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Python package for splitting arrays into sub-arrays (i.e. rectangular-tiling and rectangular-domain-decomposition), similar to ``numpy.array_split``.

Project description

array_split python package TravisCI Status AppVeyor Status Documentation Status Coveralls Status MIT License array_split python package

The array_split python package is an enhancement to existing numpy.ndarray functions, such as numpy.array_split, skimage.util.view_as_blocks and skimage.util.view_as_windows, which sub-divide a multi-dimensional array into a number of multi-dimensional sub-arrays (slices). Example application areas include:

Parallel Processing
A large (dense) array is partitioned into smaller sub-arrays which can be processed concurrently by multiple processes (multiprocessing or mpi4py) or other memory-limited hardware (e.g. GPGPU using pyopencl, pycuda, etc). For GPGPU, it is necessary for sub-array not to exceed the GPU memory and desirable for the sub-array shape to be a multiple of the work-group (OpenCL) or thread-block (CUDA) size.
File I/O
A large (dense) array is partitioned into smaller sub-arrays which can be written to individual files (as, for example, a HDF5 Virtual Dataset). It is often desirable for the individual files not to exceed a specified number of (Giga) bytes and, for HDF5, it is desirable to have the individual file sub-array shape a multiple of the chunk shape. Similarly, out of core algorithms for large dense arrays often involve processing the entire data-set as a series of in-core sub-arrays. Again, it is desirable for the individual sub-array shape to be a multiple of the chunk shape.

The array_split package provides the means to partition an array (or array shape) using any of the following criteria:

  • Per-axis indices indicating the cut positions.

  • Per-axis number of sub-arrays.

  • Total number of sub-arrays (with optional per-axis number of sections constraints).

  • Specific sub-array shape.

  • Specification of halo (ghost) elements for sub-arrays.

  • Arbitrary start index for the shape to be partitioned.

  • Maximum number of bytes for a sub-array with constraints:

    • sub-arrays are an even multiple of a specified sub-tile shape
    • upper limit on the per-axis sub-array shape

Quick Start Example

>>> from array_split import array_split, shape_split
>>> import numpy as np
>>> ary = np.arange(0, 4*9)
>>> array_split(ary, 4) # 1D split into 4 sections (like numpy.array_split)
[array([0, 1, 2, 3, 4, 5, 6, 7, 8]),
 array([ 9, 10, 11, 12, 13, 14, 15, 16, 17]),
 array([18, 19, 20, 21, 22, 23, 24, 25, 26]),
 array([27, 28, 29, 30, 31, 32, 33, 34, 35])]
>>> shape_split(ary.shape, 4) # 1D split into 4 parts, returns slice objects
array([(slice(0, 9, None),), (slice(9, 18, None),), (slice(18, 27, None),), (slice(27, 36, None),)],
      dtype=[('0', 'O')])
>>> ary = ary.reshape(4, 9) # Make ary 2D
>>> split = shape_split(ary.shape, axis=(2, 3)) # 2D split into 2*3=6 sections
>>> split.shape
(2, 3)
>>> split
array([[(slice(0, 2, None), slice(0, 3, None)),
        (slice(0, 2, None), slice(3, 6, None)),
        (slice(0, 2, None), slice(6, 9, None))],
       [(slice(2, 4, None), slice(0, 3, None)),
        (slice(2, 4, None), slice(3, 6, None)),
        (slice(2, 4, None), slice(6, 9, None))]],
      dtype=[('0', 'O'), ('1', 'O')])
>>> sub_arys = [ary[tup] for tup in split.flatten()] # Create sub-array views from slice tuples.
>>> sub_arys
[array([[ 0,  1,  2], [ 9, 10, 11]]),
 array([[ 3,  4,  5], [12, 13, 14]]),
 array([[ 6,  7,  8], [15, 16, 17]]),
 array([[18, 19, 20], [27, 28, 29]]),
 array([[21, 22, 23], [30, 31, 32]]),
 array([[24, 25, 26], [33, 34, 35]])]

Latest sphinx documentation (including more examples) at

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