# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This work is licensed under a BSD 0-Clause License.
#
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# for any purpose with or without fee is hereby granted.
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# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: François Gallard
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
Analytical test case # 3
========================
"""
#############################################################################
# In this example, we consider a simple optimization problem to illustrate
# algorithms interfaces and DOE libraries integration.
# Integer variables are used
#
# Imports
# -------
from __future__ import annotations

from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.api import configure_logger
from gemseo.api import execute_post
from gemseo.core.mdofunctions.mdo_function import MDOFunction
from numpy import sum as np_sum

LOGGER = configure_logger()


#############################################################################
# Define the objective function
# -----------------------------
# We define the objective function :math:`f(x)=\sum_{i=1}^dx_i`
# using a :class:`.MDOFunction`.
objective = MDOFunction(np_sum, name="f", expr="sum(x)")

#############################################################################
# Define the design space
# -----------------------
# Then, we define the :class:`.DesignSpace` with |g|.
design_space = DesignSpace()
design_space.add_variable("x", 2, l_b=-5, u_b=5, var_type="integer")

#############################################################################
# Define the optimization problem
# -------------------------------
# Then, we define the :class:`.OptimizationProblem` with |g|.
problem = OptimizationProblem(design_space)
problem.objective = objective

#############################################################################
# Solve the optimization problem using a DOE algorithm
# ----------------------------------------------------
# We can see this optimization problem as a trade-off
# and solve it by means of a design of experiments (DOE),
# e.g. full factorial design
DOEFactory().execute(problem, "fullfact", n_samples=11**2)

#############################################################################
# Post-process the results
# ------------------------
execute_post(
    problem,
    "ScatterPlotMatrix",
    variable_names=["x", "f"],
    save=False,
    show=True,
)

#############################################################################
# Note that you can get all the optimization algorithms names:
algo_list = DOEFactory().algorithms
print("Available algorithms ", algo_list)