NOTICE: This package has been removed from pkgsrc

./wip/py-numpy, Array processing for numbers, strings, records, and objects

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Branch: CURRENT, Version: 1.26.1, Package name: py311-numpy-1.26.1, Maintainer: pkgsrc-users

NumPy is a general-purpose array-processing package designed to
efficiently manipulate large multi-dimensional arrays of arbitrary
records without sacrificing too much speed for small multi-dimensional
arrays. NumPy is built on the Numeric code base and adds features
introduced by numarray as well as an extended C-API and the ability to
create arrays of arbitrary type.

There are also basic facilities for discrete fourier transform,
basic linear algebra and random number generation.


Required to run:
[math/lapack] [math/blas] [lang/python25] [devel/libf2c]

Required to build:
[lang/f2c]

Master sites:

Filesize: 15284.967 KB

Version history: (Expand)

CVS history: (Expand)


   2011-11-22 10:49:53 by Thomas Klausner | Files touched by this commit (11) | Package removed
Log message:
Remove py-numpy, math/py-numpy is much newer.
   2010-08-01 16:57:08 by Aleksej Saushev | Files touched by this commit (36) 
Log message:
Assume anything that wants Fortran for now actually wants Fortran-77.
   2009-10-11 12:45:10 by Thomas Klausner | Files touched by this commit (261) 
Log message:
Remove obsolete @dirrm lines.
   2009-09-09 13:22:11 by Thomas Klausner | Files touched by this commit (16) 
Log message:
Remove references to pyhon-2.3.
   2009-03-20 20:43:38 by Jörg Sonnenberger | Files touched by this commit (284) 
Log message:
Convert buildlink3.mk files to new world order.
   2008-07-27 00:00:11 by mwdavies | Files touched by this commit (2) 
Log message:
Add a MAIN__ to keep f2c happy.
   2008-06-25 12:56:02 by mwdavies | Files touched by this commit (7) | Imported package
Log message:
numpy 1.1.0

package has only been tried on current NetBSD/i386 with g95 as the fortran
compiler.

NumPy is a general-purpose array-processing package designed to
efficiently manipulate large multi-dimensional arrays of arbitrary
records without sacrificing too much speed for small multi-dimensional
arrays.  NumPy is built on the Numeric code base and adds features
introduced by numarray as well as an extended C-API and the ability to
create arrays of arbitrary type.

There are also basic facilities for discrete fourier transform,
basic linear algebra and random number generation.