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

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.

Log message:
Remove unneeded bl3.mk file.

Log message:
Add upstream bug report URL.

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
  Jofre.

* 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
  Plesivčak.

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 \ 
operators.
  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.

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-0.7.6.1.tar.gz
	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.

Log message:
Fix build. This is an egg hence has an egg-info dir not a file.

Log message:
Remove unused patch

Log message:
Update to 2.2.2. Set LICENSE. Update HOMEPAGE.

Changes from 2.2.1 to 2.2.2
===========================

* The `copy_args` argument of `NumExpr` function has been brought
  back.  This has been mainly necessary for compatibility with
  PyTables < 3.0, which I decided to continue to support.  Fixed #115.

* The `__nonzero__` method in `ExpressionNode` class has been
  commented out.  This is also for compatibility with PyTables < 3.0.
  See #24 for details.

* Fixed the type of some parameters in the C extension so that s390
  architecture compiles.  Fixes #116.  Thank to Antonio Valentino for
  reporting and the patch.

Changes from 2.2 to 2.2.1
=========================

* Fixes a secondary effect of "from numpy.testing import `*`", where
  division is imported now too, so only then necessary functions from
  there are imported now.  Thanks to Christoph Gohlke for the patch.

Changes from 2.1 to 2.2
=======================

* [LICENSE] Fixed a problem with the license of the
  numexpr/win32/pthread.{c,h} files emulating pthreads on Windows
  platforms.  After persmission from the original authors is granted,
  these files adopt the MIT license and can be redistributed without
  problems.  See issue #109 for details
  (https://code.google.com/p/numexpr/issues/detail?id=110).

* [ENH] Improved the algorithm to decide the initial number of threads
  to be used.  This was necessary because by default, numexpr was
  using a number of threads equal to the detected number of cores, and
  this can be just too much for moder systems where this number can be
  too high (and counterporductive for performance in many cases).
  Now, the 'NUMEXPR_NUM_THREADS' environment variable is honored, and
  in case this is not present, a maximum number of *8* threads are
  setup initially.  The new algorithm is fully described in the Users
  Guide now in the note of 'General routines' section:
  https://code.google.com/p/numexpr/wiki/UsersGuide#General_routines.
  Closes #110.

* [ENH] numexpr.test() returns `TestResult` instead of None now.
  Closes #111.

* [FIX] Modulus with zero with integers no longer crashes the
  interpreter.  It nows puts a zero in the result.  Fixes #107.

* [API CLEAN] Removed `copy_args` argument of `evaluate`.  This should
  only be used by old versions of PyTables (< 3.0).

* [DOC] Documented the `optimization` and `truediv` flags of
  `evaluate` in Users Guide
  (https://code.google.com/p/numexpr/wiki/UsersGuide).

Changes from 2.0.1 to 2.1
===========================

* Dropped compatibility with Python < 2.6.

* Improve compatibiity with Python 3:

  - switch from PyString to PyBytes API (requires Python >= 2.6).
  - fixed incompatibilities regarding the int/long API
  - use the Py_TYPE macro
  - use the PyVarObject_HEAD_INIT macro instead of PyObject_HEAD_INIT

* Fixed several issues with different platforms not supporting
  multithreading or subprocess properly (see tickets #75 and #77).

* Now, when trying to use pure Python boolean operators, 'and',
  'or' and 'not', an error is issued suggesting that '&', '|' and
  '~' should be used instead (fixes #24).

Changes from 2.0 to 2.0.1
=========================

* Added compatibility with Python 2.5 (2.4 is definitely not supported
  anymore).

* `numexpr.evaluate` is fully documented now, in particular the new
  `out`, `order` and `casting` parameters.

* Reduction operations are fully documented now.

* Negative axis in reductions are not supported (they have never been
  actually), and a `ValueError` will be raised if they are used.

Changes from 1.x series to 2.0
==============================

- Added support for the new iterator object in NumPy 1.6 and later.

  This allows for better performance with operations that implies
  broadcast operations, fortran-ordered or non-native byte orderings.
  Performance for other scenarios is preserved (except for very small
  arrays).

- Division in numexpr is consistent now with Python/NumPy.  Fixes #22
  and #58.

- Constants like "2." or "2.0" must be evaluated as float, not
  integer.  Fixes #59.

- `evaluate()` function has received a new parameter `out` for storing
  the result in already allocated arrays.  This is very useful when
  dealing with large arrays, and a allocating new space for keeping
  the result is not acceptable.  Closes #56.

- Maximum number of threads raised from 256 to 4096.  Machines with a
  higher number of cores will still be able to import numexpr, but
  limited to 4096 (which is an absurdly high number already).

Log message:
"user-destdir" is default these days