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Committing weighted mean modifications from rtam/weighted mean to uxarray/weighted-mean #866

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Merged
philipc2 merged 3 commits into from
Jul 26, 2024
Merged

Committing weighted mean modifications from rtam/weighted mean to uxarray/weighted-mean #866

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8ae9413
updated mean function with weighted arg
Jul 19, 2024
c8e8df6
updated weighted-mean functionality in dataarray.py
Jul 26, 2024
5e5a479
edited weights to dask array
Jul 26, 2024
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13 changes: 8 additions & 5 deletions uxarray/core/dataarray.py
100644 → 100755
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Original file line number Diff line number Diff line change
Expand Up @@ -412,14 +412,17 @@ def nodal_average(self):

# weighted_mean()
# .weighted.mean()
# weights are only on grid - not supporting custom weights for now
def mean(self, dim=None, *, skipna=None, keep_attrs=None, weighted=False, **kwargs):
if weighted:
if self._face_centered():
grid_dim = "n_face"
# use face areas as weight
weights = self.uxgrid.face_areas
weights = da.from_array(self.uxgrid.face_areas.values)
elif self._edge_centered():
grid_dim = "n_edge"
# use edge magnitude as weight
weights = self.uxgrid.edge_node_distances
weights = da.from_array(self.uxgrid.edge_node_distances.values)
else:
# apply regular Xarray mean
warnings.warn(
Expand All @@ -428,13 +431,13 @@ def mean(self, dim=None, *, skipna=None, keep_attrs=None, weighted=False, **kwar
"variables. Performing an unweighted mean."
)

return super().mean(dim=None, skipna=None, keep_attrs=None, **kwargs)
return super().mean(dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs)

# compute the total weight
total_weight = weights.sum()

# compute weighted mean
weighted_mean = (self * weights).sum() / total_weight
# compute weighted mean #assumption on index of dimension (last one is geometry)
weighted_mean = (self * weights).sum(dim=grid_dim) / total_weight

# create a UxDataArray and return it
return UxDataArray(weighted_mean, uxgrid=self.uxgrid)
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