def compute_norm_inhawp(iom, blockid=0, eigentrafo=True):
"""Compute the norm of a wavepacket timeseries.
This function is for inhomogeneous wavepackets.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
:type blockid: Integer, 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_inhomogwavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Basis transformator
if eigentrafo is True:
# The potential used
Potential = BlockFactory().create_potential(parameters)
BT = BasisTransformationHAWP(Potential)
# We want to save norms, thus add a data slot to the data file
iom.add_norm(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
HAWP = BlockFactory().create_wavepacket(descr)
if eigentrafo is True:
BT.set_matrix_builder(HAWP.get_quadrature())
# Basis shapes
BS_descr = iom.load_inhomogwavepacket_basisshapes()
BS = {}
for ahash, descr in BS_descr.iteritems():
BS[ahash] = BlockFactory().create_basis_shape(descr)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing norms of timestep "+str(step))
# Retrieve simulation data
params = iom.load_inhomogwavepacket_parameters(timestep=step, blockid=blockid)
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params)
HAWP.set_basis_shape([ BS[int(ha)] for ha in hashes ])
HAWP.set_coefficients(coeffs)
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWP)
# Measure norms in the eigenbasis
norm = HAWP.norm()
# Save the norms
iom.save_norm(norm, timestep=step, blockid=blockid)
def compute_norm_inhawp(iom, blockid=0, eigentrafo=True):
"""Compute the norm of a wavepacket timeseries.
This function is for inhomogeneous wavepackets.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
:type blockid: Integer, 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_inhomogwavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Basis transformator
if eigentrafo is True:
# The potential used
Potential = BlockFactory().create_potential(parameters)
BT = BasisTransformationHAWP(Potential)
# We want to save norms, thus add a data slot to the data file
iom.add_norm(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
HAWP = BlockFactory().create_wavepacket(descr)
if eigentrafo is True:
BT.set_matrix_builder(HAWP.get_quadrature())
# Basis shapes
BS_descr = iom.load_inhomogwavepacket_basisshapes()
BS = {}
for ahash, descr in BS_descr.iteritems():
BS[ahash] = BlockFactory().create_basis_shape(descr)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing norms of timestep " + str(step))
# Retrieve simulation data
params = iom.load_inhomogwavepacket_parameters(timestep=step,
blockid=blockid)
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(
timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params)
HAWP.set_basis_shape([BS[int(ha)] for ha in hashes])
HAWP.set_coefficients(coeffs)
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWP)
# Measure norms in the eigenbasis
norm = HAWP.norm()
# Save the norms
iom.save_norm(norm, timestep=step, blockid=blockid)
