Fast

Bottleneck is fast:

>>> arr = np.random.rand(100, 100)
>>> timeit np.nanmax(arr)
10000 loops, best of 3: 99.6 us per loop
>>> timeit bn.nanmax(arr)
100000 loops, best of 3: 15.3 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: 146 us per loop
>>> timeit bn.nanmax(arr)
100000 loops, best of 3: 15.2 us per loop

Bottleneck comes with a benchmark suite that compares the performance of the bottleneck functions that have a NumPy/SciPy equivalent. To run the benchmark:

>>> bn.benchit(verbose=False)
Bottleneck performance benchmark
    Bottleneck  0.1.0dev
    Numpy       1.5.1
    Scipy       0.8.0
    Speed is numpy (or scipy) time divided by Bottleneck time
    NaN means all NaNs
   Speed   Test                  Shape        dtype    NaN?
   2.4019  median(a, axis=-1)    (500,500)    float64
   2.2668  median(a, axis=-1)    (500,500)    float64  NaN
   4.1235  median(a, axis=-1)    (10000,)     float64
   4.3498  median(a, axis=-1)    (10000,)     float64  NaN
   9.8184  nanmax(a, axis=-1)    (500,500)    float64
   7.9157  nanmax(a, axis=-1)    (500,500)    float64  NaN
   9.2306  nanmax(a, axis=-1)    (10000,)     float64
   8.1635  nanmax(a, axis=-1)    (10000,)     float64  NaN
   6.7218  nanmin(a, axis=-1)    (500,500)    float64
   7.9112  nanmin(a, axis=-1)    (500,500)    float64  NaN
   6.4950  nanmin(a, axis=-1)    (10000,)     float64
   8.0791  nanmin(a, axis=-1)    (10000,)     float64  NaN
  12.3650  nanmean(a, axis=-1)   (500,500)    float64
  42.0738  nanmean(a, axis=-1)   (500,500)    float64  NaN
  12.2769  nanmean(a, axis=-1)   (10000,)     float64
  22.1285  nanmean(a, axis=-1)   (10000,)     float64  NaN
   9.5515  nanstd(a, axis=-1)    (500,500)    float64
  68.9192  nanstd(a, axis=-1)    (500,500)    float64  NaN
   9.2174  nanstd(a, axis=-1)    (10000,)     float64
  26.1753  nanstd(a, axis=-1)    (10000,)     float64  NaN

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: 25.7 us per loop
>> timeit bn.nanmax(arr, axis=0)
100000 loops, best of 3: 5.25 us per loop
>> timeit func(a)
100000 loops, best of 3: 2.5 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: 3.28 us per loop

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

Functions

Bottleneck is in the prototype stage.

Bottleneck contains the following functions:

Currently only 1d, 2d, and 3d NumPy arrays with dtype int32, int64, and float64 are supported.

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

Install

Requirements:

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

In order to compile the C code in dsna you need a Windows version of the gcc compiler. MinGW (Minimalist GNU for Windows) contains gcc and has been used to successfully compile dsna on Windows.

Install MinGW and add it to your system path. Then install dsna 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 10 tests in 13.756s
OK
<nose.result.TextTestResult run=10 errors=0 failures=0>