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#! /usr/bin/env python
import openturns as ot
# We create a distribution
distribution = ot.Normal()
print("distribution = ", repr(distribution))
aCollection = []
aCollection.append(ot.Normal(0.0, 1.0))
aCollection.append(ot.Uniform(1.0, 1.5))
distributionParameters = ot.JointDistribution(aCollection)
randomParameters = ot.RandomVector(distributionParameters)
print("random parameters=", randomParameters)
# We create a distribution-based conditional RandomVector
vect = ot.ConditionalRandomVector(distribution, randomParameters)
print("vect=", vect)
# Check standard methods of class RandomVector
print("vect dimension=", vect.getDimension())
p = ot.Point()
r = vect.getRealization(p)
print("vect realization=", repr(r))
print("parameters value=", repr(p))
distribution.setParameter(p)
ot.RandomGenerator.SetSeed(0)
# Generate a parameter set to put the random generator into the proper
# state
randomParameters.getRealization()
# The realization of the distribution should be equal to the realization
# of the conditional vector
print("dist realization=", repr(distribution.getRealization()))
print("vect sample =", repr(vect.getSample(5)))
parameter = vect.getParameter()
print("parameter =", repr(parameter))
vect.setParameter(parameter)
print("parameter desc =", repr(vect.getParameterDescription()))
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