Creation of a custom 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:

• getRealization

• getSample

• getMean

• getCovariance

from __future__ import print_function
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
import math as m
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]]

Instanciate our distribution

randomVector = ot.RandomVector(RVEC())

Get a sample

randomVector.getSample(5)

	v0	v1
0	0.2001577	2.751968
1	0.374857	2.529779
2	0.7119889	2.497759
3	0.9211765	2.555468
4	0.3753769	2.568851

Get mean

randomVector.getMean()

[0.5,2.5]

Compute the probability contained in an interval

randomVector.getCovariance()

[[ 1 0 ]
[ 0 1 ]]