.. 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
==============================

.. GENERATED FROM PYTHON SOURCE LINES 7-14

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 16-20

.. code-block:: Python

    import openturns as ot

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








.. GENERATED FROM PYTHON SOURCE LINES 21-22

Create the joint distribution of the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 24-29

.. 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.JointDistribution(marginals)








.. GENERATED FROM PYTHON SOURCE LINES 30-31

Create the model.

.. GENERATED FROM PYTHON SOURCE LINES 33-35

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 36-37

Create the event whose probability we want to estimate.

.. GENERATED FROM PYTHON SOURCE LINES 39-43

.. 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 44-45

Create a Monte Carlo algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 51-52

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

.. GENERATED FROM PYTHON SOURCE LINES 54-56

.. code-block:: Python

    algo.setMaximumCoefficientOfVariation(0.05)








.. GENERATED FROM PYTHON SOURCE LINES 57-58

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

.. GENERATED FROM PYTHON SOURCE LINES 60-62

.. code-block:: Python

    algo.setMaximumOuterSampling(int(1e4))








.. GENERATED FROM PYTHON SOURCE LINES 63-64

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

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: Python

    algo.setBlockSize(2)








.. GENERATED FROM PYTHON SOURCE LINES 69-70

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

.. GENERATED FROM PYTHON SOURCE LINES 72-73

Null strategy

.. GENERATED FROM PYTHON SOURCE LINES 73-83

.. 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 84-85

Use a callback to display the progress every 10%.

.. GENERATED FROM PYTHON SOURCE LINES 88-99

.. 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 100-101

Use a callback to stop the simulation.

.. GENERATED FROM PYTHON SOURCE LINES 104-111

.. code-block:: Python

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


    algo.setStopCallback(stop)








.. GENERATED FROM PYTHON SOURCE LINES 112-114

.. 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 115-116

Retrieve results.

.. GENERATED FROM PYTHON SOURCE LINES 118-121

.. code-block:: Python

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




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

 .. code-block:: none

    Pf= 0.029667519181585684





.. _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>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_probability_simulation_parametrization.zip <plot_probability_simulation_parametrization.zip>`