SALib.analyze.rbd_fast module#

SALib.analyze.rbd_fast.analyze(problem, X, Y, M=10, num_resamples=100, conf_level=0.95, print_to_console=False, seed: int | Generator | None = None)[source]#

Performs the Random Balanced Design - Fourier Amplitude Sensitivity Test (RBD-FAST) on model outputs.

Returns a dictionary with keys ‘S1’, 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:

all samplers

Examples

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

  • problem (dict) – The problem definition

  • X (numpy.array) – A NumPy array containing the model inputs

  • 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 10)

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

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

References

  1. S. Tarantola, D. Gatelli and T. Mara (2006) Random Balance Designs for the Estimation of First Order Global Sensitivity Indices, Reliability Engineering and System Safety, 91:6, 717-727 https://doi.org/10.1016/j.ress.2005.06.003

  2. Elmar Plischke (2010)

    An effective algorithm for computing global sensitivity indices (EASI), Reliability Engineering & System Safety, 95:4, 354-360. doi:10.1016/j.ress.2009.11.005

  3. Jean-Yves Tissot, Clémentine Prieur (2012)

    Bias correction for the estimation of sensitivity indices based on random balance designs, Reliability Engineering and System Safety, Elsevier, 107, 205-213. doi:10.1016/j.ress.2012.06.010

  4. Jeanne Goffart, Mickael Rabouille & Nathan Mendes (2015)

    Uncertainty and sensitivity analysis applied to hygrothermal simulation of a brick building in a hot and humid climate, Journal of Building Performance Simulation. doi:10.1080/19401493.2015.1112430

SALib.analyze.rbd_fast.bootstrap(X_d, Y, M, resamples, conf_level, rng)[source]#
SALib.analyze.rbd_fast.cli_action(args)[source]#
SALib.analyze.rbd_fast.cli_parse(parser)[source]#
SALib.analyze.rbd_fast.compute_first_order(permuted_outputs, M)[source]#
SALib.analyze.rbd_fast.permute_outputs(X, Y)[source]#

Permute the output according to one of the inputs as in [_2]

References

[2]

Elmar Plischke (2010) “An effective algorithm for computing global sensitivity indices (EASI) Reliability Engineering & System Safety”, 95:4, 354-360. doi:10.1016/j.ress.2009.11.005

SALib.analyze.rbd_fast.unskew_S1(S1, M, N)[source]#

Unskew the sensitivity indices (Jean-Yves Tissot, Clémentine Prieur (2012) “Bias correction for the estimation of sensitivity indices based on random balance designs.”, Reliability Engineering and System Safety, Elsevier, 107, 205-213. doi:10.1016/j.ress.2012.06.010)