{
    "info": {
        "author": "Andrei Shkarin",
        "author_email": "andrei.shkarin@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": ".. image:: static/ndpatch.svg\n    :height: 120\n    :align: center\n\n-----------\n\n.. image:: https://travis-ci.org/ashkarin/ndpatch.svg?branch=master \n    :target: https://travis-ci.org/ashkarin/ndpatch\n\n\n**NDPatch** is the package for extracting arbitrary regions from an N-dimensional numpy array assuming it mirrored infinitely.\n\nInstallation\n------------\n\nThe easiest way to install the latest version is by using pip::\n\n    $ pip install ndpatch\n\nYou may also use Git to clone the repository and install it manually::\n\n    $ git clone https://github.com/ashkarin/ndpatch.git\n    $ cd ndpatch\n    $ python setup.py install\n\nUsage\n-----\nTo take a patch from the array:\n\n.. code-block:: python\n\n  import numpy as np\n  import ndpatch\n  array = np.arange(25).reshape((5,5))\n  index = (1, 2)\n  shape = (3, 3)\n  patch = ndpatch.get_ndpatch(array, shape, index)\n  # patch =\n  # [[ 7,  8,  9],\n  #  [12, 13, 14],\n  #  [17, 18, 19]]\n\nTo take get a random patch index:\n\n.. code-block:: python\n\n  import numpy as np\n  import ndpatch\n  array_shape = (5, 5)\n  index = ndpatch.get_random_patch_index(array_shape)\n\nTo extract random patches from the array:\n\n.. code-block:: python\n\n  import numpy as np\n  import ndpatch\n  npatches = 10\n  patch_shape = (3, 3)\n  array = np.arange(100).reshape((10,10))\n  patches = [ndpatch.get_random_ndpatch(array, patch_shape) for _ in range(npatches)]\n\nTo split the 3D array on set of overlapping 3D patches and rebuild it back:\n\n.. code-block:: python\n\n  import numpy as np\n  import ndpatch\n  array = np.arange(0, 125).reshape((5,5,5))\n  patch_shape = (4, 3, 3)\n  overlap = 2\n  indices = ndpatch.get_patches_indices(array.shape, patch_shape, overlap)\n  patches = [ndpatch.get_ndpatch(array, patch_shape, index) for index in indices]\n  reconstructed = ndpatch.reconstruct_from_patches(patches, indices, array.shape, default_value=0)\n  # Validate\n  equal = (reconstructed == array)\n  assert (np.all(equal))\n\n\n",
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        "keywords": "ndarray patch region data development",
        "license": "",
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        "name": "ndpatch",
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