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Regression With Python, Statsmodels and Pymc3 (notebook)
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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"https://gist.github.com/dartdog/9008026\n",
"\n",
"http://nbviewer.ipython.org/gist/dartdog/9008026"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While a bit of a \"toy\" example the below is real data which uses the last quarter (already shifted) of the prior year to \"project\" the upcomming year by using a regression line of prior observations. Actually works surprisingly well..\n",
"\n",
"Since it is something I know and have the data for, I'm trying to use it as a learning base for the various Python Stats capabilities, including a recent foray into PYMC3 and Statsmodels, along with Pandas. This Notebook can be freely downloaded and modified and redistibuted. (attribution appreciated) The PYMC3 code here was largely copied with small mods, from http://www.databozo.com/2014/01/17/Exploring_PyMC3.html and http://twiecki.github.io/blog/2013/08/12/bayesian-glms-1/ and http://nbviewer.ipython.org/github/twiecki/pymc3_talk/blob/master/bayesian_pymc3.ipynb\n",
"\n",
"This is also quite helpful for OLS analysis http://www.datarobot.com/blog/ordinary-least-squares-in-python/\n",
"\n",
"Questions:\n",
"\n",
"Any comments to help interpet the Partial regression graph in cell # 6\n",
"How to better use the PYMC3 models questions above cell # 10\n",
"Any other comment as to how to improve this for others?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import io\n",
"import statsmodels.api as sm\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"content2 = '''\\\n",
"Units\tlastqu\n",
"2000-12-31\t 19391\t NaN\n",
"2001-12-31\t 35068\t 5925\n",
"2002-12-31\t 39279\t 8063\n",
"2003-12-31\t 47517\t 9473\n",
"2004-12-31\t 51439\t 11226\n",
"2005-12-31\t 59674\t 11667\n",
"2006-12-31\t 58664\t 14016\n",
"2007-12-31\t 55698\t 13186\n",
"2008-12-31\t 42235\t 11343\n",
"2009-12-31\t 40478\t 7867\n",
"2010-12-31\t 38722\t 8114\n",
"2011-12-31\t 36965\t 8361\n",
"2012-12-31\t 39132\t 8608\n",
"2013-12-31\t 43160\t 9016\n",
"2014-12-31\t NaN\t 9785\n",
"'''\n",
"df2 = pd.read_table(io.BytesIO(content2))\n",
"#make sure that the columns are int\n",
"df2['Units']=df2['Units'][:-1].astype('int')\n",
"df2['lastqu']=df2['lastqu'][1:].astype('int')\n",
"df2 #note sample data is from Statewide"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Units</th>\n",
" <th>lastqu</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-12-31</th>\n",
" <td> 19391</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-12-31</th>\n",
" <td> 35068</td>\n",
" <td> 5925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-12-31</th>\n",
" <td> 39279</td>\n",
" <td> 8063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-12-31</th>\n",
" <td> 47517</td>\n",
" <td> 9473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-12-31</th>\n",
" <td> 51439</td>\n",
" <td> 11226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-12-31</th>\n",
" <td> 59674</td>\n",
" <td> 11667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-12-31</th>\n",
" <td> 58664</td>\n",
" <td> 14016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-12-31</th>\n",
" <td> 55698</td>\n",
" <td> 13186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-12-31</th>\n",
" <td> 42235</td>\n",
" <td> 11343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-31</th>\n",
" <td> 40478</td>\n",
" <td> 7867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 38722</td>\n",
" <td> 8114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31</th>\n",
" <td> 36965</td>\n",
" <td> 8361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td> 39132</td>\n",
" <td> 8608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-31</th>\n",
" <td> 43160</td>\n",
" <td> 9016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-31</th>\n",
" <td> NaN</td>\n",
" <td> 9785</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
" Units lastqu\n",
"2000-12-31 19391 NaN\n",
"2001-12-31 35068 5925\n",
"2002-12-31 39279 8063\n",
"2003-12-31 47517 9473\n",
"2004-12-31 51439 11226\n",
"2005-12-31 59674 11667\n",
"2006-12-31 58664 14016\n",
"2007-12-31 55698 13186\n",
"2008-12-31 42235 11343\n",
"2009-12-31 40478 7867\n",
"2010-12-31 38722 8114\n",
"2011-12-31 36965 8361\n",
"2012-12-31 39132 8608\n",
"2013-12-31 43160 9016\n",
"2014-12-31 NaN 9785\n",
"\n",
"[15 rows x 2 columns]"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def fit_line2(x, y):\n",
" X = sm.add_constant(x, prepend=True) #Add a column of ones to allow the calculation of the intercept\n",
" ols_test = sm.OLS(y, X,missing='drop').fit()\n",
" \"\"\"Return slope, intercept of best fit line.\"\"\"\n",
" X = sm.add_constant(x)\n",
" return ols_test"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ols_test=fit_line2(df2['lastqu'][1:-1], df2['Units'][1:-1]) #Use the lastqu to fit to Units (so can forecast future)\n",
"ols_test.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/scipy/stats/stats.py:1293: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
" int(n))\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Units</td> <th> R-squared: </th> <td> 0.797</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.779</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 43.18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Feb 2014</td> <th> Prob (F-statistic):</th> <td>4.02e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>17:24:05</td> <th> Log-Likelihood: </th> <td> -125.17</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 13</td> <th> AIC: </th> <td> 254.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 11</td> <th> BIC: </th> <td> 255.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 1.368e+04</td> <td> 4927.353</td> <td> 2.777</td> <td> 0.018</td> <td> 2837.631 2.45e+04</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lastqu</th> <td> 3.2330</td> <td> 0.492</td> <td> 6.571</td> <td> 0.000</td> <td> 2.150 4.316</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 2.563</td> <th> Durbin-Watson: </th> <td> 1.782</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.278</td> <th> Jarque-Bera (JB): </th> <td> 0.504</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.019</td> <th> Prob(JB): </th> <td> 0.777</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.964</td> <th> Cond. No. </th> <td>4.45e+04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Units R-squared: 0.797\n",
"Model: OLS Adj. R-squared: 0.779\n",
"Method: Least Squares F-statistic: 43.18\n",
"Date: Fri, 14 Feb 2014 Prob (F-statistic): 4.02e-05\n",
"Time: 17:24:05 Log-Likelihood: -125.17\n",
"No. Observations: 13 AIC: 254.3\n",
"Df Residuals: 11 BIC: 255.5\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 1.368e+04 4927.353 2.777 0.018 2837.631 2.45e+04\n",
"lastqu 3.2330 0.492 6.571 0.000 2.150 4.316\n",
"==============================================================================\n",
"Omnibus: 2.563 Durbin-Watson: 1.782\n",
"Prob(Omnibus): 0.278 Jarque-Bera (JB): 0.504\n",
"Skew: 0.019 Prob(JB): 0.777\n",
"Kurtosis: 3.964 Cond. No. 4.45e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.45e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nextq=ols_test.predict(sm.add_constant(df2['lastqu'][-1:], prepend=True)) # the prediction for 2014\n",
"nextq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"array([ 45317.70961059])"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assigning the plot to fig fixes the duplicate plot issue"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(12,8))\n",
"fig=sm.graphics.plot_regress_exog(ols_test,'lastqu',fig=fig)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ZsWMH+/bto6qqiuXLlwNw8cUXM3fuXNatW8eWLVuYNWtWi9skIiIiItIdxR5k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Zs2UYckIpIK44CBwNjw9ULgCeC2yCISKcPSpcGgwvXqwf/7f3DooVFHJCKVoTtZwqpVq7jooovo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\nkiwREREREZEkUpIlIiIiIiKSRP8f9WQywx7WHNIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x5f6bb50>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig.clf() #clears the fig!\n",
"sm.graphics.plot_partregress_grid(ols_test, fig=fig)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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ZPQSvDNq1a1fSCtaaNWvU3t6uysrK3hWs6667TrFYTGPHjo26XAAAzlg6wetl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\naWkpQRBQVFREv379ki5RRERERN6GgleGKXhJJr3yyiuUl5eTSqWoqqp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M7C7gZXd/6pyn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\ntvbmbQAAAABJRU5ErkJggg==\n",
"prompt_number": 6,
"text": [
"<matplotlib.figure.Figure at 0x5f6bb50>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#fig.clf() #clears the fig!\n",
"result=ols_test.model.fit()\n",
"result.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Units</td> <th> R-squared: </th> <td> 0.797</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.779</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 43.18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Feb 2014</td> <th> Prob (F-statistic):</th> <td>4.02e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>17:24:06</td> <th> Log-Likelihood: </th> <td> -125.17</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 13</td> <th> AIC: </th> <td> 254.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 11</td> <th> BIC: </th> <td> 255.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 1.368e+04</td> <td> 4927.353</td> <td> 2.777</td> <td> 0.018</td> <td> 2837.631 2.45e+04</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lastqu</th> <td> 3.2330</td> <td> 0.492</td> <td> 6.571</td> <td> 0.000</td> <td> 2.150 4.316</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 2.563</td> <th> Durbin-Watson: </th> <td> 1.782</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.278</td> <th> Jarque-Bera (JB): </th> <td> 0.504</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.019</td> <th> Prob(JB): </th> <td> 0.777</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.964</td> <th> Cond. No. </th> <td>4.45e+04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Units R-squared: 0.797\n",
"Model: OLS Adj. R-squared: 0.779\n",
"Method: Least Squares F-statistic: 43.18\n",
"Date: Fri, 14 Feb 2014 Prob (F-statistic): 4.02e-05\n",
"Time: 17:24:06 Log-Likelihood: -125.17\n",
"No. Observations: 13 AIC: 254.3\n",
"Df Residuals: 11 BIC: 255.5\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 1.368e+04 4927.353 2.777 0.018 2837.631 2.45e+04\n",
"lastqu 3.2330 0.492 6.571 0.000 2.150 4.316\n",
"==============================================================================\n",
"Omnibus: 2.563 Durbin-Watson: 1.782\n",
"Prob(Omnibus): 0.278 Jarque-Bera (JB): 0.504\n",
"Skew: 0.019 Prob(JB): 0.777\n",
"Kurtosis: 3.964 Cond. No. 4.45e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.45e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this uses the statsmodels formula API (same results\n",
"# import formula api as alias smf\n",
"import statsmodels.formula.api as smf\n",
"# formula: response ~ predictors\n",
"est = smf.ols(formula='Units ~ lastqu', data=df2).fit()\n",
"est.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Units</td> <th> R-squared: </th> <td> 0.797</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.779</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 43.18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Feb 2014</td> <th> Prob (F-statistic):</th> <td>4.02e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>17:24:06</td> <th> Log-Likelihood: </th> <td> -125.17</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 13</td> <th> AIC: </th> <td> 254.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 11</td> <th> BIC: </th> <td> 255.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 1.368e+04</td> <td> 4927.353</td> <td> 2.777</td> <td> 0.018</td> <td> 2837.631 2.45e+04</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lastqu</th> <td> 3.2330</td> <td> 0.492</td> <td> 6.571</td> <td> 0.000</td> <td> 2.150 4.316</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 2.563</td> <th> Durbin-Watson: </th> <td> 1.782</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.278</td> <th> Jarque-Bera (JB): </th> <td> 0.504</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.019</td> <th> Prob(JB): </th> <td> 0.777</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.964</td> <th> Cond. No. </th> <td>4.45e+04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Units R-squared: 0.797\n",
"Model: OLS Adj. R-squared: 0.779\n",
"Method: Least Squares F-statistic: 43.18\n",
"Date: Fri, 14 Feb 2014 Prob (F-statistic): 4.02e-05\n",
"Time: 17:24:06 Log-Likelihood: -125.17\n",
"No. Observations: 13 AIC: 254.3\n",
"Df Residuals: 11 BIC: 255.5\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1.368e+04 4927.353 2.777 0.018 2837.631 2.45e+04\n",
"lastqu 3.2330 0.492 6.571 0.000 2.150 4.316\n",
"==============================================================================\n",
"Omnibus: 2.563 Durbin-Watson: 1.782\n",
"Prob(Omnibus): 0.278 Jarque-Bera (JB): 0.504\n",
"Skew: 0.019 Prob(JB): 0.777\n",
"Kurtosis: 3.964 Cond. No. 4.45e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.45e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(12,8))\n",
"fig=sm.graphics.plot_regress_exog(est,'lastqu',fig=fig)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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G39MfBwa5+07gEuDLwHqLhmV/6P3VMwRY26Cs2RkBzew8M1tp0XDvLURBW8PrG+7+CjAN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Zs2UYckIpIK44CBwNjw9ULgCeC2yCISKcPSpcGgwvXqwf/7f3DooVFHJCKVoTtZwqpVq7jooovo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\nkiwREREREZEkUpIlIiIiIiKSRP8f9WQywx7WHNIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6513910>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using Pymc3 with same data (excluding the rows with NaN) First with the Model directly specified I found I needed to set the upper bounds on Sigma to 12000 for good results. Using the GLM method below (next) I got unintelligible results.\n",
"\n",
"Questions:\n",
"\n",
"How can I determine the best regression line using the generated Bayes data? Shouldn't I be able to find the max liklihood for two points on the line,, ie be able to find the standatd parameters for the regression line and thereby also be able to perform a prediction with a new value of x? (and idealy say something about its probability?)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Remove NaN's\n",
"df2=df2[1:-1]\n",
"import pymc as pm\n",
"import numpy as np\n",
"x=df2['lastqu']\n",
"y=df2['Units']\n",
"trace = None\n",
"with pm.Model() as model:\n",
" alpha = pm.Normal('alpha', mu=0, sd=20)\n",
" beta = pm.Normal('beta', mu=0, sd=20)\n",
" sigma = pm.Uniform('sigma', lower=0, upper=12000)\n",
" \n",
" y_est = alpha + beta * x\n",
" \n",
" likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=y)\n",
" \n",
" start = pm.find_MAP()\n",
" step = pm.NUTS(state=start)\n",
" trace = pm.sample(2000, step, start=start, progressbar=False)\n",
"\n",
" pm.traceplot(trace);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_perform_ext.py:85: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
" from scan_perform.scan_perform import *\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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bYdy45n2ASbIABaUiSnkOuiYbNgRfk+XLwy0+XoKufZJ7y982rOZUkt/T3Lkt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\n1UKWsnbuDFOmmJoibubBcsnTuYX8yVstiMhYEdn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ZLuVV1YdVdb03+zQu2hfkU94Nibu9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\nkjVxovsNr584ETp3hk99qrby1YvXXoOnPT+tE0+E9u2jt5s4EXbfHXb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716xxwTEWLIiW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\nPhdSsmprs2e0c+EPLp28tbWZ9Y8cq1bZukw1NWZ5eeutTHwYtFcg3KDdZ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MpSfrZGw1LF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\n",
"text": [
"<matplotlib.figure.Figure at 0x7b54d90>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'd like to draw the most likely regression line via the MCMC process rather than the standard frequentist formula (as mentioned above)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def graph(formula, x_range, color='black', alpha=1): \n",
" x = np.array(x_range) \n",
" y = eval(formula)\n",
" plt.plot(x, y, color=color, alpha=alpha)\n",
" \n",
"plt.scatter(x,y)\n",
"\n",
"for i in xrange(0,2000):\n",
" point = trace.point(i)\n",
" graph('{0} + {1}*x'.format(point['alpha'], point['beta']), range(6000,15000), color='black', alpha=.0098035)\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Lwc/8dGGFY5us1WLZCk7FwjiRYDhfb1AwRCSkV1Or1aJarTIzM2O+gbdaLeO2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\nVUONK8IAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x5ff6090>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The process below uses the new GLM function but seems to have issues since I could not specify sigma max,, one would hope that\n",
"the formula could find it?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x=df2['lastqu']\n",
"y=df2['Units']\n",
"data = dict(x=x, y=y)\n",
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"{'x': 2001-12-31 5925\n",
" 2002-12-31 8063\n",
" 2003-12-31 9473\n",
" 2004-12-31 11226\n",
" 2005-12-31 11667\n",
" 2006-12-31 14016\n",
" 2007-12-31 13186\n",
" 2008-12-31 11343\n",
" 2009-12-31 7867\n",
" 2010-12-31 8114\n",
" 2011-12-31 8361\n",
" 2012-12-31 8608\n",
" 2013-12-31 9016\n",
" Name: lastqu, dtype: float64, 'y': 2001-12-31 35068\n",
" 2002-12-31 39279\n",
" 2003-12-31 47517\n",
" 2004-12-31 51439\n",
" 2005-12-31 59674\n",
" 2006-12-31 58664\n",
" 2007-12-31 55698\n",
" 2008-12-31 42235\n",
" 2009-12-31 40478\n",
" 2010-12-31 38722\n",
" 2011-12-31 36965\n",
" 2012-12-31 39132\n",
" 2013-12-31 43160\n",
" Name: Units, dtype: float64}"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with pm.Model() as model:\n",
" # specify glm and pass in data. The resulting linear model, its likelihood and \n",
" # and all its parameters are automatically added to our model.\n",
" pm.glm.glm('y ~ x', data)\n",
" step = pm.NUTS() # Instantiate MCMC sampling algorithm\n",
" trace = pm.sample(2000, step, progressbar=False) # draw 2000 posterior samples using NUTS sampling"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(7, 7))\n",
"pm.traceplot(trace)\n",
