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cortecs\n[![status](https://joss.theoj.org/papers/b0c4f5012351abb476081ffe9226fbf4/status.svg)](https://joss.theoj.org/papers/b0c4f5012351abb476081ffe9226fbf4)\n[![arXiv](https://img.shields.io/badge/arXiv-2402.07047-b31b1b.svg)](https://arxiv.org/abs/2402.07047)\n[![PyPI version](https://badge.fury.io/py/cortecs.svg)](https://badge.fury.io/py/cortecs)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/cortecs.svg)](https://anaconda.org/conda-forge/cortecs)\n[![Tests](https://github.com/arjunsavel/cortecs/actions/workflows/python-package.yml/badge.svg)](https://github.com/arjunsavel/cortecs/actions/workflows/python-package.yml)\n[![codecov](https://codecov.io/gh/arjunsavel/cortecs/graph/badge.svg?token=90S3STBO5X)](https://codecov.io/gh/arjunsavel/cortecs)\n[![Maintainability](https://api.codeclimate.com/v1/badges/4eb53795313af153f4cd/maintainability)](https://codeclimate.com/github/arjunsavel/cortecs/maintainability)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)\n[![CodeQL](https://github.com/arjunsavel/cortecs/actions/workflows/codeql.yml/badge.svg)](https://github.com/arjunsavel/cortecs/actions/workflows/codeql.yml)\n[![Documentation Status](https://readthedocs.org/projects/cortecs/badge/?version=latest)](https://cortecs.readthedocs.io/en/latest/?badge=latest)\n[![Paper compilation](https://github.com/arjunsavel/cortecs/actions/workflows/draft-pdf.yml/badge.svg)](https://github.com/arjunsavel/cortecs/actions/workflows/draft-pdf.yml)\n![GitHub repo size](https://img.shields.io/github/repo-size/arjunsavel/cortecs)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cortecs)\n\n\n\n\n\nA Python package for decreasing the memory footprint of opacity functions. The primary functionality is compressing opacity functions with varying flexibility. Current methods include\n- polynomial fitting\n- PCA-based fitting\n- neural network fitting\n\n\nAll fits are currently made in along the temperature and pressure axes.\n\nAdditionally, `cortecs` can chunk up opacity functions. The radiative transfer problem can often be cast as embarassingly parallel, so each chunk can be sent to a different CPU.\n\n\nInstallation instructions\n-------------------------\n`cortecs` can be installed via pip:\n```bash\npip install cortecs\n```\n\nor conda:\n```bash\nconda install -c conda-forge cortecs\n```\n\nor from source:\n```bash\ngit clone\ncd cortecs\npip install -e .\n```\nTo install from source with optional neural network support:\n```bash\npip install -e .[neural_networks]\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farjunsavel%2Fcortecs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farjunsavel%2Fcortecs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farjunsavel%2Fcortecs/lists"}