# 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
# 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
# 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: Damien Guenot
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""OpenTURNS DOE algorithms."""
from __future__ import annotations
from collections.abc import Mapping
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING
from typing import ClassVar
from typing import Final
from typing import Optional
from typing import Union
import openturns
from gemseo.algos.doe.base_doe_library import BaseDOELibrary
from gemseo.algos.doe.base_doe_library import DOEAlgorithmDescription
from gemseo.algos.doe.openturns._algos.ot_axial_doe import OTAxialDOE
from gemseo.algos.doe.openturns._algos.ot_centered_lhs import OTCenteredLHS
from gemseo.algos.doe.openturns._algos.ot_composite_doe import OTCompositeDOE
from gemseo.algos.doe.openturns._algos.ot_factorial_doe import OTFactorialDOE
from gemseo.algos.doe.openturns._algos.ot_faure_sequence import OTFaureSequence
from gemseo.algos.doe.openturns._algos.ot_full_factorial_doe import OTFullFactorialDOE
from gemseo.algos.doe.openturns._algos.ot_halton_sequence import OTHaltonSequence
from gemseo.algos.doe.openturns._algos.ot_haselgrove_sequence import (
OTHaselgroveSequence,
)
from gemseo.algos.doe.openturns._algos.ot_monte_carlo import OTMonteCarlo
from gemseo.algos.doe.openturns._algos.ot_optimal_lhs import OTOptimalLHS
from gemseo.algos.doe.openturns._algos.ot_reverse_halton_sequence import (
OTReverseHaltonSequence,
)
from gemseo.algos.doe.openturns._algos.ot_sobol_doe import OTSobolDOE
from gemseo.algos.doe.openturns._algos.ot_sobol_sequence import OTSobolSequence
from gemseo.algos.doe.openturns._algos.ot_standard_lhs import OTStandardLHS
from gemseo.algos.doe.openturns.settings.ot_axial import OT_AXIAL_Settings
from gemseo.algos.doe.openturns.settings.ot_composite import OT_COMPOSITE_Settings
from gemseo.algos.doe.openturns.settings.ot_factorial import OT_FACTORIAL_Settings
from gemseo.algos.doe.openturns.settings.ot_faure import OT_FAURE_Settings
from gemseo.algos.doe.openturns.settings.ot_fullfact import OT_FULLFACT_Settings
from gemseo.algos.doe.openturns.settings.ot_halton import OT_HALTON_Settings
from gemseo.algos.doe.openturns.settings.ot_haselgrove import OT_HASELGROVE_Settings
from gemseo.algos.doe.openturns.settings.ot_lhs import OT_LHS_Settings
from gemseo.algos.doe.openturns.settings.ot_lhsc import OT_LHSC_Settings
from gemseo.algos.doe.openturns.settings.ot_monte_carlo import OT_MONTE_CARLO_Settings
from gemseo.algos.doe.openturns.settings.ot_opt_lhs import OT_OPT_LHS_Settings
from gemseo.algos.doe.openturns.settings.ot_random import OT_RANDOM_Settings
from gemseo.algos.doe.openturns.settings.ot_reverse_halton import (
OT_REVERSE_HALTON_Settings,
)
from gemseo.algos.doe.openturns.settings.ot_sobol import OT_SOBOL_Settings
from gemseo.algos.doe.openturns.settings.ot_sobol_indices import (
OT_SOBOL_INDICES_Settings,
)
from gemseo.typing import RealArray
if TYPE_CHECKING:
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.base_doe import BaseDOE
from gemseo.typing import NumberArray
OptionType = Optional[Union[str, int, float, bool, Sequence[int], RealArray]]
[docs]
@dataclass
class OpenTURNSAlgorithmDescription(DOEAlgorithmDescription):
"""The description of a DOE algorithm from the OpenTURNS library."""
library_name: str = "OpenTURNS"
"""The library name."""
[docs]
class OpenTURNS(BaseDOELibrary):
"""The OpenTURNS DOE algorithms library."""
# Algorithm names within GEMSEO
__AXIAL: Final[str] = "OT_AXIAL"
__COMPOSITE: Final[str] = "OT_COMPOSITE"
__FACTORIAL: Final[str] = "OT_FACTORIAL"
__FAURE: Final[str] = "OT_FAURE"
__FULLFACT: Final[str] = "OT_FULLFACT"
__HALTON: Final[str] = "OT_HALTON"
__HASELGROVE: Final[str] = "OT_HASELGROVE"
__LHS: Final[str] = "OT_LHS"
__LHSC: Final[str] = "OT_LHSC"
__MONTE_CARLO: Final[str] = "OT_MONTE_CARLO"
__OPT_LHS: Final[str] = "OT_OPT_LHS"
__RANDOM: Final[str] = "OT_RANDOM"
__REVERSE_HALTON: Final[str] = "OT_REVERSE_HALTON"
__SOBOL: Final[str] = "OT_SOBOL"
__SOBOL_INDICES: Final[str] = "OT_SOBOL_INDICES"
__NAMES_TO_CLASSES: Final[Mapping[str, type[BaseDOE]]] = {
__AXIAL: OTAxialDOE,
__COMPOSITE: OTCompositeDOE,
__FAURE: OTFaureSequence,
__FACTORIAL: OTFactorialDOE,
__FULLFACT: OTFullFactorialDOE,
__HALTON: OTHaltonSequence,
__HASELGROVE: OTHaselgroveSequence,
__LHS: OTStandardLHS,
__LHSC: OTCenteredLHS,
__MONTE_CARLO: OTMonteCarlo,
__OPT_LHS: OTOptimalLHS,
__RANDOM: OTMonteCarlo,
__REVERSE_HALTON: OTReverseHaltonSequence,
__SOBOL: OTSobolSequence,
__SOBOL_INDICES: OTSobolDOE,
}
"""The algorithm names bound to the OpenTURNS classes."""
