# salib_samplers¶

Samplers for working with SALib

class ema_workbench.em_framework.salib_samplers.SobolSampler(second_order=True)

Sampler generating a Sobol design using SALib

Parameters: second_order (bool, optional) – indicates whether second order effects should be included
class ema_workbench.em_framework.salib_samplers.MorrisSampler(num_levels=4, optimal_trajectories=None, local_optimization=True)

Sampler generating a morris design using SALib

Parameters: num_levels (int) – The number of grid levels grid_jump (int) – The grid jump size optimal_trajectories (int, optional) – The number of optimal trajectories to sample (between 2 and N) local_optimization (bool, optional) – Flag whether to use local optimization according to Ruano et al. (2012) Speeds up the process tremendously for bigger N and num_levels. Stating this variable to be true causes the function to ignore gurobi.
class ema_workbench.em_framework.salib_samplers.FASTSampler(m=4)

Sampler generating a Fourier Amplitude Sensitivity Test (FAST) using SALib

Parameters: m (int (default: 4)) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition
ema_workbench.em_framework.salib_samplers.get_SALib_problem(uncertainties)

returns a dict with a problem specificatin as required by SALib