"""
Kriging : draw covariance models
================================
"""
import openturns as ot
import openturns.viewer as otv
from matplotlib import pylab as plt
import pylab as pl

# %%
# Abstract
# --------
#
# Gaussian processes are a common fixture in UQ.
# They are defined by their covariance function and the library implements several of them.
# In this example we should depict covariance functions and play with parameters
# for two families of models: the generalized exponential model and the Matern models.
#
# For visualization sake we should limit ourselves to the dimension 1.
dimension = 1

# %%
# We set the lower bound to zero for stationary kernels
ot.ResourceMap.SetAsScalar("CovarianceModel-DefaultTMin", 0.0)


# %%
# The generalized exponential model
# ---------------------------------
#
# The :class:`~openturns.GeneralizedExponential` class implements a generalized exponential with a
# parameter :math:`p < 0 \leq 2` exponent. The case :math:`p=1` is the standard exponential model
# while :math:`p=2` is the squared exponential.
#

# %%
# Various parameters p and a fixed correlation length of 0.1
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In this part we set the correlation length to :math:`\theta = 0.1` and study three different models
# with parameters :math:`p=0.25`, :math:`p=1` and :math:`p=2` and trajectories from gaussian processes
# based on these models.

# %%
# We define the :math:`p = 0.25` generalized exponential model :
covarianceModel = ot.GeneralizedExponential([0.1], 0.25)

# %%
# We define the :math:`p = 1` generalized exponential model :
covarianceModel2 = ot.GeneralizedExponential([0.1], 1.0)

# %%
# We define the :math:`p = 2` generalized exponential model :
covarianceModel3 = ot.GeneralizedExponential([0.1], 2.0)

# %%
# We draw the covariance models :
graphModel = covarianceModel.draw()
graphModel.add(covarianceModel2.draw())
graphModel.add(covarianceModel3.draw())
graphModel.setColors(["green", "orange", "blue"])
graphModel.setXTitle(r"$\tau = \|s-t\|$")
graphModel.setYTitle(r"$C(\tau)$")
graphModel.setLegends([r"$p = 0.25$", r"$p = 1$", r"$p = 2$"])


# %%
# For each covariance model we build a gaussian process and generate a random trajectory of
# on :math:`[-1,1]`.
# We first build a discretization of this interval with a regular grid with step 0.01.
xmin = -1.0
step = 0.01
n = 200
grid1D = ot.RegularGrid(xmin, step, n + 1)
nbTrajectories = 1

# %%
# We define the first gaussian process and its trajectory :
process = ot.GaussianProcess(covarianceModel, grid1D)
sample = process.getSample(nbTrajectories)

# %%
# then the second one and its trajectory :
process2 = ot.GaussianProcess(covarianceModel2, grid1D)
sample2 = process2.getSample(nbTrajectories)

# %%
# and finally the third one and its trajectory :
process3 = ot.GaussianProcess(covarianceModel3, grid1D)
sample3 = process3.getSample(nbTrajectories)

# %%
# We draw the trajectories :
graphTraj = sample.drawMarginal(0)
graphTraj.add(sample2.drawMarginal(0))
graphTraj.add(sample3.drawMarginal(0))
graphTraj.setXTitle(r"$x$")
graphTraj.setYTitle(r"$GP_{\nu}(x)$")
graphTraj.setTitle("Random realization from the covariance model")
graphTraj.setColors(["green", "orange", "blue"])
graphTraj.setLegends([r"$p = 0.25$", r"$p = 1$", r"$p = 2$"])

# %%
# We present each covariance model and the corresponding tracjectory side by side.
fig = pl.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = otv.View(graphModel, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = otv.View(graphTraj, figure=fig, axes=[ax_cdf])
_ = fig.suptitle(r"Generalized Exponential Model : influence of the p parameter")

# %%
# The blue trajectory corresponding to the parameter :math:`p=2` is smooth as expected as compared with
# the :math:`p=0.25` process which is less regular.


