class SimulationLoopHagedornInhomogeneous(SimulationLoop):
r"""This class acts as the main simulation loop. It owns a propagator that
propagates a set of initial values during a time evolution.
"""
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 prepare_simulation(self):
r"""Set up a Hagedorn propagator for the simulation loop. Set the
potential and initial values according to the configuration.
:raise: :py:class:`ValueError` For invalid or missing input data.
"""
BF = BlockFactory()
# The potential instance
potential = BF.create_potential(self.parameters)
# Project the initial values to the canonical basis
BT = BasisTransformationHAWP(potential)
# Finally create and initialize the propagator instance
# TODO: Attach the "leading_component to the hawp as codata
self.propagator = BF.create_propagator(self.parameters, potential)
# Create suitable wavepackets
for packet_descr in self.parameters["initvals"]:
packet = BF.create_wavepacket(packet_descr)
# Transform to canonical basis
BT.set_matrix_builder(packet.get_innerproduct())
BT.transform_to_canonical(packet)
# And hand over
self.propagator.add_wavepacket((packet, ))
# Add storage for each packet
npackets = len(self.parameters["initvals"])
slots = self._tm.compute_number_events()
key = ("q", "p", "Q", "P", "S", "adQ")
for i in range(npackets):
bid = self.IOManager.create_block(
dt=self.parameters.get("dt", 0.0))
self.IOManager.add_inhomogwavepacket(self.parameters,
timeslots=slots,
blockid=bid,
key=key)
# Write some initial values to disk
for packet in self.propagator.get_wavepackets():
self.IOManager.save_inhomogwavepacket_description(
packet.get_description())
if self._tm.is_event(0):
for packet in self.propagator.get_wavepackets():
# Pi
self.IOManager.save_inhomogwavepacket_parameters(
packet.get_parameters(key=key), timestep=0, key=key)
# Basis shapes
for shape in packet.get_basis_shapes():
self.IOManager.save_inhomogwavepacket_basisshapes(shape)
# Coefficients
self.IOManager.save_inhomogwavepacket_coefficients(
packet.get_coefficients(),
packet.get_basis_shapes(),
timestep=0)
def run_simulation(self):
r"""Run the simulation loop for a number of time steps.
"""
# The number of time steps we will perform.
nsteps = self._tm.compute_number_timesteps()
# Which parameter data to save.
key = ("q", "p", "Q", "P", "S", "adQ")
# Run the prepropagate step
self.propagator.pre_propagate()
# Note: We do not save any data here
# Run the simulation for a given number of timesteps
for i in range(1, nsteps + 1):
print(" doing timestep {}".format(i))
self.propagator.propagate()
# Save some simulation data
if self._tm.is_event(i):
# Run the postpropagate step
self.propagator.post_propagate()
# TODO: Generalize for arbitrary number of wavepackets
packets = self.propagator.get_wavepackets()
assert len(packets) == 1
for packet in packets:
# Pi
self.IOManager.save_inhomogwavepacket_parameters(
packet.get_parameters(key=key), timestep=i, key=key)
# Basis shapes (in case they changed!)
for shape in packet.get_basis_shapes():
self.IOManager.save_inhomogwavepacket_basisshapes(
shape)
# Coefficients
self.IOManager.save_inhomogwavepacket_coefficients(
packet.get_coefficients(),
packet.get_basis_shapes(),
timestep=i)
# Run the prepropagate step
self.propagator.pre_propagate()
# Run the postpropagate step
self.propagator.post_propagate()
# Note: We do not save any data here
def end_simulation(self):
r"""Do the necessary cleanup after a simulation. For example request the
:py:class:`IOManager` to write the data and close the output files.
"""
self.IOManager.finalize()
class SimulationLoopFourier(SimulationLoop):
"""This class acts as the main simulation loop. It owns a propagator that
propagates a set of initial values during a time evolution.
"""
def __init__(self, parameters, resultsfile):
"""Create a new simulation loop instance for a simulation
using the Fourier 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
# An `IOManager` instance for saving simulation results.
self.IOManager = None
# Which data do we want to save
self._tm = self.parameters.get_timemanager()
# Set up serialization of simulation data
self.IOManager = IOManager()
self.IOManager.create_file(resultsfile)
self.IOManager.create_block(dt=self.parameters.get("dt", 0.0))
# Save the simulation parameters
self.IOManager.add_parameters()
self.IOManager.save_parameters(parameters)
def prepare_simulation(self):
r"""Set up a Fourier propagator for the simulation loop. Set the
potential and initial values according to the configuration.
