phitter package
Subpackages
- phitter.continuous package
- Subpackages
- phitter.continuous.continuous_distributions package
- Submodules
- phitter.continuous.continuous_distributions.alpha module
- phitter.continuous.continuous_distributions.arcsine module
- phitter.continuous.continuous_distributions.argus module
- phitter.continuous.continuous_distributions.beta module
- phitter.continuous.continuous_distributions.beta_prime module
- phitter.continuous.continuous_distributions.beta_prime_4p module
- phitter.continuous.continuous_distributions.bradford module
- phitter.continuous.continuous_distributions.burr module
- phitter.continuous.continuous_distributions.burr_4p module
- phitter.continuous.continuous_distributions.cauchy module
- phitter.continuous.continuous_distributions.chi_square module
- phitter.continuous.continuous_distributions.chi_square_3p module
- phitter.continuous.continuous_distributions.dagum module
- phitter.continuous.continuous_distributions.dagum_4p module
- phitter.continuous.continuous_distributions.erlang module
- phitter.continuous.continuous_distributions.erlang_3p module
- phitter.continuous.continuous_distributions.error_function module
- phitter.continuous.continuous_distributions.exponential module
- phitter.continuous.continuous_distributions.exponential_2p module
- phitter.continuous.continuous_distributions.f module
- phitter.continuous.continuous_distributions.f_4p module
- phitter.continuous.continuous_distributions.fatigue_life module
- phitter.continuous.continuous_distributions.folded_normal module
- phitter.continuous.continuous_distributions.frechet module
- phitter.continuous.continuous_distributions.gamma module
- phitter.continuous.continuous_distributions.gamma_3p module
- phitter.continuous.continuous_distributions.generalized_extreme_value module
- phitter.continuous.continuous_distributions.generalized_gamma module
- phitter.continuous.continuous_distributions.generalized_gamma_4p module
- phitter.continuous.continuous_distributions.generalized_logistic module
- phitter.continuous.continuous_distributions.generalized_normal module
- phitter.continuous.continuous_distributions.generalized_pareto module
- phitter.continuous.continuous_distributions.gibrat module
- phitter.continuous.continuous_distributions.gumbel_left module
- phitter.continuous.continuous_distributions.gumbel_right module
- phitter.continuous.continuous_distributions.half_normal module
- phitter.continuous.continuous_distributions.hyperbolic_secant module
- phitter.continuous.continuous_distributions.inverse_gamma module
- phitter.continuous.continuous_distributions.inverse_gamma_3p module
- phitter.continuous.continuous_distributions.inverse_gaussian module
- phitter.continuous.continuous_distributions.inverse_gaussian_3p module
- phitter.continuous.continuous_distributions.johnson_sb module
- phitter.continuous.continuous_distributions.johnson_su module
- phitter.continuous.continuous_distributions.kumaraswamy module
- phitter.continuous.continuous_distributions.laplace module
- phitter.continuous.continuous_distributions.levy module
- phitter.continuous.continuous_distributions.loggamma module
- phitter.continuous.continuous_distributions.logistic module
- phitter.continuous.continuous_distributions.loglogistic module
- phitter.continuous.continuous_distributions.loglogistic_3p module
- phitter.continuous.continuous_distributions.lognormal module
- phitter.continuous.continuous_distributions.maxwell module
- phitter.continuous.continuous_distributions.moyal module
- phitter.continuous.continuous_distributions.nakagami module
- phitter.continuous.continuous_distributions.non_central_chi_square module
- phitter.continuous.continuous_distributions.non_central_f module
- phitter.continuous.continuous_distributions.non_central_t_student module
- phitter.continuous.continuous_distributions.normal module
- phitter.continuous.continuous_distributions.pareto_first_kind module
- phitter.continuous.continuous_distributions.pareto_second_kind module
- phitter.continuous.continuous_distributions.pert module
