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Source code for the COSC3000 Data Visualisation Project with some commenting and annotations. -- Jun Ling 05/05/14
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"COSC3000 Data Visualisation\n",
"=========================\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## File parsing\n",
"\n",
"A selection of files was downloaded in .txt and .tex format. LaTeX commands were stripped from .tex files, then extraneous text such as Project Gutenberg licensing information was removed from the end of all files. Files were then named in the following format [Category]-[Year of publication]-[Country of publication]-[Lines of preamble to skip] to be parsed by the code below."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" construct a dictionary containing the names of files along with their details. \"\"\"\n",
"\n",
"import sys\n",
"import os\n",
"\n",
"maindir = '/home/jun/' + 'Documents/COSC3000/DataVis/data/'\n",
"\n",
"fileDict = {}\n",
"# add filenames to a dict.\n",
"# ignore files with '.txt', these are handled individually.\n",
"for __, ___, filenames in os.walk(maindir):\n",
" for f in filenames:\n",
" if '.txt' not in f:\n",
" fileDict[f] = f.split('-')\n",
"print fileDict"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'arc-1895-US-374': ['arc', '1895', 'US', '374'], 'lit-1631-UK-385': ['lit', '1631', 'UK', '385'], 'det-1914-UK-42': ['det', '1914', 'UK', '42'], 'lit-1590-UK-0': ['lit', '1590', 'UK', '0'], 'phys-1913-DE-263': ['phys', '1913', 'DE', '263'], 'chem-1902-UK-277': ['chem', '1902', 'UK', '277'], 'bio-1932-UK-125': ['bio', '1932', 'UK', '125'], 'phys-1914-US-369': ['phys', '1914', 'US', '369'], 'chem-1908-UK-142': ['chem', '1908', 'UK', '142'], 'arc-1887-US-0': ['arc', '1887', 'US', '0'], 'mus-1905-US-115': ['mus', '1905', 'US', '115'], 'det-1896-UK-0': ['det', '1896', 'UK', '0'], 'lit-1843-UK-0': ['lit', '1843', 'UK', '0'], 'chem-1789-UK-1193': ['chem', '1789', 'UK', '1193'], 'bio-2006-US-171': ['bio', '2006', 'US', '171'], 'bio-1860-UK-31': ['bio', '1860', 'UK', '31'], 'lit-1854-UK-0': ['lit', '1854', 'UK', '0'], 'det-1914-FR-70': ['det', '1914', 'FR', '70'], 'arc-1908-US-0': ['arc', '1908', 'US', '0'], 'det-1918-UK-0': ['det', '1918', 'UK', '0'], 'det-1903-US-120': ['det', '1903', 'US', '120'], 'det-1866-FR-38': ['det', '1866', 'FR', '38'], 'arc-1930-UK-52': ['arc', '1930', 'UK', '52'], 'lit-1603-UK-0': ['lit', '1603', 'UK', '0'], 'lit-1599-UK-0': ['lit', '1599', 'UK', '0'], 'det-1920-UK-0': ['det', '1920', 'UK', '0'], 'phys-1920-UK-363': ['phys', '1920', 'UK', '363'], 'mus-1922-US-54': ['mus', '1922', 'US', '54'], 'mus-1915-US-108': ['mus', '1915', 'US', '108'], 'det-1894-UK-0': ['det', '1894', 'UK', '0'], 'phys-1964-US-97': ['phys', '1964', 'US', '97'], 'lit-1850-UK-0': ['lit', '1850', 'UK', '0'], 'det-1922-UK-0': ['det', '1922', 'UK', '0'], 'chem-1888-US-520': ['chem', '1888', 'US', '520'], 'lit-1592-UK-0': ['lit', '1592', 'UK', '0'], 'arc-1905-UK-35': ['arc', '1905', 'UK', '35'], 'arc-1910-US-0': ['arc', '1910', 'US', '0'], 'bio-1890-UK-105': ['bio', '1890', 'UK', '105']}\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"##Processing Input\n",
"\n",
"The following code sample reads in a plaintext file and calculates the frequency distribution of characters. The frequency distribution for the English version of Scrabble is also calculated and stored in an OrderedDict. NOTE TO SELF: OrderedDict isn't required, probability distributions are generated with dict comprehension anyway, so order is lost..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" General data processing functions \"\"\"\n",
"\n",
"import os\n",
"from collections import OrderedDict\n",
"\n",
"alphabet = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', \n",
" 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z')\n",
"\n",
"def fpWrapper( fileName ):\n",
" \"\"\" Returns the filePath-string in the format required by calcProbs \"\"\"\n",
" return os.path.abspath(maindir + fileName)\n",
"\n",
"def calcProbs(filePath, skip = 0):\n",
" \"\"\" Reads text file and returns dict of letter probabilities. \"\"\"\n",
" d = dict.fromkeys(alphabet, 0)\n",
" with open (filePath, 'r') as f:\n",
" for __ in xrange(skip):\n",
" next(f)\n",
" for line in f:\n",
" for char in line:\n",
" if char in d.keys():\n",
" d[char] += 1\n",
" return {char: d[char] / float(sum(d.values())) for char in d.keys()}\n",
"\n",
"def getScrabbleProbs():\n",
" \"\"\" Returns character freq. for English version of Scrabble. \"\"\"\n",
" lettercounts = [9, 2, 2, 4, 12, 2, 3, 2, 9, 1, 1, 4, 2, 6, 8, 2, 1, 6, 4, 6, 4, 2, 2, 1, 2, 1]\n",
" d = OrderedDict((alphabet[i], lettercounts[i]) for i, __ in enumerate(alphabet))\n",
" return {char: d[char] / float(sum(d.values())) for char in d.keys()}\n",
"\n",
"def getScrabbleScores():\n",
" \"\"\" Returns ratio of score values for each letter in English version of Scrabble. \"\"\"\n",
" \n",
" def getScore(char):\n",
" if char in ['e', 'a', 'i', 'o', 'n', 'r', 't', 'l' 'r', 's', 'u']:\n",
" return 1\n",
" elif char in ['d', 'g']:\n",
" return 2\n",
" elif char in ['b', 'c', 'm', 'p']:\n",
" return 3\n",
" elif char in ['f', 'h', 'v', 'w', 'y']:\n",
" return 4\n",
" elif char in 'k':\n",
" return 5\n",
" elif char in ['j', 'x']:\n",
" return 8\n",
" elif char in 'z':\n",
" return 10\n",
" else:\n",
" print \"This should never happen. Check your inputs.\"\n",
" return np.nan\n",
" \n",
" d = OrderedDict((alphabet[i], getScore(char)) for i, char in enumerate(alphabet))\n",
" return {char: d[char] / float(sum(d.values())) for char in d.keys()}\n",
"\n",
" \n",
"def getConnelProbs():\n",
" \"\"\" Returns character freq. for Cornell university study \"\"\"\n",
" lettercounts = [14810, 2715, 4943, 7874, 21912, 4200, 3693, 10795, 13318, 188, 1257, \n",
" 7253, 4761, 12666, 14003, 3316, 205, 10977, 11450, 16587, 5246, 2019, \n",
" 3819, 315, 3853 , 128]\n",
" d = OrderedDict((alphabet[i], lettercounts[i]) for i, __ in enumerate(alphabet))\n",
" return {char: d[char] / float(sum(d.values())) for char in d.keys()}\n",
"\n",
"def getOxfordProbs():\n",
" \"\"\" Returns character freq. for Oxford university study \"\"\"\n",
" letterprobs = [8.4966, 2.0720, 4.5388, 3.3844, 11.1607, 1.8121, 2.4705, 3.0034, 7.5448, 0.1965, 1.1016, \n",
" 5.4893, 3.0129, 6.6544, 7.1635, 3.1671, 0.1962, 7.5809, 5.7351, 6.9509, 3.6308, 1.0074, \n",
" 1.2899, 0.2902, 1.7779, 0.2722]\n",
" d = OrderedDict((alphabet[i], letterprobs[i]/100) for i, __ in enumerate(alphabet))\n",
" return d\n",
"\n",
"def getDictionaryProbs(dictname):\n",
" \"\"\" calcProbs function called manually on dictionary files. \"\"\"\n",
" return calcProbs(fpWrapper(dictname))\n",
"\n",
"def categoryDict(category, inputdict):\n",
" \"\"\" return a subset of a dict where key contains a search string \"\"\"\n",
" return {key: val for key, val in inputdict.iteritems() if category in key}\n",
"\n",
"def categoryAverageDict(inputdict, filter = 'category'):\n",
" \"\"\" returns a dict with frequencies averaged for each category or location\"\"\"\n",
" if filter == 'category':\n",
" catList = set([key.split('-')[0] for key in inputdict.keys()])\n",
" catDict = {}\n",
" for cat in catList:\n",
" a = [v for k,v in inputdict.iteritems() if cat in k] # list of alphabet dstr's for category 'cat'\n",
" S = {k:[ a[j][k] for j in range(len(a)) ] for k in a[0].keys()} # aggregate values under each letter\n",
" meansDict = {k:( reduce(np.add, v)/len(v) ) for k,v in S.iteritems()}\n",
" catDict[cat] = meansDict\n",
" elif filter == 'location':\n",
" catList = set([key.split('-')[2] for key in inputdict.keys()])\n",
" catDict = {}\n",
" for cat in catList:\n",
" a = [v for k,v in inputdict.iteritems() if cat in k] \n",
" S = {k:[ a[j][k] for j in range(len(a)) ] for k in a[0].keys()} \n",
" meansDict = {k:( reduce(np.add, v)/len(v) ) for k,v in S.iteritems()}\n",
" catDict[cat] = meansDict\n",
" return catDict\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic plotting functions\n",
"\n",
"The functions included below perform basic plotting using matplotlib API. So far line plots and stacked bar charts implemented."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" imports / setup for matplotlib \"\"\"\n",
"\n",
"import matplotlib as mpl\n",
"# select QT backend for mpl because wx backend doesn't work on home VM.\n",
"if mpl.get_backend() == 'WXAgg': mpl.use('QT4Agg')\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import matplotlib.cm as cm\n",
"import pandas as pd\n",
"import ggplot as gg\n",
"%matplotlib inline\n",
"\n",
"\"\"\" Plot figures at double size \"\"\"\n",
"mult = 1 # mult = 2 for double size\n",
"mpl.rcParams['savefig.dpi'] = mult * mpl.rcParams['savefig.dpi']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Define wrapper function for basic plotting in matplotlib \"\"\"\n",
"\n",
"def doLinePlots(inputDict, uselog = True, title = \"\", legend = True, legendcols = 4):\n",
" \"\"\" produces line plot of character frequencies \"\"\"\n",
" series = []\n",
" serieslabels = []\n",
" alphabet = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', \n",
" 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z')\n",
" xvals = np.arange(len(alphabet))\n",
" \n",
" fig, ax = plt.subplots()\n",
" \n",
" for k, freqs in inputDict.iteritems():\n",
" lfreqs = np.log(freqs.values()) if uselog else freqs.values()\n",
" a, = plt.plot(xvals, lfreqs)\n",
" plt.scatter(xvals, lfreqs)\n",
" series.append(a)\n",
" serieslabels.append(k)\n",
" \n",
" plt.xticks(xvals, alphabet)\n",
" plt.xlabel('Alphabet character')\n",
" plt.suptitle = title\n",
" if uselog: \n",
" plt.ylim([-9, 0])\n",
" plt.ylabel('log (frequency)')\n",
" else:\n",
" plt.ylabel('frequency')\n",
" plt.ylim([0, 0.16])\n",
" if legend: \n",
" leg = ax.legend(tuple(series), serieslabels, ncol = legendcols, fancybox = True)\n",
" leg.get_frame().set_alpha(0.5)\n",
" plt.show()\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Function wrapper for plotting side-stacked bar charts in matplotlib \"\"\"\n",
"def doBarPlots(inputDict, uselog = True, title = \"\", legend = True, legendcols = 4, cmap = 'Accent'):\n",
" if isinstance(inputDict, dict):\n",
" series = []\n",
" serieslabels = []\n",
" alphabet = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', \n",
" 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z')\n",
" xvals = np.arange(len(alphabet))\n",
" cols = []\n",
" colArray = []\n",
"\n",
" fig, ax = plt.subplots()\n",
" # colormap up in this\n",
" colormap = plt.get_cmap(cmap)\n",
" cNorm = mpl.colors.Normalize(vmin=0, vmax=len(inputDict))\n",
" scalarmap = cm.ScalarMappable(norm = cNorm , cmap = colormap)\n",
"\n",
" width = 0.55\n",
" for i, k in enumerate(inputDict.keys()):\n",
" lfreqs = np.log(inputDict[k].values()) if uselog else inputDict[k].values()\n",
" cols.append(plt.bar(xvals + (i * width), lfreqs, width = width, color = scalarmap.to_rgba(i)))\n",
" serieslabels.append(k)\n",
"\n",
" plt.xticks(xvals + width* len(serieslabels), alphabet)\n",
" plt.xlabel('Alphabet character')\n",
" plt.suptitle = title\n",
" if uselog:\n",
" plt.ylim([-9, 0])\n",
" plt.ylabel('log (frequency)')\n",
" else:\n",
" plt.ylabel('frequency')\n",
" if legend:\n",
" leg = ax.legend(tuple(cols), serieslabels, ncol = legendcols, fancybox = True)\n",
" leg.get_frame().set_alpha(0.5)\n",
" fig.tight_layout()\n",
" plt.show()\n",
" elif isinstance(inputDict, pd.DataFrame):\n",
" ax = inputDict.plot(kind = 'bar', title = title)\n",
" else:\n",
" print \"Incorrect input type, must be dict or pandas.DataFrame\"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Function wrapper for making 3D bar plots in matplotlib\"\"\"\n",
"\n",
"# NOTE: Never used because it is ugly and uninformative. :(\n",
"def do3DBarPlot(inputDict, uselog = False, title = \"\", legend = True, legendcols = 4):\n",
" from mpl_toolkits.mplot3d import Axes3D\n",
" from matplotlib.lines import Line2D\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111,projection='3d')\n",
" ylbls = []\n",
" bars = []\n",
" fakecols = [] \n",
" \n",
" # set up color map\n",
" colormap = plt.get_cmap('Accent')\n",
" cNorm = mpl.colors.Normalize(vmin=0, vmax=len(inputDict))\n",
" scalarmap = cm.ScalarMappable(norm = cNorm , cmap = colormap)\n",
"\n",
" z = 0\n",
" for i, k in enumerate(inputDict.keys()):\n",
" z += 10\n",
" lfreqs = np.log(inputDict[k].values()) if uselog else inputDict[k].values()\n",
" bars.append(ax.bar(np.arange(len(alphabet)), lfreqs, zs=i * 10, \n",
" zdir='y', color=scalarmap.to_rgba(i), alpha=0.8))\n",
" ylbls.append(k)\n",
" fakecols.append(plt.Rectangle((0, 0), 1, 1, fc=scalarmap.to_rgba(i)))\n",
" ax.set_xlabel('Alphabet')\n",
" ax.set_ylabel('Source')\n",
" ax.set_zlabel('Frequency')\n",
" \n",
" plt.xticks(np.arange(len(alphabet)), alphabet)\n",
" ax.set_yticklabels((\"\")) \n",
" if legend: \n",
" leg = ax.legend(fakecols, ylbls, ncol = legendcols, fancybox = True)\n",
" leg.get_frame().set_alpha(0.5)\n",
" plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Character frequency line plots -- unused in report\n",
"\n",
"Produce plot of mean frequencies for each category"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Produce line plots. \"\"\"\n",
"# ============================================================================================================================= #\n",
"\n",
"# Calculate frequencies for scrabble and files, and store in dict\n",
"probsDict = {}\n",
"for k in fileDict.iterkeys():\n",
" probsDict[k] = calcProbs(fpWrapper(k), int(fileDict[k][3]))\n",
"\n",
"d = categoryAverageDict(probsDict)\n",
"d['scrabble'] = getScrabbleProbs()\n",
"d['brit dict'] = getDictionaryProbs('britishdictsorted.txt')\n",
"# doLinePlots(d, uselog = False, title = 'Scrabble vs brit dict', legend = True)\n",
"\n",
"m = categoryDict('chem', probsDict)\n",
"m['scrabble'] = getScrabbleProbs()\n",
"# doLinePlots(m, uselog = False, title = \"Frequencies by category\", legend = True, legendcols = 2)\n",
"\n",
"# ============================================================================================================================= #\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison between UK and British character frequencies\n",
"A series of bar charts in descending frequency order for different dictionaries."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Produce dictionary frequency comparison plots \"\"\"\n",
"\n",
"dd = {}\n",
"dd['British dictionary'] = getDictionaryProbs('scowl_brit.txt')\n",
"dd['American dictionary'] = getDictionaryProbs('scowl_am.txt')\n",
"dd['Scrabble'] = getScrabbleProbs()\n",
"\n",
"# Sorted DataFrame by British Dictionary values\n",
"sortedBr = pd.DataFrame(dd)\n",
"sortedBr.sort('British dictionary', inplace = True, ascending = False)\n",
"\n",
"# Sorted DataFrame by American Dictionary values\n",
"sortedAm = pd.DataFrame(dd)\n",
"sortedAm.sort('American dictionary', inplace = True, ascending = False)\n",
"\n",
"doBarPlots(sortedBr, title = \"Dictionary frequencies ordered by British dictionary\")\n",
"doBarPlots(sortedAm, title = \"Dictionary frequencies ordered by American dictionary\")\n",
"\n",
"\"\"\" Produce US vs UK frequency comparison plots \"\"\"\n",
"\n",
"catav = categoryAverageDict(probsDict, filter = 'location')\n",
"# we're only interested in US and UK\n",
"catAvDict = {k: catav[k] for k in ('US', 'UK')}\n",
"sortedCat = pd.DataFrame(catAvDict)\n",
"sortedCat.sort('US', inplace = True, ascending = False)\n",
"\n",
"doBarPlots(sortedCat.sort_index(), uselog = False, cmap = 'binary')\n",
"\n",
"\"\"\" Compare scrabble, UK freq, Brit dict. Sorted by Scrabble \"\"\"\n",
"ddf = {}\n",
"ddf['British dictionary'] = getDictionaryProbs('scowl_brit.txt')\n",
"ddf['Scrabble'] = getScrabbleProbs()\n",
"ddf['UK texts'] = {'UK':catav['UK']}['UK']\n",
"\n",
"compdf = pd.DataFrame(ddf)\n",
"compdf.sort('Scrabble', inplace = True, ascending = False)\n",
"doBarPlots(compdf, title = \"British Dictionary, Scrabble and UK texts, ordered by Scrabble frequencies\")\n",
"\n",
"\n",
"\"\"\" Calculate RMSEs \"\"\"\n",
"# note, will need to execute code block further down first.\n",
"# rmses = [RMSE(getScrabbleProbs(), v) for k,v in probsDict.iteritems()]\n",
"print \"BritDict RMSE\",RMSE(getScrabbleProbs(), getDictionaryProbs('scowl_brit.txt'))\n",
"print \"UKTexts RMSE\",RMSE(getScrabbleProbs(), {'UK':catav['UK']}['UK'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BritDict RMSE 0.0183873946826\n",
"UKTexts RMSE 0.0137578235302\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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yyvFLly6hV69eMDQ0xNKlS4X9169fh7+/P1xdXSGXy7F58+Zq6zI1fbaQsKmp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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x60d0710>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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OHDkSv/76Kx4/fozY2Fj4+PigSZMmesetDu47dl59WSkaXuOSouE1LimaV8Vj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\nREdHIywsDM+fP8ekSZNgbW2NmJgYAEBYWBg8PDz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"text": [
"<matplotlib.figure.Figure at 0x60d07d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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2KISxmjMOfVNvgPrFXdb5S5uxS4wZuzrM2Ad4fcVus9kAANnZ2YiLi0N+fj6a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\ndiG5oAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x60d1590>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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PHsSlS5ewbNkyveVLS0tN6itv8HJ1aklJSU1bqPVQ4DQA5dJM0/DqS4qGV19S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\n",
"text": [
"<matplotlib.figure.Figure at 0x508bd50>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# doLinePlots(dd, uselog = False, title = \"Dictionary comparison\", legend = True, legendcols = 2)\n",
"# doBarPlots(dd, uselog = False, title = \"Dictionary comparison\", legend = True, legendcols = 2)\n",
"# do3DBarPlot(dd, legend = True, legendcols = 2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pandas and ggplot\n",
"\n",
"The package pandas allows the use of R-like object types such as DataFrames which can be used in conjunction with the plotting library ggplot (a port of R's library ggplot2) which makes producing plots like trellis graphs much easier in comparison to matplotlib's pyplot. The following plots are produced using pandas and ggplot. While the ggplot2 port is much nicer than matplotlib to work with (for me at least), it's still a work in progress and it gets hacky trying to make things like bar charts work."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Produce trellis plots demonstrating change in character frequency over time\"\"\"\n",