__DOC: Final[str] = "http://openturns.github.io/openturns/latest/user_manual/"
ALGORITHM_INFOS: ClassVar[dict[str, OpenTURNSAlgorithmDescription]] = {
__SOBOL: OpenTURNSAlgorithmDescription(
algorithm_name=__SOBOL,
description="Sobol sequence",
internal_algorithm_name=__SOBOL,
website=f"{__DOC}_generated/openturns.SobolSequence.html",
Settings=OT_SOBOL_Settings,
),
__RANDOM: OpenTURNSAlgorithmDescription(
algorithm_name=__RANDOM,
description="Random sampling",
internal_algorithm_name=__RANDOM,
website=f"{__DOC}_generated/openturns.Uniform.html",
Settings=OT_RANDOM_Settings,
),
__HASELGROVE: OpenTURNSAlgorithmDescription(
algorithm_name=__HASELGROVE,
description="Haselgrove sequence",
internal_algorithm_name=__HASELGROVE,
website=f"{__DOC}_generated/openturns.HaselgroveSequence.html",
Settings=OT_HASELGROVE_Settings,
),
__REVERSE_HALTON: OpenTURNSAlgorithmDescription(
algorithm_name=__REVERSE_HALTON,
description="Reverse Halton",
internal_algorithm_name=__REVERSE_HALTON,
website=f"{__DOC}_generated/openturns.ReverseHaltonSequence.html",
Settings=OT_REVERSE_HALTON_Settings,
),
__HALTON: OpenTURNSAlgorithmDescription(
algorithm_name=__HALTON,
description="Halton sequence",
internal_algorithm_name=__HALTON,
website=f"{__DOC}_generated/openturns.HaltonSequence.html",
Settings=OT_HALTON_Settings,
),
__FAURE: OpenTURNSAlgorithmDescription(
algorithm_name=__FAURE,
description="Faure sequence",
internal_algorithm_name=__FAURE,
website=f"{__DOC}_generated/openturns.FaureSequence.html",
Settings=OT_FAURE_Settings,
),
__MONTE_CARLO: OpenTURNSAlgorithmDescription(
algorithm_name=__MONTE_CARLO,
description="Monte Carlo sequence",
internal_algorithm_name=__MONTE_CARLO,
website=f"{__DOC}_generated/openturns.Uniform.html",
Settings=OT_MONTE_CARLO_Settings,
),
__FACTORIAL: OpenTURNSAlgorithmDescription(
algorithm_name=__FACTORIAL,
description="Factorial design",
internal_algorithm_name=__FACTORIAL,
website=f"{__DOC}_generated/openturns.Factorial.html",
Settings=OT_FACTORIAL_Settings,
),
__COMPOSITE: OpenTURNSAlgorithmDescription(
algorithm_name=__COMPOSITE,
description="Composite design",
internal_algorithm_name=__COMPOSITE,
website=f"{__DOC}_generated/openturns.Composite.html",
Settings=OT_COMPOSITE_Settings,
),
__AXIAL: OpenTURNSAlgorithmDescription(
algorithm_name=__AXIAL,
description="Axial design",
internal_algorithm_name=__AXIAL,
website=f"{__DOC}_generated/openturns.Axial.html",
Settings=OT_AXIAL_Settings,
),
__OPT_LHS: OpenTURNSAlgorithmDescription(
algorithm_name=__OPT_LHS,
description="Optimal Latin Hypercube Sampling",
internal_algorithm_name=__OPT_LHS,
website=f"{__DOC}_generated/openturns.SimulatedAnnealingLHS.html",
Settings=OT_OPT_LHS_Settings,
),
__LHS: OpenTURNSAlgorithmDescription(
algorithm_name=__LHS,
description="Latin Hypercube Sampling",
internal_algorithm_name=__LHS,
website=f"{__DOC}_generated/openturns.LHS.html",
Settings=OT_LHS_Settings,
),
__LHSC: OpenTURNSAlgorithmDescription(
algorithm_name=__LHSC,
description="Centered Latin Hypercube Sampling",
internal_algorithm_name=__LHSC,
website=f"{__DOC}_generated/openturns.LHS.html",
Settings=OT_LHSC_Settings,
),
__FULLFACT: OpenTURNSAlgorithmDescription(
algorithm_name=__FULLFACT,
description="Full factorial design",
internal_algorithm_name=__FULLFACT,
website=f"{__DOC}_generated/openturns.Box.html",
Settings=OT_FULLFACT_Settings,
),
__SOBOL_INDICES: OpenTURNSAlgorithmDescription(
algorithm_name=__SOBOL_INDICES,
description="DOE for Sobol indices",
internal_algorithm_name=__SOBOL_INDICES,
website=f"{__DOC}_generated/openturns.SobolIndicesAlgorithm.html",
Settings=OT_SOBOL_INDICES_Settings,
),
}
def _generate_unit_samples(
self,
design_space: DesignSpace,
n_samples: int = 0,
seed: int | None = None,
**settings: OptionType,
) -> NumberArray:
"""
Args:
n_samples: The number of samples.
If 0, set from the options.
seed: The seed used for reproducibility reasons.
If ``None``, use :attr:`.seed`.
""" # noqa: D205, D212, D415
openturns.RandomGenerator.SetSeed(self._seeder.get_seed(seed))
doe_algo = self.__NAMES_TO_CLASSES[self._algo_name]()
return doe_algo.generate_samples(n_samples, design_space.dimension, **settings)
