check#

This module provides functions for checking and validating Pastas models and their components.

Examples

Run a checklist of standard checks on a Pastas model:

checks = [
    {"func": rsq_geq_threshold, "threshold": 0.7},
    {"func": response_memory, "cutoff": 0.95, "factor_length_oseries": 0.5},
    {"func": uncertainty_gain, "n_std": 1.96},
    {"func": parameter_bounds},
]

ps.check.checklist(ml, checks)

Or use the list of checks defined in Brakenhoff et al. (2022):

ps.check.checklist(ml, ps.check.checks_brakenhoff_2022)

Functions#

`rsq_geq_threshold`(ml[, threshold])	Check R^2 >= threshold.
`response_memory`(ml[, cutoff, factor_length_oseries, names])	Check if response function memory is shorter than fraction of calibration period.
`response_memory_vs_warmup`(ml[, cutoff, names])	Check if response function memory is shorter than warmup.
`uncertainty_gain`(ml[, n_std, names])	Check if the gain is larger than n_std times the uncertainty in the gain.
`parameter_bounds`(ml[, parameters])	Check if the optimal parameter values are not on the lower or upper bounds.
`uncertainty_parameters`(ml[, parameters, n_std])	Check if parameter value is larger than n_std times the standard deviation.
`guess_unit_or_dims`(parameter[, return_dims])	Guess the unit or dimension of a parameter based on its name.
`acf_runs_test`(ml[, p_threshold])	Runs test to check if there is significant autocorrelation in the noise.
`acf_stoffer_toloi_test`(ml[, p_threshold])	Stoffer-Toloi test to check if there is significant autocorrelation in the noise.
`checklist`(ml, checks[, report])	Run a list of checks on a Pastas model.
`print_check_report`(df)	Print a report of the check results.