run_sensitivity_analysis

pyapprox.analysis.run_sensitivity_analysis(method, fun, variable, *args, **kwargs)[source]

Compute sensitivity indices for a model.

Parameters:

methodstring

The name of the sensitivity method

funcallable

The function to be approximated

fun(z) -> np.ndarray

where z is a 2D np.ndarray with shape (nvars, nsamples) and the output is a 2D np.ndarray with shape (nsamples, nqoi)

variablepya.IndependentMarginalsVariable

Object containing information of the joint density of the inputs z. This is used to generate random samples from this join density

args: kwargs

optional keyword arguments

kwargs: kwargs

optional keyword arguments

For more details on method specfici args, kwargs and results attributes see

pyapprox.analysis.sensitivity_analysis.sampling_based_sobol_indices()
pyapprox.analysis.sensitivity_analysis.bootstrapped_borgonovo_sensivities()
pyapprox.analysis.sensitivity_analysis.morris_sensitivities()
pyapprox.analysis.sensitivity_analysis.gpc_sobol_sensitivities()
pyapprox.analysis.sensitivity_analysis.analytic_sobol_indices_from_gaussian_process()
: func:pyapprox.analysis.sensitivity_analysis.sparse_grid_sobol_sensitivities

Returns: