.. 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_design_of_experiments_plot_monte_carlo_experiment.py:
Create a Monte Carlo design of experiments
==========================================
In this example we are going to create a MonteCarlo probabilistic design experiment.
.. 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)
Define underlying distribution, size
.. code-block:: default
distribution = ot.Normal(2)
size = 50
Create the design
.. code-block:: default
experiment = ot.MonteCarloExperiment(distribution, size)
sample = experiment.generate()
Plot the design
.. code-block:: default
graph = ot.Graph("MC design", "x1", "x2", True, "")
cloud = ot.Cloud(sample, "blue", "fsquare", "")
graph.add(cloud)
view = viewer.View(graph)
plt.show()
.. image:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_monte_carlo_experiment_001.png
:alt: MC design
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.064 seconds)
.. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.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_monte_carlo_experiment.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_monte_carlo_experiment.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_