnEUAwjfPTG484z4I/\neF4MUAeAoXSe1SURA7VD5mdZ4r0JtqjwqFTeWcPxImKmMv8ihCl8j4i0NMQDixMjmULQtVJ4b1os\nZfLTGw6PO2eCIAiCxQJy2pvi+N4nX40vfPBCXPfGZXjtBXMxv6vhpFYogBNVqZDgUSqy+aCZqrk+\nhnOWtIfmxuf/X3/j4+GZfoSm+oHhimufhmFi6fxmj2AtCmhXnDPbmaereACH+jOhAaSVsDyWCq8Q\n2DOUdYKpxXOMqIonE1XvUA59dpCxYVqOiS4smFvcxofqEQLj735qD/yECVOGYTruYJYJABb+vGof\n0rZZUZYlZPNljGaLzvE7Do5UjUHgyhBfcQ67Nw+tPoA3X7LAsXiEKRX8OC6stTXGsXBmA15z1syK\nwrgY4Mtc0VzljN8TbnH49aM7nOPClIrVW3uRzpYcxciyLFimhaXzWpCKR6AqcuDeVHPTdz1nxGtX\nub23X7fjaERxrU9CX1v2DnksSM6xvjsgC3FE3N2JWyN48cmHhZUgSfb28MVbnwcA3PHgNqzb0Y+r\nbaXW77YVFj/FrHjV8URmCPeskhteFf2j4ghV9wq7DdO7COGmtPYeE1Vl9I9402cXSiaa6nzuXb6+\nFVmqaK0kCIIgvJy1qNX5Xu0bzuOmezYjX6x9Afhk4IRUKgB4fJF5ykk/ft92UXzhZd7DAhnFn+Gy\nYaKjORFow9tJEgSh3rtSLI7vZr6xkIgpuP3BbTVbKxzhQ/IKelFVRiyi4HZ7hddvqTCEc/vVI9tx\nqC8DSWLWl3dewYJDi8VgBeZa6n0APNsQa7vr8Ehgv6cAmmXhdefNwYG+MSyzc/PLEvDrR7bjprs3\nOdfnzpU7URQUva//+iVPn353kTDrSmMqirMWtWJgNI8LlnbYCk0Qy7Lw/d9tAMDcxZbMacKs9lS1\nqgWArci4mbj4+JWtIuIq87qd/Z6MPKWS6cTKmJbrCqQoQYHQn81K/PfrdoYo8d7V6t9fSV72C8DV\n4G0V2X7uJMkTuM0UMTZSXdKtwSBL3gnwVLdPrj+Cg31jUO0LUpPrEdxrUiga+PXDwe8FUYEIs+n4\nT3MoXZhQ7YfxPjpizJVpWaExFaKF8WDvGOJRFbGId3VMkhCIuxGHtiyerIECtQmCIGohHlXxnitP\nwcxWZmnfuHsAf3hy9ys8q6nFCatUiMJbJSE44FcuCAdc+wwTAsVjyoYZqMxcbwtF3N2JC0+BmApJ\ncpQAsfCXNqcZ87uC6Uwrwd2IZPBAYDaGvw6GmAkqFlGwX7CG8KB2WZKwcfcATl/Qiled3uX4rItM\nQIZCsWxibmcdntvUE9i3akuPIwizdLUR7Dk86lhQJFlCsWwG8vBXS4OpKF4hjF/uTbv68YWfrsJt\nD7Bq6fwaRRQZfcO5QNAV38+rTXPZTlSUwrDsRtz9iQuJon++yLObjmDNNu+1OTzg1ogYTBecmB9R\nSVLloEAoS+EBJLwAD1A5xqYa/Bp+42MXu+fo72scaZmPJcuS8xy6iggr3MivsXh95RD3Nd5X2bCg\n2ha1SrVD3HPgz5CEsmHihm+sDG3ncX9yArW9MT2VmMia/7qd/ZjXFSzSJyqGlWIq0jnXCvvvFVI4\nSwg+a36XO1mSMJiuHNxOEARBeKlPRvHuK09BNMJ+ezbvHt/t92TihFQq/ELoWr3PycMPuG5Iogzm\nd5Xhq+FcWHrjRXNDxyobwfgDLhiM5coeJcKCdyWSCZ7sPXfr4AKTmDlpPJwVfydQ252HuAovesu0\nNyU8q6DinFoa4miujyEaUSBBCqz2T0R4+u6d67G/Zyw0RkMUmiwTmNnGtP89R5gbDM+OJUvAoplu\ngK4/Fe7gqO0vj8qWilyhjCvOmY3hTMFJvwswIfen92/BUxV8y+N2pWgxwLnaBXCeI9u6wEViPh5P\nmwqwOI2nNxzBu644BQDc1Wahf9O00NoQd9257H5UhQVr//rR7U5ckCR7pxYWlOxVtiufhwg/97ZG\nr0VOFFIrKVpcceCvsiRh0+5BSAAuOcMNEBYtFZb3JALn8YPfbYBpWayquOLT2qrAlPzx27g6hTuf\nSoHawjQ9/PS+zYG4DoBd/0vO6MI/v/OsQHpmwL3WM1qTMIxKMRXAFefMwofedKrn2A9/faWnYB6/\n19//3QYneYS4L0ypIQiCICozmM7j/uf2Od4sM1qD3+MnMyekUgEEhRxPBWpbEaiUihFwhSBeO+Fv\nLluMugS3QLjt7nl6jyPQNvIUlbYcMJYreoQYv6VCtJT0Dec9fYetNFbCE6gtaEeSz1Ihvq9Ug0Gc\nk4TwlVmxsGA1149soYxdh4OZeMR4F25NMS0LsYiCRbMavUqR7bMiWoP8sQTfvWs97n92LwA3ULtQ\nMvHbx3e6FgJ+ziYTLPl9cGIeQqRSff8w8rYLmptJiQl3/cM5fPmOtbj5vs1Oe+dKSawQoWVaAcWN\nr24AwBPrDmHb/mGnb9cVzG1vmBZUhQWsD6XzzrwXz27ESzv6MZZ1LSyBYn4S8INPvdqziV86HvMB\nAHM76gLn7uvGQ5hvfyWrx3fvWo9dh0ew1/78NaRc16YPX70MrQ0xZz7xqMLc0fwuicIEZrfXOQHh\nZcMMTa1bCVEorwSzSgT8n5yba5qWE79RzdLz3OYeHBEsTmI/siTh9IXhhe/44xJRZY+lS5yE/3tE\nhC+GiFXbN+0ZxPBYwXPelgUkYid3QCFBEEQtpLNFbNg1gN8+vgO33L/VqQ3VWh/BtVcte4VnN7U4\nIZWKmkz/QgpWwOtvDbCVRgCY2ZZy+60gQDuCIl/9llzhUPKtnFeKqfDPU5KkqkJLtlB28/rb/fL+\n+PnLks+HXnjPFar9vgrelZQNkXrB570aop99z1AWI5kixnIlfOoHTwvjSfjbr69EOlsMCEqycH/E\nSZV9ri7RiIKMHYzP3Z+GxwrO+bDjAdhpXkVBn7/3K0+SxILwj9iZqDyWCljYsHsAF5zagVWbewJB\n9RJcod1VRlx3Kw533eEKDZ+7Za/CA27QvAULm/cOOUJna0McyZjqPCNfueFCnBEiqKbiEd9qd9Bl\n6fqrlwaOC5xQCONlsOKUSiZe2sHcfTqbk4ioshsEbXfP7/UZC1thwXIK3smyd/hGIfi4bDCFyz8X\nZ9oSi2d64Lm9Tsat8YrO+V2IAK9rVfdgFp//31VsfDF7Wth5h7hkiQX4xJn87E9bve5tiuxk/nKP\nZa/+hA8inhgMITxfloLXaCIKGUEQxMmAaVroG85hw64BPLzmAG7701b85J7NeHD1fqcWkSQBrzmz\nA//54YvQVFe9fMHJxglZpwLwCk9LZjd6VlV/+IcN6GxJMqFb9H8Sfua5C45Yw4D/wIs/zcsXtyER\nU5jwKfzoA/aquGdO3gJl/pgHABgeK2I0W0QqobI6BJ31aAgp0nWe1o67/rLLmTpXkkzbR8YCnIrU\nHDGmgu/70s/W4LbPX+E5x/FiORKxGh8boZ8jA1n84iEdH3yj12WDC9lr9T5ce5WG69+0FJBYdWxe\nu4CJ8S5b93uzDKXiERwZyKIuEXFW+3kci+tSwwSxoXQBg+mCcx+clLbjWKy8MRXsb6Yt+B7uz2CO\ns9rvCnymxdLiVkL1ue4koirS2RIee+EgdHslxDRZ/Q59/7DnfNx0uOzYGa0p5zw5/pHfdeVibNg1\naLfzutpVI2AB4Wfqs6hUwrQsx8pnWt4MTHM76tFUF4UkscBzbp2a21FvJw7wWipU4QNULguWipDh\nFVnGvp40jvRnfAsNVkXlWbwuPFHCp3/0DD7wBi3Q9u++9QSA8OunyBKeWH8YY/kSntnYjSvPnY3L\nz55VsSDjUxuO4NrXa8794+cpKgluModwyxqbC38jxIHYKyb+0w1zSSQIgjgZKBQNDKbzGEwXMDha\nwGA6jyH7tVJWvJa6CC45owuXnTMXzePUQjtZOSGViuGxgpMlBgD+/poz8X+Puyk8z1zUhtedPweb\n9wy6P9QV+vIHTrLG4S5FnJGxor3PJxSYlsdyEWaNuHOlW3zs1ge24jytHde+PijQ+ONGFNlvqbCg\nyDJ2H3KzLomCsyxLHssFJ1soj5vOtlZR5Lkt3gDkF7f3eYSzJbMbkYip6LX9wOsT3lV1WRYtFW4/\n/srJiiw5QmtUVTxKCA/65irj1RfPw60PbHWkrzCXI/EcA4K35AbzugI+AnPkK+OiMuKHK1T8+Hdc\ntgg/vnuT5/pfeuYMLJrViIUHR/BCui+wQl01e4+vsQTJE5DOn4eJipb8lmSF7GTV4rS9KYCZiw53\nR/zkO84EwCp+G6YFSXZrKABMmRMFaNENzoJr7QkbPhaRmUWOK9rCCr8sSR4l23uC7KWlIe6kSvbH\n8fjxX8OIKmP34VGks0X0DedRsLOo+WO3RAolw6kmfuq8Zmw/OOJRohxLhVnZUiE+k/zZcMb0nYI/\nwQRBEMSJhGFaGB4rsMXEUVeBGErnkcmPn12zPqFgbkcKZy7uwJmL29DZnBz3mJOdE1Kp8Be+Y25A\nwXayb7Uy7Ida3PbuKxfjlvu3Vq5TILyvS0RgwetusfPQCGa3u+5UMtzxz9Pa0dWaxP3P7mN9WRbm\nd9VXyVzly5IjS56gYNNkgplYo0PsiglbwX437hoIBPuOs5Bdkac3HPH8n4ipoSlqnbEC7k9whKxa\nMxXFogpu/pfL8JU7XsDe7jSWzG7C4y8cdAxRXfaXAh+Jx2D4Xap4CzHAmL9aYMG8oki258godh4a\nsYOpJTBXK9NVSn1uUAAQt33a+X3jArIorHPLzmon9sB7jQolA5+85syaro0kAYd6mQXOsuNYxLlV\nopKlQlVkx02ru0pGLvGzZwE4d0k7tvmsTZLEg/IlwHKtOLzmhzMX3+p6RJFxzYqFeH5Lr2f7B994\nKtbt6IdlWegbzjuFAbnFQpYkGCGfZMuynB+b9qY4trKPYyC1dFgFa5FFMxtw7qkduONBHVddMMeT\nMlryPROcr//6Rbz91QsBAKfMbgqcbyVXTRH+TCqyhNFsCWXDtL+HgoqXSpYKgiCmOfw7ezCdZxaH\n0bzjkeCPJatERJHQ1hhFV0sSs9rrMX9GIxbMaCBrxFEw6UqFpmmfAPAZAF0A1gP4B13Xg5WpWNsV\nAFb6NlsAZui63htySCj8B/yaFQvx+yd225mURO3BfpHCV8JFAUIUqF51+gzccv9Wb3YeW1ptaYh5\nTGanL2jB/t4xz49/73AOi2c3utMQxo9H1UA2GGeVNQS/OCD7hC9VkdGQijpB5IAvpkKWsL83mJ2m\nvSmBMxeHB5EeK7IETxpboLrrTS2uWGEoMvNHv2z5TKeIH2B5gn6dFWtbsLrv2b3oGcrCMC18/G2n\nuz7/fDXf/r9YMtAzWPYEe0uShAee24f+kTyzGMEWXoX0vmFnqTjHO1OsiKjUcAzTwqH+TMX7FTam\nOIQTtC1sm9dVH8xa5OuI9zF/Rj12HgzWHwmMad/DI4NZzO6oq6i8G477k5sVzK/Q+F12IqqMqy+e\nj1U+q5jjqmgBXS1J9A3nUCqbSMZUJpTLAGz9NqrKToCzBRaUBwDL5rfgyfVMMfYrFUWhLsY9T+/B\nGy+ci77hHFZv7cEFSzsRURVoc5hi0NmcdGJ+fF6WDkvnNaMxFXXTKUv8/Nxgaq+lwtuJkxJads+7\nuT6GQslwFN1AoUiyVBAEMY3I5EroG86hdziH/pE8+kZyGBoteGpXVUKSgKZUBB1NccxsS2FWez1m\ntNWhqyXpuOASx86kKhWapr0LwLcAfATAagD/BOAhTdOW6LreX+EwC8ASAI5kMxGFAoCTRrU+yWIR\nKvlPi9vF1T8xwDLsOXthu5uekR/fmIpiYFQs5uXmgufUJyMeIUEc37TdlcR+WfXhCicZ4v4kKkuv\nv2AOrjxvNv7t5lVOG8OnVOQLrtWgsS6KOR116BvOe9OycmFcYOJivjuviSDLEmIRBZv2DGL54jYA\nwLzOepQM05Oadd3Ofme/M0crmI5TkoCZrSlc9wYtkP0JYFWsly9uc1ayAfea8fbPb+3BrkOjeOfl\ni51no3sw6wSGs8ZuoLbTvTAXx03Np1RUq4GQ9WWh4ue4eFZjRUtCGO7zJlh/hOMXzmgIKBUVXW3C\nOhbmxuHj9AxmsefwCJLxSODzyFMoszgjOAHYrH6FO5L/XLlFI9gf7PSwPlchwf2Js2x+i5OWV4yp\naG9yU+j6g67D69cAN92zGRcs7WRj2Dc/HlUcRcWCBZlbFIVjS4YJ2N8ZH7hKEyxp4jMsWDs847qB\n/WIxyagqV12lo5gKgiCmKqWyiSODGXQPZtEzlMf+7tGaXJZSMQXtTTHMaE1hZlsdZrbVoas1ifam\nBCWneBmYbEvFPwH4X13X7wAATdP+DsDVAK4H8I0qx/Xpuh7MRVojw2MsPavjZeBbpXPlPMnzQ83x\nF9+qhtvWq7lIkoRi2fAItv4feDFQ27IsqKoozHP3LLZ/NFPE5j2DjvAgwSuEetyfwIKXVVWumPZT\nliSnr7XberGgqwGffMeZODKQQTJeW3anWvnIm5fh5vu2OO5mLQ2xQFxEGEPpAgolAy0NrgnyLZfO\nx52P76xylI3lE4btU0/EVKxYPstZOQ6r1SFm13FjKtj/Tu0CwbXtj76KmryNWFGbj+IvslcrvOCf\nNxtQ9WxG/l1+haRgFzYUL4FfBv3Xa88JuVe+OJOQccVn/Qe/3+gofab/vthwAbepLubEAwH2/RHa\ni8UNRf761Qvwoz9uworlM/HEusPO/ExbeZvZlsJIpshcI63Kn2vmFuadExBUKgLCutDd4Gjec29i\nEcWbzjZkaK4oOM9eyPzEBYiwax9VhUUJ03t/whZW6AeWIIipgmFa6B7MYn9PGvu60zjUn6mYAEQC\n0NIQwSzb4jCrvQ5dLSl0tSSOu/xCTIxJUyo0TYsAOBfAV/k2XdctTdMeBXBxlUMlAOs0TYsD2ATg\nS7quPzuRsfmPp+gPL8YmiBWnvcqGLQyKkxlnMU9c7BUff33/MAZHCwFBTkSSJHQPZLGvOw3TAs70\npQQVlY7NewexryeNt166wJ2Xz1LhjCWMmfME04pKiOvS8eO7N+EU2y2LZxGqhmfht4ZV8otO68LN\n922xg9QF6804x45kirhwWSceXnPAEfAKRcMJnq0GFyY5N96xBuef2oGLT+vynEPYau1wuuC4ofhj\nKjhWBcHUfR4kx9rE/h93yjWZgMR+/NnEKrGnm+vnbuN80cBvHt3haddUFw1IntViasTNa4XiatXc\n9vh+/95ETMV3/+FSJ9OZqOyJ47RU8HE9V+sAAFx+9ixHqQCY8negd8z5HPD0f5U+l5bluryJz4Y/\nZiSgHAontMN2CXPiG5QKqasDCw7MMilJ4bmdnPogpnf+/DmfLxSzM50MV7YCGNKjWkFBIwiCmGxM\nrkT0pnGgZwwH+zOhabglCZjVlsDCGQ1YOKsZczvrMLM1hWiE6uxMRSbTUtEGQAHQ49veAyCYzohx\nBMBHAawFEANwA4C/aJp2ga7r62od2C/QVHN/SgvFw/jvrmVZWDCjHnuOpENXskXEQnEifBXY2zY4\nvn5gGL95bAcaU1Gvj7PluoLw/k6d14xFsxrtY72CmRjQKs4lFlXczDOCYqVIkucDXKkYlwQJg+l8\n6L6JwtOFOsL4OPESyxe3YcXymXh4zQFHiDt/aQeWLWjBp77/tKdta0Mcj7140Pk/ogbFqKEx0T2N\nu9cE72++ZGBuRz0GRwtuhiSfwhZwqxGQ7MX1avUEjhav5Su4Yh12SXk2ssrzlZzXwMKQFFQcm32C\n+ez2OqdmCgBIshQ6jxmtSShKsEI7R0yd7BQl5BezRvzjWpZXsQZYkLW3ngOwZE4TkjEV/3Hbalxy\nxgwA/iBpb7/+ZBCW5z27L/x4RZGd76R8oRyafIE/T/yZCbtElq+tH8WubQHAKbzI5711H1uU8LQn\n9yeCIF4GDNPC4GgevcM5FhMxlMOhKkrE7LYETl/YitMXtmPJnCZ0dTZgaCgTklCFmGpMqexPuq5v\nB7Bd2LRK07RFYG5U19XaD0//yF0oohEl4EKhqjK6WpJ4bnM3rr1KAySWfcfJk99Zj3zRcAQDVa3s\nKiDL8KSKBVyhwO+uIfaTjKtYOLMBybgKSPBq3hJzTyiWTWQLZYzlSpjZmnKO9wsE0YjsuDOoqgxV\nlaEoMhpTUfQW2cq+KMtEIoonuElV5MA5Stz6IXmvgT/t63i4xwF/WXdYWL33BZf7xv/rFQsxp5Ot\nvnLhKBZV4ZcyG1JRvPPKxXjsxYNOHx+6eikSURWqKnsUCL4/arFrvXxJG2Z1pPCd/1sPAEjnSpAk\nyblvXBiM2PdGFgTwsGfCAsu6JdvH83vCFcbGumhAyOf75JCVYz6GLIU/h4rsnYd4PYfSBc++ilmc\nnCJ8QWFWVWRnRZu3e90Fc/Hs5u5ATIg4J/9zL0nAxad3Qd8/bO+zKn6mLMAJsH/TxfOcGh1A8HkL\nXA/huvuthwC7Jl/62ZqAYlOfjKA+GcXIzqLjGid+Hv1m+Bt/9aLnf3FeN9+7BfO66p25xaMKAHaf\ndh4ewYw29jn2Kzay/XlTFdl5JiT7e4ndG9fCp8iy8/0mftekuQVFknCoL4N/u+V5AKzCt59o9MRZ\n6ZMVGV4b89RBdj7fU3eOwPSY53SYIzA95nm851gqm0hnixgaK2A4XcRQmqVuHUoXMDxWrOrKNKM1\njmXzm3HWKZ1YMqfJUwtL8f2GTkWm0xwnm8lUKvrB8qt0+rZ3AuieQD+rAVwykYH5SmDKXvlsaUlB\nUdwf0GQyhubmFJqbU1g6vxXNzSlEVBUtzSm2wqjIiMcjaGlMoLGeZWRqbg53C1JUBdGoioiqhArb\nSxe1Y/6MBuw9MgpFlQP9fO/Tl+M/b1kFw7TQ0uLuUxQZiXgEu3cP4Nt3rsfi2U3QFrah2Q4eTaW8\nbiANDQksntcMAGhsTLrj+Fa2OfX13kxTqWQ0MLdYTIWsyEjZAe98vyoIW4lEsDCfH+c4VcGWvYOY\n01nn9BOJqIF2zjnVJ5xt/DSam1PIF70rzx9+y+no6mjw9CH2pdrB8ZGIEhhjRkcDli5qd5SK9uYE\n4sko4jHml8mvGT+OKxejuTIaGxPwI4E9X7JSRqZgoM5e1efXaXZHPUbGBuy+2Lnze+G/p+K4/JqL\n81dUBbGY6tkWsYs1zmpP4VBfxrMvngj3NeXnoagyolHvV0JDQxwlyzsXAFAVxbkW/i+rWExF3meo\ni0RUpJIxyLKMeIzVI6n0mZIA1NvX7eLls7FfqDkTj3ufN38f9fXsXJKpGGIxNdQq0j+S98TpqBEF\n0Qi7lnyuANAi9D3eT27Mfl4WzmzE7sMjKBuWc3xzUxKx2Aiam1MYHivhVWfNRHOz13wvSzJiMRWJ\nZAxRVUZDQ8KZS3NzCo31MTyziWWiGiuUkUhEne+3SESFNq8Zuw6N4j9uXW1vY30XisHAxq9+7BL8\nv588g9aWusC+6UoqOfVTP06HOQLTY57TYY7A9JjneHO0LAu5QhnpbAnpbBHpTBEjmSJGxgrsz36f\nrSGIGgDiURmzO1I4dV4Lzj61C6ctaEWqwm+TCP9OnMpMhzlONpOmVOi6XtI07QUAVwK4FwA0TZPs\n/z7uMD8AACAASURBVL8/ga6Wg7lF1UzZTpmUtTOuDA9nUSq5D3w2W8DQEBNUSqUyhoYyKJXKGB5m\nftPlkoFSsYyPvfU05AplPLRqn9PeT7FYRrFYRrlseNwauPyeyxQwv6see4+M4uJlnaH9nLmwBd2D\nWYyOuH7b5bKBcslArlDGxad14q2vXghYpnN8Lud1v8hlC0iPMovE6EgOMZkJe6JIJa4UpNN5LJvf\ngi17WSrKw73pwNwKhTIMw8RYhrkN8f1lwbUr75tHGE6/JvcJt5x+isKKtn/8dDqHoSG7ErPptvG7\nlmUyeYyMZPGNj78q9PqWy6y9aZiB/SMjWSiWKzJapoWhoaxzTM7OkMWPM+ztDz+/D5ee7teX2Ypz\nLldEoWhg+74hzG5ldTHG7OQBRUHIG7Ofz1H7vo2NBYPX/ddcnH+xWEapZAS2ASxz0cBI3rOv4PvS\nf/Or5uO+Z/didCTnnHu+4I0VSKfzyGa99x8ASmXDsZbxawWwNM5HBrL4+/9Z6emnVCqjWCghXyih\nVGIuQJU+U6ZlIW9bC4aHM857AMjnvc+bv4/RNDuXbLYA0zBD3RD9Vr5S0YAC99rl7PHSaTd2J5ev\nHmDP51U22HilsuHc12y2gP3doxgayuD+Z/bg3CVt9neOgVltKUQiMkplA8ViGZlMAeWIjLRtReDf\nTwu66h2ry12P7cBfv2YhMhm3zb+9/1x86ntPOa5e/D6WDZaeV/zsm2XWZiw9fmzSdCFj3++pCFuY\niU3pOQLTY57TYY7A1J+naVrIl01YkDAwnMVYtohMvoxsvoxsvoSxHPOOyORLyOTDXTbHoz6hoK0x\njq7WJGa21WNuZz3mdNShtTHuWewp5oso5ivLEYrCFllGR3PVi72+gkynOU42k+3+9G0AP7eVC55S\nNgng5wCgadrXAMzUdf06+/9/BLAHwGYAcbCYissBvG4ig4r53AFW2Ez0ibZMy/HNsyx3P99mmCyv\nUjKmOsHMlXz5LMsVeMNqKpQNE6bJYjQuPq0rtJ9LbR9usWov76rS+P4PuQTAtI83DDN0HPEY07Tw\nmXcvx5fvWIvdh0eRK5QDx+w6NMKCyA33OgL+Wgfjf9nw4/wxH5ZleeI8/OMbhnufuHBYLpswKrRr\na4iHnrd4X6qNwedYKBoBlx7xeRGP5ce89tw5eGTtAQDsPpimhd6hLE6xaxUM2y5Pe4V0rf0j3mrN\nYV9GfFzugiTO1bQswPJu49czosgolrzPgf/5jEaYhcH/XHD+5T1no6s5icGRfGBsywJ4VI/YbVNd\nDIf7g4XwdhwcwSmzm1AsuZ/FSp+pXMFwnnvTsDzz9tdy8ffRYVtdTMNCPKpg9dZgNmpJkjyuYKZp\nwrJkvPrMGVj54iE3XkI4rzDlRMT57ArfPfx+ShawZmsvMrZrkixJ9neOhYZUFLPaUti6f8j5Dooo\nkqOoid9PIpZpOQX4nO8u4ZnN5FwFsj4ZcZ4/fqxnzicApmFO4fPhz/JUniMwPeY5HeYIvNzztCwL\nuaKBbL6EbIEpB7lC2VEU+PtcgSkNuSpFaGtBkYGGZARNdVG0NsTR1pRAa2MSLQ0xtDcl0N6UQKxC\nIDW7HhO/JpXkmqnEdJjjZDOpSoWu63dqmtYG4L/A3J7WAbhK151UMV0A5giHRMHqWswEkAWwAcCV\nuq4/OZFxw1zHM+JKoyhQWBb2do96grsty81QVGtKWX8rX1zvUcEDyfPjfAF85C3LsGROkxv05MkM\n476vlFsfCP+I83oF1eonjId4/mJAsH/beERU2Tk//yHjxHsLY42/TZYklAzTKUxXrb0kAe9//RL8\n+tEdOG1BM1MqLIDXqVAV2UnzWXKERAtNdVEMjxXRa2exqmX+779Kw3/fvtazjdW6CG+/eHYj2pq8\nAjhv+4E3aPjdyl2QJQnvvvIUIcWyO5eWhhhOtd3pKt4je3sgtjuk+ViuhC12sPCCGfXjPlEdTUmx\nR+fdJWfMwK0PbK14XIzHCUisWCUvXuefX1N9DPNnNmLt1h42F0nC/C7mQrdxN3NPE+NCDvWFW1Uq\nYVqWG4/F3cPsk26q87pw8Uxhm3YPYPOeQVyzYqFnP/dT5gHy/Bw4B+wilnxTVJXxolBLJ1CpngK0\nCWLKUzZMZjkolJHJl2wrgvs+Y1sVsgXWptbfwWrEozLq4ioaUhE01cXQVB9HS0McjakYGuui7DUV\nRV0yMqH6SMTJw6QHauu6/mMAP66w70O+/78J4JvHOmZYFeN/vfZcfPpHz2Ao7XUx6R3K4b9+vhZd\nLa4QU01Y82MB0OY2YSyXwqLZjfjzqv0QBz/aKo38C2JWe8pJUSkidpu0fcddS4AgtAvvxYrfbgKm\nKlpFLYxzeg2CACXEmfrejE+1L8xalZ6we+HfIssSDMMMBN5zFsxowDY7cFiWJKHugitg8yMNX/FD\ngD9b4X1XO8eO5iR+8KnX+NpXrlMxr7MerztvTui+REx1MhS9/vw5jsVEgpst6MNvWuoeUEmnEObh\nbJPCLXYAnJX6Wj4TkgS87rw5dv2Vo6PSj16pbKKjKYF/eNfZeM8X/2xbXULmcJTjAtySw3rgyokF\nC8sXtzlFOZ15ysyywa0JErxZuL7123XY3zuGNs1VEsVrOOCrI3LGwlZPgU7+KM9oTeLIQHZiaY4J\ngjhu8PiEUTs+YSxXRrbgVRi4dWE862gtxCIyUnEFdfEIGlIRNKRiaKyLoakujsb6GGZ11kMyTSRj\nKuoSEapdQxwzUyr70/GiklDDEX9L3dzvgguIYKmIRxV85M3LqowFvOasmc7/jlJxzLBK1nM76kIL\nxXkFM68F4GiEhWqC+fFYAbnt81fgm795CUcGsu7ca+z48rNn4akNh4UtflNFbXMIFRx9F0uRJZSN\noDLAec3ymdi2fxh7jowKArS3LVPwJE/qz+e3VC8K/5l3L8c8oc5ALYTVqeCXotozIEmsHoIjXArz\nd54DMelARUMF2+HP6lHptl51wVzcdM9m1vU490ySgPe89hT/VCbERI47VgFbrIHJ4bEb/FkKppSW\nHMXQ850leRvzau3+YnZNdf4AS68SI44DAF/+2wvx4a+v9GQwIwji+GBZFvJFgykL+TJK5jD6BjMY\nGSs6SkQ6W6qYBakWZBmoi6uoS6hoSEXRmIoyBaEuzpSGZBT1yaiTzS5SIcMewLLKNTenKFUrcVw5\nQZUK+02l30xhO2/qCWS0hBSeioyL7IJpE5rDMXxxACz3f0tDDJm+UrjbTsgxoTJCxVVmn+vKBKZr\nmhbOXdLuWQ31DxnWHRecavW+4Oczqz0VKqiqioyyYdY+9RqujyyzSuOVXEQkiFW2ed2N8HaiVUJc\ndQq4b8HCsvktNZ6EcFwVS0WoVUaw4DHF2TsfUdj3iaUVxmCvou8sW2UPvyMXLO3ETfdstovfjaf4\nu2Me7UfJfw+vWbEQv39itxOXEYsquHBZJ0bGChWu41Fp5+zFshCLKvjO318y7vPp0yEAhGebSgpp\nFiVJwhl2bZlv/N3F9tD88+VXKtxjAFfZoToVBFE7haKBdK6I0QxTEERFgb+WjiJIN6pKSMVV1Ccj\ntpIQQ2NdHI11zNWoPhlBQyqKhlTU8UogiKnKCalUVPoVD/0s8sBKX0xF7Z9b72BvuWQ+7n1mb82r\nt5W4wbaO/PT+LeENhPn53Z48K5r2ayyq4IsfOA9fsPPWu74rnpeaSMZU7D4y6tl2+TmzsPLFQ2x/\nXEUmJL2cWG2aT7ySAPrvHzzPqe7tvxWissGqkdfo/hQiJAbcn8aJqYAkuRWX7UrJTj0PcX5S5Wt6\nvH4SQowk3jlUGJdbWAI/TpLkxO+M//y7Z5eIeuurjHc7avpRFJpUqqJdlRAXRv48pe2sWxFVwSfe\nfga+cvtaz2WMRxXkQ4L1AeB9r1uCXz2yPbC9kpLUWBfDiB34XfG6+G8D2HPo+4hiXmdd6CFtdppp\nbm31K1P+555f/1T8xPz6J4iJUiwbTDnI2MpCruj9P1v01HWqlURURmNdFK31MbQ2JdDWmECzHafQ\n0hBDU12sYkAzQUxHTshflUpCpuR7BVxhQFQCqq0A+/EPtXAmC/YsC/1FQgrLVeP8UzsEF6HwNqEC\nctjK8SQsasxqTzlKBe/+/a/XHKWiEmN57lNvb7AsbNg1ENqWB83yA8JuqWFYaExFaw4YCw/UDro/\nPbLmAE6Z3RTahwz2fF2zYiE6m5NobYzjTF81cqZTsGX/sKDyjuak1w/+KFfiLcuC7H8O7L7GuyYe\n9ye7bc9gFj2DLHNTPCquiof3wTeLQqyEGtwPa3F/Et63+DI+1Qq/Bt//x1fjk997ytleLJtIZ93E\nDZZvQHFqX/rQ+fjSz9Y4/1f6bql2yrLzUfY2MkwLfcM5LJzZ6N0nAafMbsT/fOIS/OIh3e1HvM4h\nN8X03fuPv+103PP0nsDqqXjfb/38FZUnThDTHO6SlMmXMJZjf+lsCaM+C8N4yVDCiEVkNKUiaG6I\noa0xyRSGhhhaGuLoaE5g4ZwW5LIFci0iTipOSKUiFlHwuvPn1CRs8iBNMabi3CUd6GypLZ9vUJZg\nYzYkI+izv6iuuWyhJ0h6PD72ttOd980NsdDgKc/KuLONveOpQr17w485Y1Er9vWkJ2RJkSXpqPJW\nL5jRgEN9mQmbb5kMGhzvo29ZhkhEQf04hXPcrFHjj3v1xfOQzZc9Atzl58zyTMa02Co3AJyrdeBc\nrQObdgvKkeBDHzZie1MCW/cNjTuX8bBQReCvcqqSJGFmWxINyfDChTd+9CJ0NCdD9/n7EV/ZP+Nb\nKmr5XIpNjsZNhweiA0BdyPPB07ECwEfevMw7hgVcdFonIqrMqt2L/VY4N0fZCJkqf5ZM06tkrt3W\n6xzbN5z3HKMqMprrY8gXy44CxCtoV4J/Ji8/Zxae3ngEXS1JFuhuVnZmq+ZzTRBTFcuynHoKosKQ\nyZUwli+773NHF8MQUSQ0piJoro+htTGO9qakbV2Io6WeKQ5i1Wc/qiojHlORywbjIQniROaEVCq+\ndMNFGBvLY9Wm8MLdohBkWawoiPjF884rFtc81j6h7gDrm72m4hH0gQkK8ejRX+Z3rFgUuqhbTUAW\nBeL2pgQO9Y2FdMBe3v6ahdh+YBjVlo7/P3tnHidXVSX+by297yFJJyGBhCRc9i1IQGRxQBCYcXDf\nGHTUURAVdBxwRh0RBxccHRRhRP2poIPbz5+iuMCwySr7FiAXsu+ddNLppXqp7f3+uO9Vv6p6VV37\n0nW+nw+k67377j33vO2ed+85J83B1O9atpSHgdBiD8QTR+R6bIYv2wfOyy0j8NotQxmbS93W1d7M\nBy44nIGhce5/xsy8/MM5KrHfMaiyie7stxLCT2N5zV7k1It0skWSyjaT5QOu+eBqVz3JEuQSAcQd\nIS11zD/TcrRp3eRGoUv/05zYXY26jfzUmRALiw9ecDgBv98jepe35M6A/tDFvWzdnXy/OWGF3b5a\nbk49eiF/fmw6wIP73J138sGJaGMzGVeObMsWdjsVefv8lGH2UhBKQTxumRCqtkEwNhkhZCdic5Kx\njdvZnQv5sAXmPurpCNo5FtqY29vG3N72hLHQ19VCR6v4LghCIcxKo6KzvZnIVMTjq6L3QyIQKOzL\neyXw+bxDavoy/kgeuFx18Ym8+/N/Mktyknwtpv9eMKctLcxlNgL+wvTlzDYUNlPh+p3ns/5T7zyO\n6257xlOPzjXyvX85kw9//f6kNjMR9wgVm7o/496U2YtzXrOEeb3ps2IfvfAobvrtmixSwI7BEKsO\nnZfcdsKJ3OsIX9I/pSL1uppxpiInK2G6TKF5FbJdZ9FsDpWWt38SJBsmfp+Pw5f28eLGfYn7YfWR\n/XS0NXH/s9NLAZuCAU49aoG5Ljz60mnPhlx8ruLWO3XSuVvoCnXtjurknmlx6OtqSfJlytR7GSoJ\nlSYWtwjZRoExEKZnGEK28TA2YUKrFhptMODHREWyk7LN6W6jr7uVvq5Wk3Oh0+Ra6GprEoNBEMrE\nrDQqHHLNX2BZFuFIadY9Ti9FKkl1RdPpMhYyiXSR60u8F6l69Pt8RYXFW334fMJ5xODONrDLhaNs\nn4dsL5JgwJ+c1yFLVCXPL/Gu4iY/BZ7OwlZK3e86a6VnO8sP7Mkoq5s3n56cKO3vX7eMZ14dzBD9\nKU3UnMg4OLUrTF7rn+NMRQ4hZVPbSWWmELzpxsh0o1mNCncdKVUsP3Da1ycYmO5IPG7xtjOXs+iA\nDtZs2JdWT9yCG379PPP70g3IluYAPZ3NnHn8gdzq8qFI7YN7pmLDjuRACU49rztmoUcfUpY/1crD\nSah7ItF4inEwPcswPdtgMjkXSjDgo6vNJGTr62ph0bwuOlpN+NTerhZ6O5rp6ZTZBUGoBWa1UZGJ\n1AUpXW3N7I1MZihdWOWlyO2QDffArZD0YO5n74zLXVL60tfVwqK5HZ4Dm1yq6Whr4pSjFhCaiPDB\nCw7nqu8+ml3WlN+FZvL0OsytRycvwkx1WK5wrF6YbMr+4jKRF/huPKi/K+PxeS87m4GEX84hByQ5\n3M9oMORwvbpLZJqpSIo6lcJMCSy3DngsCXSOdQmQOkhxIkiBWTbpELMsVh/eT1tL0LN3w6EpNu0a\nTUqy6dDaHOS/PvY6T1ncfXf8HzrbmrxnClM32R7xyw/sYftgfhnBhcYmHrcITUYZs6MgOY7N7lmF\n0ERxCdqagz47jGoTfZ0t9HWbyEi9XS32zILJ4NzWEkjch5JbQRBqm9ltVKS8ZBNfJ1Pe+qnJooph\nwZx23vU3K/jrSwMlq9OLSJ4PVCfMaSlYfUQ/q4/o5wNfvXfmwhkGmM6sgJPYKyu23DdccRpgBlo/\nLCBqTeogc8n8TpoyGFTZlo7E4unRwdwD5Vgsbup1DWwvOOVg/vDo5qx1J8ta3MnKdvxMNace29HW\nlIhq5iYYNM7EZ61anBRmNZeZinzCPxVqRHplM3/dMQt56PmdWVt3O9inhWd1/Qz4fYl6ZvKzcXw4\ncoqm6yrk7sPRhxzAd//5DG7+3YuegR9SIww7f6c6Y/t8cNV7jp9ZEGFWYgyGSCL6kRMyNTQZITQV\nY//IJKMTXsuHc6Ot2U93exM9nc30dbUm/uvpbE4sQertaKEly0cBQRDqk1ltVBx+cB8fvODwxO85\n3dMx492Ucsp0bk8b55x0UNmNiqTlGzmJX8h8hiGfd8vXL30tV//o8awVJa1Lz2G9vCN5R2v2KE8z\n1pNynr/4gZOytJmxEuIzfAVfcWAP2wZDSXpLekHncCJai3zheqo18fU9v7qWLezmcxefmLa9tTnA\nNy47NfH7dccsZMGc9hlDyuYU/SnJpyJDmRnqce++4u3HcsiibpbM7+Sh53fO3H4islXmMu7lSDOu\nBnQlTMyH1DCyzU0B/H4f8QwfFZKXjcHB/V3M7Ul2RPcB6qC+vOQQ6gfLspgIxxgem2JodIrhUJj9\nY1PsHwszPDZVsMHQ0RpI+Cv0dbUm/BV6Olro7TKGQndH9izOgiDMbma1UWGiOkyvYXYy0qYlgypD\n2+Ve2hmNWQQDvpxD1Xa2BZOEyke+no5mDur3jrSUWs0BPTPnFHAvC2rPEpYv0UaJdFmI8Xj9x5OX\npfh8zlfp9LqCAR8fedORHL50DtsHQ/YXbw/fhhzazSUh0nf/+YyM+zx9Kkp4pXtdde89+1BamgNJ\nA5Z/OOdQfnJXcrK4XHwqvGYqbv50cn9nOp19XS2JL/LHLDd+NaHJSLZDbNxLC7PL6PQjbTlSyk/H\nBMjNoJrGMVzcYXEzhXT20ukH/9Yk0fzFvesytCDUI5ZlMTYRZe/wBEO2wbB/bIph+998E7V1tQXo\n7WxhwdwOetqbEwna+rpamNPVQm+Xd2hzQRAEN7PaqEjFb3/ydL/Xs8X6L4ZyO4zFYnHaW4KMjEdy\nGiJ0dyRHd8pngHnsirkcu2JuzuU/c9EqPu9k7nZhpf1h/DkWzGlnl510rZzkdUbswul6s5MjepQP\n+P302/kdfE6GN8fHpgj/ikw0ZzE8soXPLXQ5UVp9GTa4Z0m6O9KzYRf6td7JDZKxfReWZc5H6hf5\nAryPALjszUdz429eSGw9dEkvu4emr9lMhua0QHZteQrg95gx8ft9GWaDkreJ0+rsIB63GByeYMfe\ncQaGxtk7PMng8GTOCdsCfh99XU3M721lfl8H8/vsnAtdJqtzd0czQTtBq/grCIJQDI1lVGR4x5bj\n5Vvu9/nqIxdw2MF9Sdl+s8tTyu/Udp0Zth84d9qZNWmYYw+EPP1Js7VTspmKUtRhoj95XTMmE7v7\nt7eOcj0Tf/vapYUJyQzXdMquns7cwwlnbdP+96JzFf96819tObxky70uMH5K/3bRKo96Cjih+YYz\ntosffrDJsO4ey/t804P7pGhoHk1YRS5/Svaz8JgZScjmmo3MqyWhVrAsi4GhCTbsGGHTrlEG9o2n\nZUVPpbM1wAHdLczva2fBAcZwmNfbxrzeNno6m0v2IUEQBCEbDWVUZEwUlufz9szjD0wkRsvYVplf\n6T0dzfR05D4Y9JM8yCjHl/OZyNTijGvjS6TLvq70r+b5thnw+5iYiqVdMz7sdfUpX5Y9u5Zjd96S\nEi42H7ItVEjtm5Op+d1nreRn97ya2/3gFVXXPq5/hmzcufnRTBMM+Fmx2CPEbiE2Rf6HeB45lfKV\nONU5PVU9zu+c+u4q4gSRcB+2eH4n7Rn8i5LOnYwj64qRUJjn1g/ywoZ9jE14L9Prbg9y4Nx2lszv\nYkl/N4vndbJgTrs4PQuCUBM0lFEx/dUveXu+797u9hwchqv0Qr/2n1Z7bvf7fUkylSrkrVuXHa05\nXk6pGbp9cPqx6fH1pwvkL5cXb1x9cM5lMw2s2xy/HM/908uizDKpDHXnLEXhZI3+lGGXc38Ufm3k\n5j8yU4K87o7mrEu73PXkS25HTJcKBvxJ/gyJ7UFfkm/ITNGfpmcqprd9+/LT+MS3Hsza/vTyp+lt\n5+V4HYtNUR/EYnEefWmAv740kDQD5fPBQfPaOWLZAaxY3MshC7vp6cz9w4ggCEKlKbtRoZS6DPg0\nsAB4Dvi41jrjmh2l1JnAN4AjgS3AtVrrW0ohS6avhPksSfjIm470HGSktZVzjcXjlt4dRz+93HTJ\ncuTRuOGK07PuzzbITl0vn7q/FJRyBUDqNbN3eJJozDWw9Pns2aCEU0VFqcDKoKzHv+HEJfzvk1s9\nT57fl32m7LpLTskaQea/P3UGl37zL9kH8Znqz6mP08c2Bf18+/LTEsm7HONg1aHzufuprYmSg8MT\nWat2xop+V6lcniMOuSQVTw0pK0teap9oLM5vHtzAxp2jiW1qcRevP+EgjjpkTsYZKUEQhFqkrGNf\npdQ7MQbCF4DjMUbFnUopT69fpdRS4A7gHuBY4FvAD5RSbyiFPM6LOXVwnc8Ae/UR/XTlNFNRWy/0\n9KzU5R3leiX5cgZreS+9KpVPRWmq8azLcZpMhCIFz4zaJRckA1kzamfyLbL/zfnaSM3V4frpZJ72\njH41wwi5uSmQ1dAvZqlHLrMb2brv7Hrj6oPsc2y2DI2Gs97ziZmKHJ643n4oOV40drlvX35aTpHY\nhOry0PM7EwbF0v52rv2n1Vx10Ws46Yh+MSgEQag7yv1B/ZPAzVrrW7XWa4FLgHHgAxnKXwps0Fpf\nqQ03Av/XrqdovAe6+Q+wF8/v5L1vODRrmXNesySvOosihwGH3ze97ntuT2tOy0tmrDPL4DChU5du\nPTbZ+LJ+yS9ZtKI86pk5B0KGWS/737iVbDolp6kov1WRfdyefcYulxwZF51zKG84cXFKrV6j4Qyb\nSmDTZr0uMk1U5PLFP2tmvKTaXD9nCEPrzFTkGVLWIad7wCVbZ1uTRH+qcbbtGeMJvRuA5Qs7+LeL\nT8o60ywIglDrlM2oUEo1Aaswsw4AaK0t4G7glAyHnWzvd3NnlvJ5ceFptuNryrs23/GN3+dLCzWa\nymE1llzK5/MRjpiv6ddd+lqWzPfOO5EPX/3IKbz2KG9fCMuCow6Zw0XnqOltGWWrjON4KcdYqfkO\nEtLbbTiZuqs1rvMaUO4emgCguyO7k28uX0iXH9iTPgByNekYsJ4TNSUzEktSTRpe16KXoeFz2cLx\neEpfUw5wlj8VKvOpRy+YsYyFR6jjFD564VFFJ1YUimf7njF+8+BGLAuagz4uffOxkgdCEIS6p5xP\nsblAAEhNLT2A8a/wYkGG8t1KqZJ5qKW+eMuxFKiSg8lcmvL5fCya28G3Lz+tZO0e0NNKc5P3JRS3\nLA7u7+LEw+YX3U4pBqFtLcGSvrRDk9HkDSkhQ9OiFSWFIi2ZGBnxasMxJJcu6M5wTOGCfesTr0v6\nmt7a5O3Q/p0rTicQ8JXdhCw00hjktvxpuqzZErdMMkq7kYxH5jTj4FHkjasPmvm4HJR64mHzc4pA\nJZSHkVCYu57Yym33vJrw0/ngBYczp1uWqgmCUP/M2uhPgSwDSL+d6MfBGWgEsziHptXvejF7HReM\n+fOuE+DSC4/K+5hAwOd5jKODrvYm/H5oagrQW8Cyp2zyOAP11DIm+ZgvRc/mX7/Hdp/Puw+mH/mf\nn+Tj/fzyyxcwMjJBbIZ47w6Oo7BXmz4fNDelXENOgjb72mptDtr1BJL2Q/KysUx9KrSv7uNT6zhe\nGQMv072R6VzmQl/KoGjpom7OPWlJUlvBoJ/uzuZECNti+9jX3ZpWh/M79dpL3e+QURep5YLmnPV0\nNvMP5yqCQT9+ny/JSGltCdrbgZTree/IZKK9TDInfnuWCcx8rfjS761s7TjyzAb8AT/TectrCwt4\neeNennh5AL1lKGG0NgV8fPhNR7D6yCyR7yqIcy3U8jVRDzJCfchZDzJCfchZTzKWm3IaFYNADOhP\n2d4P7MpwzK4M5Ue01lP5NN7d3ZZxX2dHC319ZumG3+9LDPKcbbnQ4Qrt53Vcq/0VKp86Ac4/F+Ip\nFAAAIABJREFUbXle5U88vJ9lS+ZkbeeIZQcQi1t5y+KQ7bimlrBnGZ8P2tqbk7Y32wPt9vaWpO3B\nQICWlmDGdjo7W2eUIxeyXROp+ILBjG12tjXR0tKUtK+tzSyHW7Swh862JqxAIHF8S3OAVteSIvff\nmfpUbF97etqTwk9+9G3HMu+ATo5ZMTejHjrs7NfFtu3UsXLpATy1diBpm2mnuSTtfOBNRyX18dYv\nnJswbto7Wjzrd86LQyZdpB7bZOcNmDe3i3eccxhgHtLuB/X8eV34fD7a2kyyMXcdkagZSban3BNe\nbXV2tqZtmzOnI+NMm1M2EPBnvY9KcV5rlY722gm1alkW+0YmWbdtP+u3DbN++3BiVgLMs3H1EfP4\nyFuOZ25v7s+kSpHPc7Ja1IOMUB9y1oOMUB9y1oOM5aZsRoXWOqKUego4C/gdgFLKZ//+dobDHgXO\nS9l2jr09L7J9lR4fn2JoKASAFbcS5ZxtuRAam7ZxvI5zkmPlU2chfOKtR2dsJxDw093dRiQaIx63\n8pbl3Wev5Gd3v5r1uJA92EotE4tbTE1GkrZP2S/WUGgqafvJR85nTndrxnb88ThLF3QVrEtHD/nM\nVDjx4r3a9Pl8jI+Hk/aNj4dpawkQmQwzNBlO6GV4eJzvX/l6fnb3K4myU1PT/hip9Z970hLufHxr\n0dfN0FCIeGR6IBOejDAyMsG1l56aUQ/j41OeMhXD2NhkkkymnXBJ2hkZmUjqY1IbKddY4pix5G8T\nqbq49XNnc/F/3J12rOND494ei8eJRs19Pr+3jf37xwGYnIwQt5LvN2eZVOo9kVonwFhoMm3b/v2h\nxAxPKk7ZaDROOBzNqNdsz4h6JzQ+RTzHe7ss7U9E2LRrNPHfSCicVqajNcDqw+dxwWuXM6+vDax4\n2d8P+VDIc7LS1IOMUB9y1oOMUB9y1pOM5abcy5++CfzYNi4ex0Rxagd+DKCU+gqwSGv9Prv8d4HL\nlFJfA36IMUDeBpyfb8OxWJxo1PvkxmJWYp/FtBNlpvKedbiSFHkdF7UvrHzqLBdW3AIrf1mcjN3Z\njsvUz7iV3qZl66ynszlp+9mrlmRtZ8WBPfz7+19TtC6zXROZ8Crv80EkmlyXMUJ8iW2+lGvKndTK\nclWZWv9bz1jOnY9vLaqvJ6p5BP3+pDpi8XjiYZdJD87At5TXbDyWfp84uijn+YzFLc99sZiV8tu7\njtRtzu+069zjORCLW1hWcvvTkc/S5UqrM5Zc5o2rDyIWs7Di3n1NPMssc21l0kktPIvKRTwWTzu3\n5WQqEmPr7jE2D4yyedcog8OTnuXmdDVx5NI+zjr5EA6e156492v5XBTynKw09SAj1Iec9SAj1Iec\n9SBjuSmrUaG1/qWdk+IazDKmZ4FztdZ77CILgCWu8puUUhcA/wV8AtgGfFBrnRoRqijSfCXL4ahd\n8hoLp9De5aKWTGUsjxwNFnDo4h6OXDqnQIkqy7KF3g7Nfp8vaTAJ6RGDgsHUvCCuHz6TRPHm373o\nWXexDuUfffPR6RtzOJdlCXVbxhuhoKoLlKc5GGDVofPSqnLOq/ta93TTtizOXrWY049dlHfb73j9\nioz7rrvEHRgv80nu76v/2YhqEovF2bF3PGFE7NwbIu6h7o7WACsWdXLcyvkcvXwec2y/n76+DoaG\nQkQzGIaCIAizgbI7amutbwJuyrDvHz22PYAJRVsR2luDZckuXUsh4i2r8kaOZVkeCfeoLcXMwOff\nd6Ln9gtft4x+j5wn7p4F/H6uePuxaWVOOnw+Kw7s4ehDDvCs2+/38b1/ObMQcYumHKemEjk5vMgU\n0a1QaZqCfi57S4qx5puOYjUvZdCe2ryFib6VS5SffM5D2pr8DMd+5SMlicrdMFiWxe79E2zeNcrm\ngVG27Q4R8VjW0Bz0sWxBB0cvn8uxK/tZdEC75AcRBKFhmbXRn3LlqvecwFN6N7f8Wed1XL29Nir9\nnvOaqSCHOPr1wGleX5s9xrDHLE83HD70t0ckZiL++Z3HlVq0omhpCjC31FmYc8uHV7rKZzqihDeC\nO4nfFW9LNyALb7swGa2CjxTALGncPDDKuu3DbNgxwthEJK1MwA9L5rVz1CEHcOyK+Sxd2JXR10UQ\nBKHRaEijwv1y72xrojmYf5jV41bO5bpLTuHK72byIa+l13vlhxuWZZUsE3Y9kMskzMID2pOWNh25\nrILLwHI4FXO6W7nu0teWtNnWpgAdrcH0vB5lphKr693nOzX3g9e14LWtFHlchMIJR2LorfvRW/ez\nZWCUqIdfxsI5LRy5bA7HrJjPysW9tBQQllsQBKERaEyjIuV3IdmcgwF/1nCAtTSetgq0KYrJch23\n0r/M1tnqp7zwWu5VU8xwKq//xOvoaps5k3a+LD+whxuuOJ0PfPXe6Y1VVFOpT1Gme8Rr9ZWXkf3R\nC49K21awjJXzU657hkanePzlAV7aPEQkxbGytdnPkUt7WaUWcNQhB9BZhvtCEARhNtKQRkUl8Pt8\nGR19q0FBA94iBimfeuexzE1dPy6Dnpqlu7252iIUQP4XVCltCp/P5ylCplst11uwKJuihu3aWiAc\nifGX53bw7LrBJMNvTlcTJxw6jxMPW8DyA7tlSZMgCEIBiFFBWYI/4ff7Mjr6VppqOGovX9STLgel\nXdNeS/T3tXP4wX0Z95fjGqtXSuW8nUml2WsvrU9FPllKK3HtV8sxvh4YCYX55X3r2DdqcpX4fbDq\n0Dm84aRlLF/UPWufTYIgCJVCjIpGQd6XZeXEw+bPuD5eBi2Giqghg8VR0rZ9JipULrQ2B3Jfi1+g\njGYJXmHHznYi0Ti/un99wqA4amkPF593BHN7JNSuIAhCqWhIoyL1xRvw++juqMflH7nR1hKgqcjc\nB5nw5zGIyRTmsxEoxj9F8Cbj5VTBgXVzU2731TcuO5XW5tyMikJnGyLReM5GTqPxzKt72DtiEtRd\ncPJi3nrmoVWWSBAEYfbRkEZF6qjjhEPnsWJx+nKd2cJF56qkzMa5kssR7a1NfOeK03Kus5G/pDZw\n1ytORnujhCfBh1n2dtFHZh6gtrWU/1E7FYnRLJGJ0rAsi2deHQRgybw23nLGyipLJAiCMDtpUKMi\nmeamwKyeBu/tbCkodfwB3a0csTSzn4BDe2tu0VGq4dtRM8hERcXI9qW/tD4Hpq75femJEEtQbd6M\njkfKNiNZz2wZGGM4FAbgvJOXyjJEQRCEMtGQb6BSvlMufqMqXWU1xqFLevn0u44vWX0mOk0Dv9Cr\n1PWzVi1mwQElHvgWQSkugb977dLsy4kyZdQu5UyFj5ozFiX8aTovbtoHQEuTn1VK8oIIgiCUi4ac\nqSjl2O7M4w4sYW2zm4DfRzAfJ4xZxHknH8yZx1fnWnnvG2bf+vE3n35Ixn0ZIr0m9pUKY1NkylNR\nHWtDfCqSGZuIoLfsB+CElX00FZDoVBAEQciNhjQqhOpw4WnLiMdr7NNuhejraqGvq6XaYtQE5Q57\n6vP5slxnpbUqvFqp5vKaRp4I9OLhNTuJxMzSz3NXZzZEBUEQhOJpSKPC16Bfy6tNf6nXngt1wzUf\nPKlibfn9JqN7ufFlsCosyyJcgA/TdAWFH+qVtbtR2TsyyfPr9wJw3PJeDurvqrJEgiAIs5uGNCrE\nphCEyrJ4Xuf0jzLff/6sMxXl57iV8zigp3XmgmVAnJCnefiFnVgWBPzwnjccXm1xBEEQZj0NaVTI\ni1cQqke57z6/z5fRpyGXW/+yNx+dW0MZlj8dOLeDA+d25FZHiZEPJoaRUBi91fhSnHrkfOb2zt7o\nfoIgCLVCQ3r1yRIBQagi5Z6p8PuIZzAqggH/jEbDKjUvp3Z8kCUDX+EUmijxk+84FnVQb4mlqU/W\nbhlKnJoLTl1eXWEEQRAahAadqai2BILQuJTbUdvvq5BPRZYoU0VRYKVHH3JAaeWoY3YMhgDo721h\nnsxSCIIgVISyGRVKqT7gO8DfAnHg18DlWutQlmN+BLwvZfOftdbnl1I2Wf4kCNWj3Lefz+/DqoBV\n8b43HpY9V0aBNGiAtJJhWRbb9pjXzIrFPVWWRhAEoXEo50zFbUA/cBbQDPwYuBm4aIbj/gS8n+lF\nElOlFkzWHQvC7MXvy7z8CUpn1JQrmtDCGkpUWI/s2jfO+FQUgCOWza2yNIIgCI1DWYwKpdRhwLnA\nKq31M/a2jwN/UEp9Wmu9K8vhU1rrPeWQC0yuhPkS2lQQZi29nc00N9VnkrP//tQZtJRh9qNRiETj\n3PXkVgCagj6OWS5LwgRBECpFuWYqTgGGHIPC5m7MauHVwO1Zjj1TKTUADAH3Ap/TWu8rlWBvOnVZ\nqaoSBKEAyr388Mr3nEAwkLmNluZAzWaeFoOicGKxOLc/tJGBfRMAnL96CR2tTVWWShAEoXEo15t1\nAbDbvUFrHQP22fsy8SfgYuBvgCuBM4A/KqVkwZIgzBLKfTO3NAUI+DM/2o44uI+b/vmMMkshVJJ4\n3OIPf93Mhp0jAByzrIe/k6hPgiAIFSWvmQql1FeAq7IUsYCCswxprX/p+vmiUuoFYD1wJnBfPnUF\nArX5JbKSODpodF2IHgy1ooeAPYsQrOJsQa3ootrMlv4/+PxO1m4xeSkOXdzJFe9aRbBG+lYv11o9\nyFkPMkJ9yFkPMkJ9yFlPMpabfJc//SfwoxnKbAB2AfPdG5VSAWCOvS8ntNYblVKDwAryNCq6uyWM\noIPowiB6MFRbD52dJtt0X191EsS5qbYuhOJ5cdMIj6wxr5WlCzq49rIzaG2uvWjp9XKt1YOc9SAj\n1Iec9SAj1Iec9SBjucnryau13gvsnamcUupRoFcpdbzLr+IszMqHx3JtTym1GDgA2JmPnAAjIxPE\nYvF8D5tVBAJ+urvbGl4XogdDreihv6eF97zhUIaGMkaXLju1ootq4+ihnvnT48ag6GoL8Kl3ncBE\naIqJUMmDBhZMvVxr9SBnPcgI9SFnPcgI9SFnPclYbsryOUdrvVYpdSfwfaXUpZiQsjcAP3NHflJK\nrQWu0lrfrpTqAL6AyWexCzM78TXgFeDOfGWIxeJEo7V5ciuN6MIgejBUWw/d7c2cvWpxTZyLautC\nKJ79YxEA3nuOorO1qWbPZ71ca/UgZz3ICPUhZz3ICPUhZz3IWG7KucjqPcBaTNSnO4AHgI+klFkJ\nONmJYsAxmMhQGvg+8ARwutY6UkY5BUEQhDqmt7OJEw/rr7YYgiAIDU3ZFp5qrfczQ6I7rXXA9fck\n8MZyySMIgiDMTo5a1oe/3KnaBUEQhKzUrqu6IAiCIOTAkv6emQsJgiAIZUWMCkEQBKGu6elorrYI\ngiAIDY8YFYIgCEJdU6sZ0gVBEBoJeRILgiAIdY3fL/4UgiAI1UaMCkEQBKGuGZuQAIGCIAjVRowK\nQRAEoa7p62qttgiCIAgNT9lCygqCIAhCubnh06+npzXQ8EmnBEEQqo3MVAiCIAh1y9KF3dUWQRAE\nQUCMCkEQBEEQBEEQikSMCkEQBEEQBEEQikKMCkEQBEEQBEEQikKMCkEQBEEQBEEQikKMCkEQBEEQ\nBEEQikKMCkEQBEEQBEEQikKMCkEQBEEQBEEQikKMCkEQBEEQBEEQiqJsGbWVUv8GXAAcB0xprefk\neNw1wIeAXuBh4FKt9bpyySkIgiAIgiAIQnGUc6aiCfgl8N+5HqCUugr4GPBh4CQgBNyplGoui4SC\nIAiCIAiCIBRN2WYqtNZfBFBKvS+Pwy4HvqS1vsM+9mJgALgQY6AIgiAIgiAIglBj1IxPhVJqGbAA\nuMfZprUeAR4DTqmWXIIgCIIgCIIgZKdmjAqMQWFhZibcDNj7BEEQBEEQBEGoQfJa/qSU+gpwVZYi\nFnC41vqVoqQqAYFALdlL1cHRQaPrQvRgED1MI7owzJb+13I/6uVaqwc560FGqA8560FGqA8560nG\ncpOvT8V/Aj+aocyGAmXZBfiAfpJnK/qBZ/Ksy9fd3VagGLMP0YVB9GAQPUwjupgV1MXzvh5khPqQ\nsx5khPqQsx5khPqQsx5kLDd5GRVa673A3nIIorXeqJTaBZwFPA+glOoGVgM3lqNNQRAEQRAEQRCK\np5x5KpYAc4CDgYBS6lh71zqtdcgusxa4Smt9u73veuBzSql1wCbgS8A24HYEQRAEQRAEQahJymZU\nANcAF7t+P23/+3rgAfvvlUCPU0BrfZ1Sqh24GZP87kHgPK11uIxyCoIgCIIgCIJQBD7LsqotgyAI\ngiAIgiAIdUztuqoLgiAIgiAIglAXiFEhCIIgCIIgCEJRiFEhCIIgCIIgCEJRiFEhCIIgCIIgCEJR\niFEhCIIgCIIgCEJRiFEhCIIgCIIgCEJRlDNPRVVQSl0GfBpYADwHfFxr/UR1pSocpdRpwL8Aq4CF\nwIVa69+llLkG+BAmt8fDwKVa63Wu/S3AN4F3Ai3AncBHtda7XWX6gO8AfwvEgV8DlzuJCquJUupf\ngTcDhwETwCOYpImvpJSb1XoAUEpdAlwKLLU3vQhco7X+s6vMrNeDG6XUZ4AvA9drrT/l2j7r9aCU\n+gLwhZTNa7XWR7jK1LUe8n2mK6XOBL4BHAlsAa7VWt9SKzIqpd6MuYePw+j7ReBqrfVd5ZQxXzlT\njjsVuB94QWt9Qi3JqJRqxtwD77WP2YF5Jv64xuR8L+ZdvhIYBv4E/IvWel+Z5Jtx7OBxzJlU8N7J\nV8Zq3DuF6NF1bCXvm0LOd8nvnVk1U6GUeifmhvgCcDzmRr9TKTW3qoIVRwfwLPBRIC2piFLqKuBj\nwIeBk4AQps/NrmLXAxcAbwVOBxZhBgVubgMOB86yy56OSUJYC5wG3ACsBs4GmoC7lFJtToEG0QPA\nVuAq4ATMw+Ne4Hal1OHQUHoAQCn1Gkxfn0vZ3kh6WAP0Y14KC4DXOTvqXQ/5PtOVUkuBO4B7gGOB\nbwE/UEq9oVZkxOjuLuA8zH18H/B7pdSx5ZKxQDmd43qAW4C7yylfETL+CpNU9x+BQ4F3A7qW5LQH\nl7cA3weOAN6GuR+/V0Yxs44dPGRcSoXvnXxlpDr3Tr4yApW9b2wKkbPk986sSn6nlPor8JjW+nL7\ntw8zCPu21vq6qgpXApRScVKsT6XUDuDrWuv/sn93AwPA+7TWv7R/7wHepbX+jV1GAS8DJ2utH7cH\npC8Cq7TWz9hlzgX+ACzWWu+qXC9nxn5w7wZO11o/ZG9rOD04KKX2Ap/WWv+okfSglOoEnsJ8ufo8\n8IwzU9EoerBnKv4+01ewetdDvs90pdTXgPO01se4tv0M6NFan18LMmaoYw3wc631f5RDxmLktPX3\nCmaGKuO1Vg0ZlVJvxBi8h2it95dLrhLI+c/AJVrrla5tHwOu1FofVAF508YOHmUqfu/kK2OG48p+\n77jaylnGSt43Hm3ncr7Lcu/MmpkKpVQT5svtPc42rbWFsRJPqZZc5UQptQzzZdLd5xHgMab7fCJm\nmZu7jMZMbTplTgaGnAGDzd0Ya3d1ueQvgl6MbPugcfWglPIrpd4FtAOPNKAebgR+r7W+172xAfWw\nUim1XSm1Xin1U6XUEqh/PRT4TD+Z9C+Dd2YpXw0ZU+vwAV3Yz7NyUKicSql/BJYBXyyXbEXK+HfA\nk8BVSqltSimtlPq6Uqq1xuR8FFiilDrPrqMfeDvGMK8VKnrvlIJK3DuFUMn7pgjKcu/MGqMCmAsE\nMF/h3AxgXqyzkQWYF3u2PvcDYXswkanMAsyX/wRa6xjmRq0p3dkPkeuBh7TWL9mbG0oPSqmjlFKj\nwBRwE/BmeyDYMHqwjanjgH/12N0wegD+CrwfOBe4BPMie0Ap1UH966GQZ/qCDOW7lfEdKTWleO/8\nC2bpwi9LKFcqecuplFqJ8VV6r9Y6XkbZHArR5SGY5bFHAhcCl2OWFt1YJhmhADm11o8AFwG/UEqF\ngZ3AEGZpYq1Q6XunFFTi3smLKtw3hVKWe2fWOWoLs56bMGtST622IFVkLWbNaw/mIXCrUur06opU\nOZRSizGG5dla60i15akmWus7XT/XKKUeBzYD78BcJ0INo5R6D2bp3pu01oPVlsdBKeUH/gf4gtZ6\nvb3ZV0WRMuHHLC95j9Z6DEAp9SngV0qpj2qtp6oqnY1S6giMj8LVGJ+AhcB/YvySPlQ9yeqXWrx3\n6ui+gTLdO7NppmIQiGG+vLnpB6q+9rlM7MJcsNn6vAtottdOZysz371TKRUA5lBDulNKfQc4HzhT\na73Ttauh9KC1jmqtN2itn9FafxbjJHg5jaOHVcA84GmlVEQpFQHOAC63vwIO0Bh6SENrPYxZx7uC\n+r8eCnmm78pQfqRMA8yC3zv2bNv3gLdrre8rg2xu8pWzC7M07juue+zzwHFKqbAyUYKqLSOYL/7b\nnUGRzcuY635xySU0FCLnZ4CHtdbf1Fqv0Vr/L8ah9gP2UqhaoNL3TsFU+N7Jh2rcN4VSlntn1hgV\n9hfLpzDRSYDEUpmzMCFIZx1a642YB4G7z92Ydc5On58CoillFHAQZp0n9r+9SqnjXdWfhbm4HiuX\n/PlgGxR/D7xea73Fva+R9JABP9DSQHq4Gzgas/zpWPu/J4GfAsdqrTfQGHpIQxnn9RXAjnq/Hgp8\npj/qLm9zDtN9qQUZUUq9G/g/GAf5P2cqV0U5R4CjSL7Hvsv0LGnJz3uBunwYWKSUandtU5gvsNtK\nLWMRcrZj7jM3cczyxFr5kl3Re6dQKn3v5EnF75siKMu9M9uiP70D+DFmbfHjwCcxy0MO01rvqaJo\nBWOvjV6BefA8DXwKE0Ztn9Z6q1LqSkyI0fcDm4AvYdbIHam1Dtt13IQJwfaPwCjwbSCutT7N1c4f\nMV8jLwWagR8Cj2ut/6H8vcyOLf+7gTdhvsI6DGutJ+0ys14PAEqpL2Pim2/BfBVxYp+fo7W+t1H0\nkIpS6j6Soz81hB6UUl8Hfo9Z8nQgxjHwGOAIrfXeetfDTM90pdRXgEVa6/fZ5ZcCL2CWSf4QM0i6\nHjhfa12W0I4FyPgeu/wngN+4qprw8G2pmpwex2eNNFYNGe3340sY36KrMTOY3wfu01pfUkNyvg/z\nZf1yjPPzIuC/gKjW+rVlknGmsUMt3Dv5yljxeydfGT2OL/t9U4ic5bp3Zs1MBYDW+peYZDTXAM9g\nXq7n1qtBYXMipi9PYb5qfANzwXwRQJvwdTdg1mY+BrRhwsKFXXV8EhN/+v9iErHswMSkd/MejDV9\nt132AeAj5ehQAVwCdDMtu/PfO5wCDaIHMAO7W5iWcRW2QQENpYdUkr6ONJAeFmPCAq4Ffo4JD3uy\n1nov1L8ecnimLwCWuMpvwuTROBsTs/2TwAfLNSgqREbgnzCOvjeS/Dy7vlwyFihnxSngfIeAN2Ai\nAj4B/AS4HTN4ryU5b8EM8i7DDNx/gVlqknqflZKsYwcPGTdR4XsnXxmpzr2Tr4zVIt/zXZZ7Z1bN\nVAiCIAiCIAiCUHlm1UyFIAiCIAiCIAiVR4wKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAE\nQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAE\nQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowK\nQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCKQowKQRAEQRAEQRCK\nIlhtAQRBEARBEKqBUup9wI9SNu8BXgSu01r/uYA6LwXGtda3lEBEQagbZKZCEARBEIRGxgI+B1wE\n/APwNWAu8Eel1PkF1PdR4H2lE08Q6gOZqRAEQRAEodH5s9b6aeeHUuqHwADwbuCPVZNKEOoIMSoE\nQRAEQRBcaK33K6UmgKizTSnlAy4HPgQsB4aB3wKf0Vrvt8tsBA62/47bh96vtf4bpVQf8FngHGAZ\nEAceto9/3t2+UurjwEfsclPAeuAbWuufl6fHglA8YlQIgiAIgtDo9CilDgB8wHzgE0AH8BNXme8B\nFwM/BL6FGfB/HDhOKXWq1jqGMTq+A4wC/2HXN2AffwjwJuBXwEagH2M43K+UOkJrvQtAKfVPdv2/\nBK4HWoFjgNWAGBVCzSJGhSAIgiAIjYwPuCdl2yTwAa31vQBKqdcBHwTerbX+hVNIKXUfcCfwduDn\nWuvfKaWuBfZorX+WUufzWutD3RuUUj8BtF33tfbm84E1Wut3laR3glAhxKgQBEEQBKGRsTDO1a/a\nv/sxTtv/Ryk1qrX+LcZo2A/cY89oODwDjAGvZ4ZZBK11xPlbKeUHeoFxjFFxgqvofmCxUupErfWT\nxXRMECqJGBWCIAiCIDQ6T6Q4av8cYzB8Ryl1B7ACYwTs9jjWwiyZyortk3EFcClm6VTAdfygq+jX\ngLOAx5VS64C7gNu01o/k2ylBqCQSUlYQBEEQBMGF1toC7gMWAisx46UBzGD/7JT/3gD8ew7Vfhb4\nBnA/8F6Mw/bZwEu4xmNa67WAAt4JPAi8BXhIKfWF4nsmCOVDZioEQRAEQRDSccZInZjoS2cBj2it\np2Y4zsqw/a3AvVrrD7s3KqV6MQn3EmitJzAO3b9SSgWB3wCfVUp9RWsdzq8bglAZZKZCEARBEATB\nhT2QPxcIAy9jIjEF8ZiRUEoFlFI9rk0hzFKpVGIYp3D3sW8HDkzZNsf9W2sdtWXwAU359kUQKoXM\nVAiCIAiC0Mj4gPOVUofbv+djlictB76itR4DHlBK3Qx8Ril1HMbPIQIcCrwNE4L2/9nHPwVcopT6\nLLAO2K21vg+4A/i8nVjvEeBou531KfLcpZTahclhMQAcAVwG3KG1DpW894JQIsSoEARBEAShkbGA\nL7p+TwJrgUu01t93NmqtL1VKPYnJLXEtJjHeJuBWjAHgcA1wEPAvQBfwF4x/xpeBduDczc0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ZY6Oc7Qccvi+X2mvR+s3caDWb7mf/bxwnJpDE6GuWd75q/6ufL04AgbXNG/nsrwhT5f9k55R4da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\nnODEfHB3G6YBsxNHGcviIl3S0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YQQufB3nEmfVMsT4WzjTHc7DZgGzcFAVtV+n1s5P27o\nz8Tje/XbSA6mDm7uBZ7iWfNmJC2rg1QD4JDuNmaEEocxLr9Feczm7qfOLOeogPq5luLU5UkLIURl\nqctCRVdjkOUes/1mo3wFkvLfJRMLDW3BAC2u2YYXtjVz2ZGpZ7Re1dPOgTp4mlqqT6ZaCruF6Gpq\noMtjLg1RPI0Bk0uPWMzuA1l3Q6t6UqYQQgj/1WWhYnpzIx9alnmW4yRlvHPldKgSxfWexb1x/UgM\nw6ApkPpgqqsyh3T86PK5zJzWRjhhBJyKUw+lClEWdlM9KVQIIYQon7osVFSDDx40K+tZskt1Qw1m\n09ypCvQ0N9DeGEwaVjNfBnBCb1dR9uXeZ6WUKa48eqnfIYgiaA0GmJnYd6dGpZ8FXQghRDmUtVCh\nlLoJOByYCfQDdwAXaa23u7aZB/wYOAUYAm4Avqy1rv12NS5zWpv9DkGkYBgGb56f3C+iEIvaQ0xr\nroxmQZJBm1LNSdHT3MA/HrrA7zCEEELUiXLXVKwFrgS2A3OAbwO/A04AUEqZwK3ANmA1MBv4FTAO\nXFLmWJNUcf5CVLhiF1JE4Q7uaqU1mP1wzMI/8tsshBD+K2uhQmv9H66XW5RS3wT+oJQKaK0jwJnA\ncuBUZ0bt9UqpS4FvKqW+prUOlzPeaiE3VCGK74MH5dHvSgghhKhTvjWaV0r1AOcC9zkFCrBrJ9Y7\nBYqYNUAncEiZQ0zylvkzeIs8URZCiIpSzc3UhBCiVpS9UKGU+qZSaj+wG5gHvMO1ug/YmfCWna51\nvuppaqCnAofDlDbwQoh6Jr+AQgjhv4KbPymlvgFclGYTC1ihtX7OeX018J/AAuAy7D4TRZ9BO5DD\nzNCmaWAYBsFgdY52lCruWBrkkha1SNLBJukwRdLCVivnb5pm1f5+F4Ncz1MkLWySDjZJB1u5zr8Y\nfSq+BfwywzabY39orfcCe4FNSqmN2H0rjtNaPwTsAI5JeG+v8++OXILq6Ahlve3ZTXMYHg/TXaHz\nLGTS3Z0+7lzSopZJOtgkHaZIWtSG1pbGjL+D9UCu5ymSFjZJB5ukQ3kUXKjQWu8B9uT59tjQKk3O\nvw8AFyulprv6VZwBDADP5rLjwcFRIlnO7hzE7rTR3z+cyyEqRqq4AwGTjo5QTmlRiyQdbJIOUyQt\nbLF0qHajo+NV+/tdDHI9T5G0sEk62CQdbOX6rS/b6E9KqWOxayHuxZ6jYilwOfA8dmEC4M/YhYdf\nKaUuAmYBVwA/0FrnND1sJBIlHK79C+i8ZXMynme9pEUmkg42SYcpkha1IRq15HNErmc3SQubpINN\n0qE8ytnIbAR4F/aEdxuBnwFPAqfECgzOBHdnAxHgfuyJ767D7nshPCztbPE7BCGE8JV01BZCCP+V\nraZCa/00cFoW222hBB23hRBC1CZDihVCCOG7+u4OL4QQovpJmUIIIXwnhQohhBBVTcoUQgjhPylU\nCCGEqGpSqBBCCP9JoUIIIUR1k1KFEEL4rmwdtd2UUo3Aw8BhwOFa63WudfOAHwOnAEPYI0B92RkZ\nSgghhIgjZQohhPCfL4UK4GrgVeBQ90KllAncCmwDVgOzgV8B48AlZY5RCCFEFWgNBjJvJIQQoqTK\n3vxJKXUWcDrwRZIfMJ0JLAfO1Vqv11qvAS4FLlBK+VUAEkIIUaHOPWQes1qb/Q5DCCHqXlkLFUqp\nXuCnwAeBUY9NVgPrtda7XcvWAJ3AIaWPUAghRDU5ZcEMv0MQQghB+Wsqfglcq7V+IsX6PmBnwrKd\nrnVCCCGEEEKIClNwkyKl1DeAi9JsYgErgDcBbcC/OctL2rcuEJCBrWJpUO9pIelgk3SYImlhq5Xz\nr5XzyJdcz1MkLWySDjZJB1u5zr8Y/RS+hV0Dkc6LwKnA8cCYUsq97lGl1H9prT8K7ACOSXhvr/Pv\njhxiMjo6QjlsXtskLWySDjZJhymSFjVBfu8dkg5TJC1skg42SYfyKLhQobXeA+zJtJ1S6rPAv7gW\nzcbuL/Ee7OFlAR4ALlZKTXf1qzgDGACeLTRWIYQQQgghRPEZlmX5cmCl1ALsGozJeSqcIWWfwB5S\n9iJgFvY8FT/VWl/qS6BCCCGEEEKItPxuZBZXonEmuDsbiAD3YxcorgMuK3tkQgghhBBCiKz4VlMh\nhBBCCCGEqA1+11QIIYQQQgghqpwUKoQQQgghhBAFkUKFEEIIIYQQoiBSqBBCCCGEEEIURAoVQggh\nhBBCiIJIoUIIIYQQQghRkIJn1K40SqkLgC8CfcBTwGe11o/4G1X+lFInAv8MHIU9GeA7tNY3J2xz\nOfAxoAu4Dzhfa73Jtb4J+A7wXqAJeybzT2utX3Nt0w38AHuekCjwv8CFWuvh0p1ddpRSXwHeCSwH\nRrHnMLlIa/1cwnY1nQ4ASqlPAecDC51FzwCXa61vd21T8+ngppT6MnAV8D2t9Rdcy2s+HZRSl5E8\nj89GrfXBrm1qMh1q7bc+H9ncH+pBtveIWpfN/aEepbpH1INs7hHFVFM1FUqp9wLfxk7AI7BvNGuU\nUtN9DawwrcCTwKdJmCwQQCl1EfAZ4BPAscAw9jk3ujb7HvAW4O+Ak4DZ2JkCt/8GVgCnOdueBPyk\nmCdSgBOBa4DjgDcCDcCflVKh2AZ1kg4AW7Bnmz8SOyOxFrhJKbUC6iodAFBKHYN9rk8lLK+ndHga\n6MXOXPcBJ8RW1Go61OhvfT7S3h/qSMZ7RJ1Ie3+oR6nuEXUm5T2i2Gpq8jul1IPAQ1rrC53XBvaX\n7Pta66t9Da4IlFJREp5EKaW2Af+utf6u87oD2Al8WGt9o/N6F/A+rfUfnG0UsAFYrbV+2PnBeQY4\nSmv9hLPNmcAtwFyt9Y7ynWVmTsbhNeAkrfW9zrK6S4cYpdQe4Ita61/WUzoopdqAx7CfzF0KPBF7\nClUv6eA8hXq71vrIFOtrMh1q/bc+H173h3rldY+oV+77g9+xlFu6e0S9yHSPKLaaqalQSjVgl8zv\njC3TWlvAHcDxfsVVSkqpRdilTvc5DwIPMXXOR2M3c3Nvo4FXXNusBvpjGQbHHdhPvo4rVfwF6MKO\nbS/UbzoopUyl1PuAFuD+OkyHHwJ/1FqvdS+sw3Q4SCm1VSn1glLq10qpeVC76VCPv/UiZ3H3iHqU\ncH94wO94fOJ5j6hDnveIUqiZQgUwHQhgP4Vz24l9Y61Ffdg/nOnOuRcYdzITqbbpw36qM0lrHcH+\nQa6otHOeSH4PuFdr/ayzuK7SQSm1Uik1BIwB1wLvdDKCdZMOzs3ycOArHqvrJh2AB4GPAGcCnwIW\nAXcrpVqp3XSox996kaUU94i6keL+sNHnsMouwz2inqS7RxRdzXXUFjXvWuBg4PV+B+KjjcAqoBM4\nB7hBKXWSvyGVj1JqLnam4Y1a6wm/4/GT1nqN6+XTSqmHgZeB92BfJ0LUm3q/R3jeH+qpYCH3iCkZ\n7hFFbxJXSzUVu4EI9pM3t17A97bPJbIDMEh/zjuARqftdLptZrpXKqUCQA8VlHZKqR8AbwZO0Vpv\nd62qq3TQWoe11pu11k9orf8FuwPahdRPOhwFzAAeV0pNKKUmgJOBC5VS49hPrOshHZJorQeA54Cl\n1O71UI+/9SILae4RdSPN/aGepL1HOLVZdSnhHlF0NVOocEqjj2GPTgJMVoOehj28XM3RWr+IfRN1\nn3MHdjvn2Dk/BoQTtlHAfKbaWT4AdCmljnDt/jTsDMlDpYo/F87N4u3AqVrrV9zr6ikdUjCBpjpK\nhzuAQ7Grtlc5/z0K/BpYpbXeTH2kQxKnY+JSYFutXg/1+FsvMkt3j6hzJvZQ0fUk0z2idkYoypHr\nHlGSQnetjf70HuA67HZjDwOfx67+W6613uVjaHlz2r0txb6BPw58AfgrsFdrvUUp9SXsIeQ+ArwE\nXAEcAhyitR539nEtcBbwUWAI+D4Q1Vqf6DrOrdhPI88HGoFfAA9rrf++9GeZnhP/+4G3YZewYwa0\n1gecbWo+HQCUUlcBt2F3pG0HzsUep/4MrfXaekmHREqpvxI/+lNdpINS6t+BP2JXZ88Bvg4cBhys\ntd5Tq+lQi7/1+ch0f/AztnLK5h5RDzLdH/yMzW+J94h6kekeUezj1VSfCmeIxOnA5dhV4U8CZ1b5\nTeZo7JuE5fz3bWf59cB5WuurlVIt2GPGdwH3AGfFMgyOz2M3F/h/2E8sbgcuSDjOB7Ant7oDe3Kr\n/0flVJl+Cvvc/5aw/KPADQB1kg5gZ+yux57oagBYh+uGUUfpkCju6UgdpcNc7DkkpmEPDXsv9lCw\ne6B206FGf+vzkfb+4FdQPsh4j6gTae8Pda52nqDnJu09othqqqZCCCGEEEIIUX4106dCCCGEEEII\n4Q8pVAghhBBCCCEKIoUKIYQQQgghREGkUCGEEEIIIYQoiBQqhBBCCCGEEAWRQoUQQgghhBCiIFKo\nEEIIIYQQQhREChVCCCGEEEKIgkihQgghhBBCCFEQKVQIIYQQQgghCiKFCiGEEEIIIURBpFAhhBBC\nCCGEKIgUKoQQQgghhBAFkUKFEEIIIYQQoiBSqBBCCCGEEEIURAoVQgghhBBCiIJIoUIIIYQQQghR\nEClUCCGEEKKuKaVeUkr9wu84hKhmQb8DEEIIIYQoFaXUocBlwNFAL7AHeBa4WWv9A2ezKGD5E2F2\nlFJHAx8BTgEWYp/Hg8AlWuvnfQtMCIcUKoQQQghRk5RSrwPWAi8DPwV2APOA1cDngFihQmEXLCrZ\nRcDrgN8B64A+4LPA40qp47TWz/oZnBBSqBBCCCFErfoXYB9wtNZ6yL1CKTU99rfWeqLcgeXh28D7\ntdbh2AKl1I3AeuDLwIf8CkwIAMOyKrq2TwghhBAiL0qpDcA2rfVpGbZ7CVirtT7Pteww4BrgGOym\nRj8GtgE/BxZqrV9xvXcddqb/W8AhwCbgs1rru5RS7wK+BhwEPAN8TGv9pOs4hwJfAE4CZmMXgm4F\n/llrvTeLc3wUsLTWx2TaVohSkpoKIYQQQtSql4HVSqlDtNbPpNku7gmrUmo28FcgAlwJjAAfA8YT\nt3VeHwT8F/AT4FfAPwM3K6XOd97/Q8AALgZ+i93cKuZ0YBHwC+zmWYcAnwQOBo7P4hx7gaez2E6I\nkpJChRBCCCFq1bewn/o/qZR6GLgHuBP4q7sZkYcvA53AEVrr9QBKqV9i10B4WQYcr7V+2Nl2A7AG\nux+H0lpvdZbvA36slDpJa323894faq2/496ZUuoh4L+VUq/XWt+XKkil1AeBOcAlac5FiLKQIWWF\nEEIIUZO01ndgP+2/CTgMuwZhDbBVKfXWNG89E3ggVqBw9rUPuzbCy7OxAoXjIeffO2MFCtdyA1js\n2u9Y7G+lVJNSappruyNTBaiUWo7d0fw+4IY05yJEWUihQgghhBA1S2v9mNb6HKAbOBa4CmgDfudk\nzL0swLtWIlVNxSsJxxx0/nw1YbsB59/u2AKlVLdS6j+UUjuAUWAXsBm7WVWn18GUUr3ALUA/8G6t\ntXSQFb6T5k9CCCGEqHlOc6fHgMeUUs8DvwTeDVxRhN1HclxuuP7+HfYQt1cDTwH7sR/6rsHj4a9S\nqgO4HegATtBa78gzZiGKSgoVQgghhKg3jzr/zkqx/mVgqcfyg4oZhFKqC3gDcKnW+krXcq9jo5Rq\nAv7kxHaa1loXMx4hCiHNn4QQQghRk5RSp6RY9Rbn340p1q8BjneGlY3tqwf4QPGiA6ZqMhLzY58n\neUQqE7gROA44J6EPhxC+k5oKIYQQQtSqa5RSLcAfsAsQjcDrgfdg91u4LsX7rgY+CNyhlLoGGMYe\nUvZl7P4QRenDoLUeUkrdDXxJKdUIbAXOABYS30QK4DvAW4GbgelKqXMT9pWqE7kQZSGFCiGEEELU\nqn/C7jdxFvBx7ELFK9ijJl3p6lBt4SooaK1fdWo5vg98BdgN/Ai7v8P3gAOuY8S9N4/l78eeZO/T\n2AWJNU682xK2W+W8fqvzXyIpVAhfyYzaQgghhBBZUEp9D7tw0iYjLgkRz9c+FUqpLyulokqpxElf\nLldKbVNKjSil/pKqw5IQQgghRCkopZoTXk/DbhJ1jxQohEjmW/MnpdQxwCewh09zL78I+AzwIeAl\n4F+BNUqpFVrr8XLHKYQQQoi69IBS6m/ABqAPOA9opzhD0ApRc3ypqVBKtQG/xu70tC9h9YXAFVrr\nP2mtn8YuXMwG3lHeKIUQQghRx27B7tvwHeyZuF8C3qS1vs/PoISoVH7VVPwQ+KPWeq1S6tLYQqXU\nIuynAXfGlmmtB5VSDwHHYw+lJoQQQghRUlrrS4BL/I5DiGpR9kKFUup9wOHA0R6r+7BHNtiZsHyn\ns04IIYQQQghRYcpaqFBKzcUeiu2NWuuJch5bCCGEEEIIURrlrqk4CpgBPK6Uik3qEgBOUkp9BliO\nPUZzL/G1Fb3AE9kexLIsyzAS54wRQgjhoap/LOX3XgghslLyH8pyFyruAA5NWHYd9sgK39Rab1ZK\n7QBOA9YBKKU6sKek/2G2BzEMg8HBUSKRaFGCrlaBgElHR6ju00LSwSbpMEXSwhZLh2pWrb/31X4N\nVnP8Ert/qjn+Woi91MpaqNBaDwPPupcppYaBPVrrDc6i7wGXKKU2YY+0cAXwKnBTLseKRKKEw9X1\noZeKpIVN0sEm6TBF0qI2VPPnWM2xQ3XHL7H7p5rjr+bYS83Xye8ccRPIaK2vxp6u/ifAQ0AIOEvm\nqBBCCCGEEKIy+Tb5XYzW+g0ey74GfK3swQghhBBCCCFyVgk1FUIIIYQQQogqJoUKIYQQQgghREGk\nUCGEEEIIIYQoiBQqhBBCCCGEEAWRQoUQQgghhBCiIFKoEEIIIYQQQhREChVCCCGq1padQ36HIIQQ\nAilUCCGEqGKf+fe17B444HcYQghR96RQIYQQompFLdixZ9jvMIQQou6VdUZtpdSngPOBhc6iZ4DL\ntda3O+tbgX8D3g5MA14Evq+1/kk54xRCCFE9DoxH/A5BCCHqXrlrKrYAFwFHAkcBa4GblFIrnPXf\nBc4APgAsd17/QCl1dpnjFEIIUSVGDoT9DkEIIepeWWsqtNa3JCy6RCl1PrAa2AAcD1yvtb7HWf+f\nTu3GscCfyhepEEKIajF8YMLvEIQQou751qdCKWUqpd4HtAD3O4vvB96mlJrtbHMqcBCwxp8ohRBC\nVDqpqRBCCP+VtaYCQCm1EngAaAaGgHdqrbWz+rPAT4FXlVJhIAJ8XGt9X67HCQSkD3osDeo9LSQd\nbJIOUyQtbLVy/iNjYYLB6jqXar8Gqzl+id0/1Rx/LcReamUvVAAbgVVAJ3AOcINS6iSt9Ubgc8Bx\nwNnAK8BJwLVKqW1a67W5HKSjI1TcqKtYKdNi7aOv0NQQ5PWrZpfsGMUi14RN0mGKpEVtCEejdHe3\n+h1GXqr9Gqzm+CV2/1Rz/NUce6mVvVChtQ4Dm52XTyiljgUuVEp9HrgSeIfW+jZn/dNKqSOAL2J3\n6s7a4OAokUi0WGFXpUDApKMjVNK0+PODL9MeauDg+Z0l2X8xlCMdqoGkwxRJC1ssHard8Mg4/f3V\nNaxstV+D1Ry/xO6fao6/FmIvNT9qKhKZQBPQ4PyXODZghDz6fkQiUcLh6vrQS6WUaWFZFlHLqoq0\nlmvCJukwRdKiNoxPRKr2c6z2a7Ca45fY/VPN8Vdz7KVW7nkqrgJuw27a1A6cC5wMnKG1HlJK3QV8\nSyn1WeBl4BTgQ8A/ljNOIYQQ1cM0/I5ACCFEuXubzASux+5XcQf2XBVnuPpLvBd4BPg19sR4XwK+\norX+aZnjFEKIivCHuzdn3qjONQQDfocghBB1r9zzVHwsw/rXgH8oUzhCCFHx/nj/S7zzpMV+h1HR\nmhqlUCGEEH6rvnGxREWRVgdCCL+1