simpm.dist

Probability distributions and fitting helpers used throughout SimPM.

The functions in this module wrap common SciPy distributions with a lightweight API tailored for simulation modeling. They make it easy to sample, plot, and fit distributions while keeping consistent return types for downstream components.

Functions

fit(data, dist_type[, method])	Fit a distribution on data and return a structured result.
fit_beta(data[, method])	Fit a beta distribution to the provided data.
fit_expon(data[, method])	Fit an exponential distribution with location fixed at zero.
fit_norm(data[, method])	Fit a normal distribution to the provided data.
fit_trapz(data[, method])	Fit a trapezoidal distribution to the provided data.
fit_triang(data[, method])	Fit a triangular distribution to the provided data.
make_beta(a, b, minp, maxp)	Create a beta distribution with validation.
make_expon(mean)	Create an exponential distribution with validation.
make_norm(mean, std)	Create a normal distribution with validation.
make_trapz(a, b, c, d)	Create a trapezoidal distribution with validation.
make_triang(a, b, c)	Create a triangular distribution with validation.
make_uniform(a, b)	Create a uniform distribution with validation.

Classes

FitResult(dist_type, params, scipy_dist, ks)	Container for the results of fitting a distribution.
beta(a, b, minp, maxp)	Defines a beta distribution.
distribution()	Lightweight wrapper around SciPy distributions.
empirical(data)	Defines an empirical distribution based on observed data.
expon(mean)	Defines an exponential distribution.
norm(mean, std)	Defines a normal distribution.
trapz(a, b, c, d)	Defines a trapezoidal distribution.
triang(a, b, c)	Defines a triangular distribution.
uniform(a, b)	Uniform distribution defined by lower/upper bounds.