"""
Create and manipulate an ARMA process
=====================================
"""
# %%
# In this example we first create an ARMA process and then manipulate it.
#

# %%
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

ot.Log.Show(ot.Log.NONE)


# %%
# Create an ARMA process
# ----------------------
#
# In this example we are going to build an ARMA process defined by its linear recurrence coefficients.
#
# The creation of an ARMA model requires the data of the AR and MA
# coefficients which are:
#
# -  a list of scalars in the unidmensional case :
#    :math:`(a_1, \dots, a_p)` for the AR-coefficients and
#    :math:`(b_1, \dots, b_q)` for the MA-coefficients
#
# -  a list of square matrix
#    :math:`(\underline{\underline{A}}_{\, 1}, \dots, \underline{\underline{A}}{\, _p})` for the
#    AR-coefficients and
#    :math:`(\underline{\underline{B}}_{\, 1}\, \dots, \underline{\underline{B}}_{\, q})` for the
#    MA-coefficients
#
# It also requires the definition of a white noise
# :math:`\underline{\varepsilon}` that contains the same time grid as the
# one of the process.
# The current state of an ARMA model is characterized by its last
# :math:`p` values and the last :math:`q` values of its white noise. It
# is possible to get that state thanks to the methods *getState*.
# It is possible to create an ARMA with a specific current state. That
# specific current state is taken into account to generate possible
# futures but not to generate realizations (in order to respect the
# stationarity property of the model).
# At the creation step, we check whether the process
# :math:`ARMA(p,q)` is stationnary.
# When the process is not stationary, the user is warned by a message.

# %%
# We build the 1D ARMA process defined by :
#
# .. math::
#    X_{0,t} + 0.4 X_{0,t-1} + 0.3 X_{0,t-2} + 0.2 X_{0,t-3} + 0.1 X_{0,t-4} = E_{0,t} + 0.4 E_{0,t-1} + 0.3 E_{0,t-2}
#
# where the white noise :math:`E_t` is defined by :math:`E_t \approx \mathrm{Triangular}(a = -1, m = 0, b = 1)`.
#

# %%
# The definition of the recurrence coefficients AR, MA (4,2) is simple :
myARCoef = ot.ARMACoefficients([0.4, 0.3, 0.2, 0.1])
myMACoef = ot.ARMACoefficients([0.4, 0.3])


# %%
# We build a regular time discretization of the interval [0,1] with 10 time steps.
# We also set up the white noise distribution of the recurrence relation :
myTimeGrid = ot.RegularGrid(0.0, 0.1, 10)
myWhiteNoise = ot.WhiteNoise(ot.Triangular(-1.0, 0.0, 1.0), myTimeGrid)

# %%
# We are now ready to create the ARMA-process :
process = ot.ARMA(myARCoef, myMACoef, myWhiteNoise)
print(process)


# %%
# ARMA process manipulation
# -------------------------
#
# In this paragraph we shall expose some of the services exposed by an :math:`ARMA(p,q)` object, namely :
#
# -  its AR and MA coefficients thanks to the methods *getARCoefficients,
#    getMACoefficients*,
#
# -  its white noise thanks to the method *getWhiteNoise*, that contains
#    the time grid of the process,
#
# -  its current state, that is its last :math:`p` values and the last
#    :math:`q` values of its white noise, thanks to the method *getState*,
#
# -  a realization thanks to the method *getRealization* or a sample of realizations thanks to the method *getSample*,
#
# -  a possible future of the model, which is a possible prolongation of
#    the current state on the next :math:`n_{prol}` instants, thanks to
#    the method *getFuture*.
#
# -  :math:`n` possible futures of the model, which correspond to
#    :math:`n` possible prolongations of the current state on the next
#    :math:`n_{prol}` instants, thanks to the method
#    *getFuture* (:math:`n_{prol}`, :math:`n`).
#


# %%
# First we get the coefficients AR and MA of the recurrence :
print("AR coeff = ", process.getARCoefficients())
print("MA coeff = ", process.getMACoefficients())

# %%
# We check that the white noise is the one we have previously defined :
myWhiteNoise = process.getWhiteNoise()
print(myWhiteNoise)


# %%
# We generate a possible time series realization :
ts = process.getRealization()
ts.setName("ARMA realization")

# %%
# We draw this time series by specifying the wanted marginal index (only 0 is available here).
graph0 = ts.drawMarginal(0)
graph0.setTitle("One ARMA realization")
graph0.setXTitle("t")
graph0.setYTitle(r"$X_t$")
view = viewer.View(graph0)


# %%
# Generate a k time series
# k = 5
# myProcessSample = process.getSample(k)

# Then get the current state of the ARMA
# armaState = process.getState()
# print("armaState = ")
# print(armaState)

# %%
# We draw a sample of size 6 : it is six different time series.
size = 6
sample = process.getSample(size)
graph = sample.drawMarginal(0)
graph.setTitle("Six realizations of the ARMA process")
graph.setXTitle("t")
graph.setYTitle(r"$X_t$")
view = viewer.View(graph)
# plt.show()

# We can obtain the current state of the ARMA process :
armaState = process.getState()
print("ARMA state : ")
print(armaState)

# %%
# Note that if we use the process in the meantime and ask for the current state again, it will be different.
ts = process.getRealization()
armaState = process.getState()
print("ARMA state : ")
print(armaState)


# From the aforementioned `armaState`, we can get the last values of :math:`X_t` and the last values
# of the white noise :math:`E_t`.
myLastValues = armaState.getX()
print(myLastValues)
myLastEpsilonValues = armaState.getEpsilon()
print(myLastEpsilonValues)

# %%
# We have access to the number of iterations before getting a stationary state with
Ntherm = process.getNThermalization()
print("ThermalValue : %d" % Ntherm)

# %%
# This may be important to evaluate it with another precision epsilon :
epsilon = 1e-8
newThermalValue = process.computeNThermalization(epsilon)
print("newThermalValue : %d" % newThermalValue)
process.setNThermalization(newThermalValue)

# %%
# An important feature of an ARMA process is the future prediction from its current state over the next# `Nit` instants, say Nit=20.
Nit = 21

# %%
# First we specify a current state armaState and build the corresponding ARMA process `arma` :
arma = ot.ARMA(myARCoef, myMACoef, myWhiteNoise, armaState)

# Then, we generate a possible future. The last instant was :math:`t=0.9` so the future starts at
# :math:`t=1.0`. We represent the ARMA process with a solid line and its possible future as a dashed
# curve.
future = arma.getFuture(Nit)
graph = future.drawMarginal(0)
curve = graph.getDrawable(0)
curve.setLineStyle("dashed")
graph0.add(curve)
graph0.setTitle("One ARMA realization and a possible future")
view = viewer.View(graph0)


# %%
# It is of course possible to generate N different possible futures over the Nit next instants.
N = 6
possibleFuture_N = arma.getFuture(Nit, N)
possibleFuture_N.setName("Possible futures")

# %%
# Here we only show the future.
graph = possibleFuture_N.drawMarginal(0)
graph.setTitle("Six possible futures of the ARMA process")
graph.setXTitle("t")
graph.setYTitle(r"$X_t$")
view = viewer.View(graph)


# %%
# Display all figures
plt.show()
