# 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: Jean-Christophe Giret
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
Pareto front on Binh and Korn problem
=====================================

In this example, we illustrate the use of the :class:`.ParetoFront` plot
on the Binh and Korn multi-objective problem.
"""
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.api import configure_logger
from gemseo.post.post_factory import PostFactory
from gemseo.problems.analytical.binh_korn import BinhKorn
from matplotlib import pyplot as plt

###############################################################################
# Import
# ------
# The first step is to import some functions from the API
# and a method to get the design space.

configure_logger()


###############################################################################
# Import the optimization problem
# -------------------------------
# Then, we instantiate the BinkKorn optimization problem.

problem = BinhKorn()

###############################################################################
# Create and execute scenario
# ---------------------------
# Then, we create a Design of Experiment factory,
# and we request the execution a a full-factorial DOE using 100 samples
doe_factory = DOEFactory()
doe_factory.execute(problem, algo_name="OT_OPT_LHS", n_samples=100)

###############################################################################
# Post-process scenario
# ---------------------
# Lastly, we post-process the scenario by means of the :class:`.ParetoFront`
# plot which generates a plot or a matrix of plots if there are more than
# 2 objectives, plots in blue the locally non dominated points for the current
# two objectives, plots in green the globally (all objectives) Pareto optimal
# points. The plots in green denotes non-feasible points. Note that the user
# can avoid the display of the non-feasible points.

PostFactory().execute(
    problem,
    "ParetoFront",
    save=False,
    show=False,
    show_non_feasible=False,
    objectives=["compute_binhkorn"],
    objectives_labels=["f1", "f2"],
)

PostFactory().execute(
    problem,
    "ParetoFront",
    save=False,
    show=False,
    show_non_feasible=True,
    objectives=["compute_binhkorn"],
    objectives_labels=["f1", "f2"],
)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()