Statsmodels: Markov switching notebooks

markov_autoregression.ipynb

markov_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markov switching dynamic regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example of the use of Markov switching models in Statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NBER recessions\n",
    "from pandas_datareader.data import DataReader\n",
    "from datetime import datetime\n",
    "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept\n",
    "\n",
    "The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n",
    "\n",
    "$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n",
    "\n",
    "The data used in this example can be found at http://www.stata-press.com/data/r14/usmacro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAADSCAYAAACsLsN9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5b0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n",
    "dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "# Plot the data\n",
    "dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "# (a switching mean is the default of the MarkovRegession model)\n",
    "mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n",
    "res_fedfunds = mod_fedfunds.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-508.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 07 Jun 2016</td> <th>  AIC                </th> <td>1027.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:51:02</td>     <th>  BIC                </th> <td>1044.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1954</td>    <th>  HQIC               </th> <td>1034.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    3.7088</td> <td>    0.177</td> <td>   20.988</td> <td> 0.000</td> <td>    3.362</td> <td>    4.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    9.5568</td> <td>    0.300</td> <td>   31.857</td> <td> 0.000</td> <td>    8.969</td> <td>   10.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    4.4418</td> <td>    0.425</td> <td>   10.447</td> <td> 0.000</td> <td>    3.608</td> <td>    5.275</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.9821</td> <td>    0.010</td> <td>   94.443</td> <td> 0.000</td> <td>    0.962</td> <td>    1.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.0504</td> <td>    0.027</td> <td>    1.876</td> <td> 0.061</td> <td>   -0.002</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  226\n",
       "Model:               MarkovRegression   Log Likelihood                -508.636\n",
       "Date:                Tue, 07 Jun 2016   AIC                           1027.272\n",
       "Time:                        13:51:02   BIC                           1044.375\n",
       "Sample:                    07-01-1954   HQIC                          1034.174\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.7088      0.177     20.988      0.000       3.362       4.055\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          9.5568      0.300     31.857      0.000       8.969      10.145\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         4.4418      0.425     10.447      0.000       3.608       5.275\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.9821      0.010     94.443      0.000       0.962       1.002\n",
       "p[1->0]        0.0504      0.027      1.876      0.061      -0.002       0.103\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAADSCAYAAACrbB1oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm8ZGV97/vPqnHP3U33bqC7mYcHkElBQAQ0GFQEFKcY\njMkNAZOXico1JifiifGYc8+JiVeO0cQEL8bh3sTE4WAiKqABFFBAEJXxgW7Gnnc33Xv3Hmpe94+1\nVu1is4fae6+pan/frxcvuqZVT9VTtetXv/o9v8dxXRcRERERkZUkk/QARERERETipiBYRERERFYc\nBcEiIiIisuIoCBYRERGRFUdBsIiIiIisOAqCRURERGTFURAsIrMyxhxhjKkZY37u//eg/9+VSzjW\n7caYty3yNh83xnx2jstuMsacYIx5jTHmIf+8Txhj3uP/+2PGmMsWO8457muTMeYh/7GfPeOyp40x\nr1jk8f7AGPNfQhrbGcaYry/yNmcaY/7B/3fz+VvGGBacpwVu/yVjzB8vZwztWsrzJSLdK5f0AEQk\n1Sattc0gzxizAXjYGPMza+3DSQ3KWnupP56DAdc/7+MtV7kQeCSku7sQ2GGtfX0YB7PWXh/Gcfxj\nPQD8xiJvdjKwseV0ZM3ig3lKiyU+XyLSpRQEi0jbrLXbjTFPAscbY84ArgL6gf3W2tcZYz4G/CZQ\nBZ4A3m+t3e3f/G3GmGuBXuBfrLX/E8AY81HgLUDRP9afWGv/3b/NScaYHwFrgAeBP7TWThhjngbe\n3jo2Y8yXgIeBKeBM4FPGmB7g74CzrLWb/evdCnzOWvudGbf/feADQA3Y5f97I/DfgSFjzH9aa183\ny9PyfmPMaUABuM5a+yX/eJcCfw7kgUn/cd1rjPk4sNZa+0H/cXwZeB1wGPB1a+2f+bf/CPB7wBhw\nJ3C5tfaoGWN+DfB31tpT/Mc/BpziH+tx4F3W2smW628CPuE/ni8CXwUGjTFfA07w5+C91tq7jTF5\n4K+BC4Cs//x/0Fo7PstzcKIx5jbgUP+5e5e1dlcwT9bany/weF5tjHk7cDDwEPBua+3UjMf6JeAg\n4GjgJuAv5hqfMeYs4O/95/4p4AjgQ4Az4/maAl7p3+83gBHgMv/01dbaOxb5PIhIB1E5hIi0zRjz\nKuAY4F7/rJOAC/wA+ErgDcAZ1trT8TKxX2m5+SBwFvAq4D3GmDcYYw7Hy7Re4N/mz4G/bLnNMcBb\nrbWn4v29+vMFhuhaaz8P3I8XdP4LXpD5Xn/8xwDH4wVRrY/rQuBPgNdYa18OfA34trX2Drxg6845\nAmDwsuVnAK8HPmmMOdEYcyzwP4GL/cv+ALjRGNM7y+37rbUXAK8GPuCXobwB+B285/JM/7mbK2Pb\nev4r/HGcCGwA3tl6RWvt1pbHc5V/9kbg0/7j/gLw3/zzPwJUrbVn+pftwAsGZ3MU8A5r7YnAPuDq\n1guNMa9f4PFswHsdHI8XwM9VOtNrrT3FWnvtHOP7pDEmC3wT+K/+a+qzwGktx2i939OBs/EC4Q8B\nY9baV/u3+cgSngcR6SDKBIvIfPqMMT/Hy6Dl8DJl77bWbjPGAPzKWjvhX/eNwJestSX/9N8CHzXG\nBH9nbrDWusABY8w3gYustbcYY34XLyg+FjgHGGi5//9trX3B//eXgL8Brm1z7I7//38AfuRnnN/b\nMo5WbwD+Lbgva+1XjDGfMcYc0cb9XO/fZocx5ma8rG4dLyv6n8aYYBw14NhZbv/v/u23G2N24WU7\nLwa+Ya094F/n7/GCxIXcbK2tAfi1vge1cZst1tr7/X//Aghqvi8FVvkBLHhZ1V1zHOMHLfP0S2D9\njMvfxPyP59vW2rI/7odnuX3grpZ/zzW+U/C+DN0K4Gdz5yrd+Y61tgHsMsZMALf4529h+rlbzPMg\nIh1EQbCIzOdFNcGzaP1JeOYvS1m8vzFBEFhvucwBqsaYl+MFgdfhBSA/Aj7fcr2X3Kb9oXustU8a\nY34FXA68Gy8bPdNsv4pl8AKehbSOMeOPMQ/80Fp7RXCBX4qwnZdmOadmnHbwAman5bw67Wk9ljvj\nGHNpfU5bb5MFrrHW3gJgjOkDehZ5jMDMx9NY5O0Dra+3uca3YZbbz7y/QHmecSx0PyLS4VQOISLz\naSeICtwCXOkHCQAfBH5krQ0Ci98BMMasAd4FfB+vzvJn1trPAD8G3ooXdATebIxZ5f/E/fvA99oc\nS40XB7CfBz4F3GOt3TnH2N9ljFnnj/FKYE9QR7yA3/Vvczjw68B/ArcBrzd+utwY8ya8DGmxzfF/\nF3i7MWbIP3014S1gm/nczOUWvHrnvDEmA3wR+Ksl3ufMx3MVy388c43vMaAcZG79+uBTlnF/YT4P\nIpIiCoJFZD6LCRy+CPwQuM8Y8wheveV7Wo4zaox5AO8n7b+11v4Yr/Z22L/+/XiLpg4yxvT7t3sU\nL4D6JV6t6V+3HG8+3wH+b2PMb/unb8Irs/jH2a5srf0h8L+A2/wygt8GLmnjMbtAj/+4bsJbCLjZ\nWvsoXtD+r8aYB/EWo102c7HXLI8j6HRxO3AD8BNjzH14NbSTLM5cz9FPgROMMd9a4Pb/HXgGbyHY\nw/7xPryUMSzy8bRT+zzn+Ky1deAdwCf8efkQXh3vQs/fXPcbxvMgIinkuG5k3XFERFLBGHMucL21\n9pSkx9IOv/PGudbaz/mnP4TX4eKK+W+ZTnE/HmPM3wCfstaO+GUovwCOttaORXF/ItKZ2qoJNl6D\n+E9aa39txvmXAR/Dq6P6krX2hvCHKCKydMaYLwOvwcvudoongD/z27a5wLN4meVOFffjeRYvqx+U\n4lylAFhEZlowE2yM+VO8D49xa+25Lefn8GqvzsBbjHE3cIm1diS64YqIiIiILF87NcGb8RarzHQi\n8KS1dsxf+HIX3iIXEREREZFUW7Acwlp74xy9MoeA0ZbTB4BVCx2vVqu7uVx2oauJpNb4ZIUPXncH\nI/um1zhlMg4vP36Y175iE+efvpFsNr1rTr92q+Vfbnl80bc75+RD+PBvnUFPoTM7K377R5v54n+E\ntZOyBBwHNq0f5KyTDuY9F59ILuLX/ubn9/OjB7dy78M72bF3YuEbLMHLjl7Ln/3OmawZVCc0kS4w\nZ5ej5XyajeEFwoFBYP9CN9q3b7ELnDvD8PAgIyMHFr6idJyZc3vrz55nZN8UR28Y4vD1A/T15Hn0\nmRd44PHdPPD4bp545gXeesHRCY54fo9u2QPA7158Aj2FLBnHIZtxyPj/zfxr4QLfv+dZ7nl4Jx/5\n3J184B2nMtRXiH3cS+W6Lv9x9zP8+11Ps3qgwDt/7VgKuQyO47BmdR8HDkzhOLM/7hcf5yVHnuW+\nFrrGS890X3rGQjdZ0ljaGArtLJSuN1xqtQa1eoMXDpTZsm2Up3cc4Fu3b+aZ7aP8wZtfFkkgvGPv\nBN/60VP8/Amv4q5YyHLmCes56tBB+oo5eos58rnp+129qo/RUf+Lasvkts6z63qPp95oMD5VZf94\nhae3j/LIU3u55tN38EdvPYWjN7R+zEka6PO2e0Uxt8PDg3NetpggeOZnxGPAscaY1XitZy7A68Mp\n0rVc1+WOB7eRyzpc845TGWwGg8ewY+8EH/+n+/jVlr2pDoKf3z3Oqv4CF5y2oe3bnHjEGv7pe49x\nzyO7+Ot//jmf+L2zIs/4heXGO5/ipp88y7pVPfzJFS9n/erpnYv1YRqOcqXOZ77xSx6wI9xw06O8\n97KTyGbCeX08t+sAP/jZ8/zkkZ24LhyzcYhLXnUkLzvyoBcFvTMtdW5d1+Xme5/jmz/awif/+QHe\n/7ZTOfWYtct5CCKSUosJgl0AY8wVeHvd32CM+WPgVrwA+QZr7Y4IxiiSGva5/ex8YZJzXnZwSwDs\nOXRtP0ceMsRT28coV+oUC+kr+5ks1dg7VuJlR7Wzm+60XDbDey89iclSjV9t2cve0RIHH9S38A0T\nVqnW+e5Pn2XtUA/XvucM1gy2u1eFLEaxkOWad57K//r6L7nvsd1kMg7vvfQkHGcxe61Mc12XX27Z\ny833PMsTW72quw3r+nn7BUdz+nHrlnzcdjiOw8XnHMHG4QE+841fcseD2xQEi3SptoJga+2zwLn+\nv7/Wcv538RrZi6wId/xiGwCvPX3jrJcfs3GIzdtGeXrHGCccsSbOobVl64i36+xhwwOLvq3jOAyv\n8rKo1dpcu9Cmy84XJnFdOOWYtQqAI9ZTyPF/vvM0rvv6L7jnkV0cdcgQF73ysEUfZ9ueCf71h0/w\nyDP7ADj5qIP49TM3cfLRa8lEGPzOdOoxaxnqyzffMyLSfTpzhYtIAkYnKjxgR9i4rp/jNs2+BvTY\njau4hefZvG00lUHw87u9D/RN6/sXuObs8nnv5+dKhwTBO/Z6axAOXZv+rHU36C3m+KO3nsJ/+6f7\n+PrtmznusFUceUh7NbWVap3//eOn+OH9W2m4LicfdRC/ceGxbFrCF7awbBwe4LFn9zFVrtFb1Mel\nSLfpjKI+kRS461fbqTdcXvvyjXP+HHvMRi843rJtdNbLkxZktZYaWOT9OuBqrR7amKIUdA9QEByf\n1QNFrr7sJOoNl3/89iNMlWsL3ubZnQf4xJd/xq0/e551q3r44DtO5UO/cVqiATDAxmHvy+L2iLpQ\niEiyFASLtKHhuvzoF9sp5DO86mWHzHm91QNF1q3qYcv2sbZW2sdt6+5xshmHDeuWlgku5IMguDMy\nwdv9TPCGtUt7vLI0Jx+1ljedcwS7909xw02PMlmaPRCuVOv8x11P83999X527J3kdWds4hNXncXp\nx0Zb99uuIAjfNqIgWKQb6fcdkTaMjlfYM1rijOOH6euZ/21z7MZV3PPoLnbtm+KQFC0ea7guW0cm\nOHRt35I7O0xngjsjCN65d4JiIat64ARcfv5RPLl1Pw8+uYePXP9T3vzqI3ntyzeSy2ZwXZefP7GH\nf7vtSfaMllg9UOCqS05a9ILNqAWZYNUFi3QnBcEibShVvExWf29+wese4wfBm7eOpioI3rN/inK1\nzqb1S/+JOWhJ1Qk1wY2Gy84Xptg03J+KrOJKk8tm+PC7TucH9z/P9+55ln/54ZP8222byWUzOA6U\nKnWyGYc3nn04l517ZCprboNfEJQJFulO6furI5JC5apXA9vTRtuzY/264M3bRjnv1EMjHddiBIvi\nltIZIpD3d3vshEzwyOgUtXpD9cAJKuSzXPKqIzn/tA189yfPsnnbfhoN71eJ9Wt6edsFR3NoiktV\neos51q3qYZsywSJdSUGwSBvKFS8ILuQXDoI3re+nkM+wZXu6FsdNd4ZYfia4Wk9/EDzdGSK9QdZK\nMdRX4IpfPy7pYSzJpuEBfrF5D2MTFYb6O2enRBFZmBbGibRhMZngbCbD0YcOsX1kgslSNeqhtW2r\n/5PuclbcF4IguJr+7hDTnSEUBMvSBXXBygaLdB8FwSJtKFe9zGexjUwweHXBLvDU9rEIR7U4W3eP\nM9CbZ/XA0rNZHZUJ3uN3hlincghZuunFcaoLFuk2CoJF2hAsjFtMEAywJSVBcKlSY/f+KQ5bP7Cs\nRWLNhXHVDgiC906QzTgMr+5NeijSwZpt0vYoEyzSbRQEi7QhCPqKbZRDAM3Aa3SiEtmYFmNbCKUQ\n0LIwLuWZYNd12bF3kvVrepfcDk4E4JCD+shmHGWCRbqQPh1E2rDYTHCPf71yZeHdsuLw/MjytksO\nTNcEpzsIHpuoMFmuqR5Yli2XzXDI2j627ZmgkcINcERk6RQEi7Rhuia4vbdMkDEupyRYHB33MtLr\nhnqWdZzpmuB0L4zb3uwMoXpgWb6N6/opV+rsHS0lPRQRCZGCYJE2BC3SegrtdRUspiwTXPG7ORTa\nLOeYSzMITnmf4J3NzhAKgmX5tH2ySHdSECzShnLVC2YLbWaCc1mHjONQSkkrsWZNcy6cIDjtO8Zt\nV49gCZG2TxbpTgqCRdoQlDW0mwl2HIdiIUu5ko5gsVwLNvtY3lu+UzLBO5QJlhAF25/vGZ1KeCQi\nEiYFwSJtCMoh2q0JDq4bZJCT1iyHaHNh31w6JwieZM1gse0vLSLzaZY3paTGX0TCoSBYpA3lJQSR\nxUIuNR+aQTlEYZnlENlMhmzGSXUQ3HBd9o+XWbdqeYsARQLBQtdKSsqbRCQcCoJF2lCq1MllnUX1\nnC3mM80MctIqIZVDgJcNDo6XRqVyDdeFgd580kORLjGdCU7v615EFk9BsEgbKtV62z2CAz35LJVq\nPRW9RSvVBtnM4oL4ueRzmVRngsdLXglKf4+CYAlHLuv9AqIgWKS7KAgWaUOpUm97t7hAoZDFJR0b\nS1Sq9VCywJD+IHhiqgpAf6/qgSU8hXymI7YLF5H2KQgWaUN5iZng4LZJK9cay64HDuRz2XQHwSU/\nCFYmWEJUyGdT8V4WkfAoCBZpQ7lap2eRmeAgc5yGXsGhZoKzac8E++UQqgmWEBUVBIt0HQXBIguo\nN1yqtcaiM8HB9SspWBznBcHhZIIL+UyqN8uYzgSrHELCU8hlVQ4h0mUUBIssINj6eNFBcJoywWGW\nQ2Qz1OoN3BQs+JvNdE2wMsESnmIhoxZpIl1mwVSJMcYBPg+cBpSAq621T7Vc/lvAHwM14EvW2n+M\naKwiiSgFG2UsthwiJTXBDTfIZIdUDpGf3jAjrOxymCb87hADqgmWEBXzWeoNl1q9EUqXFRFJXjvv\n5MuBorX2XOBa4LoZl38KuBA4D/iwMWZVuEMUSVapvLRMcHNhXMLlEEF3irAC1rwfAFTr6fxpuJkJ\nVjmEhCj4JSXpL7UiEp52guDzgJsBrLX3AmfOuPyXwBqg1z+dzt9IRZaomQleZBBZKKQjCC4HG2Xk\nwmuRBundOjnIBKscQsI0vWtcOl/3IrJ47XwqDgGjLadrxpjW2z0CPAA8BNxkrR0LcXwiiZsKMsGL\nLIdIS4u0yhK2fJ5PkBFL6+K48VKVjOMsupuHyHyCcqKk388iEp52fi8cAwZbTmestQ0AY8wpwCXA\nEcAE8M/GmLdba78118HWrOkjF9ICnbQZHh5c+ErScZ7fuxuAg1b3LWqOh9eNA5Ar5BJ9bZT8WHXV\nYE8o4xgcLPr/D+d4YStX6wz05Vm/fqit66fxMUg4wpzb1UPej519/UW9ZlJAc9C94pzbdoLgu4FL\ngW8aY87By/gGRoFJoGytdY0xu/FKI+a0b9/kUseaasPDg4yMHEh6GBKBKb87RK1aW9Qcl6cqALyw\nbzLR18bO3d6PM/VaPZRx1P1M2K6RA/RmnWUfL2yj4xV6i7m2Hqvet90r7Lmt16Zf90PF7kzkdAq9\nb7tXFHM7X1DdThB8I3CRMeZu//SVxpgrgH5r7Q3GmC8AdxljysAW4MvLHK9Iqix1YVxw/aRbpFXC\nXhiX4ppg13WZmKoyvKon6aFIlymkpLxJRMKzYBBsrXWB9804+4mWy68Hrg95XCKpESyMW+qOcUl/\naAY1wWG1SAsW2KWxJrhcrVNvuFoUJ6FrtjyspO91LyJLo2aHIgsIMsGLzaQWU9IirRxkgsPaLMM/\nThozwc0tk9UeTUIWbDuuDTNEuoeCYJEFNDPBS9wxLvFMcNAiLazNMvxMcC2NQXBzy2RlgiVczS+1\nNQXBIt1CQbDIAkqVZbZISzgTHHaLtHyzHCJ9wYC2TJaoBEFwJeH3s4iER0GwyAKWullGPpfBIQWZ\n4NDLIdK7MK65UYbKISRkBfUJFuk6CoJFFrDU7hCO41AsZJPPBNdWzsK48ZIywRKNZiY4ha97EVka\nBcEiC1hqOQR4H5xJZ47KEbVIS2VN8JRqgiUaaVnoKiLhURAssoBSeWkt0sALnJPvExz2wrj0ZsSa\n5RC9KoeQcBXVJ1ik6ygIFllAqVIjm3HIZRf/dinm01AOsYJqgv1M8IAywRIybZYh0n0UBIssoFSp\nL7oeOFAseOUQruuGPKr2hZ4Jzqa4O0QzE6wgWMJVbPYJTt+XPxFZGgXBIgsoVWpLqgcGLxPsuslm\nTcNukRYE02muCe4rqhxCwqVMsEj3URAssoBSeemZ4J4UfHAG5RDFsMohsikuhyhV6SvmyGScpIci\nXSaXzZDNONoxTqSLKAgWWUCpUltyEFxIwYry4EM7H1Y5RIpbRU2UaloUJ5Ep5LPNbisi0vkUBIvM\no+G6Xk3wEsshelKwdXK52iCfy5BxwsmOpjoTPFVVezSJTDGfUSZYpIsoCBaZR/CBt+SFcf7tkmyT\nVqnVmxtchCGoCa6mbGFcpVqnUmtoUZxEppCCvt8iEh4FwSLzCH76XPLCOP92lYTLIcJaFAeQzTg4\npC8TrC2TJWrFfDaVXVFEZGkUBIvMoxzsFrfEetpUZIKrjVCDYMdxyOcyqasJntCWyRIxr+93I9GW\nhyISHgXBIvMIMsE9+aVlF4spqAmu1OoUQyyHAG/DjGo9ZUGwtkyWiBXzGRquS62uIFikGygIFplH\n0NWhUFjaW6UnFd0hws0Egx8Epy4T7GXtB1QOIRFRr2CR7qIgWGQewYddz3JbpCXUVqlWb1BvuKHt\nFhco5LLpC4KnVA4h0QrKm9QhQqQ7KAgWmUepsrzuEM0WaX5tcdyCLV4LIW2UEUhzJljlEBIVZYJF\nuouCYJF5NFukLWPbZEhuYVywkj3sTLC3MC5dgcD0wjiVQ0g0gvdRRRtmiHQFBcEi8ygtt09ws0Va\nMh+aQRAfVU1wmlbJa2GcRK2oTLBIV1EQLDKPYEHb0jPB3lusVE22HKIYQTmE60K9kZ4geDwoh1BN\nsERENcEi3UVBsMg8ysvOBOf84ySTCS5HVA4R1BinqS54OhOscgiJhmqCRbqLgmCReZSXXRPsvcWS\napHWXBgXcjlELhdsnZyiILhUpVjIksvqz5pEo/l+VhAs0hX0aSEyjyB4XX6LtKSC4Kgywf4CoRQt\njpuYqqlHsERquhwiPV/+RGTpFvzEMMY4wOeB04AScLW19qmWy18JfNo/uRN4j7W2EsFYRWK33HKI\njONQyGeSywT7mdqljn8u+ZRmgodX9yY9DOliWhgn0l3aSQ9dDhSttecC1wLXzbj8C8DvWmsvAG4G\njgh3iCLJWe7COPCyyIlngiNYGAfpCYIbDZdSpa56YIlU0r/siEi42gmCz8MLbrHW3gucGVxgjDke\n2Av8sTHmDuAga+2TEYxTJBHLbZEGXgCd1IdmOaJyiLQFwVP+ZiS9RQXBEh2VQ4h0l3Y+MYaA0ZbT\nNWNMxlrbANYBrwL+EHgKuMkYc7+19o65DrZmTR+5kLNSaTE8PJj0ECRkDRcyDmw4dBWO4yzpGP29\nBUb2TyXy+sgXvHZhw+sGQr3/1UNe2UHfQDEVr/v63gkA1qzqXfR40jB+iUbYczvuB7+ZXEavm4Tp\n+e9ecc5tO0HwGNA6oiAABi8LvNla+wSAMeZmvEzxHXMdbN++yaWNNOWGhwcZGTmQ9DAkZOOTFYqF\nHHv2jC/5GNkMlMq1RF4f+/Z777fSRDnU+6/6mdeRPROMrOoJ7bhLtXWn99gyuIt6nHrfdq8o5nbi\nQAmA/WMlvW4SpPdt94pibucLqtv5jfRu4E0AxphzgIdaLnsKGDDGHO2fPh94ZGnDFEmfcrVOb3F5\nv1wU81nqDZdaPf6fUKf7BIdcE5xNVznEZNkLyvtUDiERKmizDJGu0s4nxo3ARcaYu/3TVxpjrgD6\nrbU3GGOuAr5mjAH4ibX2+xGNVSR25UqdvmXuQBbUEZYqdQZ64+1KGFWf4Hw+CILTEQxMKQiWGKgm\nWKS7LPiJYa11gffNOPuJlsvvAM4Od1gi6VCu1lm7anltt4LOEuVKnYGYt/SNqk9w6jLB/pbJveoO\nIREqaLMMka6izTJE5uC6LuVKfVnt0WB6o40kPjiDPsFht0hr/iycliC4mQmO90uGrCy5bIZsxlEQ\nLNIlFASLzKFaa+Cy/LZbSfYWrTRbvHV7JrgKQJ8ywRKxYj6rmmCRLqEgWGQOzR7By80Et5RDxG26\nHCKqzTLSEQxoYZzEJcm+3yISLgXBInMo+UHrcjPBzYVxCXxwlmsNMo5DNrO0HsdzaQbBCXS8mE2w\nME41wRK1Qi5DWQvjRLqCgmCROZRCyi4GmeQkfkKtVOsU8pklb/Qxl7TtGBcsjFMmWKKmcgiR7qEg\nWGQOzUzwMrOLrS3S4lapNkIvhQAvGwbpWRjXzAQvs6ezyEIKfjmE67pJD0VElklBsMgcwi6HSKY7\nRL0ZsIYpjZngYiFLNqM/aRKtYi6D65LI5jciEi59YojMoVQJsovLC4KTXRjXaAbhYcr7LddSEwSX\nayqFkFhMd3tJx2tfRJZOQbDIHMLKBCfdIi3sjTIgfZngqXJN7dEkFknW+ItIuBQEi8wh9HKImDPB\nDdelUmuEvlEGTAfBlRS0SGu4rjLBEpvg/aQ2aSKdT0GwyBzCKodobpsc84dmkKWNYmFcEATXUpAJ\nLlfquO7y50mkHUnW+ItIuBQEi8wh7O4QcX9oTm+UEf7bPOM45LJOKsohgs4QKoeQOBQL/q8gqgkW\n6XgKgkXmEF45RDIfmsH9RVEOAd7iuDS0SFOPYImTMsEi3UNBsMgcwiqHSGphXFCvW4wgEwxeSUQa\nMsGTygRLjJo1wQl0exGRcCkIFplDqex9yC03w5jLZshmnNhXkzczwRHUBIO3YUY1BQvjpjPB+YRH\nIitBUjX+IhI+BcEicwgrEwzeT6hxf2iWI6wJhvRkgrVbnMQpeD+loRRIRJZHQbDIHEqVOvlchmx2\n+W+TQj6TWDlEZDXB2UwqAoHpcghlgiV6RZVDiHQNBcEicyhV6s3d3pbLywQntDAuonKIfD4dmeDJ\nUhXQwjiJR0GbZYh0DQXBInMoVWohB8Hd0yINvExwveHSaLiRHL9dWhgncWp2h0hBPbyILI+CYJE5\neJngcAKrQiFLpVLHdeMLGINShWJE5RBBhjnpbPB0TbCCYIleUjtAikj4FASLzMJ1Xcohl0O4xBsw\nRr4wzq+VrtaTDYLVJ1ji1Ov/TZgqKwgW6XQKgkVmUa7WcSG0THASDfanyyGiqwluvZ+kTCoTLDEK\ndpAMusdepaj3AAAYLklEQVSISOdSECwyi2C3uPAywd5bLc4gOLivYkRBcPAFYSrhn4UnSzUKuQz5\nnP6cSfR6mplgBcEinU6fGiKzCD8IDjLB8ZUOhP0YZgp+Fk46IzZVrikLLLHJZjIU81mVQ4h0AQXB\nIrMIArvQFsbl42+rFCzcKUYUBAfBdSnhYGCyXFNnCIlVTzGrTLBIF1jwk8MY4wCfB04DSsDV1tqn\nZrne9cBea+1HQx+lSMzKEWWCYw2Coy6HKCZfG+m6LpOlGutX9yY2Bll5+oo5xqeqSQ9DRJapnUzw\n5UDRWnsucC1w3cwrGGP+ADg55LGJJCaoc+0JaSveIBsba01wJeqa4ORXyVdqDeoNt7lYSSQOPYWc\nMsEiXaCdIPg84GYAa+29wJmtFxpjXgW8Erg+9NGJJCTscogkaoKjzgT3FpLPBAeBiNqjSZx6i1lq\ndTfxHtkisjztBMFDwGjL6ZoxJgNgjDkE+DjwfsAJf3giyQh7UVnQqzfOBvulSp1CLkMmE81bM8iS\nJ9kdQj2CJQnBQkxlg0U6WzufHGPAYMvpjLU2+Pr7TmAt8D3gUKDXGPO4tfarcx1szZo+chHtYJW0\n4eHBha8kHSGX994aB68bAJY/t8MHeccpFHOxvU7qrlcmENX9HTrp1URmspnEXvt7J7wxrF3Tt+Qx\n6H3bvaKa2zVDXg1670CRYf9vhMRL79vuFefcthME3w1cCnzTGHMO8FBwgbX2c8DnAIwx/wdg5guA\nAfbtm1z6aFNseHiQkZEDSQ9DQrLnhQkAyiUvyFru3JZKFQD27puM7XUyMVUln81Edn+liTIAL+yf\nSuy1v23nmPePRmNJY9D7tntFObeOv/35th2j5GPcCl08et92ryjmdr6gup0g+EbgImPM3f7pK40x\nVwD91tobQhifSOpE1yc43oVxawaLkR2/+ZOwaoJlhektJr8oVESWb8FPDmutC7xvxtlPzHK9r4Q1\nKJGkdUMQXKrUI1sUB9OLBpPsE9zcMlndISRGwRfAkmqCRTqaNssQmUX43SGCbZPjWU1eq3utw6La\nKANaNstIMBM86Zer9BXziY1BVp4gCJ5UECzS0RQEi8wiskxwTJ0Uom6PBpDJON72sUl2hwjKIZQJ\nlhg1M8EJvvZFZPkUBIvMohTylsOFQrw7xkW9ZXKgp5BN9CfhoCazVzXBEqNe/32lTLBIZ1MQLDKL\nUqVGsZAl44TTYzfumuDgfnoizASDt3Vyktmw6XIIBcESH9UEi3QHBcEisyhV6qGVQgAUchkc4ssE\nh53JnktPIZtodwiVQ0gSerRZhkhXUBAsMotSpR5qFtVxHAr5bGwL45rlEBFngnsLWSrVBo1GMr1S\np0o1shmHQk5/yiQ+fc32gKoJFulk+uQQmUWpUgutM0SgmM/EVg5RqsaVCQ4WCCWTEZss1+gt5nBC\nKlsRaUfwK5EywSKdTUGwyAyNhkul2gi1HALwM8HxBMGVmGqCg00DkqoLnizXVA8ssespZHFQECzS\n6RQEi8wQdnu0QLGQ7cKa4ORqI13XZWKqSn+vegRLvBzHoaeYUxAs0uEUBIvM0NwoI+QMYyEXXyY4\nrprgngQzweVqnVrdZUBBsCSgr5jVtskiHU5BsMgMkWWC8xlqdZd6I/rFceWYa4KT6BAxPuW1Rxvo\nVTmExE+ZYJHOpyBYZIboguBg17j4guCefLQBYrBpQCmBjNjElBeAqBxCktBbyDFVqeG6yXRGEZHl\nUxAsMkOzHCLs7hCF+DbMCAL5Qj7at3iSmeADUxUABhUESwJ6izlcN74NcEQkfAqCRWaIKhNcyMe3\ndXIzExxxOUSS3SGmyyEUBEv8gte+6oJFOpeCYJEZpjPBEZVDxBEEN7tDRFsO0ewTnEBtpMohJEnN\nrZMT3DFRRJZHQbDIDNOZ4LA3y4gxCA4WxkVdDhFkw5QJlhWm1//7MKnFcSIdS0GwyAzlCLtDAFRi\n2Dp5uiY4rh3jFATLytIsBVI5hEjHUhAsMsNU1N0hYsoEF/NZMhFvJzzdHSKJcggFwZKcoI+42qSJ\ndC4FwSIzRNUdohBjd4hypR55j2BIRyZYNcGShD4FwSIdT0GwyAzNmuBip2eCo397B9nyJAKB8akq\n+Vwm8l3xRGaT5JbhIhIOBcEiM0S9MK4SQ9a0XKlTjHijDIBMxqGYzyaWCVYphCSlL8FFoSISDgXB\nIjN0eos013UpV+uR9wgO9BSziW2brCBYkqKaYJHOpyBYZIZSpY7jQCEX7tsj2L2tHHF3iFrdpd5w\nYymHAC9jHncmuFZvUKrUFQRLYlQTLNL5FASLzFAq1+kp5HBC7qwQVya42SM44o0yAr2FbOzdISa0\nKE4SpkywSOdTECwyQ6lSi6SUILYgONgtLqYFYz2FLJVag3oj+v7HAfUIlqSpJlik8ykIFpmhVImm\nnjbYuKIScRBcamaC4wmCp7ePjS8YmA6C48l2i8yUy2bIZhxlgkU62IKfIMYYB/g8cBpQAq621j7V\ncvkVwDVAFXjIWvuHEY1VJBalSp3h1b2hH7eZCY44WGzueBdbJtgPgst1+nviycyOT3mBx0BM9ycy\nk+M49BZzCoJFOlg7meDLgaK19lzgWuC64AJjTA/wl8BrrLXnA6uNMZdGMlKRGNTqDWr1RjTlEIVg\nYVxcNcHxdYcAYu0QMVFSTbAkr7eYVRAs0sHaCYLPA24GsNbeC5zZclkZONdaW/ZP5/CyxSIdaWyi\nAsBgX/jBVTaTIZd1qNSirZ2Nuya4N4Fd44JyiCjmSaRdvYWcaoJFOlg7BXVDwGjL6ZoxJmOtbVhr\nXWAEwBjzAaDfWvvD+Q62Zk0fuVx37vA0PDyY9BBkmXaOet/njty4+kXzGdbc9hRy1BtupK+VwvPe\n23XdQX2xvCbXrukDoNhTiO090MDr3LHp0FXLvk+9b7tX1HM7NFjkud3jHLR2gGwm3G4yMj+9b7tX\nnHPbThA8BrSOKGOtbaay/JrhvwGOA9620MH27Ztc7Bg7wvDwICMjB5IehizTk8/sBaA/n2nOZ5hz\nm89lmJiqRvpa2fPCBADVci2W12Sj5mXCdo0cYGRt+LXUs9m9N3iMy3su9b7tXnHMbc5vo7h12z76\nVJ8eG71vu1cUcztfUN1OEHw3cCnwTWPMOcBDMy7/AjBlrb18ySMUSYmR0SmASBbGgVeiMOnXs0Yl\nKEuIrSbYv584ayPVIk3SoDeohy/XFQSLdKB2guAbgYuMMXf7p6/0O0L0Aw8AVwJ3GmNuB1zgb621\n/x7JaEUitnufFwSvXxNdELzvQHnhKy5D2V+gFl+f4ARqgktVHGe6PZtIErRhhkhnW/ATxK/7fd+M\ns59YzDFEOsXI/hK5rMPqgWIkxy/mM1SqdVzXDX1HukDcfYIT6Q4xVaW/J08moudQpB3NrZNjfO2L\nSHi0WYZIi5H9U6xd1UsmokUuhUIWFyLtEFGpeMeOq09wb0uf4LiMT1VVCiGJS6IUSETCoyBYxDdV\nrjE+VWV9RPXAAMVc9Fsnl6reB3Ih5prgUkzZMNd1mZiqKQiWxDUzwTF+ARSR8CgIFvGN7A8WxfVE\ndh/NrZMjrJ+Ne8e43uZPwvEEAlPlGg3XVRAsiVNNsEhnUxAs4psOgiPMBBeizwSXq40X3VfUmpng\nmAIBdYaQtOhVTbBIR1MQLOLb7QfBkZZD5IOtk6OrCS5XajhAIRfP2zsItuPKBB9QECwp0auaYJGO\npiBYxDey39vxO9JMcD6OmuA6hUI2su4TM2Uch2IhG1tN8IQfBPf3qjGNJGuV30Vm31i0bQ9FJBoK\ngkV8QTnEughrgoMguBJlOUSlHls9cKC3kI2tO4TKISQthlf3kMs6bN/bnTuhinQ7BcEivpF9Uwz1\nF5qbP0ShEEMmuFytx1YPHOgp5GLLBI9PefejIFiSls1kOHhNHzv2TuC6btLDEZFFUhAsAtQbDfaO\nlSLtDAHxlEOUq/XYdosL9BazsdUEKxMsaXLoun5KlXrkO0GKSPgUBIvg1fTVG26k9cAwvYisEtHC\nONd1KVWSyQRXaw1q9egW/AWma4IVBEvyNqztA2D73omERyIii6UgWIR4OkNAa3eIaLKmtXoD142v\nR3AgaJM2WYq+JCLIBA8qCJYU2LCuH4Ade1QXLNJpFASLEE+PYJguh4iqpVLJL0mIuxxi0/AAAFu2\njUZ+X+PKBEuKHLrWD4KVCRbpOAqCRYinPRrA+jXeT6c7I1pNHuwWF3c5xMuOOgiAh595IfL7mpiq\n0lPIksvqz5ck75CDenEc2L5HQbBIp9GniAjT5RBRB8GrBwr09+TYOjIeyfFL1WSC4KM3DNFTyPLo\n09EGwa7rsn+8zGCfssCSDvlcluHVvWqTJtKBFASL4JVD5HMZVg0UIr0fx3HYNDzA7n1TzaxtmIJa\n47hrgnPZDCccvoZd+6bY43+hiMLesRJjk1UOP3gwsvsQWawNa/sZn6oyNllJeigisggKgkWAPfun\nWLeqh0wMu6xtWj+AC2yL4OfTckI1wTBdEvFIhCURm/2a42M3rorsPkQW69B1XpnTDpVEiHQUBcGy\n4m3bM8FEqcZGf5V31A5b7y0ii6IkIqmaYGgNgvdFdh9bto0BcIyCYEmRDc3FcSqJEOkkCoJlxbvv\n0V0AnGHWx3J/G4e9D8ytuyMIghOqCQY4eE0va4eKPPbMCzQa0eyetXnbKLmswxEqh5AUCTpEaHGc\nSGdRECwrmuu63PvYLgr5DKcfuy6W+9y4rh+HaDLBzYVxCZRDOI7Dy446iIlSjWd3HQj9+OVKned3\njXPEIYPkc/rTJelxqL9hhtqkiXQWfZLIivbsrgPs3jfF6ceuiy172lPIMbyml+d3j+O64WZMt414\nH8KrB4qhHrddJx3pt0qLoEvEMzvHaLgux2xQKYSkS28xx5rBojpEiHQYBcGyot336G4Azj7x4Fjv\n97DhASZKNfaPh7eavNFwuf/x3Qz05jluUzKB4klHHoQDkbRK06I4SbMN6/rZd6Ac2UY4IhI+BcGy\nYjX8UojeYo6Tj14b630364JDLIl4cut+RicqvOL44cQ2khjozXPEIYNs3jbK2ES47aK0KE7SbLok\nQtlgkU6hIFhWrM1bR9l3oMwZxw/HXmPa7BAR4uK4+x73stqvPDGeBX5zOe/UQ6k3XL53z7OhHdN1\nXTZvG2XtUA9rBpMp9RCZzwYtjhPpOAqCZcW67zGvK8RZJ8UfNG7yg+DnQ8oE1xsNHnh8N4N9eU44\nfHUox1yq80/dwNqhHm77+Tb2HSiHcszd+6YYn6pyzMahUI4nErbgPf3gkyMJj0RE2qUgWFaksYkK\n9z3mBY0nHrEm9vsfXt1LIZ9h6+5wskZPPLefsckqZ5j1ZDPJvq3zuQxvfvWR1OoNbvrpM6EcU/XA\nknZHbxji+E2rePDJPfxy856khyMibVjw09IY4xhj/sEY8xNjzG3GmKNnXH6ZMeY+Y8zdxpiroxuq\nSDiqtQZ/d+NDjE9VeeNZhycSNGYch43rBtixd4JavbHs4zVLIU5IthQicO4ph7B+TS8//sX2ULZR\n3uIHwaoHlrTKOA6//QZDNuPwzz94otmzW0TSq51P/8uBorX2XOBa4LrgAmNMzj/968Brgd83xgxH\nME6RULiuy/97q2Xz1lHOOnE9bzz78MTGctj6fuoNl53LXEhTbzR4wI4w1F/AHJZsKUQgm8nwlvOO\not5wufHOp5a1ecaDT45wvx2hkMs0a6lF0mjj8ACvf+Vh7BktcdNPnkl6OCKygFwb1zkPuBnAWnuv\nMebMlstOBJ601o4BGGPuAi4AvjXXwcanqjRC7o06r5juKt9TZmyiEtfdTYvzuSS2p3NOzotOOLOe\n3xyj/9y44LUjO1DmkWde4K5f7eCIQwa58k0n4jgvOmKsNg17Ad3tD27j9OPWsW5Vz5wL9BxeOs5y\ntc6WbaM89PQLjE9VufAVG8lkkns8M5194sF876fP8tNHdvH4c/s59+RDOO2YdeRzGbIZh0zGIev/\n5zhOczprDZdKpc5kucYt9z3Hg0/uIZtx+M3XHZdY1wuRdr351Udx32O7uPne5xjqK7BhXT/r1/RS\nmGvx7Rx/g+Z8J89xget6X/KD/zda/u26vOS027zd7H/VHcf/q+P4d+mfDoY75+XzDr51wG1c50UD\nCv7n4OayvDBaeslT13BdGg2XevBf3ft/uVpnqlx78X/+FvP5bIZcLsNAT46h/iKr+gsU8hmy2Qy5\n4G9UNuP/nVrkmGXRCuNlDkyG11nIcRzmy8y2EwQPAaMtp2vGmIy1tjHLZQeAeX+v/ODf3tnGXYpE\nZ1V/gQ+87ZREdlVrFfy0f/uD27j9wW3LOtaqgQIXvmJTGMMKTSbjcM07TuV79z7HvY/u5Ls/fZbv\n/nTxHSOO37SK33njCWxY1x/BKEXCVSxk+a2LDJ/71q/42n8+mfRwRFa873z6LXNe1k4QPAYMtpwO\nAuDgstbl2oPA/gUGo+9S0nGGhwcXvtISjvmdT28M/bhpMjw8yInHpaNOeS5RzK2kQ1Jze9HwIBed\ne1Qi9y0i7Wvnt8W7gTcBGGPOAR5quewx4FhjzGpjTAGvFOKnoY9SRERERCREzlz1QAFjjAN8HjjV\nP+tK4Ayg31p7gzHmEuDjeBU7X7TW/mOE4xURERERWbYFg2ARERERkW6jpdYiIiIisuIoCBYRERGR\nFUdBsIiIiIisOAqCRURERGTFaadP8IpnjDkb+KS19teMMa8A/gEoAb+w1l7jX+czwKvxNgwBeAve\n8/v/4fVP3gu811q7J+7xy9zanNuLgb/wb/KAtfb9xpg1aG5TbaG5NcacBnwGb+8qBzgH7337MzS3\nqdbm+/bDwBVAHfgra+239b5Nvzbn9s+A38TbrOtT1trvam7TyxiTA/4JOBIoAP8DeBT4MtAAHrbW\n/pF/3fcCvw9Ugf8R9dwqE7wAY8yfAv8PUPTPuh74oLX2NcCYMebd/vlnAG+w1l7o/3cA+Chwp7X2\nAuDvgL+Kefgyj3bm1hgzAPwNcIm19lXAM8aYtWhuU22BuR01xrzbWvtLa+2vWWsvBP4e+Ia19lY0\nt6nW5vt2FfBB4GzgDXhfdkBzm2rtvG+NMSfjBcBn4c3tXxpjetDcptl7gD3+3LwRb36uAz7qz23G\nGPMWY8zBwAeAV/nX+ytjTJ4I51ZB8MI2A29tOb3JWnuv/++7gfP8XsrHAV8wxtxljLnSv/wk4Put\n141jwNK2heb2fOBcvA1irjPG/BjYZa3di+Y27eab25/QMl/GmD7gE8A1/lma23Rb8G8yMAE8g5c5\nGsDLBoPmNu0Wet+eD5wI3GGtrVpry8CTwGlobtPs68DH/H9ngRrwCmvtnf553wcuwvtic5e1tmat\nHSOGuVUQvABr7Y14ExbYYow53//3ZUA/0Ad8Fu/bzhuB9/nfVh8E3uxf9y1AbyyDlra0Mbd9wDrg\ntcCfAhcDHzLGHIvmNtXafN8GrgK+bq3d55/W3KbYIuZ2K95Prvfj/X0GzW2qtfk3+SHgAmNMv/+r\n3Ln++ZrblLLWTlprJ4wxg8A3gP+KV4IWOAAM4X1pHW05f9w/P7K5VRC8eL8HfNQY8wNgF7AHmAQ+\na60tWWvHgdvxvr18EjjKGHMHcDjwfDJDljbNNrd7gZ9Za0estRPAj4HT0dx2mtnmNvBbwA0tpzW3\nnWW2ub0YOAQ4Am8O32qMORPNbad5ydxaax/HK1+6Ge/LzT14c665TTFjzGHAbcBXrLX/ilcLHBgE\n9gNjeEHvzPMjm1sFwYt3CfBua+1FeFnCHwAGuNsY4/j1K+cBPwcuAL5grX0tsAUvjS/pNdvc/hw4\n2RhzkF/cfw5edklz21lmm1uMMUNAwVq7reW6mtvOMtvc7gOm/J/MK3gfpKvR3Haal8ytMWYdMGit\nPR94H3AY8DCa29Tya31vAf6LtfYr/tkPGmMu8P99MXAn3qLk84wxBb+u/wQinlt1h1i8J4HbjDET\nwO3W2psBjDFfBe4FKnjfdB4zxlSArxpjwPtp7qqExiztmWturwVuxesi8G/W2keNMWU0t51k1rkF\njserHW1l0dx2krnet/cbY+7Bqwe+y1r7Q2PMMWhuO8lcc3uiMeY+oAz8qbXWNcbofZte1+J9Cf2Y\nMeYv8D5LrwE+5ycOHwO+6c/jZ4G78MolPmqtrUQ5t47rumEdS0RERESkI6gcQkRERERWHAXBIiIi\nIrLiKAgWERERkRVHQbCIiIiIrDgKgkVERERkxVEQLCIiIiIrjoJgEREREVlx/n++cd3QfkV6tgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef0e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds.smoothed_marginal_probabilities[1].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 55.85400626  19.85506546]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept and lagged dependent variable\n",
    "\n",
    "The second example augments the previous model to include the lagged value of the federal funds rate.\n",
    "\n",
    "$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "mod_fedfunds2 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])\n",
    "res_fedfunds2 = mod_fedfunds2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>225</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-264.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 07 Jun 2016</td> <th>  AIC                </th>  <td>543.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:51:03</td>     <th>  BIC                </th>  <td>567.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-1954</td>    <th>  HQIC               </th>  <td>553.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7245</td> <td>    0.289</td> <td>    2.510</td> <td> 0.012</td> <td>    0.159</td> <td>    1.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.7631</td> <td>    0.034</td> <td>   22.629</td> <td> 0.000</td> <td>    0.697</td> <td>    0.829</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0989</td> <td>    0.118</td> <td>   -0.835</td> <td> 0.404</td> <td>   -0.331</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.0612</td> <td>    0.019</td> <td>   57.351</td> <td> 0.000</td> <td>    1.025</td> <td>    1.097</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.4783</td> <td>    0.050</td> <td>    9.642</td> <td> 0.000</td> <td>    0.381</td> <td>    0.576</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.6378</td> <td>    0.120</td> <td>    5.304</td> <td> 0.000</td> <td>    0.402</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.1306</td> <td>    0.050</td> <td>    2.634</td> <td> 0.008</td> <td>    0.033</td> <td>    0.228</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  225\n",
       "Model:               MarkovRegression   Log Likelihood                -264.711\n",
       "Date:                Tue, 07 Jun 2016   AIC                            543.421\n",
       "Time:                        13:51:03   BIC                            567.334\n",
       "Sample:                    10-01-1954   HQIC                           553.073\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7245      0.289      2.510      0.012       0.159       1.290\n",
       "x1             0.7631      0.034     22.629      0.000       0.697       0.829\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0989      0.118     -0.835      0.404      -0.331       0.133\n",
       "x1             1.0612      0.019     57.351      0.000       1.025       1.097\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4783      0.050      9.642      0.000       0.381       0.576\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.6378      0.120      5.304      0.000       0.402       0.874\n",
       "p[1->0]        0.1306      0.050      2.634      0.008       0.033       0.228\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several things to notice from the summary output:\n",
    "\n",
    "1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n",
    "2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n",
    "\n",
    "Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAADSCAYAAACrbB1oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXe0JGd57vtUVee08+zJWfPNKIzSKABCsgjCtsAWRhiD\nwRcbjK8TXGwfH+OEfc492AcbnDBGXLwEHJucLRsJhAJIoDgzmqSpyXn27Bw6d4X7R9VXXd1d1V3V\nXR2q+/utNWv23t1dXV3VXf3UW8/7vJyqqmAwGAwGg8FgMAYJvtsrwGAwGAwGg8FgdBomghkMBoPB\nYDAYAwcTwQwGg8FgMBiMgYOJYAaDwWAwGAzGwMFEMIPBYDAYDAZj4GAimMFgMBgMBoMxcDARzGAw\nLCGEbCKESISQvfq/ffq/X21iWY8TQn7B5WM+TAj5R5vbHiKE7CSE3EUIOaj/7S8JIe/Uf/4zQsib\n3K6nzXOtJ4Qc1F/7bVW3nSaE3ORyeb9BCPlDj9btZkLIV1w+Zg8h5F/0n43t18I6NNxPDR7/ICHk\n91pZB6c0s70YDEb/Euj2CjAYjJ4mK4qiIfIIIWsBHCKEPC+K4qFurZQoim/U12cSgKr/7cOmu7wG\nwGGPnu41AC6LoniPFwsTRfEBL5ajL+tFAL/o8mHXAlhn+r1tYfF0P/UKTW4vBoPRpzARzGAwHCOK\n4iVCyHEAOwghNwN4D4A4gEVRFF9LCPkzAL8EoATgGIDfEUVxWn/4LxBCPgQgCuALoih+BAAIIX8M\n4OcBhPVl/YEoit/WH3M1IeRJACMA9gH4LVEUM4SQ0wDeYl43QsiDAA4ByAHYA+BvCCERAJ8AcKso\niif0+30PwD+JovgfVY9/H4DfBSABuKL/vA7A/wSQIoT8QBTF11pslt8hhFwPIATg46IoPqgv740A\n/hRAEEBWf13PEkI+DGBMFMX366/jswBeC2ADgK+Iovjf9cf/EYBfA7AM4EcA7hNFcUvVOt8F4BOi\nKF6nv/5lANfpyzoK4G2iKGZN918P4C/11/OvAD4PIEkI+SKAnfo++HVRFJ8mhAQB/G8AdwIQ9O3/\nflEU0xbbYBch5DEAa/Rt9zZRFK/Q/SSK4t4Gr+dVhJC3AJgEcBDAO0RRzFW91gcBjALYCuAhAH9u\nt36EkFsB/LO+7U8B2ATggwC4qu2VA3CL/rxfBTAD4E367+8VRfEJl9uBwWD4CGaHYDAYjiGEvALA\nNgDP6n+6GsCdugD+VQBvAHCzKIo3QKvEfs708CSAWwG8AsA7CSFvIIRshFZpvVN/zJ8C+B+mx2wD\n8GZRFHdDO179aYNVVEVR/CSAF6CJzi9AE5m/rq//NgA7oIko8+t6DYA/AHCXKIo3AvgigG+JovgE\nNLH1IxsBDGjV8psB3APgrwkhuwgh2wF8BMDP6Lf9BoBvEkKiFo+Pi6J4J4BXAfhd3YbyBgC/Am1b\n7tG3nV3F1vz3m/T12AVgLYC3mu8oiuIF0+t5j/7ndQA+pr/uTwP4C/3vfwSgJIriHv22y9DEoBVb\nANwviuIuAAsA3mu+kRByT4PXsxba+2AHNAFvZ52JiqJ4nSiKH7JZv78mhAgAvgbgT/T31D8CuN60\nDPPz3gDgNmhC+IMAlkVRfJX+mD9qYjswGAwfwSrBDAajHjFCyF5oFbQAtErZO0RRvEgIAYADoihm\n9Pv+NIAHRVHM67//A4A/JoTQ48xnRFFUAawQQr4G4PWiKD5CCHk3NFG8HcDtABKm5/+GKIrz+s8P\nAvgogA85XHdO//9fADypV5x/3bQeZt4A4Mv0uURR/Bwh5O8JIZscPM8D+mMuE0IehlbVlaFVRX9A\nCKHrIQHYbvH4b+uPv0QIuQKt2vkzAL4qiuKKfp9/hiYSG/GwKIoSAOhe31EHjzkpiuIL+s/7AVDP\n9xsBDOkCFtCqqldslvF90356CcCqqtt/FvVfz7dEUSzo633I4vGUp0w/263fddBOhr4HAHo11866\n8x+iKCoArhBCMgAe0f9+EuVt52Y7MBgMH8FEMIPBqEeFJ9gC8yXh6itLArRjDBWBsuk2DkCJEHIj\nNBH4cWgC5EkAnzTdr+YxzlddQxTF44SQAwDuA/AOaNXoaqyuivHQBE8jzOvI6+sYBPCoKIpvpzfo\nVoRLqK1y5qp+56AJZs70NxnOMC9LrVqGHeZtan6MAOADoig+AgCEkBiAiMtlUKpfj+Ly8RTz+81u\n/dZaPL76+SiFOuvR6HkYDIbPYXYIBoNRDyciivIIgF/VRQIAvB/Ak6IoUmHxKwBACBkB8DYA34Xm\ns3xeFMW/B/BDAG+GJjooP0cIGdIvcb8PwH85XBcJlQL2kwD+BsAzoihO2az72wgh4/o6/iqAWeoj\nbsC79cdsBPA6AD8A8BiAe4heLieE/Cy0CmnY4fr/J4C3EEJS+u/vhXcNbNXbxo5HoPmdg4QQHsC/\nAvirJp+z+vW8B62/Hrv1exlAgVZudX/wdS08n5fbgcFg9BBMBDMYjHq4EQ7/CuBRAM8RQg5D81u+\n07ScJULIi9Auaf+DKIo/hOa9ndDv/wK0pqlRQkhcf9wRaALqJWhe0/9tWl49/gPA3xJC3qX//hA0\nm8WnrO4siuKjAP4OwGO6jeBdAO518JpVABH9dT0ErRHwhCiKR6CJ9i8RQvZBa0Z7U3Wzl8XroEkX\njwP4DIAfE0Keg+ahzcIddtvoJwB2EkK+3uDx/xPAGWiNYIf05f1+M+vg8vU48T7brp8oijKA+wH8\npb5fPgjNx9to+9k9rxfbgcFg9CCcqrYtHYfBYDB6AkLIKwE8IIridd1eFyfoyRuvFEXxn/TfPwgt\n4eLt9R/Zm3T69RBCPgrgb0RRnNFtKPsBbBVFcbkdz8dgMPyJI08w0QLi/1oUxbur/v4mAH8GzUf1\noCiKn/F+FRkMBqN5CCGfBXAXtOquXzgG4L/rsW0qgLPQKst+pdOv5yy0qj614ryHCWAGg1FNw0ow\nIeS/QfvySIui+ErT3wPQvFc3Q2vGeBrAvaIozrRvdRkMBoPBYDAYjNZx4gk+Aa1ZpZpdAI6Loris\nN748Ba3JhcFgMBgMBoPB6Gka2iFEUfymTVZmCsCS6fcVAEONlidJshoICI3uxmAweoh/f/govvR9\nseJvH/vAndixcaRLa9Qd/uRfnsaBE7PG79FwAF/5iJP+uUo+8PEnsJQu4LN//gbb+/zWR3+A81fK\niWC3Xr0af/ae21w/lxN+66M/wPpVSfzxu63S4xgMBsPX2KYctZITvAxNCFOSABYbPWhhwW2Dc+8y\nMZHEzMxK4zsyfAnbv2XmF7XP7S+/fgcuz2Xw2N6LOHF2HiNRf0aNN7tvl9MFBAQO73/Lbnz58RO4\nNJNpajnpbBHZvFT3selsCYloEHffuA4/ePECxLPzbXs/XprJIJ0t9c37nX12+xe2b/ubduzfiYmk\n7W1uItKqlfTLALYTQoYJISFoVoifuF89BoPR68iK1juwY8MwdurV34WV6jkD/Y+iqAgGeFy7dQzJ\naBAqAKWJhB1FUaEo9R+nKCri0SDefOdWjA9FUCg5nZfhDlVVISsqltJFyIrdTAkGg8HoP9yUcVQA\nIIS8Hdqs+88QQn4PwPegCeTPiKJ4uQ3ryGAwugwVwTzPYSSlzXtYWMnXe0hfIisqeE6rBwi89r8s\nq+ADbmaKaMtpJDhlRUVAf45QSEChJENVVXCcu+dqBBXxiqoJ4dEUG4bGYDAGA0ciWBTFswBeqf/8\nRdPf/xNakD2DwajD9EIWPz40hdfcvB6pWKjbq+MaWdYEW4DnMJKgInjwKsGSokIQtAto9H9ZURB0\nOXdIlhXIcv1KsKwo4HURHA4KUFVAkhUEPe6pMFekF1YKTAQzGIyBwZ+GPgbDJ6iqiqcOXMYXfnAc\nhaKMy3NZ/OZ913Z7tVxDhZLAcxhKhMBxgymCFUUxKsD0/0a2BitkRTWsFLxNZVdWVOM5QgFNZBdK\n3otgSa4UwQwGgzEosLHJDEabKEkK/vmbh/Dgd4+C5zhMjsbw/NFpHD270O1Vc43ZDiHwPIbioYEU\nTGZhSqu0UpMiGEDdarAsl58rHNKEb6HovS9YNq3//PLgWVwYDMbgwkQwg9Em9h2fwd5jM9i+fgj/\n49duxfvedDUA4AuPHvNdAxIVStQCMJKMYDFdwKCNXTeL4FYqwfQx9R6rmJ4rHNRFcBua48zrMD+A\nJzYMBmNwYSKYwWgTS+kiAOANt2zA2FAEW9akcMfuNbgwk8ET+y51ee3cIZvsEAAwkgxDklWs5Er1\nHtZ3yLJqVIDNjXGul0MrwTYnQ4pul6AnHe0UwTITwQwGY0BhIpjBaBOZvCYQ45Gg8be33LUN0bCA\nb/3oFFayxW6tmmsUCxEMAAvLgyWatOqs3hin/y+7rIbTSDKgUoCaoeKYCu6QLoKLbRHBZSE+iIkf\nDAZjcGEimMFoE2m9ShqPlkXwUDyEn75tEzJ5CQdOznVr1VwjVYmy0eRgJkRYeYJpcoZTzLnC9iK4\n8qQjHKSNce32BA/W/mQwGIMNE8EMRpvI5CUAQMIkggFgciQKAMi3ocmpXdBL/lSUDVMRnB4s0SQr\nCgRBt0Po/9sJWdtlmOwTdlaKWhFMK8Hee8nNnmA2MIPBYAwSTAQzGG0iQyvBkcokQuPStuQfEVxt\nhyhXggfr8rmsmDzBXHONcWbRbGelsBPBbakEm4Q4HZjBYDAYgwATwQxGm8jkSwgGeEP0Umjma6kN\nVb12QSel0WllwwNoh1BUFaoKY4pb05Vgswi2sVJQYcpXR6S12Q4BDNY+ZTAYgw0TwQxGm8jkpJoq\nMACE9GEHBR9VgmVFNUQfgIGcGlddDTca41yKYHPl2K6KXH4u7TlCHUiHCOhJFIO0T5uhJCkDFw3I\nYPQrTAQzGG0iky9VNMVRQkE/VoLLI3wBTZTFI4GBEkzl6iyv/99cY1xFJbhBOgQ98QjTiXFtGZah\nPdf4kDYumQ3MsGd+OY/f/rsn8aMDl7u9KgwGwwOYCGYw2oCiqMjmpYp4NEpQFzR+8gTLimrYACgj\nychgieCqSnCgyWEZ5sYzp+kQoVD7G+MMETxA+9QtU/NZSLKKgz5KdmEwGPYwEcxgtIFsQYKK2mQI\nwNTpL/mnEqyYGsIoI8kw8kUZuYLUpbXqLEZ1tjoirRVPsOOItPbZIejY5/FhLbWEiWB76EnI+el0\nl9eEwWB4ARPBDEYbsEuGAMqVYF/ZIeRyPi5lZMCa48qjoysb4yS3IlhuojGuA2OTR5JhCDw3cIkf\nbqBXb6YXcwNz8sdg9DNMBDMYbSCdrx2UQfFtYxxfebgYtIEZVCy2GpHmqDFOb7wK8B0Ym6wL7qDA\nYzgRYgMz6mDe/hdnMl1cEwaD4QVMBDMYbSCT06pElpVgnzbGVVeCBy0mTapOhxCaS4dwZIeQK6vO\n7RyWYbZ5jKQibGBGHczb//z0ShfXhNFuZhZzOHBytturwWgzTAQzGG0gU6cSzHMcAgLvO0+wOSIN\nGLyBGdWxZYLhCW5fOgSvV5uDHRibzPMcRpNhKKqK5UzJ8+fpB4qm7c98wf3NVx47gX/46gEsDthU\nzEGDiWAGow2kdU9wwiIdAtAGZvgtHaK6Ma48OnkwJoxR/25NY5zN6GPb5bhJh9BPPHiOQyjIt1UE\nCwJn+LxZTJo1BSaCB4aLsxmoAC7NMttLP8NEMIPRBuo1xgFaVrCf7BCSUtsYZ1SCB0QwVSc2CE1G\npDnxBFc/F6BZIoptbIwTOA6jSS0mbVAsLm6hV284Djg/k3a97xn+QJIVzCzmAGixeIz+hYlgBqMN\nZPK6J9jCDgFozXF+qgQrFiI4Gg4gFOSxMCCXC+XqxrgmI9LMaRKSjZVCrrJeANp7hlWCuws9Cdkw\nkUCxpGBaF0qM/mJuKW98Li7PMRHczzARzGC0AeoJtsoJBrRKcDuanNqFFpFWebjgOG6gBmbURqQ1\n2RhXEZFWvzHObEEJh4T2TIwzbB48RlNsYEY96Gd22/ohAMwS0a9cNlV/WSW4v2EimMFoA+V0CGsR\nHAwIvmmMU1UVilpbCQY0S8RKtoSST15LKyhVFgXatOZWBLuJSKu0Q/AotCUdovxcRiWYiWBLaCV+\n+1oqgllCRD9yxSyC55gnuJ9hIpjBaAOZfAkBQWtmsiIU4CHJii88hdU2ADPDCU00DUIHtbliCpQr\nwu7TIRw0xtHnEio9we14z5hF8FA8BJ7jsMhEsCXUDrFtXQoAcP4KqwT3I7T6m4qHMLdcaIsNidEb\nMBHMYLSBdK6EeCQIjqsVjgAQ0nNf/VBBVapsAGZSca3SvZLt/0gtuao6G2iyMc7V2GTT+yfUpoEZ\n5iEgPM8hGhaQZdPQLKFXb8aGIhiKh3B+hongfuTKfBYcgN1bx4zfGf0JE8EMRhvI5Eq2TXGAVgkG\n4IvmOCtBRikPcej919EqxgCLliPSXIjgqkow4P22lqqeKxoOsJHANhRLMgICB4HnsWFVAvPLBSMO\nkdE/XJ7PYjQVwYZVCQDMF9zPWOc3mSCEcAA+CeB6AHkA7xVF8ZTp9l8G8HsAJAAPiqL4qTatK4Ph\nCxRVRTYvYd143PY+dPiBH5rjyoKs9pyZVif9IOZbpdoT3Gw6hJuINJ6vFcHtqgTTk5xoOIDZJZZ6\nYEWhpBhjzzesSuDQ6XlcmE5j56aRLq8ZwytyBQlL6SKu2TKKNWMxAMAUS4joW5xUgu8DEBZF8ZUA\nPgTg41W3/w2A1wC4A8DvE0KGvF1FBsNf5AoSVNjHowEwvkj9IB6tMmspRkXbB2K+VWoj0rwYm2y9\n3agwDZgSOcoi2NttbYxN1k9yoiEB+YJsNOcxyhQl2fD50yohS4joL64saIJ39UgMq0c1EXyZVYL7\nFici+A4ADwOAKIrPAthTdftLAEYARPXf2ZGTMdCUB2U0tkP4wRNcPSnNTLt8qr2IVCUWm26Mk02N\ncbYRafrYZNM2D4XaMzq52uYRCQegAm2JY/M7hZJsnIwwEdyfUOvD6rEYRociCAZ4VgnuY5yI4BSA\nJdPvEiHE/LjDAF4EcBDAQ6IoLnu4fgyG70jTeLSovdvIsBH4oIJabQMwQ6tifol7awWvItIceYKt\nItIC7TnhqK5wx8La+5b5gmsplhTjs7t6LIaAwDMR3GdQwTs5GgXPcZgciWFqPguVXRnpSxp6ggEs\nA0iafudFUVQAgBByHYB7AWwCkAHw74SQt4ii+HW7hY2MxBDQD+b9wMREsvGdGL6lmf17Tj+IrhpL\n2D5+eEi7cBKLh3v+PVSCLo5ioZp1nRjTBEAoHOj511GN2/WNxeYBaPtuYiKJrKR9KYZC7l57NBYy\nfg5HgpaPjUS0+4yOxo3bR0e0S7ORaO1+aIWQLnonxrX364j+3oz64L1Zj3ase0mSEY+W99nmNUmc\nnVrB6Gjc0jPPaA/tfF8u6kk312xfhYnRGDatTeHCTBp8KIjx4WiDRzO8oJPHHSci+GkAbwTwNULI\n7dAqvpQlAFkABVEUVULINDRrhC0LC/1zWWFiIomZGRaW3q80u38vTekXQxTF9vGlonagnZ5NY2Yk\n0vQ6doKZWS0svlSSa15PPlsEAMwvZH31WWhm3y4ua81i2UwBMzMrWFrSjmWZbNHVspZNI4nT6YLl\nY1dWtPukl3PG7aWiVpmdmU17uq0zGW0fLi1mMRPkAd3ecWlqGVGLWDw/0I5jsyQrkGQVHGAse/Vo\nDCcuLOGgeAXrJhKePh/DmnZ/7569tIyAwEOVJMzMrGA0rp2QHjo+jWs2j7bteRka7di/9US1k1PX\nbwIoEEKeBvAxAB8khLydEPJeURTPAfg0gKcIIT8EMATgs62vMoPhXzJ5Oi2ujh3Cj41xFhFpg2SH\nqB5lLDQZkSaZLBCSjZ9YskjkCAfb5Amu8jpHqB2iyOwQZqh/n3qCAXtf8OnLy5Dk/v9M9BuqqmJq\