tzT6HYIQQtQ9KVQIIYSoah2tTX6HIIQQdU8KFaJgluV3BEKI\netYWavA7BCGEqHtSqBBCCFHVTEMaYgohhN+kUCEKJvdzIYSfxiYSpzcSQghRblKoEAWT5k9CCD/t\nGhj1OwQhhKh7UqgQhZFqCiGEz17cOuB3CEIIUfekUCEKU2PVFJZlsfHlfr/DEELkQG8ZYOTAhN9h\nCCFEXSvr5HdKqU8B5wMLnUXPAJdrrW93bbMC+CZwshPfM8Dfaa1fLWesoj5NhKNc/Zsn+MWX3+B3\nKEKILEWiFn+6/2Xe84alfocihBB1q9w1FVuAi4AjgaOAtcBNTkECpdQS4B7gWeAk4FDgCuBAmeMU\n2aqx5k+1Ve8iRO1bMb8dgL88uoWd/SM+RyOEEPWrrDUVWutbEhZdopQ6H1gNbACuBG7RWn/Ftc2L\n5YpPePvB79fzmXcdmnJ9TWXEa+pkhKh9bzq2F/3qfiJRixvXbuKzf3eY3yEJIURd8q1PhVLKVEq9\nD2gB7ldKGcCbgeeVUrcrpXYqpR5USr3drxiF7fHndvkdghBlNzZE/rV/AAAgAElEQVQR4a4nt/od\nRl5+97dNvLRj0O8wymJaRxPHqBkAPPH8bja8tNfniIQQoj6VvVChlFqplBoCxoBrgXdqrTUwE2jD\nbh51K3A68Afg90qpE8sV33nfXFuuQ9WM2moAJYRtz8ABrr9dl/w4N6zR/O9dLxR1n7c9+Aov7xjy\nXHfj2k3891+eK+rx/Pa6Q2fR0mRXvP/mzueJRKM+RySEEPWnrM2fHBuBVUAncA5wg1LqJCA2JuD/\naa2/7/y9Tin1OuBT2H0tshYI5F9eCga93zs4PE5Ha2Pe+y23WBoUkhYxqdLEMOz/Uq2vBLmkQ9jJ\njFTy+eSrmNdDtcsmLQLONVDqa+G1/hE6WhrTHiefGEzT9HzfroFRJsJRgkGzZq6FllADJ62axe0P\nb+HVXcOsfXwrZ61e4HdYaVX797Ga45fY/VPN8ddC7KVW9kKF1joMbHZePqGUOha4EPgcEMbuW+G2\nAXh9rsfp6AjlHWN3d6vn8g/96x388duV2RorErUYPTBBW0tyoccrLV7aPsgD67bx/jOXZ7X/VGnS\n2BCkoTGYcn22Ht2wk/m97czsaSloP+lkc02MjoWB1OdbCwr5bmRjz8AoHa1NNFRBwSxdWgyN2bM0\nl/paCAYDGb9D+cTQ2tro+b7GxiAYkZq6xltbmjjhiHmsf7Gfrbv28/u7NnPikfOY19vud2gZlfr7\nWGrVHL/E7p9qjr+aYy81P2oqEplAk9Z6Qin1CKAS1i8DXs51p4ODo0Qi+VWB9/cP57XOTw8+s4Nf\n3LKBn37p1MllgYBJR0fIMy2e2LCD//6z5k3Hzstq/6nOe2IiQtAsPF3+7YZHeNfJi3nTccV/upgu\nHRLFChW5nM+jG19j0awOpnU2FxRnqeWSDoU478o7+fhbD+b1h83K6X1DI+Nc+rOH+N6FpW/tmE1a\nDA7aszSX+jsfDkcYHw8X/XdneHjc833j42EmwlH6+4cn06HaDY+MEY1EOfPYuVx360bGJiJ89Sf3\n86Vzj6SvhA8qClGu72OpVHP8Ert/qjn+Woi91Mo9T8VVwG3AK0A7cC72fBRnOJv8O/A/Sql7gL8C\nZwFnO9vkJBKJEg7n96Gne1+++yy1sfEIB8YjnvF5pUU0ag9zlOl8LCv9dpZlEbUKTxcLiESskqZv\nNtdEbH2m7e5bv52jl8+kqSHANf+7jr8/Q3HKEXMyxrBn4ACP6tc489j52QdeZIV8N7IRtSwmwrkf\nY3h0gr1DY2X9jqVLi0gku+9IoSzL/h4V+3cnGk1xbpb9/a/U37J8RCNRIhGL6R0h3nj0PP78yBZ2\nDxzgsp8/zAdPX8bqQ3oxKnT461J/H0utmuOX2P1TzfFXc+ylVu72CTOB67H7VdyBPVfFGVrrtQBa\n6//D7j/xJWAdcB7wLq31A2WOU5RZZd7uU/v5LRvYtW805/dt2bWf367dVIKISu/up7axfc8wm14d\nyLitVWFj827fM8zewdymuynVGYQjURkQooQOXzqd04+ei4Fd8/izPz3Llb96jPWb9xC1Kuu6FEKI\nWlLueSo+lsU21wHXlTwYn619/FW625o4YtkMv0NhbDxCU2PAc11Wt2CfbtSbXh3AMGDJnE5fjj8p\nh9MvRT+D0bEwoabSf5Wvu20jJx42i3vWbc8847grTc775lp+9IWTU15j5fDTm59l0ewOPnRmYuvK\n8pN8bekdcdAMZnSFuOWBlxkYHmfztkG+e+NT9Pa0cPKq2Ry7YiY9HZXdXFEIIapN5fek9IFVhrv+\nveu2s27znpIfJxvnf+eu1CszJUURqxhyTfY/PfAStz/0StptwpFoTp9nTjHkcZmUokbmgu/ePdmc\nrVIkRpPLEJ/l+P6l8uSm3WWYlbl45/fdG59i87Yc56OotmrBPM2d0cY/vGUFpx4xh1CTXaDduXeE\nG/+6iS9eez9X/foxbnngJV7aMSg1GEIIUQRSqHC54vpHy3asSrmF/cfvnipsB0U6kXybO2c6/Oev\nuZcNL/fnt/Msjp1rkxqv0xwYHmfrrv0Z33v3U9t4IsVEhLHmRkMj45PLrr99I09u2p1TfJXghjXp\n54YIR6L5ZQKzeM8vbtnAIxtey33fjnue2paxUFTM/Ov6zXvYk+M1CJXz+1NqwYDJMctncv7bV/KW\n1QuYPW2q0/amVwf437s2c/l1j/KP37+Xn9z8DPeu207/0JiPEQshRPWq20KF1xPuF7fbT/yKdcN9\nYVvmtud+e+qF9LUlxWgbPzA8nnLdPU9tY12GGAoxfCA8OaJTKXzx2vtzSyGP0tMdj27he79bl/Gt\n967bzhPPexcSLAuGD0xw4ffvnVz2mN7Fq69lLqwAvLJzKKvrNd+rYfhAmEc3ps+sG06Ra3eGvirf\n+PVj/O2J3Ge6fvz53TySKYYCn+L/8raNFdO8KVXHZAPqrg1WMGByyKIePniG4pNvPZiTVs1iVk9o\nspC/f3SCh57dyS9u3cA//fA+vvLTB/n5Lc9y15Nb2bprv9RkCCFEFuqyUGFZFjf+NXNn2b89sXWy\noJGPK294LO/35ipSouYvGe+lGTJh1/zvOj5/zb0p1//1ia08/tyuzDsqm+zTcdBVK5Bt+LHNxici\n3Ld+u70s4b2jY2EGh8f50o/uzzoWewShhGMZ3mfz1f98KGnZrQ++zO/v2uyxde68kuLF7YNc+39P\nZ/X+TJ/AnoEDDI9O5BzX4PA496zblv0bEhL0pntfZN0Lhdf8eJ1f4mc3NhH5/+ydeZgcVbm43963\n6Z41M5N9pSsJSQgJAUIS9k3gsgiCy8WLigoqiteLgHp/qNyriCIoivuuXEVRQJBFIUDCDoEAIaks\nZN+XWXt67/r9UVXdVd3VPd2z98x5n2ee6a46VfXVqeqq7zvfcsoOAyun3e6D3dz0Y63exQitgjRU\n1NZ4OHFuK1eeM5vrLp3PRcuns2BGA0FfLi9p/5EenntrH795TOa/f/Ey1921iu/e9wYPPbeVdduO\n0N2H+08gEAhGOyNhnoohp9dXsALY4IFV73LGcZNJpjLMmlQ6Gfg7f3ydE+e2sjyvNv++Iz34PU7L\nmbgH6tXeHU3y60c3DNDeBpZiI+s6leg3yVSG7miS+qAnu2yg4+8r2dsdf3yjbxuiKvEPPbeNZfML\n53J4cPVW1m9v41BHJWEtStl9sW1fFx+/fSVXniNx8jETKjhG+fdsviwDepn6oRQb5fjZA29x2Skz\nst+TqXS2jKwVz721l1Q6w4KZTX0+vipE700+f/dqrr14HvNnNAKwZXcHv//nRm65akmfDtnVk+RA\nexR5R1u5IowJvG4n0uQ6pMl1KIpCW1ecbfu62H2wm12HuunqUb2c0XiKt989wtvvHslu2xDyMKU5\nyKTmGqY01zClpYamOh/2MW60CQSCscuo9VQ8tHor2/d1mZbpI8PlvlH1Zrf9YQ0xbXbdYuzY301H\npDAW90s/fZEHn9tKKp0xh1yVkCG/3ORrsnUcvU4lSbrd0aRlOcuNO9t54e19Ze/HikQyzZ+e2lTh\nVjb0zlBQ+OOTm/hzES/SC+v2mUbvi726e2J9H0Xsr/LbHU2WDLfS9Y18z9Lhzlh2FDyjKNbX1FY8\nHK1SudMZZfAMUYsLo8t97z839rr5jv3lhWyVS08sxfb96rPAaOw8tMrsmbnnb2/TY7x2g6Qc6n1x\n809fLHqoWCJNIpl75nREEgXPs96wur5bKk3qHkPYbDYaQl4Whcfxb8umc+1F87n2onlctGwai6Vx\ntNR7sRuu05HOOG9sPsTDz2/jngfe5qafvMin73yWb/z+NX73hMzKNbtYt+0IhztiInxKIBCMCUat\np+KBVe/icTmY2hrMLvvFI+tZNn989qXel1KciqJYxioXWw7qKGNPLMV9Kzdz7gnlTXq2aVc7kViK\nhbOa+OHf3ipdwrPC0X4rbvvDGiaNC7B0XqtpeSXvwu5oksdf3skVpx9V9jY2m3qMjKLw55VbmD+j\nEXeRsqur1u4pK8zrM3et4r0nz+CCk6Zpx1A7qKsngcthx+kYBFtauwY/euBtGkNePnr+nJLNzeE7\n6sZvbD7MgplN2LBVPJKsKIV26lDEzvfEUvg8joJ7v+Co2oJ3ykiaH+jQku37cop0fnfoRsavH12f\nPa6S13jjznbCk+sK9rv7UITagJsan4vfPSHz3pNnaNtrrs4i6DLsP9LDH5/cxPrtbSyZ3WzR0pY1\nLH7w17dKn2SZxBNpXt1wgLnT6gdkf6OdoN+FNKUeaYraX8lUhgPtUQ60RTnQ1sP+IxEOdcZJaR6u\neCLN5l0dBXO5uJx2mut9tNT7aa7z0VjrpbnBx4zJadw2BddgPJMEAoFgiBmVRkUqnVEVM0WhO5rE\n5bTjceVq5Osv9WL6lq4UWKlJxdQFRcmNNmYyClffvtJi6/JZtXYve49EWDirb6EWuw9FuP+ZLdx6\nzbIKtrKxv62Hlnp/700N5J/bv17dyWmLJuKw203L9h7p4cqzC+cJUMgLlymij+WPspaaIffZtXuy\nRoW+7xt++DxXnD6LFRWG/Oh89Laniht3mvjpjFKyfKou89NvFI/tVw2t4nfMD//6FhecNM1kMFt6\nMPL655X1+5k2yaxMrtt6hI0724seqxQZReEzdz3Lf15xDPOmN5JKZ3A67OovR6ncaDfmqKQzGRx2\nO4+8sI3zTpzaq9Hy6oYD7D0c4d+WTS8tcxGj9Nm1e4tuc+ef1/Kj/zylYPnt967h7CWTOX/pNFau\n2c3pZcyons8urerXKxsOcGlbD82G357NBqveLC5XX9CLJoiB877hctqZ2BRgYlMguyyTUWjrjnNQ\nMzb2HYlwsC1KxODdTqYy7D4YYffBiOV+A14njSEvDSEv9UEPdTVu6mo81AU96v8a1XgdqbOCCwQC\nAYxSo+Lfb3mMZFpV7G77wxoWh8dxycm52OncCzX3Zi03KVYxWg/G5ShZMyRfwSsazlHqBZGLCuoV\nq70c7ojxhiGfIRJL9jqtvM0GN//kxazifKA9ys8eWtfLsQuPfu+/NrF0XisBr920zOdxcuXZEvFk\nOmvkZbfug5Kz40AXk8bV8PfntjK1NcSCmY0l20cTKVK9eDrylfktezr4+3PbuP59x5Qlkw1rhS2j\nKHT3DMAIvAKvbTzICXNbzEaF4ZgH2qNZj5RRlP/716aCPI1Nu9pZ/dZeZvUygeCO/YWhN3pfJVMZ\n4sk0197xDL+86fTsbf3pO5/N3ktPvLKz11O7+/5cBayP3/40v7zpdO5/5l3ec8JU00/F6n7fuLOd\nDTvaTUbFS+/sZ4JB+TPKXJK8NvGEdeij3WajkvoI197xDHdfv4J9R3LzYBjPZdOuDpNRoYrSP+3/\nu/e9wdvvHuGLHzi2X/sRFMdut9EY8tIY8jJ7Ss5ojyfTtHXFs39HOqMc7ozRGUkQTZifxZFYikis\nmx0lqrU5HTZqAx7qgprBUeMxGyDan5XnUCAQCIaCITUqJEm6BrgWmKYtWgd8XZblxyza/hj4BHC9\nLMvfr+Q4EUMYQyKZtohnLXxRG5WtUu9xfV13NInP48iOxmc0W+NQR9SUSGy10+y8CYpCMpWmqydp\nObtrvhgvr99POqOw9OjWku2g0F65/+ktbN3XxWcvXQBYh0Hlv4Y6uuMF3oEtuzvIKApHTSoMBzH2\nm6Koyq0RfZT42jue4a7PLifkdxc/gTI40hnHYbex93CERCpjMioOdcRMyhsUGkCf+u4z3JM3Ap0v\nyqH2WHklb23qSHuxikvrth7hzvvWcvO/L7LaVDu4uqUeElaKRMqs6CpKTviHVm/lQHu04Hoe6SzM\n+ckoZOvyF9ND9h6O8LxFvo1RRuPvJ53JmdU/0YzSbSXyAXTvWFFFqAz9yKq7/vjUJk45ZoIpdKn3\nJ4G67I4/vcH7T59lWp5vkNlsoFhYFcWuXTyZ5hPfftp6pcZjL+3IhkjGEqmyfhoZReFIZyz7DPn+\nX3LG2Z5D1iPjgsHH43LQ2uCntaHQ85tMZYjEkiQysO9QN+1dcdq743RG4kSiSSKxdMEASCqtcLgz\n1uu8JG6X3WBkGAyOoJt6g/ExnDPcCwSC0clQeyp2AjcCm1BVhauAByVJWijL8nq9kSRJlwAnAJUX\no8/DyrFgfOlv3dtJY22hQt8bN/74BT545lHZ6j2KomC32fjij17gpzecW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LPGW4PwCCATc/+HzuZ6qHJuhtPnyuZHlt1eOpAkxqrskun5FnUNrz9pffR1bo821Yrdf357Db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\nH95zkO99bS97n2lT8RcKhUIxhyx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NMu6N88TYE6wKreby6GX8ZsvryYgMnekO2pPtTPjS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\nAWZjhFBDGLM+hFlnYtQ2o4dWEObyotTahptAeBOktXE8MY4v4gg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7hcg7xxe+zvldP9la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N82EfLmQs2+uNknm8RAAv0am52EsYIkywV+7UOONgmA0RJjBNZ4p2GrBSZ3INZakmUKY7z3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\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/EUaCJidLGuKbn04YX/XDP63nc+87M80V3PsoeeB1+kD0h398dvjaiQekJuOHf3qWRXPq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\nsvL/AbKDn8O0VzGGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b698d3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = vcsma.trace.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trace object can be sliced by (chain, parameter, index) tuples, or any subset thereof. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.48405096,  0.48405096,  0.48405096,  0.78951104])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace['Lambda',-4:] #last 4 draws of lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sigma2': [34.046017271846118, 34.215037006990748],\n",
       " 'Tau2': [1.3342090890246048, 1.5873738276194227]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace[['Tau2', 'Sigma2'], 0:2] #the first 2 variance parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only ran a single chain, so the first index is assumed to be zero. You can run more than one chain in parallel, using the builtin python `multiprocessing` library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsma_p = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                        n_samples=5000, n_jobs=3, #run 3 chains\n",
    "                        configs=dict(tuning=500, adapt_step=1.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10.42671835],\n",
       "       [ -0.18088028],\n",
       "       [  1.7756331 ],\n",
       "       [  3.60975713],\n",
       "       [ -0.07059934],\n",
       "       [ -2.7762485 ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma_p.trace[0, 'Betas', -1] #the last draw of Beta on the first chain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.84611721],\n",
       "       [-0.41804214],\n",
       "       [ 1.64652096],\n",
       "       [ 4.29570284],\n",
       "       [ 0.0309099 ],\n",
       "       [ 6.81012182]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma_p.trace[1, 'Betas', -1] #the last draw of Beta on the second chain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the chain plotting works also for the multi-chain traces. In addition, there are quite a few traceplot options, and all the plots are returned by the methods as matplotlib objects, so they can also be saved using `plt.savefig()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljw/anaconda3/envs/py3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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K4imBqwSukjhS4EiRDtDRJ68YvNiTRS7mFot4pRLZchnyeZxCCa9UxCv1zd/n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aZUJaq3SXrwZcStF2CWs8ciydV7CXKgK4k0EaTVqH6fJjRARMaGUHUBl7F87IVNNscVv/6vvkHd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Kw4MzrPvYMT5FkGypB42NgcM2onQODsl75O2Npg+X3vv+m135EptcqwvjllOKwYjRuGG2MC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I0eEqrgmBzRVz300JIYRop1avU7FsFyat9XHg51sQjhBCiC6WtG3O3Z5pdxhC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WGpnQkHDC4P2dkY7iyQJL4ARGds5ae8pmc8Llcpedev9MRJf3mbgeN+9/qH/8GN31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FFrawQTtvtcSUOahAQ77nuvNU7JRgb7zueQ97y0eYnHjj9012P+bYXtdrH3d+H+Jn+oqGqK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9lg+pdv\nPGk5o0JVB0XkNmB/4BcAIhIJvn++1uOsWfOyreDbosRiUbq721zOMWIqyDkVZASXcyyZCjJCUc6p\nxP1z7mH3u19gh7vXEy+xJSIdnaQ23ZTOTTclvXAhqfkLSM6aVTZNcw4YAAYGgdB6KZPFVGkzjeLl\nm7pM57LB9C7fRPXxLWdUBHwGuCwwLgopZduBy2o9QDabmxLxqC7n2DIV5JwKMoLLOZZMBRmnGkfe\n8DRLVg8OfY9tsoy+3Xana5vtiPf3jxhxyAJMoWsw3duMl2/qMp3LBtO/fONJSxoVqvoDEekHPoyF\nPa0CDlbVpydXMsdxHKcVKBgUsU02YeEpbyG1YOEoeziO4zjjSUsaFQCq+mXgy5Mth+M4jtOaZBbN\nZ/N//0DNi4Y6juM448f0m+LuOI7jvCLoP/BANygcx3FaBDcqHMdxnCnJjE2WT7YIjuM4ToAbFY7j\nOM6UJNbVNdkiOI7jOAFuVDiO4zhTjp49XkWit3eyxXAcx3EC3KhwHMdxphwr3/POyRbBcRzHCeFG\nheM4juM4juM4TeFGheM4juM4juM4TeFGheM4juM4juM4TeFGheM4juM4juM4TdFSK2qLyBLgg8B+\nwFzgMeA7wEdVdXAyZXMcx3Ecx3EcpzwtZVQAWwAR4AzgPmAl8A2gHXjPJMrlOI7jOI7jOE4FWsqo\nUNWrgatDPz0oIp8CzsKNCsdxHMdxHMdpSabCnIpe4LnJFsJxHMdxHMdxnPK01EhFKSKyGfA24N/q\n3TcWa217qSCfyzk2TAU5p4KM4HKOJVNBRmh9+SoxVeWuxlRpM43i5Zu6TOeywfQu30SVKZLP58f9\nJCLyceC9VTbJAytU9Z7QPguA64Hfqupbx1dCx3Ecx3Ecx3EaZaJGKj4FXDrKNvcXPojIfOC3wJ/c\noHAcx3Ecx3Gc1mZCRirqIRih+C1wK/AmVW0tAR3HcRzHcRzHGUZLGRXBCMXvgQeAU4Bs4T9VfXKS\nxHIcx3Ecx3EcpwqtNlH7QGBZ8Hok+C2CzbmITZZQjuM4juM4juNUpqVGKhzHcRzHcRzHmXpMv7xZ\njuM4juM4juNMKG5UOI7jOI7jOI7TFG5UOI7jOI7jOI7TFG5UOI7jOI7jOI7TFG5UOI7jOI7jOI7T\nFK2WUrZpRORc4F3AXOBvwHmqeuskyXIhcGHJz3er6pahbT4MnA70AjcAZ6vqveMs117Au4EdgXnA\nUar6i5JtqsolIingM8DrgRRwNXCOqj41UXKKyKXAm0t2u0pVXzNRcorI+4CjgS2Al4Ebgfeq6j0l\n201qfdYi52TXp4icBZwNLA1++ifwYVW9KrRNK7TLqnJOdj1WkPnfgY8Bn1PVfwv9Pun1WUXmuvpy\nEdkH+DSwFfAw8FFVvXy85WyEesomInOxcu0EbAZcHL6GrUid5Tsau5+2w9rYP4GLVPWaCRK3Luos\n2x7AJ7B+tx14CPiaqn5ugsStm0Z1qKCs1wP/UNUdxlXIJqjz+u0N/K7k5zwwbyL6wHppoM9MYjrq\nScE+j2PPsssalWFajVSIyOuxzvdCYHusUq8Wkf5JFOsOYA52weYCexb+EJH3Am8DzgR2AdZh8ibH\nWaYOYBVwDnaDDKNGuT4HHAYcC7wamA/8eCLlDPg1w+v3hJL/x1vOvYAvALsCBwAJ4BoRaSts0CL1\nOaqcAZNZn48A7wV2wAzJ3wI/F5EV0DL1OKqcAZPdLocQkZ2xOvtbye+tUp/lZK6rLxeRpcCvgOuA\nbYGLgW+IyIHjLWu9NPCcSgFPAR/B+sOWpoHyvRq4BjgUu6d+B/xSRLadAHHrooGyrcP63b0ww+Ij\nwH+KyOkTIG7dNKpDiUgPcDlw7bgL2QQNli8PbE6xL29Vg6KRsv0Q2Bc4FViOPae0GTmm20jFBZgX\n4AoY8igeBpwGfHKSZMqo6tMV/jsf+Iiq/gpARE4GngSOAn4wXgIFHtWCVzVSr1wi0o3V6RtU9ffB\nNqcCd4nILqp6ywTJCTBQqX4nQs6w9zk4/imYArAj8Kfg50mvzxrlhEmsT1W9suSnD4jI2cBuwF20\nQD3WKCdMcrsMnasT+DY2GvHBkr9boj4rUG9ffjZwv6q+J/iuIrJncJzfjKOcjVBX2VT1oWAfROQt\nEyhno9RbvgtKfnq/iBwJHE6JIdwC1Fu2VQw3BL8rIsdiRsY3xl/cumlUh/oq8B0gBxw53kI2QaPl\ne1pV10yAfM1QV9lE5BCsHS5T1ReCnx9uVohpM1IhIglMQbqu8Juq5jHLeffJkgvYXEQeE5H7ROTb\nIrIIQEQ2wazesLxrgJuZRHlrlGsnzCANb6NYg5xo2fcRkSdF5G4R+bKIzAj9t+MkyNmLeTaeg5au\nz2FyhmiJ+hSRqIi8AQsZuLFV67FUztBfLVGPwJeAX6rqb0vkbsn6DGRrpC/fjZFe0qurbD8ptPBz\nakwYi/IFDqQuRvZNk8oYlW37YNvrx0HEpmi0fIGjYRPgQ+MtYzM0cf0iwCoReVxErhGRV42vpPXT\nYNkOB/4CvFdEHhURFZH/FpF0M7JMG6MC6AdimKctzJPYw3MyuAk4BTgYOAu78f4gIh2BTHlaS16o\nTa45wMYylvtEy/5r4GRgP+A9wN7A/4VGNeYygXIG5/0c8CdVvTMkQ0vVZwU5oQXqU0RWishLwADw\nZeDoQJFtqXqsIie0QD0GMr4Bi1N/X5m/W6o+S2ikL59bYftusXkhrUIrPqfGkrEo37ux0NdxG61v\nkIbLJiKPiMgG4BbgS6p66fiI2BR1l09ENsfmap2kqrnxFa9pGrl+TwBvxcI/j8FCX68Xke3GS8gG\naaRsy7CRiq2w0enzgddhjqiGmW7hTy2Fql4d+nqHiNyCTdQ6Hrh7cqSaPqhq+KHzTxH5B3AfsA8j\nJ1dNBF8GtgT2mIRz10NZOVukPu/GYuJ7sA7uChF59QSdux7Kyqmqd7dCPYrIQsxwPEBVByfinI7T\nLCJyIhamd4SqPjPZ8owhewKd2IjaJ0TkXlX930mWqSlEJIqFPF2oqvcFP1cKU56SqCUyCSdduUlE\nNsVCjUqTcUw1oli42omquhZARP4N+KGInKOqA40edLrwDJDFvGth5gCrJ16ckajqi1gD3QyTKULr\nyVuLXKuBZBBzXWmbCUdVH8DawWbBTxMmp4h8EXgNsI+qPhH6q6Xqs4qcI5iM+lTVjKrer6q3q+r7\nsZjq82mxeqwiZ7ltJ6Nd7gjMAv4qIoMiMoiNmJwvIhsxD1bL1GcJjfTlqytsv6bRh+M40fLPqSZp\nuHzByNrXgeNUdTKcQqPRcNlU9SFV/aeqXgJ8FrhoXCRsjnrL14WFSH4x1Md8ENhORDaKZWNrJcbq\n3ruFYl/eKjRStieAxwoGRcBd2HNhYaOCTBujIvDG3QbsX/gtCDfYn+GxzpNGMGlyM+DxQNFYzXB5\nu7HsPJMmb41y3QZkSrYRYDHw5wkTtoTAOzsTu1lgguQMFPUjgX1VddhEp1aqz2pyVth+UuqzhCiQ\naqV6rCZnuT8mqR6vBbbGwp+2DV5/wSZtb6uq99Oi9dlgX/7n8PYBBzGJ/VE5psJzqhkaLZ+InABc\ngiUFuKrSdpPJGF67GBX6ismkgfKtAVYyvI/5KsVR3JvHWeS6GMPrtx3FvrwlaLBsNwDzRaQ99Jtg\noxePNirLdAt/+gxwmYjchlmTF2ATKC+bDGFE5L+BX2IhTwuwiUyDwPeDTT6HZY65F3gQSzf3KPDz\ncZarAzNuCkOVy8TS9z2nqo+MJpeqrhGRS4DPiMjzwEvA54EbxjhzTUU5g9eFWHrL1cF2n8BGgq6e\nKDlF5MtYGrYjgHUiUvAUvKiqG4LPk16fo8kZ1PWk1qeIfAybj/Aw5gU7CfOuHxRsMun1OJqcrVCP\nwTnWAeH5MojIOuBZVS1kqGqJ+qxA1b5cRD4OzFfVQgjCV4FzReQTwDexh+nrsFG5VqPeshH0exEs\nhGZW8H1j6Fq2EnWVLwh5ugx4O3BrqG96uQUz7tRbtnOwfqIQ7rw38E7s3mtFai5fMBG4tI95CtjQ\nou0S6r9+5wMPYGunpIEzsBSsLZeqmvr7le8CHwAuFZGLsJHtTwKXNDO6O62MClX9gVhO3g9jwz6r\ngIO1ckrX8WYhduFmAk9jqTt3U9VnA3k/GViJX8Oy8fwROFRVN46zXDthsd354PXp4PfLgdNqlOsC\nbLjtR5jX5Srg3AmU8xxgG2xCbC+2aMvVwH+UxJCPt5xnBbJdX/L7qcAVUPN1nmw5s0x+fc7Gru08\n4EXg78BBhcxFLVKPVeUUy5wx2fVYiWFrvbRQfY6ghr58LrAotP2DInIYFlrydsw4eouqtlze/HrL\nFnA7xeu3A3Ai5qxaNv4S10cD5TsD895/ieGTRC/H0mG2DA2ULQp8HFsoM4PNrXq3qn59woSugwbb\n5pShgfIlMb1jPrAe6+v3V9U/TJzUtdFAn7lObB2fLwC3As8C/8vI1ON1EcnnK60p5jiO4ziO4ziO\nMzrTZk6F4ziO4ziO4ziTgxsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4\njuM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsV\njuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0\nhRsVjuM4juM4juM0hRsVjuM4juM4juM0hRsVjuM4juM4juM0RXyyBXBeeYhIFDgPOBHYAugK/vqZ\nqh4zaYJNMUTkQuBCIK+qsVY8tohcD7wauF5V9xsj8ZwxRkT2Bn4XfN1HVf8wmfK8EhGRJcADwddT\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2h73zDU30LuGc4P1p7N4vx0VY6GsEOCNwBo0XhZHX07RMVsdgnlVBZxi3ax+MNJ1DsZ8q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\ncCe2mGeldK+nYW1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b65fa0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcsma_p.trace.plot(burn=1000, thin=10)\n",
    "plt.suptitle('SMA of Homicide Rate in Southern US Counties', y=0, fontsize=20)\n",
    "#plt.savefig('trace.png') #saves to a file called \"trace.png\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljw/anaconda3/envs/py3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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hJ/In+eXb7l/2Mff39/Hv1Yf4zGPf42zx03zito9vyDtPgrBa5Gmd3GcNJf1d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\n3If65vegdK+dPnoN4WMBoiii5gWUqz4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8lke9SSRyf4WtYiGvCs7gJ+MPMcI4t6Z+yduS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\nV8gaKA7cD5yMnz0G/EXIK8Jtmccp+LER1XZ8mJ+nnXPpPOmemLJPpY4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b64279cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcsma_p.trace.plot(burn=-100, varnames='Lambda') #A negative burn-in works like negative indexing in Python & R \n",
    "plt.suptitle('First 100 iterations of $\\lambda$', fontsize=20, y=.02)\n",
    "plt.show() #so this plots Lambda in the first 100 iterations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get stuff like posterior quantiles, you can use the attendant pandas dataframe functionality, like `describe`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = vcsma.trace.to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lambda</th>\n",
       "      <th>Sigma2</th>\n",
       "      <th>Tau2</th>\n",
       "      <th>Betas_0</th>\n",
       "      <th>Betas_1</th>\n",
       "      <th>Betas_2</th>\n",
       "      <th>Betas_3</th>\n",
       "      <th>Betas_4</th>\n",
       "      <th>Betas_5</th>\n",
       "      <th>Alphas_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Alphas_6</th>\n",
       "      <th>Alphas_7</th>\n",
       "      <th>Alphas_8</th>\n",
       "      <th>Alphas_9</th>\n",
       "      <th>Alphas_10</th>\n",
       "      <th>Alphas_11</th>\n",
       "      <th>Alphas_12</th>\n",
       "      <th>Alphas_13</th>\n",
       "      <th>Alphas_14</th>\n",
       "      <th>Alphas_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.608686</td>\n",
       "      <td>32.265190</td>\n",
       "      <td>1.828138</td>\n",
       "      <td>8.156127</td>\n",
       "      <td>-0.258890</td>\n",
       "      <td>1.796985</td>\n",
       "      <td>3.973051</td>\n",
       "      <td>-0.003564</td>\n",
       "      <td>2.108436</td>\n",
       "      <td>-1.588250</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.400545</td>\n",
       "      <td>1.117568</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>-0.169745</td>\n",
       "      <td>1.024000</td>\n",
       "      <td>0.308998</td>\n",
       "      <td>0.189192</td>\n",
       "      <td>1.873476</td>\n",
       "      <td>-0.617676</td>\n",
       "      <td>2.084861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.277064</td>\n",
       "      <td>1.211527</td>\n",
       "      <td>1.121763</td>\n",
       "      <td>3.249228</td>\n",
       "      <td>0.080627</td>\n",
       "      <td>0.205511</td>\n",
       "      <td>0.235770</td>\n",
       "      <td>0.084480</td>\n",
       "      <td>9.478349</td>\n",
       "      <td>0.866599</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825168</td>\n",
       "      <td>0.897640</td>\n",
       "      <td>0.846020</td>\n",
       "      <td>0.776934</td>\n",
       "      <td>0.860310</td>\n",
       "      <td>0.799908</td>\n",
       "      <td>0.767246</td>\n",
       "      <td>0.710348</td>\n",
       "      <td>0.868327</td>\n",
       "      <td>0.834200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.527474</td>\n",
       "      <td>27.965344</td>\n",
       "      <td>0.241872</td>\n",
       "      <td>-3.912410</td>\n",
       "      <td>-0.613551</td>\n",
       "      <td>0.886141</td>\n",
       "      <td>2.947461</td>\n",
       "      <td>-0.295581</td>\n",
       "      <td>-31.363481</td>\n",
       "      <td>-4.657230</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.289157</td>\n",
       "      <td>-2.552218</td>\n",
       "      <td>-2.767615</td>\n",
       "      <td>-2.987661</td>\n",
       "      <td>-2.569753</td>\n",
       "      <td>-3.273487</td>\n",
       "      <td>-3.087397</td>\n",
       "      <td>-0.673602</td>\n",
       "      <td>-4.875626</td>\n",
       "      <td>-0.676062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.445954</td>\n",
       "      <td>31.442746</td>\n",
       "      <td>1.099079</td>\n",
       "      <td>6.026430</td>\n",
       "      <td>-0.312824</td>\n",
       "      <td>1.660085</td>\n",
       "      <td>3.816479</td>\n",
       "      <td>-0.059833</td>\n",
       "      <td>-4.274624</td>\n",
       "      <td>-2.147018</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.931950</td>\n",
       "      <td>0.544729</td>\n",
       "      <td>-0.435577</td>\n",
       "      <td>-0.677292</td>\n",
       "      <td>0.464970</td>\n",
       "      <td>-0.201530</td>\n",
       "      <td>-0.314159</td>\n",
       "      <td>1.406189</td>\n",
       "      <td>-1.186915</td>\n",
       "      <td>1.528803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.647734</td>\n",
       "      <td>32.253578</td>\n",
       "      <td>1.575177</td>\n",
       "      <td>8.142431</td>\n",
       "      <td>-0.260495</td>\n",
       "      <td>1.797555</td>\n",
       "      <td>3.975418</td>\n",
       "      <td>-0.004856</td>\n",
       "      <td>2.077913</td>\n",
       "      <td>-1.553896</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.377612</td>\n",
       "      <td>1.106097</td>\n",
       "      <td>0.089986</td>\n",
       "      <td>-0.159915</td>\n",
       "      <td>1.043969</td>\n",
       "      <td>0.318552</td>\n",
       "      <td>0.185046</td>\n",
       "      <td>1.868410</td>\n",
       "      <td>-0.593257</td>\n",
       "      <td>2.059923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.842345</td>\n",
       "      <td>33.042655</td>\n",
       "      <td>2.256722</td>\n",
       "      <td>10.344981</td>\n",
       "      <td>-0.205082</td>\n",
       "      <td>1.938355</td>\n",
       "      <td>4.129071</td>\n",
       "      <td>0.053324</td>\n",
       "      <td>8.408304</td>\n",
       "      <td>-1.004612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145648</td>\n",
       "      <td>1.685655</td>\n",
       "      <td>0.627832</td>\n",
       "      <td>0.346805</td>\n",
       "      <td>1.592591</td>\n",
       "      <td>0.827596</td>\n",
       "      <td>0.689496</td>\n",
       "      <td>2.330962</td>\n",
       "      <td>-0.022296</td>\n",
       "      <td>2.614217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.999475</td>\n",
       "      <td>37.975548</td>\n",
       "      <td>18.930464</td>\n",
       "      <td>18.982719</td>\n",
       "      <td>0.061743</td>\n",
       "      <td>2.554069</td>\n",
       "      <td>4.799785</td>\n",
       "      <td>0.362505</td>\n",
       "      <td>36.030492</td>\n",
       "      <td>1.550131</td>\n",
       "      <td>...</td>\n",
       "      <td>2.606743</td>\n",
       "      <td>4.344976</td>\n",
       "      <td>3.047487</td>\n",
       "      <td>2.684723</td>\n",
       "      <td>4.345115</td>\n",
       "      <td>2.962645</td>\n",
       "      <td>3.355820</td>\n",
       "      <td>4.745947</td>\n",
       "      <td>2.064849</td>\n",
       "      <td>6.190255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lambda       Sigma2         Tau2      Betas_0      Betas_1  \\\n",
       "count  5000.000000  5000.000000  5000.000000  5000.000000  5000.000000   \n",
       "mean      0.608686    32.265190     1.828138     8.156127    -0.258890   \n",
       "std       0.277064     1.211527     1.121763     3.249228     0.080627   \n",
       "min      -0.527474    27.965344     0.241872    -3.912410    -0.613551   \n",
       "25%       0.445954    31.442746     1.099079     6.026430    -0.312824   \n",
       "50%       0.647734    32.253578     1.575177     8.142431    -0.260495   \n",
       "75%       0.842345    33.042655     2.256722    10.344981    -0.205082   \n",
       "max       0.999475    37.975548    18.930464    18.982719     0.061743   \n",
       "\n",
       "           Betas_2      Betas_3      Betas_4      Betas_5     Alphas_0  \\\n",
       "count  5000.000000  5000.000000  5000.000000  5000.000000  5000.000000   \n",
       "mean      1.796985     3.973051    -0.003564     2.108436    -1.588250   \n",
       "std       0.205511     0.235770     0.084480     9.478349     0.866599   \n",
       "min       0.886141     2.947461    -0.295581   -31.363481    -4.657230   \n",
       "25%       1.660085     3.816479    -0.059833    -4.274624    -2.147018   \n",
       "50%       1.797555     3.975418    -0.004856     2.077913    -1.553896   \n",
       "75%       1.938355     4.129071     0.053324     8.408304    -1.004612   \n",
       "max       2.554069     4.799785     0.362505    36.030492     1.550131   \n",
       "\n",
       "          ...          Alphas_6     Alphas_7     Alphas_8     Alphas_9  \\\n",
       "count     ...       5000.000000  5000.000000  5000.000000  5000.000000   \n",
       "mean      ...         -0.400545     1.117568     0.102000    -0.169745   \n",
       "std       ...          0.825168     0.897640     0.846020     0.776934   \n",
       "min       ...         -4.289157    -2.552218    -2.767615    -2.987661   \n",
       "25%       ...         -0.931950     0.544729    -0.435577    -0.677292   \n",
       "50%       ...         -0.377612     1.106097     0.089986    -0.159915   \n",
       "75%       ...          0.145648     1.685655     0.627832     0.346805   \n",
       "max       ...          2.606743     4.344976     3.047487     2.684723   \n",
       "\n",
       "         Alphas_10    Alphas_11    Alphas_12    Alphas_13    Alphas_14  \\\n",
       "count  5000.000000  5000.000000  5000.000000  5000.000000  5000.000000   \n",
       "mean      1.024000     0.308998     0.189192     1.873476    -0.617676   \n",
       "std       0.860310     0.799908     0.767246     0.710348     0.868327   \n",
       "min      -2.569753    -3.273487    -3.087397    -0.673602    -4.875626   \n",
       "25%       0.464970    -0.201530    -0.314159     1.406189    -1.186915   \n",
       "50%       1.043969     0.318552     0.185046     1.868410    -0.593257   \n",
       "75%       1.592591     0.827596     0.689496     2.330962    -0.022296   \n",
       "max       4.345115     2.962645     3.355820     4.745947     2.064849   \n",
       "\n",
       "         Alphas_15  \n",
       "count  5000.000000  \n",
       "mean      2.084861  \n",
       "std       0.834200  \n",
       "min      -0.676062  \n",
       "25%       1.528803  \n",
       "50%       2.059923  \n",
       "75%       2.614217  \n",
