freebsd-ports/math/py-numexpr/Makefile

# New ports collection makefile for:	numexpr
# Date created:				05 July 2010
# Whom:					Ju Pengfei <ju.pengfei@gmail.com>
#
# $FreeBSD$
#

PORTNAME=	numexpr
PORTVERSION=	1.4.1
CATEGORIES=	math python
MASTER_SITES=	GOOGLE_CODE
PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}

MAINTAINER=	jupengfei@gmail.com
COMMENT=	Fast numerical array expression evaluator for Python and NumPy

BUILD_DEPENDS=	${PYNUMPY}
RUN_DEPENDS=	${BUILD_DEPENDS}

USE_PYTHON=		yes
USE_PYDISTUTILS=	easy_install
PYEASYINSTALL_ARCHDEP=	yes

.include <bsd.port.mk>
numexpr - Fast numerical array expression evaluator for Python and NumPy. The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations. WWW: http://code.google.com/p/numexpr/ PR: ports/148372 Submitted by: Ju Pengfei <jupengfei@gmail.com> Feature safe: yes 2010-07-05 10:18:12 +02:00			`# New ports collection makefile for: numexpr`
			`# Date created: 05 July 2010`
			`# Whom: Ju Pengfei <ju.pengfei@gmail.com>`
			`#`
			`# $FreeBSD$`
			`#`

			`PORTNAME= numexpr`
- Update to 1.4.1 Approved by: maintainer(implicit) 2010-10-21 03:39:56 +02:00			`PORTVERSION= 1.4.1`
numexpr - Fast numerical array expression evaluator for Python and NumPy. The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations. WWW: http://code.google.com/p/numexpr/ PR: ports/148372 Submitted by: Ju Pengfei <jupengfei@gmail.com> Feature safe: yes 2010-07-05 10:18:12 +02:00			`CATEGORIES= math python`
			`MASTER_SITES= GOOGLE_CODE`
			`PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}`

			`MAINTAINER= jupengfei@gmail.com`
			`COMMENT= Fast numerical array expression evaluator for Python and NumPy`

			`BUILD_DEPENDS= ${PYNUMPY}`
			`RUN_DEPENDS= ${BUILD_DEPENDS}`

			`USE_PYTHON= yes`
			`USE_PYDISTUTILS= easy_install`
			`PYEASYINSTALL_ARCHDEP= yes`

			`.include <bsd.port.mk>`