5from .AbstractRKFD4ActionSet
import AbstractRKFD4ActionSet
11 Store the maximum eigenvalue over all grid cells of a patch
13 The action set runs after the final linear combination of a Runge-Kutta
14 step, i.e. it sees the solution of the next time step in solver._patch.
15 It stores the maximum eigenvalue in solver._max_eigenvalue, from where
16 exahype2.api.rkfd4.actionsets.ComputeTimeStepSize reduces it into the
24 @param solver: exahype2.api.Solver
25 Has to have an exahype2.api.rkfd4.CentralDifferencesWithRungeKutta
26 with adaptive time stepping as numerical scheme, which has already
27 constructed its kernel calls.
30 C++ boolean expression, evaluated per cell in touchCellFirstTime.
33 AbstractRKFD4ActionSet.__init__(self, solver)
39 != peano4.api.solversteps.ActionSet.OPERATION_TOUCH_CELL_FIRST_TIME
42 instance = self.
_solver.get_name_of_global_instance()
44 ::exahype2::CellData patchData(
45 fineGridCell{self._solver._patch.name}.value,
48 repositories::{instance}.getMinTimeStamp(),
49 repositories::{instance}.getMinTimeStepSize(),
50 fineGridCell{self._solver._patch.name}.value
52 {self._solver.numerical_scheme.max_eigenvalue_kernel_call}
53 fineGridCell{self._solver._max_eigenvalue.name}.setMaxEigenvalue( 0, patchData.maxEigenvalue[0] );
59 == peano4.api.solversteps.ActionSet.OPERATION_TOUCH_CELL_FIRST_TIME
65 return __name__.replace(
".py",
"").replace(
".",
"_")
Base class of the action sets of exahype2.api.rkfd4 which work against the solver.
Store the maximum eigenvalue over all grid cells of a patch.
get_guard_of_operation(self, operation_name)
Return the guard of an operation.
get_body_of_operation(self, operation_name)
Return actual C++ code snippets to be inserted into C++ code.
get_action_set_name(self)
You should replicate this function in each subclass, so you get meaningful action set names (otherwis...
__init__(self, solver, guard)