       "max       6.190255  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a `trace.summarize` function that will compute various things contained in `spvcm.diagnostics` on the chain. It takes a while for large chains, because the `statsmodels.tsa.AR` estimator is much slower than the `ar` estimator in `R`. If you have rpy2 installed *and* `CODA` installed in your R environment, I attempt to use R directly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>HPD_low</th>\n",
       "      <th>median</th>\n",
       "      <th>HPD_high</th>\n",
       "      <th>std</th>\n",
       "      <th>N_iters</th>\n",
       "      <th>N_effective</th>\n",
       "      <th>AR_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">Chain_0</th>\n",
       "      <th>Lambda</th>\n",
       "      <td>0.608686</td>\n",
       "      <td>0.065576</td>\n",
       "      <td>0.647734</td>\n",
       "      <td>0.999475</td>\n",
       "      <td>0.277064</td>\n",
       "      <td>5000</td>\n",
       "      <td>496</td>\n",
       "      <td>0.9008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sigma2</th>\n",
       "      <td>32.265190</td>\n",
       "      <td>29.986869</td>\n",
       "      <td>32.253578</td>\n",
       "      <td>34.683241</td>\n",
       "      <td>1.211527</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tau2</th>\n",
       "      <td>1.828138</td>\n",
       "      <td>0.321364</td>\n",
       "      <td>1.575177</td>\n",
       "      <td>3.844107</td>\n",
       "      <td>1.121763</td>\n",
       "      <td>5000</td>\n",
       "      <td>1007</td>\n",
       "      <td>0.7986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_0</th>\n",
       "      <td>8.156127</td>\n",
       "      <td>1.993420</td>\n",
       "      <td>8.142431</td>\n",
       "      <td>14.698938</td>\n",
       "      <td>3.249228</td>\n",
       "      <td>5000</td>\n",
       "      <td>2308</td>\n",
       "      <td>0.5384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_1</th>\n",
       "      <td>-0.258890</td>\n",
       "      <td>-0.413384</td>\n",
       "      <td>-0.260495</td>\n",
       "      <td>-0.100403</td>\n",
       "      <td>0.080627</td>\n",
       "      <td>5000</td>\n",
       "      <td>2279</td>\n",
       "      <td>0.5442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_2</th>\n",
       "      <td>1.796985</td>\n",
       "      <td>1.398922</td>\n",
       "      <td>1.797555</td>\n",
       "      <td>2.194201</td>\n",
       "      <td>0.205511</td>\n",
       "      <td>5000</td>\n",
       "      <td>4123</td>\n",
       "      <td>0.1754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_3</th>\n",
       "      <td>3.973051</td>\n",
       "      <td>3.514929</td>\n",
       "      <td>3.975418</td>\n",
       "      <td>4.435496</td>\n",
       "      <td>0.235770</td>\n",
       "      <td>5000</td>\n",
       "      <td>3187</td>\n",
       "      <td>0.3626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_4</th>\n",
       "      <td>-0.003564</td>\n",
       "      <td>-0.168442</td>\n",
       "      <td>-0.004856</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.084480</td>\n",
       "      <td>5000</td>\n",
       "      <td>573</td>\n",
       "      <td>0.8854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betas_5</th>\n",
       "      <td>2.108436</td>\n",
       "      <td>-15.912772</td>\n",
       "      <td>2.077913</td>\n",
       "      <td>21.151735</td>\n",
       "      <td>9.478349</td>\n",
       "      <td>5000</td>\n",
       "      <td>4067</td>\n",
       "      <td>0.1866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_0</th>\n",
       "      <td>-1.588250</td>\n",
       "      <td>-3.261818</td>\n",
       "      <td>-1.553896</td>\n",
       "      <td>0.132113</td>\n",
       "      <td>0.866599</td>\n",
       "      <td>5000</td>\n",
       "      <td>472</td>\n",
       "      <td>0.9056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_1</th>\n",
       "      <td>-0.124204</td>\n",
       "      <td>-2.814616</td>\n",
       "      <td>-0.127997</td>\n",
       "      <td>2.240199</td>\n",
       "      <td>1.281021</td>\n",
       "      <td>5000</td>\n",
       "      <td>2959</td>\n",
       "      <td>0.4082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_2</th>\n",
       "      <td>-0.086578</td>\n",
       "      <td>-2.101419</td>\n",
       "      <td>-0.077058</td>\n",
       "      <td>1.842112</td>\n",
       "      <td>0.989799</td>\n",
       "      <td>5000</td>\n",
       "      <td>907</td>\n",
       "      <td>0.8186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_3</th>\n",
       "      <td>0.059299</td>\n",
       "      <td>-1.462329</td>\n",
       "      <td>0.068942</td>\n",
       "      <td>1.569032</td>\n",
       "      <td>0.765519</td>\n",
       "      <td>5000</td>\n",
       "      <td>386</td>\n",
       "      <td>0.9228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_4</th>\n",
       "      <td>-2.261698</td>\n",
       "      <td>-3.675698</td>\n",
       "      <td>-2.222016</td>\n",
       "      <td>-0.829483</td>\n",
       "      <td>0.731566</td>\n",
       "      <td>5000</td>\n",
       "      <td>329</td>\n",
       "      <td>0.9342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_5</th>\n",
       "      <td>-0.931411</td>\n",
       "      <td>-2.585484</td>\n",
       "      <td>-0.909363</td>\n",
       "      <td>0.642910</td>\n",
       "      <td>0.814978</td>\n",
       "      <td>5000</td>\n",
       "      <td>286</td>\n",
       "      <td>0.9428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_6</th>\n",
       "      <td>-0.400545</td>\n",
       "      <td>-2.037184</td>\n",
       "      <td>-0.377612</td>\n",
       "      <td>1.160176</td>\n",
       "      <td>0.825168</td>\n",
       "      <td>5000</td>\n",
       "      <td>269</td>\n",
       "      <td>0.9462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_7</th>\n",
       "      <td>1.117568</td>\n",
       "      <td>-0.638643</td>\n",
       "      <td>1.106097</td>\n",
       "      <td>2.967069</td>\n",
       "      <td>0.897640</td>\n",
       "      <td>5000</td>\n",
       "      <td>485</td>\n",
       "      <td>0.9030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_8</th>\n",
       "      <td>0.102000</td>\n",
       "      <td>-1.422613</td>\n",
       "      <td>0.089986</td>\n",
       "      <td>1.981343</td>\n",
       "      <td>0.846020</td>\n",
       "      <td>5000</td>\n",
       "      <td>240</td>\n",
       "      <td>0.9520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_9</th>\n",
       "      <td>-0.169745</td>\n",
       "      <td>-1.726714</td>\n",
       "      <td>-0.159915</td>\n",
       "      <td>1.280813</td>\n",
       "      <td>0.776934</td>\n",
       "      <td>5000</td>\n",
       "      <td>419</td>\n",
       "      <td>0.9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_10</th>\n",
       "      <td>1.024000</td>\n",
       "      <td>-0.676837</td>\n",
       "      <td>1.043969</td>\n",
       "      <td>2.714632</td>\n",
       "      <td>0.860310</td>\n",
       "      <td>5000</td>\n",
       "      <td>567</td>\n",
       "      <td>0.8866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_11</th>\n",
       "      <td>0.308998</td>\n",
       "      <td>-1.290688</td>\n",
       "      <td>0.318552</td>\n",
       "      <td>1.900305</td>\n",
       "      <td>0.799908</td>\n",
       "      <td>5000</td>\n",
       "      <td>391</td>\n",
       "      <td>0.9218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_12</th>\n",
       "      <td>0.189192</td>\n",
       "      <td>-1.357847</td>\n",
       "      <td>0.185046</td>\n",
       "      <td>1.670994</td>\n",
       "      <td>0.767246</td>\n",
       "      <td>5000</td>\n",
       "      <td>399</td>\n",
       "      <td>0.9202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_13</th>\n",
       "      <td>1.873476</td>\n",
       "      <td>0.530793</td>\n",
       "      <td>1.868410</td>\n",
       "      <td>3.320154</td>\n",
       "      <td>0.710348</td>\n",
       "      <td>5000</td>\n",
       "      <td>234</td>\n",
       "      <td>0.9532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_14</th>\n",
       "      <td>-0.617676</td>\n",
       "      <td>-2.277931</td>\n",
       "      <td>-0.593257</td>\n",
       "      <td>1.104059</td>\n",
       "      <td>0.868327</td>\n",
       "      <td>5000</td>\n",
       "      <td>528</td>\n",
       "      <td>0.8944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphas_15</th>\n",
       "      <td>2.084861</td>\n",
       "      <td>0.386081</td>\n",
       "      <td>2.059923</td>\n",
       "      <td>3.698922</td>\n",
       "      <td>0.834200</td>\n",
       "      <td>5000</td>\n",
       "      <td>419</td>\n",
       "      <td>0.9162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean    HPD_low     median   HPD_high       std  \\\n",
       "Chain_0 Lambda      0.608686   0.065576   0.647734   0.999475  0.277064   \n",
       "        Sigma2     32.265190  29.986869  32.253578  34.683241  1.211527   \n",
       "        Tau2        1.828138   0.321364   1.575177   3.844107  1.121763   \n",
       "        Betas_0     8.156127   1.993420   8.142431  14.698938  3.249228   \n",
       "        Betas_1    -0.258890  -0.413384  -0.260495  -0.100403  0.080627   \n",
       "        Betas_2     1.796985   1.398922   1.797555   2.194201  0.205511   \n",
       "        Betas_3     3.973051   3.514929   3.975418   4.435496  0.235770   \n",
       "        Betas_4    -0.003564  -0.168442  -0.004856   0.157426  0.084480   \n",
       "        Betas_5     2.108436 -15.912772   2.077913  21.151735  9.478349   \n",
       "        Alphas_0   -1.588250  -3.261818  -1.553896   0.132113  0.866599   \n",
       "        Alphas_1   -0.124204  -2.814616  -0.127997   2.240199  1.281021   \n",
       "        Alphas_2   -0.086578  -2.101419  -0.077058   1.842112  0.989799   \n",
       "        Alphas_3    0.059299  -1.462329   0.068942   1.569032  0.765519   \n",
       "        Alphas_4   -2.261698  -3.675698  -2.222016  -0.829483  0.731566   \n",
       "        Alphas_5   -0.931411  -2.585484  -0.909363   0.642910  0.814978   \n",
       "        Alphas_6   -0.400545  -2.037184  -0.377612   1.160176  0.825168   \n",
       "        Alphas_7    1.117568  -0.638643   1.106097   2.967069  0.897640   \n",
       "        Alphas_8    0.102000  -1.422613   0.089986   1.981343  0.846020   \n",
       "        Alphas_9   -0.169745  -1.726714  -0.159915   1.280813  0.776934   \n",
       "        Alphas_10   1.024000  -0.676837   1.043969   2.714632  0.860310   \n",
       "        Alphas_11   0.308998  -1.290688   0.318552   1.900305  0.799908   \n",
       "        Alphas_12   0.189192  -1.357847   0.185046   1.670994  0.767246   \n",
       "        Alphas_13   1.873476   0.530793   1.868410   3.320154  0.710348   \n",
       "        Alphas_14  -0.617676  -2.277931  -0.593257   1.104059  0.868327   \n",
       "        Alphas_15   2.084861   0.386081   2.059923   3.698922  0.834200   \n",
       "\n",
       "                   N_iters  N_effective  AR_loss  \n",
       "Chain_0 Lambda        5000          496   0.9008  \n",
       "        Sigma2        5000         5000   0.0000  \n",
       "        Tau2          5000         1007   0.7986  \n",
       "        Betas_0       5000         2308   0.5384  \n",
       "        Betas_1       5000         2279   0.5442  \n",
       "        Betas_2       5000         4123   0.1754  \n",
       "        Betas_3       5000         3187   0.3626  \n",
       "        Betas_4       5000          573   0.8854  \n",
       "        Betas_5       5000         4067   0.1866  \n",
       "        Alphas_0      5000          472   0.9056  \n",
       "        Alphas_1      5000         2959   0.4082  \n",
       "        Alphas_2      5000          907   0.8186  \n",
       "        Alphas_3      5000          386   0.9228  \n",
       "        Alphas_4      5000          329   0.9342  \n",
       "        Alphas_5      5000          286   0.9428  \n",
       "        Alphas_6      5000          269   0.9462  \n",
       "        Alphas_7      5000          485   0.9030  \n",
       "        Alphas_8      5000          240   0.9520  \n",
       "        Alphas_9      5000          419   0.9162  \n",
       "        Alphas_10     5000          567   0.8866  \n",
       "        Alphas_11     5000          391   0.9218  \n",
       "        Alphas_12     5000          399   0.9202  \n",
       "        Alphas_13     5000          234   0.9532  \n",
       "        Alphas_14     5000          528   0.8944  \n",
       "        Alphas_15     5000          419   0.9162  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace.summarize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, 5000 iterations, but many parameters have an effective sample size that's much less than this. There's debate about whether it's necesasry to thin these samples in accordance with the effective size, and I think you should thin your sample to the effective size and see if it affects your HPD/Standard Errorrs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The existing python packages for MCMC diagnostics were incorrect. So, I've implemented many of the diagnostics from `CODA`, and have verified that the diagnostics comport with `CODA` diagnostics. One can also use `numpy` & `statsmodels` functions. I'll show some types of analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from statsmodels.api import tsa\n",
