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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction to Neural Networks (Psy 5038): Python & MCMC"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "MCMC with PyMC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[PyMC 2.3.4](http://pymc-devs.github.io/pymc/) is the production version. \n",
      "\n",
      "For introductions to PyMC and Probabilistic programming in Python see: https://www.youtube.com/watch?v=XbxIo7ScVzc and https://www.youtube.com/watch?v=WESld11iNcQ\n",
      "\n",
      "[A tutorial from the authors of PyMC](http://pymc-devs.github.io/pymc/tutorial.html#an-example-statistical-model ).\n",
      "\n",
      "If you want to explore PyMC 3, which is under development, see: https://github.com/pymc-devs/pymc.\n",
      "\n",
      "Also see https://github.com/aloctavodia/Doing_bayesian_data_analysis for examples from John K. Kruschke's book (1rst ed.), \"Doing Bayesian Data Analysis\" translated from R to Python."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "PyMC example: Unsupervised clustering assuming a mixture model"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Neural network motivation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the next lectures, we will begin to look at large-scale system neural architectures for vision. The basic structure consists of lateral, feedforward, and feedback computations.  A conjectured function of lateral processing is to link features of a similar type, for example similar orientations from nearby spatial positions. Texture synthesis Gibbs sampler is an example of a lateral linking process which links similar colors. These can be extended to other more abstract features.\n",
      "\n",
      "Information can also be linked based on a model of \"common cause\". Often there is more than one explanation for any piece of data, and one needs to estimate the probability of where it came from. These kinds of models are naturally hierarchical. One example is segmentation where one decides which pixels belong to one object (e.g. in the foreground) vs. another (the background).\n",
      "\n",
      "\n",
      "To illustrate a basic computation, here we look a simple toy version of the problem in which a scalar data point could come from one of two processes. We don't know which process led to any observed value, we don't know the central tendencies or variances of the processes. But we assume we have enough data to make good guesses. \n",
      "\n",
      "Specifically, we'll see how a gaussian mixture model can be modeled using MCMC sampling with PyMC. The model is simple enough that we could implement an MCMC solution without the help of PyMC. However, using PyMC will help to see how to use it to model more complex problems. \n",
      "\n",
      "Later we'll see how from a machine learning perspective, grouping operations are a form of unsupervised learning and be studied in the context of clustering algorithms such as EM and K-means. In the next lecture, we'll look at the EM solution and apply it to a clustering problem but in a different context."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This example is based on and uses code from  [Chapter 3](http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/IntroMCMC.ipynb) of Probabilistic Programming and Bayesian Methods for Hackers\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pymc as pm\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "#We'll use this next function to show the graphical relations\n",
      "from IPython.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Couldn't import dot_parser, loading of dot files will not be possible.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Synthetic data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a number of ways of drawing random numbers using Python packages. For example try np.random.normal(0,1). We'll use the scipy.stats module, because it supports a number of methods for many standard distributions. Good to know about.\n",
      "\n",
      "Our true underlying model is a mixture of gaussians, with mixing parameter truep:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?norm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Object `norm` not found.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import bernoulli,norm\n",
      "\n",
      "truemean1=100.0\n",
      "truemean2=200.0\n",
      "truestd=25.0\n",
      "truep=0.25\n",
      "\n",
      "n=2000\n",
      "\n",
      "data=np.zeros(n)\n",
      "for i in xrange(len(data)):\n",
      "    p = bernoulli.rvs(truep)\n",
      "    data[i]=p*norm.rvs(truemean1, truestd)+(1-p)*norm.rvs(truemean2, truestd)\n",
      "    \n",
      "plt.hist(data);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G5/z0p49x7NgZNyVJI0nVeLM5ya8CC1V1XXd5O/BsVX1k6Ji5uUOQpGmqqhU9szuJsF8D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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d4649d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Generative model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's where we begin to use PyMC. Suppose we were presented with the data, but don't know the generative process. So we begin to guess the possible general structure.\n",
      "\n",
      "We will assume that each data point comes from one of two normal distributions, but we don't know the probability p that it comes from the first. The true value is truep (above), but we don't know that. Let's define a PyMC random variable p that reflects our uncertainty. The true value of p is fixed for the process that generated the data; but we just don't know its value, so assume it is uniformly distributed between 0 and 1. We will want to estimate the value of p.\n",
      "\n",
      "Now imagine that for each data point we'd also like to estimate whether it came from the first or second distribution. We define a second random (vector) variable \"assignment\" that depends on p. In the language of graphical models, 'p' is the parent of the 'assignment' (vector) variable. The assignment vector thus contains n random values, one for each data point."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = pm.Uniform(\"p\", 0, 1)\n",
      "\n",
      "assignment = pm.Categorical(\"assignment\", [p, 1 - p], size=np.shape(data)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These variables are called stochastic variables.\n",
      "\n",
      "Experiment with:\n",
      "\n",
      "    ?pm.Uniform\n",
      "    pm.Uniform(\"test\", 0, 1,value=.2,observed=True).value\n",
      "    pm.Uniform(\"test\", 0, 1,observed=False).value\n",
      "    \n",
      "    p.value\n",
      "    \n",
      "    ?pm.Categorical\n",
      "    assignment.value[:10]\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We also want to estimate the two means of the normal distributions assumed to have generated the data. \n",
      "\n",
      "We'll assume the priors on the means of the two generating distributions are normally distributed. We just guess that their\n",
      "centers are 110 and 210, and allow for our uncertainty with a guess that each of their standard deviations is 10. You could explore how much bad guesses cost in terms of iterations to convergence (or in general, non-convergence).\n",
      "\n",
      "\n",
      "Call these random variables 'centers'.\n",
      "\n",
      "\n",
      "Note that these standard deviations reflect our uncertainty in the means, not the variability in data generated by either of the two assumed distributions. We also want to estimate standard deviations for each of the two distributions. \n",
      "\n",
      "PyMC works with precision $\\tau$, rather than standard deviation $\\sigma$:\n",
      "\n",
      "$$\\tau = {1 \\over \\sigma^2}$$\n",
      "\n",
      "Call the precisions 'taus'. And we'll assume a uniform distribution of the standard deviations from 0 to 100."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centers = pm.Normal(\"centers\", [110, 210], [10**-2, 10**-2], size=2)\n",
      "taus = 1.0 / pm.Uniform(\"stds\", 0, 100, size=2) ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Likelihood"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We've defined the priors. Now we need to describe how the generating variables could produce the observed data.\n",
      "PyMC has a \"decorator\" function pm.deterministic, that is used to model functions in which the inputs, if known, deterministically specify the outputs. IF we did know which distribution a data point came from, we could\n",
      "assign it to the appropriate mean (center 0 or 1) with certainty. Similarity for the taus.\n",
      "\n",
      "The functions below map an assignment of 0 or 1, to a set of parameters representing\n",
      "the `centers` and `taus`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@pm.deterministic\n",
      "def center_i(assignment=assignment, centers=centers):\n",
      "    return centers[assignment]\n",
      "\n",
      "@pm.deterministic\n",
      "def tau_i(assignment=assignment, taus=taus):\n",
      "    return taus[assignment]\n",
      "\n",
      "# OK. Here's the final summary line representing our model that relates the centers and taus to the data\n",
      "# We assume that observations, the data, can be explained by the mixture model \n",
      "\n",
      "observations = pm.Normal(\"obs\", center_i, tau_i, value=data, observed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Try:\n",
      "    \n",
      "    print \"Random assignments: \", assignment.value[:4], \"...\"\n",
      "    \n",
      "    print \"Assigned center mean: \", center_i.value[:4], \"...\"\n",
      "    \n",
      "    print \"Assigned precision: \", tau_i.value[:4], \"...\"\n",
      "    \n",
      "    print centers[assignment].value\n",
      "    \n",
      "These show current values, which may be accidents of the initial set-up, or reflect requests for random draws. Ideally, their exact values shouldn't matter to the final estimates.\n",
      "\n",
      "While center_i and tau_i are deterministic functions, their values while running the simulation will vary reflecting the stochastic property of their parents. So they are still random variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?pm.Normal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we ask PyMC to create a model class. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a model class\n",
      "model = pm.Model([p, assignment, observations, taus, centers])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model will get handed off to pm.MCMC(model) class below to run the simulations. But first, let's get a graphical view of what we've done using the directed graph function: pm.graph.dag.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Graphical view"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ellipses represent nodes to be estimated or are fixed. Triangles represent deterministic functions. It looks more complicated than it is."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pm.graph.dag(pm.MCMC(model)).write_png('dag.png')\n",
      "Image('dag.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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jY8GCBerq6nfv3kWdBQCu+vjx4+zZs7dt23bq1KmxbbmpqYn9+27ChAmjnxN+cHv37r1y\n5crr16+nTp3K0QMBMAJQ/AGBwmQyra2tS0tL8/Ly+o3G4yPbt2+/devWkydPVFRUUGcBgNsuX77s\n7e2dnJxsbGyMOssIJSUlmZubX7lyBQb8Ad4ExR8QNB8/ftTR0TEzMwsMDOy39hpfCAkJcXFxCQ0N\nXbNmDeosAKDh7OycnZ398OFDfrxnWlFRoa+vb2xsHBQUhDoLAF8GxR8QQGlpaZaWltu3bz9z5gzq\nLMOTlJS0atWqf//73zDaD4xnTU1NRkZGBAIhNTV14sSJqOMMQ0NDg5GRkbCwcFpaWr8pAgDgHfDA\nBxBAxsbGAQEB586dO3LkCOosw5CWlrZ69WpnZ+cTJ06gzgIASvhs6o2NjTY2Nlxe8Hc06urqrK2t\n29raoqOjofIDvAyKPyCYnJyc/Pz8Tp06tXnzZnztDR5Ho9EsLCxsbGyuXbvGj3erARhbioqKCQkJ\nVVVVRkZG7969Qx3n68rKygwNDWtraxMSEvjxbjUYV6D4AwLLw8MjKioqJCTEwsLiw4cPqOMMqKen\n59ChQy4uLt99993NmzdhMTcAcHPmzMnKyhIXF1+yZElCQgLqOIOJi4tbunSpjIxMZmbmrFmzUMcB\n4Cug+AOCzMLCIisrq7a2VkdHJzIyEnWcLygtLV2xYsXvv/9+48aN06dPQ58fAH1NmzYtJSXFxsbG\nysrqwIEDXV1dqBP119nZuW/fPmtra3t7++TkZDk5OdSJAPg6KP6AgNPU1Hz48KGzs7ODg4O9vf3b\nt29RJ/pf7e3tPj4+WlpaXV1dubm5GzZsQJ0IAF4kISFx7dq1mzdvXrp0SVtbm06no070fyIiIrS0\ntK5cuXL79u3Lly+Li4ujTgTAkEDxBwSfmJjYxYsXU1JS3rx5o62tvWfPnk+fPiHM09PTc+3aNQ0N\njXPnzp04cSIrK2v27NkI8wDA+1xcXIqKihYvXmxnZ2dpafno0SO0ebKzs8lksoODg4GBQVFRkbOz\nM9o8AAwLFH9gvDA2Ns7Pzz9+/HhAQICamtrBgwe5PxCwq6vrxo0bGhoaW7duNTMzKyoq+v7770kk\nEpdjAMCP5OXlg4KCEhMTGxoa9PX17e3ts7OzuR8jMzPT1tbWwMCgtbU1KSkpMDBQXl6e+zEAGA0o\n/sA4IiwsvGfPntLSUh8fn+vXr6uqqlKp1Pv37zOZTE4f+vXr1/v27VNSUvLy8jI2Nn758qWfn5+i\noiKnjwuAgDEzM3v06FFUVFRNTY2BgcGCBQuuXLnCibWA+2lubr506ZKurq6hoWF9fX1MTEx2drap\nqSmnjwsAJ8Akz2CcYjAYkZGRly9fTkxMnDp1qoODw5o1a4yMjMZwxU8mk/nixYuIiIiwsLCnT5+q\nq6tv2rTJw8MD1voEYEzk5eVduXIlKCioq6vL3Nx8zZo1q1atmjZt2hgeorq6Ojo6Ojw8PDExUVRU\nVFlZmcFgZGVlTZo0aQyPAgCXQfEHxrv379+HhobSaLTs7GwSiaSnp6evr7948WJtbW1NTU0REZGh\nN8ViscrKyp4/f56fn5+VlZWdnd3Y2DhjxgwqlUqlUpcsWQIP8wIw5trb26Ojo0NCQuh0emdnp5qa\n2rJly5YuXTp//vx58+YNt0qrr69//vz5s2fPHj58mJmZWVpaKiEhQaFQqFTqqlWr6urqjI2NpaWl\nk5KSJk+ezKGfCABOg+IPgP/14cOH1NTUjIyM9PT0oqKinp4eEok0a9YsVVVVJSUlZWXlL47saW5u\nfv/+/bt37yoqKl6+fNnS0oJhmKKi4vLly5cvX25kZDR//nyo+QDgtPr6+tmzZ69atUpNTS09PT0n\nJ6e1tRXDMCUlpW+++UZFRUVJSUlRUfHzP+e6u7srKioqKirevXtXUlJSWVmJYZikpOSSJUuMjIyM\njIwMDAz6Psb77t07Y2NjeXn5hIQEWMYD8Cko/gD4gq6ursLCwsLCwqKiovfv379//76ysvLTp0+9\nvb3s0UWioqJiYmLi4uIqKiqKioqKiopz5szR0tLS1taGLgEAuMzb2zsyMvLly5eSkpIYhrFYrNLS\n0oKCgoKCgvLycry2q66uZjAYTU1N+DBfIpEoIyMjLCysoKCgrKyspKSkoqKCn8KqqqqD/M32+vVr\nY2PjmTNnxsXFSUhIcO+HBGCMQPEHwLARCITg4GCY3AEAHvH06dOFCxf6+fm5u7tz54jPnz83NTVd\nsGABnU6fMGECdw4KwFiBp30BAADwMRaLtWPHjkWLFrm5uXHtoPPmzYuOjs7OznZxceGL1cMB6AuK\nPwAAAHwsLCzswYMHvr6+XB5cu2TJktjY2MTExHXr1vX29nLz0ACMEhR/AAAA+FV3d/ePP/7o7Oys\nr6/P/aMvW7bszp07UVFRmzZtgjFUgI/A0gIAAAD41X//+9+KioqEhARUAchkcnBwsJOTk5SU1O+/\n/44qBgDDAsUfAAAAvlRXV/fzzz/v3r1bVVUVYQw7O7tbt265uLhIS0sfP34cYRIAhgiKPwAAAHzp\nxIkTEyZMOHDgAOogmKOj49WrVz09PcXFxQ8ePIg6DgBfAcUfAAAA/vP27dsLFy6cPXuWR2Za9vDw\naG1t3bFjh4iIyA8//IA6DgCDgeIPAAAA/zl8+PCsWbO2bNmCOsj/2b59O4PB2L17t5SUFE8FA6Af\nKP4AAADwmfz8/ODg4Nu3b5NIvPVb7Pvvv6+vr9+2bZukpOT69etRxwHgy3jrtAEAAAC+6qefflqy\nZImTkxPqIF9w7Nixrq4uDw8PYWFhWAcI8CYo/gAAAPCTlJSU6Ojo9PR0Ls/qPHSnTp1qaWlxc3OT\nkJCwsbFBHQeA/qD4AwAAwE8OHTpkZma2fPly1EEGRCAQLl682N3dTaVS7927Z2pqijoRAP8AxR8A\nAAC+ER8f/+DBg8zMTNRBvoJAIFy6dKmtrY1CocTFxfFyqQrGIVjeDQAAAH9gsVg//fTTqlWrDAwM\nUGf5OiEhIX9//5UrV1IolNzcXNRxAPg/UPwBAADgD1FRUbm5uf/5z39QBxkqYWHhsLAwAwMDKyur\ngoIC1HEA+F9Q/AEAAOADLBbr2LFjNjY2CxYsQJ1lGERERMLCwjQ1Nc3MzF6+fIk6DgAYBsUfAAAA\nvhAdHZ2bm3v06FHUQYZNXFycTqcrKyubm5uXlZWhjgMAFH8AAAD4wdGjR+3s7Pir249NWlo6JiZG\nRkbG3Ny8qqoKdRww3kHxBwAAgNclJibm5OT89NNPqIOMnKys7P3794WFhS0sLGpra1HHAeMaFH8A\nAAB43S+//GJubr548WLUQUZFTk4uPj6+ra2NTCY3NDSgjgPGLyj+AAAA8LSMjIyUlBS+7vZjU1JS\nSkhI+PTp06pVq1paWlDHAeMUFH8AAAB42smTJ42MjExMTFAHGRszZ85MTk4uKytzcHDo6OhAHQeM\nR1D8AQAA4F25ubnR0dEHDx5EHWQszZkzJy4uLj8/38HBoaurC3UcMO5A8QcAAIB3nT17VldX19LS\nEnWQMTZ//vzo6OisrKx169b19PSgjgPGFyj+AAAA8KiSkpLg4OADBw4QCATUWcbe0qVLY2Nj4+Pj\n//WvfzGZTNRxwDgCxR8AAAAedeHChRkzZjg5OaEOwinLli27c+dOaGjopk2bWCzWsPZtbW2NiIjg\nx1mvAXJQ/AEAAOBFTU1NV69e3b59u5CQEOosHGRubn779u3AwMDvv/9+WDviJeOtW7c4FAwIMCj+\nAAAA8KKrV68SiUQvLy/UQTjO3t4+KCjo4sWLR44cGfpe33777aJFiziXCggwEuoAAAAAQH8MBsPX\n19fT01NKSgp1Fm5wcnK6du2ap6enmJjYjz/+OMS9hISEBHI0JOA0KP4AGLlXr15lZ2c/e/bM0NBw\n9erVqOMAIDjCwsKqq6u/++471EG4x8PDo6WlZefOnSIiInv27BnWvpmZmXFxcfPnz3d0dORQPCBI\noPgDYIR8fX0jIiKSkpLKy8tNTU2rq6u9vb1RhwJAQJw5c2b16tUqKiqog3DVjh07GAzGnj17pKSk\nhni/u6ury9bWlsVilZSUHDt2bMOGDQEBAZzOCfgdjPkDYIQuXryopaVFIBBUVVV1dXWjoqJQJwJA\nQOTk5OTm5u7YsQN1EAT+/e9/Hzx40NvbOygoaCifr6ysPHPmTFRUVEFBgb29fWBgYExMDKdDAn4H\nPX8AjFBKSoqEhASGYYWFhe/fv29ubkadCAAB8eeff+rp6RkbG6MOgsbx48e7u7vd3d2FhYWpVOrg\nH9bS0lJXV8cwjEAgeHt7R0RE3Lt3z9ramitJAb+C4g+AEVJUVIyPj4+KijIxMZk5c2Zubi7qRAAI\ngoaGhuDg4DNnzqAOgtLp06dbWlo2bNggISGxatWqIe6lr69PJBI/fPjA0WxAAEDxB8AIHT58ODU1\nNS4uTkxMLCwsDHUcAATEzZs3iUTi+vXrUQdBiUAgXLx4sbW11cnJKTo6esWKFUPZS1paWlJSUk1N\njcPpAN+DMX8AjERNTc3x48c3bNggJiaGYRgszQTAWLl8+bKLi4uMjAzqIIgRicS///7bzs6OQqFk\nZGQMZZcnT540NzfDPV/wVVD8ATASnZ2dGIbdunWrubk5PT09LS2toaGhtbW1paUFdTQA+FhWVtbz\n58/Hw8TOQyEkJBQQEGBqakqhUPLy8r74mdbWVvYfnzQabe3atWZmZlzMCPgSFH8AjMSMGTM8PT0z\nMjIWLlxYWFj4xx9/tLa22tvbMxgM1NEA4GNXrlxZsGDBkiVLUAfhFcLCwiEhIQsWLLCysiosLOz3\n7nfffScpKWlpaXn06NGtW7eSSKTAwEAkOQF/IQx3JWkAAIFACA4OdnZ2bmlpYS8/0NXVNWHCBLTB\nAOBrDQ0NioqKZ8+e3bp1K+osvKW9vd3KyqqkpCQ1NXXmzJn93u3o6KitrVVWVkaSDfAj6PkDYOT6\nLjwFlR8Ao0Sj0QgEgouLC+ogPEdcXJxOpysoKJiampaVlfV7V0xMDCo/MCxQ/AEAAOAJ169fd3Jy\nmjhxIuogvEhGRiYmJkZaWtrc3Lyqqgp1HMDfoPgDAACAHr5S9oYNG1AH4V2ysrJJSUkkEsnS0rKu\nrg51HMDHoPgDAACAXlBQkJKSEjypOjg5ObmEhITW1lYymdzQ0IA6DuBXUPwBAABAjMViBQYGuri4\nEInwW+krlJSUEhISampqbGxsWltbUccBfAlOMwAAAIhlZ2e/ffsW7vkO0cyZM5OSkkpKSuzt7fE5\nRwEYFij+AAAAIBYQEDB//nwdHR3UQfiGurp6XFxcfn6+vb19V1cX6jiAz0DxBwAAAKWurq7g4OBx\nvpjvCOjo6ERHR2dlZbm6uvb09KCOA/gJFH8AAABQSkhIaGxshOn9RmDp0qUxMTFxcXGenp6wwjgY\nOhLqAAAAAMY1Go22bNmyGTNmoA7ClwwNDe/cuWNrayssLHz16lUCgYA6EeAD0PMHAAAAme7u7sjI\nSEdHR9RB+Ji5ufnt27f9/f3//e9/o84C+AP0/AEAAEAmLS2tsbHRwcEBdRD+5uDgcOvWLRcXl4kT\nJ/7888+o4wBeB8UfAAAAZCIiIubPn6+qqoo6CN9zcnK6evWqp6enqKjogQMHUMcBPA2KPwAAAGiw\nWKw7d+5s3LgRdRAB8e2337a0tOzatUtERGT37t2o4wDeBcUfAAAANHJzcysrK+3t7VEHERw7d+5s\nbm7+4YcfpKSkNm/ejDoO4FFQ/AEAAEAjIiJixowZenp6qIMIlJ9++qm9vd3b21tSUnLdunWo4wBe\nBMUfAAAANO7evWtnZwezk4y5X375pbu728PDQ0JCws7ODnUcwHNgqhcAAAAIlJWVvXjxgkKhoA4i\nmH799VdPT08qlRoTE4M6C+A5UPwBAABAICEhQVxcfMWKFaiDCCYCgfDnn39SqVQnJ6fU1FTUcQBv\ngdu+AAAAEEhISDAyMpowYQLqIAKLSCTeuHGDwWDY2dklJCQsWbIEdSLAK6DnDwAAALcxmcykpCQz\nMzPUQQSckJBQQECAkZGRpaVlXl4e6jiAV0DxBwAAgNueP39eV1cHxR8XiEn/OWAAACAASURBVIiI\n0Gg0PT09KyurwsJC1HEAT4DiDwAAALfdv39fVlZWV1cXdZBxQUxMLCoqau7cuRYWFiUlJajjAPSg\n+AMAAMBtSUlJxsbGRCL8DuIScXFxOp0uLy9vampaXl6OOg5ADE48AAAAXMVgMNLS0uCeL5fJyMjE\nxsZKSUmZm5tXVVWhjgNQguIPAAAAV+Xk5LS0tEDxx32ysrL3798nEomWlpZ1dXWo4wBkoPgDAADA\nVampqQoKCnPmzEEdZDyaNm1aQkJCS0sLmUxuaGhAHQegAcUfAAAArsrOzjY0NIRV3VBRVlZOSEj4\n+PGjjY1Na2sr6jgAASj+AAAAcFVubu7ChQtRpxjXZs2aFR8f//r1awcHh87OTtRxALdB8QfAkDT0\ngWFYW1sb+2V3dzfqdADwjaqqqsrKykWLFqEOMt5pa2snJibm5eU5ODh0dXWhjgO4Coo/AL7u5s2b\nk/vAMMzT05P90s7ODnVAAPhGbm4ugUCAnj9eoKOjEx0dnZmZ6erq2tPTw97e3Nx88eLFDx8+IMwG\nOAqKPwC+rqGhYaDxSUQisb29nct5AOBfjx8/njlz5qRJk1AHARiGYfr6+nfv3o2Jidm4cSOTycQw\nrL6+fsWKFTt27Dhy5AjqdIBToPgD4OtcXFwGmY3Ww8ODm2EA4Gu5ublwz5enrFy58u7du8HBwTt3\n7qyurl6+fPnz588xDPP39//48SPqdIAjoPgD4OtkZWXNzc1JJNLnbwkJCTk6OnI/EgB8KicnB+75\n8hoLCws/P7/s7OyFCxe+ffuWfQvY19cXbTDAIVD8ATAk69ev7+3t7beRRCJZW1tPnDgRSSQA+E5l\nZeXHjx+h548HLV68+MOHD58+fWI/wcZgMC5cuNDS0oI2GOAEKP4AGJLVq1dPmDCh38be3t7169cj\nyQMAP3ry5AmBQNDT00MdBPzD8+fPDQwM6urqGAxG3+0dHR3Xrl1DlQpwDhR/AAyJhISEnZ2dsLBw\n341iYmIUCgVVJAD4zvPnz1VVVWVkZFAHAf/nxYsXpqamTU1N/So/DMN6e3vPnj37+U0PwO+g+ANg\nqFxdXfteHEVERFavXi0uLo4wEgD8paioaO7cuahTgH8IDw+vq6vrO9VLX+/fv79z5w6XIwFOg+IP\ngKGytraWlpZmv+zu7nZ1dUWYBwC+U1RUpKGhgToF+Ifvv//+6NGjUlJS/e5s4IhE4smTJ7mfCnAU\nFH8ADJWIiIizs7OIiAj+ctKkSRYWFmgjAcBHWCzWy5cv1dXVUQcB/yAtLX3kyJGqqqr//Oc/0tLS\n/aY1YDKZeXl5ycnJqOIBToDiD4BhWLduHf4onIiIiIuLyxcnfwEAfFF1dXVLS8ucOXNQBwFfICEh\nsX///srKyuPHj0tJSQkJCbHfIpFIp0+fRpgNjDko/gAYBhMTE1lZWQzDuru7nZ2dUccBgJ+UlZVh\nGPbNN9+gDgIGJCkpiZeAv/zyi6SkJH4juKenJz4+vqCgAHU6MGag+ANgGISEhNzc3DAMU1RUNDEx\nQR0HAH5SVlZGIpEUFRVRBwFfISUltX///tevX2/btk1ERERYWJjFYsGEz4IEblpxhJeXV2lpKeoU\ngCOampowDCORSDDgT4D5+fkpKyujTiFoysvLp0+fPraDJTZt2lReXj6GDYJ+li5dWlpaWllZeePG\njdLS0oFWOQe8TEVF5erVq323QPHHERUVFcnJyey5kTQ0NLS1tdFGAmNl0qRJMjIyEhIS7Cc/AL/7\n9OnTgwcP2PP4zJw5s6mpCYq/Mffu3TsVFZWxbbO4uPjBgwf4vxUUFJYtWzbIMtxgZOTl5dvb23t6\nevpOdwB4GZPJzMzMrKqqwl8uX7683wcILBaL66nGhfb29szMTDqdHhERUV5eLisra2pqSiaT7ezs\n5OXlUacDYLzr7u5OS0uj0+lRUVElJSUyMjIWFhYUCsXS0nLatGmo0wkmBwcHMTGxW7dujW2zFRUV\nYWFhNBotMzNz4sSJFAqFSqVaW1vD81hgvOnp6YmJiaHRaFFRUY2NjcuWLaNSqY6OjkpKSv0+CcUf\nN5SUlCQmJtLp9ISEBAaDoaenRyaTKRQK/JEKAJe9f/8+PDw8KioqKyurvb19wYIF+MloYGDQ9/FG\nwAn6+voGBgbnzp3jUPvv3r27c+cOjUZ78ODBlClTVq1a5e7ubmZmBncqgWBjMplJSUn+/v7R0dF1\ndXWGhoZUKnX16tUzZswYaBco/riK3R149+7dd+/eTZ06dcWKFRQKxdbWdtKkSajTASCY+nXySUtL\nW1paUigUCwsL6Ibnpm+++WbLli0HDhzg9IFKS0tDQkJu3LhRVFSkpKS0Zs0aKpVqaGgIVSAQJCwW\n68GDBzQaLTw8vKKiQkNDw8PDw9nZeSgP1EPxh0xJSQn+2yg9Pb2np0dXVxfvgYArFABjoqKiIjo6\nmk6nJycnt7W1LVy4ED/F9PX14YYgEhISEhcuXPjXv/7FtSMWFBTQaLRbt269evVqxowZDg4OVCr1\n8/FPAPCXjIwMGo2G9yLNmTNn3bp1VCpVS0tr6C1A8YcedAcCMFbwTj58lEVhYSHeyUcmky0sLFRV\nVVGnG9fa29slJCTodDqFQuH+0fEqMDAw8O3bt998842zs7OHhwcsNAf4S1FR0Y0bN0JCQkpLS2fO\nnLlhw4bh1nxsUPzxFnZ3YFpaWm9vr66uLl4FLliwALoDARgIdPLxvurqagUFhfT0dLQdb7m5uf7+\n/qGhoR8+fNDU1KRSqa6urrDoCOBlr169CgoKotFohYWF06dPd3Jycnd3X7hw4WjahOKPR7W1tSUl\nJUVFRcXExLx//15OTs7ExIRCodjZ2U2cOBF1OgR6enoePXq0bNmyEezb2tqanJyckZHBpysUtbS0\nBAUFlZaWzpo1y9XVVVxcHN+el5c3ZcqUMZ87g18wGIzU1FR2J5+UlJSVlRWZTDY3N4c1JHjQ69ev\n58yZk5+fr6OjgzrL/06EQaPRgoODP378iFeBbm5uM2fORB2NI0Zz/RwTra2t9+/fz8/P9/HxGXEj\n1dXVxcXFK1aswF8K/AXw7du3AQEBeM03bdq0tWvXUqnUMXtOlAV43tu3b319fclksoiIiJCQ0MKF\nC318fB4/fsxkMlFH45LGxsYTJ040NzePbHcajaaqqjpjxoyxTcUdxcXF8vLys2fPxqcVnDlzZlVV\nFf4Wg8HYunVramoq2oRcVlFRcenSJSqVOnnyZAzDFi5cuH///oSEhI6ODtTRwGAeP36M/z5DHeQf\nenp60tPTd+3aNXXqVPx/J19f38rKStS5xtIor59j4vr167Kysurq6iPbvaamZs+ePWJiYrt27WJv\nFNQLYGVlpa+vL96xN3Xq1F27duEPBoztUaD44yetra2RkZFeXl74nD1ycnJubm4hISENDQ2oo3FQ\nRUWFra1tY2PjVz9548aNgd5ydnZWU1Mb01xcYm1t/fTpUxaLVVNTs2nTJgzDPD092e/29PRYW1s/\ne/YMXUBu6O7uTkhI2L9/v6amJoZhUlJSVCr10qVLvFZJgEEkJydjGFZTU4M6yJf19PQkJCS4ublJ\nS0sTiURDQ0NfX9/q6mrUuUZr6NdPTrOyshpx8ffo0aOnT59iGNa3+GMJ1gWwurra19fX0NCQSCRK\nS0u7ubklJCSMec3HBsUfv3rx4sWpU6fIZLKwsLBgdwc6Ozv7+fl99WP379+fPn36QO+6uLjMmjVr\nTHNxw+PHjwMDA9kvP3z4QCQS586d2/czCQkJ+vr6XI/GDZWVlX07+TQ1NfFOvvb2dtTRwLBFR0dj\nGIa2/2koOjo6IiMj3dzcJCUlhYSE8CqQZ2vWrxri9ZMLbGxs+l27hqWrq+vz4o/F/xfAmpoavOYT\nEhKSlJR0c3OLjIzkwn0MGPPH9/ABbVFRUffu3ausrJw2bZqFhYWtra2FhYWMjAzqdKP16NGjlStX\nVlVVSUlJsTeyWKzU1NT8/HwhIaG5c+eam5snJyfb29sTCIRff/11+vTptra2GIbV19eHhoaWlZUt\nWrQoJCQkLy/v1atXX9z9qzEqKioiIyO9vb1TU1Pj4uIUFRU3btwoJiaGv9vS0hIdHV1UVKSsrGxh\nYcFeFiwlJeXhw4cYhuno6JiYmFy5cqWjowPDMAMDA2Nj44qKiuDgYHFxcW9v74GOW1dXN3ny5L7P\n+ixZsoREImVmZvb9mLa29rFjx9asWTPk75V39fT0pKSkJCYmJiYmPnnyRFxc3Nramkwmk8lkNTU1\n1OnAyNHpdDs7u/b2dvaJgxv85EKoo6MjMTGRRqOFhYV1dXWZmpq6ubk5ODjw0RJnn18/R/xtNzQ0\n3Lp1a9u2bTExMc+ePduzZ4+QkFC/CymdTn/79q2kpOSmTZtaWlr8/f0ZDIaCgsLatWsxDKNQKG/f\nvi0qKsrMzIyLi5s/f76jo+PQf5bu7u4JEybs2rXr/Pnz/d7ixwtgc3Pz3bt3AwICkpOTJ0yY4Ojo\nSKVSyWQy9/7P53R1Cbipb3cgiUQyNDQ8deoUX3cHOjo6ksnkfhsPHjx45coVFouVk5OzZMkSFov1\n5MkTQ0PDqVOnJicnP3nyhMViFRcXL168ODMzk8FgXLp0acKECXPmzBlo98EFBgZOmjRJTExs69at\nnp6eq1atwjBs8eLF3d3dLBYrPz9/3rx5YWFhNTU1Z86ckZSUZN997u3t1dbWFhUVxbvui4qKSCSS\ng4MDu+VNmzbdunVrWF+IvLz8sWPH+m308vLS09MbVju85sOHD3gn35QpUzAM09DQgE4+AXPnzh0M\nw/Czhm3wk4tHNDY23rhxg0KhiIiIiIqKUiiUGzdutLS0oM71df2unyP+tv/++29xcXESifTHH3/g\nz+s8ffr0ixdSLS0tJSUl/N/Nzc3S0tIGBgb4Sxsbm2+++YZCodjY2OCT7GzYsGHoP8tAPX8svroA\ntrS04P8viYqKioiI4P8vIbkpD8WfYKqrqwsJCfHy8po+fTqGYfLy8vjowKamJtTRhmf27Nnu7u59\ntzCZTFlZ2eTkZPzl8ePH8X84ODgoKyuzP7Z06dK9e/eyd1FTU8OLv4F2H9yGDRsIBMKLFy/wl4cP\nH8Yw7K+//urq6po7d+6RI0fYn3R1dRURESkoKMBfXrp0CcOwvLw8dkgVFRV2LW5lZTWsIR2pqalK\nSkqf/9Y5f/48iUTq6uoaelO8oLe3Nz09ff/+/QsXLiQSifivVRjJJ6hoNBqGYb29vf22D3RycT3g\n19XX1+O/uYWFhcXExPDf3G1tbahzDejz6+eIv+3169djGBYeHs5isYqKiga6kDo5ObGLPxaLtWDB\ngr7Fn4iISHFxMYvFYjKZ9vb2GIZFR0cP8WcZpPjj/QtgW1sb/n+OmJiYsLAw/n9OfX09wkhQ/Ak+\ndncgiUTq2x2IOtfXdXV1CQkJ7d+/v992Q0NDOTm5u3fvsliszs5OfKODgwP7ed779+/3u6ysXbuW\nPdb4i7sPbsuWLcLCwuyXbW1tJBLJ1dU1IiKi34Fu3LiBYdju3bvxly0tLVJSUuyXHh4eGIYlJiay\nWKzs7GwfH5+hfRMsFovV09NjYmLCriP7+vvvvzEMY1ecPK6qqgrv5JOVlcUwbO7cudDJNx7cvn2b\nQCB8vn2gk4uL0Yattrb2xo0bZDKZSCTKyMjg47R4rfj44vVzxN/23r17+9XuX7yQDl789e2fi42N\nxTBs+/btQ/9xBir+ePYC2NXVhY8flZGRERISwmu+2tpa1LlYLBZrLGaLAbxNS0sL/+VaXV0dFBSk\npaV1/vz5RYsW4ets0mi05uZm1Bm/rL6+vre39/MxEBcuXJCWlnZwcCCTyY2Njezt7LFx+HNh2tra\nn781yO5DJy4urqSk9OnTp8LCQgzDJCUl2W8ZGRlhGFZUVIS/lJSU3LBhg7+/f3d3d2VlZVtb28yZ\nM/38/DAMu3z5Mv707hD98MMPu3fv1tPT+/wtPEBFRcUIfhbuYDKZGRkZBw4cWLRokaKi4q5duzo6\nOn755Rd8DBD+xwkvDPMCnIPXHL29vYN/jH1ycSfVyEyZMsXd3T0hIeHdu3dHjx4tKSmxt7eXl5d3\nd3en0+kMBgN1QAwb+PrZ19C/bXxuub4zzI3yQqqvr08kEj98+DDcHT/HaxdABoNBp9Pd3d3l5eXt\n7e1LSkqOHj1aXl6Ob8RHtiAHxd84MmXKFHyCjIqKisePH2/durWkpMTV1XXKlCnLly8/ffp0bm4u\n6oz/IC8vP3HixJaWln7bdXV18/Lytm3blpKSsmDBgvr6enw7u8LDy1n8YQs29rsD7T50XV1d1dXV\nampq+FOoWVlZ7LdUVFSEhYX7rsu3devW2tra8PBwX1/fH374YfPmzeHh4SUlJW1tbfiUPUNx+fJl\nPT09Ozu7L77b0NCAYRj7QRPeUV9f7+/v7+zsPG3aNCMjo9u3bxsaGsbFxdXX19PpdC8vL3iGY/yY\nMGEChmF4/80g2CcXV0KNlqKi4nfffZeRkVFWVubj41NSUmJnZ6egoODu7p6YmMhkMhFmG+j62ddo\nvu1RXkilpaUlJSXH5D80j1wAe3t78fJOQUHBzs6upKTEx8enrKwsIyPju+++U1RURBuvHyj+xiMi\nkcieGrdfd6CamhreHTj4JYNrtLS0ampq+m7p6uoKCAiQkpK6ePHivXv3qqqqwsPDMQwjEAjsToV5\n8+ZhGJaUlPR5gwPtPizZ2dmdnZ0UCmXp0qUYhqWlpbHfevHiBYPBMDAwYG+ZP3++gYHB2bNnX716\ntXTpUk9PTyaTuXr1avwW8FDcuXOHxWK5u7uzt6Smpvb9QFVVFYFA4JFlLfp28snJyXl5eTU0NBw6\ndOjt27dlZWXnz58nk8nsRUrA+DHE4o99cnEl1JiZMWMGXgWWlpYePny4oKDA3NycvZGFaFaNz6+f\n/Yz42x7oQkoikTo7O4fSwpMnT5qbm62trYd76M+hvQCyWCy8vFNRUbGzsysoKDh8+HBpaSm+ccaM\nGUhSfR3au86Ad/T29j5+/PjUqVP4JJOioqJkMvnUqVPsocFIHDhwQFdXt++Wjo6OZcuW4c9MMJnM\nqVOn4rXRtm3bhIWF3759++bNm8bGxrlz50pKSuKTv1dWViooKEhKSj59+rSlpeWLuw9uy5YtBAKh\nsLAQf7ljxw4TExP83x4eHlJSUuXl5fjLixcvzp49u9/oH3wgYHx8PP5y7dq1qqqqn498/6KEhISl\nS5f+8f/5+vp6eXn9/vvvfT/j6elpaWk5lNY4Bx8LT6VS8ZUSZsyYsWvXroSEBF4eDg+4KT09HcOw\nz6dNHuTk4msvXrzw8fFRV1dnnw7p6elczvD59XPE3/aOHTswDGOPVxvoOoyPafHz82ttbfXz81NR\nUZk2bRr+ZIONjc3s2bPZ170DBw6sXbt26D9LdXU1hmFeXl6fv4XqAoivDYOXd+rq6j4+Pmh/XQ4L\nFH/gCz59+oQ/LCwvL49hmJqampeXV0hICPdnN6ivr5eTk3vz5g17S0dHh4KCgouLC41GO3PmDPtJ\n2+TkZBKJNHHiRLwwKi0tXbx4MR7e1dXV1tZ2+fLl//3vfxsaGr64++C2bNkiJCS0Y8eOvXv3uri4\n2Nrasueq7ejo2L59u5aW1t9//3316lUbG5t379712729vd3c3Jz9Mjk5+cSJE0M5bm5uroSERL8/\n2ERFRevq6tif6erqmjJlSkJCwlAaHFtMJvPx48c+Pj4LFy4UEhISEREhk8m+vr7wuC74HD6q5PP/\nNwY5uQQDXgXOmjULwzBVVdVdu3bl5uZy59CfXz9H9m1fvXoVv2vp7Oz88OFD1sDX4ZaWFn19fQzD\nNDQ0wsPD16xZY2lpic8IEx8fr6enRyaTf/755y1bthw6dIjBYAzxB4mOjsYnC5STk7ty5Qp7iUsW\nigtgbm7url27VFVVMQybNWsWf9V8bFD8gcH09PT07Q4UExPDuwO5+VzVX3/91e+JMAaD0dXVxe5s\nY2tsbOx3IaupqWltbWWxWH3L1oF2HwT7Ebl37959cbqcxsbGBw8evH//fqAW+j3KOoYTuIeEhNjb\n249Va0PR0NCAd/LJyclhGKasrOzl5RUZGYl/1QB8UWlpKYZhOTk5/bZ/9eQSGC9evNi/fz8+/Zam\npqaPjw8+7wlH9bt+juG3PciFlL0gyucXuvb29s//PB4Nrl0Ai4uLfXx88BUmp0+fvn//fn6s+dhg\nhQ8wVJ8+fUpJSaHT6VFRUQ0NDWpqamQymUKhmJubi4qKcu64TCZz/fr1+/bt++KDrmNl27ZtA73l\n5eX1119/+fn5dXd3c//Qurq6g+xbXFx84MCBW7ducfpRWRaLlZeXh//Xx+f0NzY2plAotra2/DI2\nH6DV3NwsIyMTGxtraWnZd/vWrVs5d3LxICaTmZmZSaPRQkJCqqurNTU1qVTqhg0b8K5BThyu7/Vz\noG97NFeh0Rvx0blwAXzz5k1gYCCNRissLJSXl3d2dqZSqcuWLev74DM/IqEOAPjG1KlTqVQqlUrt\n7e3Nz89PTEyk0+lXrlwRFRU1NDQkk8l2dnb4vO1ji0gk/v333zt37ty8eTN+J5cTTE1NB3pr6tSp\n7e3tPT09ra2tfWd14c6hB9mxvLz85MmTfn5+nLvwNTY2JiQk0On0uLi4mpoaJSWlVatW+fj4rFy5\n8vP70QAMQkpKikQiff5MKEdPLh5EJBKXL1++fPnys2fPZmVl0Wi0//73v0ePHtXU1HR3d3dzc8O7\nBsfwcH2vnwN92yO+Co2JkR2doxfAysrKwMBAf3//wsJCOTk5FxeXS5cuCUDN939Qdz0C/lZTUxMS\nEuLm5oZPboKPDuTQutTDulE7hgIDA6dNm4Zh2LZt2/C143jEhw8fOLFw3xdH8iF/9AcIgKlTp164\ncKHvFp49ubipp6cnPT3dy8tLWlqaSCQaGhr6+vr2HdY2JsrLywXs2+bEBbCqqsrX1xcf5iQjI+Pl\n5ZWenj6sdZj4Bdz2BWMD7w7Ebwvm5eXh3YEUCsXe3h4fGMu/8JEx+L8nTJggqHMRNzU1xcfH0+n0\n+Pj4jx8/4p18FAoFOvnAWNHT07O2tj5x4gR7yzg5uYaoq6srPj6eRqPdvXu3ra3NwMCASqWuW7cO\nH1w7evBtD6SmpubWrVs0Gi0rK0tCQsLBwYFKpVpYWODzEwkkKP7A2KupqUlNTaXT6XQ6vbGxkT06\nULDPJX7EYrHy8vLwO/jZ2dlEItHExAT/j6WlpYU6HRA09vb2UlJSgYGBqIPwuo6OjsTERBqNFh4e\n3tnZqa+v7+7u7uLiIi0tjTqaQGlqagoODvb398/OzhYVFV2zZg2VSh0nqw1B8Qc4qF93oJiY2LJl\nyygUioODg4qKCup041e/Tj5FRUUbGxsymWxmZoavWQIAJ+zcufPp06d9J0UHg2tvb793756/v398\nfDyBQDA3N6dSqatXr5aSkkIdjY+1tLTcuXOHRqPhE8RYWFi4u7vb2NiMq8nnofgDXPLx48e4uLio\nqKj4+PimpiY1NTX8WVEjIyPoDuSO3NxcvJPv4cOHBAIBOvkAl/36669//vlnWVkZ6iD8p7GxMTIy\nkkajxcXFkUgkMzMzKpXq6OgIQzKGrq2tLSwsjEajJSYm9vb2WlpaUqlUOzu7iRMnoo6GABR/gNsG\n6g5cvXo1766Ew7eam5vj4uISExPj4+PLysqmT59OoVDIZPLKlSt5ZH1xMH6EhISsX7++ra1NREQE\ndRZ+VV9fHxUVRaPRYmNjRUREbGxs3NzcLC0t4SsdSHd3d1xcXEBAwL1797q7u62srKhUKoVCGed3\nOaD4AygN1B1obGwM17LR6NvJh2HYihUryGQymUzW09MTnKkKAL958eLFvHnznj17hq++DUajsrIy\nNDSURqNlZmbKyMjY2tpSqVQrKythYWHU0XgCg8GIjY2l0Wh0Or2pqWnZsmVUKtXJyQlfqgRA8Qd4\nQk9PT3Z2dlRUVGJiYl5enri4uKmpqa2trbW1tbKyMup0/KGlpSU2NjYxMTEhIaG0tFRBQcHW1hY6\n+QDv6O7ulpCQCAwMxJfqAmPi/fv34eHheBU4adIkGxsbKpVqbW1NIo3HeXx7enpiYmJoNNq9e/ca\nGhrwmm/NmjXwe6QfKP4Az6muro6Pj4+KioqLi2tubobuwMHhnXyJiYkZGRnd3d0GBgZ4zQedfIAH\naWhoUKnUY8eOoQ4igMrLy+/evUuj0R48eKCoqOjo6EilUg0NDQkEAupoHMdisR48eECj0cLCwior\nKw0NDalUKjxZOAgo/gDv6tsdmJubKyEhgXcHrlq1SklJCXU6lNidfImJiSUlJfLy8nZ2dmQy2dTU\nVFZWFnU6AAa0Zs0aIpEYGhqKOoggKywsDAkJCQ4OLi4uVlZWXr16taBWgeya786dO+/fv587d+7a\ntWudnZ3xFXjBIKD4A/yhrKwsPj4+MTER7w7U1NTE+7dMTEzGzxiXwsJCOp0OnXyAf/3yyy9//fXX\n+/fvUQcZFwoKCmg0WlBQ0OvXr1VUVOzt7alU6vLly1HnGgMZGRk0Gi0iIqK8vHz27Nmurq5UKhUm\nLhg6KP4An+nXHSgpKblixQpbW1sbGxuBHMnb2toaExPD7uSbPHmyjY2Nra0tdPIBfpSYmGhubl5R\nUSGQZyvPwqvAgICAkpISTU1NKpXq4uIyd+5c1LmGrbi4+Pbt2zQarbCwUE1Nzc3NDWq+kYHiD/Cx\n0tLShISExMTE2NjYlpYWQeoOLCoqioyMTExMfPDgQVdXF7uTT1dXV0hICHU6AEaopaVl4sSJNBpt\nzZo1qLOMO0wmMzMzk0aj0Wi0qqoqvApcv3797NmzUUf7itevX9+8eROv+RQUFKhUKpVKXbZsGdzx\nGDEo/oAg6OzszMjIwCc3KSwsnDx5spmZGZlM5q/uwM7OzoSEBLxTPp4yQgAAIABJREFUs6SkZNKk\nSfiTLitWrJg6dSrqdACMDS0tLQqFcvr0adRBxi92FXj79u2amhq8CnR3d1dTU0Md7R9KSkr8/f3x\nmk9OTs7FxQVqvrECxR8QNCUlJfhN0piYmNbWVt7vDiwuLo6IiMA7+To7OxcsWIDXfNDJBwTSxo0b\nX758mZGRgToIwHp7e7OysgICAoKDg1taWgwMDKhUqrOzs4KCAsJUVVVVISEhNBotKytr8uTJ+Hg+\nAwMDuB6OISj+gMDq6Oh48OBBYmJiZGRkUVHRlClTVq5ciS9oNn36dLTZurq68Ols8E6+iRMn2tra\n2trampiYyMnJoc0GAEfRaLR169bV1NSM8yUWeAp+RaLRaHfv3m1ra8OrwHXr1nHzclRTU3Pr1i28\n5pOQkHBwcKBSqRYWFrD+JydA8QfGBbw7EH9Utru7W09PD1/xYsWKFdycChWfiCsqKiozM7OjowM6\n+cA41NDQMHXq1Nu3bzs5OaHOAvrDB5/QaLTw8PDOzk59fX13d/e1a9fKyMhw6Ii1tbX4eL7s7GxR\nUdE1a9ZQqVRzc3NRUVEOHRFgUPyB8YbdHRgREVFcXMzuDrS1teXQnY7PO/nMzc0pFIqVlRV08oHx\nSV9fX0dH59KlS6iDgAF1dHQkJiYGBAREREQQCARzc3Mqlbp69WopKakxab+lpeXOnTs0Gi0hIYHF\nYtnb27u5uZHJZDExsTFpHwwOij8wfrG7AxMSEhgMBt4dSKFQxmRA8bt37+7cuQOdfAB87vDhw4GB\ngSUlJYI37bDgaWxs/PXXX69fv15XVyckJEQmk6lUqqOjo4SExAhaa2trCwsLo9FoiYmJvb29lpaW\nVCrVzs5u4sSJY54cDAKKPwCw9vb2zMxMOp2OTxkqKytrampKJpPt7Ozk5eWH3k53d3daWhqdTo+K\niiopKZGRkbGwsKBQKJaWltOmTeNcfgD4S35+vp6eXnZ29tKlS1FnAV/BYDB0dHTU1dWvXbsWFRVF\no9FiY2OFhYXNzMzc3d3t7e2Hsupmd3d3RESEv7///fv3GQyGlZUVlUqlUCgw7hMVKP4A+IcRdAfi\nC6tHRUVlZWW1t7cvWLAA3wUeTwNgIHPmzLGzsztz5gzqIOArzp8/f+DAgYKCAvZEMLW1teHh4f7+\n/pmZmTIyMra2tlQq1crK6vPpFBgMRmxsLI1Go9PpTU1Ny5Ytc3d3d3R0nDJlCtd/DvAPUPwB8GVf\n7A6kUCj4X6vQyQfAaBw8eDAwMLC8vBzu/PKyT58+zZkzZ/v27cePH//83YqKCvwebmZm5qRJk2xs\nbKhUqrW1NYZhMTExNBrt3r17DQ0Ny5Ytw+8Uj/M12XkKFH8AfAWTyczLy4uJiYmOjs7JySGRSIsX\nL3758uWnT59UVVWtra2tra1Xrlw5shEwAIxPjx8/Xrx4cU5OzqJFi1BnAQPasmVLbGxsUVGRuLj4\nIB978eJFcHBwcHDw69evVVVVMQwrKyubM2fO2rVr165dC8uv8SAo/gAYhrq6uvj4+CNHjlhbW3t7\ne2toaKBOBABfYrFYc+fOtbCw+OOPP1BnAV+Wk5Ojr68fEBDg6uo6xF3y8vLCwsIwDHNyctLT0+Nk\nOjAqUPwBMGwEAiE4ONjZ2Rl1EAD42JkzZ44fP/7hw4fBe5UAEiwWa/ny5SQSKSUlBW7NCx5YIA8A\nAAAC7u7u7e3toaGhqIOAL7h169bDhw99fX2h8hNIUPwBAABAQE5OjkKhXLt2DXUQ0F9ra+u+ffs2\nbtwIt24FFRR/AAAA0Pj222/T09PfvHmDOgj4h1OnTnV2dp48eRJ1EMApUPwBAABAY9WqVUpKSvDM\nB095/fr1//zP/xw+fBhmYBZgUPwBAABAg0Qiff/991evXq2rq0OdBfyvffv2qaurb9++HXUQwEFQ\n/AEAAEBm8+bNwsLCV69eRR0EYBiGxcbG3r1719fXl0Qioc4COAiKPwAAAMhISUl9++23f/zxB4PB\nQJ1lvGMwGLt37169evXKlStRZwGcBcUfAAAAlLZv315VVYVPDgwQunjxYmlp6W+//YY6COA4KP4A\nAACgNHv27NWrV584cQIWHUCourrax8dnz54933zzDeosgOOg+AMAAIDYsWPHCgoKaDQa6iDj15Ej\nRyZNmnTw4EHUQQA3QPEHAAAAMU1NTXt7+2PHjjGZTNRZxqOcnJxr166dPHkSltobJ6D4AwAAgN6R\nI0cKCwvv3r2LOsi4w2KxvvvuOyMjo3Xr1qHOArgEij8AAADo6erqWlpa/vLLLzDyj8uCgoIePXrk\n6+uLOgjgHij+AAAA8ITTp0/n5+cHBQWhDjKOtLa27t+/f+PGjbq6uqizAO6B4g8AAABPmD9//oYN\nGw4dOtTV1YU6y3hx8uRJWMZ3HILiDwAAAK84ceLEx48fr1y5gjrIuPD69eszZ84cOXIElvEdb6D4\nAwAAwCsUFRW3bNly8uTJjo4O1FkE3969e9XV1bdt24Y6COA2KP4AAADwkJ9++qm9vf3XX39FHUTA\nxcbGRkREwDK+4xMUfwAAAHiIrKzs8ePHT548+fr1a9RZBBYs4zvOQfEHAACAt2zdulVdXX3//v2o\ngwgsWMZ3nIPiDwAAAG8REhI6d+7cnTt34uLiUGcRQPgyvj/88AMs4ztuQfEHAACA56xcuZJCoezf\nv7+npwd1FkFz+PBhWMZ3nIPiDwAAAC86d+7cy5cvz58/jzqIQHn06JGfn9+pU6fExMRQZwHIQPEH\nAACAF82aNevo0aOHDx9++/Yt6iwCgsViff/990ZGRi4uLqizAJSg+AMAAMCjdu/eDRPRjSFYxhfg\noPgDYCQ6OzsjIiKOHj2KOggAgoxEIl26dCkxMREW/B09fBnfTZs2wTK+gMBisVBnAIDPEAgEb29v\nGo02ZcqU4uJi1HEAEHAbN26Mjo4uKCiAVchG4+DBg5cvX3716hV8jQB6/gAYiRUrVixatAh1CgDG\nhXPnzomIiHh7e6MOwsdevXr122+/+fj4QOUHMCj+ABgxISEhAoGAOgUAgk9aWvr69es0Go1Go6HO\nwq9gGV/QFxR/AIxWZmamj49PWFgY6iAACKyVK1d6eXlt27atpqYGdRb+ExsbGxkZ6evrKyQkhG+p\nqKj4888/WSxWSkrKjz/+eOHChY6ODrQhATfBmD8Aho1AIAQHB/v7+xcWFmppabFYrJKSkqKiog0b\nNgQEBKBOB4BgampqmjdvnrGxcWBgIOos/ITBYOjo6GhoaLD/QL158+bOnTs7Ozs9PDy6u7urq6uj\no6MXL1784MEDYWFhtGkBd0DPHwAjV1lZeebMmaioqIKCAnt7+8DAwJiYGNShABBMMjIy165dCwoK\ngid/h+XChQvl5eXnzp1jb1m/fr2NjU1nZ+eOHTuuXbt27969w4cP5+Tk+Pn5IcwJuAmKPwBGTktL\nS11dHfv/z/9iGHbv3j3UoQAQWObm5nv37vXy8nr16hXqLPyhqqrq559/3rNnz4wZM/pul5CQIJFI\nWlpa+MsDBw6QSKS0tDQUGQECUPwBMDb09fWJROKHDx9QBwFAkB0/flxTU9PV1ZXBYKDOwgfwZXx/\n/PHHwT8mLi6upKT06dMn7qQCyEHxB8DYkJaWlpSUVFNTQx0EAEEmLCx87dq1goKCkydPos7C6x49\nenT9+vWhLOPb1dVVXV0Nl6/xA4o/AMbGkydPmpubra2tUQcBQMDNmzfv1KlTx44dS05ORp2Fdw1r\nGd/s7OzOzk4KhcKFYIAXkFAHAICPtba2MplMIpGIYRiNRlu7dq2ZmRnqUAAIvu+++y43N9fJySk3\nN1dVVRV1HF508+bN/8fencdD1b6PA7+HQULaCGXft4RCSRstlHZSiqdNi6I+7T0t6tGjeiotWqSS\nFoVSiJ5KWZKiEBlKjH0J2QZjGXN+f5zfZ77zEbIMZ3C9//AyZ2buuc49M+dcc859rjs+Pv7Tp0/t\nPYDBYKSnp6urqyOEHj16NH36dEj+Bg8o9QJAl+GlXkaMGLFv375Ro0ZNnTq1uLhYTEzs6NGjZDL8\noAKgL9TW1hoaGgoLC799+5afn5/ocLhLTU2Nqqrq4sWLr1692uYDNm/efOPGjS1btggKCubn59fV\n1d2/f19ERKSP4wREgR0VAN00e/bs2bNn0+n08vJyaWlposMBYHARFhYODAycNGnS7t27L168SHQ4\n3MXNza25ufnEiRMdPIaHh+fSpUv5+fmioqLDhg3rs9gAN4AxfwD0iKCgIGR+ABBCVVX1+vXrly5d\nguLq7DIyMs6dO9fJaXylpaUh8xuEIPkDAADQX9nY2GzZsmXr1q1paWlEx8It9uzZo6am1vE0vvX1\n9QwGo7a2ts+iAlwFkj8AAAD9mLu7u5qa2tKlS2k0GtGxEO/58+fBwcHu7u6saXx/df/+/ZcvX2IY\ntm/fvs+fP/dleIBLwAUfAHQZfsGHtbU10YEAABBCKC8vT09Pb/bs2Q8ePCA6FiI1NzePHz9eU1Pz\n0aNHHTysurqatesXEBD4bRVAMPDABR8AAAD6NxkZmevXry9fvnz69OmbN28mOhzCXLp0KS8v7+XL\nlx0/TFRUtG/iAVwLTvsCAADo95YuXXrgwAEnJ6fXr18THQsx8Gl8d+/eDZeggd+C5A8AAMBA4Orq\numLFiiVLlnz58oXoWAhw6NChkSNH7t+/n+hAQD8AyR8AAICBgEQieXl5aWlpLVy4sLS0lOhw+lR8\nfPzt27dPnToFA/hAZ0DyBwAAYIAYMmRIcHAwLy/v0qVLGxsbiQ6nj+DT+E6bNm3FihVExwL6B0j+\nAAAADByjR48ODg5OTU3dtGkT0bH0kXv37n38+NHDw4PoQEC/AckfAACAAUVDQ8PPz+/+/funT58m\nOpZeV1NTs2fPno0bN2pqahIdC+g3IPkDAAAw0MydO/fMmTMHDhwICgoiOpbe9ffffzMYDFdXV6ID\nAf0JJH8AAAAGIGdnZ3t7ezs7u+TkZKJj6S0ZGRnu7u4uLi6dmcYXABZI/gAAAAxM165dMzQ0NDc3\nz87OJjqWXrF79241NbUtW7YQHQjoZyD5AwAAMDDx8/MHBQUpKiqampoWFxcTHQ6HPX/+PCQk5Pz5\n8x1M4wtAmyD5AwAAMGAJCgo+e/ZMRERk7ty5VVVVRIfDMY2NjU5OTsuXL585cybRsYD+B5I/AAAA\nA5moqGhYWFhNTc2SJUsGTPE/Dw+PoqKic+fOER0I6Jcg+QMAADDAjR07Niws7MuXLzY2Ni0tLUSH\n01PFxcXHjh3bs2cPTOMLugeSPwAAAAOfhoZGWFjYq1evtm3bRnQsPXXo0KFRo0bt27eP6EBAf0Um\nOgAAAACgLxgYGNy9e9fKykpJSWnXrl1Eh9NN+DS+Dx48gGl8QbfBkT8AAACDxZIlS65cubJ3795r\n164RHUtnFRUVsf7HMMzZ2XnatGnW1tYEhgT6O0j+AAAADCIODg5eXl6Ojo6XL18mOpbf27dv37hx\n4+zt7fFSNXfv3v306RNM4wt6CE77AgAAGFzWrVtXV1e3fft2Hh4eLq+QHB4ejmHYgwcP/P39Dx06\n5OnpuXbtWpjGF/QQJH8AAAAGne3btzMYDEdHR15eXgcHB9ZyPz+/lJQUV1dXEolEYHi45ubm1NRU\n/J/m5mYXF5dhw4bp6OhgGMYN4YH+C5I/AAAAg9HOnTtra2u3bNkydOjQ1atXI4QePHiwevVqJpOp\nq6u7fPlyogNEX79+bWpqYt1kMBhVVVXbt2/39vb28PAwMjIiMDbQr0HyBwAAYJA6fPhwU1PTH3/8\ngc+Qhmd+PDw8e/bsWbx4MZlM8C7y8+fPvLy87IUJmUwmvtzY2NjT03PDhg3ERQf6MUj+AAAADF7H\njx+vqqqys7NjMpl4asVkMvPy8h48eLBmzRpiY0tKSiKTyb9WpcaXFBQUEBEUGAjgal8AAACDF4lE\nmjdvHpPJxDCMffmhQ4cYDAZRUeE+fPjw63x0PDw8JBLp/PnzLi4uRAQFBgJI/gAAAAxekZGRS5cu\nRQixJ39MJrOgoODOnTvExYWYTGZycnKrhWQymY+PLyAgwNnZmZCowMBAavVbBwDwqx8/fri7u7Nu\nenp6mpmZKSoq4jfNzMzMzMwICg0A0H1JSUkmJiZ0Oh0/4cuORCJJS0tnZWURNfLv+/fvKioq7EvI\nZPLw4cNDQ0MNDAwICQkMGDDmD4Df8/DwOHXqlICAAGtJSEgI/g+DwfD396dSqQSFBgDoPnd397q6\nujbvwjAMP/i3bt26Po4Kl5iYyMPDw8pKyWSymprav//+O3bsWELiAQMJnPYF4PcsLCwQQo1t4eXl\nxc8ZAQD6ncOHD9vb25PJZH5+/l/vxTDsyJEjzc3NfR8YQigpKYmPjw//n4eHZ/bs2e/fv4fMD3AE\nJH8A/J6RkdG4cePavKupqWnlypV9HA8AgCOUlZVv375dXFx8/Pjx0aNH49dSsO7FMKykpOT69euE\nxPbx40fW1R6Ojo4hISHCwsKERAIGHkj+APg9EolkZ2fH+hXOTl5eXl9fv+9DAgBwyujRo/ft21dQ\nUODt7a2goIAQYo3za2lpcXFxodPpfR9VUlISQohEIp06derixYt4JUIAOAIu+ACgU9LT0zU0NFot\n5OPjO3jwIBRcAGDAaG5u9vPzO3369JcvXwQEBBobG3l4eC5evOjo6NjBsyoqKn78+FH9X1VVVVVV\nVTU1NdXV1fgUHS0tLTU1Na2excvLO2zYMPx/YWFhUVHRYcOGiYqKioqK4qcU+Pn579y5s2LFil5a\n2cGMyWQWFhYWFhaWlZWVl5eXlpaWlpbW1tYihGpqathrK4qKivLw8PDz84uJiY0ePVpCQmL06NHi\n4uJycnJDhgwhbg16BJI/ADpLXV3969evrRZ+/fpVVVWVkHgAAL3n1atXp0+fDg8PRwiJiYllZ2f/\n/PkzJycnOzs7Ozs7Pz+/tLT0x48fxcXFZWVlrarxjRgxgpXGsV8o1gEajYYnjjU1Na2uQREREZGS\nkhITE5OQkJCQkJCVlZWXl5eTk5OTkxs1ahTn1nggq6ioSE5O/vLlC4VCwd/BvLw81tR5Q4cOxbtX\nRESkvRYaGhrwHLGiooK1UEpKSl5eXl5eXkVFZfz48dra2vLy8v1i2mVI/gDoLDc3t6NHj7JGf5NI\nJC0trZSUFGKjAgBwXHV1dXp6empqalRU1OvXrysqKphMJv7dFxAQkJWVlZaWlpSUFBMTk5KSGjNm\njLi4uJSUlOh/9fDVGQwGngjih6NKSkpKSkpKS0uLiopKS0uzs7OLi4vxffewYcPk5ORUVVU1NTU1\nNDQ0NTWVlZXbHKAy2DQ1NX369CkmJiYmJiYxMbGwsBAhJC0traOjIy8vL/1f48aNExMTGzp0aOdb\nbm5uLi8v//HjR15eXm5ubn5+fn5+PoVCSU9PZzAYIiIi48ePnzJliomJibGx8ciRI3ttFXsEkj8A\nOisvL09OTo71lSGTyW5ubrt37yY2KgBAz1VUVMTHx8fFxcXHx3/58iU/Px8hNGTIEHV1dQ0NDWVl\nZXl5eQUFBTk5OSkpKR4egofLNzQ05OTksA5DpqenUyiUnJwcDMP4+PhUVVV1dXUNDQ0NDAwmTJgw\nqHLBb9++hYSEhIaGxsXF0el0cXFxIyMjAwMDfX19PT09cXHx3ntpOp2enJycmJj48ePHDx8+fPv2\nDSGkoaExb968BQsWTJ06lfCpotlB8gdAFxgYGCQkJOCVt3h4ePLz86WkpIgOCgDQHVQq9fXr11FR\nUfHx8d+/f0cIjR07Fk8U1NXVtbW1FRQU+tFlFnV1dWlpaRQKJTU19ePHjwkJCXV1dQICAngiaGpq\nOn36dNYQwwEmJSXl7t27QUFB379/FxMTW7BgwcyZMydPnqykpERUSBUVFe/fv4+JiXn27FlqauqI\nESPmzZu3atWqefPmcUMWCMkfAF3g4eGxc+dOBoPBw8MzefLkmJgYoiMCAHRBRUVFeHj469evw8PD\nqVSqgICAgYGBkZERfnyovYpO/RGDwaBQKHFxcXFxce/fv09PTyeTyQYGBviMRFOmTOlHeW17Kioq\n7t27d/v27aSkJHl5eSsrq4ULF06ePJnwQ7OtUKnU4ODgwMDAmJiYMWPG2Nrarl27VlNTk8CQIPkD\noAvKysokJSVbWlp4eXkvX768adMmoiMCAPze169fHz58+OzZs6SkJH5+/qlTp+I50IQJEwZADtQZ\nNBotLi4uPDw8PDw8MTFRUFBw1qxZVlZWixYt6vkgxb6Xnp7u5ubm5+cnLCy8evVqOzu7flFyq6io\n6O7duzdu3MjMzDQ2Nt63b9+CBQsIuUAEkj8AumbmzJlRUVE8PDzFxcViYmJEhwMAaNfPnz/v3bvn\n6+v78eNHYWFhCwuLpUuXmpubd3BR52BApVKfPHny5MmT9+/fDxkyxNzc3N7e3sLCol/kwVQq9ejR\now8fPhwzZoyTk5ODg8Pw4cOJDqprWlpagoKCzp49GxsbO2nSpGPHjpmbm/d1EBgAoCtu376NEDI3\nNyc6EABA25hMZnh4uI2NjYCAgJCQkL29fUhICJ1OJzourlNUVHT16tXp06eTSKSxY8f++eefWVlZ\nRAfVrvLy8h07dvDz86uqqt6+fbuxsZHoiHrq3bt3lpaWCCFTU9PExMS+fOnWyV9/PPwLAACAC718\n+bIv92cYhjU0NFy/fl1ZWRkhpKure+XKlerq6j6OoT/6+vXrrl278AnuFixYEBMTQ3RE/4PJZN68\neXPEiBESEhJXrlxpbm4mOiJOevv2rZGREQ8Pz+bNm2tqavrmRVuf9r158yZ+rRCFQikqKmItl5GR\n0dDQ0NbWVldXh+kFAQBgkGMwGFlZWXjV3IyMDFb9SxEREfxSWU1NTSsrqz67hIJGo12/fv3cuXOl\npaUrVqzYsWPHxIkT++alB4zGxsbHjx+fOXMmKSnJxMRk//795ubmhJcszsvLc3BwCA8P37lz59Gj\nRwdkBoJhmJ+fn7Oz85AhQ7y8vObMmdPbr9jRmL+mpqbv37/jl44nJCSkpaVlZ2djGDZixAi8mCT+\nd8KECaNHj+7tQAEAABCloaGBQqFQKBR8j5CWlpabm9vS0iImJqajo8O+RxgxYkQfx9bU1OTu7n7q\n1CkMw5ydnbds2TJmzJg+jmGAiYmJOXXqVGhoqK6u7oULF6ZOnUpUJM+fP1+1apWkpKS3t7ehoSFR\nYfSN8vLy7du3+/n57d279++//+7Va5a7dsFHdXV1ZmYm+/efSqUihCQlJVnffA0NDV1dXSEhoV6L\nGQAAQC9qaWn5+vUr+y///Pz85uZmISGhCRMmsG/tCa9z+fbt2x07diQnJzs4OBw7dgyuweKgDx8+\n7Nix4+PHj2vXrj1x4kTfp9SnT58+ePCgra2tp6dn/51Ft6vu3LmzadOmWbNm+fr69t5IvJ5e7VtZ\nWcmeC6akpJSWliKEJCUl9fX12bcRgoKCHIoZAAAAxzCZzPT0dNZmnEKhZGZmNjU1CQoKamhoaGho\nsDbmhKd67Oh0+qZNm+7evWtlZXXixAl8nB/guJCQkB07dpSXl3t5eVlbW/fZ6+7atevChQunTp3a\ntWtXn70ol/jw4cPSpUslJCTevHnTS9cyc77UC54O4j8WKRRKcnJybW0tmUzGRw3iWxB9fX01NbV+\ncVU5AAAMMFQqlf1H+/fv32tqagQEBLS0tNhP4MrIyHDDVARtys/Pt7GxSU5O9vDw+OOPP4gOZ4Cj\n0WiOjo737t3bu3evq6trH3wqjh07dvz48bt3765ataq3X4s7UanUadOmycrKvnz5sjdOpfZFnb+i\noiL235RJSUn19fV8fHzKysrshwbV1dW5rSo3AAD0dz9+/MAvy8C3wBQKpaqqioeHR01NjX0LrKKi\n0l8mgU1ISJg3b56UlJSfn5+amhrR4QwWPj4+27ZtMzIyevr0aa+O7Lp169aGDRs8PT03btzYe6/C\n/dLT02fMmGFgYBAcHMzxy24IKPLMYDDy8vLYf3dSKJSGhgYBAQFFRUX2k8Xy8vKEX2cEAAD9CI1G\nS05OZp17SUtLq6ysJJFI8vLy7OdelJWV+fn5iQ62O2JjYy0sLExMTPz9/WE0UR9LTU2dPXu2kpJS\naGhoL00TTKFQDAwMnJyc3NzceqP9/iU2Nnb69OknT57k+Llvrpjho7m5OT8/n/1k8bdv31paWkRF\nRZWUlFgjTrS0tCQkJIgOdvBiMBjx8fFTpkz57cLOqK2tjYiIwK8p41yMfYpGo/n6+mZnZyspKa1a\ntWro0KEIocTExFGjRsnKynLqVVp1FJVKdXV1PX78OKcqaJSUlHz9+nXGjBkdPKaxsTEqKurz589T\np041NDTEB2xwfE1BN9TV1SUlJbH/kC4uLka/jLpWUlIaGDVcP378OGvWrHnz5vn6+nLDcUqOfx+5\nX0ZGhqmp6dixY6OiogQEBDjbOIPB0NXVFRYWjo6O5ob3lxv8/fffx44di4+P19HR4WS7fVNOsKsa\nGxtTU1P9/f2PHj26YMECBQUF/BDgiBEjjI2NHRwczp8//+rVq9LSUqIjHSyqqqr+/vvvVvUn21zY\nSQEBAXJycjIyMhwKsK99/fpVQkKCdfhEUVGxuLgYw7Dm5ubNmzdHRUVx6oVadVRAQABCKCwsrOct\nl5aW7tq1S1BQ0MnJqYOH/fjxQ15e3svLq6ysbM+ePfPnz2cwGFgvrCn4LTqd/unTJx8fH3xKUElJ\nSXwzLiYmZmZm5uTk5Onp+fbt24qKCqIj7RXV1dWKiorm5ub4J5AbcPD72I9kZmYOHz58+/btHG/5\nxo0bfHx8GRkZHG+5/2ppadHT05s/fz5nm+XS5O9X1dXV7Fs9BQUFfKsnKSnJvtWrra0lOtIBqKCg\nwNLSsqqq6rcLf+Xj49PeXdbW1goKCpwJsc+Zm5snJydjGFahjAOmAAAgAElEQVRaWrphwwaE0Lp1\n6/C7GAyGubl5SkoKp16rVUeVlZVxpNn4+Pjk5GSEUAfJX0tLy9SpUxcuXIjfZDAYsrKy+/btY93k\n7JoCdgwGo9VvYHygvbCwMOs38ABO9X6F13v78eMH0YH8D059H3tbB5vibvDz8yORSEFBQRxss7Gx\nUVZWdsOGDRxss+c422/dExoaihCKjY3lYJv9Jvn7VUVFxdu3bz09PZ2cnMzMzFgliFjpoI+Pz6dP\nn+rr64mOtN+ztra+detWZxa28vr1aykpqfbutbGxUVJS4kB8fe7Tp0/37t1j3SwqKsKHz7OWvHr1\nysjIiFMv13sd1djY2HHyFxERgRAKCQlhLTly5IiQkBDrVxZn13Qwa2lpYaV6VlZWGhoa+EFlQUFB\nfX39NWvW4Kc7CgsLiY6UGO/evUMIhYaGEh1Iv9Txprh71qxZo6ioyMGZ1sLCwkgkEpVK5VSDPdcb\n/dY9enp6nE2LufQy/s4YMWLE1KlT2SuPsxcdTEhI8Pb2ptForarM4GMHOT5SYQCLj48PDQ29ceNG\nxwsxDMOHhfHy8qqpqc2ePTsiImLx4sUkEsnT01NKSgqfvrqiouLRo0c5OTkTJ07EMIz9gp5fW+g4\nsIKCguDg4C1btkRFRb148WLs2LHr169nDQCn0WhhYWHp6enS0tJz5syRlpbGl0dGRsbFxSGEdHR0\npk+f7uXlRafTEUKTJ0+eNm1aQUGBn5/f0KFDt2zZ0sFLy8nJ6enpsW7i46vYyx+YmZnt2LEjMDBw\n6dKlHa8FQqiysvLBgwdbt259/vx5SkrKrl27yGRyex3FZDKjoqKEhYUnTZoUERERHx+PEBo1ahR+\n9BFfO3Fx8bVr1/72dTsjMDAQIaStrc1aoqWlVVdXFxYWZmVl1dU1BSwYhmVnZ7Nf95aRkUGj0Vj1\nVuzs7Li/3kpfcnd3NzY2trCw4GCbGRkZHz58SElJMTY2XrJkCWt5m9uiNheyfx/x59bW1t69ezcv\nL09ZWdnAwEBdXZ1V1Cw/Pz8wMHD79u1paWlBQUEyMjK2trZ4jQs6nR4UFLRw4cLS0tKwsDB8g8nL\ny/vjx4/g4GAeHh4rKyv2CyyKior+/fffgoICY2NjU1PTjttvc1Pcc8eOHVNWVg4KClq2bBlHGnz6\n9OnEiRPl5eV72E5tbe3Tp0+/ffumra09d+5c1mjXNjsNdbHf2mykzW14V/doHVu+fPn58+c9PT05\nVhSFg4kkFyosLHz16tX58+cdHByMjY3xIfl8fHwaGhpWVlZHjx719/dPTU1taWkhOlLutWzZMjMz\ns98uPHjwoJeXF4ZhHz9+NDAwwDAsKSnJ2NhYTEwsIiIiKSkJw7CvX79OmjQpNja2ubnZ09NTQEBA\nRUWlgxY6cO/evREjRggKCm7evHndunX4LmHSpElNTU0Yhn3+/FlbW/vx48elpaVnzpwRFhZmHbpv\naWnR0tIaMmQIPmwoPT2dTCYvXryY1fKGDRsePHjQ1V6SkJA4fvw4+xIHBwddXd3fPvH27dtDhw4l\nk8mXLl3Cx/MmJye311EUCmX58uUIoatXr+JPX7hwIULo/fv3+E0mkykvL19QUNDJsH975M/c3Bwh\n1NjYyFoSGRmJEHJ1de3qmg5yxcXF+LZozZo1+vr6eOFWHh6eVtsi/AMMWqmvrx8yZIi3tzcH23R3\nd58xYwaTyczOzpaTk7ty5Qrrrja3Rb8u/PX7WFFRoaKiEh0dXVtbi2eTkyZN2rFjB4ZhwcHB+Owj\n7u7ua9euXbBgAULo77//xjAsMjISr1B99uxZBweHvXv3Dh06dNmyZV5eXra2tjY2NiQSydLSkhXe\nmzdvNm7cmJiY6O/vLywsvHXr1o7b/3VTzClz5861trbmVGtKSkpHjhzpYSPp6ekWFhbJycnNzc0r\nV64cNWpUVlYW1k6nYV3stzYbaXMbjnVxj/Zbnz9/RghxcIzNAE/+WsFnIg8ODj558iS+CcZnjOHn\n59fQ0FizZs3JkyeDg4OzsrKYTCbRwXILZWVlOzu7jhcymczRo0dHRETgN1mZweLFi6WlpVkPMzQ0\n3LNnD+spCgoKrOSvvRY6sHr1ahKJlJqait88fPgwQujatWuNjY1qamrsG5FVq1bx8/NTKBT8pqen\nJ0IoMTGRFaSsrCzrHZ83b15Xh5NHRUWNGzeORqOxL7xw4QKZTGZPm9pja2uLEAoMDMQwLD09Heuw\no1JSUth3NllZWTw8PH/++Sd+MycnZ+PGjZ2P/LfJn56eHi8vL/sS/Fijo6NjN9Z08CgrK2P/2YlP\nd0sikRQUFBYsWMBK9aDTOgk/54tPLs8pSkpKrI/x4sWLLSws8P/b3Ba1t4Fq9X08cOCArKws/n9C\nQgKeUrBecf/+/Qih8PBw/Kaenp6+vj7+/7lz5xBCAQEB7I98/PgxfvPPP/8UEBDAD1LQaDQFBQXW\nuIv169ezfv510H6rTTGnuLm5capZJpPJz89///79njTCYDAmTJhw/fp1/GZCQgI/P39ISEgHnYZ1\nut86aOTXbXg39mgdw89QBQcH97AdlsF1NoGXl1dBQUFBQYF13LtVlZk7d+60qjKDnyyeOHEi68K6\nQaWpqYlKpbY6o/frQhKJpKqqumLFiuvXry9atGj37t3sd+H/vHnzJi4u7ujRo6zlkyZNwn/NdNxC\ne4SEhMhksqamJn5z//79bm5u0dHRkpKSX79+NTIyYj1y7ty5vr6+N2/ePHv2LEJo1apVu3fvvnfv\nnq6uLkJIVFQ0Nzf3zZs3pqamcXFxrFImndTS0nLkyJHg4GBhYWH25aKiogwGIzMzU0NDo+MW8Cmz\nFi1ahBBSU1PruKNajVhQUFCYN2/erVu3XFxcyGTyrVu3HBwcOh/8b7VaKYRQS0sLQoi96FLn13Sg\n6rjeytSpUx0cHAZSvZW+V1xcTCKRWIM3OCIyMhKvVIzPXFxTU4Mvb3Nb1N4GqtX3MSsrq6ysrKmp\niZ+fX0dHR0hIKD8/n3UvPiiFVZVaQ0PjxYsX+P/4B4M1vkJVVRUhxCrtoaam1tjYWFRUNG7cuAcP\nHtDp9L1797J6RlFRMTMz08jIqIP2EdummIPk5OTwKgc9b/znz59NTU093M+GhYV9/vx5/vz5+E09\nPT0ajcbPz48P72mz01CH7wti67cOer7VNhx1a4/WsSFDhowcObKwsLCH7bAMruTvV3x8fK3Swaam\npu/fv7O243fu3Pn69SuTyRwxYgT7wEEdHZ3BMIN4RUVFS0tLq0qqbS708PCwsrJavHixqanp/fv3\nWdffsL45+IWlWlparKe02l6010InDR06dNy4cWVlZWlpaeh/sxYTExOEUHp6On5TWFh49erVd+7c\ncXNzKysrq6urU1RUvHXrlqmp6fXr148dO9al1929e/d//vMfPI9khwdQUFDw25QIH8PBGsnx245q\nxdHRcf78+cHBwYsXL05OTu5q/B2TlpZuaWlpbGxk7eRoNBpCiH2lOr+mAwOdTmfP8xISEvBUT1xc\nfPz48fhpXHwrgR/wA9xp7NixL1++fPbs2fTp0xUVFfEDdbg2t0Wd2UDNnDnT398/JiZm1qxZlZWV\nTU1NHYz0wo+pt3kXfkqKBa94V1dXhxCiUCiSkpKXL1/+7Qq2ap/LZ0zAQ+1hkMnJyUJCQuy7Zvyq\nqc53Gmq/3zpopNU2HNfDPdqvOPsODvbk71f8/Pyampqampr4eHaEUE1Nzffv31nb+vDwcCqVihDC\n00FWJdUJEyb8epikv5OQkBg+fDi+v+944YQJExITE/fv3+/p6amnp/fly5eRI0cits8r/sM6Li6O\n/ec7+6e5vRY6qbGxsaSkZO7cufiz3r9/j+d8CCFZWVk+Pj72PfHmzZuvXr0aGBiYkJCwe/fuyMhI\nFxcXKpVaV1fXpWKt169f19XVxQfetVJZWYkQ6saxit92VCvm5uYKCgqenp5DhgzBh+hxkLq6OkIo\nPz9fSUkJX1JeXo7+N/nr9pr2Cy0tLV+/fmWfMCMvL4/BYAgLC+vo6GhqapqZmeEbAUj1es/YsWMx\nDMvLy+v51QAshw8fxq8VExQUfPz4MftdbW6LOrOB2rBhQ2Zm5ubNm0+cOBEREeHm5jZv3jxOBYzj\n5eX99u1bc3NzV2sg90byl52dPW7cOI60PHr0aAEBgYKCgp40wmQy6+rqIiIi5syZw768252G2Pqt\nq430cI/WCp1O//nzJwdricNcur83bNgwfX19Ozu7kydPhoSEZGVlVVZWvn379uTJk/r6+mlpaUeO\nHDExMREREZGSkpo9e7azs/OdO3cSEhLwk/T9naamZmlpaccLGxsb7969KyIicvny5dDQ0OLiYvwq\nURKJhJ8lRP89o/HmzZs2X6W9Fjrvw4cPDQ0NCxYsMDQ0RAhFR0ez7kpNTW1ubp48eTJryfjx4ydP\nnnzu3LmMjAxDQ8N169YxmcwlS5bY29t3/hWfPHmCYZidnR1rSVRUFOt//ERVN/ZVHXfUr0gk0pYt\nW169enX27FmOT4K+fv16AQEBfMQVLiEhYcKECSoqKqwl3V5TLsRkMikUSkBAgIuLi7W1taampqCg\noJaW1pYtW2JiYqSkpJycnJ4/f15YWEij0WJiYjw9PZ2dnadOnQqZX6/S0dEZMmTI69evOdVgdna2\nq6vr6tWr8TMYTCaTdVeb26JObqDIZLKkpKS3t/f48ePd3d05PiUXQkhHR6euru7atWusJVVVVVeu\nXOn4WeybYg56/fo1vr3tOfy0/vfv33vSCL7x9PX1ZS35+fPnkydPutdp6H/7rUuN9HyP1kpGRgZC\niJMzKnFq8OAgx1500NjYWEREBCFEJpPx8d379u3Diw42NDQQHWmX7d+/f8KECR0vpNPpU6ZMwa+Z\nYDKZYmJieGK0detWPj6+rKyszMzMqqoqNTU1YWFhfE6IwsJCSUlJYWFh/LKs9lrowKZNm0gkUlpa\nGn5z27Zt06dPx/+3t7cXERHJzc3Fb16+fFlZWbnV4HofHx+E0MuXL/GbK1askJOT6/x1369evTI0\nNLz0X/jQ/osXL7IesG7durlz53amqW3btiGEysvL8ZvNzc0ddBQ+wPyvv/5ib+Hnz5+CgoIODg6d\nDJ6lpKQEIfTrE/fs2bN+/Xr8/127dmlqauJvDZ1OV1FRSUhIYH9w59eU2zCZTPYrwDQ0NPCz2wIC\nAnhpPdYVYBwsZga6Z9myZVOmTOFUa/j3aMaMGdXV1fhA4ZEjR9JotJqamja3Re1toFp9H69cuWJk\nZBQREZGSkpKRkdFq9iM8F2TVsZs/f76IiAje5vnz59F/rxLFMMzLywshFB8fj9+8efMm696GhgZp\naWl+fv7Tp0+npaX5+flZWVnhL9RB++ybYk5NhUClUnl5eR89esSR1jAMc3R0/HVf0yX47HAIoU2b\nNoWHh587d27hwoUNDQ0ddBrW6X77+fNne4202oZj7e8Tu+3YsWPjxo3j4KWokPz1llZVZvCRxf2x\nykxFRYW4uHhmZmYHC+l0uqSkpI2NTUBAwJkzZ1hX2kZERJDJ5OHDh+NZUXZ2Nl4NS0FBYdWqVZaW\nllOnTr169SqdTm+vhQ5s2rSJl5d327Zte/bssbGxsbS0ZH2Z6XS6o6Ojpqbm7du3b9y4MX/+/Ly8\nvFZPr6+vnz17NutmREQEfnl/ZyQkJOBvKLshQ4b8/PkTf0BjY+OoUaNevXr126Zu3LgxduxYhJC1\ntXVcXBy+sL2OioyMxEtLaGlpPXv2jL2ddevWtcrJfissLGzFihUIIXFxcS8vL3zgNk5NTU1cXBy/\n6pnJZOLT6ly8ePHAgQN37txhb6Tza8oN2qy3wsvLC/VWuB/HizyvW7eOTCYrKSldu3bt0aNH/Pz8\ns2bN+vnzZ5vbojYXfvjwodX38cmTJ622DGZmZvg3KzIyEp+YasOGDcXFxQ8ePMDr9rm4uLx9+xa/\ntsPe3p5KpUZEROBlROfPn0+hUGJjY/HrEqytrfF5z9LS0liH3jU1NfHCBR2039zc3GpTzBHcWeS5\noKBg9uzZJBKJRCLNmDGDVfeqzU7DuthvbTbS5ja8G3u0jnG8yDMJa2fAKeC4oqIi9pFDFAqloaGB\nn59fSUkJHzWIjxySl5fntpG5np6eX7588fDw6GAhg8FgMpklJSUyMjLsD6uurubh4cEPheLKysqG\nDh2KzxLBPkqyvRbas3nz5lu3bjU1NeXn54uKirJXQGW9NIVCkZGRaW+cBJ1OZ79spaGhodU4624L\nCAi4f//+06dPe9JIex3Vpvr6eryMJUfU1tY2Nzezn8psaWkpLy//dcwyR9a0l5SVlSUnJ7MXUq6s\nrMTPULOP1mVN0Ay4nK2tbURExOfPn8XFxTnSII1GY22a2K9qanNb1JkNFD4Fy9SpU0tKSurr6+vq\n6h49eqStrY0XE+Gs3NxcEonUya0lamtT3BP+/v42NjZPnz5tc8Rz9zQ1NamoqMyePRs/8NkTVVVV\nTCbz1zF2Xe001Fa/dbKRru7ROhAWFjZ//vzY2Fj2wUs9BMkfYRgMRl5eHvs1g1xbZYbJZNra2u7d\nu5f9mtY2F3LW1q1b27vLwcHh2rVrePLX9y89YcKEDp779evX/fv3P3jwAM8se9JUT/TB67ZaU2K1\nV29FQUGB/Tp9ZWXlX38ngH6hpqZGT09PRUUlJCSkS/WY+kZCQsLChQvz8vLYY6uqqvL39+dsASbC\nZWVlTZw4cc2