fonnesbeck/Manatee Proportions of Mortality.ipynb

## Manatee Proportions of Mortality.ipynb
{
 "metadata": {
  "name": "Manatee Proportions of Mortality"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import modules\n",
      "from pymc import *\n",
      "import numpy as np\n",
      "from get_data import *\n",
      "\n",
      "# Constants\n",
      "JUVENILE, ADULT = 0, 1\n",
      "STARTYEAR, ENDYEAR = 2001, 2009\n",
      "YEARS = ENDYEAR - STARTYEAR + 1\n",
      "\n",
      "# Recovery rates from incidental take paper\n",
      "recovery = dict(zip(regions, (0.8625, 0.8242, 0.4069, 0.5837)))\n",
      "\n",
      "# Strong red tide years\n",
      "tide_years = (2003, 2005, 2006)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Specify Current model\n",
      "AGE = 'Adults'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Containers for variables\n",
      "U = []\n",
      "p = []\n",
      "pi = []\n",
      "X = []\n",
      "X_like = []\n",
      "Z = []\n",
      "undetermined = []\n",
      "theta = []\n",
      "\n",
      "# Red tide effect factor\n",
      "a = Uniform('a', lower=0, upper=2)\n",
      "\n",
      "# Loop over regions\n",
      "for area in regions:\n",
      "    \n",
      "    # Upper St. John never gets red tide\n",
      "    k = len(causes) - 1*(area == 'USJ')\n",
      "    \n",
      "    # Undetermined deaths by latent cause modeled as multinomial\n",
      "    # with Dirichlet prior on probabilities\n",
      "    p_i = Dirichlet('p_%s' % area, theta=np.ones(k), value=np.ones(k-1)/k)\n",
      "    p.append(p_i)\n",
      "            \n",
      "    # Proportions of mortality for region i\n",
      "    pi_i = Dirichlet('pi_%s' % area, theta=np.ones(k))\n",
      "    pi.append(pi_i)\n",
      "\n",
      "    # Loop over years\n",
      "    for year in range(STARTYEAR, ENDYEAR+1):\n",
      "\n",
      "        # Subset data by year and age\n",
      "        cdata = data[(data[\"area\"]==area) & (data['year']==year) & (data['age']==AGE)]\n",
      "        \n",
      "        # Undetermined mortality\n",
      "        undetermined_i = cdata['undetermined']\n",
      "        undetermined.append(undetermined_i)\n",
      "        \n",
      "        # Upper St. John never gets red tide\n",
      "        if area == 'USJ':\n",
      "            # Remove red tide as a cause\n",
      "            Z_i = cdata[causes[0:4] + [causes[-1]]].values[0]\n",
      "        else:\n",
      "            Z_i = cdata[causes].values[0]\n",
      "        \n",
      "        \n",
      "        # Undetermined deaths by latent cause modeled as multinomial\n",
      "        # with Dirichlet prior on probabilities\n",
      "        U_i = Multinomial('U_%s%i' % (area,year), n=undetermined_i, p=p_i)\n",
      "        U.append(U_i)\n",
      "\n",
      "        # Total deaths by cause, corrected for recovery rate\n",
      "        X_i = Lambda('X_%s%i' % (area,year), lambda u=U_i, z=Z_i: ((z + u)/recovery[area]).astype(int))\n",
      "        X.append(X_i)\n",
      "\n",
      "        # A red tide year ...\n",
      "        if (year in tide_years) and (area == 'SW'):\n",
      "\n",
      "            @deterministic\n",
      "            def theta_i(p=pi_i, a=a):\n",
      "                \"\"\"Adjust proportions in red tide years\"\"\"\n",
      "                theta = p.copy()\n",
      "                theta[4] += a\n",
      "                return theta/(1+a) \n",
      "            theta_i.__name__ += '_%s%i' % (area,year)\n",
      "            theta.append(theta_i)\n",
      "\n",
      "        # Normal year ...\n",
      "        else:\n",
      "            \n",
      "            # No adjustment to proportions\n",
      "            theta_i = pi_i\n",
      "        \n",
      "        @potential\n",
      "        def X_like_i(x=X_i, p=theta_i):\n",
      "            \"The likelihood of total deaths by cause\"\n",
      "            return multinomial_like(x, n=np.sum(x), p=extend_dirichlet(p))\n",
      "        X_like.append(X_like_i)\n",
      "        \n",
      "try:\n",
      "    del adult_model\n",
      "except:\n",
      "    pass\n",
      "\n",
      "adult_model = MCMC(locals())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adult_model.sample(20000, burn=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "[****************100%******************]  20000 of 20000 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Posterior estimate for red tide year effect"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adult_model.a.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "a:\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.772            0.105            0.005            [ 0.587  0.991]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.589            0.699           0.765          0.836         0.996\n",
