Method _reduce

xarray/computation/weighted.py:211–232

reduce using dot; equivalent to (da * weights).sum(dim, skipna) for internal use only

(
        da: T_DataArray,
        weights: T_DataArray,
        dim: Dims = None,
        skipna: bool | None = None,
    )

    @staticmethod
    def _reduce(
        da: T_DataArray,
        weights: T_DataArray,
        dim: Dims = None,
        skipna: bool | None = None,
    ) -> T_DataArray:
        """reduce using dot; equivalent to (da * weights).sum(dim, skipna)

        for internal use only
        """

        # need to infer dims as we use `dot`
        if dim is None:
            dim = ...

        # need to mask invalid values in da, as `dot` does not implement skipna
        if skipna or (skipna is None and da.dtype.kind in "cfO"):
            da = da.fillna(0.0)

        # `dot` does not broadcast arrays, so this avoids creating a large
        # DataArray (if `weights` has additional dimensions)
        return dot(da, weights, dim=dim)

    def _sum_of_weights(self, da: T_DataArray, dim: Dims = None) -> T_DataArray:
        """Calculate the sum of weights, accounting for missing values"""