.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/uncertainty/sensitivity/plot_sensitivity_comparison.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:Go to the end  to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_uncertainty_sensitivity_plot_sensitivity_comparison.py: Comparing sensitivity indices ============================= .. GENERATED FROM PYTHON SOURCE LINES 25-32 .. code-block:: default from __future__ import annotations from gemseo.uncertainty.sensitivity.correlation.analysis import CorrelationAnalysis from gemseo.uncertainty.sensitivity.morris.analysis import MorrisAnalysis from gemseo.uncertainty.use_cases.ishigami.ishigami_discipline import IshigamiDiscipline from gemseo.uncertainty.use_cases.ishigami.ishigami_space import IshigamiSpace .. GENERATED FROM PYTHON SOURCE LINES 33-45 In this example, we consider the Ishigami function :cite:ishigami1990 .. math:: f(x_1,x_2,x_3)=\sin(x_1)+7\sin(x_2)^2+0.1x_3^4\sin(x_1) implemented as an :class:.MDODiscipline by the :class:.IshigamiDiscipline. It is commonly used with the independent random variables :math:X_1, :math:X_2 and :math:X_3 uniformly distributed between :math:-\pi and :math:\pi and defined in the :class:.IshigamiSpace. .. GENERATED FROM PYTHON SOURCE LINES 45-48 .. code-block:: default discipline = IshigamiDiscipline() uncertain_space = IshigamiSpace() .. GENERATED FROM PYTHON SOURCE LINES 49-56 We would like to carry out two sensitivity analyses, e.g. a first one based on correlation coefficients and a second one based on the Morris methodology, and compare the results, Firstly, we create a :class:.CorrelationAnalysis and compute the sensitivity indices: .. GENERATED FROM PYTHON SOURCE LINES 56-59 .. code-block:: default correlation = CorrelationAnalysis([discipline], uncertain_space, 10) correlation.compute_indices() .. rst-class:: sphx-glr-script-out .. code-block:: none {: {'y': [{'x1': array([0.55555556]), 'x2': array([0.02222222]), 'x3': array([-0.11111111])}]}, : {'y': [{'x1': array([0.84696461]), 'x2': array([0.68814608]), 'x3': array([-0.29846394])}]}, : {'y': [{'x1': array([0.685388]), 'x2': array([0.09681897]), 'x3': array([-0.23027298])}]}, : {'y': [{'x1': array([0.90374102]), 'x2': array([0.76539572]), 'x3': array([-0.02232206])}]}, : {'y': [{'x1': array([0.74545455]), 'x2': array([0.04242424]), 'x3': array([-0.09090909])}]}, : {'y': [{'x1': array([0.94001308]), 'x2': array([0.55748872]), 'x3': array([-0.16157012])}]}, : {'y': [{'x1': array([1.06252802]), 'x2': array([0.60167726]), 'x3': array([-0.00959941])}]}, : {'y': [{'x1': array([0.88362459]), 'x2': array([0.31079367]), 'x3': array([0.0261049])}]}} .. GENERATED FROM PYTHON SOURCE LINES 60-62 Then, we create an :class:.MorrisAnalysis and compute the sensitivity indices: .. GENERATED FROM PYTHON SOURCE LINES 62-65 .. code-block:: default morris = MorrisAnalysis([discipline], uncertain_space, 10) morris.compute_indices() .. rst-class:: sphx-glr-script-out .. code-block:: none {'MU': {'y': [{'x1': array([0.73532408]), 'x2': array([-0.05115399]), 'x3': array([-1.6024484])}]}, 'MU_STAR': {'y': [{'x1': array([0.76770333]), 'x2': array([2.09435091]), 'x3': array([1.6024484])}]}, 'SIGMA': {'y': [{'x1': array([0.76770333]), 'x2': array([2.09435091]), 'x3': array([1.58984353])}]}, 'RELATIVE_SIGMA': {'y': [{'x1': array([1.]), 'x2': array([1.]), 'x3': array([0.99213399])}]}, 'MIN': {'y': [{'x1': array([0.03237925]), 'x2': array([2.04319692]), 'x3': array([0.01260487])}]}, 'MAX': {'y': [{'x1': array([1.50302741]), 'x2': array([2.14550491]), 'x3': array([3.19229192])}]}} .. GENERATED FROM PYTHON SOURCE LINES 66-70 Lastly, we compare these analyses with the graphical method :meth:.SensitivityAnalysis.plot_comparison, either using a bar chart: .. GENERATED FROM PYTHON SOURCE LINES 70-72 .. code-block:: default morris.plot_comparison(correlation, "y", use_bar_plot=True, save=False, show=True) .. image-sg:: /examples/uncertainty/sensitivity/images/sphx_glr_plot_sensitivity_comparison_001.png :alt: plot sensitivity comparison :srcset: /examples/uncertainty/sensitivity/images/sphx_glr_plot_sensitivity_comparison_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 73-74 or a radar plot: .. GENERATED FROM PYTHON SOURCE LINES 74-75 .. code-block:: default morris.plot_comparison(correlation, "y", use_bar_plot=False, save=False, show=True) .. image-sg:: /examples/uncertainty/sensitivity/images/sphx_glr_plot_sensitivity_comparison_002.png :alt: plot sensitivity comparison :srcset: /examples/uncertainty/sensitivity/images/sphx_glr_plot_sensitivity_comparison_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.399 seconds) .. _sphx_glr_download_examples_uncertainty_sensitivity_plot_sensitivity_comparison.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:Download Python source code: plot_sensitivity_comparison.py  .. container:: sphx-glr-download sphx-glr-download-jupyter :download:Download Jupyter notebook: plot_sensitivity_comparison.ipynb  .. only:: html .. rst-class:: sphx-glr-signature Gallery generated by Sphinx-Gallery _