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https://github.com/beltashazzer/jmpy

Quick plotting and data visualization of pandas and numpy data.
https://github.com/beltashazzer/jmpy

Last synced: about 1 year ago
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Quick plotting and data visualization of pandas and numpy data.

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README

          

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## JMPY \n",
"Jmpy is an analysis library created to simplify common plotting and modeling tasks. Simplicity, a common plotting signature, and ease of use are preferred over flexibility.\n",
"\n",
"The goal is to create mini-reports with each function for better visualization of data.\n",
"___\n",
"Currently, there are three modules: plotting, modeling, and bayes. \n",
"Plotting is used to make pretty graphs, and modeling is used to anaylyze data with visual output. \n",
"Bayes is currenlty in development to support easy bayesian analysis and plotting, relying on significant assumptions about the underlaying data.\n",
"\n",
"This module relies heavily on statsmodels, patsy, pymc, and pandas.\n",
"\n",
"___\n",
"__Overview__ \n",
"[Plotting](#plotting) \n",
"* [Histogram](#histogram) \n",
"* [Cumprob](#cumprob) \n",
"* [Boxplot](#boxplot)\n",
"* [ScatterPlot](#scatter)\n",
"* [Contour](#contour)\n",
"\n",
"[Grid Layout](#grid) \n",
"[Modeling](#modeling) "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
" \n",
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('bmh')\n",
"\n",
"import jmpy.plotting as jp\n",
"import jmpy.modeling as jm\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Plotting \n",
"___\n",
"Create some artifical data for our analysis:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" \n",
" xc\n",
" xc2\n",
" xd\n",
" xe\n",
" xf\n",
" xg\n",
" y\n",
" ytrue\n",
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"text/plain": [
" xc xc2 xd xe xf xg y ytrue\n",
"0 0.000000 0.000000 3 50 0.1 12.310639 41.696174 51.050000\n",
"1 0.401606 0.161288 5 50 0.1 -4.350195 96.030224 85.453219\n",
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"3 1.204819 1.451589 7 10 0.4 -19.704997 105.596364 120.229335\n",
"4 1.606426 2.580604 3 50 0.4 3.845414 38.828264 52.682232"
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},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nsamples = 250\n",
"xc = np.linspace(0, 100, nsamples)\n",
"xc2 = xc**2\n",
"xd = np.random.choice([1, 3, 5, 7], nsamples)\n",
"xe = np.random.choice([10, 30, 50], nsamples)\n",
"xf = np.random.choice([.1, .4], nsamples)\n",
"xg = np.random.normal(size=nsamples)*15\n",
"\n",
"X = np.column_stack((xc, xc2, xd, xe))\n",
"beta = np.array([1, .01, 17, .001])\n",
"\n",
"e = np.random.normal(size=nsamples)*10\n",
"ytrue = np.dot(X, beta)\n",
"y = ytrue + e\n",
"\n",
"data = {}\n",
"data['xc'] = xc\n",
"data['xc2'] = xc2\n",
"data['xd'] = xd\n",
"data['xe'] = xe\n",
"data['xf'] = xf\n",
"data['xg'] = xg\n",
"data['y'] = y\n",
"data['ytrue'] = ytrue\n",
"\n",
"df = pd.DataFrame.from_dict(data)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
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"outputs": [
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fwSITsMhEemjSkIT5R7DIBCwyAYv5R7DIBCwykR6aNCRh/hEsMgGLTMBi/hEsMgGLTKSH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\nRvVwDmufffaZVq+uu9Jj6NC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"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jp.histogram('y', df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets start to visualize how our artifical data looks. First, plot a histogram of the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to look at the data color coded by a categorical variable, you need to specify \"legend\":"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IdwBYOvts/uItn+HqlZ/M26qJpXTMp6Kp8askIYQQQgghJqhjIM63Ht9FytQs\nrPHxgVPrcDvs43/wJPpWrwfTxN1Qi6exbnS+2GQYhskzD21lw6v7ALjiPUuprhlb5mGyhhODPLvx\nQR5fdycH+jPfEa6sp7G6GYfDOVWWry8LkowJIYQQQohp5eGtPSTTmoZKF4vrfAwk0tTmkIyZiSRt\nv7wTAP/8Ocz5zLWjQxQnY3gowe4d3aChZoafcF1uKzKmjCSR4V6GYhGe2vBHnt/8MImRQtQ1gRk0\nVDdTH5pFpS805VZNLCal1ELgD0e8NA/4ltb6Pwr1nZKMCSGEEEKIaeXV/QMANFd5CHgcBD25/cib\n6O4j2Z1ZZMNdH855SftEwmCgL4ZSMHd+Ld6KySd2KSPJbc/+B6/ueJaewYOjry9rXsmVK67lzJa3\nkjbTOa+aWA5GVo4/HUApZQfagXsL+Z2SjAkhhBBCiGmjtWeYPX1xfE4bnzu3iZoK16TniR3mrqnC\nTGUWqnCFq3IaogiwYU1m6ODCU2dw/hXzc5onFhnuZfPe1+gZPIjd5uC8xVfynnM+ycyaeaPvsdns\nkyroXMqu/Nq8ORxT9Pmx7+3ak8evuAxo1VqPXQI1j2QBD4tITYbik5hbQ+JuDYm7NSTuxScxLy1W\nH49k2uS/Vu8HoKHSnZdEDCAdi2MmkwDYXM4x7dnsdzyWYtPaTB/PuvD4KydmI+irxkinADhn4aX8\nnyu/eVQiVmhWHPORROxJ4KPAJSN/Pjnyer58CLg9j9s7LknGLDKV6qJMFxJza0jcrSFxt4bEvfgk\n5qXF6uPxZtcwmw9GAWgMuIjEjZy2ZyaStN1yNy+c/2EwNTa3i3QsftRKijDx/dZa8+wj2zFSJpVB\nD7u2dWEYZk59dDpcuBweAC499X1FH4Zo0TG/AWg55rWWkddzNlJb+Wrgrnxs72RkmKJFplJdlOlC\nYm4Nibs1JO7WkLgXn8S8tFh5PLTW/G79AUwNs4Ju5lZ7Jz1