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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Instanciate one distribution object
dim = 2
meanPoint = NumericalPoint(dim, 1.0)
meanPoint[0] = 0.5
meanPoint[1] = -0.5
sigma = NumericalPoint(dim, 1.0)
sigma[0] = 2.0
sigma[1] = 3.0
R = CorrelationMatrix(dim)
for i in range(1, dim):
R[i, i - 1] = 0.5
distribution1 = Normal(meanPoint, sigma, R)
# Instanciate 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 = Normal(meanPoint, sigma, R)
# Test for sampling
size = 200
sample1 = distribution1.getSample(size)
sample2 = distribution2.getSample(size)
# Create an empty graph
myGraph = Graph("Normal sample", "x1", "x2", True, "topright")
# Create the first cloud
myCloud1 = Cloud(sample1, "blue", "fsquare", "First Cloud")
# Then, draw it
myGraph.add(myCloud1)
myGraph.draw("Graph_Cloud_a_OT", 640, 480)
# Check that the correct files have been generated by computing their
# checksum
# Create the second cloud
myCloud2 = Cloud(sample2, "red", "circle", "Second Cloud")
# Add it to the graph and draw everything
myGraph.add(myCloud2)
myGraph.draw("Graph_Cloud_b_OT", 640, 480)
except:
import sys
print("t_Cloud_std.py", sys.exc_info()[0], sys.exc_info()[1])
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