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astro/py-astroML: Add new port

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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 commit is contained in:
Wen Heping 2023-03-04 18:37:21 +08:00
parent a493561df9
commit 781b4dae6f
4 changed files with 36 additions and 0 deletions
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1
astro/Makefile
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@ -81,6 +81,7 @@
SUBDIR += ptiger
SUBDIR += py-astlib
SUBDIR += py-astral
SUBDIR += py-astroML
SUBDIR += py-astropy
SUBDIR += py-astropy-helpers
SUBDIR += py-ephem

26
astro/py-astroML/Makefile Normal file
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@ -0,0 +1,26 @@
PORTNAME= astroML
PORTVERSION= 1.0.2
CATEGORIES= astro
MASTER_SITES= PYPI
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
DISTFILES= ${PORTNAME}-${PORTVERSION}.post1.tar.gz
MAINTAINER= wen@FreeBSD.org
COMMENT= Tools for machine learning and data mining in Astronomy
WWW= https://www.astroml.org/
LICENSE= BSD3CLAUSE
LICENSE_FILE= ${WRKSRC}/LICENSE.rst
RUN_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}scipy>=0.19:science/py-scipy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}matplotlib>=3.0:math/py-matplotlib@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scikit-learn>=0.18:science/py-scikit-learn@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}astropy>=3.0:astro/py-astropy@${PY_FLAVOR}
USES= python:3.7+
USE_PYTHON= autoplist distutils
WRKSRC= ${WRKDIR}/${PORTNAME}-${PORTVERSION}.post1
.include <bsd.port.mk>

3
astro/py-astroML/distinfo Normal file
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@ -0,0 +1,3 @@
TIMESTAMP = 1677925316
SHA256 (astroML-1.0.2.post1.tar.gz) = 45188a7a88a36ca3ec5a3aa04e5fa227f42d17415a6e168fb523375c1aabe291
SIZE (astroML-1.0.2.post1.tar.gz) = 115119

6
astro/py-astroML/pkg-descr Normal file
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@ -0,0 +1,6 @@
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.