:raise: :py:class:`ValueError` For invalid or missing input data.
"""
BF = BlockFactory()
# The potential instance
potential = BF.create_potential(self.parameters)
# Compute the position space grid points
grid = BF.create_grid(self.parameters)
# Construct initial values
I = Initializer(self.parameters)
initialvalues = I.initialize_for_fourier(grid)
# Transform the initial values to the canonical basis
BT = BasisTransformationWF(potential)
BT.set_grid(grid)
BT.transform_to_canonical(initialvalues)
# Finally create and initialize the propagator instance
self.propagator = BF.create_propagator(self.parameters, potential,
initialvalues)
# Write some initial values to disk
slots = self._tm.compute_number_events()
self.IOManager.add_grid(self.parameters, blockid="global")
self.IOManager.add_fourieroperators(self.parameters)
self.IOManager.add_wavefunction(self.parameters, timeslots=slots)
self.IOManager.save_grid(grid.get_nodes(flat=True), blockid="global")
self.IOManager.save_fourieroperators(self.propagator.get_operators())
if self._tm.is_event(0):
self.IOManager.save_wavefunction(initialvalues.get_values(),
timestep=0)
def run_simulation(self):
r"""Run the simulation loop for a number of time steps.
"""
# The number of time steps we will perform.
nsteps = self._tm.compute_number_timesteps()
# Run the prepropagate step
self.propagator.pre_propagate()
# Note: We do not save any data here
# Run the simulation for a given number of timesteps
for i in range(1, nsteps + 1):
print(" doing timestep {}".format(i))
self.propagator.propagate()
# Save some simulation data
if self._tm.is_event(i):
# Run the postpropagate step
self.propagator.post_propagate()
self.IOManager.save_wavefunction(
self.propagator.get_wavefunction().get_values(),
timestep=i)
# Run the prepropagate step
self.propagator.pre_propagate()
# Run the postpropagate step
self.propagator.post_propagate()
# Note: We do not save any data here
def end_simulation(self):
"""Do the necessary cleanup after a simulation. For example request the
:py:class:`IOManager` to write the data and close the output files.
"""
self.IOManager.finalize()
class SimulationLoopHagedornInhomogeneous(SimulationLoop):
r"""This class acts as the main simulation loop. It owns a propagator that
propagates a set of initial values during a time evolution.
"""
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 prepare_simulation(self):
r"""Set up a Hagedorn propagator for the simulation loop. Set the
potential and initial values according to the configuration.
:raise: :py:class:`ValueError` For invalid or missing input data.
"""
# The potential instance
potential = BlockFactory().create_potential(self.parameters)
# Project the initial values to the canonical basis
BT = BasisTransformationHAWP(potential)
# Finally create and initialize the propagator instance
# TODO: Attach the "leading_component to the hawp as codata
self.propagator = HagedornPropagatorInhomogeneous(self.parameters, potential)
# Create suitable wavepackets
for packet_descr in self.parameters["initvals"]:
packet = BlockFactory().create_wavepacket(packet_descr)
# Transform to canonical basis
BT.set_matrix_builder(packet.get_innerproduct())
BT.transform_to_canonical(packet)
# And hand over
self.propagator.add_wavepacket((packet,))
# Add storage for each packet
npackets = len(self.parameters["initvals"])
slots = self._tm.compute_number_events()
key = ("q", "p", "Q", "P", "S", "adQ")
for i in range(npackets):
bid = self.IOManager.create_block()
self.IOManager.add_inhomogwavepacket(self.parameters, timeslots=slots, blockid=bid, key=key)
# Write some initial values to disk
for packet in self.propagator.get_wavepackets():
self.IOManager.save_inhomogwavepacket_description(packet.get_description())
if self._tm.is_event(0):
for packet in self.propagator.get_wavepackets():
# Pi
self.IOManager.save_inhomogwavepacket_parameters(packet.get_parameters(key=key), timestep=0, key=key)
# Basis shapes
for shape in packet.get_basis_shapes():
self.IOManager.save_inhomogwavepacket_basisshapes(shape)
# Coefficients
self.IOManager.save_inhomogwavepacket_coefficients(packet.get_coefficients(), packet.get_basis_shapes(), timestep=0)
def run_simulation(self):
r"""Run the simulation loop for a number of time steps.
