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Improve perf of xr.corr #6638

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Improve perf of xr.corr #6638

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14 changes: 8 additions & 6 deletions xarray/core/computation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,8 @@

import numpy as np

from xarray.core.types import T_DataArray

from . import dtypes, duck_array_ops, utils
from .alignment import align, deep_align
from .common import zeros_like
Expand Down Expand Up @@ -1353,7 +1355,9 @@ def corr(da_a, da_b, dim=None):
return _cov_corr(da_a, da_b, dim=dim, method="corr")


def _cov_corr(da_a, da_b, dim=None, ddof=0, method=None):
def _cov_corr(
da_a: T_DataArray, da_b: T_DataArray, dim=None, ddof=0, method=None
) -> T_DataArray:
"""
Internal method for xr.cov() and xr.corr() so only have to
sanitize the input arrays once and we don't repeat code.
Expand All @@ -1372,11 +1376,9 @@ def _cov_corr(da_a, da_b, dim=None, ddof=0, method=None):
demeaned_da_b = da_b - da_b.mean(dim=dim)

# 4. Compute covariance along the given dim
# N.B. `skipna=False` is required or there is a bug when computing
# auto-covariance. E.g. Try xr.cov(da,da) for
# da = xr.DataArray([[1, 2], [1, np.nan]], dims=["x", "time"])
cov = (demeaned_da_a * demeaned_da_b).sum(dim=dim, skipna=True, min_count=1) / (
valid_count
# `fillna` is required since `.dot` has no NaN tolerance.
cov = (
demeaned_da_a.fillna(0.0).dot(demeaned_da_b.fillna(0.0), dims=dim) / valid_count
)

if method == "cov":
Expand Down
4 changes: 2 additions & 2 deletions xarray/core/dataarray.py
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Expand Up @@ -3399,8 +3399,8 @@ def imag(self) -> DataArray:
return self._replace(self.variable.imag)

def dot(
self, other: DataArray, dims: Hashable | Sequence[Hashable] | None = None
) -> DataArray:
self, other: T_DataArray, dims: Hashable | Sequence[Hashable] | None = None
) -> T_DataArray:
"""Perform dot product of two DataArrays along their shared dims.

Equivalent to taking taking tensordot over all shared dims.
Expand Down