Source code for

# Copyright 2022 Airbus SAS
# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# 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
"""Radar plot."""
from __future__ import annotations

import logging
from typing import Any

from numpy import ndarray

from import MultiObjectiveDiagram

LOGGER = logging.getLogger(__name__)

[docs]class Radar(MultiObjectiveDiagram): """`Radar plots <>`_). 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 <>`_ 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( "radar", scalar_name, weights, normalize_each_objective=normalize_each_objective, **scalar_options, )