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Release Notes

These are the major changes made in each release. For details of the changes see the commit log at http://github.com/kwgoodman/bottleneck

Bottleneck 1.1.0

Release date: in development, not yet released

Older versions

Release notes from past releases.

Bottleneck 1.0.0

Release date: 2015-02-06

This release is a complete rewrite of Bottleneck.

Faster

  • "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

Smaller

  • 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

Beware

  • 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

min_count

  • 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

Bug Fixes

  • 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

Bottleneck 0.8.0

Release date: 2014-01-21

This version of Bottleneck requires NumPy 1.8.

Breaks from 0.7.0

  • This version of Bottleneck requires NumPy 1.8
  • nanargmin and nanargmax behave like the corresponding functions in NumPy 1.8

Bug fixes

  • nanargmax/nanargmin wrong for redundant max/min values in 1d int arrays

Bottleneck 0.7.0

Release date: 2013-09-10

Enhancements

  • 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

Bug fixes

  • #50 move_std, move_nanstd return inappropriate NaNs (sqrt of negative #)
  • #52 make test fails on some computers
  • #57 scipy optional yet some unit tests depend on scipy
  • #49, #55 now works on Mac OS X 10.8 using clang compiler
  • #60 nanstd([1.0], ddof=1) and nanvar([1.0], ddof=1) crash

Bottleneck 0.6.0

Release date: 2012-06-04

Thanks to Dougal Sutherland, Bottleneck now runs on Python 3.2.

New functions

  • 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)

Enhancements

  • Python 3.2 support (may work on earlier versions 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

Breaks from 0.5.0

  • Support for Python 2.5 dropped
  • Default axis for benchmark suite is now axis=1 (was 0)

Bug fixes

  • #31 Confusing error message in partsort and argpartsort
  • #32 Update path in MANIFEST.in
  • #35 Wrong output for very large (2**31) input arrays

Bottleneck 0.5.0

Release date: 2011-06-13

The fifth release of bottleneck adds four new functions, comes in a single source distribution instead of separate 32 and 64 bit versions, and contains bug fixes.

J. David Lee wrote the C-code implementation of the double heap moving window median.

New functions

  • move_median(), moving window median
  • partsort(), partial sort
  • argpartsort()
  • ss(), sum of squares, faster version of scipy.stats.ss

Changes

  • Single source distribution instead of separate 32 and 64 bit versions
  • nanmax and nanmin now follow Numpy 1.6 (not 1.5.1) when input is all NaN

Bug fixes

  • #14 Support python 2.5 by importing with statement
  • #22 nanmedian wrong for particular ordering of NaN and non-NaN elements
  • #26 argpartsort, nanargmin, nanargmax returned wrong dtype on 64-bit Windows
  • #29 rankdata and nanrankdata crashed on 64-bit Windows

Bottleneck 0.4.3

Release date: 2011-03-17

This is a bug fix release.

Bug fixes

  • #11 median and nanmedian modified (partial sort) input array
  • #12 nanmedian wrong when odd number of elements with all but last a NaN

Enhancement

  • Lazy import of SciPy (rarely used) speeds Bottleneck import 3x

Bottleneck 0.4.2

Release date: 2011-03-08

This is a bug fix release.

Same bug fixed in Bottleneck 0.4.1 for nanstd() was fixed for nanvar() in this release. Thanks again to Christoph Gohlke for finding the bug.

Bottleneck 0.4.1

Release date: 2011-03-08

This is a bug fix release.

The low-level functions nanstd_3d_int32_axis1 and nanstd_3d_int64_axis1, called by bottleneck.nanstd(), wrote beyond the memory owned by the output array if arr.shape[1] == 0 and arr.shape[0] > arr.shape[2], where arr is the input array.

Thanks to Christoph Gohlke for finding an example to demonstrate the bug.

Bottleneck 0.4.0

Release date: 2011-03-08

The fourth release of Bottleneck contains new functions and bug fixes. Separate source code distributions are now made for 32 bit and 64 bit operating systems.

New functions

  • rankdata()
  • nanrankdata()

Enhancements

  • Optionally specify the shapes of the arrays used in benchmark
  • Can specify which input arrays to fill with one-third NaNs in benchmark

Breaks from 0.3.0

  • Removed group_nanmean() function
  • Bump dependency from NumPy 1.4.1 to NumPy 1.5.1
  • C files are now generated with Cython 0.14.1 instead of 0.13

Bug fixes

  • #6 Some functions gave wrong output dtype for some input dtypes on 32 bit OS
  • #7 Some functions choked on size zero input arrays
  • #8 Segmentation fault with Cython 0.14.1 (but not 0.13)

Bottleneck 0.3.0

Release date: 2010-01-19

The third release of Bottleneck is twice as fast for small input arrays and contains 10 new functions.

Faster

  • All functions are faster (less overhead in selector functions)

New functions

  • nansum()
  • move_sum()
  • move_nansum()
  • move_mean()
  • move_std()
  • move_nanstd()
  • move_min()
  • move_nanmin()
  • move_max()
  • move_nanmax()

Enhancements

  • You can now specify the dtype and axis to use in the benchmark timings
  • Improved documentation and more unit tests

Breaks from 0.2.0

  • Moving window functions now default to axis=-1 instead of axis=0
  • Low-level moving window selector functions no longer take window as input

Bug fix

  • int input array resulted in call to slow, non-cython version of move_nanmean

Bottleneck 0.2.0

Release date: 2010-12-27

The second release of Bottleneck is faster, contains more functions, and supports more dtypes.

Faster

  • All functions faster (less overhead) when output is not a scalar
  • Faster nanmean() for 2d, 3d arrays containing NaNs when axis is not None

New functions

  • nanargmin()
  • nanargmax()
  • nanmedian()

Enhancements

  • Added support for float32
  • Fallback to slower, non-Cython functions for unaccelerated ndim/dtype
  • Scipy is no longer a dependency
  • Added support for older versions of NumPy (1.4.1)
  • All functions are now templated for dtype and axis
  • Added a sandbox for prototyping of new Bottleneck functions
  • Rewrote benchmarking code

Bottleneck 0.1.0

Release date: 2010-12-10

Initial release. The three categories of Bottleneck functions:

  • Faster replacement for NumPy and SciPy functions
  • Moving window functions
  • Group functions that bin calculations by like-labeled elements
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