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توضیحات

Tools for machine learning and data mining in Astronomy

ویژگی	مقدار
سیستم عامل	-
نام فایل	astroML-1.0a1
نام	astroML
نسخه کتابخانه	1.0a1
نگهدارنده	['Brigitta Sipocz']
ایمیل نگهدارنده	['bsipocz@gmail.com']
نویسنده	Jake VanderPlas
ایمیل نویسنده	vanderplas@astro.washington.edu
آدرس صفحه اصلی	http://astroML.github.com
آدرس اینترنتی	https://pypi.org/project/astroML/
مجوز	BSD 3-Clause License

.. -*- mode: rst -*- ======================================= AstroML: Machine Learning for Astronomy ======================================= .. image:: https://img.shields.io/badge/arXiv-1411.5039-orange.svg?style=flat :target: https://arxiv.org/abs/1411.5039 :alt: Reference proceedings .. image:: https://github.com/astroML/astroML/workflows/CI/badge.svg :target: https://github.com/astropy/astroquery/actions?query=workflow%3ACI :alt: Github Actions CI Status .. image:: https://img.shields.io/pypi/v/astroML.svg?style=flat :target: https://pypi.python.org/pypi/astroML :alt: Latest PyPI version .. image:: https://img.shields.io/pypi/dm/astroML.svg?style=flat :target: https://pypi.python.org/pypi/astroML :alt: PyPI download stat .. image:: https://img.shields.io/badge/license-BSD-blue.svg?style=flat :target: https://github.com/astroml/astroml/blob/main/LICENSE.rst :alt: License badge AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets. This project was started in 2012 by Jake VanderPlas to accompany the book *Statistics, Data Mining, and Machine Learning in Astronomy* by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray. Important Links =============== - HTML documentation: https://www.astroML.org - Core source-code repository: https://github.com/astroML/astroML - Figure source-code repository: https://github.com/astroML/astroML-figures - Issue Tracker: https://github.com/astroML/astroML/issues - Mailing List: https://groups.google.com/forum/#!forum/astroml-general Installation ============ **Before installation, make sure your system meets the prerequisites listed in Dependencies, listed below.** Core ---- To install the core ``astroML`` package in your home directory, use:: pip install astroML A conda package for astroML is also available either on the conda-forge or on the astropy conda channels:: conda install -c astropy astroML The core package is pure python, so installation should be straightforward on most systems. To install from source, use:: python setup.py install You can specify an arbitrary directory for installation using:: python setup.py install --prefix='/some/path' To install system-wide on Linux/Unix systems:: python setup.py build sudo python setup.py install Dependencies ============ There are two levels of dependencies in astroML. *Core* dependencies are required for the core ``astroML`` package. *Optional* dependencies are required to run some (but not all) of the example scripts. Individual example scripts will list their optional dependencies at the top of the file. Core Dependencies ----------------- The core ``astroML`` package requires the following (some of the functionality might work with older versions): - Python_ version 3.6+ - Numpy_ >= 1.13 - Scipy_ >= 0.18 - Scikit-learn_ >= 0.18 - Matplotlib_ >= 3.0 - AstroPy_ >= 3.0 Optional Dependencies --------------------- Several of the example scripts require specialized or upgraded packages. These requirements are listed at the top of the particular scripts - HEALPy_ provides an interface to the HEALPix pixelization scheme, as well as fast spherical harmonic transforms. Development =========== This package is designed to be a repository for well-written astronomy code, and submissions of new routines are encouraged. After installing the version-control system Git_, you can check out the latest sources from GitHub_ using:: git clone git://github.com/astroML/astroML.git or if you have write privileges:: git clone git@github.com:astroML/astroML.git Contribution ------------ We strongly encourage contributions of useful astronomy-related code: for `astroML` to be a relevant tool for the python/astronomy community, it will need to grow with the field of research. There are a few guidelines for contribution: General ~~~~~~~ Any contribution should be done through the github pull request system (for more information, see the `help page <https://help.github.com/articles/using-pull-requests>`_ Code submitted to ``astroML`` should conform to a BSD-style license, and follow the `PEP8 style guide <http://www.python.org/dev/peps/pep-0008/>`_. Documentation and Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~ All submitted code should be documented following the `Numpy Documentation Guide`_. This is a unified documentation style used by many packages in the scipy universe. In addition, it is highly recommended to create example scripts that show the usefulness of the method on an astronomical dataset (preferably making use of the loaders in ``astroML.datasets``). These example scripts are in the ``examples`` subdirectory of the main source repository. .. _Numpy Documentation Guide: https://numpydoc.readthedocs.io/en/latest/format.html Authors ======= Package Author -------------- * Jake Vanderplas https://github.com/jakevdp http://jakevdp.github.com Maintainer ---------- * Brigitta Sipocz https://github.com/bsipocz Code Contribution ----------------- * Morgan Fouesneau https://github.com/mfouesneau * Julian Taylor http://github.com/juliantaylor .. _Python: https://www.python.org .. _Numpy: https://www.numpy.org .. _Scipy: https://www.scipy.org .. _Scikit-learn: https://scikit-learn.org .. _Matplotlib: https://matplotlib.org .. _AstroPy: http://www.astropy.org/ .. _HEALPy: https://github.com/healpy/healpy .. _Git: https://git-scm.com/ .. _GitHub: https://www.github.com

نیازمندی

مقدار	نام
>=0.18	scikit-learn
>=1.13	numpy
>=0.18	scipy
>=3.0	matplotlib
>=3.0	astropy
<3.11,>=3.7	pymc3
-	flake8
-	sphinx
-	pytest-doctestplus
-	pytest-astropy-header
-	pytest-remotedata
-	pytest-cov

نحوه نصب

نصب پکیج whl astroML-1.0a1:

pip install astroML-1.0a1.whl

نصب پکیج tar.gz astroML-1.0a1:

pip install astroML-1.0a1.tar.gz