pyhctsa.operations.hypothesis_tests.distribution_test¶

pyhctsa.operations.hypothesis_tests.distribution_test(x, the_test='chi2gof', the_distn='norm', num_bins=10)¶

Hypothesis test for distributional fits to a data vector.

Fits a distribution to the data and then performs an appropriate hypothesis test to quantify the difference between the two distributions.

Parameters:¶

x : array-like¶

The input data vector.

the_test : str, optional¶

The hypothesis test to perform:

• ’chi2gof’: chi^2 goodness of fit test

• ’ks’: Kolmogorov-Smirnov test

• ’lillie’: Lilliefors test (only defined for ‘norm’, ‘ev’, and ‘exp’)

Default is 'chi2gof'.

the_distn : str, optional¶

The distribution to fit:

• ’norm’ (Normal)

• ’ev’ (Extreme value)

• ’uni’ (Uniform)

• ’beta’ (Beta)

• ’rayleigh’ (Rayleigh)

• ’exp’ (Exponential)

• ’gamma’ (Gamma)

• ’logn’ (Log-normal)

• ’wbl’ (Weibull)

Default is 'norm'.

num_bins : int, optional¶

The number of bins to use for the chi^2 goodness of fit test. Default is 10.

Returns:¶

P-value from the hypothesis test. NaN when the fit is not valid for the data (e.g., a positive-only distribution fitted to data with negative values).

Return type:¶

float