ndpatch

.. image:: static/ndpatch.svg :height: 120 :align: center

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.. image:: https://travis-ci.org/ashkarin/ndpatch.svg?branch=master :target: https://travis-ci.org/ashkarin/ndpatch

NDPatch is the package for extracting arbitrary regions from an N-dimensional numpy array assuming it mirrored infinitely.

Installation

The easiest way to install the latest version is by using pip::

$ pip install ndpatch

You may also use Git to clone the repository and install it manually::

$ git clone https://github.com/ashkarin/ndpatch.git
$ cd ndpatch
$ python setup.py install

Usage

To take a patch from the array:

.. code-block:: python

import numpy as np import ndpatch array = np.arange(25).reshape((5,5)) index = (1, 2) shape = (3, 3) patch = ndpatch.get_ndpatch(array, shape, index)

patch =

[[ 7, 8, 9],

[12, 13, 14],

[17, 18, 19]]

To take get a random patch index:

.. code-block:: python

import numpy as np import ndpatch array_shape = (5, 5) index = ndpatch.get_random_patch_index(array_shape)

To extract random patches from the array:

.. code-block:: python

import numpy as np import ndpatch npatches = 10 patch_shape = (3, 3) array = np.arange(100).reshape((10,10)) patches = [ndpatch.get_random_ndpatch(array, patch_shape) for _ in range(npatches)]

To split the 3D array on set of overlapping 3D patches and rebuild it back:

.. code-block:: python

import numpy as np import ndpatch array = np.arange(0, 125).reshape((5,5,5)) patch_shape = (4, 3, 3) overlap = 2 indices = ndpatch.get_patches_indices(array.shape, patch_shape, overlap) patches = [ndpatch.get_ndpatch(array, patch_shape, index) for index in indices] reconstructed = ndpatch.reconstruct_from_patches(patches, indices, array.shape, default_value=0)

Validate

equal = (reconstructed == array) assert (np.all(equal))