aNRcvXuRsyzdv3tyyZQuFQsGrXgOEEJPJnDRpkqSk5LNnzzjYLFztSxh8RGA3qsyM\nHz+eUz98O4mHh+f27dvbt2/fuHEjaxajNhdy1syZM9u7S0xMrL6+nsFgdOaoGMdfuoMn5ubmurm5\n3bp1i5UPdbupHurt1/11TfsSq94K62g61FsZ8IYNG/bgwYNZs2bZ2Nj4+vp248rNXpWSklJcXHzj\nxg0zMzNZWdmcnJz4+PiUlJQDBw4QHRonZWRkmJqaqqqq/vPPPxxv3N7e/vz583Z2dtHR0dz2/hLl\n5MmTqampt27d4myzcOSPq9FotIyMDPbzVniNe6KqzOTl5f16BLvNhb3t/v37u3bt+vHjx9atWzdu\n3Nh7x8+6qri4WEJCgttO3PeGvlzT9uqtiIiIjB8/nvVFgFRvMIiNjbWwsDAxMfH39+eGQ84sGIa5\nu7uHhIS8f/+eTCZra2uvXbv2jz/+GEiDClJTU2fPnq2kpBQaGtpLR9ApFIqBgYGTk5Obm1tvtN+/\nxMbGTp8+/eTJkxy/chySv36mqqoqKyuLdcAjNTUVv2BTUlKStf/T0NDQ09Pj4AgwLlRdXc366AoI\nCHDVPgD0EJPJTE9PZz+B+/379+bm5qFDh+rq6rJ/zvHC+mCwSUhIMDc3l5SU9PPzY03MwD26V0+O\n+/n4+Gzbts3IyOjp06e/XvHGQbdu3dqwYYOnp+fGjRt771W4X3p6+owZMwwMDIKDgzn+MxuSv36v\nsrKS/dDg58+fy8vLyWSyjIwM+8liLS2tVjMRAcANMAzLzs5mH/yamZnZ1NQ0ZMgQ9jxPU1NTVlaW\nC0d6AUIUFBSsXr3606dPHh4ef/zxB9HhDHA0Gs3R0dHX1/fgwYNHjhwhk3t9wNjff/99+PDhO3fu\n4NPmDkJZWVnTpk1TUlIKCwvrjVQbkr8BqKioiP2oyefPn+vq6vj4+JSVldn3pmpqarArBX2vuLj4\n06dPrI9oZmZmdXU1Ly+vqqoq+2Xv0tLSA/LwCeCUlpaWEydOHD9+fMaMGe7u7qyJcQEHYRh27949\nfNjivXv3ZsyY0Wcv7eLi4urqeurUqd6olc3lPnz4sGTJEkVFxX///beXxnRB8jcotKoyk5aWRqfT\n+0WVGdDftVdvRV1dnf2nCNRbAd3z+fPnbdu2ffjwwcHB4dixY4NhyvU+8+HDhx07diQkJDg6Oh47\ndkxUVLSPA7h69aqTk9OqVavw6Sv7+NWJcufOHQcHB0tLSx8fn94bvgXJ32DUXpWZYcOGKSsrs59o\nw6tfAtBJtbW1nz9/hnoroI+FhIRs3769uLh4xYoVBw4cwOekBt3T0tJy//79ixcv4mMrL168yJra\nu++Fh4dbW1tLSkreunWLU1PJca3y8vLt27f7+fkdPHjwr7/+6tVjMZD8AYQQam5uzsjIYN9nc0mV\nGcDN2qu3MmbMGG1tbdYnR0tLC59LA4DeQ6PRrl+/fu7cudLS0hUrVuzYsWPixIlEB9XPNDY2Pn78\n+MyZM0lJSSYmJvv37zc3Nyf8dFBeXp6Dg0N4ePjOnTuPHj3aN6Ut+hiGYX5+fs7OzkOGDPHy8poz\nZ05vvyIkf6BtjY2NmZmZ7CeL2avMsE4W6+jocKpkPOByjY2Nqamp7Cdwod4K4DaNjY137tz5559/\nvn//rquru3HjRltbWzjM/Fvfvn3z8vLy8fGpqKiwsLDYv3+/sbEx0UH9HwzDvL29d+/eLSAgcOTI\nkY0bN/bBRSd9JiYmZs+ePfHx8Q4ODqdPn+6bXSokf6CzqqurMzMzocrMIAH1VkD/hWFYdHS0t7f3\n48ePMQxbvnz58uXLzczMBs+4sU4qLi4OCgp6+PBhdHS0vLy8vb29vb29rKws0XG17efPn66urleu\nXJGXlz9w4MDKlSv7+0Dh2NjYkydPhoSEmJqa/vPPP703XdavIPkD3deqykxycnJZWRkvL6+srCz7\nyWJNTU3Y5nK5TtZb0dfXh1QP9CO1tbWPHj3y9fWNiIgQFBS0sLBYunSpubn5ID9fQaVSnzx58uTJ\nk/fv348YMWLRokV2dnbTpk0j/AxvZ1CpVBcXl4cPH4qLizs5OTk4OPS7USUtLS1BQUFnz56NjY01\nNDQ8evSoubl5H8cAyR/gpPaqzEhLS7NPSQJVZgjXyXorMjIyA+n0Chi06uvrX79+HRAQ8OTJk9ra\nWgUFBbP/GiSjFCgUyrt378LDw6Ojo3/8+KGoqLh8+fIFCxZMmTKFh4eH6Oi6rKys7NatW5cuXSop\nKZk5c+aaNWuWLVvWq6Wnew7DsHfv3t29ezcgIIBOp9vZ2W3ZsoWo6akg+QO9i5UO4ieLf60yg/9V\nV1fvjxug/qK0tDQlJaVVvRUeHh41NTWotwIGlcrKylevXr1+/To8PJxKpQoICBgYGBgZGRkZGRkY\nGIwbN47oADmGwWBQKJS4uLi4uLj379+np6eTyWQDAwM85Z0yZcoA+AVeX1//+PHj27dvR0ZGjho1\navHixQsXLjQ1NeWqOZ8wDEtMTAwODn7y5MmXL1+UlZXt7e3t7OykpaUJjAqSP9CnoMpMH2Cvt4Ln\n3JWVlQjqrQDwv6hU6uvXr6OiouLj479//44QGjt2rIGBgb6+vrq6ura2toKCQj/KkOrq6vBvfWpq\n6sePHxMSEurq6gQEBHR1dQ0NDU1NTadPnz5Qv/J5eXl3794NDAxMTEwUEhKaPXu2qanptGnTtLS0\niDqsUFRUFB0dHRkZGRoaWlBQICUltWjRIltbWy65kgaSP0Cw9qrMDB8+HB8viGcq2traY8aMITpY\nblRfX5+YmPhraT1WvRX8BK6iomK/GxkDQJ+pqKiIj4+Pi4uLj4//8uVLfn4+QoiPjw/fCikrK8vL\nyysoKMjJyUlJSRF+mqKhoSEnJycnJyc7Ozs7Ozs9PZ1CoeTk5GAYxsfHp6qqiid8BgYGEyZMGFQz\n5eTm5j59+jQoKOjDhw90On3EiBHGxsaGhobjx48fP368nJxc7710VVVVcnJySkpKQkJCTExMVlYW\niUTS0NCYP3/+kiVLDAwMCP/YsIPkD3Cdpqam79+/Q5WZNkG9FQD6QElJyYYNG8LCwhYtWlRfX5+V\nlZWXl9fc3IwQEhAQkJWVlZaWlpSUFBMTk5KSGjNmjLi4uJSUlOh/9fDVGQxGdXV1dXV1aWlpaWlp\nSUlJSUlJaWlpUVFRaWlpdnZ2cXExvu8eNmyYnJwca6gunqcOqmyvPU1NTZ8+fYqJiYmJiUlMTCws\nLEQIDR8+XEtLS1FRUV5eHs/mpaWlxcTEulShorm5uby8vKSkhEqlZmdn43/T09Nzc3MRQvimeMqU\nKSYmJsbGxiNHjuytNewZSP5AP8CqMsM6lYkf3BrwVWbY663ExcXl5eXh9VaEhIQmTJgA9VYA6A0p\nKSlWVlY1NTW+vr4zZ87EF7a0tBQWFrIOtuXn55eWlv748aO4uLisrKyxsZG9hREjRgwbNgxPBAUE\nBDrzoni2R6PRampq6urq2O8SERGRkpISExOTkJCQkJCQlZWVl5eXk5OTk5MbNWoUh1Z6gKuoqEhO\nTv7y5QuFQsHfwby8vKamJvzeoUOH4t3bwQGFhoaG8vLy0tLSiooK1kIpKSk8j1RRURk/fry2tnZ/\nmSUVkj/QL7VZZQYhJCkpyX70q39VmWHVW2Ed9WTVW1FRUUlNTVVVVd25c+f8+fMh1QOgl3h5eTk5\nOU2cOPHhw4djx47t5LMqKip+/PhR/V9VVVVVVVU1NTXV1dV4htHS0lJTU9PqWby8vKxBeIGBgZqa\nmosXL2YdPhw+fPioUaPGjBnDVZcvDBhMJrOwsLCwsLCsrOzmzZsfP360sbGpra1FCNXU1LS0tLAe\nKSoqysPDw8/PLyYmNnr0aAkJidGjR4uLi8vJyfWj/UsrkPyBAaJVlZnk5OTa2loymSwjI8O1VWao\nVCp7Cvv9+/eamhq83gp7CovXW0lISNi2bVtCQsKWLVv++uuvgTpwGwCi1NfXb968+f79+4cPHz50\n6FAfFznas2ePv79/Tk5OvzhuNMAoKSktWrTo7NmzRAfSdyD5AwMWt1WZ6WS9FRUVlfaG7GAYdvfu\n3d27d5PJ5JMnT65Zswb2EwBwRFpa2vLly0tKSnx8fCwtLfs+gMTERH19/ZiYGC65GnTwwHs+Li7O\nwMCA6Fj6DiR/YLBoVWUGn6GusbGxl6rMtFlvhUQiyX9Oa00AACAASURBVMvLs7+WiopKVy9bqays\ndHFxuXz5srGxsYeHh7a2ds+jBWAw8/Pz27hxo4aGhr+/v4yMDFFhaGpqzpw508PDg6gABqd9+/YF\nBgbitX4GD0j+wODVQZUZRUVF1snizlSZaa/eioSEhJaWVm/UW0lKStq2bVt8fPzWrVuPHz/e8wsM\nARiEmpqatm/ffv36dScnp9OnT3fy4oxecvz4cQ8Pj8LCQrhct89gGCYrK2tra+vm5kZ0LH0Kkj8A\n/k9FRQVeSOXLly9paWlfvnypqKjAD9fhl4/glfPU1NSKi4vxYqoUCgVP+BobGwUEBDQ0NDQ0NLS0\ntDQ1NbW0tOTk5HrvzCx+Fnjv3r0kEunUqVNwFhiALikpKbGxsYmPj7906dL69euJDgdlZWUpKyuH\nhYXNmzeP6FgGiw8fPkyePDkpKYmoadaIAskfAB1hT/JSU1PT0tJYl+zx8fEpKyvjSR7+V0lJqe+v\nJqmqqjp69OiVK1d0dXUvX748adKkPg4AgP4oJibG2tp66NChjx494p4dv6GhoZqamo+PD9GBDBY7\nd+588eJFWloa0YH0NUj+AOia3NxcKpU6atQoNTU17pkJNzk5edu2bbGxsba2tufOnRs9ejTREQHA\npZhM5vHjx11dXZctW3bz5k1hYWGiI/o/Fy5cOHz4cElJyQArWcqdWlpapKWlN27ceOzYMaJj6WuQ\n/AEwQOBngfft29fU1HTkyJHt27dz1WxCAHCDyspKOzu7Fy9eeHh4ODg4EB1OayUlJePGjfP19bW2\ntiY6loEvOjp6+vTp6enpampqRMfS1yD5A2BAqaur++eff9zc3LS1tT08PIyMjIiOCABukZKSsmzZ\nsrq6Oj8/PxMTE6LDaducOXOEhISePHlCdCADn6Oj49u3b1NSUogOhABwYACAAUVISMjFxSUlJWXU\nqFHGxsZ2dnb43CcADHI3b940NDSUkZH5/Pkz12Z+CKGVK1eGhYWxzyEGegODwQgICFixYgXRgRAD\nkj8ABiBVVdUXL148ffo0KipKVVX1woUL7LMVATCoNDU1bdq0acOGDc7Ozi9evBAXFyc6oo4sXryY\nRCIFBQURHcgAFxERUV5ebmNjQ3QgxIDTvgAMZPhZ4JMnT2pqanp4eEyePJnoiADoUzk5OcuXL8/M\nzLx9+/bixYuJDqdTli1bVllZ+ebNG6IDGcjWr1+flJSUmJhIdCDEgCN/AAxk+FngL1++iIuL42eB\nS0tLiQ4KgD4SGxs7ZcqUysrKiIiI/pL5IYTWrVsXGRmZlZVFdCADFo1G8/PzW7t2LdGBEAaSPwAG\nPmVl5efPnwcFBUVHR8NZYDBIeHl5zZw5U0dH59OnT7q6ukSH0wVz586VlJS8c+cO0YEMWI8ePWIw\nGLa2tkQHQhhI/gAYLCwtLdPS0pydnfft2zdx4sR3794RHREAvaK+vt7W1nbz5s3Hjx8PCwsbMWIE\n0RF1DZlMXr16tY+PD5PJJDqWgcnb23vBggUjR44kOhDCQPIHwCAydOhQFxeX1NRUKSkpExMTOzu7\nHz9+EB0UAJyUm5s7derU0NDQ4ODgffv29dM5D+3t7XNzcyMjI4kOZADKzMyMiYmxt7cnOhAiQfIH\nwKCjpKQUGhoaFBQUExOjqqp66tSppqYmooMCgAMiIiImTpzY0NAQHx8/f/58osPpPg0NDQMDg9u3\nbxMdyADk4+MzZswYc3NzogMhEiR/AAxSlpaWFAplx44dLi4u48ePf/XqFdERAdAjp06dmjNnjrGx\ncVxcnIqKCtHh9NQff/zx+PFj1mTigCOYTOa9e/dsbGzIZDLRsRAJkj8ABi9BQUH8LLCysvKcOXMs\nLS3z8/OJDgqALqPT6WvWrDl48KCrq+uTJ09ERESIjogDVq1ahWFYQEAA0YEMKC9fvszJyRnM1/ni\noM4fAAAhhMLDw7dv356fn7979+6DBw/y8/MTHREAnZKXl7dkyZLMzMx79+5ZWloSHQ4n2djYFBUV\nRUdHEx3IwLFw4cLq6uqoqCiiAyEYHPkDACCEkJmZWXJy8okTJ86ePautrf3ixQuiIwLg9xITE01M\nTMrKyt68eTPAMj+E0B9//BETE5OZmUl0IANETk5OaGjoli1biA6EeJD8AQD+P35+fmdn5/T0dEND\nw3nz5llaWubm5hIdFADtevz48bRp02RlZT99+qSvr090OJw3Z86ccePGQcE/Trl586aYmNjSpUuJ\nDoR4kPwBAP4HvrN5/fo1lUrV0NBwcXFpbGwkOigA/geTydy/f7+VldW2bdsiIiK4fLrebuPh4bG1\ntb1z5w4U/Ou55ubmGzdurF27Fsa0IBjzBwBoT3Nz85UrVw4fPjxmzJgLFy5YWFgQHREACCFUX19v\nZ2cXHBzs4eHh4OBAdDi9KyMjQ01N7dWrV6ampkTH0r89evTIxsaGSqXKyMgQHQvxIPkDAHSkqKho\n//79d+/eXbBgwaVLl+Tk5IiOCAxqeXl5CxcuLCoqevz4sYmJCdHh9AVDQ0MVFZW7d+8SHUj/Nnfu\nXBKJ9O+//xIdCFeA074AgI5ISUnduXMnIiIiJycHPwvc0NBAdFBgkPr48aORkVFTU9P79+8HSeaH\nENq6dau/vz9MxtMTycnJL1++3LFjB9GBcAtI/gAAvzdjxozExEQ3Nzd3d3ctLa1nz54RHREYdHx9\nfadNm6avrx8XF6eoqEh0OH3H2tpaRETE29ub6ED6sQsXLowfP37evHlEB8ItIPkDAHQKHx+fs7Pz\n169fp0yZsnDhQktLSyqVSnRQYFDAMGz//v2rV692dnZ++vTpwKjh3HmCgoL29vZXr15taWkhOpZ+\nqaSkxNfXd9u2bUQHwkUg+QMAdIGkpOSdO3ciIyNzc3O1tLT2799fW1tLdFBgIKPT6TY2NufOnfP0\n9Dx58iQvLy/RERFgy5Yt+fn5L1++JDqQfunq1avDhw+3s7MjOhAuAhd8AAC6g8FgXL58+ejRoyIi\nIidOnIANK+gNlZWVS5cuTUxMfPjwobm5OdHhEMnU1FRYWDgoKIjoQPoZOp0uKyu7ZcuWY8eOER0L\nF4EjfwCA7iCTyfhZYAsLi7Vr15qZmaWnpxMdFBhQ8vLyTExMqFRqbGzsIM/8EEKbN28ODQ2Fuutd\n5evrS6PRHB0diQ6Eu0DyBwDoPgkJCU9Pzw8fPtBoNB0dHWdnZxqNRnRQYCB49+6dnp6eqKhoYmKi\npqYm0eEQb8mSJWPGjLl58ybRgfQnGIZduHDB2tp6oJYB7zZI/gAAPTVp0qT379/fuHHD19dXXV0d\nZqMCPRQQEGBmZjZlypSXL1+OGjWKU81SqdR169YVFBRwqsG+RCaT165d6+Xl1dzc3Huv0q+76FeP\nHj1KT08/fPgw0YFwHUj+AAAcwMPDY2dn9+3bt2XLlq1du3bWrFkUCoXooEC/dPny5ZUrV65bt+7J\nkydCQkIcbDkxMdHb2/vLly8cbLMvOTg4lJWV9eqwv/7eReyYTOaxY8esrKyUlJSIjoX7YAAAwFGf\nPn0yMjLi4+NzcnKqrq7mSJs+Pj4caQdws5aWFicnJx4envPnz/fSS5SVlfVSy5zV3gd+/vz5pqam\nvfrS/b2LWJ4+fcrDw0OhUIgKgJtB8gcA4LyWlhYfHx8xMTEpKSkfHx8mk9mT1l6/fi0lJcWp2AB3\nqq+vX7Zs2ZAhQwICAoiOhWAdfOBDQkJIJNK3b9/6OCRu89ttApPJ1NXVXbp0KVEBcDleFxcXog8+\nAgAGGhKJpKOjs2HDhtLS0kOHDkVEROjr648ZM6YbTUVERCxevLi5uXnkyJHFxcWqqqoIoYyMjNDQ\n0Lt379bV1amrqyOEQkJCnj9/npqaqqenR6PRbty4ERsbixcjxNvBMCwqKurp06cfP36sqakZVFNE\ncL/y8nJzc/P4+Pjg4OD58+e3+Zhf33TU/tva5nImkxkZGVleXj527Fj8YbW1tTdv3gwMDMzLy+Pj\n4xs1ahQPDw9CKD8///bt2wYGBhQKxcvLKzc3V1tbm0Qi4c+i0+mPHz9WUFAoLCz09fUtLCxUVlbm\n4eH58ePHgwcPPn/+rKSkJCAggD+4qKgoICAgJCSEwWAoKCjgCztov80PPIuSkpKPj099ff3cuXM7\n2UXt9UYPu6jjtehSF3W1lzruIty///577tw5X19fCQkJ1sLa2lp/f/+AgIDy8vJx48YNGTKEgwG0\n2QJCqLKy0tvbe9KkSc+fPw8MDDQyMiKRSMRviwhNPQEAA19iYuKUKVPIZLKTk1NVVVVXn56UlGRs\nbCwmJhYREZGUlIRhmLu7+4wZM5hMZnZ2tpyc3JUrV/BHampqjhs3Dv+/pqZm2LBhkydPZrVz8OBB\nLy8vDMM+fvxoYGDAgRUDHIJPGy0jI5OamtreY9p709t7W39dTqFQli9fjhC6evUq/piKigoVFZXo\n6Oja2tolS5YghCZNmrRjx47g4GAxMTGEkLu7+9q1axcsWIAQ+vvvv/FnRUZGKisrI4TOnj3r4OCw\nd+/eoUOHLlu2zMvLy9bW1sbGhkQiWVpa4g9+8+bNxo0bExMT/f39hYWFt27dimFYx+3/+oFvxcXF\nZfTo0XQ6vZNd1F4v9aSLOl6LLnVRN3rpt12EYZiJicncuXPZl6Snp1tYWCQnJzc3N69cuXLUqFFZ\nWVmcCqDNFjAMu3379tChQ8lk8qVLl3R0dBBCycnJ3LAtguQPANDrmEymj4+PuLi4hIREN84CL168\nWFpamnVTSUnJ0dGRdZeFhQX+//Lly1nJH4Zhenp6rOSPyWSOHj06IiICv+nq6trdVQEc9unTJwkJ\nCV1d3aKiog4e1uab3t7b2t7ylJQU9szmwIEDsrKy+P8JCQn4bh6/uX//foRQeHg4flNPT09fX58V\nzLlz5xBCrNPT+IMfP36M3/zzzz8FBARaWlpoNJqCgkJtbS2+fP369Qih9+/f/7b9Vh/4VoqKivj5\n+W/evNmZLmqvN3reRR2vRSe7CMOw7vVSx13077//IoTevXvHWsJgMCZMmHD9+nXWuvDz84eEhHAk\ngA5awDDM1tYWIRQYGIhhWHp6Opdsi+BqXwBAryORSPi1wNbW1uvWrZs+fTq+j+lSC6z/IyMjXV1d\nEUJpaWn5+fnfv3/vzNNVVVVXrFiBXym5e/fuLq4B6BWhoaEzZszQ1dWNjo6WlJTs4JFtvuntva3t\nLWc/z4gQysrKKisra2pqQgjp6OgICQnl5+fjdwkKCiKE1NTU8JsaGhp5eXmsJ4qKiiKEtLW18Zv4\nWT/8uA7+rMbGxqKiogcPHtDp9L179zo6Ojo6OhYXFysqKmZmZv62ffS/H/hWJCUlbW1tT58+jf3v\nBF3tfS/a7I2ed1HHa9HJLkIIdbuX2uuilpaWPXv2LF26dMqUKayFYWFhnz9/Zo0owAeH4Afzeh5A\nBy0ghKSkpBBCixYtwpvikm0RJH8AgD4yfPjwCxcuJCQkYBimq6trZ2f38+fPTj6XfUM/duzY+Ph4\nJyen9PR0RUVFJpPZmRY8PDyGDRu2ePFiMzOzqqqq7qwA4Khr164tWrRo5cqVwcHBwsLCHT+4vTe9\nvbe1M2/3zJkz6+vrY2JiEEKVlZVNTU2zZ89u85G8vLxY+1Ohsg8dQwjx8fEhhOrq6igUiqSk5OX/\nCg0NzczMXL16dWfa7yD5Qwj95z//ycjIaDXVbwffizZ7g7Nd1OZasLTXRQihbvdSe11079699PR0\nNzc39oXJyclCQkL4aVwcPz8//k/PA+i4BXyIJGugJOKObREkfwCAPqWjoxMdHe3t7f3q1SsVFZUL\nFy50Jntj39AfPnzY1dX11KlTy5Yt4+Xl7eTrTpgwITExcevWrZGRkXp6ehUVFd1cAcAJJ0+e3Lp1\n644dO65du0Ymk3/7+Pbe9Pbe1s683Rs2bNi1a9fmzZsDAgKOHDni5uY2b948Tq0gQoiXl/fbt2/d\nq8nccfKnpaU1a9Ysd3d39oUdfC/a7A1u6CLUg15qs4saGxuPHj26du1aFRUV9uVMJrOuri4iIqI3\nAuhqC9ywLYLkDwDQ1/CzwJmZmdu3b9+7d6+hoWFcXFzHj29pacH/z87OdnV1Xb16NX5Ghj1xJJPJ\nDQ0NbbbQ2Nh49+5dERER/Hd5cXFxYGAg51YIdAGTyXRycvrzzz/Pnz9/5swZ9iMi7WnvTW/vbe3k\n200mkyUlJb29vcePH+/u7r5r1y6OrijS0dGpq6u7du0aa0lVVdWVK1d++0T2D3x7du7c+fLly7S0\nNPxmB9+LNnuDS7oIdbeX2uuiy5cvl5WV/VrGBD8B7evry1ry8+fPJ0+ecCSALrXAJdsiSP4AAMQQ\nEhJycXFJSUkZMWLElClT7OzsysrK2nykpKRkSUkJlUrNysr68eMHQujBgwc1NTVv376Njo6urKys\nra2l0Whz5swpLy/39vauq6vz9vb++fMnlUqtrKxECGEYdu3aNfyUzZw5c0aPHj169Oi+XFmAo9Pp\nS5cuvXHjxtOnT52cnDr5rNraWtTWm15TU9Pm29re293Y2IgQKi8vx5u9evXqo0ePmpubm5qa8vLy\n2KelrqmpQQjhY93wpzQ2NrJO+eGPxFtjhcc6foOfzWxsbFyxYoW0tPTu3bv/+eef9PR0f39/BweH\nNWvW/LZ99g883tqvLCws1NTULly40HEX0Wi0Nnuj513U8Vp0sosQQt3rpTa7qLKy0tXV1cnJCR9m\nx27hwoW6uro+Pj6bN29+/fq1u7v7unXrLCwsOBLAggUL2muBtbKsIS7csi3qw4tLAACgbcHBwTIy\nMiNHjjx//jyDwWh1b0REBJlMHj58+MWLFzEMW7duHZlMVlJSunbt2qNHj/j5+WfNmvXz508ajWZk\nZIQQUldXDwwMXLp06dy5c/GSCnQ6XVJS0sbGJiAg4MyZM0eOHCFgJQe96urqGTNmiIqKRkVFdfW5\nbb7phYWFbb6tbb7dHz58wOuYaGlpPXv2DMOwX6ePMzMzKy4ujoyMxOu0bdiwobi4+MGDB8OGDUMI\nubi4NDc3x8bG4hcu2NvbU6nUiIgIPT09hND8+fMpFEpsbCz+IbS2ts7IyEhLS2Odf9TU1ExMTMQw\nrOP2sV8+8O25ePGikJBQeXl5B1308+fPNnujh13U8Vq8ffu2812EYVg3eqnNLjp06NDw4cNZHdJK\nQUHB7NmzSSQSiUSaMWNGQUEB666eB9BmCxiG3bhxA6+YaG1tHRcX117