- phitter.continuous.continuous_distributions.power_function module
- phitter.continuous.continuous_distributions.rayleigh module
- phitter.continuous.continuous_distributions.reciprocal module
- phitter.continuous.continuous_distributions.rice module
- phitter.continuous.continuous_distributions.semicircular module
- phitter.continuous.continuous_distributions.t_student module
- phitter.continuous.continuous_distributions.t_student_3p module
- phitter.continuous.continuous_distributions.trapezoidal module
- phitter.continuous.continuous_distributions.triangular module
- phitter.continuous.continuous_distributions.uniform module
- phitter.continuous.continuous_distributions.weibull module
- phitter.continuous.continuous_distributions.weibull_3p module
- Module contents
- phitter.continuous.continuous_measures package
- phitter.continuous.continuous_statistical_tests package
- phitter.continuous.continuous_distributions package
- Submodules
- phitter.continuous.phitter_continuous module
PhitterContinuous
PhitterContinuous.fit()
PhitterContinuous.parse_rgba_color()
PhitterContinuous.plot_distribution_pdf_matplotlib()
PhitterContinuous.plot_distribution_pdf_plotly()
PhitterContinuous.plot_ecdf_distribution_matplotlib()
PhitterContinuous.plot_ecdf_distribution_plotly()
PhitterContinuous.plot_ecdf_matplotlib()
PhitterContinuous.plot_ecdf_plotly()
PhitterContinuous.plot_histogram_distributions_pdf_matplotlib()
PhitterContinuous.plot_histogram_distributions_pdf_plotly()
PhitterContinuous.plot_histogram_matplotlib()
PhitterContinuous.plot_histogram_plotly()
PhitterContinuous.process_distribution()
PhitterContinuous.qq_plot_matplotlib()
PhitterContinuous.qq_plot_plotly()
PhitterContinuous.qq_plot_regression_matplotlib()
PhitterContinuous.qq_plot_regression_plotly()
PhitterContinuous.test()
- Module contents
- Subpackages
- phitter.discrete package
- Subpackages
- phitter.discrete.discrete_distributions package
- Submodules
- phitter.discrete.discrete_distributions.bernoulli module
- phitter.discrete.discrete_distributions.binomial module
- phitter.discrete.discrete_distributions.geometric module
- phitter.discrete.discrete_distributions.hypergeometric module
- phitter.discrete.discrete_distributions.logarithmic module
- phitter.discrete.discrete_distributions.negative_binomial module
- phitter.discrete.discrete_distributions.poisson module
- phitter.discrete.discrete_distributions.uniform module
- Module contents
- phitter.discrete.discrete_measures package
- phitter.discrete.discrete_statistical_tests package
- phitter.discrete.discrete_distributions package
- Submodules
- phitter.discrete.phitter_discrete module
PhitterDiscrete
PhitterDiscrete.fit()
PhitterDiscrete.parse_rgba_color()
PhitterDiscrete.plot_distribution_pmf_matplotlib()
PhitterDiscrete.plot_distribution_pmf_plotly()
PhitterDiscrete.plot_ecdf_distribution_matplotlib()
PhitterDiscrete.plot_ecdf_distribution_plotly()
PhitterDiscrete.plot_ecdf_matplotlib()
PhitterDiscrete.plot_ecdf_plotly()
PhitterDiscrete.plot_histogram_distributions_pmf_matplotlib()
PhitterDiscrete.plot_histogram_distributions_pmf_plotly()
PhitterDiscrete.plot_histogram_matplotlib()
PhitterDiscrete.plot_histogram_plotly()
PhitterDiscrete.process_distribution()
PhitterDiscrete.qq_plot_matplotlib()
PhitterDiscrete.qq_plot_plotly()
PhitterDiscrete.qq_plot_regression_matplotlib()
PhitterDiscrete.qq_plot_regression_plotly()
PhitterDiscrete.test()
- Module contents
- Subpackages
- phitter.simulation package
Submodules
phitter.main module
- class phitter.main.Phitter(data, fit_type='continuous', num_bins=None, confidence_level=0.95, minimum_sse=inf, subsample_size=None, subsample_estimation_size=None, distributions_to_fit='all', exclude_distributions='any')
Bases:
object
Fit continuous or discrete distributions to a given dataset.
- property best_distribution
- property df_not_rejected_distributions: DataFrame | None
- property df_sorted_distributions_sse: DataFrame | None
- dict_to_dataframe(data)
- Return type:
DataFrame
- fit(n_workers=1)
Fits the appropriate distributions to the data using the specified number of workers.
