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Date:      Fri, 6 Dec 2013 15:42:33 +0000 (UTC)
From:      Mathieu Arnold <mat@FreeBSD.org>
To:        ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org
Subject:   svn commit: r335765 - head/math/py-pymc
Message-ID:  <201312061542.rB6FgXAw017932@svn.freebsd.org>

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Author: mat
Date: Fri Dec  6 15:42:33 2013
New Revision: 335765
URL: http://svnweb.freebsd.org/changeset/ports/335765

Log:
  - 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

Deleted:
  head/math/py-pymc/pkg-plist
Modified:
  head/math/py-pymc/Makefile
  head/math/py-pymc/distinfo
  head/math/py-pymc/pkg-descr

Modified: head/math/py-pymc/Makefile
==============================================================================
--- head/math/py-pymc/Makefile	Fri Dec  6 15:35:13 2013	(r335764)
+++ head/math/py-pymc/Makefile	Fri Dec  6 15:42:33 2013	(r335765)
@@ -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>

Modified: head/math/py-pymc/distinfo
==============================================================================
--- head/math/py-pymc/distinfo	Fri Dec  6 15:35:13 2013	(r335764)
+++ head/math/py-pymc/distinfo	Fri Dec  6 15:42:33 2013	(r335765)
@@ -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

Modified: head/math/py-pymc/pkg-descr
==============================================================================
--- head/math/py-pymc/pkg-descr	Fri Dec  6 15:35:13 2013	(r335764)
+++ head/math/py-pymc/pkg-descr	Fri Dec  6 15:42:33 2013	(r335765)
@@ -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/



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