apply_parallel.py

__all__ = ['apply_parallel']


def _get_chunks(shape, ncpu):
    """Split the array into equal sized chunks based on the number of
    available processors. The last chunk in each dimension absorbs the
    remainder array elements if the number of CPUs does not divide evenly into
    the number of array elements.

    Examples
    --------
    >>> _get_chunks((4, 4), 4)
    ((2, 2), (2, 2))
    >>> _get_chunks((4, 4), 2)
    ((2, 2), (4,))
    >>> _get_chunks((5, 5), 2)
    ((2, 3), (5,))
    >>> _get_chunks((2, 4), 2)
    ((1, 1), (4,))
    """
    # since apply_parallel is in the critical import path, we lazy import
    # math just when we need it.
    from math import ceil

    chunks = []
    nchunks_per_dim = int(ceil(ncpu ** (1./len(shape))))

    used_chunks = 1
    for i in shape:
        if used_chunks < ncpu:
            regular_chunk = i // nchunks_per_dim
            remainder_chunk = regular_chunk + (i % nchunks_per_dim)

            if regular_chunk == 0:
                chunk_lens = (remainder_chunk,)
            else:
                chunk_lens = ((regular_chunk,) * (nchunks_per_dim - 1) +
                              (remainder_chunk,))
        else:
            chunk_lens = (i,)

        chunks.append(chunk_lens)
        used_chunks *= nchunks_per_dim
    return tuple(chunks)


def _ensure_dask_array(array, chunks=None):
    import dask.array as da
    if isinstance(array, da.Array):
        return array

    return da.from_array(array, chunks=chunks)


def apply_parallel(function, array, chunks=None, depth=0, mode=None,
                   extra_arguments=(), extra_keywords={}, *, compute=None):
    """Map a function in parallel across an array.

    Split an array into possibly overlapping chunks of a given depth and
    boundary type, call the given function in parallel on the chunks, combine
    the chunks and return the resulting array.

    Parameters
    ----------
    function : function
        Function to be mapped which takes an array as an argument.
    array : numpy array or dask array
        Array which the function will be applied to.
    chunks : int, tuple, or tuple of tuples, optional
        A single integer is interpreted as the length of one side of a square
        chunk that should be tiled across the array.  One tuple of length
        ``array.ndim`` represents the shape of a chunk, and it is tiled across
        the array.  A list of tuples of length ``ndim``, where each sub-tuple
        is a sequence of chunk sizes along the corresponding dimension. If
        None, the array is broken up into chunks based on the number of
        available cpus. More information about chunks is in the documentation
        `here <https://dask.pydata.org/en/latest/array-design.html>`_.
    depth : int, optional
        Integer equal to the depth of the added boundary cells. Defaults to
        zero.
    mode : {'reflect', 'symmetric', 'periodic', 'wrap', 'nearest', 'edge'}, optional
        type of external boundary padding.
    extra_arguments : tuple, optional
        Tuple of arguments to be passed to the function.
    extra_keywords : dictionary, optional
        Dictionary of keyword arguments to be passed to the function.
    compute : bool, optional
        If ``True``, compute eagerly returning a NumPy Array.
        If ``False``, compute lazily returning a Dask Array.
        If ``None`` (default), compute based on array type provided
        (eagerly for NumPy Arrays and lazily for Dask Arrays).

    Returns
    -------
    out : ndarray or dask Array
        Returns the result of the applying the operation.
        Type is dependent on the ``compute`` argument.

    Notes
    -----
    Numpy edge modes 'symmetric', 'wrap', and 'edge' are converted to the
    equivalent ``dask`` boundary modes 'reflect', 'periodic' and 'nearest',
    respectively.
    Setting ``compute=False`` can be useful for chaining later operations.
    For example region selection to preview a result or storing large data
    to disk instead of loading in memory.

    """
    try:
        # Importing dask takes time. since apply_parallel is on the
        # minimum import path of skimage, we lazy attempt to import dask
        import dask.array as da
    except ImportError:
        raise RuntimeError("Could not import 'dask'.  Please install "
                           "using 'pip install dask'")

    if compute is None:
        compute = not isinstance(array, da.Array)

    if chunks is None:
        shape = array.shape
        try:
            # since apply_parallel is in the critical import path, we lazy
            # import multiprocessing just when we need it.
            from multiprocessing import cpu_count
            ncpu = cpu_count()
        except NotImplementedError:
            ncpu = 4
        chunks = _get_chunks(shape, ncpu)

    if mode == 'wrap':
        mode = 'periodic'
    elif mode == 'symmetric':
        mode = 'reflect'
    elif mode == 'edge':
        mode = 'nearest'

    def wrapped_func(arr):
        return function(arr, *extra_arguments, **extra_keywords)

    darr = _ensure_dask_array(array, chunks=chunks)

    res = darr.map_overlap(wrapped_func, depth, boundary=mode)
    if compute:
        res = res.compute()

    return res