# Copyright 2022 Airbus SAS
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
#
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Gabriel Max DE MENDONÇA ABRANTES
from __future__ import annotations

import logging
from typing import Any

from numpy import ndarray

from gemseo_pymoo.post.core.multi_objective_diagram import MultiObjectiveDiagram

LOGGER = logging.getLogger(__name__)

"""Radar plots <https://pymoo.org/visualization/radar.html>_.

Note:
This post-processor assumes the optimization has converged to a well-defined
pareto front.
"""

def _plot(
self,
scalar_name: str,
weights: ndarray,
normalize_each_objective: bool = True,
**scalar_options: Any,
) -> None:
"""Plot one radar diagram for each set of weights.

A scalarization function <https://pymoo.org/misc/decomposition.html>_ is used
to transform the multi-objective functions into a single-objective.

Args:
scalar_name: The name of the scalarization function to use.
weights: The weights for the scalarization function.
normalize_each_objective: Whether the objectives should be normalized.
**scalar_options: The keyword arguments for the scalarization function.
"""
super()._plot(