# %%
# The exponential model (:math:`p=1`)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In the case of the exponential model (:math:`p=1`) we show the influence of the correlation length on
# the trajectories.
#

# %%
# with correlation length :math:`\theta = 0.01`  :
covarianceModel = ot.GeneralizedExponential([0.01], 1.0)

# %%
# with correlation length :math:`\theta = 0.1` :
covarianceModel2 = ot.GeneralizedExponential([0.1], 1.0)

# %%
# with correlation length :math:`\theta = 1.0`
covarianceModel3 = ot.GeneralizedExponential([1.0], 1.0)

# %%
# We draw the covariance models :
graphModel = covarianceModel.draw()
graphModel.add(covarianceModel2.draw())
graphModel.add(covarianceModel3.draw())
graphModel.setColors(["green", "orange", "blue"])
graphModel.setXTitle(r"$\tau = \|s-t\|$")
graphModel.setYTitle(r"$C(\tau)$")
graphModel.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1$"])


# %%
# For each covariance model we build a gaussian process and generate a random trajectory of
# on :math:`[-1,1]`.
# We first build a discretization of this interval with a regular grid with step 0.01.
xmin = -1.0
step = 0.01
n = 200
grid1D = ot.RegularGrid(xmin, step, n + 1)
nbTrajectories = 1

# %%
# We define the first gaussian process and its trajectory :
process = ot.GaussianProcess(covarianceModel, grid1D)
sample = process.getSample(nbTrajectories)

# %%
# then the second one and its trajectory :
process2 = ot.GaussianProcess(covarianceModel2, grid1D)
sample2 = process2.getSample(nbTrajectories)

# %%
# and finally the third one and its trajectory :
process3 = ot.GaussianProcess(covarianceModel3, grid1D)
sample3 = process3.getSample(nbTrajectories)

# %%
# We draw the trajectories :
graphTraj = sample.drawMarginal(0)
graphTraj.add(sample2.drawMarginal(0))
graphTraj.add(sample3.drawMarginal(0))
graphTraj.setXTitle(r"$x$")
graphTraj.setYTitle(r"$GP_{\theta}(x)$")
graphTraj.setTitle("Random realization from the covariance model")
graphTraj.setColors(["green", "orange", "blue"])
graphTraj.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1$"])

# %%
# We present each covariance model and the corresponding tracjectory side by side.
fig = pl.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = otv.View(graphModel, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = otv.View(graphTraj, figure=fig, axes=[ax_cdf])
_ = fig.suptitle(r"Exponential Model : influence of correlation length $\theta$")

# %%
# We observe a smoother trajectory with a high correlation value.


# %%
# The squared exponential (:math:`p=2`)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#
# In the case of the squared exponential model (:math:`p=2`) we show the influence of the correlation length on
# the trajectories.
#

# %%
# with correlation length :math:`\theta = 0.01`  :
covarianceModel = ot.GeneralizedExponential([0.01], 2.0)

# %%
# with correlation length :math:`\theta = 0.1` :
covarianceModel2 = ot.GeneralizedExponential([0.1], 2.0)

# %%
# with correlation length :math:`\theta = 1.0`
covarianceModel3 = ot.GeneralizedExponential([1.0], 2.0)

# %%
# We draw the covariance models :
graphModel = covarianceModel.draw()
graphModel.add(covarianceModel2.draw())
graphModel.add(covarianceModel3.draw())
graphModel.setColors(["green", "orange", "blue"])
graphModel.setXTitle(r"$\tau = \|s-t\|$")
graphModel.setYTitle(r"$C(\tau)$")
graphModel.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1$"])


# %%
# For each covariance model we build a gaussian process and generate a random trajectory of
# on :math:`[-1,1]`.
# We first build a discretization of this interval with a regular grid with step 0.01.
xmin = -1.0
step = 0.01
n = 200
grid1D = ot.RegularGrid(xmin, step, n + 1)
nbTrajectories = 1

# %%
# We define the first gaussian process and its trajectory :
process = ot.GaussianProcess(covarianceModel, grid1D)
sample = process.getSample(nbTrajectories)

# %%
# then the second one and its trajectory :
process2 = ot.GaussianProcess(covarianceModel2, grid1D)
sample2 = process2.getSample(nbTrajectories)

# %%
# and finally the third one and its trajectory :
process3 = ot.GaussianProcess(covarianceModel3, grid1D)
sample3 = process3.getSample(nbTrajectories)

# %%
# We draw the trajectories :
graphTraj = sample.drawMarginal(0)
graphTraj.add(sample2.drawMarginal(0))
graphTraj.add(sample3.drawMarginal(0))
graphTraj.setXTitle(r"$x$")
graphTraj.setYTitle(r"$GP_{\theta}(x)$")
graphTraj.setTitle("Random realization from the covariance model")
graphTraj.setColors(["green", "orange", "blue"])
graphTraj.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1$"])

# %%
# We present each covariance model and the corresponding tracjectory side by side.

fig = pl.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = otv.View(graphModel, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = otv.View(graphTraj, figure=fig, axes=[ax_cdf])
_ = fig.suptitle(
    r"Squared exponential model : influence of correlation length $\theta$"
)

# %%
# Execpt for very small values of the correlation length, trajectories are usually smooth. It is the
# main effect of the squared exponential model which leads to smooth process.


# %%
# The Matern covariance model
# ---------------------------
#
# The :class:`~openturns.MaternModel` class implements the Matern model of parameter :math:`\nu`.
# This parameter controls the smoothness of the process : for any :math:`\nu = n + \frac{1}{2}` the
# process is :math:`n` times continuously differentiable.

# %%
# Influence of the regularity
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In this paragraph we represent three models with different regularity and generate the
# corresponding random trajectories. We shall use :math:`\nu = 0.5`, :math:`\nu = 1.5` and
# :math:`\nu = 2.5` and observe the regularity.
#

# %%
# We define the :math:`\nu = 0.5` Matern model :
covarianceModel = ot.MaternModel([1.0], 0.5)

# %%
# We define the :math:`\nu = 1.5` Matern model :
covarianceModel2 = ot.MaternModel([1.0], 1.5)

# %%
# We define the :math:`\nu = 2.5` Matern model :
covarianceModel3 = ot.MaternModel([1.0], 2.5)

# %%
# We draw the covariance models :
graphModel = covarianceModel.draw()
graphModel.add(covarianceModel2.draw())
graphModel.add(covarianceModel3.draw())
graphModel.setColors(["green", "orange", "blue"])
graphModel.setXTitle(r"$\tau = \|s-t\|$")
graphModel.setYTitle(r"$C(\tau)$")
graphModel.setLegends([r"$\nu = 1/2$", r"$\nu = 3/2$", r"$\nu = 5/2$"])


# %%
# For each covariance model we build a gaussian process and generate a random trajectory of
# on :math:`[-1,1]`.
# We first build a discretization of this interval with a regular grid with step 0.001.
xmin = -5.0
step = 0.01
n = 1000
grid1D = ot.RegularGrid(xmin, step, n + 1)
nbTrajectories = 1

# %%
# We define the first gaussian process and its trajectory :
process = ot.GaussianProcess(covarianceModel, grid1D)
sample = process.getSample(nbTrajectories)

# %%
# then the second one and its trajectory :
process2 = ot.GaussianProcess(covarianceModel2, grid1D)
sample2 = process2.getSample(nbTrajectories)

# %%
# and finally the third one and its trajectory :
process3 = ot.GaussianProcess(covarianceModel3, grid1D)
sample3 = process3.getSample(nbTrajectories)