    "#if you don't have it, try removing the comment and:\n",
    "#! pip install statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, a plot of the partial autocorrelation in $\\lambda$, the upper-level spatial moving average parameter, over the last half of the chain is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b64462b70>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DREQkKQoZveREL5KmlgwREZFkKGT0EllaXLNLREREkqOQ0UtOdOCnWjJERESSoZDRS262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\nM1OKuO7iSpZWleL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P8Hl412XTycjw8vTrh3uM0ZldWcja1XO4bNHkAf8u/alraGP3oTME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\nw1rbBOwFZo90WVx0AueKvGPq3EZYaw/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b653efe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tsa.pacf(vcsma.trace['Lambda', -2500:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the chain is close-to-first order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.81965873, -0.00715038])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsa.pacf(df.Lambda)[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could do this for many parameters, too. An Autocorrelation/Partial Autocorrelation plot can be made of the marginal effects by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7KmUXERFZtap9ne4fsUDF3jn32jLbfgxcdCqve9bmTu7deZxdh1TpFhERWYpa5WyAXD7P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M3A1cC1wL3A+cClzrnhYJV+YHvR+lPAi4Au4G7gH4CvAe84nXLsDGZEPT4+y+RM+nR2JSIiUpdq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\nSKdbEARBEARBEARBEPrE/wf1PFXSEQbPwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b653cbc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "betas = [c for c in df.columns if c.startswith('Beta')]\n",
    "f,ax = plt.subplots(len(betas), 2, figsize=(10,8))\n",
    "for i, col in enumerate(betas):\n",
    "    ax[i,0].plot(tsa.acf(df[col].values))\n",
    "    ax[i,1].plot(tsa.pacf(df[col].values)) #the pacf plots take a while\n",
    "    ax[i,0].set_title(col +' (ACF)')\n",
    "    ax[i,1].set_title('(PACF)')\n",
    "f.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As far as the builtin diagnostics for convergence and simulation quality, the `diagnostics` module exposes a few things:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geweke statistics for differences in means between chain components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Tau2': array([-1.10621591, -0.72340167, -0.39336566, -0.6434425 , -1.07029223,\n",
      "       -0.64300605, -0.55609213, -0.86085011,  0.03321543, -0.07358083,\n",
      "        0.96706966,  0.2995308 ,  0.10946477,  0.72820281,  1.03346151,\n",
      "        1.06694517,  1.46883481,  1.14800018,  1.28145411,  0.77522892,\n",
      "        1.41288455,  1.30949693,  1.13250827,  1.04737628,  0.90399709,\n",
      "        0.75503933,  0.52152212,  0.40105851,  0.79339579,  0.7520482 ,\n",
      "        0.29342595,  0.35693364,  0.8404059 ,  1.31066294,  0.11754609,\n",
      "       -1.09797389, -1.53398731, -0.86139733, -1.13148474, -0.75681619,\n",
      "       -0.32750459,  1.06321117,  1.63930971,  1.08093095,  0.84069153,\n",
      "        1.56772766,  0.24701853, -0.36473467,  0.26204387,  0.61498849])}]\n"
     ]
    }
   ],
   "source": [
    "gstats = spvcm.diagnostics.geweke(vcsma, varnames='Tau2') #takes a while\n",
    "print(gstats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typically, this means the chain is converged at the given \"bin\" count if the line stays within $\\pm2$. The geweke statistic is a test of differences in means between the given chunk of the chain and the remaining chain. If it's outside of +/- 2 in the early part of the chain, you should discard observations early in the chain. If you get extreme values of these statistics throughout, you need to keep running the chain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b5430be48>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yNjzQ7puhshkqyGipr0g6y+hUa3EJMoQNMTGTGGRslhPkM8eNLIa/soTDu663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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b5439be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gstats[0]['Tau2'][:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute Monte Carlo Standard Errors like in the `mcse` R package, which represent the intrinsic error contained in the estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sigma2': 0.014152442945278773, 'Tau2': 0.0326037034802052}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spvcm.diagnostics.mcse(vcsma, varnames=['Tau2', 'Sigma2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another handy statistic is the Partial Scale Reduction factor, which measures of how likely a set of chains run in parallel have converged to the same stationary distribution. It provides the difference in variance between between chains vs. within chains. \n",
    "\n",
    "If these are significantly larger than one (say, 1.5), the chain probably has not converged. Being marginally below $1$ is fine, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sigma2': 1.000201067966211, 'Tau2': 1.0016696419316742}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spvcm.diagnostics.psrf(vcsma_p, varnames=['Tau2', 'Sigma2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest posterior density intervals provide a kind of interval estimate for parameters in Bayesian models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Betas': [(1.9934200102974096, 14.698937734522005),\n",
       "  (-0.41338424595666518, -0.10040315322431537),\n",
       "  (1.3989223581965953, 2.1942011388972169),\n",
       "  (3.5149294335233812, 4.4354957521824439),\n",
       "  (-0.16844241462922532, 0.1574258939322527),\n",
       "  (-15.91277183831169, 21.151734866467908)],\n",
       " 'Lambda': (0.065576011337338092, 0.99947503163647056),\n",
       " 'Sigma2': (29.986869435028119, 34.683241168680439)}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spvcm.diagnostics.hpd_interval(vcsma, varnames=['Betas', 'Lambda', 'Sigma2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, you want to apply arbitrary functions to each parameter trace. To do this, I've written a `map` function that works like the python builtin `map`. For example, if you wanted to get arbitrary percentiles from the chain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Lambda': array([ 0.44595386,  0.64773363,  0.8423446 ]),\n",
       "  'Sigma2': array([ 31.44274643,  32.25357801,  33.0426554 ]),\n",
       "  'Tau2': array([ 1.09907856,  1.57517696,  2.25672175])}]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.trace.map(np.percentile, \n",
    "                varnames=['Lambda', 'Tau2', 'Sigma2'],\n",
    "                #arguments to pass to the function go last\n",
    "                q=[25, 50, 75]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, you can pop the trace results pretty simply to a `.csv` file and analyze it elsewhere, like if you want to use use the `coda` Bayesian Diagnostics package in `R`. \n",
    "\n",
    "To write out a model to a csv, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsma.trace.to_csv('./model_run.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, you can even load traces from csvs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Lambda', 'Tau2', 'Sigma2', 'Alphas', 'Betas']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljw/anaconda3/envs/py3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x7f5b5414fac8>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5b5441eb70>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f5b542afbe0>]], dtype=object))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qAZj75uYip0QIIUQyElQkU+bdCIrh3fUt+Mv8rvPGHR3s2ttT7GRkJsWlHgoX/32QbZee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\nRgIxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b5414fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tr = spvcm.Trace.from_csv('./model_run.csv')\n",
    "print(tr.varnames)\n",
    "tr.plot(varnames=['Tau2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Working with models: `draw` and `sample`\n",
    "\n",
    "These two functions are used to call the underlying Gibbs sampler. They take no arguments, and operate on the sampler in place. `draw` provides a single new sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsma.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And sample steps forward an arbitrary number of times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsma.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we did 5000 initial samples and 11 extra samples. Thus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5011"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.cycles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parallel models can suspend/resume sampling too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsma_p.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5010"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma_p.cycles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, it's the `draw` method that actually ends up calling one run of `model._iteration`, which is where the actual statistical code lives. Then, it updates all `model.traced_params` by adding their current value in `model.state` to `model.trace.` In addition, `model._finalize` is called the first time sampling is run, which computes some of the constants & derived quantities that save computing time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with models:  `state`\n",
    "\n",
    "This is the collection of current values in the sampler. To be efficient, Gibbs sampling must keep around some of the computations used in the simulation, since sometimes the same terms show up in different conditional posteriors. So, the current values of the sampler are stored in `state`.\n",
    "\n",
    "All of the following are tracked in the state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Psi_2', 'Betas_cov0i', 'Betas_covm', 'XtX', 'Sigma2_b0', 'Lambda_min', 'DeltatDelta', 'Sigma2_an', 'N', 'In', 'Delta', 'Log_Lambda0', 'Psi_2i', 'Ij', 'M', 'X', 'J', 'Rho_min', 'Betas_cov0', 'Rho_max', 'Tau2_a0', 'p', 'Psi_1i', 'Tau2_an', 'Lambda_max', 'Psi_1', 'PsiLambdai', 'DeltaAlphas', 'Rho', 'XBetas', 'Tau2', 'Y', 'initial_values', 'Lambda', 'Betas', 'Sigma2', 'Betas_mean0', 'W', 'PsiRhoi', 'Alphas', 'Tau2_b0', 'Log_Rho0', 'Sigma2_a0'])\n"
     ]
    }
   ],
   "source": [
    "print(vcsma.state.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to track how something (maybe a hyperparameter) changes over sampling, you can pass `extra_traced_params` to the model declaration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lambda', 'Betas', 'Sigma2', 'DeltaAlphas', 'Alphas', 'Tau2']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                        n_samples=250, \n",
    "                        extra_traced_params = ['DeltaAlphas'],\n",
    "                        configs=dict(tuning=500, adapt_step=1.01))\n",
    "example.trace.varnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `configs`\n",
    "this is where configuration options for the various MCMC steps are stored. For multilevel variance components models, these are called $\\rho$ for the lower-level error parameter and $\\lamdba$ for the upper-level parameter. Two exact sampling methods are implemented, Metropolis sampling & Slice sampling. \n",
    "\n",
    "Each MCMC step has its own config:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lambda': <spvcm.steps.Metropolis at 0x7f5b6a0544a8>,\n",
       " 'Rho': <spvcm.steps.Metropolis at 0x7f5b6a054c88>}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.configs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `vcsma` is an upper-level-only model, the `Rho` config is skipped. But, we can look at the `Lambda` config. The number of accepted `lambda` draws is contained in :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1177"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.configs.Lambda.accepted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so, the acceptance rate is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23488325683496308"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.configs.Lambda.accepted / float(vcsma.cycles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, if you want to get verbose output from the metropolis sampler, there is a \"debug\" flag:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                        n_samples=500, \n",
    "                        configs=dict(tuning=250, adapt_step=1.01, \n",
    "                                     debug=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which stores the information about each iteration in a list, accessible from `model.configs.<parameter>._cache`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accepted': False,\n",
       " 'current_logp': array([-9.40271195]),\n",
       " 'jump': 2.246453562495179,\n",
