Create a gaussian process from a cov. model

In this example we are going to build a gaussian process from its covariance model.

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)

define a covariance model

defaultDimension = 1
# Amplitude values
amplitude = [1.0]*defaultDimension
# Scale values
scale = [1.0]*defaultDimension
# Covariance model
myModel = ot.AbsoluteExponential(scale, amplitude)

define a mesh

tmin = 0.0
step = 0.1
n = 11
myTimeGrid = ot.RegularGrid(tmin, step, n)

create the process

process = ot.GaussianProcess(myModel, myTimeGrid)
print(process)

Out:

GaussianProcess(trend=[x0]->[0.0], covariance=AbsoluteExponential(scale=[1], amplitude=[1]))

draw a sample

sample = process.getSample(6)
graph = sample.drawMarginal(0)
view = viewer.View(graph)
plt.show()