[gentoo-commits] repo/proj/guru:dev commit in: sci-libs/ann/

[Alessandro Barbieri]

commit:     5c2b0563b86b2624b5a4aef7f96b9d7672e3fd29
Author:     Alessandro Barbieri <lssndrbarbieri <AT> gmail <DOT> com>
AuthorDate: Sun Feb 23 03:40:39 2020 +0000
Commit:     Alessandro Barbieri <lssndrbarbieri <AT> gmail <DOT> com>
CommitDate: Sun Feb 23 03:40:39 2020 +0000
URL:        https://gitweb.gentoo.org/repo/proj/guru.git/commit/?id=5c2b0563

sci-libs/ann: new package

Package-Manager: Portage-2.3.89, Repoman-2.3.20
Signed-off-by: Alessandro Barbieri <lssndrbarbieri <AT> gmail.com>

 sci-libs/ann/Manifest            |  2 ++
 sci-libs/ann/ann-1.1.2_p7.ebuild | 68 ++++++++++++++++++++++++++++++++++++++++
 sci-libs/ann/metadata.xml        | 16 ++++++++++
 3 files changed, 86 insertions(+)

diff --git a/sci-libs/ann/Manifest b/sci-libs/ann/Manifest
new file mode 100644
index 0000000..dc57b22
--- /dev/null
+++ b/sci-libs/ann/Manifest
@@ -0,0 +1,2 @@
+DIST ann_1.1.2+doc-7.debian.tar.xz 172056 BLAKE2B ca20ef1c53c02b27d8a6b1ee416267ddd719df4e9b3fc7215624dd3253a9a3a7a2e548adb58546566487927e7a5f926f6b66ea9df3d5d35a4341d691750f3d4f SHA512 d575df042676c7a074339eeeaf5fa4c4c7a1739af16c3f05c4c40e2c51df1d05d7c271bcb811b7934707f3f8ab63ac13ea71517a373e775083bafb4f591615d3
+DIST ann_1.1.2+doc.orig.tar.gz 693957 BLAKE2B 8cb9316329b2de8f0870701114f403d6ee20c842c0861f2341f41dc3e111f818b7929b35a099a723d4ec8baaed64cde19da46043a7a2909a870376615e2c322f SHA512 fb004a014add109d0b0949443c4c599795363d20ee65386421f898f5276b5df08714a3cd8d371d5a03417e7c3f7f3451335f90df2ca274ce95c658b958a253ae

diff --git a/sci-libs/ann/ann-1.1.2_p7.ebuild b/sci-libs/ann/ann-1.1.2_p7.ebuild
new file mode 100644
index 0000000..1716b3b
--- /dev/null
+++ b/sci-libs/ann/ann-1.1.2_p7.ebuild
@@ -0,0 +1,68 @@
+# Copyright 1999-2020 Gentoo Authors
+# Distributed under the terms of the GNU General Public License v2
+
+EAPI="7"
+
+inherit autotools eutils flag-o-matic
+
+MY_PV="${PV//_p*/}"
+MY_P="${PN}-${MY_PV}"
+
+DESCRIPTION="A Library for Approximate Nearest Neighbor Searching"
+HOMEPAGE="https://www.cs.umd.edu/~mount/ANN/"
+SRC_URI="http://cdn-fastly.deb.debian.org/debian/pool/main/a/${PN}/${PN}_${MY_PV}+doc.orig.tar.gz
+http://cdn-fastly.deb.debian.org/debian/pool/main/a/${PN}/${PN}_${MY_PV}+doc-${PV##*p}.debian.tar.xz"
+
+LICENSE="LGPL-2.1"
+SLOT="0"
+KEYWORDS="~amd64"
+IUSE="debug test"
+RESTRICT="!test? ( test )"
+RDEPEND=""
+DEPEND=""
+BDEPEND="dev-util/quilt"
+
+S="${WORKDIR}/${MY_P}+doc"
+
+src_prepare() {
+	export QUILT_PATCHES="${WORKDIR}/debian/patches"
+	export QUILT_SERIES="${QUILT_PATCHES}/series"
+	quilt push -a || die
+	eapply_user
+	eautoreconf
+}
+
+src_configure() {
+	use debug && append-cxxflags -DANN_PERF
+	econf --disable-static
+}
+
+src_test() {
+	cd test
+	./ann_test < test1.in > /dev/null || die
+	./ann_test < test2.in > /dev/null || die
+}
+
+src_install() {
+	emake DESTDIR="${D}" install
+	insinto /usr/include/ANN
+	doins -r include/ANN/.
+
+	dodoc doc/ANNmanual.pdf
+	dodoc doc/Copyright.txt
+	dodoc doc/ReadMe.txt
+
+	insinto /usr/share/ANN/sample
+	doins sample/query.pts
+	doins sample/sample.save
+	doins sample/data.pts
+
+	cd "${WORKDIR}/debian"
+	pod2man --center="User Commands" ann2fig.pod ann2fig.1
+	pod2man --center="User Commands" ann_sample.pod ann_sample.1
+	pod2man --center="User Commands" ann_test.pod ann_test.1
+	doman ann_sample.1
+	doman ann_test.1
+	doman ann2fig.1
+	find "${D}" -name '*.la' -delete || die
+}

diff --git a/sci-libs/ann/metadata.xml b/sci-libs/ann/metadata.xml
new file mode 100644
index 0000000..2931ad8
--- /dev/null
+++ b/sci-libs/ann/metadata.xml
@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+  <maintainer type="person">
+    <email>lssndrbarbieri@×××××.com</email>
+    <name>Alessandro Barbieri</name>
+  </maintainer>
+  <upstream>
+    <maintainer>
+      <email>mount@××××××.edu</email>
+      <name>Dave Mount</name>
+    </maintainer>
+  </upstream>
+  <longdescription lang="en">ANN is a library written in C++, which supports data structures and algorithms for both exact and approximate nearest neighbor searching in arbitrarily high dimensions. ANN assumes that distances are measured using any class of distance functions called Minkowski metrics. These include the well known Euclidean distance, Manhattan distance, and max distance. ANN performs quite efficiently for point sets ranging in size from thousands to hundreds of thousands, and in dimensions as high as 20. 
+  </longdescription>
+</pkgmetadata>