"\n",
"df = pd.DataFrame([\n",
"[c1,c2,c3] for c1, d in probsDict.items() for c2, c3 in d.items()\n",
"])\n",
"\n",
"df.columns = ['title', 'character', 'frequency']\n",
"\n",
"# add columns for category, date, location\n",
"category = [title.split('-')[0] for title in df.iloc[:,0].tolist()]\n",
"date = [title.split('-')[1] for title in df.iloc[:,0].tolist()]\n",
"location = [title.split('-')[2] for title in df.iloc[:,0].tolist()]\n",
"df['category'] = pd.Series(category)\n",
"df['year'] = pd.Series([int(x) for x in date])\n",
"df['location'] = pd.Series(location)\n",
"\n",
"a = df.groupby('category')\n",
"\n",
"print df.head(5)\n",
"\n",
"print gg.ggplot(df, gg.aes(x = 'year',y='frequency'))+ \\\n",
" gg.geom_point(colour = 'steelblue', size = 4, alpha = 0.25) + \\\n",
" gg.stat_smooth(colour = 'red', se = False) + \\\n",
" gg.facet_wrap('character', scales = 'fixed') +\\\n",
" gg.ggtitle('Character frequencies over time, fixed scale')\n",
"\n",
"\n",
"print gg.ggplot(df, gg.aes(x = 'year',y='frequency'))+ \\\n",
" gg.geom_point(colour='steelblue', size = 4, alpha = 0.25) + \\\n",
" gg.stat_smooth(colour = 'red', se = False) + \\\n",
" gg.facet_wrap('character') + \\\n",
" gg.ggtitle('Character frequencies over time, free y-scale')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" title character frequency category year location\n",
"0 arc-1895-US-374 a 0.080924 arc 1895 US\n",
"1 arc-1895-US-374 c 0.029702 arc 1895 US\n",
"2 arc-1895-US-374 b 0.017515 arc 1895 US\n",
"3 arc-1895-US-374 e 0.130416 arc 1895 US\n",
"4 arc-1895-US-374 d 0.037082 arc 1895 US\n",
"\n",
"[5 rows x 6 columns]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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NhUSEDj7Wr4Oh7FtO0EVS8+bNg0qlsljm6emJefPmCS40ZswY7NmzB5s3b8aA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\n+l//i97eXp577jnmzZvHDTfcQDAYTHVYKUUK1DTmk08+4c477+Thhx9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mTBmwT3V19YDt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\nS1GBKiIiIiIuRQWqiIiIiLiU/wPioPcc0LzgnwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x51b4950>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<ggplot: (5354637)>\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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5Zr3qjzdUTpw4we1LTEw062GhDdbGJT4+3qJnbfPmzXB1dcW6deswY8YMmvz1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qPCYnHTPznyp9LNViI1zV+zUn0E2onjMRUXc+HCBa9tV9u9LwSTsq6ptGWtRfmFArSv/pXbD+0n\nquASzoEDcWYOxfz4Y1RkZCD8VCSlNZHLpAzs2rAbT0KUmrNXaqixOegeqyU5WoMxUkXnqOZL7V64\ncAG5XE51dTWlpaUUFBT4pWtfCXbSqrHJqeYmEAKaKHpoEUkuiI6JxmaqptjpJMItoHYLuOVSigEE\nAaqra/9CIZPAghw7qcFmqkKrUpGWWKtvc3UlANarJqJlQLwaairLuXp+Wi6XkxTT9gIrAxqsXntB\nJSYmMnHiRJ+Cq+qYNm0aWVlZuFwuMjMz0el07cZvUSqVoFfLGdUnnti4OEpLStrcQK0pDBpFs9Gg\njTFy5Eivz506dWLUqFFtMsii3RWECLFMnRx6x6uRScApUVBZbUKvkuJ0OrDbrLidzb81Xy3z2kC4\nOt0/8cQTQQULtsX0R20BqUSCQibB6QaV3L+ZRLcg4HC6USlkuNxudp8uxelyM6JXDDfE++auczWt\nYfeBpC1rraXgUreC5E5GLv3uj+jnzsBqt3oe5L4+RFszp/PAlBhkcjlKhRzB5aKvH8G01+q6c+fO\njB49mt/85jchfUEIZtIKWj+wUqlQoFXKMVX+d6JIAs1OTAUjs45AJ6daW2ZLEtBg9erowEDp1q2b\nJw1NHe3Fb/FqAqmz3Z65dnYtEIIJtNi8eTM5OTnodDqWLl0adF+ud+qqWUVqFNyYFInbXTsg8MUF\n4FpCofvW4nqZVc/oqfEc54/M04VVVFudJEZp0EvclJtdSKQSTE4pBr1v8q8+z9LSUp/73BBdu3Zl\n//79nnOvrq4mJSXFa5+mZtHi4uI8s2gDBgzg8OHDrZpj1ynXcvTBX5KWEo1ap0Gta/2VBH9f6kZE\nxXCp1ERSjBatyvdBZiie774S6KSVSMcjZD6rIiK+EkygRXp6OpmZmWzatCkcXW/3BDJIbY909Fl1\ni9WK3ebGZIFOUWq6xCixO91EKgWf5Ye6Rrjb7ebs2bNERERw/vx5Bg4c6LU9kLRlrbUUnFdsQik4\nOXWxmM7REYHl2A1DXt9OOhWmqgqajqAIrUxfMRqNAU9aiXQ8AhqsdunSBYlE4rVs09DnixcvBt/D\nILleZljaisxBgwb5pPvGIkdtNhtSqdTzsOnWrRsVFRVe+zQVaJGSkkJ5ebnf5yASPDfddJNPut+/\nf384uidyFT3jI7A5XGiUMqQSCYMa8W30lVDoviXSlrXWUnCkSkKlyUmcoTbzTHtZlg1E5k033YRU\nKkUikeB2uz2V+UQ7FwknAQ1Wn332WV577TXmzZtHeno6Bw8eZMOGDTz22GNe0fltgY4+wxJqmY88\n8ghvvvkmt956K/379+fo0aNs27aNRYsWec2QNiYnLy+PyMhIz3a9Xs+lS5cYPHiwZx9fcuxeS2vW\nh4fA/Mjau8zHH3+clStXMnPmTAYMGMDRo0f5+OOPWbx4sZfPeFNyCgoK+Prrr3G73aSlpTVYKrmp\nijVut5uVK1cSGRnJ3Xff7ff5dBRkUgnaZlKd+cMvfvELn+y+Kdpz2jK9RsGNSYpWDeoKF7/4xS/4\n17/+xd13302PHj344Ycf/Na1iEioCTjP6ptvvumJ/p81axZTp07lV7/6FdnZ2T61EUyZNdFvMXys\nX7+ev/zlLx59TZ8+nXHjxvH888+TlZUVEhmBVM8Id3341iRcMjdu3Fgv68esWbP8svuNGzcyc+ZM\nT8Waq8vsQvMVa7KzszEajQEtp4o0TKXFQbnJQXK0Grms4VWo1rB7kbbB+vXrWb58ObfccgvFxcVM\nmTJF1LVI2AlosFpcXOyVhgQgKiqqXiqSpgimzJrotxg+SktL6y27RUZGUlJS4tPx8fHxfPfdd57P\n5eXl9SrRNBVo0RitmsaEIOvDt1OZly9fxu12e/2mbrebwsJCn/LN2mw2nE4nOp0Oh8NBYmIix48f\nZ8iQIZ59mnIBqaysJCcnhzFjxrB3716/z0WkYfLKLEglkF9h9UosfzXB2r1I+0HUtUhbJKDB6q9+\n9Svuv/9+li5dyqBBgzh06BD/+Mc/ePLJJ306Ptgya6LfYvh46KGHeOKJJ1i4cCGpqakcO3aMd999\nl4ceesin4+uqjRUWFhIREUFeXl69lEhNBVo0RruqD99OZT700EM8+uij9XS/ZMkSn9ouKCjAYDB4\n9jUYDBQUFHjt05QLyI4dO5gyZUq9QbfoAhKcTKNBS3mNnU5RWq/9rpa5dOlSnnzySe6//36PG8Dq\n1atZunRph1ga70g89NBDPPbYYzz66KOkpKTwww8/+HWPDydijEp4ZbYkAfVk8eLFuN1uNmzYwNNP\nP83QoUNZuHAhS5Ys8en4YMusNYb40Gp5mffddx9SqZTt27fz4osvkp6ezp133sm9997rc9ujR49m\n586duFwu0tLS0Ov1PgdabNiwgfPnz2OxWHj11VcZP3486enpfp+TiP/Mnz8fQRDYtm0bL7zwAunp\n6cydO9drmT5YGnMBOXXqFDqdjoSEBM6dO+e1TXQBCY7m8nNHR0fzxBNPoNPp2L59O3/84x8ZOnQo\nixYtYsmSJZ4X0LaCOGAJTuaDDz6IXC5n7dq1fP/99wwePJi7776bhQsXtjldX4sYoxJemb4SyH0s\noMGqQqHgkUceqZdyIpSIfottU2Z0dDTPPPMMzzzzTMDtGI1GBg0aVO+7q2ks0GLOnDkByxUJDoVC\nwf3338/9998f0PHBuIAcPHiQU6dOkZOTg9PpxGaz8dFHHzF79mzRBaSVZM6bN4958+Z57VNZWel3\nu1cTSCWb5hAHLMHLvP/++3n66acpLi72yHS5XEEV/7gWsRiIiD8EPMd75swZNm3axO7du9myZQvf\nf/89VVVVTJgwodljgy2z1hjiQ6t1ZJ49e5bt27ezb98+/v3vf/PDDz9QXV3tc7ndhmiJh5Y4wxJ6\nmWfPniUrK4vvvvuONWvWcOjQIaqrqxkzZkyzbWk0GqRSKWVlZej1egoKCupVy2nMBWTSpElMmjQJ\nqA3O3LNnj2fGXXQBaR2Z586d49NPP2Xfvn28/fbbHD58mOrqakaNGhVw2yJtk7Nnz/LOO+/w1Vdf\n8a9//UvUtUjYCWiw+tlnn3H//fczd+5c/vOf/wC1pdMeffRR9uzZ0+zxwZZZawzxodXyMr/44gue\neOIJbr75Znbv3o3D4UAmk/HSSy+xdevWgNtuCcQZltDKvFr3u3bt8rT3xz/+0WfdZ2Zm8sUXX3hy\nbQJixZp2wM6dOz26r7vHq1QqnnvuOZ8HMMFUrgMxbVlrUafrefPmsXv3bsB/XYuIhJqABqvLly/n\nww8/ZPTo0axevRqAfv36cfLkSZ/bCKbMmui3GD5WrVrF3//+dzIzM9mwYQMAvXr1Ijc3N+i2g0ln\nJtLyhEL3weTarKNbt26e4EyR1iEUug+mch2Iactai1WrVrFy5UpuueUW3nnnHSC4e/y+ffvIzc1F\npVIxYcKEepmEoLb87oYNG6ioqKBHjx7ccsstSKVSiouL2bJlC4WFhUycOJERI0YEc2oi7ZiABqsX\nL170CoaC2htNz549fW4jmDJrot9i+MjPz/dUn6qjsLCwXp3vQAgmnZlIy9OSuhdp2wSre7vdDkBC\nQgJQ6+ZVVFTk1aaYtqxtEEo7LyoqorCwkFmzZpGXl0d2djbTpk2rt9/OnTvp1asXQ4cO5aOPPuLE\niROkpqai1WqZPn26XxNhItcnTTv0NcLixYt56aWXPMuAdrud119/nQULFoS0cyJtj7vvvpvXX3/d\nS/dvvfVW0C8QV6czi4qK8qQzu5prH2Zi3r/WpaV0L9L2CVb3RUVFXjNq0dHR9fJy5+fne62U1GWA\nATxpyyQSidcxVVVVFBQUeP3Z7XYUCkWTf3WZUfz9uzozir/HtheZ9957LytWrPDoWhAE3nnnHebN\nm+d3W6WlpfTq1Qu9Xk+/fv2oqKjwbLtW9xkZGSiVStLS0jz3fp1OR1JSEjKZzKfrTOT6JaCZ1YUL\nF7Jo0SJ69uxJTU0N3bp1Y9SoUTz77LOh7p9IG2Pu3Ln86le/YuTIkZhMJoYNG8aQIUP4xS9+EVS7\nwaYzE9OWtbzMu+++m8cff9xL95mZmTzxxBNtLtemGFwXWpkLFizg0UcfZdSoUZhMJoYPH05mZiZP\nP/20T22rVCpkMpln34YGLGLasrYhc9myZZ7nu8lkYsiQIYwaNYoXXnih2SDna6msrCQtLc3zEqLX\n65HJZF7tOBwOTCaT59owGo0+VcQT7/ltT2ZL4ndv3G43hw4d4oMPPsBut7N7926GDx/eoB9KW0B8\naIVOpkwmIycnh7feegu73U52djZDhw7FYDD43ea1+BL801Q6M/Gh1bIYDAYOHTrERx991C7sXgyu\nC53M6upq9u/fz4oVK3A4HOzbt4+MjAyP3fvStsFgoLS01LNvUVGRmLasDcp0u918++23/P3vf0en\n0/HJJ58wePBgDAYDLpfLp0p1V2O1WqmsrPQc53Q6KSsrw+VyBR1vIN7zrz+ZTeH3YFUqlTJ37lyu\nXLmCXq9n+vTpLdGvkCE+tEIr87777uPw4cPI5XJPZGgocu8Fm85MfGi1rMzKykrmzJnDjz/+iFwu\n56abbsLhcPj98GoIMUiubSOVSnnooYc4fPiwJ0jGX4KpXCemLWtdmYsWLeL48ePo9XrGjx+Pw+Hw\nq40ff/zR42PavXt3iouLPb7KZrMZrVbr1Z5CoUCn02GxWNBoNBQXF5OUlNSsHPGe3/Zk+kog9/yA\n5nknTpzI+vXrueuuuwI5XKQdM2rUKLKysrjttttC2m6w6czEh1bLyxw5ciSbNm0Kue5F2j6hsPvh\nw4d7pa5Sq9Vi2rI2yKhRo9i6dWvA5VVTU1NJTU0FamfQ9+7dS+/evcnLy2t0ti4pKYkDBw4wdOhQ\njhw5woABA7y2N7SqJt7z257MliSgwWpSUhKLFy/mzTffJDMz0+P8LJFIeP7550PaQZG2RefOnXnq\nqad4//33SU9P99L9U089FVTbwaQzE2l5WlL3Im2bUOheTFvWPujcuTNPPPEE69at8xo0BmLn8fHx\ndO7cmU2bNqFSqbyW6NesWcOtt96KXq9nzJgxbNy4kX379tGzZ0/69esH1Ka0WrVqFTabDYlEQnZ2\nNsuWLUOlUoXmZEXaDRLBx7qmb7zxhifV1Pz58z2Ou3XRmYIgIJFIePvtt1uoq22PqqoqDhw4QEZG\nRr03vOtJ5l/+8hcyMjLIyMjg4YcfFnWPqHtR96LuRd1fP7q/9vkOUFpaSlxcHAqFokPq+lquV923\nBZm+4PPM6nPPPcfSpUuRSqVs3bqV6urqluxXu6CmpoadO3fSp0+fVlNqOGS+8MILPPbYY/Tp00fU\n/U+Iuu+4iLrvuFyvur/2+X7q1ClWrlzJkiVLSExMbBGZ7Y3rVfdtQaYv+DxYnTRpEkuWLCEzMxOn\n08lbb73V4H4PPPBAyDon0jYYNWoUH3/8MREREaLuOxii7jsuou47Dtc+3z/88EMOHTrEhx9+6JXx\nQ9S1SLjwebD6yiuv8N5777Fx40YcDgfvvvtug/uJF/P1x3PPPcdvfvMbtm/fLuq+gyHqvuMi6r7j\ncO3zfcOGDRQWFlJcXOzlHyrqWiRc+DxY7datG7/73e8AmDBhglcpPJHrmy5dujBmzBiWLFnCPffc\nI+q+AyHqvuMi6r7jcO3z/b333hPdAETaFLI//OEPf/D3oIULF7ZAV9ofgiCgVCrp1q1bq0Unhlvm\nkiVLWkVmWyfcehB1Hz7CrQdR9+Ej3HpoDZkLFy4My3m2dTqC7sMl0xd8zgYgIiIiIiIiIiIi0tq0\nreKvbYDNmzeTk5ODTqdj6dKlnu8PHTrEt99+i0QioXfv3p5cgNnZ2ezbtw+pVMott9xCSkoKUFuB\naePGjVitVgYMGMDEiRP9kllUVMTnn39OVVUVBoOByZMne6o+hEJmZWUlmzZtwmQyodVqycjIIC0t\nzVPKsLCwkISEBGbPnu15uwqF3LaMqHtR96LuRd3XIepe1L2o+zake0HEi/Pnz6tHH/wAACAASURB\nVAsFBQXC3/72N893ly9fFlatWiWUlJQIgiAINTU1giAIQnV1tbBixQqhvLxcOHfunPB///d/nmPe\nffdd4ejRo4LJZBLefPNNIS8vzy+Z69atE44ePSoIgiAcOXJEWL9+fUhlVlVVCQUFBZ7zWb58uWC1\nWoVdu3YJ27ZtExwOh5CVlSXs3r07pHLbMqLuRd2Luhd1Lwii7kMpty0j6r796F7aOkPi9kNKSoqn\n9GcdZ86cIT09ndjYWAB0Oh0A+fn53HDDDURFRXmqqtTV4i0tLaV///5otVr69etHfn6+XzLVajUW\niwW3243ZbPZsD5VMvV7vqdes0+mIj48nPz+f/Px80tPTkcvlpKene9oIldy2jKh7UfdXI+pe1L2o\ne1H3ou4DlxtKxMGqD+Tm5lJUVMQ///lPtmzZQlFREVCr0LqpeoDY2Fjy8/MpLS31XOAARqORvLw8\nv2ROnjyZ7OxsXn75Zfbt2+dZhmgJmaWlpRQVFZGUlERBQQFxcXEAxMXFeS7EvLy8FjvXtoyoe1H3\nou5F3Yu6F3Uv6j68uhcHqz7gdDqxWCw88MAD9O3bl+3btwO1UXMtxZYtW8jMzOTpp59myJAhbNmy\npUVk2mw2NmzYwLRp01CpVC16Tu0RUfcdF1H3HRdR9x0XUfdtE3Gw6gPJycn0798fhUJBnz59KCkp\nweFwkJycTHFxsWe/kpISkpKSiI2NpaamxvN9cXExycnJfsm8ePEi6enpyGQy0tPTuXDhgqcvoZLp\ncrlYu3YtaWlp9O3bF4CkpCRKSko8bdfl2GvJc23LiLoXdS/qXtS9qHtR96Luw6t7cbDqA8nJyeTk\n5CAIAnl5ecTExKBQKEhKSuLMmTNUVFRw7tw5JBKJJ5IuLi6Oo0ePYjKZOHHiBElJSX7J7N69O6dO\nnQLg1KlT9OzZEyBkMgVBYMuWLcTHxzN8+HDP90lJSRw6dAiHw8GhQ4c8F2JLnmtbRtS9qHtR96Lu\nRd2Luhd1H17dX/d5Vv/nf/6H6upqn/cvKSnBarXicrmQyWRERUURERFBaWkpNpsNhUKBwWBAqVQC\nUF1dTVVVFRKJhNjYWI9CHQ4HJSUluN1udDqdV31lX2SqVCoqKyux2+0olUo++ugjpk6dSkpKSkhk\n2mw2Ll++jEKhQCKRABAVFYVaraa4uNgj12g0eraHQm5j6PV6fv/73/t9XFNcL7o3GAwoFApWrVrF\n6NGj0ev1ou6b4XrTfahkirqvj6h7Ufei7tu+7q/7PKvV1dXcd9994e5G0HzxxRdMmzaNzMzMcHel\nRXjnnXdC3ub1ovs6PvzwQ2bNmnXdXQOi7jsuou47LqLuOy6B6F50AxAREREREREREWmziIPVdsjZ\ns2eZNm0an376abi7IiIi0gJMnTqVtWvXMn/+fMaOHcurr76K2WzmySefZPz48bz88suYTKZwd1Ok\nBZk6dSrr1q1j/vz5TJ48mb///e84HI5wd0uklbhy5QorVqxg6tSp/O53v+PEiRPh7lJYEQer7Yzj\nx4/z85//nGeffZZp06aFuzsiIiItgEQiYePGjTz33HOsXLmS9evXs2jRIm655RbWrVvHkSNH+Oqr\nr8LdTZEWRCKRsG7dOn7zm9/w5ptvsnXrVg4cOBDubom0Er/+9a+pqanhgw8+ICMjgwceeACr1Rru\nboUNcbDajvj+++959NFHeemllxgzZky4uyMiItKCzJgxg759+9KnTx8GDBhA586dGTduHEajkXHj\nxpGdnR3uLoq0MLfccgv9+/cnJSWFESNGsHfv3nB3SaQVKC8v58cff+Txxx8nJiaGWbNm0atXL779\n9ttwdy1siIPVdoIgCKxfv5709HRuuummcHdHRESkhenTp4/n/7GxsfTu3dvzOSYmxlNZR+T6pS4f\nJtRWCxJ13jE4cuQIycnJaLVaz3epqakdemZdHKy2EyQSCf/v//0/CgoK+N///d9wd0dEREREpBW5\nzrNMilxFWloaeXl5mM1mz3fHjh0jIyMjjL0KL+JgtR2h0+n4xz/+wYEDB3jttdfC3R0RERERERGR\nEBMdHU1qaiorVqygtLSUzZs3k5uby8iRI8PdtbAhDlbbGXq9npUrV7J7927+9re/hbs7IiIiYUAi\nkXiSd4t0DESddyz+/Oc/o1arueuuu9i/fz+rVq1CrVaHu1th47ovCnC9cHWaKoPBwIYNG8LYG5Fw\nIKYq6zhcq+s//elPXp9nz57N7NmzW7NLIq3MtdfAww8/HKaeiISDzp078/jjj/P444+HuyttAp9m\nVm+77TY2bdok5ngT4dFHH+XLL78Ur4UOiKj7jouo+46LqHuRtoBPM6tjxozh+eefZ9GiRcybN497\n772XESNGtHTfQoJMJmPlypVN7nPbbbexefNmv9vu0qULTz31FK+88gqXLl3y69j2KtPhcPDCCy9Q\nXV1Nr1696Nu3LwkJCX61J5fL0el0AFitVmw2G7GxsV77fPHFFxw7dgyNRsPs2bMxGo0A2O12tm3b\nxqVLl5BKpcycOZMuXbo0KEfUfWhlhkL3DXGt7kOBqPvQygy33R85coQDBw5gNpsZNmxYk4Emou5D\nK1O0+1rCrYf2INNXAtG9RPAjxPDHH3/k3Xff5YMPPkCpVHLPPfdwzz330LNnT78FtxYFBQXN7qPR\naLBYLH63rVAoMBqNFBcX+/3W2d5lnjp1io0bN7J582YUCgW33347s2fPplu3bs22t3HjRkaMGEFE\nRATbt29n5syZ9OjRw7M9Ly+PHTt2cNddd5Gbm8uRI0eYP38+ADt27EChUDB69GikUikOh6NRPx5R\n9y0jMxjdN0RiYmJAxzWFqPuWkRkOu7daraxatYoHH3wQqVTK6tWrWbBggWj3rSxTtPu2oYe2LNNX\nAtG9XwFWqamp/PnPf+bdd99Fo9Hw/PPPk56ezu23387p06f9Fi7SfunTpw/PPvssK1asQKPRsHz5\ncqZOncrixYvJzc1t9Di73Q5AQkICer2e5OTkerkD8/PzufHGG9FqtQwYMICSkhLPtrNnzzJ69GgU\nCgUymaxDO5yHi0B1L9L+CYfdX7p0iYSEBDQaDSqViu7du7fYjI9I44h2LxJOfA6wOnnyJO+99x7v\nv/8+SqWSe++9l3vvvZe4uDhee+017rzzTg4ePNiSfRVpI5w5c6beG/bq1auJiYlh1apVLF26lB07\ndjR4bFFREVFRUZ7P0dHRXLlyxWuf/Px80tLSPJ+1Wi1lZWXIZDKcTidZWVmUlJTQr18/MjMzUSgU\nVFVVUVNT49WO3W73LDs2hkwmQ6FQ+PsTIJfLvf71h/YsMycnhw0bNrBx40aUSiVz585l7ty5xMTE\nsHLlSpYtW8aXX37ptxyRtk+47D4lJYXt27dTXl6OXC4nJycHmUxGr169RLtvJZmi3YuEG5+u/ptu\nuolz585xxx138P777zNs2DCv7c8++2yzfiLhQqVSIZU2PYEslUrRaDR+ty2RSDCbzSgUCr9vJO1V\n5pQpU7hw4QK33XYbq1atYsiQIV77/vrXv+b9999vVI5KpUImk3m2KxSKejfTxjxTnE4npaWlTJky\nhR49evDxxx/z448/MmjQIA4cOMDOnTu99h87dizjx4/3+3z9ITo6ukXbb0syr74PrF27tt594MUX\nX2TNmjUeP8NwItp9aGWG0+6VSiXTpk1j27Zt2Gw24uPjPb+BaPctL7Ml7f78+fNkZWXhdrvJzMwk\nMzPTa3txcTFbtmyhsLCQiRMntptYGZHQ49Nd79e//jUzZ85EqVQ2us/58+dD1aeQYrPZmt0nGN+O\nqKgoTCZTq/qThFPmz3/+c6ZOneq5FhpqLzs7u1E5BoOB0tJSz/aioqJ6AVLJyckUFxdzww03AGA2\nm4mJiQEgLi7OU4ZywIABHD58mEGDBpGRkeFVnhJqZ1iKi4ubPDeVSuXTNXItcrmc6OhoysvLcTqd\nfh3bXmU+/PDDTJs2zaP7hn7b/fv3N/ubX0tLDG5Fuw+tzHDbfZ8+fTz2vX79es8+ot23vMyWtPtP\nP/2UGTNmEBUVxbvvvkv//v29ZsW1Wi3Tp0/n5MmTfp+DyPWFT4PVyMhIzp0753VTOHXqFBcvXmTy\n5MkBCW7ujQoajgytrKxk06ZNmEwmtFotGRkZXktHIi2LXq/n4sWLnocF1C4PFhQUMGbMmGaPr7vh\nFRYWEhERQV5eXr3I3qSkJHbs2MHAgQM5c+YMcXFxnm0xMTHk5eWRmJjI6dOnPQEakZGRREZGerVT\nUFDQ7INdLpcH5UTudDr9Pr69ytRoNOTm5gase6jV+65du3C73fTv35/+/fvX26exiHAAt9vNypUr\niYyM5O677w74fET8I9x2X1NTQ0REBLm5uRQVFXkCNES7b3mZobD7hrBarQCeAK2ePXuSn59P7969\nPfvodDp0Oh05OTkByxG5PvBpsLps2TK++eYbr+8iIiJYtmxZwIFVzb1R5eXlceHCBZYsWUJubi6f\nffYZ8+fPRyqVMnXqVBISEjCZTKxatYo+ffqgUqkC6oeIf/z2t79l48aNXt/pdDqeffZZdu/e7VMb\nw4cP9xqwqNVqvv/+e6DW5SQ5OZmuXbuycuVKz4CljilTprBp0yacTic9evRocLAj0jKEQvd79uxh\n9OjRnojwqx+A0Ljd15GdnY3RaAxotkgkcMJt9+vWrcNkMqFSqZg5c2boTkykWUKh+4bIz8/3eiEx\nGo3k5eV5DVabQvRXbnsyWxKfelNVVeXlHA8QFRVFWVlZQEJ9eaO6NjL0q6++Amrf8PV6PVBrMPHx\n8RQUFNC9e/eA+iLiH9XV1RgMBq/vDAYDFRUVPreRmJjIHXfc4fXdTTfd5PV58uTJDc7ax8XFsXjx\nYj96LBIqgtX91RHhgCci/Or0RY3ZPUBlZSU5OTmMGTOGvXv3Bns6In4Qbrt/4IEH/OitSCgJhe5b\nAtFf+fqT2RQ+DVZvv/123nvvPZYsWeL57sMPP2TWrFkBCfXljaqxyNA6HyaA0tJSioqKSEpKAsQ3\nrdaQOWPGDDZv3syCBQs827Oysrj55psDarslEYNsQivz1ltvZevWrdx3332e7Rs2bGDGjBk+tV1c\nXExsbKxn3/j4+HovvE3Z/Y4dO5gyZUq9WVXR7ltepmj3tYTbBtuj3TdGTEwMn3/+uefz1f7KviD6\nK7c9mb4SSJyCT1f+gw8+yLhx49i+fTsjR45k9+7dfP311y2aqqK5WgU2m40NGzZ4OX439Ka1YMGC\nZh9aEPiUt9lsRqVSBeSG0B5lLl68mGnTpvH1118zbNgw9u7dy65du9i2bZtPv3NrIgbZhFbmHXfc\nwZw5c9ixYwdDhgxh37597N27l7Vr1/rUts1mw+VyefZ1OBz1zqMxuz916hQ6nY6EhATOnTvntU20\n+5aXKdp9LeG2wfZo941RN3N3/vx5DAYDubm5jB07tsF9G7oviP7KbU9mS+LTHSwjI4OzZ8+yYcMG\nvv76a6ZPn86bb74ZcBRvUlJSs29UTUWGulwu1q5dS1paGn379vXqZ0NvWiaTqcn+dJS3nlDI7N27\nN/v27ePjjz9mz549jB07lldeeYW4uLhmf+c6CgoK+Prrr3G73aSlpZGWloZWq/Xap7Egm+XLl3tm\nTqRSqddsv0jLkpaWxt69e8nKymLv3r2MHz+ev/zlLz6XzouPj+e7777zfC4vL/c5IvzgwYOcOnWK\nnJwcnE4nNpuNjz76iNmzZ4t23woyw233Bw4c4NChQ7hcLrp27cr06dP9Ph+RwAjW7pti2rRpZGVl\n4XK5yMzMRKfTefkxV1dXs2rVKmw2GxKJhOzsbJYtWybGqHRAfH7dNhqNPPzwwzz88MNBC62rOtTU\nG1VjkaGCILBlyxbi4+MZPny41zHim1bryDQYDJ5Su3X40+4333zDqFGjPEE23bt393rxaSrIRiKR\ncN9999V7yIm0DrGxsSxcuJCFCxf6fWwwEeGTJk1i0qRJQO19Y8+ePZ4AHNHuW0dmuOzebDaza9cu\nli5dilwu5/333+fMmTN+LRmLBEcwdt8U3bp145FHHvH67mo/Zr1ezxNPPBFSmSLtE58Gqw6Hg2++\n+YbPP/+c/Px8z/cSiYR///vfAQlu7o2qscjQixcvcuTIETp16sQ//vEPACZOnEivXr0C6oeIfzgc\nDrKzs9m1axeFhYWe7yUSCStWrGj2+GCDbETCR7C6h+AiwkXCRzjtXqFQIAgCVqsVtVqNw+EQyyy3\nIqGwexGRYPFpsPriiy+yevVqpk6dSs+ePT3fSySSgAU390YFDUeGpqSk8Ic//CFguSLBsWLFCtav\nX8/YsWM92RzA92shmLKLdW4gq1evJioqivT0dI8biBhk0/Iy33jjDdatW8e4ceO8BhkSicTntlNS\nUkhJSfH6zteI8Dq6devmde2JtDzhtvsZM2bw2muvIZfLyczMJDk5GRDtvjVkhsLuG3IBuZbGXECO\nHDnCgQMHMJvNDBs2rN5qzNWIwXXhldmS+NSTt956iy+//FKcvRThww8/ZO3atV43rVDTVHDdokWL\n0Ov1FBcX8/7775OUlIRerxfTmLSCzLVr14r3gQ5KOO3eZDKRlZXFsmXL0Gg0rFu3jtOnT9O7d2/R\n7ltBZijsfuPGjcycOdOTV/3akq2NuYBYrVZ27tzJgw8+iFQqZfXq1aSmpjY6sy4G14VXpq8Eci37\nNFhNSkoKe041kbZB586dqaqqCvj4YIJsAE+OXaPRSJ8+fTh9+jQZGRliGpNWkBkfH8+5c+fq5VwO\nlpYotyoSWsJp96dPnyY5OdkT0JOamsqFCxfo3bu3aPetIDNYu7fZbDidTnQ6HQ6Hg8TERI4fP86Q\nIUM8+zTmAnLp0iUSEhI8M4Tdu3fn0qVLYXlhvlxhodhaTpTC3eqyRXwcrC5cuJDFixezYMECBg0a\n5LVtwoQJLdKxUCEuC4RW5vz58/n1r3/NvHnzGDBggNd+vpTe02g0SKVSysrK0Ov1FBQUMHLkSK99\nGguysdvtCIKASqXCZDKRm5vrCbJrrSAbt1vA5nQTqat9s2+PQTZ2h4uLZRYi1XLiDb75/jmdTubM\nmcMvf/lL5syZQ2pqqtf2UaNG+dWflka0+9DKDKfdd+3alU8++QSz2YxSqSQnJ8czMycG1zWNRCrj\n0x/yABjdx4hS3rRNNCQzWLsvKCjAYDB4+m4wGCgoKPDapzEXkJSUFLZv3055eTlyuZycnBxkMll4\nBquVNmJlGootNowRbWd5vKPg0y/+5z//GYlEwuuvv15v27U5D9sa4rJAaGUuX74ciUTCypUr6+2X\nnZ3tU3uZmZl88cUXniAbwKcgm5qaGtauXQvU3syGDx9er7JKS5NbZMJkc5IYK9DWJwTLTHbKauwk\nRau5eqxSVG3H7nJzucrm82AV4G9/+xsSiYS333673jZfdd9aiHYfWpnhtHu1Ws2YMWNYu3YtDoeD\nG264QfRZ9pHTBZWcumJCrZCSarLT2Q97r6M17L4xFxClUsm0adPYtm0bNpuN+Ph4z8taa/srdzJo\nkClkGCN1SAT/Zlfbq79yW8Kn3pw/fz7kgs+fP09WVhZut5vMzEwyMzPr7dOYw/XmzZvJyclBp9Ox\ndOnSkPfteuVCcQ1FlSa6x2rRqQO7EK9eyguUQMsuxsTEhCR1WjDUWJ2cL7MgSKQMan73sHK5wopU\nKuFypY0Yg97zfadIFVaHizidf9dAKHRfWFjolQ2gbtByNQ3ZvcPh4J133sHpdCKXy+nfv3+91HUi\nLUc47R4gPT2d9PT0oPvQ0dCo5CRGqlAoZMTrA8tNGqzug3X96tOnj8fVY/369Z59WtNf+XK5GbPg\nJhKIjws+v6y/dBQf6abw62lVVFTEN998w5w5c6ipqUEikQRcveTTTz9lxowZHofr/v37e7XVVK7N\n9PR0MjMz2bRpU0CyOyrlZgcSoMLiCHiwWkdJSQnZ2dnMmDEDk8mERCLpELlPYzVS1LYypIrI5ncO\nM/GRKspMDuIjvR9SCrmUGzpFBNxuMLrfs2cPo0eP9uTavDZXZmN2r1AoWLhwIUqlEqfTyT//+U96\n9+4dksTk/mCxO8kttuCQaWlbRUZbh45q9+0Rh8uN2eaiX5KeXp30SKX+Ze9xVVV7fQ5U98HkV4ba\nFbWIiAhyc3MpKioiMTERaL1yq5LqavjTKyiGjsYxbnyH8ldud+VWL168yF133cWZM2cwmUzMmTOH\nnTt38u9//9uzLOsPVqsVwLOU07NnT/Lz8+ndu7dnn6ZybaakpFBeXu633I6Kyy3gcLrobtRxpVwg\nPlKFw+VGIfPPfwlq9bJ06VLOnz+P2WxmxowZ7N27lw0bNnjy3rYVWsJvsY+8FN3D9yCx27EMSEM/\nKA3nwIE4BwzA2aMnMrms2TZay2+xi0ZD3fxFKGRevnyZBx98kHPnzmE2m5k7dy67du3iww8/5K23\n3mq2LZvNhlQq9USUd+vWrV7gZlN2X/fQs9vtuN3uVl+msjlcHC+oIUKtoLDCTFd988dcL3R0u68j\n3L7D/sisrrRSWFlNtcXBDYkKNOr6r1cF5RbKTHa6xWmJUCtAEJDv2oXr9b9jy82l7Kud2GvKgrJ7\ngHHjxvHVV1/hdrsZNGgQ0dHRPudXXrduHSaTCZVKxcyZMz3ft5a/cmVxJXKnQK/lf0L/1FLsI0di\nGTuWnAFDKY3pRPc4LXpN06+uHcVHuiXx6cr/61//yowZM3j66ac9bzzjxo1j8eLFAQnNz8/3enMy\nGo3k5eXVG6w2lWuzIVrbh0WQSKmy2NuMP4nd6UYqAfk1g9C9p0ooqrQwICWGPp0iOXi+7Kc37khi\nI5pfGrpa5ltvvcXUqVN55JFH6Nu3LwqFgjFjxvD000+3Ss49ALfbzcqVK4mMjOTuu+9uVE6L+C3G\nxnLyy2+xXLqMIfcMfQpOIdu0Cdkf/gddRQXWjJsQRo3ANmwYjrQ0aOA3CZffYnllNQ6X0OyNtTGZ\nb7zxBhMmTGDp0qUMGDAAi8VCRkYGjz32mE/nk5eXR2RkpGdfvV7PpUuXGDx4sGefpuze7Xbzz3/+\nk6KiIqZNm+bxV24tuz94sYpLZRaUCgczb0hG7rL6fGygMusIt+9aOO2+pKSEDRs2ePYpLy9n/Pjx\n9dIf1SH6K9eilQvYnW60MoHzVyroGV9/NaWgpAqpFPIvVdN356fo3noLXC7O33EvVa/+A7fFwZtB\n2j3UVsCaO3eu57PFYvHZBeSBBx7w+ZxbghKVjoIFSzH+4kkmJBuwbdiA8ssvGfinP2OLM1I+5w4k\n99yJ0MaWza83fLrzrV+/nmPHjiGTec8aBVMUoDmayrXZGK3lw3Imv5yTP56lEBWJsTUM7hFHgrF1\nL9Sr/UkEQeB0QQUnC8qRSiRMSktGo/zvA0RxvgatVkqlTcAhU/NDvgmdSkGaJgKj0fd0JNHR0Wzb\nto1jx44RGRmJRCLBaDSi1WqRyWQ+T+0HmnOvjuzsbIxGY0BLHKGgc6SKXYpI3GMmkaudgFEn51he\nFbKyUlJOHabz0YNEPfMMsosXsQ8ejH3YMOzDh2MfOBBauKa1yy3gcgueqN9Ki4P8Mgvd4iXkXDbh\nFgR6xOsw+DFgdbhqgwmysrL46quvWvQ+0JTdS6VSHn74YcrLy1mzZg1du3YlISGhQbtfsGCBTy5K\n/gz+IirPcvszD2Pr2w/DiSG4U1Nx9+sHTbxAByvzasxmMyqVyqe66G5BwOZwoVEGFyxRJzMrK4v9\n+/d77L7ut5VKpT67gn377bdMmzaNyMhI1q9fz8CBA722N2b3cXFx/PznP689L7ebV199lX79+gV0\nPh0BQRBwCyCTShjaM5bsU