def compute_evaluate_wavepackets(iom, basis="eigen", blockid=0):
"""Evaluate a homogeneous Hagdorn wavepacket on a given grid for each timestep.
:param iom: An ``IOManager`` instance providing the simulation data.
:param basis: The basis where the evaluation is done. Can be 'eigen' or 'canonical'.
:param blockid: The data block from which the values are read.
"""
parameters = iom.load_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 = PotentialFactory().create_potential(parameters)
# Retrieve simulation data
if iom.has_grid(blockid=blockid):
grid = iom.load_grid(blockid=blockid)
else:
grid = iom.load_grid(blockid="global")
params = iom.load_wavepacket_parameters(blockid=blockid)
coeffs = iom.load_wavepacket_coefficients(blockid=blockid)
# A data transformation needed by API specification
coeffs = [ [ coeffs[i,j,:] for j in xrange(parameters["ncomponents"]) ] for i in xrange(nrtimesteps)]
# We want to save wavefunctions, thus add a data slot to the data file
iom.add_wavefunction(parameters, timeslots=nrtimesteps, blockid=blockid)
# Hack for allowing data blocks with different basis size than the global one
# todo: remove when we got local parameter sets
parameters.update_parameters({"basis_size": coeffs[0][0].shape[0]})
HAWP = HagedornWavepacket(parameters)
HAWP.set_quadrature(None)
WF = WaveFunction(parameters)
WF.set_grid(grid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Evaluating homogeneous wavepacket at timestep "+str(step))
# Configure the wavepacket
HAWP.set_parameters(params[i])
HAWP.set_coefficients(coeffs[i])
# Project to the eigenbasis if desired
if basis == "eigen":
HAWP.project_to_eigen(Potential)
# 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):
p = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# We want to save energies, thus add a data slot to the data file
iom.add_energy(p, timeslots=nrtimesteps, blockid=blockid, total=True)
Potential = PotentialFactory().create_potential(p)
params = iom.load_wavepacket_parameters(blockid=blockid)
coeffs = iom.load_wavepacket_coefficients(blockid=blockid)
# A data transformation needed by API specification
coeffs = [ [ coeffs[i,j,:] for j in xrange(p.ncomponents) ] for i in xrange(nrtimesteps) ]
# Initialize a hagedorn wave packet with the data
HAWP = HagedornWavepacket(p)
HAWP.set_quadrature(None)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep "+str(step))
# Configure the wave packet
HAWP.set_parameters(params[i])
HAWP.set_coefficients(coeffs[i])
# Compute overall energy in canonical basis
ekin = HAWP.kinetic_energy(summed=True)
epot = HAWP.potential_energy(Potential.evaluate_at, summed=True)
etot = ekin + epot
iom.save_energy_total(etot, timestep=step, blockid=blockid)
# Transform to eigenbasis
HAWP.project_to_eigen(Potential)
# Compute the components' energies in the eigenbasis
ekin = HAWP.kinetic_energy()
epot = HAWP.potential_energy(Potential.evaluate_eigenvalues_at)
iom.save_energy((ekin, epot), timestep=step, blockid=blockid)
def compute_energy(iom, blockid=0):
"""
:param iom: An ``IOManager`` instance providing the simulation data.
:param blockid: The data block from which the values are read.
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
Potential = PotentialFactory().create_potential(parameters)
# Retrieve simulation data
params = iom.load_wavepacket_parameters(blockid=blockid)
coeffs = iom.load_wavepacket_coefficients(blockid=blockid)
# A data transformation needed by API specification
coeffs = [ [ coeffs[i,j,:] for j in xrange(parameters["ncomponents"]) ] for i in xrange(nrtimesteps) ]
# We want to save energies, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
# Hack for allowing data blocks with different basis size than the global one
# todo: remove when we got local parameter sets
parameters.update_parameters({"basis_size": coeffs[0][0].shape[0]})
# Initialize a Hagedorn wave packet with the data
HAWP = HagedornWavepacket(parameters)
HAWP.set_quadrature(None)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep "+str(step))
# Configure the wave packet and project to the eigenbasis.
HAWP.set_parameters(params[i])
HAWP.set_coefficients(coeffs[i])
HAWP.project_to_eigen(Potential)
# Compute the energies
ekin = HAWP.kinetic_energy()
epot = HAWP.potential_energy(Potential.evaluate_eigenvalues_at)
iom.save_energy((ekin, epot), timestep=step, blockid=blockid)
def plot_frames_homogeneous(iom, blockid=0, view=None):
"""
:param iom: An ``IOManager`` instance providing the simulation data.
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Initialize a Hagedorn wavepacket with the data
Potential = PotentialFactory().create_potential(parameters)
# Retrieve simulation data
grid = iom.load_grid(blockid="global")
params = iom.load_wavepacket_parameters(blockid=blockid)
coeffs = iom.load_wavepacket_coefficients(blockid=blockid)
# A data transformation needed by API specification
coeffs = [ [ coeffs[i,j,:] for j in xrange(parameters["ncomponents"]) ] for i in xrange(nrtimesteps)]
# Hack for allowing data blocks with different basis size than the global one
# todo: remove when we got local parameter sets
parameters.update_parameters({"basis_size": coeffs[0][0].shape[0]})
HAWP = HagedornWavepacket(parameters)
HAWP.set_quadrature(None)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Plotting frame of timestep "+str(step))
# Configure the wavepacket and project to the eigenbasis.
HAWP.set_parameters(params[i])
HAWP.set_coefficients(coeffs[i])
HAWP.project_to_eigen(Potential)
values = HAWP.evaluate_at(grid, prefactor=True)
coeffi = HAWP.get_coefficients()
plot_frame(step, parameters, grid, values, coeffi, index=blockid, view=view)
print(" Plotting frames finished")
