## Analyze the results¶

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.

Once you've done that, you can use the pandas project to get the info into a dataframe:

In [1]:
%matplotlib inline

import matplotlib
import numpy as np
import matplotlib.pyplot as plt

In [21]:
import pandas


(did you get an 'error tokenizing data'? If so, you probably forgot to delete the metadata from the csv file:

"BehaviorSpace results (NetLogo 6.0.4)"
"Virus.nlogo"
"exp01"
"08/28/2018 14:15:31:214 +0000"
"min-pxcor","max-pxcor","min-pycor","max-pycor"
"-17","17","-17","17"

Delete that, save, then try again.)

In [22]:
# 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
# the [run number] column. The results will be stored in 'sorted'.
sorted = data_df.sort_values('[run number]')
sorted

Out[22]:
[run number] duration turtle-shape infectiousness number-people chance-recover [step] count turtles with [ sick? ] count turtles with [ immune? ] count turtles with [ not sick? and not immune? ] ticks
0 1 20 person 45 150 75 0 10 0 140 0
972 1 20 person 45 150 75 232 47 79 173 232
975 1 20 person 45 150 75 233 49 79 172 233
978 1 20 person 45 150 75 234 47 79 174 234
981 1 20 person 45 150 75 235 44 83 173 235
985 1 20 person 45 150 75 236 41 86 172 236
989 1 20 person 45 150 75 237 42 87 170 237
993 1 20 person 45 150 75 238 43 83 173 238
996 1 20 person 45 150 75 239 47 84 168 239
1001 1 20 person 45 150 75 240 50 80 170 240
1007 1 20 person 45 150 75 241 48 81 171 241
968 1 20 person 45 150 75 231 47 81 172 231
1010 1 20 person 45 150 75 242 46 84 170 242
1016 1 20 person 45 150 75 244 47 82 170 244
1023 1 20 person 45 150 75 245 46 81 172 245
1027 1 20 person 45 150 75 246 44 78 178 246
1031 1 20 person 45 150 75 247 46 77 176 247
1040 1 20 person 45 150 75 248 43 78 178 248
2082 1 20 person 45 150 75 519 75 30 193 519
1044 1 20 person 45 150 75 249 42 82 176 249
1047 1 20 person 45 150 75 250 43 81 176 250
1050 1 20 person 45 150 75 251 44 80 176 251
1054 1 20 person 45 150 75 252 48 80 172 252
1013 1 20 person 45 150 75 243 47 87 166 243
963 1 20 person 45 150 75 230 47 82 171 230
959 1 20 person 45 150 75 229 47 82 171 229
955 1 20 person 45 150 75 228 42 83 175 228
872 1 20 person 45 150 75 205 35 112 152 205
876 1 20 person 45 150 75 206 33 114 152 206
879 1 20 person 45 150 75 207 31 113 155 207
... ... ... ... ... ... ... ... ... ... ... ...
196 4 20 person 75 150 75 53 42 63 98 53
1370 4 20 person 75 150 75 351 88 147 63 351
1314 4 20 person 75 150 75 335 115 94 91 335
212 4 20 person 75 150 75 58 44 68 96 58
1309 4 20 person 75 150 75 334 113 94 91 334
1305 4 20 person 75 150 75 333 113 92 94 333
1219 4 20 person 75 150 75 316 78 85 137 316
1223 4 20 person 75 150 75 317 74 85 141 317
1226 4 20 person 75 150 75 318 83 78 139 318
1229 4 20 person 75 150 75 319 84 81 134 319
1233 4 20 person 75 150 75 320 87 81 131 320
1237 4 20 person 75 150 75 321 90 82 128 321
230 4 20 person 75 150 75 63 46 79 91 63
1242 4 20 person 75 150 75 322 99 82 119 322
226 4 20 person 75 150 75 62 49 75 89 62
1263 4 20 person 75 150 75 323 102 80 118 323
1683 4 20 person 75 150 75 432 101 101 98 432
223 4 20 person 75 150 75 61 45 74 93 61
1272 4 20 person 75 150 75 325 111 79 108 325
1276 4 20 person 75 150 75 326 113 79 106 326
220 4 20 person 75 150 75 60 44 73 94 60
1280 4 20 person 75 150 75 327 115 81 101 327
1284 4 20 person 75 150 75 328 120 79 101 328
1289 4 20 person 75 150 75 329 118 81 101 329
1293 4 20 person 75 150 75 330 117 81 101 330
216 4 20 person 75 150 75 59 43 71 94 59
1297 4 20 person 75 150 75 331 116 83 99 331
1301 4 20 person 75 150 75 332 117 85 96 332
1268 4 20 person 75 150 75 324 103 77 119 324
1041 4 20 person 75 150 75 267 47 183 68 267

2084 rows × 11 columns

In [ ]:
# let's get the results from Run 1
# we add the .loc operator that will let us select rows where run number = 1.
# Then we store the new dataframe in run1.
# Extract the other three runs!
run1 = data_df.loc[data_df['[run number]'] == 1]

In [26]:
run1.plot.line(x='ticks', y="count turtles with [ sick? ]")

Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f65fdf951d0>

# So what does that mean?¶

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?

Explore the sample models on Netlogo Web. 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.

In [ ]: