{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "import numpy as np, scipy, scipy.stats as stats, scipy.special, scipy.misc, \\\n",
    "    pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
    "import pymc3 as pm\n",
    "from theano import tensor as tt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import mixture\n",
    "import astroML, astroML.plotting, astroML.density_estimation\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "notebook_start_time = datetime.now()\n",
    "\n",
    "SEED = 42\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Probabilistic Programming and Bayesian Methods for Hackers using PyMC3 and Jupyter Notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The easiest way to get started with an environment for [Juypter Notebook](https://jupyter.org/), [PyMC3](https://github.com/pymc-devs/pymc3), python (in version 3.6) and R is to install the [anaconda](https://www.continuum.io/why-anaconda) package manager.\n",
    "\n",
    "You will need to install several python packages as you can see from the import line above. You have two choices here, either you use anaconda or [PyPi](https://pypi.python.org/pypi), the python package manager (`pip install`).\n",
    "\n",
    "I personally use anaconda whenever possible and use PyPi if the package does not exist in anaconda. You should configure your anaconda environment to include the [conda-forge](https://conda-forge.github.io/) anaconda channel.\n",
    "\n",
    "I will not show it here, but it is possible to use R directly from within python via the [rpy2](https://bitbucket.org/rpy2/rpy2) package. This is often quite handy if you do not have to leave the python environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In general you have 3 choices for doing \"probabilistic programming\" in python:\n",
    "* [PyMC3](https://github.com/pymc-devs/pymc3)\n",
    "* [JAGS](http://mcmc-jags.sourceforge.net/) / [PyJAGS](https://github.com/tmiasko/pyjags)\n",
    "* [Stan](http://mc-stan.org/) / [PyStan](http://mc-stan.org/users/interfaces/pystan.html)\n",
    "\n",
    "All packages implement [Markov Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) algorithms.\n",
    "\n",
    "JAGS implements the so called [Metropolis–Hastings](https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm) algorithm. This can be used for all probability distributions, both, discrete and continuous, but will break down in high dimensional spaces.\n",
    "\n",
    "Stan implements the so called [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hybrid_Monte_Carlo) (HMC) algorithm, where [NUTS](https://arxiv.org/abs/1111.4246) (The No U-Turn Sampler) is an optimized version of HMC and there is bascially never a reason to use plain HMC if you can use NUTS. HMC methods have the disadvantage that they only work for continuous probability distributions. But often you can find a way around this limitation. HMC/NUTS shines in high dimensional probability spaces and is typically \"faster\" than Metropolis-Hastings.\n",
    "\n",
    "PyMC3 implements both algorithms above and allows for combining the different kinds of samplers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "All MCMC algorithms are sequential, e.g. it does not help you if you have many cores. But in order to verify if your MCMC results \"converge\" for correctness reasons you should ALWAYS run several chains in parallel and then use the [$\\hat R$ statistic](https://am207.github.io/2017/wiki/Lab7_bioassay.html#2-gelman-rubin) (aka Gelman Rubin Diagnostic; should be close to 1.0) to verify the convergence. [Michael Betancourt](https://github.com/betanalpha) from the [Stan](http://mc-stan.org) team explains this here: [Video: \"Everything You Should Have Learned About Markov Chain Monte Carlo\"](https://www.youtube.com/watch?v=DJ0c7Bm5Djk&feature=youtu.be&t=4h40m9s)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Bayes' Rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Before we start we should have at least once explicitly stated Bayes' Rule. The key insight is this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "$$ p(\\Theta, D) = p(D, \\Theta) = p(\\Theta \\;|\\; D)\\cdot p(D) = p(D \\;|\\; \\Theta)\\cdot p(\\Theta)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's take this step by step. \n",
    "\n",
    "Typically in the literature $\\Theta$ stand for the **set** or **group** (this means that $\\Theta$ typically does not stand for a single parameter, but for a group of parameters, e.g. $\\{\\gamma_1, \\gamma_2, ..., \\gamma_n\\}$) of parameters of the model and $D$ stands for the observed data. What you have is the observed data. What you want to get to are the parameters that lead to these data observations.\n",
    "\n",
    "The first two statements are just the joint probability distribution of parameters and data and because a probability distribution does not depend on the order of the parameters we can write $p(\\Theta, D) = p(D, \\Theta)$. That's basically the same joint distribution.\n",
    "\n",
    "If you then use the definition of conditional probability distributions: $p(a, b) = p(a\\;|\\;b)\\cdot p(b)$, which means that the joint distribution is the product of the \"marginal distribution\" $p(b)$ times the conditional distribution $p(a\\;|\\;b)$ conditional on $b$. That's just using the definition and nothing else.\n",
    "\n",
    "Then you end up with Bayes' Rule:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align}\n",
    "p(\\Theta \\;|\\; D)&= p(D \\;|\\; \\Theta)\\cdot p(\\Theta) / p(D)\\\\\n",
    "p(\\Theta \\;|\\; D)&\\propto p(D \\;|\\; \\Theta)\\cdot p(\\Theta)\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The second line means that $p(D)$ is typically not \"needed\". It is just a normalization constant to make sure that the probability distribution $p(D \\;|\\; \\Theta)\\cdot p(\\Theta)$ is normalized to $1$. This also means that you can calculate $p(D)$ from $p(D \\;|\\; \\Theta)\\cdot p(\\Theta)$ via:\n",
    "$$p(D) = \\int p(D \\;|\\; \\Theta)\\cdot p(\\Theta) \\hbox{d}\\Theta$$\n",
    "So all you really need is $p(D \\;|\\; \\Theta)\\cdot p(\\Theta)$, which is also your **model**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In words Bays' rule can be summarized as:\n",
    "**<p style=\"text-align: center;\">The probability distribution of the parameters of the model can be calculate by acting as if you know the parameters of the model and calculate the probability of the observed data item.</p>**\n",
    "\n",
    "**<p style=\"text-align: center;\">The probability of $\\Theta$ given Data is proportional to the probability of Data given $\\Theta$. </p>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Warmup: Solving the Bayesian German Tank problem with PyMC3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The example is taken from [here](http://isaacslavitt.com/2015/12/19/german-tank-problem-with-pymc-and-pystan/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here we will cover a [classic problem](https://en.wikipedia.org/wiki/German_tank_problem) in statistics, estimating the total number of tanks from a small sample. Suppose four tanks are captured with the serial numbers 10, 256, 202, and 97. Assuming that each tank is numbered in sequence as they are built, how many tanks are there in total?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The problem is named after the Allies' use of applied statistics in World War II to analyze German production capacity for Panzer tanks. The actual [story](https://www.theguardian.com/world/2006/jul/20/secondworldwar.tvandradio) of how that turned out is fascinating:\n",
    "\n",
    "> After the war, the allies captured German production records, showing that the true number of tanks produced in those three years was [...] almost exactly what the statisticians had calculated, and less than one fifth of what standard intelligence had thought likely.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Problem set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We start with a simplified story about how the data is generated — the generative model — and then attempt to infer specifics about that story (the parameters).\n",
    "\n",
    "Here's our model:\n",
    "1. Choose a number of tanks **N** to exist in the world.\n",
    "2. Of those **N**, choose four that we happened to observe and call that data **D**.\n",
    "\n",
    "The key is that some choices for N are much more likely than others. For example, if N was actually 5,000 then it would be a really odd coincidence that we happened to see all small numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Solving it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here's the gist: we want to know the posterior probability of the number of tanks **N**, given data **D** on which tank serial numbers have been observed so far:\n",
    "$$P(N\\;|\\;D)\\propto P(D\\;∣\\;N)\\cdot P(N)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The right-hand side breaks down into two parts. The **likelihood** of observing all the serial numbers we saw is the product of the individual probabilities $D_i$ given the actual number of tanks in existence $N$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "$$\n",
    "\\begin{align}\n",
    "P(D\\;∣\\;N)&=\\Pi_i P(D_i\\;∣\\;N)\\\\\n",
    "P(D_i\\;∣\\;N)&\\sim \\hbox{DiscreteUniform}(D, \\hbox{min}=0, \\hbox{max}=N)\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The prior over **N**, which we know must be at least as high as the highest serial number we observed (call that $m$) but could be much higher:\n",
    "$$P(N)\\sim \\hbox{DiscreteUniform}(N, \\hbox{min}=m, \\hbox{max}=\\hbox{some big number})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### PyMC3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100500/100500 [00:12<00:00, 7885.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 982 ms, sys: 83.8 ms, total: 1.07 s\n",
      "Wall time: 14.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# D: the data\n",
    "observed_tank_serials = np.array([10, 256, 202, 97])\n",
    "\n",
    "tank_model = pm.Model()\n",
    "with tank_model:    \n",
    "    # prior - P(N): N ~ uniform(max(y), 10000)\n",
    "    # note: we use a large-ish number for the upper bound\n",
    "    N_tank = pm.DiscreteUniform(\"N\", lower=observed_tank_serials.max(), upper=10000)\n",
    "\n",
    "    # likelihood - P(D|N): y ~ uniform(0, N)\n",
    "    y_obs_tank = pm.DiscreteUniform(\"y_obs\", lower=0, upper=N_tank, observed=observed_tank_serials)\n",
    "\n",
    "    # choose the sampling method - we have to use Metropolis-Hastings because\n",
    "    # the variables are discrete\n",
    "    tank_model_step = pm.Metropolis()\n",
    "    \n",
    "    # we'll use four chains, and parallelize to four cores\n",
    "    tank_model_start = {\"N\": observed_tank_serials.max()} # the highest number is a reasonable starting point\n",
    "    tank_model_trace = pm.sample(100000, step=tank_model_step, start=tank_model_start, chain=4, njobs=4) # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "N:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  380.594          171.689          3.802            [256.000, 696.000]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  258.000        282.000        323.000        409.000        870.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tank_model_burn_in = 10000  # throw away the first 10,000 samples\n",
    "# summarize the trace\n",
    "pm.summary(tank_model_trace[tank_model_burn_in:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAACECAYAAACXt2rvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmQZWV5+PHvOeeu3X1vz3RP92zMwLCEERgGAUUgiFTE\nIEmFUUGBDEkhVdFAqtSQEJ0YjRrZFCsxIdHSEg1KIE6lLCRmICTAzwiMwCDbMBuz9fR29/Xs57y/\nP+7S93bf7rm9TffMvB+Lcvrcs7xn6b7vc973fV5FCCGQJEmSJEmSJEmSpk1d6AJIkiRJkiRJkiQd\nr2RAJUmSJEmSJEmSNEMyoJIkSZIkSZIkSZohGVBJkiRJkiRJkiTNkAyoJEmSJEmSJEmSZkgGVJIk\nSZIkSZIkSTMkAypJOo6cffbZbNmypWnZ9u3bueWWWxaoRJIkSdLJTn43SSc7GVBJ0nHmpZdeYufO\nnQtdDEmSJEmqk99N0slMBlSSdJz58z//c+6+++6FLoYkSZIk1cnvJulkJgMqSTrOfPjDH0YIwbZt\n2xa6KJIkSZIEyO8m6eQmAypJOg5t2bKFb37zm1iWtdBFkSRJkiRAfjdJJy8ZUEnScejcc8/lPe95\nDw899NBCF0WSJEmSAPndJJ28AgtdAEmSZuZzn/scH/3oRznllFMWuiiSJEmSBMjvJunkJFuoJOk4\n1d/fzx/+4R/yj//4jwtdFEmSJEkC5HeTdHKSAZUkHcc++clP4jjOQhdDkiRJkurkd5N0slGEEGKh\nCyFJkiRJkiRJknQ8ki1UkiRJkiRJkiRJMyQDKkmSJEmSJEmSpBmSAZUkSZIkSZIkSdIMzWva9Pvv\nv59XXnkF13X51Kc+xYYNG7jrrrvwPI++vj6+8Y1vEAqFePzxx/nRj36Eqqp8/OMf54YbbsBxHD7/\n+c8zNDSEpmncc889rFmzhl27dvG3f/u3AJx99tl85Stfmc9TkCRJkiRJkiRJmtS8tVC9+OKL7N27\nl8cee4zvf//73H333Xz729/m5ptv5pFHHuHUU09l69at6LrOgw8+yA9/+EMefvhhfvSjH5HL5Xji\niSeIx+P827/9G5/+9Kd54IEHAPj617/Oli1bePTRRymVSjz33HPzdQqSJEmSJEmSJElTmrcWqve8\n5z2cf/75AMTjcQzDYPv27fUWpauuuoof/OAHrFu3jg0bNhCLxQC48MIL2bFjBy+88AKbNm0C4LLL\nLmPLli3Yts3g4GB9v1dddRUvvPACV1555aTlSCaL83WKkiRJ0jzp64stdBHm1Vx8Ny1d2kE2q89B\naU5s8jq1R16n9sjr1J4T8TpN9b00by1UmqbR0dEBwNatW3n/+9+PYRiEQiEAent7SSaTpFIpenp6\n6tv19PRMWK6qKoqikEqliMfj9XVr+5AkSZKkk00goC10EY4L8jq1R16n9sjr1J6T7TrNe1KKp59+\nmq1bt/KlL32paflk019NZ7mcQkuSJEmSJKk1X/jYnpxgV5Lm27wGVL/85S/5zne+w/e+9z1isRgd\nHR2YpgnA6Ogo/f399Pf3k0ql6tskEon68lrrk+M4CCHo6+sjl8vV163tQ5IkSZIkSWr2Rmonryff\nxBf+QhdFkk5o8xZQFYtF7r//fr773e+yZMkSoDIW6sknnwTgqaee4oorrmDjxo288cYbFAoFyuUy\nO3bs4OKLL+byyy9n27ZtADzzzDNccsklBINBTj/9dF5++eWmfUzl5ZFXm/6TJEmSJEk6GTjV1ikZ\nUEnS/Jq3pBS/+MUvyGazfPazn60vu/fee/niF7/IY489xqpVq9i0aRPBYJA777yT2267DUVRuOOO\nO4jFYlx77bU8//zz3HTTTYRCIe69914AtmzZwpe+9CV832fjxo1cdtll83UKkiRJkiRJkiRJU1LE\nCT4Q6b/e+H9NP1+84t0LVBJJkiSpXTLL39H19cVkJts2nMzXqdYz54L+DQTUqd+hn8zXaTrkdWrP\niXidFiTLnyRJkiRJkiRJ0olu3rr8LRaB13Y3L5AtVJIkSZK06AjPw81lCSztQVHl+965VHYMusMn\ndquvJC0k+RdLkiRJkqQFZx0+hLFvH/bg4EIX5YTjC2+hiyBJJzQZUEmSJEmStOC8cqny/3p5wmeJ\nrI5uyvmUJElanE74gGokqzf9J0mSJJ3Y9uzZw9NPPw1AoVBY4NJIs2U5HvuHC7y+P73QRZEkSWrp\nhB9DJUmSJJ08fvjDH/LEE09g2zYf/OAH+ed//mfi8Ti33377QhdNmiHfP6GTEUuS1ILr+aiqgqoo\nC12UtpzwLVSSJEnSyeOJJ57g3//93+nu7gbgrrvu4tlnn13YQkltOj4qTscjgQxKpePLy7sT7Nid\nXOhitE0GVJIkSdIJo7OzE7UhQ5yqqk0/S9LJKBqILHQRJGnaXN9f6CK0TXb5kyRJkk4Ya9eu5Z/+\n6Z8oFAo89dRT/OIXv+CMM85Y6GJJbRCOvdBFOKH44vipjErS8U6+tpMkSZJOGF/60peIRqMsX76c\nxx9/nI0bN/LlL395oYsltUEJBgEQrrvAJTkxeA0BlZA9/iRpXskWKkmSJOmEoWkat956K7feeuu0\ntrv//vt55ZVXcF2XT33qU2zYsIG77roLz/Po6+vjG9/4BqFQiMcff5wf/ehHqKrKxz/+cW644QYc\nx+Hzn/88Q0NDaJrGPffcw5o1a+bpDE98tcCq/rMcWjUzMoqSpGOmrYBKCIEi/6JJkiRJi9w555zT\n9H2lKAqxWIzt27dPus2LL77I3r17eeyxx8hms3zkIx/h0ksv5eabb+bDH/4w3/rWt9i6dSubNm3i\nwQcfZOvWrQSDQa6//nquvvpqnnnmGeLxOA888AD/93//xwMPPMDf//3fH4vTlaQ2yeBKkuZTWwHV\nVVddxXXXXcf1118v37pJkiRJi9auXbvq/7ZtmxdeeIHdu3dPuc173vMezj//fADi8TiGYbB9+3a+\n8pWvAJXvwB/84AesW7eODRs2EIvFALjwwgvZsWMHL7zwAps2bQLgsssuY8uWLfNxapI0YzKcko4n\n3nGUjKKmrTFUP/3pT+nr62PLli3ceuut/PznP8e25eBRSZIkafEKhUJceeWV/OpXv5pyPU3T6Ojo\nAGDr1q28//3vxzAMQqEQAL29vSSTSVKpFD09PfXtenp6JixXVRVFUeR35BySPWSOLdvxeOtAhqIu\nn2FpYRTKzkIXYdraaqHq6+tj8+bNbN68mUOHDvGFL3yBv/u7v+PGG2/k9ttvJxwOz3c5JUmSJOmo\ntm7d2vTzyMgIo6OjbW379NNPs3XrVn7wgx/woQ99qL5cTDIWZbrLGy1d2kEgoLVVrqn09cVmvY/F\nItDdgav5hJZE6W44L9N2iY+UgJmf74l0ndpluzYxKwpAb28nsXDXUbfp64uxbyCHEtAYzlucfmrv\nfBfzuHQyPk8zMdPrpIWDDOfMWe3jWGs7KcVLL73Ef/zHf/DKK6/woQ99iK997Ws8++yzfOYzn+E7\n3/nOfJZRkiRJktryyiuvNP3c1dXV1nimX/7yl3znO9/h+9//PrFYjI6ODkzTJBKJMDo6Sn9/P/39\n/aRSqfo2iUSCCy64gP7+fpLJJOvXr8dxHIQQ9datyWSz+sxOsEFfX4xksjjr/SwW5ZyOp5sENAO7\n4bzyJYtC0QCY0fmeaNepXbZnUyxUrlsqUMIMTR3o165TJlumUDQIqOpJed2O5mR9nqZrNtdptr/z\n82Wq4K6tgOrqq69m9erVfPzjH+erX/0qwWoGnjPOOIOnn356bkopSZIkSbN0zz33THubYrHI/fff\nzw9/+EOWLFkCVMZCPfnkk1x33XU89dRTXHHFFWzcuJEvfvGLFAoFNE1jx44dbNmyhVKpxLZt27ji\niit45plnuOSSS+b6tE5qw+nZB58nOzGDUVSyp6W0UI7Hbr5tBVTf//73EUJw2mmnAbBz507OOecc\nAB555JF5K9x8eHnk1Uk/u3jFu49hSSRJkqS5cuWVV075Jfzss89O+tkvfvELstksn/3sZ+vL7r33\nXr74xS/y2GOPsWrVKjZt2kQwGOTOO+/ktttuQ1EU7rjjDmKxGNdeey3PP/88N910E6FQiHvvvXcu\nT02SZkRh7PdhJgGVdGwI38fYt5fQ8hUEursXujiLw/EXT7UXUP3Hf/wHiUSi/ubvu9/9LmvWrOEv\n/uIvjssoUpIkSTqxTPVyr1AoTLntJz7xCT7xiU9MWP7QQw9NWHbNNddwzTXXNC2rzT0lSYtJYwiV\n1NPEQ4tjLEoiZyB8wfKejoUuyqLgpFO42SxuNkv8kvctdHEWBfU4jC3ayvK3ffv2pi+Lf/iHf+Dl\nl1+et0JJkiRJ0nSsXr26/p9hGAwNDTE0NMTBgwf58z//84UuntQG36iMmXCz2ablsm1l9gzXWOgi\n1O0fynNgZOqXHCcV+YDPCyEE9sgI/jHKuNpWC5XjONi2XR9kWy6X8TxvXgsmSZIkSdP1d3/3d/zq\nV78ilUqxdu1aBgYG+OQnP7nQxZLaMFl2xEJZpu+eGVlTPy4cf40xxwU3ncI8dBA1MULX+RfM+/Ha\nCqhuvPFGrr32Ws477zx83+eNN97gz/7sz+a7bJIkSZI0LW+88Qb/9V//xS233MLDDz/Mm2++yX//\n938vdLFOOp7vo6ltdYI5Kjn+Z/Zmcg0d7/ibXFU6tnwhKBsOXdHgnA4BamfqiaPx7cpcVr5hznpf\n7WgroLrhhhu4/PLLeeONN1AUhS984QusXLlyvss2LwKv7W653N149jEuiSRJkjTXaj0paunLzzvv\nPO67774FLtWJxXF9ApoyaQWqULbZeSgDwPvOWXEsiyY1mGmVVDfdOS2HdOI6NFJkNKtz+qpu+pdE\nF7o4TY71MKy2AirLsti5cyelUgkhRH3W+euvv35eCzcfRiaZ+2PZMS6HJEmSNPfWrVvHT37yEy6+\n+GJuvfVW1q1bR7G4eOYxOd65ns8rexLEoiHOXdfTcp3BZOkYl0pq5e3Mnvq/V3Qsb3u7oiG7WJ5M\nhOtiDQ0SWr4CNRye1rbZogVASXfmNKCakzbpYxxRtRVQ3XbbbaiqyurVq5uWH48BlSRJknTi+upX\nv0oulyMej/PEE0+QyWT41Kc+tdDFOmHYTmX8tKx0L36u59T/HVC1BSyJtJjZw8PYw8O42SxdG6c3\n1sh2K38PTHtuWzWPxyx/bQVUruvy6KOPzndZJEmSJGlWPv7xj3Pdddfxe7/3e/zBH/zBQhdHmgMB\nVcX15XieE8FcjI2R5pZvVsYYCdc5ypqTK+jNL1gMy2UkXWamYbw6J/HUsQ3K2hoxeuaZZ5Idl8ZU\nkiRJkhabv/qrv+LAgQN85CMf4U//9E/Ztm0b9jFKmyvNj85ocKGLMCPWwGGMfXsX5NgycJGsgcM4\nmczRV9SqYY8yN0lkAF57J8XuQ1kMq72WK98XvH0wU+9CeDxqq4VqZGSED33oQ5xxxhlo2li8+ZOf\n/GTeCiZJkiRJ03XRRRdx0UUX8dd//df8+te/5vHHH+dv//ZvefHFFxe6aIuKcF08QycQi8/9vud4\nf8df558Ka2gIgOiZZ83rcYQQuOkUgSVLUQKVat34rH6LJVOi682uHIXtld/j6UyAK4RoSqDiC8Fb\nBzL0LYmyYhFMLqzMwxNeGRdVef6CCzhZsOe3d7+zRYu8bpPXbd53zopF8rROT1sB1Z/8yZ/Mdzkk\nSZIkaU4UCgWefvpptm3bxsDAAJ/4xCcWukiLjr57F16pROc556LFYgtdnCmpc9P/54TlJJOYB/YT\n6O6mY/27Fro4U7Kcmc9hKmbQ7dPJpDH27qXjnHPqLw9My6VsOpRHnEURUDWyHY9QcGHGu83HsKWT\nqaW0rYDqve99L88++yxHjhxh8+bNHD58mDVr1sx32SRJkiRpWm677Tb27t3L1Vdfzac//WkuvPDC\nhS7SouSVKpn4PNNY9AHVkliYTPHYzCUzH4Tvo8zRnFyt+KYBgDdFNsuTqF7bxBoYAMAZHZ2X1ti5\nVNRtdu5NcuryGCt7Oxe6ODOimy7BwPSf9VxpHrr6Lca06d/4xjc4dOgQQ0NDbN68mZ///OdkMhn+\n5m/+Zr7LJ0mSJElt+6M/+iN++7d/u6l7ujSFeahotztuol3zF4ocG8JxUKaZjnrWx5xFBNUVDVIy\nZp6gYCpz1WLh5vMEurvnZF8LzXcqYzzL1WuezBmLIqDyhcCyPaLhtkIFfCF4fX9qRsc6EVqh2/o7\n9dJLL/FP//RPdHZWbvAdd9zBW2+9Na8FkyRJkqTpuvLKK2UwNR3z0HTheDIj3+LT/n0OzGNr2lw9\nbdPu/td04MVVeXeSyYUuQku7D+d47Z0UutlecO23OV6qlVhHc+KZufmztAiz/IWrb1Zqg/o8z8Pz\nZt4PVpIkSZKkxUU3HV6fRgVqOmzHYzhdxm+zpmQPDyHcSkvXSdpbrW328PCEZZY388yWS2Pz2Jp2\nrG/m4oqdWqolElls8uVKNzy9zRZn3Zyblmnb8RjN6nOyr2OprYDqwgsv5Atf+AKJRIKHHnqIzZs3\n8973vveo2+3Zs4cPfvCD/PjHPwZgeHiYW265hZtvvpnPfOYz9VS2jz/+OB/72Me44YYb+OlPfwqA\n4zjceeed3HTTTWzevJmBaj/YXbt2ceONN3LjjTfy5S9/+ahlKJU6KZUWvulUkiRJkhYfgWcYmAcP\nsm8gh265HBiZfCzOTO0eyHFotEgya7S1vnn4MNaRI3NejoVgOu6cd4NsRYixVpui3XwPpxXHjAtC\nXN/lYOEwpjv7cWwyOJ4ouGzZuCXHQRQ4j3bsTZLMtfd3YjFpK6D63Oc+x5VXXsmll17KyMgIt956\nK3/5l3855Ta6rvO1r32NSy+9tL7s29/+NjfffDOPPPIIp556Klu3bkXXdR588EF++MMf8vDDD/Oj\nH/2IXC7HE088QTwe59/+7d/49Kc/zQMPPADA17/+dbZs2cKjjz5KqVTiueeem8Xpjwm8tpvfPPlo\ny/9eHnl1To4hSZIkza9du3bx0Y9+lGuuuQaABx98kNdee22BS7WICTB27cQeHcEcrbR0FPXJWzdm\n2qun9vbadtvvquWVS7Ui1jWOwSmUbZxp7G+hvP5OmtfemdnYkmmZo2hlfHV+pJwgpafZlzsw+53P\npi9XddvhdJnELFow5iOb3ezMX4Fsx+Otg5ljEtAfjaHbjBzJn7CZ/9oKqAYGBjj33HP55Cc/yebN\nm3nXu95VbzGaTCgU4nvf+x79/f31Zdu3b+d3fud3ALjqqqt44YUXeO2119iwYQOxWIxIJMKFF17I\njh07eOGFF7j66qsBuOyyy9ixYwe2bTM4OMj555/ftA9JkiRJAvjqV7/K3XffTV9fHwDXXnst99xz\nzwKXan4IIfBmkEp63F7w7UoXP6WNsU/uDMdHzWQepFomwlYMy2XnoQxv7k/PqDwnn5lXYv1qy9dc\ntFCFQ7MY3ygEvvApmw5HUuVZl+VkkMjqFHWbw6Nz3+o8XUcOZikWTEqFYzR57zGOnNvquPnHf/zH\n9fFTtm2TyWQ466yz+NnPfjb5jgMBAuP6hRqGQSgUAqC3t5dkMkkqlaKnp6e+Tk9Pz4TlqqqiKAqp\nVIp4fCztZW0fkiRJkgSV757169fXf163bt2E76ITxa/fGiGRLnHeul66osGjb3A0bdQ/FsvL5Uyx\nUimzXDmeey4JIRjWhzB9jYgaBSBvF+blWCkjjYJCb7Sn5eeO62HYHvGOUEMB56Uoxy17dAStswut\nq2vejjGbiYdb3a75aKEaP3nzQmjrW+Z///d/m37eu3cvW7dundWBJ7ug01k+lzdlZIrm4/G9WyVJ\nkqTFKRAIMDAwUP9yfe65507YLiamXQkm3jqQ4ZJzls9wL9OrhAS0Bai0NNw/QaXEx9UYixk+f0II\n0nmT7q7wjOb2aeR6lZadzsjUgXfRKZEy0yQsg3XRyosJy527FoXGS3Ewfxhg0oDq9XfSOJ7Pu8/s\nm9CypTjHqJXjWJhhIOA7NubBgwDEL3nftLcXvl+du2xu/j62u5dSwSK+JDonxwTYfThLtmTx3vXL\nFzT9+oxe25111lkzSpve0dGBaZpEIhFGR0fp7++nv7+fVGqsb3EikeCCCy6gv7+fZDLJ+vXrcRwH\nIQR9fX3kcrn6urV9SJIkSRLAXXfdxe23386BAwe46KKLWL16Nffdd99CF2tezaQ73UwFtPYr9kII\nDheONFWYx1d3TNckbWZZ2Tm9gHDRDYNp4Ds2vjn77nHpvMm+oTyxaIhz17UOOtq1eyBLWHi8+6w+\nwsHJu90V7dKiGWRUS7/veD5hmsuslha+C9uCm0WacgDr0EHsRGKOCgP5FpPztkqlbkwxRnMmstXj\nmrZHR6QhrFmME/v+wz/8Q9PPIyMjFArTbwK+7LLLePLJJ7nuuut46qmnuOKKK9i4cSNf/OIXKRQK\naJrGjh072LJlC6VSiW3btnHFFVfwzDPPcMkllxAMBjn99NN5+eWXufjii3nqqae45ZZbpl0OSZIk\n6cS0fv36+uTzoVCIrnnsCnPiOXoNZKq6tvB9lIY5jHS/SEIvk9CTwMqW2+zK7MX1XcJamFDLNWYv\nX7Io6jaxjvk6QrPy66/XU74DMMN5nUyn0gJZNGZfAbUcj3CgkqRgqoDKmOU4qeF0mXBQoycemdV+\npNlpp1XerTZQ2K5HKDD7uftaTYmQKVgs6WpOw18Lsoz976B1dBBa0fpvw9Eshm5+jdoKqMZPknj2\n2Wfz2c9+dspt3nzzTe677z4GBwcJBAI8+eSTfPOb3+Tzn/88jz32GKtWrWLTpk0Eg0HuvPNObrvt\nNhRF4Y477iAWi3Httdfy/PPPc9NNNxEKhbj33nsB2LJlC1/60pfwfZ+NGzdy2WWXzfDUJUmSpBPF\nX/7lX0755Xr//fcfw9IsXqmcQWfDeCvhzM2cU04mjbF3L9Ezzqgv80UbSS78SuCRNXNM1kbVqmo4\nnXrUb/YkKRQN3nfOivY3mkKhbLPzUIZYR4hzT5vYctQUTC2QgeLgjLbTlOkHf0IIRjI6PbEIh6rJ\nD9q51rXum7ZnE9ImD3ZrwUFjkKDY0wv8hDv2nC+iOvi8qwXl2RatRwC5kkUqb7CsO8qSWb57mk7P\nViEETjKJA9g9cXJWgek0KZUMhzcPpDljVfe0yzlf2gqobr/99pbL/Wp2IbXF25fzzjuPhx9+eMLy\nhx56aMKya665pp7itkbTtJaZmc4880weeeSRdordZPxcVF1dMkOMJEnSiUK+XDs6y/bYN5QH4Jza\nsqHpVbwnS1PuVLsO2aOjoEx/5HHeLkwaUDWp1cLHVb7cfB57dITomWc1tZI12nskR7Zo8d53Lcd2\nPMqmO6NJbPcMVN7sT5VefkKZjxPRQHTaPaXSBZNDo0VGMjNLZT6Ty6M402u1cwsFrCMDhE9Zs2gS\nqywGZaMSaKbyxoSWpOma6XXdldlD0XCIsrbtbWpjKKfKXiicY/tio62A6vzzz8fzJmbSqTW3vf32\n23NeMEmSJElq10c+8pH6v/fs2cO+fftQFIWzzz6b008/fQFLduwUdBvH8entbt3dypvlmAuAA8Mz\nH7sifDGjBCHC88C2IDR5hU/fVamHuJk0wWV9LddJFyqtGr4QvLG/kvDg/NN76ThKoob55KRTlPbs\noev8jQQ7O4++wbwTWM70MicOJisvqNvZrtXtF220ZFbXnEapJrJHRgifsmZW+5iK7wt8IaY1znC2\nxzMsQeTY9GRtKaCquDOYusGxZ5edc7RlIrmFjZTbCqjuuOMOzjzzTC6//HIUReGZZ57h4MGDk7Zc\nSZIkSdJCuO+++/if//kfNmzYgO/7PPDAA/z+7//+Ubupnwh2HswA0Ns9s65too2mCWcWacqzQ0XU\nos2a5bEJn011aPet1wmli9hnnz+2/iQb+FZ7LRf1hAcNLW5vHczQ3RHilP6p+z7NpAI5mfI773Bg\nuEDQ38eGyzfO2X5nSncNcpN0D5uMYc+uJeBo1eDZVJN9Y6xroPA8dmX2EiQCzH0Q/cqeJJ7vT9rd\n0RcCdYr+hrUXHrrVXjfc0aEi6ZxPT0xhYse39q7a+NLYo6OEli/HSadRykVEZ+V31fd9Du3LsKS3\ng6W9HfX1VVWBNn4dLMfDsr16tsaD+9KMv0rtjonyhY/jOwTVqe+hcoyny2jraC+++CJ/+qd/Wv/5\n2muv5Y//+I9PmoAq8NpufvPa7gnL3Y1nA3Dxincf6yJJkiRJLWzfvp3//M//JBisfNnats2NN954\nUgRUM+ULD9tzgaOnMp5td6nJ614KpmuSMtKs6FxOQB2rntTHJPmimtFQmbSly7emFwzU9uL7gqJu\nU9RtVvdGsYcGCfYvRw3PrhvU0dQG6JuzDErmSsbIzvMRJt63tuc5muWz5/ouJbuE7RZQWT27nbUw\n1STbh0aKDGfKU2ZZnG5gWi5VXh7ky4JTGpbvO5LHd+wJAUs7zIMHCPb3Y+zbS3Awh73+gspyw8V1\nPVKjxaaAqt2/Bzv2JMgmSpx1yuzHPL2V3sURK8HayFkExFhr4IGRImet7iZUvb7KMWoprGnraLlc\njueee45yuUy5XOa5554jk8nMd9kkSZIkaVqWLVvWNJFvMBhk9eqjV5727NnDBz/4QX784x8DMDw8\nzC233MLNN9/MZz7zGWy7Unl5/PHH+djHPsYNN9zAT3/6UwAcx+HOO+/kpptuYvPmzQwMDMzDmc2f\nodIIQ6VhGB0A14U2W2B8y8LJpPF0vZ4mvFWmr0bZokW+3LoVacQvYHsOOSs/ydZj+9atSSqfDd3H\n0nmT3YenHyDYw0NYQ0MY+/ZOuo5RMCmm2h2LPfk1OVbjedpOrW9Ybd//mSgZY60vtRIdNRFGq26C\nwen3c/PE9FpXhRD4c5S0ZThTeVZKukO2aPHWgQyu52OZzqznyfPG3a5UwSBTnH3a/kbtJPKIhqfI\nFDj+FGdxzrV50fxx97Oo27wzND+TULejrRaqr33ta9x777187nOfA+C3fuu3+PKXvzyvBVtMJpv0\nV074K0nnkBy8AAAgAElEQVSStLgsXbqUj33sY7zvfe9DCMFLL73EmjVr6tN/fOYzn5mwja7rfO1r\nX+PSSy+tL/v2t7/NzTffzIc//GG+9a1vsXXrVjZt2sSDDz7I1q1bCQaDXH/99Vx99dU888wzxONx\nHnjgAf7v//6PBx54gL//+78/Zuc8W7ZXqTQ6wiIy8A6KZeCdFkc7Ssr50Rd+je14LG1Ike26Pq3y\nnxuFsZajVM6gu3OKrG6zCUAaan57ByvJI+Kx6U0iWst8OFVrl1FoDgp9XzCULtM3yYSlil7CGjxC\nePUpuJ7PzoMZTunrwrBnX6E+Gnt8jXsSTqFAYM9BumworZq6faMxCciknw8PVZKENLzgGErNLHEF\ngK+Xx1oUu+LT2tZxPZJ5A9ef2N10MuY7+3DSaTrP34gWnflEtJ7vYfkmYbXye7J7oBLkD44UsfMm\nil5izmdUrV6oSEjDBro7229pdUaGJ+6u4RF1HI9gvZVt7INgixahrmjl3nttPIMC0X5rZXX98d1v\nJ0uacyy0nZTikUceWXQ532ejMeufzPgnSZJ0YlizZg1r1owNPP/ABz5w1G1CoRDf+973+N73vldf\ntn37dr7yla8AcNVVV/GDH/yAdevWsWHDBmKxSqXswgsvZMeOHbzwwgts2rQJqGQb3LJlyxye0dw5\n2td30csTNiMoioabzRw1oKq9dW8MqFodxPdEU0BV4/iNrUwCvxrY1d5Az5uGymHLWKZ2Dm0EOrV6\n0WhW50iyRLZocWqL9YKH92F5Swj2LiNt+OiWy76X3sArH0J3BXFaJ9KYrbLlMpQq0xNsDgiE7+Nm\nswSWLEGpTo3jlkqVspbHBT22Q+Dt/fgr+6j1I2tMAtJK7XMnnSK0fCw4M1tkXmtnDFXeKqAVG1ou\ntYnjZzzfY0RP0B9dRnDc56MZnaLlkCmYbQcXTrpybn65hBaN4gvBm/szrEdpemcwfv61PQM5lvdE\niWRGCfQuY685yJA1yKrwaU0vC0zdRgUcx2/ZX8ywXIbSZU5dHpt1oovJfvUFgrJXJKyMPR/m4cNT\n7su23HpA1fgrorUoY617o2vOrkurNTKKoimE+uY89JwzbQVUu3btYsuWLei6zrZt2/jnf/5nLr/8\ncjZuXPgBlJIkSZJU82d/9mfT3iYQCDR1EwQwDINQqFJt6u3tJZlMkkql6OkZm3eop6dnwnJVVVEU\nBdu269sfC75jg+NAcHaD7X3hoyqtu+68PPIq+/UCayNnT/gsV/KJhhUCk8zn6gkXTWm+xu/kDtT/\nvSzaS1a8A4y1mLXLsFw830dT1Rm3JDS3iin1pUfj+wLb9bAdv16WKY9Tq4F6HmouhRcER8y+W5nt\nehimy/h2m8nK44yOkNu3Hy/cQe/alQSXt26RGk6X8fIlAoA6nITzx60wjcY188B+lKKDiI0fRzP1\nThzP5mDxHdRMmnVUxu9oqWGguQ56pDREUk+hOwZnLW3O7On5lbtaS3SpGDpaegTxW8vaSl4ghMCw\nXHTLYd9AjnPWVM7Bd2xKO3YQXLYMqLyAyBRNskOjvIss1tAQ5d7KPXBFc6vmwEiRU3snz+y4eyCH\nabsENZW1LRK51MymncPyLcpeEZ0Sk/W7UpRxx5jkdmnq5AWx9Hae8dY79nyft55/lc5IkLOv/Z0p\n99CY0CNXtNA8/5hlXWwroPrqV7/K3Xffzde//nUAPvzhD/OFL3yBRx99dF4LJ0mSJEnT8d3vfpfv\nf//7lKpv2+dieo/JumRNd3mjpUs7CASmGHPQjoF8vTubum8n3YMZxIaL6OtrXfnSTYd4otKqFFfG\nIp+oXQn8QmWbSChEUA2SKNg4oyUuOW9lfb2YHiWaM4l1RFAUBaWarzkYDmEVXCwDTl8dJR6olMk1\ndBTboatbIx8J1LNydcXC9PXF2K17xCKVdVcsWYoZDUKgUimLV1u9+vpiZDrDlCyPSCzKsmUxggGV\neGysteJAooySNXnXuh7iq5YRXlY5/8Z1Grv9LeuLER+sjLXo7e0kW7DIFMz6OvGwg91w/PHisTxm\n1IBymcEDg5Q8je5SknjPatRgiJgIsy9ziO5IjP7OXuKhCEokRDweYemyLnzdJ503IBrCDAQJuT6d\nHZGmYwkheHloD0okQizYPek9rRQowtsHKuPaQ9EQ3V1hYnrlXExPYLqCrnCYeChKb28X3V1hCml4\np2hD0WZlTCW+qhffhWhHiFDZJhoNEY9FyeoumbLN6R2h5utRvT7RcOsulbXnq6u3i2hfDFc3yJoF\n4ukMotqdUI1VniPRYbOsaymFt3cRWbGCcG9P0/3riIWIBaJQChPTIkSzZst7kxQBYoEokaBKX18M\ny/EwwgGi4QAdBQth+XR2hIjFIgR3DoDn0WkV6Fw5sU3RcT3eTuusXNZJTPPYcaRQfe6iTce2M1nU\neATsEvFYQyujqxMPVq/B8H6ivSvoikTo7ekiUe0uaqYN4rEotmcRD0SIpkNEGvbfMVwkfGgPUdFD\n33nvmVDGwYiGEg2iKGPl8SyLuF2E+BLioQjRkg2xKEvikQnXS+2OEk5qhNxKKBCPN78NqZWnp6eL\njqBKIV257kt7OuiKR9FUhfhQEbfana+nt5N4xmjaR29vF0vjEcIBDQJa/foJ36eDMAFNIRaPIFSV\niBJGUwMTIsR4dwfRSAi/el1iepRoxiDWESGsTXyD09cXo1C2SRZtgkWbs9Yumfr3Z460FVAFAgHW\nr19f/3ndunUT3uYdz+Skv5IkSSeGn/3sZ/zsZz9jxYqZpQ6v6ejowDRNIpEIo6Oj9Pf309/fTyqV\nqq+TSCS44IIL6O/vJ5lMsn79ehynMibmaK1T2UnG5rYrZWRwfR+9XHn7XSiaGKZNJjvCvgGF7shY\nW0VBt+mMBLAdn0LRqK9fY1QnqLVtl4JXpEPropDI49NJMjnW7a9YMDB0m4JnoCgKIbOaZSxvYhiV\nILJQMCgEK8dIDhbo6LQIZVVMswNHAXyXgaefZ0lpPUV1rPKV8osYtgeGXd1PtXzJIqWyhWHa2LkS\nqVSRgKbWz6MmZNoUCiZOukRQhKrnWFknHos2rT88nK//nE6X62Na6mVpnOw4WcRxPQKaWh/yUCga\n6IZDYGSIZPkIaBq+CkbBRaw4hUy+RLlsUi6bRLxOCmGjXj4vVSJjCgpFE1U38EIOtu1SLpskk2Nz\nfJXsMnuHhwBYF13f9Nl4hULl3gMkkkVsw6ZYqJxfqWxhGDYl10SzDFLpErZhY+T0+jaFgok9mqNn\nSRRDt7FtF8OwKWDUnw+jOpFurRy1+2MEchSccZVVzyWZLxEMqNjpMqFAEd80KBVMSoaBUyijKipq\n0EQFdpcPoQVd9AOD6G8fpOPiS4h3hur36O19JUTMQC2bFHWtXu7x1+TQ6AgARQySwSIv7hwhtD/F\nGau6KesWpuOg6zYhVSFaNgFBNlNG75x4bV9/J4Vr2uw/YqMKQSF2Gp7wsHydFUv668d2MgWM6rUo\niLFnTC2bDNtFIiGtcv06bEqexaHBbP28dMOhUDSwizp7rQGKOvjVz5LJIoW8gZovUHCtCefqOzbp\n3e/Uf659XvrNq5j7R3H7V1EImhi6RXF4FHVvmkTsEhRVxS1WXiaYeQPDqDx/ANlcCU0NIITgUMJn\nJOMSLxpkMiWMwNjv3P/bXoBIgPPOjJHOF1GqmfbS6XLT75kadTg84uFaPeiGA/hkBkfROrtAAT+t\nU/KSFJeeSkm3UV79Naqi1jML4tho6VGSS0NN97xYMDAMm6JvYqkTX14lk0UyBZNS2SRQNivP6hS/\nP9MxVWDWVjtYIBBgYGCg/sfkueeem/dBlJIkSZI0XWeddRYrVqxA07Sm/6brsssu48knnwTgqaee\n4oorrmDjxo288cYbFAoFyuUyO3bs4OKLL+byyy9n27ZtADzzzDNccsklc3pO4+WtIgfzhxjQD0z4\nLOkM80ZyT/3nTMFk58EML+1K8No7qQnrj2f6BkJANu1STLZ+udhuxji/+ubaLvvgVzJyqZaJEGAN\nNI/TGC6NIFp0Vyy99pv6v1WjoTyei2LOLChtnID2aHUZy/Z4ZU+S3YdzLT9vtfVke/R8v+nlu4eL\nW+uDNm5w/aHEZJkOp5Yv2SRyBgOJEl7DuU1IsjbL7HW66TCSLrfsnhna+yYDiRJDKb0yKXPtmMIn\n7YxyxNpfue6mjTo4im2biOr5D6XL7DzUnEW6fo1mWO2cmHlS1HfmI+otLI7j1ZMnuN7Eg43aA4za\ng5Tdscq5eWD/pMcdTpc5kijjCoFmWiimwUhm4jNbdMqU7TI5b+J4NNu3yFhJXK85o52wJ1534br1\nRCqKO/Z58Mh+MHTs4UqyCX3nTvSdO4FKV9yapFE5fsmceO6Nl9DyfHzh81Z6N4eNva1XAoaMAV4Z\nHusZoJZLOIkE9milHGWnhO5UX5y4HuNvcGBkAC2Xxh44NKE84VwB11y4jH6ttNXM9Fd/9Vfcfvvt\nHDhwgIsuuojVq1dz//33z3fZFr1AdW4qOUeVJEnS4rBp0yb+4A/+gHPPPbcpkLrnnnsm3ebNN9/k\nvvvuY3BwkEAgwJNPPsk3v/lNPv/5z/PYY4+xatUqNm3aRDAY5M477+S2225DURTuuOMOYrEY1157\nLc8//zw33XQToVCIe++9d17P0fErb2sbx94ksmNvht1qpquRjM7BkelVOhrHmbh2cyXf9fxKJd3z\nUItjlb8jqTKKGiaoaTQOfy97RbqIoGTyBJMZRH81WUit3mQ7eJk8Rnc3XdHWY79q6dhranW24P5d\neI6Bfea5BEMdLbZsT/Yok9iW05XKfa48tt5MXyjvGyywcmWZYHdHPb177V5phbEgQvg+BxMjTdu+\nsT/Nyp4OljVkEfTN5lY6qCYKcWyU4QSF8AqYJCW5mx8L2MqGQ9if/JwElaCkMSnZULqM6RskRkuU\nl55Fd6Bnwnam7SLshutWvfG1Snzg4BFUy4FAAMOcPMBrvN7jL71huWSKFqt6Z/YM7DqYxSqGeN85\nKziwJ4UCnHXucjrCAXQqqdZ1t3KdbbtEwHEplZNQnfmpPkfaJBzPI5M3iecG0bQsnDsxqYKoPgut\nnqucmwYXnvv1Di5bvZzo2rWTHsseHZn0M9fzGRnNsnrFyqblfsM0A7pTbbUtiPo9UkoFnENZrKVj\n2ymKgqjOKNf4cqXVE1QyKmniK4VwQAPftECApgJTZbKvBpGN13jPQA7fs+lIpohmfThnimQu3rGd\n262tgGrp0qX8/Oc/J5PJEAqF6DpK5p+TxWTp1EGmVJckSVoI99xzD9dddx3Lly9ve5vzzjuPhx9+\neMLyhx56aMKya665hmuuuaZpmaZpUwZsc218amEhBPmyienrQIRyNRHB4CQtTFNRlQCGpaAoJqpR\nqi93fZehdOU7LzB6hEBDxrVcSaAo9oSU4bUKolrU8Qij2CY0ZEMLvL2fVFantNImctZKtKMFKoqC\n8FyEFkTxXDJukryxj1ND4zMlTM7xHQ5mRoFKAFeuzovkCx+tVEQRflNKNOFNrPH5ja0X44rsC0Gx\nbOMjUMfdp2xJxT6UZd25kXplsZXya7+h6529FE4fe4bLpsO+oXxTQFV67bWW2wePHCBSzBNMhUlH\nKvU1y9eBXnYezPC+c8a6wwoBbx0yWNGRp7eno6lVq0YBEjljQlpsyzdQvCglL98yoAKwR0cJLFmC\nGoniiuYAXam2siTSJSI6nNLXum4ZDGqV6ZxNC2geovHmgQye7xMNafhCoLbI0CCqDVIKoJV16Brr\njuv6AlwX/e2dHEykEeEQZ7GceGcIHci6SYKlCHb36Sw5UGlVFYkEnFd5WW67HmXDZWnX5F18ay1k\nru/SmF8woCoIAUW7RJTm4GT8JMHuoTcxfIfwypWokyWeaXHvzGprrO16jNoa3mipqX4qWuSRMFyD\nolXC8lWCR/bj93SidY27v56HUmquA9cOrxhlRDha7wNnuy2edd8nkVWJdFX+TgQTaRzhEW6REMfX\nx46TKZoYohKgqahoRw7grjgFAs3XxEuMEhitdN0tGw4B0yWR01m7PNbyGZkLbQVUf/EXf8G//uu/\nNmU3kiRJkqTFZu3atTPK9Hc8iVaTPkTUKOlCgryaYYWbwRYW0Eu5rYxarUXUKKZQUBwLpaF1wfSs\nelc53yo1beML0GqZxj2vFquMU+u2JRifi0N1PRTLJVi2mPKdsuey58nnOOX0lVOthS983FQKNZ/B\n726utwxa+zmSEKwOn0ZIjeD7kHZGKLg5zhwoo6karF5S2Y8vSCcN/EAYtSGYaKyOOR6gOIiGrlOp\nnIGuOpPOteX7ojrwvnUA6ds2GrNIWFLt7qX4fv0Iutc6uHa8yv1LDucpxXR8Kww0t3zVsj46LeYS\nKpZtQmoeelyYZGy9nUjgrezjSGFsbJpAMJo1WNoZQhTymNXkAo2tNHZ+BM1XWHX2GhIuKPkSxJrv\nZy3w2HMkxwGjwFkrumCKLr6BYgmxfEnTMi2boqSWEMMpOG0VMJYXodaCM9mkwEcSJXwhCAXHng/H\ncnHyDt1i6gx8ri/YN5hH9cWEvOYHR5rH/Ijq/3y9jAiF0XftrH/WGWk/QLCd5vNoNT+U5VVawF3h\nUJtULhINkDfGnvHA4EE0ZQgt0g3hyj1zPB+lVKh0MQTiShJz1djvqlouQrgSENvJBFAJNlO/fJ0g\nAtcX9IdWjRWkevEm656qoKCW8mipIN6KU+rLddPBPTLWpTiVNxk8kMIVEA0HWL505i3aU2kroDrt\ntNO46667ePe7302wITK+/vrr56VQC03OUSVJknR82rhxI9/+9re58MILm7r8NU7ae7yrT5NU1Egl\nMmgxqsFURa2VwZmiFaSR5XgY1YHpKirhgMBxW1fShICUPcqqcf0wam/Xy9k8rBxrAfGEaAgNBKC0\nrO/22yEm+7at1bFVQ6+8aT88Oum5JPUUQ/Yh1u4vEBgxEKkR2DCWXnus21n12ihQcCvjoxxsFD9K\nKm+zrDvE4aSPt9QhZzj0nBLH8/zKXDsNle2M7qILg1hHkaVirPJYH6c1rtWgqDsUSjaoWsNInspY\nmeLQALFVlW6RAWV26e8nmKT1z3YqJ6MUCzjeUkx7rNJ82NzLmvCZNNb203mT3u6xzGrBcpmAbhMw\nD+GuPQPsiV0ohW1T2Ns6y2a2ZFX2Xn0mks4whtVLxgyjH9pRWXj2mpbbjhfO5vBHB9F+69SmCV7b\n6aJ5JFmiqNvEciUSv34Zcfq7Jl23U+tiJKNTKNv11ifTcuvTFhSTOqrhYrsQbriN3iSvCyzXp+Q2\nBw0l3Wl5z/Rdu9C6uhANrT6qAtbgYNOyVtRiHrGyOVmPPzGeGiuvcPH8ifsUCFS9RN4P4RRAK+zC\n801+469lidWwvhCVOc0EaOnR+jKoPBNQmYhX8f2pX6SML/D4P03jrtPr+9P0VVvpPd8jqGmob/8G\nfuv8ysuMeTJlQLVr16561iJN03juuedYunRp/fMTNaCSJEmSjk8vvfRS0/9Dpc//iRRQ1WoUrlGp\nDfmNw4x8H0UIym+8jmJEEF3VbH+WieJ7iOjEeW8axxGVvSIIl9qcOrW087WavzMCejGK6Gp++16r\nqLieT+DwPg73V6oXnjuxAuO6Pvq4MTNiaHjCerqrM1JKUDYdNHcpSjaLqo2vPzX2zxOMlBPQ3UnZ\n0QEFxbGhkINAZ20VhA1+sHLtGgMIgLyukimZ2I6H5aiQGIau5XiOx/7dSbqXNnS586rdHuvdGGuJ\nDvwWiRCq10kIEskSkagKQmBXg96cm2b7r57myt+5ruV2NUXdxhdM2vrVqD7erKzTNTSCetpK/Hhz\n64zpjF2/g4PNiTA84VXHyoydSypv0B0bqzoKTQMf/OpkwMFDexlPCQTwM6VxS1slPqgGJ77O/tzB\n+vL9IzncqD1hKEX54EGwFQgG0UaH6EhlyAB9+RKv7Em0PFLZcEgmSkycSa3CSmQ5YjoEusdlhWsI\neMpeiYE3dqFYVj2zW7ZkESq9hb3+Anw8AnoRV1EIB8eO7gsfyykRsfyx30sq48BK1VYY27ewPYNI\naIqBI+O6TubKHpG9++kcNw5xfCCpFnNQHXeXyhlEOivn0uIA9X8VvRzZUpiOxn3Vxli6KmgC37LI\necMU8wqdondCYJEqmChW5bieEOD7BFQVV3itG7NdBwJBSm4B1SsDY5n1QntexzpzksnuGliOR9kr\nUvaKxFlKRI023cP5MGVAdffdd/Ov//qv9b7hf/RHf8R3vvOdeS3QiSL17KsA7Ag1v0m78KPXtFpd\nkiRJmgOtxkLVsvWdKGpVYL/6ermxfhUullBcD090Ehwcwll7JhZhtH27iYYEzup1iM7JU//awsLz\nHYI0j2cRQkDJIVi2QVQq67WAKhoSGLZCvmzjuhYqFrYbBDQs16MegghAVTBtj6F0GXXyOU0BGCmN\nVYqTJZ+wqtIba/1K3ReC1wcGCcSXTVqxEULgFcDXwVFcaJhf1q/Oi1zrAZUuGqR0i+WxblS9hGt1\nkNZdLNtDtEjV3MgUzV3mlKZsez563iLSYgJi1xMIxwYhKskIRBzNsnByCXSlg/iSAHu3/S/uyrW8\n99JzJj2+4rkoKOiWy7IlKzBzrxLVOtEyCfz4EkYyOq06Pblec/5G1XEIJvejKWMT0g4YBykmwPL0\n6rE8bCdEzlIIlWwCrlMPKxsniHZFc+Da6gr+ZmAsa+Vozqi3bDq+TSZvTgio9r+2l6Cq4S1bgZZr\nyGA5SSuENy4DgueD4/gQcCk6GUCrj/cxSuWmyn7HO7tpbHvT0g0BmxC4wiWgBCh7RdL2KB1OGk+F\nqGiu/JcP76DLjuGsXkdtkFFjq0ky9QbBw1lS/efTvbY5gYUARrM6kZJFd9fYaKxUzkT4HmuXxwg1\n9Kd9Z6hFpkjXBSHIlAw8w8V1NQKBaldeRD1BRuMxTdtl70CecChKJKThVdfxfQiXS/WJmmNHhiDq\nMz6BeGnvW5S9IlGtE9PSKVoBVi7rmnCsOs+rBFR+HmF67BtyCGiVljiAxGCBiBNATPKLrhYrLc6V\nMaVg+2YloJpnUwZUMjX67OeoSthDTT+/PPJq088yC6AkSdLcGRoa4sc//jHZbGVuIdu22b59O7/7\nu7+7wCWbe1qgUnFRGr/JhUBpqK4GDu0lE16NZipEQ4Lg4AG8Jb3jx/ZPUHRzCASvjPyGFdpZjCZS\nBN7JobToI+QIC1+EMCyHzvC4z9yJ6yvKuPTIhgEdze+qR7M6Izl9wniHyaollq9T8gq4e2xWnhds\n7hUkIJHdRTksEFblE88ZN+g/DUUjSKhaFzT8Eo4vyJp5lGprQqZokSlahLsCNIYEE69Jc+YzNZuq\n7rNMSR8k3rUaRBStRbc+3xdky5WATHVd4gNDGKaKt2INfqqSsTEwfBiYPKACCCgBLFfQqUYwxw3k\nOThSqG/tCgdPuISYmBCiI5lGseOE3Ty1M7Qyo9C9nKKXQ61mEHQ8jQA+pmFiOmMtjf2hVeiWS9T1\nURsq2QFdhxaNDIY31tRaKNvU+kOFdAtRq/Q3VMIdz0PxvAn91pI5m1jYRwuouEoQ0zHJOEnCSifh\nQhEvHAZCZEsqlFKUlxeJ+CaNvxSNLW1HqwnrfpmyV6BTi2OWEmiWieK6uGGLvDtuPFq5hKGphAtZ\nKCq4XV2oDV1zI5kchh0ikM1h9TePFxNCkC7YBDVQVQXXU9BUpd6N1XV9QgENw9PR3TS9VLq8KYpa\nT8RQ0C20Qo6UM0KxEEfxInR2llGUSqbQNZ1e0+9Y2VJBFHCNHF7Oo3epxpA+wGovT8nQJlwf28oR\nHZegJJ/Zhyc6MLwyvVpnfQoFRWn990Er5vDCKyjno2i2TcGziUchVP11sQsBFCuMiAos3yQwPt36\n4EEIHfu5cqc8ojLul1AGWJIkSdJidtddd/H+97+fZ555hs2bN/M///M/J+A0H9XB2tWKhJYpN7UY\n1dS6vNh5i6g6FpioepmCopIrWi0zcNlOqL7v0VEdK5Dl8OFhgi2CKc/3KHl5bC9EPDA2JKA2RMnV\nG/oj+h5oGopSCYx8IRACtLyBiMZxfLd+dsXqZMPJnDGh+1zeyaLXu+9UPrNFZf2AaYIxdkxPeAwe\neZlQsYxYEifn9hDJ5jHdINmcQ2d/HEVxgQCOMAlVB+H7opI0wvUFIdOgsdo4ZB7Gt48ak1avT2W8\nCVRaWsI5G8uwYWXrl6mJVImRbGX97oMDDdfOx2tn3q3aGBUAZXw+SCh7BYLKWNSrV7t8ddEJKHi+\nMlahFpUxb42cos7ewTxLaQhuqqs5rjGhC9fbI4fxjyRYssSpj52LHEzhxdrvfhU9MkBHpAxByFt5\nOErCjmxSwcrtZbkwyBkaupfD9Cyq88/SkUzhBJbhE0JFJZKbes6vXMnCbzHfVo1d7XNr+xYIQSQ7\ntj9XTNyu5BWIFrvQs3ksP064VEZ0Qrk89kSpLVLilw1BQVcJBwWjWZ1UQSWgqjhKmbLnkCkG6IgE\nKXo5bNPB87tIu5VeUsuCy1EVDS2X5kAq15Tdz/PGWqkGEiWchnO1bRXbFrhOkjgxDqUyeB5YDQGo\nJca6DVrVa2F45fptClQTutRSsS/p9LEtAzSd0riuv44Laj5HwCij2WMto7XfR4BwsTq5tJvFxyPi\nx1vF5w3bQtFQCM3j+Cloc2LfmvEBliRJkiQtJpqm8Sd/8icsW7aMP/zDP+Rf/uVf+MlPfrLQxZpH\ngnCxhONMbO0oe5WKh+M3JwrwhccRUyNd9Jte7nueiu8reO5YhVV9a4DArlfx3OZ91LrG+fgTWpx8\n4eMlPTpSaaLpLLbn4+MT0AvgVyqFiaJDImegGx0YRpRsqdIqNZrVm7LJjQ+miiYULR3Lr1Q4uw8d\nIXhgN7ajYRgRhIDkW4fq2cxs30KrTnYaLBsETBPNtglkEmiZBIWdb7LkwGGC5VI9UYUIhilWYzLL\ncT2uOSwAABkXSURBVMAyIF+ZJ6qcO0jnwAihwsT5vbRsklH7CEa1O1y2ZLF/OIdvNo9TUWyTSjg8\nMUA1MllcmiuZpq+jpgYxc2MBlZgkm0Dg8L5xS5R63nBf+CTsIQatA+THzb9lux6uB6VyCNMcXz2t\nduLzFbwRl3G99/B9tdKlL9XcI0cIge7YZHWHbD6MEOC6GpYVxvzNrqbmRqOahdDzVIQHvlM5Xk2o\nVPm8aLca89NCMYPjVc49X6yVZ+zjrJui5LY3ebLleE1zvk3Fc5p7MR2t2hzOF6pla16x0kWy+dk/\nnNSr5VHqv7e252I4NmWvRMbIsW+w0t0tVCpT9sfGgaWcSmBlpvLoRphyubPSujfu2L4QYxMpN5Wz\nWMnS10KrcVhFb+zaNmZArMmaOYQA2wniedWAy1NJllxKOXvCsWpFqv1uAVjVxDkiM8qoNVD5m1Ab\nh2e79ekliuUIlqOQHZnfJHNTtlC9+uqrfOADH6j/nE6n+cAHPlAfpPrss8/Oa+EWo9l2AQyMmwS4\ncKjyRy1+yftmVzBJkiQJy7IYGRlBURQGBgZYtWoVg4ODR9/wOFJ/uWm7dI5U0w/7zZWWRH5iJSdV\nULF9G2VJEr+0irCjVMYl1Lq5GRPHGVi2SlZLEck1vzE3HYVgYGLFq+DmMH2doL4UpTohZyUBhUNI\nC+DnRijVKmAN36ejWZtq5mzSBZNlwcYsbWP7t10F04kSjo5VuBTLwLSCeL5ACAVFqbzB79TiTe0r\niu8TsKotWQSxfRPTNwjZQUL22He5EwkhjLHjF90s1vAowu1C8W18FILOxBaEjJvA0gP1uYYsx6Ps\nJlFVn55A49xPgqyTQnErkyP7voLvqwQCHl6xMKGPme1b+I5PhzbWLS+//UUEoKlqPXW45wPl8qSv\nyhWzDKIDFIVkvlr+huztumtVWxgqAbU6btJa3egAIXDG5nSuBz26V4QiBMf1HKw9U7rl0mFp9cp7\nYzjo+wpZt4SqKhhGFHFYx410oOsd1TpWpYDpgkkgoKJZCsb/b+/eY+O46j2Af+e567WT1Em8QAJE\nrYuSe6s0JaKiedAWkUfVFpAqmltVvqgS4dFQtRISeVhREoRomzaEoiIEKkVCBZqAiyBQlCAQuSrC\nNAopvrQot036xHnZjl/7mNc55/4xu7Ozu7Prfdnr3fl9/mm9zs6ec3Z2/fvN75wzpoTuBdxbV8O9\ndT0StFQakHWkzWwld2aFn5+ZJNgUYnIXCgc7e9PtrFondhk8CbtgwxTGGaacaSiSCifFoOKa3A12\nhdum/GMUVzRTZunt+gG3opZIW4hF8tMDNZUGdy7CiXcX77JXwD+bTTWtvFfLS2qn3Wo4AHR2JsG5\nDJMnAZsDdi7h54J733nTbALZylh2avPYtAwx9C6uLnobS9JdsOQ0BItB+AqZ7vkxuxWqsgnV8ePH\nZ/XFw6jwZsCXxt17CSxdESn6t7S+ihBCqrN9+3YMDg7iC1/4Aj772c9CURTcfffdzW5WQ0kABBfA\n2ETR76LjE3A6OnBu+t3iJ8IN1KKmQIpNI4JYqfX7HoeJwKDQtCUs6BBI8YQXjKZYAoylIEmAFHTP\nIme8ZODKOSBlgnNZFhi1L3m/K7x6X8i9ap0J1LkMWWbgmUDTP7NGYgyK4QZqAu4ifACwrPwd84zx\n4rETQi4Klov6IAQWvptL3jmXvGqPpOQPonTuH7nXMzrAuYSOjjSShsBkSoKk51c3REEw+OZFN3i+\nfvk1GBl3p0WOJ9wIculCDg7mrc3JFhC5cLeoFr596zXNhm251c2JVC6I7Ri9CsWyMSn5NnvINsF3\n0gghlbxHU97aGochlc5/HzmXIMsCqZQ7HTUadcuCkYlp8KW5imt2MwmbcdiMQ84kSpfSl6GqDN38\n/YHJg/d8LuA4ChyuQdVNMCZ7VVUBDtvW8iq8NrdgCQO6VHoiWYolkDJkyGwhEJHcKY/ChJZMIXuW\ncK5Akljg56fwcxd0jicn0mCmjkjEPaIpsjvlOTAYRwysqN+FSZXfFesCZHyw5O8TiU5wRQNgIGUW\nb2SeYknoF5OILC5OHfxTjkd86+hk2wZj+Z1l3D0f/PfEsm0dtu0e1+BpCNsEMpcmEmwScBREtPz1\nlLLMczcTdhywMWBcNmBzGxHZhKIrkFUHNrdgw0LKVgFcW7L/9SqbUC1fvnzWXrhd0D2rCCFk/ti0\naZP3/6dOnUIymcSiRYvKPKM1Of8aBkvmAmDHUQGYkB0GfTp4WpSXEDB3m74pZxxdUhRQ4E27CWKa\nxRf8siGSydOwbffvoCNs6JkKUWF1A0DZK9smN4BMYF34t7QwCSuMT6fZBGyeWc9hRaCqKW9qkJ5Z\nL+RON8uFPKZIl73XU3adl/eaMySe/oDStlUoCgNjucQlO/0ySLbKw4WM8YRAyrGhSxo0zfYlVSJz\nnCk4gqFDjkGTdEAIJAwbo/ZFJJxOaLIOg7vJkzo9jej/vYqUnQbkGCb4GCITDiTBMWYncI261HtL\nTJ4Gt3PvWXQ8c28u/1S3THGj89KV7K76ZZNdXrCLW8K04bs/MlKpGCKR3DnsP1bHqDvFMp2OwpQT\nACJQFAeqyjLb0ueOPT58pugYNregSbr3HhtGFIqkQlalvErstDMJXZfyXvu9iTSiUQO6Vpyk+c+D\nhMmgSFNQbA5Vdc97ZTINZPa1tG01r39+44n8czqoOgwI2LaGSMRChxLDVd/HgjMZCeSSJ9vWoOu2\nlyzKJXai5MJNtv38U3bTfOY4Vkuksvf7zWt7UAysJAz4R1GS3LsMBCVs/vfANPK/cxIGg8mveuux\nSsmuWeNgYI4E1bfeTjHLXxCp19xvg0ECFU4FBGg6ICGEVCqRSGBgYAAPPPAAAODIkSN4/vnnsWLF\nCuzbtw9Ll5a5r0vLkXDhnVTejYtLyQaAJk97C8aBXHCR5kl0ID/