XzDQMOu58lJ3/dgvx9sw8LHd9DaGz\nT8UZCowZpjjR/X7luV1sem0/SkHz/DDxWIpYNEll0DOpfgLs797Fvp5WAF7fvYqFM5cXNSGz6Jg3\nZvl6tt4BrNNaHxz3nTmSJ2MWkZoMxScxt4bE3RoSd2tI3ItPYl5arDweT+3sZV37IBUuG1+7ZM6k\naoqlY3HabrmbVRd+hE1/fyPx9oP4F8zl9Ftu5K1r7mLxd69n3heuG7OS4kT2e/P6dl54fAcoWLBs\nBi6XA4+Tb4WdAAAgAElEQVTXmdPiHaY2+cUj38E008ysaRldqKOYLDrmHVm+nq0PU4QhiiBPxoQQ\nQgghxBTXO5ziP1/MzMM6dYaf2VWerBMxM5HktY98OVNPDPA2NzL/q5+h4b2Xo+yZZfHtjXWT6l/r\nti4evWcTAJe+cxGnrZxNLJrEW+HKac7Yw6/+jtbOzbidXhY2LcfvDZTL0vXfBM7l6KGKrSOv50Qp\nVQFcAXw2121NhCRjQgghhBBiSrt5TTsxw6S2wskMf2auWG2WT5wS3X0Mbt4BQNU5yzn1p/+Irzn3\nUW+d+yM8cNs6tKlpnB3itJWzcThsOQ1NBNjRsZHbn/sZAGe0XMDVKz9BdWVdWSxd/9j3du258mvz\nLqcAqylqraNAONftTJQkY0IIIYQQYsradGCIp1v7sCk4bYZ/0nXFzFgcY2AI5XBQfcGZeGbU5Ny3\ngf4Y9/52Lem0Jlznp74pkPMcMYCh+AA/eeD/Yeo0c+oXUVVRg8PhLItE7LCRxOs6q/uRK0nGhBBC\nCCHElBRNGvzr83sB+MBpdVy9pJagxzGpIYpv3vjfALhn1DD3sx8aMy8sW/FYint+vZbhoSSBkIdZ\n86rx+lw5zRGDzDyxnz/0j3QPHCBUEWZB42nlNDxx2pFkzCJW1+EoRxJza0jcrSFxt4bEvfgk5qWl\nmMcjmTb57pO76RhIUOm288HT6vG7J/ejbfs9j3Ho8VUAeJvqMYaGcQT8E/78sfsdjxv88Tdr6Tk0\nRHVtBdd+eiXa1DnPEQN4ftPDrN+1CqfdxTkLL+eqMz9i6fBEuQZzI8mYRayuw1GOJObWkLhbQ+Ju\nDYl78UnMS0sxj8fu3hhvHBgCMsMTY4aJ353dNrTW7Pnv29n+3Z+D1lSc0kz1+SvGLF0/niP32zBM\n/vDLNXR1DuJ02Xnvx1bgr8yyYyfx9IY/ArCseSU2ZbN8eKJcg7mRZMwiUhel+CTm1pC4W0Pibg2J\ne/FJzEtLMY/HvZu6SJvQUOmiJZx9TTFjaJjNX/0hnX98HIBTvvJXNH34XbhrqrIeonjkfh/Y309X\n5yA2m6JlUS1Opz2rbZ1MR08bb3a8gU3ZCQfqS2J4olyDuZE6YxaRuijFJzG3hsTdGhJ3a0jci09i\nXlqKdTw2Hhjk6dY+HDbFVy9uzrqmmJlIsv7T36Dzj4+jHHZO+/m3OeUrf4W3qX5Sc8WO3O/2tn4A\ngtVeqmv9Oc8ROyxlJPnfJ78PQFN4Du8+++NcvfKTli/aIddgbiSNFUIIIYQQU0ZXNMl3n9gNwKJa\nH/NrfFkv2JHo7iO2N1MfuOrc06l+yxl561/r1kMAnHn+HJadOTPnOWKHRYZ76R3qAiDoq7Z8eKLI\nD3kyJoQQQgghpoRoMs03Hm0lkkgT9DiYU+UhEjey3o67por0cAwAT1N91nPETmRoIE7Hvn7sdsWC\nZTPylohBJgEz0ikAKr0hy4cnivyQZEwIIYQQQpS8WCrNNx9rZU9fnEq3nfNmB6j2OSdVUwzAGMgs\n/uHMYtXE8eze0Q0aKgIeXn95L4Zh5m3bppkmGh8EwO3y5m27wloyTFEIIYQQQpS0vliK7z+zh80H\no9T4nPzgqlPwOG2TqikGMLy3g/RwHGW3o7Um2dOPp7Eu9352RwGoqHQRj6XyUuD5sN899xOG4hHc\nDg9Ou4uBWB/hyvq8bFtYR5Ixi0hNhuKTmFtD4m4Nibs1JO7FJzEvLfk6HmlTs60ryqv7Bnh1/wA7\nujNDCl12xflzgtRXuiaVhB1mDGSSJkegAldVMOdhiof320hlnoQppfB4nXlbvOOlLY/xxPq7UCiW\nzzuPkD9cMsMU5RrMzYSSMaXUHmAQSAOG1vqsQnaqHEhNhuKTmFtD4m4Nibs1JO7FJzEvLbkcj77h\nFK/uzyRf69oHGUykR9ucNkW1z8HZMwPYlCISN6jNIdGJ7esEwL9oHnM+c+2kVlA80uH9Ho4mAWhZ\nVMeZ58/Jy5yxg337+a9Hvg3Asjkr+ehF11ta5PlYcg3mJpsnY5dorbsL1pMyIzUZik9ibg2JuzUk\n7taQuBefxLy0ZHM80qZm66E/P/3a2RM7qr0x4OLsmQHOnhVgUV0FD2zuYjCRptJtn/Q8scO6n3sl\n04dojD2/vJN5X7gup4TMMAzARue+zLL2XQcGc+rfkda2Po+RTlEbbKKpem7JraIo12BuJHIWaW9v\np7m52epulBWJuTUk7taQuFtD4l58EvPSMpHj8fyuPh7f0cvmg1GiyT8//XLZFcsbKjl7VoCzZwZo\nCrqP+ty1y+uJxI1JzxM7LLJ+C+13PAxKEThjCan+gZznjLW3txOsrGUokgDAbrflbb7Y4HAmwfN7\nAlT6giUzPPEwuQZzM9FkTANPKqXSwE1a65sL2CchhBBCCDENrd7bzw1P7xn9d2PAxcpZQc6eGeC0\nBj/ukwzrc9ltOQ1NBDCiw2z84o2gNaGVp2F3OXGGAnlZ2n5oMEE6beJyOwhWefMyXywS7eXFbY8C\nUOGp5B0rPlJST8VE7iaajF2gtW5XStUBTyiltmmtnz/caBgGbW1tYz7U1NSEw+Ggv7+fSCQi7UeI\nRCKjj3VLsX+l2P7m81uOao/vHYK0xhFy4ahyj/l8q6rH1IoKTxq/J02FJ81rbxwYbe+KOI9qr688\n5uZm+FDY0LYk2JJjtn/uBaeWVHxKtT0ej497f/BTO6Y9Sg8aExc+XFQc1dbW1lbQ/m/Zt/ak339s\n/06f9da8fn8+2k0zM4nc6uNfbu2RSKTg5+dUbT/yOp/I9QUTP3+PvcdYvf/ixP64MVO0+JSwl7nV\nXj55VkPOCdZEmYkk6//qGwxt34Uj4OeMm/8ZbWpc4VDOc8YA9rX2AjBnQQ0rL5qX03yxeHKYR9fd\nwQNrfsNwIrMEf6U3RCwVxefJ31L8wnpKa53dB5T6DjCktf7Xw6+tXr1aL1q0KM9dm97a2trkkW6W\nHvzunVm9f9/c+Uf9uz6U5GD/iW+2yxuyu7mdf/n88d8kJnSu37Xqpqy2+YELPptLl8ZVav2ZDLnH\nWEPifmKFuq5KMebr1q1be9lll41Z7Kwcfl462fHoGEjwqTu3oBS8fUGYmgon1y6vz2nIYTYOPfkS\n6z7+VTBNat92AUu//5W8LGcPsHv3Hp68p41Ib4wFS+u56oPLs07GUkaSroFOXtvxLA+9eisDw30A\n1AQaaGlYyuzaFq5e+cmSezKW7TV4ouujXI37ZEwpVQHYtNaDI39/G/DdgvdMCCGEEEJMG/dv7kID\nF88L8dEVM3Ke+5UNYzDK1m/8O5gm/iUtBJcvysvQxMNSyTTDQ5lRNE63Pev5YikjyU2PfpdXdzxL\nIpVZyOSUhmV88MLPs7DpdAbj/QS8VSWXiIncTWSYYj1wr1Lq8Pt/r7V+tKC9KgNSk6H4huJ2q7tQ\nluRct4bE3RoS9+KTmJeWEx2PZNrkiR2ZYXwe++SLNU+G1ppNf38jsbYO/ItbOOOW7+FtrMvL0MTD\nwjXVmGYrAD6/O+v5Yh29bazZ/hSpdJKAt4qPXnI9b136LkZ+/ibsLN3iznIN5mbcZExrvQtYXoS+\nlBWpyVB8UUnGLCHnujUk7taQuBefxLy0nOh49McMEuk/F0TOtU5YNg7c9yQHHnwG5XRQfd6KvCdi\nANXVIcx0ZurPWRfOzXqI4lOv30MqnaTKX8dly9/LeYuuHE3ESp1cg7kpzq8kxBiZehSimGwqu/mR\nIj/kXLeGxN0aEvfik5iXlhMdj5DXQdrM/H+40pN7nbCJSg/H2fad/wQg/NazUTZFsqc/798zPJxA\na41SsOHlvRiGOeHP7u/exRMb7gFg6ewzuerMj06p4YhyDeZGkjGLtLe3W92FslMbTFndhbIk57o1\nJO7WkLgXn8S8tJzoeKRNzUguRjHHqez891+RONiNq64a35ymvC1jf6xn/rQRAKfLQTyWIhYduwrz\nCT/7xn1obTKr5pTRFROnErkGcyNFn4UQQgghREG1jxRDdtoVMUMXZZhibG8nu3/xewBqLzmXOZ+5\nFk9D/ocovvHqPratz5RjaGoO4fE6s5ozFo0PAhDwhfB7AyVX1FkUliRjQgghhBCioHpjmdEplS47\nle7iDFPsX78F0ibuhlocAT82lyvvidjOLQd54r7NAFzyrsUsWFqPt8KV1Zyx3qFDALxl0du4aNm7\np9QQRZE7GaYohBBCCCEKandvHIDljf6i1RZz+L2Zv5gmjsqKvA5P1FrTuu0QD96xAa1hyYoazjyv\nmcqgZ8KJ2L7uVn50z5d4Y8/LAOw5tD1v/RNThzwZE0IIIYQQBZNMmzzTmlnWfjg58YUtcqWNdOYv\nygZ5WMNroD/G3tYe9rb20tbaQ3QwM/SyvinAohXhCW/nUKSDu1+8iRc2/wmtTew2Owsal+OwORmI\n9RGuLN1l7EX+STJmEanJUHxSZ8wacq5bQ+JuDYl78UnMS8vxjkd3NEXHQGZBC6/TVrRl7fvXbwXA\nUenDGIqS7OnH01g34c/HY6kjkq9u+rqHj2p3OG3UzqhkxswgXnfF+P0Z6ub253/Gqi2PkDYN7DYH\nl5x2DV63DyNtTNn5YnIN5kaSMYtITYbikzpj1pBz3RoSd2tI3ItPYl5ajnc8th2KEjdMKt12ZgXd\nRZkvlo4l2H/bAwD45s6a0CqKqVSajrY+2lp7aNvZw8GOgaOeqLncdmbNrWZ2S5iZc6vZueUgibiB\nx+uktr7m5Ns2ktx419+wt2snAOcvfjvXXvjX1IdmkjKSDMT6CHirpuR8MbkGcyPJmEUMw8DhkPAX\nk01pTD01CihOJ3KuW0Pibg2Je/FJzEvL8Y7Hw9u6AXjPkho+ePqMoswX2/vre0h29VK59BSWfO/L\nuGuqxizekUym2dvazaGOQfbv7qV9bz/pI+qD2eyKxtkhmlvCNJ8Spr4piP2Ivofr/MSiyZGVE01O\nthRDf7SHzt69AJy78HI+cvHfjQ5HdDpcU3poolyDuZHIWaS9vZ3m5maru1FWaoMpDvZPvd84TXVy\nrltD4m4NiXvxScxLy7HHY2fPMBsPRLHbwDDzMHFrAtLxBDv/7VcA+BfMPW4itmdHN/fftp5UMv3n\nFxXUNQZobgkzu6WapjlVuFwn/lHZ4bBRGfQA0Na276TnYSwZJZVO4nK4aahqnpLDEU9ErsHcSDIm\nhBBCCCEK4uW2CADNIQ+JItUXi+7aR3poGOV04AyHxswV2/jafh6/bzPa1Lg9DiqDHk4/dzYLls3A\nV6C+bd23DoCArxplk1E64s8kGRNCCCGEEAUxNPLkyeOwFa2+GOnMUEO7x40z4B+dK2aamuce3c7a\nVXsAaJwdor4pgNfnYtmZM7OqDZatZ964D4DmugXEElFZNVGMkmRMCCGEEEIURM9wptjz+XNCvGdp\nbVHmiyUO9QBg87pHF+CIDiV56I7X2berF5tNccV7l7L49MbROV+FTMR2dGxkz6HtOO0uwpV1U3bV\nRFEYkowJIYQQQhSBYRi0tbWNeb2pqQmHw0F/fz+RSGTKtx/ex7TW7DiQeX9bX4yBSITo4EDBv//A\n/Y8DYJvTxEB9kJ2vt/HEk/uIRZM4nIrzrpxFoDZNe/u+zOcrmgBbTt8PHLd9T9dWbl/zYwDm1i3h\n3NnvxOuqoKO9s2D7X+x20zRPuP8T+Xy5k2TMIlKTofikzpg15Fy3hsTdGhL34ptKMXc4HCdd6CAU\nCp10mfCp0H7knxs7h+gc7kMBdqXQ7gqaq0/8RCgf3+8zNNsffA4A26x5bN7t4tCbrQB4fU7mLapl\n8dJ5owtv5Ov7g8HgUe3dA538/rn/5KWtjwHgcfloDM+mvrHuhMMTS+H4Taa9v78/q8/39PSc8D3l\nSJIxi0hNhuKTOmPWkHPdGhJ3a0jci09iXlqOPB6PbO9GA7NDbmr9rqLMF9v1898x7K6k/+pP0F1R\nDylwuuzUNwUI1/mp8LtHlqLPr8P7PTjcz90v3czTG+4llU7itLtoaVjKrJoWQv7wtByeKNdgbiQZ\ns4jUZCg+qTNmDTnXrSFxt4bEvfgk5qXl8PHY3Rvj6dY+bAq+eMFsFtT6Cj5f7FDrAVZtjdN/zedA\nKewOG2e8ZTYr3zoPl9tR0PlhhmEwFI/wpVveRywxBMC5C6/goxdfT6giPKWLOo9HrsHcFH4WpTiu\n9vZ2q7tQdmqDKau7UJbkXLeGxN0aEvfik5iXlvb2dpJpkx8+uwdTw7xqb8ETscGBOA/9YQO33rKe\n/nmnorRJs+rjY9ct4eJ3LMI3koBVBj0FW6ijvb2d7e2vE0sM4XH5OHfhFXzs0i9RG2wYLeo8HRMx\nkGswV5LGCiGEEEKIvOmJpmjrTwAwt8pT0NpihmHywG3r6dwXAa0J7djAAnsPjSsXU91cN/4G8sg0\nM8v4V7gDNFZPr8LOonDkyZgQQgghhMib7uEkhqmpcNlpCLgLOldseChBIm4A0LD6EWaufojwvBnM\n+cy12NzFfRI1EOsDyDwBk1kRYoIkGRNCCCGEEHmzoTMzZ+rsmZVcu7y+oEMUfX43ui+TBNkTcSoW\nzMXmsGMMDRfsO4/HNNM88fo9ANQGG0cLOwsxHhmmKIQQQggh8iKtNU+82QuAqXVBv0un07T+4GaM\n1gQ0L8Izo4bqhdU4QwFc4eKu8PfKrifZ392K11VBQ9UsKewsJmzCyZhSyg68BrRrrd9VuC6Vh6lU\nF2W6kDpj1pBz3RoSd2tI3ItPYl5abG4/PcOZulNep71g88XiB7rY8Nlv0bdmA/ptHwHA/oEPMOfC\nRrx11UUdotg/1MOfNvwWgFPnrOSdZ11HdWXdtF2w41hyDeYmmydj1wNbgUCB+lJWpCZD8UmdMWvI\nuW4Nibs1JO7FJzEvLRWBAMm0xmFT1FU4CzJfLHGoh5eu+BTJrl5sPi/J2fPABGW3YwZCRZ8r9odV\n/0XCiFETmEG1vx6Hw1k2iRjINZirCQ3iVUrNBN4J/E9hu1M+DMOwugtlx6YKO1xCHJ+c69aQuFtD\n4l58EvPS0t4fA6Ax4OJ9p9blfb6YEY2x9mP/l2RXL86qINXveRtJ04ZSEKr2FaSg88nsPbSD5zY+\nCMCCpuVU+oJlNzxRrsHcTPQK+Q/gq4BZwL6UFanJUHxSZ8wacq5bQ+JuDYl78UnMS8v63QcA0Bru\n3dRFMp2/Hx2NwShrr/sKAxu24aisoO7tF5KqawKgutbPuZe0FKyO2PGkjCQ/vv+rmDrN/LrlfPCC\nz3P1yk+W1VMxkGswV+M+O1ZKvQs4pLVeq5S6+HjvMQyDtra2Ma83NTXhcDjo7+8nEolI+xEikcho\nxfIj23tfWj/6Hl9HLzbTJBnwkQz4xmzf19HLwi//ZUnu30Tbf7+hC086hteMjWnvd4TQyjba7plb\neVR7fO8QpDWOkAtHlXvM5z1btmCmNb6wB1+NB1/agz0ZH23v3Rk5ql07mo7egOFDYUPbkmBLjtn+\nra+1H9W/8frvMLYf1R6lB42JCx8uKsZ8/opzr84qvkeeOzD++dM6q+uk339s/5bMOvOk33+kLfvW\njn4+SCNb9q096fbt+8b+Vq2nfQumaeAL1FMRrD+qra2tLavzb/Wmp8fdvyP5qR33+BzZXkr3v327\ne0f+ptm/I3nC8/fw+b30rNqSuj8cPo8ncv+zmSa1n3l/Vts/fC5O9PheseyDWW0/EolkfX6WS7uf\n2tH2icQfmPD2j70Grd7/cvZKZ6a+WEPAzWAinbc5Y2YiyZpr/obBTW9i93k4+66f4ghX8cgT+2Cw\nB7u9+GvJdw100tm3F4ViWdP5ZTc8UeTHRAbyng9crZS6CvAAAaXU77TW141uxOGgubn5hBsIhUIn\nHU9aju1tbW04HI4x7ck7nxjzedfAMK6BEy/RWor7l0173O4lbveO2161e/Nx243+JEb/2B82zXRm\nWOJwT5zhnjg1C0N0b+8f877D7bXNC4+7fWW6wBx7c9XKllX/vUbXcduTDJNk7PE93vlxPIfbj3fu\nwInPHz3LPOn3H9u/E13jx+vfK/seHf17mhRDHH/fD2+/d98bJ2wfHjjI8MDBo15rbv6bk37/sf0b\n7/snsv8nay+l+9/+HZlrQTsyS0uf6Py1qn/jtR97Huf7/nfkuQnjH99st9/W1nbU+VBq8bWy/djY\nQ37iH4lEsro/FaO9p6fnhJ+Zzvb1x2mNpEfni1W67XmbM9b78usMbnoT5bBTe+WFuOvCJH2V9HRt\nA8DtdRKLJqkMevLyfRPhd2eWUbDZbAQ8obIbnijyY9wrRGv9NeBrACNPxr5yZCImhBBCCCHEw9u6\nAbhoXogPnzGDoMeRtzlj+3//EAD+BXPxn9KMKxxieDDFYCSOUjBjZrDo88VMfXgIppIiz2LSpM6Y\nEEIIIYTIScJI86dtmSeCLrvKayI29OZuDjz4NMphZ+kPv0rg1AXY3C62PJ8Znjp3QS3nXz6/qPPF\nAHZ2bgTAYXeSMlIMxPoIV9aP8ykhjpbVWau1flZqjOWH1GQovuHu+PhvEnmXJGp1F8rTSYYmisKR\ne3vxScxLw8HBJHHDxK7AbbcRiednhT0zkWTL1/8dTBP/grmjiVgqmWb96kwy5vE58/Jd2Xps3Z0A\nNFY3g9Mo22GKcg3mRp6MWURqMhTfcI8kY1Y42XwQUThKkjFLyL29+CTmpcFmy4zTc9ltBDyOvM0V\nS3T3YQxmfqln93lI9vTjaaxjz85uEnEDl9uB2+Mo+nyxjXvWsLFtDR6nj89d9R0aq5rLdvEOuQZz\nU9znuWKU1GQoPpsFKy0JUHKbsYSWSiSWkHt78UnMS0PCyNxzqrwOrllWm7chiu6aKmzOTGJn87hx\nhTM/+B/Yn1nFMljlxetzFW2+mNaaJ9bfzQ/uuR6AeTMW01jVXNb/r5NrMDfle+ZYTGoyFF/1KfIY\n3QoVhK3uQnlyyBNJK8i9vfgk5qWhK5qp5anTybzXF9M6szoy6s+/VN3bmpmfdub5zay8aF5R5ot1\nD3Ry411/wy1PfA8jnaKxeg4za+YxEOsr6/OwnPc9H2SYohBCCCGEyMmr+wYAqPfa8lpfLDNMceSX\nS6ZJsqcfMxiic38Em13Rsriu4ImY1prH19/F7c/9lHgqht8TZGnzWVRV1FLpCxLwVjFEZ0H7IKYv\nScaEEEIIIURO3ujM1DZsqiCv9cXcNVWY8UwhaVdtNa5wiE1vHAQNFX4361fvLeiTMa01v3nqX3l0\n3R0ANIXn8rX3/4xgRTUDsT4C3qqynSsm8kOSMSGEEEIIMWkHB5O09cfxOGxcNLuCJfPr8zZnDBhd\nwMNR6Qdgwyv7AKidUUk8liro4h1/eOG/eHTdHShl46xTLiJcWY+yKZwOlyxjL/