------------------------------------------------------------------- Wed Sep 27 22:20:10 UTC 2017 - arun@gmx.de - 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 ------------------------------------------------------------------- Wed Apr 19 18:37:17 UTC 2017 - toddrme2178@gmail.com - Implement single-spec version. ------------------------------------------------------------------- Mon Nov 14 14:24:23 UTC 2016 - dmueller@suse.com - 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% ------------------------------------------------------------------- Mon Apr 27 19:23:55 UTC 2015 - benoit.monin@gmx.fr - 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 ------------------------------------------------------------------- Thu May 8 10:58:17 UTC 2014 - toddrme2178@gmail.com - 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 ------------------------------------------------------------------- Tue Oct 22 12:07:46 UTC 2013 - toddrme2178@gmail.com - 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 ------------------------------------------------------------------- Fri Jun 22 13:11:43 UTC 2012 - saschpe@suse.de - %py_requires is only needed for SLE_11_SP2 (and older), newer Python package releases generate the RPM requires for the Python ABI automatically ------------------------------------------------------------------- Fri Jun 22 12:25:12 UTC 2012 - saschpe@suse.de - 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 ------------------------------------------------------------------- Fri Jun 1 09:05:36 UTC 2012 - toddrme2178@gmail.com - spec file cleanups - fix license tag ------------------------------------------------------------------- Mon Feb 27 21:55:40 UTC 2012 - scorot@free.fr - version 0.5.0 ------------------------------------------------------------------- Fri Jan 22 00:00:00 UTC 2011 - scorot@gtt.fr - Initial release