#! /usr/bin/env python

from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:

    # We create a numerical math function
    myFunction = NumericalMathFunction("poutre_fullspeed")

    dim = myFunction.getInputDimension()

    # We create a normal distribution point of dimension 1
    mean = NumericalPoint(dim, 0.0)
    # E
    mean[0] = 50.0
    # F
    mean[1] = 1.0
    # L
    mean[2] = 10.0
    # I
    mean[3] = 5.0
    sigma = NumericalPoint(dim, 1.0)
    R = IdentityMatrix(dim)
    myDistribution = Normal(mean, sigma, R)

    # We create a 'usual' RandomVector from the Distribution
    vect = RandomVector(myDistribution)

    # We create a composite random vector
    output = RandomVector(myFunction, vect)

    # We create an Event from this RandomVector
    myEvent = Event(output, Less(), -3.0)

    # Use the standard deviation as a stoping rule
    myAlgo = MonteCarlo(myEvent)
    myAlgo.setMaximumOuterSampling(4000)
    myAlgo.setBlockSize(8)
    myAlgo.setMaximumCoefficientOfVariation(0.0)
    myAlgo.setMaximumStandardDeviation(0.0025)

    print "MonteCarlo=", myAlgo

    # Perform the simulation
    myAlgo.run()

    # Stream out the result
    print "MonteCarlo result=", myAlgo.getResult()


except:
    import sys
    print "t_MonteCarlo_std.py", sys.exc_type, sys.exc_value
