{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the results\n", "\n", "Now that you have `test.csv` or some other outputs from your model, you might like to analyze or visualize the results. The first thing to do is to either make a copy, or copy the metadata from the first six lines of `test.csv` so that the first line of the file is the column names.\n", "\n", "Once you've done that, you can use the `pandas` project to get the info into a dataframe:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import pandas\n", "data_df = pandas.read_csv('test.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(did you get an 'error tokenizing data'? If so, you *probably* forgot to delete the metadata from the csv file:\n", "```\n", "\"BehaviorSpace results (NetLogo 6.0.4)\"\n", "\"Virus.nlogo\"\n", "\"exp01\"\n", "\"08/28/2018 14:15:31:214 +0000\"\n", "\"min-pxcor\",\"max-pxcor\",\"min-pycor\",\"max-pycor\"\n", "\"-17\",\"17\",\"-17\",\"17\"\n", "```\n", "Delete that, save, then try again.)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[run number]durationturtle-shapeinfectiousnessnumber-peoplechance-recover[step]count turtles with [ sick? ]count turtles with [ immune? ]count turtles with [ not sick? and not immune? ]ticks
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2084 rows × 11 columns

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" ], "text/plain": [ " [run number] duration turtle-shape infectiousness number-people \\\n", "0 1 20 person 45 150 \n", "972 1 20 person 45 150 \n", "975 1 20 person 45 150 \n", "978 1 20 person 45 150 \n", "981 1 20 person 45 150 \n", "985 1 20 person 45 150 \n", "989 1 20 person 45 150 \n", "993 1 20 person 45 150 \n", "996 1 20 person 45 150 \n", "1001 1 20 person 45 150 \n", "1007 1 20 person 45 150 \n", "968 1 20 person 45 150 \n", "1010 1 20 person 45 150 \n", "1016 1 20 person 45 150 \n", "1023 1 20 person 45 150 \n", "1027 1 20 person 45 150 \n", "1031 1 20 person 45 150 \n", "1040 1 20 person 45 150 \n", "2082 1 20 person 45 150 \n", "1044 1 20 person 45 150 \n", "1047 1 20 person 45 150 \n", "1050 1 20 person 45 150 \n", "1054 1 20 person 45 150 \n", "1013 1 20 person 45 150 \n", "963 1 20 person 45 150 \n", "959 1 20 person 45 150 \n", "955 1 20 person 45 150 \n", "872 1 20 person 45 150 \n", "876 1 20 person 45 150 \n", "879 1 20 person 45 150 \n", "... ... ... ... ... ... \n", "196 4 20 person 75 150 \n", "1370 4 20 person 75 150 \n", "1314 4 20 person 75 150 \n", "212 4 20 person 75 150 \n", "1309 4 20 person 75 150 \n", "1305 4 20 person 75 150 \n", "1219 4 20 person 75 150 \n", "1223 4 20 person 75 150 \n", "1226 4 20 person 75 150 \n", "1229 4 20 person 75 150 \n", "1233 4 20 person 75 150 \n", "1237 4 20 person 75 150 \n", "230 4 20 person 75 150 \n", "1242 4 20 person 75 150 \n", "226 4 20 person 75 150 \n", "1263 4 20 person 75 150 \n", "1683 4 20 person 75 150 \n", "223 4 20 person 75 150 \n", "1272 4 20 person 75 150 \n", "1276 4 20 person 75 150 \n", "220 4 20 person 75 150 \n", "1280 4 20 person 75 150 \n", "1284 4 20 person 75 150 \n", "1289 4 20 person 75 150 \n", "1293 4 20 person 75 150 \n", "216 4 20 person 75 150 \n", "1297 4 20 person 75 150 \n", "1301 4 20 person 75 150 \n", "1268 4 20 person 75 150 \n", "1041 4 20 person 75 150 \n", "\n", " chance-recover [step] count turtles with [ sick? ] \\\n", "0 75 0 10 \n", "972 75 232 47 \n", "975 75 233 49 \n", "978 75 234 47 \n", "981 75 235 44 \n", "985 75 236 41 \n", "989 75 237 42 \n", "993 75 238 43 \n", "996 75 239 47 \n", "1001 75 240 50 \n", "1007 75 241 48 \n", "968 75 231 47 \n", "1010 75 242 46 \n", "1016 75 244 47 \n", "1023 75 245 46 \n", "1027 75 246 44 \n", "1031 75 247 46 \n", "1040 75 248 43 \n", "2082 75 519 75 \n", "1044 75 249 42 \n", "1047 75 250 43 \n", "1050 75 251 44 \n", "1054 75 252 48 \n", "1013 75 243 47 \n", "963 75 230 47 \n", "959 75 229 47 \n", "955 75 228 42 \n", "872 75 205 35 \n", "876 75 206 33 \n", "879 75 207 31 \n", "... ... ... ... \n", "196 75 53 42 \n", "1370 75 351 88 \n", "1314 75 335 115 \n", "212 75 58 44 \n", "1309 75 334 113 \n", "1305 75 333 113 \n", "1219 75 316 78 \n", "1223 75 317 74 \n", "1226 75 318 83 \n", "1229 75 319 84 \n", "1233 75 320 87 \n", "1237 75 321 90 \n", "230 75 63 46 \n", "1242 75 322 99 \n", "226 75 62 49 \n", "1263 75 323 102 \n", "1683 75 432 101 \n", "223 75 61 45 \n", "1272 75 325 111 \n", "1276 75 326 113 \n", "220 75 60 44 \n", "1280 75 327 115 \n", "1284 75 328 120 \n", "1289 75 329 118 \n", "1293 75 330 117 \n", "216 75 59 43 \n", "1297 75 331 116 \n", "1301 75 332 117 \n", "1268 75 324 103 \n", "1041 75 267 47 \n", "\n", " count turtles with [ immune? ] \\\n", "0 0 \n", "972 79 \n", "975 79 \n", "978 79 \n", "981 83 \n", "985 86 \n", "989 87 \n", "993 83 \n", "996 84 \n", "1001 80 \n", "1007 81 \n", "968 81 \n", "1010 84 \n", "1016 82 \n", "1023 81 \n", "1027 78 \n", "1031 77 \n", "1040 78 \n", "2082 30 \n", "1044 82 \n", "1047 81 \n", "1050 80 \n", "1054 80 \n", "1013 87 \n", "963 82 \n", "959 82 \n", "955 83 \n", "872 112 \n", "876 114 \n", "879 113 \n", "... ... \n", "196 63 \n", "1370 147 \n", "1314 94 \n", "212 68 \n", "1309 94 \n", "1305 92 \n", "1219 85 \n", "1223 85 \n", "1226 78 \n", "1229 81 \n", "1233 81 \n", "1237 82 \n", "230 79 \n", "1242 82 \n", "226 75 \n", "1263 80 \n", "1683 101 \n", "223 74 \n", "1272 79 \n", "1276 79 \n", "220 73 \n", "1280 81 \n", "1284 79 \n", "1289 81 \n", "1293 81 \n", "216 71 \n", "1297 83 \n", "1301 85 \n", "1268 77 \n", "1041 183 \n", "\n", " count turtles with [ not sick? and not immune? ] ticks \n", "0 140 0 \n", "972 173 232 \n", "975 172 233 \n", "978 174 234 \n", "981 173 235 \n", "985 172 236 \n", "989 170 237 \n", "993 173 238 \n", "996 168 239 \n", "1001 170 240 \n", "1007 171 241 \n", "968 172 231 \n", "1010 170 242 \n", "1016 170 244 \n", "1023 172 245 \n", "1027 178 246 \n", "1031 176 247 \n", "1040 178 248 \n", "2082 193 519 \n", "1044 176 249 \n", "1047 176 250 \n", "1050 176 251 \n", "1054 172 252 \n", "1013 166 243 \n", "963 171 230 \n", "959 171 229 \n", "955 175 228 \n", "872 152 205 \n", "876 152 206 \n", "879 155 207 \n", "... ... ... \n", "196 98 53 \n", "1370 63 351 \n", "1314 91 335 \n", "212 96 58 \n", "1309 91 334 \n", "1305 94 333 \n", "1219 137 316 \n", "1223 141 317 \n", "1226 139 318 \n", "1229 134 319 \n", "1233 131 320 \n", "1237 128 321 \n", "230 91 63 \n", "1242 119 322 \n", "226 89 62 \n", "1263 118 323 \n", "1683 98 432 \n", "223 93 61 \n", "1272 108 325 \n", "1276 106 326 \n", "220 94 60 \n", "1280 101 327 \n", "1284 101 328 \n", "1289 101 329 \n", "1293 101 330 \n", "216 94 59 \n", "1297 99 331 \n", "1301 96 332 \n", "1268 119 324 \n", "1041 68 267 \n", "\n", "[2084 rows x 11 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's sort the data. Here, we'll use `sort_values` appended to our data_df dataframe, and we'll tell it to sort against\n", "# the [run number] column. The results will be stored in 'sorted'.\n", "sorted = data_df.sort_values('[run number]')\n", "sorted" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's get the results from Run 1\n", "# we add the .loc operator that will let us select rows where run number = 1.\n", "# Then we store the new dataframe in run1.\n", "# Extract the other three runs!\n", "run1 = data_df.loc[data_df['[run number]'] == 1]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0WUq9XsF53LhSANx+ZaAGS58xehW66Zf+PPrSAjvnzBzFnJrSbuvV95kYRmTohRDfEUJsEUJsFkL8VQiRL4SYLIRYI4TYKYR4SgjhSNRgFYmjrknrd3rcuBKgq+uPYmDcPs349IrRF9jpDIbxh1QYrCeRiOT+d/YAmtJnPP543Um8ePPp3dYpQ58Yhm3ohRDjgVuABVLK4wEbcCXwa+BOKeV0oAW4IREDVSSWo7pWyzGjVehmqLijoZse6ZW6AVPGqTfv72niQGsnEyoKokVmfXH9aZOir1W1bGIYaegmFygQQuQCTuAQcDbwjP7+Y8AlIzyGIgkYGQ2jS7TH6De2HuG9XY3pHFLG0NYZJEcQV68FUJ2m0HLmV+1qREotxv7OThd2m+DVby0Z8LO3XXQcf//6qQCqAC1BDNvQSykPAMuABjQD3wasA1qllIZ7uB8YH+/zQogbhRBrhRBrXS5VGJFqOoNhHLYcRpdqOc1Prd3H1X9cw/t7lMDZQLTqDUdyenimhgyC8kJh1a4mrvnjGpZvPwrAyh2NzK8tpzBvcBndpQXq6SiRjCR0Uw5cDEwGxgGFwHlxNo07bS6lfEBKuUBKuaC6unq4w1AMk85AmHx7DiX5dv547YLoemXoB8ZoCt4TQ5teefSw2+UB4O3tLlxuP1sPtbN4+uB/50o7KLGMJHRzDrBXSumSUgaBZ4HTgDI9lANQAxwc4RgVSaAzEI726jzj2K4f4Id1zekaUsbQ2hmMGqJYjG5TyqPvyupascPFWv2aWjStatCfL1VhsIQyEkPfAJwihHAKIQSwFNgKLAcu17e5DnhhZENUJIPOYBinQ7sf2205vP+DpZx5bHW0oYYiPuGIxOX2RydeY1FeaBf1elZXXZOXt7drodkJ5QWD/rwjN4dCh03dNBPESGL0a9AmXT8CNun7egD4L+C7QohdQCXwUALGqUgwncEw+fYuyYMxpflMrHCqqsQB+M9nNrLtUHtcj744X2+JpyYQ2dfiZfqoIkCb/4G+0yr7oszp4KhbXY+JYERaN1LKnwI/7bF6D7BwJPtVJB9fMEyBvft9vqooj1ZvkEAogiNX1dLF49mPDgBwpN3X6z2jJZ7yQrUU1NOnVXHM6GJe3nQIoNfk9UCcMLGMNXuakFKiBQ0Uw0X9mrOU2Bi9gVGx2NShvKh47ItpE+iwxf/pKL0bDY8/RGFeLtecMnHY+1g8vYqjbn90YlcxfJShz1I6g2EK7N0NvaFB4lKPy3E5rHvx15w8kf/73Ny42yi9Gy2H3hsIU5hni6s9P1iMzx618LyRUWeQbJShzzJaOgJ85u6VbDnY3i1GD10e/Xee2pCOoZkeIwPk8ydNYHRJb011UC3xAPyhCOGIpDAvlzF9fE+DwZCYsGq/hA5/iJN+9SbPfrQ/6cdSevRZxi6Xh62H2gF6xeFnjS1hSnUhu10dHGztZFzZ4LMksgEj9m6kUcajtMDeLcSTjRgdywodudhyBHdcNocZY4uHvB+j8tiq8hzv72mi0ePv02lIJMqjzzJifzQ9c5QduTnce818AFbuVNXKPTE89XgZNwZa6Ca7s26M3gZGFeznTprAnJqyIe/HaNXosahHv3JnI/n2HObXlif9WMrQZxmxj8HxUteOGV3E6JI8VuxQujc9afMGEHE0bmIxpIqzWUfd6Klb6BhZx7LCPO3zVjX0u456mDGmpFcINRkoQ59lxHr0R929UwSFECyeXs27uxoJZ7GxikdrZ5CS/N4aN7GUFtiJSPAErGmcBkM0dDNIXZu+yMu14cjNod1nzTmPRo8/rjZ/MlCGPsvw6A1GZo4t4Y7L42eOLJxcQVtnkIYsjzX3pK0zGNWz6Ysavfpzx2F3KoZkSjoCRuhm5J5qcV6uZWP0Lrcy9IoksHz7UT457EYIePmbp3PGMfFFpibpaW31TR28uPFg1secDVq98cXMYjl1ShU5gmiD62zEq3v0hsTGSCjKz7Vk6CYUjtDsDcRtq5gMlKHPEkLhCF965EOe/egARY7cfsMPtXr/znX1Ldzy1/U8uHJPqoZpaho9fioK+y/jL3XamTuhjBU7sncyO9pqcYShG2MfVvTomzsCSBm/rWIyUIY+CzjQ2sl7u7vkh3v2Ou3JqOI88nJz+N1buwDUxCxaYUtDk5eJFfGbWMeyZHo1G/a18t7u7PzevHroxjnCyVjQDL0V8+hdRv/cotR0WlWGPgtYdPtbXPvwB9Hl/rJGQJuQHR+TQ7/5YBtNWS521twRwO0PDarS81OzRgNw6983JXtYpsSToMlY0Ca3WzqsFzo0qs+VR69IGoNJ53rkSydFX0sJj6yqS+KIzE+9PjFthLX64/jxpZw/Z6wlDdRg8AZC2HIEeQkQxptQ4WRfizdlUgGpotGjXRsqRq9IGoORi40NUQgBv1++i11HszeTpKFp8IYeYFp1EW5/KCtTVDv8YQodtoQoTtZWOvEFI5aTKzY8emXoFQkh1tDk23N45munctfn5w34udgf6b3XnAjAziPZqSLY1hlk66F2hICa8sEZeqN6tj0LdW86dOXKRFAbzQCzVqpvo8eP02FL2Pc0EErrxuI0x4QPLpk3ngWTKoa8D6MFXJ3FfmyDoc0bZMGv3iAYllQX5w26ijHaP7YzSPkAmTpWoyOQOEM/SX+C2nXUw8LJQ792zUoqc+hBGXrLYzwiXndqLT88f9aQPrv6B2fj8YUozrdTUeigobkjGUM0NftavATD2lPRUCakDUOfjU1IjNBNIphY4aS6OI/Ve5q4+uTha9ubjUaPP2VhG1ChG8vz9SfWAXDRvHFD7ho1trSA6aM11cGJFc6srJSNba04FDVPI3RzyR9WEQpHEj4uM+NNoEcvhGDxtCpW7bJWqqrL7adaGXpFojjS7qPcaWfuMNQDY6kozE6ddeOJ6KcXzuKJL5886M+VxkgZN2dZ9o3HH05IVazBtNFFNHcE8FpIP6jR46eqOHUhPWXoLUyHP4QvGOHGJVPJ7aP13WBxOmx4dfnZbMJIg/v8SROG1C0pVsrYlWU1CN5AiKIE6NwYGJ5vo9saN8xgOEKLN0h1UfJ16A2UobcwRtghEZM+hY7caMVjNuFy+yl02IbsoVYVOThn5qjoPrKJDn8IZwKzSar069fl6a22mok0GTn0yqNXJALD0FcloMy6wGGL6oxnE9oj9tBvlEIIfnLBcfo+rOGJDpYOfzghOjcG1dFexpn/Pb657Qjn3r0CQMXoFYkhkWXWhXk2vIGw5SoUB6K1M0jZIArM4mF4bNnk0Ycjks5gOCE6NwbVUY8+87/H59YfiDaPH44DMVyUobcwLt2TTITn4HTkEo5I/KEIvmCYn/1jC0+v3Tfi/Zodjy9IyQDaQH3hdOSSb8/h/17fji+YHWEvb6CrX2yiqCh0IAQ0WuCGGZvFpTx6RUJwuf0IwYDSuoPByIvuDIRZ39DKI6vq+P4zH494v2bH4w+NKAwxc2wJoYjko/qWBI7KvHgD3fvFJgK7LYdyp8MSHr3L7ae0wM7p06oYU6omYxUJoNHjp8LpGHHGDXQ1kegIhGjr7IqVWr03qts3MkP/u6tOALpE0axOl3JlYvugVhflWcSjD3DJvHH8+csnY0/A73KwKENvYRJZZu3Uf7jeQLhbPn2LxbtPeXyhAfX7+2NsaQF2m7CcVktfGCm4iQzdgDbfkekevT+k/XZSWRFroAy9hUlkmbXxw+3wh6KTSWCNCbK+iEQknkCI4hF49LYcwYRyZ9bIRxgevTMJHn2mT2obaZWp1LgxUIbewrjc/oSkVoKWXglajD5Wv8UqRSzx8AbDSDlwR66BmFjpzB6PPpC4NoKxVBXl0ejxZ3TWV12jdrNPZWzeQBl6iyKlpNGTuNBN1KPvEbqxShFLPIxepUV5/TcEH4jaCs3QZ7KRGizbj2g9CxIpgQCaF+wLRjK6UfjKXY3k5ohhKciOFGXoLUqjJ4AvGGF0SWK8h5KCXH2/ftq8QUYVW6ssPR4ev3ZDG6j14kDUVhbi8Ycsr3nz8f5W7nh1O5B4j95wWDK5+GzFDhcn1pYn/LsZDMrQWxSjMfVJCfIeJlY4qSrK473dText7KCmvIC83BxLx+jdhkc/YkOvaapbPfMmNjyV6Bh9VbQ6NjOvN5fbz5aD7ZxxTHVajq8MvUVZsaORMqed48eXJmR/QggWT6/iHxsPsvVQO6UFdi1umqE/vMFgGPqRTMZCl6FvsHic3h/qkmNOdNZNl0efmdfbB3ubga4mPqlmRIZeCFEmhHhGCPGJEGKbEOJUIUSFEOINIcRO/W95ogarGBxSSlbudHH6tCpsOSPv22kwKUa98QefmUl1cZ6lPfqmDu3cRlpwVlPuRAjrtcPrSay3ncjrDjLfoz/Y2gnA5KrBK6AmkpF69HcDr0opZwBzgW3ArcCbUsrpwJv6siJFrNrVyLE/fpWjbj9Lpif2MTF2YveY0cVUWSDlrT8SpRWUb7cxtiSf+iZrp1gm09s2brY/fXELmw+0Je04icbl9nPsj/7JixsP4sjNGbacxkgZtqEXQpQAS4CHAKSUASllK3Ax8Ji+2WPAJSMdpGLwfLC3mWA4wn/+27FcOHdcQvfd0+BVF+dl7KP0YGj0BMjLzUnI5NnESqflY/TGjfHxf1+Y8H3HPiG8sulQwvefLF7bchh/KMKmA21UF+UhRGKfdAbLSDz6KYALeEQIsV4I8UchRCEwWkp5CED/OyoB41QMkvqmDsaVFvCNs6ZFc98TRc+cfM3QB3jqw4aEHscsGJXFifhx1lYUWj50c9TtY35tOUuSPOGYSB2dZLNhX2v0dSrVKnsyEkOfC5wI3CulPAHoYAhhGiHEjUKItUKItS6XawTDUMRS3+yNTv4lmp4e/UX6E8OKndbq52mQyMriiZVOGj1+OjI4D7w/AqEIm/a3MWNMcdKOcd8X5gOZFac3YvMA1QkqXhwOIzH0+4H9Uso1+vIzaIb/iBBiLID+92i8D0spH5BSLpBSLqiuTk/KkRVpaEqeoe9p9KaNKuLEiWW8/PEh/vqB9bz6RGoFRVMsLejVr9nTxBX3r6YjEE6qN3/u8WOYUlWYUQkAsQVe6ZA+MBi2oZdSHgb2CSGO1VctBbYCLwLX6euuA14Y0QgVg8YfCtPUEWBcaUFS9p9vt/HVJVP429dOja4zmnL84NlNSTlmOkmkR29kLFlR8+bzD7zPRj1EcdrUyqQeK9MSAIzqaoCp1UVpG8dIg13fBJ4QQjiAPcCX0G4eTwshbgAagCtGeAzFIGlMgWjSDz4zs9tybBNsKxEKR2jqCCTsu5xoUY8+GI50Wy7OT+71UF2cx7bD7Uk9RiJxx3j0ixOcBTcURmTopZQbgAVx3lo6kv0qhkdjAlsHDharGvrmjgBSJi6uWpJvpzgvl0Nt1tIGOtDSFYO+6/Pzkn68yiJHVAUyE/D4QtRWOplSVcgxozPXo1eYAH8ozMsfH4p6U6nUu86zd0X/fMEw+fbEZvqkCyMOnMibZqnTTnuMIJwVMJRMH7puAUtnjk768UoL7LT7gkQikpwEF2UlmlA4QmcwzGUn1nDL0ulpHYsy9Bbgnjd38oflu/nULO2Hlso0rmCoS5GxvTNoHUOfhKejMqe9m8SzFTCUTMucqXmyKy2wI6UWEjH702SH3oQlHSJmPVFaNxbAeHx+Y+sRoHe+ezIJRbpitFYyYsZ8RyKfjkoL7LRarCOXcT6pMrrGcdq85r/W2n3aGEcqipcIlKG3GHabIC83dV71mcd2TTC1ZsCPb7A0eRKjcxNLWYGjm5a/FTBCUaUFqXEujCyv1k7z3zCN1MqRiuIlAmXoLUBsO4tHv5T48vP+OHvGaJ7+qpZuaSUj5vGHECKxj92lTrulviPourmnyqM3QkSZ8D1GDX2SM5EGgzL0FiD2oj9uXEnKjz9Wb43W3JE5+c0D4faFKMrLTag2iRa6CVqq01RbZxCnw4YjNzWmxLihZMLTY7L65w4HZegzlEhEEgxHOOr2dSsgMR5tU8mY0nxsOYJ9zZ0Db5whePwjawoej7ICO6GIxBsIx30/HMm8G0BrZ5CyFE6KGsfKBI/ep/8/F5ggQSH9wSPFsJjzs9dN0z/TbsuhpryAOgvT2rTSAAAgAElEQVTJ8Hp8oYRPohlhhxZvoJcw1wsbDvCtJzew8vtnMaEiORIWyaDVG6A0hc5FSSYZ+pBm6M2QiaY8+gylp5G/6cypLP/emekZDFqrwQYLyfB6/KGEp8VVFmoZPPEKfp5ffwCA7YfdCT1mstnX3Mn4suRIbsQj324j356TGYY+qGWk5dvTb2bTPwJFQvjCKbVp614DmpbLXlcHkX7CD40eP1P/+xUm3foyz360P4WjGzpuf4iiBE+iGTn5/Wm13PXmDv7tzhUJPW6ykFJS39yRNBG9vigrcGREmqo/qHv0KcyC6wtl6DOQ2Mm8S08Yzz1XncC4FHpV8Zg3oQy3P8TWQ33rkHxyyB2NQ//2jR2pGtqw8PiCFCc4dFM1iL6nmw+0s/2IG38ofhzfTBx1+/EFIyk39MakttnxhQyPXhl6xTCIncybNqooqgufThZP15oef/be93h8dR3n3b2SGx79kEBMw+hY45WO7KChkIzJWKOQbTBdufa3mH9i2xBom5jiOYVMSVP16R59Xooykvoj/SNQDBkjPl9aYOdLiyaldzA6o0ryOXvGKAKhCL96eRvbDrXz5idHWVvfHN0m1gsze4KJx5f4GH1ero2S/Ny4oZueX0dDBqhcHm7XBNpSGaMH7brPDEMfwWHLMYUmjzL0GYhb17j++cXH4XSYJ3Hqv3UJY3+MF//9Zz7m8dV1QFemxIwxxab+oYYjko5AOCml60b7xVjW1Tfz9vbuXda+9ud1/MfTG