# 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 - API and implementation and/or documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
Optimal LHS vs LHS
==================
"""
from __future__ import annotations

import matplotlib.pyplot as plt
from gemseo.algos.doe.doe_factory import DOEFactory

n_samples = 30
n_parameters = 2

factory = DOEFactory()

lhs = factory.create("OT_LHS")
samples = lhs(n_samples, n_parameters)
samples2 = lhs(n_samples, n_parameters)

olhs = factory.create("OT_OPT_LHS")
o_samples = olhs(n_samples, n_parameters)

olhs = factory.create("OT_OPT_LHS")
o_a_samples = olhs(n_samples, n_parameters, annealing=False)

_, ax = plt.subplots(2, 2)
ax[0, 0].plot(samples[:, 0], samples[:, 1], "o")
ax[0, 0].set_title("A first standard LHS")
ax[0, 1].plot(samples2[:, 0], samples[:, 1], "o")
ax[0, 1].set_title("A second standard LHS")
ax[1, 0].plot(o_samples[:, 0], o_samples[:, 1], "o")
ax[1, 0].set_title("An LHS optimized with simulated annealing")
ax[1, 1].plot(o_a_samples[:, 0], o_a_samples[:, 1], "o")
ax[1, 1].set_title("An LHS optimized with Monte Carlo")
plt.show()