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
|
#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Instantiate one distribution object
dim = 2
meanPoint = ot.Point(dim, 1.0)
meanPoint[0] = 0.5
meanPoint[1] = -0.5
sigma = ot.Point(dim, 1.0)
sigma[0] = 2.0
sigma[1] = 3.0
R = ot.CorrelationMatrix(dim)
for i in range(1, dim):
R[i, i - 1] = 0.5
distribution1 = ot.Normal(meanPoint, sigma, R)
# Instantiate another distribution object
meanPoint[0] = -1.5
meanPoint[1] = 0.5
sigma[0] = 4.0
sigma[1] = 1.0
for i in range(1, dim):
R[i, i - 1] = -0.25
distribution2 = ot.Normal(meanPoint, sigma, R)
# Test for sampling
size = 200
sample1 = distribution1.getSample(size)
sample2 = distribution2.getSample(size)
# Create an empty graph
myGraph = ot.Graph("Normal sample", "x1", "x2", True, "topright")
# Create the first cloud
myCloud1 = ot.Cloud(sample1, "blue", "fsquare", "First Cloud")
# Then, draw it
myGraph.add(myCloud1)
# Check that the correct files have been generated by computing their
# checksum
# Create the second cloud
myCloud2 = ot.Cloud(sample2, "red", "circle", "Second Cloud")
# Add it to the graph and draw everything
myGraph.add(myCloud2)
|
