Function _average

dask/array/routines.py:2459–2509

(
    a, axis=None, weights=None, returned=False, is_masked=False, keepdims=False
)

def _average(
    a, axis=None, weights=None, returned=False, is_masked=False, keepdims=False
):
    # This was minimally modified from numpy.average
    # See numpy license at https://github.com/numpy/numpy/blob/master/LICENSE.txt
    # or NUMPY_LICENSE.txt within this directory
    # Wrapper used by da.average or da.ma.average.
    a = asanyarray(a)

    if weights is None:
        avg = a.mean(axis, keepdims=keepdims)
        scl = avg.dtype.type(a.size / avg.size)
    else:
        wgt = asanyarray(weights)

        if issubclass(a.dtype.type, (np.integer, np.bool_)):
            result_dtype = result_type(a.dtype, wgt.dtype, "f8")
        else:
            result_dtype = result_type(a.dtype, wgt.dtype)

        # Sanity checks
        if a.shape != wgt.shape:
            if axis is None:
                raise TypeError(
                    "Axis must be specified when shapes of a and weights differ."
                )
            if wgt.ndim != 1:
                raise TypeError(
                    "1D weights expected when shapes of a and weights differ."
                )
            if wgt.shape[0] != a.shape[axis]:
                raise ValueError(
                    "Length of weights not compatible with specified axis."
                )

            # setup wgt to broadcast along axis
            wgt = broadcast_to(wgt, (a.ndim - 1) * (1,) + wgt.shape)
            wgt = wgt.swapaxes(-1, axis)
        if is_masked:
            from dask.array.ma import getmaskarray

            wgt = wgt * (~getmaskarray(a))
        scl = wgt.sum(axis=axis, dtype=result_dtype, keepdims=keepdims)
        avg = multiply(a, wgt, dtype=result_dtype).sum(axis, keepdims=keepdims) / scl

    if returned:
        if scl.shape != avg.shape:
            scl = broadcast_to(scl, avg.shape).copy()
        return avg, scl
    else:
        return avg


@derived_from(np)