P9z0S1v4gVgAA6DN1dXX/\n/PPPyZMnNTU1PTw8Jk+ezH5vdXU1Dw+PiIgIfpNGo7H+b2xsZL9EsaysDB/W3dDQwD7MnMFgMJnM\nkpISGRmZXl8Z8IvS0lILC4uSkpJ///2XVXm7S9p809t7Wzvzdr969aqwsHDq1KklJSX19fV1dXWP\nHj3S1tbGC3xwUG5uLolE6tIHr9UHvk319fVycnKbNm3666+/8CXtfS/a7A2u6iLU9V5q1UXfv38f\nP378iRMn/vOf/3TwrKqqKiaTOXLkSI4H0PkWuGFbBMkfAICLZGRkODs7v3jxYvXq1WfOnBEXFyc6\nIsABmZmZc+fO5eHhefnypby8PNHhIIRQQkLCwoUL8/Ly2K+NqKqqws/ZERhYl/z111/u7u65ubkd\np4nd04+6CMOwGTNm0Gi0jx8/dv4isMEMxvwBALiIiorK8+fPg4KCoqOjVVVVL1y48NuR74DLJScn\nm5iYDBs27O3bt1yS+SGEUlJSiouLb9y4kZWVxWAwMjMzfX19T548uWLFCqJD6wJHR0cGg3H9+vXe\naLwfddHDhw9jYmI8PT0h8+skOPIHAOBG9fX1p0+fPnnypLq6uoeHh7GxMdERge6IiYmxtLTU1tYO\nDg4ePnw40eH8HwzD3N3dQ0JC3r9/TyaTtbW1165d+8cff7DKv/UX//nPf/z9/alUKscj7y9dVF1d\nraamtmDBAi8vL6Jj6Tcg+QMAcK/MzExnZ+fnz5+vXr36n3/+GTNmDNERgS4IDQ21traeN2+er69v\nq6kjuEdzczNec7ifKigoUFRUvHLlCj6rWG/g8i7auXPnvXv3vn371uZIPtAmOO0LAOBeSkpKoaGh\nQUFBMTExeGELBoNBdFCgU27duoVP4OHv78+1mR/672wT/de4ceOsrKzOnTvXyaluuoGbuyg1NfXy\n5cvHjh2DzK9L4MgfAKAfoNPpp06dOnXqlJyc3MWLFzuYYwpwgzNnzuzdu3f37t2nTp3qeLIK0HOp\nqak6OjoPHz60srIiOpY+xWAwjIyMyGTyu3fvYLRfl8CRPwBAPyAoKOji4pKamqqkpDRnzhxLS0v2\nCeYB98AwbP/+/Xv37nV3dz99+jRkfn1AS0tr5cqVf/7552A7Lu7i4vLt2zdfX1/I/LoKkj8AQL+h\nqKgYEhISHByclpamrq7u4uLCKr4PuAGDwVi7du3Zs2dv377t7OxMdDiDyNGjR7Ozsx8+fEh0IH0n\nLi7u1KlTJ06cwEsxgy6B074AgP6nqanp6tWrhw4dkpKSunjx4ty5c4mOCCA6nb5y5cqXL18+fPhw\n4cKFRIcz6Njb28fGxqanp5PJZKJj6XUNDQ0GBgbDhg2LioqCw37dAEf+AAD9Dz8/v7Ozc3p6uqGh\n4bx58ywtLXNzc4kOalCj0+lWVlbh4eGBgYGQ+RHiyJEjOTk5vr6+RAfSF/bu3Zudne3t7Q2ZX/fA\nkT8AQP/25s2b7du35+Tk7Nmz58CBA9x8YelARaPRFi5c+Pnz57CwsFbz8oG+tH79+qioqK9fv/6/\n9u48EKq9/wP4mTHDJVSKkNKVdmVr1YIoEtpdLUiLFl3atV7qlrTdyFJJKW4l3ZQlklB0yY2iErqE\nFCpUdmb7/THPz+NpLzNzzMz79Zc5M77fN/c8j0/fz/meI9qLfxcvXrS1tQ0PDxe3DS48hOIPAIQe\ng8EICAjYuXOnsrKyj4/PtGnTyE4kRt68eWNmZvb69euEhIQhQ4aQHUesFRYWDhkyJDg4eNGiRWRn\n4ZfS0lIdHZ05c+YEBQWRnUWIofgDABHx8uXLrVu3hoaGWlpa+vr69uvXj+xEou/FixdTpkxhMBg3\nb97EL7wzWLZsWXJycl5eXmd7DgdPsFisqVOnlpWVZWVl8eNxxuID1/wBgIjo3bt3SEhIUlJScXHx\n0KFDPTw8mpubyQ4lyoqKiiZOnEij0VJTU1H5dRKenp5v3rzx9fUlOwhfbN68OT09/eLFi6j8OgjF\nHwCIFGNj4wcPHuzbt+/IkSNaWlrXrl0jO5Foevz48cSJE7t165aYmKiiokJ2HPgPJSWltWvX7t27\nt6amhuwsPHby5MkjR46cP39eV1eX7CxCD8UfAIgaOp3u6uqan59vYGBgZWVlZWX17NkzskOJlOzs\nbBMTEw0NjeTkZCUlJbLjwP/YvHmzpKTkwYMHyQ7CS6mpqc7Oztu2bZs5cybZWUQBij8AEE0qKioh\nISG3bt0qLS3V0tLasmVLQ0MD2aFEQVpamrGxsY6Ozo0bN7p160Z2HPiQrKzsjh07vL29ReYpOC9f\nvrSxsTEzM9u9ezfZWUQENnwAgIhjMpn+/v7u7u5ycnJ79+61t7cnO5EQS0hImDVrlpmZ2YULF0Ry\nS4FoYDAYQ4cONTIyOnnyJNlZOqqlpcXIyKi2tvbu3bu41I9XsPIHACKORqNxu8DGxsaLFy82NTXN\ny8sjO5RQunr1qpWV1dy5c8PDw1H5dWZ0Ot3d3f3s2bNPnz4lO0tHOTk55efnR0REoPLjIRR/ACAW\nlJWVQ0JCMjIyamtrtbW1XV1d6+vryQ4lTMLDw21sbFasWIHHKgiFBQsWDB8+fNOmTWQH6RB/f/9z\n586FhYUNGjSI7CwiBcUfAIiRUaNG3b17Nygo6Pz584MHDw4JCSE7kXA4derUggUL1q5d6+3tTaFQ\nyI4DX0elUv38/KKjo+Pi4sjO8oMSExPXr1+/detWPLyb53DNHwCIo5qaml27dvn5+RkaGvr6+g4b\nNozsRJ3XkSNHNmzYsG/fPjc3N7KzwPextbXNycl5+PAhnU4nO8v3SUtLmzJliq2tbVBQEP69wXNY\n+QMAcaSgoODj4/PPP/80NTXp6uq6urrW1dWRHaoz8vHx2bBhg6enJyo/YXTw4MHnz58fP36c7CDf\nJz8/39ra2tDQ8Pjx46j8+AErfwAg1ths9p9//rlx40Y6nb5v3z47Ozv8sWmzf//+rVu3+vj4/Prr\nr2RngR+0ffv2Y8eO/fvvvz169CA7yzcpKyszMDDo27dvQkKCjIwM2XFEE4o/AADi7du3Hh4e/v7+\nEyZM8PPz09LSIjsR+TZv3nzkyJGQkJD58+eTnQV+XH19/cCBA21sbLy9vcnO8nVv376dNGkSk8m8\nc+eOsFSrwghtXwAAonv37twucGtrK7cLXFtbS3Yo0rDZ7FWrVnl7e587dw6Vn7CTlZX97bffuIt/\nZGf5iubm5lmzZlVXV8fGxqLy4yus/AEA/BeHwwkNDd20aZOEhISXl5cYdoHZbPbKlStDQkIuXbpk\nZWVFdhzgARaLpaen17t379jYWLKzfBaLxZo9e3ZKSkpKSsrw4cPJjiPisPIHAPBfFArF3t6+oKBg\n3rx5S5YsMTQ0fPjwIdmhBIfJZC5atCg0NDQiIgKVn8iQkJAICgqKj48PDw8nO8tnrV+/Pi4u7sKF\nC6j8BADFHwDAh7p16+bj45OZmclms3V1de3t7aurq8kOxXcMBmPhwoVXr169evWqhYUF2XGAl0aN\nGuXg4LBhw4bOeW/zLVu2+Pv7nzlzxtzcnOwsYgHFHwDAp+no6KSmpgYHB9+4cWPQoEE+Pj5sNpvs\nUPzCvdwqLi7u+vXruKeuSPLy8mpoaNi9e3f7gwwGg6w8bdavX3/48OGwsLAFCxaQnUVcoPgDAPis\nti7wsmXLNm/ePGbMmIyMDLJD8V5TU9OsWbNSwZaDZwAAIABJREFUU1NjY2MnTZpEdhzgCyUlpd27\ndx85cuTRo0cEQTQ1NW3btk1eXv7UqVMCy9DY2PjBEQ8PDx8fn1OnTs2dO1dgMQDFHwDAV3Tt2tXL\ny+vhw4fdu3c3MDCwt7evqqoiO9QPevjwobS09JIlS9pWMevq6szNzf/555/ExMQJEyaQGw/4atWq\nVVpaWs7OztevXx8wYMCBAwdaWlouX74smNlNTEwUFRXv3bvXdsTT0/P3338PCgqyt7cXTAb4Dw4A\nAHyzqKioPn36KCgoeHt7M5nMjz/w+vXru3fvCj7YN5ozZ46EhASVSnV0dGSxWO/fvx8/fnzPnj2z\nsrLIjgaCEBYWpqCgQBAElfqf1R85OTkWi8XveW/dukUQBIVCkZeXf/ToEYfD2b9/P4VCOXnyJL+n\nho+h+AMA+D719fXu7u6SkpJ6enppaWnt32Kz2ePGjSMIIigoiKx4X/Dw4cO2O9dISEhYW1vr6ekp\nKipmZ2eTHQ34jslkenl5SUlJffyc3ydPnvB1au7GKQkJCYIgaDRat27dduzYQaFQvL29+TovfA6K\nPwCAH1FQUGBmZkahUOzs7F69esU9GBoayq2uaDTa7du3yU34MRsbm/Z/+CUkJJSUlLDmJw7y8vJG\njhz5yZtWSkhIBAcH83X2qKio9jPS6XQZGRlXV1e+TgpfgGv+AAB+xMCBA69fvx4ZGZmSkjJ48GAf\nH5+qqioXFxfuuxwOx8LCIjc3l9yQ7RUUFPz111/td3eyWKzq6mrR3sUMXCEhIZmZmZxPPdaBSqWm\npaXxb2o2m71582bush8Xg8FgMBhXrlypqKjg37zwBSj+AAB+nJWVVU5Ojr29/caNGw0NDevr67l/\nX1ksVktLy7Rp0zrP1hBPT8/2f4C5WCzWuXPn2u//AJG0fv16IyOjtuv82mMwGImJifyb+q+//srP\nz2exWB9MWlFRYWxsLA530OyE8Hg3AAAeuHTp0i+//PLB/6PS6fRRo0YlJydLSkqSFYzr+fPnGhoa\nH/wBbm/37t07d+4UZCQQMCaTuWPHDu42iw9OVAqFUlVVxd0IwltsNnvw4MFFRUWf/NcFjUYbOXJk\neno6z+eFL8PKHwBAR3E4nIMHD368rsZgMDIyMlatWkVKqva8vLw+uepDEASdTqfRaKqqqgKOBAJG\no9G8vLzOnz8vKSlJo9E+eJdPFdj58+cLCws/WflRqVQ2m93a2sqPeeHLUPwBAHRUaGhoVlYWk8n8\n+C0WixUcHOzv7y/4VG1KS0sDAwM/fpYDjUaTlZXdtm1beXn50qVLSckGAjZ//vyMjAxlZeX2W3/o\ndDo/LvtrbW11c3P7+DidTqdQKFOnTo2Pj29/2z8QGBR/AAAd0tTUtGHDhi9cM8fhcNauXXv79m1B\npmrv4MGDHyz70Wi0nj17Hjp0qLy83MPDQ1FRkaxsIHja2trZ2dkTJ05sOytaW1v5cX6eOnWqsrKy\nfYuZTqfT6XQHB4cHDx7ExcWZmpp+bkEa+ArX/AEAdAiHw9HV1c3JySEIgk6ns1isjwtBKpUqJyeX\nlZXVv39/AccrLy/v168fd9mPQqFQKBQlJaVdu3Y5ODhISUkJOAx0HiwWa/v27QcOHCAIgsPhSElJ\n1dXVfXwLwB/W2Niorq5eXV3N4XC4J56cnNzatWtXrVrVq1cvXs0CP+bDrj8AAHwXCoWSnZ1dVVWV\nlZWVmZmZlZWVkZFRXl5OEISUlBSTyeSWg9ynqGVmZnbt2lWQ8Y4ePcpkMiUkJDgcjqqq6pYtW5Ys\nWSItLS3IDNAJSUhIeHl5DR48eMWKFUwms6Wl5dGjR3p6erwaPygoqKqqikqlUigUVVXVdevWLVu2\nTF5enlfjQ0dg5Q8AREpDQ8O5c+fITkHU1tY+f/68pKSktLT02bNn9fX13OMTJkyws7MTWIzm5uZN\nmza1trb26tXLwsJi1KhRH+9K6bSUlZWtra15MtT58+fb/hPAB0pLS/39/d+/f29ra2tsbMyrYT08\nPCoqKtTV1c3MzHR1dcW8vTtixIixY8eSneK/UPwBgEiJi4uzsLAgOwXwQLdu3aqqqjperTY2Nnbt\n2vWT23EABGPmzJlXrlwhO8V/iXUlDgCix8zM7OzZsz179lRVVT179qzgn5sEHVFbW+vi4kKn08eO\nHZuYmMiTdUoZGZl//vnHyclJWlpaVlbWycnp8ePHZP+gIMqYTGZ4ePj48eMJgujbt6+Xl1dgYGDH\nz2QewsofAIigmpqaXbt2+fn5GRoa+vn5DR06lOxE8HUhISFbt25tbW09fPjwokWLeN4ofPPmzenT\npwMCAsrKykxMTJycnGbPni1EfXDo/N6+fRsYGHjy5MmioiJTU1MXF5fp06d3wpZ3pwsEANBxCgoK\nPj4+//zzT0NDg46OjqurKy756syePHkyefJkR0fHuXPnFhQU2Nvb8+PvpaKiopub27NnzyIjIwmC\nsLGxGTRo0P79+9++fcvzuUDc5Obmrlixom/fvrt27TIxMbl//35CQoKVlVUnrPwIrPwBgGhjs9l/\n/vnnhg0bpKSkPD097e3tyU4E/6Ourm7Hjh3Hjh3T1dUNCAjQ19cX2NQPHjw4fvx4aGgojUabP3++\ni4vLsGHDBDY7iAYWixUREREYGJiYmNinT5/Vq1c7OjoqKSmRnesrUPwBgOhr6wIbGRn5+fkNGTKE\n7ERAEAQREhKybdu2lpYWPvV5v8Xr16+5j2B58eIFesHw7T7u8FpYWAjLmdMZVyMBAHiL2wXOyMio\nq6vT1tZGF5h0eXl5JiYmjo6Oc+bM4V+f91soKSm5ubkVFxe39YIHDx6MXjB8wZMnT1asWKGurs7t\n8GZlZXE7vMJS+RFY+QMAscJms4OCgjZv3iwnJ7d37150gQWvvr5++/btx44d09HRCQgIGDlyJNmJ\n/sf9+/dPnDjR1gt2dXXFbiHgat/hVVNTc3Z2FooO76eRux0aAEDwKioq7OzsKBSKiYlJXl4e2XH4\nrqioyNHRsaysjOwgnLNnz6qpqXXt2vXEiRMsFovsOJ/16tUrLy+vPn36UCgUU1PT8PBwJpNJdijR\n0XlOyG9UU1Pj5eWlqalJEAT3fGhtbSU7VIeg+AMAMZWSkjJ8+HBJSUk3N7empiay4/DRpUuXCIKI\njY0lMQO3z0uhUOzs7CoqKkhM8u1aW1vDw8NNTU0JgtDU1PTy8nr79i3ZoURBZzghv1Fubq6Tk5Oc\nnJy0tLSTk1NWVhbZiXgDxR8AiC8Gg+Ht7S0vL6+hoREdHU1iEn7fj/rNmzd8Hf8Lmpqa3NzcJCUl\nhw8fnpKSQlaMjsjKyuLeI1pOTs7JySk3N5fsRILDpzOTxBPyW7DZ7KioKFNTUwqF0qdPHy8vr1ev\nXpEdipdQ/AGAuCsvL+d2gS0tLZ89eyb4AImJiaqqqoKfVwCio6M1NDTk5eW9vb0ZDAbZcTqkrRdM\npVJNTU2joqLYbDbZofhLhM/Mz3n79u0HHd6WlhayQ/GehIeHB5mXHAIAkE1OTm7WrFnGxsbnz5/f\ns2cPg8EYO3YsjUYTzOzJyckzZ85kMBgKCgoVFRWDBg0iCOLp06fXrl0LDQ1taGjg3pgmOjo6Li7u\n8ePHenp6dXV1QUFBaWlppaWlWlpaX52CzWbfunWrqqqqd+/efP95/l9xcbG9vf3u3bstLS25iyid\n8263365Lly4TJkxwdnYePnz4nTt3Dhw4cO7cuaamJi0trZ9++onsdP9VX18fHh5+6dKlqqoqNTW1\ntmzl5eWXLl2Kjo5mMpkaGhrcg2VlZWfOnBk9enRubu7JkydLS0uHDx9OoVCIz5yZnxzk7du3wcHB\no0aNiouLi4iIGDt27Jf/W5NyQn5VXl7ejh07li5dyv3BT5w44ebmNmzYMCHaw/sdyK4+AQA6i7Yu\ncP/+/WNiYgQz6YMHD8aPH6+oqJicnPzgwQMOh3PkyBEjIyM2m11cXNyvX7+AgADuJ4cNG6ampsb9\nura2Vl5efty4cV8dPzc3d+7cuQRBHDt2jH8/RXtNTU3u7u7S0tJaWlq3b98WzKSCl5mZ6eTk9NNP\nP3F7wU+ePCE7EYfD4eTl5VlYWOTk5DAYjPnz5/fo0aOoqIjD4SQlJS1fvvz+/fvh4eGysrKrV6/m\ncDhRUVGKiooEQRw5csTR0dHS0pIgCE9PT+5QH5+ZnxzkzJkzMjIyNBrN19dXW1ubIIicnJwvJBT8\nCfllbR1eKpWqpqYmeh3eT0LxBwDwP9p3gYuLiwUw48yZM/v06dP2UlNT09nZue0tCwsL7tdz585t\nK/44HI6ent63FH8cDufhw4cC+1sbExPTv39/0ejzfovKykovLy81NbXO0AtmMpk6OjqBgYHcl1lZ\nWZKSktHR0XV1dRoaGvX19dzjS5cuJQgiPT2dw+Fs2bKFIIibN29y39LT09PX128bsP2Z+YVBFi5c\nSBBEREQEh8P5lu3zgjwhv6B9h3f8+PGi2uH9JAH1NQAAhIWKikpISMjSpUudnZ2HDh26efPmLVu2\n8Luvx220cd26datLly4EQTx58qSsrKy2traDg0tJSXVwhG9RUlLy66+/Xrt2bdGiRfv371dRURHA\npKTr1auXm5vbunXrIiMjfXx8rK2tBwwY4OzsvHTpUllZWQGHiY2Nzc7Onj59Ovcl9woBSUnJkydP\nNjU1bd68mXu8oqKif//+hYWFY8eOlZaWJghi8ODB3LeGDh0aHx/ffsy2M/PChQufG0RVVZUgiBkz\nZrQf6gsEc0J+QV5enre394ULFxgMhr29fVhYmCCfK9gZoPgDAPgEQ0PDBw8eBAQE7Ny589y5c0eP\nHp02bRr/pmtf/PXu3fvGjRsxMTGGhob9+/fPysri37w80dzc7OXldeDAAQ0NjeTkZENDQ7ITCZqk\npOS8efPmzZuXlZUVGBi4ZcuW3377zdbWdu3atYJ8lmBOTk6XLl24ndy2YARB5Obmqqio+Pv7f3UE\nCQkJzv8++qHtzPzCINwr/Dr/NZ0cDicmJubo0aNJSUmqqqrbt29fvHhxr169yM5Fgs7+nwoAgCx0\nOt3V1TU/P3/cuHEWFhZWVlalpaV8mqt98bdz5849e/bs379/zpw5nf9i87i4uOHDh//xxx/79u17\n8OCBGFZ+7enr6584caKkpGTbtm2xsbFaWlpTpkzh3kVIALOz2eyGhobk5OQPjktISBQUFDAYjB8Y\ns+3M7MggpKuvr/fx8Rk0aJC1tXVTU1NYWFhRUZGbm5t4Vn4Eij8AgC9TVVUNCQlJSkp69uzZ0KFD\nPTw8WlpaeDsFhUJhsVjcr4uLi/fs2bNo0SJuP47NZrd9jEajNTc383bqjigtLbWysrKwsBg3blx+\nfr6rqyudTic7VKfA7QUXFRWFhYU1NjZaW1sPGjTIx8eH30+UHj58OEEQ58+fbztSXV195coVbW3t\nhoaG48ePtx1/9+5dQEDAVwdsf2b+8CDkys/PX7FiRe/evbds2WJsbJyZmXnnzp158+Zx10TFF8nX\nHAIACInW1lZvb285ObkBAwbExcXxcOTVq1fT6fSioqLCwsL09HSCIIyMjN6/f5+SkqKioqKgoFBX\nV1dbW3v69GmCIE6fPl1fX3/69Gl1dfVevXrV1NR8dXzu9fW///47rwI3Nze7u7vLyMgMHTo0KSmJ\nV8OKqrZ9wfLy8k5OTvx7oiCTydTV1SUIYsWKFTdv3vzjjz+sra2bm5ubm5v79OkjKSl54MCBJ0+e\nXLx4cd68ebW1tRwOZ8OGDQRBtN3ecvr06XJycm17VtqfmdXV1Z8bZM2aNQRBVFVVfWNOnp+QH/t4\nD29lZSX/phM6KP4AAL7Dixcv7OzsCIKwtLQsKSnhyZjJyck0Gq1bt25Hjx7lcDhLliyh0WiamprH\njx//66+/JCUlJ0+eXF1dXVdXN3bsWIIghgwZEhERMXv2bDMzs5MnT3558Lt373LvrKGlpcWT+9fE\nxcUNGDBATk7O29tb2J9wKkjcfcG9e/fm677gFy9eTJkyhUKhUCgUIyOjFy9ecI8/efJk4MCB3EWf\nYcOG3b9/n8Ph3Lp1i3uvvmXLllVUVFy4cEFeXp4gCA8PD+5O7Q/OzE8OEhQUxL1dn42NTUZGxlcT\n8vyE/EBdXZ23t/eAAQMIgjAwMBCrPbzfDsUfAMB3S0xMHDJkiIyMjLu7e3Nzc8cHfPfuHXcRhav9\n1x+M//r1a+4Xgn8ecUlJCfdWcHZ2dm1VBXyXlpaW8PBwAwMDgiAGDhzo7e3ddvMUHnr79m11dfXH\nx0tKSkpLS79rqA/OzB8bRDDy8vKcnJzk5eXpdLqdnV1mZibZiTovCkcgV6ECAIgYBoMREBCwY8cO\nVVXVo0ePmpmZkZVk9erVn3vLyclJR0en41O0tLTs27fv4MGD6urqfn5+kydP7viYYi4rK8vHxycs\nLExaWnrx4sVr1679+eefyQ7FGwI4IdvjtNvDq6Ki8uuvvzo4OCgrK/N2FhGD4g8A4Me9ePFi27Zt\noaGhlpaW/v7+ffv2FXyGS5cufe4tAwODjj9BKz4+3sXFpby8fM+ePdyLwDo4ILSprKw8e/asr69v\nRUXF5MmTXVxcLC0t22/9Fkb8PiHbNDQ0BAUFBQQEPH361MDAYO3atTNmzBD3nRzfBsUfAEBHJSYm\nrlmzpqysbOPGjdu2bROZPz/Pnz93dnaOiYmxs7Pz9PRUU1MjO5Foam1tjYyMPHLkSHp6+sCBA1ev\nXr1s2TLujb7hk4qLi729vc+cOdPU1GRra+vq6ipud2nuIBR/AAA80NraeuTIEQ8PD3V1dV9f3ylT\nppCdqENaW1s9PT0PHTrUp08fPz8/ExMTshOJBRHuBfMEOry8guIPAIBnnj175urqGhMTY2lpGRAQ\n0KdPH7IT/YiEhIRff/31xYsXe/fuXbVqlcgsZAoLbi/46NGjlZWVItML7qD2Hd5x48atW7cOHd6O\nwE2eAQB4RkNDIzo6OioqKjc3d8iQIR4eHq2trWSH+g5lZWVWVlZTp04dMGDAw4cPXV1d8fdV8JSV\nld3c3IqLi8PCwhoaGqytrQcPHuzj49PQ0EB2NBIUFxe7urqqqqpu2rRpzJgxmZmZaWlpuEtzB2Hl\nDwCA95qamvbv379//37u9lhTU1OyE31FW59XSUnJx8fHysqK7ETwH229YBkZGQcHh3Xr1vXr14/s\nUHzXvsPbo0eP1atXr1y5Eh1eXkHxBwDAL0VFRa6urrGxsYsWLTp48GCnfZDozZs316xZU1pa6ubm\n5ubmxn2yHHQqlZWVx48fDwgIqK6utrCwcHV1NTExEcleMLfDe+zYsYKCgrFjx65fvx4dXp5D2xcA\ngF/69+8fExMTGRmZmprKfbork8kkO9T/ePXqlb29/dSpUzU1NR8/fuzh4YHKr3NSVlb28PAoKysL\nCwurqqqaMmXKkCFDRKwXXFJS4urq2rt3702bNo0ePTozMzM9PR0dXn7Ayh8AAN+1dYEHDRrk7+8/\nfvx4shMRTCbT39/f3d29e/fuR48eRZ9XuIhSL5jD4SQmJvr4+MTGxqLDKxgo/gAABKSwsNDFxeX6\n9euLFi3iXl1HVpK///7b2dm5oKAAfV6hVlFRceLECX9//5qaGmHsBX/c4bW2tpaSkiI7l+hD2xcA\nQEA0NTVjY2MjIyNTUlK4XWAWiyXgDK9fv7a3t584caKqquqjR4/Q5xVqKioqHh4eL168+KAX3NjY\nSHa0r/hchxeVn2Bg5Q8AQNAaGxsPHDjg5eU1ZMgQf39/AwMDAUzKYrH8/Pw8PDy6deuGPq9I4vaC\nL1y40KVLFwcHh/Xr16urq5Md6n+07/AqKCg4OzuvWLFCRUWF7FxiB8UfAAA5/v33XxcXl/j4eAF0\ngdPS0pydnZ88eaKkpJSYmDhw4ED+zQXk6py94MbGxpMnT6LD20mg7QsAQI4BAwbExcVxu8Dcu/jy\nowvM7fNOmDBBWVk5LS1NVVXV1NS0sLCQ5xNBJ9HWCw4ODi4rK5syZYqenl5gYCBZveDS0lLuXZo3\nbtyIDm9nwQEAAFI1NDS4u7tLSUnp6uqmpaXxalgmk+nt7d29e3d1dfWoqCjuwXfv3o0ZM0ZNTe3f\nf//l1UTQmWVmZtrZ2dFotK5du7q4uJSUlAhmXjabnZCQYGlpSaVSe/bs6e7uXl5eLpip4atQ/AEA\ndAoFBQVmZmYUCsXOzu7169cdHC0tLU1XV1dKSsrd3b2hoaH9W+/evRs7dqyysnJubm4HZwFhUV5e\n7u7u3rNnTyqVamlpmZCQwL+5GhoaTpw4oaOjQxCEtrb22bNnm5ub+Tcd/AAUfwAAnUhUVFTfvn27\nd+/u7e3NZDJ/YITXr1/b2dlRKBQzM7OCgoJPfqa+vt7Q0LBXr16o/8RKc3Pz2bNnNTU1CYLQ0dE5\nceLEB/8w6KCSkhIXF5euXbvSaDQ7O7vMzEweDg48hA0fAACdS0NDw8GDB/ft26elpRUQEDBmzJhv\n/Ebuft5du3bJycn5+fl9eT9vQ0ODpaVlXl5eYmLisGHDeBEchEBFRcWIESMmT54sJSV14cIFWVlZ\ne3v7ju8LvnnzJvbwChOyq08AAPiE/Pz8KVOmUKlUOzu7N2/efPXzd+/e1dPTk5SUdHd3r6+v/5Yp\n6uvrjY2NlZSUHj161OG8IARYLNakSZMGDhzIPUNevnzZwV4wOrxCCsUfAEDnFRUV1adPHwUFBW9v\nbxaL9cnPvHnzxs7OjkqlTpky5XN93s9paGiYPHmykpLSw4cPeZEXOrVDhw7R6fR79+61P8jtBY8Y\nMYIgCF1d3W/sBXM7vN26dUOHVxih+AMA6NTq6+vd3d0lJSVHjhyZkZHR/i0Wi+Xt7a2goNCnT5+2\n/bzfq6GhwcTERFFRMScnhxd5oZPKzs7mbgD63AdSU1PnzZtHo9EUFRXd3NxKS0s/+bEP9vC+fPmS\nX4mBb1D8AQAIgfz8fFNTU24XuKqqisPhZGRk6Ovrf1ef93MaGhpMTU27d+/+wZoQiIzGxsahQ4eO\nHTuWwWB8+ZPcXnCPHj0+6AVzO7y6uroEQYwYMQIdXqGGDR8AAMKBzWafPHly27Zt0tLS06ZNO3v2\nrI6Ojr+//6hRozo+eEtLy9y5c+/cuXPjxg2eDAidyvr164OCgrKzszU0NL7l87W1tcHBwX5+foWF\nhePHj584cWJISEhFRcXUqVNdXFzMzc2pVDwkQoih+AMAECZVVVVbt25NTU1dt27d8uXLefg3uKWl\nZd68eampqfHx8aNHj+bVsEC6uLi46dOnnzhxYvny5d/1jWw2Oy4uztfXNzs7e968eWvWrBk0aBCf\nQoIgofgDAID/aG1tnTdv3u3bt+Pj47/9FjPQmVVXV48YMcLAwODSpUtkZ4HOAsUfAAD8V2trq42N\nza1bt65fvz527Fiy40BHzZ49++7duw8fPuzZsyfZWaCzQM8eAAD+S1JSMjw83NjY2NzcPD09new4\n0CFnz569evXqmTNnUPlBe1j5AwCADzEYDFtb2/j4+JiYGCMjI7LjwI8oLCzU1dV1cHDw8/MjOwt0\nLij+AADgE1gslr29fWRkZHR0tLGxMdlx4PuwWCxDQ8P379/fu3fvp59+IjsOdC5o+wIAwCdISEiE\nhITMnDnT0tIyKSmJ7Djwfby8vO7duxcSEoLKDz5GIzsAAAB0UhISEmfPnqVQKFZWVlFRUSYmJmQn\ngm+SkZHh4eHx+++/c+/JDPABtH0BAOBLWCyWo6Pj5cuXIyMjTU1NyY4DX1FfX6+jo9O3b9+bN2/i\nVszwSVj5AwCAL5GQkAgODqZSqdbW1pGRkVOmTCE7EXzJpk2bampqbt26hcoPPgfFHwAAfIWEhMTp\n06e5/d9Lly5ZWVmRnQg+LTo6+vjx46GhoWpqamRngc4LbV8AAPgmHA7H2dn51KlTly5dsra2JjsO\nfKiysnLEiBGmpqbnz58nOwt0aij+AADgW3E4nDVr1gQFBaH+62w4HI6FhcWTJ09ycnK6detGdhzo\n1ND2BQCAb0WhUPz8/CgUyrx58y5evDhz5kyyE8F/nDhxIiEhITk5GZUffBWKPwAA+A4UCsXX15dK\npf7yyy9hYWGzZs0iOxEQBQUFGzZsWLdu3cSJE8nOAkIAbV8AAPhuHA5n7dq1x44dCwsLmz17Ntlx\nxFpra+u4ceNYLFZGRoaUlBTZcUAIYOUPAAC+G4VC8fb2plAoNjY2ISEhCxYsIDuR+Pr999/z8vIy\nMzNR+cE3QvEHAAA/glv/ycjI2Nvbs9nsRYsWkZ1IHN2+fdvT0/Pw4cNDhw4lOwsIDRR/AADw4zw9\nPSkUyuLFizkcjp2dHdlxxEttba2jo6OZmZmrqyvZWUCYoPgDAIAO2bt3L5VKdXR05HA49vb2ZMcR\nI66urrW1tUFBQRQKhewsIExQ/AEAQEf9/vvvFAplyZIlHA7HwcGB7Dhi4eLFi2fOnImIiFBVVSU7\nCwgZFH8AAMADu3fvplKpS5YsYbPZjo6OZMcRcc+fP1+5cqWjoyNutQM/QMLDw4PsDAAAIAqMjIx+\n+uknFxcXRUXFUaNGkR1HZLHZ7Hnz5jEYjMjISElJSbLjgPDByh8AAPCMm5sbQRBr1qwhCGL16tVk\nxxFNPj4+ycnJKSkpXbp0ITsLCCUUfwAAwEtubm5UKnXNmjVsNptbBQIPPXz4cOvWrdu2bTMwMCA7\nCwgrPOEDAAB479ChQ5s3b/bx8fn111/JziI6mpubR44cKSsre+fOHRoNyzfwg3DqAAAA723cuJFK\npbq6urLZbNyFjld27Njx/PnzBw8eoPKDjsDZAwAAfLF+/XoKhbJu3Trug4DJjiP0EhMTjxw5EhAQ\n0L9/f7KzgHBD2xcAAPjo+PHjq1ev3rNnz7Zt28jOIsRqampGjBihr68fGRlJdhYQelj5AwAAPlq5\nciWFQlm1ahWHw9m+ffu3f2NISAieF9IV6B4+AAAWOklEQVRm+fLlLBYrKCiI7CAgClD8AQAAf61Y\nsYJKpa5cuZLNZu/cufNbviUpKWnr1q0o/rhCQ0OvXLkSFxenqKhIdhYQBbjJMwAA8J2+vn7v3r03\nbtxIoVAMDQ2//OHk5OSZM2cyGAwFBYWKiopBgwYRBPH06dNr166FhoY2NDQMGTKEIIjo6Oi4uLjH\njx/r6enV1dUFBQWlpaWVlpZqaWkJ4kcSlJKSkhkzZjg6On6wb6apqeny5csaGhovX748f/78y5cv\nBwwYQKVSX716deHChezsbE1NTSkpKUJsflHw7XDNHwAACMipU6ecnJx27tz55XWH7OzsNWvWPH36\nNDw8vFu3bjo6Ot7e3pGRkUlJSaWlpcbGxps3b161ahVBEFpaWu/fvy8rKyMIoq6uTk1NbdiwYWlp\naYL5cQSAxWIZGhpWV1dnZWXJyMi0Hb99+/by5cv//fffw4cPFxQUdOvWzc/Pb9q0aebm5rdu3WKx\nWBcvXrS0tIyKiuJ+XuR/UfBd0PYFAAABWbp0KYVCWb58eXNzs5eX1+c+pqOjo6io+Pz5cyMjI+4R\nf39/MzMzCoXSr18/HR2dmJgYbvE3ZMiQu3fvcj8jJyenqanJ/x9CoA4cOHDv3r309PT2lR9BEIaG\nhqtWrVq/fn3fvn3Xr19PEASVSvXy8lqwYMGff/5JEET//v0PHTrEZrOpVCohBr8o+C4o/gAAQHCW\nLFkiLS1tb2/P4XD279//hU9SKJS2r2/dusV9lNmTJ0/Kyspqa2v5HrQT+Oeff3777bddu3bp6el9\n/G7Xrl0Jghg+fDj3Jbc5rq2tzX05ePDglpaW8vJyNTU1QeUFoYHiDwAABGr+/PkUCsXOzo7D4Rw4\ncOBzH2tf/PXu3fvGjRsxMTGGhob9+/fPysoSSFIyNTY2Ojg4TJgwYcuWLd/y+Z9++qn9SzqdThBE\nQ0MDX8KBkEPxBwAAgmZra0ulUhcuXMhmsw8dOvTJz7Qv/nbu3Hn79u34+HhpaenLly8LKiaZNm3a\nVF5eHhcXx+3bAvAQij8AACCBjY0NhUJZsGABh8M5fPjwB+9SKBQWi8X9uri4eM+ePSdOnJCWliYI\ngs1mt32MRqM1NzcLLLPAxMTEHDt27MyZM/369ePJgKL6i4Ifg39PAAAAOebNm3fhwgVfX1/uI+Da\nv6WiolJZWfns2bOioqJXr14RBHHhwoXa2trU1NSUlJS3b9/W19fX1dVNnTq1qqoqODi4oaEhODi4\nurr62bNnb9++JekH4o1Xr14tWbLkl19++fJtDuvq6giCaGlp4b6sr68nCKKmpob7ktvwbXtXJH9R\n8MNwnz8AACDN0KFDhw4d6ubmVl5ePn369LZWb5cuXUJCQs6cOaOmpjZnzpyysrLo6Ojw8PABAwbM\nnTv3woULaWlpc+bMGTFiRHJysr+//5UrV6ZPn15VVdWjRw8KhfLJHRLCYuHChdXV1TExMR9cxtde\nenr6nj17Xr161dTUpK2tnZOTs3fv3oqKivLych0dncLCwn379r148aKmpkZHR6dHjx4DBgwQvV8U\n/DDc5w8AAEh27dq1OXPmODg4HDt2rO0St/fv31OpVDk5Oe7Lurq6tq9bWlq4ty/mevPmDffRF83N\nzV8omIRCYGDgqlWrbty4YWJiwvPBRekXBR2B4g8AAMgXGxs7e/Zse3v748ePi+0Wh4KCAn19/RUr\nVnx8ESQAD6H4AwCATiEuLm727NkLFy4MDAwUw/qPwWCMGzeOyWRmZGS0X9cE4Dns9gUAgE5h2rRp\nV65cmTVrFofDOXnypLjVf3v27Hny5Mm9e/dQ+QG/ofgDAIDOwtzc/OrVq9z6LygoSHzqv/T0dE9P\nz/379w8bNozsLCD60PYFAIDO5caNGzNnzpwxY0ZoaCiNJvqLFLW1tTo6OhoaGjdu3BCfehdIhJMM\nAAA6l6lTp16/fj0mJmbRokVMJpPsOHy3du3a2tras2fPovIDwcB5BgAAnc6kSZNiY2OvXbu2cOFC\n0a7/wsPDg4ODAwMDe/fuTXYWEBdo+wIAQCd1584dCwsLc3Pzc+fO0el0suPwXllZmba2trW19Zkz\nZ8jOAmIExR8AAHRe3PrPzMzs/PnzIlb/cTgcMzOz4uLiBw8eyMrKkh0HxAjavgAA0HlNmDAhLi7u\nxo0bs2fPbntSrWg4evRoUlLSmTNnUPmBgGHlDwAAOru0tLRp06ZNnDjx8uXLonEbvIcPH44ePXrj\nxo179uwhOwuIHRR/AAAgBLKysqZOnTpmzJiIiAhhfy5tc3PzyJEjZWVl79y5Iw73soHOBm1fAAAQ\nAvr6+gkJCRkZGbNmzWpubiY7Tof89ttvpaWlf/75Jyo/IAWKPwAAEA56eno3b968d+/ezJkzm5qa\nyI7zg5KSkg4fPnzgwAFNTU2ys4CYQtsXAACESXZ2tqmpqb6+/tWrV6WlpcmO831qampGjBihp6cX\nFRVFdhYQX1j5AwAAYaKjo3Pz5s379+/PmDFD6Nb/nJycWCzWqVOnyA4CYg3FHwAACBlu/ZednW1u\nbl5fX092nE+rq6sLDg5+/fp125Fz585FREQEBwcrKiqSGAwAxR8AAAgfbW3t27dvP3361MLConPW\nf2fPnl2yZMmQIUOuXbtGEERpaamzs7OTk5O5uTnZ0UDc4Zo/AAAQVvn5+ZMnT+7fv39sbKycnBzZ\ncf6Hubl5QkICh8PhcDhOTk6PHz+urq6+f/++jIwM2dFA3GHlDwAAhNXgwYOTk5OfPXs2bdq0urq6\ntuO3bt0aP358eno6WcEaGxuTk5PZbDZ3hSU4OPjevXtr165F5QedAYo/AAAQYoMGDUpKSiouLjY3\nN6+trSUIIjU11dzcPD093c3NjaxUKSkpra2tbS8ZDAabzXZ2dt6yZQuDwSArFQAXij8AABBugwYN\nunHjRlFRkbW19bVr10xNTRkMBofDSU1NvX37NimRbty4QafT2x9hsVhsNvvgwYPGxsYvX74kJRUA\nF4o/AAAQesOGDUtKSpKWlp4zZw6TyWSz2QRB0Gg0d3d3UvJERUV9coWPzWb//fffly9fFnwkgDYo\n/gAAQBS8efMmOTmZ22DlHmEymbdv375z546AkxQVFRUVFX18nEqlUiiUX3/9dfny5QKOBNAeij8A\nABB6KSkp5ubm7Ss/LhqNtmvXLgGHiY+P//ihvZKSkt27d4+Lizt69KjQPZgERAyKPwAAEG6PHz+2\ntLT8uPIjCILJZCYmJj548ECQea5du/ZBEiqVamRklJuba2ZmJsgkAJ+E4g8AAITb06dP6+rqKBTK\nJ98V8OJfS0tLUlJSW/FHo9HodPoff/xx/fr1Xr16CSwGwBeg+AMAAOE2e/bsrKysGTNmUCgUSUnJ\nD95lMBhRUVFZWVmCCZOamtrc3Mz9mkajDRgw4MGDB66urp+rTQEED0/4AAAAEfHo0aP9+/eHhYVR\nqdT2m23pdLqlpWVERMTnvpHD4RQXF+fm5ubm5paWlr548aK0tLSysvLjHbt0Ol1FRaVv375qamrq\n6urDhg3T0tLq169fW223efPmI0eOcG/vbG9v7+vr29kePQKA4g8AAERKcXGxt7f38ePH2Ww2k8nk\nHqRQKDk5OcOHD2/7WGNjY1paWmpqakpKyr179xoaGgiCUFVVVVFRUVFR6dWrV69evT64Vx9BEAwG\no7Ky8tWrV5WVlRUVFeXl5QRBdOnSZfTo0ZMmTZo4ceLKlSsLCwt79Ohx7tw5XOEHnROKPwAAEEHF\nxcUHDx48deoUh8NhMBhUKtXW1vbcuXPV1dVRUVFXr15NSEhoamrq37+/vr6+rq6uhoZGv379vvfx\na42NjSUlJUVFRdnZ2VlZWUVFRRQKpXv37tu2bXN0dFRQUODTTwfQESj+AABAZFVWVv7xxx8BAQEN\nDQ1UKtXa2jouLo7D4RgYGJiYmEyaNIm39Vl1dXVSUtKtW7fu3r1LpVJ/+eUXZ2fnUaNG8XAKgI5D\n8QcAACIuPDx8w4YNL168GDhw4Pz5883NzWVlZfk6Y319fVxc3KVLl/Ly8saNG7dz585p06bxdUaA\nb4fiDwAARFZiYuKWLVvu378/efJkR0fHESNGCDhATk5OcHBwUlLSqFGj9u3bN3nyZAEHAPgYij8A\nABBBlZWV69evDwsLmzp16sqVKzU1NUkM8/Tp02PHjiUmJi5YsODQoUPKysokhgFA8QcAAKImLCxs\n5cqVCgoK27dvHzNmDNlx/iM9Pd3T0/Pt27eBgYE2NjZkxwHxhZs8AwCA6GhoaFiyZMnChQvnzJnz\n119/dZ7KjyCIcePGXb58efbs2ba2tsuXL29sbCQ7EYgprPwBAICIePXq1fTp00tLSz09PceNG0d2\nnM/6+++/t2/frqGhERMTo6SkRHYcEDso/gAAQBQ8ffrUzMxMQkLi2LFjKioqZMf5ivLy8pUrV1Io\nlPj4eHKvRwQxhOIPAACE3suXL8eNG9ezZ09fX9+uXbuSHeebvHv3ztnZ+f379+np6Z2/WgVRguIP\nAACE2/v37ydMmMBgMIKDg4XrQbq1tbUODg5dunRJTU0VruQg1LDhAwAAhNuyZcuqq6uPHTsmdPWT\nvLz88ePHKysrV6xYQXYWECMo/gAAQIgFBgZGRETs27dPUVGR7Cw/olevXnv37r148eLp06fJzgLi\nAm1fAAAQVq9evdLU1LSxsVm3bh3ZWTrk8OHDERERhYWFQlrCgnDByh8AAAirHTt2yMvLr169muwg\nHbVmzRoZGZnffvuN7CAgFlD8AQCAUHrx4sWZM2eWL18uJSXFj/EbGxuTk5OPHTvGj8E/ICUltXTp\n0lOnTlVUVAhgOhBzKP4AAEAonT59Wl5efsaMGXwaPyEhwd3dPTY2lk/jf2DOnDmysrLBwcGCmQ7E\nGYo/AAAQSn/++ee0adPodDqfxp8xY8awYcP4NPjH6HS6ubl5aGiowGYEsYXiDwAAhM+LFy/+/fff\nyZMn83UWKpVKoVD4OkV7kydPzs/PLy8vF9iMIJ5oZAcAAAD4bqmpqXQ6XVtbmyejNTQ0pKamPnv2\nTFlZ2cDAQFlZ+YMPZGdn//333wMHDpwyZQr3CIfDyczMzM/Pl5CQ+Pnnn3nyKGFtbW0ajXbnzh0b\nG5uOjwbwOVj5AwAA4ZOfn6+urs6TrR4FBQV2dnY0Gm3+/Pl1dXUzZsyIiopqe7e1tXXNmjUnT568\ncePG+vXrt27dyj3u6+v7/PlzOzs7bW1tX1/fjscgCEJaWrpv3755eXk8GQ3gc1D8AQCA8Kmqqure\nvXvHx2EwGJs2bTIxMTE1Ne3evbuDg4ORkZGHh0dRURH3A69fv964caO/v//Vq1eNjY1jYmLu3LnD\n4XD++uuvvn37EgQxbNgwIyOjjifh6t69e1VVFa9GA/gkFH8AACB83r17Jy8v3/Fx7ty5U1xc3L59\nPH78eAaDERERwX2pqanZr18/giAoFMovv/xCEERKSgqFQunXr9/GjRuTk5MJgli8eHHHk3DJy8u/\nffuWV6MBfBKKPwAAED4yMjJNTU0dH+fZs2fc0dqO6Ovrtx3/gLa2NpVKff36NUEQ27Ztk5WVdXFx\nWbZsWV1dXceTcDU3N3fp0oVXowF8Eoo/AAAQPj179nz//n3Hx+natStBENnZ2W1HVFRUaDTaJ5cV\nu3TpIiMjo6amRhDE4MGDw8PDbW1t7927Z2Njw5MwBEG8e/euZ8+ePBkK4HNQ/AEAgPDp27fv8+fP\n2Wx2B8cZPnw4QRBZWVltRwoLC5lMpo6Ozscfzs/Pr6+vnzhxYmtra3R0dJcuXbZv3x4QEPDmzZub\nN292MAlBEGw2+/nz5+rq6h0fCuALUPwBAIDwGT9+fF1d3b///tvBcQYNGjRjxoysrKy256rdv39f\nXV197ty53JeNjY1tJWZ8fLy5ufmYMWM4HE54eDiHwyEIwsDAoFu3bjzZfVJQUNDQ0DB+/PiODwXw\nBRTuuQsAACBEWCxWjx49nJyc7O3tOzhUS0vL4cOH7927t3jxYhaLlZiYuHPnTu6t/tLT048cOdK1\na1c9Pb03b94oKCisWrVKQkKipaVl2rRpI0eONDU1raioqK+vd3Z27vgPFRwcHBwcXFVVRaViaQb4\nCMUfAAAIpVWrVsXHx0dGRvLkIRz19fWFhYUqKiq9evX64K2Wlpa3b99+cOdnFovFZrOrqqpUVFQ6\nPjtBEBwOx9raevr06X5+fjwZEOBz8G8LAAAQSnZ2dsXFxe33anSErKysjo7Ox5UfQRBSUlIfP/ND\nQkKCTqfzqvIjCCIzM7OkpMTBwYFXAwJ8Dlb+AABAWBkYGLS2tp45c4bsIB3F4XDs7e3l5eVv375N\ndhYQfVj5AwAAYXX48OH79+/zZKctuRISEnJycg4ePEh2EBALWPkDAAAh5uDgEBsbe/nyZQUFBbKz\n/KA3b97MnTt3xowZp0+fJjsLiAUUfwAAIMTq6up0dXV79+7t6+srjJtk2Wz26tWrX79+ff/+fVlZ\nWbLjgFgQvv+dAAAAtJGTk7t48eK9e/f2799PdpYfsXfv3uzs7PDwcFR+IDAo/gAAQLjp6+tfvHgx\nPDw8ICCA7Czfx9fX98qVK5cuXfrkA0UA+IRGdgAAAICOsrKyCgwMXL58eW1t7ebNmzt//5fNZu/b\nt++vv/46ffr0tGnTyI4D4gXFHwAAiAJHR8cePXrY2tpWVlbu2bOnM3dR6+rqtm/fnpGRceXKFUtL\nS7LjgNjBhg8AABAd//zzD/exvF5eXiNGjCA7zifk5OS4ubnRaLTLly/r6+uTHQfEUWdfGAcAAPh2\no0ePzsnJGTlypIODwx9//NHU1ER2ov9qbGw8fPjw4sWLx40bl52djcoPyIKVPwAAEEEhISEbN26k\n0+kbNmwwNTUl9ypANpsdHx//xx9/sNnsQ4cO2dnZkRgGAMUfAACIppqamm3btp06dernn39evny5\nmZmZ4EtANpt9/fr1kydPlpSULF++fO/evd27dxdwBoAPoPgDAABRVlZWdujQoRMnTsjLy1tZWf3y\nyy+qqqoCmLe8vPzixYtRUVH19fVOTk4bN27s06ePAOYF+CoUfwAAIPqKi4sDAwODg4NramomTpxo\nYmJiZGQkLy/P84nev39/69atmzdv/v333z169FiyZMny5cv79evH84kAfhiKPwAAEBcMBiMmJiYs\nLCw6OprBYOjq6urr648cOVJbW/unn3764WGbm5tzcnIyMzOzsrIePHggKSlpZWVla2traWlJo+GW\natDpoPgDAACx09jYeO3atZs3b6akpBQUFFCpVFVV1Z9//vnnn39WVVVVUVFRVlZWUlKi0+mysrLc\nKwXZbHZ9fT2DwXj16tWrV68qKirKy8ufPXtWUlLy8uVLDoczePDgSZMmmZqaWlhYyMjIkP0jAnwW\nij8AABBrb968uXfv3qNHj3Jzcx8/flxaWlpTU/PV71JQUFBXV9f6f6NHj+7Zs6cA0gJ0HIo/AACA\n/9HU1PT8+fPKykoGg/H+/Xs2m00QBJVK7dq1K51OV1FR6dOnj7S0NNkxAX4Qij8AAAAAMYInfAAA\nAACIERR/AAAAAGIExR8AAACAGPk/DAHIpe8AJE4AAAAASUVORK5CYII=\n",
       "prompt_number": 8,
       "text": [
        "<IPython.core.display.Image at 0x10d464110>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "MCMC simulation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It can help to try to estimate the MAP value of the parameters. This can then be used as the starting condition for MCMC.\n",
      "We'll leave this step out for now."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#map1 = pm.MAP(model)\n",
      "#map1.fit() #stores the fitted variables' values in foo.value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcmc = pm.MCMC(model)\n",
