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

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Branch: CURRENT, Version: 1.14.5, Package name: py27-numpy-1.14.5, 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:
[lang/g95] [math/lapack] [math/blas] [devel/py-setuptools] [devel/py-cython] [lang/python27]

Required to build:
[devel/py-nose] [pkgtools/cwrappers]

Master sites:

SHA1: bafe3e48ac3d90ed5105ac0db4982b405a8a58ff
RMD160: 7f884a5d493445d204ab093ad715691de444fa0a
Filesize: 4789.672 KB

Version history: (Expand)


CVS history: (Expand)


   2018-06-18 08:47:03 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
Log message:
py-numpy: updated to 1.14.5

NumPy 1.14.5:
This is a bugfix release for bugs reported following the 1.14.4 release. The
most significant fixes are:
* fixes for compilation errors on alpine and NetBSD
   2018-06-10 14:56:54 by Wen Heping | Files touched by this commit (3) | Package updated
Log message:
Update to 1.14.4

Upstream changes:
NumPy 1.14.4 Release Notes

This is a bugfix release for bugs reported following the 1.14.3 release. The \ 
most significant fixes are:

    fixes for compiler instruction reordering that resulted in NaN's not being \ 
properly propagated in np.max and np.min,
    fixes for bus faults on SPARC and older ARM due to incorrect alignment checks.
   2018-05-16 08:53:18 by Min Sik Kim | Files touched by this commit (1) | Package updated
Log message:
math/py-numpy: Bump PKGREVISION for the new patch

The patch to turn off debugging options has changed the binary package
of py-numpy.
   2018-05-14 08:36:17 by Adam Ciarcinski | Files touched by this commit (2)
Log message:
py-numpy: Do not generate debug symbols
   2018-05-02 06:42:25 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-numpy: updated to 1.14.3

NumPy 1.14.3:
This is a bugfix release for a few bugs reported following the 1.14.2 release:
- np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15
- In python2, float types use the new print style when printing to a file
- style arg in "legacy" print mode now works for 0d arrays
   2018-04-03 20:30:45 by Min Sik Kim | Files touched by this commit (2)
Log message:
math/py-numpy: Disable openblas detection

This package should use math/blas.
   2018-03-13 12:34:08 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-numpy: updated to 1.14.2

1.14.2:
This is a bugfix release for some bugs reported following the 1.14.1 release. \ 
The major
problems dealt with are as follows.

Residual bugs in the new array printing functionality.
Regression resulting in a relocation problem with shared library.
Improved PyPy compatibility.
   2018-02-22 11:50:47 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-numpy: updated to 1.14.1

NumPy 1.14.1 Release Notes

This is a bugfix release for some problems reported following the 1.14.0 \ 
release. The major
problems fixed are the following.

Problems with the new array printing, particularly the printing of complex
values, Please report any additional problems that may turn up.
Problems with np.einsum due to the new optimized=True default. Some
fixes for optimization have been applied and optimize=False is now the
default.
The sort order in np.unique when axis=<some-number> will now always
be lexicographic in the subarray elements. In previous NumPy versions there
was an optimization that could result in sorting the subarrays as unsigned
byte strings.
The change in 1.14.0 that multi-field indexing of structured arrays returns a
view instead of a copy has been reverted but remains on track for NumPy 1.15.
Affected users should read the 1.14.1 Numpy User Guide section
"basics/structured arrays/accessing multiple fields" for advice on how to
manage this transition.