bwohlberg / Sporco
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SParse Optimization Research COde (SPORCO)
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SPORCO is a Python package for solving optimisation problems with sparsity-inducing regularisation. These consist primarily of sparse coding and dictionary learning problems, including convolutional sparse coding and dictionary learning, but there is also support for other problems such as Total Variation regularisation and Robust PCA. The optimisation algorithms in the current version are based on the Alternating Direction Method of Multipliers (ADMM) or on the Proximal Gradient Method (PGM).
If you use this software for published work, please cite it <http://sporco.readthedocs.io/en/latest/overview.html#citing>
__.
Documentation
Documentation is available online <http://sporco.rtfd.io/>
_, or can be built from the root directory of the source distribution by the command
::
python setup.py build_sphinx
in which case the HTML documentation can be found in the build/sphinx/html
directory (the top-level document is index.html
). Although the SPORCO package itself is compatible with both Python 2.7 and 3.x, building the documention requires Python 3.3 or later due to the use of Jonga <https://github.com/bwohlberg/jonga>
_ to construct call graph images for the SPORCO optimisation class hierarchies.
An overview of the package design and functionality is also available in
Brendt Wohlberg, SPORCO: A Python package for standard and convolutional sparse representations <http://conference.scipy.org/proceedings/scipy2017/brendt_wohlberg.html>
, in Proceedings of the 15th Python in Science Conference, (Austin, TX, USA), doi:10.25080/shinma-7f4c6e7-001 <http://dx.doi.org/10.25080/shinma-7f4c6e7-001>
, pp. 1--8, Jul 2017
Usage
Scripts illustrating usage of the package can be found in the examples
directory of the source distribution. These examples can be run from the root directory of the package by, for example
::
python examples/scripts/sc/bpdn.py
To run these scripts prior to installing the package it will be necessary to first set the PYTHONPATH
environment variable to include the root directory of the package. For example, in a bash
shell
::
export PYTHONPATH=$PYTHONPATH:pwd
from the root directory of the package.
Jupyter Notebook <http://jupyter.org/>
_ examples are also available <https://github.com/bwohlberg/sporco-notebooks>
. These examples can be viewed online via nbviewer <https://nbviewer.jupyter.org/github/bwohlberg/sporco-notebooks/blob/master/index.ipynb>
, or run interactively at binder <https://mybinder.org/v2/gh/bwohlberg/sporco-notebooks/master?filepath=index.ipynb>
_.
Requirements
The primary requirements are Python itself, and modules future <http://python-future.org>
, numpy <http://www.numpy.org>
, scipy <https://www.scipy.org>
, imageio <https://imageio.github.io/>
, pyfftw <https://hgomersall.github.io/pyFFTW>
, and matplotlib <http://matplotlib.org>
. Module numexpr <https://github.com/pydata/numexpr>
__ is not required, but some functions will be faster if it is installed. If module mpldatacursor <https://github.com/joferkington/mpldatacursor>
__ is installed, functions plot.plot
, plot.contour
, and plot.imview
will support the data cursor that it provides.
Instructions for installing these requirements are provided in the Requirements <http://sporco.rtfd.io/en/latest/install.html#requirements>
__ section of the package documentation.
Installation
To install the most recent release of SPORCO from PyPI <https://pypi.python.org/pypi/sporco/>
__ do
::
pip install sporco
The development version <https://github.com/bwohlberg/sporco>
__ on GitHub can be installed by doing
::
pip install git+https://github.com/bwohlberg/sporco
or by doing
::
git clone https://github.com/bwohlberg/sporco.git
followed by
::
cd sporco python setup.py build python setup.py install
The install commands will usually have to be performed with root privileges.
SPORCO can also be installed as a conda <https://conda.io/docs/>
__ package from the conda-forge <https://conda-forge.org/>
__ channel
::
conda install -c conda-forge sporco
A summary of the most significant changes between SPORCO releases can be found in the CHANGES.rst
file. It is strongly recommended to consult this summary when updating from a previous version.
Extensions
Some additional components of SPORCO are made available in separate repositories:
-
SPORCO-CUDA <https://github.com/bwohlberg/sporco-cuda>
__: GPU-accelerated versions of selected convolutional sparse coding algorithms -
SPORCO Notebooks <https://github.com/bwohlberg/sporco-notebooks>
__: Jupyter Notebook versions of the example scripts distributed with SPORCO -
SPORCO Extra <https://github.com/bwohlberg/sporco-extra>
__: Additional examples, data, and contributed code
License
SPORCO is distributed as open-source software under a BSD 3-Clause License (see the LICENSE
file for details).