dev-python/astroml: initial import

Package-Manager: portage-2.2.27

dev-python/astroml/Manifest

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+DIST astroML-0.3.tar.gz 242925 SHA256 ea6d0119593aed0e0dadc79c613ac0bddad95e6f12151237562a4fd67552b2b8 SHA512 7b9c6824ec1c09c35a0e9ea10674c93c7f301a3b15c5104357d9749534b78203c33eaac82c8ec1d1c51bdf30e1408d21002d1d2e7e243aa08c28cc7b1f1ab74a WHIRLPOOL 9f2bcc6bc9e56f7b513d7d5dc1367b3d8b1e1ae6ee4ddf9f8b917ff1f2d3952fd9d7101a876b6b820a7f30a3607997e45d0435c4929085998724c64657867cb7

dev-python/astroml/astroml-0.3.ebuild

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+# Copyright 1999-2016 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Id$
+
+EAPI=6
+PYTHON_COMPAT=( python{2_7,3_4,3_5} )
+
+inherit distutils-r1 virtualx
+
+MYPN=astroML
+MYP=${MYPN}-${PV}
+
+DESCRIPTION="Python Machine Learning library for astronomy"
+HOMEPAGE="http://www.astroml.org/"
+SRC_URI="mirror://pypi/${PN:0:1}/${MYPN}/${MYP}.tar.gz"
+
+LICENSE="BSD"
+SLOT="0"
+KEYWORDS="~amd64 ~x86 ~x86-fbsd ~amd64-linux ~x86-linux"
+IUSE="examples test"
+
+RDEPEND="
+	dev-python/astropy[${PYTHON_USEDEP}]
+	dev-python/matplotlib[${PYTHON_USEDEP}]
+	dev-python/numpy[${PYTHON_USEDEP}]
+	sci-libs/scipy[${PYTHON_USEDEP}]
+	sci-libs/scikits_learn[${PYTHON_USEDEP}]"
+DEPEND="${RDEPEND}
+	test? ( dev-python/nose[${PYTHON_USEDEP}] )"
+
+S="${WORKDIR}/${MYP}"
+
+DOCS=( CHANGES.rst README.rst )
+
+python_test() {
+	virtx nosetests --verbose || die
+}
+
+python_install_all() {
+	distutils-r1_python_install_all
+	insinto /usr/share/doc/${PF}
+	use examples && doins -r examples
+}

dev-python/astroml/metadata.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+  <maintainer type="project">
+    <email>sci-astronomy@gentoo.org</email>
+    <name>Gentoo Astronomy Project</name>
+  </maintainer>
+  <longdescription lang="en">
+ AstroML is a Python module for machine learning and data mining built on
+ numpy, scipy, scikit-learn, and matplotlib. 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.    
+  </longdescription>
+  <upstream>
+    <remote-id type="pypi">astroML</remote-id>
+  </upstream>
+</pkgmetadata>