commit: c511ece6ebd84adcb0e5211b587c9196f3f43fed
Author: Alexander Puck Neuwirth <alexander <AT> neuwirth-informatik <DOT>
de>
AuthorDate: Sat Mar 16 08:12:33 2024 +0000
Commit: Alexander Puck Neuwirth <alexander <AT> neuwirth-informatik <DOT>
de>
CommitDate: Tue Mar 19 17:02:43 2024 +0000
URL: https://gitweb.gentoo.org/proj/sci.git/commit/?id=c511ece6
sci-libs/gvar: new package, add 13.0.2
Signed-off-by: Alexander Puck Neuwirth <alexander <AT> neuwirth-informatik.de>
sci-libs/gvar/gvar-13.0.2.ebuild | 22 ++++++++++++++++++++++
sci-libs/gvar/metadata.xml | 19 +++++++++++++++++++
2 files changed, 41 insertions(+)
diff --git a/sci-libs/gvar/gvar-13.0.2.ebuild b/sci-libs/gvar/gvar-13.0.2.ebuild
new file mode 100644
index 000000000..b9d9121e4
--- /dev/null
+++ b/sci-libs/gvar/gvar-13.0.2.ebuild
@@ -0,0 +1,22 @@
+EAPI=8
+
+DISTUTILS_EXT=1
+PYTHON_COMPAT=( python3_{10..12} )
+DISTUTILS_USE_PEP517=setuptools
+inherit distutils-r1 pypi
+
+DESCRIPTION="Gaussian random variables."
+HOMEPAGE="https://github.com/gplepage/gvar";
+
+LICENSE="GPL-3"
+SLOT="0"
+KEYWORDS="~amd64 ~arm64"
+
+RDEPEND="
+ >=dev-python/cython-0.17[${PYTHON_USEDEP}]
+ >=dev-python/numpy-1.16[${PYTHON_USEDEP}]
+ dev-python/scipy[${PYTHON_USEDEP}]
+"
+BDEPEND="${RDEPEND}"
+
+distutils_enable_tests unittest
diff --git a/sci-libs/gvar/metadata.xml b/sci-libs/gvar/metadata.xml
new file mode 100644
index 000000000..31346f0dd
--- /dev/null
+++ b/sci-libs/gvar/metadata.xml
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd";>
+<pkgmetadata>
+ <maintainer type="project">
+ <email>[email protected]</email>
+ <name>Gentoo Science Project</name>
+ </maintainer>
+ <maintainer type="person">
+ <email>[email protected]</email>
+ <name>Alexander Puck Neuwirth</name>
+ </maintainer>
+ <longdescription lang="en">
+ This package facilitates the creation and manipulation of arbitrarily
complicated (correlated) multi-dimensional Gaussian random variables. The
random variables are represented by a new data type (gvar.GVar) that can be
used in arithmetic expressions and pure Python functions. Such
expressions/functions create new Gaussian random variables while automatically
tracking statistical correlations between the new and old variables. This data
type is useful for simple error propagation, but also is heavily used by the
Bayesian least-squares fitting module lsqfit.py to define priors and specify
fit results, while accounting for correlations between all variables.
+ </longdescription>
+ <upstream>
+ <remote-id type="pypi">gvar</remote-id>
+ <remote-id type="github">gplepage/gvar</remote-id>
+ </upstream>
+</pkgmetadata>
