python-Bottleneck-1.3.2-bp154.1.33

- Require numpy 1.16.0, removing Python 2 support which provides
  a lower version
- Activate test suite

- specfile:
  * update copyright year
- update to version 1.3.2:
  * Bug Fixes
    + Explicitly declare numpy version dependency in pyproject.toml
    for Python 3.8, fixing certain cases where pip install would
    fail. Thanks to @goggle, @astrofrog, and @0xb0b for
    reporting. (:issue:`277`)

- specfile:
  * update copyright year
- update to version 1.3.1:
  * Bug Fixes
    + Fix memory leak in :func:`bottleneck.nanmedian` with the default
    argument of axis=None. Thanks to @jsmodic for reporting!
    (:issue:`276`, :issue:`278`)
    + Add regression test for memory leak case (:issue:`279`)
- changes from version 1.3.0:
  * Project Updates
    + Bottleneck has a new maintainer, Christopher Whelan (@qwhelan on
    GitHub).
    + Documentation now hosted at https://bottleneck.readthedocs.io
    + 1.3.x will be the last release to support Python 2.7
    + Bottleneck now supports and is tested against Python 3.7 and
    3.8. (:issue:`211`, :issue:`268`)
    + The LICENSE file has been restructured to only include the
    license for the Bottleneck project to aid license audit
    tools. There has been no change to the licensing of Bottleneck.
    + Licenses for other projects incorporated by Bottleneck are now
    reproduced in full in separate files in the LICENSES/ directory
    (eg, LICENSES/NUMPY_LICENSE)
    + All licenses have been updated. Notably, setuptools is now MIT
    licensed and no longer under the ambiguous dual PSF/Zope
    license.
    + Bottleneck now uses PEP 518 for specifying build dependencies,
    with per Python version specifications (:issue:`247`)
  * Enhancements
    + Remove numpydoc package from Bottleneck source distribution
    + :func:`bottleneck.slow.reduce.nansum` and
    :func:`bottleneck.slow.reduce.ss` now longer coerce output to
    have the same dtype as input
    + Test (tox, travis, appveyor) against latest numpy (in conda)
    + Performance benchmarking also available via asv
    + versioneer now used for versioning (:issue:`213`)
    + Test suite now uses pytest as nose is deprecated (:issue:`222`)
    + python setup.py build_ext --inplace is now incremental
    (:issue:`224`)
    + python setup.py clean now cleans all artifacts (:issue:`226`)
    + Compiler feature support now identified by testing rather than
    hardcoding (:issue:`227`)
    + The BN_OPT_3 macro allows selective use of -O3 at the function
    level (:issue:`223`)
    + Contributors are now automatically cited in the release notes
    (:issue:`244`)
  * Performance
    + Speed up :func:`bottleneck.reduce.anynan` and
    :func:`bottleneck.reduce.allnan` by 2x via BN_OPT_3
    (:issue:`223`)
    + All functions covered by asv benchmarks
    + :func:`bottleneck.nonreduce.replace` speedup of 4x via more
    explicit typing (:issue:`239`)
    + :func:`bottleneck.reduce.median` up to 2x faster for
    Fortran-ordered arrays (:issue:`248`)
  * Bug Fixes
    + Documentation fails to build on Python 3 (:issue:`170`)
    + :func:`bottleneck.benchmark.bench` crashes on python 3.6.3,
    numpy 1.13.3 (:issue:`175`)
    + :func:`bottleneck.nonreduce_axis.push` raises when n=None is
    explicitly passed (:issue:`178`)
    + :func:`bottleneck.reduce.nansum` wrong output when a =
    np.ones((2, 2))[..., np.newaxis] same issue of other reduce
    functions (:issue:`183`)
    + Silenced FutureWarning from NumPy in the slow version of move
    functions (:issue:`194`)
    + Installing bottleneck onto a system that does not already have
    Numpy (:issue:`195`)
    + Memory leaked when input was not a NumPy array (:issue:`201`)
    + Tautological comparison in :func:`bottleneck.move.move_rank`
    removed (:issue:`207`, :issue:`212`)
  * Cleanup
    + The ez_setup.py module is no longer packaged (:issue:`211`)
    + Building documentation is now self-contained in make doc
    (:issue:`214`)
    + Codebase now flake8 compliant and run on every commit
    + Codebase now uses black for autoformatting (:issue:`253`)

- 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