Metadata-Version: 1.1
Name: Bottleneck
Version: 1.0.0
Summary: Fast NumPy array functions written in Cython
Home-page: http://berkeleyanalytics.com/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 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  1.0.0
                Numpy (np)  1.9.1
                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         36.5        4.0       36.6        9.1
                nanmean       144.5        5.2      146.1        9.2
                nanstd        253.2        4.3      253.1        8.4
                nanvar        241.4        4.2      241.2        8.4
                nanmin         30.6        1.1       30.5        1.7
                nanmax         32.1        1.1       32.2        2.9
                median         43.3        0.8       45.7        0.9
                nanmedian      58.7        2.8       67.5        6.8
                ss             14.3        3.5       14.4        3.4
                nanargmin      60.8        4.1       61.1        7.3
                nanargmax      61.4        4.1       61.3        9.0
                anynan         12.9        1.0       13.5       89.2
                allnan         13.6       98.5       13.5       97.8
                rankdata       45.5        1.4       45.9        1.9
                nanrankdata    60.7       26.3       54.1       37.9
                partsort        6.4        0.9        6.5        1.1
                argpartsort     3.3        0.7        3.3        0.5
                replace         9.9        1.2        9.9        1.2
                move_sum      276.1      121.1      283.2      330.9
                move_mean     714.4       95.7      723.7      415.8
                move_std     1102.5       56.2     1160.7      749.0
                move_min      207.0       20.9      211.0       55.2
                move_max      213.8       21.4      218.4      118.6
                move_median   457.9       43.4      452.7      208.4
        
        Only arrays with data type (dtype) int32, int64, float32, and float64 are
        accelerated. All other dtypes result in calls to slower, unaccelerated
        functions.
        
        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 1.9.1**
        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 79 tests in 70.712s
            OK
            <nose.result.TextTestResult run=79 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 :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires: numpy