# %%
# We draw the trajectories :
graphTraj = sample.drawMarginal(0)
graphTraj.add(sample2.drawMarginal(0))
graphTraj.add(sample3.drawMarginal(0))
graphTraj.setXTitle(r"$x$")
graphTraj.setYTitle(r"$GP_{\nu}(x)$")
graphTraj.setTitle("Random realization from the covariance model")
graphTraj.setColors(["green", "orange", "blue"])
graphTraj.setLegends([r"$\nu = 1/2$", r"$\nu = 3/2$", r"$\nu = 5/2$"])

# %%
# We present each covariance model and the corresponding tracjectory side by side.
fig = pl.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = otv.View(graphModel, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = otv.View(graphTraj, figure=fig, axes=[ax_cdf])
_ = fig.suptitle(r"Matern model : influence of the regularity $\nu$ parameter")

# %%
# The red trajectory is the least regular (:math:`nu = 0.5`) as it is only continuous. We see that the
# the blue trajectory is more smooth as expected.


# %%
# Variation of the correlation length
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In this paragraph we fix the regularity by choosing :math:`\nu = 1.5` so we expect a continuously
# differentiable realization.
# We then use three different correlation lengths :math:`\theta = 0.01`, :math:`\theta = 0.1` and
# :math:`\theta = 1.0` and observe the impact on realizations of gaussian processes based on these
# covariance models.

# %%
# We define the Matern model with :math:`\theta = 0.01` :
covarianceModel = ot.MaternModel([0.01], 1.5)

# %%
# We define the Matern model with :math:`\theta = 0.1` :
covarianceModel2 = ot.MaternModel([0.1], 1.5)

# %%
# We define the Matern model with :math:`\theta = 1.0` :
covarianceModel3 = ot.MaternModel([1.0], 1.5)

# %%
# We draw the covariance models :
graphModel = covarianceModel.draw()
graphModel.add(covarianceModel2.draw())
graphModel.add(covarianceModel3.draw())
graphModel.setColors(["green", "orange", "blue"])
graphModel.setXTitle(r"$\tau = \|s-t\|$")
graphModel.setYTitle(r"$C(\tau)$")
graphModel.setTitle("Matern covariance model with \nu = 3/2")
graphModel.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1.0$"])

# %%
# For each covariance model we build a gaussian process and generate a random trajectory of
# on :math:`[-1,1]`.
# We build a discretization of this interval with a regular grid with step 0.01.
xmin = -1.0
step = 0.01
n = 200
grid1D = ot.RegularGrid(xmin, step, n + 1)
nbTrajectories = 1

# %%
# We define the first gaussian process and its trajectory :
process = ot.GaussianProcess(covarianceModel, grid1D)
sample = process.getSample(nbTrajectories)

# %%
# then the second process :
process2 = ot.GaussianProcess(covarianceModel2, grid1D)
sample2 = process2.getSample(nbTrajectories)

# %%
# and the third one :
process3 = ot.GaussianProcess(covarianceModel3, grid1D)
sample3 = process3.getSample(nbTrajectories)

# %%
# We draw the trajectories :
graphTraj = sample.drawMarginal(0)
graphTraj.add(sample2.drawMarginal(0))
graphTraj.add(sample3.drawMarginal(0))
graphTraj.setXTitle(r"$x$")
graphTraj.setYTitle(r"$GP_{\theta}(x)$")
graphTraj.setColors(["green", "orange", "blue"])
graphTraj.setLegends([r"$\theta = 0.01$", r"$\theta = 0.1$", r"$\theta = 1.0$"])

# %%
# We present each covariance model and the corresponding tracjectory side by side.
fig = pl.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = otv.View(graphModel, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = otv.View(graphTraj, figure=fig, axes=[ax_cdf])
_ = fig.suptitle("The Matern model : variation of the correlation length")

# %%
# From the previous figure we see that the trajectory of the gaussian process is smoother with large
# correlation length.

# %%
# Display figures
plt.show()

# %%
# Reset default settings
ot.ResourceMap.Reload()
