def read_data_inhomogeneous(iom, blockid=0):
r"""
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
"""
BF = BlockFactory()
parameters = iom.load_parameters()
timegrid = iom.load_inhomogwavepacket_timegrid(blockid=blockid)
# Basis shapes
bsdescr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in bsdescr.iteritems():
BS[ahash] = BF.create_basis_shape(descr)
# Plot the coefficients for all timesteps
for j, step in enumerate(timegrid):
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(timestep=step, blockid=blockid, get_hashes=True)
k = []
for i in xrange(parameters["ncomponents"]):
bs = BS[int(hashes[i])]
ki = array([bs[node] for node in bs.get_node_iterator()])
k.append(ki)
plot_coefficients(k, coeffs, step, parameters["dt"], index=blockid)
def read_data_inhomogeneous(iom, blockid=0, timerange=None, path='.'):
r"""
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
"""
BF = BlockFactory()
if iom.has_parameters():
parameters = iom.load_parameters()
if "dt" in parameters:
dt = parameters["dt"]
else:
dt = None
timegrid = iom.load_inhomogwavepacket_timegrid(blockid=blockid)
if timerange is not None:
if len(timerange) == 1:
I = (timegrid == timerange)
else:
I = ((timegrid >= timerange[0]) & (timegrid <= timerange[1]))
if any(I):
timegrid = timegrid[I]
else:
raise ValueError("No valid timestep remains!")
# Basis shapes
bsdescr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in bsdescr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Plot the coefficients for all timesteps
for j, step in enumerate(timegrid):
allhashes, allcoeffs = iom.load_inhomogwavepacket_coefficients(
timestep=step, blockid=blockid, get_hashes=True)
k = []
ck = []
for ahash, coeffs in zip(allhashes, allcoeffs):
bs = BS[int(ahash)]
ki = array([bs[node] for node in bs.get_node_iterator(mode="mag")])
ck.append(coeffs[ki])
ki.sort()
k.append(ki)
plot_coefficients(k, ck, step, dt, blockid=blockid, path=path)
def read_data_inhomogeneous(iom, blockid=0, timerange=None, path='.'):
r"""
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
"""
BF = BlockFactory()
parameters = iom.load_parameters()
timegrid = iom.load_inhomogwavepacket_timegrid(blockid=blockid)
if timerange is not None:
if len(timerange) == 1:
I = (timegrid == timerange)
else:
I = ((timegrid >= timerange[0]) & (timegrid <= timerange[1]))
if any(I):
timegrid = timegrid[I]
else:
raise ValueError("No valid timestep remains!")
# Basis shapes
bsdescr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in bsdescr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Plot the coefficients for all timesteps
for j, step in enumerate(timegrid):
allhashes, allcoeffs = iom.load_inhomogwavepacket_coefficients(timestep=step, blockid=blockid, get_hashes=True)
k = []
ck = []
for ahash, coeffs in zip(allhashes, allcoeffs):
bs = BS[int(ahash)]
ki = array([bs[node] for node in bs.get_node_iterator(mode="mag")])
ck.append(coeffs[ki])
ki.sort()
k.append(ki)
dt = parameters["dt"] if "dt" in parameters else None
plot_coefficients(k, ck, step, dt, blockid=blockid, path=path)
def compute_energy_inhawp(iom, blockid=0, eigentrafo=True, iseigen=True):
"""Compute the energies 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``.
:param iseigen: Whether the data is assumed to be in the eigenbasis.
