.. 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

        :ref:`Go to the end <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 9-14

.. code-block:: default

    from openturns.usecases import stressed_beam
    import openturns as ot

    ot.Log.Show(ot.Log.NONE)








.. GENERATED FROM PYTHON SOURCE LINES 15-16

We load the model from the usecases module :

.. GENERATED FROM PYTHON SOURCE LINES 16-18

.. code-block:: default

    sm = stressed_beam.AxialStressedBeam()








.. GENERATED FROM PYTHON SOURCE LINES 19-20

We get the input parameter distribution :

.. GENERATED FROM PYTHON SOURCE LINES 20-22

.. code-block:: default

    distribution = sm.distribution








.. GENERATED FROM PYTHON SOURCE LINES 23-24

and get the model :

.. GENERATED FROM PYTHON SOURCE LINES 24-26

.. code-block:: default

    model = sm.model








.. GENERATED FROM PYTHON SOURCE LINES 27-28

We create the event whose probability we want to estimate.

.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. 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 35-36

Create a LHS algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 38-43

.. code-block:: default

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








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Retrieve results.

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: default

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




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

 .. code-block:: none

    Pf= 0.029342988609791055





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

   **Total running time of the script:** ( 0 minutes  0.039 seconds)


.. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_estimate_probability_lhs.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_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>`