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Name: Bottleneck
Version: 1.2.0
Summary: Fast NumPy array functions written in C
Home-page: https://github.com/kwgoodman/bottleneck
Author: Keith Goodman
Author-email: bottle-neck@googlegroups.com
License: Simplified BSD
Download-URL: http://pypi.python.org/pypi/Bottleneck
Description: Bottleneck is a collection of fast NumPy array functions written in C.
Let's give it a try. Create a NumPy array::
>>> import numpy as np
>>> a = np.array([1, 2, np.nan, 4, 5])
Find the nanmean::
>>> import bottleneck as bn
>>> bn.nanmean(a)
3.0
Moving window mean::
>>> bn.move_mean(a, window=2, min_count=1)
array([ 1. , 1.5, 2. , 4. , 4.5])
Benchmark
=========
Bottleneck comes with a benchmark suite::
>>> bn.bench()
Bottleneck performance benchmark
Bottleneck 1.2.0dev; Numpy 1.11.2
Speed is NumPy time divided by Bottleneck time
NaN means approx one-fifth NaNs; float64 and axis=-1 are used
no NaN NaN no NaN NaN
(100,) (1000,) (1000,1000)(1000,1000)
nansum 58.3 16.6 2.3 5.1
nanmean 258.7 46.1 3.5 5.1
nanstd 238.4 42.9 2.8 5.0
nanvar 229.9 41.4 2.7 5.0
nanmin 44.6 12.9 0.8 0.9
nanmax 41.8 12.9 0.8 1.8
median 99.6 51.4 1.1 5.7
nanmedian 102.1 26.5 5.0 31.2
ss 27.4 6.4 1.6 1.6
nanargmin 72.6 24.6 2.3 3.4
nanargmax 70.1 29.2 2.4 4.6
anynan 22.1 49.9 0.5 114.6
allnan 43.3 48.4 115.8 66.7
rankdata 50.3 8.0 2.6 6.5
nanrankdata 52.5 8.1 2.9 6.8
partition 4.1 3.6 1.0 2.0
argpartition 2.7 2.2 1.1 1.5
replace 13.7 4.9 1.5 1.5
push 3231.6 7437.4 20.1 19.6
move_sum 4173.5 8955.4 194.7 374.8
move_mean 10265.5 18540.0 222.8 372.2
move_std 8910.9 12158.5 128.7 234.5
move_var 11969.4 18323.8 202.7 378.7
move_min 2164.6 3676.3 23.9 57.2
move_max 1995.0 4206.0 23.8 108.8
move_argmin 3380.5 5559.1 40.5 180.5
move_argmax 3386.5 7278.1 43.0 227.2
move_median 1762.3 1134.9 157.9 118.5
move_rank 1203.6 223.2 2.7 7.8
You can also run a detailed benchmark for a single function using, for
example, the command::
>>> bn.bench_detailed("move_median", fraction_nan=0.3)
Only arrays with data type (dtype) int32, int64, float32, and float64 are
accelerated. All other dtypes result in calls to slower, unaccelerated
functions. In the rare case of a byte-swapped input array (e.g. a big-endian
array on a little-endian operating system) the function will not be
accelerated regardless of dtype.
Where
=====
=================== ========================================================
download https://pypi.python.org/pypi/Bottleneck
docs http://berkeleyanalytics.com/bottleneck
code https://github.com/kwgoodman/bottleneck
mailing list https://groups.google.com/group/bottle-neck
=================== ========================================================
License
=======
Bottleneck is distributed under a Simplified BSD license. See the LICENSE file
for details.
Install
=======
Requirements:
======================== ====================================================
Bottleneck Python 2.7, 3.4, 3.5; NumPy 1.11.2
Compile gcc, clang, MinGW or MSVC
Unit tests nose
======================== ====================================================
To install Bottleneck on GNU/Linux, Mac OS X, et al.::
$ sudo python setup.py install
To install bottleneck on Windows, first install MinGW and add it to your
system path. Then install Bottleneck with the commands::
python setup.py install --compiler=mingw32
Alternatively, you can use the Windows binaries created by Christoph Gohlke:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck
Unit tests
==========
After you have installed Bottleneck, run the suite of unit tests::
>>> import bottleneck as bn
>>> bn.test()
<snip>
Ran 169 tests in 57.205s
OK
<nose.result.TextTestResult run=169 errors=0 failures=0>
Platform: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires: numpy
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