      "#now use MCMC's sample method\n",
      "mcmc.sample(50000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  1%                  ] 847 of 50000 complete in 0.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 3%                  ] 1716 of 50000 complete in 1.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 5%                  ] 2615 of 50000 complete in 1.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
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     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Figure code from [Probabilistic Programming and Bayesian Methods for Hackers]( (http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/IntroMCMC.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.pylabtools import figsize\n",
      "\n",
      "figsize(12.5, 9)\n",
      "plt.subplot(311)\n",
      "lw = 1\n",
      "center_trace = mcmc.trace(\"centers\")[:]\n",
      "\n",
      "# for pretty colors.\n",
      "colors = [\"#348ABD\", \"#A60628\"] \\\n",
      "if center_trace[-1, 0] > center_trace[-1, 1] \\\n",
      "    else [\"#A60628\", \"#348ABD\"]\n",
      "\n",
      "plt.plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n",
      "plt.plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n",
      "plt.title(\"Traces of unknown parameters\")\n",
      "leg = plt.legend(loc=\"upper right\")\n",
      "leg.get_frame().set_alpha(0.7)\n",
      "\n",
      "plt.subplot(312)\n",
      "std_trace = mcmc.trace(\"stds\")[:]\n",
      "plt.plot(std_trace[:, 0], label=\"trace of standard deviation of cluster 0\",\n",
      "     c=colors[0], lw=lw)\n",
      "plt.plot(std_trace[:, 1], label=\"trace of standard deviation of cluster 1\",\n",
      "     c=colors[1], lw=lw)\n",
      "plt.legend(loc=\"upper left\")\n",
      "\n",
      "plt.subplot(313)\n",
      "p_trace = mcmc.trace(\"p\")[:]\n",
      "plt.plot(p_trace, label=\"$p$: frequency of assignment to cluster 0\",\n",
      "     color=\"#467821\", lw=lw)\n",
      "plt.xlabel(\"Steps\")\n",
      "plt.ylim(0, 1)\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.legend.Legend at 0x10d7ec7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MahdLRkEZKVkllQK9nJJyjueWUVxuo33TUI7klHBxKyPI9PUxuQNSm93Bifwy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\nS82fcOhYu+b5BlQfs4Sq3ulsunCdYWEpXKxSle/WHebyibO0LxdBswfKM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CFVKzhdQ+NF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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d72ea50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The traces don't appear to have converged yet. mcmc remembers its samples, so we can continue where we left off and gather another 100,000."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcmc.sample(100000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  0%                  ] 782 of 100000 complete in 0.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  1%                  ] 1564 of 100000 complete in 1.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  2%                  ] 2372 of 100000 complete in 1.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 3%                  ] 3145 of 100000 complete in 2.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 3%                  ] 3923 of 100000 complete in 2.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 4%                  ] 4683 of 100000 complete in 3.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                5%                  ] 5466 of 100000 complete in 3.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                6%                  ] 6265 of 100000 complete in 4.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                7%                  ] 7039 of 100000 complete in 4.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                7%                  ] 7782 of 100000 complete in 5.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---               8%                  ] 8548 of 100000 complete in 5.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---               9%                  ] 9360 of 100000 complete in 6.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---              10%                  ] 10147 of 100000 complete in 6.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----             10%                  ] 10938 of 100000 complete in 7.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----             11%                  ] 11722 of 100000 complete in 7.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----             12%                  ] 12520 of 100000 complete in 8.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            13%                  ] 13311 of 100000 complete in 8.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            14%                  ] 14090 of 100000 complete in 9.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            14%                  ] 14878 of 100000 complete in 9.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            15%                  ] 15683 of 100000 complete in 10.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           16%                  ] 16459 of 100000 complete in 10.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           17%                  ] 17271 of 100000 complete in 11.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           18%                  ] 18048 of 100000 complete in 11.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          18%                  ] 18848 of 100000 complete in 12.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          19%                  ] 19627 of 100000 complete in 12.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          20%                  ] 20413 of 100000 complete in 13.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         21%                  ] 21220 of 100000 complete in 13.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         22%                  ] 22001 of 100000 complete in 14.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         22%                  ] 22784 of 100000 complete in 14.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         23%                  ] 23570 of 100000 complete in 15.0 sec"
       ]
      },
      {
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      {
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      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(12.5, 4)\n",
      "center_trace = mcmc.trace(\"centers\", chain=1)[:]\n",
      "prev_center_trace = mcmc.trace(\"centers\", chain=0)[:]\n",
      "\n",
      "x = np.arange(50000)\n",
      "plt.plot(x, prev_center_trace[:, 0], label=\"previous trace of center 0\",\n",
      "     lw=lw, alpha=0.4, c=colors[1])\n",
      "plt.plot(x, prev_center_trace[:, 1], label=\"previous trace of center 1\",\n",
      "     lw=lw, alpha=0.4, c=colors[0])\n",
      "\n",
      "x = np.arange(50000, 150000)\n",
      "plt.plot(x, center_trace[:, 0], label=\"new trace of center 0\", lw=lw, c=\"#348ABD\")\n",
      "plt.plot(x, center_trace[:, 1], label=\"new trace of center 1\", lw=lw, c=\"#A60628\")\n",
      "\n",
      "plt.title(\"Traces of unknown center parameters\")\n",
      "leg = plt.legend(loc=\"upper right\")\n",
      "leg.get_frame().set_alpha(0.8)\n",
      "plt.xlabel(\"Steps\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x10d72e0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cw4JJ/WQzTR77P3xbNSFm7CD2TZ1P9Mj+bOs+hrD+Pcj473e0+fdsfpv2vE0+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M/rn917kyxpae9dtqWUrjyohtM8DMwA+oG7gX269fpNegDTgN+opycC/wnsA3wO+HTdfg7w2Xp6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\nSQ1h8S5JkiQ1hMW7JEmS1BD/D5cmJ2v8DCoOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d7331d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get the means of the traces and compare with the true values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mcmc.trace('centers')[:,0].mean(),mcmc.trace('centers')[:,1].mean(),mcmc.trace('p')[:].mean()\n",
      "\n",
      "print truemean1, truemean2, truep"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "91.0217581589 192.749727837 0.175733625088\n",
        "100.0 200.0 0.25\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But we shouldn't include samples from the early part of the chain when estimating the means. \n",
      "\n",
      "Try estimating the means using\n",
      "only the last 50000 steps of the traces. \n",
      "\n",
      "If there is insufficent storage, one can specify a burn-in period where the first part of the chain gets discarded. Further, there may be concern over the independence of samples. One can also just keep every mth sample. This is called thinning, and can be specified during the sampling call."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?pm.MCMC.sample"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One can check for corrrelations by looking the auto-correlations of the traces. Plotting the histogram gives some sense of whether how the space is being sampled."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymc.Matplot import plot as mcplot\n",
      "\n",
      "mcplot(mcmc.trace(\"centers\", 2), common_scale=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting centers_0\n",
        "Plotting"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " centers_1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/kersten/anaconda/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2499: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n",
        "  VisibleDeprecationWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RMgE/XgECOyEffhhGjoRLLnHnV17pPgNhfXr1cjv09u1z/qA2bIDjjkuNzIZR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IAAAC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\nDCNO/D8BCpHC4rcGZwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19f2cf310>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pm.Matplot.plot(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting p\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y3m67hm+b1M855zTeZyWuo8SThfvsA1tvndx+882pwfrEibmPOXVqEIhdckmw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\nDdcD1tiaw3eYesDKl3rA4qc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Xgvymfc1snbANhwLTyvRcEuK8plYCD5nZI8BrkbY/Cxxu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a0c44e10>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we leave toy problems, questions of convergence, adequate mixing become more of a challenge. See Chapter 3 from [Probabilistic Programming and Bayesian Methods for Hackers]( (http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/IntroMCMC.ipynb) for a discussion in the context of this example."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Further information and references on probabilistic programming using python and other software tools"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731670/\n",
      "\n",
      "Non-python, probablistic computation for cognition using Church: https://probmods.org/\n",
      "\n",
      "For tutorials, see: http://deeplearning.net/reading-list/tutorials/ and for software,including python: http://deeplearning.net/software_links/\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo(\"XbxIo7ScVzc\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/XbxIo7ScVzc\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x19f9a8350>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "YouTubeVideo(\"WESld11iNcQ\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/WESld11iNcQ\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x1a0f64510>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}