        "\t\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Matplot.plot(adult_model.a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting a\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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yvQ0Z5p4Y3rirVeYBhC23Nt2+Zkn6LJdTo7ItN6qovB7v7L3UtnRQ+7YGnoIe\nNqt/aXu6L9Bb6lCBVMNq6vqRLQsfbtq/TjNuZ0tyd5S1oqyfFd7A+wZPOub813JSP9D29OPu85YD\n4GKD4rZs+BwA+vfJjp/3d/eZ/9+zpKC0puOzN2mUbgFvtp759rebLK8QQkjnZjHAiomJQVlZGcrL\ny9Ha2orDhw8jKSnJaJ+Ghga0trYFDD///DMGDBgAHx/rT2MVMSzHYvX2qWWOsBy97TLlIL2dZ54P\nxOTQpSr2hGUAqw9eMbvZzv6myKb2/Xi2Ateqm9CqMShOyfP0S7Wq1Wj0aPf5jqngzpYUbTxD2Pad\n4bSx4eumlftNi4tWNbZga/v0WlOrBnO+LcKW42U4wJCwbxhYf33KPPD4vLDM6Eeczyk2w+r0tkw1\nSiDB0l0XOoJBbduST1x9dNT+WnTE+Sh/R/yoD4XL4hShVCpFeno6MjMz9WUaoqKikJubCwBITU1F\naWkpsrOz4e3tjejoaDz77LOcTnyd4ak0q4vjGnxdZPCo/exvzra9biEOuFRp25RIQSlzwrzhI/4A\n8ObPf2BS+/SR6fm1AHaeU6BY0WBUh8iwSCeXpXdcEd/M/PcZpMR0Yzw3n7lj9mJIweLtuIDxNK+l\nuMY06Dl6pUZfdqShRY2rVU24WsWcr2Zp+ljHnpFZLuz9GZLoR/g6WvNG7kVu76VCDYJHuTviR30o\nXFZzsAYMGICVK1cabUtNTdV/HR8fjzVr1vDfMgaOBBr/s/V3zLsnyq73GuZ+Mf7170CRofcYpkot\nnZ/rKW3V0KJBQzNzmQm+Azx7285fO9paoEVHANFgZWFoLmxqH8u+zhottL8GWxuhJaxXVFQgOzsb\n1dXVCAoKQkpKClJSUvR1+W7evIkePXpg3rx5+hH1nTt3Ii8vD1KpFE8//TT69+8PACgtLcW6devQ\n3NyMxMREpKWlufPSCCGdhFuXyrlWbb1St6EWK0Mppk8HmjJ999RPT9icD8WkIwfL5Hxa5u0AcOQK\n9+RspcHTkfoZQhfe8NRCmCKUSHgfwapvVuPzQvM1FU/cqEWLlZ8lJjzEV2ajhaoWfnLt7B2F/KGo\nbWTPrp83J/6MymQyzJw5E++//z4WLlyIL774AqWlpdi6dSvi4+ORmZmJuLg4bN26FUBbELV3716s\nWLECGRkZyM7O1gez2dnZmD17NjIzM3Hp0iWcOHHC0qkJIYQTtwZYT7dP7fFl9S+21OHRokmt1Zci\nsOSWyZM1HyiXAAAgAElEQVRtGq0Wv5V1PE2my+Mxj0M6NjSbFCG9Vt2Eyf/k9ou8vM4gwNKVFXDC\n3Yutkn6tDUVcASA6xDkV0R0dhTF1uoz5icCC0lrGHCqAeRqY+Rv7GK7zJ5FIjHLhHGFv01raS6cw\nPchh+CSiq2PwkJAQ9O7dGwAQFBSE2NhYKJVKFBQUYPTo0QCAlJQU5OfnAwDy8/ORnJwMmUyG8PBw\nREREoLi4GJWVlVCpVIiNjQUAjBo1Sv+eroDyd8SP+lC43LIWobsw3QTsqSd19mY9Mn4sxv89c6fR\n9n/8zJybogUwZdNJo22KhharI3JMnDlwtf8ic1Dh4yll3M7GWhstLRRt6Zjrj1h/go/TsTh8iN+f\nuaUPLrjS8DzGxmdfMwWn1irrW8PH6C8fysrKcPXqVcTHx6O6uhohIW1rSgYHB6O6um2kuLKyEnFx\ncfr3hIaGQqlUQiaTGT0ZzVTrrzOj/B3xoz4Uri4RYOmmAphufzU2js4A5msM6r77o9J4ylN3PqbA\nzt6bpzum6/gO6vgskuos52414GqVccX/Y6U1Fkc8bVpyyWBftqnyplYN5/U5rallCGqtLe1kSMIQ\nlfLVNkeoVCqsXr0aM2fONHt6manNhBDiKm6dIrTHT1aKkTLRMnx1tr1MxJKfrC8BY4rrAry6m+hF\nJcMTjHb+7u/tygWJBURlQzBgDdeP3jAO3HG2Aht/tTyCdugS8wggE1VrR8DzQxHzz3SLRotP8m9w\nPqat2NaXZMJY2NdKfOXs8Ka1tRWrVq3Cfffdh+HDhwNoG7Wqqmrrh8rKSgQHBwNoG5kyXGFCoVAg\nNDTUbMSKqdYfIYTYQ3QB1gc25Vm10SebG9wQbtbZv+wIl3X5AGDRzhLW1+yvpK1b2sXOt9vBtDSF\noyrquS1p5Cxcx10Mg7qLykaWRPGOjTk2TGEa5tBbWuLJllEmWy380Xgx6Re3n2fdV3eVF5Udo23u\nHIjUarVYv349oqKiMHnyZP32pKQk7Nu3DwCwf/9+feCVlJSEQ4cOobW1FeXl5SgrK0NsbCxCQkLg\n6+uL4uJiaLVaHDx4UP+eroDyd8SP+lC4rE4RFhUVYfPmzfo6WBMnTjR6vbm5GRs3bsTly5fh6+uL\nKVOmCPYXFF/3A6VJvS6OA1pGTNed48qecw3uGYBTN+xb4oWvZXIM7btYyfsx3UVXqd7WaU/D6TW2\n3DdnM21x0c16q6NwhhXh2aYImRb65tu5c+dw8OBBREdHY9GiRQCAGTNm4OGHH0ZWVhZefvllfZkG\nAIiKisKYMWOwePFiSKVSzJ07Vz+FOHfuXKxbtw5NTU1ITEzE0KFDnd5+oaD8HfGjPhQuiwGWRqNB\nTk4Oli1bBrlcjiVLlmDQoEGIiuqoJ7Vv3z54e3tj5cqVuHXrFv7xj38gKSlJkPkPRlWyeT2y86/1\ncpXK6Ez2nnFsbDfsKXFygCO8rjdiT/OKWEbxdCNMtgZYQk1De/Nn5hUMymrNpxM1WstPszrzx6B/\n//7497//zfjaK6+8wrh90qRJmDRpktn2qKgovPPOO7y2jxBCLE4RlpSUICIiAuHh4ZDJZEhOTkZB\nQYHRPn5+fmhsbERrayvq6urg5eUluOBqYns5BL4f89dxbU0qx85pusA24eaCopExYDh9sy3w4lIw\n1pAQSovZgikgZLqG8xUdy+joPhtCCOmKLI5gKZVKhIWF6b+Xy+UoKTHOK7r33ntx7NgxzJkzBxqN\nBm+//bZzWsoDwxtClUAeMbdVR8K8fYFSoLdtJRfsIvDgQffJNdtYhoGJbup1P0vdLDZCeALPFr8x\nTDEz/cHywvfnXNEcwhNd7g5NM4kX9aFwOVymYdeuXZBKpfjoo49w5coVrFixAtnZ2fDwEF7+vLNu\nae4YEzpqQyV4Q4Hezq/MoZvOFCrdKEurjfN0pmU4HHGJx2O5wsf55os2C3Wak3BHN2Xxoz4ULotR\nkFwuR0VFxyPkTI8wnz17Fvfeey+8vb0RFxeHbt264cYN5z1a7ghnrfPmjlm3Yy5Mku9sbHnaz1ls\nLWIqRM76/0QIIZ2BxQArJiYGZWVlKC8vR2trKw4fPoykpCSjfQYOHIhjx45Bo9Hg5s2bqKurQ2Rk\npFMbbS++bwdivMFQDhbhywVlI2vyPyGEdHUW54ukUinS09ORmZmpL9MQFRWF3NxcAEBqaiqSk5NR\nWlqKJUuWICgoCLNmzXJFu+3C95TGuVsN1ncSAMM40FNKARbhx79O3LS+ExE0yt8RP+pD4bKakDNg\nwACsXLnSaFtqaqr+az8/Pzz99NP8t8wJfr1qX94Sm/nb2AszChXXKvSEkM6PbsriR30oXMLLRHci\n3fI4XVlXXWqHMLuvTwgvx3nyqzO8HIcQQjqLLhVgdVWjDG6ig3sGurElxFGT+oXyerwQny6x3jsh\nhLgcBVidhKWJvz/d0d1l7SBOxvMML9/rTBJxoXXsxI/6ULjoz1ceHLrEb26XPSQS8VUHZ+PI2onE\nNrbWAiOdC+XviB/1oXDRCBYPbtY1u7sJ6O7v5e4m8KZbF5y28uLwdKfMQyL4KvmEEELaUIAlAHw8\n2Pfa/b0dP4hASDl8IPfcHuyClriOtfU7HxvSA8+PjLK4jyV/GRZh93sJIYTYzupQQVFRETZv3qyv\ngzVx4kSj17dt24ZffvkFAKBWq3Ht2jV88skn8Pf3d06L3SjrwX5OWWtNKpE4vBC1nyfzGoMvj4oG\nADx7Vy9s/NV8uRMmvYK8cL3GfaNyXAKshHB/HL7MPjU7pGcATopomtHaANac4b0AAKsPXrHr+D0C\nmEc4qWhH10Y1lMSP+lC4LAZYGo0GOTk5WLZsGeRyOZYsWYJBgwYhKqrjL+lp06Zh2rRpAIBjx45h\n586dogmu7o/phrwLle5uBi93OaYBkPFxcoyPD2V9nc3zI2/Da7svON4oO3EJsLxllveZ0C9UVAEW\nm1dTbseKfZeddnwq7N+10U1Z/KgPhcviFGFJSQkiIiIQHh4OmUyG5ORkFBQUsO7/yy+/IDk5mfdG\nCoWQ51NNb5TxYX54efTtdh3L3Wk+XIqhWhrwW5B8G0ZGi2sKkW0Jo95yX6Pv7e0b9ilIirAIIcQZ\nLMYMSqUSYWFh+u/lcjmUSiXjvk1NTTh58iRGjBjBbwsFxFl/7Rt2QniAp13HkJjcKLMe7OdAi9wb\nYkk5fNCWWjg5IQx+XsxTpkDbyKXQjHRyThmFUYQQ4lq8DcocO3YM/fv35zQ9aG8QwTfbAyYn3aZ4\niNz4XAGHSzrYiNuCHD7PvHuYk7b9vaz/WHaWkhQ6C++LZtxu2q32djPbxyWU/4vEPaiGkvhRHwqX\nxRwsuVyOiooK/fcKhQJyuZxx30OHDnGeHnT1zXHePVH48HCp2XZbb1bOGsEyPKy9nw2fbePUBB7O\nF+DNPMrkLbM/7tcl9RsK9pGhWtVq9zFdgUveGVdMuYValh+sBcnROHLlNG/nJuJC+TviR30oXBbv\nZDExMSgrK0N5eTlaW1tx+PBhJCUlme3X0NCAs2fPYvjw4dxO6uLMWrbzWXs0Xke3fp+zWm3PvfWB\n+LZAd9qAtilcwynCpW4o2ZDcOxgrJsbY9B7Tac20oT2Mvh/Ywx+JkcxL+7A9dalL6jf03N2R+q9/\nmj3Upja6gu4apw8KN3vNnp+5V8f0NtvGFjSH+tMIFiGEOIPFAEsqlSI9PR2ZmZlYvHgxxowZg6io\nKOTm5iI3N1e/36+//oohQ4bAy4tbsUs+p7O4CHPwJvLyqLZkcWc125Eiobo2GcaKw3q5fr3BAC8p\nooJtW0jax7Pjx693Nx88ndTL6HUvmQeOXatlfC9TwMBWrFPqIdHXkJJ6SNyexG9Kt+DynLt6YUpC\nmNFrpm211HZHRv7YDIoQxxPBhBAiNFbrYA0YMAArV6402paammr0fUpKClJSUjifNCLQG01qDZQN\nrpm2CRN4lXPD4IjrFKFu9I3rKJxN7IhAHJ325fL2v46IxIaj11hftxRgGNaB8nVCIOIIXQ96SCTw\n8+TetiBvKWqa1GbHYWJv/8xK6oUbNU3IPHAFT9wZgS+Ol9l3ICJIVENJ/KgPhcsta5L4eXlgVJ9u\n+P7MLZecjy0GqbE1L8dZTxEaBlgc3yMx/dfGtvXr7odztxoYX7PnXqyFgyOTBieVMGyztH98mB+8\nZRKLgbThoWYl9cTOcwo7Gul6bB/piokx8JZ54KXtxfpttvZbXGhbCYhAbylqDQI1Q326+WBQRAAy\nD1yx6cff0s8XEQ66KYsf9aFwueVP+RlDnb9sxwdT4vRfs/31zjUXTNt+63LWFKGX1PZuMA2sbG1b\ndAj7dJ7WjhDL30tq82ia4d4aDueUWAhEV02Jx5IxvY22/d8zdzIeJ8TXk7Vyur+F8g6G5L7uXS9x\nWGQQ7ugRwHqNP8wcbPQ9Y5J7+2ew9S+DzV9rF+DdcZ22dG+wjwx/GtCd+xsIIaSTcUuA1c1X5tS6\nPM/c1Qt3RATov/dkuZuaJlVbY5qUzYcQHxmeu7stP+jHWUP0wc1jQ5jbpku4N22KYduYnkjTaIy/\nD2R5gk9nYr9QPGKSdK07bLDBYsyJkYHY/OgAzB7ey6FPx5OhzaY3dMMYQWthP0Ov398bI6OD4Wsy\n9cYWzrE9bWfqvclx1nfiKC7Mj/1FOz9UX5Olk/yZ+tuJyWidrYwGIYTYym3JKIYjBTMTezr1XNEh\nPlg1xfyGGBFoOTfr0cFtAYb+ZuGEqPDePiGIbZ+qkUkl+pse2wiJLliUoC3QGdgjwKhtEYFejKMw\no/t2wwyDgDIi0AsfTGUOErRa4KX7oo1GubylEiSEtyU8/21cH/325RNj0TPIGz4yD4tThP8Y39ds\n21CDZPyYUOMg48M/xVusRB/IcaRpVN9u8JZ5YHDPAHz0cH/GffqaVEvnwlqAylVUsDdiDQIsa3FJ\nQrg/vFn+YJgQL8e4WPMiqhse6o+hPW178IEpn81L6mE0MkzEj2ooiR/1oXC5LcB63OBmH2Bys7Tn\nhmfNIIMRLR3TkR4fk5sKX0UeuTKcstTdaPt3Nw48/mdEJG4L8cawyCB88+Qg3Nv+BJrund1Znpjs\nEeiFWe1P6fWV+6Cv3A939DD/TAylxhnXPNOdQwsgMTIIt4V4W70mnf7d/bBsbB+jbaxJ6RIJ+nX3\nR6gf87X8dUQkJvQzL8dgiYdEgt7dmH+uFo6KRubkWKNtd/RwzdNzpiM9pt+b/sxN6BeK7U8zl5p4\n/p7bsCilt9n2IB8ZgnxkkPtxn9ZkiuG4ju61723DvsRd5s+fTzk8Ikd9KFxuCbC0sJx3NLin5Ru/\nIcOAQjcNZJoAzobLmneG7Hlgj6nwpSnDQE93W2K7lw3pGYhPHhmgf7Rfx5ZLWf9QgtlnbBjU6s7N\nVvxSqwUevKM7PnlkgEkb2BsRaJDLMyLasSrwYf6eRm1zNPCVSiT6aU/dx+5Iwv4QG35+k6KMR5Za\nNfwHJrprC/ExDrCmWsiRYvq/odECnhyfwHzKyaPShBAidFZ/WxYVFWHx4sV4+eWX8dNPPzHuU1JS\ngmXLluGVV17B3//+d4cbNal/qNF0liVMN3Wtyb9MhkUGmo1YsR1HR4K24pdsdInF9xisK8dU+NJ0\n+k/qIdEnKzMFVmunxePRweH64qJMOj4H+yIDLk8yWgswDV83nUKTekg6RqQYTsBldERXXJTvEOT2\nbj64vZuvURHS2yw8BABYLo9h+seD7o+A/xnRy2zfB0x+PnoFGU9bO5r394/xffXBkmnsZmkUkCm4\n1qLtycONDFOtcWHGo4P9ulP9LEJI12YxwtBoNMjJyUFGRgZWrFiBvLw8lJYaLzlTX1+PdevW4cUX\nX8R7772HhQsXcj657iZsesPs3c0Xs5J6YRhLFe9ZiT31wcZ7JlM7XP1pQHerT72p2xPDO9onwftT\n4xn3/WrGQH1i8W3B3KfOTD3AcNPrH+6PZ+6KRMYo9pwkR4u3Sli+7thotKCP1WMwPfY/wEJwai2g\nAYDuAcw5c46WAtMFIFIPiT7AHd2nG+uDBtZMSQjD+oc6gpC+7Tl28WHm12/a9j8N6I5tM9mf6nOE\nLTN8TAtuh/jKIJFIcDvDVKvct2Mkme3/LREeyt8RP+pD4bIYYJWUlCAiIgLh4eGQyWRITk5GQUGB\n0T6//PILRowYgdDQtsAgKIj79E+GyfSZ6VIrKyayB0+x7UnREYH2BzOmTJ82VOtGTNpvTGw38lmJ\nPSE3yReaalKR25Cl+9yc4b3a97FtnEY34mBvsKELNjNGRbPeIJvVlkeQDANWtoAvc3KcPhdMZ2R0\nMKdgxjRXj61EgSMMr03XF7aSekiMplxj2r9m7hvjjRKJBF4GI6uRwd5YZCHZHzDPHWTDpRSGjofJ\nIb95chAmWsp7M7iMtx+wbckk4j6UvyN+1IfCZfE3s1KpRFhYR6Agl8uhVCqN9ikrK0NdXR3eeOMN\nLFq0CAcPHuR88m7tf/XqpkUs/fpfMTHG6KbNdLOy5S90pgBmZmJPPHlnW42uOcN7YbxBkre3zMMs\nh0VH93QdVw3NbaM7HhKgp5UnGbn47LEB+tEzewdzdJ/nA/Gh8GN5Qk8X4LDXFbN+nsE9A8ymD+ua\n1UZTvWyHYTs+n+UzolhGH5eM6QhyvntqsE1nfCqxJ3bOHqp/WtSQtYBY6iHBuDj2qWGgLSGfy8+R\npf8fprmCpp9psI/MYh8Zfm9rbiMhhHRGDie5t7a2oqioCAsXLkRGRga+/vprNDc3c3qv7hf+XbcF\n46fZQxlvALoE3WGRQUaL9rIlyQ8I98eddq7FFyP3RXz7I/OPDemhf3xeCy22zxpiFHj4eXYEXGZN\nkUiMbpx/GWZcWLWpfSTo2ycH4aGB5gv82oqPUTy2pw9fTekILGTtI3xsN2qut9Xu/p54zWBBasOR\nqftjumFkdDDDu5jz7W4P8eF1Sup9ljIEY2I6ghzrxUiNPyAPiQQyDwl8PaVmDycwfWa2hif/GN8X\nGx9JsLqfpfx501xBa4Gf6Weg1rDsSAghXZTFAEsul6OiokL/vUKhgFxu/Nd0aGgohg4dipCQEPTo\n0QN9+/ZFUVGRxZPqbtCGo0hsT6xtenQAvn5iIADgwTvaghEfTw+Mi5Pr6zgZThWtnhZvuXCjSRt0\n1j3Yr604KUMzghlGrgynw8zyVUwO/pdhPRmT4wO8ZRaf1Gs7D+PLvOsrN07y1rk/tq2/JTAs08CS\ng8WxsRKJBKP7ttVr2vzoALwyumP05NUxvRHdjTkfi+nwGx9JsOuJtW6+zAGlbiSQrzpXZqyUYQBs\nX1/SU+rB+geHUeacDUO8tq4u8PuterNt/l7CWvORmKP8HfGjPhQui4VxYmJiUFZWhvLycsjlchw+\nfBgLFiww2mf48OHIyspCU1MTWlpacOnSJfTvz1zQ0ZTho/sA88hI21/KHTe7jx9JQK8gb8g8JFbr\nOJka0jMAJ2/UWdzH9Nb20+yhrEFQSkzbeopsrxsd18ZAyZ4n5fw8PTgli7Phch2AhRwslu0f/on5\nwQAA6BnEffQtzMJTiLYKD/CEoqGF8bXvnhpsdZTKUn/aMlXtzAB6SM8AoyKmtlSAeGxID0RbqHPW\nR+6D8roW3KxrG61WMxx8+uAeuD9Gjoo/LP/BRdyHcnfEj/pQuCwGWFKpFOnp6cjMzIRarcbYsWMR\nFRWF3NxcAEBqaioiIyMxZswYvPrqq2hpacHUqVPh48PtJh8d4oNts4bov+eS2G1pDT1rnrs7Eunf\nnWN8Tb+mn8kNjynomDG0BwK9ZXh4UDiaWzW43XTEhfGu2bFt7shIpxRT/ejhBLNEcK64BDr6vByW\nbvKUSjB3ZBTW/bdUH2T4yDx4e2Q/Mtj+vrcF1/UIASDnz/1Yf6a4YMsfmzG0B1rUjkWSpsv5cH1w\nYlBEAMbFdrM4krZ8Qiw0AKZtOgmAOXjz95LC30uKij84N5kQQjoNq6WdBwwYgJUrVxptS01NNfp+\n/PjxGD9+vF0NMHwCytH1y6y93dapFzaGT8G9eB9DIVErF9K7my8GW1u6pP0Q4f7ck+DDWcoYWDMz\nsSfujzFfYsWUxHJ8BYlEggfv6I51/y01e4/QcP1ZGxYZiMJrtQDapoofvKOtOKfuskJ82kbVIgK9\nUFbLLfcQaPvMNx+7wTrsZ/qkJR/enRiL+mYNXvjBckAo85BY/b/iZfLkom1V3gkhpPPjvnYGT96Z\nEMOaUM33r+jEyEDGG+msxJ5G6+AZE0ZEoEVbEjzrcjI88vX0sHhDDQ/wRIiPJ+7sFYjDl6v1DwJY\nM21AmE2jQZYYBoC2lrBgwvUId0cH6wOsb54cxLrfnwZ0x4aj19Cnm4/RtJyZ9o/5iTsj4CEBetkw\nReoo3Qjg2mnxmL/tPK/HdkIBeuICutwdmmYSL+pD4XJ5gJUUZaFOFs+/pJez1NGacWcEw1YeAyuG\nYMXWkRyZhwRBLGUhXO2jhxLgIQF8PKX40x3sy6sY0gJ44Z7beDn/bSHeeHVMb/33tiZgW/Jl2h12\nvU9m0oaW9ghjzbR4+HhyCyrThjL9HDpffHc/PDWM/dz2/E/Q0AiWKNFNWfyoD4VLGHfwdp35V7Qt\nN62sB/uhN8uTdO7AVhfLVQzzlD56qD/rU4b2CLM6Bcv8U6mf2m5vWqifJ/7zl0Gcgyt38pBI8OQw\nftcK9JZ5oKGFajUQcdBotWhs0aC+2XzFCVsEeMtcMstAxElgAZbrQ6zlE2Ow5KcLHUnuDh5vfJwc\no/uEYNd5hdH2J+6MwLDIenxacMPqMbhOwXUVhj8XvXl6OICPAZeHB4bra6FJ0PbLtquKC/Oz+oQu\nIUJxUanCM1vPOnQML6kH1kyLtzv3lXR+grojOHLTm53U0+pfEkzBU2Kk8ZSlo0nZL+uWNTEJsIb2\nCsTQXoH4tOAGaw4aEZe/GhS+FWoyv13suJaMUdHI+LEYt+qZS18QYerK+TtM66XawlMqjBHbrtyH\nQieoAMvH0/6h1sc55LNYWsKD79U92A7njPXzhOadCcJfi47raCmXoD/AS4oYhmVwmNSoWjntJzYR\ngd5I6dsNV6pV7m4KsQHdlMWP+lC4rAZYRUVF2Lx5s74O1sSJE41eP3PmDFauXIkePdoW6x0xYgQe\nfvhhuxozrFcgPp0+wK73cnFbiA/WP2ReBDVzchxub6+v1ZkGIrji+5otPsggENEhPrhVx89oy3+e\nGsz6mukagU2twvirl0l0iA+uVKnQr7t9U9TPjoi0vhMhhHQRFgMsjUaDnJwcLFu2DHK5HEuWLMGg\nQYMQFRVltN+AAQOwePFihxsjkUgQybLYLl+YCnwO7tlREd6TpyfUHC0S6UquLBVgq/gwP9zBsMyQ\noxbcG80p2DErImsj058CpornQpH1p3hoYVybjhBCiH0sBlglJSWIiIhAeHjbGoDJyckoKCgwC7A6\nk0ER/oyjXLYa2isQO88prO/oZrvmDGVcRFkosh7s55Tj+sg8OAUSwyKDHJrWNZ1iHNW3G7r5CTMH\nTwxPQBJ+Uf6O+FEfCpfFAEupVCIsLEz/vVwuR0lJidE+EokE586dw8KFC9G9e3f85S9/EXUAJpFI\neFnGZnTfEAzuOZCHFjmXkIOrzqCxxTiR9rEhPfDYkB5uag0hxuimLH7Uh8Ll8FxAnz59kJOTg5Ur\nV+Kuu+7Cu+++y0e7RE8ikUAu0JEK4jqh9DNACCFdksUASy6Xo6KiQv+9QqGAXC432sfX1xfe3t6Q\nyWS4//77UV9fj7o6qodDCAB48P14KiGEEFGwGGDFxMSgrKwM5eXlaG1txeHDh5GUlGS0T1VVlX6h\n12PHjsHLywsBAQFMhyOky+ns8dXjNN0pamvXrtXn8BBxoj4ULos5WFKpFOnp6cjMzNSXaYiKikJu\nbi4AIDU1FUeOHEFubi48PDxw++23Y9GiRS5pOCFiIOnkhT96UBVrUaP8HfGjPhQuq3WwBgwYgJUr\nVxptS01N1X89YcIETJgwgf+WESJyw6OCMCwy0N3NIIQQ4gaCquROSGfyvyKoaO+wzj1ARwghdqOK\ngoQQ0kVR/o74UR8KF41gEULsRgNY4kb5O+JHfShcNIJFCLFbgBdVfyeEECYUYBFC7JbcOwSfP3aH\nu5tBCCGCQwEWIcRuUg8JegRSqQaxovwd8aM+FC6rOVhFRUXYvHmzvg7WxIkTGfcrKSnB66+/jpde\negkjRozgvaGEEKKTk5ODwsJCBAUFYdWqVQCAb775Bnv27EFQUBAAIC0tDXfe2bZQ986dO5GXlwep\nVIqnn34a/fu3LeheWlqKdevWobm5GYmJiUhLS3PPBbkJ5e+IH/WhcFkMsDQaDXJycrBs2TLI5XIs\nWbIEgwYNMlvMWaPR4IsvvsDQoUP1Vd0JIcRZUlJSMGHCBGRlZRltnzJlCqZMmWK0rbS0FHv37sWK\nFSugVCrx1ltvYe3atZBIJMjOzsacOXMQGxuL5cuX48SJExg6dKgrL4UQ0klZnCIsKSlBREQEwsPD\nIZPJkJycjIKCArP9fvrpJ9x99936vxwJIcSZEhIS4O/vb7ad6Q+8/Px8JCcnQyaTITw8HBERESgu\nLkZlZSVUKhViY2MBAKNGjUJ+fr7T204I6RosjmAplUqEhYXpv5fL5SgpKTHbp6CgAG+88QZycnIg\nkVh+cLuwsNCB5hJCCLtdu3YhLy8P8fHxeOqpp+Dv74/KykrExcXp9wkNDYVSqYRMJjNavF4ul0Op\nVLqj2W6jy92haSbxoj4ULofrYG3atAkzZsyARCKBVqu1OEU4duxYR09HCCGMxo8fj0ceeQSNjY34\n/PPP8dlnnyE9Pd3dzRI0uimLH/WhcFkMsORyOSoqKvTfKxQKo7/4AODixYtYvXo1AKC2thYnTpyA\nTCZDUlKSE5pLCCHMgoODAQB+fn544IEH8OGHHwJo+z2mUCj0+ykUCoSGhpqNWDH9fiOEEHtZzMGK\niSAmZXgAACAASURBVIlBWVkZysvL0draisOHD5sFTllZWcjOzkZ2djbuvvtuPPPMMxRcEUJcrrKy\nEgCgVqtx6NAhREdHAwCSkpJw6NAhtLa2ory8HGVlZYiNjUVISAh8fX1RXFwMrVaLgwcPYvjw4e68\nBEJIJ2JxBEsqlSI9PR2ZmZn6Mg1RUVHIzc0FAKSmprqkkYQQYmjNmjUoKipCTU0N0tPTMX36dBQV\nFeHSpUuQyWRISEjAzJkzAQBRUVEYM2YMFi9eDKlUirlz5+pzRefOnYt169ahqakJiYmJXe4JQsrf\nET/qQ+GSaKmuAiGEWLVnzx4MGzbM3c0Qtes1TZj1dRHvxy1Y1Jbfm7RyD+/HZuMpleDT6QMQHkCF\ndjuzwsJCu/PHqZI7IYQQQgjPHH6KkCuuFeHdpaKiAtnZ2aiurkZQUBBSUlKQkpKCxsZGZGVl4ebN\nm+jRowfmzZsHHx8fAMKvDq3RaPDqq68iNDQUixcvFu21qFQqfPLJJ7h8+TJaWlowd+5cREVFie5a\nfv75Z+zbtw8tLS1ISEjArFmzRNMnTJXT+Wx7a2srNm7ciAsXLiAoKAjz589HSEiI06+LEEKcxSUj\nWLqK8BkZGVixYgXy8vJQWlrqilNzJpPJMHPmTLz//vtYuHAhvvjiC5SWlmLr1q2Ij49HZmYm4uLi\nsHXrVgDG1aEzMjKQnZ2tL1GRnZ2N2bNnIzMzE5cuXcKJEyfcck07d+40qrov1mv55JNPkJCQgJUr\nVyIzMxORkZGiu5a6ujp89913eP3117F8+XLcuHEDJ06cEM11pKSkYOnSpUbb+Gz7nj174OnpiczM\nTIwbNw6bNm1y+jURWseuM6A+FC6XBFhcK8K7U0hICHr37g0ACAoKQmxsrL6I6ujRowG03WR0lZ6F\nXh1aoVDg+PHjuP/++/XbxHgtDQ0NOHv2rP46pFIp/Pz8RHctXl5e+utpbm5GU1MT/P39RXMdTJXT\n+Wy74bFGjBiB3377zenXRNoSoyk5WtyoD4XLJVOEXCrCC0lZWRmuXr2K+Ph4VFdX66cqgoODUV1d\nDQCCrw69efNmPPnkk2hsbNRvE+O1lJeXIygoCNnZ2bh48SLi4+Mxa9Ys0V2Ll5cXnnnmGTz//PPw\n9PTExIkTERcXJ7rrMMRn25VKJUJDQwF0BNF1dXUICAhw1eUQQgivKMndhEqlwurVqzFz5kx9PomO\ntWWAhOLYsWMIDg5Gnz59WCvri+Va1Go1Lly4gBEjRmD58uVoaWnBf//7X6N9xHAtNTU1+Pjjj/HB\nBx8gOzsb58+fx7Fjx4z2EcN1sBFz2wkhxBlcMoLFpSK8ELS2tmLVqlW477779AUHg4ODUVVVhZCQ\nEFRWVuqrRQu5OvT58+dRUFCAwsJCtLS0oLGxER9++KEoryU0NBQBAQH64rXJyck4cOAAQkJCRHUt\nJSUliIuLQ0REBABg5MiR+P3330XZJzp8tl33O0Iul0OtVqOhoYFGr1yAaiiJH/WhcLlkBItLRXh3\n02q1WL9+PaKiojB58mT99qSkJOzbtw8AsH//fn3gJeTq0GlpacjJyUF2djZefPFFDBw4EPPmzRPl\ntYSEhOhzeDQaDQoLCzFw4EAkJiaK6lr69++PCxcuoK6uDi0tLTh+/DgGDx4syj7R4bPtSUlJ2L9/\nPwDgyJEjGDRokFuuqauh/B3xoz4ULpcVGi0qKsKmTZv0ZRomTZrkitNy9vvvv+Nvf/sboqOj9dMd\nM2bMQL9+/Sw+ir5nzx79o+gJCQkAOh5F11WHnjFjhtuuq6ioCNu3b+dUpkGo13L9+nVkZ2ejpqYG\n0dHRmDdvHrRareiuZd++fdi7dy+am5sxdOhQTJ8+HU1NTaK4Dl3l9NraWgQHB+PRRx/F3XffzVvb\ndWUaSkpKEBQUhAULFgiuTENXLzRa39SKJrVjt4sWtQZ/+TcVGiXi4UihUarkTgghHHT1AOv38nr8\nPfeiQ8do1WhR06TmqUUdKMAizuJIgOWyQqOEEEKExZb8HS0AZWOrk1tEbEU5WMJFARYhhHRRdFMW\nP+pD4aIyDYQQQcjKysLx48fd3QxCCOEFBViEEEH461//ipqaGnzwwQfYuXMnmpub3d0kQgixGwVY\nhBBBqK2tRXl5Ofz8/BAUFIScnBx3N6nTo3XsxI/6ULgoB4sQIgg//vgjxo8fry/Gari8FnEOyt8R\nP+pD4aIRLEKIIAwYMEAfXBUWFqJ///5ubhEhhNiPAixCiCAUFRUxfk0IIWJEU4SEEEFQq9U4evQo\nJBIJ1Gr+i1ESc1RDSfyoD4WLAixCiCA88cQTOH78ODQaDdLS0tzdnC6BbsriR30oXDRFSAgRBKVS\nCYVCgatXr2Lbtm3ubg4hhDiERrAIIYLw448/4v7779cvGE0IIWJGARYhRBCCg4MRHR0NmYx+LbkK\n5e+IH/WhcNFvMkKIIFy4cAErV67Uj2AtXLjQzS3q/OimLH7Uh8JFARYhRBAWLVqE0tJSREdHQ6FQ\nuLs5hBDiEEpyJ4QIwhdffIHdu3cDAL777js3t4YQQhxDI1iEEEHw9vaGr68vAMDT09PNrekaKH9H\n/KgPhYsCLEKIIMjlchw9ehQbNmxAz5493d2cLoFuyuJHfShcFGARQgRh3LhxuOuuu6DRaBASEuLu\n5hBCiEMowCKECMKaNWsAAI2NjZBKpXjllVfc3CJCCLEfBViEEEFYsGABAKClpQU//PCDm1vTNVD+\njvhRHwoXBViEEEG4evUqJBIJVCoVqqqq3N2cLoFuyuJHfShcFGARQgThyJEjAAA/Pz9MmTLFza0h\nhBDHUIBFCBGEmJgY/dfXr1/H9evXMWzYMDe2iBBC7EcBFiFEEHbv3o3Y2FgAQElJCUaOHOnmFnV+\nlL8jftSHwkUBFiFEECIiIjB9+nQAwKeffoqUlBT3NqgLoJuy+FEfChcFWIQQQejevTtycnIAANHR\n0W5uDSGEOIYCLEKIIEyZMgU1NTXw8/ODVqt1d3MIIcQhtNgzIUQQvv32W2zZsgUymQz//Oc/3d2c\nLmHt2rX6HB4iTtSHwkUjWIQQQVCr1ejevTsA6Bd9Js5F+TviR30oXDSCRQgRBD8/P1y5cgXffvst\npFKpu5tDCCEOoREsQojbabVaJCQkYODAgdBoNEY1sQghRIxoBIsQ4nYSiQTHjh1Dnz59KLhyIcrf\nET/qQ+GiESxCiNvl5+fj9OnT2L17NwYOHAgAWLhwoZtb1flR/o74UR8KFwVYhBC3O3HiBN566y1s\n3LgRzz77rLubQwghDqMpQkKI21VUVKCwsFD/b2FhobubRAghDqERLEKI240cORI1NTX6f4lr0Dp2\n4kd9KFwUYBFC3I7WHXQPuimLH/WhcNEUISGEEEIIzyjAIoQQQgjhGQVYhBDSRVENJfGjPhQuysEi\nhJAuivJ3xI/6ULhoBIsQQgghhGcUYBFCCCGE8IwCLEII6aIof0f8qA+Fi3KwCCGki6L8HfGjPhQu\nGsEihBBCCOEZBViEEEIIITyjKUJCiOjk5OSgsLAQQUFBWLVqFQCgsbERWVlZuHnzJnr06IF58+bB\nx8cHALBz507k5eVBKpXi6aefRv/+/QEApaWlWLduHZqbm5GYmIi0tDS3XZM70Dp24kd9KFw0gkUI\nEZ2UlBQsXbrUaNvWrVsRHx+PzMxMxMXFYevWrQDagqi9e/dixYoVyMjIQHZ2NrRaLQAgOzsbs2fP\nRmZmJi5duoQTJ064/Frcaf78+XRjFjnqQ+GiAIsQIjoJCQnw9/c32lZQUIDRo0cDaAvA8vPzAQD5\n+flITk6GTCZDeHg4IiIiUFxcjMrKSqhUKsTGxgIARo0apX8PIYQ4yqVThHv27HHl6QghAjF27Fin\nn6O6uhohISEAgODgYFRXVwMAKisrERcXp98vNDQUSqUSMpkMcrlcv10ul0OpVDq9naTz8JRK3N0E\nImAuz8EaNmyYq09JCHGjwsJCl59TIqEbHxeUv2O/FrUW/7vnD8ikjk0E/c+ISPSV+9r9fupD4aIk\nd0JIpxAcHIyqqiqEhISgsrISwcHBANpGphQKhX4/hUKB0NBQsxErhUJhNKLVFdBN2TGnyuodPoZG\no3Xo/dSHwkU5WISQTiEpKQn79u0DAOzfvx/Dhw/Xbz906BBaW1tRXl6OsrIyxMbGIiQkBL6+vigu\nLoZWq8XBgwf17yGEEEfRCBYhRHTWrFmDoqIi1NbWIj09HY8++igefvhhZGVl4eWXX9aXaQCAqKgo\njBkzBosXL4ZUKsXcuXP1U4hz587FunXr0NTUhMTERAwdOtSdl0UI6UQowCKEiM6CBQsYt7/yyiuM\n2ydNmoRJkyaZbY+KisI777zDa9vEhPJ3xI/6ULgowCKEkC6KbsriR30oXJSDRQghhBDCM4sjWEzL\nUZj68ssvUVhYCG9vb8ydOxeRkZFOaSghhBBCiFhYHMFiWo7CUGFhIS5fvozMzEzMmjUL69at472B\nhBBCnGPt2rX6HB4iTtSHwmVxBCshIQHl5eWsrx87dky/NEVcXBzq6+v1dWgIIYQIG+XviB/1oXA5\nlIOlVCoRGhqq/163BIWQaTQao+91i74SQgghhPDF4SR3PgOUs2fPYurUqZg0aRJWr15t9vrXX3+N\nadOmYdq0adi9ezeAtnXGHnnkEaSmpuK5554DAHz//fcYOXIkkpOTceDAAQDA1KlTsXjxYjz44IP4\n17/+hTlz5mDq1Km0PqKLPP7449i/f7+7m0EIIYS4hEMBFtMSFI4sNdGnTx9s374dO3bswC+//ILa\n2lqj16dOnYpt27bhiy++wIYNGwAAW7duxZQpU5Cbm4v169cDAD755BP8/PPP2LZtG7KzswG0rU2W\nnJyMbdu2QavVQqVSYfv27Rg3bpzd7SXcNTc3m40eEkLci/J3xI/6ULgcqoOVmJiI3bt3Izk5GefP\nn4e/v79D+VcVFRV46623UFpaiitXruD06dMYOXKk/vX8/Hx89NFHqKqqwpkzZwAAhw4dwltvvaXf\np7a2Fp6envD394e/vz+qqqr0r+nyxXTBFiGEdGWUvyN+1IfCZTHA0i1HUVNTg/T0dEyfPh1qtRoA\nkJqaimHDhuHs2bPIyMiAj48P0tPTHWrMli1bMG3aNEyePBlTp041m37MzMzE5s2bIZVKMWjQIADA\nvffeiz179mDmzJnQarUIDAxEc3Mz6urqoFKp0K1bN/37PT099V97eXk51FZCCCGEEDYWAyy25SgM\nPfHEE3jiiSd4aczYsWOxbNkybNq0CYGBgWavP/TQQ5gxYwYiIyPRq1cvAMDDDz+MOXPmYMuWLYiJ\nicH69evx4osv4qmnnoKnpycyMjIYz6Vbi4wQQgghhG+CWipn+PDh2LVrF+vrs2bNwqxZs4y2hYSE\nYOvWrUbbxo0bZ5ZbtW3bNv3XaWlpjjeWEEJEjtaxEz/qQ+ESVIBFCCHEdeimLH7Uh8JFaxESQggh\nhPCMAixCCCGEEJ5RgEUIIV0U1VASP+pD4aIcLEII6aIof0f8qA+Fi0awCCGEEEJ4RgEWIYQQQgjP\nKMAihJAuivJ3xI/6ULgoB4sQQrooyt8RP+pD4aIRLEIIIYQQnlGARQghhBDCMwqwCCGki6L8HfGj\nPhQuqzlYRUVF2Lx5M9RqNcaOHYuJEycavd7c3IyNGzfi8uXL8PX1xZQpUzB8+HCnNZgQQgg/KH9H\n/KgPhcviCJZGo0FOTg4yMjKwYsUK5OXlobS01Gifffv2wdvbGytXrsQLL7yAzz77DFqt1qmNJoQQ\nQggRMosBVklJCSIiIhAeHg6ZTIbk5GQUFBQY7ePn54fGxka0trairq4OXl5ekEgkTm00IYQQQoiQ\nWZwiVCqVCAsL038vl8tRUlJitM+9996LY8eOYc6cOdBoNHj77bed01JCCCG80uXu0DSTeFEfCpfD\ndbB27dqF/2/vzqOautP/gb9DIqtswS+CUFpArdLFBRAtrnXQ6tTpOF2OrXPOr850fi206nFpUada\nR3sKRZwuonR0utjWztQuY62/aotQlUVHAi4jATEqIEqIEEKAsCTh/v7wa0bKFkkCCbxff/SYcHM/\nz829SZ7e+9znIxaLsXv3blRUVCA5ORk7d+6EkxPr54mI7Bl/lB0f96H96jELkkqlqKmpMT2ura2F\nVCrtsExxcTGmT58OFxcXjBkzBr6+vqiqqrJNtEREREQOoMcEKzw8HEqlEiqVCgaDAXl5eYiKiuqw\nzIMPPoiCggK0t7ejuroajY2NCAoKsmnQRERERPasx0uEYrEY8fHxSE1NNbVpCA4ORkZGBgAgLi4O\nsbGxqKysxPr16+Hl5YXnn3++P+ImB5KQkAAAKCgogEajweLFiwc4IiICWL8zGHAf2q9ea7AiIiKQ\nkpLS4bm4uDjTv93d3bFs2TLrR0aDxj//+U/Mnj0bFRUVaGxsZIJFZCf4o+z4uA/tFyvRiYiIiKyM\nCRYRERGRlTHBIiIaojiPnePjPrRfFvfBIiIix8T6HcfHfWi/eAaLiIiIyMqYYBERERFZGRMsIqIh\nivU7jo/70H6xBouIaIhi/Y7j4z60XzyDRURERGRlTLCIiIiIrIwJFhHREMX6HcfHfWi/WINFRDRE\nsX7H8XEf2i+ewSIiIiKysl7PYMnlcuzduxdGoxFz587FggULOi2jUCiwd+9etLS0wMPDA5s3b7ZF\nrEREREQOoccEq729Henp6di4cSOkUinWr1+Phx56CMHBwaZlmpqasGvXLvz5z3+Gn58ftFqtzYMm\nIiLL3a7d4WUmx8V9aL96TLAUCgUCAgLg7+8PAIiNjYVMJuuQYOXk5CAmJgZ+fn4AAC8vLxuGS0RE\n1sIfZcfHfWi/ekyw1Go1RowYYXoslUqhUCg6LKNUKmEwGLBp0ya0tLRg0aJFmDFjhm2iJYdiNBrR\n1tbW4bn29nY0NzfDzc1tgKIiIiKyPYuL3A0GA+RyOVavXo01a9Zg//79nX5UaWg6ceIEli5d2uG5\nmpqaLuv4iIiIBpMeEyypVIqamhrT49raWkil0g7L+Pn5YeLEifDx8cHIkSMRFhYGuVxum2iJiMhq\n2EPJ8XEf2q8eLxGGh4dDqVRCpVJBKpUiLy8PK1eu7LBMdHQ00tLS0NraCr1ej7KyMowbN86mQRMR\nkeVYv+P4uA/tV48JllgsRnx8PFJTU01tGoKDg5GRkQEAiIuLQ1BQEObMmYN169ZBr9dj0aJFcHV1\n7ZfgiYiIiOxRr32wIiIikJKS0uG5uLi4Do/nzZuHefPmWTcyIiIiIgfFqXKIiAa5Ck0LqrStnZ7/\n94G9AICY3/6fXtehbOTNS/aIfbDsFxMsIqJB7kZ9KzZlXOn8B/dbLXUO/dTF38ghMLGyX5yLkIiI\niMjKeAaLiAaVl19+GW5ubnBycoJYLEZSUhKam5uRlpaG6upqjBw5EsuXLzfdjPPDDz8gKysLYrEY\ny5Yt413QRGQVTLCIaNDZvHkzhg8fbnr8zTffYOzYsXj11Vdx4MABfPPNN1i6dCkqKyvx888/Izk5\nGWq1Glu3bsV7770HJ6ehcXL/cV02AOCQO2ffcFSswbJfQ+NbhIiGFEEQOjyWyWSYNWsWAGD27NnI\nz88HAOTn5yM2NhYSiQT+/v4ICAjoNB3YYHbIfQaTKwe3YsUKJld2imewiGhQEYlE2LJlC0QiEebN\nm4df/epXqK+vh4+PDwDA29sb9fX1AIC6ujqMGTPG9Fo/Pz+o1eoBiZuGJrGTCOomvUXrcJaIMNyF\nP+f2hnuEiAaVrVu3wtfXF5WVlUhKSkJQUFCHv4tEoh5f39vfiaxp9aFLGOZk2TG3/tH7MGmUp5Ui\nImthgkVEg4qvry8AIDg4GFOmTIFCoYC3tzc0Gg18fHxQV1cHb29vALfmW62trTW9tqv5Vgcz1mAN\nvKY2o0Wvf1yXjeyvgUm8TGh3WINFNvHOO+/gyJEj3f59+vTp0Gg0/RgRDQWtra1obm4GAGi1Wpw5\ncwYhISGIiorCsWPHAADHjx9HdHQ0ACAqKgq5ubkwGAxQqVRQKpUYPXr0QIXf71iD5fgOuc9A7O+e\nH+gwqAs8g0U2UV9fb/qh60pVVRXa29v7MSIaCurr67Ft2zYAgKenJ379619jwoQJGDt2LNLS0rB2\n7VpTmwbg1lmuOXPmIDExEWKxGAkJCbxESERWwQSLiAYNf39/U4J1Jzc3N7z66qtdvmbhwoVYuHCh\nrUMjoiGGlwiJiIaox3XZpjosckyP67KR++0nAx0GdYFnsIiIhijWXzm+Q+4zkPRY+ECHQV3o9QyW\nXC5HYmIi1q5di8OHD3e7nEKhwJIlS/Dvf//bqgESEREROZoeE6z29nakp6djzZo1SE5ORlZWFior\nK7tcbt++fZg4cWKnDspEREREQ02PCZZCoUBAQAD8/f0hkUgQGxsLmUzWabnDhw9j6tSp8PLyslmg\nRERkXazBcnyswbJfPSZYarUaI0aMMD2WSqWdppFQq9WQyWSYN28eAHZBJiJyFOyD5fjYB8t+WXwX\n4SeffILnnnsOIpEIgiDwEiFBLpdDr+99bq2ysjLO+0ZERINSj3cRSqVS1NTUmB53NY3ElStX8O67\n7wIAGhoacPbsWUgkEkRFRdkgXHIECQkJiIiIgETS802qf/3rX/Hkk09i8eLF/RQZERFR/+jxFzA8\nPBxKpRIqlQpSqRR5eXlYuXJlh2XS0tJM/961axciIyOZXBEROQDORej4btVgZSOScxHanR4TLLFY\njPj4eKSmpsJoNGLu3LkIDg5GRkYGACAuLq5fgiQiIutjYuX42AfLfvXaaDQiIgIpKSkdnususUpI\nSLBOVEREREQOjFPlEBEREVkZEywioiGKfbAcH/tg2S/ORUhWo9fru2xE2xONRoPS0lKMHTvWRlER\nUXdYg+X4WINlv3gGi6ymoaEBv//97+/qNefPn+9wJyoREdFgwASLiIiIyMqYYBERDVGswXJ8rMGy\nX6zBIiIaoliD5fhYg2W/eAaLrMJgMKClpaVPrxUEATqdzsoRERERDRwmWGQV33//PV577bU+vbap\nqQlTp061ckREREQDhwkWEdEQxRosx8caLPvFGiwioiGKNViOjzVY9otnsMhiW7ZsQX5+vsXr+e1v\nf4uKigorRERERDSweAaLLFZTUwOxWGzxeiorK6HX660QERER0cDqNcGSy+XYu3cvjEYj5s6diwUL\nFnT4e3Z2Ng4ePAgACA4OxuLFixESEmKbaImIyGpu11/xUqHjulWDlY3IFSsGOhT6hR4vEba3tyM9\nPR1r1qxBcnIysrKyUFlZ2WGZkSNH4i9/+Qu2bduGCRMm4G9/+5tNAyb78vnnn1v1rFN+fj6Kioqs\ntj4i6t4h9xlMrhzcIfcZiP3d8wMdBnWhxwRLoVAgICAA/v7+kEgkiI2N7TSZ79ixY+Hu7g4AmDx5\nMmpra20XLdmdlJQUtLW1WW19WVlZOHXqlNXWR0RENBB6TLDUajVGjBhheiyVSqFWq7td/ujRo4iO\njrZedGS3mpub8Z///Mcm69bpdLh06ZJN1k1ERNQfrHYX4YULF5CdnY0lS5ZYa5Vkx27cuIFly5bZ\nZN1XrlzBli1bbLJuIvov9sFyfOyDZb96TLCkUilqampMj2trayGVSjstV15ejt27dyMxMREeHh7W\nj5LsikwmQ3a2bb+UjUYjtm3bZtMxiIY61mA5PtZg2a8e7yIMDw+HUqmESqWCVCpFXl4eVq5c2WGZ\nmpoabN++HcuXL0dAQIBNgyXr02q12LFjB0aMGIHFixfD39+/x+UzMzNRWFiI0tJSm8YlCAJ27NiB\ncePGISYmpte4Dhw4YKoFe+aZZ2waGxERUW96TLDEYjHi4+ORmppqatMQHByMjIwMAEBcXBy+/vpr\nNDY2Ys+ePabXJCUl2T5ysthPP/2E48eP45tvvoFUKkVlZSWCgoIQFhaGefPmmZbTaDQoKSnB9u3b\nMWzYsF6THWvasWMHzp49i8DAQDz88MOYMmWK6W9qtRrHjx/HZ599Bm9vb/j4+ECj0WD//v34+uuv\n+y1GIiKiX+q1D1ZERARSUlI6PBcXF2f690svvYSXXnrJ+pENMSUlJaioqMDFixcRERGBRx99FCKR\nCAaDATqdDl5eXn1a7/79+6HVauHr64u2tjZkZGRgzpw5qKioQGtra4e2Gzdv3oRWq0VmZiZCQ0OR\nkZGBhoYGaLVa5Ofno6SkBLGxsdbaZLNVV1dDpVLhwIEDqK+vx9KlSzFp0iQYDAZs27YNarXaNFm0\nIAiQy+V48cUXMX/+fOh0OhQVFSEwMBASiQShoaEIDg7GQw89dNdx6PV6tLS0wNPTEwBQVlaG8vJy\nnDp1CpMnT8asWbPg7Oxs1W0nsiX2wXJ87INlv9jJvY8EQQAAiEQii9d15coV7Nu3D1qtFuXl5Xjj\njTewZMkSeHp6orW1FWfOnMGKFSsQGhoKQRDQ1tYGlUqFGTNmwMvLCxKJBIIgoLm5GVlZWSgpKUFD\nQwNu3ryJb7/9FjNnzkRgYCBaW1tRXFyMU6dOYcqUKfD19e1226ZPn47o6GjTmPaitLQUmZmZKCkp\nQUtLS7fLnT9/HiKRCC4uLqiqqsLHH3+MyMhIhIWFQSKRoKmpCSUlJVi3bh3Gjh2LkJAQU3Kk1WpR\nVVWFqqoqtLa2QiqVorW1FQqFAgcPHkRBQQEWLFgADw8PNDc3o7i4GB988AEOHTqEe+65p8/J8J2s\neXwRdYeJlePjXIT2iwnWXdLpdDhw4AB27tyJPXv2ICIi4q5ef+DAAQiCABcXF4hEIly9ehVfffUV\nIiIiIJH8d3colUpIJBIYDAYAQGJiIqZNm4YRI0ZAq9Xi9OnTcHV1xerVq9HS0oKzZ8/i22+/xfjx\n43H//fdDEASUl5dbddsHC71ej7KyMly9ehWffvopAgIC0NLSgpqaGixatAgnT56Em5sbbt68Tp6R\nwwAAEpxJREFUiVOnTiEoKAgPPPAAhg0bBgBoaGiAUqnsNGNBUlISoqOjcd9996GtrQ1qtRrh4eG4\n5557cP/9999VjC+99BIeeOABTJgwAbNmzbLathMRUf9ggmWmM2fOQCaTQSaT4dixYxgxYgTS0tJw\n7733YubMmZg2bVqPr9doNDh06BBSU1MRGRkJLy8vqFQq5OXl4b777utzXD///DPc3d1RXV3d53XQ\nrVkLysvLkZqaiilTpsDNza1P6ykrK8P58+dRUFAAsViMyZMnY/To0Xj44Yc7TTP1S1qtFjKZDK+8\n8gqio6Nx9epVnDlzBomJiUhKSjIdN0REdxKJgJomyxo+D3MSwdttmJUiImAQJliCIEAQBDg5md/i\nq6ysDCKRCPfeey8AoK6uDm5ubnB1dQVwKznavXs3XF1dO1yWEgQBVVVV2L59O9auXYuIiAi4ubmZ\nznTcVlJSgqKiIrzzzjtW2EJyNFVVVbhw4QLUajWWLl3a6e9arRZqtRqXLl3C5s2bYTQaTX8TBAEN\nDQ3YsGEDdu3ahfHjx5uOy9raWnh6epoubebk5GDatGlmT7zdl88KDS6swXJ8j+uykfNNNo77zrZo\nPStj78HMsK7LRqhvBlWCpdVqUVRUhMTERBgMBmzduhX3338/goODTctUV1dDo9Hg5s2bSExMxKhR\noxAYGAixWAyDwQCNRoO2tjbcd999piLzw4cPIzo62vTD9kuCIOA3v/kNYmJiEBYWhmHDhsHT0xOH\nDh1CbGwsnJ2dUVdX119vA9kho9GIDRs2ICMjA4GBgXBxcYGLiwvef/99xMbGIjg4uMeaMgD44IMP\nMGLECHh4eGD//v0YNWoUZsyYARcXFygUCnz33Xd47LHHEBQUBIlEghkzZiAkJAQhISEdkv6srCzk\n5uaisbERCoUC77//PoKCgmz9FpAdYmLl+Ez7sNXY84K90LfbT63tYOHQCZZer0dzczOOHDmCy5cv\no6GhwTSVz82bN7F3714EBgZCJBJh0qRJ+PLLL+Hv7w9XV1colUqUlZVh+PDhCAgIgF6vx5UrV3Dm\nzBnMnDkTra2taGlpwblz5+4qptvF5ufOnYNKpYJKperXtgZk365fvw4PDw+IxWJcvXq1w9kqczQ2\nNuLSpUuorq7GqFGjcOPGDQDA5cuXAQBNTU3QarXQ6XRYt24dmpqa8Nhjj2HMmDEYNWoUUlNTIQgC\nHnjgAdOZr4cffhirVq3ChAkTEBwcjPDwcF6KJCKyUL8nWMnJyRCJRNBoNAgNDcWjjz6K0aNHd1jm\nxIkTOH/+PPLy8rB48WLMnz+/0xf+p59+ivLycshkMuTk5GD27NkdzlTd1tbWhvr6emzduhUeHh4d\n5lYkGgrUajWKi4uRm5uLGzduIDAwsNMy+fn5UKlUMBqNKC4uhr+/P/7+979j+PDhHZbLy8vD5cuX\nIZfL0dLS0uVl748++gjOzs7Q6/VQKBR48sknbbZtRET2qt8TLKVSiWvXruH48eN45JFHUFNTAycn\nJ5w4cQKrVq3Ct99+C3d3d9TX16OkpARvvfUWamtrTbfEGwwGnDx5stsWA0RkuVOnTuH111/Hgw8+\nCKlUigsXLsDFxQXl5eVwdnbGzZs3kZOTg7KyMixbtgwtLS3Yv38/hg0bBr1eb7rkWVRUxATLjrEG\ny/FxH9qvAb9EqFQqYTQa0dbWhhdeeAHTp0+Hi4tLh2VkMhk8PT1NX+phYWHw8vIyu5iXiO5eTU0N\nioqKoNVqUVhYCJFIhMjIyA7NVBsaGvDjjz9CLBajra0Nx44dw+zZswcuaLor/FF2fNyH9ou3DxER\nERFZGRMsIiIiIitjgkVENEQ9rss21fCQY+I+tF8DXoNFREQDg/U7jo/70H71mmDJ5XLs3bsXRqMR\nc+fO7XK6jy+++AKFhYVwcXFBQkICmxYSERHRkNbjJcL29nakp6djzZo1SE5ORlZWFiorKzssU1hY\naJrD7fnnn8euXbtsGjAREd0dJ9FAR0A09PR4BkuhUCAgIMDUiTw2NhYymaxDQ8+CggLMmjULADBm\nzBg0NTVBo9HAx8fHhmETEQ0NJaom5JVrLFrHxZu6Lp9nDyXHx31ov3pMsNRqdYfO51KpFAqFotMy\nfn5+psd+fn5Qq9VMsIiIrOBybTP+eU5lk3XzR9nxcR/aL5EgCN3O8Hjq1CmcO3cOL774IoBbU9go\nFAr84Q9/MC3z9ttv44knnsC4ceMAAFu3bsXSpUsRFhbWaX2ZmZn41a/mWnsbiMiOHT2aiblzHf9z\nn5mZicmTJ/f7uP+vuAbv5V7r93Ediey1W8dXVErmAEfiuBJn34u5o6UDHYbdKSws7PP3V49nsKRS\nKWpqakyPa2trIZVKOy1TW1vb4zJ3Uqvr+hQoETmmwsKBjoCIqP/1WOQeHh4OpVIJlUoFg8GAvLw8\nREVFdVgmMjISJ06cAACUlpbCw8ODlweJiBwAeyg5Pu5D+9XjGSyxWIz4+Hikpqaa2jQEBwcjIyMD\nABAXF4fJkyejuLgYa9asgaurK+Lj4/slcCIisgzrdxyftfZhi74d1Q2tFq3DyUmE//Fw7n3BIaLX\nPlgRERFISUnp8FxcXFyHx0uXLsXSpUutGxkRERH1i/dzr0FkYTuP3z34P/i/McG9LzhEsJM7ERHR\nECcA6P6WN/O0W/j6wYZzERIRDVGs33F83If2i2ewiGjIM2dKsMGINViOj/vQfjHBIqIh7faUYBs3\nboRUKsX69evx0EMPdZixgojM094uwNIrhU4iQGRpQZgdYIJFREOaOVOC9ZW8ugn1LQaL1nH2RoPF\ncRD1hyMXa1HazbRM5grxcUXCtCA4S8RWimrgMMEioiHNnCnB+up7+U1kXrbf5sqcx87x2dM+1Onb\ncaG6yaJ1tBkHT6V8vydYhWzrTEQO6m6/v+K8gbj+n13nLkz/3/86+I/a0aP/+w8H344+GST70KQJ\nF86fG+ggrKJfE6zBMB8ZEQ0u5kwJBvD7i4juDts0ENGQZs6UYEREd0skCJa2FiMicmxyuRyffPKJ\nqU3DwoULBzokInJwTLCIiIiIrIyXCImIiIiszGZF7idPnsRXX32F69evIykpCWFhYaa//fDDD8jK\nyoJYLMayZcswbtw4AEBlZSV27dqFtrY2REZG4tlnn7VVeP3mq6++QmZmJry8vAAAzz77LCZNmgSg\n+/dhsBlqXbJffvlluLm5wcnJCWKxGElJSWhubkZaWhqqq6sxcuRILF++HK6urgMdqsXS09NRWFgI\nLy8vbN++HQB63FZHOObNPV4VCgVef/11rFq1CjExMTYds6ioCCkpKRg5ciQAICYmBk8++aTNxgNu\nbd/evXvR0tICDw8PbN682WbjHTx4EDk5OQAAo9GI69ev48MPP4SHh4dNxmtra8OePXtQXl4ONzc3\nPP7444iOju7bxpkx3u3PREVFBby8vLB8+XIEBAT0ebyuPne/9MUXX6CwsBAuLi5ISEhAUFCQzca7\nfv06du3ahbKyMixZsgSLFi3q81jmjJednY2DBw8CAIKDg7F48WKEhITYbLz8/Hzs378fwK2bYp5+\n+mmMHj269xULNlJZWSlcv35d2Lx5s3D58mXT89euXRPWrl0r6PV6obq6WnjllVeE9vZ2QRAEYd26\ndcKlS5cEQRCEt956Szhz5oytwus3+/fvF77//vtOz3f1PhiNxgGI0LaMRqPwyiuvCNXV1YJerxfW\nrl0rXLt2baDDsqmEhAShoaGhw3OfffaZcODAAUEQBOFf//qX8Pnnnw9EaFYnl8uFK1euCKtXrzY9\n1922OsIxb+7xajQahc2bNwtJSUnCyZMnbT7mhQsXhOTkZIvGuZvxGhsbhVWrVgk1NTWCIAhCfX29\nTce7k0wmE7Zs2WLT8X788Udhz549giAIgkql6vA7ZIvxMjIyhI8++kgQBEG4ePGisGnTpj6NdVtX\nn7s7FRQUCG+99ZYgCIJQWloqbNiwwabj1dfXCwqFQvjHP/4hHDx40KKxzBnv4sWLQlNTkyAIgvDz\nzz/bfPuam5tN/y4qKjJ7/9nsEmFQUBBGjRrV6fn8/HzExsZCIpHA398fAQEBuHTpEurq6tDS0mLK\nCmfOnIn8/HxbhdevhC7K3Lp6H6zV3NCe3NklWyKRmLpkD3a/3OcymQyzZs0CAMyePXvQHNvjx4/v\ndJahu211hGPe3OP18OHDmDp1qunMdH+MaS3mjJeTk4OYmBj4+fkBgEXbebfbl5OTg9jYWJuO5+7u\njubmZhgMBjQ2NsLZ2bnPU7OYM96FCxdMVy7Gjh2LGzduQKvV9m0D0fXn7k4FBQWmz+CYMWPQ1NQE\njUZjs/G8vLwQHh4Osdg63dd7G2/s2LFwd3cHAEyePBm1tbU2He/Oqw06nQ7Dhg0za739XoNVV1dn\n+tACgJ+fH9RqNerq6jr0npFKpVCr1f0dnk0cOXIEq1atQnp6OpqabnW57e59GGy66pI9GLfzTiKR\nCFu2bMFrr72Go//bALG+vh4+Pj4AAG9vb9TX1w9kiDbV3bY6wjFvzvGqVqshk8kwb948AJbPmWbO\nmCKRCBcvXsTq1auRlJSEyspKm46nVCrR2NiITZs24bXXXkN2drZNx7uttbUV586ds+iSqznjTZ8+\nHe3t7fjjH/+ITZs2YcWKFTYdb8KECcjNzUVbWxtkMhm0Wq3FSUFvMdn7Z81ajh49atHlXXOdPn0a\nL7/8MtLT0/Hiiy+a9RqLarC2bt3aZVb87LPPDqk+Mj29D/PmzcNTTz2F5uZmfPbZZ/j0008RHx/f\n5XoGw+SWdOt48PX1RWVlJZKSkjrVPgyl/dzbtjrie/HJJ5/gueeeg0gkgiAIXZ6htrbQ0FCkp6dD\nLBbj+PHjePvtt7Fjxw6bjWcwGCCXy7Fx40a0trbizTffRExMDJydnW02JnDrzMu4ceP6XHtlriNH\njkAsFmP37t2oqKhAcnIydu7cCScn25xzeOSRR1BbW4s33ngDo0aNQlhYmM3Guq0/jsuBduHCBWRn\nZ+PNN9+0+VhTpkzBlClTkJeXh23btiElJaXX11iUYG3cuPGuXyOVSjtk7rW1tfDz8+uU9XfXTdke\nmfM+uLu7Y/78+aYvxa7eB0fZ3rthbpfswcTX1xfAreLLKVOmQKFQwNvbGxqNBj4+Pqirq4O3t/cA\nR2k73W2rIxzz5hyvV65cwbvvvgsAaGhowNmzZyGRSPr8P5XmjOnm5mb696OPPop9+/ahsbERw4cP\nt8l4fn5+mDhxoulMZFhYGORyOSZOnGiT8W7Lzc216PKgueMVFxdjzpw5cHFxwZgxY+Dr64uqqqo+\nFYKbM56LiwueeuopPPXUUwBu3Qhz+4YFW3CEz5qlysvLsXv3bmzYsMHmCfmdHnnkEXz44YfQ6XSm\ny5Td6fdLhFFRUcjNzYXBYIBKpYJSqcTo0aPh4+MDNzc3XLp0CYIgIDs7u19O+9laXd2tiV6NRiNy\nc3NNdzp09z4MNkOtS3Zrayuam5sBAFqtFmfOnEFISAiioqJw7NgxAMDx48cHxbHdne621RGOeXOO\n17S0NOzcuRM7d+7E1KlT8cILL1h0TJszpkajMZ2RKCgogLOzc5+SK3PHi46OhlwuR2trKxobG1FW\nVtbnOz7N/Q7Q6XQoLi62+LNhzngPPvggCgoK0N7ejurqajQ2Nvb5LjtzxtPpdDAYDABuXdKKiIiw\n6V3EkZGROHHiBACgtLQUHh4epmR5MKipqcH27dstvhvTXEql0vT5KywshLOzc6/JFWDDRqOnT5/G\nxx9/DK1WC3d3d4SGhmLDhg0Abt2qnZmZabpVe/z48QD+26ahtbUVkZGReO6552wRWr9KS0tDWVkZ\nJBIJxo8fjyeeeMJ0oHf3Pgw2Q6lLtkqlwrZt2wAAnp6emDZtGuLi4gZtm4b33nsPcrkcDQ0N8Pb2\nxjPPPIOpU6f22KbB3o/5ro7XjIwMAEBcXFyHZXft2oXIyEirtGnoacwjR44gIyMDTk5OuPfee7Fw\n4cIOrW+sPR4A/PTTTzh8+DD0ej0WLVqE+fPn23S8Y8eO4dy5c1i5cmWfxzF3PJ1Ohy+//BIlJSXw\n8vLCggULMHly32fl7m280tJS7Ny5Ey4uLggJCcGf/vQnuLi49Hm82587rVYLHx8fPP300zAajabx\nAGDfvn0oLCyEq6sr4uPjERwcbLPxNBoN1q9fD51OBycnJ7i6uuKdd97p83dcb+N98MEHOH36tKn2\n7XY7HFtt33fffYcTJ05ALBYjNDQUjz32GEJDQ3tdLzu5ExEREVkZO7kTERERWRkTLCIiIiIrY4JF\nREREZGVMsIiIiIisjAkWERERkZUxwSIiIiKyMiZYRERERFb2/wF5uA6SJSuFmgAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11180e390>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Proportions