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Always force dask arrays to float in missing.interp_func #4771

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Merged
mathause merged 5 commits into from
Jan 12, 2021
Merged

Always force dask arrays to float in missing.interp_func #4771

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f3e83e6
scipy.interpolate.interp1d always forces to float.
Illviljan Jan 6, 2021
a2062ca
Copy type-check from scipy.interpolate.interp1d
Illviljan Jan 6, 2021
e935f82
Update missing.py
Illviljan Jan 6, 2021
2633469
Test that pre- and post-compute dtypes matches
Illviljan Jan 6, 2021
51e0d2f
Update test_missing.py
Illviljan Jan 6, 2021
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9 changes: 8 additions & 1 deletion xarray/core/missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -734,6 +734,13 @@ def interp_func(var, x, new_x, method, kwargs):
# if usefull, re-use localize for each chunk of new_x
localize = (method in ["linear", "nearest"]) and (new_x[0].chunks is not None)

# scipy.interpolate.interp1d always forces to float.
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dcherian marked this conversation as resolved.
# Use the same check for blockwise as well:
if not issubclass(var.dtype.type, np.inexact):
dtype = np.float_
else:
dtype = var.dtype

return da.blockwise(
_dask_aware_interpnd,
out_ind,
Expand All @@ -742,7 +749,7 @@ def interp_func(var, x, new_x, method, kwargs):
interp_kwargs=kwargs,
localize=localize,
concatenate=True,
dtype=var.dtype,
dtype=dtype,
new_axes=new_axes,
)

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14 changes: 14 additions & 0 deletions xarray/tests/test_missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -370,6 +370,20 @@ def test_interpolate_dask_raises_for_invalid_chunk_dim():
da.interpolate_na("time")


@requires_dask
@requires_scipy
@pytest.mark.parametrize("dtype, method", [(int, "linear"), (int, "nearest")])
def test_interpolate_dask_expected_dtype(dtype, method):
da = xr.DataArray(
data=np.array([0, 1], dtype=dtype),
dims=["time"],
coords=dict(time=np.array([0, 1])),
).chunk(dict(time=2))
da = da.interp(time=np.array([0, 0.5, 1, 2]), method=method)

assert da.dtype == da.compute().dtype


@requires_bottleneck
def test_ffill():
da = xr.DataArray(np.array([4, 5, np.nan], dtype=np.float64), dims="x")
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