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raov5/Getting started with graphs.ipynb

Created August 21, 2017 02:48
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The notebook covers graphs in R. Specifically: * creating and saving graphs * customizing symbols, lines, colors and axes * annotating with text and titles * controlling a graph's dimensions * combining multiple graphs into one * does NOT cover ggplot2 library
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Getting started with graphs.ipynb
{
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"cells": [
{
"source": "#@author: Venky Rao [email protected]\n#@last edited: 20 Aug 2017\n#@source: materials, data and examples adapted from R in Action 2nd Edition by Dr. Robert Kabacoff",
"execution_count": 12,
"cell_type": "code",
"outputs": [],
"metadata": {
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{
"source": "# Working with graphs",
"cell_type": "markdown",
"metadata": {
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}
},
{
"source": "#In R, in a typical interactive session, you build a graph, one statement at a time, adding features, until you have what you want\n#here's an example:",
"execution_count": 2,
"cell_type": "code",
"outputs": [],
"metadata": {
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},
{
"source": "attach(mtcars) #attach the mtcars dataset that comes with R\nmtcars #view the mtcars dataset",
"execution_count": 3,
"cell_type": "code",
"outputs": [
{
"data": {
"text/html": "<table>\n<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n<tbody>\n\t<tr><th scope=row>Mazda RX4</th><td>21.0 </td><td>6 </td><td>160.0</td><td>110 </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0 </td><td>1 </td><td>4 </td><td>4 </td></tr>\n\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0 </td><td>6 </td><td>160.0</td><td>110 </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0 </td><td>1 </td><td>4 </td><td>4 </td></tr>\n\t<tr><th scope=row>Datsun 710</th><td>22.8 </td><td>4 </td><td>108.0</td><td> 93 </td><td>3.85 </td><td>2.320</td><td>18.61</td><td>1 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4 </td><td>6 </td><td>258.0</td><td>110 </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1 </td><td>0 </td><td>3 </td><td>1 </td></tr>\n\t<tr><th scope=row>Hornet Sportabout</th><td>18.7 </td><td>8 </td><td>360.0</td><td>175 </td><td>3.15 </td><td>3.440</td><td>17.02</td><td>0 </td><td>0 </td><td>3 </td><td>2 </td></tr>\n\t<tr><th scope=row>Valiant</th><td>18.1 </td><td>6 </td><td>225.0</td><td>105 </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1 </td><td>0 </td><td>3 </td><td>1 </td></tr>\n\t<tr><th scope=row>Duster 360</th><td>14.3 </td><td>8 </td><td>360.0</td><td>245 </td><td>3.21 </td><td>3.570</td><td>15.84</td><td>0 </td><td>0 </td><td>3 </td><td>4 </td></tr>\n\t<tr><th scope=row>Merc 240D</th><td>24.4 </td><td>4 </td><td>146.7</td><td> 62 </td><td>3.69 </td><td>3.190</td><td>20.00</td><td>1 </td><td>0 </td><td>4 </td><td>2 </td></tr>\n\t<tr><th scope=row>Merc 230</th><td>22.8 </td><td>4 </td><td>140.8</td><td> 95 </td><td>3.92 </td><td>3.150</td><td>22.90</td><td>1 </td><td>0 </td><td>4 </td><td>2 </td></tr>\n\t<tr><th scope=row>Merc 280</th><td>19.2 </td><td>6 </td><td>167.6</td><td>123 </td><td>3.92 </td><td>3.440</td><td>18.30</td><td>1 </td><td>0 </td><td>4 </td><td>4 </td></tr>\n\t<tr><th scope=row>Merc 280C</th><td>17.8 </td><td>6 </td><td>167.6</td><td>123 </td><td>3.92 </td><td>3.440</td><td>18.90</td><td>1 </td><td>0 </td><td>4 </td><td>4 </td></tr>\n\t<tr><th scope=row>Merc 450SE</th><td>16.4 </td><td>8 </td><td>275.8</td><td>180 </td><td>3.07 </td><td>4.070</td><td>17.40</td><td>0 </td><td>0 </td><td>3 </td><td>3 </td></tr>\n\t<tr><th scope=row>Merc 450SL</th><td>17.3 </td><td>8 </td><td>275.8</td><td>180 </td><td>3.07 </td><td>3.730</td><td>17.60</td><td>0 </td><td>0 </td><td>3 </td><td>3 </td></tr>\n\t<tr><th scope=row>Merc 450SLC</th><td>15.2 </td><td>8 </td><td>275.8</td><td>180 </td><td>3.07 </td><td>3.780</td><td>18.00</td><td>0 </td><td>0 </td><td>3 </td><td>3 </td></tr>\n\t<tr><th scope=row>Cadillac Fleetwood</th><td>10.4 </td><td>8 </td><td>472.0</td><td>205 </td><td>2.93 </td><td>5.250</td><td>17.98</td><td>0 </td><td>0 </td><td>3 </td><td>4 </td></tr>\n\t<tr><th scope=row>Lincoln Continental</th><td>10.4 </td><td>8 </td><td>460.0</td><td>215 </td><td>3.00 </td><td>5.424</td><td>17.82</td><td>0 </td><td>0 </td><td>3 </td><td>4 </td></tr>\n\t<tr><th scope=row>Chrysler Imperial</th><td>14.7 </td><td>8 </td><td>440.0</td><td>230 </td><td>3.23 </td><td>5.345</td><td>17.42</td><td>0 </td><td>0 </td><td>3 </td><td>4 </td></tr>\n\t<tr><th scope=row>Fiat 128</th><td>32.4 </td><td>4 </td><td> 78.7</td><td> 66 </td><td>4.08 </td><td>2.200</td><td>19.47</td><td>1 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n\t<tr><th scope=row>Honda Civic</th><td>30.4 </td><td>4 </td><td> 75.7</td><td> 52 </td><td>4.93 </td><td>1.615</td><td>18.52</td><td>1 </td><td>1 </td><td>4 </td><td>2 </td></tr>\n\t<tr><th scope=row>Toyota Corolla</th><td>33.9 </td><td>4 </td><td> 71.1</td><td> 65 </td><td>4.22 </td><td>1.835</td><td>19.90</td><td>1 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n\t<tr><th scope=row>Toyota Corona</th><td>21.5 </td><td>4 </td><td>120.1</td><td> 97 </td><td>3.70 </td><td>2.465</td><td>20.01</td><td>1 </td><td>0 </td><td>3 </td><td>1 </td></tr>\n\t<tr><th scope=row>Dodge Challenger</th><td>15.5 </td><td>8 </td><td>318.0</td><td>150 </td><td>2.76 </td><td>3.520</td><td>16.87</td><td>0 </td><td>0 </td><td>3 </td><td>2 </td></tr>\n\t<tr><th scope=row>AMC Javelin</th><td>15.2 </td><td>8 </td><td>304.0</td><td>150 </td><td>3.15 </td><td>3.435</td><td>17.30</td><td>0 </td><td>0 </td><td>3 </td><td>2 </td></tr>\n\t<tr><th scope=row>Camaro Z28</th><td>13.3 </td><td>8 </td><td>350.0</td><td>245 </td><td>3.73 </td><td>3.840</td><td>15.41</td><td>0 </td><td>0 </td><td>3 </td><td>4 </td></tr>\n\t<tr><th scope=row>Pontiac Firebird</th><td>19.2 </td><td>8 </td><td>400.0</td><td>175 </td><td>3.08 </td><td>3.845</td><td>17.05</td><td>0 </td><td>0 </td><td>3 </td><td>2 </td></tr>\n\t<tr><th scope=row>Fiat X1-9</th><td>27.3 </td><td>4 </td><td> 79.0</td><td> 66 </td><td>4.08 </td><td>1.935</td><td>18.90</td><td>1 </td><td>1 </td><td>4 </td><td>1 </td></tr>\n\t<tr><th scope=row>Porsche 914-2</th><td>26.0 </td><td>4 </td><td>120.3</td><td> 91 </td><td>4.43 </td><td>2.140</td><td>16.70</td><td>0 </td><td>1 </td><td>5 </td><td>2 </td></tr>\n\t<tr><th scope=row>Lotus Europa</th><td>30.4 </td><td>4 </td><td> 95.1</td><td>113 </td><td>3.77 </td><td>1.513</td><td>16.90</td><td>1 </td><td>1 </td><td>5 </td><td>2 </td></tr>\n\t<tr><th scope=row>Ford Pantera L</th><td>15.8 </td><td>8 </td><td>351.0</td><td>264 </td><td>4.22 </td><td>3.170</td><td>14.50</td><td>0 </td><td>1 </td><td>5 </td><td>4 </td></tr>\n\t<tr><th scope=row>Ferrari Dino</th><td>19.7 </td><td>6 </td><td>145.0</td><td>175 </td><td>3.62 </td><td>2.770</td><td>15.50</td><td>0 </td><td>1 </td><td>5 </td><td>6 </td></tr>\n\t<tr><th scope=row>Maserati Bora</th><td>15.0 </td><td>8 </td><td>301.0</td><td>335 </td><td>3.54 </td><td>3.570</td><td>14.60</td><td>0 </td><td>1 </td><td>5 </td><td>8 </td></tr>\n\t<tr><th scope=row>Volvo 142E</th><td>21.4 </td><td>4 </td><td>121.0</td><td>109 </td><td>4.11 </td><td>2.780</td><td>18.60</td><td>1 </td><td>1 </td><td>4 </td><td>2 </td></tr>\n</tbody>\n</table>\n",
"text/latex": "\\begin{tabular}{r|lllllllllll}\n & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n\\hline\n\tMazda RX4 & 21.0 & 6 & 160.0 & 110 & 3.90 & 2.620 & 16.46 & 0 & 1 & 4 & 4 \\\\\n\tMazda RX4 Wag & 21.0 & 6 & 160.0 & 110 & 3.90 & 2.875 & 17.02 & 0 & 1 & 4 & 4 \\\\\n\tDatsun 710 & 22.8 & 4 & 108.0 & 93 & 3.85 & 2.320 & 18.61 & 1 & 1 & 4 & 1 \\\\\n\tHornet 4 Drive & 21.4 & 6 & 258.0 & 110 & 3.08 & 3.215 & 19.44 & 1 & 0 & 3 & 1 \\\\\n\tHornet Sportabout & 18.7 & 8 & 360.0 & 175 & 3.15 & 3.440 & 17.02 & 0 & 0 & 3 & 2 \\\\\n\tValiant & 18.1 & 6 & 225.0 & 105 & 2.76 & 3.460 & 20.22 & 1 & 0 & 3 & 1 \\\\\n\tDuster 360 & 14.3 & 8 & 360.0 & 245 & 3.21 & 3.570 & 15.84 & 0 & 0 & 3 & 4 \\\\\n\tMerc 240D & 24.4 & 4 & 146.7 & 62 & 3.69 & 3.190 & 20.00 & 1 & 0 & 4 & 2 \\\\\n\tMerc 230 & 22.8 & 4 & 140.8 & 95 & 3.92 & 3.150 & 22.90 & 1 & 0 & 4 & 2 \\\\\n\tMerc 280 & 19.2 & 6 & 167.6 & 123 & 3.92 & 3.440 & 18.30 & 1 & 0 & 4 & 4 \\\\\n\tMerc 280C & 17.8 & 6 & 167.6 & 123 & 3.92 & 3.440 & 18.90 & 1 & 0 & 4 & 4 \\\\\n\tMerc 450SE & 16.4 & 8 & 275.8 & 180 & 3.07 & 4.070 & 17.40 & 0 & 0 & 3 & 3 \\\\\n\tMerc 450SL & 17.3 & 8 & 275.8 & 180 & 3.07 & 3.730 & 17.60 & 0 & 0 & 3 & 3 \\\\\n\tMerc 450SLC & 15.2 & 8 & 275.8 & 180 & 3.07 & 3.780 & 18.00 & 0 & 0 & 3 & 3 \\\\\n\tCadillac Fleetwood & 10.4 & 8 & 472.0 & 205 & 2.93 & 5.250 & 17.98 & 0 & 0 & 3 & 4 \\\\\n\tLincoln Continental & 10.4 & 8 & 460.0 & 215 & 3.00 & 5.424 & 17.82 & 0 & 0 & 3 & 4 \\\\\n\tChrysler Imperial & 14.7 & 8 & 440.0 & 230 & 3.23 & 5.345 & 17.42 & 0 & 0 & 3 & 4 \\\\\n\tFiat 128 & 32.4 & 4 & 78.7 & 66 & 4.08 & 2.200 & 19.47 & 1 & 1 & 4 & 1 \\\\\n\tHonda Civic & 30.4 & 4 & 75.7 & 52 & 4.93 & 1.615 & 18.52 & 1 & 1 & 4 & 2 \\\\\n\tToyota Corolla & 33.9 & 4 & 71.1 & 65 & 4.22 & 1.835 & 19.90 & 1 & 1 & 4 & 1 \\\\\n\tToyota Corona & 21.5 & 4 & 120.1 & 97 & 3.70 & 2.465 & 20.01 & 1 & 0 & 3 & 1 \\\\\n\tDodge Challenger & 15.5 & 8 & 318.0 & 150 & 2.76 & 3.520 & 16.87 & 0 & 0 & 3 & 2 \\\\\n\tAMC Javelin & 15.2 & 8 & 304.0 & 150 & 3.15 & 3.435 & 17.30 & 0 & 0 & 3 & 2 \\\\\n\tCamaro Z28 & 13.3 & 8 & 350.0 & 245 & 3.73 & 3.840 & 15.41 & 0 & 0 & 3 & 4 \\\\\n\tPontiac Firebird & 19.2 & 8 & 400.0 & 175 & 3.08 & 3.845 & 17.05 & 0 & 0 & 3 & 2 \\\\\n\tFiat X1-9 & 27.3 & 4 & 79.0 & 66 & 4.08 & 1.935 & 18.90 & 1 & 1 & 4 & 1 \\\\\n\tPorsche 914-2 & 26.0 & 4 & 120.3 & 91 & 4.43 & 2.140 & 16.70 & 0 & 1 & 5 & 2 \\\\\n\tLotus Europa & 30.4 & 4 & 95.1 & 113 & 3.77 & 1.513 & 16.90 & 1 & 1 & 5 & 2 \\\\\n\tFord Pantera L & 15.8 & 8 & 351.0 & 264 & 4.22 & 3.170 & 14.50 & 0 & 1 & 5 & 4 \\\\\n\tFerrari Dino & 19.7 & 6 & 145.0 & 175 & 3.62 & 2.770 & 15.50 & 0 & 1 & 5 & 6 \\\\\n\tMaserati Bora & 15.0 & 8 & 301.0 & 335 & 3.54 & 3.570 & 14.60 & 0 & 1 & 5 & 8 \\\\\n\tVolvo 142E & 21.4 & 4 & 121.0 & 109 & 4.11 & 2.780 & 18.60 & 1 & 1 & 4 & 2 \\\\\n\\end{tabular}\n",
"text/plain": " mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 \nMazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 \nDatsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 \nHornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 \nHornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 \nValiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 \nDuster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 \nMerc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 \nMerc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 \nMerc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 \nMerc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 \nMerc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 \nMerc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 \nMerc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 \nCadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 \nLincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 \nChrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 \nFiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 \nHonda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 \nToyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 \nToyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 \nDodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 \nAMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 \nCamaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 \nPontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 \nFiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 \nPorsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 \nLotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 \nFord Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 \nFerrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 \nMaserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 \nVolvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 "
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "plot(wt, mpg) #generates a scatter plot between automobile weight (x-axis) and miles per gallon (y-axis)",
"execution_count": 4,
"cell_type": "code",
"outputs": [
{
"data": {
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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "plot(wt, mpg) #generates a scatter plot between automobile weight (x-axis) and miles per gallon (y-axis)\nabline(lm(mpg~wt)) #adds a line of best fit to the chart created above",
"execution_count": 6,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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2hZaFklnZS4KuICSHLyOGZCt9TdAPhHTbnup19t/5A+6NAt5BSHdceSD6a9tOxrjqAeX7b1V+AtAuhHSXOS0otYjz7bHtZ22c/1jwXAEjgFYhpOKWxTx8Pa/GmCLr/nvh+G108BhCuseBurU/Ml2z7ZobviZmBtAihHSvzEfCbr8C2VODBM0AGoSQ7mPuFZBq+9aOP/eIqBlAexDS/RZFxDxk++au3UvCZgDNQUj3yy73p3o1f+d8SdDvwmYAzUFIJawIGdox8q3JoW+IGwE0ByGVtKVTMGMJXwqcADQHITmTd2BRbO+rQkcAbUFILhyqX2Ov6BlAOxCSKzcGRS4UPQNoBkJyyfrUu0LRQ4BGICQJ38Y9iIUSeAQhSTncMPk30TOAJiAkSTcGh38uegbQAoQkzfxecAp+XRbcQkjurCvb+YLoGUD1FA1po3XBYf6kY1xSr6WS9+xRU0j8ZItK20XPAGqnaEisu6WjxxgLT2JsrFRJqgqJ54wxfSZ6BlA5xUOaxxKWFfGLQ9kciQPVFRLn6SEp+aJnAFVTPKQhbJ51N7daV4kD1RYS31Cu03nRM4CaKR5SM3bZtj8qVuJA1YXET7WsiPtzgWuKhzSAZdj2h1SXOFB9IfGcx8M+ET0DqJeyIdVZlbGeTbXuno4aLHGgCkPCQgmkKBuSRXgQ28X5rApM6uUfVBkS/ymxwznRM4BKKRrS9R3zp49pV3Yt58mmD6UOVGdI/HTrCltEzwDqJOKZDWbOVzh5ssCJo3e8rs6QeO74sJmiZwBVUs9ThI4EsGKyZLmG/9JDU/JEzwAqpGxIF8dWiemz07bbvcRZMq/e8S+VfkWy2FS+/VnRM4D6KBrSxfLMFMNMtocZSoZUjErXSDZn2iT9LHoGUB1FQ5rEXsrLXx6RYH2SgGZD4rkTQ98TPQOojaIh1WxmfabqSjaOazkk60Jp1C3RM4C6KBpS6ETbZmTgH9oOie+o3BwvngTFKRpS+Ta2zanI1nnaDolf7FLmR9EzgJooGtJoZv8x7Efs2UJth8QLUoPTRM8AKqJoSMfiWJJ1fWQeyZIraDskzueGj8RCCW5T9udIh4ZGVLFui6bHM62HxHdWaZohegZQC6Wf2WDOtG9zNku9aLgmQuKXuiV8L3qGvcOTQ+v+6aToMUA9TxEqThshqWChtDSsz8zV/2kdt0PsGICQ/PO/UsNvCrz8+ahp1k3h6ORcgVMAR0j+2l21yTFxV3+nhv11o6+XWi5uCLBCSH663CN+rbCLjxjv2Ok4TdgMYIOQ/FVoWShJ3u1SRo9Ncux0f0XQBOCAkPw3v9TQbDFXfqW1fVuQIPUYKCgAIRH4tVqjo0IuvC9oiW2bFndNyPXhDoRE4XKv0lL3cpHP9LCpe65tezIIL8EuGkIiIWyh9EVNxlhz4T8XBoRE5MuIfplCLnzlV7Xe38JQEBKV/XXq7Bc9AwiDkMhk9o/+WvQMIApComNOC0otEj0EiIGQKC2P6Xtd9AwgBEIidaBurX2iZwAREBKtrEeiloieAQRASMSwUDImhERuRexDeMKO4SAkegfr1fxd9AygMIQkg6wBkYtEzwDKQkhywELJcBCSPFbG9b4qegZQEEKSyeEGNfaKngGUg5DkcmNQ5ALRM4BiEJJsrAulQtFDgEIQkoy+jXvgiugZQBkISU5HGlbGPVCNASHJ6tZo0+eiZwAlICSZpQen5IueAeSHkOS2vmznC6JnANkhJNmdbFFpm+gZQG4ISX45Y02fiZ4BZIaQlJAegoWSziEkRWwo1/G86BlATghJGadaVdwqegaQEUJSSM64sE9EzwDyQUiKsSyU8kTPAHJBSMrZmNj+nOgZQCYIyVsLe1cq132mT7//erp1hS1UYxR80CWh6sMrqE4HfkJI3jGPN02a+9XfYh7y6bu03AlhM2nmuNk54aWFsyeE/I3mdOAvhOSdTyK3WzdHEqf69vHpoTQLpeeqnbZu1octpjgb+M3vkGo7NOw5+iuy+32oN6QmU+zb9LI+/s7epvLtzvo/xq2Ir+w7T3X1/2RAwO+QWDHtqX58r9qQCgLX23eOsuM+nuJM2zLr/J5jN3PcWmVpjN/nAgp+h1QwoPm3l66saTHq/Ld12LtEU6k2pBzmeLTgDDvo6znyng1O83eOreymfedbk7+nAhJ+h/T3RNv/N15NfIPvYh2IplJtSLzSDPt2pemW7yf5PHyUHx9tdSnI8df2ZkP/TgRE/A6p0Uj7dmQzbo4tSzKTmkN6Kdl2X+/c1sP9OcuOys18/c7QoU8P2xrtQnm/v7gBCb9Dinb8ixoaxXn5KJKZ1BxSZoP6yy5dW9u+4mm/TnOxa5kf/TrB4TI9NmSdX1SjdY5fpwEqfofUocxF6+ZSmXb8GGtCNJV6Q+JXx4cxFjTIv44sS8tUPxdKRx8KZCz8GdV+nozG75Bms0bfXrmyugmbc6Qd+yfRVCoOifP8vbsovgzMDR9x068T3Nyxr4BgDiDhd0jmZ+wPfU80z2X9/FxC36HqkKjsqto0Q/QMQIXgmQ0/P9GpzYQ1nO/fQHZfUUOExC91T/he9AxABE8REsjvhRKohv8hXZ/eKiGm5fRsqomsDBIS5/8rNcy/hRKohN8hna/EWHwZxpIpXzfVMCHx3dWaHBM9AxDwO6RhbFAG5ycGsolUI3EjhcQv94hfI3oG8J/fISXVtT1TNb92daKJrAwUEi+0LJTMoocAf/kdUtWx9u3YBJJ57IwUEufzI4aQrjBBAL9Deqax7UHvwkb9iSayMlZIfE/1RkdFzwD+8TuknEdGneH89MjEDKqRuOFC4ld6lV4tegbwC8kv9pUry1hp66/JtieaymghWRZKgalYKGmZ3yHVrF6lYvmy8bHREaaQwHJEUxkuJM6XRffLFD0D+A7PbFCL/XVq7xM9A/gMIalGZv/opaJnAF8hJPUwpwWlkt2ICZTld0gnHyjluIcQ2UxGDYnzb2L6Xhc9A/jE75D6s5Aa9jvbkc1k3JD4wbq1/hA9A/jC75Bi610kG+YOw4bEsx6NWiJ6BvCB3yEl/oNslruMGxIWShrld0iPPEo2y10GDonzFbF9KH8lBRThd0jHy029QDbNbZoMKWvrYZrftT9Uv+bvJCcC5fgdUnZnhkftLH7rYvkURE/OpThX1sDIRRTnAeX4HdIIFopH7TjfETlgy83Tc5J6k6xvrAslsjvJgBL8Dqlcg0tkw9yhvZCaDbc95/Ro1GwvPujC9EfajJnj9OZ0q+IevOrpabI/HNLmsf/T2mdMZ/wOqToetbPYxxy/UPRcT88/aEN8nefeHBPd3ulDC4cb1PjNs9McrVn+ibf+VKG6zy+PAQT8DulpPGrHrc/ddux8VtXjj7kY97T127czDQY4ffeNwZELPDlNYaMHsiyb7L51qF6dCnzg/y/2PYhH7ThfbXL8NtEMz5eK02vbl0E7XbzUkmWh9KwH9yReXsr+E/Gr0R51B/IgfMU+spk0GNL5wA32nYHDPP6YB5937FT+1MURq0t3cf9/Uqm3v5ns+2ePLw3kyF5D1uCP2g1pbHtsYHHgJo8/pMPrjp1G77s65ETzStvdneapwY6dkeM9vjSQw69R0LjcuMLUJTNHBL3l+YcMG23f5kV97fKYnNEmdw8DpjV27LSa5vm1gRpCInLrzQ6lkwdu8OIjFkbY77H6QUyWxFHpISnSDyIcCLLfYHJD4B4vLg7EEJIw5p7Vfyji2e+GfCx52Pqync5LHvC32Ll5PH9+6acphwMvISQFmPOcvjl7QlCpKoEJxR9qyHdyK6GTLStukzp70WsRIVVDw1/BUyFEQkiym9suKrjuZKf/g86t/uznuy/OdvMf9UMi2nxWoqWcsSZXj+vZXV/32Q94wrhYCElm5onhL65Y/171+pfdHXm1SZV/rVv1csSYkl+V3C6UQDSEJLOvTFutm+uNR7o7cnw92yPouyLmlHzfT+U6nqOeDCghJJl1czwG8F2Im2++bpiW23eed3a/2lOtKvxCOhfQQkgyS1ho3+YE/Cx94E7muIHQ8ihn784dFzaLci6ghZBkVtpxL5P8gI3SB25njtd2WVnK+QHpoSnOH/0DFUBIMmv3kn27OdDN0+auhfxo35nazMURGxPbnyWbC2ghJJl9HHPYusnr1NfdkYPa5lg3GfEun3p3uk3SFrrJgBJCkllh3zIf7jm2pFXScXdHnq7cfOGxvTMSe7p+pDt3Qpj00yBAFIQkt4I3KjIWNdKDb8oujI1mLGma5EIICyWVQkgKuHLc0xcRO+72x7abyzc/4ec4IAOEpDVn2pZZJ3oGKAEhaU5BanCa6BngfghJg+aEj7zl/ihQEkLSop1Vmrl9EBAUhZA06WLXhB9EzwDFISRtwkJJZRCSVn1RasRN0TPAHQhJs3ZVbZIhega4TeGQMp7vWic6PLlj6hHJwxCSJy51j18regZwUDQk8zOBjEVWrhTJGJsodTdehOQRLJTUQ9GQ3mdNvrL9nmjm0o7sHYkDEZKH5pUahoWSKigaUstq2bd381s6+4Xq2xCSp3ZXa3xM9AzAFQ4pctTd/dRo18chJM9d7hm/RvQMoHBIrarf+T4kvxW+ItEotCyUPH1yOchG0ZA+ZE0W2NZIWUs7MKnbzSMkb3wZMSTb/VEgK+UftYuqXCWaMfa41B0PEZJX9lRvJP3jBJCd4j9H6lIrMiK5feohycMQkneuPBC9TPQMBqeiZzb8seOOyQjJO+a0oNQi0UMYmnpCOhLAipF6ySBwYln0w9dFz2BkyoZ0cWyVmD47bbvdS5wl6+od/8JXJK8dqFt7n+gZDEzRkC6WZ6YYZvrOul8ypGKMsUYqPLRsK+FX3sxHopfSnQ28o2hIk9hLefnLIxKsr0CHkL6pziIDTX/NITshFkoCKRpSzWbWnxyuZOM4QuKLg188ybOXVHyY8Kep38Q8hBccE0PRkEIn2jYjA/9ASLmJr9q2B8MXEZ71YL1afxCeDjymaEjl29g2pyJb5xk+pDUmx//EMUMoT5v1aNRiyvOBhxQNaTT70Lb9iD1baPSQ0ms5dt5uRXpeLJTEUDSkY3Esybo+Mo9kyRUMHtLcJMfOK12Iz7wytg8WSopT9udIh4ZGVLFui6bHM4OHdIjttm3NLVLJT12/xl7qc4IbSj+zwZxp3+ZsnitxlAFC4g83s90w/7Vw+ls93hgYuZD8pCBJPU8RKs4IIV1qWva5j6a3KyXHD1GtC6VCGc4LLiEkYXI/6Fe743My/f7DqrgHr8pzZnAKIenU4YbJv4mewUgQkl7dGBzx1T1vyDvt73Moik5L/TamsSEk3TK/F5xy9+aBXzUNZlH99/txvl97R7CQlsv9H0yXEJKOrSvb+YJjd2po6oaDy3tH+P6q6D+YHl15aN2fg/9JM5veICQ9O9mi0nbbzvbAldaNeVwtqRvcSrlV8c+27fxgf76q6RdC0rWcMabPrNunetn/fDlknY9n+jrCcaeiNi/7PZUeISSdSw9Jyee86z8cf6z3Hx/P82Zbx86f+xNMpT8ISe82lOt0nnd7xfGnuv/18TRprR07zz5KMJT+ICTdO9Wy4tbnutr3zwdt9PEsK8MdT+5q9or0gQaFkPQv53HTtKAF1r2iofV9feZQXrWJth9DzQzFvSidQUhGkB7SJnjSih1fdIjb7fM5Nkf2mL9z2bigjwjn0hGEZAg/JdZvG8WSRvnzTPNDQ8ux6O7rqEbSGYRkDKdbV9ji/+cUt+10CSEZRO74sJmiZ9AzhGQQf4yvFFB+aqboMXQLIRnDgrAe/341MqzqSdGD6BVCMoSMcOvLn59pFdZE9CR6hZAM4WXbPW557hD2vOhRdAohGUKvlxw7ZYNH3RI6iV4hJEPoNM2x0/CFys1PCB1FpxCSIYwbaN/eDF91sUuZH8UOo0sIyRDWhthf3u2VxBxekBqcJngcHUJIxjA6fta5wv3PBH9t/cPc8JFYKBFDSMZQ+EYsC2L11tr/tLNKU/r7uxobQjKKggPrz975w6VuCd8LnEWHEJIxYaFEDCEZ1Relht8UPYOOICTD2lW1yTHRM+gHQjIQ87E1fxS76fDlHvFrxQ2jMwjJOBZXY2Es9q27L4xZiIUSGYRkGLODpxzjF9NjJxV727xSQ7OFDaQrCMkorsX827bdFPhzsbf+Wq3xUTHz6AxCMop58Y7bfvd6rvibL/cs/Z2IcfQGIRnF6x0cO3/re8/brQslf184CRCSYbzbzLEzafB97/kyoh/u5eAvhGQUG4LP2LaFNd6+/117kuvgtVr8hJAMYuewsOCmqVe4+cXYiyXemdkv+msBM+kJQjKGz0MeeaNs6YT4F9tGOntswZwWlFrk5O3gKYRkCIdDP+D8+rQupshnXTwtaHnMw9eVnUlfEJIhvNjKvs0I2ObqkAN1a+1TahwdQkiG0HOyY6dmustjMh+JWqLMNHqEkAyh86uOnQYfuD4ICyU/ICRDmPCIfZtlWi112IrYh64pMY4OISRDWB9kf4LdCxVzJY87WK/m70rMoz8IyRj+FP1/R25uHxfyrZvjsgZELVJkIL1BSMZgfi+RMdZqk/sDsVDyCUIyjJPbPPtB0crY3ldlHkWHEBLc73CDGntFz6A5CAlKuDEocuHqp7oNfO206Em0AyFBSeY3AoL6vfJ0wyg8ldVTCAmceDEu+oEr3DzNdFD0JFqBkKCkG6YFRxpW3sF5lxTRo2gFQoKS1gXn8uzHTJ/z9+uJHkUrEBKUtCza+t/04JTZFUWPohUICUraGXDeullftkpz0aNoBUKCkoqqvmzb7guNcfn7S3APhAROLAt+1fI3sLd1nZGmz0TPog0ICZxZnBhUK4H1PsvTQ1Ly3R8OCAmcyt308YJD1p0N5TqeFz2MBiAkcONUq4pbRc+gfggJ3MkZF/aJ6BlUDyGBe5aFUp7oGVQOIYEHNiZ2OCd6BnVDSOCJ060rbBE9g6ohJPBI7oSwmaJnUDOEBB5KD8VCyTWEBJ7aVL7dWdEzqBZCAo+daZv0s/ujjAkhgedyU4LTRM+gUlvsJ8oAABMZSURBVAgJvPF5+KhbomdQJYQEXtlRudlxiXdfTRvQYsh/DdgaQgLvXOxa5keX79ydVP3Zd58oW++UggOpA0ICLxWkulwoZVcabr1J/7XObcxKTqQGCAm8Njd8xE2n75hZzv72UyHrFBxHFRASeG9XlaYZzt7++AjHTus3lBtGHRAS+OBSt4Tvnbx58FOOnV4vKzmNGiAk8IXzhdLfejh2qsxQdBoVQEjgm/+VGlZiobQ5aLtt+1Wo4R62Q0jgo93Vmhy7/21jyi7O57c+inhdxEBCISTw1eUe8Wvue1N+qimkUlDMv4XMIxRCAp8VWhZK9//A6NqPn2824l8eQgI/zI8Yki16BnVASOCPX6s3Oip6BlVASOCXK71Kfyd6BjVASJp2/nCh6BEsC6WphntmXUkISbvyXi3HWPgQ4T+yWRbdL1P0DMIhJM0qeDAxff/Jb9onlvhpjtL216m9X/QMoiEkzZoRZysov2sf0ZPwzP7RS0XPIBhC0qw2k+3bLYHiXy3CnBaUWiR6CKEQkmbFLbFv8wM2ih3EZnlM3+uiZxAJIWlWmQX2bU6AKu6RdaBurT9EzyAQQtKsHk/at6tD1fGlIOvRqCWiZxAHIWnWojDbV6JrDcZ4/aHnX+lVt+/b1J9jQy+UEJJ2PWn6y5Lv36nc6Iq3H/hLQv2X//PXSjUyqCdaEdvnGvU5NQIhadiXnWNDG091fh8SCVmJ4wssmxs9m5N//ThUv+bv1OfUBoSkbb48Q+i/STm27dkQZ/dd8E/WwMhF5CfVAoRkPKNHO3bayHCvH6MulBCS8Qy6fa+fnpPlOP2quN5X5TivuiEk43m+m31rrijPa/AdblDjN1lOrGaKhrTR+vk1f9IxLqnXUsln3iMkcvs+npK+1767NdD+d/55uAcvsJy99PX/tyzHu2vdGBS5wMvxNE/RkFh3S0ePMRaexNhYqZIQErFbowNqPFAzYKj998KfKD3nBr/yT9O/3H/gijLRHdpFJrm+bb5T1oWS8F+UUpbiIc1jCcuK+MWhbI7EgQiJ2NAq1tck35Hc3/anwmmRAfGsrAff2P0cOsXy1Sj7ufA9Xl5wdekuF7yeUssUD2kIm2fdza3WVeJAhERre+Bu23Zf8E/2N9zctni3J6+s3GWUfdu/n7eXPNG80nZvP0bLFA+pGbts2x8VK3EgQqL1WivHThfv7sl9I9AR3jcmr79Tyxltmu3tx2iY4iENYBm2/SHVJQ5ESLSeGuzYGf34ve/YMG1k6uIClx93jDlene83x//9eSU9JCXf+4/SKGVDqrMqYz2bat09HTVY4kCEROuVjo6dXs8Xf/PNR4I6jnsgsnGGq4+7HrDVvrMm2HVtrq0v20n87xwqRNmQLMKD2C7OZ1VgUjdxQki01ofYbz53yrSq+JuHV7f+BtGl7nVdLpdaPmvfjunu03VPtqy4zacP1B5FQ7q+Y/70Me3KruU82fSh1IEIiViPRhmW/55q0b74Dx32Bdj/lV+L/8zVx60KTrd8RNE/bz9I4a2csaZPfftIrRHxzAbLX80KJ4+N7t1xx2SEROtK19Bu43qY2t/zaf+wlmNn1CiXHzjTVHPYkGoR83y+slEWSup5itCRAFZMlizXMC7zd6+MnLzy3meTvtbJsfOCxH2ITv/fxCc+9Gel81O5jh48gULz1BMSv3H1jn/hK5IC0qs6doaMk/Myp1pV+EXO86uDikIqBmskJWQE2V/e6EzkQlmvkzsubJasF5DVuTUrMzw4DCEZ2HNlrCXtb9xO7l8gSg9N8eR5FCp0qm+AKYq12ev2QEVDqn0PiQMRkiIKnw2s0rNeYG8fftjqpY2J7c/KfhEZXKrWYVuhed/AWLe3ZFY0pL/EF388QeJAhKSQo59O+a8iT4k73SZpixLXIfbn+resm6Lebm8Lrey3dmeSmEe/2oKQdCd3QtjHomfwXpLjGfLrg9293obCa6QpCMmwNLhQygtw1HGRubs5ksIhLUZIxrW5fPMTomfwjjl0tX3nKHP32jkKh5R/yaMXd0NIunSmbZl1omfwTsen7dv3y7t7YBMPf4NyClKD00TP4JWvQ1ZYN7vi3nF3JEICJc0JH3lL9AzemBbUP+2fI8LGuP1JG0IytN//89zbGxS94s4qzY4rekE//TyhVdMR37g/DiEZWP6EgHr9W4R0VvS37y52TfhByespBCEZ2J/Kb7L8N6NVC0VvnaW5hZJHEJJxHQlcb9teiJ6v7IW/CB/h9UtoqB1CMq4Zt+8/M2yswlfeVbVphsKXlBtCMq5pnvxinzwudU9Yq/Q15YWQjOs/NRw7I0ZLHicH3S2UEJJxHXI8k+xy7FwBV59XapieFkoIycAer7TT8t8zHRoLuT3J7mqN3T2BTUMQkoHlDg9oPryTqfVpMZe/3DN+jZgrywAhGdqOt1Ne+1bYK1UWWhZKHj2JWQMQEgg0P2JItugZaCAkEGlP9UZHRM9AAiGBUFd6RS8TPQMFhARiFU4NShW2SqODkEC0ZdEPXxc9g98QEgh3oE7tfaJn8BdCAvEy+0cvFT2DnxASqIA5TesLJYQEqvBNTN9romfwB0ICdThYr9YfomfwA0IClch6NGqx6Bl8h5BALTS9UEJIoB4rY/todaGEkMBTBftlv2/Xofo13L+mlyohJPDMkX6hjCW+WSDvVW4MlPl1OOWCkMAjv8f1WH12338SBsj8C0TWhZKit9kjgpDAI+372x4H2B/xP7mvtCruwatyX4MeQgJPHGaOZ8M93Uv+azVM/k32i1BDSOCJFRGOndlV5L/YjcGRX8l/FVoICTyx2uRYG81MVuBqloVSisyPalBDSOCJs4GOfycjBipyvXVlOl9Q5EJUEBJ45NGWtr+SNUEK3Wr4RItK25W5Eg2EBB45Xzv5n2sXTAqZrNQFc8aYPlPqWgQQEngma3Lj0ISeHrx2HZn0kBQht4D1CUICjyl9M8cN5Top+mKC/kBIoF6nWlbcKnoGDyEkULGcx02fiJ7BMwgJVE0rCyWEBOr2U2KHc6Jn8ABCApU73brCL6JncA8hgdrljg+bKXoGtxASqF96aEqe6BncQEigAZvKtz8regZpCAm04EybpJ9FzyAJIYEm5E4MSxc9gxSEBBqRHjrqlugZXENIoBWbyzc/IXoGlxASaMbFLmV+FD2DKwgJtKMgNThN9AwuICTQkrnhI9W5UEJIoCk7qzQ9LnoGZxASaMulbgk/iJ7BCYQEGqPOhRJCAs35otTwm6JnuB9CAu3ZVbXJMdEz3AchgQZd6hGv0P31PIWQQIsK1bZQQkigTfNKDc0WPUMxCAk06tdqjY+KnuEuhARadbln6e9Ez3AHQgLNsi6UlL77qysICTTsy4jHVLJQQkigZXuS6+wXPYMNQgJNy+wX/bXoGawQEmibOS0otUj0EAgJtG95zMPXRc+AkED7DtStvU/0DAgJtC/zkaglgkdASKAD4hdKCAl04ZvYh66JvD5CAn04WK/m7wIvj5BAJ7IGRC0Sd3WEBHohdKGEkEA/Vsb2viro0ggJdORwgxp7xVwZIYGe3BgUuVDIhRES6Ip1oVQo4LoICXTm27gHryh/VYQEenOkYeUdil8UIYHu3Bhs+lzpayIk0KH04JQCZa+IkECP1pftfEHRCyIk0KWTLSptU/J6CAn0KWes6TMFL4eQQK/SQ1LyFbsYQgLd2lCu43mlroWQQL9Otay4VaFLISTQsZxxYZ8ocyWEBLpmWSjlKXEdhAT6tjGxwzkFLoOQQOdOt66wRf6rICTQu9wJYTNlvwhCAv1LD5V9oYSQwAA2lW93Vt4rICQwgjNtk3727MhLqz9c6erHuEU7Zs/e4fxGRQgJDCE3JTjNg8MKp5hK1YsM/avT5xbtbMCqVmUNdjp7H0ICg/jcNOqW24OeK72wiJuXlxvn5H0HYoaf4/zc8JiDTt6JkMAodlRudsLNIfsDf7BttwZuL/nOAb1sr/xc1Gugk49ESGAYF7uW+VH6iLcaOXba/73E+/JNK+w735icfOOncEgZz3etEx2e3DH1iORhCAnkUJDqZqH0zADHztgxJd53ljm+pTvAnDwCqGhI5mcCGYusXCmSMTZR6nfqERLIY274CKmF0pSujp1+z5Z43w3meILEzwHZJT9S0ZDeZ02+sr2ITebSjuwdiQMREshkZ5WmGa7fu9pk/2JzJXpByXc2e8G+fb6Zk49UNKSW1e6knN+yvcSBCAnkcqlbwvcu31nUsrP1LvxZD9ZzsgxaGGp7fc0loc5ePUbRkCJH3d1PjZY4ECGBbCQXSqcaxo9+dWzZWk7X8G8GdXrhhU5Bbzp7n6Ihtap+8/Zufit8RQJB/ldq2E1X78v9ZFznMR+5WEftSe3TJ3WP03cpGtKHrMkC2xopa2kH9pbEgQgJ5LS7WpNjxKdU/lG7qMpVohljj0vd4AUhgawu94hfQ3tGxX+O1KVWZERy+9RDkochJJBXoWWhZKY8oYqe2fDbjjsmIySQ2fyIoU5+HOQz9YR0xPJt310uF4MANH6t3ugo3dnUExLPvnrHaqbInV/A0K70Kv0d2clUFFIxmxESyM+yUJpKtVBCSGBgX0X0y6Q5k6Ih1b6HxIEICZSxv07t/SQnUjSkv8QXfzxB4kCEBArJ7B/9NcV5lP3W7kwSy/HkOIQESjGnBaU6v5+JVxReI01BSKA2y2P6Xvf7JAqHtBghgeocqFvrD3/PoXBI+Zc8ergRIYGSsh6NWuLnKfDwNwDBQgkhAVisiO1zzZ+PR0gAVofq1fzdjw9XZ0jbGYDGOLmlpBvyh8R/3aGQyOfmqt3zoaIncK9dN9ETuDWHfezm38L2vw5Y4Pu/pF+9/1euQEiKifP30Rr5rSwlegL3hqeInsCtIrZB9Aj3Q0iKQkgkEJKsEBIJhOQLhKQohEQCIckKIZFASL5ASIpCSCQQkqwQEgmE5AuEpCiERAIhyQohkUBIvkBIikJIJBCSrMqtED2BW2tjRU/g3tinRU/gljlki+gR7qenkI4Xip7AraIM0RO4d8WvX0FQxjHSe31T0FNIAMIgJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIKCbkDLGJIXVmnRR9BiSrj5dNzx5zAnRY7hR1Evd/yiuO14xYqPoQe6h7s+Z5w7FsdYTm7Hyai4ppyZrPrErK31G9CDS3mHq/kexg9XpbvWb6EHuoe7PmeeGsQ85N7/EHhc9iIR32d8t/32bqfueCNtDVB7SfLZK9AhOqPtz5jFzRJJ1k2eqIngQKQPZBct/L7O2ogeRklWjSw11/6OYzjJEj+CEuj9nHsusMs66yY+JET2JhFlvWf97kHUQPYiUkXEna6v7H8WY8A0Dmw//UmW3P1H358xby1kf0SNIKzy9uQv7r+gpJMxhi7jKQ2oXZHus4TF1laTuz5mXVkaYdoieQdoMxgLfVte/gHscipzI1R5SWfaPY5e+SGCfih7kHur+nHnl1CgWv1r0EG7MTzax+kdET+FSXvNa2WoPybx2l3WzjHUUPck9VP0584b548jAx8+LnsK9vCmsl+gZXJocYv2Kru6QHIpMcaJHuIcWPmeeKBrNWv0heghpBRmXrJui6EjRk7j0ELtN/XdbTUoSPcE99BLS6+zpPNEzuJEb2N26McdWFD2JS0/UtgphtWtnih7FpRXJs6ybS6yn6EnuoZOQcuKT1X/j77bB2yz//YQNET2IG+r+1u5CWMUzlv9XGsbU9dojqv6ceW4zq9DdTvQkEtYFhfRL6cQSTokexA11h8Q/YNFDR1dno9X14Ke6P2cem3vnm3vRk0j5uW/psLpPqv4REZWHxL8bVC2m02x1daSXkADEQkgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEBEAAIQEQQEgABBASAAGEpHXdqoieADhC0j61v3SyQeBvQesQkirgb0FzegRe5fwQYyc4P8N6MSvRIwFC0p732CLO0xmbzfkc9sHcRDZ3ruiRACFpzxH2BOdDa4SP5nwMy8C3duqAvwXtqVOdmxOf7FXBbK7UEGsklcDfgvY8z47uZwvfYgcPsikISSXwt6A969lH/2UXd7IZM9gvCEkl8LegPQWxAwc34EXxgwaVLUJIKoG/BQ0aGlfmGc4Hx8eP49aQCkTPAwhJk75gbIntEfCllj80ZWPfED0QICQtuhzILnN+lIVlW/6wtFpIWdEDAUICoICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAACCAmAAEICIICQAAggJAAC/x+SnkcoDclfawAAAABJRU5ErkJggg=="
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "plot(wt, mpg) #generates a scatter plot between automobile weight (x-axis) and miles per gallon (y-axis)\nabline(lm(mpg~wt)) #adds a line of best fit to the chart created above\ntitle(\"Regression of MPG on Weight\") #adds a title to the chart\ndetach(mtcars) #detaches the mtcars data frame",
"execution_count": 7,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## A simple example",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#let's create some data for our graph\ndose <- c(20, 30, 40, 45, 60)\ndrugA <- c(16, 20, 27, 40, 60)\ndrugB <- c(15, 18, 25, 31, 40)",
"execution_count": 8,
"cell_type": "code",
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": "#a simple line graph relating dose to drugA can be generated as follows:\nplot(dose, drugA, type = \"b\") # type = \"b\" means both points and a line should be plotted\n#explore help(plot) for more information on all parameters that can be specified",
"execution_count": 10,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Graphical parameters",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#you can customize many features of a graph (eg fonts, colors, axes, labels, etc)\n#one way of doing this is by using the par() function\n#values set using the par() function will be in effect for the rest of session or until they're changed",
"execution_count": 12,
"cell_type": "code",
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": "#specifying par() without any parameters produces a list of the current graphical settings\npar()\n#length(par()) == 72",
"execution_count": 18,
"cell_type": "code",
"outputs": [
{
"data": {
"text/html": "<dl>\n\t<dt>$xlog</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$ylog</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$adj</dt>\n\t\t<dd>0.5</dd>\n\t<dt>$ann</dt>\n\t\t<dd>TRUE</dd>\n\t<dt>$ask</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$bg</dt>\n\t\t<dd>'white'</dd>\n\t<dt>$bty</dt>\n\t\t<dd>'o'</dd>\n\t<dt>$cex</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.axis</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.lab</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.main</dt>\n\t\t<dd>1.2</dd>\n\t<dt>$cex.sub</dt>\n\t\t<dd>1</dd>\n\t<dt>$cin</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0.15</li>\n\t<li>0.2</li>\n</ol>\n</dd>\n\t<dt>$col</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.axis</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.lab</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.main</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.sub</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$cra</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>10.8</li>\n\t<li>14.4</li>\n</ol>\n</dd>\n\t<dt>$crt</dt>\n\t\t<dd>0</dd>\n\t<dt>$csi</dt>\n\t\t<dd>0.2</dd>\n\t<dt>$cxy</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0.0276412776412776</li>\n\t<li>0.0414364640883978</li>\n</ol>\n</dd>\n\t<dt>$din</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>6.66666666666667</li>\n\t<li>6.66666666666667</li>\n</ol>\n</dd>\n\t<dt>$err</dt>\n\t\t<dd>0</dd>\n\t<dt>$family</dt>\n\t\t<dd>''</dd>\n\t<dt>$fg</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$fig</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$fin</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>6.66666666666667</li>\n\t<li>6.66666666666667</li>\n</ol>\n</dd>\n\t<dt>$font</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.axis</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.lab</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.main</dt>\n\t\t<dd>2</dd>\n\t<dt>$font.sub</dt>\n\t\t<dd>1</dd>\n\t<dt>$lab</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5</li>\n\t<li>5</li>\n\t<li>7</li>\n</ol>\n</dd>\n\t<dt>$las</dt>\n\t\t<dd>0</dd>\n\t<dt>$lend</dt>\n\t\t<dd>'round'</dd>\n\t<dt>$lheight</dt>\n\t\t<dd>1</dd>\n\t<dt>$ljoin</dt>\n\t\t<dd>'round'</dd>\n\t<dt>$lmitre</dt>\n\t\t<dd>10</dd>\n\t<dt>$lty</dt>\n\t\t<dd>'solid'</dd>\n\t<dt>$lwd</dt>\n\t\t<dd>1</dd>\n\t<dt>$mai</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1.02</li>\n\t<li>0.82</li>\n\t<li>0.82</li>\n\t<li>0.42</li>\n</ol>\n</dd>\n\t<dt>$mar</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5.1</li>\n\t<li>4.1</li>\n\t<li>4.1</li>\n\t<li>2.1</li>\n</ol>\n</dd>\n\t<dt>$mex</dt>\n\t\t<dd>1</dd>\n\t<dt>$mfcol</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mfg</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mfrow</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mgp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>3</li>\n\t<li>1</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$mkh</dt>\n\t\t<dd>0.001</dd>\n\t<dt>$new</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$oma</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$omd</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$omi</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$page</dt>\n\t\t<dd>TRUE</dd>\n\t<dt>$pch</dt>\n\t\t<dd>1</dd>\n\t<dt>$pin</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5.42666666666667</li>\n\t<li>4.82666666666667</li>\n</ol>\n</dd>\n\t<dt>$plt</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0.123</li>\n\t<li>0.937</li>\n\t<li>0.153</li>\n\t<li>0.877</li>\n</ol>\n</dd>\n\t<dt>$ps</dt>\n\t\t<dd>12</dd>\n\t<dt>$pty</dt>\n\t\t<dd>'m'</dd>\n\t<dt>$smo</dt>\n\t\t<dd>1</dd>\n\t<dt>$srt</dt>\n\t\t<dd>0</dd>\n\t<dt>$tck</dt>\n\t\t<dd>NA</dd>\n\t<dt>$tcl</dt>\n\t\t<dd>-0.5</dd>\n\t<dt>$usr</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$xaxp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>5</li>\n</ol>\n</dd>\n\t<dt>$xaxs</dt>\n\t\t<dd>'r'</dd>\n\t<dt>$xaxt</dt>\n\t\t<dd>'s'</dd>\n\t<dt>$xpd</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$yaxp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>5</li>\n</ol>\n</dd>\n\t<dt>$yaxs</dt>\n\t\t<dd>'r'</dd>\n\t<dt>$yaxt</dt>\n\t\t<dd>'s'</dd>\n\t<dt>$ylbias</dt>\n\t\t<dd>0.2</dd>\n</dl>\n",
