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@ChadFulton
Created November 21, 2014 05:57
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Python - Econometric Modeling
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{
"metadata": {
"name": "",
"signature": "sha256:9643af9a64f9a29beabcac4facaff202ab866efec1e1e1bcdaeee6abac221bde"
},
"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stata ARIMA Examples\n",
"\n",
"See http://www.stata.com/manuals13/tsarima.pdf Examples 1-4\n",
"\n",
"and http://www.stata.com/manuals13/tsarimapostestimation.pdf Example 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"from dismalpy import ssm\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 1: Arima"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('data/wpi1.dta')\n",
"data.index = data.t\n",
"\n",
"# Fit the model\n",
"mod = ssm.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: D.y No. Observations: 124\n",
"Model: SARIMAX(1, 1, 1) Log Likelihood -134.983\n",
"Date: Thu, 20 Nov 2014 AIC 277.965\n",
"Time: 21:45:19 BIC 289.246\n",
"Sample: 01-01-1960 HQIC 282.548\n",
" - 10-01-1990 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.1050 0.057 1.835 0.067 -0.007 0.217\n",
"ar.L1 0.8740 0.062 14.192 0.000 0.753 0.995\n",
"ma.L1 -0.4206 0.120 -3.506 0.000 -0.656 -0.185\n",
"sigma2 0.5226 0.067 7.841 0.000 0.392 0.653\n",
"==============================================================================\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 2: Arima with additive seasonal effects"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('data/wpi1.dta')\n",
"data.index = data.t\n",
"data['ln_wpi'] = np.log(data['wpi'])\n",
"data['D.ln_wpi'] = data['ln_wpi'].diff()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"# Levels\n",
"axes[0].plot(data.index._mpl_repr(), data['wpi'], '-')\n",
"axes[0].set(title='US Wholesale Price Index')\n",
"\n",
"# Log difference\n",
"axes[1].plot(data.index._mpl_repr(), data['D.ln_wpi'], '-')\n",
"axes[1].hlines(0, data.index[0], data.index[-1], 'r')\n",
"axes[1].set(title='US Wholesale Price Index - difference of logs');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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PSQ5bpeigQwcNUMc5bAXYD7QBH8M20n4K7MP2sBXaX5T29vZh4RkXo2zLtmzL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\nCAQCgUAgEJQp/x9xRXo3rWbBwgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b1ac110>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"fig = sm.graphics.tsa.plot_acf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[0])\n",
"fig = sm.graphics.tsa.plot_pacf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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WFrga5XI5vbL3gH6z92DJrpIAAAAAYI0EroooivT66Jh+uecAk5OINbWsEFc7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\n17N11wc3jyUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b1ac590>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Fit the model\n",
"mod = ssm.SARIMAX(data['ln_wpi'], trend='c', order=(1,1,(1,0,0,1)))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"=================================================================================\n",
"Dep. Variable: D.y No. Observations: 124\n",
"Model: SARIMAX(1, 1, (1, 4)) Log Likelihood 386.150\n",
"Date: Thu, 20 Nov 2014 AIC -762.301\n",
"Time: 21:45:20 BIC -748.200\n",
"Sample: 01-01-1960 HQIC -756.573\n",
" - 10-01-1990 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.0025 0.001 2.058 0.040 0.000 0.005\n",
"ar.L1 0.7844 0.080 9.758 0.000 0.627 0.942\n",
"ma.L1 -0.4055 0.105 -3.875 0.000 -0.611 -0.200\n",
"ma.L4 0.3015 0.105 2.860 0.004 0.095 0.508\n",
"sigma2 0.0001 1.38e-05 7.901 0.000 8.17e-05 0.000\n",
"==============================================================================\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 3: Airline Model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('data/air2.dta')\n",
"data.index = pd.date_range(start=datetime(data.time[0], 1, 1), periods=len(data), freq='MS')\n",
"data['lnair'] = np.log(data['air'])\n",
"\n",
"# Fit the model\n",
"mod = ssm.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12), simple_differencing=True)\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==========================================================================================\n",
"Dep. Variable: D.DS12.y No. Observations: 131\n",
"Model: SARIMAX(2, 0, 0)x(1, 0, 0, 12) Log Likelihood 240.821\n",
"Date: Thu, 20 Nov 2014 AIC -473.643\n",
"Time: 21:45:20 BIC -462.142\n",
"Sample: 0 HQIC -468.970\n",
" - 131 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"ar.L1 -0.4057 0.038 -10.648 0.000 -0.480 -0.331\n",
"ar.L2 -0.0799 0.052 -1.548 0.122 -0.181 0.021\n",
"ar.S.L12 -0.4723 0.037 -12.770 0.000 -0.545 -0.400\n",
"sigma2 0.0014 0.000 8.071 0.000 0.001 0.002\n",
"==============================================================================\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 4: ARMAX (Friedman)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('data/friedman2.dta')\n",
