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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/uncertainty/sensitivity/plot_create_sensitivity_analysis.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_uncertainty_sensitivity_plot_create_sensitivity_analysis.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_uncertainty_sensitivity_plot_create_sensitivity_analysis.py:


Create sensitivity analysis
===========================

:func:`.create_sensitivity_analysis` is a top-level function
to create a sensitivity analysis from a sensitivity analysis class name,
e.g. ``"MorrisAnalysis"``.

.. GENERATED FROM PYTHON SOURCE LINES 24-31

.. code-block:: Python


    from __future__ import annotations

    from gemseo.problems.uncertainty.ishigami.ishigami_discipline import IshigamiDiscipline
    from gemseo.problems.uncertainty.ishigami.ishigami_space import IshigamiSpace
    from gemseo.uncertainty import create_sensitivity_analysis








.. GENERATED FROM PYTHON SOURCE LINES 32-36

There are two ways of using :func:`.create_sensitivity_analysis`.

The first one is to perform a sensitivity analysis
from a collection of disciplines and an uncertain space:

.. GENERATED FROM PYTHON SOURCE LINES 36-43

.. code-block:: Python

    analysis = create_sensitivity_analysis("MorrisAnalysis")
    uncertain_space = IshigamiSpace()
    discipline = IshigamiDiscipline()
    samples = analysis.compute_samples([discipline], uncertain_space, n_samples=0)
    indices = analysis.compute_indices()
    indices





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    MorrisAnalysis.SensitivityIndices(mu={'y': [{'x1': array([-0.60047199]), 'x2': array([0.51230435]), 'x3': array([-0.89800793])}]}, mu_star={'y': [{'x1': array([0.69887482]), 'x2': array([0.65136343]), 'x3': array([0.89805157])}]}, sigma={'y': [{'x1': array([0.96395158]), 'x2': array([0.6549141]), 'x3': array([0.79878356])}]}, relative_sigma={'y': [{'x1': array([1.37929075]), 'x2': array([1.00545113]), 'x3': array([0.88946291])}]}, min={'y': [{'x1': array([0.0338188]), 'x2': array([0.11821721]), 'x3': array([8.72820113e-05])}]}, max={'y': [{'x1': array([2.2360336]), 'x2': array([1.25769934]), 'x3': array([2.12052546])}]})



.. GENERATED FROM PYTHON SOURCE LINES 44-50

The ``samples`` can be saved on the disk using the :func:`.to_pickle` function,
e.g. ``to_pickle(sample, "my_samples.p")``,
in order to use them later to compute sensitivity indices.

The other way is to perform a sensitivity analysis
from samples computed from another sensitivity analysis:

.. GENERATED FROM PYTHON SOURCE LINES 50-54

.. code-block:: Python

    analysis = create_sensitivity_analysis("MorrisAnalysis", samples=samples)
    indices = analysis.compute_indices()
    indices





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    MorrisAnalysis.SensitivityIndices(mu={'y': [{'x1': array([-0.60047199]), 'x2': array([0.51230435]), 'x3': array([-0.89800793])}]}, mu_star={'y': [{'x1': array([0.69887482]), 'x2': array([0.65136343]), 'x3': array([0.89805157])}]}, sigma={'y': [{'x1': array([0.96395158]), 'x2': array([0.6549141]), 'x3': array([0.79878356])}]}, relative_sigma={'y': [{'x1': array([1.37929075]), 'x2': array([1.00545113]), 'x3': array([0.88946291])}]}, min={'y': [{'x1': array([0.0338188]), 'x2': array([0.11821721]), 'x3': array([8.72820113e-05])}]}, max={'y': [{'x1': array([2.2360336]), 'x2': array([1.25769934]), 'x3': array([2.12052546])}]})



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The argument ``samples`` of :func:`.create_sensitivity_analysis`
can be either an :class:`.IODataset` as above or a pickle file path,
e.g. ``create_sensitivity_analysis("MorrisAnalysis", samples="my_samples.p")``.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.035 seconds)


.. _sphx_glr_download_examples_uncertainty_sensitivity_plot_create_sensitivity_analysis.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_create_sensitivity_analysis.ipynb <plot_create_sensitivity_analysis.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_create_sensitivity_analysis.py <plot_create_sensitivity_analysis.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_create_sensitivity_analysis.zip <plot_create_sensitivity_analysis.zip>`


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