Note

Go to the end to download the full example code.

# Create a threshold event¶

## Abstract¶

We present in this example the creation and the use of a `ThresholdEvent`

to estimate a simple integral.

```
import openturns as ot
import openturns.viewer as otv
from matplotlib import pylab as plt
```

We consider a standard Gaussian random vector and build a random vector from this distribution.

```
distX = ot.Normal()
vecX = ot.RandomVector(distX)
```

We consider the simple model and consider the output random variable .

```
f = ot.SymbolicFunction(["x1"], ["abs(x1)"])
vecY = ot.CompositeRandomVector(f, vecX)
```

We define a very simple `ThresholdEvent`

which happpens whenever :

```
thresholdEvent = ot.ThresholdEvent(vecY, ot.Less(), 1.0)
```

For the normal distribution, it is a well-known fact that the values lower than one standard deviation (here exactly 1) away from the mean (here 0) account roughly for 68.27% of the set. So the probability of the event is:

```
print("Probability of the event : %.4f" % 0.6827)
```

```
Probability of the event : 0.6827
```

We can also use a basic estimator to get the probability of the event by drawing samples from the initial distribution distX and counting those which realize the event:

```
print(
"Probability of the event (event sampling) : %.4f"
% thresholdEvent.getSample(1000).computeMean()[0]
)
```

```
Probability of the event (event sampling) : 0.7230
```

The geometric interpretation is simply the area under the PDF of the standard normal distribution for which we draw thereafter.

```
def linearSample(xmin, xmax, npoints):
"""
Returns a sample created from a regular grid
from xmin to xmax with npoints points.
"""
step = (xmax - xmin) / (npoints - 1)
rg = ot.RegularGrid(xmin, step, npoints)
vertices = rg.getVertices()
return vertices
```

The boundary of the event are the lines and

```
a, b = -1, 1
```

```
nplot = 100 # Number of points in the plot
x = linearSample(a, b, nplot)
y = distX.computePDF(x)
vLow = [0.0 for i in range(nplot)]
vUp = [y[i, 0] for i in range(nplot)]
area = distX.computeCDF(b) - distX.computeCDF(a)
boundsPoly = ot.Polygon.FillBetween(x.asPoint(), vLow, vUp)
```

We add the colored area to the PDF graph.

```
graph = distX.drawPDF()
graph.add(boundsPoly)
graph.setTitle("Probability of the event E = %.4f" % (area))
graph.setLegends([""])
view = otv.View(graph)
```

Display all figures

```
plt.show()
```