ZKBT0u2lnHMKq7iIiL5O0SbKA\n4MHBuWVFoEFAQIAJBoflrkwXBvT+Y6RZCqriBtT+Bf/u8zhsURzsTjnjJdtXTirVCUAgGjXdBLT8\nzKrATURMIwJFydyPyNLBmIqOjmxVgsHiBnhmuqTJ04gpXRif7gD3VYhsbmHKcd8X09SRsCWYXIfJ\notA7Uui46vaPAUizNKxMdcriJlR5hvPJ159sdYmx3LkhK+77YtsaVNWBLUoHsNmEyDQjiMhRmNwI\nTM4YU7zE1LZVRKMGDGcSqqTNGMCmWAKdysL8x1LFG8YHvU2GEYWquuMoRP75o6q5JJMJBuGosCwd\nquqU/Rz5mXk3jwsmT45764BsR/E2dQhiWTokSXgXP8pd4C/cYI4xBYqSXU8VvDbPf1GFMRWsxE3D\nGZPhOCp03QJjUtF4K3KuYpr3vAqmXNqWBtu37Ym7mUbBdvyZvjHBwB0FjqNAljk4lxFLTkCkU6hs\nO5nqUULVQPWsryqcCug+RtMBCSGkEvv27fNmVbz11ls4fPgwnnrqKbz77rv41re+he985ztNbmHj\npBLpitdmWJb796NUAJcLVktXa0of262iqJoDJzNdx7Z1yHJwsFVOqWQKAGSZwXSMvD4UJkh+jLkb\nVESjBqbEJLQylaiSr6mwvM05stPiSnHfD8mbtjTTco1UKoZYLFU03dBfEWJMznufDZ7O29w8xRJ4\nc8KGyUzYvkA+m6zZtgZLkWEzDYrkeLu52bYGRXEw6aQAFMcXQXjB+5Ntv+PfwEQScDLJheOoUORJ\nIHcXMji2CkkXmbbnKnbZLhaORZDsFEpH2BUFsEk25U37ZKU2lpix+pjf93Rah677dqDLJAOFyZSs\ncG/cJEl4iYskwdtUpZzUtLvJRyLZCUlimGnxkj/psSwNssyhKAyO477fsiwCk9zCKY8zYUwuUVHL\nVavc86z4HZpyJryqsV9hzzTNnrFNphkJrJ4D+dVRkdneUUACe/s88MGessetFSVUhBBCWt57772H\nw4cPA3Bv5HvHHXdg/fr1WL9+PV588cUmt66xDDM4MDSMCCRJIBKxwHnx1eGswgCx8GJgpSxLz1yZ\n96+FUFFvaJFOd0CWmbd2RNPsiq/8u893g27DiEJRGDSt+gTPn0xV9TxW2fOEkAKTwqIdGQuu3CeT\nMUSjbjXHtnUgU8HyT5FKJjuLppoxwaABsB0Vpqkjb85WgESi07s/UVA7ADeBcAN2NfOz4iVYnMtg\nAQv0gsbHqqBaE4QxBZalIxo1IEkCjqMFJvO13vKHc3d6YLZ6k3tczvvMKAoPnDLLmVz0GYxETGha\n6UpTIDHzOsJC2eQ0N3VVR1dXsqJErhGylUoUTC80eAppUVwlcgo+b7Vc4CnXFqB8hbwRKKGaRY1a\nXzV68pWix87ol7H2njtqPiYhhLSTWCwXuJw6dQqf+9znvJ/b7ZYfjhWcIGQDW9vRyl51r3ZHsxnb\nU2HyUbguqRTGZDAmQ9etGbednvlYCgqDutkkyQKCNe58K6wECJG/BiiR6PQ2c/Dzrtz7muI4alWB\nuWFGEdFNyLIo2hofqKyiVKiRQW026LZtPbPlvxzYzkqPUyibDM2UzJdbf1jIcbTqE6o65O2QOUOV\ntZGvVU6pz3ThusPZMXt/CyihmiP1brde6Ip1AacvFSdaAE0HJISED2MMY2NjSCaTeOWVV7wpfslk\nEun03FyVnSszXnGf4deNvPpbjWoLBclkJ2KxVN3tTSQ6MFdJ1WxfBQ9SfN+oYNVU+QC3SpdyYlBV\nVlOiUqpC2mj+qWXVVnLmWuE0zrlUSwJcDX+i765Bq+75s59MAVZq9v4WUELVJI2oXgVVrgCqXhFC\nwueLX/wi7rzzThiGgYceegiLFi2CYRi4//77sW3btmY3r6FKbVM939VyBXqugnJSWqUVSFKZcuv/\nWlmjK9+zIZWeve9OSqjmgdmoXv3jxJHA39209b66jk0IIfPRbbfdhr/85S8wTRNdXe7S/Wg0iq9/\n/evYuHFjk1vXWKo1u9v/zhZ3V7hmtyJc5ne9hpC59dbEVayepWNTQjUPNaJ6FbRrIICS0wQBmipI\nCGltmqZB0/Knh7VbMgUAfGSs2U2oyVxM6SGEkFIsO3hb+EaghGqeq2b3pUqSr1LTBAHg9O2ln0fJ\nFiGEzA9sjta0R6NGxetzKtGstSOEkOYr3C2x3VBC1UbKJV+VJFtvDE8GPv4fo5fwj4AbDwOAs2Yl\nJVuEEDKH1OjsLi7PanQANN83DGhHlMOS+aLdP/+UUIVEJZUu7ZXLAIqTr0tlnjM+PIk3hk8WPf6R\n5YsAUGWLEEIabfGqXmDoX7P+Om2223woSbSKiswTlFDNQ48++iiGhoYgSRL6+/tx4403NrtJbaWa\nSlf3G28G/rvRN9z/HkfxFMOlt1eWZFEyRgghxToXxLBogYVkwT2KGknJ3KxXkgUErz4QiugqTMtB\nJGLCNCPQdQuaZs/bHc5kmQfuUlbq3lmaZkMIqeqtyFXVqfo5tZIluaKESpLErAa7hX1WFBmMzd5a\nllZS6ryrVGdnclY+U6Wm+8ZiqZp33ozFcmv7y30OVEWGkzk/dN1q6HbvN2+9tWHHKtRyCdWpU6fw\nzjvv4OjRozh//jz6+/tx9OjRZjcrNKpZ01XyGL97vaIpiEHJWKHxj1xX9Ni1Hatwy3++v6a2EUJI\nK9j8X/+Nnp4FGBmZbnZT5j0ap8rQOFWGxqkyYRunlkuoBgcHsWnTJgBAb28vJicnkUgkvG1ySWto\nRGLW1ZUMrJBN4E0c/9+6D58nri+r+jnWqpuqfg4lgoQQQgghraXlEqrR0VHccMMN3s+LFy/GyMgI\nJVQh1IikrOLXQvCGHWX99X+qfsrAX6t/mUbzVw+rmf5Z6b+t9b5rQdXIINd2rCr5u7fSZys6RjXH\nbEWUuBNCCCGN03IJVSExwz6sD+7ZPkctIYTMfx9pdgMIAeBOh5lPx2l3NE6VoXGqDI1TZcI0TrWv\nhGuSeDyO0dFR7+crV66gp6eniS0ihBBCCCGEhFXLJVQbNmzAiRMnAACvvfYa4vE4TfcjhBBCCCGE\nNEXLTflbu3YtbrjhBtx3332QJAn79+9vdpMIIYQQQgghISWJmRYhEUIIIYQQQggJ1HJT/gghhBBC\nCCFkvqCEihBCCCGEEEJq1HJrqNqRYRi4++67sWPHDqxbtw47d+4EYww9PT148sknoes6jh07hp/8\n5CeQZRnbtm3Dvffe2+xmN8yxY8fwox/9CKqq4uGHH8bKlStDMwbJZBK7du3C5OQkbNvGV7/6VVx/\n/fWh6P/rr7+OHTt24IEHHkBfXx8uXrxYcb9t28bu3btx4cIFKIqCxx57DB/60Iea3aWqBY3Bnj17\n4DgOVFXFk08+iZ6enrYeA1K9Rx99FENDQ5AkCf39/bjxxhub3aQ588QTT+Dvf/87HMfBl7/8Zaxe\nvbru742zZ8/iwIEDAICVK1fiG9/4RnM72SC1xhZhGqd64o+wjFO9cUpYxgmCNN3hw4fFPffcI154\n4QWxe/du8fvf/14IIcS3v/1t8bOf/Uwkk0mxZcsWMTU1JdLptLjrrrvE+Ph4k1vdGFevXhVbtmwR\n09PT4vLly2Lv3r2hGoPnnntOHDp0SAghxKVLl8TWrVtD0f9kMin6+vrE3r17xXPPPSeEEFX1+1e/\n+pU4cOCAEEKIl156STzyyCNN60utgsZg586d4sUXXxRCCPHTn/5UHDx4sK3HgFTv5ZdfFl/60peE\nEEKcO3dObNu2rcktmjuDg4Ni+/btQgj3b8dtt93WkO+Nvr4+MTQ0JIQQ4mtf+5o4efJkE3rXeLXG\nFmEZp3rjj7CMU71xSljGiab8Ndn58+dx7tw53H777QCAl19+GZ/61KcAAJ/85CcxODiIoaEhrF69\nGgsWLEA0GsXatWtx5syZJra6cQYHB7Fu3Tp0dXUhHo/jm9/8ZqjGoLu7GxMTEwCAqakpdHd3h6L/\nuq7jmWeeQTwe9x6rpt+Dg4PYvHkzAGD9+vUtORZBY7B//35s3boVQO7caOcxINUbHBzEpk2bAAC9\nvb2YnJxEIpFocqvmxs0334zvfve7AICFCxcinU7X/b1hWRaGh4e9Kl/2GK2untgiLONUb/wRlnGq\nN04JyzhRQtVkBw8exO7du72f0+k0dF0HACxZsgQjIyMYHR3F4sWLvX+zePFijIyMzHlbZ8O///1v\nGIaBr3zlK7j//vsxODgYqjG46667cOHCBWzevBl9fX3YtWtXKPqvqiqi0WjeY9X02/+4LMuQJAmW\nZc1dBxogaAxisRgURQFjDD//+c/x6U9/uq3HgFRvdHQU3d3d3s+t/l1QDUVREIvFAAADAwO49dZb\n6/7eGB0dxcKFC71/mz1Gq6sntgjLONUbf4RlnOqNU8IyTrSGqol+/etf46abbiq57kGU2NG+1OOt\namJiAt/73vdw4cIFfP7zn8/rX7uPwW9+8xssW7YMzz77LM6ePYv+/v6837d7/0uptt/tNB6MMezc\nuRO33HIL1q1bh9/+9rd5vw/DGJDKhfF9/+Mf/4iBgQH8+Mc/xpYtW7zHG/G90Q7j2ejYol3HCWhs\n/NGu49ToOKVdx4kqVE108uRJ/OlPf8K2bdvwy1/+Et///vcRi8VgGAYA4PLly4jH44jH4xgdHfWe\nd+XKlbxpQq1syZIl+OhHPwpVVfHhD38YnZ2d6OzsDM0YnDlzBhs3bgQArFq1CleuXEFHR0do+u9X\nzbkfj8e9q1m2bUMI4V0ta3V79uzBihUr8NBDDwFAKMeAlBZ0PvT09DSxRXPrpZdewg9+8AM888wz\nWLBgQd3fGz09Pd50Jv8xWlm9sUVYxqne+CMs41RvnBKWcaKEqomeeuopvPDCC/jFL36Be++9Fzt2\n7MD69etx4sQJAMAf/vAHfOITn8CaNWvwz3/+E1NTU0gmkzhz5gw+9rGPNbn1jbFx40b87W9/A+cc\n4+PjSKVSoRqDFStWYGhoCAAwPDyMzs5ObNiwITT996vmfd+wYQOOHz8OAPjzn/+Mj3/8481sesMc\nO3YMmqbh4Ycf9h4L2xiQ8vzfD6+99hri8Ti6urqa3Kq5MT09jSeeeAI//OEPcc011wCo/3tD0zRc\nd911OH36dN4xWlm9sUVYxqne+CMs41RvnBKWcZJEO9TZ2sDTTz+N5cuXY+PGjdi1axdM08SyZcvw\n2GOPQdM0HD9+HM8++ywkSUJfXx8+85nPNLvJDXPkyBEMDAwAAB588EGsXr06NGOQTCbR39+PsbEx\nOI6DRx55BL29vW3f/1dffRUHDx7E8PAwVFXF+973Phw6dAi7d++uqN+MMezduxdvv/02dF3H448/\njg984APN7lZVgsZgbGwMkUjEC5B7e3tx4MCBth0DUptDhw7h9OnTkCQJ+/fvx6pVq5rdpDlx9OhR\nPP3007j22mu9xx5//HHs3bu3ru+Nc+fOYd++feCcY82aNdizZ08Te9lYtcQWYRqneuKPsIxTvXFK\nWMaJEipCCCGEEEIIqRFN+SOEEEIIIYSQGlFCRQghhBBCCCE1ooSKEEIIIYQQQmpECRUhhBBCCCGE\n1IgSKkIIIYQQQgipESVUhBBCCCGEEFIjSqgIIYQQQgghpEaUUBFCCCGEEEJIjf4fOzUv40k2bi8A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28a0595e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the trace\n",
    "pm.traceplot(tank_model_trace[tank_model_burn_in:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So the mean here was 383, and the median was 323."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Linear Regression and Robust Linear Regression and the GLM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For the following examples I've combined content from different sources and tutorials on the web. Some of the sources I used were:\n",
    "* [PyMC3 documentation: linear regression](https://pymc-devs.github.io/pymc3/notebooks/GLM-linear.html)\n",
    "* [PyMC3 documentation: robust linear regression](https://pymc-devs.github.io/pymc3/notebooks/GLM-robust.html)\n",
    "* [Connect The Dots. Least Squares, Linear Regression, and Bayesian Regression](http://srome.github.io/Connect-The-Dots-Least-Squares-Linear-Regression-and-Bayesian-Regression/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Theory of a basic Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "$$\\vec y=\\bf X\\vec\\beta+\\vec\\epsilon$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "With $\\epsilon_i\\sim \\mathcal{N}(0,\\sigma^2)$ = independent Gaussian noise on the measurement error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For [ordinary least squares (OLS)](https://en.wikipedia.org/wiki/Ordinary_least_squares) regression, we can quite conveniently solve this (find the $\\beta$ values) using the Maximum Likelihood Estimate (MLE) which has a closed analytical form:\n",
    "$$\\hat\\beta=({\\bf X}^T{\\bf X})^{-1}{\\bf X}^T\\vec y$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's generate some toy data to play with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.058449</td>\n",
       "      <td>-18.123823</td>\n",
       "      <td>-27.240722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.808361</td>\n",
       "      <td>-25.752389</td>\n",
       "      <td>-42.369111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.949386</td>\n",
       "      <td>76.868235</td>\n",
       "      <td>43.969463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.599452</td>\n",
       "      <td>29.425615</td>\n",
       "      <td>-6.773289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.601864</td>\n",
       "      <td>38.229574</td>\n",
       "      <td>2.025846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y          e\n",
       "0   2.058449 -18.123823 -27.240722\n",
       "1   5.808361 -25.752389 -42.369111\n",
       "2  13.949386  76.868235  43.969463\n",
       "3  15.599452  29.425615  -6.773289\n",
       "4  15.601864  38.229574   2.025846"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_glm_data(n=20, a=1, b=1, c=0, latent_error_y=10, error_pdf='norm', nr_outliers=3):  \n",
    "    ''' \n",
    "    Create a toy dataset based on a very simple linear model \n",
    "    that we might imagine is a noisy physical process\n",
    "\n",
    "    Model form: y ~ a + bx + cx^2 + e\n",
    "    '''\n",
    "\n",
    "    ## create linear or quadratic model\n",
    "    ldf = pd.DataFrame({'x':stats.uniform.rvs(loc=0, scale=100, size=n)})\n",
    "    ldf['y'] = a + b*(ldf['x']) + c*(ldf['x'])**2 \n",
    "    ldf = ldf.sort_values('x').reset_index(drop=True)\n",
    "    \n",
    "    if error_pdf=='norm':\n",
    "        ldf['e'] = stats.norm.rvs(loc=0, scale=latent_error_y, size=n)\n",
    "    elif error_pdf=='cauchy':\n",
    "        ldf['e'] = stats.cauchy.rvs(loc=0, scale=latent_error_y, size=n)\n",
    "    elif error_pdf=='outliers':\n",
    "        ldf['e'] = stats.norm.rvs(loc=0, scale=latent_error_y, size=n)\n",
    "        for i in range(-nr_outliers, 0):\n",
    "            ldf['e'].iloc[i] = np.abs(ldf['e'].iloc[i])*latent_error_y*10\n",
    "    else:\n",
    "        raise Exception('{} is not known as a valid value for error_pdf.'.format(error_pdf))\n",
    "\n",
    "    ## add latent error noise\n",
    "    ldf['y'] += ldf['e']\n",
    "\n",
    "    return ldf\n",
    "\n",
    "n=20 # 10000\n",
    "glm_df = generate_glm_data(n=n, a=5, b=2, latent_error_y=30)\n",
    "glm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So we generate data with an **intercept** of $a=5$ and an **inclination** of $b=2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.900690</td>\n",
       "      <td>81.038623</td>\n",
       "      <td>-9.762758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.534339</td>\n",
       "      <td>64.796675</td>\n",
       "      <td>29.295394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.058449</td>\n",
       "      <td>-25.752389</td>\n",
       "      <td>-58.790104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.537339</td>\n",
       "      <td>29.248216</td>\n",
       "      <td>-32.430948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.809363</td>\n",
       "      <td>76.993812</td>\n",
       "      <td>-12.541147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.590782</td>\n",
       "      <td>128.940051</td>\n",
       "      <td>4.062235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.990985</td>\n",
       "      <td>184.501137</td>\n",
       "      <td>55.568346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x           y          e\n",
       "count  20.000000   20.000000  20.000000\n",
       "mean   42.900690   81.038623  -9.762758\n",
       "std    29.534339   64.796675  29.295394\n",
       "min     2.058449  -25.752389 -58.790104\n",
       "25%    17.537339   29.248216 -32.430948\n",
       "50%    36.809363   76.993812 -12.541147\n",
       "75%    63.590782  128.940051   4.062235\n",
       "max    96.990985  184.501137  55.568346"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Doing it with a pre-packaged scikit-learn solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.9341582334903933, 1.9573759850209729)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X, y = glm_df['x'].values[:, np.newaxis], glm_df['y']\n",
    "lr.fit(X, y)\n",
    "lr.coef_.squeeze()\n",
    "lr.intercept_, lr.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2890a536a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAIVCAYAAAAnNSfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAQQ4Q0AAAAAQAAR3gAAAAAABND/Ayw/zOuTzHvzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28a0387dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.plot(glm_df['x'], glm_df['y'], 'bo')\n",
    "plot_x = np.linspace(-5, 105, 100)\n",
    "ax.plot(plot_x, lr.intercept_ + lr.coef_[0]*plot_x)\n",
    "ax.set_xlabel(r'$x$')\n",
    "ax.set_ylabel(r'$y$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Doing it with PyMC3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Probabilistic Reformulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Rather than using the formulation above:\n",
    "$$\\vec y=\\bf X\\vec\\beta+\\vec\\epsilon$$\n",
    "the probabilistic reformulation looks as follows:\n",
    "$$\\vec y\\sim\\mathcal{N}(\\bf X\\vec\\beta,\\,\\sigma\\cdot {\\bf I})$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using ADVI...\n",
      "Average Loss = 104.85:   9%|▉         | 18885/200000 [00:01<00:13, 13794.97it/s]   \n",
      "Convergence archived at 19200\n",
      "Interrupted at 19,200 [9%]: Average Loss = 23,194\n",
      "100%|██████████| 3500/3500 [00:04<00:00, 707.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.4 s, sys: 104 ms, total: 5.5 s\n",
      "Wall time: 11.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with pm.Model() as glm_normal_model:\n",
    "    # Define priors\n",
    "    glm_normal_sigma     = pm.HalfCauchy('sigma', beta=10, testval=1.)\n",
    "    glm_normal_intercept = pm.Normal('a', 0, sd=20)\n",
    "    glm_normal_x_coeff   = pm.Normal('b', 0, sd=20)\n",
    "\n",
    "    # Define likelihood\n",
    "    glm_normal_likelihood = pm.Normal('y', mu=glm_normal_intercept + glm_normal_x_coeff * glm_df['x'], \n",
    "                                      sd=glm_normal_sigma, observed=glm_df['y'])\n",
    "\n",
    "    # Inference!\n",
    "    glm_normal_trace = pm.sample(3000, njobs=2) # draw 3000 posterior samples using NUTS sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "a:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  -1.640           10.381           0.279            [-22.713, 18.392]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -22.253        -8.257         -1.671         5.059          19.242\n",
      "\n",
      "\n",
      "b:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  1.937            0.213            0.005            [1.499, 2.336]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  1.514          1.800          1.943          2.075          2.352\n",
      "\n"
     ]
    }
   ],
   "source": [
    "glm_normal_model_burn_in = 100  # throw away the first 100\n",
    "# summarize the trace\n",
    "pm.summary(glm_normal_trace[glm_normal_model_burn_in:], varnames=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28907322e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAEUCAYAAAAspncYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f287b471a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "pm.traceplot(glm_normal_trace[glm_normal_model_burn_in:], varnames=['a', 'b'])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2879fb9128>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA98AAAIVCAYAAADI90zWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIiKiImP4JiIiIiIiIiqy/w9tg4j7F0ogAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f287a05c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.plot(glm_df['x'], glm_df['y'], 'bo')\n",
    "plot_x = np.linspace(-5, 105, 100)\n",
    "v1 = glm_normal_trace[glm_normal_model_burn_in::50]\n",
    "ax.plot(plot_x, v1['a'] + v1['b']*plot_x[:,np.newaxis], c='blue',alpha =0.1, linewidth=0.5)\n",
    "ax.plot(plot_x, v1['a'].mean() + v1['b'].mean()*plot_x, c='blue',linewidth=2)\n",
    "ax.plot(plot_x, lr.intercept_ + lr.coef_[0]*plot_x, c='red',linewidth=1)\n",
    "ax.set_xlabel(r'$x$')\n",
    "ax.set_ylabel(r'$y$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a/intercept: real: 5.00, sklearn: -2.93, pymc3: -1.03\n"
     ]
    }
   ],
   "source": [
    "# print('a/intercept: real: {:.2f}, sklearn: {:.2f}, pymc3: {:.2f}'.format(5.0, 0.66744, 0.873))\n",
    "print('a/intercept: real: {:.2f}, sklearn: {:.2f}, pymc3: {:.2f}'.format(5.0, lr.intercept_, v1['a'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b/inclination: real: 2.00, sklearn: 1.96, pymc3: 1.93\n"
     ]
    }
   ],
   "source": [
    "# print('b/inclination: real: {:.2f}, sklearn: {:.2f}, pymc3: {:.2f}'.format(2.0, 1.87342, 1.870))\n",
    "print('b/inclination: real: {:.2f}, sklearn: {:.2f}, pymc3: {:.2f}'.format(2.0, lr.coef_[0], v1['b'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Robust Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.116381</td>\n",
       "      <td>-20.144807</td>\n",
       "      <td>-29.377570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.689329</td>\n",
       "      <td>12.043284</td>\n",
       "      <td>-0.335373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.879602</td>\n",
       "      <td>-11.461403</td>\n",
       "      <td>-28.220607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.396319</td>\n",
       "      <td>7.398951</td>\n",
       "      <td>-12.393687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.447570</td>\n",
       "      <td>48.270514</td>\n",
       "      <td>14.375375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y          e\n",
       "0   2.116381 -20.144807 -29.377570\n",
       "1   3.689329  12.043284  -0.335373\n",
       "2   5.879602 -11.461403 -28.220607\n",
       "3   7.396319   7.398951 -12.393687\n",
       "4  14.447570  48.270514  14.375375"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=50 # 10000\n",
    "glm_outliers_df = generate_glm_data(n=n, a=5, b=2, latent_error_y=30, error_pdf='outliers', nr_outliers=5)\n",
    "glm_outliers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.015932</td>\n",
       "      <td>966.349825</td>\n",
       "      <td>857.317960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.675205</td>\n",
       "      <td>3431.670154</td>\n",
       "      <td>3408.339572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.116381</td>\n",
       "      <td>-20.144807</td>\n",
       "      <td>-79.624870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.328193</td>\n",
       "      <td>50.611069</td>\n",
       "      <td>-21.898572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.831566</td>\n",
       "      <td>99.020017</td>\n",
       "      <td>-2.804396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.179201</td>\n",
       "      <td>159.804638</td>\n",
       "      <td>25.821095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.875003</td>\n",
       "      <td>17675.576360</td>\n",
       "      <td>17470.854074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x             y             e\n",
       "count  50.000000     50.000000     50.000000\n",
       "mean   52.015932    966.349825    857.317960\n",
       "std    29.675205   3431.670154   3408.339572\n",
       "min     2.116381    -20.144807    -79.624870\n",
       "25%    27.328193     50.611069    -21.898572\n",
       "50%    51.831566     99.020017     -2.804396\n",
       "75%    79.179201    159.804638     25.821095\n",
       "max    99.875003  17675.576360  17470.854074"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_outliers_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1442.1865375112147, 46.303819798417791)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "X, y = glm_outliers_df['x'].values[:, np.newaxis], glm_outliers_df['y']\n",
    "lr.fit(X, y)\n",
    "lr.coef_.squeeze()\n",
    "lr.intercept_, lr.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2879ff6908>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAIVCAYAAACp726UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2879d526d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.plot(glm_outliers_df['x'], glm_outliers_df['y'], 'bo')\n",
    "plot_x = np.linspace(-5, 105, 100)\n",
    "ax.plot(plot_x, lr.intercept_ + lr.coef_[0]*plot_x)\n",
    "ax.set_xlabel(r'$x$')\n",
    "ax.set_ylabel(r'$y$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using ADVI...\n",
      "Average Loss = 324.13:   8%|▊         | 16695/200000 [00:02<00:27, 6628.52it/s]\n",
      "Convergence archived at 16800\n",
      "Interrupted at 16,800 [8%]: Average Loss = 1,459.3\n",
      "100%|██████████| 3500/3500 [00:07<00:00, 443.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.29 s, sys: 100 ms, total: 9.39 s\n",
      "Wall time: 18.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with pm.Model() as glm_outliers_model:\n",
    "    # Define priors\n",
    "    glm_outliers_sigma     = pm.HalfCauchy('sigma', beta=10, testval=1.)\n",
    "    glm_outliers_nu        = pm.Uniform('nu', lower=1, upper=100)\n",
    "    glm_outliers_intercept = pm.Normal('a', 0, sd=20)\n",
    "    glm_outliers_x_coeff   = pm.Normal('b', 0, sd=20)\n",
    "\n",
    "    # Define likelihood\n",
    "    glm_outliers_likelihood = \\\n",
    "        pm.StudentT('y', mu=glm_outliers_intercept + glm_outliers_x_coeff * glm_outliers_df['x'],\n",
    "                    sd=glm_outliers_sigma, nu=glm_outliers_nu,\n",
    "                    observed=glm_outliers_df['y'])\n",
    "\n",
    "    # Inference!\n",
    "    glm_outliers_trace = pm.sample(3000, njobs=2) # draw 3000 posterior samples using NUTS sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The only thing we've changed is in the `glm_normal_likelihood = pm.Normal` $\\rightarrow$ `glm_outliers_likelihood = pm.StudentT` line. We've exchanged the normal distribution ($\\mathcal{N}$) with the [Student's T](https://en.wikipedia.org/wiki/Student's_t-distribution) distribution. The normal distribution has \"thin tails\", while the Student's T distribution can have \"fat tails\" depending on the parameter $\\nu$. The larger the parameter $\\nu$ the closer the Student's T distribution is to the normal distribution. So a Student's T distribution with a small $\\nu$ can \"support\" outliers much better than a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "a:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  -2.150           8.273            0.161            [-18.083, 14.004]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -18.154        -7.875         -2.218         3.394          13.951\n",
      "\n",
      "\n",
      "b:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  1.963            0.175            0.003            [1.628, 2.320]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  1.619          1.845          1.960          2.080          2.315\n",
      "\n"
     ]
    }
   ],
   "source": [
    "glm_outliers_model_burn_in = 100  # throw away the first 100\n",
    "# summarize the trace\n",
    "pm.summary(glm_outliers_trace[glm_outliers_model_burn_in:], varnames=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286ed01390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAEUCAYAAAAspncYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmcXFWZ+P29t/at9y3d2ReyB0gAWUUUUBZFECViRkXf\nV+YFHZlxmZHxHR2HQXF31FFHB53XNcLwU0ABWQwCAoEQQvalk+703tW171V3ef+orr2qu7rTnUon\n5/v55JOuurfOec6951Y9z3mWI+m6riMQCAQCgUAgEAgEgikj11oAgUAgEAgEAoFAIJirCINKIBAI\nBAKBQCAQCKaJMKgEAoFAIBAIBAKBYJoIg0ogEAgEAoFAIBAIpokwqAQCgUAgEAgEAoFgmgiDSiAQ\nCAQCgUAgEAimiTCoBAKBQCAQCAQCgWCaCINKIBAIBAKBQCAQCKaJMKgEglOQBx54gGuuuYarr76a\nD3zgAwwMDNRaJIFAIBAIxO+TQFAGSdd1vdZCCASCHB6Ph7e85S08+eSTdHR08LnPfQ5Zlvn3f//3\nWosmEAgEgjMY8fskEJRHeKgEglOM5uZmduzYQUdHBwDnnXcefX19NZZKIBAIBGc64vdJICiPsdYC\nCASCQlRV5T/+4z945plnUFWVSCTCkiVLai2WQCAQCM5wxO+TQFAe4aESCE4x/vjHP/LMM8/wi1/8\ngieeeIK/+7u/q7VIAoFAIBCI3yeBoALCoBIITjE8Hg9dXV00NTXh8/l47LHHiEQitRZLIBAIBGc4\n4vdJICiPMKgEglOM66+/Hr/fz1VXXcWnPvUp7rrrLoaHh/nKV75Sa9EEAoFAcAYjfp8EgvKIKn8C\ngUAgEAgEAoFAME2Eh0ogEAgEAoFAIBAIpokwqAQCgUAgEAgEAoFgmgiDSiAQCAQCgUAgEAimiTCo\nBAKBQCAQCAQCgWCaCINKIBAIBAKBQCAQCKaJsdYCnChud6jWIggEAoFgirS2umotwqwzE79PjY12\nfL7oDEhz6nImjBHOjHGeCWOEM2OcZ8IYofw4p/P7JDxUAoFAIBCcohiNhlqLMOucCWOEM2OcZ8IY\n4cwY55kwRpi5cQqDSiAQCAQCgUAgEAimyZwP+RMIBNUz5Imwr8dH32gYbzCOomoYZIl6p4W2BhsL\nO1ysmF+Pw2qqtagCgUAgqIAn5qMvPMDappWYDOL7WiCoNcKgEghOczRN54U9Qzzz2gC9w5PndEgS\nLOus501r2nnTmnacNvFjLRAIBKcSxwI9AHgTftrtrbUVRiAQCINKIDidOToY5H8eP0DfaBhZkjhn\neQtnL29mWVc9LfVWzCYDiqLhjyQZ9kQ4NhRiX4+XIwMBjgwEeGDbES7b0Mm1Fy6i0WWp9XAEAoFA\nkI+u16RbTdc50h+gvclOvcNcExkEglMJYVAJBKchuq7z5Kv9PPDnI6iaziXrO7jxsqU01VlLzjWb\nDLQ12GhrsLFhWQvvumQxI4EgL+0f5PnX3Ty9o5/ndg1y9QULeefFizCdIYmqAoFAcKpTG3MKfMEE\n3lAcbyjOhWs6aiSFQHDqIAwqgeA0Q9N0fvGng2x7fZA6h5mPvXMNaxY3TfiZ0egYO0Z2ccB3iP7Q\nEHE1nj6wEuolK6mohScGdvPiA+184KKLOGfxgpMwEoFAIBCciugz5BnTdR1JkmakLYGglgiDSiA4\njdA0nf96ZC/b94+yoM3JXe89e8JQvYHwEH84+id2je0FQEKi3dFGi3UxJoOZaCpKIBFkTPKi2wJE\n6ePHR1/FcbSJixeczfnzzqHLOe9kDU8gOGl89atfZceOHSiKwu2338769ev57Gc/i6qqtLa28rWv\nfQ2zeW6HOiVTKr5wgrYGm1Bqp4Gm6chyba+bXuSjOhVkqpbD/X48wTgXrG5HPg3mn67r6OjIkiig\nfSYiDCqB4DRB13V+8eQhtu8fZcX8ej5589nYreUf8WAyxEOHH+XVkdfR0VlSt5DLui5iXctqHCZ7\nyfmqptIfHuSlnv280LubsM3Nk31/5sm+P9Pp6ODCeedxSecFWI2lIYUCwVzjpZde4vDhw2zduhWf\nz8eNN97IRRddxK233so111zDN7/5TR588EFuvfXWWos6LTRdA+DAcR/RhIJRlmmuF8/uVAiEE+w/\n7mPpvDraGku/M2uBL5TgYJ+P5Z31tDTYai3OpHiC6UiIY0NBlnXWZ9+PKwncMQ8djjZM8syrqSlF\nxWiQZ3wRYa/nAHElznkd585ou4K5gTCoBILThEde6GHbzgEWtDkrGlO6rvPS0Ks8dORRokqMBc5O\nrl/6dtY2r5rwx8UgG1hUt4BFGxbw7lVv42d/2sMrA3uxtA0zzCgPHXmUx3ue5i3zL+GKBZdiL2OU\nCQRzhfPPP58NGzYAUFdXRywW4+WXX+Zf//VfAbjiiiu4//7756xB9bp7D5qmoiTS3uV4UqmxRHOP\nEV8MgGFvdFYMKk3X8YcSNDgtE3qc8kPvRn3RrExzwaDK4PbHCgyqvtAAgUQAWZIqRkAomkJfaIAO\nRzu2KSzkpRSVHYfc1NvNrJ4kFL4YVVPZ6zlAh6OdVlwlx+NKfErtCWYOXdfRdA2DXLscb2FQCQSn\nAdteH+B3zx+jpd7K37+vvDE1FvPw6wMPccB3GIvBzPvOejeXdV045fAEi9nAx67bwIqdzfz6qcNo\ncoJzLozSp7/BH3ue4i8DL/Lu5ddxYccmEUYkmJMYDAbs9rSS/OCDD/LmN7+Z559/Phvi19zcjNvt\nrqWI08IXSqCoGpqmFrzf5w7T1eqclT73eQ4iSzKrmlbMSvvFJNUkCTWJyzw748mgaWlDZrbC6wbd\nEfrHwsxrctDRbCeRUqmzVxdiWqtCFTNFTEkbq0k1WfGcocgonpiXSCrKupbV1bedSM/9QLRy25UI\npcIk1STHg32sZcmUPy+YGsmUiixLGA2T6yiHfN2EkiHObdtQM6NKGFQCwRxnz1EPP3/iIE6biX+4\n5RwanIU5UylN4aneZ3mi92lSmsLa5lVsXnkjTdbGafcpSRJv3Tifhe0uvv9/drPzeTOXnfMe5q10\n88TxZ/jF/t+yZ2w/W1a/d0qrhwLBqcRTTz3Fgw8+yP3338/VV1+dfb/ahPzGRjvGGaiK2dpauhpe\niVFflHqnBYuptN99fQEAXM1p74WaynkxptLHVDBEdUCdtP2Z6v/53lcAeFPHObO64e2AL4Ymy7gc\n5gllVzQVWZKyC1fVjnM4kKDOZSOS0ugeDgNw6YImDOMGnCuavneN9XZaG9JtjgQTqJKM026itdWF\nquk8//oAdleKxiaJ5c2LpzvcEjSDgbpQsuyYqh1jnStQ9jP1STtxxUCDw05rS/m2PBhwGWxYjBNf\n/2JM1gR13tiU5MxgiKqMqJWfmcw9ma1naarouk44lsJVpSFejvyxROMpDvT4OGtR40nZn/LZ1/oB\nuHzj/EnPPRhVcFmt1DVZsJum7p2diXsmDCqBYA4z7I3yg9/vxSDLfPLmDXQ05UJPNF1jl3svv+/+\nI+6Yhzqziy3Lr2dT+zkz5jla3lXP//vB8/jOg2/w3Otu1vob+fQ1n2TrkQd53b0bd2yMO87+CA2W\n+skbEwhOIZ577jl++MMf8pOf/ASXy4Xdbicej2O1WhkZGaGtrW3SNnzjIVgnQmurC7d78g25AYLR\nJPt6vJiNBjaeVbrZazCUViSNpvH/4yniKYWWOltVfei6zoA7QnO9FZulOvUhFEz3NVH7Uxljtf2N\nuINYDOUVydGoG7vRjtPsmHY/Hm+UaCJFMBSjo85ccTuJV4d3ArCobiFrFi2uepyjYyGiicJQzJGR\nICZj2jDLjNOrRbCl0m36/VGC4QRKUsHtDhGNKwRDMXaNHmBFvB67Uj9jOUlefyw7n/LHNKX5Ov75\n4jaCwRgJJYEpFcWtl2/r2PAgAGEpjttc/dwJRpJl5a6GvtAIoUj6s+FEhFhQKzhezVyvREpN4Y37\naLW3zFhRiyFPhN6REIvaXcxrnvpcL76XB3p9+CMJAsEo65Y0T/r5lKZgko0MjEVocJpxWKdmhE12\nnxRNQdM1zAYz+7vHiCcVFpoXYzNOLYS53JydjoElSpEIBHOUaFzhu//7BrGEwofesZJlXWmjJamm\neHXkdb68/dv8ZM/PGYt5uWL+pfzLhZ/mvI5zZzwMr6nOyue2bOTsZc3s7fHxgweOcuvSv+GyrosY\nCA/xrdd+iD8RmLwhgeAEOXToEE899RQAwWBw2u2EQiG++tWv8qMf/YiGhgYALr74Yp544gkA/vSn\nP3HZZZeduMAzTCqVVvCSijrJmWmKK8RNxlggTv9YmH09vinLdqqQ0hSOB/s54D10Qu3keykzYWTF\nx1/ZP8KIP60U9gaPT6n9RtfUPfvF3+1ysYZXo02Ap8/k8s5U+fZqGImMZP8ejYzNaNvdgR76QgOM\nRmeu3WAk7UEcC8xwblcVl7w32Meu0d14wkH6RkPsPuqZWRmA10d384Y7XaE4llRqHuo6qx6qe++9\nl127diFJEnfffXc2yRfgr3/9K9/85jcxGAy8+c1v5s4778wei8fjXH/99dxxxx3cdNNNsymiQDAn\nyZRHH/JEufr8BZy/ppmdo7vZOfoGezz7SahJJCTOb9/INYvfSrtj8tX0E8FqNvKJ92xg6zNHePLV\nPr788518/KYrcCyy8XjvM3x354/59Hl3YjPOnURpwdziZz/7GY8++ijJZJIrr7yS//zP/6Suro47\n7rhjym398Y9/xOfzcdddd2Xf+8pXvsLnP/95tm7dSmdnJ+9+97tnUvyKeAIx9vZ4WbWwAV/CTyAR\nZFnD4hlpe6oGVUpJG2wptTqDbSbpd4cxGWXaqywAUWnZSNVmRvaCK1fUma7r7Hbvx51UCCpJ2qdR\nIMJoKDeCvF4laQIDSR8/ZeLFs2RKxVwmNLRaVF3FIM1svoqiKSSUxLQ+2z0QwGCQWNxRV/Gc6Srd\nvri/4LVU5EVKaSdW2CWayRvTyud2KarG/l4fnc2OSSty6rpOLKFk738m3+9EmcparHvcMAylIoCE\nrutEU7GqwvH8iQCBRBCY/bDCmWTWDKrt27fT29vL1q1b6e7u5u6772br1q3Z4/fccw///d//TXt7\nO1u2bOHtb387y5cvB+AHP/gB9fUiREggqMT/PtvNG8dGWbQqSqjtJf7xuZ+R1FIAtFibeHPXBi7q\nPJ92e2nYz2whyxLvv3IFHU02fvnkYb7269f56HXncMX8JH/uf56f7f0Nt2/4kNijQzArPProo/z2\nt7/lQx/6EACf/exn2bx587QMqltuuYVbbrml5P2f/vSnJyznVNnT7SEUTeILJuiJ9wKQUDuxGMz0\nDAcZ9kY5f1UbhhJ3RGWSioau60xVzZ+uN0BRNY4NBZnf6syGrE2Vfnc6j6hagwrAE/OiaEp2QSmc\njDAam/1iIiktRVyNE1DSXtKxYJyWuvJKsKZrZb8Ty+nAU778euWXmXCw5V31tNRP3eDzxD0cjx+h\nzdwFdADpapGD7vAJqcG9wf6Kx1RNK5nndZZcaJY7kDZK8g0qXdfZMfI6NpONtc2rpi1Xt/9YwWup\nyIqezAjMhOC32JpZ4OosOHaoz48nHqfRZSppN4M/lCAST3F4wE9zfceEfY36YhwbznnotSlMnEgq\nSlxJ0GwrzbHWdA1PagSTuXq9IjOagOpln2cYY6IZl6m+oKpjMUd8RwFIac2Y5Lmz19+sGVQvvvgi\nV155JQDLli0jEAgQDodxOp309fVRX1/PvHnpcpiXX345L774IsuXL6e7u5sjR47wlre8ZbZEEwjm\nNI+/dpAnB57Bdm4/o4YUo25os7VwbtsGzm1bz3xnZ02r612xcT4tDTZ+8Ls9/Ojhfdxw6VpWNY6w\nx7OfPxz9E+9c9o6aySY4fXE4HMh5ypYsywWv5zr5z3TGsBn2pnO0EkkVu1Wu2uPUO5LOF1hZeSG/\nLNNd6B71x0CKomo6y7umtljqifkIJkOouqGsN8Qb9xFKhllUt6Dk2LFA2gDNGFTVhPkFIklSKXXy\nsuMTXgup4LAvlChrUHnjPo76e1jeuLQkz7Sc8TqVy6/pOgf7KodmusdDEXuGQjispqpz4jL4kum2\nQ0rOc7O/x4fFZmZeg3XCDeUnIlPhDwoNyFhCYVf3GB1NdhZ31CFhRNWVEoU7pkZIqinM4wVJgsn0\nXI+lYhSj63rF38q4EsefCNBubyt7TvFGxLIk4Y8ksZmNpNRUSUGUpJpC1RRGIiMlBpU3FMcTi9Ho\nMlHZtzo5sYSCxWxgwO9B0cEopWXQxlO9dF0npSoktSROswNVU9F0rUDW/Z6DADRYSr8cgik/QcWH\nGouyic6S4+XIXKaQ4qcFO/1+Ly0mc1mDaiwQm5bH9GSGfU7ErP3ajI2N0diYs3CbmpqyZWbdbjdN\nTU1lj91333380z/902yJJRDMWeJKnJ/seJCHvfdj6jyG3WLi7Yveyt0X/D3/cuFneNeyd7DA1XVK\nlCpfv7SZu/9mE811Vn7/fC/mgfNosTXzeO8zvD66u9biCU5DFi5cyPe+9z2CwSB/+tOfuOuuu1i2\nbFmtxZoxpvpYF69Kz4TSkQn5M1VRxrhQlvHPp9IK3BH/sayiC2kPViWOBXvxxDzEtfIFPo76e3BH\nx04o5Cr/Wu3v9XJkMMCR/sp5n/5EAE9yNPu6+NZMEqxHPKmwr8dLr28IoGzejKqWMagmuIexhII3\nlM6VUTSFw2N9hOITeU2k8XM1dh4ZZTgyWnINM1Xiynk45HH1MWPEK6pGYjx3L7OvWXovrTg7j4wQ\njZ9YSFxwvMz5sDdKStE4OhjIei0zpLQUw8k+9nj2Z9/LbGKdGU8+faOFn4d0SGgoGWaf9xD9oUEC\nyWDZ8u3mIoMpklBw+2McHw0xGBme+gDHkYCB8BB7xvZPeL91XSeQCHHI142mawQjSXZ1j3Ggb4yB\n+HHcycGCc2NKnNePDfOH3a+xz3uIcDLCztE32OXekz0vEElmn0VVL5MXKI3fa738vdR0rewcAtAp\nfMaTSorB8HDB+UcGAuzr8ZZtOxpPZb9/SuTKe7oSKa1mBtZJW76rZoC/+93vOOecc1iwoHSlSSA4\nkzke7Oeel77FzsB2SFl5W9u13HvJP/OuZe+gyznvlDCiipnf6uTzHzqPJfPqeHmPD/vQRZhkE786\n8L/j8dECwczxL//yL9hsNtrb23n44Yc5++yz+cIXvlBrsWaFcDxRpExK7OvxcmQgZwQUJ6LPpIox\n1e+bzNmaDoFEEH/czyHvEUaibsb8MV7YNchYoNSDAOS5KSYeQSCSIBApVHzjKZV+d4RkqnLeVCSe\nYvv+EQbyrqema7gDlSs0HvEdxZcaQ62gWJYjkSfD8ZEwwWiSQU8EKG+AKeNuBbnAM5lW+IPJUPa6\nhJJhjgV66R7M3fuRxBAj0RF8SnF4o57Xlk5SSxtcQcVLf2iAo/6egrNHfFF2dg/z2kE3KUVjz1EP\nPZlQsnGxMobuiDdKUPERTPnRNJ2UorJ9/whPHX6NA4H9DHsj2X59cT/KJAawBiRS5c/JFF1JFN1X\njXFDLi9PLj+cUtO1AreX2x9D0RSied6r7kAPB72Hs224AxH2jx0pkSGaKny+lDxlX5lmnl40oRBP\nKgyFh4kr8YJrlD/7NU3n5f0jPH9sN8FEEF88QCSeDvl3B9PXOa7lxqTpOnvH9nPAd5C4FkPTNOJq\nofzRuML+Xi/97sj4ZwqNF13Xs56uSrzh3suu0d0V8xTzP348OMBgeIi+UOUQz8wkUzWNN4562Hm4\nfLhuvn3xRvcYhydYDJlNZi3kr62tjbGx3KrL6Ogora2tZY9lStBu27aNvr4+tm3bxvDwMGazmY6O\nDi6++OLZElMgOOV5bfQN/mfvb1A0hdTQEt6//jquWLew1mJVRb3DzGfffy7f/z+72XPIS8dZ6wk0\nvMYvDzzI/7PhtlPSEBTMTQwGA7fddhu33XZbrUWZMXxxP1ElCsgFz8qTB1+jy7IEs5wOq3rjaDkP\nR5H2U9Ye0dF1naHYAO1Jw6Sb4eq6TlyLoqeqy7nJKDoZ0TVdL1Bw+4L9qKFGXNZG3P74hLk8mbb6\nQ4MMR0ZKNvA8cNyLUTJhzAW/MOiOoOo6g55I2UIFwWSIwXFFP39z4974IUySGZhX1TjLfY8VryGX\n89DkVtZLP5/xUBlkGW28CIiuw7HgcfzjBRJ0IJgMI5NR6NPXT9FTIEklBl9S0ZB0FZPRQG/sMJFk\ngk7LIlTS7UeVGEk1RTAZpMXWzIGxYwwlxuhkMQf7fITjKcLxFLI1SiQVycmqqUiShCc1QjRuZr6z\nCX8krbCn9GTBWP2JAH85sodGWx1mSoslZc4bGosgKRqLXSnsZcttS9nzNU3HF0qUvY753ovikFhV\n09njOYCipji7bT0m2Ugwb7EvmlDwBUKMpUZZ2ObCYsrN3cHQCEsszqynSivqZ9gbRZYl2sZDR/Pn\nSCyhMDgWobPFkQ211NEZGIsQD4ZomiRFKWNQBiPpgif500+iNL+rvD+j8JyMVzGlauNensKzX94/\nQlhJZUeYQdEUwqkIDZb6rAEYVcovjkSiKVRdJqB4cKnp65YY9/7t9RxgKBHAJjtoKfqcqqaNf7Ns\nIaVomIxygRGlFd3XjKf2ZDNrBtUll1zCd7/7XTZv3szevXtpa2vD6Ux/Wc2fP59wOEx/fz8dHR38\n+c9/5utf/zpbtmzJfv673/0uXV1dwpgSnNFsH36N/9n3G9CMJA5v4vp153PF2XPDmMpgMRv4u5s3\ncP8f9vPSPh3Xujb2coAXBl/m0q4Lay2e4DRhzZo1BUqLJEm4XC5efvnlGkp1Yhz2H2MwFqODpSUh\nf3Etilm2lC1qUM5zUi5sSwfCaoB4ysdBb4LzOs4tOD4QHkLVVBbWpTfWHIkPMZQYpsXUQSTegqrp\n7O/x4bAZOWt+Q0n+Q7mcLmPRPkjhVASb2YVVNqDrOsFkCKfJUWAsZWQFGB4vXT0UGcFiyOXp6Lpe\noCNO5M9KpFS8oQSqfhh/JEk9i1H0FAe8h4lrafkyhsBUSarJ8dX9Ugl6fH3EYxrFwUHlDLJMZTa5\n4JBOYHwLinhKzYas1TvMNBoassMPJ6M04CCqhompOcNn5+FRTLKZC9d0ZOdIQotnFfCUqrLfc5iU\nliCmJAgm04bbSLIPk7SUmBYlqSXwDEawW3OCDUaGMcm5FI/ucDdaJIlVL402iqtJogmFaMLLElva\noLKa0tc8lAxniztEEwoOA/ijUQZiHqxqzlKWyIWQeWNeTIlmBsZKw/diSpxQMvd+uTmhqGkjodgj\nA4Vhl8Foktai6nqKlsvV0guMDDXryWsrysUb8cdQvOkFkEg8xYZlheaDqhWGKGYKceTLrqPTF097\nzULRFFq9nufJlAraKMt4Y/kzMX8OHh8N0WWJsnCCNClVU9l+/ACKFMdmkVneuDR7LKWlsn8XeNZ0\nnagaJqqGqU+6MOaFDkdTMeJa+l8L9eMypY/1BgYZSByj2dTOX46McuGy5djzKgbrZe5dLajKoJoo\nca8SGzduZO3atWzevBlJkvjCF77AQw89hMvl4qqrruKLX/win/rUpwC49tprWbJkydSlFwhOY3aP\n7ePn+36LpJqI7T+fq9et5YZL5+ZzYjTI/F/vXIPTbuKpXQlsG3w8ePgRVjWtoMU2+QaBAsFkHDhw\nIPt3MpnkxRdf5ODBgzWU6MSIxhWODgawWtNJ94Fwkp6REF0t6Q06dXTGUsOEFD+NxhYaTGnFzJdy\n41c8tCRWYQ4YaKyzIEsSSTVV0sdAvIdIKobTWL4u21A4nQuSMaiCqbSSmNBivNrTDbqMhTq8kTAH\nBxTWL24v+HxMKQo71AsVT3cgjj8WIEKIBbYuegIh3PExGi31rMhT0MY/DaSVMp2cYZU7WqguKxOU\ndh8Yi6Jq6ZVuddxwCSherEmNkWT1m7IW96npWnZfnHLae39wmJ4BPy1aemyZVXZ/3E9CTWIxmNE0\nPTvG0v4gY04EwjmDLxBJ0lRXqKNldLbhZN+EYzBJ5qxxdXwkSErVWNFVz0hkJBuipeoq3tQoITVt\nzJk1M3ZrTn00SIYCf0dcjWMuU81R13XQ8pRoLUhKU6i3d+CL+0sq6enoDEQGkEwJvMk40ERSSxBJ\nRQtyfELj+VXZinLjoZ97x/ZT2GBlM1ueoBiENF72u9yRDL54Lvdn0B3BTqG3N/P5YCRJ87gtoJTJ\nk8tnb4+XRBIuXFNY1S+eSmbzmIZ9UebZ4jRac8ae2SQTTaTD/qxyqddXB/YNDDAQCLBknmvcw1h4\njj9cmn/nSY2MjwWOegfp8aRD8JZ11nN02EvSoGE2ygyEh8r0qRckghaPvNKV8Csegr5Qtn9JgYO+\nI5zTug5I5+4Fk6XGdC2oyqC64ooruOGGG7j55punlN/06U9/uuD1qlW5kpXnn39+QRn1Yj7xiU9U\n3Y9AcLpxyNfNT3b/Al2TiB/cyOUrV/O+K5bP6RA5WZJ4/9tWUO8w87s9Xlj2Bve/sZVPX/C3opS6\nYEYxm81cfvnl3H///XzsYx+rtTjTIpFSC/S/IW+ElKIV5AllKqz5lLGsQRVQ0opdt3sUnx/mJ5w4\n61UOeLrxpUzUG3MLGJkcmnCs1NjKR9FUjLKhQEn2Jt2ous58Ux0DiWN4NAPraWc4MkpUiWE3WukP\nDRa0E02kGPXlVpMzSpuGxgFvN3jBYjJAa9pDI8v5CphORA0xOpg26laUVAvMXaxoUmHHoVHUCgp0\nRrkNx9KJ7mkFVyc+XjQjH03XOTYYpK3RhsueqyinA1E1jFm25p2rFRzPxyBLJBUVbzBOvU0rOesN\n917qkovxjV8Tp9VU0o6upw2lSFzJFmmYKrqup+WUMhKke/AEE6TGw0QDkST1DnPB/kUZYwrGQwrJ\nN6jkwoIB485Cr5Ir3AHQMxxi32guZ2YsOYSq6yygI2tMJVWNRFLNa0obNyF1VF1lIHEM2V9orPiT\nPmQcZAY16o/x2iE3IVMKl82U11apxwQ97d3Jz9TrGw1hNRsJRJK0muqRJKmiwg+w33sIX97m9cFo\nAntJkcOr6jahAAAgAElEQVTKLRTOudycjyYUDJIxZ4wpPiyylUSycOuAZEpDsuV9bjy8dCjRS4d5\nAQ5jkXEHDATSG+3GkxrD0VGcUnNBxUZN14nEUkTiKRxlQi7zc/YCkQRaNEZIi7C4w1WQQ5b/CE6U\ny1gJX8pNi63QMxhPpugbCae3YxgOMejZWzS62lCVQfXAAw/wxBNPcPfdd2M0Grnpppt4+9vfjtk8\nd+rDCwRzhbGYl/964/9D0VQShzdy4eLVbLn6rDltTGWQJInrLlqMxWTgwd4heunhD4ee450rL6+1\naII5zoMPPljwenh4mJGRkQpnz10yylVUrW5VNhCN4pc9oOuEVH+2lHI5do/tY13zaiRJYvfIEY4O\nBulotvPy/iE6Glzk8lYywuTJhU4oGaY/NABA+Vpd6XAi2aVgr1CmO5FS6R4MoHqHae7Mhaul9ER2\nhbwc+T6dAXeEBmMuF8aTGGMxpTlUGSPAr3gIKn6Chfo/uq7jCcRxB2K4AzEuXNORrfim67kVe1/C\nzlA8zOK6yuHYJqOcvV798aO0mjsLlM0jAwHazR7shrTymyxT0UzXdSQkBsYiJcfSRkfh63L0JY5g\nGhnOhvmFVD+gE83LOwlGkoRiqZKiD1k5ipRWHZ3+MiF3QSVXtj2lJen2DBHMU9rLGbzHR0LZ65LQ\nYkQVDafJRCQVxpMoXwZ+OD5Ip3FFwXtJRWU4FMVVYHinjcmYGsEq28f7C7O4I7eXlabrJFIaiVSh\nwVppc9xYQuH1Y4MF8zlWoSJlVgpdR0XBjIEhT4jeeK6cv0RhKF5a6nSuUma+OUZLS/BLpD2JY6lh\n8u/acLKPpYZVxPPvZd5QEimVg4OjrGgxZRdjAHoj3Rzf3oOUNHP9hsJQ4GJPs6alvXgZgzz/WEyJ\ncTw+VDakMpZUOD46gi8WwjjRFhf5obw6DI5FwBTGZ0hkx5Ahpacw6dMr2X+iVLUs3NraypYtW/j5\nz3/OF7/4RX79619z2WWX8a1vfYtEYno7WgsEglKSaoofvv4/xNQYyWNruXzZOXzkutUle17Mda48\nbwHvXPROdMXI48cfZ9/g4OQfEggmYMeOHQX/AoEA3/72t2st1rTJf+TzlZGMXldcRtyveDgWO1Ci\n7B6NHCauxCddt/WGEiSUBAk1/Zt+eCTtPRgYi3A8foRj3pGC3IhyDHhCeILpz7sDcaKJ9Ep5PKEy\nlDhOSkui65Q1CIrxKCMEE3ml1YvywqLjSfQj/hj+SBJvyl2QO+ZXPNm/R+PDJeGH+fhSFaqHUVrS\nPRvSl0dvqI9AIkg4r1BDuZCmjKGQ0pN4U6Mkx/O0MsZTUs/pU5lzNS2dL+NLuSlKEytB1ZW80LTy\nBRqKy2Gnc1oK74ckScQSE1ThKxpcOF44LwKRUr1wINZfYGAVyp2fN5T/vlp03njRgzIFPirlrYXj\nCqFxD6wO7Ow/wnCyj7CaNrhTefd3ouqzxSF/kXgKTyjK673pkMpo0fUqqZKX97dHGaEv3k1cjXFo\nqLCgjKKnshchqPjTOXA6kNfeYKwwpE4fj6bzK2MktFjZ0Mb8EvH5niJvMM5IyMer/fsLSptnWoiq\nYfyJwqp5qeKCN0VTTdVUdNKG4UjYk71v+WGEOjDijZFIqfiCCcrdu2zzRfpPStVI6olsqGc+o8mB\nMtUtTw5VF6V45ZVXeOihh9ixYwdXX301//Zv/8a2bdv45Cc/yQ9/+MPZlFEgOCPQdZ2fvvEAQ7Eh\nlNH5XHPWpdx42ZLTwjNVjuvOW8Xoi2/h1dhT/Ocrv+Tzl95BR7Oj1mIJ5ihf/vKXay3CrNGfOMpC\n6/IJzylnFETVMLqcVlRyIU3lFRdPMI4nGCcW38dlizeWHHcnB2my5K2MF30tZVaOvck4LrsJfziB\nP5xgRVc9qq6j6lG8ygh1hnRxgXRVtsqEFD/t1BOMJHFYS1WVAXeEJR0ugnkhkMfjR1hO6Yah/nCy\noJRztRsgp4sCVDq3jHclOIg7EEfTdeodhRE8ug5H88KkEloMXdMJRJLpjY9J38N6Q1PBd76ip1B0\nBb/iSfdY4fcgoPjwxAM4DRPt1lwqs1m2ZEM/s0zyk6NDNjQR4FC/lzpjriiFqukMeQsN/oRa2aDN\nFBUod6mHfVE65cLfBU8w11ZyXLnvjR+iwViajzs0Xpre1VVPXIkzGk0b2mOpIRyGnGcqkVTYNXqw\nbO5XOocq/bcGeAJxEqrOwdjuinuylc6xvPDJcS9dVA3hThQ+tyoq+rivw6+kja29x524rDmvS0Qt\nNPwGYn00OKwVizMUT5niewPpIif5G2fnG5Dlnpfid/IrC3YPBjAaZKxmY1mjp7iBtMerlMwCTrn8\ntfTiQfmmI+opXDb9qquuoquri/e973186UtfwmRKhwwsW7aMp556alYFFAjOFB498Dxv+F5Hi9Rx\nw5LrufZNxUnZpx8fvvAq+p47zIirl/sef5h/eddNNLpq464XzE0uv/zyCRcdtm3bdvKEmSVUXSmo\n1lYNOjojyX4cViO+cGLSPWQy9HuCJLsmC/XSiSfVomN55N2OfA9PfqXBseDkpY3HAnH84QQWk4yB\nMiXVy9z3jAJdzPD4ani+p2gydJ2C3KDJjMBjo96sZ0epsAlptu3xK5YxpsqdEVFDyHKunURSqWjr\nZLxMGc9LuaICA4metGyqli29XWJMpbuelPwwq4DiLQgXq0RxWGKGjCeqnPKd8WgWV8vL0DscypYz\nz/dK+lOlWwkc9nUXvM43TF7rHmYwUfkZy8yZUCSJP5zAZps45cWdHCSmRVgUkZjnaC8753x58maI\nqmF03VUwt92hECNBf8m5WdnQORY6SkiNjr8uZDqLsvnyHh8JEy0papNnIEaSyHpavoGxSPa5SSmT\n5PkV5fEVt+5ODZTIUq7/YjT0aRXTO1GqMqh+8pOfoOs6ixcvBmDfvn2sWbMGgF/96lezJpxAcKaw\n63gfj/c/ho6Bd3W9h2s2nv7GFKS/6D9xwQf4wl+/RqJ1N/c92MY/33JZQfK3QDARE/0GBYNzdwPp\nYlVgdFy5SCSrS+zOGGCxhEpkPETKIBmr8s68dthdtsx6xoiq1II7lQ7d7RvJhRdlNgpNf06v2jsE\nOaMgkdKwVVm3JlImHAxgYCzEYCJMg3N6CzYpLcUrx47SlNk6qcwwknn3pjgELKVMLSFf19PhS4Y8\npfDoUBBba3VKYnHRilA0pxAfG564kuFU7tFM4I74sZqUinlK08GnFBpUxfcD0sVQMgwmeidsL7PR\ncrHHsiT8bZyYlp73A6FBOuxtpMpsZFy+ciAEIimisfzS4xruVGnlvAIKKugVtmuxyDC19ZgCPMEY\nY6nhisdTqkad0UZCi5e9zpXIlvmPpXDaSnM7M4Z2ucWByWaKqukYDSfXoKrqK+qhhx7iRz/6Ufb1\nj370I77+9a8D07N8BQJBjp6hIP+189dgULik6W1cs3F1rUU6qTRaG3j/qhuRjAqB5pf41gM7s5sM\nCgST0dXVlf0Xi8UYHBxkcHCQnp4e/uEf/qHW4s0YmZyMSoUCisnso5RvGBXvT5Wq4EXxpdxlj2W8\nL6mCXJ/x9vWcjPl9FiqcM6uoxyoYT+VwJydRSMug6zpJLUlCizGU7JmwKMZkjAWq22xU0VPE1FxI\nVnHhhlFfJY/WxAz7Ji6UkI9BniW9rkKzntQIA+5IYeGEIiaaOdV4HAfGIiW5e2W9c8VI6a1yM55W\nTxWe1WLcsTEO+46UHqggtzcYL7gW5Qo65KNpeqHBXtTuaLy6HOWCQhOTXNNio80gTW1b26SiFRRe\nGS4ThpihXNhtcAKPaCVDdbap6gq8/PLL/OY3v8m+/s53vsPmzZtnTSiB4EyhfzTM15/6PXSN0Wle\nwq0b31ZrkWrChfM2cdB3mFfYSX9oBz/8vZVPvGc9hokq/wgEedxzzz288MILjI2NsXDhQvr6+vjI\nRz5Sa7FOObyp0UnP8ZcJRconXxGdmjejUtBXNZR+bipGQsbAnMoa8J6jXg6ESotQFKNpOsFoCm0G\nDMbBZA+armExLCt7vNxqfVqGqW1uKiFVvHeVvHwnQjXXfbLy/TNNeAq5NrpeWqCkGmJJhePBdKn4\n4vFVKutfTL4nrRzhWKqg7eJWw2r1e6vlyLUS1WZ+n6dRbxS7xTglj1Y+CW3qhu1sU5W2kkqlSCZz\n7uNIJII6waZ5AoFgckb9Mb720PNoHfswSxbuPP/WM9bjK0kSm1feSKutBdO8HvaMHeDXTx2u2UqT\nYO6xe/duHnvsMVatWsX//u//cv/99xOLTW81/5RgznwXpJ/RSqFP5c+ebi8nl2iqur2eRn0xRnzR\nqnOzJiLjjYhrpXO3L35kfA+oUhKpqSn7JzusD6rPXZsq5UrMT4XIJEp95kk8Pjp1wyKl5AZdrhhE\nNVTlSZsBCn5v8/6saouGKd5cVdcnNKZOZKrUYm5DlR6qzZs3c+2117Ju3To0TWP37t18/OMfn23Z\nBILTlmg8xbcfeJ1Ex04MBpVbV7+PBktpdaozCavRykfXbeHrr34Pafkb/HmvjbYGG1dfUHlvF4Eg\nQ2ZfxFQqha7rrFu3jvvuu2/G+7n33nvZtWsXkiRx9913s2HDhhnvYy5xImFwU6G4TPx0kSYrYZeH\nOzlQ8Vi+ylatMTkVRhKlYVrFpePnEjOYHjXjDE5Qxl/TVTImVeWKj5XJrIsoU/Qg5jMVT9qJkG+I\nTO49m70bqgPh6Il5KzVNB8Pk580kVRlU733ve7nkkkvYvXs3kiTxuc99jnnz5s22bALBaYmm6fzw\n4b24jfsx1/k4p3U957WfU2uxTgkWuDr5wOqb+Z99v8G66jW2PmektcHGuWe11lo0wSnOkiVL+OUv\nf8l5553HbbfdxpIlSwiFphPqUpnt27fT29vL1q1b6e7u5u6772br1q0z2keGueKfCqvVF/5IaDFq\n42uaHuU2aB0Yi9DRbK+BNHObyaoeTkatohVOdMEgpWgMeiKzEkZZS2bzbsQSCiNTCOctRzpf9ORa\nVFUZVIlEgn379hEOh9F1nRdeeAGAm2++eVaFEwhORx79aw97B49jW38Ih8nB5pU3nrGhfuW4oGMj\n7piHPx57EsuqV/mvxw18vvESulqdtRZNcArzpS99Cb/fT11dHY8++iher5fbb799Rvt48cUXufLK\nK4H0tiGBQIBwOIzTKeZmtUzFADsViSYUhsaiNQsrmqtMN1cmw1w1SKZTxGLGmY2pWmTgzmQX1Wz8\nPRnlqpTONlUZVB/96EeRZZmurq6C94VBJRBMjf29Pn7/fDf29XvQJI33r3oPLrNQxoq5dvGVxJU4\nz/Q9h77iBb79CHzh/W8tW1pVIAB43/vexw033MB1113Hu971rlnpY2xsjLVr12ZfNzU14Xa7hUE1\nBarKx5hFvKETV3BjogrpSSd2ggaZQDDbVGVQKYpSUOVPIBBMnVhC4f4/7MfY2YNm83N++0bOaV1X\na7FOSSRJ4qbl12M32nn02BNE5j/Ltx+Dz737KlH5T1CWf/zHf+Sxxx7jxhtvZNWqVdxwww289a1v\nzeZWzQaThSE1NtoxGqcXdmK25TYPnWwT0dOBM2GMcGaM80wYI8ytcU5XVpvNXLY2p0IMG7k2nWYL\nieSpcz2amp3U2cpvBl2O1lbXCfdZlUG1fPlyfD4fjY2NJ9yhQHCm8sC2brwpN7b5R6gz1/G+s2Zn\nFf10QZIkrlnyNhqtDfx83wMM1j/N17d5+NRb3odRntqeF4LTn02bNrFp0yb++Z//me3bt/Pwww/z\nxS9+kZdeemnG+mhra2NsLLdh6OjoKK2tlfP7fCeQBxCMJonFkthsZmKx6qrNzVXOhDHCmTHOM2GM\ncGaMMzPGiUrsZwin4sSUU+d6jLnDJGzVeTVbW1243aGS96ZKVVrJ8PAwV199NcuWLcNgyK22/fKX\nv5xyhwLBmci+Hi/bXu/DuWEPKhofWH0zdpNIbK6GC+dtwmVo4Ac7f85x0+t88fkBbj/3AyxwddZa\nNMEpRjAY5KmnnuLxxx+nr6+PW265ZUbbv+SSS/jud7/L5s2b2bt3L21tbbMW7ie2DBAIBHOFyLT2\nupo9Jtu7azaoyqD62Mc+NttyCASnLbGEwk//eABTVzeqJcAlnRewtnlVrcWaU6xtW8anz/07vvbs\nL/E193HfK9/higWXct2Sq7AarbUWT3AK8NGPfpTDhw9z1VVX8bd/+7ds3LhxxvvYuHEja9euZfPm\nzUiSxBe+8IUZ7yODKFMjEAhOBSRJmnSBJ7Nx9qmCegJl6qdLVQbVBRdcwLZt2+jv72fLli0cP36c\nBQsWzLZsAsFpwYPPduNVR7B1HqXJ2shNy6+vtUhzksVtTdy+8f1894lnsCzdzzN9z/Ha6BvcvOJd\nnNO6TlRKPMP54Ac/yKWXXloQRTEbfPrTn57V9gWnF1aTgXhKPbl9yrayGwOfKZiN8glv9ivIISPX\nxONzQtTAwV9VdvfXvvY1HnzwQR566CEAHnnkEe65555ZFUwgOB3oHQ6xbedx7Cv2oKOzZfV7hUfl\nBNiwrIV3nX0+0V0XUx9eRygZ5id7fs4P3vgpnpi31uIJasjll18+68bUyUSU5T49MBprUUTnzF5c\nsphqk2M7z7KoJv3ONnPpm6irxcHSzjps1pP/W1DVk/7KK6/wve99D4fDAcCdd97J3r17Z1UwgWCu\no+k6v3jyIIb5h9HMId4y/xLOalxea7HmPNddvJhzl7czvG8+G/X3sLJxOXs9B7h3+7fZMbKr1uIJ\nBDPCVDyuhhp5Z6UzXHE/VRH3pTZY5eqryp0M2hpmRp6Znk1Gafa2P7FbjBgkqXibrJNCVQaVxWIB\ncl/wqqqiqifXhS0QzDVe3DPMsUAPpo4e2mwt3LDsmlqLdFogSxIfvW4N7U12/rI9yJss72LLqvei\noXH/3l/yqwMPklJTtRZTIJhzTNUwazS1zJIkpw82y8n3lpzx0c81HP8i61m167yImZp7M22bWE6K\n4XnyLaqqDKqNGzfyuc99jtHRUX7605+yZcsWLrjggtmWTSCYs0TjCr/9y0HMy/YgSRJ/s+YWzIZT\nZ4+GuY7dauTjN63HYjbws8cOssC0mn867+/ocs7jhcHt/Oeu+4kpp8AO9YKTxoEDB7jpppt4xzve\nAcD3v/99du06MzyW2gwpD61TXtGe+5r7bHv3XHYTC9omrgRpk0+84qvdkOtD5vQJfZ0OtZyVsnTq\n7JMoSdBSP7UUA6OhnPxzKegvzUx9J06Fqu783//933P55Zdz0UUXMTw8zG233cZnPvOZ2ZZNIJiz\nPPzCMeItu5AsUa5ceDlL60/P2Opa0tXi4KPXriaRUvneQ7txGRr5zKaPc3brOg75u/mPnT8ilAzX\nWkzBSeJLX/oS9957b3ZfqGuvvZYvf/nLNZZq7lDnMOOym6akEObneZkq5ApNR8E8qQrxCRhUZrm6\nRTKrqbKB4zC4TjgEar5lKa2mednXsiRX9JQUey3qjU0n1PdsMp25M9+ytOoJVGc8PfZWrbTZvSxP\nfW43OErntKpXjkir9NznU/yITVUqq2yv+lnLcqqG/PX19bF27Vo+8pGPsGXLFlavXk1fX99syyYQ\nzEkG3GGeObodY+sAC5xdXL/06lqLdNpy3qo2rr1wEaO+GD9+ZB8G2chH136Ai+adz/HQAN/e+SMi\nqelvriqYOxiNRlatym1HsGTJEozGM2MD6LUtK6b92YxSaRhXvhqNrTQYWzBIk187izT56rdNnvo+\nXSezkMOJOKgMVRRKzjS/pKPyRqEnWoDEJJuRJQMmyTzep6EqY6TOYWYmzddqw92qze3pME+tmrRB\nMmAqo3hXCjGzyY6C14utK0vOaTa1T0mGgn5NBprrZr8I1fxWR9n3DZI09eq3Uzx9TfPkeeHyBDLU\nlzHgSj8vI1dXlDxLLYr6VPWt9aEPfYgPf/jDfPjDH+bWW2/lHe94B5/4xCdmWzaBYM6h6zo/e+Y1\njIv3YJJMfGTdrRjlM0OpqxU3vXkpaxc3sqvbwyMv9GCQDXxg1c28Zf4lDEdG+OEbPyMpcqpOe4xG\nI319fVkF4tlnn53Tm+NORfZ6S924clyKWbYUvO5oyoWXLbGtosnYRotpHme5VrGyaQUuQwONphZc\nhvqCz5zTVao4GaS052VBmxOHtbKXxWGobEyUo6XOWrLqXmklPOP9Mcgy9inmjJgMMpo2vTkiS3JZ\nla29sTB8L3OO0SBjNpUfQ10VXqJqCk20mbtwGupKvE4NxuZSgUjrzjPpDZQlmTpTw5Q+U2ezYDeU\nNwimK13xp4ySkS7LYtrMnUXnFZ4pSRKdlkUFnqvM39Uo/uVocuWev66WSuM8Mco5qDILJHV2U8Xv\nhgxTyWkq9qZW8/TUO9LXoMHYTJdlCa4pegYrzf3M9085TlmD6plnnuHpp5/m6aef5rnnnuN3v/sd\nb3rTm2ZbNoFgzvHy/iH6bc8hGVTev+om2uyttRbptEeWJW6/YR3NdVZ+//wxXj8yhiRJvGfFO9nU\ndjZHAz38bN+v0fQ5to+GYEp89rOf5Y477uC1115j06ZNfOMb3+Dzn/98rcWaNtoUDKqJDIkmY+EK\ne/GKtSRJuIz1WIwmXGZn9ni+Um4xG3BZCw2z8U8DaaPmrPZ5BSvlLXVW6hxmFrQ4aTV1VrXSn/Gq\nOG0mls4rNMLaGkuVvmVd9UjjiqPZVN7AmbA/uXAF32au3iDrNC8u+35JeFMVHgKLbJ2R8DOzbKHV\n3FmiaDaa0r9DLXXWAkVzJvbucxjqCl5b5EKPTIPTgtOWU8IXtRfeV1mWZ6Qim8M68b0zy1asVeSq\nWWRb1vNqyssnamuwce6SrhOS0W4xsqyzfsJzZi6lL92QLEm0TyE3crL9cItD7zILPxPlItY50vff\nKJkwy5aK1RCnMnSrbC/IFWw1d9JkyulbphosZE/Lr75ixQpRNl0gKCKWUPjVnj8gOwOsb1zPBR0b\nay3SGYPTZuLjN63HZJT58SP7GPFGkSWZv1lzC2c1LGOXew+/O/LHWospmEVWrVrFI488wl/+8hee\nffZZHn74YVavXl1rsaZNtYpVV4uDlgZbdqm4y7K44LhBkmm154wjo6F8w+1NhcqmLBloMKar+EkV\n5JHyVIgl9QsLDBKjUaa9wYbTbkKSpJJV5vzVein7f+VB28zGAsV8XpN9egpMfpsWY4HXqKtC6FQx\nlULLgIIl+0XtrooyZjyHxSvpbUUeLoMsIyHhMjacUAGNBqeFRpeljEvhxDR4l6GeFlMHHeaF6X5M\nTTiLjKzGPC9NcVqP0+QoMOzqjI00m9pxGRuQy1y94raz79vK34/8cLOz5ueMVuMEIa3ZeyLB2sVN\nWSNwotC1fDKLEZk50tniyIY5ylJ67me8hl0tjoJ2W+tttDXYJi1kUkipXMWi1hkbaDK1TdpSIqVM\nes7yrpxR6Bh/Juuclb1gGVEm8hoZJIllXfXMa5rY6DXLFhqMLbSZO7Pt2WTHuGe2mfM6zuXstvXY\njCe/hH1VJtx3vvOdgtfDw8MEg8FZEUggmKv89Pm/oDQfxk4dH1r/3hlZ/RNUz6IOFx96x0p+8uh+\nvvd/dvPPf7MJq9nIxzZ8kK+9+n2e7vsLi+sXsrFtQ61FFcwgn/nMZyZ81r761a+eRGlmDpfNTJPL\nitliJBZLlhw3SAZUXS3xTpllK40uC75QIvvaagI36Y2vKymF5at75ZgshCbjXap3mAlEkljNhqJ2\nc/2uX9CF1WRkYCxS0k45hareYUYCFrbWs+/4GABWi5EFri76xw5NKFcGk0GmpcHGkCfXZ3O9lcFx\nGUxGeUbC3/IX+M3FYYoTXMKMV6TebmLUl3tfltJtrmheQMCocWQgcELy5Ysgy1LBa0miorfIKtuI\nazEALl2wief7dmSPuYy5MD9JkmgxzSOsBjFKRlbXrWVAOZw9bjDIBdp+i7WZQHgk+zrfk5nSSkO1\ny+WG2Q0OJHTmWUs9SBaTgQYpbbw0OHJK9hUr1xKOSAyXqQeQmesS4LKbMQdL57BZNqOZVBKp0oIN\nyxoX4A214qMfAIfFCHkOXrvFSH1zHQ4tjt1sZFGHk2NDofT4ZAmXzURSyc2kDvNChpPHS/qZiOK5\n3NVSR9BXPkTOKMskxrtLpiZ2UUkYCtqud5pY1OHCZJCz3zlTwWSUsckOJFMCifRzPREyBhpNLTit\nJgbHuyv2yNbCOwVVeqgMBkPBv5UrV/LjH/94tmUTCOYMu/p62aM+haTL3HHuB7EZZz8RVVDKxevm\n8bZN8xlwR/jpHw+g6zo2o43/e/3fYDaY+cX+3zIcGZm8IcGc4eKLL+aiiy6q+G8u01xnobPVWTYH\nwiylNTS7qdQAyRhZGS/IiRoKEmA2GrAUVavLGGeSVBgatayzPi9cSqLRacnKMK/Jzrq2FRhkmSZX\n7nuyuc6KQZKxWoqUI8lMU116HPkh1JIEjdZG6uzpa+OyTVwpr5wdma8AVTIoM1XxOprsBfI2uawl\nxQBcxgaME+R1TER+6JxNdrDQuqIoRG3mt+uVperD/trzCkTMb53YeyJJEoutK1lgXV4aYlp0br4n\nodgDN5lXNEOLqZP1TeuzBVAkCeY1O6hzmGlvcGKWS3+PrSYzJjlX1TI/5DJrUE1wbdYvWEjTBAUn\nJEnK5g5ajKXhsu2NNuwThJjmd20z2FlkPYv6/Fy4KlnTvJIGawMdE6QfLO/Kzb1MQRiLbOPihecW\nnGeSzDQXebnMshmzIb0Y4Sx6Bue3OtLevfGxJLTSrUzqnRY6muxZD3nxJbdbjMS1wsJSsiRlPWOn\nElWZcXfccUfZ97XxYEu5QsnGe++9l127diFJEnfffTcbNuRWhv/617/yzW9+E4PBwJvf/GbuvPNO\nIL2auGPHDhRF4fbbb+fqq0WFNMGpTUxJcP++XyCZFK5ovpaljQtrLdIZzS1vXc7xkRCvHBhlybw6\n3nddgksAACAASURBVPGmhcxztLNl1c3cv/dX/Hj3z/nMeZ/AWuZHTjD3uPHGG7N/Hzp0iCNHjiBJ\nEitXrmTp0qU1lGzmaHRaaNTn09JkZEd/2iOTUfaqST8xViiGUDVSOvF9YZuTw+MeErvBiUEyssC6\nmA0thZ6B/LCu9F44NvoD6TczSpdOLk9MktLJ++0NZlJqzhu3rL0Zm15PQvbkxiKZUPRURizq7Cbs\n1jqMskQoVrn4TIPTUuB+yZgn+frbpvZzODzwLJDOyTBLZqzNXuIplVZHPSNB/7jsOmctaOCA113Q\nR4upg1aHzKB3X0U5KpGvvNcbmzFIBiyyjaheqITaLUaiicnDsiqTuwYuuxk10UBUjQA6KT2OWmFG\nFXuGHBYjkQnkyM1PnbXNqzg++krJOQuty3GO5+ZZTDKdzU4WWV30jqS9NasWNoLfSd9obvuLcmal\nhFRwIyVknFYjba46mk0dHAvFxmWCBmMLIdWH1WghQIL5lqXEtWhRLljla7Csqx4JaGmwcmys4vAB\naDK1sKzZjs1oY8fI6xOcWW5MpX03mVoJKOlnId8LnZk6+c9GpgG7yc7yhiVoms7BgVKB2xpshXmE\nsp0YQSTAYiw0WlrN8zBIRja0riMQ3UmjqRGn2cHyxqUc8R2lvcmO7okQiSsYZCkbAqyM2wqqXjpf\nZKChzkIwUeqFh3RIZPdgiPx7YjYZkCRoNrUxkhyoqqjLyaAqg2rDhg2oaqlbU9d1JEli//79Jce2\nb99Ob28vW7dupbu7m7vvvputW7dmj99zzz3893//N+3t7WzZsoW3v/3tjI2NcfjwYbZu3YrP5+PG\nG28UBpXglEbXdf7jpZ+jmAI0Js/i5rPfUmuRzniMBpk73r2OL/7sFR7YdoRF7U5WL25iU/s5HAsc\n58/9z/PbQ7/jg2tuqbWoghnkvvvu4+mnn2b9+vVomsY3vvENrr/+eu66665ai3bCmI0yG87qwBcL\nVTwnXwWsN9cxQCT7bv7K8crGs+gd2cFUKA4TnGdZhFW20dZgZ35rKybDxF6Z9H44hW1YDOasfAbZ\nSKdzHoqmMBrNGSktLhfznfN4w51WInVdp8O8gP7E0YK2jBPst9PgNKNp6RLh4TyDq9HUAhQqcfmK\nZSZXR5Yk7GYjq5vPYji4HWDCAjf5HpQ6i4tgInfPCsLrkHDaTGjJ9LtNLitxu6PozFIDsMFlIZpQ\nxvPbdDYuWcCx/uo3Mc/YlE6bCaMsceHaLt7otjIQHiSlJSrH/OXLnnedJgsFNRpkLAYL9cYmAooX\np9lJIJqOaTRIRprrrHQ2O0hIKlazmXnNjqxBZbeYSvbwyveGWmUbZtmCLMkFVSHPqlvFojYXBtlA\nIJwAYtljjaYWGk0t49V3ExgkY9aY6mx2MOiJZAstFM+qVlsL/njaqDYZTEhS2tDRdK3sfl6SJGU9\nyJIko1eYN8Xzt85ShzfmL3tum7kLVVe4eMFSHtv/CkoZIwXSVf7MhpxnW5YljHLhtVzRVVogI7+6\naHtz+ZxCs8HEFUvOyxrZDZZ0OzLpXMwRb7TAe+cwOoAQTaY2Np3VhqppHNtd2q7D7CCUyBnPE200\n3uiyYve6WGJbVfGck01VBtWdd97J8uXLueSSS5AkiT//+c/09PRU9FwBvPjii1x55ZUALFu2jEAg\nQDgcxul00tfXR319PfPmpTeiu/zyy3nxxRe59dZbs16suro6YrEYqqpimOTLWiCoFX84so3jyUPo\nkQY++eb311ocwTj1Tgt33rie+375Gj/4/V6+8OHzaa638u7l19IdOMbLwztY2bicN83bVGtRBTPE\nyy+/zB/+8AdMprTxkEwm2bx585w1qIoVVU3XC8JhrGYjkp77Ce+0deFT0ivWdquJJfNcDIwkMo1l\nKZfoXwmjZGRRuwun2VGgZ2fEaHCaMZfZtHZT+znsGtuLoqaQkGhwmmlvcGAhFypmko247GaCEYVr\n11yEpKrouo7L7MQdGyOYCGGSTZgNhavkmUR/Caqq3OGwmrIhkPljWL2ghdHEYMn5XZYlaFRXETT/\nHmWKDDS5LOl8rXorZzUu59Xhnbnz8wRYtbCRaCrKwHBaIV4yz0WrsoLR6BiYnShqeUMlM2KLbMVu\ncNJe18AxhieVtcncDESyEud7eprrLPSHdRw2I8FIoZHZaGotCD0syQubAKfNxLxmO4mkmr0+S+sX\nMeDz5sYjSTTXWXFHSzdhL2coF8htas+G88mylPV4GmUDhnHjweUwU283lxRdmYhKIX/OvPDadmcL\nXc4upJCKXMW+X+e2rSeQCNLtP1ZyrNXewmEKc+Py+25yWfGG0kZzJozQIBtoM3fhSY0y39lJytrK\nUOI4zXVWPME4LruZNc2F+2oVV2DMdZb702SSIZ5+02U3Z9srpni8G9vPxhf34437MbUa0bScE6bJ\n3sASWwMmgwGTUcZU9B20wDWfo1ovi1wL2JM8AKS3dDA6/n/27js6rupa/Pj3Tp/RFI2kUbMkW5Z7\nk20MBhtsnGBa6GBKcAgEAnkEHkkIYCALSPKjhpCXhF5MwBSDHcIjhGBCAD+SCDfce8OWi3qXps/9\n/THWWGUkjWRJM5L2Zy0vS6O5d/aZdu++55x9jnYYriPJQGFBGnVNPry+IEcq28/H7G8xfTK++uor\n5s+fj8ViwWw2c/7557Nq1apOt6moqMDpPD4mNSUlhfLy8JWn8vJyUlJS2v1Nq9VisYTfsMuXL2fO\nnDmSTImEtaViO38/+DGqz8gFWZfhcvTNGhOiZ0YNc/Dd+WNocPt55i+b8QeC6DQ6fjBxISatkaW7\n/kJpU3nXOxIDQlpaWquFfPV6PcOGnViZ40Si02hb9RRNyE0nOzUpMl9z4rAcMm1pTB6ZiqIo6DQa\nnFYTaY6eV7uyah2MShnO6OSRrYbIRhu6AzAhdRzjU8eiKAqBY2u/+UMBFEVhVFYKJr0W07F408yp\n5DjSuXDyDNIj8ycUnKZk8u3DybZmkZ2UybE/RP5+XPsKCsnW9sN4Wy4SnGQ+/v7oaPJ7Z2WdU03R\n57CYNBacehdWsx69TsuITFukV7DQNSlyv+Zwk62GVn1Pzf87TcmMTRnF9NFRqrGdYGnxTHMWefZc\nWmRUEdlpSYzJScZmCcfcclHnkc5hnDIqPH8qWZfKtLz8mAPLdVnRajSE1HDVyBR9eqtek2YppvC5\nYnZS5yX+w2G3eQ8co22RUGlbJGIaRWH8iBRS7Kb2nW9tbmj+fNl0yRg1Rk4dPqHDtmkUDelmFzol\nPA+reQFfk14XtZNPo2g6nJMVCLUeAda2J6mjNbCMGjPZxuFkWNIjcThtRvLSbaRYTe3WwHRYDZHF\nnwHGpowm2ZSM03i8qEhmm8TTaTOS40qKzCXsiEbRkGpOYbRzJHZD69L4CjB1VBpTCo6f9ydbjZFe\nLLPOxMTUsVj0ZjSE17WbNrr1nK+2xVMgPL8xw2khL8PGsDQrE0fEd+hfTD1UNTU1rFy5khkzZgCw\ndu1aqqqqutiqte4sUvjpp5+yfPlyFi9e3K3HEKK/FNcf5qVNb6CqCsMa5nDeOe1XWBfxd+bUbPYf\nqeNfm4/y6kc7+OGFE3BZUrlm3OW8uvUtFm95k5/PuC1uVYFE73E6nVx++eWceuqpqKrKmjVryM3N\njVSpveOOO2LaTyAQ4P777+fgwYMEg0HuvvtuZsyYwY4dO3jooYcAGDt2LL/85S/7qinA8RMIp9nB\ncEM+Oo2u1bCm7KRMDFoDaabwSYRRr42cUCie8BlHutPMqDQHu0t7dvVWURTSLeHS6foWPUWhDq7g\nW/QdJ28mnYnxqWMxacNJj0bRMMIefb6pXqsn25oZ+X2cczRHG0tIN6dxgOMXQdqeVVhN4XV+9h45\nfrW/ZbEJraJQkG0npLYvgGDVRS/H3VKqMRUojvS2NK/JE1D9ZKcmkZNuJXCsMp3u2PPV8nk7fhqk\ntIu+ZTgajcKoYQ7KPR62ltQc+/vx9bZ6qmUykm8dyYSMjMi+DQYNek943+mGbI56w1XlhmfYMBq0\nTBmZRoPHESn/3X5QYsfarqk2wTkBs+f4UEibwcr0jMJIr4dRpw1XuWvzGhmPtT3TkIs71NRq0eqW\nBVM0HQwB7apDs3lNM52i5+xxM9oVWWir5XltwTAHJoMOV7KJb45V7Iu1ikjbCxQ51mGgKuyjrlVc\nXXHps1GoCj9PURo7MttOee1wGoJ12LTJ2AxWbAZrZJitooTL9NuTDNj0pkgTzAYddouK0hRbHPn2\nPOpMzla9caY2BTgKh+VzpCF6D5RJ37oATkgNkWbIoNx/tMP17LpXZr5vxHQW8etf/5rHHnuMn/70\npwCMGTOGBx98sNNt0tPTqag4PgGurKwMl8sV9W+lpaWkp4evyHz55Zc8//zzvPzyy9hs3VtdXYj+\nUO2p4ZkNi/GrfpSDJ/FfV5whJdITlKIofO+cMZRUNfHVtlLSkk1cNqeAGRlT2Vm1m/8cXcNf9vyN\nK8dcHO9QxQnKzc0lN/d4JbIzzzyzR/v53//9X8xmM2+//Ta7d+/m3nvvZfny5Tz88MOR4kp33nkn\nK1euZO7cub0UfXvN8y30Gh2mYxX9HEY7Nq0Du86JVqMlI8aFw1uezjZfPGh5pbojOWmtT1IKkvNp\nbPwGvSf23viWV7+TolQkjIXVkMRoQ8Hx/Wjt2I02tMdOwPVaPVpFiyfgaXUeOSY7FZXWJ6saRWm3\nFhJ0PBRybMroyM9GjZERprGR7/vm3kGdosegD/cgGrQGxqWMiVrZ7bwJs1i+9nPsFkOLeTrNvW+t\n75vmMBPUhZ+vlmte6TTNVdi6V0m2ef/+YPh95fNoWx+31HBp+ZGZybg0To4eOYhVa49sZzHpsHSx\neG5HmocJNhcp0Cjth8i1/L1wdDiJbzvnyJ5kRG1UMGuTMGuPvwcLC9LQt+iJ1HZQsVGn1ZCbboua\nKCUnGclwmikuCydDzWX/AZJNybgD7nbbtI5f6fFJvd1wPJm36qwYtHryHXmsIpyQpDlM1DX4qKg7\nHkPbipsQ7l1NT3JR1hh95IVWo0GjaKMuIl2QbQcUhttzUFHbXexoX8KlY1qNFqcpudP7NPd4Ww1d\nP2daRYtBY2KYsbl3NDHPt2IuSvHWW29FilDEYvbs2fzxj3/k6quvZuvWraSnp2O1hp+4nJwcGhoa\nOHToEJmZmXz++ec8+eST1NfX88QTT/CnP/2J5OTOXwwh4sEdcPPMxsXU++sJFI/l+lPmdFo6VcSf\nXqfl9ssn8/CSdXz4nwOkOczMKcxmwZiL2Vd3kJWH/s1Y5ygKXRPjHao4Abfddluv7Oeiiy7iggsu\nAMLD0WtqavD5fBw+fDgyx3fevHkUFRX1aULVXPhAo2giGZFG0ZBmyOpyW7vBRrWnOjJZvLknRa8Y\n0Gq05JoK0HZx+J86Kq3dVWWnKZm5BVP5alt4zk4spwPaDqoAn4h0QzZjnBloFIVJaePRa8InyOvL\nNrU61ZqWOZGjjaVUearxBqKvkaPTacAbLgsfja3NCV/Lc6DMpHQKUhrwNRlbDcuyGlonnHn2XAxa\nPdlpVkYdqxLXVrTnKd3iIkVfhT4UTqzSLKlUNIULdGg7WZg2Go2itIq97fpJzfOGjAYdBvQMN41B\no2iizpHryJSRaZiTjFRXN1JW7cZ27DkxGXRMHpl6PAnoolureehdqM0zpQDjR6Rg1TjYfvD4Yl1t\nh6N1tgByywWlWxo3vHWS0bJnc1Ry+EQ+GGr7nEXX0e0tewhbFjbJsLjINg7HG/KQa80J3/dYG0wa\nCxpFYUSWLZJQnTI+I+ZFhmOhcvw5N+vMjE8Z02v77ojTmExBcn67z1dGUnq7IZCZLUr2Q+yLnve3\nmD6RO3bs4L777qOpqYmPP/6YZ599ltmzZ1NYWNjhNtOnT2fixIlcffXVKIrCgw8+yHvvvYfNZmP+\n/Pk89NBD3HnnnQCcf/755OfnR6r7tZxE/Pjjj5OdnX2CzRTixPlDAV7avISjjSUESvOYZJvBaRMz\nu95QxJ3NYuCnCwp5eMk6Xv94Jyl2I5PyU7lx4rU8sfYPvLH9XXJtP4mM5RcDzwsvvMDLL79MQ0N4\ncntnVWg701zUAuC1117jggsuoLq6Grv9+FXk1NTUyJzgvhJsmVB1U5o5hSS9ObLGT5I+iQzDMIzH\n5gbplK7XcInl4mks5zVt53H0lubHNrVY86+5qp5Bp8Fs1KFRNAyzZmHRmaMWA4Dw1X+dVkNWajhp\nCQ9t87PvSNcL6GoUDSePKCAYUjtdGLl52GTLuJtf1ySjgY6WFFYUhRRDGh5fuJfNprdSQSVpDlPb\nAoURI7PsaBSFPUdqGWbMj5zEW0w61I42ovViti3j69rx9MFi0uFKsaAEg+0uNCaZur9uULQYjHot\nDnPnS15otX1zxt3Vmlrt7t/mHjaDFafJSZo5lQZ/i1LwikKmPZnqBi8Oy/Fhsy17Q1tqm0zptBpy\nXVasZj21amzfS7oWCXykt7Szz7zSuyuhNc+XbCvX1n7ea/P3lcNioLbJ160CI/0ppm+6X/3qVzzy\nyCM8/PDDAJx33nnce++9LF26tNPtfv7zn7f6fdy44+UNTz755FZl1AGuuuoqrrpKShmLxBMMBXl1\n61vsrN5DsDodW/VUfnDDBBnqN4BkpFj478un8MTb63n2L1tYdO108jIyuWL0Rby98z1e3fo2P5l2\nS6Q6lBhY3n//fd5//30yM2O/yLFs2TKWLVvW6rbbb7+dM844gzfffJOtW7fy/PPPt5szHOucYKfT\ngq6Dno+uWHxaDgfM2IxJuJKOD3+328In+i5XV0PijyeAGoOO8vp0NBqFtDQr9rL2c6qa99e8/7Q0\na7seqrYxpKZZcdqi99DPdZxMjbeObFv0OQ8dPX5XZk3V0eD2k57efs5TWtpUfEE/a+wbW+1T0xSg\nLGRuc18bma5CtpTtItlhwW42toqhot7XLq6gRoM9yu3dYbOH4zgpexIHag6TPz7ce9XRsSS5vJEm\nTwBHsplxealUHCgjpNFg9Zox6LW4XLbI6wGQmWEn1WGmrN4HmFEUmFyQhtNuIhAKsvrAflIMLtJt\ntlZtqNFY8OqaMOoMJAVN2D3BTts5IXsUu8sOkW5ORXssaW6+b1fPjc1hprLRT0GOo+v7NpkxV4V7\nZpKSjKSkJOGytm5z2/duusuGI0qBkraagiq1bdqZklxPKKRGjSukhrC5j7+P8vNSKK50t9oe4Git\nB/XYXKS2+8lInwzA4boSGjS1mPUmXC4baWlWvP5gq8/c3BkG9DoNFpMefyCI/Uh9q8caPsyDQRd+\nDzTftq+qCbe2AZ1GG7UNI3K8VNV6GJFlj/zdmWJGWxkiz5GN1dhmsepj71dVo8GgsaDX6GN+79ua\nwts6HRZcyd3/vDRv7/dZ0CgaRuUmk5Ga1KroSG/p6ee5pZgSKp1O1yoZys/Pb1VNSYjBLKSGWLJ9\nGRvLt0B9KqH9U/nxtVO6nLAqEs+oHAc/vHACz72/hd8v38T93zuJ2dkz2Vm9h6/LNvHh/k+4uOC8\neIcpemD06NFkZmZ2qzLsggULWLBgQbvbly1bxmeffcazzz6LXq+PDP1r1nLeb2eqq5tijiWaAuNo\nXEnJlJcfn8Bfd2yR0pa3dUVVVRxmHSk2IxUVDZF9NDPqtJH9Nf+toqIh6jyNlvepqmwk4Ol4MV09\nFso9Xcfpctm61R6LVun0/vV1bsx6c+Q+1Z4G6utat7la20Sy0UHQrdDkb0Lx6ilXju8z02FEp9O0\nepzKGnePnv9mLpctEkeDxU8q6dRVe4HowxEBamvduH0B9IpKebkhsr3dqCXVYaK8vJ5hTlNkCFx1\nVSMhXyASp16rIeD1U14efp2Gm/Kpa/Lh1nhbtaGqtpF6txufLkTQ10hdvRutRtNhO7UeCymhHBob\n/YA/8pzE+lpOyHVEtulMfZ0btzucxDY2aqmsbETjNrV6D7d979ZUN+Jzd9wb16y6qrHd6zkqy9pp\nXBnaLIzHCqu0/Cy1vH/IH6Su3o3dqO1wPzrVjCVox2VIbXWftvf2AY3HSqY7LTqSzPrI/bMcpnaP\nXV3fSH2jG40m+mOnWvSo/gAmTevtXEom7roQ7hYRtHq/uv3om9zolEDM731PYxB/0Eed6qHc3/3P\nS/Nj++rdaBQNVdV69Cda7jKKaO/ZniRYMSdUxcXFkSsoK1eu7FbVPiEGKlVVeXfX/7Km9Gs0bieN\nO6dx0/kTyc/quiKUSEwnj0unYl4Byz7fy2+WbmDRd6fx3XGXc7D+MJ8c+JwcaxYnZUyNd5iimy65\n5BIuuugiJk6c2CqpevTRR7u1n+LiYpYuXcobb7yB0Rg+cdLr9YwcOZK1a9cyY8YMPvnkE773ve/1\navzRROstHZPT/fnFiqJE5o64ve1Lno/Obb/Pgdz3Pj2jsNXwpOjltzXH/m/+W+tzmkSeG6sBcloU\nQGjZG9O2rcm2rntqwtR2P+o6GToXl8EZXZRRdyWbKa9xd9izGouu5iY1z0vsTHaqBbtF3+lFV42i\nIdfWveksWR0stNtSV8Py9DpNTPtpS6tRUFBaDRXsylhnAWXuClwthrx2x6S0CfhCPrZVxX+NqVjE\n9K675557uPXWW9m/fz8nnXQSw4YN44knnujr2ISIK1VVeX/vR3x5uAidz0H9tulcMns0syZ1PSlc\nJLZzT8mjwe3n718d5MmlG7j7u9O4ZfL3+e26Z1iyfRkucxp59px4hym64dFHH+Xiiy8mIyO2IWYd\nWbZsGTU1Ndx8882R21555RXuu+8+HnjgAUKhEIWFhcyaNetEQ+6REz3Rj1ZSuscjaBI062o796bt\nujjh+xwLvp/bMNpZ0KogQay6uoY9NtdJRa07spZUs+EZrdtuNeupa/K1O9lv7nWx6q0Ej3WYxTJn\nRq/VkuowRhZP7m2T0ibgry7nm6Y96FskeBlOC6XVTa2SvoJsByOz7HEfiq8o4UVx48GkDX8/WPW9\nuy6mxagjx+aMutZbh7HoTOTZen4cNemMmDAC4YTK6wt2vkGcxfQJcDqd/PWvf6WqqgqDwRCp1ifE\nYKWqKu/t+ZDPir9EH7RTt2U6p0/M48JZI+IdmugFiqJwxdwC/P4Qn647xG/f2cBd10zj+onX8MKm\n13hh82vcNeO2mK5GisSQl5fXK5X+fvazn/Gzn/2s3e2jRo3irbfeOuH9x5tRr2V4hg2bxcC+I3U0\nef2tSk67HGbKa92tFsTtSILmU+1oFA2F6ZM52lgSKSndnHQ1Jw0xDbrphZE5DmP3RjfEmhs4bUac\nLXqjMpwWmryBdsUyctLDxQva9lxlJWVg0BhINTvZV9/18KxhaUm4vQHyMqKXIe8tJp2R6QVZNBUf\nxmLURYpndKQ7yVS600JlnZe8jMFzTptmTgmvJRXlIsKJykzp3SStu9quZ5ZoYuq7ay4ukZKSIsmU\nGPRCaoh3d73PZ8VfYgg6qNs0nYk5mVx3TvSKO2JgUhSFa84azdyp2RwsbeA3b69nuHkUFxWcS423\nlmc2vEKT/8TmwIj+U1hYyB/+8Af+9a9/UVRUFPkn2stKTcJq1jMpP4Vpo1zoWxTOKBjmYGaMZZkH\n0vehXqNrdbW8N8tOJ6L8LHtkoeeWNIpCit3Urv0aRYPLktq6d6+Tp8ig1zJhREq/zCU2GrStytK3\ndCLn2HqdhikFqd3qdWnLatZjj1NvVDSKopBicvZ6dc1Y1ovqK8Zj30/BYGInVDE94yNGjODuu+9m\n2rRprUrKXnHFFX0WmBDxEFJDvL3jPf5zdDXGQDI1m6Yxflgmt102pdOyuGJgCi/8OxZVhf/beIRH\n31jHz66aSU1OLSsP/YfnNv2J26fehEGbOAdMEd2aNWta/Q/h1/e0006LV0gJT6NRMBraz9MaSIlS\nT7UdztZVzweEe4GUowojMnv/6n+s8h0j0Gv7tihY2xLqiSDXnkNx3SGSj/XwNS8WbIry/u1Pk/JT\n4/r4/WVciwWu+9uILDs7i6sTtlx6s04/lTt27GDcuHH4/X60Wi0rV67E6Ty+ToskVGIwCYaCLNn+\nLmtK12MMpFCzaSoTczO5/bLJ3VrcUAwsGkXh++eOxWrW89FXB3jszfX8dMG3afQ3sbZ0Ay9tWcLN\nk65Dr5WqjolsyZIl7W5bsWJFHCIRiSzLmkmNt7ZHF0n0Oi0zJ5zYHL0TlWru+7Xymnt9EimvzrC4\nyLC4Ir9npSYRUiHdae5kK3GixqeO7dFaeL3JaTNy6oTEX/Oz04TqkUce4fXXX49USbruuut4/vnn\n+yUwIfqTN+jj5S1L2Fa5E70vlZrNU5k0PIPbL5vcajiMGJwUReGKMwuwmvW8+/keHntzPTdddBbu\nVA9bK3fwwubXuHnyddJTlcCOHDnCG2+8QXV1uHy0z+dj1apVnHPOOXGObPBKpBPuWA2zZjHMeryw\nUG8uVto3+j++41WcE/e50WgUctNlCkpfS9Indq9QIuk07ZTS6GIoaPA38sf1L7KtcifaxgzqNk3j\n1LE53H7ZFEmmhphzZ+Zx0wXj8QVC/HHZFnKb5jIpdTzbq3bx3MZX8QQ6Xi9GxNfdd99NcnIyGzZs\nYNKkSVRXV0s12j6WuKfbsbMdmxtii+Mckc40V2Dsz+S1eUSGrLUoROw6TajajqOWBEsMNtWeGn63\n7jn21x2E6mE0bCvkglNH8cMLJ7SqfCWGjlmTslh07XSSbUb+svIAof3TmZw6kV01e/n9+uep9dbF\nO0QRhVar5eabbyYtLY1rr72W5557jjfffDPeYYkEl5mUzpiUUWQlxXcoX0cKhjlITjKSl9F/87Zy\n063kpdsGVfU7Ifpat84Yh8JEVTF0HKw/xG/WPk1JUxmBkhH49k7mhnMncNmckfJeH+JGZtt54Psz\nGJ3jYN2OSvYXjWKio5CD9Yf5zdqnOdxwNN4hija8Xi8lJSUoikJxcTE6nY7Dhw/HO6xBbuB/GfUg\nAgAAIABJREFUT2qUcInpeM8T6YjZqGPccCfGfpzHq9NqyE5LkkJMQnRDp3Oo1q9fz5lnnhn5vbKy\nkjPPPBNVVVEUhS+++KKPwxOib2ws38KrW9/GH/TjLx6LpWEMt14zmTG5yfEOTSQIh9XIXddM4/0v\n9/P3rw5Q+WkWk04zsdu7iqfWPcuNkxYyIXVsvMMUx9x0000UFRVx4403cvHFF6PVarngggviHdag\nJtedhBAirNOE6uOPP+6vOIToF6qq8unBlby/9yMIafHumc4Y+xhuuWFSh+tciKFLp9VwxZkFTMxP\n4eUPt7HpP06yR51GXepqntv0KpeO+g7zck6XHs0EcNZZZ0V+Xr16NY2NjTgcsjCzEEKIvtdpQjVs\n2LD+ikOIPhcIBXhr+3usKl2L6jPi230S502ZzCVn5KPVyNAG0bHxw5388gen8PqKnazdAfryU0ga\nt4E/7/4re2u+YeH4KzDrpHxvPDQ0NLB8+XKuv/56AJYuXcrbb7/N8OHDeeCBB0hLS4tvgEIIIQY9\nOYsUQ0Kdr57Hi55jVelaQo12bIfmseiyeVw+t0CSKRETq1nPrZdM4r8umYTRl0bN1zMxeF1sKN/M\n42v+wKH6I/EOcUh64IEHqKysBGD//v089dRT3HPPPcyaNYuHH344ztENbtIxK4QQYX273LYQCWD1\nwe28sWspQY2bYFUGs5PP5crvj+vXSb5i8Dh5XDpjc5NZ8slO1m00YMzbQ3nmPp5c9zSXjPoOc4ad\nlrAT3Aej4uJinnrqKSC8kO+5557LrFmzmDVrFn/729/iHN3glvhrOAkhRP+QhEoMWiWVjby69iOK\ndWtBAWfdVG4+/QKGZ9rjHZoY4OxJBm69ZBJrdqTzxidGmuqS0YzawrJd/8vm8m0sHL8Ap0kKnPQH\ni+X4wpOrV6/miiuuiPwuc9v6mDy9QggBSEIlBhlVVdl3tI6P1uxma+ALtM4yNAEj52Vewvnfmi4n\nWKLXKIrCKeMzGJvnZMmKnXy90Y5p5FZ2sJuHVz/FgtEXc0qmvOf6WjAYpLKyksbGRtavX8/vfvc7\nABobG3G73XGOTgghxFAgCZUYFJo8Ab7aVsIX649wxL8XQ/5WtHofmYYcbj/tepLN0isl+oYjycCP\nL53Eqm0u3vhHEl7bNyjDd/L69ndYW7qBK8dcgsuSGu8wB60f/vCHnH/++Xg8Hm677TYcDgcej4fv\nfve7XHnllfEOb1CTSwVCCBEmCZUYsALBEFv2VVG0tYQNeyrwK00Yhu/EmHIUraLlopHf4Vt5Z8h8\nFtHnFEXh1ImZjBvu5PWPnWzcnIoxfxvb2MnDq3/LOcO/zVnD56LXyFdub5s7dy7/+te/8Hq9WK1W\nAEwmE3fddRenn356nKMb3KTzVQghwuToLgaU5iF9RVtKWL29jAa3H5Qgzvwj+NN2EiRAvj2Pa8cv\nICspI97hiiEm2Wrk9ssn89XWdN74hw1f0iG0+Tv5cP8K1pR+zaWjvsOk1PEyDLCX6fV69Hp9q9tO\nNJmqqKjgvPPO4+mnn2bmzJns2LGDhx56CICxY8fyy1/+8oT2L4QQYvCQhEoMCGU1br7aUkLR1hJK\nq8PzImxJWibOqKPUsInGQANWfRIXF1zKqVknSa+UiBtFUThtUnNv1Q42bkjDkLuH0vSDPL/pT4xO\nHsmlo77DcHtuvEMVnXjiiSfIzT3+Gj388MPcd999TJkyhTvvvJOVK1cyd+7cOEaYCOTCgBBCgCRU\nIoE1uP2s2VFG0ZYS9hyuBcCg03DShGSsOUfY2bSBfb46DCE9Zw+fx/y8uVj0li72KkT/cNqM/PcV\nU1i9PZNlX1ipLs3BPGIPu9nHE2v/yIyMqZw34iwyk9LjHapoo6ioiKSkJMaMGQOAz+fj8OHDTJky\nBYB58+ZRVFQkCZUQQghAEiqRYPyBEBv3VFC0tYRNeysJhlQUYNzwZEaNCVJr3Mumin/gq/Fj0hr5\ndu4c5g8/E5vBGu/QhWhHURRmTshg6ug0Vqw6yEdfOQiYyzGN2MXa0g2sK93ItPTJzB9+Jnm2nHiH\nKwgnT8888wzPPvssjzzyCADV1dXY7ccL26SmplJeXt7lvpxOCzrdia9353LZTngfvcluC1/gcrms\n6HuhfeF9JVYb+8pQaOdQaCMMjXYOhTZC77RTEioRd4FgiB0Hqlm9o4yvd5bT5A0AkONKYuqEJEg5\nxMbKT/hnbRUAqaYU5uScxuzsUzDrzPEMXYiYGPVaLjo9nzMKs/l41UFWbkgjYC1BP2wvX5dt4uuy\nTeRYcvj2iNOZ5pqMXqvveqfihC1btoxly5a1um3OnDksWLCgVQLVlqqqMe2/urrphOKD8IG+vLz+\nhPfTm+rqw8OuKyoa0GlPfHh1IraxLwyFdg6FNsLQaOdQaCNEb2dPEixJqERcNCdRa3aU8fWucho9\n4SQq2Wrg5MJkrJmVHHCv47PaA6iHVQwaPTMzT+LUrBmMSs6XOVJiQHLajFxz1mguPj2ftTvL+HLT\naPYf2osu4wCHOMRr25ayRP0zmZoCJiRPojBzDFkpVsxG+aruCwsWLGDBggWtbrv66qsJhUK8+eab\nHDx4kE2bNvHUU09RU1MTuU9paSnp6TJUUwghRJgcpUW/qarzsHlfJZv3VbH1myq8viAAdpuWGRM1\nmNNqOOrfz+qGo1ACCgojHSM4NeskpqVPwawzxbkFQvQOi0nHnMJs5hRmU1o9gW3fVLOp+AB7PFsI\n2os5YtjBkeod/KNCS6guFb07E5c2h2y7i0xnEulOMxlOC+lOsyRbvWzp0qWRnxctWsSll17KuHHj\nGDlyJGvXrmXGjBl88sknfO9734tjlEIIIRKJHIlFn1BVlYpaD3sP17L3cB07i6s5VN4Y/qPeQ0qG\nh+GZXhRrDUc9h9ga8kM1aBUtE1LHMjVtEpNdE7Abhsb4XTF0ZTgtZDgtzJs2DFU9jeoGD2sPbWdL\n5XYOe7/B4ywj5CyjlE2U+PWEShyE9iQTarKjuq1YdQ4ynRayUpPITksiO9VCdloSTptRyrP3ovvu\nu48HHniAUChEYWEhs2bNindIcTMszUp1vRetRt5fQggBoKixDgbvgUceeYSNGzeiKEqk3Gyz//zn\nPzz11FNotVrmzJnDj3/84y63iWYojO9MZKqq0ugJUFnr4WhlI0cqmzhUXs++0krq/fUoBg+KqRGt\npYkkhxfVWI9XbT2vIDspk3EpoxnrHMWo5JGYdMY4tUaIxFPhrmRb5U52V+9nX+0Banw1re8Q0hJy\nJxFyW1HdSaheCyGPBYNqIzvZEUm0slLDiZvTZkyIXq2hMNm5N45PQ2Eew1BoIwyNdg6FNsLQaOdQ\naCMMgDlUq1ev5sCBA7zzzjvs3buX++67j3feeSfy9//3//4fr7zyChkZGSxcuJBzzjmHqqqqTrcR\nfUdVVQJBFa8/iMcXoMkT/tfoCVDdVM83TXvx+P14/X48fj9NXh9NATeeoJeQxoeiDYDOj6L3oDi8\nKM4QbQfoeQCnIZmxtnzybDnk2XPIsw2TCn1CdCLNnMqcnFnMyQn3iNT7Gvim7iCHG0o42ljC0cZS\nSrXlBJLq2m17NKDniMfCmmIz6l4T+I2oPhMGLDiMNpxmG3azBaspnGSZDVp0Og3TRqeR5pCCL0II\nIUQs+iyhKioq4qyzzgKgoKCA2tpaGhoasFqtFBcX43A4yMrKAmDu3LkUFRVRVVXV4TZ9obrey5IV\nO/H4An2y/0QVCKn4fEG8/uZ/Iby+IKEOOit1w3ahH7Yv/Iv22L9j2ZLm2L8wBYs2Cacxi1SLg2Sj\nA4fRgcucSobFRbolDYPW0KdtE2KwsxmsTE6bwOS0CZHbQmqICnclpU3lVLirKHdXUuGupLypkkp9\nFUFrbbv91B77B6CGFPDooCn8af6oVkeSSY9GUQAFFRVVVY/9H8JusHHH9FukyqYQQghBHyZUFRUV\nTJw4MfJ7SkoK5eXlWK1WysvLSUlJafW34uJiqqurO9ymIycybMTlsvHr/0rr8fZCCJEoMnAwkZHx\nDkO00FvDGofC8Mih0EYYGu0cCm2EodHOodBG6J129lvt6Z5M1erD6V1CCCGEEEIIccL6rIcqPT2d\nioqKyO9lZWW4XK6of2te00Ov13e4jRBCCCGEEEIkmj7roZo9ezYrVqwAYOvWraSnp0eG7uXk5NDQ\n0MChQ4cIBAJ8/vnnzJ49u9NthBBCCCGEECLR9GnZ9CeffJK1a9eiKAoPPvgg27Ztw2azMX/+fNas\nWcOTTz4JwNlnn82NN94YdZtx48b1VXhCCCGEEEIIcUL6NKESQgghhBBCiMGs34pSCCGEEEIIIcRg\nIwmVEEIIIYQQQvRQn1X5GwoqKio477zzePrpp5k5cyY7duzgoYceAmDs2LH88pe/jG+AHaisrOSe\ne+7B6/Xi9/u59957KSwsHDDxBwIB7r//fg4ePEgwGOTuu+9mxowZAyZ+gNWrV3PHHXfwyCOPMG/e\nPIABFf8jjzzCxo0bURSF++67jylTpsQ7pJjs2rWLW2+9leuvv56FCxdy9OhR7r77boLBIC6Xi9/8\n5jcYDIm5+PQTTzzBunXrCAQC3HLLLUyePHnAxO52u1m0aBGVlZV4vV5uvfVWxo0bN2Dij4eB+hmL\nZtWqVdxxxx2MHj0agDFjxnDTTTdFff0/+OADXnvtNTQaDVdeeSULFiyIc/Rdi/V7JVrb/H4/ixYt\n4siRI2i1Wh599FFyc3Pj3aSo2rZz0aJFbN26leTkZABuvPFGzjzzzAHdzli/ZwdyG6F9Oz/77LNB\n9Vp255jTa21URY/ddddd6qWXXqp+9dVXqqqq6sKFC9WNGzeqqqqqP/vZz9QvvvginuF1aPHixeoH\nH3ygqqqqrlq1Sr3hhhtUVR048S9fvlx98MEHVVVV1V27dqmXX365qqoDJ/4DBw6oP/rRj9Rbb71V\n/eyzzyK3D5T4V61apd58882qqqrqnj171CuvvDLOEcWmsbFRXbhwofqLX/xCXbJkiaqqqrpo0SL1\no48+UlVVVX/729+qb775ZjxD7FBRUZF60003qaqqqlVVVercuXMHTOyqqqp/+9vf1BdffFFVVVU9\ndOiQevbZZw+o+PvbQP2MdeSrr75Sb7/99la3RXv9Gxsb1bPPPlutq6tT3W63+p3vfEetrq6OR8gx\ni/V7paO2vffee+pDDz2kqqqqfvnll+odd9wRt7Z0Jlo777nnnlbHsOb7DdR2xvo9O5DbqKrR2znY\nXstYjzm92UYZ8tdDRUVFJCUlMWbMGAB8Ph+HDx+OXEWcN28eRUVF8QyxQzfccAMXXnghAEePHiUj\nI2NAxX/RRRdx7733ApCSkkJNTc2Ait/lcvH0009jsx1fmXsgxV9UVMRZZ50FQEFBAbW1tTQ0NMQ5\nqq4ZDAZeeukl0tPTI7etWrWKb3/720BiP+cnn3wyv//97wGw2+243e4BEzvA+eefzw9/+EPg+HfO\nQIq/vw3Uz1h3RHv9N27cyOTJk7HZbJhMJqZPn87XX38d50g7F+v3SkdtKyoqYv78+QDMmjUrYdsb\nrZ3RDOR2xvo9O5DbCNHbGQwG291vILcz1mNOb7ZREqoe8Pl8PPPMM/z0pz+N3FZdXY3dbo/8npqa\nSnl5eTzCi0l5eTmXX345zz33HD/5yU8GVPx6vR6j0QjAa6+9xgUXXDCg4jebzWi12la3DaT4Kyoq\ncDqdkd9TUlISNtaWdDodJpOp1W1utzsyzCyRn3OtVovFYgFg+fLlzJkzZ8DE3tLVV1/Nz3/+c+67\n774BGX9/Gaifsc7s2bOHH/3oR1xzzTX8+9//jvr6V1RUkJKSEtlmILQ71u+VjtrW8naNRoOiKPh8\nvv5rQIyitRPgjTfe4LrrruOnP/0pVVVVA7qdsX7PDuQ2QvR2arXaQfVaNuvqmNObbZQ5VF1YtmwZ\ny5Yta3XbnDlzWLBgQasT4LbUBKlGHy3+22+/nTPOOIM///nPrFy5knvvvZdHH3201X0GQvxvvvkm\nW7du5fnnn6eqqqrVfQZC/J1JlPhjMZBi7cxAaMenn37K8uXLWbx4MWeffXbk9oEQO8DSpUvZvn07\nd911V6uYB0r88TLQn58RI0Zw2223cd5551FcXMx1113X6op4R+0b6O2G7rdtILX54osvJjk5mfHj\nx/Piiy/y9NNPM23atFb3GYjt7O737EBsI7Ru55YtWwbla9ndY86JtFESqi4sWLCg3aTYq6++mlAo\nxJtvvsnBgwfZtGkTTz31FDU1NZH7lJaWdtk13h+ixb969Wpqa2txOBzMnTuXu+++OzJ0rlkixw/h\nROWzzz7j2WefRa/XD7j420rU+KNJT0+noqIi8ntZWRkulyuOEfWcxWLB4/FgMpkS+jkH+PLLL3n+\n+ed5+eWXsdlsAyr2LVu2kJqaSlZWFuPHjycYDJKUlDRg4u9vg+kzBpCRkcH5558PQF5eHmlpaWze\nvLnd6x+t3VOnTo1X2D0W7bPZUdvS09MpLy9n3Lhx+P1+VFUdMMVZTjvttMjP3/rWt3jooYc455xz\nBnQ7Y/meHQyvZdt2DrbXMtZjTm++ljLkrweWLl3Ku+++y7vvvsuZZ57Jgw8+yLhx4xg5ciRr164F\n4JNPPumyFyJePvnkE/7yl78AsHPnTrKystDr9QMm/uLiYpYuXcrTTz8dGfo3kOKPZiDFP3v2bFas\nWAHA1q1bSU9Px2q1xjmqnpk1a1akLYn8nNfX1/PEE0/wwgsvRKowDZTYAdauXcvixYuB8HC2pqam\nARV/fxtMnzGADz74gFdeeQUIDzevrKzksssua/f6FxYWsnnzZurq6mhsbOTrr79mxowZ8Qy9R6K9\ntztq2+zZs/n4448B+Pzzz5k5c2Y8Q++W22+/neLiYiA8b2z06NEDup2xfs8O5DZC9HYOttcy1mNO\nb7ZRURO5r24AWLRoEZdeeikzZ85kz549PPDAA4RCIQoLCyOFExJNVVUVixYtorGxEZ/Px/3338/U\nqVMHTPxPPfUUf/vb38jOzo7c9sorr3Dw4MEBEf8XX3zBK6+8wr59+0hJScHlcrF48eIB8/wDPPnk\nk6xduxZFUSIXFBLdli1bePzxxzl8+DA6nY6MjAyefPJJFi1ahNfrJTs7m0cffRS9Xh/vUNt55513\n+OMf/0h+fn7ktscee4xf/OIXCR87gMfj4f777+fo0aN4PB5uu+02Jk2aFFm+IdHjj4eB+BnrSEND\nAz//+c+pq6vD7/dz2223MX78+Kiv/8cff8wrr7yCoigsXLiQiy66KN7hd6o73yvR2hYMBvnFL37B\nN998g8Fg4LHHHiMrKyvezWonWjsXLlzIiy++iNlsxmKx8Oijj5Kamjpg29md79mB2kaI3s7LLruM\nN954Y9C8lt055vRWGyWhEkIIIYQQQogekiF/QgghhBBCCNFDklAJIYQQQgghRA9JQiWEEEIIIYQQ\nPSQJlRBCCCGEEEL0kCRUQgghhBBCCNFDklAJIYQQQgghRA9JQiWEEEIIIYQQPSQJlRBCCCGEEEL0\nkCRUQgghhBBCCNFDklAJIYQQQgghRA9JQiWEEEIIIYQQPSQJlRBCCCGEEEL0kCRUQgghhBBCCNFD\nklAJIYQQQgghRA9JQiVEglm1ahXz58+PdxhCCCFEK3J8EiI6SaiEEEIIIYQQoockoRIiQT3++OOc\nc845nHvuuXz99dfxDkcIIYQA5PgkRFuSUAmRgA4fPsykSZNYsWIFP/jBD/jVr34V75CEEEIIOT4J\nEYUkVEIkIKPRyHnnnQfAeeedx/bt2/F6vXGOSgghxFAnxych2pOESogElJycjEYT/nharVYAamtr\n4xmSEEIIIccnIaKQhEqIBNTy4FRXVweED2JCCCFEPMnxSYj2JKESIgF5PB7+8Y9/ALBixQomT56M\nwWCIc1RCCCGGOjk+CdGeLt4BCCHaGzlyJOvXr+e3v/0tGo2Gxx57LN4hCSGEEHJ8EiIKRVVVNd5B\nCCGEEEIIIcRAJEP+hBBCCCGEEKKHJKESQgghhBBCiB6ShEoIIYQQQgghekgSKiGEEEIIIYTooQFf\n5a+8vD7eIQghhOgml8sW7xD6XG8cn5xOC9XVTb0QTeIaCm2EodHOodBGGBrtHApthOjt7MnxSXqo\nhBBCiASl02njHUKfGwpthKHRzqHQRhga7RwKbYTea6ckVEIIIYQQQgjRQwN+yJ8Q/anJ76bSU4U/\nFMCoNZBqSsGkM8Y7LCGE6DceXwCvP4QjyRDvUIQQIiFIQiVEF0obyyg6upbNldspaSxt9/dh1iym\npE1gdvZMnKbkOEQohBD9Z8OeCgBOGZeBRqPEORohhIg/SaiE6MCRhhL+tv8TNpRvAcCg0TPOOZp0\niwuj1oA76KGssZx9dQf4+zf/ZMWBz5mVfQoX5J+NzWCNc/RCCNG3QqqKBkmohBBCEioh2vAFffx1\n3wq+OPRvQmqIEfY8vpV7OlPSJqLX6tvd3xv0sa50A/84+AX/OvwV68s28d2xlzM1fXIcohdCCCGE\nEP1JEiohWjhYd4g/bXub0qZyXOZULh99IZNSx6MoHV+FNWoNzMo+hZmZJ7Hy0L/5YN/HvLRlCWfm\nzOayUReg1QyNSjlCiKFFVeMdgRBCJAZJqIQAQmqIfxz4gg/3f0JIDTEv93QuHnle1B6pjmg1Wr6V\nN4cJqWN5ecsbfHHo35Q0lnHzlO9j1MrkbSGEEEKIwUjKposhzx1w8+Lm1/hg38fY9FZun/pDrhh9\nUbeSqZYykzL4+Uk/ZlLqeHZU7+aZDS/jDnh6OWohhBBCCJEIJKESQ9qRhhKeWPNHNldsZ6xzFPee\n8hPGpYw+4f2adCZunnwdJ6UXsrf2G/6w/kUa/YN/xXEhxFAiY/6EEAJkyJ8YwlYdXcfSne/hC/mZ\nn3cmF448JzLfyR8IcaC0ngMl9VTVeaiq91Jd5yEYUsPzqRQw6rUkWw0kW42k2IzkptvIzbBi1If3\nodVouX7iNeg1er4qWcvTG17ijmm3YNKZ4tlsIYQQg5zXH6S+yUeawxzvUIQYEiShEkOOL+hn2a73\n+c/RNZi0Jn446Wqmpk/maGUjq7aVsu2bar4pqSMQbH31VQG0WgVVDZcLjjYhW1EgOy2JMbnJTB6Z\nyvg8J9eOvwJFUSg6uoYXNr3GrYU/6PFwQiGE6C2BYAiPL4jV3LPvIylKkbi27q/CFwhi0GuxW2QO\nrxB9TRIqMaSUNpXzypY3ONxwlFxrNgvHXsPOPX5+/fc17D9aD4BGUchNtzJqmIP8bBuuZDOpdhMO\nqwGt5vgoWa8/SE2Dl5p6LxW1nkiP1oHSeg6XN/L514fRaRXGDXcyc/xsGlKb2Fy5lVe3vsWNkxZK\n9T8hRFxt3FOJPxhk+mgXBn33v48kn0pcvkAw/L8/GOdIhBgaJKESQ8a60o28uWMZ3qCPWVkzsVUX\n8virO2n0BNAoCpNHpnLqxAymjkrDbOz6o2HUa8lwWshwWhgLzJ6cBYSv+u47UsfmfZVs3lvJln1V\nbNlXhdGQi31yFRsrtvLWjj+zcPyCTsuxCyF6bteuXRw8eJCzzjqLuro67HZ7vENKOP5g8Nj/oR4l\nVJJRJb6e9CKGVJWyajepdiN6nVz4E/HR4GukyltNrnXYgDhXSsiE6oknnmDdunUEAgFuueUWzj77\n7HiHJAYwfyjAX/Z8yMpD/8GgNfDttAtZ/X86yqoPkmTSccnp+cydNgxHUu8Mi9BpNYzJTWZMbjKX\nzy2gtKqJoq0lFG0toXz9RIzjGvmqZC1Hy/zcNP1yUuwyp0qI3vSnP/2JDz/8EJ/Px1lnncWzzz6L\n3W7n1ltv7XS7zo493/rWt8jMzESrDZ9gPvnkk2RkZPRpOxKdKhlVwlN7kFGVVbv5pqSOyloDE/NT\n+iAqIbq2o2oXAE5jMjaDNc7RdC3hEqqvvvqK3bt3884771BdXc2ll14qCZXosQp3Fa9seYOD9YfI\nMKeTVHoqHxb5UBQ/Z52UwyVn5GMx9e18powUC5ecMZKLT89n7+E6Vm7N4GvP+xwwbeTev7gptM9k\n3vQcxuUlD4irMEIkug8//JB3332X73//+wDcfffdXH311Z0mVLEce1566SWSkpL6NPa+4g8E0Wk1\nvfodI3OoEl9PXqLmYYKNHn/vBiNED4TUULxDiEnCJVQnn3wyU6ZMAcBut+N2uwkGg5GrgkLEamP5\nVpZsfxd3wM0422T2rs7lm3ofo3McLDx7LLnp/XvFQ1EURuU4GJVTyPn1w/jtumdx5+5i/X49a98u\nJzstiXNPyePUiRnotLKigRA9lZSUhKbFfEeNRtPq92gG87HH7Q2wcW8FqXYTo3OS4x2OSFD1vgbM\nOlMk6U6khLnB30i9r4GspKHdKywSV8IlVFqtFovFAsDy5cuZM2fOoDigif4TUkN8sPdj/nHwC/Qa\nHZN081j7mREFlSvnjeLsU3LRxLknKMuWxs9P/hG/+/o5GvO3kZeZxu6tCos/2s77/9rHOSfnMacw\nG6NB3vtCdFdeXh5PP/00dXV1fPLJJ3z00UcUFBR0uk0sx54HH3yQw4cPc9JJJ3HnnXd22dvjdFrQ\n9cIcFJfLdkLbl1Y1YbeZ8aut92W31QKQlmrF2o1KcJHt0qy91sN/om3sjmAoGLeiQP3VzubXKCUl\nCVda1xcPG31N7Dx6CItiIiVlOPXecC9VT+LtizbuPLADFBiVPAyLPnop+JLKRipq3Ewcmdovoz36\n6rUMhoKUNVbiSkpFF+fiVc1tVFW130fQ2JrCr3NqqhWnuW8/N73xWiZcQtXs008/Zfny5SxevDje\noYgBxBf08adtS9lYvgWXKRVL6ams2RkkxW7kRxdNYlSOI94hRmQmpXNr4Q/4/foXOGz5Fzd/dyG7\nd+j5v41HePufu/nrf77hrBk5zJ+RG1ORDCFE2AMPPMDrr79ORkYGH3zwASeddBLXXnuj8YByAAAg\nAElEQVRtTNt2dOz57//+b8444wwcDgc//vGPWbFiBeeee26n+6quPvHFvF0uG+Xl9Se0j6oaN3X1\nboBW+2q+raKyAXdj7InR8X01YDGd+HdTb7QxVrur91LrraPQNanfl6/oz3Y2v0ZVVXr0MXQ1VXtq\nqK9zU48bjTYt6vslFn3Vxvq6Y/Ho67DoA1Hvs2ZbCQB2o7bPj5l9+VoerD9EWWM5qeZU8h15ffIY\nsWhu467qvdR565iROa1fH7/5Na/UNhAw9t2onWivZU8SrIQ8S/vyyy95/vnnefnll7HZ+u+qlRjY\nar31PL/pVQ7WH2KkPZ+GbYXsOOJhwggnP7p4Uo/XWulLw+25/GjK9Tyz4RXe2vs2P55xIxfOmsU/\n1x3in+sO8f6X+/nnukNcesZI5kzNjnvPmhADgVar5YYbbuCGG27o1nadHXsuueSSyM9z5sxh165d\nXSZUg18CjQmLUa23DgBP0Ds01gPswUvUo4qP/cQb9GHRWzq9TyINVewJT8B77H9PnCMJqzv2mfGH\nAug1CZk2JISEm6hRX1/PE088wQsvvEBysoz1FrGp89XzP+uf42D9IaamTKV07WQOHPEwa1ImP1lQ\nmJDJVLMxzlH8YNJCAqEAz2x4maPeg1xyxkh+c+ssLjkjH18gxOsrdvKbt9ZT2gtXvIUY7CZMmMDE\niRMj/yZNmsRpp53W6TadHXvq6+u58cYb8fl8AKxZs4bRo0f3Wfy9rovrMF5fz9Yqitd5azAUHDAT\n1ftLVZ2HHQeqCfVCNpHI+cjhhqMx3Cu2FoRCKgdK6nF7o/d4iTbilKlW1XlYt7MMfyCx11Tr01Sz\nJ2MuP/roI6qrq/nJT34Sue3xxx8nOzu7t8MTg0SDv5E/rn+JsqYKZqbNYu0XKTQ0+bhw1gguOSN/\nQFTOK3RN5ObJ1/Hy5iU8u3Ext0y5nvEpY7hodj5zCrNZsmIn63dX8OArq7lszkjOOjn+88CESFQ7\nduyI/Ozz+SgqKmLnzp2dbhPt2DNz5kzGjh3L/PnzmTNnDldddRVGo5EJEyYMqt6pXYdqOHVCZsz3\n94Y86JTOTx/cAQ9bK7YzMnkEKSbniYbYyvqyTWg1OqalT+7V/cZDVZ2HQDBEujPc61JZ68Fq1nd7\n/uyuQzUA1Df5e20JkIGqs9N+b9CHTtGi1Wgpr3FztKqRyjoP08e4evRYgWAIRQGNohAIquh1vdFP\nkTgpbcuy+7FG5Q64OdJQSp49p1d6tPaX1GHRWqms85KZ0nnvZDz1aUI1b948Lr74Yq644gpyc3Nj\n2uaqq67iqquu6suwxCDiDrh5ZsPLHGksYXrKyaz+zEmTJ8D3zh7DvOk58Q6vWyanTeDmKd/nxc2v\n8/zGV/nehKuYkTGVZKuR2y6bzJodZbzxyS6WfraHncU13PidCb0yf0GIwcxgMDB37lwWL17MzTff\n3OH9ujr2fP/734+UYR/KAqEAR7zfoFE0FKodV1wrd1cA8E1dca8nVADBUPd7FXqyJlNn/KEAB+qK\nyU7K6HIYWkeaE6F0p4Umj5/dh2vQKAqnjI/+3JY0lhFUgwyzZsW0f48vyJGKRrJSLQPi4mJXFEVB\nVVV8oQDVtf6oiw939DKH1BCby7ei0+qZ6pqEPxju5fR1o+fjUP0RkvQWXISHBK/dWQZAqt1EZZ2H\nqaPSMBm6d1wOqSEO1R8h3eLCpDNGbq+odVNn9mGPoWCMO+AhGApiNXS+rEMopMKxBDAWnqA3pvu1\ntKdmP96AF4NWT65tWLe370hnn9+Qqsb9InOfno0tW7aMFStWcN9996HT6bjssss455xzMBiG9tUT\n0TtCaojFW97iYP1hJjum8vXn6Xi8QW44fxxnTBmYPZoTU8dx65Qf8OLm13l161tUe2o4K28uyrED\n7Lg8Jy98sJX1uyt4eMla7rxqqiwMLEQby5cvb/V7SUkJpaWlcYpmcAmEwiefXQ65S5yL7EB4WPiu\nqj29us/SxjJqPDU0+hspdE064f35g+EnrbNhe4fqDwN0mFA1efxU1x+fe3O0qhEAnU5DenL06njt\nJPQkJIUd1bs5VFWFxZtLVZ2ZCSNiW3w4eOy9Gwj2bH2tkBqipDH8PTKmTSdBZV34OW90B7qdUB1u\nOEpZUzmN/ibGp44BwBcIUV7rY5unKqbe460V2wG6LByxekdp1IS9rKkcu8GGSdf6fGJ/7YEWv8X2\nvgjG+h3RS1RVZfX2UhwWA+NjfC/0hT6dQ+VyuVi4cCFLlizhoYce4u233+aMM87gd7/7HV5v97Ne\nIVr6aP8/2Fa1kxFJBWz+v2w8viA3XTBhwCZTzcamjOJnJ/0XyUYH7+/9iKU73yNw7GqsPcnAz64q\n5KwZORytbOLRN9ZRUiXzqoRoad26da3+1dbW8j//8z/xDitulK4mUXVnXy2uAsfS49Obj90dB0rq\n2XskXDpcVdVeT6bg+AljsLdOHKM8nw1uP7sP1RAMxfYYB0rrox4Tmhfr7QtubwB/oG9Ono80lFBc\nfyTye0OTj51Hyqio9RBUAzR62vdU9nZPZF/v13cswQuonfe6Nrj9reYReXw9e94DoSD+Fkllg6+R\ng3WH2FK5o919m/x9f37hDwUiSZg/6McX9MW8rdsbiMyBq22Kfbu+0OfjhdasWcN7773HunXrOPvs\ns/n1r3/NF198wR133MHzzz/f1w8vBqlN5Vv5+zf/xKFP5ptVBXh8IW65aGKHwyQGmmHWLO6acRvP\nblzMv46s4nBDCTdNXkiy0YFWo+Gab4/GkWTgzyv38Zu313PPtdNjv/ooxCD36KOPxjuEXuUL+tlf\ne4BcW3aroWUhNYRG6bvrooFgCH8g1KoE9cHS4+WFe/v0cldxDcFgKOarzMFQiGBQjVqV7mhVI43B\nOrTWWiy69sPx1F6Mvjlp7Iu1erbur0JFxWYx4LCHX2t/MMTq7aXkZ9lxxfi97w8GWlVpCwRDvbaA\n/Ma94eGd3ZmHF6sjLYpQBEIqRyqa8LYY7hktyTmRvOdweQMqkOPqeu2uaE7kfdXq4kObt1EgGGLL\n/spI75KqqmzYU4HZoKNwVFq3HueAZxeG8tJIb1Ykkeviievor8FQkHJ3BWnmVHQ9mDO1sWwzEO5d\n21i+JfJzu8ePEkDzey8R9GlCNX/+fIYNG8aVV17Jr371K/T6cKW1goICPv300758aDGIlTVV8Nq2\nd9ApOhq2T8HTpOHGC8YPmmSqWbLRwZ0n/Zi3dixnbekGHlvze26ceC2jnQUoisJ3ThuBVqPh3c/3\n8OTb67l34Uk4bcaudyzEIDV37txOT2i/+OKL/gumF/hDAVRV5WhjCfW+enbX7KfQNRGAGm8te6r3\nke8YQaq59+coAWzcU4E/GOKUcRloNOHntVXvRy9nVFX1XZeJLqtuorzWjcthZv2uCgKhUIcn8mW+\nIziaHKSY21/FL2uqwG7oeFmWg6X1BEMq+Vl2VFVlf91BnEYHdb56MpMyMGpbT13YXb2PWm9tl0Ou\nDtYdIqAGGekY3v6PUd67zSfovqCPrRX7AWho8mNTVfYeqY05odpRu51KTMzInMb+o3WUVjdRWJAW\ndb2mli9rMBRCq4kt8VJVNTxHJ4b7lVQ1kWI3YexGifaaBi9WbTLeUHh9oo56P4vLG5iY1LOhX8Xl\nDUDPE6re0tG3WIWvlK/LSpmSOpGA6udAwxHGBmJfXijYRS9YZ5qT17YXDoobDlPRVIk74CXfkdfu\na2F39T4a/I2DoohMZ/o0oXr55ZdRVZURI0YAsG3bNiZMmADAW2+91ZcPLQapkBri9W3v4Al60B2e\nRkOVhevOGcusSbFN0B1ojFoD10+4hhH2PN7b8yF/2PASlxacz7zcM1AUhXNn5uHzB3n/X/v5w/JN\n3HPttG6P3xZisOjsuFJXV9ePkZy45ip5DdqcyEm1ikpZdRNubxC3rhyAkqayqAmVP+iPrLPU3U6T\nkKpSXNoQmbQfDIXQaLTd2ldv9gD5g350Gh2KorDu4F5qAj7SHP+fvfMOb6y+8v73VnXJsmVb7nVm\nPIUZYJihDhBCh9CWll1qSEICeUmFLPvuJvuksWETdsmbRkjCpkAgIVkICSUBJhPqANObp9rjbtnq\n/db3j2tJV9KVLHssa+y5n+eBsaRbfrdJ5/zOOd9jKjkNTqsmKcwphjMvCRiNjqHBXJ8+X7IsY8Sr\n1B51NNgR5ELwxX2YjPkw5ovCUxXCuqaVWdvzxv0gpwQT1MYmJ3IQJCEdWfTElOum6VAVOwfS7Op+\ncoknBQx5AwgIkwhGLTAZihvjoiij1EDWzsNeMEMhrGhxZL3vjftAkRSqDMr7/nBSSU30xnDKDNT1\nZBkgcypVtKIW4QKpX3mLliOD7xi2WcozExR8CA0BSx0c/PwEomII/aGB7O0UiZT6+Ym85WaSyjgZ\niOPQSBAr26thmxLLSPXNSuYIWKQc3mAyWNK2U2l/hTieK/uAMtdQ/eEPf8Bjjz2Wfv3YY4/hO9/5\nDgAsCrUZnfnntYG/oy90FFSoCeHhetx8QTfOP2XuVGSORwiCwIdazsFnT7kbVsaC3x/6E57Y81T6\nS+wjZ7fj3DUNODoexuMv7J2TPiQ6OguRpqam9H/xeBwjIyMYGRlBf38/vvCFL1R6eDMiyisGvSfq\nzZqJPzIawqgviv1DAYx4oxjzxvKeeV/Cjx0Tu9MF9GokWcYHvR68u3es4L59wURazICX+CyDi5My\nRlMp3zTH+ksfF+LYMbEb/aFBAEBMiqQ/4yUOvMRNaxAGEoH03wlehDRlSEb5GHZ4dsETncBgRBF7\nGJqIYPO+7POW2n44ziOaENA/7lfenzoDoiSgbzSMvtFMOuTOw5M4MBjAzok92OvNSPb7wkmEYwWc\nI43jEOS5caRS7Dg8CQ8/hIgYwkQiY1xn1YGV+BMSjHJZPcziXH70Yzw2gb7gURzyH0m/l6r7Sc5A\nXa+Q6zyXjvtMmauaKrU5PDwZxb6j/vRkBgBsGTqAybhXtV/lHEpI1fBlzmOMj2HL+HZ4YtqpcGEx\nOLXONOl9Gp9LsoxBj/L8eQJxzfX6RkOIxLMdWnnquDzT9NHc5tmp+X6hSGQypy5QlmXE+FjZat2m\no6xT2Zs3b8bTTz+dfv3oo4/i5ptvLucudRYxI5ExvHDkFRCCAZFDy3DNhg5cvL610sOaN7qrOvDP\n6z6Ln+3+NbZ4dmAkOoZPnHQb6s21uOXiZZgIJLDt4CRe3jyAy8+Y2eynjs5i4hvf+AbeeustTE5O\norW1FYODg/jYxz5W6WHNiEITI5yUBEMoM8PRhACeS2AymIBAB2GijXAaq+BPKEbTZNwHtyU7FTrJ\niRCKRHbGfDH0jynRvLgYwxg3gIawiO7qdgDAODeUWbjAGIUcSfNgMoyEEEe9pa7wARcgyiuGmzfu\nRb0hIzgkAxhKKkb6AT8HSZbSCmm5+08R4wQMT0RhNtDoqDdgn+8AJChRGwOt7GdoKuVLlEWQIBHi\nwpnoUPp4tQ089TWLJQXEkgLonMyzlBqcLMsIceGidXDeuB+DicNwMrWoRfa5S0oJGEhFkS1UUjF+\nZmwpAzwlquHxx7Bj4jCcdjq9pCiLRdPDREnCvqM+AIrTTRIkKIJKH5sgCSAIAoOhoYLbUOMPJ9E3\nGsLKjup0GmBqfIEoh4lAHCaWzjJaQ6IPNaQbe/p9WNZSpbHV0glEkqiyzixlXuvuz32PE3mMREfR\naGkAOxUB1UKSAX8sikDIC0mWEY4JIEBBkiUMBz0QqAgAbcGtw0NBgObRWGOBN6E4+0OREdSZ82ur\nCBCQoTQ0XueW086KP5zEZCiBNS4R+wcCiHMiLq+zI5YUEIxycFhZbNnvAUMWVunmBBEBfwwjiSg6\nGmwIcWH0BQfACxJiSQFHRkPpfmvFkICs1NG006x6vjyBOI6MZEe+AsIk9nrH0GJvRr15dn3FjoWy\nRqh4nk93lgeAaDQKUTy+Ox3rHJ+Ikohf7HkaoiwicWQlLjutGx85q73Sw5p3HAY7PnvK3Ti/+WyM\nRsfx8Pv/Dzsm9oCmSNx99Uo4bQb8ftNh7B/wV3qoOjoVY9euXXjppZfQ09OD3//+9/j5z3+OeFx7\nRvV4RWvmXRB5DCf7MMGPZr0viRJGIqM4HOgruG6ppJwpAEhIyoyyenY