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
|
#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Instantiate one distribution object
dim = 1
meanPoint = ot.Point(dim, 1.0)
meanPoint[0] = 0.5
sigma = ot.Point(dim, 1.0)
sigma[0] = 2.0
R = ot.CorrelationMatrix(dim)
distribution1 = ot.Normal(meanPoint, sigma, R)
# Instantiate another distribution object
meanPoint[0] = -1.5
sigma[0] = 4.0
distribution2 = ot.Normal(meanPoint, sigma, R)
# Test for sampling
size = 200
nPoints = 20
sample1 = distribution1.getSample(size)
sample2 = distribution2.getSample(size)
# Construct empirical CDF for each sample
data1 = ot.Sample(nPoints, 2)
data2 = ot.Sample(nPoints, 2)
cursor1 = ot.Point(2)
cursor2 = ot.Point(2)
for i in range(nPoints):
cursor1[0] = 13.0 * i / nPoints - 6.5
count1 = 0.0
cursor2[0] = 24.0 * i / nPoints - 13.5
count2 = 0.0
for j in range(size):
if sample1[j, 0] < cursor1[0]:
count1 += 1.0
if sample2[j, 0] < cursor2[0]:
count2 += 1.0
cursor1[1] = count1 / size
cursor2[1] = count2 / size
data1[i] = cursor1
data2[i] = cursor2
# Create an empty graph
myGraph = ot.Graph("Some curves", "x1", "x2", True, "bottomright")
# Create the first staircase
myStaircase1 = ot.Staircase(data1, "blue", "solid", "s", "")
myStaircase1b = ot.Staircase(myStaircase1)
myStaircase1b.setPattern("S")
myStaircase1b.setColor("green")
myStaircase1b.setLineStyle("dashed")
myStaircase1b.setLegend("eCDF1b, pat=S")
# Then, draw it
myGraph.add(myStaircase1)
myGraph.add(myStaircase1b)
# Check that the correct files have been generated by computing their
# checksum
# Create the second staircase
myStaircase2 = ot.Staircase(data2, "red", "dashed", "S", "eCDF2, pat=S")
# Add it to the graph and draw everything
myGraph.add(myStaircase2)
|
