def __init__(self, parameters, resultsfile):
r"""Create a new simulation loop instance for a simulation
using the semiclassical Hagedorn wavepacket based propagation
method.
:param parameters: The simulation parameters.
:type parameters: A :py:class:`ParameterProvider` instance.
:param resultsfile: Path and filename of the hdf5 output file.
"""
# Keep a reference to the simulation parameters
self.parameters = parameters
# The time propagator instance driving the simulation.
self.propagator = None
# A `IOManager` instance for saving simulation results.
self.IOManager = None
# The time manager
self._tm = TimeManager(self.parameters)
# Set up serialization of simulation data
self.IOManager = IOManager()
self.IOManager.create_file(resultsfile)
# Save the simulation parameters
self.IOManager.add_parameters()
self.IOManager.save_parameters(parameters)
def compute_parameters(self):
"""Compute some further parameters from the given ones.
"""
# Perform the computation only if the basic values are available.
# This is necessary to add flexibility and essentially read in *any*
# parameter file with heavily incomplete value sets. (F.e. spawn configs)
try:
# The number of time steps we will perform.
tm = TimeManager(self)
self._params["nsteps"] = tm.compute_number_timesteps()
except:
pass
if "potential" in self._params:
# Ugly hack. Should improve handling of potential libraries
Potential = BlockFactory().create_potential(self)
# Number of components of $\Psi$
self._params["ncomponents"] = Potential.get_number_components()
def get_timemanager(self):
r"""Return the embedded :py:class:`TimeManager` instance.
"""
return TimeManager(self._params)