"""
# The number of time steps we will perform.
nsteps = self._tm.compute_number_timesteps()
# Which parameter data to save.
key = ("q", "p", "Q", "P", "S", "adQ")
# Run the prepropagate step
self.propagator.pre_propagate()
# Note: We do not save any data here
# Run the simulation for a given number of timesteps
for i in range(1, nsteps + 1):
print(" doing timestep {}".format(i))
self.propagator.propagate()
# Save some simulation data
if self._tm.is_event(i):
# Run the postpropagate step
self.propagator.post_propagate()
# TODO: Generalize for arbitrary number of wavepackets
packets = self.propagator.get_wavepackets()
assert len(packets) == 1
for packet in packets:
# Pi
self.IOManager.save_inhomogwavepacket_parameters(packet.get_parameters(key=key), timestep=i, key=key)
# Basis shapes (in case they changed!)
for shape in packet.get_basis_shapes():
self.IOManager.save_inhomogwavepacket_basisshapes(shape)
# Coefficients
self.IOManager.save_inhomogwavepacket_coefficients(packet.get_coefficients(), packet.get_basis_shapes(), timestep=i)
# Run the prepropagate step
self.propagator.pre_propagate()
# Run the postpropagate step
self.propagator.post_propagate()
# Note: We do not save any data here
def end_simulation(self):
r"""Do the necessary cleanup after a simulation. For example request the
:py:class:`IOManager` to write the data and close the output files.
"""
self.IOManager.finalize()
class SimulationLoopFourier(SimulationLoop):
"""This class acts as the main simulation loop. It owns a propagator that
propagates a set of initial values during a time evolution.
"""
def __init__(self, parameters, resultsfile):
"""Create a new simulation loop instance for a simulation
using the Fourier 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
# An `IOManager` instance for saving simulation results.
self.IOManager = None
# Which data do we want to save
self._tm = self.parameters.get_timemanager()
# Set up serialization of simulation data
self.IOManager = IOManager()
self.IOManager.create_file(resultsfile)
self.IOManager.create_block(dt=self.parameters.get("dt", 0.0))
# Save the simulation parameters
self.IOManager.add_parameters()
self.IOManager.save_parameters(parameters)
def prepare_simulation(self):
r"""Set up a Fourier propagator for the simulation loop. Set the
potential and initial values according to the configuration.
:raise: :py:class:`ValueError` For invalid or missing input data.
"""
BF = BlockFactory()
# The potential instance
potential = BF.create_potential(self.parameters)
# Compute the position space grid points
grid = BF.create_grid(self.parameters)
# Construct initial values
I = Initializer(self.parameters)
initialvalues = I.initialize_for_fourier(grid)
# Transform the initial values to the canonical basis
BT = BasisTransformationWF(potential)
BT.set_grid(grid)
BT.transform_to_canonical(initialvalues)
# Finally create and initialize the propagator instance
self.propagator = BF.create_propagator(self.parameters, potential, initialvalues)
# Write some initial values to disk
slots = self._tm.compute_number_events()
self.IOManager.add_grid(self.parameters, blockid="global")
self.IOManager.add_fourieroperators(self.parameters)
self.IOManager.add_wavefunction(self.parameters, timeslots=slots)
self.IOManager.save_grid(grid.get_nodes(flat=True), blockid="global")
self.IOManager.save_fourieroperators(self.propagator.get_operators())
if self._tm.is_event(0):
self.IOManager.save_wavefunction(initialvalues.get_values(), timestep=0)
def run_simulation(self):
r"""Run the simulation loop for a number of time steps.
"""
# The number of time steps we will perform.
nsteps = self._tm.compute_number_timesteps()
# Run the prepropagate step
self.propagator.pre_propagate()
# Note: We do not save any data here
# Run the simulation for a given number of timesteps
for i in range(1, nsteps + 1):
print(" doing timestep {}".format(i))
self.propagator.propagate()
# Save some simulation data
if self._tm.is_event(i):
# Run the postpropagate step
self.propagator.post_propagate()
self.IOManager.save_wavefunction(self.propagator.get_wavefunction().get_values(), timestep=i)
# Run the prepropagate step
self.propagator.pre_propagate()
# Run the postpropagate step
self.propagator.post_propagate()
# Note: We do not save any data here
def end_simulation(self):
"""Do the necessary cleanup after a simulation. For example request the
:py:class:`IOManager` to write the data and close the output files.
"""
self.IOManager.finalize()