numba-stats 0.10.1

Numba-accelerated implementations of common probability distributions

numba-stats

We provide numba-accelerated implementations of statistical functions for common probability distributions

• Uniform
• (Truncated) Normal
• Log-normal
• Poisson
• (Truncated) Exponential
• Student's t
• Voigtian
• Crystal Ball
• Generalised double-sided Crystal Ball
• Tsallis-Hagedorn, a model for the minimum bias pT distribution
• Q-Gaussian
• Bernstein density (not normalised to unity, use this in extended likelihood fits)

with more to come. The speed gains are huge, up to a factor of 100 compared to scipy. Benchmarks are included in the repository and are run by pytest.

Documentation (or lack of)

Because of a technical limitation of Numba, this project is poorly documented. Functions with equivalents in scipy.stats follow the Scipy calling conventions exactly. These conventions are sometimes a bit unusual, for example, in case of the exponential, the log-normal or the uniform distribution. See the SciPy docs for details.

Please look into the source code for documentation of the other functions.

Technical note: pydoc numba_stats does not show anything useful, because numba.vectorize creates instances of a class DUFunc. The wrapped functions show up as objects of that class and help() shows the generic documentation of that class instead of the documentation for the instances.

Contributions

You can help with adding more distributions, patches are very welcome. Implementing a probability distribution is easy. You need to write it in simple Python that numba can understand. Special functions from scipy.special can be used after some wrapping, see submodule numba_stats._special.py how it is done.

Plans for version 1.0

Version v1.0 will introduce breaking changes to the API. Users are recommended to update their code.

# before v0.8
from numba_stats import norm_pdf
from numba_stats.stats import norm_cdf

dp = norm_pdf(1, 2, 3)
p = norm_cdf(1, 2, 3)

# recommended since v0.8
from numba_stats import norm

dp = norm.pdf(1, 2, 3)
p = norm.cdf(1, 2, 3)

This is nicer code, but more importantly, this is necessary to battle the increasing startup times of numba-stats. Now you only pay the compilation cost for the distribution that you actually import. The stats submodule will be removed. To keep old code running, please pin your numba_stats to version <1.

numba-stats and numba-scipy

numba-scipy is the official package and repository for fast numba-accelerated scipy functions, are we reinventing the wheel?

Ideally, the functionality in this package should be in numba-scipy and we hope that eventually this will be case. In this package, we don't offer overloads for scipy functions and classes like numba-scipy does. This simplifies the implementation dramatically. numba-stats is intended as a temporary solution until fast statistical functions are included in numba-scipy. numba-stats currently does not depend on numba-scipy, only on numba and scipy.