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"""
Deterministic design of experiments
===================================
"""
# %%
# In this example we present the available deterministic design of experiments.
#
# Four types of deterministic design of experiments are available:
#
# - `Axial`
# - `Factorial`
# - `Composite`
# - `Box`
#
# Each type of deterministic design is discretized differently according to a number of levels.
#
# Functionally speaking, a design is a `Sample` that lies within the unit cube :math:`(0,1)^d` and can be scaled and moved to cover the desired box.
# %%
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
# %%
# We will use the following function to plot bi-dimensional samples.
# %%
def drawBidimensionalSample(sample, title):
n = sample.getSize()
graph = ot.Graph("%s, size=%d" % (title, n), "X1", "X2", True, "")
cloud = ot.Cloud(sample)
graph.add(cloud)
return graph
# %%
# Axial design
# ------------
# %%
levels = [1.0, 1.5, 3.0]
experiment = ot.Axial(2, levels)
sample = experiment.generate()
graph = drawBidimensionalSample(sample, "Axial")
view = viewer.View(graph)
# %%
# Scale and to get desired location.
# %%
sample *= 2.0
sample += [5.0, 8.0]
graph = drawBidimensionalSample(sample, "Axial")
view = viewer.View(graph)
# %%
# Factorial design
# ----------------
#
# %%
experiment = ot.Factorial(2, levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
graph = drawBidimensionalSample(sample, "Factorial")
view = viewer.View(graph)
# %%
# Composite design
# ----------------
# %%
experiment = ot.Composite(2, levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
graph = drawBidimensionalSample(sample, "Composite")
view = viewer.View(graph)
# %%
# Grid design
# -----------
#
# %%
levels = [3, 4]
experiment = ot.Box(levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
graph = drawBidimensionalSample(sample, "Box")
view = viewer.View(graph)
plt.show()
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