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Optimize nanmean with engine="flox" #237

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Optimize nanmean with engine="flox" #237

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35a972a
Optimize nanmean with engine="flox"
dcherian Apr 28, 2023
f6d9deb
Cleanup
dcherian Apr 28, 2023
75e3466
fix
dcherian Apr 28, 2023
d5b3b82
tweak
dcherian May 4, 2023
a4d26d5
Merge remote-tracking branch 'upstream/main' into optimize-engine-flox
Illviljan May 31, 2023
65d7f87
Merge branch 'main' into optimize-engine-flox
Illviljan Jun 14, 2023
4ab01d3
Merge branch 'main' into optimize-engine-flox
dcherian Oct 15, 2023
11e2c7d
Merge branch 'main' into optimize-engine-flox
dcherian May 2, 2024
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10 changes: 6 additions & 4 deletions flox/aggregate_flox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,22 +207,24 @@ def nansum_of_squares(group_idx, array, *, axis=-1, size=None, fill_value=None,


def nanlen(group_idx, array, *args, **kwargs):
return sum(group_idx, (notnull(array)).astype(int), *args, **kwargs)
return sum(group_idx, (notnull(array)).view(np.int8), *args, **kwargs)


def mean(group_idx, array, *, axis=-1, size=None, fill_value=None, dtype=None):
if fill_value is None:
fill_value = 0
out = sum(group_idx, array, axis=axis, size=size, dtype=dtype, fill_value=fill_value)
with np.errstate(invalid="ignore", divide="ignore"):
out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0, dtype=np.intp)
return out


def nanmean(group_idx, array, *, axis=-1, size=None, fill_value=None, dtype=None):
if fill_value is None:
fill_value = 0
out = nansum(group_idx, array, size=size, axis=axis, dtype=dtype, fill_value=fill_value)
mask = notnull(array)
masked = np.where(mask, array, 0)
out = sum(group_idx, masked, size=size, axis=axis, dtype=dtype, fill_value=fill_value)
with np.errstate(invalid="ignore", divide="ignore"):
out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
out /= sum(group_idx, mask.view(np.int8), size=size, axis=axis, fill_value=0, dtype=np.intp)
return out