array_split
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 http://array-split.readthedocs.io/en/latest/.
Installation
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
Requirements
Requires numpy version >= 1.6, python-2 version >= 2.6 or python-3 version >= 3.2.
Testing
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:
and AppVeyor at:
Documentation
Latest sphinx generated documentation is at:
and at github gh-pages:
Sphinx documentation can be built from the source:
python setup.py build_sphinx
with the HTML generated in docs/_build/html.
Latest source code
Source at github:
Bug Reports
To search for bugs or report them, please use the bug tracker at:
Contributing
Check out the CONTRIBUTING doc.
License information
See the file LICENSE.txt for terms & conditions, for usage and a DISCLAIMER OF ALL WARRANTIES.
1 maintainer
Metadata
1 maintainer
Release files for array-split 0.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| array_split-0.7.0-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / array_split-0.7.0-py2.py3-none-any.whl
| Download URL | array_split-0.7.0-py2.py3-none-any.whl |
|---|---|
| Size | 30.9 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
0be45dbe8609429706fa70e3744fe962dad24028943a92966f982636716a4508
|
|
BLAKE2b-256 checksum How to use checksums |
a071b429452fb0bd7b7c656a8372b5902146e80d118f7fd574f0be0b81f271bb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 10, 2026.
Transparency logSecurity for array-split 0.7.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 1 file in this release has been uploaded with provenance metadata.
Attestation for array_split-0.7.0-py2.py3-none-any.whl
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 10, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
