Numba-accelerated implementations of common probability distributions
Project description
numba-stats
We provide numba-accelerated implementations of statistical functions for common probability distributions
- Uniform
- Normal
- Poisson
- Exponential
- Student's t
- Voigtian
- Crystal Ball
- Tsallis
- 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.
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.
Because of limited manpower, this project is barely documented. The documentation is basically pydoc numba_stats. The calling conventions are the same as for the corresponding functions in scipy.stats. These are sometimes a bit unusual, for example, for the exponential distribution, see the scipy docs for details.
Plans for version 1.0
Version v1.0 (not there yet) will introduce breaking changes to the API.
# 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 change is not only cosmetics, it was necessary to battle the increasing startup times of numba-stats. Now you only pay the compilation cost for the distribution that you actually need. The stats submodule has been removed. To keep old code running, please pin your numba_stats to version 0.7.
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.
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