#! /usr/bin/env python

from __future__ import print_function
from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:

    # Default dimension parameter to evaluate the model
    dimension = 1
    spatialDimension = 1

    # Amplitude values
    amplitude = NumericalPoint(dimension, 1.0)

    # Scale values
    scale = NumericalPoint(dimension, 1.0)

    # Covariance model
    myCovarianceModel = ExponentialModel(scale, amplitude)
    print("myCovarianceModel = ", myCovarianceModel)

    # Spectral model
    mySpectralModel = CauchyModel(scale, amplitude)
    print("mySpectralModel = ", mySpectralModel)

    # We build the second order model using covariance and spectral models
    myModel = SecondOrderModel(myCovarianceModel, mySpectralModel)
    print("myModel = ", myModel)

    # Some computations ==> call the sub models methods
    instant = 1.0
    frequency = 0.5

    #
    print("covariance matrix at t = ", instant,
          " : ", myModel.computeCovariance(instant))
    print("covariance matrix at t = ", -1.0 * instant,
          " : ", myModel.computeCovariance(-1.0 * instant))
    print("spectral density at f = ", frequency, " : ",
          myModel.computeSpectralDensity(frequency))

    # Discretize the process on a small time grid
    timeGrid = RegularGrid(0.0, 1.0, 11)
    print("discretized covariance over the time grid = ", timeGrid, " is ")
    print(myModel.discretize(timeGrid))

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