ml-finance-python

notebook.ipynb

(215395B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "#Exercises: Statistical Moments and Normality Testing - Answer Key\n",
     "\n",
     "## Lecture Link : \n",
     "https://www.quantopian.com/lectures/statistical-moments\n",
     "\n",
     "###IMPORTANT NOTE: \n",
     "This lecture corresponds to the Statistical Moments and Normality Testing lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.\n",
     "\n",
     "Part of the Quantopian Lecture Series:\n",
     "\n",
     "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
     "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
     "\n",
     "----"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [],
    "source": [
     "# Useful Libraries\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import scipy.stats as stats\n",
     "from statsmodels.stats.stattools import jarque_bera"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "#Exercise 1: Testing for Skew\n",
     "\n",
     "##a. Artificial Example\n",
     "\n",
     "Use the results from the `stats.skew` function to determine the skew of the artificial distribution named X. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skew: 0.264748310964\n",
       "The distribution is positively skewed\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAHiCAYAAADVvTJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwg0AAAAAv0C4AQAAAOAX/j99Bg6nKBQHEwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96fabe9410>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "xs2 = np.linspace(stats.gamma.ppf(0.01, 0.7, loc=-1), stats.gamma.ppf(0.99, 0.7, loc=-1), 150) + 1\n",
     "\n",
     "X = stats.gamma.pdf(xs2, 1.5)\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "skew = stats.skew(X)\n",
     "\n",
     "plt.plot(xs2, X)\n",
     "\n",
     "print 'Skew:', skew\n",
     "if skew > 0:\n",
     "    print 'The distribution is positively skewed'\n",
     "elif skew < 0:\n",
     "    print 'The distribution is negatively skewed'\n",
     "else:\n",
     "    print 'The distribution is symmetric'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "##b. Real Example\n",
     "\n",
     "Use the results from the `stats.skew` function to determine the skew of the returns of NFLX and use it to make a conclusion about the symmetry of the stock's returns. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skew: 1.84134227597\n",
       "The returns of NFLX have a strong positive skew, meaning their volatility is characterized by frequent small changes in price with interspersed large upticks.\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAHiCAYAAADVvTJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96ffb77850>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "start = '2015-01-01'\n",
     "end = '2016-01-01'\n",
     "pricing = get_pricing('NFLX', fields='price', start_date=start, end_date=end)\n",
     "returns = pricing.pct_change()[1:]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "skew = stats.skew(returns)\n",
     "\n",
     "plt.hist(returns, 30)\n",
     "\n",
     "print 'Skew:', skew\n",
     "print 'The returns of NFLX have a strong positive skew, meaning their volatility is characterized by frequent small changes in price with interspersed large upticks.'\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "#Exercise 2: Testing for Kurtosis\n",
     "\n",
     "##a. Artificial Example\n",
     "\n",
     "Use the results from the `stats.kurtosis` function to determine the excess kurtosis of the artificial distribution named Y. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Excess kurtosis of Y: -0.634472016416\n",
       "Because the excess kurtosis is negative, Y is platykurtic. Platykurtic distributions cluster around the mean, so large values in either direction are less likely\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAHiCAYAAAAktmniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA/PXVg3f2DibAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96dbce16d0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "xs = np.linspace(-6,6, 300) + 2 \n",
     "\n",
     "Y = stats.cosine.pdf(xs)\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "plt.plot(xs, Y)\n",
     "\n",
     "print 'Excess kurtosis of Y:', (stats.kurtosis(Y))\n",
     "print 'Because the excess kurtosis is negative, Y is platykurtic. Platykurtic distributions cluster around the mean, so large values in either direction are less likely'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "##b. Real Example\n",
     "\n",
     "Use the results from the `stats.kurtosis` function to determine the kurtosis of the returns of NFLX and use it to make a conclusion about the volatility of the stock's price."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Kurtosis: 9.52008087913\n",
       "The historical returns of NFLX are strongly leptokurtic. Because of a leptokurtic distribution`s fatter tails, small changes in prices happen less often and large changes are more common. This makes the stock a riskier investment.\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAHiCAYAAADVvTJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96f9c59090>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "start = '2015-01-01'\n",
     "end = '2016-01-01'\n",
     "pricing = get_pricing('NFLX', fields='price', start_date=start, end_date=end)\n",
     "returns = pricing.pct_change()[1:]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "kurt = stats.kurtosis(returns)\n",
     "\n",
     "plt.hist(returns, 30)\n",
     "\n",
     "print 'Kurtosis:', kurt\n",
     "print 'The historical returns of NFLX are strongly leptokurtic. Because of a leptokurtic distribution`s fatter tails, small changes in prices happen less often and large changes are more common. This makes the stock a riskier investment.'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "# Exercise 3: Skew and Normality\n",
     "\n",
     "##a. Artificial Example II\n",
     "\n",
     "Use the results from the `stats.skew` function to determine the skew of the artificial distribution named Z. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skew: 1.1121491036\n",
       "The distribution is positively skewed\n"
      ]
     }
    ],
    "source": [
     "xs2 = np.linspace(stats.lognorm.ppf(0.01, 0.7, loc=-.1), stats.lognorm.ppf(0.99, 0.7, loc=-.1), 150)\n",
     "\n",
     "lognorm = stats.lognorm.pdf(xs2, 0.4)\n",
     "\n",
     "Z = lognorm/2 + lognorm[::-1]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "skew = stats.skew(Z)\n",
     "\n",
     "print 'Skew:', skew\n",
     "if skew > 0:\n",
     "    print 'The distribution is positively skewed'\n",
     "elif skew < 0:\n",
     "    print 'The distribution is negatively skewed'\n",
     "else:\n",
     "    print 'The distribution is symmetric'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "## b. Jarque-Bera Calibration\n",
     "\n",
     "Ensure that the `jarque-bera` function is calibrated by running it on many trials of simulated data and ensuring that the sample probability that the test returns a result under the p-value is equal to the p-value."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "0.048\n",
       "Our answer is around 5%, which is what we would expect for a cutoff of 5% and a correctly-calibrated Jarque-Bera test.\n"
      ]
     }
    ],
    "source": [
     "N = 1000\n",
     "M = 1000\n",
     "\n",
     "pvalues = np.ndarray((N))\n",
     "\n",
     "for i in range(N):\n",
     "    # Draw M samples from a normal distribution \n",
     "    X = np.random.normal(0, 1, M);\n",
     "    _, pvalue, _, _ = jarque_bera(X)\n",
     "    pvalues[i] = pvalue\n",
     "\n",
     "num_significant = len(pvalues[pvalues < 0.05])\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "print float(num_significant) / N\n",
     "print 'Our answer is around 5%, which is what we would expect for a cutoff of 5% and a correctly-calibrated Jarque-Bera test.'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "## c. Jarque-Bera Test\n",
     "\n",
     "Use the `Jarque-Bera` function to determine the normality of Z."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1.67689917417e-07\n",
       "The returns are likely not normal.\n"
      ]
     }
    ],
    "source": [
     "#Your code goes here\n",
     "\n",
     "_, pvalue, _, _ = jarque_bera(Z)\n",
     "\n",
     "print pvalue\n",
     "if pvalue > 0.05:\n",
     "    print 'The returns are likely normal.'\n",
     "else:\n",
     "    print 'The returns are likely not normal.'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "## d. Skewness and Normality\n",
     "\n",
     "Plot Z and observe that skewness is not informative unless the underlying distribution is somewhat normal."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "The positive skew found in part a would have led us to believe values are concentrated below the mean and a tail extends to the right, however this is not the case. Because Z is bimodal, we can make no conclusions based on the skewness value alone. In order for skewness to be useful, the underlying distribution must be somewhat normal\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAAHiCAYAAADLdALcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/h88JvlhcCkdpwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96dc652550>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "#Your code goes here\n",
     "\n",
     "plt.plot(Z)\n",
     "\n",
