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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Show/Hide code</button>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import IPython.core.display as di\n", | |
| "# This line will hide code by default when the notebook is exported as HTML\n", | |
| "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n", | |
| "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n", | |
| "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Show/Hide code</button>''', raw=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Allow the created content to be interactivelly ploted inline\n", | |
| "%matplotlib inline\n", | |
| "#Establish width and height for all plots in the report\n", | |
| "#pylab.rcParams['figure.figsize'] = (18, 6) #width, height" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Import needed libraries\n", | |
| "import os\n", | |
| "from os.path import join, getsize\n", | |
| "import pandas as pd\n", | |
| "from cycler import cycler\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from IPython.display import display\n", | |
| "import numpy as np\n", | |
| "import matplotlib as mpl\n", | |
| "inline_rc = dict(mpl.rcParams)\n", | |
| "#the next cell enables plotting tables without borders" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<style>\n", | |
| "table,td,tr,th {border:none!important}\n", | |
| "</style>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%%html\n", | |
| "<style>\n", | |
| "table,td,tr,th {border:none!important}\n", | |
| "</style>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Summary report of the CO2MPAS WLTP to NEDC CO$_2$ emission simulation model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "[Visit the CO2MPAS home page](http://co2mpas.io)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Complete path to the CO2MPAS summary file used in this report:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "C:/Users/valvevi/Desktop/DataToReport\\30_Nov_2015_15_36_19_summary_damage_control_version_intermediate_steps.xlsx\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Search for CO2MPAS summary output files in given path and read the data from the SUMMARY tab into a dataFrame\n", | |
| "for root, dirs, files in os.walk('C:/Users/valvevi/Desktop/DataToReport'): \n", | |
| " for f in files:\n", | |
| " if 'summary' in f:\n", | |
| " file=str(os.path.join(root, f))\n", | |
| " print(file)\n", | |
| " else:\n", | |
| " None \n", | |
| "xl = pd.ExcelFile(file)\n", | |
| "df=xl.parse(sheetname='SUMMARY') #Select the tab with the SUMMARY" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "NOTE: 1710 NEDC CO₂ values provided and 1737 target NEDC CO₂ values provided\n", | |
| " Reporting will continue only with cases containing all the needed input\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Gather and name the basic variables used in the report according to their name in the CO2MPAS output file\n", | |
| "NEDC = df['NEDC co2_emission_value']\n", | |
| "NEDCt = df['target NEDC co2_emission_value']\n", | |
| "dNEDC = NEDC-NEDCt\n", | |
| "UDC = df['NEDC phases_co2_emissions 0']\n", | |
| "UDCt = df['target NEDC phases_co2_emissions 0']\n", | |
| "dUDC = UDC - UDCt\n", | |
| "EUDC = df['NEDC phases_co2_emissions 1']\n", | |
| "EUDCt = df['target NEDC phases_co2_emissions 1']\n", | |
| "dEUDC = EUDC - EUDCt\n", | |
| "#Obtain the case number and vehicle model from the input file\n", | |
| "cases = df['vehicle'].str.split('_').str[-1].astype('int')\n", | |
| "model = df['vehicle'].str.split('_').str[0]\n", | |
| "#Create a dataframe with this data\n", | |
| "valuesDF = pd.DataFrame({'NEDC': NEDC,'NEDCt':NEDCt, 'dNEDC':dNEDC,'UDC': UDC,'UDCt':UDCt, 'dUDC':dUDC,'EUDC': EUDC,'EUDCt':EUDCt, 'dEUDC':dEUDC,'Case':cases,'Model':model}) \n", | |
| "if (valuesDF.NEDC.count()-valuesDF.NEDCt.count()) != 0:\n", | |
| " print('NOTE:',valuesDF.NEDC.count(),'NEDC 'u'CO\\u2082 values provided and',valuesDF.NEDCt.count(),'target NEDC 'u'CO\\u2082 values provided')\n", | |
| " print(' Reporting will continue only with cases containing all the needed input')\n", | |
| "valuesDF = valuesDF.dropna()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Section 1. Performance of the model. All vehicles and test cases." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Error statistics for CO$_2$ emission per driving cycle" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Error statistics for **NEDC**, **UDC**, and **EUDC** CO$_2$ emission" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>NEDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>UDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>EUDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>1.1</td>\n", | |
| " <td>1.06</td>\n", | |
| " <td>1.05</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdError</th>\n", | |
| " <td>0.07</td>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.05</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>1.07</td>\n", | |
| " <td>1.28</td>\n", | |
| " <td>1.17</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mode</th>\n", | |
| " <td>-3.11</td>\n", | |
| " <td>-2.25</td>\n", | |
| " <td>-3.68</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>2.74</td>\n", | |
| " <td>4.75</td>\n", | |
| " <td>2.12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variance</th>\n", | |
| " <td>7.52</td>\n", | |
| " <td>22.54</td>\n", | |
| " <td>4.51</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <td>-0.01</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>0.55</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Skweness</th>\n", | |
| " <td>-0.17</td>\n", | |
| " <td>-0.22</td>\n", | |
| " <td>-0.68</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Range</th>\n", | |
| " <td>17.46</td>\n", | |
| " <td>29.74</td>\n", | |
| " <td>15</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Minimum</th>\n", | |
| " <td>-7.11</td>\n", | |
| " <td>-15.26</td>\n", | |
| " <td>-6.12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Maximum</th>\n", | |
| " <td>10.35</td>\n", | |
| " <td>14.48</td>\n", | |
| " <td>8.88</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sum</th>\n", | |
| " <td>1878.05</td>\n", | |
| " <td>1818.21</td>\n", | |
| " <td>1801.33</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Count</th>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Confidence level (95%)</th>\n", | |
| " <td>0.14</td>\n", | |
| " <td>0.22</td>\n", | |
| " <td>0.1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " NEDC [gCO$_2$ km$^{-1}$] UDC [gCO$_2$ km$^{-1}$] \\\n", | |
| "Averages 1.1 1.06 \n", | |
| "StdError 0.07 0.11 \n", | |
| "Median 1.07 1.28 \n", | |
| "Mode -3.11 -2.25 \n", | |
| "StdDev 2.74 4.75 \n", | |
| "Variance 7.52 22.54 \n", | |
| "Kurtosis -0.01 0.52 \n", | |
| "Skweness -0.17 -0.22 \n", | |
| "Range 17.46 29.74 \n", | |
| "Minimum -7.11 -15.26 \n", | |
| "Maximum 10.35 14.48 \n", | |
| "Sum 1878.05 1818.21 \n", | |
| "Count 1710 1710 \n", | |
| "Confidence level (95%) 0.14 0.22 \n", | |
| "\n", | |
| " EUDC [gCO$_2$ km$^{-1}$] \n", | |
| "Averages 1.05 \n", | |
| "StdError 0.05 \n", | |
| "Median 1.17 \n", | |
| "Mode -3.68 \n", | |
| "StdDev 2.12 \n", | |
| "Variance 4.51 \n", | |
| "Kurtosis 0.55 \n", | |
| "Skweness -0.68 \n", | |
| "Range 15 \n", | |
| "Minimum -6.12 \n", | |
| "Maximum 8.88 \n", | |
| "Sum 1801.33 \n", | |
| "Count 1710 \n", | |
| "Confidence level (95%) 0.1 " | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#Create a dataframe with the NECD, UDC, EUDC error statistics\n", | |
| "errorsDF = pd.DataFrame(index=['Averages','StdError','Median','Mode','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['NEDC [gCO$_2$ km$^{-1}$]','UDC [gCO$_2$ km$^{-1}$]', 'EUDC [gCO$_2$ km$^{-1}$]'])\n", | |
| "errorsDF.loc['Averages'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.mean(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.mean(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.mean(),2)})\n", | |
| "errorsDF.loc['StdError'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.sem(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.sem(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.sem(),2)})\n", | |
| "errorsDF.loc['Median'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.median(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.median(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.median(),2)})\n", | |
| "errorsDF.loc['Mode'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.mode().iloc[0],2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.mode().iloc[0],2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.mode().iloc[0],2)})\n", | |
| "errorsDF.loc['StdDev'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.std(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.std(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.std(),2)})\n", | |
| "errorsDF.loc['Variance'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.var(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.var(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.var(),2)})\n", | |
| "errorsDF.loc['Kurtosis'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.kurtosis(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.kurtosis(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.kurtosis(),2)})\n", | |
| "errorsDF.loc['Skweness'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.skew(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.skew(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.skew(),2)})\n", | |
| "errorsDF.loc['Range'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round((valuesDF.dNEDC.max()-valuesDF.dNEDC.min()),2), 'UDC [gCO$_2$ km$^{-1}$]':round((valuesDF.dUDC.max()-valuesDF.dUDC.min()),2), 'EUDC [gCO$_2$ km$^{-1}$]':round((valuesDF.dEUDC.max()-valuesDF.dEUDC.min()),2)})\n", | |
| "errorsDF.loc['Minimum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.min(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.min(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.min(),2)})\n", | |
| "errorsDF.loc['Maximum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.max(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.max(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.max(),2)})\n", | |
| "errorsDF.loc['Sum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.sum(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.sum(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.sum(),2)})\n", | |
| "errorsDF.loc['Count'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dNEDC.count(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dUDC.count(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(valuesDF.dEUDC.count(),2)})\n", | |
| "errorsDF.loc['Confidence level (95%)'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':2*round(valuesDF.dNEDC.sem(),2), 'UDC [gCO$_2$ km$^{-1}$]':2*round(valuesDF.dUDC.sem(),2), 'EUDC [gCO$_2$ km$^{-1}$]':2*round(valuesDF.dEUDC.sem(),2)})\n", | |
| "errorsDF" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Error statistics for CO$_2$ emission per driving cycle applying a filtering of NEDC absolute error < 25 gCO$_2$ km$^{-1}$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Removed cases and associated error of CO$_2$ emission for NEDC" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "0 cases with an absolute NEDC CO₂ emission error above 25 gCO₂/km\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#list of filtered cases\n", | |
| "fcases = valuesDF[abs(valuesDF.dNEDC) > 25]\n", | |
| "fcases2 = pd.DataFrame({'Absolute error gCO$_2$ km$^{-1}$':fcases.dNEDC})\n", | |
| "print((len(fcases.dNEDC)),'cases with an absolute NEDC 'u'CO\\u2082 emission error above 25 g'u'CO\\u2082/km')\n", | |
| "fcases2.columns.name='# case'\n", | |
| "if (len(fcases.dNEDC)) != 0:\n", | |
| " fcases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Error statistics for **NEDC**, **UDC**, and **EUDC** CO$_2$ emission (filtered)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "No filtering needed, same statistics as above\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Create a dataframe with the FILETERED NECD, UDC, EUDC error statistics\n", | |
| "#removing the cases where the absolute error for NEDC is larger than 25gCO2/km\n", | |
| "fvaluesDF = valuesDF[abs(valuesDF.dNEDC) < 25]\n", | |
| "ferrorsDF = pd.DataFrame(index=['Averages','StdError','Median','Mode','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['NEDC [gCO$_2$ km$^{-1}$]','UDC [gCO$_2$ km$^{-1}$]', 'EUDC [gCO$_2$ km$^{-1}$]'])\n", | |
| "ferrorsDF.loc['Averages'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.mean(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.mean(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.mean(),2)})\n", | |
| "ferrorsDF.loc['StdError'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.sem(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.sem(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.sem(),2)})\n", | |
| "ferrorsDF.loc['Median'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.median(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.median(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.median(),2)})\n", | |
| "ferrorsDF.loc['Mode'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.mode().iloc[0],2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.mode().iloc[0],2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.mode().iloc[0],2)})\n", | |
| "ferrorsDF.loc['StdDev'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.std(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.std(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.std(),2)})\n", | |
| "ferrorsDF.loc['Variance'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.var(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.var(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.var(),2)})\n", | |
| "ferrorsDF.loc['Kurtosis'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.kurtosis(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.kurtosis(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.kurtosis(),2)})\n", | |
| "ferrorsDF.loc['Skweness'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.skew(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.skew(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.skew(),2)})\n", | |
| "ferrorsDF.loc['Range'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round((fvaluesDF.dNEDC.max()-fvaluesDF.dNEDC.min()),2), 'UDC [gCO$_2$ km$^{-1}$]':round((fvaluesDF.dUDC.max()-fvaluesDF.dUDC.min()),2), 'EUDC [gCO$_2$ km$^{-1}$]':round((valuesDF.dEUDC.max()-valuesDF.dEUDC.min()),2)})\n", | |
| "ferrorsDF.loc['Minimum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.min(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.min(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.min(),2)})\n", | |
| "ferrorsDF.loc['Maximum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.max(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.max(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.max(),2)})\n", | |
| "ferrorsDF.loc['Sum'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.sum(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.sum(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.sum(),2)})\n", | |
| "ferrorsDF.loc['Count'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dNEDC.count(),2), 'UDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dUDC.count(),2), 'EUDC [gCO$_2$ km$^{-1}$]':round(fvaluesDF.dEUDC.count(),2)})\n", | |
| "ferrorsDF.loc['Confidence level (95%)'] = pd.Series({'NEDC [gCO$_2$ km$^{-1}$]':2*round(fvaluesDF.dNEDC.sem(),2), 'UDC [gCO$_2$ km$^{-1}$]':2*round(fvaluesDF.dUDC.sem(),2), 'EUDC [gCO$_2$ km$^{-1}$]':2*round(fvaluesDF.dEUDC.sem(),2)})\n", | |
| "if (len(valuesDF.dNEDC)-len(fvaluesDF.dNEDC)) == 0:\n", | |
| " print('No filtering needed, same statistics as above')\n", | |
| "else:\n", | |
| " ferrorsDF" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Distribution of the **NEDC**, **UDC** and **EUDC** errors for filtered cases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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5M68Ysd4uNGcL++6yjFiSllFE7EPzB6e5s7AFzedkF9cckiRJEypVfEXEHsCRwAmZOXdq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\nd0TEBRHxy4g4ISL+qIsx99FicoiIveb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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb236fd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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V3q1BO/TdvvTvsHNqLK13g8NS9z1+e0cdkSRJkjQXMjNWc//WB18RsSXVYWfvQXWI1+8P\nrHIa1bk0Bt0ZOLvu91pa79ERsc1A39fuVHvTfrJcHm0e9VGSJElS2SJWNe4CWp52GNUz+CDVQTb2\nz8yTh6x2LLBTRDyw737bUR0F8Zi+9T5FdWCNJ/SttwXV+TI+l5lXTf0JaGp6vV7bKQjrUArr0D5r\nUAbr0D5rUAbr0B1t7/l6B/B44NXA5fWZ5peck5m/oBp8nQR8ICIOoTrB6T/U67xhaeXMPDUi/hN4\nS0RsRXWQjudQTWX8s1k/EUmSJElaTtsnWT6D4X1aAK/MzFfV660B3gg8mups8icCz8/M6x2Cvj4Z\n6WHAnwNrgG8Dh2TmlzaTRzrtUJIkSdIoEbHqnq9WB1+lcPAlSZIkaTnTGHwVc6h5LTbnMpfBOpTB\nOrTPGpTBOrTPGpTBOnSHgy9JkiRJaoDTDnHaoSRJkqTlOe1QkiRJkuaEgy8VwbnMZbAOZbAO7bMG\nZbAO7bMGZbAO3eHgS5IkSZIaYM8X9nxJkiRJWp49X5IkSZI0Jxx8qQjOZS6DdSiDdWifNSiDdWif\nNSiDdegOB1+SJEmS1AB7vrDnS5K0vLVr17Fx41kzib3jjrtw3nlnziS2JGl6ptHz5eALB1+SpOVF\nBDCr7UTgNkiSyucBN9QZzmUug3Uog3VonzUog3VonzUog3XoDgdfkiRJktQApx3itENJ0vKcdihJ\nctqhJEmSJM0JB18qgnOZy2AdymAd2mcNymAd2mcNymAdusPBlyRJkiQ1wJ4v7PmSJC3Pni9Jkj1f\nkiRJkjQnHHypCM5lLoN1KIN1aJ81KIN1aJ81KIN16A4HX5IkddjateuIiJlc1q5d1/bTk6S5Ys8X\n9nxJkpY3zz1f85y7JJXEni9JkiRJmhMOvlQE5zKXwTqUwTq0zxqUwTq0zxqUwTp0h4MvSZIkSWqA\nPV/Y8yVJWt48903Nc+6SVJJp9HxtOa1kJEnSSmxdD5AkSV3ntEMVwbnMZbAOZbAO7Wu2BldS7Zma\n1WV++VlonzUog3XoDgdfkiRJktQAe76w50uStLxZ903Ndg+VPV+SNA2e50uSJEmS5oSDLxXBucxl\nsA5lsA7tswZlsA7tswZlsA7d4eBLkiRJkhpgzxf2fEmSlmfP1+jYbj8lLQp7viRJkiRpTjj4UhGc\ny1wG61AG69A+a1AG69A+a1AG69AdDr4kSZIkqQH2fGHPlyRpefZ8jY7t9lPSorDnS5IkSZLmhIMv\nFcG5zGWwDmWwDu2zBmWwDu2zBmWwDt3h4EuSJEmSGmDPF/Z8SZKWZ8/X6NhuPyUtCnu+JEmSJGlO\nOPhSEZzLXAbrUAbr0D5rUAbr0D5rUAbr0B0OviRJkiSpAfZ8Yc+XJGl59nyNju32U9KisOdLkiRJ\nkuaEgy8VwbnMZbAOZbAO7bMGZbAO7bMGZbAO3eHgS5IkSZIaYM8X9nxJkpZnz9fo2G4/JS0Ke74k\nSZIkaU44+FIRnMtcButQBuvQPmtQBuvQPmtQBuvQHQ6+JEmSJKkB9nxhz5ckaXn2fI2O7fZT0qKw\n50uSJEmS5oSDLxXBucxlsA5lsA7tswZlsA7tswZlsA7d4eBLkiRJkhpgzxf2fEmSlmfP1+jYbj8l\nLQp7viRJkiRpTjj4UhGcy1wG61AG69A+a1AG69A+a1AG69AdDr4kSZIkqQH2fGHPlyRpefZ8jY7t\n9lPSorDnS5IkSZLmhIMvFcG5zGWwDmWwDu2zBmWwDu2zBmWwDt3h4EuSJEmSGmDPF/Z8SZKWZ8/X\n6NhuPyUtCnu+JEmSJGlOOPhSEZzLXAbrUAbr0D5rUAbr0D5rUAbr0B0OviRJkiSpAfZ8Yc+XJGl5\n9nyNju32U9KisOdLkiRJkuaEgy8VwbnMZbAOZbAO7bMGZbAO7bMGZbAO3eHgS5IkSZIaYM8X9nxJ\nkpZnz9fo2G4/JS0Ke74kSZIkaU44+FIRnMtcButQBuvQPmtQBuvQPmtQBuvQHQ6+JEmSJKkB9nxh\nz5ckaXn2fI2O7fZT0qKw50uSJEmS5oSDLxXBucxlsA5lsA7tswZlsA7tswZlsA7d4eBLkiRJkhpg\nzxf2fEmSlmfP1+jYbj8lLQp7viRJkiRpTrQ++IqInSLibRFxYkT8NiI2RcTOA+vsUi8fvFwTEdsN\nrLt1RLwhIs6NiMvquA9s9llpUs5lLoN1KIN1aJ81GNfWRMRMLmvXrrMOBbAGZbAO3bFl2wkAdwAe\nD3wDOAF46DLrHgZ8amDZbwauHwE8HHgBcAbwXOBzEXHfzPzOVDKWJEnAlcxqSuPGjaua2SNJRSqq\n5ysiDgIOB3bNzLP7lu9CNZB6ZmYescz97wZ8CzggM99fL9sCOA34YWY+esT97PmSJI1kz1c7sd02\nSyqJPV839Cjgd8BHlhZk5jXAh4GHRcSN2kpMkiRJ0mKbt8HXayPiqoi4OCKOiYi7DNx+Z+CMzLxi\nYPlpwFZUUxxVIOcyl8E6lME6tM8alME6tM8alME6dEcJPV/juBJ4J/B54HxgN+ClwFci4o8z80f1\nejsAFw25/4V9t0uSJElS4+ai52vEurel2qP1ycx8er3sc8BNM3PPgXX3oRq47ZWZXxkSy54vSdJI\n9ny1E9tts6SSLHTPV2aeA3wZuHff4ouA7YesvrTH68Iht0mSJEnSzM3LtMNxnQY8OiK2Gej72p3q\nQBw/GXXHAw44gHXr1gGwZs0a9thjD9avXw9cN8/W67O7fuqpp3LwwQcXk8+iXu+fU15CPot63c9D\n+9eXlvVfryxdXz/l6/Maf2nZtOJd//pb3vIWt8ctX/f7qIzrg99NbeezyNdXa56nHe4MfBf4eGY+\no162B/BN4OmZeVS9bIt6vR95qPly9Xq9a9/cao91KIN1aN9gDZx22E7sDRs2+Flomd9HZbAOZZjG\ntMMiBl8R8bj6v/sCfwU8h+rAGudn5gkR8UZgE3AS1dTB3YAXAzcF7puZP+6L9SGqEzUfQnVusOcA\nfwLcLzO/PeLxHXxJkkZy8NVObLfNkkrSpcHXJoZ/ex+fmQ+OiGcAz6I6VPxNgF8BXwBe1T/wqmNt\nDRwG/DmwBvg2cEhmfmmZx3fwJUkaycFXO7HdNksqSWcOuJGZv5eZWwy5PLi+/cjMvE9m3jwzt87M\n22TmUwcHXvW6V2bmC+p1ts3M+y038FIZpjWPVqtjHcpgHdpnDcpgHdpnDcpgHbqjiMGXJEmSJHVd\nEdMO2+a0Q0nScpx22E5st82SStKZaYeSJEmS1HUOvlQE5zKXwTqUwTq0zxqUwTq0zxqUwTp0h4Mv\nSZIkSWqAPV/Y8yVJWp49X+3EdtssqST2fEmSJEnSnHDwpSI4l7kM1qEM1qF91qAM1qF91qAM1qE7\nHHxJkiRJUgPs+cKeL0nS8uz5aie222ZJJbHnS5IkSZLmhIMvFcG5zGWwDmWwDu2zBmWwDu2zBmWw\nDt3h4EuSJEmSGmDPF/Z8SZKWZ89XO7HdNksqiT1fkiRJkjQnHHypCM5lLoN1KIN1aJ81KIN1aJ81\nKIN16A4HX5IkSZLUAHu+sOdLkrQ8e77aie22WVJJ7PmSJKm2du06ImImF0mSpsHBl4rgXOYyWIcy\nWIeV2bjxLKq9MNO4bBi4rjb4WWifNSiDdegOB1+SJEmS1AB7vrDnS5K6YH77suz5GhXbbbOkktjz\nJUmSJElzwsGXiuBc5jJYhzJYhxL02k5A+FkogTUog3XoDgdfkiRJktQAe76w50uSusCerzbi2/Ml\naXHY8yVJkiRJc8LBl4rgXOYyWIcyWIcS9NpOQPhZKIE1KIN16A4HX5IkSZLUAHu+sOdLkrrAnq82\n4tvzJWlx2PMlSZIkSXPCwZeK4FzmMliHMliHEvTaTkD4WSiBNSiDdegOB1+SJEmS1AB7vrDnS5K6\nwJ6vNuLb8yVpcdjzJUmSJElzwsGXiuBc5jJYhzJYhxL02k5A+FkogTUog3XoDgdfkqTGrF27joiY\nyUWSpNLZ84U9X5LUFPuymo496/j2fElaHPZ8SZIkSdKccPClIjiXuQzWoQzWoQS9thMQfhZKYA3K\nYB26w8GXJEmSJDXAni/s+ZKkptjz1XTsWce350vS4rDnS5IkSZLmhIMvFcG5zGWwDmWwDiXotZ2A\n8LNQAmtQBuvQHQ6+JEmSJKkB9nxhz5ckNcWer6Zjzzq+PV+SFoc9X5IkSZI0Jxx8qQjOZS6DdSiD\ndShBr+0EhJ+FEliDMliH7nDwJUmSJEkNsOcLe74kqSn2fDUde9bx7fmStDga7/mKiB9FxIsiYu1q\nHlSSJEmSFs2k0w6vAl4LnB0Rn4yIR0aEUxe1as5lLoN1KIN1KEGv7QSEn4USWIMyWIfumGjglJm7\nA3sC7wMeBBwD/DwiDouIP5hBfpIkSZLUCSvu+YqIbYE/BQ6iGpAlcDzwbuBjmXnltJKcNXu+JKkZ\n9nw1HXvW8e35krQ4ptHzNZUDbkTEHYFXAH9G9S18MXAU8KbMPHvVDzBjDr4kqRkOvpqOPev4Dr4k\nLY7WT7IcEVtExGOAN1HtBUtgA3AS8FzgBxGx/2oeQ4vBucxlsA5lsA4l6LWdgPCzUAJrUAbr0B0r\nGnxFxG4R8QbgF8DHgHsBbwTumJn7ZuYjgN2A04HXTytZSZIkSZpXE007jIiDgAOB+9aL/gc4HDgm\nM68esv4BwLszc8vVpzo7TjuUpGY47bDp2LOO77RDSYtjGtMOJx0UvQs4D3gd8K7MPHMz63+fqvdL\nkiRJkhbapNMOHwvcLjNfOsbAi8z8emY+Y0WZaaE4l7kM1qEM1qEEvbYTEH4WSmANymAdumOiPV+Z\n+clZJSJJkiRJXTZpz9crgcdl5l1G3P4d4COZ+eop5dcIe74kqRn2fDUde9bx7fmStDjaONT8Y4Dj\nlrn9OODxK09HkiRJkrpp0sHXrsAPl7n99HodaSLOZS6DdSiDdShBr+0ExNZExEwua9eua/vJzQ2/\nj8pgHbpjJef5WrPMbdsDW6wwF0mSpNqVwAaqaY3TvWzceFaTT0SSrjVpz9dJwKbM3HPIbQF8Gdg6\nM+81vRRnz54vSWqGPV9Nx551/PmN7XZf0qTa6Pl6D3DfiHhvRNyyL5FbAkdQnXz5PatJSJIkSZK6\naKLBV2a+C/gg8DTgvIg4JyLOoTrx8tOpjnT479NPU13nXOYyWIcyWIcS9NpOQIB1aJ/fR2WwDt0x\n0Xm+ADLzKRFxLPBk4A714pOBozPzo9NMTpIkSZK6YqKer66y50uSmmHPV9OxZx1/fmO73Zc0qTZ6\nviRJkiRJKzDx4Csifj8i/iIiXh8R74mIIwYuHnBDE3MucxmsQxmsQwl6bScgwDq0z++jMliH7pio\n5ysi7g18GrjFMqslcNBqkpIkSZKkrpn0PF9fBu4CPBP4YmZeOKvEmmTPlyQ1w56vpmPPOv78xna7\nL2lS0+j5mvRoh/cEXuNRDSVJkiRpMpP2fF0C/GoWiWixOZe5DNahDJurw9q164iImVzWrl3XyHMs\nX6/tBARYh/a5XSiDdeiOSQdfHwceNotEJEnj2bjxLKrpWNO/VLElSdIsTNrztR3wOeAU4C3Az7rQ\nLGXPl6R5Muu+qVl+H9rz1XTsWcef39hu9yVNaho9X5MOvjax+W/CzMxJe8la5eBL0jxx8DUyurEb\njz+/sd3uS5pUGydZfv8Yl6NWk5AWk3OZy2AdytBuHbaeWT9ZNfCaF722ExBgHdrndqEM1qE7JtpD\nlZkHzCgPSVIRrmT2e2EkSVpME0077CqnHUqaJ/M7dW/W8Y3dfPz5je12X9Kk2ph2SERsERFPi4gP\nRMRxEXH3evn29fKdVpOQJEmSJHXRRIOviNgWOB54L7A/8GBg+/rmS4DXAc+eYn5aEM5lLoN1KIN1\nKEGv7QQEWIf2+X1UBuvQHZPu+ToUuBfwGOD29E3ez8xr8DxgkiRJkjTUpIeaPwP4VGb+TUTcHDgf\n2Dczv1jffjDw0sy85QQxdwJeDNwTuBtwY2BdZp49sN4a4I1Ue9xuDHwV+LvM/N7AelsDrwaeDKwB\nTgVelJlfWiYHe74kzQ17voxdTvz5je12X9Kk2uj5ug3w7WVuvwy46YQx7wA8HrgQOIHR37SfBh4K\n/DXwWOCtw0irAAAgAElEQVRGwIaIuM3AekcABwEvAx4B/BL4XETcdcK8JEmSJGlqJh18/QpY7oAa\nuwPnThIwM4/PzFtn5iOBjw5bJyL2B+4HPCUzP5KZnwceRZX/IX3r3Q34M+DgzDwiMzcATwTOBl41\nSV5qlnOZy2AdymAdStBrOwEB1qF9fh+VwTp0x6SDry8Az6gPvHE9EbErcCDw2WkkNmA/4NzMPGFp\nQWZeAnyKahrikkcBvwM+0rfeNcCHgYdFxI1mkJskSZIkbdakPV93AE4BfgF8CHgl8C/ANcCz6n/v\nnpk/X1EyEQcBhwO79vd8RcRXgYsz8+ED67+Q6giLN83MyyLiQ8AemflHA+s9gWoAdpfM/MGQx7Xn\nS9LcsOfL2OXEn9/YbvclTarxnq/M/AmwD3A11TS+AF4AvAj4ObDPSgdem7EDcNGQ5RfW/24/5no7\nTDkvSZIkSRrLxCdZzsxvZObdgLsCfwo8CbhnZt41M5c7GIc0knOZy2AdymAdStBrOwEB1qF9fh+V\nwTp0x5YrvWN9iPfvbXbF6biI6/Zu9duh7/alf3deZr0Lh9wGwAEHHMC6desAWLNmDXvssQfr168H\nrnvDe31210899dSi8vG610v/PFxn6fr6KV1fWjateE3Hn9Z1NnN91o83L/GXlk0r3uD1U6ccb+l6\nfa2Az3vp190+e93r17++WhP1fM3aMj1f7wEekpk7D6x/JLA+M3etr/8j8FJgTWZe0bfeoVRTI7fL\nzKuGPK49X5Lmhj1fxi4n/vzGdrsvaVKN93xFxKaIuGYzl6tXk9AIxwI7RcQD+3LZjuooiMf0rfcp\nYCvgCX3rbUF1uPnPDRt4SZIkSVITJhp8Ae8fcvkg8LX69u8AR02aREQ8LiIeB9yL6k9df1Iv26te\n5VjgJOADEfGnEfGwehnAG5biZOapwH8Cb4mIgyLiwfX1dcArJs1LzZnWrlytjnUog3UoQa/tBARY\nh/b5fVQG69AdE/V8ZeYBo26LiD2pBkTPXkEe/8V1cwsSeHv9/+OBB2dmRsQjgDfWt20DnEg15fAX\nA7EOAA4D/glYA3wbeJgHA5EkSZLUpqn2fEXEG4B7Z+beUwvaAHu+JM0Te76MXU78+Y3tdl/SpBrv\n+RrDj4F7TjmmJEmSJM29aQ++1gOXTzmmFoBzmctgHcpgHUrQazsBAdahfX4flcE6dMdEPV8R8bQR\nN+0A7As8HHjPapOSJEmSpK6ZqOcrIjZRTcAeNtfxauC9wN9l5m+nkl1D7PmSNE/s+TJ2OfHnN7bb\nfUmTmkbP10R7voAHDVmWwIXAGfM26JIkSZKkpkzU85WZxw+5nJCZ33PgpdVwLnMZrEMZrEMJem0n\nIMA6tM/vozJYh+6Y9gE3JEmSJElDTNrzdcQKHiMz86AV3K8x9nxJmif2fBm7nPjzG9vtvqRJTaPn\na6UH3IAbHnRj5PLM3GJl6TXDwZekeeLgy9jlxJ/f2G73JU2qjZMs7wicChwD7AmsqS/3B44FvgXc\nKjN/r+9S9MBLZXAucxmsQxmsQwl6bScgwDq0z++jMliH7ph08PUm4H8z87GZeVJmXlJfvpqZjwEu\nqNeRJEmSJPWZdNrhr4B/zMx3jLj9r4FXZuYtppRfI5x2KGmeOO3Q2OXEn9/YbvclTaqNaYdbA7dd\n5vbb1utIkiRJkvpMOvj6MvC8iNhr8IaI2Bt4HvCVaSSmxeJc5jJYhzJYhxL02k5AgHVon99HZbAO\n3bHlhOs/n2oAtiEiTgF+WC/fDbgXcAnw99NLT5IkSZK6YaKeL4CIWAe8BngkcJN68aXAp4GXZebP\npphfI+z5kjRP7Pkydjnx5ze2231Jk2r8PF8DDx7Areqr52fmptUk0iYHX5LmiYMvY5cTf35ju92X\nNKk2DrhxraxsrC9zO/BSGZzLXAbrUAbrUIJe2wkIsA7t8/uoDNahOyYefEXETSPi5RHx5Yj4cUTc\nr15+i3r5btNPU5IkSZLm26Tn+bol1QE3bg/8BLgj8JDM/GJ9+0+BYzLz+TPIdWacdihpnjjt0Njl\nxJ/f2G73JU1qGtMOJz3a4auBtcB9gLOB/x24/Rhgn9UkJEmSJEldNOm0w0cC78jMbzL8z1E/A263\n6qy0cJzLXAbrUAbrUIJe2wkIsA7t8/uoDNahOyYdfN2CarrhKJuAbVaejiRJkiR106Q9X2cBR2fm\nSyLi5sD5wL59PV/vAh6YmXN10A17viTNE3u+jF1O/PmN7XZf0qTaONT8Z4CDIuLWQ5K5D/A0qr4v\nSZIkSVKfSQdfrwSuBr4FvJbqT1JPj4gPAScA5wL/PNUMtRCcy1wG61AG61CCXtsJCLAO7fP7qAzW\noTsmGnxl5nnAfYGvAQdSzQl4KvBE4PNUUw4vnHaSkiRJkjTvJur5ut4dI7YD7kQ1APvJPA+67PmS\nNE/s+TJ2OfHnN7bbfUmTmkbP19iDr4i4CfBW4P9l5n+t5kFL4+BL0jxx8GXscuLPb2y3+5Im1egB\nNzLzUuBJwHareUBpGOcyl8E6lME6lKDXdgICrEP7/D4qg3XojkkPuPF9YN0M8pAkSZKkTpv0PF9P\nBN4B7JmZP5pZVg1z2qGkeeK0Q2OXE39+Y7vdlzSpaUw73HLC9XcDfg58NyI+DfwYuGxgnczMf1pN\nUpIkSZLUNZNOOzwUuBtwI+AxwCH1ssGLNBHnMpfBOpTBOpSg13YCAqxD+/w+KoN16I5l93xFxBHA\nf2Tm1+pFzwB+AGycdWKSJEmS1CXL9nxFxCbgKZn5wfr6NcBTl653hT1fkuaJPV/GLif+/MZ2uy9p\nUk0cav4CYMf+x1zNg0mSJEnSotrc4OtE4GUR8eaIeHm97LER8fJlLv8445zVQc5lLoN1KIN1KEGv\n7QQEWIf2+X1UBuvQHZs72uHBwPuAv+G6/f+PrS+jJODRDiVJkiSpz1jn+YqIrYC1wJlUA7Jjlls/\nM8+aRnJNsedL0jyx58vY5cSf39hu9yVNqrHzfGXm74CzI+J9wNfmbXAlSZIkSW2b6DxfmfmMvsPO\nS1PjXOYyWIfpWbt2HRExk4ua0Gs7AQHWoX1uF8pgHbpj0pMsS5LGsHHjWVRTplZy2bCZ2yVJ0jwa\nq+er6+z5kjRt89uXZf9Rt2LPOv78xna7L2lSTZznS5IkSZI0BQ6+VATnMpfBOpSi13YCsgaF6LWd\nwMJzu1AG69AdDr4kSZIkqQH2fGHPl6Tps+erjfjGbj7+/MZ2uy9pUvZ8SZIkSdKccPClIjiXuQzW\noRS9thOQNShEr+0EFp7bhTJYh+5w8CVJkiRJDbDnC3u+JE2fPV9txDd28/HnN7bbfUmTsudLkiRJ\nkuaEgy8VwbnMZbAOpei1nYCsQSF6bSew8NwulME6dIeDL0mSJElqgD1f2PMlafrs+WojvrGbjz+/\nsd3uS5qUPV+SJEmSNCccfKkIzmUug3UoRa/tBGQNCtFrO4GF53ahDNahOxx8SZIkSVID7PnCni9J\n02fPVxvxjd18/HmNvQ1w5Yxiw4477sJ55505s/iS2jGNni8HXzj4kjR9Dr7aiG/s5uMbe1R8f1dI\n3eMBN9QZzmUug3UoRa/tBGQNCtFrO4GF53ahDNahOxx8SZIkSVIDnHaI0w4lTZ/TDtuIb+zm4xt7\nVHx/V0jd47RDSZIkSZoTDr5UBOcyl8E6lKLXdgKyBoXotZ3AwnO7UAbr0B0OviRJkiSpAfZ8Yc+X\npOmz56uN+MZuPr6xR8X3d4XUPfZ8SZIkSdKccPClIjiXuQzWoRS9thOQNShEr+0EFp7bhTJYh+5w\n8CVJkiRJDbDnC3u+JE2fPV9txDd28/GNPSq+vyuk7rHnS5IkSZLmhIMvFcG5zGWwDqXotZ2ArEEh\nem0nsPDcLpTBOnSHgy9JkiRJaoA9X9jzJWn67PlqI76xm49v7FHx/V0hdY89X5IkSZI0Jxx8qQjO\nZS6DdShFr+0EZA0K0Ws7gYXndqEM1qE7HHxJkiRJUgPs+cKeL0nTZ89XG/GN3Xx8Y4+K7+8KqXvs\n+ZIkSZKkOeHgS0VwLnMZrEMpem0nIGtQiF7bCSw8twtlsA7d4eBLkiRJkhpgzxf2fEmaPnu+2ohv\n7ObjG3tUfH9XSN1jz5ckSZIkzYm5GXxFxN4RsWnI5cKB9dZExLsj4vyIuDQijouIu7SVt8bjXOYy\nWIdS9NpOQNagEL22E1h4bhfKYB26Y8u2E5hQAs8DTulbdvXAOp8Gdgb+GrgYeAmwISLulpnnNpKl\nJEmSJA2Ym56viNgb+CLwkMz84oh19gc+DjwoM0+ol20HnAEclZkHj7ifPV+SpsqerzbiG7v5+MYe\nFd/fFVL3LGLP1+ae7H7AuUsDL4DMvAT4FLD/LBOTJEmSpOXM2+AL4OiIuDoiLoiIoyPidn237Q58\nb8h9TgN2johtm0lRk3IucxmsQyl6bScga1CIXtsJLDy3C2WwDt0xTz1fvwbeCBwPXALcHXgpcGJE\n3D0zLwB2oJpiOGjpoBzbA5c1kKskSZIkXc/c9HwNExF3B74OvCYzXxERpwPfyMw/H1jvIOBwYOfM\n/MWQOPZ8SZoqe77aiG/s5uMbe1R8f1dI3TONnq952vN1A5n5rYj4EXDvetFFVHu3Bu3Qd/tQBxxw\nAOvWrQNgzZo17LHHHqxfvx64blev173uda+Pe/06S9fXT/n6rOIvLZtWvKbjz+o6m7l9UeMvLZtW\nvKaus5nbpxO/lO8jr3vd67PYvq/MXO/5AoiI04CzM/PhEfEeqqMh7jywzpHA+szcdUQM93y1rNfr\nXfvmVnusw/Ssbs9Xj+v/uL1B9FXE3hz3wlR6XL8G85J30/FnHXsDy38WVhPbPV/jcLtQButQhkU8\n2uH1RMS9gDsBJ9WLjgV2iogH9q2zHdVREI9pPkNJkiRJqszNnq+IOAr4KfAtqgNu3AN4MXApcM/M\nvDCqPzV/GbgtcAjVSZb/AbgLcLdh/V51bPd8SZoqe77aiG/s5uMbe1R8f1dI3bNoPV+nAU8C/hbY\nFjgP+ChwaGZeCJCZGRGPoDoq4tuBbYATqaYcDh14SZIkSVIT5mbaYWa+LjP3yMztM3PrzNwlM5+d\nmRsH1rs4M5+ZmbfIzJtk5kMzc9i5v1SQaTUxanWsQyl6bScga1CIXtsJLDy3C2WwDt0xN4MvSZIk\nSZpnc9PzNUv2fEmaNnu+2ohv7ObjG3tUfH9XSN2z8Ec7lCRJKs/WRMRMLmvXrmv7yUlaBQdfKoJz\nmctgHUrRazsBWYNC9NpOYIWupNqzNv3Lxo1nNflE3C4Uwjp0h4MvSQtr7dp1M/vrtCRJ0iB7vrDn\nS1pU9mU1HXvW8Y3dfHxjNx/ffjKpLfZ8SZIkSdKccPClIjiXuQzWoRS9thOQNShEr+0EFp7bhTJY\nh+5w8CVJkiRJDbDnC3u+pEVlz1fTsWcd39jNxzd28/Ht+ZLaYs+XJEmSJM0JB18qgnOZy2AdStFr\nOwFZg0L02k5g4bldKIN16A4HX5IkSZLUAHu+sOdLWlT2fDUde9bxjd18fGM3H9+eL6kt9nxJkiRJ\n0pxw8KUiOJe5DNahFL22E5A1KESv7QQWntuFMliH7nDwJUmSJEkNsOcLe76kRWXPV9OxZx3f2M3H\nN3bz8e35ktpiz5ckSZIkzQkHXyqCc5nLYB1K0Ws7AVmDQvTaTmDhuV0og3XoDgdfkiRJktQAe76w\n50taVPZ8NR171vGN3Xx8Yzcf354vqS32fEmSJEnSnHDwpSI4l7kM1qEUvbYTkDUoRK/tBAq0NREx\nk8vatetu8GhuF8pgHbpjy7YTkCRJ0riuZFZTGjduXNVsKkljsOcLe76kRWXPV9OxZx3f2M3HN3bz\n8e0nk9piz5ckSZIkzQkHXyqCc5nLYB1K0Ws7AVmDQvTaTmDhuV0og3XoDgdfkiRJktQAe76w50ta\nVPZ8NR171vGN3Xx8Yzcf354vqS32fEmSJEnSnHDwpSI4l7kM1qEUvbYTkDUoRK/tBBae24UyWIfu\ncPAlSZIkSQ2w5wt7vqRFZc9X07FnHd/Yzcc3dvPx7fmS2mLPlyRJkiTNCQdfKoJzmctgHUrRazsB\nWYNC9NpOYOG5XSiDdegOB1+SJEmS1AB7vrDnS1pU9nw1HXvW8Y3dfHxjNx/fni+pLfZ8SZIkSdKc\ncPClIjiXuQzWoRS9thOQNShEr+0EFp7bhTJYh+5w8CVJkiRJDbDnC3u+pEVlz1fTsWcd39jNxzd2\n8/Ht+ZLaYs+XJEmSJM0JB18qgnOZy2AdStFrOwFZg0L02k5g4bldKIN16A4HX5IkSZLUAHu+sOdL\nWlT2fDUde9bxjd18fGM3H9+eL6kt9nxJkiRJ0pxw8KUiOJe5DNahFL22E5A1KESv7QQWntuFMliH\n7nDwJUmSJEkNsOcLe76kRWXPV9OxZx3f2M3HN3bz8e35ktpiz5ckSZIkzQkHXyqCc5nLYB1K0Ws7\nAVmDQvTaTmDhuV0og3XoDgdfkiRJktQAe76w50sq2dq169i48awZPsJ89mXMZ+xZxzd28/GN3Xx8\ne76ktkyj58vBFw6+pJJ5UIwuxZ51fGM3H9/Yzcd38CW1xQNuqDOcy1wG61CKXtsJyBoUotd2Agtm\nayJiJpe1a9e1/eTmmtvn7nDwJUmSJOBKqr1q/ZcNQ5ZNfpnt9HFpfjjtEKcdSiVz2mGXYs86vrGb\nj2/s5uPPb2x/a2neOe1QkiRJkuaEgy8VwbnMZbAOpei1nYCsQSF6bSegqdVgdv1ki9BT5va5O7Zs\nOwFJkiR13VI/2Wxs3LiqmWBSY+z5wp4vqWT2fHUp9qzjG7v5+MZuPr6xR8X3t5xmzZ4vSZIkSZoT\nDr5UBOcyl8E6lKLXdgKyBoXotZ2ArEER3D53h4MvSZIkSWqAPV/Y8yWVzJ6vLsWedXxjNx/f2M3H\nN/ao+P6W06zZ8yVJkiTN0Nq16zxEvqbGwZeK4FzmMliHUvTaTkDWoBC9thOQNWDjxrOo9tpN/1LF\n3jy3z93heb6kBbB27bqxv+AnteOOu3DeeWfOJLYkSVKX2POFPV/qvln3Tc3y82PPV5dizzq+sZuP\nb+zm4xt7VPxZbYvmeRuq6ZpGz5d7viSt0tb1hkmSJEnLsedLRXAucyl6K7jPlcxqLvxs/0pasl7b\nCcgaFKLXdgKyBkXwd1J3OPhSp8zyiESzPirRLHOXJElS++z5wp6vLpntvGyY5znl8xl71vGN3Wzs\nWcc3dvPxjd18fGOPij+v22d/g84Pz/MlSZIkSXPCwZeK4FzmUvTaTkCAdShBr+0EBFiHEvTaTkD4\nO6lLHHxJkiRJUgPs+cKery6x52tk9DmNPev4xm429qzjG7v5+MZuPr6xR8Wf1+2zv0Hnhz1fkiRJ\nkjQnHHypCM5lLkWv7QQEWIcS9NpOQIB1KEGv7QSEv5O6ZMu2E5Dmy9aeN0uSpOK4fdZ8sOcLe766\npImer/mcDz+vsWcd39jNxp51fGM3H9/Yzcc3dvPx7flSxZ4vSZIkSZoTTjtUo8455xxe/eo3sGnT\n9f/Kc+6553Cb29y2pax0nR6wvuUcZB1K0MMalKCHdWhbD2vQvl6vx/r169tOQ1PQucFXRNwWeAuw\nL9V+4v8BDs7Mn7eamAD4zGc+wxFHnMxVVz1p4JYE7rDK6Oet8v6SJEnS7HSq5ysibgx8B7gceGm9\n+DDgxsBdM/PyEfez56shhx9+OAcffAqXX374DKL/ALgzzinvUuxZxzd2s7FnHd/Yzcc3dvPxjd18\nfHu+VJlGz1fX9nz9JbAOuGNmngEQEd8Ffgz8FdUeMUmSJElqXNcOuLEfcNLSwAsgM88EvgLs31ZS\nGkev7QQEWIdS9NpOQNagEL22E5A1KILn+eqOrg2+dge+N2T5aVTz0VSsU9tOQIB1KIV1aJ81KIN1\naJ81KMGpp1qHruja4GsH4KIhyy8Etm84F03k4rYTEGAdSmEd2mcNymAd2mcNSnDxxdahK7o2+JIk\nSZKkInXtgBsXMXwP16g9YmrYNttsw6ZNx3Kzm/3wessvu+x0tt32i6uKvWnTZfzmN6sKIc5sOwEB\n1qEEZ7adgADrUIIz205AwJlnntl2CpqSrh1q/gvAjTJzr4HlGwAy80Ej7tedF0GSJEnSTHio+es7\nFnhDRKyrj3JIRKwD7g8cMupOq30RJUmSJGlzurbna1uqw/JcDvxjvfhVwO8Dd8vMy9rKTZIkSdJi\n69QBN+rB1YOBHwHvB44Cfgrs48BLkiRJUps6NfgCyMxzMvMJmbkmM2+WmY/LzLP714mIP4yIt0XE\naRHxm4g4NyKOiYi7DosZEX8RET+IiCsi4ocR8VfNPJvui4jnR8SxdQ02RcTLR6zXq2/vv1wTEX/T\ndM5dNG4d6nX9PDQoIs4c8d5/VNu5dU1E3DYiPhoRF0fEryPiYxFxu7bzWiQRsfeQ9/umiLiw7dy6\nKiJ2qn8TnRgRv61f752HrLcmIt4dEed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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb443908>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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zpSI4lrkM5qEM5qF5k5iDFcuXExELeqxYvrzpcBdkEvPQNuagDOahPTZsOgBJ\nkrR4l69bt/AhkOvWLWkskqTerPnCmi9J0nBKqvkqKRZJaiNrviRJkiRpQtj5UhEcy1wG81AG89A8\nc1AG89A8c1AG89Aedr4kSZIkaQys+cKaL0nScEqqsyopFklqo7HXfEXExRFxSERMxhy1kiRJklSI\nQYcd3g58ALgiIr4UEX8UEQ5d1KI5lrkM5qEM5qF5bc/BJrDge4I1eV+wtudhEpiDMpiH9hio45SZ\nOwN7AP8J7A0cD/w0Ig6PiN9egvgkSdKI3QbkAI/LvS+YJI3E0DVfEbEZ8MfAgVQdsgROBT4KfCEz\nbxtVkEvNmi9J0jBKqrMaOJZB9o01YpI0ipqvkUy4ERGPBN4NvJjq9/mNwNHA32XmFYt+gSVm50uS\nNAw7X5I0PRq/yXJEbBARzwH+juoqWAKnAGcDrwd+FBH7L+Y1NB0cy1wG81AG89A8c1AG89A8c1AG\n89AeQ3W+ImLHiPgQ8DPgC8BuwIeBR2bmUzPzmcCOwEXA34wqWEmSJEmaVAMNO4yIA4EDgN3rVf8D\nfAQ4PjPv6NF+NfDRzNxw8aEuHYcdSpIAVixfPvDkEks17HDJYxlgvw47lKQGar4iYj1wNfBx4D8y\n87J52j8O+LPMfNViglxqdr4kSTBY3RQM1okZtAOz5LEMum/Pk5KmXBM1X88FHpaZh87X8QLIzG+X\n3vFSGRzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQHgMNB8zMLy1VIJIkSZLUZoMOO3wP8LzM3GWO7d8H\nPpeZ7x9RfGPhsENJEjjssG97z5OSplwTww6fA5zcZ/vJwPOHD0eSJEmS2mnQztcOwIV9tl9Ut5EG\n4ljmMpiHMpiH5s00HYAAj4USmIMymIf2GOY+X8v6bNsS2GDIWCRJkiSptQat+TobWJ+Ze/TYFsAZ\nwCaZudvoQlx61nxJkmBp66w2BW4bMB5rviSpHE3UfH0M2D0iPhERD+wI5IHAUVQ3X/7YYgKSJKmN\nbqPq8Cz0IUlqn4E6X5n5H8CngFcAV0fElRFxJdWNl19JNdPhv44+TLWdY5nLYB7KYB6aN9N0AAI8\nFkpgDspgHtpjoPt8AWTmyyLiBOClwCPq1ecAx2bm50cZnCRJkiS1xUA1X21lzZckCQq8t1ZJsXie\nlDTlmqj5kiRJkiQNYeDOV0TcNyL+JCL+JiI+FhFHdT2ccEMDcyxzGcxDGcxD82aaDkCAx0IJzEEZ\nzEN7DFTOCJAnAAAgAElEQVTzFRGPA74MPKBPswQOXExQkiRJktQ2g97n6wxgF+DVwDcy8/qlCmyc\nrPmSJIE1X33be56UNOVGUfM16GyHjwWOcFZDSZIkSRrMoDVfNwHXLUUgmm6OZS6DeSiDeWjeTNMB\nCPBYKIE5KIN5aI9BO19fBJ6+FIFIkiRJUpsNWvO1BXAS8B3gSODSNhRLWfMlSQJrvvq29zwpacqN\nouZr0M7Xeub/fZ2ZOWgtWaPsfEmSwM5X3/aeJyVNuSZusvzJBTyOXkxAmk6OZS6DeSiDeWjeTNMB\nCPBYKIE5KIN5aI+BrlBl5uolikOSJEmSWm2gYYdt5bBDSRI47LBve8+TkqZcE8MOiYgNIuIVEXFM\nRJwcEY+p129Zr992MQFJkiRJUhsN1PmKiM2AU4FPAPsDTwG2rDffBHwQ+LMRxqcp4VjmMpiHMpiH\n5s00HYAAj4USmIMymIf2GPTK12HAbsBzgIdTjUQAIDPvxPuASZIkSVJPg041vxY4MTP/IiK2Bq4B\nnpqZ36i3HwQcmpkPHGCf2wJvBR4L7ArcB1iRmVd0tNkeWNvj6QlsmZk3dbTdBHg/8FJgGbAGOCQz\nT+8TgzVfkiRrvvq19zwpaco1UfP1EOC8PttvAe434D4fATwfuB44jf7ng8OB3TseTwB+2dXmKOBA\n4B3AM4GfAydFxKMHjEuSJEmSRmbQztd1QL8JNXYGrhpkh5l5amY+ODP/CPj8PM3XZua3ux53ddYi\nYlfgxcBBmXlUZp4CvBC4AnjvIHFpvBzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQHoN2vr4OvKqeeOMe\nImIH4ADgq6MIbEjPAn4DfG52RV2L9hng6RGxUVOBSZIkSZpug9Z8PQL4DvAz4NPAe4C/Be4EXlt/\nfUxm/nSoYCIOBD4C7DBHzdc1wFbAr6hmXTw0M8/vaPdpYGVm/m7Xfl9A1QHbJTN/1ON1rfmSJFnz\n1a+950lJU27sNV+Z+WNgH+AOqmF8AbwZOAT4KbDPsB2vedwG/BvwGmAV8Cbg/wBnRsQjO9ptBdzQ\n4/nXd2yXJEmSpLEb+CbLmfndzNwVeDTwx8CLgMdm5qMzs99kHEPLzKsz83WZ+aXMPDMzPwbsWW8+\ndCleU+PlWOYymIcymIfmzTQdgACPhRKYgzKYh/bYcNgn1sP9zp+34RLJzCsj4gzgcR2rbwC269F8\n9orX9T22AbB69WpWrFgBwLJly1i5ciWrVq0C7v7Au7x0y2vWrCkqHpdd9niYzmW4Z8dr9vtVcyzP\nrptre6nLzLN9zvZjzseaNWvG+nou33vZ30cuu3zP5cUaqOZrqc1V89Wn/VeAh8/WeEXEO6muhC3L\nzFs72h1GNTRyi8y8vcd+rPmSJFnz1a+950lJU27sNV8RsT4i7pznccdiAhoglu2AJwFnd6w+EdgY\neEFHuw2opps/qVfHS5IkSZLGYaDOF/DJHo9PAd+qt38fOHrQICLieRHxPGA3qn+w/WG9bs96+4cj\n4m8i4rkRsSoiXkt1Q+Y7gCNm95OZa4DPAkdGxIER8ZR6eQXw7kHj0viM6lKuFsc8lME8NG+m6QAE\neCyUwByUwTy0x0A1X5m5eq5tEbEHcALwZ0PE8V/cPQIigX+uvz8VeApwAdVU9gcCm1Pd7PnrwHsz\n85Kufa0GDgfeBywDzgOevlSTgUiSJEnSQoy05isiPgQ8LjP3GtlOx8CaL0kSWPPVt73nSUlTbuw1\nXwtwCfDYEe9TkiRJkibeqDtfq4Bfj3ifmgKOZS6DeSiDeWjeTNMBCPBYKIE5KIN5aI+Bar4i4hVz\nbNoKeCrwDOBjiw1KkiRJktpmoJqviFhPNUy811jHO4BPAH+Vmb8aSXRjYs2XJAms+erb3vOkpCk3\nipqvga58AXv3WJfA9cDaSet0SZIkSdK4DFTzlZmn9niclpnn2/HSYjiWuQzmoQzmoXkzTQcgwGOh\nBOagDOahPUY94YYkSZIkqYdBa76OGuI1MjMPHOJ5Y2PNlyQJrPnq297zpKQpN4qar2En3IB7T7ox\n5/rM3GC48MbDzpckCex89W3veVLSlGviJsvbAGuA44E9gGX144nACcC5wIMy87c6HkV3vFQGxzKX\nwTyUwTw0b6bpAAR4LJTAHJTBPLTHoJ2vvwP+NzOfm5lnZ+ZN9eObmfkc4Nq6jSRJkiSpw6DDDq8D\n3pmZ/zLH9j8H3pOZDxhRfGPhsENJEjjssG97z5OSplwTww43AR7aZ/tD6zaSJEmSpA6Ddr7OAN4Q\nEXt2b4iIvYA3AGeOIjBNF8cyl8E8lME8NG+m6QAEeCyUwByUwTy0x4YDtn8jVQfslIj4DnBhvX5H\nYDfgJuBNowtPkiRJktphoJovgIhYARwB/BGweb36ZuDLwDsy89IRxjcW1nxJksCar77tPU9KmnJj\nv89X14sH8KB68ZrMXL+YQJpk50uSBHa++rb3PClpyjUx4cZdsrKufkxsx0tlcCxzGcxDGcxD82aa\nDkCAx0IJzEEZzEN7DNz5ioj7RcS7IuKMiLgkIp5Qr39AvX7H0YcpSZIkSZNt0Pt8PZBqwo2HAz8G\nHgnsm5nfqLf/BDg+M9+4BLEuGYcdSpLAYYd923uelDTlRjHscNDZDt8PLAceD1wB/G/X9uOBfRYT\nkCRJkiS10aDDDv8I+JfM/B69/2l2KfCwRUelqeNY5jKYhzKYh+bNNB2AAI+FEpiDMpiH9hi08/UA\nquGGc1kPbDp8OJIkSZLUToPWfF0OHJuZb4+IrYFrgKd21Hz9B/DkzJyoSTes+ZIkgTVffdt7npQ0\n5ZqYav6/gQMj4sE9gnk88Aqqui9JkiRJUodBO1/vAe4AzgU+QPWPs1dGxKeB04CrgL8eaYSaCo5l\nLoN5KIN5aN5M0wEI8FgogTkog3loj4E6X5l5NbA78C3gAKqRCC8HXgh8jWrI4fWjDlKSJEmSJt1A\nNV/3eGLEFsCjqDpgP57kTpc1X5IksOarb3vPk5Km3Chqvhbc+YqIzYF/BP5fZv7XYl60NHa+JElg\n56tve8+TkqbcWCfcyMybgRcBWyzmBaVeHMtcBvNQBvPQvJmmAxDgsVACc1AG89Aeg0648UNgxRLE\nIUmSJEmtNuh9vl4I/AuwR2ZevGRRjZnDDiVJ4LDDvu09T0qacqMYdrjhgO13BH4K/CAivgxcAtzS\n1SYz832LCUqSJEmS2mbQYYeHAbsCGwHPAQ6u13U/pIE4lrkM5qEM5qF5M00HIMBjoQTmoAzmoT36\nXvmKiKOAf8/Mb9WrXgX8CFi31IFJkqQybEI13Gahtt9mGy67+uqlC0iSJlTfmq+IWA+8LDM/VS/f\nCbx8drktrPmSJIE1XyNt73lVUsuMY6r5a4FtOl9zMS8mSZIkSdNqvs7XWcA7IuLvI+Jd9brnRsS7\n+jzeucQxq4Ucy1wG81AG89C8maYDEOCxUAJzUAbz0B7zzXZ4EPCfwF9w96iD59aPuSTgbIeSJEmS\n1GFB9/mKiI2B5cBlVB2y4/u1z8zLRxHcuFjzJUkCa75G2t7zqqSWGdt9vjLzN8AVEfGfwLcmrXMl\nSZIkSU0b6D5fmfmqjmnnpZFxLHMZzEMZzEPzZpoOQIDHQgnMQRnMQ3sMepNlSZIkSdIQFlTz1XbW\nfEmSwJqvkbb3vCqpZcZxny9JkiRJ0gjY+VIRHMtcBvNQBvPQvJmmAxDgsVACc1AG89Aedr4kSZIk\naQys+cKaL0lSxZqvEbb3vCqpZaz5kiRJkqQJYedLRXAscxnMQxnMQ/Nmmg5AgMdCCcxBGcxDe9j5\nkiRJkqQxsOYLa74kSRVrvkbY3vOqpJZpRc1XRGwbEf8UEWdFxK8iYn1EbNej3bKI+GhEXBMRN0fE\nyRGxS492m0TEhyLiqoi4pd7vk8fzbiRJkiSpt8Y7X8AjgOcD1wOnMfc/174MPA34c+C5wEbAKRHx\nkK52RwEHAu8Angn8HDgpIh49+tA1Ko5lLoN5KIN5aN5M0wEI8FgogTkog3lojw2bDiAzTwUeDBAR\nB1J1sO4hIvYHngDsnZmn1evOBtYCBwMH1et2BV4MrM7MT9brTgMuAN4LPHup348kSZIk9VJUzVfd\n+foIsENmXtGx/qPA0zPzYV3tPwHslZk71MvvBA4FlmXmrR3tDgMOAbbIzNt7vK41X5Ika75G2d7z\nqqSWaUXN1wLtDJzfY/0FwHYRsVm9vBOwtrPj1dFuY6ohjpIkSZI0dpPS+doKuKHH+uvrr1susN1W\nI45LI+JY5jKYhzKYh+bNNB2AAI+FEpiDMpiH9piUzpckSZIkTbRJqfk6G7ghM5/R1f4twAeB+2Xm\nLRHxGWDXzPzdrnYvAD4D7JKZP+rxutZ8SZKs+Rple8+rklpmFDVfjc92uEAXAPv2WL8TcEVm3tLR\n7tkRsWlX3dfOwG+AH8/1AqtXr2bFihUALFu2jJUrV7Jq1Srg7ku9Lrvssssut3sZqiGHqzq+p8/y\noO1LWWae7SNrX1h+XXbZZZcXu7xYk3Lla3/gi8CqzDy9XrcFcClwTGbOTjW/Evge8MrMPLpetwHw\nA+DizOw51bxXvpo3MzNz14dbzTEPZTAPzZm98jXDPTtYc7ansKtNBez7rvYjOK96LDTPHJTBPJSh\nNVe+IuJ59be7Uf3O/sOIuAa4pr6v1wnA2cAxEXEwcCPwtvo5H5rdT2auiYjPAkdGxMZU9wF7HbCC\n6v5fkiRJktSIIq58RcR6ev9T7dTMfErdZhnwYaobJW8KnAW8MTPvMQV9RGwCHA68BFgGnAccPHvF\nbI7X98qXJLXQiuXLuXzduoGeM9FXmwrY913tPa9KaplRXPkqovPVNDtfktROSzmBxqDt7XxJ0mSb\nppssq+VGVcSoxTEPZTAPzZtpOgABHgslMAdlMA/tYedLkiRJksbAYYc47FCS2sphhw3G4nlVUss4\n7FCSJEmSJoSdLxXBscxlMA9lMA/Nm2k6AAEeCyUwB2UwD+1h50uSJEmSxsCaL6z5kqS2suarwVg8\nr0pqGWu+JEmSJGlC2PlSERzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQHna+JEmSJGkMrPnCmi9Jaitr\nvhqMxfOqpJax5kuSJEmSJoSdLxXBscxlMA9lMA/Nm2k6AAEeCyUwB2UwD+1h50uSJEmSxsCaL6z5\nkqS2suarwVg8r0pqGWu+JEmSJGlC2PlSERzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQHna+JEmSJGkM\nrPnCmi9JaitrvhqMxfOqpJax5kuSJEmSJoSdLxXBscxlMA9lMA/Nm2k6AAEeCyUwB2UwD+1h50uS\nJEmSxsCaL6z5kqS2suarwVg8r0pqGWu+JEmSJGlC2PlSERzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQ\nHna+JEmSJGkMrPnCmi9JaitrvhqMxfOqpJax5kuSJEmSJoSdLxXBscxlMA9lMA/Nm2k6AAEeCyUw\nB2UwD+1h50uSJEmSxsCaL6z5kqS2suarwVg8r0pqGWu+JEmSJGlC2PlSERzLXAbzUAbz0LyZpgMQ\n4LFQAnNQBvPQHna+JEmSJGkMrPnCmi9JaitrvhqMxfOqpJax5kuSJEmSJoSdLxXBscxlMA9lMA/N\nm2k6AAEeCyUwB2UwD+1h50uSJEmSxsCaL6z5kqS2suarwVg8r0pqGWu+JEmSJGlC2PlSERzLXAbz\nUAbz0LyZpgMQ4LFQAnNQBvPQHna+JEmSJGkMrPnCmi9JaitrvhqMxfOqpJax5kuSJEmSJoSdLxXB\nscxlMA9lMA/Nm2k6AAEeCyUwB2UwD+1h50uSJEmSxsCaL6z5kqS2suarwVg8r0pqGWu+JEmSJGlC\n2PlSERzLXAbzUAbz0LyZpgMQ4LFQAnNQBvPQHna+JEmSJGkMrPnCmi9JaitrvhqMxfOqpJax5kuS\nJEmSJsTEdL4iYq+IWN/jcX1Xu2UR8dGIuCYibo6IkyNil6bi1sI4lrkM5qEM5qF5M00HIMBjoQTm\noAzmoT02bDqAASXwBuA7Hevu6GrzZWA74M+BG4G3A6dExK6ZedVYopQkSZKkLhNT8xURewHfAPbN\nzG/M0WZ/4IvA3pl5Wr1uC2AtcHRmHjTH86z5kqQWsuarwVg8r0pqmWms+Zrvze4HXDXb8QLIzJuA\nE4H9lzIwSZIkSepn0jpfAMdGxB0RcW1EHBsRD+vYtjNwfo/nXABsFxGbjSdEDcqxzGUwD2UwD3Nb\nsXw5EbHgx7BmRheyFsFjoXnmoAzmoT0mqebrF8CHgVOBm4DHAIcCZ0XEYzLzWmArqiGG3WYn5dgS\nuGUMsUqSlsDl69YNPPxNkqRSTEzNVy8R8Rjg28ARmfnuiLgI+G5mvqSr3YHAR4DtMvNnPfZjzZck\nTYCSargGbT91sXheldQy01jzdQ+ZeS5wMfC4etUNVFe3um3VsV2SJEmSxm6Shh0uxAXAvj3W7wRc\nkZlzDjlcvXo1K1asAGDZsmWsXLmSVatWAXePs3V56ZbXrFnDQQcdVEw807rcOaa8hHimddnjYe5l\nqOqxVnV8T5/lYdvPrhv1/ktZZp7tI2u/yHwfeeSRno8bXvb3URnLs9+XEs80Ly/WpA873A04G3hf\nZr6nY6r5VZl5et1mC+BS4Binmi/XzMzMXR9uNcc8lME8zG1cww5nuGcHaxT7d9jh4DwWmmcOymAe\nyjCKYYcT0/mKiKOBnwDnUk248XvAW4Gbgcdm5vVRTW11BvBQ4GCqmyy/DdgF2LVXvVe9bztfkjSH\nFcuXc/m6dQtuv/0223DZ1VcvSSzWfE1QLJ5XJbXMtHW+3gq8CNge2Ay4Gvhv4LDMXNfRbhnVrIjP\nBjYFzgLemJm9pqCffY6dL0maw1AdniX6nWrna4Ji8bwqqWWmasKNzPxgZq7MzC0zc5PM3D4z/6yz\n41W3uzEzX52ZD8jMzTPzaf06XirDqMbRanHMQxnMQ/Nmmg5AgMdCCcxBGcxDe0xM50uSJEmSJtnE\nDDtcSg47lKS5OexwNO2nLhbPq5JaZqqGHUqSJEnSJLPzpSI4lrkM5qEM5qF5M00HIMBjoQTmoAzm\noT3sfEmSJEnSGFjzhTVfktSPNV+jaT91sXheldQy1nxJkibeiuXLiYgFPSRJmmR2vlQExzKXwTyU\nYdrycPm6dSQs6DEuM2N8Lc1t2o6FEpmDMpiH9rDzJUmSJEljYM0X1nxJmj4rli/n8nXrFtx+Ket9\nBqnjKqluatD2UxeL51VJLTOKmi87X9j5kjR9lrzDY+fLWDyvSmoZJ9xQaziWuQzmoQzmoXkzTQcg\nwGOhBOagDOahPex8SZIkSdIYOOwQhx1Kmj5LOdRvU+C2AeMparjcErWfulg8r0pqGYcdSpKAwe6V\ntdT3y7qNhU0bP+7p4yVJapqdLxXBscxlMA9lGCYPg9wryw7P/GaaDmDCbQID/TNgxfLlPffj76Tm\nmYMymIf22LDpACRJUrvMXv1cqBjgtgeSNMms+cKaL0mTb5AaLiiwPqiAfS91e2OZp73nYUmFs+ZL\nkiRJkiaEnS8VwbHMZTAPZTAPzZtpOgABHgslMAdlMA/tYedLkiRJksbAmi+s+ZI0+az5Kr+9sczT\n3vOwpMJZ8yVJkiRJE8LOl4rgWOYymIcymIfmzTQdgACPhRKYgzKYh/aw8yVJkiRJY2DNF9Z8SZp8\n1nyV395Y5mnveVhS4az5kiRJkqQJYedLRXAscxnMQxnMQ/Nmmg5AgMdCCcxBGcxDe9j5kiRJkqQx\nsOYLa74kTT5rvspvbyzztPc8LKlw1nxJkiRJ0oSw86UiOJa5DOahDOaheTNNByDAY6EE5qAM5qE9\n7HxJkiRJ0hhY84U1X5ImnzVf5bc3lnnaex6WVDhrviRJkiRpQtj5UhEcy1wG81AG89C8maYDEOCx\nUAJzUAbz0B52viRJkiRpDKz5wpovSZPPmq/y2xvLPO09D0sqnDVfktRSK5YvJyIW/JAkSeWz86Ui\nOJa5DOahDDMzM1y+bh0JC35otGaaDkCAv5NKYA7KYB7aw86XJEmSJI2BNV9Y8yWpPEtZwzVo+5Lq\ng0qKZdD2xjJPe8/DkgpnzZckSZIkTQg7XyqCY5nLYB7KYB6aN9N0AAI8FkpgDspgHtrDzpckSZIk\njYE1X1jzJak81nyVH8ug7Y1lbpsCty2w7fbbbMNlV189wN4laTRGUfO14aiCkSRJGsZtDNBxXLdu\nKUORpCXlsEMVwbHMZTAPZTAPzZtpOgAB5qEE/j4qg3loDztfkiRJkjQG1nxhzZek8ljzVX4sg7Y3\nltG0955gkprifb4kSZIkaULY+VIRHMtcBvNQBvPQvJmmAxBgHkrg76MymIf2aF3nKyIeGhGfj4gb\nI+IXEfGFiHhY03FJmm4rli8nIhb02HvvvZsOV5IkLYFW1XxFxH2A7wO/Bg6tVx8O3Ad4dGb+eo7n\nWfMlaUmVVMM1aHtjGU17YxlNe2u+JDXF+3zd258CK4BHZuZagIj4AXAJ8BrgyOZCk9Qm+++zD187\n7bSmw5AkSROkbcMO9wPOnu14AWTmZcCZwP5NBaX5OZa5DOZh4S668EK+eccdXL+Ax7l33DHQvmeW\nJmQNYKbpAASYhxJ4XiiDeWiPtnW+dgbO77H+AmCnMceiAaxZs6bpEIR5GNSmVGOa53tsOuB+zULz\nzEEZSs/DILWcEcGK5cubDnlgnhfKYB7ao23DDrcCbuix/npgyzHHogHceOONTYcgzEMpzELzzEEZ\nSs/D5evWDVbbtm7dksWyVDwvlME8tEfbrnxJkiQNbZCrWYPaBAa6UnbfDTZo/ZU1adq07crXDfS+\nwjXXFTEV4rLLLms6BGEeBrHpfe7Dy+53Pzb7rfn/h3Xr+vXwy18ueN+XLSIujcZlTQcgoJk8DHI1\na9Du120MOAvk+vWNX1nzvFAG89AebZtq/uvARpm5Z9f6UwAys+fNcyKiPT8ESZIkSUvCqebv6QTg\nQxGxop7lkIhYATwROHiuJy32hyhJkiRJ82nbla/NqCZH+jXwznr1e4H7Artm5i1NxSZJkiRpurVq\nwo26c/UU4GLgk8DRwE+Afex4SZIkSWpSqzpfAJl5ZWa+IDOXZeb9M/N5mXlFZ5uI+J2I+KeIuCAi\nfhkRV0XE8RHx6F77jIg/iYgfRcStEXFhRLxmPO+m/SLijRFxQp2D9RHxrjnazdTbOx93RsRfjDvm\nNlpoHuq2Hg9jFBGXzfHZf1bTsbVNRDw0Ij4fETdGxC8i4gsR8bCm45omEbFXj8/7+oi4vunY2ioi\ntq3/JjorIn5V/7y369FuWUR8NCKuiYibI+LkiNiliZjbaCF5iIjt5zg+7oyILZqKvS0i4vkRcVxE\nXBERt9R/4xwREZt3tVvUsdC2mq+FehqwCjgK+C5wf+AQ4OyIeGJmnjvbMCL+BPg34HDg68A+wL9E\nBJn57+MOvIVeDfwCOA54bZ92CZwH/Cn3nGDqsiWLbLosKA8eD41I4KvAYV3rLxp/KO0VEfcBTqEa\ntv7yevXhwDci4tGZ+evGgps+CbwB+E7HujsaimUaPAJ4PtXfQ6dR/Y3Uy5eB7YA/p7oF29uBUyJi\n18y8ahyBttxC8wDV76YTu9YtfEpdzeVNwJXAW+uvK4H3UPUZ9uhot6hjoVU1XwsVEVtl5vVd67ag\n+kP+hMxcXa/bALgK+EpmHtDR9mPAfsCDM/POccXdZvXP+nbgsMx8b4/tpwAbdM9kqdHqlwePh2ZE\nxFrg9Mx8RdOxtFlE/CXwYeCRmbm2XrcCuAR4S2Ye2Vx00yMi9gK+Aeybmd9oOp5pExEHAh8Bdugc\nNRQR+wNfBPbOzNPqdVsAa4GjM/OgJuJtqz552J7qZ/7qzDyqqfjaKiK2zszruta9HPgEVQnTzCiO\nhdYNO1yI7o5Xve4mqlqxbTtWPwF4AHBsV/Ojga2BJy1VjFKBPB7UZvsBZ892vADqWXPPBPZvKqgp\n5QzE5dkPuGr2j0246++mE/H4UEt0d7xq51D9TprtHyz6WJjKzlcvEbElsAvww47VO9dfz+9qfgFV\nInYaQ2i622PqWozfRMR5EXHA/E/RCHk8NGe/ugbg1oj4Zv2fN43Wztz7sw3V59vP9vgdGxF3RMS1\nEXGstXeN63d8bBfVbNManw9ExO3130THW3u3pFZRDYWe7R8s+liY1pqvXv5v/fUfOtZtVX+9oavt\n9V3btfROBY6hujq5DHgF8NGIWJ6ZRzQa2fTweGjGCVT/eVsLbAO8HjguIl6WmZ9qNLJ22Yp7f7ah\n+nxvOeZYptkvqIZ/ngrcBDwGOBQ4KyIek5nXNhncFNuK6ndQt9nf/1sCziq99G6jqrv+GnANsCPV\n8XFmRPx+Zl7cZHBtExHbUtV8ndwxH8Sij4VWdL4iYh/g5AU0ncnMp/R4/tuAFwEHZOalo45vWiw2\nD/1k5mFdq06MiC8Cb4+II72VwN2WMg9anGFyk5l/2bWPLwFnA0cAdr7UKpm5hup+nbNOj4jTgW9T\nTcLx7kYCkwqQmVcDr+tYdWZEnER11eVQ4JWNBNZCEXFf4HjgN8BIR1q1ovNFNSZ/xwW0u9cf6BHx\nWqpZY96emf/ZtXn2v6BbAus61s/+h9+pb+9p6DwM6dNU42v/D/CtEe2zDZYqDx4Pi7fo3GTm+oj4\nL+CDEbFNZq6bq60GcgO9r3DNdUVMY5KZ50bExcDjmo5livU7Pma3qwGZeWVEnIHHx8hExKZUMxqu\nAPbsmsFw0cdCKzpfmXkr1XC0gdQzmPwz8KHM/GCPJrO1LDtzzz82Z8f///Bez5hiw+ZBo7WEefB4\nWCSPkaJdwN11jZ12ws+2dAGwb4/1OwFXOPpEbRERGwJfAH4PeGpmdv/+X/SxMLUTbkTEc6ju8/WR\nzDxkjmbfBK4FXtq1/uXAdVT/xVZzXkZ1T54fNB3IlPB4KEA95f+LqH7Je9VrdE4Adq+nlwfummr+\niYWXoyUAAAmwSURBVFRDT9SQiNgNeBTVcFs14wRg24h48uyKenrt/fD4aFR9I+Yn4fGxaBERVMP5\n/3979x5sZVWHcfz7FCkRqTWleUlN1BRNUUy7mKEyGWVjXjLNTC0vqGSiAlajJXkpRElMUXRyvDTT\nxSyb0VFR0T+iKSwJb5ECISAopcYdgfPrj7U2bTdnn/2e23tgn+czs+ew11rvete79tmH/dtrvWsN\nAY6JiOmtFOv0e6EpRr7aS9JhpM6dAdwl6ZCq7DV5zjkRsU7SZcBNkl4BHiVtKns6MCIivOljJ0ka\nTBrWfWdOGijp+PzvByJitaRDgVGkfRVeJi24cTpwNDDG37h1XpHXwe+H8kk6ifR7/iCwENietKnj\nIFIAZl3nNlLf3p9/zwHGAvNI++1YCSTdDcwGniYtuHEgacPT+cCNPdi0plb19/4g0gyHL0haAizJ\nS2r/gfTh/h5Jo0kby343H3Nt2e1tVo1eB0njgRbSa/E6aRr7paRNyL34WOfdTNro+kpgVU18sCAi\nFtIF74XeusnyD4DL62TPi4jdasqfRdr1ehfSh//rI+LW7m1l7yDpDtLKha35SES8LGkAMBHYj7TP\n1FpgJjAxIn5dTkubW5HXoaqs3w8lyX/4ryJNh3s/sAJ4ChgXEY/2ZNuakaSdgAmkKSUifcEwsvr3\n37qXpEtJXyzsAvQDFpO+fPihR3q7j6QW0nLatZ6sLP4jaRvSSpRfBvoC04CLIqK1ZbetAxq9DpLO\nAIYDuwP9SbNOHgPGRsSL5bW0OUmaC+xcJ/uKiBiby3XqvdArgy8zMzMzM7Oy9dp7vszMzMzMzMrk\n4MvMzMzMzKwEDr7MzMzMzMxK4ODLzMzMzMysBA6+zMzMzMzMSuDgy8zMzMzMrAQOvszMzMzMzErg\n4MvMzMzMzKwEDr7MzMysw5Rc19PtMDPbHPTp6QaYmZnZ5knSVsC3gM/2dFvMzDYHHvkyMzOrIukJ\nSS2S1ks6uKfb0x6SpkqaWNb5ImJpREwAlrbSljtyP7ZIOq6sNpmZbcocfJmZNYmqD7vrqz70tkia\nlvOfaO2DuaTTJC1ro663JL0q6XFJ50nqU1N2W0k3SHpJ0mpJ8yU9IGlY915xtwng58CHgL+29+Ci\n/SFpB0mTc/4aSQvy8x276Dp62gWkPjQzs8zTDs3MmssU4OuAqtLeyj+jjeNay6vU1Qf4IHAEcAVw\nqqQjImKVpF2AacB/gTHATNIXe0OBScCuHb6SGpLeFRFri6Z3tL5sZUQs6UCdhfpD0q653BzgVOAl\nYABwNTBd0ici4uX2nn9TEhHLgGWSGpY1M+stHHyZmTWXNR0JGgrUtQiYKWkK8DdgNCkQmwS0AIMj\nYlXVsbMk3d3oBJJGA2cDOwAvAuMi4hc5byrwArACOA2YCxzSRvoWwDjgJGBrYAZwSUT8sa36inaG\npH7ALcCxpGl2E0j3Oi2JiG/mYkX742ZgPXBkRKzJaQskDc39cBPwpaJta6PNRwL3AmMiYnJVH6wE\nzsht+BFwK3A9cEq+tu9HxD25jv7A8WwcoC+KiCmdbaOZWW/iaYdmZlZYRDwHPAQcL+l9wFHAz2oC\njUrZje4DqibpKlIAcC6wN3ANcEvN9LxT8s9DgW80SL8W+ApwOjAIeAZ4SNJ2Beor4nrgM8AxpJGs\nwfl55XoK9UdNuTU1ZVaRArNhkrZuZ/veRtIJwH3AmRExuSrra6QA62BSn98A/B6Yla/pTuD2Sr9F\nxPKIuDMi7qp5VAdeHt4yMyvAwZeZWXMZJmlZ1WOppGu6+BzPA7sBu5M+dP+jvRXkUaSRpMBgSkTM\ni4hfArcD51cVnRsRoyLinxExq156rm84MDoiHsplhwOvFqyvUXvfQwoUR0fE4xHxAmmVv5aqYkX7\nY48G5Z7P+XvkZdxHSBolaVQ72nsWcBtwXET8tib7uYgYGxGz82IZ/wbeiogbI2IOMDaf/9MFztNX\n0neAvSRdKGnLom00M+uNPO3QzKy5PAmcxdtHIt7s4nOINAWtM6MdA4G+pJGp6vQ+pOmAFfUWvKhN\nH5CPnVZJiIgWSX/K52pUXyOV+qdX1b9S0rNVZbpj9OeLwO8iYqGkeyUdEBFPNzjmWOAc4LCI+HMr\n+TNrnr9GGiUEICLWSXoD2LZR4yJiNWnk7IZGZc3MzCNfZmbNZmVEzI2IOVWP13PeUtK9ULW2IS0Q\nUdRA0kIRL5KCsL070M7K/z9HA/tXPfYhTcmrWFHn+Hrpram+V6k9x7VX0f54KZcbWCd/n5xfWYTj\n5Jw+G/hwgXbMIN2jd2ad/NpFRqJOmj8jmJl1Mf9hNTPrPWYBB7aSPjjnNSRpX+DzwG8i4g3gYWBE\nnvZXW7ate5aeB9YAu9YEinMiYn6RttSYTQogNkyVk/QO4JPAcx2or7X61wEfr6q/H7Bv5XnR/sjB\n8MPAeZL61pTpB5wHPBgRb5Lu/5qUsz8G/KVAW+cCQ4DPSZrcoKyZmZXIwZeZWXPZUtJ2NY8P5LxJ\nwG6SJkraT9KekkYCXyWtElivru1z+YuAqaSpd9flMueTpts9JemEXOdHJZ0L/L1eIyNiOTAeGC/p\nDEkDJO0v6RxJ9UZs6oqIlfn6fiJpmKS9SCsTbksKYDolIlaQ9v4aJ+kISQNJ91RVpmBWFO2PEaRp\njI9KOlzSTpKGAI/k/G/n866NiBWSPgU8ERGLC7b3X8DhwFGSbu3gZZuZWRfzPV9mZs1lKPBKTdpC\nYOeImCvpMOBK0shLX9KiDydExCNsrFLXetJ9Y88ClwO3RcQ6gFzngcD3gB8DOwL/yWUvbKuhEXGZ\npMXAxaQAaSlpylwlEKy3L1m99DH8f4PkbYCngaMi4rUGxxV1CdAPuB9YDvwU2A5YvaFhBfsjIuZI\nOojUn3eRgsQlwAPAiRGx4TWU9F5gSERcXaCNG64xn+NwYKqkWyJieKNjGqSZmVknKcJ/X83MzCry\nXljPRMQFDcptAcwj7U02oRvbczYpoBRpEY3Huutc3UFSCynAv6+n22Jm1tM87dDMzGxjZ+dl+gdX\nEiQNknRyniJ5AGnEqj/wq+5qhKQTSSOBi4DF+edmQdIkScvwKJqZ2QYe+TIzM6siaXvg3fnp/IhY\nm9MHke7z2pO0+MYM4OKImNEjDd3E5XsNt8pPF7W28bSZWW/j4MvMzMzMzKwEnnZoZmZmZmZWAgdf\nZmZmZmZmJXDwZWZmZmZmVgIHX2ZmZmZmZiVw8GVmZmZmZlYCB19mZmZmZmYlcPBlZmZmZmZWAgdf\nZmZmZmZmJXDwZWZmZmZmVgIHX2ZmZmZmZiVw8GVmZmZmZlaC/wHiFBrbHmcN+wAAAABJRU5ErkJg\ngg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb788b00>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#NEDC\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "NEDC_hist = fvaluesDF.dNEDC.hist(bins=25, color='green')\n", | |
| "NEDC_hist.set_xlabel(\"NEDC error [gCO$_2$ km$^{-1}$]\",fontsize=14)\n", | |
| "NEDC_hist.set_ylabel(\"frequency\",fontsize=14)\n", | |
| "plt.title('NEDC CO$_2$ emission error distribution', fontsize=20)\n", | |
| "plt.ylabel(\"frequency\",fontsize=18)\n", | |
| "plt.tick_params(axis='x', which='major', labelsize=16)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=16)\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "ax.set_xlim(-20, 20)\n", | |
| "plt.show()\n", | |
| "#UDC\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "UDC_hist = fvaluesDF.dUDC.hist(bins=25, color='blue') \n", | |
| "UDC_hist.set_xlabel(\"UDC error [gCO$_2$ km$^{-1}$]\",fontsize=14)\n", | |
| "UDC_hist.set_ylabel(\"frequency\",fontsize=14)\n", | |
| "plt.title('UDC CO$_2$ emission error distribution', fontsize=20)\n", | |
| "plt.ylabel(\"frequency\",fontsize=18)\n", | |
| "plt.tick_params(axis='x', which='major', labelsize=16)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=16)\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "ax.set_xlim(-20, 20)\n", | |
| "plt.show()\n", | |
| "#EUDC\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "EUDC_hist = fvaluesDF.dEUDC.hist(bins=25, color='red') \n", | |
| "EUDC_hist.set_xlabel(\"EUDC error [gCO$_2$ km$^{-1}$]\",fontsize=14)\n", | |
| "EUDC_hist.set_ylabel(\"frequency\",fontsize=14)\n", | |
| "plt.title('EUDC CO$_2$ emission error distribution', fontsize=20)\n", | |
| "plt.ylabel(\"frequency\",fontsize=18)\n", | |
| "plt.tick_params(axis='x', which='major', labelsize=16)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=16)\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "ax.set_xlim(-20, 20)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Comparative emission error per driving cycle (gCO$_2$ km$^{-1}$)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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kjZx+zfE6r5Z0AWTmH4A9gQOB9YHs03EkSZIkaeT0K/G6KiL2jYhFEbEpQGbe\nm5kfBJ6CiZfGwPT0NJdecz6XXnM+09PTgw5H0gB5PpBU4/lAnerLUMPMPDYi1gfeTFFco37d4RFx\nQT+OIw3KVTf+mXMWvYcXvuSvABx9/PpsvfbHWGe1TQccmTTeFsZCppjivIO+MehQHnDTsn/n+mee\nwMsOuAmAIw5flceeuxur3v/4AUe2pCmmBh2CNPb8fqBuRGbvnVER8TDgUcA/MvOuvkU1hCIiZ/Nc\nqXMRwY8/fsWgw3jA9PQ0R/9+F7727QtZsKC2DPbda2P23OwkFiwYnqsy7HzQevg61ThZGAsHHcIS\nppnmom0P57unXbfE+eAV2z+OJ595AAuG7Cotkzk56BCkvvF8MDueD+ZORJCZS9W46LrHKyJWA94N\n7A6sW7f8SuAY4BOZeUPPkWpeG8Zft6/lWl5w5CXU51cLFsDzX3gJJ+zzMVZn9cEF18BfuDVuJnNy\nqH6MufSa89lj648vdT7Y/Y23ccPmW/LENZ8yuOAa7HzQekziFy2pKtdxHS874Kalzgcv3f8mTj3z\nuqH6fqDh0FXiFRHPBI4HHkNRrfDPwO3AisBGwLuAvSPiJZl5Xp9jlSRJ0jw1jD/ELFjuSOD+JZYv\nWG5ZNn3ri/0hRkvpOPGKiMcAJ5XbvAk4IjPvrlv/UGAf4BDgpIjY1J4vdWvYTqpQDjX84Zns+aol\nhxqe/KMnsedH3zN0Qw09sUrVecLqm3D08euzx55Lng9+fML67LnZJoMNTtKc8nygbnXT4/Uuip6t\nrTPzd40ryyTsixHxK+Bs4J3Ae/oSpTRACxYsYOu1P8a+e72HnXcrJs/+6Pj12Gadjw1V0iWpep4P\nJNV4PlC3ukm8XgQc1SzpqpeZv4uIbwK7YOKlMbHOapuy1ioncfm5RYHO/7f5Jp5UpXnK84GkGs8H\n6kY3ide6wKc7bHse8Mquo5lDEbEm8BlgJyCAnwFvz8yrBxqYhtaCBQuGary2pMHxfCCpxvOBOtVN\nSr4YWK7DtsuW7YdSOR/tl8CTgL2BVwNPBH5RrpMkSZKkvukm8boUeE6HbSeAy7qOZu4cQNGDt1tm\nnpSZJwG7lssOHGBckiRJksZQN4nX8cDLIuIFMzWKiOcDLwOOnU1gFdsFODczHyidl5lXAmcBuw0q\nKEmSJEnjqZvE6zPA1cDxEfGRiFi/fmVErB8RhwAnANcAn+1fmH23CcU1yBpdAGw8x7FIkiRJGnMd\nF9fIzDvua/ezAAAZKUlEQVTK3qyTKKoVHhQRdwD/oCgzvyJFkYrLgF0z844K4u2XlYFbmyy/BXj0\nHMciSZIkacx1U9WQzLwkIp4G/DuwB0XP0eOB24EzKIYXfiUz7+p3oJIkSZrf1lpzbXY+aL1Bh9HC\nJLBw0EE0tdaaaw86BNFl4gUPXCj50PI2qm6lec9Wq54wAKamph7498TEBBMTE/2OS5IkSS0suvqq\nQYfQUgRkTg06DA2xrhOvMXEBRW9do42BC1ttVJ94SZIkSVKnOi6uERHLRMRHI+L1bdq9oSy+McyX\n7T4ReGZErFtbUP77WRTFQSRJkiSpb7pJjl4NvBv4dZt2vwIOAvbqNag58GXgSuCEiNg1InalKJd/\nFXD4IAOTJEmSNH66Sbz2BH6Wmb+dqVG5/mTgVbMJrEpl8Y8dgEuAI4GjgMuBHS0MIkmSJKnfupnj\ntQXwyQ7b/hJ4Z/fhzJ3MvAZ4+aDj0NKGu2IRWLVIkiQ1mpwcdAQadt0kXisDN3TY9ka8HpZ6NMwV\ni8CqRZIkaWnWYFM73Qw1vANYtcO2qwB3dh+OJEmSJI2fbhKvC4Dnddj2uWV7SZIkSZr3ukm8jgV2\niojdZmpUVgh8LnDMbAKTJEmSpHHRTeL1JeAy4OiI+O/6a2BBcR2siPgwcDRFtcAv9StISZIkSRpl\nHSdemXk38CLgCuA/gcsj4taIWBQRt1KUY39fuf7FmXlPFQFLg2bVIkmS1MjiGmonMrO7DSJWAPYH\n9gA2AVYEbgf+TDG88CtlkjZWIiK7fa4kSbMXEfz441cMOoyRs/NB6+HnljR3iqrHg45CwyAiyMxo\nXN5NOXkAyp6sQ8ubJEmSJKmNbuZ4SZIkSZJ60HWPF0BE/KJNkwTuBhYBPwVOcJyeJEmSpPmqp8QL\nWB94KLBa+f/byvuVyvsbKXrTXggcCJwVETtn5j97DVSSJEmSRlWvQw23B+4C/gd4bGaunJkrA48F\nPlGuewawKvApYFvg4NmHKw2eVYskSVIjqx6rna6rGgJExHHAPzPz1S3Wfwt4eGa+pPz/ScCTM3OD\n2QQ7SFY1VI1Vi6S5ZVXD3ljVUJIGo29VDUs7AAfNsP4M4KN1//8Z8NwejyVJmsfWWnNtdj5ovUGH\n0cIksHDQQTS11pprDzoESVKdXhMvgI3arKvP8qYpim1IktSVRVdfNegQWip6wKcGHYYkaQT0Osfr\nZ8AbIuIVjSsi4pXA64FT6hZvDlzZ47EkSZIkaaT1OsdrHeBMYHXg78Bl5aoNgMeXy56VmVdFxArA\nj4CTMvPTfYl6AJzjpRrneEmq8XwgSWrUao5XTz1emXkV8FTgk8DtwFbl7Y5y2VPLNmTmPZm5wygn\nXVI9qxZJkqRGVj1WOz31eM1H9nhJkhrZ4yWpxvOBavra4yVJkuwBlyR1rqMer4jYBrg4M2/uaucR\ny1AMQTw/M+/oLcThYI+XJEmSWrHHSzWz7fE6A3h+D8ddqdz26T1sK0mSJEljodPreAWwSkR0ezXG\nlVnyel6SJEmSNO90cwHlz5S3btnpqrEyNWXlIkmStCTnfKqdTud4zfal9I3MvHKW+xgo53ipxjHc\nkiRJaqXVHC/LyXfIxEs1Jl6SauwBlyQ1MvGaJRMv1Zh4SarxfCBJauR1vCRJkiRpQEy8JEmSJKli\nJl5Sl6xaJEmSGjnfU+04x6tDzvGSJDVyjpekGs8HqnGOlyRJfWYPuCSpU133eEXEQ4GXAxdn5nmV\nRDWE7PGSJElSK/Z4qaafPV73Al8GNpt1VJIkSZI0D3SdeGXmNHA1sGL/w5EkSZKk8dPrHK9vAHtH\nxEP6GYw0CqxaJEmSGjnnU+30VNUwInYEPgGsABwGXArc1dguM0+fbYBViYgrgbUbFifw0sw8sUl7\n53gJcAy3JEmSWms1x6vXxGu6YVHjTgLIzFym653PkYi4ArgImGpYdXFm/qNJexMvASZekh40NWUv\nuCRpSf1OvPbppF1mfqPrnc+RMvE6IzNf02F7Ey8BJl6SHuT5QJLUqFXitWwvOxvmhEqSJEmShs18\nv4DyLhHxz4i4JyLOiYjdBh2QJEmSpPHTc+IVEQ+PiIUR8aeIuLO8/SkipiLi4f0MsiInAm8Bngfs\nBdwNHBcRew00Kg09qxZJkqRGzvdUO73O8VoZOAN4MnAjcEm56knAahRFK56dmbf0Kc528ewInNJB\n01Mzc4cW+1gAnAs8JjPXbbI+J+u+cU9MTDAxMdFTvJKk8eAcL0k1ng9U0+/iGp8D3kDRY/SlzFxc\nLl8GOAA4FDgsM986q6g7j2cFli4N38xdmXnNDPt5N/BRYPXMvL5hncU1JElLsKqhpBoTL9X0O/Fa\nBPw4Mw9ssf5w4AWZ2UkyNDRMvCRJktQLEy/VtEq8ep3j9Vjg9zOs/13ZZmSUvXWvABY1Jl2SJEmS\nNBs9lZMHrgc2m2H9ZmWboRQRrwBeDPwI+BvweOBNwNMoki9JkiRJ6ptee7xOAvaLiAPLohRAUaAi\nIg4A9qWoGjisrgAeB3wS+ClwGEVVw+dn5vcGGZiGn/M5JElSI6seq51e53itApwDPIGiquHF5aoN\nKaoaXgZsk5k39ynOgXOOl2ocwy1JkqRW+jrHq0yotqQoRHEz8PTydhPwEeDp45R0SZLUjD3gkqRO\ndd3jFREPBV4OXJyZ51US1RCyx0s19nhJqvF8IElq1M8er3uBrzBzcQ1JkiRJUqnrxCszp4FFwIr9\nD0eSJEmSxk+vVQ2/AewdEQ/pZzDSKLBqkSRJ4yEihvqm8dJrVcMdgU8AK1CUYr8UuKuxXWaePtsA\nh4VzvCRJjZzjJUlq1GqOV68XUD6l7t+fBRo/dqJctkyP+5ckaejZAy5J6lSvPV77dNIuM7/R9c6H\nlD1ekiRJktrpW49XWU4+mWfl5CVJkiSpV72Wk/8ylpOXJEmSpI70Wk7+aiwnr3lqamrQEUiSJGnU\n9DrH64PAnsCWmXlv36MaQs7xUo1VzCRJktRKqzlevV7H62zgfuAPEfGWiHhBRGzXeJtVxJIkDTl7\nwCVJneq1x2u6YVHTcvKZOTbl5O3xUo09XpJqPB9Ikhr1+zper5tlPNKcqeLK7/3cpQm9JEnS+Oup\nx2s+ssdLktTIHi9JUqN+z/Gq3/FDImKNiFh+tvuSJEmSpHHUc+IVEZtHxC+AO4BFwLbl8sdExM8j\nYqc+xShJkiRJI62nxCsingacATwBOLJ+XWbeADwU2GfW0UmSNMQmJwcdgSRpVPRa1fBE4MnAZsAK\nwA3ATpn5i3L9fwF7ZuaGfYx1oJzjJUmSJKmdfs/xejbw5cy8k6VLyUMx9HD1HvctSZIkSWOl18Rr\nBeAfM6xfscf9SpIkSdLY6TXxuhzYYob1OwAX9rhvSZIkSRorvSZe3wb2bqhcmAAR8U7gBcBRs4xN\nkiRJksZCr8U1lgdOBrYD/gJsBJwPrAY8DjgFeGFmTvcv1MGyuIYkqdHUVHGTJKmmVXGNnhKvcofL\nAm8BXkVR4TCASynKy382M+/vPdzhY+IlSWoUAX40SJLq9T3xmm9MvCRJjUy8JEmN+l1OXpIkSZLU\nIRMvSZIkSaqYiZckSZIkVczES5KkHk1ODjoCSdKosLhGhyyuIUmSJKkdi2tIkiRJ0oCYeEmSJElS\nxUy8JEmSJKliJl6SJEmSVLGxSrwi4h0RcWJEXBsR0xFx8Axt94+IiyLinoj4S0QcOJexSpJG39TU\noCOQJI2KsUq8gH8HVgOOA1qWIIyI/YEvAt8Dng8cDRxm8iVJ6sbChYOOQJI0KsaynHxELAPcB0xl\n5oearLsW+GFm7lu3/KvALsDjM3Nxk31aTl6StIQI8KNBklTPcvIP2hpYFfhWw/KjgFWAbec8IkmS\nJEljbT4mXpuU939uWH4BEMDGcxuOJEmSpHE3HxOvlcv7WxuW39KwXpIkSZL6YtlBB9BKROwInNJB\n01Mzc4eq4wGYqitfNTExwcTExFwcVpI0pCYnBx2BJGlUDG3iBZwFbNRBu7u63G+tp+vRwPV1y2s9\nXbfQwpR1gyVJdfxYkCR1amgTr8y8B7ikgl3X5nJtwpKJV21u14UVHFOSJEnSPDYf53idA9wEvKph\n+d7AzRQ9bZIkSZLUN0Pb49WLiNgCWBdYply0cUTsXv77h5l5T2beHxEfBD4fEdcCPwN2BF4LvDkz\n75/jsCVJkiSNubG6gHJEfB14TYvV62Xmorq2+wPvBNYBFgGfyswvzbBvL6AsSZIkaUatLqA8VolX\nlUy8JEmNpqYssCFJWpKJ1yyZeEmSGkWAHw2SpHqtEq/5WFxDkiRJkuaUiZckSZIkVczES5IkSZIq\nZuIlSZIkSRUz8ZIkqUeTk4OOQJI0Kqxq2CGrGkqSJElqx6qGkiRJkjQgJl6SJEmSVDETL0mSJEmq\nmImXJEmSJFXMxEuSpB5NTQ06AknSqLCqYYesaihJahQBfjRIkupZ1VCSJEmSBsTES5IkSZIqZuIl\nSZIkSRUz8ZIkSZKkipl4SZLUo8nJQUcgSRoVVjXskFUNJUmSJLVjVUNJkiRJGhATL0mSJEmqmImX\nJEmSJFXMxEuSJEmSKmbiJUlSj6amBh2BJGlUWNWwQ1Y1lCQ1igA/GiRJ9axqKEmSJEkDYuIlSZIk\nSRUz8ZIkSZKkipl4SZIkSVLFTLwkSerR5OSgI5AkjQqrGnbIqoaSJEmS2rGqoSRJkiQNiImXJEmS\nJFXMxEuSJEmSKmbiJUmSJEkVG6vEKyLeEREnRsS1ETEdEQe3aHdqub7+tjgi3jrXMUuSRtfU1KAj\nkCSNirGqahgRFwL/AH4HvB5YmJkfatLul8BKwAFAfcWRKzPzhhb7tqqhJGkJEeBHgySpXquqhssO\nIpiqZObGABGxDPCGNs3vyMxfVx+VJEmSpPlurIYaSpIkSdIwms+J12YRcVtE/Csi/hgR+w46IEmS\nJEnjaayGGnbhNOCbwCUUc71eA3wlIh6XmYcMNDJJkiRJY2doE6+I2BE4pYOmp2bmDt3sOzOnGhad\nFBHHAu+LiM9k5l3d7E+SND9NTg46AknSqBjaxAs4C9iog3b9SpK+A+wGPAU4r1mDqbq6wRMTE0xM\nTPTp0JKkUWQ5eUlSp4Y28crMeyiGAg6NKT9hJUmSJPVgPhfXaPRq4G7g/EEHIkmSJGm8DG2PVy8i\nYgtgXWCZctHGEbF7+e8fZuY9EbEt8G7gWGARRXGN1wIvBt7j/C5JGl8RS13PcqikV2OWpLEV43SS\nj4ivU1QobGa9zFwUEU8A/hf4N2BV4D7gT8D/ZubRM+w7x+m5kiRJktR/EUFmLvVL31glXlUy8ZIk\nSZLUTqvEyzlekiRJklQxEy9JkiRJqpiJlyRJkiRVzMRLkiRJkipm4iVJkiRJFTPxkiRJkqSKmXhJ\nkiRJUsVMvCRJkiSpYiZekiRJklQxEy9JkiRJqpiJlyRJkiRVzMRLkiRJkipm4iVJkiRJFTPxkiRJ\nkqSKmXhJkiRJUsVMvCRJkiSpYiZekiRJklQxEy9JkiRJqpiJlyRJkiRVzMRLkiRJkipm4iVJkiRJ\nFTPxkiRJkqSKmXhJkiRJUsVMvCRJkiSpYiZekiRJklQxEy9JkiRJqpiJlyRJkiRVzMRLkiRJkipm\n4iVJkiRJFTPxkiRJkqSKmXhJkiRJUsVMvCRJkiSpYiZekiRJklQxEy9JkiRJqpiJlyRJkiRVzMRL\nkiRJkio2NolXRDwxIg6NiAsi4o6IuDYiToiIf2vRfv+IuCgi7omIv0TEgXMdsyRJkqT5YWwSL+B5\nwATwNWAX4A3AasC5EbFZfcOI2B/4IvA94PnA0cBhJl+SJEmSqhCZOegY+iIiVs7MWxqWrQhcCZyY\nma8tly0DXAv8MDP3rWv7VYqE7fGZubjJ/nNcnitJkiRJ1YgIMjMal49Nj1dj0lUuux24BFijbvHW\nwKrAtxqaHwWsAmxbVYwaD6eeeuqgQ5A0JDwfSKrxfKB2xibxaiYiHg1sClxYt3iT8v7PDc0vAALY\neA5C0wjzxCqpxvOBpBrPB2pnrBMv4HPl/Wfrlq1c3t/a0PaWhvWSJEmS1BdDm3hFxI4RMd3B7Rct\ntv9P4BXAmzLzr3MbvSRJkiQ9aGiLa0TECsDaHTS9KzOvadj29cBhwPsy86NN1n0eWD0zr69bvhpw\nPUWi9oUm8QznEyVJkiRpqDQrrrHsIALpRGbeQ1EYoysRsTdFYvU/jUlXqTaXaxOKRKumNrfrwqW2\noPmTJ0mSJEmdGNqhhr2IiJdSXMfr8Mx8T4tm5wA3Aa9qWL43cDNwVnURSpIkSZqPhrbHq1sRsR3w\nbeAPwJERsVXd6nsz8w8AmXl/RHwQ+HxEXAv8DNgReC3w5sy8f24jlyRJkjTuhnaOV7ciYhI4uMXq\nqzJz/Yb2+wPvBNYBFgGfyswvVRulJEmSpPlobBIvSZIkSRpWYzXHS/NX3eUFroiI5Vu0uTIiFkfE\nghbbtrotLoey1trv06TNHRFxdUScEhELI2KDDmLeMCIOjYjzI+K2iLg3Iv4WET+IiH1bPQ5JvYuI\n7We6FEnZZp2yzV/rlvm+l0ZUD5/zR5TLXzPDPifLNgc3LD+1Yd/3RcQtEXFRRPxfRLw2Ih7eJt6I\niD0i4piIWBQRd0fEnRFxYUR8KSK2mf2zokEYmzleEpAUlyB4O/DxFutn2naKouJlM1c2WfYH4Pjy\n3w8FHgNsBXwAeH9EHAq8KzMXN25YnqgPLo93DvBz4A7gscB2wJeB1wPPmCFmSXPP9700mrr5nE9m\n/s5Q367ZsgS+Ue4zgEcC61PUFHg5cEhE7JeZP27cOCIeCxwDbAPcDpwCXF7uZwNgT+DfI+ItmXlY\nBzFqiJh4aZzcSnGye29EfCUzb+lm48z8ry6P94fM/FDjwojYHjgCeBvwEOCNDevfR3Hyvwp4eWb+\npsk+ngcc1GU8kqrn+14aUT18zrcz06WGjsjM05doXPRovxP4L+DYiHhuZp5Zt/6hwMnAUygKxr0p\nM//RsI+Hlft4VH8eguaSQw01Tu6iOJmtBEwOKojMPA14AfAv4ICIeGptXUSsU8b2L+CFzb58lfv4\nKbDzHIQrqQ9830tqJzP/lZkfAT5M8QPNZxuavAP4N+DMzHx1Y9JV7uOuMoH8ROUBq+9MvDRuPk/R\nJX9gRDxhUEFk5sXA0RS/hu1Vt2pfYDng+5l5UZt93FddhJL6zfe9pA59ArgbeFpEPLlu+f4UI3fa\n9sx5rhhNDjXUWMnMxRHxXuB7wMeAPTrdtrwkQTP3ZObHegjnVODVLDlf41kUJ9WWE/sljbRT8X0v\nDaUKPud7kpl3RsRvKc4NzwAuiog1Keap3wecPtP2Gl0mXho7mXlMRJwDvDQitsnMszvctNV14G6j\nSOK69bfyfrW6ZY8v76/pYX+Shp/ve2l49ftzfjYazxW188TNmfmvOY5Fc8ShhhpX76QY7tPxGOjM\nXKbFbZUeY6hNuvViedL84fteGlIVfM7PhueKecjES2MpM88Fvg9sFREvH1AYq5f3N9Yt+3t5v8Yc\nxyKpMF3ez/T5V1s3PUObVnzfS6Ov0/NE0tt5ApY+V9TOE6t4Pb/xZeKlcfafwP3ARyJiuQEcfweK\nk/J5dcvOpPiVa8cBxCMJalXCZvqFe9Xy/rYe9u/7Xhp9lZ4nIuIRwBblf88DyMxrgEUU04C2a7Gp\nRpyJl8ZWZl4OHAasB7xlLo8dERtRFPZIimtx1HydYuLs7mWbmfbhL15S/10M3As8KSIe3aLNNuX9\nH7vZse97aWz8keLHkq1naLN1XdtuHURxAfbfltVQaw4vj/uBdjvwXDGaTLw07j5E8cvV+4FHzMUB\nywup/oSifPRhmXl+bV1mXkVxEdWHAD+KiC1a7GPnch+S+igz7wW+S/H+/J/G9WVlsXdTJE9HdLpf\n3/fSWDmO4rvDrhGxQ+PKiHgd8DTgMooe7Y5ExEPKi6m/j+IHoLc1NPk0RSL37Ig4KiKWukhyRDw8\nIqYo5rJrxFjVUGMtM2+NiEOAj9cWtWo7Q5lZgOMzs/FXrc3qtnkI8FhgK2BjYDHwSeA9TWL6SEQs\nQ3FB1V9HxNnAb4A7y31sBzwR+FWbhyepN+8EtgReFxHbAKcAtwPrALtR/Ejz0cw8o8m2vu+lEdXm\nc/64zPwTQGbeHhGvBb4D/DQifgL8CViGovz79sCtwKsys9n3iqA4vzyn/P8jgfUp3uePBq4F9s3M\nc+o3ysy7I+L5FHPU9wJ2iYhTKK5PGsAGFEOWHwm8ucuHryEQzV8v0miJiGng6sxcp8m65YGLgHUp\nEq/lM3O6bv3iDg7xusw8smy/D/C1hvV3UZyE/0Lx69c3M/OvbWLeEHgj8ByKa3esANwM/IHiOmTf\n8gKJUjUi4mHAW4GXAhtSDPu5mSLx+UJmntzQ3ve9NKK6/Zyv225jih9qJijKvU8DVwMnA5/MzKub\nHOuXLDlHazHFDyzXUfRm/YjiYup3t4l5d+CVFInequWxFwFnAF/LzPNm2FxDysRLkiRJkirmHC9J\nkiRJqpiJlyRJkiRVzMRLkiRJkipm4iVJkiRJFTPxkiRJkqSKmXhJkiRJUsVMvCRJkiSpYiZekiRJ\nklQxEy9JkiRJqtj/BwA4Hp+BLJmMAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc4ff710>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "The purple box represents the 1st and 3rd quartile.\n", | |
| "The dark purple line is the median.\n", | |
| "The yellow dot is the mean.\n", | |
| "the whiskers show the min and max values.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Alternatively show boxplots\n", | |
| "toboxplot = [fvaluesDF.dNEDC,fvaluesDF.dUDC,fvaluesDF.dEUDC]\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "# Create the boxplot with fill color\n", | |
| "bp = ax.boxplot(toboxplot, sym='', patch_artist=True, whis=10000, showmeans=True, meanprops=(dict(marker='o',markerfacecolor='yellow')))\n", | |
| "for box in bp['boxes']:\n", | |
| " # change outline color\n", | |
| " box.set( color='black', linewidth=1)\n", | |
| " # change fill color\n", | |
| " box.set( facecolor = '#b78adf' )\n", | |
| " ## Custom x-axis labels\n", | |
| "ax.set_xticklabels(['NEDC', 'UDC', 'EUDC'],fontsize=20)\n", | |
| "## Remove top axes and right axes ticks\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "#Set y axis title\n", | |
| "plt.title('CO$_2$ emission error by driving cycle', fontsize=20)\n", | |
| "plt.ylabel(\"error [gCO$_2$ km$^{-1}$]\",fontsize=18)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=16)\n", | |
| "ax.set_ylim(-20, 20)\n", | |
| "plt.setp(bp['medians'], color = 'purple', linewidth = 2)\n", | |
| "plt.show()\n", | |
| "print('The purple box represents the 1st and 3rd quartile.\\nThe dark purple line is the median.\\nThe yellow dot is the mean.\\nthe whiskers show the min and max values.')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Error statistics per technology type (filtered for absolute errors above 25g CO$_2$ km$^{-1}$)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Create a dataframe with the NECD, UDC, EUDC and vehicle model and case\n", | |
| "CarMod = pd.DataFrame({'dNEDC':fvaluesDF.dNEDC,'dUDC':fvaluesDF.dUDC,'dEUDC':fvaluesDF.dEUDC,'Model code':fvaluesDF.Model,'Case':fvaluesDF.Case}) \n", | |
| "#filter for absolute errors above 25g CO2 per km\n", | |
| "mod_cases = CarMod[abs(CarMod.dNEDC) < 25]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Technology type</th>\n", | |
| " <th>Technology code</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Base case</th>\n", | |
| " <td>BC</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Gear configuration A</th>\n", | |
| " <td>GCA</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Gear configuration B</th>\n", | |
| " <td>GCB</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Start/Stop</th>\n", | |
| " <td>NOSS</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Break energy recuperation</th>\n", | |
| " <td>BERS</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variable valve lifting</th>\n", | |
| " <td>VVL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Direct injection/Multipoint injection</th>\n", | |
| " <td>DI/MPI</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Thermal management</th>\n", | |
| " <td>ThM</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Technology type Technology code\n", | |
| "Base case BC\n", | |
| "Gear configuration A GCA\n", | |
| "Gear configuration B GCB\n", | |
| "Start/Stop NOSS\n", | |
| "Break energy recuperation BERS\n", | |
| "Variable valve lifting VVL\n", | |
| "Direct injection/Multipoint injection DI/MPI\n", | |
| "Thermal management ThM" | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#Print a dictionary with the tested technologies and their identification codes\n", | |
| "tec = pd.DataFrame(index=['Base case','Gear configuration A','Gear configuration B','Start/Stop','Break energy recuperation','Variable valve lifting','Direct injection/Multipoint injection','Thermal management'])\n", | |
| "tec['Technology code'] = ['BC','GCA','GCB','NOSS','BERS','VVL','DI/MPI','ThM']\n", | |
| "tec.columns.name='Technology type'\n", | |
| "tec" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Function that assigns the number of case to the specific technology tested for each vehicle model\n", | |
| "def assign_technol_perCarAndCase(df):\n", | |
| " #looks for the case # in the input file and assigns a technology\n", | |
| " df_basecase = df[mod_cases['Case'] <= 27]\n", | |
| " df_gb1 = df[(mod_cases['Case'] > 27) & (mod_cases['Case'] <= 54)]\n", | |
| " df_gb2 = df[(mod_cases['Case'] > 54) & (mod_cases['Case'] <= 81)]\n", | |
| " df_ss = df[(mod_cases['Case'] > 81) & (mod_cases['Case'] <= 108)]\n", | |
| " df_bers = df[(mod_cases['Case'] > 108) & (mod_cases['Case'] <= 135)]\n", | |
| " #some vehicles have more possible technologies than others (long vs short) and an additional technology assignment is performed for the former group\n", | |
| " In_long = (mod_cases['Model code'] == '500') | (mod_cases['Model code'] == 'A4') | (mod_cases['Model code'] == 'Giulietta') | (mod_cases['Model code'] == 'Polo') | (mod_cases['Model code'] == 'Punto')\n", | |
| " In_short = (mod_cases['Model code'] == '308') | (mod_cases['Model code'] == 'Astra') | (mod_cases['Model code'] == 'X1') | (mod_cases['Model code'] == 'Zafira')\n", | |
| " I_vvl = (mod_cases['Case'] >= 136) & (mod_cases['Case'] <= 162)\n", | |
| " df_vvl = df[In_long & I_vvl]\n", | |
| " I_dimpi = (mod_cases['Case'] >= 163) & (mod_cases['Case'] <= 189)\n", | |
| " df_dimpi = df[In_long & I_dimpi]\n", | |
| " I_short_tm = (mod_cases['Case'] >= 136)\n", | |
| " I_long_tm = (mod_cases['Case'] >= 190)\n", | |
| " I_tm = (In_short & I_short_tm) | (In_long & I_long_tm)\n", | |
| " df_tm = df[I_tm]\n", | |
| " #Append to the original DF a column with the technology IDcode\n", | |
| " pd.options.mode.chained_assignment = None # default='warn'\n", | |
| " df_basecase.loc[:,'Tecno'] = 'BC'\n", | |
| " df_gb1.loc[:,'Tecno'] = 'GCA'\n", | |
| " df_gb2.loc[:,'Tecno'] = 'GCB'\n", | |
| " df_ss.loc[:,'Tecno'] = 'NOSS'\n", | |
| " df_bers.loc[:,'Tecno'] = 'BERS'\n", | |
| " df_vvl.loc[:,'Tecno'] = 'VVL'\n", | |
| " df_dimpi.loc[:,'Tecno'] = 'DI/MPI'\n", | |
| " df_tm.loc[:,'Tecno'] = 'ThM'\n", | |
| " bigdata = pd.concat([df_basecase,df_gb1,df_gb2,df_ss,df_bers,df_vvl,df_dimpi,df_tm], ignore_index=False)\n", | |
| " return bigdata" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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0bv0GYwdy9wBMkiQtcpYSS9L49Nvp8/ELpYmIe/WzbGnaeOMjaZJYSixpktT9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\nleVnq6qI8+NaVWFsu1ElSZIkaSG1fme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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb1e2c88>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "The green box represents the 1st and 3rd quartile.\n", | |
| "The dark purple line is the median.\n", | |
| "The yellow dot is the mean.\n", | |
| "the whiskers show the min and max values.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Plot the NEDC errors per technology type in a boxplot\n", | |
| "tech = assign_technol_perCarAndCase(mod_cases)\n", | |
| "techBC = tech[tech['Tecno'] == 'BC']\n", | |
| "techGCA = tech[tech['Tecno'] == 'GCA']\n", | |
| "techGCB = tech[tech['Tecno'] == 'GCB']\n", | |
| "techNOSS = tech[tech['Tecno'] == 'NOSS']\n", | |
| "techBERS = tech[tech['Tecno'] == 'BERS']\n", | |
| "techVVL = tech[tech['Tecno'] == 'VVL']\n", | |
| "techDIMPI = tech[tech['Tecno'] == 'DI/MPI']\n", | |
| "techThM = tech[tech['Tecno'] == 'ThM']\n", | |
| "techboxplot = [techBC.dNEDC,techGCA.dNEDC,techGCB.dNEDC,techNOSS.dNEDC,techBERS.dNEDC,techVVL.dNEDC,techDIMPI.dNEDC,techThM.dNEDC]\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "# Create the boxplot with fill color\n", | |
| "bp = ax.boxplot(techboxplot, sym='', patch_artist=True, whis=10000, showmeans=True, meanprops=(dict(marker='o',markerfacecolor='yellow')))\n", | |
| "for box in bp['boxes']:\n", | |
| " # change outline color\n", | |
| " box.set( color='black', linewidth=1)\n", | |
| " # change fill color\n", | |
| " box.set( facecolor = 'green' )\n", | |
| " ## Custom x-axis labels\n", | |
| "ax.set_xticklabels(['BC', 'GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'],fontsize=20)\n", | |
| "## Remove top axes and right axes ticks\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "#Set y axis title\n", | |
| "plt.title('NEDC CO$_2$ emission error by technology type', fontsize=20)\n", | |