of mortality estimates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[p.summary() for p in adult_model.pi]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "pi_ATL:\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.582            0.017            0.002            [ 0.546  0.61 ]\n",
        "\t0.009            0.003            0.0              [ 0.003  0.015]\n",
        "\t0.01             0.004            0.0              [ 0.004  0.018]\n",
        "\t0.078            0.01             0.001            [ 0.057  0.099]\n",
        "\t0.11             0.004            0.0              [ 0.103  0.116]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.547            0.572           0.583          0.594         0.612\n",
        "\t0.004            0.007           0.009          0.011         0.017\n",
        "\t0.005            0.008           0.009          0.012         0.018\n",
        "\t0.059            0.071           0.078          0.085         0.102\n",
        "\t0.102            0.107           0.11           0.113         0.116\n",
        "\t\n",
        "\n",
        "pi_USJ:\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.596            0.12             0.011            [ 0.379  0.806]\n",
        "\t0.05             0.035            0.002            [ 0.002  0.121]\n",
        "\t0.043            0.027            0.002            [ 0.001  0.095]\n",
        "\t0.214            0.093            0.009            [ 0.038  0.398]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.364            0.505           0.606          0.695         0.796\n",
        "\t0.006            0.024           0.041          0.068         0.14\n",
        "\t0.005            0.023           0.039          0.059         0.113\n",
        "\t0.067            0.15            0.2            0.264         0.442\n",
        "\t\n",
        "\n",
        "pi_NW:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.61             0.054            0.005            [ 0.501  0.705]\n",
        "\t0.011            0.01             0.001            [ 0.     0.031]\n",
        "\t0.033            0.02             0.001            [ 0.003  0.07 ]\n",
        "\t0.157            0.05             0.004            [ 0.065  0.249]\n",
        "\t0.032            0.018            0.001            [ 0.004  0.068]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.508            0.572           0.612          0.648         0.712\n",
        "\t0.0              0.003           0.008          0.015         0.037\n",
        "\t0.006            0.02            0.029          0.044         0.084\n",
        "\t0.077            0.12            0.153          0.189         0.265\n",
        "\t0.007            0.018           0.029          0.043         0.075\n",
        "\t\n",
        "\n",
        "pi_SW:\n",
        " \n",
        "\tMean             SD               MC Error        95% HPD interval\n",
        "\t------------------------------------------------------------------\n",
        "\t0.445            0.014            0.001            [ 0.42   0.472]\n",
        "\t0.032            0.006            0.001            [ 0.025  0.046]\n",
        "\t0.008            0.004            0.0              [ 0.002  0.015]\n",
        "\t0.086            0.011            0.001            [ 0.065  0.108]\n",
        "\t0.332            0.014            0.001            [ 0.305  0.36 ]\n",
        "\t\n",
        "\t\n",
        "\tPosterior quantiles:\n",
        "\t\n",
        "\t2.5             25              50              75             97.5\n",
        "\t |---------------|===============|===============|---------------|\n",
        "\t0.418            0.436           0.445          0.453         0.471\n",
        "\t0.024            0.027           0.03           0.033         0.046\n",
        "\t0.003            0.006           0.008          0.01          0.017\n",
        "\t0.065            0.079           0.086          0.094         0.108\n",
        "\t0.302            0.322           0.332          0.343         0.358\n",
        "\t\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[None, None, None, None]"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}