nIWtYIQDN9gKwhIh+pWElWBRFFcBvVv35mOn2BwA84PF6MRi+hTbGJRzkBPuiMU6pnV5GCbXJp9qL\nlD3BfMX/7YlIq22Ma186RKX/uOwJ7v996gb6HjdPgdxoEsG3X6P97ejZBXz0i/vw5ldvwZtetaXj\n68lonqVMEYWibKRCACjHpLGEiL6EmZgYDI9J5xunQ5RzgntfaNSNSPNR3nGrVG8HY1hGG8YmWzUj\ntisnuPp1RULa87DGuEqKhgguV4LXW1SC9x2fBQC8eGymg2vH8AJa7TWL4MkRWgnOdGWdGO2FiWAG\nw2OMiLQ66RBhww7R++Kx2gZgJjxAwzKqK+LliDSX6RAm+4R9RFqtCA61aWJc9XNF9UpwnongCuiJ\nXtjU2B2PBDGWCleI4EOn5wAA566kMW/yfzN6n6mFWhEcDQcwnAixSnCfwkQwg+ExZU+wg0qwD0Rw\n/Yg0/0S9tUrtsAztfztLQ6PlVP9ccR+riLQ2T4yjrytKK8EsJ7iCglRrhwCADauSWMoUsZQpYnYp\nh8tzWWO/HTg51/H1ZDRPOR4tVvH3NWNxzC0XBsL2NWgwEcxgeEwmV4LAc8ZlZSuMxjgfHFSdTYzr\n/dfRKrRiGmhxbLKTiXFGddbcGBdqUyW4yuscjbCcYCvoiZ7ZDgGYLRErOHRai9F77c3rAQAvnZjt\n4BoyWmV+pQAAmBiqTOyZGNZ+Z5X9/oOJYAbDY9J5CfFIAJxFmgKFVpP81RhXe7gIDpAIVtTBaIyL\nhpgItsKqMQ6obI47fEoTwXfftA7rxuM4cnaBVQ99BD2OhasKGKMpTQTPMRHcdzARzGB4TCZXQrxO\nMgQABH1YCeYtRD3HcQgFeRR8IOZbxT4irX2NcYEKT7AekeaxTaHa61yOSOv992YnMRrjqoY90Zi0\ns1MrOHJ2HhPDEUyOxLB7+xhKkoKXzy50fF0ZzWG3j8dStBJc6Pg6MdoLE8EMhocoqopMvrEIDgf8\n4wkuX5q3rmyHAoIvxHyrWKVDcGiTJ7jKpwu0MR1CtvYEs8a4SozGuCo7xMRIFOGggL3HZpEryLh2\n6xgA4Ppt4wCAA8wS4RuKkoKAwNc0ARuV4CVWCe43mAhmMDwkX5CgqkCiTlMcAASD/kmHoDaAgIUn\nGNBEwWA0xukVU9N24HmuRU9wfRFsfq6AwEPgOe89waoKjitX+oMB7XmYHaKSok1jHM9xWD8RN4Zj\nXLtFmyq2bV0K8UgAL52cg6q6e48wukOxJBtDacyMpcIAmB2iH2EimMHwkLSDaXGAvxrK6kWkAZoo\nGISINCMlw1QRF4QmRHBFRFqDxriqbR4KCm2pBFN/M6BZXKLhALNDVGHXGAcAGya1sawCz2HnxhH9\nZx67t41hYaWAc1fSNY9h9B7FkmL0OZgZSbLGuH6FiWAGw0PKGcGNPMF+aoyrbAirJjQglWDJwqIg\nNFEJdtIYV92ERwkH+bZEpFWL7UhIYJXgKoymKQuRRH3BV60fMnKWAWC3bok4cna+A2vIaJWiJFue\n5AQDPIbiIVYJ7kOYCGYwPCRjTIurXwk2Lm37oIJqZQMwEw7wKJbkvr/ka1WdFXi+PXYI2XpUdTus\nJ7Ki1OzbaDiAfJGJYDMFi4lxlJ0bhyHwHG6/ZnXF39eNxwEAs4tMPPmBYkmpaYqjjA1FML9cME5Q\nGf0BE8EMhodkdTtErIEnGNBtBD6ooMoWNgAzoaAAFTA8kf2KVXW2mUpws41xgCaC2zE2uXrfRsMB\n5Asy+8I3Uc8OsWYsjk988E68eveair+PMi+pryhK1p5gQGuOkxUVy5lih9eK0U6YCGYwPITGV9Ub\nlEEJBgRfNMbVi0gDyqLA6/zaXsOozlY3xjUZkRYK2FeRZaWyWY0SCmki2Muqu6yoNWI7GtJObLyO\nY/MzdGKcnUgKB4WabPBoOIBwSGDRWj5AUVRIsmrpCQZYc1y/wkQwg+Eh9S6ZVhMK8Cj5wQ7RKCIt\n6J8mv1awqogLPOe6Wkp9wMEAX2dYRq1PF9CElqp6W3WXZbUm+YP6WpkvuEzRxWebwnEcRpNhLKww\n4dTrlNM/rPfvKMsK7kuYCGYwPIRWdu2qRWb80lBm16RFMUZA+6Cq3QpWFXGB5yrSHpwg6VVeQeCN\nZjur57La3jRVxMuqu6LWVoLZwIxaDDuEjWfUjrFUBJm8xDzWPU49uwtQHpjBsoL7CyaCGQwPoZeP\nnXxRBgP+iBazsgGYGZRKsDHFrSIirYnGOD2STKhjpZDlWmEKlMe5emlTkGWlRnBHw9rzsEpwGbux\nyY2gvmBWQext6qV/AOapcUwE9xNMBDMYHkJFbfXseStCAa0xrtdTFawGN5ihE7T8UNVuBatmNZ5r\nLiJN4Lm6VgpFtbdDAN5OjbOyXkRDWiWYTY0rU5RkCDyHgOBSBNOMWWaJ6Gno6PegrR2CeYL7ESaC\nGQwPKV8ydWaHAHo/VcEuqYBiXKL3QVW7FWjVtsIOIXBGo5vj5eiRZPWsFLKsWHqwOyaCmR2ihmJJ\nceUHpoywSrAvoP0ZdsfuRDSIUIBn+7HPYCKYwfAQt41xQO97aZ1EpAH9b4eQ9eqsOQEgwHO2zW22\ny9EjyepZKewa49qxrRWLiDSabsLsEGWKJdm1FQIwN1SxCmIv08gTzHEcRlMRVgnuM5gIZjA8xPCV\nORHBPrERGF7YOhPjgN5/Ha2ieXkrxSLfRGMcjSSrZ6WwT4egjXHeVoKrq/yxMLNDVFOUFIRdNsUB\nZi8pqyD2MuVjt70sGkuFkc6VPM/qZnQPJoIZDA+hVV0nFaOgUQnu7QMqFXmN7BD9Xgm2qpgKPAcV\ncBWTpihaJJkg2FeRNWFa+x7y2n+tqqplEgVNh8gyEWxQKDZXCR5J6nYI5gnuaWjiSrDOiQ6r6rvj\n4kwas0u5bq9GXZgIZjA8xE06BBWPpR6voMqqw8a4Hrd1tIqsqDXDK+g2cVMNppVXbdqc9TajQrka\nrz3Bis2+pekQeeYJNihKclOe4HBQQCIaZJXgHsfwBNetBLOqvlMkWcFf/dtefOahl7u9KnVhIpjB\n8JCiJCMY4G2rpmaMSWs9XwlmEWkA9fJWHjLp7258wbTyWm/ksqwo9SPSPNrWxiAUm3QI5gnWkBUF\nkqw6anhwX2seAAAgAElEQVS1YjQZxvxKvueTYAYZI+PdQSWY+YIbc2k2g2xBwswiqwQzGANDsaQ4\n/qL0SyVYaRCRRqve/e6To6kOZmhl2E1ChJbLq1WCVdXaSmHbGOfxtraLv2s2HeLgqTlcnst4sm69\nBLWfOPH6WzGaiqBYUpDJs5OKXsVJDvSYkfTBRHAjzkytAACWM0XXUzU7CRPBDIaHFErOL5n6xhPc\nKCJtgOwQ1WKReoTtJr/VW049K4Us1/qPAVNjnEc2Bbt928ywjJKk4B+/dgD/+p+9ffmzGcpe/2ZF\nMBNPvQ69klXXEzzEKsFOOauLYFlRkcmVurw29jARzGB4SLEkO64W+SVVQTIi0uzSIQbIDlEtgvXf\n3dghaIOdnZXCaFbjLCrBIW/fM7LNvg0Iml3DTTpEvihBVlScvryMbL53v/SaodjktDjKKPOS9jwl\nByPvR/UmRzY6uTFnppaNn5cyxS6uSX0Cje5ACOEAfBLA9QDyAN4riuIp0+23APiY/usUgHeKoti7\nr5jBaCOFkoLhpEs7RI9XUBvaIXwi5ltFllXwYe8a4yqtFOUTJ3rl0Oqkw/PGOJt9y3EcouGAKzsE\nrU6rKnD03CJu2jHhyTr2Am7yv60YZQkRPU+jnGBAqxKn4iF2MtMASVZwfjpt/L6ULmJ9jx4OnHxb\n3wcgLIriKwF8CMDHq27/NIB3i6J4J4CHAWzydhUZDH+gqmpTlWC/N8aFfWLraBXFIkqM/i479LyZ\nI8mo3aG6OY76iy0b4zwWwfX2bTQsuLJD5E3rdOTMfOsr10MYnuAmcoIBVgn2A4UGE+MoY6kI5lfy\nPe1z7TYXZzJaI6leVV/K9O773okIvgOauIUois8C2ENvIITsADAH4PcIIU8AGBVF8Xgb1pPB6Hkk\nWYEK59UivzTGNYpIG5hKsIUdgjcqwc5euzmSzKgi14hg++3tuQhW7f3e0VDAlQg2+5SPnFlofeV6\niNbtEMwT3OsYnuAGx++RZBiSrCLdwz7XbnNat0Jcs3kUgM/tEABSAJZMv0uEEF4URQXAOIBXAPgt\nAKcAPEQIeUEUxSfsFjYyEkOgybPpXmRiItntVWC0ETf7d1n/oCfjIUePG5/NAgCC4UBPv4/oZfnJ\nyRQCFpfoJV0Aqpy/Pg9u11VRVUSq9lUiHgIADA3HHC2PftFGI0HEYtpjh4fjmBiJGvdJZ4vGfaqX\nmUzpopTjPNnWeV27J+Lh2udKhHFuOo2xsYSjyL+LC+UopKn5LLhgAOPD0TqPaC9evhfP6p/VUYf7\nuZqR0Tg4DljJS776jPQq7diGvKDpkrWTKYzolXvL5x6NAQBCEWfH+UHkyqJ2sveqG9Zj3/FZlBR3\n+6yT29WJCF4GYF4jKoABrQp8QhTFYwBACHkYWqX4CbuFLSxkm1vTHmRiIomZmZVurwajTbjdv0aV\nR1UdPS6f1S4RLSzmevp9lNdjnebn0uAsmrUArWqZzhZ7+nWYaeazK8kKZEWpeFxRr5TOzqYRs0hz\nqIZWViVJRqmo/Tw9uwJI5Yrrsi6CZUmuWUdaSV7JeLOtZ2c1316pINUsL6C/nAuXFo3ItHpcmdGW\nNZaKYG45j6f2nserrlvT8jo2g9fH5hm6nYq128kpQ/EQrsxlfPMZ6VXa9b27ktaOxyvLOUgF+yov\n/Vycu7iICIsWsOTl03MICDzWj2knwVMzacf7rB37t56odrILnwbwswBACLkdwEHTbacAJAghW/Xf\nXw3gcHOryWD4G6N5xuGVDhrF06gx7tyVFfztl/Z17VIqnZRmJ4ABzRLRz3YIRVWhqqiZ4mbn67XD\nbHUw/MRVVgpjgIVF1Z3nOISCvOc5wZZ2iIi7gRnUDnHjVeMA+ssXXP5sN696xlIRLKwUXCWJMDpH\nwYhIq7+Pk9EgADA7hA0lScbFmQw2TiYwktBsQL1sh3Dyif4mgAIh5GloKRAfJIS8nRDyXlEUSwDe\nA+CLhJBnAZwTRfG7bVxfBqNnKXcXu0uHaNRQ9rUnTuLImQW8fLY7PktZUSwza82EgnxfR6TZpSgY\nQtahsFFMkWR0m1aLIqMxzuakIxwUPNvWkv5cVvvX7dQ42hi3dV0KqVgQR84s9M2EtGKL6RAAMJKK\nQFbUnhYEg0xRkiHwnKXly0ycieC6XJjJQFZUbF6dRDDAIx4JYDHdu41xDa9xiaKoAvjNqj8fM93+\nBIDbvF0tBsN/0EqC83QImq9rX0E9d2UFh05rFbVujbC1m15mJhwQ+npYBq3O8lXpEG4b4yoqwZx1\nFbkslO0n9HklguvF30XowAyHMWm0EhwJBXD15lE8c+QKLs1lsW487sm6dpOCyxNcK8wxaSP6z4ze\noVRSHO3fZEwTwStZdjJjxZnLWlPc5tUpAMBwItzTIpg5WhgMj3BbLaJ2iHqV4EeeO2f83MsiuN8r\nwXaJDQGXwzJolVfguToRafXTOIIB3rNsacN6YZMOAcDxwIy87nGOBAXs2jQCoH8sEfQz2kolmMak\nLbCYtJ6kICmOrGyJqNbQmsmxEdhWnNYnxW1eo/lwU/EQMnmpZ/PwmQhmMDyiYGSJOvtYhRtUgueW\n8nj2yLRRncgVuiMyFUciWDBefz9iFq9meJuYM/vllD24do9tJIJDAd6zqnu9+DvaDOe4EkyvhIQE\n7NqsieBj5xa9WM2uY+QEtyCCx/SYtFk2bawnKZZkR5XgBK0E51gl2Iozl1cQCvJYM6alaAwltJOG\n5R61ATERzGB4hFEtCrltjLMWGd9/4TwUVcXrbt4AAMh2qxIsqw0jskIBHpKs9G3Tj51FwW1jHF1O\nwNQYV+MJlu2b1YD2VIItG+OoHcJlY1wkJGA0FQGHctKF3/GiMW7dRAKAZnFi9B7FkuyoEmw0xmWZ\nJ7gaVVUxNZ/BuvG4cXwb0mMke9ULz0Qwg+ERRiXMYTpEQODAAZZVvUy+hCf3X8JIMoy7b1wHoJt2\nCKVmUlo1xsAMXdBfWcji0RfO901jlF2Kgp2v13Y5JtEp2PiJZUMoW2/zYICHrKhGdboVyhXu2udq\ntjEuHBTAcxwiYQF5F2OXe5miS7+/FZMjUcTCAZzSPZOM3qIkOfMER0KCFgnJGuNqSOdKkGQVo8ly\nzvJQXE+I6FFfMBPBDIZHOJk9b4bjONtosb3iDAolGa+9eb1x+a1rlWCHdgigvA0eefYcvvDocYh9\ncjlcskuHENylQ5StDrztxLiGjXFBZ9F6TqjfGNdcRFpEvxIScTlxrpehJ6qteII5jsOWtSlML+SY\ngOoxFFVF0aEnmOM4JKJBrLB9WMNiWqv2DifKjZ/UDsEqwQxGn1OuFjn/WAUDvGVj3KJ+wNi4KoFQ\nQBNMXW2MaxCRRn3QdBvM6c0/xy/0hwhWTOLVTFnIukyHEOo1xtWPSAsKNFrPi0qwveCO6SLYaTWX\n3o8KxUio/yrBraRDAMDWNVrH/GlWDe4pSi5PchKxILNDWEBTIIaTIeNvzA7BYAwIhSayRENBa39n\nRq8yxKNBcByHaLh7VTWnjXGA1mENAAsr2sHw2IUl28f4CWpZsG2Mk5tIh7CxUtQTpgAQ1IWY5KEI\ntvIE04g0p1cg8kUZAYE3cla7+Z71mqLLQTh2bFmri+BLTAT3EkWXnu9kNIhsQfLEktRPLOrHfWqB\n0H5mIpjBGAia6SC3y3yll0sTehNGLBzomh1CUhw0xgUrK8G0InDi4lJffFHYJTYILiPSzPYDaqWo\nHZbROB0C8KgS7GFEWqEkG1YI7fECZEXt2WgkNxQkBTzHIeBgNHY9aCWY+YJ7i3Il2JkkosdlFpNW\niWUlOME8wQxGW5lfzveE0CpniTr/WNnFXVWL4O5Xghs0xtHM45KMkiQb618oyrgwnWn7OrYb28Y4\nlxFpkmk5dFmSYt0YZ7fNg4KHnmDVvgkv6npYhlRxAhihjXVF/wuFYlGLz6o3OtwJqXgI40MRnLq0\n3DdNo/2A26t49LjMfMGVWHmCY5EABJ5jEWkMRjvYf3wW/+2TP8b3njvf7VUxxSi5sUNYN8alcyUI\nPGdU1qJh7X6Sw8lkXiLLzoZlAFp1kloh6N+O9YEv2D4ircmxyaZ0CLuINNthGca2bt1vS20eVpX+\nYEDrgnc+LKOyEkztFE4f38sUJKWlpjgzW9akkM6VMMPygnsGo6nZ4bGbNiun+yQC0CuMSrBJBPMc\nh1Q8ZAjkXoOJYIZvWc4W8dnvvgwV2rzybmPYIRzmBANaY5yiqjXiNp0rGX5goDy4oNONRqqqQlHd\npEPIhgi+Yfs4AOB4H/iC7SwKfJMRaRXpELJNY5ydCNaFd8mD4SR2qReUSEgwTu4aUSjJFe99w07R\nB81xWoasN1+XW5kvuOdwexWPTo1jKR+VLKYLCAg84pFAxd+HEyEsZYo9efWDiWCGL1FVFZ/77lEs\n6x26vTCbvJlAfXrf6kvbmVzJuOQGlDv1O+0LbtSkRQmbItIW9H1x1fphDCVCOH5+sScPfm4oN8ZV\npUMYCQ9O0yFMjXFNR6Tp7xkPrgrUi0gDtBM6JyJWkhVIslpph3AZsdbLFEtySxnBZrZQXzATwT2D\nEYHnojEOYCK4msV0EcOJUI1taCgehiQrPXksYCKY4UueOngZ+47PYufGYcQjgZ4QwcWSDA5addcp\n5goqRVFUZPMSEqaz6aj+cy7fHRHsZGIcABQkGYsr2mWvkWQYV60fxlKmiJnFXHtXtM3Q8cLV2yHg\nsjGuMiLN2kph3McuIi1QmcncCo1OcsJBZ5Vgep/qxjjAuae4lyk6HKTghE2rk+A5jsWk9RBFt57g\nGBPB1SiKiqV0EcPJcM1tqR5OiGAimOE70rkSvvjocUTDAn7t3l0YSUZ6RARrvkE3zTNWnf6ZfAkq\ntHg0SrcqweUxvw4nxpXKnuCRZBg71g8BAI6d97clgloWAtV2CNcRaabGuGYj0ujVA9kDT3CDJrxI\nSDCGYNSD3iccqq0E+90TrOgJF15VgsNBAesn4jh7ZaUrHn9GLW4HHRmNcSwr2GAlW4SiqhV+YIoR\nk9aDvmAmghm+4+JMGvmijLtuWIfxoSiGkyHkCjLyXe5CL5Rk19WioDFuuPxlWJ0MAZQ9wZ2+nOS4\nEmyKSKN2CFoJBvw/NMPONuA2HaIyIs2mMa7BNjcsNF5Ugus0xgGaYCtKSsNKN7VMRIK1lWC/e4LL\nflFvRDCg+YJLkoKLPdDLwHCfE5xgdogayskQoZrb6NS4xUz3i1XVMBHM8B1Z3RKQimkfrBH9zJNW\nILtFUXLvGwxVTVoDytmTvSSCGzbG0Uv0kozFlYLWERwLYcOqBCIhwfdDM+wj0podm2z2BFeK2UbV\n96CHOcE0Is2+MU573zWyRNDbrSrBfo9IKycHePd1WfYF+/tz0S+4HYvNRHAtRvHDshKs/W2ZVYIZ\njNbJ6CKYdqDSyy/djmChdgg3GE1OVpXgWPftEHaT0qoJV9khhhIhIwt3+7ohXJnP9mxOpBPKFgXr\nxjjXnmBzOkRNRFr9bR60aaZshkZxbGGH1Vx6e9iiEpwr+LwS3MQkyEZs1kXw2Ssrni2T0TxGtd/h\niU4kJCAgcEwEm7CKR6PQSjDzBDMYHpDNaweeWEQTiSO6EX+xy5XgQhMxSuYhExRDBEd8VAnWxXy+\nKGMxXTD2CQCsm4gDAGZ9nItqTnUwQ3291QMvbJcjW6RD1ESkNbJD0GEZHnqC6zTGAQ4qwdQOESo3\nc0ZcTpzrVQouq4ROWDMWQ0DgcfZK2rNlMprHrSeY4zgkokGkmSfYgH7/WtohWGMcg+Ed9pXg7olg\nRW2uecaqMa6XPMGN4rooVJhp0/vUiktiSd22suLjYHm7imk5Iq0ZO0SDdIgGjXGejE2mgtummZOm\nPTRqjsuXtPdlRU5wuE88wU1EHzYiIPBYPxHHxZk0a47rAZrZx4lokE2MM2F4glk6BIPRXqgnOKaL\nYFp17KYnuOSykkAJWkSkZfRKd0U6hP5asx2OSCuP+W2UDqHdPjWfBVB5IEz2QSe1XUSa3dQ3O8wn\nFY0a4+wj0jy0Q9jYPCj0/dyo6bRg0RjXL55go+nPxRAcJ2xanYQkq7g0y5rjuo1bTzCgieBcQWIn\nMTr17BDhoIBoWGDpEAyGF2QKukjU7QJUcHWzElyePe/uI0Wr2eaKQk9Wgh1OjJtb1iwPZjtEP2Rq\n2laCXUakSSarg52VoqEw9VAEK/pzV0e/UYxKsEM7RNgiJ9jvdgh6AmC2enjBpskkAODsFPMFd5ti\nE8fvhH6FK+Pj45qXLKYLCAcF25PFVCyE5R68GshEMMN3VFeCk7EgBJ4zulO7AT2IurVDjA1FAABz\nJr+slQiOhWmTUac9wc4a42h1kg6G6zc7hGJqaDPjNh3CvBz7SnCDscltaIyrF5EGOGiMs0iHCAha\n85/fh2Xk9cY+au/wio26CD7HfMFdpzwxzvk+ZlPjKrGbFkdJxUNalrDDY2WnYCKY4Tsy+RJ4jjPO\nOHmOw1Ai1NXGuEKTHeTjKV0EL5dFMK0sxKPlylMwoHUjZzvcae+0MY7nuAo/XYUdItYHdgib7cDb\nxJzZL8fF2GRbEVyOo2uVRvvXqSfYyg7BcRyi4YDvPcG5NlWC10/EwXMcS4joAZqqBDMRbCDJClYy\nRUsrBCUVD0FV0XM+aiaCGb4jm5cQiwQqzjiHE2EspotG7mmnoZWEsEs7RDIeQkDgayrB0XCgpuoY\nDQc6XwluUCk0Yz4BGKnwBPu/EmyIV8G6Mc59RJq9CG4UW2YVq9csjUSw03QIq0owoInoTr9nvYaK\neK8rwaGggLXjMZybXum56tig0UwlmE2NK7OcKUKFdVMchTbH9VpUJhPBDN+RyUuGl5YykghDVtSu\nRdaUu4vdfVHyHIexVLiiEpzOlZCI1laduiKCHVaCgcoqitkOEQ0LEHiu5yoAbrAfluEyHcJ0UmE3\ncrnRNg8KnWuMc+sJjgSrRXCg65McW4V+5ryuBAOaJaJYUnBlIev5shnOKZZkcBwQaJCCY6Yfeh28\ngiZD0Cg0K4ZiPhXBhBCOEPIvhJAfE0IeI4RstbnfA4SQj3i/igxGGVVVkc2XjIxgSrdj0pptjAM0\nX/BKtoRCSYaqqkjnpAo/MCXWBRGsNBBJZugJQCwcqKgIchyHRCzo70qwTKe4tSiCLSPS3DXGeTox\nrkFEmtNhGVaNcYB2ApQvaO9rv0I9wV6nQwCsOa5XoIOO7PysVhipN0wE102GoPi5EnwfgLAoiq8E\n8CEAH6++AyHkNwBc6/G6MRg1FCUFkqzWVIKHk9oHrFsxaTRs3W1jHACM6b7g+eU8iiUFkqxUxKNR\nouGA/vo7F8njtDEOKL/2EYtLYsloyNcVE6VBRJrrxjih+cY4juMQEHhPKsGSjc2DYtghHDbGVQvF\naDgAFY0ryb0MrWTThBYv2bRaF8HMF9xVipL7QUe0EszSIUwiOGlfCe7VrGAne/0OAA8DgCiKzwLY\nY76REPIKALcAeMDztesxphdzRg4qoztUJ0NQRrock9ZsYxxQmRBhlQxB6cbo5GbsEFa+sGQsiFxB\n9kS4dYOyT9cmHcLhiYmbxji72DJAi0nzYmJcoyY8KmrzDuwQvC7OrR7v59HJuTblBAPAhlUJACwh\notsUS4prKxvzBJeh37sjTirBPXZF0IkITgFYMv0uEUJ4ACCErAbwYQC/A8D5dQQfoqoq/u7L+/FX\n//YiC8fuIsYgCRs7RPcqwS3YIfRK8Oxy3nJkMqUbWcHNNMZZHQiTPvfP1UuH4OBxY1yDsckAEAzy\n3kyMa9CEF9Z9sA0rwUUZ4VDt5WRjdLKPfcHtygkGtM/05EgUZ6dWfG0Z8TtFSXZ97GbpEGUWV/Rp\ncT60Qzj5VC8DSJp+50VRpEfftwIYA/BfANYAiBJCjoqi+Hm7hY2MxBBwecbVC0zNZXBlIQcAOD+f\nw61XrwYATEwk6z2M4THT+odtfDRWse236u/IvKR6uk+cLisY1g6Iq8YSrp9/2yZNuOdKCgT9i3bV\neO1yRkeiAIBINNyx9138gnb+OzwUbficSf0gt24yWXPfVaNxAEAgHOyZz4yb9QjpJyDjFvtFEDhw\nAu9oecGgtpyJ8SSGEiH98ZWPDegnE6smkhiy+VKJhAKQZaXlbckLPHiew6pVKcvbkylNAKocV/e5\nJEVBLBKofc8O6+/ZWOfesxSvnk9SVAQDPNauGfJkedXs2DSKH+2/CEUQsHos3pbn6De8fi+VJM2C\n5ma5qqq9L/IluWeOad0iq58kb9s8ZmsbSqa0Y0G+1Pi41cnt6UQEPw3gjQC+Rgi5HcBBeoMoiv8E\n4J8AgBDyfwEg9QQwACz4tAv26ZcuGT9//ydnsGUijomJJGZmmJerk1yc0kQZp6gV214taV/WU7Np\nz/aJm/07r7+vc7mi6+cP6JfIz08tY1QXkpyi1C5HvwJxaWoJQ5HOnEguLmonftlMofHr0iuYIYGr\nuW9ALxCeu7iIZKj7oTRuP7uZjHaisryUxUygthpcKEiOlpfNaSdxS4sZKEWtgpSvemxWv7y6uJBF\nMWddNRF4Dpmc3PJ7PV+QwHO1+4tCvdDL6fr7P5vTmlXt3rOXryxjxCLxpF14eWxeyRQRDgptO9ZP\nDmtXgva/PIWbyaq2PEc/4fX3rqqqKBRlcIDr5SaiQSws5wdeB0zPZxANC0gv51DP2BMOCZhdyNbd\nXu3QVfVEtZNvo28CKBBCngbwMQAfJIS8nRDyXo/Wzxe8fHYBgHb5at/xWV83evgZO09wVE8k6JYd\noqD7M8NNXOUYTobBcQ48wbpFopMDM4zGKd5BOoR+ObGeHWLFRtT1OlIdi4LAc01FpFHnQLWf2IkP\nOxjgPRqWodg2xQFaakQ4KDhqjKtOhgCAiGHh8e/xMl+UPc8INrN1jVaFP3pusW3PwbBHklWoaK6f\nIxENMjsEtIi0obi9FYIyFAthqcc8wQ1PzUVRVAH8ZtWfj1nc73NerVSvoaoqXj67gKF4CHfsXoP/\n/MlZvHRiFuvXDnd71QYOKoKr0yEAOjCju+kQzXiCAwKPkaSWFZypI4KjXRidXE4zaOwJpuORJ3Tb\nhtVtfm0iqeedFXi+iYg0HhzHWQpopUFiA6CJ4JKkQFVVV7FO1SiKWrcBD9CqN/Ua4xRFRbGk1GQE\nA6bGOh97gnMFCRPDte9pr9i+fgjRsICXTsziHa+7qqX9yXAPPZl0mw4BaMfp89NplCTFiC4cNHIF\nCelcCZvXNLYwpOIhnLq0DEVVbWMZO81g7jWXXJrLYjlTxK5NI7jt6kkAwHMvT3uy7Id+fAZP7Lvo\nybIGAdoYV50TDAAjiRBWsqWuJBDQKwPNRKQBWnPcwkrBOEv2YzrEG27diN9/2w1YN17ra/T76GRq\nC7CqiPNuKsFVJxWCUPtYJ41xoQAPVXUezVZvfRo1PUaCAgp1RGzBZloc0J1mTi9R9Evl0TYkQ1AC\nAo9rt4xhdimPS3P+tAv6mVbiLVfpJ/yDnBo1rfdKrXJwopiKh6Coak/FyjER7ICjuhVi56YRrJ9I\nYN14HAdOzrW8I09cWMI3fngKX3rsOLNXOMSwQ1iY72lM2lKm89XgYgsRaYAWk6aqwIVpzVEVt5kY\nB3Q4HcKFCE5Eg7hmy6jtbQCQ7rFLYU6hlgXrSjDXVESa8VgLEcxx9gMsAO+mxsmK2nDfhkNC3eNT\nwSYjGIAhHhsN2+hVCkUZKsq2jnZx/fYxAMBLJ2bb+jx+ZP/xWfz1v72IbL49wokeu5up5JYj7gbX\nEzyt942sGok1vG8vJkQwEewAKoJ3bRoBANx69SQkWcFPDl5uepmqquLrT54EoJ2JHjw51/qKDgCZ\nBnYIoBzX0knK1YTmPlI0Jo3mhVrbIXo7Iq0evrdD1LGFCDxnVIoboVRVeQWetxiW0ViYBvWTrVZj\n0mTZmQjOF+2nvhnT4qzsEPQ961M7RL6NGcFmrts6Bo5jItiK545ewbELS0ZfjtfQz1AzBYyNqzQL\nwPnpwc15ntabwh1VgvUrgkwE+whFVXH03ALGhyKGL+y2XVoH7w/3XWh6uYfPzEM8v4g1Y9rZ0/NH\nvbFX9DvZOnaI4S4OzGhlWAZQHphRKMkICLyloOiOHcJ5Y1w9EtEgOPh3xKhhUbCozmqVYGciWKqq\n8vI8ZzTdGc8lqw23N/Uvllq8giQrSsPnigQFqCps89HzNiOTAZMn2KeNce2cFmcmGQth27ohnLi4\nxBqtqpjRK42nLi+3ZfmtZLyvm4iDw2BXgun+seoFqWaoB6fGMRHcgPNX0sjkJezUq8CAVvbfujaF\nl47PYH4573qZWhX4FADgfW+6BqtGojhwco5ZIhyQKUjgOCBi0a090sWBGUVJhsDXTsxyyrheCQaA\nRDRg2RzTjUqwm8a4evA8h3g0iBWf2iGMKW5WlWDBeWOcolQKXIHnjEY4iqNKMBXBLQ7uURx4gsMN\nLA317RD+rgTTVItoGwZlVHPD9nGoKthVwSpmFrXv2DOX2yM0qQhuJtknGg5g1UgU56fTAzvsZHoh\nBw7AquFIw/syO4QPebnKCkG58/q1UFTgRwfcWyJeFGdwdmoFt+5ahU2rk9hDVqFQknHoFDv4NSKb\nlxALBywrcrQS3BURXFKaqiRQaCUYsLZCAF32BHvQyZuMBX1vh7ASjDznLiLNLHCtPcFKQ2FKRTC1\n4TSLrKgNT3Bo6oNdTFq+jh2CJpr4tRKcM6bFtT+X+/ptmi94P7NEGBSKsiGYTl9edmw7cvUcuh0i\n2OTxe8NkEpm81LV4zheOTuOR5851bZLt9GIOw8kwgg5OIqgI7qWYNCaCG3D0nN4Ut7FSBN+6axWi\nYQE/OnDJ8chUQKsCf/up0+A5Dve9eisA4Jadmr2CWSIak8mXakYmU8Z1ITm7lOvkKgHQqmFuZ8+b\nGU01FsHBAI9ggO+OCG6xEgxoryuTK7n6vPQKtPHN0g4hcIZtpOFyFKVWBFdZKRQHwpS+11qtBDtt\njLD/ofYAACAASURBVANgG5NWrgTXVkv9PjaZivd2N8YBwNrxOMaHIjh0eq5rgqbXmDEdy/NFGVNt\nSM+gzaXNHr83Gs1xnfcFL2eK+MxDR/Dlx07gr/7tRVzp8DCykiRjYbngyA8MsEqw71BVFScuLGHV\nSNRIHqBEQgHceeN6zC8XcOi08wru8QtLuDibwZ6dE1g9qvmBN04mMDEcwUsn5oxLMwxrsnmpZlAG\nJRUPISDwmF1yb1FplWJJbjoeDdCqaDRGLG4jggGtGtzJYRlO4rqckoyFoAJIt6nLu53IqiYWrWwq\nmqXBeUSaWeBaxau5skO07Al2LoLtK8GawLV6//M8h1CQ9+2wjHwHK8Ecx+H67ePIFWQcO88GZwBl\nv+loSvv+Pd0GX3ArnmBA+/4GgHPTnfcFf/+F8yhKCtZPJHD68gr+4sHn8dzLVzr2/DOLeahw5gcG\ngFSMiuDe+Q7oORH88tkFfOXxE3j2yBXMLuY65rM5dHoOL4qVldiZxRyyBQlb9Ik+1fz07ZsBAE/u\nv2R5uxX0vnfdsM74G8dx2LNTs0QcPDXveFnff/48Xhig6nFJkrUZ7zYimOc4jA9FuiKCCy3aIYBy\nQoRdJRjQRHCugyLS8MK22BgHlLOC0z60RNRLUXDTGFedy2s1aMOJMKWNcd6kQ9Tft1Tc2laCGyQo\nREMB33qCqdWj3Y1xlD1kAgDwvefPd+T5ep1Z3Q98604tn7+dIrjZIsYGmhDR4UpwNi/hsb0XkIoF\n8ae/cjPe96arwQH4zEMvd6z3gsajTToUwZGQgFCQZ5VgO7J5CZ/69iE8/Ow5PPCdw/jDT/0Ef/Hg\n823vlp1eyOKfvn4QD3znMEqmUaRnprQzu82rrSehbN8wjI2TCbx0Ys5RIkE2X8IL4jRWjUSxc2Pl\ntDlqiXhBdCZq55fz+OIPjuPT/3EYF2czjh7jdzLGyGR7kTg+FEE6V+r45ddiSW46GYJCfcH1RHAs\nLHS2EuxRRBpgHpjROwdAp9SzKAg8BxVw5FesntAmCLVVZE0o1z80BwKt5wSrqqpNbnIwLAOwrwTX\nG5YBaFYCv+YEU+tRO4dlmNmxYRi7No3gwMm5tkWC+QlaCb5pxwQEnmuLCC7ovvpmJ74NJ0JIxoId\nrwQ/vu8CcgUZr79lA0JBAbdfsxr3vXorJFlpqlepGWb0QRlOJypyHIdULITlHvoO6CkR/J/PnMFK\ntoTX3rQev3j3dlyzZRTnp9P49HcOt81HqKoqPv+IiJKkQJJVnLpU/pA1EsEAcNf1a6GoKp5y8Kb7\nyeErKEkK7rx+bc1l1U2TSYwPRbD/xKwjS8RLegexJKv47H+97EufpVvqZQRTyr7gzlWDJVmBrKgt\n2SGAciXYzvMMaBUpSVY6NhWverhDKySi/s0KlhX7MZ902zipBtdWgmv9xLKsNBxlHPJABDsdhNLY\nDlG/khYNCcj7dGJcuTGuM5VgjuPw1ru3AQC++viJtjSC+QkqglePxbBhVQLnrqQrClVeQJfXbBGD\n4zhsXJXAzGLeGObUbgolGd97/jyi4QDuvnG98fc7rluNUJDH43svdkQTlAdlOB8rPhQPYTlT7Jk0\njZ4RwTOLOXz/+fMYTYXx1ru34adv24gP/uL12L1tDIdOz+NbT51uy/P+5PAUjpxZMAzbxy4sGbed\nubwMDsDGSXsRfNvV2pvuyf2X6jbHqKqKH750CQLP4VXXrq65neM43LJzFQpFGYdON7ZE0FD1nRuH\ncfLSMh59sfnMYr9QLyOYMq6fkXZSBNMO/WZmz5uhZ9OpeL1KcGcTIrxsjDMqwT7MQdW8vNb7l/7d\nyZeObBGRVm2HUFQndojWG+Ocxt81bIxrYIeIhAQUJcWXzV7GsAyLSMZ2sXl1CrfuWoUzUysDZXez\nYmYpj2g4gHgkgC1rU5AVFacveVsNppaiZiLSKBt0jXBhpjOWiKcOXNYKhjevq+iRiUWCuP3q1Zhb\nzuNAB6L23IxMpqTiIciKahS1uk3PiOCvP3kSkqzi/ru2GWdkPMfh1990NSaGI3jox2ew7/iMp8+5\nki3iSz84gVCQxwfu3w0AOH5Ba0hQVBVnr6xg9Visrh8sFgngFddob7p9x+yjbc5MreD8dBrXbx/H\nUCJseZ891BLR4MBXKMo4cmYB6yfi+L/vuxaJaBDfePKkMbmlX3FVCV7sXEJEUap/Odgpr7puNe7/\nqW3YQ1bZ3qfTMWleR6QB/rRDVKc6mKEVYicJEbJcmw6hqpVWCllubFEoR6Q1XxVzum8jQe09Z1sJ\nbmCHoO9ZP1oi8oYdojOVYMov3LUNAs/p34v+O3nwAlVVMbOYw8RwBBzHYavem3P8nLc2kUKLjXFA\n58cnP7n/IgICj9ft2VBz22tu0vqNHtvb/sLY9GIOiWiwbmGqml5LiOgJEXziwhKee3kaW9akcOvV\nkxW3xSNB/Pabr0MowONT3z6Mb/3olCdDJSRZweceFpHOlfALr96KLWtSmByN4eTFJSiKiumFHHIF\nua4VgvJ6/Y34/Rfsmxl++JLWEHfn9Wtt77N5ddkSUe+Sz+Ez85BkBddvH0cqFsI7Xn8VipKCv/vq\ngb7uKi5Xgu2/kMa6YIcwuotbqCQA2iXXn719U93LctEOT40rVws9aIzT7RC+bIyr06xGK6nVk9+c\nLMfKSuEkuzfopR2iwb4tD8uwfs8ZlWCb962fY9K6UQkGtMra3Teuw8xiHv/1zNmOPnevsJQpoiQp\nxhWyzboI9vo7rtiiJxgwxaR1YHxyriDh4kwG29amjLSFinWZTGL7+iEcOj2PK/PtK4wpiorZxZxj\nPzClnBDBRDBUVcWPXrqEv//qSwCAX3rtdkvf3cbJJH7rzdchHgngO0+fwZ/8f8/gmcNTjrM5qymU\nZHziGwex99gMrlo/hNfu0Tw1O9YPIVeQcX46jTO6AX/zautkCDNrx+O4dusojl9YsjTup3MlPHP4\nCkZTYVy7ZdR2OTQlIl+UcahOSsT+41rF+Ybt4wCA23ZN4qdv24jp+Sz++t/34nMPH+3L0ZvlSrD9\nWefEkPaBnOugCKaNFa2mQzih03YIKuw8bYzz4Xuz2strhgpZJ3aI6glttAGOPlZVVU0EN6jOeukJ\nbtgYRz3BDXKC7SvB/h2YQT9nnYhIq+bn7tiCkWQY337qNI4OYJMcTYagImvNaAyRkIDj573dFvRK\nXitFjNVjMQQEviMJEWcuL0MFsHWtvTah1eDH911s23rML+chK6rjZAiKUQnukSuCXRPB88t5/O2X\n9uPB7x6FrKp498/sxFXrh23vv3vbGD7yvttx7ys2YTlTxKf/4wj+6FPP4JHnzrkyo6dzJXzsS/tx\n4OQcrt0yig/+4vWGR2/HBu35j11YNJriNjmoBAPAPbfo1WCLaJvH9l5AoSTj9Xs2NPzCMQZn2KRE\nKIqKl07OIhULYov+IeA4Dr9493b88btuxrqJOJ7cfwm/94mn8I9fO4BnDk/5sgJjRdZIh7CvBCdj\nQYQCfEXIertpNWLHDfS1d+okR3HYPOWEenYIRVV7+rJvo4g0ep+Gy6mq8hqP1U/oqSuiUXWWTmdq\nJSKNDgBp1IQXdjgxzu4KRsTHo5NzRRmhAN8wRq4dJKJB/ObPXwue4/DAdw5jyUECUT9Bm+KoCOZ5\nDptXJ3FhOu1pAxq9MlUvlacRAs9j/UQcF2fTbW9aPqUX2uyiWwFgD1mFVDyEpw9ebttxdXrRXTIE\nZYhOjeuRSnBnjU4mPvPQERw9t4jd28bwK28gFROz7IiEAnjLXdvw6uvX4pHnzuHpA5fx5cdO4CuP\nn8C68QS2rk1h9WgMwQCPgKAF25ckrZN+bimPk5eWcH46DVlRcdvVk3jPvbsQMH3ZXLV+CABw/Pwi\nlrMlcFw5CLsR12wexbrxOJ4/Oo233r3dGK5RKMl49IULiIUDda0QFMMScVyzRFSPIjx1eRkr2RLu\n2L2mpmq+bd0QPvzuW/DY3ot4+uBl7D8xi/0nZhEK8Ni9bQy37prENVtGO5Z56TUZ3Q5RrxLMcRzG\nhiIdrgS31l3sBvo5mV/uzBciFUpeiOBgQEA4JFimQ3zrR6fw2IsX8ZH33W5UCnoJRbHP06V/lxt0\nOxtVXnNjnEBFsKr/r0+mc+oJbqFTXvGqElzUBsXYpWcYlWA/eoKLckemxdmxff0Q7v+pbfjyYyfw\nwHcO4w9+6UZPrsr4gbIILmuDLWtTOHpuEWenlrFrs/1VVTcsZ4sIB4WWezp2bBjGmakVHD23gOu2\njnmyblbQBKt6leCAwOOWnavwgxcv4OjZBVzbhvVpJhkC6D1PcFc+3VPzWRw9t4idG4fxgft3W05h\nqseq4SjedQ/Bm1+9FU/uv4gDJ+dwdmqlYWemwHPYOJnATTsm8DO3b6o5aE8MRzGUCOHYhSUUSjLW\njsUdR+NwHIfX37IBn/3uUTz64nm89ae2A9C6ONO5Et74ys2OxCfHcdhDVuHh587h8OkF3HDVeMXt\nNBXixu3jVg9HQOBxzy0bcM8tG3BpNoPnXr6C516exgviDF4QZ8ABWDMex5bVSWxZm8KWNSmsn0i0\n5IfqFE4qwYC2Hy/PZetOl/OSYgftELTxb6ZDjX9OY7SckowGayrBkqzg8b0XkS1IOHByDnfsXuPJ\nc3lJPU8wb1SC61dcaPObpSdY386S7Gx708+r1MGINDsRmy/JdQWEUQn2YUxaviB1LCPYjntu2YBj\n5xex7/gsvvrECbztNVd1dX06RXUlGAC26PbEU5c9FMGZYt1EHqfceNU4vvf8eew7Pts2EayqWozr\ncCLUsHBIRfAL4nR7RPACE8FNQ5vE7rphnWsBbCYRDeLeV2zGva/YDFlRcHEmg7mlPCRFhSQpUKEi\nIPAIBnikYiFsnEzUVFbNcByHHeuH8byezuCkKc7M7VdP4utPnsTDz55DMhrC6/asx8PPnkMowON1\ne9Y3XoDOzTsn8PBz5/DMkakKESwrCvYem0FA4HG1gwPA2vE47nv1Vvz8HVtwfjqN549O4/iFJZyd\nWsGl2QyePjQFQPsSvGbLKO6+cR2u2zZmW9HpNlkH6RCAuTkuh40Rd/uwGYzRqh2oBNMvhE7ZPbyM\nSAO00cnnp1egqqrx2T94cs7wex841cMi2G5YhuDME2xlLan2E1sJZSu8mBjnuDEu2KgSLNV97/u9\nEjxsk+bTKTiOw3vu3YX/9X9exCPPndea5m5y/n3iV2YWc+C4cn46UK5+nrnsTQqDoqpYyZaweU3r\n3xPb1w8hHglg//EZvPOeHW35Hl1YKWApU8RNOyYar8+6IQzFQ9h7bBbveoPiuaVnpol4NKDcGDew\ndghJVvD0wctIRIOOdqRTBJ7Hxslk3UxfJ+zYYBLBdTw3VoSCAt5//2584hsH8ZXHT+CZw1OYW87j\nNTets+zitGPrmhTWjcfx3MvTuGbzJbz6+rVQVRVfePQ4Ls9lcfs1k64u3XAcV7FtFEXF5bkMTl9e\nwempZZy8sIQDJ+dw4OQcxociuHrzCFLxEJKxEHJ5CTOLOcws5REJCRgfimBiOIrt64awZW2qo4I5\nky+BAxpenqTV0rmlfMvvByfQRq+ki33cLDQzkzaNtBujecqj/ZyMBSHJKvJF2bgy8uPD2slYOCTg\nyOl5PY6st65MyIpi3xjHVVZz7ZAspu/Rn2kDouy0EqyLTk/SIRrs24DAQ+C5uhPj6r33/VoJVhQV\nhZJsiPhuEosE8YG3Xo//9fkX8G/fP4axoQh2b7O+GugliqpiZiGHydFY25+rmpmlPEaT4QrL4kgy\njJFk2PDFtko2L0FWVFffz3YIPI8bto/j6UNTODu1Utez2yxOrBAUnudwM5nAY3sv4ui5RVzjUeWc\nMr2YQzgouLavRcMCQgEeS+kBFcH7j89iJVvCPbds6MlL8NQXDDhvijOzba3my/3ktw7hxIUl8ByH\nN9y60dUyOI7D7/zCdfh/P/8CPv+IiNGhCC7NZvD43otYPxHHu+4hrtfLDM9zWDeRwLqJhFF1Ozu1\ngsf2XsCzR67ghy85G7k4nAjhxh0TuGfPho4cJKm9oZEgowkRnYpJ86Kxwg3jw1FcnMloI2/bfBKi\n6DaAVq7YmKHNceen09ixYRiZfAkvnZjF2vE4yIZhPL7vIk5eXDaaVHsBRVWhqvYNZNW+Xjus7AeG\nn1i3UjitzgaF1tMhnA7LADRfsNWwDFXVTmjqnZRHG9gpepV8h6fFNWLVcBTvv383PvqFffiXbx/G\nn/7KHqwbj7f1OZ/cdxH/53vH8NtvvhY318kv95qSJGNxpQCysfI4wHEcrtowgueOTGExXWi5Sk+r\nkV71Idxw1QSePjSFvcdm2iuCHS57D1mFx/ZexItHpz0VwSVJxtR8FmtGY66/GziOw2gqgrnlzvXt\n1KPjn+4nHeTldpP1EwlEwwEUirIRgO2W4UQYf/j2G/FfPzmLZCzounsSACZHY/jdt+zG335pHz7x\njYMolmSk4iF84P7r29LYtml1Er/6s7vwjtftwPxKHsuZIpazJUTDAiaGoxhLRVAsyZhZzOPKQhYH\nT81h//FZPK5/wP7onTdjdZuFcCZfcuTxpXaITlkGaCU4EeuMCJ4YjuLs1AqW0kWjAbNd1KuANsMr\nrlmNpw9O4XMPH8Wfv/sWPH90GpKs4hXXTGLdRAKP77uIg6fmeksEN2gg43lnItiJHcJojGsUkRZs\nvTFOctiEB2hVeqtKcK4gQ1XL0X1WRDoc6+cV3coIrse2tUN4z7278KlvH8ZXHz+B/+et17f1+X5y\n5AoA4DtPn8FNOyY8OxluxOxSHirKE0DN7Ng4jOeOTOH05WXceFVrV5OpL9WLSjAAXLtlFMEAj/3H\nZ/GWu7Z5skwzpy4tgePg2L6xY8MwUrEgXjw2g1++Z4dnV9iOX1hCSVKwc9NIU48fG4pgaj6rNdV2\n2XPf8VLskdPz2L5+CGvbfAbbLDzP4f67tuK+V29pKfIqIPD4uTu2tOTd2rFhGL927y4UijICAo/3\nv2W3IfDaRTgkYM1YHGTjCG7ZuQrXbhnD5IiWgRiLBLFpdRK37prEe+69Gn///jvwS6/9/9s78/C4\nzvLs/85s2kf7LkuyZPm1ZHnf7Tj7RgKE0DYQtnwhhavQhY8W+pWthS5f+9FCy1ZaoEBZCiQFAg0k\nkD12YjtxvG/HsmTZ2vdltM76/XHmjMbSSHNmNJvi93dduWLNejSvZuY5z3s/993A+JSLf/rxcQbj\nXHRqneDwhWawHCIRTPgHvXIS1AkuDtI8x5ulrMGioam2gNu3VdEzNMVjz1/i0JleFLTiuLE6H4tZ\n4XQC4j4jYU6iEMYdIsxgXKgu7/zBOKPdWa07v0w5hEHpBWi64FCd3LFJzaUkL3vxImIuMW5lFcHT\nSUqLC8eOdSWsq87jVOtQIOE0HoxNzNLaOQZoOzenl/CujzUD8zyCg2mo1gqvUJ78kaIP6caqE5xm\nM9NUk0/X4GTME1w9Xi/tfQ4qiowP7JtMCltFCY4pFxc7xmJ2LGcva38L65fIPVgKXec9mALd4IQX\nwT7gxo2p2QXWuWVrFW/eW5vswwBgd1MZH31gE3/x7q2GdECJxGzSnCh+7+Z6hsdn+acfnWA0Tl6W\nLrcXp9sbdigONFlCms2cODmEvxOclUA5BJAQXbDHF9siGOB3b66nsiiL54510dI5hqjOo8CeTprN\njFiVx9X+ibj9HUVDOBcFy7xu7uKPs9BubqFFmrHCVFEUrBbTsgbjIvGATreZQw7G6bo+e9biOxK6\nxdr0CgvLmNY7wUnuVM1HURTefqPWZfzpi234wljzRcvxlkF8aHHuAL861B6X5wlFKHs0nQb/LtHl\n7uUXwbocIjeGtoxb/LNOx/2hVrGia2ASp8trWAqhs11ox3N0keyBaDh7eRiLWYl6x64wwY2qpUh4\nEby6PCcQCCExxoa6wrjoi2LFm3bX8Oa9tfSPTvOVn56Ki1n4XGRy+EJTURSKctMZHJuO2xdEMI5p\nF5lplmsGOOJJcQLlHrHuBIM2QPqBtzRh8ReAe5rLAtdt8A/7nG5LnW5wqOI1GKNyiFAJbfPvG0l3\n1mYxJ8QiDbROsMvtXZDSqRcRS3WCs9ItKIQOSUllAprgFPRVX1OVy6b6Qi52jAa6crHm2MUBAO67\nYTUb6wtp6RyLeWTxYnT57U5L8hZK7HIybZTkZ3C5x7Hsz/fxGGuCATatKUIh9kWwPgwYaTNMVOeR\nnWHldXXAUKplOMYnnVztn6ChKi/q3fLAbu312An+zEM7kq4BkcSe+/evZm9zGZd7HDz63KWYP/6k\nQXs0nSJ7OtOzHqYSoEN0TLkSpgeGuU5wIryCvV5f2CGtaKguzeG9dwoaa/KvOSneUKdtr6WSJCKc\nRMHoYJz+OJYQg3HeBZ3g8K+51gmOvrtqdAgP5obDZp3zimB/x36pTprVYqbQP9y7ktBjnpPtE7wY\n999YB8SnGzw14+L8lRFqSnMoys3g3j01APz68JWYPk8o3B4vR9UB7JlWaspCz+XUlduZmnUHvGqj\nJR5FcG6WjfrKXFo6R2O6ozXnDJEb5pbXYjaZ2Lq2mPFJZ0zkM+fatZOu5iilEBAkh0hgsutihP30\nE0IoQoivCyFeEUI8J4Som3f9g0KIw0KIA0KIf43foUpSGUVReO+dgsqiLJ491smr5/ti+vj6GWO+\nwWngREkGfD4fE1OugOtBIii0p6OQIDmE1xs3B4r9myr4+INbrtG3lRVkUpSbztn2kZSJUQ7VwQ3G\nqEWaJ4RF2lzk8jx3CAPd2eXKIYwO4cFcYMZ8ScSovp0c5n1ZUZTF+JRrRXWDp1PMHWI+1aU57Gws\n4UqfgxOXYtt1PNk6hMfrY+tabWemoSqPtVW5nGodoqN/6VCq5XKmbZiJaRc7m0oXPRnU7UuXa5UW\n68E4nV1Npfh8cPhsbL4H3R4vZy8Pk24zR+UIsn2dXxJxYWDZx7JcPTAkfm5nKYy0eN4GpKmquhf4\nBPBF/QohRDrw18BNqqruB/KEEG+Oy5FKUp40m5kP399Mms3Md568wMFTPRxvGeBc+zBdg5OLmu0b\noWtA6yIZHagsStDw2PSsG6/PR05G4qJ+rRYTeTlpiRmMWyIkIh4oikJTbT7Ts256hmI7WBIt4QpT\nvZMaXhO8sMsb7WAcaIEZy5JDeIw/l77tOX+4TdcE54XppOlf3KmypkYIdIJTyB1iPvfs1jq0r/iD\nj2LFMVUrlrYG2aLdtUuz+nzheFdMn2s+h/y+4XvWly16G10Xu1xd8PiUE4tZifka72oqxWJWePl0\nT0y69EfVfkYcs+zbUB6VW8+66nyy0i0cvdgfCOSJBp/Px5n2YeyZVqqidM8CzUHLbFJSQg5h5BT3\nBuApAFVVjwghtgddNwvsVVVV7/lbgOT/VpKkUV6Yxf+6ex3//suzfPvX5xdcn5tt43dvqmffhshS\nwboGte5DZXFkRfBAnLuljgR7BOsU5aZzqWsMt8cbVy3yUnHB8aK8UFvjvuGpqG0KY0m4Ingu8MKg\nO8QSscmRdGeXPRhnMJ0O5obD5p/I6u4QuUtogmHu5LV7cDKl7O+WYjqFNcE6q0qyKS/M5OSlIaZn\n3TGxz5x1eTh9eYjSgkwqCuc0uRvrC8nLtnH4XC8P3LImLrLGqRk3x1sGKS/MXDKxtbo0G5OicLl3\n+Z1ge5Yt5tZv2RlWNq8p4qg6QPsygzN8Ph+/ebUDBbhjx6qoHsNiNrFlbTEHT/VwqXMs6vdg1+Ak\nYxNOdq8vXdYOocmkkJ+TlhKdYCPvGDsQ7K3hFkKYVFX1qqrqAwYAhBB/DGSpqvrMUg+Wn5+JZYno\n4pVGcXH8E8lWGm++KYfaqjw6+hzMOD3MzLoZGp+hd2iSi1dH+N5vVLY2lVFdZvyDoX90BovZxPqG\nEkMaRuHSioOxadey1ijcfYcmtSK4tCgroX8LVaU5tHSOgcVCcRztBr0+SLNZEvq7idWFwCXGZ91x\nfV6jjz3l75hmZaWFvE+eP5xlset1Bvxd05ycudvZ7f77ZqdTXJxD98iM//L0sMeXmWHD5XZQVJQd\n1Zd45tXRwPGHe658/++YkXnt7zg54yYrw0pF+dJfqk1r3MB5RqaW936MhOU+j8msfU+Vl+Sk9Of8\nrTuq+eFTF7jU6+DW7ZEFM4Xi5VPdOF1e9m+upKTk2s/ou/bU8pOnL3Kha5zbIwyBMsLTR67g9ni5\nbWf1gucOprIij9pyO1f7HOQXZEXVCPD5I5Ory+1xWd97bqjjqDrAsUtD7NxYGfXjnG4d5Eqvgz0b\nylnfEL2pwG07azh4qodzV0fZtzW6Yvplv2/07g0Vy37NyoqyONs2RF5+1oLgtES+34wUweNA8BGZ\nVFUNtB+EEArweaABeHu4BxuJsXdeMikuzmFgIDYZ5m80Su1plNoX6gSPXxzgKz87zee/f5RPvXeb\noQ8vr8/H1V4HZQUZDA8bG65JU7RuWmvHaNRrZGR9O7q180MTvoT+LeT4BwTVy4NYfPHTzro9Xnze\nxP5uGf7t+bZlrF04InnvDvp3IVyz7pD3mfJ3Q0dHp5d8TP1vd3bGFbjdzLTTf98pBgYcDI1MBi4P\nd3yKv5Pb0zuGNYrGwqh/sHJqMvxzefwDeH0Djmve10NjM9gzrWHvn+E/vEtXRxLytxSLz+bhUe27\namYq/OuTTJprtBOQp49cYUOU4QU6Xq+P7//6HAqwcXX+gt9725pCHn0afnWwjU2rl/dcofjt4XYA\nNtYsfG4dfW1XlWTR1j3GsbM9UXVap2fdON1eMm3muKzvqsIM7Fk2Xni9g7fuqYk6IffR36oA3LKp\nYlnHWZmfTmaahQMnunjr3pqoOrlHTmtpsqsKM5f9mtkzrPh8cLFtgJL8uR2HeNRVSxXVRlblZeAe\nACHEbuD0vOu/gaYZfluQLEIiCcmWtcXsay7jSq+DXx0yNmk8PDbDrMsTUcCK1WKirDCTrsGJg7Yz\nJQAAIABJREFUuNqkOfxFTOLlEPrgX3x1wR6PL6aJcUYoykvHbFLoHU6NE+awg3EGLdLcS1ik6VKK\ncMEcwehfqtFKIsJZvwUzpwmek0O4PV4mpl2GPFbTbRYK7el0D60ch4iZFPUJnk9pviYdOHd5hPFl\nDh4eONVN18Ak+zaWU1W8UIpUlJvB+tUFXOoaC9iYxYqhsRkuXB1lbVVuyKS4+TRUaS4JLZ3RhUDE\nwxkiGLPJxN71ZUzOuDkZ5eBiz9AkJy4NUl9hZ01VZK4Q87GYTWxpKGLEMRtwmoiEEccs56+MsKok\nOyZJpbpDRLIlEUaK4J8Ds0KIl4EvAB/1O0L8vhBiC/AwsEEI8bzfPeK+eB6wZOXz4O1rKbCn8cQr\n7bQb0HR1+a2VKkN8KC9FZVEW07OeuIrvJ/ya4JwYTxeHQzeRj3cgiDfBg3GgfXmU5GfQOzSVEJ/n\ncIS3SPMnxsUkNjkydwiIPjUukiG8gCY4qAgeD3gEG/tCrCjKYmzCyaTf8zvVCSTGpbAmWGdXUyle\nn4/XL0QfiDA96+bnL7WRZjXz9hvrFr3djZu0sKuXTvZE/VyhePFkNwC7mxcfiAumoUrrgEdr+xWP\noIz57PUHjbx8OrrX6skjVwG4K0bSk+1+O8qjUfydPH20A4/Xx23bok/BDUYPzEh2alzYd7df9/uh\neRdfjOQxJJJgMtMtPHxPI1/48Qn++4VWPvbOLUveXvcXjdQapqo4i9cuaM4Seuc01jj8aXGJikzW\nKU6AV7DP58Pr8wUswBJJaX4mPUNTOKZdMbcvipSwg3ERWqSFdIfwzBuMi6AIjrYTHOhMG7FI0zvB\nQYNxoxO6PZqx9akoyuR02xA9g1PL7molAj0xbiX42u9sLOXR5y5x5Fwft2yNrkj59eErjE+5uH//\n6iVPbDY3FGHPtPLKmR7eflNd1IEJwVztc/Dk4SvkZdvY1Vhq6D5Fuenk56TR0jGKz+eLWBcfL3u0\nYKqKs1ldnsOp1iEOn+tld5OxAh/g0JleDp7qobwwk63+FLrl0lRbQEaamdcu9PPALWsM7/JNzbh5\n4XgXuVm2JV07IiFVUuMSHpYhkQCsry1ArMrjXPtIoNO7GJ0D0RXBeue4M8bbdsHoneBEhmXAnMVM\nPN0vPBF0CmNNmX8qvTcFLLXCBVjor49xi7QQ7hC+aCzStOLDFaX1YCQWaaE6wXNBGQY7wX7Xj2RI\nIlxuL//2izN898nzHL3Qz9RM+BCdGaebNJs5bj7ZsSQ/J421q/K42DnGcBSdtcGxaX7zagf5OWnc\nGabraDGb2L+pgskZN5/99qucal2eR7HL7eWbT5zD4/Xx/nsaDXfeFUWhoSqX8SlXVKEZunQkJyu+\nn90P3b2O9DQz//HEecOvVWv3GN958gIZaRb+6O0bYiZJs1pM7GosZcQxy6kIAolePNHFjNPDHTtW\nRa1tnk+RPTVS42QRLEkat2/XJlSffb1zydt1D05itZgC3U+jVPnt1MIV2ctBN/9PpE8waJ3CQn80\ndLwIp4WNJ2UFWhHclwK64HDd2TlNcDiLtIWPY1rQCfYXpgYt0gBcUYaKzFmkhf8aCBWWEdhONtoJ\n1t+PA4kvgq/0OXj1fD8vnezhXx8/w0e+fCDgR7sYM7OelE2LC8WuJq2D+ur5yLe6D53tw+3x8rYb\nVhvq7L55by23b6tiYHSGf3nsFF967CQT09HJXB4/0EbXwCQ3b6mkua4wovvqkoho4pz1TnBunHea\nqktz+MjvbsJsUvjaz89wum2IGad7UanX8PgMX/3paTxeLx+6b33AMjJW3LxFc6p44YQxv2eX28tv\nj3aQbjNz8+aKmB1HgX/AVnaCJdctmxsKKbSn88qZnkV1gl6fj56hScoLMiMuxoryMrBZTXT2x+9L\nd2LahdkUe7N1IxTnpuOYci0IMIgVemFmMVAkxRq9CE6F4bi51yFMEewx2Ak2B3eCr9UTR9J9D8gh\nXFEOxnmWNxg3aiAyOZhkdoJHHNqx3rK1kvtuWI3ZpPD4gbYlgwNmnO6UTYsLxVZRjElROKpGXgTr\noRMb6o0VoWlWM++6Yy2ffXgH66rzONk6xBd+csJQhz2Y19V+njpyleK8dB64pT7i417OcNy4fxcv\nXoNxwaxdlceH72/G6/Xxz4+e5MNffIkPfeFF/uGHx+gPcszqH5niCz85wdikk3fe2hDxSYERqktz\nqK+wc7p1yNBg9aGzvYxNOLl5cyWZ6bHrmlstZnKzbHGfawmHLIIlScNsMnHrtkqcLi8HFhmyGByd\nxun2BrpIkWBSFCqLsugdnoxbBK9j2kV2hjXmZutGCERDx+lDJJIwhViTSkVwqIG2YIy6Q4QcjJsn\npQgnvQjGtsxOcCRDeEsNxoWLTNbJSLOQn5MW0PgnEl0i0FSTz303rGZnYykDozOcax9e9D7TTk9K\np8XNx55pQ1Tn0dY9HlF3zefz0dY9RoE9zfCQo05VSTYfe3AL+zeWc6XXwT8/eiIwULgUfcNT/Mtj\nJ/naz89gMik8cm9TVCccVcXZZKSZuRjFcFy83SHms7G+iP/9wCb2NZexoa6QkvxMLnaM8rnvHuXY\nxQEudozyt997nZ6hKe7eVc3t22MzgBaKm7dU4mNuGHExtKCOq5hNSlyOpzA3nRHHbFgpWTyRRbAk\nqdy4qQKb1cRzxzpDvhG6ohyK06kszsbt8dEXhWbMCBNTLnISrAfW0eUhfcPx+d30TmEy5BA5mVYy\n0iwpUQSHt0gz5g6xdGKcbpFmvDtrDWiCl1cEG1nfNH+BEmowLs+gHAI0h4gRx6yhQimWDI9rneAC\nvw7xpi3atu6Lx0MXAW6PF5fbu6I6wQA79On/CLrBQ2MzjE+5AlHEkWJSFB66ex171pfS2j3Olx47\niXMRnbrP5+OpI1f5zH8c4VTrEI01+fzVwzuiTjAzmRTWVObRPzId0KgbZXzSiUlRyErgUPP62gIe\neXMTH31gE3/9yE4eubcRj8fLV392mn/80XGmZ908dLfggVvWxLWxsmNdCVnpFg6c7F6yQXThygg9\nQ1PsaCwJvHdiSaE9HY/XF9hVSgayCJYklax0K3vXlzE4NsPxloEF1885Q0QXn1tVpOsQYz8c5/Z4\nmZp1J9wjWEc3iI/WIkine3CS7/9GXfDFlczBOEVRKCvIpH9kOqzWNt7MvQ6xGowL4Q7hnTcYF5E7\nRHSDcRFZpFlDDMZNzmIxm8iMwEIsIIlIcDd4xKF1Rgv8/qZ15XZWlWRzvGUw5BfwSvEIns/WtcUo\nSmQWWG09mhSiriJ6xw6TSeH99zayfV0JFzvHeCKEB/z0rJuvP36GR5+/RFaGlQ+/rZmPvXNzSD/i\nSIhWEjE+6SQny5rUwcd9G8r59Pu2U1aQSbrNzJ8+sImbNkefLmcUm9XMvg3ljE+5OHZx4feuzvPH\nNd3wrVvi05UOOETEaTjO5/PxwvGltc+yCJYkndu2r0JR4AdPX1ww2awP0UQjhwCoLNEdImL/pTs5\nrTtDJMfCq77CjsWsoF5dXhH80xdbef54F+faR665PJIhrXhQVpCJx+tL+uBEuFAJ/UvUHW4wLkSX\nd6FFmvHurG2ZPsEReRJbTZhNyjVhDGOTTnKzbBF1rCqLk1MEDztmMZsUcvxb34qicNPmCrw+HwdP\nLZRizawgj+Bg7Fk21lXn09o9btglQg9OqKuIrhOsYzaZeP8968jPSeOpI1foCdJ+941M8bffO8pR\ndYC1q/L47MM72b6uJCbdTr2LHKkkYmzKmXT7RdAkJX/z+zv5pw/vo7G2IGHPe5N/yO2Z1ztDDumN\nOGY5dnGQquJs6iuX97exGPEOzHjyyFW+9xt1ydvIIliSdCqLsnjHLWsYm3Dy5f8+dc2gV9fgJDar\niaLc6LZi4tkJDngEJ0kOYbOaqSu3c7XfwVSUAQRjk86AVc7APKeJSDqF8aCsQJN7JFsSEa5YNNoJ\nDh2WoX0E6/rraAbjoi6CI0inMykKtWU5dPRNMD2rTbaPTTgjkkJA8objRhyz5OekXdP1291Uhs1q\n4qWT3QsG5HSP4IwVJoeAyAMR2rrHMSkKNaWLR8saJd1m4V23N+D2+Pjh0xfx+Xx0DU7yDz84Rs/Q\nFHfuWMXH3rk5pgEVq8tzsJgVWjqMd4JnXR5mnZ6E6YHDYTaZEu5HXV6YxeY1RVzqHOOFEwtlQQf8\n74tbtlbGTZoRz07w2cvD/PTF1rDpdrIIlqQEd+xYxY2bKrjaP8E3/+ccTpeHiWkXPUNTlBdmRb1l\nZc+ykZ1hjYstk2MqOUEZwYjqfC1/Pcro0ENnegOF1/zgjbmY3+R8TJT5C6beOGmejRKr2ORQj7M8\nizTtSzPq2OQIBx/X1eTj9flo6RxlcsaNx+uLuIioKNIGHjv74+fdPR+P18voxOyCL8PMdAu7GksZ\nHJvh7OVrB+R0b/GC3OXHwyaabX5JxGsGdMFuj5crfQ4qi7NiVoRtXVvMhrpCzrWP8IuDl/n8fx1j\nbNLJg7c38M7bGrAsIiuKFqvFTG2gGWBMa+5IQFDGSuC9dwmy0i385LkW+oJcKjxeLy+e7CbdZmZ3\nk7HwkmgoilMneHB0mn//5VnMJoUP39+85G1lESxJCRRF4T13rqWxJp/jLYP8wRde5E++dAC3xxv1\nUJz+uFXFWQyMTl+jZ4wFui9msjTBAOuqta1A9epImFsuxOfzcfB0T+AEY3Be8EYkQ1rxoDQ/RTrB\nnjCdYIMWae4QXV79vrqUIiC9MFAo2Kx6JzjasIzI1nddTT4AF66MBnS0kboJZKZbKc3PoK3HsaQ9\nWSwZdTjx+Qg52HOjf0v4lTPXegafadOK4vUJ3J6OFfYsG2JVHq1d4SURnQMTuNzeZUshglEUhXff\nuRarxcQvX27HMeXifXcJ7vD7wseDTfWF+Hzw1KsLtcihGJuKf2TySiA/J4333Clwurx864lzgd2q\nEy1DjDhm2dNcFldJkN4JjtXgev/oNM8c7eCLj2q+1e+6Yy31YbTusgiWpAwWs4kPva2ZPevLWL+6\ngC0NRexZX8qdO5b34VlZnI2P2G/BTuhBGUnsJtRV5mI2KVyIQhd8ucdB9+AkW9cWkZFmWdAJTqZF\nGkCpbpOWBF/ZYLwhBtqCMeoOEepxFrdIM9AJNi9PDhHJEB7AmspcLGaF81dGGJuIvoioq8hletad\nsDRA3SO4IMS2aF25nUJ7OqdahwJT8l6fj7Ptw9izbFSVLG9oK1ns8EcPHznXt+TtdH/gaJ0hFqMk\nL4P799dhMSs8fM+6QEBDvLh92yq/FrmDfgPet4m2R0tldjWVsmNdCa1d43zlp6f418fP8KNnLwJw\nS5zXLSPNQnVJNheujiyr2XGmbYjPfudV/uLfDvFfz7TQOzzF7duquGlT+HCPlSd4kryhyc6w8oG3\nNMX0MfXkuM7+iYCjQixwTCcnMjmYNKuZugo7l7rGmJpxk5lu/C198LQ2EHTDxgr6R6fpHZ7C5/MF\n9F/hOqDxJs1qptCeFjd7O6OEK0wDkgaDiXGhLdL8RbDH+GCc1RqbwTijFnja31ouLR2jgcE2o2lx\nwdRX2jl0tpe27nEqlrHLY5RhvzNEKG2goihsWVvEM0c7uXBlhOa6Qjr7JxifdLJnfdmKiEwOxc7G\nEn78bAsvnuzm7l3Vi2o6YzUUF4q7d1Vz27aqmMXsLkWazczv3VzPN/7nHI89d4k/fPuGJW8/VwQn\n77M7lXjvXYKWzlFOBkUp71hXsmznDiPcu7eWrz9+hl8fusL7722M6L7dg5M8+vwlTrUOoQAb6wvZ\nvKaIjfWFhi3dZBEsecNT7R/4aO0eY7+BM0OjpIImGDRdcEvnGC2do2xaU2ToPk6XhyPn+sjLttG8\nuoADJ7u52jfB+JQr0N1LpkWaTmlBJufaR5iedSdtUn9OorC8wbilfYIjt0izLVMTPCfPMF6kNNbk\nc7FjlFcvaB1Go0EZwegFV1v3GDdsLI/4/pEy3yN4PlsbinnmaCfHWgZprivkjF8f3Fy38qQQOlnp\nVnasK+GVM71cuDpKo1/KMp+2nnHSbeaYR/PqJKIA1tnVVMpzx7t4/eIA59uHl3RakJ3ga8nOsPLZ\n9+9k1DEbmKOJtXZ7MbaJYsoLMzl0tpe37quluHjpAU2fz8e5KyM881oHp1qH8KHJAt95W0Pguz4S\nZBEsecNTU5pDdoaV023D13Q6l8tEwB0iuR+k66rzeOIVUK8aL4KPtQwwPevm1q01mExKIHhjYHR6\nQRGczG5YbZmdc+0jXLgywpa1xUk5hpgNxoXo8i46GBeBT3C0muBI5RCgFcG/OHiZ1i6tgxiNHKKq\nOBurxRToQsYbvRNcYA9dsDesyiU7w8rxlgHec+dazrRpXaX1q1duEQyaBdYrZ3p58URXyCJ4akYb\nPG6syU9KIE6sURSFd93ewN989yj/9WwLn314x6ISpvFJf2TydT4YF4w905aU18OkKLx5Ty3ffOIc\nTx65SmNDCS63h+ePd9PR72B80sX4lBOny4PL7WXW5Qk0oOoq7Ny7u4bNDUVRf69LTbDkDY/JpNBc\nV8CIYzamfsG6Jjg7I7nnkvUBXbDx4bgLV7TbbheanVJxntYlC9YFe6PoFMaarf7C9/UlDN3jjf46\nWOIRm7yoRVoEsclRW6RFPvi4utweeF6IfDAONO1/bVkOHQMThoZVQ3mYRsKIvxOcnxO6E2w2mdi8\npoixCSfn20do6RyjuixnxRdIaypzKS/M5NjFgWv8nXUu9zgAYioRSza1ZXZu2FhO18AkLyySBgjQ\n3juOoiy+OyBJLDubSijOS+fAqW6eefUKn/7WEX78bAsvn+7ldNsQ3YOTTE678Pp8ZKRZ2L2+lE+9\nbxufft92tqwtXlZjS3aCJdcFG+sKOXy2j1Otg6zyD7uMTzr5z6cuUFaQyTZRwurynIjeTI4pF2k2\nc8CqKlmkWc2srrDT2jVmWDbQ3uPAajEFwguCO8E64UIiEkFteQ75OWmcvDSI2+NN2BZdMLG2SAsd\nlnGtO4SRzpwlkBiXGIs00LrPDVW5nG0fQSF6j+y6CjstnWO0944jqkNv1YM21PZ/v3+U/ZsqeOu+\n1VE917BjBotZWfJYt64t5uDpHn7yXAser4/mFd4FBj0QpJIfP9vCK6d7uXtX9TXX6zMB0UYWpypv\nv6meo2o/jx9oY1dT6QL3nt7hKVq7x2leXZBUZx/JHGaTiXv31PLdJy/wpZ+cwGxSuGvnKm7eUklu\nlo00qzluXsWyEyy5LmiuK0QBTgcJ/5969SrHWwZ58shV/vZ7R/nzr7/ClV6H4cd0TLuSrgfWWVed\nh88Hakd4lwiX20PX4CTVJdmBojJkEZzkwTjQtsq2ri1mcsZt6HeLB+FCJQLuEB6Dg3Hmhe4Qnnnu\nEIt1nYNZfic4ssE4Hd0qLTszet2gblsUThLxX09fZGh8lgMnu6PuCA+HCMqYT1NtPmlWc2Cn6I1Q\nBAPsbS7DYtYCQYJfvyu9Do6c66OmNGdFa59DkZtl4y17VzM54+bxA20Lrtft8PY2lyX60CRLsLe5\njLVVuWwVJXz2/Tt5x60NlOZnkm6zxK0ABlkES64TsjOs1FXaudQ1zuSMi+lZNy+e6MKeZeMP79/A\n7vWlDI3P8sQr7YYez+fzMTHtSlpa3Hx0P9PTbUNhbgkd/ZN4vD5qy+a2QQvs6Shc6xUciT41nmzz\nSyKOqcmRRBhxh1BI/GDcchPjotEEw1wRnJsVfZCEPhzXukQRfPziQEAGMzQ+G1XgjdvjZXzCuagU\nQsdmNQeKwXSbmfrKpb1FVwrZGVa2i2J6h6d4LShB7qcvtQLwuzfXr1gHjKW4fXsVpQWZPH+865pg\nFq/Px6EzvaTZzEmbMZCExmI28Rfv2cbnPrhnWdkAkSKLYMl1w4a6Qs0D9PIwL57oZnrWw+3bqtgm\nivngW9ZTWZTFydYhQxHETpcXl9tLdkZq6AbrK3PJSLNw6tJQ2I5Ze69WeNSUzU3SWi0m8u1p10Qn\np0oRrA8uHbs4kLCAhWCMyEJMJmV5cogoLNLMJhNmk4Iz2rAMrw+TokTcZakty6GiKAtRHf02eoE9\nnbxsG63dYyH/Xqdn3fzg6YuYTQpv3lsDwMnWwYifZ9Qxi4/Fh+KC0fXnjTX5SZHdxIu7d1Vjs5r4\n5v+c49jFAS5cGeFM2zCNNfk01S4uRVnJWMwmHrxtDT4f/OjZlsDfWEvHKEPjM+wQJaRZkytjk6QG\nb5x3ukQSho31hQCcaBnk6aMdpFnN3LJ1zgx8V1Mpbo/X0BCWIzAUlxqdYIvZRPPqAobGZ+gOE0LQ\n7h+IqS2/1k6mODeDkfHZQGcxFQbjwD+41FDE2KSTtq7EOAoEM/c6LF4sms3hi+AlB+MWhGUYe82t\nFtMyfIK9UdnfmU0m/uaRnbz7jrVRPa9OfUUuYxPOQJhFMD97qY0Rxyz37qnhzh3VKAqcvBR+l2M+\nw4GgjPADUNvWFnPT5gru3VMb8fOkMtWlOXz09zZhMZv4+uNn+PavzwNaFzie28zJZmO95hd7/soI\nP3z6Ij6fj5dPa1KIfRukFEKiIYtgyXVDdWkO9iwbh8/1MeKYZf+mcrLS54rYnU3GUpZgLigjVeQQ\nMFfknwrTMWvvHcdmNVFemHnN5cV5GfiAIX/Uqh7lmwrbpQFJRBJcItxhBuNAK2yjsUjTX9q5wbjI\nuu82i4mZKOPAPV5f1F3+WBRPdZULJRFTM26+/evzPPt6J2UFmdy7p4bsDCtrKnNp7R4LnHwaZamg\njPnYrGYeuntdXIIjko2ozudP37EJq8XE4NgM20TxG8oVYjEeubeRquIsnjvWxX8+pfKa2k+hPZ2G\nN9gwoCR6ZBEsuW4wKQob/Lo/k6IsiGMuycugvsLuj4Vd2J0KRjdbT6UieEOI4b/5zLr8Q3GlOQu6\njfNt0lIhLEOnqTafdJuZ1y/2L9syK1KMaGfNJlMEmuC5111RFK2A9uma4KWDOeazqjSH/pFpeqKI\nll5OERwL9Kje3752lSePXOGFE1385bePcPBUD9Ul2fzx72wIOK9sWlOEzwdn2oYjeo5AZLIBOcQb\nnYaqPD72zi3sXl/KO25dk+zDSQg5mTY+/uAWqoqzeOlkN7NOD3uaV24SoCT2yCJYcl2xqV4Lk9jR\nWEJRbsaC63c1leLzwatBQyShOOzvFgfrapONPctGbblmPTU14w55m47+CXw+Tdc5n/kOEdEOTsUD\nq8XMloYiBkZn+NlLCye+44kRiYLJpAQ6xuEf59rX02xSFoRlGHVs2O9PXDtwssfQ7YPxJrkIri23\nk5tto7VrnMeeb+V7T6mMOpy8dV8tn35o+zUpZpv8uxyR6oIDaXEG5BDXA3UVdj74lvUhP/veqAQX\nwhaziX3SFUIShPQJllxXbF1bzHvvXMu2dSUhr9/RWMqPnm3hyLk+3vWmppC3GRid5rXz/VQVZwdc\nGVKFjfWFXO4Z51z7MNtD/I7tPdrW8+qyhVuhRf4iWHeISAWLtGDecVsDbd3j/OrQFeyZNu7wd/L1\nznC89I1GQiW0QtaoRdq8IjhIT+z2+lAU4xKULQ3FZGdYeflMD2+/qS6igS6Px5fUpLA0q5nP/8Ee\n+oanGRidZmh8hrWr8kJGn1YUZVFoT+d023BEftHDfmlPvuwEX9fkZNr4zEPbGZtwBj7nJBKQnWDJ\ndYbJpHDL1qpF06Bys2w01eTT1j1Oz2DoLebfvtqB1+fjTburU26wZGOYjll7b+ihOFjYCY50SCve\n2DNt/Nk7NpObbeNHz7bw+IE2vvvkeT7+9Vf4s6+9zPE46YWNdGfNJiWsc4V3kccJllJo3Vnjr7fV\nYmJvcxmOKRcnWiLrknoifK54YLWYqSrJZsvaYm7fvipkAQzaCc6mNYVMz7q51Dlm+PFHHLNYLaaU\n8fOWJA+rxSwLYMkCwn4CCiEUIcTXhRCvCCGeE0LUzbv+LUKIV4UQLwshfj9+hyqRJIZdTdp22Td/\ncRr3vO7e+JSTA6e6KbSnsWORbnIyqSnThv9Otw2HLMraex2k2cyUFmQuuM6eacVmNQUVwcbTyxJF\nUV4Gf/rAZjLSzPzy5XZeOtnDrNPD5Iybr/zsND/87UVcUVqGLYaRYbVgScNiLNblDZZSeDyRSxR0\nScRLpxaPiQ1FtO4QyWLTGk3K9PTRDsNrrAdlpNrJqkQiSQ2MtAHeBqSpqroX+ATwRf0KIYTF//Pt\nwM3AB4UQ0oFasqLZvb6U9bX5vHauj2/88mygGAR47vVOnG4vd+6sTkkvUX34b3zSyc9famN6dk4b\nPON00zM4SU1pTsjtdkVRKM7LYGBsGp/PZ8gaLBmsKsnm4w9u4fdurufT79vOl/5kP595aDsVRVk8\ne6yTz37nNV4507PgBCZa9NfBsqRFmsmQRVqozqvZpAQG4qIZVqsszqa+0s7ZtmGGxmaYdXo4fLaX\nw2d7l/S8TvZgXKSsq86ntiyH4y2D/N33X6c/KN0wFC63l/FJJwUGnCEkEsn1iRFN8A3AUwCqqh4R\nQmwPuq4RaFFVdRxACHEQuBH4aawPVCJJFBaziT/6nY189ednOKoOYPnVefY1lzM+5eTZ1zvJSrdw\n48aKZB/moty+bRUnLw3xq0NXePFEN3fuWEVNWQ7jk058hB6K0ynOzaBrYJLe4Skm/cN15hTsotWW\n2a9JvKsqzuYzD23n0ecu8cKJLr71xHkee6GVfc3lVBVnUVqQiT3ThtPtweny0j0yQ1vHCCOOGTxe\nH6UFmZQVZGpdQwBF8f8fnC6t67hUR9ykKHi83mssvOaXxE6XN2TRaTYpuNxexiaduDzeqDrvN26s\noLVrnK/+7DR9I1MB2zSzSaGxNp/G6nxK8jMpzc8gPc2M2+PD7Ql9PKmK1WLiL969lR8+fZEDp3r4\n3Hdeo3l1AR6vdsKWb09jVUk25QWZHDrfz0vHOgHCpsVJJJLrFyNFsB0IFmG5hRAmVVVogBxeAAAG\nUklEQVS9Ia5zAG+MvEnJdU2a1cxfPrKLT37tIIfP9nH47Jx38H03rCbNlrppQzVlOfy/P9jDM0c7\neOrVjgVuCksWwX7N3Ke+eSRwmcWSeh3vUKRZzbz3LsGbdlXzzOudHDjVza8PX4nJYyssPaxmtShM\nz3r4yJcPLvk4WekLP3ItZs279aNf0e5rxNN2PjsaS/jRsy1c6XNQYE/jzh2rMJtNHFMHONM2vKi1\nmHWFrK2OzWrm4XsaaajK4wdPq9dEAYdidbn9mkAciUQiCUYJ57kphPgCcEhV1f/2/3xVVdVq/783\nAP+gquq9/p+/CBxUVfVn8T1siUQikUgkEokkeoy0AV4G7gEQQuwGTgdddx5YI4TIE0LY0KQQh2J+\nlBKJRCKRSCQSSQwx0glWgH8FNvovehjYBmSpqvotIcS9wF+h7Rj+h6qq/xbH45VIJBKJRCKRSJZN\n2CJYIpFIJBKJRCJ5o7GypiIkEolEIpFIJJIYIItgiUQikUgkEsl1hyyCJRKJRCKRSCTXHbIIlkgk\nEolEIpFcdxgJy7guEULsQvNAvkUIsRX4OjADnFBV9SP+2/wLsA8tJATgPrTX9AdADjAEfEBV1cFE\nH79kcQyu7ZuAv/Tf5XVVVf9ICJGPXNuUJ9z6CiE2Af+CFuqmALvR3ruvIdc35TH4/v0z4EHAA/y9\nqqqPy/dv6mNwbf8P8E60oK5/VFX1V3JtUxshhAX4NlAL2IC/A84B3wW8wBlVVf/Qf9sPAB8EXMDf\nxXt9ZSc4BEKIjwPfBPTopn8H/kRV1ZuAcSHEu/yXbwPuUlX1Vv9/DuCTwAFVVW8Evgr8fYIPX7IE\nRtZWCJENfB64V1XVPUC7EKIQubYpT5j1HRNCvEtV1ZOqqt6iquqtwNeAx1RV/S1yfVMeg+/fXOBP\ngF3AXWgnPCDXN6Ux8t4VQjSjFcA70db2r4UQ6ci1TXXeAwz61+dutDX6IvBJ//qahBD3CSFKgT8G\n9vhv9/dCCCtxXF9ZBIfmEnB/0M9VqqrqObIvAzf4/ZMbgG8IIQ4KIR72X98EPBl820QcsMQw4dZ2\nP7AXLRTmi0KIl4A+VVWHkGu7ElhqfV8haM2EEJnA54CP+C+S65v6hP1sBiaBdrSuUTZaNxjk+qY6\n4d67+4FG4AVVVV2qqs4CLcAm5NqmOo8Cn/H/2wy4ga2qqh7wX/YkcAfayc1BVVXdqqqOk4D1lUVw\nCFRV/TnaIum0CiH2+//9FiALyAS+jHaGczfwIf9Z6nHgrf7b3gdkJOSgJYYwsLaZQBFwM/Bx4E3A\nR4UQa5Brm/IYfO/qPAI8qqrqiP9nub4pTgTr24m23XoU7XMa5PqmNAY/m08DNwohsvy7c3v9l8u1\nTWFUVZ1SVXVSCJEDPAZ8Ck2KpuMA7GgnrmNBl0/4L4/b+soi2BjvBz4phHga6AMGgSngy6qqzqiq\nOgE8j3bG8g/AaiHEC0A10JGcQ5YYJNTaDgGvqao6oKrqJPASsBm5tiuRUOur827gW0E/y/VdeYRa\n3zcBZUAN2jreL4TYjlzflcaCtVVV9QKahOkptJObw2hrLtc2xRFCrAKeA/5TVdUfo2mBdXKAUWAc\nreidf3nc1lcWwca4F3iXqqp3oHUJnwYE8LIQQvFrVm4AjgE3At9QVfVmoBWtdS9JXUKt7TGgWQhR\n4Bf070brKsm1XXmEWl+EEHbApqpqV9Bt5fquPEKt7wgw7d8yd6J9ieYh13elsWBthRBFQI6qqvuB\nDwGrgDPItU1p/Frf3wB/rqrqf/ovPi6EuNH/7zcBB9CGk28QQtj82v51xHl9pTuEMVqA54QQk8Dz\nqqo+BSCE+B5wBHCind2cF0I4ge8JIUDbknskSccsMcZia/sJ4LdoDgI/UVX1nBBiFrm2K42Q6wus\nRdONBqMi13elsdj796gQ4jCaHvigqqrPCCHqkeu7klhsbRuFEK8Cs8DHVVX1CSHkeze1+QTaiehn\nhBB/ifa9+hHgK/4m4nngv/1r+WXgIJpc4pOqqjrjub6Kz+eL1WNJJBKJRCKRSCQrAimHkEgkEolE\nIpFcd8giWCKRSCQSiURy3SGLYIlEIpFIJBLJdYcsgiUSiUQikUgk1x2yCJZIJBKJRCKRXHfIIlgi\nkUgkEolEct0hi2CJRCKRSCQSyXXH/weiGOs9JBUvmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5b6ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the expected durations of each regime have decreased quite a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.76105188  7.65529154]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds2.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taylor rule with 2 or 3 regimes\n",
    "\n",
    "We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n",
    "\n",
    "Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get the additional data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n",
    "dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n",
    "\n",
    "# Fit the 2-regime model\n",
    "mod_fedfunds3 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n",
    "res_fedfunds3 = mod_fedfunds3.fit()\n",
    "\n",
    "# Fit the 3-regime model\n",
    "np.random.seed(12345)\n",
    "mod_fedfunds4 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n",
    "res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-229.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 07 Jun 2016</td> <th>  AIC                </th>  <td>480.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:51:07</td>     <th>  BIC                </th>  <td>517.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>495.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.6555</td> <td>    0.137</td> <td>    4.771</td> <td> 0.000</td> <td>    0.386</td> <td>    0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8314</td> <td>    0.033</td> <td>   24.951</td> <td> 0.000</td> <td>    0.766</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1355</td> <td>    0.029</td> <td>    4.609</td> <td> 0.000</td> <td>    0.078</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0274</td> <td>    0.041</td> <td>   -0.671</td> <td> 0.502</td> <td>   -0.107</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0945</td> <td>    0.128</td> <td>   -0.739</td> <td> 0.460</td> <td>   -0.345</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9293</td> <td>    0.027</td> <td>   34.309</td> <td> 0.000</td> <td>    0.876</td> <td>    0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0343</td> <td>    0.024</td> <td>    1.429</td> <td> 0.153</td> <td>   -0.013</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.2125</td> <td>    0.030</td> <td>    7.147</td> <td> 0.000</td> <td>    0.154</td> <td>    0.271</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3323</td> <td>    0.035</td> <td>    9.526</td> <td> 0.000</td> <td>    0.264</td> <td>    0.401</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7279</td> <td>    0.093</td> <td>    7.828</td> <td> 0.000</td> <td>    0.546</td> <td>    0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.2115</td> <td>    0.064</td> <td>    3.298</td> <td> 0.001</td> <td>    0.086</td> <td>    0.337</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -229.256\n",
       "Date:                Tue, 07 Jun 2016   AIC                            480.512\n",
       "Time:                        13:51:07   BIC                            517.942\n",
       "Sample:                    07-01-1955   HQIC                           495.624\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6555      0.137      4.771      0.000       0.386       0.925\n",
       "x1             0.8314      0.033     24.951      0.000       0.766       0.897\n",
       "x2             0.1355      0.029      4.609      0.000       0.078       0.193\n",
       "x3            -0.0274      0.041     -0.671      0.502      -0.107       0.053\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0945      0.128     -0.739      0.460      -0.345       0.156\n",
       "x1             0.9293      0.027     34.309      0.000       0.876       0.982\n",
       "x2             0.0343      0.024      1.429      0.153      -0.013       0.081\n",
       "x3             0.2125      0.030      7.147      0.000       0.154       0.271\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.3323      0.035      9.526      0.000       0.264       0.401\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7279      0.093      7.828      0.000       0.546       0.910\n",
       "p[1->0]        0.2115      0.064      3.298      0.001       0.086       0.337\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-180.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 07 Jun 2016</td> <th>  AIC                </th>  <td>399.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:51:07</td>     <th>  BIC                </th>  <td>464.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>425.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -1.0250</td> <td>    0.292</td> <td>   -3.514</td> <td> 0.000</td> <td>   -1.597</td> <td>   -0.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.3277</td> <td>    0.086</td> <td>    3.809</td> <td> 0.000</td> <td>    0.159</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.2036</td> <td>    0.050</td> <td>    4.086</td> <td> 0.000</td> <td>    0.106</td> <td>    0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    1.1381</td> <td>    0.081</td> <td>   13.972</td> <td> 0.000</td> <td>    0.978</td> <td>    1.298</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0259</td> <td>    0.087</td> <td>   -0.298</td> <td> 0.766</td> <td>   -0.196</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9737</td> <td>    0.019</td> <td>   50.206</td> <td> 0.000</td> <td>    0.936</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0341</td> <td>    0.017</td> <td>    1.973</td> <td> 0.049</td> <td>    0.000</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.1215</td> <td>    0.022</td> <td>    5.605</td> <td> 0.000</td> <td>    0.079</td> <td>    0.164</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 2 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7346</td> <td>    0.136</td> <td>    5.419</td> <td> 0.000</td> <td>    0.469</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8436</td> <td>    0.024</td> <td>   34.798</td> <td> 0.000</td> <td>    0.796</td> <td>    0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1633</td> <td>    0.032</td> <td>    5.067</td> <td> 0.000</td> <td>    0.100</td> <td>    0.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0499</td> <td>    0.027</td> <td>   -1.829</td> <td> 0.067</td> <td>   -0.103</td> <td>    0.004</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1660</td> <td>    0.018</td> <td>    9.138</td> <td> 0.000</td> <td>    0.130</td> <td>    0.202</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7214</td> <td>    0.117</td> <td>    6.147</td> <td> 0.000</td> <td>    0.491</td> <td>    0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td> 4.001e-08</td> <td>    0.035</td> <td> 1.13e-06</td> <td> 1.000</td> <td>   -0.069</td> <td>    0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->0]</th> <td>    0.0783</td> <td>    0.057</td> <td>    1.372</td> <td> 0.170</td> <td>   -0.034</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->1]</th> <td>    0.1044</td> <td>    0.095</td> <td>    1.103</td> <td> 0.270</td> <td>   -0.081</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->1]</th> <td>    0.8259</td> <td>    0.054</td> <td>   15.201</td> <td> 0.000</td> <td>    0.719</td> <td>    0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->1]</th> <td>    0.2288</td> <td>    0.073</td> <td>    3.126</td> <td> 0.002</td> <td>    0.085</td> <td>    0.372</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -180.806\n",
       "Date:                Tue, 07 Jun 2016   AIC                            399.611\n",
       "Time:                        13:51:07   BIC                            464.262\n",
       "Sample:                    07-01-1955   HQIC                           425.713\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.0250      0.292     -3.514      0.000      -1.597      -0.453\n",
       "x1             0.3277      0.086      3.809      0.000       0.159       0.496\n",
       "x2             0.2036      0.050      4.086      0.000       0.106       0.301\n",
       "x3             1.1381      0.081     13.972      0.000       0.978       1.298\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0259      0.087     -0.298      0.766      -0.196       0.145\n",
       "x1             0.9737      0.019     50.206      0.000       0.936       1.012\n",
       "x2             0.0341      0.017      1.973      0.049       0.000       0.068\n",
       "x3             0.1215      0.022      5.605      0.000       0.079       0.164\n",
       "                             Regime 2 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7346      0.136      5.419      0.000       0.469       1.000\n",
       "x1             0.8436      0.024     34.798      0.000       0.796       0.891\n",
       "x2             0.1633      0.032      5.067      0.000       0.100       0.226\n",
       "x3            -0.0499      0.027     -1.829      0.067      -0.103       0.004\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.1660      0.018      9.138      0.000       0.130       0.202\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7214      0.117      6.147      0.000       0.491       0.951\n",
       "p[1->0]     4.001e-08      0.035   1.13e-06      1.000      -0.069       0.069\n",
       "p[2->0]        0.0783      0.057      1.372      0.170      -0.034       0.190\n",
       "p[0->1]        0.1044      0.095      1.103      0.270      -0.081       0.290\n",
       "p[1->1]        0.8259      0.054     15.201      0.000       0.719       0.932\n",
       "p[2->1]        0.2288      0.073      3.126      0.002       0.085       0.372\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAHwCAYAAABKYcKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nScVyLEmSJBXLsSRJklS+AP8tiZumnKazAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d7e2850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, figsize=(10,7))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n",
    "ax.set(title='Smoothed probability of a low-interest rate regime')\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n",
    "ax.set(title='Smoothed probability of a medium-interest rate regime')\n",
    "\n",
    "ax = axes[2]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n",
    "ax.set(title='Smoothed probability of a high-interest rate regime')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switching variances\n",
    "\n",
    "We can also accomodate switching variances. In particular, we consider the model\n",
    "\n",
    "$$\n",
    "y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n",
    "$$\n",
    "\n",
    "We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n",
    "\n",
