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"""
Create a Gaussian process
=========================
"""
# %%
import openturns as ot
import openturns.viewer as otv
# %%
# Create a Gaussian process from a covariance model
# -------------------------------------------------
#
# In this paragraph we build a Gaussian process from its covariance model.
# %%
# We first define a covariance model :
# %%
defaultDimension = 1
# Amplitude values
amplitude = [1.0] * defaultDimension
# Scale values
scale = [1.0] * defaultDimension
# Covariance model
myModel = ot.AbsoluteExponential(scale, amplitude)
# %%
# We define a mesh,
tmin = 0.0
step = 0.1
n = 11
myTimeGrid = ot.RegularGrid(tmin, step, n)
# %%
# and create the process :
process = ot.GaussianProcess(myModel, myTimeGrid)
print(process)
# %%
# We draw the first marginal of a sample of size 6 :
sample = process.getSample(6)
graph = sample.drawMarginal(0)
graph.setTitle("First marginal of six realizations of the process")
view = otv.View(graph)
# %%
# Create a Gaussian process from spectral density
# -----------------------------------------------
#
# In this paragraph we build a Gaussian process from its spectral density.
#
# %%
# We first define a spectral model :
amplitude = [1.0, 2.0]
scale = [4.0, 5.0]
spatialCorrelation = ot.CorrelationMatrix(2)
spatialCorrelation[0, 1] = 0.8
mySpectralModel = ot.CauchyModel(scale, amplitude, spatialCorrelation)
# %%
# As usual we define a mesh,
myTimeGrid = ot.RegularGrid(0.0, 0.1, 20)
# %%
# and create the process thereafter
process = ot.SpectralGaussianProcess(mySpectralModel, myTimeGrid)
print(process)
# %%
# Eventually we draw the first marginal of a sample of size 6 :
sample = process.getSample(6)
graph = sample.drawMarginal(0)
graph.setTitle("First marginal of six realizations of the process")
view = otv.View(graph)
# %%
# Display all figures
otv.View.ShowAll()
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