.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_reliability_sensitivity/reliability/plot_probability_simulation_parametrization.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_probability_simulation_parametrization.py>`
        to download the full example code

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

.. _sphx_glr_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py:


Specify a simulation algorithm
==============================

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In this example we are going to parameterize a simulation algorithm:

 - parameters linked to the number of points generated
 - the precision of the probability estimator
 - the sample storage strategy
 - using callbacks to monitor progress and stopping criteria.


.. GENERATED FROM PYTHON SOURCE LINES 15-19

.. code-block:: Python

    import openturns as ot

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








.. GENERATED FROM PYTHON SOURCE LINES 20-21

Create the joint distribution of the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 23-28

.. code-block:: Python

    distribution_R = ot.LogNormalMuSigma(300.0, 30.0, 0.0).getDistribution()
    distribution_F = ot.Normal(75e3, 5e3)
    marginals = [distribution_R, distribution_F]
    distribution = ot.ComposedDistribution(marginals)








.. GENERATED FROM PYTHON SOURCE LINES 29-30

Create the model.

.. GENERATED FROM PYTHON SOURCE LINES 32-34

.. code-block:: Python

    model = ot.SymbolicFunction(["R", "F"], ["R-F/(pi_*100.0)"])








.. GENERATED FROM PYTHON SOURCE LINES 35-36

Create the event whose probability we want to estimate.

.. GENERATED FROM PYTHON SOURCE LINES 38-42

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 43-44

Create a Monte Carlo algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 46-49

.. code-block:: Python

    experiment = ot.MonteCarloExperiment()
    algo = ot.ProbabilitySimulationAlgorithm(event, experiment)








.. GENERATED FROM PYTHON SOURCE LINES 50-51

Criteria 1: Define the Maximum Coefficient of variation of the probability estimator.

.. GENERATED FROM PYTHON SOURCE LINES 53-55

.. code-block:: Python

    algo.setMaximumCoefficientOfVariation(0.05)








.. GENERATED FROM PYTHON SOURCE LINES 56-57

Criteria 2: Define the number of iterations of the simulation.

.. GENERATED FROM PYTHON SOURCE LINES 59-61

.. code-block:: Python

    algo.setMaximumOuterSampling(int(1e4))








.. GENERATED FROM PYTHON SOURCE LINES 62-63

The block size parameter represents the number of samples evaluated per iteration, useful for parallelization.

.. GENERATED FROM PYTHON SOURCE LINES 65-67

.. code-block:: Python

    algo.setBlockSize(2)








.. GENERATED FROM PYTHON SOURCE LINES 68-69

HistoryStrategy to store the values of the probability used to draw the convergence graph.

.. GENERATED FROM PYTHON SOURCE LINES 71-72

Null strategy

.. GENERATED FROM PYTHON SOURCE LINES 72-82

.. code-block:: Python

    algo.setConvergenceStrategy(ot.Null())

    # Full strategy
    algo.setConvergenceStrategy(ot.Full())

    # Compact strategy: N points
    N = 1000
    algo.setConvergenceStrategy(ot.Compact(N))









.. GENERATED FROM PYTHON SOURCE LINES 83-84

Use a callback to display the progress every 10%.

.. GENERATED FROM PYTHON SOURCE LINES 87-98

.. code-block:: Python

    def progress(p):
        if p >= progress.t:
            progress.t += 10.0
            print("progress=", p, "%")
        return False


    progress.t = 10.0
    algo.setProgressCallback(progress)









.. GENERATED FROM PYTHON SOURCE LINES 99-100

Use a callback to stop the simulation.

.. GENERATED FROM PYTHON SOURCE LINES 103-110

.. code-block:: Python

    def stop():
        # here we never stop, but we could
        return False


    algo.setStopCallback(stop)








.. GENERATED FROM PYTHON SOURCE LINES 111-113

.. code-block:: Python

    algo.run()





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

 .. code-block:: none

    progress= 10.0 %
    progress= 20.0 %
    progress= 30.0 %
    progress= 40.0 %
    progress= 50.0 %
    progress= 60.0 %
    progress= 70.0 %
    progress= 80.0 %
    progress= 90.0 %




.. GENERATED FROM PYTHON SOURCE LINES 114-115

Retrieve results.

.. GENERATED FROM PYTHON SOURCE LINES 117-120

.. code-block:: Python

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




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

 .. code-block:: none

    Pf= 0.029626994613273696





.. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.py:

.. only:: html

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

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

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

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

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