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apps:datastudio:comparescenarioscriptdetails [2022/01/11 10:24] – freddi | apps:datastudio:comparescenarioscriptdetails [2022/01/29 12:32] (current) – freddi | ||
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=== General advice === | === General advice === | ||
- | * Both scenarios should use the same meteorological conditions | + | * Only change one input parameter at once (e.g. for scenario 2: only change |
* The area of interest should exist in both scenarios. | * The area of interest should exist in both scenarios. | ||
* The script output should be shown together with screenshots of both model areas (scenario 1 and scenario 2) | * The script output should be shown together with screenshots of both model areas (scenario 1 and scenario 2) | ||
Line 79: | Line 79: | ||
//(lines 32 - 33 in current script version)// | //(lines 32 - 33 in current script version)// | ||
- | With index 0 we start at 23.06.2018 08:00h and with index 30 we end at 24.06.2018 | + | With index 0 we start at 23.06.2018 08:00h and with index 92 we end at 27.06.2018 |
Anyway, if the simulation periods between both scenarios do not match, we need to define the indexes separately for each folder (scenario). | Anyway, if the simulation periods between both scenarios do not match, we need to define the indexes separately for each folder (scenario). | ||
Line 97: | Line 97: | ||
Now the timespteps match again. Otherwise you would compare different timesteps. | Now the timespteps match again. Otherwise you would compare different timesteps. | ||
+ | === Advanced Settings === | ||
+ | |||
+ | There is the option to compare maximum or minimum values instead of the mean values. To do so, change lines 133 - 139 (in current script version) as follows: | ||
+ | == Mean vallues (default) == | ||
+ | <code python> | ||
+ | #vals1 = (np.nanmin(data1, | ||
+ | #vals1 = (np.nanmax(data1, | ||
+ | vals1 = (np.nanmean(data1, | ||
+ | |||
+ | #vals2 = (np.nanmin(data2, | ||
+ | #vals2 = (np.nanmax(data2, | ||
+ | vals2 = (np.nanmean(data2, | ||
+ | </ | ||
+ | |||
+ | == Maximum values == | ||
+ | <code python> | ||
+ | #vals1 = (np.nanmin(data1, | ||
+ | vals1 = (np.nanmax(data1, | ||
+ | #vals1 = (np.nanmean(data1, | ||
+ | |||
+ | #vals2 = (np.nanmin(data2, | ||
+ | vals2 = (np.nanmax(data2, | ||
+ | #vals2 = (np.nanmean(data2, | ||
+ | </ | ||
+ | |||
+ | == Minimum values == | ||
+ | <code python> | ||
+ | vals1 = (np.nanmin(data1, | ||
+ | #vals1 = (np.nanmax(data1, | ||
+ | #vals1 = (np.nanmean(data1, | ||
+ | |||
+ | vals2 = (np.nanmin(data2, | ||
+ | #vals2 = (np.nanmax(data2, | ||
+ | #vals2 = (np.nanmean(data2, | ||
+ | </ | ||
=== Output === | === Output === | ||
If all settings are done, the script is ready to get executed. The output should look like this: | If all settings are done, the script is ready to get executed. The output should look like this: | ||
- | {{: | + | {{: |
+ | |||
+ | If you would like to analyse pollutant-data instead, an output for e.g. NO2-concentration could look like this: | ||
+ | |||
+ | {{: | ||
+ | |||
+ | ==== Technical Note ==== | ||
+ | Note that the execution-time of the script may be unexpected high, especially when you compare longer time periods or large areas. This is caused by limited implementation options caused by the Delphi-Python-interface. During execution, the GUI might not be reactive. |