def compute_evaluate_wavepackets(iom, basis="eigen", blockid=0):
"""Evaluate an in 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_inhomogwavepacket_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_inhomogwavepacket_parameters(blockid=blockid)
coeffs = iom.load_inhomogwavepacket_coefficients(blockid=blockid)
# A data transformation needed by API specification
params = [ [ params[j][i,:] for j in xrange(parameters["ncomponents"]) ] for i in xrange(nrtimesteps) ]
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 = HagedornWavepacketInhomogeneous(parameters)
HAWP.set_quadrature(None)
WF = WaveFunction(parameters)
WF.set_grid(grid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Evaluating inhomogeneous 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):
"""
: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_wavefunction_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Retrieve simulation data
if iom.has_grid(blockid=blockid):
grid = iom.load_grid(blockid=blockid)
else:
grid = iom.load_grid(blockid="global")
opT, opV = iom.load_fourieroperators(blockid=blockid)
# We want to save norms, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
# Precalculate eigenvectors for efficiency
Potential = PotentialFactory().create_potential(parameters)
eigenvectors = Potential.evaluate_eigenvectors_at(grid)
nst = Potential.get_number_components()
WF = WaveFunction(parameters)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep # " + str(step))
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [ values[j,...] for j in xrange(parameters["ncomponents"]) ]
# Project wavefunction values to eigenbasis
values = Potential.project_to_eigen(grid, values, eigenvectors)
WF.set_values(values)
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
# Project this vector to the canonical basis
tmp = Potential.project_to_canonical(grid, tmp, eigenvectors)
WF.set_values(tmp)
# And calculate the energies of these components
ekinlist.append(WF.kinetic_energy(opT, summed=True))
epotlist.append(WF.potential_energy(opV, summed=True))
iom.save_energy((ekinlist, epotlist), timestep=step, blockid=blockid)
