1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
|
import openturns as ot
from math import exp
from matplotlib import pyplot as plt
from openturns.viewer import View
# Create the covariance function at (s,t)
def C(s, t):
return exp(-4.0 * abs(s - t) / (1 + (s * s + t * t)))
def covModRef(X):
return [C(X[0], X[1])]
myFuncCovarianceRef = ot.PythonFunction(2, 1, covModRef)
myFuncCovarianceRef.setDescription(["s", "t", "C"])
t0 = -4.0
tmax = 4.0
# Draw the isocontours of the discretized covariance function
myGraphRef = myFuncCovarianceRef.draw([t0, t0], [tmax, tmax])
alld = myGraphRef.getDrawables()
levels = ot.NumericalPoint(alld.getSize())
for i in range(alld.getSize()):
d = alld[i]
d.setLineStyle("twodash")
d.setLineWidth(2)
myGraphRef.setDrawable(d, i)
levels[i] = d.getLevels()[0]
# Create the time grid
# for iN in range(2, 11):
# t_00 = time()
N = 2**5
dt = (tmax - t0) / N
myMesh = ot.RegularGrid(t0, dt, N)
# Keep only time stamps in the time-grid
tmax = myMesh.getEnd()
# Create the collection of HermitianMatrix
myCovarianceCollection = ot.CovarianceMatrixCollection()
index = 0
for k in range(N):
s = myMesh.getValue(k)
for l in range(k + 1):
t = myMesh.getValue(l)
matrix = ot.CovarianceMatrix(1)
matrix[0, 0] = C(s, t)
index += 1
myCovarianceCollection.add(matrix)
# Create the covariance model
myCovarianceModel = ot.UserDefinedCovarianceModel(
myMesh, myCovarianceCollection)
# Create the non stationary Normal process with
# that covariance model
myProcess = ot.TemporalNormalProcess(myCovarianceModel, myMesh)
# Create a sample of fields
size = 10**4
myFieldSample = myProcess.getSample(size)
# Build a covariance model factory
myFactory = ot.NonStationaryCovarianceModelFactory()
# Estimation on a the ProcessSample
myEstimatedModel = myFactory.build(myFieldSample)
# Define the python function associated to myCovarianceModel
def covMod(X):
return [myEstimatedModel(X[0], X[1])[0, 0]]
myFuncCovariance = ot.PythonFunction(2, 1, covMod)
cov_graph = ot.Graph(myGraphRef)
alld = myFuncCovariance.draw([t0, t0], [tmax, tmax]).getDrawables()
palette = ot.Drawable.BuildDefaultPalette(alld.getSize())
for i in range(alld.getSize()):
d = alld[i]
d.setLegend("")
d.setLevels([levels[i]])
d.setColor(palette[i])
d.setDrawLabels(False)
cov_graph.add(d)
fig = plt.figure(figsize=(10, 4))
plt.suptitle('Non stationary covariance model estimation')
cov_axis = fig.add_subplot(111)
View(cov_graph, figure=fig, axes=[cov_axis], add_legend=False)
|
