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https://github.com/astrofrog/fast-histogram

:zap: Fast 1D and 2D histogram functions in Python :zap:
https://github.com/astrofrog/fast-histogram

histogram performance python

Last synced: 24 days ago
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:zap: Fast 1D and 2D histogram functions in Python :zap:

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README 

        
|CI Status| |asv| |PyPI|



About

-----



Sometimes you just want to compute simple 1D or 2D histograms with regular bins. Fast. No

nonsense. `Numpy's `__ histogram functions are

versatile, and can handle for example non-regular binning, but this

versatility comes at the expense of performance.



The **fast-histogram** mini-package aims to provide simple and fast

histogram functions for regular bins that don't compromise on performance. It doesn't do

anything complicated - it just implements a simple histogram algorithm

in C and keeps it simple. The aim is to have functions that are fast but

also robust and reliable. The result is a 1D histogram function here that

is **7-15x faster** than ``numpy.histogram``, and a 2D histogram function

that is **20-25x faster** than ``numpy.histogram2d``.



To install::



    pip install fast-histogram



or if you use conda you can instead do::



    conda install -c conda-forge fast-histogram



The ``fast_histogram`` module then provides two functions:

``histogram1d`` and ``histogram2d``:



.. code:: python



    from fast_histogram import histogram1d, histogram2d



Example

-------



Here's an example of binning 10 million points into a regular 2D

histogram:



.. code:: python



    In [1]: import numpy as np



    In [2]: x = np.random.random(10_000_000)



    In [3]: y = np.random.random(10_000_000)



    In [4]: %timeit _ = np.histogram2d(x, y, range=[[-1, 2], [-2, 4]], bins=30)

    935 ms ± 58.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)



    In [5]: from fast_histogram import histogram2d



    In [6]: %timeit _ = histogram2d(x, y, range=[[-1, 2], [-2, 4]], bins=30)

    40.2 ms ± 624 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)



(note that ``10_000_000`` is possible in Python 3.6 syntax, use ``10000000`` instead in previous versions)



The version here is over 20 times faster! The following plot shows the

speedup as a function of array size for the bin parameters shown above:



.. figure:: https://github.com/astrofrog/fast-histogram/raw/main/speedup_compared.png

   :alt: Comparison of performance between Numpy and fast-histogram



as well as results for the 1D case, also with 30 bins. The speedup for

the 2D case is consistently between 20-25x, and for the 1D case goes

from 15x for small arrays to around 7x for large arrays.



Q&A

---



Why don't the histogram functions return the edges?

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Computing and returning the edges may seem trivial but it can slow things down by a factor of a few when computing histograms of 10^5 or fewer elements, so not returning the edges is a deliberate decision related to performance. You can easily compute the edges yourself if needed though, using ``numpy.linspace``.



Doesn't package X already do this, but better?

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



This may very well be the case! If this duplicates another package, or

if it is possible to use Numpy in a smarter way to get the same

performance gains, please open an issue and I'll consider deprecating

this package :)



One package that does include fast histogram functions (including in

n-dimensions) and can compute other statistics is

`vaex `_, so take a look there

if you need more advanced functionality!



Are the 2D histograms not transposed compared to what they should be?

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



There is technically no 'right' and 'wrong' orientation - here we adopt

the convention which gives results consistent with Numpy, so:



.. code:: python



    numpy.histogram2d(x, y, range=[[xmin, xmax], [ymin, ymax]], bins=[nx, ny])



should give the same result as:



.. code:: python



    fast_histogram.histogram2d(x, y, range=[[xmin, xmax], [ymin, ymax]], bins=[nx, ny])



Why not contribute this to Numpy directly?

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



As mentioned above, the Numpy functions are much more versatile, so they could not

be replaced by the ones here. One option would be to check in Numpy's functions for

cases that are simple and dispatch to functions such as the ones here, or add

dedicated functions for regular binning. I hope we can get this in Numpy in some form

or another eventually, but for now, the aim is to have this available to packages

that need to support a range of Numpy versions.



Why not use Cython?

~~~~~~~~~~~~~~~~~~~



I originally implemented this in Cython, but found that I could get a

50% performance improvement by going straight to a C extension.



What about using Numba?

~~~~~~~~~~~~~~~~~~~~~~~



I specifically want to keep this package as easy as possible to install,

and while `Numba `__ is a great package, it is

not trivial to install outside of Anaconda.



Could this be parallelized?

~~~~~~~~~~~~~~~~~~~~~~~~~~~



This may benefit from parallelization under certain circumstances. The

easiest solution might be to use OpenMP, but this won't work on all

platforms, so it would need to be made optional.



Couldn't you make it faster by using the GPU?

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Almost certainly, though the aim here is to have an easily installable

and portable package, and introducing GPUs is going to affect both of

these.



Why make a package specifically for this? This is a tiny amount of functionality

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Packages that need this could simply bundle their own C extension or

Cython code to do this, but the main motivation for releasing this as a

mini-package is to avoid making pure-Python packages into packages that

require compilation just because of the need to compute fast histograms.



Can I contribute?

~~~~~~~~~~~~~~~~~



Yes please! This is not meant to be a finished package, and I welcome

pull request to improve things.



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