bugfix in corr_cov for multiple dims

Author: headtr1ck

xarray/core/computation.py

@@ -30,7 +30,7 @@
 from xarray.core.merge import merge_attrs, merge_coordinates_without_align
 from xarray.core.options import OPTIONS, _get_keep_attrs
 from xarray.core.pycompat import is_duck_dask_array
-from xarray.core.types import T_DataArray
+from xarray.core.types import Dims, T_DataArray
 from xarray.core.utils import is_dict_like, is_scalar
 from xarray.core.variable import Variable
 
@@ -1219,7 +1219,7 @@ def apply_ufunc(
 
 
 def cov(
-    da_a: T_DataArray, da_b: T_DataArray, dim: Hashable | None = None, ddof: int = 1
+    da_a: T_DataArray, da_b: T_DataArray, dim: Dims = None, ddof: int = 1
 ) -> T_DataArray:
     """
     Compute covariance between two DataArray objects along a shared dimension.
@@ -1230,7 +1230,7 @@ def cov(
         Array to compute.
     da_b : DataArray
         Array to compute.
-    dim : Hashable, optional
+    dim : str, iterable of hashable, "..." or None, optional
         The dimension along which the covariance will be computed
     ddof : int, default: 1
         If ddof=1, covariance is normalized by N-1, giving an unbiased estimate,
@@ -1300,9 +1300,7 @@ def cov(
     return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
 
 
-def corr(
-    da_a: T_DataArray, da_b: T_DataArray, dim: Hashable | None = None
-) -> T_DataArray:
+def corr(da_a: T_DataArray, da_b: T_DataArray, dim: Dims = None) -> T_DataArray:
     """
     Compute the Pearson correlation coefficient between
     two DataArray objects along a shared dimension.
@@ -1313,7 +1311,7 @@ def corr(
         Array to compute.
     da_b : DataArray
         Array to compute.
-    dim : Hashable, optional
+    dim : str, iterable of hashable, "..." or None, optional
         The dimension along which the correlation will be computed
 
     Returns
@@ -1383,15 +1381,14 @@ def corr(
 def _cov_corr(
     da_a: T_DataArray,
     da_b: T_DataArray,
-    dim: Hashable | None = None,
+    dim: Dims = None,
     ddof: int = 0,
     method: Literal["cov", "corr", None] = 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.
     """
-    dim = None if dim is None else (dim,)
     # 1. Broadcast the two arrays
     da_a, da_b = align(da_a, da_b, join="inner", copy=False)
 
@@ -1633,7 +1630,7 @@ def cross(
 
 def dot(
     *arrays,
-    dims: str | Iterable[Hashable] | ellipsis | None = None,
+    dims: Dims = None,
     **kwargs: Any,
 ):
     """Generalized dot product for xarray objects. Like np.einsum, but
@@ -1643,7 +1640,7 @@ def dot(
     ----------
     *arrays : DataArray or Variable
         Arrays to compute.
-    dims : ..., str or tuple of str, optional
+    dims : str, iterable of hashable, "..." or None, optional
         Which dimensions to sum over. Ellipsis ('...') sums over all dimensions.
         If not specified, then all the common dimensions are summed over.
     **kwargs : dict