Create a random vector

In this example we are going to create a distribution or copula.

The way to go is inheriting the PythonRandomVector class and overload its methods:

• :meth:`~openturns.PythonRandomVector.getRealization

• :meth:`~openturns.PythonRandomVector.getSample

• :meth:`~openturns.PythonRandomVector.getMean

• :meth:`~openturns.PythonRandomVector.getCovariance

import openturns as ot

ot.Log.Show(ot.Log.NONE)

Inherit PythonRandomVector

class RVEC(ot.PythonRandomVector):
    def __init__(self):
        super(RVEC, self).__init__(2)
        self.setDescription(["R", "S"])

    def getRealization(self):
        X = [ot.RandomGenerator.Generate(), 2.0 + ot.RandomGenerator.Generate()]
        return X

    def getSample(self, size):
        X = []
        for i in range(size):
            X.append(
                [ot.RandomGenerator.Generate(), 2.0 + ot.RandomGenerator.Generate()]
            )
        return X

    def getMean(self):
        return [0.5, 2.5]

    def getCovariance(self):
        return [[1.0, 0.0], [0.0, 1.0]]

Instantiate the distribution

randomVector = ot.RandomVector(RVEC())

Get a sample

randomVector.getSample(5)

	v0	v1
0	0.0991493	2.620393
1	0.1393249	2.475724
2	0.3662563	2.500463
3	0.8530635	2.912447
4	0.1050448	2.890071

Get mean

randomVector.getMean()

class=Point name=Unnamed dimension=2 values=[0.5,2.5]

Compute the probability contained in an interval

randomVector.getCovariance()

[[ 1 0 ]
[ 0 1 ]]