       " 'new_logp': array([-9.40271195])}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example.configs.Lambda._cache[-1] #let's only look at the last one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configuration of the MCMC steps is done using the `config` options dictionary, like done in `spBayes` in `R`. The actual configuration classes exist in spvcm.steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from spvcm.steps import Metropolis, Slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the common options are:\n",
    "\n",
    "### Metropolis \n",
    "- `jump`: the starting standard deviation of the proposal distribution\n",
    "- `tuning`: the number of iterations to tune the scale of the proposal\n",
    "- `ar_low`: the lower bound of the target acceptance rate range\n",
    "- `ar_hi`: the upper bound of the target acceptance rate range\n",
    "- `adapt_step`: a number (bigger than 1) that will be used to modify the jump in order to keep the acceptance rate betwen `ar_lo` and `ar_hi`. Values much larger than `1` result in much more dramatic tuning. \n",
    "\n",
    "### Slice\n",
    "- `width`: starting width of the level set\n",
    "- `adapt`: number of previous slices use in the weighted average for the next slice. If `0`, the `width` is not dynamically tuned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                        n_samples=500, \n",
    "                        configs=dict(tuning=250, adapt_step=1.01, \n",
    "                                     debug=True, ar_low=.1, ar_hi=.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4, 0.1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example.configs.Lambda.ar_hi, example.configs.Lambda.ar_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "example_slicer = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, \n",
    "                               n_samples=500, \n",
    "                               configs=dict(Lambda_method='slice'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljw/anaconda3/envs/py3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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zw0Z0bUPI59YWQH2lIvt/5RHHiYWkL06lU5x0/KUiJojw/CBxJwu63MyCzvW6rDfx0Ixh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b65ff1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_slicer.trace.plot(varnames='Lambda')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.5)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_slicer.configs.Lambda.adapt, example_slicer.configs.Lambda.width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with models: customization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're doing heavy customization, it makes the most sense to first initialize the class without sampling. We did this before when showing how the \"extra_traced_params\" option worked. \n",
    "\n",
    "To show, let's initialize a double-level SAR-Error variance components model, but not actually draw anything.\n",
    "\n",
    "To do this, you pass the option `n_samples=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsese = spvcm.both.SESE(Y, X, W=W1, M=W2, Z=Z, membership=membership, \n",
    "                      n_samples=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sets up a two-level spatial error model with the default uninformative configuration. This means the prior precisions are all `I * .001*`, prior means are all 0, spatial parameters are set to `-1/(n-1)`, and prior scale factors are set arbitrarily. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Options are set by assgning to the relevant property in `model.configs`. \n",
    "\n",
    "The model configuration object is another dictionary with a few special methods. \n",
    "\n",
    "Configuration options are stored for each parameter separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lambda': <spvcm.steps.Metropolis at 0x7f5b3f9a6400>,\n",
       " 'Rho': <spvcm.steps.Metropolis at 0x7f5b3f9a6ba8>}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsese.configs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, for example, if we wanted to turn off adaptation in the upper-level parameter, and fix the Metrpolis jump variance to `.25`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsese.configs.Lambda.max_tuning = 0\n",
    "vcsese.configs.Lambda.jump  = .25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing that might be interesting (though not \"bayesian\") would be to fix the prior mean of $\\beta$ to the OLS estimates. One way this could be done would be to pull the `Delta` matrix out from the state, and estimate:\n",
    "$$ Y = X\\beta + \\Delta Z + \\epsilon $$\n",
    "using `PySAL`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Delta = vcsese.state.Delta\n",
    "DeltaZ = Delta.dot(Z)\n",
    "vcsese.state.Betas_mean0 = ps.spreg.OLS(Y, np.hstack((X, DeltaZ))).betas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to start the sampler at a given starting value, you can do so by assigning that value to the `Lambda` value in `state`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsese.state.Lambda = -.25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, it's suggested that you start the beta vector randomly, rather than at zero. For the parallel sampling, the model starting values are adjusted to induce overdispersion in the start values. \n",
    "\n",
    "You could do this manually, too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsese.state.Betas += np.random.uniform(-10, 10, size=(vcsese.state.p,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing the spatial parameter priors is also done by changing their prior in state. This prior must be a function that takes a value of the parameter and return the log of the prior probability for that value. \n",
    "\n",
    "For example, we could assign `P(\\lambda) = Beta(2,1)` and zero if outside $(0,1)$, and asign $\\rho$ a truncated $\\mathcal{N}(0,.5)$ prior by first defining their functional form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Lambda_prior(val):\n",
    "    if (val < 0) or (val > 1):\n",
    "        return -np.inf\n",
    "    return np.log(stats.beta.pdf(val, 2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Rho_prior(val):\n",
    "    if (val > .5) or (val < -.5):\n",
    "        return -np.inf\n",
    "    return np.log(stats.truncnorm.pdf(val, -.5, .5, loc=0, scale=.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then assigning to their symbols, `LogLambda0` and `LogRho0` in the state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcsese.state.LogLambda0 = Lambda_prior\n",
    "vcsese.state.LogRho0 = Rho_prior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The efficiency of the sampler is contingent on the lower-level size. If we were to estimate the draw in a dual-level SAR-Error Variance Components iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 42.5 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit vcsese.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easy to work with the model, you can interrupt and resume sampling using keyboard interrupts (`ctrl-c` or the `stop` button in the notebook). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.3 s, sys: 103 ms, total: 8.4 s\n",
      "Wall time: 4.27 s\n"
     ]
    }
   ],
   "source": [
    "%time vcsese.sample(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vcsese.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under the Hood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Package Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the tools in the package are stored in relevant python files in the top level or a dedicated subfolder. Explaining a few:\n",
    "\n",
    "- `abstracts.py` - the abstract class machinery to iterate over a sampling loop. This is where the classes are defined, like `Trace`, `Sampler_Mixin`, or `Hashmap`. \n",
    "- `plotting.py` - tools for plotting output\n",
    "- `steps.py` - the step method definitions\n",
    "- `verify.py` - like `user` checks in `pysal.spreg`, this contains a few sanity checks. \n",
    "- `utils.py`- contains statistical or numerical utilities to make the computation easier, like cholesky multivariate normal sampling, more sparse utility functions, etc. \n",
    "- `diagnostics.py` - all the diagnostics\n",
    "- `priors.py` - definitions of alternative prior forms. Right now, this is pretty simple. \n",
    "- `sqlite.py` - functions to use a sqlite database instead of an in-memory chain are defined here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The implementation of a Model\n",
    "\n",
    "The package is implemented so that every \"model type\" first sends off to the `spvcm.both.Base_Generic`, which sets up the state, trace, and priors. \n",
    "\n",
    "Models are added by writing a `model.py` file and possibly a `sample.py` file. The `model.py` file defines a `Base/User` class pair (like `spreg`) that sets up the state and trace. It must define hyperparameters, and can precompute objects used in the sampling loop. The base class should inherit from `Sampler_Mixin`, which defines all of the machinery of sampling. \n",
    "\n",
    "The loop through the conditional posteriors should be defined in `model.py`, in the `model._iteration` function. This should update the model state in place.\n",
    "\n",
    "The model may also define a `_finalize` function which is run once before sampling. \n",
    "\n",
    "So, if I write a new model, like a varying-intercept model with endogenously-lagged intercepts, I would write a `model.py` containing something like:\n",
    "```python\n",
    "\n",
    "class Base_VISAR(spvcm.generic.Base_Generic):\n",
    "    def __init__(self, Y, X, M, membership=None, Delta=None,\n",
    "                 extra_traced_params=None, #record extra things in state\n",
    "                 n_samples=1000, n_jobs=1, #sampling config\n",
    "                 priors = None, # dict with prior values for params\n",
    "                 configs=None, # dict with configs for MCMC steps\n",
    "                 starting_values=None, # dict with starting values\n",
    "                 truncation=None, # options to truncate MCMC step priors\n",
    "                 center=False, # Whether to center the X,Z matrices\n",
    "                 scale=False # Whether re-scale the X,Z matrices\n",
    "                 ):\n",
    "        super(Base_VISAR, self).__init__(self, Y, X, M, W=None,\n",
    "                                         membership=membership,\n",
    "                                         Delta=Delta,\n",
    "                                         n_samples=0, n_jobs=n_jobs,\n",
    "                                         priors=priors, configs=configs,\n",
    "                                         starting_values=starting_values,\n",
    "                                         truncation=truncation,\n",
    "                                         center=center,\n",
    "                                         scale=scale\n",
    "                                         )\n",
    "        self.sample(n_samples, n_jobs=n_jobs)\n",
    "        \n",
    "        def _finalize(self):\n",
    "            # the degrees of freedom of the variance parameter is constant\n",
    "            self.state.Sigma2_an = self.state.N/2 + self.state.Sigma2_a0\n",
    "            ...\n",
    "        \n",
    "        def _iteration(self):\n",
    "            \n",
    "            # computing the values needed to sample from the conditional posteriors\n",
    "            mean = spdot(X.T, spdot(self.PsiRhoi, X)) / Sigma2 + self.state.bmean0\n",
    "            ...\n",
    "    ...\n",
    "```\n",
    "I've organized the directories in this project into `both_levels`, `upper_level`, `lower_level`, and `hierarchical`, which contains some of the spatially-varying coefficient models & other models I'm working on that are unrelated to the multilevel variance components stuff. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most of the `_iteration` loop is the same between models, most of the models share the same sampling code, but customize the structure of the covariance in each level. These covariance variables are stored in the `state.Psi_1`, for the lower-level covariance, and `state.Psi_2` for the upper-level covariance. Likewise, the precision functions are `state.Psi_1i` and `state.Psi_2i`. \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function spvcm.utils.se_covariance>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsese.state.Psi_1 #lower-level covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function spvcm.utils.se_covariance>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsese.state.Psi_2 #upper-level covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function spvcm.utils.sma_covariance>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.state.Psi_2 #upper-level covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function spvcm.utils.sma_precision>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.state.Psi_2i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function spvcm.utils.ind_covariance>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcsma.state.Psi_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The functions that generate the covariance matrices are stored in `spvcm.utils`. They can be arbitrarily overwritten for alternative covariance specifications. \n",
    "\n",
    "Thus, if we want to sample a model with a new covariance specification, then we need to define functions for the variance and precision."
   ]
  }
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