SALib.sample package¶
Subpackages¶
Submodules¶
SALib.sample.common_args module¶
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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
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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.directions module¶
SALib.sample.fast_sampler module¶
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SALib.sample.fast_sampler.
cli_action
(args)[source]¶ Run sampling method
Parameters: args (argparse namespace) –
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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
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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 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]
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SALib.sample.ff.
cli_action
(args)[source]¶ Run sampling method
Parameters: args (argparse namespace) –
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SALib.sample.ff.
cli_parse
(parser)[source]¶ Add method specific options to CLI parser.
Parameters: parser (argparse object) – Returns: Return type: Updated argparse object
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SALib.sample.ff.
extend_bounds
(problem)[source]¶ Extends the problem bounds to the nearest power of two
Parameters: problem (dict) – The problem definition
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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
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SALib.sample.ff.
generate_contrast
(problem)[source]¶ Generates the raw sample from the problem file
Parameters: problem (dict) – The problem definition
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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¶
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SALib.sample.finite_diff.
cli_action
(args)[source]¶ Run sampling method
Parameters: args (argparse namespace) –
SALib.sample.latin module¶
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SALib.sample.latin.
cli_action
(args)[source]¶ Run sampling method
Parameters: args (argparse namespace) –
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SALib.sample.latin.
cli_parse
(parser)[source]¶ Add method specific options to CLI parser.
Parameters: parser (argparse object) – Returns: Return type: Updated argparse object
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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¶
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SALib.sample.saltelli.
cli_action
(args)[source]¶ Run sampling method
Parameters: args (argparse namespace) –
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SALib.sample.saltelli.
cli_parse
(parser)[source]¶ Add method specific options to CLI parser.
Parameters: parser (argparse object) – Returns: Return type: Updated argparse object
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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)