[52817] trunk/dports

Tue Jun 23 13:54:07 PDT 2009

Revision: 52817
          http://trac.macports.org/changeset/52817
Author:   portindex at macports.org
Date:     2009-06-23 13:54:07 -0700 (Tue, 23 Jun 2009)
Log Message:
-----------

Total number of ports parsed:	5870 
Ports successfully parsed:	5870	 
Ports failed:			0

Modified Paths:
--------------
    trunk/dports/PortIndex
    trunk/dports/PortIndex.quick

Modified: trunk/dports/PortIndex
===================================================================

--- trunk/dports/PortIndex	2009-06-23 20:42:18 UTC (rev 52816)
+++ trunk/dports/PortIndex	2009-06-23 20:54:07 UTC (rev 52817)
@@ -8284,8 +8284,8 @@
 variants smp portdir python/py25-numarray description {array manipulation and computational library for python} homepage http://www.stsci.edu/resources/software_hardware/numarray epoch 0 platforms darwin depends_lib port:python25 name py25-numarray long_description {Numarray provides array manipulation and computational capabilities similar to those found in IDL, Matlab, or Octave. Using numarray, it is possible to write many efficient numerical data processing applications directly in Python without using any C, C++ or Fortran code.} maintainers nomaintainer version 1.5.2 categories {python devel math} revision 0
 py25-numeric 404
 variants {macosx puredarwin} portdir python/py25-numeric description {fast numerical array language for python} homepage http://numpy.scipy.org/ epoch 0 platforms darwin depends_lib port:python25 name py25-numeric long_description {Numerical Python adds a fast, compact, multidimensional array language facility to Python.} maintainers nomaintainer version 24.2 categories {python devel math} revision 1
-py25-numexpr 1048
-portdir python/py25-numexpr description {Multiple-operator array expression evaluator} homepage http://code.google.com/p/numexpr/ epoch 0 platforms darwin depends_lib {port:python25 port:py25-numpy} name py25-numexpr maintainers {blb openmaintainer} long_description {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 comp
 utations.} version 1.3 categories {python math} revision 0
+py25-numexpr 1050
+portdir python/py25-numexpr description {Multiple-operator array expression evaluator} homepage http://code.google.com/p/numexpr/ epoch 0 platforms darwin depends_lib {port:python25 port:py25-numpy} name py25-numexpr maintainers {blb openmaintainer} long_description {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 comp
 utations.} version 1.3.1 categories {python math} revision 0
 py25-numpy 392
 portdir python/py25-numpy description {The core utilities for the scientific library scipy for Python} homepage http://numpy.scipy.org/ epoch 0 platforms darwin depends_lib {port:python25 port:fftw-3 port:py25-nose} name py25-numpy maintainers {ram openmaintainer} long_description {{The core utilities for the scientific library scipy for Python}} version 1.3.0 categories python revision 0
 py25-ode 333
@@ -8684,8 +8684,8 @@
 portdir python/py26-nose description {A Python unittest extension.} homepage http://somethingaboutorange.com/mrl/projects/nose/ epoch 0 platforms darwin depends_lib port:py26-setuptools name py26-nose maintainers {mcalhoun openmaintainer} long_description {A unittest extension offering automatic test suite discovery, simplified test authoring, and output capture. Nose provides an alternate test discovery and running process for unittest, one that is intended to mimic the behavior of py.test as much as is reasonably possible without resorting to magic.} version 0.10.4 categories {python www} revision 0
 py26-numeric 412
 variants {macosx puredarwin} portdir python/py26-numeric description {fast numerical array language for python} homepage http://numpy.scipy.org/ epoch 0 platforms darwin depends_lib port:python26 name py26-numeric long_description {Numerical Python adds a fast, compact, multidimensional array language facility to Python.} maintainers {jmr openmaintainer} version 24.2 categories {python devel math} revision 0
-py26-numexpr 1147
-portdir python/py26-numexpr description {Multiple-operator array expression evaluator} homepage http://code.google.com/p/numexpr/ epoch 0 platforms darwin depends_lib {port:python26 port:py26-numpy} name py26-numexpr maintainers {blb openmaintainer} long_description {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 comp
 utations. WARNING: numpy 1.2.1, on which py26-numexpr depends, is not yet fully functional under Python 2.6.} version 1.3 categories {python math} revision 0
+py26-numexpr 1149
+portdir python/py26-numexpr description {Multiple-operator array expression evaluator} homepage http://code.google.com/p/numexpr/ epoch 0 platforms darwin depends_lib {port:python26 port:py26-numpy} name py26-numexpr maintainers {blb openmaintainer} long_description {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 comp
 utations. WARNING: numpy 1.2.1, on which py26-numexpr depends, is not yet fully functional under Python 2.6.} version 1.3.1 categories {python math} revision 0
 py26-numpy 416
 variants universal portdir python/py26-numpy description {The core utilities for the scientific library scipy for Python} homepage http://numpy.scipy.org/ epoch 0 platforms darwin depends_lib {port:python26 port:fftw-3 port:py26-nose} name py26-numpy long_description {{The core utilities for the scientific library scipy for Python}} maintainers {mcalhoun openmaintainer} version 1.3.0 categories python revision 0
 py26-opengl 557

Modified: trunk/dports/PortIndex.quick
===================================================================
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