:type iseigen: Boolean, default is ``True``
"""
parameters = iom.load_parameters()
BF = BlockFactory()
# Number of time steps we saved
timesteps = iom.load_inhomogwavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# The potential used
Potential = BF.create_potential(parameters)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationHAWP(Potential)
# We want to save energies, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
HAWP = BF.create_wavepacket(descr)
# Inner product
if HAWP.get_innerproduct() is None:
IP = BF.create_inner_product(parameters["innerproduct"])
HAWP.set_innerproduct(IP)
if eigentrafo is True:
BT.set_matrix_builder(HAWP.get_innerproduct())
# Basis shapes
BS_descr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
O = ObservablesHAWP()
KEY = ("q", "p", "Q", "P", "S", "adQ")
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep %d" % step)
# Retrieve simulation data
params = iom.load_inhomogwavepacket_parameters(timestep=step,
blockid=blockid,
key=KEY)
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(
timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params, key=KEY)
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)
# Compute the energies
O.set_innerproduct(HAWP.get_innerproduct())
O.set_gradient(HAWP.get_gradient_operator())
ekin = O.kinetic_energy(HAWP)
if iseigen is True:
epot = O.potential_energy(HAWP, Potential.evaluate_eigenvalues_at)
else:
epot = O.potential_energy(HAWP, Potential.evaluate_at)
iom.save_energy((ekin, epot), timestep=step, blockid=blockid)
def compute_autocorrelation_inhawp(iom, obsconfig, blockid=0, eigentrafo=True):
"""Compute the autocorrelation of a wavepacket timeseries.
This function is for inhomogeneous wavepackets.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param obsconfig: Configuration parameters describing f.e. the inner product to use.
:type obsconfig: A :py:class:`ParameterProvider` instance.
: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()
BF = BlockFactory()
# 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 = BF.create_potential(parameters)
BT = BasisTransformationHAWP(Potential)
# We want to save autocorrelations, thus add a data slot to the data file
iom.add_autocorrelation(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
HAWPo = BF.create_wavepacket(descr)
HAWPt = BF.create_wavepacket(descr)
if eigentrafo is True:
BT.set_matrix_builder(HAWPo.get_innerproduct())
# Basis shapes
BS_descr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Comfigure the original wavepacket
# Retrieve simulation data
params = iom.load_inhomogwavepacket_parameters(timestep=0, blockid=blockid)
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(timestep=0, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWPo.set_parameters(params)
HAWPo.set_basis_shapes([BS[int(ha)] for ha in hashes])
HAWPo.set_coefficients(coeffs)
# Set up the innerproduct for solving the integrals <phi_0 | phi_t>
IP = BF.create_inner_product(obsconfig["innerproduct"])
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing autocorrelations of timestep %d" % 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
HAWPt.set_parameters(params)
HAWPt.set_basis_shapes([BS[int(ha)] for ha in hashes])
HAWPt.set_coefficients(coeffs)
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWPt)
# Measure autocorrelations in the eigenbasis
acs = IP.quadrature(HAWPo, HAWPt, diagonal=True)
# Save the autocorrelations
iom.save_autocorrelation(acs, timestep=step, blockid=blockid)
def compute_autocorrelation_hawp(iom, obsconfig, blockid=0, eigentrafo=True):
"""Compute the autocorrelation of a wavepacket timeseries.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param obsconfig: Configuration parameters describing f.e. the inner product to use.
:type obsconfig: A :py:class:`ParameterProvider` instance.
: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()
BF = BlockFactory()
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Basis transformator
if eigentrafo is True:
# The potential used
Potential = BF.create_potential(parameters)
BT = BasisTransformationHAWP(Potential)
# We want to save norms, thus add a data slot to the data file
iom.add_autocorrelation(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_wavepacket_description(blockid=blockid)
HAWPo = BF.create_wavepacket(descr)
HAWPt = BF.create_wavepacket(descr)
if eigentrafo is True:
BT.set_matrix_builder(HAWPo.get_innerproduct())
# Basis shapes
BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Comfigure the original wavepacket
KEY = ("q", "p", "Q", "P", "S", "adQ")
# Retrieve simulation data
params = iom.load_wavepacket_parameters(timestep=0,
blockid=blockid,
key=KEY)
hashes, coeffs = iom.load_wavepacket_coefficients(timestep=0,
get_hashes=True,
blockid=blockid)
# Configure the wavepacket
HAWPo.set_parameters(params, key=KEY)
HAWPo.set_basis_shapes([BS[int(ha)] for ha in hashes])
HAWPo.set_coefficients(coeffs)
# Set up the innerproduct for solving the integrals <phi_0 | phi_t>
IP = BF.create_inner_product(obsconfig["innerproduct"])
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWPo)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing autocorrelation of timestep %d" % step)
# Retrieve simulation data
paramst = iom.load_wavepacket_parameters(timestep=step,
blockid=blockid,
key=KEY)
hashes, coeffs = iom.load_wavepacket_coefficients(timestep=step,
get_hashes=True,
blockid=blockid)
# Configure the wavepacket
HAWPt.set_parameters(paramst, key=KEY)
HAWPt.set_basis_shapes([BS[int(ha)] for ha in hashes])
HAWPt.set_coefficients(coeffs)
# Transform to the eigenbasis.
if eigentrafo is True:
BT.transform_to_eigen(HAWPt)
# Measure autocorrelations in the eigenbasis
acs = IP.quadrature(HAWPo, HAWPt, diagonal=True)
# Save the autocorrelations
iom.save_autocorrelation(acs, timestep=step, blockid=blockid)
def load_from_file(filepath, blockid=0, timestep=0, sizeK=None):
r"""Utility script to load wavepacket parameters and coefficients
from another simulation result in a form suitable for the input
configuration of a new simulation. This is (mainly) used
to start simulations with previously computed eigenstates.
:param filepath: The path to the `.hdf5` file from which data will be read.
:param blockid: The `datablock` from which to read the data.
Default is the block with `blockid=0`.
:param timestep: Load the data corresponding to the given `timestep`.
The default timestep is `0`.
:param sizeK: Load at most 'sizeK' many coefficients. Note that the order
is defined by the linearization mapping :math:`\mu` of the
packet's current basis shape. We then pick the first `sizeK`
ones.
"""
IOM = IOManager()
IOM.open_file(filepath)
# Check if we have data
tg = IOM.load_wavepacket_timegrid(blockid=blockid)
if timestep not in tg:
raise ValueError("No data for timestep {}".format(timestep))
# Load data and assemble packet
BF = BlockFactory()
# Basis shapes
BS_descr = IOM.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Create a packet
wpd = IOM.load_wavepacket_description(blockid=blockid)
HAWP = BF.create_wavepacket(wpd)
# Data
ha, ci = IOM.load_wavepacket_coefficients(blockid=blockid,
timestep=timestep,
get_hashes=True)
Pi = IOM.load_wavepacket_parameters(blockid=blockid, timestep=timestep)
HAWP.set_parameters(Pi)
HAWP.set_basis_shapes([BS[int(h)] for h in ha])
HAWP.set_coefficients(ci)
# Reformat data
C = []
for n in range(HAWP.get_number_components()):
B = HAWP.get_basis_shapes(component=n)
cn = HAWP.get_coefficients(component=n)
l = []
for i in range(B.get_basis_size()):
l.append((B[i], cn[i, 0]))
C.append(l)
if sizeK is not None:
# We load at most 'sizeK' coefficients.
# Note that this does NOT specify which
# ones in terms of multi-indices.
C = [c[:sizeK] for c in C]
return Pi, C
def load_from_file(filepath, blockid=0, timestep=0, sizeK=None):
r"""Utility script to load wavepacket parameters and coefficients
from another simulation result in a form suitable for the input
configuration of a new simulation. This is (mainly) used
to start simulations with previously computed eigenstates.
:param filepath: The path to the `.hdf5` file from which data will be read.
:param blockid: The `datablock` from which to read the data.
Default is the block with `blockid=0`.
:param timestep: Load the data corresponding to the given `timestep`.
The default timestep is `0`.
:param sizeK: Load at most 'sizeK' many coefficients. Note that the order
is defined by the linearization mapping :math:`\mu` of the
packet's current basis shape. We then pick the first `sizeK`
ones.
"""
IOM = IOManager()
IOM.open_file(filepath)
# Check if we have data
tg = IOM.load_wavepacket_timegrid(blockid=blockid)
if timestep not in tg:
raise ValueError("No data for timestep {}".format(timestep))
# Load data and assemble packet
BF = BlockFactory()
# Basis shapes
BS_descr = IOM.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
# Create a packet
wpd = IOM.load_wavepacket_description(blockid=blockid)
HAWP = BF.create_wavepacket(wpd)
# Data
ha, ci = IOM.load_wavepacket_coefficients(blockid=blockid, timestep=timestep, get_hashes=True)
Pi = IOM.load_wavepacket_parameters(blockid=blockid, timestep=timestep)
HAWP.set_parameters(Pi)
HAWP.set_basis_shapes([BS[int(h)] for h in ha])
HAWP.set_coefficients(ci)
# Reformat data
C = []
for n in range(HAWP.get_number_components()):
B = HAWP.get_basis_shapes(component=n)
cn = HAWP.get_coefficients(component=n)
l = []
for i in range(B.get_basis_size()):
l.append((B[i], cn[i, 0]))
C.append(l)
if sizeK is not None:
