{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Introduction\n",
    "  * Visualization tools for large data sets\n",
    "  * matplotlib and large data sets\n",
    "* Working with large data sources\n",
    "  * On the file system with NumPy, Pandas, PyTables, CSV and HDF5\n",
    "  * On distributed data stores with Hadoop\n",
    "* Visualizing large data\n",
    "  * Finding the limits of matplotlib\n",
    "  * Adjusting limits with configuration\n",
    "  * Decimation\n",
    "  * Resampling\n",
    "\n",
    "Before we get started, let's do our usual warm-up proceedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's bring in some of the modules we'll be needing as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob, io, math, os\n",
    "\n",
    "import psutil\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tables as tb\n",
    "from scipy import interpolate\n",
    "from scipy.stats import burr, norm\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can re-use our custom style from a couple notebook ago, too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.style.use(\"../styles/superheroine-2.mplstyle\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The term \"big data\" is semantically ambiguous due to the varying contexts to which it is applied and the motivations of those applying it. The first question that may have occurred to you upon seeing this chapter's title is \"how is this applicable to matplotlib or even plotting in general?\" Before we answer that question, though, let's establish a working definition of *big data*.\n",
    "\n",
    "The Wikipedia article on big data opens with the following informal definition: \"Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.\" This is a great place to start: it is honest, admitting to being imprecise; it also implies that the definition may change given differing contexts. The words \"large\" and \"complex\" are relative, and \"traditional data processing\" is not going to mean the same thing between different industry segments. In fact, different departments in a single organization may have widely varying data processing \"traditions.\"\n",
    "\n",
    "The canonical example of big data relates to its origins in web search. Google is generally credited with starting the big data \"movement\" with the publication of the paper \"MapReduce: Simplified Data Processing on Large Clusters\" by Dean and Ghemawat. The paper describes the means by which Google was able to quickly search an enormous volume of textual data (crawled web pages and log files, for example) amounting, in 2004, to around 20 TB. In the intervening decade, more and more companies, institutions, and even individuals are faced with the need to quickly process data sets varying in sizes from hundreds of gigabytes to multiples of exobytes. To the small business that used to manage hundreds of megabytes and is now facing several orders of magnitude in data sources for analysis, 250 gigabytes is \"big data.\" For intelligence agencies storing information from untold data sources, even a few terabytes is small; to them, big data is hundreds of petabytes.\n",
    "\n",
    "To each, though, the general problem remains the same: what worked before on smaller data sets is no longer feasible. New methodologies, new approaches to the use of hardware, communication protocols, data distribution, search, analysis, and visualization -- among many others -- are required. No matter which methodologies are used to support a big data project, one of the last steps in most of them is the presentation of digested data to human eyes. This could be anything from a decision maker to an end-user, but the need is the same: a visual representation of the data collected, searched, and analyzed. This is where tools like matplotlib come into play."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization tools for large data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated in previous notebooks, matplotlib was originally designed for use on workstations and desktops, not servers. Its design did not arise from use cases for high-volume or large data sets. However, there are steps you can take that allow for matplotlib to be used in such situations. First, though, here's an overview of tools that *were* designed with large data sets in mind:\n",
    "\n",
    "* [ParaView](http://www.paraview.org/) -  an open source, multi-platform data analysis and visualization application. ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of petascale size as well as on laptops for smaller data. Paraview also offers a Python scripting interface.\n",
    "* [VisIt](https://wci.llnl.gov/simulation/computer-codes/visit) -  an open source, interactive, scalable, visualization, animation and analysis tool. VisIt has a parallel and distributed architecture allowing users to interactively visualize and analyze data ranging in scale from small ($< 10^1$ core) desktop-sized projects to large ($> 10^5$ core) computing facility simulation campaigns. VisIt is capabable of visualizing data from over 120 different scientific data formats. VisIt offers a Python interface.\n",
    "* [Bokeh](http://bokeh.pydata.org/en/latest/) - Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets.\n",
    "* [Vispy](http://vispy.org/) - a new 2D and 3D high-performance visualization library which can handle very large data sets. Vispy uses the OpenGL library and GPUs for increased performance and with it, users are able to interactively explore plots having hundreds of millions of points. For now, knowledge of OpenGL is very helpful when using Vispy.\n",
    " \n",
    "However, all this being said, matplotlib is a powerful and well-known tool in the scientific computing community. Organizations and teams have uncountable years of cumulative experience building, installing, augmenting, and using matplotlib and the libraries of related projects like NumPy and SciPy. If there is a new way to put old tools to use without having to suffer the losses in productivity and re-engineering of infrastructure associated with platform changes, it is often in everyone's best interest to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib and large data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this spirit of adapting established tools to new challenges, the last chapter saw us finding ways to work around matplotlib's limitations on a single workstation. In this chapter, we will explore ways around some of the other limitations matplotlib users may run up against when working on problems with very large data sets. Note that this investigation will often cause us to bump up against the topic of clustering; we will be setting those explorations aside for now, though. Lest you feel that a critical aspect of the problem domain is being ignored, take heart: these will be the topic of the next chapter.\n",
    "\n",
    "There are two major areas of the problem domain we will cover in this chapter:\n",
    " * Preparing large data for use by matplotlib, and\n",
    " * Visualizing the prepared data.\n",
    " \n",
    "These are two distinct areas, each with their own engineering problems that need to be solved and we will be taking a look at several options in each area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with large data sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much of the data which users feed into matplotlib when generating plots is from NumPy. NumPy is one of the fastest ways of processing numerical and array-based data in Python (if not *the* fastest), so this makes sense. However, by default, NumPy works in-memory: if the data set you want to plot is larger than the total RAM available on your system, performance is going to plummet.\n",
    "\n",
    "Let's take a look at an example which illustrates this limitation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate a data set with 100 million points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(c, d) = (10.8, 4.2)\n",
    "(mean, var, skew, kurt) = burr.stats(c, d, moments='mvsk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = burr.rvs(c, d, size=100000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took about 10 seconds to generate, and RAM usage peaked at around 2.25 GB while the data was being generated.\n",
    "\n",
    "Let's make sure we've got the expected size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we save this to a file, it weighs in at about 3/4 of a GB: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r.tofile(\"../data/points.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 oubiwann  staff   763M Apr  2 10:04 ../data/points.bin\r\n"
     ]
    }
   ],
   "source": [
    "ls -alh ../data/points.bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That actually does fit in memory, but generating much larger files tends to be problematic (on a machine with 8 GB of RAM). We can re-use it multiple times, though, to reach a size that is larger than can fit in the system RAM.\n",
    "\n",
    "Before we go there, though, let's take a look at what we've got by generating a smooth curve for the probability distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.linspace(burr.ppf(0.0001, c, d),\n",
    "                burr.ppf(0.9999, c, d), 100)\n",
    "y = burr.pdf(x, c, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try plotting a histogram of the 100,000,000 data points as well as the probablility distribution function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Tv/nChLQSbV/dRmMKJxmWGvi8pPIHAACAERH+UBsdbV8AAADU0PRvvjAhM7vq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\nsiwunSlX9/zah9cjdm1ZAwAAYPYJf5g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a444ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots(figsize=(20, 10))\n",
    "axes.plot(x, y, linewidth=5, alpha=0.7)\n",
    "axes.hist(r, bins=100, normed=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even with 100 million points, that only took about 10 seconds to render. This is due to the fact that NumPy is handling most of the work and we're only displaying a limited number of visual elements. What would happen if we *did* try to plot all 100,000,000 points?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oubiwann/lab/python/mastering-matplotlib/.venv-mmpl/lib/python3.4/site-packages/IPython/core/formatters.py:239: FormatterWarning: Exception in image/png formatter: Allocated too many blocks\n",
      "  FormatterWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c998a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots(figsize=(20, 10))\n",
    "axes.plot(r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After about 30 seconds of crunching, the above error was thrown: the Agg backend (a shared library on Mac OS X) simply couldn't handle the number of artists required to render all those points. We'll examine this sort of situation towards the end of the chapter and discuss ways of working around it.\n",
    "\n",
    "But this clarifies the above point for us: our first plot rendered relatively quickly because we were selective about the data we chose to present, given the large number of points we are working with.\n",
    "\n",
    "So let's say we *do* have data from files that is too large to fit into memory? What can we do? Possible ways of addressing this include:\n",
    " * Moving the data out of memory and onto the file system\n",
    " * Moving the data off of the file system and into databases\n",
    "\n",
    "We will explore examples of these below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's restart the IPython kernel and re-execute the first few lines above, performing the imports and getting our stylesheet set up. To restart the kernel, in the IPython menu at the top of this page, select \"Kernel\" and then \"Restart\".\n",
    "\n",
    "Once the kernel is restarted, take a look at the RAM utilization on your system for the fresh Python process for the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gbZAcy9c1XbkFg1/0MDYMZYDaVncMX1c0cFSMLOPQiELX8iJUJpLkgeSB5IHk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\n+4xzBfAIceG6HuNfD1s3INgVY3832oaXlKp2e3DumOq1MUa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fLNOI0r2Xj7OUN/RFwH89\n9RHLlLnep7UYP+cfDxoX7qvbE0FMCt33egAE0Volvc0VW8VQLv3YiMgcJ+7ll1uenudzWXDRgbnl\nS9jdg+Dn+hnX0xZBH81QwXDxsuCVBWrddYuXb75xmzeqq668yjIJFz37vXs/dcIJJ9CxAl/r+MzB\nn1Fjxo6hwHSdddZR+OFRpd//7vdq3/32pQu03/zmN3R8wTsJcGGF9O79300XUT8/+ufq8MMPVy++\n8CK9mHTsGH1a1P2EC58DDzyQLpzOP/988h09+pD3Id4XAHuwDZP61LJUV3x9DVjDhAs4LGLKH4Iv\nJBz7saMM29d//OOjFBZRTz31T4S744471PF/PF598pOfVD/+yY/J1iO/fyQdq7DIgaDxpJNOoh0d\nWDjBYikuDL/97W+rb37zUHXZZZcpXODimIbA9I477qQLUBKu/yDQlne3GD6IfLq+YMWCCxIujNkf\nJ59yMj3a8N///d/qe0d8j/rmkn9eYpk0d85c9d3vfpcuerFYNIiEsXTAgQcojMOX579cUlnVV9iV\ng4XvXd62izrwgANpjmC+oG+QcONhzrNzdP/cQXU8649xgAAfizNYXNh///3Vz3/xc7XHHnvocfJj\n04/YIYB+vPe+e9WHP/xhE4BAUBd+8h1HhxpGTpJ/9JzEO4fwC87Z0FwOwKvlaSZ5vaCVtv3HzSjx\nB853Fp20YRiWuW1DmXMAzu+58dmChTXczKpKeDQWz99jrmJBGHP3+eefp/m2/PLLEyt242IxD4/v\n4BGASZMmqZtuuolwOF6DD0E9zkEbb7wxLYBjcQEJgTzO7ZCFYH/27Nl07MP5CYuieH8AziOQg4Rj\n+nPPPUc7C7DIjZttuHkwKlNgjtK1Oj6vV8SSJm4MwHy0gCG5vBk0+8s4hqGOxHlW69PffgX5bDzn\nVeaDhn8unQ/OMORIVXVqHw5sXSdcMHPq9FMfLLSHHFuD8HK9rlN5VbprDfXy8PwSLlr5rhJybEcG\n3Jdgs/vPR5dgI8cDbn/G1E3r+KhhAIMtdKEeF+bLLLtMlOH4pOZn/s9n6Id58tOf/FS9+S1vHpbb\nv3HHcZs3ZQsUuCuPl6ndeOONVjt32HEHCj6ffupphS3Q/7r0X5TL55rxLgEEfXhREu6cXHzRxRQQ\n4k4IFkjwfDTu8uLiBwu2CGxfeP4FKuPCSsrCndPz/3a+emTmI+qGG24wF1EwCp8lxV1UX9BmGd1j\nBRd+0C1/L75YXFS+7W1vo4s/2L7ddttR4A+VWJjYaeed6J0LuFgFHc5btOtA4597/jn1b//2bwrP\neeMuFIJ6LBYgQFxppSnqfe97n7EcF7Lb6EUXPIOKhLtAuJDE4sBQJLxzhi9YpX7cscKdMOwAwXsL\nvv71r9NdfqbBxezh3z2cFnrwmN0gE/yP3SSnnX5aSW1VX+Elvmgv7uyttPJKVMZCFQcACO7fuds7\nTb/iYh47G5Cw2wKLA7iQhwwEDxj3gHOCH/HVHdzd52uZofQT2zUUOc4lvaYuZPRkQ4MmxJw7h4qm\nJx90xIwdTQgGOdj3iUWAjblVl3BewTxEII6FSRzPr7vuOgr2wYvAH3fnIQv6Xv3qV5NIvKgPCfMT\nC8tIsAl33TnIBwzHbSwC4BiIBVCcP3HMwJyGLNzd57gF/FgAQPuwswDHJZwrR2PyxX8Gls0VxIko\n8Q9u4HJGUa6DBonpslrxl+HMX2DKJabhvEzRA6TL7fqxBsbSyWaBh3+Ac1nmXjivXFpxPmJ+5gRX\nw4RnX7FdH3fzMWl46z62ejZOHaw/YJUOB40mCauKMQkHg7bpO4d9R7007yW6eMTddiRsDeUky3gh\nElYoeSsT0/hybE+FXdipgICf7q75CDXMTOYAvicwxlBK3XgAvvQMtZ76zyMvZCwuZLy66mRU4CtQ\nuRn1FD57caJ/+uln6NjDF+VMhwsFBGscwCB4O+yww+h4hzZiq7bckudeKLAcXPi7shnXl1y74pqr\nr1Ff/8bX1al/OpV2I9xw/Q30ThGpDwEP7h67b/3HxQy2pMPu+S8XFys4PtP7SnJX00WOLuOiBm3n\ntyVDBxZHcTcDPuKExwE4LXh5AW19xhZwBJFYWDzh+BMYXR6/HR0f9t5778pFGVzcccI2Tr4gxV3+\nW/TjCeef/3dCszkI4CYtP0lf4I1X06dPJ9wz2nfwDV9EAoi7zzLttNNOtMUduxv48QpcJDZKeT80\n4vEQ43zn+4LNe9/7XnXmmWeqX/7il7SIgWD4/e97P90Vgxg8VocXGeKOGsbKoN9sjbvyhxxySGm3\nDfoKj5L8Pe8rbjL6ypfwbC/eTYBzH9qB/KijjqI+vFPvuOBn/7GA5S6Au4tnuNh35/pQ+8nXZi+s\nh/FEx/se+L32aKD3PKLhOP56E2wIoAy9B18pzzCKgkdGyFbBtcQXcUzE4jB207gJxyHc5a97iTWO\nybhmRYCPxVjcbQcMX7DBIh3mM4JsBPS85Z51AY4FS9z5x2Ot4EMgj/OXPJdDLl+vg0cevwFHHTmf\n2+WxG3KYl/WOlhztwi5OJPpKGMr2vMfMcH88KxlO7PqPhANmSwKkOkFeDE8sXbU2je0yyK9VJgjQ\nAF9ih3IOGqZ1c8ZJOPOZnAYud4vOUTcDOsbVWgsmBwfReLM+B/gyqAcMkxUXD0hyghEg8KeLiYU7\nSvJ9AFAlVxZRdlfpsKIYk3qxb+3pa5MKBB4c5OOiBc9CuglbbLHSGJtwUVMV3Bs5kX3c6GTHIy5S\nNtsSPCkzwSjLG/lUtr2hXyWrVfbIibHJ0Hj4LflcCdAFwMyl83oKQWyKuECYMGE8XeRjwUsmBPi4\n88sJx7pVp67K1VKOCxgEbG7CccwXRLl0vjpaFdUyh2i2/mLAA/q58s232Fy98Y1vpJd1yuMPyviq\nwGX/usx6BAh2YushAn1Kjlwpg+3FbgikKfqO9eOPPU5l+AoXU9j6zDxYpOQyiOBfPBrEjxHI942Q\nEPnHsQMoKUuS9qOM4B/P2uMOMqcnn3iSvryA57MnTizuPq2wwmQiwfPceG4byX2kC3eicHGJ3Q0I\nMD+vX/o4FAm7NPBOioMOOqik/vnnnqcFLrwnAG397e9+q479n2Ppjj6IsW0d72vAHX4sBuD59EEm\nLK7vs/c+tEAtvwZQ1Vc++3DOP+644+h6A+dNzAFsGcb7FNZYcw0KJsCH8ey+Hwi7YLDIRUmPUV+A\nYvnpDO2nA9r5qa/j3TO/skbF/W18nI8T25wK1xN1bQE+v+7g6whjf3ONWhRfxLRgXsJYsCiIHUxY\nEMQCGSfEBJhvMdetWDzGdTAW3HhOYGENxwDsyAEMC/Koy+MCrtuhl/mxc4qv7XlXFdsjg3YsPGDR\nWiYsVFSd09kuyTMayjRPsPFaD3kqYy7Z8w2TwffjWQccc3DZl2syk5jeAHSBeSSs7+W22/VhLH69\nJClDlqVMVwfXXXqu+/JMnnY5HxR5MMce6PDde/6EEYJpvPBKBvh4JhLfqJcBPngGlfAiHpzMOWGC\n//SnP6XndHCQQFCNC1hO2K6KuzKDSHiZHp6frUt44RNoB50wBsw/7SP4qfZXcESXxupPcBo9S0ip\ndZs9faBdVj4M1w2WYsjXUVp47ifSaWEClYCeAFgIqacQxKaIrdN4thYvTsPWXE7Y2o5tv7vvvjuD\nanNcdGDh7dxzzqVAGXcJsAX9xhturH1jcK3wOgJP87HggPecvLLwFfrMjysCwd32O2xvAvrp06er\nz3+hebCJgAjHa9wl5xcm7fr2XWnu4+5KKD0661Fa8MXd7CsuvyJE1ikcWzJffPFF6+dewPkU8rZs\n3u55z9330FZ1Pv9JHowpvMEfW8lxzkAgjZe7uQnnDbzkDTuuXv2abDupS9N1ndsPm3COwLkWF8LY\neu6mY359DH1GEnAsbmHXh2wvzn24aMajK3857y/WS2FdWf2q777H7nT3DwECp1Jf6fc84LECth0B\ngXzuF/2FO/DYTcgv8kOwgT6Tb/rGIuCFF1yoHns0ewfPQw8+RC+KrFsgt/z0l/Z+4mNpP/Lo4zM7\n2clhEyXPccgh7blqdLmSYnVrOvahKyKqHqMHNMPpF9Ww/hMhqMaxhl+6isfKcNMK5wnMOeR8jA1Z\nw9vhEWjznMbOKcjE4jSuORGA4y4+jr+gwQ40nItBBxjOU/gBBz7ckaedablSeY2PeAWPVPF2fiwS\ngIcXCEJ2jko4h+qycfZ8qBr1zMU0qNvc5TrTMJ3kZXlN89YyBnknnxvsNi4GLmlkY7ns5sbJNKHy\nNRWzigOsr+Ndy/I67spjix1eZofVOw7mJTnfSQctAvxB3snHxMUzdbj4wcTHBSAuYnwJq32g9a3e\n++h5YcSHi4HhoMQrjHgTMl72dNHFFxHrTjvupNaevjYFK6CZuPTEGJGNaPiAKpnkCde0DyMoHyeS\ntpeyV08vAkc4r/F1j+2w5IT6LAC3eCPsIPqArCr2epZ6iir52DmEOYwFPJzoeY7h7maTz63h7iy2\n9iJwO/W0U0kmLijevuvb1a677lplQhinL0L0lUgY78Fk5IvpGXy8oOzqq662qLjfEOTjbug3D/0m\nLWLiWfpTTjmFypPzu9EWY16RxwHIQh2PC+GYdMSRR9AxE0HkMb86prTryZWHu/m4U4yAchDpZz/7\nWUnN23Z5m/rwQR8uwSUAAd4svSiBTyYiKMdWUYwPlH29g0ef8AgUzg84p+CRD5zXeGxBNoJD+BvP\n7rdJ3I9NeLn9sAnBPcY3xr+8qGV5//ahf1NH//xodeGFF1IgDds/e/BnGW1yvEkaOxzwZn68hC96\n277PcUZqXAEB+yc+/gl16LcONecc9BUWkHx9Bal4HhcvScTCAK5FkBDM//73vzcvP0Swj7Ept+dD\nLrbsQxf6HXMbL5zEi7tMqmhTaz/lwuW8M/o6LNB4qrA/pKrNOAzJioGzPnlNQHwxtutrE9pm7J21\nMdo1DevBdY4v9eH6x6dmpMIwd/CeCwTNWGzDsYivoXFcRTCO3TXyZptsK+jx6BAWBPDOEFwXQxbo\neScAHrvF8/34ygaOR/hhFwFy7LjDzqorr7ySjnuIL8ALeXK+s0482w87IQvHQNgv3zXDdEtEXjHm\n8+MTKJiKy26OGcQwOZt8ZelW8DBNDFzSdFKGAXXJR1MFkziUue4rM0zm8iUIbpnryOt+iBin3Xn7\nnWejgdSZ0tVaIw6466y/DtBRCQE+B/o+hqYBPmTcc5d+M3NFWm+D9SqwNgrbKI8++ujsGVQbRTWs\nzuMCjg8qHpIS6O4777Zh6CmR1t9gfVHzF7F9GBcXuAjBnRi8RR8JWyZhC14EhQB/nXXj+8KvqQy9\n7577LGDpJAus0yaLIVWGvwfkvM6tDV388cVWk0aFZNXJ8JjlsGQUMXPIYbSqWKVHqtqKZzF4Kjg+\nYlsvXiI3ddrU+KDHI+uTn/gUfcrLgxKgeu8I4lIRgRK2juM5xbpU139LLb2UmjB+gnlDe508BLo4\nlnm/XMLNChxTsJCA7eODTFgEgl48s1mV7rnnXvr0Hi5e4V+8dwCfb/vVr35l2HB3CYEoPt0H/zdN\nuLuEC9B+J9iJMR266G6rH4H46vozhf1KsX3VVD98AZ907Y86O+69u3h0qI62Lb5ufofktjkXhGTF\nwL3XHnWM+XVqHVkUXhyTLFtC8Cih/SOavu70/gnvWDLu5CPQR8BtHhvz6MDCJG7CIcDHcRYBOMUm\nOS3wTIPg3sWBD3Acn5Gw8w5l8Eha4NDHRpZ+vI/w+flp7Lix3l0ArgzI8YaohIj4w2OLz4tgqYMB\nL+nr1LC8AB1/dUSOecx91NHeTTbdZC/Nimf25ukfNvbX/WAd06DMdV+ZYTLXLMQDGBLjslrResYz\nnGllPQQzNIO8k2+UVhRC3SXhKPMvJIrxY9CJZuBKl6EspYYkCTgH8b5An3HIm6TF+IRDQztC8rHq\nh09HXaw/iYMDDl7mg7ZjhRCrjNhqiRXFJsn4DkywU/owUpD8LBYObDzZeCWUtxtGimtEJu2nSd2m\nAY00VhNz26upRj52oBdQgTFZskHTlWARriaegI4IdkPSgQgjSxZ6Ce5ZDi4Gmh67mNfN4a82fs6m\nZpyXcHGD5/C7SFjYwK/O5smTJtPn+bCtH49KyGNLyQ5fM/Txs05HSU4HAFwE1gX4UPO3v/2VLhr3\n2nMvWpQ94cQT1I477mgsuPmmm9XFl1yscHe4TYBvBA2gUHWhPQD1rVXE9lVTBZjfgw7wYWPlHGna\niAA9zSnffAvQh8Bdz033XN+1/FA7DNy9rqzx0XC4PjK2j7AC5hYC/LrjIuYDgnT8kNz5gTp+bvDP\ntPwpP+bjz71ynYTmfzDejKzsU3EGvWghYtQiGf6aa3x3DLtjvJCYl3xjrg7mw5cEC4Cgh32WTTjn\nan8iEU4shoi2oNX8I1rPH8YjZ42yLFlCcEkzsHI/g3w0lFOozHjkkkbWGY6cy8AjMawq10SBN2dr\nAdjezhOOJNb8wYUwvjPbRXplgf5mpdsiR3BT+xDE48VC+PWaYJ81YTC0HXub2jd9+nR13B+P69W0\nKH7YllLygPGAM3YN3C3wIVzCweuDS5qackh90zlUo2bkokMOatqilv1kHes8OvHZQnx+EM84YwdW\nHX1JhLarMU9JSP8A9Jy6fv4az+XjPIJ3veDFfZzwsj1sTeUvNzA85ckDPg/Q+berOS0V+OZ3J8fn\nfhgrDW9YDpkTgjcUb8jhz65lGuHtCyPtvDhSFxatHvLNLYtAVobhoMFA1v8R2PMuBmlxoIyG1P2k\nZ5iWZ47MWQVoJI8PLmlkmWk7yfsZ5FcZiAYhuXkGLcOZjnlQD8EYR7lZxUGn639cxwBA+cUXXlRV\nz3KyQf3I8TIlDAOxolRSM9T2LVosVvzgUWfYYhvocD24wXfc3yXHDgjgXtRX9fWATBoSNX1ttzMm\nuYGWzpp5xjy+nOQEdDC9pYuBkflwnkORTbDJ4Ksaf2UMUUS27Ba1mP6LEYu38OPTbJWJmyTPToJh\nqI9HwpRSEYH9vvvuW4Iz4JOf+iQXl9i8l3m+pDmNzr/OHcR++KCL+T1U/epeH1j+wbHEdxzhY4xF\n3KyCT4pV6m4mri/UuD7GI6ajKnXQdyF/hEWXMbF9n3GW+TMbeps1sTZAVxNN+Y17uvbHtZV7znXr\nWjzFioEc6pEkDTsEMJTdHPRILpzrGXZAf7MHO3pTBsPxa5qYh3Pmd+sS7uJQ98EkjzVAMLAQ4HPC\nd5LlGyoZ3u8cOh+Z+QipqRrsoBkq+2Y+PBPeon9kKA9t4Ry8DXQo7BMmeIvkX/3JkqjkaVcUXwRR\n9MGJR/JoyyN81BNJbN8VU76Rutj+q5rDdQqH6xyqs3sk4GP7r5O2yLEoy50IT0KSB0aOB+T1TT+t\n7mp+93L8btu+WJ1dtbHSzmF4vMK1OT3OWmn4CEL20cdVorNxll1Yohw/7vrrW4xr+c/VVoVzaX11\nBPP4mkFEkleHsgzWzHG2EB+MKXz8LIdpYvMqPbEyVL/v5LsNrjNM0nOZc+ZFnX+AcZlzC2ZWbfQs\nwOolJZ4RmgMno1tvuZW+LYtnLvFCin4mepZ07nPq4YceVnieZvFYNia3L6/yRMSzM7fenNk3afIk\n603HPdtZqDaiFi7Sz7pq+/CpHbYPk80kUQRs0SuLFJ7VXHOtNWnVddzYcRkpemMIkvQvbCH/5rag\nHexXyzTgnXZZ+B4rQb0sV+j32sd0IyS3xgts1r4twbpsi6fvLH053oI10G/4PHqy5gmEKMaqCM6h\nKgFDNL+qTCpwcEK8I3jlvQlPoav3kulfiIo3u16xRxadjzzwemGjiKKL9g/r8T+K+qphU+T5F+8X\nMNdfDeU0ITfzt+W4MvxQ2lJGE3uJVo9f1hs85+fXBcAzbWM9IQa005lD0BG0JSSnj3AE+Pi6xxpr\nrEHvFOn3tXknTenz+GkzDqRJsuxvLw+Keko/f3tom7b5tC3UMdPcuXPpxbFjx46xYqbA8QiN5h9E\ncplzFyadAxrUOWdaSQNYVZK8VXStcBBel3w0EibLkMV15LLMOIa7OaJrhnHZlwMW+iHCBA45vV3/\ntltuO12XTTfgUyRU5YBfV/C2S2wNennBy0QnB5t10BPdJmmoi0mFICAt2R8+SCPHLoLx+k2X+M4t\nvURDDo/MtMxWsKKu8bAP206QW3oz8dZfH95vlcVGesj52j68adrYl5P55LIEY98r2r7iip3QctcE\n0/tyy88+gkgY9MGvbL+rP6iHfR+ppxcyrw25fi+uF2VDwGuNFT34rHrX9gQGt6vTHZexZhg5AT0s\np6185q+aQ0zjy93x7aMBrG/jKt8VVUyfohSyJYNnDjVudY4b1bzxWNN/NSy99l+N+IRu7YHq8ZQP\nv1rpfRv/tZqXLAL3/Nvv1pv5bQ4k7TQO1fyvPX7387pAy3bnRa097dzbmsucF3HtK84Rprvzgqkb\nTQ5E8BqSiIIZXzW00rYa0uL6vpawIDB2OM0ChQXCeMkB1UfOQrYJNAiUcRl9BLM0ZOLhz5qDb7x+\naUvzsm0dzBqrYwARY1ltEA7KVW222Wbv1kV+u/5CXcYzypzzW/R9OVQD7sslDGXfT4O5twxewlBG\nAi8nWa6CMa7zO/n97FfI5h8awLoYxnXTOEFD1HRAy6lQpoGs6/hm5YSlJkg+q+weCNnlciLUTXJX\nhuFlqzlnzVzXOWxbcakVGVPknu42cgsqNldAykVWR8SoCNk+mVICgmrvm3tzoW7bJS/KdXjT0y5j\nTB3tMI1Dszyr1QIfI7JXGvanafeA9fdqfyN+tE2MpUa8McQe+exfw96DfvRR3dwmfR47jP6IQnAO\nVfHm48aMowBtfy/ctPbcjkJ9CVCgqGR3SHb9ZcMchp6qUf0HDf0zoSf7E3NgPJnxX+2h/o7/at3D\nAtvruA64fzi0Leb4XGdnF8fvOh1evPYrdFcev3Xf9W38Ylw4fVtrj7ch/QP6zoul4awBNsyuZUgH\n1sDkzs8fjik0/iLsqbKDRFJfFh1alFi4UGyKISpDkDMXdSoFg3zIK2hZc1kLYxrmEG0Jk7tcLIQQ\nXGISOCr6GAHjH4iYhmHIyw0FZe+pU9m46z2oxE7y6ZM4WfbRAsY0nDMd6vJnKH0HUsDwL3aSsRLS\nQIoK9T75TO/D+WBMz/JNPVTgluZ4Xzsaj0JuUp77ZIbMKcG18sp2CrtLerhtbE9JeBzAPKJRRQ4n\nNXZUlcA4XKnNZMYQGBJn7vCjiuy3qDHgaR36h/qoZizSGB+KbtM6g/NL2Gza4WljzyCxI6q1rC5k\neJSbdgtfeMgyH3bVf5Fj0mdHgvk8UHECoPHv49Ew0edmHARIRz1Y+CK6rW14ooV3Q2j6tUdbl+Tj\nN3xoJadq4YZbJT/W1prc8vwSO74ajR+PseDnfyEXhwL83AV5nIOJkKWixJDsWoFsxcGRVubLVB7z\nCgG6ZDZElFf2czq/BGMnqKxKzlaVMb1HdDF+y21hkYvz3dtZ3SMkQ0CA/DE7chbOucS5ZUkjy1V0\nLq7Ter+fyXeNdRvt1iU94ziXOLcMGpeOYHiuBwObB0M2yPU444mf9znjXcGVdc1r8bl1l9m1sI7e\n5c/rsj1M4jsI5E1jEsoZ5jGl5EBm9MlmXF0O3tBKNPcFy3B9GTSIGWJyNNhtLPO5cHYO44cot/ww\nRDZ0qbaX8dPIjrz/XP91pT8kJwRvZHtLYuj2zi/tCxfu+qWlSputan7ZlKJmT7RB+S+kJwQXBscV\nZbNa+SVOzZJFVe3I4A0lzTaQ8T+SOkOOzzq7q91exz0k+LbzuC1fF42EbnecktxBjd+R2M+O482w\nLhcyShwkOkihcRKCl1TWmBE6P7vwshh9IUvAMoZtwKWuiy3VXYDFoZElPEt3c5+2jMbqimh5jnzw\nWdfujj6zAKE9l+vI8myuefoLAiyJed2FOYZQlZVzXkXDuCpapuks71eQX+UcFyfrsoxGch25/DGO\nYagjcR05EuWYJDiQukFlRmIH6jyhmJZzps2EFosGEs68Emb42SIbqfCOAB+fJOMyyWI5zgRB+9zB\nC1KHjEWVchbrIlyZwPtgks970pIEebm23bHGe2RLG2CvVWev9CDfo7IRiO0hH2g7uN5IyAggdseK\nW+93E1x9br1X/e4QojqfWXoVHuKnY1mBhE7f/O26rYVGe7xCjz1+Xa9ITrvsHgN4PthU/av1VX+8\nG/rXwBEp2Teai4aYa7gC5C31c/x7FY424BIwfvs6/0PjYaiP33p6metSbSN8YNX7ff4K+aUF3B2i\nbt1EeSy747b1e/x45ZfO9mi1fcx0IW6d3cF52S0lT7oqmFXkNLCyuoedL7sFQ/ui1SBRMScHnwG2\nj4RyRiDnH6O57qMBjJVLhQyHDFn21QHj5NIyvKe8X0F+nVFoTFVqg3d5qG4mie4CPpgZWG4B1zmX\nhjEPw3AwBIxo826lsibwXVQARxfAeD2DtNAZGiyD9bg562W4V1d5pmrt/hSCu9RE58iVvLJJzOuz\njXEp93sAPrMDJT/dSIL6xoEP1s82ufrceq+63fkh54YtO4yx6dyaZ4Y589HlGIo6WuextNYUeRwF\ncfA42NZ9NRZE66+RM3Toaq9XYwur691bT1FIa1LyWVjo8mILdBNFiTZ5oOSB/s9/zwge5PHboz54\nUVjyzvAHuIcCtz6QIF8oDZ6/WrrSHZ+ZGKHQlSv626VCndGl6yCX2JFbg86osTuaCTmXcghmI+Ti\nkiSVZfapGwMVrQG1lovGeeaWifv9aDAjsWuyWrnONHYDmDrLyQIbZNXq8BZxV5WhCvJhf4xTY9oJ\nOVKWrGdrpoDkXcMDhgW7dQmvHICerkaQZk1KrZfv1JMs8LAtnJNpHmFsCLGA2E6lAR8QIdTYApwa\nmybBmUj9VwiRaspWSe6sPBwD18p+LTdhIBBrfAxEY5+V0LAp71IZhO9Dc7pvLcZZxHNysfWJSWQj\nKmoRM0yewSokDQKFY0NmcdjuzAvyKJIzOaCSvRljCdwJoJ+yOzEwJCTsZ3BUY22Z9S6op7AlxtSq\nLcywNTTV6BgjEs2S7gGMobrjTysfRQzOfh6/I9T7DhKDOEe3cmfOVN9V9RS96C/x9m38aE3RTckJ\nkVX0u1ecF5i10o/yKHF1RviExxmu17js+lZey8lyEZS4im0JkdOLhSDnMgS5dVt4dQ280n1uvZq7\nQ+xQBvmhZkgng0bW3bKsu7QknwYGj0npcsLG/bEHF3pOC2LNPpmM0+JZP94BQJ/vY3qdAxca3GxZ\nHZ7pqnKYw2qr6ECT0RYlovcwiyYGRQ6XAN/1odufwQYMEEFjIfKINECz2qvSA6S0WtxeWmtO9P1A\n+ht912mgHzHDhs14yWwtLPYcMPIedDGmf8DsIt1ej6FxeWLrVbLr7IrV0ZaucKyQ4AUafDXWkFmF\nKhdkhAGKOv94jfECjT0ZtoamGm1kpULyQK0HMJbqxnGtEEkQMTj7efyOUM/XsCPh+kh6Vpbru62e\nQsprXa5S0+u4yvnL1zGBThb62KzSUDMydYsFPZVzsWV98I4kzr3FSqgqKi4p1TMgn/fpulcPxEX6\nX7Nx6AqH8txwNktXi0syH31OWM5sQZlgCXPLUjhwsl6WPmDIIIJ86ZC2zZMyUJZ1lskwxlP9wQcf\nZDzl/oFrkXgr7gA03ai11AYzXXU5tSg3z5Wp63Vtc1nKDa2gIFQRMElTXDmWr6oIXcYO66UFBmGH\nZV+HOhuJEvY04hvpxBVDrKumWfPA0Vc7VxsaYcTnhSwzUI+0KpwkrxogYgTnZFXUUmpX5ar55T88\nZ5php9U/jkGEq3JRFc6RFVM1tuRyTd1