fitdistcp 0.0.3

A Python package for making reliable predictions using calibrating priors.

fitdistcp

fitdistcp is a free Python package for fitting statistical models using calibrating priors, with the goal of making reliable predictions. Install using >pip install fitdistcp.

fitdistcp implements the method developed in Reducing Reliability Bias in Assessments of Extreme Weather Risk using Calibrating Priors, S. Jewson, T. Sweeting and L. Jewson (2024): https://doi.org/10.5194/ascmo-11-1-2025.

More information and examples are available at https://www.fitdistcp.info, including the equivalent (more comprehensive) R package.

Development of this package was funded by the Lighthill Risk Network: https://lighthillrisknetwork.org.

Example: Fitting a GEV distribution

import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import fitdistcp.genextreme

x = scipy.stats.genextreme.rvs(0, size=20)                  # make some example training data 
p = np.arange(0.001,0.999,0.001)                            # define the probabilities at which we wish to calculate the quantiles
q = fitdistcp.genextreme.ppf(x,p)                           # this command calculates two sets of predictive quantiles for the GEV, 
                                                            # one based on maxlik, and one that includes parameter uncertainty based on a calibrating prior
print(q['ml_params'])                                       # have a look at the maxlik parameters
plt.plot(q['ml_quantiles'],p, label='ML')                   # plot the maxlik quantiles
plt.plot(q['cp_quantiles'],p,color='red', label='CP')       # plot the quantiles that include parameter uncertainty
plt.legend()
plt.show()

Models

The following models are currently supported. Let us know if you have any suggestions for other models to include.

• expon: Exponential distribution
• gamma: Gamma distribution
• genextreme: Generalised Extreme Value (GEV) distribution
• genextreme_p1 & genextreme_p12: Generalised Extreme Value (GEV) distribution, with 1 predictor, 2 predictors
• genpareto: Generalised Pareto distribution
• gumbel: Gumbel distribution
• lnorm: Lognormal distribution
• norm: Normal distribution
• weibull: Weibull distribution

Methods

Four methods are provided for each model: ppf(x) (quantiles, 'percentage point function'), rvs(n, x) (random variates), pdf(x) and cdf(x), where x is the data to fit. To use, e.g. ppf for the exponential distribution, import fitdistcp.expon and call fitdistcp.expon.ppf(x).