"text/markdown": "$xlog\n: FALSE\n$ylog\n: FALSE\n$adj\n: 0.5\n$ann\n: TRUE\n$ask\n: FALSE\n$bg\n: 'white'\n$bty\n: 'o'\n$cex\n: 1\n$cex.axis\n: 1\n$cex.lab\n: 1\n$cex.main\n: 1.2\n$cex.sub\n: 1\n$cin\n: 1. 0.15\n2. 0.2\n\n\n\n$col\n: 'black'\n$col.axis\n: 'black'\n$col.lab\n: 'black'\n$col.main\n: 'black'\n$col.sub\n: 'black'\n$cra\n: 1. 10.8\n2. 14.4\n\n\n\n$crt\n: 0\n$csi\n: 0.2\n$cxy\n: 1. 0.0276412776412776\n2. 0.0414364640883978\n\n\n\n$din\n: 1. 6.66666666666667\n2. 6.66666666666667\n\n\n\n$err\n: 0\n$family\n: ''\n$fg\n: 'black'\n$fig\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$fin\n: 1. 6.66666666666667\n2. 6.66666666666667\n\n\n\n$font\n: 1\n$font.axis\n: 1\n$font.lab\n: 1\n$font.main\n: 2\n$font.sub\n: 1\n$lab\n: 1. 5\n2. 5\n3. 7\n\n\n\n$las\n: 0\n$lend\n: 'round'\n$lheight\n: 1\n$ljoin\n: 'round'\n$lmitre\n: 10\n$lty\n: 'solid'\n$lwd\n: 1\n$mai\n: 1. 1.02\n2. 0.82\n3. 0.82\n4. 0.42\n\n\n\n$mar\n: 1. 5.1\n2. 4.1\n3. 4.1\n4. 2.1\n\n\n\n$mex\n: 1\n$mfcol\n: 1. 1\n2. 1\n\n\n\n$mfg\n: 1. 1\n2. 1\n3. 1\n4. 1\n\n\n\n$mfrow\n: 1. 1\n2. 1\n\n\n\n$mgp\n: 1. 3\n2. 1\n3. 0\n\n\n\n$mkh\n: 0.001\n$new\n: FALSE\n$oma\n: 1. 0\n2. 0\n3. 0\n4. 0\n\n\n\n$omd\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$omi\n: 1. 0\n2. 0\n3. 0\n4. 0\n\n\n\n$page\n: TRUE\n$pch\n: 1\n$pin\n: 1. 5.42666666666667\n2. 4.82666666666667\n\n\n\n$plt\n: 1. 0.123\n2. 0.937\n3. 0.153\n4. 0.877\n\n\n\n$ps\n: 12\n$pty\n: 'm'\n$smo\n: 1\n$srt\n: 0\n$tck\n: NA\n$tcl\n: -0.5\n$usr\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$xaxp\n: 1. 0\n2. 1\n3. 5\n\n\n\n$xaxs\n: 'r'\n$xaxt\n: 's'\n$xpd\n: FALSE\n$yaxp\n: 1. 0\n2. 1\n3. 5\n\n\n\n$yaxs\n: 'r'\n$yaxt\n: 's'\n$ylbias\n: 0.2\n\n\n",
"text/latex": "\\begin{description}\n\\item[\\$xlog] FALSE\n\\item[\\$ylog] FALSE\n\\item[\\$adj] 0.5\n\\item[\\$ann] TRUE\n\\item[\\$ask] FALSE\n\\item[\\$bg] 'white'\n\\item[\\$bty] 'o'\n\\item[\\$cex] 1\n\\item[\\$cex.axis] 1\n\\item[\\$cex.lab] 1\n\\item[\\$cex.main] 1.2\n\\item[\\$cex.sub] 1\n\\item[\\$cin] \\begin{enumerate*}\n\\item 0.15\n\\item 0.2\n\\end{enumerate*}\n\n\\item[\\$col] 'black'\n\\item[\\$col.axis] 'black'\n\\item[\\$col.lab] 'black'\n\\item[\\$col.main] 'black'\n\\item[\\$col.sub] 'black'\n\\item[\\$cra] \\begin{enumerate*}\n\\item 10.8\n\\item 14.4\n\\end{enumerate*}\n\n\\item[\\$crt] 0\n\\item[\\$csi] 0.2\n\\item[\\$cxy] \\begin{enumerate*}\n\\item 0.0276412776412776\n\\item 0.0414364640883978\n\\end{enumerate*}\n\n\\item[\\$din] \\begin{enumerate*}\n\\item 6.66666666666667\n\\item 6.66666666666667\n\\end{enumerate*}\n\n\\item[\\$err] 0\n\\item[\\$family] ''\n\\item[\\$fg] 'black'\n\\item[\\$fig] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$fin] \\begin{enumerate*}\n\\item 6.66666666666667\n\\item 6.66666666666667\n\\end{enumerate*}\n\n\\item[\\$font] 1\n\\item[\\$font.axis] 1\n\\item[\\$font.lab] 1\n\\item[\\$font.main] 2\n\\item[\\$font.sub] 1\n\\item[\\$lab] \\begin{enumerate*}\n\\item 5\n\\item 5\n\\item 7\n\\end{enumerate*}\n\n\\item[\\$las] 0\n\\item[\\$lend] 'round'\n\\item[\\$lheight] 1\n\\item[\\$ljoin] 'round'\n\\item[\\$lmitre] 10\n\\item[\\$lty] 'solid'\n\\item[\\$lwd] 1\n\\item[\\$mai] \\begin{enumerate*}\n\\item 1.02\n\\item 0.82\n\\item 0.82\n\\item 0.42\n\\end{enumerate*}\n\n\\item[\\$mar] \\begin{enumerate*}\n\\item 5.1\n\\item 4.1\n\\item 4.1\n\\item 2.1\n\\end{enumerate*}\n\n\\item[\\$mex] 1\n\\item[\\$mfcol] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mfg] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mfrow] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mgp] \\begin{enumerate*}\n\\item 3\n\\item 1\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$mkh] 0.001\n\\item[\\$new] FALSE\n\\item[\\$oma] \\begin{enumerate*}\n\\item 0\n\\item 0\n\\item 0\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$omd] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$omi] \\begin{enumerate*}\n\\item 0\n\\item 0\n\\item 0\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$page] TRUE\n\\item[\\$pch] 1\n\\item[\\$pin] \\begin{enumerate*}\n\\item 5.42666666666667\n\\item 4.82666666666667\n\\end{enumerate*}\n\n\\item[\\$plt] \\begin{enumerate*}\n\\item 0.123\n\\item 0.937\n\\item 0.153\n\\item 0.877\n\\end{enumerate*}\n\n\\item[\\$ps] 12\n\\item[\\$pty] 'm'\n\\item[\\$smo] 1\n\\item[\\$srt] 0\n\\item[\\$tck] NA\n\\item[\\$tcl] -0.5\n\\item[\\$usr] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$xaxp] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 5\n\\end{enumerate*}\n\n\\item[\\$xaxs] 'r'\n\\item[\\$xaxt] 's'\n\\item[\\$xpd] FALSE\n\\item[\\$yaxp] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 5\n\\end{enumerate*}\n\n\\item[\\$yaxs] 'r'\n\\item[\\$yaxt] 's'\n\\item[\\$ylbias] 0.2\n\\end{description}\n",
"text/plain": "$xlog\n[1] FALSE\n\n$ylog\n[1] FALSE\n\n$adj\n[1] 0.5\n\n$ann\n[1] TRUE\n\n$ask\n[1] FALSE\n\n$bg\n[1] \"white\"\n\n$bty\n[1] \"o\"\n\n$cex\n[1] 1\n\n$cex.axis\n[1] 1\n\n$cex.lab\n[1] 1\n\n$cex.main\n[1] 1.2\n\n$cex.sub\n[1] 1\n\n$cin\n[1] 0.15 0.20\n\n$col\n[1] \"black\"\n\n$col.axis\n[1] \"black\"\n\n$col.lab\n[1] \"black\"\n\n$col.main\n[1] \"black\"\n\n$col.sub\n[1] \"black\"\n\n$cra\n[1] 10.8 14.4\n\n$crt\n[1] 0\n\n$csi\n[1] 0.2\n\n$cxy\n[1] 0.02764128 0.04143646\n\n$din\n[1] 6.666667 6.666667\n\n$err\n[1] 0\n\n$family\n[1] \"\"\n\n$fg\n[1] \"black\"\n\n$fig\n[1] 0 1 0 1\n\n$fin\n[1] 6.666667 6.666667\n\n$font\n[1] 1\n\n$font.axis\n[1] 1\n\n$font.lab\n[1] 1\n\n$font.main\n[1] 2\n\n$font.sub\n[1] 1\n\n$lab\n[1] 5 5 7\n\n$las\n[1] 0\n\n$lend\n[1] \"round\"\n\n$lheight\n[1] 1\n\n$ljoin\n[1] \"round\"\n\n$lmitre\n[1] 10\n\n$lty\n[1] \"solid\"\n\n$lwd\n[1] 1\n\n$mai\n[1] 1.02 0.82 0.82 0.42\n\n$mar\n[1] 5.1 4.1 4.1 2.1\n\n$mex\n[1] 1\n\n$mfcol\n[1] 1 1\n\n$mfg\n[1] 1 1 1 1\n\n$mfrow\n[1] 1 1\n\n$mgp\n[1] 3 1 0\n\n$mkh\n[1] 0.001\n\n$new\n[1] FALSE\n\n$oma\n[1] 0 0 0 0\n\n$omd\n[1] 0 1 0 1\n\n$omi\n[1] 0 0 0 0\n\n$page\n[1] TRUE\n\n$pch\n[1] 1\n\n$pin\n[1] 5.426667 4.826667\n\n$plt\n[1] 0.123 0.937 0.153 0.877\n\n$ps\n[1] 12\n\n$pty\n[1] \"m\"\n\n$smo\n[1] 1\n\n$srt\n[1] 0\n\n$tck\n[1] NA\n\n$tcl\n[1] -0.5\n\n$usr\n[1] 0 1 0 1\n\n$xaxp\n[1] 0 1 5\n\n$xaxs\n[1] \"r\"\n\n$xaxt\n[1] \"s\"\n\n$xpd\n[1] FALSE\n\n$yaxp\n[1] 0 1 5\n\n$yaxs\n[1] \"r\"\n\n$yaxt\n[1] \"s\"\n\n$ylbias\n[1] 0.2\n"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#adding no.readonly = T to the par() function produces a list of settings that can be modified\npar(no.readonly = T)\n#length(par(no.readonly = T)) == 66",
"execution_count": 19,
"cell_type": "code",
"outputs": [
{
"data": {
"text/html": "<dl>\n\t<dt>$xlog</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$ylog</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$adj</dt>\n\t\t<dd>0.5</dd>\n\t<dt>$ann</dt>\n\t\t<dd>TRUE</dd>\n\t<dt>$ask</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$bg</dt>\n\t\t<dd>'white'</dd>\n\t<dt>$bty</dt>\n\t\t<dd>'o'</dd>\n\t<dt>$cex</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.axis</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.lab</dt>\n\t\t<dd>1</dd>\n\t<dt>$cex.main</dt>\n\t\t<dd>1.2</dd>\n\t<dt>$cex.sub</dt>\n\t\t<dd>1</dd>\n\t<dt>$col</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.axis</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.lab</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.main</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$col.sub</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$crt</dt>\n\t\t<dd>0</dd>\n\t<dt>$err</dt>\n\t\t<dd>0</dd>\n\t<dt>$family</dt>\n\t\t<dd>''</dd>\n\t<dt>$fg</dt>\n\t\t<dd>'black'</dd>\n\t<dt>$fig</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$fin</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>6.66666666666667</li>\n\t<li>6.66666666666667</li>\n</ol>\n</dd>\n\t<dt>$font</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.axis</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.lab</dt>\n\t\t<dd>1</dd>\n\t<dt>$font.main</dt>\n\t\t<dd>2</dd>\n\t<dt>$font.sub</dt>\n\t\t<dd>1</dd>\n\t<dt>$lab</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5</li>\n\t<li>5</li>\n\t<li>7</li>\n</ol>\n</dd>\n\t<dt>$las</dt>\n\t\t<dd>0</dd>\n\t<dt>$lend</dt>\n\t\t<dd>'round'</dd>\n\t<dt>$lheight</dt>\n\t\t<dd>1</dd>\n\t<dt>$ljoin</dt>\n\t\t<dd>'round'</dd>\n\t<dt>$lmitre</dt>\n\t\t<dd>10</dd>\n\t<dt>$lty</dt>\n\t\t<dd>'solid'</dd>\n\t<dt>$lwd</dt>\n\t\t<dd>1</dd>\n\t<dt>$mai</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1.02</li>\n\t<li>0.82</li>\n\t<li>0.82</li>\n\t<li>0.42</li>\n</ol>\n</dd>\n\t<dt>$mar</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5.1</li>\n\t<li>4.1</li>\n\t<li>4.1</li>\n\t<li>2.1</li>\n</ol>\n</dd>\n\t<dt>$mex</dt>\n\t\t<dd>1</dd>\n\t<dt>$mfcol</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mfg</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mfrow</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>1</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$mgp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>3</li>\n\t<li>1</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$mkh</dt>\n\t\t<dd>0.001</dd>\n\t<dt>$new</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$oma</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$omd</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$omi</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n\t<li>0</li>\n</ol>\n</dd>\n\t<dt>$pch</dt>\n\t\t<dd>1</dd>\n\t<dt>$pin</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>5.42666666666667</li>\n\t<li>4.82666666666667</li>\n</ol>\n</dd>\n\t<dt>$plt</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0.123</li>\n\t<li>0.937</li>\n\t<li>0.153</li>\n\t<li>0.877</li>\n</ol>\n</dd>\n\t<dt>$ps</dt>\n\t\t<dd>12</dd>\n\t<dt>$pty</dt>\n\t\t<dd>'m'</dd>\n\t<dt>$smo</dt>\n\t\t<dd>1</dd>\n\t<dt>$srt</dt>\n\t\t<dd>0</dd>\n\t<dt>$tck</dt>\n\t\t<dd>NA</dd>\n\t<dt>$tcl</dt>\n\t\t<dd>-0.5</dd>\n\t<dt>$usr</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>0</li>\n\t<li>1</li>\n</ol>\n</dd>\n\t<dt>$xaxp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>5</li>\n</ol>\n</dd>\n\t<dt>$xaxs</dt>\n\t\t<dd>'r'</dd>\n\t<dt>$xaxt</dt>\n\t\t<dd>'s'</dd>\n\t<dt>$xpd</dt>\n\t\t<dd>FALSE</dd>\n\t<dt>$yaxp</dt>\n\t\t<dd><ol class=list-inline>\n\t<li>0</li>\n\t<li>1</li>\n\t<li>5</li>\n</ol>\n</dd>\n\t<dt>$yaxs</dt>\n\t\t<dd>'r'</dd>\n\t<dt>$yaxt</dt>\n\t\t<dd>'s'</dd>\n\t<dt>$ylbias</dt>\n\t\t<dd>0.2</dd>\n</dl>\n",
"text/markdown": "$xlog\n: FALSE\n$ylog\n: FALSE\n$adj\n: 0.5\n$ann\n: TRUE\n$ask\n: FALSE\n$bg\n: 'white'\n$bty\n: 'o'\n$cex\n: 1\n$cex.axis\n: 1\n$cex.lab\n: 1\n$cex.main\n: 1.2\n$cex.sub\n: 1\n$col\n: 'black'\n$col.axis\n: 'black'\n$col.lab\n: 'black'\n$col.main\n: 'black'\n$col.sub\n: 'black'\n$crt\n: 0\n$err\n: 0\n$family\n: ''\n$fg\n: 'black'\n$fig\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$fin\n: 1. 6.66666666666667\n2. 6.66666666666667\n\n\n\n$font\n: 1\n$font.axis\n: 1\n$font.lab\n: 1\n$font.main\n: 2\n$font.sub\n: 1\n$lab\n: 1. 5\n2. 5\n3. 7\n\n\n\n$las\n: 0\n$lend\n: 'round'\n$lheight\n: 1\n$ljoin\n: 'round'\n$lmitre\n: 10\n$lty\n: 'solid'\n$lwd\n: 1\n$mai\n: 1. 1.02\n2. 0.82\n3. 0.82\n4. 0.42\n\n\n\n$mar\n: 1. 5.1\n2. 4.1\n3. 4.1\n4. 2.1\n\n\n\n$mex\n: 1\n$mfcol\n: 1. 1\n2. 1\n\n\n\n$mfg\n: 1. 1\n2. 1\n3. 1\n4. 1\n\n\n\n$mfrow\n: 1. 1\n2. 1\n\n\n\n$mgp\n: 1. 3\n2. 1\n3. 0\n\n\n\n$mkh\n: 0.001\n$new\n: FALSE\n$oma\n: 1. 0\n2. 0\n3. 0\n4. 0\n\n\n\n$omd\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$omi\n: 1. 0\n2. 0\n3. 0\n4. 0\n\n\n\n$pch\n: 1\n$pin\n: 1. 5.42666666666667\n2. 4.82666666666667\n\n\n\n$plt\n: 1. 0.123\n2. 0.937\n3. 0.153\n4. 0.877\n\n\n\n$ps\n: 12\n$pty\n: 'm'\n$smo\n: 1\n$srt\n: 0\n$tck\n: NA\n$tcl\n: -0.5\n$usr\n: 1. 0\n2. 1\n3. 0\n4. 1\n\n\n\n$xaxp\n: 1. 0\n2. 1\n3. 5\n\n\n\n$xaxs\n: 'r'\n$xaxt\n: 's'\n$xpd\n: FALSE\n$yaxp\n: 1. 0\n2. 1\n3. 5\n\n\n\n$yaxs\n: 'r'\n$yaxt\n: 's'\n$ylbias\n: 0.2\n\n\n",
"text/latex": "\\begin{description}\n\\item[\\$xlog] FALSE\n\\item[\\$ylog] FALSE\n\\item[\\$adj] 0.5\n\\item[\\$ann] TRUE\n\\item[\\$ask] FALSE\n\\item[\\$bg] 'white'\n\\item[\\$bty] 'o'\n\\item[\\$cex] 1\n\\item[\\$cex.axis] 1\n\\item[\\$cex.lab] 1\n\\item[\\$cex.main] 1.2\n\\item[\\$cex.sub] 1\n\\item[\\$col] 'black'\n\\item[\\$col.axis] 'black'\n\\item[\\$col.lab] 'black'\n\\item[\\$col.main] 'black'\n\\item[\\$col.sub] 'black'\n\\item[\\$crt] 0\n\\item[\\$err] 0\n\\item[\\$family] ''\n\\item[\\$fg] 'black'\n\\item[\\$fig] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$fin] \\begin{enumerate*}\n\\item 6.66666666666667\n\\item 6.66666666666667\n\\end{enumerate*}\n\n\\item[\\$font] 1\n\\item[\\$font.axis] 1\n\\item[\\$font.lab] 1\n\\item[\\$font.main] 2\n\\item[\\$font.sub] 1\n\\item[\\$lab] \\begin{enumerate*}\n\\item 5\n\\item 5\n\\item 7\n\\end{enumerate*}\n\n\\item[\\$las] 0\n\\item[\\$lend] 'round'\n\\item[\\$lheight] 1\n\\item[\\$ljoin] 'round'\n\\item[\\$lmitre] 10\n\\item[\\$lty] 'solid'\n\\item[\\$lwd] 1\n\\item[\\$mai] \\begin{enumerate*}\n\\item 1.02\n\\item 0.82\n\\item 0.82\n\\item 0.42\n\\end{enumerate*}\n\n\\item[\\$mar] \\begin{enumerate*}\n\\item 5.1\n\\item 4.1\n\\item 4.1\n\\item 2.1\n\\end{enumerate*}\n\n\\item[\\$mex] 1\n\\item[\\$mfcol] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mfg] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mfrow] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$mgp] \\begin{enumerate*}\n\\item 3\n\\item 1\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$mkh] 0.001\n\\item[\\$new] FALSE\n\\item[\\$oma] \\begin{enumerate*}\n\\item 0\n\\item 0\n\\item 0\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$omd] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$omi] \\begin{enumerate*}\n\\item 0\n\\item 0\n\\item 0\n\\item 0\n\\end{enumerate*}\n\n\\item[\\$pch] 1\n\\item[\\$pin] \\begin{enumerate*}\n\\item 5.42666666666667\n\\item 4.82666666666667\n\\end{enumerate*}\n\n\\item[\\$plt] \\begin{enumerate*}\n\\item 0.123\n\\item 0.937\n\\item 0.153\n\\item 0.877\n\\end{enumerate*}\n\n\\item[\\$ps] 12\n\\item[\\$pty] 'm'\n\\item[\\$smo] 1\n\\item[\\$srt] 0\n\\item[\\$tck] NA\n\\item[\\$tcl] -0.5\n\\item[\\$usr] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 0\n\\item 1\n\\end{enumerate*}\n\n\\item[\\$xaxp] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 5\n\\end{enumerate*}\n\n\\item[\\$xaxs] 'r'\n\\item[\\$xaxt] 's'\n\\item[\\$xpd] FALSE\n\\item[\\$yaxp] \\begin{enumerate*}\n\\item 0\n\\item 1\n\\item 5\n\\end{enumerate*}\n\n\\item[\\$yaxs] 'r'\n\\item[\\$yaxt] 's'\n\\item[\\$ylbias] 0.2\n\\end{description}\n",
"text/plain": "$xlog\n[1] FALSE\n\n$ylog\n[1] FALSE\n\n$adj\n[1] 0.5\n\n$ann\n[1] TRUE\n\n$ask\n[1] FALSE\n\n$bg\n[1] \"white\"\n\n$bty\n[1] \"o\"\n\n$cex\n[1] 1\n\n$cex.axis\n[1] 1\n\n$cex.lab\n[1] 1\n\n$cex.main\n[1] 1.2\n\n$cex.sub\n[1] 1\n\n$col\n[1] \"black\"\n\n$col.axis\n[1] \"black\"\n\n$col.lab\n[1] \"black\"\n\n$col.main\n[1] \"black\"\n\n$col.sub\n[1] \"black\"\n\n$crt\n[1] 0\n\n$err\n[1] 0\n\n$family\n[1] \"\"\n\n$fg\n[1] \"black\"\n\n$fig\n[1] 0 1 0 1\n\n$fin\n[1] 6.666667 6.666667\n\n$font\n[1] 1\n\n$font.axis\n[1] 1\n\n$font.lab\n[1] 1\n\n$font.main\n[1] 2\n\n$font.sub\n[1] 1\n\n$lab\n[1] 5 5 7\n\n$las\n[1] 0\n\n$lend\n[1] \"round\"\n\n$lheight\n[1] 1\n\n$ljoin\n[1] \"round\"\n\n$lmitre\n[1] 10\n\n$lty\n[1] \"solid\"\n\n$lwd\n[1] 1\n\n$mai\n[1] 1.02 0.82 0.82 0.42\n\n$mar\n[1] 5.1 4.1 4.1 2.1\n\n$mex\n[1] 1\n\n$mfcol\n[1] 1 1\n\n$mfg\n[1] 1 1 1 1\n\n$mfrow\n[1] 1 1\n\n$mgp\n[1] 3 1 0\n\n$mkh\n[1] 0.001\n\n$new\n[1] FALSE\n\n$oma\n[1] 0 0 0 0\n\n$omd\n[1] 0 1 0 1\n\n$omi\n[1] 0 0 0 0\n\n$pch\n[1] 1\n\n$pin\n[1] 5.426667 4.826667\n\n$plt\n[1] 0.123 0.937 0.153 0.877\n\n$ps\n[1] 12\n\n$pty\n[1] \"m\"\n\n$smo\n[1] 1\n\n$srt\n[1] 0\n\n$tck\n[1] NA\n\n$tcl\n[1] -0.5\n\n$usr\n[1] 0 1 0 1\n\n$xaxp\n[1] 0 1 5\n\n$xaxs\n[1] \"r\"\n\n$xaxt\n[1] \"s\"\n\n$xpd\n[1] FALSE\n\n$yaxp\n[1] 0 1 5\n\n$yaxs\n[1] \"r\"\n\n$yaxt\n[1] \"s\"\n\n$ylbias\n[1] 0.2\n"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "opar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\npar(lty = 2, pch = 17) # lty = 2 means line type dashed; pch = 17 means solid triangle as plotting symbol\nplot(dose, drugA, type = \"b\") #plotting the chart again with new parameters\npar(opar) #restore original settings",
"execution_count": 20,
"cell_type": "code",
"outputs": [
{
"data": {
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fNtMzeICQoFXFOZ2/Nj2DFwgJWl3VbqXpETxBSNBq7gemJ/AGIQECCAkQQEjQ5q99TU/gHUKCLkua3WF6BO8QEjR5N6vQ9AgeIiTo8VnOZT7fS8wVIUGPWy+sMD2ClwgJEEBIgABCgvf237jd9AheIyR4ruLcToRUB0JCQ0QK2n5qegbPERK8dn3L/zU9gvcICR57qelLpkfQgJDgseIVpifQgZAAAYQEL60JyyeSkOChD1o9YXoETQgJ3ll99MWVpmfQhJDgmQ3dxpSankEXQoJX9h2ft9f0DNoQErxSfFVQtixPAiEBAggJEEBI8MQvg7aTWD0ICV749xYhOFG1NkKCB/7Y5HnTI2hGSJD3ZMC2LE8CIUFev7tNT6AdIQECCAkQQEiQteY/TU9gBCFB1OYeZ5sewQhCgqTdA4eE81NHSBBUMrJvELcsTwIhQdD0rutMj2AIIUHQB1+ZnsAUQgIEEBKkhPW7ujhCgpCHmuwyPYJBhAQZ/50ezldiDyAkiHiteYC3LE+C5pDWzBjdJ7tFbn7hatdlhOQ3H2Zda3oEs7SGFJmeplRml86ZSqmCcpeFhOQ3S64P8pblSdAa0r1qwPz4OzTtXpivZrssJCT4jNaQBnfbU31YNjjPZSEhwWe0hpQ54eBxYbbLQkLyC+X9M0/+oDWkId1r3sK2bAj/IgUBIR2gNaQ5asCC+GOk4oUj1CyXhYTkE0pRUhX9z9pldemaHf3jn1zmspCQ/EEpSjpA++tIo3plZuTmFa5yXUZI/kBINew5s2HrRefVGERIfqAUJVWTCmn/8xMbcAs7d3/3v+2+obDGqYTkB4R0kEhIpYsubdWgP07V2/33+dbOD5SipBqph1S6aFK0IpX3x4bcAiH5n1KUdFCKIZW+MLl19M8x47Y1SX2gSvIPn5DgM6mEVLp4chulmv3wkfr+gal2qlI9eseoJtELl4WEBJ9JJaRoRVkXzi+u/zu1apWz00fFfx6Zb+0QMKmEpNTYL6oOkgzJcZbltnkmiQ8gJPhMKiGd1Eg1yp+zqSEhOcXjVcEeQkLQpPRkw4Z7hiuVNvqBBoTkOPMye71HSAiYVJ/+XnvnYKVU8xkrkv/wVYOaEBICRuAF2S9+PyDa0nFuZ3Mfcp83nHmF+wpC8oFr/mZ6ApvInCK08rf9RF+UIyT7/TMtZPuWuxM7afWT36Q8y0GEZL9LR5qewCr2nP1dGyFZb2uTF0yPYJWUQ+p9wPFjJs6vlJqKkKxX/teQv//WIVIOqfbZc3luP/XaEIQEn0k5pPJzBi0u2v7y9ydsXtxH3Sk0FSHBZ1IO6cajd8Sudhx9q/O+GiE0FSFZruxD0xPYJuWQ+o+vuh4/0Im0bicyEyFZb3ZP0xPYJuWQsi+uur4wy3HaZ4nMREi2299B6pv4wEg5pBFHbo1dFR053PlSDRCaipDs9sARxaZHsE3KIf1J9V+8ffuLA9S81cPVXUJTEZLVKnreZHoE66QcUmR61VPfBZHH1VklQlMRktWeySgyPYJ1BM5seOuKkSde/rLjfPZ6hdBQhGS3rxabnsA+nCIECBA7Rcj1zUwaipDgM4KnCInNREhW277J9AQ2kvnWrmTZmT+ROs8uhpAsduaVpiewkdRjpLL+v0t9mBqEZK8PGy0zPYKNxJ5suP7ElGc5iJDsdeEY0xNYSSykn7ZMeZaDCMlaX6S/ZnoEKwmFVPFGq+4C01QjJGtNG2p6AjvJPWsnedYIIVnrpQa88VqYSL2OdMINPGuHEOPMBkBAyiH9jxfvbkZIltog+X1HoKQcUvPjxGY5iJDstLfdM6ZHsFXKIV2Vvl5smBqEZKd72u01PYKtUg6pcmbPF7aIjXMAIVmprOutpkewFietImmPZe80PYK1+DEKJK3fdaYnsBdPfyNpd28zPYG9CAkQkNpmzN8iOBUhwWdSCanqoVGOUkdE/9eJx0gBJ/7cbKCk/K3dlmPGrHScz8cO3yM2EyHZ6N30XaZHsFnKIU3sFP+a/6bTNKGJYgjJPuN+ZHoCq6UcUo9Lqq4vOVZkniqEZJ3P2DLWVepvoj+u6vqsbJF5qhCSdSayZayrlEPKy/o6drUmM09oohhCss06tox1l3JIj6ieC4uKFvZQD4rNREj2KZrJlrGuUn8T/UlVryJNlPyDJiT4jMCZDYsnD82/7BWheaoQEnyGU4RQv0r2FasPIaF+d0m++2cwERLqxZax9SMk1IstY+tHSKgPW8YmgZBQn7+wZWz9CAn1ef0x0xP4ACEBAggJEEBIcFdeYXoCXyAkuDuLp+ySQUhwxZaxySEkuGLL2OQQEtywZWySCAlu/o0tY5NDSHBzF295khxCAgQQEiCAkJAYb3iSNEJCQns7vmV6BN8gJCR0d7sS0yP4BiEhkbKut5kewT8ICYk8ypaxySMkJBDpU2h6BB8hJCRQPpGtxZJHSIAAQgIEEBIggJBQp+UdKk2P4CuEhDqxZWzDEBLqwpaxDURIqMulbBnbMISEOnzNlrENREiowydT+AmKhiEkQAAhAQIICRBASPiOP040PYH/EBIOtb/DXaZH8B9CwqHmsmVswxESDlHR42bTI/gQIeEQT2ZsMz2CDxESDvHQnaYn8CNCAgQQEiCAkAABhIRvmTjP9AT+REiobQVbxh4eQkJtF4w1PYFPERJq+SJ9qekRfIqQUMtUtow9TISEWi5ZYnoCvyIkQAAhAQIICRBASKi2d8Qa0yP4FyGhGlvGpoCQcEBZ11tNj+BjhIQD2DI2FYSEKpV92TI2BYSEKjsGbTI9gp8REiCAkAABhAQIICTEfHC26Ql8jpAQw5axKSIkOGwZmzrNIa2ZMbpPdovc/MLVrssISbOJJ5mewO+0hhSZnqZUZpfOmUqpgnKXhYSk19qmi02P4HdaQ7pXDZgfPw1l98J8NdtlISHptegHbBmbIq0hDe62p/qwbHCey0JCgs9oDSlzwsHjwmyXhYQEn9Ea0pDue6sPy4bwLxICRGtIc9SABfHHSMULR6hZLgsJSac/s49L6vQ/a5fVpWu2UmpymctCQtJoX3u2jE2d9teRRvXKzMjNK1zluoyQNGLLWAn2nNlQ9ucHa1xCSNqwZawIe0Ja26d7jRzFX5K6/FfLItMjBIHekDZcPWzE3MjqsZmdLljvto5v7fT52a9NTxAIWkP6qo2KurOvym6tcra4LCQk+IzWkCaoO7avPClN/boy8rCa5rKQkOAzWkPqdnL04k3VojR6ldfXZSEhwWe0htR8avRincqNHRc0d1lISLoUvmV6goDQGlLvQdGLirlPxY5/lOOykJA0+aDRctMjBITWkK5SN1Y/xfBu+lkuCwlJE7aMlaI1pO09VNUpdkWnpDd+x2UhIenBlrFi9L6OVHLnOQ/HrtekDfu72zpC0qNgiOkJAsPMmQ3l+91/n5D06PWC6QkCw55ThGojJPgMIQECCAkQQEihtfdyTrGXQ0ihdXe7faZHCBBCCquyrreZHiFICCms2DJWFCGFVGXf602PECiEFFIrszebHiFQCCms3DYxQIMREiCAkAABhBRKn/7O9ARBQ0ihdLbbj1XiMBBSGLFlrDhCCiO2jBVHSCG0tumLpkcIHEIKoTmD2TJWGiGFUAWnfYsjJEAAIQECCAkQQEjhorz/5IUTIYULIXmEkEIlttGb6RmCiZBChZC8QkhhohQleYSQQkQpSvIKIYUIIXmHkMJDKUryDCGFByF5iJBCQylK8g4hAQIIKQQir1aYHiHwCCkE1h29wfQIgUdIgABCCrZd1802PUI4EFKQVcw9MvdV00OEAyEF2NLjWt2x3/QQIUFIAXb6FVtMjxAahAQIIKRAKp8z1vQIIUNIQfRi39b3mp4hZAgpeL48I/3KItNDhA0hBc/cMz82PUL4EBIggJACZdGJy02PEFKEFCCfnpZ+9R7TQ4QUIQVGybT0Uz81PURoEVJgrB65yPQIIUZIgABCCoKPT/+t6RHCjpD8r+jK9DNWmx4i7AjJ9+5v3Ze9lY0jJN+7ZE656RFASIAEQvKxLVecYHoEHEBIvlU6u9VxvCGDLQjJr97okXM/D46sQUh+9dh1O02PgIMICRBASP6zf1YbfnLPNoTkO093b/dAxPQQOAQh+cymk5peu8v0EPgOQvKZz6dyWp2NCMlHKipNT4BECMk/Fhwz0/QISISQ/GJZfrPCYtNDIBFC8ompaT/50vQMSIyQfGLG66YngBtCsl+E7+jsR0jW+7/hA02PgHoRkuXWTUg7/yvTQ6BehGS3xzK+/w/TMyAJhGS3p+fxIqwvEJK92ADWRwjJVmsvSfva9AxIGiHZae/tmTw48hNCstJHHTs9zs8c+QkhWWnNH9jnyF8IyTqbaciHCMky39zYYo7pGdBwhGSVyIIuHR/klSMfIiSblA/NuGWv6SFwOAjJJpE5602PgMNDSLb45gvTEyAFhGSHykfan2Z6BqSAkKzwzoktby4xPQRSQEg2uD5t0gbTMyAlhGSDV98zPQFSREiGVb5legJIICSzlg7I3mF6BgggJJO+npA2YaPpISCBkAx6sdlIHhwFBCEZtHGx6QkghZAMebvI9ASQREhGrPpx2n+bngGSCMmAnYXNRn9gegiIIiT9drXr8TfTM0AYIekXWVxqegRIIyS9Vr5tegJ4gpB02vHzJlebngGeICR9yh9s13OB6SHgDULSZ1zru3hwFFSEpM/HvAYbXISkxfZ5vAFxsBGSBmX3HNGXb+qCjZC8t+S4NrfvNz0EvEVInnsofdo20zPAa4TkuV28YV0IaA5pzYzRfbJb5OYXrnZdFpSQyh5dZ3oE6KE1pMj0NKUyu3TOVEoVlLssDEhIz/U6YpnpGaCH1pDuVQPm74wd7F6Yr2a7LAxESJ+dkT51q+khoInWkAZ3q9lDq2xwnstC/4akav5w1qaf8ZnJSaCV1pAyJxw8Lsx2WRiEkJzPDY4B3bSGNKR7zeY/ZUMC+S9S9MGf989pwkJaQ5qjBiyIP0YqXjhCzXJZSEjwGf3P2mV16Zod/XKbXOay0K8hKUVJIaX9daRRvTIzcvMKV33394p31PgDIcFf7DmzYXUjVcue+j/APtXDm54D+tkTkvPp8hqPKj+eLH3wrwHTk0A7vSFtndS11RlVb3d9itutvElI8BetIW1tr5q3Us1fih0HKKSdz8dPd6r9nanpkaCb1pCuVNeXlj2XkbPZCU5IRQ+f3vRITk0NPa0h9RwY+4HrRWqKE5iQ9ma0m/qS2zP5CAetITUtiF+NT/s0CCGtnbs+erm6wvQcsIHWkNqfGL9alzm01O8hfXHH0EbHLDc9BayhNaSJak78+gH10wp/hzRL9bieHzXCQVpD+rKN6hB7fBQZr3I7+jWkD1+LXmz40PQYsIve15FWXZjRNXZdObOt61PE1oa0/Fe91Lmmh4CFdJ/ZENlddb3vzcddVlka0p7ejYbMcn+3CYSURacI1WJfSBXvlET/Fljwtek5YClCSkL5kiuOSl9qegrYjJDqN69tk1Mf4m1M4IaQXJVsiF68/ecdpueA7QgpsT3zz890e2cJoAYhJbLv/BZZFz69t/6FACHVLZbP/uuf3Wd0CPgJIX3HlgfGpL9s7u7hS4R0iNdOatz+qlfZXw8NQ0iHePKaf1Saum/4FyFVW/X7QSdrv1MEBSHFld/WX/X59Uq9d4oAIaS4XWNu+UTvPSJYwh5S5J1r+72j6b4QYOEOaeXPOqcNv6tEy30h0MId0q2j71uv5Y4QdCENqXTx5dO8vQeESyhDWjSxdbMzF3l5DwibMIa0NefsJ3Z5ePsIoXCFtPvJ8bxWBC+EKKTiP/2oWavxm+VvGAhTSNPaTllk/secEEyhCGnDnAeil+XlkrcJ1Bb8kL76Q15ap7vFbg6oS+BD2pjebcbb/HgRPBbkkP55V1H0kvd0hAaBDWnFb/qq43l7YWgS1JCuUQNv/ZfILEASghdS5ZuvRy/Xfik3DVCvgIVU8drVHdI4HRXaBSukje3TT7l/k/AwQP0CE9L+pdEPKV+4zYtxgPoEI6SSZy7ObvGRR8MA9QtESHdlZJz3l2KvhgHq5/eQdm6MXrz+DG+7ALP8FtK39nAuevj0pud4PgxQP5+FpNTBkraNbdxu6ktlng8D1M/HIRXftLTC80mApPgrJKVqlwRYg5AAAb4KSSlKgp38FJJSlARLERIgwEchKUVJsJWPQgLsRUiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBNgZ0jIF+MyyBn+Za3jrkRXLEzht5ONWG8l8KbF+vtMSfWWuaPhXucn38Jk0yeCdJ4H5UhOq+QgpMeZLTajmI6TEmC81oZqPkBJjvtSEaj5CSoz5UhOq+QgpMeZLTajmI6TEmC81oZqPkBJjvtSEaj5CSoz5UhOq+QgpMeZLTajmMxnS1KkG7zwJzJeaUM1nMqQdOwzeeRKYLzWhmo/98gABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiDAREhrLu3QrNeVW2OHkTlDs4bcFzEwhIsd045tkXvp17FDK+eLqhxb9Ymzcb5dB3Z0+Idj53yOs3hkRu7N5bEjufkMhLSqjRpaMFC1j5U0XXUbf4yarn8IF/t6qkEFo9URGxw754uZrao+cTbOt1z1OSXmI8fO+ZwHVM5F/VX858zl5jMQ0kVqTvSvguvV5Oi/TWnf2+vsGZC2Rv8Uid2pboxe3qGmWTpf1LImVSFZOd9T6oXqQyvn29Wyy0andHijj0Tn0x9SJKND7Kq0eVfHmaHmRw/nqxnap3BxrtoSvdymhlk6n+MU9xjVI/6Js3K+mWpN9aGV8/1RPRi9fFrNEp1Pf0i7u06JXZW1auU4o9TO6OEONVr7FC4enhW7XKlGWDqf44xvs7Z3/BNn5XyXtnj93EEX/yX2wMPK+cbE/6Lced/LovMZe9buOXWG4/TKjB9n9jE1RQIV698cpe63dWAayuUAAAS2SURBVL556mmnKiQr5xveOP5cw/kRS+fr13LHlI6dL4k9BBacz1RIizKaL4/+P+gS/0WXbENTJDJXqbQ7IpbOtyqzwDkQkpXztVM3fVn0RI561NL52rYbpjqkqZzNovOZCWndBNX2xeh1Rtf4Lzu3MDJFYk/lNlf9Vts5X+mgXnuqQ7JxvsiS92NXz6p8O+dzmqiTNzo7f6QKROczEVLkocy0yZtjR7lVfxVk5hqYwl3pr9VYO+e7oUn0n/IDIdk43wGVzdtYOl+HRrGvva2NeorOZyCkyolqyKdVh3mqOHpZHPvLyxrla4piV5XZmXbOd2bNHvY7rZyvWocOdv75OQM7xq86ZojOZyCk36lppQcOC9XC6OVCdZ3+KRLan3ZK7CrSupOd813RO6aJ6t17t5XzPZ/7cOyqSI2x88/PmdI4Vk9Jk0Gi8+kPaV/b3Irq41Uqv8wpG6k+1z6Fi2Hp70YvH1EXWDpfXNW3djbOt6VZpw3Rv44uUn+zcz7nPXVFhRO5Rs0UnU9/SG+qjqdUif5ikjrx+iHqcu1DuFnauMlZU0eqnHWOnfPFVYVk5Xz3qewLJ3ZXE2MvJNk4n3OeOn7KIHXCfkdyPv0hPV7zPX70F2W3DcsaNqtc+xCu3vrhEc2OvSr+bIiV88UcCMnK+V76SbdWI/8UPxPUyvn2zxyWefwN+2KHcvPxYxSAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAELyl958wuzE58VfCMlSfF78hZAsxefFXwjJUnxe/IWQLMXnxScij+a36nnplnhIe355fMv+v9wTPap8cOgRGcfdXhY9LJv5/RadCr42PGZoEZJPTFJZP764Y7ec6CdsXz814PKBqu8+x7lOtR13UQf1C8cpz2921tXnZrT90vSgIUVI/vCi6vGV4xT/QEU/Yb9XkyqdyilqluO0zdoR/a85xzjO/Y0/jS7bcPTFpicNKULyh8lqYexqZSykIWpT9HCjGuY43dNejVQtGJHeO6Zle4NDhhkh+cNQVRS/PiL6CWtzVPzwyBzHeeMY1fvfntoV/VWOqtLU4JBhRkj+kH8gpNhjpDZHxw+PahW9KH/jppMbtXrOcVp3MjgdCMknLqv61u7z2Ld2g9Xm6OEmNST60GlF9Oif6V2i3+81jv3HyA0PGR0zvAjJH16JP9nwzamxkG5Vl1U6lZPUrY7TtXOp42zNiP4TdbsaFz18WN1jetKQIiSfmKiyxo3vNKxj9BNW0lcNLDhB9dvnODeobpeNa6Nudpz9/VWPKaer75eYHjSkCMknIg8Mz+591Z6qF2R/cVzL42fEXpAtm3VsyzZDH4k9c1dS2L9Fj2t3Gh4ztAgJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAwP8D8FKagk7+vuwAAAAASUVORK5CYII="
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#another way of generating the same graph is to specify the options directly into the main plotting function\n#in this case, the options are only in effect for this specific graph\nplot(dose, drugA, type = \"b\", lty = 2, pch = 17)",
"execution_count": 21,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "### Symbols and lines",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "plot(dose, drugA, type = \"b\", lty = 3, lwd = 3, pch = 15, cex = 2) #lwd = line width, cex = symbol size",
"execution_count": 23,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "### Colors",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#here's some sample code to explore colors in R\nn <- 10\nmycolors <- rainbow(n) #rainbow() is a function in R that lets you create vectors of contiguous colors\npie(rep(1, n), labels = mycolors, col = mycolors) # rep(1, n) creates a vector of the value \"1\" repeated \"n\" times\n # pie() creates a pie chart with \"n\" equal parts with each part having a diff color of the rainbow\nmygrays <- gray(0:n/n) # this produces a vector of 10 gray levels\npie(rep(1, n), labels = mygrays, col = mygrays)",
"execution_count": 27,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
},
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "### Text characteristics, graph and margin dimensions",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#example of a graph where text characteristics, graph and margin dimenions are customized\n#data\ndose <- c(20, 30, 40, 45, 60) #vector with dosage levels\ndrugA <- c(16, 20, 27, 40, 60) #vector with drugA dosage\ndrugB <- c(15, 18, 25, 31, 40) #vector with drugB dosage\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize settings\npar(pin = c(2, 3)) #plot dimensions (width, height) in inches\npar(lwd = 2, cex = 1.5) #lwd = line width, 2x normal size; cex = plotted text, 1.5x normal size\npar(cex.axis = 0.75, font.axis = 3) #cex.axis = magnification of axis text relative to cex; font.axis is set to italics\n\n#create the plots\nplot(dose, drugA, type = \"b\", pch = 19, lty = 2, col = \"red\") #type = line graph; pch = symbol; lty = line type; col = color\nplot(dose, drugB, type = \"b\", pch = 23, lty = 6, col = \"blue\", bg = \"green\") #type = line graph; pch = symbol; lty = line type;\n #col = color; bg = background color\n\n#restore orginal settings\npar(opar) #restore original settings",
"execution_count": 2,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
},
{
"data": {
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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Adding text, customized axes, and lengths",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#example\nplot(dose, drugA, type = \"b\", #add data to the plot and specify the type of plot (line plot)\n col = \"red\", lty = 2, pch = 2, lwd = 2, # col = color, lty = line type, pch = symbol, lwd = line width\n main = \"Clinical Trials for Drug A\", #main = main title of the graph\n sub = \"This is hypothetical data\", #sub = subtitle\n xlab = \"Dosage\", ylab = \"Drug Response\", #xlab, ylab = axis labels\n xlim = c(0, 60), ylim = c(0, 70)) #xlim, ylim = axis ranges",
"execution_count": 3,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {
"scrolled": true
}
},
{
"source": "#install Hmisc package to enable adding of minor tick marks\ninstall.packages(\"Hmisc\")",
"execution_count": 21,
"cell_type": "code",
"outputs": [
{
"name": "stderr",
"text": "Installing package into \u2018/gpfs/global_fs01/sym_shared/YPProdSpark/user/s17c-9f3318fc11f06c-d37a4b9405b6/R/libs\u2019\n(as \u2018lib\u2019 is unspecified)\nalso installing the dependencies \u2018evaluate\u2019, \u2018highr\u2019, \u2018markdown\u2019, \u2018backports\u2019, \u2018knitr\u2019, \u2018checkmate\u2019, \u2018survival\u2019, \u2018Formula\u2019, \u2018acepack\u2019, \u2018htmlTable\u2019, \u2018viridis\u2019\n\n",
"output_type": "stream"
}
],
"metadata": {}
},
{
"source": "#invoke the Hmisc library\nlibrary(Hmisc)",
"execution_count": 26,
"cell_type": "code",
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": "#another example of custom axes\n\n#specify the data to be plotted\nx <- c(1:10)\ny <- x\nz <- 10/x\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#increase the margins of the graph\npar(mar = c(5, 4, 4, 8) + 0.1) #mar is a numerical vector indicating margin size,\n #where c(bottom, left, top, right) is expressed in lines.\n #the default is c(5, 4, 4, 2) + 0.1\n\n#create the plot\nplot(x, y, type = \"b\", #data, type = line graph\n pch = 21, col = \"red\", #pch = symbol, col = color\n yaxt = \"n\", lty = 3, ann = F) #yaxt = \"n\" suppresses the y-axis; lty = type of line; ann = F removes default titles and labels\n\n#add a line to this plot, plotting x and z\n#you use a lines() function here to add the line to the existing plot; if you used plot(), if would create a new plot\nlines(x, z, type = \"b\", pch = 22, col = \"blue\", lty = 2) #type = line graph; pch = symbol; col = color; lty = type of line\n\n#draw the axes\naxis(2, #this integer indicates the side of the graph on which to draw the axis (1 = bottom, 2 = left, 3 = top, 4 = right)\n at = x, #numeric vector indicating where tick marks should be drawn\n labels = x, #character vector of labels to placed at the tick marks (if NULL, \"at\" values are used)\n col.axis = \"red\", #line and tick mark color\n las = 2) #specifies that the labels are parallel (= 0) or perpendicular (= 2) to the axis\n\naxis(4, #this integer indicates the side of the graph on which to draw the axis (1 = bottom, 2 = left, 3 = top, 4 = right)\n at = z, #numeric vector indicating where tick marks should be drawn\n labels = round(z, digits = 2), #character vector of labels to placed at the tick marks; rounded off to 2 decimal places\n col.axis = \"blue\", #line and tick mark color\n las = 2, #specifies that the labels are parallel (= 0) or perpendicular (= 2) to the axis\n cex.axis = 0.7, #cex.axis = text size of axis text, 0.7x normal size\n tck = -0.01) #tck = length of each tick mark as a function of the plotting region\n #a negative number is outside the graph, a positive number is inside\n #0 suppresses ticks, and 1 creates gridlines. The default value is -0.01\n\n#add titles and text\nmtext(\"y = 10/x\", #mtext() function is used to add text to the margins of the plot\n side = 4, #this integer indicates the side of the graph on which to draw the axis (1 = bottom, 2 = left, 3 = top, 4 = right)\n line = 3, #on which margin line; starting at 0 and counting outwards\n cex.lab = 1, #cex.lab = text size of axis label, 1x normal size\n las = 2, #specifies that the labels are parallel (= 0) or perpendicular (= 2) to the axis\n col = \"blue\") #col = color\n\ntitle(\"An Example of Creative Axes\", #title of the plot\n xlab = \"X values\", #xlab = x-axis label\n ylab = \"Y = X\") #ylab = y-axis label\n\n#add minor tick marks\nminor.tick(nx = 2, ny = 3, tick.ratio = 0.5) #nx, ny = number of intervals into which to divide the area between the major tick marks\n #on the x-axis and y-axis respectively. tick.ratio is the size of the minor tick mark\n #relative to the major tick mark\n\n\n\n#restore orginal settings\npar(opar) #restore original settings",
"execution_count": 29,
"cell_type": "code",
"outputs": [
{
"data": {
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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Adding reference lines and legends",
"cell_type": "markdown",
"metadata": {
"collapsed": true
}
},
{
"source": "#example\n#data\ndose <- c(20, 30, 40, 45, 60) #vector with dosage levels\ndrugA <- c(16, 20, 27, 40, 60) #vector with drugA dosage\ndrugB <- c(15, 18, 25, 31, 40) #vector with drugB dosage\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize settings\npar(lwd = 2, cex = 1.5, font.lab = 2) #lwd = line width 2x normal size; cex = plotted text, 1.5x normal size\n #font.lab = font of the label, 2x normal size\n\n#generating the graph\nplot(dose, drugA, type = \"b\",#data, type = line graph\n pch = 15, lty = 1, col = \"red\", ylim = c(0, 60), #pch = symbol, lty = line type (1 = continuous), col = color, ylim = y-axis limits\n main = \"Drug A vs. Drug B\", #main title of the graph\n xlab = \"Drug Dosage\", ylab = \"Drug Response\") #xlab, ylab = axis labels\n\n#adding another line to the same graph\nlines(dose, drugB, type = \"b\", #data, type = line graph\n pch = 17, lty = 2, col = \"blue\") #pch = symbol, lty = line type (2 = dashed), col = color\n\n#adding a reference line to the graph\nabline(h = c(30), lwd = 1.5, lty = 2, col = \"gray\") #adds a reference line at y-axis = 30; lwd = 1.5x normal\n #lty = line type (2 = dashed), col = color\n\n#add minor tick marks\nminor.tick(nx = 3, ny = 3, tick.ratio = 0.5) #nx, ny = number of intervals into which to divide the area between the major tick marks\n #on the x-axis and y-axis respectively. tick.ratio is the size of the minor tick mark\n #relative to the major tick mark\n\n#add a legend\nlegend(\"topleft\", inset = 0.05, title = \"Drug Type\", c(\"A\", \"B\"), #topleft specifies where to place the legend\n #inset = amount to move the legend into the graph as a fraction of the plot region\n #title = character string for the legend title\n #c(\"A\", \"B\") = character vector with the labels for the legend\n lty = c(1, 2), pch = c(15, 17), col = c(\"red\", \"blue\")) #lty = vector of line styles 1 and 2; pch = vector of symbols\n #col = vector of colors\n\n#restore orginal settings\npar(opar) #restore original settings",
"execution_count": 36,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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wsFBsR7k/H6m7V09LvjlyfDJE5Orq+vjxY1nPAqA+cI8dgBqpvr/HyMjI3Ny8YcOG3bt3Hzhw4MiRI6u6zCd5qE/+Sc/MzNy+ffudO3cSEhJSU1OtrKw6d+48ZcqUL774gsfjiQ05Jvlzy5zi/PnzBw4cuHXrVnp6uqmpqbOz86xZs7y9vZlx5mSqh1FRUWFra5uamipcs2rVqmXLln1yxy5dusTExAhfTp48WTKlfVJeXl7Dhg1FbwL77rvvxLoDJRUVFZ05c+bixYvJyclv3rx5/fq1jo5Os2bNbG1t27dvP2HCBLGnQRmKvcdOjBZ+c6p6O5L09fVNTU3t7Ow6d+7s5eU1cOBAqZfgATQFgh0AKF1hYaGJiYnoGvzNAzWBbw6ArPDvEgBQOrGxZ+vVq8dWJaBZ8M0BkBWCHQDUio2Njei8XsOHD5dsc/78edGXLVq0UFV1oL7wzQFQBgQ7AKiVrl27ir68fPnyw4cPRde8evVq3bp1omtGjhypispAveGbA6AMCHYAUCtffvml6P3p5eXlbm5uJ06cyMnJSU5O3r9/f+fOnfPz84UNLCwspA78C9oG3xwAZcDDEwBQWz/99NPSpUtr0rJOnTphYWFyzAMBnIRvDoDCoccOAGpryZIle/bskTrvgqgePXrcuXMHv80ghG8OgMKhxw4AFKOgoCAoKOjKlSt//fVXZmZmfn6+kZFR/fr1HRwcunTp4uXl1aVLlxpOzABaBd8cAAVCsAMAAADgCFyKBQAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjkCwAwAAAOAIBDsAAAAAjtBjuwCQx4sXL9asWZOfn892IQAAABrD1NT0u+++s7e3Z7sQJUKw00g//fTT3r172a4CAABAw+jo6OzevZvtKpQIwU4jFRUVEZG+vr6trS3btQAAAGiAlJSUsrIy5geUwxDsNFiLFi2ePHnCdhUAAAAawMHBITExke0qlA4PTwAAAABwBHrsiIhev3594sSJmJiYuLi4tLS0goKCgoICIyMjExMTGxub9u3bd+vWzcPDo2nTpmxXCgAAAFAlbQ92L168+Oqrr0JDQyU3FRUVFRUVZWRkxMbGBgYGzp07d+zYsWvXruX20zQAAACgubT6Uuy1a9c6dOgglup0dXWtrKzs7e0tLS11dXVFNx07dqxLly43btxQbZkAAAAANaK9PXbp6emjRo1ino5p27btqFGj3N3dHR0dzczMeDwe00YgEOTn5ycmJoaHh4eFhUVHR+fk5IwZM+bhw4eNGjVitXwAAAAAcdrbY/frr7/m5uYS0apVqx49erR27dpu3brVq1dPmOqIiMfjmZmZdenSZcWKFVFRUQcOHNDR0cnMzNyyZQt7hQMAAABIp73BLiwsjIg8PDyWLVsmdslVKh6PN2nSpIULFxKR1HvyAAAAANilvcHu1atXRDR8+HCZ9nJzcyOipKQkZZQEAAAAUBvaG+z09fWJqKysTKa9KioqhPsCAAAAqBXtDXZt2rQhoqNHj/L5/JrvdeTIESJq2bKlssoCAAAAkJf2BjsvLy8iunnzpp+fX35+/ifbl5aWLl++fN++fUTk6emp9PoAAAC0Fo9Xo/9AgvYOd+Lv779jx46kpKT9+/eHhIQMHjx4xIgRn332mbm5eYMGDUxNTQsKCrKzs3Nycp48eRIeHn7hwoXs7Gwisre39/f3Z7t8AAAAAHHaG+zq1q178eLFQYMGpaSkFBQUnDx58uTJk5/cq2XLlmfPnjU1NVVBhQAAAAAy0d5LsUTk4OAQFxe3ePHiBg0afLJx/fr1ly9fHhMT07ZtWxXUBgAAACAr7e2xY5iamq5bt+7HH3+8du1adHT0o0ePUlNTCwoKiouLLS0trf/h4uLSu3dvPAwLAAAA6kzbgx3DwMBgyJAhQ4YMYeXsL1++ZO7ek/T27VtmegwxzCB8AAAAAKIQ7FgmEAi6dOlSVbCr3rt37xReDwAAAGguBDsiory8vKSkpA4dOoiujImJOXDgwN9//52Xl9ekSZP+/ftPmTLF0tJSsafm8XivX78uLi6WurW4uFjqpnnz5p07d05PD//7AAAA4F/angxevHgxf/78S5cujR8/fv/+/czK8vLyRYsWbd26VdjswYMH58+fX7169W+//TZ27FjF1mBsbGxsbCzTLnXr1lVsDQAAAMABWh3sLly4MG7cOMmb2FatWiWa6oRyc3PHjRvH4/G8vb1VUiAAAACADLR3uJPs7OyJEycyqc7JyWnEiBHM+qdPn65bt46IzMzMtmzZEh8fn5eXd+vWrSlTpjANZsyYId8tcQAAAABKpb09duvWrcvKyiKiH374YdmyZTo6lRl306ZNpaWlRkZG9+7dY+aTJaJu3bp169bN2dl54cKFeXl5//vf/5YuXcpa6UpWXl4eFxdXXl7OdiGgCrq6uu3bt8dQPgAA3KC9we7mzZtE1K9fv+XLl/NE5pt78OABES1YsECY6oTmz59/+PDhmJiY8PBwDge7mTNnMlPigpaYMGHC4cOH2a4CAAAUQHsvxT5+/JiI3N3deR/PIhwXF0dEPXv2lNxFR0eHGevuyZMnKqmRHbGxsWyXACr1999/s10CAMDHBIIa/QcStLfHjiE562tpaSkRNW7cWGr75s2bE5HUQYM5pnv37j/88APbVYByrV69OjIyku0qAABAYbQ32NnZ2T148OCvv/6SXP/48ePnz5937txZcq/ExEQisrKyUkWJrLKysho4cCDbVYBy7d69m+0SAECL5ecTn0/16rFdB6do76VYd3d3Ijp06NCzZ89E1w8bNoyILl68KLlLWVlZeHg4ETk5OamkRgAAAI4KCSE7O7K1peRktkvhFO0Ndn5+fkZGRgUFBQMGDIiMjBT8c6l+wYIFderU2bdv3+3bt0XbCwSCVatWMXfmYRw7AAAAOX34QLNnk5cX5eRQYSHl57NdEKdob7Br0aLFTz/9RETJycl9+/b97LPPNmzYcO3atfz8/G3btuno6PTr12/Dhg1Pnjx5//799evXR48evWrVKiJydnb29fVlu3wAAAANFBdHXbvSb78REVlYUGgotW/Pdk2cor332BFRQEBAUVHRsmXLBALB48ePv/nmG9GtFRUV33zzjdjK1q1bnzlzBpO0AgAAyOzgQZozhwoLiYj69KHDh6lpU7Zr4hrt7bEjIh6Pt3Tp0ujo6CFDhogNeiLJ2Nj466+/jo6ObtasmWrKAwAA4Ii8PBo/niZPpsJC0tWl77+niAikOmVAzxO5uLhcuHAhLS3t/PnzDx48YK69FhQU8Pl8ExMTc3Pzdu3ade3adcSIESYmJmwXCwAAoGnu3SMfH3r+nIioaVMKDKTevdmuibMQ7Co1btx46tSpbFcBAADAIQIBbdlC//0vlZYSEY0cSXv3koUF22VxGYIdAAAAKEFmJk2eTOfPExEZGtLPP9P8+fSpG5+glhDsAAAAQNGuXqWJEyk1lYioTRs6epQ6dWK7Jq2g1Q9PAAAAgIKVl9PKlTRoUGWq8/WlmBikOpVBjx0AAAAoSHIyjR9Pf/5JRGRiQjt2EEZ+VS0EOwAAAFCEU6fIz49ycoiIXFwoKIjatGG7Jq2DS7EAAABQO8XFFBBAo0dTTg7xeDR/Pt26hVTHCvTYAQAAQC3Ex9O4cfTgARGRpSXt3Uvu7mzXpL3QYwe1xauCoaFh8+bNR4wYsX///uLiYrbLlE10dHRV76sa5eXlbBcOAKBaBw+Sq2tlquvTh2JjkerYhWAHylJaWpqUlHTu3LmpU6c6OTndvHmT7YoAAEBxpM4S1qQJ22VpO1yKVV8CgcDa2jo9Pb2qBhUVFaqsp3qWlpaLFy8WvhQIBDk5OU+ePLl48WJhYeHTp08HDRp05syZQYMGsVhkzTVt2nT9+vWS67/55huSeLNCOjr4lxIAaAfMEqauEOzUF4/Hu3TpUkZGhuSm1atXR0ZG6urqqr6qqlhYWHz99deS63NychYtWsRcjfX29o6Pj2/cuLHqy5NV48aNpb4dJthV9WYBALgPs4SpNwQ7tebk5CR1/e7du1VcidzMzc337t2rp6f3+++/v3//fsmSJfv372e7KAAAkAtmCVN7uHIESsfj8X755ZeGDRsSUVBQ0Lt379iuCAAAZHf1Kjk7V6a6Nm3o9m0KCECqUzcIdqAKZmZmU6ZMIaLS0tKQkBDRTW3btuXxeG3btiWivLy8gIAAa2trHo9XUFBA/zxyy2yVxOzLk/bXSkxMzKRJk2xtbQ0NDe3s7Pz9/VNSUqrfpfauX7/OHHzr1q1SG/z3v/9lGsTHxzNr2rRpI6zn/fv3y5Yta9OmjZGRUePGjceMGXP9+vWqzpWWlvbdd9917NixXr16ZmZmnTp1WrBgwcuXL5XxvgBA22GWMM2BYAcqMnz4cGbhzp07Uhvk5+cPHjx4y5YtaWlptTzXL7/80rVr10OHDr1+/bq0tDQ5OXnHjh0uLi737t2r5ZGr16NHD+YOQrHwyuDz+UePHiUiV1fXdu3aMSsFAgGzkJKS8vnnn69Zs+bp06clJSXp6eknT57s06fPDz/8IHmoEydOtG7deu3atffv38/Ly8vPz4+Njd28eXOrVq22bdumrLcHANopOZn69qUffiA+n0xN6eBBOniQTEzYLgukwz12oCJ2dnbMwl9//SW1wbx58+7evWtoaNi5c2crKys9PTm/nIGBgcInG9zd3fv376+np3f9+vXjx49/8cUXSn2UWFdXd8yYMdu3b79x40ZaWprYYyJ//vkn02s4efJk4Uo+n88s+Pj4JCYmjhw5sl+/fjk5OeHh4dHR0US0cuXKVq1aTZgwQbjLhQsXvLy8BAIBj8cbN25ct27dDAwMoqKiDh06VFZWNm/ePB6P5+/vr7y3CQBa5ORJmj7931nCjh6l1q3ZrgmqJQAN5O3tTUQNGzZUxsE7depERB4eHjVsz3yRHBwcqm9WUlLCXHBs3Lix6HoHBwci0tfXJ6KxY8empaXV/ODMvqJf47y8vAYNGhARj8fbtWuXaOMjR46IPkdcw3cn5pNvNjIykmmzc+dOsU2zZ89m3mlmZqZwZfPmzZn2PB5v9+7dwvVlZWWzZs1iNjVu3LikpIRZX1RUZG1tTUQmJibXr18XPX5sbGzTpk2JyNTUNDU1tSZvh/kiOTo61qQxAGiXDx8E8+cLiAREAh5PMH++oLiY7ZpqpU2bNkQ0fvx4tgtRLlyKBRUxMDBo1KgREeUw//L7WFlZmaura2BgINNGbkFBQdnZ2UTk6ek5Y8YM0U0+Pj4+Pj61OXhNCK/GnjhxQnR9WVnZ8ePHiWjEiBGWlpbC9cIeO09Pz+nTpwvX6+npbd++vVWrVkSUlpZ24cIFZn1QUFBqaioR/fDDD7169RI9hbOzM/O4dH5+/t69e5Xw5gBAa8TH0+ef05YtRESWlnT6NG3eTIaGbJcFn4ZgB6pW1YMLCxcurP3IfH/++afwaJJb586dW8vjfxJzNZaIrl27Jvr876VLl7Kysujj67AkEuwWLFggeSjhypiYGGbh7NmzzALzMIqYoUOHMmXYCPcAACAASURBVF2AN27cqOUbAQDthVnCNBnusQMVKSsrY2bRqF+/vtQGPXr0qP1Znj17xix07NhRcquzs3PtT/FJ3t7e27dvr6ioOHPmzLRp05iVR44cISILC4thw4aJNhb8c3nXxcVF8lCff/45syB83JW58c7c3DwjI0Pq4NXNmzd/9eqV8KlbAAAZ5OXR7NkUFEREpKtLy5bR8uWkToPhwych2IGKvHnzhgkxzIB2kqpaLxMm2DVq1MjY2Fhyq5GRkZWVVWZmZu1PVA3mamxaWlpISAgT7AoLC0NDQ4lo/PjxBgYGoo2ZHrtGjRoZGRlJHkp4B54w2DG9gDk5OcLnaqXKz8+v/RsBAO2CWcI4AZdiQUWSkpKYhc6dO0ttIDXZVE/yEVfmBr5qhqmT+2HbmhNejb1y5cr79++J6MyZM0VFRSRxHZb+CXZVFcw8U0JEJSUlzEINn+pFsAMAGQgEtHkz9exZmepGjqTYWKQ6DYVgByoSHh7OLPznP/9RyAH5fP6bN2/EVjKPxKalpRUXF0vuUlJSUvtB8mqCedq0rKwsLCyM/rkO6+joKHm9lQl2VRX84sULZoF5ioL+eYM2NjbVPxVVyszhCADwSZmZ5OZGCxZQaSkZGtKmTRQairlfNReCHahCXl4eM0WsoaGhp6enrLsLb0QT9ebNmw8fPoit7NChA7Nw//59yV3i4uKkHkrhRJ+NzcrKYp5pnTx5smTPnPDhCanD+wlHVBYGO+YNVhUEiaioqKigoKCqrQAAH4mIwCxhHINgB0onEAgWLVrE3Ok/fvx4ps9JJq9fvxYGIKHffvtNsuWgQYOYhU2bNklu3cI8uq98wquxFy5c2LdvX3l5OY/HEx1kWEj4vjZv3iy2qaKi4pdffmGWhV19gwcPZvb6/fffJY/28uXL+vXrm5qaSh4NAOAjzCxhgwdjljCOQbAD5Xr//v20adP27NlDRObm5uvWrZNpd3t7eyIqKioKDAwUXX/37l2p0c3Pz69OnTpEFBwczPQRCgUHB4sdRKmYq7ElJSXff/89EQ0aNKhJkyaSzYQ9iMePHxcdfK68vHzevHmJiYlE1KRJEzc3N2b91KlT69atS0TffvutcGwXRn5+/qRJk8rKynR0dHx9fZXyrgCAGzBLGHfhqVhQjKysrA0bNghfCgSC3NzcJ0+enD9/vrCwkIiMjIyOHz8u66Ov/fv3Z+4zmz59+uPHjwcMGFBSUnLjxo2NGze2a9cuOzubmaRLyMLCYuPGjbNmzeLz+VOnTj19+nTfvn0NDAyuX79+9OjRVq1aFRUVvX37Vo4HNWQlfDa2qscmGEyPnbGxcUlJiZ+fX1hYWN++fXNycs6dO8eMbEJEW7duFT5FYWFhsXnz5unTpxcUFPTu3Xv48OHdu3dv1qxZYmLi77///vbtWyJauXKljY2Nst8gAGgqzBLGbUqe2QKUQg2nFKte69atb9y4IXV3yWnBRGVnZ7ds2VLygI6OjsnJyVL35fP5a9askbybrVmzZgkJCc2aNSMiOzs7WT4S8Tf7yfnTGMIJW01NTQsLC6W2MTExYQ64d+9eyfGZdXR0Nm/eLLnXpk2bpD7eq6Oj89VXX/H5/Bq+HUwpBqBdODdLmEwwpRhArejr69va2g4fPnzPnj0PHjzo2bOnHAcxNzePjY398ccfO3fuXK9ePRMTk88++2zVqlVRUVG2trYGBgaGhoaGH89yw+Pxli5dGhMTM2fOHBcXFyMjIzs7u4ULF8bExDg4ODDjwClkzLxP8vLyEi4wF4glCe+xmzp1alRUlLe3d6NGjfT19W1sbCZOnBgdHT1//nzJvQICAh4/fjx37lwHB4e6devWq1evc+fOfn5+cXFxGzZsqGa0FwDQXpglTDvgUizUlqB2z5kmJCRU38DExGT58uXLly+X3PSAmfFGmk6dOm3fvl1sZWFhIXNhVO5gJ9ObFc6KW9V1WBIJdkTk4uJy7NixGh68devWW7durXkxAKDVDh6kOXOosJCIqE8fCgwkaXf9Agegxw445cqVKx07duzYseOpU6ckt547d45ZsLOzU0Ex+/btI6LmzZtX01sp+bQvAIAi5eXR+PE0eTIVFpKuLn3/PUVEINVxGHrsgFOcnJyYEey2bt06cuRI0bvWCgoKNm7cyCyPHj1a2ZW8evWKGZN50qRJOjpV/guqlv2dAADVwSxh2gc9dsApDRs2ZB4IuHbt2pAhQyIjI3NyclJSUk6dOtW9e/c7d+4QUZ8+ffr166ekAt6/f5+Tk5OQkODn51deXm5oaDhr1qxq2qPHDgCUArOEaSv02AHX7N2798WLF9HR0REREREREWJb27dvf/jw4Wq60GopNDR06tSpwpfffvtt9SOPINgBgOJlZNCUKZXzSRga0s8/0/z5mE9CS6DHDrimbt26t27d2rNnj7Ozs/CBWR0dHVdX102bNt27d69p06bKO7uZmZmBgQERNWjQYMOGDStWrKi+fZs2bRwcHFq0aKG8kgBAu0REUMeOmCVMa6HHTq316tWLGXJWTHp6OqGzp2r6+vrTpk2bNm0an89PTU3l8/lWVlYqGJSYiEaPHp2fn5+ent6kSZOa9At+8qFgAICaKi+n1atp1Spifh18fWnHDswnoW0Q7NTaggULsrKyJNfv3r07Ojoaw5V9ko6OjtSJvJTKwMDA1tZWxScFAG2XnEzjxxMz06CpKe3YQRMnsl0TsADBTq0xE8lLioiIQLADAIBKmCUM/oF77AAAADRWcTEFBNCYMZSTQzwezZ9Pt24h1Wkz9NgBAABopvh4GjeOmDl4LC1p3z4aMYLtmoBl6LEDAADQQAcPUpculamuTx+KjUWqA0KwAwAA0DB5eeTjQ5MnU1ERZgkDMbgUCwAAoDkwSxhUCz12AAAAmgCzhEENoMcOAABA7WGWMKgZBDsAAAD1FhFBvr6UmkpE5OBAQUHUqRPbNYGawqVYAAAAdVVeTitX0uDBlanO15eio5HqoBrosSMiev369YkTJ2JiYuLi4tLS0goKCgoKCoyMjExMTGxsbNq3b9+tWzcPDw+lTh4PAADwkaQkGj+ebt0iwixhUFPaHuxevHjx1VdfhYaGSm4qKioqKirKyMiIjY0NDAycO3fu2LFj165da29vr/o6AQBAu2CWMJCLVl+KvXbtWocOHcRSna6urpWVlb29vaWlpa6uruimY8eOdenS5caNG6otEwAAtAlmCYNa0N4eu/T09FGjRhUVFRFR27ZtR40a5e7u7ujoaGZmxvvnOSOBQJCfn5+YmBgeHh4WFhYdHZ2TkzNmzJiHDx82atSI1fIBAICLMEsY1I729tj9+uuvubm5RLRq1apHjx6tXbu2W7du9erV44k8Pc7j8czMzLp06bJixYqoqKgDBw7o6OhkZmZu2bKFvcIBAICjRGcJ69sXs4SBHLQ32IWFhRGRh4fHsmXLxC65SsXj8SZNmrRw4UIiknpPHgAAgJwkZwm7cgWzhIEctDfYvXr1ioiGDx8u015ubm5ElJSUpIySAABAG927R5060dGjRERNm9LVq7RyJdWgxwFAkvYGO319fSIqKyuTaa+KigrhvgAAALXCzBLWowe9eEGEWcJAAbQ32LVp04aIjh49yufza77XkSNHiKhly5bKKgsAALRERgYNH04LFlBZGRka0qZNFBpKFhZslwWaTXuDnZeXFxHdvHnTz88vPz//k+1LS0uXL1++b98+IvL09FR6fQAAwGEREdSxI124QETk4EC3b1NAAOZ+hdrT3mDn7+9vZ2dHRPv377exsRkzZsy+ffvu3bv37Nmz7OzssrKynJyc58+fR0dHBwYGTpgwwdraevXq1URkb2/v7+/Pdvmg8QQCgb29PY/H4/F4urq6aWlpbFcEACqBWcJAmbR3HLu6detevHhx0KBBKSkpBQUFJ0+ePHny5Cf3atmy5dmzZ01NTVVQIXDbnTt3Xr58ySzz+fzg4OD58+ezWxIAKB1mCQMl094eOyJycHCIi4tbvHhxgwYNPtm4fv36y5cvj4mJadu2rQpqA84LDAwUfRkUFMRWJQCgIidPUqdOlanOxYViYpDqQOG0t8eOYWpqum7duh9//PHatWvR0dGPHj1KTU0tKCgoLi62tLS0/oeLi0vv3r3xMCwoSnl5eXBwMBHZ2NjY2dndvn37zp07L168wEzEANxUXEyLFxMzuD2PR/Pm0f/9Hxkasl0WcJC2BzuGgYHBkCFDhgwZovpTCwSCefPmZWRkSN36999/FxcXS67Pysqif8Ze0Qo1vKFYIFByHQpz+fLlzMxMIho/fnyzZs1u375NREePHl26dCnbpQGAosXH09ix9PAhEWYJA6VDsGMZj8dr0KBBVcPpmZmZSe0mzMvL+/Dhg46OVl9J12jC67ATJ060srIKCAgQCARHjhxBsAPgmoMH6csvqaiIiKhvXzp8GPNJgFIh2En39u3b/fv3X79+PTU11cTEpGXLlsOHDx8zZowyrsb++OOPsu4yduzY4OBgHh6M10yFhYXMrHSfffaZk5MTj8fr3bt3ZGRkXFzcw4cPO3TowHaBAKAIeXk0a1blfBK6urRsGS1fjvkkQNm0N9gxY9ENGDDgyy+/FF0vEAi2bt26ePFi0Wugt27dOnToUKtWrQIDA7t27arqWoFbzpw5U1hYSES+vr5MOvf29o6MjCSiI0eO/PTTTyzXBwC1d+8ejRtXOZ+ErS0dPoz5JEA1tPda3okTJ06cOBETEyO2/tdffw0ICJB6Z9uzZ8969+59i3mgCUBezHVYHo/n4+PDrBk9ejRzYT0oKEigOXcKAoAUYrOEeXjQ338j1YHKaG+PnVTPnz9fvHgxszx16tTJkyc7Ojry+fy4uLht27adOnWqpKTE29v76dOnxsbG7JaqMT58IGkpWSlycmp7BCMjUvL/2Xfv3l28eJGI+vbta2try6xs3Lhxnz59rl27lpSUdPv27e7duyu1BgBQlowMmjy5cj4JQ0P6+WeaPx/zSYAqIdh9ZOvWrcyjpiEhIWPGjBGub9SoUf/+/f/v//5v8eLFb9682bNnz9y5c9krU3MEBZGvL6ns6d0ajEf4Cbq6FBhIY8cqohrpgoODy8vLiWjix+NXeXt7X7t2jYiCgoIQ7AA0UkQE+fpWzifh4EBHj1LHjmzXBFpHey/FSnX58mUiGjdunGiqE/rqq6+cnZ2J6PTp06quTEM9eKC6VKcQFRX04IFSz3DkyBEiMjQ0FPuOCa/GCpMfAGgMqbOEIdUBG9Bj95GkpCQicnd3l7pVV1d38ODB9+/ff8gMRwSftHw5tWihgCukS5bUqNm6dbU9kbm5UgeCf/Xq1Z9//klEI0eOrFevnuimhg0b9uvXLyIiIiMj4+rVq4MHD1ZeGQCgSJglDNQJgt1HmEcUW7duXVWDNm3aEFF2drbqatJoderQzJkKOE4Ng90/90eqLaa7joiOHz9ezWg1QUFBCHYAmuHkSZo+vfKfry4udPQoVf0LAqACuBT7EWZCp+Tk5KoaMJGuJnPLAohhhiCuScuTJ09KfS4bANRIcTEFBNCYMZSTQzwezZ9Pt24h1QHrEOw+0rt3byK6e/duVQ3++OMP+qffDkAmDx48iIuLI6Jhw4bFV2HEiBFElJeXFx4ezna9AFC1+Hjq2rVy7ldLSzpzhjZvxtyvoA60/VLsxYsXZ86c2eYf8+fP/9///rdlyxZPT0/JgYjPnDlz/vx5InJzc2OjWNBswmnEJk2a1LZtW6ltJk6cePbsWSI6cuTI6NGjVVccANQcZgkDNabtwe7169e7d+8WvmQeSywvLx81alRSUpKeXuXnk5KSsmPHjvXr1xNR/fr1p0+fzkq1oLn4fH5QUBARGRsbj6h6/u/hw4cbGRkVFxefPXs2Ly/PzMxMhTUCwKdgljBQe9p7KXbdunV+fn69e/e2trYWruTz+Xw+n4jevn0rOuTE1KlT161bV1FRwePxdu7caWFhwULF2kwgqNF/auzGjRuvX78mIjc3NxMTk6qamZqaDh06lIhKSkpOnTqluvoA4JOioqhTp8pUZ2tL167RypVIdaButLfHbrHIE5QFBQXPnj17+jHJXaytrbdu3Sp1iDuA6gmvw4791OjHnp6eoaGhRBQUFDR58mSlVwYAnyQQ0JYt9M03VFZGROThQXv2EP6FD2pJe4OdKBMTk44dO3asejBJb2/vgICAAQMG1KlTR5WFATeUlJSEhIQQUd26dYcPH1594xEjRhgYGJSWll65ciUjI6Nhw4YqqREAqoBZwkCjaO+lWJnMnDnT3d0dqQ7kc+HChZycHCKqybeoXr16zCB2FRUVx48fV0V9AFCViAjq2LEy1Tk40J07FBCAVAfqDD12AErn4eEhkOUWwLCwMOUVAwA1Ul5Oq1fTqlXE5xMR+frSjh1U9Q2yAGoCwQ4AAOBjmCUMNBaCHQAAgIgTJ2jGjMpZwjp3pqAgzCcBGgT32AEAABAR0YcPFBBAnp6YJQw0F3rsAAAAiB4/pnHj6OFDIiJLS9q3j6oeSxxAbaHHDgAAtN7Bg+TqWpnq+val2FikOtBQCHYAAKDF8vLIx4cmT6aiItLVpe+/pytXMPcraC5cigUAAG0VFUU+PvTiBRGRrS0FBlKvXmzXBFArCHZqbffu3dnZ2ZLrExISiEimodEAAOBfkrOE7d1LDRqwXRZAbSHYqS+BQHD58mVmxgIx6enphGAHACAfzBIG3IVgp754PF5wcLDUTWPHjg0ODtbRwS2SAAAyunKFJk2i1FQiIgcHOnqUqp4oHEDjIBkAAIB2KC+nlStpyJDKVOfrS9HRSHXAMeixAwAALYBZwkA7INgBAADXYZYw0Bq4FAsAANyFWcJAy6DHDgAAOAqzhIH2QY8dAABwEWYJA62EYAegIm3btuVVoU6dOk5OTqNGjdq1a1dxcTHblQJouNxczBIGWgvBDjQSx0YS/fDhw8OHD0NDQ2fNmtWtW7fXr1+zXRGAxoqKIhcXOnqUiMjWlq5do5UrSVeX7bIAVAT32IHm0fRUt379etGX5eXlmZmZCQkJ58+fFwgEsbGxbm5u0dHR+vr6bFUIoJEwSxgAgh1oLh6PNHROta+//lrq+vv37/fp0yc3N/fBgweBgYFTpkxRbV0AmgyzhAEQES7FgsYR/YuaY39pOzs7b9y4kVm+xQyjCgA1ceUKdexYmeocHOjOHQoI4NpfEAA1g2AHoEb69evHLMTGxrJbCYBmwCxhAB/DpVjQJJL/AtfcC7JSmZmZMQsmJibsVgKgAcRmCdu5kyZMYLsmAJYh2AGokb/++otZcHFxYbcSAHUnNkvY0aPUqhXbNQGwD5diQWNUdcMMZ26k+fDhwzfffENEhoaGc+bMYbscAHUldZYwpDoAIkKPHWiK6tObZl2QTUhIEH1ZUVHBDHeycePGxMREPT29Q4cO2dvbs1UegFoTmyVs/35yc2O7JgA1gmAHSnTmDI0fT4WFVTbQ06NvvqG1a+XZV0nnVYF27dpVtcnIyOjSpUu9evVSZT0AGuPgQfrySyoqIiLq25cOH8Z8EgBicCkWlOjGjU8ks/JyunxZzn3FiHbp1ea87CouLnZzc1u1ahWfz2e7FgB1kptL48ZhljCAT0KPHSjRd9+RhQW9f19lAwMDGj/+0/v+/LPqzqsCAonLxgKB4N27d/Hx8StWrIiMjFyxYkV8fHxgYCCPM/cPAtRGVBT5+NCLF0REtrYUGEjo1QaoQm2DXWpq6l9//fXu3bv8/Py5c+fy+fzc3Fxzc3OFFAearn59WrKktvvWPNsI77SrzXlZwePxrKysrKysrl692qdPn5s3bwYFBY0fP37EiBFslwbAKswSBiAjOS/FCgSC06dPOzk52djYjBgxYsqUKfPmzSMiPp/fvHnzH3744cOHDwqtE0Ar6Ojo/Pjjj8zy4cOH2S0GgGUZGTR8OC1YQGVlZGREmzbRqVNIdQDVk6fHrqKiYsKECceOHZO6NS8vb+XKlXfu3AkNDTU0NKxdeaDtZL0UqVmPx0rVqVMnZuH58+fsVgLApitXyNeX0tKIiBwc6OhRzCcBUBPy9NgFBARUleqELly4MHv2bLlKAqgk3w1mmn5bWlZWFrPQAD0ToJ2Es4QxqQ6zhAHIQuYeu8ePH2/fvl340sDAgM/nl5eXi64pLS0logMHDixdurR169YKKVQ7RUZGZmRkSK5PSUlRfTGqp+l9b/I5e/Yss9ARv2SghZKSyMeHbt8mwixhAPKQOdht3bpVuLxt27YZM2Y4OTk9efKk8nB6elFRUX369MnNzRUIBJs3b962bZvCitUyAoFg9uzZ6enpkpsKCwuJqKKiQuVFgXLduXNn+fLlzLKHhwe7xQCo2okTNH165QPtmCUMQC4yB7u7d+8yC35+fv7+/pINnJ2dv/rqqxUrVpDIxJcgBx6PFx8fL3XT2LFjg4ODdXV1VVwSKMSGDRvE1ggEgqysrMePH589e5YZDMXf37979+5sVAfAhg8faMkS2rKFiIjHo3nzaP16MjBguywAzSNzsHvBjCREVM1ADD169GAWnj17Jl9ZABzGTAhbjWHDhq1bt041xQCwD7OEASiOzMGujBlMiKhu3bpVtcnJyWEW8vPz5SsLQNvUqVPHwcGhbdu2o0aNGjNmjI4OZoUB7SA6S1i/fnT4MNnYsF0TgAaTOdhZW1szozBcuXJl0KBBUttcvHiRWWjcuHFtigPgkoSEBLZLAFAnubk0axYxYyzo6dF339Hy5YQ7TABqR+ZegX79+jELGzdu3L59u9hAxOXl5Tt37ty9ezfzEnOZAwCAFFFR5OJSmepsbenqVVq5EqkOoPZkDnYzZ85kFsrKyubOnWtubi68687JycnCwmLOnDnCxpMnT1ZIlQAAwBECAW3eTD17Vs796uFBsbGY+xVAUWQOdq6urqIPw5aUlAjvunv48GFeXp5w05gxYwYMGFD7EgEAgCMyMmjYMMwSBqA88kwptmnTptLSUuH1VqmGDh164MABeasCAADOwSxhAMonz5N3enp6u3btunjx4sCBAyWHUuvQocOePXvOnTtXzWOzAACgRTBLGICqyNNjxxg8ePDgwYMLCgru37+fmZlZUlLSoEGDzz77zNraWoH1AQCAZsMsYQAqJH+wY5iYmAiHI9Zcr1+/PnHiRExMTFxcXFpaWkFBQUFBgZGRkYmJiY2NTfv27bt16+bh4dG0aVO2KwUA0CiYJQxAtWob7DTdixcvvvrqq9DQUMlNRUVFRUVFGRkZsbGxgYGBc+fOHTt27Nq1a+3t7VVfJwCAhsEsYQBskHN0+6dPn06cONHa2joyMpJZk5iY6OHh0aBBg+bNm/v6+j548EBxRSrLtWvXOnToIJbqdHV1rays7O3tLS0txe4gPHbsWJcuXW7cuKHaMgEANM3jx/T555WpztKSwsJo82akOgAVkKfH7tatW4MGDSoqKiIiZsLyzMzM/v37v3nzhohycnKSkpJOnToVGBjo4eGh2HIVKD09fdSoUcy7YOZxcnd3d3R0NDMz4/F4TBuBQJCfn5+YmBgeHh4WFhYdHZ2TkzNmzJiHDx82atSI1fIBANTVzp20aBEVFxMR9e9Phw5hljAAlZG5x47P50+dOpXJQ0I7duxgUp1QYWHhpEmTcnNza1ug0vz6669MeatWrXr06NHatWu7detWr149YaojIh6PZ2Zm1qVLlxUrVkRFRR04cEBHRyczM3ML889QAAAQc/06zZlDxcWkp0erV9Ply0h1AKokc7ALDQ1NTEwUW3nkyBHJlnl5eTt37pSzLuULCwsjIg8Pj2XLlkkO2iKJx+NNmjRp4cKFRCT1njwAAKCmTcncnFq0oD/+oO++Ix05b/gBECPS6wLVkfmPnDDD1a9fPzIysnfv3ikpKcKo9+2337548UL49OiVK1cUVajCvXr1ioiGDx8u015ubm5ElJSUpIySAAA0nr09paXR06ek+QMmAGgimYPdy5cvmQU/P7/evXvr6OgI05uBgcGSJUtatGgxcuRIZk1ycrKiClU4fX19IhLOh1ZDFRUVwn0BAEAKAwOqwWUQgJpjuuvQaVcTMgc7YVZzdnZmFiIiIpiFPn36mJmZEZGjoyOz5vXr1wqoUTnatGlDREePHuXz+TXfi+mwbNmypbLKAgAAgCog232SzMGusLCQWdDT0yMigUAgDHbDhg1jFho2bMgs1KlTRwE1KoeXlxcR3bx508/PLz8//5PtS0tLly9fvm/fPiLy9PRUen0AACzi8Wr0H4Dy4YsmE5mDna2tLbPw8OFDInrw4EEaM/cf0dChQ5kF4Rp1nqrB39/fzs6OiPbv329jYzNmzJh9+/bdu3fv2bNn2dnZZWVlOTk5z58/j46ODgwMnDBhgrW19erVq4nI3t7e39+f7fIBAAC4TzLVIedVT+Zx7Ozs7JhHJU6dOuXv779x40ZmfbNmzdq2bUtEAoHg7NmzzMomTZoorlQFq1u37sWLFwcNGpSSklJQUHDy5MmTJ09+cq+WLVuePXvW1NRUBRUCAAAAyETmHrshQ4YwCwkJCU2bNj1w4ADz0s3NjcfjZWdnT5gw4dKlS8xKd3d3RRWqDA4ODnFxcYsXL27QoMEnG9evX3/58uUxMTFMfgUAAAClqqpzDp121ZC5x27mzJmrVq2SHHnYx8eHiN6+fRsUFMSsqV+/vq+vb+1LVCpTU9N169b9+OOP165di46OfvToUWpqakFBQXFxsaWlpfU/XFxcevfujYdhAQAAQJ3JHOxMTU337NkzYcKEkpIS4coePXr0kBiyaP369XXr1q1tgSphYGAwZMgQYWekimVkZBQUFEjdlJWVJTbJh3AXJRcFAADApuq75Xg8EghUVYpGkWeu2DFjxlhbW//666+3bt3i8/lDhgxZv369zj/Di9etW7d7c0TS1AAAIABJREFU9+7z588fMWKEQkvlJoFA0K5du+zsbDn2LS0tVXg9AAAArKvJxVZkO6nkCXZE1L179+7du0uud3BweP/+PTMSCjckJCS0a9eOiATK+frweLzExMSqJtV9//691KFYVq5c+ccffxgYGCijJAAAANBQCk5guAtNDhYWFhYWFjLtIhwpEAAAgGNq/mwEOu0kYXpmAAAAAI6Qs8fuypUrmzdvTkhIePnyJTN9alWUdAWz9mo4aonwGRGx9gkJCYqvCQAAgOsEAsrMpKquPMk6lAk67cTIE+zmzp27fft2hZeiYk+ePFFqe00XHh5ek+H9QKMJZwgEAFA2gYDu3qWQEDpxgl69onXraPFixRwZ2U6UzMHu7NmzHEh1RDRt2rS9e/eyXYU6MjMzIyJmUjW2awFVYP6PAwAoA59Pd+5QSAiFhFBKyr/rX76U3h4RrZZkDnbbtm1TRh2qt2fPnsGDB8+cOTMvL8/S0nLv3r1S58lQ9lOxamj79u27du0qLi5muxBQBUNDQz8/P7arAPWjNX/jgZIIBHTzZmX/3Js3/67X0aFevcjTk6ZNY684TpM52MXHxwuX586du2jRombNmunq6iq0KhUZO3bs559/Pn78+Nu3b48cOXLOnDnr16+vU6cO23WxrH379ps3b2a7CgAA0GCzZ9OuXf++1NWl3r3J05NGj6bGjdkrSwvI/FRseno6s9C5c+ctW7a0aNFCQ1Mdo3nz5tevX1+2bBmPx9uxY0eXLl1iY2PZLgoAAECzMVdd9fRo4ED63//o7Vu6epXmzEGqUzqZg521tTWz0KlTJx4npuHV09NbtWrV1atXmzRpEh8f//nnn2/cuJHP57NdFwAAgKYKDKTTpyk1lS5fplmzqnwGFhRO5mDn5ubGLHBsutK+ffvev3//iy++KC0tXbRo0dChQ1NTU9kuCgAAQO2UldHFizR9OllZkasriUwd/y9zcxo5kiwtVV6c1pM52C1ZsoSZJiEiIuKN6P2Qms/CwuLkyZM7d+40MjK6fPmyk5PTmTNn2C4KAABALZSWUng4TZtGjRvT0KG0Zw+9e0d//01yzXYOyiJzsGvatOmlS5eaNm1aWFg4bNiwyMjIoqIiZVTGCh6PN3v27Ojo6M8+++zdu3ceHh5ff/0120UBAACwpqSEwsJo8mRq1Ijc3GjfvsokZ2REI0dSWBj9c4sWqAWZn4oVva/u4cOHffv2rb69Jo4S0r59+6ioqK+//nrHjh3nzp1juxwAACXLzaW+fSkri+7cIRsbtqsBNbJnD331FeXm/rvG2JiGDiVPTxoxgjAIphqSc0oxzjM2Nt6+ffvgwYNnzZr1/v17tssBAFCmuXOJGRDg+XMEOxC1b19lqqtTh4YPJ09PcnMjExO2y4KqIdhVx8PDw8PDg+0qAACUKTiYDh8mInJzo1692K4G1MvGjXToEPXqRW5upPXDvGoGBDsAAC325g19+SURkaUl/f4729WAqhUW0rlzFBpKdna0di1JDmLm6kqurmxUBvKSOdhp4j1zAAAghUBAM2ZU3gm/cyeGjtUe+fl09iyFhND58/ThQ+XKWbOoeXM2qwKFQI8dAIC22ryZzp8nIpo+nTw92a4GlC43l86epePH6eJFEp0PvH59mjaN7OzYqwwUB8EOAEArPX5MS5cSEdnb06+/sl0NKNfff9P339OlSx8NJmxuTh4e5OlJAweSoSF7xYFCyR/sysrKgoODz5w5ExUVlZmZWVFRYWVl5erq+sUXX/j4+OjpITICAKirsjKaPJk+fCAdHdq3j0xN2S4IlCsggG7cqFy2sCAPD/LyogEDSF+f1bJACeSMX9HR0ePGjXv+/LnoypSUlJSUlJMnT65evTo4ONjZ2VkRFQIAgKItW0bR0URE331HvXuzXQ0ona8vZWZSr17k6Un9+xP6XjhMnv+30dHRvXr1Kha9Pv+xxMTEHj163Lp1y8nJqRa1AQCAEty8Sb/8QkTk4kLLl7NdDShGZiaFhVGTJjRkiJStM2bQjBkqrwnYIHOwKy0t9fb2ribVMQoLC8eOHfvw4UNckwUAUCO5ueTrSxUVVKcOHTmCS3GaLj2dTp6kkBCKjKSKCtLRoaQkatqU7bKAPTLPFRsUFPTy5Uvhy4kTJ16/fj09PT0zM/P69esTJkwQbkpISAgJCVFMmQAAoBDz5tGrV0REGzaQgwPLxYC8UlNp+3bq14+aNKE5c+jqVaqoICLq0IEsLNguDlglc3famTNnhMsHDhyYNGmS8GWvXr169erVr1+/6dOnM2tOnTo1bty42leptWxtbV+/fl3V1grmzzEAQA2dOkWHDhERDR5Ms2ezXQ3ILD+f9u2jkBD680/i8/9db2tLo0eTlxd160Y6MvfYAKfIHOyimfttiQYNGiSa6oSmTZt2+PDhP/74g4iioqJqV562Cw0NzcnJkVy/evXqyMhIXV1d1ZcEAJrq7dvK26wsLWn/fimTDIDamzSJQkP/fWlnR2PGkKcn/ec/+P8JlWQOdpmZmcxCz549pTbg8Xh9+/Zlgl1GRkYtagPq3Lmz1PW7d+9WcSUAoNkEApo+nbKyiIh27CBra7YLAnkw/99atCBPT/L0JFdX5DkQJ/+UYrwafJsw/xgAgFrYurVykolp08jLi+1qoDopKWRsTJaWUjZt305Ll+LZCKiOzJfiLf/5rl2/fr2qNsJNDRs2lK8sAABQmPh4WrKEiKhFC9q4ke1qQLpnz2jdOurShZo1ozZtKqfwFcPjIdXBJ8gc7FxdXZmFK1euHDhwQLLB/v37r169yix37dq1NsUBAEBtiU0yYWbGdkHwkSdPaM0a6tSJWremb7+lmBgiotJSwtNxIB+ZL8V6eHicOnWKWZ4yZcrVq1dnzpzZtm1bIkpISPjtt98OMY9cERHRyJEjFVUoAADIY8UKunePiGjpUurTh+1qoFJ8PB0/TiEh9PDhR+vbtydPT/L1JSsrlioDDSdzsPPx8fnxxx9fvHjBvDx48ODBgweltmzVqtXYsWNrVR0AANTGn3/S+vVEmGRCvSxbRmvWfLTGyanyeYh27ViqCbhC5mBnYGAQHBzcq1evDx8+VNPM2Nj42LFj+hjTHACALQUFNGUKVVSQkREdPEgGBmwXBJViYysXOnYkLy/y9KQ2bVgtCDhEnvm+OnfufPPmTS8vL2G/nRh7e/vg4GAXF5fa1QYAALXg70/PnhER/fILtW/PdjXwrz176Px56tmTWrViuxTgHDkncnVxcUlISAgKCjp9+vS9e/eYwe2srKxcXV09PDx8fHzQVwcAwKZTp4i5T2bwYPryS7ar0S4xMRQSQqGhZG1N58+ToaF4g0aNaMoUFgoDbSBnsCMifX39SZMmSZ18AgAA2IRJJlROIKDo6MrnIYQTqick0Js3ZG/PamWgZeQPdgAAoI4EApoxA5NMqIZAQHfvUkgIhYRQUtK/63k86taNZs1CqgNVkz/YVVRUhISEnDt37unTp0+fPi0qKmrUqJGrq6u7u7u3t7ehZNczAACowLZtFB5ORDR1KiaZUKrQUJo/n1JS/l2jo0Pdu5OXF40ejZGE/5+9Ow+LquzfAH4PICLK6q6YCgUIuOTyBi64AiWaoihukUiWhmVmpv5et7TMsszMJU0T5XVXXHDPRMw9MBdQoEJQXEAFgWGRbX5/HBoIBmFgZs4wc3+u93qvw3OeM+crJ/TmOec5D4mjhsFu79698+fPj42NLduYmJiYmJi4Z8+eefPmbdy40cPDQxUVEhFRtd2+jdmzAaB9e6xcKXY1Om7VqpJUZ2iI3r3h64sRI9CqldhlkX6rSbD74YcfPvzwwxd0uHv37uuvv759+3a+x46ISHMKC7nIhCYtXowff0SvXvDxQYsWYldDBKAGwe727dszZ86ssltxcXFAQECPHj1s+XwBEZFmyBeZmDOHi0zUXlERzpzBgQNo0QL//a+CDr17o3dvjZdF9EJKrxW7atWqgoICYdvIyGjy5MkXLlxITU19+vTpxYsXJ0+ebGhoKOzNzc1dsWKFKoslIqLKnD+Pr78GgFdfxcKFYldThxUW4pdf8N57aNkSgwZh9WrMm4d/P3lEpL2UHrE7ceKEfHvnzp0jR46Uf+nq6urq6jpw4MAxY8YILceOHat9iUREVAUuMlFrBQX49deS988JU4oFxsYYPZpvEqY6Q+kRu/v37wsb/fv3L5vq5Pz8/Nzd3ct1JiIiNfrgg5JFJpYvh4uL2NXUMX/9hYAANG+ON97Apk0lqa5+fQwZguBgpKQgJARGfDkY1RFK/6dqbW396NEjAM6VL1DTqVOns2fPAmjcuHFtiiMioqodOIDgYADw8EBQkMjF1EHTpkF+L8rEBF5e8PXF0KGwsBC1LKIaUXrEbuDAgcJGdHR0ZX1u3rwpbAwaNKhmZRERUbWkpuK99wDAygqbNnGRiRoYOhTNm2PECGzfjtRUHDiACROY6qiuUjrYzZ8/38zMDMCZM2f27dtXsUNoaGhERAQACwuLefPm1b5EIiJSTCbDpElITQWAH39EmzZiF6SlcnJw8CDOn1e8NygIjx5h3z6MHQszM81WRqRqSgc7BweHkydPtmvXDsCYMWOmTJly+fLltLS09PT0K1euvP/++8LMCTs7u19++eWVV15RecVERFRi7VocOQIAb7+N0aPFrkbrZGdjzx6MHo1mzTB8OPr2/deqX0Q6Seln7CRlxvkLCwvXr1+/fv36it3+/vvv//znP5V9iEwmU/a8RET0L7Gx+PRTAGjXDqtWiV2NFpFKcfgw9u7FsWPIySltt7GBpaV4ZRFpBOf5EBHVQcIiEzk5XGRCLj8fe/Zg716cOIHc3NJ2CwsMHQpfX3h5wcREvPqINILBTnvJZDJvb+9U4emZf0tISABQXFys8aKISDssWoQrVwDg00/Rr5/IxWiHyZOxdWvpl1ZWePNN+PrCwwP164tXFpFmMdhpL4lEMnr06JSUlIq7tm/fnp6eLuH0NyL9dOECli0DgFdfxWefiV2NthBG46ytMXw4fH0xcCDf00z6SOlgx8fjNGnixIkK269evXrjxg0GOyJ9lJ1dusjEli36Fl4yM2FoiIYNFexaswbTpsHREfXqabwsIq2h9KxYIiIS04cf4s8/AeDrr9Gxo9jVaMiTJ9i4Ea+/jiZNYGuLtDQFfYyM0LEjUx3pO96KJSKqOw4exM8/A4CHB6ZNE7satUtNxf792LsXZ86gsLCkMS0NUimsrUWtjEhbMdgREdUR8kUmLC11e5GJR49K8lxEBIqKSttbtsSIEQgIwEsviVcckXar4a3YP//8c8KECS1bthQWmQAQHx8/bNgwa2vrdu3avfXWWzdu3FBdkUREek9YZEKYTaXTi0ysXAkbG7z/Pk6fLkl1rVvjww9x9iySk7F6Nbp1E7tEIi1WkxG7CxcueHh45OTk4J+5FI8fPx4wYMD9+/cBpKenJyUl7d+/f9u2bcOGDVNtuUREemrdupJFJvz94ecndjVqJM9zbdpg5Ej4+sLNDQZ8IJyoepQOdsXFxQEBATllX+YNrF27Vkh1ctnZ2f7+/nfv3rXgQspERLX011+YPRsAbGywcqXY1ajXmjXo3x9ubnjtNR2+20ykLkr/EnTgwIH4+Phyjdu3b6/YMzMzc926dTWsi4iIBIWFmDABUikMDBASAisrUapQVcZKTMQ336B3bwwbhufPFXRo0wYzZsDVlamOqCaUDnbyDGdpaRkREeHu7n7v3j151Js7d25CQoKNjY3w5alTp1RVKBGRnvrsM1y+DACzZom1yETtM1ZCAr76Cj16oH17zJqF8+dx6BD++ksVxRFRGUrfir1z546wERgY6O7ujjLpzdjYeM6cOebm5m+++ebatWsB3L17V3WlEhHpn4sX8eWXANClCxYvFrcWiQTKvqL+zz+xdy/27sXVq/9qd3DA5MlwdlZhdUQE1CDYybNa586dhY1ff/1V2Ojbt6+5uTkAJycnoSU5OVkFNapfcnLyvn37oqKiYmJiHj16JJVKpVKpiYlJo0aNWrVq5ezs7ObmNmzYMPlIJBGRJmRn4+23UVSE+vWxdatYi0yUHa6rfraLiMD06bh+/V+NHTrA1xe+vujUSZUVEpGc0sEuOzu75EgjIwAymUwe7N544w1ho1mzZsKGqampCmpUp4SEhJkzZx44cKDirpycnJycnNTU1GvXrm3btm3atGl+fn5Lly61tbXVfJ1EpI+mTy9ZZOKrr+rcIhNfflma6lxcSvIch+iI1E3pYNemTRvhibqbN2+OHTv2xo0bjx49Ena9/vrrwoa8RcuHuMLDw4cMGVJuhq+hoaG1tbWZmVlmZmZ6enpRmZdj7tq16+TJkwcPHuzTp4/GiyUiPXPwIDZtAoBBg/Dhh2JVUfHpumoO2s2aBUNDuLnB1xeOjuoojYgUUDrYtW3bVgh2+/fvDwoK+u6774T2l156ydHREYBMJjt8+LDQ2Lp1a9WVqmIpKSk+Pj5CqnN0dPTx8Rk6dKiTk5O5ubnkn7/JZDJZVlZWfHz80aNHw8LCIiMj09PTR44cefPmzebNm4taPhHptMePtWGRidqcduBADByoulKIqHqUnhXr5eUlbMTGxtrY2GzZskX40tvbWyKRpKWljR8//uTJk0Lj0KFDVVWoyq1YsSIjIwPAkiVLoqOjly5d6ubmZmFhISnzN5lEIjE3N+/evfuCBQuuXLmyZcsWAwODx48fr1q1SrzCiUgPyBeZWLdOC9fP4otIiLSW0sHu3XffVfjO4bFjxwJ48ODBjh07hBZLS8u33nqrlvWpT1hYGIBhw4bNmzfP0NCwyv4SicTf33/GjBkAFD6TR0SkGj/+COG+x4QJGDNGrCqY3ojqIqWDnZmZ2aZNm+rXr1+2sVevXr169SrXc/ny5Q0bNqxVdeqUmJgIYPDgwUod5e3tDSApKUkdJRER4e+/MWsWANjYQItvDjD2EWmnmiy/N3LkyNOnT48cObJly5bNmzf39/cPDQ01+Gclv4YNG3p4eISFhb3zzjsqLVXF6tWrB6CgoECpo4S5FMKxREQqVliI8eNLFpnYulWsRSZQvdzGbEekhZSePCHo2bNnz549K7Y7ODg8e/ZMeBOKlrO3t4+MjNy5c+fUqVMNqr2+tLDwhp2dnTpLIyJ9tWRJySITM2eif3+xqyGiuqcmI3YvUK9evTqR6gCMGjUKwLlz5wIDA7Oysqrsn5+fP3/+/M2bNwPw9fVVe31EpG8iI0sWmXB2FneRieoPxXHQjkjb1DaEPXz48OrVq0+ePMnKypo2bVpxcXFGRoaVeLcPqi8oKGjt2rVJSUnBwcF79+719PQcMmSIi4uLlZWV8B47qVSalpaWnp4eFxd39OjR48ePp6WlAbC1tQ0KChK7fCLSLdnZGD8eBQWoXx/bt8PERKxCmNWI6rQaBjuZTHbo0KH58+ffvHlT3igEu3bt2n388ceffvppgwYNVFSkWjRs2PDEiRMeHh737t2TSqWhoaGhoaFVHmVnZ3f48GEzMzMNVEhEemTGDMTHA8CXX9at9bZqsIAsEalPTW7FFhUVjR07dvjw4WVTnVxmZuaiRYtGjBjx/PnzWpenXg4ODjExMbNnz7a2tq6ys6Wl5fz586Oiohz5DnUiUq3jx7FxIwC4u2P6dHFrkcmU/h8RaY+ajNhNnz59165dL+5z/PjxKVOmCE+kaTMzM7Nly5YtXrw4PDw8MjIyOjr64cOHUqk0Ly+vSZMmLf/RtWtXd3d3ToYlItV7/BgBAZDJYGmJkBBUey4XEVFFEpmSv23dunXLucwyzsbGxsXFxYWFhQBkMllhYWHDhg3z8/MBSCSSuLi4V155RbUV6565c+cKT+9VdOXKFYUDn/fv38/MzPzss8/6V5g317Bhw65du5ZtycnJuXbtWtlFb9mZndlZizp/9hkOHQKAHTswZkzdqJmd2bkOdk5PT8/Ozj5z5szatWsrHqUzlP7V8IcffpBvr169Oisrq+y7P4yMjK5cuSIsTSGTyb7//nuVVElEpJsePUJYGACMH6/5RSaKizV8QiJSO6VH7Lp27frHH38ACAwM3LhxIwBHR8e4uDgA8o9asmTJggULALi5uV24cEHFJRPg5+e3e/fuZs2apQirSRJRXfT333j1VWRlwcYGN25o8nXEly9j0iTcuoW1azF1qsZOSyQmBweH+Pj4cePGbdu2Texa1EjpEbuEhARhY8iQIZX1kS8v9tdff9WsLI3Jyspas2bNpEmTevToMWjQoK+//loqlQq77t69O23aNDs7uwYNGjRu3Lhfv36bNm0SbjoTEdVWYSEmTEBWFgwMsGWLxlJdbi4+/RS9euHWLQBIT9fMaYlIQ5SePCFfg+sF68Cm//NXRXVe/CuimJiY119/PTk5Wd7y66+/7ty589dff3327FmfPn3u378vtOfl5UVERERERGzevPnQoUPVmUVLRPQin3+OS5cA4OOPMWCAZs55/jwCAxEXBwAmJli4EJ98opkzE5GGKD1i17JlS2Hj1KlTlfU5ceKEsNGiRYualaUBRUVF48aNK5vqBH/88cfChQvnzJkjT3WtWrWqX7++sH3+/PmPPvpIo4USke6JisLSpQDg7IwlSzRwwtxczJmDvn1LUp2rK65exZw5qCNLBRFRdSkd7OTTML/77rs1a9bk5uaW3VtYWLhu3bqffvpJ+LJPnz61L1FNtm3bduPGDQBdunQ5depUWlpadHT0smXLjIyMfvzxxz179gD4+OOPnz17dv/+falUGh4ebm9vDyAkJOT3338XuXoiqruyszFunCYXmTh/Hl264KuvUFSEBg2wbBnOnUOHDuo+LRGJQaakK1eulD28fv368re7dezY0dzcvOzeU6dOKfv5GvPmm28CaNmyZWZmZtn2T/65MzF06NByh9y9e9fU1BRAYGCgBitVYPTo0QCaNWsmbhlEVBPvvisDZIDs2281cLavv5ZJJCUn7N1bFh+vgXMSaSNhdGbcuHFiF6JeSo/Y9ejRo+xKqc+fP5c/dXfz5s3MzEz5rpEjRw4cOFDZz9cYYWJHYGBgufXBRowYIWz4+/uXO6RNmzYTJkwAEBsbq5EaiUjnHD4M4Z5G//7QyHMdBw5AJkPDhvj+e0REgK8WJdJtNXm8YuXKlfn5+fL7rQq9/vrrW7ZsqWlVmnDnzh0ADg4O5drlb1Ru3759xaNcXFwACK93ISJS2qefliwysWWLZhaZWLcOBw9i/HjY2mrgbEQkspr8tWJkZLRhw4YTJ04MGjTI0NCw3N6OHTtu2rTpyJEjL5g2qw0aN24MoNwzgigz21dh/QYGBgBycnLUXB0R6agePWBsjJ9+Qps2mjlhp06YP5+pjkhf1HxClKenp6enp1QqvX79+uPHj58/f25tbe3i4iKfNqvl7O3tk5OTT58+PXny5LLtJiYmFy9eBNC2bduKRwljdXXlz0hEWmfLFmzaxMmoRKQmtf3LpVGjRvLXEdctLi4up0+f3r1795QpU/r27Stvl0gkrq6uCg95/PhxSEgIgB49emioSiLSPepJdefOISsLb7yhjs8mojpDE094aKcpU6YYGhoWFxd7enp+8MEHBw4ciImJqayzTCa7cOHCoEGDnj17BmD8+PEarJSI6EWePsWECejTB4MH4/JlsashIlHpb7Dr0KHD999/DyA/P3/16tU+Pj4zZ85U2POHH36wtLTs1auX8N67YcOGeXt7a7RWIqJK7NsHZ2cIS182bozWrcUuiIhEVa07AsnJyV9++WVkZOStW7dMTU3t7e0DAgL8/f2N/rmhkJ+f//fffycmJubm5ubl5T1//jwzMzMqKiokJEQmk6mz/loJCgpq167d3Llzb968+YJu9+/fl7/GZezYsRs2bJBIJBopkIioUqmp+OQThISUfOntjfXrGeyI9F3VwW7r1q1BQUFSqVT4UiqVpqamnjt3bv/+/bt375bJZLNnz163bl1RUZGaS1ULb29vb2/vJ0+e3Lp1q+IMWUGTJk0GDBjQqVOncePGde/enamOiES3Zw+CgvD4MQBYWWHZMrz7rtg1EZEWqCLYxcbGTp48OT8/v+Kuw4cPf/7558J9TPXUpjlNmjRxd3evbO8nn3zyCRfKJiLtkJKCoCDs21fy5ZAhWL8erVqJWhMRaY0qgt1nn32mMNUJvvnmm+LiYlWXRERUd1R/CF8Vz6VERsLLC2lpANC8OdaswciRtf9UItIdVQS7sg+fDR8+/Ouvv27RokVUVNSUKVPi4uLKZr5WrVo5OzsbGBjk5+cbGhpaWFjY2Nj4+fmpq3AiIv1z6lRJqhs3DqtWoXFjsQsiIi1TRbATFlQFYGZmtnXrVmFZ1X79+q1bt27AgAHybhMmTNi0aZOxsbH6CiUioqlTkZcHNzd4eYldChFppSqC3fPnz4WNl19+WUh1gi5dupTtNn/+fKY6IiJ1s7DAokViF0FEWqy677Erm+oAWFhYlP2yXbt2qiqIiIiIiGqmusGu3Ds+DAz+dSCH64iIVCg5GTt2oJJXMBERVUp/V54gItJCxcX44Qc4OmLcOHzxhdjVEFFdo5a1qImIqAb+/BOBgfjtNwCQSODoKHZBRFTXMNhpteDg4JSUlIrtsbGxALR5uTYiUkpxMTZuxMcfIzsbANq3x08/YeBAscsiorqGwU57yWSy3bt3p6amVtx17949MNgR6Yq//0ZgICIiAEAiweTJ+PZbNGokdllEVAdVN9hFRES8YI3UF+xi+KgxiURy9OhRhbv8/Px2795dbgoLEdU5hYVYswb/93/IyQEAW1ts2oR+/USuiojqLo7YERFVLj0daWmws1PHZ+flYcAAXLwIAIaGmDEDixejQQN1nIqI9AWHfIiIKiguxokT8PNDy5Z45RWcPKmOkyQklKQ6JyecO4fly5mp3YTlAAAgAElEQVTqiKi2OGJHRFTGnTsIDkZwMO7eLW0ssy52eRWeNhGeTKnOQyhOTli7Fvn5mDIF9evXpFgionKqCHZ8Qo6I9EJeHkJD8fPPCA9HcXFJo6kpRo7Eu++id+9qfoz8eWOJpFrZburUGtRKRFQpjtgRkX6LiUFICDZtwpMnpY3duuGttzBhAho3Fq8yIiKlMdgRkV569gy7d2P9ely9WtpoaYnRozFlCl59tQYfWe71ANUctCMiUiEGOyLSJ8XFuHABISH43/9KXjECwMAAAwbgrbcwahTnLxBRncZgR0T64f59/O9/2LABCQmljW3aYNw4TJmCdu1q+fEK3+bJQTsi0jAGOyLSac+f49AhbN2KY8dQVFTSaGKCoUPx7rsYOFBxIiMiqpsY7IhIRymcFeHkBH9/vPOOamdFvCAcctCOiDSJwY6IdEtGBnbtwoYNiIoqbazdrIgXq3LIj9mOiDSGwY6IdAJnRRARMdgRUZ2ncFaEjQ3Gj8d776F9e7WevJpP6HHQjog0g8GOiOomhbMi6tfHm29yVgQR6S0GOyKqa27dwtat+PlnPH5c2qieWREvplR05KAdEWmA0sFOovwvwVZWVh07dgwICPD39zcwMFD2cCIi4J9ZEVu34vz50kZhVsR776FrVw2XU4MBQWY7IlI3TYzYpaennz179uzZs6GhoXv27Klfv74GTkpEOuLFsyJ8fWFqKmp9RERaRKO3YsPCwubNm7d8+XJNnpSI6ipRZ0VUiWNvRKSFNP2M3cqVKz/55JPmzZtr+LxEVGc8f46TJxESgv37UVhY0ijMinjrLQweDENDUesjItJeSj/xJpPJ7t696+rqKnw5cuTI06dPP3z4MC0t7eLFi4GBgcJTdObm5hcuXJDJZM+ePYuIiBg4cKDQv7CwMDQ0VIV/ACLSHbduYc4ctGmDN9/Enj0lqc7JCcuWITkZu3dj6FB1pLriYpw6hXHj0KgRgoJU/vFERJqj9Ijd06dP+/Xrl5CQAOCHH36YNm2afJerq6urq2ufPn0mTpyYmZn5xhtv3Lhx46WXXnJ3d//ll18GDx58/PhxAHFxcSr8AxBRnSferIi7dxEcjM2bkZhY0nL6tPrORkSkdkqP2C1evFhIdT169Cib6uTefvvt1157DUBGRsaiRYuERolEIt9OSkqqabVEpEOKi3HuHN57D61b4733SlKdgQF69cL69bh/H+vXqynVPX+OXbvg5YX27bFwYUmqMzHBuHHYs0cdJyQi0hClR+zkN1L/85//VNanW7duly9fBiAM0QmcnZ2FDQY7In334AFCQvDTT/j779JGYVbEu+/C1lZ9Z05KwooV2LYNT5+WNnbtikmTMG4crKzUd2YiIk1QOtilpqYKG7GxsZX1SfhnCtuTJ0/kjXfv3hU2LCwslD2p3oqKikpPT6/YnpKSovliiGorPx8nTog7K2LECFy9WrJtbY3x4zFpErp0UfdpiYg0ROlg16pVq8TERACnT5/ev3+/j49PuQ6//vrrqVOnhO2mTZsKGwUFBfPmzRO27ezsalyuvhk+fHhycnJle4vkyygRabnbt7Fli+K1IgID0aSJxgrp2BHXrmHQIEyahOHDwbdqEpGOUfoZO29vb2FDJpONHj168uTJFy9efPLkSWZm5h9//DFr1qyhQ4cW/vO7uLu7O4ALFy506dJl//79QqOtOu+z6Jh79+7JFBk9ejQAQ770gbRcRgY2bEDv3nBywldflaQ6Cwu8+y4iIxETg9mzNZnqAAQHQyrFiRPw82OqIyIdpPSI3SeffBIcHJydnQ2gsLBw48aNGzdurKyzMLvixo0bt27dkjfKX31CRDorKgobNmDbNmRnl7QYGMDNDf7+mDBBfWtFPH+OgwcREgJTU2zZAhMTBX0aNFDTyYmIxKd0sGvXrl1ISMjo0aPlw3KVmTt3bq9evco1enp6CnNmiUgHKZwV0bo1JkxQ96yIGzewadO/ZkV88gl69FDfCYmItFFNVp7w8fEJDw8PCAj466+/FHYwMTFZunTpRx99VK69U6dOW7ZsqcEZiUiriTcr4tkz7NiBn39GZGRpo7U13n0X3bqp6ZxERNqrhkuK9e7d+/bt2wcOHNi/f//ly5cfPXoE4OWXX7a3t+/WrdvEiRPLLhpmZGTUsWNHT0/PJUuWNOBdECJdIsyK2LwZ/8yXBzQ0KyIiAhs3Yt8+5OaWtBgYYODAklkRCm/CEhHpvJqvFWtkZOTr6+vr61tlz3feeeedd96p8YmISOtkZmLnzvJrRVhYwM8Pb72F3r3Vff6NGzF5cumXbdti4kQEBKBtW3WfmYhIq9U82BGRPhJpVkQ5BQUAYGKC4cMxaRIGDoSB0lP8iYh0EIMdEVWDMCti40aUfbJWI7MiFJo6FT16wNYW1tYaPjMRkVZTOthJJJLqd65fv35eXp6ypyAibfHiWRFvvAEjdf1ymJGBnTtx8ybmzkXr1go6dO+upjMTEdVhHLEjIkVeMCti0iT8s6iMyslkiIjAzz9j796SWRGmpvj6azWdjYhI1zDYEVEZmZk4cAAhIfhnYUBAQ7MikpNLkmTZV+C1bYtqTNAiIqISDHYAkJycvG/fvqioqJiYmEePHkmlUqlUamJi0qhRo1atWjk7O7u5uQ0bNszGxkbsSonU5gWzIsaPR8OGajptZTd7PTzg7w8fH/Xd7CUi0kFK/5Upk8kq25WXlxcZGfnBBx9cu3bN0tJy//79/fr1q1V16peQkDBz5swDBw5U3JWTk5OTk5Oamnrt2rVt27ZNmzbNz89v6dKlXOuWdMrDh9i6VfGsiMmTYWen1pM/e4auXXHnTmlL586YNAkTJnBWBBFRTajyd2ETE5PevXsfPXr0lVdeefbs2Ztvvnn9+vX27dur8BSqFR4ePmTIkJycnLKNhoaG1tbWZmZmmZmZ6enpRUVF8l27du06efLkwYMH+/Tpo/FiiVSqqAjh4diw4V8DZcbG8PTU5EDZ48dITAQAS0uMG4dJk7hcBBFRraj+7+6WLVsOGDAgLCwsKytr2bJl69evV/kpVCIlJcXHx0dIdY6Ojj4+PkOHDnVycjI3N5fP/JXJZFlZWfHx8UePHg0LC4uMjExPTx85cuTNmzfLLq1BVJfExiI4WPOzIhR65RUcP46MDAwdyrUiiIhUQC2/lMtDz8mTJ9Xx+SqxYsWKjIwMAEuWLJk7d66horUsJRKJubl59+7du3fvPn/+/JCQkICAgMePH69ateqLL77QeMlEtSDerIj793HkCNzd4eioYK+np/rOTESkd1T/snaZTHb9+nVh++HDhyr/fFUJCwsDMGzYsHnz5ilMdeVIJBJ/f/8ZM2YAUPhMHpGWiorCe++hdWu8/XZpquvWDevX4/59rF+vplSXn4/QUAwZgrZt8d578PFRx0mIiOhfVDliV1xc/ODBg1WrVv3+++9CS4sWLVT4+aqVmJgIYPDgwUod5e3t/e233yYlJamlJiIVeviw5Jbrn3+WNrZpg4kTMXGiWteKiInBpk343//w+HFpo6ur+k5IREQl1LvyhKPCWy/aoV69erm5uQXCkpPVJsylqFevnnqKIlKRlBR06ICMjJIvhbUiJk2Cp6f6FlUV72YvERGVUO/EtylTpqj182vD3t4+MjJy586dU6dONaj2P3Xbt28HYKfmd0CQvqv+r0+VvX4oPx/Can4dOyIwEOPHo0kT1dRWie++w7x5kE8xl0jQvz8mTcKIEWjQQK1nJiKiUmoMdgEBAcOHD1ff59fSqFGjIiMjz507FxgYuGrVKjMzsxf3z8/PX7JkyebNmwH48l34pOXatMHNm8jNRadOmjnh+vUlqU4jN3uJiEgx1Qe7l156ydHR8f333x82bJjKP1yFgoKC1q5dm5SUFBwcvHfvXk9PzyFDhri4uFhZWQnvsZNKpWlpaenp6XFxcUePHj1+/HhaWhoAW1vboKAgscsnqsorr2jybOvWITQU3t5qvdlLRERVUOXKE3VLw4YNT5w44eHhce/ePalUGhoaGhoaWuVRdnZ2hw8frnJ4j0gnxcfD0FDxahT9+6N/f40XRERE/6bXv1k7ODjExMTMnj3buhqrF1laWs6fPz8qKkqbZ4QQqUNmJn76CW5ucHCAkxMSEsQuiIiIKqHvy2ubmZktW7Zs8eLF4eHhkZGR0dHRDx8+lEqleXl5TZo0afmPrl27uru7q2ky7NOnTzPksxf/7dmzZ1lZWRXbU8uuGUCkHjIZzp3Dpk3YuxfZ2SWNhoaaWWyMiIhqQum/oVu3bi1fPnX//v1ubm6qLkkExsbGXl5eXl5emj+1TCazt7cXnt5TVn5+vsrrIQLw8CG2bMHmzYiPL220scHEiQgMxEsviVcZERG9kNLBrmHDhn/+875Tvs6t9iQSye3bt6VSqcK9T58+zZG/QKKMRYsWnTlzxtjYWM3Vkd5JTsa0aThyBIWFJS3GxqWvwKvGEi1ERCQmpYPdxIkT//vf/wrbiYmJ3bt3V3VJmnblypXg4OBr164VFRU5OjqOHj168ODBlb2Hed68eXv37gUQGxurqgKaNWvWrFkzhbtsK3ljRGX9iWpp/XocPFiy7eKCwEBMmKDuV+AREZHKKB3sZsyYER4efurUKQDBwcF1+o1uhYWF06dPX7t2rbzlypUrW7du7dOnT2hoaBNF/5o9evQoLi5OgzWSrigowPXruHQJly4hOhqTJuHDD8WuSQE/P5w9CycnBATgP/8RuxoiIlKS0rNiGzRoEBYWtmDBAlNT0yNHjvj7+1+/fl3h7ULt93//939lU53cb7/91q9fP05QoNq6fx+hoZg1C336wMICPXrggw+wbRuuX8f69eKWVtmPrIsLIiKwbh1THRFRnVTbtWJDQkJCQkJe0F9r33sXFxe3cuVKAMbGxgsXLhwxYkSrVq2ioqIWL1585syZmJiYKVOm7Nu3T6m1cUnfPX+OqChcvoxLl3DxIu7dU9CnaVO4umLGDI0XBwAPH2LrVvz8M/7+G9u2wc9PlCqIiEhd9Pe9Bd98801BQQGAsLAwT09PobF///59+vSZOHHitm3b9u/fv2fPntGjR4taJmm9Bw8QFYXz53HuHKKiSlZoLcvICPb26NYNvXujVy84OSmxFGwZEshkqOGvGQUFOHoUmzbh2LHSWRGqe0yUiIi0hf4Gu5iYGAA+Pj7yVCcwMjLasGFDVFRUbGzs7Nmzhw8fzsmn9C85Obh6FVFRiIrCb78hMVFBn5Yt0a1bSZjr2ROmpsqdosI4d0kUVH78OzYWP/+MrVuRklLa6OyMyZMxZYqyH0ZERNpOf4NdfHw8AHd394q7TE1Nf/jhBw8Pj8TExA0bNkybNk3j1ZGWefCgdEwuMhLPn5fvIAzLCWNy3brB2VkdVUgkSkS73bvx/fe4cKG0xdwcY8Zg0iS89po6qiMiIvHp71qxwoSPpk2bKtw7aNCgN95449ixY59//vnEiRMbNWqk2epIbFIprl0rucd65gweP1bQRxiWE8Jc9+4wMVFTLTW4cxsXV/r8nESCPn0QGAhfX6WHDomIqG7R3xG7Fi1a3LlzJzo6urIOX3/99cmTJ1NSUhYuXPjtt99qsjYSR0JCyZjc+fP44w8UF5fvUK8eOnUqGZNzd0e7dhooqlyqq+agXatWcHFBRgYmTEBAAF55RU3VERGRdtHfYOfq6nrnzp3NmzfPmjXL2tq6YgcXF5fZs2cvXbp05cqVPj4+vXv31nyRpF6Zmbhxo+Qe68WLePpUQZ+WLUtvsPbogfr1NV5ledXJdmZmuHlTI9UQEZE20d9gN2bMmB07dqSkpAwZMmTHjh1t27at2Gf+/Pn79++/ffv2sGHDTpw4oQPLbOi7oiLExpZOYo2NVTAs16gROncuucfaty9EXeSDL9shIiKlKBfsMjMzDx48eP78+d9//z0lJeXp06cSicTa2rpFixY9evTo1avXsGHDzMzM1FSrag0dOtTT0/PkyZMXL15s3759z549HR0dZ86c2aFDB3kfExOTkJCQAQMGpKWlubq6jh8/Xr5OLtUZGRn4/ffSe6zp6Qr62NqWjMl164bXXoPWL4Ks1CwKIiLSH5JqToZ48uTJwoULt27dWtly9QIzM7OAgIBFixZZWVmpqEI1evbs2YgRI8LDw+UtYWFhQ4YMKdft2rVrXl5e5RaiEHcSiZ+f3+7du5s1a5ZS9iUWJCgsRFxc6STW27cVhCAzM3TqVHKPtWdPNG4sRqFVePFwHYMdEZFSHBwc4uPjx40bt23bNrFrUaNqjdgdOXJk0qRJ1VliKysra9WqVXv37hUGumpdnnpZWlr+8ssvYWFhq1evvn79+pMnTxR269Kly++///7tt99u3rw5KytLw0VStTx8iMjIkjG58+eRm1u+g6EhHBxK3y336qswUHo9Pa3CQTsiIqqo6mB37Nix4cOHF8pfV18NDx488Pb2Pn78eN++fWtRmyYYGhoOHz58+PDhAHJycipbQOyll176/vvvlyxZcvHixYSEhL///luzZVIFBQW4caNkTC4qCrduKegjLM8q3GPt3Rt1YRRZjk/XERFRDVQR7LKzswMCAsqmuiZNmrz//vs9e/a0sbGxsbGRSCTJycnJyckXLlxYs2aNfNArLy/v7bffjouLq68FswirybSqd3yZm5t7eXlpphhSQFOLd6mVTIbsbLz4xYjVrJqDdkREVE4VwW7Lli1ln+JatGjRrFmzygUgJycnJycnT0/PTz/9dPny5YsWLRLak5KStm3bNmnSJFXXTHojOxt//FEyJnf2LJKSFPSp5eJdalZcjMRE3L6NmJjS/5dKsWIFZsxQwecz2xERUVlVBLtTp07Jtz/44IOFCxe+oLOpqenChQtTU1PXrl0rtPzyyy8MdqScsot3/f478vPLd9DI4l21lJuLWbNw6RJu30ZOjoIOt29Xeqz2DTISEVGdUUWw++OPP+TbH330UXU+ccaMGfJgFxkZWePKSF9o0+JdqnL0KNasKd/YvDmcndGhAzp2xLhxKjsXB+2IiEiuimCX/s9Lv8zNzdu3b1+dT7S1tTUzMxNmjz5V+Cp/oioX7zI1xauvltxj1dTiXS9WUIC//vrXHdW4OLRsiagoVFy4pF8/vPkmcnLg5FT6v2q+U4UpjYiIaqyKYJeZmSlsdOjQobIZo+UYGBg4OTldvnwZwLNnz2pZH+mIurl41++/4/Bh3L6NW7cQH4+CgvIdkpLw6JGCYNe4MQ4e1EyNREREpaoIdvLX8Jooc/NL3lnct/iSmOra4l0VSaXo21fBG/EkErz0Ejp0gIsL+vWDk5MYxRERESmiv2vFkuqVXbzr3DkoHK+VL97Vuze6dIGhocarRG4uYmNx61bJHdW8PKxeDTu78t1MTGBvj5s30a5dyb3UDh1KHpJ78ctKiIiIxMJgp0+qOd+y+uOsdWHxruJi/PEHYmJw61bJ/+7cKT96uGcP5swpf6CREa5dQ34+jI01ViwREVGtMNhpL5lM5uTkpHA12OzsbABFRUUaLwp4/Bhnz+LiRVy6hKtXFS/e5ewMNze4usLVFQ4O4r7AIyAAW7cq3mVkBFtbdOuGt9+u9HCmOiIiqkOqG+wiIiKqOXmCVEUikfz4448Kl+j97rvvLl68aKj5+5ipqXBwUHCPtVkzvPYaXF3h5oYePTR2q/LZs5I5qg8eYNw4vPyygj7y71+9enj55ZJ7qcL/OzoytxERkU7hiJ1Wq2yx3b179168eFHDxQDA8+clQ3T16qFz55IxOVdXBU+oqUFaWuntVOF/9++X7r10CUePKjhqxw5cuoQ2bWBvj3r1NFAmERGRaBjs9JoEMhmUGYht0wY3buDpU3TpggYN1FZXeRcuYMwY3LtXaYcGDTBkiOJdlpZ4/XU11UVERKRdGOxISfb26vjU1FTEx8PBAU2bKth79Oi/Ul2DBujQofSOqrMzbG1FmV9LRESkXar7HjvSPRLIUINBO1V4+PBfd1RjYkreWNy6Ne7ehYFB+f4ffQSJBI0albx2pH17BX2IiIiII3akIYWFmD8fv/2GW7fwz0p15TVooHgGbZMmWLJErdURERHpAgY7PSUM18m3NTBod/Ysli0r32hhUXpH1cUFbm7ivhqFiIiobmOw00dlU11tyGRITCxdwiEmBrGxsLTEpUto1ap85x498OabSE+HoyOcnErCnI2NSgohIiIigMGOBNUftLt1C4cOlTwbFxuL7OzyHbKykJCgINiZmeHgQVXUSkRERJVgsNM7lQ3XVSfbFRWhTx+kpSnY1axZySCcuzt69659mURERKQ0BjtSgqEh7O1x6RJatiyZoCp/4YjG14AlIiKi8hjs9MuLn66rzqDdxYuQSjW2ZhgREREpgW8D0yfVeSthNfow1REREWknBjs9Up03ifBtI0RERHUXg52+YGIjIiLSeQx2VB4jIBERUR3FYKcXlM1qzHZERER1EYMdERERkY7g6070QnWmwxIREVFdxxE7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoKvO9FeMpnMz88vPT294q6bN28CKC4u1nhRREREpL0Y7LSXRCLx8PBIS0uruCs1NTUlJUXCBSKIiIioDAY7rTZ58mSF7VevXr1x4waDHREREZXFZ+yIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEV54AgOTk5H379kVFRcXExDx69EgqlUqlUhMTk0aNGrVq1crZ2dnNzW3YsGE2NjZiV0pERERUKX0PdgkJCTNnzjxw4EDFXTk5OTk5OampqdeuXdu2bdu0adP8/PyWLl1qa2ur+TqJiIiIqqTXt2LDw8M7duxYLtUZGho2bdrU1ta2SZMmhoaGZXft2rWre/fuv/32m2bLJCIiIqoW/R2xS0lJ8fHxycnJAeDo6Ojj4zN06FAnJydzc3OJRCL0kclkWVlZ8fHxR48eDQsLi4yMTE9PHzly5M2bN5s3by5q+URERETl6e+I3YoVKzIyMgAsWbIkOjp66dKlbm5uFhYW8lQHQCKRmJubd+/efcGCBVeuXNmyZYuBgcHjx49XrVolXuFEREREiulvsAsLCwMwbNiwefPmlbvlqpBEIvH3958xYwYAhc/kEREREYlLf4NdYmIigMGDByt1lLe3N4CkpCR1lERERERUG/ob7OrVqwegoKBAqaOKiorkxxIRERFpFf0Ndvb29gB27txZXFxc/aO2b98OwM7OTl1lEREREdWU/ga7UaNGATh37lxgYGBWVlaV/fPz8+fPn79582YAvr6+aq+PiIiISEn6+7qToKCgtWvXJiUlBQcH792719PTc8iQIS4uLlZWVtbW1mZmZlKpNC0tLT09PS4u7ujRo8ePH09LSwNga2sbFBQkdvlERERE5elvsGvYsOGJEyc8PDzu3bsnlUpDQ0NDQ0OrPMrOzu7w4cNmZmYaqJCIiIhIKfp7KxaAg4NDTEzM7Nmzra2tq+xsaWk5f/78qKgoR0dHDdRGREREpCz9HbETmJmZLVu2bPHixeHh4ZGRkdHR0Q8fPpRKpXl5eU2aNGn5j65du7q7u3MyLBEREWkzfQ92AmNjYy8vLy8vL1HOvmDBgpSUFIW7IiMjc3NzK7bfv38fgEwmU29lREREVKcw2IlMJpMJUzQU7s3MzMzLy6vYLrx+T6kXtRAREZHOY7ATmUQiWb16tbJH+fn57d69uzoroREREZH+0OvJE0RERES6hMGOiIiISEcw2BERERHpCAY7IiIiIh2hv5Mnavme4djYWFVVQkRERKQS+hvs4uLixC6BiIiISJX091bspUuXgoKCjI2NxS6EiIiISDX0N9i99tprq1evjoiIMDc3F1qysrJk1SZu8UREREQV6W+wE7i6uk6ePFnsKoiIiIhUQN+DHYAxY8aIXQIRERGRCjDYwcnJSSKRiF0FERERUW3p76xYOVNTU39//ydPnnDpVSIiIqrTGOwAIDg4WOwSiIiIiGqLt2KJiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjuCSYlpt3759T58+rdiekJAAQCaTabwiIiIi0l4Mdlpt5cqVDx48qNiekpICBjsiIiL6NwY7rfbbb78pbPfz89u9e7eBAe+kExERUSkmAyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHGIldAL3IjRs3UlNTK7anpKRovhgiIiLScgx22ksmk3l6er4gwxUVFWmyHiIiItJyvBWrvSQSyaNHj2SKjB49GoChoaHYNRIREZEWYbAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDrCSOwCtEJycvK+ffuioqJiYmIePXoklUqlUqmJiUmjRo1atWrl7Ozs5uY2bNgwGxsbsSslIiIiqpS+B7uEhISZM2ceOHCg4q6cnJycnJzU1NRr165t27Zt2rRpfn5+S5cutbW11XydRERERGKjtv4AACAASURBVFXS61ux4eHhHTt2LJfqDA0NmzZtamtr26RJE0NDw7K7du3a1b17999++02zZRIRERFVi/6O2KWkpPj4+OTk5ABwdHT08fEZOnSok5OTubm5RCIR+shksqysrPj4+KNHj4aFhUVGRqanp48cOfLmzZvNmzcXtXwiIiKi8vR3xG7FihUZGRkAlixZEh0dvXTpUjc3NwsLC3mqAyCRSMzNzbt3775gwYIrV65s2bLFwMDg8ePHq1atEq9wIiIiIsX0d8QuLCwMwLBhw+bNm1ed/hKJxN/f/8aNG99+++2BAwe++OILVVWSm5ubl5encFdeXp7CXdnZ2ao6OxEREekM/Q12iYmJAAYPHqzUUd7e3t9++21SUpKqypDJZDY2NmlpaTU4trCwUFVlEBERkQ7Q32BXr1693NzcgoICpY4qKioSjlVVGRKJJDIysrJg9+DBA+F+cTnr1q27cOFCkyZNVFUGERER6QD9DXb29vaRkZE7d+6cOnWqgUF1nzXcvn07ADs7OxVW0r59+/bt2yvc1a1bN4Xtx44du3DhggprICIiIh2gv5MnRo0aBeDcuXOBgYFZWVlV9s/Pz58/f/7mzZsB+Pr6qr0+IiIiIiXp74hdUFDQ2rVrk5KSgoOD9+7d6+npOWTIEBcXFysrK2trazMzM6lUmpaWlp6eHhcXd/To0ePHjws3TG1tbYOCgsQun4iIiKg8/Q12DRs2PHHihIeHx71796RSaWhoaGhoaJVH2dnZHT582MzMTAMVEhERESlFf2/FAnBwcIiJiZk9e7a1tXWVnS0tLefPnx8VFeXo6KiB2oiIiIiUpb8jdgIzM7Nly5YtXrw4PDw8MjIyOjr64cOHUqk0Ly+vSZMmLf/RtWtXd3d3FU6GJSIiIlI5fQ92AmNjYy8vLy8vL7ELISIiIqo5vb4VS0RERKRLGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkI7jyRB12584dOzu72nxCenq69qyTJpPJZDKZgYG2/LJRVFRkaGgodhUlZDJZYWGh9lys4uJiiUQikUjELqSEVl2soqIiAFpVj/YUo20/5gUFBUZGRvwvWSFt+zEvKCiwsrKqzSfcu3dPVcVoMwa7OsnU1BRAQUFBQkKC2LUQERFpQnp6eu0/RPgHVIcx2NVJc+fOLS4uzsrKqs2HJCcnX7x48Y033mjUqJGqCquNq1ev5ufnu7q6il1IiT179vTt27dZs2ZiFwIAly9fTk5OHjlypNiFlPjll1/atm1rb28vdiEA8PTp09OnT48YMUJLhjqOHTsG4I033hC7EAAoKioKDQ0dMGBA48aNxa4FAOLj45OSkjw8PMQupMS+fftsbGxee+01sQsBgNTU1IiIiFGjRoldSIlLly4ZGxt37dpV7EIAQCqVHjt2zM3NzcbGpjafY2ZmNnfuXFVVpaVkpK/27NkD4K+//hK7kBJTp0718/MTu4pSEonk9OnTYldRYvz48cbGxmJXUerVV19dsWKF2FWUuHjxIoDc3FyxCynx8ssvv/zyy2JXUSI3NxfAxYsXxS6kxIoVK1599VWxqyhlbGw8fvx4sasocfr0aYlEInYVpfz8/KZOnSp2FSX++usvAHv27BG7kDpAWx50ICIiIqJaYrAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3B99jpLzMzMwAmJiZiF1KiadOmWvIeMkGLFi2sra3FrqJEo0aNtOqb07Rp06ZNm4pdRQkrK6umTZsaGWnL32ba8zMFwMjIqGnTprV8X78KadV/OQAMDQ215EWeAKytrVu0aCF2FaWaNm2qPX8HCj9Wwj9b9GISmUwmdg0kjhs3bnTu3Pnp06fa86NLlVm/fv13330XGxsrdiFUtYCAAACbN28WuxCqmqOj44wZM9577z2xC6EqpKWlNW7c+Pr16506dRK7Fm3HW7FEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY7Qlld6kuZZWFg0btxYq16mSpXRtte60gvwStUh/MmqK0xMTBo3bmxhYSF2IXUAX1BMREREpCN4K5aIiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwU43yWSysLCw8ePH29nZNWzYsGnTpr169Xr33Xfj4+MrOyQnJ2f58uWvvfaapaVlw4YNXVxc5syZk5KSosmy9VNcXNzHH3/s4eHRtm1bU1NTFxcXHx+fkJCQ4uLiyg7hxdIekyZNkkgkjo6OlXXgxRLFV199JXkhX1/fikfxYokoMTFxwYIFXbt2tba2trKy6t2795w5c9LT0yvrz4tVKRnpnPT0dC8vL4WXu169erNmzSooKCh3SGxsbLt27Sr2t7CwCA8PF+MPoS/WrFljaGio8GJ17tw5Jiam4iG8WNpj586dwjffwcFBYQdeLLEEBga++N++kSNHljuEF0ssxcXFK1eubNCgQcVvfpMmTY4dO1bxEF6sF5DIZLIX/9dPdUtRUZGHh0d4eDiAZs2aTZw4sWPHjo8fPz579uyBAweEPu+///6aNWvkh2RlZTk7O9+7dw9Ar169/Pz8jI2Njxw5EhYWBsDU1DQ6Orp9+/Zi/Gl03NGjR729vQEYGBiMGjWqf//+pqamsbGxmzZtEn7vbNOmza1btxo1aiQ/hBdLeyQmJnbp0iUjIwOAg4NDbGxsuQ68WCLq27fv2bNnbWxsKvstt1u3blOnTpV/yYsloqVLl/73v/8F0LBhwwkTJnTr1i0zM/Pw4cNnzpwBYGFhcfXqVVtbW3l/XqwqiJ0sScX2798vXFlXV9cnT56U3XXixIl69eoJe69cuSJvnz17ttA4ceLEoqIiefvixYuF9uHDh2vuD6BPOnXqBMDY2Ljcr5gZGRmdO3cWvvmrV68uu