def plot_frames(
iom,
blockid=0
): #, view=None, plotphase=True, plotcomponents=False, plotabssqr=False, imgsize=(12,9)):
parameters = iom.load_parameters()
if not parameters["dimension"] == 2:
print("No wavefunction of two space dimensions, silent return!")
return
G = BlockFactory().create_grid(parameters)
V = BlockFactory().create_potential(parameters)
print(G.get_extensions())
WF = WaveFunction(parameters)
WF.set_grid(G)
BT = BasisTransformationWF(V)
BT.set_grid(G)
timegrid = iom.load_wavefunction_timegrid(blockid=blockid)
u, v = G.get_nodes(split=True, flat=False)
u = real(u)
v = real(v)
N = WF.get_number_components()
for step in timegrid:
print(" Plotting frame of timestep # " + str(step))
wave = iom.load_wavefunction(blockid=blockid, timestep=step)
values = [wave[j, ...] for j in xrange(parameters["ncomponents"])]
WF.set_values(values)
# Transform the values to the eigenbasis
# TODO: improve this:
if parameters["algorithm"] == "fourier":
BT.transform_to_eigen(WF)
else:
pass
Psi = WF.get_values()
fig = figure()
for level in xrange(N):
z = Psi[level]
subplot(N, 1, level + 1)
plotcm(z, darken=0.3)
savefig("wavefunction_level_" + str(level) + "_timestep_" +
(5 - len(str(step))) * "0" + str(step) + ".png")
close(fig)
print(" Plotting frames finished")
def plot_frames(iom, blockid=0):#, view=None, plotphase=True, plotcomponents=False, plotabssqr=False, imgsize=(12,9)):
parameters = iom.load_parameters()
if not parameters["dimension"] == 2:
print("No wavefunction of two space dimensions, silent return!")
return
G = BlockFactory().create_grid(parameters)
V = BlockFactory().create_potential(parameters)
print(G.get_extensions())
WF = WaveFunction(parameters)
WF.set_grid(G)
BT = BasisTransformationWF(V)
BT.set_grid(G)
timegrid = iom.load_wavefunction_timegrid(blockid=blockid)
u, v = G.get_nodes(split=True, flat=False)
u = real(u)
v = real(v)
N = WF.get_number_components()
for step in timegrid:
print(" Plotting frame of timestep # " + str(step))
wave = iom.load_wavefunction(blockid=blockid, timestep=step)
values = [ wave[j,...] for j in xrange(parameters["ncomponents"]) ]
WF.set_values(values)
# Transform the values to the eigenbasis
# TODO: improve this:
if parameters["algorithm"] == "fourier":
BT.transform_to_eigen(WF)
else:
pass
Psi = WF.get_values()
fig = figure()
for level in xrange(N):
z = Psi[level]
subplot(N,1,level+1)
plotcm(z, darken=0.3)
savefig("wavefunction_level_"+str(level)+"_timestep_"+(5-len(str(step)))*"0"+str(step)+".png")
close(fig)
print(" Plotting frames finished")
def compute_autocorrelation(iom, obsconfig=None, blockid=0, eigentrafo=True):
"""Compute the autocorrelation of a wavefunction 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.
Value has no effect in this class.
: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_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
WFo = WaveFunction(parameters)
WFo.set_grid(grid)
WFt = WaveFunction(parameters)
WFt.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_autocorrelation(parameters, timeslots=nrtimesteps, blockid=blockid)
# Preconfigure the
values = iom.load_wavefunction(timestep=0, blockid=blockid)
values = [values[j, ...] for j in range(parameters["ncomponents"])]
WFo.set_values(values)
# Project wavefunction values to eigenbasis
if eigentrafo is True:
BT.transform_to_eigen(WFo)
# Fourier transform the values
WFo.set_values([fftn(value) for value in WFo.get_values()])
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing autocorrelations of timestep %d" % step)
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [values[j, ...] for j in range(parameters["ncomponents"])]
WFt.set_values(values)
# Project wavefunction values to eigenbasis
if eigentrafo is True:
BT.transform_to_eigen(WFt)
# Fourier transform the values
WFt.set_values([fftn(value) for value in WFt.get_values()])
# Compute the prefactor
T = grid.get_extensions()
N = grid.get_number_nodes()
prefactor = product(array(T) / array(N).astype(floating)**2)
# Compute the autocorrelation
# TODO: Consider splitting into cases `fft` versus `fftn`
valueso = WFo.get_values()
valuest = WFt.get_values()
acs = [prefactor * ifftn(sum(conjugate(valueso[n]) * valuest[n])) for n in range(parameters["ncomponents"])]
iom.save_autocorrelation(acs, timestep=step, blockid=blockid)
def compute_autocorrelation(iom, obsconfig=None, blockid=0, eigentrafo=True):
"""Compute the autocorrelation of a wavefunction 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.
Value has no effect in this class.
: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_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
WFo = WaveFunction(parameters)
WFo.set_grid(grid)
WFt = WaveFunction(parameters)
WFt.set_grid(grid)
# We want to save norms, thus add a data slot to the data file
iom.add_autocorrelation(parameters, timeslots=nrtimesteps, blockid=blockid)
# Preconfigure the
values = iom.load_wavefunction(timestep=0, blockid=blockid)
values = [values[j, ...] for j in range(parameters["ncomponents"])]
WFo.set_values(values)
# Project wavefunction values to eigenbasis
if eigentrafo is True:
BT.transform_to_eigen(WFo)
# Fourier transform the values
WFo.set_values([fftn(value) for value in WFo.get_values()])
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Computing autocorrelations of timestep %d" % step)
# Retrieve simulation data
values = iom.load_wavefunction(timestep=step, blockid=blockid)
values = [values[j, ...] for j in range(parameters["ncomponents"])]
WFt.set_values(values)
# Project wavefunction values to eigenbasis
if eigentrafo is True:
BT.transform_to_eigen(WFt)
# Fourier transform the values
WFt.set_values([fftn(value) for value in WFt.get_values()])
# Compute the prefactor
T = grid.get_extensions()
N = grid.get_number_nodes()
prefactor = product(array(T) / array(N).astype(floating)**2)
# Compute the autocorrelation
# TODO: Consider splitting into cases `fft` versus `fftn`
valueso = WFo.get_values()
valuest = WFt.get_values()
acs = [
prefactor * ifftn(sum(conjugate(valueso[n]) * valuest[n]))
for n in range(parameters["ncomponents"])
]
iom.save_autocorrelation(acs, timestep=step, blockid=blockid)
