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 1.2.1¶
Release date: 2017-05-15
This release adds support for NumPy’s relaxed strides checking and fixes a few bugs.
Bug Fixes
#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
Older versions¶
Release notes from past releases.
Bottleneck 1.2.0¶
Release date: 2016-10-20
This release is a complete rewrite of Bottleneck.
Port to C
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%
Redesign
Besides porting to C, much of bottleneck has been redesigned to be simpler and faster. For example, bottleneck now uses its own N-dimensional array iterators, reducing function call overhead.
New features
The new function bench_detailed runs a detailed performance benchmark on a single bottleneck function.
Bottleneck can be installed on systems that do not yet have NumPy installed. Previously that only worked on some systems.
Beware
Functions partsort and argpartsort have been renamed to partition and argpartition to match NumPy. Additionally the meaning of the input arguments have changed: bn.partsort(a, n) is now equivalent to bn.partition(a, kth=n-1). Similarly for bn.argpartition.
The keyword for array input has been changed from arr to a in all functions. It now matches NumPy.
Thanks
Moritz E. Beber: continuous integration with AppVeyor
Christoph Gohlke: Windows compatibility
Jennifer Olsen: comments and suggestions
A special thanks to the Cython developers. The quickest way to appreciate their work is to remove Cython from your project. It is not easy.
Bottleneck 1.1.0¶
Release date: 2016-06-22
This release makes Bottleneck more robust, releases GIL, adds new functions.
More Robust
move_median can now handle NaNs and min_count parameter
move_std is slower but numerically more stable
Bottleneck no longer crashes on byte-swapped input arrays
Faster
All Bottleneck functions release the GIL
median is faster if the input array contains NaN
move_median is faster for input arrays that contain lots of NaNs
No speed penalty for median, nanmedian, nanargmin, nanargmax for Fortran ordered input arrays when axis is None
Function call overhead cut in half for reduction along all axes (axis=None) if the input array satisfies at least one of the following properties: 1d, C contiguous, F contiguous
Reduction along all axes (axis=None) is more than twice as fast for long, narrow input arrays such as a (1000000, 2) C contiguous array and a (2, 1000000) F contiguous array
New Functions
move_var
move_argmin
move_argmax
move_rank
push
Beware
median now returns NaN for a slice that contains one or more NaNs
Instead of using the distutils default, the ‘-O2’ C compiler flag is forced
move_std output changed when mean is large compared to standard deviation
Fixed: Non-accelerated moving window functions used min_count incorrectly
move_median is a bit slower for float input arrays that do not contain NaN
Thanks
Alphabeticaly by last name
Alessandro Amici worked on setup.py
Pietro Battiston modernized bottleneck installation
Moritz E. Beber set up continuous integration with Travis CI
Jaime Frio improved the numerical stability of move_std
Christoph Gohlke revived Windows compatibility
Jennifer Olsen added NaN support to move_median
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