romtools.workflows.sampling_methods#

Functions

LatinHypercubeSampler(number_of_samples[, ...])

Generate UIID LHS samples

MonteCarloSampler(number_of_samples[, ...])

Generate UIID Monte Carlo samples

RandomizedQuasiMonteCarloSampler(...[, ...])

Generate randomized quasi-Monte Carlo samples using Owen-scrambled Sobol.

Classes

Sampler(*args, **kwargs)

Generate UIID samples
romtools.workflows.sampling_methods.LatinHypercubeSampler(number_of_samples, dimensionality=1, seed=None)[source]#

Generate UIID LHS samples

Conforms to the Sampler protocol

Parameters:

  • number_of_samples (int)

  • dimensionality (int)

Return type:

ndarray

romtools.workflows.sampling_methods.MonteCarloSampler(number_of_samples, dimensionality=1, seed=None)[source]#

Generate UIID Monte Carlo samples

Conforms to the Sampler protocol

Parameters:

  • number_of_samples (int)

  • dimensionality (int)

Return type:

ndarray

romtools.workflows.sampling_methods.RandomizedQuasiMonteCarloSampler(number_of_samples, dimensionality=1, seed=None)[source]#

Generate randomized quasi-Monte Carlo samples using Owen-scrambled Sobol.

Randomization preserves low-discrepancy structure while ensuring unbiased integral estimation in expectation over the scrambling.

Conforms to the Sampler protocol.

Parameters:

  • number_of_samples (int)

  • dimensionality (int)

Return type:

ndarray

class romtools.workflows.sampling_methods.Sampler(*args, **kwargs)[source]#

Bases: Protocol

Generate UIID samples

Returns np.ndarray of shape (number_of_samples, dimensionality)