python-Bottleneck-1.2.1-bp150.2.4

- update to version 1.2.1:
  * #156 Installing bottleneck when two versions of NumPy are present
  * #157 Compiling on Ubuntu 14.04 inside a Windows 7 WMware
  * #159 Occasional segmentation fault in nanargmin, nanargmax,
    median, and nanmedian when all of the following conditions are
    met: axis is None, input array is 2d or greater, and input array
    is not C contiguous.
  * #163 Reducing np.array([2**31], dtype=np.int64) overflows on
    Windows

- Implement single-spec version.

- update to 1.2.0:
  This release is a complete rewrite of Bottleneck.
  - Bottleneck is now written in C
  - Cython is no longer a dependency
  - Source tarball size reduced by 80%
  - Build time reduced by 66%
  - Install size reduced by 45%

- update to version 1.0.0:
  * "python setup.py build" is 18.7 times faster
  * Function-call overhead cut in half---a big speed up for small
    input arrays
  * Arbitrary ndim input arrays accelerated; previously only 1d,
    2d, and 3d
  * bn.nanrankdata is twice as fast for float input arrays
  * bn.move_max, bn.move_min are faster for int input arrays
  * No speed penalty for reducing along all axes when input is
    Fortran ordered
  * Compiled binaries 14.1 times smaller
  * Source tarball 4.7 times smaller
  * 9.8 times less C code
  * 4.3 times less Cython code
  * 3.7 times less Python code
  * Requires numpy 1.9.1
  * Single API, e.g.: bn.nansum instead of bn.nansum and
    nansum_2d_float64_axis0
  * On 64-bit systems bn.nansum(int32) returns int32 instead of
    int64
  * bn.nansum now returns 0 for all NaN slices (as does numpy
    1.9.1)
  * Reducing over all axes returns, e.g., 6.0; previously
    np.float64(6.0)
  * bn.ss() now has default axis=None instead of axis=0
  * bn.nn() is no longer in bottleneck
  * Previous releases had moving window function pairs: move_sum,
    move_nansum
  * This release only has half of the pairs: move_sum
  * Instead a new input parameter, min_count, has been added
  * min_count=None same as old move_sum; min_count=1 same as old
    move_nansum
  * If # non-NaN values in window < min_count, then NaN assigned
    to the window
  * Exception: move_median does not take min_count as input
  * Can now install bottleneck with pip even if numpy is not
    already installed
  * bn.move_max, bn.move_min now return float32 for float32 input
- increase required numpy version to 1.9.1

- Update to version 0.8.0
  - This version of Bottleneck requires NumPy 1.8
  - nanargmin and nanargmax behave like the corresponding functions in NumPy 1.8
  - nanargmax/nanargmin wrong for redundant max/min values in 1d int arrays

- Update to version 0.7.0
  + bn.rankdata() is twice as fast (with input a = np.random.rand(1000000))
  + C files now included in github repo; cython not needed to try latest
  + C files are now generated with Cython 0.19.1 instead of 0.16
  + Test bottleneck across multiple python/numpy versions using tox
  + Source tarball size cut in half

- %py_requires is only needed for SLE_11_SP2 (and older), newer Python
  package releases generate the RPM requires for the Python ABI automatically

- Update to version 0.6.0:
  + replace(arr, old, new), e.g, replace(arr, np.nan, 0)
  + nn(arr, arr0, axis) nearest neighbor and its index of 1d arr0 in 2d arr
  + anynan(arr, axis) faster alternative to np.isnan(arr).any(axis)
  + allnan(arr, axis) faster alternative to np.isnan(arr).all(axis)
  + Python 3.2 support (may work on earlier verions of Python 3)
  + C files are now generated with Cython 0.16 instead of 0.14.1
  + Upgrade numpydoc from 0.3.1 to 0.4 to support Sphinx 1.0.1
  + Support for Python 2.5 dropped
  + Default axis for benchmark suite is now axis=1 (was 0)
  + #31 Confusing error message in partsort and argpartsort
  + #32 Update path in MANIFEST.in
  + #35 Wrong output for very large (2**31) input arrays

- spec file cleanups
- fix license tag

- version 0.5.0

- Initial release