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freebsd-ports/math/py-numexpr/Makefile

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- Update to 2.0.1 - Cleanup Makefile header - Sort PLIST Changes: http://code.google.com/p/numexpr/wiki/ReleaseNotes
2013-01-30 20:51:53 +01:00
# Created by: Ju Pengfei <ju.pengfei@gmail.com>
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
# $FreeBSD$
PORTNAME= numexpr
- Update to 2.4 - Update WWW Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2015-03-21 21:07:17 +01:00
PORTVERSION= 2.4
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
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
- Pet portlint - Take maintainership
2011-09-21 19:07:19 +02:00
MAINTAINER= sunpoet@FreeBSD.org
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
COMMENT= Fast numerical array expression evaluator for Python and NumPy
- Add LICENSE - Use PYDISTUTILS_AUTOPLIST - Remove post-patch:
2014-02-14 20:43:20 +01:00
LICENSE= MIT
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
BUILD_DEPENDS= ${PYNUMPY}
- Update to 2.2 - Do not use setuptools Changes: http://code.google.com/p/numexpr/wiki/ReleaseNotes
2013-09-02 14:50:07 +02:00
RUN_DEPENDS:= ${BUILD_DEPENDS}
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
- Convert PYTHON_FEATURES to USE_PYTHON (which was landed several hours ago) Notified by: koobs, wg
2014-08-15 05:43:54 +02:00
USE_PYTHON= autoplist distutils
- Use USES=python
2014-08-15 05:01:58 +02:00
USES= python
- Update to 2.4 - Update WWW Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2015-03-21 21:07:17 +01:00
GH_ACCOUNT= pydata
GH_PROJECT= ${PORTNAME}
GH_TAGNAME= v${PORTVERSION}
USE_GITHUB= yes
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
.include <bsd.port.mk>
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