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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).
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