consolidate

• OpenTURNS_interface
• SALib_interface
• fit
• parametric_analysis
• sensitivity_analysis
• sensitivity_analysis_evaluation

Package Contents

Classes

Functions

Attributes

class Option_class

add_option(idOption, theOption)

get_option_name(idOption)

get_option_value(idOption)

set_option(idOption, value)

_pack_options()

__str__()

Return str(self).

class Option_int(name, based_value, choices=None)

Bases: Base_Option

set_value(value)

class ListDataStruct(compress_save=False)

Bases: ListDataStruct_Interface

__len__()

get_length()

clone(filename)

Clone the datastructure to a new location

save(filename)

Save data using json format. The data to be saved are automatically detected, see obj_to_json()

extract_collection_from_indices(indices)

Extract data from the collection at specific indices, and return it as new collection

_format_str_save()

Save data using json format. The data to be saved are automatically detected, see obj_to_json()

_format_data_lines()

_get_json_module_tree()

add_data(data_in)

Add a data to the list

get_data()

Get full list of datas

get_data_generator()

Get a generator to all the data stored

get_data_at_index(index)

set_data(theData)

Set full list of datas

set_data_at_index(data_in, index)

Replace data at specific index

extract_collection_from_attribute(attributeName)

Convenience class to create a sub-collection from an attribute of all the items.

Parameters:attributeName – Name of the attribute to extract Returns:ListDataStruct

reset_data()

delete_points_at_indices(indices)

Delete several elements from the Collection

Parameters:indices – list of indices to delete

merge(collection)

Merge a collection with the current collection

Parameters:collection – Collection to merge

get_nbr_elements()

Returns:the number of elements contained inside the structure

class AutosaveStruct(dataStruct, filename='', change_filename_if_exists=True)

Structure that provides automated save of DataStructures

__str__()

Return str(self).

get_filename()

Get set filename

set_filename(filename, change_filename_if_exists)

Parameters:

• filename – Filename to set
• change_filename_if_exists – If already exists, create a new filename

stop_autosave()

Stop autosave

start_autosave(timer_autosave, safe_save=True)

Start autosave

save(safe_save=True)

Save

get_datastruct()

Return :class:’~DataStruct_Interface’

__getstate__()

__setstate__(state)

getPath_workspace()

Get workspace path (i.e., location where optimeed files will be created). Create directory if doesn’t exist.

rsetattr(obj, attr, val)

setattr, but recursively. Works with list (i.e. theObj.myList[0].var_x)

rgetattr(obj, attr)

getattr, but recursively. Works with list.

class Parametric_Collection(**kwargs)

Bases: optimeed.core.collection.ListDataStruct

class Parametric_parameter(analyzed_attribute, reference_device)

Abstract class for a parametric parameter

get_reference_device()

get_analyzed_attribute()

class Parametric_minmax(analyzed_attribute, reference_device, minValue, maxValue, is_relative=False, npoints=10)

Bases: Parametric_parameter

Abstract class for a parametric parameter

get_values()

class Parametric_analysis(theParametricParameter, theCharacterization, filename_collection=None, autosave=False)

Bases: optimeed.core.Option_class

run()

Instantiates input arguments for analysis

evaluate(theDevice)

initialize_output_collection()

leastSquare(function, functionArgs, x_data, y_data)

Least square calculation (sum (y-ŷ)^2)

Parameters:

• function – Function to fit
• functionArgs – Arguments of the function
• x_data – x-axis coordinates of data to fit
• y_data – y-axis coordinates of data to fit

Returns:

least squares

do_fit(fitFunction, x_data, y_data, *args, fitCriterion=leastSquare)

Main method to fit a function

Parameters:

• fitFunction – the function to fit (link to it)
• x_data – x-axis coordinates of data to fit
• y_data – y-axis coordinates of data to fit
• args – for each parameter: [min, max] admissible value
• fitCriterion – fit criterion to minimize. Default: least square

Returns:

[arg_i_optimal, …], y estimated, error.

evaluate_sensitivities(theSensitivityParameters: SensitivityParameters, numberOfCores=2, studyname='sensitivity', indices_to_evaluate=None)

Evaluate the sensitivities

Parameters:

• theSensitivityParameters – class`~SensitivityParameters`
• numberOfCores – number of core for multicore evaluation
• studyname – Name of the study, that will be the subfolder name in workspace
• indices_to_evaluate – if None, evaluate all param_values, otherwise if list: evaluate subset of param_values defined by indices_to_evaluate

Returns:

collection of class`~SensitivityResults`

evaluate_sensitivities_fast(theSensitivityParameters: SensitivityParameters)

Deactivate multicore and save management for fast results

class SensitivityParameters(param_values, list_of_optimization_variables, theDevice, theMathsToPhys, theCharacterization)

Bases: optimeed.core.SaveableObject

Abstract class for dynamically type-hinted objects. This class is to solve the special case where the exact type of an attribute is not known before runtime, yet has to be saved.

get_device()

get_M2P()

get_charac()

get_optivariables()

get_paramvalues()

get_additional_attributes_to_save()

Return list of attributes corresponding to object, whose type cannot be determined statically (e.g. topology change)

class Restrained_SensitivityParameters(*args)

Bases: SensitivityParameters

Class to perform Sensitivty Analysis on a subset of the full parameters

set_selected(selection)

get_optivariables()

get_paramvalues()

class SensitivityAnalysis_LibInterface(theSensitivityParameters: SensitivityParameters, theObjectives)

Interface a library for sensitivity analysis

Parameters:

• theSensitivityParameters – optimeed.consolidate.sensitivity_analysis.SensitivityParameters
• theObjectives – array-like objective associated to evaluation, using Sobol sampling

get_summary()

Display a summary of the sobol indices

get_convergence_S1(stepsize=1)

Create dictionary for convergence plot - First order index

Parameters:stepsize – increments of sampling size Returns:Dictionary

get_convergence_ST(stepsize=1)

Create dictionary for convergence plot - Total order index

Parameters:stepsize – increments of sampling size Returns:Dictionary

_get_convergence(method, stepsize)

get_SA_params()

set_SA_params(theSensitivityParameters)

set_objectives(theObjectives)

prepare_embarrassingly_parallel_sensitivity(theSensitivityParameters, studyname)

Initialize sensitivity analysis folder :param theSensitivityParameters: :param studyname: Folder to be created in Workspace :return:

gather_embarrassingly_parallel_sensitivity(theSensitivityParameters, studyname)

Gather the results. If some are missing, display the indices.

Parameters:

• theSensitivityParameters –
• studyname –

Returns:

launch_embarrassingly_parallel_sensitivity(theSensitivityParameters, studyname, base_index, mult_factor=1)

Single job launcher for an embarrassingly parallel evaluation :param theSensitivityParameters: :param studyname: Name of the folder in Workspace in which the study is performed :param base_index: start index (Formula: index to evaluate = base_index*mult_factor) :param mult_factor: Multiplication factor of the base_index. Allows to overcome QOSMaxJobPerUserLimit in clusters. :return:

launch_missing_embarrassingly_parallel_sensitivity(theSensitivityParameters, studyname, missing_list, base_index, mult_factor=1)

Same as launch_embarrassingly_parallel_sensitivity, but using the ‘missing_list’ arg used for mapping

condition_aborted_sensitivities(foldername)

printIfShown(theStr, show_type=SHOW_DEBUG, isToPrint=True, appendTypeName=True, end='n')