4sXS0sUFBS4ubnJ/yJVOGLHiyWili1bApg8eXI1+/NiiSUqKkoikQB46aWX4uPj5e3FxcWLFi0Svvne3t5lD+HFejEGO10zcOBAABKJ5M6dOxX3fv7558J/91OnThVa8vPzra2tATRq1CgzM7Ns58LCQgcHB+HTkpOTNVC8Xnn8+LFwLaZNm1Zx7y+//CLsDQgIkDfyYmmPefPmAWjSpEllwY4XS0RZWVnCdVm+fHl1+vNiiUg+pHrmzJlyu4qKijp27Cj89puRkSE08mJViZMndM2NGzcA9OzZU+HzByNHjhQ2rl27JmycPn06LS0NgKenp5mZWdnOhoaGPj4+AGQy2b59+9RZtT66deuWsCFk8XK6desmbNy8eVPeyIulJSIiIr744gsAP//8c2V9eLFE9Ndffwkb9vb21enPiyWWR48eCb/EdujQwd3dvdxeAwODBQsWvP3222PHjk1KShIaebGqxGCnU3JycoRxIOG3lopatWolbBQVFQkbkZGRwobwsFc5Q4cOLdeNVCUvL8/Ly8vLy+uVV16puPfBgwfChoWFhbyRF0sbpKWljR8/XiaTvf/++/LveUW8WCL6888/hY1qBjteLLEcO3ZMmP4vzC6v2MHX1zc4ODg4OFgYugMvVjUYiV0AqZJEIhGeHrWzs1PY4erVq8KG/O0M0dHRwoaLi0vF/s7OzsJGTEyMakslT09PT0/PyvYePnxY3k3eyIslOplMFhgYeP/+/Q4dOixfvvwFPXmxRCQEOwMDAyMjo+nTp0dERPz9998SiaRt27aDBg364IMPyj6JD14s8Qi3mFDmHkWVeLGqxGCnUxo0aDBkyJDK9hYWFn722WfC9qhRo4SNhw8fChvCUwvlmJubGxoaFhUVPXr0SNXF0r/IZLL8/Pzs7Ow///xz37593333HYAuXbqUnbjHiyW69evXHzhwwNjYePv27aampi/oyYslIvmIXYcOHQoLC+Xt0dHR0dHRP/zww1dfffXxxx/Lh4h4scQSFxcnbLRo0eL+/fsrV648duzY3bt369Wr5+Li0qlTp+nTp7/88stlD+HFqhJvxeqL3NzcgIAAYfZ479695YPYUqlU2FD4QyKRSIR2eTdSk8uXL5uYmDRu3NjV1XX58uUSiSQoKOj06dNlHyLhxRJXTEzMjBkzACxbtqxLly4v7syLJSIh2BUXFxsaGs6cOXPPnj1XrlwJCQnx8/MDUFRU9Mknn/zwww/y/rxYYnn27JmwcenSJXt7+2+++SYmJiYrKystLe3s2bOrV692cXFZuHBh2XTOi1UlBju9cOrUqa5du/7vf/8D4OTktH//fvmvqnl5ecJGuadQ5YR2ff4hEUVhYeHTp08zMzPLNvJiiSg3N3fMmDF5eXkeHh7Tp0+vsj8vloiEYNeyZcuIiIhvvvnG19e3R48eEyZM2Llz5549e4Q+s2fPlj/JyoslFvm3dPLkybm5uRMnTtyxY8fvv/9+6NCh2bNnm5iYPH/+fPHixTNnzpQfwotVNfEm5JImREdHDx48WH65hw8fnpKSUrZD3759hV1paWkKP0F4oYOpqalG6tVfOTk5t2/fjoyM3LVrl3w81cbG5u7du/I+vFgiev/99wE0btz4wYMHZduFK1LxdSe8WCLKzc3Nzc2tuMSOYOLEicKl+eKLL4QWXiyxyB+Jq1+//vnz58vtjYuLMzc3FzpERUUJjbxYVeKInc56/vz5/PnzO3fufPToUQDt2rXbtWtXaGhos2bNynYTXuMJQOGSfDKZTGgvu/gBqUODBg0cHR27des2evTow4cPf/zxxwCSk5O/+uoreR9eLLEcOnRo7dq1AH7++Wf5VXgxXiwRmZiYmJiYGBkpfohcHuzOnj0rbPBiiUX+LV2yZEnPnj3L7bW3t5e/e3XLli3CBi9WlRjsdNPVq1e7du36+eefFxUVNW/efNWqVbGxsaNHj644n1z+QyK8GaicrKws4cUo8vekkGYsWLBA2Dh27Ji8kRdLLO+++y4Ab29ve3v72H8TOjx//rxcCy+W1nJychI25I/h82KJxcrKSth48803FXbw8PAQNuSvdODFqhJnxeqg69evDxgwICMjw8DAYPr06YsXL37B7y7yH5K4uLju3buX2xsfHy9syAfMSVW++eabJ0+etG7d+oMPPqi418LColWrVg8ePJA/BgReLPEIj3gfOXLkyJEjCjskJiZ26NBB2JbJZODF0mLCi9MANGzYUNjgxRJLhw4djh8/DqBFixYKO7Ru3VrYkD9wzItVJY7Y6Zq7d+96enpmZGRYW1ufOXNmxYoVLx6R7tq1q7Ah3LEtR97o6uqq8lL13LFjx7766qt58+bJ/nlOqyxh8gSAsu/U4MWqQ3ixxLJhw4ZBgwYNGjRIWCS+IvmiL/L3aPBiiaVHjx7CxvXr1xV2kL+1Tj4Cx4tVNRGf7yN1+PDDDwEYGhpeuXKlOv2fP38urG1gbW39/PnzsruKi4vlvw+VfYSfVOKjjz4SvrcVHxmWyWTh4eHCXnd3d3kjL5YWEr7nFSdP8GKJ5eDBg8L3du7cuQo7CKtOAdixY4fQwosllszMzAYNGgAYMWJEcXFxxQ4BAQHCN//rr78WWnixqsRgp1MyMzOFmd7+/v7VP+q///2v8JOwYMGCsu0hISFC++jRo1VdKcmuXLkifHs7d+78+PHjsrvu3bsnXwrpp59+KruLF0vbVBbsZLxYIpFKpcJ9PUNDw0OHDpXdVVhYKJ+NZGdnl5+fL9/FiyUW+atMFixYUPaKFBUVff/998Iuc3Pz1NRU+S5erBeTyBTdBqI66uTJk15eXgDc3NzkDwgr1KJFC/lso2fPnnXr1i0hIQFAYGDg2LFjjYyMDh8+/N133xUVFVlaWkZFRZVbgYdqTyaTjRs3bufOnQCsrKz8/f3t7e2Li4tjYmK2bt2ak5MDoGfPnhEREWUn9/FiaRthQpKDg4N82oQcL5ZY9u3bN2rUKOFfN29v7379+llZWf35558nTpy4du0agPr160dERLz22mvyQ3ixxJKZmdm9e3fh1YPOzs7e3t6vvPJKUlLS8ePH5eu9bt++fezYsfJDeLGqIHKwJJUS3shQHeUGGG7dutWuXbuK3Ro3bhwRESHWH0fnZWZmVjYXDMD48eOfPn1a8SheLK2i8AdKjhdLLDt27LC0tFT4k9WyZcvffvut4iG8WGJJSkrq1KmTwotlZma2c+fOiofwYr0Ag51O+eSTTxT+bFRU8d+hjIyMzz//vEuXLubm5qamph06dJgzZ87Dhw9F+YPoj+Li4kOHDvn5+bVv375BgwaNGzfu2bPnpEmTLl++/IKjeLG0R2U/UHK8WGJ58uTJl19+2atXr2bNmhkZGTVt2rR///7ff/99dnZ2ZYfwYoklPz9/48aNnp6eLVq0MDIysrKycnNz+/zzz8s9plLW/7d370FRVo8fxx8UZGFBwIzLMCz3i6iYjDp2GXKIW9GMDmoa0UCNCWU66kzaX0Y1TTZoTWmZjSIG+ocTI5pNZlAJgUVgUnHxAtKEaFSOyL3ddn9/7HR+zyzstrKrfjm8X389nOec85xzmh4/s88+Z/mPZQ2PYgEAACTBdicAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACScL3bAwAw4bm4uFgr9/HxmTFjxvz585OTk3Nzc7Va7R0em3NNnpkCmKBcTCbT3R4DgInNWtyxEBgYeODAgYyMjNs9nttn8swUwARFsAPgKDvjjqIobm5uFRUVjz322G0dz+0zeWYKYIIi2AFwlDruPPzww+J4eHi4paWlr69PXdnf37+lpeWee+65c+NznskzUwATFMEOgKPUccfilmI0Gjs6Ot5+++09e/aIwnXr1u3evfvOjc95Js9MAUxQBDsAjrIRd4TCwsJXX33VfOzl5dXd3e3t7X0nBudUk2emACYotjsBcCds2bJFPJTs7++vqKhQn3VRURRlaGho+/btsbGxwcHB1upYsH32119/3bBhQ0REhEaj0el0mzZt+uuvv/6z1e2YqZlery8tLV2xYkVYWJinp6dWqw0LC1u5cmVpaalerx+z276+vnfeeScjI2PWrFlardbf33/hwoVPPPHEsWPHjEbjmE16e3vfeOON5OTkyMhIjUYTFBS0cOHCvLy8xsZGG+Mf31o1NDQUFBTExMRotVo/P78FCxasW7eura3N9loBcD4TADjGzlvKtm3bRLX8/HxrPQwNDaWmppqPAwIC7LyKjbNHjx718vKyuPVFRUX99ttvt3ozdHymJpPp7NmzUVFR1u7JERERDQ0NFk1KSkp8fX2tNYmNjT1z5oxFk88//9xGk7Vr1xqNxtEjH8da6fX6F1980dqF1q9fbzAY7FlbAE5BsAPgKDvjzsmTJ0W1xMREaz2oU5Hjwa6mpsbVdewNO++//347R+7EmZ49e9bT09NaDDLTaDSNjY2iyalTp2zXVxRFq9VevHhRNLl8+bKfn5/tJh9++KHFsMe3VmvXrrV9odWrV48ZIgHcDnzHDoCj7PnmmaIozc3Nc+bMMR8HBwd3dXWN2YOnp+fg4KD5OCAg4Nq1a/ZcZcyzBoMhPj7+4sWLos6SJUtmzZpVX1/f0NBg0YM9N0MHZ6rX6+Pj4y9duiRqJicnJyUlKYpSU1NTVVUlyqOiolpbW80xa8WKFeXl5eby2NjYZcuWJSQkGAyGpqam4uLiGzdumE/l5OSUlpaajzdu3Pjuu++ajzUazbPPPhsXF9fZ2XnixIkLFy6Yy5csWfL111+LK45vrU6dOpWenm4+dnNz27hxY0pKSn9//2effVZcXCzqV1RULF261NpyAXCmuxgqAcjBzluKiCCKomg0Gms9KIry6KOPHjlypL6+vq6uzs6rjHn2k08+EYWurq7l5eXmcqPRuGvXrnHcDB2c6ccff6zu4cCBA+pWJSUl6rOHDh0ylwcFBYnCrq4udZPTp0+LU4GBgaI8MTFRlO/YsUOUX7lyRZT7+PiouxrfWomH5oqi7Nq1S92h+nXguXPn2lpWAM5DsAPgKDvjTm9vr6jm4eFhrYecnJwxn9zZvsqYZ7Ozs0XhmjVrLJpkZWXZOXJnzVR9xaeffnp0w6eeekpUWLVqlbkwICBAFObl5TU1NYn1MRgMX//rm2++Ef0cPny49F+///67KK+urrY2hXGsVX9//9SpU80l7u7ug4OD6iYjIyPTpk0TTbq7u22sGABn4VEsAEfZ+YCypaVl9uzZ5mMbj2LPnTs3b968W73KmGejoqLa29vNx7W1tQ888IC6yVdfffXII4/YM3I7xyBYm6lOpxNvIXz77bcPPvigRcPq6mqx73F4eHhHR4eiKLm5uRYf9QUFBT3yr5CQEBsDNplMHR0dTU1NP/zwQ01NTX19vfqtW/UUxrFWtbW1Dz30kLnEzc0tIiLC4urt7e0Gg8F8fOLEiczMTBtDBeAUY39PFgCcTp3k1J9CWYiOjnbWFcX38xRFiYmJsTgbHx/vrAtZsDbTP/74QxzHxcWNbqguFJWLioouXbpUV1cnTl29erWsrKysrExRlJiYmJycnIKCgnvvvVfd1cDAwOuvv37kyJHLly/bM+ZxrJV6Onq9/vz58zb6//PPP+0ZBgAHsY8dgDtEHU0WLVpkrdp/vjQ6PurHgmbu7u6340KK3TMdbcqU/78ni8+6/P39a2pqTp8+XVBQoNPpLJpcuHBh27ZtiYmJ4q0IRVFu3LiRmJj41ltviVQXGxubnZ198OBBe4Zh51qNjIzY05vZzZs37a8MYNwIdgDuhMHBwffff1/8Ofop5C2x2JJ3eHh4zGrq7eLMjzXVbH/CNG42Zqr+UG3MzXtbW1vHrDxlypSkpKQ9e/Z0dnY2Nzfv3LkzJSVFHb+6urpeeukl8WdhYaHIeUuXLm1qamprazt06NDy5cutDXscazVjxgxxnJCQYPt7P+vXr7d2aQBORLADcCfs2LFDPIzz9vZ2cPOL7u5u9Z/WfkpB/Ujx8OHDFmf37dvnyBissTHTBQsWiOOPPvpodNu9e/daVNbr9b/86/r16y4uLvHx8Zs3b/7yyy97enpefvllUb+mpkYcqzfSKy4uTkhIMB+rg6OFcayV2NJFUZTOzk7xEaNgULH28xgAnOy2vpoBYDKwcUsxGo3t7e0Wn9Zs2LDB/h4E9VPILVu2iDdDr169On/+/DF72L59uyj08PCoqqoSp0pKStTPPe28GTo4U4sNTUpKStRn1Ru/KYpy8OBBk8mk1+u1Wq255Mknn7R4X1j90VpoaKgo12g0ovyXX34xFxoMBhsvAo9vrebOnSsKP/jgA3WHTU1N4jPF6dOnX79+3Z4VBuAg3ooF4Cj1u6LipU5FUYaHh1tbWy2+XBUUFNTc3Gzxuwj2vG2anp6u/g2GRYsWzZs3r6enp6qqqr+/X11T9HDt2rWwsDDxVTBXV9e0tLTw8PDGxsbvvvvOon97boYOzvTvv/+Oi4tTv82QkpKSlJRkMpmqq6vVGxRHR0c3Nze7ubkpipKamlpZWSkW4fHHHw8MDOzp6WlpaTl+/Lh4zVa9QXF0dLTYBjk0NDQ/P19RlE8//fTMmTPqERqNRjGj8a3V/v3716xZI8rz8/PT09N9fX3r6uqKiorEti+vvPJKYWHhWCsKwNnuZqoEIAX7bzju7u6VlZW2e7B2lYqKCmvd+vj4WOth586d1lp5e3vf6s3Q8Zk2NDR4eHjYbuvp6Xnu3DnR5NixY/95Oa1Wq27y/PPPW6um/vPKlSsOrtU///wjfnnCmvT09IGBAXuWF4DjCHYAHPWfscNMp9OpN9G11oONCz3zzDOju/Xy8lL/NJZFD0ajcevWraNb+fj41NbW2nldJ87UZDI1NDSEh4dbaxsTE6OOaGZ79+61CK9q06dPr6ioUNfv6emJjIy0qDZz5sy6ujp1RCsqKnJ8rfr6+pYtWzbmwFxcXPLy8oaHh+1ZWwBOQbAD4ChrgcPFxcXX1zcyMnLVqlX79++38Q+8tdAw2tGjR9PS0nQ63bRp03Q63XPPPdfe3m4ymdxVRreqrq5euXJlYGCgq6trQEBAbm5uZ2fnLV3XWTM1GxkZKSkpycrKCgkJ0Wg0Go1Gp9MtX768rKxMr9eP2aSvr2/37t0ZGRlz5szx8/PTaDRRUVHJyclvvvnmjRs3Rtfv7+/funXrfffdp9FowsLCNm3a1NPTYzKZMjMzxUIFBwc7Za2MRuMXX3yRnZ2t0+nc3d1nzpy5ePHiF1544fz58/asKgAn4jt2ACap3t5eX19f8Sc3QxtYK2Ci4JcnAEhr3759Yg+OhIQEi5/JampqEscWL3NMQqwVIAeCHQBpvffeez///LP5ePbs2T/++KP53gkS4QAAAbJJREFUPVNFUUwm02uvvSZqqrftmJxYK0AObFAMQFppaWniuLm5efXq1W1tbQMDA/X19ZmZmertRfLy8u7C+P6XsFaAHPiOHQBpDQ0NLV68+KeffrJdLTMz8/jx4xZ78E42rBUgB/7nBCAtDw+PkydPpqamWqswderUzZs3l5eXk1RYK0AOfGIHQHImk+n777+vrKzs6Ojo6Oi4efNmSEhIaGhobGxsVlZWUFDQ3R7g/xDWCpjoCHYAAACS4BN1AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkMT/AfYqNgpWQAcrAAAAAElFTkSuQmCC"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Text annotations",
"cell_type": "markdown",
"metadata": {
"collapsed": true
}
},
{
"source": "#example using the text() function\n#text() function is used to place text within the graph\n#mtext() function is used to place text within one of the four margins\n#this example plots the car mileage versus care weight for the 32 automobile makes provided in the \"mtcars\" data frame\n\n#attach data\nattach(mtcars)\n\n#create the plot\nplot(wt, mpg, #data, wt on the x-axis and mpg on the y-axis\n main = \"Mileage vs. Car Weight\", #main = title of the plot\n xlab = \"Weight\", ylab = \"Mileage\", #x and y axis labels\n pch = 18, col = \"blue\") #pch = symbol; col = color\n\n#add text\ntext(wt, mpg, #text is placed at each (x,y) location\n row.names(mtcars), #the text field is populated by the names of the cars\n cex = 0.6, pos = 4, col = \"red\") #cex = plotted text, 0.6x normal size;\n #pos = position relative to location. 1 = below, 2 = left, 3 = above, 4 = right\n #col = color\n\n#add marginal text\nmtext(\"This is marginal text\", side = 4) #side = 4 means right margin\n\n#detach data\ndetach(mtcars)",
"execution_count": 4,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#another example using the text() function\n#this example displays various font families\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize settings\npar(cex = 1.5) #cex = plotted text, 1.5x normal size\n\n#create the plot\nplot(1:7, 1:7, type = \"n\") #x-values and y-values are the vectors 1 through 7;\n #type=\"n\" option in the plot() command is used to create the graph with\n #axes, titles, etc., but without plotting the points\n\n#add text to the plot\ntext(3, 3, \"Example of default text\") #this text is displayed at position 3,3\ntext(4, 4, family = \"mono\", \"Example of mono-spaced text\") #this text is displayed at position 4,4 using mono font family\ntext(5, 5, family = \"serif\", \"Example of serif text\") #this text is displayed at position 5,5 using serif font family\n\n#restore orginal settings\npar(opar) #restore original settings",
"execution_count": 2,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Combining graphs",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#R makes it easy to combine several graphs into one overall graph using either the layout() or the par() functions\n#with the par() function, you can include graphical parameter mfrow = c(nrows, ncols) to create a matrix of nrows x ncols\n#plots that are filled in by row. To fill in by column, use the mfcol = c(nrows, ncols) parameter",
"execution_count": null,
"cell_type": "code",
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": "#example: the following code creates 4 plots and arranges them into 2 rows and 2 columns\n\n#attach data (the \"mtcars\" dataset)\nattach(mtcars)\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize parameters\npar(mfrow= c(2, 2)) #creates a matrix of 2 rows x 2 cols filled in by row\n\n#create plots\nplot(wt, mpg, main = \"Scatterplot of wt vs mpg\")\nplot(wt, disp, main = \"Scatterplot of wt vs disp\")\nhist(wt, main = \"Histogram of wt\") #hist = histogram\nboxplot(wt, main = \"Boxplot of wt\")\n\n#restore orginal settings\npar(opar) #restore original settings\n\n#detach data\ndetach(mtcars)",
"execution_count": 5,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#another example: 3 plots in 3 rows and 1 col\n\n#attach data (the \"mtcars\" dataset)\nattach(mtcars)\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize parameters\npar(mfrow= c(3, 1)) #creates a matrix of 3 rows x 1 cols filled in by row\n\n#create plots\nhist(wt)\nhist(mpg)\nhist(disp)\n\n#restore orginal settings\npar(opar) #restore original settings\n\n#detach data\ndetach(mtcars)",
"execution_count": 6,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#example using the layout() function\n#the layout() function has the form layout(mat) where \"mat\" is a matrix object\n#specifying the location of the multiple plots to combine\n#in this example, one figure is placed in row 1 and 2 figures are placed in row 2\n\n#attach data (the \"mtcars\" dataset)\nattach(mtcars)\n\n#create layout\nlayout(matrix(c(1, 1, 2, 3), 2, 2, byrow = T)) #creates a layout that includes a matrix of 2 rows,\n #with 1st row having 1 col and 2nd row have 2 cols\n #byrow = T = fills in the matrix by row\n\n#create plots\nhist(wt)\nhist(mpg)\nhist(disp)\n\n#detach data\ndetach(mtcars)",
"execution_count": 9,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "#example including the \"widths\" and \"heights\" options in the layout() function\n#to control the size of each figure more precisely\n\n#attach data (the \"mtcars\" dataset)\nattach(mtcars)\n\n#create layout\nlayout(matrix(c(1, 1, 2, 3), 2, 2, byrow = T), #creates a layout that includes a matrix of 2 rows,\n #with 1st row having 1 col and 2nd row have 2 cols\n #byrow = T = fills in the matrix by row\n widths = c(3, 1), #figure in row 1 is 1/3rd the height of the figures in row 2\n heights = c(1, 2)) #figure in the bottom right cell is 1/4th the width of the figure in the bottom left cell\n \n#create plots\nhist(wt)\nhist(mpg)\nhist(disp)\n\n#detach data\ndetach(mtcars)",
"execution_count": 10,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
},
{
"source": "## Creating a figure arrangement with fine control",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "#example: 2 box plots are added to a scatter plot to create a single enhanced graph\n\n#attach data (the \"mtcars\" dataset)\nattach(mtcars)\n\n#identify settings that can be customized\nopar <- par(no.readonly = T) #save the list of settings that can be modified to the \"opar\" object\n\n#customize parameters\npar(fig = c(0, 0.8, 0, 0.8)) #sets up the scatter plot going from 0 to 0.8 on the x-axis and y-axis\n\n#create the scatter plot\nplot(mpg, wt, xlab = \"Miles Per Gallon\", ylab = \"Car Weight\") #data and x, y labels\n\n#add a box plot above the scatter plot\npar(fig = c(0, 0.8, 0.55, 1), #sets up the box plot going from 0 to 0.8 on the x-axis and 0.55 to 1 on the y-axis\n new = T) #adds the scatter plot to an existing plot rather than creating a new one\n\n#create the box plot above\nboxplot(mpg, horizontal = T, axes = F) #creates a boxplot of mpg horizontally (default = vertical)\n #axes=FALSE suppresses both x and y axes\n\n#add a box plot to the right of the scatter plot\npar(fig = c(0.65, 1, 0, 0.8), #sets up the box plot going from 0.65 to 1 on the x-axis and 0 to 0.8 on the y-axis\n new = T) #adds the scatter plot to an existing plot rather than creating a new one\n\n#create the box plot to the right\nboxplot(wt, axes = F) #creates a boxplot of wt vertically\n #axes=FALSE suppresses both x and y axes\n\n#add text in the margin\nmtext(\"Enhanced Scatterplot\", #text in the margin\n side = 3, #side = 3 = top margin (1 = bottom, 2 = left, 3 = top, 4 = right)\n outer = T, #outer margin\n line = -3) #line in the margin; 0 is closest. Moves outwards\n\n#restore orginal settings\npar(opar) #restore original settings\n\n#detach data\ndetach(mtcars)",
"execution_count": 11,
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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"
},
"output_type": "display_data",
"metadata": {}
}
],
"metadata": {}
}
],
"nbformat": 4,
"metadata": {
"kernelspec": {
"name": "r-spark20",
"language": "R",
"display_name": "R with Spark 2.0"
},
"language_info": {
"codemirror_mode": "r",
"name": "R",
"pygments_lexer": "r",
"mimetype": "text/x-r-source",
"version": "3.3.2",
"file_extension": ".r"
}
}
}
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