l7livKz5YD3ZsEEGiqrFBbea01pgi5J+gIUGEVi3Gt8cQ\ni9OqeIgTKHmgwgNmvtfN/woZflTVwBTjOyerovbLr4FWCdQ4YUFJUBWuRDwEgNChBqZkOEEhimRq\nEfV1Ynn0+HHtiNBuZINW81t1w1/V0YaICibQz20pTCpKGUdxnW9LKNPZ+OzM71IVNGUMxhra5Y45\nf1sLSUXJbX9WN20lwrLegt+UwMg/AF3BDAOcBcoy8G1SFzIq9Q4iyIcBPodJw3x4F+bW6/hJ7+57\n7C7pUjl5IHkgeSB5IHkgeSB5IHkgeSB5IHkgeWDJ9kAotgzB4S3gONj31UMwwDm5MhjeaT62U2nd\nC6tyMmuTNKEy06Y8eSB5IHkgeSB5IHkgeSB5IHkgeSB5IHkgeSAUO0p4yEsxNCHevsOHW5DfxFmg\nlfS+soT13ZlJQfJA8sDQeeCxxx4bOuXDTHOdL1566SX10EMPqQULFgwzy5M57IG6PmS6lGceSP4a\nzEho4udnn31W3X///erll18ejHGjSEsTP4+iZlNTRlPbR1JbRpKtHY55jhM5h2i3LOt1qpvQ1snq\nGT/cgnxuUMhJMfAQDcl+5JFH1EYbbaR22GEH+r3lLW9RhxxyiHriiSdY97DMTz/9dLXVVlspOQlx\n4tx0002Hpb3JqOSBQXtg0SJ8p7I+Ya5vuOGG6kc/+lGJeP/991e77z7yH/EJ+eLJJ59UH//4x9U+\n++yjvvSlL6ntt99enXzyySU/jDbA3nvvrXDsl2m4j4NQH8o2uOcz9OfHPvYxdfXVV0uyRmXw33jj\njSWec845h+bNv/71LwuHQA7nocMOO8yCD7oS4y/Y9LWvfU1tvfXWdP7fdttt1QEHHKDOOuusoLlb\nbrllEFeHOPfcc9UDDzxQR2bhR4Of58+frz73uc+pD33oQ+qrX/2q2m677RSuYTgdeOCB6t577+Vq\nX3Pf3IfCkern448/3ly/brzxxgpjGNez++67r8Lc/MIXvtBXf7LwuXPnqp133pmrneYxc/nMM89U\nb3jDG6jtOO7tuuuu6qijjqp/R5bH0jbz1CPGC4ppCxjdY7kbm3znO99RxxxzDOmQbd9xxx3Vu971\nLnXsscf2vGjvs/Xuu+9W0OEmzKtrr73WBY/0uowfZVm2qylc8g68PFyD/CpHwME9OXnSpEnqkksu\nod+FF15IB4UTTjihSuewwE2YMEEdeuihw8KWZETywEj2AI4BF1xwgZInNZzMEASP1oSXDn7qU59S\nu+yyizr//PPVqaeeqk477TT1X//1X2rGjBmjtdmV7RoN40Cez/75z3+q97znPep73/teZbvbIqdN\nm6bOOOMMi/28885TU6ZMsWDDvYIAFNcAV1xxhTr88MPJX88//7zX7KoFAC+DAOL6YubMmQISVxzp\nfsbC4dJLL02B9CmnnKL++Mc/0iLQnDlz4hwwIKqR6OcPfvCD5vp13XXXVUcffTTVEfgtaQmLR5jH\nOO6ddNJJVL788ssbu6HtPG2sqIZBHsvrYhNu+8UXX6yw8HPDDTfQubxGRULXe6Aqvgzh6qUOEcVQ\nB/kxDgNNFV0IF4Jbrl5mmWVoJfS2224j+Kc//Wl16aWXKqxS3XTTTXRHAyv973jHO9QHPvABhUCA\nE05ku+22G61yffnLX1ZYvUa65ZZb1Hvf+166I4hV1RdeeIHgONm9853vpBVH6KmDE5P4g1VTbK/9\n/+ydBZxVxdvHH0ARFQVBQkIQERFFBQPFwtfuDsDu7u7Ov12oqBhgB3aL3YnYgWKhIjYGeN/zfeC5\nO/fsOWfvLrvL7r3P8/mce+bMmVO/mXnmqZmLBTqJEEawZq655poq6JnX/4ADDpDrrrtOtthiCz0P\nU8Bbg7C/++67V/neSc/yPEegMSNAv8cLEAoEKC8bbbRRwWfRpzbeeGPZYIMN5OKLL86fK7ZP4RFN\n4h/PP/+8CmcnnXSSHHLIIXLEEUcUvMuZZ56pgkv+gVEiNEgUkx+WIc0zmzZtKoMHD86f6ty5s/KG\nVq1aaR7eNrwC8Ds8/XhroGK/F7557rnnKl8kIgJjAhT/3mnTpsnZZ5+tPBIPlJV78803NcoAPkn+\nRx99pNen8c40Xvvyyy8rD4cXwpvTwoVnph3gxcB7vd9++8naa68txxxzjNx2223aVlZffXV59NFH\n9d3r+2fAgAEFkWlpbThtvMh6X7zaH3zwgfz222/5Ytwn7DdpddtQ8Vp00UWlbdu28tlnnwmK/h57\n7CHW3vhI6heirp988klN83P88cfL66+/LiiuXMP4TL0PHz5cy9C+kSWOPfbYfKRAWl3kbzoj0dhx\n/vPPP5V32HSgXr16yfXXXy+zzVax1jMGRnhE3MsP78NzCJYHH3yw0J6S6iUNy2L7PlA3dpzj7YZj\n2uMuu+yiHu4ddthBj8mPy7aTJk2SfffdV+sA2Zb2D6W1Z85Rh4wNyJlXXXUVWUo//fST7Lzzzirf\nwrsx/NU3zT///Dqmv//++/poDPZ8H+/LGG78ON6W4v20IXwLHxDXTbLwbN26tfKnUaNGydSpU7OK\n1vo55BJ4o+keOA2M0sbneFu08rNwn6YvpuXzqpzLOm+fU0wZK1vr+1mt5PNBxQJQVbnwvKVtXwAc\njXLs2LG6EabDoIyQBn377bdqFbvwwgsFKynCLeFmCKEoxgz4DDoYAGB4WA+feOIJzcOaxlxXBHYY\nx4MPPih9+/ZVoZd7n3POOULEAB5E7m1hj2n5XBMSf1uF14GQJBhRSAixCNfcHwvg0ksvLQ899JAW\nmThxosDQEeD5HrwYTFHgvTE0IHxlvXf4HE87AqWCAEqkeSXp0/QHBBQjBij6N8a80aNHy4cffqh9\nmvPF9CkE3TT+QX+DX8AfCMMbOHBgXhFAMIZHLL/88vYquifM/tprr82HI+KZ55j8YuiTTz4Rwjvj\nxDvAj+BpV1xxhSr98Du8XGeccYYWL+Z7KQhG7du3V95DaOFpp50m33zzjfKX8Hvx7GEUhUdec801\ncsEFFyimXEO0wcMPP6z7e++9V5+fxCPTeBY8DcMJPBheiJDHvOA0qmk7+Ouvv/Q9UeLgtRh0wJC2\nAp/GsFofZIZfjL9EZ2Cc2WefffTRaW04a7zIemeMRKEQjxFmrrnmki5duuQvS6vbhoIXL0qbRAZ4\n9dVXte3xbkw5QDYgqgVjPt8BmSeeEH9rj5THcIWhEMF6oYUWUgMA3lQLqT3yyCNVgSWqgukxaXWh\nD4n9NHacMSTCU1HgDzzwQMWoR48eMvfcc+e/lDZo/R+ZBqI+MPTBizGoMNUBo1K8XtKwrG7fb+w4\n58EMErRrlC6cOW3atMmPWaFsC8/HSIVxjjrAEGph/mntGTkR4xc8hvEpNJxicOnevbvyQ/g5xs76\nIIzQfC9ebGRfxi0MuxB8GTmYvMsvv1x5Mn+pHW9L8X46q76Fd87STTifRRg55p133jy/yipbm+dw\nkNJvaROMg/RX2lra+Myz422xNt+nBvcyPdH23CJMJ92yqvN2TbHlrHyt7yvMqrV+62rfsLbAqPI+\nCJfDhg3TF6RTIIgzp8cIKyiD9ksvvaSMq3///noKSz3CKHPJYKBYB7GgQTYXiMZOR4VxQjR0Biw8\ndjxj7733VqETjxfWbSgtX0/GfhCmsM4iPJ911ln5s82bN9dBEUaMFRUDQuhd4V0hvgsGzx5acMEF\n9R0RTtPeWwv6jyNQYgjg8UQYwitJv8Grg8Ji9NRTTwnhcxjzIMJP6cs2Z7+YPoXgk8Q/uN8iiyyi\n0TWkUZzgLSgPhBCvvPLK0qJFC07lCeMc0QQIpjvuuKO+F55C+FexlGTlRxin78PTUHjbtWunt8M7\nGRo9qvpeLoIPEU4Kde3aVT1yREvAJ8PvBUeEfowKEHyY58ML8QRgLFhhhRVU+OR8Eo/EY5fEszDY\nwltZdwHCw8C7pFFN2wFYofgtsMACemt4MxEfeCvhrxh56oOoP/NeUb94sBDuoaw2jBKVNl5kvTcK\nK4ZsotVQajm2KDauS6tbokYaAl68I21ywoQJJFW5xwBlhPLC99GWQyLyjTGXPkq/w9NMXTOmozxg\n3MFAkBb2n1UX4XMs3Zhxpr+j7GEcZH0Ivp2wclMg+cbNN99cP5W+CqYQ7QMjCW2TNk0dIUNBYb1g\neE3izTy3On2f+zZmnHn/OIGh8TuMUKFDyGRbDIP0AWRB48E4gjCGprVnkynh1RD3wogK8UwMzhD8\n1ORrzajDH97XngX/JbqAMZfvY3ywb+Mc4wL9lnEsbEvx15tV38J7VKWbxN81fsx3hzJM/HxdHDOO\n81zkEIx6OAYw9KeNz+hCkLVFPWg4P01q6VW4T/gXe7V02+rfpiEp+fb2xYJMuWLL2r11TxjMZZdd\nVpAXHpjQhsAUH+iZF0+DpjO2bNkyfxmDFJt1MgQaIyyFEEI8yjTMFUbKojQ77bRTar5dH98j4LNw\nDcKU0eTJk3U6AcYDGDfTCEJq1qxZ/jD+TZzIeu/8hZ5wBEoIASJjzCtJnzTl1D6RPoHQaH2ZPYOX\nUVV9Kot/cI9OnTrZrVShx4iIMMw2ZMiQ/DlL9OvXT8OCEVywluONJOyN/GIIbz0hsnEiAggFnO+F\nNxoZr7Pjqr6XcmAaluMe5vEJv5dndezYMY8t2LMgKu+BgQOB8qKLLlJvKrw6iXf27NlTBRqrH54P\nr0Xx57khJfE8O1/TdkD9YnAJKf7c8FxdpTEGoZQa0ZapUwwOaW24qvHC7pW0xzDMd+PFR8jH+GQe\nbsqn1S0KX0PAi3dkgU2M5UnE+gJJ7QUjH8Ym5gAz9hLiDKFYYJhjOhz3JDowidLqIqkseY0ZZxR6\npv1g7MLZwMZ6QmBDe4HiRkzyUAyIgkE2QmHAS2sU1ksalvTJeB9Mqku7J/vGjHP4HZamnYZExJeR\nybbwSPhenHeCcVp7xpgYYhmmkTmpW5RoPLpEXsETQhnZ3qE29xgxLr300kq35F3hNfGxiHYDhW0p\nfvGs+hbeoyrdJP6u4TEGD+o6lFHC8zVNM5UPQxHGS6tP+hneeAw+GBWIIqPvMm7j8ERGAWvOxduY\nvYe1RTtuwPvq6JqUrehwDeCjCiWUBvBCwStUR4GvTtngEclJYwyEthKKYqsyI1hj7US4JAwIAQdm\nAhGSR/ge1xBChDCAxZCGTGgZXh0TZBHMmWuGFyUtP/nNpufCvHhe6MknVBShmVBXQn+JNgiZe9b9\nOJf23lVd5+cdgcaMAF4cvEuE58bD4zlGGUJZoi/jIQ29IlV9dxb/4Nq4wsOCaUypGR+FqMJfkoiV\nlOEfKEzs6evFEsqzhfjbNfyNHkoLQjgeGMLtTCknbDGOiV2Xtsf4Sag9ROgsCyJZJEP4vdyXdwFX\nNjx3CP2E9iIcYORAyc/ikWk8i/xx48apEMJ7wH9tvinHSVSX7SDpeXWZhyfT/i0mrQ3P7HiBF5Zp\nbPHoF74rrW7r8ptr8942/ifdEyM6Ai3tCQUDQqnBi4VSizwQRnCgSJmMkFYXSc+xvMaKM32faZAo\nkxB9HaWgKsEefgGOODFox+BsckxYL2lY1qTv836NFWfevTpkGGIIINIJwyv8l9X5UdKJjkhrz2AO\nbzdFOTTsEbWBcZpxhH+twQCAEXpWEWMJ7SccizDM4aWHDAd7v7CfNrRvsXfM2iMPMD0B73htE0YD\npgIwVcP6InICGHbv3l0jddBFMMrxzyVEzuHMTBuf7f3idWD5DWhfHb2yOmXr9RMboic/BKC6wIXl\nw3R4z6LTePFOPPFE9bajQCPk05hhkDRkPCYwNaxVhKwQxosgy/wmBik6BoRCThkYDAwVxsqAx1zc\ntPyqXhIGhufAFvlBKWAOKoYEBtY+ffqo1y7NWxG/P9+a9N7xcn7sCJQSAt26dVPrNKHhcUIpxkNH\nH6d/YP1nrY5iKYt/JN2DPotRIR6FEy+LYl8d5d6uR5DBS4PXl7UGGLzhQyhrCGV8L9ON+F7CveEj\nNlfW7lHVHuGK6UI33HCDKpoYLhAmbV6zXc+Cn8yb5zyEgECIKfwR4wXeNRYOZeGkNB6ZxrPIR+CB\nF8K3eafQm2DvEO5r0g7wXDREwvPCFBQEv7Q2jBI2M+MFbZQpYxalFuKQVreEtDd2i24ftQAAQABJ\nREFUYgxHkMUoZMS4zzoItFn6FNEoI0aMULmBKBs82JynbVeXnzRWnDHWgRPzo1EEUPjAjikuWUQ0\nEw4Q+hay1OKLL65rW4QODa5Pa9d48avb97lfY8WZd68pMVWNsYB1EjDsMj0LxSurPePIwgjD+GBT\nAng+8jC8muk7GLlY34J+MCsJuRuZlrU1MHqyToQpnvH3CvtpQ/yW+PtyTNQExhnaPMYZ+lax8n7S\n/bLyiKRDdkDRR/9hXEUWQm7A2IlhBH2EtoShH6Nn2vic9ZxZeC7UF8N0Ma9E+QblvQ9fupiPSSoT\n5iWlLY+9bTzX0rYnkiCetjz28bTlsSf+3I4tHe6JO+0QWZ5GRfuZJv4LmEYbJ6yaWLfC0CUrE4a3\nWB4dAAGM1XwRuo3S8u18MXveAyXB5mMiyIUL3RRzD8okvXex13o5R2BWIYAgGYaE19Z7EJpGPw9D\n2at77zT+Eb8PygOD6cyG3FWFBbyBVZTxrIV8iPfB88g8WAvNi79j1jGeXVYc53u5PlxNO+k6BEKE\nhtCqz/PhYyirYehtFo9M4lnwQ3itzSFNen518mqjHVTneVXVYXXulfTutTVepL1HUt2mla2N/NrE\nq7rvQ3+iH2GQQmEiHbbd8H5JdRGer266IeMMLj/++KOu8wE2xRC8lm+yf/wgnXZtGpa13fd574aM\nczG4ppWBR6IkhpTVnqkfcE8aD4l0w6iaVl/hM7LStdmXiaxlLAojybKebeca4rfYu9XVvircGYPh\nb/H2wvswBtMukvSkpPG5rr6huveNeDVzIydGG4t/EJpN+FF8H+aRDjcUfDu2dLiPpzlmgywdHlu+\nFphRJimdlWfnpKF78vMvOiNhBoF4ftpxhRadVqLI/KSGy6VpAznnkoRkBFq2OKXlx8tlHSNYmIJP\nuZoo+FyX9N7kOzkCDRmB6g7ixX4LimpVympV90rjH3Ydc0+xhjMfcGYVfO5ZFRbwhjT+gMI9szyg\nqu+1704SBnm+RUFZOfZZPDLpfeGHtaXg8/zaaAfcp1iqqg6LvQ/lkt69tsaLtPdIqtu0srWRX5t4\nVfd9wr6UZPAP75dUF+H56qYbMs5ZfCbtO5GpTMGnTNb3pWFZ232/qvdI+5aZya+v9pyksGW1Z+on\nTe4N5c+G8u01HQMa4rfMDKbFXFtVm6vuGGzPTBqf7VwD2FdHT6yuDjrLP6+Yj0sqE+YlpS2PvW18\nrKVtb556ji0d7uNp89STH26Wb3uMFyxN3TGy6I6M9k6OgCPgCDRYBPAasOYH8+JDr3aDfeGUF2NF\nbBbQc3IEHAFHwBFwBBwBR6AhIxAZBFlB9bto4y8+pkZb6LU3j7556m0f5uOFt3xLh/t4mmM2yNLh\nseVrgRllktJZeXau0Xny8y8eJDAQQLaffuS/joAj4Ag0EgTwGtRknn1D+zxX8Btajfj7OAKOgCPg\nCDgCjkA1EDB90vbVuLRhFW1s4frFoFdQKfb/wcVc6GUcAUfAEXAEHAFHwBFwBBwBR8ARcATKCoEC\n/bEUvrwUlfyCemHVZCdHwBFwBBwBR8ARcAQcAUfAEXAEHIHSQYDFI1kHiLU42JLI8lmU04jrSp2Y\n1+7kCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCJYCAK/klUIn+CY6AI+AIOAKOgCPgCDgCjoAj4Ag4\nAo4ACLiS7+3AEXAEHAFHwBFwBBwBR8ARcAQcAUfAESgRBEp+Tn7Wf6yWSB36ZzgCjoAj4Ag4Ao6A\nI+AIOAKOgCNQVgiUw9z6mlaoe/Jripxf5wg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag0MARKWclPXmKx\ngVWAv44j4Ag4Ao6AI+AIOAKOgCPgCDgCjkC9I1Cy+mIpK/n13kr8gY6AI+AIOAKOgCPgCDgCjoAj\n4Ag4Ao7ArETAlfxZib4/2xFwBBotAlOmTJHevXsX9f7XXnutDB06NLHs119/rf/tetRRR1U6v+yy\ny8piiy2m+ePHj5emTZtKly5ddOvQoYMsscQSMnbs2ErXeUZxCHgdFofTmDFjZMUVV9T2TjuePHmy\nXnjwwQdLv379CraRI0fqubRr4k/s3r27vP322/ns0aNHC3nvvvuueJvPw5KaeP7557VuFl10Udli\niy3k+++/TyzrOE+H5cMPPyxor7TfVVddNY/ZjTfeqHiusMIKMnz48Hz+K6+8IgMHDhRw3nrrrWXS\npEn5c2HCcQ7RSE9vs8028vLLL6cXaGBn3nrrLdl0000L3or2EfK/YcOGFZyv64Nx48ZJz54984/5\n77//5JBDDlGZYckll5Sbb745f67Y9pu/oIYJ+P5GG22UePULL7wg6667buI5z5x1CBDGEN8wDtjW\nLErbxkJ+bLPP2JpH+zmircWMbc5oP1e0zR1tLaNt3mhrFW2to61NtLWNtnbR1j7aOkZbp2jrHG1d\no61btPWINlp0r2hDuu4TbX2jbalo6x9ty0bbgGgbGG1rRNuQnFO9IdCyZctae9aAAQNyEQOrtfv5\njRyB2kJg6tSpucMPPzzXp0+f3Oyzz17lbT/55JNcJAjkIgEhsexXX32Va9WqVW6RRRbJcW+jSHnP\nderUKRcZEjTr888/z7Vu3dpO6/7ss8/ObbDBBgV5flA1Al6HVWNkJSKFXtvh+++/n4uEyNxBBx2U\n22efffT0r7/+mouUHd0+++yzXGQIyH355Ze5rGvsvrbv1q1bLhKg9fD222/XfkCfgbzNKwyZP507\nd8699NJLyjv222+/fN3EL3KcpyNC37c2yx4eeswxx+jJZ555RtvwTz/9lGOD906YMCE3bdq03IIL\nLpiLFBgtd/TRR+d23XXXOMR67DgnwpLPvOKKK3JrrrlmLpLPc5GBKp/fUBPwon333TfXrl273Fpr\nrVXwmkOGDMk999xzuX/++Uc32kl90d9//53beOONc5GxP//Iq666Khcp0TnO0W6RH+DL1Wm/+ZvV\nMPHUU0/lNtxww8SrI6N6Dnmntum7777L/fXXX/rdVhfx/b///ptjC/Pp/+iJM/RF9Eb0R/RI9En0\nSvRL9Ez0TfRO9E/0UPRR9FL0U/RU9FX0VvRX9Fj0WfRa9Fv0XPRd9F70X/Rg04nRj9GTTWc2Hdp0\navama7OP6+IcZxIXOTkCjoAj4AgUiQDedCzVWO0jJT/zqmhwFbydp5xySmY5/gUEz9Fjjz2WL3fd\nddfJdtttlz9OSswzzzwy77yMIU7VQcDrsHi0IqFMImFSvfhNmjSRtddeWz799FO9Ae2vTZs2utHO\nzzjjDOnatatkXZP2ZLxOJ598sjz55JOy8MILpxUTb/OF0MCDIkVfmjVrJgsssIA0b47MmE7ljjM4\nWZuNFDh54okn5LTTTlPArr/+ejnssMMUw0hRkvfee0+xxWNKu1tttdW03N577y0PP/xwOsjRmXLH\nOQ0cIoJOOOEEWX755dOKNKj8tm3bClEHSWM47aJjx46Ch5oV3hlX6ouOP/54OeCAA7Tf2zNpk5Hx\nSdsvEX94zWnfNWm/dk+iEBdffHGJnBAyePDgfBQXUUP0HyMiB4wi46/KSEQZUN88H6I/HXfccRIp\n+Vr/kXKu+UceeaREjhNN//zzz7Lyyitr+rXXXtNIiR49emjEwqOPPqr5kTFO9t9/f41cInLGKR0B\nrAZOjkCtIEDHpaPee++9yuxOPfVU2XLLLfXeDJ4wScrANO+66y4VBq+88ko577zzBGWIsORRo0bl\nlZZrrrlGHnjgAfnll1/k9NNPl1122aVW3tNv4gjMDAIoOqussoreoqpBnXa7ww47CKH1VdGOO+4o\nV199tQ7MkbdJCFtGUKQ/Gf3555+y22676WFkqZfISi+R5dxO+75IBLwOiwQqKsaUkMj7phdEnhC5\n7LLLZL311iu4wUMPPSScGzRokOYXc014gxtuuEEuvvhiiTyqOhUlPOdtPkSjcjry3uXD9ZnekBUC\n7ThX4IcscuCBB2q7gx9AH330ke7/97//qdIWeeXlnnvukYkTJ6oypyejHxQ78tLIcU5DJnKPLoWD\nVGS++eZLL9SAzkTRczreY0xDbjWKPMLy8ccfq7IZefkFBfTWW28tmPphZWt7z5jPVLM11iBguYLS\n2mlafsWVySl4CQYwpgRhQMQAFkVyCfI80wzBwCiKurKkGj24BkPO3XffrXoAin7kbZdvv/1WpydG\nkZDy+OOPqyPj6aefzt+Lb+McFEXMqEFos802U+wvueQSNTIzJjCt5vXXX5eFFlpIfvjhh/yzPVGI\nQP2ZnQqf60cliMAff/whUXiQMOft/vvvV+b3xRdfaMc+4ogjBOsbSgmWvdtuu00RgGk8++yz6hlC\nyTdLHSexpEchovLII49oZy9ByPyTShgBBkiEADN0VfWpq6++urz55puCJRulCWt2NAWm4DIEDeYF\nsu21114qUCCoOtUNAl6HFbjC1/Fk9urVSz1IFWdEDbjmiQnzs64Jy+G9xxuGASEKPQ9PabSMt/kC\nSPIHGAOJoMCrhXd06aWXFjx8aeQ4VyCDrDHHHHPIcsstl89Ehpltttm0Lb7zzjtquMLQikGALaT4\ncXjOcQ7RKM0089/vu+8+efDBB1XphXcde+yxdf6xyAc40M4888xKz0prp2n5lW4Qy0COx/mAgg+x\nbhB5VRFOEIvUQEGHTyH7h0Q+fRADRPv27TViBscF0Yzwe4jz6BRETbLRP414BhFfVTlarHy57t2T\nX641XwffTZggwgZE54vm5WiHxfOIhwFl/pZbblGl3RYhW3/99TUUFEUIj2ffvkyBmU4777yzWvwI\nA8Jy5+QINCYEzj33XGHQWmeddTQaBUs3Ri28REnEYLXVVltpH2Ggs74UlkXJp18ZbbLJJmoI+PHH\nH2X++ee3bN/XEgJeh9OBvPPOO+XEE0+USy+9NO+tN4gxZEXz8Ct5sLKusWttzwJWyyyzjFxwwQUS\nzXMVFrmyaSje5g2lyvtXX31VccKIDqHkR3PH5fLLL69cOMpxnCtgwRO47bbbVmREKZQNjK0Q3n0M\nrXj3CVcOFzREMSEiMY0c5zRkSiefyCXzOPNVKLVxRbYuvvamm27S6VCbb7653j6aV64yBqH6yADx\ndgpfpa3G87Par7033zj33Ewln07I+KH33vLZo8gbxacMcYzTLiSmfR166KHq/LMIMLz4GHkZByCm\nG+CpJ3IsWhdBLrroovwt5pyTqe1OVSHgnvyqEPLzRSOAksJ8NyPr2ITSsAIp82tQ2JnfZITST5gm\njIN5zueff76dEu/EeSg80UgQYI4a884gBG3C95iSQtgZAjhKfhZhNccjwMriNv8zqzwh/XijTCHK\nKuvnikPA67AQJ8Irab9jxoyppOBTkpBLFKPQo1LVNYVPiFbrjbyn0Pbbb6/CMlEqaeRtvgIZlFI8\nzhj5IIwj5nWrKFWRcpwrsMCQGi0AV5ERpVA8aM8QnlqUDvg2009o0za3OFogMj9vWAvHfhznGCAl\nePjBBx9ItBBfXulFyUahrmvCGYaHG7mCjekE7DFKrbTSSkLbhH7//XddNwJDVXXbr30DyjdRt4TZ\nQ0xDIQ9C5rA5+YwNVoZzOPSI4oXgT/wbC3P6Q0K+Z+oGchL3ZBsxYoSWQ6bh/Yn+RR7CmYEhOSt6\nJrw3aaYzUEdE47GRJq/cyD355Vbjdfi9dPI77rhDvZEsQkLI8e67764djMWYmG9JJ4Uh4bGnE+PJ\nZ7CNVtNXix0Mir8AcXIEGiMCGKwQBmnHCOBGGLpYXI+5nFnEQMjgyVw7mycalmd9ChbUgTCMYY3H\nUBa3nIfXeLp6CHgdFuLFdCk8l+HfjKHUI3xBKD72N492ZVXXWLmkPfP/MQYj8CH4eZtPQml6HhFz\nRFjwV57UAd461rIphsoZZ4witCu8hCHhLSTKEK8s8gxTCwkrxoCFMgJfZpFDwobDtVLCe8TT5Yxz\nHItSOmaaB1F1ePMJKcfrzZpSdU3IB6FRH8da9+7d9bG0X5xlvBOKNYvwWXRsTdovnnQUbe7BugMY\nvpA3oOgfVoRoWwwITOEK1x3imKjEFi1a6Nx9Fu9LklHoW9E/gqghjXsyXdHWf2GqIs/HQYghg4WJ\n+atV3qcqIvKXcSmMLgAP9BKwQRYrF2pSxIcmlQnzktKWx942HmVp2zcN8iwd7uPp+N8JcN7ywj1p\nYjn4b4mR0d6pHhCg49ApEQhRdAjTR7FnQMXCCBNggb3+/fvromJY+AhdRiFigRtCmzEAIEzSoWEM\nFg7FyrZ0UCdHwBFwBBwBR8ARqEAAYRYhFkHcaeYRQEnAGx9XTJBfwNmnRs08xqVyB8LQMQhFf4Pb\nYD4JAxYKNh7xkGrafuEvGLbi34ijgW8PjQ72PBx6ODdCZ4edq84e/YHnMm0LHQDlP+4AQefAEEA+\nG157+mlIdg3lMIpC3C9ylAyNkqyiiZt/2oztv4Q9ebZRztIs1hFPWx77eNryolN6Ln5s+ewhzhuF\n6aw8O6dKd/4gJYFCHqcwLylteext4x6Wtr0p8RxbOtzH06bIk2+b5YV7V/JBexYRc4To9HRKIyyA\ndFaEEDobf7FhjIEQGpgSzCAM+bRrfe8IOAKOgCPgCDgCjoAj4Ag4Ao5AiEBcySc8H50jJFPy2RM5\nDJWDku/h+mEr8HStIJC0oAfKe2jRMwWfBzI3x+ff1wr0fhNHwBFwBBwBR8ARcAQcAUegLBGIK/gh\nCFnnwnKlksYb7uQIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAIlgIAr+SVQif4JjoAj4Ag4Ao6AI+AI\nOAKOgCPgCDgCjgAIuJLv7cARcAQcAUfAEXAEHAFHwBFwBBwBR8ARKBEESn5OPqukOjkCjoAj4Ag4\nAo6AI+AIOAKOgCPgCDgC5YCAe/LLoZb9Gx0BR8ARcAQcAUfAEXAEHAFHwBFwBMoCAVfyy6Ka/SMd\nAUfAEXAEHAFHwBFwBBwBR8ARcATKAQFX8suhlv0bHQFHwBFwBBwBR8ARcAQcAUfAEXAEygIBV/LL\nopr9Ix0BR6C2EZgyZYosvfTSRd32+uuvl5133jmx7DfffCNzzz23HH/88ZXOr7zyytKvXz/N/+KL\nL7Rcz549ha179+6y7LLLyrvvvlvpOs8oDgGvw+JwslLvvfeeLLHEEnZYsN9zzz1l+PDh+bwXX3xR\nBg0aJEsttZQMHjxYvv/++/y5MNG7d29555138ln333+/kDdu3DjxNp+HpcpEVt1wseM8HcKPPvpI\nVlxxxYJtrbXWyuM7atQobberrbaaXHfddfn81157TVZffXVtz9ttt51MmjQpfy5MOM4hGunp7bff\nXl599dX0Ag3szNtvvy1bb711wVvRPsK2FPK/goIZB4899pgwzsNXd9ttN/njjz8KSic9N4238h/w\nRx55pMoMyy+/vNx22216r6rafMEDZ/LgmWeekS233DLxLi+99JJssskmiec8s24QcCW/bnAty7u2\nb9++1r6bAfb999+vtfv5jRyB2kJg2rRpcuyxx+rA/Pnnn1d5288++0yuvPJKyVoEdN5555XRo0cL\n9zZCyfn222/tUPetWrWSTz75RLfx48fLkCFD5MQTTywo4wdVI+B1WDVG8RL//POPtrW4EEq5O+64\nQ1544QX5+++/85chxJ977rnyxhtvyAILLCBnnHFG/lxa4u6775ZjjjlGHnzwQVl88cW1mLf5NLQq\n8rPqpqJURaqccV544YXlgQceyG9bbbWVrLTSSgrO888/L1dffbWAzz333CMXX3yxfP3114LyNHTo\nUDnllFMEpYt7JBllKxCenipnnONY2DGK8IYbbih33XVXwXhn5xvanvH74IMPlo033lgwCoeEQnv+\n+ecLe7ZddtklPF1l+rfffpP99ttPRo4cKa+//rr8/vvvctlll+l1Wc9N460jRoyQDz/8UF5++WVt\nw0cffbQgJ2S1+SpfshYL4BS54ooravGOfquqEHAlvyqE/Lwj4Ag4AgECTZs2lfXXX18FwNlnnz04\nUzmJMnnEEUfIcccdV/lkkDPXXHMJlvcnnngin3vjjTeqBzSfkZCYZ555hM2pegh4HVYPL0qj4Oyz\nzz4CdiGhBN1+++2y0047hdlC3+jUqZM0a9ZMOnbsKM2bNy84Hz/A64Qh4KGHHpIePXrET+ePvc3n\nocgn0uomXyBIlDvOtMc2bdrohiI1ZsyYvKEUZeuggw7StorBCgMVbZgoCdrdKqusokjuvvvu8uij\njwaoVk6WO86VEZmewziH8rnMMsukFWlQ+bQVPNNJRh0cUR06dBA81Cjscd5Y1Yf8+uuvcthhh0m3\nbt1kjjnmUMMmBiUo67lpvJWogB133FHbb+fOnWXttdeWp556SnlwWpuv6h2JQqSu+vbtq/eePHmy\nXkJ01qeffpq/nHo14rvAjOiEQYMGaf/hHHidfPLJksvltC999913egnyEcZd6Oeff5Y11lhD0/Q/\nIiX69Omj93r88cc1/7nnnpNDDjlE5SMiZ5zSESj5v9BL/3Q/U9sI0HHpqFjJYXYnnHCCbLbZZvqY\nm266Sc4880zt3DCbW265Rbp06SLXXHONXHTRRWrRpbMS/oRXE4K5PPzwwwLDwFsJ83JyBGY1Ak2a\nNMl7fqoa1M8++2z1ACEIVEV4imj/DMxTp04VwpbpA/QnI6IBULSgr776SogkoI84VQ8Br8Pq4fX0\n00+rF4tw5ZDg+Xi5zjnnHPUchecuvfRSFfB69eqlYffcI40Ikb788svVIIZwGpK3+RCNyum0uqlc\nUsRxrkCFtouCdd555wn8APr44491f+GFF6pXdcEFF5Rbb71Vp5qEPJx02vQTbuA4K4yJP0suuaTm\nzzfffInnG1pm69atdbxHsSbazojoGaLqDj30UJl//vnVQH/DDTdohJ+VqWoPr8NgNHbsWI16Qmm2\n8T7tudwzjbfSJrPaaVKbz3pHplMgw+B8IBoL4wxOC6JdmGYIBkZEDBhh9HjyySd1OuG9996rMhCR\nChjOUOzpb4sttpgaIDAWPPvss/Lvv//q5UREcA5Ch+CZRFGA/bBhw2TNNdfUqMibb75ZiLzpHk1b\nTIou0xv4jxSa5B0QR2AmEEAYgxEQznbnnXcq8/vyyy/lr7/+Uk8mVkYs4gMGDNDzPIoOTD6hyYsu\numiBJxOG8Oabb2rn9pDkmagYv3SWIMAAyaBthq6qXoIpKvQdLNl4iQYOHKhz8MPrEDQIdWRj/h5z\n+RBUneoGAa/D6Z6Vs846Sz35cZQvueQSWWeddVTQCs9hpGJu6N577608HsEeb3MaoajicWJayyuv\nvFJQzNt8ARwFB/CKtLopKDjjwHGuQAWvIN7T0KOMDIOnn7ZIO0TxwCOPcsQWUvw4POc4h2iUZhqP\nO9OUmJKB0nvBBRfISSedVKOPxeG11157adSIheun3SiLt1bVTpPafNpzyCeqCucDcj2EQYO8qojp\nL6wXBKGg887xqY3kYzyYOHGitGvXTr8dxwV5yDfQfffdp89migdbqMzzDCK+qnK06I3K+Mc9+WVc\n+bX96QhjCHUQnW+99dbTDsuCY4TpYHUjrBNGs80222g5BETmxKEIMb84XNRphx12UIsfYUIMvk6O\nQGNCgEGfQYvB7JdfflFvJkYtIlqSiMFq8803V8GBgc76UliWPsZUASMGQwbIH3/8Ub0Jlu/72kHA\n61A06oqQfDwu0E8//ZT3rDAfFf6MlwVPDm0YDxQLQxLeTEglhJKPEZeorSTCi88Ck3iNdorC/vEE\nWUSXt/kkxKbnERGXVjfmnQ6vdpwr0MATiOwRErwUYysEfoQK463Fs/jDDz/ki+Ixbdu2bf44nnCc\n44iU3jEGoDBUHKU2rshW9dUsmjthwgSVlTHqd+3aVSP5WPMnjfCIp/FW2mS8ndrCvdwvqc2nPYd8\nvpGphEZMuQq995bPHkXeKD41i+NwvRbKEZJ/1FFHqV6w6qqr6qUYxzCsExkGsUgfUxnQE1jYNTSA\ntGjRQsv4TzYC7snPxsfPVgMBBDys4EYIZzAEmA6DJfNrUOLDlTcJbyJcjnLks9CNkXdiQ8L3jQUB\nPPe2YCQhn8yrx9t5+OGHq6Jz4IEHZn4KVnO8mUS82PzPrAsI6ccbZQpRVlk/VxwCXoeFOGF8xaNC\nO2ZDiWePEkSYJaGjHG+66abKwzfYYAM1PCHAYnyCWD2feflpZOMGz0JYPuCAA9KK6jQWb/PT4cmq\nmyQAHecKVAgnjk8/QfHAiw/hqSV0GAMVi0ASZgxfhlg0DpkmjRznNGRKJ58V6zfaaKO80kv0XahQ\nF/OlzOPHEGoeahR485qnXY8hKo230iZpmxCL+PFOYTtNavNpzyEfGYT7EY0LMQ3FFHJkDta0gOgn\noRKPQ48oXoipCEQcYfgNac4551QD8VVXXaXP4VlM62WRQPg778/ce+QonBkYQ7KiZ8J7e7oCAffk\nV2DhqZlEgE5O6BLeSJgXDAYvPpY5wpGYy0MnZR4+gyadGA8+ispyyy2nIZ8wlCwBbyZf0S93BOoU\nATw4CIMs4BT+2wTKDoNalqLDi5kHFOEzyRNHRIANlljZWd+Ceftxy3mdfmSJ39zrsLCCEeZCIxLG\nXLwrEJ4nI+bY0g4xArCxPgtCLx58DL3FrqqMt59FnDCQIVB6mzeEK++z6qZy6cKccsYZfky76h7N\n5w0JbyEyCwoH8gxTC4nEos2jbGDAYiE+lDKiEouhcsa5GHwaaxmmeRCt2r9/f1XMGY9HRKvbV4dQ\nwJGB4ZFstMlrr7028xZEyabxVtovzjLeCcWadawsOjatzWc9jPWBUNiRz1l3AMMXjjlojz32UO86\ni+ItssgiBfIOxzgscNQxdx/enySj0LcwcvA3qxDTFS3aq2XLlsJfW4IR/7DCmIDBAMXfqXgEpq82\nkl0+qUyYl5S2PPa28RRL255Ignja8tjH07iJLd/2lhfuSc8ZbR0iZjwy2jvVAwKECtEpCWVD0SHs\nEsUe5oKFHKWH1cb5Gw0Ue+a80aFR7FnghtBmPELMMyZkjkU2bAEOrs1a6KYePs8f4Qg4Ao6AI1AN\nBAjhZDVmvE9OjkBjQYDpgbPNNlslxQT5hfaMwuPkCIAAxiA83SiiNSUiWXGMZU0Bid87i7diLEDB\nxiNeG8SzMGzFv5H35ttDI7A9D4cext3Q2WHnqrNHf+C5RAaDEcp/3AFCPoZl8tlefPHFSo+wa3gv\ni26YgfnQqPDEaJsSbdNmbPzFAelwT9o2O8cxi3VYvqXDfTzNMRtk6fDY8rXAjDJJ6aw8O6cKdv4g\nJYESHqcwLylteext4x6Wtr0p8RxbOtzH06bIk2+b5YV70q7kRyDMCpo0aZJ2ejqlERZAOivCHp2N\nFfONMfDfozAlmAEWcydHwBFwBBwBR8ARcAQcAUfAEXAEshBwJT8dHQ/XT8fGz9QQgSRrJMp7aNEz\nBZ9HEMbM5uQIOAKOgCPgCDgCjoAj4Ag4Ao6AIzBzCLjbdObw86sdAUfAEXAEHAFHwBFwBBwBR8AR\ncAQcgQaDgCv5DaYq/EUcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgZlDwJX8mcPPr3YEHAFHwBFwBBwB\nR8ARcAQcAUfAEXAEGgwCJT8nf6655mowYPuLOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIzDwCLLznlIyA\ne/KTcfFcR8ARcAQcAUfAEXAEHAFHwBFwBBwBR6DRIeBKfqOrMn9hR8ARcAQcAUfAEXAEHAFHwBFw\nBBwBRyAZAVfyk3HxXEfAEXAEHAFHwBFwBBwBR8ARcAQcAUeg0SHgSn6jq7LKL3zPPffIzz//XPmE\n5zgCjkCdITBlyhTp3bt35v3PPfdcWWKJJaRXr15yxhlnJJb9+uuvpUmTJnLUUUdVOr/sssvKYost\npvnjx4+Xpk2bSpcuXXTr0KGD3nvs2LGVrvOM4hDwOiwOp+HDh0u/fv3y27Bhw/TC//77Tw455BBt\no0suuaTcfPPN+Ru+8sorMnDgQFl00UVl6623lkmTJuXPhYnu3bvL22+/nc8aPXq0kPfuu++Kt/k8\nLFUmxo0bJz179kwt5zhPh+bDDz/Mt2Nr06uuumoetxtvvFFWXHFFWWGFFYR2b+Tt2ZConf0222wj\nL7/8cu3crB7u8tZbb8mmm25a8KQ0vlhQqMiDeP+dOnWq7L///spb+/btKzfccEP+TmPGjNE2ivwx\ndOhQmTx5sp77888/Zdddd1W5ZOmll5b7778/f40l4s+x/Nra824bbbRR4u1eeOEFWXfddRPPeWbd\nIOBKft3gWq93/fTTT+X666+XF198Uf755596fXb4sHnmmSc8nKk0A+x77703U/fwix2BukBg2rRp\ncsQRRwgK+GeffZb6iGeeeUbuvvtuQTikb15xxRXCIJdErVq1krvuuku4txFKzrfffmuHuqfcV199\npdvEiRNlhx12kKOPPrqgjB9UjYDXYdUYhSWeeuopufTSS7Ut05732GMPPX3NNdfI+++/r0r6gw8+\nKIcddph8/vnngvK/1VZbyZlnnikoVSifRx55ZHjLxPQdd9whhx9+uDzxxBNqwKKQt/lEqAoyGfeP\nOeYY+f333wvy0w7KGWfaIu3LtsGDB8sqq6yiUD377LPKp2nLDz30kJx33nnKa709p7Wk6udjIFxr\nrbXktttuKxjvqn+n+rkC+Xq//faTtddeW1CiQ0rji2GZYtJJ/XfEiBHyxRdfCEZ8lHXeAXkAhx6K\n/XXXXae8t3379nLcccfpY/73v//JvPPOKx988IFceeWVQtv+999/86+Q9Jz8yXpI9O/fXxgznOoP\nAVfy6w/rOn0SVj8UCKzQMCUnR8ARqBsE8KZjqUZYmX322VMfksvl5KSTThL+4aNt27ay0EILqfKT\ndAFlMGw99thj+dMM4tttt13+OCmBYY1B3al6CHgdVg8vvD8dO3bUMYaVjMEPevjhh9Vz1Lx5c40u\nwUuD8kR52uZqq62m5fbee28tqwcpP0QBnHzyyfLkk0/KwgsvnFJK9L7e5gvhOf744+WAAw6QZs2a\nFZ5IOCp3nMGoTZs2un3yySfaXk877TRFCmcJhira899//62Ohs6dO3t7TmhHNc0iSuKEE06Q5Zdf\nvqa3qNfrGLuJOjjllFMqPTeNL1YqWEVGUv+dbbbZBAXe9sgac8wxhxqdNt54Y/XWEwGI8cFkftoq\n3n9o8cUXlxYtWhQYUpKeU8WrybXXXqv3WmSRRdRoYFEDW2yxhdB/jIjkMvr1119VRsKgRn2DE4Tj\nDoMEshH1/91332k+BmCMuxBGjJVXXlnTr732mkbd9OjRQw3Fjz76qObjQOE7eQcilJzSESj5v9BL\n//TSPEMHIXwfIWnQoEHSunXrevtQOi4d9d5771Uh8NRTT5Utt9xSn8/gCZOkDEwTr2XXrl3V2oi1\nHM8aYcmjRo3KKy1Y/B544AH55Zdf5PTTT5dddtml3r7FH+QIpCHAwGqeH1N2ksqagoPXDC8+Sv5K\nK62UVFTzdtxxR7n66qs1nA2jHWHLCOT0JyM8CbvttpseTpgwQSMJ8CY4VQ8Br8Pi8cL78/HHH6tQ\n1a5dO0HQuvXWW4UQZ6JJUP6NSJOXlm/l4ntCUS+++GL1RjMdJSRv8yEaldP0f6adrLHGGpVPxnIc\n5wpAkEUOPPBAbXfwA+ijjz7SPR5RjFndunVTecrbs8JSKz9LLbWU3me++earlfvV9U2QoRnvUbKR\nW42y+KKVKWaf1n+HDBkiN910k/ZrIgaZzmfGKeQJCC/9ZZddJuutt54eE6oPIS8jS5999tmq6JOX\n9hzOpRHTKTCAPf/887LAAguoAeyggw7SyGGmGYKBERFcRjgcuQZFnmhG9AAU/b/++kujEehvffr0\nkccff1wdGU8//XT+Xrwn5yCiFDEIbbbZZor9JZdcko+owKH5+uuvq1z1ww8/2KN1j1xG9E0SZcls\nSeUbe950c3xj/wp//0oIYNkbOXJk6jzIShfUQsYff/whnTp10vBMwouwtBFuRMcmvBnrG8wKyx6h\nWhBWc0LkeF+UfLPUcQ5LOqGgjzzyiIckA4hTo0SAgY52TtgdbTmNVl99dXnzzTfVkk2oKNbsli1b\nFhRH0GBeINtee+2lihaCqlPdIlDOdYiwdN999wkhzBhrESqPPfZYBRxFiS0ky0vKD8uFabz3CIbc\n+6WXXgpPqXDtbb4AkvwBRn2M6UyLKIYc5wqUkDXwjC633HL5TGQYPKe0xXfeeUeVKAyt1qbzBaNE\nvH2H5xznEI3STGfxxWK/OKv/IiPjEUe5x0F2+eWXq2xt92YaFI4E1vshiiekTTbZRGVulPxvvvlG\nZYrq8Am7F3I8zgcUfIh3SZrnb+Vtj1GEMRNCQcdpEZ/aSD59EAMaEQtEIeC4IJrR1j7gPDoFUZNs\n9E8jnoEzM0lpZ4pXGmWdS7umMee7J78x117Gu+M1RGmoT2spIW4WKkTn23DDDbXD4nlkASWU+Vtu\nuUUVHeYUQeuvv74QeoSlj/nFLDBitPPOO+uCZIQBxedCWRnfOwINFQEGw+5RKBkL7y244IKCtRkP\naNrCMwxWzGOmjzDQWV8Kvw8ln35lxGCOIeDHH3+U+eef37J9X0sIeB1O9xaZZwVYEd5MYKPNff/9\n93m0EdiWWWYZjdaK5xPBlUYsYMV1F1xwgeDBYpErC8n3Np+GmqinjzU6Nt98cy3E4obrrLOOTo0w\n73R4teNcgQaewG233bYiI0qhbCA3QeCHoRXvPqHP3p4VFv+ZgQBe9DS+WCxIeOrT+i/tEy866xdA\nOAAwtLKQ6Z133iknnniirpNCxK4RhgCm+CFzsOEhR5YgKiXtOUl8wu7HN84999x2qNNYQu99/kSU\nQJE3QhcIyaa/hHlMMzj00EPV+WffgBcfIy/jAISshC5DpMK+++4rF110Uf4Wc845Zz4dTyBv8c3h\nO1EGAx7nyonck19itY1ghNLMoF+fCj4woqSEcwKtY6PcsIot82tQ2JnfZIRCQ5gmjIN5zueff76d\nkqxOnC/kCUegASHAHDVbMJIVwxmkLWzs1VdfVat01utiNcebyaJ7Fu6fVZ6QfrxRphBllfVzxSHg\ndViIE4s4IWiacMc8fBRyiOknt99+u6ZZ9I1zKEYImCwSZXMxKWPzLLVw7AfhC9p+++3ViECUShp5\nm69ABsM43i4W2WIjtJh9muDuOFdgh/Kz5pprVmREKRQPQogh+DZKB6uUe3tWSPwnQCCLLwbFMpNZ\n/Zd/z2F9E4i2SGQJXm34KmHsY6JV7E05tocQGo9xAIJfI4NwTdZz7NqkPfcnooBoXIjpPvZMZA6b\nk8+7WBnK4dAjihfivZnHz5z+kJDvmbqBYYJ7so2IFhukHDIN4wnRv8hDODO+/PLLzOiZ8N6scYRh\nDh0IvYSNNHmcKydyT36J1DaDN4IXXpa4Fa2+PpFOzvxjvJFY0Qg53n333fVvUph/z+q/hLghhOCx\npxPjyWewHTBggFrsEAb5SyYnR6AxIoDBikGYdkwIHQvDYIlmgGG+sk1TSfs2BjgGT+bXJgnqrE9h\nc5YZxPGOYiibVX0+7Tsac77XYWHtEc5M9AheKwRGvDvM94TwrmCc5RyCHHNCLRoL4Y12TBgmYZbh\n2hKFTyg8Yr4pxmAEPgQ/b/OF+IRH8IrQwIeRneihYqiccSbyiXYFbw6J9kyUIXIU8gxTCwkrRknw\n9hwi5eksvlgsOln9FwcBzjr7C13aIrI1ijYRU+HfPhJ9gjKMk4yoWNb2Qb4m7N0iAWrCJ/Cko2jD\n01mPBWMD8ga0zz77CNG2GMCYMoBRwohj3pWF/5i7z+J9STIKfYt/DcCQBhGtYOsNEKHI83EQYrxk\nYWKMFrxPMYQyH/7FsclTWdNsirlvYyvTpIgXTioT5iWlLY+9bTzK0rZvGuRZOtzH0ywdS164WV64\nJ00sR4eoQkdG+5ImFgOp79D8JEDpVHRKGBCKDmH6KPYMqHh8YAIssMffaOCNwcLHAjcoRCxww3wc\nDAAwLzo0jMHCoVipGcOBkyPQ2BCg3TLAxOfXN7bvKOf39TqcvkYKik/SnEYUJgQ6PDAhwe9R/n0q\nSYiKpxs6AkwPxHESV0y8PTf0mqv/92PtqDS+WBtvA/+Et1YnspVpO8jM8fZb0/ch7B1DbZz342jg\n20MDgj0DZZooXqbAzAyhP/Bcpm0xDiNHmcJu90XnwBBAfvyclbH8UMnnfpGjhLnDE6NtSrRNm7Gx\nah/pcE/aNjvHMYvSWL6lw308zTEbZOnw2PK1wIwySemsPDunSnf+ICWBQh6nMC8pbXnsbeMelra9\nKfEcWzrcx9OmyJNvm+WF+7JS8gG2IREMhk5PpzTCAkhnxRpIZ2NBEWMMrAyMkAgzwGLu5Ag4Ao6A\nI+AIOAKOgCPgCDgCjkAWAq7kp6Pj4frp2PiZGiKQtMASynto0TMFn0dgoayOlbKGr+WXOQKOgCPg\nCDgCjoAj4Ag4Ao6AI1DyCLjbtOSr2D/QEXAEHAFHwBFwBBwBR8ARcAQcAUegXBBwJb9catq/0xFw\nBBwBR8ARcAQcAUfAEXAEHAFHoOQRcCW/5KvYP9ARcAQcAUfAEXAEHAFHwBFwBBwBR6BcECj5Ofms\nkurkCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCJQDAu7JL4da9m90BBwBR8ARcAQcAUfAEXAEHAFHwBEo\nCwRcyS+LavaPdAQcAUfAEXAEHAFHwBFwBBwBR8ARKAcEXMkvh1r2b3QEHAFHwBFwBBwBR8ARcAQc\nAUfAESgLBFzJL4tq9o90BByB2kZgypQpsvTSS2fedtiwYbLCCitI//795bTTTkss+80338jcc88t\nxx9/fKXzK6+8svTr10/zv/jiCy3Xs2dPYevevbssu+yy8u6771a6zjOKQ8DrsDicrNR7770nSyyx\nhB3Kf//9J0ceeaS20eWXX15uu+22/LkXX3xRBg0aJEsttZQMHjxYvv/++/y5MNG7d29555138ln3\n33+/kDdu3DjxNp+HpcpEvG7iFzjO0xH56KOPZMUVVyzY1lprrTxco0aN0na72mqryXXXXZfPf+21\n12T11VfX9rzddtvJpEmT8ufChOMcopGe3n777eXVV19NL9DAzrz99tuy9dZbF7wV7SNsS8OHDy84\nX1cHjz32mCAbwIt32203+eOPP/RRv/76q+yzzz4qb3D+qaee0vyq2nxtvuczzzwjW265ZeItX3rp\nJdlkk00Sz3lm3SBQ8gvv1Q1sDeeuDzzwgHz55ZepL7TgggvKBhtskHq+Nk+0b98+VZCr7nMYYFGQ\nFltssepe6uUdgTpFYNq0aXLCCSfIww8/LJ9//nnqsxjsrr/+enn00UelWbNmsvHGG0ufPn1k8803\nr3TNvPPOK6NHj5aTTjpJy1IAJefbb78Vzhm1atVKPvnkEzuU888/X0488US5884783meqBoBr8Oq\nMYqX+Oeff7StmUDJ+REjRsiHH34oL7/8svzwww+y6qqrCso+BiiE+JtvvlkFzsMPP1zOOOMMufDC\nC+O3LTi+++679RkPPvig9OjRQ5V8b/MFECUeJNVNYsEZmeWM88ILLyzITUa0YZQj6Pnnn5err75a\nwAf6v//7P