Bayesian t-Test

Bayesian t-test

{
 "metadata": {
  "name": "",
  "signature": "sha256:40d1e33fbcc5eeb5b32043afa79b7ee4af7041e599111a112a8864e2fbcfcd5c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Implementing the Bayesian t-Test"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In January I started my internship as a Data Analyst at Shopify. In the first few weeks of working I was tasked with my first bayesian adventure; to implement the t-test in the context of the bayesian framework.\n",
      "\n",
      "First, here's Bayes Theorem:\n",
      "\n",
      "$$\n",
      "P(A|B) = \\frac{P(B|A)\\cdot P(A)}{P(B)}\n",
      "$$\n",
      "\n",
      "where:\n",
      "\n",
      "* $P(A)$ is the prior, our initial idea of what A should be.\n",
      "* $P(B)$ is the evidence\n",
      "* $P(A|B)$ is the posterior probability, what our belief of A is given B.\n",
      "* $P(B|A)$ is the likelihood.\n",
      "\n",
      "But where does everything come together? Where do I go to get these probabilities in the context of a t-test? These are the questions that I didn't really have an answer to at the start of the project.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Understanding the Problem"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With some suggestion, I followed the ideas of John K. Kruschke of Indiana University's model outlined in the paper [*Bayesian estimation supersedes the t test*](http://www.indiana.edu/~kruschke/BEST/BEST.pdf) (or the *BEST* paper). I think Kruschke's paper does an excellent job motivating the test and setting out how to do it.\n",
      "\n",
      "What we wanted to do was for two populations, estimate the means ($\\mu_1$ and $\\mu_2$), variances ($\\sigma_1$ and $\\sigma_2$), and normality ($\\nu$) of the populations. Mathematically what we wanted to find, the posterior distribution, is $P(\\mu_1, \\sigma_1, \\mu_2, \\sigma_2, \\nu | D)$ and from Bayes Theorem we get:\n",
      "\n",
      "  \n",
      "$$\n",
      "\\begin{equation}\n",
      "    P(\\mu_1, \\sigma_1, \\mu_2, \\sigma_2, \\nu | D) = \\frac{P(D | \\mu_1, \\sigma_1, \\mu_2, \\sigma_2, \\nu) \\cdot P(\\mu_1, \\sigma_1, \\mu_2, \\sigma_2, \\nu)}{ P(D) }\n",
      "\\end{equation}\n",
      "$$\n",
      "\n",
      "where $D$ is the data we observe. Once we have the posterior distribution, then we can compute the probability of $\\mu_1 \u2260 \\mu_2$ and other measures of interest like effect size (a normalized measure of difference in populations) and the differences in variance of the groups.\n",
      "\n",
      "This looks like it's going to get pretty ugly. Luckily, there are some great tools on our side.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The Mathematical Tools for the Job"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I've spent a lot of time reading my textbooks and then reading papers trying to figure out a nice way to do all this Bayesian stuff above. What you end up with is this complicated sum of integrals and complicated distribution functions. Fortunately, Kruschke suggests using [Markov chain Monte Carlo (MCMC)](http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo), a method of random sampling and moving data between different states of our model. For this we used the Python library [PyMC](https://pypi.python.org/pypi/pymc) to do the heavy work.\n",
      "\n",
      "MCMC is a method with the goal of generating an accurate representation of the posterior distribution of a model. [Monte Carlo methods](http://en.wikipedia.org/wiki/Monte_Carlo_method) are ways of using random number generators and deterministic computations to solve mathematically complicated equations. MCMC then is the process of creating a [Markov chain](http://en.wikipedia.org/wiki/Markov_chain) which has an equilibrium that is the same as the distribution we wish to study. \n",
      "\n",
      "PyMC isn't the most friendly library when you aren't familiar with it, so I also consulted with [*Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) by Cam Davidson-Pilon. Chapters 1 and 2 should be enough to understand what comes next."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Building the t-Test\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Preparing for the test:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, we need to setup our environment for the computations we need to do:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import pymc as pm\n",
      "\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "If you have some data that you want to play with, go ahead and load it up into a dataframe. If not, numpy can generate some with `np.random.normal` for testing purposes.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generate some random normal data\n",
      "group1 = np.random.normal(15,2,1000)\n",
      "group2 = np.random.normal(15.7,2,1000)\n",
      "\n",
      "# Generate Pooled Data\n",
      "pooled = np.concatenate((group1,group2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setting up PyMC:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How PyMC works is that you specify the model you want to work with, and then you run MCMC on that model. It's not too complicated when you know the priors that you're working with. \n",
      "\n",
      "The *BEST* paper outlines some good starting values for our prior distributions. The idea is that we don't want to weight our priors too heavily or else we might introduce bias into the test, but we also don't want fixed values because the data we are using could be extremely large or small. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Build our model from the pooled data\n",
      "\n",
      "# Setup our priors\n",
      "mu1 = pm.Normal(\"mu_1\",mu=pooled.mean(), tau=1.0/pooled.var()/1000.0)\n",
      "mu2 = pm.Normal(\"mu_2\",mu=pooled.mean(), tau=1.0/pooled.var()/1000.0)\n",
      "\n",
      "sig1 = pm.Uniform(\"sigma_1\",lower=pooled.var()/1000,upper=pooled.var()*1000)\n",
      "sig2 = pm.Uniform(\"sigma_2\",lower=pooled.var()/1000,upper=pooled.var()*1000)\n",
      "\n",
      "v = pm.Exponential(\"nu\",beta=1.0/29)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we want to setup our posterior distributions. Since this is where the data we already have comes in, they're a little bit different than the priors. We inform these distributions with our priors and our given observations and this will be used by the MCMC."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Include our observed data into the model\n",
      "t1 = pm.NoncentralT(\"t_1\",mu=mu1, lam=1.0/sig1, nu=v, value=group1[:], observed=True)\n",
      "t2 = pm.NoncentralT(\"t_2\",mu=mu2, lam=1.0/sig2, nu=v, value=group2[:], observed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Generating the results:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We've finished generating our model, now we just put it together and run MCMC on it. There are different algorithms for MCMC, but PyMC's default algorithm works just fine. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Push our priors into a model\n",
      "model = pm.Model( [t1, mu1, sig1, t2, mu2, sig2, v] )\n",
      "\n",
      "# Generate our MCMC object\n",
      "mcmc = pm.MCMC(model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we run the MCMC to generate our resulting posterior distributions. When running MCMC longer chain lengths result in more precise outcomes. Unfortunately we also have to worry about autocorrelation, which is clumping up of results which will bias our outcome. So here sampling 40000 times, we throw out (burn) the first 10000 results and keep every 2nd result (optionally we sometimes choose every k'th result, but this really depends on the tradeoff you wish to make between computing time and precision). This means that our sampling might take a long time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# Run MCMC sampler\n",
      "mcmc.sample(40000,10000,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  1%                  ] 434 of 40000 complete in 0.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                  2%                  ] 838 of 40000 complete in 1.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 3%                  ] 1268 of 40000 complete in 1.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-                 4%                  ] 1699 of 40000 complete in 2.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                5%                  ] 2121 of 40000 complete in 2.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                6%                  ] 2527 of 40000 complete in 3.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--                7%                  ] 2923 of 40000 complete in 3.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---               8%                  ] 3314 of 40000 complete in 4.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---               9%                  ] 3721 of 40000 complete in 4.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---              10%                  ] 4119 of 40000 complete in 5.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----             11%                  ] 4511 of 40000 complete in 5.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----             12%                  ] 4895 of 40000 complete in 6.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            13%                  ] 5289 of 40000 complete in 6.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            14%                  ] 5654 of 40000 complete in 7.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----            15%                  ] 6039 of 40000 complete in 7.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           16%                  ] 6405 of 40000 complete in 8.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           16%                  ] 6746 of 40000 complete in 8.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------           17%                  ] 7110 of 40000 complete in 9.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          18%                  ] 7485 of 40000 complete in 9.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          19%                  ] 7852 of 40000 complete in 10.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------          20%                  ] 8136 of 40000 complete in 10.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         21%                  ] 8494 of 40000 complete in 11.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         22%                  ] 8866 of 40000 complete in 11.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------         23%                  ] 9211 of 40000 complete in 12.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------        23%                  ] 9577 of 40000 complete in 12.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------        24%                  ] 9935 of 40000 complete in 13.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------        25%                  ] 10208 of 40000 complete in 13.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------        26%                  ] 10522 of 40000 complete in 14.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----------       27%                  ] 10846 of 40000 complete in 14.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----------       27%                  ] 11175 of 40000 complete in 15.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [----------       28%                  ] 11541 of 40000 complete in 15.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------      29%                  ] 11843 of 40000 complete in 16.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------      30%                  ] 12208 of 40000 complete in 16.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------      31%                  ] 12556 of 40000 complete in 17.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------------     32%                  ] 12928 of 40000 complete in 17.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------------     33%                  ] 13298 of 40000 complete in 18.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [------------     34%                  ] 13633 of 40000 complete in 18.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------------    34%                  ] 13983 of 40000 complete in 19.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------------    35%                  ] 14363 of 40000 complete in 19.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-------------    36%                  ] 14723 of 40000 complete in 20.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------------   37%                  ] 15077 of 40000 complete in 20.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------------   38%                  ] 15421 of 40000 complete in 21.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [--------------   39%                  ] 15781 of 40000 complete in 21.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------  40%                  ] 16105 of 40000 complete in 22.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------  41%                  ] 16479 of 40000 complete in 22.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------  41%                  ] 16779 of 40000 complete in 23.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------- 42%                  ] 17154 of 40000 complete in 23.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------- 43%                  ] 17510 of 40000 complete in 24.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [---------------- 44%                  ] 17868 of 40000 complete in 24.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------45%                  ] 18230 of 40000 complete in 25.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------46%                  ] 18557 of 40000 complete in 25.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------47%                  ] 18890 of 40000 complete in 26.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------48%                  ] 19218 of 40000 complete in 26.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------48%                  ] 19577 of 40000 complete in 27.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------49%                  ] 19900 of 40000 complete in 27.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------50%                  ] 20266 of 40000 complete in 28.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------51%                  ] 20589 of 40000 complete in 28.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------52%                  ] 20910 of 40000 complete in 29.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------53%                  ] 21269 of 40000 complete in 29.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------53%                  ] 21578 of 40000 complete in 30.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------54%                  ] 21895 of 40000 complete in 30.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------55%-                 ] 22216 of 40000 complete in 31.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------56%-                 ] 22553 of 40000 complete in 31.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------57%-                 ] 22934 of 40000 complete in 32.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------58%--                ] 23283 of 40000 complete in 32.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------59%--                ] 23661 of 40000 complete in 33.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------60%--                ] 24046 of 40000 complete in 33.5 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------61%---               ] 24421 of 40000 complete in 34.0 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------62%---               ] 24805 of 40000 complete in 34.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------62%---               ] 25183 of 40000 complete in 35.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------63%----              ] 25564 of 40000 complete in 35.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------64%----              ] 25945 of 40000 complete in 36.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------65%-----             ] 26330 of 40000 complete in 36.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------66%-----             ] 26713 of 40000 complete in 37.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------67%-----             ] 27092 of 40000 complete in 37.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------68%------            ] 27474 of 40000 complete in 38.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------69%------            ] 27855 of 40000 complete in 38.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------70%------            ] 28239 of 40000 complete in 39.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------71%-------           ] 28620 of 40000 complete in 39.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------72%-------           ] 28990 of 40000 complete in 40.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------73%-------           ] 29292 of 40000 complete in 40.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------73%--------          ] 29564 of 40000 complete in 41.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------74%--------          ] 29943 of 40000 complete in 41.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------75%--------          ] 30308 of 40000 complete in 42.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------76%---------         ] 30668 of 40000 complete in 42.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------77%---------         ] 30997 of 40000 complete in 43.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------78%---------         ] 31340 of 40000 complete in 43.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------79%----------        ] 31683 of 40000 complete in 44.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------80%----------        ] 32053 of 40000 complete in 44.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------80%----------        ] 32366 of 40000 complete in 45.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------81%-----------       ] 32739 of 40000 complete in 45.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------82%-----------       ] 33108 of 40000 complete in 46.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------83%-----------       ] 33420 of 40000 complete in 46.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------84%------------      ] 33710 of 40000 complete in 47.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------85%------------      ] 34068 of 40000 complete in 47.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------85%------------      ] 34376 of 40000 complete in 48.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------86%------------      ] 34731 of 40000 complete in 48.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------87%-------------     ] 35029 of 40000 complete in 49.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------88%-------------     ] 35372 of 40000 complete in 49.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------89%-------------     ] 35687 of 40000 complete in 50.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------90%--------------    ] 36058 of 40000 complete in 50.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------91%--------------    ] 36430 of 40000 complete in 51.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------91%--------------    ] 36745 of 40000 complete in 51.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------92%---------------   ] 37116 of 40000 complete in 52.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------93%---------------   ] 37442 of 40000 complete in 52.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------94%---------------   ] 37769 of 40000 complete in 53.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------95%----------------  ] 38064 of 40000 complete in 53.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------95%----------------  ] 38385 of 40000 complete in 54.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------96%----------------  ] 38738 of 40000 complete in 54.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------97%----------------- ] 39011 of 40000 complete in 55.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------98%----------------- ] 39317 of 40000 complete in 55.6 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------99%----------------- ] 39679 of 40000 complete in 56.1 sec"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------100%-----------------] 40000 of 40000 complete in 56.5 sec"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Checking for convergence:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When testing one might want to test whether or not the sampler converged to some value. PyMC lets up plot a trace for each parameter and examine this. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymc.Matplot import plot as mcplot\n",
      "\n",
      "mcplot(mcmc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plotting mu_2\n",
        "Plotting"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " mu_1\n",
        "Plotting"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " sigma_2\n",
        "Plotting"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " nu\n",
        "Plotting"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " sigma_1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAFwCAYAAADwl6zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREJAA0CBMREREJAA3CRERERALg/wOIR2m5MKARDgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1011e66d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAFwCAYAAADwl6zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcVHX+P/DXgQG5CeOgiIqICCreEPKSSWrSumbectPS\nXIu0C1aatZX6rba+5rrYomES+dN2y+2yft0N0zKrJVPQSnAkNdBQUUPFCWYAuTPM/P4YZpjLmStn\nzpzDvJ/72EfMzDnn855zkHnP5/M+nw+j1Wq1IIQQQgghvPLxdACEEEIIId6IkjBCCCGEEA+gJIwQ\nQgghxAMoCSOEEEII8QBKwgghhBBCPICSMEIIIYQQD6AkjBBCCCHEAyS2XszJyYFcLkdoaCgyMzMB\nAHv37kVeXh5CQ0MBAEuWLMHYsWNN9mttbcVrr72GtrY2qNVqjB8/HkuWLHHTWyCEEB1rf3vq6+ux\ndetWVFVVoU+fPlizZg2Cg4MBALm5uTh8+DB8fHyQlpaGxMREAMClS5eQnZ2NtrY2JCUlIS0tzZNv\njRDSDTG2JmstLS1FQEAAtm/fbpKEBQYGYvbs2TYP3NLSgh49eqC9vR2vvvoq/vjHP2L48OHcRk8I\nIWbY/vYUFRWhZ8+emDdvHvbt24eGhgY89NBDqKioQFZWFjZt2gSlUokNGzZg27ZtYBgG69atw/Ll\nyxEXF4dNmzbhnnvusfjCSQghXWFzODIhIcHwbdGYI5Ps9+jRAwCgVquh0WgQEhLiYoiEEOI48789\nwcHBKCoqwtSpUwEA06ZNQ2FhIQCgsLAQkydPhkQiQUREBCIjI1FWVgaVSoXm5mbExcUBAKZMmWLY\nhxBCuGJzONKaQ4cO4ejRo4iNjcWyZctYEzWNRoOXXnoJN2/exIwZMxAVFWX1eHl5ea6EQQgRsdTU\nVLcc1/xvz8CBA1FbWwupVAoACAsLQ21tLQBApVIhPj7esG94eDiUSiUkEglkMpnheZlMBqVSydoe\n/f0ixDtx8TfM6SRsxowZuP/++wEAe/bswe7du5Genm6xnY+PD9588000NjZi48aN+PnnnzFy5Eir\nx01OTnY2FE4UFBQgJSXFI21T+55t35vfu6fbl8vlbju2+d+es2fPmrzOMAznbXrq7xcRDn3Sbi1Z\nJ90LV3/DnL47MiwsDAzDgGEYTJ8+HRcuXLC5fVBQEJKSknDx4kWXgySEEGfp//ZcunQJYWFhqKmp\nAaDr/QoLCwOg++Csrq427FNdXY3w8HCLnq/q6mqTnjExKigo8HQIFrZt24Zt27ZZPC/EWNl01zit\nXRd3E8v55JLTSZhKpTL8fOLECURHR1tsU1dXh4aGBgC6u5XOnDmDwYMHdyFM9/FkTwS179n2vfm9\nC6F9d7D2t2fcuHH47rvvAABHjhzB+PHjAQDjxo3DsWPHoFaroVAoUFlZibi4OEilUgQGBqKsrAxa\nrRb5+fmGfQh3Vq1ahVWrVnk6DGKGrgt/bA5HZmVloaSkBHV1dUhPT8fChQtRUlKCy5cvg2EYRERE\n4LHHHgOg64LdsWMH1q1bB5VKhXfeeQcajQZarRZTpkzB6NGjeXlDhBDvVVNTg+zsbIu/PYMHD8bW\nrVtx+PBhwxQVABAVFYVJkyZhzZo18PX1xfLlyw3DlStWrEB2djZaW1uRnJws+jsjxZR0iyVWipNb\nYomTSzanqOBLXl4e1YRR+17Vtre3L5fL3VaYzzdP/v0iwkE1Yd6Fq79hNGM+IYR4ASHW21BNGD+o\nJky4XJqiojvxdPcntU81Yd7aPiFUdyRMdF344/VJGCGEeAMxJd1iiZUtzoqaZof39/P1Qd+e/lyG\nxErM57O78/okzJvrcry9fW9+70Jon5Du6NF/lzq87RMT++MPo/u6MRoidFQTRgghXkCI9TZUE8YP\nqgkTLq/qCdu3bx82bdqEkJAQw1Ijxj0BBw8exIQJE9C7d2/eYvJ0T4Q3t+/N710I7RNCtUfCRNeF\nP17VEzZ16lSbmfYXX3yBqqoqi+cFMIsHIYR0iZiSbrHESnFySyxxckkUSdjHH3+MZcuWYcmSJZg9\nezY+/fRTLFiwAPPnz4darUZBQQFeffVVAEBpaSmefvpp1uP06tULfn5+Js/pk7IrV67g22+/xdNP\nP43XXnsNn3zyCZYvX44lS5YgLy8PL7/8MubMmYPf/e53hrXoTp48iVmzZmHu3LnIzs4GAGzZsgVz\n5szB7NmzUVpqvzbA092v3ty+N793IbRPuLFv3z5MmjQJvXv3xk8//cS6TUFBARYvXsz62urVq3H+\n/Hmrx//kk09QWVnJSayEEFOiSMIYhkF4eDg+/vhj3H777Th16hQ+/fRTjBw5Et9//z0nC/IOGjQI\nqamp2L59O1577TUAQI8ePfDxxx/j7rvvxv/8z//gwIEDyMzMxNtvvw0AePnll7Fr1y7s378fK1eu\nRGlpKS5evIgDBw5g586d2LhxY5fjIoQQW0aMGIF//vOfuOOOO2xuZ20S0aysLAwbNszqfu5Mwqgm\njB9UEyZcoqkJGzFiBAAgMjISwcHBAID+/fujpqYG4eHhhu2cHTq01f1pvEzJtm3bcPToUQAw9Ka1\ntbWhf//+AHSJ4vnz53HixAnMnTsXACCR2D+9nu5+9eb2vfm9C6F9bzRnzhyMGTMGP/zwAxoaGvDO\nO+9g69atOHfuHO677z6sX78eV69exeLFi3Hs2DEAwPbt29HQ0ICXXnqJ9ZhDhw612y7DMGhubkZa\nWhpKS0uRmJiIHTt2GGJ64403MHr0aDzzzDMoLi4GwzB46KGHMGDAABQXF+OJJ55AYGAgDh06hICA\nAM7OB9UeCRNdF/6IJgmz1tul1WohlUpx/fp1ADAMFbpCIpGgvb3d8NjHR9dRqFQqceTIERw8eBDF\nxcWGoU9/f3/cuHED/fr1g1arxdChQ3HHHXcgKysLAKBWq12OhXBPq9WiRa1BgJ+vp0MhXophGPTo\n0QN5eXnYsWMHli5diu+++w5SqRTJyclYuXIl6z5dpdVqceXKFfzrX/9CZGQkZs6ciRMnTmDChAmG\n458+fRqVlZWG5K+urg6hoaHYtWsXNmzYgMTExC7H4SixfEGgOLkllji5JIrhSMD0D5H5zyNGjEBT\nUxMWLFhg+BbH5tixY1iwYAEuXLiAP/zhD6isrDTp/rz77ruxfv16bNmyxWS/Xr16oVevXpg7dy72\n799vOP4bb7yBRx99FHPnzsU777yDESNGYMiQIZgzZw7mzZtnGLa0xdPdr97Ufn55DeZ+cNojbbPx\n9va91T333AMASEhIQEJCAiIiIuDv74+YmBhcu3bNbe3edttt6NevHxiGwejRo3H16lWT1wcPHozL\nly9j7dq1yMvLQ8+ePQ2v0c1JhLiHKHrCjAtKV6xYYfj5qaeeMvz80Ucf2T3O5MmTMXnyZJPnLly4\nYPh59uzZmD17tsV+DMOwHj85ORlffvmlyXOrVq1yuSv3nKIBwyOCXdqX2KdoaPV0CITA3183Q7qP\nj4/hZ/1jtVoNX19faDQaw/NNTU2ctFtfX2/42dfX16KnPiwsDEePHsW3336L999/H5999pmhLoiL\n3jg2+uOb/80Uy0TC3TVOa9fF3cRyPrlkMwnLycmBXC5HaGgoMjMzAQB79+5FXl4eQkNDAQBLliwx\nqZ0CgKqqKmRnZ6O2thYMwyA1NRWzZs1yKrCt+VeREiPF+IGhTu2nt2HDBhQWFhoeT5s2Dc8995zF\ndnxe8IvVTYiVBZj8QdO3r6hvxar9v+DrFUm8xWPcvqcIsSbskrIJskAJpIF+rK+7u32+eLp9wi4i\nIgJVVVVQqVQICgrC119/jdTUVIf2dbXHSqvVQqlUQiKRYM6cOYiLi0N6ejoAICQkBLdu3XLpuPZQ\n7ZEw0XXhj80kbNq0aZg5cya2b99u8ry1HiPDQSUSPPzww4iJiUFzczNeeukljBkzBlFRUQ4H9uX5\natS3truchL3yyisu7edO6bnnkD1/GOJ7B1m8pqHufrdj4Ni3+Sc/PYcJA0Pxxu+HuDki4s0YhmHt\nYfLz88MLL7yAu+++G/369cOwYcNs9kR9/vnnWLt2LZRKJR588EGMHj0ae/futWjL+AYmtlhu3LiB\np59+2tALp699Xbx4MZ5//nm3FOZbI5YvCBQnt8QSJ5dsJmEJCQlQKBQWz9v7tiWVSiGVSgEAAQEB\niIqKgkqlcioJ4wvf3Z/tGtNzZ96+RquFj5u6/tl4uvtXqGtHqtvdnxR787n3Vvv37zf8bF4eYfza\n448/jscff9yhY9r7UszWVkZGBmu7hw8ftth3zpw5mDNnjkOxEEKc41JN2KFDh3D06FHExsZi2bJl\nhikj2CgUCpSXlyM+Pt7mMY0/EHQFw8EmrwEwe118j4FgaK28XtPGAAjCzPeK8erwBkHE6+xjv+jR\n+PcZBeaF3RREPBbnP2yo6eMO5tsDgKqmxurr3P0+uPf4Qm4/KMiyN5i4lxCTbqoJ4wfVhAkXo7XT\nraVQKJCRkWGoCautrTXUg+3ZswcqlcpQO2CuubkZr732GhYsWIAJEyZYbSMvLw/Jyckmz83YdQp3\nDpbildTBTr0hIZux6xSy5g5FAkvxfeWtFizbUwIAvNeFcWVr/lV8eb6ak/gbWtsR7O+L/5xR4P2i\n6ziQNtb+Tnb854wCO368Zje+GbtOIbl/T/x1VlyX2zT3VsFV3Gpp71a/166Qy+UO1zkJHdvfL3d4\n8cUX8eOPP5o89+STT1qdCd+cmD7gxBKrcZwymQwAMG5znsP7PzGxP/4wuq9bYjMmxvMpdFz9DXO6\nJywsLMzw8/Tp0026tY2p1WpkZmbizjvvtJmA2bPt2K9Qa7R47s5ol48hJNYGGh2tVxKDdV9eQNKA\nnlg0xrU/Lqdv1ONPX5Th6xVJOKdoQAsPQ4N8OXxRhaY2jf0NBeBabTPeK7yOV++O9XQoBMDmzZu7\ntL9YPtwA8cRKcXJLLHFyyel5wlQqleHnEydOIDraMjnSarV49913ERUVhXvvvbdLAR48V4VD56u7\ndAxb3DVXUrtGixm7Tlk8b55OeGKupvoWNa7WNHPevj6NPHntFo5eqrG5rR5b+zVNbYaf3Zl+eeLc\nG6fanp6ny177hRW3UHC5lqdoCBGOC1WNOHJJ5dT/CXGFzZ6wrKwslJSUoK6uDunp6Vi4cCFKSkpw\n+fJlMAyDiIgIPPbYYwB0s8rv2LED69atw/nz55Gfn4/o6Gi8+OKLANinsujO2gV8t2PWsV9x5FKN\nw8OG7RotTt+oR9KAnvY37qB1a/rkGrb7HdQaLSQ+3PZCzth1Crv+kIDoXqZ3kV2taUajQHvBmtva\nceiXagwIDXD5jmQibEIc6hFqTdgPV2uxW959Fi2nmjDhspmErV692uK56dOns24rk8mwbt06AMDw\n4cOxZ88eDsJzPy4ueFu7BnkXVJg5zPot4GxO37iFSXdMtr8hdDVSNU1tGBDW9dvDm40SAeP3r9Zo\n8XlpFeaP7GN47oertdiYV46Wdq39pM1GLnP+twYM62NZC+fpecJm7DqFg4+OtZmIfX+lFon9QhDk\n7/hyRxV1zRZJ2JOfnrNo39y+nxU4ee0WNsxw//QY+vbLlU14oiO2WBklYYQ/NB+VMNF14Y/gly3i\nu1Lq2OUafHGuyql9yqqasCX/qv0NzfzpiwsoqqhzaNu38q8ibW+p020Y02q1aGxtt9pHdbWmGe98\nX2Hy3DdlSk5qsp757BdUuTBjvastb8grx1VVs8lz1Y1trNvam6Ptz99cwle/uDYkrtVq0dTWbvjZ\nng9OVuLHq479TnCltpl9jVNr//aa1Rq8+vVF9wVE3EJMPQxiilUMxHI+xRInl4SdhGndt1yGnr4u\npvj6LRy9pMLbx35FVsGvTh2DbejN0ah//ll3R6S9t9nY8UH+4sEyZ0Iz8e1FFebvPm3ynHFdkLsT\nXg1LDsJal8RBIPnlNThR0VnPpNVqsfe06Zx3XanJeuCjM6i81WJ3u4PnqjGvY71K899ltvYbWtst\nnnMXtvYdGUVX3GrFDzwnioQQ0h0JOwnjUcZ3V/DGt5f5b9hOwlHfosb3VzqTieLr9Ta2tu23el1P\nlLUPWrZE0Fp4z3x2Hr9UNVrflqUNl8rkOvbZmFdud9N/FF13qbfNlZxP1aTGsj0l2HXC9oLLlfXC\nX6+S7bpvP/4rss16RYm4efpGEDbbtm0z1B8ZE2KsYubs+bR2XdzNG6+7oJOw/Ms1bu+dMe/+rOex\nJwIARowYYfP1z0qq8OdvLnFc5t55NOP3z3aurZ3/8781Qn6t6+vJ6ds/feMW3j5mvQfySLn9uy0/\nKb6Jgsud29mb9sN8MXdHtKg1JsOX+Q7E1RlPp2/KlMi6HGZ1Wz7Y6vr/+WaD4edfqhpNh1I73sj5\n3xpASFesWrWK6o8EiK4LfwSdhPFJ/wHZaqX+qa1dg1e+vojKWy0O1fY4OozKtlV1g9EUDVbaKlc2\n4VptM+tr9liL3umhX7PY7O3PNmwrv1aHs5X1OHiuGgdKLWvx7N1lef8/T+Noeeft4e0aIO+CEgCw\n40frvVTtGi0qaq0PJ1prdc77PyH37G+srx06X22YlsT896igvAZtRuOxZyvrDbVYv9Y0Y8auUzjx\nK3dDfGcr6/GfM5ZLjulptFrD7xbbdCLGV/LpfedxptIy4RLLfGdER0z1NmKKVQzEcj7FEieXvC4J\nq21W4+atziEiR7s/61ra8ePVOizbU2LxgXSr2bT3rKG1HWq2AigWJSW6mjDj7Vva7X+4PfHpOaza\n/4tDbRiYJUkzdp1irQkzmd/MibzMlV7