039PRk3rFSK6IGWYmnm78XAFwx3E/1LJ+YYhWJIdFXcmcfIA0yoKKBnfdH+lk5+8sIWQMu5FgJqyp2m1irZps8zJCP2XFmYR3NHd0N/4+Prem3nD0X4+0f7efjdvQkfQ6Jo9PjJzREpFxfLlBi9P2QeNVdl6DOQe9/eBUBRXvpLq2PJy7UxtiQfh633ZbXste3c8+ZOnHYblYWa5ssHe5v556ZDKR3jX9Y0sOuop8/3/aEw3/zregAWTatK+PFLCrorWK7e3URTD8P/4LVdLR7M7Lm63FoHrlSHJsqcdjqDYdNPWPuDEVOkVoIy9BlHMBzhtS2aSqUZ5E97cvEJ47nprKmcPLmCn110XHT975drN6eOQJhSp53WzgCfu381X3/io5SNLRCK8N/PbeLi37/b5zYb97Wxt7GDGWOKGVWcn/AxlDnttMWkBl714PvR11OqC/nqGVMYXdIVCjFzM/FGj5+q4tRXYteUa09aO4/0fcM2A75Q2BSplaAqYzOO2IwbMxr6/zp3BgDfPkdbPn58CZfdu7rbNqUF9mgxSbJ5ZdMhFk2rorTATpOuxdPRhwQBdMWd77oyOd2S+ppIPOOYah77d02Q7lBbV8bNuoYW/rB8F5+ZPTatdRLxcLn9jEpDw+vTpml9ad/Z4eL48aUpP/5g8QUjKnSjGB7egDm6yg+W6aOLuy1fOHccM8cWY0vB435Dk5ebnviI7/1tI9B/oZKBy61lkiRrgrGswE5HINwt7RTgutNqo6+NClqAPa4OfvPadu5/Z3dSxjMSEtk8fSiMKs5nSnUhH+9vTfmxh4IvGDZFaiUoQ59xGB793VfOo8BhDm+hP0ry7Wz86aejy7+76gTOnjGa7b84lxuXTEmq6uH+Fi1F0YjJx+avxxMQ23qwnT2NHdhyBOVJ0m8p1fVujrT7WFffjBDwraXTOXtGVxu+2O9k56/OY9bYEo4O4iaVKlxuPzuPuGn0JK55+lAZU5LfK3vJbJiptab5nv0V/eLV25MVmiitciCMrIzYwq5cWw6FjlwCoQjhiEyKh1+va+8YlcSxHv3B1s5u4mFSSj5zz0pAe1JK1hOH8V1c+/AH7G3UROD6av04d0IZdlsO1cV5g3oaSRW/ee0TXv74EOGITItHD9oT14Z9ZvfoI1QUmsOXzhxroQCgI2Aejeuh8MkvzsXeIxunwKEt+4LhXmqOIyEUjrD5YDsf1mnFWkaYJNYDfHXzYS6aN47RJfn6GLpCKck0XoahN4w8QHWc423/5bnY9FzV6uI8dhwxj9jZwVZfdJ4jXR69Vo9gnptfPPyhMHkm8ejNcbtRDBojRp9JHj1oeh89vWRDp7szmNg0uefWH+CSP6zi2Y80RcjD7T6C4Ug3r/hXr2zjwt+9G/X2Y1vTjStNfLaNwdjS3lWksVk2Bnm5NnL1G6Nh1MzSsCT2e0ynR+8NhE2dldThD5tCix6Uoc8IQmEtvBGOyGhVbGGGefTxMOKXnf1kwQwHI579yJdO4qYzpxKR0NwRoK0z2K1c/6jbz86jHqSU0UyY606t5Y7L5yR0PLEcO6aY57+xiIev78qVnzWA7k9VUR7BsDRNTn2sJ51Oj77nWMyEPxTmiNuXcnmIvsgstzALafL4mf/Lf/VaX5BhHn08jMlkX4I9ercvhCM3h7OOHYVfD8m43H5avQEqCh0caO1KX/z0nSv4zOwxXHvqJG35uDFUJtlLnTehrNvyQM3cDaN21O1PSwexWELhCM3eAAV2G/5QmFFxnkZSgSEQ53L7qa00V9opaDr9UsKkKnM0kcl8a2Fxdrvid20qzICMm4FIVujG4w9GlSer9YIel8dPW2eQMqed17+zhAK7jaW/fYdAKMIrmw5z8bzxQOqaXAO8cstiygsHPl5NueYV7mv2ckyPdNVU09wRQEr49jnTmTuhjJI0Nb42u0ff0Kz9bidWmOMmpAy9idl+2M3n7l8d9z0ziZkNF8Oj76uH6nCJbQNYXaTF2xvdflo7g4wtK4gaS1uMMI+hvZNKQz9QyMagVs8OqjeBoqUhD1FbWcgpUyrTNg5jAttM2UixGP9Xqdbq7wsVozcxj62u6/O9ZOafp4pkefTuGIlho0S/0ROgzdt3I2sj/m30dTUTFYUOivJy096q0eMPsa6+hdpKJ0uOSbwO0FCoKHQgBLhMmkt/pN2Pw5ZDZWF6Q20GmW8tLMwn/XRrsgLRGH2CPXp3TL9XpyOXQocNl1sL3cR67LHZLs3eALYcYUpZCSEEtZVOHn2vLq09ZT/Y20QoIvnfS2en/Yky15ZDhdNhWo9eqxp2IHrKuaYJ813VCkALJWzY18rcCWUsmloZlf8945hR/aovZhJJi9H7Qowr60qRrCrOo76pg1BEdvPYH77+JD53/2oaPQFW7nQxfVSRaX6YPbnm5Fr++7lNrNrVyLFj0hOnN7peTRtdlJbj98TMufQut7/PQrh0oAy9yegMhPnZP7Zw3PhSIhJ+dP5MTppU0W2bhZMr+vh0ZmEY+qHG6KWU3P7PT7h43vi4cW6PP0RxzCRhdVEeO/WbY6xHP6W6iB+dP4tvP7WBzQfauXHJlOGcRkq4auEE/ueVbWkN3xipvemagO1JVZG5KoZjcbn9jE1iPcZQUaEbk/H61sM8+eE+fvz8Zmw5grk1ZQN/KEMpKbAjxNAzJ1q9Qe5fsYdr/vh+3Pc9/u5tAKuK8qIGclKPVLxpo7q80yXTq4c0jlQihGBihZP6pvhZWKnA4w9htwnTCHWZ2aNPl+BbX5jjf0wRJTanelxZviUmXfsi3641Khlqf9R2nzZx6umjKjI26wa6UvEKHTZOmFjebdtZY7ueCBZM6v6e2ZhU5aS+yUsgFOH3b+1MeP3BQBh9dM0S3qoq0mL0ZqkYNohEJE0dgbRo9feFCt2YjNiS7p7epxWZWOmMio8NFiNDJhju/QNvaPISCEe6PTYbntWpU6t63ThzcgTf/dQxuH1B0ygN9sUxo4t5dfNhHly5h2Wv7yAUkXz7nGNSdnyPP5SUPrrDpbo4D38o0itUl252HvUQjkhTFXKN2F0UQtiEEOuFEC/py5OFEGuEEDuFEE8JIcxzW8sAYtvMTawwRw5uMqmtKBxyfnjPfqEHWzu5753dNDR5WbFTa7J9ekwbQMOj7ysl8Jal0/nh+bOGNIZ0cPq0KiISln9yFICWjtSmFmppq+YxqFUmzaVfqV+Di6enNwU1lkTEBb4FbItZ/jVwp5RyOtAC3JCAY2QNsXomPSdhrUhNeQGNHv+Q+n/21Hx56N293P7PT7j3nV181NDC6JK8bt2YZo8vZVRxHktnju65q4xi3oQyHLYcthzU0m4DcZ5okklsxbEZ6KqONVcu/UcNWq1BPAG7dDEiQy+EqAHOB/6oLwvgbOAZfZPHgEtGcoxMZF19C+/tahzWZ9u8AUoL7Oz61XlccsL4BI/MfJTpBSWG8Q6EIqyrb+n3M7FPPVJK6nTJ372NHdQ3eZlcVdgtjjy7ppQPfniOaQSmhkuurk1vpKOGwqlpx2hgttCNWT36ukYvU0zW9nGkHv1dwPcB44qrBFqllEageT8Q11oJIW4UQqwVQqx1uVwjHIZ5CIUjXHbve1z9xzVRAzQUWvWinlybdSdhYzHSHdt14/2j5zdx2b3vRbtDxaM9xtC3eIPRGH9Dk5f6Ji+1JtEXSQaGmBf0PRmdLDy+kKkKyox+tUfafWkeSRdSShqavaaKz8MIDL0Q4gLgqJRyXezqOJvGfb6UUj4gpVwgpVxQXW3etLah0u7r+vE99O7eIWcEGMJb2YIhSdDqDbLziJun1+4H+td1afV2ParXNXVEUycPtvlo9PiZaBJ9kWQQKwuc6tRCs3n0FYUOCh22tEtDxNLcEcDjD5lufm0kbuMi4CIhRB3wJFrI5i6gTAhhXA01wMERjTDDiDVCf3q/no8ahtburKUjkFJhrXRj3NTW1rfwqTtXRNfX9ZMvfri9y8B9VN9CIBThlCld8xnTR5mjcjMZxOZmpzJk4QuGNSfERNemJg1RmNbagp4YT5dmkSc2GLahl1L+QEpZI6WcBFwJvCWlvAZYDlyub3Yd8MKIR5lBGLHmLy2aBNBnCCJec2qAhmZvt16mVqesQAtFPL9e6wZ17zUnAvSZWx+JSFbvbuTfjtMmVtfrfUOvPrmWJ288haduPCXjJ137I9ajT6WhX1ffQjAsTVdrUFvpZNOBNtPk0hs3HbPIExskIxD8X8B3hRC70GL2DyXhGKbFmChccowWjor3Y2xo8jLzx6+y+UBbt/VtnUFavMGoLG02YDy9fHLYzfRRRZw3eyxTqwv7fBzf7fLQ6AmwdOZoxpbms1E39JWFDk6ZUsnJUyqT1tjbDBg9bgE6AuFoa8lks2KHC7tNpFWaOB5Tqgtp9AS4+82d6R4KoIUchYAJFeaa+E+IoZdSvi2lvEB/vUdKuVBKOU1KeYWU0lxT4knG0DWfUO7EYcvBFSeOuqfRQyAc6dXFviGqYW0ubyCZFMfEfBfrEgSVRXk095EjbqTS1ZQXMLHCGRXaypZw16UnjOc3l8/hm2dPA6DRnZrUwhU7G1lQW5F21cqe3HC6pk+00yRCfw1NXsaW5A/YNSzVZEdqRwqJ1TU3SrT72qa+qYNL/rCK/31FK0P44sNrAPPF95JJToz3vVgvaCotsPfZH9XINCnOs3dr6pAthr4wL5crFkzgxFothBLPkUg0R90+th1qjz6lmomKQgcLJ1eYJsWyodlrymQAZegTTGtMpyJNdKm3x9Vl6L1s2NfK/Sv24A+FafUGmVjh5Ng0t4tLNfdecyK3njeDxXo1a1m/hl5bX5Sf2+3JpzSLMpUgtR2W3t2p1YSYqdIzluoi84ibHW73mapQyiCrDP1v39jB799KXiyvrTPInf/aQb49B7stp08ZVSO8s/1IVxOJJv2G8PUzp5pGNCpVnDd7LF87Y2q0dqDMaedQm49zfvtOr8Izj56+WpSX2y2FzUwVm6nAmJR1uX3c985u7klijHrFDhdVRY5uAnBmorrYHHLFUkoaPf5uE+ZmIasM/T1v7mTZ6zuStn9jcvXTs8YAWipcPE+jNcajNzjUpsWaq00kbZoujDDMrqMenl67r9t7biN0k5/LnJrS6PpsuzlWF+VRUehgfUMrt//zE377RnKu60hE8u6uRk6fVtUtzGYmqoocuH2hlKt59sTjD+ELRroVtZmFrDL0ycYw3N8/91hA8zSaPP5eqZTxwhKX3as1ATdTV5p0Uers+qG8u6uRSMz35/F1aaJn06R1T3JyBKdPq+JZPS01WWw73E6jJ2DK+LyBUVuQ7vCNEaZVHn0a6StvPZHUN3fgsOVEY3RVRQ4iElq83eP0PdUXYzHjRZJqCh1dGQuNngBHYx7LjaYihge/7Iq5/Oj8mSkfoxn4wim1ST+GIaA2v9Zc+fOxVOqGvqWj799VKjDCR2ZqOGKQNYa+p7EdLkbTB7ev+0XV5PFz/zt7qKkoiOZxVxdrOc8944dtnYE+24yZpWt8OjEyawwRstjKR3ePpiKXz6/hy4vN2wIwmSycXMFpU7vy2pMRuqhv6iA3R5haEM4I9bV2plfFUhl6E5CoyZq3PjnKstd3sHx7dyE2Qwf9xJgORkasrucj5VG3n3kTypg3oYz5teV8+5zplDntfHrWaNM3v0gF5x43hilVhfzy0uOB7nMZZtNETzc/vfC46Ov2PjKVRkJ9k5fx5QWmFtkzZDT6ytRKFZ8cbidHwPhy890UsyZVIVGG3jDojT32Z2SD3HrejOg6IwxzoKWT59cf4KK54whLyYGWTi6YM5Z7vzA/um0qOwWZnVEl+bz1vTMJhiPYcgT1zV0evdk00dPNsWOK+f3VJ3DzX9bT2hlkVEliG1I3NHtNJ9DVk1hhvHSyYmcj8yaUmaZ5eixZ84vZ7Rp55ZyUkhU7NEPfs1DFyAaJlXE1ytXveG07zR0BguEICydXEIpIS0vpJgq7LYea8oJuHn1zRyArWiwOBUMvKBmGrr7Jy0VzxyV8v4mkpCD9Hr0/FGbT/la+fubUtI2hP8z7PJZgVupFHwUjCI3UNXmjJffxPHojG8SgMC+XykJHtJz/d2/tYucR7YZTa8LqOTMyscJJQ7OXjxpa8IfCGeFhpppkhS7e2eGirTNo+ms1324j356TVkO/v6WTiIQpVeZUTs0KQy+l5MO6ZgACI+jK8+FebR+lBfbeHr2vezaIQeyPpKHZy//p+c7ZnBo4FGornXy8v43P/r/3+Pk/tuILRkxveFKNMfnXX7OWodLcEeC6hz8AMqN3cVmBo5tEeKrp0qky53eVFYa+uSOA2xei3GknHJG9Ui2llGw+0DZgs+U6PQNhTk1pr5h/X00ZDINe5rRT6LCx7VA7ebk50e44iv6JDXE99aFWPDVR3SS7MaY0n9pKJ6uG2b4yHntjuqNlglNSWmBnbV1Lt5qLVBKVJ1aGPn0YzQCmj9I0ZAKh7l79OztcXPC7d7nqwfcH3E9NeQE15QUcaO3sdlH1lQ1ixJPLCuxcNE/rqlhb6TRtlaHZmD6661E4pH/f0yzcWGS4nDa1ijX6E2ciaIiZAM8Ej760wM6exg7+te1IWo5f3+zF6bCZtrI9Owy9frc1jIY/1D3fePthTXPmk8PuPuVxQXs8m1hZyILaClq9Qd7f00QoHCEckVo2SByP/mS981Fdk5czdHVGszUlMDNLplfz1I2nRJe/fc50U+d0p4uJFU7cvhAdCegjGwpHqGvUnKPXv7OEAof5U36XXTEXgB0x+lGppL5JmzsyqxRHVmTd7HF1IARMqdYMfU+Pvj6mycW7uxr7zDKob+pg3oQyFk+vQgi4+o+arPDECiclBbmMLu6d2mbk1U+pLuTUqVXk5gimVitDP1hycgQnT6lkfm056+pbuOH0yekekimJrdkoHEH66XPr9/OdpzZy0qRyxpcVcEyGKKlOrHRSXZzXb6/hZFLf1GHqJ82s8Ojf293EnJqyqMft72HoG5q8zB5fSpnTHk2f7IkvGKbdF2JMaT6jSvJ55PqTomXhDc1e6hq9cWP0jtwc/nHz6fz1K6dQWmDnqa+eylfPMGcKlpm5/4vzefam0yg2YY6yGTBqNkaq9/KvrUcB+LCuxdSyB/GorXB2c9pSRSQi2dfSaeq0X8sb+rbOIBv2tbJkelU09bFn5s3exg4mVxWyaFoVz6zbz18/aOi1H+MHZHhOZx47inNiepN6/KG4oRuA2TWl0Zz6+bXlVCiZgyFTVZTXrepY0Z2qBOnTx1Z1mlV/vi8mVjrZlwZDf6C1k0AoYtqJWMiC0M3q3Y2EI5Ilx1RHc99jQzf7mr0caO3ky4sns3h6FS9/fIjn1h/gqoUTu+3H+AHFio59adEknA5btGnIZ08cn4IzUih6Y2RxueI0uhkKhozCdz91DBfMMXehVE9GFefT5AkgpUxprNyYBJ83oSxlxxwqljf0K3c2UpSXy7wJZdGwTKyhXxntnlPNtFFFfP3Mqdz79m7ufXt3tyo3Q4I0VrAo327jutMmpeAsFIr+qSh0IMTIPfq2ziDTRhVxy9LpCRpZ6igtsBMIR/AFIymdQNYas+Qxc4w5G7NAFoRuGpq9TB1VhN2WgyNO6OaTw+0U5+VGJ0ivXjiRHAGPvVeHlF3pk/E8eoXCLOTacqhwOkYco2/1BqPaMZmGUSGcShVLozHL4unmbcwCWWDo2zq7LlyHrsAXCEV4fHUdr2w6pKVFVXalRU2ocPI/l87mcLuPXXpn+UAowm3/2AJAZaEy9ApzEtu6cvOBNr70yAdc+/AHPPZe3YCffWbdfv7j6Y2s3tMUNZiZRjrEzbYcbKe5I2D6+QzLh27aOoNMrtK89ahHH4rwkxc0wz25qpCZY7unkC3Wu+m8s8PF9NHF7DjiJhCKMHNsSXQfCoXZ0JrRa4b+T6vrWbW7iUKHjQMt3n5DjFJK7nj1k2iDl2A4PdWlI6U0DeJmhprt6SY39Ja3WrGPooaRvu+d3dH365o6ehUwjS8rYGp1Ib98eRv1TR006DP5y66Yk6JRKxRDp6pI6yH73u5GVu50sXTGKM49fuyAhu+Tw+5uXby2HGxL9lCTQqkztR79HpeH37y2nZljSxgVp4bGTFja0EciknZfMHqnryl3Mm1UUbdS8dHF+XEfu4ysm2fW7adOr6zNBM0PRfZitNS7+sE1HGzzsXByBWVOO22dwW7zTT1ZubN77cj/XDo7qeNMFmV6r+G2FMXoDe2lqxZOSMnxRoKlDb3bF0LKrmbTpQV2/vXdM6L59C98YxHv//dSFk3rbei/vHgKNeUF7Dji5v539lBV5OimNa9QmI2eGTeTqgopLbATDEte2xJfA+Zwm4//e31HdPmcmaP49HFjkjrOZJHq0E1dUwdTqwu59tRJKTneSLC0oTf+w3tmEfzvZzWPZeoAJcu1lU5e23KEts5gVD5BoTArl/ao46itcEav/a/9eV0v1VaAv6ypxx+KcPXJ2hPsFzPAaPVFocNGXm5OwrrJDUR9kzdjnvItbeiNNKvSHob+syfWUHf7+QN66LH/iQ9dtyDxA1QoEshZx47id1edEF2uKXd2y6CJJ9jX7A1QnJ/L/1w6m7rbz+cMPREhExFCMLHCmRK9GyllRjXBsbShP6B3g6oaZu77lKouQ680VhSZwMyxWtHOhIoCHLk5lBZ0yW3E83RbvUHTSusOh9pKZzR5Ipm0eIN4A2EmZIiht3TQeeUurSr2uHHDq1g7ZUplgkekUCSXaaOKeP8HSynM0ypDY/WX4hVTtXUGoz1XrcDEikLe292UdBkE46Y5uiQzbpKW9ug/2NvMwskV2G3DO81Zund0/HjzljYrFD0ZU5offQItL+zfo2/rDGZsgVQ8JlYU4A2E++0rkQiilfIZ8jRkaUN/pM03ohhaTo7gvVvP5okvnzLwxgqFCRlfVsCLNy8CNM30UA/l1lZvsNccViZjpFi2+0begKU/XB4fMPywcKqxrKH3BcO4/aERa9OMKyuw1A9BkX3MHl9KcV4u97y1i5v/sr7be63eQMZq28TDSLDwJNnQN7q1J4ZM0b4atqEXQkwQQiwXQmwTQmwRQnxLX18hhHhDCLFT/5sWEfFMe7RSKJKFEIIHr1vAmcdWs3z7UXxBrZVmOCJx+0PROhMrYDT/cfuTm0vv8vhx5OZQnCG1NSPx6EPAf0gpZwKnAN8QQswCbgXelFJOB97Ul1OOy6PUJhUKg1OmVHLtqbX4QxHW1bcAmjcvZe/040zG8OjdyQ7duP1UF+WZtkdsT4Zt6KWUh6SUH+mv3cA2YDxwMfCYvtljwCUjHeRwMJqMVCmPXqEA4PjxpQDs1Btor9UN/vHDzEozIyX6JHSyQzf7W7zdunGZnYTE6IUQk4ATgDXAaCnlIdBuBsCoRBxjKIQjkm8/tQGAqmLrPJYqFCOhuigPp8MW7av67s5GCh02TrBQi0YjdOPxJ9fQ1zd5qc2QHHpIgKEXQhQBfwe+LaVsH8LnbhRCrBVCrHW54jfkHi6bDrThDYQ5aVI5Y0rMrSqnUKQKo3K0Qa8c3dvYwfTRxZaS3jbqB5Jp6L2BEEfdfmpN3CO2JyP6HxZC2NGM/BNSymf11UeEEGP198cCR+N9Vkr5gJRygZRyQXV1YsuuV+xwIQTc/8UFGRNDUyhSQW2lM6rG6nL7LTeHlZdrw5Gbk9QYvVF5myk6NzCyrBsBPARsk1L+NuatF4Hr9NfXAS8Mf3jDY8vBNiZXFVJRqMI2CkUsM8eWsKexg1ZvgEaP9Qw9QHFeLp4kZt2sb2gF6NWwyMyMxKNfBHwROFsIsUH/9xngduBTQoidwKf05ZRS3+RlUgbdbRWKVLF4ejVSwq1/30RTR8CSyQpF+blJnYxdscPF2NJ8pmaQou2wk0CllO8CfcVFlg53vyPFUJU7darSqVEoejK3phSnw8arWw4D1kw/Lnc6ounViSYUjvDurkY+c/zYjAoLW2cWRsfl8eMNhDNqRlyhSBW5thycDlt02WHLHGM1WGorkydVvHF/G25fiCUZJudsOUO/9aCW+DNQUxGFIlv50fmzoq/n11ontdKgtsLJwdZOAqHIwBsPkY37tPj8wskVCd93MsmM+t0hsHJnI47cHBbUZtZ/hEKRKi45YTyXnDB+4A0zlImVhUQkHKMFsCQAAAqESURBVGjtZHJVYufqXB4/dpugMsMSPSzn0a/Y4WLhpAoKYh5PFQpF9mDkt9fraaSJpNHtp7Iwj5yczAp5WcrQH2rrZOdRD0uO6d3sW6FQZAddhj7xcXqXx5+R1faWMvQrdzQCZNxEiUKhSBxRqYckGPpGjz8jFXEtY+illLy06RCjivM4dnTmFDIoFIrEEpV6aE586Mbl9mdk7YFlDP297+xmxQ4Xi6dXZ1R+q0KhSDwTKxKfYukPhWnyBDKy9sAyhv7VzYdx5Obw/XOPTfdQFApFmplUVUhDs5dIRCZsn+vqWghFZEaqfVoivbK5I8CmA21855xjGK3UKhWKrGdihRN/KMIRt4+xpcPXjX9hwwFu/fsmIlISjkhyc0RGVt1bwtC/u6sRKWHxdJVto1AoumfejMTQv7uzkVyb4OqTawGYNbYk2sUqk8i8Ecdh5Q4XpQV25tSUpXsoCoXCBNRWaIVSDU1eTpkyfA+8vtnLsaOL+cF5MxM1tLSQ8TF6KSUrdro4fVoVtgwrYlAoFMlhXFk+uTmC+hFm3jQ0eZmYQQ1G+iLjPfqdRz0caferIimFQhEl15ZDTXkBf1i+m5MmVXDmsUPraPrgij2s2t3I4XafJSTPM96jX7FDa0N4+nRVJKVQKLooc2oVrNc/8uGQP/v/3t7F5gPtzK8t5+wZKW97nXAy2tCv3Onily9vY2p1IePLMqcju0KhSD4HWzujr1/ceHDQn2v3BWnxBvny4sn8/euncfz40mQML6VktKHf29hBZaGDqxZOTPdQFAqFyfj1ZXMo1jNkfvfmzkF/zmiebqWeFhkdo7/21Elce+qkdA9DoVCYkLNmjGLTz/6NX760lT+9X08kIgelOvnAij1AZjX/HoiM9ugVCoViIGorteKpo+6B2wseauuMhnkSrWWfTpShVygUlmai7pm/vOlQn9tsPtDGSx8fjHrzL33zdEv1tMjo0I1CoVAMhKFm+4uXtnLNyRPJt3c34L5gmCvuW01nMAxATXkBx40rSfk4k4ny6BUKhaUZU5rPry+bDcDf1u3v9f4He5vpDIb59WWzeeM7S3j5m4stp4CrDL1CobA8F84dhxDw4+c3s6+5u3zxih0uHLYcLpw7jumjiyl12tM0yuShDL1CobA8Tkcu931hPgAvfdw9Vr9yZyMnTS7H6bBuJFsZeoVCkRV8etZoRpfk8etXP2HDvlYADrf52H7EzRKLV9YrQ69QKLICIQS/ukSL1W860AZo1fUAiy1u6K37rKJQKBQ9OHvGqGis/sfPbwagqiiPmWOt3WdaefQKhSJryMkRyB7dBX964SzLZdn0RBl6hUKR1Vw4d1y6h5B0VOhGoVBkFX/5ysnscXUggckW0rPpD2XoFQpFVnHa1CpOm5pdjYpU6EahUCgsjjL0CoVCYXGUoVcoFAqLkxRDL4Q4VwixXQixSwhxazKOoVAoFIrBkXBDL4SwAX8AzgNmAVcJIWYl+jgKhUKhGBzJ8OgXAruklHuklAHgSeDiJBxHoVAoFIMgGYZ+PLAvZnm/vq4bQogbhRBrhRBrXS5XEoahUCgUCkiOoY9XSyx7rZDyASnlAinlgupqawsKKRQKRTpJRsHUfmBCzHINcLC/D6xbt65RCFE/zONVAY3D/GymkS3nmi3nCdlzrtlynpDac60dzEZC9lT4GSFCiFxgB7AUOAB8CFwtpdyS0AN1HW+tlHJBMvZtNrLlXLPlPCF7zjVbzhPMea4J9+illCEhxM3Aa4ANeDhZRl6hUCgUA5MUrRsp5SvAK8nYt0KhUCiGhhUqYx9I9wBSSLaca7acJ2TPuWbLeYIJzzXhMXqFQqFQmAsrePQKhUKh6IeMNvRW09QRQjwshDgqhNgcs65CCPGGEGKn/rdcXy+EEPfo5/6xEOLE9I18aAghJgghlgshtgkhtgghvqWvt9S5CiHyhRAfCCE26uf5M339ZCHEGv08nxJCOPT1efryLv39Sekc/1ARQtiEEOuFEC/py1Y9zzohxCYhxAYhxFp9namv3Yw19BbV1HkUOLfHuluBN6WU04E39WXQznu6/u9G4N4UjTERhID/kFLOBE4BvqH/31ntXP3A2VLKucA84FwhxCnAr4E79fNsAW7Qt78BaJFSTgPu1LfLJL4FbItZtup5ApwlpZwXk0Zp7mtXSpmR/4BTgddiln8A/CDd40rAeU0CNscsbwfG6q/HAtv11/cDV8XbLtP+AS8An7LyuQJO4CPgZLRimlx9ffQ6RktJPlV/natvJ9I99kGeXw2agTsbeAmtQt5y56mPuQ6o6rHO1Nduxnr0DFJTxwKMllIeAtD/jtLXW+L89cf2E4A1WPBc9XDGBuAo8AawG2iVUob0TWLPJXqe+vttQGVqRzxs7gK+D0T05UqseZ6gSbq8LoRYJ4S4UV9n6ms3k3vGDkpTx8Jk/PkLIYqAvwPfllK2CxHvlLRN46zLiHOVUoaBeUKIMuA5YGa8zfS/GXmeQogLgKNSynVCiDON1XE2zejzjGGRlPKgEGIU8IYQ4pN+tjXFuWayRz9kTZ0M5YgQYiyA/veovj6jz18IYUcz8k9IKZ/VV1vyXAGklK3A22hzEmW6VAh0P5foeervlwLNqR3psFgEXCSEqEOTJT8bzcO32nkCIKU8qP89inbzXojJr91MNvQfAtP1mX0HcCXwYprHlAxeBK7TX1+HFs821l+rz+qfArQZj45mR2iu+0PANinlb2PestS5CiGqdU8eIUQBcA7aZOVy4HJ9s57naZz/5cBbUg/smhkp5Q+klDVSyklov8O3pJTXYLHzBBBCFAohio3XwKeBzZj92k33xMYIJ0U+gyagthv4YbrHk4Dz+StwCAiieQI3oMUu3wR26n8r9G0FWtbRbmATsCDd4x/CeZ6O9vj6MbBB//cZq50rMAdYr5/nZuA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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run1.plot.line(x='ticks', y=\"count turtles with [ sick? ]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# So what does _that_ mean?\n", "\n", "Well, that depends. We often get to the end of a process (or more appropriately, the end of the beginning), and are left with a distinct sense of 'so what'. Embrace that. It's telling you something. Perhaps we're graphing the wrong thing, or asking the wrong question. Or perhaps our experiment was set up poorly. There clearly is _some dynamic_ at play here: try more exploratory visualizations of your data. Can you explain what is happening in this model? How does it force you to ask _other_ questions - and what _kinds_ of questions? \n", "\n", "Explore the [sample models on Netlogo Web](http://netlogoweb.org/). Are there any that seem like they'd be useful for archaeology? Try to get them running here. Run some experiments! These three notebook files should get you going asking some _good_ questions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }