Source code for gemseo.algos.doe.scipy.settings.poisson_disk

# 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.
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# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
"""Settings for the Poisson disk DOE from the SciPy library."""

from __future__ import annotations

from pydantic import Field
from pydantic import NonNegativeFloat  # noqa: TC002
from pydantic import PositiveInt  # noqa: TC002

from gemseo.algos.doe.scipy.settings.base_scipy_doe_settings import BaseSciPyDOESettings
from gemseo.algos.doe.scipy.settings.base_scipy_doe_settings import Hypersphere
from gemseo.algos.doe.scipy.settings.base_scipy_doe_settings import Optimizer




class PoissonDisk_Settings(BaseSciPyDOESettings):  # noqa: N801
    """The settings for the Poisson disk DOE from the SciPy library."""

    _TARGET_CLASS_NAME = "PoissonDisk"

    optimization: Optimizer | None = Field(
        default=None,
        description="""The name of an optimization scheme to improve the DOE's quality.

If ``None``, use the DOE as is. New in SciPy 1.10.0.""",
    )

    radius: NonNegativeFloat = Field(
        default=0.05,
        description=(
            "The minimal distance to keep between points when sampling new candidates."
        ),
    )

    hypersphere: Hypersphere = Field(
        default=Hypersphere.volume,
        description="""The sampling strategy to generate potential candidates.

The candidates will be added in the final sample.""",
    )

    ncandidates: PositiveInt = Field(
        default=30,
        description="The number of candidates to sample per iteration.",
    )