JC5owJIYQQQohJ\nW7Mvs5hGtdfB/v5EXrcd3b2fVGQQAGVTdGzv5GD7AHa7ojLkweN1FmzxjrtfvJn7Xv4VNmXnLQsv\npyYwY3RYohD5Ik/GLCI1GYpP6oxZQ+qMWUSWtreE3NuLT2Juva2HMvf5GQE3vabO23wxgL7V68E0\n8TTW4a4Ls2VHJvFbfEYT517cgrfCVZAhik+sv5u7X7wJgLMXXMyn3/YNYqnoCYcllvN5WM77ng/y\nZMwiUpOh+KTOmDWkzpg1pM6YNeTeXnwSc+t1DmSehnkdNmwef97miwF03PMYAM2f/gA1H3s/m18/\nAIDdpgqWiO3o2MhvnvoRAGfMu5Bqfx2xVJRwZf0J54eV83lYzvueD5KMWURqMhSf1BmzRjnXXrGS\n1Bmzhtzbi09ibi2tNXv6Mr/svHZ5HX+xrCZv88WirXvpf20TyuEg2T/EY/dvJZ02CVZ5sTtsxKLJ\nvHzPkbojB/i3e7+MYRq0NCxhRtVM/N7AuEMTy/k8LOd9zwf5KckiUpOh+KTOmDWkzphFpM6YJeTe\nXnwSc2t1DiYYTpm47Iq1+wfp7Mjf8eh7bRMAnsY6WqMe9u3uw+6wMWtedUHmiqWMJDfe9Xn6oz2E\nAzP4h/f9lHet/BhXr/zkuCsmlvN5WM77ng8yZ0wIIYQQQkzK/khmiKLPaWcwkSZh5O+pvBHJ1C6L\nVdezVdcB8Pa/OJWZc6oKMkSxe/AgnX2ZlRpPaz6HlJmUFRNFwUkyJoQQQgghcuKwKyrddtx5SpDM\nRJKDjzxPPFTL3iWXoFHUNwWYv7S+YDXFeiIH0Nqkwl1JTXCGrJooikKGKQohhBBCiEmJpzJPwpoC\nbq5ZVotd5Wd+dteug2ytWszOaz5H0uPH43HQMDNYkHlikBmi+NBrtwJQ5a/lHSs+IsWcRVHIkzEh\nhBBCCDEpL7ZllpofTBjcu6mL86p1TtuLDiVY8+wuNqzZS3rGfFQ6TdAcYuaKRfj87oLVFIsM93Jw\nZIhisCJMLBXF5/EX5LuEOJIkYxaRmgzFJ3XGrCF1xiwiS9tbQu7txScxt84TO3p4prUPu4Kl9X4G\nE2nsk0xgEnGD11bt5rVVe0gl0wAEd77BjC0vcfkz/4vh8hZsKXuAgLeKvqFuABqqZmc9RLGcz8Ny\n3vd8kGTMIlKTofikzpg1pM6YNaTOmDXk3l58EnNr7O6N8dMX9wOwclYAhy0zZ6yhtjqr7WhT89pL\ne1jzzC7isRQAs5t81Lz4CMnnn8PdUEf7b+9l3heuw1agRAzgUKSdeGoYp91FpS/7c6qcz8Ny3vd8\nkGTMIoZh4HBI+IvJZleY6dyGT4jsKWxS88oCGlNqvFlA7u3FJzEvvmjS4GuP7CRhmMyp8vB/L5pN\nzNAEPQ5s2iSbJQmee+xNXnthNwCVFQ4WdW8mdsOdJLVGOZ1Un7+CVP8AyZ5+PI11BdojeGrDvQDU\nh2YSTw4zEOvLaiXFcj4Py3nf80H+T20RqclQfFJnzBpSZ8wiUmfMEnJvLz6JefG9tCdCb8ygwmVj\nWX0FMUNTW+HCZbdldTzeeHXfaCI2Z3A3s2+6gdjtf0DZbcz+1F8w5/98EFdVAGcogCtcuKcv0fgg\nL9gg5bQAACAASURBVGx+GIDG8NwJFXk+Vjmfh+W87/kgaawQQgghhJiwvrgBQF2Fi2qfk6An+x8n\n9+zo5on7tgDQ8Mpj+DetyWzzqotZ+M2/pmLeLMxEkmRPP65wCJu7MEOvU0aSXzzybYbiEYIVYT7y\n1r8lHKiXlRRF0UgyJoQQQgghJqxjIFPo+dzmINcur8dlz26g1cGOAe6/bT1aa2o3rya8aQ2OujBL\nb/wyDe+6ePR9NreroEMTIbOKYnt35unczPBcnE6XJGKiqCQZE0IIIYQQE5JMm6xvHwTg4GAi68/H\n4wZ33fIqqWSaYFcbdWuewDNzBk0feif1V5yX7+6OK+irJpXOLBxS5a+VQs+i6CQZE0IIIYQQE9If\nMxhIZIYp2pQiEjeozaL212svZFZN9KSGafrTbbjra1jxmx/iP2V2wYYijieRigHgdnot+X5R3sZ9\nrqyU8iilXlFKbVBKbVZK/VMxOjbdSU2G4pM6Y9aQOmMWkaXtLSH39uKTmBdXyOsgYWRWJq6pGDtf\n7GTHIx5LsX51GwD1z9yHMk3Cl77F0kQsMtxLPJlZ8Ehrk4FY36S2U87nYTnvez5MZJBvArhUa70c\nOB14u1Lq3MJ2a/qTmgzFJ3XGrCF1xqwhdcasIff24pOYF1fa1BimxmlTXHta3Zj5Yic7Hs/cuopE\nPE1Fx278nbupvfICXH4vyZ7+Qnf7hCrcAVLpJKCorpz8MMVyPg/Led/zYdxkTGcMjfzTOfKfFGvK\nkWEYVneh7NjsyuoulCWpdWUNqe1mDbm3F5/EvLgODSUBcNoV923uJpk++l5zvOOR6h9gzfU/YMvO\nzEiJhs6tzPrE+6iYNaPgy9aPJ5bM/Ijr9wS46szrJr14Rzmfh+W87/kwoTljSik7sBY4Bfi51nrN\nke2GYdDW1jbmc01NTTgcDvr7+4lEIkVrf6Mzc2H1O0JoZcOTjuE1Y2M+f7j96hbvhLd/eNvZbP94\n7Z50jAPuhjHtM2fWjL7H19GLzTRJBnwkA74x2/d19Ga+p4Dx3f3jX5/0+4/tX/V5Z2S1/aEtB6jw\npPF70mPauyJOTK1G29NNR/ehd2cEM63xhT34ajxjPm/bmfr/7N13fFzVnfD/z5k+I2lm1CzbktwL\nHWLT3GVjegvNSWghIclvNzGkPYHsZjfZfTZLSEgWkvBsNgESINmwSy8JEGywDAZsYxvjgiu2ZVmW\nbbVRHWna+f0xlpA06p6ZO9b9vl+vvGLmzL06Zc6de+aec7490j35rh5Px3offyRYP+Df763tSPOA\n6b2P9+QW9khvpQ5NDAceHGQlHN8ZRHGo7dfS7bMDg39+AmtXEItF8HiLyPIlBrasq/q4R/qa8g97\nZdATD+hsCYEl1CMp21bYVT4vY4kSTjh/9/IXlp416N/v7rU1zw9af93TrZWJXxQDnd9dOm5Y5x/u\n9a/+vQ+H1L870w+envj57i9/TcH4tUShONRwgByXD6878UanOnCImI5yeuDrw7o+fFy5ccC/3zt/\nF5/xuWGdf2tJfEOAgdqne3ph4MZhnT+bwmHlf7jXz8bGRnw+X0q+3yr3d7tGDdD/OtPnL52Z9u/f\ngdI76x6GVv/PrPntkPpfNmOYVnqK4eUzi1018dkO2Q4rzR3RhDVjVVVVTJw4seu/W/dWsPaav6PG\nX4ouOxtvuJmcr3+N0rkTcIbbU7pt/VAcqt0HgEVZeG3Tn7nm/DtGNCDrXW4zMXPZk2FIgzGtdRQ4\nRynlB15QSp2htd7WdRKbbcBG8Pv9Az7CTHb66trqHuntVjft1v4XZQ7n/L3PPZTz95WeSz1aWRLS\nTzlUm3C8o6kNR1P/U71SXb+D/f3u6X19DgY7f2u7ldZ266Dpjl19T2Noq2sfcApiZ3rBTD+1fZyj\nMz3kKD6h/A2WXphb02d6iLY+p/J1RrMfavuFnl7RZ3p/7RfT8QFKW9NR2pqO9nv+zvSiwkv6TFcx\nR8L6pBbbp2WNEqaFvssO8fLXV24Z9O93l1f66aWrv/pL5/mHe/3r3lZD6V8tNA/497vnr6qhDoDi\n3HiemtsbaW7v/2ZxuNeH9ZWvD/j3T/T8r/9vz7Ya7POZafmvqKjo8XlI5vX50J7EQVdf/e9E8p/K\n9N51D0PrX4Ont/bbB40qf11dXb/HnOwqAvHvW5/bRo7TOmCMsbaKw6y/6W7C9Y1ETomvcHEVj8Gd\n7SIrNxubzZuWPA/k/Z1vAFCUW0pLsImmYAP5OYk/AAmRKsPaTVFrHVBKrQIuA7YN9n4hhBBCCBGX\naTOJRpL+4cH4E9qFRXBeXgfVhyoTjq+oqCBUdZSKr/+YyJFanOPGoGbMAGDslCzGTbFRVVVpePm0\n1mzauwaIb2uf6xpLU20rLfUVQzq+ezqkdqZSJqfHYrETKr/ZDToYU0oVAuHjAzE3cDHw05TnTAgh\nhBBiFMm0mUTDTbe5s9nXGJ9V0WLNoaR0XI8NPPx+P42NjeSHNB/8/b8ROVqH/9wzOOvRn/DEY5uh\nNUws5KS0dAI2W+J64nSX78DRXTQG63DaXBR4i1g6+7N4XNlp+/ujJb3zB4ahHj+anxyPxFBW1o8D\nVimltgAfACu01n9JbbaEEEIIIUQmWV/ZREyD32UjFNU0tieux9XRKB/9/b/QcbQOV3ERs574GW3K\nQVsw/l5lUQRb+1jnaICapvjSE48rB601wbCEYhHpN+iTMa31FuAzg71PDE/QIoEF003ijBlD4owZ\noylo3FbRZibxdtJPrjHps+NYvK7HZNv7XS/miSnCgSYAvGfMINYR4mBFEB3TZHud5PhcuIcRJDqV\nPI745jDRWAS3I2vE29qDufu+mcueDMNaMyaSZ6ANP0RqSJwxY0icMWMMtGmHSB2Jt5N+co1Jnw8O\nxQdZN545hrmT/AkxxgAKSotBxyMg2bzZ2PN8bHtuDwAXLJrCmeeV9jlF0QjRWOdOyApOMPqNmfu+\nmcueDJnRG0xIaYkBlG4SZ8wYEmfMGBbV/w6fInUk3k76yTUmPXbXtHK4KYTdqjgY6P/HzWg0Six8\nfEqizcbhykaOVTfj8tgzaiAG8MmR7QD4s/IJdrTSFGwY8bnM3PfNXPZkyJweYTL+iEwhSre8afIY\n3QhZ5BudBVMa5y8xOgumVFVVZXQWTEeuMakXDEf5aXl8k4aJfhetoVif68UADuzcTbgufo