import logging
from dataclasses import dataclass
from typing import Callable, Literal
from feectools.linalg.stencil import StencilVector
from struphy.io.options import LiteralOptions, OptionsBase
from struphy.linear_algebra.solver import SolverParameters
from struphy.models.variables import FEECVariable, PICVariable, SPHVariable
from struphy.pic.accumulation.filter import FilterParameters
from struphy.pic.accumulation.particles_to_grid import ParticlesToGrid
from struphy.propagators.implicit_diffusion import ImplicitDiffusion
from struphy.utils.utils import check_option
logger = logging.getLogger("struphy")
[docs]
class PoissonSolve(ImplicitDiffusion):
r"""
Weak discretization of the (stabilized) Poisson equation: find :math:`\phi \in H^1` such that
.. math::
\epsilon \int_\Omega \psi\, \phi\,\textrm d \mathbf x + \int_\Omega \nabla \psi^\top \, \nabla \phi \,\textrm d \mathbf x = \sum_i \int_\Omega \psi\, \rho_i(\mathbf x)\,\textrm d \mathbf x \qquad \forall \ \psi \in H^1\,,
where :math:`\epsilon \in \mathbb R` is a stabilization parameter. Boundary terms from integration by parts are assumed to vanish. The equation is discretized as
.. math::
\left( \epsilon\,\mathbb S + \mathbb G^\top \mathbb M^1 \mathbb G \right)\, \boldsymbol{\phi} = \sum_i(\Lambda^0, \rho_i )_{L^2}\,,
where :math:`\mathbb M^1` is the :math:`H(\textnormal{curl})`-mass matrix and :math:`\mathbb S` is a stabilization matrix.
"""
[docs]
@dataclass(repr=False)
class Options(OptionsBase):
"""Configuration options for :class:`Poisson`.
Parameters
----------
stab_eps : float, default=0.0
Stabilization weight used for the mass-like term in the Poisson
operator.
Internally mapped to ``sigma_1 = stab_eps`` in the parent
:class:`ImplicitDiffusion` formulation.
stab_mat : {"M0", "M0ad", "Id"}, default="Id"
Stabilization matrix multiplied by ``stab_eps``.
- ``"M0"``: standard weighted 0-form mass operator.
- ``"M0ad"``: adiabatic-electron weighted 0-form mass operator.
- ``"Id"``: identity operator.
diffusion_mat : {"M1", "M1perp", "M1gyro"}, defaults="M1"
Diffusion matrix.
- ``"M1"``: standard weighted 1-form mass operator.
- ``"M1perp"``: weighted 1-form mass operator perpendicular to magnetic field.
- ``"M1para"``: weighted 1-form mass operator parallele to magnetic field.
- ``"M1gyro"``: weighted 1-form mass operator used in gyrokinetic model.
x0 : StencilVector, default=None
Initial guess for the iterative linear solver.
solver : LiteralOptions.OptsSymmSolver, default="pcg"
Name of the symmetric iterative solver passed to
:func:`psydac.linalg.solvers.inverse`.
precond : LiteralOptions.OptsMassPrecond, default="MassMatrixPreconditioner"
Name of the preconditioner configuration.
Currently this class inherits the same behavior as
:class:`ImplicitDiffusion`, where ``pc=None`` is used internally.
solver_params : SolverParameters, default=None
Iterative-solver controls (for example ``tol``, ``maxiter``,
``verbose``, ``info``, ``recycle``).
If ``None``, defaults to ``SolverParameters()``.
filter_params : dict[PICVariable | SPHVariable, FilterParameters], default=None
If not None, specifies a filter to the accumulation of a specific variable.
Notes
-----
``Poisson.Options`` reuses :class:`ImplicitDiffusion` internals by
enforcing
``sigma_2 = 0.0``, ``sigma_3 = 1.0``, ``divide_by_dt = False`` and
``diffusion_mat = "M1"`` in ``__post_init__``.
"""
# specific literals
OptsStabMat = Literal["M0", "M0ad", "Id"]
OptsDiffusionMat = Literal["M1", "M1perp", "M1para", "M1gyro"]
# propagator options
stab_eps: float = 0.0
stab_mat: OptsStabMat = "Id"
diffusion_mat: OptsDiffusionMat = "M1"
x0: StencilVector = None
solver: LiteralOptions.OptsSymmSolver = "pcg"
precond: LiteralOptions.OptsMassPrecond = "MassMatrixPreconditioner"
solver_params: SolverParameters = None
filter_params: dict[PICVariable | SPHVariable, FilterParameters] = None
def __post_init__(self):
# checks
check_option(self.stab_mat, self.OptsStabMat)
check_option(self.diffusion_mat, self.OptsDiffusionMat)
check_option(self.solver, LiteralOptions.OptsSymmSolver)
check_option(self.precond, LiteralOptions.OptsMassPrecond)
# defaults
if self.solver_params is None:
self.solver_params = SolverParameters()
# Poisson solve (-> set some params of parent class)
self.sigma_1 = self.stab_eps
self.sigma_2 = 0.0
self.sigma_3 = 1.0
self.divide_by_dt = False
def __init__(
self,
rho: FEECVariable | Callable | ParticlesToGrid | list = None,
rho_coeffs: float | list = None,
):
"""
Parameters
----------
rho : FEECVariable or Callable or ParticlesToGrid or list, default=None
Right-hand side source term(s) of the Poisson problem.
Accepted entries are:
- ``None``: zero source.
- ``FEECVariable`` in ``H1``.
- ``Callable`` to be projected to ``H1`` via ``L2Projector``.
- :class:`~struphy.pic.accumulation.particles_to_grid.ParticlesToGrid`, describing a
particle-to-grid (charge/current) deposition.
- a ``list`` containing any mix of the entries above.
rho_coeffs : float or list, default=None
Multiplicative coefficient(s) applied to ``rho``.
If ``None``, coefficients default to ``1.0`` for all sources.
"""
super().__init__(rho=rho, rho_coeffs=rho_coeffs)
@property
def options(self) -> Options:
if not hasattr(self, "_options"):
self._options = self.Options()
return self._options
@options.setter
def options(self, new):
assert isinstance(new, self.Options)
self._options = new
logger.info(f"\nNew options for propagator '{self.__class__.__name__}':\n{self._options}")