"data.index = data.time\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':'1981', 'consump']\n",
"exog = sm.add_constant(data.ix['1959':'1981', 'm2'])\n",
"\n",
"# Fit the model\n",
"mod = ssm.SARIMAX(endog, exog, order=(1,0,1))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 92\n",
"Model: SARIMAX(1, 0, 1) Log Likelihood -340.508\n",
"Date: Thu, 20 Nov 2014 AIC 691.015\n",
"Time: 21:45:20 BIC 703.624\n",
"Sample: 01-01-1959 HQIC 696.105\n",
" - 10-01-1981 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const -36.0583 34.244 -1.053 0.292 -103.175 31.059\n",
"x1 1.1220 0.033 34.455 0.000 1.058 1.186\n",
"ar.L1 0.9348 0.043 21.894 0.000 0.851 1.019\n",
"ma.L1 0.3091 0.146 2.110 0.035 0.022 0.596\n",
"sigma2 93.2554 13.516 6.900 0.000 66.765 119.746\n",
"==============================================================================\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Postestimation: Example 1 - Dynamic Forecasting"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"raw = pd.read_stata('data/friedman2.dta')\n",
"raw.index = raw.time\n",
"data = raw.ix[:'1981']\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':, 'consump']\n",
"exog = sm.add_constant(data.ix['1959':, 'm2'])\n",
"nobs = endog.shape[0]\n",
"\n",
"# Fit the model\n",
"mod = ssm.SARIMAX(endog.ix[:'1978-01-01'], exog=exog.ix[:'1978-01-01'], order=(1,0,1))\n",
"fit_res = mod.fit()\n",
"print fit_res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 77\n",
"Model: SARIMAX(1, 0, 1) Log Likelihood -243.316\n",
"Date: Thu, 20 Nov 2014 AIC 496.633\n",
"Time: 21:45:21 BIC 508.352\n",
"Sample: 01-01-1959 HQIC 501.320\n",
" - 01-01-1978 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 0.6732 14.307 0.047 0.962 -27.367 28.714\n",
"x1 1.0379 0.019 54.578 0.000 1.001 1.075\n",
"ar.L1 0.8775 0.062 14.043 0.000 0.755 1.000\n",
"ma.L1 0.2771 0.106 2.623 0.009 0.070 0.484\n",
"sigma2 31.7073 5.045 6.285 0.000 21.820 41.595\n",
"==============================================================================\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Prediction\n",
"npredict = data.ix['1978-01-01':].shape[0]\n",
"mod = ssm.SARIMAX(endog, exog=exog, order=(1,0,1))\n",
"mod.update(fit_res.params)\n",
"res = mod.filter()\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Personal consumption', xlabel='Date', ylabel='Billions of dollars')\n",
"dates = data.index[-npredict-npre+1:]._mpl_repr()\n",
"ax.plot(dates, data.ix[-npredict-npre+1:, 'consump'], 'o', label='Observed')\n",
"\n",
"# In-sample one-step-ahead predictions and 95% confidence intervals (forecast without data)\n",
"predict, cov, ci, idx = res.predict(alpha=0.05)\n",
"ax.plot(idx[-npredict-npre:], predict[0, -npredict-npre:], 'r--', label='One-step-ahead forecast');\n",
"ax.plot(idx[-npredict-npre:], ci[0, -npredict-npre:], 'r--', alpha=0.3)\n",
"\n",
"# Dynamic predictions and 95% confidence intervals\n",
"predict_dy, cov_dy, ci_dy, idx_dy = res.predict(dynamic=nobs-npredict-1, alpha=0.05)\n",
"ax.plot(idx_dy[-npredict-npre:], predict_dy[0, -npredict-npre:], 'g', label='Dynamic forecast (1978)');\n",
"ax.plot(idx_dy[-npredict-npre:], ci_dy[0, -npredict-npre:], 'g:', alpha=0.3);\n",
"\n",
"legend = ax.legend(loc='lower right')\n",
"legend.get_frame().set_facecolor('w')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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T7Gs2dy588YVJuP7tb2HqVO83Tk83w219+5rFGxuYGuUcKaW6Ar2Bb7XWAZWsJDlHQggh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"text": [
"<matplotlib.figure.Figure at 0x10b1f30d0>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Prediction error\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Forecast error', xlabel='Date', ylabel='Forecast - Actual')\n",
"\n",
"# In-sample one-step-ahead predictions and 95% confidence intervals (forecast without data)\n",
"predict_error = predict[0, -npredict-1:] - endog.iloc[-npredict-1:]\n",
"predict_ci = ci[0, -npredict-1:] - endog.iloc[-npredict-1:][:, None]\n",
"ax.plot(idx[-npredict-1:], predict_error, label='One-step-ahead forecast');\n",
"ax.plot(idx[-npredict-1:], predict_ci, 'b--', alpha=0.4)\n",
"\n",
"# Dynamic predictions and 95% confidence intervals\n",
"predict_dy_error = predict_dy[0, -npredict-1:] - endog.iloc[-npredict-1:]\n",
"predict_dy_ci = ci_dy[0, -npredict-1:] - endog.iloc[-npredict-1:][:, None]\n",
"ax.plot(idx[-npredict-1:], predict_dy_error, 'r', label='Dynamic forecast (1978)');\n",
"ax.plot(idx[-npredict-1:], predict_dy_ci, 'r--', alpha=0.4)\n",
"\n",
"legend = ax.legend(loc='lower left');\n",
"legend.get_frame().set_facecolor('w')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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B8W1TLd4Ve/ZIrMdllzVNCzdNkyfXZrIv7wz/ujaNzlFNv9QvvpD7hQtDIyYt\n0LF3pmmSU1rF/vxyDuSVcyC/nEqLjaSEWIb36kJSQixJvbq0OkW+NWw7UcJDH+7hl4tGsmCk667k\nq1eL0yaxaS9lt1FuNR/gkzpHVVVyBmoomnbtwqytpTZ5LGeHj6N04FgK+o4lt+cYBqR2ZfLkFvZZ\nWytnsbp/XGllLX9bm8m+3DP85uIUJgxo2b136JC4N0aPhgkTXF/FmaZUHD12TK5omxNTGTml/Oqj\nPVyd1p9bpw9xGRRtsYj5f/Bg78yiIFrUkyvPaouNFRkneXXLcaYP6ckPZw1lQPfgT6Epr7bw8d48\n3t15EtC4Jq0/S0f3bZXozM2V0gthYeKyTE5u/RXs5mPFPLUuk/jOUTywYITb1sLycvl9JSbKb7Ex\nJ05IfEFMjMQsOO47dQody8OXWYU88dkB/q2neWyF9Rc7dkhG4mWXyXfSENM0eeJzKfD69FXjm1yw\n2WwSMB4TIyVHOqpAysmRz2nIEM/jYrzFbpqcKKniQP4Z9tcJoYOnK4iNCielTxyj+sYxqk8csVER\nHC48S1ZBhdwKzxIdEVYvmHp1YUSvLgyJ7+zzsI3GrM8q4PFPD/D4paOZNsRJkJ2PUeLIB7RGHFks\n4mMF54Li+Lenyf18N73ydtHjxG5ij+wm6vA+6N0bbexYqVJ4//1NjzzNkJ5ZwF++OMiC5N7cecHw\nFttAnD0LGzfKCWrePDH1Qr0/uqpKrmbDw8XN5SqDzDRNjB05vPjNMX57cSqzhrlWUGVlcmDs00cs\nFd6a/3fuFKPc1KmeBZxX1Fj539YTvLUjh0Uje/ODGUPOc1EES+zJ/rwzrMg4ydpDBcwYGs/Vaf2Z\nMKC7V3WhGlNeLt9948/N2dzLy8V94s7bWu123s04xX+/Ocr8Eb25ffbQVl+NHj8ulq3qarlVVcn9\n5MnO+38dOya/sYZCKiZGLBzN/da8/d4dWYqOYPzG5ObKccBRwsFxS052L8XeUY366avG+80t7O3c\nv/1WxOullzaNHLDZTR75ZB/l1Vb+euXYJi5Xq1UyN2tq5LMYNSowLiZf/99NU36zjizWCRPE0u4P\nAWizm2SXVHKgziK0P7+cQ6fL6dYpkpG940ipE0Kj+sQ1+R82nrdpmuSX19QJpQqyCs5yuKCCE6VV\n9O0ac866lFRnaerXvZNXGcGN+XhPLv9ooU+ar1ExR21McbGcsM+ckZvFIsF2w4Y5F0eDp/Rm8JSF\nQAPxZbNCcYKoAAAgAElEQVSJ/2r3bnjjDcmLfOcdynoMISND9FJzKe7zRvRiwoDuPLU2kxte3sxv\nlqQwqZlg5NhYiU/IzBQ327Jl5x/gS0rkBDppkus/d7XFxh8+P0BWwVn+e9PkZq0xx46JxWLKFDkY\ntoZx4+SEt26dxMFPmVIv7pqjS3QEP5w1lGsn9OeVLdlc/9JmrhjXj5unDqa7mzWofI3NblJrtVNl\nsfHV0SJWZJyk+Gwty8b34+3vT2+29EGz+3XRADQuzv22Djt3ygl+xgzXjSAdRISFoU8cwEUpffh/\nXx9Ff3Ezt04fzLUTBnjdBmfwYM9jC6qq5LfrEFJVVfLfcdYa8fBhEYCZmV3o0qVevIwa5fz3tGGD\n/I4d24WFiTCaP995uYfCwvp+1Y5bTIx7FwW7Tpbx8Mq9/PHyMU5PHFVVga0fNGWK/PecuUPDwzQe\nXZrCQx/u5dcr9/LHy0eflzkZESHZeKdOyXfQEa6pLRapLxceLqJo8GDfiSKb3eRY8Vlxi50WMXTo\ndAU9OkeS0rcrI/t04fszhjCyT5xXxzFN0+jbNYa+XWOYPbz+h2+x2TleXHnOuvT+rlNkFVRwptrK\n8IRYhveKJSmhXjR5Eoj/5tZs3tx6guevd15EtKPhleVI1/XbDcN4wQ/jaRVr1qwxp0xZeE7wnDkj\nVzeTJjXd1lGkq2tXuTkr0e4RpglPPw1/+Qu2l15lc7cLOXLEdb8jqqqkd8fUqRAZyYbDhfzp84Nc\nkJTA3XOHE+vE99/47Tz9I+eUVvHg+7tJ6hXLry4c1az5df9+uZpatEgKBfoKu71+34mJYuHyZB6n\ny6UlyReHTnP9xAFcN3EAYWEaNRY7NVYbNVZ7g5vr5VqrnepGy4711Y2WG7/eZjeJjgwjOjyMMf26\ncXVaf2YO7el1j6yyMvHkZmaK4G1tf6vjxyUIt0cPERnulik4WnSWp9dlcrK0mvvmJzFrWE+fWL58\nydGjkv2iaecLmAEDnH9u1dXyX3Fs56/pHMwv5+53Mnh0aSozhza9ujpyRCqo63pwuxdrrXZ+9v4u\nusZEetz3zW6X+2CeX2NOn2798c1qt3OsqPK8GKHMggoSYqMY1TeOlD5xjKyzCDWuct9WlFdbyKpz\nyx0uOFtnbaqgU2Q4Sb26MLzO0jSiVxeG9Ox8Xl0+0zR5fuMRvjhYwL+uHe+0r6ZpyjHMH83C25Vb\nTdf1DYZhBF2f+TVr1pjm+gii+sQTnRhPp/7xxPfv1OIVtE9Zvx5uuAHuvJO8Wx9i/YYwevWSeOzz\nPG42m/jITp+GCy+Ebt0or7bw9Lostp0o4VcXjvKpP/erI0X8btU+vj9DrDAtnfQqKuSKyl9Xular\nmPiHDvXu9Tkllbzw1VHWHDpNZFgYURFhREeEER0RXnd//nJURBgxjZYd62PqHkc1WB/TaLnh/iLD\ntVaLBrtdRMy+fWI1GTlSrB++avxps0kg7s6dUvPJWd0nV3x1pJCn12WR2DWG++aPYFhCx79KbA1H\ni87yk7d28ODCZKfBqdnZUn186VLndZCCjWqLjXtX7GRAj078+sJRbv/WT52S4O1hwyQ2sblq3e0V\nq83OkaKzHMgvPxcjlFVYQe8uMefcYil94kju3cVl9l+wYJomeWeqz4kmh7XpZGkViV1j6txysZwo\nqeJw4Vn+4aJPms0mGWkWi5zKfJ2oEXTiSNf18mZe18kwjKBzya1Zs8Zc2K9ffRnQ4mLxM110UdsO\n5ORJuUTs2RPri6+yNas7x465uGrcv1+CAebMOWdi+vpoEX/47AAzhvbkp/OSWgzmbc4PbzdNXvzm\nGO/uPMkfLhtDmhvB3+2JYIk58pSdO+WkmZoqAtGbqy135l5VJW4oT6+OrTY7xo6TvLTpGBel9OGH\nM4e63UqnLQiW7z2ntIrbl2/nzguGsXR005ScU6ckDnDJEt/ViWqLuZ+ttXL32xmk9u3KAwtGuC2Q\nyssl8DsrSy6AkpJ8K/o9nbvFIodYi8W5B8EdqmptvLfrFJ/tz+NI0VkSu8acFyOU3DvO71m+bfl7\nr7XaOVZ8VuKYCiuorLVx55zhTudosUhManS0uKr9UZIiGGOOMoLROtQiKSnnL1sszrfLyZG85IYt\njePifGN3799fgmt+9jMiZkxh+ooVpF05zvkJMCVFgiVWrxZ/wZQpzBzak+W3TuPv6Vnc8PJmfnXh\nKGY4MdW3RHm1hUc+2c+Zaguv3jyl3VTCPXZMXG4ducfXuHGeWXO8xZEp5ikR4WHcOHkgS1P78PxX\nR7n2xU38YMZQrkrr59Mq3u2Z/PJq7jR2cOu0wU6FkaMO0qJFwVVAsyEWi1gY09LOP/TFRkXw96vH\nc4eRwbMbjnDnnGbqgTQgLk7idyZMkOvTzEzJXvSVOHKXmhopX7B3r9RcmzDB831U1Fh5e0cOy7ed\nIG1Ad+6em0Rq3zin5Q46ElERYST3FtHXHNXVsGqV/LZnzep4WYzNWY4eMAzjyTYeT6vwKFutslIi\nV4uL661MNTXyL2pcRLI1vP463HcfPPMM3HST6+2qq0WwJSWd9/TmY8U88dkBJg/qwX3zk9w21WYV\nVPDg+7uZOUysT64CbO12OYA5yyQKFBs3SozGuHFSI6O91tOpqZF5NNbrwYDNJn8Bd09aWQUVPLU2\nk8KztTywYESbpPAGM8Vna/nR8u1cMTaRm6c6j0DPyZFYjEA1TnYHm02y0OLipL9g4xNcaWUtt7+1\ng4tG9eG2GUN8+t6elvdwl61bxV09eLAcyj1tDVRaZWH5thO8k3GSmUPj+d60IQFxLZeXt5y1GUg2\nbpQL2ClT/Ps+QedWa4+0us5Rba0cLZxdamdliYhyWJm6d3f/V7trF1x1lfQYePLJ8/Joa2patpCc\nrbXyz/WH2XC4kIcWjzwvO8EZnx/I569fHOK++SNYOtp13nxlpcQIREfL1W0w/QnLyuQgl5sretUf\ngX7+4vRpOTgfOyaeUm+KNfqb/Hypej1qlHy+7ozPNE2+zCrkmfQsoiPC6NUlmu6dIunROYoenSPr\nH3eKpHvdc3HREUEX1N1azlRb+PHyHcxJSuDHs52k1LUzLBaxACQkSGxkYworarh9+XauTuvPjZOd\npPh5gd0Ob74pFuKkJBGQvvp/Hzok1qLGFcFborCihte/zWblnlzmJ/fmlqmDGOBlax9PqaiQ44aj\nb1thoRyPlyxxnj29cqX8Z3v2lMQLxympLf9q/hK3jVHiyAf4pAikKwoKJJaouFhuZ87I5db06e5d\nGpaWwne/KwLr7behXz+KiqQZ3/Tp7hVX3Jpdwu8/3c+EAd25f8GIc5kPDn+01W7nn+sPS/2kK8Y2\nW405L09M/qmpTU3qwURRkYRkxcdLYl9jgiX2BEQMbd8uGjs1VerBeFD2ymNaO/fKSsmgysmRq7/k\nZPd+B7VWO1mFFZRUWiitrKWkykJJZS2lVZYmz9VY7XTrFHmeYOrRKYruneW5Hp0dj2Vd15hItzKk\nfPm9W+12qi12qi02qq1yX1N3X9XgOcf61QfyGd+/O/fNTwqI8PPHb762VpqN9u/v/H+Wd6aaH/1v\nO1MH9+DSMYmM69+t1XVzHJZVh+vNnUBuf8w9t6yKV7dk8/mBfJaO7st3pgyiT5wf/7hOqOtVTkJC\nfe+2htfojeddVlZ/KnLczp6VU0x7tbS7IhhjjgDQdX0kcD+QCGh1tz6GYfjZmBZk9Op1fuCAzSb/\naHdrAHTvDu+/D3/8o1TE+9//6Dl3LhdfLDWLjhwRs3aT3RUXi104PJzJg3rwv+9N5dkNR7j+pc38\nYvFI5ibJmIrO1vKrj/YQHRHGqzdPaTZ4du9eOYnPmxfcJn+QK6MlS+rThIOZ8HA5sXjbaaat6dxZ\nfgMFBfD112LtWrq0ZUtmVEQYqX3dKwBXa7XXiabac/clVSKgDp2uOPfYcV9RYyMuJqLOEtVITDV4\nfKjMRMsqoMYiJRkc4uY8MeN4vu6+psG6htvZTZNOkeGSuRgZRkykZDDKfd1zDe4vGZ3oVsZneyIq\nSr77jz6Sw1zjLNK+XWN48aZJfLQnlz98fpBqi40lqX1YktLXa5dTdLRYhFNS6gO5s7Pdy3IrLZVr\nVWdV2t3leHElr2w+zpdZBVw5vj9v3zadeC9rlDnD0by1oKDeGjR8uHM3+4wZnu3b0fes4ffkqk6a\no4Bnw/DaHj2Cz5odbLRoOdJ1fTvwKjAS2AZMAvYbhvEP/w/PM/xqOfIln38uEv/BB+G++7CbGjt2\nyMlp2jS5gj/HV1+JD2Tx4vMCRLafKOHxTw+QmtiVpaP78sRnB7hsTCI/nDm02Stvu10K402c2PZB\nkoHGYpFERqu1vlKyzSYHaWcisbQUMjKabh8fL/WrOho5OS0XjvQ3Vrudsirr+WKq0kJpldw7ng/T\nNBEvjYRL/X1DodNofaPnfFGaoapKrmP6O++/2W6oqRGh1NzHYZomh05X8On+fD7fn0+PzpEsSe3L\nRSl96OWnpA9HXbeiIqmRlpsrLT68CQ/NPF3BS5uP8e3xEvS6Wmm+rj906JBccERHi9hsaBFq60QT\nu70+vNYRYltaKuO5/HL39nHqlIjWQIReBK1bTdf1jYZhzNZ1/btAIfApsNowjKBTIUEjjg4ckHLW\nzZlljh2Da66RS4n//he6dKGoSFK8581r5Mvds0eOCI1MPdUWG89tOMJnB/J56MJ6K1KoUFgo9TXg\nfPHSq5dU83W2/caN9a0jHPfx8c4PspWVIhgc2zq2j4kRQ6BCASIoVq6U+DJv08XbKza7yfYTJXy6\nP5/0zAJG9o5jSWofFiT39ml6+9q19Tkz48aJ9cVT99He3DO8uOkYe3PPcNPkgVyV1r/FYrvOME0R\nFwUFcjxw1ti7pkbugzXj1jTl+BbrxOhXUiLnIYeV6cwZ8TRcemlgjntB61YDHPWOdgE/BdYCQe6M\nCTDdu0tAT0qKRLs6uwwbMkTO1HfdJb6Yd9+l56hRLFjgZH9jxojMb7TPmMhw7lswgglhJ0NOGIG4\n3KKjNzJ37uzzxI6rg2ZCAlx5pfv779y5kRUvyGireKuSkrYP9myJYIk1s1jEZdGvX9sJo2CZO0jb\nkSmD45kyOJ6fL0zmqyNFfLovj6fWZjJjaE+WpPRh5rCeXrejcTB3riPRIZ2xY+d59NrtJ0p48Ztj\nHCuu5LtTB/PEpaM9bs5aXi55NYWFYnmJjZWLMGctaMD3osjX37mmORdGIBd/ffuKGM3Olt/4ZZd5\nnvXX3nFHHL2o63pPwzAydF0HOAU87t9htXP69pU+EF98If/o+fOd/1tiYuA//5HbBRfACy9IVltz\n+1y7VqxHbhROMU2xpHS0AD0HmgbdulmVFcfPbNwov6OZM33bSqa9Y7NJxl+PHp7HjLQn3G1VFBMZ\nzsKRvVk4sjdlVRbWHDrNG1uz+f1nB1iY3IuLU/t6HcgdHi6ZbQcPujtmk2+OFfPSN8coOlvLLdMG\ns3R0X69FWlSUZL85em82btzbkejUqfW9NDsCKlvNn9jtsGmTyO+LLmq+rfe338K118J118ETT5yn\naGprpaeUu9lEjtesWyd/5MmTWzkPRUhjmpJR9O23YiGZOtX1VWco8cUX4v6ePz+4rGq+5tNPxWDt\naUNhB7llVXx2IJ9V+/Kpqm19IHdz2E2T9ZmFvLTpGDU2O7dNH8zCkb1V4dJ2TNDGHLUngk4cOTh8\nWCw9XVvI8CkshBtvlOCZ5cvPXaaXl4tHLTJSuoy0FEhdXCwFtwcOlDIB6rig8AUWiwSo798vbg5n\nJ0uLRa7yQ+E3V1Qk1zsdfa4FBSKQFixoXcC5aZpkFlSwap/vA7mtdjtfHDjNS5uPEx0exm0zhjAn\nKcEjK5VpykVo585iqFcEB0ErjnRdf8DJ06ZhGE/5Z0jeE7TiyBNsNvjtb+G116Qe0rRpgPxxd+2S\nQLlJk6SOjuN/n56ezrz4eIiK4nh+DFt2RjNxZgzDU6PdLzXQTgmm+Iu2JlBzLy+Xn6kzd+amTZI/\nEBEh5vmYGLkfPdr5ibW2VsSUp1kw/p67wyVttcotJiZ43NOB+N7z8iTJdsoUCUBurVvJZjfZkVPK\nqn15HgVyN567xWbn4715vLL5OAmxUdw2YwjTh8R7lH1ot0sZgYwMiX6YNi34xFEoH+eCOSA7Dmio\noKYApf4ZjoLwcHGrTZ0qUXC/+x3cfjuapjF+vFytr18vVzgXX1x3UqlLPcjdV8LRXTVclFpN16M1\ncMgilqjGmKaUCIiJkaNBdHT9447YSlvhU5qzXE6fLieX2lrpiFNVJfeuXrN1q1iiwsLqhVRMjPSd\nS2zaroyaGtnWatWoqqoXL7Gxzk/Yx46JJdWxneM2dqzzsL116+S/5agZ48hSnDu3/afptwZH/+6d\nO2HzZokAaI1rNTxMY/KgHkwe1IMHFyWz8bBngdzVFhsf7D7Fa1uyGdozlt8sGcXEgc2ELTjBbpcY\npowMMerPni1uY4UCvHCr6boeCfzNMIyf+mdI3tPuLEfFxZIr6YrMTAnQnjQJnnvuXMlU05QCaI1r\n0tjt4tZoMVPCbpdyA9XVcrapqZHHNpvzHHiLpb7PSENB1bmzRCgqFK3EYqkXUlVVEivnrP3Dxo3y\nt9C0euESESEB0c7ES2amVBMODxe3tGP7xETnJ3eLRfYdHt6x44haQ22t/wKSHYHcn+7L40hRZZNA\n7rO1VlbsOMmb204wJrErt04fwuhE9wqSNsZmk5pvKSnqmjCYCVq3mjN0Xf+fYRg3+GE8raJdiaPy\ncvjgAynaMW6c6+3OnoUf/UgqRK5YERgxYreLGnOIKIegsts7ZjVEhULhMaWlIkaTkprPPXGXxoHc\nUwb34MusQqYO7sGt04eQ1MvD5mmKdknQutV0Xf+o0VO9gZO+Hoiu6y8jVbirgZcNw3il7vlFwCN1\nmz1iGMZaX793QIiLk6I7q1dLuv/cuc7rucfGwuuvw7/+JZfHL70kdf4b4Hd/dFiYZ31GKipEOLUU\ngO4DQtkXr+Y+L9DDCAjBOvfISPnbf/KJGJhHjJD4JG/db4ndOvG9aUO4ZepgMgsq2HysmFFJVq5d\nMsaj/VRXyyEpofl+3UFNsH7nHRl3Yo6ebLRcbBjGLj+MxQSuMwwj2/GEruthwGPAorqnPtN1fZ1h\nGB0jxa5LF6nf/vXX8N57cOGFzqNcNQ3uvlt6flx3HfzgBxK0HaxpMgUF4v/o1k3qDwwb1rELgygU\nCmJjJd5s6lRpV5GVBe+8I/Wx3Gms7QpN00juHUdy7zjS04+6/brKSkliOXhQjPPtWRwp2p6gSeXX\ndf0l4FHDMI43eG4k8EvDMG5tsM0fDMPIdLaPduVWa8zBgxIJumRJ89vl5YGui7B6/fXmY5YCid0O\nJ05Ik6GTJ6WU7JQpodfQTaEIYWw2ORS0ZZPT8nIJHD98WK7Nxo1TdbnaM0HrVmuMrusaMMUwjC3e\nvKGu64uBBxs9/QDSpuRNXdeLgfsMw8gC4oFSXdefrtuuDOgJOBVH7ZqRI93rVdG3rxQ9+sUvpLrj\nihXSTiTYCAuT1LrBgyU+KSsreHKhFQpFm9BcmYZvvxVvfZ8+vg1+T0+Xw6Sun8thUSg8xp2Yo/cN\nwzjXkcowDFPX9ceBC715Q8MwVgOrnay6p+790oC/AsuAIqA7cAegAc8izW9d0tA3m56eDtB+ltev\nd3/7p55ib5cujJg/n+zvfIeka67h2yNHsHTtysxLL4WYmMDPp+Hy6NHO15sm82bOhOhor/afkZHB\nvffeG/j5BWD5mWeeIS0tLWjG05bLjsfBMp62XHY8Fyzj8WbZboedO7fyySedSE4eTVIS5OVtJC7O\n2ur/+2WXBX5+vl4O9d97IHCnCOQGwzAuaLAcBuwwDGO8Pwak6/oo4HeGYei6rocDXyIxRxqw2jCM\nWa5e267dat6ydy+n77yT3qYpJXuLi+U+IkJcbj171t83fOxsXY8ebWv/Bulq+sEHkoednCyXkmHu\nx1KlNxDDoYaa+7xADyMgdLS5FxWJYTkrS+oMzZ/vetuGc6+ulsDvUKCjfeeeEHSp/Lqu/wSx2AwF\nGkbB9QTW+zqVX9f15UAi4l670xF7pOv6hcBv6zZ7rM7y5JQOKY6+/FIiCVNT3X+NaUoJAIdQanzv\n6rmSEnHONyegGj+XkND6vN3aWjhyROKTysokxSUlxTf5wAqFol1QV8u2xfig3FzYsUMOG1de2fy2\nivZPMIqjbkAPwACuRSw3ANWGYeS1zfA8o0OKozNnpG5/QoLUFPJn3I7dLu/nrpgqLob8fBFut9wC\n11/f+gDxM2ekWEqPHqrApEKhAOSQYLXKfVUVpKVJBpwHRmZFOyXoxJEDXddnGIbxTRuNp1V0SHEE\nclTYsEHEyOLFTeoHBdTkarWKeHvlFelOuXixCKUlS/zjojPN86I3Q9ncrOY+L9DDCAihOPcjR+SW\nn7+JG2+cHnLVy0PxO3cQKHHUou5uL8KoQxMRIY74UaMkPqeoKNAjqiciQopSvvWWNLJavBj++Efp\nbXLffZJT6ytMU5rxbtwoFiuFQhESDBsGixZB//7VISeMFIEhaOoc+YIOazlqSGGhuK6C3Z586BC8\n+qrc4uPFmnTjja1vYnT2rOw7M1PE0siRIhpDJTJToVAoQoigtRzpuv5co2WtrhijIhAkJAS/MALJ\nPHv8cbEmPfWUtL4eORIuu0zK5tbUeLff2Fip66TrYk0rKxNLkkKhUCgUPsKds+x5jWzqWncM989w\nFN6Qnp4upWiDkbAwWLBAYpJycuCaa+Df/5bU/TvugM2bxQLkDb17k26aYm8PQQJdBySQqLmHJqE6\n91CddyBxJ/XpvPqmdRWyo/0zHIVXmKaID4DoaLnFxMjNlXAoKZH10dFtZ4nq0kXca7fcIhal116D\nm26SwO3vfhduvllilXzFyZPQu3fb125SKBQKRbvGnWy1Z4AK4HFETP0eCDMM46f+H55nhETMUXNY\nreKuqq6W+9paGDKk6XY2G7z7bv124eEilGJjpRFuY0xT+qQ5RJdDgPkiMtI0pfHuK6+Iu23yZBFK\nV10FnTu3bt/r1sm4k5Nh9GjV102hUCjaGcHcW+1XSDsPRyHI94Cf+W1ECu+JiJBbS1XUwsPh2mvr\nly0WEUq1tc63t1ph3756MeUQXl26SG2j1qBpMGuW3P7+d8nGe+UVuPtuEUi33CL1nbyxbs2fL10o\n9+6F996T8rtjx7Y+KFyhUCgUHRqVrdYBCEgNDNMUURUV5Z/9nzoFb7whQqmyUqxJ3/1uk8KQbs/d\nYpEst6oqsU51AEK59oma+7xADyMghOrcQ3XeEMTZao3RdX2oruu/8MdgFO0ITXMtjPbvh1Wr4PBh\n7wPF+/WDn/8cdu8Gw5DaTtOmwZw58N//SiVtT4iMFNdaBxFGCoVCofAfblmOdF0fBOhIG5Fw4GPD\nMB7x89g8JlQtR0GH1SoB14cOQUGBWHuSk1vvzqqthY8/FmtSejpccgncdptkw7U2/mnPHhFkrW1/\nolAoFAqfEXQxR7qu90fEkA7YkaawFxiGcaqNxqZor0REQFKS3M6elYKN69fD3LmtE0hRUbBsmdwK\nCuB//4Of1uUF/PSn8J3vQKdO3u3bYhFrV/fuMGYMDBrkm4BzhUKhULQ7mnOrZQPTgBsNw5gNnFbC\nKDgJ6hoYsbHSJVLXfRsI3asX3HMP6f/8pwRyf/ghDB4Mv/61pPB7yoQJcMMNUqhy+3Zx5R086Lvx\n+oGg/t79jJp7aBKqcw/VeQeS5sTRj4CewAe6rj8MqP4MCt9z9qy4yE6d8q4YpKbBwoXw0UdSKfvM\nGbH83HQTbNni2b7CwsTatWwZzJsnWX0KhUKhCDncqXOUAFyNuNe6AauAlYZhbPb/8DxDxRy1Q2pr\nxUJz6JCUCEhOhhEjoFs37/dZWgovvgj/+IfEEd17r5QFiHCncoVCoVAogoVAxRx5lMqv63of6oSS\nYRjz/DUob1HiqJ1TVCTxSVlZMH681CRqDVaruNueeUYCxO+6C37wg9YHXa9fL6Jr2DD3rUumKZl7\njptpOq9HZbNBdnb9dna7zCMsTLLtFAqFIoRoF+Io2AlVcdThamA4BIEbNZTcnvu2bRKb9NFHElt0\nzz0wapR34ztxQkoMFBeL0LLZpFTAkiVNt62qknpNpilCKixMLFidO4s1qzG1tSK+wsPrb2FhUpF8\n0qSmcx8/XoRfSopULw8ROtxv3gPU3OcFehhtTqjOG4IwW02hCBhhYa6F0e7d0n+tRw/P9jlpErz6\nKuTmwnPPSebcxInicrvwQs8y0wYOlFtpqVTgdlQmd0anTlJuwN0K31FRsHix+2OJiJA4q7feEkvW\nmDGefzYKhUKhOA9lOVK0H0wTvv1WXG+dOkl8UlKSdxaT6mopBfDMM5LG/9OfSuPb1vZzCxRVVVJ8\nc98+sWbNnCllCRQKhaId024qZCsUAUPTYOpUuPFGqZZdUCAWk2+/9XxfMTFw662QkQH//jd88omU\nAnjoIcjJ8f3Y/U2nTmIJu+EGCWj3V1sXhUKhCAGUOOoAhFwNDE2D/v1h/ny+TEyUgo2t2df8+dLw\n9ptvpI/buHEiMjZt8t2Y/YDT7z08XMRRe7WAuUnI/eYboOYeeoTqvAOJEkeKdo09Ksp3xSWTkiRo\n++hRsUzdeCNMnw7Ll4vrrb2TmwtffAH5+YEeiUKhUAQ1KuZI0XF5/30RTqNHQ9eunr/eZpPstmee\nkSa6d94JP/wh9Ozp+7G2BRaL1JPas0fcbmPHShC3u8HiCoVC0caobDWFwtcsXgx794pI6ttXxEBi\novuvDw+HK6+U244dYlVKSoLrrpNSAKmpvhmnaUoKf02N3KqrW35cWytWrREj3H+fyEgRiqmpUktp\nzx7YvFmy9Xr18s1cFAqFogOgxFEHIJRrYDQ799hYCeCeOFEy3DZsEBEwf77nbzRhArz8MuTlwfPP\nwwssXKAAACAASURBVIIFUqhy6lT3xExL6yIjpZZRdLQEizt73HA5PJzae+4havZsKUewYIH75Qg0\nTYLPBw+WWk1xcZ5/HgFG/ebnBXoYASFU5x6q8w4kbosjXddvNwzjBX8ORqHwCxERUiRx1ChJeW8N\nffvCo49KVpthSHxSXFzLYqYl0eOFa2vTZ58x58QJKUOgaSKSbrxRMtfcxVW1cIe73ZP6TwqFQtFB\ncDvmSNf1DYZhXODn8bQKFXOk8Jrq6vZbYdo0Yc0aiY369luJi7rjDmlx4i3Z2ZK9N2aM1JOKjPTd\neBUKhcJNVJ0jhSKQfPIJrFwprTjaW5KCpsGiRTL+L7+Uyt2jR8N3vgNbt3q3z0GDpIp4bq4Uy9y0\nSaqBKxQKRQjgiTiq9NsoFK0ilGtg+GzuV14prredO6Ww5O7dEvQcxDid+8iR8K9/wZEjkJYGV18N\ns2fDO+9IvzpP6NtXRNeyZSLA3nsvaMoAqN98aBKqcw/VeQcSt2OODMO4yBdvqOv6BcCTwHrDMH7e\n4PlFwCN1i48YhrG2uecVCp8SFgbDh8vt9GnJ5MrPF3HQHunRA372M4lDev99cbk98ADcfTd8//ue\n9V+Li5O6TxMnuu4hp1AoFB2INq9zVCd24oCZDnGk63oYsAFwnIk+MwxjjrPngbmGYTgdtIo5UvgU\n0+xYAclbt0o5gpUrJXD7nnvE0qRQKBRBSsjEHBmG8QVQ3OjpEcAhwzCqDMOoAg7ruj7C2fNAUtuO\nWBGyuBJGeXlSILKtME3fvN/kyfDaa1L7KT4eLrgALrkEVq9uXZzVwYNw/Hjrx6dQKBRBgt9s5Lqu\nLwYebPT0A4Zh7HKyeTxQquv603XLZUBPQHPxfKYfhtxuCeUaGAGZ+549EqickiJ1k2w2EVJDhzbd\n1mIRi43Ndv4tIsJ5vaXqaokPcmxnt4tw6dJF+r01ID09nXmzZ8t7eJK+368f/P738KtfwZtvwv33\ny3v89KcSxO3JvkDcbhs2SPXtmTOlvpSfUb/5eYEeRkAI1bmH6rwDid/EkWEYq4HVbm5eBHQH7kAE\n0bNAIWLZcva8Sxr+iBxBbB19ueHcg2E8bbmckZHR9u+/aBGUlbH9tdeIqKxk3MSJEBNDep31pOH2\nms3G3N69ITyczVu3QlgY02bNgshI5/s3TeYtWwYREazfsAEzLIx5CxY4HU9GRgZRhYXMPHsWhg5l\nY2kp1q5d3Z/P5s0wfDjzdu2CtWsp/M1v6Prgg0TdcQfccQfpmZnufz7XXMPW//yH2LVrSbnxRhgz\nhvT169vm+wixZQfBMp4O/39XywFdDhQB6a2m6/o84JIGMUfhwJdIbJEGrDYMY5ar513tV8UcKUKS\n6mrYvx/27YNu3aRNyqBB3sVLZWbCP/8Jr78OF18s1qSpU91/fVkZbNwo45g92/P3VygUigaETMyR\nruu/AB4FLtN1/QUAwzBswGOIpenzuvUun1coFA2IiZH2JjfcIFXAd+yQuChvGDEC/vEPKQUwaRLo\nurjKDMO9UgDdukkckyeCSqFQKIKMgFiO/EWoWo7SQ9gfreY+z79vYrXChx9KKYBjx+Cuu6QCtyel\nAPyA+t7nBXoYASFU5x6q84YQshwpFIoAUVUlgeSeEBEBV10llbffe0+C0YcNk5pJZ854tq/KSs9f\no1AoFAFAWY4UilChoADWrRPBM2aMFLwMD/d8P7m58OtfSwmAZ54R8eROfFN2NqSny3uPH+/deysU\nipAiaC1Huq5f3mg5TNf1f/pvSAqFwi/06gXXXgtTpkBWlvRM27ZNAro9ITERXnwR3ngDfvMbuPRS\nOHq05dcNGiRCqrAQVqyAU6e8m4dCoVD4GXfcaj9vuGAYhh0Y45/hKLwh0CmPgUTN3UM0DQYOhKVL\nJXC6slLcbd4wZw5kZEhW2pQp8Kc/Sc2l5ujSBS68UNqRpKdDXbq/p6jvPTQJ1bmH6rwDics6R7qu\npwCpQE9d169CUulNoA8wsG2Gp1Ao/EaPHlIluzVERcFDD8F118Gdd0oJgOefbzmNf/BgKUbpbVad\nQqFQ+BGXMUe6rl8BLAOWAKsarKoG3jAMY6P/h+cZKuZIofARpaVw4oSUBoiMdO81pinusnvvhSVL\n4M9/hp49/TtOhULRoQlUzJFLy5FhGB8AH+i6/h/DMH7QhmNSKBSBRtPg9GnYvh2Sk2H0aOjateXX\nXHONuM0eflhe8+c/w3e/63lBSrsdwlQyrUKhCAwtHn2UMAp+QtkfrebuJ7p1g4ULReyEh8P778Pn\nn0sF7Jbo2lUKSa5cKdW258+XCt7uUlwMb73VbDNb9b2HJqE691CddyBRl2YKhcI1sbFS7frGGyWQ\n210XG8DkybB5swisOXPEmuRO8Hd8vGy/aZOUCzh71vvxKxQKhRd4VOdI1/WhwCjgU8Mwgq5Akoo5\nUiiClFOnJBZp2zZ49lm46KKWX2OzSTbc3r3SHmXMGO/6xSkUinZLMNc5+rzuPgH4ArgH+Iufx6VQ\nKNoLFRUiZJqjXz/pz/avf8FPfiLZbS1V6w4Pl/5uV1whtZHc6e0WSnz1lYjNkydbLqGgUCg8wh23\nWpe6++uBvxqGcTEwz28jUnhMKPuj1dyDgP37JUvt5MmWt734YmlBkpQE48aJWGpJWHXrJnFLDVx6\nQTN3f1Nb26RY5rm5Dxwon922bfDaa/DuuyKYWvo82zEh8703IlTnHUhcZqs13EbX9QjgcuDmuuc8\nLKmrUCg6LFOmQO/e0n+tb1+YPh06dXK9fefO8MQTcNNN8OMfwyuvwAsvwMSJbTfm9kBmpsRsDRsm\nlrfGDBokNxBBVFQkLWKctWUxTbmpDECFwi1ajDnSdf1+4JfAKsMwbtF1PRz4wjCM+W0xQE9QMUcK\nRQCxWsWKceiQCKQRI1p+jWmKOPrlL+H66+F3v2u5ZEDD1370kYiHlJSO06utqKjeAjRrlgjP1lJW\nJpalhAQRsH36yC06uvX7Vij8SNDGHBmG8RSQbBjGLXXLNkApEIVCcT4REdIW5JJLpHK2O2gafO97\n4morL4fUVHjnHRE+7rx21ixx5731Fhw86N7rgpl9++CTT0RYXnmlb4QRiGvyppsksD0sDHbvlt56\nyl2jUDjFLRurYRiljZbt/hmOwhtC2R+t5h6ExMdLexBPSEiA//4X3nwTHnmkxWa25+bes6dkvi1c\nKBart9+GnBzvxx5oBg0CXRdLmIvMPK+/96goGDBAgtwvuQRuuUXErDNKSyVgPsjil4L2N+9nQnXe\ngcSdmCN0XU8E+iL91TSgr2EYH/tzYAqFIgSZMwd27ICnnpJYpp/9DO6/v2VLVJ8+cNllIozac7p/\nly4tb+MrNM11bFhpqZRRKC4WsetwxQ0Y4FmtK4WineJOzNETwPeAGqAAGAp8aRjGNX4fnYeomCOF\nIojJzBRr0MyZ7omAo0elmW12tnvNbNsTNTXSIqW5wHVnFBdLTFaEW9e1rcdqlSDv/HxxX6akSIyX\nQtFGBG3MEXANkAQ8BdyPxBuV+3NQCoWiAzJsmLjP3n0Xdu0ScdAcQ4fCxx/Do49KsPYPfiDByt5g\ntYqwCDSmCQcOSM2n7Gz3X1deDg8+CEOGiCVn4UL4zW/g00/FyuMvIiIgMRHS0sQVp4SRIkRwRxwd\nNwyjCjgGjDUMYzdSJVsRJISyP1rNvR0RHi7p+ldcIe6v996T5rbN4Whmu2+fWJtGj4aXXyZ93TrP\n3rukRAKd162DM2e8n0NrKCiADz6Q2KilS2HkyJZfY5oSOJ2SIp/VoUNsfOMNeOABWffnP0u9ozFj\n4PbbJfMvK6v9B6a7oN395n1EqM47kLhjmz2p63o88CWwQdf1QaiebAqFwlu6dRNxcPiwxLVceGHL\nr+naFZ55Bm6+GX7yEyaVlUlT28WL3Ysx6tVLqnLv3i1NdIcNE6HWuXPr5+MOX30lbsJp09wrcQCS\nwXf33WIZeustycwDrHFxMG+efIYg1bF37oSvv4ZVq6SHXW2tuC9nzpTXTZqk0vYVCg9wJ+YozjCM\n8rrH4xC32nLDMFqo/d/2qJgjhSIEsNvFNffwwxIo/Mc/wowZ7r++ulpE2cGDcNVVEBfnv7E6OH5c\n3FPulDgoK4PHHoPXX5fMvR//2PMaTidOiCD76isRTQcOiGvMIZZmzvRNmYCSEvksL7ig7eKgFCFF\noGKOPGo8G+wocaRQhBBWK7z6qgiJ8eOl6vbYse6/vqrK84Bof2KaIoh+8QuxCv3xj2Lx8gUVFbBl\niwilr76Cb76RfTcUS6mpnlfQttlgwwaJ57roIoiN9c14FYo6gjkgWxHkhLI/Ws29A1JdLen8zdTY\nSU9PF0vFbbeJBWjBAnGx3XSTuOvcwR/CqLbWu9ft3CllDP7+d4nF+s9/XAojr773Ll3kM3r4YXG9\nFRfL+8yaJWJp2TKpGXXxxfD738PatSKoWiI8XFx8w4eLu7KgwPOxeUCH/c23QKjOO5C0KI50XX+2\n0bKm6/rLfhuRQqEIbUxTTrLvvONeM9uYGLj3XikVMGqUxPX85CdNGra6za5dEjTtiVXdNGHvXli+\n3LOA79JSuOceibv6znekl5qrwoy+JCxMgrh/9CMJ4s7MFNfb7bfL+H/zG6lrNGmSxD0tX958dt34\n8SK0Vq1qtninQtFecCfmaINhGBc0eu5LwzDm+HVkXqDcagpFB+L4cXEDudPMtiFFRfCnP8GLL0r6\n/y9+Ienv7pKfLy6o6mqYPFlKCjRHXp5YX6KjRSD06NHye9jtIkp+9SvJ3nviCbHcBBPV1bB9e33c\n0ldfwdy58PLLrt1nRUVSdmDIkLYcqaIDE8xutfMiAXVd1wCV9qBQKPzL4MFw7bWSUbZihfsuq549\n4a9/FQtQWRkkJ8Pjj7vnJoL6atvTp4t77733nFuwamqkNMDatdKz7NJL3RNG27eLiHrhBWmc+/zz\n3gmjggIZg7+IiZFYpJ////bOPD7K6mr83wRCAsgiyI+1hggk2tIgO4SEJKyCgCDlgiBVUJGlIKj0\nVUHRt2prFfS1ihapFSq2XiwUi+yBqFBRwAW1IIsJIJsGWcKSQJL7++NOxkkyk0zWmcmc7+fzfMhz\n732e55y5M8yZc889Z7Z9DY4csXPRu7dnj17jxmIYCdUCb4yjT5RSTyqlIpRSV2GTQW6vZLmEUhDM\n69GiezUnv5jtyJEFdnp5pXvLltbw2L7d5klq1w5efNF7g+JnP7OxOB06uK/XVqOGTUswapR3yRF/\n/NEu9w0eDPfcY70xXbqUfF0h775T9927bQ4kreGDD2zsVWUmhAwPt16jUaOs4fjpp5X3LA8ExXve\nDcGqty/xxjh6BLgaSAP2Y71GD1emUIIgCAUoT/B027a2oO26dbBhg/Uk/fWvdrdbSYSEWMPHXRxQ\nzZo2V1JJtcby8uC112wixxo1YM8eG0he0s6wK1dsDNK6de77+/a1xWP79rWZx48ds/pVZrHYkBB4\n6CEbOD5woE2p4A3evNaC4EdU+VZ+pVQCMB94X2s926X9DSAGyALe0FovcbT3A+Y5hs3TWm/2dG+J\nORKEIOPAAevhKU2Cw23bbKzP99/b5bZbb628YrWffAK/+Y01oF5+2eYa8oaDB63Hq2VLa5iVd2dd\n/g7Apk1tDFdFJL/ctQuGD4epU63B5Ok1zM62y6IJCXauBKEU+CrmyBdZu8KB3wNxhdoNMFpr7dwS\noZQKBZ4A+jma1iultmitq09yJkEQyoYxNhj6P/+x+Y3at/euYnyvXpCaCuvXWyPp97+Hp5/2Ptu2\nN2RkwMMPw+rVtsTH+PHe3fv0aWu8Xb4M/fpZY6aiqF3b7krbutW+Tk2b2riuNm3Kdr/Ona0BN2yY\n3em2aJF7IzU83Hq3Nm60sVm/+EX59BCEKsCbrfzXKKX+opRa7zgPUUpNL+sDtdabAE8VIAv/79EO\n2Ke1vuSo73YQWwRXcCGY16NF9+AkNTXVGhvx8dZ7cfq0LbHx1VfeLSuFhMBNN8HOnXY32/TpNg/Q\n9nKGU+bmwiuv2ISKdevaJbRf/9p7oysjw+6OGzHCo2FUpnmPiLBeq4EDrTyDBlmvVHmXu1q2tPFO\n589bY85TnqOmTe2uvD17rHFWUtFhDwTrez5Y9fYl3niOXgOWAdMBtNZGKTUa+FNxFyml+gO/LdT8\ngNZ6t4dLMoG3lFI/ArO01geARsAZpdTzjjFngcbY2CdBEARbd61PH7uNfOdOu4zTubN314aG2gDj\nESPs1nqlrHfjySdLl20bbNbpadNswsVNmyA2tvS6eFt3rbw0bGgPT3z5pc311LTpT0txnsqs1K0L\ny5fb3Eg9etgdeD//edFx9epZAyklxR79+1eMLoJQCXhjHDXSWr+jlJrm0laix0lrvRHY6K0gWusZ\nAEqpG4FngRHAKaAhMBXrVVoIZBR3n9TUVJKSkpx/A3Jezc/z8Rd5quo8v81f5KnK86SkpKL9X34J\n4eEkdepU+vvXrElqmzaELl5M76+/hn79OBkbS9qdd9Jj3Ljir//5z+F//ofs1as5OGkSP3/ySQgJ\n8avXq9Tnv/gF2w4epNa+fXTNyoKPP2bPnj2cbd+eHrfdVnR8aCip/fvT1BhuSEqCpUtJjYhwf/+B\nA+H0afm8l/f9Xs3PP1y9mqs/+8ymk/AB3iSB3AyMA97SWicrpUYA92itB5f1oUqpJOBm14Bsl77r\ngf/VWiulVA3gA2zMUQiwUWvdy9N9JSBbEIQKITMTnn/ebv0fNcp6RVq0KDgmJ8cuof3v/9pdY489\nZr1YJXH5svVwNWpkM3oHCmfO2EDuWrWKH7d1q33N5syxweiCUFqOHrXexW7dSDl61G+TQM4G1gEd\nlFKfAk8CM8r6QKXU/wCPA0OVUn92af+HUup94DnHM9Fa52IDsjcCGxzXCYUo/IsqmBDdg5NS637i\nhA2O/v5778bXq2eNnb177bJR+/Y2NulHR7jkhx/apbuVK+H99+G550o2jIyxS1Va29ikMiZL9Nm8\nN2xYsmEENg5s2zZrOE6bVqHb+IP1PR90ejdoYOPifPjjocRlNa31LqVUV+B6IAf4xmG0lAmt9TPA\nM27ax3gYvwFrGAmCIJSNpk1tPM+mTTYnUJcu3pUUueYaa/jMnGk9RNHR0K2bjcmZP996SLwJts7I\nsAaDMTYo2kNR2WrDddfZXYRjxsDNN9tg+eJinMAark2aVF5aBSFwuOoqe/iQKs9zVJnIspogCMWS\nm2uzZX/+ObRqVbqabWC3wm/caHd8leY/7w0b4NprISamen355+baHWrNmrnvz8mBBx6w+q9e7Tlt\ngDGwZo1NMZCc7F1KBiEo8Nvaao6daYIgCIFPjRp2F9qYMbYOWs1Spnpr184mPSztr9oBA+wSQXUy\njADOnbPeuK+/dt9fs6bNpj19us0v9cEH7seFhNhllIgIu9vtwoXKk1nwLw4fLlIixx/wJubogUqX\nQigXQbce7YLoHpyUW/ewMJv3JwA9FH4171dfbbfn//e/Py0bumPqVPjb3+BXv7L12dwRGmqL2rZp\nA//6l9ucSX6lexVSLfXOyYHUVJtFvjILKJcRb4yjS0opDwkuBEEQqhlnzti6ZqUlK8vG2Vy6VPEy\n+TP5+YvOnYO1a+1uPHf072+D13/3O1tuxFMiyA4dbFD3li2VWydO8B3nzsGqVdaYHj7cegzd4UOP\nkjdb+edgt9L/iZ8yWButtZcVB6sOiTkSBKHc7NhhK9zfeONPxWKLwxib+XnXLuv16NLFu11d1Q1j\nbCLM0FAby+WJjAwYOdIGxL/5pt0N6I7c3JJfeyHwOHzYGsmdOhVfSiYzE+66i5R77/Xb2mrtgEPA\n0ELtfmccCYIglJuuXe1uqx077K60zp1trJG7eKGTJ+1yUliY3ZXlzQ646kpIiE3YV1JpkGuusUHt\nkyfbYrTvvmuD4wsjhlH1wxi7qWHAgOLrBu7dawtC9/KY1rDyMcZUm2PTpk0mGNmyZYuvRfAZontw\nUmW6Hz9uzLvvGrN2bdG+8+eNWbbMmP37q0YWB9Vm3vPyjHnmGWNatjTmk0+8uiR10yZjcnMrWTD/\no9rMuTe8844xTZoYs3ixMcYYx/d6ldsTpdyqIQiCEEQ0awZDh7qPI6pb1+56C/UmdFMoQkgI/Pa3\nNnfU4MGwcKHNG1UMESdO2KW4666z3rzivA9CYJGTAw8/DO+8Y2PXvK2PWEl4ledIKdUHGAQYYI3W\nOrWS5SoTEnMkCILgZ+TkwKef2hgTT6kTPvvMBnVPmmTLjhSX8iAzEw4csEdODrRtawvdeopdEnyH\nt3FjJ0/aHxrh4bBsGTRu7Ozy5zxH04GngH3AQeAZpZQUzBEEQRBKJiTEet7efddz/qKOHWH7druD\nafx4u/PPE/Xq2fGjRtnYlby84Nsh6O/k5toSO9u3lzz2o4/sJobeveG99woYRr7EG3/weCBZa/2a\n1vrPQDJwR+WKJZSGapkDw0tE9+BEdA8gatSAxMRi8xcBtrDv++/bVAB9+7qtg1dE98aNoXt3G+Tt\njrKkZPBDAmrOz5+3hnB2ti214wlj4OWX7Vb+V16BJ57wqyB8b4yjHK2104zXWl/E1lgTBEEQBO/o\n0MHuPlq3DtLS3I+pUwf+8Q9rHHXvDl99VfbnZWXZJZqNG+3zJGdS5fPdd9YAbtsW+vXznGT14kVb\ngue112xusCFDqlZOL/Amz9FrwGngz1hjajJQT2s9qfLFKx0ScyQIguDnnDpl44W6dy9+3LJlMGuW\nzag9eHDZnpWdbQ2jAwfsc6OibH07CeSueI4etYk7+/aF5s09jztwwOa56tABXn3VGsSe2LePlCNH\n/DPmCLgPuAK8DfwduOhoEwRBEITSkb8UVhLjxlkvxF132fpsZcmWHB5ua9oNGWJLlzRsaI0koeJp\n3twaPcUZRv/+t82FNXkyLFni2TC6cqX4mn1VgFe71QKFYPUcpaamkpSU5GsxfILonuRrMXyC6J7k\nazGqjvR0m06hZ0/+M3AgcSNHVs5z/Dgjd8DPeW4uzJsHS5eC1sVnUAfr7fvuO4iLIyU11W89R4Ig\nCILgG1q3tlnIga533mlrtL3xhq3PVZGsXWsDif/73+J3ywmlIyMDBg2ysUU7d5ZsGIFd/kxI8Kmx\n6tE4Ukppl79nVI04QlkI6F8U5UR0D05E92qGMbB6NRw54r6/fn1YtIiw77+He+6BlSvhZz8Dpez2\nf0/FbkvD4MG2nt6JEzYofN06OHjQp8VP8/GrOc/Ls0aONwbkzp12m36nTrBhA/y//1f58lUQxWXI\ndl04HAG8WMmyCIIgCMFISIitabdxozVQ2rd3P652bWsQKQU//gjLl8P8+TYuaeRIG6cUH1+2rOWh\noXDttfa4csUu5x09alMQFObiRfj2W2s45eXZwxibiDImpuj4s2fh889/Gpd/TcOG7re7Z2TYtAb5\n40ND4aqrbMb2G28svW4VxYULNhaodu2SX+PFi+GRR2zQ9a23eh5nTPFJP32ELKtVAwIqB0YFI7oH\nJ6J7NaRpU5sle+9e2LrVbQHbAro3agT33gsffAC7dtmlmGnT7DLcQw/ZosFlJSzMlifp3dt9f06O\nXda7cMF6UHJyii+4W6uWNWxatYLISFv+JDraGmLuaNAAkpLsdvgBA9gWFgY33OA5QeLRozaWZ80a\na1Tt3Al79tjM0xXFsWPWYxcZaZc2a9VyP+7SJWusPv+8TQRZnGF08qSV2w+XMYvzHLVUSt0PhAA/\nc/kbwGitF1S6dIIgCELwUK+eNZBSUuyy1sCB3sWdREZag+ihh2D3bpsG4OabrWdm3Di47TbPhkhZ\nqF/f7rryltq13XuUPBEWVsAQutKggdXRE82aWYPlwoWfjowMu9zoLm3BDz9Yz1fdugWPOnXce3F2\n77ZHcjK0bOlZjvR068Fr1w4+/th6uzzx1Ve2bExSEkREeB7nIzzuVlNKPY6tpQbWKCowUGv9RKVK\nVgaCdbeaIAhCtcIYu2PpuuvKfo+8POu5WLYM/vlPu1Q3bpzd0t+oUcXJGoicOWMNGVdj6sIFa4Al\nJBQdv2ePjfEqzthZvx7uuMMWj50xw/NSWU6O9fadOWMNunr1ihXVV7XVZCu/IAiCUL3JzraeqGXL\n7Jd4crI1lIYMsV4doezk5cFTT8Gf/wx//7t74yofY+yOwAYNvN6N5reFZwX/p9rGIHiB6B6ciO7B\nSZl1Dw+3y3Va2x1xw4fDokW2ntuECTbI2I/Li/jtnJ8+DcOG2Z1oO3YUbxiB9SYlJ9ulND/NKZWP\nGEeCIAhCQFDr1Cn7JXz6dNlvUr8+3Hmn3Rn33/9CbKyNVWrVCu6/3wZ3V6MVlUrjiy/sDsPoaNi8\nufjM2K7Ur1+5clUQsqwmCIIgBAbnzlmD5uBBuxzWtq3dal+3bvnvvXcvvPWWXXoLC7PLbmPHut/K\nH+wsXQoPPAB/+hOMGVOpj5KYowpAjCNBEIQgwBg4ftwWMU1Lgz59bMBwRd3744+tkaS1DQofO9Y+\nwxgbUOzpuHKlfP2exoSHW8+W69Gypd2NV5U5grKzbTHglBRYsQJ+8QvPY3/4wRqz5TQufWUcFbeV\nXwgQAr7uTjkQ3ZN8LYZPEN2TfC2GT3DqHhJi44VatIBevSr2ISEhtsRFjx6wYIGNR1q2DF56CWrW\n9HyEhRXfX9yY8PBix+z7/HOic3NtCY7vvvvpyM0tajAVNqKuuaZsSTEL8913dqdfixZ2abO45bE9\ne2yuJU95ogIAMY4EQRCEwMVTYG9uLnz0kfX8NG9eNg9LWJitCzZoUPlkLCfHrr2WaHcG8blzNgGk\nq8G0e7dNBpl/nplpDRp3nqf8v5s1s4aYJzZvtsuMs2bB7NmeX8vcXJvA84cfbKB2gwYVor8vCJpl\ntWPHjnHp0iVC/DBNuSD4K8YYWrZsSYQfJmkThGK5csXGJx04YDMwt21rD09Zpqsrly7Z7NaubXUx\n2QAAHVhJREFUBlRhgyojw9Y9K+x5atnSxnctXGi9Z336eH5OZqYNcm/Y0HqMijO2SoEsq1UiZ86c\nAaCNBNYJQqnIy8tj//79REZGioEkBBZhYdChgz1On7ZG0oYNNtFhabJbBzq1a9u4n+K+/65csQV3\nCxtQ+Tv3Pvmk5Jiu3FybBby4OKQAosqNI6XUq0AMNo3ABK31t472fsA8x7B5WuvNxbWXhlOnTnFd\neTKtCkKQEhoaSrt27UhLS/OrHxcSd5PkazF8Qpl1v/pqu+28SxdbUiPAqPQ5Dwuzxk95gtobNrRH\nNaHK8xxprSdrrZOBJ4DZAEqpUMf5AMfxuKd2pVSp3WshISGynCYIZSQ0NFQ+P0L1ICTEBj+744sv\n7BJSTk7VyiT4Jb5cVssE8k34dsA+rfUlAKXUQaVUO6zxVqAdaAvs94G8giD4CcHqOQHRvdK46irY\nt88GFEdG2vikli2rdqu8B/xuzjMzS6yJFuhUmnGklOoP/LZQ8wNa692OvycC/+f4uxFwRin1vOP8\nLNAYW/DWXbsYR4IgCELFkR+Xc+mS9SDt3GkNpdGj3RtIxtgjL++nf8G9Z+rKFTh7tuDYvDyoVQua\nNCk6/tIlOHTop7E1athEl/Xr+34H2Dff2DxQw4cHTLbrslBpxpHWeiOw0V2fUmoo8I3Weq+j6RTQ\nEJiKNYgWAhlYz5G7do+4rs3m16O59tpry6NKpbJ//34mTpzIxYsXqVWrFosWLeKXv/ylT2U6e/Ys\nb731FlOmTPGpHGDncP78+fz73/+u1OcMGTKE2bNnk5iY6NX4jIwMBg0aRO3atYmPj+fpp5+uVPkq\nkxdeeIF7772X2iUU4Mz/PBX+fPni3LXWlD/IU5Xn+W3+Ik9Vnn/++efMnDmz8p/Xvj2pGRmEXr5M\nb4dhVKD/xAm+efZZCA0l5oYbIDSUPXv3cqVRI2J/+9ui48+e5auFCyE0lPaxsRASwu6vviKnQQM6\nTZ1adPzly3y2fj2EhtKxUyc+27GDGllZ5NapQ8cZM4qOz8zkk3feIbd2bXr26wd165K6fTuEhFTc\n65OSQoOvv6Zjs2YwdCipn35aea+/y7mvqPKt/EqpzsBtWusHXdpqAB8A/bBG0EatdS9P7Z7u7Wkr\n/7fffuu3Adl9+vRh5syZDBs2jE8++YSpU6eyc+dOn8qUnp7O0KFD+fLLL30qB1SdcTR06FBmz55N\nby+TlmmtWbNmDW+88UalylUVREVFsXPnThoXs8XZ3z5DqRKU7GsxfEKw6l6i3qdPw9dfw4ULPx2X\nL0O7du4TMV6+bI86dbxLEHn+vE2GWbeuLRobFlZWVUqNr7by+6Lw7HKgq1Jqi1LqRQCtdS428Hoj\nsAFHQLan9urCDz/8QFpaGsOGDQOgW7duhIaGsm/fPgAef/xxZs6cydixY+nSpQtjx44tcP2bb75J\nr169iI+P5/777/f6uWlpaQwYMICEhAQ6derEihUrnH0fffQRSinS0tJISEhg+PDhXj+zbt26PPHE\nE8TFxdGxY0c+++wzr+TJzMxkwoQJDBgwgJiYGObMmVOgPysriwcffJDk5GS6dOnCqVOnnH27du2i\nT58+JCYmMmLECDIyfnIsfvnll4wYMYLk5GRiYmJYuXKls+/UqVMMGjSIuLg4br/9ds6cOYO3PxSG\nDx/Oo48+ysaNG0lISOD3v/+9sy8tLY3BgwcTHx9PXFxckV8/rVu3ZvHixfTo0YMOHTpw6NAhZ19x\nr+2hQ4e49dZbiY+Pp1evXixevNgrPbOysrj77rvp0aMH3bp145FHHinQFx8fz4kTJxgyZAgJCQkc\nOXLEq9fA1wTjF2Q+onvwUaLeV18N8fEwcCDceiuMH2+L63bv7n788eOwahW8/jq8+SasXGnTHDi+\ne4rwwQcQFQX9+1epYeRTjDHV5ti0aZNxx8GDB92259PljykVcpSWnTt3mj59+hRoGzVqlNm4caMx\nxph58+aZ5ORkc+7cOZOXl2eioqKcunz11VcmMTHRXLlyxRhjzG9+8xuzdOlSr547a9Yss2DBAo/9\n6enppn379kXaS3pmzZo1zfvvv2+MMWbdunWmS5cuXsljjDGnTp0yxhhz8eJF06JFC3Ps2DFjjDFb\ntmwxrVq1Mnv27DHGGHPHHXeYxYsXG2OMyc7ONrGxsc6xy5cvNxMnTnTeMzMz02RnZxtjjPnss89M\ndHS0s2/atGnmscceM8YYc/z4cRMZGemU3RveeOMNM3369CLtcXFxZs2aNcYY+zpGRkY6dTPGmNat\nW5v777+/yHXFvbY5OTmmQ4cOZt26dW5lKU7Pf/3rX2bYsGHF6tK6desCMrqjpM+QIAgBSF6eMefP\nG3PypDHffmvM8ePux+XmVq1cLji+16vcngiKJJAlsWN2MVk/fUhISAhDhgyhnmNXQGRkpDOhZUpK\nCocPH6Z///4AXLx4kUaNGjmvffvtt3nppZec55s3bybMYfGPGjWKKVOmkJ6ezogRI4r8KjEePCgl\nPTMiIsK5LDVw4EDGjRvHlStXCAsLY9++fdx1113OsfPnz6dbt27O85o1a7J69WrS09MJDw/nxIkT\nNG/eHIAbb7yR66+/HrBLQPmvwd69ezly5IjTo5aXl1cgUeFVV13F4cOH+eSTTzh06BDHjx939m3d\nutXpYWnWrFmp47zyP0CuZGZmcvjwYQY5Sg1ERkbSq1cvPvroI26++WbnuMKeMSj+tf3mm2+oXbs2\nAwcOdCtLcXr26tWLZ599lttvv52hQ4cyfPhwwj1tZQ4ggnV5BUT3YNS90vQOCbFLZXXrFj/Om6W3\naoYYRz7k2muvJT09vUBbenp6gQByT4ZKWFgYw4cPZ8GCBW77R48ezejRo9329ezZk08//ZRt27bx\nwgsvsGLFCl588cUS5S3pmYUJCQmhhqPuUXR0NB9++KHbcbt372b8+PFMmTKFjh070qRJE6+WuGrW\nrEnr1q3ZsmWL2/7XX3+dJUuWMHXqVBITEwvcs0aNGl4vo7nDU96fwvc0xhDqxX8sJb22ubm5Hq8t\nTs9rrrmGrVu3smfPHt58803+8Ic/eL3cKQiCEKwEnznoRzRp0oSoqCjee+89AD7++GOMMURHR5d4\n7U033cTy5cs5ePCgs83bL/u8vDxCQ0NJSEjgwQcfZPv27QX6IyIiOHXqFHmOran59y3pmRcvXnTq\nsnLlSjp06OCVYZCSksLNN9/M5MmTqV+/PmlpaV7pEhMTQ3Z2doEYG9frVq1axZw5cxg9ejT79+8v\n0JecnMzf//53AA4cOMCnjp0X3uJOvnr16hEVFcW7774L2CDmbdu20bNnzxLvV9xrm6+na2yYK8Xp\nme/huuGGG3j44Yc5duwYFy5cKHB9REQEJ0+e9KiXPxKM3oN8RPfgI1j19iXiOfIxr776KhMnTuSx\nxx4jPDyc119/vUC/Jw9FVFQUixcv5vbbb3d6Qf74xz/Sq5fHzXxO3nrrLRYuXOj06rz88ssF+ps1\na0ZiYiIdO3akadOmPPnkk3Tr1q3EZ9apU4edO3fyhz/8gZycHJYuXerVazBmzBiGDx9Oz549uf76\n6+nduzcnTpxw6l/4Ncg/r1GjBqtWrWLGjBk8++yzhIaGMnr0aKZPnw7ArFmzuPfee2nevDkDBw6k\nUaNGXLhwgbp16zJ37lzGjh1L9+7dadOmDW3btvVKVlcZ3M3N3/72N6ZOncozzzxDXl4eS5cupaFL\nSv2yzGe+njNnzmT+/PmEhoYyatQoZji29Ban5969e5kwYQJhYWFkZ2fz7LPPUreQC33KlCkMGzaM\nyMhIxowZw913312q10IQBKG6UeVb+SuTQNzKX52oV68emZmZvhZDqAT87TMUrLEnILoHo+7BqjcE\n11Z+oZoi9bcEQRCE6oAYR0KFce7cOV+LIAQJwforGkT3YCRY9fYlYhwJgiAIgiC4IMaRIAgBh6/r\nLvkS0T34CFa9fYkYR4IgCIIgCC6IcSQIQsARzDEYonvwEax6+xIxjgRBEARBEFwQ40gQhIAjmGMw\nRPfgI1j19iViHPmYpKQkOnbsSMeOHZk4cSJnz571tUgA7Nu3j3nz5lX4fceMGUP37t0ZNmyYszxJ\nILJq1Sr27NlT5uu3b9/OQw89VKBt69at9OnTh9jY2CLj165dS/fu3enUqVOR6yZPnkxCQoLzCAsL\n4/vvvwdsqZjf/OY3dO/enW7dujlLpuQza9YsvvjiizLrIQiCUC3Jr71UHY5NmzYZdxw8eNBtuz+Q\nlJRkdu3aZYwxZuHChWbw4ME+lqjyOHnypImMjPS1GBXCHXfcYd55550yXXv06FHTp08fk5WVVaD9\nd7/7nVmxYoVp3759gfazZ8+aNm3amFOnThljjPnVr35lVqxY4fbeW7duNf3793eev/3222bkyJHG\nGGPOnz9voqKizMmTJ53958+fN8nJyebHH38sVmZ//gwJglB9cXyvV7k9IZ4jP8A4SrhMmTKFM2fO\nsGvXLsB6lT7++GPnuFtuuYUNGzYA1s3ar18/HnzwQZKTk+nSpQunTp1yjl2+fDmDBg0iPj6eTp06\n8c033zivGzBgAMOHD2fo0KG89NJLREVFOfuzsrJISEggNjaWoUOHFpH1/fffJzk5mYSEBOLi4ryu\n8P7EE08wZMgQMjIySEhIQCnl7Lt48SL33HMPcXFxdOvWjT/96U8Frr3zzjt56qmnSExMpFu3brz9\n9tvOvl27dtGnTx8SExMZMWIEGRkZBe47c+ZM4uLiSEhIYOrUqc6+zMxMJkyYwIABA4iJiWHOnDkF\nnrlgwQK6detGXFwcAwYMKNB39913s27dOh599FESEhKchWa95bHHHmPu3LmEh4cXaJ87dy4dO3Ys\nMn7fvn20bt2aRo0aAdbbs2TJErf3fu6557j//vud53Xr1iU3NxeAnJwcatWqRc2aNQv033fffTz9\n9NOl0kEQBKFa4wuLrLKOMnuOoGKOMuDqOTLGmJkzZ5q//vWvxhhjli1bZu69915jjDEnTpwwMTEx\nznFbtmwxrVq1Mnv27DHGWE/G4sWLnf0ZGRnOv59//nkzadIk53Xt2rUzly5dMldffbXZvHmzmTlz\nplm4cGEBuVJTU82QIUMKtKWlpZm2bduaQ4cOlUnX9PT0Il4RY4x55JFHzOzZs40xxly6dMn06NHD\npKSkOPvvuOMOk5SUZM6dO1fguuzsbBMbG2uOHTtmjDFm+fLlZuLEic7+qVOnmjlz5niUJ98Tc/Hi\nRdOiRQvnfU6fPm2aNGlirly54vHaO++80/zzn/8sSeUi5OXlmZYtW5q8vDy3/WlpaUVeo9OnT5vI\nyEiTlpZmjDFm7dq15pe//GWRaw8cOOD29Z07d65p166dadWqlXnvvfeK9GdlZZXo0fM3z9GWLVt8\nLYLPEN2Dj2DV2xjfeY5qlmw+BQHGf4rvGmOcNcpGjhzJY489RlZWFm+++SYTJkwoMPbGG2/k+uuv\nB2xV9zNnzjj7GjduzOeff87u3bv55ptvOH78uLMvJiaGiIgIGjRoQGxsLB988AEXL14sIkdh1qxZ\nw6hRo7j22mvLrJs71q9f7/QGRUREMHHiRNauXUufPn0AW7Nt+vTp1KtXr8B1e/fu5ciRI4wdOxaw\n8TURERHO/hUrVpCWluZRnpo1a7J69WrS09MJDw/nxIkTNG/enIYNGzJo0CAGDx7MsGHDGDNmDNdc\nc43X+hRHRkYGderUKVUduoYNG7Js2TImT55MdnY2UVFR1K5du8i4559/nvvuu69A23vvvce2bdtY\nvXo1R48e5YEHHqBTp040a9bMOSY8PJzLly+TnZ1dxJslCIIQjIhx5Gfs2LGD8ePHA/ZL65ZbbuGd\nd95h2bJlrF+/3uv75BtSo0aNonPnznz33Xflli0kJIScnJxy38cdrsHZeXl5RYwHd4ZIzZo1ad26\nNVu2bPF4X0/y7t69m/HjxzNlyhQ6duxIkyZNCjxjyZIlnDx5kpUrV9K9e3c2bdpEVFRUgXuUpdBu\n3bp1ycrKKvV1vXr1Yt26dYANzg4LCyvQf/r0adasWcOCBQsKtC9atIh58+YRHR1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"text": [
"<matplotlib.figure.Figure at 0x10bcfa190>"
]
}
],
"prompt_number": 12
}
],
"metadata": {}
}
]
}
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