- Parameters:
n_workers (int, optional) – The number of workers to use for fitting the distributions (default is 1).
- get_n_test_null(id_distribution)
- Return type:
int
- get_n_test_passed(id_distribution)
- Return type:
int
- get_parameters(id_distribution)
- Return type:
dict
- get_sse(id_distribution)
- Return type:
float
- get_test_anderson_darling(id_distribution)
- Return type:
dict
- get_test_chi_square(id_distribution)
- Return type:
dict
- get_test_kolmogorov_smirnov(id_distribution)
- Return type:
dict
- property not_rejected_distributions
- plot_distribution(id_distribution, plot_title='HISTOGRAM', plot_xaxis_title='Domain', plot_yaxis_title='Probability Density/Mass Function', plot_legend_title=None, plot_height=400, plot_width=600, plot_bar_color='rgba(128,128,128,1)', plot_bargap=0.15, plot_line_color='rgba(255,0,0,1)', plot_line_width=3, plotly_plot_renderer=None, plot_engine='plotly')
- plot_ecdf(plot_title='EMPIRICAL CUMULATIVE DISTRIBUTION FUNCTION', plot_xaxis_title='Domain', plot_yaxis_title='Cumulative Distribution Function', plot_xaxis_min_offset=0.3, plot_xaxis_max_offset=0.3, plot_legend_title=None, plot_height=400, plot_width=600, plot_line_color='rgba(255,0,0,1)', plot_line_width=2, plot_line_name='Empirical Distribution', plot_bar_color='rgba(128,128,128,1)', plotly_plot_renderer=None, plot_engine='plotly')
- plot_ecdf_distribution(id_distribution, plot_title='ECDF', plot_xaxis_title='Domain', plot_yaxis_title='Cumulative Distribution Function', plot_xaxis_min_offset=0.3, plot_xaxis_max_offset=0.3, plot_legend_title=None, plot_height=400, plot_width=600, plot_empirical_line_color='rgba(128,128,128,1)', plot_empirical_line_width=4, plot_empirical_line_name='Empirical Distribution', plot_empirical_bar_color='rgba(128,128,128,1)', plot_empirical_bargap=0.15, plot_distribution_line_color='rgba(255,0,0,1)', plot_distribution_line_width=2, plotly_plot_renderer=None, plot_engine='plotly')
- plot_histogram(plot_title='HISTOGRAM', plot_xaxis_title=None, plot_yaxis_title=None, plot_legend_title=None, plot_height=400, plot_width=600, plot_bar_color='rgba(128,128,128,1)', plot_bargap=0.15, plotly_plot_renderer=None, plot_engine='plotly')
- plot_histogram_distributions(n_distributions=10, n_distributions_visible=1, plot_title='HISTOGRAM', plot_xaxis_title='Domain', plot_yaxis_title='Probability Density/Mass Function', plot_legend_title='DISTRIBUTIONS', plot_height=400, plot_width=600, plot_bar_color='rgba(128,128,128,1)', plot_bargap=0.15, plotly_plot_renderer=None, plot_engine='plotly')
- qq_plot(id_distribution, plot_title='QQ PLOT', plot_xaxis_title='Theoretical Quantiles', plot_yaxis_title='Sample Quantiles', plot_legend_title=None, plot_height=400, plot_width=600, qq_marker_name='Markers QQ', qq_marker_color='rgba(128,128,128,1)', qq_marker_size=6, plotly_plot_renderer=None, plot_engine='plotly')
- qq_plot_regression(id_distribution, plot_title='QQ PLOT', plot_xaxis_title='Theoretical Quantiles', plot_yaxis_title='Sample Quantiles', plot_legend_title=None, plot_height=400, plot_width=600, qq_marker_name='Markers QQ', qq_marker_color='rgba(128,128,128,1)', qq_marker_size=6, regression_line_name='Regression', regression_line_color='rgba(255,0,0,1)', regression_line_width=2, plotly_plot_renderer=None, plot_engine='plotly')
- property sorted_distributions_sse
- summarize(k=20)
- summarize_info(k=10)