# We load at most 'sizeK' coefficients.
# Note that this does NOT specify which
# ones in terms of multi-indices.
C = [c[:sizeK] for c in C]
return Pi, C
def plot_frames(PP, iom, blockid=0, load=False, limits=None):
r"""
"""
parameters = iom.load_parameters()
BF = BlockFactory()
if not parameters["dimension"] == 2:
print("No wavepacket of two space dimensions, silent return!")
return
if PP is None:
PP = parameters
if load is True:
# TODO: Implement reshaping
raise NotImplementedError("Loading of 2D grids is not implemented")
#G = iom.load_grid(blockid=blockid)
#G = grid.reshape((1, -1))
else:
G = BF.create_grid(PP)
u, v = map(squeeze, G.get_axes())
V = BF.create_potential(parameters)
BT = BasisTransformationHAWP(V)
wpd = iom.load_wavepacket_description(blockid=blockid)
HAWP = BF.create_wavepacket(wpd)
# Basis shapes
BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.iteritems():
BS[ahash] = BF.create_basis_shape(descr)
timegrid = iom.load_wavepacket_timegrid(blockid=blockid)
N = HAWP.get_number_components()
for step in timegrid:
print(" Plotting frame of timestep # " + str(step))
hi, ci = iom.load_wavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockid)
Pi = iom.load_wavepacket_parameters(timestep=step, blockid=blockid)
HAWP.set_parameters(Pi)
HAWP.set_basis_shapes([ BS[int(ha)] for ha in hi ])
HAWP.set_coefficients(ci)
psi = HAWP.evaluate_at(G, prefactor=True, component=0)
fig = figure()
for level in xrange(N):
z = psi[level]
z = z.reshape(G.get_number_nodes())
subplot(N,1,level+1)
#plotcm(z.reshape(G.get_number_nodes()), darken=0.3)
plotcf2d(u, v, z, darken=0.3, limits=limits)
savefig("wavepacket_block_"+str(blockid)+"_level_"+str(level)+"_timestep_"+(5-len(str(step)))*"0"+str(step)+".png")
close(fig)
print(" Plotting frames finished")
def compute_energy_inhawp(iom, blockid=0, eigentrafo=True, iseigen=True):
"""Compute the energies 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``.
:param iseigen: Whether the data is assumed to be in the eigenbasis.
:type iseigen: Boolean, default is ``True``
"""
parameters = iom.load_parameters()
BF = BlockFactory()
# Number of time steps we saved
timesteps = iom.load_inhomogwavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# The potential used
Potential = BF.create_potential(parameters)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationHAWP(Potential)
# We want to save energies, thus add a data slot to the data file
iom.add_energy(parameters, timeslots=nrtimesteps, blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
HAWP = BF.create_wavepacket(descr)
# Inner product
if HAWP.get_innerproduct() is None:
IP = BF.create_inner_product(parameters["innerproduct"])
HAWP.set_innerproduct(IP)
if eigentrafo is True:
BT.set_matrix_builder(HAWP.get_innerproduct())
# Basis shapes
BS_descr = iom.load_inhomogwavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BF.create_basis_shape(descr)
O = ObservablesHAWP()
KEY = ("q", "p", "Q", "P", "S", "adQ")
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing energies of timestep %d" % step)
# Retrieve simulation data
params = iom.load_inhomogwavepacket_parameters(timestep=step, blockid=blockid, key=KEY)
hashes, coeffs = iom.load_inhomogwavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params, key=KEY)
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)
# Compute the energies
O.set_innerproduct(HAWP.get_innerproduct())
ekin = O.kinetic_energy(HAWP)
if iseigen is True:
epot = O.potential_energy(HAWP, Potential.evaluate_eigenvalues_at)
else:
epot = O.potential_energy(HAWP, Potential.evaluate_at)
iom.save_energy((ekin, epot), timestep=step, blockid=blockid)
