Python SphericalModel Examples

Example #1

myDefautModel = ot.MaternModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.MaternModel([2.0] * inputDimension, [3.0], 1.5)
test_model(myModel)

myDefautModel = ot.ExponentiallyDampedCosineModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.ExponentiallyDampedCosineModel([2.0] * inputDimension, [3.0], 1.5)
test_model(myModel)

myDefautModel = ot.SphericalModel([2.0], [3.0], 4.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.SphericalModel([2.0] * inputDimension, [3.0], 4.5)
test_model(myModel)

myDefautModel = ot.FractionalBrownianMotionModel(2.0, 3.0, 0.25)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.SphericalModel([2.0] * inputDimension, [3.0], 4.5)
test_model(myModel)

myDefautModel = ot.DiracCovarianceModel()
print('myDefautModel = ', myDefautModel)

Example #2

File: t_CovarianceModel_std.py Project: louisblanchard/openturns

myDefautModel = ot.MaternModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.MaternModel([2.0] * spatialDimension, [3.0], 1.5)
test_model(myModel)

myDefautModel = ot.ExponentiallyDampedCosineModel([2.0], [3.0], 1.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.ExponentiallyDampedCosineModel([2.0] * spatialDimension, [3.0],
                                            1.5)
test_model(myModel)

myDefautModel = ot.SphericalModel([2.0], [3.0], 4.5)
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

myModel = ot.SphericalModel([2.0] * spatialDimension, [3.0], 4.5)
test_model(myModel)

myDefautModel = ot.DiracCovarianceModel()
print('myDefautModel = ', myDefautModel)
test_model(myDefautModel)

amplitude = list(map(lambda k: 1.5 + 2.0 * k, range(2)))
myModel = ot.DiracCovarianceModel(spatialDimension, amplitude)
test_model(myModel)

myDefautModel = ot.ProductCovarianceModel()

Example #3

myModel = ot.ExponentiallyDampedCosineModel([2.0], [3.0], 1)
ott.assert_almost_equal(myModel.getScale(), [2], 0, 0)
ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0)
ott.assert_almost_equal(myModel.getFrequency(), 1, 0, 0)
test_model(myModel)
myModel.setFrequency(3)
ott.assert_almost_equal(myModel.getFrequency(), 3, 0, 0)

myModel = ot.ExponentiallyDampedCosineModel([2.0] * inputDimension, [3.0], 1)
ott.assert_almost_equal(myModel.getScale(), [2, 2], 0, 0)
ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0)
ott.assert_almost_equal(myModel.getFrequency(), 1, 0, 0)
test_model(myModel)

# 6) SphericalModel
myModel = ot.SphericalModel([2.0], [3.0], 4.5)
ott.assert_almost_equal(myModel.getScale(), [2], 0, 0)
ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0)
ott.assert_almost_equal(myModel.getRadius(), 4.5, 0, 0)
test_model(myModel)

myModel = ot.SphericalModel([2.0] * inputDimension, [3.0], 4.5)
ott.assert_almost_equal(myModel.getScale(), [2, 2], 0, 0)
ott.assert_almost_equal(myModel.getAmplitude(), [3], 0, 0)
ott.assert_almost_equal(myModel.getRadius(), 4.5, 0, 0)
test_model(myModel)
myModel.setRadius(1.5)
ott.assert_almost_equal(myModel.getRadius(), 1.5, 0, 0)

# 7) FractionalBrownianMotionModel
myModel = ot.FractionalBrownianMotionModel(2.0, 3.0, 0.25)

Example #4

File: openturns-SphericalModel-1.py Project: ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
covarianceModel = ot.SphericalModel()
if covarianceModel.getSpatialDimension() == 1:
    scale = covarianceModel.getScale()[0]
    if covarianceModel.isStationary():
        def f(x):
            return [covarianceModel(x)[0, 0]]
        func = ot.PythonFunction(1,1,f)
        func.setDescription(['$tau$', '$cov$'])
        cov_graph = func.draw(-3.0 * scale, 3.0 * scale, 129)
        fig = plt.figure(figsize=(10, 4))
        plt.suptitle(str(covarianceModel))
        cov_axis = fig.add_subplot(111)
        View(cov_graph, figure=fig, axes=[cov_axis], add_legend=False)
    else:
        def f(x):
            return [covarianceModel([x[0]], [x[1]])[0, 0]]
        func = ot.PythonFunction(2,1,f)
        func.setDescription(['$s$', '$t$', '$cov$'])
        cov_graph = func.draw([-3.0 * scale]*2, [3.0 * scale]*2, [129]*2)
        fig = plt.figure(figsize=(10, 4))
        plt.suptitle(str(covarianceModel))
        cov_axis = fig.add_subplot(111)
        View(cov_graph, figure=fig, axes=[cov_axis], add_legend=False)

Example #5

File: openturns-SphericalModel-1.py Project: ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.SphericalModel().__class__.__name__ == 'ExponentialModel':
    covarianceModel = ot.ExponentialModel([0.5], [5.0])
elif ot.SphericalModel().__class__.__name__ == 'GeneralizedExponential':
    covarianceModel = ot.GeneralizedExponential([2.0], [3.0], 1.5)
elif ot.SphericalModel().__class__.__name__ == 'ProductCovarianceModel':
    amplitude = [1.0]
    scale1 = [4.0]
    scale2 = [4.0]
    cov1 = ot.ExponentialModel(scale1, amplitude)
    cov2 = ot.ExponentialModel(scale2, amplitude)
    covarianceModel = ot.ProductCovarianceModel([cov1, cov2])
elif ot.SphericalModel().__class__.__name__ == 'RankMCovarianceModel':
    variance = [1.0, 2.0]
    basis = ot.LinearBasisFactory().build()
    covarianceModel = ot.RankMCovarianceModel(variance, basis)
else:
    covarianceModel = ot.SphericalModel()
title = str(covarianceModel)[:100]
if covarianceModel.getInputDimension() == 1:
    scale = covarianceModel.getScale()[0]
    if covarianceModel.isStationary():

        def f(x):
            return [covarianceModel(x)[0, 0]]

        func = ot.PythonFunction(1, 1, f)
        func.setDescription(['$tau$', '$cov$'])
        cov_graph = func.draw(-3.0 * scale, 3.0 * scale, 129)