SALib.analyze.fast module#

SALib.analyze.fast.analyze(problem, Y, M=4, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)[source]#

Perform extended Fourier Amplitude Sensitivity Test on model outputs.

Returns a dictionary with keys ‘S1’ and ‘ST’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

fast_sampler : SALib.sample.fast_sampler.sample()

Examples

>>> X = fast_sampler.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = fast.analyze(problem, Y, print_to_console=False)
Parameters:

  • problem (dict) – The problem definition

  • Y (numpy.array) – A NumPy array containing the model outputs

  • M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 4)

  • print_to_console (bool) – Print results directly to console (default False)

  • seed (int) – Seed to generate a random number

References

  1. Cukier, R. I., C. M. Fortuin, K. E. Shuler, A. G. Petschek, and J. H. Schaibly (1973). Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. J. Chem. Phys., 59(8):3873-3878 doi:10.1063/1.1680571

  2. Saltelli, A., S. Tarantola, and K. P.-S. Chan (1999). A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output. Technometrics, 41(1):39-56, doi:10.1080/00401706.1999.10485594.

  3. Pujol, G. (2006) fast99 - R sensitivity package cran/sensitivity

SALib.analyze.fast.bootstrap(Y: ndarray, M: int, resamples: int, conf_level: float)[source]#

Compute CIs.

Infers N from results of sub-sample Y and re-estimates omega (ω) for the above N.

SALib.analyze.fast.cli_action(args)[source]#
SALib.analyze.fast.cli_parse(parser)[source]#

Add method specific options to CLI parser.

Parameters:

parser (argparse object)

Return type:

Updated argparse object

SALib.analyze.fast.compute_orders(outputs: ndarray, N: int, M: int, omega: int)[source]#