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#! /usr/bin/env python
import openturns as ot
import math as m
ot.TESTPREAMBLE()
def printPoint(point, digits):
oss = "["
eps = pow(0.1, digits)
for i in range(point.getDimension()):
if i == 0:
sep = ""
else:
sep = ","
if m.fabs(point[i]) < eps:
oss += sep + "%.6f" % m.fabs(point[i])
else:
oss += sep + "%.6f" % point[i]
sep = ","
oss += "]"
return oss
# We create a numerical math function
myFunction = ot.SymbolicFunction(["E", "F", "L", "I"], ["-F*L^3/(3*E*I)"])
dim = myFunction.getInputDimension()
# We create a normal distribution point of dimension 1
mean = ot.Point(dim, 0.0)
mean[0] = 50.0 # E
mean[1] = 1.0 # F
mean[2] = 10.0 # L
mean[3] = 5.0 # I
sigma = ot.Point(dim, 1.0)
R = ot.IdentityMatrix(dim)
myDistribution = ot.Normal(mean, sigma, R)
# We create a 'usual' RandomVector from the Distribution
vect = ot.RandomVector(myDistribution)
# We create a composite random vector
output = ot.CompositeRandomVector(myFunction, vect)
# We create an Event from this RandomVector
myEvent = ot.ThresholdEvent(output, ot.Less(), -3.0)
# We create an AnalyticalResult based on fictive results
result = ot.AnalyticalResult(sigma, myEvent, False)
print("result=", result)
digits = 5
print(
"standard space design point=",
printPoint(result.getStandardSpaceDesignPoint(), digits),
)
print(
"physical space design point=",
printPoint(result.getPhysicalSpaceDesignPoint(), digits),
)
print(
"is standard point origin in failure space? ",
result.getIsStandardPointOriginInFailureSpace(),
)
print("importance factors=", printPoint(result.getImportanceFactors(), digits))
print(
"importance factors(classical)=",
printPoint(result.getImportanceFactors(ot.AnalyticalResult.CLASSICAL), digits),
)
print(
"importance factors(physical) =",
printPoint(result.getImportanceFactors(ot.AnalyticalResult.PHYSICAL), digits),
)
print("Hasofer reliability index=%.5f" % result.getHasoferReliabilityIndex())
print("graph importance factors=", result.drawImportanceFactors())
print("graph sensitivity=", result.drawHasoferReliabilityIndexSensitivity())
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