./math/py-numexpr, Numerical expression evaluator for NumPy

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Branch: CURRENT, Version: 2.6.4, Package name: py27-numexpr-2.6.4, Maintainer: pkgsrc-users

Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.

Required to run:
[devel/py-setuptools] [math/py-numpy] [lang/python27]

Required to build:

Master sites:

SHA1: 691949d4a7f396db7ddfd17821398171c0c6e599
RMD160: 02e7274ee8761c98e7217949dca6d4ed5203bcd7
Filesize: 88.926 KB

Version history: (Expand)

CVS history: (Expand)

   2017-09-18 19:03:45 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-numexpr: update to 2.6.4

Changes from 2.6.3 to 2.6.4
- Christoph Gohkle noticed a lack of coverage for the 2.6.3
  `floor` and `ceil` functions for MKL that caused seg-faults in
   test, so thanks to him for that.
   2017-09-14 12:52:01 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-numexpr: update to 2.6.3

Changes from 2.6.2 to 2.6.3
- Documentation now available at readthedocs.io_.
- Support for floor() and ceil() functions added by Caleb P. Burns.
- NumPy requirement increased from 1.6 to 1.7 due to changes in iterator
- Sphinx autodocs support added for documentation on readthedocs.org.
- Fixed a bug where complex constants would return an error, fixing
  problems with `sympy` when using NumExpr as a backend.
- Fix for 277 whereby arrays of shape (1,...) would be reduced as
  if they were full reduction. Behavoir now matches that of NumPy.
- String literals are automatically encoded into 'ascii' bytes for
   2017-05-21 09:06:57 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
Changes 2.6.2:
Updates to keep with API changes in newer NumPy versions
Removed several warnings
Fix bugs in function stringcontains()
Detection of the POWER processor
Fix pow result casting
Fix integers to negative integer powers
Detect numpy exceptions in expression evaluation
Better handling of RC versions
   2016-11-17 08:26:47 by Alexander Nasonov | Files touched by this commit (3) | Package updated
Log message:
Update to 2.6.1.

Prompted by beta.repology.org.

Changes from 2.6.0 to 2.6.1

     * Fixed a performance regression in some situations as consequence of
       increasing too much the BLOCK_SIZE1 constant. After more careful
       benchmarks (both in VML and non-VML modes), the value has been set
       again to 1024 (down from 8192). The benchmarks have been made with
       a relatively new processor (Intel Xeon E3-1245 v5 @ 3.50GHz), so
       they should work well for a good range of processors again.
     * Added NetBSD support to CPU detection. Thanks to Thomas Klausner.
   2016-07-15 09:24:22 by Thomas Klausner | Files touched by this commit (1)
Log message:
Remove unneeded bl3.mk file.
   2016-07-15 09:21:36 by Thomas Klausner | Files touched by this commit (2)
Log message:
Add upstream bug report URL.
   2016-07-15 09:18:46 by Thomas Klausner | Files touched by this commit (4) | Package updated
Log message:
Updated py-numexpr to 2.6.0.

Fix CPU detection patch while here.

Changes from 2.5.2 to 2.6.0

- Introduced a new re_evaluate() function for re-evaluating the
  previous executed array expression without any check.  This is meant
  for accelerating loops that are re-evaluating the same expression
  repeatedly without changing anything else than the operands.  If
  unsure, use evaluate() which is safer.

- The BLOCK_SIZE1 and BLOCK_SIZE2 constants have been re-checked in
  order to find a value maximizing most of the benchmarks in bench/
  directory.  The new values (8192 and 16 respectively) give somewhat
  better results (~5%) overall.  The CPU used for fine tuning is a
  relatively new Haswell processor (E3-1240 v3).

- The '--name' flag for `setup.py` returning the name of the package
  is honored now (issue #215).

Changes from 2.5.1 to 2.5.2

- conj() and abs() actually added as VML-powered functions, preventing
  the same problems than log10() before (PR #212).  Thanks to Tom Kooij
  for the fix!

Changes from 2.5 to 2.5.1

- Fix for log10() and conj() functions.  These produced wrong results
  when numexpr was compiled with Intel's MKL (which is a popular build
  since Anaconda ships it by default) and non-contiguous data (issue
  #210).  Thanks to Arne de Laat and Tom Kooij for reporting and
  providing a nice test unit.

