Metadata-Version: 1.1
Name: Bottleneck
Version: 1.2.1
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 1.3.0.dev0; Numpy 1.12.1
                Speed is NumPy time divided by Bottleneck time
                NaN means approx one-fifth NaNs; float64 used
        
                          no NaN     no NaN      NaN       no NaN      NaN
                           (100,)  (1000,1000)(1000,1000)(1000,1000)(1000,1000)
                           axis=0     axis=0     axis=0     axis=1     axis=1
            nansum         67.3        0.3        0.7        2.5        2.4
            nanmean       194.8        1.9        2.1        3.4        3.1
            nanstd        241.5        1.6        2.1        2.7        2.6
            nanvar        229.7        1.7        2.1        2.7        2.5
            nanmin         34.1        0.7        1.1        0.8        2.6
            nanmax         45.6        0.7        2.7        1.0        3.7
            median        111.0        1.3        5.6        1.0        4.8
            nanmedian     108.8        5.9        6.7        5.6        6.7
            ss             16.3        1.1        1.2        1.6        1.6
            nanargmin      89.2        2.9        5.1        2.2        5.6
            nanargmax     107.4        3.0        5.4        2.2        5.8
            anynan         19.4        0.3       35.0        0.5       29.9
            allnan         39.9      146.6      128.3      115.8       75.6
            rankdata       55.0        2.6        2.3        2.9        2.8
            nanrankdata    59.8        2.8        2.2        3.2        2.5
            partition       4.4        1.2        1.6        1.0        1.4
            argpartition    3.5        1.1        1.4        1.1        1.6
            replace        17.7        1.4        1.4        1.3        1.4
            push         3440.0        7.8        9.5       20.0       15.5
            move_sum     4743.0       75.7      156.1      195.4      211.1
            move_mean    8760.9      116.2      167.4      252.1      258.8
            move_std     8979.9       96.1      196.3      144.0      256.3
            move_var    11216.8      127.3      243.9      225.9      321.4
            move_min     2245.3       20.6       36.7       23.2       42.1
            move_max     2223.7       20.5       37.2       24.1       42.4
            move_argmin  3664.0       48.2       73.3       40.2       83.9
            move_argmax  3916.9       42.0       75.4       41.5       81.2
            move_median  2023.3      166.8      173.7      153.8      154.3
            move_rank    1208.5        1.9        1.9        2.5        2.8
        
        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, 3.5, 3.6; NumPy 1.12.1
        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