LtV9exP985XqRd11LO0qMem12/HgNGd9dMdmmrV2DP39z\nyeS5Q+ersfzfnTc6vF90Hd92JG/2XK/rTN60gOHmCuObM/RrhOp7zS4pm0yOYfzbca0jGXy5C+fB\n3IfySptJ6P/+txyvfloIAKzJr/m1vFjdaPU1QgghrhF+EtbxF1+r1eKnG10f/nrlq4v4456fLZux\n88li/HKz2jRJetXsA/6+3afx/8w+ADd+y17TdFzpBwAmtUXGHUxsqVyrums9EMbHv1jvi6a2drRr\ntIZkwJjNIT0bJ60rw6e22lzx71KLux7tdUzWNbdbFJI3tHUmzlUNbfi4+Cb++t0VQ9Jk69fhV6Me\nyMpbrVh/yHryZH4zgN53F21P7ph7VoF9P+t63Bpb2y0m/n0r/ypeMbpDUT/B7bXaZt3vZ8cb+KZM\niWaj96qob0VjazuOX6nFjyo/i99lA7MTkPND5+8nj+vLEw4Jsd6GasL4QTVhwuXSAt580v+9v1jd\nhBe+uNDldQn1NV9fnKvCIGkAwoc6f7xvLyhxWdVksSxPu0YL3475pox7SwDgSMeQj3nCcKVRN/dU\nldEQZLtWC41WCx8rn3aXO2a7v9XS+eH6y2+NiO8dCGWTGgx0tUr3DAuHv8R2nv1RRQA++uA0lozt\nyzo7uiMfuG8VXEVlXSsGhPWwuZ21ZKmpTYNvjZIS4yFI812u1jTj6c/Oo1mtwd8XJrBuY6y6sQ01\nVqZg0PvL4cuGn7OPsxej5/xwzTCc6MrNEebn0WryY9QeANwzLBx/+sLyjtij5TUm9YuLPjqLjxeP\nRNreUiwY1cfw7+bNI1fg5xuDabG9AABL//Uz7oyRGvb7oOgGe7xW0lBHhuIJcRTVHQkTXRf+iCYJ\nc7TvJ++CEncOlsLf13byYTwNhSOJnfGH6LcXVfj2ogofnLyBd+YPMzz/P4cuGhZ91n9WabVa1lop\n494J8+M//3kZhsgCnVpA+unPzmPrnHisOdD5gX2zvhWPTxxgsS3bx+i1OsteMFUT+7xa5o5frkVN\nsxry6509lWxtaAH87cgVpE+KQrATE5+a0ycwjzowb9pDn5xFgJ1E9IrKuGfLep3YgRLn5o/jwvJ/\nl0BRr7sOF6sbMSQ8yOq2+jq0ylutNm8wMU5KG9vYt7OWfD/yfyWICPG3FzYRIDHV24gpVjEQy/kU\nS5xcEv5wpJMyvruC4uvODVvuL/kNLVZ6Jhpb23H4opK1X6CtXYtyow/wMqO6GXsTgD6yt8Tqa7XN\nahTbGHrNMOq5MdZuVodWZ9YDpH8PrHc1moWr1mjxwEdnbQ716Y/HtsnFal0NVENrO5478Ishvq/L\nlLhsVh9lzYxdp0zad7TOzphGC7tLBRn3Spk38cHJzp6im05ON2F+/q2y0duoT8AA4MSvdYYifluO\nX6nF+d8arb5uXN9lfkavd9RLWgvpxq1Wkxo8QgghrhN+EtbxlVz/oXCmst6hSTJ3/niNtXaKLanY\nfrwCdS3sPQKHL6qw6fAV1tcA4PCFzmE0tuFDa2mDsrHzA5rtw9o8TuPHv9q4q8+YcTharRY3WHq7\nDK+bPX6/6DoA4DsbC9PWt9hOMt7IK8e1uhac7fjQdnQky9pQmPya+ycI1fcgXe1Idj465fr6cfd/\neIarsAAA+0uqTIatXWWclCrNVhHQfxlh6wkz9IxSTZgoCbHehmrC+EE1YcIlmuFIvec/L8Pt0aH4\nX7O19U5du4XhEZ1DNXvPKDA9ToarNU24VN2E5RMsh+WcisPK+Mz3VzvrqIyXH3Smz6a1XWOReOj3\nZyuWd8XZmw04eN760jvm8f5m5cO+5GYD/u/0TQDAntMK7DmtQFgA+6/R0fIaLErsrJvr6qLeL391\nifV5LsuU9D1IVVaWOHKVrbzlaweXRKpubGOtLdMnzK027qo9WVHHmlDfsPKFhi0RfuCjswB0PcAA\n8OLBrtdoEu9GtUfCRNeFP4LvCWMrYK5n6bV66csL+HfHvEidH8pabDp8BXus3KFmy4xdp1CqaICy\n49u/I1/+TXuedP81nu1eb6ETPST6t3LSxsSoP1fWm2xrHgPg/JxO1oZTCy7X4DjLe7LK6DCP/eec\n+VN2cZWEmvvJySFrd2K7IcIa/YS2+h67//3vJXzaMW/Z4/85x7qPVqvFukMX8dUvllNw/Fpj5fxS\nb1e3I6Z6GzHFKgZiOZ9iiZNLNnvCcnJyIJfLERoaiszMTADA3r17kZeXh9DQUADAkiVLMHbsWIf2\ntaWtXYNV+39Bzn3D7W5r7UP8n3LToaNbVoYYHVWubDIcU1/jZItp74Euytf/W27RW2BtwWRzG/PK\nHZopfs3numL8C1WmMX5dpsTjEwcg1EpPlTmTuxKdWNrI1h1z5vNzGctjmZfrTKXpnYdXauxPSNvY\n5vi8bHqFFdwmYWcrXV9OyhnmQ9GVt+zXqTk7CWxjaztqmxysZyOEEOIymz1h06ZNw/r16y2enz17\nNjZv3ozNmzezJmC29rWmsU2Di9VNUDhQ/NxmZVZ7c2xr37k6zPTSlxfsbuPD0hMG6D7U7GEbqjMe\n6nQE2+ScSgfvcDRvni2vatdYnzbDmmor5zvvgpL1BoHnP3d+gfJvypTIL7c975a7PWcWt/kUJe5e\niN6WvAvOnZvX/1vOercsETch1ttQTRg/qCZMuGx2kSQkJEChsBzKc2SuIGv7WqP/iGpsbWct+P70\nbOex9D1Jn/38G7K/r8Azd0SZxtfx3zaWGhlrd0GyuepAL4wx48/ZdqNz9KjRnZDW6qLsrXPoqsf/\ncw5p4/rZnNoAsIyLLc57/l5sZV/nmc9qb8yVM+HCjZNu9dnP7EsbmTt+xfG1J/lS1Sj8RcdJ90C1\nR8JE14U/LtWEHTp0CC+88AJycnLQ0MDN7erGs5Av+KdlzZTxt3l9gvDJT7qhwrfNJ9js+EC+Xte1\nD5NPrawRaI1xIlWq6JwGQOnA0A7bECVXadk/im7YnTKj4HKtSX2QwHIa0TOe6sLYa9+wr6TgKFvr\nX7qMLn63JKZ6GzHFKgZiOZ9iiZNLTidhM2bMwPbt27F582b06tULu3fv5iSQH+S6xMuRv/8tzS02\nuy316zEa46Ob05ERpxssieGOb06xD/9wOIS146hzw3xVVY7dsQc4X3v3r2PW50gDgP/Ncz4xsdWz\nZo6P34U2tXhrqhydAkWvoKDA5Jw68pgQQogLSVhYWBgYhgHDMJg+fTouXLBfK+WI4Qn2l6DR8+/R\nAykpKVaH8L6oCrZ4jo8M28eBnKlEYdlzeKSKfQZyZ4ZO7bmldW6W814yGWdtm7uhCXHbsR3Bx++C\nn0Tws79wJiUlxeScOvKY8E+IyS/VhPGDasKEy+lPCpVKhV69dOvQnThxAtHR0ZwE4spcT9bqq6z1\nzNQ4WqTuIkfqujy19p61yWitcWeUzva0cM3euo1coBE9Quyj2iNhouvCH5tJWFZWFkpKSlBXV4f0\n9HQsXLgQJSUluHz5MhiGQUREBB577DEAgFKpxI4dO7Bu3TqTfW/duoX09HQsWrQId911l9W2NDx8\nbFU3uneIiGHsL1fEumRQFzg63YXTunEWMff9nzwdAiG8E1MPpJhiFQOxnE+xxMklm0nY6tWrLZ6b\nPn0667YymcyQgFnb15bOBa8d39bZOwo35Fmfs4oLDIAP5baXubnhwLxOznBm4ldn/Ojk3FLEVFfn\nqCOEENL9CX7GfDZaw3+d666pbXbvByPDMPjVyWktCCGED0Kst6GaMH5QTZhwCaZ6WMvyk/VtXRsr\na3Bg0tSuYNCtR/EIIYRTVHskTHRd+COYJKyuo7bJkeFIZaPaYwXutnhwUnRCCICqqipkZ2ejtrYW\nDMMgNTUVs2bNslhubfHixUhK0i0nlpubi8OHD8PHxwdpaWlITEwEAFy6dAnZ2dloa2tDUlIS0tLS\nPPa+uCCmehsxxSoGYjmfYomTS4JJwrZ3TLgqd3Bh5YbWdqfuqPxH0XVXwnKKI1NUEELcRyKR4OGH\nH0ZMTAyam5vx0ksvYcyYMQB0y63Nnj3bZPuKigocP34cW7ZsgVKpxIYNG7Bt2zYwDIOdO3ciPT0d\ncXFx2LRpE4qLi60u00YIIa4QXE3YrhOOJUsaLaByYpHhT4pvuhqSw5SNahx1YMFtQoh7SKVSxMTE\nAAACAgIQFRUFpVK3UDxb73lhYSEmT54MiUSCiIgIREZGoqysDCqVCs3NzYiLiwMATJkyBYWFhby9\nD3cQYr1wJsS3AAAgAElEQVQN1YTxg2rChEswPWHOsjcVhCfUsax5SQjxDIVCgfLycgwdOhTnz5/H\noUOHcPToUcTGxmLZsmUIDg6GSqVCfHy8YZ/w8HAolUpIJBLIjCYslslkhmSOcIdqj4SJrgt/RJuE\nVTW4d+JVVwhtEWlCvFVzczO2bNmCRx55BAEBAZgxYwbuv/9+AMCePXuwe/dupKenc9ZeQUGBoZ5F\n/22eHnftsR7f7f/4449o1fYDX77//nu0t7d32/PpzOOUlBRBxWPrcVBQELjAaAVQ4Z6Xl4e1ciqo\nIkRsvl6R5NJ+crkcqampHEejo1arkZGRgbFjx+Lee++1eF2hUCAjIwOZmZnYt28fAGD+/PkAgI0b\nN2LRokXo06cPXn/9dWzduhWA7g9vaWmpYXJqY3l5eUhOTnbLeyGe8aH8BnbbmfPRXNGLut/ncZvz\nHN7niYn98YfRfZ1qhwgDV3/DBFcTRgghrtJqtXj33XcRFRVlkoCpVCrDz8bLrY0bNw7Hjh2DWq2G\nQqFAZWUl4uLiIJVKERgYiLKyMmi1WuTn52P8+PG8vx8uCbHehmrC+EE1YcIl2uFIQggxd/78eeTn\n5yM6OhovvvgiAN10FMeOHWNdbi0qKgqTJk3CmjVr4Ovri+XLl4PpmGtmxYoVyM7ORmtrK5KTk+nO\nSDeg2iNhouvCH0rCCCHdxvDhw7Fnzx6L5/VzgrFZsGABFixYYPF8bGwsMjMzOY3Pk8Q0B5OYYhUD\nsZxPscTJJRqOJIQQQgjxAErCCCHECwix3oZqwvhBNWHCRcORhBBCPIJqj4SJrgt/bCZhOTk5kMvl\nCA0NNdRGmK/BtmTJEtaC1eLiYrz//vvQaDSYPn264RZwQggh/BNTvY2YYhUDsZxPscTJJZtJ2LRp\n0zBz5kxs377d5Hm2NdiMaTQavPfee3jllVcgk8mwbt06jBs3DlFRUdxETQghhBAicjZrwhISEhAc\nHGzxvL35XS9cuIDIyEhERERAIpFg8uTJKCoq6lqkhBBCXCbEehuqCeMH1YQJl0s1YWxrsBlTKpXo\n3bu34bFMJsOFCxe6FikhRJBcWfaDqyU/iLhR7ZEw0XXhj9NJmLvXYCOEiIt5HYcjj+VyudvjIqbE\nVG8jpljFQCznUyxxcsnpKSrCwsLAMAwYhsH06dNZe7hkMhmqqqoMj6urqyGTyboWKSGEEEJIN+J0\nEmZtDTZjQ4YMQWVlJRQKBdRqNY4fP45x48Z1LVJCCCEuE2K9DdWE8YNqwoTL5nBkVlYWSkpKUFdX\nh/T0dCxcuBAlJSWsa7AplUrs2LED69atg6+vLx599FFs3LjRMEUF3RlJCCHEmLfXHl1RteDnm/Ww\nc6+bCR+GwWBZAAL9fN0Wl7dfFz7ZTMJWr15t8dz06dNZt9VPRaGXlJRkc702Qggh/BFTvY2YYu2K\nQ79U49Av1U7t0zfEH9vnDUOgn+P7iOV8iiVOLtGyRYQQQgghHkBJGCGEeAEh1ttQTRg/qCZMuGjt\nSEIIIR5BtUfCRNeFP9QTRgghXkBM9TZiilUMxHI+xRInlygJI4QQQgjxAErC3CSmV4CnQyCEEAMh\n1ttQTRg/qCZMuERRE+bnw6BN48REKgJgHO2DiX3xr59ueiwWQggRIqo9Eia6LvwRRU+Yny9jd5vf\nxcsgCxJOTmkc8qIxEZ4LhBBCIK56GzHFKgZiOZ9iiZNLgkvC+of6AwAeSop0aj+tM1MO86B/aA9P\nh0BEzN+BLx6EEELETXBJWGK/ngCEl1Q5iwF9iBLXzR3RBwAwI54WvifcEGK9DdWE8YNqwoRLOON3\nHTRdSL6ElPhoIe4k0t+XQWu7sN4DA4j8rDquZw/dunBJA3ri6zKlh6MhxD2o9kiY6LrwR3A9YdKO\nBbFsfdjeOzwcEwaGmjwnlA/nu+N6WTzHMMJJDh01Y2i4yeMQf+4Wi105aYBL+4UG8P+dYVqs5fV0\nxqaZQ1zaT/8bw3eH8K4/JPDbIOGNmOptxBSrGIjlfIolTi4JJgn7Y7KuBmzWMN2Hv7+v9dCiwgKQ\nEiM1PJ4wMBSPTXDtg51rUpZVVcU4tGqeNqYMlrJuZ+6eYeH2N3IR23lMv30Ats6Od1+bXUzv43sH\nubajpxJ38X1fIIQQ0bKZhOXk5OCxxx7D888/b/HagQMH8MADD6C+vp5134MHD+L555/H888/j4MH\nD9oN5I/J/QDo7oR87/4ETI3t/NBn+xg0/kBO7BcCWZATS8oTALrzZk201LV5znx9uv4p/uj4/qzP\nj+gbbPFcRIg/RkaG4NmUgVaPN6+jvsolPOfP7r6hg+0cEu8gxHobqgnjB9WECZfNJGzatGlYv369\nxfNVVVU4ffo0evfuzbrf1atXkZeXh02bNuHNN9/EyZMnUVlZ6XBQA6UBFvVdk2PCTB4bfzZ6qqPJ\n1lDVvJGdH/zuDC/Qz7nOzAeMpsuwFdfcEabX1tHePHd1pGybOxSv/S7Wsr2OBmcNZ/9dBICn7ohy\n+W7D8GDHkvvQHtwM14YF6I5jGI60sl2mC71/if1C8NacoTa3oY4wwqdVq1a5tf6oua0d12pbnPr/\njboW1Le2uy0mMXD3dSGdbBbZJCQkQKFQWDy/e/duLF26FJs3b2bd7/r164iPj4e/v266iREjRuDE\niROYO3eu3YAC/dg/zMZEhuD7K7XQz9lqnBNoOv7bt6c/qhvb7LZh2p4Pmto09jdkERpg/YNXf5en\nu42ICMbJa7cc38FomMtaXrX7gRFgGAZPTYpC9vcVAHSJsZOH55QPw8DH7OAbfz8ESQMcO8+uJsLL\nx/fHp2d/w+BeAShXNVvd7t9/HIMZu06xvvbAmAjsOW3574hNRIg/ShWNdpOhuPBAh45n7M17dYnb\nuKieKKpg/53Rn+KB0h74tabF6TaIcImp3oarWBvbNHj+81+gbFJzcjyxEsu1F0ucXHK6JqywsBDh\n4eEYNGiQ1W0GDhyI0tJS1NfXo6WlBXK5HNXV1TaPW1BQgK9XJCHY3xcFBQU4ebIIAPDy9BjM69uI\nPjW/4NDyJADApfJLKLtwwbBvefllFBQUYJADicIrqYNNHkf62U/aBgSwfyvytZFxGHer/vDDD3bb\ncEVivxC8PsOyd8iYvtbOQKvFipgm/QMAne9PP5J4Vl6IgoICw+Pn4hrRdOOSQzE5cofqxYuWx0oO\n67wOly9ftni9+Kdii+fGDwyFxIdBQUGBzW7sgoICJIe2Wn39T/ENVl/z66hNbGy0vo2+DTZaAD7K\nqzb3BYCHo3XXRJ9olnecA/MeyHV3DcKKQU04+eP3do9pTWhLNcIktr94NDU22XwdAFZP1g0Bm59/\nRx4TQghxMglraWlBbm4uFi5caHiObZhqwIABmDdvHt544w385S9/weDBg+HjY7sp4ww4JSUFt902\nDgAwJbYXHp05yeT12MGxGDKk866zO8YMdTiDTogwLZRmrPSRGN/VdvfIKNZt2HphInv6G96D3u23\n326yTU8Ohq6euSMKG2cOsXkDA9BZa6enBTBl4m0AAP0MFD17mr6PyXeYnu+Z0yZj5MiRDsXlSE/Y\nkCGWieP6OcmGnwfHxFi8PnbsWKvHS0lJsXr9o8J6ICUlBWvn3GZ1/xlT7f/uBAdbr5/Tx2BNwogR\ndo8/daLu/ev/OaUkDmPdLiosAIt+d0eXvjGunTcRex9hPx/6y/f7kex1ecbuTdANAZuff0ceE/4J\nMfmlmjB+UE2YcDmVhN28eRO//fYbXnjhBTz11FNQKpVYu3YtamtrLbadPn06/vrXv+L1119HcHAw\n+vXrx3JE1+lTpy8fHYspHXfuLRkbabf2x7ynJlzGXtd1W1Qo6/N6dwwKQ1J/yyTs3uG98dnDYwyP\nI3v6mwz7+fkwyOwYFhoREQw/FwvZ+4T4203A9AaGsRd7myfQ+oc9JJbH5bLuju1Y1qaf0J8dVyeQ\n//tCXQIUbGeKjd0P2E6UjJNLZ5JorVZrMRbqyFuZHMN+N6rxvi9Otd4b7Sr9eXr4tn7ozXKzy7go\nfobZiXeg2iNhouvCH6eSsOjoaOzcuRPZ2dnIzs6GTCZDRkYGwsLCLLbVJ2ZVVVU4ceKE099+A1gS\nAT3jaQN8fRjDPFx9e/rjriGmSdWW2fEY1ieIdV+gc/jH3t2A/1iomz/p/tG214H0YUzr2szv2ps3\nsg+iewXg1dTByJgVh533czcv09crkliflwZ2JjhsNzSkT9L19OkTWPPaK91+jmVhzt4owMa4h/H+\n0REY2TcYsTLna6CcEdnTNFE1PwMjje4qDPb3haO5c0gPCfqE+Js8pwUwoOMuyCmDpTbv7DRnfGnY\nkuWuCguQWP09AgAJB3e/Es8QUw+kmGIVA7GcT7HEySWbf8WzsrLwyiuv4MaNG0hPT8fhw4dNXjee\nhFSpVGLTpk2Gx1u2bMFzzz2HjIwMLF++HEFBzs2XZOvDnAHjcM/MqMgQmxONqlS62ci3zxuKOwbp\nksmlLOtWBrEcQx/Cv5eO7ozNLIFhwBg+uB4d1w+PTxwAH4ZBymApekh8cFpe6Ngb4VBoD11S9nLq\nYCyLbkJChC7BsDW9hJ+d4WS9qV2c3BQAhhrNrfXYxAHYOmeoUxPeuiNhWzSmr+HndXfFYNPMOLv7\n/H6oDBIfBkNsFNG/nDoYs4b3tvo7avlr3nkeJg2y/PLjqmfu0CXits7z1yuSBLUqBSGEiJ3NuyNX\nr15tc+ft27cbfpbJZFi3bp3h8euvv96lwIL8fU2SG2P9Q3vgZj17obXxh4g+p7CVsPX21+AXAAF+\nvnjtd7GYseuUxdJJWq0WvQL9kLtsDD46VWn8AgD7M7kH+fti/fQY3MZSQ9bDxc6Mwb06P9j/dm+c\nSeJik1YXj763IyZIY/ySVWP7hyDnvmFIzz1v8/D2bo44+OhYHCj5jfW1P4yKwH/O6u4ivDuuF/57\nQWXzWNZEhPjhkpK9sPzl1BhkHr1q947YiBB/w+/Y+ukx6G00VYU+abXH+HdRf75nvncKGssRSkgD\n/fD1iiR8fMp0Kpd2TeeW6bcPwKBenedX4sPAz4dBm6brY8WzE3rj7E3Tmw/EN8UwsaWgoEBwPQ36\nuiPzoS8hxipmzp5Pa9fF3bzxugtu7UhjbMnNwUfHQuLDIPcs+y3/ER0flgkRQdgwQ1dcr7HycRIX\nHoi1s8dYfPu39uET7O/r8nIy1uYUu2tKCm71/g3bj1fY3H/h6AjsPdP5nvv27BziGmN3OgzrvRfG\nv/C23hPDMBgSbj/RY+tI+ffS0bj/wzP4v4dG2RzOMn7JnQlA72A/w/QLxvPP3RkjRf7lGvx76Whc\nr2vBqv2/AOj60kXGpAEStNhYk3Px2L5YlNjZ6ybxYdA/tAeu17XgvlG2h8K7gmEYrLsrxm3HJ4QN\n1R0JE10X/ghm2SJH6T/Ekwf0RDJLz9LisbqhxCA/X0MSJ2NZSggA3rlvOAL8fC1qa8yTEbahSADw\nMcoalozty7qNI+aO6IO106wXWYf28HV42SB7FnDwQT6VJRZbPXGOrvlo3KFjLSFcPLav3QGxMBvt\nMWBMpin5892dd2qumKC7IzA0QIIAluHwPg5O3NrZlqV37hvesT4j+xtkGMYkUbW3AoGtGw6md9RH\nTovtZTlVCfE6YuphEFOsYiCW8ymWOLkkuiRMb1CvQPz1Hsu6HP2HlnFx+bN3RmPPQ6MwI16G0B4S\n/H6ozPCa+S2xoyODMd5scfC5I9hnv/f39cHBR3VTJzwyrj9rQbO9UiZ9+yFGd9ztWzbGZJvdD4zs\n0nI2T0wcgD9NicbXK5IsZoA3fv/O9D6Z3yHozCSt1tqZOUyGBzt6gaxtkzauP0ZF2p4uYuGYvnjJ\nSlLLAIjpFYhegc53Ao83u2P2VbM558yvP9s5kQX5OTwLvyPeuW8Yts9nn85i7V0xAHTzyZlPVeKq\nrq6lSQghpJNokzB7jOvIAyQ+6BXohz9NHQR/O3eUZc4eitFGH/J+vozNITTu7hazPI7UqEfHeDqJ\nCQNtT59hbmifIMwYan9h7dGRwYa79mxiTO8WNHvJZYN6BRrWjezKlBjR0gCrvZ+MA3WC1qxOGYj/\n94fhhsdsh/jbvZ1fDGydC0fa3zAjFncOlmJYnyCrvXC9g/3Rr6c/62t6fULY9105uNF+ENDV+RnX\nohFxEuIcTDRPGD9onjDhEnRNmKtkQRKTRMoWW92fK8b3h7+E/aP0QQeHH+0laWzt64c/rbVt7wN8\nTkJvlCubLAqt7bX/bEo0IkJsf6BbY2+tQ2P6+H2Zzgljza2Y0N8wnGbRlgOZnr1tfj9UZrGUkL3Y\nGYZBTC/bd17q6/MGSntYnevLUROjdfVqL04dZDO2nj0k+GTxKCz+5CyemDgAO368Znjt87REq/PJ\nzU+djHesLLVk7O15ttebJMRVVHskTHRd+NMtk7B/LWG/q1JvwagIDAyz/83euEDanCN3yG2fNwzx\nvZ2bLuHl1BjDzyH+ElQzbfCX+KC13fH1LYP8fdEvtIdDSZgtfYLZEzKm438mzzHAv5aMYp1jDADi\newca6pf0NXifLBmFRR+dZd0+IsTf5YTQFn14Axy4/naPZeO1TTPjbMYvDZTgxi3rSykZs1cXBnQu\nNK6fnkM/3OrohL5s9EOPASzruQ6Uuj48TjxDTPU2YopVDMRyPsUSJ5e67XCkLYNlgYYEy9nuT2eG\n24b2CbI7v5V5+1MG63p/PnxwJP56zxB8uTwJEqMJaQFuF8k2bt/8uLcPCsP+h03r0xaNicDcEb0t\njsOAgYxlhnW97PnDDeswzhwWjncXDIc00A8+LtQYdeXt69f7ZFtui8tVAezZMGMInh3i2HCgM/Q3\nFDiSfLnS9a8/Re/db38pJkIIIbZ5ZRImROaJRUSIP6RGdU1sSYNVLmQTW+fEs/Z8mfeCrJgwAKMi\nQ7qUCEp8GEOPDd9l3tZq2bhm7/yEBkgQ6sftu9/9wAiH5zAj3keI9TZUE8YPqgkTrm45HOkMT3d/\nOtu+D8PtjPD69kf2dayGTs/PaDHH/qE9sGBUHxtbW8cwjq9+4Ignbx9gtz2A2+SP9a5YB/bj+nfP\neOklR+5iTElJAc7Zrwkz9sTEAfhdvMz+hoQ4gGqPhImuC3+8PgkTCkd7lr5IG+vQuoXuXlzmmTsG\n4sHESKTnnsOTtw/A7dHsS+iYr+XpTgESH6R0FMOznc9Zw8MNa5Ky1651n+kX3DW0GhUWgCgO6ukI\n/zz9hdMZYopVDMRyPsUSJ5e8fjjS2e7PYRFB8ONwEWN9+44e0desPoyNM5+/rnb/hgZIbK6JqGdv\nFvbFUc3Y+PshLsVgbv8jiTaL4Z9NiTYUuU+P64U/DmRf2ogLjqyx6Omud0+3Twgh3s7rkzBnTRnc\nC190TNDKp9AACZ6aFOXQtloA94+JsDs0JwRDgtstJse1x6EpKuy87u/rg8HBpnecOtt5ZDOO7rDO\ndffpGCQQZtJNNWH8oJow4fL64UhPd38a2rfzoe3DMJg30vG6q5hegXbntDJpvwvYQp8aK8Ujt/Xn\npX1XCebac+zOGCn6h9mfQsKVmjBCuES1R8JE14U/NpOwnJwcyOVyhIaGIjMz0+S1AwcO4MMPP8R7\n772HkBDLou7c3Fzk5+eDYRhER0dj5cqV8PPjbrmW7iYypIfTaxMKmZ8PgwEOJAKucGSoLyosALIg\n575jGNdR2VqTUW9k32CrReqe7Ah75e7B9jdyhAh786qqqpCdnY3a2lowDIPU1FTMmjUL9fX12Lp1\nK6qqqtCnTx+sWbMGwcG6O0lzc3Nx+PBh+Pj4IC0tDYmJiQCAS5cuITs7G21tbUhKSkJaWpon31qX\nefpLhzPEFKsYiOV8iiVOLtkcjpw2bRrWr19v8XxVVRVOnz6N3r0t54sCAIVCgby8PGRkZCAzMxMa\njQbHjh3jJmKOebr7U9/+gLAe+GjxKG4O6sQwEhfvn22xa3t1a1y2z0YW5Gd30l5bbfcJ9scXaYk2\n95cG+uGFqVbWqHTg7Qvld88qEQ5HSiQSPPzww9iyZQs2btyIr776ChUVFdi3bx/GjBmDrKwsjBo1\nCvv27QMAVFRU4Pjx49iyZQvWr1+PXbt2GaaD2blzJ9LT07Ft2zZUVlaiuLjYk2+NENIN2UzCEhIS\nDN8Wje3evRtLly61ul9QUBB8fX3R0tKC9vZ2tLa2Qiaj29q7o/cXjcAYB5eIEhu/Lsw2TzxDKpUi\nJiYGABAQEICoqCgolUoUFRVh6tSpAHRfLgsLCwEAhYWFmDx5MiQSCSIiIhAZGYmysjKoVCo0Nzcj\nLk63FuiUKVMM+4iVp5N+NlQTxg+qCRMup2vCCgsLER4ejkGD2HsAACAkJARz5szBypUr4e/vj8TE\nRIwZM8bq9p7k6e5PrtufNSwcdwxiny7CHe33d2TBbze2z2XbXHb8eGKeMGd195owhUKB8vJyxMfH\no7a2FlKpbvqSsLAw1NbWAgBUKhXi4+MN+4SHh0OpVEIikZh8cZTJZFAqlfy+AS9AtUfCRNeFP059\n1W9paUFubi4WLlxoeI5tJvfKykp88cUXyM7Oxo4dO9Dc3Iz8/HybxzbOgAsKCuixi4+fvTMaqgvF\nHo3nzvBWRLdec3l/u8cPvIll0U0u7+/uxwDw448/CiYeW4/1c+7y3b67NTc3IzMzE4888ggCA01v\nUHF0qNwZQrmeth7rk36hxGPrsTEu/j12N+WXLxl+5vt8uvNxSkqKoOJx5HFXMVo76+EoFApDbdfV\nq1exYcMG+Pvr5mJSKpWQyWT4y1/+grCwzt6X48eP4/Tp03jyyScBAEePHkVZWRmWL1/O2kZeXh6S\nk5O5ek9OMf7jRO17V/vmbV9VNWPFf0pZZ8B3xpOfnsPb84baHc4UwrnPrYlAi1qL7fOHWbx+sboR\nDa0ajOnH/XCzXC5Hamoq58cFALVajYyMDIwdOxb33nsvAODZZ5/Fa6+9BqlUCpVKhddffx1vvfWW\noTZs/vz5AICNGzdi0aJF6NOnD15//XVs3boVgO5clZaW4rHHHrNoz5N/v4htysY2rMw9B2WT2u1t\nFb2o+30etznPre30DfHH9nnDEBbo9ZMbeBRXf8Oc6gmLjo7Gzp07kZ2djezsbMhkMmRkZJgkYADQ\nv39/lJWVobW1FVqtFmfOnMGAAcKfs4oQLry7YLho6skyZsXjrblDWV8bEh7klgTMnbRaLd59911E\nRUUZEjAAGDduHL777jsAwJEjRzB+/HjD88eOHYNarYZCoUBlZSXi4uIglUoRGBiIsrIyaLVa5Ofn\nG/YRKyH2DlFNGD+cPZ9UE8Yfm6l0VlYWSkpKcOvWLaSnp2PRokW46667DK8bd+srlUrs2LED69at\nQ0xMDKZMmYK1a9eCYRjExsbi7rvvdt+76AJB1OVQ+4JoW+LL75wM3nzu3eX8+fPIz89HdHQ0Xnzx\nRQDAkiVLMH/+fGzduhWHDx82TFEBAFFRUZg0aRLWrFkDX19fLF++3PB3bcWKFcjOzkZrayuSk5Mx\ndiz/kzR3d1R7JEx0XfhjdziSD9SdT4TiqqoZ0b1obUR3c+dwJN/o75dw0XAkcRePDEd2R57u/qT2\nPdc+W9t8JmDefO4JIYRQEkYIIV5BiEk31YTxg2rChMvr+zM9XRdD7QunJozaJ4RfVHskTHRd+EM9\nYYQQ4gXElHSLKVYxEMv5FEucXPL6JMzT3Z/UvrBqwqh9QgghfPH6JIwQQryBEJNuqgnjB9WECRfV\nhHl5XY43t+/N710I7RNCtUfCRNeFP9QTRgghXkBMSbeYYhUDsZxPscTJJa9Pwjzd/UntU02Yt7ZP\nCCHezuuTMEII8QZCTLqpJowfVBMmXFQT5uV1Od7cvje/dyG0TwjVHgkTXRf+UE8YIYR4ATEl3WKK\nVQzEcj7FEieXvD4J83T3J7VPNWHe2