# 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 and can be scaled and moved to cover the desired box.

[1]:

from __future__ import print_function
import openturns as ot


We will use the following function to plot bi-dimensional samples.

[2]:

def drawBidimensionalSample(sample, title):
n = sample.getSize()
graph = ot.Graph("%s, size=%d" % (title, n), "X1", "X2", True, '')
cloud = ot.Cloud(sample)
return graph


## Axial design¶

[3]:

levels = [1.0, 1.5, 3.0]
experiment = ot.Axial(2, levels)
sample = experiment.generate()
drawBidimensionalSample(sample,"Axial")

[3]:


Scale and to get desired location.

[4]:

sample *= 2.0
sample += [5.0, 8.0]
drawBidimensionalSample(sample,"Axial")

[4]:


## Factorial design¶

[5]:

experiment = ot.Factorial(2, levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
drawBidimensionalSample(sample,"Factorial")

[5]:


## Composite design¶

[6]:

experiment = ot.Composite(2, levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
drawBidimensionalSample(sample,"Composite")

[6]:


## Grid design¶

[7]:

levels = [3, 4]
experiment = ot.Box(levels)
sample = experiment.generate()
sample *= 2.0
sample += [5.0, 8.0]
drawBidimensionalSample(sample,"Box")

[7]: