diff options
author | mat <mat@FreeBSD.org> | 2013-12-06 23:42:33 +0800 |
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committer | mat <mat@FreeBSD.org> | 2013-12-06 23:42:33 +0800 |
commit | 995f2d898637763d87c61c9138c1f95b9b5e2003 (patch) | |
tree | ba3d4ff86445b8bc8e8e00d6ee986ab5a228d973 /math/py-pymc | |
parent | 981d985df07ab4247960f4347d2e8e49c1114c9a (diff) | |
download | freebsd-ports-gnome-995f2d898637763d87c61c9138c1f95b9b5e2003.tar.gz freebsd-ports-gnome-995f2d898637763d87c61c9138c1f95b9b5e2003.tar.zst freebsd-ports-gnome-995f2d898637763d87c61c9138c1f95b9b5e2003.zip |
- Update to 2.3,1
- Add license
- Add matplotlib dependency as per docs/INSTALL.rst
- Add DOCS option
- Add stage support
maintainer timeout.
PR: ports/183951
Submitted by: Johannes Jost Meixner
Diffstat (limited to 'math/py-pymc')
-rw-r--r-- | math/py-pymc/Makefile | 35 | ||||
-rw-r--r-- | math/py-pymc/distinfo | 4 | ||||
-rw-r--r-- | math/py-pymc/pkg-descr | 18 | ||||
-rw-r--r-- | math/py-pymc/pkg-plist | 238 |
4 files changed, 40 insertions, 255 deletions
diff --git a/math/py-pymc/Makefile b/math/py-pymc/Makefile index 217e52645bc5..c005b188a158 100644 --- a/math/py-pymc/Makefile +++ b/math/py-pymc/Makefile @@ -2,8 +2,7 @@ # $FreeBSD$ PORTNAME= pymc -PORTVERSION= 2.0 -PORTREVISION= 1 +PORTVERSION= 2.3 PORTEPOCH= 1 CATEGORIES= math python MASTER_SITES= CHEESESHOP @@ -12,11 +11,37 @@ PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} MAINTAINER= wenheping@gmail.com COMMENT= Markov Chain Monte Carlo Sampling Toolkit -BUILD_DEPENDS= ${PYNUMPY} -RUN_DEPENDS:= ${BUILD_DEPENDS} +LICENSE= AFL +LICENSE_NAME= Academic Free License +LICENSE_FILE= ${WRKSRC}/LICENSE +LICENSE_PERMS= auto-accept dist-mirror dist-sell pkg-mirror pkg-sell + +BUILD_DEPENDS= ${PYNUMPY} \ + ${PYTHON_PKGNAMEPREFIX}matplotlib>0:${PORTSDIR}/math/py-matplotlib \ + ${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy +LIB_DEPENDS= liblapack.so:${PORTSDIR}/math/lapack \ + libblas.so:${PORTSDIR}/math/blas +RUN_DEPENDS= ${PYNUMPY} \ + ${PYTHON_PKGNAMEPREFIX}matplotlib>0:${PORTSDIR}/math/py-matplotlib \ + ${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy + +OPTIONS_DEFINE= DOCS +DOCSDIR= ${PREFIX}/share/doc/py-${PORTNAME} USE_PYTHON= yes USE_PYDISTUTILS= yes +PYDISTUTILS_AUTOPLIST= yes +PORTDOCS= * + +post-install: + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/flib.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/gp/cov_funs/distances.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/gp/cov_funs/isotropic_cov_funs.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/gp/incomplete_chol.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/gp/linalg_utils.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/Container_values.so + ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/LazyFunction.so + ${MKDIR} ${STAGEDIR}${DOCSDIR} + ${INSTALL_DATA} ${WRKSRC}/docs/*.rst ${STAGEDIR}${DOCSDIR} -NO_STAGE= yes .include <bsd.port.mk> diff --git a/math/py-pymc/distinfo b/math/py-pymc/distinfo index b7893c02af9e..a32fba127cac 100644 --- a/math/py-pymc/distinfo +++ b/math/py-pymc/distinfo @@ -1,2 +1,2 @@ -SHA256 (pymc-2.0.tar.gz) = 15bbf1e0df9e080d367d639462c4b2b9290826ca069a76ac2bc79d795fe6e653 -SIZE (pymc-2.0.tar.gz) = 1200416 +SHA256 (pymc-2.3.tar.gz) = db029c59d6a04f0c97d88993151c4ee58c76b9ad5c71ead7ee250088ba4cce0d +SIZE (pymc-2.3.tar.gz) = 13080085 diff --git a/math/py-pymc/pkg-descr b/math/py-pymc/pkg-descr index 611e0330b425..a817c5a91a2e 100644 --- a/math/py-pymc/pkg-descr +++ b/math/py-pymc/pkg-descr @@ -1,11 +1,9 @@ -Bayesian estimation, particularly using Markov chain Monte -Carlo (MCMC), is an increasingly relevant approach to -statistical estimation. However, few statistical software -packages implement MCMC samplers, and they are non-trivial - to code by hand. pymc is a python package that implements -the Metropolis-Hastings algorithm as a python class, and is -extremely flexible and applicable to a large suite of problems. -pymc includes methods for summarizing output, plotting, -goodness-of-fit and convergence diagnostics. +Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an +increasingly relevant approach to statistical estimation. However, few +statistical software packages implement MCMC samplers, and they are non-trivial +to code by hand. pymc is a python package that implements the +Metropolis-Hastings algorithm as a python class, and is extremely flexible and +applicable to a large suite of problems. pymc includes methods for summarizing +output, plotting, goodness-of-fit and convergence diagnostics. -WWW: https://pypi.python.org/pypi/pymc/ +WWW: http://pypi.python.org/pypi/pymc/ diff --git a/math/py-pymc/pkg-plist b/math/py-pymc/pkg-plist deleted file mode 100644 index cdb4af11460f..000000000000 --- a/math/py-pymc/pkg-plist +++ /dev/null @@ -1,238 +0,0 @@ -%%PYTHON_SITELIBDIR%%/pymc/CommonDeterministics.py -%%PYTHON_SITELIBDIR%%/pymc/CommonDeterministics.pyc -%%PYTHON_SITELIBDIR%%/pymc/CommonDeterministics.pyo -%%PYTHON_SITELIBDIR%%/pymc/Container.py -%%PYTHON_SITELIBDIR%%/pymc/Container.pyc -%%PYTHON_SITELIBDIR%%/pymc/Container.pyo -%%PYTHON_SITELIBDIR%%/pymc/Container_values.so -%%PYTHON_SITELIBDIR%%/pymc/InstantiationDecorators.py -%%PYTHON_SITELIBDIR%%/pymc/InstantiationDecorators.pyc -%%PYTHON_SITELIBDIR%%/pymc/InstantiationDecorators.pyo -%%PYTHON_SITELIBDIR%%/pymc/LazyFunction.so -%%PYTHON_SITELIBDIR%%/pymc/MCMC.py -%%PYTHON_SITELIBDIR%%/pymc/MCMC.pyc -%%PYTHON_SITELIBDIR%%/pymc/MCMC.pyo -%%PYTHON_SITELIBDIR%%/pymc/Matplot.py -%%PYTHON_SITELIBDIR%%/pymc/Matplot.pyc -%%PYTHON_SITELIBDIR%%/pymc/Matplot.pyo -%%PYTHON_SITELIBDIR%%/pymc/Model.py -%%PYTHON_SITELIBDIR%%/pymc/Model.pyc -%%PYTHON_SITELIBDIR%%/pymc/Model.pyo -%%PYTHON_SITELIBDIR%%/pymc/Node.py -%%PYTHON_SITELIBDIR%%/pymc/Node.pyc -%%PYTHON_SITELIBDIR%%/pymc/Node.pyo -%%PYTHON_SITELIBDIR%%/pymc/NormalApproximation.py -%%PYTHON_SITELIBDIR%%/pymc/NormalApproximation.pyc -%%PYTHON_SITELIBDIR%%/pymc/NormalApproximation.pyo -%%PYTHON_SITELIBDIR%%/pymc/PyMCObjects.py -%%PYTHON_SITELIBDIR%%/pymc/PyMCObjects.pyc -%%PYTHON_SITELIBDIR%%/pymc/PyMCObjects.pyo -%%PYTHON_SITELIBDIR%%/pymc/ScipyDistributions.py 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