{
    "info": {
        "author": "Shane J. Latham",
        "author_email": "array.split@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Utilities"
        ],
        "description": ".. start badges.\n\n.. image:: https://img.shields.io/pypi/v/array_split.svg\n   :target: https://pypi.python.org/pypi/array_split/\n   :alt: array_split python package\n.. image:: https://travis-ci.org/array-split/array_split.svg?branch=dev\n   :target: https://travis-ci.org/array-split/array_split\n   :alt: TravisCI Status\n.. image:: https://ci.appveyor.com/api/projects/status/oprsw5rfr07m77o0?svg=true\n   :target: https://ci.appveyor.com/project/array-split/array-split\n   :alt: AppVeyor Status\n.. image:: https://readthedocs.org/projects/array-split/badge/?version=stable\n   :target: http://array-split.readthedocs.io/en/stable\n   :alt: Documentation Status\n.. image:: https://coveralls.io/repos/github/array-split/array_split/badge.svg\n   :target: https://coveralls.io/github/array-split/array_split\n   :alt: Coveralls Status\n.. image:: https://img.shields.io/pypi/l/array_split.svg\n   :target: https://pypi.python.org/pypi/array_split/\n   :alt: MIT License\n.. image:: https://img.shields.io/pypi/pyversions/array_split.svg\n   :target: https://pypi.python.org/pypi/array_split/\n   :alt: array_split python package\n\n.. end badges.\n\nThe `array_split <http://array-split.readthedocs.io/en/latest>`_ python package is\nan enhancement to existing\n`numpy.ndarray  <http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>`_ functions,\nsuch as\n`numpy.array_split <http://docs.scipy.org/doc/numpy/reference/generated/numpy.array_split.html>`_,\n`skimage.util.view_as_blocks <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#view-as-blocks>`_\nand\n`skimage.util.view_as_windows <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#view-as-windows>`_,\nwhich sub-divide a multi-dimensional array into a number of multi-dimensional sub-arrays (slices).\nExample application areas include:\n\n**Parallel Processing**\n   A large (dense) array is partitioned into smaller sub-arrays which can be\n   processed concurrently by multiple processes\n   (`multiprocessing <https://docs.python.org/3/library/multiprocessing.html>`_\n   or `mpi4py <http://pythonhosted.org/mpi4py/>`_) or other memory-limited hardware\n   (e.g. GPGPU using `pyopencl <https://mathema.tician.de/software/pyopencl/>`_,\n   `pycuda <https://mathema.tician.de/software/pycuda/>`_, etc).\n   For GPGPU, it is necessary for sub-array not to exceed the GPU memory and\n   desirable for the sub-array shape to be a multiple of the *work-group*\n   (`OpenCL <https://en.wikipedia.org/wiki/OpenCL>`_)\n   or *thread-block* (`CUDA <https://en.wikipedia.org/wiki/CUDA>`_) size.\n\n**File I/O**\n   A large (dense) array is partitioned into smaller sub-arrays which can be\n   written to individual files\n   (as, for example, a\n   `HDF5 Virtual Dataset <https://support.hdfgroup.org/HDF5/docNewFeatures/NewFeaturesVirtualDatasetDocs.html>`_).\n   It is often desirable for the individual files not to exceed a specified number\n   of (Giga) bytes and, for `HDF5 <https://support.hdfgroup.org/HDF5/>`_, it is desirable\n   to have the individual file sub-array shape a multiple of\n   the `chunk shape <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>`_.\n   Similarly, `out of core <https://en.wikipedia.org/wiki/Out-of-core_algorithm>`_\n   algorithms for large dense arrays often involve processing the entire data-set as\n   a series of *in-core* sub-arrays. Again, it is desirable for the individual sub-array shape\n   to be a multiple of the\n   `chunk shape <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>`_.  \n\n\nThe `array_split <http://array-split.readthedocs.io/en/latest>`_ package provides the\nmeans to partition an array (or array shape) using any of the following criteria:\n\n- Per-axis indices indicating the *cut* positions.\n- Per-axis number of sub-arrays.\n- Total number of sub-arrays (with optional per-axis *number of sections* constraints).\n- Specific sub-array shape.\n- Specification of *halo* (*ghost*) elements for sub-arrays.\n- Arbitrary *start index* for the shape to be partitioned.\n- Maximum number of bytes for a sub-array with constraints:\n\n   - sub-arrays are an even multiple of a specified sub-tile shape\n   - upper limit on the per-axis sub-array shape\n\n\nQuick Start Example\n===================\n\n\n   >>> from array_split import array_split, shape_split\n   >>> import numpy as np\n   >>>\n   >>> ary = np.arange(0, 4*9)\n   >>> \n   >>> array_split(ary, 4) # 1D split into 4 sections (like numpy.array_split)\n   [array([0, 1, 2, 3, 4, 5, 6, 7, 8]),\n    array([ 9, 10, 11, 12, 13, 14, 15, 16, 17]),\n    array([18, 19, 20, 21, 22, 23, 24, 25, 26]),\n    array([27, 28, 29, 30, 31, 32, 33, 34, 35])]\n   >>> \n   >>> shape_split(ary.shape, 4) # 1D split into 4 parts, returns slice objects \n   array([(slice(0, 9, None),), (slice(9, 18, None),), (slice(18, 27, None),), (slice(27, 36, None),)], \n         dtype=[('0', 'O')])\n   >>> \n   >>> ary = ary.reshape(4, 9) # Make ary 2D\n   >>> split = shape_split(ary.shape, axis=(2, 3)) # 2D split into 2*3=6 sections\n   >>> split.shape\n   (2, 3)\n   >>> split\n   array([[(slice(0, 2, None), slice(0, 3, None)),\n           (slice(0, 2, None), slice(3, 6, None)),\n           (slice(0, 2, None), slice(6, 9, None))],\n          [(slice(2, 4, None), slice(0, 3, None)),\n           (slice(2, 4, None), slice(3, 6, None)),\n           (slice(2, 4, None), slice(6, 9, None))]], \n         dtype=[('0', 'O'), ('1', 'O')])\n   >>> sub_arys = [ary[tup] for tup in split.flatten()] # Create sub-array views from slice tuples.\n   >>> sub_arys\n   [array([[ 0,  1,  2], [ 9, 10, 11]]),\n    array([[ 3,  4,  5], [12, 13, 14]]),\n    array([[ 6,  7,  8], [15, 16, 17]]),\n    array([[18, 19, 20], [27, 28, 29]]),\n    array([[21, 22, 23], [30, 31, 32]]),\n    array([[24, 25, 26], [33, 34, 35]])]\n\n\nLatest sphinx documentation (including more examples)\nat http://array-split.readthedocs.io/en/latest/.\n\n\n",
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