Source code for gemseo.algos.ode.lib_scipy_ode

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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 - API and implementation and/or documentation
#        :author: Isabelle Santos
"""Wrappers for SciPy's ODE solvers.

ODE stands for ordinary differential equation.

from __future__ import annotations

import logging
from typing import TYPE_CHECKING
from typing import Any

from numpy import inf
from scipy.integrate import solve_ivp

from gemseo.algos.ode.ode_solver_lib import ODESolverDescription
from gemseo.algos.ode.ode_solver_lib import ODESolverLib

    from numpy.typing import NDArray

    from gemseo.algos.ode.ode_result import ODEResult

LOGGER = logging.getLogger(__name__)

[docs] class ScipyODEAlgos(ODESolverLib): """Wrapper for SciPy's ODE solvers. ODE stands for ordinary differential equation. """ __WEBSITE = "{}.html" __WEBPAGE = "scipy.integrate.solve_ivp" LIBRARY_NAME = "SciPy" def __init__(self) -> None: # noqa:D107 super().__init__() self.descriptions = { name: ODESolverDescription( algorithm_name=name, internal_algorithm_name=name, description="ODE solver implemented in the SciPy library.", library_name="SciPy", website=self.__WEBSITE.format(name), ) for name in [ "RK45", "RK23", "DOP853", "Radau", "BDF", "LSODA", ] } def _get_options( self, first_step: float | None = None, max_step: float = inf, rtol: float | NDArray[float] = 1e-3, atol: float | NDArray[float] = 1e-6, jac_sparsity: NDArray[float] | None = None, lband: int | None = None, uband: int | None = None, min_step: float = 0, ) -> dict[str, Any]: """Check the options and set the default values. For more information, see Args: first_step: Initial step size. If ``None``, let the algorithm choose. max_step: Maximum allowed step size. rtol: Relative tolerance. atol: Absolute tolerance. jac_sparsity: Sparsity structure of the Jacobian matrix. lband: Lower boundary of the bandwidth for the "LSODA" method. uband: Upper boundary of the bandwidth for the "LSODA" method. min_step: Minimum allowed step for the "LSODA" method. Returns: The options of the solver. Raises: ValueError: When the LHR and RHS shapes are inconsistent, or when the preconditioner options are inconsistent. """ return self._process_options( first_step=first_step, max_step=max_step, rtol=rtol, atol=atol, jac_sparsity=jac_sparsity, lband=lband, uband=uband, min_step=min_step, ) def _run(self, **options: bool | int | float | NDArray[float] | None) -> ODEResult: if self.problem.time_vector is not None: options["t_eval"] = self.problem.time_vector if self.problem.jac is not None: options["jac"] = self.problem.jac solution = solve_ivp( fun=self.problem.rhs_function, y0=self.problem.initial_state, method=self.algo_name, t_span=self.problem.integration_interval, **options, ) self.problem.result.is_converged = solution.status == 0 self.problem.result.solver_message = solution.message if not self.problem.result.is_converged: LOGGER.warning(solution.message) self.problem.result.state_vector = solution.y self.problem.result.time_vector = solution.t self.problem.result.n_func_evaluations = solution.nfev self.problem.result.n_jac_evaluations = solution.njev return self.problem.result