j4hhHg7r0/CCCHEGwgx6aaaMH5QTZhw2awJy8nJgVwuR2ho\nKDIzM01eO3DgAD788EO89957CAkJMXnt+vXreOuttwyPb968iQceeACzZs3iMHRueLr7k9qnmjBv\nbZ8Qqj0SJrou/LGZhE2bNg0zZ87E9u3bTZ6vqqrC6dOn0bt3b9b9+vfvj82bNwMANBoNnnzySUyY\nMIGjkAkhhDhLTEm3mGIVA7GcT7HEySWbw5EJCQkIDg62eH737t1YunSpQw2cOXMGffv2tZqweZqn\nuz+pfaoJ89b2CSHE2zk9RUVhYSHCw8MxaNAgh7Y/fvy4Q9mtXC53NhROBAUFeaxtat+z7XvzexdC\n+4RfBQUFgutp0NcdmQ9/CTFWMXP2fFq7Lu7mjdfdqSSspaUFubm5ePnllw3PabVaq9ur1WoUFRXh\noYcesnnc1NRUZ8IghBDSDVDtkTDRdeGPU3dH3rx5E7/99hteeOEFPPXUU1AqlVi7di1qa2tZtz91\n6hRiY2MRGhrKSbCEEEJcI6YeBjHFKgZiOZ9iiZNLTvWERUdHY+fOnYbHTz31FDIyMizujtQ7duwY\nJk+e3LUICSGEEEK6IZs9YVlZWXjllVdw48YNpKen4/DhwyavMwxj+FmpVGLTpk2Gx83NzThz5gwm\nTpzIcciEEEKcJcQbMWieMH7QPGHCZbMnbPXq1TZ3Np66QiaTYd26dYbHAQEBeO+997oYHiGEkO6K\nao+Eia4Lf2jGfEII8QJiqrcRU6xiIJbzKZY4ueT0FBVcKy4uxvvvvw+NRoPp06dj/vz5XT5mVVUV\nsrOzUVtbC4ZhkJqailmzZqG+vh5bt25FVVUV+vTpgzVr1hjmQcvNzcXhw4fh4+ODtLQ0JCYmAgAu\nXbqE7OxstLW1ISkpCWlpaQ7FoNFosHbtWoSHh+Oll17ite2Ghga8++67qKioAACsXLkS/fr14639\n3Nxc5Ofng2EYREdHY+XKlWhpaXFb+2wrO3B5vtva2rB9+3aUl5ejZ8+eePbZZ9GnTx+rbf/zn/+E\nXMCTfFgAACAASURBVC6HRCJB3759sXLlSgQFBXHetrX29dhWteCr/S+//BJff/01fHx8kJycbLhD\nmuv2CSFEzDzaE6bRaPDee+9h/fr12LJlC44dO2ZIHLpCIpHg4YcfxpYtW7Bx40Z89dVXqKiowL59\n+zBmzBhkZWVh1KhR2LdvHwCgoqICx48fx5YtW7B+/Xrs2rXLMPXGzp07kZ6ejm3btqGyshLFxcUO\nxXDw4EFERUUZHvPZ9vvvv4+kpCRs3boVf/vb3zBgwADe2lcoFMjLy0NGRgYyMzOh0Whw7Ngxt7Y/\nbdo0rF+/3uQ5Ltv79ttv0bNnT2zbtg333nsvPvroI5ttJyYmIjMzE2+++Sb69euH3Nxct7RtrX2A\nfVULvto/e/YsioqK8OabbyIzMxNz5sxxW/vEcUKst6GaMH5QTZhweTQJu3DhAiIjIxEREQGJRILJ\nkyejqKioy8eVSqWIiYkBoKtNi4qKglKpRFFREaZOnQpA9+FRWFgIQDcB7eTJkyGRSBAREYHIyEiU\nlZVBpVKhubkZcXFxAIApU6YY9rGluroap06dwvTp0w3P8dV2Y2MjSktLDW37+voiKCiIt/aDgoLg\n6+uLlpYWtLe3o7W1FTKZzK3ts63swGV7xseaOHEizpw5Y7PtMWPGwMdH908rPj4eSqXSLW1bax9g\nX9WCr/a/+eYb3HfffZBIdB3t+ilq3NE+EbdVq1ZR/ZEA0XXhj0eHI5VKpck3dZlMhgsXLnDahkKh\nQHl5OeLj41FbWwupVAoACAsLM8xvplKpEB8fb9gnPDwcSqUSEokEMpnMJD79B6otH3zwAZYuXYqm\npibDc3y1rVAoEBoainfeeQdXrlxBbGwsHn74Yd7aDwkJwZw5c7By5Ur4+/sjMTERY8aM4a19PS7b\nUyqVCA8PB9CZ1NbX11udmsXY4cOHDdO08NW2tVUt+Gr/xo0bKC0txSeffAI/Pz/88Y9/xJAhQ3g/\n98SUmOptxBSrGIjlfIolTi5168L85uZmZGZm4pFHHkFgYKDJa8bTa3Dp5MmTCAsLw+DBg62uJuCu\ntgGgvb0d5eXlmDFjBjIyMtCjRw989tlnvLVfWVmJL774AtnZ2dixYweam5tx9OhR3tpnw3d7ep9+\n+ikkEgmvf1j0q1osXLjQ8JytVS3cob29HfX19di4cSOWLl2KrVu38to+IYSIhUeTMJlMhqqqKsPj\n6upqk2/EXaFWq5GZmYkpU6ZgwoQJAHQ9IjU1NQB0vQJhYWGGOKqrq03iCA8Pt+h9cSS+X375BUVF\nRXjqqaeQlZWFs2fP4u233+albQCGffVDO7fffjsuXboEqVTKS/uXLl3CsGHD0LNnT/j6+mLixIko\nKyvjrX09Ls+38e9pe3s7Ghsb7fbEfPfddzh16hSeeeYZw3N8tG1tVYuamhre3nt4eLhhfsC4uDgw\nDIO6ujre2ifshFhvQzVh/KCaMOHyaBI2ZMgQVFZWQqFQQK1W4/jx4xg3blyXj6vVavHuu+8iKioK\n9957r+H5cePG4bvvvgMAHDlyBOPHjzc8f+zYMajVaigUClRWViIuLg5SqRSBgYEoKyuDVqtFfn6+\nYR9rFi9ejJycHGRnZ+PZZ5/FqFGj8Mwzz/DSNqCrh+vduzeuX78OADhz5gwGDhyI2267jZf2+/fv\nj7KyMrS2tkKr1eLMmTMYMGAAb+3rcXm+x40bhyNHjgAAfvjhB4wePdpm28XFxdi/fz9eeOEF+Pv7\nm8Tk7rb1q1pkZ2cjOzsbMpkMGRkZkEqlvLQPAOPHj8fZs2cBANevX4darUZoaChv7RPxoNojYaLr\nwh9Gy/dYhZlTp06ZTFFx3333dfmY586dw5///GdER0cbhqKWLFmCuLg4q9MWfPrppzh8+DB8fX3x\nyCOPYOzYsQA6b51vbW1FcnKyw9M0AEBJSQkOHDhgd4oKrtu+fPkyduzYAbVabZgiQaPR8Nb+Z599\nhiNHjoBhGMTGxuKJJ55Ac3Oz29rPyspCSUkJ6urqIJVKsWjRIowfP56z9synSVi9ejUiIiJY2164\ncCH27dsHtVpt6LEZOnQoVqxYwXnbxu3funULYWFhWLRoEe666y7D608//TT++te/GmLho/0777wT\nOTk5uHz5MiQSCZYtW4aRI0e6pX0hyMvLQ3JysqfDICyUjW1YmXsOyia129sqejEVADBuc55b2+kb\n4o/t84YhLNDjM0x5NblcjtTU1C4fx+NJGCGEiBklYcJFSRhxF66SsG5dmE8IIURHiPU2VBPGD6oJ\nEy5KpQkhhHgE1R0JE10X/lBPGCFE0LZv345Tp055OgzRE9McTGKKVQzEcj7FEieXKAkjhAjaE088\ngbq6OmzduhUHDx5Ec3Ozp0MihBBOUBJGCBG0W7duQaFQICgoCGFhYcjJyfF0SKIkxHobqgnjB9WE\nCRfVhBFCBO3zzz/HjBkzEBkZCQCGZYyI+FHtkTDRdeEP9YQRQgRtxIgRhgRMLpdj+PDhHo5InMRU\nbyOmWMVALOdTLHFyiZIwQoiglZaWsv5MCCFiR0kYIUTQ6urqcObMGZw9exa1tbWeDke0hFhvQzVh\n/KCaMOGimjBCiKClpaWhoKAAWq0WjzzyiKfDIRyi2iNhouvCH+oJI4QIWlVVFRobG1FXV4eDBw96\nOhzRElO9jZhiFQOxnE+xxMkl6gkjhAja559/jtmzZ0MioT9XhJDuhXrCCCGCNnDgQERHR6N///7o\n37+/p8MRLSHW21BNGD+oJky46KslIUTQfv75Z5SUlMDPzw8A8Nxzz3k4IsIVqj0SJrou/KEkjBAi\naM8++ywqKioQFxeH6upqm9vm5ORALpcjNDQUmZmZAIC9e/ciLy8PoaGhAIDFixcjKSkJAJCbm4vD\nhw/Dx8cHaWlpSExMBABcunQJ2dnZaGtrQ1JSEtLS0tz4DvkhpnobMcUqBmI5n2KJk0s0HEkIEbQP\nPvgAR44cAaBLmmyZNm0a1q9fb/H87NmzsXnzZmzevNmQgFVUVOD48ePYsmUL1q9fj127dkGr1QIA\ndu7cifT0dGzbtg2VlZUoLi7m+F0RQgglYYQQgQsICEBYWBgAwN/f3+a2CQkJCA4Otnhen1wZKyws\nxOTJkyGRSBAREYHIyEiUlZVBpVKhubkZcXFxAIApU6agsLCQg3fiWUKst6GaMH5QTZhw0XAkIUTQ\nevbsidLSUuzevRsMw7h0jEOHDuHo0aOIjY3FsmXLEBwcDJVKhfj4eMM24eHhUCqVkEgkkMlkhudl\nMhmUSqXN4xcUFBiGUvQfJEJ7bByrEOJJSUnBqlWrUFBQYHH+zpw5w1l73Y1Gq0WVUon2Xr0AAFev\nXgEAREcPsvq4od0HDa3tCPb3dej8JScnC+L3Q8iPg4KCwAVGy/YVkRBCBOTatWvQarWIioqyu61C\noUBGRoahJqy2ttZQD7Znzx6oVCqkp6fj73//O+Lj43HnnXcCAN59912MHTsWERER+Oijj/DKK68A\n0C2VtH//frz00kus7eXl5SE5OZmLt0k4pmxsw8rcc1A2qd3eVtGLqQCAcZvz3N6WNEACZ7+PvP67\nWAyPsOwlJq6Ry+VITU3t8nGoJ4wQImhZWVkAgNbWVgDACy+84NT++qFMAJg+fToyMjIA6Hq4jAv9\nq6urER4ebtHzVV1dbdIzRoin1TQ7n1RqqLtFkKgmjBAiaKtXr8bq1avxpz/9CQkJCU7vr1KpDD+f\nOHEC0dHRAIBx48bh2LFjUKvVUCgUqKysRFxcHKRSKQIDA1FWVgatVov8/HyMHz+es/fjKUKst6Ga\nMGGimjD+UE8YIUTQfv31VzAMA7VajV9//dXmtllZWSgpKUFdXR3S09OxcOFClJSU4PLly2AYBhER\nEXjssccAAFFRUZg0aRLWrFkDX19fLF++3FBztmLFCmRnZ6O1tRXJyckYO3as29+nN6L5qISJrgt/\nqCaMECJoe/fuBQD4+flh7NixiImJ8WxAZqgmTLi6a02YK96aMxQj+lJNGFeoJowQ4hWGDBli+Fmp\nVEKpVFLSQwjpFqgmjBAiaHl5eaioqMC1a9eQl5eHuro6T4ckSkKst6GaMGGimjD+UE8YIUTQBgwY\ngLlz5wIA6urqMG3aNM8GRDhDtUfCRNeFP5SEEUIELycnBwzDmEw3QZwjpslLxRQr4Y43XndKwggh\ngvbggw9CqVQiKCgIfn5+ng6HEEI4QzVhhBBB++CDD7B3714EBQXh73//u6fDES0h1ttQTZgwUU0Y\nf6gnjBAiaAzDoE+fPgDAujg3ES+qPRImui78oZ4wQoig+fn5oaKiAl9++SUaGho8HY5oianeRkyx\nEu5443WnnjBCiGBptVpMnDgRt27dglarxe9//3tPh0QIIZyhnjBCiGAxDIOff/4ZSUlJSE5Oho8P\n/clylRDrbagmTJioJow/1BNGCBGswsJCFBUV4aeffkJISAgA4LnnnvNwVIQrVHskTHRd+ENJGCFE\nsIqLi7Fhwwbs3LnTsPA2cY2Y6m3EFCvhjjded+rbJ4QIVlVVFeRyueG/crnc0yERQghnKAkjhAjW\npEmTUFdXZ/gvrRvpOiHW21BNmDBRTRh/aDiSECJYtE5k90a1R8JE14U/1BNGCCFeQEz1NmKKlXDH\nG687JWGEEEIIIR5ASRghhHgBIdbbUE2YMFFNGH+oJowQQohHUO2RMNF14Q/1hBFCiBcQU72NmGIl\n3PHG605JGCGEEEKIB1ASRgghXkCI9TZUEyZMVBPGH6oJI4QQ4hFUeyRMdF34Qz1hhBDiBcRUbyOm\nWAl3vPG6UxJGCCGEEOIBlIQRQogXEGK9DdWECRPVhPGHasIIIYR4BNUeCRNdF/5QTxghhHgBMdXb\niClWwh1vvO6C6AnLy8vzdAiEEJ6lpqZ6OgRCCPEoQSRhAJCcnOzpELqsoKCg22Ty9F6Eqbu8F7lc\n7ukQvI4Qf3f0dUfmw19CjNWbWLsu7uaN110wSRghhBDvQrVHwkTXhT9UE8ah7pTB03sRpu70Xgi/\nxPS7I6ZYCXe88bpTEkYIIaRbYjwdACF20HAkh7rTeDa9F2HqTu+F8EuIvzvO1IT9WtOM0zfqnTp+\nm0aDWy3tXQvSC1FNGH8oCSOEEOIRznzI1zarkXXsVzdGQ/SoJow/NBzJoe6UwdN7Eabu9F4Iv8T0\nuyOmWAl3vPG6u9QTlpOTA7lcjtDQUGRmZrJu849//AOnTp1Cjx49sHLlSgwePLhLgRJCCCGEdCcu\n9YRNmzYN69evt/q6XC5HZWUltm3bhscffxy7du1yOUAx6U7rXtF7Eabu9F4Iv4T4u0NrRwoTrR3J\nH5d6whISEqBQKKy+fvLkSUydOhUAEB8fj4aGBtTU1EAqlboWJSGEkG6Hao+Eia4Lf9xSE6ZUKhEe\nHm54HB4eDqVSaXMf4wy4oKDALY/37duHiRMnYsKECW45vn48213x8/nYmBDi6cpj8/fk6Xi68jgl\nJUVQ8bj6mPBPTPU2YoqVcMcbrzuj1Wq1ruyoUCiQkZHBWhOWkZGBefPmYfjw4QCADRs24KGHHkJs\nbCzrsfLy8uDr64vExERXQnGYSqVCSEgIZs6c6bb1KjUaDXx8OnNbrVYLhqHZaggxJpfLu83akXl5\ned1i2TWhO1tZj+c+L/N0GFYVvaj7fR63WZhrIb81ZyhG9A32dBjdBld/w9zSEyaTyVBdXW14XF1d\nDZlMZnMfW7ngxx9/jGXLlmHJkiWYPXs2/n979x4XVZ3GD/wzDN5AAQdE0slWhS5upSCmRl7C8Lfr\neqFc3c38laLl3cQ0NXOrRbfShCxJrUhrc+1Vq1CWZUakArqKMF5S08nLqjQQd0Vuw5zfH6zzkwBh\nhuGc7zl83q9XvTjDOTPP4/k683i+z3zPjh078NhjjyEyMhJWqxWpqan429/+BgA4deoU5s6dW+/z\ndO7cGW3atGnwdT799FOMHTsWDz/8MD799FMAQF5eHv76179izJgxmDlzJgBgx44dGDlyJEaOHInv\nv/8eADBmzBhMnz4dEyZMwLZt2zBt2jRMmjRJtTcn19LVCuZCJObYYU+YmNgTJp8WWSesf//+2L17\nN8LCwnDmzBl4eno2qx9Mp9PB19cXcXFxWLlyJbKysrBjxw4sX74cBw4cqHXlqTnGjBmDiRMnoqys\nDKNGjcLEiRMRFxeHyZMnY/To0QCA6upqvPnmm/juu+9QUVGByMhIhIeHQ6fTISQkBLNnz8a2bdvQ\nrl07JCQkuCQuIiItYu+RmHhe5ONUEbZu3TqcPHkSJSUlmDVrFiZMmIDq6ppViSMiIhASEoKsrCzM\nmzcP7du3x6xZs5odaJ8+fQAAAQEB8PSsuaTarVs3FBUV1eo/c3J2FUDNtMK7774LSZJw/vx5AMDZ\ns2exaNEi+z55eXkwGo1o27Yt2rZtC3d3d3vuTz75pH2/fv36OR2HCLQ0N89ciNQ1dtQUK7lOazzv\nThVhzz77bKP7TJs2zZmnblBDfVWSJMHHxwfZ2dkAgBMnTjj9GrGxsfjqq68gSRL69+8PALjzzjuR\nlpaG0aNHQ5Ik+Pn54dKlS6ioqEBFRQWqqqqg1+sBoNYVOVddnSMiIiJtUk2lcHMR9tuf+/Tpg7Ky\nMjz22GMwmUwNFmxpaWl47LHHYDabMX78eFgsllq/Hz16NEaNGoVly5bZp0+jo6Px8ccfY8yYMZg1\naxb0ej0WLFiA0aNH489//jOWL19uP/7AgQP1xqhGWpqbZy5EYo4d9oSJiT1h8lHFvSMff/xx+8/T\np0+3/zxnzhz7z1u3bm30ecLCwhAWFtbg7xcuXIiFCxfWeszX1xeffPJJrcfGjx+P8ePH13rsiy++\nsA+gm+MlIqL6sfdITDwv8lFFEeaMmJgYHD582L49fPjwOgWWq2lpPpu5iElLubSE+m6pdu3aNcTF\nxSEvLw9dunRBdHS0va80MTERKSkpcHNzw9SpU+3L5Jw7dw7x8fGoqqpCcHAwpk6dqlhOrqKmsaOm\nWMl1WuN512wRtmLFCqVDICKZDR8+HH/4wx+wfv16+2NJSUm4//77MW7cOCQlJSEpKQlPPPEELl++\njPT0dMTGxqKgoAAxMTF46623oNPp8N5772HWrFkIDAzEq6++CpPJpPov2xCReFTTE6YGWprPZi5i\n0lIuLeGee+6xX+W6ISMjw34bteHDh9uvkB8+fBhhYWFwd3eHv78/AgICcPbsWRQWFqK8vByBgYEA\ngKFDh9a6qq5WIo4d9oSJiT1h8mERRkSaVlxcbP+ijbe3N4qLiwHU3EGjvturFRYW1lpc2mAwCHHb\nNS1uz58/HyEhIXV+f/z48QaPp+ZpyvkJCQmx94WJNF5E3G4up29b5ErJyclwc3Pj5X6iVqIlb1v0\n21uqTZ06FZs3b7b//sb2Bx98gKCgIAwZMgQAsHHjRvTr1w/+/v7YunWrvaXh1KlT+OKLL7BkyZJ6\nX4+3LZIHb1vUPLxtkWsJfdsiIiJReHt7o6ioCEDN1S9vb28A9d9ezdfXt86Vr6bcdo2IyBkswlxI\nS5fMmYuYtJSLXEJDQ/HDDz8AAPbu3YsBAwbYH09LS4PVakVubi4sFgsCAwPh4+ODDh064OzZs5Ak\nCfv377cfo2Yijh32hImJPWHy0ey3I4mo9fntLdUmTpyIyMhIxMXFISUlxb5EBQAYjUYMHjwY0dHR\n0Ov1mDZtmn2R5enTpyM+Ph6VlZUICQlhq0QL4XpUYuJ5kQ97wohIdi3ZEyY39oTJgz1hzcOeMNdi\nTxgRERGRirEIcyEtzWczFzFpKReSl4hjhz1hYmJPmHyc7gkzmUzYsmULbDYbwsPDERkZWev3JSUl\nePvtt1FUVASbzYYxY8Zg+PDhzY2XiIg0gr1HYuJ5kY9TRZjNZkNCQgJWrFgBg8GAZcuWITQ0FEaj\n0b7P7t270bNnT0yaNAklJSVYsGABhgwZAr1e77LgRaOl+14xFzFpKReSl5rGjppiJddpjefdqelI\ns9mMgIAA+Pv7w93dHWFhYcjIyKi1j4+PD8rKygAAZWVl6NSpk6YLMCIiIiJHOFWEFRQUwM/Pz75d\n3209RowYgUuXLmHGjBlYvHgxpkyZcsvnNJlM9p+Vvg2Bs9s3HhMlnuZsb9iwQah4mrO9YcMGoeJp\nzvZvx5rS8Ti7TfIT8c+dPWFiYk+YfJxaouLgwYM4evQoZsyYAQDYt28fzGYzoqKi7Pts374dV69e\nxZQpU2CxWLBy5UqsWbMGHTp0qPN8WlmiIjU1VTOXU5mLmLSSC5eokJ+axk59sXKJiuZRwxIVahqj\nii5RYTAYkJeXZ9+u77YeZ86cwaBBgwDAPnWZnZ3djFDFp5bB0xTMRUxayoXkpaaxo6ZYyXVa43l3\nqgjr3bs3LBYLcnNzYbVakZ6ejtDQ0Fr7dOvWDcePHwcAFBUVITs7G127dm1+xEREREQa4FQRptfr\nERUVhVWrViE6OhoPPvggjEYj9uzZgz179gAAHn30UZw7dw6LFy9GTEwMJk+ejI4dO7o0eNFoaT6b\nuYhJS7mQvEQcO+wJExN7wuTj9DphwcHBCA4OrvVYRESE/WcvLy8sWbLE+ciIiEjTuB6VmHhe5MMV\n811IS/PZzEVMWsqF5KWmsaOmWMl1WuN5ZxFGREREpAAWYS6kpfls5iImLeVC8hJx7LAnTEzsCZOP\n0z1hREREzcHeIzHxvMiHV8JcSEvz2cxFTFrKheSlprGjpljJdVrjeWcRRkRERKQAFmEupKX5bOYi\nJi3lQvISceywJ0xM7AmTD3vCiIhIEew9EhPPi3x4JcyFtDSfzVzEpKVcSF5qGjtqipVcpzWedxZh\nREREGufuplM6BKoHpyNdKDU1VTOVPHMRk5ZyIXmJOHZu9B39dvpLxFjVbnNGNvw7tm3SvvqsJABA\n+MQo3Bsg3z2fW+N5ZxFGRESKYO+RfI5cudr0nT2GAADuLC6XtQhrjTgd6UJaquCZi5i0lAvJS01j\nR02xkuu0xvPu1JUwk8mELVu2wGazITw8HJGRkXX2+fHHH/Hhhx+iuroanTp1wssvv9zcWImIiIg0\nw+ErYTabDQkJCXjhhRcQGxuLtLQ0XL58udY+paWlSEhIwJIlS7B27VosXLjQZQGLTEtrnDAXMWkp\nF5KXiGOH64SJafT1/Rh9fb/sr9saz7vDV8LMZjMCAgLg7+8PAAgLC0NGRgaMRqN9n9TUVAwcOBC+\nvr4AAC8vLxeFS0REWsGeMDF9+b+esNZx+URZDhdhBQUF8PPzs28bDAaYzeZa+1gsFlitVrzyyiso\nKyvDqFGjMHTo0OZHKzgtzWczFzFpKReSl5rGjppiJddpjee9RRrzrVYrzp8/j2XLlmH58uXYvn07\nfvnll1seYzKZ7D+npqbWuizJbW5zW1vbREQE6CRJkhw54MyZM/jss8+wfPlyAEBiYiJ0Ol2t5vyk\npCRUVVVhwoQJAICNGzeiX79+GDRoUL3PmZycDDc3N/Tr18/ZPISQmqqdNU6Yi5i0kktmZiZGjBih\ndBgukZycjJCQEKXDaJSIY8eRdcJOWK5h4ZdnZYvNURnP14zn0NXJCkfSfDf6we78P5Pwh7v8Gtnb\ndUQcow1x1XuYw1fCevfuDYvFgtzcXFitVqSnpyM0NLTWPgMGDMDp06dhs9lQUVGBs2fP1uoZa0hJ\nSQnOnhX3LxkREbnO/Pnz2RcmoC89htj7wqhlOdwTptfrERUVhVWrVtmXqDAajdizZw8AICIiAt27\nd0ffvn2xaNEi6HQ6jBgxoklFWFZWFuLi4pCUlOR4JgJQSwXfFMxFTFrKheSlprGjpljJdVrjeXdq\nnbDg4GAEBwfXeiwiIqLW9tixYzF27FjnIyMiIiLSMK6Y70JaajhmLmLSUi4kLxHHDtcJExPXCZMP\n7x1JRESKYD+YmLhOmHx4JcyFtDSfzVzEpKVcSF5qGjtqipVcpzWedxZhRERERApgEeZCWprPZi5i\n0lIuJC8Rxw57wsTEnjD5sCeMiIgU0VBPmKenZ53H3N10LR0O/Q97wuTDIsyFtDSfzVzEpKVcSF4i\njZ2MyyX49kz+Lfbwwdffn6/1yK/Xqlo2KFKcSGNULizCiIhIVpeLy/HDuSKlwyBSHHvCXEhL89nM\nRUxayoXkJeLYUar3iG6NPWHy4ZUwIiJSBO9PKCb2hMmHV8JcSEvz2cxFTFrKheTFsUOia41jlEUY\nERERkQJYhLmQluazmYuYtJQLyUvEscOeMDGxJ0w+7AkjIiJFsCdMTOwJk49TRZjJZMKWLVtgs9kQ\nHh6OyMjIevczm8148cUXER0djYEDBzYrUDXQ0nw2cxGTlnKR25w5c9ChQwe4ublBr9fj1VdfxbVr\n1xAXF4e8vDx06dIF0dHR9oVCExMTkZKSAjc3N0ydOhV9+/ZVOIPm4dgh0bXGMepwEWaz2ZCQkIAV\nK1bAYDBg2bJlCA0NhdForLPf1q1b0a9fP0iS5LKAiYic9fLLL6Njx4727aSkJNx///0YN24ckpKS\nkJSUhCeeeAKXL19Geno6YmNjUVBQgJiYGKxbtw5ubuzgICLXcfgdxWw2IyAgAP7+/nB3d0dYWBgy\nMjLq7Pf1119j0KBB8PLyckmgaqCl+WzmIiYt5aKE3/6DMCMjA8OGDQMADB8+HIcPHwYAHD58GGFh\nYXB3d4e/vz8CAgJgNptlj9eVRBw77AkTE3vC5ONwEVZQUAA/Pz/7tsFgQEFBQZ19MjIyMHLkSACA\nTtf4Pb9MJpP959TU1Fong9vybx8/flyoeJqzffz4caHi4bYyb7Q6nQ4xMTFYunQpvvvuOwBAcXEx\nfHx8AADe3t4oLi4GABQWFsLX19d+rK+vb533uZsp/eeppu309HRUVFQAqOk9Yl+YeG4+L0qPF9G3\nm0snOThXePDgQRw9ehQzZswAAOzbtw9msxlRUVH2fWJjYzFmzBgEBQUhPj4e/fv3x6BBgxp8oyZ8\nowAAFspJREFUzuTkZLi5uaG4uBhxcXFISkpyMh0iUoPMzEyMGDFC1tcsLCxE586dUVJSgpiYGERF\nRWH16tXYvHmzfZ+pU6di8+bN+OCDDxAUFIQhQ2o+iDZu3Ijg4OB6e1uTk5MREhIiWx5akPRjLt45\ncEXpMFwq4/ma8Ry6OlnhSFxn4ZDb8Ye7/BrfsRVy1XuYwz1hBoMBeXl59u38/HwYDIZa+5w7dw5v\nvvkmAODq1aswmUxwd3dHaGhoM8MlInJO586dAQBeXl544IEHYDab4e3tjaKiIvj4+KCwsBDe3t4A\nat7n8vP//w2m63ufIyJqLoenI3v37g2LxYLc3FxYrVakp6fXKa7Wr1+P+Ph4xMfHY9CgQZg+fXqr\nKMCUmmZpCcxFTFrKRU4VFRUoKysDAJSXl+PYsWPo0aMHQkND8cMPPwAA9u7diwEDBgAAQkNDkZaW\nBqvVitzcXFgsFgQGBioVvkuIOHbYEyYm9oTJx+ErYXq9HlFRUVi1apV9iQqj0Yg9e/YAACIiIlwe\nJBFRcxQXF2PNmjUAar65/dBDD6Fv377o3bs34uLikJKSYl+iAgCMRiMGDx6M6Oho6PV6TJs2rUm9\nreQY9oOJieuEycfhnrCWwJ4wotZFiZ6wlsKeMMexJ0wd2BPWMFe9h3HRGyIiIiIFCFuEzZ07FwcP\nHlQ6DIdoaT6buYhJS7mQvEQcO+wJExN7wuQj7L0jf/nlF1y/fl3pMIiIqIWwJ0xM7AmTj7BXwtRI\nS/e9Yi5i0lIuJC+OHRJdaxyjLMKIiIiIFMAizIW0NJ/NXMSkpVxIXiKOHfaEiYk9YfIRtieMiIi0\njT1hYmJPmHx4JcyFtDSfzVzEpKVcSF4cOyS61jhGWYQRERERKYBFmAtpaT6buYhJS7mQvEQcO+wJ\nExN7wuTDnjAiIlIEe8LExJ4w+fBKmAtpaT6buYhJS7mQvDh2SHStcYyyCCMiIiJSgNNFmMlkwoIF\nCzB//nwkJSXV+f3+/fuxePFiLFq0CCtWrMDFixebFagaaGk+m7mISUu5kLxEHDvsCRMTe8Lk41RP\nmM1mQ0JCAlasWAGDwYBly5YhNDQURqPRvk/Xrl3xyiuvwMPDAyaTCe+++y5WrVrlssCJiEjd2BMm\nJvaEycepK2FmsxkBAQHw9/eHu7s7wsLCkJGRUWufO++8Ex4eHgCAwMBA5OfnOxXgmTNn8Mwzzzh1\nrNy0NJ/NXMSkpVxIXhw7JLrWOEadKsIKCgrg5+dn3zYYDCgoKGhw/++//x7BwcG3fE6TyWT/OTU1\nFYWFhQCA8vJyZGZm1rpMmZqaym1uc1vF20REBOgkSZIcPejgwYM4evQoZsyYAQDYt28fzGYzoqKi\n6ux74sQJJCQkICYmBh07dqz3+ZKTk+Hm5obi4mLExcUhKSkJ48ePx5w5c+Dn54d58+Zh7969joYp\nu9TUVM1U8sxFTFrJJTMzEyNGjFA6DJdITk5GSEiI0mE0SqSxk/RjLt45cMXed6SFacmM52vGc+jq\nZIUjab4b5+XobY+g7231f243xNejDR691x8ebfUOv65IY7QxrnoPc6onzGAwIC8vz76dn58Pg8FQ\nZ7+LFy9i06ZNWL58eYMFGBERtU5aKL60yH5eiitwqbjCoWPv6Nwekff6t0BU2uTUdGTv3r1hsViQ\nm5sLq9WK9PR0hIaG1tonLy8Pb7zxBubNm4eAgACXBCs6tVTwTcFcxKSlXEheHDskutY4Rp26EqbX\n6xEVFYVVq1bBZrMhPDwcRqMRe/bsAQBERETg3//+N0pLS/H+++/bj3n11VddFzkRERGRijl926Lg\n4OA6zfYRERH2n2fOnImZM2c6H5kKqWk+uzHMRUxayoXkJeLY0VJPmJYodV5EHKMtjfeOJCIiRbD4\nEhPPi3xUddui5cuX49tvv1U6jAZpqYJnLmLSUi4kL44dEl1rHKOqKsIsFguuXbumdBhEREREzaaq\nIkx0WlqEkrmISUu5kLxEHDu8d6SYeO9I+bAnjIiIFMHeIzHxvMiHRZgLaWk+m7mISUu5kLxaYuxU\nVdtQVGZ16BidDqi0OnyjFmoFWuP7myqLsJKSEiQmJuKpp55SOhQiolarrMqGv+05h19KHFtVvdxq\na6GIiNRFlT1hJSUleOONN5QOow