ggt.modules.result_aggregator

Module Contents

Functions

calculate_cdf(data[, n, warnings])

find_idx_nearest_val(array, value)

calculate_ci(data[, n, warnings])

expit_custom(x_input[, scaling_array])

Performs a custom implementation of Scipy's expit

unscale_preds(df_input[, scaling_df_path, drop_old])

file_loader(args)

bayesian_inference_file_gobbler(data_dir[, num, ...])

create_summary_df(data)

This function takes the xarray produced by

save_pdfs(args)

main(data_dir, num, out_summary_df_path, ...)

A function which performs all the above steps

ggt.modules.result_aggregator.calculate_cdf(data, n=1000, warnings=True)
ggt.modules.result_aggregator.find_idx_nearest_val(array, value)
ggt.modules.result_aggregator.calculate_ci(data, n=1000, warnings=False)
ggt.modules.result_aggregator.expit_custom(x_input, scaling_array=None)

Performs a custom implementation of Scipy’s expit function. If the scaling array is supplied, it does the unscaling keeping in mind how 0s and 1s were mapped to different values while the scaling was done. expit is the inverse of the logit function.

ggt.modules.result_aggregator.unscale_preds(df_input, scaling_df_path=None, drop_old=True)
ggt.modules.result_aggregator.file_loader(args)
ggt.modules.result_aggregator.bayesian_inference_file_gobbler(data_dir, num=300, unscale=False, scaling_df_path=None, drop_old=True)
ggt.modules.result_aggregator.create_summary_df(data)

This function takes the xarray produced by bayesian_inference_file_gobbler and then produces a dataframe with summary statistics.

ggt.modules.result_aggregator.save_pdfs(args)
ggt.modules.result_aggregator.main(data_dir, num, out_summary_df_path, out_pdfs_path, unscale, scaling_df_path, drop_old)

A function which performs all the above steps necessary to prepeare the data for analysis