| "plt.ylabel(\"error [gCO$_2$ km$^{-1}$]\",fontsize=18)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=18)\n", | |
| "ax.set_ylim(-20, 20)\n", | |
| "plt.setp(bp['medians'], color = 'purple', linewidth = 2)\n", | |
| "plt.show()\n", | |
| "print('The green box represents the 1st and 3rd quartile.\\nThe dark purple line is the median.\\nThe yellow dot is the mean.\\nthe whiskers show the min and max values.')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Descriptive statistics for **NEDC** CO$_2$ emission error per technology type" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>NEDC error</th>\n", | |
| " <th>BC</th>\n", | |
| " <th>GCA</th>\n", | |
| " <th>GCB</th>\n", | |
| " <th>NOSS</th>\n", | |
| " <th>BERS</th>\n", | |
| " <th>VVL</th>\n", | |
| " <th>DI/MPI</th>\n", | |
| " <th>ThM</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>1.41</td>\n", | |
| " <td>1.09</td>\n", | |
| " <td>1.03</td>\n", | |
| " <td>2.65</td>\n", | |
| " <td>-1.39</td>\n", | |
| " <td>2.71</td>\n", | |
| " <td>0.73</td>\n", | |
| " <td>1.11</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdError</th>\n", | |
| " <td>0.13</td>\n", | |
| " <td>0.14</td>\n", | |
| " <td>0.19</td>\n", | |
| " <td>0.18</td>\n", | |
| " <td>0.18</td>\n", | |
| " <td>0.19</td>\n", | |
| " <td>0.22</td>\n", | |
| " <td>0.14</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>1.34</td>\n", | |
| " <td>1.29</td>\n", | |
| " <td>0.78</td>\n", | |
| " <td>2.41</td>\n", | |
| " <td>-0.99</td>\n", | |
| " <td>3.09</td>\n", | |
| " <td>0.39</td>\n", | |
| " <td>1.24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>2.09</td>\n", | |
| " <td>2.17</td>\n", | |
| " <td>2.89</td>\n", | |
| " <td>2.83</td>\n", | |
| " <td>2.74</td>\n", | |
| " <td>2.2</td>\n", | |
| " <td>2.53</td>\n", | |
| " <td>1.99</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variance</th>\n", | |
| " <td>4.37</td>\n", | |
| " <td>4.69</td>\n", | |
| " <td>8.33</td>\n", | |
| " <td>8.01</td>\n", | |
| " <td>7.52</td>\n", | |
| " <td>4.86</td>\n", | |
| " <td>6.42</td>\n", | |
| " <td>3.95</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <td>-0.54</td>\n", | |
| " <td>0.34</td>\n", | |
| " <td>0.27</td>\n", | |
| " <td>-0.52</td>\n", | |
| " <td>-0.41</td>\n", | |
| " <td>-0.59</td>\n", | |
| " <td>-0.96</td>\n", | |
| " <td>-0.51</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Skweness</th>\n", | |
| " <td>-0.32</td>\n", | |
| " <td>-0.5</td>\n", | |
| " <td>0.32</td>\n", | |
| " <td>-0.23</td>\n", | |
| " <td>0.04</td>\n", | |
| " <td>-0.35</td>\n", | |
| " <td>-0.04</td>\n", | |
| " <td>-0.08</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Range</th>\n", | |
| " <td>10.07</td>\n", | |
| " <td>12.43</td>\n", | |
| " <td>16.08</td>\n", | |
| " <td>11.46</td>\n", | |
| " <td>13.27</td>\n", | |
| " <td>9.64</td>\n", | |
| " <td>10.46</td>\n", | |
| " <td>9.33</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Minimum</th>\n", | |
| " <td>-4.45</td>\n", | |
| " <td>-6.37</td>\n", | |
| " <td>-5.73</td>\n", | |
| " <td>-3.81</td>\n", | |
| " <td>-7.11</td>\n", | |
| " <td>-2.38</td>\n", | |
| " <td>-4.36</td>\n", | |
| " <td>-4.03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Maximum</th>\n", | |
| " <td>5.62</td>\n", | |
| " <td>6.06</td>\n", | |
| " <td>10.35</td>\n", | |
| " <td>7.65</td>\n", | |
| " <td>6.16</td>\n", | |
| " <td>7.26</td>\n", | |
| " <td>6.1</td>\n", | |
| " <td>5.31</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sum</th>\n", | |
| " <td>354</td>\n", | |
| " <td>265</td>\n", | |
| " <td>250</td>\n", | |
| " <td>644</td>\n", | |
| " <td>-339</td>\n", | |
| " <td>365</td>\n", | |
| " <td>99</td>\n", | |
| " <td>239</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Count</th>\n", | |
| " <td>252</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>135</td>\n", | |
| " <td>135</td>\n", | |
| " <td>216</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Confidence level (95%)</th>\n", | |
| " <td>0.26</td>\n", | |
| " <td>0.28</td>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.36</td>\n", | |
| " <td>0.35</td>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.44</td>\n", | |
| " <td>0.27</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "NEDC error BC GCA GCB NOSS BERS VVL DI/MPI ThM\n", | |
| "Averages 1.41 1.09 1.03 2.65 -1.39 2.71 0.73 1.11\n", | |
| "StdError 0.13 0.14 0.19 0.18 0.18 0.19 0.22 0.14\n", | |
| "Median 1.34 1.29 0.78 2.41 -0.99 3.09 0.39 1.24\n", | |
| "StdDev 2.09 2.17 2.89 2.83 2.74 2.2 2.53 1.99\n", | |
| "Variance 4.37 4.69 8.33 8.01 7.52 4.86 6.42 3.95\n", | |
| "Kurtosis -0.54 0.34 0.27 -0.52 -0.41 -0.59 -0.96 -0.51\n", | |
| "Skweness -0.32 -0.5 0.32 -0.23 0.04 -0.35 -0.04 -0.08\n", | |
| "Range 10.07 12.43 16.08 11.46 13.27 9.64 10.46 9.33\n", | |
| "Minimum -4.45 -6.37 -5.73 -3.81 -7.11 -2.38 -4.36 -4.03\n", | |
| "Maximum 5.62 6.06 10.35 7.65 6.16 7.26 6.1 5.31\n", | |
| "Sum 354 265 250 644 -339 365 99 239\n", | |
| "Count 252 243 243 243 243 135 135 216\n", | |
| "Confidence level (95%) 0.26 0.28 0.37 0.36 0.35 0.38 0.44 0.27" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "grouped = tech.groupby('Tecno')\n", | |
| "gNEDCmean = grouped.dNEDC.mean()\n", | |
| "gNEDCsem = grouped.dNEDC.sem()\n", | |
| "gNEDCmedian = grouped.dNEDC.median()\n", | |
| "gNEDCstd = grouped.dNEDC.std()\n", | |
| "gNEDCvar = grouped.dNEDC.var()\n", | |
| "gNEDCskew = grouped.dNEDC.skew()\n", | |
| "gNEDCrange = grouped.dNEDC.max()-grouped.dNEDC.min()\n", | |
| "gNEDCmin = grouped.dNEDC.min()\n", | |
| "gNEDCmax = grouped.dNEDC.max()\n", | |
| "gNEDCsum = grouped.dNEDC.sum()\n", | |
| "gNEDCcount = grouped.dNEDC.count()\n", | |
| "gNEDC_CI95 = 2*grouped.dNEDC.sem()\n", | |
| "NEDCerrorsTec = pd.DataFrame(index=['Averages','StdError','Median','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['BC','GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'])\n", | |
| "NEDCerrorsTec.loc['Averages'] = pd.Series.round(gNEDCmean,2)\n", | |
| "NEDCerrorsTec.loc['StdError'] = pd.Series.round(gNEDCsem,2)\n", | |
| "NEDCerrorsTec.loc['Median'] = pd.Series.round(gNEDCmedian,2)\n", | |
| "NEDCerrorsTec.loc['StdDev'] = pd.Series.round(gNEDCstd,2)\n", | |
| "NEDCerrorsTec.loc['Variance'] = pd.Series.round(gNEDCvar,2)\n", | |
| "NEDCerrorsTec.loc['Kurtosis'] = [round(techBC.dNEDC.kurtosis(),2),round(techGCA.dNEDC.kurtosis(),2),round(techGCB.dNEDC.kurtosis(),2),round(techNOSS.dNEDC.kurtosis(),2),round(techBERS.dNEDC.kurtosis(),2),round(techVVL.dNEDC.kurtosis(),2),round(techDIMPI.dNEDC.kurtosis(),2),round(techThM.dNEDC.kurtosis(),2)]\n", | |
| "NEDCerrorsTec.loc['Skweness'] = pd.Series.round(gNEDCskew,2)\n", | |
| "NEDCerrorsTec.loc['Range'] = pd.Series.round(gNEDCrange,2)\n", | |
| "NEDCerrorsTec.loc['Minimum'] = pd.Series.round(gNEDCmin,2)\n", | |
| "NEDCerrorsTec.loc['Maximum'] = pd.Series.round(gNEDCmax,2)\n", | |
| "NEDCerrorsTec.loc['Sum'] = pd.Series.round(gNEDCsum)\n", | |
| "NEDCerrorsTec.loc['Count'] = pd.Series.round(gNEDCcount)\n", | |
| "NEDCerrorsTec.loc['Confidence level (95%)'] = pd.Series.round(gNEDC_CI95,2)\n", | |
| "NEDCerrorsTec.columns.name='NEDC error'\n", | |
| "NEDCerrorsTec" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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HNgCPzswvzz63kpomIn4NuFdmnlyOHrk2i2aT0twpm5+eCWzKzN+qNzcaRtl075vAv2Tm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\nTcCaMUlS21gzJknTZc2YJEmSJDWMwZg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XqO9rNM0Wpil1kDgk08kcjJsoHgm8DUyQ9L/d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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xbfbabe0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "The blue box represents the 1st and 3rd quartile.\n", | |
| "The dark purple line is the median.\n", | |
| "The yellow dot is the mean.\n", | |
| "the whiskers show the min and max values.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Plot the UDC errors per technology type in a boxplot\n", | |
| "techboxplot = [techBC.dUDC,techGCA.dUDC,techGCB.dUDC,techNOSS.dUDC,techBERS.dUDC,techVVL.dUDC,techDIMPI.dUDC,techThM.dUDC]\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "# Create the boxplot with fill color\n", | |
| "bp = ax.boxplot(techboxplot, sym='', patch_artist=True, whis=10000, showmeans=True, meanprops=(dict(marker='o',markerfacecolor='yellow')))\n", | |
| "for box in bp['boxes']:\n", | |
| " # change outline color\n", | |
| " box.set( color='black', linewidth=1)\n", | |
| " # change fill color\n", | |
| " box.set( facecolor = 'blue' )\n", | |
| "## Custom x-axis labels\n", | |
| "ax.set_xticklabels(['BC', 'GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'],fontsize=20)\n", | |
| "## Remove top axes and right axes ticks\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "#Set y axis title\n", | |
| "plt.title('UDC CO$_2$ emission error by technology type', fontsize=20)\n", | |
| "plt.ylabel(\"error [gCO$_2$ km$^{-1}$]\",fontsize=18)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=18)\n", | |
| "ax.set_ylim(-20, 20)\n", | |
| "plt.setp(bp['medians'], color = 'purple', linewidth = 2)\n", | |
| "plt.show()\n", | |
| "print('The blue box represents the 1st and 3rd quartile.\\nThe dark purple line is the median.\\nThe yellow dot is the mean.\\nthe whiskers show the min and max values.')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Descriptive statistics for **UDC** CO$_2$ emission error per technology type" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>UDC error</th>\n", | |
| " <th>BC</th>\n", | |
| " <th>GCA</th>\n", | |
| " <th>GCB</th>\n", | |
| " <th>NOSS</th>\n", | |
| " <th>BERS</th>\n", | |
| " <th>VVL</th>\n", | |
| " <th>DI/MPI</th>\n", | |
| " <th>ThM</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>1.55</td>\n", | |
| " <td>1.05</td>\n", | |
| " <td>0.46</td>\n", | |
| " <td>4.66</td>\n", | |
| " <td>-4.38</td>\n", | |
| " <td>4.65</td>\n", | |
| " <td>0.83</td>\n", | |
| " <td>1.17</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdError</th>\n", | |
| " <td>0.19</td>\n", | |
| " <td>0.21</td>\n", | |
| " <td>0.28</td>\n", | |
| " <td>0.31</td>\n", | |
| " <td>0.3</td>\n", | |
| " <td>0.24</td>\n", | |
| " <td>0.3</td>\n", | |
| " <td>0.24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>1.61</td>\n", | |
| " <td>1.31</td>\n", | |
| " <td>0.06</td>\n", | |
| " <td>4.09</td>\n", | |
| " <td>-4.89</td>\n", | |
| " <td>4.91</td>\n", | |
| " <td>0.58</td>\n", | |
| " <td>1.69</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>3.09</td>\n", | |
| " <td>3.28</td>\n", | |
| " <td>4.43</td>\n", | |
| " <td>4.83</td>\n", | |
| " <td>4.73</td>\n", | |
| " <td>2.84</td>\n", | |
| " <td>3.5</td>\n", | |
| " <td>3.59</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variance</th>\n", | |
| " <td>9.54</td>\n", | |
| " <td>10.79</td>\n", | |
| " <td>19.6</td>\n", | |
| " <td>23.28</td>\n", | |
| " <td>22.37</td>\n", | |
| " <td>8.07</td>\n", | |
| " <td>12.24</td>\n", | |
| " <td>12.87</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <td>0.6</td>\n", | |
| " <td>-0.28</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>-0.67</td>\n", | |
| " <td>0.47</td>\n", | |
| " <td>-0.51</td>\n", | |
| " <td>0.17</td>\n", | |
| " <td>0.83</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Skweness</th>\n", | |
| " <td>-0.42</td>\n", | |
| " <td>-0.34</td>\n", | |
| " <td>0.23</td>\n", | |
| " <td>0.12</td>\n", | |
| " <td>0.36</td>\n", | |
| " <td>-0.1</td>\n", | |
| " <td>-0.33</td>\n", | |
| " <td>-0.7</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Range</th>\n", | |
| " <td>19.8</td>\n", | |
| " <td>17.26</td>\n", | |
| " <td>29.72</td>\n", | |
| " <td>19.52</td>\n", | |
| " <td>25.27</td>\n", | |
| " <td>13.54</td>\n", | |
| " <td>19.08</td>\n", | |
| " <td>22.03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Minimum</th>\n", | |
| " <td>-10.59</td>\n", | |
| " <td>-8.93</td>\n", | |
| " <td>-15.26</td>\n", | |
| " <td>-5.04</td>\n", | |
| " <td>-14.97</td>\n", | |
| " <td>-1.6</td>\n", | |
| " <td>-10.1</td>\n", | |
| " <td>-12.43</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Maximum</th>\n", | |
| " <td>9.22</td>\n", | |
| " <td>8.34</td>\n", | |
| " <td>14.46</td>\n", | |
| " <td>14.48</td>\n", | |
| " <td>10.3</td>\n", | |
| " <td>11.94</td>\n", | |
| " <td>8.98</td>\n", | |
| " <td>9.59</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sum</th>\n", | |
| " <td>392</td>\n", | |
| " <td>254</td>\n", | |
| " <td>111</td>\n", | |
| " <td>1132</td>\n", | |
| " <td>-1065</td>\n", | |
| " <td>628</td>\n", | |
| " <td>112</td>\n", | |
| " <td>253</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Count</th>\n", | |
| " <td>252</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>135</td>\n", | |
| " <td>135</td>\n", | |
| " <td>216</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Confidence level (95%)</th>\n", | |
| " <td>0.39</td>\n", | |
| " <td>0.42</td>\n", | |
| " <td>0.57</td>\n", | |
| " <td>0.62</td>\n", | |
| " <td>0.61</td>\n", | |
| " <td>0.49</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>0.49</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "UDC error BC GCA GCB NOSS BERS VVL DI/MPI ThM\n", | |
| "Averages 1.55 1.05 0.46 4.66 -4.38 4.65 0.83 1.17\n", | |
| "StdError 0.19 0.21 0.28 0.31 0.3 0.24 0.3 0.24\n", | |
| "Median 1.61 1.31 0.06 4.09 -4.89 4.91 0.58 1.69\n", | |
| "StdDev 3.09 3.28 4.43 4.83 4.73 2.84 3.5 3.59\n", | |
| "Variance 9.54 10.79 19.6 23.28 22.37 8.07 12.24 12.87\n", | |
| "Kurtosis 0.6 -0.28 0.9 -0.67 0.47 -0.51 0.17 0.83\n", | |
| "Skweness -0.42 -0.34 0.23 0.12 0.36 -0.1 -0.33 -0.7\n", | |
| "Range 19.8 17.26 29.72 19.52 25.27 13.54 19.08 22.03\n", | |
| "Minimum -10.59 -8.93 -15.26 -5.04 -14.97 -1.6 -10.1 -12.43\n", | |
| "Maximum 9.22 8.34 14.46 14.48 10.3 11.94 8.98 9.59\n", | |
| "Sum 392 254 111 1132 -1065 628 112 253\n", | |
| "Count 252 243 243 243 243 135 135 216\n", | |
| "Confidence level (95%) 0.39 0.42 0.57 0.62 0.61 0.49 0.6 0.49" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "gUDCmean = grouped.dUDC.mean()\n", | |
| "gUDCsem = grouped.dUDC.sem()\n", | |
| "gUDCmedian = grouped.dUDC.median()\n", | |
| "gUDCstd = grouped.dUDC.std()\n", | |
| "gUDCvar = grouped.dUDC.var()\n", | |
| "gUDCskew = grouped.dUDC.skew()\n", | |
| "gUDCrange = grouped.dUDC.max()-grouped.dUDC.min()\n", | |
| "gUDCmin = grouped.dUDC.min()\n", | |
| "gUDCmax = grouped.dUDC.max()\n", | |
| "gUDCsum = grouped.dUDC.sum()\n", | |
| "gUDCcount = grouped.dUDC.count()\n", | |
| "gUDC_CI95 = 2*grouped.dUDC.sem()\n", | |
| "UDCerrorsTec = pd.DataFrame(index=['Averages','StdError','Median','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['BC','GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'])\n", | |
| "UDCerrorsTec.loc['Averages'] = pd.Series.round(gUDCmean,2)\n", | |
| "UDCerrorsTec.loc['StdError'] = pd.Series.round(gUDCsem,2)\n", | |
| "UDCerrorsTec.loc['Median'] = pd.Series.round(gUDCmedian,2)\n", | |
| "UDCerrorsTec.loc['StdDev'] = pd.Series.round(gUDCstd,2)\n", | |
| "UDCerrorsTec.loc['Variance'] = pd.Series.round(gUDCvar,2)\n", | |
| "UDCerrorsTec.loc['Kurtosis'] = [round(techBC.dUDC.kurtosis(),2),round(techGCA.dUDC.kurtosis(),2),round(techGCB.dUDC.kurtosis(),2),round(techNOSS.dUDC.kurtosis(),2),round(techBERS.dUDC.kurtosis(),2),round(techVVL.dUDC.kurtosis(),2),round(techDIMPI.dUDC.kurtosis(),2),round(techThM.dUDC.kurtosis(),2)]\n", | |
| "UDCerrorsTec.loc['Skweness'] = pd.Series.round(gUDCskew,2)\n", | |
| "UDCerrorsTec.loc['Range'] = pd.Series.round(gUDCrange,2)\n", | |
| "UDCerrorsTec.loc['Minimum'] = pd.Series.round(gUDCmin,2)\n", | |
| "UDCerrorsTec.loc['Maximum'] = pd.Series.round(gUDCmax,2)\n", | |
| "UDCerrorsTec.loc['Sum'] = pd.Series.round(gUDCsum)\n", | |
| "UDCerrorsTec.loc['Count'] = pd.Series.round(gUDCcount)\n", | |
| "UDCerrorsTec.loc['Confidence level (95%)'] = pd.Series.round(gUDC_CI95,2)\n", | |
| "UDCerrorsTec.columns.name='UDC error'\n", | |
| "UDCerrorsTec" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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5269YQURM9AWrJpr/9itWVL0ZZ4L3sWrS+uqmGBG795i9BXAUcCzw2dYZmXna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\nIk4Bds/Mxvba66aRHzgzD87MtXu8dui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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xdc97898>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "The red box represents the 1st and 3rd quartile.\n", | |
| "The dark purple line is the median.\n", | |
| "The yellow dot is the mean.\n", | |
| "the whiskers show the min and max values.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Plot the EUDC errors per technology type in a boxplot\n", | |
| "techboxplot = [techBC.dEUDC,techGCA.dEUDC,techGCB.dEUDC,techNOSS.dEUDC,techBERS.dEUDC,techVVL.dEUDC,techDIMPI.dEUDC,techThM.dEUDC]\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "# Create the boxplot with fill color\n", | |
| "bp = ax.boxplot(techboxplot, sym='', patch_artist=True, whis=10000, showmeans=True, meanprops=(dict(marker='o',markerfacecolor='yellow')))\n", | |
| "for box in bp['boxes']:\n", | |
| " # change outline color\n", | |
| " box.set( color='black', linewidth=1)\n", | |
| " # change fill color\n", | |
| " box.set( facecolor = 'red' )\n", | |
| "## Custom x-axis labels\n", | |
| "ax.set_xticklabels(['BC', 'GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'],fontsize=20)\n", | |
| "## Remove top axes and right axes ticks\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "#Set y axis title\n", | |
| "plt.title('EUDC CO$_2$ emission error by technology type', fontsize=20)\n", | |
| "plt.ylabel(\"error [gCO$_2$ km$^{-1}$]\",fontsize=18)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=18)\n", | |
| "ax.set_ylim(-20, 20)\n", | |
| "plt.setp(bp['medians'], color = 'purple', linewidth = 2)\n", | |
| "plt.show()\n", | |
| "print('The red box represents the 1st and 3rd quartile.\\nThe dark purple line is the median.\\nThe yellow dot is the mean.\\nthe whiskers show the min and max values.')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Descriptive statistics for **EUDC** CO$_2$ emission error per technology type" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>EUDC error</th>\n", | |
| " <th>BC</th>\n", | |
| " <th>GCA</th>\n", | |
| " <th>GCB</th>\n", | |
| " <th>NOSS</th>\n", | |
| " <th>BERS</th>\n", | |
| " <th>VVL</th>\n", | |
| " <th>DI/MPI</th>\n", | |
| " <th>ThM</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>1.25</td>\n", | |
| " <td>1.05</td>\n", | |
| " <td>1.29</td>\n", | |
| " <td>1.44</td>\n", | |
| " <td>0.28</td>\n", | |
| " <td>1.49</td>\n", | |
| " <td>0.59</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdError</th>\n", | |
| " <td>0.12</td>\n", | |
| " <td>0.13</td>\n", | |
| " <td>0.16</td>\n", | |
| " <td>0.12</td>\n", | |
| " <td>0.13</td>\n", | |
| " <td>0.19</td>\n", | |
| " <td>0.21</td>\n", | |
| " <td>0.12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>1.12</td>\n", | |
| " <td>1.21</td>\n", | |
| " <td>1.54</td>\n", | |
| " <td>1.28</td>\n", | |
| " <td>0.44</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0.55</td>\n", | |
| " <td>0.88</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>1.9</td>\n", | |
| " <td>2.01</td>\n", | |
| " <td>2.56</td>\n", | |
| " <td>1.89</td>\n", | |
| " <td>2.07</td>\n", | |
| " <td>2.17</td>\n", | |
| " <td>2.41</td>\n", | |
| " <td>1.74</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variance</th>\n", | |
| " <td>3.63</td>\n", | |
| " <td>4.05</td>\n", | |
| " <td>6.57</td>\n", | |
| " <td>3.58</td>\n", | |
| " <td>4.29</td>\n", | |
| " <td>4.71</td>\n", | |
| " <td>5.8</td>\n", | |
| " <td>3.01</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <td>0.07</td>\n", | |
| " <td>1.92</td>\n", | |
| " <td>1.18</td>\n", | |
| " <td>-0.02</td>\n", | |
| " <td>-0.25</td>\n", | |
| " <td>-0.7</td>\n", | |
| " <td>-0.73</td>\n", | |
| " <td>0.29</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Skweness</th>\n", | |
| " <td>-0.7</td>\n", | |
| " <td>-1.33</td>\n", | |
| " <td>-0.63</td>\n", | |
| " <td>-0.7</td>\n", | |
| " <td>-0.6</td>\n", | |
| " <td>-0.49</td>\n", | |
| " <td>-0.45</td>\n", | |
| " <td>-0.66</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Range</th>\n", | |
| " <td>8.54</td>\n", | |
| " <td>10.53</td>\n", | |
| " <td>15</td>\n", | |
| " <td>7.83</td>\n", | |
| " <td>9.43</td>\n", | |
| " <td>8.38</td>\n", | |
| " <td>9.04</td>\n", | |
| " <td>7.87</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Minimum</th>\n", | |
| " <td>-3.68</td>\n", | |
| " <td>-5.74</td>\n", | |
| " <td>-6.12</td>\n", | |
| " <td>-3.56</td>\n", | |
| " <td>-5</td>\n", | |
| " <td>-3.22</td>\n", | |
| " <td>-4.12</td>\n", | |
| " <td>-4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Maximum</th>\n", | |
| " <td>4.87</td>\n", | |
| " <td>4.79</td>\n", | |
| " <td>8.88</td>\n", | |
| " <td>4.27</td>\n", | |
| " <td>4.43</td>\n", | |
| " <td>5.16</td>\n", | |
| " <td>4.93</td>\n", | |
| " <td>3.87</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sum</th>\n", | |
| " <td>316</td>\n", | |
| " <td>256</td>\n", | |
| " <td>314</td>\n", | |
| " <td>349</td>\n", | |
| " <td>69</td>\n", | |
| " <td>202</td>\n", | |
| " <td>80</td>\n", | |
| " <td>217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Count</th>\n", | |
| " <td>252</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>243</td>\n", | |
| " <td>135</td>\n", | |
| " <td>135</td>\n", | |
| " <td>216</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Confidence level (95%)</th>\n", | |
| " <td>0.24</td>\n", | |
| " <td>0.26</td>\n", | |
| " <td>0.33</td>\n", | |
| " <td>0.24</td>\n", | |
| " <td>0.27</td>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.41</td>\n", | |
| " <td>0.24</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "EUDC error BC GCA GCB NOSS BERS VVL DI/MPI ThM\n", | |
| "Averages 1.25 1.05 1.29 1.44 0.28 1.49 0.59 1\n", | |
| "StdError 0.12 0.13 0.16 0.12 0.13 0.19 0.21 0.12\n", | |
| "Median 1.12 1.21 1.54 1.28 0.44 2 0.55 0.88\n", | |
| "StdDev 1.9 2.01 2.56 1.89 2.07 2.17 2.41 1.74\n", | |
| "Variance 3.63 4.05 6.57 3.58 4.29 4.71 5.8 3.01\n", | |
| "Kurtosis 0.07 1.92 1.18 -0.02 -0.25 -0.7 -0.73 0.29\n", | |
| "Skweness -0.7 -1.33 -0.63 -0.7 -0.6 -0.49 -0.45 -0.66\n", | |
| "Range 8.54 10.53 15 7.83 9.43 8.38 9.04 7.87\n", | |
| "Minimum -3.68 -5.74 -6.12 -3.56 -5 -3.22 -4.12 -4\n", | |
| "Maximum 4.87 4.79 8.88 4.27 4.43 5.16 4.93 3.87\n", | |
| "Sum 316 256 314 349 69 202 80 217\n", | |
| "Count 252 243 243 243 243 135 135 216\n", | |
| "Confidence level (95%) 0.24 0.26 0.33 0.24 0.27 0.37 0.41 0.24" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "gEUDCmean = grouped.dEUDC.mean()\n", | |
| "gEUDCsem = grouped.dEUDC.sem()\n", | |
| "gEUDCmedian = grouped.dEUDC.median()\n", | |
| "gEUDCstd = grouped.dEUDC.std()\n", | |
| "gEUDCvar = grouped.dEUDC.var()\n", | |
| "gEUDCskew = grouped.dEUDC.skew()\n", | |
| "gEUDCrange = grouped.dEUDC.max()-grouped.dEUDC.min()\n", | |
| "gEUDCmin = grouped.dEUDC.min()\n", | |
| "gEUDCmax = grouped.dEUDC.max()\n", | |
| "gEUDCsum = grouped.dEUDC.sum()\n", | |
| "gEUDCcount = grouped.dEUDC.count()\n", | |
| "gEUDC_CI95 = 2*grouped.dEUDC.sem()\n", | |
| "EUDCerrorsTec = pd.DataFrame(index=['Averages','StdError','Median','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['BC','GCA', 'GCB','NOSS','BERS','VVL','DI/MPI','ThM'])\n", | |
| "EUDCerrorsTec.loc['Averages'] = pd.Series.round(gEUDCmean,2)\n", | |
| "EUDCerrorsTec.loc['StdError'] = pd.Series.round(gEUDCsem,2)\n", | |
| "EUDCerrorsTec.loc['Median'] = pd.Series.round(gEUDCmedian,2)\n", | |
| "EUDCerrorsTec.loc['StdDev'] = pd.Series.round(gEUDCstd,2)\n", | |
| "EUDCerrorsTec.loc['Variance'] = pd.Series.round(gEUDCvar,2)\n", | |
| "EUDCerrorsTec.loc['Kurtosis'] = [round(techBC.dEUDC.kurtosis(),2),round(techGCA.dEUDC.kurtosis(),2),round(techGCB.dEUDC.kurtosis(),2),round(techNOSS.dEUDC.kurtosis(),2),round(techBERS.dEUDC.kurtosis(),2),round(techVVL.dEUDC.kurtosis(),2),round(techDIMPI.dEUDC.kurtosis(),2),round(techThM.dEUDC.kurtosis(),2)]\n", | |
| "EUDCerrorsTec.loc['Skweness'] = pd.Series.round(gEUDCskew,2)\n", | |
| "EUDCerrorsTec.loc['Range'] = pd.Series.round(gEUDCrange,2)\n", | |
| "EUDCerrorsTec.loc['Minimum'] = pd.Series.round(gEUDCmin,2)\n", | |
| "EUDCerrorsTec.loc['Maximum'] = pd.Series.round(gEUDCmax,2)\n", | |
| "EUDCerrorsTec.loc['Sum'] = pd.Series.round(gEUDCsum)\n", | |
| "EUDCerrorsTec.loc['Count'] = pd.Series.round(gEUDCcount)\n", | |
| "EUDCerrorsTec.loc['Confidence level (95%)'] = pd.Series.round(gEUDC_CI95,2)\n", | |
| "EUDCerrorsTec.columns.name='EUDC error'\n", | |
| "EUDCerrorsTec" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Error statistics for engine parameters" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>param a</th>\n", | |
| " <th>param a2</th>\n", | |
| " <th>param b</th>\n", | |
| " <th>param l</th>\n", | |
| " <th>param l2</th>\n", | |
| " <th>param t</th>\n", | |
| " <th>param trg</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>0.426</td>\n", | |
| " <td>-0.002</td>\n", | |
| " <td>0.017</td>\n", | |
| " <td>-2.047</td>\n", | |
| " <td>-0.003</td>\n", | |
| " <td>3.645</td>\n", | |
| " <td>84.422</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdError</th>\n", | |
| " <td>0.001</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.01</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.03</td>\n", | |
| " <td>0.187</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>0.429</td>\n", | |
| " <td>-0.002</td>\n", | |
| " <td>0.016</td>\n", | |
| " <td>-2.146</td>\n", | |
| " <td>-0.002</td>\n", | |
| " <td>3.958</td>\n", | |
| " <td>81.907</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mode</th>\n", | |
| " <td>0.366</td>\n", | |
| " <td>-0.007</td>\n", | |
| " <td>0.021</td>\n", | |
| " <td>-2.598</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.77</td>\n", | |
| " <td>70.128</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>0.043</td>\n", | |
| " <td>0.001</td>\n", | |
| " <td>0.021</td>\n", | |
| " <td>0.405</td>\n", | |
| " <td>0.004</td>\n", | |
| " <td>1.243</td>\n", | |
| " <td>7.748</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Variance</th>\n", | |
| " <td>0.002</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.164</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.546</td>\n", | |
| " <td>60.03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <td>-0.749</td>\n", | |
| " <td>-0.699</td>\n", | |
| " <td>-1.029</td>\n", | |
| " <td>-1.473</td>\n", | |
| " <td>-0.175</td>\n", | |
| " <td>-1.162</td>\n", | |
| " <td>-1.298</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Skweness</th>\n", | |
| " <td>-0.401</td>\n", | |
| " <td>-0.528</td>\n", | |
| " <td>0.304</td>\n", | |
| " <td>0.169</td>\n", | |
| " <td>-0.916</td>\n", | |
| " <td>-0.429</td>\n", | |
| " <td>-0.223</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Range</th>\n", | |
| " <td>0.205</td>\n", | |
| " <td>0.007</td>\n", | |
| " <td>0.033</td>\n", | |
| " <td>1.832</td>\n", | |
| " <td>0.019</td>\n", | |
| " <td>4.976</td>\n", | |
| " <td>28.098</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Minimum</th>\n", | |
| " <td>0.312</td>\n", | |
| " <td>-0.007</td>\n", | |
| " <td>0.003</td>\n", | |
| " <td>-2.98</td>\n", | |
| " <td>-0.019</td>\n", | |
| " <td>0.872</td>\n", | |
| " <td>69.841</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Maximum</th>\n", | |
| " <td>0.518</td>\n", | |
| " <td>-0</td>\n", | |
| " <td>0.036</td>\n", | |
| " <td>-1.149</td>\n", | |
| " <td>0</td>\n", | |
| " <td>5.849</td>\n", | |
| " <td>97.939</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sum</th>\n", | |
| " <td>727.827</td>\n", | |
| " <td>-4.056</td>\n", | |
| " <td>28.496</td>\n", | |
| " <td>-3499.94</td>\n", | |
| " <td>-5.899</td>\n", | |
| " <td>6233.26</td>\n", | |
| " <td>144361</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Count</th>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " <td>1710</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Confidence level (95%)</th>\n", | |
| " <td>0.002</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.02</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.06</td>\n", | |
| " <td>0.374</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " param a param a2 param b param l param l2 param t \\\n", | |
| "Averages 0.426 -0.002 0.017 -2.047 -0.003 3.645 \n", | |
| "StdError 0.001 0 0 0.01 0 0.03 \n", | |
| "Median 0.429 -0.002 0.016 -2.146 -0.002 3.958 \n", | |
| "Mode 0.366 -0.007 0.021 -2.598 0 1.77 \n", | |
| "StdDev 0.043 0.001 0.021 0.405 0.004 1.243 \n", | |
| "Variance 0.002 0 0 0.164 0 1.546 \n", | |
| "Kurtosis -0.749 -0.699 -1.029 -1.473 -0.175 -1.162 \n", | |
| "Skweness -0.401 -0.528 0.304 0.169 -0.916 -0.429 \n", | |
| "Range 0.205 0.007 0.033 1.832 0.019 4.976 \n", | |
| "Minimum 0.312 -0.007 0.003 -2.98 -0.019 0.872 \n", | |
| "Maximum 0.518 -0 0.036 -1.149 0 5.849 \n", | |
| "Sum 727.827 -4.056 28.496 -3499.94 -5.899 6233.26 \n", | |
| "Count 1710 1710 1710 1710 1710 1710 \n", | |
| "Confidence level (95%) 0.002 0 0 0.02 0 0.06 \n", | |
| "\n", | |
| " param trg \n", | |
| "Averages 84.422 \n", | |
| "StdError 0.187 \n", | |
| "Median 81.907 \n", | |
| "Mode 70.128 \n", | |
| "StdDev 7.748 \n", | |
| "Variance 60.03 \n", | |
| "Kurtosis -1.298 \n", | |
| "Skweness -0.223 \n", | |
| "Range 28.098 \n", | |
| "Minimum 69.841 \n", | |
| "Maximum 97.939 \n", | |
| "Sum 144361 \n", | |
| "Count 1710 \n", | |
| "Confidence level (95%) 0.374 " | |
| ] | |
| }, | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#Gather and name the engine parameters used in the report according to their name in the CO2MPAS output file\n", | |
| "param_a = df['co2_params a']\n", | |
| "param_a2 = df['co2_params a2']\n", | |
| "param_b = df['co2_params b']\n", | |
| "param_c = df['co2_params c']\n", | |
| "param_l = df['co2_params l']\n", | |
| "param_l2 = df['co2_params l2']\n", | |
| "param_t = df['co2_params t']\n", | |
| "param_trg = df['co2_params trg']\n", | |
| "#Create a dataframe with this data\n", | |
| "paramsDF = pd.DataFrame({'param a': param_a,'param a2':param_a2, 'param b':param_b,'param c': param_c,'param l':param_l, 'param l2':param_l2,'param t': param_t,'param trg':param_trg,'NEDC':NEDC,'NEDC error':dNEDC}) \n", | |
| "paramsDF = paramsDF.dropna()\n", | |
| "#print the basic automatic statistics\n", | |
| "#paramsDF.describe()\n", | |
| "paramsDFstat = pd.DataFrame(index=['Averages','StdError','Median','Mode','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['param a','param a2', 'param b', 'param l', 'param l2', 'param t', 'param trg'])\n", | |
| "paramsDFstat.loc['Averages'] = pd.Series({'param a':round(paramsDF['param a'].mean(),3), 'param a2':round(paramsDF['param a2'].mean(),3), 'param b':round(paramsDF['param b'].mean(),3),'param l':round(paramsDF['param l'].mean(),3),'param l2':round(paramsDF['param l2'].mean(),3),'param t':round(paramsDF['param t'].mean(),3),'param trg':round(paramsDF['param trg'].mean(),3)})\n", | |
| "paramsDFstat.loc['StdError'] = pd.Series({'param a':round(paramsDF['param a'].sem(),3), 'param a2':round(paramsDF['param a2'].sem(),3), 'param b':round(paramsDF['param b'].sem(),3),'param l':round(paramsDF['param l'].sem(),3),'param l2':round(paramsDF['param l2'].sem(),3),'param t':round(paramsDF['param t'].sem(),3),'param trg':round(paramsDF['param trg'].sem(),3)})\n", | |
| "paramsDFstat.loc['Median'] = pd.Series({'param a':round(paramsDF['param a'].median(),3), 'param a2':round(paramsDF['param a2'].median(),3), 'param b':round(paramsDF['param b'].median(),3),'param l':round(paramsDF['param l'].median(),3),'param l2':round(paramsDF['param l2'].median(),3),'param t':round(paramsDF['param t'].median(),3),'param trg':round(paramsDF['param trg'].median(),3)})\n", | |
| "paramsDFstat.loc['Mode'] = pd.Series({'param a':round(paramsDF['param a'].mode().iloc[0],3), 'param a2':round(paramsDF['param a2'].mode().iloc[0],3), 'param b':round(paramsDF['param b'].mode().iloc[0],3),'param l':round(paramsDF['param l'].mode().iloc[0],3),'param l2':round(paramsDF['param l2'].mode().iloc[0],3),'param t':round(paramsDF['param t'].mode().iloc[0],3),'param trg':round(paramsDF['param trg'].mode().iloc[0],3)})\n", | |
| "paramsDFstat.loc['StdDev'] = pd.Series({'param a':round(paramsDF['param a'].std(),3), 'param a2':round(paramsDF['param a2'].std(),3), 'param b':round(paramsDF['param b'].mode().iloc[0],3),'param l':round(paramsDF['param l'].std(),3),'param l2':round(paramsDF['param l2'].std(),3),'param t':round(paramsDF['param t'].std(),3),'param trg':round(paramsDF['param trg'].std(),3)})\n", | |
| "paramsDFstat.loc['Variance'] = pd.Series({'param a':round(paramsDF['param a'].var(),3), 'param a2':round(paramsDF['param a2'].var(),3), 'param b':round(paramsDF['param b'].var(),3),'param l':round(paramsDF['param l'].var(),3),'param l2':round(paramsDF['param l2'].var(),3),'param t':round(paramsDF['param t'].var(),3),'param trg':round(paramsDF['param trg'].var(),3)})\n", | |
| "paramsDFstat.loc['Kurtosis'] = pd.Series({'param a':round(paramsDF['param a'].kurtosis(),3), 'param a2':round(paramsDF['param a2'].kurtosis(),3), 'param b':round(paramsDF['param b'].kurtosis(),3),'param l':round(paramsDF['param l'].kurtosis(),3),'param l2':round(paramsDF['param l2'].kurtosis(),3),'param t':round(paramsDF['param t'].kurtosis(),3),'param trg':round(paramsDF['param trg'].kurtosis(),3)})\n", | |
| "paramsDFstat.loc['Skweness'] = pd.Series({'param a':round(paramsDF['param a'].skew(),3), 'param a2':round(paramsDF['param a2'].skew(),3), 'param b':round(paramsDF['param b'].skew(),3),'param l':round(paramsDF['param l'].skew(),3),'param l2':round(paramsDF['param l2'].skew(),3),'param t':round(paramsDF['param t'].skew(),3),'param trg':round(paramsDF['param trg'].skew(),3)})\n", | |
| "paramsDFstat.loc['Range'] = pd.Series({'param a':round((paramsDF['param a'].max()-paramsDF['param a'].min()),3), 'param a2':round((paramsDF['param a2'].max()-paramsDF['param a2'].min()),3), 'param b':round((paramsDF['param b'].max()-paramsDF['param b'].min()),3),'param l':round((paramsDF['param l'].max()-paramsDF['param l'].min()),3),'param l2':round((paramsDF['param l2'].max()-paramsDF['param l2'].min()),3),'param t':round((paramsDF['param t'].max()-paramsDF['param t'].min()),3),'param trg':round((paramsDF['param trg'].max()-paramsDF['param trg'].min()),3)})\n", | |
| "paramsDFstat.loc['Minimum'] = pd.Series({'param a':round(paramsDF['param a'].min(),3), 'param a2':round(paramsDF['param a2'].min(),3), 'param b':round(paramsDF['param b'].min(),3),'param l':round(paramsDF['param l'].min(),3),'param l2':round(paramsDF['param l2'].min(),3),'param t':round(paramsDF['param t'].min(),3),'param trg':round(paramsDF['param trg'].min(),3)})\n", | |
| "paramsDFstat.loc['Maximum'] = pd.Series({'param a':round(paramsDF['param a'].max(),3), 'param a2':round(paramsDF['param a2'].max(),3), 'param b':round(paramsDF['param b'].max(),3),'param l':round(paramsDF['param l'].max(),3),'param l2':round(paramsDF['param l2'].max(),3),'param t':round(paramsDF['param t'].max(),3),'param trg':round(paramsDF['param trg'].max(),3)})\n", | |
| "paramsDFstat.loc['Sum'] = pd.Series({'param a':round(paramsDF['param a'].sum(),3), 'param a2':round(paramsDF['param a2'].sum(),3), 'param b':round(paramsDF['param b'].sum(),3),'param l':round(paramsDF['param l'].sum(),3),'param l2':round(paramsDF['param l2'].sum(),3),'param t':round(paramsDF['param t'].sum(),3),'param trg':round(paramsDF['param trg'].sum(),3)})\n", | |
| "paramsDFstat.loc['Count'] = pd.Series({'param a':round(paramsDF['param a'].count(),3), 'param a2':round(paramsDF['param a2'].count(),3), 'param b':round(paramsDF['param b'].count(),3),'param l':round(paramsDF['param l'].count(),3),'param l2':round(paramsDF['param l2'].count(),3),'param t':round(paramsDF['param t'].count(),3),'param trg':round(paramsDF['param trg'].count(),3)})\n", | |
| "paramsDFstat.loc['Confidence level (95%)'] = pd.Series({'param a':2*round(paramsDF['param a'].sem(),3), 'param a2':2*round(paramsDF['param a2'].sem(),3), 'param b':2*round(paramsDF['param b'].sem(),3),'param l':2*round(paramsDF['param l'].sem(),3),'param l2':2*round(paramsDF['param l2'].sem(),3),'param t':2*round(paramsDF['param t'].sem(),3),'param trg':2*round(paramsDF['param trg'].sem(),3)})\n", | |
| "paramsDFstat" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Error statistics for engine parameters applying a filtering of NEDC absolute error < 25 gCO$_2$ km$^{-1}$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "No filtering needed, same statistics as above\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#filter for absolute errors above 25g CO2 per km\n", | |
| "fparamsDF = paramsDF[abs(paramsDF['NEDC error']) < 25]\n", | |
| "fparamsDFstat = pd.DataFrame(index=['Averages','StdError','Median','Mode','StdDev','Variance','Kurtosis','Skweness','Range','Minimum','Maximum','Sum','Count','Confidence level (95%)'], columns=['param a','param a2', 'param b', 'param l', 'param l2', 'param t', 'param trg'])\n", | |
| "fparamsDFstat.loc['Averages'] = pd.Series({'param a':round(fparamsDF['param a'].mean(),3), 'param a2':round(fparamsDF['param a2'].mean(),3), 'param b':round(fparamsDF['param b'].mean(),3),'param l':round(fparamsDF['param l'].mean(),3),'param l2':round(fparamsDF['param l2'].mean(),3),'param t':round(fparamsDF['param t'].mean(),3),'param trg':round(fparamsDF['param trg'].mean(),3)})\n", | |
| "fparamsDFstat.loc['StdError'] = pd.Series({'param a':round(fparamsDF['param a'].sem(),3), 'param a2':round(fparamsDF['param a2'].sem(),3), 'param b':round(fparamsDF['param b'].sem(),3),'param l':round(fparamsDF['param l'].sem(),3),'param l2':round(fparamsDF['param l2'].sem(),3),'param t':round(fparamsDF['param t'].sem(),3),'param trg':round(fparamsDF['param trg'].sem(),3)})\n", | |
| "fparamsDFstat.loc['Median'] = pd.Series({'param a':round(fparamsDF['param a'].median(),3), 'param a2':round(fparamsDF['param a2'].median(),3), 'param b':round(fparamsDF['param b'].median(),3),'param l':round(fparamsDF['param l'].median(),3),'param l2':round(fparamsDF['param l2'].median(),3),'param t':round(fparamsDF['param t'].median(),3),'param trg':round(fparamsDF['param trg'].median(),3)})\n", | |
| "fparamsDFstat.loc['Mode'] = pd.Series({'param a':round(fparamsDF['param a'].mode().iloc[0],3), 'param a2':round(fparamsDF['param a2'].mode().iloc[0],3), 'param b':round(fparamsDF['param b'].mode().iloc[0],3),'param l':round(fparamsDF['param l'].mode().iloc[0],3),'param l2':round(fparamsDF['param l2'].mode().iloc[0],3),'param t':round(fparamsDF['param t'].mode().iloc[0],3),'param trg':round(fparamsDF['param trg'].mode().iloc[0],3)})\n", | |
| "fparamsDFstat.loc['StdDev'] = pd.Series({'param a':round(fparamsDF['param a'].std(),3), 'param a2':round(fparamsDF['param a2'].std(),3), 'param b':round(fparamsDF['param b'].std(),3),'param l':round(fparamsDF['param l'].std(),3),'param l2':round(fparamsDF['param l2'].std(),3),'param t':round(fparamsDF['param t'].std(),3),'param trg':round(fparamsDF['param trg'].std(),3)})\n", | |
| "fparamsDFstat.loc['Variance'] = pd.Series({'param a':round(fparamsDF['param a'].var(),3), 'param a2':round(fparamsDF['param a2'].var(),3), 'param b':round(fparamsDF['param b'].var(),3),'param l':round(fparamsDF['param l'].var(),3),'param l2':round(fparamsDF['param l2'].var(),3),'param t':round(fparamsDF['param t'].var(),3),'param trg':round(fparamsDF['param trg'].var(),3)})\n", | |
| "fparamsDFstat.loc['Kurtosis'] = pd.Series({'param a':round(fparamsDF['param a'].kurtosis(),3), 'param a2':round(fparamsDF['param a2'].kurtosis(),3), 'param b':round(fparamsDF['param b'].kurtosis(),3),'param l':round(fparamsDF['param l'].kurtosis(),3),'param l2':round(fparamsDF['param l2'].kurtosis(),3),'param t':round(fparamsDF['param t'].kurtosis(),3),'param trg':round(fparamsDF['param trg'].kurtosis(),3)})\n", | |
| "fparamsDFstat.loc['Skweness'] = pd.Series({'param a':round(fparamsDF['param a'].skew(),3), 'param a2':round(fparamsDF['param a2'].skew(),3), 'param b':round(fparamsDF['param b'].skew(),3),'param l':round(fparamsDF['param l'].skew(),3),'param l2':round(fparamsDF['param l2'].skew(),3),'param t':round(fparamsDF['param t'].skew(),3),'param trg':round(fparamsDF['param trg'].skew(),3)})\n", | |
| "fparamsDFstat.loc['Range'] = pd.Series({'param a':round((fparamsDF['param a'].max()-paramsDF['param a'].min()),3), 'param a2':round((fparamsDF['param a2'].max()-paramsDF['param a2'].min()),3), 'param b':round((fparamsDF['param b'].max()-paramsDF['param b'].min()),3),'param l':round((fparamsDF['param l'].max()-paramsDF['param l'].min()),3),'param l2':round((fparamsDF['param l2'].max()-paramsDF['param l2'].min()),3),'param t':round((fparamsDF['param t'].max()-paramsDF['param t'].min()),3),'param trg':round((fparamsDF['param trg'].max()-paramsDF['param trg'].min()),3)})\n", | |
| "fparamsDFstat.loc['Minimum'] = pd.Series({'param a':round(fparamsDF['param a'].min(),3), 'param a2':round(fparamsDF['param a2'].min(),3), 'param b':round(fparamsDF['param b'].min(),3),'param l':round(fparamsDF['param l'].min(),3),'param l2':round(fparamsDF['param l2'].min(),3),'param t':round(fparamsDF['param t'].min(),3),'param trg':round(fparamsDF['param trg'].min(),3)})\n", | |
| "fparamsDFstat.loc['Maximum'] = pd.Series({'param a':round(fparamsDF['param a'].max(),3), 'param a2':round(fparamsDF['param a2'].max(),3), 'param b':round(fparamsDF['param b'].max(),3),'param l':round(fparamsDF['param l'].max(),3),'param l2':round(fparamsDF['param l2'].max(),3),'param t':round(fparamsDF['param t'].max(),3),'param trg':round(fparamsDF['param trg'].max(),3)})\n", | |
| "fparamsDFstat.loc['Sum'] = pd.Series({'param a':round(fparamsDF['param a'].sum(),3), 'param a2':round(fparamsDF['param a2'].sum(),3), 'param b':round(fparamsDF['param b'].sum(),3),'param l':round(fparamsDF['param l'].sum(),3),'param l2':round(fparamsDF['param l2'].sum(),3),'param t':round(fparamsDF['param t'].sum(),3),'param trg':round(fparamsDF['param trg'].sum(),3)})\n", | |
| "fparamsDFstat.loc['Count'] = pd.Series({'param a':round(fparamsDF['param a'].count(),3), 'param a2':round(fparamsDF['param a2'].count(),3), 'param b':round(fparamsDF['param b'].count(),3),'param l':round(fparamsDF['param l'].count(),3),'param l2':round(fparamsDF['param l2'].count(),3),'param t':round(fparamsDF['param t'].count(),3),'param trg':round(fparamsDF['param trg'].count(),3)})\n", | |
| "fparamsDFstat.loc['Confidence level (95%)'] = pd.Series({'param a':2*round(fparamsDF['param a'].sem(),3), 'param a2':2*round(fparamsDF['param a2'].sem(),3), 'param b':2*round(fparamsDF['param b'].sem(),3),'param l':2*round(fparamsDF['param l'].sem(),3),'param l2':2*round(fparamsDF['param l2'].sem(),3),'param t':2*round(fparamsDF['param t'].sem(),3),'param trg':2*round(fparamsDF['param trg'].sem(),3)})\n", | |
| "if (len(paramsDF['NEDC error'])-len(fparamsDF['NEDC error'])) == 0:\n", | |
| " print('No filtering needed, same statistics as above')\n", | |
| "else:\n", | |
| " print('Filtered statistics')\n", | |
| " fparamsDFstat" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Distribution of the engine parameters values for filtered cases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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fr5Icc6aS7N9UmjGnLhm1rPGRwFcy843jaIwkSZIkrVWjXjkL4OJxNERaS6yP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\nJc6htcnPVHWJZY2SJEmS1AI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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc72ed30>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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7mblDZt41M58/WKy1216fma9pt7ltZu49U7Gm2pwLXpv51WV2tZlfbeZXl9mpq5EXbJIk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\n1JVTIqVlZmJigqmpqVEPQ5IkSbNgwab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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc6d3d68>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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bI28EHg/83NJirbcTOD4ibrRk/Z3oLkby+dk2TyvlQagG57DVYH9ozwxqMIf2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\n//X//jxwNnBsRJww+saIOJru6pFnzbG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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb907f60>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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f8trMry6zq838ajO/usxOXbCIkyRJkqRCHBPXckycJEmSpMU4Jk6SJEmSNDKLOJVn3/La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\nM3GSJEmSVIhFnMrz26zazK8us6vN/Go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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc034c88>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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m3HiRkGd1m28H/APwbODPl6mPkiRJkrRuTHW6Y5JHAW8E/g34SeBPRrdX1eeA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\naZLGWHsgNdakSY3jgtS/m82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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc941710>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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SJE2sUW+c/YuIWAn8A9VCHs/rO3wX8DHgVZn5i7meMyIeBLyZalXHnSNiZ+5e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\nz8ptIuPtYuBrwKMHvsd+datjafvDbPA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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xc0f3470>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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EfIFyp2trvwMz88RFtk2SJEmSJtagydZnKNO6B/DK6tE7zXtU20y2NHEcmy8w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\nIZRfsr8K/LFF0CtLRGyjDA/pdW5mPnP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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb90ceb8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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pq82+V5v51WV26jLs8MU/As4Edk6zfWe7/TVzPWBEHBsRn4iISyPiuoi4OCLe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\nf3XZ99TFokySJEmSxsiiTOU5rr4286v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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xdc41320>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#Histogram for each engine parameter\n", | |
| "#create a list with all the available engine parameters\n", | |
| "paramlist = list(sorted(fparamsDF.columns.unique()))\n", | |
| "for p in range(2,(len(paramlist))):\n", | |
| " tit = paramlist[p] + ' distribution'\n", | |
| " fig = plt.figure(1, figsize=(14, 7))\n", | |
| " plt.title(tit,fontsize=20)\n", | |
| " plot = fig.add_subplot(111)\n", | |
| " # We change the fontsize of minor ticks label \n", | |
| " plot.tick_params(axis='x', which='major', labelsize=16)\n", | |
| " plot.tick_params(axis='y', which='major', labelsize=16)\n", | |
| " par_hist = fparamsDF[paramlist[p]].hist(bins=25, color='grey')\n", | |
| " par_hist.set_xlabel(paramlist[p],fontsize=20)\n", | |
| " par_hist.set_ylabel(\"frequency\",fontsize=20)\n", | |
| " plot.get_xaxis().tick_bottom()\n", | |
| " plot.get_yaxis().tick_left()\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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zWjtJkjQOsxbXiIjlFCVGAJKiiuFMfgU8p4vVDbtQXKML90ZY7GyDdrAd2sF2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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xdef8e10>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "The purple box represents the 1st and 3rd quartile.\n", | |
| "The dark purple line is the median.\n", | |
| "The yellow dot is the mean.\n", | |
| "the whiskers show the min and max values.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Alternatively show normalized error boxplot for all engine parameters\n", | |
| "paramsbp = fparamsDF.drop(fparamsDF.columns[[0,1]], axis = 1)\n", | |
| "paramsbp_norm = (paramsbp - paramsbp.mean()) / (paramsbp.max() - paramsbp.min())\n", | |
| "# Create a figure instance\n", | |
| "fig = plt.figure(1, figsize=(14, 7))\n", | |
| "# Create an axes instance\n", | |
| "ax = fig.add_subplot(111)\n", | |
| "# Create the boxplot with fill color\n", | |
| "bp = ax.boxplot(paramsbp_norm.values, sym='', patch_artist=True, whis=10000, showmeans=True, meanprops=(dict(marker='o',markerfacecolor='yellow')))\n", | |
| "for box in bp['boxes']:\n", | |
| " # change outline color\n", | |
| " box.set( color='black', linewidth=1)\n", | |
| " # change fill color\n", | |
| " box.set( facecolor = '#b78adf' )\n", | |
| "## Custom x-axis labels\n", | |
| "ax.set_xticklabels(['param a', 'param a2', 'param b', 'param c', 'param l', 'param l2', 'param t', 'param trg'],fontsize=20)\n", | |
| "## Remove top axes and right axes ticks\n", | |
| "ax.get_xaxis().tick_bottom()\n", | |
| "ax.get_yaxis().tick_left()\n", | |
| "#Set y axis title\n", | |
| "plt.title('Normalized CO$_2$ emission error per engine parameter', fontsize=20)\n", | |
| "plt.ylabel(\"normalized parameter error\",fontsize=18)\n", | |
| "plt.tick_params(axis='y', which='major', labelsize=16)\n", | |
| "ax.set_ylim(-1, 1)\n", | |
| "plt.setp(bp['medians'], color = 'purple', linewidth = 2)\n", | |
| "plt.show()\n", | |
| "print('The purple box represents the 1st and 3rd quartile.\\nThe dark purple line is the median.\\nThe yellow dot is the mean.\\nthe whiskers show the min and max values.')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Correlation between all engine parameters and NEDC error. All vehicles" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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79OjRA/v378fx48ehUChw5coV2NnZaX02TZkyZdC1a1epW+OdO3ewfPlynD59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JwYN26cTQMUERERERG5WVYlPL169TL/d7169Vi3bh0xMTEEBwfj6+trs+BERERERG4X2ofHNm5o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\nzZo1i6ybiemuWkBAAAMHDgSgd+/eREREsHz5cl544QUqVqxIly5diI6O5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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xd9d3860>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#Create a heatmap with the correlation of all the engine parameters and the NEDC error\n", | |
| "fparamNEDCerror = fparamsDF.drop('NEDC', 1)\n", | |
| "#from seaborn.apionly import heatmap, diverging_palette\n", | |
| "import seaborn as sns\n", | |
| "sns.set()\n", | |
| "# Compute the correlation matrix\n", | |
| "corr = fparamNEDCerror.corr()\n", | |
| "# Generate a mask for the upper triangle\n", | |
| "mask = np.zeros_like(corr, dtype=np.bool)\n", | |
| "mask[np.triu_indices_from(mask)] = True\n", | |
| "# Set up the matplotlib figure\n", | |
| "f, ax = plt.subplots(figsize=(16, 12))\n", | |
| "# Generate a custom diverging colormap\n", | |
| "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", | |
| "# Draw the heatmap with the mask and correct aspect ratio\n", | |
| "sns.heatmap(corr, mask=mask, cmap=cmap, center = 0, linewidths=.1, annot = True, annot_kws={\"size\":14}, square = True)\n", | |
| "plt.title('Engine parameters vs engine parameters. Correlation heatmap.',fontsize=22)\n", | |
| "plt.yticks(fontsize = 14) \n", | |
| "plt.xticks(fontsize = 14, rotation = 1)\n", | |
| "cax = plt.gcf().axes[-1]\n", | |
| "cax.tick_params(labelsize=16)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Avoid using seaborn templates and go back to matplotlib templates\n", | |
| "mpl.rcParams.update(inline_rc)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Section 2. Performance of the model. Statistics per vehicle model and case test." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Glossary of vehicle models and number of test cases considered in the report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Brand and model</th>\n", | |
| " <th>Model code</th>\n", | |
| " <th>Case</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Peugeot 308</td>\n", | |
| " <td>308</td>\n", | |
| " <td>163</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Fiat 500</td>\n", | |
| " <td>500</td>\n", | |
| " <td>190</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Audi A4</td>\n", | |
| " <td>A4</td>\n", | |
| " <td>217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Opel Astra</td>\n", | |
| " <td>Astra</td>\n", | |
| " <td>163</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Alfa Romeo Giulietta</td>\n", | |
| " <td>Giulietta</td>\n", | |
| " <td>217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Volkswagen Polo</td>\n", | |
| " <td>Polo</td>\n", | |
| " <td>217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Fiat Punto</td>\n", | |
| " <td>Punto</td>\n", | |
| " <td>217</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>BMW X1</td>\n", | |
| " <td>X1</td>\n", | |
| " <td>163</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>Opel Zafira</td>\n", | |
| " <td>Zafira</td>\n", | |
| " <td>163</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Brand and model Model code Case\n", | |
| "0 Peugeot 308 308 163\n", | |
| "1 Fiat 500 500 190\n", | |
| "2 Audi A4 A4 217\n", | |
| "3 Opel Astra Astra 163\n", | |
| "4 Alfa Romeo Giulietta Giulietta 217\n", | |
| "5 Volkswagen Polo Polo 217\n", | |
| "6 Fiat Punto Punto 217\n", | |
| "7 BMW X1 X1 163\n", | |
| "8 Opel Zafira Zafira 163" | |
| ] | |
| }, | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "mod_cases_stats = mod_cases.groupby(['Model code'],as_index=False).count() \n", | |
| "mod_cases_stats['Brand and model'] = ['Peugeot 308','Fiat 500','Audi A4','Opel Astra','Alfa Romeo Giulietta','Volkswagen Polo','Fiat Punto','BMW X1','Opel Zafira']\n", | |
| "cols = mod_cases_stats.columns.tolist()\n", | |
| "cols = cols[-1:] + cols[:2]\n", | |
| "mod_cases_stats = mod_cases_stats[cols]\n", | |
| "mod_cases_stats" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "**NEDC**, **UDC**, and **EUDC** CO$_2$ emission error per vehicle model (filtered for NEDC CO$_2$ emission absolute error < 25 gCO$_2$ km$^{-1}$)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>308</th>\n", | |
| " <th>NEDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>UDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>EUDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>2.62</td>\n", | |
| " <td>2.52</td>\n", | |
| " <td>2.64</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>2.93</td>\n", | |
| " <td>2.78</td>\n", | |
| " <td>2.76</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>1.94</td>\n", | |
| " <td>4.25</td>\n", | |
| " <td>0.73</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "308 NEDC [gCO$_2$ km$^{-1}$] UDC [gCO$_2$ km$^{-1}$] \\\n", | |
| "Averages 2.62 2.52 \n", | |
| "Median 2.93 2.78 \n", | |
| "StdDev 1.94 4.25 \n", | |
| "\n", | |
| "308 EUDC [gCO$_2$ km$^{-1}$] \n", | |
| "Averages 2.64 \n", | |
| "Median 2.76 \n", | |
| "StdDev 0.73 " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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SJEkjZiOlmeX4Z4E5UJ9ZEJgDNcyBumAjJUmSJEktOUdKkqR1yDlSkta7mZgjleSoJKcm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\n6jsDdVwHPGOB+0zj6+t6SQ7h/2/v3mL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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xe054b38>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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oqt4vn2kt5B+dGWN6AO8ALaoOZdVyqb5TIiIiNWRmZFLxbUW1YxUNKzizw5lR\n6pGIiDgtnDVbjwKtgGlAZ+B71toUH1+pjvRUREQkgUy4bgLtStpVO9aupB0TrpsQpR6JiIjTwpkU\nPgB4yVp7n1OdERERSRadOnXigrYXsK14m+dYx7Yd6dixYxR7JeAqWvLrh3/N/XfdT+9evaPdHRGJ\nYyGv2TLG7AbyrbWTnO1S7NCaLRERkfgVbGiqWbSkw+cdVLREkpLWbAXH35qtcMLWUuAUa23Cln9X\n2BIREYk/oYYmFS0RcVHYCo6/sBXOmq27gO7GmCkm1nYqExERkaQ16qZRPLH7Cbb33A4NYHvP7Tz+\nzeOMmjDK732ZGZlUNPRRtCRDRUtEJDThhK3/A9YDvwX+a4xZZoyZ6+Prj850VUREROpTqBsuR3uj\n5lBDk4qWiIjTwplGWBngpTZeKxJqGqGIiCSjUKfh1deap7rWYn311Vec86tz2Nljp+dYuw3tKHis\noM4CJNfceg3b9nsVLUnvyAtPvqCiGZJUunTpwpYtW6LdjbjRqFEjDh065Piarc6BXmutjcvvlsKW\niIgEKpE+jIe6dinY+yJZwMJXaJr0i0lBf49UNENE6hKRTY3jNUCJiIg4qeaH8aKZRZ4P4+v/sz4u\nA1ioGy4Hep+/98xfgBl10yhXmOvpasO9Fqt4QvEJYe6FJ184ob2h9w4Nqr1g2xQRqSnkNVvGmEYB\nXtcl1DZERERina9iDI/teozuOd0Zeu9QXuvyGpfMvIRJMydRUVFR9wNjQKhrlwK9r74LWITaXjht\niohAeAUyXqjrAmPMqcDbYbQhIiIS03x9GD9WcIz/9v5vSB/uY4F7w+XBxYM9Xxe0vYBvvv3Gb+GL\n2u6ruU6qvgtYhBOYVDRDRMIRboGMJ621t9dyvh3wL+A0a21Ao2CxRmu2RMQJE6dN5JMtn+C9S4a1\nlrM7n81jMx+LYs8kVN5rjVq1aHVCMYa0V9IoH1QOTb1uKoO7W9/Nb6f+tv47HCan1y1FooBFpNoL\ntU0Rb9Zaps6cygPTHkA7JiWeSG1q/ARwCzDZWvtIjXNtgFVAV+Aaa+3ikBpxmDFmOjC9xuGd1toO\ntVyvsCUiYVv68lLG/m0s5Z3LPcfSvkxjwYgFjBw2Moo9k2DVFjq++vordhzY4bmuhWnBh999GPKH\ne1+iWYAjEpv91neAUWCSaFr68lLGzxpPfl6+/t53UKyE2EiFLQMsBS4DrrXWvlh1vCWwEugBXG+t\nXRBSAxEo3SWCAAAgAElEQVRQFbauAgYD7jfkmLX221quV9gSkbBZaxlw5QDW9Fjj+pvHwrkbzmX1\nX1brJ5xxJpjQ4dSH+1gop373/XfzwLcPJMxInUh9eTb/WWY/P5ujbY6yufdmuq7ryve+/h633XAb\nv7z+l9HuXtyLlRDrL2yFvGarKoWMBj4E5hljcowxzYAVQE/gplgKWl4qrLXfWGu/rvryGbRERJxi\njCFvTB5pW9MASNuSxqTrJiloxaFg1v688OQLrJy30vMV6ihKOMUdoO4NhisqKsi7J89vMQ+tWxIJ\nTe64XGbcNYNDRw6BgUNHDnHP5HvIHZcb7a7FtWfzn6VHdg/uzr+b/UP2M3XuVHpk9+DZ/Gej3bUT\nhFMgA2vtYeCnQDHwV1zFMLKAidbaOeF3LyJON8ZsM8b8zxizyBiTEe0OiUjiGzlsJL329wILvQ70\nYsSlI6LdJQlBNEJHqMUd/IUo7wAWSJgLtPCFiFRnjMEYQ+mBUrqv7U7pgVLPMQldPIXYsMIWgLV2\nD3AxUA70AaZaa58I97kR8iEwDld/bwDaAR8YY1pEs1Mikvjco1vp76RrVCuORSN0hBrwAi1J/9W2\nrwIKc06N1AWrrpG5YK8TqW+bizeTn5fP+uXryc/LZ3Px5mh3Ke5FM8Raa5lyzxQCXWoU8JotY8zc\nOi75Aa6CGH8/sU/25wE1Us+MMWm4RuUesNY+7uO81myJiGNiZSGvxJ/a1n8Fvc7qNTD9DLbN8X/b\nUr9MpcH2BhweeNhzLNxiHqHyfj09uvUIaK1afa1pE5HY8sATD5B5eiYjLh3BS6+8xObizUy5bUrE\n2/W1TsyRAhlVpd5DYa21qSHeG3HGmLeBz6y1N/s4Z6dPP168MCcnh5ycnHrsnYiIyIkCCRi+yp3X\nVpK+e2F3TjntFM+h+q7U5+v1NNnRhOKs4jqLkYRbKTGaVR5FJH74K3Zy4/gbaw1bwfzIJ+HWNhlj\nGuMakat14+UZM2bUW39ERCTxOfHhftRNo1wBo6crYLjXWRVPKPYEDPeUx23Fx0fEWvRswYclNUrS\nl7TjH3/6R1TXX/l6PeYbg21Y/QfCFQ0rOLND9emNmRmZVHzrYxpkh7rXtHkHvKKZRRoRE5Fa5Y7L\npWXLltw5507POrH7J9/PyGEjuXH8jbXeF/DfJtbaLY70NIqMMQ/jmua4FWgL/AZIA+ZHs18iIpL4\nnPxwH2jA8DU6dc2t11QLYB3bdox6oQtfr8dmWpp83oTvfvid51i7knZM+FX1tWoTrptA/q/yTwiQ\nNa+rKZDAKiLiVnOdWMmBkoDWiSXbj246AX8GWgPf4CqY0d9aWxLVXomISMJz8sN9qAEDfAewaPP5\nekrb0b99f/YW7/Uc8xUMfY3gBRIgQx0RE5Hk5S524r1OrC4hb2qcDFQgQ0REnOL0xsBObZocK+r7\n9fha0xatwiCSfFQwKT4E+n1ypEBGMlLYEhERp+jDfexJtMAq8cNXRTuJPYF+nxS2QqSwJSIiTtKH\ne5Hk5q+i3S+v/2W0uydVgv0+KWyFSGFLRERERJxirWXpy0u5c86dlPywhFM/OpVHf/moZ9REUwtj\ng7/vk6/vjb+wlRLx3oqIiEi9KPp3EUPHDGXdp+ui3RUR8aFmRbvSA6WeY8v+voyn336al155Kejn\nWmuZcs8U3IMENX8fb6Ldf3/fp2ApbImIiMS5iooK8u7JY+i9Q3mty2tcMvMSJs2cREVFRd03i0jI\nQgkF7op265evJz8vn4WLF9Ijuwd359/N/iH7mTp3Kj2ye/Bs/rMBP7NmUAsnuNU3X+9hpINnIN+3\nmt+nQCoP+qJphH5oGmHyqKyspLCwEICsrCxSUvRzCBGJH1f84gpXWfnWx8NVg90NuOzwZdozSiSC\nnCh0EeyUNW811xad8top7Nu2j+ZnNufrQV/HxZow7/dw9+7dYa9pq/k98fU98nUsnAqR9TqN0BjT\n3hhzhTGmt9exzsaYc40xJzvdnki4CtcV0nd4XwY9NohBjw2i7/C+FK4rjHa3REQClpmRSUVDH3tG\nZWjPKJFIeDb/2bBHo9zCmbKWOy6XGXfN4NCRQ2CgUetG3HL9LTQ6qREYOHTkEPdMvofccbmhvMyI\n8vUePvHcE2Sfk+15PcH0v+bzbrr3Jhp1bMTNj9/seX77H7SnQ48OPr9vkRoNdHRTY2PMIOB14CTA\nGmNmWWvvAnYCWcAHQKqTbYqEo7KykvHTxlPUp8jzo4eiyiLGTxvP2r+u1QiXxK2J0ybyyZZPqv1j\nba3l7M5n89jMx6LYM4mEcDY5FpHg5Y7LpWXLltw5505PKLh/8v0hj26FslkunBjUSr4rqf77AyUh\nrzWKNF/v4aNTHsVay4uFLwbd/5rPa9S6Ebf8+BaWFC7xPH/2g7Ox1pL3XJ7n2JB+Qzyjae4ANu13\n05wbDbTWOvYF/AO4AkgHugMLgN9VnWsHVDrZXqS/AOvra/r06dZaa48dO2YLCgpsQUGBPXbsmJ0+\nfbrf62vS9bF5PYOxadek2YKCgpjoj65Pnuvv+M0ddtB1g+zgsYNt596dHfvzzAzXV9q4NLv05aV+\nrz/trNPsHb+5IybfH13v//rRt4y2g8cOdvTPj67X9bo+8Ot/NupnUenPBRdf4PP6adOm2aUvL7UP\nPPFARPqTPTjbVlZWhtX/JcuX2IanNXSs/0uWL7Hpg9Jt92HdbfqgdHvn/91Z6/NbZ7a26YPS7ZLl\nS+xf/vYXe+olp1pmYJue0TSk98fWkiccXbNljJlhrZ1R49j4qk6+Bmy31sbNyJa/NVuF6woZP208\nm9I3AZC5P5O5M+eS1TurPrsYFq1TgrVr1zLosUGUdy2vdjxtcxrvTnyXvn37RqlnkoyWvryUsX8b\nS3nn438e075MY8GIBUH/tNRay4ArB7CmxxowgIVzN5zL6r+s9vyE0Mn2JPlUVFQw5bdTWLR+Edt/\nsJ0On3dgdK/RPHD3AzRo0CDo60TizQNPPEDm6ZnVRqOm3DYlrLU/8cKpTZlrew+det5zf3qOX4z5\nRbXnW2tPaPOMLmcwftZ4Tm12KiX7SoJ+XfW2z5Yx5lfW2keNMadba//ndXwo0BZ4LhHCVmVlJX2H\n96029YxK6FPUJ26mniVCWHRCInwvJXEEEpCC4R2mfIUop9tzkqZBxr5Ai3KoeIckG6eCCBBzwS1R\nN2UON/TVZ4GM94wx9wObjTH93Qetta8CXwDfOdxeVBQWFrpCive7lwKb0jd5Ropimfc6pfKu5ZR3\nLaeoj2udUmVlZUTbXbt2LWvXro1oO8FISUlh7sy59CnqQ9rmNNI2p9G7sDdzZ85V0JJ6Z4whb0we\naVvTAEjbksak6yaF/A/syGEj6bW/F1jodaAXIy4dEdH2nJTdL5uC1AJWZazyfBWkFHDeOedFu2v1\nIh72ywq0KIeKd0iycLJohls0SrhbW3tZ9JoFOeqzAIe/foVr6u1TPdUfRw4bGdboWk2Ofpq01n4E\n3Av0sdZ+WOPcKqC3zxsTiJOBIlLhJBphMZYr/mX1zmLtX9fy7sR3eXfiu3zyt0+SboRPYkddASkY\n7jCV/k56rSHKyfac5N0vIOb6FynxtF/WhOsm0K6kXbVj7UraMeG6CSFdJxLvnAwikQhugfIX8Jzc\n7NfJfsUyxydLW2sPAp/Wcq7Y6faiISsri8z9mRRVVp961ml7J35x3y/YnO6qIBPO1LxEmuZXXxX/\nwlmDlpKScsL6LK1pk2hwB6Txs8YzKS/8UaaRw0ZSUFhQa0hxuj2nuPvlmQa5JY3WjVqTMy4nYlML\nY2Hq4qibRrmm3PV0havtPbfz+DePUzyhOOam3HXq1IkL2l7AtuJtnmMd23akY8eOIV0nEu9OqAwY\nRiVAp6sdBsJ7iqC/qnyhVk6MdL9iVVhhyxgzGMgGOlQd2g68XzWKlbDcU8+8w9AZ+87gUINDrOuz\nLuxAEelwUltYzNyfSVaW82GurpE0J4pQBBNOAwlRiRR2Jf7UFZCCYYzhd9N/V2/tOWnksJE88qdH\nWGPX0OtAL8ZeO5Zxy8edUNDjtnNuc6S97H7ZzPlqTsSeH4jMjEwqvvUx5a5DbE65e+HJFxy9TiTe\nORVEnAxugQo04E29farnv+ujmFI0gqeTQgpbVSHrGcD9t7/7O2+rzn8O3GStfTfsHkZZZWWlzw/j\n7qln7g/tlZWV5DyRE1CgqOvDfqTDia+w2LWsK3PvPXGdUqCjO9EcBQomnAYSorT3lgQjEqMhgQSk\nUITa10Dvc/q9qDnqNuLSEcxaOIs19nhBDyenFnqHu0g8PxDaL0skvjkZROp7BKm2gAcw5Z4pUSvS\nEY3g6aSgw5YxZiSwqOreHcA7QEnV6VOBHKAb8E9jzChrbXxNrKyh7/C+tY5oeE89W7t2bUDPi5UR\nk5ph0WfoC7CvdV0X6ZE0f+F07drj4ah3794Bhaj6GImT4OROzmXTrk0nHM9sm8mcB+dEoUfHxcJo\nSKBC7Wug90XivfAedfM1tdDJgh6Rfn4gNOVORNzqewQJfAc891qpc84+J2qjSfUdPJ0UVOl3Y0wH\nYBOuj6ETgeettcdqXJMC/Bx4HNdIV6a1drtjPa5HxhjLtMDKgAdSQjzQMuOxUI7c6b7WDGRdy7qS\nf28+vXv1DntErLa9shoVNKLz4c581eYrADqVdGJrx60cOvNQtetq7qmlvbdiT864HFZlnDg7eXDx\nYFbOW1n/HfISy+XTawq1r4HeVx/vhXcbkXifI/18EZFw1Vc5+EQt8x4JTpZ+vwNIA66x1j5bM2gB\nWGsrrbXPAddUXXt7sB2OKV4jJP4qAwZSQjzQKoC1Pev5Gc9TWFgYsdLp3tUP165dG1BfA31Nvir+\nAY5UKHSPnOH9lhyDlC0pbBqwyVPeflPmJg4fPRza8yK4pk3iWyyXT68p1L4Gel99vBeBVFiM5eeL\niISrvqryRbPMeyIJdhrh/wPWWGv/WteF1tq/GWPWAD8BJofSuVhxbN8xrv31tZ4Rktqm0wUyNS9Q\nNZ9FKtww/YaITT+sOfLUqaQTlR2dDXTe0y79rYv6eNnHrFvn2lsmkPfQ1xq0jls7UtKzpHoI7AB8\nDHTH73TGYNa0icCJhRxirdCEt1D7Guh9oTw/2LVe/gp6OLFuLFYLhohIcgu3Kl+wI2JOrJXy1Was\nbdQcacGGrc7A80Fc/wHwiyDbiC3uEZLhmwIqluCrhLhbsGuX3M/yNVXPyYINvoLPptM3cdLfToKu\n+O1rqOuxahsR+6zyM3pc0qPOYFtTrQVLvKVAw1Mb0nn18amFtYUoJ4OzJL5YLZ/uS6h9DfS+UJ4f\n7FovfwVEnFg3FqkCJSIi4Qi3Kl8oa6/CXSvlq81YWANWn4INW98DjgRx/VEgNcg2YkrXlV1PHCEJ\nsVhCqCMmThRs8Fct0OfzU6GycyWZqzP9BhNHR4Eq4UjJETZdFliw9fWavEfOfIXAbind+Pi1wEbO\n/AVnkZqCHQ2J5p5OoY7cBHpfsM93sgpgLFQUFBGJhFBHmsIZEQumSIf3iNWceXNOaPOWSbdgUg0n\nZ54cl/tlhSrYsLUD6BXE9T2AnXVeFcNemPXCiSMkYahtxCSSpdNDrYCY2iyVhbcv9PSltn6FMgrk\nc0RsO9VH0qDWUFnXa/IXAhs0aKAQFWcy22aCjy3RM9tm1n9nahHsaEg0qxiGOnIT6H3BPt/JKoCB\nPisWNjAWEQlWKCNN9bVPlfeIla82Zz84G2stec/lxeV+WaEKthphPjAa6G2t/byOa7sB64AXrLXX\nh9XLKDHG2GPHjoVVGdCJDXT9Vfyra32Tk1USnVbzdXfc2pGSTiV1VgsMpr/R3P9LxJ94qmLoi9Nh\nxckqgIE8a+nLSz2BzC3tyzQWjFiQ8P/wi0jyWfryUsbPGs+pzU6lZF8J+Xn5jv1dV1vVwuwfZvPi\nJy9Wa9NaG7F+RJO/aoTBjmw9BYwFXjHG/NRa+59aGuwG/B3XFMLfB9lGTAlnmpxTG+jW1oe7xtzF\nOSPP8fv8QKYgRqsgRM0Rsd69e3POyHPqXP8VzLRKTQWsXSzvXZUM4n0ExumROSfXvQXyLE03FJFk\nEsl9qmobOdv8v83kX1C9TWtt3O6XFaqgwpa1dq0x5mFgEvCJMeYl4C2qb2r8I2A40BCYZa0tcLC/\nURHKNLlAQhQEHhxqDSYOFc2IVkGImmFIVQDrz6Zdm3zuXeVrup64OB18AqncF6ubJkcirDhZBbCu\nZ8XCBsYiIvUlkhsk17aWbOod/ttMhBGtQAQ7soW1drIx5jvg/4BRwFU1LjHAMeBeYEa4HYwVwY6Q\nOFHUwl8f6toHyxPSgqgWGAujQIGEvlArIIqEKxqjOSeEmvcgtTSV2ZWzeXLZk0B0RroiEVaCXetV\nV/it61nxVLJfRCSWRXLkLN4FHbYArLUzjTHzgfFANtC+6tRO4D1gnrVWPx8PQKSDQzzuGVVX6IvH\n1ySJIRqjOTVDTcOUhhzpfoR3T3/Xc020RrqiHVbCDb91hd1YncIpIhJrIjlyFu+CKpCRbIwxdsaM\nGQAMHjyYnJycaudXrlwJ4PP4qlWu6Vkr7UpWGtd17iIOj97xKMYYz33utV2ndD6F7FbZAGSemcnV\no66utT3vIhE5KTnk4HrW5zs+54WnX6gWOlauXIm1lqZNmwLHR4u8+xnq6/N336BBg6qNUL377ruO\ntucufrFx40bat2/PkCFDQupnqK8vEe5bWfU/b2P3jmXc5eNiqp+xdN/ust3HR3O+TOPh/g/zzc5v\nItpP74IP13x9DV3bdnVdw0pW2pXVikDU9/vy9LNPs3z1cnJH5lb7B7Y+vn/e70uOOf734KBBg3z+\nfeCrvXfeeYd3333XZ3tLX17KnHfmcLT5Uc//T9xFNFqlt4rJP5+6T/fpPt2n++r/Pn8FMhS2/DDG\n2HDen5oFMrqWdSX/3nyfJddDqZoXzPPrW6jl5uNJPBWY8NXXos+L2PeTfSdcO7h4MCvnraynnsUf\nJ6vmBcNdScpdzck78EWzgp73virRWO/kXVXQ6fci3itGiohI/XAsbBljGuKaJlgG/MRae9TPdW8A\nacD5tV0X68INWxD50uOxWNo8WqXk61vOuByfBSZiMaz47Ou/oNmBZvT5QZ9qh2MxLMYa7+BT84N9\npKaeeYcaICqBLxZFOvxGMsyJiEhicLL0+7VAX/wELQBr7ZGqqoWvAtcA84JsJ2FEuuhELBS1qCkS\nxUEkAs6HPsV9gg6G8TSiFyn+1llFqnpgzeIRTpVJryne1ik5WTLel2ivSxMRkfgWbNgaAWy21v6j\nrgutta8bYzYDPyOJw1YiisXRNKk/Khnvv2pefe3f5GSZdG+xWmren0i9FxD5MCciIokt2LCVhWu0\nKlDvApcE2YbEsEDWYqk0e/yK9KhVMoyK1df+TcGWSQ9UPG72G6n3wi2SYU5ERBJbsGGrNbAriOt3\nAa2CbENiVKAbNas0e3WBBIxYCSFOjloFU5Qj0UbF4nnqmTb7PVGkw5yIiCSuYMPWQSA9iOtPBg4F\n2YbEqGDWYgWyOXG8y2yb6TMkZLbNrPb7QAJMpKfmBdpXn/4FRQeKyBmXA7gCExl13+bzNX1Z932J\nIN6nnsVzWBQRkcBFu6JsMgg2bJUA/YK4vh+wNcg2JEE4WbwjFteJxdO0t7D6WgH7frKPVVQFpy8d\n6VKtYmWUL1zxPPUs3sOiiIgEZtnfl/H0209zztnnqNJqhAQbtlYCE4wx/ay1Bf4uNMb0BQYCT4bY\nN4kx0VqLlQx7dsWVBsA70OxQ9bLxAY2SBcDpUb6a4W3jZxs5aA5yUsOTOPP0Mz3HnQ5z8T71LJ7D\nooiI+Pds/rPMfn42R9scZf+Q/UydO5Vpv5vGbTfcxi+v/2W0u5dQgg1bTwE3AUuMMZdYaz/zdZEx\n5gfAEuAY8HR4XZRYEY21WIGuE0tINabvuUV9hOd81y+hlI2PhhPC25fAENjHPnay8/jxBFs3Fq54\nD4siIlK73HG5tGzZkjvn3AkGDh05xP2T79foVgQEFbastRuNMTOBGUChMWYp8DbwVdUlHYELgZFA\nI2CatXajc92VaKvvtVhJvWdXzel74ApgnxexaVz1aXZOBTBfa7uKDhWxDx9FLULRAJq97nsjZRER\nEakfxhiMMZQeKKX72u6UHCjxHBNnBTuyhbV2pjGmApgOjAaurnGJAY4Cv7bWPhB+FyXWxOJGyrHM\nV4DZ+NlGNjba6Bm12vjZRpp93qza1DafIcdXAINqzw9nzZOv8znjck5sLwA+i3J0ioGROREREWFz\n8Wby8/IZcekIXnrlJTYXb452lxJS0GELwFp7vzHmBWA8kA20rzq1A3gPyLfWbnGmi5LMEmHPrloD\nTMaq49PYqqr7eU/NCzXkOL3mKdRKhtEIVLWVm6+zemKsTtlMQBOnTeSTLZ9U++mptZazO5/NYzMf\ni2LPRESSy9Tbp3r+W9MHIyeksAVQFaamO9gXkRNoz67oq++wEU6Z+pDLzUdhymayyu6XzZyv5lDe\nudxzLO3LNG4757Yo9kpERCQyQg5bIvUlGfbskuMcDzI11oltPLiRg68fdGTKpgTPew8vDGDRXl4i\nIpKwFLYkLiTjOrGIF6tIFufXXTkx1CmbEjz3Hl5j/zaW8s7lpG1JY9J12stLREQSU8hhyxjzvwAu\nqwTKgM+Al6y1y0JtTyTZ+BrhyZ2cy6Zi38UvJHnF2zoo79EtjWqJiEgiC2dkK6Xq/g5Vv68AvgVa\neT13O9AG6AOMMsa8BlxurT0WRrtSh8rKSk25i3GRLDoRzpqnaAineqK4xNs6KPfo1vhZ45mUp1Et\nERFJXOGErbOAFcAXwFTgQ2ttpTEmBRgA3I9rr62LgHbA48AlwO3Ao+F0WmpXuK6wWjGJzP2ZzJ05\nl6ze8VG5L1lEMkTEW0BxsnpiqEEz3qdsxuM6qJHDRlJQWBDTfRQREQmXsdaGdqMxT+IKUj2ttRU+\nzjcE/g38w1p7mzEmDfgc+MZaGxeLb4wxNtT3JxoqKyvpO7wvRX2ql0nvU9SHtX9dqxGueqBRmuC5\ny+DXNLh4sN91VpEWb9/LpS8vPb4O6ss0FoxYoFK+IiIi9cAYg7XW5zSNcEa2hgN/9hW0AKy1R4wx\nf8e16fFt1tpyY8xbwBVhtCl+FBYWuka0vDNVCmxK30RhYWHSFZiIBqf3uJLoicVA5Y/WQYnEptLS\nUnLzcpnzyByaN28e7e6ISD0LZ6ijFdCwjmu+V3Wd205UAVFExHHudVDp76Srup9IjCgtLeWisRex\n5OQlXDT2IkpLS8N+3pU3XBn2c+JRMr92iW/hBJ//ASONMb+x1u6vedIY0xQYSfWf6bcH9oTRpviR\nlZVF5v5MiiqrTyPM3J9JVpbWbCWqeJvuJpGjdVAiscMdtAq6FcBJUNC4gIvGXsSK+StCGuHyPC+j\ngOKxxSE/Jx5F47VrRFKcEk7YmgM8BqwxxvwWeB/YBbQFzgN+jatS4a8AjOvHrDlAURhtih8pKSnM\nnTm3WoGMrmVdmXvvXK3XSmCxMHUxnMAXb9UTY5kxht9N/120uyGS9GoGLcAVuLqFFricDm7REkqA\nicZrT+ZgK84LOWxZa58wxpwJ3Ags8HGJAeZYa5+o+n0bYBGuCoYSIVm9s1j717Uq/e4wjR75F07g\n0/snIokmNy+XggyvoOV2EhRkFJCbl8tfnv9LQM9yOrhFSygBJhqvPVGCrcSOsNZPWWsnGGP+DIzD\ntZdWM1ybGBcCC6y173pduwtXifioM8ZMAPJwTWvcANxhrX0vur1yTkpKiophOCzQMKFRGhERmfPI\nHIrHFlPQuEbgOgj9ivsxZ37gP2RyMrhFS6gBJlKvvbYRtkQJthJbwi5WURVS4iaoGGOuwrXn1424\npj7eDLxujOlmrf0qqp2TuKdRmvqj0UYRiVXNmzdnxfwV1T+4H4R+n/UL+gO7k8EtGsIJMJF47f5G\n2BIh2ErsScb5ZROBudbaudbajdba24AdwE1R7peIBME92ljzy1cAExGpb+7A1e+zfrA3tKB1wnMO\nVh0MMbhFQyABpjZOv/Zqwa/F8cDnrnA455E59Cv2asvNHe4eie1gK7EpqcqwG2O+B/QFHq5x6h/A\nwPrvkUj4NHVRYtXEaRP5ZMsn1crQW2s5u/PZPDbzsSj2TKR+uMNCbl4uc+b7Lwrhr3hEtZGyjAL6\nFcdH0ILwR6eceu2BjrA5NSIp4hZw2DLG/Ad4ylr7dCgNhXu/Q1oDqbiqJnrbBVxY/90RCV8sTJlT\n4BNfsvtlM+erOZR3LvccS/syjdvOuS2KvRKpX82bN69z6lkgxSOCCW6xxIkA48RrD3SKYDwHW4lN\nwYxs/QBXWAlVuPeLRI3ChH+xEPgk9owcNpJH/vQIa+waV31aC70O9NI+YCJegikeEUhwcz+zrhLr\n9bmPlBMBJtDXXptgRtjiNdhKbAp2GmGO93SQINlQb3TQbuAYrr3AvLUFdtZ/dyReKEyIBM8YQ96Y\nPMb+bSzlnctJ25LGpOsmEca/IyIxJdzAEonqd4GMkkVjH6loB5hgR9jCDXcibsbawDKQMabSgfZm\nWGtnOvCckBljPgSKrLU3eh3bCCyx1v5fjWvt9OnTPb/PyckhJyenvroqIn6oGmF8sNYy4MoBrOmx\nhnM3nMvqv6xW2JKE4B1YQp1qduUNV7Lk5CXQwsfJvfCzAz8LaAqiO/ABdYaJEwKe1zVAvY12RYsT\n3zeRmowxWGt9/uMWTNga7EBfvrTWbnHgOSEzxlyJaxPmm3GVfr8JuB7oYa0tqXGtDfT9ERER35a+\nvJTxs8aTn5fPyGEjo90dkbD5CyzBfHD3ObJF4M/zDg59NvXBpBgKexbW+iyg1vb6/Lvq/jMKEz6E\n1OcUSkkOjoStRGKMuRG4C9emxutxbWr8vo/rFLZERMJkrWXqzKk8MO0BjWpJ3As3INX5vGCDlvu+\n1/cHSz4AACAASURBVIAB+B0lA3yPpB3EVZf5x9R7BT4ng49ClESLwlaIFLZEROKfStCLk5yY+ldT\nsFPbfAa+g8AK4CKCG9kK4D5fa76cCDVOTunT9ECJJn9hKxk3NRYRkSSS3S+bgtSCaptfF6QUcN45\n50W7axKH/G1822dTH44ePerZJDdQ7uINPzvws4BCgs8y5ifhCkz/oNYNgH1uEvwOcD4BbzrsDjVL\nTl5SbUPgYPnbYLi0tJQrb7gy4GfXtVmxSDQpbImISEIbOWwkvfb3Ol4TVyXoJQw+A4vXmqe/tfhb\nnR/0fYUJd/U776BVW+ioNfABfdr1IWt9FuytHrTczwKO938v9GnVh6z/ZvkMj/2K+3kKb7j740So\n8VeFccjoIVxw7QUBh7m6KjoqcEm0KWyJiEhCc5egT9uaBqAS9BK2aoFrr1dxiZ6FdYaQQEeG/F1X\nW+Dr91k/3vnzO7y98O1qo2Q1nwV4RtLc1/t6lnvq4ZU3XMmWLVuCCjX+Rqdq3WAYKNpZFND7WOez\nahmZq02wo2kigdKaLT+0ZktEJDGoBL1EQmlpKeNuHcfWvVv9VgEMpOx6zRGtoK7zs04pnGcBnmMt\n3mzB3ov3BrRWra5+hbrmzNf0SicKlmi9l4QrImu2jDGDjDF9Qu+WiIhI/XCPbqW/k65RLXFM8+bN\nadioIYVnFNY5shLodLdgpsXVtdYrnGcB1aYM7r14L2kr0uqcbhjIVEMn1o75fZaPkbm6RhADmRqp\n0S8JRcgjW8aYY8Cz1toJznYpdmhkS0QkcagEvURCoCMrgVYxdLLaYajPqvU17YW0FWmUDyv3uSFy\nMKN81doJcJ+wQPcc8zUyF/AIWy1tavRL/IlI6XdjzC5gobX2znA6F8sUtkRERKQugUzVC/SDvZP7\neIX6LL8hbSe0+KAFe4fsPTHUbCuA8wgq3HmXkfc8J8TNooN9VqBh1KlNrCVxRSpsLQZOs9YOCKdz\nsUxhS0RERAIRkfVTDny4D+VZdYW0pbOXMuneSSeGGghp3ZXPtsMYQXIy2FZ7fQ5sYi2JKVJhqyuw\nBvg9MNNaezT0LsYmhS0REREJVCCb/QYaJqK94W/Io3U1C12EOCIXzqbJwUyfrOt1RmITa0k8kQpb\nc4EzgGxgF7AO2MnxnUzcrLX25yE1EmUKWyIiIuK0QMNEuKEj3GfVFdJqDSIHcW2uPIiorG8Kdvqk\nv9fp5LROSVyRCluVAV5qrbWpITUSZQpbIiIiksz8hTR/QaTPv/vQ5ZQu5D+RH5UwEuz0yaBep4KW\n1BCpsNU50GuttVtCaiTKFLZEREREahfLQSTaUzEleUQkbCUDhS0RERER/2I5iER7KqYkh3oJW8aY\ndKA5sM9aW+bIQ6NMYUtERESkbgoikswiFraMMQ2APOAGIMPrVDHwPPCItbYi5AaiTGFLRERERET8\nidSarYbAG8BgXBUIvwJ2AO2BToAB/gX82Fp7JKRGokxhS0RERERE/PEXtlLCeO6vgBzgVaCbtbaL\ntXaAtbYLcCbwd+D8qutERERERESSSjgjW/+u+s8+1toTysAbY1KAoqo2eoXexejRyJaIiIiIiPgT\nqZGtM4DXfQUtgKrjrwPfD6MNERERERGRuBRO2DoCnFzHNU2Ao2G0ISIiIiIiEpfCCVv/Bq4wxpzi\n66QxpjVwBbAujDZERERERETiUjhh6yngFOAjY8zPjTGnG2NOMsZkGGOuB9ZUnX/KiY6KiIiIiIBr\nX68rb7iS0tLSaHdFxK9w99m6H5iCq/T7CaeBh6y1U0JuIMpUIENEREQktpSWlnLR2IsoyCigX3E/\nVsxfoY2UJaoitqlx1cP7Az8HsoBmwD6gEJhrrV0d1sOjTGFLREREJHZ4gla3AjgJOAj9PlPgkuiK\naNhKZApbIiIiIrHhhKDlpsAlURaR0u/GmEHGmD6hd0tEREREJDC5ebkUZNQIWgAnQUFGAbl5uVHp\nl4g/4RTIeAfQn2oRERERibg5j8yhX3E/OFjjxEHoV9yPOY/MiUq/RPwJJ2zt5sQ/7iIiIiIijmve\nvPn/b+/e46uqzoSP/x4jIkJCVQoiNYitttYZneJlpEqJVSwFeS06gzpDbceOUxV55xXtjFQHgjiW\ntkitFUc7bbVTxWo1XnAqFmsBsYO2Ig5jrbcW8I4UBSlquaz3j32SScLJISfJ4ZDk9/18zifn7P3s\nvdd5kk+S56y112LBDxdw1DONCi6HEGoX1+Z7tiLidqA6pTSsY5u064iIVFtbC8CIESOoqalpsn/h\nwoUAebcvWrTI4zzO4zzO4zzO4zzO4zr4uPnz5/PYY48B8NxrzzFn5pwmhdau0k6P6z7HlWSCjIg4\nmGwtrTnAFSmlzW060S7MCTIkSZJ2PW+//Tb/cMk/8N1Z37VHS2VXqmLrB8BHgOOAN4CngNfZfs2t\nlFL6UpsuUmYWW5IkSZIKKVWxta2VoSmlVNGmi5SZxZYkSZKkQgoVW7u347xD2nGsJEmSJHVp7Sm2\nBgMbUkrLO6oxkiRJktRVuM6WJEmSJJWA62xJkiRJUgm0p9haCHyyg9ohSZIkSV1Ke4qty4GPRsSM\niOjRUQ2SJEmSpK7AdbYKcOp3SZIkSYW4zlYbWWxJkiRJKsR1tiRJkiRpJ2tzz1Z3YM+WJEmSpEJK\n1bPV+AK9gUOAPimlRzrinJIkSZLUmbVnNkIi4kMRcRfwFvBrsoWO6/cdHxG/iYia9jVRkiRJkjqf\nNhdbETEQeAw4Fbgf+C+gcffZY0B/4Iz2NFCSJEmSOqP29GxNIyumRqaUTgMWNN6ZUtoMPEI2Nbwk\nSZIkdSvtKbZGA/ellH5RIGY1sH87riFJkiRJnVJ7iq0BwPM7iNkM9G7HNSRJkiSpU2pPsbUOOGAH\nMYcAr7fjGmUXEds9amtr88bW1tYab7zxxhtvvPHGG2+88d0ovpA2r7MVEXcCnwEOTim9HhHTgKkp\npYrc/oOBp4FbUkrntOkiZRausyVJkiSpgIiW19lqT8/WN4E9gUUR8Vlgr9zFeudezwO2AVe34xqS\nJEmS1Cm1uWcLICLOAf6N/IsjbwHOSSnd2uYLlJk9W5IkSZIKKdSz1a5iK3fyg4ELgGOBfYH1wFLg\nupTSs+06eZlZbEmSJEkqpKTFVldmsSVJkiSpkFLdsyVJkiRJakG3KrYiYmFEbGv02BoRc8vdLkmS\nJEldT76JLbqyBPwAmALUd/W9W77mSJIkSeqquluxBbAppfRmuRshSZIkqWvrVsMIc86MiDcj4n8i\n4psR0afcDZIkSZLU9XS3nq1bgVXAq8BhwEzgz4FR5WyUJEmSpK6n00/9HhEzgMsKhCTghJTS4jzH\nHgU8DgxNKS3Ps9+p3yVJkiS1qEuvsxUR+wD9dhC2OqX0Xp5jA/gT8DcppZ/k2Z+mTZvW8Lqmpoaa\nmpr2NViSJElSl9FhxVZE7AEsATYAn00pbS4QNx/YCxjeUly5RcQRwJPAp1JKS/Lst2dLkiRJUos6\nclHjCcCRwDcKFVAppT8B3wSOAf62yGuUREQcFBH/EhFHRsTgiBgN3AY8ATxa5uZJkiRJ6mKK7dm6\nH/hISuljrYx/FnghpTSmje3rMBHxIeAWsokx+gAvAfcDV6SU3m7hGHu2JEmSJLWoUM9WsbMRfgL4\nzyLiFwOji7xGSaSUXgZqyt0OSZIkSd1DscMI+wFvFBH/BrBvkdeQJEmSpE6v2GLrXaCyiPg+wHaz\nAEqSJElSV1dssfUScFQR8UcBq4u8hiRJkiR1esUWWwuBYbnFgAuKiCOBTwK/aEO7JEmSJKlTK7bY\nug5IwE8i4tCWgiLiY8BPgK3A9W1vniRJkiR1TkXNRphSejYirgBqgScj4k7gYeDlXMgg4ETgdKAn\nMDWl9GzHNVeSJEmSOoei1tlqOCjiq8A0oAdZT1eT3cBmoDal9LV2t7CMXGdLkiRJUiGF1tlqU7GV\nO+lg4BzgOGBgbvNrwBLgppTSqjadeBdisSVJkiSpkJIUW92BxZYkSZKkQgoVW0Xds9XCyQcDHyQb\nTvhmSsmp3iVJkiR1e8XORghARPSLiNkR8RrwO+Ax4HHg9xHxakR8MyL26ciGSpIkSVJnUvQwwog4\nGFgAHEA2GcYW4A+55/uQ9ZYlYBVwUkrpdx3Z4J3JYYSSJEmSCik0jLConq2I2A24FagGFgEnAX1S\nSgNTSvsBlcDJwGLgQOCWdrRbkiRJkjqtonq2ImIU8FPgDuCslrp9IiKA28nW2xqVUlrQAW3d6ezZ\nkiRJklRIh/VskRVP7wOTClUhuX0Xkq239VdFXkOSJEmSOr1ii62hwKMppTd3FJhSWkO25tbQtjRM\nkiRJkjqzYoutA4Cni4h/Ghhc5DUkSZIkqdMrttiqAt4uIv5tskkzJEmSJKlbKbbY2gPYWkT8ttwx\nkiRJktSttGVRY6fnkyRJkqQdKHbq9220odhKKVUUe8yuwKnfJUmSJBVSaOr33dtyviLjrVYkSZIk\ndTtFFVsppbYMO5QkSZKkbsfiSZIkSZJKwGJLkiRJkkqgqGIrIj4VEdVFxB8eEWcX3yxJkiRJ6tyK\n7dn6BfDFxhsi4p8j4g8txI8DbmpDuyRJkiSpUyu22Mo3E+GewAc6oC2SJEmS1GV4z5YkSZIklYDF\nliRJkiSVgMWWJEmSJJWAxZYkSZIklUBbiq3U4a2QJEmSpC4mUmp97RQR22hDsZVSqij2mF1BRKRi\n8iNJkiSpe4kIUkr5Zm1n97acr8h4qxVJkiRJ3U5RxVZKyXu8JEmSJKkVLJ4kSZIkqQSK6tmKiDYV\nZymlbW05TpIkSZI6q2Lv2drchmukNlxHkiRJkjq1Yougl2j9hBd9gH2LPL8kSZIkdQnFTpBx4I5i\nIqIHMAm4LLdpZdGtkiRJkqROrkMnyIiIvwaeAb5JNkX8PwGHduQ1JEmSJKkz6JB7qSLik8As4C+B\nLcC1wBUppbc64vySJEmS1Nm0q9iKiA8DXwfGkfVk3QlMSSm92AFtkyRJkqROq03FVkTsA0wDvgzs\nAfwXcHFKaWkHtk2SJEmSOq1i19naA/h/wKXAB4AXgUtTSneVoG2SJEmS1GkV27P1LFANrCMruuak\nlLZ2eKskSZIkqZOLlFq7bBZExDaydbbeAja18rCUUhrchraVXUSkYvIjSZIkqXuJCFJKkXdfG4qt\noqWUOnSK+Z3FYkuSJElSIYWKrWIXNe6URZMkSZIk7WwWT5IkSZJUAhZbkiRJklQCFluSJEmSVAIW\nW5IkSZJUAl2m2IqIcyPi4Yh4KyK2RUR1npgPRMSPIuLt3OM/IqJvOdorSZIkqWvrMsUWsBfwIDCN\nbC2wfG4D/gI4GfgMMBT4j53SOkmSJEndSlHrbHUGEXEk8DgwJKW0utH2jwG/AT6ZUlqa23Yc8Ajw\n0ZTS83nO5TpbkiRJklpUaJ2trtSztSPDgHfqCy2AlNKjwB+BT5atVZIkSZK6pO5UbO0HvJln+5rc\nPkmSJEnqMLt0sRURM3KTXbT02BoRnyp3OyVJkiSpud3L3YAd+Bbwox3ErN7B/nqvAx/Ms71/bl9e\ntbW1Dc9ramqoqalp5eUkSZIkdWfdbYKMp4HjGk2Q8UmyCTI+5gQZkiRJkopVaIKMXb1nq9UiYgDZ\nvVcfBQI4LCL2BlanlN5KKf02Ih4EboyIL+dibgDm5Su0JEmSJKk9dul7top0HvAk2bDDBNwPLAPG\nNoo5C3gKmA88kIs/e+c2U5IkSVJ30OWGEXYkhxFKkiRJKsR1tiRJkiRpJ7PYkiRJkqQS6DITZOxM\nBx54IKtWrSp3M9TJDR48mJUrV5a7GZIkSSoR79kqoKV7tnLjMsvQInUl/hxJkiR1ft6zJUmSJEk7\nmcWWJEmSJJWAxZYkSZIklYDFliRJkiSVgMWWJEmSJJWAxZa6nSFDhvDwww+X9Brr16+nrq6Or33t\nayW9jiRJknZdFlsC4E9/+hN///d/z4EHHkjfvn0ZOnQo8+fPL3hMTU0NvXr1oqqqisrKSg499NB2\ntWHu3LkcffTRVFZWMmjQIMaMGcOjjz7asP/mm2/m8MMPp3fv3uy///5ccMEFrF+/vl3XLJW+ffty\n5JFHsnnz5nI3RZIkSWVisdUNTJ8+nSuuuKJgzJYtW6iuruaRRx5h/fr1zJgxg/Hjx7N69eoWj4kI\nrr/+ejZs2MA777zDM8880+Y2zp49m8mTJ3P55ZezZs0aVq9ezcSJE5k3bx4AV199NVOmTOHqq69m\nw4YNLF26lFWrVjFy5Ei2bNnS5utKkiRJpWKxJQD22msvpk6dygEHHADAmDFjGDJkCE888UTB41q7\nKO+yZcsYOnQoffv2Zfz48Zx55plMnToVgA0bNjBt2jSuv/56Tj31VHr16kVFRQWjR49m5syZvPPO\nO9TW1nLdddcxcuRIKioqqK6u5o477mDlypXccsstbX7fzzzzDAcddBC33347kA0xnDVrFkcccQSV\nlZWce+65rFmzhtGjR1NVVcXJJ5/c0Ju2du1a7rrrLurq6hoeixYtanNbJEmS1LVYbCmvN954g+ef\nf57DDjusYNyUKVPo378/w4cPb7HQ2Lx5M6eddhrnnHMO69at46yzzuLuu+9u2P/LX/6S999/n899\n7nN5j6/fP27cuCbbe/fuzejRo1mwYEGR7y6zbNkyRo0axZw5czjjjDMattfV1fHzn/+c5557jvvu\nu6+h6Fu7di1bt27l2muvBaBfv36cfvrpnHbaaQ2PESNGNLlGa4tRSZIkdT27l7sBKo2xY8eyZMkS\nIoL33nsPgGuuuQaA448/nvvuu6/FY7ds2cKECRP44he/yCGHHNJi3De+8Q0+/vGPs8cee3Dbbbcx\nduxYnnrqKYYMGdIkbunSpWzdupULL7wQgHHjxnHMMcc07F+3bh39+vVjt93y1/5r165tcf/AgQNZ\ntmxZi21syeLFi/n+97/P3LlzGT58eJN9kyZNol+/fgAMHz6cAQMGcPjhhze0vTWTa2zcuJE777yT\nJ554gqeffnqHRaskSZK6HoutLqr+XifI7tmKiIZhe4WklJgwYQI9e/bkO9/5TsHYo48+uuH52Wef\nzW233cZPf/pTJk6c2CTu1VdfZdCgQU221Q9XBNh3331Zu3Yt27Zty1tQ9evXr8X9r732WkNh9MIL\nL7BixQpWrFjBKaecwtChQ1ts+4033siIESO2K7QABgwY0PC8V69e273euHFji+et16dPHy6++GIu\nvvjiHcZKkiSpa3IYYYksXLiQ6dOnM336dBYuXJh3f0vbWzq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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xb15ab70>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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SpsQeqeqx/PyWNCv2SEmSJEnSjFlIqbOc/yxwHGiJY0HgOFDhOFAdLKQkSZIkqSJ7pCRJ\nmhJ7pKrH8vNb0qzYIyVJkiRJM2Yhpc5y/rPAcaAljgWB40CF40B1sJCSJEmSpIrskZIkaUrskaoe\ny89vSbNij5QkSZIkzZiFlDrL+c8Cx4GWOBYEjgMVjgPVwUJKkiRJkiqyR0qSpCmxR6p6LD+/Jc2K\nPVKSJEmSNGMWUuos5z8LHAda4lgQOA5UOA5UBwspSZIkSarIHilJkqbEHqnqsfz8ljQr9khJkiRJ\n0oxZSKmznP8scBxoiWNB4DhQ4ThQHSykJEmSJKkie6QkSZoSe6Sqx/LzW9Ks2CMlSZIkSTNmIaXO\ncv6zwHGgJY4FgeNAheNAdbCQkiRJkqSK7JGSJGlK7JGqHsvPb0mzYo+UJEmSJM2YhZQ6y/nPAseB\nljgWBI4DFY4D1cFCSpIkSZIqskdKkqQpsUeqeiw/vyXNij1SkiRJkjRjrSikIuKgiPhARHw/InZE\nxKOHbHNkRJwfEVdGxMkRcYcmctX8cP6zwHGgJY4FgeNAheNAdWhFIQXsAZwOPBW4cvmVEfEs4OnA\nk4E7AxcDH4+I688ySUmSJEmCFvZIRcSPgSdn5vED6y4AXpOZL+svX49STD0jM980JIY9UpKkxtkj\nVT2Wn9+SZqXzPVIRcStgE/DxxXWZ+XPg08CBTeUlSZIkaf1qfSFFKaIS2L5s/fb+ddJQzn8WOA60\nxLEgcByocByoDrs0ncC0bN26lc2bNwOwYcMGtmzZwsLCArD04nG528uL2pKPy80sb9u2rVX5uLy+\nlosesDDwb1qwzBrXNxWvPIfT/P/Ztm1ba8aHyy673PzyJCr1SEXEa4B/zcyvT3zPK9/HtXqk+lP7\nvgPcJTNPG9juv4BLMvMxQ2LYIyVJapw9UtVj+fktaVZm3SP1FOBrEfHpiHhkROw27h2PKjPPAS4C\nDl1c1z/ZxEHAKdO+f0mSJElarmoh9VDgk8A9geOBCyLilRGx3yRJRMT1I+JOEbGln9Pe/eVb9jc5\nGnhWRDw4Iu4IHAf8GHjnJPerbqvjkK3mn+NAixwLAseBCseB6lCpkMrM92XmfYF9gVcAvwD+Fjgz\nIk6KiIdFxK5j5HFn4MvAacD1gBcCX+r/JTNfAbwaeB1wKrARuG9m/nSM+5IkSZKkiUz0O1IRsQvw\nQODxwH36qy8F3gK8KTO/M3GG4+Vlj5QkqXH2SFWP5ee3pFmZtEeqth/kjYi7Au8DfrO/agfwEeAF\ngyeJmAULKUlSG1hIVY/l57ekWWn8B3kj4pCIeAfwKUoRdQmlp+mzwB8Cn4+Ih096P1JVzn8WOA60\nxLEgcByocByoDmP9jlRE3AjYCvwVcFvK11GnAP8KvCczf9nf7q7AfwJHAu+aPF1JkiRJal7V35E6\niFI8/RnlpBA/Ad5G+W2p01e4zVHAszJz6qdKH7hPp/ZJkhrn1L7qsfz8ljQrk07tq3pE6lP9v2dQ\njj4dn5k/WeM25/cvkiRJktQJVXukTgAOyczfzszXj1BEkZn/lpm3Gi89aXzOfxY4DrTEsSBwHKhw\nHKgOlY5IZeafTysRSZIkSZoXVXuk9gJuD3w5M3885PobAFuAMzPz0tqyrMgeKUlSG9gjVT2Wn9+S\nZmXWpz9/PvAh4JoVrr+mf/1zxk1IkiRJktquaiF1KPDxzLxy2JWZ+VPgY8D9Jk1MmpTznwWOAy1x\nLAgcByocB6pD1ULqlsB31tjm7P52kiRJktRJVXukrgDenJlPX2WbVwOPy8w9ashvLPZISZLawB6p\n6rH8/JY0K7PukTqLVabtRfnEuB/w7XETkiRJkqS2q1pIvRe4XUS8LiJ+bfCK/vLrgP2Ad9WUnzQ2\n5z8LHAda4lgQOA5UOA5Uh0q/IwW8BngE8ETgQRHxaeB84DeBg4GbA18Bjq4zSUmSJElqk0o9UgAR\nsQF4PfAwrn1EawdwAvCUzLy8tgzHYI+UJKkN7JGqHsvPb0mzMmmPVOVCauCO9wLuAmwALgdObfJH\neAdZSEmS2sBCqnosP78lzcqsTzbxK5l5SWb+d2a+o/+3FUWUtMj5zwLHgZY4FgSOAxWOA9Vh7EJK\nkiRJktarcXqk9gQOB+4K3AjYechmmZn3mTy98Ti1T9IoNm3azPbt59USa+PGfbjoonNriaXucGpf\n9Vh+fkualZn2SEXE7YAesBflnXMlmZnDCqyZsJCSNIq6d3J939FyFlLVY/k6kjQrs+6ReiVwU+Dl\nwK2BXTNzpyGXxoooaZHznwWOAy1xLAgcByocB6pD1d+ROgj4cGY+dxrJSJIkSdI8qDq17wrgXzPz\nWdNLaXJO7ZM0Cqf2adqc2lc9lq8jSbMy66l9pwH7jXtnkiRJktQFVQupFwF/GBELU8hFqpXznwWO\nAy1xLAgcByocB6pD1R6pWwIfAD4WEe+kHKG6fNiGmXn8hLlJkiRJUitV7ZHaQZkIPTiXcHmAwNOf\nS5oD9khp2uyRqh7L15GkWZm0R6rqEanHjHtHkiRJktQVlY5IzQuPSAnK/OeFhYWm01DDVhsHHpFa\nX5p4T/CIVPVY034d+dkgcByomPVZ+yRJkiRp3RvriFRE7AX8GXB74PqZ+X8H1t8KOD0zf1ZnohXz\n84iUpDV5RErT5hGp6rF8HUmalUmPSFUupCLiscBrgOux7MQSEXFH4CvAX2Xmm8dNalIWUpJGYSGl\nabOQqh7L15GkWZnp1L6IOBR4I/BN4MHAvw5en5lfA84AHjRuQlJd/I0IgeNASxwLAseBCseB6lD1\nrH3PAi4EDsnMKyLid4ds81XgHhNnJkmSJEktVfV3pC4HTsjMJ/SXXwAcMfibURHxMuCvM/P6dSc7\nKqf2SRqFU/s0bU7tqx7L15GkWZn1Wft2A366xjYbgGvGS0eSJEmS2q9qIXUucMAa29wNOGusbFYQ\nETtFxFERcXZE/Kz/96iI8PTtWpHznwWOAy1xLAgcByocB6pD1ULkA8BBEfHQYVdGxGOA3wHeN2li\nyzwbeCLwFGA/4KnAk4Dn1Hw/kiRJkrSmqj1SNwK+BNySUizdEDgU+BvgIOAhwHeAAzJzrSmAVe73\nQ8ClmfmYgXXHAXtm5p8O2d4eKUlrskdK02aPVPVYvo4kzcpMe6Qy84fAIcBngYcC96W8g76mv/w5\n4D51FlF9nwXuHRH7AUTEHYDfBz5c8/1IkiRJ0poq9xhl5nczcwHYQplu93zgr4G7ZOYhmXl+vSlC\nZr4ceBtwZkT8AjgdOC4z31D3fak7nP8scBxoiWNB4DhQ4ThQHar+jtSvZOZXKb8ZNXURcRjwF8Bh\nwJmUIu41EXFOZr5lFjlIkiRJ0qKxC6kZewXwisx8T3/5jIjYTDnZxNBCauvWrWzevBmADRs2sGXL\nFhYWFoClbyFcdtnl7i8vrlv5+l7/76TLS/c1zcfj8vjLCwsLI23/kIccxg9/uJ369Jh8fNW9zBrX\nNxVv135f2eQ2btyHE044rkRf9v+9qE3js0vLhx22le3bz6MON7rRRi677KLa8x31/cDl7i9PourJ\nJo4YcdPMzKPGS2no/V5K+eHf1w+sew7w2Mzcd8j2nmxC0po82YSG8QQR3Ynla7IZvrdqXkx6somq\nhdSOVa5eDBSUQmrncZMacr9vAe4DPAE4A/g94A2UPqm/H7K9hZToDRyF0Pq12jjww359GfU9wUKq\nO7GGvSb9bJi+eXhvdRwIJi+kqk7tu/cK6zcAd6H8vtOHgX8bN6EVPAU4CvgX4KbAhZRCqrajXpIk\nSZI0qkpHpNYMFvHbwKnAYZn5gdoCV8/DI1KS1jQP35pq9jwi1Z1Yviab4Xur5sVMp/aNFDDiBOBW\nmXm3WgNXy8FCStKa/LDXMBZS3Ynla7IZvrdqXsz0B3lH9F3gjlOIK1VSx9lYNP8cB1rkWBA4DlQ4\nDlSHaRRSdwN+NoW4kiRJktQKVc/at/cKV+0C3BJ4HPAI4N2Z+YjJ0xuPU/skjcLpJxrGqX3dieVr\nshm+t2pezPqsfeey+isjgG8Bzxw3IUmSJElqu6pT+45f4XIc8GrgMOB3MvP8GnOUxuL8Z4HjQEsc\nCwLHgQrHgepQ6YhUZm6dUh6SJEmSNDdqP/15G9gjJWkUzuPXMPZIdSeWr8lm+N6qedHG059LkiRJ\nUqdVKqQi4qQxL5+c1gOQVuL8Z4HjQEscCwLHgQrHgepQ9ax9C/2/STn+vtxq6yVJkiSpE6r+jtRu\nwLuBOwJHAT3gImATcG/gecDXgIdl5i/rTnZU9khJGoXz+DWMPVLdieVrshm+t2peTNojVbWQOgp4\nDHDHzLx8yPV7AqcDb87MI8ZNalIWUpJG4Ye9hrGQ6k4sX5PN8L1V82LWJ5t4JPC+YUUUQGZeBrwX\neNS4CUl1cf6zwHGgJY4FgeNAheNAdahaSN0c+MUa2/wSuNl46UiSJElS+1Wd2vdtYAdlat91CqqI\n2J3SIxWZuW9tWVbk1D5Jo3D6iYZxal93YvmabIbvrZoXs57a91ZgX+CkiDg4InbuJ7FzRBwCfBK4\nNXDcuAlJkiRJUttVLaReBnwQOBA4Gfh5RGwHfg6c1F//of52UqOc/yxwHGiJY0HgOFDhOFAdKhVS\nmfnLzHwQ5WQSJwE/Avbs//0k8MjMfFBmXl17ppIkSZLUEpV6pOaFPVKSRuE8/mo2bdrM9u3n1RZv\n48Z9uOiic2uLVxd7pLoS63rAVbVEautYrVPdr2/fWzUPZvo7UvPCQkrSKCykqqn3+YK2PmcWUsYa\nFquNY7VObR73XX/u1ZxZn2xi8U5/JyJeFhEfiIhPDKzfHBEPi4gbjZuQVBfnPwscB1riWFDRazoB\ntYDvB6rDLlVvEBEvAp7LUhE2+DXBTsA7gacBr504O0mSJElqoaq/I3UY8A7go8CzgIcDz87MnQe2\n+V/gisw8tOZcR+bUPkmjcGpfNU7tGyuasToSq41jtU5tHvddf+7VnFlP7Xsq8G3ggZn5VeA6P8oL\nfB24zbgJSZIkSVLbVS2kfhv4aGYOK6AWXQBsHD8lqR7OfxY4DrTEsaCi13QCagHfD1SHqoVUADvW\n2GYj5Qd6JUmSJKmTqvZIfRm4OjPv0l9+AXDEYo9UROxEmdp3SWbeawr5jpqnPVKS1mSPVDX2SI0V\nzVgdidXGsVqnNo/7rj/3as6se6TeDfxeRDxjheufC+xLOSGFJEmSJHVS1ULqaOArwCv6Z+d7AEBE\nvLK//ELg88Aba81SGoPznwWOAy1xLKjoNZ2AWsD3A9Wh0u9IZebPIuLewDHAI4HF057/LaV36m3A\nUzLz6lqzlCRJkqQWqdQjda0bRuwJ3AW4MfAj4NTMvKTG3MZmj5SkUdgjVY09UmNFM1ZHYrVxrNap\nzeO+68+9mjNpj1TVk008GtiemR8d9w5nwUJK0igspKqxkBormrE6EquNY7VObR73XX/u1ZxZn2zi\nWOD+496ZNEvOfxY4DrTEsaCi13QCagHfD1SHqoXURWPcRpIkSZI6perUvn8H7gpsycy1fpi3MU7t\nkzQKp/ZV49S+saIZqyOx2jhW69Tmcd/1517NmfXUvucBvwG8OSJuMu6dSpIkSdI8q1pIvZNyhr5H\nA9+LiK9HxMkRcdKyyyfrT1WqxvnPAseBljgWVPSaTkAt4PuB6lDpd6SAhYF/7w7s178sV/sx2IjY\nBLwM+EPKUbHvAE/MzM/UfV+SJEmStJpVe6Qi4qnA5zPz1NmlNDSPGwJfAj4NvA64FLg1cEFmnjVk\ne3ukJK3JHqlq7JEaK5qxOhKrjWO1Tm0e911/7tWcafdIHc3A6c4j4pqI+Idx72wCz6IUTY/JzNMy\n87zMPHlYESVJkiRJ07ZWIfVzyhS+RdG/zNoDgf+NiBMiYntEfDkintxAHpojzn8WOA60xLGgotd0\nAmoB3w9Uh7UKqXOA+0XExoF1TRxfvTXwJEpf1H0pR8peFhFPaiAXSZIkSevcKD1SR7NUPI066TUz\ns+qJLFbL4yrg1Mw8aGDdS4AHZeb+Q7a3R0rSmuyRqsYeqbGiGasjsdo4VuvU5nHf9edezZm0R2rV\nYiczXxMRFwN/BNwcuDfwXeDcce9wTBcCX1+27uvAU1e6wdatW9m8eTMAGzZsYMuWLSwsLABLh3Nd\ndtnl+VsNOENcAAAgAElEQVTec89N/PCH26lPr/93YcLl/pLPV6Xlpp+flZaXVHs8111eXDfu7ae1\nzBrXNxVvcd2k+dS9vHu/0JhcxO5kXtW6WEWv/3dhwuXpxGvL+4PL3VqexKpHpK6zccQO4MjMfNHE\n91xBRLwduEVmHjKw7ijgwZl5xyHbe0RK9Hq9X71Y1B3VvzXtce2dtGtFqxhrNe381rS93zKXeLN8\nzkZ9T2jvc2asemL1WPk9oWqscRiraqxpvE+4jyCY/ln7lnsh1/26YRZeDdw9Ip4bEb8VEQ8F/ppy\nKnRJkiRJmqlKR6SaFBEPAF4K3JYyvfC1mfkvK2zrESmpo9p8tKCN7zvtfb5KPJ8zYxnLWGvFauP7\nhLph0iNSc1NIVWEhJXVXm3dy2/i+097nq8TzOTOWsYy1Vqw2vk+oG2Y9tU+aG3U0EaoLek0noJbw\nPUFFr+kE1AK+H6gOFlKSJEmSVJFT+yTNlTZPu2rj+057n68Sz+fMWMYy1lqx2vg+oW5wap8kSZIk\nzZiFlDrL+c8qek0noJbwPUFFr+kE1AK+H6gOFlKSJEmSVJE9UpLmSpv7V9r4vtPe56vE8zkzlrGM\ntVasNr5PqBvskZIkSZKkGbOQUmc5/1lFr+kE1BK+J6joNZ2AWsD3A9XBQkqSJEmSKrJHStJcaXP/\nShvfd9r7fJV4PmfGMpax1orVxvcJdYM9UpIkSZI0YxZS6iznP6voNZ2AWsL3BBW9phNQC/h+oDpY\nSEmSJElSRfZISZorbe5faeP7TnufrxLP58xYxjLWWrHa+D6hbrBHSpIkSZJmzEJKneX8ZxW9phNQ\nS/ieoKLXdAJqAd8PVAcLKUmSJEmqyB4pSXOlzf0rbXzfae/zVeL5nBnLWMZaK1Yb3yfUDfZISVIr\n7E5E1HLZtGlz0w9m7mzatLm251+SpFFYSKmznP+sojej+7mK8g3s5Jft28+bUc7dUZ6ztZ7bk0fY\nxm++u6/XdAJqAfcRVAcLKUmSJEmqyB4pSXNlvfSv1PUe1t7nq8Tr/uM0lrGMNWks9+k0LfZISZIk\nSdKMWUips5z/rKLXdAJqjV7TCagVek0noBZwH0F1sJCSJEmSpIrskZI0V9ZLL0z3e4dKvO4/TmMZ\ny1iTxnKfTtNij5QkSZIkzZiFlDrL+c8qek0noNboNZ2AWqHXdAJqAfcRVAcLKUmSJEmqyB4pSXNl\nvfTCdL93qMTr/uM0lrGMNWks9+k0LfZISZIkSdKMWUips5z/rKLXdAJqjV7TCagVek0noBZwH0F1\nsJCSJEmSpIrskZI0V9ZLL0z3e4dKvO4/TmMZy1iTxnKfTtNij5QkSZIkzZiFlDrL+c8qek0noNbo\nNZ2AWqHXdAJqAfcRVAcLKUmSJEmqaC57pCLiOcBLgNdl5lOHXG+PlNRR66UXpvu9QyVe9x+nsYxl\nrEljuU+naVl3PVIRcXfgccBXms5FkiRJ0vo0V4VURNwQeBvwGODyhtNRyzn/WUWv6QTUGr2mE1Ar\n9JpOQC3gPoLqMFeFFPBG4N2Z+ammE5EkSZK0fs1Nj1REPA74K+BumbkjIk4GTrdHSlpf1ksvTPd7\nh0q87j9OYxnLWJPGcp9O0zJpj9QudSYzLRFxW8rJJe6ZmTuazkeSJEnS+jYXhRRwD+DGwJnlW0cA\ndgYOjognANfPzF8O3mDr1q1s3rwZgA0bNrBlyxYWFhaApXmxLnd7eXFdW/JxuZ7logcsDPybVZaP\nBrZU2H7cZda4frx4s3++Zrtc5+t97fsb3Hat/EaJN8ry4rpxbz+tZda4vql4i+smzWe15W3A0yre\nnjWur7pcV7zFdZPmU/cya1xfZXlXBvb9JhKxO5lX1RJr48Z9OOGE44D2fD66PN7yJOZial9E3AC4\nxbLVxwHfBF6SmV9ftr1T+0Sv11u2M6kuqD6Fq8e1dzquFa1irNW0cypLe6e8lXizfZw9Vh4L14o2\nQqxRGat9sXqMNg5GiTUOY7UjVo/q4+DasdzXnH+TTu2bi0JqGHukpPWpvYWBhdQ48br/OI1lLGN1\nNZb7mvNv3f2O1ABHryRJkqRGzG0hlZm/P+xolLSojrmv6oJe0wmoNXpNJ6BW6DWdgFqh13QC6oC5\nLaQkSZIkqSlz2yO1GnukpO5aL70w3e8dKvG6/ziNZSxjdTWW+5rzbz33SEmSJElSIyyk1Fn2SKno\nNZ2AWqPXdAJqhV7TCagVek0noA6wkJIkSZKkiuyRkjRX1ksvTPd7h0q87j9OYxnLWF2N5b7m/LNH\nSpIkSZJmzEJKnWWPlIpe0wmoNXpNJ6BW6DWdgFqh13QC6gALKUmSJEmqyB4pSXNlvfTCdL93qMTr\n/uM0lrGM1dVY7mvOP3ukJEmSJGnGLKTUWfZIqeg1nYBao9d0AmqFXtMJqBV6TSegDrCQkiRJkqSK\n7JGSNFfWSy9M93uHSrzuP05jGctYXY3lvub8s0dKkiRJkmbMQkqdZY+Uil7TCag1ek0noFboNZ2A\nWqHXdALqAAspSZIkSarIHilJc2W99MJ0v3eoxOv+4zSWsYzV1Vjua84/e6QkSZIkacYspNRZ9kip\n6DWdwBh2JyJquWhQr+kE1Aq9phNQK/SaTkAdsEvTCUiSlruKeqeySJKkutkjJWmu2AvTlVglnj1S\nxjKWseY1lvua888eKUmSJEmaMQspdZY9Uip6TSeg1ug1nYBaodd0AmqFXtMJqAMspCRJkiSpInuk\nJM0Ve2G6EqvEs0fKWMYy1rzGcl9z/tkjJUmSJEkzZiGlzrJHSkWv6QTUGr2mE1Ar9JpOQK3QazoB\ndYCFlCRJkiRVZI+UpLliL0xXYpV49kgZy1jGmtdY7mvOP3ukJEmSJGnGLKTUWfZIqeg1nYBao9d0\nAmqFXtMJqBV6TSegDrCQkiRJkqSK7JGSNFfshelKrBLPHiljGctY8xrLfc35Z4+UJEmSJM2YhZQ6\nyx4pFb2mE1Br9JpOQK3QazoBtUKv6QTUARZSkiRJklSRPVKS5oq9MF2JVeLZI2UsYxlrXmO5rzn/\n1kWPVEQ8JyJOjYgfRcTFEfHBiNi/6bwkSZIkrU9zUUgBBwOvA+4B3Bu4GvhERGxoNCu1mj1SKnpN\nJ6DW6DWdgFqh13QCaoVe0wmoA3ZpOoFRZOYDBpcj4i+AHwH3BD7cSFKSJEmS1q257JGKiJsB5wP3\nyszPDbneHimpo+yF6UqsEs8eKWMZy1jzGst9zfm3LnqkhjgG+BLwP00nIkmSJGn9mbtCKiJeBRwI\n/JmHnbQae6RU9JpOQK3RazoBtUKv6QTUCr2mE1AHzEWP1KKIeDXwMGAhM89bbdutW7eyefNmADZs\n2MCWLVtYWFgAlnawXe728qK25ONyPctFD1gY+DerLG9b4/q6llnj+qbiLa6bNJ/pLNf9evf5H3WZ\nNa5vKt7iuknzWW152xi3Z43rm4q3uG7SfOpeZo3rm4q3uG7SfMpy05+HLtezPIm56ZGKiGOAhwIL\nmfnNNbb1YJXUUfbCdCVWiWePlLGMZax5jeW+5vybtEdqLo5IRcS/AI8CHgj8KCI29q/6SWb+tLnM\nJEmSJK1HOzWdwIieCOwBfBK4YODyjCaTUrvVcchWXdBrOgG1Rq/pBNQKvaYTUCv0mk5AHTAXR6Qy\nc14KPkmSJEnrwNz0SFVhj5TUXfbCdCVWiWePlLGMZax5jeW+5vxbr78jJUmSJEmNsZBSZ9kjpaLX\ndAJqjV7TCagVek0noFboNZ2AOsBCSpIkSZIqskdK0lyxF6YrsUo8e6SMZSxjzWss9zXnnz1SkiRJ\nkjRjFlLqLHukVPSaTkCt0Ws6AbVCr+kE1Aq9phNQB1hISZIkSVJF9khJmiv2wnQlVolnj5SxjGWs\neY3lvub8s0dKkiRJkmbMQkqdZY+Uil7TCag1ek0noFboNZ2AWqHXdALqAAspSZIkSarIHilJc8Ve\nmK7EKvHskTKWsYw1r7Hc15x/9khJkiRJ0oxZSKmz7JFS0Ws6AbVGr+kE1Aq9phNQK/SaTkAdYCEl\nSZIkSRXZIyVprtgL05VYJZ49UsYylrHmNZb7mvPPHilJkiRJmrFdmk5AmpZer8fCwkLTaczMVVdd\nxRFHvJjLL7+ilnj3uMdd2Lr1UbXEalYPWGg4B7VDD8eCHAcqejgONCkLKakjvv3tb3PMMW/iqque\nU0O0i/ngB19UWyG1adNmtm8/r5ZYkiRJbWCPlNQRZ5xxBgce+DCuuOKMGqJ9k02b/pgLL/xmDbHs\nXzHWyvHskTKWsYw1r7Hc15x/9khJkiRJ0oxZSKmz/B0pFb2mE1Br9JpOQK3QazoBtUKv6QTUARZS\nkiRJklSRhZQ6az2dsU+rWWg6AbXGQtMJqBUWmk5ArbDQdALqAAspSZIkSarIQkqdZY+Uil7TCag1\nek0noFboNZ2AWqHXdALqAAspSZIkSarIQkqdZY+UioWmE1BrLDSdgFphoekE1AoLTSegDrCQkiRJ\nkqSKLKTUWfZIqeg1nYBao9d0AmqFXtMJqBV6TSegDrCQkiRJkqSKLKTUWfZIqVhoOgG1xkLTCagV\nFppOQK2w0HQC6gALKUmSJEmqyEJKnWWPlIpe0wmoNXpNJ6BW6DWdgFqh13QC6gALKUmSJEmqyEJK\nnWWPlIqFphNQayw0nYBaYaHpBNQKC00noA6wkJIkSZKkiuaqkIqIJ0XE2RHxs4j4YkTcq+mc1F72\nSKnoNZ2AWqPXdAJqhV7TCagVek0noA6Ym0IqIh4OHA28GNgCfA44MSJu0Whiaq1t27Y1nYJawXGg\nRY4FgeNAheNAk5ubQgp4OnBsZh6bmWdl5lOBC4EnNpyXWuryyy9vOgW1guNAixwLAseBCseBJjcX\nhVRE7AocAHx82VUfAw6cfUaSJEmS1rNdmk5gRDcBdga2L1u/HbjP7NPRPDj33HObTmHmrrnmKuCs\nGiKdU0OMtji36QTUGuc2nYBa4dymE1ArnNt0AuqAyMymc1hTRNwMOB84ODM/O7D+H4A/z8zbL9u+\n/Q9KkiRJUqMyM8a97bwckboUuAbYuGz9RuCi5RtP8oRIkiRJ0lrmokcqM38JnAYcuuyqQ4FTZp+R\nJEmSpPVsXo5IAbwKOD4ivkApnp4I3Ax4Q6NZSZIkSVp35qaQysx3R8SewPMoBdTXgAdk5veazUyS\nJEnSejMXJ5uQJEmSpDaZix6pUUXE4yLipIj4YUTsiIi9h2xzbv+6xcs1EfGPTeSr6RhxHGyIiP+I\niMv7l+Mj4oZN5KvZiYjekNf/O5rOS9MVEU+KiLMj4mcR8cWIuFfTOWl2IuIFy173OyLigqbz0vRF\nxEER8YGI+H7///3RQ7Y5MiLOj4grI+LkiLhDE7lqetYaBxHxliHvEZ8bJXanCing14GPAi8AVjrU\nlsCRlDP+baJME3zxLJLTzIwyDt4JbAHuC9wP+D3g+JlkpyYlcCzXfv0/vtGMNFUR8XDgaMr7/Bbg\nc8CJEXGLRhPTrH2Dpdf9JuC3m01HM7IHcDrwVODK5VdGxLOApwNPBu4MXAx8PCKuP8skNXWrjoO+\nj3Pt94g/HCXw3PRIjSIzjwGIiAPW2PQnmXnJDFJSA9YaBxFxO0rxdGBmntpf93jgMxFxm8z81syS\nVROu9PW/rjwdODYzj+0vPzUi7k85YdHzmktLM3a1r/v1JzNPBE4EiIi3Dtnkb4CXZub7+9v8JaWY\n+nPgTbPKU9M1wjgAuGqc94iuHZEa1TMj4tKI+HJEPDcidm06Ic3UPYAfZ+bnF1dk5inAT4EDG8tK\ns3JYRFwSEV+LiH+KiD2aTkjT0X9vP4DyTeOgj+Frfb25dX/61tkR8c6IuFXTCalZ/TGwiYH3h8z8\nOfBpfH9Yj+4VEdsj4qyIeGNE7DXKjTp1RGpExwBfBn4A3BV4ObAZ+KsGc9JsbQKGfetwcf86ddfb\ngfOAC4D9gZdRpvjcv8mkNDU3AXYGti9bvx24z+zTUUM+D2ylTO+7KfAPwOci4g6Z+cMmE1OjNlGm\new97f7j57NNRg04E3gecQ6kJXgJ8MiIO6P+W7YpaX0hFxFGsPv0igXtn5qdHiZeZRw8sfi0irgDe\nFRHP8g21veoeB+qOKmMjM/99YP0ZEXE2cGpEbMnMbVNNVFIjMvOjg8sR8XnKDtNfUvrnJK1jmfnu\ngcUzIuJLlC9d/wh4/2q3bX0hBbwa+I81tvnuBPFPBQLYF/jCBHE0XXWOg4uAYYdsb9q/TvNlkrFx\nGnANcBvAQqp7LqX8/25ctn4jvtbXrcy8MiLOoLzutX5dRNn/2wh8f2C97w/rXGZeGBHfZ4T3iNYX\nUpl5GXDZFO/idynfWF84xfvQhGoeB/8D7BERd1/sk4qIAyln+xvpdJdqjwnHxu9Qpn75+u+gzPxl\nRJwGHEqZtrHoUOA9zWSlpkXE9YDbASc1nYuak5nnRMRFlPeD0+BXY+Mg4BlN5qZm9fujfpMR9g1a\nX0hVERGLpy3cj/Itw/4RcSPgu5n5w4i4O3B34GTgR5QeqVcBH8jM768QVnNmrXGQmd+IiI8Cb+if\nrS+AfwM+5Bn7uisibg08EvhvypGK/YFXUj5AT2kwNU3Xq4DjI+ILlP/nJ1JOe/+GRrPSzETEPwEf\nohyZ3kjpkfp1YKWzd6kj+qcx35fyOb8TsHdE3Am4LDO/R5na+ZyIOAv4FvB84MeUn0hRR6w2DvqX\nIylftl0I3Ar4R8pRyf+3ZuzMlX5mZ/5ExAsY/ttBj8nM4yPid4HXU3awd6fMf3wn8E/9M7WoA9Ya\nB/1tbgi8FvjT/nUfAP46M6+YWaKaqf7vBr2NUkDtAXwP+C/gRZl5eZO5aboi4gnA31MKqK8BT+uf\nqVPrQES8k3KU4SaUEw19HviHzPxGo4lp6iLiEMqX58v3B96amYf3tzmC8nuCNwL+F3hyZp4500Q1\nVauNA+BJlD6oLcAGSjF1EnBEZp6/ZuwuFVKSJEmSNAvr9XekJEmSJGlsFlKSJEmSVJGFlCRJkiRV\nZCElSZIkSRVZSEmSJElSRRZSkiRJklSRhZQkSZIkVWQhJUmSJEkVWUhJkiS1SETsERHviYhbNJ2L\npJVZSEmSJLVERDwWeAbwENxPk1rNF6gkiYjYJyJ2RMSxXbgfLYmIQ/rP+eLlzKZzmrV5GneZ+ebM\nfCEQK20TETde9n96zQxTlNRnISXpVwZ2uE5aZZvFHZKzl63fsezy84i4OCJOi4g3RcT9I2LN95yI\n2C8iXhsRp0fE5RFxVUScHxH/FRGHR8RuFR/TWPEi4oCIeEtEfCciroyIH0XEVyPiFRFx8yo5zJHs\nX7pyP7q2HnAk8Lq6Avr6asyVlP/LI4HzGs1EWsci088ySUVEHAKcDPQy8/dX2GYf4Bzg3My89cD6\nHZSd4yMp36TuDGwA9gfuCewOfBF4ZGZ+a4XYRwBH9G//P/3tfwxsBA4GbgOclpl3HfHxjBUvIl4O\n/B3wS+DjwOnAbsCBwN0oOzF/mZnvGyWPeRARuwC3Bn6Umdvn/X60ZOB1fWRmvqjGuHPz+hp43zou\nMw+vK+409d9TN2fmd9fY7mTg4MzceTaZSVq0S9MJSOqWzDxq+bqI2At4LfAw4OMRcefMvHTZNs9l\n6dvVh2bmF4fEuS/w96PkMW68/s7h3wFnA3+cmd9Ydv2DgbcD74yIQzPzU6Pk03aZeTXwza7cj6bL\n11c1EfFEyhcIy7+9jv660zLzXTNPTNJkMtOLFy9eyEyAQ4AdwEmrbLNPf5uzl63fAVyzyu0COAm4\nBnjVkJhXAT8Hbr9GjruO8DjGite/3S/6t7vDKrd5fP/xnjmF/4O7Ae8FLuw/hu8C/wbcbIX/h2Mp\nO2jvBS4FrgA+Cuzf3+4mwBuBC4CfAacCC6v8vx67bP2fAp/s3/7nwPmUKWJPHBJjzW1Xup+B6x8G\nfBq4nHJk4qvAs4HdVnn8+wAnAJf0H+MXgD+a0XN/G+BdwPb+2D541G0meLwrxlvjdX3EKtv8DXBG\n//n7PuWLjxsA53Ld1/rcvb5WGd8BHNO/7r3A7tN4fY2Z8w5g7xG2O5lV3nu9ePEyvYs9UpJmIjMT\neDFlx+URy64+HNgVeG9mfn2NOL8c4e7GjXc45Uj9f2bmag35/07Z2d6vP22qFhFxOPBZ4H6UovPV\nlKLgscAXVzgV8q2A/wX2At5C2cn7A+DkiNgX+DxwAKXQeBdwJ+C/RzmtckT8FfB+4HbAB4FXAh8G\nrgdsHXfbVe7vH/t57kc5KvHa/lX/CHykPy1wuc2Unde9geP7t98feH+V/5sxn/t9Kc/93sDbgDdQ\ndrRH2mbMxzvKfVYSEa+nPN4b9OO9AziUMu1uWA5z+fpaLiJ2pxRITwFem5n/JzOvWrbZ1F5fkjqg\n6UrOixcv7bkwxSNS/W12o3wjfQ2wz8D6T/TXHV7T4xgr3sDtHjvCtm/rb/vcmnK+DeVb/rOATcuu\nuzdwNfC+If8P1wDPXrb98/vX/QD4l2X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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xd9e6b70>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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bb7yRXbt2+RVj9P85SkRExEF1XeVwchibv5W5aGwSEevVOpFgqOtht1//+te5\n5557WLhwId/85jfp0qULDz30ECNGjGDVqlX07t2bf/7zn6SnpzN48GAGDRoEwKRJk7j//vu5+uqr\nKSkpYfHixXTp0oXmzZvzxz/+kZMnT5KSkkKzZs1499132bJlCwsXLvQrRmO1OnSNjDFWz0dERJxU\n1/OeqhsxIouyPwID5fPLsmo9Z9++ffTu/YdqSdoicnNTI3bu0t13P1ZlSCVA/fofcOedK0PaGEQk\nHBhjCNffgTt37sxvfvMbbrzxRsD9/SkhIYG+ffvy+uuvY63l6aefZtmyZRQVFdGqVSuGDx/OjBkz\nKhpdjB07lg0bNnDw4EEuueQSrrvuOubOnUvXrl158803mTFjBu+//z6nT5+mTZs2jBo1igkTJtQY\nU9nz8jihK+BkyxiTYa1dHNBFwpSSLRERcUrVSspPaNt2CcOHEzGVFH+StHA2ZcoiZs1KBSoni/uZ\nMmUdTz75SKjCEgkL4ZxshaOAki1jTHtqn9v1lLV2eADxhS0lWyIi4hRVUsJLNFbrRJyiZMs3tSVb\n3vwp7RkgFffCxJ5YICqTLREREadE47ynSKaW7iJSF7ypbF0KTLDWzqhh/2JrbUYwggs1VbZERMQp\nqqSISKRQZcs3AVW2rLUnjDH7ajnkLb8jExERiRGqpIiIxB51I6yFKlsiIiIiEmtU2fJNbZUtrxc1\nFhERkcijRXtFRELHr2TLGHOP04GIiIiIc0pLS5k48RmGDHmNV16ZzW23bSIzcwGlpaWhDk0k5llr\nmTRpjqpHMcDfylaSo1GIiIiIo4YNm8qzz6ZQVDQOaEBR0TgWLBjMsGFTQx2aSMxbu3YjixcfYN26\nTaEORYLM32SrpjbwIiIiEgbUal4k/Cxduppu3W5nypQ3OX58PpMnb6Vbt9tZunR1qEOLCjNmzOD+\n++8PdRhV+LtkvWqeIiJeGj9+Ljt2nMCYc3+nstbyrW9dyjPPZIYwMok2BQW7mTr1OWbOHE1GxlCy\ns1+u1mp+HRkZqSGMUCS2jR49ghYtWjJhwlbAcOqUi5kzx5CWlhLq0CJC48aNK36WfvnllzRo0IB6\n9ephjGHp0qUAVX7WVtexY0cOHjxIUVERLVq0qNiemJjIzp07+eSTT7jiiiscjdnfZEtERLzUv393\nli0zlJSc+2EaH7+BsWM1SECcUVpayqRJi1izxlBUNJuCgiUMHw7JyYc5cCCr4ji1mhcJLWMMxhiK\ni09x7bVJf7kJAAAgAElEQVSPUljoqtgmF3b8+PGKzzt37sxvf/tbBg0aVLFtxgyPywJXMMbQqVMn\n1qxZw8MPPwzArl27OHnyZND+DdSNUEQkyNLSUujefQPnBgVYunffSGrq4FCGJVGkpvlZZ86cZvPm\nrIrX889nhTpUkZi3d28h2dm3sGvXPLKzb2Xv3sJQh+S4umgAYq31eP3Tp08zcuRImjRpQvfu3dmx\nY0eV/ffffz8rV66s+HrlypWMHDkyaHEq2RIRCTJjDBMnphAf754IHR+/kczMW/SXTAlYeVv3pk0b\nan6WSISYPHkUaWkpGGNIS0th0qQfhzokx4WyAcif/vQnhg8fztGjR7njjjsqKljl+vTpw/Hjx/nw\nww9xuVy8+OKL3HfffUFLDNUgQ0SkDlSubqmqJYGq3tb9L38xxMdXnWCv+VkikSUa2sGHQwOQ66+/\nnpQUdzJ7//338/777593THl167XXXuOaa66hbdu2QYvH3zlb/3E0ChGRKFde3UpPf1RVLQnYsGFT\nWb9+JKWl1wLw+edZwHhatbqfbt2+Dmh+lkikKa8G9e69KWIbZoRDA5DWrVtXfB4fH8+pU6dwuVzE\nxZ2rMd13330MGDCAjz/+mB/84AdBjcevypa1donTgYiIRLu0tBQyMtqoqiUB89TWHSYyevS3NT9L\nJMKEQzXIKdUbgBQXnwzLBiBXXHEFnTp14tVXXyU1NbgjADRnS0SkjhhjeOqpn4XdDx2JPBkZQ2nd\n+uUq2zRsUCQyjR49gqyshzl1ykV5NWjGjDGMHj0i1KH5JdwagNQ0LHP58uW88cYbXHLJJUG9v1q/\ni4iIRJj27dtz442H2L8/q2Kbhg2KRKZgtIO31jJ58lxmzcqs8z/wTZ48quLzYA4f9PZ9VT6u8ued\nOnWiU6dOPl/PVyaQSXjGmPbAeKAn0B64yMNh1lr7db9vEkLGGBvJkxRFREREJPzNmvUcCQlXkJo6\nmHXrNrF3b2FAXQpzcjaQnr6R7Oxb/Ep4jDER3aijrpU9L4/Zmt/JljEmGXgFaAiUAp+XfTyPtbaT\np+3hTsmWiIiIiESKpUtXs3DhC5w504O9e5+gS5efc9FFOxk7dhgPPnif19dRsuWb2pKtQIYRzgHq\nAT8AfmetdQVwLRERERERCUA4dAOUqgJpkNEdWGOtXa1ES0REREQktCKlG2AsCaSydQQ47FQgIiIi\nIiISmPJugJXnf0noBDJnaxmQZK1Ncjak8KE5WyIiIiISazRnyze1zdkKZBjhFKCpMeZXxphGAVxH\nREREREQk6vidbFlrDwG3AMOAg8aYPGPMGx5erzsWrYiIiIhImLDWMmnSnCpVIE/bJHb5PWfLGNMN\n+BvQvGxTYg2H6r80EREREYk6a9duZPHiA/Tuvami45+nbZHmyiuvVFMNHzRo0KDGfYHM2doIfAeY\nDqwEiqy1Z/26WJjSnC0REZHaFRTsZurU55g5czQ9elwb6nBE6oSn9ayOH38dYy7i0ksHBLTGlUSe\nYK2z1RdYZ619IoBriIiISAQqLS1l0qRFrFljKCqaTUHBEoYP38SsWWOoXz+QXy9Ewp+n9awWLpyO\ntZaJE99Ea1xJuUAaZHwFfOJQHCIiIhJBhg2byrPPplBUNA5oQFHROBYsGMywYVNDHZpI0Hlazyou\nLo64uDitcSVVBPKnp83Atx2KQ0RERCJIQkJ7SkubVtlWWtqUrl3bhygikbrlaT0ra63WuJIqApmz\n1RnYDswDZkfj5CbN2RIREfFs37599O79Bw4eHFOxrXXrReTmptKuXbsQRiYiUreCNWfr58Au4Elg\nlDGmADjq4Thrrf1RAPdxjDFmOu6GHpUdtNa2DUU8IiIikap9+/bceOMh9u/PqtjWrh1KtERCwFrL\n5MlzmTUrU8MWw0wglS2Xl4daa209v27isLJk6x5gIFD+X+JZa+0XNRyvypaIiIiIhLWcnA2kp28k\nO/sWNeQIgdoqW4E0yOjk5atzAPcIhlJr7f+stf8te3lMtEREREKloGA3Q4aMY+fOD0IdioiE0IUW\nSF66dDXdut3OlClvcvz4fCZP3kq3brezdOnqOo5UauJ3smWt/dTbl5MBO6CzMWa/MeY/xpg1xphO\n/l7I5XKRl5dHXl4eLpe3hT4RERHPSktLmTjxGYYMeY1XXpnNbbdtIjNzAaWlpaEOTURCoHyB5HXr\nNnncP3r0CLKyHubUKRfl7eZnzBjD6NEj/L7nhRK8UAnXuC7E72TLGFPzUslVj+vo7z2C4B3gASAF\n+DHQGvi7Maa5rxfKz99NUtI4Bgz4lAEDPiUpaRz5+budjVZERGKK2qmLCHhfsfLUgj7QdvMXSvBC\nJVzjupBAhhE+f6EDjDEdgDcCuIejrLUbrbU51tpd1to3gCG4n8FIX67jcrlIT19KQcECSkpSKSlJ\npaBgAenpS1XhEhERv6mduoiAbxWr8hb0u3bNIzv7Vr/bzYfrkMRgxFWXVbJAuhGmGmOetdb+1NNO\nY0xr3IlW2LYlstaWGGN2A11qOiYrK6vi8+TkZJKTk8nPz2fPnmSq5qpx7NkzkPz8fJKSkoIVsoiI\nRLGMjKFkZ79crZ36OjIyUkMYlYjUteoVq8JCV40Vq8mTR1V8HkhzjNGjR9CiRUsmTNhKeYI3c+aY\nkDfcCEZc5VWy3r03Bf39BZJsLQLGGGMKrbVPV95hjLkM+BvuBhn+DxoNMmNMQ+Bqaqm+VU62RERE\ngima26kXFOxm6tTnmDlzND16XBvqcETCnqdFk4PJlwSvLjkZ19Klq1m48AXOnOlRViX7OdOmLWLs\n2GE8+OB9QYg+sGRrHNAeeMoYs89a+wKAMaYF8FcgAfihtfbFwMN0hjFmLvAn4DPgcuAXQDyw0pfr\nJCYmkpCwkoKCuzhX3XKRkLCFxMShToYsIiIx5vnns0IdgqNKS0uZNGkRa9YYiopmU1CwhOHDNzFr\n1hjq1w/k1xCR6OZUxcoXdZ3gecupuEJRvfN7nS2oaJLxOtALuAXIx10lSgQestYucyJIpxhj1gA3\nAK2A/+FumPELa+2/aji+xnW28vN3k56+lD17BgLQpctmsrMfIjGxW1BiFxERiUR33/0Y69ePpLT0\nXDWrfv0PuPPOleTkzA5hZCISi8rXJOvQwVBY6CI7+9aAk63a1tkK6E9K1trTxpjvAm8DLwP/wZ1o\njQ+3RAvAWnuvU9dKTOxGXt4C8vPzy75+lri4QPqNiIiIRB81/RCRcFLnwzOd6MJhjLkC2Ia7lfoU\na21U/KmqtsqWiIiIXNi+ffvo3fsP1Zp+LCI3NzUq5qKJiDhS2TLGLL/AIZ8CFwNdqx1rrbU/8vY+\nIiIiEj2iuemHiMiFeF3ZMsb4u4CUtdbW8/PckFJlS0REREREauPUnK1ODsUT1VwuV6V5XImax1WH\nAnn2+ncTERGRaGatZfLkucyalRnydu6xxOtky1r7aTADiQbnOhQmA5CQsJLlyx8M2w6F0ZRgBPLs\nI+3fTaLX+PFz2bHjRJUfgtZavvWtS3nmmcyIv5+IiISOvwv5KkkLjCMNMqKVL8MIXS4XSUnjKChY\nQOW1t3r2HEde3oKwS2TOTzA2Bz3BCFZyF8izj7R/N4luOTkbGDnSUFJy7odgfPwGVq0yQVkDpK7v\nJyIida/yQr579z5Bly4/56KLdnq9kG95q/Ts7Fv0s6EGtQ0jVLJVC1+Srby8PAYM+JSSktQq2+Pj\n17J1a0eSkpKCEaJfQpFgBDO5C+TZR9K/m0Q/ay19+z7K9u3zAQNYrrvuUbZtm+/zXxO9qVo5eT8R\nEQlP1lpycjYwYcJWCgtn0aHDZObPH0haWkqt3+sDTdJiSdDW2ZLIlJ+fX5b0VE6q4tizZyD5+fmO\nJxgul4v09KVVkruCgrtIT1f1SKQyYwwTJ6YwcuQmSkpSiI/fSGbmLX4lPv37d2fZsvOrVmPHnruW\nk/cTEZHwZIzBGENx8SmuvfZRCgtdFdtqM3r0CFq0aMmECVsBw6lTLmbOHKPqlq+stXrV8AKsp9f0\n6dNtdWfPnrWXX/5tj8dPmzbtvGNzc3PtqFGjvL6+tdZOnz7dkeNHjRpl4+PXWrBVXvXrfy8o8eTm\n5pbdz5n4qx9/9uxZ27PnIxamOXJ9mGZ79nzEnj17NijPX8fr+NqOd7lc9rrrxjn637P7/3H3dV0u\nV63HV/9+FW7PR8freB2v43W878fPnLnM5uRssC6Xy37veyO8vv5LL71qL774upDHHwnH25ryiZp2\n6GUpe3Be27Fjl+3Z8xEbH59j4+NzbI8eY+yOHbtqOGatjY9fa3v2fOS8Y4LtXHJy1lKRbJ31mGA4\n4VyyZau84uNzbG5uriP38ObZB+NckWB46aVXbePG42xOzoaArxMfv6Hs/7dXa7yeU/cTEZHoUjlJ\ny8nZYGfNeq7GY10ul33ssdkVf9SzNnZ+vtSWbGnOVi38WWertiYQ4dSM4dwcqoEAdOmymezsh4LS\nIKOu3rfTrd+jqVujRBZrnen8ZO25OVm1zcVy6n4iIhK7KjfSOHTofzE130sNMvxkjLFnz5517Jfs\ncGvG4E0y4VTCUZfJ3YV4855C0a1RJBgitYuU2tKLiEQGT4006tcv4Prru7Nhg/GpKUekqtMGGcaY\nNkB/YK+1dmfZtiuB1sBua+0Jp+8ZTElJ46L2l+y4uLhaEzxf1p+6UAKTmNiNvLwFlY55NiSVIm/e\nkxp6SDRJS0shN/d9UlMHhzoUn3jT4ENERELPUyON+fMfwVrLCy9s8qkpRzRytLJljBkAvApcgnsy\n2Txr7c+MMQ2AW4G11tp6jt0wyIwxFs46NtwtnIYRXogvsUZKFcjb9xRuFUiRWFR5CGR5W/rLL7+d\nrl2TMCauynGqdomIhFb5KIoOHQyFhS6ys29lz57PSEi4gtTUwaxbt4m9ewuZNOnHoQ41KOqysvVz\nYCSwEegATDLGPGWtnWSMeQf3T8wI41xL9Li4OJYvf5D09HFVhtMtX/5QUIfw+cPb9vDhXgWq/Axd\nLledtryX2BBJw90iKVZPbenvu+96liz5lqpdIiJhZu/eQrKzb6mSWE2ePKpifyQNY3ea08nW3621\nOWWffwD8wBiTboz5IfAK7mpXTPN2OJ0vQ/hCqa7X7PJF9WfYvv3vcbnuvuB5iYmJJCSspKDgLipX\nwBIStpCYODRo8UrNRo9+ij17Tp23PSGhIcuWTQpBROdE0nA3f2MNVZKWlpbC008/yvbtg+nefSNz\n5szjrbcmsH37YMqrXd27byQ1dX7QYhARkQtTYlUzp5OtYwDGmM7W2v8AWGuXG2OGAEMcvlcdcf6X\n7AvNlQqHalGkJxyenuGePd/lkku+D6RS23vytQIpwbdnzym2bMnysMfTtrpVOSEI9wTA31i9TdKc\nTsrKq1vp6Y+SmXkLcXFxQV2EOZIqfyIiEiFq6gnvzwv4NjATOAv0qbZvIHDMyfsF+wWEZM2luliX\nyhverD9V12t2eaumZ9igwQKbkPAjr9bUKl98Ojc3N6TvRawdOHD6ef+W4N4eDrxdzyoc+BPruYWW\nXWXPvuoCyZ6ufe77VmDPo/q6LZVj8RRDIIIRv4iI0zytZyWhRV0uaoy7OUb3GvZ1cvp+wXwBIfkl\nO1ySLWu9SzjCcVHg2p7hu+++qyQqwoR7shXMBMBp/sbqTZLmbVIWqGAtkllX8YuIBCLSFwqOxmSx\ntmTL8TFR1tqT1tp/1LDvY6fvF2yhGDbmHsK3GXBV2lo+3C2xTmMpH/KYlJRU47Mon4e2dWtHtm7t\nyI4dz4Z8blltz7D8/dT2nkR8UT7crXHjRx0d1hYM/saalpZC9+4bODf08PxW8uXXjo/fBOD4ML/K\nsWRktHG8nX1dxS8i4o+lS1fTrdvtTJnyJsePz2fy5K1063Y7S5euDnVoPlm7diOLFx9g3bpNoQ6l\nTmhR41oYY2z586nrzoDhtAhwpNIzjB7JyVke52wNHJjF5s3nbw8Fay2TJ89l1qzMsP/l3N9YvVkg\n2dpzLduvu+5Rtm2bf8F7hNNcKX/iFxGpC9ZacnI2MGHC1ohcKNjT4scXXbSTsWOH8eCD94U6vIAE\nrfW7MWYg7gWM25ZtKgLettZuCeS64SYUnQE9dS0E9xpQ7q/rthV8JAqXhZTDVTh3+KsuIaEhnpph\nuLeHB2MMTz31s1CH4RV/Y/VmgeTqTS28+QUgnDo6+hO/iEhdKF8UuLj4VEQuFOxp8eOZM8dEffdC\nv5KtsiRrCdC1fFPZR1u2/1/AT6y1WwOOMMQC7QwYSEWsctfCSGkFH24u1PkxloVzh7/qwi35i1Xe\nJmneJGXVjw+njo6+xi8iUlc8rWcVKSI9WfSXz8MIjTFpwBrcidoB4G9A+b90ByAZd6WrFBhmrV3n\nVLB1zRhjc3NzGTDgU0pKUqvsi49fy9atHWv9Rf78BGmzXwmSy+UiKWlclYQPXPTsGR4LB0tkioSh\neRI7cnI2MHKkKWvpvoFVq4xff+0MpyGJIiJS1axZz5GQcEWVZHHSpB+HOqyAOTaM0BjTFliJO5F6\nBPiNtfZstWPigB8BC4BVxph3rLVFfkUewZxcK6u2hYPz8vIqrhXo0MK6npcmIlKu+gLG/la1wmlI\nooiIVBWLix/7+tv0OCAeGGGtXVo90QKw1rqstc8BI8qO/WngYYaOv50Ba0uQyhOaQJ09u4/77lvK\ngAGfMmDApyQljSM/f7df18rP301S0jhHriUi4iunOjpW7proVnP3RBERkWDzdc7WLcB2a+3LFzrQ\nWvsHY8x24FbgMX+CCwdxcXEsX/4g6enjqnS1W778oTqr/LgTvpUUFNzFueStlLi4LezZk0OglTMn\nq3C+UjWtbnhqhlFQ8EloghGpgRNzpcqTtpEjN5UNSVT7dhERCR2f5mwZY47iHjo4wcvj5wGjrLVN\n/IwvpAJp/e70PKvqbczbtfs9hYXf49Spu6sc581csury8vL8npcWCKfmtMmFeZ6f9RRNm/6Lnj07\nVtkajt0IRXzhTft2ze0SERGnONn6/SLgKx+OPwPU8/EeYWXGjBkADBw4kOTk5Cr7Nm/eDOBx+5Yt\nW7jrrpZcffVk/vjHbwPnKmJbt26t9TxP90tM7Mb8+UM5cOAAXbt2xeWaSHJyYdl1NpOc7D7v7be/\nBnT0KU6A/v0v47XXqr73/v0/58MPT5+XbNUWp7f3GzBgAI8++nKVZLRZs+bMmbOK55+fVSUZdeJ+\nOu+cyv+9fPxxR1asyDrvvM2bN4c8Tp3nPm/9+veqJAUdO0KnTibkcZYnK+WxfPJJ1WQl1M+zevv2\n6ueVz+369rcbVPr+2Z7rr+9Qp3HqPJ2n83Sezouu885jrfX6BXwE/NmH4/8EfOTLPcLp5X48gTl7\n9qzNzc21ubm59uzZswFfr/J1e/Z8xMJZC7bs5d7m632cvJa3cnNzbXz82kr3c7/i43Nsbm6u13EH\n49lGo4EDp5/3rMG9XcLbSy+9auPjN1T7/+RVm5OzQXHVwuVy2ccem21dLleN+6+7bpwFV1n87q9r\nOl5ERKQmZTmDx3zC1wkyW4GbjTFXX+hAY8w1QErZOTGrfJ2npKQkR+cjlc8l69lzHPHxa4mPX0uP\nHj9l+fIHPd7H5XKRl5dHXl4eLpcroGuFAzX0kFgRrg0fwjWucuVrgtU0V6t8bld8/CYAze0SEZGg\n8HXOVhLwHvAf4LvW2g9qOO4a3FWtTsB11tpcB2Ktc5XnbIUrb+aSeTs3qi6bVQQypy1c1h3z1HQC\nwnPOk9bUimxOrUEVK3F5y3oxt0tERORCapuz5c+ixrOBTNxzt9YBr1N1UePvAEOBi4F51tqInWkc\nCcnWhYRLYuJJ9aYfXbpsJjv7oQs2yAhVQ4/qIimBiaTEUM4XrklBuMbli5ycDaSnbyQ7+5aIShRF\nRCR8ONkgA2vtY8aYL4GfA8OAe6rfDzgLPA5k+Xp9cdaF1vuqq8TEk8TEbuTlLahUTXs2bIctRjpv\nEyolZbVzsoOdL9cqH/JWueFDOAjXuHzhRLt5ERGRmvicbAFYa39pjFkJpAP9gTZluw4CbwErrLUf\nOxOiRLPyOW2VXWg4o+d1x8oXmh5aB1FHrz17Tnms1unvJm7lHexKSs5VQOLjNzB2rO9Jhq/XCtek\nIFzj8lb53C4REZFg8LuMYK391Fo73Vr7HWttt7LXTWXblGiFCXdishmo3BSjPDFJDE1QtfCm8UUk\nNvSQ6OBkUwhfr3Whhg+hEq5xiYiIhAO/KlsSOcoTk/T0cVXmRi1f/lDYJSYul4v09KVV5pcVFNxF\nevr588s0BDEyRfowxfJhcyNHbiprCuF/BzsnryUiIiLhyadkyxhzMe5hgseAW621Z2o5bgMQD9xQ\n03FyTjA7AUZKYuLr/DJPQxDrUkJCQzwNr3NvF0+iYZhiWloKTz/9KNu3Dy6rRM0Pi2uJiIj4ylrL\n5MlzmTUrU3/sCxJfK1v3AUnUkmgBWGu/MsbMBf4CjABW+B1hDDi/NftKj63ZAxHqxCQaRUIlRpzn\nZFOIaGgwISIikWvt2o0sXnyA3r03qSNrkPiabKUCe621my50oLX2VWPMXuB7KNmqkS9D56KdGl+E\nnqp13nGyKUSkN5gQEZHIs3TpahYufIEzZ3pw/Ph8Jk/+OdOmLWLs2GE8+OB9oQ4vqviabCXirlZ5\naytwm4/3iCnh3Jq9rtU2vwzc62tB8BdcjmWxUK1zYt6Ykx3s1A1PRETq2ujRI2jRoiUTJmwFDKdO\nuZg5c4yqW0Hga7LVCvjch+M/B1r6eA+JEv7MQ/M0v2znzn+SlDQuqMMsQ81TAvDhh7uBxnTtekWV\n7XXdTKJ6bB9+uJuTJxtxySVxVWKLlCYX0TBvTEREJBDGGIwxFBef4tprH6Ww0FWxTZzla7J1Emjs\nw/GXAuf/CVkqROLQOW+SqEDmoVWeXxYNwyy9qaR4TgCygCwOHqx+ZvXjguv82LKALI4epVpslY/x\nTMMURUREwsPevYVkZ99Caupg1q3bxN69haEOKSr5mmwVAr18OL4X8JmP94gpkdSaHbxLopxMkKJh\nmKUqKeeEovJVPdktKPikzmNw0vjxc9mx40SVvz5aa/nWty7lmWcyQxhZ3dOzEBHx3+TJoyo+1/DB\n4PE12doMZBhjellrc2s70BiTBPQDFvkZW8yIlNbs3iZR0ZAgBd9TFBR8QnJyFhD5CYAn4bKmlufK\nXOTq3787y5YZSkrO/WCMj9/A2LGxN/RDz0JERMKdr8nW/wN+ArxkjLnNWvtPTwcZY64GXgLOAosD\nCzE2REJr9lAkUZE4zNI7pzh6dAVbtpR/nRXCWIJDFb3gqLw2FxjAOrpGVyRVi/QsREQk3PmUbFlr\nPzTG/BL3b0v5xpgc4A1gX9kh7YCbgDSgATDNWvuhc+FKJHAyQYq0YZYSCdzzxpo2/YSePTtWbI2U\neWPla3ONHLmJkpIU4uM3OrpGVyRVi/QsREQk3Pla2cJa+0tjTCkwHRgO3FvtEAOcAaZaa2cFHqKE\nC2+TKKcTpEgZZhmY8xMAdzfCdI/dCOtS9aYW7m6ED3jsRhgZ3EMYe/bMYvPmrNCG4qfKFR0nKznV\nrx2MapHT9CxERCSc+ZxsAVhrZxpjngfSgf5Am7JdB4C3gGxr7afOhCjhwpckyukEKRKGWdbEUwe+\ngoJPOHq08pbwTQAioZ17rCmv6KSnP+poJafytYNVLXKanoWIiIQzv5ItgLJkarqDsUgE8CWJiuQE\nyUmekpXk5KxK87WcFS6NKcJFtLabT0tLITf3fVJTBwfl2sGqFgWDnoWIiIQrv5MtiV1KogIXzAQg\nXBpThEuSU9cJZl01VTDG8NRTP3PsetWvHaxqUTDoWYiISLhSsiUSArFQYYqF9+hJtDRVCGa1KNLo\nWYiIiL/8nkRjjPmPF69/G2N2GGOeN8akORm4iEg4SktLoXv3DYAt21LeVCGyflEvrxapkqNnISIi\n/gukpVsccDHQsezVHrik7GP5tobAVbg7Fv7eGPMnY0y9AO4pIhLWyoedxcdvAlBTBRERkRgWyDDC\nbwKvAR8Bk4F3rLUuY0wc0BeYiXutrZuB1sAC4Dbgp0BIZxgbYzKAibi7KO4Gxllr3wplTCISOk43\nFVFTBREREYHAkq0ngaZAf2ttaflGa60LeNsYczPwPvCktXasMeZ7wL+AEYQw2TLG3IM78XsIeBt4\nGHjVGHONtXZfrSeLRIBwaUzhr1B0U3S6qYiaKoiIiAgElmwNBX5XOdGqzFr7lTHmT7iHEI611pYY\nY14H7g7gnk4YDyy31i4v+3qsMeYW4CfA1NCFJeKMSG9MES7dFAOlpgoiIiISSLLVEvecrdpcVHZc\nuYMB3jMgxpiLgCRgbrVdm4B+dR+RiG+0hlZdeoqCgk9ITs6qstXbZx3MduQiIiISGQJJfP4DpBlj\nfmGtPV59pzGmCZAGfFxpcxvgcAD3DFQroB7webXtnwM31X04Ir6JlqpPZDjF0aMrPCw+nRWCWERE\nRCQSBdKNcBnQDthujBlhjOlojLmk7ON9wHagLbAUwLgnLSQDBQHGLCIiIiIiEvb8rmxZa581xnTF\n3WhilYdDDLDMWvts2deXAWtwdzAMlUPAWeDyatsvxz3EUUQimL/DLD01FSko+ISjRx0OUERERGJK\nQPOnrLUZxpjfAQ8APXF3JzwG5AOrrLVbKx37Oe4W8SFjrT1jjMnD3Y5+baVdNwMveTonKyur4vPk\n5GSSk5ODGKGIBNJN0d9hlp4SseTkLA9DCEVERES8F3CzirL1qSJpjar5wCpjzHu4W7//BPdcsqWe\nDq6cbIlI8KnRh4iIiESLkHUGDBVr7e+NMS1wt3lvA+wCbrXWFoY2MpELi/Q1tCKJnrWIiIgEKuaS\nLQBr7a+BX4c6DhFfqepTd/SsRUREJFBedyM0xnxgjMnw90aBni8iIiIiIhJJfKlsXY17nSp/BXq+\niH0BOtAAACAASURBVEitNPRPREREwomvwwiT3ctl+cX6e6KIiDc09E9ERETCic/JVtlLRERERERE\nauFLsjXIgft94sA1REREREREwp6xVqP7amKMsXo+IiIiIiJSE2MM1lqPc6287kYoIiIiIiIi3ovJ\ndbZEgmX06KfYs+fUedsTEhqqeUMN9MxEREQkWinZEnHQnj2n2LIly8MeT9sE9MxEREQkeinZikIu\nl4v8/HwAEhMTiYvTaFERCQ/jx89lx44TVF5GxFrLt751Kc88kxnCyERERJynZCvK5OfvJj19KXv2\nJAOQkLCS5csfJDGxW2gDExEB+vfvzrJlhpKSlIpt8fEbGDvW7zUcRUREwpbfJQ9jzABjTE8ng5HA\nuFwu0tOXUlCwgJKSVEpKUikoWEB6+lJcLleowxMRIS0the7dN3BunXtL9+4bSU0dHMqwREREgiKQ\nytbfgKVAhkOxSIDy8/PLKlqVc+g49uwZSH5+PklJSSGKTESCKZKG5hljmDgxhZEjN1FSkkJ8/EYy\nM2+pEruIiEi0CCTZOgScdCoQkWiQkNAQT40d3NvFEz2zwEXa0Ly0tBSefvpRtm8fXFbVmh/qkERE\nRILC70WNjTEvAldYa/s6G1L4iLRFjV0uF0lJ4ygoWMC56paLnj3HkZe3QI0yopRap4u1lr59H2X7\n9vmAASzXXfco27bND9uKUU7OBtLTN5KdfQtpaSkXPkFERCRM1baocSCVrZ8D240xjwO/tNaeCeBa\n4oC4uDiWL3+Q9PRx7NkzEIAuXTazfPlDSrQCFM4JjVqnSyQOzUtLSyE3933N1RIRkagWSLI1GdgF\nTAF+ZIzZCRzk3KznctZa+6MA7iM+SEzsRl7egkqt359VouUAJTQS7iJtaJ4xhqee+lmowxAREQmq\nQJKtByp93rrs5YkFlGzVobi4ODXDEIkx5dWt9PRHw76qJSIiEisCSbY6ORaFiIgETEPzREREwovf\nyZa19lMnAxGRyBXOc9piiYbmiYSf4uJiRo9+nGXLfkGzZs1CHY6I1LFAKltVGGMaA82Ao9baY05d\nV0RqFw6t0zWnTUTkfMXFxdx88xRyczP5+OMpvPbaTCVcIjEmoGTLGFMfmAj8mErDCo0xHwO/AZ62\n1pYGFKFIGAiHhKYmqhyJiISfc4nWk0BzcnOf5OablXCJxBq/ky1jzMXABmAg7iYYhcABoA3QEXgS\nuMUYM9ha+1XgoYqEjhIaERHxVvVEy00JVyBDKjUcUyJVID3BHwWSgb8A11hrO1pr+1prOwJdgT8B\nN5QdJyIiIhITRo9+nNzcTM4lWuWak5ubyejRj4cirJAqT0BfemkMN988heLi4jo511/FxcV8//sT\n6uReEt0CSbb+f3t3H2ZVWS98/PsTUUFmgCQQSRAre/E5WjiaZAamqGE8hiTqybSnjqSi51yiPCd8\nAQwzCiUz8Sin0sqwEPGtJzHM8K3UEPWYmZov4LsSbxJqvNzPH2vPODPs2TN7Zu/ZM8P3c13rYvZa\nv7Xue9/MNTO/fd/rt/6V7DlbX0wpPVP/QErpWeAY4Angy21oQ5IkqVOZO/cCampmAasbHVlNTc0s\n5s69oFXX7awJQMOZvqF1M3wteR9tObet/W3P5E5dV1uSrQ8Bt6eUtuQ7mNt/O/DBNrQhqRPYa6+d\nGDFi+lZbR7inTZLaW58+fVi8+GJqas7jvYRrNTU15zW5hLC5RKqzJgDNLaks9D7acm5p+ts+yZ26\nuJRSqzZgDTCnmZgryKoTtrqdSm7Z8EiSJBVv9erVqabmtATPpZqa09Lq1atbFffe8VUJUoJVBa/X\nkRx77KQEz+X63Xh7Lh177KSynNsaW49z6nTjrcrI5Qz584mmDjS3AfcArwPvb+J4P+A14J7WtlHp\nzWRLkiS1xerVq9Oxx05qQaKVP5Hq7AlAW/rf3u+9vZM7dR2Fkq3IjhcvIsYDvwSWAxcBvyerRrgr\nWeGM88mqEp6QUprfqkYqLCLS9OnTARgxYgQjR45scHzJkiUAeffffffdnud5nud5nud5nud5TZ5X\nu2StV68xQA+WLKk9L1tyOGPG/+bBBx/MXWNEvePk2rmJj3/8T8yZc3GHfH+1Fi1axM9//jvmzTuX\nbDng1u+vqfbeeecdLrjg1npLCVfzr/96MXvt1asd+knuOrczcuRDJW/P87rOeRFBSinIp6ksrCUb\ncDGwBdicZ9sCzGzL9Su94cyWJEkqk5bMpHT2ma1aLV1SWepzm7peU7ONnXnJpiqHcsxs1YqIA4Gv\nA58EegNrgUeAn6SU/timi1dYRKS2jo8kSVI++QtAQONiGlvHFS620VF1hOdsvTeWk6mpmZV3DFsS\nI9VXaGarzclWV2ayJUmSyqmliZQJQNsVk7T6EGUVoyzJVkR8FliXUnq0LZ3ryEy2pI5rwoSZPP30\nO1vt32uvnZg795sV6JEktU5LEykTgNZr6Syi1BrlSrY2A1enlE5vS+c6MpMtqeMaOXI6d989fav9\nI0ZMZ8mSrfdLUkdmIlVe48efzQ03nAEMzXP0eY499grmz7+0vbulLqJQstWWhxqvBN5uw/mSJEki\nexDy/PmXmmiVydy5F1BTM4v3HjJdazU1NbOYO/eCSnRL24C2JFtLgE+XqB+SJEkqgTVr1jB+/Nms\nWbOm0l3pMPr06cPixRdTU3Me7yVcLiFU+bUl2Tof+EhEzIiI7qXqkCRJklqn9t6kG244g1GjzjXh\nqqdhwvW8iZbaxfZtOHcK8GfgXODrEfEY8BrQ+CanlFL6ehvakSR1QmedNYtly9YT8d4y9pQSw4b1\n4vvfn1zBnkldU+MiEEuXfptRo841oainNuHK7o9zXFR+bSmQsaWFoSml1K1VjVSYBTKkjstqhB3f\nggWLOPnkYMOGI+r29ey5iJ/9LBg37ogCZ0oqVimq7VmkQ2qdclUjHNLS2JTS8lY1UmEmW5LUeikl\nhg+fxIMPzgYCSHzqU5P44x9nN5jtktR2ba2253O8pNYrVzXCIUDflNLy5rY2tCFJ6qQignPOOYKe\nPX8LQM+edzB58pEmWlIJ1RbDmDXr31tdba/hrNjQuuWH3u8ltV1bkq3fAxNK1RFJUtczbtwR/Mu/\nLAIS//Ivd3DMMYdXuktSl1G/GMaXvvRdFiz4z6Kr7eVfftjXhEsqEZ+zJUkqm9rZraqqSc5qSSWU\nbzaqYcLVsmp7EybMYOnSyTS8zwuyhGsyEybMKOO7kLq+ttyz9StgcEppeGm71HF4z5YktV1KiSlT\nZvGd70w22ZJKoLliGAsW/CeTJ1/eokIXpSisIW3rynXPls/ZkiQ1KyKYOfP/mmipy6nUw4Obm42a\nPPly5s+/tGCSVNt3oOiH/frQZKnl2jKz9RPgQ8BBwOtAl3vOljNbkiQpn0pW72vrbFS+vgMtej9W\nLZS2Vq7S7z5nS5IkbXO2Tnbaf8lda/tQ6Dyg4HO2OsL7ljoin7PVSiZbkiSpvo50j1Oxs0xt6XtH\net9SR1Mo2SKl5NbERrYkcqtt2rRpKZ9p06YZb7zxxhtvvPHbSDxMS5Dqbc+lY4+d1En7n/W9cPy/\nN3q/tdu/d4r3a7zx5YxPTeQTrZ7Zqi8idgb2AnqllO5t8wU7CGe2JElSfYVmeD7xiXPYY4+eXHPN\njJLM8qxZs6bgsr7WXM+ZLan0ylWNkIj4QETcSFa+ZinZg45rj30mIv4SESPb0oYkSVJH0adPn7zV\n+z7xiXOI2J6bb55UkocB139gcakeLtxU31tSeRCKr1rYWlY7VFfS6mQrIgYCDwJHA78G/gjUz+ge\nBPoDx7Wlg5IkSR1Jw6Tl+bpE65FHZlL7gOG2JEj5Hljc1oQrf9JU+MHHjRO+Ys4ttl+1760cSaZU\nUU2tL2xuA64CNgKH5F5PAzY3irkJ+J/WtlHpLRseSZKkra1evTodffTp6ZOfnJBgVaP7mFalmprT\n0urVq4u+Zk3NaSW7XsNrPld3jdWrV6djj53U5PW27seqFp/b2n698MILTbbZ0uuVol9SsShwz1Zb\nEpEVwI31XudLtn4ArGxtG5XeTLYkSVIhxx47KcFzTRSOeK/oRKWuVyhpavk5bU/4mm/j+dSz5+hW\nt5kvoZTaS6Fkqy33bA0AnmkmZiOwcxvakCRJ6rDmzr2AmppZvHcfU63V1NTMYu7cCyp2vfxFLfo2\nuyxxwoQZLF06mYaFMGrPncyECTNa3IeW9+uHbNhwRavaLMeyS6lU2pJsrQJ2byZmL+C1NrRRUhGx\nJCK21Ns2R8S8SvdLkiR1Tq0pOtFe12tt0lSKhK9QkYv8/boAKL7N1iaUxfRXapOmprya24AFwFvA\nrinPMkLgw8A/gZ+0to1Sb2TVEn8EvJ+seEd/oKpAfNvnFSVJUpdX6mVspbheW5YDtmb5YUv73nS/\nGi8lbL7NUiy7dAmi2ooy3bP1KbJlgk8Bnwe+C2wmWzb4eeCvwDvA3q1to9RbLtm6vIj4Ng++JEna\nNpS6QEMprlfOpKkt7TUV916RjJa12db7y9oyPlKtsiRb2XX5GvBuLslqvL0LfLkt1y/1lku23gDe\nBP5MNl/dq0B8CYZfkiTpPe1dNa8tMzfF9LXYxKepfhU7Pq1NmNqjEIi2DYWSrciOt15EfBg4HTgQ\n2AVYCzwAXJFSeqpNFy+xiPg3YDnwCrA3MBN4OqV0ZBPxqa3jI0mSVOu9e4wmU1Mzq+QPBC7U7oQJ\nM5g794KytTd+/NnccMMZwNA8R5/n2GOvYP78S8vSr9aMa2v6K+UTEaSUIu+xzp5MRMQM4LwCIYns\nWWD35Dm3BngIGJZSejTPcZMtSZJUElsXc2h9IY2OKH+xCmiv91ls4lbp/qrr6OrJ1vuAfs2ErUgp\nvZPn3CAr4vGvKaUb8hxP06ZNq3s9cuRIRo4c2bYOS5Kkbc628od9Z0soO1t/1TF16WSrLSJiX+AR\n4LMppfvyHHdmS5Iktdm2tGStUkslm+pLc7NdHam/6pxMtoCI2BP4MvAbYCXZPVuXAP8ADsiXVZls\nSVLnd9ZZs1i2bD3ZYoZMSolhw3rx/e9PrmDPtC3p6jNbjZOa9rhHrCV9amkS1RH6q87LZAuIiA8A\n15ElWb2AF4FfA99KKeV9gp3JliR1fgsWLOLkk4MNG46o29ez5yJ+9rNg3LgjCpwplVZXXbLWEWeG\nyjHWJmRqislWK5lsSVLnl1Ji+PBJPPjgbCCAxKc+NYk//nF2g9kuqT10xMSkLTpiAlmOWcSu9v+m\n0iqUbG3X3p2RJKk9RQTnnHMEPXv+FoCePe9g8uQjTbRUEX369GHx4os59tgrOv0f7PmTmr4sXfpt\nRo06lzVr8i4cKrsJE2awdOlkGiZakPVtMhMmzCjqeg3f59CKvz91Ls5sFeDMliR1DfVnt5zVkkqj\noxb9KOXMVle/106lUbKZrYjYISIeiog7I6J7M3F3RcQDheIkSWoPtbNbVVWTnNWSSmTu3AuoqZkF\nrG50ZDU1NbOYO/eCSnSrbvawpuY83utb65KjUs+SadtT7DLCE4H9gO+llDY2FZRS+icwCziArAKg\nJEkVNW7cEZx++kCOOebwSndF6hJKmdSUt2/Pt7pPHTWhVOdR1DLCiPg18KGU0kdbGP8U8LeU0lGt\n7F9FuYxQkiSpsI5cPKIUFQQ7YhEQdSwlq0YYES8D/y+lNKGF8f8NjE4pDWpxIx2IyZYkSVLzunpZ\n9I6cUKrySplsvUu2hLBFc6YRcRFwTkpppxY30oGYbEmSJAm6fkKp1iuUbG1f5LXeBqqKiO8FvFNk\nG5IkSVKH0qdPn4pUV1TnVmyBjBeBmiLia4AVRbYhSZIkSZ1escnWEmB4RDSbcEXEfsCngd+3ol+S\nJEmS1KkVm2xdASTghoj4WFNBEfFR4AZgM3Bl67snSZIkSZ1TUfdspZSeiohvAdOBRyJiAXAX8FIu\nZBBwKDAO2BGYmlJ6qnTdlSRJkqTOoahqhHUnRZwLTAO6k810NTgMbASmp5S+0+YeVpDVCCVJkiQV\nUrLS740uOgT4GnAQMDC3+1XgPuCalNLyVl24AzHZkiRJklRIWZKtbYHJliRJkqRCCiVbxRbIkCRJ\nkiS1QLEPNd5Kbjnh+8nu3XozpeRztSRJkiRt81o1sxUR/SJidkS8CjwHPAg8BDwfEa9ExKyIeF8p\nOypJkiRJnUnR92xFxIeBxcDuZJUHNwF/z339PrLZsgQsBw5LKT1Xyg63J+/ZkiRJklRIye7Ziojt\ngF8Ag4G7gcOAXimlgSmlXYEq4HDgHmAP4Lo29FuSJEmSOq2iZrYi4kjgN8B84ISmpn0iIoBfkT3c\n+MiU0uIS9LXdObMlSZIkqZBSViMcB7wLnFkoC8kdO4Ps4cZfKrINSZIkSer0ik22hgH3p5TebC4w\npfQG2QOOh7WmY5IkSZLUmRWbbO0OPFFE/BPAkCLbkCRJkqROr9hkqxpYU0T8GrKiGZIkSZK0TSk2\n2doB2FxE/JbcOZIkSVJJrFmzhvHjz2bNmmLmAKT215qHGlueT5IkSRWxZs0aRo06lxtuOINRo841\n4VKHVmzp9y20ItlKKXUr9pyOwNLvkiRJHUdtorV06beBvsBqamrOY/Hii+nTp0+lu6dtVClLvwNE\nkZskSZLUJlsnWgB9Wbr0285wqcMqKtlKKW3Xiq1TzmpJkiSp45gwYQZLl07mvUSrVl+WLp3MhAkz\nKtEtqaCilhFua1xGKEmS1DHkn9kClxKq0kq9jFCSJElqV3369GHx4oupqTkPWJ3ba6Kljq3YAhnP\nNROyhezZWo8B16aU7m1D3yrOmS1JkqSO5b0ZrsnU1Mwy0VLFFZrZak01wpZKwMyU0nlFnNOhmGxJ\nkiR1PGvWrGHChBnMnXuBiZYqrpTJ1pBmQrYD+gGfBiYDA4HRKaU7WtxIB2KyJUmSJKmQkiVbRTb6\nAeAJ4PcppS+WpZEyM9mSJEmSVEhFCmSklF4CbgEOKFcbkiRJktRRlbsa4XJglzK3IUmSJEkdTrmT\nrWrg7TK3IUmSJEkdTrmTrVHAU2VuQ5IkSZI6nLIkWxHRNyJ+BHwEuKkcbUiSJElSR1Zs6fe7mgnZ\njuwerb2A7mTVCD+VUtrQ6h5WkNUIJUmSJBVSiYcavwv8Ejg7pbSqxQ10MCZbkiRJkgoplGxtX+S1\nDmnm+BZgLfBUSundIq8tSZIkSV1G2R5q3BU4syVJkiSpkIo81FiSJEmStmUmW5IkSZJUBiZbkiRJ\nklQGJluSJEmSVAYmW5IkSZJUBiZbkiRJklQGJluSJEmSVAY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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xe107470>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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8s8A8UJe5IDAPVJgHaoKFlCRJE2TtmjVExM23tWvWjDyGbaHV/UvSOLBHSpKkCTKwt2mI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\nTQkcyy3f/we1GpFWVEQ8ETi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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xe11d860>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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4MNAMmAScCBxhrc2K8qqRQUtERERERMQNNz1bZcAKa+0vvG1S+lDPloiIiIjU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\nuymSJEkqE4utncDEiRO56qqrmh1/8cUXc/TRR2817qKLLmL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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xfb56198>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>500</th>\n", | |
| " <th>NEDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>UDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " <th>EUDC [gCO$_2$ km$^{-1}$]</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Averages</th>\n", | |
| " <td>3.74</td>\n", | |
| " <td>4.31</td>\n", | |
| " <td>3.33</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Median</th>\n", | |
| " <td>3.81</td>\n", | |
| " <td>4.92</td>\n", | |
| " <td>3.25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>StdDev</th>\n", | |
| " <td>1.77</td>\n", | |
| " <td>3.41</td>\n", | |
| " <td>1.14</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "500 NEDC [gCO$_2$ km$^{-1}$] UDC [gCO$_2$ km$^{-1}$] \\\n", | |
| "Averages 3.74 4.31 \n", | |
| "Median 3.81 4.92 \n", | |
| "StdDev 1.77 3.41 \n", | |
| "\n", | |
| "500 EUDC [gCO$_2$ km$^{-1}$] \n", | |
| "Averages 3.33 \n", | |
| "Median 3.25 \n", | |
| "StdDev 1.14 " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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5oHpUPSL1GeChEfGKBda/Crg3NV6sV5IkSZLGTdVC6jjg+8BbI+JbwBMAIuLt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\nrfoKsHb4PZIkSZK0nE3KBXn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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xfb35c18>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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777N9+3Z69uzJyJEjq6698MILWbx4Mbt27WLx4sXMmzePRx991JVxOwlbu4Fd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\nGDRoUJO533XXXQwdOnSXQgugb9++9d/36NFjl/ebN29u8ro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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xe162080>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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zvwXc4aa8wPeA3UYNSJIkSZLarmoh9RDg5MwcVEDNuwJYO3pIUj2c/ywwD9Rj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\navoHp+vVvTZpFR0AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10f9c9e8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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iiy6qPjZu3DgWLVpU/f2SJUu49tpr631Ox44dufjii3nvvffCjl3JloikFGMMM2bcoapW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\nkiS1RlnP2SrQ+cBzZIYdJuB+YAkwpk7NacDzwHzggWz9Ge3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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x110ac8d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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64M7AxyPi7it9UEmSJEkaZ3ULqWMoM/S9ELgsIv4zIk6LiFMX3U5pPlSpHsc/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\nw42+/qfKq4EjM/PIavlVEfE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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10f96ef0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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