xarray.dot#

xarray.dot(*arrays, dims=None, **kwargs)[source]#

Generalized dot product for xarray objects. Like np.einsum, but provides a simpler interface based on array dimensions.

Parameters:

*arrays (DataArray or Variable) – Arrays to compute.
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) – Additional keyword arguments passed to numpy.einsum or dask.array.einsum

Returns:

DataArray

Examples

>>> da_a = xr.DataArray(np.arange(3 * 2).reshape(3, 2), dims=["a", "b"])
>>> da_b = xr.DataArray(np.arange(3 * 2 * 2).reshape(3, 2, 2), dims=["a", "b", "c"])
>>> da_c = xr.DataArray(np.arange(2 * 3).reshape(2, 3), dims=["c", "d"])

>>> da_a
<xarray.DataArray (a: 3, b: 2)>
array([[0, 1],
       [2, 3],
       [4, 5]])
Dimensions without coordinates: a, b

>>> da_b
<xarray.DataArray (a: 3, b: 2, c: 2)>
array([[[ 0,  1],
        [ 2,  3]],

       [[ 4,  5],
        [ 6,  7]],

       [[ 8,  9],
        [10, 11]]])
Dimensions without coordinates: a, b, c

>>> da_c
<xarray.DataArray (c: 2, d: 3)>
array([[0, 1, 2],
       [3, 4, 5]])
Dimensions without coordinates: c, d

>>> xr.dot(da_a, da_b, dims=["a", "b"])
<xarray.DataArray (c: 2)>
array([110, 125])
Dimensions without coordinates: c

>>> xr.dot(da_a, da_b, dims=["a"])
<xarray.DataArray (b: 2, c: 2)>
array([[40, 46],
       [70, 79]])
Dimensions without coordinates: b, c

>>> xr.dot(da_a, da_b, da_c, dims=["b", "c"])
<xarray.DataArray (a: 3, d: 3)>
array([[  9,  14,  19],
       [ 93, 150, 207],
       [273, 446, 619]])
Dimensions without coordinates: a, d

>>> xr.dot(da_a, da_b)
<xarray.DataArray (c: 2)>
array([110, 125])
Dimensions without coordinates: c

>>> xr.dot(da_a, da_b, dims=...)
<xarray.DataArray ()>
array(235)

xarray.dot

Contents

xarray.dot#