#! /usr/bin/env python

from __future__ import print_function
from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:

    # Default constructor
    myDefautModel = UserDefinedStationaryCovarianceModel()
    print("myDefautModel = ", myDefautModel)

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

    # Amplitude values
    amplitude = NumericalPoint(dimension)
    # Scale values
    scale = NumericalPoint(dimension)
    # Spatial correclation
    spatialCorrelation = CorrelationMatrix(dimension)
    for index in range(dimension):
        # constant amplitude
        amplitude[index] = 1.0
        scale[index] = (index + 1.0) / dimension

    # Sample a ExponentialModel
    referenceModel = ExponentialModel(
        spatialDimension, amplitude, scale, spatialCorrelation)

    print("reference model=", referenceModel)
    size = 100
    timeGrid = RegularGrid(0.0, 0.1, size)
    covarianceCollection = CovarianceMatrixCollection(size)

    for i in range(timeGrid.getN()):
        t = timeGrid.getValue(i)
        covarianceCollection[i] = referenceModel(t)
    # Create a UserDefinedStationaryCovarianceModel
    myModel = UserDefinedStationaryCovarianceModel(
        timeGrid, covarianceCollection)
    print("myModel=", myModel)

    for i in range(timeGrid.getN()):
        t = timeGrid.getValue(i)
        # We look for cov(s,t) ==> when adding to the collection, we compute cov(t,s)
        # Because of symmetry, we check the right index computation
        print("t= %.6g" % t, "myModel =  %.6g" %
              myModel(t)[0, 0], ", referenceModel=  %.6g" % referenceModel(t)[0, 0])

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