1l77bVlgQUWkKFDhwpK3CqrrKJtFKPs5ZdfruXSfsoZ5zRMwPCee+5RBXTfffdNK9Zg\n8j/77DO55JJL5K677pIll1yy4L0Y4xmDMbZDjPN1Tb/99pvst99+Kld07NhRdtxxR7nsssvkiCOO\nkOOOO07mn39+eeONN+SDDz5QmQPenNXm6/p9w/vjFLniiivCLE/XMQLuya9jgOv69iuttJI0adIk\n8THkc97JEXAEag+Bpk2byvrrry8XX3yxzD777Kk3xvi2xx57qJKOpx6LPwJDEs0111yqHD3xxBP5\n0zfeeKN6QPMZCYl55plH2Jyqh4DXYfXwovQpp5yiXiKwM8KjhJDZvHlz6dy5sypE5j2ib3Tq1EkF\nX4RRymQRUQAYAh566CFV8NPKepuvjExS3VQuNT2n3HFGEWvTpo1u8OMxY8ao0g46I0eOlIMOOkjb\n6t9//63KEm2YKAnaHQo+tPvuu6uSpQcpP+WOcwosOs4dffTRsswyy6QVaVD5tBU800mRdu+//750\n6NBB8FCjfIe8sa4+AoPUYYcdJt26dZM55phDFl98cY2o4nlET22zzTb6aCJKcJKRl9Xmq3pPHBXU\nVd++fZXXT548WS8hOuvTTz/NX45x14h3BDMiDQZF0Vz0Hwi8Tj75ZMnlctqXvvvuO83HOHHMMcdo\n+ueff5Y11lhD0xgrkJtwjnCvxx9/XPOfe+45OeSQQ1Q+4jud0hFwT346No3iTOvWrTVk+M0336z0\nvljNOF9fRMelo2Ilh9nh7dxss8308TfddJOceeaZ2rlhmrfccot06dJFrrnmGrnooosEzxqdlfAn\n81zCXPCWwjDwViJMOjkCsxqB0HiWNagT0mnEYIa3Hc9mGuEpov3jOZo6daoQtkwfCL1O/CUo4XjQ\nV199pZEE9BGn6iHgdVg9vJ5++mlhagPhyiERgo+Qa0TawvIvvfRSFfB69eqlHnnukUaESOMVxRuF\nsSAkb/MhGpXTaXVTuaSI41yBCvIKytJ5552Xd5R8/PHHWoCIk99//12IhLz11lu1Tae184o7VqQc\n5wos4inzhs8333zxUw3yGBkaZxlGS6LtjIieIaru0EMPVe85BvobbrhBw+itTF3s4Y8YmcaOHSvn\nnnuuKtomIyBTv/XWW6r4//XXX6pUf/311/nXSGrz+ZMJCaZTnH322cK3Ec2CcQYeTbQL0wzBwGj8\n+PGWVKPHk08+qREO9957r0bBvP7664LhDFmI8RcDBAZhjAXPPvus/Pvvv3o90REWwYsOwTOJggR7\nonvXXHNNYUxAliLypnsUNRZGl+VfwhOKQIVJ3gFptAgQKkRIY0gcWwhRmF+XaToejIC5Syg0MD+8\nmTAbLHV4fbDoDRgwIB9eTAcmn9DkRRddVJmJvSMMAeMFnRsl38kRaIwIPPLII7LOOutoKH7WHH6m\nqNB3sGQT4j9w4ECdgx9+M4LGhhtuqBtz8Zh3h6DqVLcIlHMd0h7POuss9eTHUUZoZAuJY4xUzNXf\ne++9VUhDsMfbnEYoqgh8V155pbzyyisFxbzNF8BRcJBVNwUFZxw4zhWo4BXEExp6lJFh8HrSFmmH\nKB545NPaecXdClOOcyEepXjEeiR33HGHTu1A6b3gggt0jK+vb0Wh32uvvTRainB9CCcbivEuu+wi\n6623nq7dA/80Smrzdi5pT1QVzgfkegiZnryqCKOI6R8o6IwH8amN5GM8mDhxorRr106/A8cFecg4\n0H333afPZooHW6jM8wymdGU5Wqp6z3I47578Eqjl2WabTRWCsPOhIJBfnwQzQaiD6HwwGTrszjvv\nrBZFrG633367htxYSBHKz1ZbbaUe/yFDhhQs6rTDDjuoxY8wIQZfJ0egsSFw7LHHquKOoQqLcxYx\nWDFfH8GBfmN9KbyGPsZUASMGQwbIH3/8Ub0Jlu/72kOg3OuQqCu8QXhcoJ9++invWWnbtq3OxTe0\n8eKzUCReG8KbCamEUPIx4hK1lUR48bkOr9FOO+2kniCL6PI2n4TY9LysusFbFifHuQIRPIHIHiHB\nSzG2QuBHqDDeWjyLrDlhRDun7aeR45yGTOnkYwAKQ8VRauOKbF18LQvtTpgwQeVr5PyuXbtq9B/j\n1HLLLaeOMebj02YxApiCzrsktfmsd+QbmUpoxJSr0Htv+exR5I3iU7M4xmkXEiH5Rx11lHrjWcsF\nwjhG9MA555yjxyzSx7QE9IQ999xT1x7QE9FPixYtLOn7DATck58BTmM6hQJBaBnEnuP6JpSUcOER\nhDMYAoMjgyXza5hXE668SXgT4XKUI595zkbeiQ0J3zcWBJijxrwziBA6IlQIVyu2P2I1x5tJxIvN\n/8z6dkL68UaZQpRV1s8Vh4DXYSFOGF/xqLD4FBvhq+xNCWJBKojwZiJQ4PUoSwijGJ8gVs9nXn4a\n2bjBsxCWDzjggLSiOo3F2/x0eLLqJglAx7kCFcKJ49NPUDxsTQk8tYQOY6Bi3jNhxja3mDZPO08j\nxzkNmdLJZ8X6jTbaKK/0wvvsn3Dq8iuZ+4/x1LzaGFRNkYcvX3vttbruAQYHFt0zBZp3SmrzWe+K\nDEJbJxoXYhqK3Q+Zw9YYop+ESjwOPVsQnGkFRBzxj0AhzTnnnDrP/6qrrlJZh2cxrZdFAuHvjCfM\nvWfqDM4MDBvxqLHwfp5ORqB+Xb3J7+C5tYQA4SvMk2E/K4hOzmqyeCNhRDA9vPhY5ggtYi4PnZR5\n+AyadGLm7KOoYIFEEYKhZAl4s+K7/JmOQLEI4MFBGGQBJ7z3KDcWtsY9GJzDufrx+zIQ4gFF+Ezy\nxP3yyy/5wRIrO+tbMG8/bjmP39ePi0fA67AQK4S50IiEMRfvCoR3BeMsfxGJIMe6KRhyIUJHEXrx\n4GPoLXZVZbz9LOLEwpMIlN7mFc7En6y6SbwgyCxnnDE+0a7ixlfaMzILCgfyDFMLCSumzaNs8E9F\nLMSHgkVUYjFUzjgXg09jLcM0D6JV4X0o2YzHI6J/aqhrwriE3AxfZaMdo9hDyBZEXBENyN+PovSj\nTENpbV5PpvywPhAKO/I5q/Zj+MIxB7GoMP2FRfEWWWQR4d+1jDjGYYGjDp0E3p8ko9C3kIn4m1WI\n6YoW7dWyZUvhry35XqYfMyZgMEDxdyoegcrxXJWvTSoT5iWlLY+9bdzZ0rYnkiCetjz28TT/T2H5\ntre8cE+alt0hYsYjo33ZEP/dmhVGVpdA8Fw6JaFsKDqEXaLYw1ywkMMEWGCPecko9sx5o0Oj2BN9\nwHwcmBLzjON/oVebf89Xlxj4vR0BR8ARKDcEEDQR6PDAhEQIJ6sx49l3cgQaCwJMD2S6Y1wxQX6h\nPaPwODkCIIAxCE93fF2sukaH6FecaUnyPvkoyUmOgpq8F3wcw1b8G3kHvj00Atv9cehh3A2VfztX\nnT36A88lMjjtu8gnwozvTftmyw+jAWbgNzR6n4nRNiXaps3Y/kvYk2cb5SzNojTxtOWxj6ctLzql\n5+LHls8e4rxRmM7Ks3OqYOcPUhIo4XEK85LSlsfeNu5hadubEs+xpcN9PG2KPPm2WV64J12WSn70\n3bOcMDTQ6emURlgA6awIe3Q2Vsw3xsCqzQiJMAMs5k6OgCPgCDgCjoAj4Ag4Ao6AI+AIZCHgSn46\nOh6un46Nn6khAkmWRZT30KJnCj6PIJzIQopq+Ei/zBFwBBwBR8ARcAQcAUfAEXAEHAFHIELA3abe\nDBwBR8ARcAQcAUfAEXAEHAFHwBFwBByBEkHAlfwSqUj/DEfAEXAEHAFHwBFwBBwBR8ARcAQcAUfA\nlXxvA46AI+AIOAKOgCPgCDgCjoAj4Ag4Ao5AiSBQ8nPy55prrhKpKv8MR8ARcAQcAUfAEXAEHAFH\nwBFwBBwBEGDhPadkBNyTn4yL5zoCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjQ4BV/IbXZX5CzsCjoAj\n4Ag4Ao6AI+AIOAKOgCPgCDgCyQi4kp+Mi+c6Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao0OAVfyG12V\n+Qs7Ao7ArERg+PDh0q9fv/w2bNiwSq+z8847S5j/yiuvyMCBA2XRRReVrbfeWiZNmlTpGjK6d+8u\nb7/9dv7c6NGjNe/dd9+V8ePHS9OmTaVLly66dejQQZZYYgkZO3ZsvrwnikMgrQ7//PNP2XXXXaV3\n796y9NJLy/3335+/IXU2ePBg6dOnj2y33XYyYcKE/LkwUYp1+Mgjj8iyyy4rPXv2lB122EH++OOP\n/CffeOONsuKKK8oKK6wg4BqncePG6XXxfDsuRVeT2rgAAEAASURBVLzs26q7v/7666Vv376y2GKL\nyVFHHSW5XK7SLcaMGSMbbbRRpfysjO+//14uv/zyrCJldQ4MabP086FDh8rkyZP1+6dMmSL77bef\nLL744srf77zzzjwuzsPzUNRKYptttpGXX365Vu41q27ywAMPyPLLL6/taM899xTGj+pS2lg0depU\n2X///ZUXwBNuuOGGSrdOw/Ctt96STTfdNF/+ww8/zMsrJrusuuqq+fO1mcjiTy+88IKsu+66tfk4\nv1ctINAkukd8wzhgW7MobRsL+bHNPmNrHu3niLYWM7Y5oz0r4c0dbS2jbd5oaxVtraOtTbS1jbZ2\n0dY+2jpGW6do6xxtXaOtW7T1iLae0dYr2npHW59o6xttS0Vb/2hbNtoGRNvAaFsj2oZEg6RTBgLR\nIJb73//+l7pxvrHQWmutlXvxxRcby+v6ezZSBIYMGZJ77rnncv/8849u06ZNK/iSW265JRcpQ7kL\nL7xQ8zm/4IIL5qLBT4+PPvroXKRIFlxjB926dctFA7Qe3n777blFFlkk98knn+jx559/nmvdurUV\n1f3ZZ5+d22CDDQry/KBqBNLq8OSTT84ddNBBeoOXXnop17JlS61jMtZcc83cqFGjctTnxRdfnNt7\n770TH1Rqdfjrr79q+6X9/fXXX7nNNtssd/rpp+u3P/PMM7lIWcr99NNPukVKUy4yfuRx+fvvv3Mb\nb7xxLjJI5fPiiVLDK/59xR6///77ua5du+a+/fbbXKRs5lZfffVcZECpdPlTTz2V23DDDSvlZ2VE\nhpbcgAEDsoqUzblIoc916tQpB97//fef9vd99tlHv//EE0/M7bbbbtrHP/3001z79u1zX3/9tR47\nD6+dJnLFFVcoL43k89zzzz9fOzedBXeJjO65yOCe+/LLL7V97LHHHrkTTjih2m+SNhZdffXVuciY\nl/v3339zPGueeebJffPNN3r/NAyRFfbdd99cu3btcsjDRpHBIBcZqfMbcsMxxxxjp2t1n8Wf4Gtf\nffVVrT6Pm3333Xc6NjHemFwW34MjW5gPJuiJM/RF9Eb0R/RI9En0SvRL9Ez0TfRO9E/0UPRR9FL0\nU/RU9FX0VvRX9Fj0WfRa9Fv0XPRd9F70X/Rg04nRj9GTTWc2Hdp0avama7OP6+IcZxIXOZUxApEg\nIU2aJLcT8jnv5Ag4AhUI4Jns2LGjYJVmVVe860bRACaRki+RoGhZQvlogJbVVltN8yLlUB5++OH8\n+aTEzTffLJHCKU8++aQsvPDCSUU0j/vOOy9jiFN1EEirw86dO6v3hHvhzWvRooVESr167fGG4smP\nhH7Ba3PppZdmPrJU6vCXX35RrzIe9znmmEOjRyLlSL8dz/Nhhx0mzZs3l0jAkvfee0/A0Oj444+X\nAw44QJo1Q1bJplLBK/sr088+9thjEhlElLfQ7ogGSuMTkeFFvflEVuCRpj1D22+/vXAfo8hgJddd\nd52sv/76GiFENAaEl/DQQw9VLyGevTvuuEPzaeORMUt69eqlG3VSagSPBme8+Mg4a6+9tkQKvX4m\n0TqRkqQ8vUePHkKbj5Q45+G12Ahor5EyrB7wWrxtvd/qtddek8hwJpFhTtsL/RXPfnUpbSyabbbZ\nJDIyie1nn3125b/cPw3Dtm3bCt79U045peA14L9t2rTRLTIEyBNPPCGnnXZaQZm0g2uvvVbHwsjh\noOOfRb1sscUWwr2MllxySUtKGn9ifDjuuOM0QokIiEg512uOPPJIOfzwwzX9888/y8orr6xpMIY/\n0RfhdY8++qjmR8ZlHad5B/qoUzoCWA2cyhiB+eabT5ZbbjkhFC1O5HO+PggBkTAehJAHH3xQlaKT\nTjpJImujTJw4UUNEzzzzTGUOhxxyiAolDNAwu5EjRyojDN+TayPvkkTeVPnhhx9UKP/ggw8ksnDK\nVVddpcJNWN7TjkAxCERWYPn44491gKEtMejceuutQuhbZFDWUM8LLrggLzRzT9ovRgEj0uSlEWF5\nkadY2z6h+SERDmgGBMLFP/vsM4ks52ERT1eBQFYdEqoPRZ5qibz2Enk8VNGnzlu1aiWrrLKKClqR\nF1Cuueaa1NDDUqpD2iCGqXfeeUfOOOMMbf8IidBHH32k+ygaTA1ekVde7rnnHuXHtEvCn9dYYw0t\nk/VTSnhlfWfWuerwCQyMkRdUFaW7775bttxyS1VEUdAxvERePKGdc446QxnZZZddJIpO0VeAv0RR\nGWqUYXxEqGa6BQZKhPnHH39cIu+hGhowbJUSMcUp8oTqJ0WePbnssstkvfXW0+NLLrkk/6koEigh\nSy21lGLtPDwPzUwlwBOqL9lypl4242KUe6bKYTBDEX/jjTdSp3Cl3SZrLIo8/HLTTTcp/2ScZ/oO\nijqUhmEU6adjFAaBu+66q9JjkVEOPPBAlS/SnHvhRUynwBgAr1lggQXUoIvhEB6DsZv3N4oivSyp\nDpAk/gTPiSKV1LjGtDf4DFPfnn766fy9GDc4B0VRj2oQiqLH9HvonxjlkIOYJvb666/LQgstpDJ+\n/uGeKECgwgVVkO0H5YQAijLMISSOya8vgvkwl2fQoEEqrDAH6YgjjlCLI5ZOvGYwCAZeBBwYCkII\nQmY4h5n7YBVkDtJ5552nFtbdd99dYJgo+QjtzIl2cgRqggAezPvuu08NUQx0CIjHHnus3grlHmGR\nQSck2iRbSPHj8Bzee9o49zah3M4zeDPXjm2vvfZS4wKDtlPxCGTVod1lk002Uf4Dv4hCJHUOOoZQ\nBAsEk9tuu009flY+vi/FOkSoZb4ySv9FF12kn8zcfARc2itGAJQmvL94Y0499VTBMFsMlSJexXx3\nWKY6fAJjE54wCAEYRQNFYKutttJxFCGYdRTWWWcdmWsuIkQLibU+uAZjFkZvFC68kBgrMQxg1Hn1\n1VcL1qQovEPjP0JGILqKqAWiTYzgD8gOROvce++9Mueccyr/jvPs+LFdz97bc4hGaabpfzjC2GMI\nQ1Eloqk6lDUWMcbgEUe5xzPPmhq02ZkhnBJEY/HexRBr0uy4446q4FOedwnXqUm7Rxp/CsvDt3gf\njJtELBABhuOCSCRbT4Dz0dQaXd+INY7CtWB4BlGOYSRleH9PT0fAPfneElRIQ7nGA2PEMcJbfRJW\nymh+sT6SgZeFxczayzm8QgzKMBkGXxYXIcwu7PgoXNG8fPniiy8EhQhLI0I5jNjCkwhJhFmyCJqT\nI1AdBFBizMrMdbQrhGsIhRALOxZ0jFAMPrRfPEe0OSMGNcLq0oiFeJZZZhnBaIBxinZuIfm06Wg+\nbv5SlNFo3rj8+OOPMv/88+fzPZGOQFYdIkjhWaDO2BDcEDoI7cV4Y6GBLKJIGK95ceJPK6U6xFvF\nt8Kb8fhGc5OVD0fzl1U4syldeIY4j+EVDxRh0ZtvvrlCQxg0Cifh50kepFLCK94Wij2GJ4TesCw+\nEVcmbLoECj2h6BgiGSejueaJj2dcxFhD5AWE8YawWNo8RnXqCT7GVAyMN0SxlBKxoB7tF+cBso7R\n77//rgoGPB6jHtOhIOrGebih5HtDAD6H84gpXRh9LBLMzle1zxqLMCgjsxKVA7355pvqXJgZuZV7\nbrvttlW9Vv487zf33Ewln07wmdB7b/nsGQuN0viTnWePR54pQzjurA/ixcexgewDEd3LuIvzhGk0\nZlzmHMY3p6oRaFp1ES9RDghgETMPJPusecB1hUd83macUfBcmAAMgRBDGADCdkj9+/eXgw8+WAgp\ngmC+CJUIM7YRWpqlZIX387QjECLAgG6hsOQjDKOQQ3i+8IpdeeWVwlwx5sYhcCM4E6Jm82ajBfXy\nc870wtiPGdeYX4sRAY99GuGRwzJvRoC0cp5fgUBWHeLFRBCCEGaIEsKTQB0zN90MOij/KP5WVxV3\nn56y/FKoQ7xJKIFmTKWdgwmEoIYRFcIrBX/mXwlYgR8vDH2Bjcgw9kkKPteWEl58T00IAwmKORFr\nEPPkyUuiZ599Vg3ZnEMJZ54sYfYQ03mYR4uxxaLxwJ2x0IgxFKWE9smGUQCh+ZxzztE0K84TkQFv\nwWBeSgQvJgx4zIzIwfDbmCv+f//3fxrObAo+552Hhyh5GgSIlmFcwFhG32MKTHX/9SJrLMLJZdOi\n4K30c+O7Na0BDNZM6SmW4BNEFBhPYloVeRAyh83Jpy9ZGc5l8SfOQ/AbnCIY1rkn24gRIxRL+A4G\nNwwARDTizMDQnBU9M/2u/htHoH5dtfGn+3GDQgCPDOEy5plpUC8342WYr493k1A6Fj2DSYYdn7mJ\nCJlsKEAwBxgJCj6hiMzTx1NXavMMG2JdleI7EeaGJx1PDwMulm7mbkN4OI2IPMFIZdNgGMiYm0xI\nGsoSkSjFEHNHWdCGwY9BEEUToQJCCcVYxTzaJINYMfcvxzJZdXj++efr/OZoZWMVMggbNE8KdYAQ\nR8QEnmkUqWKosdfhSiutpJjQvvlrN0LxWQcFwruCQogxCiEPpZIwTKJYQsMTBtzuRS6Q1NjxKqZN\nJJUBQ4yDGNhRMMGLqWZJRKQbofks0MfcWNqi8QAM3UT2hNPSmGqBIYBn4KFmShtjIMcQBnOeS+QF\nbZ62jpDNeRTcUiKmMRAlEf6FGDIPygTRC7RVPLRGeP1p987DDRHfgwBRM9GK+gJ/pK+g6DN+VIey\nxiK8+PRH2h4Eb6XP15TgCcgP5swr5j7wBRRtps+yBhHGBuQNiCghFhuEP1jkrd0ziz9ZGfaMFRiQ\nkdchohVsvQwiFHk+EUbIUawZgtGd93EqHoEmRRRNKhPmJaUtj71tPMrStm8a5Fk63MfTLNFLXrhZ\nXrgnTSwH/9szXRqJDpyqRgAPOZ25vgkBESHSQuLOPfdcDc/Hsg6hpBO2zP+F4yFFyUKQRBhC0UJp\nwqvE3CWYQfQXZ1qO8oSN4q2gLM/Bim9hpPX9nf680kCAhSJpS9UJY8WThqDtYfUNow1k1SFKPPzC\nFKfwjalDm0YU5pd6GqMShtWkKCi8Wnjjk/AqdVxq+/vgK7TNqngLxm3Ga+azxgmhGYN4VZ4/lBMM\nBRZJwX3svigxCNpOFQg4D6/AwlPTEaC/0I8YL2pKWWMR4w19dFaGpxOKj3MizpMYE+BXoUHXMDA+\nksSfrEwxe4wTPJepiow/8KR4RBhGOwwB5MfP2TMsn/cymjGeDY2OJ0bblGgj3Intv4Q9ebZZGY65\noeVbOtzH0xyzQZYOjy1fC8wok5TOyrNzqnTnD1ISKORxCvOS0pbH3jbuYWnbmxLPsaXDfTxtijz5\ntlleuHclH7RLlGAsMFW8pXRYmE8xwggeKPOslig0/lmOgCPgCDgCZYoAXnqmoyEUE43i5Ag4Ao5A\nqSPgSn56DXu4fjo2fqaBIoC3CAUfwjpXjIJPWVfwQcHJEXAEHAFHoBQRsBD/hjzlrhRx929yBBwB\nR6AhIuBKfkOsFX8nR8ARcAQcAUfAEXAEqoEAobG2hkQ1LvOijoAj4Ag4AiWIACHvTo6AI+AIOAKO\ngCPgCDgCjoAj4Ag4Ao6AI1ACCLiSXwKV6J/gCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCIBAyYfrs+qv\nkyPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCNQDgi4J78catm/0RFwBBwBR8ARcAQcAUfAEXAEHAFHoCwQ\ncCW/LKrZP9IRcAQcAUfAEXAEHAFHwBFwBBwBR6AcEHAlvxxq2b/REXAEHAFHwBFwBBwBR8ARcAQc\nAUegLBBwJb8sqtk/0hFwBGoLgeuuu05WXHHF/DZ8+HC99X///SdHHnmk9OvXT5Zffnm57bbbKj3y\nvffekyWWWKJSvmX07t1b3nnnHTuU+++/X8gbN26cfPHFFzL33HNLz549deM/sZdddll599138+U9\nURwCNanD1157Tfj/8aWWWkq22247mTRpUuLDSrEOH3vsMVl55ZW17e62227yxx9/5L991KhRMmjQ\nIFlttdUEXOPkbT6OSPrxTTfdJMstt5zykOOPP15yuVylws8884xsueWWlfKzMr7//nu56qqrsoqU\n1TkwpM0uvfTSsvPOO8vkyZP1+6dMmSIHH3ywLLPMMsrf77nnnjwu5dz/8yDUYmL77beXV199tRbv\nWP+3euihh2SVVVbRdrT//vtLTdYASxuLpk6dKocccojyAnjCyJEj8x+Y1n5//fVX2WeffaR///7K\nr5966qn8NS+++KK2ecavwYMHCzyhLiiLP7300kuyySab1MVj/Z4pCLiSnwKMZxeHwAMPPCBXXHFF\n6sb5xkIbbbSRvPLKK43ldf09ZxECDGLnn3++sGfbZZdd9E1GjBghH374obz88sty9913y9FHHy3j\nx4/Pv+U///wjJ554YoGClD+ZkOAexxxzjDz44IOy+OKLa4lWrVrJJ598ohv3HjJkiN4z4XLPykCg\nunWIAWfo0KFyyimnyNtvvy0LL7ywoIRVRaVQh7/99pvst99+KmS+/vrr8vvvv8tll12mn/7888/L\n1Vdfre0dhejiiy+Wr7/+Og+Lt/k8FFUm4B20r/vuu08QyFEqb7nlliqvK6YABqlQSSjmmlIt8/PP\nP6tif+WVV8qbb74p7dq1k5NPPlk/F75Om0X5xHh10EEHybfffivl3P9rux1gFN9www3lrrvukmnT\nptX27evtfl9++aUccMABcvPNN8sbb7yRbz/VfYG0sejGG28UnkFbvPPOO1Xhpy1mtd/jjjtO5p9/\nfn0fcN5zzz3zBiyMKueee66eW2CBBeSMM86o7qvOdHmMaugLTvWHgCv59Yd1ST5ppZVWkiZNmiR+\nG/mcd3IESgmB999/Xzp06CBYpVGAmjadzkbxdu64447SvHlz6dy5s6y99toSWtIR4LGyW/ksTIgC\nYBDGU9CjR4/UovPMM4+wOVUPgerWId5ocMZrA+2+++7y6KOPZj60VOoQ79Bhhx0m3bp1kznmmEMN\nTig9EIojihBt/u+//1YBslOnTnlcvM3noagy8eSTT8oGG2wgHTt2lBYtWghCOTwliagTvPlEBeGR\npn1Cu+66qzzxxBP5Sw4//HC54YYbZLPNNpOxY8eqd4+TeAmPOuoo9RISlYQxCsK7xzssueSSuiVF\nI2nBRvyDEYpvXHTRRVV2WWONNeTzzz/XL8IYssceeyiPXmihhWTBBReUCRMmKL7l2v9ru6qJcsMA\nTrREYyYUezzsXbp00fZCf3344Yer/UlpY9Fss80m7du3F/YYomaffXblv1ntF+PgNttso+9ARNli\niy2mBkMyuB7e3KxZM+Ux8Oxi6Prrr9e66tu3r8o3FvVCNMCnn36avwX1apTGn/hWDGpEKDGWfvfd\nd3oJxgkcGhBGDPokBMbwpz59+iive/zxxzX/ueeeU6MH78B3OqUjUPJ/oZf+6X6mNhBo3bq1hiph\nEY8TVjvO1wchYBIGRJjSI488Ii1btpRjjz1WTjrpJJk4cWLeCwdzIaQaoQYjxIABAzTEFEYa0umn\nny4//fSTWj5/+OEHIRTro48+UivppZde6owlBKuM0nh58KQfeuih2hYQqBGiCWVGQEb5NyJtIXFP\nP/20EApKuHdVhAfp8ssvlyOOOEKNBWF5wgExFEBfffWVCqc1ESzCe5ZbuiZ1mFW3SfiVUh1isMKo\ngZKIJwjBziK0Pv74Y/38Cy+8UD38KEW33nqrCqbe5pNaRnpeddoYBkaMAkzXuffee3V8I8oC/oLh\nBSGZds45hGqUkb322kuoEwj+8tdff6kQzfi25ppr6hSj22+/XaNUqF+mB+EJ3HrrrdNfuhGeISqK\niBPo33//1WkMGGQhPPlGKBLIDig3KE9pvN3Kh/tS6v/hd9VGGgMSNN9889XG7WbZPVDumUaHwQz5\n8a233tIxuTovlDUWoawTybP++uvrOI/M0aZNG93S2i/vxHvQxunfKNUWWYXcikGwV69e2reNF2S9\nL1EEZ599thoO8f5jnEEuIXrrm2++UR5j14dRi2n8CTkdxR7ZGwMEThAU9WeffVb7IvcisoFz0Akn\nnKDP3HjjjWX06NEybNgw5VXIQURQEEnWvXv3oqMj9aZl9jPdBVVmH+2fW7sIIGgQRhwSx+TXF6G8\nwyiwDiLs4OHAOoiwwjHMAabHwA0Dgvl98MEHqrAhvBpxH65DoT/rrLPUQrvvvvuqdRTmedppp6mH\nxcr7vrwQwIN5xx13qJGIge6CCy5QQxIo0HbYQuIYyzRtCa9mMcTgy+BHOGl8+gjWeEId2ZgbjXEB\nL6tT8QjUpA7T6jbtqaVYhwiQKIp4gyxcH2ELzxDtlbaK0oT319t8WstIz69OGyNCzsZXBGAUDbzR\nm2++uQrJ1Ater7XWWkvmmmuuSg9lrQ9CpRHgr732WjXGYyyEnzBdgDBkxk3ChEuVGONR7lnjxAyn\nfCv84aKLLlLDPkaPOeecM5W3p2FTiv0/7VvLNZ/+RzTCqquuqh5u5E/G5+pQ1lhE38MjjnLP1DDW\n1KDNGiW1X7zhKMZMIVxvvfW0bfNO8AecW3vvvbcqzRhaipFHiCRkmhoKPsS7kFcVpfGn8Dr4Fk4S\nDGlEKjCu4LggD/kGghfxbKYesIVrwfAMohyLiYwMn1tu6UL3Zbl9vX9vrSCAFXPgwIEFnZ/juHe8\nVh6WcRMswzA2iIGbUCezFrPHk4oRAOaJ8s8CZ5999lkB4zgp8vwzpxoDAMwRSysCLAwdRQ3C8wGD\nxSLqVF4IoMSE4WG0Cwv1bNu2rbYNQwTPHIvwYY3Hmo7FGiJCxCzTSVNd8LJxHQL4TjvtpEapeeed\nV6+lTWLZN2IwZID88ccfNbLA8n2fjkBN6hAPCv3eiLqlvtOolOqQhR0JWYa3wte7du2qyhGRUrQ9\nFtyDaMuEVhLp4m0+rWWk59PGQm8YbYy8JIqH2tp0CRR6QtFZxwNhnNDzJGJcQ6gm8gLCeMOCXHgA\nWQuAaQJ4zvDcYbyJG/GT7tmY8lg/AoM9nnuUNCPWm8CDCo/Ho2hTocq5/xs2vq+MAAYy1tLAYIaR\nDiW6OpQ1FuGpZg0fC11nLRiiVJE709ovETtE1SK/4g2nX6MkY7CjLbOQH4SSz3QVjFlZxPuFRkL4\nDLwjiTAkGKXxJzvPnu9iyhDeeOuDGMeIHjjnnHO0KNG5TBNbZ511NKrIjMucZEqTU9UIuCe/aoy8\nRBEIEDJjAgN7juub8CiFlGRVhYmsu+66qhTh5VhhhRXCS/KrpBKSBMG8EV75JtuYK50mfBXczA9K\nDgGMOyzQaAMd87JRyCEUHBYTghAWOUcei+Nhkb7kkkt0YwoL6SQFn2utHXMdRgS8ammER4550mYE\nSCvn+RUI1KQOUX4IM7S5z9QzdZtGpVSHrDuBcGheFARG8+wgqGEEhfBKoRghQHqbT2sZ6flmKCfi\nDGJKGXlJhGDMolwQkWhETmDYhjAMMoWIqRRMR4PgNeEiZwjVKCXUExtGATzWKL2kt912W2EuLrzF\nnqM3KoEfFi/D20nkgikX9lmnnnqqGq3OO++8vILPuXLu/4aN7wsRIFoGbzLTmeh7LCgXGuALSycf\nZY1FOKnGjBmjF8JbMbbCd7PaL3IFhgfmx+N8wGFFG8cYy/U4AyAcXKz9URXhFGOsM57ENBTrM8gc\nOMkg+D6h+EZZ/MnKwG+YCkOEAs9h499FWNQWvoMMReQtU8FwZmBohmc5VQ8B9+RXDy8vnYEADI95\nOuwbKmEJZY4hCxQhvGKFDRkHCxQhpKL8o0DBXGBEKPiEMuKF5dpSm6fYUOurob0X4Xl4NFn7gQEX\nS/eIaFV9iPmrLIbFOYRuFuGzv8sLlXDCy7BOF0NY2hmwWWmXwfWXX37JC/M8G2MTwnjccl7Mvcu1\nTE3rEGEDLykeUBReQnmLocZehxgz4It4fthogwiSEG2evyBDQEPIQ6kkSoU27m2+mNZRUQaD3qab\nbqoKJV43eATYJtEiiyyiYbR4sxhzUTCMB2B0ZAE5QveNmGoBT6KeCCvGaIMxgGOIsHVCX/Gc4clG\n2KaN804ouKVETGMgSsLm4fNt8Fb6N9ELGOjCfzVAscGzX679v5Tqvja/BQ83YfEYOlFIUfQt2rPY\n52SNRXjxif4zJwK8lT7Nmhtp7Ze/duUaphSypgZKP8o0fZtQfu4FDycqrZhV7ukjKOxECLBqP8YG\nDIgQUULwfxbFgx9hlDDK4k9Whj1jBbyIKCKIaAWLLmBdLRxxjD9EEiEHYdBE8XcqHoEmRRRNKhPm\nJaUtj71tPMrStieSIJ62PPbxNK5ay7e95YV70nNGW4dooBoZ7Z3qCQGEi6ww1rp6DSyNMC8YG8Rc\nacLzbcVOzjEXH0/cDjvsoIM2giiMBGUJpoiHlrlPMJMXXnhB594TukioNfOfKctzWLHY/+uzrmqy\ncdwXhYa2kBTGihKE8I012qnhIlCTOsQbyurCCDzlRkSvYBhN4u94tZieZYpmuWFTm98LX6FtJvGW\n8DkYpxHWQ+HazmOkJhLAIi4sP75HOYFXhVPr7L4oMYx5ThUIlHP/r0DBUyEC9Bf6kU3tCM8Vm84a\nixhv6KMo68USfJq+G48WJKSe++HZrw5xHUa/OE9iTIBfhQZdu6/xkST+ZGWK2RN9wHOJzE37LvKJ\nkOR7499sz7B83stoxng2NDqeGG1Tom3ajO2/hD15tlHO0twwnrY89vG05UWn9Fz82PLZQ5w3CtNZ\neXZOFez8QUoCJTxOYV5S2vLY28Y9LG17U+I5tnS4j6dNkSffNssL967kg7ZTIgIwJpgyXlA6PMyr\nGGEGT0h9/VtA4ot7piPgCDgCjoAjkIIARmk8zgjF4fzVlOKe7Qg4Ao5Ao0fAlfz0KvRw/XRs/EyJ\nIoC3yebUY90rRsEHClfwS7RB+Gc5Ao6AI1ACCDCtjBB/WwyxBD7JP8ERcAQcAUeghgi4kl9D4Pwy\nR8ARcAQcAUfAEXAEGgoChMbaatwN5Z38PRwBR8ARcARmDQKEvDs5Ao6AI+AIOAKOgCPgCDgCjoAj\n4Ag4Ao5ACSDgSn4JVKJ/giPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCMAAiUfrs8KsU6OgCPgCDgCjoAj\n4Ag4Ao6AI+AIOAKlgwAL7zklI+Ce/GRcPNcRcAQcAUfAEXAEHAFHwBFwBBwBR8ARaHQIuJLf6KrM\nX9gRcAQcAUfAEXAEHAFHwBFwBBwBR8ARSEbAlfxkXDzXEXAEHAFHwBFwBBwBR8ARcAQcAUfAEWh0\nCLiS3+iqzF/YEXAEGgIC48aNk549e+Zf5eCDD5Z+/foVbCNHjtTzkyZNksGDB0ufPn1ku+22kwkT\nJuSvCxPdu3eXt99+O581evRoIe/dd9+V8ePHS9OmTaVLly66dejQQZZYYgkZO3ZsvrwnqodAdepw\nzJgxsuKKK0rv3r1l6NChMnny5MSHlWIdDh8+vKBdDxs2rODb33rrLdl00/9n7zzApKjZOP7Se+8d\nQURQLICiqIBiw4IV/QQrilixU0REsGCvgGJFxQYoioqKDVAU7KKioHSQ3kE6++WXI3uzczN7e8dx\n3O2+7/NkJ5Nk2n8yyVuzZ8aUaZ+PgSOhnZdfflmaNWsmTZo0kd69e0skEslwHP3w9NNPz1Aer2DZ\nsmUydOjQeE1Sqi7sW960aZNcd911csABB9j+/vbbb0dx+e6776R169bSuHFjOe+884T+HUTJ+P0H\nPefulp1//vkyderU3T3NXj3+ww8/lMMPP9zOCd27d5f//vsvy/cTNrZu375drr/+ejsWMCa88sor\n0XOH9d9169bJFVdcYfmMli1byueffx49JifuNXqyOJl449M333wjJ598cpyjtWpvIFDAXNSfUA64\nVMjkXWIhP1KRXamo2RYzqfiuVMJsWQmvlEmlTSprUjmTyptU0aRKJlUxqapJ1U2qaVItk+qYVM+k\nBibBVe9n0v4mNTWpmUkHm9TcpJYmtTKptUntTepsJkmlPIyAmUQjDz/8cGiifm+SmfQjVapU2Zu3\noNfOgwhs2bIl0rFjx4gRtKN3ZybYiGH8bJo9e3bECISR+fPn2/rjjz8+8vrrr0d27NgRefLJJyNX\nX3119Dhvpl69ehEjMNmiUaNGRRo1ahT5559/7P6cOXMi5cuX9zaPPPDAA5FTTz01pkx3EkMgK+/Q\nCPSRmjVrRv7888/Izp07IzfeeGPkmmuuCbxQMr7Dzp07R77++uvI1q1bbaIfQ/TNa6+91o6RJ5xw\nQgwe2udj4Mh0h75Vp06dyOLFiyPMO8cee2zk1VdfzXDcl19+GTnttNMylMcrMMqsSKtWreI1SZm6\neN9y//79I0ZIsuP0rFmzIlWrVo0sWrTI7tetWzdiBBiLU58+fSKXX355IGbJ+P0HPmg2C59++ukI\nY4PhzyOTJ0/O5ln2/mFG6R4xCnc7xzMeXnnllZE777wzyzcWNrY+99xzEaPMi2zbti3CtcqUKRP5\n999/I/H671VXXRUxykF7D9OnT7fjyapVq+zxOXGviTxcvPGJcW3hwoWJnCZLbZYsWRLZvHlzhDnd\nzVH+LTiSvOXwa8iJu+RF5EbkR+RI5EnkSuRL5EzkTeRO5E/kUORR5FLkU+RU5FXkVuRX5FjkWeRa\n5FvkXORd5F7kX+RgJxMjHyMnO5nZydBOpmbrZG22flmc/bjEQUqKwF5DwDAyUqBAcD+lnHolRSCv\nIdCvXz/p0aOHFCrEGJxGZhKWihUr2oRV/7777hPDtFurPZY0LPmGYRQ0/oMHD3aHBW7feOMNGTBg\ngHzxxRfSsGHDwDYUcs2yZZlDlLKKQFbeoWFMxCh1rMWGcenEE08UIwTEvWQyvUM8HqpXry5YYljJ\nGI8SqFKlSoJFbuDAgTFY4KmifT4Gkkx3Pv30U9vHwLl48eJy2WWXyccffxx4HBY7rPl4EuFdwvuB\nLrroIuE8jowySl566SU55ZRTrIcQ1j0IK+Ett9xirYR4H40ePdqW886MACb77befTfThZKN43zLW\neaO0sv27QYMGUr9+fTGKWosvY23btm0tHEZJG/puHF7J9P27Z8qJLf3VCMPWAp4T59tb5/jhhx/E\nKM7sHM94yPeKtTyrFDa2Fi5cWIySSdy2SJEiUqxYMYnXf40i1nqZcQ94A+E5aBQpsjv3+uKLL1rP\nFmNwsDyM82A755xzxCh5o4970EEHRfNh45NRPMgdd9xhPZTwgDDCuT2mV69ectttt9n8mjVr5Oij\nj7Z57pvxiW+RsW78+PG2fNKkSdbLgXvgG1UKRwCtgZIisNcQqFChghx22GGCK5yfKKc+N8hoYq2b\nHoOIsdRJp06d5MEHH7SXNipFMVY7+eijj4SB1lhkxTFLuXFveo28hYDRVAtune3btw+8MfqJ0RhL\nu3btbP3ff/8t5cqVk2OOOcZO0sZiJy+88EKo2xpuecbaL7fffrt1y/deBHdA3PEgBCnjMSDcj1LW\nEMjqOyQswlig7EV4t0OGDJEOHTqEXjSZ3qGxfAh9GNdR49VkGa233npL2rRpI8azxPZrxsV33nkn\niof2+SgUCWeWLl1qFSnuAIR9yoIIZQvMO4zymDFj5Nxzz7WCKAI6Lv/Gq0J4b9ShbEQY6dq1q0yZ\nMsWejvHFWL4Epnv58uWWqT7iiCPkzTffFJj5zz77TIz10AouKCeTieJ9y0899VT0UREkEEIOPvhg\nizXvw1G8d0ObZPr+3TPn1BY8odzi7XLqvv3nQYFPqBwKMwTxn376KTQMz3+s2483thoLv4wYMcLy\nGczzhO84I0LYXMQ9cR/0cfd9wye0aNEiW/dKOMU999xj+3+NGjXk1ltvFRSHjDEYLLh/R8bT0GWt\nMjhofOKejKeSNeyhgGCcIXxx4sSJ0XMxN1MHGY8ZqxA666yz7PzC94mCHT7IeDnJjz/+KPvss48d\nw6IX10wMAirkx8ChO3sDATS7M2fOFDR4jmAeKc8t+vnnn4VJHc0kAxdWOzT4aFJXrFhhPQqIaWSQ\nQfgfOXJkbt2aXicPIUAfvfvuu+X9998PvSusmoMGDYrWb9y40Sqx/vrrL6t1ZvK7+OKLQy3BWO9h\n4hEiSTDfjhCmXOwzwuYHH3wgN9xwg3hjR11b3QYjkJ136M40Y8YMK/jwTvDkCKNkeocoPenvxx13\nnH1chPm+ffvKV199Ffb4on0+FJrQCpTJJC/5910dCkMEfAgGuGfPnlbhh3KadwMTTDzuSSedJCVL\nlnSHRbes9cHaEvfee68tQ+DCConixoTP2TLeN+NLslLYt0x/f+yxx4RY6bFjx0qJEiXse/G/C/++\nF6dk+v69z6X5dAT4/jBEsWWdBvpN0aJ4XidO8cZWeEws4nyjKJvwfoAv5VpQUP814SZy9tlnW0Us\nigEUdtxTdu+V7/+SSy4RBHwIRYO7vi0I+Qkbn7zNGbfgW1BIwmfzPaGQwBPJ8TgY3TAAsgYM8w4K\nFUdcI56Xo2uX6lsV8lO9B+SB50cLitXz3Xffjd4N+5TnFsHwINxjEWFhEFwcTQyu1YaWLl3aWva5\nFxbe8Vqscuv+9Dp5AwE067jLMZFCuHfCSONWixs3FkyUQzDLjpjA0DbX3+VWRh+ijbMAuHZuC3OJ\n5h1GE20+i5o5l3yEfBOP65rKGWecIfRPFFGVK1eOlmsmHIHsvEPOBkMCE0WoBeNTPEqmd4gyyVlW\neGYYRhjIeKR9Ph46wXWEPnitYVjxKQsivzDBvolHtQI9ggBKGRh0PNCCiLmOBTxN/LitZrE53GKx\nAOI+zHjGPIflbtq0adYTKeg8+bUs7FvesGGDFTDo7wgXuOhDvAdCGRzFeze0Sabv3z2zbjMiwFyC\n8h5PUIRUs05DxkZxSuKNrViqsaIjBEMYosaNG2eF7LD+i8cOnoJ46LB4JN478LFQdu6V+ytVilDy\nNGKc8VrvXTlbrwAeNj5522ORJ2QI45qbT7Hi420E7wPBi8M7YewgjOaJJ56IngLlm1LmCBTMvIm2\nUAT2PAJo5PiYIba5raFDUML1CldoBkjii7799lt7P/7BJJ4G3x6gP0mLABZ4tMvDhg2zCY8T8m5d\nCdzPWEfCxSwDBAL72rVro4IRrmkolcKUWK6c+FoEKrOYTiieWOSI03NKgNCGWhFFIDvvEBdDXAcn\nTJgQZUiiJwzIJNM7hIl17t88KgIgfToeaZ+Ph05wHXGoCOa4tELEybvYVP8ReFHMmzfPFiOEEyeL\n1Q5iDiOOFu845w3H+IQg4gimmnmMMYaEUoB5Di818vx7BDHljC3uOu7Y/L6N9y1jLcWDgXAGJ+Dz\nvCg/OM6tfWAWRQ19N7RPpu+f51HKiADeMoxzKMv49ugzWf3Xi3hjK/+e41bHx+LPd47AHq//Ihw/\n++yz1vsPRSw8LN96du+VY/EocGMSYSiUQfAcLiafedG1oS7e+EQ9xHhD6AYespyTNHz4cIsl4w4K\nNxQAhMZhzMAworx3GnZZ+c09U2lW7krbpiQCCEe46+yNxfYYpJi4XaytczfFuqGkCDgEmNi8AjUL\n7zkLPW1gAlnwxkswfExeMABY27H+w4QnQsTeoXDieCZBlAUwFRAadSxMxNH6NeeJnDtV22TnHX7y\nySc2PtrrocE4xXiRGeX3d4hLKt4jWDdhMrHusC5JPNI+Hw+d4DoUeiwkhYIbAZNxpVu3boGNWRgP\n13wW6CM2lvHEjQHNmze3nj38zZsjYnVRBHANLNQsdEWsPfsQFjOui4cSrrKMNzDZ1CPgJhPF+5bx\nXmBMx+rpCKspYzrCCOuw1KpVy4aj4MqfCOX37z+RZ0zFNoTBmBX15aijjrLfCoL+o48+miUo4o2t\nWPH5Hh0/gcKObx5BG0+SoLno0ksvtcewZgpeQRggnJEqO/fKuICgzV/4sR4Lygb4DQgvIRYbZHxg\nPEIp4Sje+OTasMVlHy+iQw45xBbjreDWG8DwxvXhwTGmECLH3wtzP0qJI1AggaZBbbxlQXlXxtYl\nLuXyblvQU+by3q0/X8i0p8ybXJl3Sx5fDv7f6jWzVconCLAIEINJbhMLqeGqT+wTDBZaScIHGFzM\nX+dEXfVY6AMXI5QCSopAVhGA0c7vCw5l9Zm1fXIggDs44yKLSGaFtM9nBS2xGIN1Zjhj1WK+JDTC\nTzDNuPY6V11/vdtHkEdR4CzPlLvzIsTAaCulI4A3BP1ZQ6PSMUn1HN8L35HX8yOrmMQbW+lvfKNO\nWE/k3PwDCt+u8zB0x2T3XnHFx/DlH5MwNDAneA0f3muFjU+uTSJbQhG5LqGKYc+F0gNenef1P7O7\nhisHA0eczxhKuph9VjjdZBLuTqSdAVvKXHJt2OeErtzlvVt/nn0S5PLefVduG+xqE5SPV+bqrNAd\n3QnJIJD7yVsWlHdlbF3iHC7vtk6IZ9/lvVt/3gnylLvkyrxbFfJBWynLCGApxVKlk3iWodMDFAFF\nQBFQBPYiAljpWTgPptj8z/ZevBO9tCKgCCgCuYOACvnhOKu7fjg2WpOCCPg1lSkIgT6yIqAIKAKK\nQD5EwLn4742Qt3wIl96yIqAIKAJJjYAK+Un9evXhFAFFQBFQBBQBRSAVEMB1363GnQrPq8+oCCgC\nioAiEI4ALu9KioAioAgoAoqAIqAIKAKKgCKgCCgCioAikAQIqJCfBC9RH0ERUAQUAUVAEVAEFAFF\nQBFQBBQBRUARAIGkd9fn/yGVFAFFQBFQBBQBRUARUAQUAUVAEVAEFIFUQEAt+anwlvUZFQFFQBFQ\nBBQBRUARUAQUAUVAEVAEUgIBFfJT4jXrQyoCioAioAgoAoqAIqAIKAKKgCKgCKQCAirkp8Jb1mdU\nBBQBRUARUAQUAUVAEVAEFAFFQBFICQRUyE+J16wPqQgoAllFYPbs2XL66afLgQceKKeeeqrMnDkz\neoqHH35YDj/8cGnevLk89dRT0fKgzKZNm+SQQw4JqrJl//77r5QqVUr69euXoc3RRx8thx56qC2f\nN2+ebbfvvvsKif/Ebtmypfz+++8ZjtOCNAQWLFgg5557rsWwQ4cOMVgl+g4fe+wxi/NBBx0kDz74\nYCC0yfYOn376aXn88cdjnjURHN5880058sgjY1LPnj1jzsNOsuGV4QF3oyDRfvniiy/afsnY0qdP\nH9m5c2eGqyrOaZAE9edE8OPoRN6H4pyh64UWfPrpp8K8xrx6xRVXyMaNG0Pb5qWKX3/9Vc4777zA\nW5o+fbp9nsDKTApfeumlmPHy+eeft0ds375dbr75Zjt3HXbYYfLaa69FzxSGIWNAr1697DHwJyNH\njowe88MPP8ixxx4rBx98sFx44YWycuXKaF1OZiZNmmTn3KBzTpkyRc4444ygKi3bQwiokL+HgNXT\n5g4CH374oTCBhyXqc4v2339/K3ghgNWuXdsOdN9++23cyy9btkyeffbZaJu2bdvKn3/+Gd3XzN5D\n4Nprr5Urr7xSfvvtN/vf07fccou9mdGjR8tnn30mTGZffvmlvPXWWzJx4sQMN7pjxw7p27evZWjm\nzJmTod5bULZsWXnvvfeEYxz98ccfsnjxYrdrt+XKlZN//vnHprlz50rnzp2lf//+MW10Jx0BmMiz\nzjpLfv75Z7nmmmvkkksusZWJvsOvv/5axo4dG33Xzz33nMCoBFEyvMPPP/9cLr/8cundu7ds3bo1\n+piJ4nDmmWcKYy7pgw8+kHr16snxxx8fPY83kwx4eZ8nJ/KJ9kvGgEGDBsknn3wikydPlq+++kre\nfffdwFtIZZzD+nOi+CX6PgA+lXEO7HgBhevXr5frrrvOCqw//vijbNiwQYYMGRLQMu8Uoey/6aab\npGPHjoLC3k+Mk8zB2VVWwEc8+uijdo4h37VrV3uJV199VebPny/ff/+9vP3221bghx+Ih+Hw4cNl\nxowZMnXqVBkzZoxV/sEnIPx36dJFBg4cKCgrGjZsGGhU8D9bTu+jkIRXV8o9BFTIzz2s9Up7AIGj\njjpKChQoEHhmyqnPTUIggIFgYD3llFPk9ttvj3t5tKleDW3cxlqZqwi0atXKWvLpR0xOzlL2zTff\nCMJM8eLFBaGb/Mcff5zh3goWLGj7wJNPPilFihTJUO8tKFmypPUMgCl1xCR/wQUXuN3AbZkyZYSk\nFIwA7wxFCNS+fXtxypZE32EkErGKGt5PpUqVrBLP9QP/FZPhHaKghMm8+OKLYx4vURz4JipWrGjT\nG2+8Ic2aNZMTTzwx5lxuJxnwcs+SU9tE+yXjCfhVqFDBevewLVq0aOBtpDLOYf05UfwSfR8An8o4\nB3a8gMJ169bJrbfeapV/xYoVkwMOOCA6rwY0zxNFjGd4gwV52nGDCM4okJnvs0MYdapVq2aVxwjw\n7jyFCxeWqlWrCtsqVapYHgLM4mGIhR9FNmNBrVq17NiLIQJPA/iEY445xt5it27dZPz48Qnd7ssv\nvywtWrSwYznnXr16tT0O3mTWrFnRc+A54Ih7BDO8Ndq1a2evTx3POmDAAGE+4V6WLFliD7njjjui\nvPKaNWvsXE3FTz/9ZL0cmjZtas+FcQVC6YyXA/eAcU0pHIGk/wu98EfXmmRAoHz58lYAw1LnJwQz\n6vcGMTDjHoxFDA0v1qxx48ZZpoz7QeB46KGHrACCNR/3NQYuiEEVoZGBEg2xsz5SjsCI5hg3cdxp\nYe7QAiN4fPTRR1HlQmYu5PZC+hMXgbvuustq7rHgf/HFFzJq1CjbHi+NX375JXos+SBFk1fJ5Cbu\n6EEBGTTtuO4hFOGqhyWUd+71RuEvQWEooIULF1qhNUjBEHD6lCyC6XE0ePBg65HBfqLv0DFFWEWw\n4tc3IRK4o4dRfn+HWN5JeCB5lRlZxWHp0qUybNiwUK8Hh19+x8s9R05tE+2XvKOzzz7b9kUE1ho1\natj5Juw+UhXnsP6cKH6Jvg+He6ri7J4/sy2CJwIm3nHwPwiJ3vkts+P3Rj08JMYivjO87byEBx/W\nfdzgs0PwchiF4DEqV64sKPlfeeUVyw+ef/75QvgTxiKU07RB4QCFYQgvicLAEXnKwspdu7AtXgQP\nPPCAvS/GGMKCCL9iLiRExevthWHLEd5u8EyEE2L44rvAc2PLli1WsIc3atKkifWERFDHE2nbtm32\ncLwZqIPuvPNOe028KMD+mWeesbw0fBBKZLyYmJOz60VhL5LkP9lTPSU5KPp4+QsBBhIsql5in/Lc\nJjSmxD4heKFpJJabeGvieZ07JQNniRIlrKCO8IC1ywn43C8DIUoLBjXniu0GWwQ/JsiaNWvawZb2\naFbff/99O5hShwunuvyDzO4TGvFLL73Uaoxvu+02e0IsnUxYMNnE7DPRhVnRsnIHhGrgSocmGy17\n69atbd/xngNG47TTTrMJV3SUQ1hGlMIRWLt2rVx11VWWoYBJgLL6DhlLbrjhBiGEwqs48F812d9h\nojjggvu///0vQ/9NNbz8z5vZfqL9kvEdV3K+feYI1p7ApTeMkr1fhj13WHmi+CX6Ptx1FGeHRPwt\nyhPGZPiYvO