UQ_UTILS

uq_utils.UQDistDict	Definition of the dictionary structure expected for the parsing of UQ parameters for generating distributions.
uq_utils.generate_index_distribution(...)	Generates a vector of indices to partition the data for training.
uq_utils.generate_index_distribution_from_fraction(...)	Generates a vector of indices to partition the data for training.
uq_utils.generate_index_distribution_from_blocks(...)	Generates a vector of indices to partition the data for training.
uq_utils.generate_index_distribution_from_block_list(...)	Generates a vector of indices to partition the data for training.
uq_utils.compute_limits(numdata, numblocks, ...)	Generates the limit of indices corresponding to a specific block.
uq_utils.fill_array(blocklist, maxsize, ...)	Fills a new array of integers with the indices corresponding to the specified block structure.
uq_utils.compute_statistics_homoscedastic_summary(df_data)	Extracts ground truth, mean prediction, error and standard deviation of prediction from inference data frame.
uq_utils.compute_statistics_homoscedastic(df_data)	Extracts ground truth, mean prediction, error and standard deviation of prediction from inference data frame.
uq_utils.compute_statistics_heteroscedastic(df_data)	Extracts ground truth, mean prediction, error, standard deviation of prediction and predicted (learned) standard deviation from inference data frame.
uq_utils.compute_statistics_quantile(df_data)	Extracts ground truth, 50th percentile mean prediction, low percentile and high percentile mean prediction (usually 1st decile and 9th decile respectively), error (using 5th decile), standard deviation of prediction (using 5th decile) and predicted (learned) standard deviation from interdecile range in inference data frame.
uq_utils.split_data_for_empirical_calibration(...)	Extracts a portion of the arrays provided for the computation of the calibration and reserves the remainder portion for testing.
uq_utils.compute_empirical_calibration_interpolation(...)	Use the arrays provided to estimate an empirical mapping between standard deviation and absolute value of error, both of which have been observed during inference.