numba-stats 0.9.0

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
• 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 limited manpower, this project is poorly documented. The documentation is basically the source code. pydoc numba_stats does not really work at the moment, because Numba does not show the docstring of the wrapped function but the docstring of the wrapping function. The plan is to fix this (either in Numba or locally). The calling conventions for those functions which have a scipy.stats equivalent, are identical to those in SciPy. 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.

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.