Exploring the datacube output of the SAMIR model
There are two notebooks
, one for each running mode, they are otherwise identical in their content. They are divided in 3 parts.
Plotting maps of cumulated or averaged variables
This first part processes the output dataset to sum (for the extensive variables) or average (for intensive variables) along the time dimension to produce a two dimensionnal (spatial) dataset. The resulting maps are then plotted on the same image (and saved in the output directory).
Calculating variable statistics for each land cover class
With the two dimensionnal processed dataset and the land cover raster, statistics for each land cover class can be calculated. The result is a pandas
dataframe that can be saved. Here is an example:
DP |
Irr |
E |
Tr |
SWCe |
SWCr |
ETR |
NDVI |
Rain |
|
---|---|---|---|---|---|---|---|---|---|
Unit |
mm |
mm |
mm |
mm |
[0-1] |
[0-1] |
mm |
[0-1] |
mm |
Operation |
sum |
sum |
sum |
sum |
mean |
mean |
sum |
mean |
sum |
no_sim |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
0.525 |
465.725 |
Strawcereals |
0.000 |
0.000 |
158.894 |
290.473 |
0.410 |
0.411 |
449.367 |
0.483 |
467.686 |
Oilseeds |
0.000 |
0.000 |
156.194 |
291.650 |
0.349 |
0.434 |
447.844 |
0.514 |
467.544 |
Soy |
0.000 |
199.290 |
247.010 |
334.268 |
0.484 |
0.539 |
581.279 |
0.416 |
469.850 |
Sunflower |
0.000 |
0.000 |
218.971 |
217.039 |
0.400 |
0.418 |
436.009 |
0.357 |
468.605 |
Corn |
0.000 |
154.943 |
246.207 |
286.925 |
0.426 |
0.565 |
533.131 |
0.397 |
469.100 |
Grasslands |
0.000 |
0.000 |
96.462 |
361.480 |
0.393 |
0.361 |
457.942 |
0.629 |
467.333 |
Plotting variable time series for a chosen coordinate
With this last part, you can enter a point coordinate (in lattitude/longitude coordinates) and extract a time series of the output variables and plot them on a graph.