1coNzsoqbYRd43fagpmtH//BwlbNuIYPJiq3/8e2+jR6GRyItV6sNW0\nit23ZSwON1KJgEYphfh4bHPnUnPbbZgsdoRd35K8ZQPqESOwTpyIef587MOGQRv5bZwud73Jq/aK\nT3ewn/3sZ/znP//xmoL//PPPmTNnTkBCk5KS+Pzzzz2fr3asrqMph+vGaC0flhPZR5iyYCa43Fg7\nJaBIScaSnERFlJGK+AQiRwxBOeBGaOIBEUp/kgqznVMFVRy5UEF8pIrvT7jpm/jf5ZH0JDUHzlmQ\n4+KNrB+osTpJjNagEazN/jbXyhw/fjybN29m+vTpCIJAcXEx27dv5+abb/aprWBy7gFUVlaSk5PD\nmDFj2Lt3rz8/XchQKWT0StAjUynRq2t10KuTDlOUCs2AblTNvQ0ASXk5yv37Ue3dS+Tvf4/8zBkc\nAwfiHj0a1003YR88GAJYFmyKk4XV2J1uuht1RGkVfHOqhLwyC/vPlWO2uYiNkNMz3neZ54pqcJe7\n0EnsTJgwgT179jB16lTP9m+++Yabb77Zp7aCDbSoIzo6ml69epGXl0dCQkKjdm8ymZrsj7822Ldv\nAtVPPYXs5Emq9h4g8r33UJ48iaDX4+rbF2dqKs4bb8R54424evWCn9wWgpFZR1N+ZMVVNhQyCQq5\nFKvdRUyEkpwrNZisTiK1crRqNUadzO+H1tUyJ0yYwGeffeaxe5PJxPbt2z3+i81hs9lwu90eXSYl\nJXHu3DmvFTZfyiyfPXuW6OjoVs8C0lYprrRSVGMnKUpNlK72ejt1uQarw00Po5ZOMZEkRKmw2t2U\nVFkpqrKRGKUmSgG6d96h0+uvY50xg5KtW3F17+5pVyGTYozSUlxsCtruW5OWcAGJjXQhlyuIj4rw\ncsfQaDQw82fYZv4Me1kZyrVrif7tb8HlwnbPPdjvugvhpwmccLidFJRbKKywERehIsXoe2xRu65g\ntXTpUsaNG8ecOXOw2+08/PDDfPTRR+zYsSMgoXXVJ86fP4/BYCA3N5exY8d67dOUw3VjtJYPS/KN\n3fhg8zdE2qyMjFYg5J2FS5cwnzpP1Hd7iFq9ClXRZZw33oh94EAcAwdi1fIF/AAAIABJREFUHzgQ\nV48e8JMhhdKfRCkV0ErdTP/+U7SWGoTZs3AY/2sYMiApSkWZWUCjkKKUKRnYJRIENw6H7yk4nE4n\n9957L3PnzuWzzz7Dbrfz5JNP8sknn7BmzRqfzieYnHsxMTHs2LGDKVOm1Hvgt7YLSN8kjdfgQaFQ\nEHFtrEF8PO6bb8Zy881YAElVFYp9+1B/9x2Gl19GfuIEzgEDsI8YgWP4cBxDhkAT/W1uKVgQBCTS\n2kFJ3Xm5BXC4Baosbi5XmDlx2UlemZ1F47sTp78mjU1NDfKzZ5GdPo3szBnkOTkYTpxGbjFz/Ktv\nWbRoEbNmzeKWW27B4XDw7LPPsm3bNtauXevTb6hQKJBIJBQXFxMREUF+fn69LCCN2b3JZPLc8M1m\nM7m5uYwYMQJoxVybCjnayWP5sX8GsdHRyJwWjDoZsrw85CdOoDhxAsWnn6J59VXk+fk4u3fHceON\nOH4awDr69cPVtWtI/ciqzA4ulJhxCwLCT7Npdocap8OJ0+XiQrGNhGiorqn1WW8I10+5g7XKhv2t\n24Ld13Hs2DGvPK8dudyqxe7kwMUqqiwOCiptjOwVh0GrxI0Uk91JzhUzGq2WfknRHDhXxo+XzXSP\n1SL57Avil/8Zd0oKlVu34urdGykgbURmsHYPwbl+HThwgEOHDuFyuejatSvTp09vVE5LuIAk6GV0\njlCjVErRarUNu2NoNJjvuw8WLkTx/ffo1qwhcsgQbGPGYJ4/HyZPxhLAS2pjLiAut4BEAtImZnCt\nDhdOh50qsxuLxfcX1XZdwSotLY2cnBw++ugjKisrGTRoEC+99FJQqQ2mTZtGVlYWLpeLzMxMdDqd\nzw7XGzZs4Pz581gsFl599VXGjx/fqmlNOhk0TDFoUCgUGI1Giou74XA4sFscFJkdJEVrUNRUozh6\nFMWRI6g/+wz9K68grajAMWAAjoEDYcgQ7P364erSJaglA0l1NYaNGxm7ejXu6GicvXqhmnMrpr43\n4vj5EuwTJ3Cx3EpZjQ21VMr0aCfueCNKjRqTzYlWKfNrOefGG29k165dfPLJJ1RVVZGamsozzzwT\n0qpGjS0Fnzp1Cp1OR0JCQr38vq21FHw1/izLArUD0Zkzcc2ciQtw1NQgzc5GuXs36uXLkR45Uru0\nPGoUrlGjcA8fDpGRlNfYsLtcdNKpGpRptjkoqrKiksvonRSLruQKEV98guw//+G+k6fJGz0RU0wc\n35YKUHiZASVnif8IDCopkqIiJEVFCJevgN2Gq+cNSPv1RejdG/fttyPc0IvixK70io9GmhzLkSNH\n2Lp1K2azmYyMDF588UW/7gOTJk3is88+w+12k5GRQVxcnM85djdt2oQgCERERDBixAhPNbPWplus\nDplahVYi4HY5cXXpgqtLF2xTpnj2kVgsyE+fRnH8OPITJ1B/8QWKEydAoUDTrx/Ofv1qB7L9+uFs\nZBbWF1QKKVJJ7Ww/goDNKaBRyIiJV2J1uimssHDqcjUywYXLLdA1RkOV1Um0Tonsp+jwM1dqMNvd\ndDYoSYhqePYnnHZfh9Pp5PTp015+jR253GqFyU63RBdHL5TRI8mIVK3DaIxkpCGaz3+4xMXSas4e\nLGBQtzgKi6qIvnCezP/vX8RevoT09ddh+nSac2CJjo5m3LhxnDlzho8++gibzcbw4cNZvny5X79B\noK5fZrOZXbt2sXTpUuRyOe+//z5nzpyptxLb0vj8jJRIcAwZQsWQIUgqK9Fs2kTkH/+I9JlnkN15\nJ+Z583B36hRUX8x2J6cv16CQSembEIGskZnkbnE6VFIX+iAz/7QVJEIgzqHtiGvf3hsiGMf32sFq\nsU9vINKyMhSHD6M4fBj1sWPIDh0Cmw1H3ezroEE40tJwd+7crMyyr79G9dZbaLZuxTZqFKYFC7CP\nHIkAHDtbQuKXn9Jz3bvIrBYuDMok4koBsUcOYpcrUdpt5KcPJW/8VNSzbiG5c9NLav6eZ1PY7XY+\n/vhjbr/9dqDWj61Lly5eN6/vvvsOt9vtyaP517/+lccee4wvvviCI0eOIJVKcTqd2Gw2+vXrx+zZ\nswOeYWlTKUUsFhQHDqDYswfF3r0oDh3CccMNFPTPwNQ1hQS1lBilHEt5OYLZjMRqxWkyU15ajd1q\nI6KsmJj8C0gFAdeY0VhHjcaamEzkV58jr6nBllfARamW8tSBpKZ2QarR4DYacRuN/OhQ4dQb0GsV\npMT99zdrq2lMmqM17f5CiZkqq5NucdqmHwyCgLa8HNfBgyhOnKidjT1+HPmlS7WzsD8NYOsGsm6j\nkWqrE6dbID5K16gNutwClRYHURoFXDPbUlZjI7/SyZYDl5BJJXSL1dI7QUeMTuWZaT19uRqT3UWi\nQU2nq5LGtxW7r+PkyZPs37+fe++91/PddWH3AcqUnTiB4tXXUB47iuB2I9OocffogatnT46V2on+\nPpvo8hIMFcVI3G4q4jpTOmU6FY//kh5JTQ9TQ2n3NpuNzZs3M2/ePKDWhaBr165erl+N6d7hcPDG\nG2+waNEi1Go1a9asYfLkySQnJzcoqy097z0IAhEnTyJ7+200WVnYhg3DPH8+tnHjQNZ09piGZFZa\nHJwtMlFWYydapyQp2ttuy2rsVFud9EyMZtfxAiwON30766i2ukmMVjc7eA2l3TdGXfoxf/BpyP3c\nc881+mbx/PPP+y20o+KOicE2fjy28eNx/WQw0suXURw5gvKHH9CtXo3i8GFQqbCnpXkGsY6BA3HH\nxIDNhmrLFlizBsPZs5jmz6foq6+8BrcSICJSS+HNM9EuvJPiT3aiPXSA4rQM3rzjl+TLdfSUWBl4\n/DvSP15HzIqXsE+aiHXKFGzjxyNENJ1/83//938bvRaeeuqpZn+DYHLuTZo0iUmTJgG1LiR79uzx\nzLxdF2UX5XIcmZmQmQm//CXYbMgPHcL9+TdEnvgRZZwB4mJxAy6DAaFTJ6xyJcVmN0UWN9X6aNT9\ne5OS2gNjpJrvz5VxudJG14cGodeqySmoQKuWo5RJqEqJ8vJh7Gx3Ul7joJNe0WDfnE4nL774YlC6\nv16psjqRSqDK4mj6QSCRICQlYYuJwfbTdQzUvqScOYP8+HEUx4+j/uorFMePI8hkKLv3wpyYjKJT\nDCR0RgMoNBoEnQ5Bp8Ot1ZJnlVCt1mJK7EyXnv99CNRYndRYnVgdTgSXm1Kzi0i1jPJKGSlYkZlL\nkFRXk1pZictsRR4ViTsiAiGy9l+umjkLp93XcfTo0XqlXq8Lu/dDprOqCnVWFto1a5BfvIjpgQeo\nfPQRkMmQWK3Izp5FOHUaRVUF3996N5q+vdAkJ5BjEig2OcjsHo3cLvjcj1DYfbAuIDNmzOC1115D\nLpeTmZnpGai2JxcQhgzBnJaG5Y9/RLV5M5HLlyP9zW+w3nUX1vnzcScl+SwzTqFAJpNz7MAp3CY5\ntsguXueTV1GN0y2gKbdwpdoBCOQWW+kcpaHM7CRG37TvbLuuYHXp0iXPxSoIAocOHeLkyZPceeed\nLdq5UNDmc+517w7du+OcORMnYBEEpBcvIjt0COWhQ2j++U/kP/yAOzoaicWCKzUV27JlWCZNQpDL\nuXYB2mRzEqHT0CdZi0wq4Xj6UM4kpZIco0FXYsZYY6dX92QGzMkkv2IJzuoyDDu/RL9uHVG/+hWm\n9Ayko0fizszEOXgw6HRUW538kHuZzno5RUVFXvKOHDlCTk4Os2fP9vk3DCbnXodCpcI5bBjqn2af\nLAoFEUYj5mveeOOcbsqLTMRSu0hijKx9y662OnG7BU4V1mBz1WB32lHKZPRNiKCw0kaXmP/qS6eU\no4tp+houKCjwug8cO3aM3Nxcbr311pCedihoTbvvlyyn0uIgMUrTbOL1BmVqNDB0KAwdigNwAAgC\nQkEhl7/Zhzw/n2iVG7vdjryyEkVNDZhMSEwmJDU1aKpqoLwCdVkJUrsNd0IC7sRE3EnduTBsAlEx\nUYwruYw+/yI3XDhJt33fIBUEJIZIhMjaP1Sq2vaqqpBUVyOpqgKTCXtyFzh4MOx2b7fbOXv2bNiu\ntUqLg7IaOynGMCXXPXKEiBUrUG3ciH3wYCoeXMLh1EzkaiV9OkdwudKGye6i242p7O5dSrXViV4j\nZ9TAZLJPFVJRWk3vzhEgldDd6F8Rh9aw+8YWeE0mE1lZWSxbtgyNRsO6des4ffo0vXv3bp8uIEZj\n7UTEL38Jhw+jW7UK3cSJMGwYLF4MN9/cYLrD6OhocDrhhx/gm28wfP4F3ffuBbcbaUYGsvvvg1mz\nqJarKbaUgSDBUWrDEBlBjcVOTJSBmBgtPTpFolM1P1B3uYU2V8EqYDeAf/3rXxw8eJC//e1voe5T\nSGmTywL+ynS7MR8/jQMpcempTco8XlBdu+ygltM1Vst3Z8q4Um3D7nTTK8GAVi7Qs1MElysslJoc\ngIQBybUzEydPFxKzbw/xPx4m7tih2gCg3r0pSh2Ec8QYCseMJSW2/oPpgw8+4OjRo7z00kt+/QZX\nE8iyQHNcF7r3UWalxUFRlY3Ohv8u81hsTi6UWiirsXLqigWz3cGQHlFEqpWkxGo8aY0ClQmi7lta\n93W3Z6VS2aRMT4oaiwXZ5cuUnDyP+T+7Sdy/mwjBhV2royimExX9+uOaOB5zUlfSujTt/pNzuYYo\npRKrRKBLdH2/7I6k++P5VUgkoFQoGDGgB2WlJS2je7e79qXBZEKorkG5fx+RH36I4vJlTHfeSc0d\nd+BKTqbip8A6AUhN0vNjfjVyae2LanGNjXNFJjrpldTYJVwsqSJCJSclTsuNSZHoVK1r98G4gJw+\nfZrDhw97crTu37+fiooKJk+efP24gJjNqLZuRfPee0gvXPjvbGtiIqqjR4k8dAjHl18i27cPd0IC\njuHDsWYO4+SATFwKJcZdX9IpazORB7/n3OARHE3qQ1G33tQMGoxKKUeplKNRyhjbx4i6kSDKq7lU\nZsUhUWJQuojVtUydvharYNUQt99+O7/97W/b/GD1esApwGl9AgAKs4Om1KxXyyg3OYj8acCSGK1G\noZAikwgoZFJyi6v4/nwlXWPVaBRyDNr/Xry6OAOXx05COesW0CnBYkF55AiKPfvQ7PoPzhlTG5T5\ns5/9jJdffjmoh5ZIcBg0inq5UzUqOX0T9ThdOmocZUgEJwa1kj4JoZsdEnXfsjQV2GFzuGoDLeRS\nEg1qyi1WkqLU0L07x52R7KMLmml3odOosdudlJttGPVqMrpEkxjZfFBgVIQap0xNlMze4PaOpHut\nSsaBcxUYdEqi8sroXPfO7nYjqaxEetVMt8Rk8vpc93+F1YqyqgrpT/tITCav/0tqapBYrQgaDUJE\nBFaVhuqUHpT/4lHi59+Nubwc108DxyitAme0GqVcikImpVOkCpO9dnARr1fSt1ME50vMlJdYsdoF\njJEy+ibofRqo+oI/ug/GBaRr16588sknmM1mlEolOTk5nkHudeMColDguP12am6/Hfnp02jXrCFq\n6lQkVmttBqGJEzHffTeWV1/FfVV56RTgWH4VX/UZTuSg0QzUuih+Zx1Jx48ydNc29K+XcnDkVC5m\njkU9aiiXK0wkRTeyCuJyIS0uRlZYiOrUBaLcAmU330xkYHGfLYJPV+7Zs/8/e2ceH0V5P/737H3m\nPsgBhHCEG2KUgHIIqKDiyVF7KFbFFqxatWpr68/it/bbqt9arVYRqlJERcIlQcULBUSgQsqlkANC\nyAG5k81u9p7fH0tGllybTTYHzPv1yivZ2Znn80w+88x85nk+x3G/z6dPn+bNN99kypQpXd6hwsJC\nsrOz8Xq9ZGZmNkttA21XN7oQUSgEdBoFLrcXfTtvRv2jDPQ/x3e+f7SB/tG+ZR+XqOLwqWpqrU5i\nzRouTTH7oohbORa9HmdmJurJk4mOjcVbUUF+fr6fvPLyct5//30mTJjQ6fPsanq9C0g3yayyOEiI\nMlBRb2dwv0i0OhV2pwd9AJkgzpVZXFzs992ZM2d45513mDRpUsDnU1JSwueffy4tBY8fP77ZPi2N\n77q6OjZs2IDVasVgMJCRkdFi+puLCbvLi1f0/S6ssqFSCpTW2hkQbSDcoCbKrKbW5gS7mwa7E7fb\nd+1F6FXNksO3RGKkXppdu9jHvcFWwbXvvoJYWUWsvR5zXTVCRQVCVRXo9YhmM6LJ9MOP0ejz/z/n\nbzE2FoxGPE3bz/3u7N8YjaBQ4PZ4OVBUi9cLI/uHYXM6m437/uf0P7WFcxms1qJQNWDSqYgwaDAZ\nDegDNFa7etwH6wKi0+mYOnUqa9asweVyMWTIEKlM+4WIe9gwap76I7sW/grsDkaOHEByYj+cFRV4\nWzB2DRolOrUSvUaJITGamEd+yd7jNRwEhIITJGevY8aK5wl/oQbXddeijgjzuflYLCgsFhS1tShO\nn0ZZUYE3MhJPQgKmhES8aSMwRhnweEITVBsMAV2556eJSExMZObMmSEJrvr444+ZM2eOlOJi9OjR\nftP67VU3uhBRCALDz86GqVXBV6Mw6VQMijEQbdKQMTDCz1ANlCuuuMLvc3x8PJMnT+5QgE1ZWRk7\nduzA6/UyevRoRo8e3WyflgwWl8vFW2+9hdvtRqVSMXr0aGnZqCXksos+FF43GoWC5HA1Slx8d6oO\ni81NtEktvcgEIvP82ZAm3T/88MMBn8+2bduYPHkyJpOJDz/8kIEDB/r5RrU2vhUKBbNmzSIhIQGr\n1cry5ctJS0sLPG3YBUi4QU1ChG92zepwU2tzEWnwza6PTDATY9Ji1AhsPlCBCi9x4VqiTDp0fWzc\ng295d8uWLZw6dQqFQsFNN93UrKBEE6EY9+FGFWK/OKpSh+FKGYg5KRp1v1g8UVF41T+kAeu0zHP6\nPjhGi8frRaug9fyebSAAo5IjSInS4BVB8LpobAzs+K4e950pt5qent6tqSl7GlEUcSnViEa1VD2w\nNVJjjaTEGKQMIDq1ktlj4jlda+eoZhhbtYt4//q7uEKoYWbhfiLUAt7+/RHDwihHTa3GRFTaQIwD\nkqT0eU0uILaKCjyekJ9uwARkrHq9gSeO7wx2u6/WdtOb0+DBgykpKWHYsGHSPoFUOZFpGYVC4LLU\njpWGPJ/z37KDYdeuXUyZMkUyWM5/GWrNYFGr1SxcuBCNRoPb7WbZsmUMGzaM6HOWRmSao9OoGJ9i\nlh4sZ0MlOpzet7O6dzp9y8kJCT6XluTkZMrLy0lNTZX2aW18m81mKa+q0WgkLi6O0tJSBp1Tdedi\nJO7scn6EQe23xKdQCPQL9303pn8EdVYbKdEGYsKCM1Z7ctyD7yUnPDycOXPmoFAoQpZSpzW00ZHw\n2K+pK7P6siUITqIMSvLPNGB12EmJ1ksVpLpMpur8VP3BoRAEArClW6UrdN9dXCiraeMGafF6RcIM\nmqBk1pfb8YoCZqMWpcqNPW4w4s1T8Rh+uEZPF9UiAFaDmpjwHyYE+3QFq+6ipKTEL11JbGwsxcXF\nzYzV1lJc9KVUFheLzPNxOBwIgsCAAQMAn09SVVVVQAYL/OD/5HQ68Xq9vWow9RVSYgw4XF506u6t\nGV1eXu6XRD4yMpIzZ8747RNIFaOqqirKy8tJOpvuRR73bTMuJZrKOh2xYdo2K950pczz6ey4P378\nOPfcc4/0vzu/Tn13kdbPhMEchsdu8eV6dnkR8FULkmmdYGfVKysrycrKkvZpKvl9flGBJi6U1bSm\n1xSXSwhKpsfjwu31MiTWgFGrRK1UUlxZT4xZKwXgJpqVVNtcROsFv7716QpW/fv3RxAEv/QSLX0u\nKirqcAc6SlvJC/pkKos+JrOz10JBQQH9+vWTlvcGDBiAxWLx26ctg8Xr9bJs2TLKy8uZPXu2VCNc\nNlg6JlMb4CTQuTLHjx8fkO5zcnJabUuhUPgZHOcbHe0lJ3E4HGRlZTF79mzpxUUe9+3TLzb4FZWe\nHvdKpRK32012djaVlZWMGDGCzMxM1Gp1t497vUpFZJieGo8dQRAYmRyB1eGrCNae/3dvGPfByOzs\nuAf45ptvmD59OiaTic2bNzN8+HC/71ubVY+JieGXv/wl4Fvh/dvf/saIESM6fD4XG2n9zESavShF\nNwJwpt6OzemhsNImZf8x6lQY+1B1q4B6+sQTT/D3v/+dH/3oR6Snp7N//36ysrJ48MEHm110nSEp\nKYlPP/1U+lxRUdFsqSg5Odlvu81mk2ZdMjIySEtL89vf6XRSUVHRptxel8qiF8u8//77ef3117np\nppsYM2YMhw4dYvPmzSxatIihQ4dKx7T2P6+rq8PhcEjfNzQ0YLPZ/PZpy2BRKBQsXryYmpoaVq9e\nzYABA0hISJANlm6Q+Yc//CGg+0BrAY9ms5m9e/dK3zc2NvqtmkDb49vj8bBmzRrGjh3rJ08e96GX\n2ZPj3u12U1VVxTXXXENqaiqbN2/myJEjjB8/Xh733SCzs+PebrejUqkYN24cAMOGDZNc/poIxL3v\n+PHjREZGShMUMq2jVAikxpmkGVOdRklhpU3KEtQXCajnb731FitWrJCi/2+55RZmzZrFb37zG3bv\n3t1lndHpfMnMCwsLCQ8Pp6CggGnTpvnt01aVkwsmlUUvlvnee+/x3HPPSVkarrnmGqZMmcLTTz9N\ndna2tG9rcqKioqiurpa+r6ysbBYo0ZbB0kRkZCRDhw6luLiYhIQE2WDpBpkrVqzg+eefl5bgJk+e\nzIQJE1i6dCkfffSRdExb/3O3282BAwcwmUzk5uY2Ww5sbXyLosimTZuIi4trFlQnj/vQy+zpcR8T\nEyON7zFjxnDgwAHGjx8vj/tukNnZcX/q1CnMZrP0vV6vlxL7NxGI+8/hw4f9KpjJq2mBy4xSq9ut\nXNUVMkNJQL2pqKjw8zUDiIiIaOZv1hXMnj2b7OxsPB4PmZmZGI1GubpRL6KqqqqZYRAWFkZlZWVA\nx3cm557VapUc1W02GwUFBVx++eVSH2SDJbQyKysrMRgMfu0YjUbKy8sDbnvixIls27ZN8l1TqVQB\nje+ioiIOHjxIfHw8r732GgAzZ870m9WTCR09Oe7BZ+wWFxeTmJhIbm6u5Osqj/vQy+zsuHe73Xi9\nXmlfj8eD57ww8/bcf9xuN7m5uX7ZAuRZ9QtPZlsEZKz+5je/4ec//zlLlixh/Pjx5OTk8Nprr/HI\nI490eYdSUlL41a9+5bct0BQXMqHnF7/4BQ8//DALFy5k1KhRHD58mFWrVvGLX/wi4DYmTZrk52yv\n0+kCMlgaGhrYsGEDoihiMpm4/PLLpQhxmdDTFbpPTExkwYIFftsCGd8DBw7kj3/8Y9B9l+kcPTnu\nwTeTu2HDBtxuN6mpqS0G6MiEhs7qPi4ujj179kifa2pqOryalp+fT0JCgt+sqTyr3vtkBkrIKlgt\nWrQIr9dLVlYWjz32GBMmTGDhwoXce++9HRYo07f56U9/iiiKbNmyhT/96U+kp6czf/78DuW6DdZg\niY+Pl5ztZbqfrtC9TN+kJ8c9+NwAFi1aFFznZTpFZ3Xf2Vl1gEOHDvm5AIA8q94bZYYSQWxv/r2P\nU1VVFVDetWByyQqCgEajwel0truMcbHKDJRQLDlcKPXh+6rMQAlFfXh53PeszECRx/2FJ7MlSktL\n2blzp1/qqiY9Nb2wfPrppxw5cqTFghAvvPACv/71r9stAiLrvmdlBkow9/yAPWjz8/PZsGEDO3fu\nZNOmTXz77bfU19czY8aMDgvtTi6UvGu9SeaJEyf4+OOP2bt3L2+++SYHDhzAYrEwefLkDrfdRCge\nWhdKgujeJPP48eNkZ2ezZ88eVq9eTU5ODhaLhalTp3a47VAij/uulymP+94xBntCZmfH/eDBgxk8\neLDftkBn1TUaDY8//niHz0HmwiKgK/+TTz7h5z//OfPnz2fbtm2AL3L/gQceYNeuXSHtoEzv4quv\nvuLhhx/m+uuvl3Sv1Wp58sknO/XQCgWywdK1Mj/77DNJ9zt27JDa+5//+R8++OCDDrfdRG9z5Jdp\njjzuffT0GJTHfdvILyo9KzOUBNSTF154gffee48pU6awcuVKAEaMGMHRo0eDEtpa/edzsVgsZGVl\nUVtbS2pqKjfccAMKhYKKigo2bdpEWVkZM2fOlKLBZbqH5cuX889//pPMzEypssjQoUMpKCgIuI1g\nq5nU1dWxYcMGrFYrBoOBjIwMv3QnMqGlJ3UPsHHjRvLy8jAajSxZsqRrTkomIHpa9y+88IJkiCgU\nCjleohvpad07nU62bNnCqVOnUCgU3HTTTc0CtJqQX1R6VmaghKyCVVFRUbPk3cXFxc2m9QOhrfrP\n5/LVV18xdOhQJkyYwPr16/n+++8ZNWoUBoOBa6+9NmhDWaZzlJSU+JVIBN+NaODAgQG3EWyNcIVC\nwaxZs0hISMBqtbJ8+XLS0tLa9WOS6Rp6UvcA6enpZGZmsmHDhs6fjEyH6GndC4LAnXfeicFg6PzJ\nyHSIntb9tm3bCA8PZ86cOSgUil4V9CPTfQRUHHzRokX8+c9/lqx0p9PJP/7xD+64444OCzy/UkVr\nefpKSkrIyMhAo9EwduxYSkpKAF9+t6SkpB6rDX2x85Of/IR//OMfftfCG2+8wbx58wI63ul0ApCQ\nkIDZbCY5OZny8nK/fVq7RsxmMwkJCYDvOoiLiwvIoV6ma+hJ3YMvfVVT4RCZ7qWndS/Tc/S07o8f\nP86UKVNQq9UolUr5HnCREtDM6sKFC7n77rsZPHgwDQ0NpKSkMHnyZJ544okOCwykUoXL5cJqtUr+\nFrGxsQFVypIrWoRe5k9+8hN+/etfc8UVV2C1Wpk4cSKZmZk8/PDDAbV9+vRpoqKipH1jYmKaPZQC\nuUaqqqooLy8nKSkJkHXfHTJ7i+7PR9Z96GX2Bt2vXLmSiIgI0tPTpTKfsu5DL7Mnda9UKnG73WRn\nZ1NZWcmIESPIzMxErVbLuu+FMkNJu73xer3k5OTw7rvv4nQ62bm9/q+8AAAgAElEQVRzJ5MmTWpW\n0SpQQpkpS65oEVrCw8PJyclh/fr1QV8L9fX1aLVayR/JZDI1uybau0YcDgdZWVnMnj1byuEn6z60\n9Bbdt4Ss+9DSG3R/9913SyU733nnHZKSkjCbzbLuQ0xP697tdlNVVcU111xDamoqmzdv5siRI4wf\nP17W/QUosy3aNVYVCgXz58/nzJkzmM1mrr322g4L2bt3L/v37wd8gVnt1X1Xq9UYjUYaGxvR6/VU\nVFRIM2htIVe0CK3Muro65s2bx5EjR1CpVFx66aW4XK52/7/n9+H06dPSMUVFRQwYMMBvn7aqmXg8\nHtasWcPYsWOl2RWQdR9qmb1B960h6z60MnuD7psq1cXGxpKWlkZubi4ZGRmy7kMsszfoPiYmRtLx\nmDFjOHDgAOPHj5d13wtlBkrIKljNnDmTtWvX8uMf/7jDAgAmTJjAhAkTAJ8jdVuVKppISkpi3759\nTJgwgYMHDzarXtHSm5hc0SL0Mq+44go2bNjAzTffHFQbCoUCURQpKirCZDJRVFREenq63z6tVTMR\nRZFNmzYRFxfHpEmT/I6RdR96mT2p+7aQdR96mT2p+6aiBFqtFqvVSkFBgTT+Zd2HXmZPj/uoqCiK\ni4tJTEwkNzdXCvaSdd/7ZIaSgCpYPfTQQyxfvpzMzEwyMzOl4CZBEHj66ac7LLS1ShWrV6/mxhtv\nxGw2U19fz7p166ipqWHw4MFS6iqLxcLy5ctxOBxShZX77ruvRyLC6+vr2bdvHxkZGc0GzYUq86mn\nnur0tVBYWEh2djYej4fMzEwmTpzoVyMcWr5GTp48yZtvvkl8fDyCIAC+F6mhQ4eG4Kzbpqf1cLHp\nHiArK4vCwkIaGxsxGo1Mnz692UOvO+hpPVxsuq+urmbNmjUAUgDOJZdcEoIzbp+e1sPFpnuAyspK\nNmzYgNvtJjU1lenTp0vuX91JT+vhQpYZCK0aqy+//DK/+tWvAF9t4CZH3SYjQRRFBEHgzTff7Kau\n9j5KS0t5/fXXuffee0NSMrK3yHzmmWdwuVzce++9PProo/K1gKx7Wfey7mXdy7qXdS/L7C5adQN4\n8sknWbJkCQqFgg8++ACLxdKd/ZLpRTz33HM8+OCDAPK1cJEh6/7iRdb9xYuse5neRqvG6lVXXcW9\n995LZmYmbrebN954o8X97rrrrpB1TqZ3MHnyZDZv3ozJZJKvhYsMWfcXL7LuL15k3cv0Nlo1Vp97\n7jnefvtt1q1bh8vlYtWqVS3uJ1+sFz5PPvkkv/vd7/jwww/la+EiQ9b9xYus+4sXWfcyvY2AAqxm\nzJjBF1980R396VNcLM7P58q8+eab5WuBnteDrPueo6f1IOu+5+hpPci67zl6Wg8XssxACMhYlZGR\nkZGRkZGRkekJelc9rV7Axo0bycvLw2g0smTJEml7Tk4OX3/9NYIgMGzYMK6++moAdu/ezd69e1Eo\nFNxwww0MHDgQgIqKCtatW4fdbmfMmDHMnDmzQzLLy8v59NNPqa+vJzw8nKuvvlpK5dEVMuvq6tiw\nYQNWqxWDwUBGRgZjx47F4XCwfv16ysrKSEhI4NZbb5XSgnWF3N6MrHtZ97LuZd03Iete1r2s+16k\ne1HGj8LCQrG0tFR85ZVXpG2nT58Wly9fLlZWVoqiKIoNDQ2iKIqixWIRX3rpJbGmpkY8ceKE+Oqr\nr0rHrFq1Sjx06JBotVrFFStWiMXFxR2S+f7774uHDh0SRVEUDx48KK5du7ZLZdbX14ulpaXS+bzw\nwgui3W4Xd+zYIW7ZskV0uVxidna2uHPnzi6V25uRdS/rXta9rHtRlHXflXJ7M7Lu+47uFd1jEvcd\nBg4ciE6n89uWn59Peno60dHRABiNRgBKSkoYMmQIERERpKSkAEjlzaqqqhg9ejQGg4ERI0ZQUlLS\nIZk6nY7Gxka8Xi82m036vqtkms1mEhISpPOJi4ujpKSEkpIS0tPTUalUpKenS210ldzejKx7Wffn\nIute1r2se1n3su6Dl9uVyMZqABQUFFBeXs6yZcvYtGkT5eXlgE+h59a4jY6OpqSkhKqqKukCB18d\n3OLi4g7JvPrqq9m9ezd//etf2bt3r7QMEQqZVVVVlJeXk5SURGlpqVTqLiYmRroQi4uLQ3auvRlZ\n97LuZd3Lupd1L+te1n3P6l42VgPA7XbT2NjIXXfdxfDhw/nwww8BXxWPULFp0yYyMzN57LHHuOyy\ny9i0aVNIZDocDrKyspg9ezZarTak59QXkXV/8SLr/uJF1v3Fi6z73olsrAZAcnIyo0ePRq1Wk5aW\nRmVlJS6Xi+TkZCoqKqT9KisrSUpKIjo6moaGBml7RUUFycnJHZJZVFREeno6SqWS9PR0Tp48KfWl\nq2R6PB7WrFnD2LFjGT58OABJSUlUVlZKbTeVWwvlufZmZN3Lupd1L+te1r2se1n3Pat72VgNgOTk\nZPLy8hBFkeLiYqKiolCr1SQlJZGfn09tbS0nTpxAEAQpki4mJoZDhw5htVr5/vvvSUpK6pDMQYMG\ncezYMQCOHTvG4MGDAbpMpiiKbNq0ibi4OCZNmiRtT0pKIicnB5fLRU5OjnQhhvJcezOy7mXdy7qX\ndS/rXta9rPue1b2cZ/U8srKyKCwspLGxEaPRyPTp0xk3bhxbtmzh5MmTxMTEMGXKFElBu3fvZs+e\nPSiVSr/0DuXl5axfvx673c7o0aO56qqr2pVps9kwmUxMnz6dpKQktm/fTkVFBXFxcUydOtUvlUVn\nZZ48eZI333yT+Ph4BEEAYObMmQwYMKDNVBadldubkXUv617Wvax7Wfey7mXd9z7dX/DG6tKlS7FY\nLD3dDZl2MJvNPPXUU13apqz7voGs+4sXWfcXL7LuL16C0f0FXxTAYrFw55139nQ3ZNrhrbfe6vI2\nZd33DWTdX7zIur94kXXfPrNmzeLpp58mMzOzp7vSpQSje9lnVUZGRqaP4Ha7e7oLMjIy3YQgCH0q\nYj+UXPAzqxcKs2bNYuHChWzevJna2lp+/vOfc8stt6BWq3u6azLdQGlpKa+//jpffvklV155JXa7\nnaSkJO6///6e7ppMiJk1axY///nPyc7OJjc3l927d6NQyPMMFwPr169n3bp1HD9+nJiYGP7whz9c\ncLNsMi3zu9/9jrKyMu6//34UCgWLFy++oGaNO4psrPYh3n33XZYuXYrRaOTJJ58EYMGCBT3cK5nu\n4KGHHuKyyy5jw4YNbNq0iZdeeom77rqrp7sl0w0IgsB7773Hk08+yZgxY2RD9SKhpqaGf/7zn/zr\nX/9i4MCBlJWVyTPrFxH/+7//S05ODkuXLpVfUJDdAPoMgiAwa9YsLrnkEtLS0pg/fz7bt2/v6W7J\ndAOVlZUUFBRw//33ExkZyZ133imVApS5OJg9ezYZGRloNJqe7opMNyEIAg6Hg5MnT+JyuUhISKB/\n//493S0ZmR5BNlb7EE2JfAFGjBjBgQMHerA3Mt3F4cOH6d+/v5RSBHz6l7l4GDt2bE93QaabiYiI\n4M9//jP//ve/mTFjBn/961+prq7u6W7JyPQIsrHah/j++++lv7/77jvGjx/fg72R6S5Gjx7NqVOn\nsNvt0rZzrwWZCx+lUtnTXZDpAaZMmcKKFSvYuHEjxcXFvPnmmz3dJZluRKFQyAFWZ5GN1T6CKIp8\n9tln5OTkcOzYMbKyspg6dWpPd0umG4iJiWHw4MH885//pLq6mpUrV1JVVdXT3ZKRkQkhhYWF7Nmz\nB6fTiVqtRqPRYDAYerpbMt3IyJEj+e6773q6G70C2VjtIwiCwG233cZzzz3Hgw8+yK233srNN9/c\n092S6Sb+7//+j7q6Om655RZOnDjBrFmz5DduGZkLGKfTyYsvvsjUqVO57bbbMJvN3HHHHT3dLZlu\nZMGCBXz11VdcccUVrFy5sqe706PI2QD6EKmpqbzzzjs93Q2ZHiA5OZmlS5dKn//whz/0YG9kupOP\nP/64p7sg0wMMGzZMvt9f5EyYMIEJEyb0dDd6BUHNrN58881s2LABl8vV1f2R6WU88MADfP7557Ku\nexk9MavaFddCRUUFW7du5eOPPyY/P7/FfT777DP+/ve/s2zZMioqKqTt+/btY8WKFSxbtoyPPvoo\n6D7IdBz5PnDxIutepjcQ1Mzq1KlTefrpp7n77rv50Y9+xO23387ll1/e1X3rEpRKJa+//nqb+9x8\n881s3Lixw23379+fRx99lOeee45Tp0516NiOyrRYLGRnZ1NdXd1tMgFcLhfPPvssS5cuJS0tjeTk\nZBISEjrURiCEIhXThaL7lmTm5eVRVlbW7vl1pUyXy8Wf/vQnLBYLQ4cOZfjw4R2+FsLCwrDZbHi9\nXurq6ti2bRtRUVHS98XFxZw8eZJ7772XgoICPvnkE376059is9nYsWMHS5YsQaVS8c4775Cfn8+Q\nIUNalHMh674nZHaF7ltCHve9X6asex89rYe+IDNQgtG9IHZiiubIkSOsWrWKd999F41Gw89+9jN+\n9rOfMXjw4GCb7HJKS0vb3Uev19PY2NjhttVqNbGxsVRUVHT4rbOvySwvL2fZsmWsX78etVrN3Llz\nufXWW0lJSelwey2RmJjYJe2ci6z70Mg8duwY69atY+PGjR26FpxOJ5s3b2bu3LkAfP311/Tv35+J\nEydK++zZswev18ukSZMAePHFF3nwwQdxuVy8/PLL3H333eh0OlavXs3VV19NcnJyi7Jk3YdGZrC6\nbw153PcdmbLue4ceerPMQAlG98o//vGPfwxWYFxcHFdddRUZGRl8+umnZGVl8cYbb/Dtt9+Snp7e\nKxKXWyyWdvdRq9VBVQZRKpUYjUZppqgj9DWZKSkpXHrppYwaNYodO3aQnZ3NmjVrOHjwIKNGjfKb\nHQsGs9ncqeNbQtZ9aGTGxMQwZcoUxowZ06FroaysDKvVSmpqKgANDQ3U1NT45Q/eu3cvqampUhuH\nDh1i0KBBmEwmYmJiWLFiBbt372bUqFFS6rb6+nqqqqqwWCzSj9PpRKfToVQqW/1Rq9WIotjmPi39\nqFQq9Ho9TqcTQRA6dGxflxkXF8eVV17JuHHj+OqrryTdHzp0iHHjxhETE9MhGUajscPXZHvI4z40\nMoMd960h3/MvPJmBEozugw6wOnr0KG+//TbvvPMOGo2G22+/ndtvv52YmBj+/ve/c9ttt7F///5g\nm5fpReTl5fHiiy+yatUq6Y165cqVREVFsXz5cpYsWcLWrVt7upsy3UB+fn6z2ZWuvBZaW+ixWq1k\nZ2dz3333odfref/998nNzWXYsGHs27ePr776ym//adOmMX369KD7EQiRkZEhbb+3yWzvnr9kyRL5\nnn+BEupxLyPTHkEZq5deeiknTpxgwYIFvPPOO37LeABPPPFEwL50oUar1bZbS1uhUKDX6zvctiAI\n2Gw21Go1KlXH/pV9ReaMGTM4efIkc+fOZcWKFVx66aV+3z/++OO88847QZ2LTN/i2muvpaioiBtu\nuIGXX36ZjIwMv+8feOABVq9e3erxcXFx7NmzR/pcU1PTrHxkcnIyFRUVki+qzWYjKiqK3NxckpOT\npdWaUaNGcfLkSYYNG0ZGRgZpaWl+7TidTr/grJbQarU4HI72T/w8VCoVkZGR1NTUdHjmoq/KnD59\nOkVFRdx444288sorfvcBq9XKokWLeO2119r9n59PbGxsh/sl0710dtyDb1Vlx44deL1eRo8ezejR\no/2+r6ioYNOmTZSVlTFz5ky/GJiNGzeSl5eH0WhkyZIlXXdiMn2KoIzVxx9/nJtuuqnNOtWFhYXB\n9qlLCeQm3RnfjoiICKxWa7f6k3SnzF/+8pfMmTOHpKQkKioqWjx+9+7dQZ3LufTEjJFMx1iyZAmz\nZs1qc9yfa4yeT9NxZWVlmEwmiouLmz34kpKS2Lp1K+PGjSM/P5+YmBgABgwYwEcffYTNZkOj0ZCX\nlye9JIeFhREWFubXTmlpabvjQ6VSdcony+12d/j4vipz8eLFfrpvqb3du3e3Kac9gwV8mSAOHz6M\nXq/n1ltvlYzZffv2kZOTg8fjYcCAAVx77bVBn49Mx+jsuAfYtWsXU6ZMwWQy8eGHHzYLjDQYDFx7\n7bUcPXq02bHp6elkZmayYcOG4E5A5oIgKGM1LCyMEydO+M1mHDt2jKKiIq6++uqA2igsLCQ7Oxuv\n10tmZiaZmZnN9mnpxuVyuXjrrbdwu92oVCpGjx4tBWPIdD1ms5mioiKSkpKkbfn5+ZSWlsoVtC4y\nmq6Fcx80Hb0WJk2a5Gew6HQ6vv32W8C3YpOcnMyAAQN4/fXXpXEPoNPpmDp1KmvWrMHlcjFkyJAu\nC+6TaZ+u0H17BktXZYKQ6Vo6q3un0wkgZRBITk6mvLxc8l0HMBqNGI1G8vLymh0/cOBAampqOnsa\nMn2coIzV++67j+3bt/ttM5lM3HfffeTm5gbUxscff8ycOXOIiIhg1apVjB492s/ZvrUbl1qtZuHC\nhWg0GtxuN8uWLWPYsGG9IpjrQuT3v/89H3zwgd82o9HIE088wc6dO3uoVzI9we9//3vWrVvnt62j\n10JiYiILFizw23a+a8nVV1/d4ktveno66enpHey1TFfQWd0HYrCUlJQwcuRIDAYDY8aM4YsvvgCQ\nAsTsdjs6nQ6Xy4VOp+uqU5Nph87qvry8nIiICOlzZGQkZ86c6XS/6uvraWho8NvmdDrbDdprCjrs\nKE1udx11v7uYZIaSoHpTX1/vd/EBREREUF1dHdDxdrsdQJoZGTx4MCUlJQwbNkzap7UbF/ywnOh0\nOvF6vb3un3ohYbFYmi2xhoeHU1tbG3Sbhw8fZvfu3YDvrbklw2TFihWUlpaiUChYsGCBdG289NJL\nVFdXo1QqefLJJ4Pug0zHsVgshIeH+23r7LUg0zforO4DMVhKSkoYO3as9NlgMFBdXU1UVBRz5szh\n73//OyqViszMTCllmWywhF5mQ0MD0dHRfu1ER0dTV1cXUNsqlQqFQiHt25QJorPIgZUXnsy2CMrK\nmzt3Lm+//Tb33nuvtO29997jlltuCej4kpISyRcNfE72xcXFzYzV1m5cXq+XZcuWUV5ezuzZs6Wb\nqHzj6nqZc+bMYf369Tz00EOSzOzsbK6//vqg+g8+/6YrrriCmJgYNm3a1Exn+/fvp7y8nPvuu489\ne/awceNGHnvsMQAyMzMxGAxBJTuW6RzXXXcd69at42c/+5m0bdOmTb3Sf1AOrOxamTfeeCMffPAB\nd955p/R9VlYWc+bMCahtrVaLUqmU9lWr1c3uH3ImiN4pc968eWzdutXvef/BBx/4+RS3hdlsZu/e\nvdK+jY2Nfs/6YJEDK3ufzEAJJrAyKGP1nnvu4corr+TDDz/kiiuuYOfOnXz55Zd8/vnnwTTXIm3V\nKlAoFCxevJiamhpWr17NgAEDSEhIaPHGdccddwSUyy/Y2VmbzYZWq0Wr1Xb42L4gc9GiRcyePZsv\nvviCiRMn8s0337Bjxw62bNkSVI7E+vp6BEGQ/Iyjo6MpLCz0u3nl5eUxaNAgoqOjue666ySfRvAZ\nqydPnuywXJnO85Of/IR58+bxxRdfcNlll7F3716++eYb1qxZ09Nda4YcWNm1MhcsWCAZLefrPpC2\nw8PDqaqqkvYtLy+XM0H0EZlz586VSqxPmDCBvXv38vXXX7Nu3bqAsz+43W4OHDiAyWQiNze3xeA6\n6FgZaTmwsvfJDCVBWUsZGRkcP36crKwsvvzyS6699lpWrFgRsLWclJTEp59+Kn0+9wbVRGs3rnOJ\njIxk6NChFBcXk5CQ0OqNy2q1ttmfvnoT6Q6Zw4YNY//+/Xz22Wd88sknTJs2jeeee46YmJh2/68t\nceDAAXQ6nXRsREQEJ06c8NunrKyMSy65RPqsVCo5fvy4n3/b+ZSWlja7cRoMBr8Z/JYIdna4aRlL\nq9V2+AWgr8rMzMwkJyeHDz74gJ07dzJr1ixefvnldv/H51JSUsLnn3+O1+tl/PjxUmL/c2kpsLKy\nspKsrCxpn6Z0SuenzZMJDWPHjuWbb74hOzubb775hunTp/P8888HHCsgZ4LouzJHjhzpp/tp06bx\n7LPPEh0dHXDbEydOZNu2bVJgpUql8gustFgsLF++HIfDgSAI7N69m/vuuw+tVktWVhaFhYU0Njby\nt7/9jenTp8u+6xchQTt7xsbGsnjxYhYvXtzhY5uc4wsLCwkPD6egoIBp06b57dPajctqtUrLWjab\njYKCAiknm3zjCo3MiIgIFi9ezLx586Tjg+27x+PxO97r9QZUJUMQhDa/37p1a7MZ12nTpjF06NCg\n+hko519v3UFPyoyMjOSRRx7hkUceCaqdNWvWcNNNN0mBlRMmTPD7vrXAypiYGH75y18Cvmvmb3/7\nGyNGjOjcScl0iOjoaBYuXMjChQuDOl7OBNF36azu2wusNJvNPPzwwy0eO2/evKBkylxYBGWsulwu\ntm/fzqeffkpJSYm0XRAE/v3vfwfUxuzZs8nOzsbj8ZCZmYnRaAzoxtXQ0MCGDRsQRRGTycTll18e\nkrJtMj5cLhfffPMN//nPfygoKJAMS0EQeOmllzrc3pAhQ9i3b5/0uaamptmDJyEhwe+68ng8DBo0\nqM12Z82a1eLManspT9RqdVCGt1KpJCwsjPr6eskAD5S+KtNut7Nr1y6+/PJLysrKpH0EQeDVV19t\nty2Hw4HH4yE8PBxRFElOTubYsWN+s+htBVY2cfz4cSIjI5sF/MiEDpfLxe7du9mxY0cz3Qd6H5Az\nQfRNukL33YXsq+4vU6vVkX/GgiAIDI43oWhn0qezMkNJUD155plnWLlyJbNmzWLw4MHS9vZmv84l\nJSWFX/3qV37bArlxxcfHSzMsMqHnpZdeIisri2uvvZaUlBQ/YzUYTCYT4MsIEB0dTW1tbTMXkKFD\nh/Lxxx9TVVXF7t27A0pTk5iYSGJiot+20tLSgPzpgvXnA58BFowR2Bdl/vWvf2Xt2rVMmzZNisYG\n37UQSNvFxcWEhYVJ+5rNZk6dOtXMWG0tsLKJw4cPM2bMGOmzHFgZepkvv/wy77//PldeeaWfO44g\nCEG7mMj0DV566SVp3J87sRDsM+Bc2su33lZlq5aQfdV/wO5VUFReR4PdgyBAjEFAp24/C0NnZAZK\nMMGCQRmrb7zxBp9//nnIl1hlep733nuP9evXk5mZSUVFRZdcvBMmTGD37t2IokhKSgoRERFSHr+5\nc+dyySWXsG/fPl555RUUCgXz58+Xjn3hhReor69HFEWWLl3KuHHjuPnmmzvdJ5n2ee+991izZk2b\nvsOdpb0AC7fbTW5urt9LrBwRHnqZa9as6TP3fHl2rWtlrlmzho0bN/pNTHUV7eVbb6uylUzbFFZY\ncbu9CIJIXJguIEO1NxOUsZqUlNRncivKN67OyUxMTMRqtXbpssCECROa+SrOnTvX7/OiRYtaPPah\nhx7qtHyZ4OjXrx/19fVBHx8XF+dXlrGmpibgiPAm8vPzSUhI8HugyRHhoZcZFxfHiRMnmuXX7gil\npaV8+eWXeL1exo4dy9ixY5sF5XZFcJ08u+Yv8+ipKix2N4NiDBh1gd2/z5UZHx9PeXl5s5WrztL0\nLGor33pbla1k2ibKqKGi3k1anBGDpvcs5wdLUGewcOFCFi1axB133NEsmnfGjBld0rGuQr5xdU7m\n3LlzeeCBB7jrrrtISUnxe1BOnjy5Q221RW9LQCzTnPnz5/Poo48yb948Ro0a5fddINdCZyLCmzh0\n6JCfCwDIgZWdlVlnc1JtdZEcqUetav5i73a7mTdvHg899FDQugfYvn07kydPlsqtDho0yM9YlYPr\nQkNdowulQqCu0RWwsXounR33rRFIvvW2kN1/2ia1n4mkyI5XerugKlj95S9/QRAE/vGPfzT77vw0\nRDKdRxRFjpdbcXlERiS3PbPhFUXcHhHN2YeOKIq4vSJqZduzywClNY1UW130j9YTrvcNrKal+H/8\n4x94vV6/ZdqmKlQyFwevvPIKgiDw5ptvNvsu0Gsh2Ihw8D2Ijh8/zo033tg1J9RBRFHE4nATrvBf\nTjt/zPU2iqqsVNZaSYnRo1UpKauzE6ZXYz5ruBRX21EooKTWTkqMocU2Oqv7zpRbPRc5uC5wvF4R\nq8PNgGg9DXYP/cKDK1HbFeM+FMjuP23j8njJKbKg0yi5bGh8QMFVnZUZSoIyVgsLCzstuD3Hamh5\nSaiuro4NGzZgtVoxGAxkZGT4BWRciLi9voekADQ42l4CPF5upcHhITFCR1yYlhOVNuobXSSE60jR\n6/GKonTROt1eVAoBhcL3ucbmQqmAWqtLMlb37NmDWq0mNja2y3xWZfom5y7hB0tnIsI1Gg2PP/54\np/sQLKW1diotTgxaNwn94qXtuacbaHR6GBClJ9rc8UIdoaa6wYEoilRbXYCLWpuLqgYnY/v7DL5o\ns4Yaq4tYk6bVNjqr+86WW21CDq5rn/pGFycrrdTaXESY9ETqlQyK71gBl0aXSF5ZHWalwP79+zvc\n50CIiopqN996W8juP83xekVOVVtRKxVYnCJfHChBpVRiVrqIDQvsZeWCqmDVRHl5Odu3b2fevHk0\nNDQgCELAVY3ac6xubUlIoVAwa9YsEhISsFqtLF++nLS0tKCqOfUV1EoFyXoBl90hGZGt4faICPje\nqgBcbi8CAnaXh+1HyymtaiBjYDgGnYqTlTZ0agUjEn1LqAOiDVRbnSRFNL+oy8vLyc7OZvbs2Vit\nVgRBwGBoeRZG5sKmsrKS3bt3M2fOnF57LYTCV93sFKm2edFpVX4+3Cq1A63gRaXRttted/qq11gd\nVNQ7iYvQ43Bo6B9toLC8gaIaKwNjjFI/Bun1tJQYriWZFRUV7Nq1i5tuuqlD9/zOlFttQg6ua1nm\nifJ6qiwOhiaEEW7Qcup4BQ0eNR4FGAx6oqNMxMaYOiTrv+yHFXEAACAASURBVCcqAReiVsWwRN9L\nRmee923RVr71Jlq6NmT3n+acqrLxTX41FruHEclhGDQKdGoBrVLssPwLooJVUVERP/7xj8nPz8dq\ntTJv3jy++uor/v3vfwdUetFutwNtO1a3tiRkNpulvKpGo5G4uDhKS0vbzcPZJxFFNHv3ol+7ln4f\nfQRA4113wZNPtnrIkH4mbE43Zq1PtYPjTRwtsVBU1ciR0gYaGp1UNjiYOTIGAfCccw8w61TS0mAT\nJSUl3HfffZw8eZKGhgby8vL45ptvyMrK4rXXXuvyU+5K5OC6rpV5+vRp7rnnHk6cOIHNZmP+/Pns\n2LGD9957jzfeeKPDbYeSUPiqV9TacDid6MI1GAwGyW88JVKN3eXBpG0/PVh3+arbnG4OnqonXK9C\nqTCQGKbC5XRgaXSQEqVBq/S2249zZRYWFrJkyRIKCwux2Wxcc801bNu2LeD7QGfKrTYhB9e1LPNE\ncS2CIJDn9L2E1NTUYrM6iDVrSI3WIngbKT1txe7yYNYFNrNrLMzDtH499sd+Q05ODr/4xS+kcT9t\n2jQ+/fRT3n//fZYvX97h82kiNja23XzrbVW2koFGp5u6Rg9xZg0KhYBRp8Lu8tJgd3G4uJ60eD2T\nhkSjUfXtTAAQpLH64osvMmfOHB577DHJQfrKK69sNYL7fAJxrA5kSaiqqory8nKSkpKAC2dJSFFY\niO7999G9/z6iwYB9wQJqfvc7cLkwvfACDB2K6Re/wHr33Ygm/zdmNaDX/rCcZ7e7KK13UF7nQKkU\nsLndxCp0VFo9pMWbCTNoUJ31Z1WcOIF6zx6EhgYEqxXBauUvn3/ODUolT9x4I9Fr16JWq5k6dSqP\nPfZYr8+vKAfX+cusqrXgcnsJN7a+3NuWzJdffpkZM2awZMkSxowZQ2NjIxkZGTz44IMBn09ZWZmf\nz+ro0aObzRi15P4DvrG8ZcsWTp06hUKh4Kabbmpm8IQSxekypvzmfpQREZDUD5NWizssDDEsDK/Z\njGg0Sj9eg8H3d9NvoxExgHzB53Omzo7LK5IS2/Y9RhRFv7yXeWesuNxe6mxuVGoXewrqiTFpGBit\no6q6gaSaM2i/K0VZUoKyuBhFdbXvHMLD8Z79UURHw5AhkJzMihUruOqqqyTdA1x++eU89thjAZ2H\nHFwXOplJEVpqrU7izL7CH9EGJQJq+kfqUCug3urg2GkLbg8kRvrcw1rF5cK0bBmDly1D8dvfUqGG\nv7z2GjNnzpR073K5mDBhAg8//HCnZ97ay7feVmUrGSiosIEo4vF6SYrUE2XUcOulCXz2XTleUYnb\ng/R8D5RGp5uTFRY0nvYrS3YnQRmra9eu5fDhw1Ld8Ca6IklwE+0tCTkcDrKyspg9e7Z0I+zTS0L1\n9bB2LaxcCUePwm23wfr1cMklmAQBySRdtQpyc9EvXYp+4kR47DFYsgRamTU7szOHSWtXIpw8Sc4V\nszCOySQmQkd0RDhqrY6EY4cgO9v3U1sL06dDdDQYjRAdzcZTp9j1299TbI6GrCxiY2MxGAwolcqg\n/E5kega7083Hh8oRRZHpw2OICsK3Mjs7my+++KJT437Xrl1MmTJFigg/30etNfcfgG3bthEeHs6c\nOXNQKBTdvkTVf0gSNf/v/+GpsaDSKvGUlyHW1CDU1aEuKkJobESwWlGcfdFr+ixYrShsNnA6wWAg\nzGCQjFhvkyGr1/9g6J41ct06PUqnAk9sHNZpl0NcXIv9Kq1ppMLiJD5cKwXR6NUKRL2KGKOGo+WN\nnDhTh06tYJjSzpz7b0dQq/AkJ/t+kpJwDR+OwmJBUVuL8uRJ32+LBQwGWLWqS3Tfl4PrzqW9Z1N3\nc/6KWFy4jrhwHY1ON58dLKPW5sCkVRBuUKNStq4v9bffEvH443gSE6n55BOiMzKgoqJLdN9dXGyr\nadFhHmptTqLDjej1vnu6Xqfjx8IRKksq0EzMwGgwUF5np9zioH+UnnBD25MVR07Voq8uRkhMYFBs\n5908uoqgjNXrrruObdu2cdNNN0nbPv3004Br+CYlJbXrWN3WkpDH42HNmjWMHTuW4cOHS8f0uSUh\nmw319u3o1qxB89lnuCZPxn733TivugrOGuBUVjaXOWwYNS+/jHjoEMZnn0X1/PPYHnwQ++23+47z\nelF/+SX6t95i2K5vKJxxHc6RI5i1/K/YDCZKJs/AISiJ37oOR0QY7tmzcb70Eu6xY+G8gT49L49V\n+jCuu+5GvL/7NRUVFXz44Ydcf/317f5fO4Js+IYYARQCeEAKqDsXl8eL1eEhXK9q9SE0Y8YMdu3a\nxaxZs6Rt27dv5/rrrw+oC52NCD9+/Dj33HOPNKN//sMz1KgMespGjMPmAktEOP3NdMxg9njQe704\nqqokY1YybK1WBJsNwWaTtmmqq4mtrke77RMi/vQEmEyEjRmDY8wYXOPG4Ro7Fk9kJA0ONx5RpLSm\nkbgwLQpBQCkIlNbYUQoQZ9ZQVa9C4XEz+U+PU3HdDbC0dVeiJpRKFaaISMS6mk7rHvp2cF1TVpXC\nKgsnakVidW60Ib786htdeLwicREdW8HyeH19tTk9uEURp9tNv/gwUmOMLaYmE+rqCPvLX9Bt3Urd\nU09hv/FG1JofDJqu0H1LKyrn09qKysGDB9m3bx82m42JEyc2m5E/lwthNc0riuSebsDjFRnVP7JN\nmQlmJf1MOgTB59Zjc7gw/OVZYj/KxhGfTPgTuRS8v4EyczRn6h3UW6ykp/hPrinOnEF94ACa/fvR\n/Pe/TPzvf3EMGMSpzVuC+j8FQrdVsFqyZAlXXnkl8+bNw+l0snjxYtavX8/WrVsDOr6pfGZbjtWt\nLQmJosimTZuIi4tj0qRJfsf0lSUhVW4umg0bML7/Pp74eGzz51O7dCnec1wcaKc9t9uNa8gQ7K+/\njpBzAPX/PIP236vxTp2MafMHeKOisN1xB8ee+gu7zzgQRYiavYBBe7ah27uXSDzkL3mIutnXM6Qp\nUtTj8f2cw+233868efPI2fM1bpeLRx55hI8++ojVq1f3KufrC5nyOjvOAJaC20KnVjFrTDxWhwu1\nuvkDK/+MFafbS4xZQ1JkyzMACxcuZP78+Vx//fU4nU5++9vfStdCQOfRiYhwpVKJ2+0mOzubyspK\nRowYQWZmJmq1ulvdf6LD9DTWOogwalGpPO0fcC5qNUqtFuU5wWji2Z/WUOF7wahTKomsrcX15Zeo\n9u9H9+qrqA4cwGEOY+iYS6iL7Yde9KBRiJgFD0ln6hhaVow1PgHH4vsYnD6K8EcfA50O4aknUQVw\n3scrbIh1IgaFi7vvvptbbrmFG264AZfLxRNPPMGWLVtYs2ZNr3cH6gqOV9qwNLqwOkUGJZposLvR\nGlW4vSJOtxeDpmstV5fHS0G5DRDR6zS09ypfZXFgc3oYYKtG8cxfSfryM4oeeJgxd91No01Lvwg9\ndTYX5RYHSRE6zHo1uN0Y1q7F/Pzz2GfOpPyLLxBbKPrQ2XEPwa+o2O12vvrqK+655x4UCgUrV65k\n1KhRAZXg7qt4PCJ2lwdEX2B0ezRNLijz84l+6DcobTa+fPVt8jDR/+1/MeX2n1Dywut4o+PxIoLL\nhXbXLjzrN2HesR2NoxFXejqu8eNpuOcexEsvJWbkSKJ7WfafoJ5+Y8eOJS8vj/Xr11NXV8f48eP5\n85//3CFruT3H6taWhIqKijh48CDx8fGSY//MmTN7fRlAoboa/aZNGNauRXnmDK4FC6h65x3c580E\nB0Nd2kiOPfcqmn+/TUxNJbV/eglx1ChS4wz016g4aimn0elBb9BwcPxkygZdgl6rZmJqFMnhbS8H\njxw5kj179rB9+3ac1jpGjRrFb3/7205VsmmNYNOZXXC43Shqa1FUVeGtrMSbX4JSpcI28RI4z4/P\n7zCvSKPTjUnrmx11ub3U2lxEn01JpFAIFFXbQYRRyWFoz5ll0agVONxev23nM3LkSHbs2MFHH31E\nfX19SK6F1pZY3W43VVVVXHPNNaSmprJ582aOHDnC+PHju9X9JxYYMrDLmw2MqChMqalw110A1NQ3\ncuyr/+D+cjsRlmocOi1Ks4GYxChiUZDv1qAqyGPIPXfQYA7HrdKw85W3yVCbSI0Na0cYVDqqqbO5\nCA8PZ+yQK8nPz2f9+vU4HA4mTZrECy+80OtyMUJoloIVCgc6rYJok4JIhYtIjQLB5eL7k1W47U6S\nzCpiDD4DsOlHOPdvjwfB7UblcsHZv3G5pO/OP050uRlR3YBbqSJ66EDs/ZPQRkaijotDDA+Hc1Y/\nRFGkfn8eA977N3Fb1lM87yd8/KdXmPnMo/xHNKD76QJMRgPfn66itMZBrc3DNQV70f/5z3jj4rCu\nXInnsss41/w7d/k5IyODb7/9ls2bN2O1WklPT2fp0qUBj3uHw4FCoZBWUFJSUppVwGxtReXUqVMk\nJCT8kLli0CBOnTrV65/3wSLU1WHIy2Ps4e/xutyEXTIWjO0Y5g4HpldewfjGG5y6dwlHb7wNr6BA\nXedixzXzCTfrmXb/Qopvu4PIkwVEbt+GOzWVwstnkP/yv1AMG8KAmB9e7Hvry6cg9jYHnC6mtLS0\n3X06syzQZv5RpxPdtm3o165F+/XX2GfMoHH+fBxTpqA3mbpU5olKK/tP1KBQCOjUSpKj9MSH+Zzp\nG50eiqqs9IsK4+P/nuJwiYUYs4YbxvUjNa79lCbdkWc1MTGR1157jdmzZ0vpzO66665m6cy2bt3K\nj3/8YwoKCjh48KDkz9gSodC9VxSxNLqIMhuIj4+T/ieWRhe1jW4SI3Qoz11mF0WEhgYUVVUoqqtR\nVFejs1hwnznj+3x2u/Lsd4rqagSLxRfkEhWFNyoKiykcwW4norAAZX09rrQ0nGlpuIcPx5WWhiNt\nOGUqgy/6W6ckOkzHsH5mvvi+gsJKGw6XE6dHQCWASikSY9IzZVg00WYtSsUPxSM8XrGZM75HFPBq\nTGg8NkRvB2cSz8PpdLJ582aptO7XX39N//79/cpm7tmzB6/XK62avPjiizz44IMAvPzyy1IwRl5e\nHgcOHGDevHlBz6xeCBHhxdVWCitsGHVKPG7fS0dcmG8mraC8gaMldZSW1hC+fy+HBo4kvl80P5o4\ngNi2gmzOolAq0RvDcNgsXZJvMdhyq9Cx4LqQ3PMLjpMwbSqCKIJajahWIyqVeBQKvIIShUaNoFGD\nSoWoVIJK5ftbpQKlElGlQqHR4FEofN+d3Xb+PiiVvvab2rDbUVdXo62txV1aiqK8HMHhwBMdjS0i\nisZBg4koL0VRUEDZNXPgkQegXz/yy62Ubf+WW/74K7Y+8wrxUy+j+lgh5n3/YdymdzCoBCy/+x1V\nmVdQY3OREKlHdc59qyvv+cXFxRw7doyZM2cC8N1332G1Wv3cCNevX8/YsWOlGdfly5czd+5cTCYT\nr776KnfccQcqlYpVq1YxfPjwVitl9ujzvgMyFVVVqHJzfT/5+ajP/hbq63EPHYp7yBBEtRpNXh7q\n3Fw8ej3uYcNwpaXhHjYMd1oazrFjURUUEPngg3iSkqh95hm8SUm4PF6OnW7AKyj5vqgKhSAwpzaX\npHdXYZ82Dfvs2XgTErDY3dRYnSSGUPetEUzp3qBmVp988slW/dqefvrpYJrsU1gaXVQ2OBkYa27+\npSiiPnwY/dq16DduxD14MI3z51P7t78hhrU/mxEsg2KMRBo0NNhdGLQqGp0eYsy+GTW9RolOraKk\n2oZRoyIpUke0QUNkG0nAm3j22WdRKpUYjUasVite7w8Rgo8++miX9b8z6cy6k5zDJSg/+ohCu53U\ncD39VE509kYcFXUkNtowWOowNdRJhqeiuhpRo5EMT290NEJsrC+CPDoa18CB0nZpn/Bw30PrPKrV\namJVKhp27kQ4fBj10aNoPthM2NGjGHUG4iNjUbqcGEQ3eq+bm62NeB1OXEoV1aZIag3hWIxhNIZH\nciYuBkW4hkFGhc9XsrERwW73mxES3G5sNgdCeAS5z7/Eu8tfbHXcB3ItdDYiPCoqiuLiYhITE8nN\nzZVmarrb/cfrFXG4PL0iIjzerCFCp0ShECirtWNzejBpBFwuFyq8uNweImPDODD8UrxeL/HhaiL0\ngQWnqQGDVoW13s0zzzzTKd1D8OVWoeeD60gdxP4DR6mwiwxKjCNK49OD1yvi8optrkg00VkjqabJ\neLDbaThVRmX+KYx5uVhTbkQ9ayaqc7LApPUzYbw6k331T3Hdb3+BR6FCQMQ+Ph37fUuw3XwjKBSc\nLKlHEECsaWRAdMu5kp999tlO6749Wpsz02g0zJ49my1btuBwOIiLi5MCjbrT/cft8XLgVB2RdTAg\nQhHY8aKIoqwMZW4uuuPH0R05gjIvD1VuLng8eM4anZ6hQ2m85ho8w4bhTUz0ixtxqlRERkRgOXwY\nvvsO1dGjaHJyUL61ClNpCQCVv/89qoW3oxQEbHYXB07WY9QqiTLpGNIvnKKaRg4MyUC38ho0KgVK\nQAlEqdVEmZuvLlxQ5VZPnTolXbyiKJKTk8PRo0e57bbburRzXUEoloQKKh14RCWnLS6ibDY0Z86g\n/89/sO34BsOOr9A5GnHddhsNn3yC92z+1/Mn8kMRHdhWe2ajSGO9k7SkSCalxZMcHViUX3l5OYIg\noFAo8Hg8HDx4kLy8PG699dag+t8aXZXOLNQIDjsD/vM1drUWIToSe6QBQ5gBZVQkDap+KJPjEfvF\n/mB4RkXBef5VwT60AIiMxD1xIq6zRl5Do4sjJXVU/vcosQ4LxjA9WpOJpIQIat0Cm7+rwtFgQzhT\njt5Si9lST5yznhR7A0qVDjEyCm9EhC8aXafzzeqcM+NT4wS3IZwwvZrS0lK/cX/48GEKCgo6FKHd\nmYjwa665hg0bNuB2u0lNTW0xSKM7+L7Ugq7OS6TajVnb8yVWtWrfi835xoZKpcCoVWEy6KhtsFPV\n4CbGqMXrFVsMsmuLzuq+rwfXnal3cOSMDa8oEB/tJuqsXahQCGg7+L/sNDodxiEp1ETHk5Ochk6j\noH+9i0GxPxirjS4PTo9Iw+zreClhKKUVFrRJicy7NInEqB/u2xF6FTU2N+GG1o2vzuo+Li7OrwJa\nTU1Nh3LspqWlSYHTa9eulfbpLvcft8fL18dOU1zroqqxjtiIeAacO1Hl9UJhIXz3HXz/ve93098G\nA4wYASNHQmYm3Hmn7+94X/nTQE3miDFj4Gzatsr6RnJOVHJ8zyGikmLQxMWSUAejBkRworYGQa3H\nrVDQPz6alIRo1CcqiTTqiIuLCajsehO9zcUnKGP1rbfearbtX//6V8jKsnWGUEQHijWVqFe9S/j3\nB1EWfIfW7cSZkUHd4FGcfPwpvBMuo3+TD0gr7XZ3RGKUXiDGbGb/8XKsdgdKPES0cYNq4vnnn2+2\nLPDuu+9y6NChkEUKtkZbHivd9ZZ9aeZwyt9aTmWtA09YGJHRauyiFz3Q9AgQAQGkN9jOymyipTfe\nSLWacWo1itTJGDQqRHwzfyqlgjBg8XjfLKDdq+DUmVoMWjURBhVuL4QbNTjUbT/0o89Zfn7llVea\nfb969WoOHjwY8PkMHDiQgQP9nT4DjQiPiYkJOJdzKPn/7J13eFRl9vg/0yczmfRCSAJBhICEQAgQ\nUAi9CogUu6KyFsCyi2vXr6uubXd/oqi7KKziIixoaAoo4qIUISBFmhCSQIAUSCF1Jpl6f3+EXBnS\nJjOTBvfzPDw8M3Pve96bc997z/ue857jcAgIyGhjaQhrkVdSRahBTbC/jo5+4WRcMOKrVWK2OfBp\n4oag9957r9Z3Nc8BV2iuzXUthQAE6dWAnJ5RgZSXFreY7LqQyWQYtCrkChmFFVZiQpwN5uyiSqps\nDs6XW8gSdFTolcTqVCiVzsdFBumIbGSu76nuPfWoVFRU4OvrS2ZmJvn5+aILuaWy/2QVGDHnFaA+\nmkksRgK25WM6d7Y6P3FWFsrMTBxBQdUrpd27Y+/dG9u0adi7d0e4ZHDXknlFlp/6qCv8x+EQ8FNa\nCe3ZGYVChsJeRWmZlbM5FmxVFnwVdmKCdegVNsoqTJQUl3Ihv5hIvUOc2DZVprdp8XKrlzN9+nRe\nfPHFOl9odeHJZpp169aRnp6OXq9n7ty53roEl9H7qPG9kM1vCTeS9uTzRPW5njCDCkelFYfJSsd6\ndlO3NmqlHI1SjtkmoFa6vxowceJE3nnnHd58802v9c3TdGYtucmmQzjEN35Ys3HljNfVYd89Orzx\ng1yUWcP9999Pjx492lwFq+akZ6QBX/9AqipK29Ru2SsJM2goq7LRIUCL1SyjegpFkw3V+vD2c8Cd\nzXUtNUmNDlER7KdDr1WjVSup9HIhmIaozy0b6KsgJtSAQgbXhRucXPXlFgd5JVV06+BPbpGJUo2S\n+Gh/IgJ9XVpVb8wVfMstt/DOO+/w97//3aVrGDp0KNu2bcNutxMfH4/BYHDZo/Lll19iNBrRaDRO\nca4tFf4TmJdFwuSJWCOj8el+PZXh4Vg7dqTkht6UhEbg3/cGZH51hASCmNXH2+E/0YFaogOrPXbl\nlVYqrQ78NDL8L/Pi2e12sosqOHmhHDnwa1YR/WJcXy29Ksqtnjp1yunz+fPn+eyzzxg6dKjLbXz3\n3XdMmjRJ3EwTFxdXazNNffFLCQkJJCUlsXbtWne67zEdIkPIefNNHKUWAgMDiAhQYrfbMPioqlOC\ntFFkMhk9IgwIgNzFhM5nzpxBqVRSXl5OUVERubm5fPnllwwcONCrffMknRm0wxy77VBmRkaG02/5\n+fmsXLmSgQMHepRztzkyOjR3cnCFYGtTycHr4rpLck7klmE02+gSqifI1/ViEJfLzM7OdvrtwoUL\nrFixgsGDB7t0PZ06deKXX34Rjy0vL6+1wt7QZDQkJEQc37179+bQoUMtngniclrDRVqXzIgOdU9A\nO5ZDSJCdiior/gZfukVpuTEuqs4YxcZk1ve+HzZsmMtjNzQ0lL59+9b67nLq86g8eCn7RWvhE3s9\nOSfS0Plo8QkNpeKSh/FIdhlyGRjtSiJbsX/Vdkfdv4X7aejgp6HK6iAmpG0uormKW8bqlSteHTt2\nZNSoUS5vrvJ0M03nzp0pLm49N4xMJiMqWEeXDv6ie9zu2UbpFkMmk9GUNdWbbrrJ6XN4eDhDhgzx\n6uaqGtxNZwbtJ8due5Z5pfej5l546qmnXG7bk+TgCxYsEI1QuVzOww8/XK+cqyE5uLdkmm12LBYL\nFUY5PgrXYxcul3ml27ZG9/Pnz3c92bnDwalTp/D19SUrK4s+ffo4/e7O5jppklo3HXUOrHY4a3Hg\no3SgxkplRRkFVRWNn3yFzCvf9x06dGDo0KH88Y9/bHOT1ObgVKGJ8iobnUPg8i4btErKq2z4teEF\nKo1KwaS+Ea3dDa/glrF6+Y5wd2gvm2kkqle4WyKVBTReJxrqn31LND9Xrq65gyflVmUyGffffz86\nXd27liXq5oZIf4rLKjBo3X+pekP3zbG5TpqkNiBPBrERvkQY5NWhH4IdqwtJ5q+U2ZDu25KbGJrH\noyJTmNFoFDhkcifvRs/opuTpvXpKvLYWbacnV+BO+teWTGUBnqV4uFZkSkjU4OmOcAn3UCnkHhmq\n3sKTcqttZXNde0OpkHstRtkTPPGo7N+/n4MHD2K32+nUqRMTJkyoV05zeFQ6BaioMNsJMajQ6XTt\nxqPSGjJdpcXKrUZHRyOTyZwMyro+nz17ts7zPd1MUx/XevxSc+CpriWuHvr37+/SvfDLL7/Ueb4n\nO8Jrxv7nn39OQEAACQkJ9OjRA5AmqS0hs2/fvi7p/uDBg02WI9G28XTcg/seFZPJxI4dO5g7dy5K\npZIVK1aQkZFR6/zmRKmQE6CT15trVqJlcMtYfeGFF3jvvfe4/fbbSUhI4MCBA6SkpPDkk0+KL5CG\n8HQzTX1I8Uvel/n444+zePFi7rzzTrp168avv/7KN998w0MPPeRU8s6T2CVoP/FL1zKPP/44S5Ys\nYcqUKcTFxXHkyBE2btzI7NmzvfbyaMijMnv2bAwGAwUFBaxYsYLIyEgMBoM0SW0BmS+99JJLz/y2\nMI6be3Nde3LLekPmU089xaJFi5g6dSrx8fEcOnSIr7/+mkcffdTpHVCfHE/KrapUKgRBoKqqCq1W\ni9VqFe0HiWsLt/OsLlmyRNz9f+uttzJu3Dj+/Oc/k5qa6lIbnmymSUlJISsri8rKSt59911GjBhB\nQkKCFL/UDDJXrlzJu+++y+TJkykoKGDUqFEMHTqU1157jQ0bNojHtbXYJQnv89VXX/GPf/xD3Gg1\nYcIEhg8fXuteqA9Pk4MbDNXpYUJDQ4mNjeXkyZMkJiZKk9QWkLlkyRL+8Y9/iKVxhwwZwsCBA3n1\n1Vf59ttvxXOaOmltDuNW2lznXZnLly/nb3/7mzjuR40axZAhQ2qN+/rkZGdn4+fnJ/5uMBg4d+4c\n/fr1E49pyKMyadIk3nvvPZRKJUlJSURFRQGSR6UtymxO3OpNQUGBkzsPICAgoJZLryE82UwzY8aM\nJvRWwhOKiopqTQD8/PwodDGpscTVg6f3gifJwS0WC4IgoNFoMBqNZGZmMnjwYLEP0iS1eWUWFhai\n0+mc2tHr9eTn57e5TBAS3qUl3gH1eVSMRiMbNmxg3rx5+Pj48OWXX3Ly5Em6d+8ueVSuQpkN4Zax\n+uc//5kHHniAuXPn0rdvXw4ePMiiRYt46qmnvN0/iVbmkUce4cknn+SJJ56gc+fO/PrrryxbtoxH\nHnmktbvWKJI70LsyH3/8cf785z/z4IMP0rt3bw4fPszSpUt57LHHXG57+PDhbN26FYfDQd++fQkM\nDHTJo1JRUcGqVauA6lWXwYMH4+/v3+TrkXCPRx55hPnz5zNr1ix69erF0aNHm/wckDJBtE881b0n\nHpWTJ08SFRVFcHAwAL169eLMmTN0795d8qi0QZmuLwIcWAAAIABJREFU0mIVrB566CEcDgcpKSk8\n88wzDBw4kFmzZkmz3auQu+++G7lcTkpKCnv27KFv377MnDlTfIm0ZSR3oHdlzpw5E7PZzLp163jl\nlVdISEhg+vTp3HbbbS63HRwczMyZM8XPlZWVLnlUgoKCmDNnTpP7L+Ed7r77bgRBYOPGjfz1r38l\nISGhSc8BKRNE+8VT3XviUenUqRPffvstJpMJtVpNenq6GIoieVTanszmxC1jVaVS8dhjj9Vy47dF\npNU1z2T6+Pjw6KOP8sQTT4iuWIlrE5VKxQMPPMADDzzQ2l2RaGE81b2UCaL9ytTpdDz88MMeLUa5\nW25Vq9WSnJzMqlWrsFqtXH/99WIxIYlrC7cjaDMyMli7di07d+5k/fr17Nu3j7KyMkaOHOnN/nmM\ntLrmucxz586xfft2tm7dyr///W8OHTpEeXk5Q4YMaXJb9dHW4mMk6ub06dN899137N27l88++6xZ\n7gVvIE1SvS/z1KlTbNiwgT179rB8+XIOHjxIeXk5ycnJjbal0WhQKBRiP1QqVS0jSsoE0XZlevq+\n96TcakJCAgkJCW5egcTVglvG6vfff88DDzzAzJkz+fHHH4HqGdATTzzBrl27POpQVlYWGzZswOFw\nkJSUVKvEI9QfhC/hfbZt28b8+fO5/fbb2blzJ1D94nn55ZfbnIEi0bzU3As333yzOM6bei94sskG\nqqvnffLJJ/j5+XHXXXfVK0eapHpX5g8//CDqfseOHWJ7r7/+Ol9//XWjbfn7+1NUVCSel5+fL2WC\naCcyt2zZwpNPPsmUKVPYunUrBQUFmEwmnn32WTZt2tTktmtojve2NEltXZnNiVs9WbBgAStXrmTo\n0KF8/vnnAPTs2ZMTJ0543KHvvvuOSZMmERAQwLJly4iLi3Ny6TQUhC/hfRYvXswnn3zC5MmTWbp0\nKQDdunUjMzPTrfb27t1LZmYmGo2GkSNH1soqAVBeXk5KSgolJSVcd911TJ48GblcTkFBAevXrycv\nL49Ro0Zx4403enJpEk1k8eLF/POf/yQpKYmUlBSg6feCJ5tsAFJTUwkNDXXrBSzhPp7qXsoE0X5l\nLlq0SNR9jTu+S5cuZGRkNHsmiMLCQvF+g+rNWSNGjBDjVq9EmqS2rkxXabEKVmfPnqV79+5O32Vn\nZ9O1a1d3mhOpqqoCEGNSunbtSk5OjpMsKQi/ZcnJyXHaBAHVD57OnTs3ua38/Hzy8vK49dZbyc7O\nJjU1lfHjx9c6btu2bXTr1o2BAweyZs0ajh8/Tq9evdDpdEyYMMErkyKJpuPpveDpJpvS0lLS09NJ\nTk5m9+7dnl6ORBPwxnNg8ODBTgaLVquVMkG0A7yhe3cnqSEhITz66KNAtVfl3XffpWfPnp5flES7\nw+1sAG+++SZvv/02UP0S+uCDD7jvvvs86kxOTo5TparQ0FCys7NrGav1BeFLwfbel3nvvfeycOFC\n3nvvPaA6rmzp0qXcfvvtTW6rqKiIbt26YTAY6NmzJ/v27auzjZycHEaNGoVarSY+Pp7s7Gx69eqF\nXq9Hr9eTnp7eJLkS3uGuu+7igw8+4Pnnnweqx9ann37qct5jTzfZbN68mbFjx9ZaPZHGffPLvPfe\ne/noo4946aWXAPeeA507d65l4EiZINo+no57b2WCOHXqFIGBgdJE5RrFLWN11qxZzJ49m65du1JR\nUUFMTAxDhgzhhRde8Hb/atFQEL4UbO995s2bJ+raaDQyYMAAhgwZwl//+lcxnsxVSktLiY+PF2OV\nDAYDCoXCqR2r1YrRaBRjbUJDQ12qiiYZLM0v86677uKPf/wjN910E0ajkUGDBpGUlMT8+fNdalup\nVCKXy8VjFQoFCoXC6Zj6xndaWhp6vZ6IiAhOnz7t9Js07ptfZs1zYNCgQR4/B5obKW7RuzLvu+8+\nnnjiCYYMGYLRaGTw4MEkJSXxzDPPuNR2QUEBwcHB4rFhYWFcvHjR6ZjGMkEAHD16lN69e4ufpWd+\n25PZnDS5Nw6Hg4MHD/Lf//4Xi8XCzp07GTx4cJ2xh00lMjKSLVu2iJ8vD7avoaEgfCnY3rsyHQ4H\nP//8M//85z/R6/V8++239OvXD39/f+x2e5NLK1ZVVVFaWiqeZ7PZuHjxIna73eNge8lgaV78/f05\nePAga9ascXvcGwwG9u7dK+q6srKyVjhRfeP7wIEDpKWlkZ6ejs1mw2w2s2bNGqZNmyaN+2aWWVRU\nxLZt21i4cCFWq5U9e/bQv39/t58DlyOVW23bMsvLy/nll19E3e/du5fExERxddOVts1mM3a7XTzW\narXWuo7GUiLabDZOnjzptOouPfOvPpkN0WRjVS6XM3PmTC5cuIDBYGDChAle64xWqwWqMwL4+/uT\nmZnJsGHDnI6pLwgfpGD75pA5e/ZsfvvtNwwGAyNGjKjzQdMQx44dE2NMu3TpQkFBgegOMplMtUo4\nqlQq9Ho9lZWV+Pj4UFBQQGRkZKNyJIOleWWWlpYyY8YMjh07hlKppH///lit1iYbKjabjUOHDuHr\n68vJkydrbbSob3yPHj2a0aNHA9XPh127dokxjdK4b16ZDoeD2bNnc+jQITQajZiqqi0lDJdoHuRy\nOY888oioe3dSU3pSwaqGjIwMIiIinFZNpWd+25PpKi1WwWrUqFF89dVX3Hnnne6c3iDjx49nw4YN\n2O12kpKS0Ov1LgXhSzQPQ4YM4euvv3a7vGqvXr3o1asXUB2zuHv3brp37052dna9M7fIyEj279/P\nwIEDOXz4sJPrB+qehUsGS/PLvOmmm1i7di1Tp051u51Bgwbx448/iptslEqlNL7bAUOGDGHDhg0e\n6V6ifeKp7j3JBFHDkSNHar0HpGd+25PZnMgEN0oS/elPf2Lx4sViHtSauDOZTMZrr73m9U62VcrK\nyti/fz+JiYm1Bs3VIrNG1927d2fEiBFibW53db1lyxaOHTtWK4fm8uXLmTJlCgaDgbKyMlavXk1x\ncTFdu3YVU1eVl5ezePFizGYzMpkMtVrNvHnz0Gg0Xr1mV7gWdH+lzFdeeUUa97S+HiTdtx6trYf2\nqvua/Ok1i1CDBg1ymqRC/e8Gi8XCggUL+OMf/9gqz/oaWlsPV7NMV3B5ZfXDDz8Uy6vm5+eLOwHz\n8vKA6tUumUzWDF1su1RUVLBt2zZiY2NbTKktIfNKXU+cOJGTJ0+SnZ2NTqfzSNf1VSm5PJemn59f\nnWUdDQYD8+fPd0uut7ladX8lH3zwAVarldjYWGncX0LSvaR7SfdN031MTEyt8uyuZIKA6pXZZ599\ntqmX4HWuFd23hkxXcNlYffnll5k7dy5yuZyvv/6a8vLy5uyXRCtypa7T0tL45JNPePjhh+nYsWNr\nd0+iBfn73//Ok08+CSCN+2sMSffXLpLuJdoaLhuro0eP5uGHHyYpKQmbzcann35a53EPPvig1zon\n0TpcqeuVK1dy8OBBVq5c6bT7W9L11c+QIUP45ptv8PX1lcb9NYak+2sXSfcSbQ2XjdW///3vfPHF\nF6xevRqr1cqyZcvqPE66eds/V+o6JSWFvLw8CgoKnGKGJF1f/bz88ss8//zzbNq0SRr31xiS7q9d\nJN1LtDXc2mA1cuRIqcwp10bw88iRI1m3bl2bDLhuTa4F3V8pc+rUqdK4p/X1IOm+9WhtPUi6bz1a\nWw9Xs0xXcMtYlZCQkJCQkJCQkGgJ2lY9rTbAunXrSE9PR6/XM3fuXPH7gwcP8vPPPyOTyejevbu4\nazE1NZW9e/cil8uZPHmyWPu6oKCA1atXU1VVRe/evRk1alSTZObn57NlyxbKysrw9/dnzJgxYioP\nb8gsLS1l7dq1GI1GdDodiYmJxMfHi5WB8vLyiIiIYNq0aaLr3xty2zKS7iXdS7qXdF+DpHtJ95Lu\n25DuBQknsrKyhNzcXOGjjz4Svzt//rywePFiobCwUBAEQaioqBAEQRDKy8uFhQsXCsXFxcLp06eF\nf/3rX+I5y5YtE44cOSIYjUZhyZIlQnZ2dpNkfvnll8KRI0cEQRCEw4cPC1999ZVXZZaVlQm5ubni\n9SxYsECoqqoSduzYIWzcuFGwWq3Chg0bhJ07d3pVbltG0r2ke0n3ku4FQdK9N+W2ZSTdtx/dy1vG\nJG4/dO7cWSz7WkNGRgYJCQkEBwcDiCXfcnJyuP766wkICCAmJgb4vS51UVERcXFx6HQ6evbsSU5O\nTpNkarVaKisrcTgcmEwm8XdvyTQYDGLZU71eT1hYGDk5OeTk5JCQkIBSqSQhIUFsw1ty2zKS7iXd\nX46ke0n3ku4l3Uu6d1+uN5GMVRfIzMwkPz+fjz/+mPXr15Ofnw9UK/TyGrfBwcHk5ORQVFTkVMM4\nNDSU7OzsJskcM2YMqampvPPOO+zdu1d0QzSHzKKiIvLz84mMjCQ3N1csdRcSEiLeiNnZ2c12rW0Z\nSfeS7iXdS7qXdC/pXtJ96+peMlZdwGazUVlZyYMPPkiPHj3YtGkTUHeNem+xfv16kpKSeOaZZxgw\nYADr169vFplms5mUlBTGjx+PRqNp1mtqj0i6v3aRdH/tIun+2kXSfdtEMlZdICoqiri4OFQqFbGx\nsRQWFmK1WomKiqKgoEA8rrCwkMjISIKDg6moqBC/LygoICoqqkkyz549S0JCAgqFgoSEBM6cOSP2\nxVsy7XY7q1atIj4+nh49egAQGRlJYWGh2HZNxarmvNa2jKR7SfeS7iXdS7qXdC/pvnV1LxmrLhAV\nFUV6ejqCIJCdnU1QUBAqlYrIyEgyMjIoKSnh9OnTyGQycSddSEgIR44cwWg0cvz4cSIjI5sks0uX\nLqSlpQGQlpZG165dAbwmUxAE1q9fT1hYGIMHDxa/j4yM5ODBg1itVg4ePCjeiM15rW0ZSfeS7iXd\nS7qXdC/pXtJ96+peyrN6BSkpKWRlZVFZWYler2fEiBH06dOHjRs3cubMGUJCQhg6dKiooNTUVPbs\n2YNCoXBK75Cfn8+aNWuoqqoiLi6O0aNHNyrTZDLh6+vLiBEjiIyMZPv27RQUFBAWFkZycrJTKgtP\nZZ45c4bPPvuM8PBwZDIZAKNGjaJTp04NprLwVG5bRtK9pHtJ95LuJd1Lupd03/Z0f9Ubq6+++irl\n5eWt3Q2JRjAYDLzyyitebVPSfftA0v21i6T7axdJ99cu7uj+qi8KUF5ezv3339/a3ZBohKVLl3q9\nTUn37QNJ99cuku6vXSTdX7u4o3spZlVCQkJCQkJCQqLNIhmr7YT4+HjOnTsnfn7xxRf54IMPWrFH\nEi3Bv//9b+bPn+/03dtvv83bb7/dSj2SaAnWrl3L448/Ln6++eabeeqpp8TPo0ePFjdkSFx9nDt3\njiFDhnD8+HGgOk4wOTmZffv2tXLPJJqbzz77rNYz/6233uKdd95ppR61DSRjtZ0ik8nEQGmJq5eJ\nEyeyc+dOTCYTUJ1+5Pvvv+fmm29u5Z5JNCcDBgxg//79QLWhYrPZOHz4MFBtyFRWVhIbG9uaXZRo\nRqKjo/nTn/7E888/T1VVFS+//DJTp06lf//+rd01iWZm8uTJ7Ny5U4y9tdlsfPfdd0yZMqWVe9a6\nSMZqO+Yq3xsnAURERNCzZ0/+97//AbBnzx60Wi29e/du5Z5JNCdRUVHo9XqOHz/O/v37ufHGGwkL\nC+P06dPs27ePxMTE1u6iRDMzffp0oqOjufPOOykqKnJaaZe4egkJCaFfv358//33APz8888EBQXR\ns2fPVu5Z6yIZqxISbZyJEyeKVVQ2bdokrapeI/Tv359ffvmF/fv3079/fxITE9m3b5/4WeLqZ/r0\n6WRmZnLXXXehUqlauzsSLcQtt9zChg0bANiwYQOTJk1q5R61PpKx2k4IDQ2lqKhI/Hz8+HEpDOAa\nYcyYMezbt48LFy6wdetWJk6c2NpdkmgBaozVAwcOMGDAAPHzvn37JGP1GsBkMvHOO+8wbdo0Pvro\nI0pLS1u7SxItxIgRIzh58iTp6els375dWqBAMlbbDYMGDWLdunWUlZWxZs0aTp8+3dpdkmghgoKC\n6N+/Py+99BJRUVF06dKltbsk0QL079+fvXv3YrFYCAsLo1+/fvz888+UlZVd8y7Ba4G3336b3r17\n85e//IXk5GRef/311u6SRAuh1WoZM2YMzz77LL1796ZDhw6t3aVWRzJW2wmzZ8+mrKyMKVOm8Ntv\nvzFhwoTW7pJECzJx4kT27NkjzbCvITp37oxer6dfv34A+Pr6Eh0dTd++fSWvylXO1q1b2bVrFy+9\n9BIATz/9NMePHxfDgSSufqZMmUJGRgaTJ09u7a60CdwqCjB16lRmzZrFpEmTpDiaFuK6667j3Xff\nbXG5TzzxBLfccgvJycmSrluRyZMnt/pDS7oXWp6tW7c6fV65cmWLyJV03bqMHDmSkSNHip91Oh0b\nN25slb5I90LrEBERIa6wSri5spqcnMxrr71GeHg4c+bMYdeuXd7ul0QbITExkUWLFjF8+HBef/11\nfv3119bukkQr4Y17oaCggM2bN/Pdd9+RkZFR5zE//PAD7733Hh9//DEFBQVOvzkcDhYtWsSKFSvc\nugYJ15DGvUQN0rhveRwOB4sXL2bmzJnodLrW7k6bwK2V1fnz5zN//nyOHTvGsmXLuPPOO1Gr1dxz\nzz3cc889dO3a1dv9dBuFQsEnn3zS4DFTp05l3bp1TW47Ojqap59+mr///e9OCftdoT3JfPDBB5k4\ncSKPPfYYc+fORS6XExsbS48ePfD3929ye3URHBzslXYuR9K9d2WazWbGjBlDUVERJ06ccOte8PPz\nw2Qy4XA4KC0t5ccffyQoKEj8PTs7mzNnzvDwww+TmZnJ999/z9133y3+npqaSmhoKGazuUE5ku49\nlymN+9pcK7qXxn1tWkoPVquVf//734SGhjJnzhzi4uLazf3mKu6Me5nghWSd27dv57HHHuPYsWPo\n9XrGjBnDW2+9Rffu3T1t2mNyc3MbPcbHx4fKysomt61SqQgNDaWgoACr1dqkc9urzB07dvDSSy+R\nlpaGTqcjOTmZ5557zuMJSseOHT06vy4k3TevzNTU1CbdCxaLhW+++Ybp06cD1fkDo6OjGTRokHjM\nnj17cDgcDB48GID333+fJ598EoDS0lLWrVtHcnIyu3fv5q677qq335LuvStTGvfVtLYe2oJMady3\nDT20RZmu4s64d2tlFeDEiRN88cUXrFixArVazb333su9995LSEgI7733HnfccQcHDhxwt3mJNkR6\nejrvv/8+y5YtQ6VSMX36dD7//HOCgoJYvHgxc+fOZfPmza3dTYkWICMjg9WrV7Nu3bom3wv5+fkE\nBASInwMDA7lw4YLTMTk5OcTHx4ufdTodFy9eJCgoiM2bNzN27NhaqytlZWVUVFQ4fWexWNDr9Q1e\ni0KhcCsGT6lUOv3fFNqTzFOnTvH+++/zxRdfoFKpmDlzJjNnziQoKIhPPvmEefPmicUqJK5upHF/\n7Yx7T2Q2J271pn///pw+fZrbbruNFStWOM2QAF544YVGl+Il2gcTJkzg3Llz3H777SxatIg+ffo4\n/f7EE0+wfPnyVupdw2g0GuTyhsOy5XI5Pj4+TW5bJpNhMplQqVRNHtTtVebYsWM5c+YMU6dOZfHi\nxQwYMMDp2GeffZYVK1bUK0ej0aBQKMTfVSpVrYdpfY6etLQ09Ho9ERERtdK27d+/n23btjl9N2zY\nMEaMGNGka20qgYGBzdp+a8q8/Bm/cuXKWs/4N954g+XLlxMaGtoi/ZFoPSZMmMDZs2eZPHkyH374\nYa3qad54B0jjXpLZGG4Zq88++yy33HILarW63mOysrIabCMrK4sNGzbgcDhISkoiKSmp1jE//PAD\nR48excfHh2nTpokPxgULFoiGiFwu5+GHH3bnMiRcYO7cuUyaNInIyMh63QJ79uxphZ41TmPxTeCZ\nqyQgIACj0dii7pnWlPnoo48ybtw4cdzX1V5qamq9cvz9/SkqKhJ/z8/PJzo62umYqKgoCgoKuP76\n64HqxOhBQUEcOHCAtLQ00tPTsdlsmM1m1qxZw7Rp00hMTCQ2NtapHYvFUmuTxpVoNBqX7pErUSqV\nBAYGUlxcjM1ma9K57UXmnDlzmDRpEuHh4RQXF9f5t/zll18a/Rs3hmTstn3mzp3rNO7roqF3QFhY\nmNPvxcXF0ri/SmW6ijvj3i1j1c/Pj9OnTzvdKGlpaZw9e9blNAvfffcdkyZNIiAggGXLlhEXF+e0\nfN9QwLVMJuP++++Xdsm1AAaDgbNnzxIZGSl+l5GRQW5uLsnJya3YM4mWpuZeqHmhQNPuhZqXXV5e\nHr6+vmRnZ9dapYmMjGTz5s306dOHjIwMQkJCABg9ejSjR48Gqie6u3btYtq0aUD188jPz8+pndzc\n3EYNeqVS6VFMls1ma/L57UWmj48Pp06dIjw8XJQpjftrE2ncO3M1j3tPZTYnbqWumjdvHgaDwek7\nX19f5s2b59L5VVVVAMTExBAQEEDXrl3JyclxOiYnJ4cbbrgBnU5H7969KSwsdKerEh7y4osv4uvr\n6/SdXq/nhRdecLvNvLw8vvzyS1auXMnRo0frPKauNCalpaUsXbqUjz76iM8++4zDhw+73QeJpuON\ne2Hw4MHs2LGDjRs30qtXL7RaLfv27WPfvn1A9QpLp06d+OSTT9i9ezdjx4716jVIuEZzjHuJ9ok0\n7iXaAm6trJaVlTkFTAMEBARw8eJFl87PyckRZ05QvSScnZ3tlD2goYBrgM8//5yAgAASEhLo0aOH\n2C8p4Nq7MisqKsS/eY3M4OBgSktL3U4QvXv3bkaMGIGvry/ffPONqL8a6ltVl8vljBs3joiICIxG\nI4sXLyY2NhaNRuNWPySaRnl5ea00Nf7+/pSUlLjcRseOHbntttucvruyzv2YMWMa9NDExMQQExPj\nskyJplNeXl5r1aqpupa4OpDGvURbwC1jdfr06XzxxRdOsaIrV67k1ltv9VrHGsqoNXv2bAwGAwUF\nBaxYsYLIyEgMBoMUcN0MzJgxg40bN/Lwww+LMr/++munGOKmUFVVhVKpFDdqde/eXVxpr+HKVfWa\nKj4Gg0Fc0dfr9YSFhZGbm0uXLl08uUQJF5k4cSKrV6/mnnvuEb9bv369VPr3KmTixImkpKTw1FNP\nid95quujR4+SmpoKVJeSrcswWbJkCbm5ucjlcm677TZxAWPhwoVcvHgRhULByy+/7HYfJJqONO4l\n2gJuGat/+MMfGD58OJs2beKmm25i586d/PTTTy6nMYmMjGTLli3i58sDq2uoL+AaEA2W0NBQYmNj\nOXnyJImJiVLAdTPInD59OrfeeiubNm0iISGB1NRUfv75Z1avXu3W5opz586JEw2ojo07efJkk1bV\nAYqKisjPzxdjaaVV9eaXed999zF16lR+/PFHBg4cyN69e8V7oa2VYZQyQXgm84EHHmDKlCn89NNP\nDBgwgNTUVHbu3Mm6devc6j/A3r17GTp0KGFhYaxevbrWJPXAgQPk5+czb9489uzZw7p163jmmWcA\nSEpKQqfTuZXkXMIz7rrrLmbMmMHWrVsZMGAAe/fuZffu3axataq1u1YLady3rszmxK2eJCYmcurU\nKVJSUvjpp5+YMGECS5YscXmlTavVAtUB0/7+/mRmZjJs2DCnY+oLuLZYLAiCgEajwWg0kpmZKSYS\nlgKuvS/zhhtuYO/evfz0009s3ryZYcOG8be//Y3g4GC3+m+z2XA4HOK5drsdu93udExjdSrMZjMp\nKSmMHz9eDN6va1X9vvvua9RYBffzyZlMJjQajVthCO1R5o033sixY8dYt24dO3bsYMKECSxatKhJ\nK+znzp1j8+bNOBwOcYJ5JXVlAbFarSxduhSbzYZSqSQuLk4c93UhZYLwTGZsbKzTuE9OTuadd94h\nODjYrf5XVFQgCIK4mBAQEMDRo0e57rrrxGPS09Pp0qULwcHBTJw4UYxnhGpj9cyZM02WK+E58fHx\n7N69mw0bNoghXP/4xz+aVIUoLy+PHTt24HA4iIuLIy4urtYx0rhv/zJdxR3PsNtmc00psDlz5rh1\n/vjx49mwYQN2u52kpCT0er34cOrfv79TwHXNzQvVD72aGZ1Op2Pw4MFeK/0nUTchISHMmTOHGTNm\neHzzepLGBKqN21WrVhEfH+8U61rfqrrRaGywP+1lhbutyNTpdNx1111OVWQa+xtfzpYtWxg2bJgY\nr9y5c2c6deok/l5fvLJKpWLWrFmo1WpsNhsff/wx3bt3b5ZynRLVeHPcZ2RkOK3yBAYG1tpUm5eX\nR79+/cTPCoWCU6dOORm0V5Kbm1vLw6PT6Zz2RNSFu54AhUIBVI+npk7+2rPMqKgoHn30UR599FG3\n2ktNTWX06NEYDAbWrVtXy1iVxr1EY7hlrFqtVrZv386WLVucHjgymYz//Oc/LrURExPDY4895vSd\nKwHXQUFBbhvIEk3HarWye/dufvnlFzIzM3E4HEC1rhcuXNjk9jxJYyIIAuvXrycsLKzW7FpaVW9+\nmSaTidTUVHbs2EFeXp74m6v3Qo1XpGYlNjIyktzcXCdjtb54Zfj93rFYLDgcjjblorra8Pa4dxeZ\nTNbg75s3b6614jps2DC6devWnN2q9axpCVpLpqfv+6qqKhQKhWigduvWrVbIljTuJRrDLa2/8cYb\nfP7554wbN86pHnBjD5bWQIph8Uzm+++/z8qVKxkzZgxdu3YVXfQymczt2LXhw4ezdetWHA4Hffv2\nJTAw0KVV9bNnz3L48GHCw8NZtGgRAKNGjWr2F5NENQsXLuSrr75i2LBhTrtyXR33npZddDgcfPzx\nx+Tn5zN+/HjJo9KMLFy4kJSUFCZMmEBMTIyTseoO119/Pfv37xc/FxcX19rZHRER4WQM2e32RjdP\njhs3rs6V1eLi4gbPU6lUbk3eFAoFfn5+lJUyftu0AAAgAElEQVSV1Qpfaoz2KvONN95g5cqVjBw5\n0qmmu0wma/TvDNXPbT8/P/FYvV5PRkaGy/sUpHEvAW4aq59++in/+9//PDISPKlgBeBwOPjkk0/w\n8/NzckleSXPGsORX2JBr9AQqLShkDcdZektmS8ewfPHFF6xZs4akpKRaFazc6T9Up76aOXOmUzuu\nrKp37tyZv/zlL27JlPCclStXsmrVqgbdsp7SULyyXC5nzpw5FBcXs3z5cjp16kRERIS0ua4ZZK5a\ntYp169bRv39/r1SyqYlRO378OCEhIZSUlNRKWdetWze+++47ioqKSE1NFfc2NETHjh2dDCio9qi4\n8mxy9/kL1e8Vd4zA9ijziy++qHfcu9K22WzGbreLx1qt1lrX0dbHvclsQy2vDo24mse9pzKbE7d6\nExkZ6XG+PU8qWEF1DExoaKhbcXjeorDcTLBSR0GVhQ4GFcYqGyWVViL8tcjlbW+V2R06dOhAaWlp\na3dDog3QoUMHysrK3D7f03jlGgIDA+nWrRvZ2dlERERIm+uaQWZkZCSVlZUeybySYcOG8dNPPwHV\nK61RUVGsXr0aqM460q9fP/bv389HH32EXC53mtAuWLCAsrIyBEHg1VdfpU+fPkydOtXjPkk0zrU+\n7rMvGskrtqI3OtD5XN3j3hsymwu3rmTWrFk89NBD3HffffTt29fpt5EjRzZ6/uUVrACxgtWVboH6\nYlhKS0tJT08nOTmZ3bt3u3MJHmNzCAiCgENw0NFfi+Cwc7rQhEwGggBRQe65yNsaM2fOZP78+Tz4\n4IPExMQ4rbAMGTKkFXvWOFIIiHdl3n333Tz77LPcfvvt9O7d2+k4V8ou+vj4IJfLuXjxIgaDgdzc\nXG666SanY+qLVzYajeI1mEwmMjMzufHGGwFpc11zyJwxYwZz587lgQceoGvXrk7u56FDhzaprRp6\n9uxJz549xc9Go5Hp06c7HfPQQw/Vee6f/vQnt2RKeM7MmTN5+umnmTFjBr169XL6zZV3gCf7FNrC\nuC8qLifrQgXXhRnEEJOrddx7KtNVdDpdk89xy1h9++23kclkfPDBB7V+O336dKPne1rBavPmzYwd\nO7aWIlrSLZBbaESjViEgQ6tRYbPZCPTVUFZpI9jPp9H22otb4J///Keo65q4tRouTy3TFpHSmHhX\n5oIFC5DJZHzyySe1jqtJ9t4YSUlJ/PDDD2IKG8DlLCBr165FEAR8fX258cYbxXzL0uY678tcuHAh\ncrlcHPeXu2ld1bXE1cFHH32ETCbjs88+q/Wbq/dCTbnVmnFfU24V2v64r6iyEOarRKWoHgNX87j3\nVGZz4paxmpWV5eVu1Ka+GJa0tDT0ej0RERG1DOMWcwcWF9P7T3PIHjwcYeJENBFd0Wg09HJBjtsy\nL6Ml3QInTpxosozGcDfXJsC6detIT09Hr9czd+5cr/ftaie3uBK7AA6HgMFHSZBe3eDx+aVV5FQU\n4iu3OLny3MXdsovh4eFup825mhEEgQulVaiUcoJ9634eFFeY2ZtZgL9GTY+OvmhUikbb3bNnDyqV\nitDQ0Fqx6hLXFtf6uA/Bgu9/PkM+PBl718hW7cu1jEcRtPn5+Wzfvp0ZM2ZQUVGBTCZzyTD0pILV\ngQMHSEtLIz09HZvNhtlsZs2aNUybNq3l3IE2G5pbpxK9ZQvq99/B1rkz5rFjsYwbhy0uDlzYMdve\n3AJWq5VNmzZx8803N0nXdeFurk2AhIQEkpKSWLt2rVuyr3ZOFRgxWex0DdXho1ZeClWBHSfyKa2o\nRO+jpKLSRrCvmrIqa4PGqvzCBeRbthN+4TxnHniYTkHVxlBhYSGpqalMmjQJo9GITCZzy60j4Rk2\nu4NDZ0spMloJ9VPj76NCqfg97KWsykruxSpMViiusGKzQaXV4ZKxWkN+fj4bNmxg/Pjxkq5bmIoq\nGzaHQKh/26gOd62O+witDJmxFO3LL8KD9+Lfrx9VAwdiSUrC0q8fuJkVR6JpuGWsnj17ljvvvJOM\njAyMRiMzZsxg27Zt/Oc//3GpBJsnFaxGjx7N6NGjxfN37dolugxazB2oUmGdPBnztGmEBgRQsXEj\nyk2bMMyeDXY7pjvuwHTHHTg6dPCezCtoKbdATk4O8+bN48yZM1RUVDB27Fh27NhBSkqKmD6qKXia\na7Nz584upUtpLoxmGxcrLHQKNbRaH+ojt7iSYznlhPupKa+yU1Fl41ShCbVczoUyK/klRvQaNV1C\ntZSaLMSEXPaiEQQU586h3rMH9Z49aFJTkRcXYxgwEOONw4gK1JKTk83cuXPJysrCZDIxadIkdu/e\n7fa90Jw0R7xyYbmZogoznULUTrHDNruDSosdg0/jRoU345WLjRY0GjWmEjMVZgG5Uo2P9vc+bD1x\nkQullVRZ7UT464kM9iHA1wcfTeP9zM7O5g9/+AOnT5/GZDJx7tw5duzYwcqVK/n000+b3H+JpmGz\nO8jINyIgoNWocL1GnPfJycm5psc9UVHk/eWv7DpZiN5iYlhRBkEHf0H3zjso0tKwDRqEddw4rGPH\n4rhi45jbMi/h7j4FQRCqJ6ZaLfImppu7qsqtvv/++0yaNIlnnnlGNCKHDx9eb3B8XbhbwarNoVJh\nvekmTAMHUvbKK6iOHEG3fDlhI0diHjQI0913Yx4+HBSur2a0JZYsWcLYsWN59dVXxaohN954o1iz\nu6l4mmuztcm4UEFBuZnSKjsRHcJbuztOlJisdPBTg0xGiEHNthOFlJisGLQKIgN1nC0opaM/BB/a\nT6/jR9BUlBGYn4MyKwtFVhYOgwFrUhLmQYMwzp6NLTYWlUZD2CVX8JIlSxg9ejRz584VN1h5ci80\nJ80Rr3zmQhkyGZwrcNAxLESMHf4tpwyLXSAiQEu4X8OhOd6MV1bLBPw1MjoY1ATolZw6X8L14b7i\nOTqVQJXFRlSwHoMG9mQUsPVIHncOjiagkfCPDz/8kDFjxojjvrKyksTERJ588km3U9bVhTtlFxvj\nathY6XAI6HVWbHYBXx9Nk2SeKTRSbDTTKViP3gvXuXTpUiZMmMATTzzB9ddfj4+PDyNHjuTZZ591\nO9d2c9Fc+xQCNBAVpMVgCKM8LgrGjiS/tApjwUW6HdmL749b8X3rLRzh4VSNGoV59OjqVddL7/3m\n2KdwptBIaaWNbuF6fNS/3xf55WbSz1fgr9eiV0GX0KZ5QK+qcqtfffUVR48eFUuy1dCUhNHuVrC6\nso0rE0u3FCaLHb38CgNUJsMaH09pfDxl//d/+Kxfj+Hdd/F/7jlMd92F6fbbcVyRE7AlMZpt5Jea\nCfNTuzzb2rBhAzt27PBI102loZx79dFSm+scggybIMfqqL7+Vs1/Z7GgOHsWR1o6Rb8cJionh0DB\ngp/djKKkhFsuFFKk0nG2/00Ivr4k5BfT/+fNqJUyTEmD0XSJxjJ0EFVdunBSF0ypwoeOgT6E+2uR\nAaorZG7cuJHt27eLnpGal6dMJnP5enJzc/npp59wOBzEx8c7TUpqqCteubS0lLVr12I0GtHpdCQm\nJtZ5bnMSalBzusBEdIiv0/dyuQzB5kDZwtnq5DIZnXygx9PPYDbbKHn9deD3vg3qGkxiTAAo1Fwo\nKuPH3wrRKGUczytn8PUNl6tsjXHvLdrzxkqZ0Yjq119R79/PwP37cfj5UfXss+gGDnRZZmGpEUEQ\nyC+pQKeWU1hixGS2IZPLiAz0cdKhyWKjxGQl3E+L4rJ0i5df57p169i6dSsWiwWozq1ak9HH1b9h\nXl6e0waruLi4WgZLWx33NfTtFEBAYBDlpcVYrVYulJtR6Hw5PWwc0bdOBbsd1YEDaP/3P/yffx75\n+fOYR4ygavRoZBMmgLrhCWJTKDFZOZpTgSA40CgVxEb8Pu7zSqqwWB0Um6z4BqgpLDdTVmWjU5CP\nU5hQe8MtY3XixIn8+OOP3HLLLeJ3W7ZsYcaMGV7rmLdojln2gawiMi9UEB2sx9/PUPeM18cHYfZs\njLNnozh8GPV//oPvmDHYBg3CMmsW9rFjW3xmf/hcCYIgoK6CSBdTa40dO5bU1FQ6XApp8PHxYevW\nrUyZMsWt/nfq1IlffvlFPLe8vJzOnTs7HeNKzr0rqWtz3bBhwxgxYkST+9gQI/wCOHWhXNzM0hwr\nQ5fjEASOHzmF+tBBupSch1OZBJ48CSdPwrlzOCKjKI6IpiIkGken69BHh+MTEwHBwRSh5sDGnQQc\n/RX56TNobAJLxj1A0eCh3DYklmG9fp84+WQVgtWOwV9HaB0hDoGBgUyaNIkjR45wyy23IJPJCA0N\n5eeff+a2225zKtjREKtXr+aWW24R8ysPGjTI6ff64pXlcjnjxo0jIiICo9HI4sWLiY2NbdE8gPac\nPEbMfQiT3o/K6I74+umxGwwk+vtjDQxG6BmLLTYWwde38ca8gHr7dvz/7/+w9uuHPDKSwFsnceqV\nN9BPvVnM86xSKNBoVaRV2ogI1KBVK+gV0XgIy8iRI9m5c6dTIvjt27dz8803N9v1XHMIAoqsLNT7\n94v/FKdOYbvhBiyJiVTOnIkyM5PACRPg7ruRzZkDdVRvsjsELDYHPurqiUWXEB+KLxmgPxy7QFFZ\nJb5aJR39tQT5qtFdtgp3qsCEDLDbBaKD644/HTlyJLt27WLcuHHid029F3bt2sXQoUPx9fVl06ZN\ntfaotOVxX4NCLkOrVlJ+6XO4QUNZlY1wwyUjVKHAOmAA1gEDKH/uORQ5OWh++AHd6tVonnkGn969\nqRo9GvPo0di6dnVpb0t9+Kjk+PsoqDBDmMF5oSDEV40ciO0YSJnJxNHsMkL91JwvNbfrlJpuGatz\n585l+PDhzJgxA4vFwpw5c1izZg2bN292uQ13K1hZrVaWLl2KzWZDqVQSFxdXq0785TTHLLu4vBKH\nzU6FyYxOp2t8xtutG7z+OrLnn0f79dfo//Y3lE89hfGOOzDecQeOSNd3GHoys9epFBSVGlH6Kly+\n3rvvvpuZM2fy008/YbVaefLJJ/n2229Zvny52+5Ah8PBqVOn8PX1JSsriz59+jj9Xl+8ckPUt7nu\nylKMV+LOprNwH1DKLIC+eTa6WSyo9u9HtW0bqh9/JPbkScpie1HZMxZDQh8qBgzAEhODvXNnSm0y\nMgsqOFdgIshXhTbCD61/9cpnWk4pF0aO4aeeSVRaHJitNkBGjK+aYI3N6W8TqnVglNnxESopKKgS\nv798Q98dd9zBrbfeyuTJk7FYLNx///1s3LiRVatWNfp3huqxaLPZ0Ov1WK1WOnbsyG+//caAAQPE\nY+qLVzYYDGLKGr1eT1hYGLm5uY2W4/Qmmg4hbH/iRXxNRjpr5YRaShEuXkReWoouIwPl8mUo09Nx\nhIRgi43F2qMHth49sMbGVr+cvPSCVWRl4ffaa6hOnKDslVeoGjsWATjWvS99X36a4vQ0hGfmg0yG\nze7gVE4Z50ur6BMdwHVhevxciK2dNWsWM2fOZOfOnVitVp577jlx3Eu4idGIOjVVNExVBw4gaDRY\nExOx9OuHacYMrHFxte4Ty/33E/KvfxE0ZAgVDz6I8ZFHEC7b2JRxoQKTxU7HAC3h/tpLLmEZZqud\nEqMFOdXGjVwh4+T5CgxaJV3DqidUflolJUYrBm0dpsClVIU198LNN9+MxWJp8r1QsyIbEREBVC9G\n5OfnO02E2vK4r48wfy1hDVR+tUdGYpo1C9OsWfgIAo4ffkD7ww/43nEHglotGq7mpKQmPxs0KgVD\nugaCXI7NIVBltaO9tHFSEATsDsgtraKotBJjlQ2dWkHXMO+t7LYGbhmr8fHxpKens2bNGkpLS+nb\nty9vvvlmk1aZ3K1gpVKpmDVrFmq1GpvNxscff0z37t3FeMqWIKGTP+dLzcQ0cZONoNNReccdVN5x\nB76ZmSg+/ZSwsWOxJCZivOcezCNHghcCmu0OAZtDQKN0XlG+voMBmcNKUYUFH7UCm11Aq5I36Nq7\n4YYbSE1NZfv27eTn59OrVy+ee+45p7jTpuJuzj2AlJQUsrKyqKys5N1332XEiBEkJCS071ybgoAy\nMxPN9u1otm1DvWcPti5dMCcnU/b881y4IR6zXF0dexgWRuVlqYR0KogwqOgcGCgaITW/RRhU0MEX\nPx8lDoecQL2cED8NWoWCIF3t69apqNfwttlsdO/enR07dvDtt99SUlJCz549eeaZZwgICHDpb5Cb\nm4u/v794rL+/P7m5uU7HuBKvXFRURH5+PpGXJnktFQISFhaEYepIjucayfc3YAhSguCcexi7HcWZ\nMyiOH0d54gQ+W7ZgWLgQxdmz2Dt3RrjhBtTdu2Pr2RNbjx44Ond2KZ5dqVRCeTmGN95AvWwZprlz\nqVi8GLRaaq5AMXQIB75YTeKzjyNkZZD7zj84VVRJx61biPl5G/JbJqOa4VrVpz59+rBv3z62bt1K\nQUEB8fHxvPzyyx6N+2sKQUBx5szvhun+/ahOncLasyeWxERMM2ZgeestHJcMuAabCgmBhQspvuce\ndG+8QdiQIZT/6U+Y7rzT+X1x6Tmedr6cM4UmLDYHHfw17D9VSFmlFYUcZIUXCTqfha6qCMWZM/Q5\nfRpFXh6yysrqf1VVyCorobIKS1AwRbv3csMNN4jjvqysrMnvAG/tU2itcV+DRznONRpsEydimjgR\nkyCgOHYMzZYt+P2//4ciLQ1rcjKWMWMwjxqFEP77XghRpkKB7MIFlMePozx+XDzf0aEDR+Y8xYWB\nN9E13FAdiy5TYHFYSD9fwekLJdgFCPTTYNA1Xr7Y0+tsTtzuTXBwMA899FCTNlXV4GkFq5qKGBaL\nBYfD0eJ/VB+1ki6hSlRNSAFzJfa4OCreeIOyF19E+803GD78kIDnn6/OJHDnndijoprcZl5JFRfK\nqiivsuGrUdIlVE+A7vdBabM7yC+3IJfBb9llCECIQU1UUMPpR4KCgnjooYeYOnWqVwKu3c25B7TJ\nUBN3kF+8iHrHjmoDdft2AMzJyZimTaNkwQIclz2ka6aA9U0qQgx1z8oNPioMPiq6RxhQqTVYLZ6X\nJg4KCuLuu+92Kn3sTRqLVzabzaSkpDB+/HjxOdBSISA1nDddQKmQU4m67qwQHTrAlZ4isxllWhoc\nOYLq6FFISYEjR6CgAHr2hN69IS6u+l/v3hAR8bub0OGAZcvghRfQjh4NR47g27EjVwYbhIaGwg1d\nIHkXF+95gOjEBDoJAoVxCVRMmETXf7yNkJlGxatvYNCpG40/DQ0NpWvXrm4945uCu162lsRksVFQ\nZiEySFfnPSozmcRY05pVU9RqLImJ1cbp9OkoBwyg8orCKk3B0aULxf/6F6pDh/B74w30ixeT/fzL\nhA9NpjD9LLb9p1AVniXy4DFCzuQgmM2YffT4q3To7GaiL+YRkZ+NsXss6m5dscXEUDlpEvaOHRH0\negQfHwStFsHHh/QK8A0OxWaqwE+jkcb9ZXgl9CssDEaMgDffhIICNN9+i2bDBgyvvALdu8OkSRAZ\nWf2MOHKEwCNHwG6H3r2x3dCLc3EDqJz3DLFFZ+nx1NN0iYxG9vln+IdGEhwSwtmCcr7/9RynL5pB\nEIgMFTD4B6JVu24rNXeIW1Nxy8p7+eWX633Qvfbaa42e72kFK4fDwccff0x+fj7jx4/H/1IcT7ua\nadXI9PfHds89lN5zD4rffkP7xReEjh+PtV8/qu67D8uYMU6z54ZkVtmq0KrVFFZYCdApRRklRgtn\nCo0oVRbsDigx21Ar5PhqlSiUygav/e233xZjeisrK52qWD333HNNvu5rFrMZ9b59aLZvx2fnTvwz\nM7EkJWEeNgzjnDkexzA1hlIhx9Npxt/+9rd6x/3TTz/d6Pme1gi32+2sWrWK+Ph4evToIZ7TkiEg\nABqZFeQ+qOxVTiETjRIRgSYmBvPYseJXsvJyFGlp1aslJ06gXL8e5fHjYLNh69EDe48eKA8fRgYo\n16yhuHv36tXvRq7t0POvUX7nw1QqVJRr9FhsdmKSkhn2/DyKcwpIf+VVOgXX/1xsqXEfGhrqtpet\nJTly8jy2n/dw/uJFjBo70dYydEWF2PILUZw7h+7MaWw1q6bTp2N5881am2mVGg14IZOCtU8fcr5Y\nQe6qDfR+4Xn0JRfpotNTGdMFR2wsqp49+Dk6ngqHDHmFEXlZKVUKDTuH3Iz/4AEM7dep0WIgMf4y\n7CoNWr2Dd95+Sxr3NHOO8wkTqv9Zraj27MGx8VtkBw6hSYhH9/TTlERHYw0NBZmMUpOVzPwKZMDF\nrn2o+GI9CSsWE3LjIEo+/BDrsGFoBYHeR3Yx5l/vURAUQcUdd1LaJ4xyFzZYtUS5VXcmm24Zq+fO\nnRNvXkEQOHjwICdOnOCOO+5wp7k6aWimJZfLmTNnDsXFxSxfvpxOnToRERHRchWsLsOr1aQGDIAB\nA6h66y0Ua9fiu2gRsueew37ffZjvvRd7p86oFPI6ZdrsDuQyE3ZkTEyMAWToNErsDoGs7AouVjrQ\n2CxkFVSSU2wiJlRPYrcOhPlpRV1WWWyUVloIMfiIO0MvXLjgpOtDhw5x8uRJZsyY4XZRgGsCQUB5\n8qS4cqreuxdbt26Yk5Op/OtfKe/Vy6u7Q1uC3Nxcp3vh6NGjZGZmMmXKFJfO96RGuCAIrF+/nrCw\nsFox6i0dAtLRX0NoaLBblZ1qydRqoU+f6n+XIS8sRHniBKq0NCoHDsQ+bRqh4eHY6pF50WihxGQl\nKkCLWqWga4iW86ooSiut5OWZyC8xYTGoOf/8u9z9x7tRhIZgnf94vffguXPnUCgUaLVaKisrOXz4\nsKhrb6az8dTL1lLoKyuIXf05Zb4BKKMjKe8Ygk9cHFn4YOnQAeJ7E9Wh5UIkzDaBopuSWdhxJdG+\ncsIigujgpyHMX4tGJiP+YiV7s4qRyxT8ll1EiclOhyAtI27o2KihCtXew9BQPwoKzNK4v4LmznFu\nShzAsYhYEGT0iPLnuuiOWK8I/YoK1KCWyzhTZEJQyDk753EUQwcT+NhjlE6aTKXJQsIPmzn4x+eo\nSD9DwoI30L73V4T+/RC6XoftuuuwdI7BFBWNNjjQaaGkxGghzwgGWRUKWdMz8zQXblloS5curfXd\nv//9bw4cOODS+Z5UsLqcwMBAunXrRnZ2NhERES1XweoSzTrTmjoVpk5FceIEmmXLUN80BKtfAGql\nDKUg4LBaEWw2ZHY72O3YrTaSTCbsej32/onYBw3CMngwv0Vcz/lSKwq5jPBgP46fKcRWeJHrNv4H\nv+f2Yu12PYpBA7H1788Rv45YUXDRt0JcdVmwYEGt61y+fDmHDx9u9O/aFJqjEkpz5lt0CGA0Gp2y\nMsgKClBe2hSl+uknBJUK24gRWO+9l6rFixEuuVXkcjk+brgDWyrHY30yP/7441q/L1u2jMOHD7vc\n9vDhw9m6dSsOh4O+ffsSGBjoUrzy2bNnOXz4MOHh4WIi8lGjRtGtW7cmX1N7wBESgmXIECxDhgCg\nauQ+ziuuQi6H3DIzMcE6CisslFbauFBqIT46gIt+KoqMVgJDgsn97yqi3nwNxejRlP71r1iSk2u1\n995772GyClgVOnwvvbT++9//cuTIEa9ep6detpbypvUdHMfFr9dSUmHDYPDFz1eOFQcUV1JlshIT\nqkOlanhMetODF+KvArmCbhH+VNkEOvhrUF22atYpXEWncD8UShUVpg7Y7IJLRSvqkvnRRx/V+r3m\nHeDq9QwdOpRt27Zht9uJj4/HYDBI474eFAoZPmoFdruATz2hhoGXwvu6hftSbrYRqFNhGTqUw6vW\nE/rS8xT6BPDdP74gR6HFFt2f/w2dyvCqbLpcOEPHi3n4bNiAKj2TkP/f3p3HN1Wnix//JG26JN03\nlhTaUtYRCrHYsmNBZBnXAURZR4QygDgObr9xGZd7r46XGZmr4qCA4AVHRsrSURTE0cFBpxShDKCA\nUGhL05ampXRNlzT5/VGba1iTNKFp87xfL1++cnLOeb6H53ybb07O+T