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import openturns as ot
from matplotlib import pyplot as plt
import openturns.viewer as otv
mesh = ot.RegularGrid(-5.0, 0.1, 101)
class GaussianConvolution(ot.OpenTURNSPythonFieldFunction):
def __init__(self):
outputGrid = ot.RegularGrid(-1.0, 0.02, 101)
super(GaussianConvolution, self).__init__(mesh, 1, outputGrid, 1)
self.setInputDescription(["x"])
self.setOutputDescription(["y"])
self.algo_ = ot.GaussKronrod(
20, 1.0e-4, ot.GaussKronrodRule(ot.GaussKronrodRule.G7K15)
)
def _exec(self, X):
f = ot.Function(
ot.PiecewiseLinearEvaluation(
[x[0] for x in self.getInputMesh().getVertices()], X
)
)
outputValues = ot.Sample(0, 1)
for t in self.getOutputMesh().getVertices():
kernel = ot.Normal(t[0], 0.05)
def pdf(X):
return [kernel.computePDF(X)]
weight = ot.Function(ot.PythonFunction(1, 1, pdf))
outputValues.add(self.algo_.integrate(weight * f, kernel.getRange()))
return outputValues
N = 5
X = ot.GaussianProcess(ot.GeneralizedExponential([0.1], 1.0), mesh)
f = ot.FieldFunction(GaussianConvolution())
Y = ot.CompositeProcess(f, X)
x_graph = X.getSample(N).drawMarginal(0)
y_graph = Y.getSample(N).drawMarginal(0)
fig = plt.figure(figsize=(10, 4))
x_axis = fig.add_subplot(121)
y_axis = fig.add_subplot(122)
otv.View(x_graph, figure=fig, axes=[x_axis], add_legend=False)
otv.View(y_graph, figure=fig, axes=[y_axis], add_legend=False)
fig.suptitle("Composite process")
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