Note

Click here to download the full example code

# Create a random design of experiments¶

## Abstract¶

Random designs of experiments can be generated from probability distributions.

```
from __future__ import print_function
import openturns as ot
import openturns.viewer as viewer
import math as m
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
```

We create the underlying distribution: a standard 2-dimensional normal distribution.

```
distribution = ot.Normal(2)
size = 50
```

## The Monte Carlo design of experiments¶

We build the experiment with the `MonteCarloExperiment`

class :

```
experiment = ot.MonteCarloExperiment(distribution, size)
sample = experiment.generate()
```

We draw the design of experiments as a `Cloud`

```
graph = ot.Graph("Monte Carlo design", r"$x_1$", r"$x_2$", True, "")
cloud = ot.Cloud(sample, "blue", "fsquare", "")
graph.add(cloud)
view = viewer.View(graph)
```

## Latin Hypercube Sampling¶

We build the LHS design of experiments with the `LHSExperiment`

class :

```
experiment = ot.LHSExperiment(distribution, size)
sample = experiment.generate()
```

We draw the LHS design of experiments as a cloud :

```
graph = ot.Graph("LHS design", r"$x_1$", r"$x_2$", True, "")
cloud = ot.Cloud(sample, "blue", "fsquare", "")
graph.add(cloud)
view = viewer.View(graph)
```

Display figures

```
plt.show()
```

**Total running time of the script:** ( 0 minutes 0.146 seconds)