    "The application is to absolute returns on stocks, where the data can be found at http://www.stata-press.com/data/r14/snp500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAADSCAYAAACsLsN9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXe4JEd57//tMOHkTWdXOUuNEJggsgOGa2wDFrbxtfHP\n5t6fba4x2H6MA3AJDnCxjbk2MmAQMkIGCRkDAkQSCIG0Cmi1Wq12pd3V7vbmcPbkOHmm0/2juqqr\ne3rCOWfCOTvv53n06OxMT3d1dXf1t7711luK53kgCIIgCIIgiPMdtdsFIAiCIAiCIIhOQMKXIAiC\nIAiC6AlI+BIEQRAEQRA9AQlfgiAIgiAIoicg4UsQBEEQBEH0BCR8CYIgCIIgiJ6AhC9BEARBEATR\nE+jdLgBBEEQUwzB0AGcAPG2a5hv8z14N4NOmaT6/RcdwAWwxTXO+zjYvAfA20zTf2Ypjxux/GMA9\npmn+t3bsfxnl0AH8A4BfBsCTu3/VNM2PStu80d8mBeDbpmm+T/ruCwBeB2Da/30SwDEAf2ia5qxh\nGM8D8DiAo9Jh32Ka5lF/v//o/2YfWH3nDMNQAdwM4JcAaAA+bprmv7f+7AmC6CXI8SUIYi3y6wCe\nAXCjYRiG9HkrV9xpZl/PA3BxC48ZZROAl7Zx/83y5wCuBPBC0zRfCOBnAfyGYRj/S9rmMwDeDuCF\nAN5hGMZVkX3cbJrmi03TvNHvnBwHcIv/3asA/Kf/Pf/vqGEYWwD8B4BfN03zegAnAXzM/807AFwD\n4LkAXgbgz/2OCEEQxIohx5cgiLXIHwP4LzCH8C/ARBAADBmGcTeYIFoA8HbTNI8ZhvEzAD4O1pn3\nAHzUNM17fEf1M2BizQVwH4D3m6bp8gMZhvH/A/jvpmneJP8bwDsBfBjAsGEYt5um+TbDMG4C8EEA\nCQAFAO8xTXOnXHDDMC4H8CiAQwAuB/BqAFcD+CcA/X45PmSa5vfBRF+/YRh7ALwEgA3JheauNIDn\nA/gkgLy/j/8N4G8BnAAT50kAf2Ka5sO16qJBfV/on1MfgIJpmlnDMN6KsDmyD6xDshHAOICxBvt8\nAIGIfRWAKw3DeMIv08f8Mv0igF2maZ7wt/ssgKcB/AmAXwPw76ZpegAWDcP4CoC3Atjd4LgEQRA1\nIceXIIg1hWEYzwXwcgBfBXAngLcahrHR//oSAP9imuaLwITxXf7nHwIbCn8pgLcBeK3/+b8BmPUd\nyJcAeAGAd/vfKdJho+6vZ5rmGJi4fNQXvdeADfW/3jTNGwH8EYBvGobRF3MalwD4sGmazwFQBhO4\nbzVN8yUAfhXArYZhXALg98GE5ot9MV5VDunvG8DCA17k7/NlAP7ZNM0X+/v/UIO6qMfNfplnDMPY\nbhjG3wNIm6Z5UNrm62CC+18B/IxpmpVaO/Pr5H8CeND/KAfgy6ZpvhzA7wH4rGEYLwJwKYCz0k/H\nwDoagzW+u6SJcyEIgqgJCV+CINYa7wBwr2maS6Zp7gZwCkxkAsA+0zSf8P/+IoCXGIYxBOBrAD5j\nGMZdAG4E8AF/m18G8GkAME3TAnArgNevsFyvA3ABgAcMw9gL4D/BHNprYra1AHAn+JVgjuq3/N99\nH4AD4KdifqfU+fdZX4xzTpumud//ew9Y2ARQuy5qYprmOV8o3+j/3gCwwzCMdwCAYRjvB3PAf9k/\nzpsNw/gzwzDeLO3mLw3D2OOf4xMAZvmxTdP8Ux6fa5rmYbBOza+i9jvIqfGd0+hcCIIg6kGhDgRB\nrBkMw+gHcwqLhmGcABN+Q2BD37vBwgQ4iv9vyzTNzxmG8R2wofPXA/iQYRg/hWrxpIIN6ct4CAvM\nZI3iaQAeME3z/5PKewmAczHblqVwCg3AQdM0Xyn97kKwiWBxDqbib5NA2PHNRbYrxp1Djbp4vmma\n2RrnBcMwPgbg874oPQzmyP4umMN7K4D3AnipH1byRgA/9o8ndyJuNk3z5ph9qwDeB+CTpmnm/Y9V\nABWwCYwvlza/BMCCaZpFwzDOgHUYOBejcXgFQRBEXcjxJQhiLfFWADOmaV5omuZVpmleCRYfOwhg\nK4AX+IIWYC7wo6ZplgzDeAzAi03TvNP/fATABrCY3j8BAMMwUmCTs+6PHHMGwPMMw0j62Q1ukr6z\nEQjlBwH8Ip9sZxjGG8Am4KVjzkMW0jsBXGsYxs/6v3shWOzyRf7+NWnbabCQDAD4jdrVVJtIXbwd\nrC421v8VtgL4PzxswzAMBcz1fcr/3gQT0gBz4I+DdRC2NSqP3wF4k18WHgP9ZrDQifsBvNwwjKv9\nzf8IwLf9v78N4A8Mw9AMw9gA4LcBfKvR8QiCIOpBwpcgiLXEO8AmZglM01wC8CkA7wJwEMDfGYbx\nNIBfAYsXBYD3gAm3p8AmVX3INM0z/m+2GYaxH0ykHgZLnQUEbur9AB4GE3cPg03i4jwO4DmGYXzD\nj3d9O4Cv+MP5HwZwk2masvPKEU6taZqzYCL2n/1y3wHgd03TPAtgAsBewzAO+nHM7wJwi2EYu8Hi\nkSeaq7YQ75Xq4kFeF4Zh3OiXO453+sfa59fVQQCbAfyp//1vA7jJ//2jYGEerwfwtzVinKP8DoA3\nGIaxD8C9AN5lmuYR0zRnwOKcv2EYxrNgE/X+yv/NZ8EE9jNgoRO3mab5aPPVQBAEUY3ieY0z+hiG\n8XIA/2Sa5mt8t+JTYE5FGcD/9BsvgiAIYg1jGMZ3efYKgiCIXqSh42sYxnsA3AaWtBwAPgGWNue1\nAO4Bi90iCIIg1jCGYVyEIK8uQRBET9LM5LZjYLkbv+T/+y2maU5Jv48b5iMIgiDWEKZpjoPl3yUI\nguhZGgpfPwn85dK/pwDAMIxXgU0a+blG+/A8z1OUaJYegiAIgiAIgmg5NUXnitKZGYbxFgDvB/AG\n0zTnGh5dUTAzUzOTzppjdHRoXZW3nVBdhKH6CKC6CKC6CKC6CKC6CKC6CEP1EdCOuhgdHar53bKF\nr7+M5dsB/LxpmourKBdBEARBEARBdIxlpTPzE5F/Eiyn5j2GYTxoGMbftaVkBEEQBEEQBNFCmnJ8\nTdM8DeBV/j83t684BEEQBEEQBNEeaAELgiAIgiAIoicg4UsQBEEQBEH0BCR8CYIgCIIgiJ6AhC9B\nEARBEATRE5DwJQiCIAiCIHoCEr4EQRAEQRBET0DClyAIgiAIgugJSPgSBEEQBEEQPQEJX4IgCIIg\nCKInIOFLEARBEARB9AQkfAmCIAiCIIiegIQvQRAEQRAE0ROQ8CUIgiAIgiB6AhK+BEEQBEEQRE9A\nwpcgCIIgCILoCUj4EgRBEARBED0BCV+CIAiCIAiiJyDhSxBET+N6XreLQBAEQXQIEr4EQfQss0tF\n/MnNj+C+J850uygEQRBEByDhSxBEzzI+m0fZcvC17ceQL1ndLg5BEATRZpoSvoZhvNwwjO3+31cb\nhvGoYRgPG4bxmfYWjyAIon2oiiL+/v7jp7tYEoIgCKITNBS+hmG8B8BtAFL+RzcD+IBpmq8GoBqG\n8attLB9BEETbcNwgvtc8u9ix4+ZLFk5OZDp2PIIgCILRjON7DMCvS/++0TTNR/2/fwDgF1peKoIg\niA4gT2xz3c5Ncvvmwyfw93fuRq5I4RUEQRCdRG+0gWma9xiGcbn0kSL9nQUw0syBRkeHllm07rLe\nyttOqC7CUH0ErPe6GJrMir81XV3V+Sznt7YHeB7QP5jG6Kb+FR9zrbLe74tWQnURQHURhuojoJN1\n0VD4xuBKfw8BaGp8cGYm23ijNcLo6NC6Km87oboIQ/URcD7UxcJiUfxdqTgrPp/l1kWhWAEAzMxm\noTrOio65Vjkf7otWQXURQHURhuojoB11UU9IrySrwx7DMH7O//v1AB6ttzFBEMRaRQ5v8DqYz5cf\nt5PhFQRBEMTKHN93A7jNMIwEgEMAvt7aIhEEQXQGOca3k+tY8GM5JHwJgiA6SlPC1zTN0wBe5f99\nFMDPt7FMBEEQHUF2XDu5gptDji9BEERXoAUsCILoWcLCt4PH9UU2Ob4EQRCdhYQvQRA9SzjUgWJ8\nCYIgzndI+BIE0bPIurOTIpQfixxfgiCIzkLClyCIniWc1aFzx3U8cnwJgiC6AQlfgiB6lpDwRedE\nqMcd306qbYIgCIKEL0EQvUu3lix2yfElCILoCiR8CYLoWbqVx9ehGF9iDUAdL6IXIeFLEETP0r2V\n26qPTxCd5JuPnMB7b90B23G7XRSC6CgkfAmC6Fm6nceXhC/RLcamc5jPlFEo290uCkF0FBK+BEH0\nLLLu7EYeXwp1ILoFrR5I9CokfAmC6Fm65viS6CC6jOvH29A9SPQaJHwJguhZQlkdOun40pLFRJch\nx5foVUj4EgTRs3RrcpsQHZTHl+gSNt2DRI9CwpcgiJ6Fv/QVJci00MnjOjSjnugSItyGdC/RY5Dw\nJQiiZ+FiV9fUjjq+Hk1uI7qM49A9SPQmJHwJguhZuOula0pXFrCg+EqiWzh+r8+je5DoMUj4EgTR\ns/CQA01VOzy5jf3fofhKokvQ6oFEr0LClyCIniUQvgqAzk1wo3RmRLehCZZEr0LClyCInkUOdQDQ\nsXAHSmdGdBse40udL6LXIOFLEETPwl/6msaawk65X+T4Et1GLJtNji/RY+gr+ZFhGDqAOwBcAcAG\n8IemaR5pYbkIgiDaDn/p677w7XSoAzm+RLfgqfSo80X0Git1fN8AQDNN86cBfATAP7auSARBEJ2B\nv/N5jG8nNIDreeCHIdFBdAvKLEL0KisVvkcA6IZhKABGAFRaVySCIIjO4ERifDshAuRjkONLdItg\ncluXC0IQHWZFoQ4AcgCuBHAYwGYAv9LoB6OjQys8VHdYb+VtJ1QXYag+AtZ7XSQSGgAgnUoAADZv\nHsRAX2JF+2q2LiqWI/5OpRPrvg7jOB/PaaWs1brgHbDBoXTHyrhW66JbUH0EdLIuVip8/wLAfaZp\nftAwjIsBbDcM43mmadZ0fmdmsis8VOcZHR1aV+VtJ1QXYag+As6HuiiVLACA5yfzn5nNopBevvBd\nTl2UKrb4O5cvr/s6jHI+3BetYi3Xhe1ndVhYLHSkjGu5LroB1UdAO+qinpBeqfCdB2D5fy/6+9FW\nuC+CIIiuUJXVoSOhDtXHJ4hO4nlekNWB7kGix1ip8P0EgP8wDOMRAAkA7zdNs9i6YhEEQbQfvnKa\nrnYuj6+cPopifIluIN93JHyJXmNFwtc0zTyAt7S4LARBEB3Fizi+PJ3ZyYkMRjf0YXCF8b71cEl0\nEF0mJHwpjy/RY9ACFgRB9Cxx6cwWc2V85I7d+OvbdrblmOS2Ed2GOl9EL7PSUAeCIIh1T/WSxR7K\nFgvCzRSsmr9bDR6FOhBdhhxfopchx5cgiJ7F8TwoCqAqQR5f2wlmn7XDDSO3jeg2jnSPU+eL6DVI\n+BIE0bN4rgdVUaBw4QvAsgNRMJsptfyYDjm+RJeR7zsyfIleg4QvQRA9i+t5UFUFvu6F53kh4Tsx\nm2/9MWnlNqLLOHQPEj0MCV+CIHoWx2XCV1WDUAdLGgaemCu0/JjhUAe3zpYE0R5ogiXRy5DwJQii\nZ3FdhEIdPA+wJcd3fK4Njq+kM8htI7oBCV+ilyHhSxBEz+J5HlQFUGuEOky23fEl0UF0HnlyG2V1\nIHoNEr4EQfQsrudBU6XJbR5CoQ6T820QvvLkNhIdRBegdGZEL0PClyCInsVxPSh1JreVKk7Lj0mO\nL9Ft6B4kehkSvgRB9Cyun85MlWJ8ZeFrO27LHTGaUU90G4rxJXoZEr4EQfQsLMY3EL5uxPEFAMtq\nbeYFeeU2Eh1EN6AFLIhehoQvQRA9i+vBj/Hl/w7SmaWTGgCgYrc23IEcX6LbyLHldAsSvQYJX4Ig\nehYe48vz+MqhDgNpHQBQabHj65LjS3QZx6F7kOhdSPgSBNGzsBhfBI6v64k8vgPpBIDWO760chvR\nbSirA9HLkPAlCKJn8fiSxVDEv7nj298ux1faHYkOohtQVgeilyHhSxBEz+K4HjQlEurgMId3oI85\nvtHJbqsllMeXRAfRBWyp90XCl+g1SPgSBNGzuF44j6+c1YHH+JbbGOpAooPoBqEYXxp1IHoMEr4E\nQfQsrguoSrByW3hym+/40uQ24jyDOl9EL6Ov9IeGYbwPwJsAJADcYprmF1pWKoIgiA7g+UsWqzGO\nr4jxpXRmxHkGTW4jepkVOb6GYbwawCtN03wVgJ8HcGkrC0UQBNEJHD+rQ7ByW5DHl8f4tn5yG7lt\nRHehzhfRy6zU8f0lAAcMw/gWgCEA72ldkQiCINoPd7pUNQh1cP1QB0UB+pKtdXxtx8XXth8TIhsg\n0UF0h/CSxV0sCEF0gZUK3y0ALgPwKwCuAvAdAM9pVaEIgiDaDXdbFSUIdfBcFuqQ0FUkE2xArFWO\n74N7zuHHu8dCnzmuB8/zhPAmiE7gyFkdKNSB6DFWKnznABwyTdMGcMQwjJJhGFtM05yt9YPR0aEV\nHqo7rLfythOqizBUHwHruS4qFnNy+9IJDA2lAQBDw2l4AFIJDVu3DAIAEim9qfNstM1SwYr9fPOW\nIWjq+SV81/N90WrWYl2k00nxdyKhdayMa7EuugnVR0An62KlwvcnAP4MwL8ahnERgH4wMVyTmZns\nCg/VeUZHh9ZVedsJ1UUYqo+A9V4XpYoNALBtB4VCBQCwuFhEqWxDUxUU8mX22VKx4Xk2Uxfj0/Hf\nT01lkNDPnwQ76/2+aCVrtS6y2ZL4u1iyOlLGtVoX3YLqI6AddVFPSK+otTVN814Aew3D2AXg2wD+\n2DRNGi8hCGLdwEd7WToz/zN/cltCV5HUNQBA2WpNjO9CrlyjHNR0Ep3FpgmWRA+z4nRmpmm+r5UF\nIQiC6CQ8tlFRUJXHN92fFDG+rVq5bT4TL3xpghvRaRxauY3oYc6f8TWCIIhlwF/4ch5fz8/jm9AC\nx7fSAsfXdT3kiuEYX11jzS9NLiI6jSx2Hbr/iB6DhC9BED1JfDqzIKtDgmd1aIHjOy/FVHJ4XC85\nvkSnkZcs9uj+I3oMEr4EQfQk3PWSY3wdx4PjetA1BSnh+K5e+E4vFKs+S2hKqBwE0SloAQuilyHh\nSxBETyI7vnxRCe7uJnRNOL5WCxawiBO+unB8aQUBorOElyzuYkEIoguQ8CUIoieRHV8hfP143oSu\nQlUU6JqKcgsc35nFOMdXDZWjVdiOi9OTWXgUu0nUgCa3Eb0MCV+CIHoS/r5XVYhQh7IkfAEgqast\ncXznMtUxvnqbYnx/sn8CH/7ikzh+LtPS/RLnD2HHl4Qv0VuQ8CUIoicJOb5qJNTBd2OTCbWpGN9S\nxcbt3zuI05PxSdjns2UoCrBlJC0+a5fjm82zxThq5Q0mCJfy+BI9DAlfgiBWxfhsHlMLhW4XY9nE\nZXWodnw1VJpwfJ85MoPHDkziR7vPxn4/nylh41AKqaQmPmuX42v7M/bLldYsvLEWeWz/BO59/FS3\ni7FukbM6kPAleg0SvgRBrIp/+8Y+3PqtZ7tdjGUTyurgf1aJCt8mHd8zU8zpPXZuKfY4i9kKNg2l\nkZSWJk60KY+v7cdvtmrFubXIj548i+/uONXtYqxbeGdLUSjUgeg9SPgSBLEqckWranGG9UAoq4PK\nHV+e1UH1/681lceXC9/phSIyfqgBZzFXhut52DScEmJXPkarHV/u5rVi4Y21iuW4IdeSWB78nkvo\nKqUzI3oOEr4EQawK2/VgO+svJRef2C7n8RWOry9QUwkVtuM2HA4+OxXE9h6PuL58qeJNw2khduVj\ntHqomQvC89nxdVyWb5ncypXBszokNJXSmRE9BwlfgiBWheO4sFqwulmnEaEOanw6MwBIJlhMbr3z\ncz0PZ6dy0HzXOBruwFdt2zycRkIPYnz5MVqezqwHQh0cv6PlrMMO11qAd44SugqX8kgTPQYJX4Ig\nVoznebCdder4ilAHSJPb2HnoItSB/b9cZ4Lb7FIJFcvB86/aDEUBjo+H04jxVGabhlJivwCg+46v\n3YY8vgBakn94rcIn8Fl27brzPI9yGdfA8TyoigJNVUC6l+g1SPgSBLFieHygtR6Fb2gBC/YZz+Ag\nZ3UAAKuOiByfzQMArrpoGP0pHflSON5ZDnUITW5rk+Pr9EBWBy7u7Tqq7Y77TPzjXU91qkjrCsfx\nRGw7hYsQvYbe7QIQBLF+4SLL81jcoKaun760cHwVBYoaCXWQ8vgCqJvSbMIXvhdtGYCuqcKN5Mxz\nx3c4FYrx5Y5v69OZ9UCog19n9Sa4nRjPYGIu36kirSsc14WmsRAfCnUgeo3185YiCGLNIS99atcZ\ndl6LhGJ8/c94BgcuSrnjWy+lGc9oMTKYhK4psCPxwIu5CnRNwWBfIjarQ8sdX/f8n9wmQh3qjDRU\nbAeOS+EOcbiuB30Zjq/tuPiHL+3G9j1jDbfdvmcMX3ngaCuKSRBtgYQvQRArRnY311u4Q9wCFly0\n8olqaX/BiWLZrrkfXge6qjLHN+KgVSwHqYQGRVEiji87Rssnt53nWR08z2tqcht37yldVzWOK4U6\nNFE/2YKF4+cyOHByvuG2j+ybwANPNRbIBNEtSPgSBLFi5Elt6y2zg5zOjOfx5eKdO77DA0kAQKZQ\nqd6Bjy1+o0DX1Krh94rtiOwQiZgY33aFOlTO0xhf1/PAaywaViLDXXrK91uN43jQVAWa0pzjy0N9\nanWmimUbP3jiNAolC47jslRz1OEg1igU40sQxIqRMxKst8wOYceXfcbFu+a7sUL45psQvroKTVOq\n6sGy3Xjh27Y8vud3jK8sduvdc1yssXAcreZ2vYjjetBUFYqqiI7XXfebKJRtvP2mG6q2589FrXvq\nmWOzuHv7cQz1JUMTXlMq1Tux9iDHlyCIFSMPNa874SuyOgTpzLj5xUMdhvsTAJp0fHmoQ8RhtGw3\ntBIcp22O73ke4+vUEb5T8wX8011PYWw6J65DPVe4V2ETUfnkNvbZg3vOYeezU7HbC+FbiX/G+b1W\nsR1xTdbbCBDRO6xK+BqGsdUwjDOGYVzXqgIRBLE8frjrDE5OZBpv2AZCMb7r7EUXWrKYW74+Qvg2\n5fj6Mb46F75uaEJVxXZFGrP4rA6trbfzPY+vHEMdFbVHxhZxZGwJ+0/MSducn/WwGhzXg6bxPL5e\nw2eXf19rGWx+HRzHCxzfddYeEL3DioWvYRg6gFsBFFpXHIIglsNSroyvPngM9z95tivHt53aImSt\nI+fxjeheaNEY33w4N69MOMaX7Yi//D2PiQoe1hDK6pCo7/hatoPpheU3r3x/tUTKeqee48vvwWwh\nuF40ua0a1/V8x5d1APnqggBis2Dw2PdSjXvKkfIqN5NxgyC6yWoc338B8FkA4y0qC0EQy6TcwIlp\nN07I8V1fQstxazu+XMD2p3TomoKlZhxfTQ1cXDHMzq5PIibGN6XXXw75Gw+fwAdve0KkS2sW4b65\n63NFvUbYdcJr+L/l0JTzsQ5Wiy1ldQCAmcVi8J30TE/M5bHz2cmGMb48vMZ2gowb5PgSa5UVCV/D\nMH4PwLRpmj8CoDTYnCCINsFfLt1ytda14+vVcXz9hTgURcFQf7K5yW3+0DEQDMfz65MUK8Gp4pjc\n8a0lEBZzZTiuh2yd+OI45Ljr0nmY2aHePRcnfCmrQzUsq4MqCd/A8ZXr99s/OYnPffcgFrJs9cFK\nxYl1hPlvHMcNRDAJX2KNstKsDr8PwDUM43UAXgjgTsMw3mSa5nStH4yODq3wUN1hvZW3nVBdhFlL\n9bFUZsJGVdWulGtgKC3+7h9Mram6acTAACv7yEgfNm8eDH03umVQnMvmkTTOTGaxZcugmAQno/hD\nxhdsG8FAPwuNGNnQj41DabFq2+AAq5stc8xZ0zQFo/4xEyk9tt5U3z0eHOpbVr3KfaDBoT6Mbuxr\n+retQi6v47gidKQVFKSFUvr7k6FjpdJsMmKhHAj+oeHl1V+rWWvPhOexkYD+vgRSfp7qnFRfIxv6\nMTKYAgBU/Lp2fH/LAzCycQCpRDhbQyrF6j2VTogQooHBdNW5r7W66DZUHwGdrIsVCV/TNF/N/zYM\nYzuAP6onegFgZia7kkN1hdHRoXVV3nZCdRFmrdXHzGwOAFAsWR0v1+joEObmgyVh5+bya6puGpHJ\nMBGaz5WwuBBuCpeWCkj7Wq0/paNiuzh7bhF9qeoms1iyoGsqZmaycHyXa2oqC7tkYdofQnYdBzMz\nWRTyTAgrCpDPMRdtKVOKrbec71pOz2QxlGSFmZjL40dPnsVvvuaa2LIA4bCXiaklwK69+EY7kJ+R\nux86hl0Hp/DRP3qlCANZLfyeB4D5xUKo7paWWP0uZAIHc3Yuh5F0d9JqrbX2AghCkhTPg+3fK6fH\nl8T3U9NZVIrs3svk2T06PRfU+dj4Iob9Dh4n48cIZ7Il4fTOzGaxZTAhtlmLddFNqD4C2lEX9YR0\nK1oiGkciiC7Bh8mjq4V1ivWcziwU46vGZ3UAIF7ytcIdbMcTsbs8Nrg61IEJL93fTlODVdxqhTpw\nASF/v9ucwUNPj+PYuaXY38jnBXQ/pdnYdB5zmXLdle+WS91QB7/e5Wu13u7LdsOX5U7oqrjPpxeC\nGF8rJlQmXwyuX9zCKPw6VGxXCAKa3EasVVa9gIVpmq9tRUEIglg+Qvh2KY7xvFiyWFGqQhhkd5Jn\ndljKV7BtU3/VfmzHFYKW/07MbPfdNZHHV5NifBsIX16fFel7vr96kxnlzki5yzG+7cjpWndymx1M\n7BOf9XhWh1LFFktmA8G1SOjBKoOhyW3SteIdlnwpmGAZ15niHQ5ZFNPkNmKtQgtYEMQ6Rp5U0kk8\nz8PcUjEsQtbZi84LZXUIfxdyfBvk8rWdIF0ZX/EtOrOdi1y+gpuiKOI3NYVvjOPLhV2lTl3LnZFu\n5/IV9dDC+1MWstGJa3EjH40mty3lK3jgqbGW51NeCxTLNv7i04/hnkdPiM9kx1fx73NZzNqxjq8s\nfOPquDrrAwlfYq1CSxYTxDqmW47vo/sm8MUfHMZrX3xxUJZ1Nnue6ye2ZHE01EF2fOuv3mY7npgk\nxB3fqFuFK05jAAAgAElEQVQbdXxDoQ41RGEgfCUxUcNB9TwPt33vIJ5z2UbhZAPdz+Vrt3AxA8/z\nsJAthzp50bqL63w16hT+ZN84vvHwCVy0ZQDXX75x1eVcS2TyFZQrTjiUQdyTWmy7wT/zPA+lCnN8\nc6Ug1CHW8fV/UyLhS6wDyPEliHVMIHw7+5KZ9ScRyS/U9RZLyR2++AUsgg82+jPcj47Fx9XajivF\n+Ibz+FpWOMaXb6dKwreWUx4XJmDHOGsAUCjb2PnsFH6yfyL0eatifL/ywFF86X5z2b/j5W3FvbH/\nxBzefcsOPH10VnwWFbWxQq5BqEOcq3m+ENdR4vdbUlerRjqA4FpVLFcs4R1yfGNjfGNCHdZZe0D0\nDiR8CWIdw18unc7jy53EgjRpqZOhDgvZMv7m80/gyNnFZf1OFgCB44uaSxYDwNUXj+DybUN44uAU\nnj42iyi24wrBKya3RYb4E5Eli8MxvvHiVDi+MeEkUTet5KejKpbCk8haJXx3HZrCk4fqJu6JJYh1\nXv29wTtbUzUmYrHjLd/x5WU7H3Mex40IVaS48+ikTrYt+02xEtxLhQaOr0OOL7GOIOFLEOsYLoQ6\nHePLX2ryC7GTDs/xc0s4N5vH4dMLTf/m4Kl5vONfHsLJiQwAKcY3ZnKbLHx1TcXbfuV6aKqCex45\ngSjhrA7hyW28g1AlfNXgGI1ifCtSTKUIoYiIDz4kzTsi/DitmtxWKNsrEoatnNxWjulsVcX4xtyD\njcKAxHK8lc6mfesEcSNCItRBU0MdvpHBZGhb+XrL4TPxoQ58JKL6OASx1iDhSxDrGMupdnQ6QZzj\n28kXHT/ucsTYuZk8PACnfOHLXXItMrlNi4n5vWR0ENdeMoKx6VxohrvnebBtyfFV4ye3JaU0ZorC\nFhxRfNe3VodBuKUx6buqHF+/HnhHpN/P8bsSx/erDx7FzmcnpWO6qFgubMdddshCK5ev5R0AOTVa\n9ZLFcaEOvev4RkceAEn4JtRQB2/ET9tn+RMoa3UE4rM6hDt68nEIYq1Bwpcg1jHdyuNbthuLkDgq\nloP7d51Z9jK8UbjAKy1D2PGXf7bAhCt3sRRFEbPbgXB8r8y1l2yAh3Csr+t58BA4rFpkcps8kYgf\nKyHFViY0tYmsDtViIprVgYs2Lkr60ysTvpbt4Ie7zmL73nPiM9nVX644bGWoAz+X+sJ3+Vkd7PNY\n+Ir2wY4Rvpoauu+HI45vsRxfH3ETJp0Yl5hifIm1CglfgljHBOnMOuv4Wv7LL27iVT0OnlrAVx48\nhicOTq3q+IUyE6/lZQxP87JGhS9LZyYJXzW+Wbzusg0AgKNSXDFPL8YdXy6AHZHQPxzqAAAbBlNi\nUYyEHi98HdcV5Yur42hccHSBiMG+hP95nXy/rovb7z0I80wQLsKFiyxgZFd/ueEArZzcJkIdSrLw\nbRzq0GyMb/E8DHWo5/gmE1o41GEgGuoQXx9xHYRoaA8QTOwkiLUGpTMjiHUMf4k5rgfP86qG6NtF\nXB5ZPkRaD/4yXe2kK+H4LsOlE8LXX47V808hmtVBr+H4Xn3RMFRFCU2o4057QgplAKpjW5OS8H3f\n7744JJRj41KlupSFb9yiFkB1PXARUyjVzlQwMVvAY/snoSgKjMtYGi9+XUo1JjYt2/FtYTozLqpC\ni1P49XFqMoNUokZ6rgYTP0WMb51OwnolzvEVnTEtEuowwLKX2A3qo16Mr3xtLOf8q89GZAsVLGTL\nuGxb7eVyie5Djm+baeVSncT6oWw5cDuQaUEWFJ3M7BA33NmMqxe3KMNKWInw5S987vjKi0uEHd94\n4ZtO6rj8gkGcmsyK8+dCK8jq4E9uiwi+qOPLHdlajq/s0MkiV2R1sKLCN9zO8EU38qXa7U8lZoif\nT4Yrhxzf+qms6tHKBSziFk6wHdbhu/mrz+AL3z9cY3JbszG+519bXXdyW+S+Dxzf+jG+8c9+ddvT\nizG+X3ngKP7+zqe6vlQ4UR8Svm3k+Lkl/Om/PoK9R2a6XRSig+RLFv7y04/h+ztPt/1Y4YlPnXvR\nxDm+zRy/VbP8+fD7cl4wtgh1YI4vF8JJXQ05vrVCHQBg68Z+OK4nBKUdEbYinVkkFlcWvjIJLX5y\nW1zeVUDKl9rA8U0lNPSlNOTq5Kbl4RIlOZTBigl1WKHj63leS2N8a3W2ShUHuaKFbKGyohjf3pjc\nVr20eDSdWb2sDjKxK7fFzDHoReGbKVjinlyrzC4W8Y2Hj9dMo9gLkPBtI9MLRXgI550kzn9mFoso\nlm2Mz+XbfqywKOqu41vvRff00VnsOz7bcsd3OQ4kP2aGO76SAJBDRGpNbuPbAoFo5qEOuhae3MY/\nl+Mpa+2vkeMbDnWojqUEqkWKpikYSCdCGSii1HN8K7YrxMxKY3xDw95tEr6O42IxV2bf2/FZJ+Im\nfs4uFeHxGOoGQq8ZXM9bk0IidnKbJQvfYNs+PxNIXB5fILjH6y1gEXfsXiKY+7D27gXOjmcnce/j\np2GeWV4O9POJnhG+t3zrAL7+0PGOHpO/HGnYo7fgLlulA5M7QqEOa9jx/dQ39uETd+8Lsh2ssqxc\n0C0rxtc/Zq5gMaEiC4AmQh2AIFaXX1suKPTIksSB0+mEPo/CYny9UJ5U9rv6oQ7R+q8SKarKhG+x\nTqiDVS1wyiER7H+/QsfXbvFoRFw7ajkeFrO+8LWc2M5f1PF98vA03vvZx/Hjp8ZY2VoQ6vDFHxzG\nB297QojpbuB5Hs7N5MJhDTGTC/lnST08uU0st83rIxLjO9hXO1NIbKiDtMrgelvVcaVUWtSxbyf8\nGa83GnS+0xPCN1OoYPfh6Y4MPcvwh4CEb28hhG8Hev1xOV47QazjW+PlJt//tRzf+UxpWS+LII/v\n8rM6uJ6HQsmOOL7BdnoNkQoEzq1wfP06j4Y6OJGQhEQifp9cMGcLlnAugWj6qaD+amV1iIoUTVMw\n0KejbDmhei2U7Kp9yL+V08NFF8Vgny1H+Lba8Y0LY3CxmGOhK2Ur3vEtlGx85pv7cXSMOVw89OxH\nT54Nla3Zczs5kcGZqWzos4nZPGaXSl1t609PZfE3t+/CI8+Mi8/k583zquPO5VCHaKct2plKJXXo\nmlJj5bbaju9Hv/QUPvWNfSs+r/VE3BLRaw3+HBXW6PyjQ6cX8G/f2Bf7jmkV60L4up63qt74mcls\n443aQLDyEgnfXoK7bJ1I5xOKBW1BLt/DpxdCixfcdb+Jbz5SPVIS6/jWyOqwkA0EXdyLYSFbxvv+\nfSd+8ETzHVPuQi5HaMjHzBYq4rlM6lpIADTj+PJry4VWIhrqEIltTdaJ8QWAz3xzP/7y04/hvifO\nsN/VCnWomce3elh6IM0m0PHMDq7r4QOfexx3/OBwaB+lGo5vdFGMuOPUw6lxDisl3vENQh1sx429\nL09MZPDUkRnsPswE74A/sZB3UJe7cttH7tiND33hyUjZfHe8i5kh+HM25y/tDATn5iEIPeF1pEdC\nfHQ9nJGEX3++RUJTkUpozTu+/nEm5wuYmi+s9LTWFbxdWNPC1+/w1pv42k12H57G3qOzODOVa9sx\n1oXw/a8fHcV7btmx4pvptNQ77+RQFH+xdssFKFsO/vrzT4QcAKL9BI5vB4SvJC5akcv3yz8+gtvv\nPSQyUjz+7BQe2z8Z2obFM4bPjaWSij/fuYz0Io5xfKcXCrAdt+mXo2UHAsd2PNiOi7PTOXzkjt2Y\nl44VpRISvhYs22WrtqkKZKm7rBhfJxzqICa3VS1gUTvUAQCOnWOLYnxt+zGcmszEit24/XKibqWu\nqYHAkzJgZAqWmHMg8teWHdEuluKEbysc3zaFOjiOhwXJKZeFe1+KufM8LKZssfPgGTX4uciO73Le\nD5l8sAiLcM9XmRnix7vP4lNf37ei95RYRlxe4EPqjEad/qSuir8VhYXHyNvxSY+D/UEGkmRCi4/x\njZvc5rBc1BXbbdnS2WuddeH48vukTvx/Nwmy76xukaN6rAvhOzGfR75kr/hCnZIc306IEQ6/+eNm\nwXaC2aUSxmfzoQT1ax3X9fCx/9yDB/z4u/WIcJI6EOoQN+N/pbiuh8n5IhzXQ1bEKTvI5CuhGNS4\nRj2d1GqKG1mM8mE2edslX0A0K6qiQ3Rly8HeozM4OZGBebb2hI2o42vZrhCeihKI33pZHUSogxUI\nbyBwbkU6M+kFqKlKzX3GCeLFXCU2d6/8dzOT23hMZr7IhV9YnPEXjNyRkcUlXxxkpVkdZDFkt6Dd\njQt1sKVQBw6/BnyyVjT1XTTVNT93z2scly8LUjncoSJ1IlbDniMzePrY7Iom2okhbOl61VvuOqGr\norx9SV103mTHV1MV9KcD4VvL8Y3rdFu2K+7T5ayw2E4WsuW2ilJ+vp3UGcuFl3GtOr78Ps62MQZ5\nXQhf/nCu1Dk9LQnfQgcvNr+A3Qp1EA/hGl5BJ3pNs0UL5tlF7Ds+16USrZ58txzfVebxnc2UxEtv\nKVeG7bhwXA+O6+HkRAYfvG0nTks5bDmaqtTMTgAA85nAkeOiVd42s1zhG+kAl8qOOEa9fVgRx7di\nu6EQBB7u0FSoQyQ/ahDjGw51qNiOEBRx8KWMo+WUOzH8+XVcF1x3VTu+8ZPbgOB+5NsIp1NqF4ox\n+XsDx9eSPmu+/Wyl4+t5Xux9Z0uhDhwu+Pt94RuN4Y3WnVzXjVZvk7eVRxJ52Va7+hsv40raDt6R\nCS3pHDNyIIffcFc3ndKk0YrA/U8nNaQSwWIrA2kdhZIdSl/mel5s22NJTm/FcjuaZzyOhWwZ//vW\nx/GDNs71aTZrjed5mFvqTrYn0Ulbs8KXHF8A8mpPy28MckULs1LMUyftfd4QdV34rtHe5+xSEX/6\nr4/g5q89HbhQMStHrTeyxfACCe1kuUsG12NSSr+WyYddx8cPTGJiroD9J+aqzkvTFOha/ApkQNjx\n5c9fSPgWwsKsEdHOa8lyMJ9lxyjVmbAhiy/m+Dohx5U7gXUnt/lCtRwNdeCOrxqe3GZFxHWUuGwP\nlciENDEzXxq2rviTlZ49OY/Pf++gELcc3U9nBgC5UtTxrW4X4lbUk2N8ed0sx4lsZYyvZbuIyqZ0\nkq3UtpiNCl923lz4ckqVaiMgX7JCgqzR+cnvoNMxI4mrFRO8/q0VvDPiJi3F5fkOOb6VwPFNREYr\nihUbfSldjHIkNBXbNrE81rOLwTNdK8TKst3YEYR2wEOz6oWITM77IVVtSi/qSSMnjVate+b4HH7v\n/9yPAyc7b/BYwvGtr4XyJQvv/ewO7Dgw0dR+M4UKnj42u+ry8WeJLzTUDtaF8OUP8koE5ORcOG6w\nkzMZux3qUO6y49yIyfkCHNfDgRPzuP3eQwBk4Ru4F//3y3u60kCslJwUJtBu7JihzCiu5+H2ew/i\nycPTdfclPytL+Uqo/CcnMgCAhVy5yqXXVXUZwrcFjm801KESOL7FOvuwpdATHuMrO658ok9dxzcR\nndwWzuqgxazcVlf4xnxn2W5sjG/UNbUdF488M44dByZF54GjaSoGRKhDOOdxIHwlkVsOfwcEw9OF\nso0Ng6nQPpohuvrc3duPVYVdbd8zhr1HGy/wEzfal0pqsaEOm4bTuHh0ANdfsSm8D+H4Bvuajoig\nRp0v+ZngIXRyqMhqHV9+nuUWOb71Jkbqmiq2Tac0ce/K6czSSQ0p/x5NJlRcuLkfAEI5yms995bj\nht59rVjFdHaxWCXEnjKn8c6bH8axsSV89K49uOWe/bG/5SMD7ZpzYzue6Jw16ujNLrL7bmax9pyE\ndsHvrUaj35NzBcwulXBsbKmp/f71bU/gU1/fh/HZ1eWvX7OOr2EYumEYdxqG8YhhGDsNw7ip1QWT\nKa0i1IE3ZLz338m4Ft7AdN/xXZvCV37weLycSAHnv6TGZnI4fGYRzxxdP8K3o6EO0jFq5fFdzJbx\n2P5JPPL0ubr7mpgPC1/55ctn2C5my1WhM7rGQx3ihfd8NibUwVmF8PXvG74sb7liC3FdT7hYtiuE\na64UE+rAhW/dyW3RdGYRxzdm5TY9Jpwh2F9wfH4+0YUYrMixOGXLxVJkmJ+jq4Hjm484vrbD9i/f\nO8WYlfB4XRZLNoYHklCU5Tq+wf0wPpvHD544g499ea84D8/z8OUfH20qv3pc259O6shL6dk4qYSG\nj7zt5XjjKy8PfR7ENgfbTy2EjZFoWrh65ZhdKiFfskL12Oj3jQhCAxy4nresSW5WjKCJy6XMY9sV\nRcHVF48AAJ535eZQOjPX9VAs2+hPJ0KO70WbBwCEO8m1QhjsiOPbCuH7/Z2n8fnvHRLCEQD2HWej\nULd+5wCOnVvCbjPckeL1KITvMjsnzb6/5Q5VxXLxue8+i2dqOKC8XlpRJ8uF3yeNtBB/1pvRXbbj\nCsNntfmBy2s4xvetAGZN0/w5AK8H8OnWFSmM63pBL3gFApJX4sYh5lh0I9ShW1kdglCLtRnqID94\nvLGOhjpEJ+SsB3JSqEO7s4jIL918ycah0wtVx+QN2FymWiSVKjam/Zd/yPHNVULDrfzltpirnhyi\naSoSGou3jB7b87xwjK9Y6jfYd8bv2Tf7EuDP8Cb/mV7IlcU51o3xdVwM9XGx7IQmtwFBqMOyFrCo\nGePbZKiD9N0GLnwjoQ58gYtovVu2i0Ups4C8L03K6pCPTO7if8vtgnjJSc9ZqeyIDBoDaR3ppL7M\nGN/qzg3AwmYA9mw7rofZpVLVAh5R4tqwVI3V8HjnI3oduYMdzigSdXzrt9XRckQnIrYqxncpX8G7\nPvkofrjrbOh7x3XFsH6tstV0fKWsDlzkvumnr8Bf/tYL8MZXXh5KZ5YvWfDAwkaE8NVVXNCk46sq\nChzXCy+H3YJUb/xelt8dZ6ZZp1xuZzzPw/QiW53vzvtMvP9zOzG/5M8DWMb7eGIuj3d8/OGm4oLl\nuh6fy2Pns1N4/NnJ2G3LMdeqU1SkkRyApb+LGw0MOsON9cNRaVLxaudRBVkdLH+eSev1y0qF79cA\n/I20j7apSbmhXYmA4xd5gxC+HQx1qBNq8Oi+cfECaBe8vlYrvNvlWMudEJ5SKbqMqjw8WyzbOLfK\nYZR2E12nvd1xvvL+79t1Bv/8X3txdjqc/5C/jOczpSphes8jJ/G3t+9CrmhhYr4gHNGlfDnWsV7I\nlkV8K0fXFGlGeHj/+ZIduv/qhTqUY9JJ3ffEGXzi7mdCwog32LwzOz4bCPZ6wqViuRjyUzMVyzYc\n14sIXy6YGmd1CCa3sXLpUeErQh2cpmN8eRtVsd2qsIbohDe2nYMlaZh/uD8p/tY1BYNpNsqVi2R1\nAFi7Kh+D3yOyKChbjnCU+1M60kltxenMZFfwB36uYj5p2bLd0HnEEW3DFIRzI/PrCgTXQFEUIYKB\n+MltUeHbSLhGy1GxnFD7uBpxxyeTAkxw5Uu2SHMHMDH315/fhS/edzj298LokEJlQjG+0sQrvqCK\nrql43lWboWts9UIFYfdusC8h6lnXVYxu6IOmKpiQOslxwpenkpPdv9V2CgBUmWC24+LcTK7qGTt4\negHvu/Vx3HHfYew4MInphaKoy+WE6/Bh/rsfOt7QxJDby5yYtxB/rEoXHV/52Jbt4j2f3YHPfutA\nKP8zEFyvZvTDXsnZbhQ7zNl5cBLHz1WHUfDyZfIVfPC2nfjcdw42tb/lsCLha5pmwTTNvGEYQwDu\nBvDB1hYrQE4P0+gCnJnK4r2f3YFzM8GLn7+kNwyyl0JHszrUifH9xsMnYhcGaOnxWzC5bY85jT++\n+REcqZMmaqXwXvtAWmf5Hi03VGbHdUOO77cePYkPf2FXzeHdtUB0klE7wx2is6kX/OH+pXxYRMiT\nmaLDUNMLBVRsF2enssjkK7j6Ijb0mYnE+HKW8pWql7uuqVVOpyhTZOIRF7CW4yJbqGBqviCEr4fq\nZ/zJw9PYd3wu9LLiz/Cm4TQAJhKCc41/vl0/O0U6qUHXVDFxQo7xVcXktiYc30j4QbCARTC5zXU9\n2I5XM4cvO74kfP02yrIdIVK4gIjG/QLMEZHra2RQFr6S48uFb8Txtazwv6Pb7Dkygw/cthMAC8NY\nrvCtFXoz6V9z+aU/vVA/h3NVRgdNDWXLuGR0UPwtT06UOzGViuPnlZVifP0hcx4K19jxdfz9sutc\nrjihZ3w14k6+lvz+XMoHz0/FYrmuT45napStOnQlOnIAsGcvblKlorAOrCx8h/rDjq+uqdi6sQ8T\ncwUhBOMmt6WTrD7l4epaE0+XYw5ER3Ym5wqwHQ+vuGEb3v6m5+LaS1j7dfDkPADgkWcmxDPKw+mW\ncw/LC3w0egfK58Hrr9Y5B6EOnR8Nlu/X7+04Jf7ORGJqlxNi+qxf30D1OzAO23Fx23cP4qvbj1WX\nz7+PF7JlzCyW8JQ5U1W2ehwdWwyNMMWh1/22DoZhXArgmwA+bZrmVxttPzo6BIC9UI+eXcCNz9nW\n1HEKUtxgIqXj4NklXHvZBly0ZbBq24f3T2J2qYRzCyW88LkXAgCSKdb4X7xtGNg/CU9VRVmaKe9q\n4CWv2A62bBkMPUSlsg1bb64sK0X3Gx/bcVd8nB2HpuF6HnKVle+jFq6fOXXb5gGcOLeEvsEUUn3B\ny3touB8J//rZHpApWrAdDyUXuKaN9daIevVQiLwEhkf6sHmkry3liDZIvCORSCVCZUxOBLPPXU0L\nfccfryl/mPCyC4dxbjaPXMlGuj9VdUzPA0rRmMqkjgHfbRzZ0I+RweB3Z+eZsIiKJsv28M9feRpj\nEXd6YKhPCFogeHEODKVFPfL3++UXjQBPjQnxAgCOG399uCAe6E+iL6WLuhocSIrt+eSewYFUzWts\ncVdYZ/WY8uNodelZ1lQFUBSMbOwXx6y1v40b+sXfF20dBjABTdeQ8IXYYH8SxXIRwyN9sBAW5OXo\nvSbV+5bNA7jwghH0pTSUbfbs8vYAAPr6U4HSl87HcjxoqiJCEADgpp+9Cm/5hevw4c/vxOxSqen2\nsz9GoF20ZQDjs3ksFG0MSg51yan/XJ2eDQvjhK6ivy9wea+5bCMOnWYT54aGguuX0FXxnHgAhkf6\n4Un1yGPDNwylUCjb0BNa3XKk/QltI4MpzGdKSPUnMTAY3K8elKrfN91uSu6z5Xdos0Vb/J6nv8qV\nrNh9KlKHrW8ghdHRwVAmjP5BVi+242FkMBG7j6SuAooCNcHulW1bBoXRsGG4D6OjQ7jiohE8vn8C\nejqJTcPpqjYPAIYGkpjLlOApgcAulu2qY85nSnjHx3+MP7jpBrzxp6+sWz0A4PhiO9XHyr/fv+bP\nvXoUb/zpK3FmpoCjY0uIm27AP7Idr+lroieDeOEnzBn8zI2X1dx2KWbpb7tGe6T4bY2L1miN5SAL\n3+9KwlePvDc0v8Pj1jgHjut6mJHaYE9rrGsWs2V4HtODfFv+/6hZ5Hoejoxn8fpXXlH/xACcnsjg\no3ftwRUXDuPf3v2amtutSPgahrENwA8B/Ilpmtub+c3MDGswvrb9GO574gz+/n+9HBdtGWj4u/HJ\noPE8cWYRD+wZwytvuAB/eNNzq7Y9Pc5s88nprDjevO8kpPznb3YhL76rxejoUMNtmoEP5XseMD6x\nJHrOfGlNy3YxNZ0Rk2pazYLfUJYrTuz5ZPIVfPexU3jzq68Syd6j8B7rzFyuJXUiM7fIrs1GP7bx\n7PgSZucD925sfBFz/oSrXL4CxW/0jp+Zx7bhalHWCRrdG2fPhV2BiakMXH/y1YbBVGhp3GaYXixi\nZCAZG88YHVLi7u/0bPhaTUkjICdOz2MkFewr47/UDvhDVX0JFUP9CcwvlTAzG79k5Anf+ehL6cxZ\n8jy4vhienMqgUgyEwNlxtu3m4XQoTMV23CrRCwDnJpbglPlz4wkXe3yS1SMATM6x3w0k2UN9bibY\nbzZfib0+3IHxXA+phCpWk/NcN9jev78qFbvmNc75DvZStoSZmSyW/P0kdFX8RtMUFpYz4Q/juV7N\n/ZWKgTPhnw4y2TJs/8WZ8h3pyalMVfaCE2fDGRKWJHc9m2Hl60/pWMqWMTOTxZzkqk5OZ5GXMkHw\ndrFQsjA8kAw59a978cWoFCvQVQWW7WJicqluyjf+jMzHuLjGpRswPpvHM+YUrrpwWHx+/OwCXnDl\nxpr75PciX0BBUxW40gty00Aggq1ycP2iz9u58UUUihaSCRW27Ynz5I7v7Hyh7vM949/Dg2kd8xlW\nLkdyeRf8eo/WRTPIIxczfru3kClhejoDRVHE95lcBZNTS1UhOTnJ5RqbWEIC4RjbuTl2jcuWAxVK\nbLlUVUGpbIv3ruK6cPgoXNnCzEwWo37b++T+cbz4ulHMzFQ/xzyt3/Rc8F2xXP0eOnJ2ERXLwaET\ns3jZdVuq9jO1UMD3Hz+N3/mF65BKaiKEgLdxz/rt1qb+BGZmslA8dk+clXTDQFoPzyfxz6MZ5PfR\nibHFur+blr7jKfZyhfj2KBNpR1qFeWYB9zxyAn/85ueHQp84tlM7RnxiKoOZTYFJM+s/v/kSq69S\nxcYPd53Fzzz/QmweCdr4xVwZtuPhws39mJgrYGauscaa9O/vuSV2f2/dOoyZmSwctzqkCwAe3HUa\nL7lmc8Pz33WArVJ7aiJ+VISz0hjf9wPYAOBvDMPYbhjGg4ZhNKVE+GzMekuLyshDR/xllcnHD3Xz\nGBV5OJf39rsS4ys1zHH5MT20JuC/FvyYjuvF3kyPPzuJB/aM4emjtXPv8SGzlawk1Ah+LfhDVCzb\nVRNugqT7ttg+bpLWWiFXDN9fluXizFQW775lB3YsM6Y7V7Twvlsfxz/dtSf2ez4cHpXS0WtVinmG\nOPz68nRlG4dSGBlIolC2q4T11o2sUeTD0kN+h0XXVCkEIHyf8bALuaGsh1zWXDHIsSoPwS/lKkgl\nNZFii6OgdqiDnLs0ldTEQhDykK/SzAIWPJ1ZZAELWQgmNBWO42LBv0952xNHONSBx/g6IjZzIB0s\nwgSo5jEAACAASURBVMA/42WIpkKSZ5XzkIuBdKIqqwPA6ikunVm54oRemEP9CZEXN53U/N821xbE\ntTnPvYKJ21MT2VBaOtkxioOXnZdFk+LKAYRGCeRrEQ1bKVUcVGwHSV1Df1oX98EmX8xFszxUlcOv\ns6GBYJKkHIaxmlAHuV55KifLdsVwOK8vD0EMqYxcjriFYoJ80G7N8BtdYwvR8HfoQGhyG/v/C65h\nAnXXoSm2v5jJR+mYGN+4Z1NOWxnHP9z5FB7dN4FH9jFBwydf8vaAhzVePMpMNN6B4ffTW157Dd79\n2y8KtZEVKyz+vv7QceysMQlNviaNshVEF8hhv68f6tDq9Krf3XEKR8aWamaTiJsnddlWNnoezfLA\nO038vtp3fA7f/slJvOezO0Kpxvg75dIa+4mDt0nyKqG1yrdtYx/MM4tNhVw0u+z9SmN8/9w0zYtM\n03ytaZqv8f/flBrhL8JmkxPLDwRfkz0qLjizfCgopiL5S6WzK7eF05tw5HNqZ3C7fEz577PTOcxn\nSsLtqPfwBcK39eUslGykEpqYmFIo2aGXd9lyQkn35VmoaxX+QMupqXhew0Yv1Sj8fj49la0SocfH\nl3Dv42ymcToVdoOj10rObTsf6TTwhp3fC5uG0hjxyz4bqWce/8sTwA8L4auIv6Px1zzWakuM8JVF\nSYqLqnJY4HKyhQr+4Uu7sfPZSSzmytgQccETuootG9I1RZklxeKmpd8lQzG+jdOZRQV+NKsD+70K\n2/HE9Ys7d7ncnA1DPMY3iOft58LXcUVHJ/py/63XXIPLtg3i999wvdgXF38DfQmUKg5sx62O8Y3E\nproum1zal9KE+L9wczAqFwjf5tqCuLzSF20ZwIbBJE5PZUPXqpHw5W0pbyt0VRFl7EvpoXtBvq+q\nMjv4551MqKJTAbCX9ubhNPafmK+7EEylEi5H2QrH+NZbQEVmx4GJqnZMvj6ZfPC88zhf+V0RzdsM\nhDudxZhJpLY/byI6qVMmoalwXC+I8e1LiI4Wv/evuGAIWzf24eljsyhXnLoxvnK8Z9y7TgjAGu9l\nXg7+zEYnt+WK7B3CRyx5Cj/ezr3ihgtw+QVDVR1v/vuK5eD7O0/jh0+Gs2dw+L3O5wXUm+Am17+8\n7HMc4r3WQuNrIVvGoVNsFOhEjThwKya16XP9fNdVK2JG4v5lQfu1B4PYXF7XgfBtrO3khV7kBWii\nxsnwQBJXXjRcs7MXZcwf/eMmTS06soDFvT85ISqcv8yaTU4sPyyLQvhWV4DneaLnEepB+MdNJzX0\npbSuLGABhN0W+ZzaWZ6Q8La5++vio3c9hdvvPSTqsznh23rHN1+y0J/WRaNVLNuhiYAl6d88qwNQ\n7VoC7H5qR9qT5cIf4lG/oa1YjnhJLfday0Pbuw5Ohb67d8dp/PipMQDBS4ZT5fjKw51S3XmeV/Uy\nYo4v6yRyMcKFxNUXs6FpLoi52NU0VWRYiE5mWxLCt7ohkoWR5otOueyL0sjO6aksjp/LYOfBKWQL\nFjYMpoQQA4AXXbsFg77Ii4NP5Eroauh3sgAQk9vqZHXQNRUKgmeLr6YWdRltxxWObNy5i+NLv+P1\nXrGrRW7FksUwe7nP+ML6motH8KHffxmuvHBYXCs+1MzFXTS7Bk9nxsVMqRJ0MlMJTTjtfMECgMVy\n822bIU5A9qd0XHHBMBay5dDQfjS7QhTeDgz2c8c3WHxh41AqdB1lJzgaksGcbrZwCa9HgHWAXnjt\nFhTLdmgSU65ohdpRntuau+LlSFaHZiYrTczl8fnvHcJ3HjsZOUfJ8ZVCYPg7U9533OQd2dzgbU00\nj6888hGHprFwlqyU1YF3hIf8c1YUBS+/fhsqlou9x2Zil6Pu85+xWpPb5pZKGJvOic/i2kbZlXX8\nXLzRyW2FsiU6h0DQUeS/5Pf/BdJ9DFTn0a01As3rfHRDmqX2q5NZKm6SHp+kXfU5d7qb6EQePDUv\nRuTqsevQlDjvWsKX379yh5CPwkSd2mhub1kYy6vf8Q7cBZsGoGuqWDCnHoWQrgvuZf4s8dJdunUQ\nA6lwPvJaeJ4nJjDWG7UDOiR8b71nP545xhYgEI5vk8mJ4x72XEwFZIuWuCnjQh1SCQ39Kb1teXz3\nHZ/DB2/bKV78cmouuRxA847v7sPT2N1gta16lEPCl5VlPsNyno7P5YVIq1eGRkNRq6FQsjGQ1sXL\nnYU6hF/OfGjLcT3xYEYbqflMCX/1mcfw491jLS/jcpn1y8Yb2ortik5es24QR3ZPt+8dD/XW5dne\nspAD4kIdZMc3qDvLdquSz28cSglxwUXyL73sMvy3Gy/BDZGVsPjLX1MVbBwMcurK1HN8ASYSL9s6\niNfeeIlf1qCOZMeXN448vdDIYDJ03q+44QKkk7pYnCGKJTmzKamjEJvOrI7jqygKEgk1cHzdaiGh\nq2xmPHd8Rzc05/gODySggIl0Xt4+2fF1wmKYOy1yNgfufIqJelJmh7Djy0ZXeOeFdTL9tlKqV9nx\n5RkmmhW+cU5gfzohhqVP+pMuUwkNuaJVdzROOL7++eiaKtrajUMpMRwPhDsu0ThY5viyFHOy45vQ\nVbzoWjaEv9cP/bJsBx/43E58+cdHq8shOb6hBSyaEDL8Xo4unRtqryWBtRjn+MYJX6l9iEsbaDnB\nO6me42s7rnBqB/sTeOG1W/DB/3GjqB8AeP5VLN7y9GRWZO+QXXd+38miRi7/7fcexMe+vKfu+yWU\nrcXPKc0N17L4nROanyIvU80zuADABZtYeyxn4wACAZYthDs44rj+9RzdwDqvcockSpybKh8r9FmT\n6czmMyV8/CtP4yN37Mat3z5Qd9u9R2agKGxUZWwmF3svcgNAbjO4Gx7VRnykkIdKysJYFqH8nbJl\nJO3HUzfWWPK+FqV3RjT97KVbB8UKlI1G6xeyZSnEpH4b1bEli+ezZZQqQeParOMrXzz5po++3ORh\nI9kS5w1IMsF6+O1wWD3PwyfufgYTcwXsP8EEflXOzVAcWHUsVnR/AHDLtw7glm8daJjcvRay8ObH\n5zPgl3IV8Xe9h48PSdS6kVzPqxksXw/XC1YG4g1XoWyHhw0rTmwquLmlcD7a6YUibMcLTXLqFOWK\ngzvuOyzc0aD3yxpay3bES2q5qWu48BsZTGJsJodb7gnuBfnFV+34RkMdguG6WUn4Rq87GxXRhSDg\nHaPnX7UZv/u667BpOB3qScsxvhvqOL79KV0IuCjPu3IzPvQHL8NFfkch5PhKDeJiJCxnw2BY7Dzv\nyk2hGNRi2Q4ttRmK8ZVDHRLLW8ACYO6giPGVln/laJrihzoEL4RacAHCwgtUJHQ1lIeVv8gtK4jx\n7Y/U5QbpJcbrhDu/wSIWVrXja7voS+nQNQXFiiNe0OmQ8A2csr5k0EGNY/ueMdzn5+gF5PhnVpak\nzs6Ph53xCS6XX8Bmc9cLBQpifP17TlUC4TuYEsvqAmHHN9qJYQ4tc7rletQ1FdddugHppCayQyxk\ny8gVLZyeyoZ+D0iOb8Wtcnwb5Xvlxkw0vKNWG5sRjq8c6lD9/oyuxBdNd2hL4TK1ckvzdGbZogVF\nYWEkmqri6otHQhMFuRgplh0xciN3mPgIkNxOyeUfm2F5irmQjBM1fElogLVpco7pkuWIEas+KdRL\nbmcGJEf/p67ejL6UhudcztzNuBjb+Wx1tCa/JvwZrhfnWyt1ZVy7H+Qhrl6YRubkRFa4uLsPz9S9\nt+azZWwYTOH5V22C57FOSa0yyu1VsMJjfIwvwPSDHLYi1xs3SDYNpzDQl2gqnFQW2XGhDi+6dgt+\n+WWX4RduvESMzDQS1PJzumaE71KuHMov2nyMb/wJRG/AkPAtWrjzvsP4xN3PiEYpqavoT+kolp0V\nCbV6HJVesPzhjj4E0SH86Pacx/ZP4G0f2x7KRdwo/q0WcTHG8r54b7zejdoo1OGeR07gr255bNmL\nXBTLNjywoSj+AqpyfC0ndnnJaD5a/kA0mzi7lTx7ah4PPx0sRjKfKWGoPyEEB3N86090qAVfletd\n//2nYFy6Ac8cn8OJ8Qw8z8OSFANY5fhGnhn+78svGMRSriIEZTFyTfnLijeE/P5ISXk8r7ggSFMj\nx/jy3y5myzg+viRcgEy+guGBZM0XLY9PTscMo8vtRdRJ3jCYgq6p+I1XX4V3/trzoGtqKAb1eztO\n4R/vekq4RrLwDYU6aHKoQ+MFLPg+8iULH/7Ck3jMv+6yg8biJF3MLBbRn9JDQ+px+wKAfn84jy39\nXB3WwBxfz/8s7HDJuYjFYgM8xpe/NIp2eOU230FL6qq/Ipsjvk8lgv3zDhyAUEhSHN/feQbf3REM\n33Phy68tFyV86JyLIp7dQXb4olRiQh14eS7c0h9xfAOBFp3cVigFC5fIwkjOUTu7VPSXuGXlC72Y\nueM7IIU6SG09z0deD952LWbL4TkNNdrYpXyM8I0NdQgbKnZkWNu2G4c66CrrtOUKFgb7EjUzDsmj\ndOI6S9dAnmzIEeEJJVvUAZ8AGndPnZoICxm5fsp+qI7jeuLZkcsFBK4zwDrYn/mLV4v2K85pjps7\nUqo4rGPvd9bqxZnWErBx7X6tOT9RuJhL6Kowi+LwPI+1tf1JXOXPxeAduPBx2XnfeN0oXnHDNnzg\nrTeK9iSqA+T2omy5wWT04XRo2/lMGUldxWBfQji+jcy6QgPHdyCdwG+99hpsGk6HwrXqMSONoDR6\n13ZM+C7mKpHJKtU30LnZfNWFrRUDExW+3F1RwBqjJw5N48CJeZQtBwr8vI/pwFlslsOnF/D4gUmU\nKw7+495DsbEz2/eeE39zFyLa+NVyfKPne/u9hwAAjz8bxHTG9dyaITy5jR0zTkTXdXylrAoc23Hx\ngydOYz5TwvFzS0xMNUgYHYXfxHKMb6EUdXztWMcXCMeq8n11cuIih9+H2SJ72OcyZWweTgsBYtmu\ntCQvuwae5+Epc0ZM1qoFD3XYMtKHlzxnKwDWODOXJaiXaCq66EPP//3ia0cBsJgxVp7wdnwJYP6c\ncLdIdkWv8RPEA0HMn6aqGO5PQlUUnJvN42P/uQf/+aMjcFwXuYKFkYFkzRctdxG5GJXLFA51CAtf\nPlT3xldegZf6dZOWFiHgzymPHa0pfKWXdTOhDgBzVZdylZDDEHZ8VVi2h7mlErbUCXMAAuHN6zyZ\n0FCx2fVVlKBe5EUt5Jf7tk3h2MVU1PEVL42w41soW0IADvUnMJ8piXYxlVTxP37xOrz0OVtDk4K4\ns1arvciVLBTLwdA/v3/477jQjGbjuPIiJnwn68zI5mUPQh0UvOs3fwqvf8VleN1LLg3do6EY30gn\nho80shHA6pCXzcNpVCw2rMvvuUy+Ip433qbyUAd55TZe143iNnOFYMEWeQJprVnrwqBoGOrghicK\n83AZPmrgp9Fk51tjuWc9WLlRFo5R5E6QU8fxleH3jfwO4h1aeZSDc3a6jvC1gsnOsuMrdzIH+6pH\nmfjzFDepLm7uSKliI53URIerXohmbeFb2/EF6t8v/N3Pw8xqOc58BGd4IInrL9+IgbSO+3adqVoY\npiI61DreftMNuOaSEegaGwWLGkdyucqWI77n8c680zaXKWHTcBqKomAgnWC53htoLPleXohxfOXn\neaBJx5dfm2RCRaNB8s45vvly6GGNhjpk8hV86D924e6Hjoc+r1WB0dVBxBCzPzTHh3oy+QqSCY1d\nlMhKRs3wf/9rL2773kFs33sOP9k/gUeeOVe1zbGxYDIEv4j8puCv0NAwYxMxvrJQaCR8t+8Zw599\n8tGq4a9wjC8XvtUPd13hyx1fyUXcc2QGd28/ju17zwk3Qh66GJvJ4c77DtcdwuHbD6QTtWN8y05V\no8Hr5TPfPICH/A5H4Ph2XviKYxctZP0X5OaRtMgWULGkUAe/Idl1aBqfuWc/jp/LCMcwjqV8Bbqm\nYCCtC7GwkC1XXedojt9ixcGOAxMipU2x7CCZUHHDlazxPOjP/K2e2MZEzkDEoZQzH1xz8QbxNx/u\n1TUFqqpgZDCJCX8lpemFIpsFDeYM13rR8hdROiZ+VI5xji5pu2GgOkdlWsoMMeaPmPD7UwhfTQ29\noEPpzMTKbfWbxTj3OhTj609uq9guRhssXsJ/x0VTUgp1SPihAQATXHZEyADAm3/uqnDZeIyvGmR1\nAPwYX8sR586Nh2RCw7WXbECp4uDQadYhSiU0vObFl+Cdv/a8kOMXiJ3qF7mcNYK37aK8fueGP+fD\ng+Frd+WFzIXjy+DuOz6Hx/ZPhLapSmemKti2sR+/+fPX+On04q8p78TwZyRYsa/a8QVYVhOAjdzw\ne85DcP9VhTpIji8fAWkUtymLJ1kE1hK+mSayOrgeGzLnEySLkuMrwmWacXz9uiuWnbrCN6Gr0FQl\n5PjyOlYVJdS54dtOzOZx67cPhJxIWfRE600e5SlbTjjUQZrs3B+K8Q3ug4GY8vMylmMc37gJbqWK\ng3RSEx2ueo5vrVHP6DtMXpo6WgYZz/NwejKDzcMpbPPz69bKaMXfCcMDLP3gW3/RQMVy8V9SfLpc\nxmSkPe5P61XGUWjV3IqDQol3AoJVcMuWg1zRwuZhPloYdmfLFQc7D05WOcDhGN/qyW3yCE4tRzoK\nf7YbtblAh4TvQF8iNMQKVDu+c5kSHNeDeSZsz3N3NOpqTS8UQ7nq5rPspr1sW3jFkIVcWfQe+M3b\n7MQ6mW/7M3Cr1nYv25jLlHH95RuhKkHcGW8MxZB3jR6efDHlzgCfGAOEY1fiOHRmEbmiJWY0cuTJ\nDiLUIWb2dD0HnIv0UsXG3iMzePjpc9h/nMUxyy8H+UZ+5OlxPPT0OI6M1V7iMdbxjQjf6IxpAHjO\nZSxGay5TEnkkeR0WyuHruvPg5Ird8maRHV8eP7t5OC1eLJYc6uDX5UPSCEHUubn5a0/jqw+yxmop\nV8bIQBKKoog8o4u5ctVvog1nqWzjjvtM3PlDk/27YqMvqeOSrYMY7k/g4Kl5nJ7MinLxGNGNEceX\nI/e+r5Uc32Epxlf+PcDizfi9UT/UgTu+7P8Tc3nsOMBEjzyKEJ2EF5cbl+9jcr4g7i8unuXJbeka\nMb5qE3l8gWaEb/B3I8c3ndKR0FVs9oeGE7rGMjj4y8ryz8/N5sU58Ik2F48OiM4M56XXb8UrnrtN\nnAt/EeVKbHKbCDPw25qEruI5l7POzI+eZJNDr5QWlpCpF+Mrmwm8znloBr/G/ZFQB4DV5ebhNFJJ\nTTi+n7j7Gdx+76HQgie8o8yve7RzoqqK+Ex27Plnw/4CF/y8ozG+XCxvGmH7n8uUQllFhDMZM7mN\nt638vBrFF+ZDwjcQW3G/SyZU8RzUy+rABe3IAHd8LfEZb19txxXvnrgli6Of1xO+iqKwBWwqDmw3\n7PjqmiLqB2BiM53UMDVfwK5D06FsFrWEr+d5WMpVsM1PS1Uq21WhDsLxDTn3WtCZjBO+kVzUcbGq\nMszx1UVdxE1us2wXP9p9Nvb3fB8y0XdarXDOxVwFmYKFyy8YFsev5fjy+4G3yS+7fiu2buzD8cgI\ndZyjClQv8hGdJMwdX3kyeqFsC8ORh7bIcwoA4NF94/jcdw5i75FgBTwAKPrfbxxKRUIdqidKNhvq\nwPUTbx/r0RHhu2mYnRx3X3RNZTFIUsXyQk/OFaqyHnDXS+buh47jk1/fJ9J85Pxg/K2Rk2YTGVgl\nNrp5ZKJB5PyhCy3N53kiR+slo4PYMJTEgi/A+QXkN0I5FNMTH+og36RyvNHpyWzdoHa+utVsxM0N\nhTrYQfmjddlsOrN/++Z+3HGfiaf8m3h6oSh+GzdklMlVNxLimP72A+kE0kkNihI3uS08oQEAXnDN\nZnziz34GyYQqer/8RRJanadk47bvHMTXYtYCjyNXtHD/rjN1c3gC7Jo//PQ5MRGHp27JFS1xzTaP\npEWPNVsMhpiLFQezS0WYZxdx3aUbcOHmfsxnSnjKnMbf3P4E5jMlHDgxj2eOzflxvBWx/C93UOaz\n1cJ3KbKgy0K2DMt2sZAtYz5TQrHiIJ3SoSoKrr9iExZzFXz4i0/i2z9hL6ArLmBCh4+WRO8P2R0Y\nlkTLZl/UcQGxUXJ5imVbPCsjdYQvT3vE/3/g5Dw+/71DODOVFQtVxMGdLRnu+B6TVs9bjDq+uiqE\nGP83R0GTwjdmFT05LlgWXvVSmQGsgf/Q778Uv/O66/x9qyKsQddVXHnhMBK6CvPMojiHCzb14yNv\nexn+7vdeWrW/173kUrz9TTeIfweOL4vx5XGbwvHVVVx/WTDhZ+NQSnQuowjHN2ZoNic9e7OLRXzp\nflPE7PLr8v/a+/LwOK4q3191d1Xv3erWvlibbZUsy1u8JU4cZ98IibMQ8kgIa0hCBoYlDxhmwvAN\nA8yagXkMDDzgCzMMw8DwwgBDCEtgsEP24CTeyku8yZasfVe3ulv9/rh1bt2qrm61pJYS4/p9X77I\nrerSrVt3Ofd3fuccGlcs7yox/R5IkoTaeABnB6dM8Rc/Fgyk8UTalHvXjpX36pu5ifHV3yWNF4Px\ndZsYX08O45vE8JigMdcNtGRqBi5JgldmuY6TepYIAHyuFuuSBQozvm6XhMoyPw/mFdO3Wb0+NDa8\nupE2NpUSpA5GxdA9hxhZlO9wI47dQoYv3ddO4+t2s0MIfd8ru03z2KQ1zxPoPaXvBVUxI+jV5DVN\n2TO+4r9DNtp6OhzbZVWw5jin9Gk+r8Fy2jG+33vyCP79l4fx1KuzF8Fgf9u8x/zXrtc42SGCCK+m\n6tCs3mraE6JCyrmqMj/Gp1I4cGII7/vbX0M7OVSA8ZWZl1yff9Y2T6czmEiwYHTRED3VSwVEWA5f\n/jt9XyRbwEreTSbTkD0uVJb5TVIiKhAj7hfFeuopIDNeRFXXJTF8Y2EfJhJp3gkUwS0aoLQgZWF2\n7U8l2YnL6s6l75JLc3wqhaBPNm3MBJp0oSLcFQCw51AvPvCFXVwLKWJwlBkVe47044++sAu/eIEl\nvq6vDOqnl2nd7WTWpE2nMugemMBXf7TP5FIRJ/vR00aQnMgEiH1nB4pG7Rsxs7nW4LaJRAqTyTSW\n10dNQR/5XC1pIaDGmm0BAB/0rI1CehMqx1hA90vXB3xs4wvo5W9z0plZNgO/14NIQEE0qORIHMRs\nHyMTSWRhZuiTqQwef+YEZ4pFPP7sCXz3ySN4/mAvntnXg2f22y9iJ3rG8K2faXj8GVY8gibj+GSK\nv6MKQeMrHmBS6RnsfoWxmds6a1AeYfNi9yvdON03gT26B2NkYhoTiTTSmSxnkaJBpqEdFqQOb7qo\nCfUVQdy6Y7mpjeK7OnJ6BIlkmhsfV21sQHsjY/iIYdvWWYOPvnU9tq6qBqAbI8L9ZAs78NCd6/Hu\nG1ahoSqMD9+xDtduaQSQy8JSVHbEovEV2QafYmZ8CYdOsUo9dZb8mwBbFP3eXOPTMHyNAySxzobh\n684pfEEge3c2ja+dm1iMeBcPnBe0VRa8F8BShpFRqXhY8YtkKgPZzaQOy+siON03zp/F43ahvjI0\nqyQDMNa8kYlpZGaynH0b44yvG9GQl2dvENliKwppfMVN6TcvduHXL53mkhp6NjEIiQxR+l1NeQDp\nzAy6BZ3v8wd6uWE4oa/vtGHbvSMu83Dnsu+0L4wJjG/QhvElhn1wNGE6UBqxGxl4FRck3fhlRXbY\n+yZjklJ3ErLZLH78u+PcQzk+meLzy7Q+WYwNr+xGfUUQiemMru1PQ9GzYoxOTJsYcTGIOxpUMDox\nncP4TiWZ2zkaVLBmudlTYO0HwAgkzAe/14NJG40vHQKpz72KO2d+22HSxv0dC3vhld02wW1pPg6t\n3mA6iNtLHShvNfvuVMK4p3WPnU6x9Gk+xSiyZPUWp9Iz+NVL5jSadkVTRNCeRvPsUNcInnjuVE5w\nJ42N2vIgN+KLZXwBg/n8nz2nkc5kcbhrpCDjCxj2SE6slS47FIPRJxNpbqs1VTPD15qBgdrV1Wt+\nNmZEe1BZ5kMWQI/+7LR2itI43rZECmOT03nJKQrItI4HOywR48sWk1NnzacDUe4g/ny8ZwyHTg3j\nH77HUoT5vW5blgUwBsfEVApBv2x7SiUjhCZyIcY3m83i4a8+jclkGk++ZNbz1sQDyAI4eHIIX//x\nfkwl03juAMuzW1cRRCzkZSX4Jqb5AAv5DZfY7/b24Nn9Z7mBA5jZX7G+NEk3aLOxsrmEzMwMdxWI\n18xY8ghPpzK8r6rK/HyBDwdkk35QxGwuO/H+4qI1JET054PB+OoR32T4pmeEQKcMT/RNoEEd8su8\nko5odNPEpb89OJZgOVWHp/CpbzyL7//mKB59/GBOZo+9r7FDzoETQ3j08YP4zi8OYzKRwtd+tM9U\nBpGybQxziYdu+E6lMDjC3kN51JA6WBdT2hQ3qpV8XhzUE+aT90JkS4npJQ3tkMD4djTH8Zn3bkWD\nPp/scPgUW+yIUV1eH8VD/2uD6eDj93mwuiXOF2KXJPHFTfa4ciK7O5rjuGRtLQCW6ozGuDWg5bUz\nI/ozmDW+4sJE49u6EL+kexVaa6Omz9cuL8ea5eU8EE0EueLPCAYBlzrk0fiKzAeVLC5UwIJ9p/Dv\n6bBz3dZG2yCfQqB+mkyk+RhqW1aGLID9ugY3nz7TDjS/6LDtU9zwed38QEvPsn5FBdwuCdvW1Oa9\nF703u2pTouF7xCJxorEnSgvoQEeazFo9SO+QIHXLwpgTEwm2qUVDCuorg1hZbx4X4rPYaXyj3PA1\nNL52wW3EFolSHQDck5dMZfiY8SpuPY8v64/N7VWIhb146tXunGwBj/32NfxUPyyPT6VQFmYFWPqG\npzCVTOObPz3ASRw6mHkVN6+GdapvXE/d5UFdRQCZmSwe/vqzXBooGjSRoIKJRK5h+PzBXkwk0tjW\nWZM3c4kYGNjWUGZ7DcGveFh2hTRlAzEfSiIBYnxds6Z4A6yBrXq5bz1ft9X7l0xl+B6Sl/G1Aiwp\naAAAIABJREFUCW7jGl/dwCJ5XCSooG94Cp/71xf52kvGsV9hKR4l5JJmdsHJVo9ZPqmDdW14Zp+Z\nkKExF4t4Z/VWj9gYviSzor1teDzJ/7Y15iIgGJeA8S5oXtDBL+iTuXE7mUjheM8oJBgSU0pzR+sB\n7VUiSca+m0bA6+F5wk/rv6f2eYX9gOQrPYOT+NhXnsYP/ucoUukZU+YrgB1qwwFzfvd8WBrGV9Cp\nKbKLJ3QXNa2iduaJ50/ir//tJZ4Tl5W21fVbltPU2cFJ3fhJI+T32Bu+cn6pQ//IFH60+xg3/MSq\nPTQILltfh0+/azO2ddYAAL7xk/2YFFg0AKgrD/LgoMGxZE76nenUDDeCaA2QJPNk7xmc4idLuoaM\nGvruU6924zu/OIRsNovjPaPoGZjk14q64FTKnMommZ7hE7qyzI+qWACSBNRXsIFnZ+TaBRbWVwZx\nydpazhoSyABMpY1KZVYXvAh695SX0+9l4vrpVMY4XU9OIwvzOzcWNYUF1KQyORIH9rf1wJQscLpv\nAv/w/ZfRN5xAPOJFYtoIfgLYgkAT85l9Z3m6tOcO9OKZ/Wd5nXgA6NaNKmo/jaVkKoOeQfa7eMSQ\nOlhT5HQPTiDg9SDok/kmS0zGMSF9D+m1RT0k6aGsi5w1ZZOIvcfYHBIZF5ckmVzwfhs2htzAsxl5\nImghp0AHSvNXXxGCxy1xlsuUZF7/2WrIavo8bK41a/Y/ePtaPHjLGtu/b63IFg0pQnCbvuDLLm6I\n0XWEYhlf8RD+gVvX4H1v7jD9/h3XqbhsQz1u2d5q/eqsEEsiU9YMkh6QK7bQ+7ZC9rihyC5u+FrZ\nN2Lzd25vxefvu5CvB3YQtfhWiGuqePAQv2cyfHVdObWFslMc0b1eVOSiZ2CSaVOTjG3yuF34zHu2\n4hrdy2B9VsCi8bWwj6K22S64LRpinpWB0QSGx5O87YbUIcONJ0V2I5kyMiX4vW5ctqEeiWlGchAO\n68/ECzBNpRD2y6iI+tE/msDLR/ux+5VuHtwnSgRo/e/qNQzfe65VcdsONra0k2yeiAYN9S3tGdb5\nvUX37NhBJAQ6W+1ZYQL1DRmDXONrZXxlNyddKJ2YXZq04fEk38NIohTVKzRaGV8xuM3K8JHm1xqk\nCwhSB6EABgBcsaEeYb+MI6dH8Niu1/jfYN9hObYDPk+O4bnPxitsTV+Yj/GNWVK+Pb2vx3RAEPNU\nc9slj4yG9lxxv6AgL5qvI+PTgobWyvgauXwzMzOGdEK/HxnhAUHjO5FI48TZcVTHA/wd0Jpl2ADs\n/wOjCb43Z7NZZvj6PDxdYhcZviR1sBCdQZ8HA6NJJFMZvHSoD4/teg2f+sZznCXPzLBMLGG/XJR3\nYYmkDsbJpq48yDvHjvGVwF5QVcyP+25ajZbaCC5dV8c7wpoM/uzQFKaSGWRmsgj57Blfb47haxjZ\nP3/uFH64+xj2HhtEKp0xRUGSKykSVNBYHeb1n0cnU6iK+fGmi5oAMJYg4POYyrZapQ7JVMZkBMk8\nrzAbDOnMDAbHEqiJB0wbOC18g6MJjE5O4xv/fQC/fLELJ8+O4zPfegFf/dF+fi3JI073jeNZ3Z0v\nBtdxwzfmxx1XrMD7d67JW7UFQI6+FmDM0LtvWGXK78m+z55DTAJeSOpAmzgPqPIauURJEzbCFz9j\nMouML8A2EZFtIgNcZGv+48nD6B6YxNWbluHmS1oAmHMv7ztmLF4i831QZ1NENw25ZEYncnXFx7rH\ndKPWww0YegZ6p9OpGS4JKLcsfN2CwXBS946Izx4LM48CFeqgRakQQ0nVoazSADHoyk42IKbXKhYX\ntFXiTRc1celFZiaLoM+DeMTLK56J9wZycxADbG6IBz+avz7FnTevKPu9cd8L2ioR16VH2WzWCG6z\nZnWwq9xWJOMb9Hmwoa0SF66uMf2+s7Uc91yrzomZ5e0RNiQyHFrrIiZjN19gUj4EfTLfiHyyWepB\n7KXscc2qR/bo0gu7HJmFAk98NowveTJoE6V1iOIc2pcxY//MwAS/92yaUzuNL/VbwMcKddC4ypfO\nzO1yoSysoFcPkGysCkGSzGkqaU54ZZcpAFfxuLFjXR08bglPvtTFjRhaa0YnWcBZYjqDUEBGecSL\n5HSGe0JZu4zxaWZ8JzCpVymTPW5s62TM/ImzYzh5dowfFEnqABip0vw+wasBc1ESK0ietLolPus8\n4IbvlDG2AJFl9/K+puDUbZ01WNNaju3rcj0L3/nlYXzya89iYCTBvZhlIQVe3fBN8EI87D3S2poj\ndbDsESKob5MpCohOQwJw48XNeOQDl2BZVQjP7j+L7oEJrmWndSUUUHKkDqd6x+H3uk1pBYnxJcLG\n6iEhw5f05AArB98/kjBpYYfGkpAktgcUq/G1kzoQhseTJsmXCFG+8I2fHMAj33sZgLH/iIwvPd+J\nnjFMJdOm3O4xIfsQYC60QmRTMpXBTDaLoE/mY5HYW6PgmL1hDjA757d7ziALY7ySpjgUkN94jC/A\nSouKjB6BTo13X6vilu0t+PS7t2BrRzUefscmvOXyFXyxZvnijHv3Dk1xQ5ZcYfQzgTrRMJaMRfqk\nftI43TeO7z55BCd7x3Hl5mWQYBjjZKiLA2mTWoUtq6ohSUBjFXvxouFrJ3UQ3d5+vUoWncb6RxLI\nZpkMQXSVNFQR45vEE88ZVZEOnBhCNgsTczk+lcKx7lE8/I3n8OjjB1nb/QbjTIZxZZkf9RVBbFQr\nC6YosmOBaVG16jmNcsKFDd9j3aMYnZjG0FgSLkni9+Nu1OkMFF2HyCuXCYFMAX6yNAzfSRvGV5xw\nB3VW5MZtTdx9d1hwx5Lhu3Z5uamtlHZH7GPO+E6ZGV+ALaKVMT8z8ixGDx2aACOLQtzSh6IzkJgv\n0RihReXE2TG4XYYcQWS4RHZcZOV9ls1BHMt2mqjgPAxfr+zGbTuW880aYC4wMih5zlpB6ymyUX90\n6xo8eEsnqvWAFklimzRtVrNpt8QFb+f2FkSDXqQzM5hMpvNXbjMxvsXl8aVNIxqam4yhGIjtoUAV\nRXbzIg+A2R1dDMRNQ5Hdpoj7uTD