4tzWczFzFpKReSV0uNnbKqalyvsjn0n+1/F8LYEyYm9oTJR5VX\nwoiISP3YeyQmnhf5qPJKmKi0NJ/NXMSkpVxIXhw7JLrWOEZVX4RlZ2cjPz9f6TCIiIiIHKL6Iiwu\nLg6JiYlKhwFAW/PZzEVMWsqF5CXi2GFPmJjYEyYf9oQREZEi2HskJp4X+ThdhJlMJmzZsgU2mw3h\n4eGIjIyss8/mzZuRlZWFdu3aYfbs2ejZs2ezghWdluazmYuYtJQLyYtjh0TXGseoU9ORNpsNCQkJ\neOGFFxAbG4u0tDRcvny51j6ZmZmwWCx466238Mwzz+D99993ScANKS0txcMPPwwAuHDhAv773/8C\nAI4dOwabjWvSEBERkVicKsLMZjMCAgLg7+8Pd3d3hIWFISMjo9Y+R44cwbBhwwAAQUFBKC0tRVFR\nUfMjboAkSTCbzQCAbdu24ZNPPgEAjBw5ElVVVfjiiy+QkJDQ4PGVlZWw2WzIycnBoUOHnIrBmfns\no0eP4ptvvnHq9ZrKZrPh4MGDDh2jpbn5puZisVhw5coVAEBWVhasVsdWAv+t8vJynD9/vlnP8Vta\nOi8kLxHHDnvCxMSeMPk4VYQVFBTAz8/Pvm0wGFBQUFBnH19fX/u2r69vnX3kdOXKFfz8888AgOee\new6FhYXIzc3FTz/9BAAYPnw4fvrpJxw7dsy+EOzTTz+NjIwMHD16FJMnT671fEVFRdi6dWuTXvv5\n558HUDPA9uzZAwAYPHgwCgsLcezYMezatQsAsGzZMlgslmblmZOTgwcffBAA8OKLL2LTpk2oqqqy\nTxfv3r0bX3/9NQBg+/btqKqqQl5enr340DKbzWYfg6mpqThy5AhsNhsmTJgAoKZ4v1GoP/rooygt\nLcWuXbuwbt06ADXnvLq6utHX+eqrr3Dp0iWYzWY8+eSTAID4+HikpaW5LJecnBycPn3aZc/nKnl5\nefjxxx+VDoNU4kuPIew/EhDPi3x0kiQ5fBOvgwcP4ujRo5gxYwYAYN++fTCbzYiKirLv8/rrr2Pc\nuHG4++67AQAxMTF44okn0KtXrzrPl5ycjEceGeFsDkSkMt99l4wRI7Txdz45ORkhISFKh6GIknIr\n5n/xE7JLKpUORXEZz9eM59DVyQpHoqw7OrfHm2PuhGdbvdKhtKjMzEyXvIc51ZhvMBiQl5dn387P\nz4fBYKizz83rd9W3z80KCgqdCUUoZWVl+Oyzz+xXP1zp9OnTSEpKwtKlS/Hhhx9i4MCB9gK3Jeza\ntQspKSlYs2YNhg0bhrfffht5eXmIj4/H9u3bERkZiejoaHh7eyM6OhopKSmYOnUqxo0bh+DgYIwb\nNw4mkwnPPfccfv/732PChAl45JFH8J///KfR1z5y5Ajuu+8+tG3bts7vsrKysHfvXixYsADPPvss\npk2bhs6dO+Pbb7/FtGnTWuKPwqVsNhuuX7+Ojh071nrMZDIhJCQE//rXvwAAkyZNQmVlJdq0aQOd\nTueS13733XfRv39/+Pv7Y9asWfjyyy+xatUq9OnTB+Hh4ejbty8uXLhQ65j9+/fjoYcewpUrV1BZ\nWYlevXohKCgIBw4cgCRJuHbtWqNfuKmqqkJZWRm8vLxQUlKC9u3b48QJl6RERKRqThVhvXv3hsVi\nQW5uLgwGA9LT0/Hss8/W2qd///7YvXs3wsLCcObMGXh6esLHx8clQYvqyJEjLVKAAcDdd9+NpUuX\nAoAsNy738vLCmjVrnDq2bdu2CA4OBgDMnDkTnp6e6NSpU5MKMKBm7DQkODjY/tw3pgkB3LIAS01N\nFeZbN25ubrUKsBuP3biSMmnSJPvj9RWhzcnlmWeesf/85ZdfAgCWL18OoKYQzMzMrHPMkCE1UxJG\no9H+2KZNm9CpUye0a9cOXbp0afR127RpgzZt2gCoGVekDJH+Htxwo++IU19iUeq8iDhGW5pTRZhe\nr0dUVBRWrVplX6LCaDTa+50iIiIQEhKCrKwszJs3D+3bt8esWbNcGjjJJzo6Gt26dat19fOG9u3b\n44477gAArF69Gh4eHvD09MTmzZsB1Hwpg8Tn5uZ2yyvVNwsPD2/haKi1YPElJp4X+Ti9TtjNVyRu\niIiIqLWthukhV9JSBX9zLjea+rt06YIHHngAADBjxgwEBgaie/fu2LJli/33ItLqeSFyBMcOycFa\nLaGsqhrXKhz7drlHG32rHKNcMZ+a7L777sN9990HAPjjH/+ocDRE5EpOfEcLejfX9CuSdlwpqcD/\n/cTxb0i/Ne4udGrf+kqS1pdxC9LSfDZzEZOWciF5NTZ2zPll2HjQ0aVqJFiuOv/NSPaEiam556Xa\n8XoeQOt8f2MRRkTUgKbcnk0rqm0SjluuyfqaLL7ExPMiH6cWa6X6aamCZy5i0lIuomvK7dnUhGOH\nRNcaxyivhBER1ePm27MBsN+e7eblOojINSqrbfhvYblDx7Rz16Frp3YtFJE8WIS5kJbms5mLmLSU\ni+jquz3bjfvTiu5iYTn+W1T7A620tBSenp4NHvNLSUVLh1UHe8LEpMR5id551uFjlgy/g0WYq9S3\nUKTaeHh4aCIPgLmISku5aImI5+S35ZYnANyihz4QwGuy333pof/938lObpF8993/ftBALmo5LyUX\nkJl5QekomkWIIkwr95AjIu1oyu3ZAL5/EZHz2JhPRFSPm2/PZrVakZ6ejtDQUKXDIiIN0UnOrNBH\nRNQKZGVl1Vqi4tFHH1U6JCLSEBZhRERERArgdCQRERGRAhRvzFfritR5eXmIj49HcXExdDodRowY\ngVGjRuHatWuIi4tDXl4eunTpgujo6Ft+LVwkNpsNS5cuha+vL5YsWaLaXEpLS7Fx40b7wpqzZ8/G\nbbfdprpcEhMTsX//fuh0OvTo0QOzZ89GRUWFKvLYsGEDMjMz4eXlhbVr1wLALcdTYmIiUlJS4Obm\nhqlTp6Jv375Khg+g/hxu2LlzJz7++GMkJCSgY8eOtX6XnZ2NN998076dk5ODv/zlLxg1apRwsQL1\nj7M2bdoIF+euXbuQnJwMAPb325ZSX5yfffYZkpOT4eXlBQCYNGkS+vXrV+dYOT/TmhPnrc6FSLE2\n9FkrWpyVlZV4+eWXUVVVBavVigEDBmDSpEmNv6CkoOrqamnu3LlSTk6OVFVVJS1atEi6dOmSkiE1\nWWFhoXT+/HlJkiSprKxMmj9/vnTp0iXpn//8p5SUlCRJkiQlJiZKH3/8sYJROmbnzp3SunXrpNde\ne02SJEm1uaxfv15KTk6WJEmSrFarVFpaqrpccnJypDlz5kiVlZWSJElSbGyslJKSopo8Tp48KZ07\nd05auHCh/bGGYr906ZK0aNEiqaqqSsrJyZHmzp0rVVdXKxL3zerLQZIk6ddff5VWrlwpzZ49W7p6\n9eotn6O6ulp6+umnpV9//bUlQ3U61obGmWhxXrx4UVq4cKFUUVEhVVdXS3//+9+lX375RdY4P/30\nU2nnzp23PE7uzzRn42zo2JbkbKwNfdaKFqckSVJ5ebkkSTWfOy+88IJ06tSpRo9RdDry5hWp3d3d\n7StSq4GPjw9+97vfAQDat28Po9GIgoICZGRkYNiwYQCA4cOH4/DhwwpG2XT5+fnIyspCeHi4/TE1\n5nL9+nWcOnXKnoder4eHh4fqcvHw8IBer0dFRQWqq6tRWVkJg8GgmjzuueeeOlfoGor98OHDCAsL\ng7u7O/z9/REQECDEoqj15QAAH330ESZPntyk5zh+/Di6du1aa9HXluBsrA2NM9HizM7ORlBQENq2\nbQs3Nzf06dMHhw4dkj1OqZEWark/05yN81bHthRnY63vs7awsLAlQgTQvD/Tdu1qFo61Wq2w2Wz1\nXtH9LUWnI9W8IvXNcnNzcf78eQQFBaG4uBg+Pj4AAG9vbxQXFyscXdN8+OGHmDx5MsrKyuyPqTGX\n3NxceHl54Z133sHFixfRq1cvPPXUU6rLpWPHjhgzZgxmz56Ntm3bom/fvrj//vtVl8fNGoq9sLAQ\nQUFB9v18fX1RUFCgSIyNOXz4MHx9fXHHHXc0af/09HTF7nDQlFgbGmdyakqct99+O7Zt24Zr166h\nTZs2yMzMRGBgoIxR1vjmm2+wb98+9OrVC08++WSdD2tRPtMai1MkjsR682et3JoSp81mw5IlS5CT\nk4ORI0c26RZnbMxvpvLycqxduxZTpkxBhw4dav1Op9MpFJVjjhw5Am9vb/Ts2bPBal8tuVRXV+P8\n+fMYOXIkXn/9dbRr1w6ff/55rX3UkIvFYsFXX32F+Ph4bNq0CeXl5di3b1+tfdSQR0Mai13E3Coq\nKpCYmIgJEybYH7vVv46tVisyMjIwePBgOcKrpamx1jfO9u/fL1yc3bt3x7hx47By5Ur84x//QM+e\nPeHmJu/H18iRI7F+/XqsXr0anTt3xkcffSTr6zeVWuIEHIu1vLwcsbGxmDJlCtq3by9jlE2P083N\nDWvWrMHGjRtx6tQp/Pjjj40+t6JFWFNXpBaV1WrF2rVrMXToUDzwwAMAav6FX1RUBKDmX/je3t5K\nhtgkZ86cQUZGBubMmYN169bhxIkTePvtt1WZi6+vLwwGg/1fyYMGDcK5c+fg4+OjqlzOnTuHu+66\nC506dYJer8fAgQNx9uxZ1eVxs4bGk8FgQH5+vn0/Ud8HcnJy8Ouvv2Lx4sWYM2cOCgoKsHTp0gav\nRmZlZaFXr172Zl45NTXW+sbZmTNnhIsTAMLDw/Haa6/hlVdegaenJ2677TbZ4gRqxq9Op4NOp0N4\neHi9V7hE+ExrSpyiaGqsNz5rhwwZYv+slZOjf6YeHh4IDg7Gzz//3OhzK1qEqXlFakmSsHHjRhiN\nRvzpT3+yPx4aGooffvgBALB3714MGDBAoQib7vHHH8eGDRsQHx+PBQsW4N5778W8efNUmYuPjw/8\n/PyQnZ0NoKYn5/bbb0f//v1VlUu3bt1w9uxZVFZWQpIkHD9+HN27d1ddHjdraDyFhoYiLS0NVqsV\nubm5sFgsikw1NaZHjx547733EB8fj/j4eBgMBrz++usNFsJpaWkICwuTOcoaTY21oXEmWpwA7IVZ\nXl4eDh06JPs07819SIcOHUKPHj3q7CPCZ1pT4hRFU2Jt6LNWTk2Js6SkBKWlpQBqvil5/Phx9OzZ\ns9HnVnyxVrWuSH369Gm89NJL6NGjh33qZNKkSQgMDFTFEgINOXnyJHbu3KnqJSouXLiATZs2wWq1\nomvXrpg9ezZsNpvqcvn888+xd+9e6HQ69OrVCzNmzEB5ebkq8li3bh1OnjyJkpIS+Pj4YOLEiRgw\nYECDse/YsQMpKSnQ6/WYMmVKvV+pl9uNHK5evQpvb29MnDgRDz/8sP33c+fOxWuvvYaOHTuioKAA\nmzZtwrJlywDUTJ3MmTMH69evr9OmIFqs9Y0zd/eWaRduTpwvvfQSrl69Cr1ej6eeegr33ntvi8R4\nc5w3xu+ECRNw8uRJXLhwATqdDv7+/nj66afh4+NTJ045P9OaE2dj50KUWBv6rG2p9whn47x48SLe\neecd2Gw2SJKEoUOHYuzYsY2+nuJFGBEREVFrxMZ8IiIiIgWwCCMiIiJSAIswIiIiIgWwCCMiIiJS\nAIswIiIiIgWwCCMiIiJSAIswIiIiIgX8PxY9Dn+cM3UbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106d0ef50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAFwCAYAAABkcQUaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106b46290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAFwCAYAAABtmqVgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107003950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAFwCAYAAABkcQUaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1071bbb50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If the lines converge from wavy or noise to approximately flat, then we can see that the sampler converged properly. Lots of oscillations might indicate that the sampler was not converging nicely and maybe changing the number of burns or choosing every $j$'th value for $j>k$ might return better results."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Getting the resulting data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The reason that we set string representations of each object in our model is so that we can pull out the results from our sampler."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Grab our data from the trace\n",
      "mus1 = mcmc.trace('mu_1')[:]\n",
      "mus2 = mcmc.trace('mu_2')[:]\n",
      "sigmas1 = mcmc.trace('sigma_1')[:]\n",
      "sigmas2 = mcmc.trace('sigma_2')[:]\n",
      "nus = mcmc.trace('nu')[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You'll notice that the results from MCMC aren't a singular value, but rather a collection of numbers. This is because we are sampling the distribution of our parameters, not computing them exactly. The result is that we have a measure of confidence on where we think a paramter should be. This allows us to do things like compute the distribution of the differences of means.\n",
      "\n",
      "Here are some parameters of interest we can generate:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff_mus = mus1-mus2\n",
      "diff_sigmas = sigmas1-sigmas2\n",
      "normality = np.log(nus)\n",
      "effect_size = (mus1-mus2)/np.sqrt((sigmas1**2+sigmas2**2)/2.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Estimates of $\\mu_1$ and $\\mu_2$ and their difference:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can calculate estimates of parameters using the expectation of their posteriors. So for estimating our means we can simply do:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"mu_1\", mus1.mean()\n",
      "print \"mu_2\", mus2.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mu_1 15.0106417435\n",
        "mu_2 15.6880183953\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting the densities of the posteriors and their differences:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import beta, gaussian_kde\n",
      "\n",
      "figsize(16,9)\n",
      "\n",
      "minx = min(min(mus1),min(mus2))\n",
      "maxx = max(max(mus1),max(mus2))\n",
      "xs = np.linspace(minx,maxx,1000)\n",
      "\n",
      "gkde1 = gaussian_kde(mus1)\n",
      "gkde2 = gaussian_kde(mus2)\n",
      "\n",
      "plt.plot(xs,gkde1(xs),label='$\\mu_1$')\n",
      "plt.plot(xs,gkde2(xs),label='$\\mu_2$')\n",
      "plt.legend()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.legend.Legend at 0x1071b0b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA6AAAAIWCAYAAABJIeBtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmM5Od93/lPVfVRR3cdfd9z8hyeo1CS7ZFsk5HX0irO\nxoihSI7gjb0wvLLXWMlBGAQBokCIZdkSIiOWcyDetZFg114kRhLJpiVrGFmaSLZID0UOR7zm7unp\n6buOrq6jq+q3fzSbGnJm+nqqftfzfv1X7J6qL/XRjz3ffp7v80Qcx3EEAAAAAECHRb0uAAAAAABg\nBxpQAAAAAIAraEABAAAAAK6gAQUAAAAAuIIGFAAAAADgChpQAAAAAIArunb64o0bN/TFL37xrdcL\nCwv6yEc+og996EMdLwwAAAAAEC6Rvd4D2mq19Eu/9Ev69V//dQ0NDXW6LgAAAABAyOx5C+65c+c0\nOjpK8wkAAAAAOJAdt+De6tvf/rZOnTp1x6+dPn26bQUBAAAAAPznqaeeMn6PPTWgjUZDzz//vH72\nZ3/2rt9z8uRJ42IQTGfOnLnrLycQfuRvL7K3G/nbi+ztRv72Onv2bFveZ09bcF944QUdPXpU6XS6\nLR8KAAAAALDPng4h+uIXv6jHHntMP/ZjP3bHr58+fZoVUAAAAAAIqbNnz7ZlC+6uK6DValXnzp3T\ne97zHuMPAwAAAADYa9cGNB6P6/d+7/eUSCTcqAcBdObMGa9LgIfI315kbzfytxfZ2438YWrPp+AC\nAAAAgO0cx9Hi4qKazaYikYjX5bSN4ziKxWIaGRnp6L/XnmZAd8MMKAAAAAAbLCwsqL+/X8lk0utS\n2m5jY0OlUkmjo6O3fc21GVAAAAAAwJZmsxnK5lOSksmkms1mRz+DBhTGmAWwG/nbi+ztRv72Inu7\nkb9Cte32Tjr970cDCgAAAABwBTOgAAAAALBH8/PzGh8f97qMjrnbvx8zoAAAAACAQKEBhTFmAexG\n/vYie7uRv73I3m7kD1M0oAAAAAAAV9CAwtipU6e8LgEeIn97kb3dyN9eZG838g+GZrOpD37wg2+9\n/tVf/VVdunTJw4p+oMvrAgAAAAAgLH7i37/Qlvf52v/2+IH/7HPPPafp6WlJkuM4eu6553T06NG2\n1GWKBhTGzpw5w2/DLEb+9iJ7u5G/vcjebuS/O5PGsV1Onz6tJ598UpL00ksv6YEHHpAkFYtF/cVf\n/IUuXLigT37yk57UxhZcAAAAAAiRZ599Vo8++qgk6Wtf+5p+9Ed/VM8884zS6bQee+wx1et1z2pj\nBRTG+C2Y3cjfXmRvN/K3F9nbjfz9b3l5WdevX9czzzyj69evK5lManl5WYcOHfK6NEk0oAAAAAAQ\nGs8++6w+/vGP61Of+pQk6QMf+IDHFb0dW3BhjPug7Eb+9iJ7u5G/vcjebuTvf2fPntWHP/zhu37d\ncRwXq7kdK6AAAAAAEBK/8Ru/cdevra+v68tf/rJefPFFvfLKK28dTuSmiNOGFvj06dM6efJkO+oB\nAAAAAN+an5/X+Pi412V0zN3+/c6ePaunnnrK+P3ZggsAAAAAcAUNKIwxC2A38rcX2duN/O1F9nYj\nf5iiAQUAAAAAuIIZUAAAAADYI2ZAzbACCgAAAABwBQ0ojDELYDfytxfZ24387UX2diN/mKIBBQAA\nAAC4ghlQAAAAANgjZkDNsAIKAAAAAHAFDSiMMQtgN/K3F9nbjfztRfZ2I3+YogEFAAAAALiCGVAA\nAAAA2CNmQM2wAgqgrZotR395taDXlspelwIAAACfoQGFMWYB7HZr/s2Wo3/+9Uv6Dy/M65//+WV9\n+ftLHlaGTuPZtxv524vs7Ub+wdBsNvXBD37wrde/+qu/qkuXLnlY0Q90eV0AgPD4yivLWq819ds/\ndZ+W1uv6P/7razo52a/JTNzr0gAAAFzx+X/yZ215n3/46z954D/73HPPaXp6WpLkOI6ee+45HT16\ntC11maIBhbFTp055XQI8tJ1/tdHS//O9m/rsB4+rKxrReLpXf+ehEf3hiwv6tfcf8rhKdALPvt3I\n315kbzfy351J49gup0+f1pNPPilJeumll/Tggw9Kki5evKhXXnlF58+f10/+5E/q0Ucfdb02tuAC\naIszl/O6ZyipowOJt/7Z33pgSP/jSkH5yqaHlQEAANjl2Weffau5/NrXvqb3v//9euaZZ/TVr35V\n4+Pj+sQnPqHf+Z3f8aQ2GlAYYxbAbtv5f/X1Ff3EvQNv+1o63qUnptP65uW8F6Whw3j27Ub+9iJ7\nu5G//y0vL+v69et65pln9Od//udKJpNaXl5WIpHQJz7xCb3rXe/S3NycDh3yZocaW3ABGJsv1nRl\nrar3zmRu+9qTx3L6oxcX9FMPDntQGQAAgF2effZZffzjH9enPvUpSdIHPvCB277nT/7kT976uttY\nAYUxZgHsdurUKX39wqp+/FhOPbHb/5Py+ES/Lq1WVKo1PKgOncSzbzfytxfZ2438/e/s2bP68Ic/\nfNevP/PMM/rFX/xFzc/Pu1jVD9CAAjD2V9eK+pHDt69+SlJPV1QPjfXphbmSy1UBAADY5zd+4zf0\n2GOP3fFrX/nKV/T5z39eP/dzP6f/8l/+i8uVbaEBhTFmAez2Z984o7liTSdG++76PX9jql/P04CG\nDs++3cjfXmRvN/IPtg9/+MM6ffq0/viP/1i/9mu/5kkNNKAAjFwsd+nkRL+6opG7fs/fmErr+dmi\nHMdxsTIAAAD4DQ0ojDELYLd8YlRPTKd3/J7JdK+6YhFdXau6VBXcwLNvN/K3F9nbjfxhigYUwIE1\nW47OzpX0xNTODWgkEtFjE/166ea6S5UBAADAj2hAYYxZAHu9slhWSnUNprp3/d4ToymdXyi7UBXc\nwrNvN/K3F9nbjfxhigYUwIF9d7aoe/qae/reh0ZTepkVUAAAAKvRgMIYswD2+u5sUT/9Qyf29L0T\n6V5tNh0trtc7XBXcwrNvN/K3F9nbjfwV+kMVO/3vRwMK4ECWy3Utleu6fyS1p++PRCI6MZbS+QVW\nQQEAQHDFYjFtbGx4XUZHbGxsKBaLdfQzujr67rDCmTNn+G2YhZ6/XtK7JtP6zrf/x57zf2i0Ty/f\nLOvHjw10uDq4gWffbuRvL7K3G/lLIyMjWlxcVD6fVyRy92vogsZxHMViMY2MjHT0c2hAARzId2cL\n+uFDWWlh73/mwdGUvn5htXNFAQAAdFgkEtHo6KjXZQQWW3BhzPbfgtlos9nSCzfW9Tem+veV/7GB\nhK7nq6o1Wh2sDm7h2bcb+duL7O1G/jBFAwpg384vlDWV6VU2sfv1K7fq6YpqJhvXxZVKhyoDAOBg\nlhdKevmvr+vqhWW1mvyiFOgUGlAY4z4o+zw3W9S7p9OS9p//fSMpvbbEfaBhwLNvN/K3V9iybzRa\neuY/ndN/+r+f17WLq/rWV1/Xf/jSd7S2zM+qOwlb/nAfM6AA9u27s0X92vtnDvRn7xtO6oW5Upsr\nAgBg/xzH0Z/80YtyHEe/8Kn3qbunS47j6MXvzuqP/v139bP/+w+pPxP3ukwgVFgBhTFmAexyo1hT\nqdbQvcNJSfvP/77hpF5bCufR5bbh2bcb+dsrTNm/8J1rKhWq+lt/7zF192yty0QiET32nhk9/kOH\n9JU//J6cVrjvfNyvMOUPb9CAAtiX71wt6L0zGUUPeOz4dCautcqmitVGmysDAGDvyqWavvPsBX3o\nZx5RrOv2vxK/+31H5DjSub++7kF1QHjRgMIYswB2+fbVgn7oUOat1/vNPxaN6PhQUm8sswoadDz7\ndiN/e4Ul++fPXNYDj01oYDh1x69HohH92Ifu01/+94tqcnr7W8KSP7xDAwpgzwrVhi6ubOjxiX6j\n92EbLgDAS7Xqps49P6d3/cjhHb9vYian3FBK3//eDXcKAyxAAwpjzALY46+uFXRysl89t2xVOkj+\nNKDhwLNvN/K3Vxiyf+m56zp8z5AyucSu3/tDTx7TX37jIrOgbwpD/vAWDSiAPdvafps1fp/7hrau\nYnEcfpgDANzlOI6+91fX9K4fObSn7588lFNPb5euXVrtcGWAHWhAYYxZADvUGi1970ZJ73nz/s9t\nB8l/pK9bLUda3thsV3nwAM++3cjfXkHPfn62oFg0qrGpzO7frK1TcR9+1xSHEb0p6PnDezSgAPbk\nr+eKumcoqXTc/PrgSCSi+0aSem2RbbgAAHe98uINPfDYuCL7OM39gcfGdfm1JVUr/OIUMEUDCmPM\nAtjhzOW8Th2+ffvtQfO/byip15bKpmXBQzz7diN/ewU5+1azpdfO3dT9j47v688lkj06fM+QXn1p\nvkOVBUeQ84c/7NqAlstlfeELX9AnP/lJffKTn9Trr7/uRl0AfGSz2dJfzRb1I4f3tl1pL+4bTuk1\nrmIBALho9vKq0tmEcoN3vnplJw88NqFXX6QBBUzt2oD+/u//vh5//HH9y3/5L/X5z39eU1NTbtSF\nAGEWIPxenF/XVKZXQ6me27520PzvG07q9aUNtTiIKLB49u1G/vYKcvYXX13S8QdHDvRnD98zpOWF\ndZUK1TZXFSxBzh/+sGMDurGxoVdeeUVPPvmkJCkWiymZTLpSGAD/uNv2WxPpeJeyiS5dz9fa+r4A\nANyJ4zi6+Mqijt13sAa0qyuq4w+OsA0XMLRjA7q4uKh0Oq3f/d3f1dNPP61/+2//rWq1O/9l8dbf\nhpw5c4bXFr3e/md+qYfX7X39zW+d0V9cWH6rAW1n/vcOpfTlb3/PV/++vN7761OnTvmqHl6TP6/d\neb09A+iXevb6+s//7FuqVKoaGus78Ps1YytvNaBe//uQP6/dft0uEWeHi/guXryof/pP/6k+85nP\n6Pjx4/r93/99JRIJfeQjH3nb950+fVonT55sW1EA/OOl+ZL+9V/O6V//nfvb/t7/+dyi5ks1/coP\nT7f9vQEAuNV3v3lJxXxVf/OnHjzwe7SaLf2bz31DH/3F9yg3tP85UiDIzp49q6eeesr4fXZcAR0c\nHNTAwICOHz8uSXrve9+ry5cvG38owqWdvxGB/3zrcmHH7bcm+d8/nNRrSxxEFFQ8+3Yjf3sFNfvL\nry/r6L1DRu8RjUV138NjVm/DDWr+8I8dG9BsNquhoSHduHFDknTu3DlNT7NSAdjCcRx9+2pe72vz\n/Oe2Y0NJXVmrqt5sdeT9AQCQpM3Npm5eL2jqyIDxe93/yLhefXFeO2wiBLCDrt2+4R/8g3+gf/Wv\n/pUajYZGR0f1iU98wo26ECDcBxVel1Yr6o5FNZOL3/V7TPKPd0U1me7VpZWK7