def compute_evaluate_wavepackets(pp, iom, blockid=0, eigentrafo=True):
"""Evaluate a homogeneous Hagedorn wavepacket on a given grid for each timestep.
:param pp: An :py:class:`ParameterProvider` instance providing the grid data.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
:param eigentrafo: Whether or not do an eigentransformation before evaluation is done.
"""
parameters = iom.load_parameters()
if pp is None:
pp = parameters
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Prepare the potential for basis transformations
Potential = BlockFactory().create_potential(parameters)
grid = BlockFactory().create_grid(pp)
# We want to save wavefunctions, thus add a data slot to the data file
d = {"ncomponents": parameters["ncomponents"],
"number_nodes": pp["number_nodes"],
"dimension": parameters["dimension"]}
iom.add_grid(d, blockid=blockid)
iom.add_wavefunction(d, timeslots=nrtimesteps, flat=True, blockid=blockid)
iom.save_grid(grid.get_nodes(), blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_wavepacket_description(blockid=blockid)
HAWP = BlockFactory().create_wavepacket(descr)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationHAWP(Potential)
BT.set_matrix_builder(HAWP.get_innerproduct())
# Basis shapes
BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BlockFactory().create_basis_shape(descr)
WF = WaveFunction(parameters)
WF.set_grid(grid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Evaluating homogeneous wavepacket at timestep %d" % step)
# Retrieve simulation data
params = iom.load_wavepacket_parameters(timestep=step, blockid=blockid, key=("q", "p", "Q", "P", "S", "adQ"))
hashes, coeffs = iom.load_wavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params, key=("q", "p", "Q", "P", "S", "adQ"))
HAWP.set_basis_shapes([BS[int(ha)] for ha in hashes])
HAWP.set_coefficients(coeffs)
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWP)
# Evaluate the wavepacket
values = HAWP.evaluate_at(grid, prefactor=True)
WF.set_values(values)
# Save the wave function
iom.save_wavefunction(WF.get_values(), timestep=step, blockid=blockid)
def compute_energy(iom, blockid=0, eigentrafo=True, iseigen=True):
"""
:param iom: An :py:class:`IOManager: instance providing the simulation data.
:param blockid: The data block from which the values are read. Default is `0`.
:param eigentrafo: Whether to make a transformation into the eigenbasis.
:type eigentrafo: Boolean, default is ``True``.
:param iseigen: Whether the data is assumed to be in the eigenbasis.
:type iseigen: Boolean, default is ``True``
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavefunction_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Construct grid from the parameters
grid = BlockFactory().create_grid(parameters)
# The potential used
Potential = BlockFactory().create_potential(parameters)
# The operators
KO = KineticOperator(grid)
KO.calculate_operator(parameters["eps"])
opT = KO
if eigentrafo is True:
opV = Potential.evaluate_at(grid)
else:
if iseigen is True:
opV = Potential.evaluate_eigenvalues_at(grid, as_matrix=True)
else:
opV = Potential.evaluate_at(grid, as_matrix=True)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationWF(Potential)
BT.set_grid(grid)
# And two empty wavefunctions
WF = WaveFunction(parameters)
WF.set_grid(grid)
WF2 = WaveFunction(parameters)
WF2.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
nst = Potential.get_number_components()
if eigentrafo is True:
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep # " + str(step))
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [ values[j,...] for j in xrange(parameters["ncomponents"]) ]
WF.set_values(values)
# Project wavefunction values to eigenbasis
BT.transform_to_eigen(WF)
ekinlist = []
epotlist = []
# For each component of |Psi>
values = WF.get_values()
for index, item in enumerate(values):
# tmp is the Vector (0, 0, 0, \psi_i, 0, 0, ...)
tmp = [ zeros(item.shape) for z in xrange(nst) ]
tmp[index] = item
WF2.set_values(tmp)
# Project this vector to the canonical basis
BT.transform_to_canonical(WF2)
# And calculate the energies of these components
ekinlist.append(WF2.kinetic_energy(opT, summed=True))
epotlist.append(WF2.potential_energy(opV, summed=True))
iom.save_energy((ekinlist, epotlist), timestep=step, blockid=blockid)
else:
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep # " + str(step))
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [ values[j,...] for j in xrange(parameters["ncomponents"]) ]
WF.set_values(values)
# And calculate the energies of these components
ekinlist = WF.kinetic_energy(opT, summed=False)
epotlist = WF.potential_energy(opV, summed=False)
iom.save_energy((ekinlist, epotlist), timestep=step, blockid=blockid)
def compute_energy(iom, blockid=0, eigentrafo=True, iseigen=True):
"""
:param iom: An :py:class:`IOManager: instance providing the simulation data.
:param blockid: The data block from which the values are read. Default is `0`.
:param eigentrafo: Whether to make a transformation into the eigenbasis.
:type eigentrafo: Boolean, default is ``True``.
:param iseigen: Whether the data is assumed to be in the eigenbasis.
:type iseigen: Boolean, default is ``True``
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavefunction_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Construct grid from the parameters
grid = BlockFactory().create_grid(parameters)
# The potential used
Potential = BlockFactory().create_potential(parameters)
# The operators
KO = KineticOperator(grid)
KO.calculate_operator(parameters["eps"])
opT = KO
if eigentrafo is True:
opV = Potential.evaluate_at(grid)
else:
if iseigen is True:
opV = Potential.evaluate_eigenvalues_at(grid, as_matrix=True)
else:
opV = Potential.evaluate_at(grid, as_matrix=True)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationWF(Potential)
BT.set_grid(grid)
# And two empty wavefunctions
WF = WaveFunction(parameters)
WF.set_grid(grid)
WF2 = WaveFunction(parameters)
WF2.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
nst = Potential.get_number_components()
if eigentrafo is True:
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep # " + str(step))
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [
values[j, ...] for j in xrange(parameters["ncomponents"])
]
WF.set_values(values)
# Project wavefunction values to eigenbasis
BT.transform_to_eigen(WF)
ekinlist = []
epotlist = []
# For each component of |Psi>
values = WF.get_values()
for index, item in enumerate(values):
# tmp is the Vector (0, 0, 0, \psi_i, 0, 0, ...)
tmp = [zeros(item.shape) for z in xrange(nst)]
tmp[index] = item
WF2.set_values(tmp)
# Project this vector to the canonical basis
BT.transform_to_canonical(WF2)
# And calculate the energies of these components
ekinlist.append(WF2.kinetic_energy(opT, summed=True))
epotlist.append(WF2.potential_energy(opV, summed=True))
iom.save_energy((ekinlist, epotlist),
timestep=step,
blockid=blockid)
else:
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep # " + str(step))
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [
values[j, ...] for j in xrange(parameters["ncomponents"])
]
WF.set_values(values)
# And calculate the energies of these components
ekinlist = WF.kinetic_energy(opT, summed=False)
epotlist = WF.potential_energy(opV, summed=False)
iom.save_energy((ekinlist, epotlist),
timestep=step,
blockid=blockid)
