# 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
"""
Pareto front
=============

In this example, we illustrate the use of the :class:`.ParetoFront` 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.sobieski.core.design_space import SobieskiDesignSpace

# %%
# Import
# ------
# The first step is to import a high-level function for logging.

configure_logger()

# %%
# Description
# -----------
#
# The :class:`.ParetoFront` post-processing generates
# a plot or a matrix of plots (if there are more than
# 2 objectives). It indicates in red the locally non-dominated points for the
# current objectives, and in green the globally (all objectives) Pareto optimal
# points.

# %%
# Create disciplines
# ------------------
# At this point, we instantiate the disciplines of Sobieski's SSBJ problem:
# :class:`.SobieskiPropulsion`, :class:`.SobieskiAerodynamics`,
# :class:`.SobieskiStructure` and :class:`.SobieskiMission`.
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 an MDO 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 SLSQP optimization algorithm
# and a maximum number of iterations equal to 100.
# and a maximum number of iterations equal to 100.
scenario = create_scenario(
    disciplines,
    "MDF",
    "y_4",
    design_space,
    maximize_objective=True,
)
scenario.set_differentiation_method()
for constraint in ["g_1", "g_2", "g_3"]:
    scenario.add_constraint(constraint, constraint_type="ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 10})

# %%
# Post-process scenario
# ---------------------
# Lastly, we post-process the scenario by means of the :class:`.ParetoFront`.

# %%
# .. 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`.

scenario.post_process("ParetoFront", objectives=["g_3", "-y_4"], save=False, show=True)