"plt.tight_layout();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x2686ff10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+ianvvpk7F559FhYvrrdb1Bv58Ny0aGERsY491uzZv/3t+r9nPvSL\nU2t+T1UhK5ZTkrjGI8CfgUej9inwB1XNueQDIi5gOU4uUlAQSUYsYgIWmPa5bVsTwo4/3kLCf/zx\nnvMMD/uefVKNLng9ew5Au1f6qhlAklUxxUqOirXxKMyUcK6I3IPl6/pVbOWxY8fSs2dPAAoLCxkw\nYMDuyVDorO5lL0eXQ+rj+hUVcOONRdx1F7z3Xm583lTKCxYsyKn2JCp36AC//GUx11wDBx5YxNCh\nudW+hlwOt0tKSmgI1GEhMfoar4tIzziHkloZzVAQQ8dx8pSCAhg1as998+fbgslxx1mQjPLy7LTP\niZCqueB/sMiCk7ABYxTmX/URgKrelqDeEGBclLngTUClqt4ddc79QLGqTgzKobmgALNV9cBg/1Dg\nRlUdFXMPNxd0coo//9l8hYqLfeKUCaZOhUsvhTfecBOJbJHv5oIhQWTBHwJDscW+14H7VDWpteFA\nyHo+ylzwVuAy4EtgHnC9qm6JU0+nTVNOOy0dn8JxnMZCmOfsiCMsMulHH1lqBvcDT510jmOpuor3\nAI5S1etV9SfAIGB/Vb0tkYAVMA/oLSI9RaQ5MBoT1KKZBFwCu4WyLaq6TlXXAitFJEy/eBrgmYac\nnGbVKrjtNhg/3gWsTHHGGfCrX9kK3+bN2W6Nk+c8CvQH7gX+AhwK/KsO17sPOBAYAKzBzBLj4v8X\njuOkSvPm9h6miCkrqxqBcOtW8+tyMkuqKQA7AmVR5bJgX7WoarmIXA1MBQqAh1R1iYhcGRwfr6ov\nichIEVkO7MBW/UKuAR4LBLSPY445NVDsPiQJqa+++dGP4KqroG/ftF86Y+Tjc/PDH8KyZRZ1acqU\nyMCTTvKxX5yUOVRV+0eVXxORWntWqur6cFtEHgSeT3Tuo4+O4/XXbbuoqMifNcdxaiRcnAlTQPTq\nBe+/D2vW2PumTba/qAg2bDD/rnbtstLUnKO4uHgPF5J0kaqQ9Sjwtog8g5nxnQf8M5mKqjoZmByz\nb3xM+eoEdd/DzBQdJ+d5/nkLn/qYp+nOCv/3fyZkXXEFPPKIawacWvGuiBynqrNht3XFO7W9mIh0\nUdU1QfF84INE544dO45Tkgmv4TiOE8WoURYQAywoVJMm5q4QzdKllsy4RQsLnPH553BktUmYGj6x\ni1m33VadYV5qpOSTBbtD3A4NijNVdX7aWlMH3CfLyQW2bIHDDoN//QufKGWRHTtsxe7ss82E0MkM\nDcgnaynQB4t4q8D+WK7HcizI0xHV1J2A+RO3B9YBtwJFmKmgAiuAK1V1XZy6On264sorx3Hqyvr1\n8Oqrtt2vHxx0ELz8spkSHnkkfPKJ+W15jryqZC2Ee0BrYFuQGLiDiByoqivS0RjHyXeuvx7OOccF\nrGyz116mUTzuOIu2dOml2W6Rk2eMqG1FVY03ZXk42fqueXUcJx107AgnnwwzZlhS4pYtTcACeO+9\n7LatsZBS4IsgoeLPsRDqAM2Bf6e5TU6aqS9b04ZAOvtmyhR47TW4++6az80H8v256dwZXnoJfv5z\neOWV9F033/vFqRlVLcEiAe4NtAtfqloSHKs3XMhyHCdddO1qmqoDDzQf5bPOgmPc+SZjpKrJOh8Y\nSGCbrqqrRaRt2lvlOHnGl19GfIDa+i8iZ+jXD558Ei64AKZNc9tzJzlE5A5gLPAJUBl1qN511C5k\nOY5TX+y9t73mzk2+zsaN5grhqVFSJ9UQ7qWqunvAEZG9kq0oIiNEZKmILBORGxKcc29w/D0RGRhz\nrEBE5otIwqhMTnw8OlVi0tE3qvCDH5jT6bBhdW9TrtBQnpuTToK//hVGjoQVaTBsbij94lTLaOBg\nVT1ZVU8JX5m4sQtZjuPUN2edlfy5ixbB22/DypX1156GSqqarCdFZDxQKCJXAN8BHqypkogUYLlG\nTgNWA3NFZJKqLok6ZyTQS1V7i8ixWF6RIVGX+RGwGMh7PYGqPazz58OCBfDFF7ZfxBwTDz/cVtzb\nt89uO53kePRRC5GaysqQk1kuvBDWrbNcWm+8Eckl4jgJWATsiwWuyCguZDmOU980axbZrqy0SISJ\n2LrV3mfN8iAZqZK0JktEBHgceDp49QF+qar3JlF9MLA8sGcvAyYC58accw5BOHhVfQsT5DoF9+4O\njMQEurwdgtassfDShx8OgwfDAw9Ylu4+fex14IGwfDncfrvlOCgqgr/9zXIa1AX3IUlMXfvmo4/g\npz+FCROgVav0tClXaGjPzdVXm7A1cqRFVKotDa1fnLj8FpgvIi+LyPPBa1ImbuxCluM49U20kLVm\nTeLzwISwEA/inRqparJeUtXDgJdTrNcNC4Ubsgo4NolzumEriX8EfoY5Iecd69fDHXdY3qTzzzfB\naejQ6lcOSkst1Objj8MvfmGrBz/7GfTsmbFmOzXw1Vf2vdx2mwnOTu7z61+b5viccywoRkMTjJ20\n8ShwF7CQiE9WRqYX0ZMfx3Gc+qBpUzj1VAvjPnMmHH+8JSiuqLAAGWDWVrt2WUqUgw+Gjz82gWzv\nvaFNm+y2P19IWshSVRWRd0RksKq+neJ9kh2cYtfwRERGAetVdb6IFFVXeezYsfQMpJDCwkIGDBiw\n238iXH3OZLmiAubNK+J3v4OiomIefhjOOy+5+rNnF9O2Lfz730WsWwfXXVfMEUfAt75VxO23w6JF\nybenqKgoK5+/IZenTy/m7rvh4IOL+MEPst+e+iqH5Ep70lH+29/g9NOLGTYMZswoolmz1Or776nq\n81FcXExJSQkNjO1JWmmknQEDsnFXx3EaG506RdxV3nwzsr9JExO4PvvMyi1awKBBJmTNmGH73Gww\nOVJKRiwiHwK9gE+BHcHuahMzBvWGAONUdURQvgmoVNW7o865HyhW1YlBeSmWwPFa4NtYEsiWmDbr\naVW9JOYeOZWMePly+Pa3LV/P/feb+V9d2bTJtCb/+Q/ccouZPzWtTaYzp87ccw/84x/m37NX0uFf\nnFyhrMy0yraQAQUF2W5Rw6ABJSP+A1AKTAreAVDVd+v5vjk1jjmO0zhYtw7WroXCQouWvGiR7Y8W\npl58MeKfdfHFEdPmjRutXkMZR9M5jiUlZInI/qr6mYj0xLRSVW5eU94QEWkKfAgMAz4H3gbGxAl8\ncbWqjgyEsntUdUjMdU4GfqqqZ8e5R84MTv/6F1x3Hfzyl3DNNdWbBdaGxYvtulu2wEMP1bzyWVxc\nvHsF2qlKbfrmlVdMgJ4zxxLdNlQa+nOza5eZDXbpYqH3kx0gGnq/1IUGJGQVE8cCo74jDObSOOY4\nTuMl9MOKnr9u2wYvvGDb554LrVtbftDNmy2Kb7dumW9nfZDOcSxZPchzwEBVLRGRp1X1G6ncRFXL\nReRqYCpQADykqktE5Mrg+HhVfUlERorIckxLdlmiy6Vy70xSXm6JT59/HoqL4bDD6uc+/fvbRP8f\n/4Dhw+F734Nf/cpUuk798s478M1vwlNPNWwBqzHQqhU895yFsv3e9+DBB9O/IOLkJ6palO02OI7j\nZIt4Y2Hr1pHtsjJ737zZ3isq6r9N+Uiymqz5qjowdjuXyPYK4JYtcNFFtj1xIrRrl5n7rlkD3/++\n5f959FG3569Pli6FU06xwCXnn5/t1jjpYvt2izjYqxf8/e8Nx+QhGzQUTRZA4A/cHzNTB0BVb0+i\n3sPAWZgv8eHBvnZYdN4DgBLgIlXdEqeua7Icx8lZdu2C116D446Dffe1+W7nzubD1bt3tluXHtI5\njvm6bRpYvRpOPBH69rWIZZkSsMBMnZ591sKIDx9uUQzDFQYnfZSUWP/efbcLWA2NNm1g8mT49FO4\n5BLTSDuNmyAf5EWYT7AE28nqrh8BRsTsuxGYpqp9gFeDsuM4Tl7RqpVFH6ysNPPBVq0sp2tpac11\nGyPJCllHiMg2EdkGHB5uB6+t9dnAXGfJEjjhBPPR+dOfshOIQsQmh+++a4EYjjsu4rQIntenOpLp\nm4ULTYi+4Qbr58ZCY3pu9trLbM03bYLRo6sfMBpTvzRijg+CK21S1duAIcAhyVRU1deBzTG7d+eB\nDN7PS1dDHcdxMklBAXz+uQXCaN7cXFW++irbrcpNkhKyVLVAVdsGr6ZR221VNS9zV6WDt98287E7\n7jBfrGwnkeze3Vbkr7zSEhn/+tfw9dfZbVO+88YbMGyYabCuuirbrXHqk1atTCsM5qdVl4TFTt6z\nK3jfKSLdsOi2netwvU6qui7YXgd0qkvjHMdxssWGDZGF/BNPtLxZq1fnp1/W8uVmAllfpBTCPZfJ\ntC17cbH5YD38MIwalbHbJs1nn8EPf2hmbg88YInmnNT417/g+ustxPfw4dlujZMpKirst/POO2b+\n27FjtluUPzQUnywR+SXwF+BU4G9YwKW/q+ovk6zfE3g+yidrs6ruG3V8k6ruYVguInrrrbfuLod5\n2RzHcXKFCRPsvW9fGDjQhJRwgTKf8mdVVMATT8DHHxezY0cxX3xhAT8eeOC2zIZwzwcyKWS98AJ8\n5zvw+OOmycpVVC0K3o9/bO28807o0SPbrcp9du2yEPmzZln/1VeUSCd3UYVx4+Cxx8wk4pCkDMWc\nhiJkRSMiLYCWqvplCnV6UlXIWgoUqepaEekCTFfVvnHqeeALx3FympUrI75YIaHgFStkTZhggaX2\n2Sdz7UuW7dth+nTYf39LjRTyzW/mYeALERkhIktFZJmI3JDgnHuD4++JSBjNsIeITBeRRSKyUESu\nzVSb4zFxIlx+uQlauSxggZkvXnghPPhgMQcdZJEHb7wR1q/Pdstyh1j/mtmz4ZhjTNCaN69xC1iN\n2fdIxBJ/33KL5f947bXIscbcLw0dERkcCEFh+VLgSeCOIEJgbZkEXBpsXwo8W4drOY7jZI0ePaoK\nWIkIg0i99FL844sWZccsf/t2m+stXWqaqyOOsP2HHQbnpdlbNiNClogUYKYXI7CQuGNEpF/MOSOB\nXqraG7gCuC84VAZcp6qHYs7HV8XWzRR//7uZj02bBoMHZ6MFtaNVK7j9dliwwLJ19+0LV18NH32U\n7ZblDps2wRVXwAUXwC9+YSaCbdpku1VOtrnsMtNYjxkD999vGi6nQTMeKAUQkZOAu7BAFVuBB5K5\ngIhMAN4EDhGRlSJyWXCd00XkI8wE8a56aLvjOE5WOPNMCyAVzdY4YfHmzTPt0Zo18P77prCYOdPK\nb7xhATXqm08/tfstW2bh50VsjD/8cJsvp5OMmAuKyHHArao6IijfCKCqd0Wdcz9mQvF4UF4KnBzl\nLBye9yzwZ1V9NWZ/vZpZTJ1q+aimTbN8OvnM2rUWCfGRR6BnT4uYd9ZZjTO57qZN8Mc/Wu6rMWPg\nN7/JTbW2k12WLbPQ/YMHw1//mv4/4oZCvpsLish7qnpksP1XYIOqjos9Vo/3d3NBx3HykgkT4Mgj\noX9/K69ebdqqzZvh7LMtmXFoVhjNAQeYcFVWZhqy009PT3t27jTTxrZt7d6FhVaeNQsGDYI+feLX\nS+c4lqmA492AlVHlVcCxSZzTHYvEBOy2cx8IvFUfjayO006DOXOgUwOICdW5s/ln3XGHCY2PPWam\nUW3bmmnUoYdCv35w4IHQoYMlnGtoCVoXLICHHoL//Af+539g7lw46KBst8rJVXr3tt//5ZfD0KHw\n5JP+vDRQCkSkmaqWAadhVhUhWUjQ4TiOkx8cfrhpiEIhq7TUFq2bNzetVZcupjWKXkdq184Cs1VU\nWN1PPklfe158sWrey1atIpEEMzV+Z2rQSHZpLlZy3F1PRNoATwE/UtXt8SqPHTuWnj17AlBYWMiA\nAQN2R2YK/ShqW379dSt36pSe62WyHO1DEnv8zDOLOPNMmD69mBUr4Ouvi1iyBP7zn2LWr4cdO4rY\nuhUKCopp1gxatixCFUpLi6moAJEiysuhoqIYEWjatIgWLez8Vq2ge/ci9tsPKiuL6dABTjyxiAMP\nhA0biunUCYYNy0x/TJ9ezMcfw7p1RTz9NKxeXcyZZ5oGa/Ro66PPPsuN7ytXygsWLODHP/5xzrQn\nF8oTJhRx7bXFDBxoJre/+U1utS/T5XC7pKSEBsIEYIaIfAHsBF4HEJHewJZsNsxxHCeX6dHDTPGm\nTTN3i332gZYtTbD6+GNYscLOO/dceOst+OILW7QEW8gvKLAkx+lg9mwTsIYNs/fOnWHLFmvHgAGZ\ny2mbKXPBIcC4KHPBm4BKVb076pz7gWJVnRiUd5sLikgz4AVgsqrek+AebmaRgOLi4t2To9pQXm4r\nEmVl9mrSxF4FBfagFhREVicqK+3cXbvMuXDTJvshrV0Lq1aZqvaTTwgEHjNX7NPHorf16mUag4MP\nhm7dav8jULU8DkuWWILm2bPN1rdFC1NZn3OO5RErKKh73zRkvG/iU1xcTGFhEWPGmMnBn/9s2l4n\n/80FYbd5e2fgZVXdEezrA7RR1Xfr+d4+jjmOk5eEIdGjGTjQ8rUuWgQnnwwzZphrRvg3F51fdt06\n03h16mRBKS66aM97LFlimrFmzey6O3fatbp3h/32i5w3YYIJeGeeae+pkM5xLFNCVlPgQ2AY8Dnw\nNjBGVZdEnTMSuFpVRwZC2T2qOkREBHM83qiq11VzDx+c8oyvvjJh68MPLQjHsmX2WrHCfmxdukDX\nrvaD69jRzBnbtImsjKjaNbZtMwfLtWvNrvfTT+1Yv35mH3z88XDccaYeznbCaKfhsGOHRet85hnT\niJ57brZblH0agpCVTXwccxwnn5k3zxbLw4iCJ51kWqStW20xUjXxPGzDBnjllUg5DAcfJjneudMC\nZRx8sC34N21q88GtW+26J50Uqfv44xZdu0ktwvvlnZAFICJnAvcABcBDqnqniFwJoKrjg3PCCIQ7\ngMtU9V0RGQrMBN4nYj54k6pOibm+D04NiK+/Ns3XmjUmcG3YYMLU9u0mWIW0bGnCV9u2EaGse3cT\nylygcjLBzJnw3e/ait3vf9+4c9G5kFU3fBxzHKch8N57Nhbuu2/yc7GNG+HllyPlE0+0oBkLF0b2\nHXCALZxHs3mzmSgWFZnwtffeJozVNjFyXgpZ9Y0PTolxs6/EeN8kxvsmPvH6ZdcuuOsuizx43XWW\n6iFVE4WGgAtZdcPHMcdxGiubNlkk7zZtbEEdzJKpTRs46qjELiSqFpjq00/ND2xL4D2bC0KWR0ty\nHMepI61aWYTOsWNNwOrTB371KytnysHWcRzHcfKVUON13HFm5teqVXLpUkSszuDB5mtfXh4/R1c2\ncE2W4zhOmpkzB26+2Uxe33zTcn80BlyTVTd8HHMcp7Hy1Vdm5veNb2TX3cPNBePgg5PjOLmEquVf\nG1hSiwoAACAASURBVDw42y3JHC5k1Q0fxxzHcbJLOsexWsTdcPKN6Jw2TlW8bxLjfROfZPtFpHEJ\nWI7jOI7jRMiYkCUiI0RkqYgsE5EbEpxzb3D8PREZmEpdJzELFizIdhNyFu+bxHjfxMf7xaktIlIi\nIu+LyHwReTvb7Wls+MJR/eN9XP94H+cPGRGyRKQACMOz9wfGiEi/mHNGAr1UtTdwBXBfsnWd6tkS\nhlpx9sD7JjHeN/HxfnHqgAJFqjpQVV3PmWF8clr/eB/XP97H+UOmNFmDgeWqWqKqZcBEIDZ15zlY\n0mFU9S2gUEQ6J1nXcRzHcfIB91lzHMdpBGRKyOoGrIwqrwr2JXNO1yTqOtVQUlKS7SbkLN43ifG+\niY/3i1MHFHhFROaJyPey3RjHcRyn/shIdEER+QYwQlW/F5T/FzhWVa+JOud54C5VfSMovwLcAPSs\nqW6w30MyOY7jZBmPLpgYEemiqmtEpAMwDbhGVV+POu7jmOM4TpbJt2TEq4EeUeUemEaqunO6B+c0\nS6KuD+yO4zhOTqOqa4L3DSLyX8wc/vWo4z6OOY7jNBAyZS44D+gtIj1FpDkwGpgUc84k4BIAERkC\nbFHVdUnWdRzHcZycRURai0jbYHsvYDjwQXZb5TiO49QXGdFkqWq5iFwNTAUKgIdUdYmIXBkcH6+q\nL4nISBFZDuwALquubiba7TiO4zhpohPwXxEBG3sfU9WXs9skx3Ecp77IiE+W4ziO4ziO4zhOYyFj\nyYjrC0/uGEFEHhaRdSLyQdS+diIyTUQ+EpGXRaQwm23MFgn6ZpyIrAqenfkiMiKbbcwWItJDRKaL\nyCIRWSgi1wb7G/2zU03fNPpnR0RaishbIrJARBaLyJ3B/kb/3KSKiIwQkaUiskxEbsh2e/KZeHOC\n6p5JEbkp6PelIjI8ey3PTVKdVyTqTxEZJCIfBMf+lOnPkcskOT85M+qY93EK1GaOk64+znshC0/u\nGM0jWNLmaG4EpqlqH+DVoNwYidc3CvwheHYGquqULLQrFygDrlPVQ4EhwFViCb/92UncN43+2VHV\nr4BTVHUAcARwiogMxZ+blBCRAuAv2P9Tf2BM8Iw5tSPenCDuMyki/TE/7/5Y//9NRBrCvCidJD2v\nSNCfYTCX+4DvqmpvzM++0S1MVUMy85PJ4H1cS1Ka46SzjxvKn4lHZAKCUMCbY3bvTvIcvJ+X0Ubl\nCAn6BvzZQVXXquqCYHs7sATLRdfon51q+gb82UFVdwabzTGf2c34c5Mqg4HlqlqiqmXARODcLLcp\n34n9bSZ6Js8FJqhqmaqWAMux78MJSHFeEa8/jxWRLkBbVQ2tjR7F/xd2k+L8xPs4RWoxx0lbHzcE\nIcuTO1ZPpyBKI8A6zPnaiXCNiLwnIg+5WROISE9gIPAW/uxUIapv5gS7Gv2zIyJNRGQB9nxMV9VF\n+HOTKt2AlVHlVUQEeSd14s0JEj2TXamaEsb7PjlS7c/Y/avxfk6GeGOM93EdSHKOk7Y+bghC1gmq\nOhA4E1MBnpjtBuUqalFOPNJJhPuAA4EBwBrg99ltTnYRkTbA08CPVHVb9LHG/uwEffMU1jfb8WcH\nAFWtDMwFuwMnicgpMccb9XOTJN4/6aXaOUESz6R/Hyngv/F6w8eYNJONOU7eC1nRyR2BMLmjE2Gd\niHQGCFSd67PcnpxBVddrAPAgjfjZEZFm2J/Pv1T12WC3PztU6Zt/h33jz05VVPVL4EVgEP7cpMpq\noEdUuQdVV0udFEgwJ0j0TMb2ffdgn1M9qfTnqmB/95j93s/VUM0Y431cC1Kc46Stj/NayBJP7pgM\nk4BLg+1LgWerObdREfyoQs6nkT47gUPnQ8BiVb0n6lCjf3YS9Y0/OyAi7UMTFhFpBZwOzMefm1SZ\nhzlQ9xSR5pjD9aQstykvqWZOkOiZnARcLCLNReRAoDfQqKMUJ0lK/amqa4GtInJs8J/6bfx/oVqq\nGWO8j1OkFnOctPVxXufJCj78f4NimNzxziw2KauIyATgZKA9Zl/6K+A54Algf6AEuEhVt2Srjdki\nTt/cChRhqngFVgBXRtnnNhqCiHAzgfeJqMtvwiYbjfrZSdA3NwNjaOTPjogcjjkLNwle/1LV/yci\n7Wjkz02qiIVnvgcLHvJQYx7H6kKiOUF1z6SI3Ax8ByjHzIimZrzhOUyq84pE/Skig4B/AK2Al1T1\n2ox+kBwm1fmJ93Fq1GaOk64+zmshy3Ecx3Ecx3EcJ9fIa3NBx3Ecx3Ecx3GcXMOFLMdxHMdxHMdx\nnDTiQpbjOI7jOI7jOE4acSHLcRzHcRzHcRwnjbiQ5TiOkweIyIUiskhEKkTkqATntBSRt0RkgYgs\nFpE7Y45fIyJLRGShiNwV7PuWiMyPelWIyBFJtuleEdlW85mO4ziO07homu0GOI7jOFURkSLgUlW9\nLGr3B1i+lPGJ6qnqVyJyiqruFJGmwCwRGaqqs0TkFOAc4AhVLRORDkGdx4DHgvseBvxXVd9Poo1H\nA4VEQuI6juM4jhPgmizHcZzcYw/BRVWXqupHNVZU3RlsNsfyLm0Kyj8A7lTVsuC8DXGqfxOYGBZE\nZLiIvCki74jIE0GCV0SkAPgd8HNAkv9YjuM4jtM4cCHLcRwn96i14CIiTURkAZbUcrqqLg4O9QZO\nEpE5IlIcaKJiuQiYEFynPXALMExVBwHvAD8JzrsaeE5V19a2nY7jOI7TkHFzQcdxnBxBROYALYA2\nQDsRmR8cukFVX07mGqpaCQwQkX2AqSJSpKrF2P/9vqo6RESOwTLdHxR172OBnVFC2RCgP/CmiIBp\nxt4Uka7ABUCRBAccx3Ecx6mKC1mO4zg5gqoOARCRk4GxMT5ZqV7rSxF5ETgaKAZWAc8Ex+aKSKWI\n7KeqG4MqFwP/ibnMNFX9ZvQOERkJ9AKWB7tai8hHqtqntm11HMdxnIaGmws6juPkHjVpiOIeF5H2\nIlIYbLcCTgdCbdizwKnBsT5A81DAEpEmwIVE+WMBc+D/s3fmYVJU18N+zzAzguyIgmyCLCKLAipg\n1DgoGkAiIajALy6onxtBokYjxqi4RI3GxKiJIXGPESQaFRVUUEbFBUTZFxGBsCOILMIAs5zvj1Nl\n9zQ93T0zvdTM3Pd5+um+VfdWna6Zrlvnno1TRKS916euiHRU1amqeqSqtlPVdpj1yylYDofD4XCE\n4ZQsh8PhCB5KRPILERkqIuswN743RWSat72FZ7ECaAG858VkzQZeV9V3vX1PAUeLyCIs7urisMP/\nGFirqmt+EEB1GzAKmCgiC4CPgWPKkNXhcDgcDkcYourmR4fD4XA4HA6Hw+FIFs6S5XA4HA6Hw+Fw\nOBxJxClZDofD4XA4HA6Hw5FEnJLlcDgcDofD4XA4HEnEKVkOh8PhcDgcDofDkUSckuVwOBwOh8Ph\ncDgcScQpWQ6Hw+FwOBwOh8ORRJyS5XA4HA6Hw+FwOBxJxClZDofD4XA4HA6Hw5FEnJLlcDgcDofD\n4XA4HEnEKVkOh8PhcDgcDofDkUSckuVwOBwOh8PhcDgcScQpWQ6Hw+FwOBwOh8ORRJyS5XA4HA6H\nw+FwOBxJxClZDkcGEZH2IvKtiPT02i1EZKuI/DjTsjkcDofDEQ83jzkc0XFKlsORQVT1a+Bm4HkR\nqQM8DTytqh9kVjKHwxEkRORuEVkgIvNF5F0RaR2lT2sRmSkiS0RksYiMjTdeRM4SkbkistB775fO\n7+Wo+rh5zOGIjqhqpmVwOGo8IvIacDRQDJykqoUZFsnhcGQIEckDLlHVS8O21VfV3d7na4HjVfX/\nRYxrDjRX1fkiUg/4HPiZqi4ra7yI9AA2q+pmEekKvK2qrdLyRR3VCjePORylcZYshyMYPAF0BR51\nE5PDUeM5aPXTV5A86gHbovTZrKrzvc/fA8uAFrHGq+p8Vd3sbV8K1BGRnGR8CUeNw81jDkcY2ZkW\nwOGo6Xgrzg9jE9SdIvJfVf0uw2I5HI7MIVE3ivweuAjYC/SNeQCRtkBPYHY5xg8DPncPyI7y4uYx\nh+NgnLugw5FhRORJ4FBVHSkiE4BGqjo803I5HI70IiKfAodglqYmwFpv129UdXpYv3HAMeHuhBHH\nqQfkA/eo6qtR9h803nMVfA04S1VXJ+cbOWoKbh5zOA7GKVkORwYRkSHAY0B3Vd0hInWB+cDtqjox\ns9I5HI5MICKnA6NiKFFtgKmq2i3KvhzgDWCaqj6cyHgRaQW8653zkyR9DUcNwc1jDkd0UhaTJSJP\nicgWEVkUtq2JiEwXkRUi8o6INArbd4uIfCUiy0Xk7LDtJ4jIIm/fX1Ilr8ORCVT1NVVtrao7vPYe\nVe3oJiaHI7WIyABvvvlKRG4uo88j3v4FfnrqWGNF5EERWeb1/6+INPS2lzeD30HugiLSMaw5BJgX\npY8ATwJLIxWsssZ78/CbwM1OwXJUBDePORzRSWXii6eBARHbxgHTVbUTtmo2DkBEugDDgS7emL95\nkwXA48DlqtoR6Cgikcd0OBwOhyNhRKQWtvI+AJt3RorIsRF9BgEdvLnnSmwuijf2HaCrqh4PrABu\n8bZvBQar6nHAJcC/4oioHJz84j5vwXE+kAf82pOnhYi86fU5BbgQ6Cci87zXgFjjgTFAe+COsDFN\n48jncDgcjjik1F3QC7x9XVW7e+3lwOmqusVLNZuvqp1F5BagRFX/4PV7CxgP/A94T1WP9baPAPJU\n9eqUCe1wOByOao2InAzcoaoDvPY4AFW9P6zP34GZqvqi116OKSft4o31tg8FhqnqhRHbBcvs19wl\nmHA4HI7qS7pTuDdT1S3e5y1AM+9zC2B9WL/1QMso2zd42x0Oh8PhqCgtgXVhbX/OSaRPiwTGAlwG\nTI2y3WXwczgcjhpAxlK4q6qKSNLMaMk8lsPhcDgqhqpGTT8eMBKdLyr0XUTkVuCAqr4Qsb0rcD9w\nVhnj3DzmcDgcGSZZ81i6LVm+myAiciTwjbd9A9A6rF8rbHVwg/c5fPuGsg6uqoF8XXLJJRmXoarK\n52SrnvIFWbagyxdk2aoQkXNOa0p7TUTrEz4vlTlWREYBg4BfhB/My+D3X+AijZEiPdN/w+r8uuOO\nOzIuQ3V/uWvsrnFVfyWTdCtZU7CgX7z3V8O2jxCRXBFpB3QE5qhVod8lIn08P/aLwsZUGdq2bZtp\nEWISZPmcbBUnyPIFWTYItnxBlq0KMRdLpNRWRHKxxEtTIvpMAS4GEJG+wA41d/cyx3pJJm4Chqjq\nPv9ALoOfw+Fw1DxS5i4oIhOB04GmIrIOuB1zk5gsIpcDa4ALAFR1qYhMBpYCRcBoDamTo4FngDpY\nXY+3UiWzw+FwOKo/qlokImOAt4FawJOqukxErvL2T1DVqSIySERWAnuAS2ON9Q79KJALTPcS5H6i\nqqMpncHvDq/vWaq6LS1f2OFwOBxpJ2VKlqqOLGNX/zL63wvcG2X750D3JIqWdho1ahS/UwYJsnxO\nttjs2wcLF0LLlvYKJwjylUWQZYNgyxdk2aoSqjoNmBaxbUJEe0yiY73tHaN0R1XvAe6psLCOpJCX\nl5dpEao97hqnHneNqw4ZS3xRk+jRo0emRYhJkOVzsh3Mtm3wxz/C+++bgtWhA2zYAI0bQ14eXHYZ\nnHyyu3aVIcjyBVk2hyPIuIfT1OOucepx17jqkNI6WelERLS6fBeHIxqq8Nxz8JvfwPnnw3nnwUkn\nQd26UFICS5bAu+/C/ffDdddZv6x0R106ajQiglaN7IJ+/NTDmMvfE+rVaYzo8wgwENgLjFLVebHG\nisiDwGDgAPA1cKmq7vT23YKldS8GxqrqO1HO5+Yxh8PhyCDJnMeckuUIJKtXwxdfwNdfw8qVsHEj\nFBZCcbEpG82bQ+vW0KYN9OwJJ5wAubmZljp1bNgAF10EO3fCP/5h37cs1q2DESOgQQP417+gadP0\nyemo2VQVJUtEagFfYu7rG4DPgJFhsVWIyCBgjKoOEpE+wF9UtW+ssSJyFvCuqpaIyP0AqjpORLoA\nLwAnYTW1ZgCdVLUkQi43jyXAxIlw+unQokWmJXE4HNWNZM5jbp07DeTn52dahJgEQb7CQnjtNbjy\nSjj6aHN3e+45mDs3nx49bPv118O4cfYaMMCUiAULYPRoOOww6N8fHnoItmyJf75kkK7r9uWXcMop\ncMYZMHt2bAULTPnMz4eGDfP50Y9g+/a0iFkugvA/F4sgyxdk2aoQvYGVqrpGrSjwJGBIRJ9zgWcB\nVHU20MgrQVLmWFWdHqY4zSZUgmQIMFFVC1V1DbDSO44jgnnzYM8e+P772P327k2PPA6Hw1FRXEyW\nI6OsXAlPPAHPPmuxReedB2PHQteuIGLKQiLuxzt2wKxZ8PLL0LkznHYaXHEFnHNO1XaZ++wzOPdc\nuPdeuPTSxMfl5MDVV5viOmIETJ0K2e7X7nD4tATWhbXXA30S6NMSaJHAWDDXwIne5xbAp1GO5Yhg\n+XLzYCgshJFe+qwDBw72VKjOngsOh6N64B670kDQgxQzId/q1XDHHTBtGlxyCcycacpRRWVr1AgG\nD7bXo4/CSy/Z8W+7DcaPhyFDTGlLJqm+bjNnwgUXwJNPmqJVXvLy8jj1VBg40Kx/f/xj8mWsKO43\nUXGCLFsVIlGfvArdNUTkVuCAqr5QXhnGjx//w+e8vLwa+fcuLLR3VXMRf/nlkMJVVGTvtWplRjaH\nw1G9yM/PT5mHiFOyHGnl22/h9tth0iS49lpbsWzQILnnqFcPRo0y5W3KFFO27r4bHnnE3O6qAp9+\nagrW5MnQr1/Fj5OdDS++CL17w3HHwcUXJ09Gh6MKswFoHdZujVmXYvVp5fXJiTVWREYBg4Az4xxr\nQzTBwpWsms6SJRZ3C6ZwiZjXgt8OGgcOwFdfQZculgV22TKbc5xC6HAEl8jFrDvvvDNpx67CjlRV\nh6DHUKRLvpdegm7dzH3vyy/NwhRPwaqMbCJmwfriC7jpJlNarrjCFL1kkKrrtnixyf3MM5VTsHz5\nmjQxt8EbbrAHgCDgfhMVJ8iyVSHmAh1FpK2I5ALDgSkRfaYAFwOISF9gh6puiTXWyzp4EzBEVfdF\nHGuEiOSKSDugIzAndV+valFSElKa6tYNba9Vy+KzILS/oKB0OygsXWoWt4ULba6bMcMSFq1Zk2nJ\nHA5HpnBKliPlfPONxVr97nc2CT36aHoz3mVlWVzS0qVQp47Fe02enL7zl4dVqyypx8MPWzxZsuja\nFW65BX75y+A9nDgc6UZVi4AxwNvAUuBFLzvgVSJylddnKrBKRFYCE4DRscZ6h34UqAdMF5F5IvI3\nb8xSYLLXfxow2qURDPHii+bdAOYe2KGDxZV+8w1s3Wrb/avluxKWlJjbeVmohvomm2h/uYULoWNH\n86QoKgrFwM6ZE7K+ORyOmkVGUrh79UIuBEqARcClQF3gReAoYA1wgaruCOvv6otUQT7+GIYPN3/6\nu+6C2rUzLZFl6LvoIujb1xS+hg0zLZGxaROceirceCNcc03yj19YCL16mbI7fHjyj+9wZCKFu4gc\nCrRW1S/Ted5UUFPnsYleepDhw03hGjbMFKhFi8wj4cABW6jLyYEVK+Dzz+Goo+B//7O+fhKM/ftN\n+dq2zRIhidgCWzIpKTEZO3UKZXotKIBXXzVviVq1TL4VK0Jjunc3Lw6HwxF8qnQKdxFpC1wB9FLV\n7lgxxxHAOGC6qnYC3vXaePVFhgNdgAHA30TEWeACjqrFQA0dCn//OzzwQDAULIA+fSxN8KGHQo8e\nNhlnmh07zIJ16aWpUbDAHlD+/ndzG9y1KzXncDjSiYicC8zDrEqISE8RiXT7cwScnBx737TJ3rOy\nzM25sNAULDjYkuW7Dfr3sq+/hv/+15SdlSutFIiqbU8mvjzhStT+/bZY58dede4MRx4ZcocvLIS1\na+GDD5Iri8PhCDaZUFZ2AYXAoSKSDRwKbCSsJon3/jPvc5WvLxL0GIpky7d/vyVYePpp+OSTyrm9\npera1a1rCscjj9gK6b332gplJmQrKLDsgaefDrfempRDAtHlO+UUU+Zuvz1556kINe03kUyCLFsG\nGI+lT/8OQFXnAUcnMlBEBojIchH5SkRuLqPPI97+BSLSM95YETlfRJaISLGI9ArbXltEJorIQhFZ\nKiLjKvZ1qye+a52vhGRlhRSW7t3NUqVqCpVf98/PMjhzpr1/+60VqT/vPItl7eMl1d+/394LC+HD\nD0NKUkUJd0H87DOLuZo2DfaFReDVrWulRwYOhJNOsjGbNlmM1v/+V7nzOxyOqkPalSxV3Q48BKzF\nlKsdqjodaOYFFQNsAZp5n1tQOuuTqy8SYLZvh7PPNsXh449tNTHI/PSnMHeu1ZEaONBiANJJYaG5\ns7RubXFYyU4zH40//MHccxYsSP25HI4UU+i7lYcRd7lERGoBj2HeEV2AkSJybESfQUAHVe0IXAk8\nnsDYRcBQINJmMQJAVY8DTgCuEpE2iX7J6kxJScgqBdC2rSlZdeqYctWtmylcJSWmzKxfb+3CQovt\nrV/fChd//bVlUfWtYmAWpaVLbd/y5Ta2sokowpWslSttIRHg+OMP7puVZd+hoMDibXNzbV7ctMlc\nGh0OR/Um7SncRaQ9cB3QFtgJ/EdELgzvo6oqIrEc06PuGzVqFG3btgWgUaNG9OjR44e0jP7qbyba\neXl5GT1/uuTbuBHuuiuPwYNh0KB8Zs8OxveL127VCu68M5+nnoJevfJ4/nmAxMb7VOT8hYXw17/m\noQqjRuXzwQfJ/37R5GvaFM4/P59rroGPP07u+RJt+9uC8PevavIl6/eajLb/eU3mUqgtEZFfANki\n0hEYC3ycwLjewErPOwIRmYR5TSwL6/ODd4WqzhaRRiLSHGhX1lhVXe5tizzfJqCup6DVBQ5gXh01\nnsJCOOSQkMWpuNje69SxeCswZUU1pGzVrm2Wo8aNzQ3v9dehXbvSmQnB3PUKCy35BIRcxY86ys5Z\nUXmj0apV9O05ObBxo8narx+8+Sbk55vC5X8/h8NRPUl74gsRGQ6cpar/z2tfBPQFzgD6qepmETkS\nmKmqnX23ClW93+v/FnCHqs6OOG6NDBgOCvPmmVvgrbdaBruqyttvW42tq66yQsapqm+yf7+5tfh1\nrHJzU3Oesti3zwK3J0+2BCAORzJId+ILEakL3Aqc7W16G7g7In16tHHnAT9R1Su89oVAH1W9NqzP\n68B9qvqx154B3IwtEA6IM3Ym8GtV/SJs2/OenIcC16nqE1HkqnHz2O7dpnR8/721f/ITi8cK5/XX\nTUHJz7f+DRvCzp1mqWrd2vpnxfDL8RNrjBxp7oXHHAMtWlRM3nXrzOVv3brQtj59yvba2LYNpk+3\n+fHQQ+GVV0Kujn6BZYB33zXLXDSLmMPhSB9VOvEFsBzoKyJ1xJb7+mNpbV8HLvH6XAK86n2u8vVF\nIq0KQaOy8n3wgU2Mjz6afAUr3dfuJz+xuloffghnnhnbf76ishUUwM9/biupkyenTsGKJV/t2qZE\n/u53qTl3PKr7byKVBFm2dKOqe1T1t6p6ove6NZ6C5Q9N8BRJmWg9RawOcCRmCbvRm89qPMXFoZis\ntm0PVrDAFLBPPzUFy+8HIZfBWAoWQP/+oSyDjRuXnVJ9zx5TyPwEHNEoLDTrlL8A17WrWcbKwteZ\nGzSwMb6lDuxcBQVmnfvmG9iyJfoxHA5H1STt7oKqukBEnsMKOpYAXwD/AOoDk0XkcrwU7l7/pSLi\n1xcpwtUXCRSvvw6XXw4vvGATWXXgyCPhnXfgj3+EE0+Ee+6BK69MTrzUqlXmItK9Ozz5ZOn4gXQz\napTFZ82cWbmixw5HpvAsRpGoqp4RZ+gGoHVYuzWlY3+j9Wnl9clJYGwkPwJeUdViYKuIfAScCBxU\n6Wn8+PE/fPbdQ6szJSUhJSnW/dCvl+Wncm/TJvGMtYcfHvrcsGFIiVq/3ixa/vmneHkply+3eSAa\nvpLVuLFZqY47Lva5mza1ZENgc0itWiFLFlg2RF++aAqmw+FILfn5+SlbvMxInaxUUBPdLDLN889b\nTacpUyzguDqyZIkpI40awd/+ZsUmK8rUqZai/Xe/gzFj0pPkIh7PPw+PPx6qKeNwVIYMuAueGNas\nDQwDilT1pjjjsoEvgTOxBExzgJFhRYX9xBdjVHWQiPQFHlbVvgmOnQncqKqfe+2xQA9VvcxzcZwD\nDFfVxRFy1bh5bNs2mD/fXO7q1AlZtcLZsME8Jnr1Mle/yrB5syXDOOMMsyTVqQODBpmi9eabcNhh\n5mVw0kmlx+3fby6KW7bYvXLzZlP8wl3+EsF3XYxG+/bVdy51OKoKaXUXFJHuyTiRo3rxl7/Ab39r\nVpDqPCl07WrZo84+G04+Ga6+2oKYy8OmTaZUXXml1XG59trgKDQjR5rrzLRpmZbE4Sg/qjo37DVL\nVa8H8hIYVwSMwWK4lgIvquoyEblKRK7y+kwFVonISmACMDrWWAARGSoi67A44zdFxP9lTQByRWQR\npmA9Falg1VSKi03BqV8/uoIFoYQW7ZLgYJmba4qSfx8vKIAZM8x7QcQsT34SjnCWL7e4qWXL7Bh1\n61YsecaZZ5oFLvK7Hn20KXGLF4dcDB0OR9UmkZisx0XkMxEZLSINUy5RNSRRM+SOHeY6MGaMpRa/\n5BK4/np46CFLFZtp+cBu/rfdZladDz+EY4+NP6YyBCH+JDsbbrrJik82bGiufldfDX/6U34pt49I\ntmyxcV272qQ8b57VqUoXiVy7WrXgjjvg7rvTO7EH4e8aiyDLF2TZ0o2INAl7NRWRAUCDRMaq6jRV\nPUZVO6jqfd62Cao6IazPGG//8eFJLKKN9ba/oqqtVbWOqjZX1YHe9v2qeqGqdlfVrqr6UNIuQhUn\n3F2wLPy41WTEr9avb+/vv2/vTZuacrNzp8Vk1a1rtbT8+C8f/15fXGxKUp8+MGRI+c9/xBEwl1pb\njgAAIABJREFUdCgMHhxyDxw50hJe7NwJixbBd99V7Ls5HI5gEVfJUtVTgV8AbYAvvIKKZ8cZ5igH\na9aYUtW6tbluHXWUxTn162fbVq6EU081V4kHHggVY0w3hYVwxRXm9jZrVuxg3+pIkyYWw7Roka2o\nTphg/vwjR5oyfM89phBfcom5tHToYJP2okXwpz+VjgsIEsOG2aTunt0dVZAvgM+91yfAr4HLMyqR\nI2H27rWED/GUrEMPLb9bXlnk5Jhbnk94vaq6dS1BxZYt8MYbVkPRT9kenrrdj+OqTPbZOnXMO8IP\nuatdO3SOt98unSDD4XBUTRKOyfL80H8GPILVt8oCfquqL6dOvMSpir7sxcWWke+eeyy26brryg7k\nLS42n/TnnrNkE2PG2IN9wzTZFnfssIDj2rVh0iSoVy895w06a9bY32XbNlN+d++24pl9+pgFK1Up\n4JPNM89YfNaMGZmWxFGVSXdMVnWjKs5jlWHuXPjqK1NszjknfeedP9/c/sA8FXwrVdu2VtJi0qRQ\n386dzco0a5bFhkHyFL5IwuO1atWCCy5IzXkcDkfZJHMei5tdUESOB0YBg4HpwGBV/UJEWgCfAoFQ\nsqoaO3aYu0B2tsX8xEuoUKuWWbb69bPq9XffbZaSceMsxieVdZZWr7YJ8KyzzCJTVRSHdNC2bSid\ncFXmF78wt8HZs01BdDiCjIgMI0YadlX9bwLHGAA8DNQCnlDVP0Tp8wgwENgLjFLVebHGisj5wHig\nM3BSRJ2s47DYrPpYZt2TVDVK9E/N4cABey8rFitV+DGxffrYPc8nK8v2jRxpyTG++85isXbtsjTy\np5xiSZBSxRFHmBfERx/Zwury5ZZFsVatihdPdjgcmSORmKxHgHnA8ao62p80VHUjkKEqO1WLyBiK\nnTutHtOJJ8J775U/Y1379mZ5+PBDG9+tm2VFqugCaKwYj5desolo9GhLdpFuBSvI8SdBlg3KJ19O\njsWP3Xtv6uQJpzpdu3QTZNnSyE/jvGIiIrWAx4ABQBdgpIgcG9FnENBBVTsCVwKPJzB2ETAU+CDi\nWNnAv4ArVbUbcDpQSA1n7157b9Mmvec9+mizWLVrZzG2LVva9vD5rUsXU6q6dbMkGbt2medIg4Qi\n/irGmWdCq1YhC9a8efDaa+Yt4XA4qh6JrB+dAxR49T38Caa2VwTyuZRKVw3ZvdvSxZ54Ivz5z5XL\nMte5sylXU6fCDTfAI49YzFCPHpWXc88ec1+cOdN806tzBkGHcfnl8PvfWwxZd5dT1BFgVHVUJQ/R\nG1ipqmsARGQSMARYFtbnXOBZ73yzRaSRiDTHiglHHauqy71tkec7G1ioqou849X41Abz5lkK9D59\n0q9k1a8fSoDRrZu5e2/YED02zO8HFkeVDiL/fVx8lsNRNUnEkjUDq1TvcyjmNuhIEL+Y5L595iLY\ntavFYiUrjfegQbBwIZx7rhU9vPBCc/Err3xg1rCXXjJFrbDQJsJMKlhBLsQZZNmg/PLVqWOK9X33\nxe9bWarbtUsnQZYtE4jIYBH5jYjc7r8SGNYSWBfWXu9tS6RPiwTGRtIRUBF5S0Q+F5GYdbxqAitW\n2HvTpul3F4zEV66iKVnhBYJT6ZYfyeDBoc+JFl12OBzBIpFbW21V/d5vqOpuETk0hTJVW2680TLM\n/f3v8bMplZfcXPjlL+Hiiy1u6oQTLGvRNdeYC0K885WUwPTpcOutpmg99pi5NDpqFtdcY640X39d\nOgOXwxFERGQCtgh4BvBP4HxgdsxBRqLO1clK4pEDnAqcCBQA74rI56r6XmTH8ePH//A5Ly+v2irV\n2dkWk5XsubAi+BaqaMpegwYWC51OBQtKW9BcHLTDkTry8/NT5oafiJK1R0ROCKtc708SjgTJz89n\nx448pk41y1AqJ5X69S2BwQ03wL//bYrdnj3Qv79VsO/d2wJ39+2z1/Ll8OST+SxYkMfhh8Ptt1tK\n76AUy83Pzw/sQ0aQZYOKydegAVx1FTz4oC0GpIrqeO3SRZBlywA/UtXuIrJQVe8UkYeAtxIYtwFo\nHdZujVmkYvVp5fXJSWBsJOuAD1R1O4CITAV6ATGVrOqMPw8GQYHwlayy5r3mzdMnSzQKa3z0nsOR\nOiIXs+68886kHTuRx/3rgMkiMktEZgEvAtdW5qSeb/tLIrJMRJaKSB+vmOR0EVkhIu+ISKOw/reI\nyFcisrwq1ujassUeXF94IX0p1+vXt4K58+fDiy+ai2J+vqVhP/lkGDgQ/u//4OmnLZvRrFnmcnje\necFRsByZ4Ve/gsmTYdOmTEvicMTFX/DbKyItgSIgkUfiuUBHEWkrIrnAcGBKRJ8pwMUAItIX2KGq\nWxIcC6WtYG8D3UWkjpcE43RgSULfsJriW41ycjIrh8/hh5d2DQwCxx4Lxx0XjJisJUtCqe4dDkdi\nJFQny5tIjsFcLL5U1Uqtq4jIs8D7qvqUN+HUBW4FtqnqAyJyM9BYVceJSBfgBeAkzO99BtBJVUsi\njhnI+iJFReZqcM45lm7d4agKjB1rKYMffDDTkjiqEumuk+XFXz2KuQv+1dv8T1W9LYGxAwmlYX9S\nVe8TkasAVHWC18fPIrgHuNTPrhttrLd9KJaRtylWT3Keqg709v0CuAWbR99U1YNmhKDOY6lg+nRz\nTXZuybH5/nvLInzuuZmVY+JEK+PStGlm5UgWhYUWJuFS4zsiSeY8lqiS9SMso1I2ni97RTMLikhD\nbOI5OmL7cuB0Vd3iZXDKV9XOInILUBJWh+QtYLyqfhoxPpCT0x/+YJPJO+8Ew/fc4UiEtWst+cnX\nX0PjxpmWxlFVyGQxYhGpjcUQ78jE+ZNBUOexVPD22+bCHjTrUdBQNc+CCy6wbIxffQX798NRR6VP\nQS0stIRYAwZUn/ngtdfMs8h5XjsiSeY8FvexX0SeB/4InIIF7Z7kvSpKO2CriDwtIl+IyD9FpC7Q\nzHPFANgCNPM+t6C0v3simZwCwdq1Zgm47LL8QCtYQa6742SrOJWRr00bWzl97LHkyRNOdb52qSbI\nsqUbEVkoIr8Vkfaquq8qK1g1ia1bYfv2YMRjBR0Rc60sKIB337Xnii1bLOV8uvDrmZWUxO6XaQoK\nYMGC+P127rTvtGmTlSxxOFJFIokvTgC6JHF5LRsL+B2jqp+JyMNAKbcJVVURiXW+qPtGjRpF27Zt\nAWjUqBE9evT4IZjNfzBJZ/u222Ds2DxatMjM+atD2yco8oS358+fHyh5ki1fv35w0015XH89zJ2b\nXPnmz5+f9utRneQLStv/vGbNGjLEuVhM1GRvzpgETFbVtfEGisgAQi5/T/jeEhF9HgEGAnuBUao6\nL9ZYETkfGA90Bk7y3QvDjtcGWArcoaoPVegbVwO++cbec9Ocsa+qImKWl9q14cc/htmz7fN338Gh\nh6be5e3bb+09CLFhsVi/HpYuheOPj93vvbB0M4sXu7qQjtQR111QRP4D/EpVNyblhOYK+ImqtvPa\np2J+6kcD/VR1s4gcCcz03AXHAajq/V7/t7AJanbEcQPlZjFtGlx7rf2AXY0LR1XlggvMpeemGlrV\n58ABqzl34IC57ahaHEl4emVHiAy7C3YEbgN+oaoxbSQiUgv4EuiPZRH8DBipqsvC+gzCFgMHiUgf\n4C+q2jfWWBHpDJQAE4BfR1GyXgKKgTnRlKygzWOp4oMPzBIzfLhzo0+Et94yhWrgQMsOvGEDLFtm\nFsEjj0yty1txsbkKlpTAKadYtuJjj7U6Zzt32vyQ6HFiWS5LSux4RUWWhKQirFgBn38OI0ealapJ\nk+gK6GuvhaxzYAm/9u1z93WHkcx5LBFL1uHAUhGZA+z3tqmqVigM01Oi1olIJ1VdgU1US7zXJcAf\nvPdXvSFTgBdE5E+Ym2BHYE5Fzp0u9u0zBevRR52C5aja3H67pf8fPRrq1s20NKlnzx544w17qJk/\nH778Elq0sN9xVpY9CKxeDa1aWS26QYPg/PNd8HQmEZG2mDXrAkyB+U0Cw3oDK1V1jXeMScAQYFlY\nn3OBZwFUdbaXFbc55vIedayqLve2RZPzZ8AqLIlGjaSkBFatMuXgjDOcgpUo/fubUtCggbX9MiyQ\nmvTuW7ea9Wz+fNi2zRaXAD76yN49Qz+QmJK1ejV8+qkpP2Xx+eewcqV9zs21UjKRfPWVKZX16pXe\nvncv7NgRuiZg2ZTbtYO+fQ8+ziGHwGmnmeI3Y4aVslm8OLZ8DkdFSOQWNx74GfB74KGwV2W4Fvi3\niCwAjvOOfT9wloiswDJF3Q+gqkuByZiLxTRgdNCX+h54wNKuDhxo7UjXt6ARZPmcbBUnGfJ162bu\nKY8/Xnl5wgnStVO1xDQjRphC9cwz0KhRPhMm2APGypU2AS9caO87dtjK7llnwXPPWfzab39rrirp\nIEjXLtOIyGzgFWwuO19VeyfohtcSq13lEy3Wt6w+LRIYGylnPUz5G5+AbNWWuXPhs8/MMnzYYZmW\npuqQnR1SsMAWvAYPtkWeAweSd55lyyyL4IwZlrBr61a7P7Zubdaeirp3FiRQWTX8exw4ED1d/Ny5\nZq3ascMWxIqKzPV09mz44gtLMw8hxXP16pCCGE5RkZUOaNLErq3f58svy/e9HI54xLVkqWq+t1LY\nQVVniMihiYyLc8wFRE+e0b+M/vcC91bmnOli82b4y1/sZuBwVAduu80UimuuqV7WrOJiePlluP9+\nm5R/+UtL9NG0qa2C9u4dfVxOjvnwd+8Ol15qE/Pjj1s2xltusTpj2ZW6QzrKwSW+9aicJLpQlyzX\nx/HAn1V1r0Qzc4V3DCtGnBdRJLOqs20bHHGEPRi730jlyc2FXbtMSYj1X7VrlyWEyMoyl79Iiovt\nHhhuoerWzWpo5uSEjj1smFmcVqwI9VuxwhTmWEpzWX/rrVvNutmsmWVMBPMS2L7d7s1Dh4YUOz/R\nh4iFY/iIWJbFvn3hVc//6aWXQvt9hcrf3qVLyHUxKyukqIEpam3bOs+EmkZ+fn7KFi8Ticm6ErgC\naKKq7UWkE/C4qp6ZEokqSFB82UePth/on/+caUkcjuRx/vk2if3615mWpPKowptvwo032oPBLbfY\ninBlXZdWrrSi4zt3whNPmNJV08hkTFZ58IoLj1fVAV67VKkQb9vfsVIik7z2cqyIcLsExs4kLCZL\nRD4AWnu7G2FxW7ep6t8i5ArEPJYqpkyBM8+sXos1mWbixLLd68L7hDN8OPz3v6ZYjRwJr7xirnYN\nG5prYiyL1axZsM6z4x5ySEg5iuVqt3ixZfH7+c9tTEmJWateecX2d+hgyTVOOMEWuXbsMJdtsMWu\nxYtDMVRt24KfZ+ekk8wl0I/1mjjR4rm2bg2du3dvi6OdOzfkjgh2vXJzbUxOTsj61aZNdEU0U5SU\nOLfadJPumKxfYv7rnwKo6goROSIZJ69urFhh9SyWV2Rd1eEIMLffbtasq6+u2g9IS5bA9dfbQ8Kf\n/2x1X2LbFRKnQwdzs3n6aTj7bHjoIbjoouQc25F05gIdPS+NjVhMV+Rj4hRgDDDJU8p2eHUcv01g\nLIRZwVT1xz9sFLkD2B2pYNUEioqcBSvZdO5slp/y4CtYAO+/H4pl6to1vktgs2YhJctXsOKxx4tC\nXLDAvH38dk6OuSJ+841Z2+rWtftx48bmDvnGGzAnLAK/ZcuQggUWnxWeTGPkSEtvH549cM4cu+/v\n2WMK1/79Joc/7rjjQmnc69Qp/7VMNS++CH36mKLoqHokoh/vV9Uffkoi8kNBYkdpbr3VVvojK6IH\nPYYiyPI52SpOMuXr3h1OPx0efjg5x0v3tdu3z+Km+vWzyXvhQouZLEvBqqh8InDZZTBzpimmd9wR\nPSagMgT9/64qoKpFmAL1Nhbv+6KXHfAqEbnK6zMVWCUiK7FsgaNjjQUQkaEisg7oC7wpItNw/EC8\nDHOO8tO4saVxT5RevUony9gYljc6EYtJx44hS0+i5/XP9/XXFvfapYv9HxQWmmK3a5ftr1MnNKZ+\nfejUyT639CIew934hg+PvuDXrJnd47t3N6vcaaeZF8bPfmZuhV262IKh/39Yr57doxs1sv5BTFO/\np8amyqn6JLKm9L6I3AocKiJnYRPN66kVq+oxezZ88gk8+2ymJXE4UsO999qK2uWXQ/PmmZYmcT76\nyGTu1s2Uq3TI3rWrZdMaMsRcVJ56yvn5pwKvkP0NQBtVvcJL436Mqr4Rb6yqTsOSKYVvmxDRHpPo\nWG/7K1gijljnvTOebNUVp2QlH5HyLeSEZzw+9NCQG17nzqagJELr1rZg1by5xdnNnFl236Ki0tah\nnj3tf+D440vHkh1xxMGLXk2bmodQ+/YWk+Ura2eeGVshrF/f7vdlEb4Q7qdtb93a7tHREm5kmmR5\nWzjSTyKWrHHAVmARcBUwFfhdKoWqaqjCuHEwfnz0lZ2gBy4HWT4nW8VJtnzt28OoUWadqSzpuHb7\n98NvfmPxZPfcY0HPiSpYyZCvWTN7+Nizx9xYkjV5B/3/Ls08DRwAfuS1N2LZah0B48CB0kkUHMmh\nvErWgQOWMXbo0JDC2727KT+JZg8UCd1LfYWlrPvbunV2D+zTxzK4hivZ/v/CMcfY/BJJq1bmet2i\nhdUCa9jQlKsjkhiw4i9+rVtX/muZLlSDqfw54hNXyVLVYlX9h6qe573+Wa0jcyvAu++ayX3UqExL\n4nCklltvtQxOixdnWpLYLFsGJ59sq6ALF1r64UxQp4751BcUwCWXBNMVpYrT3ks4cQBAVZ1jTQAp\nKrJscX6WN0fyyMqKrRj4iRNGjjQ3uXbtzP2udm1zx+vQIbbVJxHCE2BEOz/YOctSsHv1soQWkdSq\nZcmJRCz+qls3uOCCyskaib8w3q5dqBZi0Ni5E/7zn0xL4agIcZUsEVkd5bUqHcJVBVThd7+DO+8s\nO6A36DEUQZbPyVZxUiFf48amaN14Y+WOk6prpwr/+Iet1F59tWWvioyRTIRkynfIIRZovnGjpcGv\n7BJV0P/v0sx+EfkhkkNE2gMJheOLyAARWS4iX4nIzWX0ecTbv0BEesYbKyLni8gSESkWkRPCtp8l\nInNFZKH33q9C37aKUFBQ+mF19257d7ElyUcktmIQnp2uadPSzymdOiVWTDgee/ZYoeBoFBaaK2Iy\n3KVFkm8JFTEFtHPn4Fmydu60d7/OWJBkcyRGIu6CJ4W9TgP+Avw7lUJVJd58024wyV5dcTiCytVX\nw6pVpWuVBIHdu+EXv7BaVx9+CFdeGRzXpDp1LH31woWmpDqSxnjgLaCViLwAvAdEVZjCEZFawGPA\nAKALMFJEjo3oMwirD9kRuBJ4PIGxi4ChwAeUThC1FRisqscBlwD/Kku2lSutiGpV5tVXzYK7d69Z\nb/103H36ZFau6kikYjBtWukMx1u3li8xRkWIrC31/feh+CnfTbQq4M8XQVFmpk61923b7D2Zhacd\n6SERd8FtYa/1qvowcE4aZAs8JSWWQeyuu2IHYQY9hiLI8jnZKk6q5MvNhUcesZpw/gp1eUm2bAsX\nwoknWqao2bNtVbIypOLa1a8Pr78OkybBv8p8xI5P0P/v0omqvgMMAy4FXgBOUNUYYfg/0BtYqapr\nVLUQmAQMiehzLvCsd57ZQCMRaR5rrKouV9UVEcdBVeer6mavuRSoIyJRHz0/+8yKolYHXnvNypqA\nuaW5NNTJJ1LJ2rED5s2zBeCdO+F//4vuipdM6tULuUKr2n3OV/R27YIGDVJ7/mQSz/0yk3z3XaYl\ncJSXuNkFPZcH/18uCzgRcPmBMFckEUsN6nDUJAYMsAxPN94IEybE759KnnoKbr7Z6l5deGFmZYnH\n4YebRatfP3voPPnkTEtUNYmYlwA2ee9tRKSNXwQ4Bi2BdWHt9UCknSVan5ZAiwTGxmIY8LmnoEWl\nqsfuHXaYJX5p29ascuvX2yKII/n4cUSRikG9eiFLSL8UO6dmZ4dqbfnWFt+StXt3KINfVcB3v8xk\nAeDVqy07bSSbN1etzL6OxFK4P0RoMisC1gCVdo7zXC7mAutV9aci0gR4ETjKP4eq7vD63gJcBhQD\nY73Vy4xSXGxWrD/+Mb5LUn5+fqBXn4Msn5Ot4qRavj/9ybJSvf02/OQn5RubDNn27oUxY2wy+uAD\nOPbY+GMSJZXXrls3K1g8bJjJ3qZNcGSrQoTPS9GI91iZ6Fp1Uh1ORaQrcD9wVll9XnppPABLl5rV\nsir+rYuKTMFq2BB69LCXIzX4lqxXX7WY2cMOM0t+mzaWMe/7721xJ5Xk5sL8+XDUUTYfgCnWxxxj\nlrV69VJ7/mQSaRksKDB3vdat0yfD1q2hzz//uV3DHTss4ZT7LSWf/Pz8lMU6x1WyVDUvJWeGX2Fu\nE/4axzhguqo+4AUSjwPGiUgXYDjm+94SmCEinVQ1ozlgJk2yCWTAgExK4XBkjgYN4Mkn4dJLYdEi\nK+aYLlassNTs3brBnDlVaxIHK5Z5ww2WRvmjj0rXrnHEJwnz0gYg/LGpNWaRitWnldcnJ4GxByEi\nrYD/AhepaplRV+edNx6wYPzKEF6DKJ1s325uau5/Oj2ImPVo3z677nv3hu6H6VIMjjrK7sO+ggUW\nqz5liil7VSUmC8wCt2wZHHectZcssaQew4YlnuK+svhFljt1sli3Zs3sd3XgQOZ+19WZyMWsO+9M\nXilDiZeNXUR+zcGrfv6fWFX1T+U+qU02z2D1TG7wLFnLgdNVdYvn956vqp09K1aJl6YXEXkLGK+q\nn0YcM22Z5QsLrWr4hAlwxhlpOaXDEVhGj7ZVtn//Oz03/4kTYexYuPtuuOqqqjvhqMLw4dCkCfz9\n75mWJjmICKqatr+Il1lwNHAqNk99CDyuqvvijMsGvgTOxGprzQFGquqysD6DgDGqOkhE+gIPq2rf\nBMfOBG5U1c+9diPgfeAOVX01hlz6wgs2j8VTsgoL7XcXzUqxd6/FQ512mtUaShabNtnDfLt2ZfdZ\nssRW/517YHrYsMEs+eEMGZL6ZBeRqJoSUKuWfd6+3ZSFqrYANnGiKVPDhln7/fctM+yPfmTKZDpY\nuNCuY9euoW3791uW2g4dzEJ44EDFMueGs3evKXGuQHhpkjmPJeJ1egJwDWZFagVcDfQC6hGyQpWX\nPwM3AeHWqGaqusX7vAXwa4+3oPQqoe8XnzGee85WiJyC5XDAgw9aRrTx41N7noICU6ruuAOmT7cs\nh1VVwQKT/Ykn4L334PnnMy1NleU5zMvhESzjX1diZO7zUdUiYAzwNuZR8aKqLhORq0TkKq/PVGCV\niKwEJmDKXJljAURkqIisA/oCb4qIn4NzDNAeuENE5nmvSj0irVsHM2bYA21xsVl3i4th7VrY4s2k\nfgroZDF3bvRYkXD27DG3NUd6iCzMe8wx6VewwO5nhxxi8Vk5OWZ9qWoKlk+LFqHP+/dbTFll62ft\n2hX6XcaioMBiryJT3h9yiLng7tplv8Hp063v+rCn48JCm4sT5bXXLEmKI3UkEpPVGuilqrsBROQO\nYKqq/qIiJxSRwcA3qjpPRPKi9VFVFZFYZqmo+0aNGkVbL41Oo0aN6NGjxw8mQN/fsrLtk0/O4667\n4MYb88