6uH/YkzNP333+/teSHtcmsHEUq37FTIrPuCcYFiO8ZQw/CPV4EhHV61wUKwhALOclL\nriyo3NsuKI/hCAEdAR/iXijLjFCKOP4bAR2jhfOic8dSjlIDhTCeCvQFDBeUweNA8LVcm3UKSF5h\nnms0aNAg6vngzqvbWATUkh+Lh+7lQwSwmiMQeQcf9inPbcISS2weQh8WWVy5IQTFu+++Wy677DI7\neF900UWht4arLJpOhH80llDQYIungiMETpQJEAqFFStWuCrdZgMBBEMWrUFjjhsaCZcxYuTq1q0r\nuHGy4F316tWtcgUme3cJaz/vkUmfie7qq6/OcEqEfDT7jpgMmSB531gClGIRID4Rd32wJBbQeVxg\nnUnkHeJ9g9UPt9I6depYnGG+wlzQk/UdZgUHGFcsUDBomVGy4pXZc4fVJ9ovUfYyVnTq1MmeCkXj\ncBOPG7YwmOIci3ii+CX6PtzZFWeHRPCWOZO5Ei9HeDTGVMZS1q7Jb8QYt2jRomhI3apVq2zcPsYZ\nN89k9kxYr73u5gjGThjGUo0CD69PCAMABhy+9TAMCSlbvnx59LJY8Fm4Fw8AfzltMyPujzAUR/C1\nXuu9K2eLIO/Ib/RgH+OVl3guPF2xxrdp08ZW4RmBQcstcMsifcy/J510knTv3j1GIURomFLmCKgl\nP3OMtEU+QACXHYQviC37e4NwQcOtmzh7BHrc6SFcfBlkcXHCjQktZhgFDV4MtsRjOfIPtngGeMmv\ntfXWaT5zBMATxgPPDAgNM4I/kyWeGqxOy+RZqFAhIXbeCd64H+6OFwVac9ycua5zkY53tzCr9AsU\nS0oZEcASh6UIrwcv45XoO8QzBndF57qOB4ezamS8WlpJMr7DeDj4+zzWJvBq3LhxGEQx5cmIV8wD\nZmEn0X5JrC5urW6hTgSAVOyXWYA2pmk8/Lz9Od77iDmhZ0f7swcMX5aYczwcnUU2kfHUd4o8s4vy\nGEUmoZEkFEJsvfNMZjfLWOm8AWmLB5/7Nx366IQJE+wpGE9RkPCNx8MQPvOdd96xx7CoIeejDCU1\n8e+On6FNvLAzewLzAw9C282bN9ui119/PSqQw3OwKCHEWOQV4hHcMYhAzB14PbA+hpfgsTBk4aHA\ndUgjRoywiwLC03D/eLgSlooxA8WG8rVeBBPL576pM7H70laKQJYRwH0HIZptXiSs97h8s6CbE8qZ\nEByjFu+eGQBx42JlWpQA3sE23nFalz0EUKIMHTpUTj75ZDs5MUHiSle6dGnrhob1nH6GJh+BH6sE\nxDG0ze5iikyELJBDjF8Qs4CiwU2WKH5QOhC379ecZ++pk+so/nJw2rRp0TUMeDq8XYj/w2KSyDtk\n/QOYOWIEUdgR44hSJx4l4zuMh4O/z6Pk8lqn4mFFXTLildkzh9Un2i/pkyiLGzVqZK2hWJCx/MUj\nxTkdnXj4eftzvPeRfrbYnOIci4d3D8GSfztBAUhiPuOvDPMjIeR6let8g1ids0IsaIdXA2ssIcAz\np+ORA2HFJ17dCf3wjXjvcJ0wDLF2s+Ad50OwZj0nFr+DEJYJH8UtHiWLW2PIVob84GWBwM5f+OEp\niLKBNQMg/nmI6+HhyDiEUsIR+yi74FXhyfGiC+JRMHah9OFv/SCUlU888YTNw2udcMIJVhlB+C3e\nlCgMvKGttqH+xEWgQNzatMqgNt6yoLwrY+sSZ3N5t8WTwJ93ZWz9+UK7ylwdW1fm3ZLHtFnNdObX\nzFYpRRBgtfpE3JD2BBwwtrh44y4fRLhUwwAQ08ugCaGtPOKII6wAgfBBTB9xw27hEQZOXK4gBn20\nqt7BlkmFeC3c3hh0IQb5Hj16RDWutlB/soUAk5pzhWdy9ZLTXHs9LLz1ms/7CCT6DrGeoHSB8Uhl\nUhxy5+0n2i8J58J9FiuiUtYRSBS/RN9H1u8gNY+gzzKW7C1eLa+hTv/CWu5fW4r7ZM0lhGVnGHL3\nHg9DlCcc4+dNMChxvqyG9uGKj2LAf3/cA/ftVXa4+8PqjveqV/h3dVnZwn9xXUIV6TPMwX4DCOWM\ngZT769y1XLnXG2BXH+xi2iw1if9H3LEr7QzYUuYS7VyeRRD8eVfG1p93ZabK1vn3XTlbiHpH3ny8\nMldnBezoTkgGIdxP3rKgvCtj6xLncHm3dUI8+y7v3frzTpCn3CVX5t2qkA/aSnkKAf7jlBh9/sM0\nuxQ22Gb3fHqcIqAIKAKKgCKgCCgCioAikB8RUCE//K0hKCspAorAHkYA1yhcr2688cbduhKLCfq1\nqbt1Qj1YEVAEFAFFQBFQBBQBRUARUASSCgGNyU+q16kPk1cRIC7q4Ycfjv6tSF69T70vRUARUAQU\nAUVAEVAEFAFFQBHI3wiokJ+/35/efT5BwMXY55Pb1dtUBBQBRUARUAQUAUVAEVAEFIF8ioC66+fT\nF6e3rQgoAoo/ztvnAABAAElEQVSAIqAIKAKKgCKgCCgCioAioAj4EUh6S36TnvP8z6z7ioAioAgo\nAoqAIqAIKAKKgCKgCCgC+RiB7/pVzMd3v2dvXS35exZfPbsioAgoAoqAIqAIKAKKgCKgCCgCioAi\nkGsIqJCfa1DrhRQBRUARUAQUAUVAEVAEFAFFQBFQBBSBPYuACvl7Fl89uyKgCCgCioAioAgoAoqA\nIqAIKAKKgCKQawgkfUx+riGpF1IEFIGkRKB4kQLy7q31pcfwf2Xm4i32Ga8+oZKc2bKsFCpUQN6Y\nvEZe+HJV4LN3b19Rzj2ivBQx7UZNWSNDxq/M0K5aucLy3b2N5OnxK+T+sctj6j/oWV9KFC0o7e+Z\nLbUrFpGvBzSUpWu32zaFzTlXbdgh1720SGb8m3ZfMQfrjtSsUFjuPq+61K9SVJat2y53jV6aAavz\njignRzUuJTe8/G8gYs3qFpdep1eRKmULy1d/bZT73l0mOyOxTZPlHdatXETuO7+61K1cVBas2ib9\n3lois5dtjT5sMfMtfNR7Hznu7tnRMm+Gb+LK4yt5i2TKzI0y8J1lMWXJglfMQ2VhJyf65b3mPR26\nT4noVXu9vlh+m785uk8m1XF2YFzatoIULVxAnv08fZy+oHV56XpsRSlkTF2f/77BftcR33fN8YmM\n9YqzQzrzbcsGJaTvWVWlfMlCMsPMp33fXCIrzTyW16lprWJy06lVpNuzC6O3etwBpeXGUypL6eIF\nZerf/8mAt5fK5m0BnSh6RGymQdWiMrhrrZjCjZt3SqfH5wljbd8zq8qR+5WSbTsi8tTHK+SjX9bb\ntm2alJKeZk4qW6KQ/DBnk8FwsWzaGrH30e/satLCYLx5604ZZOaqyTP/s8cc0aik9OpYRcoZ3Bkn\n7hy5RNZu2hlz7ZzY4Trdjqsolw9Lx8mdt4UZr27oUFkuHrrAFel2DyOglvw9DLCeXhFIFIHJA/eV\nHwc1kqn37Cu/PrCfvNC9tjAhxqNKpQvJRcdUiDZBGG1UvWh0XzO7j0DvM6rayRNmEDqteRlpaybZ\njg/NlbMeNumwsnKkmdj8dHjDEnLSwWXk9AfnyJmPzLXviUkuiNZt2iEnH1JWChZIr92vRjGpagRL\nL60zk3KrO/6xqUWfv+XtqWvsxO1to/l0BB67uKZ8+PN6qyR5acIqGXxZLEOFUHtJ24pWkZJ+VHoO\n5o1jEFJPM++xevki0uGQMukNPLlkeIcPdK4hr3y1WtoMmCUTp2+QgZ2q2SekX/YxDOcHPfexCgDP\nY8dkYUIveHKeTZ2fmi8LVmyViX9ujGnjdpIBL/csWd3ubr/keiimzjTjT0fTL0l+Ad/dUyrjfPT+\npQSs7zCCD4pWR/WrFLHCBsLUqQ/MkSP2LSmnBHzXiY71nDeVcXa4JrIdYoTaAUbZiuIahTVCcl4m\n5gjGwRHX1ZUSRvB2VMsokO/7X3Xp/txC+yyUoxDKCs1dvjU6XjJujv1hrXw3K00o51wopk68b7Zc\nZa5xj1HqVTUGgVLFCsr9F9SQq55fJMffO1tKFS0gXdulLTzXx/AqqzZsl+MNtjcapfXDF9WUciUK\nGmVAQXnykppy64jFVkG7cv12udUoCXKbfl+wWW57bXFuXzalr6dCfkq/fn34vIbAhYPnWwGueZ+Z\n1rpwu2Gs41FFI+Sf06pcvCZatxsIoKlf+98O+XtJujXzsIYlrUZ9y/aIrDdad4TI4w4sneEqBQoU\nkMfGrbCa/TUbd8iCldvEFAXSJqN1/2nuJjnGMKWOsDC/8/06txu43WCuT1IKRqCgAfyd79bayklG\n2IRhc4Tg2v+cavLoh7HeE66e7QnNSsuXf2yQWUu3SOUyheX64Yvs+/a2cflkeIc/GavQ+Gkb7CPB\nkBXYpXXCc+Gz39bL7cZitN1YlcKIb2LtfzttOvvwcvKn8TAJE/KTAa8wHDIr391+Wb5UIcvM161U\nRA6uV0IKerWDvounMs5zjRfK65NXy8hv18Sgst0YjsGFsR0LKNutpu/6KdGxnuNSGWc/bvH2sUov\nMcI9Y8oys90WgHu843O7jrn7/Z/WySO+eeIg8939bObsxWu2Cx4gI6eslfYBfEC8+wUDN17ibYbi\n7qH30+ajCuYbf2XSanvu+YZ3WGhSrQpFpIwR2Icar7+FxtOKPos3hPv8DzOGhTG7eIZ/lm6Vv01d\nS8OvoJz+9DfmsTQ+ZpLxSKtnvLUSIfiQT/s2kIn9G8pTl6YpDTjumStqmXOkz6ef9NkneroyxsMA\nI9Ukc8yYW+pFDU+NjOHitl3KhbG31ZcqZQrZYzCkOF4XhcTom+rZcrzoxhnPsa/uamjP5fgjDCgD\njOKFe8A4phSOQKyZKLyd1igCikAuIrDDyG2fGRdCLBC4i79zS3254Il5UfcqBsH+o5fIs93qGOGj\nkLGw1TeWxrn2Ds8/srwVOhloHxq7zE4+VDBYd2ufph2eNm+T3GFccXHXuur4ikb4LGAnKISgz81k\n0Me40KU64SWBm2fXZxbIc93rROFYvHqbHFineHS/mcmbuToDTf0nTSOPhehC422xYOVW+WH2pgzt\nXMHbxp3/gqMqWKEIr4ETDypjXfERNB3huo+1FcLlF7fq802/UApGAEudo8uNa+7E6elW5etPrmw8\nIdbKcuPGH0b7GHdK+sHY2/aRjVt22m/t4iELLIMVdEx+f4cwmLiJDuxUXbCCXm76vqPvZ6X1XX+o\ngqv3bhmTLmlTQU4eFOzW79rmd7zcc2R1u7v9sknNYoKr742nVJGSxQrIPkZAOOexeTZ8J+heUhVn\nBCFSywYlo4IQ+FCGcpbQE4RMLMq47Psp0bHeHZeqOLvnT2Tb+40l8q7hZ2YZBUwdE4LW0Xij5GXC\ne46xD8VQB+OZ5+hfwwfsb75D5mr4NXiCGkYIzy7ddW416T9qafRwbx6hlnCx6Ys2yxYTDjDi6zX2\n2tedVNmEohURvKagf1dvt/dBWGEx4wWAUF2jfGHbt/safg8qbO73YjM2f2k8tTKjQ+oVF+bJcx6d\nZ8PdCLPob+7z5lcXW8UBngaO6niUBngsnv3oXPl13mbrzfjMFbWt1wH35LwTUUAwx6CUwMXfedoQ\nnvD3krTww94dq8oTH62QT35dLycb7OHHCJmDD0KJjBcOxpOpd6R5Mrh70W06AuZ1KykCikBeQaCx\nGZQPNgPrsU1LWRexT6ett5bg6Qs3S4dDy9rbZOBloP9jwRa5dOh8+XPRlqiATwMGXmJmLzJeAb2M\nhhRyg3UXMxm0Na64aNIZrCFi43Arv2jIfGlz1yxpZ6zX6vIvcrdxj7vXxLRt9xnKXzcx+Lyjl66u\nIyOurSNFDN5BViALrvn5xShUnv18pTSuWdy6+bty//YbE9N3QO1i1rWuXdPShrH4T/4zgqWXsKKO\nN32CxESPIgHmQCkcgTLG5f6hLjUsQ3HbiLS4e74HLCfjdsU4hh1d0jAThE2c+fAcOc8oDPAK6GFi\nCsMoGd4hQs+b36yRMeZZs9u3UKiM+X6ttZKGYUV5MuAV7/ni1e1Ov2SdBBh71uPo+sxCqxi8bJfL\nbtA1UxnnIDyY305vUdaukfLQB8ullhE2T2ueNr9622d1rFecvehlzCMQ4z01fOIqeWLccvnD8DW3\nnpb7buMZ7yzrJQiwv5rYdhTAWLgRVLPrlUCMPTzENN+aGnj+Ed8+yLjnXz5sgeX73J2igALHJcaT\nwLnrP24w7W3i7h83rvlv9Kgrc8w4geeEIxSDI2+sZ8tfmrDaFYdu2zcrYxXhrGcDDTVrCrU/MF3R\nEXYgIQfgAyGgs34QXkde+tiUH9OktFWcEz6wZM02q5DAWj/e1EFdDA+71JR3Obq8XGhSSROq4Og7\nw/vMX7HNejq4Mt1mREAt+Rkx0RJFYK8hcMFR5WW90RxvNQPzBGN1/OjnNHdtmO6bzaIvbGFGRhqr\nbxiNMm5j0F/GVRaNJxQ0WE+4s6GpSYuP+tC4o+G2CKFQqFiaoSHdRd1WpNAPgl1rM2mXKZ6mJEFL\nz0JXaMNRqpxy/xxpUqu4LDWTHxrmmgEafFz30DKjVUfDXrH0KstYhrkv4/KHdQnmk4lu+MSMkzAT\nttfihLD/1yONBde+1catUCkWgYbVihqXvtqWGfLGAnY3i8Pxzl41Sho8XuoYBgQrxb1jYheIY0Eo\nvC+27YIWpQoKmDDKz+8QofMMs3AeyiPcUEm4SRJ7usj030QJxvTMw8oJoUeZUX7GK7Nni1e/u/1y\nh3GnwIXX0a9GkZis/dI9Y05uTzBeUsx57/+YNr8WLbxc/mc84Mbu2nfXwoqbyFjv2qdqf3bPn9mW\n0JL1Zv2ZZz5LWwBx+qLF8u3d+1qvwsyOzYv1xL3zLeMujzckyuTsEFZpf98raWLt8SDE4s36P3iS\nQY2N9wBu+1+YMDLmpjtHLbGC++PG4v2LEayPM/H4jaoXs3zHQxfWiC7Uy1oy8JD9zIJ7U4xBIREq\nYtjH/7akKwngPzBqBJFbs4g6v7IDBYbX6k8bQgZYJLCVWQ/j2133gxX/0PolZKBZwBBifoaHImTu\nZRO6wEKZjjB0KWWOQLpaJPO22kIRUAT2MAKs/n2FWb31mhcWWYHerX7KYI4LLKv4IgCiBQ2jzdti\nrb+0Y7AmXtaRf7D2rwgLo57KNGdZmndEH+NaSGL1+ifN6razTUwbVvyhl9eW30zM8g4z6XUyayIQ\nrwwRo+a8IJqY1Xh7GFc3hyXHuZXxw7AdbRbSw80Zj45EJmJc+nmvGzargB+E6RPGotHbrDr+mhFc\nvUSoyrUvLrLvdsgnK6xi69nPVtom3nc46c8NQpyjY1CImcT6FI/y6ztk3Lj9rGrR/oubJysxZ6Y8\n8uIFLg2NtQim18V/xsOKuvyKV2bPFa9+d/vlOUYoQCnlqK1RPDEexaNUxDkMjxVGOYvl1cUyH1A7\nfWz29ud4Y33YuRXnMGREVhiLLcpxlNIQK9YTl58fiTBKwiSxqLOA3mXtKkT5gKw+Dzzd10bo9dIt\nxsPhmxkb7T/COAGfepSxA8+rZgw4aUwaihPHV+BB1dkYilDQEnqJ2zwCNC7yvYzrO95oifAV7j44\nFqMSbvYQ48635t9SoA1GWVPPeMNBfEvFYTJ30eFGcEc5DBHSUK5kQYvRrmq7QUgn/ICFo7knUicT\nUgqWKAVQcnAe5mrWE8DbJsXZUi98CefVkp8wVNpQEdi7CLCwCy72P5rFsZwWE8uBZ9Hg0BtksO5p\nBvkXzV+9IRR6B+vQg1K4AsstcZuONpkJiYkU7HBDW2lWsGWCJwYPzwkXa4/LLCvgoqTBHW4YC8MM\n2Ncu7ETs9zVGsIxHc5dvs4v5TTaTexCxKA3/vgARw4YAhsuuszQHHZOqZfzlYFPDvD/gsa4Q/oBV\nJO0vm9IUIyxeiRfL8vVp+953+MfCLfLeD+vsu2aRRSwUKAfiUX59h/Shnq/9a61CuIPDGMJg/bfL\nwyfsmb140QYvmH88C1WGHefK8yte7v6zus2Jfsk/IOChwqJUzAEsDMkiXfEo1XCOhwVhNwhWU81f\nly4y4/xO4xnBKumQtz/HG+vDzq84hyEj1r0al/Iv+zUQFoZjvROvh1X4kXmvBsMI4Rysl8SK97jG\n373LAp2Vu0XhgeCOxdpL/NsO/fIsI1g7YpV9eI1xxuNvyt2NbOw6itgeZkFYaLTx8BzWrbacbgRz\nvNNYWwlesU3zUtZINGrXgna0RVjvNzJ9DQDK/ITXYat9N8h4s/AeLvUs8HndrvmPMeiRC2vahf9m\nm/HHu7YN88fTZnzi2tWNsvg2s6p/EI+CK/9A8xe3TnGOss2tHcC8wxo6rJuBRw0LEzOfsz6BUuII\nJKIYCWrjLQvKuzK2LnFXLu+2qH78eVfG1p9H/efK3daVebfk6QnV6l47/TWzVVIE8jwCrBJ6hYm7\nwh08iJgM+D/1To/NtW5ZtEHb+XGfBrLGrA6M8MJf6BF37FaDn25cuZveMsOejv9VPdUM/t7BGkGW\nGC7ccV81gzbEqqjPfbEqSxpfe2CK/Tjrbrx4fCCBAYgYTjwzYSnF4MtXj4vFj9AXr0UlXz1AFm4W\nz5OKZqxZZRRICJBKeRcB/uIRxZPXSyvv3m3euzOssYzjCBHxKNGxPt45tC4dAVy7EU5XmXCoZCDm\n+NyeG4oYKadU8ULC6v9+yun74X2xPg2Kbi9xD8WMBT/sH35Q4qQp1L1HZS0P30uIB2sjhT3Xd/0q\nSvny5W3IBGETQeTK4cUcrV+/XipVqtTF7KPt2GQSYJJ4UP+WMpdcHfuc0JW7vHfrz7NPglzeu+/K\nbYNdbYLy8cpcXULeD0GIecuC8q6MrUtc1OXdFkHdn3dlbP15J8i7OrauzLslr0K+AUEpeRDAsvaK\niVE6edCcbD9U2GCd7RPqgYqAIqAIKAKKgCKgCCgCisBeQECF/HDQ1V0/HButUQTyDAIsxtbVxHwN\n2xU3nN0b469e/NrY7J5Lj1MEFAFFQBFQBBQBRUARUAQUgbyHgAr5ee+d6B0pAhkQ+MssUMKifO5v\nSTI00AJFQBFQBBQBRUARUAQUAUVAEVAEDAIq5Gs3UATyAQIuxj4f3KreoiKgCCgCioAioAgoAoqA\nIqAI7EUEkl7Inze4yV6EVy+tCCgCioAioAgoAoqAIqAIKAKKgCKQ0wgsXcq6eUpBCCS9kP/ff/8F\nPbeWKQKKgCKgCCgCioAioAgoAoqAIqAIKAJJhwCr0yspAoqAIqAIKAKKgCKgCCgCioAioAgoAopA\nEiCgQn4SvER9BEVAEVAEFAFFQBFQBBQBRUARUAQUAUUABFTI136gCCgCioAioAgoAoqAIqAIKAKK\ngCKgCCQJAirkJ8GL/Oijj2Tt2rVJ8CT6CIpA/kFg06ZNcsghh8S94WeeeUaOOOIIad68udxzzz2B\nbf/9918pVaqU9OvXL0P90UcfLYceeqgtnzdvnm237777Cql+/frSsmVL+f333zMcpwWJIaDvMDGc\nXKuLLrpIvv/+e7cbs+3evbs8//zz0bJvv/1W2rVrJwcffLBccMEFsmzZsmidN7P//vvLtGnTokUf\nfPCBUPbHH3+I9vkoLJlmpk+fLgceeGBoO8U5DZqZM2fKkUceGZNOOOGEKG6vv/667bdt27aVl156\nKVr+ww8/yLHHHmv784UXXigrV66M1nkzirMXjfB8vLEk/Ki9V/Prr7/KeeedF3MD9A9vX/KOfzEN\n99CO/5vfuXOn9OrVy/IMhx9+uIwcOTJ65U8//VTgJxgjrrjiCtm4cWO0LiczkyZNknPPPTfwlFOm\nTJEzzjgjsE4L9wwCKuTvGVxz9axz5861HzOT0NatW3P12t6LVa1a1bu7W3km2D///HO3zqEHKwJ7\nAoEdO3ZI37597YQ5Z86c0Esw2b388ssyfvx4+eqrr+TLL7+Ud955J7B92bJl5b333hPO7QghZ/Hi\nxW7XbsuVKyf//POPTXz3nTt3lv79+8e00Z3MEdB3mDlG3hYwr6eddprtv94+6tqMHj1avvnmG9my\nZYsrEpj4hx56SH766SepUaOG3HfffdG6sMyYMWPk9ttvl3HjxskBBxxgm2mfD0MrvZx5n3EgUcY9\nlXFu2LChfPjhh9HUqVMnOeqooyyYkydPlueee07A591335Unn3xSFi1aJAhPXbp0kYEDBwrCHucI\nUsqmv5G0XCrj7MfC7Wc2lrh2eWU7e/Zsuemmm6Rjx46CUthLzPGPPvqosCV17drVW71H80Hf/PDh\nw2XGjBkydepU24f79Okj8Anr16+X6667Tl577TX58ccfZcOGDTJkyJA9en9BJ8co8vTTTwdVadke\nQkCF/D0EbG6fdvv27dbCArPFR62kCCgCewaBggULyimnnGIZwCJFioReZP78+XLllVcKAjyWejT+\nMAxBVLJkSUHz/vnnn0erX331VWsBjRYEZMqUKSMkpawhoO8wa3jRN2EYW7RokeFAhKBRo0bJpZde\nGlPHt1GzZk0pVKiQVK9eXYoWLRpT79/B6oQiAM+0Bg0a+Kuj+9rno1BEMwif11xzjdCvM6NUx5n+\nWLFiRZsYjydMmBBVlCIE3XjjjbavorBCQUUfxmJKvzvmmGMsvN26dbPK23hYpzrOYdjEG0vCjtmb\n5fQVLNNBSh0MUdWqVRMs1AjSiXx/OfUsQd881vpLLrnE9t9atWrJiSeeaI0L69atk1tvvVXq1asn\nxYoVswpUFFeJEIYKxv1mzZrZc69evdoehnfWrFmzoqfgvTriemCG10C7du3s90MdeA0YMEAikYj9\nlpYsWWIPueOOO6xyl501a9ZI+/btbTnfH3xT06ZN7bk+++wzW/7111/LzTffbPkjPGeUwhFI+r/Q\nC3/05KzBbR8mCVfe1q1bC1aQ3CI+XKwwaMkZ7O68804566yz7OVHjBghgwYNsh83g+abb74ptWvX\nlhdeeEGeeOIJa8HkY8X9CaEIYnD5+OOPhQEDKwWDl5IisLcRKFCgQNTyE29Sx6XTEZPZ22+/LW+8\n8YYryrDFUkT/Z2JGaYfbMt8A35Mj/hIUZh5auHCh4EnAN6KUNQT0HWYNr4MOOsgeUKFChZgDGfOx\ncj344IPWcuStHDx4sGXw9ttvP+t2P3HiRG91TB4X6aFDh0rPnj0F5tRL2ue9aGTMgysWRlzJMyPF\nOR0h+i6CzyOPPCKMB9Dff/9tt48//ri1dtatW1feeustG2qCMOeIfFj4CW0UZ4dUxm3YWJKxZd4o\nKV++vJ3vUVribecISzpedbfccotUrlzZKuhfeeUV6+Hn2uypbdg3T58M6qeMqSimfvvtN+tdhXDu\n5SvC7pPQrAceeMA+G95YKHoZo/F2IczQ6znsNS6i9Pjiiy9sOOHYsWOtFwweBCjO4IX43po0aWIV\nECgL8HTctm2bvQ08IqiDkCG4Jl4UYE/44/HHHy/MCfBSeN7UN7JOoh5M9qQp9pO52jfFAEmWx+WD\nQ6hwWrfceC4+PAYC3Nm4NoMf1szNmzcLmjq0jGjEW7VqZeu5Jz5gynFNbty4cYwlkwHh559/th+3\nuiTnxhvUa+wJBD755BM56aST5K677oobw0+ICt8Ommxc/FHS4QHgJRgN3KZJxNURYwejqrRnEdB3\nGIzvU089Zfs2jJaXUFIRG3r11VfbMR7GHstTGMG0Es4ybNgw+e6772KaaZ+PgSNmh7Hi/vvvj4ut\n9wDFOR0NrIJYNb3eKfAwWPrpi/RDBA8s8igESF7y73vrFGcvGsmZxxKO5ywhGQi9jz32mJ3j9/TT\nxvvmM+unGNauuuoq652SiLs+BkOMD/D1EDw9ZZkR4S+sFwQhoDMf+EMbKcdzcenSpVKlShV7Txgu\nKIO/gd5//317bUI8SF5hnmvg8RXP0GJPkuI/aslP0g6ABpqPAC1kbhHMGEwdxMfXoUMH+8Fedtll\n1k0HrRtunUyu559/vm2H8ENMHBZ/4ou9CwddfPHFVuOHmxCTr5IikN8QIHYfwR0ttF8Q8j8Lk9XZ\nZ59tGQcmOvctedvxjREq4IjJkAlyxYoV1prgynWbcwjoOwzHknhUxmf6N4pl+jBzDgtD4t6MSyWE\nkI8SF6+tIMKKzwKTWI0uNW7/WIKcR5f2+SDE0srwiCNcAmsYtGrVqqjVy1mn01qm/SrO6WhgCYT3\n8BJjKcpWCPxwFcZai2Vx+fLl0aZYTCtVqhTd92cUZz8iybePAsjrKo5Q6xdk98RTx/vm6ZP+fsq4\nyuK8CxYssDw5xoM6depYj0HmtnjEMxJK6IiQK6/13pWzRZB35A/NYt+7XgvtcMnv3bu3tca3adPG\nHopyDO8BPMMgFukjxAA5gYVdvYqJ4sWL2zb6Ex8BteTHxyff1cJY8UGceuqpuSrgAxQMHlpwRzBn\nDAgMOkyWxNcgxHtX3sS9CXc52lHOQjeO9CN2SOg2vyCAG5xbMBJ3ODxUcFfLTMB3z4fWHGsmHi8u\n/tPVBW1x6cca5QSioDZaljUE9B0mjhdulrjlY9E/88wz7RjO3IOwBGOJ8gli9Xzi8sPIzRsoemGW\ne/ToEdbUhrFon0+DB7ywdoE/CQUL2yABnyMU5/RuhTuxP8QBwQMrPoSlFtdhFFQsAombMeMyxAKq\n8DRhpDiHIZM85fxLw+mnnx4VevG+c/+EsyefMt43T590i/uyuB73RBnrBaBwdZZwXOeddT7evcKD\ncD68cSHCUJxADs/h1hjiO/EK8Rj08OKFCBHA+wDFr5dKlChhFcTPPvus5XW4FmG9LGrJ+M79E3tP\n6AzGDJQU8bxnvOfWfDoCaslPxyJf5woXLmwnIwYZvxYttx6MjxzXJayRDCoMMFjx0czhJkQsDx8p\ncfhMmnzEWPARVA477DArCDGgxGPwcutZ9DqKQHYQwIIDM8gCTlg3EW6c2xrnY6L1xur7r+EsoDCf\nQYw6a264yRItO+tbELe/t755//0nw76+w8TfIhYhR8Tr0w8RNEmsz8J8hAUfRW+iqypj7WcRJxae\nhKHUPu8QzriF0fYq+FC0Y/lKhFIZZ5RP9Cu/8hVrITwLAgf8DKGFuBWDK8IGCiwW4kNYwisxEUpl\nnBPBJ7+2IcwDb1X+HheBmfl4uFndfk9TvG+e/ouxjHtCsGYdK+cdC6/NWEyi77/44ouZ3irrAyGw\nw5+z7gCKLwxzEIsKcz0WxWvUqJF4/12LfQwWGOqI3WfsD+JR+LbgifibVQivR+ftVbp0aeGvLVFS\nsLYYcwIKAwR/pcQRSFttJH77oDbesqC8K2PrEldxebfFk8Cfd2Vs/XnMxK7cbV2Zd0u+hEnVzGD8\nmtkmNWExzG3X/CBAcRXio8SVDUEHt0sEeyZUNOQMAvz9En+jgWBPzBsfNII94QXE42CFIM4YlzkW\n2XALcHBsvIVugu5HyxQBRUARUAT2HgK4cLIuDJZ9JUUgvyBAeCCGE79gAv9Cf0bgUVIEQABlEJbu\n3FzkOjPkEeIRsLGIewmPWQxw8UJNvO1dnnEcxZb/GTkfz+5VNLpjMOih3PUK/64uK1vkB66LZzD3\njvDvN4BQjmKZcn+du5Yr93oD7MKii2mz1KRNJu3YlfjrAfLeLXmXXB37LNbhyl3eu/Xn2SdBLu/d\nd+W2wa42Qfl4Za7OCtjRnZAMQrifvGVBeVfG1iXO4fJu64R49l3eu/XnnSBPuUuuzLslnzJCvnnW\nPEUrV660Hz0fpSM0gHysMHt8bKyY7wYGVgZmUGIwQGOupAgoAoqAIqAIKAKKgCKgCCgCikA8BFTI\nD0dH3fXDsdGabCIQpCVEePdq9JyAzyWIzSEpKQKKgCKgCCgCioAioAgoAoqAIqAI7B4CajbdPfz0\naEVAEVAEFAFFQBFQBBQBRUARUAQUAUUgzyCgQn6eeRV6I4qAIqAIKAKKgCKgCCgCioAioAgoAorA\n7iGgQv7u4adHKwKKgCKgCCgCioAioAgoAoqAIqAIKAJ5BoGkj8kvWbJkngFbb0QRUAQUAUVAEVAE\nFAFFQBFQBBQBRWD3EWDhPaVgBNSSH4yLlioCioAioAgoAoqAIqAIKAKKgCKgCCgC+Q4BFfLz3SvT\nG1YEFAFFQBFQBBQBRUARUAQUAUVAEVAEghFQIT8YFy1VBBQBRUARUAQUAUVAEVAEFAFFQBFQBPId\nAirk57tXpjesCCgCeQGBTZs2yf777x/3VgYPHiyHHHKING3aVPr37x/YdtGiRVKgQAHp3bt3hvqW\nLVtKkyZNbPncuXOlYMGCUrt2bZuqVasmBx54oPz2228ZjtOCxBDQd5gYTq7VH3/8Ifvuu6/bFfC7\n7rrr5IADDpBDDz1U3n777Wjd5MmT5cgjj5TGjRvLOeecI8uWLYvWeTP169eXX3/9NVr03nvvCWW/\n//67aJ+PwpJpxv9u/AcozmmIzJgxw/ZV+qtLbdq0icL16quv2n57xBFHyPPPPx8t/+6776R169a2\nP5933nmycuXKaJ03ozh70QjPn3/++TJ16tTwBnms5pdffpEzzzwz5q7oH64PsX3mmWdi6hPZmTBh\ngu1v8BJdunSR1atX28PWrVsnV1xxheUd4AM+//xzW55Z/6VR0L0m2n/tRXbjh+c5/fTTA8/wzTff\nyMknnxxYp4V7D4EC5tL+hHLApUIm7xIL+ZGK7EpFzbaYScV3pRJmy0p4pUwqbVJZk8qZVN6kiiZV\nMqmKSVVNqm5STZNqmVTHpHomNTAJDmM/k+Cum5rUzKSDTWpuUkuTWpnU2qT2JnWOKOUaAqVLl86x\na7Vq1SpimJYcO5+eSBHIKQS2b98eue222yJGcI8UKVIk9LRffvllxAj4kbVr10Y2bNgQOeqooyIj\nR47M0H7hwoWRcuXKRRo1ahTh3I6M8B6pWbNmxEz+tmjOnDmR8uXLu2q7feCBByKnnnpqTJnuZI6A\nvsPMMfK32LJlS6Rjx44Ro1yKVhnFVcQwopEdO3ZEZs2aFalatWrEKK1sfa1atSJTpkyxfdooAiLX\nXHNN9Dhvpl69ehHDlNqiUaNG2e/gn3/+sfva571IheeD3o2/teKchgjfvhHQo4kx9Pbbb7eVkyZN\nihjFVGTVqlU2MfYuWLDA9u+6detGjABj2/Xp0ydy+eWX+yG2+4pzICzRwqeffjpy/PHHRwx/HjGK\nwGh5Xs0wFl177bWRKlWqRE444YSY2+zcuXPk66+/jmzdutUmxsGskBHo7Rz/559/Rnbu3Bm58cYb\no+PkVVddFTGKf3u66dOnR+rUqWP7ZLz+G3av3Fei/Tcr9x/UFr7ntNNOC6qKGKVwBH4np2nJkiWR\nzZs3RxgH3bvwb7dt2xYhecsZB5ATd8mLyI3Ij8iRyJPIlciXyJnIm8idyJ/IocijyKXIp8ipyKvI\nrcivyLHIs8i1yLfIuci7yL3Iv8jBTiZGPkZOdjKzk6GdTM3Wydps/bI4+3GJg5QUAUVAEVAEEkQA\nazqaarT2RsgPPWrevHlimAMpW7aslCpVSoyQL0YQCmzPv4BgOfr000+j9S+99JJceOGF0f2gTJky\nZez5g+q0LBwBfYfh2ITV9OvXT3r06CGFCsF3pBHWTPo4eDZo0MBa4OfPn28r+TaMoG/b16hRQ4oW\nhZcJpzfeeEMGDBggX3zxhTRs2DC0ofb5jNAEvZuMrdJKUh1n+m/FihVtMkKRtZDec889FpyXX35Z\nbr31VttXjcAgRriyfRgvCfpd27Ztbburr75aPv744zCIbXmq4xwGDt49d955pxx++OFhTfJUeaVK\nlQSvg4EDB2a4L/pF9erVBQs1K7wzDmaFjMArRnFqPQLx5jvxxBOjPIJRHljLPufDmw9vQLyj4vXf\nsHvNTv91z/Hiiy9aTy1jhJALLrgg6mmAdxbfj6ODDjrIZQUvBHgkvL5431wf4nu64447xAj59v0b\n4dyW9+rVS4zhxObXrFkjRx99tM3/8MMP1lOCuYVzjR8/3pYbZZxcf/311kMMzxmlcATQGigpAjmC\nAB8uH+rYsWPtYHf33XfLueeea8/N5MkgSRsGonfeeUeMZlKGDRsmjzzyiBhNox3IXn/99ajQ8sIL\nL8iHH34oxhIq9957r3Tt2jVH7lNPogjsDgJMxsccc4w9RbxJ/ZJLLolehsnMWPFtv48W+jK0f+65\n56w7m9HWC27LMIp8T47+++8/68LHvrEwyezZs8Vozl21bhNEQN9hgkDtakYfwzW/ffv2MQc+9dRT\n0X0YL/r5wQdjABF59tlno+76uN3Hc8195ZVX5MknnxRjUbWhKNGTmoz2eS8aGfNh7yZjSxHFOR0V\neJEbbrjB9jvGA2jmzJl2+/DDD1uhzVjl5d1335WlS5daYc5Wmh8EO8rCSHEOQ8aYR3eNDxUqVAhv\nlIdqjPecne9RWsK3OjIWYfn777+tsGms/FYAfeutt8Qb+uHahm0JtzOeDbbaWJllyJAh0qFDB7sP\nf/zTTz/ZkDxjpbYCMnO+o6D+G3avWe2/7hqM2SjAUC6gqEUBZrwNBH6eMEMwcGS8rlzWKj04BkXO\nmDFjrByAoM9zLF682IYnorT47LPPrCFj4sSJ0XMxnlEHGY8ZqxA666yzLPbMNyhCmBMIq/nxxx9l\nn332keXLl0evrZlYBLKmdoo9VvcUgRgENm7cKMa9WIgZ+uCDD+zghzWTD7tnz54CE4hQgmYPgQdi\n0Pjqq6+s9hJtpdPUUYcm3bgxySeffGI/dsqUFIH8hsBHH31kLUAoqojbC6Njjz1Wfv75Z0GTzTFo\ns00ITExzGA3iAknGnc8yFDCqSnsWgVR+h/RHFLaDBg0KBNm4mVpFbffu3a1CqkSJEoKS6qabbrJz\nAFY71qXA2hxGWO+xhsHkGhf/mGba52PgiNnJ7N3ENDY7inM6IvAaxYoVk8MOOyxaCA9TuHBh2xen\nTZsmCF4oWhGoSF7y73vrFGcvGsmZZ9x7//33Zdy4cVboZezq27dvth4Wnhkvkf322896S3ES1vBB\nyMWb77jjjhMs6V5vqKD+G3bxrPZfdx74eIwPCPgQ6wZRlhlhBHGeGgjozAfw/l6inGdAAWHCvKzH\nDEoMvBnd2gfUI1PgNUni+3TENfD4imdocW1TeauW/FR++zn87AxAuNBAfHwmLsd+sCwegiUHYf7N\nN9+0QjsLjECnnHKKdVfC4n/xxRdLs2aEwKTRZZddZjV+uAGhuVNSBPIbAii3ENxx7UTjHI+YrDp1\n6mS/ESY69y15j0Hg4btydMYZZ1hFwIoVK6Ry5cquWLc5iECqv8MRI0YIbqVnn322RRUX/ZNOOsn2\naZguGDIsLyzshEsz9P3331uPLLCDEPJNTKgMHTrU7vt/WMCqRYsW8thjj4mJc7ULRxHmAmmf96OV\nvh/v3TjrdHprsQvJKc5piGAJ/N///ueFxwobKFsh8EPRinWfhSW9C0cimOCRGEban8OQSZ5yFEDO\n4sxTIdT6BdlEnpbFShHoWaS3Xbt20UPMulTWyIWLO/0PT1YEXkdB/dfV+bf01az0X3c8z0iooSN4\nfK/13pWzRZB35FVGUMY+RjsvYZG/5ZZbrPHPPTdWfJS8zAMQi/TBN+HdQFjYE088ET0FymSlzBFQ\nS37mGGmLBBFASPHGa7oPG1caLJjE1yCwE9/kCKEfN00GDmJ4Hn30UVcl+hFHodBMPkGAGDUmZQg3\ne1a+xxMlMwHfPR5acywCrCzu4j9dXdAWl36sUU4gCmqjZVlDQN9hLF4oX7GoEFpFwiWULUIQVnqs\nTIzhTsDnaCwzWEJRPkGs9uysQbbA94P1FLrooosss4yXShhpn09HJt67SW+VnlOc07FAkWoWgEsv\nMDkED1yIISy1CB0oqHCrxs3YxRabBSKjccO2se9HcfYBkoS7f/31l5iF+KJCL4p8FGhZIfoU1voJ\nEybECPicA0GXkCfW6kF58O2338a0Ceq/YdfOav9150H4xusWb1yIMBTKIHgOF5PP/bs21GHQw4sX\nYh7gHwPwRPAS/D2hGyh+OSdp+PDhth08jVms2CoA4IcwZrDWSzzvGe+5NZ+OgFry07HQ3G4iwEc+\nevRoa41kERJcXLt162ZjMYkvIt6SjxQGEYs9HzGWfAYrtJYIQkyeN998827eiR6uCOwdBBB2mLjp\nx8TvIdwwwTpigRlvrL4rd1smQiZPYp+DLHGsT8Ff6EEoxtDQoyjza87d+XSbdQT0HcZiRn/0KpFQ\n5NavX982oo+zj0XZEZYpQq+wTvG3UOSxIrHGSiJEjCrKYBg+GD/t8+GoxXs34Uel1aQyziif6Fd+\n5SvWQrwMscrCzxBaiFsxBgyEEcZlFpPEg8W7Vko8rFMZ53i45Pc6wjzwqsOaj4UdqzdrSmWFMADg\nFeKN48eTBMH20ksvtd5TxPkT7w7f7AxfYf037NrwB9npv1jSCbOFX2fdARRf8BuQ+bcUwdsW/oYw\nA/7S1xH7eCUWL17cxu6zeF8Qj8K3xV+wokiD8Hp0axQQqsj1MRCiWEbZwV+tcj9KiSNQIIGmQW28\nZUF5V8bWJS7l8m5b0FPm8t6tP1/ItKfMm1yZd0seXw7+6+c1s1XKBQRYIZyPkkELQQc3fQR7BiRW\nFmcQYIG95s2b20XF0PCxwA0CEQvcEI/DQMaAxwfNwODcobASoThQUgQUAUVAEcgfCODCiRUHBlFJ\nEcgvCBAeiDXeL5jAv9CfNTQqv7zJPX+fuKGjEDJ/g7tHLgbfi8AbpPTP6gWz238Zx1Fs+Z8RQwPP\n7lUCu3vCoIcXL15du0PID1yXsK0wLJA5UASAURhOrtzrDcD5jKGE2GFW0dxk0o5daWfAljKXaOfy\nLNbhz7sytv68KzNVts6/78rZQtQ78ubjlbk6K3RHd0IyCOR+8pYF5V0ZW5c4h8u7rRPi2Xd579af\nd4I85S65Mu9WhXzQ3ktEzCYfPR+lIzSAfKwwe3xs/MWGGxhYtRmtOoOBLqLhENOtIqAIKAKKgCKg\nCCgCioAioAiEIaBCfhgyIuquH46N1mQTgaAFaRDevRo9J+BzCVyQnBtSNi+phykCioAioAgoAoqA\nIqAIKAKKgCKgCBgEsIYrKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKQBIgoEJ+ErxEfQRFQBFQBBQB\nRUARUAQUAUVAEVAEFAFFAARUyNd+oAgoAoqAIqAIKAKKgCKgCCgCioAioAgkCQJJH5PPKqlKioAi\noAgoAoqAIqAIKAKKgCKgCCgCikAqIKCW/FR4y/qMioAioAgoAoqAIqAIKAKKgCKgCCgCKYGACvkp\n8Zr1IRUBRUARUAQUAUVAEVAEFAFFQBFQBFIBARXyU+Et6zMqAoqAIqAIKAKKgCKgCCgCioAioAik\nBAIq5KfEa9aHVAQUgZxGYNOmTXLIIYfEPe0zzzwjRxxxhDRv3lzuueeewLb//vuvlCpVSvr165eh\n/uijj5ZDDz3Uls+bN8+223fffYVUv359admypfz+++8ZjtOCxBDQd5gYTi+99JIceeSR0fT888/b\nA3fu3Cm9evWyffTwww+XkSNHZjjh9OnT5cADD8xQ7gr2339/mTZtmtuVDz74QCj7448/RPt8FJZM\nM4pzphDZBjNnzoz2Y9enTzjhhOjBr7/+urRr107atm0r9HtHP/zwgxx77LFy8MEHy4UXXigrV650\nVTFb7c8xcITuXHTRRfL999+H1ue1il9//VXOO++8mNsKGxdjGmWy8+2339r+Rr+64IILZNmyZfYI\n5qabbrpJWrRoYfvru+++Gz3TRx99JMccc4zlP66//npxa49t375dbr75ZjseH3bYYfLaa69Fj/n0\n008FfoKx+IorrpCNGzdG63IyM2nSJDn33HMDTzllyhQ544wzAuu0cM8goEL+nsE1Jc9atWrVHHtu\nJtg///wzx86nJ1IEcgqBHTt2SN++fe2EOWfOnNDTMtm9/PLLMn78ePnqq6/kyy+/lHfeeSewfdmy\nZeW9994Tzu0IIWfx4sVu127LlSsn//zzj01z586Vzp07S//+/WPa6E7mCOg7zBwjbwv68qOPPips\nSV27drXVw4cPlxkzZsjUqVNlzJgx0qdPH6FfOtq6davtn4kylJzj9ttvl3HjxskBBxxgT6N93qEZ\nvlWcw7Hx1zRs2FA+/PDDaOrUqZMcddRRttnkyZPlueees30ZoerJJ5+URYsWCcqsLl26yMCBAwVh\nj3MEKWX919L+7EdEBAXhaaedZudC73yXsWXeKJk9e7YVtjt27CgI3l4KGxe9bTLLo+x46KGH5Kef\nfpIaNWrIfffdZw9hvOW7RhGC4unGG2+0/MD8+fOlR48e8sYbb9hjaExb6NVXXxXqOebtt9+2Aj88\nxPr16+W6666zQv+PP/4oGzZskCFDhthjcvMHo8jTTz+dm5dM+WupkJ/yXUABUAQUgawgULBgQTnl\nlFMsA1ikSJHQQ5lsr7zySkGAx1KP1QiGIYhKliwpWEI///zzaDUTNpr9eFSmTBkhKWUNAX2HWcML\nhWu1atUESwwMI/hBWIcuueQSKVq0qNSqVUtOPPFEq8xyZ0couuaaa6LtXXnQFi8AGFysVA0aNAhq\nYsu0z2eERnHOiElYSaFChaRixYo2MR5PmDAhqijF8okwRX/esmWLFaJq1qwpeEnQ77CeQt26dbPK\n27BrUK79ORgd5jmUgVio8wPRV7BMByl1wsbFrDwXPAR9jH5ZvXp12/c4Hk8R+AfG2n322Ufq1q0r\nCxYssH0SK33t2rVtHUqCjz/+2F6ycOHCgrGNbZUqVYRzFytWTNatWye33nqr1KtXz+6jQEVxlQhh\nqOBdNWvWzI71q1evtofBm8yaNSt6Ct6rI64HZngNtGvXzn4/1IHXgAEDJBKJ2G9pyZIl9pA77rjD\nKnfZWbNmjbRv396Wo/iAb2ratKk912effWbLv/76a6vA4B7wnFEKRyDp/0Iv/NG1JqcR4MPFCoOW\nnIHpzjvvlLPOOsteZsSIETJo0CD7cTNovvnmm3aQeuGFF+SJJ56wFkw+VtyfEIogBhcGLwYMrJUw\nk0qKwN5GoECBAlHLjxN2gu4Jl05HTGZo1tG+hxGWIvo/ghJud7gt8w3wPTnCLQ+hCVq4cKHgSeAm\neNdGt5kjoO8wc4xcC6xJeI/ccsstUrlyZauIeuWVV6wnC66lCP+OyDt304kTJ1rLFy7OmRGWqqFD\nh0rPnj2tssDbXvu8F42MecU5IyaJlMCvIPg88sgjwngA/f3333b7+OOPW2sngtVbb71l+3RYP7cH\n+H60P/sA8ewedNBBdq9ChQqe0rybLV++vJ3vEZjxtnMUb1x0bRLZDh482ArC++23nw1P4nuGnHWe\nPELt0qVLraANz4GXHzwCwvwvv/xieQHanX/++Za3xggBb8CYDb8NoZj67bffrNcAwrmXr7ANAn7w\nCHjggQfsmI+XAcoZxmi8XQgzBANHXg8ulMFffPGFDSccO3as9YLBgwDFGbwQ31uTJk2sQhhBHU/H\nbdu22VPhHUEdhAzBNfGiAHvCH48//ngbngAvhedNfRO2mKinmD1piv2oJT/FXviefFyYMQYC3NkQ\naBhgsGZu3rxZ0NRh9UEj3qpVK1vPvfABU86g1bhx4xhLJgPCzz//bD9udUnek29Oz70nEfjkk0/k\npJNOkrvuuituDD8hKnw7aLJx8W/durX1APDeG4wGro4k4uqIsYNRVdqzCKTyO8TiM3r0aOvCDHP3\n2GOP2b4M4ghKJC+xTx++//77rXuzty4sD2NLOMuwYcPku+++i2mmfT4GjpgdxTkGjiztYBXEyum1\nKMPDYFGlL9IPETywyIf187ALan8OQyZ5yuONi4k+JYI6a5pcffXVlhdGAYJXjiOugRGMuPtRo0ZJ\niRIlrOBMn23Tpo01fCEgM0ZC8N0YxeC98Tx49tlnhTUoHGH9v+qqq6znQCLu+nhVYXyAr4c4L2WZ\nEeEvrBcEIaDznP7QRsrxXER5gdcB3gwYLiiDv4Hef/99e21CPEheYZ5r4PEVz9BiT5LiP2rJT/EO\nkJOPz0DDYAXx8XXo0MF+sJdddpl100HrxkDF5IrGEUL4ISYOiz/xxd4Fmi6++GKr8cNNiMlXSRHI\nbwgQu4/gjhYajXM8YrI6++yzrUDFROe+Je8xfGNo6R0xGTJBrlixwlpZXblucw6BVH+HCDpel0iY\nN8ewVapUSZYvXx4FGys+C0XiqUUssws3WbVqVdQa46ym0YNMBis+x2E1uvTSS21YgPPo0j7vRSo2\nrzjH4pGVPSyB8B5eYixF2QrRT3EVxosFy6K/n9P3w0j7cxgyyVMeb1xM9CmxbhMGwmJ5EEI+xi4E\ne+Lm4ZMZe7Fue8PyXnzxRbsWCmsaoIByvAJ9GoOYc3eH90BBjcUdV394cowHderUsR6DzG3xiGck\nlNARYSxe670rZ4sg74h2XnLhL94y7rF3797WGo/CAkI5hvfAgw8+aPdZpI8QA+SE7t27x6wjULx4\ncdtGf+IjoJb8+PhobRYQQEhBC+4I5owBgcmRyZL4GoR478qbuH3iLkc7ylnoxpF+xA4J3eYXBHCD\ncwtG4g6HhwruapkJ+O750JpjzcTjxcV/urqgLS79WKOcQBTURsuyhoC+w1i8sASdfvrpUeYOLxP3\njw+M624xSZhS6ihDYYsV5qmnnrIJl1fyQQI+V3PzBsehRGBhqTDSPp+OjOKcjkVWc7gT+0NJEDyw\n4kNYURGuELyIYcbNmHEZos/Tz8NI+3MYMslTHm9cTPQpUSrx7zgo6SH+ZYS4fOjuu++2Cif4Y6+A\nj8ELKzZroPAvOyxk5xT/xONPMGtMQPRfzo0VnnVUUCQ4SzjKBWedt41DfuBB6Ot440KEoTiBHJ7D\nrTHEd4LnrSMMenjxQoQI4HHEvXoJrwQMeHgbcB0SYb0saglPw3xCmAKhMxgzUFL4vca859N8MAJq\nyQ/GRUuzgQAfOavJYo1kUIHhw4qPZg43IWJ5+EiJw2fS5CPGgg/TxkIiCEIMKPEYvGzclh6iCOQa\nAlhwYAZZwAnrPZO2c1vjJphovbH6/htjImRCh/kMEojWrl0bnSzRshNvR9y+X3PuP6/uJ46AvsNY\nrHANxQLE30DCGNLvhptV9SGsKyhnqYORY90U543lVTyhAMYikwhhxWIRJxaehKHUPh+OGhgrzuH4\nhNUgVNGv/MpX+jM8CwIH/AyhhbgV038RNk499VTrVoywhFdiIqT9ORGU8l+beONiok+DxyvrWKE0\nxYKPQcytPg//gLIIbx1HCNlY9vl3ExRS8NDwDIRGQVjx8Z5ySlj6L/w4/Rdem2uQ6Pt4A2RGrA+E\nwA5/znosKA4wzEEsCsj3wqJ4jRo1sgv+ufOxj8ECQx2x+zxTEI/CtwVPxN8HQnge8L1ApUuXFv7a\nEmUa/7DCnIDCAMFfKXEECiTQNKiNtywo78rYusSlXN5t8STw510ZW38eM7Erd1tX5t2SL2FSNTMY\nv2a2SrmAAO5rfJS4bCLo4HaJYM+EyoCElhH3Iv5GA8GemDc+aAR7FrghHgdrD3HGuMyxyIZbgINj\n3YJOufAoeglFQBFQBBQBDwIIPVh0YLj8BNMIQ4cFRkkRyO8IYC1lUTO/YAL/wuriCDxKigAIxBsX\nE0UIV3f6FZb9RAmDGUK+18rvjuVcjMdYy72ExywGuHihJt72Ls/9odjyj/2cjznBq2h0x3B/KC3g\n3XeHkB+4Lp7B3DvCv98AQjneYpT769y1XbnXG2AXFl1Mm6UmbTKJ/zAm8dcD/i1lLrk69lmUxpW7\nvHfrz7NPglzeu+/KbYNdbYLy8cpcnRWwozshGYRwP3nLgvKujK1LnMPl3dYJ8ey7vHfrzztBnnKX\nXJl3S16FfAPC3iD++oOPno/SERpAPlYGMT42FgdxAwP/PQqTyGCAxlFJEVAEFAFFQBFQBBQBRUAR\nUAQUgXgIqJAfjo6664djozXZRCBIS4jw7tXoOQGfS6Bt9Gscs3lpPUwRUAQUAUVAEVAEFAFFQBFQ\nBBSBlEZAzaYp/fr14RUBRUARUAQUAUVAEVAEFAFFQBFQBJIJARXyk+lt6rMoAoqAIqAIKAKKgCKg\nCCgCioAioAikNAIq5Kf069eHVwQUAUVAEVAEFAFFQBFQBBQBRUARSCYEkj4mv2TJksn0vvRZFAFF\nQBFQBBQBRUARUAQUAUVAEUh5BFh4TykYAbXkB+OipYqAIqAIKAKKgCKgCCgCioAioAgoAopAvkNA\nhfx898r0hhUBRUARUAQUAUVAEVAEFAFFQBFQBBSBYARUyA/GRUsVAUVAEVAEFAFFQBFQBBQBRUAR\nUAQUgXyHgAr5+e6VZbzhd999V9asWZOxQksUAUVgjyGwadMm2X///eOef/DgwXLIIYdI06ZNpX//\n/oFtFy1aJAUKFJDevXtnqG/ZsqU0adLEls+dO1cKFiwotWvXtqlatWpy4IEHYIJ6bwAAQABJREFU\nym+//ZbhOC1IDAF9h4nhNGHCBDnyyCNtf+/SpYusXr06euCrr75q64444gh5/vnno+Uu88cff8i+\n++7rdjNs69evL7/++mu0/L333hPKfv/9d9E+H4Ul04zinClEtsGMGTPk0EMPjUlt2rSJHhzWn7/7\n7jtp3bq1NG7cWM477zxZuXJl9BhvRvuzF43w/Pnnny9Tp04Nb5DHan755Rc588wzY+6K8c7bl555\n5pmY+t3Zuemmm2LOzXVee+01e0r63gUXXGD5igsvvFAWLFgQvVRY/3UN9jTuzBWnn366u1zM9ptv\nvpGTTz45pkx39j4CBcwt+BPKAZcKmbxLLORHKrIrFTXbYiYV35VKmC0r4ZUyqbRJZU0qZ1J5kyqa\nVMmkKiZVNam6STVNqmVSHZPqmdTAJLiF/UyCu25qUjOTDjapuUktTWplUmuT2pvUOZIC9PDDD0ce\nf/zxiPmAIlu2bNlrT1y6dOkcu3arVq0ihmnJsfPpiRSBnEJg+/btkdtuuy1iBPdIkSJFQk/75Zdf\nRoyAH1m7dm1kw4YNkaOOOioycuTIDO0XLlwYKVeuXKRRo0YRzu3ICO+RmjVrRowiwRbNmTMnUr58\neVdttw888EDk1FNPjSnTncwR0HeYOUauhRHobT/8888/Izt37ozceOONkWuuucZWT5o0KWKE/8iq\nVatsoq8ahtMdauejjh07RoxCKlrmz9SrVy9iGGhbPGrUKPsd/PPPP3Zf+7wfreB95n3FORgbfynf\nvhGSookx9Pbbb7fNwvrzjh07InXr1o0YAca269OnT+Tyyy/3n9rua38OhCVa+PTTT0eOP/74iOHP\nI5MnT46W59UMY9G1114bqVKlSuSEE06Iuc3OnTtHvv7668jWrVttop/kFK1bty7aR2fPnm3H2fnz\n59vTg9/rr78e4XpPPvlk5Oqrr7blYf2XytzCHb7ntNNOs/fj/zFK9Qj8Tk7TkiVLIps3b7bzjXsX\n/u22bdsiJG854wBy4i55EbkR+RE5EnkSuRL5EjkTeRO5E/kTORR5FLkU+RQ5FXkVuRX5FTkWeRa5\nFvkWORd5F7kX+Rc52MnEyMfIyU5mdjK0k6nZOlmbrV8WZz8ucZBSEiBgJi5BS4YWb9asWUnwRPoI\nikDeRABrOppqtPZGyA+9yXnz5olhDqRs2bJSqlQpMUJ+6LfJv4BgCf3000+j53vppZcELX08KlOm\njD1/vDZalxEBfYcZMQkrMUyZGAHSWvHxODnxxBOj/fjll1+WW2+9VYoWLSpG0JTp06dLrVrwPWnU\nr18/6dGjhxQqBK8Sn9544w0ZMGCAfPHFF9KwYcPQxtrnM0KjOGfEJKyEvlixYkWbjAAnn3/+udxz\nzz22eVh/xkuCfte2bVvbzghV8vHHH4ddwpZrfw6GB4+gO++8Uw4//PDgBnmstFKlSoL1e+DAgRnu\njH5RvXp1y3uzwjvzSk4R/c31U6z69913n9SpU8da7ZctW2Yt+XgBdu/eXfAYhML6L3XZxf3FF1+U\nAw44QIwRwl7TeXGdc845wvfj6KCDDnJZMQoKyyPhwcV1wQlifrjjjjvECPn2/Rvh3Jb36tVLjOHE\n5vFKPvroo23+hx9+sN4MDRo0sN5g48ePt+VGmSHXX3+9cA94ziiFI4DWQCmJEOADwX0fJqldu3Zi\nLH+59nR8uHyoY8eOtYPd3XffLeeee669PoMPgyRtGDTfeecdO2ANGzZMHnnkETEaSeuWbLSTUaHl\nhRdekA8//FCMJVTuvfde6dq1a649i15IEQhDAEHnmGOOsdXxJvVLLrkkegomM2PFt/0+WujL0P65\n556z7mwo7XBbhlHke3L033//yRVXXGF3cdEzGn4xmnNXrdsEEdB3mCBQphkhIcYKZA8wlhAZMmSI\ndOjQwe7PnDnTbo03mcDkGiumnX8KFy5s+yXhEO3bt7dt4v288sorYixSYiyqNhTF21b7vBeNjHm+\nf8U5Iy6ZlcCL3HDDDbbfMR5AYf156dKlVphz50SwoyyMtD+HIWPMowdjIBWpUKFCeKM8VAMPzXyP\nQh++1ZGxCMvff/9thU1j5RcE0Lfeeku8oR+u7e5sP/roI2HchZ+HuKbx/LP3VKxYMTEeVgKvjBt8\nWP9lPM4O7oRToAAzHhdSo0YNq9A1nlxWmYCCAQwcGa8rl7VKD45BkTNmzBgrByDoG2u7LF682IYn\nEsL42WefWUPGxIkTo+diPKMOMh4zViF01llnWeyfeuopq2RmTsCg+eOPP8o+++wjy5cvj15bM7EI\n5JzaKfa8ureXEcCaT/xOWNzYnri9jRs3inEvFmLePvjgAzv4Yc3kw+7Zs6egfUMoQbOHwANhBfrq\nq6+sZYjYY6epow7LEAPYJ598Yj92ypQUgfyGAJM0FiAUVcTVhdGxxx4rP//8s11fg2PQZpsQmJjm\nMBrEBZKuuuoqy1DAqCrtWQT0HYod1+nH++23n7XOgzhjPgwkXmTTpk2zzCiKKZTNKHkHDRqU0IvB\nes85UCBMmTIl5hjt8zFwxOwozjFwZGkHXgMh6bDDDoseF9afUQiQvOTf99Zpf/aikZx5E7ok77//\nvowbN84KvYxdffv2zfGHxTjmrNycnD7K+hAIuQjJ8NJ4DLq6oPHYVmbjBz4e4wMCPsS6QZRlRihF\nnKcGAjpGC3h/L1HON4iyrGrVqtYDDMMF3oxu7QPqkSnwmiTx7I64BsbMeIYW1zaVt2rJT9K3j3YL\noSE3taW4bOJCA/Hxmbgc+8FieWQBJYT5N9980wrtLN4EnXLKKdYVFIv/xRdfLM2aEQKTRpdddpnV\n+OEGhOZOSRHIbwig3EJwx7WTbzIeMVl16tTJfiNMdO5b8h6DwMN35eiMM86wioAVK1ZI5cqVXbFu\ncxABfYcib7/9tl04ErdQZ1ECYpgz5hkIayiKKaxJI0aMENz8zz77bFuHsvmkk06y34GzmtqKXT8s\nYNWiRQt57LHHxMS5CotcEeYCaZ/fBVLARnEOACXBIoSk//3vfzGtw/oz7sq4SDtCMMEjMYy0P4ch\nkzzlWNedxZmnQqj1C7K7+7RY7U0cfox3AH0UXqL+Ljd1FoOkDYJ0WP/N7n3wjIQaOoLH91rvXTlb\nru+Idl5y4VzeMsK+brnlFmv8c3MKVnyUvMwDEN4JPCueYygynnjiiegpSpQgtF0pMwTUkp8ZQvms\nHsaI+EmYq9wU8IEJIcUbe+k+bFxpsGASX4PATnyTI4R+3DQZOIhzfvTRR12V6EcchUIz+QQBYtSI\nO4Nws2flezxRMhPw3eOhNcciwMriLv7T1QVtcenHGuUEoqA2WpY1BPQdxuKFeyVukxMmTIgR8GkF\no4Y1CcKyBZPGv0mgsMUKQzgWCZdXtkECPsdifYIuuugiyyzjpRJG2ufTkVGc07HIag5FqlnALOaw\nsP5MyArfgYstNgtERuOGY06wa0f7cxAqyVX2119/iVmILyr0oshHUZmTxNiKEtVrreYahLA6hQKu\n7vzLD30urP9m954QvvEUwBsXIgyFMgiew8XkMze4NtRh0MOLF8LDizh+Yvq9BH9PCMHQoUPtOTnv\n8OHDbTt4GrNYsVUAwA9hzECREc97xntuzacjoJb8dCzydY4PnI8fbaJfi5ZbD8ZHPnr0aGuNJD4T\nF9du3brZv0lhwRDiLflIYfaw2PMRY8lnsjWr6VtBiMnz5ptvzq1b1usoAjmKAAormEH6MfF7WCRh\nEB2xwIw3Vt+Vuy0TIZMnccxBAhGTO3+hB6EYw5qEomxvffPuvpNpq+8w9m2ipMJy6Y01hfGE+cK6\nglcW8w7jP6FYuGHClHoVTyh/neUp9uwZ94j/RxkMwwfjp30+I0auBIwVZ4dG4ls8n+hXfuVrvP6M\nMMK4zMKSuA1710qJd2Xtz/HQyb91hHngVYc1H5dyrN6sKZWThFLJ/YWuOy+8PmMjRjG89/CSYnE8\nKKz/umOzusWSTpgt/DrrDqDIhd+AzD+sCN628DeEcPGXvo7YxyuxePHiQuw+9xfEozBXXHfddVYx\nzLF4Pbr1XwhV5PoYCFESszAxf7XK/SgljkCBBJoGtfGWBeVdGVuXuJTLu21BT5nLe7f+vP/vBKh3\nZd4teXw5+N+e18w2qQlhIrdd84MAZYVwPkoYQgQd3PQR7JlQWVmcQYAF9po3b24XFUPDx4JNCEQs\n2EQ8DgoAmEk+aAYG5w7FSqMoDpQUAUVAEVAE8hYChFPBfAYxcnnrTvVuFIHMEQjrz/AvWCU1NCpz\nDFOlBWtHoeBkMbzcJvpikMduWP/N7v3hio9iy/+MGBp4dq+i0V0Dgx5evIQQ7A4hP3BdwraQARD+\n/QYQZA4UAZT769y1XbnXG4DzGUMJscOsornJpB270s6ALWUu0c7lWazDn3dlbP15V2aqbJ1/35Wz\nhah35M3HK3N1VuiO7oRkEMj/z96ZwNtUtX/8MVNChFDxishUSSkN9EczpUEZSmke3+aQBpoHDZpD\nQmnQXEqDSGkeaC6S5lQi5VWJ89/fxTp3n3P3Pvfc617ce37P57PvXnvt4ez93WutvZ5hrZsu4byo\ntM9j7Reu4dN+7ZV4tn06vE5Pe0WefL/4vPA6p5R8wK5PgmWRSk+l9IIFkMqKNZDKxr/Y8A0DMwNj\nVacxCIcl+XO1FgEREAEREAEREAEREAEREIEwASn5YRqpaYXrp/LQVjEQiJqQBuU9bNHzCj4/x9gc\njb8vBvC6hAiIgAiIgAiIgAiIgAiIQM4TwBsuEQEREAEREAEREAEREAEREAEREAERKAMEpOSXgZeo\nRxABERABERABERABERABERABERABCEjJVzkQAREQAREQAREQAREQAREQAREQgTJCoMyPyWeWSYkI\niIAIiIAIiIAIiIAIiIAIiIAI5AIBefJz4S3rGUVABERABERABERABERABERABHKCgJT8nHjNekgR\nEAEREAEREAEREAEREAEREIFcICAlPxfesp5RBERABERABERABERABERABEQgJwhIyS8Dr/nZZ5+1\n33//vQw8iR5BBEoPgWXLltm2226b8YbvuOMO22mnnax9+/Z22WWXRR77ww8/2IYbbmgXXnhhvv27\n7rqrbbfddi7/66+/dsc1a9bMWJo0aWIdOnSwjz76KN95ysiOgN5hdpxmzJhhXbp0ceX96KOPtkWL\nFrkT//33XzvrrLNcGd1hhx3svvvuS17wnXfesT322MO22WYb69+/vy1cuDC5L5xo2bKlffDBB8ms\np59+2sj7+OOPTWU+iaXAxCeffGJt2rSJPU6cV6H54osvbOedd05ZunfvnuQ2ceJEV9Y7d+5sY8eO\nTearPCdRFEviiCOOsLfffrtYrrU2LjJ79mzr3bt3yk9RPsJlafTo0Sn7C7ORXn8zta20pQMGDHD9\nioEDB9p3333nfuq8885LuR/u7YEHHnD7XnjhBaM/QRtx7LHH2tKlSwtze1kfy7fikEMOiTz+jTfe\nsAMOOCBynzJLhoCU/JLhulavOn/+fHvooYeMj9A///yzVn87/GP16tULb65Rmg/sp59+ukbX0Mki\nUBIEVqxYYRdccIH7YH711VexP8HHbty4cfb888/bK6+8YtOmTbNHH3008vgaNWrYE088YVzbC0rO\njz/+6DfdumbNmjZ37ly3UO/79u1rF198ccox2iiYgN5hwYz8EYsXLzYU+zvvvNPef/99q1u3rg0b\nNsztnjBhgn3zzTeus/7II484hZ8yu3LlSuvXr58NHz7c6BxvueWWkUYs/xt+/dhjj9mQIUPsmWee\nsdatW7tslXlPJ37Nd592INuOey5zpixOnjw5uRx66KG2yy67OLgzZ860UaNGGXwef/xxGzlypH3/\n/fcqz/FFr9B7UIT3339/9y0Mf+8KfaG1dMK8efPszDPPtJ49expG4bDwjb/++uuNNQsKd1Ekqv7G\nta1cHwV/v/32c31+jKvXXnut+1kcBb5s33///Va1alXXT/njjz/s1FNPdUbYd9991/7880+79dZb\ni3Kra3QOTpHbb799ja6hkwtHQEp+4Xitt0dj9cMq+vDDDxudf4kIiEDJEChfvrztu+++rgNYqVKl\n2B9B+Tn++OMNBR5PPVZ1OgxRssEGG9iOO+5oU6dOTe7mI9+nT5/kdlRio402MhZJ4QjoHWbPCyWH\nDmWLFi2sXLly1rVrV/PGrYoVKxrGXdYo/9SHKlWqGF4pyuVuu+3mfui4445zxq5Mv4qh+oorrjAi\n05o2bRp7qMp8fjQYU04++WSjXBckuc65QoUKVrt2bbfQHk+fPj1pKCUS5YwzzrDKlSvb33//be+9\n9541bNhQ5bmgQlWI/XznBg8ebNtvv30hzlp3h1JW8ExHRdrhiKpfv77hoUaRzqb+RT1JVP2Na1vx\n2v/yyy8uqoAowGOOOcZuuOEGd1naRl+28epjjN1ss81syZIlds4551jjxo1d+4wBFUNsNoKjgnfV\ntm1bZ1zwUVz0Tb788svkJXivXvg9mBE10KVLF1d/2Acv7imRSLhvw08//eROGTp0qDPusoFRmW8M\nQv2j39SqVSt3rRdffNHlv/rqq86gzD0QoSSJJ1Dm/4Ve/KOXzT2E7dNJIpS3U6dOhhdkbQkVFy8M\nlkQau4suush69erlfv7ee++1K6+80lVuGiFCiGh8xowZYzfddJPzYFJZCX9CKUJoXKZMmeIaKLwU\nWC8lIrCuCaDoeM9Ppo86Icpe+Jjh6cS6Hid4Pin/e+65p2G0I2yZOkB98sK/BKUzj/CxR9mijkgK\nR0DvMHtedAjxaCLLly+3u+66y5VRtg877DDXlmP0oiyeffbZrpM5a9Ys1/nlGISO8M8//7xqI+Iv\nIdK33Xab0TFt1KhRyhEq8yk48m28/PLLzsPI0IiCRJzzCNFfQfEZMWKEM16xZ86cOe6AG2+80Xk7\nt9hiC3vwwQdd2aUMe1F59iQKv27Xrp07aeONNy78yevgjFq1arnvPQZMou284H0nqo42b5NNNnEG\n+vHjxzvPuT8mm3Vc/Y1rWxnaRB+5W7duTmH/7LPPnHecfoOX5557zvUhdt99d5dFm4qh9cMPP3Re\nf5TzcL/Cn5e+xnF49dVXu2dr0KCBM87QRhPtgoEhHDkcdi5i9HjppZfccMInn3zSRXURQYDhjL4Q\n39+tt97aRTeiqBPpyLcFISKCfQg6BAYhoihgz/BHnptvAn0pIm/QdbKNYHIXzbE/BZt9cwxIWXlc\nKhxKhbe6rY3nouLREBCeyW/T+OHN/OuvvwxLHWOC8PB07NjR7eeeqMDkE5qMpyjsyaRBIDyUyq2Q\n5LXxBvUbJUGAD+5ee+1ll1xyScYx/AxRoe5gySbEHyMdEQBhoaNBqCML4+oYY0dHVVKyBPQOzRjL\nTEeS+SC8oYl2Hq8NbT2eLgwAHIcCxRKW9O3wPjq6DGdhSMBbb70V3uWiA1TmU5AkN2grrrrqKjcs\nIpmZISHOeXDwChJ1EvYo04fB009ZpByieBD5oPKcx02pVQTwhBM5y9AOlF686XzjCyOZ6m9c20oZ\nZWguDjIUdRxoDCcICw41IlLSBcfaiSee6KJTsgnXx2GI84F+PUI7T15BghOE+YIQFHScFj76y59L\nPv39BQsWuCgwImZwXJBHe4889dRT7rcZ4sESVub5DSK+Mjla/G/l8lqe/DL69rFAUwmwQq4tQQE5\n6aST3M9R+fbZZx9XYRnPSZgOVrdJkyYZH1eslAjKD2Pi8Pgzvjg8cdCRRx7pLH6ECdGwSUSgtBFg\n7D6KO4YqLM6ZhI/VQQcd5DoOfOh8XQqfQx3Da+qFjyFh0r/++qvzJvh8rYuPgN6hufHJTBzJ+FPv\nHYIw3hQMsD68krKOQQTjEyGlXvDi16lTx2/mW+PFZ4JJvEZHHXWUC3/1EV0q8/lwJTOIiGM4hR/W\n89tvvyW9XnjL0kWc84hQdul7hIW2FGMrAj9ChfHW4llUeQ6TUhoDUDhUHKU2XZEtiFKm+hvXtjKR\nL30JQu8Rtr/99lunSBPiT3llmzbYC5PzkkefHOfB5ptv7gy2fNsyCc/IUEIvDGMJe+99PmsUeS8c\nFxY//CWcxzdj0KBBTi/w3xSMkEQPXHPNNe5QJunjOdETTjjhhJR5BJhvQFIwAXnyC2ZUqo5gTA4V\ngjGUa1PBBxJKClZwL3TOaBD4OPKxZHwNSnx45k3CmwiX4zjyfVgo11Al9iS1Li0ECIPzE0ZiZSdC\nhXA1PsrZCFZzvJlEvPjxzJnOI6Qfb5RXiDIdq33ZEdA7TOXERHqETTIsxHfG/BGMx58ejGlG8GzR\nmcTrQ4g/YZmUY4QJJ/kGxIn/bmDopbN8+umnxx3qhrGozK/CAy+8XTfffLNb+OaTjlLwOUOc84oV\n4cTpQxxQPPDiI5RnQocJL1d5zuOm1CoCRCz16NEjqfQSfef/E062jDLV37i2ld8gesobFAh1JwoW\nBR+h/GKoCnu4mS+A/4LiPeGEznvvfKZ7pQ9C2000LsJwH/8NoM/h5xiinhB56wWHHlG8CEMEiFgg\nAiws1apVc+P8if7id1iISmBiTNp3Jgdk7D1DZ3BmYKTIFA0WvrbSeQTkyc9jUapTVHA+RjQA6Va0\ntfVgVHJCl/BG0qjQ6OHFxzJHmBBjeaikhBnx0aQS48FHUWGGUBQhGpRMHby19Sz6HREoCgE8ZSg3\nTOCE957xcz5sjevxoQ2P1U//DT6EGOrofEZ11Jlzw38ssbIzvwXj9tdVnU+//7KwrXeY+haJvMIT\nHx7zSUePzhdefLzIvnPLUCzafzqY7MfYTBgmnUuiuLIR5mhhEicmnuR3VObjqdHRDhv44O49fPFn\nrdqTy5yJfKJcpRtf8RbSZ0HhoD9DeSasWOW5oNKUe/sZ5oFnnH+Pi8LM9/iee+4pFIhM9TdT24oj\nAKcY0VFE7zBW3QuGVZT+sGBgpa9NPgtl/+677w4fEpmmzUdhp3/OvAMYvnDMIUwqTH1hUrzmzZu7\nCVj9RdjGYYGjjrH7zKgf1UehbtEn4t+sIkSC0S4h1atXN/61JffO3GJ8EzAYoPhLsieQP54r/7lR\nx4TzotI+j7VfuLJP+zWRBOlpn8c6PY2b2Of7tc8Lr0lXC5b6Qeci7x/3BhllUfAYru3Q/CiONDhU\nSjqEKDqEXaLY80HFQo5lkn+Zwr/RQLFnzBsVGsWe4QWMx8ELQZgRlkgaLj8BB+dmmrgp6n6UJwIi\nIAIiUPIEmPuFDh3embDQ3rOPDqJEBEoLAYYH4jhJV0xUnkvLG1x794kxCE93SU1yHde28oTsK8wE\nhkTM4oDLNHQqihyh+Bhq05+R6/HsYUOjPx+HHlG89N3XRNAf+F0ig7l3lP90Bwj5RDGRn77P/7bP\nD0cDrGbRLzhmQbAsCxb+hzEL/3ogfU2eX/w+tpl8xuf7dHidnmabBfHp8LbPdwesPiYqnSnP73MK\ndnIjJoESni7hvKi0z2PtF67h037tlXi2fTq8Tk97RZ58v/i88Jp0zij5wbOuV7Jw4UJX6amUXrAA\nUlkZ80ZlI9zINwz871EsizQG4RAjf67WIiACIiACIiACIiACIiACIhAmICU/TCM1rXD9VB7aKgYC\nUVZClPewRc8r+Pwc3p90D1Ax3IYuIQIiIAIiIAIiIAIiIAIiIAI5RwBvuEQEREAEREAEREAEREAE\nREAEREAERKAMEJCSXwZeoh5BBERABERABERABERABERABERABCAgJV/lQAREQAREQAREQAREQARE\nQAREQATKCIEyPyZ/gw02KCOvSo8hAiIgAiIgAiIgAiIgAiIgAiIAASbek0QTkCc/motyRUAEREAE\nREAEREAEREAEREAERKDUEZCSX+pemW5YBERABERABERABERABERABERABKIJSMmP5qJcERABERAB\nERABERABERABERABESh1BKTkl7pXphsWARFYHwgsW7bMWrZsmfFWbrnlFtt2222tVatWdvHFF0ce\n+/3331u5cuVs0KBB+fZ36NDBtt56a5c/f/58K1++vG222WZuqV+/vrVp08Y+/PDDfOcpIzsCeofZ\ncYo7asmSJXbssce68k1ZnTp1avLQ6dOn28477+zqSL9+/WzRokXJfeFEkyZNbPbs2cmsJ554wsj7\n6KOPTGU+iaXAxMcff2zNmjWLPU6cV6H5/PPPbbvttktZdt999yS3CRMmuHK700472ejRo5P5b731\nlnXq1MlatGhhvXv3toULFyb3hRPiHKYRnz7ssMPszTffjD9gPdsza9YsO/DAA1PuivIRLkt33HFH\nyv6S3oir81H3mm17vKb3zO/06NEj8jKvvfaa7b333pH7lFkyBKTklwzXnLzqRhttVGzPzQf2k08+\nKbbr6UIiUFwEVqxYYeedd56h1MybNy/2snzsxowZYzNmzLC3337bKUCTJk2KPL5mzZr26KOPGtf2\ngpLz448/+k235rjvvvvOLQsWLLAjjzzSBg8enHKMNgomoHdYMKNsjjj//POtbt26rq1GOTr66KOd\nMr948WJDsR87dqx9+umnVq9ePRs6dGiBl3z44Yft3HPPdXUFAxaiMl8gNvvnn39syJAh9ueffxZ8\ncHBELnPGEIIxyi99+vSx3XbbzXF75ZVX7Pbbb7dnnnnGnn32WRsxYoRra1euXGmHHnqoXXnllYaR\ngGtQ9guSXOYcxwZFuHv37vbQQw+lfO/ijl/X+V9++aWdeuqptueee9r//ve/lNuZNm2aYcjHAMRy\n/PHHp+wvyY2oOh93r0Vtj4v7/tu3b+/6RMV9XV0vnoCU/Hg22iMCIiAC+QjgTcdSTWelUqVK+fb7\njK+//tpOOeUUq1Gjhm244Ya2yy67GB/hKOG/gGDYeuGFF5K7UZD69++f3I5KYFjj+pLCEdA7LByv\nuKNfffVVp8yzn4gTIlZmzpzpFKOePXs6Lz5RKnSQ48q+v/b9999vw4YNs5deesm23HJLn51vrTKf\nD4ldeOGFdvrpp1uFChXy70zLyXXOMKpdu7Zb5s6d65T9yy67zFEaN26cnXPOOVa5cmX7+++/nfGq\nUaNGhseUcte5c2d33EknnWRTpkxJI5u6meucU2nkbRHdc9FFF9mOO+6Yl7kep+rUqWNEHQwfPjzf\nXVIuNt10U8NDzQzvfFfWlkTV+bh7xTFQ2PbYP8fdd99trVu3tubNmxsGMR+RdfDBBxv1x0u7du18\n0ojwoo+EMYz3DScExx3G3kQi4d7/Tz/95PIxmGHcRTBI7Lrrri79zjvvuEiJpk2bums9//zzLh/H\nyWmnnWbcA5EzkngCZf5f6MU/uvYUNwEqLhX1ySefdI3dpZdeaocccoj7GT6eNJIcQ0OE13LzzTe3\nO++801nL8azRSZw4cWJSacELOnnyZPv999/t8ssvt4EDBxb3Let6IlBoAigt3vOT6aM+YMCA5LX5\nmOG5oNzHCcePGjXKhbP9+++/RtgyHUXqkxc8CYRHI99++62LJMCbICkcAb3DwvGKO5o2/L333nPD\nRv766y/XiaNc7r///s4jynnLly+3W2+91fbZZ5+4y9j48eNt5MiRzhvNcJSwqMyHaeRPU/8ZdtK1\na9f8O9NyxDkPCH2R//73v67c0R4gX3zxhVtfd911Tmlr3LixPf7440bUFMqcF9LkxYk4x5Ex22ab\nbdzOjTfeOP6g9WhPrVq13Pceg374+40nfc6cOU7ZJJoJBfTBBx+08NCPknqMuDofd69ERRGhgmTT\nHvv7ZjgFBjAMtw0aNHAGsDPOOMPozzPMEAZevvrqK590Rg/OwZDz2GOPOT0ARZ9vBNGJ1DcMwi++\n+KJzZLz88svJa/Fs7EOIUsQg1KtXL8f+5ptvTkZUEDn27rvv2n/+8x/75Zdfkr+tRCqBtWd2Sv1d\nbZVBAkuXLrWGDRu6cLann37aNX54M6nYhDdjfSO8GcseCg+C1ZwQObw8KPneUsc+LOmEej733HMK\nSQaIpFQSIOwTDxCGKsbvxckee+xh77//vrNkcw7W7OrVq6ccTkeDcYEsJ554outQ0FGVlCwBvcNo\nvswzQUeMiJP/+7//c94evKBeCG2m7G+11VbO0+zz09d47/GGYQx44403UnarzKfgSNnA64UxnTDy\nbESc8yjR16hSpYrtsMMOyUz6MBUrVnRl8YMPPnAKEYZWDAIsYUnfDu8T5zCNsplmCMdTTz3lhnag\n9NJ2XXDBBSX+sIWt8+EbyrY99ufQj8f5gIKPMG8QeQUJThAfqYGCjtMifWgj+dRBjGUM5yJiBgMx\n0Yx+7gP2o1MQNclC/fTCbxDxlcnR4o/N5bU8+bn89ov52encEUKDUPnw5lBh8TwygRLK/AMPPOCU\ndsZrIvvuu68LI8Ljz/jitm3bunz+ML4Tix9hQOljoZIHKSEC6zEBjFso7oR2YnHOJHysGPdJHaHe\n+LoUPgeFh3rl5YADDnCGgF9//dU22WQTn611MRLQO4yH2bFjR2eIJQyTkE6ireiUIY888oibbJIx\nq126dHF5cX+YwGr77be3G264wfr27WtMHOWHoajMx1Ezu/fee93QiIMOOsgdxGRwe+21l2tvvHc6\nfLY459HAE3j44YfnZQQplA2MrQj8MLTi3ads//zzzy6fPygmRCTGiTjHkSk7+XjEvceZp0KpTVdk\nS+JpC1vn/T0Upj325/CMDDX0Qh8/7L33+axR5L2EDb3ksY3TLiwM4Tr77LOd889/H/DiY+TlO4Aw\nSR/9JqLAGPp40003JS9RrVq1ZFqJeALy5Mez0Z5CEkBJCY8J9BWbUBo8mIyvQWFnfJMXFBrCNGk4\nGMNz/fXX+12mSpxEoUQpIcAYNT9hJGH2zHxPJEpBCr5/PKzmeASYdM+P//T7otaE9OON8gpR1DHK\nKxwBvcPsedEZu+uuu9x8EnRwX3/9dafQE5KJh396MPmk78BluireU+SII45wnWWiVOJEZT6PDIZx\nvF0Me2MhXJd1lILPWeKcxw5Dardu3fIyghSKByHECJ5alA7+OwrhzpRpP7aYCVT9uGF3cNofcU4D\nUgY3P/vsMzeBoFd6MeRjqCxpKWyd534K2x77Z6DtJuqWaFyEYSi+PafP4cfk0877YzgOhx5RvAgR\nMYzjZ0x/WOjfM3Tjtttuc9fkuvfcc487jj4Nk4gS/Ut/CGfGN998ky+aJnw9paMJyJMfzUW5RSBA\nJWc2WbyRTEJCiOtxxx3n/k0KYzeZ/ZcQNzoheOypxHjy+djiEUIR4uN51llnFeHXdYoIrHsCGKz4\noFKOGb+HR9LPEs7dMcFMeKx++h3zIeTjyfjaqI4681P4Mct0LvAmYShLt5ynX1fb2RPQO8ye1VFH\nHWV4kRmLyphM2nY6b2zj7QyPT8VDSoetIGHsKMZgOnx0/FTm44nRVoQNfBjZmzRpEn9CaE8ucyby\niXKVbnzFW0iUIV5Z+jMMLSSsGAcGygjtMmHFhA2H50oJYc2XzGXO+WCUoQyGeRBVhzef6CW83swp\nVdJSlDqPo6Eo7TGedBRt+uvMO4Dhi/4GcvLJJ7toW/o3DMfiX/p6YRs9oGrVqm7sPpP3RfVRqFv8\n5wIMaQhRj37uAIYq8vs4CDFeMjEx/2qV+5FkT6BcFodGHRPOi0r7PNZ+4ad82q/Lh/J8OrxOTzN1\nLHnhxeeF16SJ5agfKJX3BWvJWiDADOFUShoTFB3C9FHs+aAysziNABPs8W808MZg4WOCGxQiJrhh\nPA6dRDqGVGgaBh8Oxcy2GA4kIiACIiAC6xcB2mY6ZVGGqfXrTnU3IlAwAYYH4o1PV0zov+CV1NCo\nghnmyhGEoWMQ4l99llUhFB/DVvoz4mjg2cOGRs8Ahx5RvAyBWRNBf+B3GbYV951B58AQwPcn7hvk\n88NzaXC9wFHC2GFm0VwWLPwPY5aVEWvy/OKPYZvJOny+T4fX6Wm2WRCfDm/7fHfA6mOi0pny/D6n\ndCc3YhIo5OkSzotK+zzWfuEaPu3XXoln26fD6/S0V+TJ94vPC6+l5EN7HQnjAqn0VEovWACprFgD\nqWz8iw3fMDAzMFZ1GgNNouGJaS0CIiACIiACIiACIiACIhBHQEp+HJlgiFb8Lu0RgaIRiJqQBuU9\nbNHzCj6/QHinxt8XjbXOEgEREAEREAEREAEREAEREIEwAbzhEhEQAREQAREQAREQAREQAREQAREQ\ngTJAQEp+GXiJegQREAEREAEREAEREAEREAEREAERgICUfJUDERABERABERABERABERABERABESgj\nBMr8mHxmSZWIgAiIgAiIgAiIgAiIgAiIgAiIQC4QkCc/F96ynlEEREAEREAEREAEREAEREAERCAn\nCEjJz4nXrIcUAREQAREQAREQAREQAREQARHIBQJS8nPhLesZRUAEREAEREAEREAEREAEREAEcoKA\nlPyceM16SBEQgeImsGzZMtt2220zXvaOO+6wnXbaydq3b2+XXXZZ5LE//PCDbbjhhnbhhRfm27/r\nrrvadttt5/K//vprd1yzZs2MpUmTJtahQwf76KOP8p2njOwI6B1mx8kf9cknn1ibNm38pltPnDjR\nunTpYp07d7axY8cm973++usuf5tttrE+ffrYzz//nNwXTrRs2dI++OCDZNbTTz9t5H388cemMp/E\nUmAi6t2ETxLnVTS++OIL23nnnVOW7t27J1HFled33nnH9thjD6M89+/f3xYuXJg8J5wQ5zCN+PQR\nRxxhb7/9dvwB69me2bNnW+/evVPuivYuXJZGjx6dsj+bjYKuEfW7cWWROchOOukk1y+h3/HMM8+4\nWyiozGdzn9keM2PGDDvkkEMiD3/jjTfsgAMOiNynzJIhICW/ZLjm5FXr1atXbM9Nh/HTTz8ttuvp\nQiJQXARWrFhhF1xwgaGAf/XVV7GX5WM3btw4e/755+2VV16xadOm2aOPPhp5fI0aNeyJJ54wru0F\nJefHH3/0m25ds2ZNmzt3rlvmz59vffv2tYsvvjjlGG0UTEDvsGBG6Uf8888/rqwtXbo0uWvmzJk2\natQoe+yxx+zxxx+3kSNH2vfff+/204m/9tpr7b333rMGDRrYFVdckTwvLsF1hgwZ4jqnrVu3doep\nzMfRysuPejd5e/OncpnzlltuaZMnT04uhx56qO2yyy4OUlx5XrlypfXr18+GDx9uKF1cI8oom046\nlzmns/DbKML777+/+xaGv3d+//q2njdvnp155pnWs2dPwygcFr7x119/vbFmGThwYHh3Vum4a8T9\nbqayeOONN9pGG21ks2bNsptvvtmOOuooW758uSuvcWU+q5sspoNwitx+++3FdDVdJhsCUvKzoaRj\nREAERGA1gfLly9u+++7rFJpKlSrFcvnmm2/s+OOPNxR4PPVY/PlwR8kGG2xgO+64o02dOjW5e8KE\nCc4DmsyISPBBZ5EUjoDeYeF4cTQKzsknn2yw83LffffZGWecYZUrV7a///7bKfQNGzZ0u6kbpCtU\nqGCbbrqpO8afF7V+6KGHnCHg2WeftaZNm0Yd4vJU5vOjiXo3+Y9alZPrnCmPtWvXdgvt8fTp05OG\n0rjyTJQE5W633XZzEI877jhnvI1jTH6uc45jw3du8ODBtv3228cdsl7lU1bwTEcZdXBE1a9f3/BQ\n//HHHyltY7YPEXeNuN/NVBZpb/HkI1tvvbVVrVrVOQ4ylfmC7hNHBe+qbdu2NmDAAFu0aJE7heis\nL7/8Mnk679XLkiVLHDOivrp06WLcM8KzDhs2zBKJhKtLP/30k8sfOnSoM+6ysXjxYuvatavLx0BM\nv6lVq1YuguzFF190+a+++qqdddZZrn9E5IwknkCZ/xd68Y+uPcVNgIqLFwaLIR3Biy66yHr16uV+\n5t5777Urr7zSVW4arwceeMA222wzGzNmjN10002uIaKyErqEUoTQuEyZMsVoMPBW0sBIRGBdEyhX\nrlzS8xNWeNLvi5BOL3zMHnnkEbv//vt9Vr41niLK/5577mn//vuvEbZMHaA+eSEcD0UL+e6771wk\nAXVEUjgCeoeF4/Xyyy87LxbhymGZM2eO28SD9Oeff9oWW2xhDz74oFWsWNFuueUW18HbaqutXNg9\n14gTQqRvu+02O++886xRo0Yph6nMp+DItxH3bvIdGGSIcx4V+ivnnHOOjRgxwmgPkLjyzFATlDkv\npOOGn3CMOHtS+dft2rVzmRtvvHH+nethTq1atdz3HqMl0XZeiJ4hqu7ss8+2TTbZxBnox48f7yL8\n/DEFrTNdI+53M5VFPPfI1Vdf7YxMl156qVP0XWbwJ6rM+31Ra4ZTcC2cD0RjYZyhjSZ6i2GG3L8X\nIgu9YPR46aWX3HDCJ5980kXBvPvuu84QTF+I+oYRguhGjAVEOhJxgBDZwD4EHYLfJIoC9gx/7Nat\nm/FNoC9F5E2TYNhiOLrMnag/SQJ5JvlklhIiUDQCVDwaAsLZUGho/PBm/vXXX4al7oUXXnAWvY4d\nO7r9/AoVmHxCk1u0aJHiycQz9P7777vKrZDkor0TnbXuCTz33HO211572SWXXJJxDD9DVKg7WLIJ\n8e/UqZOLAAg/AR0NQh1Zjj32WNehoKMqKVkCufwOKY9XXXWV8+SnU6bNx0tEZ+2tt95yHTU8mBip\nzj//fOdVoo2nY4+3OU5QVLnGnXfe6a4TPk5lPkwjNZ3p3aQeuWpLnPOo4BWsUqVKikc5rjyjHLGE\nJX07vE+cwzTKZpqw+YcfftgNVULpveGGG9w3vjBPW5RrZFMW6R8wxIChBOEhf1FlPtP9ElWF84F+\nPUKfnryChOEvzBeEoKDzPUgf2kg+xoMFCxZY3bp1XdQXjgvyuH/kqaeecr/NEA+WsDLPbxDxlcnR\n4i6S43/kyc/xAlCcj09nzIcKUfn22WcfV2GPPvpoF6aD1W3SpElGQ3PYYYe5n0b5YUwcHn/GF4cn\ndTryyCOdxY8wIT6+EhEobQQYu4/ijhUai3Mm4WN10EEHuY4DHzpfl8LnUMcYKuCFjyEfyF9//dV5\nE3y+1sVHINffIVFXjLPH44L89ttvSc8KZQ/jFIJ3htBKvFt4bQhvJqQSQcnHiEvUVpTgxWeCSbxG\neKPwBPmILpX5KGKr8jK9G++dDp8tznk08ATS9whLXHnGs/jLL78kD8WbWqdOneR2ekKc04mUvW08\nz+FQcZTadEW2oKcuyjWIhI0ri3fddZcdfvjhxnwmLIS105fwUYVRZT7TPXJ/DCX0wrCssPfe57NG\nkffCcWHxw7nCeYTkDxo0yHnjd999d7cL4xjRA9dcc43bZpK+xo0bOyfJCSecYLfeemvyEgxFkBRM\nQJ78ghnpiCwJoKTg1fFC54wGgQaJzh/ja1DiwzNvEt5EuBzHkc/ETV5UiT0JrUsLAcao+QkjCbMn\nQoVwtYIUfP98WM3xZjKGzY//9Pui1oT0443yClHUMcorHAG9w1ReGF/xqDCREwthpKxRIumo4YFH\n8EoRaolCj7LEf33A+IQwez7j8uPEfzf4LTrLp59+etyhbhiLyvwqPJneTRRAcc6jQjhx+vCTuPKM\nwkSYsR9bzASq9GniRJzjyJSdfGas79GjR1LpJfrO/yecbJ+yKNfIVBbpa/ghgfSpP/zww6QXnnuK\nKvOZ7pU+CGWdaFyEYSheIafP4ecYot0n8tYLDj2ieBHugYgj/iNQWKpVq+bG+WOY4HdYGNbLpJa0\n7wz/wkjBUDCcGd9++22+aJrw9ZSOJiBPfjQX5RaBAJWc2WTxRjIJCY0eXnwsc4y/ZywPoUaMw6eh\nohLjwUdR2WGHHZwiRIOSqYNXhNvSKSKw1gjgwaEzyAROeO9RbnzYGjeBZ9Nb1aNuig8hHlA6n1Ge\nuN9//z35scTKjlWfcfvplvOoaysvOwJ6h6mc6MyFjUgYc/GuIHhXaOPpoNH+MxSLMEyOYX4WOr14\n8DH0ZjurMt5+JnFi4kk6lCrzqe8jvJXp3YSPi0rnMmeMT5SrdONrpvKMsrHffvu5sGLCholKzEZy\nmXM2fErrMUxGR7Qq/x6XcHa+x/fcc0+hHqco1+BbH1cWGVbl5/ahf40Rwk9iF1fmM90w8wOhsNM/\nZ94BDLk45hAmFaa+MCle8+bNLfzftdjmPnDUMXaftj+qj8K3gj4R/5YSIerRR3tVr17d+NeWGNP4\nDyt8EzAYoPhLsiewaraRzMdHHRPOi0r7PNZ+4Vd82q+JJEhP+zzW6WncxD7fr31eeE26WrDUDxrj\n+4K1ZC0QIHyNSkkoG4oOYZco9jQuNDQ0AvzLFP6NBoo9Yzip0Cj2TNjEeBw8RPxrMkJAmWTDT8DB\nuZkmulkLj6efEAEREAERiCDAcCom20vvyBHCyWzMePYlIlBaCMSVZ/ovlGcUHokIQADjJp5uFNGi\nSlGukaksLly40DkL0tvjot4f7TiGrfRnJFqAZw8bgf1v4NDDuBtW/v2+wqzRH/hdIoNxHqL8pztA\nyCfCjPz0ff63fH54Lg3OC/SWfsExC4JlWbDwP4xZVkasyfOLP4ZtJuvw+T4dXqen2WZBfDq87fPd\nAauPiUpnyvP7nIKd3IhJoISnSzgvKu3zWPuFa/i0X3slnm2fDq/T016RJ98vPi+8Ji0lP4CwLoQG\nhkpPpfSCBZDKSmePysaM+b5h4H+PYlWnMcADJBEBERABERABERABERABERCBTASk5MfTUbh+PBvt\nKSKBqAlpUN7DFj2v4PMTjM1hkYiACIiACIiACIiACIiACIiACKwZAblN14yfzhYBERABERABERAB\nERABERABERCB9YaAlPz15lXoRkRABERABERABERABERABERABERgzQhIyV8zfjpbBERABERABERA\nBERABERABERABNYbAmV+TP4GG2yw3sDWjYiACIiACIiACIiACIiACIiACKw5ASbek0QTkCc/moty\nRUAEREAEREAEREAEREAEREAERKDUEZCSX+pemW5YBERABERABERABERABERABERABKIJSMmP5qJc\nERABERABERABERABERABERABESh1BKTkl7pXphsWARFYHwgsW7bMWrZsmfFWbrnlFtt2222tVatW\ndvHFF0ce+/3331u5cuVs0KBB+fZ36NDBtt56a5c/f/58K1++vG222WZuqV+/vrVp08Y+/PDDfOcp\nIzsCeofZcfJHHXbYYfbmm2/6zeR61qxZduCBBya3SUyfPt123nlnV0f69etnixYtStnvN5o0aWKz\nZ8/2m/bEE08YeR999JGpzCexFJj4+OOPrVmzZrHHifMqNJ9//rltt912Kcvuu++e5DZhwgRXbnfa\naScbPXp0Mv+tt96yTp06WYsWLax37962cOHC5L5wQpzDNOLTcW1J/Bnrdk9UG0f5CJelO+64o9A3\nGddOnnnmmSnX5nfuu+8+d/3nnnvO6BtQ34888khbunRp8nfXtPwmL1TEBM/To0ePyLNfe+0123vv\nvSP3KXPdESgX/HT6gnHALxWCtF+YyI+l0uqlcrCuEixVVy/VgjUz4W0YLNWDpUaw1AyWWsFSO1jq\nBEvdYKkXLJsGS8NgaRQsmwdL42BpGix8xbYKFnrXrYKlbbBsEyztg6VDsHQMlk7B0jVY+iYka41A\n9erVi+23OnbsmAg6LcV2PV1IBIqLwL///ps499xzE4HinqhUqVLsZadNm5YIFPzE77//nvjzzz8T\nu+yyS+Khhx7Kd/x3332XqFmzZqJ58+YJru0lUN4TDRs2TASGBJf11VdfJWrVquV3u/XVV1+d2G+/\n/VLytFEwAb3DghmFj7j99tsT3bp1SwTf1MTMmTOTu+bOnZs45ZRTEnXr1k107949mR8o9K7sfvrp\np4mVK1cmzjjjjMTJJ5+c3B9ONG7cOBF0oF3WpEmTXD3guojKvMNQ4J+///470bNnz0Rg+Is9VpxX\noaHuBwp6cqENHTJkiNs5Y8aMRGCYSvz2229uoe399ttvEytWrEhsscUWiUCBcccNHjw4ccwxx0Sy\nFudILMnMuLYkecB6lohr47jNvn37Jl599dXEP//84xbKSWEkUzu5ZMmSZBmdN2+eK5fffPNNgnzK\nIm3jX3/9lejVq1fi8ssvdz9bHOW3MPcfdSz9nv333z9qVyIwqifo7xS3/PTTT44F7aB/F+nr5cuX\nJ1jC+bQD6Imr9UX0RvRH9Ej0SfRK9Ev0TPRN9E70T/RQ9FH0UvRT9FT0VfRW9Ff0WPRZ9Fr0W/Rc\n9F30XvRf9GCvE6Mfoyd7ndnr0F6nZu11bdbpujjbGYWTJCIgAiIgAlkSwJuOpRqrfaDkx5719ddf\nW6AAWY0aNWzDDTe0QMm3L7/8MvJ4/gsInqMXXnghuX/s2LHWv3//5HZUYqONNnLXj9qnvHgCeofx\nbKL24JG/6KKLbMcdd0zZXadOHcMjN3z48JT8oCNngdLpvPhEqey5556xZd+feP/999uwYcPspZde\nsi233NJn51urzOdDYhdeeKGdfvrpVqECfcLMkuucYVS7dm23BAqcTZ061S677DIHbdy4cXbOOedY\n5cqVLVAY7JNPPrFGjRoZURKUu86dO7vjTjrpJJsyZUpG0LnOOQ5OXFsSd/y6zo9r47gvysWmm25q\neKiZ4Z3vSmEkUztJefPlFK/+FVdcYZtvvrkFTgMX9UfESJUqVVw0X2BIdT9bnOXXP8fdd99trVu3\ntsAJYX369ElGZB188MFG/fHSrl07n7TAEOH6SEQa8L7hhFCfhg4daoGS774lgXLu8s8//3wLHCcu\nvXjxYtt1111d+p133nHRDE2bNnVRC88//7zLD4wZdtpppxn3AAdJPAGsBhIRKBYCVFwq6pNPPuka\nu0svvdQOOeQQd20aHzqCHEOj+eijj7oG684777QRI0ZYYAF1YckTJ05MKi1jxoyxyZMnu0YtsFTa\nwIEDi+U+dRERWBMCKC277babu0Smj/qAAQOSP8PHLPDiu3KfzExLcPyoUaNcOFvgbXJhy3QUqU9e\n/ve//9mxxx7rNgMPkwUWfgss53631lkS0DvMEtTqw7bZBqeG2cYbb7w6Z9UqiCxxdQFjF226F4aR\nBB47txl4T+zWW2+1ffbZx+/Otx4/fryNHDnSAo+qG4oSPkBlPkwjf5r6z7CTrl275t+ZliPOeUDo\ni/z3v/915Y72APniiy/c+rrrrnNKW+CVt8cff9wWLFjglDm3M/iDYkdenIhzHJnAPRrTlsSfsW73\nxLVxgUfY5syZ45TNIJLJUEAffPBBCw/9KOjOs2knn332WaMN7dKli7scw/UwMn3wwQdO8eceMFQh\nxVV+3cWCPwzNwgAWRG9ZgwYNnAEsiMoy+vMMM4SBlyCywCed0YNzMAo/9thjTg9A0Q8iD+zHH390\nwxMZwvjiiy86R8bLL7+cvBbtGfuQIGLGGZeDaAX3fbn55pudwZhvAsMS3n33XfvPf/5jv/zyS/K3\nlUglUDizU+q52hKBFAKMCwrCi40xb08//bRr/PBmUrHPO+88w/qGUoJlD4UHwWr+yiuvOC8PY4+9\npY59WNKDcE9j/BGVXSICpZEAH2k8QBiqGFcXJ3vssYe9//77hiWbc7BmB0NgUg5HmWLsM8uJJ57o\nOhR0VCUlS0DvsGh8+RZQ9rfaaivnaY67Ct57vGEYA954442Uw1TmU3CkbNBWYEy/8sorU/LjNsQ5\njwx9DTyhO+ywQzKTPkzFihVdWUSJQrnC0IpBgCUs6dvhfeIcplE203jPn3rqKXvmmWec0kvbdcEF\nFxTpYTO1kzjHvJc7fHG8+qeeeqozit50001uV3GVX/879ONxPqDgI8wbRF5BghPER32hoOO0oO8f\nFvKpgxjL6tWr5yJmcFwQzejnd2E/OgVRkyzhuQf4DSK+Mjlawr+Xq2l58nP1zZfAcxPiRggNQuUL\nxuW4CovnkQmUUOYfeOABp7QzEROy7777umrYzDEAAEAASURBVLBOPP5MINK2LUNgVsnRRx/tLH6E\nAWG5k4hAaSOAcQvFndBOLM6ZhI/VoYce6uoIHzpfl8LnoPBQr7wccMABzhDw66+/2iabbOKztS5G\nAnqHRYP5yCOPuMkmmXzSe6HirsQEVttvv73dcMMNFoxzNSa5YpgLojIfR83s3nvvNUJ+DzroIHcQ\nk8Httdderr3x3unw2eKcRwNP4OGHH56XEaRQNjC2IvDD0Ip3lHDln3/+2eXzB8WEiMQ4Eec4MmUn\nHwOQ9zjzVCi16YpsNk+bqZ3ESx+Mw0+JDmCiXfKCuXhc+QzG5ztDKhP7Flf59ffNMzLU0At9/LD3\n3uezRpH3wnFh8cNfwnkM4Tr77LOd889/H/DiY+TlO4AwSR/9JqLAGProjRnsq1aNoe2SggjIk18Q\nIe3PmgBKSnhMoK/YhNLgwWR8DQo7Yzi9oPQTpknDwTjn66+/3u9SJU6SUKK0EGCMGuPOEMLs+SAT\niVKQgu+fD6s5HgFmFvfjP/2+qDUzkeON8gpR1DHKKxwBvcPC8Yo6mpBMoq+mT59eoILP+XhPkSOO\nOMJ1lolSiROV+TwyGMbxdjHsjYXQYtZRCj5niXMeOwypwWSSeRlBCsWDEGIETy1KB/8dhbBqyrQf\nWxxMEJkcN+wOTvsjzmlAyuDmZ599ZsFko0mlF0M+hsrCSEHtJGURo1PYW814dzz43qv99ttvO283\nv1tc5dc/A8o3UbdE4yIMQyEPoc/hx+TTzvtj2IdDjyhehIgY/rMKY/rDgpLO0I3bbrvNXZPr3nPP\nPe44+jTBZMXOAEB/CGcGho1M0TPhayudR0Ce/DwWSq0hASr5ww8/7LyRTEJCiOtxxx3nxvUQWsR4\nSyopnRA89lRiPPl8bIPZ9J0ixMfzrLPOWsM70ekisG4IYLDiw005ZowyHkk6iF6YYCY8Vt/n+zUf\nQj6ejK+N6qgz6Q5j8hAMY3iTMJSlW8799bQuPAG9w8IzSz8DwxbezvD4VDqrdNgKEsbyYwymw0fH\nT2U+nhhtRdjAh5G9SZMm8SeE9uQyZyKfKFfpxle8hUQZ4pWlP8PQQsKKUbJQRmiXmYgPBSs8V0oI\na75kLnPOB6MMZTDMg6g6vPmElOP1Zk6pwkhB7SRGJf8vdP11mcCXyFc8+OxjyI7/13rFXX7xpDPM\nlv468w5g+KK/gQT/LcWItqV/w3As/qWvF7aJSqxataobu8/kfVF9FOoWBgsMaQhRj34uF4Yq8vs4\nCDFeMjEx/2qV+5FkT6BcFodGHRPOi0r7PNZ+4ad82q/Lh/J8OrxOT6f/OwH2+7zwmjSxHPw/mfuC\ntWQtEGCGcColnTsUHcL0Uez5oNIw0QgwwV779u3dpGJY+JjgBoWICW4Yj4MBgI4hFZqGwYdDMdMo\nhgOJCIiACIiACIiACJQUAYYH4o1PV0zov+CV1NCokiJf+q7L3FEYhIJ/g7tWbx4jP33iqGEjxV1+\nCcXHsJX+jNwDzx42NHoIOPSI4mUIwZoI+gO/y7AtnhflP90Bgs6BIYD89H3+t31+OBpgNT/GDjOL\n5rJgWbF64d8VkA6vSfvF72ObyTp8vk+H1+lptlkQnw5v+3x3wOpjotKZ8vw+p3QnN2ISKOTpEs6L\nSvs81n7hGj7t116JZ9unw+v0tFfkyfeLzwuvpeRDex0J4wKp9FRKL1gAqaxYA6lshBz5hoGZgbGq\n0xiEw5L8uVqLgAiIgAiIgAiIgAiIgAiIQJiAlPwwjdS0wvVTeWirGAhEWRZR3sMWPa/g83OMzdE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Cmf9uvyoTyfDq/T00w3S1548XnhNWliOfgfNPcFa8laIEDnmUpJ5w5FhzB9FHs+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\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vepGH1/lwkYwO8m0vk3PLjk9v16GVJASEgBAQAkJACAgBISAEhIAQ6N8I1PPbfFwjmXiz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\nfPRGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image at 0x1067a8550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"memory-before.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load the array data in and then re-check the memory utilization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.fromfile(\"../data/points.bin\")\n",
    "data_shape = data.shape\n",
    "data_len = len(data)\n",
    "data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A8kDyQNkDnS6SY/napiuXoP+bHsaGgVygdqo7hq8tGjgqRpZxaESibXkRKhNJ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vfT5vAuvEBcN8nCs2PHjsqfRo0aJePHj88qWvFzyCXwRtM9cBoYpY3P8bZo5afgPk1fTMvnUTmX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\nbGgL0cY5x6LsCMsUS6+wTvG3UMS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n64YJc4kXVyKEFYtNnNh4EoZS23w4ajDaXgUfinYs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HvGvFGhUewZXsB4HBolxhkTMsckG34CDs7luhIREAEREIE1T4BoK4y3UWHKtPEYnFCGJCJQ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\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Hnrgy2XTGfR71ZPI3Gy/X02ELYdcn1FtOPg3EAS5yGm2NNwcZHVTL1uKjYWbrdXSiOeye\nY1MWR9lmh1ysi9eZ7Dn5Wthkz2vJxBmRRyPUqF0jeclGCAgBISAEhIAQEAJCQAgIASFQDQHz16rF\nm76andeXyaaDF2XTFfX4dFDRefdhk42Th8lF7supJscCk/98XUxn3NL6MHKtNGbbNN4TTj4NMCfU\ny41W2gAgD0tvOvLweVueFm8cvcl0oDnwcMjryM/ikYthbE2fiLlcprN4z71MmiI1qiumU1gICAEh\nIASEgBAQAkJACAgBIdDXCJjf5etRS+fjTPa8KBOupjNn3WzgXlcme51PVyZbm3wcOsKQ6dNQ4/9b\nesuj8ZQNWDbi5FOBMke0gew7mFg+xoksyuiKZWFjRJwPo/c6H0fnmVNvenTYe6fey8TxQCYbr6ez\n+FqcOMjKSEOV/6vpKxap1KhdMZ3CQkAICAEhIASEgBAQAkJACAiBriBgvlS9NNXsinoL1+I+Dtke\n6mAyvJrD7vVebiRtsQzSFHWZKmeWryksjaXz3Gy6y33epXk04uT7CpU5l1ZIWZylLYsjnem9bGng\nkNkgW1nIRl5HB5pjT7zFoScfi/N2Fke8PYmYy9V03qZMRgeR3sjLtXQWBy9L4+MlCwEhIASEgBAQ\nAkJACAgBISAEehMB87OqlVkW73VlMjrTl8nFOML4cmZbxn28l4u2teLM1rfVdHBPPuzlRm2qpSF9\nrTiff2jUye+QqIEAFWjUOfW2VnGf1nTFYs2mGO/DdJY59qQnTLqy3fsyO2ytHJOL4cQk2ng9OqhM\n5/XRyP1n9k4lUQgIASEgBISAEBACQkAICAEh0O8R8H6Yr2xRb2Hj2JoMryUX48zeeJmz3qjO8rAy\nrF5ebzJxRkWdT282vcp7ysmv1ggajCNb5NibzmQ4VM3xrQZeUe8d+zTHirNP3ubcmx264kM6r/Nh\nk2tx4iDyKFKZztvUi/e2koWAEBACQkAICAEhIASEgBAQAj2NQNHnKpZXFl/UWbgW93HI9lCeyWW8\nzLHHrpq+LA901cjbm423N7nIzbZHeU86+TSoUQfV23qZxhswjQJBx5nj7tN4J97Hez31LT7k4XU+\nXCajg3zbq8mpZeV/b1fRShICQkAICAEhIASEgBAQAkJACPRvBGr5bT6unky82ZTJxTizqcbrOfb1\n4svy9T1h9UHnZW9TJnfFtix9VV0znXwqWc1J9XEmFzmVNJ3JcMuTuEaJjvKOfDFdPceeMu0hrclW\nFwtbXBn3uqJMGLL80lD5/43YlKeUVggIASEgBISAEBACQkAICAEh0HwEGvHNymy8rkw2nedFuSyM\nrjtPVx18Q9LK8uGi7OtZjLOw52bvdd2Sm+nk16sAla7lsFq8NQ5bk8nbp/V64sqozJGv5vhb2ZRR\nfMjb6yzcCPc2yJBvR6qp/F8rrmIlSQgIASEgBISAEBACQkAICAEh8P/bM68luZEYCOr/v/oOS1Wo\nFoN2NMMx2REaAAXDZnJfEHotAr0dLec8lj9jo8brFLsNKh7LX13o1eeUpYWNo1j+j9j4UU8jfZ58\n9ZIfL1ItrtJbNt5QOflhdTQzalZOtfjHLM2LWXqu9LDSIi9dfmVdy37EcfyZm1L/ztbV3agQgAAE\nIAABCEAAAhCAAATOJTC7h1V1rlW+NLfux5tE7P8qzfN7/JgZJ/du6qZnP2rjZLup/36V/6ec6J29\n5Mdle0tple9pennNVCwEoWdNuRmrZ8cc/5f1mKU7qC5rEcdRXfarOLQ43rMp/EIAAhCAAAQgAAEI\nQAACEHg/Aq39LOseV760sO4HEWmuu9bTc10V52donuvyw/rxWumVplzYUd5rh/7ZS371wLhwtcRm\nXXG2MTO0ODFH/o/wVws/68rLam7M8H+tvNdHjd5Bva65r7rQ4ozirer3b+75nSWCAAQgAAEIQAAC\nEIAABCDwGgRGe1jcMte0Ytflh3Vf81zLNYqz9d6ci1gn50KX5jXS3ea8Yll/jrRT7eqSrwv1ltCo\nWc2rJ9t4Wdci1uzQZ4761ec9not8K44e9Y+s14YfRz1btP1Wmud7/pHe3lxyEIAABCAAAQhAAAIQ\ngAAEnMDs3uU98qverHksf8VGrde3YulVrXJxb/mqc01+2DiqyXbLbr/Kueb+0bzP+vFXl/yHAZNC\nXLxaTCtdml42+qTF47IeWhzVbVH7V7OiXndyzf2YojrpYeOoN1vP/RT+/VGda/J7OdVgIQABCEAA\nAhCAAAQgAAEIvDoB7UvVPauca/KzjVmuzfpRl2uruNL0TOUUy0qXDV2n0iLX0tV3ir1qyY/L58XV\ntcp3zV9Oetg4Mbfyf5LpR72SW7HuqrysnqW826iJ49qmbL/SpeVYutuZGq/HhwAEIAABCEAAAhCA\nAAQg8AoEtB/17pJrWrHr8ivb0lwP/0gc75NnVO/oz1BemmZIl/W8tMP2qiV/5mLxQnmpldazMTv6\nKiAtPXo0M/w4HstXv9uojThOZXUP5bbKf7WKvd+1ys+zqho0CEAAAhCAAAQgAAEIQAACdxPQPjS6\nR1WXNY/lr1ivzX6O476hZd3jqkZaZUPT0RzFT7NHlsmZ3qrGtZGvfGVHmudn/IAedV4rza37qnUt\n/Die25RaU65lqzmtWnQIQAACEIAABCAAAQhAAAJXE9izwFY9WfNYvmy8k/zKjjTPz/h6ntdKq6xr\nPT9ycTR3i+rfmZqHzqML5Ki/ymfN48qX1rNVzjX3A0LEs5rqw8ZRX8/PuYh1vF9az67W92aRgwAE\nIAABCEAAAhCAAAQgcJTA6vLZqs+6xyNfebfuxztGPNJGec3pWc9lv4pbWug6upfiaXt0gZzpr2qy\n5nHlS5uxVU2lBaTQezmBzDXqzflWPNKVr6yeXeXQIAABCEAAAhCAAAQgAAEIPJvAngW01ZN1jyu/\npykX1v3g47H7ylWacj3ruexXcUsL3Y/u49qUf3SBnO2v6rLmsXzZeBn5e+yeHn/mrJ/rItbRHRT3\n7Eptbw45CEAAAhCAAAQgAAEIQAACVxJYWUZbtVn3eOQrv2JXaoNdrh9p4q0+xd7nWuVXvVXdg3bG\nMjkzo1XjuvtxUY/lZ+t1Oeex+72eKtfSXA8/jp6zRdtvpXne/ZVa78OHAAQgAAEIQAACEIAABCBw\nJ4GVpbSqzVordl2+bLy//J7t5XyG+7nHc+HHUU32f5J/f7zGdfdnarz+l3/GUjk7o6rLWi9WTjZe\nRP6qnen1mp6fcxHH0Z226PF3lH/sQIEABCAAAQhAAAIQgAAEIPA+BEbLapXPmscjX/lsg1jWZuOq\n17Xw42jeFj3GVY1qs82zcr4bn7Vozs6p6rLWi5WTjZeTL1tpysn2ajzX83OuikPT8WdLw0IAAhCA\nAAQgAAEIQAACEPgWAr3lNed6secqX5ps8JXfslXNSIt8HM3cose4qlFttnlWzg/jMxfPmVmtmqz3\nYs/Jl40Xli+7R/Oenh+5OP6sTdl+W7rXuL9a7734EIAABCAAAQhAAAIQgAAE7iKwupy26ivdtRW/\nqj2iBVvvn4n1PXKfdLczNV5f+mculbOzWnVZ78Weq/xKCwDSZV2b9aMujs/YlFpTrtXjeXwIQAAC\nEIAABCAAAQhAAAKfSGC0wFb5keb5kX80H9/EZ8zE+o65T3q2s3W571dcLaq/ChaD2Xmtuqz34lbO\n9bP8wOCzhKXSWrXq6dnWvF4POQhAAAIQgAAEIAABCEAAAncT2LugtvoqPWsen+H7jODpsfs5V8Wh\nxcl9m/r4O1v32JmUK5bK2Zmtukp3zf14HY9X/V5/zlVxSwtdx+8kDQsBCEAAAhCAAAQgAAEIQOBb\nCYwW2iqftV48m/O6GT++l9fp+1Vaq1Y9blv9XjPtX7GArsxs1VZ61nqx59wPMB67n3NVvKJFbXXy\nM6saNAhAAAIQgAAEIAABCEAAAp9CYGWJrWpntFzjsfvB1OOWn+uquKX19Mjl43fIueX4qoVzZW6r\nttKzdiRe7RXc3DfSlW/Z1rxWPToEIAABCEAAAhCAAAQgAIFXJrB3aW31zeq5zmP3g93RuJqhb5Jn\nS6/sSm3V/6BduWCuzO7VVrmsnR0HqDxT8Fp6r0e9s7b3jNkZ1EEAAhCAAAQgAAEIQAACELiawFlL\nam9OK1fpWTs7Dp55pjPu5bxuNCfXTsdXL5Mr83u1VW5GO6tGQKt5yoUd5b12xb9q7sodqIUABCAA\nAQhAAAIQgAAEILCyxK7QGs1t5Ss9azmOe2Utx1VNSws9TjVjyzz+rtQ+dneUq5fH1fm9+lau0me0\nqiZQrerC2+pT/kz7zGedeW9mQQACEIAABCAAAQhAAALvReCyZbTAMHpWKz+rV3WzWly3qtVr9HKq\ncbta771d/xnL4uozRvWtfKXPagGpqu3pDrbV6zV7/Stn770TfRCAAAQgAAEIQAACEIDA9xK4bEH9\nH+nM7FbNil7VVlp85Zauv4BRXnWyq/Xqm7LPWiD3PKfXs5pr1bf0gNfLzeSnPsDfotGzVmZRCwEI\nQAACEIAABCAAAQhA4C4CZy6wo1m9fCtX6ZUmfntz6s+2Ny/X7oqfuVzuedaop5dv5Vp6AOzlZvK9\njzCa3eslBwEIQAACEIAABCAAAQhA4N0JHFlwR729fCvX0oNzLzeTr77VaGbVs6w9e/Hc+7xRXy+/\nNyeYvX7VuF2t9158CEAAAhCAAAQgAAEIQAAC30Zgdfmdqe/V7M3Fd+n19r7b3r7ezDJ3x0K695kz\nfaOao3mHOJrltfgQgAAEIAABCEAAAhCAAAQgsEZgZTEe1R7Nx81HM1pvt7evNa+r37mo7n32TN9Z\nNYI3M0+1I3vmrNG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      "text/plain": [
       "<IPython.core.display.Image at 0x10c99ef28>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"memory-after.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took just a few seconds to load with a memory consumption equivalent to the file size of the data.\n",
    "\n",
    "That means ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8192"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 * 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.73656618610747"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filesize = 763\n",
    "8192 / filesize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... we'd need 11 of those files concatenated to make a file too large to fit in the memory for this system. But that's if all the memory was available. Let's what how much memory we have available right now, after we delete the data we just pulled in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll wait for a few seconds, to let the system memory stabilize, and then check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1856.99609375"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psutil.virtual_memory().available / 1024**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 2.5GB. So, to overrun our RAM we'd just need a fraction of the total:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2096985583224114"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2449 / filesize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which means we only need 4 of our original files to make something which won't fit in memory. However, below we will still use 11 files to ensure a data that would be much larger than memory if loaded *into* memory.\n",
    "\n",
    "So, how should we create this large file for demonstration purposes? (Knowing that in a real-life situation, the data would already be 1) created, and 2) potentially quite large.)\n",
    "\n",
    "We could try to use ``np.tile`` to create a file of the desired size (larger than memory), but that could make our system unusable for a significant period of time. Instead, let's use ``np.memmap`` which will treat a file on disk as an array, thus letting us work with data which is too large to fit into memory.\n",
    "\n",
    "Let's load the data file again, but this time as a memory-mapped array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = np.memmap(\"../data/points.bin\", mode=\"r\", shape=data_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the array to a ``memmap`` object was *very* quick (compared to bringing the contents of the file into memory), taking less than a second to complete.\n",
    "\n",
    "Now let's create a new file for writing data *to*, sized to be larger than our total system memory (if held in-memory; on disk it will be smaller). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "big_data_shape = (data_len * 11,)\n",
    "big_data = np.memmap(\"../data/many-points.bin\", dtype=\"uint8\", mode=\"w+\", shape=big_data_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That creates a 1GB file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 oubiwann  staff   1.0G Apr  3 12:43 ../data/many-points.bin\r\n"
     ]
    }
   ],
   "source": [
    "ls -alh ../data/many-points.bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is mapped to an array having the shape we requested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100000000,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and just contains zeros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fill the empty data structure with copies of the data we saved to the 763 MB file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([ 90,  71,  15, ...,  33, 244,  63], dtype=uint8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for x in range(11):\n",
    "  big_data[x * data_len:((x * data_len) + data_len)] = data\n",
    "big_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you check your system memory before and after, you will only see minimal changes, confirming that we are not creating an 8GB data structure in-memory. Furthermore, that only took a few seconds to do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data_len = len(big_data)\n",
    "big_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[100000000 - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data[100000000 - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempting to get the next index from our original data set will throw an error, since it didn't have that index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 100000000 is out of bounds for axis 0 with size 100000000",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-9866a08d74d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m100000000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 100000000 is out of bounds for axis 0 with size 100000000"
     ]
    }
   ],
   "source": [
    "data[100000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But our new data does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data[100000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then some!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_data[1100000000 - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot data from a ``memmap``ed array without significant lag-times. Note, however, that below we are creating a histogram from 1.1 million points of data, so it won't be *instantaneous* ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CAAAA3NHPm3457ZifDb45BysBKs3tYwAAAAAJuVIIAACARelub3873XmuyiuB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\nAAASEoUAAAAAEvr/g1KrWgfpwukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cab7630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots(figsize=(20, 10))\n",
    "axes.hist(big_data, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took about 40 seconds to generate.\n",
    "\n",
    "Note that with our data file-hacking we have radically changed the nature of our data, since we've increased the sample size linearly without regard for the distribution. The purpose of this demonstration wasn't to preserve a sample distribution, but rather to show how one can work with large data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas and PyTables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few years ago, a [question was asked](http://stackoverflow.com/questions/14262433/large-data-work-flows-using-pandas) on StackOverflow about working with large data in Pandas. It included questions such as:\n",
    "\n",
    "<blockquote>\n",
    "What are some best-practice workflows for accomplishing the following:\n",
    "\n",
    "<ul><li>Loading flat files into a permanent, on-disk database structure?</li>\n",
    "<li>Querying that database to retrieve data to feed into a pandas data structure?</li>\n",
    "<li>Updating the database after manipulating pieces in pandas?</li></ul>\n",
    "</blockquote>\n",
    "\n",
    "The [answer given](http://stackoverflow.com/a/14268804) by [Jeff Reback](http://stackoverflow.com/users/644898/jeff) was exceptional and is commonly referenced as *the* way to work with very large data sets in Pandas. The question was framed around a desire to move away from proprietary software which handled large data sets well, and the only thinking keeping this person from making the leap was not knowing how to process large data sets in Pandas and NumPy.\n",
    "\n",
    "Using the scenario outlined, one can easily use tens of GB of file data using the high-performance [HDF5](http://en.wikipedia.org/wiki/Hierarchical_Data_Format) data structure. Pandas [provides documentation](http://pandas.pydata.org/pandas-docs/dev/io.html#hdf5-pytables) on its use of HDF5 in the \"I/O\" section of its docs site. Jeff provides the following question to consider when defining a workflow for large data files in Pandas and NumPy:\n",
    "\n",
    "1. What is the size of your data? What are the number of rows and columns? What are the types of columns? Are you appending rows, or just columns?\n",
    "2. What will typical operations look like? For example, will you be querying columns to select a bunch of rows and specific columns, then doing some in-memory operation, then maybe creating new columns, and finally saving these?\n",
    "3. After your typical set of operations, what will you do? In other words, is step #2 ad hoc, or repeatable? \n",
    "4. Roughly how many total GB are you expecting to process from your source data files? How are these organized? (E.g. by records?) Does each one contain different fields, or do they have some records per file with all of the fields in each file?\n",
    "5. Do you ever select subsets of rows (records) based on specified criteria (e.g. select the rows with field A > 5)? and then do something? Or do you just select fields A, B, C with all of the records (and *then* do something)?\n",
    "6. Do you need all of your columns -- as a group -- for all of your typical operations? Or is there a good proportion that you may only use for reports (e.g. you want to keep the data around, but don't need to pull in that column explicity until final results time)?\n",
    "\n",
    "Jeff's response boils down to a few core concepts:\n",
    " * creating a store\n",
    " * grouping your fields according to your needs\n",
    " * reading large file data in chunks, to prevent swamping system memory\n",
    " * reindexing the data (by chunks) and adding it to the store\n",
    " * selecting groups from all the chunks that have been saved in the store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HDF5, PyTables, and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Hierarchical Data Format](http://en.wikipedia.org/wiki/Hierarchical_Data_Format) is a set of file formats (namely HDF4 and HDF5) originally developed at the [National Center for Supercomputing Applications](http://en.wikipedia.org/wiki/National_Center_for_Supercomputing_Applications) to store and organize large amounts of numerical data. (Some may remember NCSA as the place where they downloaded their first graphical web browser, code which is the ancestor not only to the Netscape web browser, but also Internet Explorer. A web server was also created there, and this evolved into the Apache HTTP Server.)\n",
    "\n",
    "HDF is supported by Python, R, Julia, Java, Octave, IDL, and MATLAB, to name a few. HDF5 offers significant improvements and useful simplifications over HDF4. It uses B-trees to index table objects and, as such, works well for write-once/read-many time series data with common use occurring across fields such as meteorological studies, biosciences, the finance industry, and aviation. HDF5 files of multi-terabyte sizes are common in these applications, typically constructed from the analyses of multiple HDF5 source files, thus providing a single (and often extensive) source of grouped data for a particular application.\n",
    "\n",
    "The [PyTables](https://pytables.github.io/) library is built on top of the Python HDF5 library and NumPy, thus not only providing access to one of the most widely-used large-data file formats in the scientific computing community, but then links data extracted from these files with the data types and objects provided by the fast Python numerical processing library.\n",
    "\n",
    "Pandas, in turn, wraps PyTables, thus extending its convenient in-memory data structures, functions, and objects to large on-disk files. To use HDF data with Pandas, you'll want to create a ``pd.HDFStore``, read from HDF data sources with ``pd.read_hdf``, or write to one with ``pd.to_hdf``.\n",
    "\n",
    "One project to keep an eye on is [Blaze](http://blaze.pydata.org/). It's an open wrapper and utility framework for working with large data sets, generalizing such actions creation, access, updates, and migration. Blaze supports not only HDF, but also SQL, CSV, and JSON. The API usage between Pandas and Blaze is very similar, if various methods are spelled differently. [This page](http://blaze.pydata.org/en/latest/rosetta.html) outlines the basics and is a good review. \n",
    "\n",
    "In the example below we will be using PyTables to create an HDF5 too large to comfortably fit in memory. We will follow these steps (for more details and to examine steps that we have left out, be sure to see Yves Hilpisch's presentation [Out-of-Memory Data Analytics with Python](http://hilpisch.com/TPQ_Out_of_Memory_Analytics.html)):\n",
    " * Create a series of CSV source data files taking up ~14 GB of disk space\n",
    " * Create an empty HDF5 file\n",
    " * Create a table in the HDF5 file, providing schema metadata and compression options\n",
    " * Load our CSV source data into the HDF5 table\n",
    " * Query our new data source, once the data has been migrated\n",
    "\n",
    "Remember the temperature precipitation data for St. Francis, KS USA from a previous notebook? Let's create fake data sets we can pretend are for temperature and precipitation data for hundreds of thousands of towns across the globe for the last century:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "head = \"country,town,year,month,precip,temp\\n\"\n",
    "row = \"{},{},{},{},{},{}\\n\"\n",
    "town_count = 1000\n",
    "(start_year, end_year) = (1894, 2014)\n",
    "(start_month, end_month) = (1, 13)\n",
    "sample_size = (1 + 2 * town_count * (end_year - start_year) * (end_month - start_month))\n",
    "countries = range(200)\n",
    "towns = range(town_count)\n",
    "years = range(start_year, end_year)\n",
    "months = range(start_month, end_month)\n",
    "for country in countries:\n",
    "    with open(\"../data/{}.csv\".format(country), \"w\") as csvfile:\n",
    "        csvfile.write(head)\n",
    "        csvdata = \"\"\n",
    "        weather_data = norm.rvs(size=sample_size)\n",
    "        weather_index = 0\n",
    "        for town in towns:\n",
    "            for year in years:\n",
    "                for month in months:\n",
    "                    csvdata += row.format(\n",
    "                        country, town, year, month,\n",
    "                        weather_data[weather_index],\n",
    "                        weather_data[weather_index + 1])\n",
    "                    weather_index += 2\n",
    "        csvfile.write(csvdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took about 35 minutes on my 2009 iMac. Here are the files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/0.csv, ../data/1.csv, ../data/2.csv, ../data/3.csv, ../data/4.csv, ../data/5.csv, ../data/6.csv, ../data/7.csv, ../data/8.csv, ../data/9.csv, ../data/10.csv, ../data/11.csv, ../data/12.csv, ../data/13.csv, ../data/14.csv, \r\n",
      "../data/15.csv, ../data/16.csv, ../data/17.csv, ../data/18.csv, ../data/19.csv, ../data/20.csv, ../data/21.csv, ../data/22.csv, ../data/23.csv, ../data/24.csv, ../data/25.csv, ../data/26.csv, ../data/27.csv, ../data/28.csv, \r\n",
      "../data/29.csv, ../data/30.csv, ../data/31.csv, ../data/32.csv, ../data/33.csv, ../data/34.csv, ../data/35.csv, ../data/36.csv, ../data/37.csv, ../data/38.csv, ../data/39.csv, ../data/40.csv, ../data/41.csv, ../data/42.csv, \r\n",
      "../data/43.csv, ../data/44.csv, ../data/45.csv, ../data/46.csv, ../data/47.csv, ../data/48.csv, ../data/49.csv, ../data/50.csv, ../data/51.csv, ../data/52.csv, ../data/53.csv, ../data/54.csv, ../data/55.csv, ../data/56.csv, \r\n",
      "../data/57.csv, ../data/58.csv, ../data/59.csv, ../data/60.csv, ../data/61.csv, ../data/62.csv, ../data/63.csv, ../data/64.csv, ../data/65.csv, ../data/66.csv, ../data/67.csv, ../data/68.csv, ../data/69.csv, ../data/70.csv, \r\n",
      "../data/71.csv, ../data/72.csv, ../data/73.csv, ../data/74.csv, ../data/75.csv, ../data/76.csv, ../data/77.csv, ../data/78.csv, ../data/79.csv, ../data/80.csv, ../data/81.csv, ../data/82.csv, ../data/83.csv, ../data/84.csv, \r\n",
      "../data/85.csv, ../data/86.csv, ../data/87.csv, ../data/88.csv, ../data/89.csv, ../data/90.csv, ../data/91.csv, ../data/92.csv, ../data/93.csv, ../data/94.csv, ../data/95.csv, ../data/96.csv, ../data/97.csv, ../data/98.csv, \r\n",
      "../data/99.csv, ../data/100.csv, ../data/101.csv, ../data/102.csv, ../data/103.csv, ../data/104.csv, ../data/105.csv, ../data/106.csv, ../data/107.csv, ../data/108.csv, ../data/109.csv, ../data/110.csv, ../data/111.csv, \r\n",
      "../data/112.csv, ../data/113.csv, ../data/114.csv, ../data/115.csv, ../data/116.csv, ../data/117.csv, ../data/118.csv, ../data/119.csv, ../data/120.csv, ../data/121.csv, ../data/122.csv, ../data/123.csv, ../data/124.csv, \r\n",
      "../data/125.csv, ../data/126.csv, ../data/127.csv, ../data/128.csv, ../data/129.csv, ../data/130.csv, ../data/131.csv, ../data/132.csv, ../data/133.csv, ../data/134.csv, ../data/135.csv, ../data/136.csv, ../data/137.csv, \r\n",
      "../data/138.csv, ../data/139.csv, ../data/140.csv, ../data/141.csv, ../data/142.csv, ../data/143.csv, ../data/144.csv, ../data/145.csv, ../data/146.csv, ../data/147.csv, ../data/148.csv, ../data/149.csv, ../data/150.csv, \r\n",
      "../data/151.csv, ../data/152.csv, ../data/153.csv, ../data/154.csv, ../data/155.csv, ../data/156.csv, ../data/157.csv, ../data/158.csv, ../data/159.csv, ../data/160.csv, ../data/161.csv, ../data/162.csv, ../data/163.csv, \r\n",
      "../data/164.csv, ../data/165.csv, ../data/166.csv, ../data/167.csv, ../data/168.csv, ../data/169.csv, ../data/170.csv, ../data/171.csv, ../data/172.csv, ../data/173.csv, ../data/174.csv, ../data/175.csv, ../data/176.csv, \r\n",
