c9fd2c425cb873508ba16221819574400ad2443e,dask_image/ndmeasure/__init__.py,,extrema,#,75

Before Change

        ("max_val", input.dtype),
        ("max_pos", numpy.int)
    ])
    default = numpy.zeros((), out_dtype)[()]

    extrema_lbl = labeled_comprehension(
        input, labels, index,
        _utils._extrema, out_dtype, default, pass_positions=True
    )

    extrema_lbl = {k: extrema_lbl[k] for k in extrema_lbl.dtype.names}

    for pos_key in ["min_pos", "max_pos"]:
        pos_1d = extrema_lbl[pos_key]
        if not pos_1d.ndim:
            pos_1d = pos_1d[None]

        pos_nd = _utils._unravel_index(pos_1d, input.shape)

        if index.ndim == 0:
            pos_nd = dask.array.squeeze(pos_nd)
        elif index.ndim > 1:
            pos_nd = pos_nd.reshape(
                (int(numpy.prod(pos_nd.shape[:-1])), pos_nd.shape[-1])
            )

        extrema_lbl[pos_key] = pos_nd

    result = (
        extrema_lbl["min_val"],
        extrema_lbl["max_val"],
        extrema_lbl["min_pos"],
        extrema_lbl["max_pos"]
    )

    return result

After Change

    )

    extrema_lbl = collections.OrderedDict([
        (k, extrema_lbl[k]) for k in type_mapping.keys()
    ])

    for pos_key in ["min_pos", "max_pos"]:
        pos_1d = extrema_lbl[pos_key]
        if not pos_1d.ndim:
            pos_1d = pos_1d[None]

        pos_nd = _utils._unravel_index(pos_1d, input.shape)

        if index.ndim == 0:
            pos_nd = dask.array.squeeze(pos_nd)
        elif index.ndim > 1:
            pos_nd = pos_nd.reshape(
                (int(numpy.prod(pos_nd.shape[:-1])), pos_nd.shape[-1])
            )

        extrema_lbl[pos_key] = pos_nd

    result = tuple(extrema_lbl.values())

    return result

Instances