- Fix that allows numexpr-powered apps to be profiled with pympler.
  Thanks to @nbecker.

Changes from 2.4.6 to 2.5

- Added locking for allowing the use of numexpr in multi-threaded
  callers (this does not prevent numexpr to use multiple cores
  simultaneously).  (PR #199, Antoine Pitrou, PR #200, Jenn Olsen).

- Added new min() and max() functions (PR #195, CJ Carey).

Changes from 2.4.5 to 2.4.6

- Fixed some UserWarnings in Solaris (PR #189, Graham Jones).

- Better handling of MSVC defines. (#168, Francesc Alted).

Changes from 2.4.4 to 2.4.5

- Undone a 'fix' for a harmless data race.  (#185 Benedikt Reinartz,
  Francesc Alted).

- Ignore NumPy warnings (overflow/underflow, divide by zero and
  others) that only show up in Python3.  Masking these warnings in
  tests is fine because all the results are checked to be
  valid. (#183, Francesc Alted).

Changes from 2.4.3 to 2.4.4

- Fix bad #ifdef for including stdint on Windows (PR #186, Mike Sarahan).

Changes from 2.4.3 to 2.4.4

* Honor OMP_NUM_THREADS as a fallback in case NUMEXPR_NUM_THREADS is not
  set. Fixes #161. (PR #175, Stefan Erb).

* Added support for AppVeyor (PR #178 Andrea Bedini)

* Fix to allow numexpr to be imported after eventlet.monkey_patch(),
  as suggested in #118 (PR #180 Ben Moran).

* Fix harmless data race that triggers false positives in ThreadSanitizer.
  (PR #179, Clement Courbet).

* Fixed some string tests on Python 3 (PR #182, Antonio Valentino).

Changes from 2.4.2 to 2.4.3

* Comparisons with empty strings work correctly now.  Fixes #121 and
  PyTables #184.

Changes from 2.4.1 to 2.4.2

* Improved setup.py so that pip can query the name and version without
  actually doing the installation.  Thanks to Joris Borgdorff.

Changes from 2.4 to 2.4.1

* Added more configuration examples for compiling with MKL/VML
  support.  Thanks to Davide Del Vento.

* Symbol MKL_VML changed into MKL_DOMAIN_VML because the former is
  deprecated in newer MKL.  Thanks to Nick Papior Andersen.

* Better determination of methods in `cpuinfo` module.  Thanks to Marc

* Improved NumPy version determination (handy for 1.10.0).  Thanks
  to Åsmund Hjulstad.

* Benchmarks run now with both Python 2 and Python 3.  Thanks to Zoran

Changes from 2.3.1 to 2.4

* A new `contains()` function has been added for detecting substrings
  in strings.  Only plain strings (bytes) are supported for now.  See
  PR #135 and ticket #142.  Thanks to Marcin Krol.

* New version of setup.py that allows better management of NumPy
  dependency.  See PR #133.  Thanks to Aleks Bunin.

Changes from 2.3 to 2.3.1

* Added support for shift-left (<<) and shift-right (>>) binary \ 
  See PR #131. Thanks to fish2000!

* Removed the rpath flag for the GCC linker, because it is probably
  not necessary and it chokes to clang.
   2015-11-04 00:33:46 by Alistair G. Crooks | Files touched by this commit (262)
Log message:
Add SHA512 digests for distfiles for math category

Problems found locating distfiles:
	Package dfftpack: missing distfile dfftpack-20001209.tar.gz
	Package eispack: missing distfile eispack-20001130.tar.gz
	Package fftpack: missing distfile fftpack-20001130.tar.gz
	Package linpack: missing distfile linpack-20010510.tar.gz
	Package minpack: missing distfile minpack-20001130.tar.gz
	Package odepack: missing distfile odepack-20001130.tar.gz
	Package py-networkx: missing distfile networkx-1.10.tar.gz
	Package py-sympy: missing distfile sympy-
	Package quadpack: missing distfile quadpack-20001130.tar.gz

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.