6AFu3JoW14ujpEXz8n3/Hx6sdOlriWFEfRdsyYzxa5z2Nc84M\n1oahyC70DEzyzd5OsylCkXMZXzLeqGQ9gfI50zsX52s87OMHhXjEi7KQ18L4smt9shvTenVJSukX\nCSrY3F6F7oFJ7NfXD1prppJpnh0i5Je5sS96GWW3i78Tr+xGLOxFwOvB8e5RpDMz/JBaFlIQCcg4\ncXYM//D9lzlho8huvl4S0SLOG9EbZYed21tQWx7APdeqBXoa+n0pRZyRGxnIzaRBWmUAUBtj+PAd\n63DDhU2290xnZrDv+CBf+8tCXvgUD0tnpu9HBgvJ+tKahaauPMgOcjbR/TT3xWIaPi8bBy5Jwk0X\ntyCbBX7+/ClusNK6EvbLGNfldQAjk3oGJ9FQGTKt5cT4ki1iPSgmp9laFBOCsC7WDzIiyTKkV2Jz\nu1yzSh1GJ6bhElK2Arml0ofHk0bwWF7GN2UihcqChvSHPZtRhZOkV2ImLV7IaDyJmZmsycajfVSU\nqFSW+eGSJIPxLZBuTQTZTWcsHthwQMmJB7PDkmV1IGztqDZFqBPGpqbhdkm4bH0d3nxxS07j6UUF\nfR7+kuorg0imMpyZDfpllIW8uO+m1Xi7MHGpE4M+WdfpkBs8y13cr50ZxW/3nEF1zI8HbltnEvaT\nbkU0Xjaqlags8+MTd12Au69hEdn00nuHpvhGSwN2IpEypW7xeZnLgF4gLfiVMb/JVdKgu/x6hyZN\nmuOjlo2GjKnPfOsF0+e0yaXSBuMrsuaBAu5LcgeJmyO9u5hl0yfGWAzcy80+MI3P/euL+O6vDmNo\nLIGysMJ1pSHLZuyT3Tz3n+i+oUWcJvjoRMokF7FjfAGgOuZHOKCgKuZHJKjg0KlhvoCd6h2HT3Hj\n0nV1AIzcy3TwodKlgFFdajo1g7FJI4BE/DvsGdyWzw1GgDameIFStrQQUP5GwBhfKT1RvVFpzNjo\nxRM/PQ+Qy6yKuQ7tDEoag955sJbiJtBYbRjBtDGaSox6zSztRrWKZ5eoLPOzQ5BcnOEbj/gQ8Hqw\nfW0tqmMBvvEMj0+bszqIrn5R40t5fGfN6qC7xW0CaRcKsT1ivts2IdNCMUFtIkSto09xQxUORHM1\nov1ej0kCtedIP/qGE/j94b6832mpieCTb99ongMkddDHQiQg56wzNfEAegYnBVnULIZvAY2vV3ab\nDnuKhwWo0YZqSkEnzMtoyIvyiA8Dowk8s68HmZksH8eK4kYWbM0R39uVG5cBAB796UH87bdfMJVS\np4Nz2K9ww1cszTuRSPPnYNluJDRUBg32VpAGNVaHMThq1iIrusQHMBhfcS2qiRdm9TesrMRn772w\nqJRQ1BZaJ2mNofVIlDo8dOd6fO79F/NDsTiXre91//FBzvhGg4Zm05B46QSTfo1V43vDRU34m/sv\nsp2fvICFkMdXbMuGlRUIB2TsOdxvML7CYWMmm+X7+On+CWSzjJwSy4bT36B1fu+xQXztx/v4HkLG\nXaW+V4T8Mur0g8GZgQn8zXdewqOPH8DweJLvt3RgL2T4hoPmSnsBIUc+wNZBuzy57Fp23cj4tGkP\n54yv/lnQL3P7i0gukfH1ez3wKm4MjSUxNpViZJ7+nLSnccNXly5Vxvzo0ouVJItgfEWQPIiTlH6Z\nv69CWBLDV2StokGFGwdiTtkxPSLPLmgFME5qfq+Hp0da28oYOqrSRBNoa0e1iSGhE7rLxU5ElHqH\nImUBljQ9M5PF+pUV8AqnZoAtUgDr/FjYi6oyP3/ZKxvKeJBSU00YAa8Hz+7v4YZgOMgyEBzuGjFV\nE/ErLH9tcpqVUCajtCrmNw3WWMiLoM+Dw10jSE5nuNFgZVhUYWOMCEakwTgzjW80ZD4RicUjrKCT\nqpiMPGrJ+UowGF+9gp7EWHxROnDq7BgyM1mcODuG4fFp0z1Mm5Js1iHSoiLBWISIye4bnjIxpVMW\njS9hhR4MI0kSVjZEMTw+jf6RBGayWZwdmkJ1PIBVTTGsbIjiio0NOX3R1TuOVJoFyRFo0omgSS66\nrD1uCcuFYJyycC7ja9VtZsE2Y7H0p9hf4ueSJPHviwvwBiGjgFX3RP3tkiRb1m8+jC/B7zWysIhs\nAC/PKyxidm4pktEQS2QwvoXbEvB58IUPXoJ3Xt8OwDBMR8aTQsUqV44WmGBUbitO6hANld7wFRd8\nceMW2fu5Sh3E/vbKbnS2GJ6NuTK+PkGeBTANLiDELdh8xy5LBO0J9IySJJmqxEWCCmrLg5hOz3Cv\nXL7Nj0CbtyhlIYPWK7tNxIVBhuhBnEKfXrfV0A+XBRXs3N4CRXbjaz/ez+8l/n98MmV6b611Eaxf\nUYGB0QR+q+fsJmPwzACRNB4udbLmqFYs9xfnkGikWXPW03epT4nxFcd4TTy/hnuuoL2D3P9iHl/A\nMPy8MsscsmZ5hfAcxjuy7iX7jw9heCwJCWwccMNXMIYBo5S09UDscbvyemOM4DbD8BX3W5dLwtrl\n5RiZmMbBE4yJp7WzWbcpyN4g0mxZVSinYIfYzpGJaTyz7yxPWUZ/uzoexJ/dswmfvXcrN3z3vjaA\ngyeH8dSrPUhnsqY9IuT35JU6jExOcw+RCFrnPW4JmZksN1at856u23ts0LSf0vwZ5FIHT85BwzoO\n42GvKQi7vTEGt0vCKWJ8k4bhC7DA1rFJlrGhkMYXYHtDJCDzdZwzvlPknX8DaXzrKkN4YGcn/u79\n2wCwweyV3Vx/COiGb4H0KTRgAz4P3nZ1G+67eTVnhoj9FE+OIjUudmLQL3PGV4w0pMWHXOHipibe\n96E71+Ojd663NdC9MmMNRydTPKef4nGhtS6Swwz6vW5TXkwj40LA1PaAjy2Q1D5Kj5TPsAOAB281\ngn8o/yArtJHIyXMslgu2gtxBojFllTq49WwDk4kUnt1/Fq/oAYn1FUFkwd5r3/AU9hzuR5fOfHQP\nTCIzk+XBgOy5zYavyHQSc0AuKcB4J5Rxgd5X30gCr51hcgrF4+KL8HKh8MJKQe4wOMqyPtToAv0/\nuXsjrhc2PnrLXX3j6B+ZMh1eiP0NmdxLhkFJaK2NcLcf6086xbsRCciQJJgOaoTquN90H1FHdueV\nK03XkrFGm03Qx4xPWrispSvJDeb3um3HMhlL1pN3MZAkiW9m4qJIi624cNrl6aUI/zqr4VvEguZx\nu/jz0OY3MjHNq1jJ7kJSB/b/2fL4cqnDIjC+ppzJwka2vD7K2+XxFB/cBhha8qDPg7UrKrgXCZhb\nhgiAvbvptJEFxlpmOB7J78UQsbIhigdv6cTlG4xDppUgIf3fEd31GrSJ0hdx8yUtuP/m1VyaBhhj\nJ+Dz8JywgPHcsbAXfq/bZJw3Vofxd+/fhhu3NWHr6hp0NMfxodvX8t/TnKBxNJlM5xgSH7x9Lb7+\nscvxyIcuxW07WnHNZsYCn9HXwHBAMRn64mHMavhetqHe9jrRmyI+l1XjKx6qq2dhfOeCHMbXUjWw\noTIIxePCMpt2ul3GPBQN36AeQHaoawThgAyP2/DQUCYdUXcqSfZrSD64XEyGRjpTK+MLsDgWADyl\nJQXDUro6yjRiMnzDuYavlcl+6tUeHDgxxA1fn+JGa10E4YCCkF9GOCDzPZL2e/G+Qb9sW7ltSi/u\nYZfKlVhl2vOIYLN6JIlkoOqNG9sqsXZ5ObZ2sEBI2vcCQjozgHk4rTKEspCX5bUfpeIWXtSUB9DV\nN4GZbJaz+UQoEtHRPTBpBN9Z2kce3vqKIO67uRMP3tKJ+sogeoemkM7McJsuFJBNBYryYfadpETY\n3F7Ff5YkCVUxP84OsYwMmRk2AMP+/GmZFIHxXdXE2E3STr3WbWZ8ASMQZiabNRm+Yb+MvqEpZLNZ\nziTEwoaGa6WuQxNLoor3pfQb+XDFxno88fxJ9AoDrLUugt8fZinMKqI+9I8k4Pd6+ISeTKRNjC8x\nG4zh9CAe8fG2bmyrMiXKliS22V+ythYDowlctqHelvHtHmBuGasLy1+Q8TUnZhd/psUqGlLgVzw4\n3c9yFBOaayPo6pvA6MQ0/mv3Mew50s9LkxJEuYSZjXGhqTrMU8X5vR49d2uue+yskKJtZHwaP3v2\nJJ549iSyYMady+XC2cFJ08GAqo8d7hrhz1Nt0uB64XaxE/Ly+iiOnB5BV9847zuqckOMb008wBl4\n0Z1LaGuMmYw28bkvXF2D8akUXJKEQ10jprFovVdzTRh/cvcFaKgM5bIcLglJGIcUMq7vfXMHvvLD\nvTwYhkDPkk8+YJTQnTvjC7BNIpWeMblWyWDkxQvyBKttaKvAlRc04LL1bMMvVupgBc1hUXoke1xw\nuSQosgvTqRmTq1+ykY7YwZA6LK7GV9zIvLIb7U0xdPWOzxp0ZMWbL27GmtY42ptiOTKJQgGoduBl\noaczCHglE3nh97pRFlIK5hwnSJKEjWqV6TOSGEgSm9+01h6xITbsUBULmIxbANixrg6RgIwV9VFT\nu+gdvuP6dtu87vGID7deupz/W22MIeSXMT6V4kyW6QBls9m6XBJWLouhzOfBLj0zzBnhsFwhHBLa\nG2MYmUhiTWs5J0HIoKurCKIq5mfxLEJbW+sikCS2TtABhEkd2LgkqZjYJ9ag5IXAJxTGAJj0AzAM\n7XjEhy99+NK88yngY9pdkVi5etMy/FAvrEPaI0PqwNZFOhBmZrII6IV55gIxL3AWuevK6pY4PG5J\nkHDojG9NGBIMw/fk2TFIABoqQhgSYltkt/lgBLCUhIdODeOFg738ea1yztryIMYmzakAzYyvjJMp\n5nn8lycOIhJU8JbLVvDy3q11ueTJTRc3o72xDBN6IQsuf7EQGn6vh9smALCpvQpbO6pNVdUA5nXx\nuF18/WyqyfU6kG1AmYkiQQXLKkM43TeB/pGEUPSCzW+Svxw5PWJkJ7G0j7PDFUFu/72o9eHo6VEc\nPDHE16E3VDozO1TF/JhOzWBkYprrVsI2VD2BM77CIKVTirVKGgCTfktM3RHyy/ykR3lzL9RPNfUV\nQb64isbebAuuiIqoHxcJUd6yXn2JQKmJfIqHL/TdA5PoHZ6CX88IQO0mhpMYG5/i5i8dYMbJ1lXV\n2KDLM+64fAWqyvwmqt9qIFoNX/pbxUgd/F636T2UhRTUxgM5J75Y2MsNyeHxJD+giOm66DqCyfCV\n3SZpgFdxo74yZMpXS94BqqAmPheRspGggs6WOBoqQ6gTDiyN1SxTwOGuEc4Yi2yqy2WUCF7TGofs\nceHomVG+aVHbevh3jb9tp41TG8tMi6voFrvzypV4740dvC/qKoJ8o7CyM0ymUWZrAHLGV783GU2b\n26vw1Yd2mMYNwBa62vJA3o2Q5tJ8GF8AeNcN7fjLe7eajDSr1CGfIetTPLjrmjbOiBUb3GbFioYo\nvIobT77Uhf0nBhHwGuWxfTobLm6avGTxLFICWnesB7lSQDYZvuZ15/6bV+NTNtXaZkPIL6Oztdxk\n9D78jk1YUR81rVXFQJRGDejeEkJQyKozHxkIrYeRANP+Uz+LEeVzRSSoYMf6erhckmmNoUNYdSxg\nWmsKgTxBND7M+aALjxliYSlbS3nEh3BQ4UZiRZkPD79jM3Zub+VzTtzA//edG7C6JY6rdeYYYHvN\nw+/YhI/csc5oh56tgtaQoM9jGqelNHzFvViSjIOmaOiKHhgraC6K8/pN25pw2XoWm0BeMDGvu7WK\n61zXBIC9i96hKbyoMV26df/yKR5TphafsP7UVgRxrGcMh7uGcbhrBK11EXgVt5nxlXPHx85LWuB2\nSTjZO8Z1rFaXfJ1NKsGycK4d0js8hade7cEvnu/CZCKFnz13EorHZSvRa6gM4YoLGkz3cbsk28OI\nuL/SXLEa53TooLlYyPCl4hbRgMKD9Lt6x01MOQCsbo1DkpjcdDo9k7MuA4bXqkX4e3QwfuR7L+Pn\nz58CQBrfIjyDs16xSKCOPd03wV2M4QJSBzpdiotfQM+FSroVq4FKefdE1oqu+cFvX8PLRwdQHQ9g\nTWs5Hn/2pG3UsexxzXnzv/XSVp7HUZFdqIpFIIEZZJ2tcex+tRvhgMxzlB7vHkXf8BTqyoOQJGNi\n08JC9eNbatkkI+YhHvGZypPaITMzA9nj4oyXNdKTFo4f7j6G0/0TeGBnJ/8duWRIkxoRGC5JkvCn\nb98EWXbh0Z8e5J+/76YOtDWU8fyGh04N5011JAY9miLPPS6TG88nu/GJuzZAVBCSxlcsymFFNOjF\nXXopWBFulwvL6yPYf3wIR/UqX1Z2tSLqR99wArXlQbQ1RLHv+BCe1ROMX9BWiVeODnB9EW0mrKpS\n7oa/oi7KPQCAvVFAEb5lIQXRkILB0SRqbNjjfOC6Ov3e4t+wpq4hfPLtG/OyJTT25qPxZe1xwfpn\nrVKHYk7mgKjvn1tbwgEF121p5KWZ77lONTSfihupjFlbWWxwm9oYw5c+dGnOhlkKiP1tlVKwVEKl\n+TsttSzgbK7wCYYvscXkHQn6ZE5etNRETIV6igEddOjQVhMP8OpowNwICDuIc3yu2mYAuHZLIxTZ\nzbO/2KWFy4ewwFJKABqqgnBJEuIRH3qHpkzsL91LHAvlUR8++tb1OfelcuPGd5nRQK7yjmZzWrJi\npSjFQDQ66yqC/EA728HR+L4h6bhtRysUD8uXe8917Vi/spJLXUQD0Vr+eD5z8M6rVuKL338Z3/zp\ngZzn4NdcsZJX2RR/31obwZn+CfzzfzHv5lsuXwHALAc0GF8X6iqCONM/gbZGVqK+q3cCTbr8yxqE\nRc/bVBPG6b4JpDMzJsaXyAjKQpTOzODRxw9icDSJqzY2mKRRVohkS3tTzPYwUl8Z5HOWCBxxjDdU\nBvkcDfg8GBpLotlGZ84NX4HxpTHR1TuOk2fHEAt7+ZyIBBS0NcZw+OSwHoOUO362dlQjHvGZYh3E\n7EGEYrM6vG6GLy1Cf/8fe/hnhRa2jW2VGB2fxroVFabP6ytCeQ1fXhteyTV8f/3SacTCXnzo9rWo\nivnxzuvbTfcW06LlO7HmQzziw93XtGH3K92oiPoge9yo1SfAuhUVuO+m1SYje9crZ5BKz3AtlJUR\no9MOGcqxMNPQWNNhiWB11DPoH05AEQzfqjKzMSWenp8/2IvbhiaN+uhJ0vjqRpllI6ZJIC4MHc1x\nvaQw+84LWq/pO001YX4SzFfVSva4TOxUFrnGmzW1kR3Taq2gI2JlQxn2Hx/C03rlHSu7Wlnmx4ET\nQ6iK+dHRHMe+40M42TuO1roon3DkFqKxTKnMCO+6vp3lJVbcpqIJdsYxveNY2IdIgBm+1XNgZyiX\nL79PEam2CjFohtShdE4hg/Elw7e45We+jC8AXLN5GXa/0o2auN+U5WJjW5WpaA5QfDozYH4bbjGg\nPqJ8rW80iNIoCmxb1RTD3mODCPk9WLksir3HBrG6JT5nw9ca8OZySWisDvH0SrOlM5sNphLF8xjX\nLpeEKwVWzRQkPMt4EA8x1fEAH/vluuEr6n1lIV3aXGHdqzpbWBGKB2/pxGQynbcc9Xwgzsfm6jA/\nfBcbfMm9L4rHFFAImFNLigdkn2I2fOezJqxujuN9b16NL/9wLwDAbRvj4MGn37UZzx/sNRlYq5pi\n2P1qN4bGkriwo5rv43bBbV7Fgz+7ZyMyM1m4JAnLqsLo6pvgjKdXcUMMGmnU/86qxhg8LglHz4ya\n9kjaf587YFR4e0Hrg9sl8bLx+SC27xrBayCCGN+gzygEJo4XsfBJ2M8yZDXaMb6UtUu3yyJBhR+4\nWMaOaayzpA7dvKoa2okhDI0l+cFAhMftyvFarmqO4f6bV0NtjOGpV7tx7MwoL0Azm1xtXqu3qqoS\ngC8DWAcgAeC9mqa9Npd72DF01mhBEeGAgpv04gMiGiqDvMKbtUQhD9ARFjlxgXpgZydBvWAhAAAP\neElEQVQ3LsRNETAGynxZhisuaMAVFxiL5F1XrUTfSAJe2S0IxrOIBhUM6ANknR71amV81y2vwFWb\nGnCVniYnFvbiVO94wdP7Azs78eXH9uKqTQ34tR5Z3FgdyqmEVVnmx43bmjE4msDv9vZgz5EBXLNZ\nN3x1pqUi6oOE3OIhBHFDoVNnY3UIssfFcwdvaq/CCwd7sUmtxMmeMWSRa/hSejcKtGupjeBY96jt\nCc7jdnHjHjAbkxG9Vn3/SH6t4drl5ZwJZM9gfs83bmvCyoYoGqvD+tp0FABwQXtVzsk6Fvbi2i3L\nclxV28V0YpSGzWdv0HQ0x3DbjlZs66xFz8AETpwdm5Nbkoy1VU0x3HH5CmxZVTXLNwqjOh6A2yXZ\napbnC2KwyBVbyMMjgmt8izSURfi9Hnz+vgshSeaAwzuuWJFzrZEe7nVTgPG1yi5Q5Y0AQ+qQQY8u\nM9qwsgJ7jw0i6JexfW0dbr1Sxa4XTha6jS1qywMI+WWTNKypOmwYvgs8bIhG4VxTwtlBZKZoTc8H\ncayL3iw6YIsHdwrsmUvQlldxsxRd+iNWxwM4OziJjmZm+Fr11KWAOB+basIIBxTcuK0ZqxoLlzom\nFOv5EX9fEfVjRV0UDZVB9I0k0GExhorFpvYqbFhZgd8f7s+7jzZWh3MyFmxdXY36yiCyWUOjCrDx\ndNfVTJ5FXkifpUpiU3UIT+8zCpp4ZTdmhBy/bcvKcP/Nq9HZEseBExG8fHTAlIu4s7Ucj+06xrNK\neNwupDMzuGh1jengZAdxr6XDkBUU9JovlZ24p9x+2QoMjCZsyRNRVkGZORSPC0GfB4f0Z19m6dct\nq2vw7Z8dRCzsxX03F/ZiE1ySxI1xa17o2cbUfFeSnQC8mqZtU1V1K4BH9M+Khqi3ojyJNbMEjtmB\nBp8iu3Krkdi4a+mE2FAZNAU8WWFXCGMhWNUcxyrLZ5IkoakmjFeODsDjlrC6hU1iMth5LlXFjbdd\nZbjsiekVpQJWdLaU48sf2QGABW6dHZrC/Td35iz4kiTh1ktbMTyexO/29uDlI/3YpFbi5SP9vA58\nVZkfH75jna0GCQAy+qlVlISUhby46eJm/OB/XoNXceOea1XUxAO4fEM9fvP7MxgcTZhOoQDr88lk\nmgvqH7pzPfYc6ceGlWaWn0BGb31FECuXlWFTexWQzWLbmlr843++gsuFaGgrWmoj2Lm9BT/cdcxW\nelAR9aNiDRujy6pDPKBtY3sVwsIBS5FdqCkP4K1XrMy5hwiaiNbCHwS3y4U3XdQMgLnPdmyon5Px\nI+sBBx63K4c9mQ/iER+++MHtc5YXFAIxIT7Fgz++fa1tgnk7LITxBYo3csguKobxXSzwjBFFHgqW\nGuSe7RuewktaHzxuF7Z0VGP/iSGT8VfsoUaE3+vBI390sYllIg2hT3GXxFileVyszKYQxDRkFwip\nA+0ge1gWn6lk2sRo3XBRE5pqwiZW0ZrVoRh85j1b8PvD/dwQ/PjbNmBoLDmrQbQQmBhfXXJx66Wt\nc/7+bAa+aDxu6ahGRZkff/GerXNpqi0e2NmJPYf7sTqPIWgHl54/2Q7kDaBsEFapkmjsMUmbF0OD\nhuErCYbcRrUq57DSXBPmwc9BnwfrV1Tgmf1ncf2Fs6/3Ib+M9+/sRG15IK8Hu6Y8AHVZWU4hp41t\nlUhMp01Bkq11EdtgOsAckHfFxgY+jq/ZvAyP7WJkU6NFptBSF8WH3rIOjdWhHLtgPpjNmzhfw/cS\nAD8DAE3TnlVVddNcb1AW9jKdh1/Gp965Gcd6Rk0n/WJB9LydgcqlDsICcvGaGmQyM7hkbV3O9SLi\nYR8qoj4sr597m+aCZt3wbW+M8ZdF7c5ndNDpTSx5WAgffut6ZPSUXflQFvKipTaMAyeG8NCXf8c/\nX7O8AuGAgs7W8rzfpXyR1pKn125phHZqGHXlLGiQFsVrNi/DwGgiZyNbt7wC3QMneZo6v9dTMPiG\ntIVvu7oNHrcL7xf0yf/04Utn3dzevK0ZisdtK9AX4ZIkbF9Xh/3HB9HeHMfQoJGQ/upNy4oKuvG4\nXdiyqgr1lfkzlxAqy/xFJZAXceO25jlH6M+GUrvzL+qoRiaTRU3czwOXigHN7fkYU3NBe1MMY5Op\nBTOLC8G5wvj++69YTtK3XLYcQZ+MB29ZY7quUKByIVjXBMqXXioC4rP3XoiewcmSaF1pndqxvq4o\nozwSkDGVTJvSe1WV+VFlOaCvaIiiviKY450rhIqoH1dvMlzYZSFvSQyIQhA1qnZ6y9ngnwfjK2aH\nWig8bhcjS0qMTWoVyu/2YXmdmVgT++hdN6ya80FOkiRsbKvEL1/sQnNNGHdd04Y3X9Ji6z23bdcs\nz+p2ufDxuy7I+VxMj1oMwkEF9ZVB1MQCuPNKw7N247ZmdA9O4iWtzzag1GpwLwQUl5QP813hIwDE\nCgppVVVdmqbN5PuCFS5Jwmf1qG+XS8oZJMWitpy5ZCknnAhi18xpzjy4ZhY9DMDYqb95YNu82jQX\ndDTH8aOnjpvYkqqYHz7FjWVV9gvfxWtqMTSWxPo8TKgVxU6MHevrcab/MFY2RLFuRQXWrSjHqhVV\n6OsbK/i9G7c145WjAzmBZB63Cx+5Izcg4+o8GqPbLmtFY3WoaLfcQ3euR99wIkf7AxTHDkqSVDQ7\neocexECLFbE318+BXb3/5s7ZL5onFmMBLzVWNcexqrl4doVw0eoa+L2eOTEz88HVm5aZjIfXA2RM\nLEY55FJAPGyvbonn1RaWylCt1Q/N+WRWc0UkqJTsULG6OY4/f+dm23y6doiGvDg7NJWXMSSsqI/i\nM+9dOKO52BClQ3ORZRCIiJntkG8Xo/NGhsft4nlzRYT8Mq7a2IBoSMGaAkRSIWzpqMYvX+xC27Iy\n+BRP0XESSwmXJOEv3r0lh1mWJAn33tiB6etm5uTNmA/ev7OwsS5ls9mCF9hBVdW/B/C0pmn/qf/7\npKZpC/evOnDgwIEDBw4cOHCwSJivaOopADcAgKqqFwJ4tWQtcuDAgQMHDhw4cOBgETBfnvwxAFer\nqvqU/u93lag9Dhw4cODAgQMHDhwsCuYldXDgwIEDBw4cOHDg4FzD65ew0oEDBw4cOHDgwIGDJYRj\n+Dpw4MCBAwcOHDg4L+AYvg4cOHDgwIEDBw7OCziGrwMHDhw4cODAgYPzAm+87MeLBFVVPQC+CaAZ\ngALgswD2A3gUwAyAvZqmPahfey+A9wFIAfispmn/rX/eBeCQfsunNU370yV8hJJhoX2hqqoLrEz1\nRgBeAJ/WNO2nS/wYJcMC+uMvNU37qaqqHwdwHYAsgBiAak3TCpcGfIOiBGMjAuC7AEIAEgDu1jSt\nd4kfoyQoQV/EAHwbQBjAAIB7NU3rX+LHKAnm0hf69ZUAdgNYo2natKqqPrC+qAIwCuAdmqYNLOUz\nlAoL7Qvh81sA3K5p2l1L1vgSowTjIgI2LiIAZAAf1TTtmaV8hlKjBH0SAPAdsL0kCTZXupfyGUqB\nEs6TdgDPAKgSP18IzifG924A/ZqmXQpmpHwJzHj7pKZpOwC4VFW9WVXVagAfAHCRft3nVVWVVVVd\nDuBFTdOu0P87J41eHQvqCwBvB+DRNG07gJ0AVtj9kXMI8+2Pv1JVVdY07a81Tbtc07QrAHSB9c+5\nioWOjXcCeEX//vcAfOx1eIZSYaF98UkAu/TvfwnA51+PhygRiuoLAFBV9RoATwCoFr7/AIxx8a8A\nHl7KxpcYC+0LqKr6BTBDwFze6tzDQvviIwB+qWnaZWBpUf9pCdu+WFhon9wL4AX92n8D8PGlbHwJ\nUYp5Egbwd2AkSslwPhm+34Ox2LoBpAFcoGnaLv2zxwFcDWALgN2apqU1TRsFcBjAWjB2s0FV1SdV\nVf2Jqqrm+rznFhbSF+sAXAvgjKqqPwHwNQA/XsrGLwIWOjYAAKqq3gpgUNO0Xy1Zy0uPhfbFq2Ds\nDfT/l+SE/jphofOkQ78GYEV/Llmqhi8CiumLq/SfMwCuBDAofP8SAD+zufZcxEL7AmDj4YFFbudS\nYKF98QiAr+o/ywCmFrW1S4MF9YmmaV8EOxQBQCOAocVu8CKhFPPkawD+BMBkKRt23kgdNE2bBPgJ\n4vsA/hTsJEEYA9uowwBGhM/HAUQBnAHwOU3TfqCq6sVg7pktS9D0kmOBfREBUAFguaZpN6qqeimY\n62LH4rd8cVCCsUH4BIA7F7Wxi4wS9EU/gGtUVd0H5qrbvgTNXhSUYJ78HsBNAF4GcDMA/+K3enFQ\nZF9E9Wt/pV8rspkRGH1E/XZOogR9AU3Tvq+q6jm7ZhIW2hf6QRGqqtaAeQI+uCQNX0SUaHxkVVX9\nFYBOsMP1OYeF9oOqqn8O4Ceapr1q7Z+F4nxifKGq6jIATwL4lqZp3wXTmRDCAIbB9GcRm89fBPAj\nANA07SkAtUvR5sXCAvtiAMBPAEDTtN8COJfZbwAL7g+oqroKwJCmaa8tTYsXDwvsiz8H8Neapq0G\n8wz8vyVp9CJhgX3xVwBaVFX9DRhzc2op2rxYKLIvRIjVkUb1a/Jde05hgX3xB4WF9oWqqmsA/ALA\nJzRN272YbV0qlGJ8aJp2JYBLcQ6voQvsh7sBvEdV1V8DqAHw81K167wxfHUd3hMAPqZp2rf0j3+v\nM5YAcD2AXQCeB3CJqqqKqqpRAO0A9oJt6B/S77UO5/AmVoK+2A3gBv1e6wCcWMr2lxol6A+AuWwe\nxzmOEvTFIAxmrw+GsXPOoQR9cSmAr+n6xaNg7u1zEnPoCxEiS/MU9DVD/7/12nMGJeiLPxgstC9U\nVe0Ac4m/TdO0khk2rydK0CefUFX1bv2fE2ASgXMOC+0HTdNW6vFUlwPoQQmZ7/NG6gCmEykD8LCq\nqp8CO1n8MYD/oweiHADwn7qL4R/BjDsJTIg9rarqXwH4tqqqbwKL3H7n6/EQJcJC++L/AviKqqpP\n6/e7f+kfoaRYUH/o92gDYy3OdSx0bHwKwNdVVX0QbH157+vyFKXBQvtCA/AvqqoCLOjxPa/HQ5QI\nRfWF5Tsie/MVAN9SVXUXWKT62xa/yYuGhfbFHxIW2hefA8sM9EXdnT2sadoti9/sRcVC++SbYHPl\nPWDk5LsWv8mLglLOkyxKeHiUstk/1PnowIEDBw4cOHDgwIGB80bq4MCBAwcOHDhw4OD8hmP4OnDg\nwIEDBw4cODgv4Bi+Dhw4cODAgQMHDs4LOIavAwcOHDhw4MCBg/MCjuHrwIEDBw4cOHDg4LyAY/g6\ncODAgQMHDhw4OC/gGL4OHDhw4MCBAwcOzgv8f4AsRiMkxTQZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daa8950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n",
    "dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))\n",
    "\n",
    "# Plot the data\n",
    "dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "mod_areturns = sm.tsa.MarkovRegression(\n",
    "    dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)\n",
    "res_areturns = mod_areturns.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>520</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-745.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 07 Jun 2016</td> <th>  AIC                </th> <td>1507.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:51:09</td>     <th>  BIC                </th> <td>1541.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>05-16-2004</td>    <th>  HQIC               </th> <td>1520.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 04-27-2014</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.7641</td> <td>    0.078</td> <td>    9.761</td> <td> 0.000</td> <td>    0.611</td> <td>    0.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.0791</td> <td>    0.030</td> <td>    2.620</td> <td> 0.009</td> <td>    0.020</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3476</td> <td>    0.061</td> <td>    5.694</td> <td> 0.000</td> <td>    0.228</td> <td>    0.467</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    1.9728</td> <td>    0.278</td> <td>    7.086</td> <td> 0.000</td> <td>    1.427</td> <td>    2.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.5280</td> <td>    0.086</td> <td>    6.155</td> <td> 0.000</td> <td>    0.360</td> <td>    0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    2.5771</td> <td>    0.405</td> <td>    6.357</td> <td> 0.000</td> <td>    1.783</td> <td>    3.372</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7531</td> <td>    0.063</td> <td>   11.871</td> <td> 0.000</td> <td>    0.629</td> <td>    0.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.6825</td> <td>    0.066</td> <td>   10.301</td> <td> 0.000</td> <td>    0.553</td> <td>    0.812</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  520\n",
       "Model:               MarkovRegression   Log Likelihood                -745.798\n",
       "Date:                Tue, 07 Jun 2016   AIC                           1507.595\n",
       "Time:                        13:51:09   BIC                           1541.626\n",
       "Sample:                    05-16-2004   HQIC                          1520.926\n",
       "                         - 04-27-2014                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7641      0.078      9.761      0.000       0.611       0.918\n",
       "x1             0.0791      0.030      2.620      0.009       0.020       0.138\n",
       "sigma2         0.3476      0.061      5.694      0.000       0.228       0.467\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.9728      0.278      7.086      0.000       1.427       2.518\n",
       "x1             0.5280      0.086      6.155      0.000       0.360       0.696\n",
       "sigma2         2.5771      0.405      6.357      0.000       1.783       3.372\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7531      0.063     11.871      0.000       0.629       0.877\n",
       "p[1->0]        0.6825      0.066     10.301      0.000       0.553       0.812\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_areturns.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAADSCAYAAACrbB1oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOfx/iS7OKPz/OzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb58d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_areturns.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in a low-variance regime', figsize=(12,3));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}