#! /usr/bin/env python

from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:

    # We create a NumericalMathFunction
    myFunction = NumericalMathFunction(['x1', 'x2', 'x3', 'x4'], ['y1', 'y2'], [
                                       '(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1)', 'cos(x2*x2+x4)/(x1*x1+1+x3^4)'])

    # We create a distribution
    dim = myFunction.getInputDimension()
    R = CorrelationMatrix(dim)
    for i in range(dim):
        R[i, i] = 1.0
    for i in range(1, dim):
        R[i, i - 1] = 0.5

    m = NumericalPoint(dim, 1.0)
    s = NumericalPoint(dim, 2.0)
    distribution = Normal(m, s, R)
    ref_distribution = distribution
    print "distribution = ", repr(ref_distribution)

    # We create a distribution-based RandomVector
    X = RandomVector(distribution)
    print "X=", X
    print "is composite? ", X.isComposite()

    # Check standard methods of class RandomVector
    print "X dimension=", X.getDimension()
    print "X realization (first )=", repr(X.getRealization())
    print "X realization (second)=", repr(X.getRealization())
    print "X realization (third )=", repr(X.getRealization())
    print "X sample =", repr(X.getSample(5))

    # We create a composite RandomVector Y from X and myFunction
    Y = RandomVector(CompositeRandomVector(myFunction, X))
    print "Y=", Y
    print "is composite? ", Y.isComposite()

    # Check standard methods of class RandomVector
    print "Y dimension=", Y.getDimension()
    print "Y realization (first )=", repr(Y.getRealization())
    print "Y realization (second)=", repr(Y.getRealization())
    print "Y realization (third )=", repr(Y.getRealization())
    print "Y sample =", repr(Y.getSample(5))


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