SALib package

Subpackages

• SALib.analyze package
  • Submodules
  • SALib.analyze.common_args module
    • create()
    • run_cli()
    • setup()
  • SALib.analyze.delta module
    • analyze()
    • bias_reduced_delta()
    • calc_delta()
    • cli_action()
    • cli_parse()
    • sobol_first()
    • sobol_first_conf()
  • SALib.analyze.dgsm module
    • analyze()
    • calc_dgsm()
    • calc_vi_mean()
    • calc_vi_stats()
    • cli_action()
    • cli_parse()
  • SALib.analyze.fast module
    • analyze()
    • bootstrap()
    • cli_action()
    • cli_parse()
    • compute_orders()
  • SALib.analyze.ff module
    • analyze()
    • cli_action()
    • cli_parse()
    • interactions()
    • to_df()
  • SALib.analyze.hdmr module
    • analyze()
    • cli_action()
    • cli_parse()
  • SALib.analyze.morris module
    • analyze()
    • cli_action()
    • cli_parse()
  • SALib.analyze.pawn module
    • analyze()
    • cli_action()
    • cli_parse()
  • SALib.analyze.rbd_fast module
    • analyze()
    • bootstrap()
    • cli_action()
    • cli_parse()
    • compute_first_order()
    • permute_outputs()
    • unskew_S1()
  • SALib.analyze.rsa module
    • analyze()
    • cli_action()
    • cli_parse()
    • plot()
    • rsa()
    • to_df()
  • SALib.analyze.sobol module
    • Si_list_to_dict()
    • Si_to_pandas_dict()
    • analyze()
    • cli_action()
    • cli_parse()
    • create_Si_dict()
    • create_task_list()
    • first_order()
    • second_order()
    • separate_output_values()
    • sobol_parallel()
    • to_df()
    • total_order()
  • Module contents
• SALib.plotting package
  • Submodules
  • SALib.plotting.bar module
    • plot()
  • SALib.plotting.hdmr module
    • plot()
  • SALib.plotting.heatmap module
    • heatmap()
  • SALib.plotting.morris module
    • covariance_plot()
    • horizontal_bar_plot()
    • sample_histograms()
  • Module contents
• SALib.sample package
  • Subpackages
    • SALib.sample.morris package
      • Submodules
      • SALib.sample.morris.brute module
      • SALib.sample.morris.local module
      • SALib.sample.morris.morris module
      • SALib.sample.morris.strategy module
      • Module contents
  • Submodules
  • SALib.sample.common_args module
    • create()
    • run_cli()
    • setup()
  • SALib.sample.directions module
  • SALib.sample.fast_sampler module
    • cli_action()
    • cli_parse()
    • sample()
  • SALib.sample.ff module
    • cli_action()
    • extend_bounds()
    • find_smallest()
    • generate_contrast()
    • sample()
  • SALib.sample.finite_diff module
    • cli_action()
    • cli_parse()
    • sample()
  • SALib.sample.latin module
    • cli_action()
    • sample()
  • SALib.sample.saltelli module
    • cli_action()
    • cli_parse()
    • sample()
  • SALib.sample.sobol module
    • cli_action()
    • cli_parse()
    • sample()
  • SALib.sample.sobol_sequence module
    • index_of_least_significant_zero_bit()
    • sample()
  • Module contents
• SALib.scripts package
  • Submodules
  • SALib.scripts.salib module
    • main()
    • parse_subargs()
  • Module contents
• SALib.test_functions package
  • Submodules
  • SALib.test_functions.Ishigami module
    • evaluate()
  • SALib.test_functions.Sobol_G module
    • evaluate()
    • sensitivity_index()
    • total_sensitivity_index()
  • SALib.test_functions.lake_problem module
    • evaluate()
    • evaluate_lake()
    • lake_problem()
  • SALib.test_functions.linear_model_1 module
    • evaluate()
  • SALib.test_functions.linear_model_2 module
    • evaluate()
  • SALib.test_functions.oakley2004 module
    • evaluate()
  • Module contents
• SALib.util package
  • Submodules
  • SALib.util.problem module
    • ProblemSpec
      • ProblemSpec.samples
      • ProblemSpec.results
      • ProblemSpec.analysis
      • ProblemSpec.analysis
      • ProblemSpec.analyze()
      • ProblemSpec.analyze_parallel()
      • ProblemSpec.evaluate()
      • ProblemSpec.evaluate_distributed()
      • ProblemSpec.evaluate_parallel()
      • ProblemSpec.heatmap()
      • ProblemSpec.plot()
      • ProblemSpec.results
      • ProblemSpec.sample()
      • ProblemSpec.samples
      • ProblemSpec.set_results()
      • ProblemSpec.set_samples()
      • ProblemSpec.to_df()
  • SALib.util.results module
    • ResultDict
      • ResultDict.plot()
      • ResultDict.to_df()
  • SALib.util.util_funcs module
    • avail_approaches()
    • read_param_file()
  • Module contents
    • avail_approaches()
    • read_param_file()
    • scale_samples()

Module contents

class SALib.ProblemSpec(*args, **kwargs)

Bases: dict

Dictionary-like object representing an SALib Problem specification.

samples

Type

np.array, of generated samples

results

Type

np.array, of evaluations (i.e., model outputs)

analysis

Type

np.array or dict, of sensitivity indices

Attributes

analysis

results

samples

Methods

analyze(func, *args, **kwargs)	Analyze sampled results using given function.
analyze_parallel(func, *args[, nprocs])	Analyze sampled results using the given function in parallel.
clear()
copy()
evaluate(func, *args, **kwargs)	Evaluate a given model.
evaluate_distributed(func, *args[, nprocs, ...])	Distribute model evaluation across a cluster.
evaluate_parallel(func, *args[, nprocs])	Evaluate model locally in parallel.
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
heatmap([metric, index, title, ax])	Plot results as a heatmap.
items()
keys()
plot(**kwargs)	Plot results as a bar chart.
pop(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
sample(func, *args, **kwargs)	Create sample using given function.
set_results(results)	Set previously available model results.
set_samples(samples)	Set previous samples used.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
to_df()	Convert results to Pandas DataFrame.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

property analysis

analyze(func, *args, **kwargs)

Analyze sampled results using given function.

Parameters

• func (function,) – Analysis method to use. The provided function must accept the problem specification as the first parameter, X values if needed, Y values, and return a numpy array.

• *args (list,) – Additional arguments to be passed to func

• nprocs (int,) – If specified, attempts to parallelize model evaluations

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

analyze_parallel(func, *args, nprocs=None, **kwargs)

Analyze sampled results using the given function in parallel.

Parameters

• func (function,) – Analysis method to use. The provided function must accept the problem specification as the first parameter, X values if needed, Y values, and return a numpy array.

• *args (list,) – Additional arguments to be passed to func

• nprocs (int,) – Number of processors to use. Capped to the number of outputs or available processors.

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

evaluate(func, *args, **kwargs)

Evaluate a given model.

Parameters

• func (function,) – Model, or function that wraps a model, to be run/evaluated. The provided function is required to accept a numpy array of inputs as its first parameter and must return a numpy array of results.

• *args (list,) – Additional arguments to be passed to func

• nprocs (int,) – If specified, attempts to parallelize model evaluations

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

evaluate_distributed(func, *args, nprocs=1, servers=None, verbose=False, **kwargs)

Distribute model evaluation across a cluster.

Usage Conditions:

• The provided function needs to accept a numpy array of inputs as its first parameter

• The provided function must return a numpy array of results

Parameters

• func (function,) – Model, or function that wraps a model, to be run in parallel

• nprocs (int,) – Number of processors to use for each node. Defaults to 1.

• servers (list[str] or None,) – IP addresses or alias for each server/node to use.

• verbose (bool,) – Display job execution statistics. Defaults to False.

• *args (list,) – Additional arguments to be passed to func

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

evaluate_parallel(func, *args, nprocs=None, **kwargs)

Evaluate model locally in parallel.

All detected processors will be used if nprocs is not specified.

Parameters

• func (function,) – Model, or function that wraps a model, to be run in parallel. The provided function needs to accept a numpy array of inputs as its first parameter and must return a numpy array of results.

• nprocs (int,) – Number of processors to use. Capped to the number of available processors.

• *args (list,) – Additional arguments to be passed to func

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

heatmap(metric: str = None, index: str = None, title: str = None, ax=None)

Plot results as a heatmap.

Parameters

• metric (str or None, name of output to analyze (display all if None)) –

• index (str or None, name of index to plot, dependent on what) – analysis was conducted (ST, S1, etc; displays all if None)

• title (str, title of plot to use (defaults to the same as metric)) –

• ax (axes object, matplotlib axes object to use for plot.) – Creates a new figure if not provided.

Returns

ax

Return type

matplotlib axes object

plot(**kwargs)

Plot results as a bar chart.

Returns

axes

Return type

matplotlib axes object

property results

sample(func, *args, **kwargs)

Create sample using given function.

Parameters

• func (function,) – Sampling method to use. The given function must accept the SALib problem specification as the first parameter and return a numpy array.

• *args (list,) – Additional arguments to be passed to func

• **kwargs (dict,) – Additional keyword arguments passed to func

Returns

self

Return type

ProblemSpec object

property samples

set_results(results: ndarray)

Set previously available model results.

set_samples(samples: ndarray)

Set previous samples used.

to_df()

Convert results to Pandas DataFrame.