.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/sensitivity/morris/plot_12_dimensional_gfunction.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_morris_plot_12_dimensional_gfunction.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_sensitivity_morris_plot_12_dimensional_gfunction.py:


12-dimensional g-function
==============================================

To be compared with results from "An effective screening design for sensitivity analysis of large models",
Campolongo et al, 2007

.. GENERATED FROM PYTHON SOURCE LINES 12-13

Initially we have to import the necessary modules.

.. GENERATED FROM PYTHON SOURCE LINES 16-23

.. code-block:: default

    import shutil
    from UQpy.run_model.model_execution.PythonModel import PythonModel
    from UQpy.run_model.RunModel import RunModel
    from UQpy.distributions import Uniform
    from UQpy.sensitivity import MorrisSensitivity
    import matplotlib.pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 24-25

Set-up problem with g-function.

.. GENERATED FROM PYTHON SOURCE LINES 28-55

.. code-block:: default


    a_values = [0.001, 89.9, 5.54, 42.10, 0.78, 1.26, 0.04, 0.79, 74.51, 4.32, 82.51, 41.62]
    na = len(a_values)

    model = PythonModel(model_script='local_pfn.py', model_object_name='gfun_sensitivity', delete_files=True,
                        a_values=a_values, var_names=['X{}'.format(i) for i in range(na)])
    runmodel_object = RunModel(model=model)

    dist_object = [Uniform(), ] * na

    sens = MorrisSensitivity(runmodel_object=runmodel_object,
                             distributions=dist_object,
                             n_levels=20,
                             maximize_dispersion=True)
    sens.run(n_trajectories=10)

    print(['a{}={}'.format(i + 1, ai) for i, ai in enumerate(a_values)])

    fig, ax = plt.subplots()
    ax.scatter(sens.mustar_indices, sens.sigma_indices)
    for i, (mu, sig) in enumerate(zip(sens.mustar_indices, sens.sigma_indices)):
        ax.text(x=mu + 0.01, y=sig + 0.01, s='X{}'.format(i + 1))
    ax.set_xlabel(r'$\mu^{\star}$', fontsize=14)
    ax.set_ylabel(r'$\sigma$', fontsize=14)
    ax.set_title('Morris sensitivity indices', fontsize=16)
    plt.show()




.. image-sg:: /auto_examples/sensitivity/morris/images/sphx_glr_plot_12_dimensional_gfunction_001.png
   :alt: Morris sensitivity indices
   :srcset: /auto_examples/sensitivity/morris/images/sphx_glr_plot_12_dimensional_gfunction_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    ['a1=0.001', 'a2=89.9', 'a3=5.54', 'a4=42.1', 'a5=0.78', 'a6=1.26', 'a7=0.04', 'a8=0.79', 'a9=74.51', 'a10=4.32', 'a11=82.51', 'a12=41.62']





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

   **Total running time of the script:** ( 0 minutes  0.875 seconds)


.. _sphx_glr_download_auto_examples_sensitivity_morris_plot_12_dimensional_gfunction.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/SURGroup/UQpy/master?urlpath=lab/tree/notebooks/auto_examples/sensitivity/morris/plot_12_dimensional_gfunction.ipynb
        :alt: Launch binder
        :width: 150 px



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

      :download:`Download Python source code: plot_12_dimensional_gfunction.py <plot_12_dimensional_gfunction.py>`

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

      :download:`Download Jupyter notebook: plot_12_dimensional_gfunction.ipynb <plot_12_dimensional_gfunction.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_