     "print 'The positive skew found in part a would have led us to believe values are concentrated below the mean and a tail extends to the right, however this is not the case. Because Z is bimodal, we can make no conclusions based on the skewness value alone. In order for skewness to be useful, the underlying distribution must be somewhat normal'\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "# Exercise 4: Out of Sample Test\n",
     "\n",
     "##a. Testing for Normality\n",
     "\n",
     "Plot a histogram of the historical returns of AMC to ensure it is unimodal and vaguely normal before testing it for skewness in part b."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "The returns of AMC from 2014 through 2016 are unimodal and vaguely normal, so a skewness measure would be relevant.\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAHiCAYAAADVvTJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96f98bbf10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "start = '2014-01-01'\n",
     "end = '2016-01-01'\n",
     "pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
     "returns = pricing.pct_change()[1:]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "print 'The returns of AMC from 2014 through 2016 are unimodal and vaguely normal, so a skewness measure would be relevant.'\n",
     "\n",
     "plt.hist(returns, 30);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "##b. Test for Skew\n",
     "\n",
     "Find the skew of the historical returns of AMC between 2014 to 2016. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skew of AMC: -0.128642043604\n"
      ]
     }
    ],
    "source": [
     "start = '2014-01-01'\n",
     "end = '2016-01-01'\n",
     "pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
     "returns = pricing.pct_change()[1:]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "print 'Skew of AMC:', stats.skew(returns)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "## c. Out of Sample Test\n",
     "\n",
     "Find the skew of the historical retunrs of AMC from the first half of 2016 to determine if the skew from part b holds outside of the original sample."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Skew of AMC: 0.971754222772\n",
       "The negative skew of AMC between 2014 and 2016 did not hold outside of the orignal sample, meaning the skew of AMC might be volatile and not reliable enough for predictions about future behavior.\n"
      ]
     }
    ],
    "source": [
     "start = '2016-01-01'\n",
     "end = '2016-07-01'\n",
     "out_pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
     "out_returns = out_pricing.pct_change()[1:]\n",
     "\n",
     "#Your code goes here\n",
     "\n",
     "print 'Skew of AMC:', stats.skew(out_returns)\n",
     "print 'The negative skew of AMC between 2014 and 2016 did not hold outside of the orignal sample, meaning the skew of AMC might be volatile and not reliable enough for predictions about future behavior.'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "## d. Rolling Skew\n",
     "\n",
     "Plot the rolling skew of AMC using the `pd.rolling_skew` function. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "This confirms our result from part c, that the skew is too volatile to use it to make predictions outside of the sample.\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHxCAYAAACiQxf/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlgW+WVNvDnarUsybsl73biOPuKk0DIShrIUKDQhbVN\nS0uXIe2Uln5lWpiBtlOWdtoydGhLZ2inU0rJdIGyk1BCAmQjcXZncZx4l21ZljfZ2nW/P+QrO4kX\nWdau5/cPSSRfvbko0j33nPccQRRFEURERERERClKFusFEBERERERxRKDIiIiIiIiSmkMioiIiIiI\nKKUxKCIiIiIiopTGoIiIiIiIiFIagyIiIiIiIkppili9cF1dHb761a/i7rvvxqc//emLHtu4cSOK\nioogCAIEQcBPfvITGAyGGK2UiIiIiIiSWUyCIrvdjh/+8IdYtWrVmI8LgoBnn30WaWlpUV4ZERER\nERGlmpiUz6nVajz77LPjZn9EUQRnyhIRERERUTTEJFMkk8mgUqkmfM4jjzyC1tZWLF++HPfff3+U\nVkZERERERKkmZnuKJnLfffdh7dq1yMrKwtatW7Fjxw5cd9114z6/pqYmiqsjIiIiIqJEVF1dPeaf\nx2VQdPPNNwd+vW7dOtTV1U0YFAEjf8Gamppx/7I0gucpODxPweO5Cg7PU/B4roLD8xQcnqfg8DwF\nj+cqOPF0niZKpMRdS26bzYZ77rkHbrcbAHDw4EFUVVXFeFVERERERJSsYpIpqq2txRNPPAGTyQSF\nQoHt27dj48aNKCkpwaZNm7BhwwbcfvvtSEtLw/z587F58+ZYLJOIiIiIiFJATIKiBQsW4Lnnnhv3\n8S1btmDLli1RXBEREREREaWquCufIyIiIiIiiiYGRURERERElNIYFBERERERUUpjUERERERERCmN\nQREREREREaU0BkVERERERJTSGBQREREREVFKY1BEREREREQpjUERERERERGlNAZFRERERESU0hgU\nERERERFRSmNQREREREREKY1BERERERERpTQGRURERERElNIYFBERERERUUpjUERERERERCmNQRER\nEREREaU0BkVERERERJTSGBQREREREVFKY1BEREREREQpjUERERERERGlNAZFRERERESU0hgUERER\nERFRSmNQREREREREKY1BERERERERpTQGRURERERElNIYFBERERERUdCa2vux55gp1ssIKwZFRERE\nREQUFLvTg+89ux9P/P4gWjoHYr2csGFQREREREREQfnj9jOw9NoBAO8daYvxasKHQREREREREU1q\n73ET/rb7PIw56VCr5Nh9pBWiKMZ6WWHBoIiIiIiIiCbUah7Az144jDSVHA/evRJXzi9Au2UQ9a29\nsV5aWDAoIiIiIiKicXl9Iv5j2xE4XV58/fZlmFmcifVXlAAAdh9OjhI6BkVERERERDSul3fX42xT\nD9YtLcbapcUAgGVzDNBplHj/aCu8vsQvoWNQREREREREY2ru6Mcf3jqDLL0aX/nE4sCfKxUyrF5S\nBGu/E7UXLDFcYXgwKCIiIiIiost4vT78x7YjcHt8+OqnliBDq7ro8fXL/CV07xxsicXywopBERER\nERERXeYvO8/hXEsvNlSX4KqFhZc9vmBmLow56dhz3IQhhzsGKwwfBkVERERERHSRdw424w9vnUFO\nRhq+fMuiMZ8jkwm4dmUZnC5vws8sYlBEREREREQBXp+IZ148Dq1Gie9/eRX06apxn/uRFWWQCcDb\nHzZFcYXhx6CIiIiIiIgCnC4PHC4v5s/IQUVhxoTPzcvSYNkcA+qae9HU3h+lFYafItYLICIiSjb1\nLb0422SF2+uD2+ODIAi4proEuZmaWC+NiGhSdqcHAKBRBRcqXHtlOWrOmLHjwyZ86eaxS+3iHYMi\nIiKiMHI4PfiXZ/Zg0OG56M/f2NuAR/9xNQrztDFaGRFRcBwuLwAgTR1cqLByfgEydSq8e6gVd98w\nH0qFPJLLiwiWzxEREYXR+0fbMOjwYOPyUjz0+ZX43peuwm2bZqOrx44nXzgc6+UREU1KyhSlqYML\nbpQKGa6pLsXAkAsHajsiubSIYVBEREQURtsPNEEQgE9vnourFhaieq4RW66fh+XzjDjdaEVdc0+s\nl0hENCHncKYo2PI5ALjuynIAwNsHmiOypkhjUERERBQm7ZZBnG3qwbLZBhhy0i967GNrZwIAXv3g\nQiyWRkQUtJFMUfBBUalRj7nl2ThSZ0bPgCNSS4sYBkVERERhcrTODAC4amHBZY8tnZ2P4nwd9hwz\nweX2RntpRERBc7ikRgtT2xu0uCofogi0mm2RWFZEMSgiIiIKk6PnugAAS2bnX/aYIAhYPs8It8eH\nM03WaC+NiChojhAyRQBgyPZnyDu7h8K+pkhjUERERBQGXp+IE/UWGLI1KMwdu8Pc4ll5AIDj9ZZo\nLo2IaErszql1n5MYc/xjB8w9DIqIiIhSUkNbHwaG3FhSlQ9BEMZ8zoKZuZAJwAkGRUQUx0bK56aY\nKRreS9lpZVBERESUks61+LvKzZ+RO+5ztBolZpZkoa65J1CeQkQUb6bakluSn5UOQWBQRERElLLa\nugYBAKVG3YTPWzgzFx6viPrW3mgsi4hoyqThrZopls8pFTLkZqSxfI6IiChVtXX5uy0V508cFM0u\nzQYAnGthUERE8UnKZKun2H0O8JfQdffa4fH6wr2siGJQREREFAZtXTZk6lTQpasmfN6s0iwAQD2D\nIiKKU1L53FT3FAGAMScdPhGw9NrDvayIYlBEREQ0TW6PD53WIRTlTZwlAoCC3HToNEqcY/kcEcUp\nqXxuqt3ngMRttsCgiIiIaJo6ugfh84koMUweFAmCgFmlWWi3DMI25IrC6oiIpkbKFKmVUy+fMw7P\nKupIsFlFDIqIiIimSdpPVDTJfiJJ1XAJXR1L6IgoDjlcHqSp5JDJxh4vMJHCPP+ctnaLLdzLiqiY\nBUV1dXW49tpr8fzzz1/22N69e3HrrbfijjvuwC9/+csYrI6IiCh4bebgmixIllTlAwBefu98xNZE\nRBQqh9MTUukcMHJzyGQZDOeSIi4mQZHdbscPf/hDrFq1aszHH330UTz99NN44YUXsGfPHpw/zy8N\nIiKKX13DG4oLctODev7iWXlYWpWPw2fMOHS6M5JLIyKaMrvTG1KTBQDI1quhUcvRzqBocmq1Gs8+\n+ywMBsNlj7W0tCArKwtGoxGCIGD9+vXYv39/DFZJREQUnIHhvUH6STrPSQRBwD03L4RMAH7zysmE\na11LRMnN4fJMeXCrRBAEFObqYLL491omipgERTKZDCrV2F8cFosFOTk5gd/n5OTAbDZHa2lERERT\nZrO7AQA6jTLon6kozMDmqyrQarbhrX2NkVkYEdEUiaLoL58LMVMEAIX5WrjcXlj7HWFcWWSF/reN\nElEMLsKsqakZ89c0Pp6n4PA8BY/nKjg8T8FLlHPV2dULmQw4eeIoBCH4jckLC73YqRTw+9drkSnr\nRro6tHuViXKeYo3nKTg8T8FLxnPl9ojwiYDLORjy30/mGQAA7Np7GDOMaQlxnuIuKDIYDOjq6gr8\nvrOzc8wyu0tVV1cD8L85pV/T+HiegsPzFDyeq+DwPAUvkc7Vf7/9d2SkC1i+fPmUf9biqsf/vFaL\nM+Y0fOmWRVP++UQ6T7HE8xQcnqfgJeu56rM5AbTBmJ8T8t+vx9uM92uPQJ9TDKA7bs7TRMFZ3LXk\nLi4uxuDgIEwmEzweD3bt2oU1a9bEellERETjGhhyQzuF0rnRblo7AwW56XhjbwMcLk+YV0ZENDXS\njKLplM8V5fvbcpu6EqfZQkwyRbW1tXjiiSdgMpmgUCiwfft2bNy4ESUlJdi0aRMeeeQR3H///QCA\nG2+8EeXl5bFYJhER0aREUYTN7kbR8GyOqVIq5Fg8Kx87DjTBbB1CWUFGmFdIRBQ8h8sLANCE2JIb\nAApz/Z+HHdZBLC4KrWFDtMUkKFqwYAGee+65cR9fvnw5tm3bFsUVERERhcbu9MDnE6ELsvPcWKRW\n3h0MiogoxuwOKVMUejCTpVdDpZSj0zoEQB+mlUVW3JXPERERJZJQOs9dypjjD4o6u4fCsiYiolD1\nDzoBABladcjHEAQBxhxNQn2mMSgiIiKahsEwBEUFo0pNiIhiqdfmD4qy9KEHRQBgyE6Hze6Gw5UY\nc9gYFBEREU2DbcgfFGnTmSkiosQXCIp00wuKpM+1nsHEaCDDoIiIiGgabHYXAECnCX1PUYZWBY1a\nqr8nIoqdPpv/My1TF/pnGgAYc/wZ8F6bd9prigYGRURERNMwMJwp0k8jU+Svv9ei0zoY9NByIqJI\n6B0IT/mccbiBTI+NmSIiIqKkJ5XPTWdPEeAvNbE7vegfdIVjWUREIekbLp/LDFP5XC/L54iIiJJf\noHxuGi25gZFmCyyhI6JY6hlwQp+uhEI+vTChQNpTxPI5IiKi5BeOltzAyF3Vjm52oCOi2OmzOaed\nJQL8N4pyMtLQ0OlEU3t/GFYWWQyKiIiIpmFQKp+bxp4iYGSAKzNFRBQrXq8PA0OusARFAPCPn1gM\nj1fEv/32APadMMHni989kwyKiIiIpkHKFGnDliliUEREsdE/6IIoTr/JgmTVokKsX6hHV88QHvvd\nQXzzyd3Yd6I9LhvKMCgiIiKaBpvdBYVcBrVSPq3jGFg+R0QxFq4ZRaNdszgTv3hgIzZcUYKG9j48\n9rsP8cftZ8N2/HBhUERERDQNA0Nu6NOVEARhWsdJUymQrVezfI6IYiZcnecuVWLQ41ufrsYvvr0R\ncpmAo3XmsB4/HBgUERERTYNtyD3t/USSglwtunrt8Hp9YTkeEdFUhGtG0XhKjXrkZWni8uYPgyIi\nIqIQiaKIQYcbOs302nFLjDnp8PlEdPXaw3I8IqKp6Buek5apDc9n2liMOenoGXDC6Y6vVt0MioiI\niEJkd3rg84nTbrIgkSbAd7LZAhHFgN3pH7SqUSsi9hpSUxlznGWLGBQRERGFyBamdtwSadhhR5xd\nLBBRanAMB0VpqsgHRfFWQsegiIiIKEThGtwqMeZqAQCdVnagI6Lok0ra0tTT66Y5kUCmqIdBERER\nUVKw2f319+HcUwSwfI6IYsPp8gdFalXkgiJDnH7OMSgiIiIK0cBw+Zw+TOVzuZkaKOQCOpgpIqIY\ncAwHRSyfIyIioqCFe0+RXCYgPzs97i4WiCg1OFzSnqLIZYqy9WlQKmToZPkcERFRchgMc/kc4G+2\n0GdzBbpAERFFy0j5XOQyRTKZgMI8LVo6B+KqLTeDIiIiohBJjRbC1ZIbGN1sIb7uohJR8nO4PFAq\nZJDLhIi+zop5RjhdXhw5a47o60wFgyIiIqIQhbt8DhjVlrub+4qIKLocLm9ES+ckVy8uAgDsOW6K\n+GsFi0ERERFRiMLdkhsYGeDaEWedmYgo+Tlc3oiWzkmqSrOQn63Bh7UdON1gjfjrBYNBERERUYhs\nQ8N7itLDuaeIs4qIKDacLk9UMkWCIOBja2diyOHBA0+/j5PnLRF/zckwKCIiIgqRze6GSiGDWhm+\ni4iCPC1kAnDkbBfcHl/YjktENJlolc8BwC3rZ+GBLcsBAO8fbYvKa06EQREREVGIbEPusO4nAvyl\neNddVYG2Lhteff9CWI9NRDQen0+EM0rlc5JViwqRnqbA4ThouMCgiGKuZ8CBP79TxzuiRJRwbHYX\ntGFsxy3Zcv086NOV2Pb2GXT32cN+fCKiS7nc0uDW6GSKAEAhl2FJVT46uodgstii9rpjYVBEMfeH\nN8/g92+cxr4T8dOBhIhoMj6fiEG7O6xNFiQZWhU+d8N82J1e/PaV2rAfn4joUo7AjKLoBUUAUD3X\nAADYvq8JoihG9bVHY1BEMSGKIv62ux5/212P9460AgDOt/aN+/wPaztw30934ZtP7oLXy4wSEcWe\n3emBTwxvO+7Rrl1ZjqrSLLx3tA3H67si8hpERBKHyz8wOi2K5XMAcOWCQmRoVXhxVz2+99/7Y5Yd\nZ1BEUefziXjmxeP4zSu1+M0rtYE7Exfaxg6KHE4PfvT7g7hg6kN9ax9MFnZkIqLYi0Q77tFkMgH/\n+InFEATgmRdPwMMbQkQUQc4YZYqy9Gr8/FsbcMUcAw6fNeNr//4u3j8S/cYLDIooqnw+Eb/86zG8\nsbcRFYUZKMrTQqWUQ5+uwvm2vjHTpkfquuDy+KCQ+6crt5oHor1sIqLLSO249WFsx32p2WXZuO7K\ncrR0DrDpAhFFVKwyRQCQm6nB9750FbZ+cjHcXh9+/IdD+P0bp6K635xBEUWN1yfiqf87gu37m1BZ\nkolH712Np761Ab96YCMWVuZiYMgFS68j8PwhhxtNHf34sLYDAPCJa6oAAC2dsd2IR0QERD5TJPns\nR+dDn67ECzvYdIGIIscZg0YLowmCgOuvnoGn7t+Awjwt/vzOOdzx0Ot48d1zUXn96IeClLJe2H4G\nOw+1YHZZFr7/5asDFxJpOQpUFmdi34l2/OmdOrjcXpxr6UGr2QYpcaTVKLFxeSn+9Pc6tDBTRERx\nwDbkD4q0EdpTJMnQqrDlo/Pxy78cwyvvXcDnb1oQ0dcjotQkbWeIVVAkKc7X4cdfW4v/e/ss9hw3\n4X9eO4USox4r5xdE9HWZKaKosPY78NLu88jJSMMPRgVEksqSLADAW/sasfNQCyy9diycmYfFs/IA\nAOuWFqMgVwulQobWTgZFRBR7Nru/fE4XgZbcl1q3tBgA0GAavyENEdF0OJ3SnqLY50yy9Gp85ROL\n8cgXr4JSIcNj//MhXth+JqLd6WL/t6aU8Ke/+zNAd92yENoxSk2WzTHgno8thE6jxOyyLBQb9JDL\n/HuIGtv7UZCbDrlMQHG+Dq1mG3w+EbLhx4mIYkHKFEWq+9xoWo0SuZlpaOFNISKKkJE9RbHNFI1W\nWZKFR+65Ck/96Qj+uOMs5lTk4Io5hoi8FjNFFHFen4j3jrQiJyMNm1aUjfkcuUzALesrsWllGcoK\nMgIBEQBUFGYENv2VGHRwuLywsK6eiGIsWnuKJKVGPSx9Dgw53FF5PSJKLSPlc/GVM1kyOx8P3b0S\nAPDHtyKXLWJQRBF3rrkHA0NurJhvhFw+vbfcjKJMAMCO/U3hWBoRUciiHRSVGfUAgFYzm80QUfhJ\nmaJot+QORmVJFlYtKsTZ5h7UnDFH5DUYFFHEHTrTCQConmuc9rE+unoGDNka/OmdOhw7x2GGRBQ7\n0WjJPVrJcFDU3MESOiIKP2ecNFoYz12b5wIAnn/rdESyRQyKKOJqTndCIRewpCpv2sfSaZT49pbl\nkAkCfvp8DXoHnGFYIRHR1AUyRVHYUwSMZIq4r4iIIiFey+ckFYUZWLOkCPWtfYFxLeHEoIgiyjbk\nQn1rH+bPyEV6WnguHOaW52DL9fPQM+DEky8chs8XuU4kRETjsQ25oFLKoVRE565qqZQpYlBERBEQ\nz+VzkjuvmwNBAJ7ffibs138MiiiiTjdaAQDzZ+SG9bgf3zALV8wx4PBZM37wm/145sXj8DI4IqIo\nstndUdtPBPjnFWXp1MwUEVFEOOM8UwQAZQUZWLe0BA2mfuw/2R7WYzMooogaCYpywnpcmUzAN++8\nAjkZatScMeP1PQ1oau8P62sQEU3ENuSOWumcpNSoh7lnKHBHl4goXKSSYK0mfoMiALjjutmQCcDf\ndp8P63EZFFFEnWqwQiYAc8qzw37sLL0aT35zA9YODzU09wyF/TWIiMbi84kYdEQ3UwQApUYdRBFo\nYwc6Igoza58DGrU8bNsdIqXEoMe8Gbk402QN695yBkUUMW6PF+eae1BRmBmxf2A5GWm4enEhAMBs\nZVBERNEx5PRAFKPXeU7CZgtEFCnWfgdyMtJivYygrJxfAFEEDp3uDNsxGRRRxDSY+uHy+DAvzKVz\nlzJkpwMAOpkpIqIokdpxa6OcKSphswUiigCP14demxM5GZpYLyUoKxf4x7x8eCp8XegYFFHENJj8\ne3xmFmdG9HWMOf6giJkiIoqWaLfjljBTRESRYO13AEDCZIpKDHoU5WlxtM4cti50DIooYpo6/EFR\nRWFGRF8nQ6uCWiWH2WqP6OsQEUmkTJFOE93yuSy9GjqNkkEREYVVICjKTIygCABmlWTB7vTC0hue\n6z8GRRQxTe39EISRO5uRIggCDNnpLJ8joqgJZIqiXD4nCAJKjXq0Wwbh9nij+tpElLysfYmVKQKA\nEoMOANAapsYzDIooIkRRRGN7PwpytEhTR761ozEnHYN2d+BChYgokmxDsSmfA4CyAj18ImDqGoz6\naxNRcpIyRbkJFRT5b7q3msOTOWdQRBHRO+BE/6AL5YWRzRJJDNn+jYFdzBYRURTEKlME+GcVAWy2\nQEThk4jlcyVGZoooATQOD1Itj/B+IonUbKGjm3dOiSjypD1F0W7JDYwERdxXRETh0j1cPpebQEFR\nUb4OggC0MFNE8ezwWTMAYGZRZDvPSaTg61xL70V/7vH6cLbJCm+YOpMQEQGjJ7/HIFNkYKaIiMJL\nyhRlJ1D5nFophyE7PWyZoshv9qCUY+134I09DcjL0mDFfGNUXnNeRQ5kAnCqwQoA6B90Yfv+Rry+\npwHdfQ589qPzcOtHZgPw73caGHIjQxv9O7xElBxi1ZIbAPKy0qBRy5kpIjjdXnT1DMHcY0dXzxAy\ndWpctbAw1suiBGTtd0CnUUKtlMd6KVNSYtCh5owZtiEXdNPM3DMoorD78zt1cHl8uH3TbCgV0fnH\nlZ6mREVRJuqae/C712rx6gcNcLm90KjlUCnleO2DBuRkpOF4vQXnWnrQ0mnD125dis1XlUdlfUSU\nXGLVkhsY6UB3oa0PXq8v6q9P8aGuuQff+cUHcHsufg/8+jsfQVG+LkarokRl7XMk1H4iSalRj5oz\nZjS092NRZd60jhWToOjxxx/HsWPHIAgCHnzwQSxatCjw2MaNG1FUVARBECAIAn7yk5/AYDDEYpkU\nAnPPEN7a1wRjTjo2rSyL6msvmJmLC219+Ou79cjL0uDmdZW4dmUZ/vDWabz2QQP+Y9sRAIBCLoNG\nLcev/noMRflaLKrMQ11zD97c24iPrZuJGVEq+SOixGWzu6FWyaFUxKYKvdSoR11zL9q5jzJlHTvX\nBbfHhxXzjagqzUa/zYnX9jRg95E23HndnFgvjxKI0+2Fze7GrNKsWC9lyuaUZwMAzjb1JF5QdPDg\nQTQ1NWHbtm04f/48HnroIWzbti3wuCAIePbZZ5GWlnjRKgF/+nsdPF4f7rh2DhTy6F4sLJiRi1ff\nvwC1So7Ht65GQa4WAHDD6hl4c28jcjPT8N27V6IkX4dzrb3412f24vHfHcTPvrEOz758EqcbrdhZ\n04LHt67G/Bm5UV07ESUW25A7Jp3nJGWjmi2wEDg1NXf4yye/fMsiFORqMeRwY8eBJrx3pBV3XDsb\ngiDEeIWUKHr6E29GkWRueQ4A4EyjddrHivotrn379mHTpk0AgMrKSvT392NwcOROlyiKEEVuik9E\nHd2D+PuHzSjO1+Ka6pKov/7S2fmYPyMHX/vUkkBABPj72D/1rQ146v4NmFWShTS1Aosq83DvJxdj\nYMiFh361B6cbrcjSqeHziTh0ujPqa6fY6egeRIOpL9bLoARjs8c2KGJbbmrq6Ida5d9oDvjLyJfP\nN6LVbAt0gCUKRiJ2npPkZWmQl5mGs009044foh4UWSwW5OTkBH6fnZ0Ni8Vy0XMeeeQR3HXXXfjZ\nz34W7eXRNLyw4yy8PhF3XjcX8ihniQB/F6gffW0tNlSXXvZYeUHGZRvwNl9VgZvWzoS5xw4A+Mz1\ncwGM3H2j5Odye/GdX3yAh3+9L9ZLoQTi84kYcrinval3OgJtuTvC03WJEovX60Or2YZSox4y2UhG\naPlcf3OjM009sVoaJSBrX+JmigBgTkUOem1OdFqnN6sy5o0WLo3q7rvvPqxduxZZWVnYunUrduzY\ngeuuu27S49TU1Iz5axpfOM/TkNOHd2tMyM9UIN3bgZqaxMi2LC0Wca4kDX2DXmTLLdCoZKhvtvD9\nFKJEO1f7zwwE7pDtO3AQqijtD0m08xRL8Xiu7C4fRBHwuAZjtj6fKEIhF3C2sRPXzDPG5XmKR8ly\nniz9brg9PmgVrov+To5+fwOQD4/Vw6DqDvn4yXKeoiEZztWxM/6bwT0WE2pqIhNQR/I86eT+irM/\nv3kQV8/Th3ycqAdFBoPhosyQ2WxGfn5+4Pc333xz4Nfr1q1DXV1dUEFRdXU1AP9Jl35N4wv3eTrd\nYIUomrBmWQVWrFgQtuNGw8oVI7+eceB9nGm0YvGSpVAq5Hw/TUGinSuHy4P/ePXvgd/PrFoQGAIc\nSYl2nmIpXs+Vf0i0CSWF+aiuviJm6yh7bxdaOwfg84lYsWJ5zNaRKOL1/RSKPcdNADpxxYIZqK6e\nFfjzRW4vnt3xOgbd6pD/rsl0niItWc7VMVMtgD6sWDY/sEcnnCJ9nipm2bH3zLvYeXwA665cgCVV\n+eM+d6LgLOo1TqtXr8b27dsBALW1tTAajUhP91+I2Gw23HPPPXC7/fMfDh48iKqqqmgvkULQ1uUv\n4SjO107yzPhWatTDJwKmLnZ0SnZv7GlE74ATaSp/2/jeAUeMV0SJwjY0PKMoBu24Rysz6uHy+NA7\n5I3pOij6pDLvsoKL74qrlHIU5+vQ2N4PH4eWU5ASvXwuN1OD//eZ5fD5fPiXZ/biZ3+sQXeffcrH\niXqmaNmyZViwYAHuuOMOyOVyPPzww3jppZeg1+uxadMmbNiwAbfffjvS0tIwf/58bN68OdpLpBCY\nLP6gKNFnI4zevFxemBHj1VCkDDnc+Ou756BNU+Cjq2fgz++cQ5/NFetlUYIYkGYUxWBw62jS51VX\nnzum66Do6+rx750ozLv8RuSMwgy0dA7A3DN0UdMhovFYh7vPZesTMygCgCvmGPD4V9fg1y+dwLs1\nrdh3oh23bZqNW9ZXBj0zMyZ7iu6///6Lfj9nzkg//S1btmDLli3RXhJN00imKMGDIoP/IqOVHZ3i\nksfrQ0eXpesDAAAgAElEQVT3IEoModcMA8BrHzSgf9CFuzbPhSFbAwDoGXCGY4mUAmx2KVMU26Bo\nRpH/xk29iVnOVNNr839eZenUlz1WUZSB9462ocHUz6CIgmLttyNTp4rZ3LVwmT8jFz/7xnq8faAJ\nz715Gr9/4zTePtCM7335KhTlTX59mth/e4obpq5BaNRyZOsv/4BOJGxzG9/+8OZp3PujnThe3xXy\nMQbtbry0qx46jRIfWzsTWcPv2T4bgyIKTrwERcvmGGDISceRC4PoYflnSukdcEIhlyE97fJ729IA\ncrblpmBZ+x3IzdDEehlhIZcJ+IdVFfj1dz6CzVeVo717EG/tawrqZxkU0bT5fCJMXTYU5esSflhc\nXlYaNGo5Ws1scxtvhhxuvLmvEQDwf2/XhXycdw41w2Z34+MbZkGrUSJz+E5rL4MiCpLd4QHgnwsT\nSwq5DJ+6ZhY8XuDl3edjuhaKrj6bE1l69ZjfudLNvVYzb+7R5IYcbtidXuQk4IyiiejSVfj0P/hH\nrfib40yOQRFNm6XPDpfHh+IgUpPxThAElBj0aDXb4PX6Yr0cGmXnoRYMOTxQyGU4Xm9BXXNobUPP\nNfcCANYsLQKAQHazl+VzFCSn29/YQK0Krk49kj6yogw6jQxv7G0I7HWi5CaKInptLmTpxm70kZ+l\ngUohC5S1E01E2k+UqE0WJpKlU0OjlqPdwqCIosTUlRxNFiSlRr1/78o0h4BReH1wzASZAHzzzmUA\ngD+/E1q26HxbHzRqOQpy/LX2GVqWz9HUOF3+TFE8BEUqpRyr5+lhd3rx6vsXYr0cigKHywuX2xvI\ncl9KJhNQlK+Dqct22SxIokslc1AkCAIKcrVo7x4M6t8CgyKatrbh9tWJ3o5bEpgUz31FcaXdYkNe\ndjrWLi3GnLJs7D/ZgaaOqdXMO91etJkHUFGYGZgCr1TIoNMo2WiBguby+LPIamXsgyIAqJ6lRYZW\nhVffv4AhBzvRJTspq501wR7e4nwd7E5v4IKXaDzSAPNkK5+TFOZp4XR5g/qOZ1BE05Z0mSKD/+/B\noCh+OFweWPudKMxNhyAIuPUj/vllf915bkrHaWrvh08EKoszL/rzLL2a5XMUNKdruHwuToIilUKG\nj62bCZvdjTf2NsZ6ORRhfRN0npMUD3+PcX8sTUaaUZSbhJkiACgc7sAYTAkdgyKatrZkC4qYKYo7\nncOljFJ72RXzC1BWoMfuI21Bb6AEgAZTHwBgxhhB0cCQi/vIKCiBoCgOyuckN6yeifQ0BV7efR5u\nD9/HyUxqCjNe+RwwMh6DQRFNJpnL54CRWV4Miigq2rpsyNKpY96eNlyMOelQKmRoMLGdabzoGP4w\nk4IimUzArRur4POJeHFXfdDHOd/mD4pmFl0cFEkXF32D3KhOk3O6h/cUxUmmCPC3B1+zpBi9Nie7\njiW5QKZogvK5kuFMEZst0GS6+5O7fE66bgjmBiqDIpoWt8cLs3UIRUmynwgA5HIZllTlo7G9Hw2d\nrMeOB1LTi8JRgwjXLi1Gll6NAyfbgz5Oo6kfMgEoK7h4+Gum1t/Fic0WKBgutz8To4qjoAgYyXLz\nQji5SaW+k2WKBAE41dDNZgs0IWufAzJh4vdTImOmiKKmo3sIPnEkVZ8s7rxuDgDg3eP9/EKJA1Km\nyJibHvgzuVyGwlwtem0u+HyT/z8SRRHNnQMozNNddjGrS/cHRYN2blKnyTndXggC4m76eyA7wJKp\npNYbxJ4irUaJqxYW4nxrH46dC33YNSU/a78DWfo0yGWJPWdyPLmZGijkMrQzU0SRlmz7iSSzy7Kx\ncn4BmrtcOFrHL5RYkz7MRmeKAH/5iM8nBjWfxdrvwKDdfVmWCECg9JNBEQXD6fJArZTH3bDqwD4S\nZoqSWp/N/3mXOc6cIsltH5kNANg2jWHXlNxEUYS135G0pXMAIJcJKMhNZ6aIIk/qPJcs7bhHu2uz\nP1v0/PYzzBbFWEf3EPTpKmgv2bcm3SkNpnNcc4d/n8VYQZF0XBuDIgqC0+2NqyYLEkNOOhRygZmi\nJBdM+RwAzCrNQvVcA2ovdONEvSUaS6MEY7O74fb4krbznKQgVwub3T3pDVQGRTQtdS29AIDywowY\nryT8KkuyMK9Ug7NNPag5Y471clKW1+tDp3UIhXnplz0mbTQOKiga7iZYbrz8vcpMEU2F0+2Lu/1E\ngP+OaGGelkM7k9ygww2NWg6FfPJLOKkU/I87zkR6WZSApHbcydp5TlIU5L4iBkUUMlEUcepCN7L1\n6svKmpLFhkX+C+jn3zrNi4wY6bAOweP1ocRweYZHulPaE0SDhECmqJCZIpoel8sbV53nRivO12HQ\n4QnsO6Hk43J7oVYqgnrunPIcVM814OR5ZovoclLnudwkLp8Dgu9Ax6CIQtZuGUTPgBMLZubGXW19\nuBizlFizpAj1rX04eKoz1stJSVIwI3XWGk3KFAXTNa6pox9ymYCivMv3vzFTRFPhdHvisnwOGNlX\nxBK65OV0e6FSBn/5dtfmuQBYCk6XG3L4v/PS05JjpMp4Ah3oGBRRpNRe6AYALJiZG+OVRNbN6yoB\nAIfPsoQuFqSZK2VjBUVB7ikSRREtnQMoyteN2TFM6j7HTBFNRhRFOF1eqBTxGRRxPk3yc01xT9vs\nsmwsn2f07y06z2wRjfB6/UGyQp6cN7YlwbblZlBEIattSI2gSNovxTuvsSHtBRorU5Qd5J6irl47\nhhweVIyz903LTBEFyeMV4RMRx5ki/7+TVn5eJS2nyzvlPW2BvUXbzzJbRAFen3/mmjyI/WmJzJCd\nDpngb9o0keQ+CxRR51p6oVHLUV6QfE0WRtOoFcjNTOOU+Bhp6RyAUiGDIWeCRguTlM81tfcDAMrH\n6DwHAOlqBQSBmSKanNPtBYD43VPETFFSE0VxeE/R1N5/o7NFx88xW0R+nhTJFCkVMuRlpwc6Jo+H\nQRGFxO3xoc1sQ1lBBmRJOvBrtOJ8HSx9DjicnlgvJaX4fCJazTaUGHRjDpbTqBVQKWToHXBMeJym\n4X1J43VJlMkEpKcpmSmiSTld/s+AeM0UZWhV0KermNlOUh6vDz4RIXU/vH2Tf27RzpqWaa/D6/XB\nG8TQbIpvXu9wpkiW/OFAiUGHnkmqSpL/LFBEtJoH4PWJ45YjJRvefY2Nrl47nC4vSsfoPAcAgiAg\nS6+etHxuJFM0/vtVp1HCFsQQWEpt8Z4pAvxf/h3WIbg9vlgvhcLM6fb/Pw3l/VdVmgWFXBYoSQ5V\nu2UQW3+8E/c+8Q4ahz9bKTFJmSJ5kmeKgLH3JV8quJ6ORJeQPghTJSgqyR8JiipLsmK8mtQh3e2W\nNo+PJUuvxoW2foiiOG4XxKaOfqhVchjHKMGTaDVKtFsY9NLEXNO4KI2WonwtTjda0WkdHLOVPSWu\nQKYyhPefXC5DiUGHls4B+Hxi0FUedc09eLemBR3dQ+i0DqGjezAQcD/wn+/jp/etG3PPJ8U/KduX\nCpmiYIKi5D8LFBGBO++pEhQNX1iwJCW6TMNBSlH+BEGRLg0er2/c0jev14eWThvKjPoJLwJ0GiXs\nTi88Xt5dp/HFe/kcwLbcySwQlIf4/isz6uF0eWHumXjDuaSxvR8P/WoPXvugAYdOd8LaZ0epUY+v\n37YU37xzGexODx7/3w9ZWp6gpPK5ZN9TBABl4+wpHo2ZIgpJqmWKpPI5dnSKLqlcsSh//OHAUrOF\nngFnoLX2aCbLIDxe36QNQUZ3oJOGwhJdKlHK5wCW+yYj6f0Xyp4iYOTCsKVzIDDQcjw+n4gn/vdD\nOFxefP22pVi1qPCyz9hzzb14bU8D3j3ciutXVYS0JoodT4p0nwPG7mB7qeQ/CxR2oiiiwdSH3Mw0\n6Me4CE1G+VkaZGhVOF5vCdxZocgzDc8UGGvgqmSyDnRNHcFlNTnAlYLhdE3vojQapEwRb+IkH1eY\ngiJpKPZE6pp70NY1iA3VJbj2yvIxbzrdsmEWAGDfcVNI66HYSpU5RYB/QG1+tmbC5zAooik7dLoT\n1n4nFs/Ki/VSokYmE7B6cRF6bU4Ov4siU5cNWTp1IIszlskGuDZO0o5bIr0G23LTRKZbvhQNhXla\nyARmipKRFJSHmqksG86YB9NsYd+JdgDA2iXF4z7HmJOOWSWZOF5vYaOaBORJoe5zwOTZotQ4CxRW\nf323HgDw8eE7RKli3TL/F8N7R9pivJLU4Pb4YLYOTVg6B4zKFI0TFDVP0o5boktnUESTc7pD3+ge\nLUqFHMYcLYOiJDRSPhfa5VtBrhYqhQxnm6wTDnEVRRH7TrYjTSXH0tn5Ex5z1aIieH0iPjzVEdKa\nKHZ8vtTpPgdMHOADDIpois40WlF7oRvVcw2YUZQZ6+VE1fwZucjNTMPuw604cLI91stJeh3dg/CJ\nE5fOAUGUz7X3Q5+uQrZ+4n1CujSWz9HkAnfq4zhTBPj3QfbZXLx7n2QCe9pCfP/JZQKuXlKEtq5B\nHDrdOe7zmjsG0G4ZRPU846SlemuWFAEA3tjTOGGgRfEnMLw1RTJFm1aWTfh4apwFCpu/7DwHAPjk\nxqoYryT6ZDIB/3TbUggyAY/97kO8vqch1ktKau3SfqLJMkUTlM85XB60dw+ivFA/brtuiWY4KLKz\nixJNYLob3aMlsK+I2aKk4gpDo49PXuP//pa+z8eyb/jG36qFhZMeryhfhysXFOBscw9qL3SHvC6K\nvsDw1hTJFE2GQREFraVzAAdqOzCnLBsLZ+bGejkxUT3XiMfuXY0MrRrPvHgc//STd/Hnd+rQaQ2u\nvSkF7+8HmwEAlcUTz4XKnqB8rqVzAKIIVEzSeQ4Y2WjKRho0kUToPgcAxcM3E0wMipJKOIKiisIM\nLK3Kx6kGK7r77GM+Z9/xdijkApbPMwZ1TCnQksrrKTF4fFKjBYYDAIMimoIXhz/sPrlx1qR33ZPZ\n7LJs/PvX12LVokK0mm34/Run8cVH38Yj/7UP51t78e/PHcJXHv87fvCb/TjTaI31chPS8fou7DvR\njnkVOVg2Z+J6dq1GCYVchl6b46I/77M58cu/HgcAVJVlT/qa0peCm0ERTSCRyucAdqBLNuHqfrhk\neJ/Q6TG+o3psHlww9WFxVf6ETW5GmzcjB/MqcnDodGdgjiHFv0CmKMhBvsmOQREFxdJrx67DLSjO\n1+HKBZOn05NdQa4WD969Es99bzP+6balmFeRg8NnzfjGk7vx3tE29NqcOHiqEw89szfoIXnk5/WJ\n+O+/nQQAfOmWhZMG4IIgIEunuihT1N1nx3d/uQf1Lb247spyrL+iZNLXlYIiqUUp0VgSJ1PEWUXJ\naLp7iiTzKnIAjB0UnTP5bzBdFUTp3GifGi6rf3EXs0WJwhtotMBwAGBQREF6Y28DPF4Rn7hmFmS8\noxCgS1fhuivL8cRX1+CmtTOhkMvwlY8vwrYffhRfu3UJXG4vfvtKbayXmVB2HGhCY3s/Nq0oQ1Xp\n5BkewN9sodfmgiiK6OgexD8//QFaOgdw87pKfO3WJUHdBZOCIg8zRTSBRJhTBAA5GWnQqOVoY6Yo\nJiLVcCBce9pmlWZBLhPGrGZosfibcyyqnFqZ/PJ5RpQa9dh9uJU3AxOE9H2XCnOKgsGgiIJyrrkX\nwEiXGbqYTCbgy7cswp8euwE3rpkJQRBw7cpyzKvIwZ7jJhytM8d6iQnBZnfjD2+ehkYtx2c/Oi/o\nn8vSp8Hl9mLQ4cG//novOq1DuOu6ObjnYwuCLvVUKPzP8zBTRBNwhelOfaQJgoDifB1MlsHA3WCK\njkG7G1//6S786PcHw37swJysaQZFaqUclSWZON/aFwi0JK0WJ3QaZSDbGCyZTMAnNsyC1yfilfcu\nTGt9FB1SZUSqzCmaDM8CBaXVPIC8LA3S04KrL05VSsXIPymZTMBXPr4IggD8+qUTcHuYgZjMth1n\n0T/owm2b5iA7Iy3on5M60NWc7kRH9xA2XFGCOzfPndLeN+lLgZkimog0xyoRPguL8/Vwe3zo4l37\nqBFFEf/5p6NobO/H3uMm9I0zKiBUTlf45mTNrciB1yfiTMNItqh3wIkemxdzyrND2ju8/ooS5Gam\nYfv+Rs58SwDMFF2MQRFNasjhhqXPgRLD1O4aEVBZkoV/WFWBVrMNr77PO2fjsfY78MT/HsTL751H\nQW46bl43c0o/L80q2nW4FQCwcn7BlNcgBbTsPkcT6R1wQqmQQZumiPVSJiU1W+C+ouhpbO/HnuMm\nKBUy+ERMOAsoFIFMURgylSuGO8vtOWEK/Fldcw8AYE55TkjHVCpk2Li8FA6XN3Asil9SFpnbIvwY\nFNGkpC9UBkWh2XL9POjTVdj29plx25+msgZTH/7pJ+9iz3ET5pRl48G7V0KpmNoXfl6mP6t0+Ky/\nTHFxVd6U1yHtO2L3OZpIz4AT2Xp1QnTgLJGaLXBfUdRIe3RuWuO/sXOgtiOsxw/nnKxFlXnI0Kqw\n70Q73B4vjpw14+X3zgMA5pYHt59zLDOL/YPdmzsGpr1GiixvIFPEcABgUERBkFq6lhj0MV5JYtKn\nq/C5G+bB7vTipV3nY72cuPP6ngb0D7pw9w3z8e9fX4sZRZlTPsb6K0qQrVfD5xMxszgTmcPldFOh\nGM4UeVjmSOMQRRG9A05k64Mv7YwlafAxM0XRc3Y4O7JxRSmK87U4fNZ82Z6d6XCFMSiSy2W4enER\negec+PTDb+Lh/9qH4/UWZGnlmFsRWqYIAMqH58I1d7A1d7xj97mL8SzQpEaCImaKQrVxeSmUChmO\n13fFeilx51SDFWkqOW5ZXxny3Xddugr3fnIJgNBK54BRLbm5KZ3GMWh3w+P1Bco14x3bckdfXXMP\nNGoFSgx6XLmgEE6XF8fOhe9zPzAnK0zdDz+yohQyAdColbhx9Qw8du9q/NNNBdCoQy8PLczTQiGX\noYlBUdzzeH0QBM4pksR/UTTFXKvZnwJnUBQ6pUKOOeXZqL3QDduQC7p0VVA/J4oiBh0eeL0+eLw+\niCKQm5mWEKU7wRgYcqGlcwBLqvKmfadq1aJC/OqfN8KYkx7SzweGtzJTROPoGZ6FlShBUZpagbzM\nNLSabRBFMWk+N+LVoN2NVrMNiyrzIJcJuHJhAV7cVY8DJztCvllzKafbC5kQvo3xc8tz8PwPrkd6\nmjKwr6Smpmlax1TIZSgx6NDcMQCfT+R+lTjm9YrsPDcKgyKaVKvZBo1agZwpdAOjyy2cmYeT57tx\nqtEa1BekKIr40XOHsOeY6aI//9TGKnzuhvmRWmZEiaKIxvZ+HDlrxtG6LliG91jNq5jaPIzxTKfE\nU7rI4PBWGk/PgH+oZaKUzwH+DfN7jptQe6EbCyunvteOgneupQeiCMwu8+/HmVOegyydGh/WdsDr\nE8NyN97p9kKtkoc1wA32Jt1UlBdkoLG9H+aeIRTkasN+fAoPj88HOTvPBTA8pAl5fSJMXYMoNuh4\nl3GaFs70X/ifPN8d1PN3HGjGnmMmlBp1WL24COuWFkOpkAWaCSSao3VmfO772/H1n+7C/7x2Ckfq\nutDS6S/rmT8j9Pr1cOHwVppMT39iZYoA4Jb1lQCAv+w8F+OVJL8Gk79cbFZJFgB/SdKK+Ub02pyo\nawpPJzaX2xv3g4MBoLzQf4OKzRbim9crQsFMXgCDIpqQ2ToEj9cX6GJEoZtTkQ25TMCphsmDou4+\nO3776kmkpynwgy9fje98bgW+vWU5Kosz0djeH9aNu9Hy/Ftn0Gdz4prqEtx/1xV4+tvXQJ+uQprK\nX1oYa3IGRTSJ3uGZM9kJFBTNrcjBgpm5qDljRoOpL9bLSWotnf4AoNQ48n151cJCAMCB2vawvIbT\n7Q3bfqJIKjP6gyLuK4pvXp+PTRZGmfRM7NixAzYbN2mmKmmDbjH3E01bmkqBUqMeje39E27mF0UR\nv/jLMQw5PPjCTQuRl6UJPDa7LBs+n4gLrYl1cWPtd+Bscw/mzcjF/XdV45rqUpQXZODZhzbhqW9t\niItBmFL5HIMiGk9Pf+KVzwH+klsA+OvO+hivJLm1mm2QyQQU5o18Xy6ZnQ+1So79J8PTmjtxMkVS\nBzpmiuKZxytycOsokwZFe/bswe23344777wTTz/9NI4dOwZRZM19qmDnufCaUZQBp8uLzu7BcZ9z\nrqUXB091YvGsPFx3ZdlFj1UN16rXtSTWULwDtR0QRX8zhNHS05QoyouP91ag+xz3FKUsh9ODJ184\njNoLY2dzE63RgqR6rgEVhRl4/2grOib47KHQiaKIls4BFOamBwZBA/4ucVfMMaCtyxZoWmTtd4Q8\n1NXp8oZlcGukGbLToVbJmSmKc14vM0WjTXomvv/97+P111/HU089hfLycvzqV7/CqlWrorE2igOB\nTBHL58KiotA/g0eqPR/L6eHhf9deWX7ZPq7Zpf5a9USbFL7/pL90RColiUcc3ko7a1qw81ALtu04\nO+bjUvlcogVFgiDgkxur4BOBl3YxWxQJfTYXbHb3mM1erlzgb6zz+p4GiKKILz36Nr7/7H6YLFOr\nwvH5RH+maIrDrWNBJhNQZtSjpdMWGBBK8cfjDU8DkGQxaVDU3t6Ol19+GU899RSef/55qFQqbN26\nNRprozjQZrZBEIAiBkVhMaPIX1IwUW2/tCF3dlnWZY8V5mmhT1fiVIM1YTK2Hq8PtRe6UWrUh9wu\nOxoEQYBCLvALPEWJooi39jUCAI6ft2BgyHXZc3r7ndCo5dOa4RIra5cUwZCTjr9/2Bzookfh02KW\n9hNdHhStWVqM4nwdXvugAb977RRcw23/61t6p/QabV02+ETAmBu/n6OjlRdkwOP1wWRhdjJeeX0+\ntuQeZdIzsXHjRrz22mu46aabsG3bNvz85z/HZz/72WisjeJAW9cA8rM0CbGxMxFUDAdFje3jZ4rq\nWnqgT1ehcIw2poIgYPk8Iyy99sDk9HhX39ILp8uLRZXhabsdSQq5jHuKUlRdcw8aTP1QKmTw+UQc\nPHXxHhBRFNHZM4TcTM04R4hvcrkMn1hfCZfHhzf3NsZ6OUlnolJztVKOb336CijkAl4clam70Da1\nvaFnm/xVBHPLY9+tMxjsQBf/vNxTdJFJg6KXX34Z69evx/PPP4877rgDDz/8MF5//fVorI1ibMjh\nhrXfydK5MMrWpyFLrx43U9Rnc6Kjewizy7LGbYG+ZmkxAOCDo6YxH483J85bACAhZqTI5TJ4uKco\n5fh8In7zSi0A4Es3LwQAvHek7aLndPXYMWh3o2J4A3ki2lBdCgA4G6b20DSitXP8TBEAVJVm42ff\nWI/NV5Xj5nX+NulTDYrODP9/mxsH3TqDUWaUmi1wX1G8Yve5i016JmbPno3PfOYzeOKJJ7B161aY\nzWY8+OCD0VgbxVjgztc4H/IUmtml2TD32HFyOFgY7dxwOYU0/G8sy2YboE1T4INjbfBN0MUuXpwc\n3rQuzWmKZ0pmilLSOwebcbrRitWLi3D91TMwryIHNWfMFzVckG5kzCjKjNUyp02rUSJLp57yXhaa\nnNSOe6KmRDOKMvG1W5fiizcvhCEnHRdMfROWQXd0D+JUQzdcwyMYzjRaoVbJEyYwl8rFE6WqIRWx\n+9zFJg2KnnjiCXzqU5/CnXfeiQ8++AB33HEH9u3bF421UYyxyUJk3H7tbADAs6+chLX/4tr+I8OD\nWedWjF8eoVTIsHJBAbr7HBOW4cUDr9eH0w3dKM7XITsj/tsY+/cUxX+gSeH13lF/Vuiejy0c/u8C\nAMCzL58I3HhoGP63Jl3oJarCPC3M1iG4PQz+w6nFbENORlrQ4wUqizPRZ3Nd9h3g9Yk422SFw+nB\nA//5Pv756Q9w+0Nv4Ns/fw/NnQOoKs1KmDv72RlpKDXqUHuhm++3OOX1ck/RaJPuFp09ezY+//nP\nw2AwjFvOQ8mpTcoUMSgKq9ll2dhQXYJdNa24+wfbseGKEmy5fj5yM9PwwTETtBolFk1SarZ0tgHv\n1rTiaF0XZhbH753r5s4B2J1ezJ+RGDXwcrmM3edSjCiKqG/pRWGuFvnZ/v1Cc8pzsH5ZCXYfacW7\nNS34yIqypMgUAUBRvhanG63otA6O2SmNps7u9MDSa8eSquBLhGcUZWLfiXb8v6feQ0VRJsqMepQV\n6HHwdCf2HDOhOF+HngEn5pZnw+MTUdfSC1EEllTlR/BvEn5LqvLx2gcNONtkTYgS6lTi84nwiYCc\nmaKASYOiefPm4d5778XQ0BDeeust/OIXv8CaNWuwZMmSaKyPYqiVg1sj5mu3LsWskiy8c7AZ79a0\nYs8xE65eUgRrvwObVpRdNOdiLNKX77H6LnzimlnRWHJIgikHjCcKuQwutzvWy6Aoau8ehM3uxhVz\nDBf9+edumI99J9vx+zdO4erFRWg09UOnUSI3M/4znhOR5oKZLAyKwkW6gVg6hfN5TXUJzjRa0WDq\nw6HTnRfNLZIJ/koNmUzAA1tWID9bA7vTg1bzQMKUzkmWDgdFR891MSiKM9IQeQUzRQGTBkX/9m//\nhsceewyPPvooAOCjH/0ovvvd72Lbtm0RXxzFVpvZhjSVPOEvAuKRWinHzesqceOamXj3UAuee/M0\ndtW0AgBWLyma9OdzMzUXlSVMFkTFijRPKXGCIoF7ilLMuWZ/4F51SQv8/GwNPr6hEv/3dh3++28n\n0N49iIUz8xK+YqIo39/V0tTFNsnhIrXjnsqQ84JcLb7/Zf/Mx4EhF5o7BtDcOQCv14fZZdn47i/3\nYO3SokD2UqNWoKo0MT5HR1tYmQeZTMCxui585h/mxXo5NIo0foKZohGTBkUKhQJz584N/H7GjBlQ\nKBJvRgNNjc8nwtRlQ2mBPuEvAuKZXCZg08oyrFlShJffOw9LnwNLZwdXHiGVJZw4b7nsLne8ONfc\nC5VSjrKCxLgjrVCw+1yqkbKZY11wfvKaKrx9oBlvf9gMwH93P9GNZIrYbCFcAk0WQmxKpE9XYcHM\nXExkl20AACAASURBVCwY1Yzmf/71uoSch3UprUaJmUUZqG/ti+sbeKnII2WKEmSPWjRMeiYUCgVa\nWloCF8a7d+9OmKGRFLquXjtcHh+bLERJmlqB26+dg69+aknQH1Drr/BfoL36/oVILi1kDpcHjR39\nqCzOTJgPXYWM3edSidcn4sR5C2SCf+P7pTRqBT5/43wAwD+sqsCmlWXRXmLYFeb5M0XtzBSFjdSp\ndbx23KHI0KqSJoCYXZYNj9c34dByij5mii436W2IBx54AFu3bkVDQwOqq6tRXFyMH//4x9N60ccf\nfxzHjh2DIAh48MEHsWjRosBje/fuxZNPPgm5XI5169Zh69at03otCg2bLMS/ueU5WDAzF4dOd+LD\n2g5cMdcQV8FHo6kfPp+IqtKsyZ8cJ/yZIgZFqeJ3r9XiQlsfrlxQgLRx7spvqC7F/Jm5yM/SJEXW\nXKNWICeDbbnDQRRFvPZBAz6s7UC2Xo1svTrWS4pLs8uy8cbeRpxr7kmYUupUIH3XsfvciEmDopKS\nErz66quwWq1QqVTQ6XRoaWkJ+QUPHjyIpqYmbNu2DefPn8dDDz100f6kRx99FL/97W9hMBjwmc98\nBps3b0ZlZWXIr0ehae3ylwOwyUJ8+9TGKtRe6Ma//fYA0tMUWFKVj2VzDKieY4AhJz2ma2sdrrMv\nK0icjcFymQBR9GcQ5LLEvwCm8b25twF/230epUYdvnHnFRM+15Ad239L4VaYpwvMv1Ep5bFeTkJy\nur345V+OYeehFmTp1Pjnz65IiqA5EqRAqK6lFzfEeC00Qmq0wEzRiEnDwy984QuwWq3IycmBTqfD\nK6+8gs997nMhv+C+ffuwadMmAEBlZSX6+/sxOOhP47e0tCArKwtGoxGCIGD9+vXYv39/yK9FoZMy\nRSyfi2/L5xnxw3+8GjesnoEMrQr7TrTjl385hi8+9jaO1plDOmZH9yDe2Nsw7YxJ23B5TvHwxu5E\noBguV2G2KLkdPmPGMy+dQKZOhYfvuQo6TXCzZZJFUZ4Wouj/t05TZ+134DtPv4+dh1pQVZqFn31j\n/UX7gehixfk6aNSKQOMdig/STD52nxsxaabovvvuwxe/+EU8/vjj+M1vfoP29nb88Y9/DPkFLRYL\nFi5cGPh9dnY2LBYLtFotLBYLcnJG5pnk5ORMKytFoWtlUJQwllTlB2ZXmCw2vH2gGX/ZeQ7H6y1Y\nOntqDRj6bE489MxemK1DkMsEbL6qIuR1SeU5ifQekr4cvF4fwDvoSamlcwA/eu4g5DIBD919JQpy\nEydoD5ei/JG23ImUyY0HoijiqW1HUN/ah00rynDvJxcz2zYJmUzA7LIsHDtnQYOpL+FnfSULD/cU\nXWbSoGj16tXIy8vDV77yFaxbtw7PPfdcWBcwUdOGqTR0qKmpGfPXNL6JzlNDWw8y0uWoPXksiiuK\nT4n2fqrI9AIAjp9pQY3RHvTPebwifr+zC2arCwDwwvZa5CgskE2hJGT0uapvtkClEHC+7mTClJUM\nDPg3Ah+qOQJtWuQudBLtPRVL4T5XrxzowZDDg1uuysaQtQE11oawHj9WpnKe7P3+z4UPj56F0mmK\n1JLi0nTfT8cbh3D4rBWVBWqsnuXFieNHw7Sy+BLuf3eLioFj54BHf7MHX9psgCKJLsQnOlcer4j9\nZ204cmEQH1uZjXJD/Ow76+z1z+Szdlui8p2UCN974wZF3/72ty+6kCkrK8Pu3bvxwAMPAEDIzRYM\nBgMsFkvg92azGfn5+YHHurq6Ao91dnbCYAjuTnd1dTUA/0mXfk3jm+g82Z0eDPyxFUur8lP+XCbq\n++m/334TfQ5Z0GsXRRE//7+jaO5yYe3SYigVMuw81AKkl6B6fkFQxxh9rnw+Eb1/eg2lBRlYvnx5\nyH+PaNt56hBOt7Rh4aLFyMmIzHyuRH1PxUK4z5Uoivj5azuQoVXh7k+uTZp9Y1M9T7lF/fjT++9C\nps5CdfXSCK4svgR7nmxDLiiVcqjHyAD9ef8HkAnAd76wNmmzjJH4jKquBrocR7F9fxPqurXYcn1y\nzCya6FwdOt2J3/7tBEwWf5lqXZcSn7g+fj7761t7gTc6UVhgRHX1osl/YBri6XtvouBs3KDo6quv\njshiVq9ejaeffhq33XYbamtrYTQakZ7u38RaXFyMwcFBmEwmGAwG7Nq1Cz/96U8jsg4aX1vXcNkT\nmywkrPKCDByvt8Du9AQ16+KlXefx94PNmFWahfvuWIbTDd3YeagFdc29WBFkUDRad5/D39I9L7He\nQ4E9RR7uKUpGF9r6YO134JrqkqQJiEJRkOv/zm23cE/RpRwuD+790U4U5Wvx2NY1F71PPF4fzrX0\noqwgI2kDokj6wk0LcKSuC395pw4r5xsxpzxn8h9KQKIo4skXDuPdmlbIBODGNTNw6HQnak53wun2\njhlsx4Iv0GiBe4ok414tffzjH4fdbodG45+mPDg4iH379qGkpOSiYa5TtWzZMixYsAB33HEH5HI5\nHn74Ybz00kvQ6/XYtGkTHnnkEdx///0AgBtvvBHl5eUhvxaFhk0WEl95oT8oaukcmLQFartlEL97\nvRY5GWn4l8+vhFopD3Tb6uodCun1TcOBdWECNVkAELgA8vgYFCWjg6c7AQAr5k090E8maSoF8jLT\nAv9OaUTNaTN6bU702px4a18jblg9I/BYY3s/XG4v5pSzrXQo0tOUuO/2pXjoV3vx5P9n777j26rv\n/fG/jrYseVuyvGfixBlO4uyEDAhhBwgJI5TVywW+3HLbAi3tLb2F9teW771QSn9tGS2lLRBIm1DK\npgRCIDtxhjO995RtSbZk2Vrn+8fRkePYsvb0+/l48CCxZZ0PB/lI7/N+f97vt07ghcfWRU2AEExn\nGvuxp6odxdnJ+M4dC1GUnQyp+Cx27anH8Qu9WDEvK9JLBDC2pyieShkD5TYo+uijj/Dyyy/jn//8\nJ6xWK7Zs2YLU1FTo9Xo88MADuOmmm/w+KB/08MrKylx/Xrx48bgW3ST8zjb1AwDygziIjoRXgYb7\nf9fSNegxKDrd0AeWBW7dMBPpydxNkPQU7t99eu/3JF2s1TnhPdYCa8oUxbdj53ogEDBYOMu3BiTx\nKFulRHV9H0YsNsgknrPJ08VXJ9sBABKxEH/96ByWz9W4rot897RZFBT5bX6pCjdcVoz3v27EW59e\nwL3Xz4n0koLu3S8bAAAPbZ7vaiqxqiIbu/bUY9+pjqgJivjuczSnaIzbM/Hqq6/i5ZdfBgB8/vnn\nSExMxPbt2/G3v/2NgpY4Nmiy4POjbVClyjGnhFqMxqqCLK6jVHPXoMfHXmgeAADMLhwrZZCKhUhW\nSvwKiliWxe4jrRAwQHlRbL2G+OG3/PwGEj/0Q6OobdOhvCht2rXgnkxWBpfF7e73Lxscj4ZHrDh2\nrgd5mUrcf+NcDI/Y8Id/nnF9v6aFC4poAGlg7r52NjKSZfhgfxOGhi2RXk5Q9Q4M48i5bswqSMXs\norH31NLcFGRnKHDodBdMZmsEVziGMkUTuQ2KEhISoNFwJQb79u3DlVdeCQBQKpWQSqOnewYJro8O\nNMFitWPTZSWuD4gk9hRqkiBggIYOg8fHXmjRQSYRurJLvIwUObQ6s09dIAHgVJ0WjZ0GrJyfjcwI\nD5D1Ff+at1KmKO5UXegBy1LpHC/bud+PSujGnG8egMXmwPK5WbhqWQFmF6Zh/6lOHD3XDQCoaRlA\ngkyEXDVVUQRCJhHhxrUlGLXY8dGB+Oj+yGvp5m5EXroXl2EYXLEkHxabA/tOdURiaRPwN/8E03h/\n5aXcfup1XFRTf/DgQSxfvtz1d4slviJ7wrFY7fhgXyMUMhE2LsuP9HJIAGRSEXLUiWjs0Ls2U07G\naLa69h1dutkyI1kOi82BQZNvv+/v7KkHAGxeX+r7wiOMv2PGlxWQ+HH0nHM/UXlmhFcSHbKd+/06\nqdmCS4szs16amwKBgMF/bKmAUMDgxXeq0asbRofWhJn5qfQhMgg2LiuAQibCR/ubpnyPijV85jVr\nkkYcly/OA8MAnx+NjvmbdlemiG6A89yeieLiYvziF7/Aj370IyQkJGDePK5d37vvvovUVEodx6Mv\njrXBYLTgmpVFSJBReUmsK81NhnnU7uomOJlaZznIZBuHVX7sK2rsMOBErRbzSjIwIy/2rhP8mwNf\nVkDiQ12bDkfOdSMrXYFc6qoJAMh2ls9RpmgMX25c6Cw/LshKws3rSqHVmfG7ndzMvjIqnQuKBJkY\nK+ZlY2BwlGsNHSe6B7ibDJnpE6skMlLkKM1NQV2bLiqqEWyu7nMU5PPcBkVPPvkk0tLSkJSUhFde\neQUAMDo6irfffhtPPvlk2BZIwsPhYPHu3nqIhAxuuKw40sshQVCalwIAaJjiDedCC7efaFbhxNao\nqlQuKNL6EBT948vYzRIBFBRFu7o2HV7/+DzMozavHm+12fHZ4Rb87NXDsNkdeODmeTEzSDjUNOkK\nMAxlii7W3DUIiViIzIvu8vPvh8cv9AKY/AYS8c9iZ9a2ytkVMh70ODNF7krHS3JTYLOzaO32vN83\n1ChTNJHbljNSqRQPPfTQhK9Rk4X4dORcNzq0JmxYkh+yoZUkvEpzuaCovt2AdZV5kz6Gb7Iw2d3P\nDB8zRXqTDV+d7EG+JhGVMdrdiy+fo6Ao+jS06/HkSwcwPGJDU6cBP7p3qdv5GsZhCz4+2Iz3v26E\nbmgUQgGD+zfNxeLZVDrHk4iFUKXI0dVHmSKA+51v6zGiMDtp3GyitCQZyvJTUdNKTRaCbcEMFYQC\nBkfP9+COq/wf9RJNegaGIZeKkKSQTPr94hyuG11jhwElzvfoSLFR97kJqA8nATC2D+TmdSURXgkJ\nluLsZAgYuC1NcDhY1LbqkJ2hQLJyYvMUX4OiQxeMcDhYbF5XGrN348cyRfFT4x4PWJbFr98+AfOo\nDYVZSTh6rgevvHsaD22eP+G1tuOzGuz8og4jFjvkUhFuXleKG1YXuzKfZEx2hhIn67ReD3mOZ51a\nI2x2Bwo1SRO+t2yuBjWtOmS5uVYS/yjkYswpTkd1fR90gyNIjeEbst39JtS16dHdb0JWhsLte2DJ\nRUFRpPGZouk8yPpSFB4SdPWZcL55AJWz1Mif5A2BxCZPzRbaeodgGrFNWjoHjKX/Pz/Whn9+1TDl\n3gPzqA3HG0xIT5ZhzcLc4PwHRICQyueiUn27Hs1dg1gxLwv/91urUZiVhI8ONOMfznkgvNbuQbzx\nyQXIpSLcd/0cvPbjjfjmDXMoIHKDH67cRSV0aOniZqvx4wwutnxuFgQMMLc4tkYMxAJ+Zs/eE+0R\nXklg/veNY/if149hxGKfsutqQVYSBALGq86wocZ3n6OW3GMoKCJo7+XeDObQBT/uzMhLcdts4ULz\n1IMI05PluO/6cpjMVvzxn2fwyLN70Omm1KauTQeLjcXahbkQi2L3skJ7irz37BtV+PdffIbdR1pC\nfiy+W9OGJflIkInxk/uXIz1Zhtc+OIv9pzpdj9tf3QUA+OYNc7B5fSkUNI9oSq623FRCh9o27npY\nnDMxKMrLTMRz316Lb94Qf4NGI23NwlyIhAw+P9rm8/iHaNLYMbZHSDNJ5zmeVCxErlqJ5i5DxLvu\nuTJFtKfIxWO+fNu2bRPSgEKhEEVFRXj44YeRmRkdNdo2u4M2i/mJ32jLD/Mj8aMkNxlfHGtDfbse\neZnjZ2vw+4ncZYoAYPP6GVi7KBcfH2zGjs9q8e7eBjx8S8WEx9W3cSV6sV5vP9aSm4KiqbT1DLnu\n7L6w4yRy1YlTvo4CYXew+OpEO1ITpVhUxu1Vy0iR4yf3L8f3//+v8cq7p7FyfhYYhsH+Ux0QCQVY\nOodmEXnD1ZZbS5mi880DEAoYzHTTNZNvXEOCK0khwZJyDQ6e7kJDuyFmz3NqkhRaHVdq7qkUtSQn\nGa3dQ+jqNyFHFblumDbKFE3gMYpYuXIlNBoN7rnnHtx3333Iy8tDZWUlioqK8MMf/jAca/QK30qT\n+I4vnZisrz6JbWPNFsbvK2JZFifrtEhMkHgsmUxPluOOK8ugTkvA50daYTCOTnhMrTMoitU3NJ5r\neCvtKZrSR/u5gYtXLuXmme09HrrSl55+E4aGraiYoRp3R7MoOxnL52ZhYHAEzV2DaOsZQkv3EBaV\nqWmkgJdcbbmneaZo1GpHQ7sexTnJkE3zvVWRcNXyAgDAW/+qifBK/GN3sBgwjAAA8jWJWLto6hLy\n4hzufbKxPbIldJQpmsjjmaiqqsJzzz2HjRs3YsOGDXjmmWdw9uxZ3HvvvbBareFYo1fq2uKnz324\ndVGmKG7xzRYaLrn4dmiN6NObUTEjw6tNlkKhADeuKYbF5sCH+ydOIK9v00MuFUAd43s3+KCIMkWT\ns1jt+PvntfjX4RakJcnw4Ob5SFJIsO9UZ8jO2aWzYy620Jk5OlHTiwPVXBndqorskKwjHmWmKSAQ\nMHGVKfpwfxPe+Pg8dh9pwZmGPvTpzR7LlOpadbDZWZQXUQl5JCwqU2NeSQaOnOvGqTptpJfjM/3Q\nCOwOFqsqsvG7713uMfvDN1to6Ijs59ax7nOUKeJ5vCXS39+PgYEBpKVxpRFDQ0Po7OzE4OAghoaG\nQr5Ab9W16nDNisJILyMmdfWZkJIopburcYhvttDQznXF4WudT9RwbzwLZnrfOvvKpQV469MafLCv\nCZvXl0Im4S4fgyYLegaGUZIljdmuczxqyT05lmVx6Ew3/vT+GXT3DyNJIcG3tlZAKhZiVUU2Pj7Q\njNMNfT69nrzlCoqyJwuKVACAqgu9GBq2QCRkqHTOB2KRAJmpCXHTaMFktuKld6onfF0iFuKe62Yj\nx83+93NNXCnx7KLQlICSqTEMg2/eMAePvrAXv9pehee+vdbV/TQW8B1aVV6uucgVFEU4U8SXz1FL\nbhePZ+Luu+/GNddcg82bN+OWW27Bhg0bsHnzZuzZswe33XZbONbolVrnDAHiG5vdgR7dMJXOxbEN\nS/IxYrHj0V9/hdMNfQCA4zXcIMKFM1VeP49cKsK1q4owNGxxbXwHuCYLAJCdNvlchlgipJbcE1ht\nDjz7RhV+8ecj0OrMuHFNCV7+4QYsKeeCj8WzuH2lNSG6Bk+VKUpNlKE4JxlnGvvR1DmIBTPVUFJz\nBZ9kqRTQG0cxPBI9lR/+sljtALimQY/cugBbr5iByxbkQC4V4g/vnsG5tsnHC/DZifIQ7YsjnpXm\npeCbN8zBwOAofvbqYa8HNEeDPj1XOpee7F1QpJSLkZmWgMYOQ0SbS4yVz8X2zcxg8pgp2rJlC66+\n+mo0NzfD4XAgPz8fKSnRt2+gtWcoLi7q4dY7MAyHg6XSuTi2eX0p5DIRXn6nGj9+6QCuW1WEY+d7\nUJyTDPUUrUMnc/3qIvzjy3r8c28Drl5RCKGAweEz3QCAoszYn98hpu5zEzz/1nF8fbIDswvT8J+3\nLUCuenzDjnwN9/e27tDsS2nuGkRigtjtUOm7rpmN/3n9KMyjdqyaT6VzvsrOUOA4uGYLsb4n0Or8\nvVWlyrFxWYHr602dBjz+wlfYfdKAu24a/zP6oVGcaejDrILUmJ6TEw9uXFOC9l4jPj3UguferMIP\n710aE6VdfQbfMkUAN8T14Oku9BtGIpYV49/nqEnZGI9nQqvVYteuXfjiiy+wZ88e/OUvf8ELL7wQ\njrX5hGWBM439kV5GzOE7z2VTUBTXrllRiJ89tBIJMjHe+7oRAgZ4+Jb5Pj9PaqIMly/OQ1e/CYdO\nd8Fmd2B/dSdSEqUoVMd+UCSk8rlxalt1+PpkB8oKUvHTB1dMCIgAQJ2aAIlYiLae4JdTW2wOdPeb\nUJCV5LY0c/HsTPz28cvx7dsWYP3ivKCvId7FU1tuuzPDK77kQ15RdjIqZ2diYMg2Yd7awdOdcLDA\nqoqcsK2TTI5hGDy0eT4WzFDh8Nlu/PmDs5Feklf48rmMFO+D6hnOGxA7dtdGrDX3qDOzKpUII3L8\naOQxKHrwwQdx4cIFCAQCCIVC1z/R6NDprkgvIea093JvENkRbAtJwmNeSQZ+9Z01WDhThftumIuy\nAv9KRW5aWwIA+PhgE07VaTFosmD1/GwIYuCOnici54wlm42CIgDYtacOAJeN4feQXUogYJCXqUR7\n75CrRj1YtAYbWBYo9NAhUZ2WgA1LC2LirnK04TeF10e4E1YwTHXnu3IWt9/t2IUe19ccDhZ7qrjO\niZRljA4ioQBP3LMEuWol3t3b4CrPjmZaV1DkfcbnquWFKMxKwicHm/HCjhMRae4zPMKVKHpqIT6d\neDwTCQkJ+OUvfxmOtQQkJVGKo+d6sKI4I9JLiSmt3Vy9Pl8CQ+KbJl2Bnz64MqDnyFUnIl+TiJoW\nHQ44h2WuXpCDEV1zEFYYWfyG02B/uI9FusERHDzdhdK8FMwvnfq6mp+ZiIZ2A3oGTK7MQzBoDVxJ\nNF2fQqe8OA2JCRJ8drgF2zaWxXRLaldQNMkA6Urn3req873YdBl3Y+evH53D+eYBLJ6dCVWMd86M\nJ0q5GLdumIlfbT+OmhYdZriZHRUt+vVmCAUMUhK9zxQlKST4xcOr8NQfDuKLY20Ytdjx2J2VYR1+\nzu/boiZbYzye/YqKCjQ0NIRjLQFZWq6B3jiKtj5LpJcSU1p7hiAUMEH9IEPiX1l+KkYsduw90Q65\nVIRZBdH9puUtyhSNaegwgGWBZXM0HrsK8oOB27qDW0LXN8i9aU9WtkeCQyYR4bpVRTCarfj8aGuk\nlxMQq/P3drKMYUaKHJkpYpxu6IPBOIpPDzVj15565KgUeHTbonAvlXhQnM11aGuMcIc2b3T1m5CR\nIvc5U52YIMHPHlyJuSXp2F/diee2V4VohZPj9+EnyGL3RkiweQyKvv76a2zatAmrV6/GunXrsHbt\nWqxbty4MS/PNyvlZAIAzLcMRXknsYFkWbT1DyFYpw3p3gsS+MmcQNGqxY05xetwMf+NbcltpT5Gr\n61uBh9I1gMsUAUBL0IMi7k07N5Nu2oTSdauKIBYJ8O5XDTGdJXXtKXLzfrawOAFWmwO/2XESv99V\njcQECf77/uVITIj9zpnxJlethEQkiHjbak+MZjsMRsuk3TG9kSAT4yf3L0dhVhL2n+oMa8Mw04gN\nErGQGi1cxGN4+OKLL4ZjHQFbMEOF1EQpTrcMw2qzQyyKzn1P0aRPP4LhERsWltFdWOKbmfljmaF5\nJfFTssqXEZhHYqcdbKg0d3JBUdEk84EuVZqXAoGAwbt7G7BiXpYrcxQo7aANSrkYKcrYb+IRzVIS\npVhfmYd/HW7B4TNdWBmj+2s8ddNaWKLA1+dMOHKuGyKhAD+6bylVSUQpoVCAgqwkNHUaYLU5ovbG\nbY+eC2L8DYoALls7rzQDzV2DaO81jnt/DSXziJWyRJdw+yrbu3cvAODgwYOT/hNthEIB1i7KxYiF\nxeGz3ZFeTkxo7XHeCQ7SBxgyfeRrkiBzdqzxtN8kliicM26M1N4fLd2DkEuFUKd6btuenizHf2yp\nwNCwBf/98gH06gLP2FttDgwM2ZCrVsb8UOBYwDdQ+ceX9RFeif+sHoIiqViAG9eWQsAA3759IeYU\np4dzecRHxTnJsNlZtPcGv7NlsLiCIi9uHk2Fz7bz+7zDYXjEhoQY3kMYCm6DopqaGgBAVVXVpP9E\now1L8wEAv9lxEofOUCc6T1qdpS75XpTHEHIxoYBB5exMZKUrXNO544FEJIBIyMBknt5BkdXmQFvP\nEPI1SV53Fdy4rAD3XV+OPsMI/vvlA9APjQa0hu5+E1gWQcs6kanlZSZi8exMXGjR4XzTQKSX45ex\nTJH71+ztV87EGz+9BusW5YZrWcRPxc73loYo7ozIB0XelBlPhW8mE+wS5KkMj9ooU3QJt2fjgQce\nAICY6DzHK9Ak4ZaVaXj/qAE/f+0IbrtyJrZtnBUXrYJDYSwoog8dxHeP31kJh4ONqzbIDMNAIRdP\n+6CIb6/ta0nI5vUzMGiyYNeeejz1x4P4xf9Z5XdnI37uUa6aypvCZfO6Uhw734N/7K3H7KKlkV6O\nz+xeDKNkGIb2EMUIPihq7IzuoEgiEgQ865HPFIVi3ttk7HYHRi126jx3CbdB0dq1a6csWfjyyy9D\nsZ6AzStMwNoV8/Hz145gx2e1aGg34LE7K6GU0//4S7X2DEIkZJBFg1uJH0RCARCHW/cUMnFYN7tG\nI77JQpEfdfL3XFeOoWEr/nW4BT/702E89e8rIBX7/kI56Jw7VxxHmchoN7ckHSW5yTh0pgv9BjPS\nk2OrTbXNxjVamKwlN4k9hVlJEDDR24HObndAa7CiMDs54GZDygQJ0pKkaA1TUMS346YZReO5PRvb\nt28P5zqCqig7Gc9/dy2efaMKx8734NHn9+JnD61EZprn2vjpgu88l6NSUucRQi6ikIvRZxiJ9DIi\nim+yUJjte0DCMAwe3lIBo9mCA9Vd+PRQs2sujLdaugex90Q7MlPEmF+q8nkNxD8Mw2BZuQYN7Qa0\ndA/FXFDkaU8RiS0yiQjZKiWaOg1wONioq/rp7DPB7gisycLF8jOTcLJOi+ERa8gzOPzgVgUlDMZx\nGxR5aqawZcuWoC8mmPhWm3/98Bze+bIe7+6tx4M3z4/0sqKGYdgO86id9hMRcgmFTAyL1T6tu1g2\nd/PtuP0rrRUKGGzbOAsHqrtcZbq+eOvTGrAscPl87/c0keDIVnHlip1aIxaVqSO8Gt94s6eIxJbi\nnGS09xrRMzAcdVUtfEa9MCs42ew8TSJO1mnR1jOEsoK0oDynO8P84FbKFI3j9mx4aqYQ7UERwL0x\nf+Oa2fjwQBNO1fVFejlRpVdPk+IJmQx/58xktiElcZoGRZ2DyEiRQxnA3gu1MzPfM+BbJ7qGdj32\nV3diZn4KZuZ4PyGeBEe2ivvg2aE1RnglvvNmTxGJLSU5yfjqRAcaOw1RHBQF53PUxfuKQh0UJVHu\nswAAIABJREFU8ftm5dRoYRy3Z+PSBgt6vR4MwyA5Obbqu8UiAeYUp+P4hV4MDI4gLYneZAFAa+Du\nEuRTZydCxnEFRSNWpCROv/k4BuMoBgZHsHh2ZkDPI5eKkKyU+BwUvfnpBQDAnVfPBmtsC2gNxHf8\n3J5OrSnCK/Edlc/FH1ezhQ4DVkXZ/CxXmXGQMkXh7EDH7ymiRgvjebxyHD9+HBs2bMA111yDq666\nCldffTVOnz4djrUFTYWzJv1UnTbCK4kevQbKFBEyGb5F6XTtQNfSzb/RB15am5mWAK1uGHYH69Xj\nL7QM4Oi5HswpTsfCmbSXKBIUcjFSEqXo7Iu9TBE1Wog/Rc59jfVt+givZKLm7kEoZIKg3TxzzSoK\nQ7MFvpkQteQez+OV47nnnsPvf/97HDx4EIcOHcKvfvUrPPPMM+FYW9BUzOCGS1JQNKZHb4VIKEBW\nenSlowmJNKWrfG56BkVjdz+DERQpYLOzGPCyccWbn3BZom9cPYsGtkZQjkqJ3oFhWG32SC/FJ649\nRQIKiuJFslKKHJUCF1oGvL65Eg7DI1b0DgwjMyV4mRa+A1042nLzjRZoT9F4Hq8cAoEAM2fOdP29\nvLwcQmFs1dkXZSdDKhFGbVvHcDMYR9Gts2J2YVrAbSQJiTcXl89NNxarHR8daAbDADPzUwN+vkzX\nviLPpVhfVrXhZK0WC2aoMLckI+BjE/9lZyjgYIHuft9KHyPNtadIRAF1PCkvSsfwiA2tzix2NGjp\n4gKXYAZFANeBTqszh3wshCsoovK5cbwKiv71r3/BaDTCaDTio48+irmgSCBgkKdWor3XGFV3GiKl\n2tl0YmEZlacQcinFNM4Ubf/0Ajq0Rly/ujgom5ozvWy20N1vwu93VUMuFeLhLRUBH5cEJsfZgS7W\nmi3QnqL4VF7ENR0419gf4ZWMae7ibrKrgx0UacIzxHV4lMrnJuPxyvH0009jx44dWL9+Pa644gq8\n++67ePrpp8OxtqDK1yTBanOguz/2No8G24naXgDAAqrZJ2QChWys+9xk7A4WI6OTf89b9W16vPRO\ndVSVJ9W26vCPL+uhSU/A3dfMDspzatI9B0U2uwPPvlEF86gND22uiLoOU9MR34Eu1pot2OzOPUUU\nFMWV8qJ0AMC55oEIr2RMk7PzXNAzRWEKisyUKZqUxxCxsLAQr776ajjWElL8vI3W7kHXXbDpyGZ3\n4ERNL+RSAYpzUiK9HEKizlTlc8drevHirlMYNFnw0hNXINXPbpbv72vEF8fasHSOJipmwVhtdryw\n4wQcLPDIrQsgC1KdeWYa9+F6qqDon3sbUNOqw9qFuVhfmRuU45LAuGYVxVizBRtliuJSVoYCKUop\nzjVFT1DU0jUIAQOokoIbVLi6P/aF9obEWPkcZYou5vbKYTQa8eyzz+Khhx7Cyy+/DIeDu9j09PTg\nwQcfDNsCg4UfUhqOVofR7NX3zqDPMILyPDmENBSRkAnclc85HCye334c3f3DGB6x4ej5Hr+PwdfG\nN0XJPscdu2vR2j2Ea1YUYn5p8DLIqlQ5xCKB225KNrsD733dCLlUhIdumU/NFaJEVroCDBN75XM0\nvDU+MQyDsoJU9OnNGBj0rmmLP1iWRXW91mMGn2VZtHQNIlulhDjI+9f4THlXiIMi/qafnBotjOM2\nKPrJT34CANi6dSsuXLiA3/72t9i5cyduvfVWrFmzJmwLDBZXq8NpHBTtPtKCD/Y1oUCTiI0LY2ve\nFCHhMlY+Nz4oauw0QG8cxawCrgHBMT+DIgfLoq3X6HrOSGvsMGDn53VQpcpx7/XlQX1ukVCAAk0i\nWroGXR9YL3awugsDgyPYsDTf1fWPRJ5ELIQqNQGdsRYU2fhGC5Qpijcz8rjKlrpWXciOcb55AD96\n8QB+/PLBKR+n1ZthGrEFpUPnpdKSZBCLBCHf6mGmTNGk3J6Nrq4uPPfccwCAtWvXYtmyZVi6dCl2\n7NgBjUYTtgUGiypVDrlUGFXdS8KptlWH3+2shlIuxo/uW4bOlguRXhIhUUkh5y6LxkuCohM13F68\n61cXQ288j1N1WtjsDp9LdQwmO0Yt3J3IpggHRTa7Ay+8fQJ2B4tvbV0Qkvryouxk1Lcb0N5rnPAh\n4tPDzQCA61cVBf24JDA5GQqcqNVieMQaM/sOqHwufs1wdsOsbdNj2dyskByDD0TONvajT29GRop8\n0sc1d108tiC4Nw4EAgaadAW6+kxgWTZk2XPzqA0ioQBiUWw1Tgs1t1eOizvMiUQilJeX48UXX4zJ\ngAjg0q95mYno0JqmXQc63eAIfvHnI3A4HPjeNxbTRmZCpiCTiCBgMKEl6okaLRiGa1CyeFYmhkds\nOO/Hxl+tYex5O3qNGLVGrtnCl1XtaOw04Mql+SHb21Timkg/fvii1WbH+aYBFGYlufawkOjB/z8J\ndRlPMPGNFsQUFMUdPlNUG8JM0aDJ4vrz33bXun1cQzt3M6sgBJkigCtfNY3YMDQcug6ow6M2Kp2b\nhNsrx6XRaTzUemdnKGGzO6DVxdbshUD99aPz6DeM4O5ry7FoVuQ3dRMSzQQCBslKKTq0Rlid5Tij\nVjvON/ejJDcFyUopFjoDiNP1fT4/v3aQK1tIT5bBwXIbdiOlwRmoXLOyMGTH4Bu6NFyyf6q2VQ+L\nzYG5JekhOzbxXyx2oOMzRTR/L/4kJkiQnaFAXZsejhDd2NYNjrr+/MmhZpxpmPz6fqC6EyKhIGTz\n1Pgb16EsoRux2CCn0rkJ3F452tvb8cILL7j+ufTvsSjb+UILdVePaNPYYYBELMTN60ojvRRCYsKa\nhbkwGC04UN0JAOjUGmGzsyjN5T7glxelgWGAMw2+z83gM0VrF3Kd1kJ559OTDufeplB25CzMTgLD\nAE0d44O/M43cBw4a1BqdXLOKYqgDHTVaiG+zCtNgMlvx9cmOkDy/bohr4vC9b1SCAfDCjhMwXzJ+\noaVrEM1dg1g8Wx2yfZBZzlEGofysah6xQS6h0rlLuQ2KNm/eDKFQ6Prn0r/HIleb0RjbPBoIlmXR\n1W9EdoYCAuo2R4hXrnVmTj7c3wRg7M0px3n3XJkgQWFWEmpaBnyaNTQ8YkVD1ygkIgGuXVUEhgH2\nneoM7uJ90KE1Ii1JGtI9I3KpCPmZiTjf3O/aQ2U0W3GyVgsAmFtMmaJoFIsDXPlGC2JqtBCXbrty\nJqQSIV58pxp9enPQn183xGWKls3Nws3rStHdP4y/fHhu3GP2nmgHwN04CxVNuDJFVD43gdsz8q1v\nfSuc6wgLVznANMoU6YdGYR610z4iQnyQrVJi0Sw1jl/oRWOHwXUjhZ8hAQBzitPR1DmI2lY95nj5\nwf5P75/FkNmO2zbMRGZaAuYUp+NMQz+0OjNUqZNv6g2VUasdWr0Z88KQqbnnunL89NXD+O+XD0Ig\nAAacZSr5mkQkK6UhPz7xnSo1ASIhg66YKp/jyqqEAgqK4lF2hhL3b5qL3+08hV+/fRw/fWBlUG/2\n6odGoZCJIBULse2qWThyrgcf7m/CinlZqJjBjSo409APgYDBkvLMoB33UqFuy2212WGzs0GbRxdP\nptWVg/9AE0t3vgLFB4DZFBQR4pPrnB3RPtzf5HpzuvjmAl/2dbbRuxK64RErPjvcgowkEW67sgzA\n2N3GUJWDTKVTawTLhrZ0jrekXIPrVxVBbxyFSChA5Sw1blpbgu/cvjDkxyb+ETq7YLVrjWDZ2GhO\nZLM7IBAwVBURx65aXoDFszNxqq4PH+xvDOpz64dGkZLIDeSWiIX4zu0LIRAw+M2OE67GO939JqhT\n5ZBJQhdQqFMTIBAwIQuKzKNcdQNliiaaVkGRQi5GilIaU3e+AtXlrAfPyqDuToT4onJWJjLTEvDl\n8XbUtuogYABN+lhQNKeIyw55GxTVt+vhYIGZOTJXec+q+dkQChhXSUY48TeHctThuTY8cPM8vPN/\nr8erT27EU/++Av+2aS5m5KWG5djEPzkqJUxm67iuXNHMnxb5JLYwDIP/vHUBkhQS/OWDc0Ebs2K3\nO2AwjSIlcSxzPTM/FTevLUGvzoyvT3ZixGKDbmgUmrTQ3mQWCQVQp8r9Lp/T6sz47q/34sP9TZPe\n0OD3SVFQNJHHq8dbb70FozF+MitZGQr0DJhcXaXinStTpKJMESG+EAoYXLuyEBarHS3dQ1CnJYzb\nq5CSKEWuWonzzf2wTzKY9FJ1rVynt5x0ietrSQoJFpap0dhhQHtveAdLt4ehycLFGIahmRgxZmwf\nbmzcSLTZHRBTk4W4l5okw7e2VsBic+BXbx0Pyuc5g8kClgVSE8eX866Yx81EaukeRE8/17lYE4bK\nG026Arqh0QmNHrxxqk6L+jY9XnqnGn/7fGJrcQqK3PMYFNXU1GDTpk144okncOzYsXCsKaRyVEo4\nWKBnIDYu8oHi38yofI4Q321YWgCJMxDKniTbOrckA+ZR+4R205Opa+OCouw0ybivr12YAwD46kR4\nS+jae7igKDdMmSISe3Jc+3Bj48aoze6AiJosTAsr5mVjw5J8NLQb8PZnNQE/n26Q6zyXmiQb9/W8\nzEQAQGv3oCtzo0lLCPh4nmSl+99s4eLf1/2TNPIZcQZFMuo+N4HHq8dTTz2F3bt346abbsJ7772H\n22+/HX/4wx9gMER2Eru/pluzha4+E6QSIdIu+UUnhHiWpJBg7SJu389kNxb4BgvetOaua9MhSSFB\nimL8G9GyuVmQiIXYe7w9rHs3Gjr0SJCJoE4N/Rs8iU3ZMdaBzmZjqcnCNPLvN82FOi0BOz+vxfkm\n3wdpX4zvPHdppihBJoY6VY6W7iF0DzgzRemhv8kcSLMF/mZ4Roocrd1DEzqkDvOZIppTNIFXVw+B\nQID8/HxoNBpYLBacPXsWd955J3bv3h3q9QUdf7d3OrTlZlkWnX1GZKUr4mL4LiGRcNPaEmSkyFE5\ne2K3Ib6dtKd9RQbjKHp1ZszIS5nwuyiXirBsjgadfSbXpPRQGx6xokNrRGluCm1KJ265ZvvFSvmc\ngzJF00mCTIxH71gEFsCv3qpyZUD8oXfOKLo0KAKAfE0S9EOjrhJoTXrobyRpAsgUdWiNkEmEWDI7\nE3YHi5au8aXZ/HmSh7BZRKzyePV49913cdddd+Ghhx6CUqnEa6+9hl//+tfYvn07fvOb34RjjUEV\ni1O6/aUbGsWIhdpxExKIfE0SXvvxRiyeJCjKSJFDk56As039U05ZP3i6CwBQXjR56+41zhK6cDVc\naOgwgGXhGkZLyGTSkmSQSYQxlCmiPUXTzZzidNxwWTG6+4dx6Gy338/DZ4r47nMXK9BwJXRHznHX\n8XBkilw3JHzMFDkcLLr6TcjOUKLEeX1v6NCPewztKXLPY1C0b98+fPvb38b777+Pu+++G8nJyRga\nGkJSUhLuueeecKwxqPgAIVYu8oHoonbchITcnOJ0mMxWtLjpgsSyLD7c3wShgMEVS/ImfUzlLDUU\ncjH2VLVhT1XblAHWVJq7BvHOnnqPm3PrnfubSvMoKCLuMQzXlru73xQTbbmp+9z0tHFZAQDg4Gn/\nB2HzHRaTFJIJ38vXJAHgWlknJoihkIdu2DUv05mNcpcp6jeY8fGBJhw52w37Re8XA4MjGLXYka1S\noCQ3GQAm7Hl17SmioGgCj1ePZ599FikpKTh69CiOHj2K/fv349ZbbwUA3HLLLSFfYLDJJCJkJMum\nxZ4i18DJMHWXImQ6mlvMzStyt6/oXNMAmrsGsXxeFtKTJx/QKhYJceOaEhiMFvxq+3GcrNP6vI5+\ngxk/fukAXvvgLB55ds+Um+P5oGgGBUXEg6wMBUYsdhiM0d+W22Z3QEhB0bSTn5mIHJUCVRd6MWLx\nr4RuaJh7fScmTAyKirKTXH8uzkn2b5E+kklE0KQnoKZFN2lL/NfeP4ff76rGz/50GK++d8b1df66\nn6NSokCTCKGAQUP7+EzRMGWK3PJ49fj5z3+ORx55BA8//DCeeeYZfPe738WNN94YjrWFTLZKiT69\n2e9fnljROcnASUJIcM0tmXpf0Qf7uAGD1zuHwbpzx8YyfPu2BQCAli7fZm9YbXb88s9HoTeOYuFM\nFXoGhvHsG1Xo7jdNyBo5HCzONfVDKRcjMwxdlEhsC2RvQ7jZ7CzEFBRNOwzDYMW8bIxa7DhR4/sN\nJQAwDnPDWRMnyRQVZSfjsTsr8YN7luC/7l0a0Fp9cd2qYoxY7K73EJ7dweJ4TQ/SkqTIy0zE+183\nurrMdWjHxrCIRULMKkxDbat+XCOKEQsNb3XH49WjuroaH3/8MWbNmoVdu3bhT3/6E8xmczjWFjJ8\nkNDt7Dkfr6h8jpDQy0xLQHqyDGcb+yeUGPUbzDh4ugsFmkRXp7qp8MNM+RlC3nrl3TOoadVhXWUu\nnn5gBdZV5qKuTY9//8Vu3P3UJ/jt30+67haeqO1Fn2EEK+dnUwMW4hG/qbwryoMilmWpJfc0tmCG\nCgDX5dMfgyYLBAyQ4CZQWLcoF6vmZyNBFvrSOd7VywuQmCDB+183YnjE6vp6fZsOQ8NWLJ6twQ/v\nWQKpRIgXdpxAp9aIU84qg8IsLqN1z7XlAICX3ql2ldnRniL3PF49JBIuarZarWBZFnPnzsXx48f9\nPqDNZsPjjz+Obdu24a677kJ7+8SNxXPmzMHdd9+Nu+66C3fffXfQa5n5YYWNXswWiWWdfUZqx01I\niDEMgwJNEvTGUYxax7c+/fRQC+wOFtetLvYqAMnKUEDAcIMCv/W/X+AP/zzt8Wf+dbgFnxxsRnF2\nMv5jSwUYhsGDN8/HZQtycNmCHCQpJPj0UAu+8/xePP7CV3jjkwsAgKuWF/j130umF1emKMpLzh0O\nFiwLiKjRwrSUmxnYoOGhYQuUCZKo6sYpk4pw45piGM1WfHKw2fX1485s2KJZauRlJuI/tlTAPGrD\nz/98BIfPcDfh+JK/2UVpuHxxHho7Da7nGNtTRHOKLuUxTCwqKsKbb76JxYsX47777kNRURGGhvyf\nvP7BBx8gOTkZzz77LPbv34/nnnsOzz///LjHJCUl4a9//avfx/Bk8exMvPbBWby/rxHrK3Pj8m4p\ny7Lo6jNRO25CwkDp3HhrMlshc7Y5tdoc+ORgMxQyEdY5Zx15IhELkZmmQE0Ld7ezZ2AYd19bDql4\n8jev9t4hvLirGkq5GD+8d4nr2Eq5GN+/azEAZ6nFhR58dKAZVRd6wLJcXTztJyLe4IdIRnumyGp3\nAADtKZqm0pJkkEv975Q4NGyZdD9RpF23uhi79tTjH3sbcN3qYkjFQpys7YWAASqc2bH1lXk429iP\nTw+1AAA2Li8Y97nv3uvLcehMF17/+DxWV2TTnqIpeLx6PP3007juuuvw6KOP4pZbbkFBQQFeeukl\nvw948OBBbNiwAQCwcuXKSbNOoe5yk5eZiBXzslDfpsfJWv/qT6Md346bb0FOCAkdxUVBEe/Q6S7o\nhkZxxZJ8n958ctRjjVFGLHYcv9Dr9rGfHW6Fze7AgzfPc9smVihgsKRcg5/cvxx/+K8rce915fj2\nbQvpZgnxiipVDoGAifpyc7ud+9xAe4qmJ4ZhkK1SolNr9Ll7J8uyGBq2Ttp5LtKUcjGuW1UE/dAo\ndh9pBQC09QwhK0PpuhkHAA/cNA8luclIkImwvnJ8l9PURBnuvGoWTGYr/vLhOZpTNAW3Z+To0aMT\nvqbRaKDRaNDW1gaNRuPXAfv6+pCWlgaAexELBALYbDaIRGNLGR0dxeOPP47Ozk5s3LgR9957r1/H\nmsrWy2fiQHUX/vZ5LRaWqYP+/JHm6jyXQZ3nCAm1saBorKnBhweaAADXemiwcKlctRLHzve4/n7g\ndCdWzMsa9xjd4Ah6dMPYe6IdCpkIK+dne/XcmWkJuOXyGT6th0xvIqEAqhR51DdasDkzRdSSe/rK\nUSnR0G5An94MtQ9NZEwjNjgcLJQJ4dsv5ItNa4rx3lcNeGdPHVZXZGNo2IqygrRxj5GIhXjm4dUw\njVgnzXhdt6oInx1pxWdHWiESCsAwgFRC5XOXchsU8SVtFosFtbW1KC4uht1uR1NTEyoqKvDmm296\nfPK///3v2Llzp+uOJMuyqK6uHvcYh8Mx4ed+8IMfYNOmTQCAO++8E0uWLMGcOXOmPFZVVdWkf55K\naZYUZxr68Y9PDiBfNXGKcSw73sC9gVlMfW7Ph7fnabqj8+S96Xqu9ANct7iTp89heECO/iEbzjb2\noyhTiu7WGnS3jn/8VOfJbuZuaOSmSzA0YseBUx1YVcJCLOKuoyMWB17+pAc6I7d/aVGJAqerT4bg\nvyo6TNfXlK9CeZ6UUjsaBkbx+VeHkaKIzrvLg8Pc78OgQTfluaDXk3di8TwxVu46vOfACZRmeb+X\nesDI3cyymIf8+u8Ox7laUJyAI7VG/Hb7PgCAwG5ye9xmN8+xtlyC5i7uBoJExATUH8AfsfCacnt1\n2759OwDgiSeewIsvvgiViqtd7OrqwgsvvODVk2/duhVbt24d97Uf/vCH6OvrQ1lZGWw27oV4cZYI\nAG677TbXn1esWIHa2lqPQVFlZSUA7qTzf/ZEltqPH/xuH6rbBbj5au9+Jlac6T4HQIcVleWYW5Ix\n4fu+nKfpjM6T96bzueq1NGP3yVPIyi1E5aJcvPHJeQDduOnyOai8pJTB03lK1Rjw/pEvccXyUgyP\nWPH3z+tglWRh+cIcAMCzb1RBZ7QjNVEKg8mCbdctmnDXMF5M59eUL0J9nvSOVvz67RNoG1TiijVT\nvxdHSne/CXi3CypVBiorF036GHo9eSdWz5ORacfeM1VISM5CZWWx1z9X26oD0I2i/CxUVs716Zjh\nOlcZOYM48r97cLplBABQMbsQlZUlPj3HIpbF7urP0aE1QSQShvX/cTS9pqYKzjzmmVtaWlwBEQBk\nZWVN2jHOW6tWrcInn3wCAPjiiy+wbNmycd9vamrCY489BoDrVHf8+HGUlpb6fbypzClOx5zidBw7\n3xN3nej4AV40uJWQ0FPKxvYUORws9hxrg1wqxMpLyt68UZyTjBefuBw3ry1x1YZ/UdUGALDbHdh3\nqgN5mUq8+uRG/PUnV8VtQESiB9/F8F+HW6J2vh/fblhMLbmnLX4/Zku3b3PephrcGi3y1ImQS0Uw\njXC/fxo/Rq0wDIPVC7iba8Mj0fl7HGkerx6pqal49NFH8eabb2L79u34/ve/D5nM/xbP1157LWw2\nG7Zt24a33nrLFQC98sorOHXqFIqKipCVlYUtW7bgzjvvxPr16zFv3jy/j+fJrVfMBAD87fPakB0j\nEjq1JsgkQqQmxldZICHR6OJGC2cb+9GrM2Pl/GzI/Ozuk6tOhFAoQF5mIkrzUnC8phe6IW4fkd3B\nojQ3BWKRAMlK+v0moScRC3H1ikIMDVux93hHpJczKZuN9hRNd/mZiUhLkmL3kVZcaBnw/ANOQyZn\nUBSFjRZ4AgEzrmNolpvGOp5c5gyKyOQ8vmM///zzeO+991BbWwuWZbFgwQLceOONfh9QIBDgl7/8\n5YSvP/DAA64/P/74434/v68WlqlQlJ2Eg9WdGLHYXC1tYxnLsujqNyE7g9pxExIOCjl33TCZrdh9\nlNtAdMWS/KA89+WVeahv0+OrEx2uGWs5lAEmYXbNikLs/KIOH+xrxMZl+VH33mKlRgvTnkQsxKPb\nKvHjlw/gf18/hhceXQelF9mfQVemKDobLfBm5qeiur4PAHxqJHGxAk0Sbllfilx1YjCXFjc8Xj1k\nMhluvfVWPPnkk1ixYgW2bdsGhSJ+2jwzDIPZhWlwsP4P/Yo2A4MjGLXYqfMcIWHCTznvM5hxoLoT\n6rQEzClKD8pzr1mYA6GAwRfH2sbKYul3m4RZRoocK+dloblrEGcb+yO9nAnGus9FV7BGwqtihgq3\nX1mGXp0ZL+w44dWIlyETN0ohmsvnAC4oAoD0ZJnb2XXeuPf6OdiwNDg37eKNT7dUQjlQNZL4iLmt\nx/+htNGk0zl5nGYUERIe/LyII2e7MWKxY92i3KBNRk9WSlE5KxONHQYcOt0NAMii320SAdev5jav\nv7+vMcIrmYifU0SZInLblWWYV5KBQ2e6vXqtGp2ZomicU3Sxmflc+RzdFAsdn64eoR6qGil5mdwL\nrL3Xv0nI0eaLo9ym7JJcmlhPSDjwe4pGLFxb4NLc5KA+/+WLuYYLpxu40olsPzbZEhKo8qI0FGcn\n49CZbmh15kgvZxzXniJqtDDtCQUMHrtzEZKVErz2/lm09059w9tgiv5GCwCQnizHd25fiHuvL4/0\nUuKWT1ePH/zgB6FaR0TxmSJPvzixoK1nCF8ca0W+JhHL5/re+YoQ4juJWDiu65XGz02w7iwpz3QF\nXimJUle5HiHhxDAMbrisCA4Hi48PNkV6OePQniJysfRkOe69rhw2OztuGPalWroHcehMF1KUUqTE\nQGOqK5bku8roSPBN2VXgww8/xN69e9Hb2wuGYaDRaHD55ZfjyiuvDNf6wiI9WQaZRBgXmaI3PjkP\nBwt84+rZEAapfIcQ4plCLoZ+aBQAkOnnJlh3JGIhVldk49NDLZQlIhG1ekEOfvO3kzjf7H13r3Cg\nPUXkUvNKuXEyF5p1wNrx37Pa7PjiWBve/qwWVpsD/7G1ggJq4j4o+ulPf4ru7m5cffXVUKlUYFkW\nvb292LFjB44fP44nnnginOsMKYZhkKtWoqV7CHYHG7PBRF2bDgequ1CWn4rlczWRXg4h04pCxgVF\nKcrQZHKuWJyPTw+1oECTFPTnJsRbMokIaUky9A4MR3op49CeInIpdaocKYnSce25LVY7PtjXiH9+\n1YCBwVGIhAJsu2oWVdYQAFMERefPn8dbb7014eubNm3Ctm3bQrqoSMhVJ6K+3QCtbjjopS/h8vpH\n5wEAd107O+rapRIS7/hmC5r04GaJeLOL0vD0AytQnB3c/UqE+EqdmoCaVh3sdgeEURICzXpAAAAg\nAElEQVSEUPkcuRTDMJhVkIpDZ7rRpzcjI0WOv+2uxY7dtZBLhdi8rhSb1hQjPVke6aWSKOH26mGz\n2WA0TiwnGxoags0Wf5Nw8zK5fUWt3bG5r+h0fR9O1GqxYIYKFTNUkV4OIdOOwhUUhe6myqIydUzU\nvZP4pk5NgMPBot8wEumluNDwVjKZWQVpAODKFlXX90HAAH/4rytx3w1zKCAi47jNFN16663YtGkT\nli1bBpWK+5Dd09ODo0eP4rvf/W7YFhguBRouKGruGsTSObFVesayLP7y0TkAXJaIEBJ+4QiKCIkG\n6jTug2SPbtjvIZLBRnuKyGTKCrimBDUtOqyYl43GTgPyMhORrKSbS2Qit0HR1q1bsWbNGhw4cAC9\nvb0AgFWrVuF73/seMjIywrbAcCnI4ur0W7oGI7wS3x052+38hc+iriSERAgfFGVlRMeHREJChW8k\n0jswDJREeDFOdju15CYTleSmgGGA+nY9OnqHMGqx07gS4pbboIhlWej1etx8882w2+3YtWsXTp06\nheHhYWzZsgUi0ZSN62KOOjUBcqkIzd2xFRTZHSxe//g8BAxw1zWUJSIkUjJSZACA/ExqhEDimzr1\noqAoSlip0QKZhFwqQq5aiYZ2A+rb9QCAUgqKiBturx5PPvkkduzYAQB49tln8dlnn6GoqAhHjx7F\nU089Fa71hY1AwKBAk4iOXiOsNnukl+O1ulYdWrqHsGZhrmtfFCEk/G5YXYyf/5+VKM2jN1wS3/iS\nud4oGuBqo0YLxI2S3BSYR234+mQnAAqKiHtu0z01NTXYuXMnAOD48eN44403IBaL8Y1vfAN33HFH\n2BYYTgVZSbjQokN7rxFFMdLhqa2HawwxtyT+ShoJiSUJMjHml1KTExL/VCncnqJeXfRkimhPEXGn\nNDcFX1a149j5HggYoCibsvlkcm5vqVitVnR0dAAACgoKYLVaAQBmsxmjo6PhWV2YFTr3FTXH0L6i\nDi3XITBHRZu7CSGEhJ5ELERakhQ9UVQ+R93niDsXZ4bWVeZBJo2v7R8keNy+Mh577DHcfvvtqKio\nQGJiIu644w7MnTsXJ06cwKOPPhrONYZNcQ6XHapr02N9ZV6EV+Odzj4TACBHrYzwSgghhEwX6tQE\n1LXpo2bguY0aLRA3+M92AHD/jXMjuBIS7dwGRWvWrMHHH3+MAwcOoKWlBcXFxVCr1XjssceQlpYW\nzjWGTWluCkRCAc439Ud6KV5r7zUiQSZCCrWXJIQQEibq1ARcaNFhwDACVWrkZ73YqNECcUMuFeGn\nD6xAWrIMiQmSSC+HRLEpc4hKpRIbN24M11oiTiIWYkZeCmpadTCP2iCP8hSr3cGiq8+EouwkMEzk\n79QRQgiZHsaaLQxHSVBE5XPEvYVl6kgvgcQAunpcorwoDQ4Hi9oWXaSX4pFWNwyb3YEcFZXOEUII\nCR8+KIqWfUXUaIEQEigKii4xu5ArDTwXAyV0riYLtJ+IEEJIGGWmjmWKogFligghgaKrxyVmF6VD\nwAAnarWRXopHbT3OoCiDgiJCCCHho05ztuWOlkyRjdtTJKZGC4QQP9HV4xJJCgnmlmTgfPMA+vTR\nM5huMvVt3HTmktzYmKlECCEkPqiiNFMkFNDHGkKIf+jqMYnVFdkAgP3VnRFeydTq23VQyETQpNOM\nIkIIIeEjFQuRkihF70B03DykltyEkEDR1WMSK+ZlQ8AAX51oD/pzP/3HQ/if14+BZdmAnsdotqJD\na0JpXgoEUTAjghBCyPSSmZoArX4Ydkdg72fBQI0WCCGBoqBoEimJUiyalYnaVj1O1/cF7XkNxlEc\nO9+Dr0924MjZ7oCeq6GdK52bkZcajKURQgghPlGlymGzs9ANjkR6Ka6gSEyNFgghfqKrhxu3XzkT\nAPDWv2qC9pw1rWNtvl997ywMxlG/n6vOuZ+oNC8l4HURQgghvlI79xVFw/5bfngrVU4QQvxFQZEb\nZQVpWFSmxumGPpxpCE62qMY5+2hWQSq6+k34rxf3w2i2+vVcfJOFGRQUEUIIiYCMFK4DnVYXBUGR\nzQGRUECDzAkhfqOgaAp3bCwDELxsET8Q9sf/thzXrCxEa/cQ3v+60a/nqmvTIVkpgSol8pPECSGE\nTD+qVGdQFA2ZIocDYhEFRIQQ/1FQNIVZhWlYOFOF6vo+nG0MbJirw8Gitk2HHJUCSQoJ7rt+DpRy\nMT7Y14gRi82n5zIYR9GrM2NGXirdFSOEEBIRrkyRPvJtuflMESGE+IuuIB7csXEWAODtALNFp+q0\nGB6xoawgDQAgl4pw3aoiDJos+M9nv8Tvd57C/lOdGBq2eHwu136iXCqdI4QQEhl8pUJ07ClyQEhB\nESEkAHQF8WB2URoWzFDhZJ0W55r8yxbZ7Q788b0zYBjghsuKXV+/aV0pLluQA71xFB8fbMYzfz2K\nf/v/PvMYGPFB0Yx8CooIIYRERpJCAolIEBXlc1Y7S5kiQkhARJFeQCzYcsUMnKzT4usTHSgvSvf5\n5z8+2IzW7iFsXFYwLrujlIvx/bsWw253oK5dj7/vrsORc91o6jRgfqnK7fO5mixQpogQQkiEMAwD\nVao8KjJFdrsDEpEw0ssghMQwuq3iBb7DW4fW6PPPDposePOTC0iQiXDXNbMnfYxQKMCsgjSsqsjm\njtPr/jgOB4ua1gFkpMiRmiTzeT2EEEJIsGSkyGEwWjBqtUd0HTa7AyJqtEAICQAFRV5IkImRliRF\nR5/J559985PzMJqtuGNjGVISpVM+NkelAAC0TxF8NXcNwmC0YH5phs9rIYQQQoJJlcLNKuqPcLbI\nZnNAKKCPNIQQ/9EVxEvZKiW0umFYfLgb1tw1iE8ONiNHpcR1q4o9Pj5HnQgA6NS6D75O1moBAAtm\nui+vI4QQQsLB1ZY7wrOKrHYWIhF9pCGE+I+uIF7KUSnBskBXv/fZotc/Og8HC9x/41yIvbhYK+Vi\npCilU5bPnaztBQAsmEFBESGEkMgaa8sd2aDIbndATI0WCCEBoCuIl7IzlACATi/3FdkdLE43aJGX\nqcTi2ZneH0elQM+ACVbbxIyUcdiCs00DKMxKov1EhBBCIk4VBUGRw8HC7qDuc4SQwNAVxEv8fp+p\nStsu1qk1wjxqx4y8VB+Po4SDBbom2b/04jvVsFjtWF+Z69NzEkIIIaGQEQWziuwOBwBAKKRGC4QQ\n/1FQ5KVsFZcp8rYDXV2bDsBY5zpv5aq547T1jD/O3uPt+OpEB8oKUnHjmhKfnpMQQggJBVemSDcc\nsTVYbVxQRJkiQkgg6AriJU26AgLGh6Co1TlLyMegqLyYm4N05Fy362t9ejNefKcaMokQj25bRFO7\nCSGERAWZVITEBDH6DJHMFLEA4NXeXUIIcYeuIF4SiwTIylCgrWcILMt6fHxdux5CAYOi7GSfjjMz\nLxUZyTIcPtsNq80Bh4PFr98+DpPZivtvnOva20QIIYREA1VKArQ6s1fvjaFgo0wRISQI6Arig7zM\nRAwNW2EwWqZ8nM3uQFOHAQWaJEjEvk3YFggYrKzIhslsxak6Ld77uhGn6vqwpDwTG5cVBLJ8Qggh\nJOgyUuQYsdhhMlsjcnyrnQ+KaE8RIcR/FBT5IC+TmyPU1jM05eM6eo2w2BwoyfUtS8S7bEEOAOB3\nO0/htQ/OIlkpwSO3LgDD0AWfEEJIdHHNKopQswWbnTJFhJDA0RXEB/nOoKjVQ1DU1GkAAJ9L53iz\nCtJw64aZ6NObIWAY/OjeZUhNpBbchBBCok+kZxXZ7VzZHgVFhJBAiCK9gFjCZ4pauwenfFxjJ/f9\n4hz/giIAuOua2SjMSkJakgyzi9L8fh5CCCEklMY60EU4U0SNFgghAaCgyAc5aiUYZmK77EvxmaLC\nrKSAjseX0RFCCCHRii+f6xmITFtuaslNCAkGuoL4QCYRITMtYco9RSzLoqnTgMy0BCjk4jCujhBC\nCAm/wqwkiEUCVF3oicjxbdRogRASBBQU+SgvMxF64ygMxtFJv68bGoXBaEFRdmBZIkIIISQWJMjE\nWFSmRmv3kMdGRKFAjRYIIcFAVxAf8c0W2nsnL6Gra9UBAIpzfBvaSgghhMSqVRXZAID91Z1hP7aN\nGi0QQoKAriA+yvPQge5s0wAAoLyQmiMQQgiZHpaWayASCrD/VCSCIsoUEUICR1cQH3maVXSusR9C\nAYOygtRwLosQQgiJGIVcjIVlKjR3DaJDO3UzomCz2WhPESEkcBQU+cgVFHVPDIpGRm2ob9ejNDcF\nMik19iOEEDJ9rJrvLKELc7aIWnITQoKBriA+kktFUKfKJy2fq2nVwe5gUV6cHoGVEUIIIZGzbI4G\nIiET9n1FtKeIEBIMdAXxQ15mIgYGR2A0W8d9/eDpLgDA3BIKigghhEwvygQJKmao0NhhQFefKWzH\npT1FhJBgoCuIH0rzuM5yv9pehbON/dAPcS26PzvSCnWqHIvK1BFeISGEEBJ+rhK6MGaLaE4RISQY\naOOLH25ZPwM1LTocPdeDo+e4YXVikQBWmwM3ryulu1WEEEKmpeXzsvC7naewv7oTWy6fEZZjjjVa\noPdeQoj/InIFOXLkCFauXIm9e/dO+v333nsPW7ZswW233YadO3eGeXWeyaUi/Pe/Lcej2xbhlvWl\nWD5XA026AmUFqdiwND/SyyOEEEIiIjFBgvmlGahv06N3YDgsx3TtKaJGC4SQAIQ9U9TW1oY///nP\nqKysnPT7ZrMZv//977Fr1y6IRCJs2bIFGzduRFJSUphXOjWxSID1lXmRXgYhhBASVeaWZOBErRYt\n3YNQpyWE/Hi0p4gQEgxhv4Ko1Wr87ne/g1KpnPT7p06dwvz586FQKCCVSrFo0SIcP348zKskhBBC\niD/UqXIAQK/OHJbj0Z4iQkgwhD1TJJVKp/x+X18f0tLSXH9PS0uDVqsN9bIIIYQQEgSqVC47pNWF\np3zOYrUDACQiYViORwiJTyENiv7+979j586dYBgGLMuCYRg88sgjWLVqldfPwbKsV4+rqqqa9M/E\nPTpP3qHz5D06V96h8+Q9OlfeiabzpDfZAAA1jR2oqhoJ+fHaO/UAgPq6Gpj6JVM+NprOUzSj8+Q9\nOlfeiYXzFNKgaOvWrdi6datPP6NWq8dlhnp6erBw4UKPP8fvUaqqqnK7X4mMofPkHTpP3qNz5R06\nT96jc+WdaDtPdrsDv3n/A9gZeVjWdajpFAAjKubPRV5motvHRdt5ilZ0nrxH58o70XSepgrOIror\ncbIsUEVFBc6cOQOj0QiTyYQTJ05EzYkkhBBCyNSEQgHSk2VhL58TU/c5QkgAwr6naO/evfjjH/+I\npqYmnD17Fq+//jpeffVVvPLKK1i2bBkqKirw2GOP4Zvf/CYEAgEeeeQRt00ZCCGEEBJ91KkJON/U\nD5vdEfKucFbnnCKpmPYUEUL8F/agaO3atVi7du2Erz/wwAOuP2/cuBEbN24M57IIIYQQEiSqFDnO\nskC/YQSZIW7L7coUUVBECAkA5ZoJIYQQElQqV1vu0JfQ8ZkiCZXPEUICQFcQQgghhATVWFvu0M8q\nGqU9RYSQIKArCCGEEEKCih/gqtWHI1Nkh1gkAMPQ8FZCiP8oKCKEEEJIUKlSnEFRGDJFFquDSucI\nIQGjqwghhBBCgiqc5XNWm52aLBBCAkZBESGEEEKCSi4VITFBHJZGCxYbZYoIIYGjqwghhBBCgk6V\nmgCt3jzpoPZgslodkFCmiBASIAqKCCGEEBJ0qhQ5Ri12DJosIT2OxWaHRERBESEkMBQUEUIIISTo\n1M6hrVp9aPcVWawOiMX0cYYQEhi6ihBCCCEk6MLRgc7uYGGzOyhTRAgJGAVFhBBCCAk6FT+rKITN\nFqw25+BWyhQRQgJEVxFCCCGEBJ06NfTlc1abAwCo+xwhJGB0FSGEEEJI0PGZop6B0GWKLFYuU0Tl\nc4SQQFFQRAghhJCgS1FKIZMI0d1vCtkx+EwRlc8RQgJFVxFCCCGEBB3DMNCkK9DdbwrZrCJXpojm\nFBFCAkRBESGEEEJCQpOeAPOoHQZjaGYVWVx7iigoIoQEhoIiQgghhISEJl0BACErobNanUERlc8R\nQgJEVxFCCCGEhERWBhcUdYUoKOLL58SUKSKEBIiCIkIIIYSEhCtT1BeioMjGd5+jjzOEkMDQVYQQ\nQgghIZGVHuJMEXWfI4QECV1FCCGEEBISqlQ5BAIG3f2hmVVkpTlFhJAgoaCIEEIIISEhEgqgTpXj\n/7V35/FVFff/x983K1mIJMRERPSHUDcUBBeWoIBoWi2oFQJISNKoCKIgGhCiX0GrVgtVW1C0PggN\nuwhFS6mC1gJqWVLAStmKCAgESSCEkI3cJHd+f0COhIRAkrsc4fX8K55772FmPHfmfs5nZs6Bw0Ue\nOb+1+xyZIgCNRC8CAAA85rKYpioocupYsfu35S7nOUUA3ISgCAAAeMzlsU0lSftyCt1+bp5TBMBd\nCIoAAIDHtPJkUGRtyc3PGQCNQy8CAAA8plVsuCQPZ4qYPgegkQiKAACAx1RlivaSKQJgY/QiAADA\nY0KbBCr6oiba74GgqJxMEQA3ISgCAAAedVlsUx0uOK7CEvfuQOe0nlPEzxkAjUMvAgAAPKp922hJ\n0upNB9x6XjJFANyFoAgAAHhUz06t5HBIn/97n1vP66xgTREA96AXAQAAHnVxZIjat43Wtj1H9MPh\nYred11lOpgiAexAUAQAAj7vj5sslSf9c775sEWuKALgLvQgAAPC4rje0UJMgf/1zwz65XMYt5yyv\ncMnPzyF/f37OAGgcehEAAOBxIcEB6tb+UuUeKdHW3XluOaezopIsEQC3oCcBAABeccfNrSS5bwqd\ns9ylwADWEwFoPIIiAADgFTe0iVZ0sxB99c0BHXdWNPp85RWVCgrkpwyAxqMnAQAAXuHn51Cvmy5T\naVmF1m0+2OjzOctd7DwHwC0IigAAgNe4cwpdOWuKALgJPQkAAPCay2Ka6urLI/WfHbnKKyht1LnK\nyl0KJFMEwA0IigAAgFfdcUsruYy0amN2g89hjCFTBMBt6EkAAIBX3XZjSwX4+2nFhoZPoauoNDJG\nCmL3OQBuQFAEAAC8qmlokK75f5Ha88MxlVe4GnSO8opKSVIgu88BcAN6EgAA4HVREU0kSUcLyxr0\neWf5iWCKTBEAdyAoAgAAXlcVFOUXHm/Q551kigC4ET0JAADwusimJ4KiI8caFhRVTbsLZvc5AG5A\nUAQAALwuKiJYkpTfwKDIWX4yU8TucwDcgJ4EAAB4XaQ1fa6ha4pOBEWsKQLgDgRFAADA6yKbnsgU\nNXT6nPPk9DnWFAFwB3oSAADgddZGC8calikqZ/c5AG5EUAQAALwuLCRQgQF+OtLI3eeCyBQBcAN6\nEgAA4HUOh0OREU0avNFCVaYokEwRADcgKAIAAD4R1TRYRwvL5HKZen/WyhSx+xwAN6AnAQAAPhEZ\n0USVLqPCEme9P1u10UIQzykC4AYERQAAwCcaswOdtSU3a4oAuIFPepKsrCx169ZNq1atqvX1du3a\nKTk5WUlJSUpOTpYx9U+rAwAAe2vMDnQ/PryVTBGAxgvw9j+4b98+ZWZm6qabbjrjeyIiIjRr1iwv\nlgoAAHhb1QNcG5IpKremz5EpAtB4Xu9JYmJi9Pbbbys8PPyM7yEzBADA+c/KFDVgW25r+hyZIgBu\n4PWgKDg4WA6Ho873lJWVacyYMRo8eLAyMzO9UzAAAOBVzU6uKcovrP/0uapMUSC7zwFwA4fxYFpm\n4cKFWrRokRwOh4wxcjgcGjlypOLi4pSenq5f/OIX6tGjR43PLViwQPfee68kKTExUS+99JLatWt3\nxn9nw4YNnqoCAADwkMLSSr3+4Q+67vIQDejevF6f/VtWvjbsLNbjv4zVxRcFeqiEAM43Z1rC49E1\nRQkJCUpISKj35wYOHGj93bVrV+3YsaPOoEj6sYIbNmyoc70STqCdzg3tdO5oq3NDO5072urc/JTb\nqdJl9OZHSyT/kHrX4YsdGyUV68YON+iS5mFnff9PuZ28iXY6d7TVubFTO9WVSPFpzrm2JNXu3buV\nlpYmSaqoqNDGjRvVtm1bbxcNAAB4mL+fQxeFBzdo97mq6XPBPKcIgBt4ffe5VatWafr06dq9e7e2\nbNmi2bNnKyMjQ++99546d+6sDh06qEWLFurfv7/8/f3Vu3dv3XDDDd4uJgAA8ILIiCbKPlRkTbM/\nV9aW3ARFANzA60FRjx49al1H9Oijj1p/jxkzxptFAgAAPhIV0US7sgtUWlah0Cbnvjbox93n2GgB\nQOPRkwAAAJ+JbOAOdE52nwPgRvQkAADAZxr6ANfyikoFBvjVa8odAJwJQREAAPCZqKpMUT2DIme5\ni6lzANyG3gQAAPjMj5mi+k2fK6+oZJMFAG5DUAQAAHwm6mRQVO9MUQWZIgDuQ28CAAB8ppm10UI9\n1xSVuxREpgiAmxAUAQAAn/kxU3Tu0+eMMSopq1BwEEERAPcgKAIAAD4TFOivsJBAHalHpuhYsVPO\n8kpd3CzEgyUDcCEhKAIAAD4VFRFcrzVFOUdKJEkxUaGeKhKACwxBEQAA8KnIpk1UWFKu8orKc3p/\nbv6JoCg2kqAIgHsQFAEAAJ+KbFq/dUW5ZIoAuBlBEQAA8KnIiPrtQFc1fS6WoAiAmxAUAQAAn4qq\n5wNcc/NLJUkxTJ8D4CYERQAAwKciq7blrkemKDwkUGEhgZ4sFoALCEERAADwqaiT0+eOnMMOdMYY\n5eaXsJ4IgFsRFAEAAJ+q2mjh0MlpcXU5VuxUmbOS9UQA3IqgCAAA+NSl0WFqGhqo/+w4JJfL1Ple\n6xlFrCcC4EYERQAAwKf8/f10y3WX6Mix49q5/2id7/3xwa0h3igagAsEQREAAPC5Lte3kCSt3fxD\nne+rekYRD24F4E4ERQAAwOc6Xn2xggL9tea/dQdFOfk8uBWA+xEUAQAAn2sSFKBOV1+s/blF2pdT\neMb35bKmCIAHEBQBAABbOJcpdLn5PKMIgPsRFAEAAFu45bpL5Ofn0LrNB2t93RijnCOlim1OlgiA\nexEUAQAAW4gIC9L1VzbX//bmK6+g5jOLCoqccpZXMnUOgNsRFAEAANvofP0lkqR1W2pmi3JPbrLA\ng1sBuBtBEQAAsA1rXVEtu9Dx4FYAnkJQBAAAbCMmMlRtLrtIm3YeVlFpebXXrGcUkSkC4GYERQAA\nwFZuvjZWlS6j7XuOVDtuZYoIigC4GUERAACwlUtOBj35x45XO249uDUyxOtlAnB+IygCAAC20qxp\nE0nS0aKyasdzj5SoaWigQpvwjCIA7kVQBAAAbKVZeLAk6Wjhj0GRMUa5R0qYOgfAIwiKAACArTRr\nWjMoOlpUJmeFi53nAHgEQREAALCVi6oyRadMn2PnOQCeRFAEAABsJTDAT01DA5VfeGpQVCqJoAiA\nZxAUAQAA22nWNLja9Dlr5zmCIgAeQFAEAABsp1l4ExWWOFVR6ZIk5Z4Mii5uxnbcANyPoAgAANhO\n1WYLBSfXFRUWOyX9uN4IANyJoAgAANjO6TvQFZeWS5LCQ3hGEQD3IygCAAC20+y0HeiKSssVGOCn\noEB/XxYLwHmKoAgAANjO6ZmiotJyskQAPIagCAAA2E5VUFS1LXdRSbnCQwmKAHgGQREAALCd2MgT\nW28fzCuWMUbFx8sVHhLk41IBOF8RFAEAANu59OIw+TmkfTmFKi2rkMtlFMb0OQAeQlAEAABsJzDA\nXy2iw7Qvp1BF7DwHwMMIigAAgC1dFtNUhSXlys4tkkRQBMBzCIoAAIAtXX5JU0nS9j1HJElhbLQA\nwEMIigAAgC1dFnMiKNp2MihiowUAnkJQBAAAbOny2JOZou+rgiIyRQA8g6AIAADY0mUx4XI4pNKy\nSkli9zkAHkNQBAAAbKlJcIA1hU4SD28F4DEERQAAwLauvjzS+pvpcwA8haAIAADY1lVXnBoUsdEC\nAM8gKAIAALZ1aqYoLCTAhyUBcD4jKAIAALZ1xSU/rikKCSYoAuAZ9C4AAMC2/P391L3DpSo5XiGH\nw+Hr4gA4T3k9KKqsrNRzzz2nvXv3yuVy6ZlnnlGnTp2qvWfJkiWaNWuW/P39lZCQoP79+3u7mAAA\nwCbGJd/i6yIAOM95PSj661//qtDQUM2bN087d+5Uenq6Fi5caL1eWlqqadOm6S9/+YsCAgLUv39/\nxcfHKyIiwttFBQAAAHAB8Pqaovvuu0/jx4+XJEVFRamgoKDa6998843at2+vsLAwBQcHq1OnTtq4\ncaO3iwkAAADgAuH1TJG/v7/8/f0lSTNnzlSfPn2qvX748GFFRUVZ/x0VFaVDhw55tYwAAAAALhwO\nY4zx1MkXLlyoRYsWyeFwyBgjh8OhkSNHKi4uTnPnztXKlSv17rvvWkGSJC1dulSbN2+2skl/+MMf\n1LJlSyUkJJzx39mwYYOnqgAAAADgPHHTTTfVetyjmaKEhIRag5mFCxdq5cqVmjZtWrWASJJiYmKq\nZYZycnLUsWPHOv+dM1UOAAAAAM7G62uK9u3bpwULFuitt95SYGBgjdc7dOigzZs3q6ioSMXFxfr6\n668JegAAAAB4jNfXFC1atEgFBQUaOnSoNaVuxowZmjFjhjp37qwOHTooLS1NDz30kPz8/DRy5EiF\nh4d7u5gAAAAALhAeXVMEAAAAAHbn9elzAAAAAGAnBEUAAAAALmgERQAAAAAuaLYIipYuXarrr79e\nR48ebfA5Zs6caW0BPm/ePElSUVGRhg0bpsGDB2vo0KE6duyYJMnpdGr8+PHq16+f9fmsrCx17dpV\nycnJSkpK0ssvv9y4SnnA0KFD1b17d61atarB5ygqKtKIESOUlJSkIUOGaNeuXZKk1atXKyEhQYMG\nDdK0adOs9+/YsUN33XWX5s6dax1LT09X3759lZycrOTk5EaVxxPccT1VWbt2rSDzwFEAABPjSURB\nVAYOHKjBgwfrueees46/+uqrGjRokB588EH997//tY7PmjVL119/vUpLS61j7dq1s66r5ORk2WUZ\n3759+zR8+HAlJCTogQce0Msvv6yysrIzvv+HH37Qpk2bahw/X9soOztbnTp1ssqVmpqqNWvWNOqc\nBw8eVGpqqpKSkvTQQw8pLy9PkrRkyRL1799fAwcO1KJFi6z3Z2VlqVu3btW+Y0lJSUpISLDaauvW\nrY0qk7uc3l7Jycl69dVXz/j+9PT0s/YdkyZN0qBBg5SQkKDPPvtM0ok2rOq/nnrqKZWXl0uSjh07\npkceeURPPvlktXNkZGTo/vvvV0JCgjZv3tzIWrqPHcY96cS1d99996lfv36268sl+4x7FRUVSktL\nU0JCglJTU1VYWNjwSnmA3ca97du3q1+/furfv3+1tvU1O417dmwju457RUVFGjp0qAYMGKBRo0ZZ\n/b7bGRsYNmyYufvuu83777/foM/v3bvX3H///cblchmn02l69eplCgsLzdSpU01GRoYxxpgFCxaY\nyZMnG2OMeemll0xmZqbp16+fdY5169aZUaNGNb4yHjZ+/HizcuXKBn9+ypQpZvr06cYYY1auXGlG\njx5tjDHmnnvuMQcPHjQul8sMHjzY7Ny505SUlJikpCTz/PPPmzlz5ritDJ7W2OvpVPHx8SYnJ8cY\nY8yoUaPMqlWrTFZWlhk2bJgxxpidO3eagQMHGmOM+fDDD80bb7xhevXqZUpKSqxzdOnSpdHlcDeX\ny2Xuu+8+s3btWuvYjBkzzNixY8/4mcWLF1e7Dqqcr220f//+an3E3r17zT333GP+97//Nfic48aN\nM8uWLTPGGDNnzhwzefJkU1JSYn7+85+boqIic/z4cdOnTx9TUFBg9u7dax577DHzxBNPVPu+DRky\nxOzcubPhFfOQ09vrbM7Wj6xdu9Y8+uijxhhj8vPzTc+ePa3PLV++3BhjzBtvvGHmz59vjDFm9OjR\n5p133qnWj3/77bemX79+xuVyma1bt5qpU6fWu16eYodxLz8/38THx5uSkhJz6NAh8/zzzze+Yh5g\nh3Fv7ty55pVXXjHGGPPBBx+Yf/7zn42okfvZbdxLSEgw27ZtM8YY8/TTT5vjx483ulyNZbdxz45t\nZNdxb9KkSWbmzJnGGGPefvtts2nTpgaXpy4+zxQVFBRo8+bNGjdunJYuXWodT0pK0qRJk5ScnKxB\ngwbphx9+UFZWloYPH17j7mirVq00b948ORwOBQYGKjQ0VMXFxVq7dq3uuusuSVKvXr20evVqSdLT\nTz+tO++8s0ZZjA3uTp+rDz/8UL/73e8kSSUlJbrjjjskSfHx8crIyNCQIUM0cOBAlZSUVPvcsGHD\nlJKSIkmKjIzU0aNHtW/fPjVr1kyxsbFyOBzq0aOH1q5dq+DgYE2fPl0xMTHerVwj1HU97dy5U5I0\nd+5cvfXWW6qoqNDo0aM1aNAgvfbaa+rRo0eN8y1evNiqf1RUlI4ePao1a9ZY10+bNm107NgxFRcX\nKz4+Xk899VSNc9jxuvrqq6/UunVrde7c2TqWmpqqTZs26ciRIzpw4IB1V/WZZ55RXl6epk6dqlmz\nZmnFihXVznW+ttHpWrVqpccee8y6ezx37lw9+OCDGjJkiDIzMyVJhYWFGjZsmBITEzV8+PBqdwUl\n6YUXXlB8fLykH9vqm2++Ufv27RUWFqbg4GB16tRJGzduVExMjN5+++1aH0nwU2ivU7355ptKSkrS\n4MGD9fHHH1vHP//8c6Wmpur+++/Xtm3bqn3m1ltv1R//+EdJUkREhEpLS+VyuZSVlaVevXpJqt6v\nv/LKK+rUqVO1c6xYsUJ33323HA6Hrr32Wj3xxBOerOY5s8u4t3r1asXFxSkkJETR0dH6zW9+44Xa\nN5wvx70VK1aob9++kk48mL7qGrQDu417eXl5Ki0t1TXXXCNJev311xUcHOyRuteHncY9u7bR6ewy\n7q1YsUJ9+vSRJI0YMUI33HCDR+rr86Bo2bJl6tWrl2677TZ9//33ys3NtV6LjIzUrFmz1KdPH6vx\nd+zYoRkzZui6666rdp6QkBBJJy76yMhIxcbG6tChQ4qMjJQkNW/eXIcOHZIkhYaG1lqW7777TiNG\njFBiYqI1kNiZw+Go8XdFRYXatm2rOXPmqGXLljXSnkFBQQoIOPF4qqq2PXz4sKKioqz3REVFKTc3\nV35+fgoKCqr1354zZ45SUlKUlpbmlnS9u9R1PZ3uyy+/VHl5ud5//3116dLFuj5OFRYWJknKzc3V\n6tWr1aNHjxrtFRkZqcOHD5/xuiorK9OYMWM0ePBg6zr2tV27dunaa6+tcfyqq67Snj179Oabb+rh\nhx/WnDlzFBMTo+zsbD3wwANKTk6u8WPgfG2j2rRr107fffed9u/fr+XLl2v+/PmaM2eOli1bpoMH\nDyojI0O33Xab5s6dq65du9boR5o0aSKHwyGXy6V58+ad8ft36NAhBQcHV/uOn2rKlCkaMmSIJk6c\nKKfT6dE610dtwdr69et14MABzZ49W5mZmZo2bZpVZj8/P/35z3/W6NGj9c4771T7nMPhUJMmTSRJ\nCxcuVM+ePeXn56fS0lLrwd9n69ezs7N14MABPfLII0pNTdX27dvdWt+Gssu4l52drdLSUj322GMa\nMmRIo6fJeIOvxr3s7GytWrVKSUlJSktLs6Yl2oHdxr3s7GxFREQoPT1dgwcP1syZM91Qy8az07hn\n1zaqjR3GvcOHD+v9999XYmKiJk6c6LHpcz4PipYuXap77rlHfn5+io+Pr3YXsVu3bpKkG2+8UXv2\n7JEkXXPNNVbndrr//Oc/mjx5sn7/+99Lqt55mpMPij2TK664Qk888YSmTZum1157Tc8995wqKioa\nWz2fuOmmmyRJsbGxZ5z3PHnyZAUHB9eYXy6d/S70fffdp7S0NM2cOVNXX321pk6d2vhCu0ld19Pp\nvvvuO+vOco8ePeTv71/r+/Ly8vTYY4/phRde0EUXXVTj9bO11/jx4/XSSy8pIyNDS5Ys0ZYtW+pR\nI8+o6qBO53K55O/vr61bt6pjx46SpDFjxqh9+/Z1nu98bKPaFBcXy8/PT5s2bdL3339vzbsuLS3V\n/v37tXXrVuuaSklJUe/evWucw+VyaezYseratau6dOlS4/WztVVKSorGjh2rOXPmyOFwVFv34Gu7\nd++utqboT3/6k77++mtt2rRJycnJevjhhyXJ+tFWdce2ffv22r17d63n/Mc//qHFixfr+eefl1Sz\nX6+LMUYul0vTp0/XE088of/7v/9rdB3dwS7jnjFGR48e1bRp0/Tqq6/q2WefbWzVfMbT454xRm3a\ntNHs2bPVtm1bvfvuu40vtJvYbdwzxig7O1vp6emaMWOGFi9erO+++66etXI/O417dm2j2thh3Csr\nK1P37t01d+5cuVwuLVy40D2VO03tvayX5OTk6JtvvrHS4cePH1dERIR+/etfS5J18Z7asVfdITzd\n9u3b9fzzz+u9995TbGysJCkmJkaHDx9WeHi4cnJy6pwGFhsbq7vvvlvSiXRhdHS0cnJy1LJlS7fU\ntTEKCwsVEhKigIAA68t76kB3evB2pk6uypQpU5Sfn6/f/va3kk6006l3i87WVqde0L1799YLL7xQ\nn+p4TF3X06ntdeodBj+/H+8L1PbjoWpxX1pamrp27Srpx+uqSm5uri6++OIznmfgwIHW3127dtWO\nHTvUrl27hlbTLa688krNnz+/xvGdO3eqdevW8vPzq3XwqM352ka12bx5s6677joFBQWpZ8+eevHF\nF6u9Pn369LO2W3p6ulq3bq0RI0ZIqv37VzUw1+bUKVC9evXSsmXLGlIVj7jyyis1a9asascyMzPV\nr18/PfroozXeX9td/1N9+eWXeu+995SRkWHdmQ0NDZXT6VRQUNBZ+6ro6Gi1adNG0okfzQcOHGhQ\nvdzJTuNedHS0OnbsKIfDoVatWiksLExHjhypdgfXV+w27kVHR+uWW26RJHXv3l1vvfVWvevkCXYc\n95o3b662bdsqIiJC0onv3rfffmt9F33FTuOeXduoNnYY91q0aGEFqXFxccrKympoderk00zR0qVL\nlZiYqI8++kgfffSRli1bpoKCAu3bt0+StGHDBkkn7oTVdaG4XC49++yzmjp1qlq0aGEd7969u/WD\n4dNPP9Vtt91mvWaMqRaZ/u1vf9OMGTMkSYcOHVJeXp41yPjaiy++qM8++0zGGO3atUutW7dWeHi4\ndbd1/fr153yu9evXa9OmTdbAIEktW7ZUcXGxDhw4oIqKCq1cuVLdu3c/4zlGjRpl/T9at26drrrq\nqgbWzL3qup6aNm1qfQE3btwoSbr88sut3WG++uorVVZW1jjna6+9ptTUVMXFxVnH4uLitHz5cknS\nli1bFBsbWy09fup1tXv3bqWlpUk6MYhv3LhRbdu2dXPN6y8uLk7Z2dn64osvrGOZmZm6+eabFRER\nofbt22vt2rWSTvyYWLNmjRwOR63Z0/O1jaTq5dy7d68yMzOVmpqqdu3aad26dTp+/LiMMXrllVfk\ndDp1ww03WO22YMECffTRR9XOt2TJEgUFBVVb29KhQwdt3rxZRUVFKi4u1tdff23d9a6tHKfufJWV\nlaWf/exnbq93Q9V2t69Dhw5asWKFjDEqKyurtrNnVd/19ddf1+jji4qKNHnyZL377rtq2rSpdbxr\n167WtbV8+fI6+/Xbb79dX375paQTd8gvueQSN9Sycew07sXFxWndunUyxig/P18lJSW2CIgk+417\nt99+u9VfbtmyRa1bt25gzdzLjuPeZZddpuLiYh07dkwul0vbtm2zRXvZadyzaxtJ9hz3unTponXr\n1kny7PfPp5miv//975o0aVK1Y/fff7/+/ve/y+FwWHPBi4qKNGXKFGsqwenWrFmj7OxsTZgwwbq7\nNnbsWA0ZMkRjx45VYmKiIiIiNHnyZEnSk08+qYMHD2rPnj1KTk7WwIEDdccddygtLU2ff/65Kioq\n9OKLL55xuoK3jRw5UuPGjdOsWbPUo0cPtWzZUhdddJHeeecdJScnV0uBn+3O6/z583Xw4EFr2+PI\nyEhNmTJFEydO1NNPPy1J6tOnj6644gpt2bJFr732mg4cOKCAgAAtX75cb731lhITE/XUU08pJCRE\nYWFh1QYaXzrT9fTxxx9rwIABeuGFF9S6dWu1atVKktSzZ08tWrRIiYmJuvXWW9WsWbNqnz1+/LiW\nLFmivXv36oMPPpDD4VDfvn2VkJCg6667ToMGDZK/v78mTpwoSXr33Xf1r3/9S3l5eRo6dKhuvPFG\njRkzRi1atFD//v3l7++v3r17e2yBYH04HA5lZGRowoQJmjJlilwul66//npretHIkSOVnp6uefPm\n6dJLL9XIkSNljNH48ePVvHlza8Hj+dxGkqw+wul0yuVyaeLEidbNkpSUFCUmJiogIEC9e/dWUFCQ\nUlJS9MwzzygpKUnh4eF6/fXXq51v3rx5cjqdSkpKksPhUNu2bTVhwgSlpaXpoYcekp+fn0aOHKnw\n8HCtWrVK06dP1+7du7VlyxbNnj1bGRkZGjBggFJSUhQWFqaYmBiNGjXKF01Tq9r6nI4dO6pz585W\nNnDw4MHVXh8+fLhycnJqfHc//vhjHT16VKNHj7b69UmTJln94YIFC3TppZfqV7/6lVwul1JSUlRU\nVKScnBwlJyfr8ccfV+fOnfXFF19o0KBBkmRdh75kp3Hvl7/8peLj4zVgwAA5HA5NmDDB09U/Z3Yb\n95KSkjRu3DgtWrRIYWFhVmbG1+w67qWnp+uRRx6Rn5+funfvrquvvto7DVIHu417dmwjyZ7j3qhR\nozR27FhNnTpVzZs31+OPP+6RujuMTbcxSkpK0sSJE21zxxjnn4KCAq1bt07x8fHKyclRampqnXOx\nAcCTGPfgaYx7wJnZIxVSi7oWhwLuEBYWpk8++UQZGRkyxvykFxkD+Olj3IOnMe4BZ2bbTBEAAAAA\neIPPt+QGAAAAAF8iKAIAAABwQSMoAgAAAHBBIygCAAAAcEGz7e5zAACcSXZ2tn7xi1+oY8eOMsao\nsrJSN998s0aMGKEmTZr4ungAgJ8YMkUAgJ+k5s2ba9asWZo9e7YyMzNVVFSktLQ0XxcLAPATRFAE\nAPjJCwoK0rPPPqvt27dr586dGjVqlFJSUtS/f39Nnz5dkvTggw/q3//+t/WZoUOH6osvvvBVkQEA\nNsL0OQDAeSEgIEDt2rXTypUrdeedd+ree++V0+lUt27d9OCDD2rgwIFavHixbrnlFhUUFGjPnj26\n/fbbfV1sAIANEBQBAM4bRUVFio6O1vr16zVv3jwFBgbK6XSqoKBA99xzj6ZMmaLS0lJ99tln6tu3\nr6+LCwCwCabPAQDOC6Wlpdq2bZsOHjyo8vJyvf/++5o9e7ZCQ0MlnZhid9ddd+nTTz/V8uXL1a9f\nPx+XGABgFwRFAICfJGOM9Xd5ebleeeUVxcXFKS8vT23atJEkff755yorK5PT6ZQkDRgwQPPnz5ck\ntWzZ0vuFBgDYEtPnAAA/Sfn5+UpOTlZlZaWOHTum7t27a8KECdq1a5eefvppffXVV+rdu7f69u2r\nMWPGaNGiRWrTpo0qKyv1wAMP+Lr4AAAbcZhTb7UBAHAe279/v4YPH66//vWv8vf393VxAAA2QaYI\nAHBB+NOf/qRPPvlEL7/8MgERAKAaMkUAAAAALmhstAAAAADggkZQBAAAAOCCRlAEAAAA4IJGUAQA\nAADggkZQBAAAAOCC9v8B8+0EhtpGCkIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f96cdb718d0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "AMC = get_pricing('AMC', fields='price', start_date='2015-01-01', end_date='2017-01-01')\n",
     "\n",
     "#Your code goes here\n",
     "rolling_skew = AMC.rolling(window=60,center=False).skew()\n",
     "plt.plot(rolling_skew)\n",
     "plt.xlabel('Day')\n",
     "plt.ylabel('60-day Rolling Skew')\n",
     "print \"This confirms our result from part c, that the skew is too volatile to use it to make predictions outside of the sample.\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "---\n",
     "\n",
     "Congratulations on completing the Statistical Moments and Normality Testing exercises!\n",
     "\n",
     "As you learn more about writing trading algorithms and the Quantopian platform, be sure to check out the daily [Quantopian Contest](https://www.quantopian.com/contest), in which you can compete for a cash prize every day.\n",
     "\n",
     "Start by going through the [Writing a Contest Algorithm](https://www.quantopian.com/tutorials/contest) tutorial."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
     "editable": true
    },
    "source": [
     "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
    ]
   }
  ],
  "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": "2.7.12"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }