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Set summed bin without data to fill_value #364

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Set summed bin without data to fill_value #364

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e60e3e0
set bin without data to fill_value
zxdawn May 28, 2021
f2e171b
use np.isin which supports dask
zxdawn May 31, 2021
f4e0f69
Merge branch 'main' into sum_fillvalue
pnuu Dec 15, 2023
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14 changes: 12 additions & 2 deletions pyresample/bucket/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,13 +200,17 @@
target_shape = self.target_area.shape
self.idxs = self.y_idxs * target_shape[1] + self.x_idxs

def get_sum(self, data, skipna=True):
def get_sum(self, data, fill_value=np.nan, skipna=True):
"""Calculate sums for each bin with drop-in-a-bucket resampling.

Parameters
----------
data : Numpy or Dask array
Data to be binned and summed.
fill_value : float
Fill value to mark missing/invalid values in the input data,
as well as in the binned and averaged output data.
Default: np.nan
skipna : boolean (optional)
If True, skips NaN values for the sum calculation
(similarly to Numpy's `nansum`). Buckets containing only NaN are set to zero.
Expand Down Expand Up @@ -241,6 +245,10 @@
# TODO remove following line in favour of weights = data when dask histogram bug (issue #6935) is fixed
sums = self._mask_bins_with_nan_if_not_skipna(skipna, data, out_size, sums)

# set bin without data to fill_value if fill_value exists
if ~np.isnan(fill_value):
sums = da.where(sums == 0, fill_value, sums)

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return sums.reshape(self.target_area.shape)

def _mask_bins_with_nan_if_not_skipna(self, skipna, data, out_size, statistic):
Expand All @@ -251,7 +259,7 @@
statistic = da.where(nan_bins > 0, np.nan, statistic)
return statistic

def _call_bin_statistic(self, statistic_method, data, fill_value=None, skipna=None):
def _call_bin_statistic(self, statistic_method, data, fill_value=np.nan, skipna=None):
"""Calculate statistics (min/max) for each bin with drop-in-a-bucket resampling."""
if isinstance(data, xr.DataArray):
data = data.data
Expand All @@ -268,6 +276,8 @@
shape=out_shape,
dtype=np.float64)

# set bin without data to fill_value
statistics = da.where(np.isin(statistics, (0, fill_value)), fill_value, statistics)
return statistics

def get_min(self, data, fill_value=np.nan, skipna=True):
Expand Down
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