Event manipulation

In this example we are going to define an Event from a scalar variable in the form:

with a scalar threshold

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

Create model f(x) = x1 + 2*x2

model = ot.SymbolicFunction(['x1', 'x2'], ['x1+2*x2'])

# Create the input distribution and random vector X
inputDist = ot.Normal(2)
inputDist.setDescription(['X1','X2'])

inputVector = ot.RandomVector(inputDist)

# Create the output random vector Y=f(X)
outputVector = ot.CompositeRandomVector(model, inputVector)

Create the event Y > 3

threshold = 3.0
event = ot.ThresholdEvent(outputVector, ot.Greater(), threshold)

Realization as a Bernoulli

print('realization=' , event.getRealization())

Out:

realization= [0]

Sample of 10 realizations as a Bernoulli

print('sample=' , event.getSample(10))

Out:

sample=     [ y0 ]
0 : [ 0  ]
1 : [ 0  ]
2 : [ 0  ]
3 : [ 1  ]
4 : [ 0  ]
5 : [ 0  ]
6 : [ 0  ]
7 : [ 0  ]
8 : [ 0  ]
9 : [ 0  ]

Build a standard event based on an event

standardEvent = ot.StandardEvent(event)