The array_split python package is an enhancement to existing numpy.ndarray functions (such as numpy.array_split) which sub-divide a multi-dimensional array into a number of multi-dimensional sub-arrays (slices)
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The array_split <http://array-split.readthedocs.io/en/latest>_ python package is
an enhancement to existing
numpy.ndarray <http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>_ functions,
such as
numpy.array_split <http://docs.scipy.org/doc/numpy/reference/generated/numpy.array_split.html>,
skimage.util.view_as_blocks <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#view-as-blocks>
and
skimage.util.view_as_windows <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#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 <https://docs.python.org/3/library/multiprocessing.html>_
or mpi4py <http://pythonhosted.org/mpi4py/>) or other memory-limited hardware
(e.g. GPGPU using pyopencl <https://mathema.tician.de/software/pyopencl/>,
pycuda <https://mathema.tician.de/software/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 <https://en.wikipedia.org/wiki/OpenCL>)
or thread-block (CUDA <https://en.wikipedia.org/wiki/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 <https://support.hdfgroup.org/HDF5/docNewFeatures/NewFeaturesVirtualDatasetDocs.html>).
It is often desirable for the individual files not to exceed a specified number
of (Giga) bytes and, for HDF5 <https://support.hdfgroup.org/HDF5/>, it is desirable
to have the individual file sub-array shape a multiple of
the chunk shape <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>.
Similarly, out of core <https://en.wikipedia.org/wiki/Out-of-core_algorithm>
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 <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>_.
The array_split <http://array-split.readthedocs.io/en/latest>_ 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:
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 http://array-split.readthedocs.io/en/latest/.
.. end long description.
Using pip (root access required):
pip install array_split
or local user install (no root access required):
pip install --user array_split
or local user install from latest github source:
pip install --user git+git://github.com/array-split/array_split.git#egg=array_split
Requires numpy <http://docs.scipy.org/doc/numpy/>_ version >= 1.6,
python-2 version >= 2.6 or python-3 version >= 3.2.
Run tests (unit-tests and doctest module docstring tests) using::
python -m array_split.tests
or, from the source tree, run::
python setup.py test
Travis CI at:
https://travis-ci.org/array-split/array_split/
and AppVeyor at:
https://ci.appveyor.com/project/array-split/array-split
Latest sphinx generated documentation is at:
http://array-split.readthedocs.io/en/latest
and at github gh-pages:
https://array-split.github.io/array_split/
Sphinx documentation can be built from the source::
python setup.py build_sphinx
with the HTML generated in docs/_build/html.
Source at github:
https://github.com/array-split/array_split
To search for bugs or report them, please use the bug tracker at:
https://github.com/array-split/array_split/issues
Check out the CONTRIBUTING doc <https://github.com/array-split/array_split/blob/dev/CONTRIBUTING.rst>_.
See the file LICENSE.txt <https://github.com/array-split/array_split/blob/dev/LICENSE.txt>_
for terms & conditions, for usage and a DISCLAIMER OF ALL WARRANTIES.
FAQs
The array_split python package is an enhancement to existing numpy.ndarray functions (such as numpy.array_split) which sub-divide a multi-dimensional array into a number of multi-dimensional sub-arrays (slices)
We found that array-split demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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