Create a functional basis process¶
The objective of this example is to define a multivariate stochastic process of dimension where , as a linear combination of deterministic functions :
where is a random vector of dimension .
We suppose that is discretized on the mesh which has vertices.
A realization of on consists in generating a realization of the random vector and in evaluating the functions on the mesh .
If we note the realization of , where , we have:
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)
Define the coefficients distribution
mu = [2.0]*2 sigma = [5.0]*2 R = ot.CorrelationMatrix(2) coefDist = ot.Normal(mu, sigma, R)
Create a basis of functions
phi_1 = ot.SymbolicFunction(['t'], ['sin(t)']) phi_2 = ot.SymbolicFunction(['t'], ['cos(t)^2']) myBasis = ot.Basis([phi_1, phi_2])
Create the mesh
myMesh = ot.RegularGrid(0.0, 0.1, 100)
Create the process
process = ot.FunctionalBasisProcess(coefDist, myBasis, myMesh)
Draw a sample
N = 6 sample = process.getSample(N) graph = sample.drawMarginal(0) graph.setTitle(str(N)+' realizations of functional basis process') view = viewer.View(graph)
Total running time of the script: ( 0 minutes 0.075 seconds)