The Elementary class

class pylife.materialdata.woehler.Elementary(fatigue_data)

Base class to analyze SN-data.

The common base class for all SN-data analyzers calculates the first estimation of a Wöhler curve in the finite zone of the SN-data. It calculates the slope k, the fatigue limit SD, the transition cycle number ND and the scatter in load direction 1/TN.

The result is just meant to be a first guess. Derived classes are supposed to use those first guesses as starting points for their specific analysis. For that they should implement the method _specific_analysis().

analyze(**kwargs)

Analyze the SN-data.

Parameters:

**kwargs (kwargs arguments) – Arguments to be passed to the derived class

bayesian_information_criterion()

The Bayesian Information Criterion

Bayesian Information Criterion (BIC) is a criterion for model selection among a finite set of models; the model with the lowest BIC is preferred. https://www.statisticshowto.datasciencecentral.com/bayesian-information-criterion/

Basically the lower the better the fit.

Note that the BIC is not suitable to compare the results of two different likelihoods.

pearl_chain_estimator()

use_all_fractures()

Use all fractures to determine the likelihood in the finite zone.

Return type:

self

use_custom_likelihood_estimation(likelihood_class)

Inject a custom Likelihood calculation class to determine the likelihood.

Parameters:

likelihood (Class implementing Likelihood) – The likelihood calculation class

Return type:

self

use_highest_mixed_level()

Use fractures of pure fracture levels and the highest mixed level to determine the likelihood in the finite zone.

This is the default

Return type:

self

use_old_likelihood_estimation()

Use the old (until pyLife-2.1.x) likelihood estimation.

That uses all fractures for the finite likelihood and only the mixed levels for the infinite.

Return type:

self

use_only_pure_fracture_levels()

Use only fractures of pure fracture levels to determine the likelihood in the finite zone.

Return type:

self