kpriv/freebsd-ports
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity
freebsd-ports/math/py-numexpr/Makefile

25 lines
628 B
Makefile
Raw Normal View History

- 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
PORTNAME= numexpr
math/py-numexpr: Update to 2.8.2 - Change MASTER_SITES to CHEESESHOP Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2022-01-11 18:15:56 +01:00
PORTVERSION= 2.8.1
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
math/py-numexpr: Update to 2.8.2 - Change MASTER_SITES to CHEESESHOP Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2022-01-11 18:15:56 +01:00
MASTER_SITES= CHEESESHOP
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
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
- Update to 2.4.4 - Update MASTER_SITES: move back to CHEESESHOP - Add LICENSE_FILE - Reformat pkg-descr and remove trailing space - Add PyPI as primary WWW Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2015-10-04 21:01:07 +02:00
LICENSE_FILE= ${WRKSRC}/LICENSE.txt
- Add LICENSE - Use PYDISTUTILS_AUTOPLIST - Remove post-patch:
2014-02-14 20:43:20 +01:00
Change the way *_DEPENDS are set
2018-11-17 18:31:57 +01:00
BUILD_DEPENDS= ${RUN_DEPENDS}
math/py-numexpr: Update to 2.8.2 - Change MASTER_SITES to CHEESESHOP Changes: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst
2022-01-11 18:15:56 +01:00
RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}numpy>=1.13.3,1:math/py-numpy@${PY_FLAVOR}
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
math/py-numexpr: Update USES=python math/py-numpy requires Python 3.7+
2021-11-05 20:04:42 +01:00
USES= dos2unix python:3.7+
Change the way *_DEPENDS are set
2018-11-17 18:31:57 +01:00
USE_PYTHON= autoplist concurrent distutils
- 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
- Allow concurrent installation (USE_PYTHON=concurrent) - Strip shared library - Bump PORTREVISION for package change
2015-09-02 19:04:14 +02:00
post-install:
math/py-numexpr: Cosmetic change
2021-10-25 17:08:13 +02:00
${FIND} ${STAGEDIR}${PYTHON_SITELIBDIR} -name '*.so' -exec ${STRIP_CMD} {} +
- Allow concurrent installation (USE_PYTHON=concurrent) - Strip shared library - Bump PORTREVISION for package change
2015-09-02 19:04:14 +02:00
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>
Reference in a new issue Copy permalink