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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_lhs.py>`
        to download the full example code

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

.. _sphx_glr_auto_reliability_sensitivity_reliability_plot_estimate_probability_lhs.py:


Estimate a probability with Latin Hypercube Sampling
====================================================

.. GENERATED FROM PYTHON SOURCE LINES 6-7

In this example we show how to use the `LHS` algorithm to estimate the probability of an event. We consider the :ref:`axial stressed beam <use-case-stressed-beam>` example.

.. GENERATED FROM PYTHON SOURCE LINES 9-16

.. code-block:: default

    from __future__ import print_function
    from openturns.usecases import stressed_beam as stressed_beam
    import openturns as ot
    import openturns.viewer as viewer
    from matplotlib import pylab as plt
    ot.Log.Show(ot.Log.NONE)








.. GENERATED FROM PYTHON SOURCE LINES 17-18

We load the model from the usecases module :

.. GENERATED FROM PYTHON SOURCE LINES 18-20

.. code-block:: default

    sm = stressed_beam.AxialStressedBeam()








.. GENERATED FROM PYTHON SOURCE LINES 21-22

We get the input parameter distribution :

.. GENERATED FROM PYTHON SOURCE LINES 22-24

.. code-block:: default

    distribution = sm.distribution








.. GENERATED FROM PYTHON SOURCE LINES 25-26

and get the model :

.. GENERATED FROM PYTHON SOURCE LINES 26-28

.. code-block:: default

    model = sm.model








.. GENERATED FROM PYTHON SOURCE LINES 29-30

We create the event whose probability we want to estimate.

.. GENERATED FROM PYTHON SOURCE LINES 32-36

.. code-block:: default

    vect = ot.RandomVector(distribution)
    G = ot.CompositeRandomVector(model, vect)
    event = ot.ThresholdEvent(G, ot.Less(), 0.0)








.. GENERATED FROM PYTHON SOURCE LINES 37-38

Create a LHS algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 40-45

.. code-block:: default

    algo = ot.LHS(event)
    algo.setMaximumCoefficientOfVariation(0.05)
    algo.setMaximumOuterSampling(int(1e5))
    algo.run()








.. GENERATED FROM PYTHON SOURCE LINES 46-47

Retrieve results.

.. GENERATED FROM PYTHON SOURCE LINES 49-52

.. code-block:: default

    result = algo.getResult()
    probability = result.getProbabilityEstimate()
    print('Pf=', probability)




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

 Out:

 .. code-block:: none

    Pf= 0.029342988609791055





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

   **Total running time of the script:** ( 0 minutes  0.055 seconds)


.. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_lhs.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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