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 \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/Autostronomy/AstroPhot/blob/main/media/AP_logo_white.png?raw=true\"\u003e\n  \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/Autostronomy/AstroPhot/blob/main/media/AP_logo.png?raw=true\"\u003e\n  \u003cimg alt=\"AstroPhot logo\" src=\"media/AP_logo.png\" width=\"70%\"\u003e\n\u003c/picture\u003e\n\n[![unittests](https://github.com/Autostronomy/AstroPhot/actions/workflows/testing.yaml/badge.svg?branch=main)](https://github.com/Autostronomy/AstroPhot/actions/workflows/testing.yaml)\n[![Documentation Status](https://readthedocs.org/projects/astrophot/badge/?version=latest)](https://astrophot.readthedocs.io/en/latest/?badge=latest)\n[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Autostronomy/AstroPhot/main.svg)](https://results.pre-commit.ci/latest/github/Autostronomy/AstroPhot/main)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![pypi](https://img.shields.io/pypi/v/astrophot.svg?logo=pypi\u0026logoColor=white\u0026label=PyPI)](https://pypi.org/project/astrophot/)\n[![downloads](https://img.shields.io/pypi/dm/astrophot?label=PyPI%20Downloads)](https://libraries.io/pypi/astrophot)\n[![codecov](https://img.shields.io/codecov/c/github/Autostronomy/AstroPhot?logo=codecov)](https://app.codecov.io/gh/Autostronomy/AstroPhot?search=\u0026displayType=list)\n[![Static Badge](https://img.shields.io/badge/ADS-record-2A79E4)](https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.6377S/abstract)\n[![DOI](https://zenodo.org/badge/473209170.svg)](https://zenodo.org/doi/10.5281/zenodo.10798979)\n\nAstroPhot is a fast, flexible, and automated astronomical image modelling tool\nfor precise parallel multi-wavelength photometry. It is a python based package\nthat uses PyTorch to quickly and efficiently perform analysis tasks. Written by\n[Connor Stone](https://connorjstone.com/) for tasks such as LSB imaging,\nhandling crowded fields, multi-band photometry, and analyzing massive data from\nfuture telescopes. AstroPhot is flexible and fast for any astronomical image\nmodelling task. While it uses PyTorch (originally developed for Machine\nLearning) it is NOT a machine learning based tool.\n\n## Installation\n\nAstroPhot can be installed with pip:\n\n```\npip install astrophot\n```\n\nIf PyTorch gives you any trouble on your system, just follow the instructions on\nthe [pytorch website](https://pytorch.org/) to install a version for your\nsystem.\n\nAlso note that AstroPhot is only available for python3.\n\nSee [the documentation](https://astrophot.readthedocs.io) for more details.\n\n## Documentation\n\nYou can find the documentation at the\n[ReadTheDocs site connected with the AstroPhot project](https://astrophot.readthedocs.io)\nwhich covers many of the main use cases for AstroPhot. It is still in\ndevelopment, but lots of useful information is there. Feel free to contact the\nauthor, [Connor Stone](https://connorjstone.com/), for any questions not\nanswered by the documentation or tutorials.\n\n## Credit / Citation\n\nIf you use AstroPhot in your research, please follow the\n[citation instructions here](https://autostronomy.github.io/AstroPhot/citation.html).\n\n## Thanks to our contributors!\n\n[![Contributors](https://contrib.rocks/image?repo=Autostronomy/AstroPhot)](https://github.com/Autostronomy/AstroPhot/graphs/contributors)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautostronomy%2Fastrophot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fautostronomy%2Fastrophot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautostronomy%2Fastrophot/lists"}