6FBSgUSszdu2Hu1o3mbt1o\nConEv+8A9FPuoGf4jZ914WoUFmcmtbyCixcv0r9/f0pKSuxaf/Xq1daf8Ddt2sT8+fMv++ln9+7d\n1kpZR48eZdasWdTW1lo/eOvq6tiwYQNz5syxPjF4qUsf4LiStsy552ztbEdiNpstfHf6PP7FRfTW\nhtEjtjvllZU0mc3g60tlg5n8inrOW1QMDGwm7vQx/LKz8cvORnniJOf6DiJv7O34B/gTkH+Gvp9u\nJ3fIMKrvvpvQslJ65R3H79AhlIV6Kvv2p/nmm/FJSaZJp6NZq0Xl52dznJWVlYwdO5bDhw87/G92\nNd3dMP+su3J/obaRw+eqiKqtIbngMOrs/Si+/heq8yXUDxtOc9pYGsaOpTk+/oo/7Xv6+eZITFec\nCx0p93Dj8mC2WDh9vhZTs5mbeoTTrWuXq8Y8X1nfcmU1IhCNvy/nyus4XlJDj/AAEruFo2hu5EJd\nyxfq1sIF/p991jL11ZAhVD7//GUP+5wqrSMsLBxjTRU9I/zd0u+NRiM5OTnWe9EPHDhAdXU148aN\ns66zdetWBg8ebL1wsWbNGqZOnUpERARffvllu9y36O0c/by/UTpDv29LzNzSGrJOXyDI34eaJgX1\njQ2Eq1XEBPtz0WgiNjyQIXEtvwD8UFJNbb2JHioTXWsu4FNcjLKkhKaCIiwmM8annsDPxz1XVp35\nm+/UldUzZ87YvC4pKWH9+vWMHj3a7n04W8GqpqaG7du3Y7FYCAoKYsSIEVcdqHYWPkoF/XrF0BwX\nRUBQIERHYzYYMP948mosFsI09cT4KIkJ8ae+Tw/qJ08GYF9OHqpPdtPrwDdYVCqaoqPZ/cd1qAf1\np7rehLpLEJU/TnWkqKrCNycH/3//G9X27YT+7necMZloGvAzipJ0GOfO4vz5Ej788ENSUlJcfpwd\n4UELZVkZXX/3Inft34+/sQ7jLSmYRvx7dtQAACAASURBVA7j+3F3Udu3HyHBgR16Lrtryc/Pt3ld\nWlrqtnOhrTpDFaOmZjMm6lH6+NBovnbM+Ev22WhpICE6BE2AL8UXG6htaKJXtIbwoJ9cKbn7bqon\nTCBg5Uq6pKVh0ulouv12miZN4oxSiam2gTNlpUQHqThSWM5f/vIXhg8f7tKqRREREW36la1T3bfo\noTFPnz5t815paSmbN28mJSXFrinrrsYdf7s7Q79vS8ySqov4+amICAkgArhQ40NSXDhhahUllfX4\nKhXWdgyKC6Cx2fzjlFc9YcgQAPwUCvz8/PD/sTS6p3BqsHrpH5Pu3bszfvx4ux6uauVsBasuXbrw\nq1/9ysEWd3z+vkrwvXInVCoUxIa3nID6C3VU1JnoERlIaKCKmK6R/DDlTo6MvB1QYjKZ6K8NIkih\nQNczDM1P5tezhITQNHYsTWPH/rjAQr8ePeCbr+HrfbD6Tbp06cKoUaPsurHeUR3hQQtzUBA+w24h\nf/4ifAcnEx7QjLnZRES9CZ+6JrqF2Tc9SEc0cuRIm9fuPBfaqiNXMfqpLholjc1mAn2xb07nH6l9\nLVyoaSQm2IeS+maO5JVzpuQiEwd1sfm5GIUC4/LlKH71K/z27WuZB/KNN0hunVrox18GWnO9fPly\nt5RbzcvLIzQ0lNzcXMa2/v350bXmXe6s9y16UsxLLxq0nguPPfaY28pxOquz9HtnYxobGtCoFEQH\n+YDSj9AAJYE+FsL8gWBfNCrlZe0wXvL9p1OVWzW3YfqNG83bvmkZzQ0EBPhQ36yka2Agg3sF0qz0\nJSq0kTOltRRXmiitbiYhJoCo8OtfkS4vL6fwQh0Vxma6haiICnbPYKyjPGhBQADGuXPpqlJZH7Ix\nN0NwgO+VJ9buRAoLC9u8jyuVXbzU1a6e79ixg1OnTqHRaFiyZEmb29IRRP44LZmjZU67hQVYvzip\nfP04ZLFgMpkpq2684hcqi1pNw+2303D77VSazZw/dgz13/+OJjcXw9tvu3VQ4uyvbOLGkH7fcaT2\niqC8tglteAA+Kn/KLiqICm6Zpi5c7fj90p6k3T5dnf3J19Fawd72TatbkA8XahuJVPtZ9x/ka6HR\n14I2IpDKunoMlfWUXKyha4ivterFtXQN8WNQYst9M668qvJTFy5c6BAPWrRyybRlnTzmlfzrX/8i\nLS2NoKAgPvroI5upaODaV891Oh2pqals377dJW3xFhHB/vTuosFkgTC1L7mGWkIDfK86Py9KJU1J\nSdQlJ6OJjr7uNFlt5eyvbKLjcLbcKki/d0Tr3NoAgf6+KEM85wGptnLqE6hHjx4oFAqb+xmu9Lqg\noOCq+3D2J19PqhXsiTQBvjY/7QP0iFQTGxFIblkDgT4WjE3NBAWo8LVjzrWhQ4eiVLZUuTKbzVgs\nlivm+sCBAy4/lktd6/6ZDj1BdAeJ2dZ+X19fj6+vr7W8bt++fa1X1Ftd6+p5XFwcFRUVrjwsr6BQ\nKLilV8s9noUXjDQ0NVNUb7IOVusaTVz8sZZ865R1ntTvRfsaOnSoXf3+audCW8qtgvR70cKpwerT\nTz/Nn/70J2bMmIFOp+PQoUNkZGTw61//+rIrJVfSlp98PblWsCdTKBT014ZyIdiHkAD7r7AtW7aM\ndevWMXPmTHr16sXhw4fZuXMnDz300GXfjtuirdOZyYMW7o+5bNky3nnnHe6++24GDRrE0aNH+eij\nj1i4cKHNVDJX+zc/d+4cwcHB1vcDAwP54Ycf7L56fjVyVd3+mN3CFTSU1REdorIuO3e+DosFFMom\n65R1jz76KGvWrOGBBx6gT58+HD58+Iq5duZYRMeybNky1q5dy1133cXAgQM5evSoQ58Briq3einp\n954X052cnmd17dq11qf/7733XiZOnMjjjz9OVlbWdbdv69x6rTp0reB2iOnvpyIy2LH5TDMyMnj9\n9deZMmUKFRUV/PznP2f8+PG8+OKLfPrppw634WoCAlruo5MHLTw35ubNm1mxYoX1lp3bb7+d0aNH\n89JLL/Hxxx9b171aHJPJhNlstr7f3NxMc3OzzTrOPH0qV9Ud00Nr+7pnsx8XqhuI7xpC5I/3pG/d\nupV3333X+jd+zpw53HvvvXb/jRedx5YtW/jDH/5g7feTJ0/m1ltvvazft4X0e4l5PU4NVg0Gg803\nJYCwsLDLvi21hdfVCvbQmD8tjdcaMyEhgfLycpdPPSIPWni28vLyy74QhISEUFZWZtf2bS27eDVy\nVb1tMUN8ICRMgbm+GkN9NdAyHWHrg6mtMc1mM8XFxW2aruhSN3rqOeE46fctOlu/d0dMe92wcquP\nP/44Dz74IEuWLGHIkCHk5OSwevVqHnvsMbu2b+tPvl5RK9hDYi5atIi5c+eybNky620A7733HosW\nLXL5h5Y8aOHZFi1axPLly5k3bx433XQTx44dY+PGjSxatMiu7dtSdvFa5Kq662MuWrSIpUuX8sgj\njxAXF8fhw4fZuHEj6enpHjdd0aW8bQYYd8dctmwZjz/+OPPnz2fQoEEcOXKEDRs28PDDD9u178DA\nQJRKJRcuXCA4OJiioqLLpsGTft85YrqTU4PVhQsXYjabycjI4MknnyQlJYV58+aRnp5u1/Zt+cnX\nG2sFt2fM+++/H4vFQkZGBvv372fIkCFMmzaN+++/36NOZOF+s2bNwmKxsHPnTv7zP/8TnU7H9OnT\nHZrrdvjw4TZT2AQEBNh99TwjI4O8vDyMRiOvvfYaaWlp6HQ6lx+nwPow60/7vaO5bi/eNgOMu2NO\nnz6dhoYGduzYwfPPP49Op2Pq1Kncd999du87NTWVzz//3NrvAen3wiEuK7fqqNapq1p/8h02bJjN\nyQuwZ88evvvuO5upq/Lz81m/fj1dunSxzj14rVrB3l5+rb1j2ktKbna+mPaS3He+mPaS3He+mPaS\n3He+mPa6YeVWAU6fPs327dvZt28fmZmZfPvtt1RVVdnUdL4WZ3/yjYuL44UXXnC22cIJZ86cYcOG\nDXzxxResW7eOf//731RXVzNq1Kj2bto1yc+Bro955swZPv74Y/bv38/7779PTk4O1dXVjBkzxuF9\nC8/WUfu9cL2zZ8+ya9cusrOzWb9+vZwL4oZzarD62Wef8eCDDzJ9+nS+/PJLoOWn/UceeYRvvvnG\npQ0U7Wvv3r0sX76cGTNmsG/fPqBlEPjcc895/B8q+TnQtTE///xzli9fzs9//nP++c9/Wvf3H//x\nH/ztb39zeN+t3PGwoHxRaVvML774gmXLlnHvvffy9ddfExgYSEhICM8//7zcM+5lWj8Dfv7zn1s/\n3z31M0D6ffvGdCenWrJy5Uo2b97M6NGjee+99wAYMGAAJ06ccGnjXEFO3rbFXLduHevWrePWW29l\nw4YNBAYGkpSURG5urlPt//rrr/nhhx8ICAhg4sSJV3zis7q6moyMDC5evEivXr248847USqVGAwG\nMjMzKS4uZvz48YwYMcLh+MJ5a9as4a233iI1NZWMjAwA+vTpQ25ubpv37WxFu6uRLypti7lq1Sre\nfvtt7rzzTt5//32MRiM9e/bk1KlTTrU/Ozub3Nxc/P39GTdunHU2mZ9+UZF+75mk37fwhn7f1pj2\ncuYChVOD1YKCAps5UaGl4lRiYqLd+2jLSepIrWA5edsW89y5c8TFxaFWt8zPajQaKSwsJC4uzuF9\nlZaWcu7cOe655x4KCwvZu3cvkyZNAmxP3r1799KnTx9SUlLYtm0bx48f56abbkKtVjN58mSP/FLk\nDfR6vU3VGWh5sj8uLq7N+3a2op1wD1fmurS0lOLiYu69914KCwvJysqy9vufkn7vmaTfC09w/Xqb\nV7Bw4UJefvll62ClsbGRN954g7lz59q9j9aTdO7cuWRnZ1NbW2vz/k9P0hEjRvDZZ59Z39PpdMye\nPduZpgsHzZw5k//5n/+xyfW7777LtGnTHN5Xa4m9gIAAevfufdUSenq9nuTkZPz8/EhKSkKv1wMt\nFcu0Wi0+Pj7OH5Bw2syZM3njjTdcci781E8r2oWFhVkr2v3UpRXt7J3jUThH+r1oJf1eeAKnrqzO\nmzePhx56iMTERGpqaoiPj2fUqFE8/fTTdm3flnKrILWCb6Tp06fzxBNPkJiYSG1tLcOGDeOWW25h\n2bJlDu+rtLTUJseBgYFUVVXZTDfW1NREbW2t9RaD6OhouyrmSPUy98ecOXMmjz76KCNHjrSeC6mp\nqSxfvrxNZTfbWtFOcu/6mDNnzmT58uU2/d7ZXJeVldGvXz/rdmq1mrq6OkJDQ63rSL/33JjS71u0\ndx46Qkx3crg1ZrOZnJwcPvjgAxobG9m3bx/Dhw+/rKLVtbiq3Oql5OR1bUyz2cyJEydYt24dGo2G\nTz/9lJtvvtnmQ8YRSqXSpg0KhQJfX1+X1BeX6mXuFRoaSk5ODtu2bXO6319NVVXVdde51gx7knvX\nMpvNHD16lIyMDJfk2t/fn9DQUOttXL6+vkRERFy3QpE9JPfuJf3+ct6S+w5fblWpVDJ9+nTOnz9P\ncHAwkydPdke7pFawh8RcuHAh58+fx9fXl/vuu8/h7bOzszl06BDQ8hBeY2Oj9UOrsbHxssplKpUK\njUaD0WgkMDAQg8GAVqu9bL+Xkupl7o1ZWVnJtGnT+O677/D19WXo0KE0NTW5pIpZWyvaSe5dH3Pa\ntGmcPHmS6Ohohg0b5nCujx49yvfffw9Ar169OHv2rPVLbnV1Nc3NzRgMBuvfAun3nhlT+v3/8bbc\nd4pyq+PHj2fLli088MADzmze5pP0auTkdX3MUaNGsWHDBhYsWOBUzISEBBISEgA4f/48X3/9NbGx\nsRQUFBASEmLNzU9PXq1Wy8GDB0lJSeHIkSMMGjTIZp9X+iIj1cvcH3PkyJFs376de+65x+n9XElb\nKtqB5N4dMUeOHMm2bdtYtGiRUzH79+9vLYVdWlrKv/71LxITEyksLCQsLOyK+5N+75kxpd/b8qbc\ne1KVSqcGq1qtloULF7J27VpSU1OtN74rFApeeuml627f1pP0auTkdX3MmJgYfv3rX/PBBx/YfHgo\nFAqeeOIJh/YVERFBTEwMH374If7+/qSlpVnb8/7773PXXXcRHBzMmDFj2Lp1K9nZ2SQmJjJgwACg\n5YrMmjVraGhoQKFQkJWVxdKlS/H393eoHcI5Xbt25YknnuAvf/kLOp3Opt87ei5catKkSTYV7TQa\njd3lGIXrde3aleXLl/Phhx+2ud/HxMTQtWtXtm/fbu33raTfez7p98IT2F1u9c0337RWnJo1a5bN\nfYfQ8q1XoVCwfv16uwI7W24VbGsFazSadqsVXFVVxcGDB0lOTr5skNyRY16aa4Dy8nKioqJQqVQO\n57oz6qy5v9Qf/vAHkpOTSU5OZvHixW3u951BZ8299Pvr66y5v5T0+8t5S+7bI6ZdLHYKCwuzNDc3\nWywWiyUoKMjezTo1vV5vef755y16vb5Txbw01+1xnJ6us+b+UqGhoZbf/e53Fr1eL/3+R50199Lv\nr6+z5v5S0u8v5y2599R+b/dtALfddhvp6emkpqZiMpl49913r7je/PnzXTaQFu3j0lxv3ryZnJwc\nNm/ebPMUqOS68xs1ahQfffQRQUFB0u87Oen3opX0e+Fp7B6srlixgk2bNrF161aamprYuHHjFdeT\nk7fjuzTXGRkZFBcXYzAYbO4Tk1x3fs899xy//e1v+eSTT6Tfd3LS70Ur6ffC09g9WI2Pj+fZZ58F\nYNy4cTaT9IvO5dJcb9q0iXfeeYf09HS6d+/ezq0TN1KPHj0YM2YM6enpzJ49W/p9Jyb9XrSSfi88\njc8LL7zwgqMbzZs3zw1N6XgsFgt+fn7Ex8ffsCdTb3TMefPmtctxejpvyP2lMdPT029ITE/X3nmQ\nft9+vCH3l8aUft+ivfPQmWPaw+7ZAIQQQgghhLjRPKv4qwfYsWMHp06dQqPRsGTJEuvynJwcvv76\naxQKBX379mXChAkAZGVlkZ2djVKp5M477yQuLg5oKXSwdetW6uvrGTRoEOPHj3coZmlpKXv27KGq\nqorQ0FAmTJhgnbrLFTErKyvZvn07tbW1qNVqkpOTSUpKoqGhgW3btlFcXEy3bt34xS9+Yf125Yq4\nnkxyL7mX3EvuW0nuJfeSew/K/Q2ff8DD5eXlWYqKiiyrVq2yLispKbGsWbPGUlZWZrFYLJaamhqL\nxWKxVFdXW15//XVLRUWF5ezZs5Y///nP1m02btxoOXr0qKW2ttaydu1aS2FhoUMxP/zwQ8vRo0ct\nFovFcuTIEcuWLVtcGrOqqspSVFRkPZ6VK1da6uvrLf/85z8tO3futDQ1NVk+/vhjy759+1wa15NJ\n7iX3knvJvcUiuXdlXE8mue84uVfemCFxxxEXF2etsNXq9OnT6HQ6IiMjAdBoNADo9Xp69+5NWFgY\n8fHxANZypuXl5QwcOBC1Ws2AAQPQ6/UOxQwICMBoNGI2m6mrq7O+76qYwcHBdOvWzXo8MTEx6PV6\n9Ho9Op0OX19fdDqddR+uiuvJJPeS+5+S3EvuJfeSe8m983FdSQardsjNzaW0tJS3336bzMxMSktL\ngZaE/rSmfWRkJHq9nvLycusJDi117wsLCx2KOWHCBLKysnj11VfJzs62/gzhjpjl5eWUlpai1Wop\nKiqylraNioqynoiFhYVuO1ZPJrmX3EvuJfeSe8m95L59cy+DVTuYTCaMRiPz58+nf//+fPLJJ0DL\nU3PukpmZSWpqKk8++SS33HILmZmZbonZ0NBARkYGkyZNwt/f363H1BFJ7r2X5N57Se69l+TeM8lg\n1Q6xsbEMHDgQlUpFv379KCsro6mpidjYWAwGg3W9srIytFotkZGR1NTUWJcbDAZiY2MdillQUIBO\np8PHxwedTkd+fr61La6K2dzczF//+leSkpLo378/AFqtlrKyMuu+W+dXdOexejLJveReci+5l9xL\n7iX37Zt7GazaITY2llOnTmGxWCgsLCQiIgKVSoVWq+X06dNcvHiRs2fPolAorE/SRUVFcfToUWpr\nazl+/DhardahmAkJCZw8eRKAkydPkpiYCOCymBaLhczMTGJiYhg+fLh1uVarJScnh6amJnJycqwn\nojuP1ZNJ7iX3knvJveReci+5b9/cd/p5Vl988UWqq6vtXr+srIz6+nqam5vx8fEhLCyMoKAgysvL\naWhoQKVSERoaip+fHwDV1dVUVVWhUCiIjIy0JrSpqYmysjLMZjMajcamtrY9Mf39/amsrKSxsRE/\nPz9CQ0NRqVQui9nQ0EBJSQkqlQqFQgFAWFgYAQEBGAwGa9zo6Gjr+66IezXBwcE8//zzDm/nShkZ\nGeTl5WE0GtFoNKSlpTF48GB27txJfn4+UVFRjB492to5s7Ky2L9/Pz4+PjZTe5SWlrJt2zbq6+sZ\nOHAgt91223Vj1tXVERQURFpaGlqtlq+++gqDwUBMTAxjxoyxmcakrTHz8/NZv349Xbp0seZ2/Pjx\n9OzZ85rTmLQ1rieT3EvuJfeSe8m95+a+0w9WH3/8cX75y1+2dzPEdWzYsIE//OEP7d0MIYQQQngY\nuQ1ACCGEEEJ4LBmsCiGEEEIIjyXlVjuIXbt22dzT2dTUxJAhQ3j33XfbsVVCCCGEEO4lg9UOYtKk\nSUyaNAmA2tpaZs6cyZQpU9q5VUIIIYQQ7iW3AXQwZrPZOnHwtGnT2rs5QgghhBBuJYPVDub111/H\naDTy29/+tr2bIoQQQgjhdnIbQAfy6aefsmvXLjZv3oyPj097N0cIIYQQwu1ksNpBHD9+nFdeeYU1\na9Y4Nem+EEIIIURHJIPVDuIf//gH1dXVzJ0717osOTmZt956qx1bJTyZyWTC11e6uDeS3Hsvyb33\n6sy5t+ue1XvuuYft27fT1NTk7vaIq1i8eDE5OTns37/f+p8MVDuuFStWXPaA3COPPMKjjz6K0Whk\n3bp1pKSkMGrUKLZs2UJrobnc3FzGjRtHVFQUSUlJvPrqq9TU1Fj3ER8fz1tvvcWIESMICwvDbDbf\n0OMS1ye5916Se+8luW8bu8qtvvbaa2zcuJH8/HxmzJjBnDlzGDFixI1oX5s99dRTGI3Ga65zzz33\nsGPHDof33aNHD5544glWrFjBuXPnHNrWW2LaKzIy0mYe2c6upKSE3r17o9frCQ0NxWQyodVq2bVr\nFxs3bqS4uJhXXnmFyspKZs+ezWuvvcaECRPIzc0lLy+PMWPGcPz4cR588EEWL17MggULAEhISECt\nVrN69WpSUlKsdZ6F55Dcey/JvfeS3LeNXYPVVt999x0bN27kgw8+wM/Pj9mzZzN79mwSExPd2cY2\nKSoquu46gYGB1x3QXolKpSI6OhqDweDwVWdviWmv7t27u2W/nmzy5MlMnTqVBQsW8PHHH/P//t//\n49ixYyQkJPDVV1/Ro0cPAP70pz9x6tQpVq1addk+1q5dS2ZmJh999BHQ8odr/vz5PPfcczf0WIRj\nJPfeS3LvvST3znPo5oabbrqJ3//+90yZMoWHH36Yl156iT/+8Y9MmDCBV155hb59+7qrnUJ0OvPm\nzWP16tUsWLCATZs2MWfOHE6cOEF+fj5JSUnW9cxmMwkJCQDU1NTwzDPP8M033/DDDz9gNpuJiIiw\n2W9qauoNPQ7hOMm995Lcey/JvfPsnmf1xIkTPPvss/Tq1Yv09HRmzJjB2bNnKSkpITk5mfvvv9+d\n7RSi07n77rs5cuQIx44dY+fOncyaNYu+ffvSo0cPvv/+eyoqKqioqKCyspLDhw8DsGrVKk6ePMmH\nH37IxYsXWbly5WX3KHXWG+w7E8m995Lcey/JvfPsGqwOHTqUkSNHUl5ezl/+8hdOnDjBM888Q8+e\nPVGr1Tz99NNcuHDB3W0VolMJDAxk6tSpzJw5k9TUVGJjY1EqlcyYMYOnnnqK48ePYzabyc3N5auv\nvgJabmsJDw8nJiaGAwcO8Oabb7bzUQhnSO69l+Tee0nunWfXYPWpp56iuLiYP//5zwwbNuyK6+Tl\n5bmyXUJ4hXnz5nHs2DHmzJljXfbCCy+QlpbG4sWLiYiIYPr06ZSUlADwm9/8BqPRSFxcHI899hhL\nlixBoVC0V/NFG0juvZfk3ntJ7p1j1wNWu3fvJj4+nn79+lmXnTx5koKCAiZMmODWBraVPGDVvjHt\n5Y0PWAEUFBQwYMAAzp8/T1BQUHs3R9xAknvvJbn3XpJ759h1ZXXp0qUEBwfbLAsKCmLp0qVuaZQQ\n3sBsNvPyyy/zq1/9Sv5oeRnJvfeS3Hsvyb3z7Lort6qq6rISn2FhYXKfqhBOqq2tpWvXruh0Oqfm\nvhUdl+Tee0nuvZfkvm3sGqxOnTqVTZs2kZ6ebl22efNm7r33XqcDZ2VlsWfPHiwWC/369WPGjBmX\nrbN27VqKiopQKpXcd9991qmxXn/9dS5cuICPj0+nn1tMdE4ajYbq6ur2boZoB5J77yW5916S+7ax\na7C6YMECbr31Vj755BNGjhzJvn37+Mc//sHf//53pwN//vnnTJgwge7du7NhwwYMBgPR0dHW9w8d\nOkRpaSlLly5l//797NixgyeffBJomVNMrVbLtxMhhBBCiE7OrntWk5OTOXPmDBMnTuTbb79l8uTJ\nnD59mqFDhzoV9OLFiwAMGzaMnj17Eh0dzbFjx2zWOXXqFAkJCURGRjJlyhTq6+ut76WmphISEuJU\nbCGEEEII0XHYPZNsdHQ0ixcvZvHixW0OeuzYMTQajc2+z549S1pamnVZcXExN998s/W1j48PZ86c\noVevXlfdb1FREQaDwWaZWq0mKirqmu1RqVSOHoK1TQD+/v4OT8rrLTGFEEIIIdrCrpFHU1MTX331\nFXv27EGv11uXKxQK/vd//9dtjbvU9eYW2717N/n5+TbLxo4dS58+fdzZrHa5yustMYUQQgjh3ewa\nrP7Xf/0X7733HhMnTiQxMdG63NmJaQcOHMg//vEP6+vS0lIGDBhgs063bt1sBsbNzc3WWrlXM3Hi\nxCteWa2oqLjmdiqVyqn5Q318fAgJCaGqqorm5maHtvWWmPYKDw93y36FEEII0bHZNVh99913+fvf\n/+6yK5St02BlZWXRrVs3ysrKGDhwoM06ffr0YdeuXZSXl5OVlUVAQMB199u9e/fLJpcvKiqyayJ8\nZyfLB2hoaHBqEOgtMe0hg1UhhBBCXIldg1WtVmt9KMpVbrvtNuvUVX379iU6OpqtW7cCLVNl3Xzz\nzRw8eJBVq1ahVCqZPn26dduVK1dSVVWFxWLhxRdfZPDgwdxzzz0ubZ8QQgghhGh/dpVbXb16NatX\nr2bu3LkMGTLE5r1x48a5rXGuIOVW2zemvby13KoQQgghrs2uK6u///3vUSgUvPHGG5e9d/bsWZc3\nSgghhBBCCLBzsJqXl+fmZgghhBBCCHE5u4oCtCotLSUjIwOAmpoaamtr3dIoIYQQQgghwM7BakFB\nASNHjmTQoEH88pe/BGDv3r3Mnz/fnW0TQgghhBBezq4HrB577DGioqJ48skniYqKoqKigtraWvr0\n6WPXA0ztqby8HKXy2mNypVKJ2Wx2eN8KhQI/Pz8aGxux45/RK2PaS6auEkIIIcSV2HXP6pYtWzh2\n7Ji17GYrZ4sC3EgNDQ3XXactT8mHhYVRW1t7Q5/M70gx7SWDVSGEEEJciV23AUyZMoUvv/zSZtme\nPXuYNm2aWxolhBBCCCEE2HlldcmSJdx6661MmzaNxsZGFi9ezLZt29i9e7e72yeEEEIIIbyYXVdW\nk5KSOHXqFLfccgt33HEHQ4YM4cSJE5cVCBBCCCGEEMKV7LqyChAZGcnChQtZuHChO9tDXl4eH3/8\nMWazmdTUVFJTUy9b5/PPP+fYsWMEBgbyi1/8gujoaLe2yRM0my1YLBZUKse2a2o2U1nXRITGj/Kq\nBsoqa6k0NqFUKomPUqPxt/sUae4ZJQAAEOJJREFUEEIIIYS44ewaqTz33HNXfZjqpZdecmmDdu3a\nxR133EFYWBgbN25k4MCBaDQa6/uFhYXk5+eTnp5Obm4un332GbNmzXJpGzxCczOK2loUtbVQXUP+\n2fOYFEoShvSFyMjLVi+rbkDlqyQ4wBdTswU/35aL5rnna6msa+RQvYnyGhNHC8ppNivo0zWI85X1\nJPUIJSrYHwCzxWKzbStTs5kLNfWY3TQTgBBCCCHE1dg1WD137px1sGqxWMjJyeHEiRPcf//9Lm1M\nfX09APHx8QAkJiai1+vp27evdR29Xs/PfvYz1Go1gwYN4osvvnBpG+xRXlPPt2cvYjaZ6RV9kf5h\noGxsoKyilrrKGrqrzPjX16Gsq0NRU2MddCpra1te19WhMhoJqKxE8eM6yh/XUbSu09iIRa3GEhSE\nWa0m0C8QTCaCLhigpoaImBiau3aluWtXjFEx+AZFci4okhp1MH4+CiL8fegdFUBA0UVOnLlA8MUy\nEg0lDKkoJ8hYQ6ClieDgQHw1asIjgrD4+VHZrKBZ4YMq0I/AABUolaBUcrHBTF1oOMXpi9GG+d3w\nf28hhBBCeC+7BqsbNmy4bNm6des4dOiQSxuj1+uJioqyvo6OjqawsPCywWpSUpL1tVqt5sKFC0RE\nRFBVVUVNTY3NPhsbG22uzF6Jj48PKgd+Xz97+Ht+ef/tWACLSgUqPxT+foQrfbGoVJiDglCFBmPR\naLAEBV3+/y5dMIeF0eTvb11u1mhs1kOthp9cza5vNGG2gFkTQHhgIDXHj2MpLERZXIyyUI//yTzi\ns/bjV1eDRaEgMNCPgEA/4hotBNebOB8YiiGsC/kxPan0DyIkTEN8qB9xGh9CI/yhsZFqQxXN9Y34\n+CkJDPABs7nlv9pGTH5qNAF+Dv07CSGEEEK0ldM3LE6dOpVnnnmGVatWubI913WtSekPHjzI3r17\nbZaNHTuWtLQ0l7bhZ6OHsv2fJ7AolXSPCEKXEEWAny8XKuq4UFNPYpcQVH7X/6cNbEMbQnU60Oms\nr9WmZg6dMXChuoEu4YEEqnyJ6BZKeXktP+SWYmxqpra+EbVSSUJwAKHBgfSI0hAVHECIuuU2AHWz\nmdqGJkIC/Wxu+9BYLDQ1m/Hz9bmsHUIIIYQQ7mTXYPXMmTM2r0tKSli/fj2jR492aWO0Wi179uyx\nvjYYDPTu3dtmndjYWJvldXV1REREAJCcnEy/fv1s1m9sbMRgMFwzrr+/v13FA1qF+sLovuH4+voS\nHh5ORUUF1ZUmfIGYAKiurKD6OvtwNGarn8Y0mUw27yWG+5AYrv7xVTOVFRcIUsKYPqEA1ipU/6eR\nhtpGDLW2McoueX2tmK7iDQ/JCSGEEMJxdg1WLx0wdu/enfHjx7v84aqAgACgZUaA0NBQcnNzGTt2\nrM06Wq2W3bt3M3jwYE6fPm1z20BISAghISE26xcVFV236pKvr2+bKjOZTCaHt/eWmEIIIYQQbWHX\nYNWZevLOmjRpEh9//DHNzc2kpqai0Wj49ttvARg6dCixsbH07NmTd955xzp1lRBCCCGE6JwUlmvd\nBNoJFBUVXXedwMBAjEajw/tWqVRER0djMBgcvuLoLTHt1b17d7fsVwghhBAdm11XVnv06IFCobB5\nuOlKrwsKClzfQiGEEEII4bXsGqw+/fTT/OlPf2LGjBnodDoOHTpERkYGv/71r+nfv7+72yiEEEII\nIbyU3fOsrl271vr0/7333svEiRN5/PHHycrKcmsDhRBCCCGE91Jef5WWKaTCwsJsloWFhXH+/Hm3\nNEoIIYQQQgiw88rq448/zoMPPsiSJUsYMmQIOTk5rF69mscee8zd7Wszf39/lMprj8mVSiWBgY5P\n0a9QKKirq0OlUuHr61h9BW+JKYQQQgjRFnbNBtDU1MTbb7/NJ598QnZ2NikpKUyZMoX09HT8/Dy7\nVrzMBtC+Me0lswEIIYQQ4krsukymUql4+OGHefjhh93dHiGEEEIIIazsumcV4PTp06xYsYK7774b\ngG+//ZYvvvjCbQ0TQgghhBDCrsHqZ599xtixY9Hr9Xz55ZdAS2nUZ5991q2NE0IIIYQQ3s2u2wBW\nrlzJ5s2bGT16NO+99x4AAwYM4MSJE04F/fzzzzl27Ji1XGp0dPRl61RXV5ORkcHFixfp1asXd955\nJ0qlEoPBQGZmJsXFxYwfP54RI0Y41QYhhBBCCOH57LqyWlBQQN++fW2WFRYWkpiY6HDAwsJC8vPz\nSU9PZ8SIEXz22WdXXG/v3r306dOHpUuXYjQaOX78OABqtZrJkyfLIFUIIYQQwgvYNVhduHAhL7/8\nsvVJ8sbGRt544w3mzp3rcEC9Xs/PfvYz1Go1gwYNoqys7KrrJScn4+fnR1JSEnq9HgCNRoNWq8XH\nx8fh2EIIIYQQomOx6zaAefPm8dBDD5GYmEhNTQ3x8fGMGjWKp59+2uGAer2epKQk62u1Ws2FCxeI\niIiwLmtqaqK2ttY6J2h0dLRdlbKqqqqoqamxWdbY2IhGo7nmdj4+PqhUKkcOA8A656gzc496S0wh\nhBBCiLa47ujDbDaTk5PDBx98QGNjI/v27WP48OGXVbSylx3Tujrt4MGD7N2712bZ2LFjSUtLc1tM\ngPDwcLfu35tjCiGEEMK7XXewqlQqmT59OufPnyc4OJjJkyc7HCQ7O5tDhw4BLQ9mGQwGevfuDUBd\nXZ3NVVVomddVo9FgNBoJDAzEYDCg1WqvGyc5OZl+/frZLGtsbMRgMFxzO39/fxoaGhw5JKDlSmN4\neDgVFRWYTCaHtvWWmPa60kN2QgghhBB2/a47fvx4tmzZwgMPPOBUkJSUFFJSUoCWB6x2797N4MGD\nOX36NFFRUVfcRqvVcvDgQVJSUjhy5AiDBg2yef9KV2hDQkIICQmxWVZUVHTdqku+vr5tqsxkMpkc\n3t5bYgohhBBCtIVdg1WtVsvChQtZu3Ytqamp1oebFAoFL730kkMBY2Nj6dmzJ++884516qpW77//\nPnfddRfBwcGMGTOGrVu3kp2dTWJiIgMGDABaprRas2YNDQ0NKBQKsrKyWLp0Kf7+/g61QwghhBBC\neD6F5So3kb755pvW8qqzZs2yPpijUCiAliubCoWC9evX36Cmep6qqioOHjxIcnLyZVd0JaYQQggh\nRNtd9crqc889x5IlS1Aqlfztb3+jurr6RrarQ6ipqWHv3r3069fvhg3ivCWmEEIIIQRcY7B62223\nkZ6eTmpqKiaTiXffffeK682fP99tjRNCCCGEEN7tqoPVFStWsGnTJrZu3UpTUxMbN2684noyWBVC\nCCGEEO5y1cFqfHw8zz77LADjxo3jiy++uGGNEkIIIYQQAuwstyoD1SsLCgpi7NixBAUFSUwhhBBC\nCDe46mwAQgghhBBCtDcp9n6JHTt2cOrUKTQaDUuWLLEuz8nJ4euvv0ahUNC3b18mTJgAQFZWFtnZ\n2SiVSu68807i4uIAMBgMbN26lfr6egYNGsT48eMdillaWsqePXuoqqoiNDSUCRMmWKs8uSJmZWUl\n27dvp7a2FrVaTXJyMklJSTQ0NLBt2zaKi4vp1q0bv/jFL6xz2LoirhBCCCGEI+y6DcCb6HQ6Zs+e\nbbPs/PnzHDx4kAceeIClS5cyYsQIoGVKpwMHDjB37lzuuOMOPv30U+s2u3fvZtSoUaSnp5OXl4de\nr3co5t69exk8eDCLFy9m0KBB7N2716UxlUolEydOZOnSpdx333188cUXNDQ0cODAAUJDQ3nkkUcI\nCQnh22+/dWlcIYQQQghHyGD1EnFxcQQEBNgsO336NDqdjsjISAA0Gg0Aer2e3r17ExYWRnx8PAAN\nDQ0AlJeXM3DgQNRqNQMGDLjmAO5KMQMCAjAajZjNZurq6qzvuypmcHAw3bp1sx5PTEwMer0evV6P\nTqfD19cXnU5n3Yer4gohhBBCOEIGq3bIzc2ltLSUt99+m8zMTEpLS4GWAVzrT/MAkZGR6PV6ysvL\nrQNagOjoaAoLCx2KOWHCBLKysnj11VfJzs623nbgjpjl5eWUlpai1WopKioiKioKgKioKOvAs7Cw\n0G3HKoQQQghxNTJYtYPJZMJoNDJ//nz69+/PJ598ArSUnHWXzMxMUlNTefLJJ7nlllvIzMx0S8yG\nhgYyMjKYNGkS/v7+bj0mIYQQQghHyWDVDrGxsQwcOBCVSkW/fv0oKyujqamJ2NhYDAaDdb2ysjK0\nWi2RkZHU1NRYlxsMBmJjYx2KWVBQgE6nw8fHB51OR35+vrUtrorZ3NzMX//6V5KSkujfvz8AWq2W\nsrIy6767d+/u8rhCCCGEEPaSwaodYmNjOXXqFBaLhcLCQiIiIlCpVGi1Wk6fPs3Fixc5e/YsCoXC\n+uR8VFQUR48epba2luPHj6PVah2KmZCQwMmTJwE4efIkiYmJAC6LabFYyMzMJCYmhuHDh1uXa7Va\ncnJyaGpqIicnxzrwdOexCiGEEEJcjcyzeomMjAzy8vIwGo1oNBrS0tIYPHgwO3fuJD8/n6ioKEaP\nHm0dkGVlZbF//358fHxspnMqLS1l27Zt1NfXM3DgQG677bbrxqyrqyMoKIi0tDS0Wi1fffUVBoOB\nmJgYxowZYzN1VVtj5ufns379erp06YJCoQBg/Pjx9OzZ85pTV7U1rhBCCCGEI2SwKoQQQgghPJbc\nBiCEEEIIITyWDFaFEEIIIYTHksGqEEIIIYTwWDJYFUIIIYQQHksGq0IIIYQQwmPJYFUIIYQQQngs\nGawKIYQQQgiPJYNVIYQQQgjhsWSw2smZTKb2boIQQgghhNNksNqOVqxYwbRp02yWPfLIIzz66KMY\njUbWrVtHSkoKo0aNYsuWLbQWG8vNzWXcuHFERUWRlJTEq6++Sk1NjXUf8fHxvPXWW4wYMYKwsDDM\nZvMNPS4hhBBCCFeRcqvtqKSkhN69e6PX6wkNDcVkMqHVatm1axcbN26kuLiYV155hcrKSmbPns1r\nr73GhAkTyM3NJS8vjzFjxnD8+HEefPBBFi9ezIIFCwBISEhArVazevVqUlJS8Pf3b+cjFUIIIYRw\njlxZbUddu3Zl9OjRbNmyBYBdu3YRHR2NTqdj+/bt/Pd//zfx8fEMHjyYhx56iB07dgCQmJjI+PHj\nUalUJCUlsXjxYjIzM232ff/99zN69GgZqAohhBCiQ/Nt7wZ4u3nz5rF69WoWLFjApk2bmDNnDidO\nnCA/P5+kpCTremazmYSEBABqamp45pln+Oabb/jhhx8wm81ERETY7Dc1NfWGHocQQgghhDvIldV2\ndvfdd3PkyBGOHTvGzp07mTVrFn379qVHjx58//33VFRUUFFRQWVlJYcPHwZg1apVnDx5kg8//JCL\nFy+ycuXKy+5L9fWV7yFCCCGE6PhksNrOAgMDmTp1KjNnziQ1NZXY2FiUSiUzZszgqaee4vjx45jN\nZnJzc/nqq68AKCoqIjw8nJiYGA4cOMCbb77ZzkchhBBCCOEeMlj1APPmzePYsWPMmTPHuuyFF14g\nLS2NxYsXExERwfTp0ykpKQHgN7/5DUajkbi4OB577DGWLFmCQqFor+YLIYQQQriNzAbgAQoKChgw\nYADnz58nKCiovZsjhBBCCOExZLDazsxmM0uWLEGj0fDHP/6xvZsjhBBCCOFR5CmcdlRbW0vXrl3R\n6XTWaamEEEIIIcT/kSurQgghhBDCY8kDVkIIIYQQwmPJYFUIIYQQQngsGawKIYQQQgiPJYNVIYQQ\nQgjhsWSwKoQQQgghPNb/B0T/wvEqMoxOAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x878c9d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<ggplot: (8874741)>\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\" Data source summary \"\"\"\n",
"\n",
"# get a list of categories\n",
"categories = pd.unique(df['category'])\n",
"# df['category'].drop_duplicates().tolist() also works\n",
"\n",
"# Assigns numerical values to category labels for plotting.\n",
"# To prevent overplotting, NaN all but the first listed category entry.\n",
"df['catAsInt'] = pd.Series([(list(categories).index(cat) if df['character'][i] == 'a' \n",