h9hK1PQ8Ozbjfzt\nFcTs56/lNTTar57eXf/qu6uJ6aw2N5tavrmu4fH+NlQXLEHMH/6y61N4+PBhffazn3WjFgA+89xs\nUU9MpTv6GfcPJ/X68gYNKACgY65dWtX0UfPVT0mKRCO675FxvfLSvJUNKGBq13tAgd0wCxBez10v\n6onpnRtQ0/zvYw40sHj27Ub+9gpi9rOXVjXTpgZUkh54ZFyvvmTnNtwg5g9/oQEFcEflelMXVip6\nZLyvo59z73BKry6VO/oZAAB7bdabWpwvavJQ+84zGB7vV1csqvnZQtveE7AFDSiMMQsQTmfnSjox\nmlK8a+f/TJjmfzgX19L6psr1ptH7wH08+3Yjf3sFLfu5q2saGe9Xd4/5/Oe2SCSi+x8dt/I03KDl\nD/+hAQVwRy/cKOnkZOdnW2LRiI4PJvQ623ABAB0we2lV00cH2/6+9z8yrtfO3VSrZd82XMAEDSiM\nMQsQTi/Nr+uRPRyu0I787xtO6bVlGtCg4dm3G/nbK2jZX2vz/Oe2geGUUv29un55te3v7WdByx/+\nQwMK4Db5yqZWNjZ1bCDhyudtHUTEHCgAoL3qtYaWF0oan+nMfdYPnZzUC9+51pH3BsKKBhTGmAUI\nn3M3yzoxmlIsGtn1e9uR/33DSb22yApo0PDs24387RWk7Oeurml0Iq3u7lhH3v/hJ6Y0fz2vhTl7\nDiMKUv7wJxpQALd5aX5dj4x19vTbW43196jebGmlvOnaZwIAwu/apVXNHGv//Oe27u6Y3v3+o/of\nX7/Qsc8AwoYGFMaYBQifczdLe75+pR35RyIR3cd1LIHDs2838rdXkLLfOoCo/fOft3rkiSmtrZT1\n6ot2nIgbpPzhTzSgAN6mXG/qRrGu40NJVz/3wdGUvr9IAwoAaI9adVMri+san8p09HO6umP68Ece\n1emvvKLLry939LOAMKABhTFmAcLljeUNHRtMqGsP859S+/J/YCSpVxZoQIOEZ99u5G+voGR//cqa\nxqYy6urQ/OetRicz+ts/+5i++sfn9F//4ws6f3ZOG+u1jn+uF4KSP/yrfTfyAgiFVxfLum/Y3dVP\naesqlgsrFW02W+qO8bsxAICZ2Q5dv3I3U4cH9POffJ9efWleF15Z1LNfeUWjk2k9+T8/oKGxzt+r\nDQQFf8uDMWYBwuXVpQ3dP5La8/e3K/9UT0wT6V5dXKm05f3QeTz7diN/ewUl+635z84dQHQnPb1d\neuSJaf3tn31cn/gnT+q+h8f1R7/3nBZuFF2to5OCkj/8iwYUwFscx9GrS2Xd78EKqMQcKACgPaqV\nTa0ulzs+/7mTWFdUj757Wk9++H595f/9nhqNlme1AH5CAwpjzAKEx1J5U62WNNrXs+c/0878HxxJ\n6fvMgQYGz77dyN9eQcj++uVVTcxkFevy/q+6Dzw6oYHhlF78q2tel9IWQcgf/ub9UwnAN15b2tB9\nI0lFIns7gKjdHhxN6RVWQAEAhq65cP3Kfvzw37xHz5+5olaTVVCABhTGmAUIjwsrG7p3n9evtDP/\n8f4e1ZuOFtfrbXtPdA7Pvt3I315ByH72srsHEO1mdCKtvnRvKK5pCUL+8DcaUABvubhS0dGBhGef\nH4lEdII5UACAgcpGXYXVDY1Oejf/eScnTk7q1XPzXpcBeI4GFMaYBQiPiysVHRvcXwPa7vwfHElx\nH2hA8Ozbjfzt5ffsZy+vavJQTjGfXel1/IERXXp1Sc2AH0bk9/zhf/56MgF4Jl/ZVLXR2tcBRJ3w\nACugAAADsz6b/9zWl44rN5TS3NU1r0sBPEUDCmPMAoTDpdWt7bf7PYCo3fnfO5TUlbWqagH/DbEN\nePbtRv728nv21zy4/3OvDh0b1LWLK16XYcTv+cP/aEABSDrY9ttO6O2K6nAurteWNrwuBQAQMBvr\nNa0Xqhod7/e6lDuaOTaoqxdXvS4D8BQNKIwxCxAOB21AO5H/w2N9evnmetvfF+3Fs2838reXn7Of\nvbymyUM5RX02/7lt4lBWSzdL2txsel3Kgfk5fwSDP59OAK67uFrRMQ9PwL3VQ2MpnaMBBQDs0+yl\nFV/Of27r7o5pcCSlxRtFr0sBPEMDCmPMAgRfvdHSfLGmmVx833+2E/k/NNqnVxbLaractr832odn\n327kby8/Z3/tkr/u/7yT8ems5mfzXpdxYH7OH8FAAwpAV/JVTaZ71eOTLUvpeJeG+3p0caXidSkA\ngIBYL1ZVLtU0PJ72upQdjU9nND9b8LoMwDP++NsmAo1ZgOC7ZHAAUafyf2Ssj224Psezbzfyt5df\ns5+9vKqpIwOKRvd3mrvbRsbSWr5Z8rqMA/Nr/ggOGlAAuriyoWODSa/LeBvmQAEA+zEbgO23kjQw\nnFJhraJGgA8iAkzQgMIYswDBt30H6EF0Kv+Hxvp0fqEsx2EO1K949u1G/vbya/azl1Z9fQDRtlhX\nVJnBpFaWyl6XciB+zR/BQQMKWK7lOLq4UtFRH9wBeqvhVI8S3VFdy1e9LgUA4HOlQlXVyqaGR/15\n/+c7DY/1B3obLmCCBhTGmAUItoVSXamemDLxrgP9+U7mv3UfaDB/Q2wDnn27kb+9/Jj97KWt+c+I\nz+c/tw2P9mkpoA2oH/NHsNCAApYz2X7baQ9zEBEAYA+uXVrR9BH/b7/dNjTWr+WFYDaggCkaUBhj\nFiDYLhqcgCt1Nv+H3zyIiDlQf+LZtxv528uP2c9eDsYBRNuGx/q1FNBfsPoxfwQLDShgOT/Of26b\nSPeq0XK0sF73uhQAgE8V8xXVa00NjvZ5Xcqe9Wfiamw2tVHm5xvsQwMKY8wCBNvF1Q0dGzj4FSyd\nzD8SiTAH6mM8+3Yjf3v5Lftrb55+G4kEY/5T2vr5lhtKaW05eD/f/JY/gocGFLBYqdZQqdbUeLrH\n61Lu6iHmQAEAOwjK/Z/vlBtMam1lw+syANfRgMIYswDBdWm1oiMDCUUNfmvc6fy3VkBpQP2IZ99u\n5G8vP2XvOI5mA3YA0bbcUEr5AK6A+il/BBMNKGCxiysVHfPpCbjbDufiWqs0tFbZ9LoUAIDPrK1s\nqNVyNDCc8rqUfcuyAgpL0YDCGLMAwdWOA4g6nX8sGtGJ0RRzoD7Es2838reXn7K/+sayDt8zFKj5\nz21B3YLrp/wRTDSggMWCsAIqMQcKALizK28s6/DxIa/LOJDtQ4i4agy2oQGFMWYBgqnebGmuUNUR\nwwbUjfwfGksxB+pDPPt2I397+SX7ZqOl2ctrmjk+6HUpBxJPdKurK6qNgF015pf8EVw0oIClrqxV\nNZHuVW+X//8zcM9QUtcLNZXrTa9LAQD4xI3ZvAaGkkqm/HuS+26ygymtrTBiArv4/2+e8D1mAYLp\nwvKGjg8d/P7PbW7k3xOL6p6hpF5Z5Ie0n/Ds24387eWX7K+8saxD9wRz++223FBSa8vBmgP1S/4I\nLhpQwFIXVio6bngAkZseZhsuAOAWV948gCjIgnoQEWCCBhTGmAUIpnatgLqV/4nRPr28wAqon/Ds\n24387eWH7DfKda0tb2hiOut1KUYyA0kV1oLVgPohfwQbDShgoWbL0eW1qo4G4ATcbQ+OpvT60obq\nzZbXpQAAPHbtwoqmj+QUC8A5BjvJ5BIqrlW9LgNwVbCfWvgCswDBM5uvaijZrVRPzPi93Mo/1RPT\nVKZXF5Yrrnwedsezbzfyt5cfsr8cgu23kpTOJlTMB+vnmh/yR7DRgAIWurBS0fGh4Kx+bjsx2scc\nKABYznEcXb0Qjga0r79X1cqmNjc55R32oAGFMWYBgufCyoaOD5rPf0ru5v/wWEovL9CA+gXPvt3I\n315eZ7+ysK5oLKpsm36OeSkSjag/HVcpH5xtuF7nj+CjAQUs9MZyQFdAx/p0fqGsluN4XQoAwCNX\nLizryD1DikQiXpfSFulcQoW1YG3DBUzQgMIYswDB0nIcXWzjCqib+Q8mu9XfG9M1DmzwBZ59u5G/\nvbzO/sobKzp0z6CnNbRTJhesOVCv80fw0YAClpkv1pXqiSkT7/K6lAN5iOtYAMBam5tNzV1d08zR\n8DSg6VxCRVZAYREaUBhjFiBYLqxs6J423P+5ze38HxrjICK/4Nm3G/nby8vs566saXisX/FEt2c1\ntFsmm1AhQCugPPswRQMKWObCSkXHB4M3/7ntIQ4iAgBrheX021uxAgrb0IDCGLMAwXJheUPH27gC\n6nb+k+le1RuOFtfrrn4ubsezbzfyt5eX2W/d/xme7beSlM7GA3UIEc8+TNGAAhZxHCfwK6CRSEQP\njaV0jm24AGCV9WJV64WaxiYzXpfSVn3puKobdTUaLa9LAVxBAwpjzAIEx1J5UxFtnSbbLl7k/9Cb\n17HAWzz7diN/e3mV/dULK5o5OqBoLFx/fY1GI+rLxFUKyBwozz5MhesJBrCjiysV3TOUCPzdaQ+N\n9rECCgCWufLGsg6FbP5zW4a7QGERGlAYYxYgOC6sbOhYm+7/3OZF/scGE1par6tYbbj+2fgBnn27\nkb+9vMjeaTm6cmEldAcQbUtng3MXKM8+TNGAAhZ5Y3lDx4eCO/+5LRaN6P6RlL7PNlwAsMLizZLi\niS5lcsH/GXYnnIQLm9CAwhizAMFxcaWie9q8AupV/g+NpnSO61g8xbNvN/K3lxfZX3kjfNev3CpI\nd4Hy7MMUDShgiXxlUxubLY3193hdSls8NNanl5kDBQArhL0BZQUUNqEBhTFmAYJh+/qVdh9A5FX+\n9w8ndWm1qhrH1nuGZ99u5G8vt7PfrDd083pB00cGXP1cN23NgFa9LmNPePZhqmu3b/jlX/5lJRIJ\nRaNRxWIxffazn3WjLgBtttWAtnf7rZfi3TEdzsX1+vKGHh7r87ocAECHzF5e0+hEWj29u/61NbD6\n0r0qr9fUbLYUC9k1M8A77en/4Z/+9Kf1m7/5mzSfuCNmAYLhQocOIPIy/xOjKZ1nG65nePbtRv72\ncjv72Usrmjk26Opnui0WiyrV16v1Ys3rUnbFsw9Te2pAHcfpdB0AOuzCyoaOD4br9MATo306z0m4\nABBq1y6tauZYeLffbuvPxANzFQtgYtcGNBKJ6DOf+Yz+8T/+x/r6179+1++79bchZ86c4bVFr7f/\nmV/q4fXtr7/+F2e0utHQVCYeqvxPjKb04lxB3/qWv/73tuX1qVOnfFUPr8mf1+683p4BdOPzvvHf\nv6XVpbLGprK++ffv1OtaY13Pf/dF39Tjh/x57a/X7RJxdlneXFtbUy6XU7FY1Gc+8xn9/M//vB54\n4IG3fc/p06d18uTJthUFoL1emi/p/3puXl/8qXu9LqXt/tf/77w+/YGjOhzSu+EAwGYXvr+gF/7y\nmn7m55/wupSO++afvaaeeJfe+2PHvC4FuKOzZ8/qqaeeMn6fXVdAc7mcJCmdTuvd7363Lly4YPyh\nCJd2/kYEnfHGckXHOrT91uv8T4z26fxNtuF6wevs4S3yt5eb2V+7tKrpo+HffisF5yoWnn2Y2rEB\nrdVqqlS2HoRqtaqXXnpJMzMzrhQGoH0uhnD+c9uJ0ZReXuAgIgAIo7mra5o6bEkDmomrVAjGVSyA\nia6dvlgoFPRbv/VbkqRWq6VTp07p0UcfdaUwBAf3QfnfxZWK/peHRjry3l7n/9Bon/7wxQVPa7CV\n19nDW+RvL7ey39xsamVxXaOTaVc+z2v9AbkLlGcfpnZsQEdGRt5qQAEEU73R0lyxpsPZuNeldMR0\ntlcb9abbHNN4AAAgAElEQVRWypsaTHV7XQ4AoE0WbxQ1ONKn7u6Y16W4Ip1NqJivyHEcRSIRr8sB\nOoabbmGMWQB/u5qvaiLdq56uzjzuXucfiUT04GhK59mG6zqvs4e3yN9ebmU/P5vX+HTWlc/yg954\nl6LRiKqVTa9L2RHPPkzRgAIhd2m1cwcQ+cVDo316mftAASBU5mcLGp/OeF2Gq9IB2YYLmKABhTFm\nAfzt4kpFRwc614D6If8Toymdv8kKqNv8kD28Q/72civ7rQbUnhVQSerPxlXK+/skXJ59mKIBBULu\n4kr4V0DvGU5qtlDTRr3pdSkAgDYol2qq1xrKDSa9LsVVrIDCBjSgMMYsgH85jqNLq51dAfVD/j2x\nqI4PJvTqEttw3eSH7OEd8reXG9kvzhc1MpG27jCedDauos9XQHn2YYoGFAixhfW6El1RZRPhPx32\nxGhK55kDBYBQWLpZ0vBYv9dluC6dSajIXaAIORpQGGMWwL/c2H7rl/xPjPXp/E0aUDf5JXt4g/zt\n5Ub2Ww1oX8c/x2/SubiKa/5eAeXZhykaUCDELq1WdDTk85/bHhxJ6dWlspotx+tSAACGlm+ua8jC\nFdD+TEIlVkARcjSgMMYsgH9dXKnoWAfnPyX/5J+Od2k41aNLq/7+zXGY+CV7eIP87dXp7JuNltaW\nyxocsW8FNNXfq+pGXY1Gy+tS7opnH6ZoQIEQu7Ra0ZEON6B+cmIspfMLXMcCAEG2ulxWOptQd3fM\n61JcF41G1JeOq1Tgl6kILxpQGGMWwJ+qm02tbWxqIt3b0c/xU/4nRpkDdZOfsof7yN9enc5+6WZJ\nQ+P2bb/dls4mVPLxVSw8+zBFAwqE1LVCTZOZXsWi9hxh/9BoSi8vlOU4zIECQFAtW3oC7rb+AFzF\nApigAYUxZgH86dpaVTPZzm+/9VP+Y/09chxHC+t1r0uxgp+yh/vI316dzn55cV1DFs5/bktnEyr6\neAWUZx+maECBkLqar+pQLu51Ga6KRCJb17FwHygABNbaclm54ZTXZXgmzQooQo4GFMaYBfCnq2sV\nHcp2vgH1W/4nRlN6+SYHEbnBb9nDXeRvr05m32y2VMxXlR1Iduwz/M7vK6A8+zBFAwqE1LV8VTOW\nrYBK0kOjrIACQFAV1irqS/eqq8vev6L2Z+MqsQKKELP36UbbMAvgP7VGS8vlTU12+ARcyX/5HxtM\naHG9rlKt4XUpoee37OEu8rdXJ7NfWy5rYMje7beSlM7EVSpU5bT8eaAezz5M0YACITSbr2oibdcJ\nuNti0YjuHU7qlUVWQQEgaFaXyspZ3oB293SpuyemjTIH6iGcaEBhjFkA/7mar7oy/yn5M//7hlN6\nfWnD6zJCz4/Zwz3kb69OZr+2TAMqvTkHWvDnHCjPPkzRgAIhdHXNvhNwb3XvUFKvL9OAAkDQrC1v\naGDI3gOItm0dRMQcKMKJBhTGmAXwHzcPIPJj/vcOJfX60oYcx5/zM2Hhx+zhHvK3VyezX2UFVJK/\nDyLi2YcpGlAghK6uVXU4m/C6DM+M9HWr6UgrG5telwIA2KN6raFataH+tL07eLZt3QXqzy24gCka\nUBhjFsBf6s2WFst1TWQ6fwKu5M/8I5EI23Bd4Mfs4R7yt1enss+vbig7kFDEwgP03snPW3B59mGK\nBhQImZvFukZSPeqy/Af4vcNJDiICgAAprFaUGWD+U5L6swmVWAFFSNGAwhizAP4yV6xp0qXVT8m/\n+d87lNQbrIB2lF+zhzvI316dyn57BRRSJhv37Qoozz5M0YACITNX3LoD1HbHBhO6uOLPH94AgNsV\n1irK5FgBlaREqkeb9aY26w2vSwHajgYUxpgF8JcbxbomXWxA/Zr/cKpb9aajtQoHEXWKX7OHO8jf\nXp3KvrC6oQwroJK2zjLoz8R9eRcozz5M0YACITNXcHcLrl9FIhEdG0zoEqugABAIzIC+XTqXUHHN\nfw0oYIoGFMaYBfAXt7fg+jn/owMJXVylAe0UP2ePziN/e3Uie6flqJCvKGPxFWLv1J+Jq1Tw388w\nnn2YogEFQqTeaGmt0tBoX4/XpfjCUVZAASAQ1ks1xRPd6u6JeV2Kb2ytgPIzDOFDAwpjzAL4x3yp\nptG+HsVcvILFz/kfHUjoEiugHePn7NF55G+vTmRfWNtQJsfq563SmQQzoAglGlAgROaKNU7AvcWh\nbFw3ijXVmy2vSwEA7CC/WuEAondIZ+PcBYpQogGFMWYB/GOuUHP1BFzJ3/n3dEU1nu7VNQ5x6Ag/\nZ4/OI397dSL74lpFWa5geZt0NqGCD7fg8uzDFA0oECI3ipyA+06Hs3Fd5TfIAOBrhbWK0mzBfZv+\nTFzlUlUtdvEgZGhAYYxZAP/wYguu3/OfycV1jQa0I/yePTqL/O3VieyL+YrSnID7NrGuqJJ9vSr5\nbA6UZx+maECBEPFiC67fzWRpQAHA77Ya0LjXZfhOJufPbbiACRpQGGMWwB9qjZby1YZGXL6Cxe/5\nH8rGmQHtEL9nj84if3u1O3un5Wi9UFV/hgb0nTIDSd81oDz7MEUDCoSEF1ewBMFEplc31+vaZIYG\nAHxpvVRTb6JbXd3cAfpOmYGE8qsbXpcBtBUNKIwxC+APN0t1jfe7v/3W7/n3xKIa7evRXLHmdSmh\n4/fs0Vnkb692Z18qMP95N37cgsuzD1M0oEBI3CzVNdbv7vbboGAOFAD8q7hWZf7zLjK5pAqr/mpA\nAVM0oDDGLIA/LJTqGnV5/lMKRv4zzIF2RBCyR+eQv73anX2RFdC72loB9dcWXJ59mKIBBUJiYb3G\nCuhdbK2AsgUXAPyomK/SgN5FX3+v6tWGNutNr0sB2oYGFMaYBfCHhVJdox40oEHIf+suULYwtVsQ\nskfnkL+92p09V7DcXSQaUX82roKPfobx7MMUDSgQEjfXvdmCGwQzmV7NFWpqthyvSwEAvMNWA8oK\n6N1kckkVOQkXIUIDCmPMAnivXG9qs+koE+9y/bODkH+8O6Zsols3S3WvSwmVIGSPziF/e7U7+1K+\nqn5WQO/Kbyfh8uzDFA0oEAIL61vbbyMR7gC9m5lsr2Y5CRcAfKVW3VSr5Sie6Pa6FN/KDPirAQVM\n0YDCGLMA3rtZqmnMo+23Qcl/OhvXtQINaDsFJXt0Bvnbq53Zbx9AxC9Q7y4z4K+rWHj2YYoGFAiB\nBe4A3dVkulc3CpyECwB+wgFEu/PjVSyACRpQGGMWwHvbW3C9EJT8JzK9mivSgLZTULJHZ5C/vdqZ\nPVew7I4ZUIQNDSgQAjdLdY329Xpdhq9NprdOwgUA+AcroLuLJ7rlOFK1sul1KUBb0IDCGLMA3rvp\n0R2gUnDyH071qFhrqNpoeV1KaAQle3QG+durvTOgFfWzArqjSCSydRCRT65i4dmHKRpQIAQW1uue\nHUIUFLFoRGP9vbrBNlwA8I1SvsoK6B5kcgnlfbQNFzBBAwpjzAJ4a73WkOM46u+NefL5Qcqfg4ja\nK0jZo/3I317tnQGtMAO6B5mcf07C5dmHKRpQIOC25j+5A3QvJjmICAB8o9lsaaNcV18/ZxjsJpNL\nqMhJuAgJGlAYYxbAWwvrdY15+MM7SPlPpmlA2ylI2aP9yN9e7cp+vVhTqq9X0Rh/Hd1NZsA/J+Hy\n7MMUTzwQcAvrdY0w/7knkxlOwgUAv+AE3L3L5BK+2YILmKIBhTFmAby1uF7XSF+3Z58fpPwn0r2a\nK1a9LiM0gpQ92o/87dWu7Ev5Kifg7lEml1QxX5HTcrwuhWcfxmhAgYBbKm9qmBXQPRlKdatca6qy\n2fS6FACwXrFQUTrDCuhedPfE1BPvUnmdXTwIPhpQGGMWwFuL63UNp7xbAQ1S/tFIRONprmJplyBl\nj/Yjf3u1K3tWQPcnk0v6Yg6UZx+maECBgFsqMwO6HxNp5kABwA+KBe4A3Y/MAHOgCAcaUBhjFsA7\nm82WitWmBhLMgO4VV7G0T9CyR3uRv73aNwNaUTrDCuheZXIJFXxwFQvPPkzRgAIBtrKxqYFkl2JR\n7gDdq0m24AKALxTzVfWzArpnWw0oK6AIPhpQGGMWwDuL65saTnm7/TZo+XMVS/sELXu0F/nbqx3Z\n16qbkhz1xrvMC7IEM6AIiz01oK1WS//oH/0jfe5zn+t0PQD2gfnP/ZtMswUXALxWfPMAokiEHTx7\nxQwowmJPDeif/umfampqqtO1IKCYBfDOkscn4ErBy38w2a2NzZbKda5iMRW07NFe5G+vdmS/Nf/J\n9tv96M/EVS5V1Wy2PK2DZx+mdm1AV1ZW9MILL+jJJ590ox4A+7BY9n4LbtBEIhFNpnuYAwUAD22d\ngMsBRPsRi0WV6o+rlK96XQpgZNcG9A/+4A/09//+31c0uvO33vrbkDNnzvDaotfb/8wv9dj0emm9\nruVrF8h/n697N9ff2obrh3qC+vrUqVO+qofX5M9rd15vzwCavF8xX9VqfsEX/z5Beq1Y/a2TcIOc\nP6+D+bpdIo7jOHf74l//9V/re9/7nn7hF35B58+f11e+8hU9/fTTt33f6dOndfLkybYVBWBvfumP\nX9E/fP8hHR9Kel1KoPzed+eU6I7pY4+PeV0KAFjpT/7oRR25d1gPPj7hdSmB8mf/+ZwmZrJ65Ilp\nr0uBhc6ePaunnnrK+H12XNZ8/fXX9fzzz+uXf/mX9du//dt6+eWX9Tu/8zvGH4pwaedvRLA/i+ub\nnh9CFMT8JzNxtuC2QRCzR/uQv73akT1XsBxMZiDp+UFEPPsw1bXTFz/60Y/qox/9qCTp+9//vr78\n5S/rV37lV1wpDMDONupNbTZb6u+NeV1K4Eyke/TV12lAAcArpUKFGdADyOQSuvTqktdlAEa4BxTG\nuA/KG9tXsHh9hH0Q859MswLaDkHMHu1D/vYyzb7VbKlcqqkv3dumiuyRHUgov7rhaQ08+zC14wro\nrR588EE9+OCDnawFwD4scQLugQ0ku966iiXVwwoyALhpvVRTItWjWIx1kP3K5JIqrHEXKIKNJx/G\nmAXwxuJ6XcN93t4BKgUz/0gkoon+Hs2zCmokiNmjfcjfXqbZl7iC5cCSfT3arDdUrzU8q4FnH6Zo\nQIGAWip7fwBRkE1metmGCwAeKOYr6s9wANFBRCIRZbIJFVkFRYDRgMIYswDeWFqv+2ILblDzn0j3\nvnUXKA4mqNmjPcjfXqbZl/JVpTkB98AyA0nlPWxAefZhigYUCKjFsj+24AbVZJoVUADwQrFQVX+G\nLbgHlRlIqODxQUSACRpQGGMWwBtL6/44hCio+U9kejVXoAE1EdTs0R7kby/T7Iv5CiugBjK5hKcH\nEfHswxQNKBBAjuNsXcOSYgX0oCZYAQUAT5TyVfVzCNGBcRIugo4GFMaYBXBfodpQb1dU8W7vrxAJ\nav6DyW6V601VNptelxJYQc0e7UH+9jLNnhVQM15vweXZhykaUCCAFjkB11g0EtE4q6AA4KpataFm\n01E8wQ6eg9peAXUcx+tSgAOhAYUxZgHct3UCrj9+eAc5/8kMJ+GaCHL2MEf+9jLJvlTYWv2MRCJt\nrMguvfEuxWJRVTY2Pfl8nn2YogEFAmip7I8DiIJuIt2rGxxEBACuKXIFS1t4vQ0XMEEDCmPMArhv\nab2uYZ9swQ1y/pPcBWokyNnDHPnbyyT7ElewtEVmIKnCqjcHEfHswxQNKBBAi5yA2xachAsA7iqs\nbiiTowE1tXUVCyugCCYaUBhjFsB9S+VN36yABjn/CVZAjQQ5e5gjf3uZZF/IV2hA28DLu0B59mGK\nBhQIID8dQhRkQ6lulWtNVbmKBQBcUVitKE0Daoy7QBFkNKAwxiyAu5otR2uVhoZ8cghRkPOPRiIa\nS/dqrlj3upRACnL2MEf+9jLJvsgKaFtkBhLKe3QIEc8+TNGAAgGzsrGpTLxLXVGOsG+HSeZAAcAV\nm/WG6tWGUn29XpcSeOlsQuuFqlot7gJF8NCAwhizAO5aKvtr+23Q8+ck3IMLevYwQ/72Omj2hXxV\n6WxCEX6BaqyrK6pkX69Kharrn82zD1M0oEDA+OkAojDgJFwAcEdxdYP5zzbK5BIqMgeKAKIBhTFm\nAdzltwOIgp7/ZIYG9KCCnj3MkL+9Dpp9YY35z3bK5LyZA+XZhykaUCBgFtc3NeyTA4jCYCLdq7kC\nDSgAdBoNaHtlBjgJF8FEAwpjzAK4a6lc14iPtuAGPf+hVLdKtQZXsRxA0LOHGfK310GzL9KAttXW\nXaDur4Dy7MMUDSgQMH47hCjoopGIxvt7daPEVSwA0EmFNe4AbafMQEKFVVZAETw0oDDGLIC7ltb9\ndQhRGPKfSPfqBttw9y0M2ePgyN9ezID6QybnzRZcnn2YogEFAqTeaKlcbyqX6PK6lFDhICIA6Kxa\ntaFGo6UEZxi0TV9/r2qVTW0yQoKAoQGFMWYB3LNU3tRgqlvRiH/uUAtD/hPcBXogYcgeB0f+9jpI\n9tvznxEf/fwKukg0ov5s3PWrWHj2YYoGFAiQrflPfnvcbpPcBQoAHVVY22D7bQd4tQ0XMEEDCmPM\nArhnqbzpuwOIwpA/V7EcTBiyx8GRv70Okj0HEHVGJpdQweW7QHn2YYoGFAiQpfW6rw4gCouhVLeK\ntYaqjZbXpQBAKHEAUWdwFyiCiAYUxpgFcM+iD69gCUP+sWhEY/29usk23H0JQ/Y4OPK3l8kMKNrL\ni6tYePZhigYUCJCl9U2NsALaERPpHg4iAoAOYQW0MzK5hApr7m7BBUzRgMIYswDu8eMhRGHJfzId\nZw50n8KSPQ6G/O213+wdx2EGtEMyuYTyqxU5juPaZ/LswxQNKBAgfjyEKCymsr2aLVS9LgMAQqda\n2ZQkxRP8/Gq3eKJbkcgP/jcGgoAGFMaYBXBHud5Uo+WovzfmdSlvE5b8pzO9mmUFdF/Ckj0Ohvzt\ntd/suQO0cyKRyJvbcN2bA+XZhykaUCAglsp1jfR18wO8Q6azcV3PswIKAO3G9tvO4i5QBA0NKIwx\nC+COpfVN381/SuHJPxvvUtORCtWG16UERliyx8GQv732m31+dUO5wWSHqsHWSbjuHUTEsw9TNKBA\nQCz58AqWMIlEIprO9LIKCgBtll/ZUGaABrRTMjn3r2IBTNCAwhizAO5YKvvzCpYw5T+djTMHug9h\nyh77R/722m/2+dUNZWlAOyYz4O4WXJ59mKIBBQJicd1/V7CEzdZBRKyAAkA75VcqbMHtIO4CRdDQ\ngMIYswDuWCrXNdznvy24Ycp/KhPXLFtw9yxM2WP/yN9e+8m+0WipXKqqPxvvYEV2S+cSKuarclru\n3AXKsw9TNKBAQPj1EKIwmc726jpbcAGgbYprFfVnEorF+Ctnp3R3xxRPdGu9xM8vBAP/NYAxZgE6\nz3EcLZfrGvHhIURhyn883auF9bo2my2vSwmEMGWP/SN/e+0n+/xKWdlBrmDpNDe34fLswxQNKBAA\nhWpDvV1RxbtjXpcSaj2xqIZTPZov1b0uBQBCIb9aUXYw5XUZobd1FQsn4SIYaEBhjFmAzrtZqmu0\n35/bb8OW/3SmlznQPQpb9tgf8rfXfrLfOgGXFdBOy+TcOwmXZx+maECBAFhYr2usr9frMqwwnY0z\nBwoAbbK2whUsbsgOJFRY5SRcBAMNKIwxC9B5C+t1X94BKoUv/ylWQPcsbNljf8jfXvvJvrCyoSxX\nsHScmyugPPswRQMKBMCCj7fghg0roADQHq2Wo0K+ogwroB2XziVca0ABUzSgMMYsQOdtbcH1ZwMa\ntvynM72aLVTlOO7cpxZkYcse+0P+9tpr9qVCVYlkt7o5QK/j+jNxbazX1Gh0/hR3nn2YogEFAmBh\nva4RVkBdkYl3Sdo6eRgAcHB55j9dE41G1J9JqJhnFRT+RwMKY8wCdJbjOFtbcH26Ahq2/CORyNYc\nKNtwdxW27LE/5G+vvWafX2X+001uXcXCsw9TNKCAzxVrTXVFI0r1sIXJLdOZuK5xEBEAGMlzAJGr\nMrmECmuchAv/owGFMWYBOmth3d8HEIUx/5lcnJNw9yCM2WPvyN9ee81+dbmsgaFUh6vBtkzOnatY\nePZhigYU8Dk/b78Nq8O5uK6s0YACgIm1JRpQN2WHUlpbZgUU/kcDCmPMAnTWwnrN1w1oGPOfycZ1\njQZ0V2HMHntH/vbaS/bNZkuFfIUtuC4aGEppdbnc8c/h2YcpGlDA5xZKm77eghtGI309Wq83Va43\nvS4FAAKpsFpRX3+vuriCxTXZwaQKaxW1mp2/igUwQQMKY8wCdJbfV0DDmH80EtFMNq6rrILuKIzZ\nY+/I3157yX51uayBYbbfuqm7O6ZUf68KHb6KhWcfpmhAAZ9jBtQbh3KchAsAB7W6RAPqhYGhlNaW\nOr8NFzBBAwpjzAJ0juM4vj8FN6z5H2IFdFdhzR57Q/722kv2a8tl5TiAyHW