def _ensure_filter(self, symbolic):
self._ensure_parser(symbolic=symbolic, for_filter=True)
if len(self.parser.get_nonlin_funcs()) > 0:
if symbolic:
raise CircuitException("ciruit is nonlinear: symbolic formula generation not supported")
p = dk_simulator.Parser(self.S, self.V, self.FS, not self.backward_euler)
sim = dk_simulator.SimulatePy(dk_simulator.EquationSystem(p), self.solver, self.dc_method)
J = sim.jacobi()
else:
J = None
self.sim_filter = dk_simulator.LinearFilter(self.parser, J)
def _ensure_sim_py(self):
if self.sim_py is None:
self._ensure_eq()
try:
self.sim_py = dk_simulator.SimulatePy(self.eq, self.solver, self.dc_method)
if self.sys_reduce_tol >= 0:
self.sim_py.balance_realization(self.sys_reduce_tol)
if (isinstance(self.eq.nonlin, dk_simulator.PartitionedNonlinEquations)
and self.transform_opts.decompose):
self.eq.F, self.eq.C = self.eq.nonlin.decompose_blocks(self.eq.F, self.eq.C)
except dk_simulator.ConvergenceError as e:
raise CircuitException(e)
def check(self, name, args):
p = dk_simulator.Parser(self.S,
self.V,
self.FS,
not args.backward_euler,
create_filter=True,
symbolic=False)
p1 = dk_simulator.Parser(self.S, self.V, self.FS,
not args.backward_euler)
sim = dk_simulator.SimulatePy(dk_simulator.EquationSystem(p1),
self.solver)
J = sim.jacobi()
f = dk_simulator.LinearFilter(p, J)
b, a = f.get_z_coeffs(samplerate=48000,
subst_var=f.convert_variable_dict({}))
res = np.array([[float(v) for v in b], [float(v) for v in a]])
if np.allclose(res, self.result):
return "Ok"
else:
return "Fail"