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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 2.2.2 - Support STAGEDIR Changes: http://code.google.com/p/numexpr/wiki/ReleaseNotes
2013-09-28 21:47:45 +02:00
PORTVERSION= 2.2.2
Update to libmpc version 1.0.1 which brings the following fixes: - Switched to automake 1.11.6, see CVE-2012-3386. - #14669: Fixed extraction of CC from gmp.h. - Fixed case of intermediate zero real or imaginary part in mpc_fma, found by hydra with GMP_CHECK_RANDOMIZE=1346362345. This is on top of the following changes from version 1.0 - Licence change towards LGPLv3+ for the code and GFDLv1.3+ (with no invariant sections) for the documentation. - 100% of all lines are covered by tests - Renamed functions . mpc_mul_2exp to mpc_mul_2ui . mpc_div_2exp to mpc_div_2ui - 0^0, which returned (NaN,NaN) previously, now returns (1,+0). - Removed compatibility with K&R compilers, which was untestable due to lack of such compilers. - New functions . mpc_log10 . mpc_mul_2si, mpc_div_2si - Speed-ups . mpc_fma - Bug fixes . mpc_div and mpc_norm now return a value indicating the effective rounding direction, as the other functions. . mpc_mul, mpc_sqr and mpc_norm now return correct results even if there are over- or underflows during the computation. . mpc_asin, mpc_proj, mpc_sqr: Wrong result when input variable has infinite part and equals output variable is corrected. . mpc_fr_sub: Wrong return value for imaginary part is corrected. Convert to the new LIB_DEPENDS standard and remove hard-coded .so versions from a couple of dependent ports. Bump PORTREVISIONS of all dependent ports. PR: 183141 Approved by: portmgr (bdrewery)
2013-10-26 02:52:33 +02:00
PORTREVISION= 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}
- 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
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:= ${BUILD_DEPENDS}
numexpr - Fast numerical array expression evaluator for Python and NumPy. The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations. WWW: http://code.google.com/p/numexpr/ PR: ports/148372 Submitted by: Ju Pengfei <jupengfei@gmail.com> Feature safe: yes
2010-07-05 10:18:12 +02:00
- Pet portlint - Take maintainership
2011-09-21 19:07:19 +02:00
USE_PYTHON= yes
- Update to 2.2 - Do not use setuptools Changes: http://code.google.com/p/numexpr/wiki/ReleaseNotes
2013-09-02 14:50:07 +02:00
USE_PYDISTUTILS=yes
post-patch:
@${REINPLACE_CMD} -e 's|\(import\) \(setuptools\)|\1 do_not_use_\2|' ${WRKSRC}/${PYSETUP}
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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