def compute_norm(iom, blockid=0, eigentrafo=True):
"""Compute the norm of a wavefunction timeseries.
: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``.
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavefunction_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Construct the grid from the parameters
grid = BlockFactory().create_grid(parameters)
# Basis transformator
if eigentrafo is True:
# The potential used
Potential = BlockFactory().create_potential(parameters)
BT = BasisTransformationWF(Potential)
BT.set_grid(grid)
# And two empty wavefunctions
WF = WaveFunction(parameters)
WF.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_norm(parameters, timeslots=nrtimesteps, blockid=blockid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing norms 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
if eigentrafo is True:
BT.transform_to_eigen(WF)
# Calculate the norm of the wave functions projected into the eigenbasis
norms = WF.norm()
iom.save_norm(norms, timestep=step, blockid=blockid)
def compute_norm(iom, blockid=0, eigentrafo=True):
"""Compute the norm of a wavefunction timeseries.
: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``.
"""
parameters = iom.load_parameters()
# Number of time steps we saved
timesteps = iom.load_wavefunction_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Construct the grid from the parameters
grid = BlockFactory().create_grid(parameters)
# Basis transformator
if eigentrafo is True:
# The potential used
Potential = BlockFactory().create_potential(parameters)
BT = BasisTransformationWF(Potential)
BT.set_grid(grid)
# And two empty wavefunctions
WF = WaveFunction(parameters)
WF.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_norm(parameters, timeslots=nrtimesteps, blockid=blockid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing norms of timestep %d" % step)
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [values[j, ...] for j in range(parameters["ncomponents"])]
WF.set_values(values)
# Project wavefunction values to eigenbasis
if eigentrafo is True:
BT.transform_to_eigen(WF)
# Calculate the norm of the wave functions projected into the eigenbasis
norms = WF.norm()
iom.save_norm(norms, timestep=step, blockid=blockid)