5oWTH50B59mGKBhTw\nsfU3ZxD7uAPUdTO5uK52eBsTAITVKifgemJgKKVVVkDhczSgMMYsQOdsb7+NRCJel3JXYc2fFdDd\nhTV77A3522tPM6BL62zB9cDAcOevYuHZhykaUMDHbpRqGk/3el2GlUb7e1SscRIuAOxXZaOuZrOl\nVD8/v9yWc+kqFsAEDSiMMQvQOfPFuiZ83oCGNf9oJKKZTK9mOYjorsKaPfaG/O21W/Zry1vbb/28\neyes+tNx1aoN1aqNjn0Gzz5M0YACPjZfqmnMxwcQhd2hXFxXaUABYF9WlzaUY/utJyLRiHJDSa2x\nCgofowGFMWYBOicIK6Bhzn8ml2AOdAdhzh67I3977Zb96jIHEHlpoMPbcHn2Yaprpy/W63V9+tOf\n1ubmphqNhp544gl97GMfc6s2wHo3ijWNM0PjmUPZuL7yyrLXZQBAoKwtlXX/I2Nel2Et7gKF3+24\nAtrT06N/9s/+mX7rt35Ln//853X+/Hm9+uqrbtWGgGAWoDMaLUerG5sa6ev2upQdhTn/Q7m4rrEF\n967CnD12R/722i375cWSBkf7XKoG75Qb7uwKKM8+TO26Bbe3d2v1pdFoqNVqqa+P/6AAblgo1TWQ\n7FZ3jJ3yXhnt61G+2lBlk5NwAWAvNjebKuWryg2yBdcrg8MprSyte10GcFc7bsGVpFarpaeffloL\nCwv6iZ/4CU1NTd3x+86cOfPWnvDt34zw2o7X2//ML/WE5XX88COaSPf6ph4b849FI8rFNvXfvvFX\n+sgHftjzevz2+tSpU76qh9fkz2vvX5cLTWUHk4p1RX1Rj42v3/OeH1J+eUPf/Oa3FI1GPK+H1+F5\nnUwm1Q4Rx3GcvXzjxsaG/sW/+Bf62Mc+phMnTrzta6dPn9bJkyfbUhCALf/t+0u6tFrR/3lqxutS\nrPa5b1zRYxP9+p/uHfS6FADwve+/cEOXXlvSh//eo16XYrXf+8I39bc/flJDI+xcRPucPXtWTz31\nlPH77HlvXzKZ1OOPP66LFy8afyjCZfs3JGivm6V6IA4gCnv+R3IJXV6teF2GL4U9e+yM/O21U/bL\nCyUNMf/puaHRfq3cLHXkvXn2YWrHBrRYLKpc3hpirtfrOnfunI4cOeJKYYDtbhRrGk9zB6jXjgwk\ndHmVg4gAYC+WF9ZpQH1gaLRPSwvMgcKfunb6Yj6f15e+9CW1Wi05jqP3v//9evjhh92qDQGxvTcc\n7TVfrGkiACugYc//yEBcl1gBvaOwZ4+dkb+9dsp+qwHtd7Ea3MnQWL9eefFGR96bZx+mdmxAZ2Zm\n9LnPfc6tWgC8yXEczZfqGk/7vwENu8Fkt1qOo7XKpnIJf1+JAwBeqlUbqmzUlcklvC7FekOjfVpm\nBRQ+xf0OMMYsQPvlqw31xCJK9cS8LmVXYc8/EonoyECCVdA7CHv22Bn52+tu2S8vlDQ4nFIkGnG5\nIrxTdjCp9WJVm/VG29+bZx+maEABH9qa/2T10y+O5BK6whwoAOxo8UZRIxNpr8uApFgsqtxQSsuL\nZa9LAW5DAwpjzAK033yxromANKA25M8c6J3ZkD3ujvztdbfsF+dLNKA+MjKe1tJ8se3vy7MPUzSg\ngA/NFWuaygSjAbXB0YGErtCAAsCOFm4UNUoD6hujE2ktzLW/AQVM0YDCGLMA7Xe9UA3MCqgN+R/K\nxXUtX1Wz5Xhdiq/YkD3ujvztdafsm42WVpc4AddPRib6tdiBFVCefZiiAQV8aK7ACqifJLpjGkx1\na65Y87oUAPCllcV1ZXJJdQfg8DxbjIyntXRzXa1my+tSgLehAYUxZgHay3EczRVrmgzICqgt+R/O\nJXSZbbhvY0v2uDPyt9edsl+4UdTIBKufftLT26X+dK9Wl9t7EBHPPkzRgAI+s1ppqDcWVV/vjtf0\nwmVHB2hAAeBuFueLGhln/tNvRibTWrjBHCj8hQYUxpgFaK+5QjVQ229tyf/wQFyXuYrlbWzJHndG\n/va6U/bzswWNTWU8qAY7GR1Pa/FGqa3vybMPUzSggM9cL9Q0GaAG1BZHBxK6vMYKKAC8U2OzqeWF\nksYmWQH1m9HJjG5eL3hdBvA2NKAwxixAe80VaprMxL0uY89syX+8v1drlYY26k2vS/ENW7LHnZG/\nvd6Z/eJ8SQNDKXX3MDriN2NTGS3OF9Vs40FEPPswRQMK+Mz1Qk1TATmAyCaxaESHsnFWQQHgHeZn\n8xqfznpdBu6gN96lTC6hpZvt3YYLmKABhTFmAdrrepEZUL86NpjQxRUa0G02ZY/bkb+93pn9/GxB\n4zM0oH41Pp3V/LV8296PZx+maEABH2m2HN0s1TXOCqgv0YACwO1uzOY1Ps0BRH41MZPV/CxzoPAP\nGlAYYxagfRbX6xpIdKu3KziPpk35HxtM6MLKhtdl+IZN2eN25G+vW7Mvl2qqVTY1MJjysCLsZHw6\noxuz7VsB5dmHqeD8LRewACfg+tvRgYSurVXVaDlelwIAvnD96pomD+UUiUa8LgV3MTjcp431ujbK\ndUU2Y3AAACAASURBVK9LASTRgKINmAVon7liVZMB235rU/6J7phG+no0m+c+UMmu7HE78rfXrdlf\nv7yqqSM5D6vBbiLRiManM5pv0yoozz5M0YACPjKbrwXqACIbHR1M6AJzoAAgSbp+ZU1Thwe8LgO7\naPdBRIAJGlAYYxagfa7lq5rJBucOUMm+/I8PJnWROVBJ9mWPtyN/e21nX61sKr+yodHJtMcVYTcT\nM1ndaNNBRDz7MEUDCvjIbKGq6YA1oLY5zkm4ACBJmru6pomZrGIx/jrpd+PTGd28nler2fK6FIAG\nFOaYBWiPcr2pjXpLw6lur0vZF9vy376KxXE4iMi27PF25G+v7eyvX17T1GHmP4MgkexRfzqupZsl\n4/fi2YcpGlDAJ67lq5rO9ioS4SRBP8u+eU3OwjqnCQKw2/Urq5o6wvxnUEwezmnuKnOg8B4NKIwx\nC9AeQZz/lOzM//hgQheW2YZrY/b4AfK316lTp1SvNbS8sK6xqYzX5WCPJg7lNHd1zfh9ePZhigYU\n8InZgDagNjo2mNDFVRpQAPaan81rZDyt7u6Y16Vgj6bebEAZIYHXaEBhjFmA9riWr2o6E7wG1Mb8\njw0mdYGTcK3MHj9A/vY6c+bM1vwn938GSmYgIceRioZ3WfPswxQNKOAT1/I1VkAD4vhgQhfZggvA\nYtz/GTyRSESTh7Kau2K+DRcwQQMKY8wCmKs3Wloq1zWR6fW6lH2zMf+x/h5VGi3lK5tel+IpG7PH\nD5C/vd773h/WzbmCJmayXpeCfZpswxwozz5M0YACPnCjWNNYf4+6opyAGwSRSERHB5gDBWCnxRsF\n5YZS6o13eV0K9qkdDShgigYUxpgFMBfU+U/J3vzZhmtv9thC/vY6840XNHmI1c8gGhnvV2GtoqrB\nDh6efZiiAQV8IKhXsNiMk3AB2Kq01mT7bUBFY1GNT2d0g1VQeIgGFMaYBTA3WwjuAUS25n98iJNw\nbc0eW8jfTo7jqLoe0+QhTsANqq1tuPkD/3mefZiiAQV8gBXQ4JnJxrVYqqu62fS6FABwTX5lQ11d\nUaWzCa9LwQExBwqv0YDCGLMAZlqOo+uFmqYCeAKuZG/+XdGIZrJxXVo1u08tyGzNHlvI305zV/Pq\nSTa8LgMGJmayWrhRVKPROtCf59mHKRpQwGOL63Wle2NK9sS8LgX7dGwoqYuWb8MFYJcb19bUl+Ov\nj0HW09ul3FBKizcKXpcCS/FfEBhjFsDMtXxN0wHefmtz/scGErq4Yu9BRDZnD/K31dzVvN734+/y\nugwYmjyU1fUrB5sD5dmHKRpQwGOzzH8G1nFOwgVgkcpGXcV8RSNj/V6XAkPMgcJLNKAwxiyAmStr\nFR0KcANqc/5HBhK6slZVs+V4XYonbM4e5G+jm9cLGpvK6Nvf+bbXpcDQ5KGcblxdk+Ps/+cXzz5M\n0YACHruyVtXhXHAbUJsle2IaSnZrtmDvQUQA7LEwV9TYZNrrMtAG/Zm4unu7tLpc9roUWIgGFMaY\nBTi4luPoWr6qQwFuQG3P/9igvXOgtmdvO/K3z8JcUaMTGbIPiclDWc1d2f82XPKHKRpQwEOL63Wl\numPq6+3yuhQc0LHBhC5Z2oACsMvCjYJGp1gBDYutOdCDHUQEmKABhTFmAQ7u6lqwVz8l8rd5BdT2\n7G1H/nbZKNdVqzaUHUiSfUhMHfAgIvKHKRpQwENX16o6nEt4XQYMHBtM6sLKxoEOcgCAoFiYK2hk\nIq1IJOJ1KWiTwZE+Vcp1lUs1r0uBZWhAYYxZgIO7slYJ/Aqo7fkPJLoUjUS0vLHpdSmusz1725G/\nXW49gIjswyESjWjiUE5z1/a3Ckr+MEUDCnjoKifgBl4kErF6Gy4AOyzc2DqACOEydSiruSvMgcJd\nNKAwxizAwTRbjq4VapoJ8B2gEvlL9s6Bkr3dyN8uSzdLGh7vl0T2YTJ5eP9zoOQPUzSggEdulurK\nxruU7Il5XQoMHR9M6uLKhtdlAEBHbNYbWi9WlRtMel0K2mx0MqPlhXVt1htelwKL0IDCGLMAB3Nl\nrRKK7bfkLx21dAWU7O1G/vZYXljXwHCforGtvzaSfXh0d8c0Mt6v+dnCnv8M+cMUDSjgkTBcwYIt\nk+lerVYaKtebXpcCAG23vLCu4dE+r8tAh0zMZHXjGnOgcA8NKIwxC3AwV0JyBQv5S7FoREdycV1a\ntWsVlOztRv72WLpZ0tBY/1uvyT5cxiYzWpgr7vn7yR+maEABj1wNwRUs+IHjQ8yBAginpZslDd/S\ngCJcRifTWrix9y24gCkaUBhjFmD/Gi1Hc8Xgn4Arkf+2YwP2zYGSvd3I3w6O42j5HQ0o2YdLdiCp\nWrWhjXJ9T99P/jBFAwp44EahpqFUj3q7eATDwtarWACEW7lUkyQl+3o8rgSdEolGNDKR3tc2XMAE\nf/uFMWYB9i8sJ+BK5L/t8EBCs/mqNpstr0txDdnbjfztsLJY1tBovyKRyFv/jOzDZ3Ri79twyR+m\naEABD1xaDU8Dii3xrqhG+3s1W6h5XQoAtM3q0roGhlNel4EO2+9BRIAJGlAYYxZg/y6tVnR0MPgn\n4Erkf6tjgwldWLbnICKytxv522FlqXxbA0r24TM6ufctuOQPUzSggAcurVZ0bCDpdRlos2MDCeuu\nYgEQblsroNwBGnZbBxFtqrKxt4OIABM0oDDGLMD+lGoNlWpNjafDcaAD+f/AsSG7DiIie7uRvx1W\nl8oaHHn7CijZh89+DiIif5iiAQVcdnm1oiO5hKK3HOiAcNi+isVxHK9LAQBjteqmatWG+tOcWWCD\n0Ym0Fua4DxSdRwMKY8wC7E+Y5j8l8r9VNtGt3q6oFtbt2MJE9nYj//BbXSprYCilSPTtvzAl+3Da\n6xwo+cMUDSjgsosrFR0dCE8DirfjPlAAYbGyVNbACCfg2mJ4PK3FmyWvy4AFaEBhjFmA/bm0Gq4G\nlPzf7vigPQcRkb3dyD/8VhfvfAAR2YfTwGBS68Wa6rXGjt9H/jBFAwq4qNlydG2tqiMDzNOE1VFW\nQAGExMpSWYPcAWqNaCyqwZGUlhdYBUVnde30xeXlZX3pS19SoVBQJBLRU089pQ996ENu1YaAYBZg\n764XqhpM9SjRHfO6lLYh/7c7NpDUv1uZ87oMV5C93cg//LauYLm9ASX78Boe69fifEkTM7m7fg/5\nw9SODWhXV5d+7ud+TocPH1a1WtXTTz+tRx55RFNTU27VB4TK1v2f4dl+i9uNp3u0XmuqWG0oHd/x\nP7EA4FvNRkvFfFXZQVZAbTIyntbSPCug6Kwdt+Bms1kdPnxYkhSPxzU1NaW1tTU36kKAMAuwd5dW\nwnUCrkT+7xSNRHR0wI45ULK3G/mH29rqhtKZuLq6bv+rItmH1/B4v5Z2OYiI/GFqzzOgi4uLunz5\nsu655547fv3W/zOeOXOG1xa9PnfunK/q8fPrN1Yqqi9c9k097XhN/re/TtTzb82B+qEeXvOa17ze\n7+vVxXUpVvNNPbx25/XFK+e1dLMkp+X4oh5e++t1u0ScPdyYXq1W9elPf1o//dM/rXe/+923ff30\n6dM6efJk24oCwshxHP3d/3hO//7vPqBcotvrctBBf/bail6cL+npHzvsdSkAcCB/+Y2LqlUa+tEP\n3ud1KXDZv/vNb+hnfuEJ5dh+jXc4e/asnnrqKeP32XUFtNFo6Atf+ILe97733bH5BLA386W64l1R\nmk8LHB9M6AIn4eL/b+/Oo9ss73yBf19JXiRZiyXLq+zEa+zsCWYnEAhlgNABOgOdtgzD7ZmeTinn\ntnd6SmHO4cy9f3SmtEMZZkiBmcsM5bYzpzOdKYVCyxK2hEBDVpI43hLb8S7Lki1btiRLeu8fSYAQ\nJ15eSe/yfD/ncFI5svXr+cb2+9P7/J6HSMfCY9F5NyAi4/OVOzgHSll10QZUlmU8/fTT8Pv92L59\ne65qIp3J5C15I+sMzqDJZ1O7jIxj/udbUVyIkUgc8WRa7VKyitmLjfkbWygYRXHJ/A0oszc23wIb\nETF/UuqiDWhHRwd27dqFo0eP4sEHH8SDDz6IQ4cO5ao2IkPpGptBU4nxGlA6X57ZBL+7ED0CbERE\nRMYjyzJCvAMqLF+FA4EFNiIiUsJysb9sbm7GL37xi1zVQjrF86AWpzM4gz/ZUKZ2GRnH/OfX4LXi\nxPgsmkuNewHH7MXG/I1rdmYOAGC1zT8ywuyNrbTcgXcucgeU+ZNSi94Fl4iWLy3L6ArOoJF3QIVR\n77Whe3xG7TKIiJbs7N1PSZLULoVU4PbYMDuTQGx2Tu1SyKDYgJJinAVY2MBkHK5CC5yFF110oEvM\nf34ibETE7MXG/I0rHIzCc4H5T4DZG51kklBSduHzQJk/KcUGlCgHuoKc/xRNnceK3nAMqfSCJ10R\nEWlKaCyKYs5/Cq20gjvhUvawASXFOAuwsM6xGTQacAdcgPlfiC3fjBJbHvonYmqXkjXMXmzM37hC\nC9wBZfbG56twIDAcmffvmD8pxQaUKAc6eAdUSCIswyUi4wlf5AgWEoOv3IEgd8KlLGEDSopxFuDi\nEqk0TozPYpVB74Ay/wurLzH2RkTMXmzM35jSqTQmw7Nwey/8O4vZG5+v3IFgIIp06vzzrJk/KcUG\nlCjLuoIzqHYVwJpnVrsUyrHGM0exEBHpxWR4FnZHAfL4O0to+QUW2B35CIeM+yYqqYcNKCnGWYCL\naxuNYk2ZcZcyMf8Lqz/TgMqyMTciYvZiY/7GtND8J8DsRVFa7px3IyLmT0qxASXKsmOjUawpK1K7\nDFKB25qHQosJo9MJtUshIlqUs2eAEvm4Ey5lCRtQUoyzABcmyzKOjUax2sB3QJn/xdWXWNEdNOYy\nXGYvNuZvTOFgFMUXmf8EmL0ofOXznwXK/EkpNqBEWTQUiSPfLKG0KF/tUkglDV5jb0RERMYSDs7w\nDigBOHMHlDvhUhawASXFOAtwYUa/+wkw/4U0GHgjImYvNuZvTKFFHMHC7MXgclsRj81hdubcMRLm\nT0qxASXKojbOfwqvnmeBEpFOxGNJJOJJOJyFapdCGiCZJJSUORAcmVa7FDIYNqCkGGcBLuyYwXfA\nBZj/QsqK8pFIpRGenVO7lIxj9mJj/sYTOjP/KZmkiz6P2YujtMKBwHDknI8xf1KKDShRlkzGkhiL\nJlDnsapdCqlIkiTUeYy7ERERGUd4LIpizn/Sp/gqnJwDpYxjA0qKcRZgfoeHp7C2vAjmBd5J1jvm\nv7AGrw0nDLgREbMXG/M3nnAwCo934QaU2YvDV150XgPK/EkpNqBEWXJ4aBobKzn/SUBDCedAiUj7\nQkHeAaVzlZQ5MB6YRjqVVrsUMhA2oKQYZwHmd2h4ChsrHGqXkXXMf2ENXqshj2Jh9mJj/sYTCkbh\nWWAHXIDZiyS/wIIiZyFCn/odxvxJKTagRFkwHp3DxGwSdV7OfxLgdxUiPJvEdDypdilERPOS0zLC\nwZkFj2Ah8fjKHQgOcw6UMocNKCnGWYDzHRqewvqKIpgkY89/Asx/McwmCfUe4y3DZfZiY/7GEpmY\nhdWWh4JCy4LPZfZi8VU4EPjUHCjzJ6XYgBJlweHhaSGW39LiNfls6Bwz3jJcIjKGYGAa3lLuW0Dn\nKy13YOwzR7EQKcEGlBTjLMD5Dg1NCbMBEfNfnMYSGzqDxmpAmb3YmL+xBEenUVK2uN9bzF4snz2K\nhfmTUmxAiTJsZCqOeDKNGneh2qWQhjSW2NBlsAaUiIxjfJR3QGl+TnchEvEUZmcSapdCBsEGlBTj\nLMC5Dg1NY0OFA5IA858A818sv6sAk7EkIjHjbETE7MXG/I0lGFj8HVBmLxZJkuArd2DszEZEzJ+U\nYgNKlGGHh8VZfkuLZ5Ik1Htt6DLgcSxEpG/ptIzQGO+A0oX5KhznLMMlUoINKCnGWYBPyLL88R1Q\nUTD/xWsqsaEraJydcJm92Ji/cUyGZ2Cz5yO/YOEdcAFmLyJfuQOBM3dAmT8pxQaUKIP6wjHkmSVU\nOvPVLoU0qMlnRRd3wiUijeH8Jy2klHdAKYPYgJJinAX4xL7BCFr9TmHmPwHmvxRG2wmX2YuN+RvH\nUuY/AWYvopKyIoTGppFOpZk/KcYGlCiD9g9M4ZIqcZbf0tJUOgswFU9i0kAbERGR/p2+A8rfXXRh\nefkWOJyFCAWjapdCBsAGlBTjLMBp8WQabYEoNlaK9Uuc+S+eSZIMdRwLsxcb8zeOYGAa3iXcAWX2\nYvJVnN4Jl/mTUmxAiTLkyMg06j1W2PPNapdCGtZUYkMn50CJSCPSaRnhYBRen13tUkjjfBVOzoFS\nRrABJcU4C3DavoEILvE71S4j55j/0jT6jDMHyuzFxvyNYSI0A1tRwaJ3wAWYvah85ac3ImL+pBQb\nUKIM2T/I+U9aWJOBluASkf6NDUVQWs7fXbSwTx/FQqQEG1BSjLMAwFg0gdDMHBpLbGqXknPMf2kq\nHPmYnUsjPDundimKMXuxMX9jGB2eQmnl0lbvMHsxOd2FSM6l8NbOXWqXQjrHBpQoA/YPTGFzpQNm\nkzjHr9DySAbbiIiI9G1sOLLkBpTEJEkSfOUOzEyl1C6FdI4NKCnGWQBg/6CY858A81+OphIruoKz\napehGLMXG/M3htGhCEorlrYEl9mLy1fhQKl3hdplkM6xASVSKJWWcXBwCpf4OUNDi9PInXCJSAOm\nIzHIaRkOV6HapZBOlFW6MDoUUbsM0jk2oKSY6LMgXcEZFFvz4LPnq12KKkTPfzmaDLITLrMXG/PX\nv8CZ+U9JWtr4CLMXV3mVE73dI2qXQTrHBpRIof28+0lLVFaUj0QqjfEZ/W9ERET6FVjG8lsSm8dn\nRyImIx5Lql0K6RgbUFJM9FmQ/QMRtAo6/wkw/+WQJAlNBliGy+zFxvz1b3RoeRsQMXtxmcwmlFe5\nERjmMlxaPjagRApEEymcCM1ibXmR2qWQzqzy2dAxFlW7DCIS2MjAJCr8brXLIJ0pq3JiZGBS7TJI\nx9iAkmIiz4IcGppCS6kdhRZxv5VEzl+J5lI72gP6vgPK7MXG/PVtOhJDci4Fl8e65M9l9mKLREe5\nEREpIu5VM1EG7B+cQivnP2kZmn02dARnkJZltUshIgEN90+i3O9a8gZERHaXCaODvANKy8cGlBQT\ndRZElmXsG4jgkipx5z8BcfNXym3Ng6PAjIGJuNqlLBuzFxvz17fhgUlUVC9v+S2zF9uNN23BdCTO\njYho2diAEi3TUCSOuZSMlcU8P42Wp9lnRzvnQIlIBcP9Eyj3u9Qug3TIZDbBV+7gRkS0bGxASTFR\nZ0H2D07hkiqH8MuXRM0/E5pLbWjX8U64zF5szF+/0mkZo4OTqKheXgPK7MW2e/dulFU5uQyXlo0N\nKNEy7RuI8PxPUqTZZ0dHgHdAiSi3xkenYS8qgNWWr3YppFNllS6MDPIOKC0PG1BSTMRZkLlUGh8N\nT2Oz4POfgJj5Z0q914pTEzHEk2m1S1kWZi825q9f/T0h+Gs9y/58Zi+2a665BuW8A0oKsAElWobj\ngSj8rkK4Ci1ql0I6VmAxoaa4EN1B/S7DJSL9GegNwV9brHYZpGMen50bEdGysQElxUScBdk3MMXl\nt2eImH8mnd6ISJ8NKLMXG/PXJ1mWMdAbRrWCO6DMXmy7d+/mRkSkCBtQomXYz+NXKEOaS+1o5xwo\nEeVIaCwKi8UMp9uqdimkc+V+F4b7uQyXlo4NKCkm2izIxOwcBiNxrC6zq12KJoiWf6Y1+/S7Ey6z\nFxvz16fTdz+VLb9l9mI7m39ltRvDpyZUrob0iA0o0RLtH5zCxkoHLCaxj1+hzKhyFSCaSCE8O6d2\nKUQkAKUbEBGdVVHjxlD/BGRZVrsU0hk2oKSYaLMgH/ZHcGk1l9+eJVr+mWaSJLSU2nFsRH/LcJm9\n2Ji//siyjIGekKL5T4DZi+5s/k53IQAgMhFTsxzSITagREuQSsvYNxDBpX42oJQ568rtODo6rXYZ\nRGRwk6FZAIDLw/lPUk6SJFRUuzDcz2W4tDRsQEkxkWZBOoMz8NjyUFrEw7vPEin/bFlbXoQjI/pr\nQJm92Ji//pw6OY7qWg8kSdkICbMX26fzr6xxY4hzoLREbECJlmAvl99SFjT5bOifiGMmkVK7FCIy\nsN7OIFY2lqhdBhlIRbWbd0BpydiAkmIizYJ82B/BZWxAzyFS/tmSbzahscSK4zo7joXZi43560s6\nlcapk6GMNKDMXmyfzr+8yoWxkWkk5/gGKi0eG1CiRQqfOX5lTVmR2qWQAel1GS4R6cPwwCQc7kLY\nHQVql0IGkpdvhtdnR2A4onYppCNsQEkxUWZB9g1MYVNlEY9f+QxR8s+2tWVFODqqrzugzF5szF9f\nersyt/yW2Yvts/lXcA6UlogNKNEi7e2fxGXVLrXLIINaXWZH59gMEqm02qUQkQH1dgVRy/lPyoLK\najeG+ifVLoN0hA0oKSbCLEgqLePA4BRa/Q61S9EcEfLPBXu+GX5XAbqCM2qXsmjMXmzMXz9mZxIY\nD0yjckVxRr4esxfbZ/OvrHFjmHdAaQnYgBItQnsgCp89HyV2Hr9C2bO+ogiHhjgHSkSZdap7HP6V\nHlgsvOyjzHN5rEjOpTA1GVO7FNKJBX8SPfXUU/ja176G73znO7moh3RIhFmQvQM8fuVCRMg/Vy6p\ncuLA4JTaZSwasxcb89ePkx1jqG3K3PJbZi+2z+YvSRIqVxRjsC+sUkWkNws2oFu3bsVf/dVf5aIW\nIs3a2x/BpX42oJRd6yqK0D0+w/NAiShj0mkZJzvGUNdcqnYpZGD+lcUY6GUDSouzYAPa0tICu92e\ni1pIp4w+CxKYTiAwncCaMn4fzMfo+edSocWEVT4bPtLJcSzMXmzMXx+GTk2gyFUIV7E1Y1+T2Ytt\nvvz9tR4M9IRUqIb0KGPDAJ/+x7h7924+FujxkSNHNFVPph//7K0DqC2IwXzm+BW169HaY6Pnn+vH\n3rkQfvNhh2bq4WM+5mN9P373jQOoP3P3Uwv18LExH5dVOBAen8Zbb+7SRD18nJ3HmSLJsiwv9KRA\nIIBHH30Ujz322Lx/v3PnTmzevDljRRFpyUOvdGN7Swm21LrVLoUE0B2cwfff7MW/3NUCSeKZs0Sk\nzL88vgu33LUeFX4eI0bZ9ct/3YcNl1ejcXWZ2qVQlhw4cADbtm1T/HW4HRrRRUzHkzg+FuXxK5Qz\n9V4rEqk0+ifiapdCRDoXDkYRjyVRXsk9DCj7qmuLuQyXFoUNKCmWyVvyWvPhQATryotgzTOrXYpm\nGTl/NUiShCtXuLDnlPYP9Wb2YmP+2neiPYD6Zh8kU2ZXUzB7sV0o/9NzoNyIiBa2YAP6xBNP4JFH\nHsHw8DC+8Y1v4K233spFXUSasKdvEleu4LIlyq0ra1x4v4+HehORMieOj308/0mUbeVVLoSCUcRj\nc2qXQhpnWegJ3/rWt3JRB+mYUc8Dm0ulsX9gCn9xhV/tUjTNqPmraX1FEfon4gjNzMFjy1O7nAti\n9mJj/to2O5PA6NAkauq9Gf/azF5sF8rfbDGh3O/CYG+Yx/7QRXEJLtEFfDQyDb+rAF4NNwBkTHlm\nEy6rdmJ3L++CEtHy9HQGUV3rQV4+R0god6rrPDh1knOgdHFsQEkxo86CvN83iau4/HZBRs1fbdfX\nF+PNE9qepWH2YmP+2naiPYD6luzchWL2YrtY/isbvOjrHs9hNaRHbECJ5pGWZbzXO4mrVvDoFVLH\nJX4nBifjGJnibrhEtDSpZBq9nUHUrfKpXQoJprzKhcjELKL83UUXwQaUFDPiLEjbaBSOAjNqigvV\nLkXzjJi/FlhMEq6tdePNbu3eBWX2YmP+2tXfE0JxiR1Fzuz8DmP2YrtY/iazCdV1HvSd4F1QujA2\noETzeOdkGNfVFatdBgluW4MHr3eFIMuy2qUQkY50HRtF05oytcsgQa1oKOEyXLooNqCkmNFmQVJp\nGbt6JnBdHZffLobR8teSllIb8swSDg9Pq13KvJi92Ji/NslpGd3HA2jIYgPK7MW2UP6n50CDfPOU\nLogNKNFnHBudRrEtD34Xl9+SuiRJwm0tJfjN8aDapRCRTgwPTKDQlgdPiV3tUkhQbq8NJpMJ42NR\ntUshjWIDSooZbRbknZMTuK6Wdz8Xy2j5a822Bg8ODE4hPKu9g72ZvdiYvzZ1HRtF4+rsLr9l9mJb\nKH9JkrCiwYu+Lr55SvNjA0r0KWeX317L+U/SCHu+GVtq3Xi1g/M0RHRxsiyj61gAjauzc/wK0WKt\nbCxBLxtQugA2oKSYkWZBjoxMw1eUh0pngdql6IaR8teq7S0leLl9HKm0tuZpmL3YmL/2BEemkU7L\nKK10ZvV1mL3YFpP/ykYvBvvCmEskc1AR6Q0bUKJPeZu735IGNZXYUGy14Pf9k2qXQkQa1tU2isY1\npZAkSe1SSHAFhXko97vRy91waR5sQEkxo8yCJFJp7O6ZwFY2oEtilPy17vY1Pvz6mLaWMzF7sTF/\n7TndgGb/+BVmL7bF5l/f7MOJ44EsV0N6xAaU6Iy9pyKo9VhRWpSvdilE57m21o2+8Cz6wrNql0JE\nGjQRmkF0Ko7KGr6JStpQ31yKkx1jkDU2PkLqYwNKihllFuSN7hC2NXjULkN3jJK/1uWZTdjeUoJf\nt2nnLiizFxvz15auY6Ooby6FyZT95bfMXmyLzd/ttcFqzcPIIMdH6FxsQIkARGJJHB6exhYev0Ia\ndmtzCd4+EcZ0nJs6ENG5unO0/JZoKepaSnGifUztMkhj2ICSYkaYBXn7ZBiX+p2w55vVLkV3jJC/\nXnhtebi02olXO0NqlwKA2YuO+WtHdCqO4Og0auq9OXk9Zi+2peRf31zKOVA6DxtQIgA7u0O4sZFz\nM6R9d6zx4cW2Mc0dyUJE6uk+HkBtkw8WCy/rSFsqa9yYiSYQCkbVLoU0hD+pSDG9z4IMTsYw7OFX\n7AAAGC5JREFUMpXAJVXZPTfNqPSev940+2xwFFjw4UBE7VKYveCYv3Z0HRvJ6fJbZi+2peRvMklo\nXFOGziMjWayI9IYNKAnvje4wttYVw5yDjRuIlJIk6cyRLJypISIgNjuHoVMTqG0qUbsUonmtWleO\njqNsQOkTbEBJMT3PgsiyfGb5LXe/XS49569X19W5cTI0i1MTMVXrYPZiY/7acLJjDNW1HuQXWHL2\nmsxebEvNv2pFMWamuQyXPsEGlIR2bDSKArMJDV6r2qUQLVq+2YRbVnnxYhvvghKJrusYd78lbTOZ\nJDSt5TJc+gQbUFJMz7Mgb3SFsK3RA0ni8tvl0nP+enZbSwneOhFGNJFSrQZmLzbmr765RAp93eOo\nay7N6esye7EtJ/+mtVyGS59gA0rCSiTT2NU7gRvqufst6U+JPR8bKxx460RY7VKISCUnO8dQUe2C\nzZ6vdilEF1W1ohiz0QTGA9Nql0IawAaUFNPrLMievkk0eG0oLeIvbiX0mr8R3NrsxcvtQciyOkey\nMHuxMX/1dRwZwap15Tl/XWYvtuXkbzJJaNlQibaDQ1moiPSGDSgJ63cd47h5FTcfIv3aVOXATCKF\nzuCM2qUQUY7NJZLo7Qxy/pN0Y82mSrQdGoLMc6yFxwaUFNPjLMjIVBzd4zO4eoVb7VJ0T4/5G4VJ\nknBLcwlebh9X5fWZvdiYv7pOtI+hssYNqy33q3iYvdiWm39JuQNWWx76e0IZroj0hg0oCen1rhCu\nry9GvoXfAqRvf9Dkwe6eCVU3IyKi3FNr+S2REqs3VeEYl+EKj1ffpJjeZkHSsoxXO8dx8yqv2qUY\ngt7yN5piax42VTmwszv37ygze7Exf/Uk4kn0dY+rtvyW2YtNSf4tGyrQ3TaKuUQygxWR3rABJeEc\nHJqCs8CCeq9N7VKIMmJ7sxevqLgZERHl1on2AKpWFqPQmqd2KURLYncUoGpFMTqOjKpdCqmIDSgp\nprdZkFc7Qrz7mUF6y9+INlY6EEum0TGW282ImL3YmL961F5+y+zFpjT/9ZdV4/De/gxVQ3rEBpSE\nMhlLYt9ABNfz7E8yEJMk4dZVJXi5Pah2KUSUZbHZOfSfDKGhpVTtUoiWpa6pBNORGALDEbVLIZWw\nASXF9DQL8ruOcVy90gVHgUXtUgxDT/kb2U1NHuzuncR0PHdzNcxebMxfHe0fDWNlU4mqy2+ZvdiU\n5m8ym7Cu1Y+PeBdUWGxASRiptIyXjo/h86t9apdClHFuax5aqxzY2R1WuxQiyqKj+wexdnOV2mUQ\nKbKu1Y/2j0aQyOGbpqQdbEBJMb3Mgvy+fxJeWx6aSrj5UCbpJX8RbG8pyelmRMxebMw/94KBaUxH\nYljRUKJqHcxebJnI3+EqRNXKYrR/NJyBikhv2ICSMF48FsQf8u4nGdiGiiLEUzKOB3K7GRER5cax\nA4NYvbESJpOkdilEim24rJrLcAXFBpQU08MsyKlwDCdDs9hS61a7FMPRQ/6ikCQJtzZ7c7YZEbMX\nG/PPrXQqjeOHhrBGA8tvmb3YMpX/ysYSzETnMNw/kZGvR/rBBpSE8IuPRnH7Gh/yzfwnT8Z2U6MH\ne/omMcW5GiJDOdExBqfbCm9pkdqlEGWEySRh81U12Pder9qlUI7xapwU0/osyMhUHB+cmsQfrlZ3\nZsaotJ6/aNzWPFzqd2Jndyjrr8Xsxcb8c+vAnj5surJG7TIAMHvRZTL/da3V6Osax2R4NmNfk7SP\nDSgZ3i+PBHDrKi+PXiFhbG/x4uX28ZxtRkRE2RUcmUJoLIqmNeVql0KUUQWFFqy9pAoH9vSpXQrl\nEBtQUkzLsyChmTm8dSKML6zjgd3ZouX8RbW+vAiptIy20WhWX4fZi435587+PX3YcHk1zBZtXLYx\ne7FlOv/NV63AsQODiMfmMvp1Sbu08ZOMKEt+cXgUN9QXo1jFA7uJci3XmxERUfZEJmbRdWwUm67Q\nxvJbokxzuq2oXVWCgx+cUrsUyhE2oKSYVmdBhiJxvNEdwpc3cclSNmk1f9F9rtGL909FMDGbvXeU\nmb3YmH9u7H23B+sv9cNqy1e7lI8xe7FlI/8rr2/A/t29iGXxdxZpBxtQMqx/+XAIX1hbyrufJCRX\noQXX1xfjv44E1C6FiJYpMjGL9sPDaL1mpdqlEGWVx2dHXXMp9nNHXCGwASXFtDgLcjwQRdtoFH/E\n2c+s02L+dNqfbCjDKx3jmIxl50gWZi825p99777aic1XrYCtqEDtUs7B7MWWrfyvvKEeB98/hdmZ\nRFa+PmkHG1AynFRaxk/eH8B9rRUo1MiGDURqKC3Kx7W1bvzH4VG1SyGiJRo6NYGBnhAu3bJS7VKI\ncsLtsaFpbRl+//ZJtUuhLOPVOSmmtVmQl46PId9swucaPWqXIgSt5U/numdzBV7tHMdQJJ7xr83s\nxcb8syeVTOO1F47iuptXIS9fe0eIMXuxZTP/q29sxLEDgxgPTGftNUh9bEDJUALTCfzswAi+dU01\nJElSuxwi1XltefjjdaX4p98Pql0KES3SB2+fgNNtRfOGCrVLIcopu6MAV1xfj50vHedZ1gbGBpQU\n08osiCzL+Mc9/bh9jQ817kK1yxGGVvKnC/vC2lL0hWPY1TOR0a/L7MXG/LNjoDeMw3v78bnb12j2\njVRmL7Zs57/pihrEZ+dweG9/Vl+H1MMGlAzj5fZxjEfn8MUNZWqXQqQp+RYTvnvdCuzY05/VY1mI\nSJmZ6The/sVh3PxH6+Bw8Y1UEpPJbMKtX1yP917vwvgYl+IaERtQUkwLsyCnwjH8dP8wHrp+JfLN\n/GedS1rInxa2usyOP1jlxd++1YdUOjPLmpi92Jh/Zs0lkvjv5w9g7eYq1K3yqV3ORTF7seUif6+v\nCNfevAov/L8D3BXXgHilTroXT6bxt2/34r7WCi69JbqIezdXwCQBz3AelEhTUqk0Xvy3Q/CWFuGq\nGxvULodIE9a1+tHQUopf/+wgEvHsHCdG6pDkDEz47ty5E5s3b85EPURLIssy/uatXpglCd/bukKz\n8zJEWjEVT+Ivf9OFrXXF+MqmcrXLIRJeKpXGb395BPFYEnfcswlmruIh+piclvHaC8cwNjKFL9y7\nWXNn4ormwIED2LZtm+Kvw59ypGv/dmgUo1MJ/OWWGjafRIvgKLDg0Vsa8GZ3CM/tG+Iug0QqSs6l\n8OK/HUI8lsQffnkjm0+iz5BMEm66cw1qm0rw/JN7cKI9oHZJlAH8SUeKqTUL8mrnOH7bEcT//lwd\n8i38p6wWzgLpj8eWh7+7rRH7B6fww3f6kEill/V1mL3YmL8yszMJ/NdP98OSZ8IdX9mEvDyz2iUt\nGrMXW67zlyQJV9/YiO1f3IC3X27Hfzy7Fz2dQaQztJ8B5R6v2kmXXu0cx3P7hvGDWxrgseWpXQ6R\n7hRb8/Cj7Y1IJGU8+HI3AtPc5IEoV4KjU/j5Tz5AeZUT2+/eADPfRCVaUHWtB/d9+xq0bKjE7tc6\n8X//7h2893oXwsGo2qXREnEGlHQlLcv4+cERvNYZwvdvruemQ0QKpWUZvzwSwH8dCeB/banBFTUu\ntUsiMixZlnHs4BDeeaUdW7c3Y82mKrVLItKt0aEI2g4O4vjhYbiKrVi9sRKr1lfAZs9XuzTDytQM\nqCUDtRDlRDCawN+9ewqxZBr/cHsTiq2880mklEmScPf6Mqwps+Nv3uzFkeFp3NdagTzOohFl1Ew0\ngZ0vtmE8MI27//wy+ModapdEpGtllU6UVTpx3c2r0HdiHG0Hh/DeG91o2ViBy66t41m6GsYrDFIs\n27MAU/Ekfn5wBH/x3+1YW2bHY9sb2XxqCGeBjGFNWRGeurMZ/ZMx/M8XO9ETml3wc5i92Jj/4qTT\nMg79/hT+9e93w+EqxFfuv1L3zSezF5vW8jeZTaht8mH7Fzfgf3z7GlgsJvz0H97D7tc6MZfg8S1a\nxDugpElpWcbxQBQ7u8N4+0QYV9Q48Y+3r0KFk9tvE2WLs9CC//O5OrzWFcKDr3TjC2t9uHt9Gcwm\n7jBNtFTptIyOj4bxwVsnYLXn4+6vXgpfhb4bTyKtszsKcN0tzdh81Uq887sO/Ovf78bWW5vRuKaM\npyVoCGdASRPiyTROjM+iYyyKjrEZHByagrvQgi21btzSXAIvNxoiyqnAdAI/fvcUphMp3H+lH6vL\n7GqXRKQLk+FZHDs4iKP7BlHkLMBV2xqwosHLi18iFfSfDOGNl9pgLyrA9dubdb/6QG2ZmgFlA0o5\nJ8syBibjaAucbjY7AlGcmoihprgQzT47VvlsWFtehEre7SRSlSzLeKM7jOf2DaGl1I4vbSxDvdem\ndllEmiHLMqYmYxgdjGCofwI9nWOIRuJYtb4C6y6pQlkVN/UiUlsqlcZHe/ux580TWLW2DFfd2MiN\nipaJDShpxu7du3HNNddc8O8TqTS6gjM4NhLFsdEo2gJRFFpMWF1mR0upDat8dtR7rDzLU6cWyp/0\nL5ZM48VjY3jh2BiqXAW4qcmLK2ucOPThB8xeYCJ+70en4hgdnMTIYAQjA5MYGZyELAPlfhcq/C6s\naPCiotoNk8GXrYuYPX1Cr/nPziTw/psn0HZwCC0bK3DJ1Svh9vBN1aXgLrikWbG5FNoCURwZieLo\nyDQ6xmbgdxVgTVkRrm8oxgNX++HjO09EulFoMeHuDWX4wrpS7O6ZwNsnw9ixpx8+SyE6C4awymdD\nlbMAFc4CFPCNJDKIdCqNkcFJ9PeETzebA5NIxJMo97tQXuXC2tYq3Hj7ajhchVxeS6QDVls+brit\nBZddW4sD75/Cz3/yPjw+O5rXV6CmwQtPiZ3fyzmy4B3QQ4cO4bnnnkM6ncYNN9yAO+6447zn8A6o\n2CKxJNpGozgyMo0jI9PoCcfQ4LViXXkR1pUXYXWZHfZ8s9plElEGzc6lcGz09KqG7vEZDE3GMTKd\ngLPAghJ73un/bPnw2fPgteed/tOWjxJ7HptU0iRZljE+FsWp7nH0dQcx0BuGs9iKmjrP6abT74Lb\nY+MFKpFBpJJp9HYH0Xl0FP0nx5FMpuFfWQxfuQPesiJ4fUVwuAqRX8D7dWfl5A5oOp3Gs88+i0ce\neQQejwcPP/wwWltb4ff7Fb8w6VMyLeNkaBbtgejp/8ZmMD4zhxafHesqivDnl1Vilc/OC0wig7Pm\nmdHqd6LV7/z4Y6m0jPGZOQSjcwjOJE7/GZ1D9/gsgtEExqJzCM3ModhmQYPXhnqv9eM/ffY8XthT\nTp2d3+w/GULfiXH0dY/DbDFhRb0XLRsr8Qd/tI5zYkQGZraYUN9civrmUgCnNxAb7A0jODqFo/sH\nER6LYioShyQBNns+LBYTTBYTzGYTTCYJJpME6cyfJpOEvHwz7I4CFDkKUOQshLPYCqfbCoezACae\nrX2Oizag3d3dKC8vR2np6WCuvvpq7Nu3jw2oQA4NTeHIyDROhWM4NRHDUCSOcmcBms9sFPTH68sw\ncGw/rt2yQe1SSSV6nQUh5T6bvdkkobQoH6VF+QDm3zU3LcsYjsTRPT6L7vFZ/Ob4GLrHZ5FKy6j3\n2tDgtaLOa0WD14oqVyEsBp+l07Nsfe8nk2kk4snT/8WSiH/mf8vpMwu3JAmSBEif+XPejwOIxZKY\njSYQnY5jPBDF2HAEAFBd78WKei+u2tbAebBF4s99sRk1f1exFa5i6zkfk2UZiXgSM9EEUikZqWQa\nqWQa6bQMWZaRTp35M336edGpOKJTcQQDQUTCs4hMxDAzHYfdUQhXsRVO9yeNqdNdCKstH85iKwoF\nO9/+oktwP/jgAxw+fBhf//rXAQDvvvsuuru78dWvfvWc5+3cuTO7VRIREREREZGqNLMJUSYKISIi\nIiIiImO76IJkj8eDYDD48ePx8XF4PJ6sF0VERERERETGc9EGtL6+HiMjIwgEAkgmk9izZw9aW1tz\nVRsREREREREZyILHsBw8ePCcY1juvPPOXNVGREREREREBrJgA0pERERERESUCTyUhoiIiIiIiHJi\nwV1wn3rqKRw4cABOpxOPPfbYOX/30ksv4Wc/+xmeffZZFBUVnfe50WgUTz/9NAYGBgAA3/jGN9DU\n1JSh0inblGT/q1/9Crt27YIkSaipqcH999+PvDyxzjjSu/ny/8///E/s3LkTTqcTAPDlL38ZGzdu\nPO9zDx06dM7S/TvuuCOntZNyy80/GAxix44dmJychCRJ2LZtG2699dac10/Lp+R7HwDS6TQeeugh\neL1efO9738tZ3aSckux5zad/SvLndZ/+Xei6/7e//S1ee+01mEwmbN68GV/5ylfO+9ylXvct2IBu\n3boVN998M5588slzPh4MBvHRRx+hpKTkgp/73HPPYdOmTfjOd76DVCqFeDy+0MuRhiw3+0AggJ07\nd+Lxxx9HXl4eHn/8cbz33nvYunVrDqqmTLlQ/rfddhtuu+22C35eOp3Gs88+i0ceeQQejwcPP/ww\nWltb4ff7s10yZdBy87dYLPizP/szrFy5ErFYDN/73vewfv165q8jy83+rFdeeQV+vx+zs7PZKpGy\nREn2vObTv+Xmz+s+Y5gv/6NHj2Lfvn340Y9+BIvFgkgkct7nLee6b8EluC0tLbDb7ed9/Pnnn8c9\n99xzwc+bmZnB8ePHccMNNwAAzGYzbDbbQi9HGrLc7G02G8xmM+LxOFKpFBKJBI/v0aEL5b/Q2Hh3\ndzfKy8tRWloKi8WCq6++Gvv27ctWmZQly83f7XZj5cqVAIDCwkL4/X6Ew+FslEhZstzsgdPHtR08\nePDj3/2kL8vNntd8xrDc/HndZwzz5f/666/jzjvvhMVy+p7l2Tvhn7ac674F74DO58MPP4TX68WK\nFSsu+JxAIACn04mf/OQn6OvrQ11dHe677z4UFBQs5yVJIxaTfVFRET7/+c/j/vvvR35+PjZs2ID1\n69fnsErKpt/97nd49913UVdXh3vvvfe8H1ahUOicu+Mejwfd3d25LpOyZKH8Py0QCKCnpweNjY05\nrJCyZTHZ//SnP8U999zDu58Gs1D2vOYztoXy53WfcQ0PD+P48eP493//d+Tl5eFP//RPUV9ff85z\nlnPdt+RNiOLxOH71q1/hrrvu+vhj870zkkql0NPTg5tuugmPPvooCgoK8MILLyz15UhDFpv9yMgI\nXn75ZezYsQPPPPMMYrEYdu3alctSKUtuuukmPPnkk/jhD3+I4uJiPP/882qXRDm0lPxjsRh+/OMf\n47777kNhYWEOq6RsWEz2+/fvh8vlQm1t7aLulpI+LCZ7XvMZ12Ly53WfcaVSKUxPT+P73/8+7rnn\nHjz++OMZ+bpLbkBHR0cxNjaG7373u/jmN7+JUCiEhx56CJOTk+c8z+v1wuPxoKGhAQBwxRVXoKen\nJyNFkzoWm/3JkyexatUqOBwOmM1mXH755ejs7FSpasokl8sFSZIgSRJuuOGGed/h8ng8CAaDHz8e\nHx/nUhyDWEz+AJBMJvHYY49hy5YtuOyyy3JcJWXDYrLv7OzEvn378M1vfhNPPPEEjh49et4sGenP\nYrLnNZ9xLSZ/XvcZl9frxeWXXw4AaGhogCRJmJqaOuc5y7nuW3IDWlNTg3/+53/Gjh07sGPHDng8\nHjz66KNwuVznPM/tdqOkpARDQ0MAgCNHjqC6unqpL0castjsKysr0dXVhUQiAVmWceTIEVRVValU\nNWXSp2f59u7di5qamvOeU19fj5GREQQCASSTSezZswetra25LJOyZDH5y7KMp59+Gn6/H9u3b89l\neZRFi8n+S1/6Ep566ins2LED3/72t7F27Vo88MADuSyTsmAx2fOaz7gWkz+v+4zr0ksvxdGjRwEA\nQ0NDSCaTcDgc5zxnOdd9krzAOpknnngCbW1tmJqagsvlwt13343rr7/+479/4IEH8IMf/ABFRUUI\nhUJ45pln8PDDDwMAent78cwzzyCZTKKsrAz3338/h9J1REn2v/71r/HOO+9AkiTU1dXh61//+scD\nzKQPZ/OPRCJwu92466670NbWht7eXkiShNLSUnzta1+D2+0+L/+DBw+esx33nXfeqfL/G1qq5ebf\n3t6Ov/7rv0ZNTQ0kSQJw8SM7SHuUfO+f1dbWhpdeeonHsOiMkux5zad/SvLndZ/+zXfdv2XLFjz1\n1FPo7e2FxWLBvffeizVr1ii+7luwASUiIiIiIiLKhCUvwSUiIiIiIiJaDjagRERERERElBNsQImI\niIiIiCgn2IASERERERFRTrABJSIiIiIiopxgA0pEREREREQ58f8B6Bw49NC0thwAAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1011ec0d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import beta, gaussian_kde\n",
      "\n",
      "figsize(16,9)\n",
      "\n",
      "subplot(121)\n",
      "\n",
      "minx = min(min(mus1),min(mus2))\n",
      "maxx = max(max(mus1),max(mus2))\n",
      "xs = np.linspace(minx,maxx,1000)\n",
      "\n",
      "gkde1 = gaussian_kde(mus1)\n",
      "gkde2 = gaussian_kde(mus2)\n",
      "\n",
      "plt.plot(xs,gkde1(xs),label='$\\mu_1$')\n",
      "plt.plot(xs,gkde2(xs),label='$\\mu_2$')\n",
      "plt.legend()\n",
      "\n",
      "subplot(122)\n",
      "\n",
      "minx = min(diff_mus)\n",
      "maxx = max(diff_mus)\n",
      "xs = np.linspace(minx,maxx,1000)\n",
      "gkde = gaussian_kde(diff_mus)\n",
      "\n",
      "plt.plot(xs,gkde(xs),label='$\\mu_1-\\mu_2$')\n",
      "plt.legend()\n",
      "\n",
      "plt.axvline(0, color='#000000',alpha=0.3,linestyle='--')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA50AAAIWCAYAAADULOnlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB044NSwsuZBuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106ff8a50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Testing a Hypothesis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want to test the hypothesis $H_0: \\mu_1=\\mu_2$ vs $H_1: \\mu_1 > \\mu_2$. Well, in the bayesian model we have, it's simply $P(\\mu_1-\\mu_2 > 0)$, which is easily computed:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = (diff_mus > 0).mean()\n",
      "print \"p-value\",p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "p-value 0.0\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similarly, to test $H_1: \\mu_1 \u2260 \\mu_2$:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = (diff_mus > 0).mean()\n",
      "p = min(p,1-p)\n",
      "print \"p-value\",p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "p-value 0.0\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython import utils\n",
      "from IPython.core.display import HTML\n",
      "import os\n",
      "def css_styling():\n",
      "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
      "    base = utils.path.get_ipython_dir()\n",
      "    styles = \"<style>\\n%s\\n</style>\" % (open(os.path.join(base,'profile_default/static/custom/custom.css'),'r').read())\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
        "@import url('http://fonts.googleapis.com/css?family=Kameron');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:200');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:300');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:400');\n",
        "@import url('http://fonts/googleapis.com/css?family=Source+Code+Pro')\n",
        "\n",
        "/* Change code font */\n",
        ".CodeMirror pre{\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "\n",
        "/* Change input prompt size */\n",
        "div.input{\n",
        "    /*width: 105ex;*/\n",
        "}\n",
        "div.input_area{\n",
        "    border-color: rgba(0,0,0,0.10);\n",
        "    background: rbga(0,0,0,0.5);\n",
        "}\n",
        "div.text_cell {\n",
        "    max-width: 105ex /* instead of 100%, */\n",
        "}\n",
        "\n",
        "div.text_cell_render {\n",
        "    font-family: \"Crimson Text\";\n",
        "    font-size: 12pt;\n",
        "    line-height: 145%; /* added for some line spacing of text. */\n",
        "    /*width: 105ex; /* instead of 'inherit' for shorter lines */\n",
        "}\n",
        "\n",
        "div.text_cell_render h1,\n",
        "div.text_cell_render h2,\n",
        "div.text_cell_render h3,\n",
        "div.text_cell_render h4,\n",
        "div.text_cell_render h5,\n",
        "div.text_cell_render h6{\n",
        "    font-family: 'Kameron';\n",
        "    font-weight: 300;\n",
        "}\n",
        "\n",
        "div.text_cell_render h1 {\n",
        "    font-size: 24pt;\n",
        "}\n",
        "div.text_cell_render h2 {\n",
        "    font-size: 18pt;\n",
        "}\n",
        "\n",
        "div.text_cell_render h3{\n",
        "    font-size: 14pt;\n",
        "}\n",
        ".rendered_html pre, .rendered_html code{\n",
        "    font-size: medium;\n",
        "}\n",
        ".prompt.input_prompt{\n",
        "    color: rgba(0,0,0,0.5);\n",
        "}\n",
        "\n",
        ".cell.command_mode.selected{\n",
        "    border-color: rgba(0,0,0,0.1);\n",
        "}\n",
        "\n",
        ".cell.edit_mode.selected{\n",
        "    border-color: rgba(0,0,0,0.15);\n",
        "    box-shadow: 0px 0px 5px #f0f0f0;\n",
        "    -webkit-box-shadow: 0px 0px 5px #f0f0f0;\n",
        "}    \n",
        "\n",
        "div.output_scroll{\n",
        "    -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
        "    box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
        "    border-radious: 2px;\n",
        "}\n",
        "\n",
        "#menubar .navbar-inner{\n",
        "    background: #fff;\n",
        "    -webkit-box-shadow: none;\n",
        "    box-shadow: none;\n",
        "    border-radius: 0;\n",
        "    border: none;\n",
        "    font-family: lato;\n",
        "    font-weight: 400;\n",
        "}\n",
        "\n",
        ".navbar-fixed-top .navbar-inner, .navbar-static-top .navbar-inner{\n",
        "    box-shadow: none;\n",
        "    -webkit-box-shadow: none;\n",
        "    border: none;\n",
        "}\n",
        "\n",
        "div#notebook_panel{\n",
        "    box-shadow: none;\n",
        "    -webkit-box-shadow: none;\n",
        "    border-top: none; \n",
        "}\n",
        "\n",
        "div#notebook{\n",
        "    border-top: 1px solid rgba(0,0,0,0.15);\n",
        "}\n",
        "\n",
        "#menubar .navbar .navbar-inner,\n",
        ".toolbar-inner{\n",
        "    padding-left: 0;\n",
        "    padding-right: 0;\n",
        "}\n",
        "\n",
        "#checkpoint_status,\n",
        "#autosave_status{\n",
        "    color: rgba(0,0,0,0.5);\n",
        "}\n",
        "\n",
        "#header{\n",
        "    font-family: lato;\n",
        "}\n",
        "\n",
        "span#notebook_name{\n",
        "    font-weight: 200;\n",
        "\n",
        "}\n",
        "#site * .btn{\n",
        "    background: #fafafa;\n",
        "    -webkit-box-shadow: none; \n",
        "    box-shadow: none;\n",
        "\n",
        "}\n",
        "\n",
        ".rendered_html ol {\n",
        "    list-style:decimal; \n",
        "    margin: 1em 2em;\n",
        "}\n",
        "\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x10bc554d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}