" else np.nan)\n",
" for i, cat in enumerate(df['category'])])\n",
"\n",
"# debug printing\n",
"print df.head(5)\n",
"print categories, type(categories)\n",
"\n",
"# print gg.ggplot(df, gg.aes(x = 'year',y='catAsInt', shape='location'))+ \\\n",
"# gg.geom_point(size = 70, alpha = 0.25) + \\\n",
"# gg.scale_x_continuous(limits = [1550, 2100]) + \\\n",
"# gg.scale_y_continuous(name = 'Category', labels = tuple(categories)) + \\\n",
"# gg.ggtitle('Summary of data sources')\n",
"\n",
"print gg.ggplot(df, gg.aes(x = 'year',y='catAsInt', colour = 'location'))+ \\\n",
" gg.geom_point(size = 20, alpha = 0.75) + \\\n",
" gg.geom_point(size = 2, colour = 'black') + \\\n",
" gg.scale_colour_brewer(type = 'Qualitative', palette = 'Set2') +\\\n",
" gg.scale_x_continuous(limits = [1500, 2000]) + \\\n",
" gg.ylab('category') +\\\n",
" gg.scale_y_continuous(breaks = range(len(categories))) + \\\n",
" gg.ggtitle('Summary of data sources')\n",
"\n",
"print categories"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" title character frequency category year location catAsInt\n",
"0 arc-1895-US-374 a 0.080924 arc 1895 US 0\n",
"1 arc-1895-US-374 c 0.029702 arc 1895 US NaN\n",
"2 arc-1895-US-374 b 0.017515 arc 1895 US NaN\n",
"3 arc-1895-US-374 e 0.130416 arc 1895 US NaN\n",
"4 arc-1895-US-374 d 0.037082 arc 1895 US NaN\n",
"\n",
"[5 rows x 7 columns]\n",
"['arc' 'chem' 'det' 'lit' 'phys' 'bio' 'mus'] <type 'numpy.ndarray'>\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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YaG9Dj4YfMUeVs9mzZyshIUG9e/dW9+7dlZ+fr8LCwkrrxMTEyOOxb2y32y2v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\n",
"text": [
"<matplotlib.figure.Figure at 0x65c2190>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<ggplot: (6668873)>\n",
"['arc' 'chem' 'det' 'lit' 'phys' 'bio' 'mus']\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating Word Clouds\n",
"\n",
"Following the principle of visualising common occurence of words in text, the same approach can be repurposed to visualise character occurences in text.\n",
" \n",
"Python GCD code\n",
"\n",
" def GCD(a,b):\n",
" while a:\n",
" a, b = b%a, a\n",
" return b\n",
" \n",
" \n",
" def GCD_List(list):\n",
" return reduce(GCD, list)\n",
"\n",
"Some bash\n",
"\n",
" # concatenate files. e.g. combine files beginning with 'lit'\n",
" cat lit* > allLit\n",
" \n",
" # add spaces between each character. e.g. add spaces to file \n",
" # 'allLit' and output to file 'allLitSpaces'\n",
" sed -e 's:\\(.\\):\\1 :g' < allLit > allLitSpaces\n",
" \n",
" # count occurences of a character in a file. e.g. count all a's\n",
" tr -cd a < allLitSpaces | wc -c\n",
" \n",
" # copy file to clipboard\n",
" xclip allLitSpaces\n",
"\n",
"Generate pseudo-file\n",
"\n",
" nums = [802334,150166, 273084, 414232,1285476,237024, 187300, 607010,698432, 7194, 68908, 396306, 254682, 691930, 779770, 180398, 11718, 600834, 19758, 920544, 286318, 95726, 205282, 19758, 194778, 6138]\n",
" pseudo = ''.join([alphabet[i] * nums[i] for i in xrange(len(nums))])\n",
" with open (\"pseudofile\", 'w') as f:\n",
" f.write(\"%s\" % pseudo)\n",
"\n",
"After making all counts even the GCD is 2. I could have guessed that myself and saved myself all this effort. I want to cry."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deviation from Scrabble\n",
"\n",
"Following sections use root mean squared error (RMSE) to identify deviation from Scrabble and Dictionary distributions\n",
"over time or by category. This condenses the distribution into a single value for ease of comparison."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Takes a pair of lists or dicts of equal length containing numerical values and returns\n",
"# root mean-squared error.\n",
"def RMSE (a, b):\n",
" \"\"\" Takes a pair of lists or dicts and calculates RMSE.\n",
" Expects raw list of frequencies. e.g. {a:0.02, b:0.02 ...}\n",
" \"\"\"\n",
" if not len(a) == len(b):\n",
" print \"Mismatching lengths!\"\n",
" return np.nan\n",
" if isinstance(a, dict) and isinstance(b, dict):\n",
" return np.sqrt(sum(map(lambda x,y: (x - y) ** 2, a.values(), b.values()))/float(len(a)))\n",
" elif isinstance(a, list) and isinstance(b, list):\n",
" return np.sqrt(sum(map(lambda x,y: (x - y) ** 2, a, b))/float(len(a)))\n",
" else:\n",
" print \"Types must match and be list or dict.\"\n",
" return np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More plotting -- unused in report"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# print [v for v in distByYeardf.iloc[:,i] for i in range(len(distByYeardf))]\n",
"rmses = [RMSE(getScrabbleProbs(), v) for k,v in probsDict.iteritems()]\n",
"\n",
"print rmses\n",
"\n",
"# construct a new dataframe with new unique entries for categories...\n",
"rmsedf = pd.DataFrame()\n",
"rmsedf['titles'] = pd.Series(probsDict.keys())\n",
"rmsedf['rmse'] = pd.Series(rmses)\n",
"rmsedf.sort(['rmse'], inplace = True, ascending = [False])\n",
"\n",
"\n",
"\n",
"testdf = pd.DataFrame([\n",
"[c1,c2,c3] for c1, d in probsDict.items() for c2, c3 in d.items()\n",
"])\n",
"\n",
"#split columns seperately into category, year,location... then facet_grid('cat', 'year')\n",
"\n",
"testdf.columns = ['title', 'character', 'frequency']\n",
"\n",
"\n",
"# add columns for category, date, location\n",
"category = [title.split('-')[0] for title in rmsedf.iloc[:,0].tolist()]\n",
"date = [title.split('-')[1] for title in rmsedf.iloc[:,0].tolist()]\n",
"location = [title.split('-')[2] for title in rmsedf.iloc[:,0].tolist()]\n",
"rmsedf['category'] = pd.Series(category)\n",
"rmsedf['year'] = pd.Series([int(x) for x in date])\n",
"rmsedf['location'] = pd.Series(location)\n",
"\n",
"# plot of RMSE vs title -- this plot is really lame, just leave it out\n",
"\n",
"# print gg.ggplot(rmsedf, gg.aes(x = 'year', y = 'rmse')) + \\\n",
"# gg.geom_point(colour = 'b', alpha = 0.65) +\\\n",
"# gg.stat_smooth(colour = 'red', se = True) + \\\n",
"# gg.xlab(\"Titles\") +\\\n",
"# gg.ggtitle(\"YOLO\") \n",
"# gg.scale_x_continuous(breaks=range(len(rmses)), \\\n",
"# labels= probsDict.keys())\n",
"\n",
"# trellis plots of rmse\n",
"\n",
"print gg.ggplot(rmsedf, gg.aes(x = 'year', y = 'rmse'))+ \\\n",
" gg.geom_point(colour='steelblue') + \\\n",
" gg.facet_wrap('category', scales = \"fixed\") + \\\n",
" gg.stat_smooth(colour = 'red', se = False) + \\\n",
" gg.ggtitle('Change in RMSE over time by category')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0.012722952888471089, 0.012701355685668703, 0.01421998253249462, 0.014805102956014731, 0.01346135669990267, 0.014734736899399595, 0.012888923480323259, 0.013428315080952062, 0.013462066544815703, 0.014297743458537434, 0.014547311481582852, 0.014366789787076851, 0.014546750442145805, 0.013874863149380662, 0.013957677131416931, 0.013880407654801348, 0.014803235916089915, 0.01393767116375908, 0.014026509502276893, 0.014510987594932398, 0.013913747388330589, 0.014273698647043255, 0.015561611751901589, 0.015070907247858104, 0.014390828404944673, 0.014471353820401087, 0.013692212963566385, 0.013928913419322829, 0.01393335362161895, 0.014222821734889892, 0.013083654953788281, 0.013391990000913147, 0.01430941478748304, 0.012473498865741627, 0.015104546887196951, 0.01440173565940887, 0.014925897445309429, 0.014616899265047584]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fE9ZQY3LIwBzyy1CO9WUdrym55JBDBjnlAKpfY2ZpUF1dXQHcvpPf19cX169f\nR/PmzY3GhISEYMuWLcjJyUFycjLs7OwAAHq9HosWLUKdOnUwdOhQo2X8/Pzg5+dnNC0jIwMajabK\n2R5k7N+VH0IvKSmp0QvBGnLIIYO15rDkL1BrqTE5ZGAO+WUAWF+A9bym5JJDDhnklEPWDSoA9OzZ\n0/AxU6GhofDy8sK6desAALGxsYiIiEBKSgoWLlwIhUKBuLg4AMCBAweQl5cHlUqFWbNmAQA6d+6M\n6Ohoc0UnIiIiIjMyW4MaFRWFqKgoo2mxsbFGj0ePHl1hufbt26N9+/YmzUZERERE8sG/JEVERERE\nssIGlYiIiIhkhQ0qEREREckKG1QiIiIikhU2qEREREQkK2xQiYiIiEhW2KASERERkaywQSUiIiIi\nWWGDSkRERESywgaViIiIiGSFDSoRERERyQobVCIiIiKSFTaoRERERCQrbFCJiIiISFbYoBIRERGR\nrLBBJSIiIiJZYYNKRERERLLCBpWIiIiIZIUNKhERERHJSpUb1MLCQqxduxavvvoqAODEiRNITU01\nWTCimvjlWBYW/nwWXyVfhrZMb+k4FsO6JTId1heR6VSpQd27dy8aNGiAr776CitXrgQAFBQU4PXX\nXzdlNqJqWfPbafx3z0UcOH8T245mY/62dAghLB3L7Fi3RKbD+iIyrSo1qPPmzcOXX36J9evXQ6lU\nAgBatWqFw4cPmzQcUXXsO52NYu1fR00zcouRX1xmwUSWwbolMh3WF5FpValBTUlJQadOnYym5eTk\nwMfHxyShiGpCoZCMH0sS1MpH73Jr1i1ZytW8Yry36RSmrj+BRdvPodQKL7NhfRGZVpV+a8fHx2Px\n4sVG01avXo0RI0aYJBRRTYzoEgIPJxsAgJ1KQvsgVzjYKC2cyvxYt2QJer3Awu3ncOrqLVy8ocH+\nc3n4/LeLlo710LG+aretadfw7x9O4b1Np3D0Sr6l41AlVFUZlJCQgH79+mHVqlUoKipC06ZN4eTk\nhMTERFPnI3pgrYM80a9lXSQezIBeL3Dq2i3c1GhRx15t6WhmxbolSygoLkO+xviSmqz8EgulMR3W\nV+2158wNfH/oKjSlt4/sf/brRfxf30bw83i0fkfIXZUaVF9fXxw8eBCnT59GUlISunfvjrCwMFNn\nI6qW7Ucu4397L6OkTA/b0mKcK9Fh5a8X8VrvYEtHMyvWLVmCo60S9jZKo+u+HW2r9KumVmF91V6H\nL940NKcu+ltoAAAgAElEQVQAcOOWFkeuFMDPw9msOZLTb2DTH9eg0ws08HDA6OgAsEX+ywP9rxES\nEoKQkBDk5uYiPz8fLi4upspFVGWFxWX4Kvkyikp1sFEpcTQjHyVaHZ7fthJP7NuE+U+9ggyfvpaO\naTGsWzInlVKBwe398M3+DJRo9XB1VOO5zg0sHctkWF+1j/ufl4CVs1VJ8KtjZ9YMN26VYu2+DNwo\n0gIArt0sgbujGsM7BZo1h5xV6RrUIUOGYPfu3QCAxMRE1K9fH40aNcK6detMGo7oboQQ2JaWhU+S\nzuJf3x3HnvRc/HEpHynpOQg8dQST1n6I8HNHMHXYZIz6eTXi1y8ESkstHdusWLd0N3vP5mJ64glM\nWX8c/91z6aF/DFvrAFe8G9sE78Y2wZQnw+DqYH3HhVhftdfANr5oVs8ZLnYquDqo8FiIBxr7mffo\naUZusaE5BQD9n9PoL1U6gvrzzz/jiy++AAB8+OGHWLduHRwdHTFt2jTExsaaNCBRZdbuz8DPx7JR\nqhNQ6soQce4IOh3fjQ7Hk3HTsQ5+a9oR8556FVp7e3z+788wft08qGJjcWPJEujr1bN0fLNg3VJl\nrt0sxtd7LyOv6PYp+Ks3S+DuaIPHwx/u3ecKSYK9Fd+cyPqqvdRKBd7oE4ybmjKolBKcLHAJiq+r\nHdwc1Mj9s0mVANR1tTV7Djmr0k9FrVZDp9PhwoULOH/+PGJiYqDX63HgwAFT5yOq1LGMApSW6TH+\n+wXoeDwZVzz8sLtJB0x4/n1ke9eHVidgo1KgSyM3PNupAfL6fQanxYvh1a8fdkyYgf85hUIAiAio\ng6da+1p6c0yCdUuVOZWZb2hOAUCrE0jPumXBRNVzKiMPn24+hlKtHkGeDhjeoX6Fj5gzJdZX7SZJ\nkkWP7Hs42SC2rS9+TM1CmV4Pf3cHPNPur4MnV/M0+OFQBtRKBZ5qXRdOdtZ3Hff9VGmLu3XrhqlT\npyIzMxPx8fEAgIyMDDg7m/eQOFE5hSQBkoR9oe3wRffhuOHiCYHb70r/1bsZjl+4Bl8XG7RsUOfP\nBRQoHDsW54Iao/Xr43C53eNY02UwsvJLUNfFFpHB7hbdHlNg3VJlAjwd4WSrRGGJDsDt67zqmvn6\nu5rSlOrwwfdHcTnndmN9MacItmoFBrU339kR1hfVVKcQD3QK8agw/XJOIT7YdBLZBbcvSzuRWYC3\n+4XAwQpvNryXKl2DOmfOHAgh4O3tjQkTJgAA9uzZg+HDh5s0HNHd9G3pDVcHNX5v2hHFnj7o3sQT\nbz7eCNMHNEWHsLp4orXfX83pHXb7NsXYMfPR5swhDN+xBsVaPY5cLrDAFpge65Yq08DTEY+H+8Db\n2QaeTjZoHVgHA9rUrrMImXkaZN3UGB7rBHAhp8isGVhfZCr/+z3d0JwCwOXcYuxJz7VgIsuo8sdM\nzZ4922haXFwc4uLiTBKK6H7aBrohwMMBp64Wor6bPQI8HQAAavW9X9INPR2wxcMLb/7j37ArLYZK\nKSHQ094ckc2OdUt30zfcB4+38IZeAMp7nBY/c60Q3+zPgFanR7CXI4Z1qG/GlHfn7mQDF3sbXC/4\n66YSZzOfAmV9kalUVpMqpfkuX5GLKlf05cuXkZSUhCtXrkCSJAghIEkSJk2aZMp8RHfl5WwLL+cH\nu6i8hX8ddA3zxIELeYBwQKu6TujR1MtECS2PdUt3I0kS7vU7r6ikDMt2XUBW/u0jOZduaGBvo8SQ\nDgFmSnh3rg42GNwpGN/8fgZanQ6eTjZ4tqO/2XOwvsgUnu0aij/OZSMj7/YbsGAvB3SwwsvQ7qdK\nDerHH3+MKVOmIDIykn9nmGq9wZH1ENfOD3oAKjPeVGFurFuqiYy8Elwv/Os0o04PnL9u3tPo9/Jk\nu0C087dHQVEJHG2VkCTz1jLri0zF3ckOk59qgl+OXYONSoHOoR6wUVXpikyrUqUG9aOPPsKvv/6K\n8PBwU+chMguFQqraBdi1GOuWasLdSQ0XO5XRHf917OV1k4ZKqbDY3c2sLzKFYxn52PnLBeh0WgyM\nqPvAZwmtSZUqu169eg/9g5yJyLRYt1QT5Z+NmnQ0G9oyPbxdbDG8g/lPo8sV64setjPXCrFo+3nk\n/fnZqOeybmFS/xC42FvfH7qoiio1qMuWLcPEiRPRp08ftGzZ0mhely5dTBKMiGqGdUs11buZN7o3\n9kRpmf6R+4ib+2F90cP28/HrhuYUAK7ll+DA+Tx0b2K990ncS5X+xzl8+DB27NiBX375Be7uxhfq\nXrp0ySTBiKhmWLf0MKiUCqiU1n5BzINjfdHDZq82/strSgkW+StXclGlLZ80aRI+/fRTjBgxAirV\no7uziGoT1i2R6bC+6GGLbeuLs9eLcOF6EZQKoImvM9oGulo6lsVUqapsbW3Ru3dvFiFRLcK6JTId\n1hc9bI62Kkx+qgmuFAIltwoR7G1/+68mPqKqdN5m1qxZmDhxIg4dOgS9Xm/0RUTyxLolMh3WF5mC\nnVqJqNC6aFq/ziPdnAJVPII6aNAgAMCaNWuMpkuSBJ1O9/BTEVGNsW6JTIf1RWRa921Qy8rKYG9v\nj5SUFNjZ2ZkjExHVEOuWyHRYX0Smd98GVaVSoWXLlsjOzkbnzp3NkYmIaoh1S2Q6rC8i06vSKf4R\nI0bgxRdfxJAhQxAZGWl0UXj37t1NFo6Iqo91S2Q6rC8i06pSg/rhhx9CkiR89tln+Oyzz4zmnTt3\nziTBiKhmWLdEpsP6IjKtKjWo58+fN3EMInrYWLdEpsP6IjIt/nkQIiIiIpIVNqhEREREJCtsUImI\niIhIVsz2N9qSk5Oxbds2CCEQFhaGwYMHVxizfPlyZGRkQKFQYNCgQQgNDQUALFiwADdu3IBSqcTk\nyZPNFZmIiIiILMBsR1CTkpLQq1cvjBo1CidPnkR2drbR/IMHDyIrKwtjx45FREQEEhMTDfMiIyMR\nGxtrrqhEREREZEFmaVDz8vIAAFFRUWjQoAG8vLyQlpZmNOb06dMICgqCh4cH+vbti+LiYsO8yMhI\nuLi4mCMqEREREVmYWU7xp6WlwdHR0fDYy8sL586dQ7du3QzTMjMzERERYXisVCpx9uxZNGzY8K7r\nzcjIqHAk1sHBAZ6enlXKpVarq7oJlVIqlQAAW1tbow9pflDWkEMOGawxh6VZQ43JIQNzyC+DHFhD\nfTGH/DLIKUdNyLqyJUm65/ytW7fiwoULRtOio6MREhJiylgVyOXorhxyyCEDwBwPC2tMXhkA5pBb\nhppgfRljDnllACybwywNavPmzbFjxw7D46ysLDRp0sRojK+vL65cuWJ4rNPpEBQUdM/19unTp9J3\nn7m5uVXKpVarodVqqzS2MkqlEi4uLsjPz4dOp6v2eqwhhxwyWGMOAHBzc6vR8jVhDTUmhwzMIb8M\n5Vhf1vGakksOOWSQUw6g+jVmlgbV1dUVwO07+X19fXH9+nU0b97caExISAi2bNmCnJwcJCcnw87O\n7r7r9fPzg5+fn9G0jIwMaDSaKmd7kLF/V34IvaSkpEYvBGvIIYcM1prDkr9AraXG5JCBOeSXAWB9\nAdbzmpJLDjlkkFMOWTeoANCzZ0/Dx0yFhobCy8sL69atAwDExsYiIiICKSkpWLhwIRQKBeLi4gzL\nzp8/H/n5+RBCYPr06WjZsiWefvppc0UnIiIiIjMyW4MaFRWFqKgoo2l//+io0aNHV7rs+PHjTZaL\niIiIiOSFf0mKiIiIiGSFDSoRERERyQobVCIiIiKSFTaoRERERCQrbFCJiIiISFbYoBIRERGRrLBB\nJSIiIiJZYYNKRERERLLCBpWIiIiIZIUNKhERERHJChtUIiIiIpIVNqhEREREJCtsUImIiIhIVtig\nEhEREZGssEElIiIiIllhg0pEREREssIGlYiIiIhkhQ0qEREREckKG1QiIiIikhU2qEREREQkK2xQ\niYiIiEhW2KASERERkaywQSUiIiIiWWGDSkRERESywgaViIiIiGSFDSoRERERyQobVCIiIiKSFUkI\nISwdorbKz89HSkoK2rRpAxcXl0c6hxwyMIf1kcN+lEMG5pBfBmsgl/3IHPLKIJccPIJaA4WFhdi5\ncycKCwsf+RxyyMAc1kcO+1EOGZhDfhmsgVz2I3PIK4NccrBBJSIiIiJZYYNKRERERLLCBpWIiIiI\nZEU5bdq0aZYOUVsJIWBjY4PAwEDY2to+0jnkkIE5rI8c9qMcMjCH/DJYA7nsR+aQVwa55OBd/ERE\nREQkKzzFT0RERESywgaViIiIiGSFDSoRERERyQobVCIiIiKSFTaoRERERCQrbFCJiIiISFbYoBIR\nERGRrLBBJbMoKyuzdAQiq8YaIzId1pf5sUElzJ49G88884zRtFdffRXjxo2DRqPBihUr0L59ezz2\n2GNYu3Ytyv+2Q3p6Orp37w5PT0+Eh4fjgw8+QGFhoWEdgYGBWLRoETp27AhXV1fo9XqzbheRXLDG\niEyH9WWlBD3yMjMzhaOjo8jLyxNCCKHVaoW3t7c4ePCgGD9+vBgyZIg4d+6cOHz4sGjevLn46aef\nhBBCnDlzRiQlJYnS0lLxxx9/iIiICLFs2TLDegMDA0XTpk3Frl27RHFxsUW2jUgOWGNEpsP6sk5s\nUEkIIURMTIyhMH/44QfRrFkzIcTtAr148aJh3Pz588XLL79c6TqWLVsm+vfvb3gcGBgoZsyYYcLU\nRLUHa4zIdFhf1kdl6SO4JA/x8fFYsmQJEhISsHr1aowcORInTpzAhQsXEB4ebhin1+sRFBQEACgs\nLMS//vUv7N69G6dOnYJer4e7u7vReiMjI826HURyxRojMh3Wl/XhNagEAHjqqaeQmpqKtLQ0bNq0\nCcOHD0doaCj8/f1x7Ngx5ObmIjc3Fzdv3sThw4cBAAsXLsTJkyfxzTffIC8vD/Pnz69wjY5KxfdA\nRABrjMiUWF/Whw0qAQDs7e0RGxuLYcOGITIyEvXr14dCocDgwYPx1ltv4fjx49Dr9UhPT8euXbsA\nABkZGXBzc4O3tzf279+PTz/91MJbQSRfrDEi02F9WR82qGQQHx+PtLQ0jBw50jBt2rRp6NatG156\n6SW4u7sjLi4OV69eBQCMHz8eGo0GAQEBeOONN/Dyyy9DkiRLxSeSPdYYkemwvqyLJMSfn7dAj7yL\nFy+iSZMmuHbtGpycnCwdh8jqsMaITIf1ZV3YoBKA2xeOv/zyy3B0dMTcuXMtHYfI6rDGiEyH9WV9\nePUv4datW6hbty5at26NxMRES8chsjqsMSLTYX1ZJx5BJSIiIiJZ4U1SRERERCQrbFCJiIiISFbY\noBIRERGRrLBBJSIiIiJZYYNKRERERLLCBpWIiIiIZIUNKhERERHJChtUIiIiIpIVNqhEREREJCts\nUImIiIhIVtigEhEREZGssEElIiIiIllhg0pEREREssIGlYiIiIhkhQ0qEREREckKG1QiIiIikhU2\nqEREREQkK2xQiYiIiEhWVJYO8LBNnz4dBQUFlo5hdbZs2QJnZ2d06tTJ0lEeOc7Ozpg6daqlYxiw\nxh6+ZcuWoXfv3rh69Spu3ryJ3r17WzrSI4P1RWRa1a0xq2tQCwoKMGrUKEvHsDpnzpxB3bp177tv\n+/TpgxkzZiAyMtI8wR4BK1eutHQEI6yxh+/rr79GTEyMUd1cuXIFjz/+OA4fPgyFgie7TIX1RWRa\n1a0x/q9HVSaEuO8YSZKqNI6Iqob1RESPIjaoVKmMjAxMmzYNXbt2xbRp06DT6QzzTp48iRkzZqBX\nr16YPXs2MjMzAQBvv/02MjMz8corryAyMlJ2RyaI5EwIgUWLFuHtt98GAMNRtI4dOyIyMhKpqakW\nTEckf3369MH//vc/DB8+HNHR0Zg3bx6KiorwxhtvoFu3bvjggw9w69Yt7N+/Hz179qyw7N69ewEA\n6enpGDduHKKjo9G1a1fMnj3bEpvzyGODSpUaP348nJycsH79egQGBmLr1q2QJAl5eXl47rnn8Nhj\nj2H9+vVwc3PDm2++CQB477334Ovri08//RR79+7laSqiByRJkuH7VatWAQD27NmDvXv3Ijw83FKx\niGoFSZKwbt06TJ48GUuXLsXatWvx/PPP44knnsA333yD1NRUbN++/a7Lllu4cCHatWuHpKQk/Pjj\nj+jTp4+5NoHuwAaVKrh+/TrS09PxyiuvwM3NDaNGjYKHhweEEEhKSkKvXr3QvXt3ODk54R//+Acu\nXryInJwcS8cmsio8tU/04Pr374/GjRsjLCwMLVq0QN26ddG1a1d4eXmha9euSE5Ovu86hBCGGxbt\n7e355tBC2KBSBWlpafD394etra1hWpMmTQAAe/fuxebNm9GxY0d07NgRnTt3hkajQUpKiqXiEhER\nAQDCwsIM33t4eCA0NNTw2N3dHVlZWfddx5tvvgmNRoOBAwfixRdfxP79+02Sle7N6u7ip5pr3rw5\nLl26hOLiYtjZ2QEAjh8/jpCQELRr1w4uLi6YPHlypcsqFAoe+SF6CJRKJQAeSSV62Hx8fJCfnw+d\nTgelUonc3Fxcu3bNMN/X1xfvvPMO3n77bWzYsAFvvfUWkpKS+GkaZsa9TRV4enoiODgYixYtwo0b\nN7Bq1Srk5ORAkiTExMQgKSkJP//8M4qKilBUVIRdu3ahqKgIANC0aVMcO3bMwltAVDvd2Yz6+PjA\n3d0dR48etWAiIuvToEEDuLq6IjExETdu3MDChQuN5v/www+4ceMGhBCwt7eHg4ODhZI+2tigUqXm\nzp2LmzdvYsCAATh37hxiYmIAAC4uLvjPf/6D/fv344knnkD//v2xYcMGw3KDBg3Czp070alTJ8NN\nHkR0f5IkGb7KH48ZMwZTp05Fx44dceTIEQsnJKrd7qyvd955B+vXr8ewYcMQGhoKHx8fw7jdu3dj\nwIAB6Nq1KzZu3IipU6fy6KkFSMLKzh9NmDCBd4+TVVm5ciXmzJlj6RgGrDGyJqwvItOqbo3xLQER\nERERyYrV3SSlVCqxdOnSKo19+umnkZiYWO3n8vf3x8SJEzF79mxcunSp2uuxhhxyyGCNOYDbd6LK\nSW2rMTlkYA75ZSjH+rKO15Rccsghg5xyANWvMas7xZ+RkVHlsfb29tBoNNV+LrVaDS8vL2RnZ0Or\n1VZ7PdaQQw4ZrDEHAPj5+dVo+YetttWYHDIwh/wylGN9WcdrSi455JBBTjmA6tcYT/ETERERkayw\nQSUiIiIiWWGDSkRERESywgaViIiIiGSFDSoRERERyQobVCIiIiKSFTaoRERERCQrbFCJiIiISFbY\noBIRERGRrLBBJSIiIiJZYYNKRERERLLCBpWIiIiIZIUNKhERERHJChtUIiIiIpIVlbmeKDk5Gdu2\nbYMQAmFhYRg8eHCFMcuXL0dGRgYUCgUGDRqE0NBQXL58GatXr4ZWq4VKpUJ4eDj69etnrthERERE\nZGZmO4KalJSEXr16YdSoUTh58iSys7ON5h88eBBZWVkYO3YsIiIikJiYCABQqVTo168fJk+ejISE\nBKSkpCA/P99csYmIiIjIzMzSoObl5QEAoqKi0KBBA3h5eSEtLc1ozOnTpxEUFAQPDw/07dsXxcXF\nAIC6deuiRYsWAAAvLy/Y29tXWJaIiIiIrIdZTvGnpaXB0dHR8NjLywvnzp1Dt27dDNMyMzMRERFh\neKxUKnH27Fk0bNjQMC09PR0ajQbh4eEAgIyMjApHYh0cHODp6VmlXGq1ulrbc2dGALC1tYVKVf1d\naQ055JDBGnNYmjXUmBwyMIf8MsiBNdQXc8gvg5xy1ISsK1uSJMP3+fn5WLNmDTp27AgnJycAwNat\nW3HhwgWjZaKjoxESEmLWnC4uLmZ9vruRQw45ZACY42FhjckrA8AccstQE6wvY8whrwyAZXOYpUFt\n3rw5duzYYXiclZWFJk2aGI3x9fXFlStXDI91Oh2CgoIAAKWlpVi8eDGCg4PRs2dPw5g+ffpU+u4z\nNze3SrnUajW0Wu2Dbo6BUqmEi4sL8vPzodPpqr0ea8ghhwzWmAMA3NzcarR8TVhDjckhA3PIL0M5\n1pd1vKbkkkMOGeSUA6h+jZmlQXV1dQVw+05+X19fXL9+Hc2bNzcaExISgi1btiAnJwfJycmws7MD\nAOj1eixatAh16tTB0KFDjZbx8/ODn5+f0bSMjAxoNJoqZ3uQsX9Xfgi9pKSkRi8Ea8ghhwzWmsOS\nv0CtpcbkkIE55JcBYH0B1vOakksOOWSQUw5ZN6gA0LNnT8PHTIWGhsLLywvr1q0DAMTGxiIiIgIp\nKSlYuHAhFAoF4uLiAAAHDhxAXl4eVCoVZs2aBQDo3LkzoqOjzRWdiIiIiMzIbA1qVFQUoqKijKbF\nxsYaPR49enSF5dq3b4/27dubNBsRERERyQf/khQRERERyQobVCIiIiKSFTaoRERERCQrbFCJiIiI\nSFbYoBIRERGRrLBBJSIiIiJZYYNKRERERLLCBpWIiIiIZIUNKhERERHJChtUIiIiIpIVNqhERERE\nJCtsUImIiIhIVtigEhEREZGssEElIiIiIllhg0pEREREssIGlYiIiIhkhQ0qEREREckKG1QiIiIi\nkhU2qEREREQkK1VuUAsLC7F27Vq8+uqrAIATJ04gNTXVZMGIqOZYt0Smw/oiMp0qNah79+5FgwYN\n8NVXX2HlypUAgIKCArz++uumzEZENcC6JTId1heRaVWpQZ03bx6+/PJLrF+/HkqlEgDQqlUrHD58\n2KThiKqiTKfHqasFOHOtEHohLB1HNli3RKbD+iIyrSo1qCkpKejUqZPRtJycHPj4+JgkFFFVlWh1\neH/zaXzw4xl8+OMZzNl8Cjo9m1SAdUtkSqwvItOqUoMaHx+PxYsXG01bvXo1RowYYZJQRFX1/aGr\nSM8qgl4PaHUCaZdv4ufUy4b5hSVlOJFRgGv5JRZMaRmsWyLTYX2Rqew/k4W5P57CJ9vO4kquxtJx\nLEZVlUEJCQno168fVq1ahaKiIjRt2hROTk5ITEw0dT6ie7JNP4NhOzah4/FkXPTyx9KY53H5xi00\ndFMi++YtLP7lArLyS+Bkp0K3xp4Y0MbX0pHNhnVLZDqsLzKFYxn5WPzzOeTeun1Q5VKuBpP6h8LV\nQW3hZOZXpQbV19cXBw8exOnTp5GUlITu3bsjLCzM1NnoEVemF1i7/woyc4vh7WKLwZH1oFZIUB0/\nDvtNm2C3eTOey7uJn0OjsDTmebQ7dQDLPh2L9ceGYEqnJ1CiUKKwRA8AKCguwy8nstGnuRccbKv0\nsq/1WLdEpsP6IlPYcTzb0JwCQHZBKXaezEHrBi6oW8cONqpH59NBH+g3dUhICEJCQpCbm4v8/Hy4\nuLiYKhcRlu88j/1n86AXAo0y03F25X50PrYb0GpR3K8f8ubMgbZ1a9heLYTiaDa2tG6HbW16IeHH\nFRiyZSWuePjhbN0gnPENxsZ2fVEIGxQUlz0yDWq52lq3BcVlKCrQwsVVd89x1/KL8e3+TOj0At2a\neKJF/dqxfeb2U1oWUi/nw85GhSHtfOHpbGvpSFahttYXyZNTJb+ffkq7hs2p1+DtbINxvRrC4xGp\n3Sq14kOGDMHu3bsBAImJiahfvz4aNWqEdevWmTQcPdou5mgg6cqwYsEYTPrmAxQWl+HGwoXISk5G\n/pQp0LZpAygUaOLngtd6BaORjxMuevpjyshpeObtNZg7YBz+CArHY8d2I3b37dNuAo/ODVS1uW6T\njmZhWuIJTF9/DK+s+A2Zd7kOK1+jxfyt6ThwPg+HLt7E8l0XcCwj38xp5W/b0WysT8nE0SsFSDmX\ni/k/nUWx9t6NP91bba4vkq+49vUR5ucKpQJQKyWoFBKKSvUoLdPjcm4xVu+5fP+VWIkqHUr6+eef\n8cUXXwAAPvzwQ6xbtw6Ojo6YNm0aYmNjTRqQHl0qpQI6pQrvjJyOTLe68HOzR5uWTe46vn1DVxy8\ncBMFxWUoVdvifP1GOOPXCEcCm+PjpW9ga0QvzN2Sjrh29dC+oZsZt8Qyamvdlmh12JqWhRu3tACA\nC9mFWPnbBUyICa4w9tDFm7iWX4rW6YcRnbYLWqUaDj87wjnYC1CrIdRqqBwdoZAkCLUasLGBUKsh\nbGz++l6tBmxtK0wv/1dydAQcHACtFhACkCRz75JqK9MLrE/JwG+nclBcpjdMv5pfjEs3NAjxcbJg\nutqtttYXyZu9jRJz4qOw79h5ZN8sxsrfL6Lsjk+mOZ9ThF0nr6NzqAekWvR/UXVUqUFVq9XQ6XS4\ncOECzp8/j5iYGOj1ehw4cMDU+egR9lTruliz9zKuwRfujmr0a+l9z/FN/VwwNMofu9PzoNVq0SnE\nDb+euoHT8MVPrXviH0mrMG/AOCzdeR4+LjYI8HQ005ZYRm2t21ulOpSUGR/pLr2jubqTs60SA5O/\nxzO7vsX/usRBLyng4GED4eIIaLWQSkqg0Gig1GgglZbenlZaWvn3f44v/x5/zpO0WkCrhWdJCaDX\nV9rY4s/mVtjY/PV9eZP75/dKe3vYKJWGxlnY2v71/Z0N8x3ruPO5lA4OgI8PlIWFEErlXxnKn7f8\nOZVKQxO96OdzOHzxZoXzBvYqBZztHq1LXR622lpfJH82KiXCfF3g72aLTak2uHSj2DAvr6gMX+65\njBOZhXiha6DlQppBlf6H6tatG6ZOnYrMzEzEx8cDADIyMuDs7GzScPRoaxPoikY+jrh2swTeLrZV\nuouxS2MvxHZuiuzsbGi1WhRodDh97Ra+ih6CZZ+8iMaXTuCEf2N8ufsy3nnSum9oqK1162qvhofT\n7euFAUClkBDkVcmbidJSdPtkFm4e2Y8JL87DVRcv+HvYI+bxRii84zoue3t7aDTV/6gWtVoNLy8v\nXM/Ohra4+K9mtrT0dhP792b2z3/vnC+VlsIGgPbWLUPjXN74ShoNFDdvVto83zlNUVYGCAGXW7cg\nyrRNCYAAACAASURBVNdbSUMNIQyN8yS9AqUKJbQqNUpVtihR26BEbYuUppH4rt5zeLFrIBQK6z4K\nYyq1tb6o9lArFRjfuxFW776E45kF0Ghvv1Ev0wmcyCxAUakODjZKC6c0nSo1qHPmzMG8efPg7e2N\nCRMmAAD27NmD4cOHmzQcUR17NerY37sxFUJgU+o1nLl2C57OtnjtSXfDPM2f19kV2TlgRe9/YOym\nJXhlzHzoHoG/OFXb6lZTqsOavZdRoClDmwAXuDuqodHq0bSBJ54K94ROV2YYq7h+HW6jR0Pv7g4k\nbcaoW7ePsob4OJn2LlelElAqIezsHvxq5ofUKOf++ebrrnQ6oLQUoqQU7647gps3NVDrtLDRlsCu\nrBRqbSkKHJxx+Xwevj909ZH66LWHqbbVF9VObo5qvNKrIWZtOIn07CJLxzGrKn/M1OzZs42mxcXF\nIS4uziShiB7E//Zdwfbj16HV3W4Zcor2G65X7NrYA8npN3C9UIvt4V1xwbsBIEkIcHewZGSzqE11\nq9cLzNuajjNZtwAAxzML0a+lDwa284eXlxeys7Oh+/OeHtWxY3B/7jloBgxAwcSJgEKBEF5K+Rel\nErC3h2Rvj9ZtG2HLkSxcL9HBwUaBotI7LpUQQEZe8d3XQ/dUm+qLar8uYR7Iyi9BQYkOaqWExr7O\nVn30FHiAj5m6fPkykpKScOXKFUj/396dR0dd2Hsf/0wWshIgyQSyCAlLIDUkJC6hVHZSrJRKZVXh\nYi29h6UP1VvR59pa5R7P8QhV1CsulVLvIw+eGpDwFHrhklbAqpEYQIxiGkISSCaYhYSQkIQk83v+\n4DK3Iy4DYWZ+M3m/zuk5zm9+M/nMNF/z8bdaLDIMQxaLRY8++qg78wHfqvRMq6OcStLpxla1dXar\nX4AUExmih34wUnmHbCqvb1N3erqmxUXo7vFJXkzsOb4yt41tF3Wm5X/KUme3XSU1LbrrFuf1Qvfu\n1YCHHlLLv/2b2n/8Yw+n9D2zMoco84YoVTd1KCo0SL87UKVz7Ze2RPcLtGjUYP8+DtvdfGW+4Psm\njY5VXFSIDled0w3RobptVIy3I7mdSwX1+eef129+8xvl5ORwn2GYTtCXjqELCgy4tJvXfmmT2+Co\nUP18xnBvRPMqX5rb0OBABQVY1K+rU2MrS3R0eKYC//EMVcNQ5L//uyJef11n33hDXePGeS+sj0mK\nDlfSf+8xWHBrggo+a1R3T4/GxPdX7o1WL6fzXb40X/APY+L7a0x83znG2aWC+txzz+ndd99VRkaG\nu/MAV+3HN8XrD++eVkPrRfUPDdIdWUMVHBigLnvfvs6jL81t/9AgfXdEtErfr9GSd7bqf7/9rC78\n4AeyD5yvzzNuVfya/6XQqgrV79olezzHTF6rCSNjNH1sUq+OhcUlvjRfgC9yqaAmJibK6AMnlcA3\nfSchSr+enaqqxgtKiI5Qxqihqq+v93Ysr/O1uZ1/S4Kea2rXr37+nOLPfaEfln2gGx9YoxFnTunQ\n2Nv06WMb9UPKKUzC1+YL8DUuFdTXXntNa9as0cyZM5WZmen03KRJk9wSDLgaA8KDlRE+QMHB334p\nqr7C1+b2QGmjPrOdV7ddOhEWq+czZsvImK0Brc06FzFAgyrbNCWrW5FcvxMm4GvzBfgal/5Nf/To\nUe3fv1/vvPOOoqOjnZ47ffq0W4IB6B1fm9vTZ9vV7XySuSTpXORASZcuI3XhYg8FFabga/MF+BqX\n/k3/6KOP6sUXX9TixYsVFMQfB8AX+Nrc3pw8QIdONqm189Kxw/2CLOrpMXT5Ag3WqBDFRPbzYkLg\nf/jafAG+xqWpCgkJ0fe//32GEPAhvja3aQlRmntzgv5WdlaSocmpMTp/0a6TDZ0Ktth1z/hEBXLX\nI5iEr80X4Gtcmqwnn3xSa9as0UMPPXTFsTYBAW68awuAa+aLcztlTKymjIl1PL5896T6b7t7EuBh\nvjhfgC9xqaAuWLBAkvTmm286LbdYLOrp6duX8gHMirkF3If5AtzrWwtqd3e3wsLCVFxcrNDQUE9k\nAtBLzC3gPswX4H7fWlCDgoKUmZmp+vp6TZw40ROZAPQScwu4D/MFuJ9Lu/gXL16s5cuXa9GiRcrJ\nyXE6KHzatGluCwfg2jG3gPswX4B7uVRQ161bJ4vFos2bN2vz5s1Oz1VUVLglGIDeYW4B92G+APdy\nqaBWVla6OQaA6425BdyH+QLci2thAAAAwFQoqAAAADAVj90Co7CwUPv27ZNhGBo9erQWLlx4xTqb\nNm2SzWZTQECAFixYoNTUVEnSCy+8oLNnzyowMFCPPfaYpyIDAADACzy2BbWgoEC5ubm67777VFpa\nqvr6eqfnDx8+rLq6Oq1atUrZ2dnKz893PJeTk6O5c+d6KioAAAC8yCMFtbm5WZI0fvx4DR06VFar\nVSUlJU7rlJWVKSUlRTExMbrjjjvU0dHheC4nJ0dRUVGeiAoAAAAv80hBLSkpUUREhOOx1Wq94jIc\ntbW1SkxMdDwODAzUyZMnPREPAAAAJuKxY1CvhcVi+cbnbTbbFYcKhIeHKzY21qX3Dw4OvuZs0qUS\nLUkhISFOF2m+Wv6QwwwZ/DGHt/nDjJkhAznMl8EM/GG+yGG+DGbK0Rse+anp6enav3+/43FdXZ3S\n0tKc1omPj1dNTY3jcU9Pj1JSUr7xfffu3auqqiqnZZMnT9aoUaN6H/oqmOXwAzPkMEMGiRzXCzNm\nrgwSOcyWoTeYL2fkMFcGybs5PFJQBw4cKOnSmfzx8fFqaGhQenq60zqjRo3Snj171NjYqMLCQoWG\nhn7r+86cOfMr/+uzqanJpVzBwcHq6upy8VNcKTAwUFFRUWppaVFPT881v48/5DBDBn/MIUmDBg3q\n1et7wx9mzAwZyGG+DJcxX/7xO2WWHGbIYKYc0rXPmMe2286YMcNxmanU1FRZrVZt375dkjR37lxl\nZ2eruLhYGzduVEBAgObPn+947YYNG9TS0iLDMLR27VplZmZqzpw5SkhIUEJCgtPPsdlsam9vdznX\n1az7ZZc3oXd2dvbqF8Efcpghg7/m8OYfUH+ZMTNkIIf5MkjMl+Q/v1NmyWGGDGbKYfqCOn78eI0f\nP95p2ZcvHfWzn/3sK1/74IMPui0XAAAAzIU7SQEAAMBUKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIA\nAMBUKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBUKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBU\nKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBUKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBUKKgA\nAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBUKKgAAAAwFQoqAAAATMViGIbh7RC+qqWlRcXFxbrpppsU\nFRXVp3OYIQM5/I8ZvkczZCCH+TL4A7N8j+QwVwaz5GALai+0trbqwIEDam1t7fM5zJCBHP7HDN+j\nGTKQw3wZ/IFZvkdymCuDWXJQUAEAAGAqFFQAAACYCgUVAAAAphL4xBNPPOHtEL7KMAz169dPycnJ\nCgkJ6dM5zJCBHP7HDN+jGTKQw3wZ/IFZvkdymCuDWXJwFj8AAABMJcjbAQD0Lfn5+SorK1NERIRW\nrlzpWH7kyBG99957slgsSk1NVW5uriSpsLBQhw4dUkBAgGbPnq1hw4ZJkurr67V9+3Z1dHRo7Nix\nmj59eq9z5OXlqbGxUZLU0dGh0NBQLV++3G05vipDXV2d9u3bp5aWFg0YMEC5ubmyWq0e/y4aGhp0\n4MAB1dbWavDgwZozZ46Cg4PdluPcuXPasWOH2traFB4erptuukkZGRnq7OzU22+/rdraWsXHx+uu\nu+5ybNFx1/fh68wwY2aYr6/L4ekZM8N8ST44YwYAeFBlZaVhs9mMjRs3OpadOXPGeO2114yGhgbD\nMAyjtbXVMAzDOH/+vPHCCy8YTU1NRkVFhfHyyy87XvPGG28Yn3zyidHW1mZs2rTJqK6u7nWOf7Rn\nzx5j//79bs3xVRneeust45NPPjEMwzCOHTtm5OXluTXD1+XIy8szSkpKDMMwjIMHDxqFhYVuzdHS\n0mLYbDbDMC79/79hwwajo6PDePfdd43du3cbXV1dxq5du4y//e1vbv8+fJ0ZZswM8/V1OTw9Y2aY\nL8PwvRnjJCkAHjVs2DCFhoY6LTtx4oSysrIUExMjSYqIiJAk1dTUaOTIkRo4cKCSk5MlSZ2dnZKk\nxsZGpaenKzw8XGlpaaqpqel1jssMw9Cnn36qsWPHujXHV2UIDQ1Ve3u77Ha7Lly44Hje099FZWWl\nUlNTJUmjR4/WqVOn3Jqjf//+io+Pl3Tp//+4uDjV1NSopqZGWVlZCgoKUlZWluM93fl9+DozzJgZ\n5uvrcnh6xswwX5LvzRgFFYDXlZeXq66uTq+++qp27typuro6SZf+BXl515skxcTEqKamRo2NjY4/\nsJJktVpVXV193fJUVVUpMjJS0dHRHs+Rm5urwsJCPf300zp06JBjN6ynv4sRI0bo6NGj6u7u1scf\nf6zTp09Lkqqrq92eo7GxUXV1dUpMTJTNZlNsbKwkKTY21vGH0BM5/ImZZsyb8yWZY8a8OV+Sb8wY\nBRWA13V3d6u9vV3333+/xowZoz//+c+SLm1p8YaSkhLH1h1P59i5c6dycnL08MMP65ZbbtHOnTs9\nnkGSpkyZorq6Om3atEl2u91xfJy7dXZ2atu2bbr99tsVEhLitd8Bf2OmGfPmfEnmmDFvzZfkOzNG\nQQXgdUlJSUpPT1dwcLBGjx6thoYGdXV1KSkpSfX19Y71GhoalJiYqJiYGKdb8NXX1yspKem6ZOnp\n6dHx48d14403OuXzVI5Tp04pKytLgYGBysrKUlVVlcczSNKgQYM0a9YsLV++XMOHD9eIESPcnqOn\np0d//OMflZGRoTFjxkiSEhMT1dDQ4PhZCQkJbs/hj8wyY96eL8kcM+aN+ZJ8a8YoqAC8LikpSWVl\nZTIMQ9XV1YqOjlZwcLASExN14sQJNTc3q6KiQhaLxXF2aWxsrD755BO1tbXp+PHjSkxMvC5ZTp48\nKavVqqioKMcyT+ZISUlRaWmpJKm0tNTxh8vT30VbW5skqaWlRUVFRRo5cqRbcxiGoZ07dyouLk7f\n/e53HcsTExN15MgRdXV16ciRI44/hN743fBlZpkxb8+XZI4Z8/R8Sb43Y1wHFR7R3d2toCCuagZp\n27ZtqqysVHt7uyIiIjR16lRlZmZq9+7dqqqqUmxsrCZOnOj4F15hYaE+/PBDBQYGOl3mpK6uTm+/\n/bY6OjqUnp6uGTNm9DpHVlaW8vPzlZSUpJtvvtlpfXfkuJzhwoULioyM1NSpU5WYmKiDBw+qvr5e\ncXFxmjRpktMlcNz5XVzOMWXKFF28eFFFRUUyDEPjxo3TpEmT3PpdVFVV6Q9/+IMGDx4si8UiSZo+\nfbqGDh36jZfAccf34evMMGNmmK9/zOHNGTPDfEm+N2MUVGj9+vX68MMPtW3bNsey1atXKyAgQE89\n9ZS2bt2qV199Vf369dMvfvELzZs3TxaLReXl5frZz36mY8eOKSEhQffee69WrVqlyMhISVJycrIe\nfvhhbdmyRceOHVNLS4sCAthoDwAAvhkFFTpz5oxGjhypmpoaDRgwQN3d3UpMTNSePXv0xhtvqLa2\nVk899ZTOnTunxYsX69lnn1Vubq7Ky8tVWVmpSZMm6fjx4/rJT36iFStWaNmyZZIu7UYJDw/XK6+8\noltvvZVbEgIAAJewOQsaMmSIJk6cqLy8PEnSnj17ZLValZWVpR07dmjdunVKTk5WZmamfvrTnyo/\nP1/SpctkTJ8+XcHBwcrIyNCKFSscZ0NetmjRIk2cOJFyCgAAXMZBgZAkLV26VK+88oqWLVumLVu2\naMmSJfr8889VVVWljIwMx3p2u10pKSmSpNbWVv3qV7/S+++/r7///e+y2+2O69pdlpOT49HPAQAA\nfB9bUCFJuvPOO3Xs2DGVlJRo9+7duvfee5WamqobbrhBn332mZqamtTU1KRz587p6NGjkqSNGzeq\ntLRUb731lpqbm7VhwwbZ7Xan9+XEKAAAcLUoqJAkhYWFae7cubrnnnuUk5OjpKQkBQQEaOHChXrk\nkUd0/Phx2e12lZeX6+DBg5Ikm82mQYMGKS4uTkVFRXrxxRe9/CkAAIA/oKDCYenSpSopKdGSJUsc\ny5544glNnTpVK1asUHR0tObPn68zZ85Ikh588EG1t7dr2LBh+uUvf6mVK1c6Ll0BAABwrTiLHw6n\nTp1SWlqavvjiC8elogAAADyNggpJl05+WrlypSIiIvTMM894Ow4AAOjDOIMFamtr05AhQxx3+QDg\ne7hbG+A+zJfncQwqFBERofPnz+vgwYNXXCYKQO+tX79e8+bNc1q2evVqPfDAA2pvb9fvf/973Xrr\nrbrtttuUl5enyzu2ysvLNW3aNMXGxiojI0NPP/20WltbHe+RnJysl156SRMmTNDAgQOvuIoG0Bcw\nX37KAAC4VW1trREREWE0NzcbhmEYXV1dRlxcnHH48GHjwQcfNBYtWmRUVFQYR48eNdLT043/+q//\nMgzDME6cOGEUFBQYFy9eND7++GMjOzvbeO211xzvm5ycbHznO98xDh48aHR0dHjlswHexnz5J7ag\nAoCbcbc2wH2YL//EARUA4AHcrQ1wH+bL/7AFFQA8gLu1Ae7DfPkfCioAeAB3awPch/nyPxRUAPAQ\n7tYGuA/z5V+4UD8AeAh3awPch/nyLxRUAPAA7tYGuA/z5X84+hcA3Iy7tQHuw3z5J7agAgAAwFQ4\nSQoAAACmQkEFAACAqVBQAQAAYCoUVAAAAJgKBRUAAACmQkEFAACAqVBQAQAAYCoUVAAAAJiK391J\nau3atTp//ry3YwDXTf/+/fX44497OwYAAB7jdwX1/Pnzuu+++7wdwy8UFRXpX//1X1VQUODtKH3a\n66+/7u0IAAB4FLv4AQAAYCoUVAAAAJgKBRWaOXOmtm7dqrvvvls/+MEP9NZbb6mrq8vxfF5enm6/\n/XYtWbJE7777riSppKREU6ZMkWEYjvUKCgo0b948SVJ5ebkeeOABTZ48WVOmTNH69es9+6EAAIDP\n8rtjUHFt3nzzTa1du1YRERF67LHHJEkpKSlqbGxUaWmptm7dqnfeeUdr165VQUGB0tPTNXDgQL33\n3nu67bbbJEl/+tOf9KMf/UiStHHjRt1yyy1av369uru7VVZW5rXPBgAAfAtbUCGLxaKZM2cqOztb\no0eP1vz583Xw4EFJkmEYWrVqlaKjo3XnnXfq/PnzqqiokCTNnj1bu3btkiSdO3dOH3zwge644w7H\n686cOaNz584pLCxMGRkZ3vlwAADA51BQIUkaM2aM45/T0tL08ccfS5JiY2M1aNAgSVJQUJAGDRqk\nuro6SdKsWbN04MABtbe3a+/evcrOzlZsbKwk6eGHH1Z7e7vuuusuLV++XEVFRR7+RAAAwFdRUCFJ\nOn78uOOfP/vsM40bN+5bXzNkyBBlZGSooKBAu3bt0uzZsx3PxcfH69e//rXeeecdzZw5U4888ojs\ndrtbsgMAAP9CQYUMw1BBQYGOHDmi0tJSbdu2TZMmTXLptbNnz9bmzZt14sQJzZgxw7H8T3/6k86e\nPSvDMBQWFqbw8HB3xQcAAH6GggpZLBYtWrRI69ev1y9+8QvdddddmjNnjuO5bzJjxgydOXNG06ZN\nU0hIiGP5+++/rx//+MeaMmWKdu3apccff1wBAfy6AQCAb8dZ/JAkDR8+XFu3bnVadsstt2jfvn1O\ny/bs2eP0OCQkRNHR0U679yXpqaeeck9QAADg9/yuoAYGBup3v/udS+vOmTNH+fn51/yzbrjhBq1Z\ns0br16/X6dOnr/l9vJ3j/Pnz2rVrl1JSUq46R2lpqTo6OnT27Fmlp6f7/HdhthySFBMT06vXAwDg\nayzGP15p3Q/YbDaX1w0LC1N7e/s1/6zg4GBZrVbV19c7Xdj+ank7x/jx4/Xb3/5Wubm5V5Vj3rx5\nqqur0zPPPKMJEyb4xXdhthySlJCQ0KvXAwDga/xuCyquXmFh4TW9btu2bdc5CQAAACdJAQAAwGQo\nqAAAADAVCioAAABMhYIKAAAAU6GgAgAAwFQoqAAAADAVCioAAABMhYIKAAAAU6GgAgAAwFQoqAAA\nADAVCioAAABMhYIKAAAAU6GgAgAAwFSCPPWDCgsLtW/fPhmGodGjR2vhwoVXrLNp0ybZbDYFBARo\nwYIFSk1NVXV1tbZs2aKuri4FBQUpIyNDs2bN8lRsAAAAeJjHtqAWFBQoNzdX9913n0pLS1VfX+/0\n/OHDh1VXV6dVq1YpOztb+fn5kqSgoCDNmjVLjz32mJYtW6bi4mK1tLR4KjYAAAA8zCMFtbm5WZI0\nfvx4DR06VFarVSUlJU7rlJWVKSUlRTExMbrjjjvU0dEhSRoyZIjGjh0rSbJarQoLC7vitQAAAPAf\nHtnFX1JSooiICMdjq9WqiooKTZ061bGstrZW2dnZjseBgYE6efKkhg8f7lhWXl6u9vZ2ZWRkSJJs\nNtsVW2LDw8MVGxvrUq7g4OBr+jz/mFGSQkJCFBR07V+lP+QwQwZ/zAEAQF9k6r+cFovF8c8tLS16\n8803NWHCBEVGRkqS9u7dq6qqKqfXTJ48WaNGjfJozqioKI/+vK9jhhxmyCCRAwAAX+aRgpqenq79\n+/c7HtfV1SktLc1pnfj4eNXU1Dge9/T0KCUlRZJ08eJFvfzyyxoxYoRmzJjhWGfmzJlfuQW1qanJ\npVzBwcHq6uq62o/jEBgYqKioKLW0tKinp+ea38cfcpghgz/mkKRBgwb16vUAAPgajxTUgQMHSrp0\nJn98fLwaGhqUnp7utM6oUaO0Z88eNTY2qrCwUKGhoZIku92ul156SQMGDNDdd9/t9JqEhAQlJCQ4\nLbPZbGpvb3c529Ws+2WXdwN3dnb2qsz4Qw4zZPDXHBRUAEBf47Fd/DNmzHBcZio1NVVWq1Xbt2+X\nJM2dO1fZ2dkqLi7Wxo0bFRAQoPnz50uSPvroIzU3NysoKEhPPvmkJGnixImaPHmyp6IDAADAgyyG\nYRjeDnE92Ww2l9cNCwvr9VYyq9Wq+vr6Xm0l84ccZsjgjzkkXbGXAAAAf8edpAAAAGAqFFQAAACY\nCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUV\nAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAA\npuJyQW1tbVVeXp5Wr14tSfr888917NgxtwUDAABA3+RSQf3www81dOhQbd26Va+//rok6fz58/qX\nf/kXd2YDAABAH+RSQX322Wf1xhtvaMeOHQoMDJQkjRs3TkePHnVrOAAAAPQ9LhXU4uJife9733Na\n1tjYqMGDB7slFAAAAPoulwrq0qVL9fLLLzst27JlixYvXuyWUAAAAOi7glxZadmyZZo1a5b+4z/+\nQxcuXNB3vvMdRUZGKj8/3935AAAA0Me4VFDj4+N1+PBhlZWVqaCgQNOmTdPo0aPdnQ0AAAB9kEsF\n9bJRo0Zp1KhRampqUktLi6KiotyVC7huDMPQvk/r9ZntvCJDg3TP+CSF9wv0diwAAPA1XDoGddGi\nRXr//fclSfn5+UpKStLIkSO1fft2t4YDroddH3+ht4tr9fHpFr1XdlbP7j0hu93wdiwAAPA1XCqo\nf/nLX3TzzTdLktatW6ft27dr+/bteumll9waDrgeSmpa1Nltdzw+09yphtaLXkwEAAC+iUu7+IOD\ng9XT06OqqipVVlbq9ttvl91u10cffeTufECvBQVYnB73C7IojF38AACYlksFderUqXr88cdVW1ur\npUuXSpJsNpv69+/v1nDA9bAwJ0kv/aVCX7R0KiIkUBNGRqt/6FUdfg0AADzIpb/Sv/3tb/Xss88q\nLi5ODz30kCTpgw8+0L333uvWcMD1MDQ6TL/+Uaqq6i8oOrKf4geGejsSAAD4Bi5fZmr9+vVOy+bP\nn6/58+e7JRRwPXT32PV/3jutmuYOhQQFaOn3btDgAZRTAADMzuX9nNXV1SooKFBNTY0sFosMw5DF\nYtGjjz7qznzANdvyfrX+VnZWl8/Xf/EvFXp8zpgrjkkFAADm4lJBff755/Wb3/xGOTk5Gjx4sLsz\nAddF2Ret+seLSTVduKjmtouK7R/itUwAAODbuVRQn3vuOb377rvKyMhwdx7gurjYbVdDm/OlpLq6\nDU6OAgDAB7h0HdTExEQZBhc2h+9oae9ScIDzr/fA8GCFBHN5KQAAzM6lzUmvvfaa1qxZo5kzZyoz\nM9PpuUmTJrklGNAbA8KDFRUWpLaLPY5lydbwa3qv6rPtqqi/oOFx4UocFHa9IgIAgK/hUkE9evSo\n9u/fr3feeUfR0dFOz50+fdotwYDeCA4M0P0Th+r/flCtzm67rFEh+sltQ6/6fQo+rdf/O3pG5zu6\nFRUaqDk3xWvqGKsbEgMAgMtcKqiPPvqoXnzxRS1evFhBQRzDB98wcnCkHp8zplfv8dfP63W+o1uS\n1NLRo7981kBBBQDAzVw6BjUkJETf//73Kafoc+x24xsfAwCA68+lgvrkk09qzZo1OnLkiOx2u9P/\nAH82Mi5SQf89JUEB0qjBkd4NBABAH+DSJtEFCxZIkt58802n5RaLRT09PV/1EsAv3D9pqAYP6KdT\nje1Kjg3XHRlcBxgAAHf71oLa3d2tsLAwFRcXKzSU20SibwmwWDR7XLy3YwAA0Kd8a0ENCgpSZmam\n6uvrNXHiRE9kAgAAQB/m0i7+xYsXa/ny5Vq0aJFycnKcTpaaNm2a28IBAACg73GpoK5bt04Wt7xe\nmAAAC85JREFUi0WbN2/W5s2bnZ6rqKhwSzAAAAD0TS4V1MrKSjfHAAAAAC5x6TJTAAAAgKdQUAEA\nAGAqFFQAAACYisfuXVpYWKh9+/bJMAyNHj1aCxcuvGKdTZs2yWazKSAgQAsWLFBqaqok6YUXXtDZ\ns2cVGBioxx57zFORAQAA4AUe24JaUFCg3Nxc3XfffSotLVV9fb3T84cPH1ZdXZ1WrVql7Oxs5efn\nO57LycnR3LlzPRUVAAAAXuSRgtrc3CxJGj9+vIYOHSqr1aqSkhKndcrKypSSkqKYmBjdcccd6ujo\ncDyXk5OjqKgoT0QFAACAl3lkF39JSYkiIiIcj61WqyoqKjR16lTHstraWmVnZzseBwYG6uTJkxo+\nfPjXvq/NZrtiS2x4eLhiY2NdyhUcHOzqR/hKgYGBkqSQkBCnmxdcLX/IYYYM/pgDAIC+yNR/OS0W\nyzc+v3fvXlVVVTktmzx5skaNGuXOWFcwy9ZdM+QwQwaJHAAA+DKPFNT09HTt37/f8biurk5paWlO\n68THx6umpsbxuKenRykpKd/4vjNnzvzKLahNTU0u5QoODlZXV5dL636VwMBARUVFqaWlRT09Pdf8\nPv6QwwwZ/DGHJA0aNKhXrwcAwNd4pKAOHDhQ0qUz+ePj49XQ0KD09HSndUaNGqU9e/aosbFRhYWF\nCg0N/db3TUhIUEJCgtMym82m9vZ2l7Ndzbpfdnk3cGdnZ6/KjD/kMEMGf81BQQUA9DUe28U/Y8YM\nx2WmUlNTZbVatX37dknS3LlzlZ2dreLiYm3cuFEBAQGaP3++47UbNmxQS0uLDMPQ2rVrlZmZqTlz\n5ngqOgAAADzIYhiG4e0Q15PNZnN53bCwsF5vJbNaraqvr+/VVjJ/yGGGDP6YQ9IVewkAAPB33EkK\nAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAA\npkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJB\nBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAAgKlQUAEAAGAqFFQAAACYCgUVAAAApkJBBQAA\ngKlQUAEAAGAqFsMwDG+H8FUtLS0qLi7WTTfdpKioqD6dwwwZyAEAgH9gC2ovtLa26sCBA2ptbe3z\nOcyQgRwAAPgHCioAAABMhYIKAAAAU6GgAgAAwFQCn3jiiSe8HcJXGYahfv36KTk5WSEhIX06hxky\nkAMAAP/AWfwAAAAwlSBvBzCb/Px8lZWVKSIiQitXrnQsP3LkiN577z1ZLBalpqYqNzdXklRYWKhD\nhw4pICBAs2fP1rBhwyRJ9fX12r59uzo6OjR27FhNnz691zny8vLU2NgoSero6FBoaKiWL1/u8Rx1\ndXXat2+fWlpaNGDAAOXm5spqtbotx1dlaGho0IEDB1RbW6vBgwdrzpw5Cg4Odut3ce7cOe3YsUNt\nbW0KDw/XTTfdpIyMDHV2durtt99WbW2t4uPjdddddzm2mrorCwAA/oxjUL8kKytLixcvdlr2xRdf\nqLi4WHfffbdWrVqlCRMmSLp0KaGioiL90z/9k374wx/qP//zPx2v2bt3r2677Tb98z//syorK1VT\nU9PrHPPnz9fy5cu1fPlypaWlKS0tzSs5Dhw4oMzMTK1YsUJjx47VgQMH3JrjqzLs379fY8aM0c9/\n/nMNGTJEhw8fdvt3ERAQoJkzZ2rVqlVasGCB/vrXv6qzs1NFRUUaMGCAVq9eraioKH300UduzwIA\ngD+joH7JsGHDFBoa6rTsxIkTysrKUkxMjCQpIiJCklRTU6ORI0dq4MCBSk5OliR1dnZKkhobG5We\nnq7w8HClpaVddQH5qhyXGYahTz/9VGPHjvVKjtDQULW3t8tut+vChQuO592V46syVFZWKjU1VZI0\nevRonTp1yu3fRf/+/RUfHy/p0u9AXFycampqVFNTo6ysLAUFBSkrK8vxvu7MAgCAP6OguqC8vFx1\ndXV69dVXtXPnTtXV1Um6VEAu79qWpJiYGNXU1KixsdFRYiXJarWqurr6uuWpqqpSZGSkoqOjvZIj\nNzdXhYWFevrpp3Xo0CHH4Q6ezDFixAgdPXpU3d3d+vjjj3X69GlJUnV1tUcyNDY2qq6uTomJibLZ\nbIqNjZUkxcbGOsqmp7IAAOBvKKgu6O7uVnt7u+6//36NGTNGf/7znyVd2pLpDSUlJY6tp97IsXPn\nTuXk5Ojhhx/WLbfcop07d3o8x5QpU1RXV6dNmzbJbrc7jj/1hM7OTm3btk233367QkJCvPZ7AACA\nv6KguiApKUnp6ekKDg7W6NGj1dDQoK6uLiUlJam+vt6xXkNDgxITExUTE+N0i8v6+nolJSVdlyw9\nPT06fvy4brzxRqd8nsxx6tQpZWVlKTAwUFlZWaqqqvJ4jkGDBmnWrFlavny5hg8frhEjRngkQ09P\nj/74xz8qIyNDY8aMkSQlJiaqoaHB8fMSEhI8kgUAAH9FQXVBUlKSysrKZBiGqqurFR0dreDgYCUm\nJurEiRNqbm5WRUWFLBaL4+zt2NhYffLJJ2pra9Px48eVmJh4XbKcPHlSVqtVUVFRjmWezpGSkqLS\n0lJJUmlpqaMcejJHW1ubJKmlpUVFRUUaOXKk2zMYhqGdO3cqLi5O3/3udx3LExMTdeTIEXV1denI\nkSOOsumN3w8AAPwB10H9km3btqmyslLt7e2KiIjQ1KlTlZmZqd27d6uqqkqxsbGaOHGio1AUFhbq\nww8/VGBgoNNlhOrq6vT222+ro6ND6enpmjFjRq9zZGVlKT8/X0lJSbr55pud1nd3jgsXLigyMlJT\np05VYmKiDh48qPr6esXFxWnSpElOl5m63jm+nGHKlCm6ePGiioqKZBiGxo0bp0mTJrn9u6iqqtIf\n/vAHDR48WBaLRZI0ffp0DR069BsvM+WOLAAA+DMKKgAAAEyFXfwAAAAwFQoqAAAATIWCCgAAAFOh\noAIAAMBUKKgAAAAwFQoqAAAATIWCCgAAAFOhoAIAAMBUKKjwiO7ubm9HAAAAPoKCCq1fv17z5s1z\nWrZ69Wo98MADam9v1+9//3vdeuutuu2225SXl6fLNx8rLy/XtGnTFBsbq4yMDD399NNqbW11vEdy\ncrJeeuklTZgwQQMHDpTdbvfo5wIAAL6JW51CZ86c0ciRI1VTU6MBAwaou7tbiYmJ2rNnj9544w3V\n1tbqqaee0rlz57R48WI9++yzys3NVXl5uSorKzVp0iQdP35cP/nJT7RixQotW7ZMkpSSkqLw8HC9\n8soruvXWWx33pwcAAPgmbEGFhgwZookTJyovL0+StGfPHlmtVmVlZWnHjh1at26dkpOTlZmZqZ/+\n9KfKz8+XJI0YMULTp09XcHCwMjIytGLFCu3cudPpvRctWqSJEydSTgEAgMuCvB0A5rB06VK98sor\nWrZsmbZs2aIlS5bo888/V1VVlTIyMhzr2e12paSkSJJaW1v1q1/9Su+//77+/ve/y263Kzo62ul9\nc3JyPPo5AACA72MLKiRJd955p44dO6aSkhLt3r1b9957r1JTU3XDDTfos88+U1NTk5qamnTu3Dkd\nPXpUkrRx40aVlpbqrbfeUnNzszZs2HDFcaZBQfw3EAAAuDoUVEiSwsLCNHfuXN1zzz3KyclRUlKS\nAgICtHDhQj3yyCM6fvy47Ha7ysvLdfDgQUmSzWbToEGDFBcXp6KiIr344ote/hQAAMAfUFDhsHTp\nUpWUlGjJkiWOZU888YSmTp2qFStWKDo6WvPnz9eZM2ckSQ8++KDa29s1bNgw/fKXv9TKlStlsVi8\nFR8AAPgJzuKHw6lTp5SWlqYvvvhCkZGR3o4DAAD6KAoqJF06+WnlypWKiIjQM8884+04AACgD+MM\nFqitrU1DhgxRVlaW4xJSAAAA3sIWVAAAAJgKJ0kBAADAVCioAAAAMBUKKgAAAEyFggoAAABToaAC\nAADAVP4/bXrzShSdykQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x87a0310>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<ggplot: (8640449)>\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation heatmaps -- unused in report\n",
"\n",
"\n",
"Produce correlation heatmaps for year and frequency using pearson correlation."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Strictly speaking, Pearson correlation coefficient should be applied to normalised data.\n",
"# So perform normalisation first.\n",
"\n",
"from scipy import stats\n",
"\n",
"# normalised df.\n",
"categories = pd.unique(df['category'])\n",
"locs = pd.unique(df['location'])\n",
"\n",
"cat_v = pd.Series([list(categories).index(cat) for cat in pd.unique(df['category'])])\n",
"loc_v = pd.Series([list(locs).index(loc) for loc in pd.unique(df['location'])])\n",
"\n",
"df_n = pd.DataFrame()\n",
"# df_n['character'] = df['character']\n",
"df_n['year_norm'] = (df['year'] - df['year'].min()) / (abs(df['year'].max()) - abs(df['year'].min()))\n",
"df_n['freq_norm'] = (df['frequency'] - df['frequency'].min()) / (abs(df['frequency'].max()) - abs(df['frequency'].min()))\n",
"df_n['loc_norm'] = (loc_v - loc_v.min()) / (loc_v.max() - loc_v.min())\n",
"df_n['cat_norm'] = (cat_v - cat_v.min()) / (cat_v.max() - cat_v.min())\n",
"\n",
"# print stats.pearsonr(df['freq_norm'], df['year_norm'])\n",
"corrs = np.corrcoef(df_n)\n",
"\n",
"\n",
"# df = DataFrame(abs(np.random.randn(5, 4)), index=Index, columns=Cols)\n",
"\n",
"# plt.pcolor(df_n)\n",
"# plt.yticks(np.arange(0.5, len(df.index), 1), df.index)\n",
"# plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)\n",
"# plt.show()\n",
"\n",
"\n",
"\n",
"# print np.corrcoef(df['freq_norm'], df['year_norm'])\n",
"print df_n.head(5)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" year_norm freq_norm loc_norm cat_norm\n",
"0 0.733173 0.551849 0.000000 0.000000\n",
"1 0.733173 0.202551 0.333333 0.166667\n",
"2 0.733173 0.119444 0.666667 0.333333\n",
"3 0.733173 0.889353 1.000000 0.500000\n",
"4 0.733173 0.252878 NaN 0.666667\n",
"\n",
"[5 rows x 4 columns]\n"
]
}
],
"prompt_number": 24
}
],
"metadata": {}
}
]
}
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