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# Various design of experiments in OpenTURNS¶

The goal of this example is to present several design of experiments available in OpenTURNS.

## Distribution¶

```
import openturns as ot
import openturns.viewer as otv
ot.Log.Show(ot.Log.NONE)
```

## Monte-Carlo sampling in 2D¶

```
dim = 2
X = [ot.Uniform()] * dim
distribution = ot.ComposedDistribution(X)
bounds = distribution.getRange()
```

```
sampleSize = 10
sample = distribution.getSample(sampleSize)
```

```
fig = otv.PlotDesign(sample, bounds)
```

We see that there a empty zones in the input space.

## Monte-Carlo sampling in 3D¶

```
dim = 3
X = [ot.Uniform()] * dim
distribution = ot.ComposedDistribution(X)
bounds = distribution.getRange()
```

```
sampleSize = 10
sample = distribution.getSample(sampleSize)
```

```
fig = otv.PlotDesign(sample, bounds)
fig.set_size_inches(10, 10)
```

## Latin Hypercube Sampling¶

```
distribution = ot.ComposedDistribution([ot.Uniform()] * 3)
samplesize = 5
experiment = ot.LHSExperiment(distribution, samplesize, False, False)
sample = experiment.generate()
```

In order to see the LHS property, we need to set the bounds.

```
bounds = distribution.getRange()
```

```
fig = otv.PlotDesign(sample, bounds)
fig.set_size_inches(10, 10)
```

We see that each column or row exactly contains one single point. This shows that a LHS design of experiments has good 1D projection properties, and, hence, is a good candidate for a space filling design.

## Optimized LHS¶

```
distribution = ot.ComposedDistribution([ot.Uniform()] * 3)
samplesize = 10
```

```
bounds = distribution.getRange()
```

```
lhs = ot.LHSExperiment(distribution, samplesize)
lhs.setAlwaysShuffle(True) # randomized
space_filling = ot.SpaceFillingC2()
temperatureProfile = ot.GeometricProfile(10.0, 0.95, 1000)
algo = ot.SimulatedAnnealingLHS(lhs, space_filling, temperatureProfile)
# optimal design
sample = algo.generate()
```

```
fig = otv.PlotDesign(sample, bounds)
fig.set_size_inches(10, 10)
```

We see that this LHS is optimized in the sense that it fills the space more evenly than a non-optimized does in general.

## Sobol’ low discrepancy sequence¶

```
dim = 2
distribution = ot.ComposedDistribution([ot.Uniform()] * dim)
bounds = distribution.getRange()
```

```
sequence = ot.SobolSequence(dim)
```

```
samplesize = 2 ** 5 # Sobol' sequences are in base 2
experiment = ot.LowDiscrepancyExperiment(sequence, distribution, samplesize, False)
sample = experiment.generate()
```

```
samplesize
```

```
32
```

```
subdivisions = [2 ** 2, 2 ** 1]
fig = otv.PlotDesign(sample, bounds, subdivisions)
fig.set_size_inches(6, 6)
```

We have elementary intervals in 2 dimensions, each having a volume equal to 1/8. Since there are 32 points, the Sobol’ sequence is so that each elementary interval contains exactly 32/8 = 4 points. Notice that each elementary interval is closed on the left (or bottom) and open on the right (or top).

## Halton low discrepancy sequence¶

```
dim = 2
distribution = ot.ComposedDistribution([ot.Uniform()] * dim)
bounds = distribution.getRange()
```

```
sequence = ot.HaltonSequence(dim)
```

Halton sequence uses prime numbers 2 and 3 in two dimensions.

```
samplesize = 2 ** 2 * 3 ** 2
experiment = ot.LowDiscrepancyExperiment(sequence, distribution, samplesize, False)
sample = experiment.generate()
```

```
samplesize
```

```
36
```

```
subdivisions = [2 ** 2, 3]
fig = otv.PlotDesign(sample, bounds, subdivisions)
fig.set_size_inches(6, 6)
```

We have elementary intervals in 2 dimensions, each having a volume equal to 1/12. Since there are 36 points, the Halton sequence is so that each elementary interval contains exactly 36/12 = 3 points. Notice that each elementary interval is closed on the left (or bottom) and open on the right (or top).