# 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.
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# 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
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# 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
"""
Use a design of experiments from an array
=========================================
"""
from __future__ import annotations

import numpy as np
from gemseo import create_design_space
from gemseo import create_discipline
from gemseo import create_scenario

# %%
# Let us consider a discipline implementing the function :math:`y=a*b`
discipline = create_discipline("AnalyticDiscipline", expressions={"y": "a*b"})

# %%
# where :math:`a,b\in\{1,2,\ldots,10\}`:
design_space = create_design_space()
design_space.add_variable("a", 1, design_space.DesignVariableType.INTEGER, 1, 10)
design_space.add_variable("b", 1, design_space.DesignVariableType.INTEGER, 1, 10)

# %%
# We want to evaluate this discipline over this design space
# by using the following input samples:
sample_1 = [1.0, 2.0]
sample_2 = [2.0, 3.0]
samples = np.array([sample_1, sample_2])

# %%
# For that, we can create a scenario and execute it with a :class:`.CustomDOE`
# with the option "samples":
scenario = create_scenario(
    [discipline], "DisciplinaryOpt", "y", design_space, scenario_type="DOE"
)
scenario.execute({"algo": "CustomDOE", "algo_options": {"samples": samples}})

# %%
# Then,
# we can display the content of the database as a :class:`.Dataset`
# and check the values of the output,
# which should be the product of :math:`a` and :math:`b`:
opt_problem = scenario.formulation.opt_problem
dataset = opt_problem.to_dataset(name="custom_sampling", opt_naming=False)
print(dataset)