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-*- mode: rst -*-\n\n=======================================\nAstroML: Machine Learning for Astronomy\n=======================================\n\n.. image:: https://img.shields.io/badge/arXiv-1411.5039-orange.svg?style=flat\n        :target: https://arxiv.org/abs/1411.5039\n        :alt: Reference proceedings\n.. image:: https://github.com/astroML/astroML/workflows/CI/badge.svg\n        :target: https://github.com/astroML/astroML/actions?query=workflow%3ACI\n        :alt: Github Actions CI Status\n.. image:: https://img.shields.io/pypi/v/astroML.svg?style=flat\n        :target: https://pypi.python.org/pypi/astroML\n        :alt: Latest PyPI version\n.. image:: https://img.shields.io/pypi/dm/astroML.svg?style=flat\n        :target: https://pypi.python.org/pypi/astroML\n        :alt: PyPI download stat\n.. image:: https://img.shields.io/badge/license-BSD-blue.svg?style=flat\n        :target: https://github.com/astroml/astroml/blob/main/LICENSE.rst\n        :alt: License badge\n\nAstroML is a Python module for machine learning and data mining\nbuilt on numpy, scipy, scikit-learn, and matplotlib,\nand distributed under the BSD license.\nIt contains a growing library of statistical and machine learning\nroutines for analyzing astronomical data in python, loaders for several open\nastronomical datasets, and a large suite of examples of analyzing and\nvisualizing astronomical datasets.\n\nThis project was started in 2012 by Jake VanderPlas to accompany the book\n*Statistics, Data Mining, and Machine Learning in Astronomy* by\nZeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.\n\n\nImportant Links\n===============\n- HTML documentation: https://www.astroML.org\n- Core source-code repository: https://github.com/astroML/astroML\n- Figure source-code repository: https://github.com/astroML/astroML-figures\n- Issue Tracker: https://github.com/astroML/astroML/issues\n- Mailing List: https://groups.google.com/forum/#!forum/astroml-general\n\n\nInstallation\n============\n\n**Before installation, make sure your system meets the prerequisites\nlisted in Dependencies, listed below.**\n\nCore\n----\nTo install the core ``astroML`` package in your home directory, use::\n\n  pip install astroML\n\nA conda package for astroML is also available either on the conda-forge or\non the astropy conda channels::\n\n  conda install -c astropy astroML\n\nThe core package is pure python, so installation should be straightforward\non most systems.  To install from source, use::\n\n  python setup.py install\n\nYou can specify an arbitrary directory for installation using::\n\n  python setup.py install --prefix='/some/path'\n\nTo install system-wide on Linux/Unix systems::\n\n  python setup.py build\n  sudo python setup.py install\n\n\nDependencies\n============\nThere are two levels of dependencies in astroML.  *Core* dependencies are\nrequired for the core ``astroML`` package. *Optional* dependencies are required\nto run some (but not all) of the example scripts.  Individual example scripts\nwill list their optional dependencies at the top of the file.\n\nCore Dependencies\n-----------------\nThe core ``astroML`` package requires the following (some of the\nfunctionality might work with older versions):\n\n- Python_ version 3.6+\n- Numpy_ \u003e= 1.13\n- Scipy_ \u003e= 0.19\n- Scikit-learn_ \u003e= 0.18\n- Matplotlib_ \u003e= 3.0\n- AstroPy_ \u003e= 3.0\n\nOptional Dependencies\n---------------------\nSeveral of the example scripts require specialized or upgraded packages.\nThese requirements are listed at the top of the particular scripts\n\n- HEALPy_ provides an interface to\n  the HEALPix pixelization scheme, as well as fast spherical harmonic\n  transforms.\n\nDevelopment\n===========\nThis package is designed to be a repository for well-written astronomy code,\nand submissions of new routines are encouraged.  After installing the\nversion-control system Git_, you can check out\nthe latest sources from GitHub_ using::\n\n  git clone git://github.com/astroML/astroML.git\n\nor if you have write privileges::\n\n  git clone git@github.com:astroML/astroML.git\n\nContribution\n------------\nWe strongly encourage contributions of useful astronomy-related code:\nfor `astroML` to be a relevant tool for the python/astronomy community,\nit will need to grow with the field of research.  There are a few\nguidelines for contribution:\n\nGeneral\n~~~~~~~\nAny contribution should be done through the github pull request system (for\nmore information, see the\n`help page \u003chttps://help.github.com/articles/using-pull-requests\u003e`_\nCode submitted to ``astroML`` should conform to a BSD-style license,\nand follow the `PEP8 style guide \u003chttp://www.python.org/dev/peps/pep-0008/\u003e`_.\n\nDocumentation and Examples\n~~~~~~~~~~~~~~~~~~~~~~~~~~\nAll submitted code should be documented following the\n`Numpy Documentation Guide`_.  This is a unified documentation style used\nby many packages in the scipy universe.\n\nIn addition, it is highly recommended to create example scripts that show the\nusefulness of the method on an astronomical dataset (preferably making use\nof the loaders in ``astroML.datasets``).  These example scripts are in the\n``examples`` subdirectory of the main source repository.\n\n.. _Numpy Documentation Guide: https://numpydoc.readthedocs.io/en/latest/format.html\n\nAuthors\n=======\n\nPackage Author\n--------------\n* Jake Vanderplas https://github.com/jakevdp\n  http://jakevdp.github.com\n\nMaintainer\n----------\n* Brigitta Sipocz https://github.com/bsipocz\n\nContributors\n------------\n\n* Alex Conley\n* Andreas Kopecky\n* Andrew Connolly\n* Asif Imran\n* Benjamin Alan Weaver\n* Brigitta Sipőcz\n* Chris Desira\n* Daniel Andreasen\n* Dino Bektešević\n* Edward Betts\n* Hans Moritz Günther\n* Hugo van Kemenade\n* Jake Vanderplas\n* Jeremy Blow\n* Jonathan Sick\n* Joris van Vugt\n* Juanjo Bazán\n* Julian Taylor\n* Lars Buitinck\n* Michael Radigan\n* Morgan Fouesneau\n* Nicholas Hunt-Walker\n* Ole Streicher\n* Pey Lian Lim\n* Rodrigo Nemmen\n* Ross Fadely\n* Vlad Skripniuk\n* Zlatan Vasović\n* Engineero\n* stonebig\n\n.. _Python: https://www.python.org\n.. _Numpy: https://www.numpy.org\n.. _Scipy: https://www.scipy.org\n.. _Scikit-learn: https://scikit-learn.org\n.. _Matplotlib: https://matplotlib.org\n.. _AstroPy: http://www.astropy.org/\n.. _HEALPy: https://github.com/healpy/healpy\n.. _Git: https://git-scm.com/\n.. _GitHub: https://www.github.com\n","funding_links":[],"categories":["其他_机器学习与深度学习","Python","Others","Uncategorized"],"sub_categories":["Uncategorized"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FastroML%2FastroML","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FastroML%2FastroML","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FastroML%2FastroML/lists"}