# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This work is licensed under a BSD 0-Clause License.
#
# Permission to use, copy, modify, and/or distribute this software
# for any purpose with or without fee is hereby granted.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL
# WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL
# THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT,
# OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING
# FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT,
# NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION
# WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
Scatter plot matrix
===================

In this example, we illustrate the use of the :class:`.ScatterPlotMatrix` plot
on the Sobieski's SSBJ problem.
"""

from __future__ import annotations

from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace

# %%
# Import
# ------
# The first step is to import some high-level functions
# and a method to get the design space.

configure_logger()

# %%
# Description
# -----------
#
# The **ScatterPlotMatrix** post-processing builds the scatter plot matrix among
# design variables and outputs functions.
# Each non-diagonal block represents the samples
# according to the x- and y- coordinates names while the diagonal ones approximate
# the probability distributions of the variables, using a kernel-density estimator.

# %%
# Create disciplines
# ------------------
# At this point, we instantiate the disciplines of Sobieski's SSBJ problem:
# Propulsion, Aerodynamics, Structure and Mission
disciplines = create_discipline([
    "SobieskiPropulsion",
    "SobieskiAerodynamics",
    "SobieskiStructure",
    "SobieskiMission",
])

# %%
# Create design space
# -------------------
# We also create the :class:`.SobieskiDesignSpace`.
design_space = SobieskiDesignSpace()

# %%
# Create and execute scenario
# ---------------------------
# The next step is to build a DOE scenario in order to maximize the range,
# encoded 'y_4', with respect to the design parameters, while satisfying the
# inequality constraints 'g_1', 'g_2' and 'g_3'. We can use the MDF formulation,
# the Monte Carlo DOE algorithm and 30 samples.
scenario = create_scenario(
    disciplines,
    "y_4",
    design_space,
    formulation_name="MDF",
    maximize_objective=True,
    scenario_type="DOE",
)
scenario.set_differentiation_method()
for constraint in ["g_1", "g_2", "g_3"]:
    scenario.add_constraint(constraint, constraint_type="ineq")
scenario.execute(algo_name="OT_MONTE_CARLO", n_samples=30)

# %%
# Post-process scenario
# ---------------------
# Lastly, we post-process the scenario by means of the :class:`.ScatterPlotMatrix`
# plot which builds scatter plot matrix among design variables, objective
# function and constraints.

# %%
# .. tip::
#
#    Each post-processing method requires different inputs and offers a variety
#    of customization options. Use the high-level function
#    :func:`.get_post_processing_options_schema` to print a table with
#    the options for any post-processing algorithm.
#    Or refer to our dedicated page:
#    :ref:`gen_post_algos`.

design_variables = ["x_shared", "x_1", "x_2", "x_3"]
scenario.post_process(
    post_name="ScatterPlotMatrix",
    variable_names=[*design_variables, "-y_4"],
    save=False,
    show=True,
)