def std_blocksum(data, block_sizes, mask=None):
    """
    Calculate the standard deviation of block-summed data values at
    sizes of ``block_sizes``.

    Values from incomplete blocks, either because of the image edges or
    masked pixels, are not included.

    Parameters
    ----------
    data : array-like
        The 2D array to block sum.

    block_sizes : int, array-like of int
        An array of integer (square) block sizes.

    mask : array-like (bool), optional
        A boolean mask, with the same shape as ``data``, where a `True`
        value indicates the corresponding element of ``data`` is masked.
        Blocks that contain *any* masked data are excluded from
        calculations.

    Returns
    -------
    result : `~numpy.ndarray`
        An array of the standard deviations of the block-summed array
        for the input ``block_sizes``.
    """

    data = np.ma.asanyarray(data)

    if mask is not None and mask is not np.ma.nomask:
        mask = np.asanyarray(mask)
        if data.shape != mask.shape:
            raise ValueError('data and mask must have the same shape.')
        data.mask |= mask

    stds = []
    block_sizes = np.atleast_1d(block_sizes)
    for block_size in block_sizes:
        mesh_values = _mesh_values(data, block_size)
        block_sums = np.sum(mesh_values, axis=1)
        stds.append(np.std(block_sums))

    return np.array(stds)
Calculate the standard deviation of block-summed data values at
    sizes of ``block_sizes``.

    Values from incomplete blocks, either because of the image edges or
    masked pixels, are not included.

    Parameters
    ----------
    data : array-like
        The 2D array to block sum.

    block_sizes : int, array-like of int
        An array of integer (square) block sizes.

    mask : array-like (bool), optional
        A boolean mask, with the same shape as ``data``, where a `True`
        value indicates the corresponding element of ``data`` is masked.
        Blocks that contain *any* masked data are excluded from
        calculations.

    Returns
    -------
    result : `~numpy.ndarray`
        An array of the standard deviations of the block-summed array
        for the input ``block_sizes``.