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Fast NumPy array functions written in Cython
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README.rst

Bottleneck

Bottleneck is a collection of fast NumPy array functions written in Cython.

Let's give it a try. Create a NumPy array:

>>> import numpy as np
>>> arr = np.array([1, 2, np.nan, 4, 5])

Find the nanmean:

>>> import bottleneck as bn
>>> bn.nanmean(arr)
3.0

Moving window mean:

>>> bn.move_mean(arr, 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 1.1.0dev; Numpy 1.11.0
    Speed is NumPy time divided by Bottleneck time
    NaN means approx one-third NaNs; float64 and axis=-1 are used

                 no NaN     no NaN      NaN        NaN
                   (10,)   (1000,1000)   (10,)   (1000,1000)
    nansum         34.0        3.9       34.1        6.4
    nanmean       117.1        5.6      118.5        7.3
    nanstd        196.5        4.2      202.6        7.0
    nanvar        190.0        4.5      195.6        6.8
    nanmin         29.9        1.1       30.4        1.4
    nanmax         29.8        1.0       30.6        1.8
    median         52.6        0.7       60.0        2.9
    nanmedian      48.0        2.4       55.9        6.0
    ss             13.4        3.8        3.1        1.1
    nanargmin      54.3        4.0       54.3        5.5
    nanargmax      54.7        4.0       55.3        6.6
    anynan         13.9        1.5       14.2      179.1
    allnan         14.4      209.0       14.1      194.1
    rankdata       34.4        1.5       36.1        2.2
    nanrankdata    47.4       20.8       41.6       34.6
    partsort        4.5        0.8        4.9        1.1
    argpartsort     2.5        0.7        2.5        0.5
    replace         8.2        1.9        8.2        2.0
    push          149.9       10.4      152.7       14.5
    move_sum      294.7      219.7      301.4      336.1
    move_mean     709.9      248.0      729.5      489.8
    move_std     1191.6      133.3     1223.3      363.8
    move_var     1165.8      183.3     1202.6      379.1
    move_min      237.6       22.2      235.3       48.6
    move_max      242.4       22.6      239.9       73.1
    move_argmin   364.6       79.9      385.0      235.4
    move_argmax   368.7       80.7      384.2      257.4
    move_median   435.8       48.7      435.2      138.5
    move_rank     587.4        3.8      653.6       10.8

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 http://pypi.python.org/pypi/Bottleneck
docs http://berkeleyanalytics.com/bottleneck
code http://github.com/kwgoodman/bottleneck
mailing list http://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; NumPy 1.11.0
Compile gcc or clang or MinGW
Unit tests nose

Optional:

tox, virtualenv Run unit tests across multiple python/numpy versions
Cython Development of bottleneck

To install Bottleneck on GNU/Linux, Mac OS X, et al.:

$ python setup.py build
$ 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 build --compiler=mingw32
python setup.py install

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 114 tests in 18.978s
OK
<nose.result.TextTestResult run=114 errors=0 failures=0>
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