SALib.sample package

Submodules

SALib.sample.common_args module

SALib.sample.common_args.create(cli_parser=None)[source]

Create CLI parser object.

Parameters

cli_parser (function [optional]) – Function to add method specific arguments to parser

Returns

Return type

argparse object

SALib.sample.common_args.run_cli(cli_parser, run_sample, known_args=None)[source]

Run sampling with CLI arguments.

Parameters
  • cli_parser (function) – Function to add method specific arguments to parser

  • run_sample (function) – Method specific function that runs the sampling

  • known_args (list [optional]) – Additional arguments to parse

Returns

Return type

argparse object

SALib.sample.common_args.setup(parser)[source]

Add common sampling options to CLI parser.

Parameters

parser (argparse object) –

Returns

Return type

Updated argparse object

SALib.sample.directions module

SALib.sample.fast_sampler module

SALib.sample.fast_sampler.cli_action(args)[source]

Run sampling method

Parameters

args (argparse namespace) –

SALib.sample.fast_sampler.cli_parse(parser)[source]

Add method specific options to CLI parser.

Parameters

parser (argparse object) –

Returns

Return type

Updated argparse object

SALib.sample.fast_sampler.sample(problem, N, M=4, seed=None)[source]

Generate model inputs for the Fourier Amplitude Sensitivity Test (FAST).

Returns a NumPy matrix containing the model inputs required by the Fourier Amplitude sensitivity test. The resulting matrix contains N * D rows and D columns, where D is the number of parameters. The samples generated are intended to be used by SALib.analyze.fast.analyze().

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

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

SALib.sample.ff module

The sampling implementation of fractional factorial method

This implementation is based on the formulation put forward in [Saltelli et al. 2008]

SALib.sample.ff.cli_action(args)[source]

Run sampling method

Parameters

args (argparse namespace) –

SALib.sample.ff.extend_bounds(problem)[source]

Extends the problem bounds to the nearest power of two

Parameters

problem (dict) – The problem definition

SALib.sample.ff.find_smallest(num_vars)[source]

Find the smallest exponent of two that is greater than the number of variables

Parameters

num_vars (int) – Number of variables

Returns

x – Smallest exponent of two greater than num_vars

Return type

int

SALib.sample.ff.generate_contrast(problem)[source]

Generates the raw sample from the problem file

Parameters

problem (dict) – The problem definition

SALib.sample.ff.sample(problem, seed=None)[source]

Generates model inputs using a fractional factorial sample

Returns a NumPy matrix containing the model inputs required for a fractional factorial analysis. The resulting matrix has D columns, where D is smallest power of 2 that is greater than the number of parameters. These model inputs are intended to be used with SALib.analyze.ff.analyze().

The problem file is padded with a number of dummy variables called dummy_0 required for this procedure. These dummy variables can be used as a check for errors in the analyze procedure.

This algorithm is an implementation of that contained in [Saltelli et al. 2008]

Parameters

problem (dict) – The problem definition

Returns

sample

Return type

numpy.array

SALib.sample.finite_diff module

SALib.sample.finite_diff.cli_action(args)[source]

Run sampling method

Parameters

args (argparse namespace) –

SALib.sample.finite_diff.cli_parse(parser)[source]

Add method specific options to CLI parser.

Parameters

parser (argparse object) –

Returns

Return type

Updated argparse object

SALib.sample.finite_diff.sample(problem, N, delta=0.01, seed=None)[source]

SALib.sample.latin module

SALib.sample.latin.cli_action(args)[source]

Run sampling method

Parameters

args (argparse namespace) –

SALib.sample.latin.sample(problem, N, seed=None)[source]

Generate model inputs using Latin hypercube sampling (LHS).

Returns a NumPy matrix containing the model inputs generated by Latin hypercube sampling. The resulting matrix contains N rows and D columns, where D is the number of parameters.

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

SALib.sample.saltelli module

SALib.sample.saltelli.cli_action(args)[source]

Run sampling method

Parameters

args (argparse namespace) –

SALib.sample.saltelli.cli_parse(parser)[source]

Add method specific options to CLI parser.

Parameters

parser (argparse object) –

Returns

Return type

Updated argparse object

SALib.sample.saltelli.sample(problem, N, calc_second_order=True, seed=None)[source]

Generates model inputs using Saltelli’s extension of the Sobol sequence.

Returns a NumPy matrix containing the model inputs using Saltelli’s sampling scheme. Saltelli’s scheme extends the Sobol sequence in a way to reduce the error rates in the resulting sensitivity index calculations. If calc_second_order is False, the resulting matrix has N * (D + 2) rows, where D is the number of parameters. If calc_second_order is True, the resulting matrix has N * (2D + 2) rows. These model inputs are intended to be used with SALib.analyze.sobol.analyze().

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

  • calc_second_order (bool) – Calculate second-order sensitivities (default True)

SALib.sample.sobol_sequence module

SALib.sample.sobol_sequence.index_of_least_significant_zero_bit(value)[source]
SALib.sample.sobol_sequence.sample(N, D)[source]

Generate (N x D) numpy array of Sobol sequence samples

Module contents