8e4z5HAkexXbPLkyoZsgP\n+g9hMDxc8syxLMuIxJXIGKVyNlJRs1xCXBhRPoqEkIQnNontnl0YTCiqfv5IEgOeMCQAPK8Y6LGk\nAIfBDsgy/OEkRiajiGSCS5BkCQOJgxhMHsIB3yEMpJyCEjJrRFnEAf8hxEXtsaYYj01gv/8wfHxh\nOf8gpxiMESFjOMoAJkOJLGdnb79v2nGl19e4BkdGQ4hEs98fSh7GcLIv7XzxggRfKFPjpt7MUPIw\nBhIH068lWcaOid3YPiU2kIskS5BkMescHRgMgBNEePxxCKKEeFJIR80mg8qzq0TA1NFSxb4Mx7is\nseXiCyUQjCiRVU8gjnA8P+rXO+Avug0tNAUxcl4PhIcwGfPi6FSEtRCj3iiGJ6J5z3ZmX4Vf85KU\npXYoiBIiGk62Opoo5tS6TU4dezDCIZLg0+m0w5NRROI8PP54ge8kCR5fHLwoZY3JG0ognIxhPDqp\n+dhIRerthjwRvNeb/1yol/ZrXKtU9DqVzjvflNWhuvnmm3H55Zfjc5/7HO677z5ceeWVeoRKZ1a8\n1P8ahqIjECaacGbLalx/XtcJmzZKkRRuWHo1bl9xM0RZxE92/QIv978Gm4nBp65eCQIEfvz8nvQP\niI7OiUaq1yHPK0b5qlWrsHXr1gqPamZQRdT7omJp9WCJqWafx8qYP67pVGlJcfviOca96nu6lL40\noixg0BPB7j7vVFoRgXCch5jbuFPO//NQ4AgGpgzXlHLZZDCBJC8pY1WtT8lKNCIxJbmcSKojOMLU\nvzM/d96EH6FkGGNcvgEd4TIOxM6jYzg8HER8ymmVZDnPyE1FwtSGbKhQumCJ9Cf2Z45LdTrNlDW9\nF1mW09dKlmXwIo93Dh3C/kF/Sb8rxfzm8dgE9gT2YDB5GGPcIAI59TWyLGPrgQnsODwJSUqNQXtb\nnJwAL2lH52IJAUOeiCKZPxTAvgE/BFFEMMphzBdDNMFnpdUBSMtvl0ohhyAQSabruFLP33T3Umxq\n3zKk9ERGqeRue2giilFfLOt4Do8Ese3QRJbd5E8E0R8ayLq/Cj2hanl2WZYhTx37mDcGbziBUW8M\nUT6afnaSvIj+sTD6RkNZDtXRsTC2DR7Bq7t68aedWxHVkGVP8iIEmZ82LTGfypY7lDXl74YbbsDZ\nZ5+NXbt2gSAIPPjgg2hoWJziATrlYyA0hJf7XoPEGdFNnInbL+05YZ0pNevdp6LJ2oAf7XgCLxx5\nBcFkGDcsvQrXn9+F3248hMf+uAdfvPnkkhWadHQWCx0dHXjyySdx2mmn4c4770RHRwfC4fD0Kx5H\nWBkLAKDOUoNQMAFOkMDQhZ7lwoZETIhj9j/1ynbjSQH9wQG4TDVZnx4aCUBgQnCZagqmM6VSiryh\nJF4Z34OzlnagyqQthODjPQgKPhCBDsSEBAIRFkRSMYBtJhbu6uLf+wkhASnX8coaS4Y8Z3DqQ05K\nYlIjSuAJxBGMZhvvHC9CkGRQ5PS/R5zIYf9opo4oMVVvlDo/Ow5N5tWEqDp+pZfjZ1B3pKYU2zQU\n5SDJMihZbeQS2O87guHoKKqZOiR5x9T2Zma8JngRAz5POgKTctjiQgJVBgcIQhmjLMsQJQkEQUxb\nDyzJEoaSR9Bh6sn7bHefkjpnNmrf+7uOeNPHMZzsg42uQjOKK/sNT0YQ4wSYp4Sfhia0o5C9Uxki\nZ6xwZzkrKbVIl6m6qMKksnDxj9Woo5UyZPCikkaqdtAnNGqe+oL9kGRgZFwV7Snhug4mDyMQYdCN\n09NOqTDlcI1xA8VWxagvir54Zpm93gOaCbQBfhJMmEONXZWGOc3QZBlI8hKGw1G02+auLUCplHVv\nyWQSe/fuRSQSQSgUwltvvZXXwV5Hpxi8JOAHW34FmZDhDKzDZ64+dd4fkuOZJmsDvnTavWi0uPH3\n4bfx891P4oLTGnDKEhd6BwJ48Z2j029ER2eR8bWvfQ1XXHEFvvCFL+C6665DW1vbAux7qJgZBEEg\nGOEKNmwFMnaGOsIwF+RupS+Y/X0SFLwYiYziSLC/4DZMtFLn4wsnEBR82D62T3M5jz+GoKBEt0YT\nA5jgRzGWGEE8qTgQKQckc2z5x+gLJ3F4JISERuSMQLZDlZ8WpGzPww0jKeWnE+U6UwCwf9CPvtFQ\nQbVBdfSEE/kcZyh7/ElehCRLSEiZWq7UxKEMICaWp8FqLJGJeEXjPCaDCQQSmQioDBm7BxQBE7Xh\nPuotPB6t22/QE8GuwVFEc9LtZFlGgufSztPB8H4MJg8DOLam0Knt5Uf9suHkJHiZg4/3FF0uyYsY\nmAhhWOVEjYe9+aIhRZ69EBeGL+7LajLNizx8cXWKvqz6v/JX6j5QR6JCGvfjmD8+rT+kjhILkoxo\nnEcgpkrlLLJuTAwrDq8sQJoaC0EoqcGiLEMQpXQapqw+70U2emg4AK5Ac2JOECHJStSuGLIsw8dP\ngJc5RBM8glEu7z6bD8oaobrrrrtAkiSamrJV2K6//vpy7lZnEfHY5j8gIvtB+zvwwEcu1uyZcaJT\nZXDg86d+Go/t+h9sm9gFcY+E2y69Cf1jYTz/Zj9WdFSjq9Ex/YZ0dBYJN954I66++mpcccUVuOqq\nqyo9nFmRNqan6okApP/VIs4JGJqIosMUR64Fo47opwyulGGVEkSI8XHs8+1HTLTCTGnP1Htz0vkU\nY5JApEjNQq7gQkpAgRM5jMcmwAoO2KvMODIaShfMp2b1E2IcBLJVHXhZWX/cr5pxlwEQmT5WKSUy\nNQlOxEQws44/6YfVlImqpc8LSosARbhoWjFNre6nrh0bSh7BEkxFdCBjYCw/SqoOpnn5MUTEEOrY\nRgx6ZDA1U1EUSBiNjsBpM2jU0hS2Vv38BFjSCAtlgwxgXCNK4Y9kDPOU2aqVrpg74FQ6mdb+40KR\neqZIIuu8D0dGsWl/L1yMGwABkRbSjtt0hvRM0T5TxQ3/4YkIbGYWLEPCz2eubTgZQV/4KCiCQqtx\nyTT7SH2W/2lfnuR54TGrnb5Uvyw14RiHYLRwSmZuym3faAj1TnPWefbEPACM4KQk3hvanrW8X5jM\nOoah8AjiQqaP2PBkxtnUOtZCcu7hGI8auwGiLMMXSk6tr8AJIjzcCDpMdgDKxIo/PCXTLsUxyY+B\nAJnuTTb1VVARymqdCoKQpfKnozMT/rpvB/bGPgDBmfH5c26esQLPiYSZMeHeNR/Hj3c+gZ2Te0CR\nz+JjV1yGR57egcf/uBdfvXOd7ozqnDB8+ctfxksvvYRrr70WPT09uPrqq3HBBReka6sWAmqjoJQU\n52BUcbb6xvyw1eYbohljRsYkN4qwGAQBAu2mZQCA0eg4ZFmGlx8v6FDlIsgCeJECo8oa4EUJk8EE\nauxGsBopitGEAG/ch8m4D+NhPyIBL0YifvBSgZ44qpeTyYzMdzHnUotCMs/TEYzxmn2NxmMTGksr\naYeFUM/EZwzOzLVNFdUnpDhIUEi5HVq1ZxExCCNpwpb92uOQZRkBQXHIOkw9CEe5PBM3t6eTljeg\nFjgKCj70hY+grmpl0UhIUkyCEyUEI0rKVkomPnUc6tqj1HWc5JUomNOY+Z3XCi75hOKRpFwifASC\nzIMmtFNS5Zy/PZwin7/9EI3GGnPacTm525WORnkCcUSTilFfrDZKq8Ywl9z7JTVhoAwsM6niDSYg\nyOrfcO0LIIrK+5IsIRzLdl61SPICZGS+F8fiYyDFGsTECJzJ/O9LTs6Mdyw6DlbKVxIEAK2grQwJ\nBPIbOfvCCciQEYpwWXVTud8FMoC9/X5wU5FeDzdSuK1ABcpCypo71d3dDb9fVxvTmTkHRybxv/1/\nAADc0PUPaK/Pb1ypkw1LMbh79R3ocnRgm2cn3o/+FZec3gJPII7fvHpw+g3o6CwS1q5di3/913/F\n66+/jjvuuANvvPEGzj333EoPa0bIMjDgCePg2DhCfPHGmOpZ2aPxQ3kRI17i0Z/Yj+HkEWw5NJqO\nrMiQ4eGGERFDM1Y4S3F0PDvqMhlMIBLns2o2hJzi/5HoGARZQJIXERGDOBoexAjXn5fileDEjDNJ\nAJ5EYUU8ZZHSjajRqfRJdWoVkG8gK42Us8UKCMysGao206+vdhpz0zmjYhhefryoBH4pe0vVvhDQ\nrlmayIlKRqb6oxU/fhlj3hgCkST84Uz6VUQMIp4U0tLxQP69kbudWM65LyRscmQ0X6hFlAXs9R7E\nYOLw1Jgzn6UM8YiQ6VEmSTKiYhhRMYwEJ2Rtk1DdXcEohy37CtyLqn305ayfIhznsWNESfvjxOkk\n72VEE0KJ0vjIOrdvDygqi7GEEvXTijwGIjl1gYKUdir7hgt/7yR5EQleLOi3aN1LAaGwGmWuM5U6\nbjXDkxFwgiJYERL8KHxXVyZGVdYp67GxMVx88cXo6uoCRWW80ieffLKcu9VZ4EwG4/jeO78B4Yxj\ntfV0nL/0pEoPacFgoFjcs+ZOfH/7T/H++FZ8uMWGtv46vLlrFCd11WBdz8z7wOjoLERCoRBeffVV\nvPzyyxgcHMRNN91U6SHNCI4XkeQljHhDYKaZ+pRluajBFeEVB4vTUERLGZBUvAa2GQbw1GpkoiRh\nh2dfOvqgdgDinMZMvURiMpgy/qjs2o4pI5svUFuRt6mp6NtMm5oLolTyPtTIkEsSechFbeal1lcb\n2qlzEBL8YBkDJjVq5tS7Vdd68RKHUW4ALsadF2GMitqCLCJEHJnwwEQpAihaqnWFaou0zpqfn4BF\nVko8Uo7adMqO8URxZb1UCthskGQpp9ZIrVQHgMg0ugWynZE8iJlryMU5DqMTkxTNhWEAACAASURB\nVLCZGQBhhDnlOoz5YhhDDAZj4S1qpcyV4iYk+cz55CQO7+0bhzQVrUylxRUjJegSk8IwyPkugqBS\n8Rv0RFBV79Yev0YmpSAX/o4qpOindcwpB1l7x/n1kvNFWR2qT37yk+XcvM4iJBLn8e0/vwjJPQgH\nWYu7Tru60kNacBhpIz695mP47pYf4LXBTbj87Csx+gKJX7zUi65GO6rtxkoPUUenrNx11104ePAg\nLrroInzqU5/CqaeeWukhzZxpLAJ1hEAdyeBFpYnm8GQUVhMDQzIMks44NIXqY7R7V2Xe40UpK7Uv\nlw/6+nAkkEk/Sw3/wGAAIQ1pZEkqfIBq26oUqfU+jehEKYz54lkREo+q/qkYgswjyM1sn3EhDorK\ncqmm/tU+D6kUuBQpZ1Gz4SkyTY4n+BG0UUuzPivkFPmTfoxxMdSxSsPYXBnxYoS4AKKiAAuZUasL\nCF4YEiQk//TNW1NEcyJQ6muf5MWS0uZEWYSXH0MV7QJLZlIGEyoHIiB4MZyl/K/dUylFqoF2Kem2\nkiyCTDU1Vr0/GBlGREwiEEliSVN+HXMoGUE4xsFqLp6Wdyyk75tisu0azkyh546Tsh3cQpFK9f2X\nK7leCpECrSFiYmV6TJVCWVP+1q9fj1gshgMHDmD9+vVwu91Yt25dOXeps4DhBRH//dzbiLq2gpQp\n3LfuNtCkXvczGyyMGZ9e/TFYGQteHn4RH9rAIpYU8NM/7S3YO0NHZ7Fw2223YePGjfi3f/u3helM\nYfr0tbikpF5JspT3TKeKwyNxHt5wPJ3eBqBgzUEqiiHIPEaTA1kF+ADQryGooOZIINtRS42+UEra\nTBXcyjHjnFIOBJQI2/bhQ0WWzkaQZtazaCA0lCXnXtydyscfTuLQcDCndqyAMSvLOJo4kBlroTqT\nKXKN5GKEYxx2Hx3DWGIYE9xIniMeEoLYO35UFXEgZhTZCaj6XO3u85bUiyggTCIqhjHODaXfS0qJ\nLPl7Pz+BraO96dcyZExwOQ2yVfsaTvYVF+dQrwdth0GaRlTDF07AE4hjMqAdGUtIsayoUnZC3LQS\nGznvl+d3Pyzkfy9IspQ1kNnWLwKZzUiyiKDgm/Ze9ocTCItBJMX578NZVofqP//zP/Hss8/iD39Q\namFeeOEFfOMb3yjnLnUWKJIs47E/7cKw9e8gaB439VwDt6W+0sNa0NSaa3D36ttBEiTei7+EFcto\n9A4E8Mp7xftE6OgsdM4777ysNPOFTjGbUpD5WaWf5RKJ80jyigGYkGIICJNpGfP0OGawvWhSwKgv\npjmjnBSSJTdRzT62uTUK1VtLcELxlK8c/OHSaloK7S/XpfLz2uIShdcvbCSXqlSYcuYCgjftoE9H\nPClg12TvtHLkKaJxPq8Oaq6JpGsCJYiyAB8/MW16m5cfT6+Xoi/em/U6ldIaFLwYDA1lfaautRtM\nHE4vm6op5CUuS2Z+IhjHiDeaFQVMRd+KpeuOT9UzqcdTjLCqJkxNMYcqLBav0SzGUZ929FM73i0X\njDwVIvWd5OU98PEe+IXiz0laHVCaf9n0sjpU77//Pr7//e/DYlHyc++9917s2bOnnLvUWaA88/pB\n7OL+BtISxpnudTin6fRKD2lR0Olox23Lb0RCTCJY9xbsDuAPfz+C/rHZpcjo6OjMD1lF7NMUpBc3\nbQlMcvmNarUY1pBiVlOoRkmr7xNQXIlv1De9Ac/LHAJFZKDnkpkaeupIipqDw0EIJTgbqSUSUgx9\n8d60It9MkCHDz0/kOb6zYbapVNMZ+TNJI8ylVMdQnZ42yY8iKHjh5YsLmJTinACKY+TjJ3A0OA71\nkzaeHMlabjQ5kHZmQoIfQ8kjWTVugQiHaEJQ1Q0WR5hFbd9cUOi+niu0mmYXw+NXrlNK/bCUFGCg\nIiJ/5XWoDAYllzXdlEwUIYqz6/Kts3j5y3sDeH3gTdCuEbRaW3BTz7WVHtKiYm39ybi0/cPwJf2o\nW7MXoizisef3lDxDrKOjM/8cS1PTXEpN95kuverIiLbTodX3qRgzyTpOizcQilrZQkCrpkuQsgUw\nePnYDVdJlhAQvNM2pS0HqUuoTrOba8aSpaXcpRBlETGxtEhbqaRq01KGfTFSdW8BoXAtXkrWfDoK\nTVJkkEtygueqyfcxMQdDmOueZOWgrA7VqaeeigcffBAejwdPPPEEbrnlFqxfv76cu9RZYLzf68Hv\ntr0JtrUXFtqCT66+FYxeNzXnXNFxEdbUrsJwYgBd64Yw7o8r9VTHw5etjs4c09vbi+uuuw6XXnop\nAOAHP/gBduzYUeFRzZAZ+FNBrnB7ksQcG5hzweGRmacYSXJ2H6eFxvBE9nUodaa9nOQKQsyE4nLn\nx45Hoz6r0pQ6mmK9qebKwRFLvH8yfbsqdy5TUUpOml1rBmXdmU1AzOWEVKmU1aH6/Oc/j/POOw9n\nnnkmxsbGcOedd+L+++8v5y51FhAHBgP46d/+DqZrJ1iKxWdOuQtOY1Wlh7UoIQkSty2/CU3WBoxg\nL5p6JrHt4CT+9HZ/pYemozPnfO1rX8O3vvUt1NbWAgAuv/xyPPTQQxUe1cyYiUEwlhgp+Jm/yGx5\nJSkk5V2I2cibHw+kon7z6QyW6ohkS4ofX0RnmIK5UFBHnnL7LJWDkOAHL3FzUmd5rAjy/EWYF51s\n+uDgIFauXImVK1dmvdfS0lLO3eosAIYmInj0z38H1bUFFEngU6vvQKutudLDWtQYaQPuPukOPPzB\n9xCwb0WV+ww8/0Yf2uptWNOt3e1cR2chQtM0enp60q87OjpA04s38p0q3F6MxKSZOV7HI+EitWRz\nzfEQ+dKZD0r3kIaSR2AgT7R2KfPvUpX1F+b2229P109xHAefz4clS5bgueeeK+dudY5zvMEEvvvH\njZA73wVBibhz1S1YVt1d6WGdENSYnPjESbfhe9t+AqJ9K+jw6fjJC3vxr7etRUONpdLD09GZE2ia\nxuDgYPr3Z9OmTSWl2jz88MPYsmULBEHA3XffjYsvvjj92dtvv41HHnkEFEXh3HPPxb333lu28QPl\nkzleaISEwumMCwFZVn7zdBY+pTTGnS9mOtGQPIZ0u4UIWQFVirI6VK+//nrW64MHD+LZZ58t5y51\njnMicR4P//GvSLa+DZKScPuKj+LUutWVHtYJRXdVB25edh2e7P0dqlfvwvh7p+C7z2zHv9yyVm/6\nq7MoeOCBB3DPPfegr68Pa9euRVNTE7797W8XXefdd9/FwYMH8cwzz8Dv9+Paa6/Ncqi+8Y1v4Gc/\n+xnq6+txyy234JJLLkF3d/kmgipRA6BTDuRjUrqb1R51X3zREyogj64zxWJzqHJZsmSJLpt+ApPk\nRPzHCy8j3PAWSBL4+Em34uTaVZUe1gnJWY3rMBodw+uDb6B13UEMvLsc331mOx68ZS2spvJ1bdfR\nmQ96enrwwgsvwOfzgWVZWK3WaddZt24dVq9WJnfsdjvi8ThEUQRFURgcHITD4UBDQwMApc/VO++8\nU1aHqjJVADpzTkWcG92j0jmxWXQ1VI8++mjW67GxMYRCi7PQUKc4gijh2y8/D1/NZpAgcffq23CS\na3mlh3VCc03X5RiNjmOf7wC611Xh0PuNePR3O/Clm0+BgV08TVF1Thzuv//+dJqfFg8//HDBzyiK\ngtlsBgA8++yzOPfcc9PNgScmJlBdXZ1etrq6GoOD00s6O51m0PTsnqVYgofJxAJA+t/FzGI9RgFE\n1rHNx3GGCU9Fz+divZa5aB0nbRJhwuI5/oV6LWtqrKh12Epevra29GULUVaHKrdT/bJly/C5z32u\nnLvUOQ4RJQn/8dffYty6FZTE4r5T78KS6o5KD+uEhyIpfGzlP+E7W76P4dhOLFtjw/4dwPd+vxP3\nXb8aBkZ3qnQWFmedddYxb+PVV1/Fs88+i5///OfHvC1/Cb1rChFPCojHOZhMLOLx41eNbS5YzMc4\nqDqu+TrOOCp3LhfztVRT6Dg9vBdxYXEc/0K+lhOTIZBcaULmtbU2TEyE896bKWV1qO655x7N9yVJ\nyScmSe2DPXDgAO655x7ccccduOWWW8o2Pp3yI0oivvHaL+Bhe0EKZnxp/SfRVtVY6WHpTGFmTLh7\n9R34zw++j2HiXfT0XIh9vX48+rsduO/61TCyi1cZTWfxce21mabgBw4cwKFDh0AQBJYtW4bOzs5p\n13/jjTfw4x//GD/96U9hs2V+UOvq6jA5mZEfHx8fR11d3dwOXkdHZ8ETFHyVHoIOFNtmvimrtbR6\n9WqIYn6DM1mWQRAE9u3bl/dZLBbD17/+dZx55pnlHJrOPJAUOHxz00/hpfpBcQ7881mfQqO9ptLD\n0smh3lyLu1b9E3644+fw1byF1T0XY2dvAP/12x343A1rYDLoTpXOwuLb3/42XnvtNZx00kmQJAnf\n/e53ceWVVxbNkAiHw3j44YfxP//zP6iqyu6H19zcjEgkgqGhIbjdbmzcuBHf+c53ynoMurCAjo6O\nzuwgibK22dWkrJbSvffei+7ubpx99tkgCAIbN25Ef39/wcgVALAsi8cffxyPP/54OYemU2bCXAQP\nvfkTBIkx0PFa/MuGT6Le7qj0sHQKsLx6Kf6h+yP43cHn4Wh8F6cR5+GDfX488sx2fPaGNbpQhc6C\nYvPmzfjzn/8MhlHuW47jcPPNNxd1qF588UX4/f6sZU4//XQsW7YMF110Ef793/8dX/ziFwEojYI7\nOvS0ZR0dncUPSZB6f7MSKKtD9e677+LTn/50+vXll1+O22+/vahDRdP0om7AeCLgjfvw7Xd/jCgC\noMPN+LcP3QWXXe9xdLxzXvNZGImO4q2R92Bt3YLTydOxec8Evv3UVnzhxpPhtBkqPUQdnZJwuVxZ\nvyMMw6CpqanoOjfddBNuuummgp+vW7cOzzzzzJyNcXr0EJWOjk7lWYgtHBadyl8gEMCmTZtw2mmn\nAQA++OAD+Hx6fuliZjA8jP/64HEk5RgYXzf+9aJb4LKbKz0snRIgCAI3Lr0GwWQIu729WN3G4EPG\n07Fxywge+vUWfPHmk1Hv1K+lzvGP0+nEP/zDP+CMM86ALMt4//330dLSklae/exnP1vhEU6PtEj9\nKQKE3rRYR0enbFTKASxrkuHXv/51PPbYY9iwYQM2bNiAH/3oR/jqV79azl3qVJB93gP4z/d/iIQU\nAz22Cv/3wlvhcugG+EKCJml8fNWtWOrsxs7JPeAbtuLqs9swGUzgoV9vxaAnUukh6uhMS0tLCy64\n4AKYzWZYLBacf/756OrqAkVReeqzxyvSIi2iKiZrr6Ojo7NQKbsoxVNPPZUWodBZvLwz8gGe7H0W\nkgQYRtfhwSuvQG3V/Kus6Bw7DMXg7pNuxw92/BRbPNuxtk7GRy88B7959TD+48mt+NwNq7GkuWr6\nDenoVIjPfOYzlR7CMbNI/akFmT6ko7OQYCgSvDiXNU/H3zNbzdTBx3s0P1P8jfkfc1kjVL29vbju\nuutw2WWXAQB++MMfYseOHUXX2b17N2699Vb87//+L375y1/i1ltvRSAQKOcwdY4BWZbxUt9r+HXv\nbyEJFExDZ+H/fuRK1OmpYQsaI23APWs+hi5HO7Z4duAQ8zruunIpOF7Ed5/ejp2HJ6ffiI5OhXjs\nscewbt06LF++HMuXL0dPTw+WL19YjcTlxepRHYfGWSlQ8zwpbKXs87o/ncIYyYU1OTzXt2opm6OJ\nuRWuanIVrru3UDbYqONP5KysEaqvfe1r+Na3voVvfvObAIDLLrsMDz74IJ5++umC66xatQq/+tWv\nyjksnTmCF3k82fss3h/fBilphG30bPzz9eeh2m6s9NB05gATbcK9J38cP9n5C+z27gPn5PGpa6/E\n488fwP/7/S7cdcVynLHSXelh6ujk8dxzz+G5556D271w70+LkYbZQC+6aiOKoCDKwpxsq8exDL3B\n/XOyrbmAIgmIc1T8ZqUciIihkpe3GGhEk3NzXo9HXA4jJoOJiuz7RK/5I8obe8ljSZOj6BlPSLNv\nmF5OynqWaJpGT09P+nVHR4eu4LdICHFhPLrtMcWZijhQNXoBHrzhfN2ZWmQYKBafWnMn1rhW4oD/\nEDYG/4B7rl8GlqHw+At78dqWoUoPUUcnjyVLlsDtdqdrphZS7VQKhqaKztIuVOyUc862xZDsjJZv\nqj2G80kohl5bvQ2NBa4LOYehAQkzS9laiCb/dKfLSJpBEZV/bk90h4oiprfbCRBwsy2oosvfa1RJ\nGy5+81Siyqis3g1N0xgcHEzXT23atGkRpzGcOAxHRvGjHU/AnwxAmGxAbfh03H/zaXBYZvbjprMw\nYEgad626Bb/a91u8P74Nz4tP4VPX34ifPXcUT/71AEJRDtds6NDrJHWOG6655hpcddVVWLlyZZYj\n9dBDD1VwVIsTF+PGJD9W0rIm0jLnDTerbUb4wqVFLswsjY4GG8Z8ccRLjObUsU0wkEaMcEcAACxN\ngqXLP2MvyWLZtm03swjFuKLLkARxTMIoFiONaKL4OSYJAuI0+6AJBmIZz0VpLCy7daY/xdP1mSrV\nqTVRFrCkAQHBCwCwmhhE4rzmskaGQoKf3XVl6OnHUwlRn7I6VF/+8pdxzz33oK+vD2vXrkVTUxMe\nfvjhcu5Sp8xsGd+BJ3t/h6TIgR9cAre4Gl/66Cmwm3VnajFDkRRuW3ETrKwFGwffxJP9T+DWa2/G\n7/7kxQtv98MXTuD2S3tAU/PfnVxHJ5eHHnoIV199Nerr6ys9lDmHwNyZd3NRvF6pCEKToQMgAJOB\nAsKlr0eTJJpdFhwcDpa0/EyiJIqzqH0+Zx69yl/eQdcgKgYhaKRMzuSecFiLO1RzMTdmM7FZDpW7\n2owxXyZVy8hS4AUZ0428td4GiTQgGEnOaP/FRAu0cLMtGOMGNT9L2eYNNRZAljE6dRxNhg4EBe+0\nqZkG0oSkFEed0wyPXztdrZjzMR1WEwOWpkqeWMgbH2FEXC6cRmcgTYiKM3jIpmioNsMbSmqOy25h\nkQjEC65LAGg0tGMk2Z/3WX2VGfI0wzEZ5j8brqx7dDqdeOGFF+Dz+cCyLKxWazl3p1NGBEnAc4de\nxMahN0HINJKHTkabcSk+/9E1sJrmthhR5/iEJEhcv+Qq1Jpc+N2B5/GrQ/+DGy6/Hq9vpPHWrjEE\nIxw+fc2qinyR6eioaW1tXRRKf1rUHGMtSQ1TDy8/PmfjsZmMGC9gm5sNNGJ5kaCMtW5gKCRnMUvd\nYcqUEpBk+SLjTS4LMINOEUbShGQB746aZpwMwaKWbUwbkFpLG0kTojOoq5otRBm0GGdznSSIIAjt\nCp4auxHe0NzVVDEkCztjQ4jPvn4mlgbPyzAbaFiNWvfz9NQyjYhJITjMHDz+zPvqKGAxZ6qKdsFK\n2TGUPKL5OUUSII9pLrPwtWk3LkNYnF4YrpATXmjCw2Sc3k4gNa48RRCwWwwIhYvfT3OZflsqZZ1O\n/tKXvgQAqK6u1p2pBYw/EcB/b30MG4feBM3bEd91BpY7luP+j56sO1MnIOc1n4VPrb4DJEHiqUNP\n49RzQjipqxq7+3x46NdbMVFk1klHZz5Ys2YNvve97+HNN9/EO++8k/5vMXAshkJuup06NsBMRZeN\nZGkKrRRJwmZmsarFDTvtRKOhLW9fuZMruUNvrTt2u8DIlC9CRpGkytQsfN6rrAb0uNpRZ64tuIxN\nlcXRZlwKF5MRTKmia9Bs7ISBLF6D3OiyYj7Sz47VFNWyC2azTU7KRKVyU8qrbQZ0uG3obLTDUOI9\n0GBozd4GU5f+W5aBU5t6sj6vshoA4thqqGrZRjAkA5rIz+IpNU2eIIhpl7WzdljTynfZyzbkPJv5\n21dETQrtm9W4L1nSkP5b+e5I7bPEYyppqdLWPl5a9JR1Krm9vR0PPPAATjnlFDBM5gG7/vrry7lb\nnTmk13cQT+x5ChE+CkOkBYHeZThtaSM+ceUKMPOQR65zfLLKtRxfOPUe/GjnE3jx6CtYtXQ5NtjX\n4o1tk/j6Lz7Ap69eieXt1ZUeps4Jyvvvv5/1L6AYBmeeeWalhjRnmAw0LJRtVik4uTO+6jKDFTU9\nmAzGwJJG9MV789Y1kiYkpMxkCU0BDdUWmAwMahgltbKlzopoXIAvnECrYQlI62jJUQTLlEx4KgKT\nW9fBkiw4STsURlMkhDntu6NgYEgIU4ZsPduIahMDX9yXt5zFSGNVcxN29hepJVPZgIpjW9yk1DLh\nLUZt1UeGYIEiNTAz5hjzSrWiUQaaBRDN3s3UYgbSiKSkfZ+QBFAohllKinlno3JfHRkJKedJhY2q\nSqcFNrksaKixoItzQJQkEISSHjo0GYVyMmbnAnS4bZiYAEo9oYq6Y3Y6qom0ZMUMc5+F7ppm2Mhq\neHwH87ZXzdTCSJrQYuzCYOKw5j4JkEjwhe8fI2lCk6Edw6r0u9R4rCYGDdVmyCKDBsYKmpYx0FfS\noZYMQRTvy3e8VG+XxaHq7e1FT08PeJ4HRVHYtGkTnM6Mso/uUB3/SLKEvxzdiD8d+QsIgoTBswaB\nfjfOP7kJt1y8rKxpFjoLg2ZbI7687j48sec32O3dB5dtHB+58BK8+HoA331mB268oBsXndasi1Xo\nzDtarTdeeeWVCoxk7nBXm8FMCSI46KpZOVSKWahVm1MNljSAJbWNqiq6poBUcbaVY2QoGBmlloMg\nCFAEgboqEzzpqHXh74I6tjErFbGOacYYN5DZ09Suqm1GkKKSKpU6ltRXDEuykEkBvDB3zoWryoTJ\nQBJW2oFOR23aoVKnKxppI2hyphL36qW1zkvmc0VqXgRDMjAwFGKckC7qN5BGNBra0d5gxcYjH5S0\n5yWObgx6ts9otDTBQJC109LMlAV2h5xVI6U+pEaXBQY2O4pEqLy2erYZA4lDMxpPKdjoKlBE9lVJ\nOep2OmOTdjc50OgyT30OkFRuJHf2DpXi9E0f48o4DXLW6zbjEpA5rQYa2DZExBC8/Di6Gh2osVrB\nx0lNNT6HhupeQ40FsiTDH0lCkmQQINFp7UKSkwteB60olcthhNOqRKosRgbNNVaIUo77W+C0MSQD\nm4lFOF5MHEVZmSLzJ0yK2RW5jvN8URaH6lvf+hZ++ctfphWVbrvtNvz4xz8ux650ykCMj+EXe5/G\nbm8vbLQdsf2rEfBaceVZbbh2Q6duIOuksbM2/J+TP44/H/kLXj76Ov7G/Q6XXX4RNm2k8fRrB7G3\n34c7L1+uK0DqzCsjIyP49a9/Db9fKVjgOA6bN2/GJZdcUuGRzY5athGNNhZhQTkeAiQaDW2gCQaS\nLIGTk/BwwwXXtxho1NeYcXQ0BkaVqpMy3qzTNMm0004kuGyHSj1jbDOzSCQFdDk7MRoZRx2bSeVz\nWFhE4zxEnkU1XQdOVtK4DIxitDqZWrTamhEKx7OcPROlnXpYYzfAkmQRjGbSwRqqLRjwhFHPtiBM\nDyEwQwGDFHVOM/gEBX8846wWmhlX/wouq1oGgiBgpEwgCRJWyoGQ4NdesQRymwg3GtqRlBIw0SY0\n1ZqR4BkEIxwSvAgTaYHTaoDbaZ22F5XZQMPI0rCyheXjmw2dCEoeJOUYRCFz8DbKAb+Qaehe7zRj\nfEpgwWE2ocNWjzHfHgD5NrTFQMNIGbKU/+yME17Rm17G7TTDbKIhSTJ84SRC0WxDezZWh4txo7PK\ngCOB/qLLTReQk+WMQ8QyamdLRl2VGRGvElXV6pVlMdJoqGZhNptxNHYYZsqKmKgU5hEE0Oa2geNE\nyEDaIe1stIMAgcMjQZAEBQNNIcZnritJULDTTnj5caTmtmVZRhVdAwJAHMVrnlJKlTYzA0GUUE3V\nwUJXYdAzk0kaAk5r5nedJrXTLmuNLowTSQiykBdpctpYRBN8liKfVtorRRIQcvy05W3V6O9Vrpva\nWa+iXbDRlWn6W5acLV0afeEyEBrCf7z/KHZ7e9HAtsH3wXrEfDbcevFSXHdul+5M6eRBEiQ+0nUp\nPr36TrAkg9c8L6HrrANY2mnAzsNefOVnm7H94OT0G9LRmSMeeOABVFVVYfv27Vi1ahX8fv+CVJhd\n6epBq7kTVsqOVltz1mcG0gSKoMGQrGbxdso5sRhpNLos6dorI6mqN5Dz/kjDZM3SZ/d9MZDGrDVW\ntlfj1KW1qDI4sLxmKc5c0oE1Xa6s7TUYWqfGSqDJZZmqBwKWN7vgrlGcp9wZ9jq2ESaWRpPLUnSO\nP+Wc0QSDGrshz/gmoETK1FCqKn52qp8VCcBlzB53ehs5G9XK0rCbDGgzLoWDzk93JglgSUtV+rWZ\nyjid7c7iDahpgoGFsqX3q64bI0Bg6dR2iSKZIy11VjS5LIpDaixc+8yQLAhQaHBZYM+ZCEuldlIk\nAQdrz2y71gYba0ObcUnB7XY42nBqaxecNsWhX+luTV8np9UIm5kBRRBgKBL1U9fKTFk1I6q5NVpN\nxvaC+801R9XbK0V6w2ZmIUNOKxnTJImOBjtcDsXwr3FkHAB1fSNFEHBXm0EQBNrcNtTa7TipdiXq\nmKb0Misam1BtssNqYmBWiTRQBAH1pVyzxFVSHRdJkHAytenvg9Q9QxVRrDipbjmWuptm7LDmLk9O\nKWHmjtNIG9Fi7AZLGqCuSJRlJcrb1WjPWr6Bza/3St0zKRot9XBYWHQ1OdDmtmU9D07GBZqoTG1/\nWRyqXKNbd7COf2RZxlvDm/HdrT+ELxFAJ7kWR97sAUMY8LkbVuNDpzZPvxGdE5pVruX453WfwzJn\nN/YH92PS/QrO3MAhnhTwvd/vxE/+uAf+8OxmjnV0ZgJFUfjkJz8Jl8uFf/qnf8KPfvQjPPnkk5Ue\n1owx0SYYqfyC6+kMwVq2EU2GTgAANeUYaa1BT9XBqn+hDQyFploLmgsIRrCkAUbSAsg5SWuq332z\nkS6q9mk20KDJVA0Gi2Vt1WhyWfOa/looO5prLTDnbMtdrThgDTX5USySf1M/BAAAIABJREFUIFBf\nnXm/zmnG9aecgw91rstarrkuE6VpYNvhYtxoq3bnzZBThPb503KommsLi2zYLWxWzQ9F0Ogw9aDD\n1AO7KbNPljSApIiCRqGcUyvF0GTBiU6nzZB2gowMhRZ7M7qdnZrLqrfQVGsBO+XYpK8jQaTT5CiS\nyLLrUsdFElQ6OtBp64SbbcmMk6TRbGuAy25Ed5MD1TYjapi6orL0JEgYKEPe+w3V2de9UDQTAJJi\n5jdHhox6tgUm0pLXgJYhte9Xh5lBZ6MddnPmetAkgVqnGactqwdFKk5grpPX2WiHzcRkP1sUC2aq\nL16VlYXLake7XTlHqaiklrWce3VPWVJY/AQAGo1tMJLm9LU3MDmmvmqDFkY5d7m3ULPNDRud7exU\nM5n9msjSmmQX+qZipsZkZsyoZRoyy2vcy2qFTCNlhtNYld42S5Fod7TCTFmz7rdKMC+qAnpU4/gm\nLsTxxJ6n8NT+34MlGTSEzseed2vhcpjwL7esxarO8ne+1lkc1Jic+D8nfwIfXXYdZFnG9uTraDln\nBxpbOby7dxz/8vi7eHnzQFkKyHV0UiSTSYyNjYEgCAwODoKmaQwPF06JWwiof0Z72pxppwLIzA6n\nsFJ2MCSD9Y1rUGNXOwjZ5pphKv2vxmLJMnwcJhNokkBLnRWtdVYwFIUOl2JMmUkbHLQTBtKIZc7u\nGR+HmNenSRlTS50V63pK6xvmtBlw+vJ6VNsLqOKpDtNhZkCTVJ7CIatybkiChI2uQmdVa9bKJ9Wu\nRHOtFQ4zi6WtVZgOkiRQYzdqOrzpeq+pf1O1J0DGsQWUfkinty6HidI2WJttTakNFtwHMKXcxhiz\naoWqDA5UGfLTodzVZixpduL09iVoq7dpqvTVmDPOIstQWYIh6v02mJqx3LEKdoOt4DGklq5iatBg\naM1yTqkcx6bZ2ogGqxuddm1HUItGl+IQdjZmHysJEgbSCLehJS8aShdwqNodrXBbCjkwyr3S7rbl\nOXmFSD3HKX8099ktRL1T/bwXX9ZIGdFgaFUdY/YKqVcus7Zt5642Y31HF9a1LUddlVm1XuY+ddA1\nWFadH5EsdB4JEHCoIk0MqdxjBEHAqkrRy46e5ruX9Wz+5L6dtaGebS54v80XZamh2rZtG84///z0\na6/Xi/PPPx+yLIMgCPztb38rx251ZkFf8Cie2PMUvAk/mkzN8O1egcNeEis7qvHJj6zIknrV0SkF\ngiBwTtMZWFnTg+cOv4gPxrcD7hEsbV2K4d2N+O3GQ9i0fRhXb+jA+uX1FekXobO4+fjHP4533nkH\nd911F66++mpQFIUrr7yy0sOaM2wmBrU2Y7rmwkAa0WZtwdFIdmPSrgYnWkQLBsLDCCTy6ypaTB0I\nIpFnBK2s6cF2z650Ks3JS12gCAqBoNIElCAINBs7YGNnLnvOEgbkqr2poQgKNMGgw20Hy1A4HB0F\nAHQ02CGolM1yJ2rb6m1YU+vCHv/ojMcEQLM5vYFiAQpZiqUdjnb0BftntQ9ASdUElPQ/XyiBJCei\ntsqEvlGlDociaNRZqjGMCc316821qDO58HLwXQDZqZmdlqWYlCIY4wfQ7rYimdA2pAFkiYXYTAxW\n1vTASCsG7yH1rSIrqXdWxg6eEOEWW1BjNoNiksDUGEmCzLN9HVYWdjMLt7GIo6HpFGZDkzSaTA3w\nikptktbPReo9hiLRUGOBgSHR7KpGncWE4YhyMF2NdpxW34j3esdV6xFoNXbjlLqG/I2qaLY2YiKm\nnbae66gDGcVKLVwOEw5EkI66shSDrqoOHA6kpPEyAhitdVYsdVSBIAhYsvo2EehosCPqz5zboip4\nBNDZYMdgX2ptJQWzxqStxGs1MaApEs2NVbAbKHgCyveM2vElCCLr+TdQmefHStk1mx2bDRRSGpkM\nSUHdYMVK2dHRbMbKmmpsGR/IW1d1NHnvSHOpcHkMlMWhevnll8uxWZ05RJIl/PXo3/Cnvr9AlmUs\nZU7DnjddEETgqrPbcdXZHbqSn84x4TRW4c6V/4gNTWfi9wf/iIHwARBLD6JZasfYvib85I9x/Pmd\no7h2QydOXuLSHSudOePCCy9M//3ee+8hGo3C4ahMofJckR31yH9W1nd3oMonYXxSBGRgRZtiLLEU\niyarG8FkMKv3EaAYgwzJgCAyxhJFEnkOlgzFgFLXX802kb9YuiJNkVhbvxoOCwtXquZpyvcyMBQg\n5Buv9ZZ6jEfHcXL9CpgYGstrlmGcimDMv3dG40rVIbHT9DSyG2x576mPqGC9SioyMXXmSIKAy1Gg\nf87UstVMbVZUIGU1EwSBarsBsiyj1Z0ZD0VQYEkjaIoASRAwMwbwBdoCplL5ShEMUqd3mSgLjJQR\nTqMDQH/BdUiCwIr2asTGpq9nIUCAJClIkpjnoKR+FqrtBjhtBthM+eMlCQJWyg6w8bz0tlRqIkmS\nIEkCbfU2HB3PiC9QBA2qgKBCepmCnxNotDZAlCUAMvgpkQ2lBiwlTpH9pLTWWzEs2MCqopJOYxUI\nDces3laTjsKyJAsbXZW+FvVOMwaT2ufWZmazGhATAFwOlWOr6ZQqb9YyDagzm7OOOaVoydAEkJO1\nX2WsQoyPodGS+W5pqDEjzGeLo9gpJ2RoN6Ve2lyFTsmRV+eYGVvmb3WqqZE2IiEk8qKalaIso2hq\napp+IZ2KMR714Ne9v8OR4FHYGBuMY6dhx2EDbGYGd12xHKu7tItydXRmQ3dVBx447T7smtyLl/pf\nxUC4D8zKPriEVoweaMT3/xBFk8uCy85oxfrl9SX1FtHR0SISieDZZ5/FHXfcAQB4+umn8Zvf/AZt\nbW34yle+Apdr8X630RSJFbVLsLRaQjwpZGUXmGgT1tafjHe92X2S1KZec60FgigVrQNSU6xGKpei\nCmo5r7uatB1fK2NBUkiiOmdWvcXWiBZbY/q1hTHDypQ2OVNtN8IXSsBuztQ3NdSYgCLtpBiSRo2p\nBjA5cTDen/d5S50VnMQhYVTLxavRPhuNNRaMeLMjd1qS1ylSkvQGJv86GCnFCLdZWPiybFhVLyOa\nRGejPV27U6g0I+UAEqr/p5ZPSZATBFGSwEMu6XUIYI1rJXhJwKHAEQCZZrGp60IQBM7tWANfIoB6\ncy12TOxOb6ez0Q6SbENdLYP+cB9kWUpP0FlZCxAFXFP3jSUnnTFXPGUmmBnFAeh0tGEkMgaGItHd\n5ECb3YWB0JD2MRNExplSOQda57/TkRFoqLYb8yZE1DhtBgx4wmiuteYIykztk2JQa3RhIjGpGVVL\nYaUdaLNn7+ekzhpwvIRtA/kOUXdVR9571XYjEM++9620A7UWG4YnDihjQqo2kSicujuFOvrWXp+Z\nQFhRswycyBesf5tvjo9R6MwLoiRi49Cb+NORV8BLApqYbgxu7YAnTuHUpbW47ZJleao+OjpzAUEQ\nWF27Eie5VmCPtxcv9r2Ko+EBGFYMwCLWY/xwM376pwj+9+9HcMn6VmxY06jMSOvozICvfOUr6Qm9\nvr4+PPLII/jv//5vDAwM4Jvf/Cb+67/+q8IjLA8mNvNTTlPkrFK1GZrCkubidUJ1VeZ0+k93AcdH\nC7UL4bQZoRaWtjAFojQ52Fk73JY6TZGCXIwMpaQO5lg4uel6TptBUd9rzhwLVcKEToejFUJYW5qa\noUl0NtgR8rCaDlUhtbbWelvaoSrkmpQiWJBLu9uOREi7tiRXnr3QMgbCCJoiweX093KzLQgIk6g3\n15ZcC5QmZ98USYEiqbRj0VpvhYNxZE2wmRkzzFMiCnaDHaFkCDTFwGSgsbxNcT5NzFJMxCfhMimv\nqwwOrFClM6pZ3Vkzo4mBYphoxSkgANSaCjtUxWiutaDKYEdSVmTV1Y5Pdrqdxv4NNE5fXg+CIDDu\ny+kZN7X8BctX4r3RrSCJ/HupmHgcTZGgKbIkpUFlW6q/Ve/XGJXnrIapR6u9GQMhGS22wgGYJc1V\nEP3ZKaPumsyED0mQmte1UugO1QnCQf9h/PbA8xiJjsFEmWGeWIdDfQ6YDDQ+fuUSnLnSrYuH6JQd\ngiCwyrUcK2t6cMB/GH8d+Bv2+Q6AWToOh+xE5GgLnnotjj++1Y8L1zbjgrXNmgXSOjpaDA4O4pFH\nHgGgNPK99NJLcdZZZ+Gss87Cn//85wqP7thQGzOp7+qTu13oGw2ju6lwzUbRbZaYt5dSuqtzmtIO\nFUNP73hUm6rTjXBTLG1yYufkMIy0ImrBUKU93yRBwESX6HxZWHy4+zRM8qNwWzOCAjUmJ0iCSNer\nkFDEIRg64wwYpxw2a5E6mBQNhlaQyHckSILMEw6ws1YAIqrZ6mnTm2f2U6wShHBZ4I8kUeswAVCa\n8LqrzRji6Jwl86FVDlHKkK8yVmFJ11J4/SJa6iyIJQT0DgTQ6LIgkRRgIE2oZ1vSKaIpBbtS4aaa\nC0lS5kbssLdhr7cXJEEUTb9M1+zk3MRmxoQ2piXvPS3MReTjp6PKmJ36mVKeA2YgxKZars7kwpg0\nDretGkdDkVmNqdB+U++SJHFMGSBmWnFm7LQTTa6Z109WWSzoMPUAUK7fEmdX8eWtBlgTDCJTvcu0\nGhenWN1Zg0icx5HRUJaQxnyiO1SLHE9sAi8ceQVbPTsBALXSUgxubYYssDhjZT1u+lA3HNbjx8PX\nOTEgCALLqruxrLobQ+ERvDqwCVs8O0C2+1HVfhjcSCueezuOFzcfxTknNeCidS1ZKkc6OlqYzZl7\n5L333sP111+ffr0YJozaHW0IJINpY9LI0lje5pxmrekpdm5OrV9TNEWoGO32FsWhmrJ5HRYDWJrB\n6tpVmsp7c0mN3YQalK4Ml4IiKbQbl017vxAgYCS1v5NoksYSZze8nkH4eA8AYJmzG1XVRgRMCc11\nAKWWJBjlshy86VCP0m5mccYKN/Z6g4jxfFEHqtbswkRsEqtrV0GSpayamRZrI2iSQoPFDYakkSr1\nclgpnL5CUWJMaDYQzvfQu52diAuJotdafarNjAkMxYIXuYLLHwuzrdVN1XilaHY0gi/SB5elWHAi\nl1az00TlDDbbGlFndqGY27usxYlonE87Rc22RgyFR2Bns5273MmOQsmcalITl7WF6voA1JjtaI13\nw+20oaVAWwVly6pGvRSLlHM/W0ppvWQ2MjAbGVTbjRUrG9AdqkXKZNyHl/pfxXtjWyHJEuxEHQK9\nSzAQtKGhxoxbLl42Jz/EOjrHSrOtEXes/Cg+0nkpNg6+gbdGNkNu3Adb42Fgsh2v70xg49ZhnLzE\nhUvWt2JJs2NRGMc6c48oivB6vYhGo9i2bVs6xS8ajSIeL1Cdn8OBAwdwzz334I477sAtt9yS9dkF\nF1wAt9sNamom/jvf+Q7q60uT+p4LXKbqdC3IfHEsTg9JkOhwtAORKAQuY9ixJUal1MznM99cmy88\nMVMcBhvslDPtUCm1MywIonAvvmq7sWg9SakRRRNtQoyPwUgX3labvQWttmbN88pQTF4j6dlSSKod\nyBzPfKZ3W00Mml1WVNm0J5KbbU0YCqtbLCjnZ41rJTiJx37fQQiSAJZiwEPLqVToqV6KEBeCw5Af\n6Vzi7MJwZDRPZY+l2LRiXW6tIKCkqKqb3Lot9XBb8r9/8q5pCc+OzczilO5asLk9q1S01FlhNFA5\nrRim2S5jA5CJUp+6pHaGEVikfT8nPX29WyVrsHWHahEhyzIO+A9j0/Db2DmxBzJk2Egnokc7MT7m\ngt1iwI0Xt2PDmka98F/nuKPG5MT1S6/CZR0X4o3hd7Bx8E1EXAdgq+2DIdiFbYc4bDs4iXa3DZes\nb8XaZbX6fayTxSc+8QlcfvnlSCQS+MxnPgOHw4FEIoF//Md/xI033jjt+rFYDF//+tdx5plnFlzm\n8ccfh8VS2X4ns6WzwQ6SJNB3KPVOYQu9q6qj5JqJYtSYnBglJUTAz9yQUkHMT9tMACg6+15JTIaM\n49Ht7NQ0ygGg1dYEC2POd75zLkAlJqa6qjoQ5iJgSDp9f8UShR2TclCoaTUAuC11qDfXYsv49qz3\nKZKCiaSwoqYHcSEBM2NCFIVDVCzFpOu4cnEY7JqOFqBMQqytP3lOr406/bTB6sZoZAw2Nn/SwMAW\nd2xJkphRpoiWQz+dimYu3c5ODNNjsHKtaHMf30qtukO1CEgICbw3thWbht7GWEyZDaum6xAbbIVn\nsAYGlsY1G1px8boWGFn9kusc31gYMy5t/zAuaNmAN0c2469H/4aQvRe2tYfhiC/B0b31eOyPYVTb\nDbhwbQvOXdMIs1G/r3WA8847D2+++SaSySSsVsVoMhqNuP/++3HOOedMuz7Lsnj88cfx+OOPl3uo\nFaFuBsaQuiYkhcVIo8pigMtR+gw1oDgo+wb8aHDN3hGdK/syJWphZszgRA7SHDiNsxC4mxXq3+9i\nkR+KpKbSxxRa7S0IceHjQg3NaazKu7cC0eyo3UxO5xxcvTwIgkinRFqZ7GeGpZiCEVaaYiCIx5be\nltr/sa2f+bvKYkC7O+O8NVkb0GiZn5r5udhH+j5fAAKtlX+6dGbNWNSDvw+/g82jHyAhJkERFLrM\ny+E97MbwIAuKJPHhtU34yFntunqfzoKDpVhc0LIB5zSegbdH3sNfjm6EV94L69oDqBV7MLynHr/d\neAjPv9WHc1c34sLTmlFboI+FzokDwzBgmGyDpxRnCgBomgadKw2Xw1e/+lUMDw9j7dq1+OIXv1h2\nw6SzyYGD/cKcpkVZKDskWSg5hSwFQRDomUWquMNqwBkrCks+l7TvOfJazIwJy2uWwUgZ5qyGyzaN\ncI67wvWfdWZXloNVLlIZA9Q89LCcq/uhEG32FjRbG6ftUaVmtWtFSfU+5UZ9ZrSe13J/ZzVZGxAX\n4mizt0A+dv9ywaA7VAsMSZawa3If/j70Nnr9BwEAVQY7VlrX4egeJ3aPKIWoZ6yoxzUbOmY0I6mj\nczzCUgzObzkbZzeuxzuj7+OVoxsxmtwNdnUvOqnlGNnrxl8/GMSrWwaxdmktLlnfWrCXjY7OsXDf\nffdhw4YNcDgcuPfee9NKgoVwOs2gZyAwUIiW+mOv6VGzYdlqGFkK474YmIQAp9OE2jmoGzoWCu3f\nFlMmSWpdNvz/9u49tqn77uP4+5xjO44T54qdG8lCebiE62BAW0JDYcDabs86VVopWkQ39bKqhbWd\nujZLkUCTBrSDStMqdaXqpqmtVKYqk3i0Cqo90D2MBsqlpQ1ry2UNDSEkcS6+JHbi2L/nj5Q0gOPc\nYzt8X5JV23HS78c/fuf45/M7v2NPGpupeA5Gn7WlI0i36l2u2umMPH0rPc1NsmYhPzetL99Q3+c0\nu/uG52LdRv2FDYM0b+/CEVfrWm63cqnJx7T89CGtAtk/Y/9s6d3JBHoMMlJsOKZEzuw1bPhNyZh0\nI2bvSzy1R3+mJDNpbb2Ln4y2xpH9vp2ivN7VNd2+LtJa/MP6W9/0+VSs5uEdDR+psWhLGVAlCF93\nBx80fMjh+qO0BtoAmJFxC8Wm+XzykYl/NXSgEWRZiZP/Lp1GwSimVggRj8yGmbKpy7k9fxnHGk5w\n4OIhagOfYsz6Nwut83CdLeDEF82c+KKZ6QVprF1SyOKZcp6VGDs/+tGP+u6XlZVx9uzZqAOqtrbO\nAX82VA6HnebmKMuJjUBmcu+u3+324+/uwawpmptjN4shWsYpWg7ubg+d7hABbWzfh9Fod3fi8foJ\ndpkGrL3YYaNdS8Lr8dPc7B1WW3q8Ny6iMtb/Dkajtd3fV2P/umYWZQ47oyMj+ZrfaXf7CYa6sQQ7\naVaR/1abpxNvpx9DH/j9H0/j0S/HirujO2LbDNdYZPR0Dr8WrzcAStFq9WPWx/8QV6ScIxlgyYAq\njiml+NJzkX/VH+Nk0+nelWV0Myvyb8MRms3Rk53su+wBulgy28m9pcUUDPEq90IkKrNuYkXBbdyW\nt4QPr5xif+1BzvpPYxTVsHTuArxfFfD5WQ8X6s9gt5kpnZ9H2cJ8crPkaK0YOa/Xy1NPPcUrr7yC\nxWLh+PHjfO9734t1WaOSZDbwd/dgGYOjaOMlOzmT7OT4W5G20JFKT0+YQufAH7xsVjMWk44+jGlj\n1/tWjp3kJNM112qabLLswz8KkZWcSVNnMwWpo5tKOhmZv/4ScaRLxMfagilz6Qp1xcU5f8ORWNXe\nJDqDfj68coojl49xueMK0HvRt+W5t9HTUsA//9lEU1vv89+Z5eDe0mlRV60RYjIy6SaW5y/j1tzv\ncLzxI/bX/i81no8g4yOmr8wnubOYLz9PYf+xr9h/7CtmFWZwx8I8Fs1wkJwkmz5xo5qaGl544QXq\n6+sxmUwcOHCA1atXM3XqVNauXUtZWRnr168nKSmJOXPmRD06lQimF6TR2OonN1u+bBgui9lgVtHg\nA72FjnnDOg/nqqWznfSE1IQuKT7RCh2p1DX7brh4+1CGAanmlFFdI20ys1lNzCjIuOF9jYWRDOmi\nLfwRzzQVD2fQjUK8HnIdrrAKc779S442nOBU0ycEw0EMzWCBYy7FprlcvJDEiS+a6eoOYTJ0ls/L\nYe3SIpnaJ8TXQuEQp11nqG44zmctZ/uW5M025xJqc9BYl0zYl45JN7NgejbLSpzMm5YtKwTGSLye\nfzCWxmL/FM9Ti8bKzZARJldOV7uf85d7z4Hqv+DIWGQ81/Yf3F1uclNymGrPH9XfGi+TqS0HMhYZ\nlVJ82eAlOy2J9NTI1/6KNZnyNwkopaj11HGy8WNONZ3G3d3boNnWLG5JmkfYNZXP/q+TDzy950xl\np1m5a1kRqxYVyKp9QlzH0A0WOxew2LkAd5eHU02f8Knr35xr/w/h1CsklfRey0YPZPBJWxqnD6fD\ne2nckp3L/FsczCnOoignVc65EkKIGCq0F5BkspCT4ox1KWKUNE3jlvzIi7ZMNjKgmmBtgXa+aDvf\ne2s9j7vbA4BFszKlZyZBVy4Nl2xcCgO0kpxkYvm8XFbMz2NmUUbCzokVYiKlJ6WxqnAFqwpX0Bn0\nc7btPBfctVxw11Kn1WO2fnPl9rqQzlcuO//zlR29K43cFAfTpxQwKyePolw7joxk6XdCCNHP1Qu0\nJo3D+XdWUxJF9qlj/neFGE9xOaDavn07p0+fRtM0KisrWbBgQaxLGlRPKIzPH8TT0Y3XH8Td4cfV\n2UaTv5GWYDOecAsdtBA0fH2/o4IWQu58Qi25+D1TcCsds0nnW7mpzCnOYv4tWdySn4ahyzfmQoyU\nzZzMt53z+bZzPgBdoW4ueuq45LtMvbeBrzz1XDGaCKf2Tl9pAprC8EG9jrpgQ+tOxW5kMsWWgTMl\ni6npDoqznRRmZ2Axx+UmVAghxlVaioX/KkgnzSazZYSAOBxQffjhh1y8eJG9e/dy4cIFKisr2bt3\n74TX0RUM0ebroMnrxhvowuvvwhcI4A100xEI4O3upKO7k85QgK6Qnx660cxdaJYAmiUA5q5vrlat\nAQaoHjPhdgemTgfW7hyyk5xMSUtmylwrOVnJFOXYycu2yQBKiHGUZFiYmTmdmZnT+54LhUNc6Wzi\nsu8KlzyN1LY10Ox34U1uI2zz4eMKPqA2AB8GgEZQIQM9ZMWCFathw2ayYbekkp6USmaynSxbGhlW\nO2nWZNKSU0g2WUkyLBNyhXohhBhvU9LlQupCXBV3A6rq6mrWrFkDwPTp03G73fh8PlJTx34Vu8a2\nTl75Ww0dgSA9IUUorAiFwwR7FD3hHqzfPoRmjrAGvvnrW7/1IK6+kZrSsWoppBjZpFnScVqd5Kfm\n8a20fHLTsrAlmdEn4CriQoihM3SDgtQ8ClLzWNpvFV6lFO5uD40+F1+2NHHZ7cLlb6O9y01n2EtQ\n9xPQW+nSW3AraOgCugDPAP8jBYRN6MqMpkxoSkej96ZjkOWfhz2Uj6FrGIaGoesYhobJ0DHpvf/t\nff6b+yZdx2RoGIb+zfO6hv717ep9Q7v2sa5phJUirBQqTO/9sEIpRVj1f/zN/auvT7dZKCnOGv+G\nEUIIIRJA3A2oXC4Xc+fO7XuclZVFc3PzuAyowmFFMBQGNKwWvd8HEg2b1URHeA4q3InFZMJiMmM1\n995sFgvpySlkWFNJsdhIMduwmZJJMadgt6TIMp5CTBKappGRlE5GUjqzsqdHfE2gu4fGdjdXPO00\nd7hp7fDS1uWlI9hBV9hPd7ibHtV7CxEkpAUJ6z0ovQv0MGgKtDBoUHu5gVBTYkyh2f1EKZn2+Fy1\nSQghhJhIcTegut5gq7qPZuldh8POnspoF4UrG/HfFkLcPAoL4u/CoyL2xmpp+JthifmbISPcHDlv\nhoxwc+S8GTLC2OSMu0MpTqcTl8vV97ipqQmHwxHDioQQQgghhBAisrgbUJWWlnLgwAEAzpw5g9Pp\nHJfpfkIIIYQQQggxWnE35W/x4sXMnTuXBx54AE3T2Lp1a6xLEkIIIYQQQoiINDXYSUpCCCGEEEII\nISKKuyl/QgghhBBCCJEoZEAlhBBCCCGEECMUd+dQjbezZ8/y+OOP89Of/pTy8vKIr9m9ezcff/wx\nb7zxxgRXNzzRsjQ0NPDLX/6SYDDInDlz+M1vfhOjKocmWpa33nqLffv2oes68+bN4/nnn49RlYN7\n8cUXOXnyJD09Pfz85z9n3bp1fT/74IMPeOmllzAMg7KyMp544okYVjq4aFmOHj3KSy+9hK7rTJs2\njd/+9rfoenx+PxMtx1WJ0uejZUm0Pi+i2759O6dPn0bTNCorK1mwYEGsSxqxY8eO8eSTTzJjxgwA\nZs6cycMPP8yzzz5LKBTC4XDwu9/9DovFwr59+/jLX/6Cruvcf//9/PjHP45x9YO7fv/V0NAw5GzB\nYJCKigouX76MYRjs2LGDwsLCWEeK6PqcFRUVnDlzhoyMDAAeeugh7rzzzoTOef02dv78+ZOyLa/P\nefDgwUnVln6/n4qKClpaWujq6uLxxx9n9uzZ49uW6ibS0dGhysvyKzZWAAAII0lEQVTL1ZYtW9Qb\nb7wR8TXnzp1T69evV+Xl5RNc3fAMluUXv/iFeu+995RSSm3btk3V19dPdIlDFi2L1+tVq1atUsFg\nUCml1M9+9jP10UcfxaLMQVVXV6uHH35YKaVUa2urWrly5TU/v/vuu9Xly5dVKBRSGzZsUOfOnYtB\nlUMzWJa1a9eqhoYGpZRSmzdvVu+///5Elzgkg+VQKnH6/GBZEqnPi+iOHTumHn30UaWUUufPn1f3\n339/jCsanaNHj6rNmzdf81xFRYV69913lVJK7d69W7311luqo6NDrVu3Tnk8HuX3+9X3v/991dbW\nFouShyzS/ms42aqqqtS2bduUUkodPnxYPfnkkzHLEk2knM8995w6ePDgDa9L1JyRtrGTsS0j5Zxs\nbfn3v/9d7dmzRyml1KVLl9S6devGvS3j8yvlcWKxWHjttddwOp0Dvmbnzp08/fTTE1jVyETLEg6H\nOXnyJKtXrwZg69at5OfnT3SJQxYti9lsxmw209nZSU9PD36/n/T09BhUObilS5fy+9//HoC0tDT8\nfj+hUAiAuro60tPTycvLQ9d1Vq5cSXV1dSzLjSpaFoCqqipyc3svip2VlUVbW1tM6hzMYDkgcfp8\ntCyJ1udFdNXV1axZswaA6dOn43a78fl8Ma5qbB07dozvfve7AKxatYrq6mpOnz7N/PnzsdvtWK1W\nFi9ezKlTp2JcaXSR9l/DyVZdXc3atWsBWL58edzmHcrnJyChc0baxk7GthzKfhESuy3vueceHnnk\nEaB39kZOTs64t+VNNaAymUxYrdYBf15VVcWyZcsoKCiYwKpGJlqW1tZWUlJS2LFjBxs2bGD37t0T\nXN3wRMuSlJTEE088wZo1a1i1ahULFy5k2rRpE1zh0BiGgc1mA+Cdd96hrKwMwzAAaG5uJisrq++1\nWVlZNDc3x6TOoYiWBei7NlxTUxNHjhxh5cqVMalzMIPlSKQ+Hy1LovV5EZ3L5SIzM7PvcbxvL4bi\n/PnzPPbYY2zYsIEjR47g9/uxWCwAZGdn09zcjMvlSqjtJETefw0nW//ndV1H0zS6u7snLsAQDbSf\nfvPNN9m4cSNPP/00ra2tCZ0z0jZ2MrblQPuSydSWVz3wwAM888wzVFZWjntb3nTnUA2kvb2dqqoq\n/vznP9PY2BjrckZFKUVjYyMbN26koKCARx99lPfff58777wz1qUNm8/n49VXX2X//v2kpqby4IMP\n8vnnnzN79uxYlzagf/zjH7zzzjv86U9/inUpoxYtS0tLC4899hhbt2695sNfPIqUI1H7fKQsk6nP\nixupBL+6SXFxMZs2beLuu++mrq6OjRs3XvON+ED5Ej03DD9bImW+9957ycjIoKSkhD179vDyyy+z\naNGia16TiDn7b2P7n6c62dqyf86amppJ2ZZvv/02n332Gb/61a+uqXM82vKmOkIVzdGjR2ltbeUn\nP/kJmzZt4syZM2zfvj3WZY1IZmYm+fn5FBUVYRgGt99+O+fOnYt1WSNy4cIFCgsLycrKwmKxsGTJ\nEmpqamJd1oAOHz7MH//4R1577TXsdnvf806nE5fL1fe4sbFx0KkTsTZQFugd6D7yyCM89dRTrFix\nIkYVDs1AORKxzw+UZTL1eXHj9qKpqQmHwxHDikYnJyeHe+65B03TKCoqYsqUKbjdbgKBAPDN9jBS\n7njfTkZis9mGnM3pdPYdhQsGgyil+r5Fj3e33347JSUlAKxevZqzZ88mfM7rt7GTtS2vzznZ2rKm\npoaGhgYASkpKCIVCpKSkjGtbyoDqa3fddRfvvvsuf/3rX3n55ZeZO3culZWVsS5rREwmE4WFhdTW\n1gJw5syZuJ0mN5iCggIuXLjQ1wlqamooLi6ObVED8Hq9vPjii7z66qt9K+VcNXXqVHw+H5cuXaKn\np4dDhw5RWloao0oHFy0L9J539OCDD1JWVhaD6oYuWo5E6/PRskymPi+gtLSUAwcOAL1t6XQ6+6bZ\nJqJ9+/bx+uuvA73Tn1taWrjvvvv6Mr733nvccccdLFy4kE8//RSPx0NHRwenTp1iyZIlsSx9RJYv\nXz7kbKWlpezfvx+AQ4cOceutt8ay9GHZvHkzdXV1QO95YzNmzEjonJG2sZOxLSPlnGxteeLEib5Z\nHC6Xi87OznFvS03F87G6MVZTU8MLL7xAfX09JpOJnJwcVq9ezdSpU/tOPgO4dOkSv/71r+N6CeXB\nsly8eJGKigqUUsycOZNt27bF7bLWg2V5++23qaqqwjAMFi1axLPPPhvrkiPau3cvf/jDH675IHvr\nrbcya9Ys1q5dy/Hjx9m1axcA69at46GHHopVqYOKlmXFihUsXbr0mukAP/jBD1i/fn0sSo1qsDa5\nKhH6/GBZEqnPi8Ht2rWLEydOoGkaW7dujetpzoPx+Xw888wzeDwegsEgmzZtoqSkhOeee46uri7y\n8/PZsWMHZrOZ/fv38/rrr6NpGuXl5fzwhz+MdflRRdp/7dq1i4qKiiFlC4VCbNmyhdraWiwWCzt3\n7iQvLy/WsW4QKWd5eTl79uwhOTkZm83Gjh07yM7OTtickbaxO3fuZMuWLZOqLSPlvO+++3jzzTcn\nTVsGAgGef/55GhoaCAQCbNq0iXnz5g15mzOSjDfVgEoIIYQQQgghxpJ8fSmEEEIIIYQQIyQDKiGE\nEEIIIYQYIRlQCSGEEEIIIcQIyYBKCCGEEEIIIUZIBlRCCCGEEEIIMUIyoBJCCCGEEEKIEZIBlRBC\nCCGEEEKMkAyohBBCCCGEEGKE/h91x1n5rYVapQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286eccbda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "pm.traceplot(glm_outliers_trace[glm_outliers_model_burn_in:], varnames=['a', 'b'])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f286aa726d8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAIVCAYAAACp726UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIv5f2yvoBIpAmCgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286aaa80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.plot(glm_outliers_df['x'], glm_outliers_df['y'], 'bo')\n",
    "plot_x = np.linspace(-5, 105, 100)\n",
    "v1 = glm_outliers_trace[glm_outliers_model_burn_in::50]\n",
    "ax.plot(plot_x, v1['a'] + v1['b']*plot_x[:,np.newaxis], c='blue',alpha =0.1, linewidth=0.5)\n",
    "ax.plot(plot_x, v1['a'].mean() + v1['b'].mean()*plot_x, c='blue',linewidth=2)\n",
    "ax.plot(plot_x, lr.intercept_ + lr.coef_[0]*plot_x, c='red',linewidth=1)\n",
    "ax.set_xlabel(r'$x$')\n",
    "ax.set_ylabel(r'$y$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Please notice that the line that you perceive as a single blue line is actually a set of lines like in the graph above. Only because the $y$ scale is this time much larger you cannot visually se the difference that well. But if you could zoom in into the above graph you would see it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Mixture Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Finite Mixture Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following example is taken from the [PyMC3 documentation](https://pymc-devs.github.io/pymc3/notebooks/gaussian_mixture_model.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nr_centers=3\n",
    "ndata = 500\n",
    "def generate_mixture_data(nr_centers=3, n=500, spread=5):\n",
    "    labs = nr_centers//2\n",
    "    lstart = -labs\n",
    "    lend = labs + 1\n",
    "    lcenters = np.array(range(lstart, lend))*spread\n",
    "\n",
    "    # simulate data from mixture distribution\n",
    "    v = np.random.randint(0, nr_centers, n)\n",
    "    ldata = lcenters[v] + stats.norm.rvs(size=n)\n",
    "    return ldata\n",
    "\n",
    "data = generate_mixture_data(nr_centers=nr_centers, n=ndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8YAAAIECAYAAADbxjfKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286a7be780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.hist(data, bins=50)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# setup model\n",
    "with pm.Model() as fixed_mixture_model:\n",
    "    # cluster sizes\n",
    "    p = pm.Dirichlet('p', a=np.array([1., 1., 1.]), shape=nr_centers)\n",
    "    # ensure all clusters have some points\n",
    "    p_min_potential = pm.Potential('p_min_potential', tt.switch(tt.min(p) < .1, -np.inf, 0))\n",
    "\n",
    "    # cluster centers\n",
    "    means = pm.Normal('means', mu=[0, 0, 0], sd=15, shape=nr_centers)\n",
    "    # break symmetry\n",
    "    order_means_potential = pm.Potential('order_means_potential',\n",
    "                                         tt.switch(means[1] - means[0] < 0, -np.inf, 0)\n",
    "                                         + tt.switch(means[2] - means[1] < 0, -np.inf, 0))\n",
    "\n",
    "    # measurement error\n",
    "    sd = pm.Uniform('sd', lower=0, upper=20)\n",
    "\n",
    "    # latent cluster of each observation\n",
    "    category = pm.Categorical('category', p=p, shape=ndata)\n",
    "\n",
    "    # likelihood for each observed value\n",
    "    points = pm.Normal('obs', mu=means[category], sd=sd, observed=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/disk2/home/cs/local/install/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:3: DeprecationWarning: ElemwiseCategorical is deprecated, switch to CategoricalGibbsMetropolis.\n",
      "  app.launch_new_instance()\n",
      "100%|██████████| 10500/10500 [00:57<00:00, 181.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.6 s, sys: 1.26 s, total: 1min\n",
      "Wall time: 59.6 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with fixed_mixture_model:\n",
    "    fixed_mixture_model_step1 = pm.Metropolis(vars=[p, sd, means])\n",
    "    fixed_mixture_model_step2 = pm.ElemwiseCategorical(vars=[category], values=[0, 1, 2])\n",
    "    fixed_mixture_model_trace = pm.sample(10000, step=[fixed_mixture_model_step1, fixed_mixture_model_step2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "means:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  -4.910           0.412            0.038            [-5.277, -3.823]\n",
      "  -0.671           1.248            0.124            [-3.486, 0.083]\n",
      "  4.682            0.558            0.055            [3.360, 5.099]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -5.243         -5.115         -5.066         -4.991         -3.700\n",
      "  -3.655         -0.183         -0.110         -0.048         0.069\n",
      "  3.295          4.835          4.912          4.981          5.073\n",
      "\n",
      "\n",
      "p:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.302            0.077            0.007            [0.113, 0.379]\n",
      "  0.327            0.031            0.002            [0.270, 0.393]\n",
      "  0.371            0.065            0.006            [0.300, 0.530]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.109          0.306          0.330          0.347          0.378\n",
      "  0.274          0.308          0.324          0.344          0.402\n",
      "  0.302          0.331          0.348          0.373          0.534\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fixed_mixture_model_trace_burn_in = 2000\n",
    "pm.summary(fixed_mixture_model_trace[glm_outliers_model_burn_in:], varnames=['means', 'p'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286a5531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAEUCAYAAAAspncYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f287b8e0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "pm.traceplot(fixed_mixture_model_trace[glm_outliers_model_burn_in:], varnames=['means', 'p'])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Please note that on the right hand side of the above graph you can nicely see the effect of \"burn-in\". It takes roughly 2000 samples until the values \"converge\". This is why you typically throw away the first 2000 samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Infinite Mixture Models: Clustering the McDonalds Menu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This example is taken from here: [Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process](http://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/). This is also by far the best explanation I've found for the so called **Stick-Breaking Process**, and how it is equivalent to the **Chinese Restaurant Process**, the **Polya Urn Model** and the **Dirichlet Process**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>dietary_fiber</th>\n",
       "      <th>sugars</th>\n",
       "      <th>protein</th>\n",
       "      <th>vitamin_a_dv</th>\n",
       "      <th>vitamin_c_dv</th>\n",
       "      <th>calcium_dv</th>\n",
       "      <th>iron_dv</th>\n",
       "      <th>calories_from_fat</th>\n",
       "      <th>saturated_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>carbohydrates</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.256972</td>\n",
       "      <td>-0.043135</td>\n",
       "      <td>0.623001</td>\n",
       "      <td>0.737522</td>\n",
       "      <td>-1.187087</td>\n",
       "      <td>0.563394</td>\n",
       "      <td>-0.317138</td>\n",
       "      <td>-0.139953</td>\n",
       "      <td>-0.485008</td>\n",
       "      <td>2.002784</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.059024</td>\n",
       "      <td>2.078751</td>\n",
       "      <td>-0.525870</td>\n",
       "      <td>Hamburger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.426906</td>\n",
       "      <td>0.216793</td>\n",
       "      <td>0.912521</td>\n",
       "      <td>0.531005</td>\n",
       "      <td>-1.237093</td>\n",
       "      <td>0.639829</td>\n",
       "      <td>-0.220048</td>\n",
       "      <td>-0.142032</td>\n",
       "      <td>-0.170976</td>\n",
       "      <td>1.527630</td>\n",
       "      <td>0.455109</td>\n",
       "      <td>0.574437</td>\n",
       "      <td>1.660648</td>\n",
       "      <td>-0.751855</td>\n",
       "      <td>Cheeseburger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.948294</td>\n",
       "      <td>0.594870</td>\n",
       "      <td>0.990855</td>\n",
       "      <td>0.202455</td>\n",
       "      <td>-1.288236</td>\n",
       "      <td>0.900404</td>\n",
       "      <td>-0.206808</td>\n",
       "      <td>-0.145340</td>\n",
       "      <td>-0.286954</td>\n",
       "      <td>1.311651</td>\n",
       "      <td>0.971714</td>\n",
       "      <td>1.003947</td>\n",
       "      <td>3.846183</td>\n",
       "      <td>-1.280131</td>\n",
       "      <td>DoubleCheeseburger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.797274</td>\n",
       "      <td>0.476721</td>\n",
       "      <td>0.815308</td>\n",
       "      <td>0.292716</td>\n",
       "      <td>-1.262738</td>\n",
       "      <td>0.884814</td>\n",
       "      <td>-0.242453</td>\n",
       "      <td>-0.144432</td>\n",
       "      <td>-0.352148</td>\n",
       "      <td>1.588547</td>\n",
       "      <td>0.778464</td>\n",
       "      <td>0.618489</td>\n",
       "      <td>2.786308</td>\n",
       "      <td>-1.161608</td>\n",
       "      <td>McDouble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.893391</td>\n",
       "      <td>0.553171</td>\n",
       "      <td>0.797632</td>\n",
       "      <td>0.409525</td>\n",
       "      <td>-1.266509</td>\n",
       "      <td>0.902107</td>\n",
       "      <td>-0.221952</td>\n",
       "      <td>-0.140197</td>\n",
       "      <td>-0.263338</td>\n",
       "      <td>1.481046</td>\n",
       "      <td>0.848911</td>\n",
       "      <td>0.877620</td>\n",
       "      <td>3.259275</td>\n",
       "      <td>-1.261429</td>\n",
       "      <td>QuarterPounder®withCheese+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_fat  cholesterol    sodium  dietary_fiber    sugars   protein  \\\n",
       "0   0.256972    -0.043135  0.623001       0.737522 -1.187087  0.563394   \n",
       "1   0.426906     0.216793  0.912521       0.531005 -1.237093  0.639829   \n",
       "2   0.948294     0.594870  0.990855       0.202455 -1.288236  0.900404   \n",
       "3   0.797274     0.476721  0.815308       0.292716 -1.262738  0.884814   \n",
       "4   0.893391     0.553171  0.797632       0.409525 -1.266509  0.902107   \n",
       "\n",
       "   vitamin_a_dv  vitamin_c_dv  calcium_dv   iron_dv  calories_from_fat  \\\n",
       "0     -0.317138     -0.139953   -0.485008  2.002784           0.237144   \n",
       "1     -0.220048     -0.142032   -0.170976  1.527630           0.455109   \n",
       "2     -0.206808     -0.145340   -0.286954  1.311651           0.971714   \n",
       "3     -0.242453     -0.144432   -0.352148  1.588547           0.778464   \n",
       "4     -0.221952     -0.140197   -0.263338  1.481046           0.848911   \n",
       "\n",
       "   saturated_fat  trans_fat  carbohydrates                        name  \n",
       "0       0.059024   2.078751      -0.525870                   Hamburger  \n",
       "1       0.574437   1.660648      -0.751855                Cheeseburger  \n",
       "2       1.003947   3.846183      -1.280131          DoubleCheeseburger  \n",
       "3       0.618489   2.786308      -1.161608                    McDouble  \n",
       "4       0.877620   3.259275      -1.261429  QuarterPounder®withCheese+  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILENAME = \"mcdonalds-normalized-data.tsv\"\n",
    "\n",
    "# Note: you'll have to remove the last \"name\" column in the file (or\n",
    "# some other such thing), so that all the columns are numeric.\n",
    "df = pd.DataFrame.from_csv(FILENAME, sep='\\t', header=0, index_col=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_fat</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>sodium</th>\n",
       "      <th>dietary_fiber</th>\n",
       "      <th>sugars</th>\n",
       "      <th>protein</th>\n",
       "      <th>vitamin_a_dv</th>\n",
       "      <th>vitamin_c_dv</th>\n",
       "      <th>calcium_dv</th>\n",
       "      <th>iron_dv</th>\n",
       "      <th>calories_from_fat</th>\n",
       "      <th>saturated_fat</th>\n",
       "      <th>trans_fat</th>\n",
       "      <th>carbohydrates</th>\n",
       "      <th>name</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.256972</td>\n",
       "      <td>-0.043135</td>\n",
       "      <td>0.623001</td>\n",
       "      <td>0.737522</td>\n",
       "      <td>-1.187087</td>\n",
       "      <td>0.563394</td>\n",
       "      <td>-0.317138</td>\n",
       "      <td>-0.139953</td>\n",
       "      <td>-0.485008</td>\n",
       "      <td>2.002784</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.059024</td>\n",
       "      <td>2.078751</td>\n",
       "      <td>-0.525870</td>\n",
       "      <td>Hamburger</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.426906</td>\n",
       "      <td>0.216793</td>\n",
       "      <td>0.912521</td>\n",
       "      <td>0.531005</td>\n",
       "      <td>-1.237093</td>\n",
       "      <td>0.639829</td>\n",
       "      <td>-0.220048</td>\n",
       "      <td>-0.142032</td>\n",
       "      <td>-0.170976</td>\n",
       "      <td>1.527630</td>\n",
       "      <td>0.455109</td>\n",
       "      <td>0.574437</td>\n",
       "      <td>1.660648</td>\n",
       "      <td>-0.751855</td>\n",
       "      <td>Cheeseburger</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.948294</td>\n",
       "      <td>0.594870</td>\n",
       "      <td>0.990855</td>\n",
       "      <td>0.202455</td>\n",
       "      <td>-1.288236</td>\n",
       "      <td>0.900404</td>\n",
       "      <td>-0.206808</td>\n",
       "      <td>-0.145340</td>\n",
       "      <td>-0.286954</td>\n",
       "      <td>1.311651</td>\n",
       "      <td>0.971714</td>\n",
       "      <td>1.003947</td>\n",
       "      <td>3.846183</td>\n",
       "      <td>-1.280131</td>\n",
       "      <td>DoubleCheeseburger</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.797274</td>\n",
       "      <td>0.476721</td>\n",
       "      <td>0.815308</td>\n",
       "      <td>0.292716</td>\n",
       "      <td>-1.262738</td>\n",
       "      <td>0.884814</td>\n",
       "      <td>-0.242453</td>\n",
       "      <td>-0.144432</td>\n",
       "      <td>-0.352148</td>\n",
       "      <td>1.588547</td>\n",
       "      <td>0.778464</td>\n",
       "      <td>0.618489</td>\n",
       "      <td>2.786308</td>\n",
       "      <td>-1.161608</td>\n",
       "      <td>McDouble</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.893391</td>\n",
       "      <td>0.553171</td>\n",
       "      <td>0.797632</td>\n",
       "      <td>0.409525</td>\n",
       "      <td>-1.266509</td>\n",
       "      <td>0.902107</td>\n",
       "      <td>-0.221952</td>\n",
       "      <td>-0.140197</td>\n",
       "      <td>-0.263338</td>\n",
       "      <td>1.481046</td>\n",
       "      <td>0.848911</td>\n",
       "      <td>0.877620</td>\n",
       "      <td>3.259275</td>\n",
       "      <td>-1.261429</td>\n",
       "      <td>QuarterPounder®withCheese+</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_fat  cholesterol    sodium  dietary_fiber    sugars   protein  \\\n",
       "0   0.256972    -0.043135  0.623001       0.737522 -1.187087  0.563394   \n",
       "1   0.426906     0.216793  0.912521       0.531005 -1.237093  0.639829   \n",
       "2   0.948294     0.594870  0.990855       0.202455 -1.288236  0.900404   \n",
       "3   0.797274     0.476721  0.815308       0.292716 -1.262738  0.884814   \n",
       "4   0.893391     0.553171  0.797632       0.409525 -1.266509  0.902107   \n",
       "\n",
       "   vitamin_a_dv  vitamin_c_dv  calcium_dv   iron_dv  calories_from_fat  \\\n",
       "0     -0.317138     -0.139953   -0.485008  2.002784           0.237144   \n",
       "1     -0.220048     -0.142032   -0.170976  1.527630           0.455109   \n",
       "2     -0.206808     -0.145340   -0.286954  1.311651           0.971714   \n",
       "3     -0.242453     -0.144432   -0.352148  1.588547           0.778464   \n",
       "4     -0.221952     -0.140197   -0.263338  1.481046           0.848911   \n",
       "\n",
       "   saturated_fat  trans_fat  carbohydrates                        name  \\\n",
       "0       0.059024   2.078751      -0.525870                   Hamburger   \n",
       "1       0.574437   1.660648      -0.751855                Cheeseburger   \n",
       "2       1.003947   3.846183      -1.280131          DoubleCheeseburger   \n",
       "3       0.618489   2.786308      -1.161608                    McDouble   \n",
       "4       0.877620   3.259275      -1.261429  QuarterPounder®withCheese+   \n",
       "\n",
       "   cluster_id  \n",
       "0           9  \n",
       "1           9  \n",
       "2          15  \n",
       "3          15  \n",
       "4          15  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[:,:-1].values\n",
    "\n",
    "gmm = mixture.BayesianGaussianMixture(n_components = 25, weight_concentration_prior_type='dirichlet_process')\n",
    "gmm.fit(x)\n",
    "clusters = gmm.predict(x)\n",
    "\n",
    "# https://github.com/echen/dirichlet-process/blob/master/plots.R\n",
    "df['cluster_id'] = pd.Series(clusters, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8YAAAIFCAYAAAAQmuRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286d930668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.hist(df['cluster_id'], bins=50)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39                  ChickenMcNuggets®(4piece)\n",
       "40                  ChickenMcNuggets®(6piece)\n",
       "41                 ChickenMcNuggets®(10piece)\n",
       "46    ChickenSelects®PremiumBreastStrips(3pc)\n",
       "47    ChickenSelects®PremiumBreastStrips(5pc)\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cluster_id']==2]['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Have a look at [Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process](http://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/) and scroll down to \"Let’s dive into one of these clusterings\" to see some of the clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![Cluster 1 (Desserts)](http://i.imgur.com/oM1aPCE.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![Cluster 2 (Sauces)](http://i.imgur.com/3bOYEBz.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![Cluster 8 (Sugary Drinks)](http://i.imgur.com/MNdKmrr.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![Cluster 9 (Breakfast Foods)](http://i.imgur.com/m67oVsY.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Have a look at [scikit-learn](http://scikit-learn.org)'s [Gaussian mixture models](http://scikit-learn.org/stable/modules/mixture.html) documentation to learn more about this functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Switchpoint analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Single switchpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The example is taken directly from the PyMC3 documentation: [Case study 2: Coal mining disasters](https://pymc-devs.github.io/pymc3/notebooks/getting_started.html#Case-study-2:-Coal-mining-disasters). You can look there for more details and explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disaster_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
    "                            3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
    "                            2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,\n",
    "                            1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
    "                            0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
    "                            3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
    "                            0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)\n",
    "year = np.arange(1851, 1962)\n",
    "disaster_data_df = pd.DataFrame({'disaster_count':disaster_data},index=year)\n",
    "\n",
    "#with pd.option_context('display.max_rows', None, 'display.max_columns', 3):\n",
    "#    print(disaster_data_df)\n",
    "#print(pd.get_option('display.max_rows'))\n",
    "#pd.set_option('display.max_rows', 60)\n",
    "# trick to show full dataframe all entries:\n",
    "display(HTML(disaster_data_df.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f286d4c6978>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9AAAAIVCAYAAAA5/BdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286d930f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.plot(year, disaster_data, 'o', markersize=8);\n",
    "ax.set_ylabel(\"Disaster count\")\n",
    "ax.set_xlabel(\"Year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as disaster_model:\n",
    "\n",
    "    switchpoint = pm.DiscreteUniform('switchpoint', lower=year.min(), upper=year.max(), testval=1900)\n",
    "\n",
    "    # Priors for pre- and post-switch rates number of disasters\n",
    "    early_rate = pm.Exponential('early_rate', 1)\n",
    "    late_rate = pm.Exponential('late_rate', 1)\n",
    "\n",
    "    # Allocate appropriate Poisson rates to years before and after current\n",
    "    rate = pm.math.switch(switchpoint >= year, early_rate, late_rate)\n",
    "\n",
    "    disasters = pm.Poisson('disasters', rate, observed=disaster_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigned Metropolis to switchpoint\n",
      "Assigned NUTS to early_rate_log__\n",
      "Assigned NUTS to late_rate_log__\n",
      "Assigned Metropolis to disasters_missing\n",
      "100%|██████████| 10500/10500 [00:40<00:00, 257.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 43 s, sys: 377 ms, total: 43.3 s\n",
      "Wall time: 42.9 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with disaster_model:\n",
    "    disaster_model_trace = pm.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f286d7bde80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f284cff1b38>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f284cfa9e48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f284cf82630>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f284da8f8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f284e5160f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f286df83cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f286d441f60>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAI0CAYAAAAa4mizAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nidD/A7cuwhAK/JTsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f284d23ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disaster_model_trace_burn_in = 2000\n",
    "pm.traceplot(disaster_model_trace[disaster_model_trace_burn_in:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "switchpoint:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  1889.703         2.283            0.125            [1885.000, 1893.000]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  1886.000       1888.000       1890.000       1891.000       1895.000\n",
      "\n",
      "\n",
      "disasters_missing:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  2.225            1.770            0.095            [0.000, 6.000]\n",
      "  0.853            0.903            0.025            [0.000, 3.000]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.000          1.000          2.000          3.000          6.000\n",
      "  0.000          0.000          1.000          1.000          3.000\n",
      "\n",
      "\n",
      "early_rate:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  3.089            0.285            0.005            [2.550, 3.672]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  2.552          2.894          3.082          3.273          3.678\n",
      "\n",
      "\n",
      "late_rate:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.932            0.119            0.002            [0.709, 1.170]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.712          0.850          0.926          1.009          1.174\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pm.summary(disaster_model_trace[disaster_model_trace_burn_in:]) # , varnames=['means', 'p']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Multiple switchpoints: Bayesian Blocks / Data Binning / Data-Bucketing / Discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In a similar way like with our mixture model above where we treated the case with a fixed number of mixtures within PyMC3, but used pre-packaged algorithms for the infinite case, here I simply point you into the right direction.\n",
    "\n",
    "We will look at a very specific case here: often you will need to [discretize a continuous feature](https://en.wikipedia.org/wiki/Discretization_of_continuous_features), also called [binning](https://en.wikipedia.org/wiki/Data_binning) or bucketing, e.g. you have some continuous data, but you want to make the representation more compact by treating the data as some countable number of cases.\n",
    "\n",
    "There have been many rules of thumb used in the literature, e.g. [Scott’s Rule](https://academic.oup.com/biomet/article-abstract/66/3/605/232642/On-optimal-and-data-based-histograms?redirectedFrom=fulltext) and the [Freedman-Diaconis Rule](https://link.springer.com/article/10.1007%2FBF01025868). Both are based on assumptions about the form of the underlying data distribution. A slightly more rigorous approach is [Knuth’s rule](https://arxiv.org/abs/physics/0605197), which is based on optimization of a Bayesian fitness function across fixed-width bins.\n",
    "\n",
    "A further generalization of this approach is that of [Bayesian Blocks](https://arxiv.org/abs/1207.5578), which optimizes a fitness function across an arbitrary configuration of bins, which need not be of equal width. This paper is quite accessible and **the method can be understood as a switchpoint analysis with an infinite number of possible switchpoints**.\n",
    "\n",
    "The [astroML](http://www.astroml.org) package includes an [implementation in python](http://www.astroml.org/user_guide/density_estimation.html#bayesian-blocks-histograms-the-right-way) for all the above algorithms and an explanation of the algorithm is given in the blog post [Dynamic Programming in Python: Bayesian Blocks](https://jakevdp.github.io/blog/2012/09/12/dynamic-programming-in-python/).\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.24126734, -2.96907699, -2.67392823, -2.63535746, -2.5456948 ,\n",
       "       -2.25652824, -2.07765982, -2.04048352, -2.03867836, -1.71902618,\n",
       "       -1.52435604, -1.51501914, -1.43331863, -1.4079865 , -1.40706244,\n",
       "       -1.02545147, -0.57689776, -0.57572803, -0.43988777, -0.07577381,\n",
       "        0.13185513,  0.16865882,  0.38469141,  0.47144196,  0.47371268,\n",
       "        0.83816783,  1.20386144,  1.90310481,  2.67627562,  3.85273149])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEED = 42\n",
    "np.random.seed(SEED)\n",
    "x = np.random.normal(size=1000)\n",
    "bins = astroML.density_estimation.bayesian_blocks(x, p0=1.5)\n",
    "#bins = astroML.density_estimation.bayesian_blocks(x, gamma=0.999)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8kAAAIICAYAAABdD6w8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAJEIyAAAAJP8flQSQl5BeufAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f286d8dae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.hist(x, bins, normed=True)\n",
    "ax.hist(x, histtype='step', linewidth=2, bins=30, normed=True)\n",
    "ax.set_ylim(0.0,0.6)\n",
    "#astroML.plotting.hist(x, bins='blocks', p0=0.5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Supervised Learning: Data Binning / Data-Bucketing / Discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Just as a side remark, in case you have a supervised learning task where you want to classify data according to some features and you want to discretize some of the continuous features then a good approach for finding the bin boundaries is [binning data using decision tree regression](https://sites.google.com/site/kittipat/programming-with-python/binningdatausingdecisiontreeregression). The decision tree internally already builds up decision tree boundaries and those you can use as good bin boundaries points.\n",
    "\n",
    "More theory is given in [A Simple Guide to Entropy-Based Discretization](http://kevinmeurer.com/a-simple-guide-to-entropy-based-discretization/).\n",
    "\n",
    "Also have a look at [decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html) and its associated [gist](https://gist.github.com/cstrelioff/8fefa9a43e82d96e9f0c) to learn more about how to use decision trees in scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Stochastic Volatility Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following example is taken from [Probabilistic Programming in Python using PyMC](https://arxiv.org/abs/1507.08050) by John Salvatier, Thomas Wiecki, Christopher Fonnesbeck. A similar [example](https://pymc-devs.github.io/pymc3/notebooks/stochastic_volatility.html) is also part of the PyMC3 documentation. And [here](https://blog.quantopian.com/probabilistic-programming-quant-finance/) is another similar discussion of this example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One elegant example of how Probabilistic Programming can infer unobservable quantities of the stock market is the stochastic volatility model. Volatility is an important concept of quantitative finance as it relates to risk. Unfortunately, as Tony Cooper reminds us:\n",
    "\n",
    "> \"Volatility is a strange thing - it exists but you can't measure it.\"\n",
    ">\n",
    "> -- Tony Cooper on [Quantopian forums)(https://www.quantopian.com/posts/system-based-on-easy-volatility-investing-by-tony-cooper-at-double-digit-numerics)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If we can't measure something, the next best thing we can do is to try and model it. One way to do this is in a probabilistic framework is the concept of stochastic volatility: If we assume that returns are [normally distributed](https://en.wikipedia.org/wiki/Normal_distribution), the volatility would be captured as the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of the normal distribution. Thus, the standard deviation gives rise to stochastic volatility. Intuitively we would assume that the standard deviation is high during times of market turmoil like the 2008 crash.\n",
    "\n",
    "So the trivial thing to do would be to look at the rolling standard deviation of returns. But this is unsatisfying for multiple reasons:\n",
    "* it often lags behind volatility,\n",
    "* is a rather unstable measure,\n",
    "* strongly dependent on the window size with no principled way of choosing it.\n",
    "\n",
    "These properties are outlined in the plot below. The larger the window size, the more lag, the smaller the window size, the more unstable the estimate gets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we will see, the stochastic volatility model does a better job at all of them. But before we look at that we need to establish one more insight into the nature of volatility: it tends to cluster. This is nicely demonstrated by looking at the returns of the S&P 500 below. As you can see, during the 2008 financial crisis there is a lot volatility in the stock market (huge positive and negative daily returns) that gradually decreases over time.\n",
    "\n",
    "So how do we model this clustering property? The Stochastic Volatility model assumes that that the standard-deviation of the returns follow a [random-walk process](https://en.wikipedia.org/wiki/Random_walk). You can read the Wikipedia article I linked but essentially this process allows for slow, gradual changes over time.\n",
    "\n",
    "What is interesting is that we can model the standard deviation itself to follow a random walk. Intuitively, we allow standard deviation to change over time but only ever so slightly at each time-point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The plot further down below shows the latent volatility inferred from the model based on the market data. The lines represent the standard deviation of the Normal distribution we assume for the daily returns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As you can see, the estimated volatility is much more robust. Moreover, all parameters are estimated from the data while we would have to find a reasonable window length for the rolling standard deviation ourselves. Finally, we do not just get a single estimate as with the rolling standard deviation but rather many solutions that are likely. This provides us with a measure of the uncertainty in our estimates and is represented by the width of the line below.\n",
    "\n",
    "In summary, Bayesian statistics and Probabilistic Programming is an extremely powerful framework for Quantitative Finance as it provides:\n",
    "* a principled framework for modeling latent, unobservable causes, as demonstrated by the stochastic volatility model;\n",
    "* a measure of uncertainty in our estimates;\n",
    "* powerful sampling methods that allow automatic estimation of highly complex models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This example has 400+ parameters so using common sampling algorithms like Metropolis-Hastings would get bogged down, generating highly autocorrelated samples. Instead, we use NUTS, which is dramatically more efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Asset prices have time-varying volatility (variance of day over day returns). In some periods, returns are\n",
    "highly variable, while in others they are very stable. Stochastic volatility models address this with a latent\n",
    "volatility variable, which changes over time. The following model is similar to the one described in the NUTS\n",
    "paper ([Hoffman & Gelman, 2014](http://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf), p. 21).\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\sigma&\\sim\\hbox{exp}(50)\\\\\n",
    "\\nu&\\sim\\hbox{exp}(.1)\\\\\n",
    "s_i&\\sim\\mathcal{N}(s_{i-1},\\sigma)\\\\\n",
    "log(\\frac{y_i}{y_{i-1}})&\\sim t(\\nu, 0, e^{s_i})\\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Here, $y$ is the daily return series which is modeled with a Student-t distribution with an unknown degrees\n",
    "of freedom parameter, and a scale parameter determined by a latent process $s$. The individual $s_i$ are the\n",
    "individual daily log volatilities in the latent log volatility process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Sometimes some of the probability distributions are parametrized with different parameters, e.g. in the below `pm.GaussianRandomWalk` may be parametrized in terms of $\\tau$ where $\\tau=1/\\sigma^2$ (there is no documentation in the PyMC3 documenation about the `pm.GaussianRandomWalk`; you have to look this up in the sourcecode).\n",
    "\n",
    "The same is true with [different parametrizations](https://en.wikipedia.org/wiki/Student%27s_t-distribution#In_terms_of_inverse_scaling_parameter_.CE.BB) of the Student T distribution, e.g. $\\lambda=1/\\sigma^2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Build the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Our data consist of daily returns of the S&P 500 during the 2008 financial crisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00553 , -0.001764,  0.005191, -0.003814, -0.002478])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_model_n = 400\n",
    "sp500_model_returns = np.genfromtxt(pm.get_data(\"SP500.csv\"))[-sp500_model_n:]\n",
    "sp500_model_returns[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f286d7d6cf8>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f287e9440b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sp500_model_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as sp500_model:\n",
    "    sp500_model_sigma = pm.Exponential('sigma', 1./.02, testval=.1)\n",
    "\n",
    "    sp500_model_nu = pm.Exponential('nu', 1./10)\n",
    "    sp500_model_s = pm.GaussianRandomWalk('s', sd=sp500_model_sigma, shape=sp500_model_n)\n",
    "    \n",
    "    sp500_model_volatility_process = pm.Deterministic('volatility_process', pm.math.exp(sp500_model_s))\n",
    "\n",
    "    sp500_model_r = pm.StudentT('r', \n",
    "                                nu=sp500_model_nu,\n",
    "                                sd=sp500_model_volatility_process, \n",
    "                                observed=sp500_model_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using ADVI...\n",
      "Average Loss = -1,130.4:  34%|███▍      | 67761/200000 [00:18<00:36, 3594.10it/s]\n",
      "Convergence archived at 68100\n",
      "Interrupted at 68,100 [34%]: Average Loss = 505.57\n",
      "100%|██████████| 2000/2000 [01:30<00:00, 22.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 54s, sys: 418 ms, total: 1min 55s\n",
      "Wall time: 1min 54s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with sp500_model:\n",
    "    sp500_model_trace = pm.sample(1000, tune=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2840dabcc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f283f8f9630>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f283f8bdcf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f283f89bb38>]], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2840d9eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAEUCAYAAAAspncYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njJBCRRAEQRAEQRAEMUb+PzXAJil0yIhiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2840dab4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500_model_burn_in=100\n",
    "fig=plt.figure(figsize=(12,6), dpi= 80, facecolor='w', edgecolor='k')\n",
    "# pm.traceplot(sp500_model_trace[sp500_model_burn_in:])\n",
    "pm.traceplot(sp500_model_trace[sp500_model_burn_in:], sp500_model.vars[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f283f821080>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAGsCAYAAAAog7OlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAMTQAADE0B0s6tTgAAIABJREFUeJzsnWuMpGl1309duqq6qm/TM7MLWQfJDiJ2EBskkG0JWwiL\noF1igxfJikXEh/gDiU3ywRGWI4wNMvJNsoWEhTayVoqFkshRjC3bBENivhCIY2SLZJWIfMCJxO6y\nuzM70911v3RX5cPxb8+pd7p7epiqqe6e/09qdXfVW2899dR7Of/n3Eqz2WxmQgghhBBCCLFCyqse\ngBBCCCGEEEJImAghhBBCCCFWjoSJEEIIIYQQYuVImAghhBBCCCFWjoSJEEIIIYQQYuVImAghhBBC\nCCFWjoSJEEIIIYQQYuVImAghhBBCCCFWjoSJEEIIIYQQYuVUVz0AIYQQosjR0ZH92q/9mv3X//pf\nrVwuW61Wsw9/+MP2wz/8w6semhBCiCUhYSKEEOLc8ZWvfMW+/OUv2+c+9zmr1Wr2l3/5l/b7v//7\nEiZCCHGJkTARQghx7rh27ZrdvHnT/uAP/sDe8Y532Fvf+lZ761vfuuphCSGEWCKl2Ww2W/UghBBC\niCL/7b/9N/vMZz5jf/EXf2Hf9V3fZR/+8Ift7W9/+6qHJYQQYklImAghhDjXTCYT++xnP2u/+qu/\nav/9v/93azabqx6SEEKIJaCqXEIIIc4d//E//kf7xCc+YdPp1NbW1uwtb3mLTadTK5d12xJCiMuK\nckyEEEKcO971rnfZn//5n9u73vUuq9frVq1W7ZOf/KQ1Go1VD00IIcSSUCiXEEIIIYQQYuXIJy6E\nEEIIIYRYORImQgghhBBCiJUjYSKEEEIIIYRYORImQgghhBBCiJUjYSKEEEIIIYRYOZe2XPDNm51V\nD8HMzHZ3W3b7dm/Vw7hUaE4Xj+Z0sWg+F4/mdPFoTheP5nSxaD4Xz6rn9Pr1zVOfl8dkiZRKZpVK\n2UqlVY/k8qA5XTya08Wi+Vw8mtPFozldPJrTxaL5XDwXYU4lTIQQQgghhBArR8JECCGEEEIIsXIk\nTIQQQgghhBArR8JECCGEEEIIsXIkTIQQQgghhBArR8JECCGEEEIIsXIkTIQQQgghhBArR8JECCGE\nEEIIsXIkTIQQQgghhBArR8JECCGEEEIIsXIkTIQQQgghhBArR8JECCGEEEIIsXIkTIQQQgghhBAr\nR8JECCGEEEIIsXIkTJbIeGw2Gq16FEIIIYQQQpx/JEyWyGwWP0IIIYQQQoiTkTBZIqWS/5YwEUII\nIYQQ4nQkTJaIhIkQQgghhBBnQ8JkiUiYCCGEEEIIcTYkTJaIhIkQQgghhBBnQ8JkiUiYCCGEEEII\ncTYkTJaIhIkQQgghhBBnQ8JkiUiYCCGEEEIIcTYkTJaMxIkQQgghhBB3R8JkyZTLZtOphIkQQggh\nhBCnIWGyZGYzs/HYbDhc9UiEEEIIIYQ4v0iYLBk8JdOp2WSy2rEIIYQQQghxXpEweUDMZmZHR/4j\nhBBCCCGEmEfCZMngMSn/zUxLmAghhBBCCHEnEiZLBFFSKplVKv63EuGFEEIIIYS4k+oq3/zZZ5+1\n3/iN37ByuWzD4dB+6qd+yp588sm5bUajkX3kIx+xF154wSaTiX3gAx+wH//xH1/RiO+N6dR/V/9m\nlkslFyXTaQiV05jNotywEEIIIYQQl5mVCpNnnnnGfu7nfs7e/OY323PPPWfvec977IknnrBSssY/\n85nPWKPRsN/7vd+z27dv2/ve9z5729veZtevX1/hyM8GwqRSCZFBrslpwoRE+dnMrFaLMDAhhBBC\nCCEuKysVJp/61Kde/fv555+31772tXOixMzsy1/+sv30T/+0mZnt7u7aW97yFvvqV796Jq/JefE2\nVCohRmaz+RAvMxci06l7VnL1LoTMefkc5wHmQnOyODSni0XzuXg0p4tHc7p4NKeLRfO5eC7CnK5U\nmJiZffOb37Sf//mft729Pfv0pz99x/M3btywa9euvfr/I488Yi+//PJd97u727JKZbWuhvHYhcWj\nj27aeOyej1LJRUq1GiFe47ELklrNfx8ehiipVMzW1lb6Mc4lV69urnoIlw7N6WLRfC4ezeni0Zwu\nHs3pYtF8Lp7zPKcPRJh84QtfsKeffvqOx//oj/7IXv/619tnP/tZe/bZZ+1DH/qQ/cmf/Im1Wq0T\n9zWbze7wqhzH7du9lSvCUsm//Fde6dhw6P/Xamajkf9dr/t2o5GLkLU1FyYIFwQK24mY01u3Oioi\nsCA0p4tF87l4NKeLR3O6eDSni0XzuXjOw5xeu3a6KHogwuSJJ56wJ554Yu6x6XRqn//85+3JJ5+0\nUqlkjz/+uLVaLfvrv/5re/zxx1/d7jWveY3duHHD3vCGN5iZ2YsvvmhvfOMbz/S+5+FAns1ceBwe\nRl5JqRSekUolclEI6ZrNIq+Ex1Ytss4bOSROLAbN6WLRfC4ezeni0ZwuHs3pYtF8Lp7zPKcri3Uq\nl8v26U9/2r7yla+YmYds3bhxw173utfNbfeOd7zDPve5z5mZ2c2bN+3rX/+6ve1tb3vg4/1OOTqK\nhPecS5Kfg2L+CWLkvB48QgghhBBCLIqVJmH81m/9lv3rf/2v7QMf+ID9s3/2z+xjH/uY7ezs2De+\n8Q37+Mc/bmZm73//+83M7Cd/8iftZ37mZ+yjH/2o7e7urnDUZ2c8Nuv3zYbDSGrnb7M7e5rwP4Ik\ne02EEEIIIYS4zJRms8u5Hn/zZmfVQ7DDQ7Nmc9Nu3uxYuRxd36vVqNLVaPh2ZiFKyuVIhCdpvlZb\n3ec4T5RKHp/4yiuKOV0UmtPFovlcPJrTxaM5XTya08Wi+Vw852FOr18/PcdEHTKWSLnsCe2Hh9FU\nsVz2/4+O/DeixMxsMHAhUiznphNSCCGEEEJcdiRMlggJ7uVyVN1aW5tvmEjPEjrCHx3F8+SZnOck\nJSGEEEIIIRbByvuYXGYmk0h2p7lipeI/47FX6zLz52czzz8phmxlYaLKXEIIIYQQ4rIiYbJEyA2p\n1yOxnRLBeEWo2FUqRWJ89o4onEsIIYQQQjwMKJRriSBK6F9i5p4SxEmpFP8jSMg9AQkTIYQQQgjx\nMCBhskSosFUUF4R31Wr+3NGRCxQzFyX9foiT7FkRQgghhBDisqJQriUyGpn1euEVQWzgHalWQ3iM\nRlFOmGaMZiFq1MtECCGEEEJcZiRMlki7HUnvo5GLi3LZH6OPSbkc1bh4jKR5M4VyCSGEEEKIhwOF\nci2RUsm9JO22Wafj4gIPyWQSogThQrJ8qeTPU7WrKE4QMUIIIYQQQlwW5DFZIlTjGg69eeLhodmj\nj0Z/E6pwjUYhVKp/843QaLFa9d8Il2JPk5xYL4QQQgghxEVFHpMlUqu5wMhd3klyPzoKsTEa+XMk\nxJtFONfRUTRfHA7n+5lMJgrxEkIIIYQQlwMJkwcAfUqmU6+4hceE8sCIkLU194BMpxHilfuakKOS\nSxDn0sJCCCGEEEJcVCRMlgid3s1CgIzH880Wx2P3hCBUshAhbAthQ9d4Mxcx7FdeEyGEEEIIcdFR\njskSqVZdQFSrIURu3XLxsbnpooRQLhiNIkmeHidHR+4lMYvKXWZR3Ws6Va6JEEIIIYS42EiYLJGD\ng/kGivQzOTpywYKo6HZ9u37fbH3dt6Mz/GTiP6WS55/kfialUnhUJEyEEEIIIcRFRsJkieztuajo\ndqO8LyJjf99FxWDgzx8emrVa7hkh7IsQLrwtJMYjTkioJxleCCGEEEKIi4qEyRKpVNxrQiWu0cis\n0QixMhr5D40YqeBFF/hKxcVKTojPvU8y8poIIYQQQoiLjITJEmk0Qmjg5djf99/9vlmv50Kj2zVr\nNt17cutW5J4QvlWve4L8aOShXlmY0NtEwkQIIYQQQlxkJEyWSLttdvu2i4pGw0VEv+9io9OJSlwI\ni709fwzvyWjkifPXr/v+xmN/LaJkNPJtsxdFYV1CCCGEEOIiImGyRIZDFyCdjntEEBCIEKptTSbR\njHE49Mc6Hf97Y8Ps6lUXKHhNplPfplQKcVOr+f6qhW9UYuV0EHTyNgkhhBBCrBYJkyVCJa4XX/QQ\nrEcfdWHR77vIaLfNtrZCnBwd+Xb0M6EhI13hZ7PYrt/3/JPJxCt8EfqVhQnb0rhR3MlkMl+eWQgh\nhBBCrAYJkyWyvu7CoNPx/JHNzWiq2O97YryZ2c6OG8aTiYuN7W33gJTL/vrhMARLqRQNGZvNKDlM\nKeHsIcmVwMwkTooQRsffEiZCCCGEEKtDwmSJrK2F96PbdQ/J4aELi37fxUqt5qKDkKzDw+hdMp36\nPm7e9JAu9mUWhnS57Ps7OvI8Fpo6IkooNzyZSJgUyT1hqHgmhBBCCCFWg4TJErl1y8VIv+8/JLzj\nMaH5IuWDySNhu+nUxctLL5lduzYvLKjGhZjBa9Lve3gYRne1Gs/L+J4H8WZ2Z/llIYQQQgjxYJEw\nWSL0IzGLZoidjguE0chFSKVi9sorLiomE6/iNZmE54MwLvYzHEZOCaFbORwpe1t4nPfmdcLJHhMJ\nEyGEEEKI1SJhskRGIxci4/F8iBaNFKfT8J6sr7s4abfdk9JohJdjMHBBsrXl++x2PS8FY5pqXNVq\n7DP/bRaipVi162EFUUKom4SJEEIIIcRqkZm6RAij6vcjZKtScWO4UnFjeDCIxPfp1LdfX/dtBoMQ\nJ1ev+mv6fRcnGxu+z9nMvSzNZiTDdzqeRG/m+0EI8Z7HJXk/bGFeCJNKJeZRCfBCCCGEEKtDwmSJ\nkIS+tuaCo9t1T0izGSKk1/Pns3djY8MFCP01Wi3fDwnyvZ7vo9dzEbK3Fz1PqlUXGISC9XrzVbqm\n0zuT4HOoV6USjRur1RBMtdrlMdrJyzHzz5lD4i7LZxRCCCGEuGhImCyRl15y4dBoRI+RyST6ltBs\nkW7wpVKEbhF21WiEgCmVXDTMZi5Eej3ftlJx0XN4OF9muFLx/ZRK/p5mLn7W1+fHifdgOo3QJnqn\nkNtynKA578xmPl+EuQGfCe8VYiTn6lzEzyuEEEIIcZGRMFkiL77olbkqFfdwdLv+OF4Uck4mExcg\ndHVvt8NjgWhArJiFMT2ZRLjXeOx9UXZ2/PW5VPFs5l4XPCNm8x3PszDJ4VxsO5uF0LlI8LkQWWbz\n3hIeKwoTRCPlmIUQQgghxPKRMFkidG5HdLTb/hilf2czf346jaaJrPJvbPg+skA5OnIBgiE9HEYe\ny3jswoecEkLFSKSfTGL/OWwsgwABEvaPjtzj8qBCnRjn2tr97SdX3UJY8Vju9I74IJwr94oRQggh\nhBAPBq0HLxHEwNGRezbG4/By3LoV1bboXWLmooTtEBhmsbrfbnsI196e/zZzbwyChP3gHalUwtAn\n0Xs0ijFmIcLrqCJmNh/elQ39ZcKcFcd2r2RhkUPSzOa9PwgUCg0c93ohhBBCCLFc5DFZIiSjD4cu\nHkiGHwyiAzzND0ej8HAcHUU+SaUSYVqNhu/35s3wuDQa/jehW7dv+3viaUGMUAlsNPIf9jUe++N4\nExjPbBZiCI9NMe+C/JVFliDOwgBhlUGwnEU0sA25O1l45BAtQrb4/MXXCyGEEEKI5SNhskR2d12Q\n4B1ZX78znApDu9/3cCkM5n4/RANd4REQvZ6LE3JTtrbMtrf9ucHA7JFHQvg0GpH8vbbm+0TwmPn2\nJNlnrwheknLZfxAsQGiZ2enC5F7LEGcxkJPw+Z+cnNHo9IaRucoWXqPJJD5TMSQtV+fKYxdCCCGE\nEA8GCZMlsrER3dspxzsauVFMN/diTxM8LGbhXaEiF2WFDw8jwb3T8ZwVs6jWRVlghAMGOVWoECbl\nchj7jUZ4cur1EC68hv1j7GfPwknio1iG+CwgDPD0FIVJ5jTRk70lWZjwuYtQwYzXqOmiEEIIIcSD\nRcJkidBUEaFAjxEz94KMx2784sXAc0EJYDwp7XaETSEsEBvkgBCmRcL9cBjJ4yS1d7shLDD6Ae8N\nxnml4qWHETe5lK7Z2XIxcrWvswgTRFIWawgs/jebzwk5bV9sy/6Yb0onF2HOCetSbxMhhBBCiAeH\nhMkSef5592iQbJ6T0Ul6z7kdk0kYxQiZSsWFyXjsP7WaCxdCxLa2og8KHBzMlyMm/Ktej1LFxf4d\nvCfiAEFAPgfelRzulJPii8KDMCw8MGeBxHvGV61GIn7ef66idRI8x7a59PJJXpacU4PwkzARQggh\nhHgwSJgskW7XRQRigxV7OrbnfBOMeIzx6dTFx2DgogNjGU8HnoVOx3/q9XjdeOyvxUtQLocgMvP/\nCQsjhGk8DgHBWAgpM5v3YORKYRjwRRA2JPbX675PclaOI4duIZyy+Cl6TE4TJjlXhm1535NCwAiX\nY46KOSdCCCGEEGJ5SJgsEfJG+n03eHu9EA94IvAS1Grz5YVZqSfECyP56CgS1QcDFyW3b3uiPc0Z\n19b8Nc2mP4YIGY38fVotFx2DgdmVKz7W7KWh+3yjEQnmeA/wwPCDd6VIFjDTaYiq3IW+CJXJcogZ\ngqooTHIeyHEejeNEzFkEDeRwsYvWWFIIIYQQ4iKyUmHy7LPP2m/8xm9YuVy24XBoP/VTP2VPPvnk\n3Da//du/bX/4h39ojz32mJmZ1et1e+aZZ1Yx3HsGQYBhPxiEuBiPXQSQx4HRTjhXpxOeg3Y7vA/1\nuj9ObsrNm16Ra33dt93bc0GxsRHVq1otf81oNN/PhBwWqnXhmcHoPzry1+awLgz13DX9OIFQDAMj\n5+UkUZCbO2ahk70c7L8YhlYUJjlxPQuMs4SAwb2IGCGEEEIIcf+sVJg888wz9nM/93P25je/2Z57\n7jl7z3veY0888YSVCpbmU089Zf/iX/yLFY3yOwdDm5AqwqXG4+gI32jMJ53n5oejkXs9qLZFPw4a\nKpIUPxi4ICH/hGRvwrp6PTfK+32znZ15EbS/72Oq1yO3g3yVXFo3N2wsdk3PgsVs3iNESBTveVJp\n4WLYVFHsFMOvThMOxzVRLO7rbkiYCCGEEEI8WFYqTD71qU+9+vfzzz9vr33ta+8QJffDqpOWu90w\n2vEs0KEdD0qxOlYGzwoJ6+Ox2dWrvt9Gw4WGmQuPGzfcu9HtRj+UWs3fj0T7UinGdHgY+SvkpDSb\nIZBy1S+2oTpWUZgcl8/BZ6apJJ8HwZFLEvOaLB7wuCBGEGW8N2KP0LL8Xef+JXlMvB/jOe34yF6Z\nVR9Hy+Zh+ZwPCs3n4tGcLh7N6eLRnC4WzefiuQhzuvIck29+85v28z//87a3t2ef/vSnj93mq1/9\nqj377LPW6XTsH/2jf2RPPfXUXfe7u9uySuUeOvstAZLDzTZfTXAfDsP4N3Pj+iQI1zKLXJHBIBK0\nc+7GeBzNFNfWPOeEql5XrrhwqVRcEJRKHuqFd6RaNXv0UQ8HI/ek3Y7Xtlr+f6fj77m5Ge+FV4bu\n82aRy2Lm200mIaxIgG+15nNNRqMIeeMzkDCfPTf8vnZt0zqdEGC52SJJ+1QhwzuEuBmNIn/mtPwR\n8oFqtXtrEnlRuXp1c9VDuFRoPheP5nTxaE4Xj+Z0sWg+F895ntMHIky+8IUv2NNPP33H43/0R39k\nr3/96+2zn/2sPfvss/ahD33I/uRP/sRardar27z97W+3xx9/3N7+9rfbyy+/bD/xEz9hf+/v/T37\nu3/37576nrdv91auCD1ZfNMGg44dHrqH415CgwaD+DtX2ppMQiysrZm9+KIbz/v7YYT/v/8XRjUl\nhdfWQjzk/Jf19fg/N3BE8OzsuKel3bZXPwfColwOUUECf78fjRVz/opZNIskFwZx0uvF+BEg5KTU\n6/MemO/6rk27datjBwfzyfS8R65ERnI+/5PYT1hZo3GyOEH03UuDyItIqeQXqVu3OgpdWwCaz8Wj\nOV08mtPFozldLJrPxXMe5vTatdNF0QMRJk888YQ98cQTc49Np1P7/Oc/b08++aSVSiV7/PHHrdVq\n2V//9V/b448//up2+e9HH33U3vzmN9v/+T//567CxGz1+QE0VBwO77/0LNW7CLeiS7uZ9y1ZX/f9\nNxr+/7e/7Qb15mb0S1lb89fcvOmP53wTBMfRkT+OwCGki8/Cb4z/en2+nO9wGKFqxTAvwsCmUxci\nvDeVtxABs5mLHQRNrTZfWhmxwXuQ5zIaRchcriRGWBglj/mh2tdJ3hCqhFEJ7bJ7TdTtfrFoPheP\n5nTxaE4Xj+Z0sWg+F895ntOVmVrlctk+/elP21e+8hUzM7tx44bduHHDXve6181t98u//Mv2Z3/2\nZ2Zm1u127X/9r/9l3/u93/vAx/udgHFNCNP9gLE9HkfOCEb3aOT/9/tRnrjdvrOxI56FTie2OTry\n/7vd6CiP6MjVuHIiPyFciIXcayT3LyEhnp/jGjr2eiFm8IoQrsV78NocjpW3rVYjqX40mh8H73d4\nOJ83Yja/TRH2n3vHCCGEEEKI5bHSHJPf+q3fsk984hP2O7/zO9br9exjH/uY7ezs2De+8Q37D//h\nP9jHP/5xe//732+/9Eu/ZL/7u79rg8HA/uk//adn8pacB/r9KNG7CKZTFxFU6ppOI4cDY54E840N\nN+hHIy8njEggLImeJBj0nc58JS0Szw8PfR8Y8KNReD4QRrm8MKIge1d43Cy8IVQiw/sxHEZzRcZ1\nXClgvBi8B/tne/bFXPBZmT9CuvASnVShi8cZu7rACyGEEEIsl5UKk+/93u+1f/fv/t0dj3/f932f\nffzjHzczs9e//vX27//9v3/AI1sM9AtZFIRkkQCPIU54Fyv806nZrVv+GgxwGiuWy57QjhchlyEe\njz0kjGT20cjfB9GAp2YwiHAvPBpmEfZEbgo5JogWkurN7jT2yUmpViNBnnGxDWKJz0i+DMIEIXV0\nFKFtuf8JAoWyyGwLWayRJJ+bX9JH5jsJ6UL0mZ1cMlkIIYQQ4mFGJtISycboIkBMYNCT3E3ZX4RE\nvx+hW3hIWq0Ic1pbc3FCqBdeF5LC8YIMBhGKhnChUtbh4Xxy+NpaNHQcj6NjfaMRc4G4QezwHtvb\nUYVrbc3DyqjMhQeGqlt4SGaz+ecpZ8zr8OLghSnm+CBwik0hCe/Cm5K9QVnk3Q3GVa3O96nhvS97\nvooQQgghxL0iYbJEDg6Wk5uAYV6rhdFc/E0y+/a2/242XSz0+1HVq9+fN+rxUPT70SNkNHLvCGWG\nCeM6OPBwscNDzxPZ2AjDHjFAWFZOjM+hYb2ebzMYhNBAEJDkTslkwq/Io8mJ74yV8RX7oSBqjmvg\nOBj4Z2MuETN4fpgTs6jqdZaQLsZeLACAF0fCRAghhBBiHgmTJbK/P79Svkhycvlw6MKDsKTJxHNG\n6vUw/sdjHw/J8deu+eP0JhkO3btx7ZqHW9G8kaR6vBaNRoQ84aHo912oNBohLOp1fy7ncpCwP5n4\n9qORh1zVavE5Dg6i9HCzGaIDTw2CJvctQTyQDJ+rTSAEcnPFnEMzGMSc5LAyBAUJ8+zHLMLOTiK/\nP9+TWVQfO82LJtEihBBCiIcVCZMlQuf1ZZRkyxWj1tfdsC6XXRyQcD8cmt2+7Ub03l70IdnedgFw\n5Yrvp9cLzwQr/ZQ4NvPQqpwHQif4djsaNPJaXkNzRzq0I0So+tXrRR7JeBwel8HAf29uuleHvJei\np6KYaI8Y4H1z0johVdmrhGfl6CjCyobDEEl4abJA4XvEC3MSOW+F98tlkU9KuOc7QJgJIYQQQjxM\nSJgskfvtXXIaeB7IM8leB7MoIby25j1N+v3I96DBYbXq4oImieSKkJOBwY+no1LxZo14GvBkUIaY\n/AsMeTwP5Fj0+/MiZnc38lHoxE4YWbUa70tIFKFhZvM9VhAZCCd6x4zHIXwYz2QSTRVz6eHDQxdg\nGxsuirK3BO9K9mScFM7F98JzeT/FJPwiCJazhosJIYQQQlwmJEyWSO7cvixyFSxW+FnlH48jVMvM\nPRDVqnsvvvUtf2w4nPc4EMJFtS/yRW7fdoN+MAhx0e+HMOG9Ce8i0d3MvSMHB7795mZ0dcebgIgi\nOb/b9ddlkUS/FkQN3enH40jkL1b2Ojz0sfE+hFEhoLLoQWjxOQCBVOyBkvu3IJDIkSHxn//zPvlu\n7haydbdwMSGEEEKIy4aEyRLByF82eCQoZVuthrFPzxMSzq9fd6P3lVeiEla16oY61b0aDX+s1/Pn\nDw5CkORyv92ui53NzRBhiIBWK8bX7Xq3+fX18EyQ04LI6XbnPR+EirVaUWWL6mBHR+FZKZe9NHIW\nMMNhFAAgJyYnsueeKlTNMou5I/wr93yBYr7JeBzPMT7IYWA5H4b5ozQx+S/5fRB48poIIYQQ4mFB\nwmSJEOK0bGiwSAgRSeJUzCqVYhU/Vwm7edNDl65fj+pWvZ4LCLPwoJA7gheGPAm8FuSsIJDW1vz9\nqKJV7OxOiBbjRGDwvuybcC1EFoKjXo/QsPV1f4x8nuy1YV+EY+FFIWwt53KQD5ObQWYvCcILjw+C\npFSK98J7xeN4onIvFcbH95D7wBRDvJhLiRMhhBBCPAxImCyR3E9j2dCRHc/AcBi5JxjqjYYLCRoy\nUnaXClh0Sn/llSgPjKAgMZx8FN6PSlrdbiS2NxoRHlUuu9jB04KXgDEeHobRn8Of9vYiv4Uk+9yT\nhAaI+TNXKh5yVqtFvgzPjUbxPngrEASEauEtQTTlilzkpoxGIQIJmyO/J4eMEYZV7BhfFBnHlQ/O\nZZPpHSOEEEIIcdmRMFkirMovsvv7aZBngpcBwx4DfDJxg9/MxQK5D3Q2r1TcON/b8zFvbYX3As8C\nYgKDv1RyzwsCo1yOnBMzFyyEkRGWRe8S+qqUSlHpazaL8DHmkOpcOSG/243mjHR8Z6757AgTRA1J\n9YSCVashqKgulkshk4SOJwSRgReKMKwcGlZsymg2L0bwaJlFoQCS6xGwpZLPAZ9DJYSFEEII8TAg\nYbJEcmjRg6DYGR5jFy8FXoDBILwE29tuaCNASELv90OAYLDzOvIv1tb8tYiMHB5FHglJ5VTCIkmd\n5HnGTJmP4qaEAAAgAElEQVTgRx6JcXc6YaRTShcx0W7H+zUavi0Chc7y06knxpuFkMKrQ/4IXpRS\nyfdDwQAS5+loz+vY1+GhC6bcib5YurjYB6XoNSHkrVi9jefP0vdECCGEEOKyIGGyROr1k3tWLANW\n+XOC9WAQXhQM8243yuxS4er27fAkmEWDRhLCSVLv9SKkC/EyGPj2JNrnBHeqe02nHiJWqXheyK1b\nIQ7IHyFpHiEyGPh7bm5GmJeZi5JOx0UVn5PGizn06uDA90fDSRL62Q+5K4DXhr4uw6GPtd2OAgLM\nLaFWiC0znxPKEOdwMMQKyfd4k3gNYWnklADvxXcnr4kQQgghLjMSJksk5x48KPAGAGKEEr3jceSL\n0Ddka8u3RUxQkpcQq2YzPB58FjwuhDYR4kW3dpLec+L5/r7vp9v1n+1t92iwba3mYWQ0jOx2Ix9k\nOvXXki8yHPq4SZzHuKfCGCKNPi0IHz5TpRLeHpLiCb8irI3X4i1ZX/e/19fD04FXySyqfJEcT2J7\nzjPKQpXtETnFSlx8Lnq3IBCFEEIIIS4jMnOWSLH3xYOAkCcg9yKHF5F/gQGNBwUDnL4cGN2Vinsf\ndnZceJBLUqtFNSx6j6yvR68UDGqqaLXb/lpCwFqtebHT7fprm01/Hi/CbOavnU6jDHGnY3b16nyH\necaLl+fw0D0zCJXh0B/f2Yl54vMinnJyfakUniQ8LY1GCL3cw2Q6DU9SzlNBTEEWJnSkx1NTBA8L\nogYPTPa4CCGEEEJcFiRMlgjVqVYJIUh5HHhREAwHB2505zyJ3GdjPHZBQENFqmHhaTGbr8BF0vlo\n5Eb3wUGEQeWwplzWGGN+fz+8GrdvR6gXIWjkkEwm/l4kzCMEMNqpONZuR0Wy4TA+J54ePB+Eru3u\nhueCjvEIOLalUhaigp4qV6/GfCAoyFXJ+Sf5uznu+zoOvC7k8qj5ohBCCCEuGxImSwSPwHmAsCPC\nhigZTML75mZ4UsibIJ9kczOSwjGQ6/VokojHgxLEeDFyrw4S5hEseJM6Hf9BDOC1IRdlOHSBwnjx\n3Mxm7g359re9F0uv52Mxc3HT67lgoGnkI4/E5yfh/dYt347wKapgUckLQdfvx3yQbH90NB9yNhhE\npa4sfnLfFvq0MIdFEYLwoEIYj5nNz43ZnR3qhRBCCCEuOhImS4RqV+cFwo7M3JOwvh5eEsoHk5yd\nywnv78eq/+Fh5FhQBYt8DjwCeDJIUKcS19qaG/GIkn7ft0Xw0CxxYyM60JNY32j4ti+/7J+j2fTx\n7e9H40U8CeOxC5jhMELGEFOjkQuSzc3or8K+8MrQR6TX87mi3DFNH81CYJGAPxyGEGKO2SchW3z2\n7e07q23lPBSEHu+JBys/rxLCQgghhLhsSJgskX4/qlydB7IhTEngzc3I5cj5EuRq0Fwx99ogxGs6\nDc+JWfxNLgR5J+R2IBQqFRcet26Fgb+56UY9VcR4nNLC5bJX9cK4v3IlkutJ0EdAUC2MKmAbGz4O\nqoYhOPgbT4qZv9eVKyEeut0oGGAWeR+TiT/WakXCPOFkWYzw+VutSIbHI1T0mJDXQnd6RC2eEoRI\n7k4vhBBCCHFZkDBZIjQTxOg9byBAckUrPCYYvhjJrP6buXjAa0Lvj5w0fnDgYqHdDm8LORm8T7sd\nOSGEPtEIsdOZ7zi/tub73N+PJPZKxbfb2HABsrHhIV7dbpT7RUQgbjY3XbSwfwRXu+0/k4nZo4/G\ne/Ne9bq/jmpdzabPA3kp9Xp4N5jL3LuE8DAqdHE8IPwQHIgZclsIDaPsM54kkvbX1h58cQUhhBBC\niGUhYbJE2u3zK0rM3LDOK+8kt5tFTw4MZZK+zSKsanMzygsTRoWX5PZtFxJmbkxfvRoehdyhfjh0\nQUHYV7nsIoSkcxoZYvTT7LHXixyZTsdfs7vr+67VouoWlbYQDoSe7e1F+eIbNyIRHiFJ1bKDg2hC\nubYWIW2DgX/WWs2fz8LMzMeP4Mqd43PTTULlajV/TQ7VorEk5YopxVyvRzlmkvqFEEIIIS4DEiZL\nhCTr8wpVsEgsJ0SJkrSEJ+XwInJDCJnKSfCtViSIIxwQM4ggvAEIBZLKaah4dBSd5Ekkx3hH1DSb\nLgqOjlxUEG713HPuydnYiK7zecx4PprNKD+cc0PIm6nXfWx4V2j4yPgPDuJ5xAW9Xswi3wVhhThB\nmBCmRTgXoWt4VwhRIweI9zYLbxYVv3JHeSGEEEKIi4yEyRJ5kI0V74ecB5PHTPgVK/yIDLwqlBwm\nKbxWi2aLWdRQapdck3J5Psxqby9KGFerLhL6fX+MTvCMYTh0YYBHhPeazcxefNGrb9Fl/uAgwqdI\nhscjkkULXi2e7/Vc7IxG/h7ttn82Qsjo9I7npNHwH/JvBgMfN3kveGDwFOXwt+EwQrl4PaIMD8ra\n2nyZYrbH+6Omi0IIIYS4DMikWSIXfSUbw5sEb3qGEI5kFoICkUETRbwAlAjGy1Cr+f4ozcuqP+FM\njUaIGLPwGDSbYcQjEMgRobxuv+8J9YOBV+8iqb7bNXv+efdAkGdy40Z4YUjWz93rh8PIRUFIIKgO\nDqIHDE0UNzYieT+X9D048P0gungdgoXPR1lpwr4IR5tOfdx4s9gPYlBJ8EIIIYS4LEiYLJGL3gSP\nZO/cCT4b6KzqUw3r1q0wutfWIsSLPh54JDCmyRXBezIchmDBK8H7sN+NjTDmyX0hnAovC/vhb7qy\nv/SS2bVrvt9eL8QWHhryZtpt3z9eEEKr8K6wHf9vbMS46f7O/LXbEd42m/nrcrUtPjvzSv8UGjfO\nZtFnhXnJhQQuildOCCGEEOJuSJgskcsQYoOBn0vU9vtujJM3gdcBQcBqPnkTJK6bRRWs9fUI88qe\nAF5DIjm5KVSg6nb9tTRyZB+5WzvvjQgajz0Zn2T7Wm2+p8tkEnkz5KuMRp6wz7jxXCAW8HxQtatc\njj4s6+sxFpLq8fIQNlav+//0jJlMoqM9c0nIHOOkkWOzGUKHcK7z0shTCCGEEOI75RKYzueXjQ03\nIs9TL5N7haT0TK4+hZAgBIlKWEBYU4a8DLP50CY8AFSiWluLhHRCqnJ1rVYrwrHwjuDNQQiQQN7p\nxH4QDXhaBoMIler1QhDg3UCAILAQBYiwW7f8Map3Xb0aQqrdjlyY9XXf1+6uHxeEyJFEz3vR5JF5\nJ/ek243PxHeAkMGjY3bxQwiFEEII8XAiYbJEqlU3IgknOsv22eg/r8xmbkyTB5FFyXFC5Dj6/ejj\nkSG3xCy8NIgJs/mmj9mTQ8f4XLGKhoYknnc6vo9SyY1+PB0kuRNy1e36+92+7Z+RMshU9trdjTLJ\nNEzE65GbP1LKmH4vdHGnH0wOxUKIITbMQogQspYFICKNPBPmnTLDQgghhBAXDQmTJXLlihuJVHS6\nG3gLSBw/z1AhK3cvv5eeLVSeOi0ECdFCvgniBPGBN4ewp8EgKmDlXijNZogXvCm9ngsNEvEREnwH\nlDButXyMVPFqNr3vCmPDQ2MWzST7/WggOZtFRTE+S7/v3hi8LrmaF7kkiBNyaaguBniTqBI2m0VD\nz9y0UQghhBDioiBhskQIsTnJi1D0GNTrsWpOeNF5JosSs3sXUwiBk8gCjcR7yg3jFTELDwgigRwR\njPxyOaqDZS8LBn32fjDneE/o6UIFMKpukcuCh4vO8K2W2be/HSFkNJZcWwvvGX1MSKQnPM0sws1o\nWpk9aNn7xvaIGMSPhIkQ9wchm1QKFEII8eCQMFki5CYcJzDK5QjvQbjUauFdIQ/islMUN5ksWhAF\ndJgnQRwPE00UCWs6PIxQLbwqJOUjXA4OzLa2opoXkLBPw0e6vSOkCEM7PIx8j243vGIIjWYzKnXh\n7RmNvFRxqxUlhnkescVxUy7PFwSgVDPJ/TmMDrHGWIUQ3xmcWxL4Qgjx4JEwWSJbW/47r8RjiG9s\n+OO1mj/XaoU3gNeIO6GCl5kbEM3mvDcFb0O16sIPoUIjxpygT6f6nJ9Rq0V3ejwbZi6GEAI3b0bZ\nXkQMYW1UFGOseGjIZcFL0+1G0n+tFmWZGVu/Px/qhkcnzwNzgIDjt4SJEN85XKMlTIQQ4sEjYbJk\nWNHP4Ufr61FalvKyNOnrdiMkh//F8RCO1WiEYKGKV6USORx4oXI+B54Uck6Yc7wslCAmrAPPyOGh\n55Kw/0bD94MImUzcI7Kx4VW6traiX0vuan/tWjSLLJd9O5pEMi6S+c1CLJEvQ3gZCf7FogkyqoS4\nd7IHVwJfCCEePBImSwRjNye0Y3DyOD/kQFQq4QUg3OssifMPK4gBwEDvdsMTQWgGIXK8ZjiMnJFa\nLXKCSJRHnBAihjjBA9NsejgYVb12dqIS12QSpaKbzUim7/ejmWOtFo/jPRsOo+IZjSrxnJDvQh8T\nszhO+OyEfEmYCHHvFIUJXlAhhBAPBgmTJYJhzI2NBGg8KNVqGMO5kzjGNYJFwuRkSBI/jm43eoWQ\n65O3R3zgWcnfR55zKmORJ0K+C94KOsVvb0cDxNEoOr/TXb5ScdHS6cT/vZ6Pq90ODwmCCMFEnolZ\nHE+lUox3bS28PDSq1GqvEPdOMedNwkQIIR4sEiZLZHMzvCVmkfdA4vTWlhuT/E/PDMKD1tb8tXQl\nF8eTPSYZckdyHkbenhVR/ua7OM7TgJcFbwSVwI6OwvuBt4KQMQQLyfY8RtW1Xs9/8JBduxY5Lr1e\ndLgnv4UcmTymXHmMz0nCvRDi3uB6IM+jEEKsBgmTJYJnJN/YCL1BjLC6zio+BiWr39wgxXfGaZXN\njhM0JzW45PswC2/XwYH/j3eFpo94Mrrd6Gx/61aEi5n5c6NRJMFPpyFQSOandwqNLBEmk0nkuJiF\nFyU3njRTo0UhjoOFheM8Idkziff6buTXCCGEuD8kTJYIies5h4Q8EsJv1tbcgKzXffucd0JIDnkm\nCs9ZHSTM09+AHiQ0OESo4DmhWzzf4e3b/h2vr0cC/Wzmr6lUIjEeY4gqXBg7k4m/H8n0CFxyUMwi\nZI1O91rtFWKefG4VQ0APD+eblbL93cgLFgr7EkKI+0PCZIlQ7YmbIAKjWnUDlYaKVGTC2GU7VsTr\n9TA0SYC+ly7rYjFgpBB2RwI930enE3lFrda8N4SKXuVyJLlnr0qrFf1W6Oo+nUaRBDw2uYdJLiXM\ndpQmJuwsg6ChAIAQDxucE0dH88KEc2M2i/BLzpHT8kzyOaZ8FCGEuH8kTJZI7klCJ/DpNLwklYr/\nbjbjhsbq+fa2G6f9fjTN6/V8m0ZDZYRXSU5QJ4yj0Yjn6FnSbJpduTLfqb1SiZwTygMjMnJi+8aG\nd5BvtcwefTSMpnbbhU2rFcJ2OJyvOMb7FYUJ4yXcTIiHmexR5PykrDv9h8zi/+OER1GYCCGEuD/O\nhXny3HPP2Xvf+157+umn7Qd+4AfmnhuNRvaRj3zEXnjhBZtMJvaBD3zAfvzHf3xFI703RqMI68ms\nrcXK+saGPzaZuLEJ5BVQNapSiU7xVGqS12R1FHNXCLUjryMnpdfrEaJVr0feCJ4QM3/MLKpstVoe\n5vXKK/5dd7suTEul+fcgRDAn6+JZwzsCxSafQjxsFMsBI0TwQhJ6SS6XWSwMHR7GuUf1OwkTIYRY\nLCsXJtPp1H7xF3/R/s7f+TvHPv+Zz3zGGo2G/d7v/Z7dvn3b3ve+99nb3vY2u379+gMe6b1TLkfv\nC1bHazX3hhCPvL7u29KlnG7mdCqnR8b6eoT3jEb+f6ez2s8n5sGLgrGDuKRSl1mEc2UPB93kNzf9\nsWrVtz889NwUclqOjtxj0mhEojzd7mm+iEAZDCJMLIekmKk/gxBm80Kdiny5zxHkSnj8nUMzi/sT\nQgjxnbNyYfK7v/u79ta3vtWef/75Y5//8pe/bD/90z9tZma7u7v2lre8xb761a+eyWuyasOLDt3E\n/1N5aXvbRYrZfFWu7W03JDFMSWzG+Mx9UarVyEvQDfH8kMO7qKxFo8xu159rNEJw0l1+NvNjYWMj\nEupHIxcipZLZ9esuXKjIdXAQHphc1pR8JMoRb2yEODbzfXW7fmxRzprzZNXny2VB87l4FjmnxdwR\nvCV5USFvy7mVKyTmcZz093lHx+ni0ZwuFs3n4rkIc7pSYfLNb37T/vN//s/2b//tv7WPfvSjx25z\n48YNu3bt2qv/P/LII/byyy/fdd+7uy2rVFZbkmhjw29kjcamzWYuJLa3zR57zKsyEQ6AGKlW/bHB\nwP/e348+FnhTRiPfnuRNuonnzvIPR0jB5qoHcFdoolipeFd4GiXyPUP+7vh/fd232dvz7/PRR/34\n6fejjDTChuPnypXoMj+d+rG2teVCCMHEqnC97sdnDjO8evX8z+lFQvO5eO53TikiAlSwazbjMc5T\nilpQsOI4itdbcs0uEjpOF4/mdLFoPhfPeZ7TByJMvvCFL9jTTz8999jh4aFtbm7aJz7xCaveQybu\nbDaz0hmk3u3bvZUrQl/R3rThsPNqJS4MTrwfBwfh/RiN/MZ29aobpOQTzGb+e2sremYcHkbFJ4xf\nVv4uP5tmdv7j2BAWhOwdHroHhOR4GjQiStfWXFS0Wr6dWcS6I2x6PRes6+u+zWQSIV2DgYuN4TAE\nrVkIWI4PqnhRWKFU8ovUrVudh+T4WS6az8WzqDlFmHC9xDt5eBgio14PzwnPra+fXno7v3bV952z\nouN08WhOF4vmc/Gchzm9du10UfRAhMkTTzxhTzzxxNxj//N//k/78Ic/bL/wC79gZmbf+ta37Nln\nn7Vf+qVfsh/8wR98dbvXvOY1duPGDXvDG95gZmYvvviivfGNbzzT+676QM6ioVRyg3NrK0J36HWx\ntuY/GxtRlWlry+zGjTBaj4489KZWm++XQSI8q+CECInzA7lClCTd2/PHyRvBW4KYoPkixlC/7z8Y\nRhhOVGojJKXR8OOh04nk9+xFy7HyjCWvBJMPIxaD5nPx3O+ckl+FN4RzxCyS2RuNWPThNSwmAeef\nWVyz8WrfrdEirzsvPYZ0nC4ezeli0XwunvM8pysL5fr7f//v23/5L//l1f//1b/6V/bUU0/dUZXr\nHe94h33uc5+zH/qhH7KbN2/a17/+dfvYxz72oIf7HYGhyA2wVnPDk14kVG0y8zAcusGTl5LDvOhv\nQQgXVZ2azfm8BkITxPkBYUGpYL7z4TCaac5mkaxuFh4QjKTbt12orq+7wJ1OfZ+9Xhwr/X54VfDM\n9XohXjneKpUwooR4GMn9oHIOCaICrwfnJtdltiPvj23ZHk821+ciuUcK57oQQojgnKzZzPONb3zD\nPv7xj5uZ2fvf/34zM/vJn/xJ+5mf+Rn76Ec/aru7uysc3dmh03ez6X9vbkbvCcK6EB71ut/MyBlo\nNHz7ZtMff+wxD+dpNiOBfmvL90U/FP6+iHHOlxk6sne70USRBoiIBQya/X33ltC8cTTy33t7/nri\n3REYNOLk73Y7ykjTyRrhSunpnPyrwgniopL7A52VvEJYqcx7M0ulEBP5dy4uQbPcLDqyMClWvqMh\nKuTeKOd1tVIIIVbJyqtywa//+q+/+vf3fd/3vSpMarXa3HMXiccf9yZ5//f/zntLECmEC1B9azIJ\nL0i365WY6AJPLDSGKTdIVstJ0mRFj1wUcT4gt2RtLcJHqLxGPxLEC14VwrGmU3/88NCFTbvtx0ij\n4WFbiFdEz+Ghe1Xw0CB+MYrwmGBw3S30RIjzCOKe697dKFbUyuFYPMd+sgeE8wThgrc7N13kfGUB\nAC8lnsn8fB7PRclHEUKIB8W5ESaXkUrFjcatLf+fG1Oz6d6Pfj9CBAjBqtXc4ByPwyty82YkLHOj\n3NiI7QktKJV8n9Ope2MGg9V+fhGQ44H3BANoMvHvGOOHFWDCPegMXy57s0UMI0QqDRXb7XgN+Sqd\nThhIZiFG8MwdHcljIi4+uYP7WchiAPFQFCQ5b8QsPBycp53OfCl4FpzGY/9hkQmOa8h4r+MWQoiH\nAQmTJVKvx8q2mYsJ+li89rVuaHJDyw3yqlU3Hnd3/Sa3vx+ekeHQ8wauXPGb3EsvRflYGjkSK83q\nnTh/5MTaXi+8G9DrRcdpkm9v3Yq8JX4QIXhM8LhR9QvDC7Gb80xybpIQFw2MfCrbnXX7/H/2dpjd\nGaLF+YeX0cyvweT8nVStK++X0uCc3ywoKJRLCCHuRMJkiVA+ktCrK1d8RRyhUqt5yA3J7ONx9J2Y\nzSL8ptEIA/PgwPe1seH7QtRQGrbdjipg7FOcb/r9+bLBR0dRbQvvRq/nQhbxeuVK5JqMRndW3iJc\nbDLx1d18vBD6gnEkcSIuGtmox5Nx1rCovB2v4zHCKzk/8GCWy/O5WmYRolUcF41Pc4I8EM45Gslb\nKYQQxyFhskSoxLSx4TcpSgWvr/vz6+t+Q9zYiHjpZjOS5EnKxNMyHvtruJnWarEvSg7nUKG8Mi7O\nL4RgTadhGBHmUS5HLhL9SxAs9LjJJU4RtYT8YVixj1yBiNVcVegSF42it+EsuVLHeUzMotR2Drcs\nlyNfi6akJLLn8K5cjru4DedULlgBJzXCnUyi+pcQQjyMSJgskcnEf/CKkKyOUDGbL/+7tuai5MqV\nCMWhIRhJ0eSgbG9HrwtinTFUKxVfJadpn6ovnW8QF6yuEvpBTDpi4ujIj6Vm00sHI0yHQ99mMPDj\nYmfHDZ+DA39NrRbFFno9DxEsJuJynCkZV1xEztI/pJj8nj0tVMYzi9BJPIrk9hEayWNZ+JtFuBeV\nvHIFLopQ5ByWfG7zGWiCelKn+SLkKOq8FUJcFiRMlgg9JxAL+XFW0DAGp1MXLI2Gi4nJxG9krZZv\n/61vRVdwEuErFTdCCQsgv4CbIkn13BgxbsXFYDDw73U8jlA9EtoPD90DRzlhwvboZ4IQHY9DuBLK\nNRr5c4eH4Z2hXDFVwoQ4zyAy8AgjIs5y7J4UykXoI+cKz3N+5LwR3n80in4kiIpcYjhX9CJU0+z4\nPJN7zTk5Oppv1iuEEJcBCZMlUq+bXbsWK2k57IpkZRLV19Z820YjHkPMrK/7ivdw6MLjuefC8CT5\nkptnvR4N9kiizx3hcyNGVuYzJ4UYiAfP0ZGLDDP/bhEdlAfudOJ7JfxjOPSQL5rH8f03m/4c+8CY\nwtCi1DD5SlqJFffKgyx/m70fuZDDaUnwx4VyUTDEzI9/wipzLygER66eiJAhXJfcsCxMcuU7FoiA\necohaDx/1utvLqAhhBCXBQmTJUJ4FlWScjL65mascJM3cPWqP5c7CpNAv73tj9PdezTyFXMS6Nvt\nEB0IoJ0dN17JVWB1nLjoXMcfMGjF+SOHesxmEbrV6cR3u7YWDTpHo2iyiCjt9TwskBDAvLJLbxWz\nWDk+a0iJeLght+KkjufLguvYWYRJfo1ZXPuoiMf1E09lrTYfbsXnysIBr0Wu2lX04pDbddwY7sdj\nkoWMeqIIIS4LEiZLJscQIziyQNnaml/dphsxXYPpWbG+7kZnoxE5K+Wyv55GfNwMd3bcAN3cDEHy\nyiv+fpubvi0dw4seEm6yCvk6f4zH4dlotVxg0E+h243vcjYze81r/FhgFZjjqVTyLvIbGy5gOG4I\nJyRkhWNQwkTcjdzd/EH15sgekxxeddr75/LCh4dR+S4f7zyfk9CLeSE5VDY3skV85LEcHoa3M4uT\n44RJcZwnfYbslcmPS5gIIS4DEiZLhFW8Xi+8E7Wae1C4YeWGirj1m00Px8kN9Go1Xx3f23PPSr/v\nxik3VF5/9aonPZtF6E6lEnkHhOnkTvGj0XxXcAmT8wkigu7vpZLZ7dt+bHAc5HyiRiO+y8EgPGG3\nbrnQfeyx+RAVwlEI8dMxIO5GsaLbWb0W90vReGdx57gSvvk1OReFSoeEWeUkeLYjzIqQsexxJi8L\njwuem1otKuzlMK27CZO7eUw4t/Giq1mjEOIyImGyRK5edSNwfz/ERbXqwoN+I/w0m/E6VsRYraOT\nMEmOm5tudJJTcnjoouX6dRdBe3suWnLp2c3NSNRExEwmkQBNWVryCo4L8xKrp98PwVGvR4GEdjuq\nwLGiu7Xl3+PGRoQLEr61s2N28+Z8Rbh63T0wVB0y00rswwqr/XcrhsA1IudYPIhjplhhKwsTmszm\nbfES02w054FwzuSeQGZRhYu8v+wJYTGApPZc3ITwSQQO83JcOFf2vpzmAcmixOzORQNdq4UQlwUJ\nkyXDzYWbU6vlhiJ5ARiLjUaUDs6CgoourNjl5PaDg7hpttv+HEJnMAiDlGTnRsMFEAmcuXoMoil3\nkM8x471efJ57iYU+6/anhTaIebpd/36o0JWr+1AmmnyRzU3//hEhrVYcMxhqhLYgTsgz4di4WxlW\ncfnIvTjOmlCeKww+qGMmh1mdlASPYCGskaa1vHY4jGswlelYvMkltFmsyUVDONeGw2heyhwUyxIX\nc/dYdKIh40lkcQO5R8pxOSxCCHFRkTBZIpSHrNXcsM8CpFSKyllskxsyErucV+22ttzAvHrVH9/a\n8pskVZlIhL9yxeyll8Ldz0ogJYjrdX8vwrkGg+iHYRa5Ca1WrAgSmnBcN+OTQNSwKkkX8+NChBgr\nN2+FEZ3MeBxhIrlxJw05WcGl8MFg4PPLCi/9FMhfmkz8eMglrAmJkVB8ODnr9852xfCnZQuT48Z3\nkjDBK0H4Vb8fXmh6QxUT1fk8XO/wsvT7sTBAYRM8z3hjaH6bhQ3iJJcPRsAcV8Y95/8xVnph5XwY\nwrdy81QhHjTcM1S2WiwCCZMlksMcqtWI+x8MIrGSviU5gZPcEG5+lBre2vIb4be+FV6O6dRvlggJ\nQr3a7egsj8BgP9yQu10fS6MRDRqLJS9rtRBReTWd8rMnrdQRssY81Otxo883WVYzid/e3HQRl2/U\nCiu7E8Rou+3hgtVqlPvFOOl0fN5u3w5v2XgcHa0Hg/C+MeeHh/Plh/O8I1YU2nX5OWvp2vx8uRwe\niY55YF0AACAASURBVGUaKMUwrvz4cUnwCBOupaWSnxvDoW+DpzmHcWVvMgsreDfILZlOo3Q32+Xt\nc7lt9tXrhWiiSSMLMseJilwxLxdHAQSOEKskh0PKwy7ulzMJk6eeesre85732Lvf/W579NFHlz2m\nSwOhVyRaciPD8OPmzc2Fxl54DcgLyE3y1tfnRQGr44QRYPDv7PhvDExCe/DaHBz4xSSHJ2BYEGJG\niENe+cMz02zGDZEwr7W1WJlstWKVns9I3gzGAN3IWcHM3evbbX9dsxmhEiIYDKJc8O3bkWdSr5s9\n8og/R7jewYGL2vHY55xy06weE6rHqi/x+LmrPJWF8nErLicYy/x9lm2zgUzTv2UZzFloQM4VOU6w\n8BqumZTQrtXCk0sxEvbB+YDA4JzJHd3X1+e9LCTPH+fRMIvrHGNmPyxWsUiV55/tiiFl+TG2FeJB\nk68XCv0Vi+BMwuRDH/qQffGLX7Snn37a3vCGN9g//If/0J588knb2dlZ9vguNBjxrDRTfQvBsLHh\n27FyllfM8KQgSszixtlquSFJCAHJ81tbURZ2a8vfF8N+YyNW69bW/EbIBYUclCwiEDTcNFkpp4pY\nNgp4HV4cfgitIJQsCyVyXVotH9PeXjxfq8VnwUAgCTeHMYj5sqd8p3SDJ7xrf9/nm5yj4dDs29+O\nOeW7zyElhO5RFIHvUaEil597DePKwoRFmGUKk1wNC4oe1WwoTadRZnsw8OsLlQgp/DAeh/geDPya\niCgg1JbFFcLVhkM/9/CYsLCTKyniKcHTzKJMFkp4eRA0XAP5HEURUhQmys8TiyCXor4XcnSDIhvE\nIjiTMHnnO99p73znO20ymdif//mf25/+6Z/apz71KXvTm95k733ve+1d73qXrWkZ9Q5ItsSTwU2P\n7tq5Kle+mRGSZRYXCgx0vBVU3SIvpdn0n5s3w3syHEZOCxcdbsh4TnJYBLHT3IzxvpjNhzlUKtFH\ngxAy9l2pRO6Mmf9Nbwxu6vTQyCFk7ANjAe8Rcdt8hkYjvCki8oYQJUdHLvKIta9U3Hh66SWzRx91\njwnH5e6uz3un414VGnzSnJHkeTgp3ERcLs5awrboVeHaQTjXso6VLJ4BEY7XgW3o/cMP23KucA3M\nSeTdbpw7k0mEfHU6fr4MBv48uXpUtsud41kUMgvP8+Ghv1+nMx8uiYDh/MrFLBAvJ6FQLrEoqPp2\nXKW508jHZ/ag5uIZdztGVf1RZO7p1lEqlWwymdjh4aEdHh7a/v6+felLX7If/dEftb/6q79a1hgv\nLKzsDYdxoyRMixVuVtOy+z/nnXBSc+PqdPx3rq5ULkeITrXqz5n5752dKBm7s+M3U1bK8VgghDY2\nYmWwUvF95mpdiJm8csfFbG3NRQiNIBFjtVqs1LPySE8WEkerVd+G7dfX4715PR4hqtcwN8fxMGlk\ncoUwigYDD+0ivG48duOp0/Hfk0n0QSG/qN/3/1klRjTjyTOLXihnLXwgLi7ficfEbD6+vFiB6n4h\nxDWHNh1XpYrjezSK62u7fWejUWBRiBCtbtfPHbal0ATn1N5e5GHhmeSHvBPGyuLAdBoeGRZwGGe3\nG8/lssK93ny1LRZ1cun440LWhPhOKIZvcv5ynJ/2uhzmbRbHLJ724fD0fWADqeCNgDN5TP7H//gf\n9sd//Mf2n/7Tf7IrV67Yj/3Yj9lnP/tZe93rXmdmZv/7f/9v+5f/8l/aF7/4xaUO9qJRr4exzg2L\nqkkY1tn4q9fnE8apwMKJnxuIlUpusLNSSEjU9nYkPROGg1eCPINez0WKmb+20fAxckMnfpta/bVa\n3OSJt87PEX6wsTFfkYbPd3Tk+8dIpvpTsQngxkYkxhNWMR77nBBORmNJQuSO856sr0f42MMAK9Ss\ntLIyS66Qmc9rt+s/hGwR7oXhhNGGuMTg2tiYb0an6j+Xm+IK6EkUn8vGyb0YGRyzp62sUg2raMDk\nSldmkauRe4MQwkU4Kcd/qxW5d4ju6dRDH7mGUCSCwhJcAxE0nFc0u8VbUiwhzHk2GsX1kQUr9sc5\nlRuc5jA58veKK9oK5RL3y3E5UZwPZuHh45zKXkZex3nFY7l4xGn3Dc5nzgUhziRM/vk//+f27ne/\n25555hl705vedMfzb3zjG+37v//7Fz64iw5x+4QzcTPLTRK3tvzGRkUqDEKz8Gzk5Hlc/6WS36x3\ndqJZI8nxm5v+PJWWMBjwiEwmfiMl8ZmbtVnkc+BN4QKFEYsIIVeBLvaIGwSTWYguDObsfQHESU6O\nJ3dmNIokfuKyCdPIFYCoYkYoRy7LzHxfdpGSjTUELDcDM5+jfj/CUMz82EMoYmTNZvOGFgYZ39my\nw3TEg4Vcjfx9Fg3ck8Is8mopr88hHKz4360TfO7TQW5FBqM+exJyzyW2abcjPAvPXqMRApvrF4Id\n0ZCvb7dvx4JLzk3pduMaur0dYVyUUB+NQrTw2XNuXPbKlEp+3SRPkBw/wib5TrhuZqMvV/rKnz9/\nJyxG6RwVZyWHdFcq86KEhQPOde4TnCNm8/mHWZxkr99J942cD6aQLmF2DzkmH/nIR+54/IMf/KD9\nzu/8jpmZfeITn1jsyC4BGIHNZiQSY2Q3Gm78Ef7AjSSf6IRbmc3nmNTrfqM08xMZA5PtECi9Xpzk\nrLARImXmeQW7u3HhoWIMbly8EpubceM387HjgeECRTgYHiI+Exc4QhjoicLnZXxXrkSvF8QE4V1b\nW2Y3bkRTQFZtMFKyCOGmjwua98MYQdicFJKUSyJfVDjWEIasRhOyhbhst2Pb8diPBwojEBOPGN7a\nCvGSPW7i4sLKPnHgGL159fS0/IbjKnJlDy/nWPak5PfO56XZyYYJHgS8H4w152NMJtG3h2OXcRH+\nRGgJCz1c36hIx3tkTwQht1S8K5f9WnftWpQQJowLQw0vdDbKyAHDe8K9AKE/GMR1lvHlhSXGylwS\n0oaRmCssMr8SJuKs5HOenyxMeD7fN1lMMJvvy8OxR7REPr+PIz+uql7C7C7C5Gtf+5p97Wtfsy9+\n8Yt27dq1uefa7bbySu4CNzYqH5HsTU+SfBPmdxYmZvPGAqKjXo9+IBifNE5ECDUaYeizUohnYzz2\nGyslMK9f95XCfj/6j+TVx93duKnncAJCfkhKR9TwOOIE0UTSPI0A8Ratr7uhy0WNvBdESq3mY5hO\nfbWyWo1EfZLyc7lPPAXMU63mRovZ/A2bimXMDaWdi9RqFy+3gvkwC0OG1S6OiXbbw1ZYUe73zf7W\n34pkX0QgIYlmkUTMKjlFEu62Ki7OH8VY8Pw9zmZ+zpAcfpLHJFfxQUDgNSAs87gQo7xtNkwQSXmM\n2es3HIbHmefJq+Icn0x8UYMmozn/I3syuC7duhX9nQhZJXer3/fraKcTIuDatTCexuOoasdiUPYk\n1eshjPK1nu15P8adPR7jcTR3ZG5yOE2uzMW1k88mUSLuBRadzOK+nUXycecwxxwFfsxisbG4yJDz\nV/K1pChWJEyE2V2EyfXr161Wq9lkMrEXXnhh/oXVqv3mb/7mUgd30Vlfj5vfzk6EGJGXAbnU5nGl\nIfFKYEBUq35jRoTkk5tuxNOp97MgRjqXoCQBvtfz/ezu+vPdbpT5NYvme41G3NgHg0jqbzT8c1H9\nq1p1Y7fRCIOGFXdCvPAS7e35/7NZhJ+RF0K+CTdkxjQYxIr+0ZGv8BM+Nh670MFgzuU2ERwknZLz\ngmcFY53EejP/3jAsLmJSHt6vbNRwkyHhvdNxYUKIHccElYXwRuFpIlRuNvPXr6+HWCV8T1wc8kom\nBgFidjiczzk66fUYGjnHiVAQPAPHeUuK+8jPZWGSKwpy7cm5HrwvYVUsIFDwg21HI/8chL1Op34N\n6vf999Wrvk27Hd6Jg4MQNrlvFOFdvHcWKTmJHrjGcC0l1wWDDqOQRqgs1iB4WOQplmXFGKzVwuPN\nPJ0UFsdryXUUwiw8cLl1AedBMaeEqANESdFu4fkc5pkXXvP1AK983ifXlIepiI2Y59RL03d/93fb\nBz/4Qfue7/kee+c73/mgxnRp2N6O5oEkp3NzyjcWvCI5jKLoSckrDeSrkJPCCjbG5daW3+RIur95\nM1ZEMTLX1nx8hD6Vy/6aZjNubtOp/0/JWW7eBwdhyCM0zHw8iKBstDabPratrfD0DIf+/+6u/725\n6YYE4gFxRd1/s1jVJOmfFUUMJ5JYMb65+a+vz4fAIbJ4nlCwnG+T+7pc1KRSGiXmctMYTlTounEj\nQvPM4hjC21T87LlrNXOU85/ExSF7I/g/l6zlsZNWS82Oz03hPMIDgIGBoZIrdmVhkkv25v3iweC6\niXcv50HlhPFiCAoLJAiKLAYQGHhEEDHTqZfY7vej+AOva7f9WnlwEPNVKnk4KtfjXs//p48Qoh6D\ni7AzQtPYDwszOUSNXMKcc8hnYm5YeMiekwz7J4cvh6zludI5/HBSXBAFjjWOlyx2Oee4Z+Tcryyk\n8/Ujt08wi3Mt9/bJoY7i4eRUYfLJT37SfvZnf9a+9KUv2Ze+9KVjt/m1X/u1pQzsMrC+7jfP8dhF\nAAZzvhGZxYldjO82iwsFbvwcTkVMczEkrFoNETEa+Y2Vmyd1/ukqz+taLQ/jKZUiTIo8EMKzNjbm\newHs7MRqKSIIEUJyPCt/29uRtN/t+mvxkmDsklNCqBc38cce8xv9/n4In8EghA5ih/0jqogJR0TR\nXA2xVCpF4j0X0XyBJfwiC8iL5D1BuJlFTH1Odt/fD2G6uxvzcu1aFDbgpkQxguKNhF4PhNGJiwHl\nc4vVnViUyCucpwmTvHDCdsSWY/xT0SeX6T1uP8dVl5rN5j0OzWaUxyYstd2O46/fj5XcHO/OdRNP\nH0Ue6ENycBCGPeO+dSuuK2ZxLcTbeHAwX+ErX4NYBLp1y+yVV+ZLoXNebm7GtSznjbBajZDJ+X9c\n+xEt5LNkzxSfG4GHscc947iYf0TZSeGs4vJSFKXHhZgXQ7CI5MhFa4qeUfLA+v04FjlH8rGaF2OL\nHlTxcHKqMHnkkUfMzOyxxx57IIO5bBByRaw+AiSHOB13UuYLQL4wFE/8fEEgPCHnZeTGhvW6P765\nGfkuGAoYqtvb4Q3BcM+Jldy0NjcjDprPyGoKJTJ5jP+3tvz5bteN4LW1CG87OPAbO0KiXPbHdnZi\n1X9ry7e5ccPHevVqhDz0ejE2VlSJMc/d5Bnz9rbvC2ObpmdcnClbvL0dK5Z4ZXI/hYsEYXCtlhty\nrLS+8oo3XsQwozcDRhE3DkIS+dx4trLXRFwMMFwx4Ov1uIZQnQpj/KTvlcfztSoLE35T4pfVT0KT\ncgNV9nOcQYyng2sbK7IkjB8cRENE8snwPOQ+UvT4yZ4DRAbvi2DA003C/N5eLKLgNel0/IcKiHhd\nyK+r1Xxs+/v+s7sbXoxeLxZtuN5znjGv2XORKxUiMghDZc65vkPui0KBC67leaU7zzPfK55SCZSH\ng5PyQcxOt0tyE2hymzi2ctGe4oIEZedz7spZ8lHOO3iEL9KYzyunCpN//I//sZl5uWBx71BekiR0\njGQuBMddAPg7P57J1Vn4oWQvN6qcyGYWOSWzWTQ7rNcjBIIbIit6pVI0cOSmnz06xeTKHM7VbPqN\nnbCrVsv/z7kONIikgSI3VXIiCLPK+TP5c+LJaTYj14bKUa+8EmFkGFcYLK2W74fCA4NBPIZoyjf4\n7E2iwhcFAvAcmN29etF5gV4QrOpyvBH+hkBllZVjqdOJfKBsZBaTlhXOdT5hgSI3P8zGCOd37hGC\nx5BjgGsOBi6vxajmeELUcB6T78BPrgCWw42OEzhmcZ6xAMO1IeeL0bcJbyehrjxPNTq8vf1+lPw9\nOoowK/bJ+c05MByGp4HrIU0b8RbTH4qqh+T2EcbF/wcHUSZ4f3++jxOFJShFjOgvlyNnJXtDWKzC\n2wLME72fEDLMP98x+0H48d2yaMHCk7jcFIVHDn8shmoeZ3RzDckRBbkxdF6EQDDnstd5UTXbRRdp\n8Y9iH9h54v449bLzgQ98wEp3kX+f+cxnFjqgy8TRUcTqYxhkLwk38lzJwuxOYZKNSG5EvJ7Eb/bL\njYjVRSph4c3Ak9Bo+M2KPhUYrHgL6NSey38SJkX8J5+JxOc8bgz4xx7z99rY8HHWah6fnat3lUq+\n/eFheJhIlK/XQ3RRQtjM94NXp92O8W5v+/+IJwwkkulbrfhuzCKfIpfRxfBBqHBxpTlavniycpmT\nXc8rGEeIQIwcVnDr9SjBzPdOpSK8Rqyok8TLscfNRcLkfHFcdbYsShAbHAtcX1jN73TcY0COHOeT\n2byAzTlMxUTYYkU7rndZ0J8UyoUwQXRwbuZrHt4ErgcsypRKkcCO1w8PEZ5Yri18rsPD2J7znpBO\nmjQiNNrteK8rV6IqGGOlzDBzPx77XI5Gvm2umMjY+R9BhJBg0QixmAua5L4SecyILLP4ncsK813l\nvJx8r7kIiy3i/jkulCtfN3LYOWRvR859hSw0cilyQrsQ4DzHscbxedHI5cbF/XOqMHnf+973oMZx\nKcGAy71DcMubxU01l+hkm0zRm1LUijlZmwsMRjkrX9ntiiGRKykxDkKWSOLkNTnM6uWXI2G8VIqV\nR4yDViuMfMQF4SJm/h7drm/DiiH5H3hFuLBdu+ZeEMpnbm2FgczYdnc9tKtajWpC5EhsbEQc+ZUr\nsWIzmbjRYuZjuHLFBROGyyuvRF8BDJG8soPXAa/ORel/Qgw+oSmE81A+FS9arxchKcxnTpyl1Cmh\nM1RpE+eLYqI5xz/ioLjSyaIFXsmcwE6O3EmJshjV2WtBzhslu1nkKCbe5+sant/cByFX7yFBHcNn\nPPZjt9kMQx0DhxAuFodITL91y0UD+SnMQ7nsz5vFGI+OYgEHI63TiTwRBA+iCQ8H1bzMfPvd3RjT\n3p5fM4i3z6WCGTvXcj7v9rbvi2shC0QsFjE/uccLiwXMb+7jlBPl+b54bdELIy4vJ3lM+Dt7RHks\nL3ZAPp+zMDEL7xsLfJxjm5txnud7Ee9zESiGpIn751Rh8tRTTz2ocVxKuCkfF0ucT3huIKdVxjju\nb8g3nnwjyjcpQMCQG8JNiRW7ctn7mjA+blaNRhge1WpUxhoMIjSs03HhQPd6KjflEDDeC3HGKiDC\nhf4EZvG+jz3mYogkWsQOq/pXr7qwwEPF56ZqGJW8trYikbbbjcaXzaa/BzH25OjgIeE9WWXNni+M\nLoyH8w5x9CT+57AQVrLwkmTRuL8/f5zhceFCjJdLnB+KN0yajpLfwPXmpOsPwhWvLN6P40JOuTYU\nDWuOl5wfwf+EWuVyohjoeHE5HrNhzfMvvxz7wGvCMd3tRr5Z9uowxk7HFx8I/czvTcPF7F1AnHDt\nHAx8H48+GrkljJlFDwQLc3/7tufN8R57e+GFoo8S3iWq5CGE8O4gJgmDZWz5nkJJeq6x+bsoGqAc\nE5OJX3u5xkqYXG7yOVwUJvl5zs18fOWqbsXiGGzLtaS4oJoXGRDeuagPx19+7XmG6xpchDFfBE4V\nJv/kn/wT+zf/5t/Yj/zIj5wY0nVStS5xZ5f2nJuQQ7KKOSEnCZOToupyaETxZM+ihPwM8ihqNfdI\nEBrBajr7Wl8Pb8zWlu/j6MiFy+6uGwbcLDc33cCnag6eFIRBXt2jdDBlgQlLYMUdjwuCjrh04rmp\n5EXIQr3uRvHBge/vypW4ENKxfGsrGjqyL97zkUfcYOj3ozRyLo1Ig0lEjVnksbDSiHFBRSO+Y+L3\nj6tItEp6vTtzBlgJJqyN49XMV2s7nfC0cWPB24fAVWWu8wOrmtmwJeeDBOcc3kV4RTZcOb8pxW02\nn7PG4koWJoRsHlcVajaLpqgYKAgPzudc0YtQqPwYoph8DzwzvV5cVwhDu3EjBA3vx3k+HLpYoOz6\nbObHNsnynMeIg1otBLtZzBnbERZJBcBeL6rgIXa4vvLZWMSZzfy6xTmHN5LrNHOY+0yY+b7wynC9\nzOXn8XwSwooBynlPBUTmOAvZopARl4csgE/ymBCqncVGvqZw7JrFNtzz2EcxCiR7X3KRBRpCm80X\nl8lC6LyB19HsztxhnTP3x6nC5Gd/9mfNzOzXf/3XH8hgLit55T0fyPzOblKzexcm3GjyCicGBxeE\nYiNBbkit1nwYDzdA4j+pwIWAwDuCkU/oExcqDH/EQrkcYUPkncxmEbJFLsz29nyoFBW7qLBDLxLC\npshbQdTk8V29GuKs2fTPT9gEnpnsxdnZiSaPrL5eu+ZGC0n2eEQwzq9f9/+p6kPyPd9RcfWZyl/n\nBZKC6WmD4be/H71NCDe5etW/Z8Lj1tbuTNzFC0XpUrFaMA74LhCO9PVBcOLxI1wPbwBhSeQXIc4R\nNuRHUI0nr5ziqciPIz7Mou9GNoLxduIRyCGXbIMRnr3DGEYsULCffj+6wec8udxAlCpd/CCqKKON\nByH3O8HLkI95DHyz8NQgMLjG4XFhoYKCEyyScA6S/4IHK4dXFRevuJby+mK+Ys6juXEj5oeS7ni3\nsjc/FzrJ8y4uD1kwcJzlyA1siWLeF/c0FgWzN4QE91z2Ol8T8jGUc9TMYgGVhZCcBH9e+2Tlawr5\nrHhHdc7cP6cKk8cff9zMzP70T//UPvaxj93x/Ac/+EH7/u///uWM7BJQq51ciQuhggjIF4UinKjF\ncsGQhUm+eHCjOu59c0wyFwbyBUolFyy3b3tvE262lNDNQoreI6y84CkhnhpRwwlMdSzCB/jM1WoY\nvcSDU9IYg4GxIThyQuxs5sLgpZfcoCBsy8zDtAg14nPQgBHhVKuFsGg23Tgx833s7kaY095eiCNW\nXcnnoat0sSQ03/95y0Nhbnd2YhUbwy3Pd6cTVdIwWPPNBWFLAzvCWngPPCk5ZEcsF4zbYoU1szDw\nZzM/9jFIzaLnQA4TykUT2u3YHwsN29vzyfOEKrFAwbWC852wDa5bnEvZ60KuB+Mi/IrPRcgloUh8\nJnI88Er0++EBxcPBIgbChHki9r3f93EwdvL0GBOLKVTn2tqKRYtbt+Ic4LMxf4gBjDrE/mwW1bqy\nZ4NVZQqa5H4nXE8pGsI8EeLFfCJmmK/ZzPeDd4bjgTEVe2qdN4NQ3D/FXBCOp2IRhOwF4VjIuaJZ\ndOQIEDys7JeFgOyd4R5DpAHHONcbxE1e6Dsvx2G+fpFrajYv4sT9caow+drXvmZf+9rX7Atf+IJd\nvXp17rl2u21/9Vd/tdTBXQbW1uLkI34SY84sQl/yiX0cdyvbmFfacl5JDu8iqZVKGNnlzw0zex6I\nfyZxFa8H+8RYzaWFEUjXrkX4QqcT27Ra8d45qZR9Eu+dq2GxT7w7VOdCvDQaEX6xuRn9Twh9wHPT\naoVRQ+7PYBDPs28upFQy29mJeXvNa8LlXK9HjgzhHSSiclHm83BhR0jl0qyrJsfB05/h5k3//Ds7\nPmbmm7j5HIaINwpDCA8aNx9ueBiq4u5wk843vrOCsZFDDc3mw3PM4nkWCTh22RaRMBiEgMFor1Y9\nP4MFjpzATWltPAAcWwgNvGoYPVwD6eieQzpyUj7XN/JIGDfi//DQDXwWejiWEdkct3gXEOG8R863\nIMEcI2Q4DO8IhjteCUTCaORCBeHONSEbWUdHMaaciN7t+uPXrsW8I2So/JeFJhUD2QbDjnljH1x3\nssDjh3OSBZ5878jeLHF5wCsBxepaxZyTvCBatFGO84bk54imYH857ySLDrPwxHJeMc7zmAhfLIkM\nJ82DFuPunVPN3evXr1utVrPJZGIvvPDC/AurVfvN3/zNpQ7uspCry+CZWDR5v4RmcBEpVv0yiwR0\nbnZcDHLvD7wXrPLn1cmcgInxRK8LVuxyc0mMma2tCGkgjAShRC4OfRBYTSRRF+MnJ/Vvb8eFj5VA\nwk4IpSCs5PAwGjUiPjY353usXLkSBgg37u1tf/3eXhhWGBZ4GTC2EFLMfw6xwDDHE3SehAkx/6OR\ne4tYBe/15r0ilUqE3WG8FQ217I7nGCt6kYSTDcgsQrKX47QFi+PguOI8ZUUcUcFqJl4IjFIMeERm\njvXGC0DoIt4GM7MXX4zQy1xOvGgM83nwHrAwkivf4ZkhX4NzkFVZ8uF6vWjeiBAulVyYHB66sL51\nyz9fTuDn3OUzZa9QrzcfksW5jhDI1z16N81msWgzHLowQTxkD1G/HwU08C7mHI522z3UhE1yrcth\nsngec/8p5jbPN8dAXrXGI4LBxzyzUMBYWEDjPoCBaab8scsA0Qc5aiILEa7jLAZy78o5SUQanCZM\nigLHLK4XxegRzokcjs6xfFzPrFVTDEWDoseE+cIOEGfnVGHy3d/93fbBD37Qvud7vsfe+c533vH8\n5z//+aUN7DKRky8xXJdBXvU3i5XwHEKTV/K5uGCY8BxhHKWSGxxUZRoO3XA/OAhjH4OcHJDJJMQK\n4iYbp6y859ADTnRyHthXuRw38I0NHwOrg4gJ+mmsrbnBTL+S2cyNiYODGB8X3d3d+RwL9jGdhpgi\n/Kzf931evRqln+ntwGfkgk7ZYcInuAnwvTPe87aKklfQqBa0sxNzSZ4BYoPvBkMmG3jkJ+SbHjea\n4woyPMxg9OUbNAKOx04TJpyjxWOJeSe/oNWK7wBDnn3ynY1GcWwiMPv9+f1hfCNSyRvBAN7amo8X\nx+BHhJPgyuo/1yH+p7QveR0cc7khIyFPBwchUjifKhV//ODAj2F683Q680IADzDnJgIcA5zjGg8w\nc8FxjVAxC28HnhQ8wJVKCD2uP7kiGcZ/bmTZ7brIq1b9epPz4Jgfs3nDEIHB52CRgbFxznJs4fll\n3nKsPMdMnk8tJFwuiiHjx3lMckEJs/nzGcGcRepx97KiwOB982tyqBdj4b7BghfjKFarWxV8ntPC\nkvP13Cw+9/14wR827hIg5PzIj/yIfe5zn7NvfetbNv2bWe73+/b7v//79u53v3upA7wsLMtTtbxU\nWQAAIABJREFUksnxmPyd4z7zxQCxRA4A/5MgSSgBCfAHB/53qxWroiS403+kXI6cE5LOaajI3zmZ\nkxAgLmBU4MnVvXLhAPZBpa1840cMYdTxGKFeCKIcwkAOUBZLCBVWGflpNMz+9t82e+EFf1/CxbK3\nhfwUqhcRppErmfD95Js9gvI85J90u/F9YzTyHRJfzzySxEvYXzGUJOfXnJcby3kiV6/JIUvZs3nS\nSmHRwwF51RvDGg8dwpztsleLc5CxTKdhfBPeSLgFIU4Y62ZxHGCAEKqIwU/Y48HBfFgS+Szlsp//\ne3sxzsEgwigRWXgWer2YJ8a5sRGVuChcwbWM64hZlEwmPItjGSFGbhzjzKWD8VAgBMhlW1ubz4nZ\n359fuNnfj+a2fD/8znkmu7shRnO8PT8k5vOZOO8w6ng8G4VcyzD0yMNptyPXLh8bfPZiqEqu2CYu\nHhwP+R6ZF43yQiVk70D2hOfFrOIxkffNeZIrzRG6zbY5z4sFXM6pfH3CY3jaMbhs4z9fp4vkxV/O\n6xyKlq/3Oo9O50zC5KMf/aj95V/+pb3pTW+yL3/5y/ZDP/RD9vWvf91+5Vd+ZdnjE/dAMT7cLAzD\nvBrCdrlyFzcvszDMiS/nplavu9G6txdejWYzQntYNeW9KKPLzTSHMGFYkVCdm/XxWi5aeV+8H2Ee\nfB7CyDA0ms0QMHh+plMfC0YKYozXcsOm9DGGCiu2VCMjvCm7vbtd39e1a9F/gfFzIWLllNXbTK6U\nBDkf4EGC12Rjwz8zScUIrexZy2GDVHxiG7xfXKiZd63COtkrUsxZKG5TpLgax7bkWnEj57giNItr\nAJWmWD3HOCcHazCIxqZm8f0R+oXAJGkcA8Is3rtcju1nMz+ecv8bSuaycNBuu3HOud/v+/lG6NHe\nnp+/r7wS3hSqW/X7HrpFpUDOzxzLTngWSeyU9B0OoxRwXrXFQCJ/j/nmPOBzcl3K/VaYC86P27dD\nyPT78+WXOe8nE/98tZp7TqZTXwja3o7PiGjJHhcEKOfejRvRo4nyx9mzxgo0VfS2t+N44Djks2Qv\nP+G34mLCeWcW5wbXBpogF721ZvNVsnKe00lGdnHRg0ITZnEM5TBOtud+zDUs2wscj3cz6hlXzk9Z\nJCeFcZmFwMr3Oz5TcV51/zudM11m/uIv/sI+//nPW71etyeffNI++clP2rPPPmt//Md/bP/gH/yD\nZY9RnJGTPCbFkI98YuMNQIywupHjQHPieb0e3hEuZrVarIjTg2Q2i3wKVgHxjvC+2T1sFquL2bNw\ndDSfEI/3hDh4+gqwMsyKCaKC+E5W+6i2ZRZNF2u16CaPQNvZiRUkVhUxtgkt4zGSdY+OXNTQOT6v\n+HChxMOSE28Jgcv9TlgJpxzqg4ScGXJNRiM3+jC++B7L5Qh5q1R8zrm55bAvwlW4EeXiDA8b3Djz\n8Y9oRbDn8xOjlePG7M5VzRwmR1hWLsDAOc2xzQ00r8azP0KD9vb8GEa04/3C6GY1nTAnDHSuBTnc\niOOg3Z7ve4CBzj6oupVDiBBTo5HnjTz/vB+Xh4c+NvJkEBDklHCsctwhIhBjeGSIAR+NIjSMeSqK\nG65xxfywHBplFuMmF4QqguSp8Jnx3uJF6fVcwDCPfC/PP++ftdudL8dOqBbFCbIRyLUml33m2ME7\nncO3yINk/pl35kPCZPWc1WsAeC/5znI4Fcd0LhKTPXQ53JprS9HTwv2S+zNwHrDQkaMyOKcQDwh+\n7ufkYnY6EV2Rt0OwHEc2/vl8XGsX4aHIouS48FkW7/JiDd7QvMB6nvJlzitnusysra1Z/W+WyKbT\nqc1mM3v88cftF37hFxYyiOeee87e+9732tNPP20/8AM/MPfcb//2b9sf/uEf2mOPPWZmZvV63Z55\n5pmFvO9lgxM/r4xkDwTbHLfCwYUJIwcvCReknCRJgvj6ejQ25EaOaCBps1r17bmgMaaixwFhkY0B\nwMCnhwoiI+eGcAGmFHExB4ZVTz4HifiNRnh/MOy4EJNfQdIqBkmrFWMs9qfJRgM3fEIjspGyuRkr\nsngVsjFmFmN90OIkr0Bfvx6fm5UgjM/NzViF3t2NZm54t/L3y34xWjM5wfKyg/Asxv4XBUI2BnLY\nhFkIWsQNc8xKY+4ZwnlEd/K84kiuBK9nDJ2OG/8Y75ALH5TLcexyzpLASpU6xoZhhDHDTfvgIAx1\nxDme1OEw9oEBtL/vPwcH/hzjowt7vr5sbMyHcSH68IxigHHtyO9tFnNBXgbe2Ho99mkWoSeEt2I0\nInqonJVF4/9n705DNc+zu4Cfu9Tdn7tVdXVnxmZCJmhm4nRGMriTIJi2cWLIxG0YZkRfiGQyiYqK\nY0jaNkEMyOig4ARREmKQDIivRMKMgkuGgG9CBqHjnkx6eqmquy919+uLXz59zvPUrepbXXet/h8o\nqureZ/kvv//5ne/3fM857qnrKIBCsoyOZlZW/UtENi/o9fp17IDR0FC7DjLKgEn143t7ObOK9E5m\nyjWKyPvpGa5yw84ux+qaPUlKVIkIPrvutRVcVAIASYHQiMi1d1JMYb1WyWEFrF6n4YY1WvcCoMh7\nZVFqPZmW471eP5hB4p10/s67ZjlJtPnFdysnflS2ROwkW8vvAfeO1+86e7SdCph85CMfib/4F/9i\n/It/8S/i27/92+Mf/sN/GB/+8Idjk7j3Cezo6Ch+8id/Mj74wQ8+9DWf+MQn4kd/9Eef+LveC1aD\nZCCkDlAb1LBXJ2Jj4ig8xBg2r52ezpacgAhN8tFRyrOYWg6bvs8WxHCg2HYBloAVcyK7oyi7bpQj\nI+3nZpVUyZbvI7VaXU1piWuAYZ2Y6JdR1MJQ11T2R5CwvZ3OkwOcm8trpHhcC94qTRP42Dwwv5y4\nTM5FmyBsaSmZaQGaYHV9vf2OrIZzHpwgXmsXbEiuQ2WfSfmeRqsyuKrlttm6RtZQJQ+8VyCLhast\nwrH9nhuSuZGRFqySK3n+ARif5bOHh1O+qWhePQdJpJoPG/3sbEoxnUcN+KenkzWNyPoGYMLaxzSS\nkwHy6l3UhSh8j0jpJjmTDKlrYbYOsMPnCHA81xWARaSvAjJqswAAK6KdG+AUkbJUBfF7eylhW1lp\nz0pt3OE+uaaeHb64djrzLLpn1tLsbH87ZQGd++damEHjeH2GTIvvBGAHn1kB3+N2ievsya0Cg5Pk\nneqhrKea6eJjatvsOi/MH4OMZVWrhKv6Zc+nn9fmLnWftG/Xz4lImfPwcHsefKefVbBem8fUPeYk\nYOJ3dT8FyEirIjKb+7hWz+2k+xORPtU5OV/nVYmos8jiPK12KmDy9//+349f+qVfitHR0fj85z8f\nr7zySvzqr/7qmWRMfv7nfz4+9rGPxWuvvfbEn9VZ/wNXpVsyDDZcVtOutb0klo0z4GBsljTMVT6G\nycD61eJL2RObXE2zDrYW9XeVh2BUd3Za0D87m9PHOWQSDBPJBf9VKkW2Jki0+ao12djoLwg1p4Qc\npRbtKuidmmqb/sFBTrRfX0+gpuiWtA2IM3DRRg+cjY31F+VeFstitoLg8vCwATsgBaAkHakNBgRB\n1lJEriVM8mBmDCh7Gh12lVEAq9bgYAFzZSorU4jZrGuwFmrLZlXmEGiUFdzeTiLh/v2s6QAuquzg\nmWeyAcXSUtYbKRR3/NZvLTCP6AccdXbGwUGTLK2vpwxExy71V8DR8nIGXZh+LXl129rdzWwNQmGw\nIx7AhEurUqpHAX8ADmgWQDEss+CrBm0R+d0yRXyR4ImMVNCvVg2hgoDZ2EgfU4GlOVKklPy3GhKA\nhB8DYPnyycnMesoQ1e5h/t7fz85iHTC5eBusJRv8XQ2M+RL3qWZdNZ+Ync33nxTwIxr8v4L4Wnsq\n3vCcDQbvFSBZuzKYXjM93d+inL/iKyIeBD71/FiNNxCZ/m/NexbrEODT2qMyJuIfc5BkKvnJ2tFP\nXNA9Rw+3UwGTsbGx+At/4S9ERMTzzz8f//Jf/ssz+fL//b//d3zlK1+JX/zFX4yf+ImfeOjrvva1\nr8XXv/712NjYiD//5/98fOITnzjV5192gHNSTcdFfGcNGut31//Xn3uPIMiGh9mzgVZWl1yCk5ON\nsaliaur3cnL14cT8CiQG08ZY3tp2eHg4JUPeD0yMjTXgsrmZTm5+Poc99nr9mltF7Zzw0FDK1I6P\nMxUd0X5vyrPM0Pve14I20q+joxZMCRDViWxvt9f7brUZ9XxrxkCQA2RdBjABsIDWw8OU5Ql8FhdT\nTz81lYEXkKENrc0TY7e2lrVKtMg1Y3ed7aRnrEpvIjLzATxH9AMOm766Kc8O8qCuibrRA8xqCGqQ\nUoNSG7R7pQbC/837IYeqYAmoEYg4JplHx+5cgAZAd3o6AS9CYWsrhxMqRNeAApMqM1K7iNWp7qSV\nEe196jFkUACaChAH60VOMufo+tb1qRtZzRrWOjb3bmOj/X9rq1+2NjWVAYrP3tho39XrpfQKKVJr\nYVZWEsgcHTU/NDnZ1szduzk8VubJZ9V7VwGbToV13WoPy0f5rG4uw8n2pHs+EDFYF+H5i+ivxai/\ns3/zBWrCqgzP73Z3s4ucvXhjI1UA5I7z8w/KQvkEe6+1UTMU9jtEo2enytFWV/sVGbW2iT8aJEV9\n1kn1TrWOo94Dz4fso252jyPpqrHMScCkyrXtY66Rva+SF5eZMbmMuPRx7ZEhwGc+85kYeoej/4Vf\n+IV3/JJf/uVfji996Ut9Pzs4OIherxc//dM/HaOPiES+93u/N1544YX43u/93njrrbfiz/7ZPxsf\n/vCH4/f8nt/zyO9cXJyOkZGroQu5ebN3od9XsxeDD5E0voeyshQ2PwPEvAbDrYbAZjs+3hyQojmF\n7s89l87Pd9VbLCCyEcu+CHCwKzILpsavr7d/j462ayol6jgXF3OeSUTbnCcn289fey3rXcwqUQcx\nN9cyAYeHLWCOyHqW11/PdLMuZOQMN25E3L6dXbyqw6vFhOQeNgpZq16vnVMNviLav2tAOjvbgo7z\nt/51KnitbWP391NyZ4OYmWnnPz+fk6t3drKGZ3GxnTtwiKl1X22M2LWnpaVwfe7JiczcOTpKVk1d\nFNAiI+n5rYGK2g9ruIJ5rKNnRqCBaBgZiXjrrXbP5uayUDyiX4p1eNjuncYH7tH8fHu2DSJECIyP\npxTo2WfzWXdegHsdpgrERmTQAjS5FlrxWhcte9N7G6zX7IHPcU3X1/tbGPM3rj9Zx0mymIdZZXuZ\nIMS6dm+rVZmY1wq0AKtah1WzuPWa1Xk0giA6/tqFa3Ky3VvkTdX3y8pqYtIImd7bWdGJiczA7O9n\nl0Uyn16v/e2zOzvZ3u2ebz9GBlrj1py1jExzbyIyyLfX1LoxDRBk89xX68E6np3N+tCjo1xPR0ft\nmQJE7N03b2awbT+OyCxIRNZLOc5eL7PG6lRr0X2Vfttr6vWUeej1+tcgv6Ou1PfzLRHpW+yrp7Xq\nn9R0eS79Xqbn/v225xmtQBq/sJDPIr95mXbRcenj2COByQ/90A+dyZe89NJL8dJLL/X97Nd//dfj\nb/7Nv/m2HOwb3/hGfP3rX4+XX345/uAf/INvv+6FF154+9/PPvtsfPSjH43f+I3feEdgsry8demI\ncGio3fylpY3H2gDP08gWsLQQPlYPG6vDUmVXODxp/YgEEKQKgu2qqa9p0xqAYnoErKQkGPatrfZ7\nYApTODbWrqkBamYcbG1lYajWoTdutO5CtTiW3KKCBee1spKdP1ZXM5DzPvptWR5act2BRkdTX2oz\nEEAIDsh4FN47HoxT7ZYUcVFyrl5EbDzw07W13Axr7QLH+n//bwNn8/O5yajv2dxsQMX1FVSrncEe\n13UGaF72s/skNvjcCwwACYGA89/a6u/VD6zbYDFvx8fZtcn7rWOBAnCxsZEBAaZue7u1kiVnfOON\niP/3/xJEzs+3173xRmbElpfbGsC0b2+3329stM1XwKot8PJyBipTU+3/KyvJfDrW1dX+WoaNjfZa\nYGZvrz23a2vt7zYUsRdraxtvH4fnxrX1fPNXro/gvUq2nsQfDzYj0AEw4kGp7MPer9gYIeTeY3Wr\nJIc8LSLPQUcz/m57u10r935nJ0mD0dH0OcDxzEy7PrOzvVhe3nh7vRwdtWBTsEhm65oCpADT02Su\n/5P4nifd82sHKxk45vmRna91C4gyvsaxRKTUcXOz7YtDQ80va6LhXls3wD6p57PP5mdEZG2IbCT5\nknV2eNjW6/BwDmP2PAO1SIL19Xyv44nIxjObmxHT0724f3/jbUAiC6Tdtetmb671sREpBfU6z6ix\nB6epN/Hs1O9zT46PG4Hpc2RHtSMXBznPiP5Zb4M1POdtVyEuvXXr0aDoka5lUDL12muvxfLycty6\ndSve9773PdGBfdd3fVd89atfffv/n//85+MTn/jEA125fuqnfir+8B/+w/HH//gfj83Nzfjv//2/\nx4/8yI+c6juuChioQeZVsHo8g2lYwERw4TW1iE2gvL+fgY/NioSpdt/wndg+v6/aet83mI6urE9E\nv7TApj40lJkbmaKpqQyGMPRVYlY/U+FpDQw5XIG079EmWHBIjqJDmM+bn2/OeXU19flTU/mdpsQD\nVZxeZY8rcBLIX4Ztbua1Nt9EtuTgoP1sbi71tMvL7eeTk8lc376dG15lfznsOuTtJP3wdbRac2Dt\nRyTb7/wrEMVGVumGIMGGXutNZKf29to1tekKYMkXPVeVfb13r61PhfFLSynV+sY3MrPpGb9/v73G\n5r69nXJOGUhZw/39tg7u3WtAZmysARnBBiDh+VJDQYJ6cJDkggDJM1g79giYMKhAjcBHpq5mLd5J\nunWRVtdFRN4/PgFjDIRsb7drizAAWPhxIHJsLK8Fn17r+gRWGxuZsZURd42tt/v3MyNe1+dV2tOe\n1Kpk8Sxkaqfd861/RhZbsw/uCZ9b6yzch8GGGo5BppFvdl+rpNT9df6O4+AgO7NV2aTvApwAFr4c\nKACw1IwhJGUR+DB1nXyNuq3axCIiSTzn61pUtUXNYNhv+FLZWOdXMyCP2m9koWo8AUSRZdY4ACHq\nvWSefGT1/+5hzUDaV89rSCS7anFptVNxHq+++mr8tb/21+LevXvR6/VifX09nn/++fhH/+gfPbKb\n1ru1V199Nb785S/HK6+8Ep/61Kfi5Zdfjp//+Z+P+/fvx1/5K3/lHbMlnb2zcVoWZtWkchqVoacb\nx8xW3SQnV/XSEfl6jsFm6Hf1WAZ/LkBROxKRmRyO0mwVjpLzUNNigvzQUDoMbFNtVSgYq7UeEenE\nKwDCTHG4Nm91LBzW7dvtMxrTmxPVDw6SaabH53zJ4zhMQQQQN8igXZQdH/dr6XUO0iZaVmt3NzNM\nMkBbW1k0azNxHhhzP/P7qpm+zpmTiP5MSCUABCSuheeusnueBc9NfU6AZprpCmxlJQSa6oSszePj\nBibVeZBpKIonaVSj8PzzKen77d9OIsDzYL4GMHTzZmYE1tcz8wnAyJyRquhkVeWVa2s51wR4F7wo\nvhX81KJaAUclXi6jq93j2OZmZiD4MsGY4EVth+wQZloDENdA3Re/9frr7XfPPttfH8JH3rmT8tXj\n45TKkqDWhgUAj6zp02gXGahZu7Ve0n23t9agWdDPVwITVbI9eF8qcQEYeN5k9mVk+R+A4vCwv514\nVTrUInl+CJhC6vEBjt2sorm5jBlqXaF9D/ExP98/MLaSigAMv7K3l+SjZ6HWxiFMK2jzuZQLJ2Ut\nXIPaRKMqOOo1d21J1WqGUiE8RUA9Tt9TgYl98WnLSp7WTnXaf+/v/b34kR/5kfhTf+pPxdDQUBwf\nH8e//bf/Nl555ZX4V//qX53JgfzMz/zM2//+0Ic+FK+88kpERHz7t397/Ot//a/P5Ds66wcjEf1F\nWBgxhZKDmYrBLEhEf9eMwYd7EJj4HMGsB7oeQ2WFajtDn0t3LSXs5x5qgR15mpQy1vnwMDtnaeNX\nN//KGNqcnZfjEkxOTKTkTdC+sZHypNrFCAip50ruUluvDg/ndGjHY7Oi5R8ElRdhNouhoUxVT083\nJ3z/fguAgM/p6Sxctp7cD8y7teaa1AJv9+M86k0qED9vq4ym73aPbTr13G1CleWzlpm16X4A2xG5\nUWtvK6AAJKwpoEWAG5Gf6XNlNGqHus3NlIcJJGzaPluHL88bAELWWJlbxwlMyAQ5nloj49nTmQrg\n4KfqeyPy30DNVTbnJzuibmh7u1+eIiMCsJGomRaPQLGOyLusiePj9lySZlo37ltEPncR/dJSx4RQ\nuapM67u1+ow+jtV797g+ZVCRUANVWXvrWmDtftSGD4PDV6vZ10i0Dg8TRNgXdX/TfGJ9vYEHBMbK\nSoLRmgHmy+zZtZGLdaxTFT/hMwAVoKHXy2MdzJzLEPKbaji9FnFDEeE+AFi1sU9VY/AbSMHaUZDV\nY3avnE8lRyPyWB1TzYpQWNRMbu1iWJ9J5/20PWOPY6cCJuvr6/EDP/ADb/9/aGgo/vSf/tNn1p2r\ns4u3kxZ9lWtF9AdVtd0dMFCLlunWBzMmPoezqI6j/n6wkwbHPMhyVMavHkvtAmQTVcQXkd26ODGb\n/WAKvGYmRkebwzTPRCq7Orx6rPPz/Ww/ydKtW8k6eo/Pmp5ubVk5LAFGPb4qfbIxXLTTslHoMFbl\nbJihu3fzXk5M9NczSXW7P5hezlx9xODmZkMAbJ7EsOrnAXhOsgraSR9lJWuwLCivMoR63hhC4AEo\n8Fobes36aVSAUSTDAjLeeCMlUNaVz1K/4DopXpehIOOqGRMAXRc6x2LOjXN0vthGWdZa5KuWwXNm\nzcuYVr9k/VUf41oMzia5yub6C4QET85heroFZzUDFdHkcqSeGn/U4BG7S06qeLl2TaoNDkZHM8tJ\n4lNlpYDe0zaL4d0Ck5PItcf9TuvZs16zxVV2XPfCQcKjfqb7rjW9Pc+asm/u7yeonZ3t74yHVABk\n+C51JLu72cpbBqRKj4FYBARi5OCgvX5vLxs0VMJPgB6RYE/NImDOZzhXsqfaHtj5Dsq03K/6vZUc\nGryHXlPJw62tBFr2kZrxd83sdVWeXsFYRO57QExEP/A8K6ufdR1k0qcCJiMjI/F//s//6ZNt/eZv\n/mbc6HoGXjurDx6HNpjCrE4xIhkHmZQqyalOZHDBAyGDwGTwNSdlVWph36ADrm0SmToXGymwUG1q\nKh213wn2fIaZAlVa5XxrC2CdUZ55JgPoqnHncGZmIt7//pSfCI6rVOv559u/l5Yym0OvWovkanoY\nMBLgXYS5Jhsb7Vxdv/v32+ZRjw2gA/K0rzVL4/Aw26Ji7pwvaYCMymAh72ltMDtS19F517HY8Aaf\ntypXsObVZUT0g/Wq7xfwV8miIEBAW2s0SA8Fqaur/RmR5eUMWq33KiMCPnZ22rrs9fqnmjsGxyhT\ntrqawLSyqjdu9Gc4BBoKZwVBghlF/bUOrUrPtKJ2nWpQL7i5bnIj90GWiCxPoEMKqibIOa6s5Doj\nuVNQz5djvSPSv8pammUCqNy71/69uJjBEl/u/vB5NdNykQFPzYo/jtX6hbOwd0sQDRJugMZJQbR/\nD7L9FRTVzwXU61wiMqT63Z49A1T9v9aiyKpOTqafunEjs6fWwNpafo89c2WlvwOnoB35wMcfHydR\nZR2qXZQdAY5r3Vs9p1qPwe/VmGLwmiMb+UT+Y3BdOL4qD/NvqopKKtn3dTz03BwfJ0EzN5ezVJBO\ngwSpbPdZGOKKXXY3sNPYqbb5H/uxH4s/9+f+XHzsYx+LhYWFWFlZiV/7tV+Lf/AP/sF5H19n52T1\nIT2J6anARACEha3dMQQeD1vsVQJ1Us1AZchrari23Rx0/h5kgVHN4GBZOa7Bc2KAlOCG5ItTA0AE\nQICAzb1ma7DJEf1TbG/cSOezsdE2fLp/DmlkpDni27fbsZvtYbOISOaralmr/OKiwEktHLx/Pze+\no6Mscj84aADC/ZRF2tho10jzAI6aDK4GOkAwp18lJ6cFJjZaQXpE/0Z/EcCkGsAsg4ERc64YPODX\ne2r9hPeRVQ0P5xA9wLA+GxE5CwMQ2Nlp92JlpV/KWOcSYPYErbJ4IyPZfYs+e2MjgfjQ79SG7O+3\n73XtAXCsKRmZDRyLWIETkFXrSOp6UA8je8CPYFDrRnxdzCygSkDI9AJpfKl1gbzhu8heyIAQILXY\nNyLBnXvqvi4sZHGy5gZYZ4SKIJE0zH2JuDhwAoTyoYNWSYBKZNWAfvD19d+nyX7Uz3kngFJZ+cH3\nOh/3szL+nuNB6a79p9ZdDAIWgXfNwHhO1KEhCxAK/PrMTNZ7WQ9VYq0Tn5+99Va2l759u5+QqkE4\nKfDSUrbhR2RVQE5+ur/fALI9xDkDE1oL21OqHMr5WwMVRPBXtXblYeuiZk3cJxJrnRWBdrJvcQTT\nuMa19v21hlWWqd7fJwUorknEg6qUq2yn2ua/7/u+Lz7ykY/E1772tVhaWopbt27FT/3UT8Wzzz57\n3sfX2RlbTTfWn530uhoo+tlgn35O4WEMVA2SHvY9jJSg1pZEnJyyrudR5VyOjXymWv3MoaEEI0BW\nLXqvwIHTc1zDw+29la2uAM17Ofbp6VZ8Wtnkeq6jo61gGACKaFkWgaNMDnCiP7rzuyhggrmNyO++\nfbtfMqTN4+hobiirqznoDVgQOOzvp2bdOqlMew0wazBSzWc5rkF2kFUAjFE7K1Zq0AYDlVr7NJhx\nNA9EcaTnDpjAfu7u5vwBm6RMHGmBDlyuLebcZ6yvt6BAF6zaCa4WlnsvBrMW1pNJkSAoaEVcCA4E\n2eqs7t5tmZo6s8VmDKgAJUCW7xWAu8/AjWvqvRH9WdbrZGoBgKuIrOlx/nUNRWRwdXSUEhd+hs8Q\nnPhbjQpJrDlFZF6khjVAHwR+nlcEClnvRQCTweB78DsrUBokIxBrj2KNTwtMHmeN1QwsGCfFAAAg\nAElEQVQp4uqkz6pgowLsKunkFwXCEf2sPfBeJUgIhwpM1tf7m7K47/X+HhxkG2ryP3EAf4V0Q7JU\nYk3bbiQW6SciRevqWltSiZn9/aZKiMg5KhVIkJseHSVZ49rZO33exkY7DnPO+BXXfzDLXcENH+V9\ngAQybmcnSTcNbHTjAlT82dpqr9WmefD+RzyclH1cq6BEjPbUAJNPfvKT8fGPfzxeeumleMYq6exa\n2uDCrz+rxslVp1+DxbphCbQf9X0P+56a6qwPj++LeBCYcICcfc3GVGDysOOpr8P4CdwEdT6jSlEG\n61kGM0COCwvkugiSa1tXDhU4GhvL3vKCiGefTQZLwI2Z4YwvWi9a2dfNzWSIZJcicrq3biRVukOH\nXAuerSdBsA0RO2tNCNxI2xiAOsgYOl4gABNd64ieFJgMSloGr1MN2hy/n09PPxgEAG4CR61yb9xI\n1m16Oqeouy6rq/01Bnr8e8/WVmM1FbeurubkZ1I6MgX3pN7nmZl+7bRrLvBRqG5dVr22mpXl5Zyv\nopED9t0zpyj24CDXlu9zzyoQsXbqdbvONkgymIgt2CG3sa4Au/X1nPouMKyfZQ3KlJFJkkrqnGd9\ner1nz/WNyKCP37SWLwoMDkqcBs0a5idroGv91Hq/iAczJqex075nEEjJFtsjalbEflPJQ+QXQDU8\n3F9nCJwCIySBarIi2rVQT6JOcG0t/dbxcevQJghXd1QJAF0nq9w5ImVdCwvt5ysr7fV372a7bxkT\n1xwBIhPLD5GPCfrHxzOb7Dj5eevfnl3lcBV8AhxeA7xEPOi3AVcgvsrb7GNVNWGei/0ZGHKNh4cf\nrIkhn+v1+p+vWrdV46z63sft0mXdXbeqi1Od5qc//en4yle+El/84hfjO7/zO+PjH/94/Ik/8Sdi\nfn7+vI+vszO2ms6NeHhghtk56b0R/cVZj+pIUr/vYd9VsxaD74140OlX9oGzAGqq03rYd2GGMPy6\nBmGNMFP1YdY1iTMGbgS51aHUlLxUMllKTfeOjmaQXZ2Oc5udze81TJKTr+zWYJr/vI1zFajOzKQk\njUMXBHHUWo9ubKSmXWcVjC0mSrqfFE7RvEC2DuyMSNmL7jRYZ6CzggPfE/Hk1wzQ8e8KKGrNg3vl\nmgm8BX6ukd8LHtSBkLwBOwIEHZuOjpLNlO1YX+8fkmqIKRCg+QKwBPzWbAxG9MaNFrRgZ23wAhjn\nLeAVGCui1npY22zgo0pEav2MgLpmkLymSrUq2/gwKcbTYCsrLfuow5bny73HgNf2pVXjPjPTnr/1\n9Xz9yEgDqp5b69j6EGTu7iZ5Irhz//2JOBuQf1qr2f469Twin2nrhF8QrJL0VNnjoFlPEY9ml0/r\nP+rxWssnMeI1m89nVMINAVSzIXwk8E/6WIkF/oGMcn+/ranNzZzcTlrl2V1ZSckTyabjvX8/MyS1\nza96EJmL5eWUZEZkbScJIf8DRNR5T/ycIN89kckdvPbqXyg81MLIXIgT3APPAD+tZuv4OPcb2QbZ\nH4SP+k/XVV2NrLUaPMdk/1FjgmAAcmQeZc6dg+NGHB0fP5oEHrR3ivGusp3qFL//+78/vv/7vz92\ndnbiV37lV+KrX/1q/NN/+k/jwx/+cPzzf/7Pz/sYOztjOw0wOckE2HUY0GDW4CR7pw4uDwMmPv+k\nNKvPrQV1tZPXo0CQzxTYD8rTOLjKrAyep6A6IhkU/6d9tUEIJObmkgHR8pgT4kz9Xm1Ar5eskw1A\n4R5HNT2drBZnWzfD8zBBkYCk6v9JekZGIt58szngD32oBVZ7e23TA7gEs66ZeweIcNSVKdvby0AE\nc1fBhg0Ze299AQmP0hSf1g4PE1zJADkGDJuMkI0IGMHmTkwkwyfQd7xHRzlfosrnsK0mOWPMyQYE\nHhFZCD083K756mq/7CsiN9Iqc6nFoIIVv5flqXI43yfYc78ArTt3WmCDfeRHgPR6z9S2VCJDtsjn\nVab+vWICQ8+I587zAfC6T6R/Y2MNeMiU8C/ux/Fxy9ROTjbw4/lTO3T/fsS3fVsGoJ7zKiW1Xt2T\nmsE+D6sEg3UqICeHrYDpJHlvvYb+z/iYiHeWfJ3070cdb2XuBzsv+X31UQitmlG097jXlRypfmdl\nJckBMibPrppArelXV/trku7caXvP1FRe06GhzOIO+mb+vBIavgOpUIlAnfzsvcAH+eHubgNLajLV\nrVUJm3VX/UpEP+ixd0bkoGCZ1lo3U30OEOOzXTfntrvbnhnnxP8719o+2HVTe2cIrc+Zns6stmfH\nNXE/a2wk438aOw24vqr2WImhiYmJ+OAHPxi/+Zu/Ga+//nr8j//xP87ruDo7R3snedXDjPyIA8JA\nneZ9j3o46gN80ntrEFlfg/GyIXqAH3VM0ueYR++xYUckyBm8TjX4tTlg5CuD67MEgUdHjbX02UdH\nOdRJkSqWfG0tX8v50Y2vr6dkQieUmuK1IQtQzssEh5gcIGBlJTcyHVtIcEyMHx7O83Cd/DG5vMr0\ngAn3QCAsWK61CnXztmFXcFmDXZ/5qEzeo8xGWiVnMj6yEAqKbYAVFFfZhrkCuqyRWNWJzENDOVtA\nsfLmZt5/Uh5ABcspK+Gz6L1rgahAAMDGzNbMSK21cp61+BwbKuCiW4/IjVjARFPuProPNuuIZBJr\nRx+s4XmD7qto1oAgHBFR15QWv9UHeF6Oj5u/4HMEgxhpGc4PfrCtJetrfr79XEBlPVQQSkpUg7sq\npTlLGyTUABMZBIFbDfD4qpqdFJA+7Dv87lGE2mmIjZpV4rNq69mIfiBSCRh+odbLVNlWzaLX7KVr\n4N6T9WH8q8zUmvJMAS582OJiWzOvvdbvJ6vcdnw819jKSvpCAbesifuhPgVg4Kt0AZRtsQZlWPxd\n5c61q19EqibqtSL1rM8QYOKc1K+YLu91ZGoR6TePj9NH7+xkRnFrqz1jhpQ6rvHxtn8fHrY9vUq9\nqAxkoxEDiFH7Z33mH7UmAV++2vq6bnYqYPL1r389/sN/+A/x1a9+Nba2tuLFF1+Mv/pX/2p87GMf\nO+/j6+wc7N0CEyaQPEt7lKQs4tEPWWXEH/aawc+sQMQmK9iS0WD1XKssjQOsmSROAcNCbgNQYKjG\nx5vTF2zZTHq9JqPwcxsMSQyGHvCJSOdbJR7n3ZVofb1dg8nJDIgFuCRrNsqZmZQW0Ta7ZxXcjo1l\noXxEbj7uvfa2dMicL8nB+npuOBH9HVgi8tpUOy3rDnzaOATQNj33xJogVwI2BjcUwdLERGqy6fxt\nfCd1plLzYTO3TgflbhhusjGM+9paBnCyIz7b9bIpRvQHuEdH/bIeBazah2LiMZzW4fJyaupr0FOz\nRMDV4WG7JuPjybxW0H/da0iexOpzEJFMNiCgJTBQfniY9UiyCRHtepM5CnI3NvrZcdIS2ZeZmVzr\n29sNsAjeKmAVuPn3We8T7n8NUK212vyA+b0GLZ7RSnZVH+B6VFnwSfvJoN94mB/hhwHqCgLsGzpL\nOX7Agc8AIO05W1vpW5zv8nL/PjA31+7p6mpmWcfGkkhAmtTnspInnttv+Zb0wYeHzX/Ietjf9veb\n71JQX9cpErPWRtkbAADHsbqaEmYF8hMT7TimpnKNqjOs3eEcH4JnYiLriJA/g5mrenx8X83AHx31\ngzpkm4wTQrGSo2py7OGDNXTWgHXgmlBPWBfHx5nxB1qA7EqmDsZNnteauXlqpVyf/exn46WXXoqf\n/umfju/+7u+OoesIwTrrs5OmnF5Fs9RqgeCgvRtgEtHP9gl4I95ZdsYJSeGOjDRnqhuODbtmgmZm\n+guDtR6u6fODg/Y5NhVpbIXAjquynDZcga6N9SKGy2GNKjuHeeXw1YOQAC0ttWO/ebM/iMY+GZRl\ng5RK1zpyaCjf49zrRhORILAGKO5DZSkFJ48KngCFKlNRJwHMVmBpfdho6Y8j8nc1s2Y2h3sqaK8b\ndkR/8KWfPymFIlZtpQVtOsmYY4IxFbjWbA8AYLMDREjQbNq1dSzAMMiYY+cFgo7Fe+vzY2Ou8kPt\niatEUKDdWb8JWgW3rpF76hmtUhDPgZojM4bW1iK+8Y3G6taZFZW1XVtra6kSKCZ3W0PsNMDEe05D\ndlU5ZkS/P9jcTN9YyQNrWHAGOPNPCqwj0t8gnmReTvITlSirGdmdnQbaHF+VFTt2GUfA3b5TgYtm\nEoBe9bPVPwik1Yn5TFKoe/fa3iOLquA8IjP6VQ0gWCYNFYADePagiYlW3C5LPjycWZDqx2Zn2/Eh\noyqBZb966622fl5/vX2W7x4dbZKy1dUGkObmskOYa84v6FbIZ9ibreNaV+Q8AFvkDQljRPpjGSZ7\nFB9HjizjYe/xnbVNPoAEfLpfa2vtvMUFfLf1VTPYnuGqEqBgqSYGcg287rrZqYDJf/2v/7UDI0+Z\nXRcUfZIGeNAGl+Zplmplwzj401yTejxVMkC64DNtBHNzLTjv9ZqD1eo3IjckLOfkZKarbZ4jIy1Q\nuHOnOTvOC7CRHifX4dQvyrSNtXGTpN29mwysrMHmZtsoa7DgWG26dLw2FoGE60njrEuUAJYMSLG8\njaiCOYXgVS7xKDlGBRH1/zaoGuD4E5GbWS0UtoHVgF9AKDgjY6jsWp0VIzOjPkNRpmDDOnAtbMSs\nDjWzGWMWHSsGUNAHfGBcAXjfOyipcdw2+pGRPJ7KuleQ4fuZYEtWsP6uswfN9d7YSElkBZoR/etY\nFgxIQajU4LsWAG9vZ82JzyRDsZampx+cF1SlnA8zmYGId/ZbzqEGiRqD+J4KJCLyOsgQKV7WWUrW\nwfuqNJdPAuIRD7XTl6CxyorqLIqI/myIa6KI2WfzcVh4QKBmMtwzpAhgX+tNFLgfHWWXraGhbGjB\nRzpvc5CmphLkkVodHkb81m+118v8WxfWhjXHf5FjuSf8y+Fh+kWEHdkUEqeCFVl4UuixsYj3vS/r\nUyoJsriYUuaarZHNsccDnLXuUxbfPkLyOjragIMMkH1tfT39/tRUA+fLy+09OiK6Zxp+OH7H6Fmd\nn09Qbl1UMMoG15rfV9VBRH5vXc9+ft3sVMCkAyWdXZYNgoWTluKTAJOTtL2P+oxBoOR1lWXj4Ccm\ncgZERG469PdkOjaaqanm3OihaVXJobQCdRzqfWpAQf5S5zqclw0WPftO0iNzMqT8l5by3wJhaXdM\nm2sKeBjCVYuiZYiwjcPDmYmZm8vfYZlqoALwMBvBSWnxuiF432AgZYOvci3Bj9fOzrbXCD62t3OD\n87m1u4uOLt5vo3/rrfZZOzst6FC8XNcd8OF61hoDAEF2SiCFgXUv63PgOlStt9fI5ljvNcAisaiB\nVc06+U7nay15Hmvmp7N3NtKrup792/2vA3IFY+6/wHRtLf3K7dvZCUwA6tmrAZ3ANyKDrAq4ZdWq\nbLMGcBEnE0/W7qCsSsbO9w02V7HGanAW0d8kw+s8CwLm+t3AhtepFzgpO1+Bea0ricggvTZZGazP\nQTZUYOLeOdbh4QQYEek3/c7zTRLp2KsPtC955gTPiIQaQO/stMyAY6ndpPgt2RBT2wEz83ccq26B\nm5vNR6uNATTcT8XvQIJrvbycREiVlR0ft/00op23DA3ixZoHUqzFCtSGhjKjIUsS0X4n81sl13t7\nSTZaI9Y9n+v5qBnEjY1cCxH9QFvmfXDtWE9MltNzMJhhsd6vMyiJeMzi9846uwwT1EQ8HJjUjeu0\nwKTa4zAMNcjyvTYHzk0gQDtaax8WFrJGgvMl46r1LRwv56MGQRCrmNxxz8xkNqYWCJ63AVkcPSBW\nWe+RkUz5KxScmsqCXK+9d69dl4WFvJZYJ8CALroCIZt7ddyucc2I2UxlT7x+eLhd/yqbqMCkXufB\nwsI6WNDmZT3YwGUbfB/2VoZjc7OdM0Dn/XThe3stSKhFrDIcwKmieG2GBX97e+0zBT6Ai3sUcTJD\nN2gCE+cY0V9rY6OtnYIYBrFKEU5ao65HxPkUTz+tVuUkgjnrvWYFrKfDw6zlEvCSBU5NNVJEwS5W\n9/btLIKv7LGmDDJwERk4YXcFXbWj1CBIGDwf67VmViv7zddPTPR3PKqyX8+R7J/zrd3N7C01SxKR\nQKaCZedRmX/vrd9pjddzr+dbpaWyj1h8WVaEk4xGzWTYU1ZW2vM+N5eBPzmS1/kevyd/cq1WV7PR\nQZXFjo62z5+eztePjbXvQxatr2cra4Xv5GEjI+21lcDSGe7WrSSezK46POwvNK/SOrIy90Tbeffc\nd66v5/WNaODBWq1NPBB+5MZIIODFHlWlrAAyAosPt44qqXPnTvq3+rmaT9Q1Y9/2zALAMiJVlWAf\nsybrunJfaxbGXlX98XXwqx0w6ezKWwUmj3rN4GZ3GqsP+qPqWB52PINpYulnAwc5QNmRWj9Rj3tq\nKlPSg0V4AoqJieZgvUYGRTerra3cXGxwAsfztMryCIQxs/fv5/C/yii67vfvNye+sNB+h6W7dSvl\nCDdv9kuVfJeuNRW8kWK4vlivhYWUetgoame1Wv8xGCwP6sgFXRG5KWFxMXIR7V6NjaW0pgJXs3O2\ntrJwvE4RrrU3a2tZc2Qzp0+OyJ/pzqPY/Pg4QZuCeUFlvXenNcHi4JoSxDiHh9WCVGldZYIHX8Mu\nYu0+jVafs4iso7D2a8BasyoyGoC9jJpsJf+Ekd7fb2AeWz4z018MXGuKqj5/EAhUcqdmWgSrAi3P\nqWe2NmkQyNXuSz7b75yj57dKYyKSTKr1egLSWpN1EjDh//kjn/uojLz3upaefeBherr/PiJbAP+t\nrSaZPThoPhOpQepUswP8zOpq7lMIDtm23d12/GrWfIZgH3iSba1F3X7muyOShJDJ8TdZVET6K63T\nay3h9HT67O3tRlq5Jo5nYSEzHIrmyaU1mCFtcw/sG8gia2N1tWXerT9ZGsDJDDHXdWurfX4FjLUe\np4JTPhiIPDrKbBdCb38/j8mz4/eIn0rAVim554Qcru6Dnj+/uw71xacCJn/n7/ydE38+NDQUc3Nz\n8ft+3++LF1988UwPrLPO2CDT9KjXnBaYVDZPkPk4wCSiX7N6eJgF3oIAzIhgGigBKhQr6tqlEE6R\nOEZofLw5YPIcjNr4eHMy9P9kT9UBX0RwVwtq64AvU3ttojZlv5+ainjjjfbvhYW8brID9+9nHQZH\nrYmAQAVTKhWPXZMex3rVGhVOXmBhQ1Zo79hqJxzHXrXtg/In0ggZEjIRdRYyJTVIqSxsRG5qVf6h\nu07VLA8GHXUA6Pp6f3ME3cpkIp4kiza4npx3lSw8yk77us7OzgRptT7q+DhZ54j0ZWNj2ZDB8yw4\nkmVbXs5AigS1srA3byaxIiiqc4cqGVCzBr5HgMcf12wof13BSs1IVsDAF6jbIhFyTp4/r5V99NzK\nyiIqqtSmAoz6/7o3WOs1+zr4Gr6x+vCaCQV0ZBIF9wJq8iHvk2F2Tch99vbafZORsP/VYYC+0+wP\ntYEybjUD7rl3b4Eq5AwzrFFGApDB7h8fNyBEdkXqa8+0bo6OGgjjX+1zx8f93eRc7wqiNEmR7ZWt\nR2RaJ1oW83GGlFrn1feRxFXQ5ue6Z9b1D4zpnOc6kukilqgsSJyBUPtpJd3sifYiWReE28ZGFtbb\ne9Q6XnU7FTC5efNm/Jt/82/ie77ne+LZZ5+Nt956K37lV34lfuAHfiCOjo7iC1/4Qvz6r/96/K2/\n9bfO+3g7ew9a1f4/DDQMFm+e5jNJgR5WN/Ko91ata9XoY8Y5CxtI1RQLWkdGsrsHplHgzDl5nQFo\nWBvBtEJBDn9sLOsbBKsn6VbP0qT+1cJw5s41IgNm514HvMlCcOK6WdlQvW9yMlnW1dX2uWp0MImc\ncJVB+b1N2iZPgkXnjXmzCQnMHDMpAtApvS64YAJBGnybDzYSMyawEHhVptHa2trKbInMhHMBpGzy\nBhFiHW3Qvn9wcz2re9/Z1Tddq/gpRIHmDZ41tURIFEETWc/MTHsfCUvNMgpIBT+1jTa2tsqnjo5y\ngrYgs2Yw/LxKnDx7fu4zzfTwvRHpf2qdlYDaZ9fvqnKZKrWqWVR+NeJkKW9E+nC/40tqbVpE/3EI\nHLe2+jM2/DxC6uCgkTnT00mEKXgfGmq+cmWlvVcW+uio+Z2lpQyM1WKYdTQx0X7PL7nWukc980ye\nh2cesLt7tx90IEUOD5MUIaF1PWoheEQG0RG5J+/utnOYmEi5q/f7TGuoNkXhc7Wu1zTGtVb/Qo62\ntdXeRwpb74W9XeBf99X79/Oz7GnkZpWccp207V5czGeL9NZ5y6ZXcF73bkRfrfHxHa4HsrRmHgEc\nRftX3U4FTP7X//pf8eUvfzk+8IEPvP2z3/qt34ovfOEL8U/+yT+Jz33uc/Fn/syf6YBJZ+di1ck/\nKmPyuCnKmtbkJD3Y72SAEKdRA2wMv8+NSPZahqRqobGWnB6AMj+fG7r33b6dGy0mRQEhm55uaW8S\nhloEeJ4mXU8+VPW1m5tNlmV2i2utCFKwLW2vq9fRUc428frR0WR7sYjuhewIhkq9CJYP4PNdQKPM\nw/R0putr0a6NkKPH0mGmgCx/AKXNzWQMh4fbJk86Yd0IxjBeapQODlLGJVgi/5Ilqhp0rKVjkNon\n6euKyd+7VgttK8MviIxI36d2ZHg4/Qh2eHo6A+9ayA7ACMiQPkNDLRMMENRAXkBVmygw2RSZmhoU\nDxaN+yw+RWalZmQ8KwAZrf/ISNZ6ec4Bnir1UlReOxBG9Bcc12OvrX2RAs7HdcOS1yyR6yB7sb6e\ndYdra9nilxxNcOu5399PFr/XS7JCFlXmlk8kvVR3p67o8LC9n29VC+i9VaZ2505/8E7OVWVftSZC\nFtwf+6b9yTpEILnXis4j8rjN6bFPPPdc2yOBrs3NzJY4Jn6QlNj9ADJk1/jO9fX+WhW1WTqH2WMO\nDjJbtb7e9m9xApBjjd26lbI7ZEAlqaxh66k+H9Y7uW7t8ulcXG97SpXYAqxX2U4FTP7n//yffaAk\nIuIDH/hA/MZv/EZERExPT8fhw4TFnXV2BnZeusgKQjBhp7GqecZ8c+5md0Q8yJRJ69uodGKqOtyh\noexmtbGRDlxnrvHxxrLUTevgILMVpE7z88nQa0VcAcxZG4cKmNSCcfIPgGBjozF0alJef705/14v\nA/gqVajM6MZGAi5BFpZpaandR0WRWGEO34YkyK9tImuhaAU2g8WiwAlgwum77lXWhTWMyPMCWGTM\nbCb37iWrZS3UgkZrTCZGgBeRmZzqhgGTi5L0dXb1rcpsIvrnHRleB1QI8gWAgiss8vBwEgTWvIDw\n9u1+uVbtWlQzHvyCoHViIvX7tQZGTYLga3KyPUOOxTNeZ3PUxhOMv6nNSIB1PsLzJlPB11TQT75W\nyZf6PXy886oF4LOzWd8BCCFW+J5eL+U+iKoa9JMMaXShjqIGoK7xykoL2mtdx+rqg/V4gERt0Q00\n6naIINFumY/h9+wvssiuNRDl2KyziDx35E9t6MHPuj/2RySVe6wNsjVb/1AdbG5mXSZyjAKhzg2z\nF8tkTExkRl02hRROswf/lzkB6BT5I7XI5GT/V1baZzz7bN4bx7W4mLWIlQCt96yqPABtci7Xzj1B\njl6HUP1UwORbv/Vb43Of+1x8/OMfj4WFhdja2op//+//fTz7O1fzL//lvxzf9V3fda4H2tl7205b\nO3KRBsgMDaUjqprbiP5CeSwbNkgwPNg21qYa0TZAhcs2dJKu5eVkL3W9stkqHFQsSFN73t26MFQC\nFenmKiUScN+40QKYqo/mYDFQa2v9qXGdUGyOS0sJwnRu0Q4TO4m5dY1t7DIf9LwVhNS5KjZVgYMg\nIaIfKPR6CRBkRkhg3HesskwPmcTBQQsWtN7EPgoQ3VfTkW1Aa2sp9wD4KnAalJl11tnDTL3W4WHq\n5IGDu3fbOn3zzX5SBuuOoAHWPTsCoRqo85Grq0nm+LkgVycitSO1UxXfpRkEZnhiIgfwzc2113h9\nLX53jACVALueR2357Q8ww4eQ2dRgspIItY6MH0FSzM9n9mZ9PTPNMjCV7ALYyJCGhtq1m5rK4F/B\nOiBxcNCIjrm51mb83r0EQQCLe+gYI3J/ADIAtJotroQTf6Ptt/e7Hoi1iP49RwabTKseh1oIeySy\ni9+se6bsRwWlS0tZWyg7bS26pjIlsin37mW7+Xv3MksNiNmfar0kCVhEvyzM/Zedcl1lguwBEZk1\n80zZ20jtZOfEGmpnfK9rD5x7joBpcrPFxf7M4VW3UwGTL37xi/GzP/uz8XM/93OxtrYWMzMz8dGP\nfjQ+//nPR0TE933f98UP/uAPnuuBdtbZVTOFlDa9iGRnbJg1Y1KL1o6PMwC14dnMq77b67EzNkOB\nqeOYnMxJzNvb2a1EUblAd34+dcXnYWpEMH40wc4New9oqI9ZXW2skYxBLQCX6q6bA+mS6zw7267D\n0lI/A2uTNBeGFKEySjY2NR26nNX+9DIv5AwCKuBCpxWbvzR9HZZ240YO4xoaahKI2p3LsddNpabx\nqzQNCKrtLP3fZg4sd9bZaUxwyp/xGzKgr72WTSWGhtpzUv2XTB/pZdXX1ywlILK/n0HeYAG1QFdQ\nXGUugm8ZGn5AS1jZZQM9Sai2tvJ5k+mpPqoOEBW8AhdY+V6vX0ZZ54o4Rs8uf+1Z5DfGxjI76ngE\nk9XHyGjUWg3MP4LHNaiZDhIf2a319cyo1EwRoOHeyUbI2iCVZmZybSA/AJVaY+G6YuS1LT6JBOOX\nBOn2gUE5tHvrWsmmqLVTz7e1lT4R4NU8ha+2Jg0L1eBkfz8zSltbzZ8bGmpv9TMZIs1EDg/Tx/su\nyojamKTW1/DTjsXwZOesVgbJJ04g2SMnrHsDIFtrNcUbwDZC6zrUBZ4KmMzNzcXf/tt/O46OjmJl\nZSUWFhZiuGhgPvnJT57bAXbW2VW3GzeaU9zYSDAioBVkRvRLiiYm2mvW1trvZFcdTGcAACAASURB\nVBBq3Yu07cREFp1ifCYmUuOKAZmfb+yY13PYdXNWoyFgwBiepTl/QUstlpTlOTpq/56dTVZHce3g\nZk2uFpH65LqZaAagbWmVDdhA5+ayw9Xxcf8gRhuiQGR+vh+kYDkXF/vZJlkOLSW3ttqfe/civvnN\nBBakJVVetrLSNiSbXz0OrThrYTENcmVTBWxkJAIFQU1XT9LZ4xjWGdD3f5mRN99s0hRzmDxDhtAt\nLqZcperYtcsWZJJjGgaqtTrZiQJ3oECAXaVT/JdAEdDZ2EhiQy0JQFCLgWsW27Ompq12VOJ/ajAn\nkyRTOVjv4r38K1DjGZadcc0F9jIItUYNUSEIF9SuribpIrtRi8N9tgBaJrbK6xAvrkOdkVJbqVe5\nkGAb+EEw8Vt1hso7Gf8kuyCktL8hqSrgsweqA0RcbW0lOWQPiEjpFIk1ECfj4jXqUZBo7iMAKtNR\n6/aAJK91T6amUqolw8FHA62VZPPM1L3m4KBlKYeHG2GHJKhZNASldS2+sO7ce6Bchukqqk8G7VTA\n5LXXXouf/MmfjP/23/5bHB0dxcjISHzP93xPvPLKK3H79u3zPsbOOrvyVtOtVXrj/xEpa+D4sCER\nCRqqDIqO12dzzJziwkJ/60OOipRhdzc7gHBYgI5OJhG5mZyVyXbUPuuDNSGcs04we3vZoQyoM9zL\npq6AX0bDcetfT/K2s9M2idpNyzRkUgl6YUBSRsm8GPeLUwcGql5cYLW9nUDTJgMcCARsQrrPYBit\nFcBI9gOTbCNx7SIyQJH+l02yuTpPgV1nnZ3GBJNYXmBa5k3ADqxMTbXnyjPiuRHQ1oC8rumI/ras\nXitw8xpBZq0BsdY1uajDSJljRNwImmVeBXgC6/qc8IlVrulYnEe9BoZLyjbU9sr8OFlRzQSoLfAa\noEoNhLbgmP06JwRzDrzIlso4AV8IEeBH8F+vlYwMHyPYX1pqBA2fV0GX+4ipr77RuZ/GKnECcLjW\nWt9XiTOyyXVDBrm/fOLWVtsbZRBIdvnrWrM4OppyOL58MIOkCYDnwN5lrZLsVjDmenkPUCqrqHMa\nv728nNeP4iAinwmZNIBQV0mfa71VCblrCczXrMpVt1MBk5dffjl+/+///fGFL3wh5ufnY3V1NX7x\nF38xXn755fjZn/3Z8z7Gzjq7FkaOUNOslW2KSOAguCXbmp3NAD2i/Xt+vl9Tq8+5z5mZ6S9glCmp\ndRq01hsbGZRz6jZLTNtZGmdoU6y1NjICMkbqQ3Z2mlxE95W6yfZ6GYRgTre2GoM7P9/OQSrdNZV5\nOT5uLTaPjyO+5Vsy26TLDgmFLI0NPiJBAIDk2DUmsJHVWhAgyUYpkKp95rHRtT6kgoraPcg9I9PY\n2UkNcu0iRN9s0+2ASWfv1g4OkmEXUKuH0HJU3Rsiok6NVy+GrVf4PjPT36ZbtrAC9krg+Nv7yaNk\nWYEboKEWfnveFEWT2dRuW1UuhkQQyFZwVWW7EVls7fdYav4B6bC2lkE3X+G4+Hrny9+plRCk8tM6\na3m2fY8ZHMgOx1QbXtTCckFtrc2pUmLmeCug9J38TA2Ma+H9Way/Su64B+6V2pB6/2p9U0TuKfYe\nhJFsxcZGyoetB/f/+Dj34/X1lHLxrdaBzL3mMjIqslqDvpj89+7dzKosL6eSwf6+tpagSO1XRL/M\nmQSuZniqnMt3ix+qHPiq26mAyZtvvhk//MM//Pb/FxcX48d+7MfiT/7JP3luB9ZZZ9fNgI+q638Y\nMBmsD+FI6qZJWlAdiQzEjRttkJlUrUC1Moim0laJkM0XkyPFfNZdm0iJsIicpx72siKcbC1uXFpq\nwQ2HDDzZkEjUdCibnGzO3es533peGCnHA5hx7jaBnZ32/RH98irAYG8vWVzgsrKeChhJzIaHs7hV\nwaU2oFtb7b5pN1wBF30xWQPgRNrlfEjYrJua4u/qSzp7t2Y98j2C8q2tXNc6ygn2BNozMxnwVjIl\noj+DSFIkaKvPE3mVz93YaD6B5Aop4PhqB6yI/HfNcAqmkUBVZusYvEc7Yc9clWwJkJ0HUFGlulht\nxzc5mUBKEG2AniwpUHJ01GRzCBb1JBpfAGlY/NqSFulVmfM6G6Vmn6rV+8fIg9RkCKpri2ky0/qd\nT2J1fxwEV1V+5uekb/YW5BIQEJFDZq23GzfatZQJv3MnpU4UCvV6LS+31/PJfl47pdVaGFk9wA8h\nCFCTQT73XF7ze/eSWDw6anuQ41cbhWyzH1oLYgREGNLg8DDbK0ckQLoOdipgEhFx9+7deOaZZ97+\n//Lycl+dSWedvddtMEtS/10L0WhbORXMYUQ6RRug4NpmqAAwItnHg4PMnmCu6tySquVVc6LOY1AG\ncVZWmUuMoqBccEEHjl2V/an6ZRsQkLa4mAydTcg1tLHIYOhYVXXPb73VfnfrVgtQtIpUHInZdL3I\nHGiwHd/OThb/bm5m6n1pKYEJYLG2lswpPbYCUvI1xbc2XffMeWKhBR46bQFJwCbr2gN39iSG+SXH\n5IfUOszP97eonpjI9qparuocRGc/MdHWseLkOgHbe5A0fJWAV5ttPo+kyTp/9tkMloF3wX6vl1Id\n8p3Z2f5ZU0CK4I2fOqlgmG8m9XK8mHjDAD3LMiu6DyI+ZHOx5QBOLeoWVG9uZlMBmSgERkT6gRrY\nC7Zru9iTTMA/aLUBiz1Cu2IGUJ4FKIl4NJsPcKgVrI1itHdHEDlngPPevdZVTqYIYFtdzdbJESnR\nAxbVEd6719/kADFl/ZPOac5AqiizAfDY+9fXc96K/X13t+1ztZbFvTZfy95fG7M4LgSffV3Ga2am\nfd7SUtaqXnU7FTD59Kc/HZ/4xCfixRdfjIWFhVhdXY2vfOUr8aM/+qPnfXyddXZtrLJ/g1KaGvxj\nEAEJv6+6XZ/HqURkEbg6CLIHQb8i/Lm5DGB1/sCcTE01R0s+QLJxXl26IvL7pZsnJlKXy7kKGjh7\nkjjsLEAFjOzs5ETf3/7tTM3LgGxupkYY8Ilo779zJ6+n7IuABaipM2ewdxhNwMHxynbt7zeggWGk\nPybBktUANrDKmM7JyZwX4d4BRgAXtlR3lho8XocUfWfXx0gYZQSBCOvSPCFyRcE4WZUAcHOz+bGp\nqWSbZVlGRrKWThZXQCwotv4F/NqEa2ns2VKMX4ulgXU1AYK19fVGctSBk/yODIDnW0GzY8Nk63gl\nOJb15Z/4DnViExPtmq2sZKZXAByRkiPHzCfcudNf2yBzIcvBxwjUa83FaYLQSmhUA6pk5dX2VXuc\nmpKzMIG4e+7Y7S3WqqGeVe5muC6Znr2Er5Xhqh3aFNbXBimOo2boyMHs6bIltWbKM1El2wBJJSgV\nvs/Otte89lr7+fvel01OHHdtYGPNDmbitECucrWrbqcCJp/61Kfi9/7e3xv/6T/9p7h3717cunUr\n/tk/+2fxkY985LyPr7POrpVVKZZNIuLkFGp1ELUwDbNi05+ZyZS9TVGGoc4xMbfEd25vp2yBg7Lx\nYVYEEVLM52W1RbAAWvChtbGghdO3oQMJu7utVgST2Ou1177+ettsVlZSunH/fvt9LQKXyvcdmDdZ\np4jc6CPyWmtbKfsiIABidnfbRrK21oIIAYSJ77VX/dRUvl92y0ZjY7LJ1Dokxfk207qh1pqlzjo7\nK6s1IDKwpCtra+lXPI8CMNp6bU+x0N/6rf1zHRAWPsczQV5qXdcifJ2PSCGHhpLkUHgekc+muhRz\nhcwPmZ5uGZ7aiYpEid8WBMuA3ryZz+XoaHv/xkZ2/CMtrYXmuu5V3w1A8GuIixs3GuBaWWnP+cJC\ndpIi03Vsmg6QzMrmCp5rN8R3a1j3iASiV8EqMK5zUBCDAnNdtryGPI7wR71NRAb3shz2HdfdnhHR\nv5fw14gr+1atydG9C1gF+HSQq2tfbY19SH3l0lLbOxYXE+zUhjYkyrUDZ0SSXaS/lQy9ynZqKdcL\nL7wQL7zwwnkeS2edXXuTSgdMBqVc1Tg3jo6DsTlyaDVYxezLlNgs1apgDLXg9L02yPX19joZFNkX\n8qTzKpgGgmpAzam6Zs5d4EBqYWMfHm7Mz8hICzCOj1M6ZWMQoJN7TE2lDlm2AfNJPkFO9sYbybYe\nHmbLZRuKrle1004tWHzzzfYZS0vtc8wswebRHleJWkQet3sFKNWOOmpxnKdrFdE/S6Gzd2eVxTwP\nu46NCHTbkk3QHWpnp61tk6mtccBgdbX9TapSg0TPOka/Ei0ynZ5TxfbT05nFACaWl/vJgToR3Pd5\njjSwGBrK+6yb4cpKf/cn/pOMRvG3z5meboHt3l4jIUjWbt5MX0NyWYFJzcrUbKyM0vp682nOzzGQ\ncd2/3/wKH+j8a1OMiGwRex521dYvmRW/aa+MSAAsE1KVC+S0tbnC2lpKscgMt7fb+qj+tTYw8VnA\nhD1/eTl/Ts5HtheRe8rdu01SbJ9zPFUeR+YLyPd6eZy1bnN8vL/lthqYCoZlU65DZv2RwOQ7vuM7\nYugh1TLHx8cxNDQUr7766rkcWGedXUerHagqa/WworMahFbtrjqQiJRwcVg1kDc7AGMpjc1JkUNx\nqNhH79Pyc2mpv6/6eV0b4EsQYQhWrc+pXXIEEzIF5AnS9CQkZG2CGZkJAVWVig0NtQ3t7t32HnIF\nmxkQ5b5sbLSNa3k5v/utt9p3jo9n++U7d7LQ3d82sYi8ru4DrTIgVNeMoENqX8AheK5BQgdKntzc\na0XK7KwAxXUEJhH9MlK1TJ65mZlsk12H7lWZC/Z2aCg7EfFlgiWBWy14BhSwvHNzyWrXTIds5tpa\nFsjXduGep83NbCKh7kt72NnZzL7KstQaA5IZMjZNMnTJIv0hyxFIynYINrHWgkNF0+RCKyvtuGtG\nWXOM1dV2zPyS7EmtbRBUv5dMcbk6p4iU9Wq5X6VVVbIHpPrj95qUWCfawZOJRfR38PInor/e42Gm\nY5e9o0rJpqZy70ei1f3B+tTVUi0KIFT/eGZ3drJT5MzMUyDl+o//8T9e1HF01tlTY5xa7RjzMMO6\nRPQXyAve/RzLVpk22mTp3QpsZFzu3s0JtzZum7kAYnY2Bx1GnB8wEZhpkcuxa5eom4jX6gjjtWtr\nCcjIM2oRIwZX60xsU53AjsFaXs7U+De/mZuXzAyApFB9dTWZMN+JhdUV6/XX87U1SKgAtdYRHR4m\n4yc1PzXVLwH0c5vodWC7rqN5JnRoUzxbm0gICoHqR92LCkQEooKi62iIgYhs9oCt97s6jA7Irt2J\nSBtlcmu9huL2Gvh5f2W5NzebT1PoK6DUYlstm0zI0FAOVZ2aSkmX43IfJyYaOSNzI0gllZI18Z0y\nphVMjY42wgKxgm0X0N69m2sAKAK81taSHJqfz6zq7m7LkCiodt6uvRq997JfkB2Qla7PqEye62Vd\nAX32RfUg7qfnv8rXBhsEAArvxkinkSDkiePjbY92DrJmyLV79/qbTqhpqg1vKrhWTyXTX+OGq2yP\nBCbvf//7L+o4OuvsqTEPv8169JFPWVptKSzzwvQir+zJ0FDWlHC6GBfTlicmkg0cHm6MycpK1q5w\ncDUVfF7sLu14Ldjzb4FDla1F5P+XljKYIOv45jfb5qLd49BQk1RsbGSxo/a/ildtYDTKIyPt/VhZ\ntTdYXRvDnTv97GdlKnu9DCAwrLWrChZZNzIF9zY617pKvAQadeN7LwcfZ21mCJBLCKoN+KzyOs9U\nbTJQi5Sr1WeHtMlalwE9C/3/ZZggfX09gyAD7YaG2nMgUJqaSnJhbCznjrAbN5qPWljIda+YvdaH\n1IL7iEYOLC31dzKqUkd/BJvmhhhQJ0tLeuMPUsT33r+fWZf6nJKPCvqAm5WVzHwAUCRBMjY1s0Im\nJ/Dd2sohsLWJytZW/k4tRWX3nyQ4fppM3Y96PpkpagJrCPCISIKPLM5+C2hXlUOtMTkLk73XjGFo\nKKV6AFVd05652mzBc8c3yaStr7fzvX27nRvZ5P5+gvSrbqeuMemss85Ob2aNPI4BI4KjiH5trFQ1\ndieiv7UlICTAp3GW6p6eboH88nJ73fR0tjWcmOjvVnIe7WZ9NmaqFp3XYllB4dFRHmPVnY+N5Sa+\nvp4gwBR5bTK1NOXcDVY0BwET6VwNiAMmtDC9e7e/OQCgR8ZRgwXgxUY2mMnCfD7Mascv7F9EMnid\nPbnpbEfqUIdsqr2qrWVnZh5s8V2Z72q11qLKewSa2npflULixzXnq32tZ0Q7U9fJusfgRuTsEtlf\n11U2krSUnM5Axpo50SJXlqNKXByP+oCadZyf7++2BUiR1VTCYGUlNf0yqbKnmGiZFbUDWs72ekmm\n8AHDw0msKJIntyEHU2ciOzoyksX0GPxaD8dndaAkrc7MoQ5QG1Q7tsl0Cf51k3OvvK4+2/aDsyKH\nDg8z018zjVpHaxN8eJjHblq8Dncyeho3RLQ9TLF+r9c+S8MI0rTr0CilAyaddXZFTD3F4M90+Tg+\nTtbPJqUrl82S7ljgrxOXOpWFhSY5qu0fZSsETcDDWTP0HCnQUVPKOl7ZuM0osPlHZDMAwYW2ojqL\n6YSF8dJhheThmWdyMxoby6nKmDKZo9qti3ZdgaMslABU4SlpF6bN/Xk3LBswirG/DgzXdTCSICCc\nxE8bUY0MZNpI+8bH832065h4Uj4ZBGtLETQZoudSEHJdgUlEPl9377bgB0N9fNyKeYGx9fUHs0Um\nWstS1dbYe3s5p8PAQoCADMVgxYhsJBGRRfqAJbABBHmO1BNEZB3awUFrWmHg3Z07CYI8555r9TWj\now3sYL0XFvKZB5h2dvqH3a2s5JyKOmjx3r3+tr+IKRlbMrKIlPco9u98Q7/VoNu6ikgfrm7IGkAa\n2D/cq5Pq9s46oNdEReZN9gPo1tzB3siP1DjBQGVkCELSWr1zpz1z9jTfc9WtAyaddXbFbbDXvmCp\nghOdtThYciS/I22KyNkEBo+RC2g5ODOTcoaztqohr9KOiGyHi3kFLjBV09PJDmkTSlJhkwYOaLox\npwZwuZYyF7UlcERjNhWtYlN1MgOKdA4SlNhgBKoY0ydN/Z+ldKCzfG4UgHoeBMlAxdhYsq1jYzmY\njawHAPF5CmNlKKen89mMSHYTo1ubH1xHU3dFIiOgBjgWFjKDaWq14Yrj4/3DBwEFz6OgyrWtLHZE\nBpXuFyabBGtuLucJ7ey0wYuAaG0JqzOSFsiaHxwf53FgmfkfbLsWsOpEtrdzdhTChX9YW0vfXLuc\nAVf376fsrNa0mFXiOgk61RNEdKDknaxOXa9Bu2dYwXgFI5ub+drzNlItpEhdEwsLebxmmtjDgY6F\nhZRSbmy0Na5JhGYLCDXSVcDsqlsHTDrr7IqbDd/mj8GtPf9r7/QqT8GmkIFNTLRBTW+80V4zM9MC\niFq7IfNyGba1lTI4rBaghYEe7PwzPd1ed3ycARP5g2BiaipfJ02P/QTy6vyXiBbcrK9nbQj2DXvr\nc2qARapStemdXY7RXQ8NZevnyckMNAFQXdU8N35+fNzAO3nRwUG2zPX5OsF53mRQInKNeFa175St\nrC2hz3OG0HmYGow6b6lObsfo3rnTrsmtW+3akX4hJerU9729/gL0iAysPGc1SyybMDubUtA33si6\nlP39BI3z81l7IluhHiQiA/6JifbcR7R7RSq6uNjfdnZoqIEO+n/NAASEd++2P4DtxkZ73c2bOY9F\n0TsfUxt/yEqpjZP1jbi+gPaiDRgkta2T1yMSjKjXrLVfF3GNyUIRIAAnMk5m3t7+/ve3Y1Qzafiw\n2UIa2djz1ND5jDo48qpbB0w66+waWC2oxyhK7ZIdcGS1nzvdqunK4+Nto8QWSh8rhBdUk7NUxu4i\nrDLNWFEgiTa/1m4Iamoqnoa4tnDEYpKX1MJYLGavl6+XPlf3Anxg3Dh/36ELDKb0Ojj/p9lq84OJ\nibamNTcAOkgodEITENdOPjdv9uu+DdSL6N/0ddMBgpEJhsBhbz2b5F9V3njdmhvIHjgXYGFlpV23\n2vlqY6MNWMTwynDIhFbQv7TUrpPsh4GOVdaqc5r6lrGxbO1qMOvubuuSVe+novSZmZz+vrCQs1nc\nM6QOmRqgxR8hIkjQyLt0Qbp7t303v1vby66tNWCys9P+TU7qWmHx1ZOQowqwO2ByekNuCci3t9s1\nj2jr1j5Ra6Eu0nZ3s4GNjEZE7i/Hx2096UpXi97v3k0Qbq1YQ4C+DJ4aRZndq24dMOmss2tiVdIl\n0ObIgJQ60wTDOz2dxZNYPezu1FRjE2nASbswNl5/Uex/7cHOWdchhrqYVGAgYLG5kHtJ4TsHzDVd\nrnkJXgfo6KrDmUu51044NrPKbmJzu0zJ5drQUGMOZSmAkpmZttZXVhL8joy0Z0A76YgEGTJ0wLnn\nwSwOTRYmJxuoraSA+gayI6QCLbiuQePj2YziugETQQ6plcBZBhZAkBFaXc3Mp9lFssDkVDKiAvWD\ngxbgm0nhGpNPypgYZLi3l8BQDdrSUv+xeLYPDxtJY9Cilr8RGdT5mw+QYQNGBYQrK9kFirxzZSUL\nmNW21cnutTNY7dwXkWC1Dgy8zvK/yzZAstZw1ntzmfKmev+tQf4J0Wa9VoKkKgp6vcz6IfDsk0D7\n7Oy7a8pzGdYBk846uyam6A0gqUXgMiMC+4isvxgbyz7ogmq69+HhZCZlYRgnznFfROB0dNSCjTon\nQTAQ8aA+WGCDOZIG15msSk1kOurgqloo6DqaQG1yPEZUbcvhYf8AONfM53R2eVZBLOnU9HRb45OT\nKaGyhtQZmUVTa1BkIK0XdVm6wM3NpYxSbYpjIBmrRa1Ait+RcExPZ1BrLV0HHbjrWBtCIEbo2rG3\nOlP5v2taiQD3oxbobm+nfl4Qr2jeM+we1lbq/IXAznDH4eHsunV01GpQgKGVlfbvW7dSmoboULuG\nxeYjatej+fl+X6SWRGYFabG83I5NHRvQIWvGtwxmqTtQ8uRWC9jJCasM+qJNQwaA+/797EqnbklG\nzTyTShRaK2RaBofK8Nc9yZq/Dtn8Dph01tk1MhtfRD+DRqqABbLhc3jS1joTKf6MaHKGlZX2s/n5\n1D9HpLQA43sRVh1obbeLURx0tICWriM6rWiTPDOT7T/9TAakzjBQj0D7XcHL3l5/sa2iWcGmAOa6\nsd7X2TR3oKW2znVFstYF/jo+AemV8VcgDcxE5KyZkZH2GTduRPyu35VzbSLyM7WenZlJYIw5PzrK\n4tbDwxb4khmpScGS6iJ1fHz119LxcQMb8/MJyjyfZiWZvRAR8eabKZPSulT2w7MJ3Biueu9eNsRw\n/WQqgIRKECAuIjLgl9GRKSMli8gMyupqkg9Y6Tph21rZ20tQAkTUuRivvdbAjqnuNfDEaFt7D5Nn\ndQDk/OwkAFIbC1yG2WfUwNhzdV4bHm7/BoRlFqens45qYqL93nqqr0Vibm+3Z/A6kB6XCkw+85nP\nxPr6eszOzkZExEc/+tH4G3/jb/S9Znd3N378x388vvnNb8b+/n585jOfiR/8wR+8jMPtrLMrZYCJ\njUyaVqBWW3XazGu9yfp6OikDGcfHcxq5DjqyChdlgANwFdFfXF5noNjwB1tn1vbJ/gYeZE4q4+t7\nBToCXTUJjoezB2ywVVjRzi7GZChkLKxXa1VWke5fVxvPi+fAWiDnUgQbkXIuz4VMSp2XICDXkMJE\nacFoHYBai297vcwOCEpqhq/Xy1a6V92ca60BId2an8/sg3lAsiuyHevrKTXhs8jk7tzJTAgiBQHj\nGSax8n//lj31LN+7177b8zo2ljUnOhvdv9+O0xqS5aid1A4Ps8kG/T8fUSfY80nb23kc5Fu1s1hn\nl2uXDQQBIz7F+ojon122utqyKFVOjICxNgdrkjwH1qjhi1fdLj1j8uM//uPxB/7AH3jo73/hF34h\nJiYm4pd+6ZdieXk5fuiHfij+yB/5I/HMM89c4FF21tnVsyohqsMZyQJqK+GIlLLYOGdmsisVXfbR\nUZsYS2pRi+xprS/CHNeg1c5ZFYgM1nVw3ADJ6GgGmYIGjr1mS+qfkZEEbzI4MzMZOGGhfFZn52+1\nq46MSW357JmwdtVxqAERbHpmAJTFxeyktrGRnZ5IhiYmsjnC/n6Cdy2sMZW1uF3AsbPTXlflR/v7\nrUDcc4b1jEiAZfjgVV9bZFDIjQo6gDmZSHNgIrJO4+7ddu1mZ7NeS7tc0iw1HhEJ9Aw9nZhonyFD\n7Lvr4FlEzOZmAgYtxTUt2Nlp71tZSUIDobO/n+tIwTqAtL6eQ2ndc+tsbS2BUq1H29zMLPZlB8ad\nXb4B0XyJ/Rv5IvNm7o5MrmeFDFTHN3u/minr+byGJ5+1XToweSf7L//lv8QP//APR0TE4uJifPd3\nf3d87Wtf67Imnb3nTQBUTQcgWm7ZgtrH3L/NATk+bvIS/dK3txtTyYHJXGAkL0qP+ygH+k6buQCV\n897aSvkaNkkQg6HSHUgQIqjc2srAU4bJlOYuqLg46/Xa+tzayroFYJyU0RonYSTd0llNEbuN3mcq\nrFbYrNDd8E4Av7ayrow8SWBEEgCYdt2eFhYyc7K/375XxqUWsxp0iv3f2Ljaw/QQHYeHGUjJLKr/\nACIwt1rwbmy0IH13N1v6yr6sreXkbs8aqZUMJYlcnditFsz90IQAWAFMh4bavdF22LPvuleSh9SH\nf3UcsiCCvoj0r7V7Vz02mVbNNTrrLCLbbVvXdRo8GaCulNY0ks2/t7baGrt1qxEfQD/S0ededbt0\nYPJzP/dz8aUvfSmGhobir//1vx4vvPBC3+/v3LkTt27devv/t2/fjrfeeutUn33ZRT6+/7KP42my\n7po+2kZG2gYvZXt42AKg4+P+lpbYfx243ve+1MjfvNkcJMkKHatZDYPZiqtoAoPahlTgJ+DEXgJy\nJCkRWSuA4RUoqgPoitwv3silai0HEKHZQe3uZBMGRDCQpF8RDZho/3t8Qw9Y/wAAIABJREFU3P4N\nPBwctGcJC67IfXQ04tu+rYF3zL3AemSkARCzUMxSIAUzs2JjI2ejTEy091qHpoqbg6HjTm1PfRVN\n9qfOTXBOVXZKSrK3l4GTGhA1HYYrymLU7l3qPARkZF9V9ol4qEP16ucCDfv7CRzNN6mzkMg91QIB\nkIApckNNCYAyPNx+Zj1G5Odct5k1nV2MeXZq1pGEUdvsQXCvnk7dFDKyPguaTcieAOxXOYa6EGDy\ny7/8y/GlL33pgZ9/7nOfi+effz6+4zu+I371V381PvvZz8Z//s//OUYeAemOj49j6BRXdHFxOkZG\nrkbD5ps3e5d9CE+dddf0ZBNAz89nwEWmpdgUo2c+w+ioIKIXQ0Mt6DI8ze9r20zzAK6y0ZXLhihW\n/51ytreDpDozpfaSF0yQcXidwOl09nSv0Ytg8WUwch323i5y13FueroF75OTEa+/nqAUCNFxa24u\nwQqQIgjQsem55zII9hkRbR3NzzdwIbAcH2/PlGdhdrY9ayYyCyAU0fus115rx3zrVnZ0+sAH2u/J\niAAVwYfAt87NObtrf3brVNtl2YNBIAUUKEKnqzffxDO4tpZtv2UbgJ3aXpWk0rUVlAGSBilWUKLG\nJKK//TAAE5HkTURmRGRYHD+Z2P37/Vm6tbWI8fHe2/ePVFTWRS1SZ49jT7cvjUgQbd+yzshOJyeb\nv5idbT7BIGBg154ly6uZB3JREw/A5CrHUBcCTF566aV46aWXHvmaP/SH/lAcHBzE3bt347nnnnv7\n588991zcuXMnfvfv/t0REfHGG2/Ed37nd77jdy4vb106ImwDunqxtLRx5Rnm62LdNX201fkaJBaC\naXMEer2UK0S0n83M9GJ3d+NtecrNm62Dh81cgflFF8I/iSkEJLmIaAytwBYjip2ifTdw0WcMBlen\nY617EbFxRmdyOSYIv8wuLjW7ceNGL3Z2Nt7+uRklU1NZo0GSRVKE+V5bazMr5ufbhk7S5d9qAsgp\njo8bOy7bsb+fU8BJxgwHtIZIfwxTrHMzPDuC4LGxBkyOj3Po59RUDk9ToC/Qr22pI/rn8zyZne06\nda67uym7UwxPchXRDyAmJtIfCc68ZrBRRUTOdRBwRTyYwazgxf9rwEeqNT3dn2Uj6Ros5Pd5Mma1\nSJ+sDBjd3e3F1tbG29mdWn901Qmdq2nX35eexmTtazv/msXXOtseXZszyLyScN+40br+bW013+eZ\nWl5uccDt25cbQ9269WhQdGlSrsPDw/j0pz8d//gf/+N47rnn4tVXX43R0dEHitr/2B/7Y/Hv/t2/\niz/6R/9o3L17N37t134t/u7f/bun+o6rErhi5To7O+uu6cONhIFeNaJ/4qvrdnzcQEqdgyIwW1ho\nm7b/26Qxi6QM18EGA+t30ndz7u+V+hGZs0EQJrBXOFztSYa9kdHVVq6DVutGtH6enGy/Ozpq61O9\nwORkZkV6vcyEACoRWWdCbjUzk5lE3bIwlKSPMmq1G5Ni0zq0Uyc3DCXJ48REy4DU+UM7O002OTTU\njmVvrwUKETnFXBYBKKoNGaqRtV0141/W1nIeEWABQLimAnxySWzv/fuZxVQXdniYoDHinRtxDK7P\nnZ0Emf4AgOrJHKvuYUCR36svkg2TwZGJIWNTv1drAE5q5tFZZ9U8H5ubrQbLutLSHlGkiY3sGwJH\n1o4c0Xusv5rhvcox1KUBk5GRkfhLf+kvxWc/+9mYmpqKg4OD+OIXvxgjIyPx6quvxpe//OV45ZVX\n4lOf+lS8/PLL8clPfjIODw/jJ37iJ2JxcfGyDruzzq60ARhVZyq4qpkDmQ+pXhv01FTq9RcXW9A0\nPp4bssBAd6raXvVpsvcKKCFZsmYw0YJrgyy1PQVcsM71cx61yQEAaj98zsREAiJBnRaYNlsyrqmp\n7Mu/sJAdk2ingXH1BrOzeZxjYzn5WBbQWjdPpNfrH+Q5Odl+LyDVBIE8TDZucrL/GKan23NjQrwB\njopT1XeNjKQsQ1A8Pd3es7qaGRigSetvAbvnsoLseh8Ez5dpVYJWj6X6JnUoEVmn4XoMznR5EiBW\nG3gwHbzIxhxDRJIxsh2kYQZnqn2pMjDSO6Ckftd1yTR3dvmG7LD2d3aaz1CjxSd6vviCo6P23PMz\nJI61fTCy5irbpRa/v/jii/Hiiy8+8PMPfehD8corr0RExNjYWPzMz/zMBR9ZZ51dX7MB1v7mtN21\nja5Wqlhg8ibM8+JidufSllNtytFRf+vhzq6nmdGhlgN7jI2rs0HGx1O6YsN7FHtdh4GZAg58kL1E\nZC0IeZLZOYDJ1FSCGgXjk5MJktVM6aSlPe/8fLLxMjPAhaD3xo1k9Xu9tvmrLTEokZxoYyOvEVAC\n8Pt8AUJtR+w5kYURoJsADnAY0jg+3r4Lu08mBHRhT7UZVsOga5RCb9dNIHMZkrzaavdhIKmSANaR\neyZTcdJr3415P/Dq+1wbwE4RfESy0tY6iVmVw7pPdbbEoOSz85OdPY4Z9Gu4qLUpi8K32b9lFZGI\ndc+vbf+vcgMNdulduTrrrLPzM606WZVjYcYFNAsLuflOTLRi4Zs3+zWsOhAJ7joW8PoagOBeC7Z3\nd5MhFvy7z9YPoBCR8hcbYkQGZjIUAmN1BYAOkCPjQBJVZ/IocK/yrt3ddsyCXlIubLZMoKyBDJCA\n1L/39/u/7+bNdiyrqwlU1KMARa6LgN/APVIuxwz0uUYyPqur2aK2ZgMq0O/1+qeH+522xe6HOhdB\nik5lMkkyUP49mHF4EkneaY0U6nFeH5HF7WdpdYaKc6+SlkFAwcfV+UmyxO6dNcEeNpvkOgSEnV0d\nQw7xCUgdPri2C+bLdnf7iRCEibWuUcNVtw6YdNbZU2x1EjwmT4egOpBucrIFejrWSBWPjzfm2WcI\njGqtSWfXy2QDsP/z8xnwy6QtL2dwDbzaACvgqN3Njo5yFsXgIMSIDOK9f2QkW+Fir+tckrGxBE21\nSLp2lwGuSJxsyILbqan2M3IruuuIDNgBk5mZJq2amUnpw9BQZmcM8tvfb98/Pp66bTU45posLCTb\nXwFer9eAyeZmPo+ur3MWNFcgVLuHYUsdHwaVtE2mYTCLc9JwtYvqEPU4fuIidO/WK6t1RIOvq5kO\ncq46NyWiAx2dnb3Vttfm5dQumUAxgqdmbhFBiBiETcT1WKsdMOmss6fYBHqCQQGM4CcimRiynaqj\n13VIRyLyHrp7TE1n18MEyZh980DURkxOtntbW03WQnCBfJU4qXOQeRsdbcycTIm1Vls3ex2ZgS5W\nAnV/q4MCkoEYsq4KgGQ2FH8CLArlb97MLkzOBYDwmcBYraG5dasfuJFQVQAhy2R+CcCm81PNjMzO\n5qwULCZAMz2d7YHn5vL3Mi8mqW9tZRYH2ADszNyo2VLMv8YD1sLjZDKeJjspo/GwOqn6WrVQWlK/\nV2rROrsc4x9lRGVL1WapI5FBRaKYYcZ3a2KjFfpVtw6YdNbZU26KZwELLQirhMFrBHujoy1IWlxs\nwczycmquFevS8lfdfGdX3wT94+NZ8D062gJhQyUPDrKTlO5PdXaDTAWZAf2yTJwMgCxcDfy3tjIz\ng+lTdC5DEZEZjkFZlq5awEFEZvSmpxPgYLbn5rL70/37mUXp9SJu3+6vHxGoAyhVdmZauQAB2FpY\nyAGBgBy5mRkriqynp7OG5/Cwvcf31Not7ChQpW4EcTAz015382Z7/927/QX5gFztwCfIUTgPrNS2\nu+9Vo8d/pzocwV7E5bbR7uy9YbVGLiIBsaGktbidH0csIjv4js3N9MVX3Tpg0llnT7kJIgERGvsK\nTDg0AVlEdt7SPWhpKVlxMhaylg6YXG2rTLq5HzMzLdDu/U5L+ZrhkB3Y2OgvSAYEjo9bwL++ntk4\nRd8C4omJ9tm9XmYpTM1WFyKIpouu7YEBp9HR7EhDvuV95E7kSLJ4JFTmmSiQj+ifxzM3l5kca1/3\nOc+I5yKiPQfmCJBa+L/rdutWZhQBjv39JnMDlsbHm6RreDgHntW5BLKYiAIyyvX1rO1xD6em+psR\nCFoELjVzIpNDzmdtaFn8XrbTSlyuQ2DX2dNh1bfWYaNANIk2WWr1SyS5sszVL15164BJZ5095SYg\nNROApj0iZwlgY+tcB0FY7dDFwXmd1PLs7IOzLjq7Gub+C1RrwfvNmwlIbGYyGNrbRiRYrcE2WVHt\nvEXzHJGtcEmHyApnZ/vn7NQ6j7W1fuA7M5NDFLXNxPaTH8qCAM1aYvr//Hz7e2Mjj31qqq1r4MV3\nyQgBCJhHxwk81A45tY7EtQbW19f75ZT1PpDUCRhkg1ZW8pouLGRWyXMr4+HP7GxKN27cyInx7leV\ncWFfNQ1wfh0wubozHTp7bxvCBiDe2sosp6xIbU/Nd2gookaFr74O67wDJp119h4xgWcdCkYa4vek\nHoI+rQpp9HX2oWOtRcedXU3T9lZHFvd1djaleNPTGaRqQbu6moXnpmObmK2lNJuejnjuuQYsItpn\nACFvvNE/U6PXy7VnrQm8JyayC5WMCbkZoGAOiI0XGAFopqYaU4gZnJpKkA1Y1HbFWuuqv6ggTGam\nDjt07LWdr8J1/5aN0AK4DpI0r8SE5jo8cmWlP0tZW3rPzvazn2p9aMyBtVrXQ/qhc9fYWPsOHb/M\nKiID66yzszSySeu2s3dng9ducD5ObdxgLg+yRvdAUq6lpYs77ndrHTDprLP3kNU2vxWQ+BsTMzGR\nweD6etYO6E60tpapY0PQ1tYe7HbT2eVbHZqpsBsgdY8F6+ZpuMe9Xga1W1ttLQC2umrNzWVWQqbB\nMEbBLvCj7bRAv2r2ZS5kFBYXEzAJvm2+jtV5CHx0ETO93Tnv7SU4iUgQHpHtha1b7WBrHU3NPAj8\nSb9kcUwMrwNNt7ayMD4i/w1A7e216wc0Hh834ADomK+iPoysA8Ak+SK7NAMmItlR140kzfE7Xxmp\nDpi8t0xRdbXzGMpp7b/bDnDvNLz1vWCD9Uz1eihmVwxPrrm9nfIvpISak6tuHTDprLP3mAm46iDG\niH5JF7lIr5cTtA3Ji2iBqLbDx8fZEWl4OIvkO7s8q5s5qRY5DxnT7Gwr/l5aavd9bi4lXhU4CHrV\nOywtZVeYxcX2GWRdsmf7+ylPqgMHR0ba2tnbazKlzc32ObW+aWsr5WYmdde6J9ItGYXV1VzLMidm\nq9TOXTqNWf/1GahtNV07WRy6bj93XZxrra/Z389mEI7bZwNhOmn1elkQL1sjcwM0RvQzoFtbeU5A\nlxqVra1+YCSrCZzU5xq4VHtyHaZBd3Z2NvqQyM/6PovgtZJf6poUcw+azO3QUFvv1ep8pM76DejQ\n7bASg7V1uCYX8/Ptz1W3Dph01tl7zARndRheRH+dSURzbLWdrFoAfdO3t/vlQVhnAVFnF2+CeZs/\n1l3XFgH9zZutSHthIbMZvV4GJlh02TP3UzcsbWzX1trrFK/XlrlbW1mYqTOVYxoba5+FQR0dbcdl\nJgcQ41jUxujRr7BeNsffTPAvoHfMpFk7O/0zWARRde3WzJGsoGOtAyFrcwl1KCRhMj5ra5ktEoCN\njrbvIJcE4gBJwA0wdJ3dB93EZC3VuVRANjubwGZ7O8EXUOSZJeHrsiZPv01NJbOujTXQjRA4C2BS\ns5n2GOtXRrU2Y0GeAM3sOhRrX5a5f8iPOpDUUFf+SqOOh4HSq2TX4BA766yzszYApM4xqMXvEenY\nxsdz/oMWwXt7EW+9lUHP5GR/IXIHTC7e3Kea7RBQ628/Pd0yIrJhN2/mNGtB9uFhBvWABknS2Fh7\nn0Di4KAFyTZG9RLkWoJtP1eEeetWtg6+fz/rPRR0W4uyFkCPLnBzcy3o1rVrYyNZQ2uUzArgEPiQ\nq6gDIdsCXOrcn+HhzA5GPBgkCbyqfKsOnJyayiyO1sCaA6jRUTCv4cDt2/3PE/Aoo0TqNjTUgIu5\nLwcH7ZqsreU1d90cj2yUQn3s+N5eJ+d62o2/r/OqEEy1WUJd47JsJ1n9XZWFed74jzr0T1A8NJRN\nGup+A7TXz3esnUT4ZDNoUY2ejNNgZz7Z5+tQD9oBk846e4+aAIlVWYsgjoRnbq4FNYLF6en+LkEz\nM6mzH2SeOztfqxI8wbTguDLnU1NNevXss1kbUgPY2pVrZ6f9fns7P0fgMT/f7rfMxvR0ZkwE91NT\nmSHQOndiIjMfs7PtsyYnc0P1HTIcVbIk6BdU62glwAakBFmkVN4vOK9rXHaiTrevEqoaMJnNMsg2\nVgmZ71LzEZEDzZw/YCITRIY2NdUPpGZnU7aFPBDYYUgPD9v9VFdy82ZK4jY3cyYMJpzkUhe93d0M\nVJz3edQYdHY1DEgAyOvcnp2dzAgiCoB3c6+qVZLh4KCf0AKydYeS0fTd6h/UdkVk/ZS6NM+6456Z\nae/pZsc8aIAG31S77fEVZKW1tu4qWwdMOuuss4jod1qCOjMdZmaS4RX8qCkBZEgAFBt3dj42WLQq\nnQ8s3riRndRMEifNmp2NeP/7MxMh9U/egU2dmmrM/Te/2T6r10v9982bLSC+e7cFMc88k73zBSOO\no0oGDQWMSEmTDmGCpjpjpTZbkOkwD2RhoZ/5nZ5u5yJ7UDMuFTTLujj/k4K0iH5JY0Q/21utyh4V\nx5sPVEFQbTBRi839TocvmSWkgSyV60tKt7KSgSOQUY8BqWDgJLkeELOwkIy1GS6+G7Pa2dNlwLyB\nqTVzpl6pFkhj2WXpasZCTZI6JT6odtfzfGv2ENFfA+FZRHAgMYB0UkvPh/qz65Y5Oc3gzic1tW6y\nJZV0rHI6ZNFVtw6YdNZZZ29bnQxfhzspit7cTE29mgVD72RJzGM47cCyzh7PxsdzA8KECzoi2t/P\nPJOZAhmMycmWLXnmmWTUTU8HXhYX+9vb6pMfkcMIb9yIeP759h7rwbEAOt47P5/SKlPMq459drb9\nXgBewYYaJ8XyJtHPzbXPAh6AqogsrhWkA0dmAUT0Z5iqtEXRut9XYPIwq8Akor9D2KA8zIyRra3M\nmNSMiJqU2knr1q32s7t3c5K8dsPDw+1+HR+3++BcJydbJoaMQ7eeOueFhK9213NMXabz6TGZC22l\nsemVgJDpFPwjCerMnIODnAOESADAZ2bamq7gHcAFUtSAyZYgDeqgVRlS8zh8bwVIU1NtrV8n4wvO\nez8crAuqflnd28ZGIzWuunXApLPOOouI/qnbEamdlw7GfkXkHAyp+dnZZHpN9u6yJmdjg5ua+xGR\n0igBRUTbgGQJDCiUVdDb/saNrDXx/rGxlGRh0O/fb7VEmLb5+WRFn322vd8QQQBIAwTBy/h4AxKk\nBVhaBepa2OoWtrnZ/77p6fYdsjbkXwCy4KXWrDCgWaBVQYnA6KR22djfdwIng8DEvar3SKZDdqJm\nVmSDsKp+fnDQzhcAOzjoL1b3vl6vfebRUfv3/HzEa68lYFxc7L+/OzvJSt+82Y7P7JShoWxVPDmZ\nM2k6u55WfXVtOAEga3pA/snHa2yh2xNywrrExgPvtaj6xo32HZou6AJ4+3Zbd+vruTfwSXXwaETu\nM1pyk2j6mSzLZRk/8U7HMFgrc95W9wiSTX6ldvG7DsRDB0w666yzt61Ofheg+X9ttSr1rrOSQGp1\nNV87NZXM/qBzJv+6Dj3Vr5Jh+AUIuqEJDGqXG2ykzID2uoLumzezgxMgUOs21Jro5uTzZNCGh7Ml\nsMJrNRMLC1lUbc6JbICARm99HaZ0i7JOer0skt/ebp8TkWAjIplUQY5rQ7ZlfotrUrMoNVvBBPHa\ncL5TB5tB+VZtj1oHKmKWZUPcL9KuiP4BjgCYxgNqVhSrV/mLwMMwyto9TKMBRfwR/QHj7Gz7eb33\nzrkDJtfPape1Sg5YY7KrsooAOumkzGKtW6jPq+dpeDhrk8h4/a19uGdicbGtsbW1/Pzt7Tw+BFet\nbZEBBMAF3epSLmPfqL6DL3lY+2PXqPqii5agkYq6f2rf+NGrbB0w6ayzzt62GmhhzyKaM1NvgqWe\nnm4bzhtvJJsr2MXACpqGhrKwV9Hke9kepTuWWcCMR+TGbBJ4rc8QdHutoGQQmNy6ld26BCo3bjQQ\nQV5BJiSbsLGRczJkWQQQQ0PtfguGzBjZ22sBMgaWjGR2NrvwYPTn5zPLIOhYWMhsgO/WiYsMLCJr\nReqkc9KwiATICsYxhjVrcpJZ36cBJhEPyreqLAwTLThR1M5ktBQfA1y7uxm4mSY/MZFdtSqAsp58\nD/mcImKZMdlPGRN1BDJqMk4r/7+9c4+1/KzK/9rnNue2z31myqWIioXa2BRB5GYIpWJbmggaASuo\nxKKRW4KIoY3W2kYQsIKCokKUWyoSIiUBwiUiSCpFQNQAbUIjaaBMO23ndu7X/fvj/X26nv2dfc6c\n25y9z8zzJJM5Z599+X7f/V7Ws9az1jqe65skZWN/gGgm0U9ySZhXRE+QBBLBJBrJ2kGOS7ltiAzE\nhs9CBkbUZXz8dJnl8HAS7e7uiGPHynOqkmDIPNet1fFoMjo7m3IyTdg/26AHEnstZxiyWqSrRK5x\n+FAyvV05W6ur5fqQ7NJvqdNhYmIYxiPAkMKwImLCgYchQwWhmZly0NBgkd4KNNnD2OIAi2gOOe9F\nYmAnoVbLvhQ6tlqmtb+/eez18NVyunjA1tay8hVyirGxTGYnlwSiiJ67p6d8f5T8xVihQhXkhMgC\nRkpEGjczM+W7RRKGZ5TeJ3hYMX4w+Gdn08gZGSkSD6R/4+NJfIjm4KkcHU3irFXIMKypFgYJ1mpY\neDY16rfedwS5wBDZCBATjS5yn5qrwrVwHUSfyJ3R8qrqqcaoJBeH7wbiyvcEURkdLZ9x//0pxYHA\nPupRWVoZoxX53IEDRZ5H2WWSkYlKGZ0Pcs0oVkLEWmVdFEwgMrG2lsUQiABo7yr2HHUEHDiQ85Hc\nNpwhRFx6e8sewZrEmTI6mtFTiBFGPmtHC1ZAUFj3RFwosnI2IxFUsKvX8x56elIKR3R5djYdJiph\n7e7O3Bzdg/YSKsnmHjodJiaGYTwCjCi8YYADaWgojcTjx1M+gyGHYYuhhKemalxrw7rziZioIaod\nuru701DEKCd5GSMRUoN8DqkP3k3K/2okJSI9lhgMJJTTj0ab9w0PpzSLgxdvakR69/Gya0NAvn9K\nBRMtmZ7ORO+IiIceOr0/AcYJ8kCMaSoIcU9a7SoiPbNEXejlwuMYVxgGfAfVHj5VcF3aHG49VIk3\n44yHmcfwuFJ1SGV3kH2MCJXeqGeaz9D5w/fAd9bfnxp/pDOQm8OHy+/ItIjiQGDq9ZwDROeYD0Zn\ngu9XewgRkcDJQeL48HBWWVxeTrkt/YlOniwRcEgx85P9RXPZNEcFJ8nISM5dDPrR0bLmV1ezbPna\nWjojWNOsU+Y45GZysjx3ebm8F89hf8SJtptgPUZkpUCNoLLWcPqwdolWcf+zsxldgfDvNSg4MDSU\nsutOh4mJYRiPoFoilc0YwwVpEEYn3bcxggi54zFiA8cI4wDBkMVY2w9Nn3YCDEg6fePRRzaFcYlX\nUSuqYFwjsxoby8OFfgJDQ+UAn5/PJPfFxfJ5JECTZK4GBJ5RDFK+XwxhurEjP8LD2d1dPgtiBTnR\n+QNh5e9DQ81NGGn4SLQAMkOUjX/kP1Dhh/lFQi7RBQx6ogjo0DFkSKTF+7qRpEGjHniEI9LQr0q8\n1FhS2Rjvpfkj3AP3EZHvByHFINRr1Ugl78vr0PtDPLX3EI4CvLyalA/5iEiJl34n/G0/GDPnI4hy\nsvbIG5qYSGINCR4by3WPPCui/G1kJAmw9saZn8/IHnvUwkJGA9VoJ8pBhHNoKCNw7G1E3onw6pnA\n++HQWl7OqnGsAfYTzhPyIHeTmHDtSMXYJ8mb1DOL8spahYwxZc9nndZq7Us8Z7zIF+x0mJgYhvEI\nMNYwTPRAwjtOU7ahoXJwTEyk92ppKeKCCzI/YXY2tcEcLKq5RW50LhMTjSBF5CFFsmjVQNYcAoxH\njMqhoSzLjFGCbIuyuZAdqqMdPpzXoYUJiARQoUnzL/iukGxgeJ86lcSE99e+IJT6hIzwt3o9pUGU\nKZ2YKM/TEtTaKR15GcbHyEizbIq5qWNK9AdPLJETTX5njDeCyrlUUocMUSVa/K7EpBpFIqrCc5F1\nVCVSECJex73q99fdnVEroj89PemdVdJKNIqxx+iDfFCKWcknRhkJ93jdTU46C+zJ5DYQCTl8OL34\n5Br19kY89rE5HwYHs/+IRugoG46DAlKiexFkAlknjpbBweZ+QRp90+7jWvSBdUR0BDkUJbKRnrFv\nYVQToSGnjWp6vFbzuLYC9iZy8bhe9lecKUtL5R+OAR0jpF6MJc49Cl60Yx1RjUvXeSfDxMQwjEdQ\nNZLZaMlf0I7uVD1CIjA/n4cOuQ94lpAcoVnHoMRQYqM/F4GHkggUsh5IGUnSWm1KDVDGfmGhudkg\n+T7T05kzgpQAY1qbHfI3TUTFY44Rq15+ojXDw6XaFpEAjAUIq1Zsw6DnfvHgc1+aq0TlHjygGCV4\nYbUbfL1eiAk9ObhGgEcVQw2ZiFbHQhKnhQI2AmSDsVRPYzUxXqvw6O8qA+P16nmurjdIBusGgqJV\n7lZWstQvJYZZdxFJyrRPRNUYIgpVr5frIBI0Pd38eE9PkWziGTY6A+QGaVSLvBKipNPTOW9xaPCY\nevFHR5Nw9PamwU3uhFb2Yi+qVtHjM7QYw8hImadHjuT+QHRmaSkrga2slPXKa5Grahns2dniBIPk\nM39xWiBlVGfPdvqdQI74n6gsxAMnB/JYyJA69DgviVCTiwOpaVe1OxwtNLntZJiYGIbRBJIM+Tki\nQ9LIjyhXSuUkiAdGFEY4j2EocfDx3lqmFHnQuQQaC+JJZBwx5tF6z8wk0SOZfHi4GBJEr7q7k4BQ\nIQvjHLKCUc+BOj6enu/h4fI7cjESVxl7rdJD6B+D9+TJNE40coDSNnCyAAAgAElEQVRRrIZHRHO/\nFCJtVHUjkVQT2DF8uXfmBdfF/RFxicjoghZs4G8QJBJk1TuLp/dM0NwRxgNUiYlKrarPjcjHuRfV\nplcT7PW70J4ymk/DveP1Jq+G74vnYkhhXKqsjOvi+6GgAX+nSMHKSvmfRGqjvSDXj++J77RezzWz\nutqc/zc62pyPMTVV5haRDhLMV1cLqVFHUVVuyNqmYSvzQvPhJibKP6IPkB2isEi16GNCUYeI/H94\nuOw76sTifkdGStEVlb9qEQnefytlhXG4QLAgO0oycIwcPFh+f+CBdDDo3oNjhXxAzsPR0fYRE0iJ\nSlM7FSYmhmE0QYmJbrYYTHi50MMTLanV0vvPAUQCc0R5jnriSajnwCEvBY/vuQCS/zk4kTZFpHSJ\nQ02rn/X3Z08KjPixsTRSNWcDXTZ5Gz092auCClcR6YnEOMGop78JHkpKzFIqGE+kdmHmwNdoTETm\nx3AQc38Y4d3d5VA/dqy5xK0SLKRoSnQ0rwMDR+cpXlyVwKlXWEuabgUYecg6uJZqjguPcT1VYqLR\nEn1vzScBkHsaTaoETSWAVDaD8FWTiEdHS7SD+9cqbktLuR6RnTDXpqbK9zM4WCJllAI3OgMYz5B5\nilPg1SfKx7ocGiprnL2XqOHgYKnStrBQnkMZ8IGB/M4x7pmD7M2Qg3o9SQp7V70e8aM/2tzbBNki\nexgGOp8V0bw/Etnl/XRea44U98M61age+XdaBbJ6trEuIXHkZxE9pJw3n8G5R5SKta/5girFxAkz\nPZ1nJLLKvQbR4/0Q+TQxMQyjCVphSyMm/LyyUg61hx5KqQ8SLWREHCp4+TGcIS4YOnjIISkYUfst\n50QPPKCyIcgEBj1eOeQ26I85/PDQEWnh9XhFkWMhrcKzz2PT0ylp4CDHe0pCLO9BJCIiv4fu7pQL\ncS/MB6IW9MQgoR4DNyKvH/334GBWEoOoqfSLKBtGAAY/xr5KlbgOjUKobArDQaN0eD63S0w0J4rv\niMeIJFXJiHZ/xziqEhBe3wrcJ/p0PoPvHEKn1bwg/ZCyqalMVub7pVIa3xNRmUYj5XXMz76+QpCO\nHm3W8RtnF2o0K/h+I3LfrEadIzLihTNiYqL8rj2IyE87ejTlSRHlfR56qJBS7WVFqVkikOxv7Ons\nHSMjhXgQ8YDEY+RTOIX3phog10ZEaG6uXB8NGjUHijwyctrYS06caI6s1OtlrmtuGONLQQj2H92b\n2EshYESh2Yc44yD7GrFeWyv3rWWZ5+czOo5Msh1YXNwfa9jExDCMJqh3FmDsqAGOLAuvHJ50POsL\nC1kVaHW1HC5UBiF5mqRHDClC/hxm1WvC2OwUrw8Ge1dXc1IhsgCqlnHwcXD19ZXDamwsDzkiJMvL\n5XGiDTyGNInnHjpUxuLkyeIZR1qHx47PjGiu/BWRMiUiHRHNMjOF5gzxXrVa0YVrxARjCOMX763K\nuTCkOeh5TwwHyEO1LHC1Clb1+qpQ76V6bLdKTBgTJRZa2UurA+lr8FrjYdWk3ep1t5J9EW1irJHW\nELlZXMziAzxfE/zxVFOpi3Gg8trycpl/4+Pl9TSCm5zMe11ZKZ+BoXniRHMCv3qijd0Fe6tGBJXo\nQkJZz0iHIAc8jiySuY9RjxxLq9VR9IC9mLW4spKPR6QToF7POUwUh8IKVOjivShRXq+nLJFEbHKc\nIMQqIUUGy+tIuie5fnU1+yZFnH4+EaGgMSPrF4cM14fzjMgJzpmenrImtLIYTpSlpdzbIWcLC5kP\nx1jjcGFPIM+kHTmVx45tL/dmr2FiYhhGE3QD18dGRk5PVKwmHePF7u1Nby3kZWYmK4MgI+L1HBYR\nKelS7xyHI5+F7r/qbV7P07hb41J9b665VktSxkE3OJgVytSzubiYRjfEhl4fNC1EloVRCknhUKRi\nFYbnzEyzdACpF8YKr8NTqIYyPytJUUC8NL8hIpPa8d4jbeIzMVqqZX5VzqCNCPUadG4wfno9Ec3S\nqvW+L4iQ5ndsFRiFel1IOZBSVZuGauO3qixS76Ma+YloTrhVA5M1h4GDlxZCSCRreLiZuPf2lnWn\nkjsIJPOPNdfTU4yXahRrcDAreGkEpl04l4kReVB9fenswMivShRxAGjuxtRU7kfqEKjVyt8wvhcX\nU/LJ/kKTUo0GEo2FLGsey9xc5o3RPJEqgexd5CFeeGFKQ+mrgbyJKmGQISSd7JWQbtYExIjrInLL\nPsPcoJEjhBpJLcSEs449DMKDY4mEdeScRHqIMBJRmZkp/2geOTZWSBU9XMbHM3KjMry9Lt2726WV\nzxZMTAzDaAIHVKvHORS6uvI5aJDxHiEDY5NfWkqvVrWbNNEUSI3mFiD/6u3NbtXIAZCgaNJkRBr2\nmreyG8A7qSF48howmulwTJUyjDktJYkhzsHLAcc90VwwIo380dGUevX1FWkD44o3UcttIpnCa6md\nwvluKTKg0o/1ACHRkqIR2ZgNQkUkDMNHS5NyPxgYkJpTp/KaOOgZWwzu6vXxs3pwWwHJhVbK2g6U\nDFejSfxNSRfrQ5POlXxVX4/hBAFifigZ0/wulaoQeURix3pRbzevRyaj768NNJljGLsakWEezsys\nL0PBo70XOHDg3E3G14pqfC9ablwr6SHHHBnJnA8kQ0TrkEZCqIlyQk7IdaPYhpKbhYXcf7TpKtFw\njXZzXZwDJLUTcRsYyOIbXV0lAkcBkMOHS3lgjUBgRFN+m6p83DuVrrg2iATzkHlLNUOtJEg0Rc8t\nxmpgoOzb5JzUas0lzSFeWhENp9DYWPmnRB+yxD6I44ny3XuZTzk8XPKKOh0mJoZhbBps7BxQeMU4\nbDBsarXcjKmlT8UWQuja9Tei2SBSDzoGNdIUHif6gCyBA314OI2yzXbbPZM8TKtiobUmSqBlITn0\nJibKgTc0lN5FktLxEI6NZVRpbi5lXz09xUDgwJqYKIc4kQdei3GGR/TAgUyI5rPV24+BzGHNwcz3\nuhEgOMvLmWOhFcJ6ewv51PwRlYYxP/i+IV8zMzk2quHme29FKlpVsGqFaoL8dqIl+jmtiAWfA1Hm\nOUpG+b0VMMKIDnKdREKA5npFJIljzayuFmkfawQiqiVYMc54H/rg4DFmnEZH03vNd4OxduJEmZvV\nNcX3rXlUZwsaSTpXwPdASW7+J3LC+HLfRBEOHCj7w6FD5fvBcUPUbWio7DGnTqUDSavosZfTmJH9\nEocH+RvsseQg6bqkKSf7E9HemZncG8lZo/z3yZPlM5D3NhqFnNAYlrkI6RkczMpi/f0ZvVtezgpd\n3A8d2NkbWEvcu14TOYBjY1kEgLMFWRr5IjiNeM7x43m+kctTr5f7YKyRdkXkdSwsNFcR28t8k97e\n/UHoTUwMw9g0dEOdm8uD6dSpJBV47znY8MpCRpDw4LXHMCIiQWM+lasgU9GeDJrMrCUvOfw4dCPS\nW0jIH682hyAH2XpGFcYnHi49TDRvg/sYGSneQRK/MTrxouHlZIxGRrIiDCSLQ5eDuasrew5MTeXB\np4nfjM34eBqiWjlGk825Z+5vI6hkD4OXA57PHxlJ0sb3hnGg+Q94GxkHjS7wd0gN47YeMeHa1rtm\n5lKraMduohpl3KxsTK9Rq2pVX4uxWs2/gggS3dCIiEZY1NDDqcDno5tnXQ8NpSSQdYVXuV7PXBMl\nIdpv5WwTk+r4rJers59AbwkiGBjX3KvmabEX8W9kpBjWo6NZBhip0+hoIS61WvnekNASeUAKRb4I\nBjt7ekQa8PqeDz1U9n8q+GnHd6RbfAaEFccEcxbjPSLnL59LNGR2NssPQwaYj7wXVccgUZqY3mgk\nqdLeJ0SaIH5EmGlGipOLcwgCiLSLx7VEMVGmWq2MCffCeTg/n/mXIyNl/Ihs7VXOpCodOhkmJoZh\nbBp4jrXKVn9/KQH78MPNunpyDSj5iIcfTTBdeznYqFiCYcPhQNUpDC0OV7xnasxxgKrXXftB0EiQ\n6I3q+yE21UNCjUzVtmv5SmRHkCe03BAD8g6IkCCdwFtIUnxEswHPayFFkDy6D3NvkI3+/mbpA1K5\niDyYeV/1ym+WmBAFYB7oGOOx5HPw5vN9qU6c7xfCBTkk0sZ7YUhXk9/1ezgTAdgP3vUzXaNGbBhT\njK9qBKEVqYGYUIVIoyraFI5IGoYmhIPo6NBQWa8QGhwAzLm9kKWQp8Dnsf4ocdvTkxWeyG3rZCB9\nxMgGGOCQQL7jtbWytiEBkAoM3Ho9JUQ4OVi3rGMqKyqRhFgwLyAX7KVUt8OpgpTz8OEspjE2lvse\nc1ajeHxvJKRDiCEg7M981sGDxcly7Fgm1ePxZ15OTBQCQFSeqPDqaubeab7d8nKeW3wm0rnR0XS6\nkcNC5S2S2oeHy+chx8IZhkyNXB4IUUR+XydOlJ+PHy/XdOpUc/7Y2YZKazsZJiaGYWwaVf08B09/\nfznEZmbS2Kl2I0eyMjOT3nVKzGopVK0IQ4Iin8OhpVVpVA6mlU94Lz1Yu7tTphKRhIZIBZIsIiJU\nftGSlEQNNMkbA5vP4nqRRKBzhhwhE5ifT682nkVIGIY/HjdttqdGJRVhVPI2PJyJmDRvxGvJYQtJ\n2Yx3H9JHtETzgYhIqKwLQ6taJIExhExpUr16vjGoInIOVOfhuZr8fCa0knpF5Pejz4vI75mypcxD\nzWNh/jHXKZU6MNBckQsDGaKtDUH3CiRE48hgTmGcsx9oL4+9yn2pShCrwNGgEVf2C9Z1RBI9JHZU\nd+J96VEEMdGIKOuf3DMcRnznlOyFUGK8I4MlOV2dDxRAgFRobsrkZCEWKluF3B46VH7GScI+OjSU\nJavJDyHCS4RncbEQk+Hh/C7pJ6I5ciMjzSSLaC6SYMoPs+dzfRF5zVQYnJgo762Eg/GamChjrnse\neUADA+VaURGQr8V5hax2bCyv58SJ5gjnXpB6reTWyTAxMQxjS+DwRL5DbgNaY6p2qSa5q6scFBxg\nGO333ZdeLIwnog4aSeBA4qDk4FCSgkECwcFYxogiP4PIBsYLER0OGwwtJGZ4DbUEr8oe8CbjWYME\nYcBp0zCt2nXgQDmckTDQq2JysuiaMc4x4CE7HJZ4zYlC8By+F4ghJIjrJEqDQbdZyZGOmUY4GBui\nOhBAXsf/kDoO9moOBYezkkydbwolKjvJHdmvqN4/32eVmGjCO55xzcvBKMYoYn5iJGLkzs+XuToy\nUv6fnm6el/qdDw+fvSgFe0ArqRNkmTnO84nsnm1oxLYVManV0lutjg++J62gx1hTaAJSwGdMTmai\nOfJRLRnM+6lzhr0Qx4oSIYzrAweKwYzTgx5VEek4wdgfGSlVB5kDkAP2epwhJIQjm4U8jY+nvCyi\n2fnEd8q9kw918GDm5HF/FBqhYajKwpj7lE2G3LEnE1mBHBw8GPGDH+T+T9lf9mwtMzw/n+SFNcB5\nwTlVq5XveHy87OlE+8j5mp/Pff9sSyBBu3qobAUmJoZhbAkYqBHpIeSgQr+M14rNeW2tPD4wUA7V\nqaly+M7OZn4KxgQyAj3k8MzihVfPIQeZdlXHI49uGO8cpIdkbnJmOMhPnizPwzCDGGm0AO8wBxGG\nz+Bg1tvnMzAK9FCkjwC5Nhy8VFfi8OfAhABxvRzI/J2IAocehAhvoMp9dOw0P2QzUN22SsD4TrTy\n13rERJ/PGGquUbV/ib5X9VqqROZ8hSZLV4kmETzkkzzGc7SULAaX5mlR+nR2tqxZ1jWOA/Uea5GD\nKs5UXGIzYE2pXh9PeLWoAgSAaNHQUHOfobMFPq+V8ac5cQBSoQ0HGVskU11dKffiHtlnxsaKMd3d\nnflnEbm/QRhwzuBQIL9jYqI8ny7n5B5BZNhzIBmMf19fypV0/+eekBti0BP5JV9uaSk/a3o6KzVq\n9JR5TT8QHD8jI7n3EYlW0qYSR84jzXWjUiJNRDlXBgczh4T+W/V6IV99feU6IYD0BVpdLc/l2iGN\njAMFKTi/cOyQN7O4mFGevQC5bJ0OExPDMLYE9bhqzgeH1qlTRRMckYbDgw82J0DizR8ba/YaITPB\n60dJYB7Do6ZeKfVeUQqXHBLeB3AwU12Lw5bqKBAXrR7G3/Eeam7J+HhGIjicVS6hyZE8B8MCSQq6\n/ePHM+Gdg2xhIctTYiTQdAyDgLHEIKCsMsYl35kSAh7bLCmJaI7O0JyNx/FUY+jqa/gfslf16mtE\nROV8Wj64FYjQnO/ERAscVAkb36/mQgG+DwgF8xKpY09PmWs4FPju+U4woiHKROJafR9bmWetoOWv\nKVvL/oBjQfORWIPkaMzNlTV1tioSMSbkvC0tNVeXwrDXXA+tpkdp2tXVLHgBGenqSqOW1+FxR645\nMhLx2McWxwr7jUZqVc5G9IHrZt/VvXdwsPxOMRMaLPL8+fm8N6K+XD9RYhxD2oRW852oSMe+joSM\ncWEPJI+E8roTEzkHenqyJLFG3CEOzAPybDRhnn07IiPdx45llId8vqmpjCAxj0dHm/cmclt0r0dJ\nQGPJEydyHSIbg9io0yDi7BVyIKLU6TAxMQxjS8B4J4kyIskBXszp6ZQH9PeXA0e9+Rwgo6MZzVAv\nrCaxKhEgiXBmJj1i9MTQKlMReSihk5+ebjZ4IQ/IqE6cSDkIBzUVjzjgkY9xuDEOvA8J31NTKemC\nzEDCKKfM4yTC03+A5HjNO2GM8dhpda+IrAJDwzP1JGNQVCMRWzUWeT2yOg5PrejUSnal8ybidI+d\n5vtggG0mkrNZGdr5ACWAEZkroj1kNGKFcVzNLSHCwrwZHS1OhYjyOIQXQgmhIScgIj381QgFZaq3\nCox0IoA05dPID/0m8IATLcDoP3EivfREJiO27qlWw5x7Yk/BeNWCF3Nz5W+QDS2/q/elMlOthMZ3\nB7kg8skexP5GPkpEkhMipOwbIyO5RjGi2ZPZ27S628pKRlSolDUxkUSk0Uhvv8q3IAjcE1FWohoQ\npu7uLNGLgU5ETgkTpLS7OyVcGPXM54mJvJ6enqwoRiGArq7y/jhxcGJBHpFjLS6mfDEik/GR4UGO\ncBSxf0OUKFEP+YNYjo/n2nn44TLmELljxzL/UstFb2d+ngnkwHQ6TEwMw9gSNFdAPVEYk9V8EKpx\ncWjRkXh5uVln3NNTDhjNP8ArijGOAX78eG6w6hEkCoK8iYOIn5VU8V4RaVQhq+BQxciiyhgGCJEP\nCACJmvV6+X1qKkkVkaTJyfTYYoBgQJF4evRokrrJyWYZ19BQlgFGO88/7gUvNgSOMVLp1U5L50L4\nOEw3IhKa00LkSr3IGE4YyypJMraPKvnTPjIRzcY0kTTGHnLO3KPqE958jGDkNxiSGG2ax8X702QO\nsqwGFwRJwf5QrZyk78faYi309mbVv4jMrcIQZg0z1zBeNwKyIC0YAfkmwoAHenk575Hrp4IWBvLs\nbOYddHeXfYL7Y01yP9wv+5s2wKTvyORkrnX62UBUMLR1PCjnjVNBc9W4B13PBw5kCWIIB9FaCAmv\n5zGcN8jTmH98r8xPnStcU62WkTGIKGNB/hoEjT2c+1hbK0a/Sogh0xHl2sbGSoPBhx5K8kYSe0RG\nzSngwb1SzUrvG+KBRI4x5vyh5DvzZ3Q0HVBUITt8OOKBB5LA4rDTaNtuYnk5SXUnw8TEMIwtgUNM\nvWAYPnhS8QhCTLRqj+ZeqHcVL5Mms+MhRd4TkQaHSqH6+4uhQcI9Hi0OQLS1SBXQK1O2ks+F/GAk\n0E0bA0QlI9VEYbyfeDwpYcn1EgXhPdFKr62Ve6nXi/cM4kYHYTxwHHrIWdBEQ4bw/GEQYFwq8BZu\nFNlYDxinjA2GJ//Iv6mSCu0Yzbzh9XqAc32OhGwfKrFU8olhDRnRykRIaiKac6n42/BwVncbHU0S\nTLlv1imae0g2hr/mODBPtZACr1ODqasrjXcSkLkurbDHc5n77BMY10Th8GpDNHAMUH2Kea2V3iAQ\njAXzFuKm+WsYlJBsjE+8/kRJqBrGXkMJ3L6+bGyJca6yGxw4tVr5mX0V7z0kH7LG2sdZQJSoqytl\nWUom+C4ikoBRmpe9BilpvV7ujTGcn88mgrwvBKJqXCsJxSHDuLGfqzSROaj5U0jMiAghdeP/xcUy\nlnwOjqX5+cwr4bGIrDimJBTnEgSPM4Gmkcw5xp/3nZ3N70LXHJFsyg3TMJdIjHaFJ4pFdKMq62pF\n5DcLJVGdDBMTwzC2BCUmehjjJcTLpdpvDinkCxwsyJFI3sZYUKKAZ1YPDzykXV3lAEEypbkbWu8e\n7yPJ4xHNEQsS0PGC6qFVr2cFFap2LS9nB2BN+kUuQR8RkjTpRo/0jKRMZGQRqXuOKM+hDCdGF4c/\nBkajkf1hiOZoSc1W2EqyexVKFqvzQAsXVD8bbyfgZyUgHOT6vsbWoZEx/V50PtPUTaOSeGxZd/o9\n4uVtNIpRfO+9ScaZuxASjDyM8oiMeuAoIIJGdHNkJNc6JcSRgbJHEHGBJEDSWR9awAIjlmvBWJ6b\ny72F+0bCFJHvDUHC0YAxxx7C84mWMnYQAi1OgeSTZHVKNrPfVPcdjH8tb0uUAlkszhMcHiqrJDrL\n3sxex3M0R0cjlBAEdTCxzxKJJko1OtpMBlXGp0QJ54e+p85T3YeUaBHR4nU4MDDkG43s/M71Eznm\nu2Dsp6czV4WkdEgtESDySRhLHGs0yB0by31+cjLJjzZy5BxUh0B3d3aUp9gAMjQKvgwPl/Pr5Ml8\nDmsT4oMzSYnxdrG46M7vZ8TLX/7yOHXqVIz8/3IOl112WbzhDW9oes673vWu+PjHPx6PecxjIiLi\nwIED8b73vW/Pr9UwjAI1SDEuIRhohXXjr3qYVE51wQVlI37ooTxse3rKZk64nsOA0poYRSTrPv7x\n2bAKTxtJ5BxQ4+PN+mXyOk6dymRuJFwYWVSworRlRHMCpHYN1upYeC6pqKOfi5cP4waZDAcbmmS8\n1Gj/ie5EpM464vSICQbd2TLuNdLCOKiXeT0Jkf7cioBoYr6Jyfah5BEpEUaNeoT1O1MZHWSfktnM\nSdYb8wvvPpECXjs6mlFAzedC+qg9GyAOumb4G4Yyxj4RmYGBbEqHIc/6UicH14qTgetbWUniQ0QG\niRQ5FvR6gRgxdpq3QdRGDW6IGY8zbiMjZVz4jIhc33195e/j4ylDo4ElUSqS2/G6T02VPZHPoDAH\nhCiiuR8SBEehDhW+U+R57NGsVz6DfUa71COTgkAqQWIvZS4xhhHNEZCI5rnJHqN7CYUMaKJJV/mI\n3CfJ5VhbK+NGhIL5TmRXozQLC6VylhZWoXIXc1n3vNHRlFlRGIC5SVNPiA8yyMXFdMT19aXDSUvF\nc3ZwlnIGrKxkhGu7fZs0woIsrtPR9ku84YYb4md/9mc3fM6LXvSieO1rX7tHV2QYxplQDSc3Gtkw\nkOpQbMgQBTVKVE4xMpIHAbImoiPUhSdplsaJRBYmJ9NIwjNblRpxYJ08Wa4HQx/Da26uuYoXHl1k\nIHzu8eN5gKgnl6gKxEmT8tfWkpxhDA0OllD+wkJ6CpF4IAPjcBobS2kahgf5OiRmaiECLfm5F3Og\n1TxQVP+m0RKFicnuAcOjKsvCaMVwwuhiPkMWiHxCCtDDz85mVFA195Q8pZoRRSQg7BBvJR7V6KXq\n9jF8iVRq2VmMYV7PvgMx0apv7DNEblRmQwRDc5wiyrUjlyTvg/FZWEgDmschLaxZHBnaOLVeT289\nCdjkMGA8M9bIMEnUZ8z4W19fxKMfXf5G1BbSxXokGkGUgRwbzSNCygQp43tX453rYC8bHs5IAUY3\npX818qwFOqjeVZXKVYmJRgYgKMxdJUvcp+atEUnr7i75IxFJSGluCzlhzEjaZ7zpWL+2Vs4JeshA\n3ADEn72X73t4OB4p1sLnM49ZW5CQiOzr0tNTzqaJiYzo8Tlra/l7qypdrNMzQffgzTy/E9B2YmIY\nxv4DXlI1AjiQ8HCyCbKR0xQRzS6G/gUXFO8R3lmMHaQEEBY8kByKjUYmfuJ9JYeD55KgODVVDooH\nH2zu/K4HIgYMB5968Dh4MIQgG5oET/lOdPMR5W8YHysrKYPQAwipgX72ykqOAQSFKj963bzHwEAe\nkq08pGcLVWJSPUA3S0wwpjTCYuwMjCNjXo12EcmIaJY0YahimCkB7+kpRjbJzRiImm+F4Y68UL3n\n7Bm6xtToJIJB+VnNV8A7H5HPGRwsRScg9t3dWTmKzt8RWVZY369ez6gEZEmjv5ovVa83S7ggbEqm\ncFroumR9E4Uimjk0VPYkyo0rUdCoFzK3tbVCSLT6H/kJ5ODx3anUUr979g9IkconiaCoM4a9E5kW\nRBPJEXsZydrcM/OHscYZpNelcifmqBYW4DHGXAuiQAaRPA0Pl8+Ymytzob8/4vvfz3yQqmR0cLAk\nnENMyX+DmDBX6OTO5zCeOkc0WrW2lkRMc+fq9bwWxoc1MTiYcrPBwUKKVDZYdSJUi0bwt83mnSws\n7E0/n52i7cTkH//xH+M973lP1Gq1eP3rXx+XXnrpac+544474n//939jeno6XvKSl8SLXvSiTb13\nuz1vGqY0dgce093HdsZUIwvVcHxEHnxs/BzslJQktwKjhsovJC3yvNHR9KZyWJNDMjKSuu2urhKS\nX1jI0Lc2ScQQmZ3NQ44Dgt+1HDCeSkgOUQmIAt5OvJ9TU835Idqk64ILymPHjzcbQEi7OBC12lFX\nVx5SXJsesMhiMOC4rqqW+2wDI0oPxlako/p7q+tk/Fvlx3jdbx3IKSOaK7SB6rrl75pfQBM75jPe\n9tHRTIBeXU0Zknq3I5or2UXkvhCR/YlUjsXrqLLE85HdcH0YfgcPZileIjt4qNlfcKIQOcCBMj6e\nxSlmZlI+yl5B5BenAzlmGKHknqiciagmThHAPZDIT6SYqlyso3o9m7VOTmYCPAYt+w2khJ9VchfR\nnNTOdUAQdPwYK5WfaWU81jTzQ+cTUR7yFtjPMMA1eqx5S2QjeBoAACAASURBVOxlzA8S/DVXhtfo\nXOU8YZ/jHolMVKNt9LpijJCAcTYQVYLsca29vWVecS8QGJUUVpsl8loikJxvRP11PZDnBLEbH89c\nKJU9871TnhoSAvHhjNL8kzMBuaWu/07EnhCTz3zmM/Ge97zntMdf85rXxIUXXhhPetKT4itf+Uq8\n6lWvii996UvRLSfTc57znLj00kvjOc95TjzwwAPxK7/yK/GTP/mT8cQnPnHDz5yYGIru7s5wvU1O\n1tt9CeccPKa7j62OKQc/Xm711vMzEqmIJBFs4sgQkFLhodTICfrfkyfTUMBDODqazat4/4WFjGbo\n50BwTpxI4wI5x9RUiaQgVcCLubRUjB1051TQQaJw8GAaCD/2Y0lkDh0ir6Ueg4MRP/Ij6XEm1E9y\nJaQFKRNJxo1GuS6MN0gP70OzRzzGvEaNu70ABpomaKqnuVr2EoNCqyptBV73WwORhI2iaJOT9Udy\nwyJS6nL4cMoGIesLCyUi8cMfZi8hlWhNTzfLesjzwiBmn1hcLJ/H3D92rLmXBwY1RjtzipLFJJU/\n7nHp1KBPEWseR0CjkdIicgImJkpu2uJi2Vsw/DHKp6aaI0r1esqLiCIw78n/4HpLflv9kb2RyAwG\nLtGoiYkkSBMTGZ2CjIyNpQSWPWBwMPctLZtLRASjHyLGOiMCS9SG7xgnCLK4RiObHOKh52cIFeXM\nJyZyb+Z7Imlfk9/JlYAk6FyEQOrvVYloT0/E4cP1mJ8v+zckaW4u9++FhUxwRz5MpJ6xWlgo+Ukq\n3yPacuhQSveQ5TJPtQw0OYBUckRWDMlhDCYmUs5HNbbp6fI6zi/mWl9fxI//eLkfKkvqGEDIUAQw\nvjjpNIpypjwUziPWfadiT4jJlVdeGVdeeeWGz3nGM54RKysr8eCDD8YFF1zwyOMaQTl8+HBcdtll\ncffdd5+RmBw7Ntt2RlirlS//4Yend1RJwUh4THcf2x1TNky8SX192SxxeLgYLkgHIsqmOjOTjaU4\nQDBcqMHPz4uL6cnEm9bX19wHRfNRlpayugmyJg6htbVybVT9IZESQ2J0NL2ISmqICv3gByltQF5R\nreOvHujV1XosLEzH2lo5TIeHy+fPzZVr4ADC61yrZbgfLzL3wX1qvsrcXPldG35xyO01+Hz6mig5\n0o7GKvNqVVZ4I3jd7z6qY0qpUtXjMwfJIyBxnOge3u6BgfJ6ZD4RmfNF41WqS2lUg7lPNAAPOnsA\nhj37BOtdS+2OjRXHAl51kp+7uzNBnz0DMoynn78TWUVGQ4SX36mARQQVjzl7ADkfMzMREfWo1aYf\n2Xcw+Bm7Mu7Z9JUiBThn2FNnZpolQJAfqqFheGteCQRBo0UYuIuL+fyFhSxvq4nwAEnS8nLznlOr\nZSU03p/cCPJwILKMNdey3prXppvVfaJWi3j0o+tx7Nh0zM2V8SLygiRX90dkaCdOlOcy5kT4FhaS\nbHd1lb4nzNWI5jwbqmmxBiDbXLNG7nt6Crk+diwja8wrGkfOzpbz6cSJJCXM6er941BQuSOkGsKo\nxRe0/PdG6O8vMraLL27vXjo1tTEpapuUa3V1NV72spfFO97xjrjgggvirrvuip6enjh48GDT826+\n+eZ45jOfGVdccUXMzMzEt771rXj1q1+9qc/olANMtdXG7sBjuvvY6phqiF71wGyQWi4yIj1L5J8Q\nFq+G24mM8Fp01BgIeBNV4z41VTZ8rRyEJwkjh+v6/0UAmypj4WlU7fTx45kzwuF2+HAeBMhLNPxO\niUmiKujLNRKEkYGOnYMGg44eCNXqSREpPWBMOZzwALZrTfC5KlVjPqk2POL03Ietfo7X/e5CjUz9\nzjCOcDqMjKS3t17PKGStVuYzhmKjkb1LtAeQlo2FRGsFL/ryjI4WA48cMtbjyZNJSIhUIhXT/I6Z\nmYzisAdAiObm0tGBcU4RDhwjFOPA2OT+Dx/OhGQM4Yj0vFMClojg+HjmwmjSNPsgFfmIkh46lN3F\nMZ4ZU4gP96nSTU1s1xwI7ZnCGPD9RCSRUVkuj+uezucwFzThXfOSIprfR9+v+lh1/oHqvqHESqXC\nej4Q0eA++O4x/MkFwWnC50SkbA6HEPsonwkx4HfkVJrIr3k6kETyKdmrIbJVSTCRvB/+MKXLSr60\nYqRKp+mvQ2SKx1slySvGxvL86+S9tG3EpLu7O17xilfEq171qhgcHIyVlZV45zvfGd3d3XHXXXfF\nP//zP8dNN90U1157bdx4443x/ve/P+bn5+N3fud3zhgtMQzj7EOjBOr9VGLChs7hpsmaHPgqTSIU\nzsGKUYO3DuOew31lJUPw6OB5Ld4tuj+TP8Jhy+aPcYDxRFlM8lGojY+RRHMsDiiIFPdK3xK03Egp\n9OAYHU0jJSKTYzHaGR/ugUMVUoe8g3+dgCoBUcMEIyFiexIu4+yDKALEkvmIYYYTYW2trAV09Fqd\nirmPwYRBNTOT60XfF+8/5XmJVkK4qUzFXsH6qSYca14HZVy5DiKhJBhrx3YiA6xjJJaHD5fPYe6y\nh5BXguGJLJR9iOdTqUklnOw7VNrr6Sk/k+NAztzwcPHkI6PSillIifCckyuBRx0wTtU8EQX7UxV8\nx7qPc1/qANF8EJVy8t46rzZC9RrZK4kKcD/ImDhjuE4tqKAlr4lsKemGRBKp4NzQprrVa+IeWAf8\nYz5WiR5ni5Yo5qxhnus5wP3292eRFKqy4biq1VIGDOnicyBEG3V053p7e93H5Ix4/vOfH89//vNP\ne/ziiy+Om266KSIinvCEJ8Rtt922x1dmGMZmwCapXi71LlH3X6uLROSBzWPocHt7y2E9O5vyC5JT\nqUyCtIMoA+SCZPapqewHoHkPfAaGDIerJjCiWe7uLh5aEtBHR4v8YmyseEaJyuCRwyPGYaE5IRpJ\n4nkYIZokSeIrsgHuTT1jatyvZ1i0C1ViosaRGiomJp0JNbxYGzq/8NZTiUhLedMThMRhZF6skYmJ\nZi82c5p9A2KAoYghT+ST9U1u2OhosxGHF39xMUvEIimq18ua1UpeVWcBJACCwL1rlASjFpkVpEsN\nc4xwoqbkk0DGSLLG6IRQjY42Xxfjh8xJ9zrtV6Led56nJZO1ytZG607Xpxq9JF/TMwRohSglDypn\n3Sw0IsKephEQ3ps5yJhT9IToTVXuV68XggdZxYlDjsjaWpmXhw8niVHHUCsHi44P1wkpVEeUkhMI\nqVa7I5+Q59XrOZ9nZjLSgnyMeUDJaiWKjE+1ald1fBm3M0VVOgFtr8plGMb+BZtjq3C8etowSDiw\nMMq7utKwUdkFmyid4DFQOGgp2YtRj46ZUDURBfS9eKS0GRdeMIwIkh0xxvBsaeMzEu5JFB4ezugH\nhxQHIPeAzALDRaup4OHDIOE90PGrbhojgfFuZTC0E1WJlhITnQdGZwIjt9HIyICSCeYuxv+hQ8WI\nwiCDhEMyMMCQMJFrgtGn/Y40kbu3t7z3sWPpWGg0igFJRFQ7uisgAyRz12qZGzI3V9YrfVaWl5sb\nudJRnRK37APkV7BPQLhUIoRhiFSKKAvdxNVY5R55fGgoHRnsmewNVelWRO4pOHX0e1H5J99nNZK9\n3nevkS91sLSKeCix0yR59v2tzjv9n2gH/UcimiVjKqWtGt3cK/NvZCR7r+B4oqAJJI4cRZUatpJF\n6XUyN5ibmivF69mb+Vkly9wTuSgk8Edk/iBlrCl8gESQecsZxv0zB1kzKsnjO4vIfJpOhomJYRjb\nBoelHpwRebBW+xaoXlk10gMDSQCIXqC11Z4ks7MZbdADigNIkwepggMxoM8IIW+iMX195XGkYERq\nkGBoxZqIlGlRlYfDjYOFA0Q13xphqVbOwvum+mjGjGRY3oPxYsw7CetFTLjOToruGKcDg4t5RuKw\nGtXqHVdpIwY2OVdEBu6/Pz3F9OtZWsrqTXwO0ic82RTPIBeNyAcSqqWlTDpmrSDV6usrkc6Zmdw/\nxsfLvCTvi6T9iCy9Sk+kgYHce+j0zXplDDSPIaK8H/kurHXGJyL3PK1Sp0VDMBYhdxTvqCZCq6NC\n8zEicl/g71zbZtcdkreINPrX22PU8NZrqfYi2QzWi7IwZtw/14OEi3NCJbl8J9o7hzGkoAHkrVbL\n0u7Im5SYrBcxaXWNkE2KRkAoNCcFx5LOE84bSkMvLpYoD2cO84DvGUKligQKwjDuShh1nlMkYD/A\nxMQwjG1DD2wMbrytEI9qfoUeRKpjJkKAZEMjCAMDWaKRhEEOg0YjD3HNZeA9V1ezQsvISJa75H2o\nDkaeC/IQQvtEVtbW0khBB68eN3JftE8K44OHlL9h4OnP2h+Be2GM9WewFbnEXoEDvWocGfsDGKdq\nZOKlJf8CwkJZ1OPHy5wdHs5+C0Q0NT8F4o+MSROLScLGCIQsYNwRIYlI4wqSz74CEcDAo2kr6/Tk\nyZRRnjiRUREq7VHSF6lUV1fKRpFtqQHOmGiyPw4RurlrvkxEGSt6bUCatIoVpYgZ356ejM6yx7I3\nqIwnIskS2I7jQvfmM71eP0vlWDv5TIXK/iKaHRzsm8jgyEFkz+W9hobKmE5P5/zk3NHyv63ugXWg\n71e9bj5T5cTqSKr+6+sra4jIJNEWSmVTqp45SJ8d5H0nTqRDTgkU+S0QV6qvKTHRvJdOh4mJYRg7\ngnruqhs4SZoqL2Aj56DhMXTVqjnH69Pbm43MiIKox47XqHwEjXp3d1bXwcihLwFGBgc9ZU/5nEaj\nPHdwMKsB8Xkk0iKlUIMLI4v753V4+HisGlbnfvVz1DNbjbR0GpDaOdF9/wJDTA2uiHQQYDQhqYxI\njzQlgTGaiGBoT4ixseyjQeUi1dITHUXKyXogEoERX6tlBIK1j/ebaCh5CKy1lZXMP+H6xsfL61jn\nVPbq7W3uyE71J64VBwXOD6pokf+CYwSCMT+f/TGIzLA/UKwDQgD5GBtLT7k6ctQIVrmOkqC9njPq\nTNkK+C6rr1UnE8+LyO9Of+fz+X40XwpyqnkeRMWJcqhkUOd7df9d7/pUOaCRJ6AOOC0aMT3dHGVS\nIs1aookkUZiI0/MOKcvNGkEaSISR+9kvMDExDGNHYINWD6geoITStaKKljhUj5V6GDn4V1fLQU8U\nA8+TVo+hoheh7ewp0NwZvVbLBlq1WpbgxKDGSKLkKJV1quU66WeAPrzRyOaLJF+eOnW6tw15gY6R\ngsNW9dOMkZKb7XonzzbQnm8kfzA6G2ooqhQRDz9rAYKAUTQ6mmsI44wcLErpajU6ZC6stYGB5p4l\nPT0ZxRgdLe8F6dVEXuRgvb1Fq490U6OhtVo239M8EZwUWjKWvDIq4vX2lv2HiMvKSiYqE+XgMxYX\nS9NUSFpEGqMaRaZYANegTQfZ0yBTIyPNDf0gYqwtcuYi2rcnaNWq7WA9w19/1siKysU4D+jpBDlh\nzmoSOvs739vqauayaNQjojlvY72IScTp1btUrqyyO712zhHmQUSSGi0Prb1L6LfFuaJjghyYfC1k\nzOoUjGguF9zJMDExDGNHqBITIhmEtNFHa1hcjY/qAYS2ligG+lmMJLxG1ZKP8/OZJM/z9Jr41yrh\ndGYmX0/khj4DExNp4PD+3CPJsir34D70fwBpUmNCoYYOh1GV3HR6FGK96zb2B5CFEL3kMeYscxjj\naHQ05z99QoigYFhhiHV1ZdSDUsFacEKTvalaNDxcohqQF3JMNAqr+S/IKvls9p7BwbKWMdh4nEgL\nxuz8fMqpMC6RUVUjQ1olq6cnex1RxYtmhERWVbpGLgSGLx56oriUJOdzdb/AiIacdYI852ysdTXa\nNwLRKWR+Ws6Z6yIvZWjodCkc3wvn0WbzS3h9q8eZl/ys98Qa0ka+GmWkGSTXxZmAc44ziIqV1egQ\npIuked2P90Oun4mJYRg7wkYbtFah6u5OzWx10yZMrdIEtMT1enMCKofywkI2XqzXU45BqVEiIouL\nqT8fGCivo3u06tbn5po9vtPTWZ6Ubr0cJPQhQPtOdOXUqbz39QxzTZ5tBUosV7XV+8XI53odLdm/\nYO6qYYbRzNzF2CFHA4kT8qqpqawURaUh1tbkZHqE1VgiQqqR06mpQihwTJDHsrRUShOzlyCDxEgl\ncoKHnFLkXV3ZEBIJFfsU1aB0zSGloqIfEUEil4cONUeGcV6QfM9eBhEbHs5KS5CViOaKWpo43Whk\nH5Xl5cyN43tRZ8h+2SM2C82/2UiKRKS2tzcjIES0iFBUqxsinYMoEnGJaJ0P2QobyZh5r+rjkCKV\n8XG+ECEjOkZSPnNzbi6b93KOHDiQ5fSRHnOWRuSZurZWzqdjx8487u2GiYlhGDuCJuFVN2jV7PJc\n9QzynIj0eLKRYuiQ2IjUi8Mbwwi5B8YH73HgQHqexsaKEbOw0GxkqLYX4wuvMGSKyl3UkOdzqhVX\nMELUm7nRmJ3pbzouWmN/P6DTozrGmaHRTtYG810jm9q0dHg4ow2sIRLjiUpQgQup5cGDzdWX8CRT\nRQlDfG2tRE4mJ4usam6u/L62VioZra5monhXV1bvw8tMjszSUl4T0QyVqpE7xrrr6SmfGZEFOTQR\nvqenkJOjR5Po0JQV41FlOqOj6dRgjDQROiJzz9Q5AYlTydROJVSdDs0lORO0fwt7MfNQIw08l4ah\nRCMg1MwFlTGuN75EErdKTPifAgkQ6wMHcg2Rswi5p2rdyZPNeVgQYZxqROcg+0QO5+ZSGdDpMDEx\nDGPHqMq5qC5FeByPlxr0HMgYNVp1hYo+GPhqGEXkZo3nkUNmaKhs0vydRFeSXI8fz9C45r5ozXkt\nKYmXrb8/Sc7QUOp0CZXrAaXJ9NsdS8DBbEPf2Gvgjaf4RFVyqd58PNXj47lmWKuaeFw19rq6ilF/\n6lRW0UKCAnHAcMOwR2ZGNBVCQinisbHyuZo3QKW74eH0HKusjP2JiOrAQBIpKnfRof7BBzO/jOvg\nGtlbqCzIXqjNFsnTiUgnDARGc8pwcqjHm78pzlVSslWQk8h3jSyLHEJ62bCXalSd7wzJHORzsw6m\nVon/VVmvXmdEcySN84frnJ1NORbRRqTEVBdjTuAQ0JwmPgfyQxlvjaR0MkxMDMPYMarEhM0PLbXm\nfXAY4I3lNdWohRowVa2xbrokzEKCtIwoERWS2JFt4E2iCzzyKQgJ3tyINBzUmOjpydKOeIjxRHEA\nqBRmK9AI1H6KkhjnFrQvgkbsVJp54ECRWc3NFdKPIUV0kUiIRkBZ8zgf+vtLxGN5ORPcMfYHB5v3\nDq3SNTRUXjs9nRIywNrHS66NVdXLzP6ANBQi1Ntb1jdEiPsmysrfkY0i28IIHhgo5AfJKBEYyAq5\ncRFZspa9gugIxjXfgV6/0RqMPVXQNDlco2I4lPgONNKt+/ZG0RL+zuurWC9iArkkKqOV32g2fPx4\nkmVKRi8tlfk0NlaeOz/fXPmRJsVdXRn9oQeKlm93g0XDMM4LaJ4JhANjoqsrE/SQWlElBd20eo0i\nTpeGsZlDPgi99/Wl/pqNHFkXcgq8mxg6bM4kBfI59EohkoJRQeIsSb0YB6oTh5ToPe4E3L8jJUY7\noYZxRBpBzM2pqRI9xPmwvFwMJP7OepybS2kK64XqdTwXMoFMhUgFUrHh4VyjdGmPyLXI4/yu+QJU\n9SOpfna2XBNOBK3OpGV++/vLPwjO7GySC/YQ3mt5ubnpI84WctsiktBhVGrpY6COnWrOw0a5aUaB\nRpk5Z5hXyAg5ayhEolIsrWIVsTERXC+/Uh9br8gJZyURfc6wgYFmEn7yZK67/v4y1ygswfxgrTCX\ntBQy5xv5SfsBJiaGYewY6uXXJMOIjFBwYGCAaFf2auImEQzeB08Xz0WLy3vqwaKlIjmg8IqiJ+da\n6a+AgYJGHenFyEiWC+a5ajxVK+VgeO0UJNbaO2q0G2ooY3TjcNBKQo1GNrLjdRHludUeJxhhEInu\n7uIJhogg06rXI44cSTLEmqDQBf0o2ANojsoahWTU61khiwZ0lP8minPyZEZMSVAnOsJ+pXkjGvkh\nojQ4WF5LnsDgYJIV9g68+uwfyEqVzAHdKxl/Y2No/g1RrlqtfA8Y7EAj/SotVpxJysXrmItaKGK9\niItGWlR6xeMDA/k75e6Z+zjMRkez+hhzDMeZEl/kyRSSgCR3MjzNDcPYFagemt+BEgLdgJVQIJGK\naN7MyVHBMKjKPLRCTkRz2B75SFWnzgZO7XdKo7L5UzYYDxMeKSIwGC3V+9+NaAn3b8+o0QkgSkmV\nI6IZSI7UmEZipYY7hSiQXEaklAXPNfkXg4OF3IyOloRz3S9wUESU15EzFpFe8BMnMvpKyV0iHBFl\nnVPyVyuMjYyUezl5MquCcT1ajY99DHLC/kNUZng4I0fDw+X1vIdK4bieer05B0AdMhHNJGUneWvn\nE3ScmTt65lRz+CKazyEt+LAZxxBFEiDGyIirn6Vg7mj0kfchEs85g3MN+Z/mPFHpbWmprAd+Zp5z\n5uEUeOxji/Sy02FiYhjGroCDAE24GtatDgP15vC/eo+qr9XwtHrF+J8DHY9VrZZSDO2jop5ePLYq\nDaPkIhKOtbU8FKoHnJZQJSpkGOcatHodBpUSfCIBeJ6JoKyuZqIuyezkbEFySOodGsroBY0HkUJh\nsGGoUzkLDT2RB95HIyJjY/k5J07kmh8by6Z7XDPlgC+4oBhw5J4g3aJaWESWJsa5gdQGEkW0BGIH\nGcMhwmfyfJwpeOyRIkU071vGxmCM2N8381z2cKrOgc0QQeYeBVeY4/xtvddoPxGkWsybgYFCNFhf\nrKWhofJZDz6Yr6O6GzLlao6S9gvTJPlOhomJYRi7huqGu9HzVL6gh3JVc60HDa+NyE1XSQeg0opW\nuIlojqy0IkFjY+X9SJJHoxyRnlf1HPO33ZJwGUanQp0AEc35XjQ4JUpJxGFxMdc00URkT0Qi+Jsa\nViofo4RvRFYII2LJa4liaCd1pFN0eieSgsE/P5+5LvQfQZpFWeJTpzJacvBgyZUjR0SJRURW+CJK\nMjaWZcYhItWqhDxWq6WsFacK+w9Gcyc0UdwP0Hm5mecixcKRpXKurUSomNOcSTy2ETTKj/yQPChy\nGpmr9Xr26pmaKp/BmmOOII9UgsxZW6uVCKOrchmGYbRAlXgg09BKKbqhkkSLRhyjRj1ebO6E1QmT\n8x48nwMIQDK0szTSL/U+KeFSDyjvYRjnAyAYFIwgkknEAsMHIw0ioX9DFoUhRj6Avj+5aro/QDB4\nDsYalfEwyCBDWrJ3ba08f2YmDVES4dHfQ5rQ+JPLov1bKN/KngCp0WaAkCPGAUcJhqhWgWI8eC6/\na/WmasM/Y2NsxUlULbSyHVLC84m4RWwc4ao+zhogyjM5WeYl5FolxBSSUGmkVmyjZLKed6wPnHWd\nDhMTwzD2HHiX+JmDuaqp5kCGeOC91H4pPBdoPwGtDnamQwJJSUTK0fgcDi4+C6NBDyHDOB9Q9TLj\nCCBHgnwttPY8rlEVJFQjIyk5GRnJJGKcCisrSWC0VC9Gf5WIaPNVlVfqXkNEhjwU1jM9RtiPtGoW\nuSS8VmU/9E7CcGR8uAeVjuJY0T4bel1VR4fugYy9sbvQ6H1EzpWtRsD5njU6vx5UFaAKACL6RN40\n52R2tjmSNjRUIntHjiSJp4T1+HhZhxAlev9E7I85ZGJiGMaeoxox4Xc9hDmoq1prfa3mgbTKS1Gj\nYLPAKFHtsJZdrD5vP2z0hrGboPxuRDGEkJz09+fPREEgEvxMxJPE9GqFLiRdEH8q8M3MZB4HEY2e\nnoxGzM+nE2F1taxbzQ1jLyB3hYR5IhpEVGjKGtHsNGEfQadP2WMkNOSecI1Eb9WZggHcSm7UypBt\nlZtn7C707FHCvZ3x1hysM72+6syq9uiq18v8omEoFbiYx5DgsbGyNlgPlO9GqojzQMlXp8PExDCM\nPcd6JEK72bL5tmp2pX0EIpqTBPU9q2H6VoCEaHdlXqsGTdVzuV4DLcM416H9Pai8RZQEnT6kPiLJ\nAfKSpaViQFF1CLkXpXpZh/yNqAQyK57Pe6Prr0ZJtU9SRF6jVtXT6khU48NIZA8AWpVME6YjMkel\nmswekZ9P7yTu+0xwJPbsgzGuOsW2A6Jg2/neOGeQFDcahXAsLRWScupU83XyP2XtNVeKCKP2CTp+\nvFmp0MkwMTEMoy3AGKgmt1e9OlUCoGSDA0BLDUc0ExPeYz0CoaUhlZjoe1SrjVVLpBrG+QiVV7Ie\nhofLWqSLNdHGhYXytxMnirEFAUASRaQD+dTSUpINjUKoU4BroDQqa5ZcMZLKISP8DKFYWUkjjs9T\n2WfVaUISuvYZmp9PuZoW84hozg3RCmYYoJsdY92XjN2F5vJoZH672O5rmTeNRjYDpUJXT0/JgXro\noZRBkv84OFhIydxc3gvraHQ056nmQnU69sElGoZxLqK6QbKhal7H8vLpxGW9KIhGWqqvwSBpBd5H\nyzJyPbyH/tOeJvo8wzjfAEnXClIY6Bj4jUYxmpB3sb6QW7GuWPu9vYXArK4WXT3NCMlZUQ+3yimJ\nUPA7xny1ohWPK+mA2PD8qmOD5xAJIvKBLIymjq32BI2eYCSSq7IZmJicfVDtKuL0KNleARUAci1N\nwqd/CXNAz6CenrK+xsZKVIUmwRHl74ODxSkwMFAiK/sBJiaGYXQEqhETbdaof68+r/p/tQ49Bs/S\nUmtywmdod2n9HE12p/pQ9SAzjPMZ5G7Mz5fftRkqf+dxIo8RmZcCMdHEYaILKo1R0qAkBYJEvgnO\nja6ulIhpZCai+X3wUnNd5AjoHkCSf39/XodKx7q6mntE6H4EaYGYbKakOuCaXfnv7EEj5u1yNDGf\nmGfaADSifP9IvOjPQlSQaOLMTPPaIe+KHj3Dw/vjvLKvzzCMjkDVUKhqYdcjJtVSmtWEUTxNWnZT\n0YrQVI2SVpIwS7mM8x3VdVGrNRtG1SgG+R5EGrTUow939AAAGGJJREFUsHqIISqU7SYSo13TVWuP\nPIscFjpnUx61qv2vVvMjIV8jL1UioA4Q7hNCglRLn4vmX8dImzBuZYwZU+PsQUtXtwtERzi39FrI\na+rvLxKtsbEkKERYUAZMTpa8rFqtzMOBgdIwdHTUUi7DMIwtAa8Vnkctl1klHlXZlerdFaq7bVWV\nBENDm7pVyY3+TV+n12UY5xt07mM4IcfSwhQqr+rrK7knSFdYt8ijMMwogUq0QqMmRFOrpV1xPpAw\nr1IvJRq6dvVnoqHsQRAizSup7iHVQhv8T5SGMeG56vQwOgcQwHZfg/7PvKFSHGQYSSSltGu1iAce\nKHNtaCjiUY8q8/27381CE8vLWXCi02FiYhhGxwCJFIc/RkJVthHR3JCR0qTryR3WIyZKejQJsmo4\n0MRNDSze1zDOZ6hxj+yoqytLB/MPSdPQUFbV0iZwGs1krWrnal2rrGdtwghh0SgNZEkbslZ7EpGU\nTh+Tvr6yB1HRa2kpk40j1neUaKRXu39rcQ2t9mUYVVRzmiiZz/wh4kHBhoWFMjcXFkqeSaNRyAcR\nk4cfjjh2LIn8RmdkJ8HExDCMjgH6a5JJW5VebCWlIqS9HqoeKFCNeqyn5+b3KjGxgWEYBa0q31Vl\nXf39zX1KiEgQ0cApQCQEqZfmbvBcKhdFNOeTLCwk+WAf0eaMaPgjmh0WXBf7iV7LwkJ6mltVCdRK\nYisrWbQDDzz37n3D2Ag6L4gkIn+cmMioHblUrB+ijzRTpGTw0FDJO4nI9dVKztxpMDExDKOjgPdy\nI6iXdjPh9yqZAa2ISSuPkhKkqvzDMM5naBRCk9xZI3SwhvBDNvAKQzxounjgQBIRckOIhvB5VcLB\n42jzkU9BEohWVHNMMOz09TzOe1FGmOR5vW/+19wAnBv9/c39W6pRH8NoBdZTT0+RPB4/ngnsEYVo\nUP4a0kKFONQGRCPHxjLHRIs8dDpMTAzD2Hdg895sM6szEZPNvIeWCLaUyzAKdG2p3InIIhEIyAHr\niLwt9eRCJiiZyvtpzyMlQdr7IaI5hwO5C9eo/UU0kb0V2dBKfVQ3imjOQavmA/CZBw6kxxr5DPKz\n6ucYRhWaq0QOFjKuiPK32dnyGMSDecn6ghwPD5fnjoxkQQZLuQzDMM4C2Hi3kqyoWvWq53QzhgIH\nhno+TUyM8x0aJYlIox3CQSRE81DIHVMHAyRBE9p1XbLW9XN0LWpEhL9XE9f1ddXS4Brx0QphXAcJ\n8Vw3UV2t/Eene618VI2stJK8GQZg3TDfaRbaaOTPEUVeCEEhtwoSg9SrVsvqXL29EePjJiaGYRhn\nBVtpUAbU6FheTi+s/m0jYNCoF9YwzndARNC+K7QaFgm4Skgw5tW414pDrdCq75BGQ6rRVKI11bW+\nuJj7wHrVkCA6dI3HQKxW59JcFe2JUUW7qz4ZnY/q2UZ/oOXlQjKGh0sH+PvvbybmfX2ZHK8NQ4m6\ndHdnNbxOh4mJYRjnBareUNWsbwYYKdXO8IZxPgPjn+pbrA9tPEgkBINeDf8q0Viv7HdEvrYqjVJo\nzoj+XyUmej1go4p7GgVZXj691wnXRVTEeWjGboAckojMqRocLGvu1KkkJ5rwrqW2p6YKCe/tLcRk\nP8xHH62GYZwXqBoaW8kvafW8/bDBG8ZeoZpLgSGPHErlk60klNqVfT2vbqs+Q1VUC1WA9Tq+V6VV\nG+0L3B/VupTUtOqNYueFsVMo2dUy1ORuLS8X4tHXV8iJShp5fGqqRFoi9sec3AeXaBiGsXNUjaBW\nj58JVQ27YRgJJSdaypdqXI1Gc6JuNf9Cu6kDTYivNlTcDFr1LoloJhZKKjbKO4OU8PlcD9dZvXbv\nEcZOoXMKuSTlrZFmsXZoKsochsiQb+JywYZhGB0E7SugSbZbJSZOfDeM1tCoR0SSEZVzRWRyrybs\nUsyiSjrQy+t7bnbNavQiIqMj+vpq+e+N1je5MBHZs6Raoavai8IwdgLmE2uESN/QUImIUEZYK25p\nHhdrrZr/1ckwMTEM47yBRkpIst2K8UCJU8u4DKM1kG0tLWUC+/JyWTtDQ81Eo1Yr8hPtr1At6V3t\n+L6V5N2qUYdGv5V0a71clCpUqkafFMhUdS8xMTF2A8x9+pTQLJR+JaurJfGdxqDknkQ056VoMYpO\nhomJYRjnJXp6tiYJiWiuHmQYRmuQEF+NRiix18gEv1eT0VV+RaRiK1BighRMy//qZ5wpWlIFjg2V\np2nExKTE2E1oCW7I/8BA+VtPT5KUlZXyM713WkkWOx0mJoZhGFuADQ7DODPW69+B91er25HIu7jY\nbDzttFN6K8kWMhf099o5fquVtOha3yrZ3fuEsVtgbkJAeOzAgRKVXFrKBozz8yl3nJ/Peb60VP5t\nh+DvNUxMDMMwDMM4a+jtLeRkaakYUxGnd2PXHiJ0hN9pyV2twEW0RglRtULYVvPHqj1LNFF5P0hm\njP0BjcLp3OztLd3faWKqTT6Zf6urEXNzGTF08rthGIZhGOc9kGNtZPTjGYZA7FQWRYSGBPyIzCur\ndoonirLVz0New2f19BQv9n6RzRidD5VwKXp6To9GQvaJsDQapUs883s/RPLaSkwajUa85S1viTvv\nvDO6urrila98ZbzgBS9oes7i4mLccMMNcd9998Xy8nK8/OUvjxe+8IVtumLDMAzDMLaDMxlFfX0Z\nMdmtJoXIxtbWMrrRqrmq9jTZyuepYWgYZwPkbFUBEaYIA/Owr69ESahCeepU6WOy1ZzKdqGtl/nx\nj3887rnnnrj99tvj2LFjceONN8bVV18dNVnhH/zgB6O/vz8+8pGPxLFjx+KXfumX4lnPelYcPHiw\njVduGIZhGMZuQvNRWiWUbweUKVZiUm2GqCRoOxKs/WLwGecWtMliRFa8Y73095doydJSmdf0Pel0\ntDWo87nPfS5e/OIXR1dXV0xNTcXf/M3fNJGSiIh///d/fySKMjExEU95ylPijjvuaMflGoZhGIZx\nlrHdBqitoHkm2qiu2rjO3dqN/QiVJZJPAvleXc38LvqZ7Ae0leffe++9cd9998V1110XMzMzcd11\n18UVV1zR9JyjR4/G1NTUI78fOnQoHnjggU29f7tDq9WkPmPn8JjuPjymuwuP5+7DY7r76OQxRaKy\nutqcIL9daB4IxIOcF/T32ghyJ9et/xs7g8fzzGDOEmFE2qXNFiEqGk3p5DHdE2Lymc98Jt7znve0\n/Nvi4mK8733vi+9973vx0pe+NH76p386JiYm1n2vRqNxWlSlFSYmhqK7uzNcH5OT9XZfwjkHj+nu\nw2O6u/B47j48pruPTh1T5FzVhojbwcpKs7E2P58yGLq3R5S/74ZXuVPHdL/C47k+VlcjZmdLpGRw\nsMi2kHRpM9OhofJvZKT83sljuifE5Morr4wrr7zytMd/4zd+I57xjGdERMSP/uiPxoUXXhj/93//\n10RMLrjggjh69GhcdNFFERFx5MiRuOSSS874mceOzbadEdZq5ct/+OFpV+jYJXhMdx8e092Fx3P3\n4THdfZxPY9polDKqtVohHtPThfBATACyl+3ifBrTvYDHc3OYnY2YmSm5JpS9XlzM6Mn8fJYRXl6O\nOHSovWM6NbUxKWqrlOuKK66IL3zhC/HkJz85jh8/HkeOHIkf+7Efa3rOc5/73PjkJz8Zz372s+PB\nBx+Mb37zm/HHf/zHm3r/TpnI1W62xs7hMd19eEx3Fx7P3YfHdPdxPo0pkpbe3ub8EqBNFneC82lM\n9wIez43R15fjMzRUoibkl/T0lN8jmsewk8e0rVqnl770pXHy5Ml4yUteEq985SvjTW96U0xMTMRd\nd90VN910U0REXHvttY8891WvelX84R/+4YZSL8MwDMMwDIX2MaHSl6oq6EBvGPsN9M4ZGiokhQ7x\n9D4ZGir/a45JJ6PWaHQqZ9oZHnxwut2XELVaCVk99JDDkLsFj+nuw2O6u/B47j48pruP821M19bS\ncxxRJFt0o48ohttOy/6eb2N6tuHx3BrIpaKvycJCc67J2Fj5d+hQe8f04MGNpVydkR1uGIZhGIZx\nlkD1Iu0mX42YGMZ+BvO5r6/0LKGyHRW5SITvdLgtkGEYhmEY5zxaVdxSomIY+xmUxaaAA1HBiEJW\n9oOMK8LExDAMwzCM8xR9fe2+AsPYPUC+G40kJV1dJXrS1dW5Ce8K+wgMwzAMwzgvUU2CN4xzATQT\nhZCsrmZVuk6HIyaGYRiGYRiGcQ6hq6tEBJeXy+/9/Tvr07NXMDExDMMwDMMwjHMIEBFyT/ZLSWxL\nuQzDMAzDMAzjHAIkhDLYRE46HSYmhmEYhmEYhnEOQSvOkfi+H0oGm5gYhmEYhmEYxjkEiEmtVqp1\nWcplGIZhGIZhGEZbUKuVSEmtViRdJiaGYRiGYRiGYbQd+6FcsImJYRiGYRiGYZxjqEZITEwMwzAM\nwzAMw9hz7Edi4j4mhmEYhmEYhnGOQStz0Q2+07EPLtEwDMMwDMMwjK2gWpnLye+GYRiGYRiGYew5\nICJra/tDxhVhYmIYhmEYhmEY5xy6uiK6uwspWVraH+TExMQwDMMwDMMwzkHQXLHR2B/ExMnvhmEY\nhmEYhnGOoq+vkBInvxuGYRiGYRiG0Vbsh8T3CBMTwzAMwzAMwzA6ACYmhmEYhmEYhmG0HSYmhmEY\nhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYm\nhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0\nHSYmhmEYhmEYhmG0HSYmhmEYhmEYhmG0HbVGo9Fo90UYhmEYhmEYhnF+wxETwzAMwzAMwzDaDhMT\nwzAMwzAMwzDaDhMTwzAMwzAMwzDaDhMTwzAMwzAMwzDaDhMTwzAMwzAMwzDaDhMTwzAMwzAMwzDa\nDhMTwzAMwzAMwzDajp52X8C5ir/7u7+Lz3/+89Hd3R2XXnpp3HDDDVGr1dp9WfsKX/3qV+PVr351\nXHzxxY889id/8ifx/e9/P9797ndHb29v1Ov1eNvb3hajo6NtvNLOx6lTp+LGG2+Mr33ta3HHHXdE\nRMSXvvSlluP4/e9/P2644YZYXV2NtbW1+KM/+qO45JJL2nwHnYdWY/rEJz4xnva0pz3ynF/91V+N\nq6++2mO6Cfz93/99fPazn43u7u543OMeF29+85vjK1/5iufoDtBqTH/qp37Kc3SbWFtbi7e//e3x\njW98I3p6emJycjLe8pa3xDe+8Q3P021gvfF8ylOe4jm6C7j55pvju9/9bnzoQx/aX+d9w9h1/M//\n/E/jmmuuaczPzzdWV1cbv/mbv9n47Gc/2+7L2ne48847Gy972cuaHltYWGg861nPatx7772NRqPR\neNe73tW45ZZb2nF5+wq/9Vu/1bjtttsaz3zmMxuNxsbjeN111zVuv/32RqPRaPznf/5n45prrmnP\nRXc4qmPaaDQaF110Ucvnekw3xte//vXGNddc01haWmo0Go3Ga17zmsaHP/xhz9EdoNWYfvSjH/Uc\n3QG+9rWvNX7v937vkd/f+MY3Nv76r//a83SbaDWe73vf+zxHdwF33HFH49prr2287GUv23fnvaVc\nZwFf+tKX4vLLL4/+/v7o6uqKq666Kr74xS+2+7LOCfz3f/93XHjhhfG4xz0uIiKuueYaj+0m8I53\nvCN+7ud+7pHf1xvH5eXl+OpXvxpXXXVVRET8zM/8TJw4cSKOHDnSluvuZFTHdD14TM+Myy67LP7p\nn/4pent7IyJifHw8ZmdnPUd3gFZjevz48ZbP9ZhuDk996lPj1ltvjYiIpaWlOHr0aDz+8Y/3PN0m\nWo3nox71qJbP9XhuHtPT0/Hnf/7n8aY3vSki9t95b2JyFnD06NE4ePDgI78fPHgwHnjggTZe0f7F\nfffdF6997WvjJS95Sbz97W+PI0eOnDa2999/fxuvcH+gXq83/d5qjt5///1x7NixGBgYiL6+vtP+\nZjSjOqbg+uuvj2uvvTbe8IY3xIMPPugx3QS6u7tjeHg4IiLuvffe+OIXvxhra2ueoztAqzG9+uqr\nI8JzdKd429veFpdffnk84QlPiNXVVc/THULH03N057jlllvid3/3d2N8fDwi9t95b2KyB2g0Gu2+\nhH2Jxz/+8fG6170ubr311vjgBz8Yd99992lMvtFoOHdnF3CmcfQYbw433nhjvPGNb4zbbrstfuRH\nfiRuueWWdZ/rMT0dd999d1x33XXx5je/OR7zmMc0/c1zdHvQMX3sYx/rOboL+IM/+IP4whe+EA8/\n/HD84Ac/aPqb5+nWoeP5D//wD56jO8DnPve5aDQa8fM///PrPqfT56iJyVnABRdcEEePHn3k9yNH\njsSjH/3oNl7R/sThw4fjhS98YfT19cWBAwfi8ssvjzvvvLNpbO+//36P7TbwqEc9quU4Tk5OxsLC\nQiwuLjb9bb3wutGMX/u1X4uJiYmIiLjqqqvirrvu8phuEt/5znfida97Xbz97W+PZz/72Z6ju4Dq\nmEZ4ju4E3/3ud+Puu++OiIi+vr648sor1z2TPKZnxnrj6Tm6fXz605+Oe+65J1784hfHa17zmvj2\nt78dH/vYx/bVHDUxOQt47nOfG//6r/8a8/PzsbKyEp/+9KfjiiuuaPdl7Tvcfvvt8Rd/8RcRURj+\nnXfeGZdffnkcOXIkvve970VExCc+8Yl43vOe187L3Je49NJLW45jT09PPOtZz4pPfepTERHx5S9/\nOR796EfH4cOH23m5+wL33HNP/PZv/3YsLy9HRMR//Md/xMUXX+wx3QTm5ubi9a9/fbzrXe+Kyy67\nLCI8R3eKVmPqOboz3HPPPXHzzTfHyspKRET813/9VzzpSU/yPN0mWo3nwMCA5+gO8M53vjM+/vGP\nx0c/+tF497vfHZdcckm8973v3VdztNawzuis4P3vf3988pOfjK6urnjGM54Rr3/969t9SfsOs7Oz\ncf3118cDDzwQjUYjLrnkkrjhhhvi61//erzjHe+I7u7uOHjwYLz5zW9+REttnI4TJ07Ea1/72lhc\nXIzvfOc78eQnPzkuuuiiuOKKK1qO45EjR+L666+PpaWl6Orqiptuuime8IQntPs2Ogrrjeno6Gj8\n27/9WwwNDcXg4GDccsstcfjwYY/pGfDRj340br311rjoooseeeyZz3xmXHbZZZ6j28R6Y7q8vOw5\nuk00Go249dZb46tf/Wp0d3fH1NRU/Omf/ml85zvf8TzdBtYbzw984AOeo7uAH/zgB3H99dfHhz70\nofjKV76yb+aoiYlhGIZhGIZhGG2HpVyGYRiGYRiGYbQdJiaGYRiGYRiGYbQdJiaGYRiGYRiGYbQd\nJiaGYRiGYRiGYbQdJiaGYRiGYRiGYbQdJiaGYRhGW/CRj3wkIkrn589//vNtvhrDMAyj3XC5YMMw\nDGPPsbq6Gk996lPjm9/8ZrsvxTAMw+gQOGJiGIZh7Dl+/dd/Pebm5uLKK6+Mpz/96fEv//IvERFx\n+eWXxwc+8IH45V/+5Xj6058et912W/zt3/5tvOAFL4jnPe958e1vfzsiIqanp+NNb3pTPP/5z4/L\nL7883vve97bzdgzDMIxdgImJYRiGsed461vfGhERn/nMZ+InfuInmv72rW99Kz72sY/FzTffHG99\n61tjcnIyPvWpT8Vzn/vceP/73x8REX/2Z38W8/Pz8elPfzo+8YlPxO233x5f/OIX9/guDMMwjN2E\niYlhGIbRUfiFX/iFqNVq8aQnPSkWFhbi6quvjoiIJz7xiXH06NGIiPjCF74Qr3jFK6Knpyfq9Xr8\n4i/+Ynz2s59t52UbhmEYO0RPuy/AMAzDMBT1ej0iIrq6iu9saGgoIiK6u7tjbW0tIoqU6/d///ej\np6ccY0tLS3HppZe24WoNwzCM3YKJiWEYhrHvcOjQofjLv/zLuOSSS9p9KYZhGMYuwVIuwzAMY8/R\n29sbEREnT57c1uuvuOKK+PCHPxyNRiPW1tbir/7qr+LLX/7ybl6iYRiGsccwMTEMwzD2HAcPHoyn\nPe1p8bznPS9WVla2/PrXve51ERFx5ZVXxlVXXRU//OEP46lPfepuX6ZhGIaxh3AfE8MwDMMwDMMw\n2g5HTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzD\nMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzDMAzDMAzDaDtMTAzD\nMAzDMAzDaDv+HwqbqpeQ/KdRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f283f7e9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500_model_thinning=10\n",
    "fig=plt.figure(figsize=(12,6), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "ax.set_title('s')\n",
    "ax.plot(sp500_model_trace[sp500_model_burn_in:]['s'].T, 'b', alpha=.03);\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('log volatility')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f286d5a9828>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyUAAAGeCAYAAACKBq4AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f283e697fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,6), dpi= 80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "# ax.plot(np.abs(sp500_model_returns))\n",
    "ax.plot(sp500_model_returns.cumsum()*.2+0.03)#\n",
    "sd = np.exp(sp500_model_trace[sp500_model_burn_in:]['s'][::sp500_model_thinning].T)\n",
    "ax.plot(sd, 'r', alpha=.03);\n",
    "sp500_model_returns_df = pd.DataFrame({'r': sp500_model_returns})\n",
    "ax.plot(sp500_model_returns_df.rolling(10, min_periods=1, center=True).std(), label='10d ma')\n",
    "ax.plot(sp500_model_returns_df.rolling(25, min_periods=1, center=True).std(), label='25d ma')\n",
    "ax.plot(sp500_model_returns_df.rolling(50, min_periods=1, center=True).std(), label='50d ma')\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('absolute returns')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Finite mixture model with time-varying probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "* [Finite mixture model with time-varying probabilities](https://modernstatisticalworkflow.blogspot.de/2016/10/finite-mixture-model-with-time-varying.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Bayesian Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "* [Neural Networks in PyMC3 estimated with Variational Inference](http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Appendix: Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Usage of Bayesian Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "By far the most instructive book that teaches you Bayesian methods is the book [Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan 2nd Edition by John Kruschke](https://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884). I highly recommend the book!\n",
    "\n",
    "* [Book Site](https://sites.google.com/site/doingbayesiandataanalysis/) with [source](https://sites.google.com/site/doingbayesiandataanalysis/software-installation/DBDA2Eprograms.zip?attredirects=0&d=1).\n",
    "* [Book Blog](http://doingbayesiandataanalysis.blogspot.de/)\n",
    "\n",
    "Some people have translated the examples in the book from R and JAGS to python and PyMC3:\n",
    "* [Doing Bayesian Data Analysis in Python](http://doingbayesiandataanalysis.blogspot.de/2014/09/doing-bayesian-data-analysis-in-python.html)\n",
    "  * [Osvaldo Martin: aloctavodia/Doing_bayesian_data_analysis: GitHub](https://github.com/aloctavodia/Doing_bayesian_data_analysis)\n",
    "    * [jupyter nbviewer](http://nbviewer.jupyter.org/github/aloctavodia/Doing_bayesian_data_analysis/blob/master/IPython/Kruschkes_Doing_Bayesian_Data_Analysis_in_PyMC3.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I also can highly recommend to read the [Stan manual](https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf) from cover to cover. I postponed reading it for too long, because I thought it is a \"boring\" manual, but actually it explains quite a lot of modeling techniques!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Another quite good free online book is [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) by Cameron Davidson-Pilon (also available as a printed [book](https://www.amazon.com/Bayesian-Methods-Hackers-Probabilistic-Addison-Wesley/dp/0133902838))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "While the book [Machine Learning: A Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020) is not directly related to Markov Chain Monte Carlo methods it gives a good overview over many different probability models. The art in making best use of MCMC is in finding the right model and this book looks in detail into many different probability/likelihood models and especially important sets them into context and compares them. The book is not always easy to read and sometimes the notation is confusing. Nevertheless it helps a lot by pointing you into the right direction!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## MCMC internals: how it all works"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you want to understand what is going on under the hood then I recommend to start with the following video from [Michael Betancourt](https://github.com/betanalpha), a member of the [Stan](http://mc-stan.org) team: [Video: \"Everything You Should Have Learned About Markov Chain Monte Carlo\"](https://www.youtube.com/watch?v=DJ0c7Bm5Djk&feature=youtu.be&t=4h40m9s).\n",
    "\n",
    "And the paper [A Conceptual Introduction to Hamiltonian Monte Carlo](https://arxiv.org/abs/1701.02434) also by Michael Betancourt goes into more details. I suggest reading this paper together with [Logic, Probability, and Bayesian Inference: A Conceptual Introduction to the Foundations of Applied Inference for Scientists and Engineers ](https://github.com/betanalpha/stan_intro/blob/master/stan_intro.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## People to follow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following list mentions some people who are very active in the MCMC area. It is worthwhile to look at some of the things they've done. Below I've given links to one of their web-sites, but often they are active in many different places in the web, e.g. simply use Google to search for them and their work:\n",
    "* [Michael Betancourt](http://betanalpha.github.io/) (some of his case studies are [here](http://andrewgelman.com/2017/03/07/fistful-stan-case-studies-divergences-bias-identifying-mixtures-weakly-informative-priors/))\n",
    "* [Austin Rochford](http://austinrochford.com/posts.html)\n",
    "* [Chris Fonnesbeck](https://github.com/fonnesbeck)\n",
    "* [Thomas Wiecki](https://twiecki.github.io/) (you can support him on [patreon](https://www.patreon.com/twiecki), so that he can do more quality work on PyMC3)\n",
    "* [Jim Savage](https://modernstatisticalworkflow.blogspot.de) (he is currently in the process of writing a [book](https://www.amazon.de/Bayesian-Econometrics-Stan-James-Savage/dp/1138626511))\n",
    "* [Cam Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## News from PyMC3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you want ot learn more about PyMC3 then the best way is to follow some of the examples that others have done. Very noteworthy are the recent improvements in [PyMC3 verion 3.1](http://twiecki.github.io/blog/2017/07/05/new-in-pymc3-31/).\n",
    "\n",
    "All around MCMC is an extremely active area of development. It is worthwhile to check regularly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notebook_run_time: 0:04:32.994270\n"
     ]
    }
   ],
   "source": [
    "notebook_end_time = datetime.now()\n",
    "notebook_run_time = notebook_end_time - notebook_start_time\n",
    "print(\"notebook_run_time: {}\".format(notebook_run_time))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}