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Fast NumPy array functions written in Cython

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README.rst

Bottleneck

Introduction

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

NumPy/SciPy median, nanmedian, rankdata, nansum, nanmin, nanmax, nanmean, nanstd, nanargmin, nanargmax
Functions nanrankdata, nanvar
Moving window move_sum, move_nansum, move_mean, move_nanmean, move_std, move_nanstd, move_min, move_nanmin, move_max, move_nanmax

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 nanmean:

>>> bn.move_nanmean(arr, window=2)
array([ nan,  1.5,  2. ,  4. ,  4.5])

Fast

Bottleneck is fast:

>>> arr = np.random.rand(100, 100)
>>> timeit np.nanmax(arr)
10000 loops, best of 3: 90 us per loop
>>> timeit bn.nanmax(arr)
100000 loops, best of 3: 12.6 us per loop

Let's not forget to add some NaNs:

>>> arr[arr > 0.5] = np.nan
>>> timeit np.nanmax(arr)
10000 loops, best of 3: 133 us per loop
>>> timeit bn.nanmax(arr)
100000 loops, best of 3: 12.6 us per loop

Bottleneck comes with a benchmark suite. To run the benchmark:

>>> bn.bench(mode='fast', dtype='float64', axis=0)
Bottleneck performance benchmark
    Bottleneck  0.4.0
    Numpy (np)  1.5.1
    Scipy (sp)  0.8.0
    Speed is NumPy or SciPy time divided by Bottleneck time
    NaN means one-third NaNs; float64 and axis=0 are used
    High-level functions used (mode='fast')

                 no NaN     no NaN     no NaN      NaN        NaN        NaN
                (10,10)   (100,100) (1000,1000)  (10,10)   (100,100) (1000,1000)
median            9.34      14.40       7.29       8.27       3.64       2.84
nanmedian       219.65     127.95       8.21     226.79     176.69       8.10
nansum           12.16       6.40       1.72      12.10       7.34       1.71
nanmax           12.78       6.29       1.69      13.56      10.45       1.69
nanmean          21.97      13.98       3.00      21.93      28.89       4.99
nanstd           30.06       9.69       2.69      30.61      17.62       3.71
nanargmax        10.68       6.05       2.68      10.85       9.04       2.88
rankdata         23.11      12.51       8.33      22.71      14.09       9.36
move_sum         11.13       8.71      14.53      12.15       8.63      14.11
move_nansum      29.39      19.52      29.45      28.00      25.40      29.83
move_mean        11.11       4.25      14.43      11.23       8.36      14.30
move_nanmean     31.65      11.81      29.86      32.81      14.41      30.93
move_std         17.33       3.33      22.82      22.30      20.77      29.94
move_nanstd      34.82       6.18      34.94      40.44       7.06      36.09
move_max          4.06       3.61       9.26       4.71       5.54      11.65
move_nanmax      22.16       5.95      19.57      24.74      14.69      27.07

Reference functions:
median         np.median
nanmedian      local copy of sp.stats.nanmedian
nansum         np.nansum
nanmax         np.nanmax
nanmean        local copy of sp.stats.nanmean
nanstd         local copy of sp.stats.nanstd
nanargmax      np.nanargmax
rankdata       scipy.stats.rankdata based (axis support added)
move_sum       sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nansum    sp.ndimage.convolve1d based, window=a.shape[0]/5
move_mean      sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nanmean   sp.ndimage.convolve1d based, window=a.shape[0]/5
move_std       sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nanstd    sp.ndimage.convolve1d based, window=a.shape[0]/5
move_max       sp.ndimage.maximum_filter1d based, window=a.shape[0]/5
move_nanmax    sp.ndimage.maximum_filter1d based, window=a.shape[0]/5

Faster

Under the hood Bottleneck uses a separate Cython function for each combination of ndim, dtype, and axis. A lot of the overhead in bn.nanmax(), for example, is in checking that the axis is within range, converting non-array data to an array, and selecting the function to use to calculate the maximum.

You can get rid of the overhead by doing all this before you, say, enter an inner loop:

>>> arr = np.random.rand(10,10)
>>> func, a = bn.func.nanmax_selector(arr, axis=0)
>>> func
<built-in function nanmax_2d_float64_axis0>

Let's see how much faster than runs:

>>> timeit np.nanmax(arr, axis=0)
10000 loops, best of 3: 24.7 us per loop
>>> timeit bn.nanmax(arr, axis=0)
100000 loops, best of 3: 2.1 us per loop
>>> timeit func(a)
100000 loops, best of 3: 1.47 us per loop

Note that func is faster than Numpy's non-NaN version of max:

>>> timeit arr.max(axis=0)
100000 loops, best of 3: 4.78 us per loop

So adding NaN protection to your inner loops comes at a negative cost!

Benchmarks for the low-level Cython functions:

>>> bn.bench(mode='faster', dtype='float64', axis=0)
Bottleneck performance benchmark
    Bottleneck  0.4.0
    Numpy (np)  1.5.1
    Scipy (sp)  0.8.0
    Speed is NumPy or SciPy time divided by Bottleneck time
    NaN means one-third NaNs; float64 and axis=0 are used
    Low-level functions used (mode='faster')

                 no NaN     no NaN     no NaN      NaN        NaN        NaN
                (10,10)   (100,100) (1000,1000)  (10,10)   (100,100) (1000,1000)
median           14.72      14.75       7.09      11.90       3.64       2.83
nanmedian       337.43     133.27       8.92     352.73     182.65       8.20
nansum           20.75       6.72       1.73      20.61       7.96       1.72
nanmax           20.03       6.58       1.72      22.44      11.11       1.69
nanmean          38.55      14.44       3.00      39.35      30.52       5.00
nanstd           41.78       9.85       2.70      44.16      18.17       3.71
nanargmax        17.97       6.33       2.70      18.50       9.64       2.91
rankdata         24.43      12.43       8.37      24.37      14.06       9.21
move_sum         18.29       8.60      14.52      18.13       8.87      13.62
move_nansum      45.98      20.80      29.33      48.56      26.25      29.29
move_mean        16.33       4.35      14.33      16.21       8.64      14.15
move_nanmean     50.79      11.92      29.36      51.63      14.93      30.32
move_std         23.45       3.36      22.88      33.20      20.18      29.18
move_nanstd      48.02       6.16      34.61      57.20       7.03      36.13
move_max          5.82       3.63       9.31       6.70       5.62      11.77
move_nanmax      29.09       6.02      19.55      36.57      14.83      27.02

Reference functions:
median         np.median
nanmedian      local copy of sp.stats.nanmedian
nansum         np.nansum
nanmax         np.nanmax
nanmean        local copy of sp.stats.nanmean
nanstd         local copy of sp.stats.nanstd
nanargmax      np.nanargmax
rankdata       scipy.stats.rankdata based (axis support added)
move_sum       sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nansum    sp.ndimage.convolve1d based, window=a.shape[0]/5
move_mean      sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nanmean   sp.ndimage.convolve1d based, window=a.shape[0]/5
move_std       sp.ndimage.convolve1d based, window=a.shape[0]/5
move_nanstd    sp.ndimage.convolve1d based, window=a.shape[0]/5
move_max       sp.ndimage.maximum_filter1d based, window=a.shape[0]/5
move_nanmax    sp.ndimage.maximum_filter1d based, window=a.shape[0]/5

Slow

Currently only 1d, 2d, and 3d input arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other ndim/dtype combinations result in calls to slower, unaccelerated functions.

License

Bottleneck is distributed under a Simplified BSD license. Parts of NumPy, Scipy and numpydoc, all of which have BSD licenses, are included in Bottleneck. See the LICENSE file, which is distributed with Bottleneck, for details.

URLs

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
mailing list 2 http://mail.scipy.org/mailman/listinfo/scipy-user

Install

Requirements:

Bottleneck Python, NumPy 1.5.1
Unit tests nose
Compile gcc or MinGW
Optional SciPy 0.8.0 (portions of benchmark)

Directions for installing a released version of Bottleneck (i.e., one obtained from http://pypi.python.org/pypi/Bottleneck) are given below. Cython is not required since the Cython files have already been converted to C source files. (If you obtained bottleneck directly from the repository, then you will need to generate the C source files using the included Makefile which requires Cython.)

GNU/Linux, Mac OS X, et al.

To install Bottleneck:

$ python setup.py build
$ sudo python setup.py install

Or, if you wish to specify where Bottleneck is installed, for example inside /usr/local:

$ python setup.py build
$ sudo python setup.py install --prefix=/usr/local

Windows

You can compile Bottleneck using the instructions below or you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck

In order to compile the C code in Bottleneck you need a Windows version of the gcc compiler. MinGW (Minimalist GNU for Windows) contains gcc.

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

Post install

After you have installed Bottleneck, run the suite of unit tests:

>>> import bottleneck as bn
>>> bn.test()
<snip>
Ran 46 tests in 41.457s
OK
<nose.result.TextTestResult run=46 errors=0 failures=0>
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