``OpenTURNS_interface``
==================================================

.. py:module:: optimeed.consolidate.OpenTURNS_interface


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   optimeed.consolidate.OpenTURNS_interface.Collection_Metamodels
   optimeed.consolidate.OpenTURNS_interface.Metamodel_PC_Openturns
   optimeed.consolidate.OpenTURNS_interface.SensitivityAnalysis_OpenTURNS_Chaos
   optimeed.consolidate.OpenTURNS_interface.SensitivityAnalysis_OpenTURNS




.. py:class:: Collection_Metamodels(collection_to_fit, inputs, inputs_as_optivariables, name_collection='Chaos Expansion Fit')


   .. py:method:: _do_callbacks()


   .. py:method:: add_callback(theCallback)

      Method to call when this item has changed


   .. py:method:: get_list_attributes(attributeName)


   .. py:method:: refresh_attribute(attributeName)


   .. py:method:: get_metamodel(attributeName)


   .. py:method:: get_fitted_attributes()


   .. py:method:: __str__()

      Return str(self).



.. py:class:: Metamodel_PC_Openturns(inputs, outputs, degree_fitted, inputs_as_optivariables=None)


   .. py:method:: add_callback(theCallback)

      Add a callback method, to call everytime the metamodel is changed


   .. py:method:: refresh()


   .. py:method:: _do_callbacks()


   .. py:method:: _end_training_index(outputs)
      :staticmethod:


   .. py:method:: get_FunctionalChaosResult()

      Perform the fit (if not performed before).

      :return: FunctionalChaosResult, from openturns. Check https://openturns.github.io/openturns/latest/user_manual/response_surface/_generated/openturns.FunctionalChaosResult.html


   .. py:method:: get_metamodel()


   .. py:method:: get_metamodel_as_python_method()


   .. py:method:: evaluate_metamodel(inputs)

      Evaluate the metamodel at inputs. X (as array [[x1i, ... xni], ..., [x1j, ... xnj]])

      :param inputs: list of variables combinations [x1i, ... xni], ..., [x1j, ... xnj]
      :return: list of corresponding evaluations [output_1, ... output_j]


   .. py:method:: set_fit_degree(degree)


   .. py:method:: set_inputs(inputs, inputs_as_optivariables=None)


   .. py:method:: set_outputs(outputs)


   .. py:method:: check_goodness_of_fit()



.. py:class:: SensitivityAnalysis_OpenTURNS_Chaos(theSensitivityParameters, theObjectives, theMetamodel: Metamodel_PC_Openturns)


   Bases: :py:obj:`optimeed.consolidate.sensitivity_analysis.SensitivityAnalysis_LibInterface`

   Polynomial chaos expansions based.
   Sobol indices are computed from metamodel.

   .. py:method:: sample_sobol(theOptimizationVariables, N)
      :staticmethod:


   .. py:method:: get_sobol_S1()

      Get first order sobol indices

      :return:


   .. py:method:: get_sobol_S1conf()

      Not available using Chaos Expansion


   .. py:method:: get_sobol_ST()

      Get total order sobol indices

      :return:


   .. py:method:: get_sobol_STconf()

      Not available using Chaos Expansion


   .. py:method:: get_sobol_S2()

      Get second order sobol indices

      :return:



.. py:class:: SensitivityAnalysis_OpenTURNS(theSensitivityParameters, theObjectives)


   Bases: :py:obj:`optimeed.consolidate.sensitivity_analysis.SensitivityAnalysis_LibInterface`

   Interface a library for sensitivity analysis

   :param theSensitivityParameters: :class:`optimeed.consolidate.sensitivity_analysis.SensitivityParameters`
   :param theObjectives: array-like objective associated to evaluation, using Sobol sampling

   .. py:attribute:: coefficients
      :type: List[List[float]]

      

   .. py:method:: sample_sobol(theOptimizationVariables, N)
      :staticmethod:


   .. py:method:: get_sobol_S1()

      Get first order sobol indices

      :return:


   .. py:method:: get_sobol_S1conf()


   .. py:method:: get_sobol_ST()

      Get total order sobol indices

      :return:


   .. py:method:: get_sobol_STconf()


   .. py:method:: get_sobol_S2()

      Get second order sobol indices

      :return:


   .. py:method:: _get_SA()