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)
test_model(myDefautModel)
amplitude = [1.5 + 2.0 * k for k in range(2)]
dimension = 2
spatialCorrelation = ot.CorrelationMatrix(dimension)
for j in range(dimension):
for i in range(j + 1, dimension):
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.FractionalBrownianMotionModel().__class__.__name__ == 'ExponentialModel':
covarianceModel = ot.ExponentialModel([0.5], [5.0])
elif ot.FractionalBrownianMotionModel(
).__class__.__name__ == 'GeneralizedExponential':
covarianceModel = ot.GeneralizedExponential([2.0], [3.0], 1.5)
elif ot.FractionalBrownianMotionModel(
).__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.FractionalBrownianMotionModel(
).__class__.__name__ == 'RankMCovarianceModel':
variance = [1.0, 2.0]
basis = ot.LinearBasisFactory().build()
covarianceModel = ot.RankMCovarianceModel(variance, basis)
else:
covarianceModel = ot.FractionalBrownianMotionModel()
title = str(covarianceModel)[:100]
if covarianceModel.getInputDimension() == 1:
scale = covarianceModel.getScale()[0]
if covarianceModel.isStationary():
def f(x):
return [covarianceModel(x)[0, 0]]
import openturns as ot
from math import exp
from matplotlib import pyplot as plt
from openturns.viewer import View
a = 4.0
covarianceModel = ot.FractionalBrownianMotionModel(1.0, 1.0, 0.25)
cov_graph = covarianceModel.draw(0, 0, -a, a, 512)
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,
square_axes=True)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
covarianceModel = ot.FractionalBrownianMotionModel()
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)
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, square_axes=True)