monte_carlo_analysis.analysis package¶

Submodules¶

monte_carlo_analysis.analysis.Analysis module¶

monte_carlo_analysis.analysis.MelbaAnalysis module¶

Author : Robin Camarasa

Institution : Erasmus Medical Center

Position : PhD student

Contact : r.camarasa@erasmusmc.nl

Date : 2020-11-20

Project : monte_carlo_analysis

class monte_carlo_analysis.analysis.MelbaAnalysis.MelbaAnalysis(df: pandas.core.frame.DataFrame, metric: str)[source]¶

Bases: monte_carlo_analysis.analysis.Analysis.Analysis

Implement MelbaAnalysis

Parameters

• df – Dataframe containing the following columns [‘uncertainty_map_a’, ‘uncertainty_map_b’ …]

• metric – Metric to analyse

generate_credible_interval_latex_table(caption: Optional[str] = None, label: Optional[str] = None) → str[source]¶

Display the 95% equally tailed uncertainty credible interval in a latex format

Parameters

• caption – Caption of the latex table

• label – Label of the latex table

get_statistical_test_a_b(uncertainty_map_a: str, uncertainty_map_b: str) → dict[source]¶

Get the statistical test of the uncertainty map a and b

Parameters

• uncertainty_map_a – Name ot the uncertainty map a

• uncertainty_map_b – Name ot the uncertainty map b

Returns

The statistical test data of the uncertainty map a and b

get_statistical_tests() → dict[source]¶

Plot the statistical tests

Returns

Return all the values of the statistical test in the form

of an dictionnary

plot_statistical_test_a_b(uncertainty_map_a: str, uncertainty_map_b: str) → dict[source]¶

Plot the statistical test

Parameters

• uncertainty_map_a – Name ot the uncertainty map a

• uncertainty_map_b – Name ot the uncertainty map b

plot_statistical_tests() → dict[source]¶

Plot the statistical tests

monte_carlo_analysis.analysis.MelbaDiceAnalysis module¶

monte_carlo_analysis.analysis.MelbaDistributionAnalysis module¶

Module contents¶