      "../data/177.csv, ../data/178.csv, ../data/179.csv, ../data/180.csv, ../data/181.csv, ../data/182.csv, ../data/183.csv, ../data/184.csv, ../data/185.csv, ../data/186.csv, ../data/187.csv, ../data/188.csv, ../data/189.csv, \r\n",
      "../data/190.csv, ../data/191.csv, ../data/192.csv, ../data/193.csv, ../data/194.csv, ../data/195.csv, ../data/196.csv, ../data/197.csv, ../data/198.csv, ../data/199.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls -rtm ../data/*.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just take a look at one of those:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 oubiwann  staff    72M Mar 21 19:02 ../data/0.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls -lh ../data/0.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each file is about 72 MB, at 200, that makes about 14 GB -- too much for RAM!\n",
    "\n",
    "Running queries against so much data in .csv files isn't going to be very efficient: it's going to take a *long* time. So what are our options? Well, for reading this data, HDF5 is a very good fit, designed for jobs like this, in fact. Let's use PyTables to convert our CSV files a single HDF5 file. Note that we aren't using the Pandas ``HDFStore``, since Pandas isn't currently designed to handle exteremly large data sets out of memory. Instead we'll be using PyTables which *has* been designed for such use cases. We'll start by creating an empty table file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "File(filename=../data/weather.h5t, title='', mode='w', root_uep='/', filters=Filters(complevel=0, shuffle=False, fletcher32=False, least_significant_digit=None))\n",
       "/ (RootGroup) ''"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_name = \"../data/weather.h5t\"\n",
    "h5 = tb.open_file(tb_name, \"w\")\n",
    "h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll need to provide some assistance to PyTables by indicating the data types of each column in our table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_types = np.dtype(\n",
    "    [(\"country\", \"<i8\"),\n",
    "     (\"town\", \"<i8\"),\n",
    "     (\"year\", \"<i8\"),\n",
    "     (\"month\", \"<i8\"),\n",
    "     (\"precip\", \"<f8\"),\n",
    "     (\"temp\", \"<f8\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also define a compression filter to be used by PyTables when saving our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filters = tb.Filters(complevel=5, complib='blosc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create the table inside of our new HDF5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tab = h5.create_table(\n",
    "    \"/\", \"weather\",\n",
    "    description=data_types,\n",
    "    filters=filters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that done, let's load each CSV file, reading it by chunks so as not to overload our memory, and then append it to our new HDF5 table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for filename in glob.glob(\"../data/*.csv\"):\n",
    "    it = pd.read_csv(filename, iterator=True, chunksize=10000)\n",
    "    for chunk in it:\n",
    "        tab.append(chunk.to_records(index=False))\n",
    "        tab.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took about 7 minutes on my machine, and what started out as ~14 GB of ``.csv`` files is now a single, compressed 4.8 GB HDF5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4758762819"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5.get_filesize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the metadata for our PyTables-wrapped HDF5 table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/weather (Table(288000000,), shuffle, blosc(5)) ''\n",
       "  description := {\n",
       "  \"country\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"town\": Int64Col(shape=(), dflt=0, pos=1),\n",
       "  \"year\": Int64Col(shape=(), dflt=0, pos=2),\n",
       "  \"month\": Int64Col(shape=(), dflt=0, pos=3),\n",
       "  \"precip\": Float64Col(shape=(), dflt=0.0, pos=4),\n",
       "  \"temp\": Float64Col(shape=(), dflt=0.0, pos=5)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (1365,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0, 69, 1947, 5, -0.2328834718674073, 0.06810312195695747),\n",
       "       (0, 69, 1947, 6, 0.4724989007889187, 1.9529216219569407),\n",
       "       (0, 69, 1947, 7, -1.0757216683235205, 1.0415374480545465),\n",
       "       (0, 69, 1947, 8, -1.3700249968748606, 3.0971874991576405),\n",
       "       (0, 69, 1947, 9, 0.27279758311253083, 0.8263207523831321),\n",
       "       (0, 69, 1947, 10, -0.0475253104621414, 1.4530808932953758),\n",
       "       (0, 69, 1947, 11, -0.7555493935762871, -1.2665440609117482),\n",
       "       (0, 69, 1947, 12, 1.540049376928493, 1.2338186532516708),\n",
       "       (0, 69, 1948, 1, 0.829743501445512, -0.1562732708511116),\n",
       "       (0, 69, 1948, 2, 0.06924900463163704, 1.187193711598589)], \n",
       "      dtype=[('country', '<i8'), ('town', '<i8'), ('year', '<i8'), ('month', '<i8'), ('precip', '<f8'), ('temp', '<f8')])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab[100000:100010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.23288347,  0.4724989 , -1.07572167, -1.370025  ,  0.27279758,\n",
       "       -0.04752531, -0.75554939,  1.54004938,  0.8297435 ,  0.069249  ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab[100000:100010][\"precip\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you're done, go ahead and close the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to work with the data again, simply load it  up:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/weather (Table(288000000,), shuffle, blosc(5)) ''\n",
       "  description := {\n",
       "  \"country\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"town\": Int64Col(shape=(), dflt=0, pos=1),\n",
       "  \"year\": Int64Col(shape=(), dflt=0, pos=2),\n",
       "  \"month\": Int64Col(shape=(), dflt=0, pos=3),\n",
       "  \"precip\": Float64Col(shape=(), dflt=0.0, pos=4),\n",
       "  \"temp\": Float64Col(shape=(), dflt=0.0, pos=5)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (1365,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5 = tb.open_file(tb_name, \"r\")\n",
    "tab = h5.root.weather\n",
    "tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the first million entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jOTt8IUmy6YUN2biuN5ON5JPP/pSh8yNJkmXPLs72rZvT0tKcwXMX8smhI0mS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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9c0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots(figsize=(20, 10))\n",
    "axes.hist(tab[:1000000][\"temp\"], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from this example and from the previous ones, accessing our data via HDF5 files is *exteremely* fast (certainly compared to attempting to use large CSV files).\n",
    "\n",
    "What about executing calculations against this data? Unfortunately, running the following will consume an enormous amount of RAM:\n",
    "\n",
    "```python\n",
    "tab[:][\"temp\"].mean()\n",
    "```\n",
    "\n",
    "We've just asked for all of the data: all 288,000,000 rows of it. That's going to end up loading everything into RAM, grinding the average workstation to a halt. Ideally, though, when you iterate through the source data and to create your HDF5 file, you also crunch the numbers you will need, adding supplemental columns or groups to the HDF5 file for later use by you and your peers.\n",
    "\n",
    "If we have data which we will mostly be selecting (extracting portions) and which has already been crunched as-needed, grouped as needed, etc., HDF5 is a very good fit. This is why one of the common use cases you see for HDF5 is that of sharing/distributing processed data.\n",
    "\n",
    "However, if we have data which we will need to process repeatedely, then either we will need to use another method besides that which would cause all the data to be loaded into memory, or find a better match for our data-processing needs. Before we move on, let's give HDF5 another chance...\n",
    "\n",
    "We saw above that selecting data was very fast in HDF5: what about getting the *mean* for a small section of data, say the first 281,250 rows? (We chose that number since it multiplies nicely to our total of 288,000,000.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0030696632864265312"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab[0:281250][\"temp\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that was fast! What about iterating through all of records in a similar fashion? Let's break up our 288,000,000 records into chunks of that size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 281250), (287718750, 288000000))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit = 281250\n",
    "ranges = [(x * limit, x * limit + limit) for x in range(2 ** 10)]\n",
    "(ranges[0], ranges[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = [tab[x * limit:x * limit + limit][\"temp\"].mean() for x in range(2 ** 10)]\n",
    "len(means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That took about 30 seconds to run on my machine.\n",
    "\n",
    "Of course, once we've taken that step, it's trivial to get the mean value for all 288,000,000 points of temperature data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.3051780413782918e-05"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(means) / len(means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at another option for handling large data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've looked two ways of handling data too large for memory:\n",
    " * NumPy's ``memmap``\n",
    " * and the more general HDF5 format wrapped by PyTables\n",
    " \n",
    "But there is another situation which may come into play for projects that need to use matplotlib to visual all or parts of large data sets: data which is too large to fit on a *hard drive*. This could be anything from large data sets like those created by super-colliders and radio telescopes to high-volume streaming data used in systems analysis (and social media) and financial markets data. All of these are either too large to fit on a machine or too ephemeral to store, needing to be processed in real-time.\n",
    "\n",
    "The latter of these is the realm of such projects as [Spark](https://spark.apache.org/), [Storm](https://storm.apache.org/), [Kafka](https://kafka.apache.org/), Amazon's [Kinesis](http://aws.amazon.com/kinesis/). We will not be discussing these in this notebook, but will be instead focusing on the former: processing large data sets in a distirbuted environment, in particular, [map-reduce](https://en.wikipedia.org/wiki/MapReduce). Understanding how to use matplotlib and numpy with a map-reduce framework will provide the foundation necessary for the reader to extend this to the streaming-data scenarios.\n",
    "\n",
    "Even though we have chosen to example map-reduce, there are many other options for solving problems like these: distributed RDMSs and NoSQL solutions like [Riak](http://basho.com/riak/), [Redis](http://redis.io/), or [Cassandra](http://cassandra.apache.org/) (to name but a few)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MapReduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what is “MapReduce” and why are we looking at it in the context of running computations against large sets of data? Wikipedia gives the following definition:\n",
    "\n",
    "<blockquote>\n",
    "MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a cluster. A MapReduce program is composed of a ``Map`` procedure that performs filtering and sorting, and a ``Reduce`` procedure that performs a summary operation. The \"MapReduce System\" orchestrates the processing by marshalling the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for redundancy and fault tolerance.\n",
    "</blockquote>\n",
    "\n",
    "A little context will make this more clear and why it is potentially very useful for visualizing large data sets with matplotlib.\n",
    "\n",
    "(Note that some of the content in the following sub-sections has been taking from the Wikipedia [MapReduce article](http://en.wikipedia.org/wiki/MapReduce) and the Google [MapReduce paper](http://research.google.com/archive/mapreduce.html).)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Origins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Between 1999 and 2004, Google had created hundreds of special-purpose computations for processing the huge amounts of data generated by web crawling, HTTP access logs, etc. The many kinds of processing developed were in large part used to create Google search's page ranked search results -- at the time, a vast improvement over other search engine results. Each computation required was pretty straight-forward; it was the combination of these which was unique.\n",
    "\n",
    "However, in the span of those five years, the computation tasks needed to be split across hundreds and then thousands of machines in order to finish in a timely manner. As such, the difficulties of parallelizing code were introduced: not only the decomposition of tasks into parallelizable parts, but the parallelization of data and hanlding failures. All of these combined with the legacy code being maintain (and created) made for an approach that was becoming less maintainable and growing more difficult to easily add new features.\n",
    "\n",
    "The inspriation for a new approach to Google's problem came from the second oldest programming language still in use: Lisp (Fortan being the oldest). The authors of the Google MapReduce paper were reminded of the fact that many of their processing jobs consisted of a simple action against a data set (using a [modern Lisp](https://en.wikipedia.org/wiki/LFE_(programming_language)) as an example):\n",
    "\n",
    "```cl\n",
    "> (set data \"some words to examine\")\n",
    "\"some words to examine\"\n",
    "> (lists:map #'length/1 (string:tokens data \" \"))\n",
    "(4 5 2 7)\n",
    "```\n",
    "\n",
    "And then the \"folding\" of those results into a secondary analytical result:\n",
    "\n",
    "```cl\n",
    "> (lists:foldl #'+/2 0 (4 5 2 7))\n",
    "18\n",
    "```\n",
    "\n",
    "The function above is called \"folding\" due to the fact that there is a recursive operation in place, with each item in the list being folded into an accumulator. In this case, the folding function is addition (with arity of \"2\", thus the ``+/2``); ``0`` is provided as an initial value for the first fold. Note that if our folding function created items in a list rather than adding two integers for a sum, the initial value would have been a list (empty or otherwise).\n",
    "\n",
    "The map and fold operations can be combined in the fashion typical of higher-order functions:\n",
    "\n",
    "```cl\n",
    "> (lists:foldl\n",
    "    #'+/2\n",
    "    0\n",
    "    (lists:map\n",
    "      #'length/1\n",
    "      data\n",
    "      (string:tokens data \" \")))\n",
    "18\n",
    "```\n",
    "\n",
    "As you might have guessed by now (or known already), there is another term that folding is known. It is named not for the process by which it is created, but by the nature of the results it creates: *reduce*. In this case, a list of integers is reduced to a single value by means of the addition function we provided.\n",
    "\n",
    "In summary: given an initial data set, we've run a length function (with an arity of one) against every element of our data which has been split on the “space” character. Our results were integers representing the length of each word in our data set. Then, we folded our list with the + function, element at a time, into an “accumulator” with the initial value of zero. The end result represented the sum of all the elements. If we wanted a running average instead of a running sum, we would have supplied a different function: it still would take two arguments and it would still sum them, it would just divide that result by two:\n",
    "\n",
    "```cl\n",
    "> (defun ave (number accum)\n",
    "    (/ (+ number accum) 2))\n",
    "ave\n",
    "> (lists:foldl\n",
    "    #'ave/2\n",
    "    0\n",
    "    (lists:map\n",
    "      #'length/1\n",
    "      (string:tokens data \" \")))\n",
    "4.875\n",
    "```\n",
    "\n",
    "The average word length in our data is 4.875 ASCII characters. This example makes more clear the latent power in solutions like these: for completely new results, we only needed to change one function.\n",
    "\n",
    "Various Lisps and other functional programming languages have fold or reduce functionality, but this is not just the domain of functional programming: Python 3 has a library dedicated to functional programming idioms: ``functools``. Here's how the above examples would be implemented in Python 3:\n",
    "\n",
    "```python\n",
    ">>> data = \"some words to examine\"\n",
    ">>> [x for x in map(len, data.split(\" \")]\n",
    "[4, 5, 2, 7]\n",
    ">>> functools.reduce(operator.add, [4, 5, 2, 7], 0)\n",
    "18\n",
    "```\n",
    "Similarly, these may be composed in Python:\n",
    "\n",
    "```python\n",
    ">>> functools.reduce(operator.add,\n",
    "...                  map(len, data.split(\" \")),\n",
    "...                  0)\n",
    "18\n",
    "```\n",
    "\n",
    "And to calculate the running average:\n",
    "\n",
    "```python\n",
    ">>> def ave(number, accum):\n",
    "...     return (number + accum) / 2\n",
    "...\n",
    ">>> functools.reduce(ave,\n",
    "...                  map(len, data.split(\" \")),\n",
    "...                  0)\n",
    "4.875\n",
    "```\n",
    "\n",
    "The really important part to realize here -- given the context of Google's needs in 2004 and the later fluorescence of MapReduce -- is that each ``map`` call of ``len`` is independent of all the others. These could be called on the same machine in the same process, or in *different processes*, on *different cores*, or on *another machine altogether* (given the appropriate framework, of course). In a similar fashion, the data provided to the ``reduce`` function could be from any number of sources, local or remote. In fact, the reduce step could be split across multiple computational resources -- it would just need a final reduce step to aggregate all the results.\n",
    "\n",
    "This insight led to an innovation in the development process at Google in support of the tasks which had steadily gown in complexity and reduced maintainability. They created infrastructure such that engineers only needed to create their own mapper and reducer functions, and then these could be run against the desired data sources on the appropriate MapReduce clusters. This automated the parallelization of tasks and distribution of workload across any number of servers running in Google's large computation clusters in their data centers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Optimal Problem Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MapReduce programs are not guaranteed to be fast. The main benefit of this programming model is to exploit the optimized algorithms which prepare and process data and results, freeing developers to focus on just the map and reduce parts of the program. In practice, however, the implementation of these can have a heavy impact on the overall performance of tha task in the cluster. When designing a MapReduce algorithm, the author needs to choose a good tradeoff between the computation and the communication costs, and it is common to see communication cost dominating the computation cost.\n",
    "\n",
    "MapReduce is useful in a wide range of applications, including:\n",
    " * distributed pattern-based searching\n",
    " * distributed sorting\n",
    " * web link-graph reversal\n",
    " * Singular Value Decomposition,\n",
    " * web access log stats\n",
    " * inverted index construction\n",
    " * document clustering\n",
    " * machine learning\n",
    " * statistical machine translation\n",
    "\n",
    "Moreover, the MapReduce model has been adapted to several computing environments:\n",
    " * like multi-core systems\n",
    " * desktop grids\n",
    " * volunteer computing environments\n",
    " * dynamic cloud environments\n",
    " * mobile environments\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The Future for MapReduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2014 Google announced that it had stopped relying upon it for its petabyte-scale operations, having since moved on to technologies such as Percolator, Flume and MillWheel that offer streaming operation and updates instead of batch processing, to allow integrating \"live\" search results without rebuilding the complete index. Furthermore, these technologies are not limited to the concept of map and reduce workflows, but rather the more general concept of data pipeline workflows.\n",
    "\n",
    "MapReduce certainly isn't dead, and the frameworks that support it aren't going away. However, we've been seeing an evolution in the industry since Google popularized the concept of distributed workloads across commodity hardware with MapReduce, and both proprietary and open source solutions are offering their users the friuts of these innovations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open Source Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most readers who have even just passing familiarity with big data in general will have heard of Hadoop. A member project of the Apache Software Foundation, Hadoop is an open source distributed storage and processing framework designed to work with very large data sets on commodity hardware computing clusters. The distributed storage part of the project is called HDFS, Hadoop Distributed File System, and the processing part is named MapReduce. When a user uploads data to the Hadoop file system, the files are split into pieces and then distributed across the cluster nodes. When a user creates a code to run on Hadoop MapReduce, the custom mappers and reducers – similar in concept to what we saw in the previous section – are copied to MapReduce nodes in the cluster which are then executed for against the data stored in with that node.\n",
    "\n",
    "Hadoop's predecessor was created at the Internet Archive in 2002 in an attempt to build a better web page crawler and indexer. When the papers on the Google File System and Google's MapReduce were published in 2003 and 2004, respectively, the creators of Hadoop to re-envision their project and create a framework upon which it could run more efficiently. That was the birth of Hadoop. Yahoo! invested heavily in the project a few years later and open sourced it while at the same time providing its researchers access to a testing cluster – that last was the seed for Hadoop's very strong role in the field of machine learning.\n",
    "\n",
    "Though Hadoop is the primary driver for the big data market, projected to generate 23 billion USD by 2016, it is not the only big data framework available in the open source community. A notable, if quiet, contender is the Disco project.\n",
    "In 2008, the Nokia Research Center needed a tool that would allow them to process enormous amounts of data in real-time. They wanted their researchers – many of them proficient in Python – to be able to easily and quickly create MapReduce jobs against their large data sets. They also needed their system to be fault-tolerant and scalable. As such, they built the server on top of the Erlang distributed programming language, and created a protocol and Python client which could talk to it, thus allowing their users to continue using the language they new so well.\n",
    "\n",
    "Since then, Disco has continued evolving and provides a generalized workflow on top of its distributed file system: Disco pipelines. The pipeline workflow enables data scientists to create distributed processing tasks which go far beyond the original vision of MapReduce.\n",
    "\n",
    "The functionality of MapReduce is no longer available only in the domain of MapReduce frameworks: the rise of NoSQL databases which then extended their functionality to distributed data have started offering MapReduce features in their products. Redis clusters, for instance, make it trivial to implement MapReduce functionality. The Riak distributed NoSQL key-value data store, based upon the Amazon Dynamo paper (not to be confused with the DynamoDB product from Amazon), offers built-in MapReduce capabilities. Riak provides an API  for executing MapReduce jobs against nodes in a cluster, and this is supported by the Python Riak client library. MongoDB is another NoSQL database which offers built-in support for MapReduce.\n",
    "\n",
    "In our case, though, we will be focusing on the Hadoop implementation of MapReduce, utilizing its support for Python via its streaming protocol. In particular, we will take advantage of a service provider which allows us to quickly and easily set up Hadoop clusters: Amazon Elastic MapReduce, or EMR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Amazon EMR (Elastic Map Reduce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will be using Hadoop on Amazon Elastic MapReduce, performing the following tasks:\n",
    "\n",
    "* Creating a cluster\n",
    "* Pushing our data set to the cluster\n",
    "* Writing a mapper and reducer in Python\n",
    "* Testing our mapper and reducer against small, local data\n",
    "* Adding nodes to our EMR cluster in preparation for our job\n",
    "* Executing our MapReduce job against the EMR cluster we created\n",
    "* Examining the results\n",
    "\n",
    "We're going to create an Amazon EMR clusster from the command line using the ``aws`` tool we've installed:\n",
    "\n",
    "```bash\n",
    "$ aws emr create-cluster --name \"Weather\" --ami-version 3.6.0 \\\n",
    "    --applications Name=Hue Name=Hive Name=Pig Name=HBase \\\n",
    "    --use-default-roles --ec2-attributes KeyName=YourKeyName \\\n",
    "    --instance-type c1.xlarge --instance-count 3\n",
    "```\n",
    "```\n",
    "j-63JNVV2BYHC\n",
    "```\n",
    "\n",
    "We're going to need that cluster ID, so let's export it as a shell variable. We're also going to need to use the full path to your ``.pem`` file, so we'll set one for that too:\n",
    "\n",
    "```bash\n",
    "$ export CLUSTER_ID=j-63JNVV2BYHC\n",
    "$ export AWS_PEM=/path/to/YourKeyName.pem\n",
    "```\n",
    "\n",
    "We can check the state of the cluster with the following:\n",
    "\n",
    "```bash\n",
    "$ aws emr describe-cluster --cluster-id $CLUSTER_ID |grep STATUS\n",
    "STATUS  RUNNING\n",
    "STATUS  RUNNING\n",
    "STATUS  WAITING\n",
    "```\n",
    "\n",
    "The first ``STATUS`` is the master node, and once it returns as ``RUNNING``, we can start copying files to it:\n",
    "\n",
    "```bash\n",
    "$ for FILE in data/{0,1,2}.csv\n",
    "  do\n",
    "    aws emr put \\\n",
    "      --src $FILE \\\n",
    "      --cluster-id $CLUSTER_ID \\\n",
    "      --key-pair-file $AWS_PEM\n",
    "  done\n",
    "```\n",
    "\n",
    "Or we can move them all up there (changing to a volume that has more space):\n",
    "\n",
    "```bash\n",
    "$ for FILE in data/*.csv\n",
    "  do\n",
    "    aws emr put \\\n",
    "      --src $FILE \\\n",
    "      --dest /mnt1 \\\n",
    "      --cluster-id $CLUSTER_ID \\\n",
    "      --key-pair-file $AWS_PEM\n",
    "  done\n",
    "```\n",
    "\n",
    "Login to the server and copy the data to HDFS:\n",
    "\n",
    "```bash\n",
    "$ aws emr ssh --cluster-id $CLUSTER_ID --key-pair-file $AWS_PEM\n",
    "```\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ hdfs dfs -mkdir /weather\n",
    "[hadoop@ip-10-255-7-47 ~]$ hdfs dfs -put /mnt1/*.csv /weather\n",
    "```\n",
    "\n",
    "Let's make sure the files are there:\n",
    "\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ $ hdfs dfs -ls /weather|head -10\n",
    "Found 200 items\n",
    "-rw-r--r--   1 hadoop supergroup   75460820 2015-03-29 18:46 /weather/0.csv\n",
    "-rw-r--r--   1 hadoop supergroup   75456830 2015-03-29 18:47 /weather/1.csv\n",
    "-rw-r--r--   1 hadoop supergroup   76896036 2015-03-30 00:16 /weather/10.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78337868 2015-03-30 00:16 /weather/100.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78341694 2015-03-30 00:16 /weather/101.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78341015 2015-03-30 00:16 /weather/102.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78337662 2015-03-30 00:16 /weather/103.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78336193 2015-03-30 00:16 /weather/104.csv\n",
    "-rw-r--r--   1 hadoop supergroup   78336537 2015-03-30 00:16 /weather/105.csv\n",
    "```\n",
    "\n",
    "Before we write our Python code to process the data now stored in HDFS, let's remind ourselves what the data looks like:\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ head 0.csv\n",
    "country,town,year,month,precip,temp\n",
    "0,0,1894,1,0.8449506929198441,0.7897647433139449\n",
    "0,0,1894,2,0.4746140099538822,0.42335801512344756\n",
    "0,0,1894,3,-0.7088399152900952,0.776535509023379\n",
    "0,0,1894,4,-1.1731692311337918,0.8168558530942849\n",
    "0,0,1894,5,1.9332497442673315,-0.6066233105016293\n",
    "0,0,1894,6,0.003582147937914687,0.2720125869889254\n",
    "0,0,1894,7,-0.5612131527063922,2.9628153460517272\n",
    "0,0,1894,8,0.3733525007455101,-1.3297078910961062\n",
    "0,0,1894,9,1.9148724762388318,0.6364284082486487\n",
    "```\n",
    "\n",
    "Now let's write the *mapper* (saved as ``mapper.py``). This will be used by Hadoop and expects input via ``STDIN``:\n",
    "\n",
    "```python\n",
    "#!/usr/bin/env python\n",
    "import sys\n",
    "\n",
    "def parse_line(line):\n",
    "    return line.strip().split(\",\")\n",
    "\n",
    "def is_header(line):\n",
    "    return line.startswith(\"country\")\n",
    "\n",
    "def main():\n",
    "    for line in sys.stdin:\n",
    "        if not is_header(line):\n",
    "            print(parse_line(line)[-1])\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "```\n",
    "\n",
    "Next we can write the *reducer* (saved as ``reducer.py``):\n",
    "\n",
    "```python\n",
    "#!/usr/bin/env python\n",
    "import sys\n",
    "\n",
    "def to_float(data):\n",
    "    try:\n",
    "        return float(data.strip())\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def main():\n",
    "    accum = 0\n",
    "    count = 0\n",
    "    for line in sys.stdin:\n",
    "        temp = to_float(line)\n",
    "        if temp == None:\n",
    "            continue\n",
    "        accum += temp\n",
    "        count += 1\n",
    "    print(accum / count)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "```\n",
    "\n",
    "Make them both executable:\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ chmod 755 *.py\n",
    "```\n",
    "\n",
    "Let's test drive the mapper before using it in Hadoop:\n",
    "\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ head 0.csv | ./mapper.py\n",
    "0.7897647433139449\n",
    "0.42335801512344756\n",
    "0.776535509023379\n",
    "0.8168558530942849\n",
    "-0.6066233105016293\n",
    "0.2720125869889254\n",
    "2.9628153460517272\n",
    "-1.3297078910961062\n",
    "0.6364284082486487\n",
    "```\n",
    "\n",
    "Let's add the reducer to the mix:\n",
    "\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ head 0.csv | ./mapper.py | ./reducer.py\n",
    "0.526826584472\n",
    "```\n",
    "\n",
    "A quick manual check confirms that the generated average is correct for the values parsed by the mapper.\n",
    "\n",
    "With our Python code tested and working, we're ready to run it on Hadoop... almost. Since there's a lot of data to process, let's switch to a local terminal session and create some more nodes:\n",
    "\n",
    "```bash\n",
    "$ aws emr add-instance-groups \\\n",
    "    --cluster-id $CLUSTER_ID \\\n",
    "    --instance-groups \\\n",
    "      InstanceCount=6,InstanceGroupType=task,InstanceType=m1.large \\\n",
    "      InstanceCount=10,InstanceGroupType=task,InstanceType=m3.xlarge\n",
    "```\n",
    "```bash\n",
    "j-63JNVV2BYHC\n",
    "INSTANCEGROUPIDS\tig-ZCJCUQU6RU21\n",
    "INSTANCEGROUPIDS\tig-3RXZ98RUGS7OI\n",
    "```\n",
    "\n",
    "Let's check the cluster:\n",
    "\n",
    "```bash\n",
    "$ aws emr describe-cluster --cluster-id $CLUSTER_ID\n",
    "```\n",
    "```\n",
    "CLUSTER\tFalse\tj-63JNVV2BYHC\tec2-54-70-11-85.us-west-2.compute.amazonaws.com\tWeather\t189\t3.6.0\t3.6.0\tEMR_DefaultRole\tFalse\tTrue\n",
    "APPLICATIONS\thadoop\t2.4.0\n",
    "APPLICATIONS\tHue\n",
    "BOOTSTRAPACTIONS\tInstall Hue\ts3://us-west-2.elasticmapreduce/libs/hue/install-hue\n",
    "BOOTSTRAPACTIONS\tInstall HBase\ts3://us-west-2.elasticmapreduce/bootstrap-actions/setup-hbase\n",
    "EC2INSTANCEATTRIBUTES\tus-west-2b\tOubiwannAWSKeyPair\tsg-fea0e9cd\tsg-fca0e9cf\tEMR_EC2_DefaultRole\n",
    "INSTANCEGROUPS\tig-3M0BXLF58BAO1\tMASTER\tc1.xlarge\tON_DEMAND\tMASTER\t1\t1\n",
    "STATUS\tRUNNING\n",
    "STATECHANGEREASON\n",
    "TIMELINE\t1427653325.578\t1427653634.541\n",
    "INSTANCEGROUPS\tig-1YYKNHQQ27GRM\tCORE\tc1.xlarge\tON_DEMAND\tCORE\t2\t2\n",
    "STATUS\tRUNNING\n",
    "STATECHANGEREASON\n",
    "TIMELINE\t1427653325.579\t1427653692.548\n",
    "INSTANCEGROUPS\tig-3RXZ98RUGS7OI\tTASK\tm3.xlarge\tON_DEMAND\ttask\t10\t0\n",
    "STATUS\tRESIZING\n",
    "STATECHANGEREASON\tExpanding instance group\n",
    "TIMELINE\t1427676271.495\n",
    "INSTANCEGROUPS\tig-ZCJCUQU6RU21\tTASK\tm1.large\tON_DEMAND\ttask\t6\t0\n",
    "STATUS\tRESIZING\n",
    "STATECHANGEREASON\tExpanding instance group\n",
    "TIMELINE\t1427676243.42\n",
    "STATUS\tWAITING\n",
    "STATECHANGEREASON\tWaiting after step completed\n",
    "TIMELINE\t1427653325.578\t1427653692.516\n",
    "```\n",
    "\n",
    "We can see that the two we just added have a ``STATUS`` of ``RESIZING``. We'll keep an eye on this until they've finished.\n",
    "\n",
    "Back on the Hadoop cluster, let's execute our map-reduce job against the data we've updated to the cluster and saved to HDFS:\n",
    "\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ hadoop \\\n",
    "  jar contrib/streaming/hadoop-*streaming*.jar \\\n",
    "  -D mapred.reduce.tasks=1 \\\n",
    "  -files mapper.py,reducer.py \\\n",
    "  -mapper mapper.py \\\n",
    "  -reducer reducer.py \\\n",
    "  -combiner reducer.py \\\n",
    "  -input /weather/*.csv \\\n",
    "  -output /weather/total-mean-temp\n",
    "```\n",
    "\n",
    "To see the results:\n",
    "\n",
    "```bash\n",
    "[hadoop@ip-10-255-7-47 ~]$ hdfs dfs -ls /weather/total-mean-temp\n",
    "Found 2 items\n",
    "-rw-r--r--   1 hadoop supergroup          0 2015-03-29 20:20 /weather/total-mean-temp/_SUCCESS\n",
    "-rw-r--r--   1 hadoop supergroup         18 2015-03-29 20:20 /weather/total-mean-temp/part-00000\n",
    "[hadoop@ip-10-255-7-47 ~]$ hdfs dfs -cat /weather/total-mean-temp/part-00000\n",
    "-5.30517804131e-05\n",
    "```\n",
    "\n",
    "This is within an order of magnitude of the result obtained by manually slicing the HDF5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.3051780413782918e-05"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(means)/len(means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without an in-depth analysis, one might venture a guess that the difference between these two values could be due to floating point calculations on different platforms using different versions of Python (the Python version on the Hadoop cluster was 2.6; we're using 3.4.2). At any rate, the mean calculation meets with expectations: close to zero for a normal distribution centered around zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hadoop and matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard use case for matplotlib is on a workstation, often at an interactive Python or IPython prompt. In such scenarios, we are used to crunching our data – such as getting means, standard deviations, etc. – and then plotting them, all within a few commands (and seconds) of each other.\n",
    "\n",
    "In the world of big data, that experience changes drastically. What was an implicit understanding that one's data is in-process, trivial to copy and perform analytical operations upon, is now an involved process comprised of cluster deployments, configuration management, distributed data, communication latencies, and the like. The only thing remaining the same is that its our data, and we need to plot it.\n",
    "\n",
    "When the data was too large for memory, but still able to fit on a single hard drive, HDF5 and PyTables gave us the means by which we could use our old approaches with very little change in our analytical workflows. Once our data is too large for a hard drive or a file server, those workflows have to change: we can't even pretend it's the same data world we lived in previously. We have to think in terms of partitioned data and our jobs running against those partitions.\n",
    "\n",
    "We still get to use NumPy, but the work is not being done on our machine in our IPython shell: it's being done remotely on a cluster comprised of distributed nodes. Our work in interactive shells is transformed instead to a testbed where we operate on a small sample set in preparation for pushing out a full job to the cluster. Additionally, every new big data project has the potentially to be legitimately different from any other one. For each organization that needs to work with big data, and for each set of data, the particulars of the day-to-day analytics workflows are likely change.\n",
    "\n",
    "In the end, though, our jobs will run and we will have distilled from the octillions of data points, the few tens of millions needed in the final analysis, and it is this data which we provide to matplotlib for plotting. Though big data requires that the preparation of data for plotting move outside of the familiarity of an interactive Python prompt, the essence remains the same: we need to know what we have, we need to know how to distill that, and we've got to be able to visualize it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing large data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of this notebook has been dedicated to processing large data sets and plotting histograms. This was done intentionally, since using such an approach limited the number or artists on the matplotlib canvas to something on the order of 100s vs. attempting to plot millions of artists. In this section we will address the problem of displaying actual elements from large data sets. We will return to our fast HDF5 table for the remainder of the notebook.\n",
    "\n",
    "As a refresher on the volume we're looking at, our data set has the following number of data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_len = len(tab)\n",
    "data_len"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding commas to more easily see the value -- 288,000,000 -- we're looking at almost a third of a billions points.\n",
    "\n",
    "Let's start with establishing a baseline: is there a practical limit we should consider when attempting to render artists? Let's use the data from our HDF5 file to explore this, starting with 1000 data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DN26c/J7ueGKVxBLdCVmyY/Ej/t6v9nF+xHCzTDBhibW208H+f/t62Md9oHUb3ckSAGAp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\n2HuvWkrUCW5zV21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wF1QOE9rBS2q0rOc7KS32A9Yph4IGnnpLtLwWnue4we1GJnpwNJSQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15705b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 1000\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:limit][\"temp\"], \".\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was quick; maybe something under a second. Let's try 10,000 data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vwbl1/iGfdb27iF759OAF9DBkkiXjoemDLvSct2jvWCyPtEq5g2YbYYxHKtgZ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\nR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mQOMDl3y8Mafu3iIn505rYmyQbp2Crp2DjniEFwMxIpCub9MczYDoHCENWvVCex+lvk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\nvgcPHjzsInibvgcPHjzsIvz//Enipy9/an8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1576d8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 10000\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:limit][\"temp\"], \".\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was still fast -- again, under 1 second to render. 100,000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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26CbeM3ADjwzMe9ZgPdmn4EotjuOpsvfZVCWB1yppz7pjtCm6sWEmqFgp36Dq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\nGQAGBgaGNgUzAAwMDAxtiv8HEuHFqCR9pR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1590c6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 100000\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:limit][\"temp\"], \".\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're starting to see some more time taken; that was about 1 second. Let's keep going:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LwC3L7lNU9GaA9ca7XnnUiphr0Uy5CHCCBQQHUoC372ldQkgwYUJAWDDddeoJ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\nb4RVmrI72MdTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158c8ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 1000000\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:limit][\"temp\"], \".\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ksZ6wgbEez2PvW8uvjZS7mpn8msm6OinCzZRzmr1vPbySjPWg3ez2VqfRtjXT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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15da6bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit = 10000000\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:limit][\"temp\"], \".\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A million data points only took about 2 or 3 seconds -- not bad for a single plot, but if we had to plot 100s of these, we'd need to start utilizing some of the techniques we discussed in the cloud-deploy notebook. 10 million points took about 15 seconds. Note that if we had used lines instead of points an exception would have been raised -- ``Exception in image/png formatter: Allocated too many blocks`` -- indicating that the shared library we're using for the Agg backend couldn't handle the number of artists required.\n",
    "\n",
    "100,000 data points looks like a good limit for the number we'd want to plot simultaneously, with no or no appreciable delay in rendering, but that still leaves us with only a fraction of our total data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2880"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frac = math.floor(data_len / 100000)\n",
    "frac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How should we procede if we want to plot data points representing our full range of data? We have several options:\n",
    " * adjusting matplotlib configuration to use plot chunking\n",
    " * decimation\n",
    " * (re)sampling\n",
    " * data smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``matplotlibrc`` Agg Rendering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using the Agg backend and are having issues rendering large data sets, one option is to experiment with the ``agg.path.chuncksize`` configuration variable in your ``matplotlibrc`` file or from a Python interactive prompt using ``rcParams``. Let's try to graph the 10,000,000 data points as lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oubiwann/lab/python/mastering-matplotlib/.venv-mmpl/lib/python3.4/site-packages/IPython/core/formatters.py:239: FormatterWarning: Exception in image/png formatter: Allocated too many blocks\n",
      "  FormatterWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160587240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:10000000][\"temp\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That generated the error in the backend we talked about above. Let's tweak the chunksize from 0 to 20,000 (a recommended starting point offered in the comments of the ``matplotlibrc`` configuration file):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mpl.rcParams[\"agg.path.chunksize\"] = 20000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now re-render:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oi9+S+mNwoDKVQaN/7q7DWxO5eHU8D+9ORR7cvxuJrZODk5EHcrvLKtn5OW3P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cf6f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(tab[:10000000][\"temp\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this feature was marked as experimental in 2008 and it remains so. Using it may introduce minor visual artifacts in rendered plot.\n",
    "\n",
    "More importantly, though, this is a workaround that will allow you to squeeze a little more range out of matplotlib's plotting limits. As your data sets grow in size, you will eventually get to the point where even this configuration change can no longer help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another approach carries the unfortunate name of the brutal practice employed by the Roman army against large groups guilty of capital offences: removal of a tenth. We use the term here more generally, indicating \"removal of a fraction sufficient to give us our desired performance\" -- which, as it turns out, will be much more than a tenth. From our quick calculation above, we determined that we would need no more than every 2880th data point. The following approach brings this into memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "decimated = tab[::frac][\"temp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you know this is something you will be doing, it would be better to generate this data at the time you created your HDF5 table.\n",
    "\n",
    "Let's sanity check our length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(decimated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the 100,000 data points which represent the full spectrum of our population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/Kla4kJTsJBUJnh1d8MvJOpl0PLQZ27oiAEII1nSDRbGhGzoqUDqJEYF9m/LP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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16056dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xvals = range(0, data_len, frac)\n",
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(xvals, decimated, \".\", alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to keep in mind:\n",
    " * potentially important data points can be eliminated in this method\n",
    " * depending on the type of distribution, standard deviation, variance, and other statistical values could very well change in the decimated data set\n",
    " \n",
    "If these are important to you, you still have some other options available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your data is random, then you can simply take a slice of the desired number of points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[size] = tab[0:limit][\"temp\"].shape\n",
    "size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZjBDA2+UR/zxfW9rOInnYxlql8/BBIpDKK67IBZuqxWv4tBoAAiNjyeRc2iLm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\ngiACBIk6QRBEgPg/gZmPLA8vWocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160566da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(figure, axes) = plt.subplots()\n",
    "axes.plot(range(size), tab[0:limit][\"temp\"], \".\", alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This, by nature of PyTables, is a very fast option.\n",
    "\n",
    "If your data is periodic, it might be worth trying ``scipy.signal.resample``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important thing to keep in mind when working with large data sets and matplotlib is this: use the data wisely and take advantage of NumPy. As we saw, even with data sets approaching billions of points, matplotlib's use of NumPy was intelligent and didn't do anything untoward under the hood: when we used ``memmap``, matplotlib used it. More importantly, in the early art of this notebook we decided to plot the histogram, not the entire data set. A little discernment on our part went a long way and made matplotlib highly usable even under circumstances that it was never designed to handle."
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}