.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_reliability_sensitivity/design_of_experiments/plot_mixed_design.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py: Create mixed deterministic and probabilistic designs of experiments =================================================================== .. GENERATED FROM PYTHON SOURCE LINES 6-15 In this example we build a mixed deterministic and probabilistic design of experiments in which levels are defined from the probabilistic distribution of the input random vector. More precisely, we show how to create an `Axial` design of experiments whose points are updated so that they match the mean and variance of the distribution. The example here is an axial design of experiments where levels are proportional to the standard deviation of each component of the random input vector, and centered on the mean vector of the random input vector. .. GENERATED FROM PYTHON SOURCE LINES 18-25 .. code-block:: Python import openturns as ot import math as m import openturns.viewer as viewer from matplotlib import pylab as plt ot.Log.Show(ot.Log.NONE) .. GENERATED FROM PYTHON SOURCE LINES 26-27 Define the underlying random vector. .. GENERATED FROM PYTHON SOURCE LINES 29-34 .. code-block:: Python dim = 2 R = ot.CorrelationMatrix(dim) distribution = ot.Normal([2.0, 3.0], [0.5, 2.0], R) rv = ot.RandomVector(distribution) .. GENERATED FROM PYTHON SOURCE LINES 35-36 Define the structure of the design of experiments. .. GENERATED FROM PYTHON SOURCE LINES 38-42 .. code-block:: Python levels = [1.0, 2.0, 3.0] experiment = ot.Axial(dim, levels) sample = experiment.generate() .. GENERATED FROM PYTHON SOURCE LINES 43-44 Scale the design proportionnally to the standard deviation of each component. .. GENERATED FROM PYTHON SOURCE LINES 46-51 .. code-block:: Python covariance = rv.getCovariance() scaling = [m.sqrt(covariance[i, i]) for i in range(dim)] print("scaling=", scaling) sample *= scaling .. rst-class:: sphx-glr-script-out .. code-block:: none scaling= [0.5, 2.0] .. GENERATED FROM PYTHON SOURCE LINES 52-53 Center the design around the mean point of the distribution. .. GENERATED FROM PYTHON SOURCE LINES 55-59 .. code-block:: Python center = rv.getMean() print("center=", center) sample += center .. rst-class:: sphx-glr-script-out .. code-block:: none center= [2,3] .. GENERATED FROM PYTHON SOURCE LINES 60-61 Draw the design as well as the distribution iso-values. .. GENERATED FROM PYTHON SOURCE LINES 63-70 .. code-block:: Python graph = distribution.drawPDF() doe = ot.Cloud(sample) doe.setColor("red") doe.setLegend("design") graph.add(doe) view = viewer.View(graph) plt.show() .. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_mixed_design_001.png :alt: [X0,X1] iso-PDF :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_mixed_design_001.png :class: sphx-glr-single-img .. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_mixed_design.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_mixed_design.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_mixed_design.py `