nPT2x8uK9nsuVJRjvI8kXKmGl5wtvz58/nuuuuC4w8mZKvbl245ZZ8xoyBVq3yuOKK+OMf\nfvjhcv0+J0zI5/e/h1NPzWPuXPjii8R/fxVpl1e+yrRffhlOPTWfwkK49NL4/YP0e/U/r1mzhgzR\nVVW7hLXfE5GliQxU1WnAtIhtEyLaYxId621/BXglyvZ7gHsSkStRfMeNzZtNwdq4ET7/PLS/du1Q\n8oFkkRVnWba4GL7+uvIr7I7EyckxpWDjRmu7a185TjihdNbcAwdMWazsb2nWLFv0+PnPy64ZNnFi\n6HPfvgfvb9fO4rJ8+V71bOInnggdO5r18LPPoH37xBcgk70Q4yhNIu6CXwLH++4XIlIbWKCqx1To\nhCL3AhdhSTRqAw0wP/WTgDxV3SwiRwIzPXfBcQCqer83/i3M5WJ2xHHT4i74179aalQ/a08i5Ac8\nUD3I8jnZKk665fvqKysC/MQTcE6cIg+JylZUBPfdB48+amnjR4xIjqzxSPe1e+YZ+MMfzFLg++OX\nRZD/7zLgLvg88FdV/cRr9wV+qaoXpUuGZFIed8EVK0JK1SGHQM+eFie5erW9eva0lM/JKDbr89Zb\ndsyyZPvuO+vTq5dZVBzpwX8479o1FEvkqBirVlniCb+m26uvmlWuUaPKJVf66COzMmdlmaU3Nxd6\n9w5ZHf0YNoCf/jS6FfCbb+Ddd0Ptrl1Nyf76a4vz3bwZZs6E885LLA5u4kRzN+7fv+LfqzqSbnfB\n54A5IjJeRO4EZuPVDqkIqvpbVW2tqu2AEcB73oQ4BSvSiPfu+61PAUaISK6ItAM6Yj7waWfvXvj9\n7y0WpDwE9YHIJ8jyOdkqTrrl69jRJqRRo+DZOHeIRGSbNcseED/80OoGpUvBgvRfu1GjbFIfOzZ+\n36D/36WZE4GPROR/IrIG+Bg4UUQWicjCWANFZICILBeRr7xkS9H6POLtXyAiPeONFZHzRWSJiBSL\nSK+IY93i9V8uImdX5kuDLUB06WIPZOecY6vchx1mq9rnn28Pb7t2VfYspfFjN/bsib6/oMDesxPx\nkXEkjYEDTal2ClblqVUrVEJh61b7n871sifuixnpGZ9TToFBg8zStGmTueu9/bYpO6+/bn3atSvb\nzfKww6ywPdjv/rjj7Fj799u2oiJ799ux8G0SQa0JVl1IJLvg7z3r0aneplGqmkwvTv9PfD8wWUQu\nJyxNvKouFZHJmO97ETA6bRkuIvjrX82Ee8IJmTi7wxF8+vSxlbTzz7dg7EcfLX98wIYNVvfq/fct\n3mv48Kode5Uojz1m95aJEyufWa4GUaH8rl4JkceA/lgWwc9EZEqUxBcdVLWjiPQBHgeYftJpAAAg\nAElEQVT6xhm7CBiKxXCFny8pWXK/+87cgfbvt9TcPXqU/UDWqJH1SWYx2Hr1LJ31lCn2sNiwYen9\nRUXmruaKDqeXRo3Sm921OpOdbf/HM2aUTqUO8O230LICGQGKisyKddRRFt9Vv74pU1u2mMVp7VqL\nt9q5M3a6/Vq1TLnavz80r+bm2vGLikLKYUFB6ftCSYntC7du+S6HvtLmSA2Jlls7FNitqn8B1nsW\npUqjqu+r6rne5+2q2l9VO6nq2X6NLG/fvaraQVU7q+rbZR8xdXz3HTzwgFmyykt4/EIQCbJ8TraK\nkyn5unWzB8F9+2xRYtq0g4urRpPt88/NotO1q6X9XbbMrFeZULAyce3q1bPSEGPHmvtHWQT9/y6d\nqOoaYCfmdt7Ef6nqGm9fWfQGVnr9CoFJwJCIPufieW147umNvMy3ZY5V1eWquiLK+YYAE1W10JNr\npXechCgstNiQNWvsQa99e8sg17lz2WNq1zbvizffNDejZJAblsLaLzobTnGx/R/XhEURR/UkO9t+\nb76C1bJlyNrjW2oToajIxm3YYLFSUDpJSVYWHHmkLUr06mVZZmNZsXxq1Tp44bKkBP7zH/j4Y2vP\nmFF6/8KF8NJLIUsX2H0BnCUr1cS1ZInIeCzD4DHAU0Au8DxwSkolCxj33Wc1bZJZ8NThqK7Uq2cZ\n8yZOtGyAV18Nl18OZ55pk8n27ZYVae5cq6/y3nvmPjF6NDz0kLlF1ER69oTf/c4sWbNmpa8uS1VF\nRO4GRgGrKJ2tNl4x4pbAurD2eqBPAn1aYhlv442NpAUQnpevXFlyX3rJ3jt1Mmtnp07xx2RlWZzG\ne+/ZqnVkFrqKEJ5hrSwly7kKOqoytWqFFiV+8hOz9CxaZO1ElawvvoAvvwy1W7e2uK5U3c+HDYOX\nX46+b+dOW7AEi9Xs0KF0Eo/KZk10xCaR2+FQoCfwOYCqbhCRiqZur5KsW2dFV/0fWnkJegxFkOVz\nslWcTMsnAv/3f/aaPx/++U+46SZTsLZvz0PEHhh794Y77zQFLCgPaJm8dmPH2krkrbeau2Qkmf67\nBozhQHtVjfLIH5NE129TaZOJKsOUKeM5cMAe0vIiimRu3Fi6dk486tc3N6Q5c8xVqbK/r5ISq7u1\na1fZSparueOoyviZ/845J+Rme/jh5l2QiEV4+3b77darZ5bmHTssrXpF3AwTJTcX+vUzV/1IVntl\nz1u3tkXNAwdKu5Y6S5Z5h6TKQySRW+5+VS0Rz/4vInFyX1U//JX48NoJDocjcXr0sJhGR3xE4Omn\n7Zr172+rqY4yWQI0xmorlocNWHkSn9aUrscYrU8rr09OAmPjna+Vt+0ghg8fT0FBKMPYpk2hfd9/\nX34lpmVLWLrUxlY2bqekxB7Wtm+PrmTt3Wtuig5HVaV+fYsDDi9X0K6dufW9/37Z43btspint72A\nlsGD7V6+fbspWcmKiyyL5s3tvVkzi/V65RWTuUkT2962rRkMFi8ubb0qjyWrsDCxrIVVjcjFrDvv\nvDNpx04kJus/IjIB80e/EngXeCJpEgScxYvNd/U3v6n4MYIeQxFk+ZxsFSfI8gVZNsi8fE2bwr/+\nBZdeaml5w8m0bAHjXmCeiLwjIq97rykJjJsLdBSRtiKSi1nEIsdNAS6GH1LD71DVLQmOhdJWsISz\n5Ea6JFX2z920qb3C4zEqSkmJPXzm5kZXsnbtOjgZhsNR1YhWDy4r62Crz6ZN9lq+3J4T33nHtnfr\nFopLbNLE3L979EitzGDn6dfPZN23z1zyFy60LKStWtkCSbhSVb9+bEvWokWmIL77ri3U+G7LjsSJ\nackSM1+9CHQGdgOdgNtUdXoaZMs4qnDjjXDLLW7icDgc6aVfP4tju/hiqz0UrxBsDeU5LDPtYkIx\nWXEdYFS1SETGAG8DtYAnVXWZiFzl7Z+gqlNFZJCIrAT2AJfGGgsgIkOBR4CmwJsiMk9VB1YkS+6O\nHZZAJpJ4ddSikZOTnMLEvpKVlQULFkD37qX3791b/myiDkdVQORgq0/kAsjevfaeyXu1iP3ew9O4\n+3L36GFJ3OrVs/wC2dnmyh/Ovn0mf06OGRl8fFdJ5xJcPmIWI/aUrEWq2i19IlWMVBQjfu01U7AW\nLKieJlKHwxFsioogL89cT8aNy7Q08clAMeLPVDWJ5XYzi4jop58q335rmcdWr7a4xSOOsDo6xx9f\nsSK/H31kq9h+aueKxoe8/77Vw9uwwVa4Bw8OKX0rV9oD25AhobgWh6O6UFBgroA/+1lo28SJpqgM\nHGjPiN9/b8rN4Ydn9plx0qTSFqpjjzUFa98+cyMEc0XPyrKsvmd7Vft27LBswA0aWHKduXPhggss\n1svPtjh0aPV3CU7mPBbTkqWqKiKfi0hvVc1IAeBMUVAA118P//iHU7AcDkdmyM62ifzEE62Q5Wmn\nZVqiwPGhiNyHueP9sHarql9kTqTKcfTRsGqVZQXr0MESVoA97FSU3FzLMLZ9OzRunJiStXatKU6n\nnGIPjtnZIUtWt2627403op/L4ahuZGUdbMnKzjalw08oE5TFhZ/+1JSixo0tS207r+hSbq4pgDt2\nmCK1Z499p40bLefAtm32HXbtMgWrbl2zWh17bEjJKiys/kpWMknEqNkX+EREVonIIu+1MNWCZZoH\nH7TaBf37V/5YQY+hCLJ8TraKE2T5giwbBEu+1q0tEcbIkeayESTZAkAvbI66F3go7BUXERkgIstF\n5CsRubmMPo94+xeISM94Y0WkiYhMF5EVXpxYI297bRGZKCILRWSpiJRplwx3xUnWAl9OTqhWz3ff\nRY+n8ikuNvfUjz6yAPrp0y0m4623bFxWlqW1btPG+vuuUT17wo9/7GpkOaonIgfHL/mLDkGjbl1L\ndNGwoWVJ9MNdsrLsmfa880yZysqy+8H771udrc8+s4yIhx9uFvOTT7ZxLVua1bp584MLNDtiU6Yl\nS0TaqOpa4CeYj3uNuXWuWQN/+YvVOnA4HI5MM2gQXHSRvW6Oqg7UTFQ1ryLjRKQW8BjQH8vy95mI\nTPFjq7w+g4AOqtpRRPoAjwN944wdB0xX1Qc85Wuc9xrhyXuciNQBlorIC94cW4qsLLNczp2bPCWr\naVN7z8uzOJJ9+8q2OO3caQ9eYJkt/WxpYBYtfzW/Tx8LqG/c2JSxZs2SI6vDEUQiLVmqwVWyEsVf\nEPGtVcXF5ibYpcvBfevXt33Llpm13ZEYsdwFXwN6quoaEXlZVYelS6hMc8MNcN11ITeNyhL0ujZB\nls/JVnGCLF+QZYNgynf33XDGGTBrVh5nnJFpaYKDiAwGugA/OLGo6l1xhvUGVqrqGu8Yk4AhwLKw\nPucCz3rHmy0ijUSkOdAuxthzgdO98c8C+ZiStQmo6ylodYEDwK5ogmVlhZJHJEvJ8tM4H3GEpXGP\nFb4cniAjWsInPwYrO9sULHAKlqP6E2nJ+vbbzMmSLHwF8fTTE/sNH344fPpp/H6OEInq4EnTW0Wk\ntYjMFJElIrJYRMZ626O6WXj7bvHcMpaLyNnJkiUakydbqsqbbkrlWRwOh6N8ZGdbQPM//hE9FqYm\n4pUXuQAYi3lbXAAksjzWElgX1l7vbUukT4sYY5t5ad7Banc1A1DVtzGlahOwBnhQVXdEEywrK+Qy\nmKwsXocealapWrXs+MXFZfctKrL4jNNOC53fjzkZMMBWtB2OmkZ4Cvf9+82Ntqrj/64TjbHKzjaX\n4Vj3D0dpMmHoLASuV9WumC/9L0XkWEJuFp2wWlzjAESkC1aHpAswAPibiKRE7i1bYOxYePbZ5Ab2\nBT2GIsjyOdkqTpDlC7JsEFz5WrSA3/42n8sus9osDn6kqhcD21X1TmxOSST/XqKpaBNxk5dox/PS\n3SqAiFwI1AGOxCxhN3r1sg4+WNiKeb16CUqZAL41y3d7evNNmD3b3APDV+h37TKlrFUra48YYTEc\nJ5yQ+oKqDkdQCU/h/t//ZlaWZOE/5yZadsG3fLlQmsSJ5S54nIjs9j7XCfsMNn9U6HarqpuBzd7n\n70VkGbYKWJabxRBgoqoWAmu8miW9gaQaLVXh6qut+GefPsk8ssPhcCSPLl3g/vstVfacOTW+hp9f\nuneviLQEvgWaJzBuA9A6rN0as0jF6tPK65MTZfsG7/MWEWmuqptF5EjAqy7Dj4BXVLUY2CoiHwEn\nAqsjBbv33vHs2QPr1sGRR+bRrFleAl8ncURMsdq1y16rVlms1pFH2v6CgtLKlB+30alTUsVwOKoc\n4XFZzZtbLcOqjAicf37IopUo33+ffFmKizOXuTA/Pz9lC6sx62SlGhFpC7wPdAPWqmpjb7tgK5ON\nReRR4FNV/be37wlgmqq+HHGsStXJ+ve/4b77rGZAUNJwOhwOR1lce609IL/2WvknyVSRgTpZt2FJ\nKM4A/oZZjv6pqrfFGZcNfAmcCWwE5gAjoyS+GKOqg0SkL/CwqvaNNVZEHgC+VdU/eBkEG6nqOM8t\nvoeqXiYidb0xw1U1rNynzWN79yo5OeZZUdF6VrF47z2LrVi1KlQ89bTTQpar2bPhsMMsfbzD4Qgx\ncWLo88iRmZMjk2zaZF4UsRTMr76y+8uZZ8LChZapOx4ffgjr11v6+WRa8CtC2upkpRIRqQe8DPxK\nVXdLWN5Xrz5XLI0p6r5Ro0bRtm1bABo1akSPHj1+CGD3tdRo7bVrYcyYfO6/Hw45JH5/13Zt13bt\nTLeHDIEHH8zjl7+EESPyEUm/PP7nNWvWkAlU9W7v48si8gZQW1V3JjCuSETGAG8DtYAnPSXpKm//\nBFWdKiKDPO+JPcClscZ6h74fmCwil2OxV351qwnAkyKyCHPTfypSwQonOzs1ChZYnNXevaZIDRgA\nCxZYevdVq8yatWqVJchwOBylOeoo+N//Mi1FZqlXz+pr+Xz0ERx3XOlYzeXLzdr12WeWrTsRJcu3\nYL3+uiV4atKketSozYglS0RygDcwi9TD3rblQF6Ym8VMVe3s1xNR1fu9fm8Bd6jq7IhjVsiSVVBg\nq3gjRsCNN1bue5VFfn7+Dw8nQSTI8jnZKk6Q5QuybBBs+cJl273bXL2GDIHbb8+oWED6LFki0htY\np6qbvPYlwDBMsRmvqttTLUMqEBHdt09T6k3xwQeW3KJhQ4uzmj/frGbbw67YSSc5S5bDEY3p061o\nb021ZJWUwEsvmZthYSG8/LLFdJ1xRkjRmjnTSkSsXWvtESPi189btszuRVmeS2b37lb0PNl8+62V\nsDjiiLKVuGTOY2lPfOG5Aj4JLPUVLI8pwCXe50uAV8O2jxCRXC9QuCPmalFpVOGaa6BjR/j1r5Nx\nRIfD4Ugf9etbAoNnnoEnnsi0NGllArAfQER+jFmQnsUy+P0jg3IFntq1rai1/0DkFypu0yYUjxxe\nD8jhcITo1Qt69860FJkjK8tCagoK7AVmGQ/PeFtSUnqRJpH7SUmJWdeTee85cMCOt3u3uUmvXw/v\nvGMLTfn5Jv/u3aY0p4pMuAueAlwILBSRed62WyjDzUJVl4rIZGApUASMrlTwVRh/+5tlSfnkk9RW\nqQ/qirhPkOVzslWcIMsXZNkg2PJFyta8Obz1ltU6adAALrgg+rhqRlaYtWo4MMGL031ZRBZkUK5K\nk8q5CMzdRzWUMMW3mrVqZe5QrVpVDzcdhyMVHHaYvWoyOTlmxdq2zRZnfItVYaHtKykpXX6isBBm\nzYJTTik7frikpPS+ZKSJfzksc0NursV9AZx1lrk5vvpqaP+wYWUXaK8MabdkqeosVc1S1R6q2tN7\nvaWq21W1v6p2UtWzw2uIqOq9qtpBVTt79UYqzXvvwV13wSuvhIorOhwOR1WkUydTtH71K3jhhUxL\nkxZqeW7nAP2BmWH7Elo8FJEBXu3Fr0Tk5jL6POLtXyAiPeONjVPv8TgR+cSrD7lQRKI6BaZayfKV\nq8MPt/d2XiJ5X9nKzU29DA6Ho+riu/Tt3AlNm5ryBKHaYSUl1udnP7P2mjWwcSP85z9lF0IvKQkp\nOYcfbrFv+/YlJk9JCSxaZFYpMAVty5bQ/iZNzKV+2DBzc2za1Npnnhnq8+WXlm012WSiTlbG+egj\n8xF98UVo3z715wsPEg8iQZbPyVZxgixfkGWDYMtXlmzHH2+T3I03wnPPpVemDDAReF9EpgB7gQ8B\nRKQjELXIbzgiUgvLSjgAq8E40qvXGN5nENBBVTsCVwKPJzC2rHqP2cC/gCtVtRtWrqQwumwJXoEK\n0rKlzX9Z3uzvvyer8LHD4aje1Kplik1RkVmfGje27Tu9lEO+JatOHWvPmxcaO2tW9GOuXRtyFaxT\nx5JrJFoL8ttvYfFic51fs8ae8d97z/a1bg1nn21y5uaWtpYdcYQpXd26hcYnm4Ak/k0fc+bA0KHw\n/PMWMO5wOBzVhW7d4N13zR2ifn2711VHVPX3IvIeVhPrHVX1PfkFuDaBQ/QGVqrqGgARmYTVZFwW\n1udcLM4LVZ0tIo1EpDlWTLissWXVezwbWKiqi7zjfVeWYOmwIoWfw/+cVSOXXB0OR3nxLVm+khXp\nXuxbssL58Y9h9Wqr/+ezebO5u4NlI2zXDjZssKQXa9daMozVq82i1a6dWaAKCiwGLCvLXBVFrE+L\nFqY0ffKJHe+EE8w1+sgjY99Ts7NL1+YqjLr0VXEyWicrmSSSXfCLL2DgQHjySRg8OE2CORwOR5pZ\nudJcJJo0Se95010nq6KIyHnAT1T1Cq99IdBHVa8N6/M6cJ+qfuy1ZwA3A22BAdHGish3ZdR7vA7o\nBRwBHA5MUtUHo8ilRUWadqvSxIm22lvTY00cDkd8Zs6EY481F7v27c06PmlS6T5DhljWwZISs0rV\nr29K2X/+Y/tPO81ipM491xLvzJr1/9u78zg5qnrv459vFnZkCEIgEBzgBgVREpawKDKgYBAElU0e\n5RIQ9IqyeHlkUy9BH1bX63W5iMAFlbiAIGiQTQa4ikBCAlkIiDBAWMIaCFvI8nv+qOpMT6dr1l6q\nu7/v16teU1Vdp/rXZ6rmzOk6C3zqU93NliNg7tyk4nTrrTB6dPJ07Omnk9FPFyxIzjdsWFLJGz8+\neY9yFby+LFoEN9yQrI8YAYcd1gTzZNXaNdfAF74AP/uZK1hm1tw8/Haf+vvtYn8KWpU7X8l8jyOA\nDwI7AW8Ct0qaERF/KU33zW9OWfnNa0dHR80GYXE/LDPrj2HDkooWJJWb0r8du+ySVLAKxxZGMi1u\nqrcobdR9yy1JxWjnnekxdYXUPYR78XD5b76ZNCfMKuMG80R+1qxOZsxI5pp89NGBp+9N0zcQWLEi\nGeDixBNh2rTujni1lOf+HZDv+Bzb4OU5vjzHBvmOL8+xNZCngLFF22OBBX0cs1l6TLn9T6XrC9Mm\nhaTzPT6X7n8SuCMd4OlNYBrJk61VTJkyZeVSqwrWDjtAW1vfx5mZFY/8VxhIB5JK0sYbJ834snR0\nwLbbJk+hIGn6N5B5+Qr9vCqpo6OD73xnChdcMIUvfnFKRc/d1E+ynnsO/u3f4Jlnkr5Ym2xS74jM\nzCwHpgPjJLUDT5MMA186veh1wJeBX0vaFVgUEQslvdhL2sJ8jxfQc77Hm4BTJa1JMuDFnsD3ygVW\njydK73537d/TzBrTuHHJzz337N7XnwmHIfk//MUXk0EyRo5MmgjmpT/osGHwkY9U9pxN2ScrAi67\nDE4/HY46Cr71rZ4d28zMrPIapU8WgKT9gB8Aw4FLIuI8SV8AiIiL0mMKowi+DhwdEfdlpU33jwJ+\nC2xOOt9jYToSSZ8hmRMygD9FxOllYqrUNJBmZrk0bx7cf38yL9/uu9c7mlVVshxrukrWCy8kQzIu\nXgwXXwwTJvSd1szMhq6RKll55EqWmTW7hx5KBqLbffekopU3lSzHcvKQrnLa2mDyZLj77vxUsPLe\nhyLP8Tm2wctzfHmODfIdX55jMzMz602heWDp0O/NqGEqWZImSZov6R+STss6bsSIpIlgniZWnDVr\nVr1D6FWe43Nsg5fn+PIcG+Q7vjzH1kj6U6ZI+mH6+v2SJvSVVtIoSTdLeljSTZLaSs63uaTXJJ1S\nvU9mWfwFRfU5j6uv0fO4lSZAb4hKlqThQKFt/LbAEZK2qW9U/beoMFZlTuU5Psc2eHmOL8+xQb7j\ny3NsjaI/ZYqkjwH/EhHjgM8DP+1H2tOBmyNia+DWdLvY94A/VeVDWZ8a/Z/TRuA8rr5Gz+PVVkt+\nupKVHxOBRyKiKyKWAr8GDqpzTGZm1pj6U6YcCFwOEBF3A23p8Oy9pV2ZJv25ctIQSZ8AHgXmVecj\nmZnl36abJj+Lh4JvVo1SydqUZJ6RggXpvobQ1dVV7xB6lef4HNvg5Tm+PMcG+Y4vz7E1kP6UKVnH\njOkl7eiIWJiuLwRGA0haBzgVmFKB2M3MGlahuWA15rzKm4YYXVDSwcCkiDgu3f4ssEtEnFB0TP4/\niJlZk2uE0QX7WaZcD5wfEX9Nt28BTgPaS9IeCewcESdKejki1i86x0sRMUrSd4C7I+J3kqYAiyPi\nu2XicjlmZlZnlSrHGmUy4qeAsUXbY0m+PVypEQp2MzPLhT7LlDLHbJYeM7LM/qfS9YWSNo6IZyVt\nAjyX7p8IHCzpQqANWCHpzYj4SfEbuhwzM2sejdJccDowTlK7pNWAw4Hr6hyTmZk1pv6UKdcB/wog\naVdgUdoUsLe01wFHpetHAdcCRMSHImKLiNiCZBLjc0orWGZm1lwa4klWRCyT9GXgRmA4cElEPFjn\nsMzMrAFllSmSvpC+flFETJP0MUmPAK8DR/eWNj31+cBvJX0O6AIOq+kHMzOz3GiIPllmZmZmZmaN\nIjfNBfM8MWStY5P0fkl3SZoj6QFJq+clPklrSJqaxjVPUuk8MLWI7VBJcyUtl7RDybnOSI+fL2nf\n3mKrUXw7Fu3fR9L0NO+mS9qrzrHtUOZ89bwnevu95uGeKBtfTu6Jb0t6MD3+95LWK3otD/dE2fgG\nek80q/7kufWPpK70epop6Z50X2/l7YDuj1Yj6VJJCyXNLto34PyUtKOk2elr/1nrz5FnGXk8RdKC\n9DqeKWm/otecxwMgaayk29Lye46kE9P91b+OI6LuC0mTi0dIRm0aCcwCtik55mPAtHR9F+DvfaUF\nLgROTddPIxkpqvicVwG/AU7JS2wkTTjvB96Xbq8PDMtRfJOBqen6msBjwOY1ju09wNbAbcAORefa\nNj1uZJrukTrlXVZ844GN0/X3AgvyEltO7omsfMvLPZEV32Tqf0/sU8gTkiZrhfs1L/dEVnz9viea\ndelPnnsZUH4+Bowq2ZdVng3o/mjFBdgDmADMHmR+FlpM3QNMTNenkYzQWffPl4clI4/PAv69zLHO\n44Hn78bA+HR9HeAhYJtaXMd5eZKV54khax3bvsADETE7Pd/LEbEiR/E9A6wtaTiwNvA28GotY4uI\n+RHxcJn3O4jkn92lEdFFcmNMzIit5vFFxKyIeDbdnAesKWlkHmKD+t8TvcSWi3uil/jycE/cXJQn\nd5OMeAf5uSfKxjfAe6JZ9SfPbWBKR2nMKs8Gen+0nIi4E3i5ZPdA8nMXJSNtrhsR96THXVGUpuVl\n5DGseh2D83jAIuLZiJiVrr8GPEgyt2HVr+O8VLLyPDFkTWMj+aY8JP1Z0gxJX81TfBFxI8k/kM+Q\ndOz+dkQsqnFsWcbQcxjmvtLUOr5iBwMz0n+q6h5bTu6JLOPIxz1RVg7viWNIvmGDfN4TxfEV6+ue\naFb9yXPrvwBuSZufHpfuyypvB3p/WGKg+Vm6/ymcz/1xQtrE+pKipmzO4yGQ1E7y1PBuanAd52V0\nwf6OvtGfOURU7nwREeqe6HEK8P2IeENSX+esdWwjgA8COwFvArdKmhERf8lDfEom7VwT2AQYBdwp\n6daIeKzKsQ1WbzHUJT5J7yVpMrVPL4fVOrYp1Oee6I+R1O+e6PskObonJH0NeDsiruzlsLrdE1nx\n9fOeaFYefaqyPhARz0jaELhZ0vziF0vK23L8+xiAfuSnDc5PgW+m698Cvgt8rn7hNL70y+SrgZMi\nYnHxvzrVuo7zUsnK5cSQdYrtSeCOiHgJQNI0YAcg6x/KWse3O3BNRCwHnpf0V5J/fsv9Q1nJ2Mql\n7ev9ij9Pf46vdnxI2gz4PXBkxj/h9YqtXvdEf2Kr5z3Rn/hycU9ImkzSX+rDfZyrLvdERnwDuSea\nVX/y3PopIp5Jfz4v6RqSv21Z5dlA7w9LDCQ/F6T7NyvZ73zuRUQU8hRJPweuTzedx4OQNkO/GvhF\nRFyb7q7+dRz56JQ2AvgnSQez1ei7s/WudHe2zkxL0qnttHT9dEoGvoheOhfWKzaSTv0zSL4ZHwHc\nDOyXo/hOBC5N19cG5gLb1TK2orS3ATsWbRc6K64GbJGmV63zrpf42kgGcPhEve6JrNjycE/0kW91\nvyd6ia/u9wQwKX3fd5acKxf3RC/x9fueaNalP3nupd95uRZJn4nCvfhXkj6dWeXZgO6PVl3Sa7N0\n4IsB5SdJ86xdSJ6Ce1CGvvN4k6L1rwBXOo8Hnbci6T/1/ZL9Vb+O6/7hiz7sfiQjfjwCnJHu+wLw\nhaJjfpS+fj89R/daJW26fxRwC/AwcBPQVuZ9e/2Hsh6xAZ8B5gCzKVMxrGd8wOrAL9PY5tLLKHRV\njO2TJE833gSeBW4oeu3M9Pj5wEfrlHdl4wO+DrwGzCxa3pmH2HJyT/T2e83DPZH1e83DPfEP4PGi\n6+onObsnysbHAO+JZl2y8s3LgPNxC5J/jmalfy8K129v5e2A7o9WW4CpwNMkA/o8STIp94DzE9gx\n/Rv5CPDDen+uPC1l8vgYkkrBA+nf2GtJ+g85jweXvx8EVqR/FwrlzKRaXMeejNjMzMzMzKyC8jK6\noJmZmZmZWVNwJcvMzMzMzKyCXMkyMzMzMzOrIFeyzMzMzMzMKsiVLDOzJiXpW/Ny8U8AACAASURB\nVJLulzRL0q2SxpY5Zqyk2yTNlTRH0ol9pZe0j6Tpkh5If+5Vy89lZmaWdx5d0MysCUjqAI6KiKOL\n9q0bEYvT9ROA7SPi2JJ0GwMbR8QsSeuQzEn2iYh4MCu9pPHAs5FM4vhe4MaIKJ6k0czMrKWNqHcA\nZmZWEat8Y1aoIKXWAV4oc8yzJPN+ERGvSXoQGAM8mJU+ImYV7Z8HrClpZEQsHfKnMDMzawKuZJmZ\nNQeV3SmdAxwJvAHs2usJpHZgAsms9v1NfzAwwxUsMzOzbm4uaGbWwCT9HVid5EnTKOCJ9KVTI+Lm\nouNOB95d3Jyw5DzrAJ3A/4uIa8u8vkr6tKngH4B9IuKxynwiMzOzxudKlplZE5C0JzC5l0rU5sC0\niNiuzGsjgT8CN0TED/qTXtJmwK3pe95VoY9hZmbWFDy6oJlZc1iluaCkcUWbBwEzyxwj4BJgXmkF\nKyu9pDbgT8BprmCZmZmtypUsM7PmEKw6+MV5kmZLmgV0AKcASBoj6U/pMR8APgvsJWlmukzqLT3w\nZWAr4KyiNO+s2iczMzNrMG4uaGZmZmZmVkF+kmVmZmZmZlZBrmSZmZmZmZlVkCtZZmZmZmZmFeRK\nlpmZmZmZWQW5kmVmZmZmZlZBrmSZmZmZmZlVkCtZZmZmZmZmFeRKlpmZmZmZWQW5kmVmZmZmZlZB\nrmSZmZmZmZlVkCtZZlUk6QxJF9c7DjMzs8FwOWY2OIqIesdgZmZmZmbWNPwky8zMzMzMrIJcyTKr\nEEmnSVog6VVJ8yXtLWmKpF8UHfOvkh6X9IKkr0vqkrR3+toUSb+T9Iv0HA9IGpc21ViYptun6FxH\nS5qXHvtPSZ+vx+c2M7Pm4HLMrHJcyTKrAEnvBr4E7BQR7wD2BbqAKDpmW+DHwBHAJsB6wJiSUx0A\nXAGsD8wEbk73jwG+BVxUdOxCYP/0/Y4Gvi9pQkU/mJmZtQSXY2aV5UqWWWUsB1YH3itpZEQ8ERGP\nAio65hDguoj4W0QsBf6DosIrdUdE3BwRy4GrgA2A89Pt3wDtkt4BEBHTIuKxdP0O4CZgjyp+RjMz\na14ux8wqyJUsswqIiEeAk4EpwEJJUyVtUnLYGGBBUZo3gRdLjnmuaP1N4IXoHp3mzfTnOgCS9pP0\nd0kvSnoZ+BhJYWZmZjYgLsfMKsuVLLMKiYipEbEH8C6Sb/YuoOc3fE8DmxU2JK3JIAsTSasDVwMX\nAhtFxPrANHp+42hmZtZvLsfMKseVLLMKkLR12kF4dWAJ8BZJ04tiVwMfl7SbpNVIvi0cbGGyWrq8\nAKyQtB9J+3kzM7MBczlmVlmuZJlVxurAecDzwDPAO4Ez0tcCICLmAicAvyb5NnAxSbOKJUXHlbZt\nL7sdEYuBE4HfAi+RdEL+Q8U+jZmZtRqXY2YVlKvJiCVdCuwPPBcR70v3TQR+BIwElgHHR8S99YvS\nrDIkrQO8DPxLRDxe73jMLLMcGkXSYf9dJKOtHRYRi9LXzgCOIfnG/8SIuKnMOTPTmzUyl2Nm2fL2\nJOsyYFLJvguBb0TEBJJRbC6seVRmFSLp45LWkrQ28B3gARdMZrlSrhw6Hbg5IrYGbk23C8NZHw5s\nm6b5iaRy5WrZ9GaNyOWYWf/kqpIVEXeSfCNS7BmSeRgA2oCnahqUWWUdSHINPwVsBXy6vuGYWbGM\ncuhA4PJ0/XLgE+n6QcDUiFgaEV3AI8DEMqfNSm/WiFyOmfXDiHoH0A+nA/8r6TsklcLd6hyP2aBF\nxHHAcfWOw8wGZHRELEzXFwKj0/UxwN+LjlsAbDqA9GYNx+WYWf80QiXrEpJ27tdIOhS4FNin9CBJ\n+elcZmbWoiKiqYdfjojoo7zptSzqLb3LMTOz+qtUOZar5oIZJkbENen6VZRvigFARHgpsxx11FF1\njyGvi/PGeeN8qdzSxBZK2hggnZy1MNnqU8DYouM2o3yT9qz0q4gIurqCK68MHn00ydfFi4Nnngle\neGHgv5Ply4OlS7u3V6xIfi5Zkvx87rngn//MTv/qq8ETTwRvv13Za2X69GDatGR90aLgH/8Inn++\nO97XX++Ou3j92WeD2bOTfa+/PrD3POuss1auv/12cs4lS4Jly8rnUWF5+unkfZctS/KjsN6f91yx\nInmvFSu6z/v668nnLD6u9HyF15cs6f1zLluWHPvii8Frrw3surj99mDOnOQaeOGF4JVXso8vXC+F\n/Mi6ZorzuLAsWtR3fi1e3PvrS5cm7/nKKz1jKc3rgV4Tzz4bXH99cN99yfbcud33XbkYKnkPlPud\nFK6Rws9XXw0eeCCYObP7/cvlceF3VLhPC7/Pt98OFi4M3nqr5/s891zyd+bxx/sX25Ilyd+gxx9f\n9f546aUk/0p/h4sWJftefbXn9b5iRbBgwap/U0rviXotldQIT7IekbRnRNwO7A08XO+AzMyspVwH\nHEUyMetRwLVF+6+U9D2SZoLjgHsGkL6sv6cNENdcM/m5zjrJMhjDhiVLgdLvZ1dbLfm54YbJkmXd\ndZOl0nbcEQr/z6y3XrIUDBsGI4r+O1lrre710aOTZahGjsx+TSXfYW+ySff6QPNDWvW9ij9PwfDh\nPbcLv7PVVuv+XZVTSDdqVP9jKpz/Qx/q//HFMRTnR38U/26z9HV9jxgBW27Z+zFS+bztzejRcMAB\n3dvbbtt7DNVU7j5dd1143/v6l774d7RB0fTQG2206vv0ds9nnXvjjcu/tv76yVIq6/cuwaZlGlUP\na4THPgOUq0qWpKnAnsA7JT1JMprg54Efp5PjvZlu2wC0t7fXO4Tcct5kc96U53xpbhnl0PnAbyV9\njnQIdoCImCfpt8A8uqcYifQ8FwP/HREzstKXU6h4fPjDA/9HqNGUVmbMzJpJripZEXFExku71DSQ\nJtPR0VHvEHLLeZPNeVOe86W59VIOfSTj+HOBc8vsP65o/aWs9KWWLUu+MS/99tmGzvdu9TmPq895\n3Dia8OGcmZlZY1qypPfmYTZ4/ue0+pzH1ec8bhy5qmRJulTSQkmzS/afIOlBSXMkXVCv+MzMzKrp\n7bddyTIzawaq9EgaQyFpD+A14IqIeF+6by/gTOBjEbFU0oYR8XyZtJGnz2Jm1kqWLYORI0U0+RDu\n1SQpnnkmePBB2GuvekdjZtZ6pMqVY7l6khURdwIvl+z+InBeRCxNj1mlgmVmZvX14IP1jqA5vP02\nrL56vaMwM7OhylUlK8M44EOS/i6pU9JO9Q6o0XR2dtY7hNxy3mRz3pTnfClvxox6R9Acnn++9+HF\nzcysMeRqdMEMI4D1I2JXSTsDvwX6mC3BzMxqafr0ekfQHN5+G97xjnpHYWZmQ9UIlawFwO8BIuJe\nSSskbRARL5YeOHny5JVz2LS1tTF+/PiVo7AUvn1uxe2Ojo5cxePtxtkuyEs8edj2/dTz+ujs7KSr\nq4s//QmrkMFOPGxmZvmRq4EvACS1A9cXDXzxBWBMRJwlaWvglojYvEw6D3xhZlYHS5dCWxu88YYH\nvhgKSfHXvwabbQabr1LKmZlZtTXtwBeSpgJ/A7aW9KSko4FLgS3TYd2nAv9azxgbUelTCevmvMnm\nvCnP+bKqefNcKaiUFStArqaamTW8XDUXjIgjMl46sqaBmJlZv82YATvuCPPn1zuSniStBYyNiIfq\nHUt/RbiSZWbWDHL1JMuqo9CPwlblvMnmvCnP+bKq6dNhp5yN+yrpQGAmcGO6PUHSdUM850mSZkua\nI+mkdN/2ku6S9ICk6yStm5G2Kz1mpqR7st4jAoa5ZDYza3j+U25mZkNSeJKVM1OAXUjnXoyImQxh\nZFpJ2wHHAjsD2wMHSNoK+DlwakS8H7gG+GrGKQLoiIgJETEx6338JMvMrDnkqpIl6VJJC9P+V6Wv\nnZKOLDiqHrE1Mvchyea8yea8Kc/50tPSpTBnDkyYUO9IVrE0IhaV7FsxhPO9B7g7It6KiOXA7cDB\nwLiIuDM95pZ0X5Y+q0/uk2Vm1hxyVckCLgMmle6UNBbYB3i85hGZmVmmuXPhXe/K5bDjcyV9Bhgh\naZyk/yIZWGmw5gB7SBqV9vX6GLAZMEfSQekxhwJjM9IHcIuk6ZKOy3oTNxc0M2sOuR/CPd33O+Bb\nwB+AHSPipTLpPIS7mVmN/fzncMcdcMUVlR36dqgkrQ18Ddg33XUj8K2IeGsI5zwGOB54HZgLLAH+\nG/ghsAFwHXBiRLyzTNpNIuIZSRsCNwMnFD0BKxwTRx55FqNHw9prd8/LZmZm1dHZ2dmjhcrZZ59d\nsXIs95Ws9BvCjoj4iqTHcCXLzCw3vvhF2GYbOPHEfFWyqk3SucATEfHfRfu2Bn4REbv0kfYs4LWI\n+G7J/rjppmDCBHjnKtU0MzOrtkqWY7kawr1U2iTjTJKmgit3Zx0/efJk2tvbAWhra2P8+PErvwUs\n1FJbcbu4hp6HePK0XdiXl3jytD1r1ixOPvnk3MSTl23fTz3vnz/8oZOuri7uu49ckXRbmd0REXsP\n4ZwbRcRzkjYHPgnsImnDiHhe0jDg68BPy6RbCxgeEYvTJ2z7AmeXew8PfGFm1hxy/SRL0vtIOhK/\nkb68GfAUMDEinitJ5ydZGTo7O1f+c2Q9OW+yOW/Kc750i4A114QXX0yat+XpSZak4kHl1yAZkGJZ\nRGSN/tefc95B0ixwKfCViLhN0onAl9JDro6IM9NjxwAXR8T+krYEfp8eMwL4VUScV+b8MW1asNtu\n0NY22CjNzGywKlmOVaWSJel9EbHKCIH9TNtOSZ+sotfcXNDMLCdefRXGjIHXXku281TJKkfSvRGx\nc73jyCIprr8+2HNPWLfsbFtmZlZNlSzHqjWG0U8l3SvpeEnr9TeRpKkkoz9tLelJSUeXHOJalJlZ\nTrz8Mqy/fr2jKC8dBbCwvFPSJOAd9Y6rL8uXw/Dh9Y7CzMyGqiqVrIj4IPAZYHPgPklTJe3bRzIi\n4oiIGBMRq0fE2Ii4rOT1Lcs9xbLeFfefsJ6cN9mcN+U5X7q9/DKMyu/MhfcBM9LlLuAU4HN1jagf\nli+HEbnuLW1mZv1RtT/lEfGwpK8D00mGtx2fdgw+MyKurtb7mplZbeT5SVZEtNc7hsFYssRPsszM\nmkG1+mRtD0wGDiCZD+TnEXFf2hH47xGxeUa6S4H9geeKhnD/dnqet4F/AkdHxCtl0rpPlplZDV19\nNfzyl3DNNcl2HvpkSTqYXpqWR8Tvs16rN0nxu98FhxxS70jMzFpTIwzh/kPgEuBrEVEYGZCIeDp9\nupXlMuC/gCuK9t0EnBYRKySdD5wBnF6FmM3MbABy2lzw4/Tefze3lSxIRmk0M7PGV62BL/YnGaL2\nDQBJw9O5QYiIK7ISRcSdwMsl+26OiBXp5t0kw7jbALgPSTbnTTbnTXnOl255bC4YEZMj4uispd7x\n9cVNBc3MmkO1nmTdAnwESAf2ZS3gRmD3IZ73GGDqEM9hZmYVkMdKVjFJBwDbksyTBUBEfLN+EfVt\nWLW++jQzs5qqViVrjYgoVLBIZ7lfaygnlPQ14O2IuDLrmMmTJ9Pe3g5AW1sb48ePXzlpaOHb51bc\n7ujoyFU83m6c7YK8xJOHbd9P3dtz5sDy5Z1MntxF3ki6CFgT2Bu4GDiUpDVErim3s4yZmdlAVGvg\ni78CJ0bEjHR7J+C/ImK3fqRtp2QyYkmTgeOAD0fEWxnpPPCFmVkNHX44fPKT8OlPJ9t5GPiiQNLs\niHifpAci4v2S1gH+nE4xkkuS4i9/Cfbaq96RmJm1pkaYjPhk4LeS/lfS/wK/AU4YzInSCSS/ChyU\nVcGy3pU+lbBuzptszpvynC/dct5c8M305xuSNgWWARsP5YSSTpI0W9IcSSel+7aXdJekByRdJ2nd\njLSTJM2X9A9Jp2W9xzA3FzQzawpVaS4YEfdK2gZ4N8koTw9FxNK+0kmaCuwJvFPSk8BZJKMJrgbc\nrKQdxV0RcXw14jYzs/576aVcV7L+KGl94NskExJD0mxwUCRtBxwL7AwsBf4s6Y/Az4F/j4g7JR1N\n8qXgf5SkHQ78iKSv8lPAvZKui4gHS9/HlSwzs+ZQleaCAJJ2B7YgqcgF9D6yYAXez80FzcxqaKut\n4M9/hnHjku08NRcsJmkNkr7Ci4ZwjkOASRFxbLr9dZL5G8+MiLZ031iSJonvLUm7G3BWRExKt08H\niIjzS46Lv/412H2oQ0SZmdmg5L65oKRfAt8BPgDsRPLN387VeC8zM6uPnM6TBUDafO9MSVtFxFtD\nqWCl5gB7SBqVDuT0MZIpReZIOig95lBgbJm0mwJPFm0vSPetYkS1hqMyM7OaqlbDhB2BD0TE8RFx\nQmGp0ntZH9yHJJvzJpvzpjznS2LFCnj1VWhrq3ckmQ4ElpP0D54u6f9K2nywJ4uI+cAFwE3ADcCs\n9PyfA46XNB1Yh+Tp1irJ+/s+rmSZmTWHav05nwNsAjw9kESSLiWZyPi5wuiCkkaRDJzxLqALOKwC\n30iamdkQvPIKrL12fifPjYgukkrRBZLGAd9ItwcdcURcClwKIOlc4ImIeAj4aLpva5IyrNRT9HzC\nNZbkadYqLr54CqNHJ+uFKQPMzKw6Ojs7q/blabWGcO8ExgP3AEvS3RERB/aRbg+SCYyvKKpkXQi8\nEBEXpiMyrR8Rp5dJ6z5ZZmY18uijsPfe0NXVvS9vfbLSKUEOBw4jeer0m4j47hDOt1FEPJc+EbsR\n2AVYPSKelzQM+B/gLxHxPyXpRgAPAR8m+fLxHuCI0oEvJMXDD8fKPm5mZlZblSzHqvUka0r6MwAV\nrfcqHZ2pvWT3gSQjDgJcDnQCq1SyzMysdvLcHwtA0t0kI9P+Fjg0Ih6twGmvkrQByeiCx0fEq5JO\nlPSl9PWrCxUsSWOAiyNi/4hYJunLJBWz4cAl5UYWBFhnnQpEaWZmdVeVPlkR0UnStG9kun4PMHOQ\npxsdEQvT9YXA6KHG12rchySb8yab86Y850si53NkARwVERMi4rwKVbCIiA9FxHsjYnxE3Jbu+2FE\nvDtdziw69umI2L9o+4b0mH+JiPOy3iOvzS/NzGxgqvIkS9LngeOAUcBWJCMw/ZSkqcSgRURIynwi\nNnnyZNrb2wFoa2tj/PjxK9uzF/4x8ra3i7cL8hJPnrZnzZqVq3i8na/tO++EZctgypROuorbDOZE\nOlBFwxnmebLMzJpCtfpk3Q9MBP4eERPSfbML/az6SNsOXF/UJ2s+0BERz0raBLgtIt5TJp37ZJmZ\n1chFF8GMGfCzn3Xvy1ufrEYjKV56KfL+hNDMrGnlfp4sYElEFAa8KHT6HWwN6DrgqHT9KODaIcZm\nZmZD1ADNBRuSn2SZmTWHav05v13S14C1JO0D/A64vq9EkqYCfwPeLelJSUcD5wP7SHoY2DvdtgEo\nbRpn3Zw32Zw35TlfEnmvZElaW9I3JF2cbo+TdEC94+qL+2SZmTWHao0ueDrJBI2zgS8A04Cf95Uo\nIo7IeOkjlQvNzMyG6qWXYMst6x1Fry4DZgC7p9tPA1cBf6xbRP3gJ1lmZs2hKn2y6sF9sszMaueQ\nQ+Dww+HQQ7v35alPlqQZEbGjpJlFfYPvj4jt6x1bFknx1lvB6qvXOxIzs9aU+3myJD1WZndExKC/\n95R0BvBZYAXJE7Kji/t9mZlZ7eS9uSCwRNKahQ1JWwG5LzP8JMvMrDlU68/5zkXLHsB/Ar8a7MnS\nEQePA3ZIRx0cDnx6yFG2CPchyea8yea8Kc/5knjppdxXsqYAfwY2k3Ql8BfgtLpG1A/uk2Vm1hyq\n8iQrIl4o2fUDSfcB3xjkKV8FlpIMpLEcWAt4agghmpnZEOT9SVZE3JSWO7umu04sUzbljp9kmZk1\nh2rNk7Uj3UO2DwN2Ar44lLbw6QTH3wXeBG6MiCNLXnefLDOzGllvPXj8cWhr696Xhz5ZJeUPQCGe\nAIiI+2oeVD+5HDMzq6/c98kiqQwVSoplQBdw2GBPlralPxloB14BfifpMxHRowni5MmTaW9vB6Ct\nrY3x48fT0dEBdDfx8ba3ve1tbw9t+9ZbO3ntNbjvPrjjjk66urrIkeLyp5y9BntiSScBx5JU3C6O\niP+UNBH4ETCSpLw7PiLuLZO2i6RVxnJgaURMHGwcZmaWfw0xuqCkw4F9IuLYdPtIYNeI+FLRMf4G\nMENnZ+fKf46sJ+dNNudNec4XeOEF2HrrpF9WsTw8yaoWSdsBU0n6Gi8l6e/1b8AlwHkRcaOk/YBT\nI2KVilw6INSOEfFS6WtFx7gcMzOro9w/yZJ0Cqt+k7iyyUZEfG+Ap5wPfCMdKeotknmz7hlalGZm\nNhh5748FkJYXxwMfJCmP7gR+GhFvDfKU7wHuLqSXdDvwKZL5t9ZLj2mj9/7CTVkBNTOzVVWrT9aV\nJN/2XUdSqBwA3As8DBARZw/inKcCR5EM4X4fcGxELC163d8AmpnVwD33wJe+BPeWNIrL05MsSb8j\naZ73S5Jy6P8A60XEob0mzD7fe4A/ALuRfNl3K8mXfd8F/kZSNg0DdouIJ8ukf5Skufty4KKIuLjM\nMS7HzMzqKPdPsoCxJMOtLwaQdBYwLSI+M9gTRsSFwIUVis/MzAapEZ5kAe+NiG2Ltv8iad5gTxYR\n8yVdANwEvA7MJKlYXQKcEBHXSDoUuBTYp8wpPhARz0jaELhZ0vyIuLP0oClTpqxc7+joaPmmqWZm\n1dTZ2bmy33GlVetJ1kPA9kXNKtYA7o+Id1f8zbrf098AZnAfkmzOm2zOm/KcLzB1Klx7LfzmNz33\n5+xJ1i+BH0fEXen2rsCXSkemHcL5zwEWABdExDvSfQIWRcR6faQ9C3gtIr5bst/lmJlZHVWyHKvW\njBxXAPdImiLpbOBu4PKhnFBSm6SrJD0oaV5aYJqZWY01yJOsnYC/Sno8Hdnvb8BOkmZLemAwJ5S0\nUfpzc5L+WFcCj0jaMz1kb9Jm8SXp1pK0brq+NrAvMHswMZiZWWOo2uiC6VwlH0w374iImUM83+XA\n7RFxqaQRwNoR8UrR6/4G0MysBs45B15/Hc49t+f+nD3Jau/t9YjoGsQ57wA2IBld8CsRcZuknYAf\nA6uTzON4fETMlDSGZJj3/SVtCfw+Pc0I4FcRcV6Z87scMzOro0bokwWwFrA4rRRtKGmLiHhsMCeS\ntB6wR0QcBRARy0g6EJuZWY29/DKMHl3vKHoXEV2S1ifpIzyiaP+gJyOOiA+V2Tcd2KXM/qeB/dP1\nR4Hxg31fMzNrPFVpLihpCnAqcHq6azWSEZ4GawvgeUmXSbpP0sWS1hpimC2jWh36moHzJpvzpjzn\nSzI/Vt6bC0r6FvAA8F8kIwAWFjMzs6qr1pOsTwITgBkAEfFUoT36II0AdgC+HBH3SvoBSQXuP4Yc\nqZmZDcjLL8OoUfWOok+HA1tFxNv1DsTMzFpPtSpZSyJiRTLQ0sqOvkOxAFgQEYVZWa6i+ynZSpMn\nT6a9vR2AtrY2xo8fv3IUsMK3z6243dHRkat4vN042wV5iScP276f4LHHOnn8cejsTF7r6uoih+YC\n6wML6x2ImZm1nmoN4f5V4F9IRlA6DzgGuDIifjiEc95BMgHxw2lzxDUj4rSi191h2MysBt7/frji\nChhf0ssoZwNf7EwyefAcYEm6OyLiwPpF1TuXY2Zm9ZXrIdzTeUJ+A1ydLlsD3xhKBSt1AvArSfcD\n7wfO7eN4S5U+lbBuzptszpvynC/w7LP5H/iCZCqR89PFfbLMzKymqtVccFpEbAfcVKkTRsT9wM6V\nOp+ZmQ3c66/D4sWw8cb1jqRPr1Xgyz0zM7NBqVZzwcuBH0fEPRU/efZ7upmFmVmVzZsHn/oUzJ+/\n6ms5ay74PZJmgtfR3VxwSEO4V5vLMTOz+mqEebJ2BT4r6XHg9XRfRMT7q/R+ZmZWA11dkI4vlHc7\nAEFSHhXbqw6xmJlZi6lonyxJm6erHwW2BPYGPp4uQ+5sLGm4pJmSrh/quVqJ+5Bkc95kc96U1+r5\n8thjjVHJioiOiNirdKl3XGZm1hoq/STrD8CEiOiSdHVEHFzh858EzAOGMueWmZkNUgM9yULSAcC2\nwBqFfRHxzfpFZGZmraLiowsW2bKSJ5O0GfAx4OdALtr8N4rC3Da2KudNNudNea2eL11dsMUW9Y6i\nb5IuAg4DTiQpMw4D3jXEc54kabakOZJOSvdNlHRP2sri3nTo+HJpJ0maL+kfkk4rd4yZmTWPalay\nKu37wFeBFfUOxMysVTXQk6zdI+JfgZci4mySvlnvHuzJJG0HHEsyyu32wAGStgIuJJmmZALwH+l2\nadrhwI+ASSRP1o6QtM1gYzEzs/yrdHPB90tanK6vWbQOycAX7xjMSdMmH89FxExJHVnHTZ48mfa0\n9G9ra2P8+PErv3Uu9KNoxe3iPiR5iCdP24V9eYknT9uzZs3i5JNPzk08edlu9fupqwueeqqTQjZ0\ndnbS1dVFDr2Z/nxD0qbAi8BQBp5/D3B3RLwFIOl24FPA08B66TFtwFNl0k4EHomIrjTtr4GDgAeH\nEI+ZmeVYVYZwrzRJ5wJHAstI2ta/A7g6/ZaycIyHvs3Q2dm58p8l68l5k815U14r58trr8FGGyVz\nZalMo+2cDeH+DZKnR3sDPyEZafDiiPjGIM/3HpJ+x7sBbwG3AveQTHD8N5JWFsOA3SLiyZK0hwAf\njYjj0u3PArtExAklx7kcMzOro0qWYw1RySomaU/g/0bEx0v2u3AyM6uiuXPhkEPgwYznL3mqZBWT\ntDqwRkS8MsTzHAMcTzI1yVyS+be2A34SEddIOhT4fETsU5LuYGBSfypZZ5111srtjo6Olq3Qm5nV\nQmdnZ48WKmeffXbLV7JOiYgDS/a7kmVmVkV/+hP86Edwww3lX89DJUvSRODJiHgm3T4KOBjoAqZE\nxEsVep9zgAXABYWm8JIELIqI9UqO3TV970np9hnAioi4oOQ4l2NmZnVUfVQUwgAACwdJREFUyXKs\nkQa+ACAibi+tYFnvimvo1pPzJpvzprxWzpcGGfTiIpInTEj6EHA+cDnwKvCzoZxY0kbpz81J+mNd\nCTySfvkHSdPEh8sknQ6Mk9QuaTXgcOC6ocRiZmb5VumBL8zMrEk1SCVrWNHTqsOBiyLiauBqSfcP\n8dxXSdoAWAocHxGvSPo88OO0SeKbwOcBJI0h6QO2f0Qsk/Rl4EZgOHBJRHjQCzOzJtYQzQUljQWu\nADYi6bz8s4j4YckxbmZhZlZFhxwChx4Khx9e/vWcNBecA0yIiKWSHiLpI3V7+trciHhvPePrjcsx\nM7P6qmQ51ihPspYCX4mIWZLWAWZIutnfBJqZ1U6DTEQ8Fbhd0gvAG8CdAJLGAYvqGZiZmbWOhuiT\nFRHPRsSsdP01krlFxtQ3qsbRyn1I+uK8yea8Ka+V86URmgtGxDnAKcBlwAcjojCBvYATMhOamZlV\nUKM8yVpJUjswAbi7vpGYmbWOxYvhjTdgww3rHUnfIuKuMvvKDUhhZmZWFQ1VyUqbCl4FnJQ+0eph\n8uTJtKdfs7a1tTF+/PiVc4wUvn1uxe2Ojo5cxePtxtkuyEs8edhu1fvpscegvb0Dqef10dnZSVdX\nF2ZmZtatIQa+AJA0EvgjcENE/KDM6+4wbGZWJX/8I/zkJzBtWvYxeRj4opG5HDMzq6+WmycrneDx\nEmBeuQqW9a70qYR1c95kc96U16r50gj9sczMzPKiISpZwAeAzwJ7SZqZLpPqHZSZWatwJcvMzKz/\nGqa5YF/czMLMrHoOOgg+8xk47LDsY9xccGhcjpmZ1VfLNRc0M7P6eegh+Nvf4MMfrnckZmZmjcGV\nrBbQqn1I+sN5k815U14r5svpp8Opp8IGG9Q7EjMzs8bQMJUsSZMkzZf0D0mn1TueRjJr1qx6h5Bb\nzptszpvyWi1f7rwTZs6EEzyNL5JOkjRb0hxJJ6X7flPUV/gxSTMz0nZJeiA97p7aRm7Qml+Q1Jrz\nuPqcx42jISpZkoYDPwImAdsCR0japr5RNY5FixbVO4Tcct5kc96U10r5smIFnHIKnHsurLFGvaOp\nL0nbAccCOwPbAwdI2ioiDo+ICRExAbg6XcoJoCM9dmJtorZi/ue0+pzH1ec8bhwNUckCJgKPRERX\nRCwFfg0cVOeYzMyaUgT8859wzjlJRevTn653RLnwHuDuiHgrIpYDtwOfKryYTjVyGDC1l3N4UBAz\nsxYxot4B9NOmwJNF2wuAXeoUS0PZYw+YPbuLP/wBRoyA1VZLlpEjYdiwZJGSpUAt9G/AzJldzJjR\nc1/x4F4RybJiRbIsWwbLlyc/C/siYPjwZBkxonu9OG+L87jws9z7FL9X4dzFCr+zwrkLPwt6O/fy\n5d2fobAU3g96xj5iBMyb18Xdd6+aD8U/C+9ZLqZyn7kvhXOW5sXy5d35XvgcEcl5R4zoGXsh70vf\nNyt/y32OwlLYX3yeWbNWvWYqIet6iOj5OyvkRUR3nIXPXHo9lCp8luJzF6xYAUuXJssTTyR/J3bb\nDS65pDsvWtwc4BxJo4C3gP2B4mZ/ewALI+KfGekDuEXScuCiiLi4qtGamVldNcQQ7pIOBiZFxHHp\n9meBXSLihKJj8v9BzMyaXDMP4S7pGOB44HVgLrAkIr6SvvZT4OGI+H5G2k0i4hlJGwI3AydExJ0l\nx7gcMzOrs0qVY43yJOspYGzR9liSp1krNXPBbmZm9RcRlwKXAkg6F3giXR8BfBLYoZe0z6Q/n5d0\nDUkz+DtLjnE5ZmbWJBqlEch0YJykdkmrAYcD19U5JjMzayGSNkp/bk5SqboyfekjwIMR8XRGurUk\nrZuurw3sC8yufsRmZlYvDfEkKyKWSfoycCMwHLgkIh6sc1hmZtZarpK0AbAUOD4iXk33H07JgBeS\nxgAXR8T+wMbA75OxMRgB/Coibqpd2GZmVmsN0SfLzMzMzMysUeS+uaAnf8yWkTcTJd2TfuZ7Je2c\nkbapJ3ceYt604nWzvaS70s99XaFpU5m0rXjd9Ddvmuq6kXSppIWSZhftGyXpZkkPS7pJUlvRa2ek\n18V8SftmnDMzfStr9vuqlsrdh0O9bltZpf4OSNox/dv6D0n/WevPkWcZeTxF0oKi/3X3K3rNeTwA\nksZKuk3S3LRsPzHdX/3rOCJyuwDbkbRbX4OkmeDNwFYlx3wH+HpG+seAUfX+HLXMG6AT+Gh6zH7A\nbWXSDgceAdqBkcAsYJt6f6Y85E0LXzf3AnukxxwNfNPXTf/zphmvG5IhyScAs4v2XQicmq6fBpyf\nrm+bXg8j0+vjEWBYmXOWTd/KS7PfV3XIz1Xuw6Fet628VODvQKHF1D3AxHR9GsmI0XX/fHlYMvL4\nLODfyxzrPB54/m4MjE/X1wEeArapxXWc9ydZnvwxW1bePA2slx7TRjIyY6lmn9x5KHlT0ErXzcHA\nuOgeTvqWdF+pVrxu+ps3BU1z3aSf+eWS3QcCl6frlwOfSNcPAqZGxNKI6CIplCaWOW1W+lbW7PdV\nPZTeh0O9bltWBf4O7CJpE2DdiCg84b8C3/srZeQxlC9PnMcDFBHPRsSsdP014EGS+Xerfh3nvZI1\nB9gjfaS3Fsnkj5sVvd7fyR+nSzquyrHWWlbenA58T9ITwLeBM8qkLTe586ZVjreWhpI30FrXzcdI\n8maOpMI/dofSc8qEgla7bgaSN9Dc103B6IhYmK4vBEan62PoOa1G1rWRlb6VNft9VWvl7sOhXrfW\n00Dzs3T/Uzif++MESfdLuqSoKZvzeAgktZM8NbybGlzHua5kRcR84ALgJuAGYCawouiQI+geQrec\nD0TEBJKmYV+StEe1Yq21XvLmEpJJLjcHvkI6p0tp8lrFWQ9DzBtoretmFrAc+BxwvKTpJI/T3y6X\nvFZx1sMQ8waa+LopJ5L2Er1dE71eL/1I3yqcB5XV63041OvWevJ9XDU/BbYAxgPPAN+tbziNT9I6\nwNXASRGxuPi1al3Hua5kQTL5Y0TsFBF7AotI2lIWT/74m17Srpz8EShM/tg0SvLmZeBhYJeIuCY9\n5CrKf+Y+J3dudEPIm1a7bhYBD0XEQxHx0YjYiaS5Urmnw6123Qwkb5r+ukktlLQxQNp04rl0f+m1\nsRnlm+NmpW9lTX9f1VLGfTjU69Z6Gkh+Lkj3b1ay3/nci4h4LlLAz+kuT5zHgyBpJEkF6xcRcW26\nu+rXce4rWfLkj5lK8uZTJHnziKQ900P2JqlclGr6yZ0HmzcteN18ErhS0obpvmHA10m+RSvVatdN\nv/OmFa6b1HXAUen6UcC1Rfs/LWk1SVsA40g6CPc3fStr+vuqVnq5D4d63VpPA8rPiHgWeFXSLmlf\n+iPxvd+r9J/+gk/SXZ44jwcozY9LgHkR8YOil6p/Hfc2KkYeFuAOYC5J0529ivZfBny+5NgxwJ/S\n9S3TNLNI+lqcUe/PUou8AXYiaWs6C7gLmFCaN+n2fiRPBR9x3vi6AU5Mr4eHgHOLjvV104+8acbr\nhmRAoadJmkc+STKy4iiSwT8eJmlW2VZ0/JnpdTGfdBTPdP/FwI7pemb6Vl6a/b6qYT5uUe4+HMx1\n62Vl/lTq78COJBWFR4Af1vtz5Wkpk8fHkAyq8ABwP8k/8qOdx4PO3w+SdBmZRdJ9ZCYwqRbXsScj\nNjMzMzMzq6DcNxc0MzMzMzNrJK5kmZmZmZmZVZArWWZmZmZmZhXkSpaZmZmZmVkFuZJlZmZmZmZW\nQa5kmZmZmZmZVZArWWZmZmZmZhX0/wGCpWVe6LRDGQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x26e73710>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"point['sigma']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"7436.1618154575099"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"point['beta']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"4.2154869439146863"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"point['alpha']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"4.6985952299713603"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!pip list"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
}
],
"metadata": {}
}
]
}
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