Propagator base class#

Propagator base class.

class struphy.propagators.base.Propagator[source]#

Bases: object

Base class for propagators used in StruphyModels.

Note

All Struphy propagators are subclasses of Propagator and must be added to struphy/propagators in one of the modules propagators_fields.py, propagators_markers.py or propagators_coupling.py. Only propagators that update both a FEEC and a PIC species go into propagators_coupling.py.

class Variables[source]#

Bases: object

Define variable names and types to be updated by the propagator.

property var1#
class Options(opt1: str = ('implicit',))[source]#

Bases: object

OptsTemplate#

alias of Literal[‘implicit’, ‘explicit’]

opt1: str = ('implicit',)#
abstract property options: Options#
abstract allocate()[source]#

Allocate all data/objects of the instance.

update_feec_variables(**new_coeffs)[source]#

Return max_diff = max(abs(new - old)) for each new_coeffs, update feec coefficients and update ghost regions.

Returns:

diffs – max_diff for all feec variables.

Return type:

dict

property init_kernels#

List of initialization kernels for evaluation at \(\boldsymbol \eta^n\) in an iterative Pusher.

property eval_kernels#

List of evaluation kernels for evaluation at \(\alpha_i \eta_{i}^{n+1,k} + (1 - \alpha_i) \eta_{i}^n\) for \(i=1, 2, 3\) and different \(\alpha_i \in [0,1]\), in an iterative Pusher.

property rank#

MPI rank, is 0 if no communicator.

property derham#

Derham spaces and projectors.

property domain#

Domain object that characterizes the mapping from the logical to the physical domain.

property mass_ops#

Weighted mass operators.

property basis_ops#

Basis projection operators.

property projected_equil: ProjectedFluidEquilibriumWithB#

Fluid equilibrium projected on 3d Derham sequence with commuting projectors.

property time_state#

A pointer to the time variable of the dynamics (‘t’).

add_time_state(time_state)[source]#

Add a pointer to the time variable of the dynamics (‘t’).

Parameters:

time_state (ndarray) – Of size 1, holds the current physical time ‘t’.

add_init_kernel(kernel, column_nr: int, comps: tuple | int, args_init: tuple)[source]#

Add an initialization kernel to self.init_kernels.

Parameters:

  • kernel (pyccel func) – The kernel function.

  • column_nr (int) – The column index at which the result is stored in marker array.

  • comps (tuple | int) – None or (0) for scalar-valued function evaluation. In vector valued case, allows to specify which components to save at column_nr:column_nr + len(comps).

  • args_init (tuple) – The arguments for the kernel function.

add_eval_kernel(kernel, column_nr: int, comps: tuple | int, args_eval: tuple, alpha: float | int | tuple | list = 1.0)[source]#

Add an evaluation kernel to self.eval_kernels.

Parameters:

  • kernel (pyccel func) – The kernel function.

  • column_nr (int) – The column index at which the result is stored in marker array.

  • comps (tuple | int) – None for scalar-valued function evaluation. In vecotr valued case, allows to specify which components to save at column_nr:column_nr + len(comps).

  • args_init (tuple) – The arguments for the kernel function.

  • alpha (float | int | tuple | list) – Evaluations in kernel are at the weighted average alpha[i]*markers[:, i] + (1 - alpha[i])*markers[:, buffer_idx + i], for i=0,1,2. If float or int or then alpha = [alpha]*dim, where dim is the dimension of the phase space (<=6). alpha[i] must be between 0 and 1.