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Libraries"],"readme":"[![Python \\\u003e= 3.8](https://img.shields.io/badge/python-3.8+-green.svg)](https://www.python.org/)\n[![Package License](https://img.shields.io/github/license/lanl/scico.svg)](https://github.com/lanl/scico/blob/main/LICENSE)\n[![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Documentation Status](https://readthedocs.org/projects/scico/badge/?version=latest)](http://scico.readthedocs.io/en/latest/?badge=latest)\n[![JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04722/status.svg)](https://doi.org/10.21105/joss.04722)\\\n[![Lint status](https://github.com/lanl/scico/actions/workflows/lint.yml/badge.svg)](https://github.com/lanl/scico/actions/workflows/lint.yml)\n[![Test status](https://github.com/lanl/scico/actions/workflows/pytest_ubuntu.yml/badge.svg)](https://github.com/lanl/scico/actions/workflows/pytest_ubuntu.yml)\n[![Test coverage](https://codecov.io/gh/lanl/scico/branch/main/graph/badge.svg?token=wQimmjnzFf)](https://codecov.io/gh/lanl/scico)\n[![CodeFactor](https://www.codefactor.io/repository/github/lanl/scico/badge/main)](https://www.codefactor.io/repository/github/lanl/scico/overview/main)\\\n[![PyPI package version](https://badge.fury.io/py/scico.svg)](https://badge.fury.io/py/scico)\n[![PyPI download statistics](https://static.pepy.tech/personalized-badge/scico?period=month\u0026left_color=grey\u0026right_color=brightgreen)](https://pepy.tech/project/scico)\n[![Conda Forge Release](https://img.shields.io/conda/vn/conda-forge/scico.svg)](https://anaconda.org/conda-forge/scico)\\\n[![View notebooks at nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/lanl/scico-data/tree/main/notebooks/index.ipynb)\n[![Run notebooks on binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lanl/scico-data/binder?labpath=notebooks%2Findex.ipynb)\n[![Run notebooks on google colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lanl/scico-data/blob/colab/notebooks/index.ipynb)\n\n\n# Scientific Computational Imaging Code (SCICO)\n\nSCICO is a Python package for solving the inverse problems that arise in\nscientific imaging applications. Its primary focus is providing methods\nfor solving ill-posed inverse problems by using an appropriate prior\nmodel of the reconstruction space. SCICO includes a growing suite of\noperators, cost functionals, regularizers, and optimization routines\nthat may be combined to solve a wide range of problems, and is designed\nso that it is easy to add new building blocks. SCICO is built on top of\n[JAX](https://github.com/google/jax), which provides features such as\nautomatic gradient calculation and GPU acceleration.\n\n[Documentation](https://scico.rtfd.io/) is available online. If you use\nthis software for published work, please cite the corresponding [JOSS\nPaper](https://doi.org/10.21105/joss.04722) (see bibtex entry\n`balke-2022-scico` in `docs/source/references.bib`).\n\n\n# Installation\n\nThe online documentation includes detailed\n[installation instructions](https://scico.rtfd.io/en/latest/install.html).\n\n\n# Usage Examples\n\nUsage examples are available as Python scripts and Jupyter Notebooks.\nExample scripts are located in `examples/scripts`. The corresponding\nJupyter Notebooks are provided in the\n[scico-data](https://github.com/lanl/scico-data) submodule and symlinked\nto `examples/notebooks`. They are also viewable on\n[GitHub](https://github.com/lanl/scico-data/tree/main/notebooks) or\n[nbviewer](https://nbviewer.jupyter.org/github/lanl/scico-data/tree/main/notebooks/index.ipynb),\nand can be run online on\n[binder](https://mybinder.org/v2/gh/lanl/scico-data/binder?labpath=notebooks%2Findex.ipynb)\nor\n[google colab](https://colab.research.google.com/github/lanl/scico-data/blob/colab/notebooks/index.ipynb).\n\n\n# License\n\nSCICO is distributed as open-source software under a BSD 3-Clause\nLicense (see the `LICENSE` file for details).\n\nLANL open source approval reference C20091.\n\n\\(c\\) 2020-2025. Triad National Security, LLC. All rights reserved. This\nprogram was produced under U.S. Government contract 89233218CNA000001\nfor Los Alamos National Laboratory (LANL), which is operated by Triad\nNational Security, LLC for the U.S. Department of Energy/National\nNuclear Security Administration. All rights in the program are reserved\nby Triad National Security, LLC, and the U.S. Department of\nEnergy/National Nuclear Security Administration. The Government has\ngranted for itself and others acting on its behalf a nonexclusive,\npaid-up, irrevocable worldwide license in this material to reproduce,\nprepare derivative works, distribute copies to the public, perform\npublicly and display publicly, and to permit others to do so.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fscico","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flanl%2Fscico","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fscico/lists"}