# kwgoodman/bottleneck

Fast NumPy array functions written in Cython http://pypi.python.org/pypi/Bottleneck
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# Bottleneck

Warning

This version of Bottleneck requires NumPy 1.8.

## Introduction

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

 NumPy/SciPy ```median, nanmedian, rankdata, ss, nansum, nanmin, nanmax, nanmean, nanstd, nanargmin, nanargmax``` Functions ```nanrankdata, nanvar, partsort, argpartsort, replace, nn, anynan, allnan``` Moving window ```move_sum, move_nansum, move_mean, move_nanmean, move_median, 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.nansum(arr)
10000 loops, best of 3: 43.7 us per loop
>>> timeit bn.nansum(arr)
100000 loops, best of 3: 7.98 us per loop
```

Let's not forget to add some NaNs:

```>>> arr[arr > 0.5] = np.nan
>>> timeit np.nansum(arr)
10000 loops, best of 3: 74 us per loop
>>> timeit bn.nansum(arr)
100000 loops, best of 3: 30.6 us per loop
```

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

```>>> bn.bench(mode='fast', dtype='float64', axis=1)
Bottleneck performance benchmark
Bottleneck  0.8.0
Numpy (np)  1.8.0
Scipy (sp)  0.13.2
Speed is NumPy or SciPy time divided by Bottleneck time
NaN means one-third NaNs; float64 and axis=1 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            8.10       1.04       0.82       8.43       1.70       0.93
nanmedian       140.72      35.97       7.01     151.70     101.35      13.24
nansum           16.20       5.49       3.98      15.94       7.83       6.93
nanmax            7.29       1.98       1.10       7.29       3.49       2.80
nanmean          34.49       9.23       5.25      34.77      11.82       8.05
nanstd           53.70       7.82       4.26      54.83      10.12       6.68
nanargmax        13.76       4.45       4.38      13.75       7.56       7.65
ss                6.23       2.49       2.40       6.26       2.50       2.38
rankdata         65.57       8.87       1.46      64.45      21.63       2.17
partsort          1.21       1.96       2.84       1.33       2.37       4.44
argpartsort       0.40       1.96       2.22       0.45       1.70       1.42
replace           3.64       1.87       1.83       3.66       1.87       1.83
anynan            4.47       2.14       1.50       4.67      10.05     116.63
move_sum         58.31      54.99      85.41      58.22      57.03      87.54
move_nansum     168.52     173.22     263.86     167.18     187.21     409.86
move_mean        98.78      33.89      33.00     102.37      95.10      92.41
move_nanmean    288.63     105.86     133.04     288.69     110.75     185.44
move_std        112.53      23.80      35.88     153.28     192.38     291.89
move_nanstd     307.70      53.50      68.04     341.50      56.74     101.22
move_max         46.32      24.87      21.93      47.65      68.60      65.75
move_nanmax     103.47      36.94      33.36     105.58      81.05      90.14

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
ss             scipy.stats.ss
rankdata       scipy.stats.rankdata based (axis support added)
partsort       np.sort, n=max(a.shape[1]/2,1)
argpartsort    np.argsort, n=max(a.shape[1]/2,1)
anynan         np.isnan(arr).any(axis)
move_sum       sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nansum    sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_mean      sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nanmean   sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_std       sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nanstd    sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_max       sp.ndimage.maximum_filter1d based, window=a.shape[1] // 5
move_nanmax    sp.ndimage.maximum_filter1d based, window=a.shape[1] // 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.nansum_selector(arr, axis=0)
>>> func
<function nansum_2d_float64_axis0>
```

Let's see how much faster than runs:

```>>> timeit np.nansum(arr, axis=0)
10000 loops, best of 3: 21 us per loop
>>> timeit bn.nansum(arr, axis=0)
100000 loops, best of 3: 1.18 us per loop
>>> timeit func(a)
100000 loops, best of 3: 841 ns per loop
```

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

```>>> timeit arr.sum(axis=0)
100000 loops, best of 3: 3.79 us per loop
```

So, in this example, adding NaN protection to your inner loop comes at a negative cost!

Benchmarks for the low-level Cython functions:

```>>> bn.bench(mode='faster', dtype='float64', axis=1)
Bottleneck performance benchmark
Bottleneck  0.8.0
Numpy (np)  1.8.0
Scipy (sp)  0.13.2
Speed is NumPy or SciPy time divided by Bottleneck time
NaN means one-third NaNs; float64 and axis=1 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           11.50       1.06       0.82      12.19       1.76       0.95
nanmedian       186.96      35.89       6.99     196.92     102.88      13.15
nansum           25.95       5.91       3.99      25.30       8.44       6.88
nanmax           10.33       2.05       1.11      10.38       3.67       2.80
nanmean          49.86       9.70       5.20      49.81      12.44       8.04
nanstd           69.55       7.98       4.28      71.26      10.33       6.70
nanargmax        20.24       4.72       4.37      20.35       8.04       7.72
ss                9.28       2.65       2.41       9.38       2.64       2.40
rankdata         81.74       8.98       1.45      80.86      22.08       2.14
partsort          1.77       1.97       2.83       1.95       2.46       4.52
argpartsort       0.54       2.00       2.18       0.58       1.75       1.37
replace           5.06       1.91       1.84       5.04       1.91       1.83
anynan            6.59       2.25       1.51       6.92      13.72     125.85
move_sum         81.81      56.10      86.25      81.73      58.49      88.22
move_nansum     240.43     177.71     266.71     239.01     190.94     409.71
move_mean       129.18      33.87      33.16     134.26      96.73      93.24
move_nanmean    394.05     106.12     132.25     395.97     112.24     185.82
move_std        140.03      23.97      36.54     197.33     198.96     298.69
move_nanstd     378.62      53.62      68.33     419.77      56.99     101.56
move_max         61.32      25.00      22.00      61.65      69.32      65.81
move_nanmax     138.46      37.20      33.64     138.35      83.78      90.45

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
ss             scipy.stats.ss
rankdata       scipy.stats.rankdata based (axis support added)
partsort       np.sort, n=max(a.shape[1]/2,1)
argpartsort    np.argsort, n=max(a.shape[1]/2,1)
anynan         np.isnan(arr).any(axis)
move_sum       sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nansum    sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_mean      sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nanmean   sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_std       sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_nanstd    sp.ndimage.convolve1d based, window=a.shape[1] // 5
move_max       sp.ndimage.maximum_filter1d based, window=a.shape[1] // 5
move_nanmax    sp.ndimage.maximum_filter1d based, window=a.shape[1] // 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.

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.

## Install

Warning

This version of Bottleneck requires NumPy 1.8.

Requirements:

 Bottleneck Python 2.6, 2.7, 3.3; NumPy 1.8 Compile gcc or MinGW Unit tests nose

Optional:

 SciPy portions of benchmark suite tox, virtualenv run unit tests across multiple python/numpy versions

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.)

Bottleneck takes a few minutes to build on newer machines. On older machines it can take a lot longer (one user reported 30 minutes!).

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 124 tests in 31.197s
OK
<nose.result.TextTestResult run=124 errors=0 failures=0>
```
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