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Python Versions](https://img.shields.io/pypi/pyversions/sporco.svg)](https://github.com/bwohlberg/sporco)\n[![Package License](https://img.shields.io/github/license/bwohlberg/sporco.svg)](https://github.com/bwohlberg/sporco/blob/master/LICENSE)\n[![Documentation Status](https://readthedocs.org/projects/sporco/badge/?version=latest)](http://sporco.readthedocs.io/en/latest/?badge=latest)\n[![Test status](https://github.com/bwohlberg/sporco/actions/workflows/pytest.yml/badge.svg)](https://github.com/bwohlberg/sporco/actions/workflows/pytest.yml)\n[![Test Coverage](https://codecov.io/gh/bwohlberg/sporco/branch/master/graph/badge.svg)](https://codecov.io/gh/bwohlberg/sporco)\\\n[![PyPi Release](https://badge.fury.io/py/sporco.svg)](https://badge.fury.io/py/sporco)\n[![PyPi Downloads](https://static.pepy.tech/personalized-badge/sporco?period=total\u0026left_color=grey\u0026right_color=brightgreen\u0026left_text=downloads)](https://pepy.tech/project/sporco)\n[![Conda Forge Release](https://img.shields.io/conda/vn/conda-forge/sporco.svg)](https://anaconda.org/conda-forge/sporco)\n[![Conda Forge Downloads](https://img.shields.io/conda/dn/conda-forge/sporco.svg)](https://anaconda.org/conda-forge/sporco)\\\n[![Binder](http://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/bwohlberg/sporco-notebooks/master?filepath=index.ipynb)\n[![DOI](https://img.shields.io/badge/DOI-10.25080%2Fshinma--7f4c6e7--001-blue.svg)](https://dx.doi.org/10.25080/shinma-7f4c6e7-001)\n\n\n# SParse Optimization Research COde (SPORCO)\n\nSPORCO 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).\n\nIf you use this software for published work, please [cite it](http://sporco.readthedocs.io/en/latest/overview.html#citing).\n\n\n# Documentation\n\n[Documentation](http://sporco.rtfd.io/) is available online, or can be built from the root directory of the source distribution by the command\n\n\tpython setup.py build_sphinx\n\nin 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 Python 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.\n\nAn overview of the package design and functionality is also available in\n\n\u003e Brendt Wohlberg, [SPORCO: A Python package for standard and convolutional sparse representations](http://conference.scipy.org/proceedings/scipy2017/brendt_wohlberg.html),\n\u003e in Proceedings of the 15th Python in Science Conference, (Austin, TX, USA), doi:10.25080/shinma-7f4c6e7-001, pp. 1--8, Jul 2017\n\n\n# Usage\n\nScripts 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\n\n\tpython examples/scripts/sc/bpdn.py\n\nTo 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\n\n\texport PYTHONPATH=$PYTHONPATH:`pwd`\n\nfrom the root directory of the package.\n\n[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).\n\n\n# Requirements\n\nThe 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.\n\nInstructions for installing these requirements are provided in the [Requirements](http://sporco.rtfd.io/en/latest/install.html#requirements) section of the package documentation.\n\n\n# Installation\n\nTo install the most recent release of SPORCO from [PyPI](https://pypi.python.org/pypi/sporco/) do\n\n\tpip install sporco\n\nThe [development version](https://github.com/bwohlberg/sporco) on GitHub can be installed by doing\n\n\tpip install git+https://github.com/bwohlberg/sporco\n\nor by doing\n\n\tgit clone https://github.com/bwohlberg/sporco.git\n\nfollowed by\n\n\tcd sporco\n\tpython setup.py build\n\tpython setup.py install\n\nThe install commands will usually have to be performed with root privileges.\n\nSPORCO can also be installed as a [conda](https://conda.io/docs/) package from the [conda-forge](https://conda-forge.org/) channel\n\n\tconda install -c conda-forge sporco\n\nA 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.\n\n\n# Extensions\n\nSome additional components of SPORCO are made available in separate repositories:\n\n-   [SPORCO-CUDA](https://github.com/bwohlberg/sporco-cuda):\n\tGPU-accelerated versions of selected convolutional sparse coding\n\talgorithms\n-   [SPORCO Notebooks](https://github.com/bwohlberg/sporco-notebooks):\n\tJupyter Notebook versions of the example scripts distributed with\n\tSPORCO\n-   [SPORCO Extra](https://github.com/bwohlberg/sporco-extra):\n\tAdditional examples, data, and contributed code\n\n# License\n\nSPORCO is distributed as open-source software under a BSD 3-Clause License (see the `LICENSE` file for details).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbwohlberg%2Fsporco","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbwohlberg%2Fsporco","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbwohlberg%2Fsporco/lists"}