NetworkDistribution#
- class fracability.Statistics.NetworkDistribution(parent, obj: rv_continuous = None, parameters: tuple = None, fit_data: NetworkData = None)#
Class used to represent a fracture or fracture network length distribution. It is essentially a wrapper for the scipy rv distributions.
- property AD_distance: float#
Calcuate the Koziol and Green distance between the empirical and the fitted model :return: Float. The KG distance
Notes
Kim 2019, Tests based on EDF statistics for randomly censored normal distributions when parameters are unknown
- property AD_rank#
- property AIC: float#
Property that returns the classic Akaike Information Criterion (1974) of the distribution :return:
- property AICc: float#
Property that returns the Akaike Information Criterion (for small number of values) of the distribution :return:
- property Akaike_rank#
- property BIC: float#
Property that returns the Bayesian Information Criterion of the distribution :return:
- property KG_distance: float#
Calcuate the Koziol and Green distance between the empirical and the fitted model :return: Float. The KG distance
Notes
Kim 2019, Tests based on EDF statistics for randomly censored normal distributions when parameters are unknown
- property KG_rank#
- property KS_distance: float#
Calcuate the Kolmogorov-Smirnov distance between the empirical and the fitted model :return: Float. The KS distance
Notes
Kim 2019, Tests based on EDF statistics for randomly censored normal distributions when parameters are unknown
- property KS_rank#
- property Mean_rank#
- property b5: float#
Property that returns the 5th percentile of the distribution :return:
- property b95: float#
Property that returns the 95th percentile of the distribution :return:
- cdf(x_values: array = None)#
- property distribution: rv_continuous#
Property that returns or sets a frozen ss.rv_continuous class :return:
- property distribution_name: str#
Property that returns the name of the given distribution :return:
- property distribution_parameters: tuple#
Property that returns the parameters of the frozen distribution :return:
- log_pdf(x_values: array = None) array#
Property that returns the logpdf calculated on the data :return:
- log_sf(x_values: array = None) array#
Property that returns the logsf calculated on the data :return:
- property max_log_likelihood: float#
Property that returns the log likelihood of the distribution. The likelihood is calculated by adding the cumulative sum of the log pdf and log sf of the fitted distribution. :return:
- property mean: float#
Property that returns the mean of the frozen distribution :return:
- property median: float#
Property that returns the median of the distribution :return:
- property mode: list#
Property that returns the mode(s) of the pdf of the given distribution :return:
- property n_distribution_parameters: int#
Property that returns the number of parameters of the frozen distribution. the args method returns the shape parameters. This means that except for normal and logistic the loc counts as a parameter. To fix this we subtract -1 to all except normal and logistic. :return:
- property std: float#
Property that returns the standard deviation of the distribution :return:
- property var: float#
Property that returns the variance of the distribution :return: