.. only:: html

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

        Click :ref:`here <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:


Parametrization of a simulation algorithm
=========================================

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.



.. code-block:: default

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








Create the joint distribution of the parameters.


.. code-block:: default

    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)








Create the model.


.. code-block:: default

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








Create the event whose probability we want to estimate.


.. code-block:: default

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








Create a Monte Carlo algorithm.


.. code-block:: default

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








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


.. code-block:: default

    algo.setMaximumCoefficientOfVariation(0.05)








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


.. code-block:: default

    algo.setMaximumOuterSampling(int(1e4))








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


.. code-block:: default

    algo.setBlockSize(2)








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

Null strategy


.. code-block:: default

    algo.setConvergenceStrategy(ot.Null())

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

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









Use a callback to display the progress every 10%. 


.. code-block:: default

    def progress(p):
        if p >= progress.t:
            progress.t += 10.0
            print('progress=', p, '%')
        return False
    progress.t = 10.0
    algo.setProgressCallback(progress)









Use a callback to stop the simulation. 


.. code-block:: default

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









.. code-block:: default

    algo.run()





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

 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 %




Retrieve results. 


.. code-block:: default

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




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

 Out:

 .. code-block:: none

    Pf= 0.03252214870472134





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

   **Total running time of the script:** ( 0 minutes  0.035 seconds)


.. _sphx_glr_download_auto_reliability_sensitivity_reliability_plot_probability_simulation_parametrization.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_probability_simulation_parametrization.py <plot_probability_simulation_parametrization.py>`



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

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


.. only:: html

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

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