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Adds option for parallel weight generation with xESMF #145

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Adds option for parallel weight generation with xESMF #145

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5 changes: 4 additions & 1 deletion ndpyramid/regrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,7 @@ def pyramid_regrid(
target_pyramid: dt.DataTree = None,
levels: int = None,
weights_pyramid: dt.DataTree = None,
parallel_weights: bool = True,
method: str = 'bilinear',
regridder_kws: dict = None,
regridder_apply_kws: dict = None,
Expand All @@ -216,6 +217,8 @@ def pyramid_regrid(
Number of levels in pyramid, by default None
weights_pyramid : dt.DataTree, optional
pyramid containing pregenerated weights
parallel_weights : Bool
Use dask to generate parallel weights
method : str, optional
Regridding method. See :py:class:`~xesmf.Regridder` for valid options, by default 'bilinear'
regridder_kws : dict
Expand Down Expand Up @@ -283,7 +286,7 @@ def pyramid_regrid(
grid = target_pyramid[str(level)].ds.load()
# get the regridder object
if weights_pyramid is None:
regridder = xe.Regridder(ds, grid, method, **regridder_kws)
regridder = xe.Regridder(ds, grid, method, parallel=parallel_weights, **regridder_kws)
else:
# Reconstruct weights into format that xESMF understands
# this is a hack that assumes the weights were generated by
Expand Down
6 changes: 5 additions & 1 deletion tests/test_pyramid_regrid.py
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Expand Up @@ -16,7 +16,11 @@ def test_regridded_pyramid(temperature, regridder_apply_kws, benchmark):
temperature = temperature.isel(time=slice(0, 5))
pyramid = benchmark(
lambda: pyramid_regrid(
temperature, levels=2, regridder_apply_kws=regridder_apply_kws, other_chunks={'time': 2}
temperature,
levels=2,
parallel_weights=False,
regridder_apply_kws=regridder_apply_kws,
other_chunks={'time': 2},
)
)
verify_bounds(pyramid)
Expand Down