Metadata-Version: 1.1
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 1.2.0; 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