8Ts1go\nf3UXALkFifHijFZVdwAAtyObbLf3hKYpmrnvm7nsySCDMSGEEEII0SetNQ+tqaSysQOv08ophZ4B\n44uF9h5ER6PYc72ooiJCkfiURYfDmjEbdwA0ttaz6ZO3AcjLKeTyWTePKNizECdK1owJIYQQQog+\nPbv1GKs+acBls3D/FdPwuWz4XLY+14vFOkLU/6UcAHfJOGZ85Xo2PhkP5O502TJm445INMwvXvw/\ntHW0kO324XX7CYZbB9zWXohUkSdjQgghhBAiwc5jrTy6/jAAF5R6meB3UZjl6HMgBtBeU0/7R7sB\ncI4vhPZ2vH4XANNOK8qI9WId4XYefOledld9hNPuZvbUhXizck9oiqIQJ0KejAkhhBBCiAR/3VmL\nBqblu8nz2Glsj1A4wNOt4IFDRI/WYXG78J4+HYvPS0NtfLfL/btqmHHGWEMHZOFIiAee/xbbKj7A\nZrXz/Rt+xZjcYrzuXJmiKAxj/E8UJiVxxtJP4owZQ2IAGUPijBlD4u2kn1xjUkNrzaaqZgDGe50D\nrhPrVPnHlwEoXnY5U7/5RdrDmrbj68Q0GL5m7EhDJTsrNwNw9uS5jMktJj+nKCkDMTP3fTOXPRnk\nyZhBJM5Y+kmcMWNIDCBjSJwxY0i8nfSTa0xq7DjWSk1rmFy3ja9eMJ5ct73f6YkAwcpqjvzlLQAs\nzvjgRmsIdUSwWBS5eR7D14yt3bmCSCxMbvYYpow9JalTE83c981c9mSQJ2MGkThj6SdxxowhMYCM\nIXHGjCHxdtJPrjHJV9sa4mer41vZF2bZBx2IAXzy0BMQjeGZNhG0JlQXoLoy/oS+qMTHBYunGjpF\nsbW9mb9s+BMA08efkfTdE83c981c9mSQK5hBJM5Y+kmcMWNIDCBjSJwxY0i8nfSTa0xyra9s5O9f\n2MXhphA2i6LE5+o3plinYGU1h599HQDH5fOw+71YfF42vRcf0Fktxv8Yuq3iAzrCQXLcfvKyxxAM\nJ3d6q5n7vpnLngwyTVEIIYQQwuTaQlF+u66K13bVATAm284543IYm+MYcK1YrCPER3//I2IdIbKm\nTqDgqkVMOWUmLcEoLU3x5QEOl41ga4gcnystZenLkYaDAHg9eeR4fLJ7osgYMhgTQgghhDCxTVVN\n/OLtg9S0hrFbFF86dxxXnlpASyjab0yxTh21DTTv3AdAzhnTUVYrFqcDjzVGOBSNv+5zGbpeLBwJ\n8e6O1wDIzS6QAM8io8hgTAghhBDCpDZVNfEPr32CBvLcNv7t0qlML/AA4LYPvvbUkesl2hoEIGvK\nBKye+NMvq1URicTXx89ZMs3Q9WI1jYepro8/GfN58iTAs8gosmZMCCGEEMKkntx4pCuW2ILJfvzu\n4f1OHw40g9bYc71M/vsvoKzxAVx7MEw4FMXusOIcZEv8VNt6YB3haAivJ4/x+RNliqLIKDIYM4jE\nGUs/iTNmDIkBZAyJM2YMibeTfnKNGbkdx1r5+FgrdotiZqEHn8s2aCyx3lr2HABAWa0ceORpvJ4s\nAOpq4u1itVr44O39XU/J0i0U7uCFtb8HYHLRzJRNUTRz3zdz2ZNBpikaROKMpZ/EGTOGxAAyhsQZ\nM4bE20k/ucaM3J8/PALA1acVcMOZYwZdH9aXw8/Ed1F0TxhPONCEOxwfdB3cG98IJMfnoj0YNmwD\njx2HNhForcVhc1LkL03ZFEUz930zlz0Z5MmYQSTOWPpJnDFjSAwgY0icMWNIvJ30k2vM8MW05k8f\nVrOusgmLAqtiRAOxcGMzx/72DgCeycXY/V6s/hwAao+1AOD22HG57YZt4LFl//sAjPGV4MvKTdkU\nRTP3fTOXPRnkCmYQiTOWfhJnzBgSA8gYEmfMGBJvJ/3kGjM8LR0R/nXFfp7cGH8qNmt8DpEYg8YS\n6y3WEWLbd+8n0tyKc0w+07/3FaYsv5XDNccAqD4Yv8+ZvWAy5y+aYsgGHqFwB+XbXgGguGBSSndR\nNHPfN3PZk0EGY0IIIYQQJrCvLsjyl3bx/sFGshxWlkz1U+p3keO0DnutWEdtAy279wPgmVKCxeHA\n4owPdOpqWmlubMdqVRypNO7H5/1Hd9La3oTD5iQ3qyDpgZ6FSAZZMyaEEEIIMcr9bXcdv363klBU\nMzXfzQ8vmkx+lp3G9siIpig6C3KJNManInoml+LI/3Td0PZNhwDw5XnoaI8Ytl7Mao3f5jrtLnI8\nftlFUWQkGYwJIYQQQoxi5fsa+MXb8ThbU/Pc/OyKaeQ447eAhSNcy6VsVsKBJgCmfedLXU/FAPbt\nrAHAn+cxdL1YNBqO5yOrUAI9i4w16M8gSqlSpdQqpdTHSqntSqlvpiNjQgghhBDixBxrCfHLNZUA\nnDMumzPHZtGehG3mm3fsI9YRwprlpurp14h1hABoqAlSe7QFp8vGkqtONWy9GEBLML6rbFtHC69t\n+jPhSMiQfAgxkKH0jgjwXa31acCFwDeUUqelNlujn8QZSz+JM2YMiQFkDIkzZgyJt5N+co3pXzSm\n+Vl5Ba2hKMVeJ5NyXXhHEEusL8feWAOAu2Qs4UAToboAkUiMjzfGt7T353vI9rkMG4gBHAnEp0t6\nnNm0BJtoCjak7G+Zue+buezJMGhv1FpXA9XH/92slNoBFAMfpzhvo5rEGUs/iTNmDIkBZAyJM2YM\nibeTfnKN6d+fNx9hy5EW/G4bP71iKkqpEa0P60vggy0AOArzsPu9OPL9tLR0UF8T/671+l2GrRXr\n1B6KD9QtFivZbm9K14yZue+buezJMKyfRpRSk4DPAOu6vx6JRKioqEh4f3FxMTabjUAgQGPjpzcG\nW6rjCz4DNj9aWXBFg7hjwYTjO9Ovmerucfxg588ND+/8T6/fN2B69+NzB0nvfbznz89gL8jBUZjT\nI01ZFK27qtGRWI/0T/zerve07TmSkN5d254j8MCjhLweQl5PQrrncD2WWKxH+qHJ04eV/wthWOd/\n8bG/dKXX720kFtV48l14ChIvxvV7G+G008hyRcl2RRPSaxrtxLTqSrfO9CccP9j5u6crBVr3nx71\n95y+0PvvD5a/wdJVZc9tg+uqPiYWi+DxFpHlK0o4PhKJ9Pn57tT7899SUtAjva/26c5SZRvw7/fO\n35Fg/ZDL33EUDh3aRTQaIS93LDnexK2nO9N9vkJ8RacnpB88sI1oNIw/dyy5eeN6pLV/0jFgeu/j\nS0rPGrR8PVRGBm2f7umv2LYnpA/Uv0pKCgZtn+7p1srEX3P7y19x7kQAjjZWE4mFyHH58LoTvyir\nA4eI6Sgv3/dYv9eXvq4/1uzIgH+/d/7WhLaDpY+pQREPCgvaEuqRXni8rYZ6/qH2j06xhuwe5R+s\nfl7ijzjISkhvpQ5NDAeeHukKhUb3m979+Bvnf3VY+T8UXJ+Qv4HyP497h10/qUzPprBH+Qern6Gm\nO8lOuAcxonyZpqkjwn8fD+o8uzgHv9uelEEYQPuRGurWbATAM6mESV9dhsXpQHW0094WRinIL8w2\nbK0YQDgSYtO+eAw0t8OT8jVjnd/ZZmTmsifDkGtOKZUNPAd8S2vd1OMkNhsTJ07s91i/399j1Ly6\ntrpHervVPeCTot7HJ/v8qU4P1zYTrm3u8VrWqePRx+ds95U+2PHdOZracDT1/8tg9/SGGRcMO//D\nOX9tTeKgqK2ufcCnUq3tVlrb+w9Q25nu2NX3tKvBzt+ZXjDTT20f5+hMDzmKTyh/g6Vbq7f0nb+m\no7Q1HU14vfPCNtTPf+jpFX2m99d+MR0Z8O/3zl903Nw+0/sqf0Plp4OTHG8+FRWJg5VOjY011B/d\n1296oOEIgYYjJ5TuDNf3mz7U8g+Uru15/ab31b9OOVTb9e+h9K+axr4/O33lr3Vs/FpcnDuRqoYK\nmtsbB3xKNtzrT+3YHQP+/d5UzAGx/m+AeqfXVPYs62DnH+73w3Or/7tH+mD1E6JtwCcvvdOzKaSF\nmiEfP5z89877UPI/3PpJZfr6ytcT0odbv32lO8jq9x7EqPLX1dX1e0y6HKgPEtOQ5bDiddpobI+M\neLOO7sJNLWy8+bvocATX+DFY3U4iLW3YvNlUVcS/Y4uKfVy4ZJqhUxQDrXUcrotvWuLLyicYbsXj\nyk7Z36uqqhrwXng0M3PZk2FIvUQpZSc+EPtvrfXzqc2SEEIIIYQ4EZFYfCqI06pGFEesz3O2Btl0\n2/do/ngvNl8O+WXnd01RjERibFwTjztm5CCsU0NLDcFQC3abk9KCybKtvchYg/ZMpZQCHgN2aK3/\nI/VZEkIIIYQQJ6KpPT59vNTv4rozCk94imK0vYMP77iXhnUf4RxXyPnP/Aqrx40j34/F6aAlECRQ\nF3+K6fLYDV0vFo6EeGbNbwAo8pdwxexbZVt7kbGG0jPnAbcBS5RSm4//74oU50sIIYQQQoxQVVN8\n+n5zR5QXttUQio58O3utNVu/+e/UvbMBi9tJ0RWLcJeOwzV+TFd8Ma0h2BbGYoGCMcauF2tsq6eh\nJT4d3OfJJRiWHTdF5hrKboprAJWGvAghhBBCiCTYVBVf6zne66C5I3pCa8Y+efBxjry0EmW3Mf7G\ny1AWC6G6AK7xY7rec2h/fG1uwTiP4evFfJ48wscDPue4c2WKoshoxk/qNalQTf8L5kVqSJwxYwQC\nx4zOgilJnDFjSMyr9JM6TxSKxNhVE58ymGW3ntCascPPvs7enz0CSjH26iVYXc6udWLd7dsd37hm\n0ozCjFgz1h6Ol9/tTNytNhXMHGvLzGVPBtmH0iAD7V4mUkPijBmjsbFm8DeJpJM4Y8aQmFfpJ3We\naMuRFkJRjddpJdtpHfGasZY9FWz91r8DUFB2Pqff/3+ItLR1rRPrFInE2L8rfq0PBTWRSMzQAVlj\nWz3tHfFButbQFGwgPycxREYymTnWlpnLngzG/3RhUioDfjUyG4tVZtsawWqV33yMYFH9h1sQqaPk\nazXtpM4T/W13fGv9Yp+TmIa28MjWi9W8+R46EsUzuQTP1AlEWtp6rBPr1NbSQTjUGW9SE2ztI7Zg\nGuW4fHREOgDwe/LTMk0xEokM/qZRysxlTwa5ghnEM32s0Vkwnbxp8hjdCCUlM43OgimN85cYnQVT\nyiIxwLlILanzno42h1izP4ACSrzOE5qiGK6PP2G35WThyPUlTE3s5PI40PGd9HHmdBi6eQfEn4yB\nxm51YElSoOvBVFVVpeXvZCIzlz0Z5CdrIYQQQohR4qmPjhDVsHCyjzvOG4/PZRvRFMVYR4jaVWsB\nsPu9TPrqsoQnYl2Oj8RsNgvjJ+QavmbsjQ+fAcCfXUiwozUt0xSFGCl5MiaEEEIIMQpUN3fw+q74\nFEW/yz7igRhA0/Y9tOypAMCa7SHS0v/avPZg+NM8VAaIREa+jf6Jampt4K0tLwAwecwMst1e2U1R\nZDR5MiaEEEIIMQq8urOWmIZSnxOrRY14O/tYR4gdP/gPYu0duIqLyDl1ar9TFIGuYM9Wu4VoJGZY\nwOdwJMSv/vIPtHW0kO8dy82Lvkm+t0gCPouMJoMxIYQQQohRYGt1CwDjTnCt2Mc/+A8aP9yBa/wY\nZj35ANnTJvQ/RZGuWYrYbFasNotha8Ya2+qpOLYbgMljZmK3O2QgJjKeDMYMInHG0k/ijBlD4owZ\nQ+KMGUNiXqWf1HlcTWuIHTVt2CyKr10wnrE5zhFNUTzy8psc+tPLYLVQcNHcQQdiAB3t8WmKuQUe\nZpw2wbA1YwpFc7ARi7JQnD8lrdMTzRxry8xlTwZZM2YQiTOWfhJnzBgSZ8wYEmfMGBLzKv2kziEU\njfGf7x1CaxiTbR/xQAxg/2+eAiB//rlYXQ5CdYP/sNPcFN9Gvr0tzN6tDYatGdtxaBMAXk8eVmt6\nw3uYOdaWmcueDPJkzCDKZkEbuMDVjCxWRSyqjc6G6VitNqJRiUGSbhZlJaajg79RJJXCgkau7el0\nMsUZi0QiVFRUJLxeXFyMzWYjEAjQ2Jj4Q8pg6U7/GA40xH9wnOyO8Mn+A3js1iEf35le9e4GGj/8\nGOV0wJJzafflcKS9hZJI3oDHB+ri0yOdHgthHWD/vv04nLaE84+0fENN37D77Xgd5J+OK5LLJ/v3\n4nZkpeXvA7S0tKS0fJmaPnbsWJxO54iPNzsZjBnEM30srTsOG50NU8mb5qN2l0zdSreSkplUVGw3\nOhumM85fQlVD4k2fSK0s8mlBngan08kUZ8xmszFx4sR+0/1+/4BPGfpLD4aj1BwPtDymII+pk4v7\nfDI20PljHSEq/u9/ApBzyhROv7QM17ieAZ77O37Xpm0AKKxYbVYmT5nc51TFkZZvqOkVNbsAsDtt\nhB1NTJ08rceasVT+/YqKCiZOnJjS8mVqeveyD+X4urq6ft9jRjIYE0IIIYQ4ie2uaSMU1RRlO7jz\n/L4HYoMJVh+jeccnAGRNm4jF4Rh0rVjXsW3xNWOnnDkOX1HMkDVjtY1HqG6owGKxku8t5PJZN8vm\nHeKkcPI82xdCCCGEED2EojGe3FQNQK575L+xt+7ajw6Fsef5yJpSOuBW9r21tcTXjPnyPVgsasR5\nOBHbD34AQH72GEARDMvGLuLkIIMxIYQQQoiTVCAY4UhzfIqi32WjsX1ka3SPvv4OAOOuu5gpy28d\n8lOxSCRG3bH4wOeTHceIxYxZm90Rjq+Zc9hdEuhZnFRkMCaEEEIIcZLyu200d8Q36xnvdY4otlj7\n4WNUv7ACAB0d3gY0rc3tXVvba62JhI3ZOKihJb5W8/TSc7nm/DtkiqI4achgzCASZyz9JM6YMSTO\nmDEkzpgxJOZV+pm9zttCUdojMZw2C7fOKhr2ejGtNVu//e/E2jtwlY5F2axD2s7+UwqtwWazkJXt\nJC8/b3gFOEGhcDv/+85veGnd4wDUtxrznWPmWFtmLnsyyAYeBpE4Y+knccaMIXHGjCFxxowhMa/S\nz+x1vrcuCECW3cKL22tZdvbwBmSVjz9P3eoPsDgd5M+fjSPXN6z1Yq3N8e9Wb66bWfMm4RrBk7mR\n2rj3bZ548+cca6wCYELhdPKzi2gKNpCfU5S2fIC5Y22ZuezJIIMxg0icsfSTOGPGkDhjxpA4Y8aQ\nOGPpdzLFGUuFg4HjgyFXfLpiY3uEwqyhTdFr2rqbHT/8JQAFS+Yw9ZtfTNjOfjD1tfHBcCym2fTu\nAWbPn4jTaR9mKYbnSEMlj75xH9sq1gNQkj+FaeNPx+3INmy9WCQSwWYz5221mcueDFJzBpE4Y+kn\nccaMIXHGjCFxxowhccbS72SKM5YKdmt890KtIcdpHfKasfYjNWy87XvocITsU6bgLika1nb2nZob\n40/mnC4b7cEwBysOMn3G1OEVYhjCkRA/eWY5RwOHsFntfG7B17l89hfQWtMUbMDrzjVkvVhVVdWA\nMeRGMzOXPRlkMCaEEEIIcZLq3G9jgt/JdWcUDmmKYriphY03f5eOIzU4x40hd8452P3eYU1P7KTU\np4NBl9uOzW4d9jmGo7Gtntb2+FKPc6ctYu6pl2Kzxp/EpXtqohDJIIMxIYQQQoiTVFVTfJpidXOI\nF7bVDLpmLNYRYtPt99D88V48U0s5/9lfgwZHvn/YT8UAwqH4dOjiiX7OXzSFqqrKkRVkiHyevK4p\n2D5PnmxhL056g/58opT6vVLqmFJqWzoyJIQQQgghBqa15k8fHuGl7bUAFHudXWvGBnLgd/9Lw9rN\nWN0uCi+aiyPPj2v88NaJdYpEYlTuqwegviY9u1pGomFC4XiQaZtsXy9GgaGsen0cuCzF+RBCuEi0\n/AAAIABJREFUCCGEEEMQjWl+/e4hntxYjUXBBaVe8jz2Ia0Zq1n5HgD5iy8ArYe5jX1PbS0dhELx\nwV80GiPYGhrxuYZq1daXiMTCeN25RKMRmoINKf+bQqTSoNMUtdZvK6UmpT4r5iJxxtJP4owZQ+KM\nGUPijBnD7DGvjGC2Om/uiHDfWwfYWNWM3ar4h8WTOL/US2N7BJ/LNvAUxVCYxi07AbB7s0e8TqyT\nJ9uJ5fiaMafLjjvLgU+nLuaU1pryrS8DUFo4jRyPL2OmKZo51paZy54MSVkzVt/awStrE3dLC9j8\naGXBFQ3ijgW7Xs8dJL338YFAgMbGxJg5xcXF2Gy2hPTccMuwzu//26s4CnMS0tv2HEFHYtgLckae\nDv2mRwKtJ3z+LdUtwzp+zKZ3hnV+Cj2EvPH/9eY5XI8lFuuRXpD36Ueqfm8jsajGk+/CU+BKOL5+\nbyOOrdsHTO9x/Ez/wOmDHQ893tc7vV6v7XH8voqdRKMR8nLHkJ87JuH8IcsFxLQiyxUl25W4hXhN\no71HetAyq0f6oUO7iEYj+HyF+P2J5392zSNoYjjw4CArIb2Vuh7pdeN75mGw81fucw1Yvt7ltw+j\nfsaVnNqV7ojEGFdy6oDnH1s8LSH94IFtRKNh/Lljyc0b1yMtrOyDtk/3dPsQytedXYcH/Pu98+c8\ntjohfX/rYaI6Rp7DS57D2yNt7Zi2YZ0/b8yshPT+2td+fDX/vl1Dq5+xRYuH3H8A7HZnj+MHO7+r\noXFI/aNTVvGsAcvXu/xr3toOlj5+jY944tvMW0I90sdY84d1/oDLh9edeLNaHThETEfJ6ZbuzXUD\n4CAroX/21kod767ck5C/gfLfmfeh5v8/nryvR/4Gy/9w04sn+4d8fQLIpnDA9MGOHyj9tTXPD+v4\niy+8ps/7h0697y8+rtw4rPMXeSYlvJYMoWiMb7+ym4OBDhxWxb9dOoXPjI9fX4aynX3dOxuIBTuw\n+XKw+71M+uqyEU1P7GSzWfDlezhW3cyUUwqx2SwpjTn1ceVGDtbswWFzUlo4lctn3WzIzol9MXOs\nLTOXPRmSMhjTWGiw9x9xvd3qpt3qHnG63+8fsKF7p6+urR7W+cO1zQMGYU5Fevc4Y0b8/eGkO5ra\ncDT1H1Sze3ptTeJNXVtd+4ABl9OV3l+csc70Tzx9L4usbzhGfUPi053SqRcC0NpupbW9/92jOtMb\nKvve3r2xsabPwMgzJp4OxAOaDhTUtDO9v+3j+zt/NBq/Ae6vfJ1ONL2xqX7Q44N1B/pNDzQcIdBw\npMdrQcunPw6kIv/u2Kf13dff752/Yy39b61dH2qiPtTU4zXHJ5+21VDO3xzs/5f/3u3bcnwxvdUa\nv7wPVv7h9q/e/WSw8w+1f3Tq3U/6+/x2UjEHxPq/Geud3rufDHb+5vbGAQNod0+35OYnxBkbrP8O\nJ/999fFk5n8k6UO9PqUyPUx7v7Hd+ju+MybSUO8v1le+PqzzFzGp33OeiJ3HWjkY6MCqYMEkPyW+\nxO/c/gQPHWHrt/4dgOwZk9DRKJGWNmze7BHnJxKJUXc0/gP4wb11nP6ZYiCWsphTm/e9C0BpwTQU\nimC4FY9r5PlPJjPH2jJz2ZPB3JESDeSZPtboLJhO3jR5jG6EKRNPMToLpiT1bgyzx7wygpnqfO3B\n+MC42Oek2OccckyxcKCJDV/4DqGaelylY/GeOeOEpyjC8TVjHT3XjFVVVZ3QOQdyqHYfAG6nccGd\n+5PKcmc6M5c9GWQYK4QQQgiR4cLRGCv3xDeruH3WWC6c6B9STLFYR4hNX7yX1j0HyJoxifOf/TU6\nEh3xVvbdubMctAfDAPhy3bizHJCi5arhSIidhz4EID+nMKOmKApxIoaytf1TwPvATKXUIaXUnanP\nlhBCCCGE6PTO/gCB9ghep5WKhqFvSFX9wgoa1n2E1eOmYPGF2H05I97KvreOYJhIOIbTZWP+JTOw\n2VI34aqu+SjBUCugcDuyCIbNtXGLGL2GspviF9KRESGEEEII0bdXdsTjic0o9NASitHYHhnSph3N\nu/cDkHPGdIjFCNUFcI1P3PRlJI5Vx9eb5xVmoY7vqpgqFhUf6NltDjzOnIyaoijEiZA1Y0IIIYQQ\nGayqsYPtR1uxKijKdgwpnlinaNvx3aS1Tso6se4O7IkPECORGOtX7yMS6XsjlWSoazoKgMPqgNSO\n+4RIK1kzZhCJM5Z+EmfMGHUD7LQnUkfq3Rhmi3mVCcxQ53/ZEd8xc9GUXG7+zNhB44l1inWEaFi3\nBQBbdtYJb2XfXaC+jY/WHQSgqNhLezBMsDWUkphT7aE2Hl1xHwAF3nEEO1ppCjaQn1OU9L81UmaO\ntWXmsieDDMYMMtBW8iI1Btq+W6TOQNuei9SRejfGQNuwi9QY7XXe0hHhrzvrAHDa1JAHYgAdtQ1E\nW+P1o+zWE97KvrvVr+0iGtUUjs3B4bDhcseDPttsQ99ufyg6wu08+NI9VNXtJ9vtY/r4MzNuJ0Uw\nd6wtM5c9GWQwZpDuccZEevQXZ0ykltVqIxqNGJ0N05F6N0bvOGMi9UZ7nb+2q472SIxctw2n1TLk\ntWIAzoJPByy2nKykTVHcv7uWPduPYrNbuPbWz2C1Wo4PxCxJjTnVGmzivme+wSdHPsZudfCPN/4/\nfNl5eN25GbeTopljbZm57Mkga8YMInHG0k/ijBlD4l0ZQ+rdGGaKeZUpRnOda61ZsacegCl5brwu\n25DXinWKheM/yihrcm6WI5EYrz+3FYBxpX6yvS5yfK6unRSTFXNqe8UHfP+JW/jkyMcoFGdPmYsv\nO4/8nKKMG4iBuWNtmbnsySDDWCGEEEKIDLSxqpkDDe34XTbumldCQZZjyFMUAdoOHSHc0ASAjkaT\nspNiU0Mbrc0doCC/MItga4gcX3KmJoYjIaobKnj1gz9Tvu1lALyeXE4rPZfSwikZNzVRiGSQwZgQ\nQgghRIYJRWM8uj7+xGFirmvYAzGA2jffJ9begSPfj2dySVKmKUaOT/d3OGx4sp3xQM9JEI6EeOSN\nH7Nu55t0RNqxWe1cd+GXueLcW2gLtWTk1EQhkkEGY0IIIYQQGSYQjNDUEQUg22Ed1loxgFgozIH/\negqAaf/nTkpuvjopOym2NsU3w8ot8DBr3qSkBXre9Mka1mx/nZiOkptVwPKrf8zpE84DwO3MSsrf\nECITyZoxIYQQQogM43fbiMXiT6F8I1grdvAPz9F++Bh2v5eOukDS8tVQF9+dMRKOsendAyccWywc\nCfHR/vf57ev/SkxHKcmfwkXnXM+M8WcnI7tCZDx5MmYQiTOWfhJnzBgS78oYUu/GMEPMq0wzWuvc\nYbVgPz4t8cpT84e3VqziMLvv/x0AeQvPJdLYnJT1YgBVBxoAyMpxdMUW675mbDgxp8KREE+9/Wve\n3Pw8HZF2Zk9dyBeXfo/crIKTbkqimWNtmbnsySCDMYNInLH0kzhjxpB4V8aQejfGaI95lYlGW51r\nrdlY1cz/bD7K0ZYQAKv3BZiY6x7SgCwabGfLN/6FWLCd7JmTcRUVYPd7k7JeTMc0lfviuztmZTu7\nYot1N5yYU41t9ew69BEdkXZyswu5bcl3GOMbf8L5NIKZY22ZuezJIIMxg0icsfSTOGPGkHhXxpB6\nN8Zoj3mViUZLnUdjmncPBPifj46yty4IgM2iOKMoi3BUD2nNWKwjxOav/hOBDduwelzMeuKnWBwO\nHPn+pKwXqz7USFtrCK/fxYJLZ+DJdiasGRtOzCmfJw+LJX78hMJp5OcUnXAejWLmWFtmLnsyyJox\ng0icsfSTOGPGkHhXxpB6N8ZojnmVqU72Og9FY7y6s5Y7n93Bj986wN66ILluG3fMHseNZxYyOc9N\njtM6pDVj7TX1NKz7CIDcOZ/B4nDgGj8mKQMxgD3bjwIw5ZQxeP3uPjfvGE7MKbvNweQx8WvVKcWf\nOemmJnZn5lhbZi57MsgwVgghhBAizWJa89zWYzyz5RiB9vhT7LE5DpadVcTF0/Nw2iyEojEa2yP4\nXLahTVFsaSPS3IrF6cB3zqlJmZrYqbmpnc3rDgIQ6ogQicROeCfFcCTE/mM7ANhZ9SHhSOikHpAJ\nMRIyGBNCCCGESLPNh5t5ZP1hAHLdNr5y/niWTM3DalFd73FYLcPazr6ufD0A+QvPZerdtyftiRjA\nihe3Ew5FyfG5cLpsSQn23NhWT7AjvuYvGo3QFGw4qacqCjESMhgTQgghhEiz3TXxQcgEv4tZ47M5\nZ3xOj4HYcDWs38LeB38PgEry+p2dW6rZt7MGi1UxYWoebo8jKcGefZ48tI6v5fY4c/C6c0/4nEKc\nbGQwJoQQQgiRZg3B+NREt82CdwRxxDrpaJR9v/4jex94DB2N4ijMwzkmP2lb2Tc2BFnx4nYAyq44\nhemnFeHOciQt2HMoEt/pWKYnCrOSwZhBJM5Y+kmcMWNIvCtjSL0bY7TGvMpkJ2Odh6IxNlU1AeB1\nWbnujMJhxRGD+M6JgY92svv/PkxgwzYAfLPPwHfWTBwFuUlZLxaJxHju8Q10tEfw57k589wS7Hbr\ngMcMJ+ZUQ0sNDS21ADhszpN6mqKZY22ZuezJIIMxg0icsfSTOGPGkHhXxpB6N8Zoi3l1MjgZ6/xv\nu+o42NgBQLbDRls4xnBm/cU6Qmz77v1Uv7QSHY7gLCrgzF//M3kXnE2oLpC0rex3bammvqYVi1VR\nMjmP9rYwdt/Ag7HhxJxqDjYSiYVx2t3kZhee1NMUzRxry8xlTwYZjBlE4oyln8QZM4bEuzKG1Lsx\nRkvMq5PJyVbnr+6s5eH3DqGBafluSv3OYU9RDB4+xtHX30aHI3gml/CZx+4j57RpAEmZmgjxp2Lv\nv/UJAONKfHj97iGtExss5lRMx9hTtYX3d61g3c6VAPg8+TDy5XIZwcyxtsxc9mSQmjOIZ/pYWncc\nNjobppI3zUftroDR2TCdKRNPYc++bUZnw3Sk3o2RRT4t1BidDVM5meq8tjXEQx9WAnDbrLFcOiMP\nv9s+7CmKNW+sIdrShj3Xy7jrLyFr6oSk53X96n0E6tvILcji8pvOItvrGtI6saqqKiZOnNjjNa01\ne6u38d6ON1i3ayX1LZ8+uXc7sjitdBbBjtaTeppiX+U2CzOXPRlkMCaEEEIIkQb1bRFA8fU5xXz2\n9JE9wYq0Btn38B8BmH7v1yj5wlVJ3cIeoL62lbWr4k/Fxpf6hjwQ6y4U7mDLgbVsP/gBH+xZRW3T\nka60/JwiLjzlYs6bVsa2g+tpbW8m2+09qacpCjFSQxqMKaUuA34JWIFHtdb3pzRXQgghhBCj0Lkl\nOcybNPI1NhWP/C+hmgYcBbl01DYkMWef2rfzGLGYJn9MFk63fdgxxcKREN985FoaWj59YpmXU8SF\nM5cy55SLmTbuDJSKz0ucOu50moINeN25sqOiMKVBB2NKKSvw/4CLgUPAB0qpl7XWH6c6c0IIIYQQ\no0W2w8ppWVkj3sYewD2pBEdBLvmLziPS2Jy0Ley7O+fCiTTUtRGNxHC57cOOKdbYVk+2y0d7qI0i\nfwk3zPsqs6ctwqISn67ZbY6TdmqiEMkwlKvB+cBerfU+AKXU/wDXAjIYE0IIIYQYosJsO/NPKRr2\nGrHuxl6+kNa9FYQDTdj93qRsYd+bzWZh8ZWnEmwNjSimmM+Tx+xpC2gPBcnx+Dhn8rw+B2JCCFCd\nkc/7fYNSNwKXaa2/cvy/bwMu0Fov73zPm2++WQNUpDKjQgghhBAniYkXXXRRYe8Xk3W/pGMxRTRq\nw2qNKIslI7cJ1jqmojpqsyprRKnMzKMwTJ/9w6ySsoGHVKgQQgghxMDkfkkI0dtQnhlXAaXd/rvk\n+GtCCCGEEEIIIUZoKIOxD4DpSqnJSikH8Hng5dRmSwghhBBCCCFGt0GnKWqtI0qp5cDfiG9t/3ut\n9faU50wIIYQQQgghRrFBN/AQQgghhBBCCJF8SdnAQwghhLFUPILq+UDx8ZeqgPVafnFLGalzY0i9\ni0xg1s+hWcudSvJkLA2UUj7gH4DPAmMADRwDXgLu11oHDMzeqCYXjfSTOk8/pdQlwH8Ce/h0g6US\nYBrwda31G0blbbSSOjeG1HvmMeM136yfQ7OWO9VkMJYGSqm/AW8BT2itjxx/bSzwReAirfUlRuZv\ntJKLRvpJnRtDKbUDuFxrfaDX65OBV7XWpxqSsVFM6twYUu+ZxazXfLN+Ds1a7lSTaYrpMUlr/dPu\nLxwflP1UKfVlg/JkBr8ElvZ30QDkopF8UufGsAGH+ni9CrCnOS9mIXVuDKn3zGLWa75ZP4dmLXdK\nyWAsPSqUUvcQfzJ2FEApVQTcAVQambFRTi4a6Sd1bozfAx8opf6HT68ppcRDkTxmWK5GN6lzY0i9\nZxazXvPN+jk0a7lTSqYppoFSKhf4PnAtUER8zdhR4vHafqq1rjcwe6OWUuofgGVAXxeNp7XWPzEq\nb6OV1LlxlFKnAdfQc93Gy1rrj43L1eimlDqV+HVd6jyN5LOeOcx8zTfr59Cs5U4lGYwZQCm1gPhi\n162jdT51ppCLRvrJDaoQQpiHfM8KcWJkMJYGSqn1Wuvzj//7K8A3gBeBS4BXtNb3G5k/IcTJTXZs\nTT+l1GVa69eP/9sH/IL4j2zbgG93TkkXySWfdZEJzPo5NGu5U81idAZMovu86f8PuERr/a/EB2O3\nGJOl0U8p5VNK3a+U2qmUqldK1Smldhx/zW90/kYjpdRl3f7tU0o9qpTaopT68/F1kiI1ngYagDKt\ndZ7WOh9YfPy1pw3N2eh1X7d//wI4AlwNfAD81pAcmYN81jOIib9nzfo5NGu5U0qejKWBUuojoIz4\n4HeF1npWt7QPtdafMSpvo5mEFEg/pdSmzs+3UupR4jeojwDXA4u01p81Mn+jlVJql9Z65nDTxMj1\n+qxv1lqf0y2tx3+L5JHPemYx6/esWT+HZi13qsmTsfTwARuBDYBfKTUOQCmVDSgjMzbKTdJa/7Tz\nCwLiIQWOhxmYaGC+zOJcrfU/aa0rtNYPApOMztAoVqGUuqf700elVJFS6l5kx9ZUGaOU+o5S6ruA\n73jg207y3Zo68lnPLGb9njXr59Cs5U4p+cJIA631JK31FK315OP/X308KQZcZ2TeRjm5aKSf3KAa\n43NAPrBaKdWglKoHyoE84judieR7BMgBsoHHgQLoeiqw2bhsjXryWc8sZv2eNevn0KzlTimZpihG\nrV4hBcYcf7kzpMD9WusGo/I2WimlftTrpf/UWtccv0H9mdb6diPyZQZKqVOAEmCt1rql2+tdG02I\n5Dpe58XAOqnz9FFKnQ9orfUHSqnTgcuAHVrrVw3OmumY+XvWrNdc6X/JJ4MxYUpKqS9prf9gdD7M\nROo8dZRSdxPfpXUHcA7wTa31S8fTutY2ieRRSt0FLEfqPK2O/+BzOfFgwyuI72BZDlwM/E1r/e/G\n5U50N5qv+Wa95kr/Sw0ZjAlTUkod1FpPMDofZiJ1njpKqa3AHK11i1JqEvAs8Eet9S9lk6DUkDo3\nxvF6PwdwEt8gqERr3aSUchN/QnmWoRkUXUbzNd+s/V/6X2rYjM6AEKmilNrSXxIg26yngNS5YSyd\n02S01geUUmXAs0qpicgmQakidW6MiNY6CrQppT7RWjcBaK2DSqmYwXkzHRNf883a/6X/pYAMxsRo\nVgRcSjz+RXcKeC/92TEFqXNjHFVKnaO13gxw/Nfaq4DfA2cam7VRS+rcGCGllEdr3QbM7nxRxYPR\nys1g+pn1mm/W/i/9LwVkMCZGs78A2Z0Xy+6UUuXpz44pSJ0b43Yg0v0FrXUEuF0pJQGIU0Pq3BgL\ntdYdAFrr7jd/duKxrUR6mfWab9b+L/0vBWTNmBBCCCGEEEIYQOL+CCGEEEIIIYQBZDAmhBBCCCGE\nEAaQwZgQQgghhBBCGEAGY0IIIYQQQghhABmMCSGEEEIIIYQBZDAmhDipKaW+p5R6rtdrv1JK/dKo\nPAkhhBBCDIVsbS+EOKkppcYBe4FirXVAKWUDDgOXa603Gps7IYQQQoj+yZMxIcRJTWtdDbwN3HT8\npcuAWhmICSGEECLTyWBMCDEaPAHcevzftwJ/NDAvQgghhBBDItMUhRAnPaWUC6gGFgBrgdO01geN\nzZUQQgghxMBkMCaEGBWUUo8AFxCforjE6PwIIYQQQgxGpikKIUaLJ4AzkSmKQgghhDhJyJMxIcSo\noJSaAOwExmqtm4zOjxBCCCHEYOTJmBDipKeUsgDfAf5HBmJCCCGEOFnYjM6AEEKcCKVUFnAUqCC+\nrb0QQgghxElBpikKIYQQQgghhAFkmqIQQgghhBBCGEAGY0IIIYQQQghhABmMCSGEEEIIIYQBZDAm\nhBBCCCGEEAaQwZgQQgghhBBCGEAGY0IIIYQQQghhABmMCSGEEEIIIYQBZDAmhBBCCCGEEAaQwZgQ\nQgghhBBCGEAGY0IIIYQQQghhABmMCSGEEEIIIYQBZDAmhBBCCCGEEAawGZ2BTnPmzPldaWnpDKPz\nIRIdPHiwaMKECUeNzodIJG2T2aR9Mpe0TeaStsls0j6ZS9omc1VWVu5+//33v9ZXWsYMxkpLS2c8\n/fTTi4zOh0h00003NT799NOnGJ0PkUjaJrNJ+2QuaZvMJW2T2aR9Mpe0TeZatmxZv2kyTVEIIYQQ\nQgghDCCDsWEoLy/HZrNx7NgxAD744AOUUhw4cMDYjAmampq48sorKSsr48ILL2TDhg08//zzCe87\ncOAAd9xxR/ozKHrory/913/9F3/9618Nzp15ffvb32bBggV885vfBMDn81FWVkZZWRn19fXEYjGu\nvfZa5s2bR0VFBQDLly+nsbHRyGyPegcOHKCoqIiysjIuueQSAB544AHmz5/PLbfcQjgclrYxSCQS\n4fOf/zyLFy/mnnvuAaTfZBq5dzu5lJeXk5OTQyAQAOCOO+5g7969BucqtWQwNkznnHMOL730EgAv\nvPAC5557rsE5EgBPPvkk119/PeXl5axZswan09nnYExkjr760mWXXcaVV15pcM7MadOmTbS0tPDO\nO+8QCoX44IMPOPPMMykvL6e8vJy8vDw+/PBDLrjgAh544AGeffZZtm3bRmlpKT6fz+jsj3oXX3wx\n5eXlvPHGGxw7doxVq1axZs0azjrrLF588UVpG4O88MILnH322axatYpgMMhHH30k/SYDyb3byaW0\ntJRHH33U6GykjQzGhmnJkiW8+eabAGzfvp3TTz+dYDDIF77wBZYsWcLnPvc5wuEwmzdvZtGiRVxw\nwQXcd999ADz++OPccMMNXHHFFVxxxRVorY0syqji8Xh4//33qa2txWaz8dRTT7FixQrKysqoqanh\nhz/8IQsWLOAnP/lJ1zF/+ctfWLhwIXPnzuX111/nvffe49577wWgvr6ea6+91qjimEJffam8vJxH\nH32UAwcOsGDBAm644QZmz57NoUOHDM7t6Ld27VouvvhiAJYuXcr777/Pjh07WLBgAd///vfRWuPx\neGhvb6e1tZWsrCwefvhhli9fbnDOzWHVqlUsWLCABx98kA0bNlBWVgZ82lbSNsbYt28fZ511FhC/\n4X/vvfek32Sgvr5vAoFAwn3aww8/zMMPP0xbWxtlZWU0NzcbmW3Tuvbaa3nllVeIRqNGZyUtZDA2\nTA6HA5fLxdq1azn11FMBWLlyJddccw1vvfUWZWVlPPvss8ycOZPy8nLWrVvHihUrCAaDAJSUlPDq\nq69SXFzMli1bjCzKqHLbbbcxYcIEFi9ezNKlS7nmmmu6fkmORCKsX7+ed955h0WL4nvExGIxfv7z\nn/PWW29RXl7OAw88wJw5c1i7di0AL7/8sgzGUqyvvtRdS0sLzzzzDN/5znd47rnnDMihuQQCAbxe\nLxCfZhUIBNizZw9vv/02DQ0NvPLKK5x66qlEIhH+/Oc/M2PGDGbOnMmDDz7I3XffTVNTk8ElGL3G\njRvH7t27WbVqFStXrmTDhg0JbSVtY4yZM2eyevVqID5gln6Tmfr6vnE6nQn3ad/4xjf461//yle/\n+lW+973vkZOTY3DOzclqtXL11VebZoaTDMZG4IorruDv/u7vuP766wF47bXXeOihhygrK+OJJ57g\n2LFj7N+/nyuuuIJFixaxY8eOrrnKZ5xxBgDFxcVd82HFibPb7fzwhz9k69at3HnnnTz00ENdaRUV\nFV2/XM6ePRuA2tpaduzYwdKlS7nkkkuorq4G4Oyzz+bDDz/k5Zdf5rOf/Wz6C2IyvftSd6eddhoW\ni0X6Spr4fL6uG8Ompib8fj95eXkopfjsZz/Ltm3bALjvvvv4wx/+wFNPPcXixYux2+3ccsstPPXU\nU0Zmf1RzOp1kZWVhs9m46qqrmDp1akJbgbSNEa6++mqCwSAXXXQRTqeToqIi6TcZqvf3jdY64T5N\nKcWtt97Khg0bZMq8wb7yla/wyCOPGJ2NtJDB2AhcccUVzJ49m/POOw+ASy+9lHvuuYfy8nLWrl3L\n17/+dX7zm99w7733snr1aqZNm9Y1JVEp1XUemaaYPBUVFYTDYQDGjBlDTk5O1+PtiRMnsnXrVgA+\n/PBDAAoKCjjzzDN58803KS8v56OPPkIpxY033sgf/vAHIpEIeXl5xhTGRHr3pe6kr6TXnDlzuqbx\nrFy5krPPPrurD7377rtMnTq1673vvfces2bNIhqNEg6HCYfDtLS0GJJvM+g+Verdd99l2rRpXU9j\nVq5cyYUXXtiVLm2TXlarlV//+te8+eabWK1WLr30Uuk3Gar39829996bcJ/W2trKo48+yrJly3ji\niScMzrG5+f1+Zs6cyfr1643OSsrJYGwEsrOzeeyxx7puFi+55BJeeOEFLrroIpYsWcKmTZu48sor\nWb58OcuWLcPhcBic49Fv8+bNzJ8/n7KyMu6//37+6Z/+ifr6em688UacTiezZ89mwYIYisb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W1jPraL2dguZmPbmI/tYja2KzY2tqCreTxNkYiIiIiIyAqsMhgrKiqCVquFTqfD0qVL0ZunStqK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\ngv8HUaj935eirOwAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jp.histogram('y', df, legend='xd', bins=50, cumprob=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"You can also create cumprob plots:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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