.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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 `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_probability_simulation_parametrization.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_