work on documentation

anemoi/datasets/data/grids.py

@@ -101,6 +101,10 @@ def check_same_grid(self, d1, d2):
         # We don't check the grid, because we want to be able to combine
         pass
 
+    def check_same_resolution(self, d1, d2):
+        # We don't check the resolution, because we want to be able to combine
+        pass
+
 
 class ConcatGrids(Grids):
     # TODO: select the statistics of the most global grid?
@@ -125,7 +129,7 @@ def tree(self):
 
 class CutoutGrids(Grids):
     def __init__(self, datasets, axis):
-        from anemoi.grids import cutout_mask
+        from anemoi.datasets.grids import cutout_mask
 
         super().__init__(datasets, axis)
         assert len(datasets) == 2, "CutoutGrids requires two datasets"
@@ -140,7 +144,7 @@ def __init__(self, datasets, axis):
             self.lam.longitudes,
             self.globe.latitudes,
             self.globe.longitudes,
-            plot="cutout",
+            # plot="cutout",
         )
         assert len(self.mask) == self.globe.shape[3], (
             len(self.mask),

anemoi/datasets/data/subset.py

@@ -29,6 +29,7 @@ class Subset(Forwards):
     def __init__(self, dataset, indices, reason):
         while isinstance(dataset, Subset):
             indices = [dataset.indices[i] for i in indices]
+            reason = {**reason, **dataset.reason}
             dataset = dataset.dataset
 
         self.dataset = dataset

docs/using/grids.rst

@@ -23,6 +23,16 @@ So for 2D fields, the thinning will apply to both the latitude and
 longitude dimensions. For 1D fields, such as reduced Gaussian grids, the
 thinning will apply to the only dimension.
 
+The following example shows the effect of thinning a dataset with a 1 degree
+resolution:
+
+.. image:: thinning-before.png
+
+Thinning the dataset with ``thinning=4`` will result in the following dataset:
+
+.. image:: thinning-after.png
+
+
 ******
  area
 ******

tools/.gitignore

@@ -0,0 +1 @@
+*.png