#! /usr/bin/env python

from __future__ import print_function
from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:
    # We create a numerical math function
    myFunction = SymbolicFunction(
        ["E", "F", "L", "I"], ["-F*L^3/(3*E*I)"])

    dim = myFunction.getInputDimension()
    # We create a normal distribution point of dimension 1
    mean = 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 = Point(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)

    # We create an OptimizationAlgorithm algorithm
    myCobyla = Cobyla()
    myCobyla.setMaximumEvaluationNumber(400)
    myCobyla.setMaximumAbsoluteError(1.0e-10)
    myCobyla.setMaximumRelativeError(1.0e-10)
    myCobyla.setMaximumResidualError(1.0e-10)
    myCobyla.setMaximumConstraintError(1.0e-10)
    print("myCobyla=", myCobyla)

    # We create a Analytical algorithm
    # The first parameter is an OptimizationAlgorithm
    # The second parameter is an event
    # The third parameter is a starting point for the design point research
    myAlgo = Analytical(myCobyla, myEvent, mean)

    print("Analytical=", myAlgo)
    print("physical starting point=", myAlgo.getPhysicalStartingPoint())
    print("event=", myAlgo.getEvent())
    print("nearest point algorithm=", myAlgo.getNearestPointAlgorithm())
    print("result=", myAlgo.getAnalyticalResult())
    myAlgo.run()
    print("result=", myAlgo.getAnalyticalResult())

except:
    import sys
    print("t_Analytical_std.py", sys.exc_info()[0], sys.exc_info()[1])
