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import openturns as ot
from math import exp
from matplotlib import pyplot as plt
from openturns.viewer import View
N = 512
a = 20.0
# myMesh = ot.IntervalMesher([N]).build(ot.Interval(-a, a))
myMesh = ot.RegularGrid(0.0, 2 * a / N, N + 1)
covarianceModel = ot.ExponentialModel(1, [1.0], [1.0])
myProcess = ot.TemporalNormalProcess(covarianceModel, myMesh)
mySample = myProcess.getSample(1000)
myCovarianceFactory = ot.StationaryCovarianceModelFactory()
myEstimatedModel = myCovarianceFactory.build(mySample)
def f(x):
res = covarianceModel(x)[0, 0]
return [res]
func = ot.PythonFunction(1, 1, f)
func.setDescription(['$t$', '$cov$'])
def fEst(X):
res = myEstimatedModel(X)[0, 0]
return [res]
funcEst = ot.PythonFunction(1, 1, fEst)
funcEst.setDescription(['$t$', '$cov_{est}$'])
cov_graph = func.draw(-a / 4, a / 4, 1024)
cov_graph.add(funcEst.draw(-a / 4, a / 4, 1024))
cov_graph.setColors(['blue', 'red'])
fig = plt.figure(figsize=(10, 4))
plt.suptitle('Stationary covariance model estimation')
cov_axis = fig.add_subplot(111)
view = View(cov_graph, figure=fig, axes=[cov_axis], add_legend=False)
view.show()
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