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):
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)
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()
for level in xrange(N):
z = Psi[level]
# Plot the probability densities projected to the eigenbasis
fig = mlab.figure(size=(800,700))
surfcf(u, v, angle(z), abs(z))
#mlab.contour_surf(u, v, abs(z))
#mlab.outline()
#mlab.axes()
mlab.savefig("wavefunction_level_"+str(level)+"_timestep_"+(5-len(str(step)))*"0"+str(step)+".png")
mlab.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 plot_frames(PP, iom, blockid=0, load=False, eigentransform=False, timerange=None, sparsify=10, view=None, interactive=False, path='.'):
"""Plot the wave function for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
parameters = iom.load_parameters()
if not parameters["dimension"] == 2:
print("No wavefunction 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")
else:
G = BlockFactory().create_grid(PP)
if eigentransform:
V = BlockFactory().create_potential(parameters)
BT = BasisTransformationWF(V)
BT.set_grid(G)
WF = WaveFunction(parameters)
WF.set_grid(G)
N = WF.get_number_components()
timegrid = iom.load_wavefunction_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!")
u, v = G.get_nodes(split=True, flat=False)
u = real(u[::sparsify, ::sparsify])
v = real(v[::sparsify, ::sparsify])
# View
if view is not None:
if view[0] is None:
view[0] = u.min()
if view[1] is None:
view[1] = u.max()
if view[2] is None:
view[2] = v.min()
if view[3] is None:
view[3] = v.max()
for step in timegrid:
print(" Plotting frame of timestep # {}".format(step))
# Load the data
wave = iom.load_wavefunction(blockid=blockid, timestep=step)
values = [wave[j, ...] for j in range(parameters["ncomponents"])]
WF.set_values(values)
# Transform the values to the eigenbasis
if eigentransform:
BT.transform_to_eigen(WF)
Psi = WF.get_values()
for level in range(N):
# Wavefunction data
z = Psi[level]
z = z.reshape(G.get_number_nodes())[::sparsify, ::sparsify]
# View
if view is not None:
if view[4] is None:
view[4] = 0.0
if view[5] is None:
view[5] = 1.1 * abs(z).max()
# Plot
# if not interactive:
# mlab.options.offscreen = True
fig = mlab.figure(size=(800, 700))
surfcf(u, v, angle(z), abs(z), view=view)
mlab.draw()
if interactive:
mlab.show()
else:
mlab.savefig(os.path.join("wavefunction_surface_block_%s_level_%d_timestep_%07d.png" % (blockid, level, step)))
mlab.close(fig)
def compute_evaluate_wavepackets(pp, iom, blockid=0, eigentrafo=True):
"""Evaluate a homogeneous Hagedorn wavepacket on a given grid for each timestep.
:param pp: An :py:class:`ParameterProvider` instance providing the grid data.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param blockid: The data block from which the values are read.
:param eigentrafo: Whether or not do an eigentransformation before evaluation is done.
"""
parameters = iom.load_parameters()
if pp is None:
pp = parameters
# Number of time steps we saved
timesteps = iom.load_wavepacket_timegrid(blockid=blockid)
nrtimesteps = timesteps.shape[0]
# Prepare the potential for basis transformations
Potential = BlockFactory().create_potential(parameters)
grid = BlockFactory().create_grid(pp)
# We want to save wavefunctions, thus add a data slot to the data file
d = {"ncomponents": parameters["ncomponents"],
"number_nodes": pp["number_nodes"],
"dimension": parameters["dimension"]}
iom.add_grid(d, blockid=blockid)
iom.add_wavefunction(d, timeslots=nrtimesteps, flat=True, blockid=blockid)
iom.save_grid(grid.get_nodes(), blockid=blockid)
# Initialize a Hagedorn wavepacket with the data
descr = iom.load_wavepacket_description(blockid=blockid)
HAWP = BlockFactory().create_wavepacket(descr)
# Basis transformator
if eigentrafo is True:
BT = BasisTransformationHAWP(Potential)
BT.set_matrix_builder(HAWP.get_innerproduct())
# Basis shapes
BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid)
BS = {}
for ahash, descr in BS_descr.items():
BS[ahash] = BlockFactory().create_basis_shape(descr)
WF = WaveFunction(parameters)
WF.set_grid(grid)
# Iterate over all timesteps
for i, step in enumerate(timesteps):
print(" Evaluating homogeneous wavepacket at timestep %d" % step)
# Retrieve simulation data
params = iom.load_wavepacket_parameters(timestep=step, blockid=blockid, key=("q", "p", "Q", "P", "S", "adQ"))
hashes, coeffs = iom.load_wavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockid)
# Configure the wavepacket
HAWP.set_parameters(params, key=("q", "p", "Q", "P", "S", "adQ"))
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)
# 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 plot_frames(PP,
iom,
blockid=0,
timerange=None,
view=None,
plotphase=True,
plotcomponents=False,
plotabssqr=False,
load=True,
gridblockid=None,
imgsize=(12, 9),
path='.'):
"""Plot the wave function for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param view: The aspect ratio.
:param plotphase: Whether to plot the complex phase. (slow)
:param plotcomponents: Whether to plot the real/imaginary parts..
:param plotabssqr: Whether to plot the absolute value squared.
"""
parameters = iom.load_parameters()
if not parameters["dimension"] == 1:
print("No two-dimensional wavefunction, silent return!")
return
if PP is None:
PP = parameters
if load is True:
if gridblockid is None:
gridblockid = blockid
print("Loading grid data from datablock '%s'" % gridblockid)
G = iom.load_grid(blockid=gridblockid)
grid = real(G.reshape(-1))
else:
print("Creating new grid")
G = BlockFactory().create_grid(PP)
grid = real(G.get_nodes(flat=True).reshape(-1))
# View
if view[0] is None:
view[0] = grid.min()
if view[1] is None:
view[1] = grid.max()
timegrid = iom.load_wavefunction_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!")
for step in timegrid:
print(" Plotting frame of timestep # {}".format(step))
wave = iom.load_wavefunction(blockid=blockid, timestep=step)
values = [wave[j, ...] for j in range(parameters["ncomponents"])]
# Plot
fig = figure(figsize=imgsize)
for index, component in enumerate(values):
ax = fig.add_subplot(parameters["ncomponents"], 1, index + 1)
ax.ticklabel_format(style="sci", scilimits=(0, 0), axis="y")
if plotcomponents is True:
ax.plot(grid, real(component))
ax.plot(grid, imag(component))
ax.set_ylabel(r"$\Re \varphi_{%d}, \Im \varphi_{%d}$" %
(index, index))
if plotabssqr is True:
ax.plot(grid, real(component * conj(component)))
ax.set_ylabel(
r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" %
(index, index))
if plotphase is True:
plotcf(grid, angle(component),
real(component * conj(component)))
ax.set_ylabel(
r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" %
(index, index))
ax.set_xlabel(r"$x$")
# Set the aspect window
ax.set_xlim(view[:2])
ax.set_ylim(view[2:])
if "dt" in parameters:
fig.suptitle(r"$\Psi$ at time $%f$" % (step * parameters["dt"]))
else:
fig.suptitle(r"$\Psi$")
fig.savefig(
os.path.join(
path,
"wavefunction_block_%s_timestep_%07d.png" % (blockid, step)))
close(fig)
def plot_frames(PP, iom, blockid=0, view=None, plotphase=True, plotcomponents=False, plotabssqr=False, load=True, gridblockid=None, imgsize=(12,9)):
"""Plot the wave function for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
:param view: The aspect ratio.
:param plotphase: Whether to plot the complex phase. (slow)
:param plotcomponents: Whether to plot the real/imaginary parts..
:param plotabssqr: Whether to plot the absolute value squared.
"""
parameters = iom.load_parameters()
if not parameters["dimension"] == 1:
print("No wavefunction of one space dimensions, silent return!")
return
if PP is None:
PP = parameters
if load is True:
print("Loading grid data from datablock 'global'")
if gridblockid is None:
gridblockid = blockid
G = iom.load_grid(blockid=gridblockid)
G = G.reshape((1, -1))
grid = G
else:
print("Creating new grid")
G = BlockFactory().create_grid(PP)
grid = G.get_nodes(flat=True)
timegrid = iom.load_wavefunction_timegrid(blockid=blockid)
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"]) ]
# Plot the probability densities projected to the eigenbasis
fig = figure(figsize=imgsize)
for index, component in enumerate(values):
ax = fig.add_subplot(parameters["ncomponents"],1,index+1)
ax.ticklabel_format(style="sci", scilimits=(0,0), axis="y")
if plotcomponents is True:
ax.plot(squeeze(grid), real(component))
ax.plot(squeeze(grid), imag(component))
ax.set_ylabel(r"$\Re \varphi_"+str(index)+r", \Im \varphi_"+str(index)+r"$")
if plotabssqr is True:
ax.plot(squeeze(grid), component*conj(component))
ax.set_ylabel(r"$\langle \varphi_"+str(index)+r"| \varphi_"+str(index)+r"\rangle$")
if plotphase is True:
plotcf(squeeze(grid), angle(component), component*conj(component))
ax.set_ylabel(r"$\langle \varphi_"+str(index)+r"| \varphi_"+str(index)+r"\rangle$")
ax.set_xlabel(r"$x$")
# Set the aspect window
if view is not None:
ax.set_xlim(view[:2])
ax.set_ylim(view[2:])
if parameters.has_key("dt"):
fig.suptitle(r"$\Psi$ at time $"+str(step*parameters["dt"])+r"$")
else:
fig.suptitle(r"$\Psi$")
fig.savefig("wavefunction_block"+str(blockid)+"_"+ (7-len(str(step)))*"0"+str(step) +GD.output_format)
close(fig)
print(" Plotting frames finished")
def plot_frames(PP, iom, blockid=0, eigentransform=False, timerange=None, view=None,
plotphase=True, plotcomponents=False, plotabssqr=False,
load=False, gridblockid=None, imgsize=(12, 9), path='.'):
"""Plot the wavepacket for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
parameters = iom.load_parameters()
BF = BlockFactory()
if not parameters["dimension"] == 1:
print("No one-dimensional wavepacket, silent return!")
return
if PP is None:
PP = parameters
if load is True:
if gridblockid is None:
gridblockid = blockid
print("Loading grid data from datablock '{}'".format(gridblockid))
G = iom.load_grid(blockid=gridblockid)
grid = real(G.reshape(-1))
else:
print("Creating new grid")
G = BlockFactory().create_grid(PP)
grid = real(G.get_nodes(flat=True).reshape(-1))
if eigentransform:
V = BF.create_potential(parameters)
BT = BasisTransformationHAWP(V)
timegrid = iom.load_wavepacket_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!")
# View
if view is not None:
if view[0] is None:
view[0] = grid.min()
if view[1] is None:
view[1] = grid.max()
for step in timegrid:
print(" Plotting frame of timestep # {}".format(step))
HAWP = iom.load_wavepacket(step, blockid=blockid)
# Transform the values to the eigenbasis
if eigentransform:
BT.transform_to_eigen(HAWP)
values = HAWP.evaluate_at(G.get_nodes(), prefactor=True, component=0)
# Plot
fig = figure(figsize=imgsize)
for index, component in enumerate(values):
ax = fig.add_subplot(parameters["ncomponents"], 1, index + 1)
ax.ticklabel_format(style="sci", scilimits=(0, 0), axis="y")
if plotcomponents is True:
ax.plot(grid, real(component))
ax.plot(grid, imag(component))
ax.set_ylabel(r"$\Re \varphi_{%d}, \Im \varphi_{%d}$" % (index, index))
if plotabssqr is True:
ax.plot(grid, real(component * conj(component)))
ax.set_ylabel(r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" % (index, index))
if plotphase is True:
plotcf(grid, angle(component), real(component * conj(component)))
ax.set_ylabel(r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" % (index, index))
ax.set_xlabel(r"$x$")
# Set the aspect window
ax.set_xlim(view[:2])
ax.set_ylim(view[2:])
if "dt" in parameters:
fig.suptitle(r"$\Psi$ at time $%f$" % (step * parameters["dt"]))
else:
fig.suptitle(r"$\Psi$")
fig.savefig(os.path.join(path, "wavepacket_block_%s_timestep_%07d.png" % (blockid, step)))
close(fig)
def plot_frames(PP,
iom,
blockid=0,
load=False,
eigentransform=False,
timerange=None,
sparsify=10,
view=None,
interactive=False,
path='.'):
"""Plot the wave function for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
parameters = iom.load_parameters()
if not parameters["dimension"] == 2:
print("No wavefunction 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")
else:
G = BlockFactory().create_grid(PP)
if eigentransform:
V = BlockFactory().create_potential(parameters)
BT = BasisTransformationWF(V)
BT.set_grid(G)
WF = WaveFunction(parameters)
WF.set_grid(G)
N = WF.get_number_components()
timegrid = iom.load_wavefunction_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!")
u, v = G.get_nodes(split=True, flat=False)
u = real(u[::sparsify, ::sparsify])
v = real(v[::sparsify, ::sparsify])
# View
if view is not None:
if view[0] is None:
view[0] = u.min()
if view[1] is None:
view[1] = u.max()
if view[2] is None:
view[2] = v.min()
if view[3] is None:
view[3] = v.max()
for step in timegrid:
print(" Plotting frame of timestep # {}".format(step))
# Load the data
wave = iom.load_wavefunction(blockid=blockid, timestep=step)
values = [wave[j, ...] for j in range(parameters["ncomponents"])]
WF.set_values(values)
# Transform the values to the eigenbasis
if eigentransform:
BT.transform_to_eigen(WF)
Psi = WF.get_values()
for level in range(N):
# Wavefunction data
z = Psi[level]
z = z.reshape(G.get_number_nodes())[::sparsify, ::sparsify]
# View
if view is not None:
if view[4] is None:
view[4] = 0.0
if view[5] is None:
view[5] = 1.1 * abs(z).max()
# Plot
# if not interactive:
# mlab.options.offscreen = True
fig = mlab.figure(size=(800, 700))
surfcf(u, v, angle(z), abs(z), view=view)
mlab.draw()
if interactive:
mlab.show()
else:
mlab.savefig(
os.path.join(
"wavefunction_surface_block_%s_level_%d_timestep_%07d.png"
% (blockid, level, step)))
mlab.close(fig)
def plot_frames(PP, iom, blockid=0, load=False):
r"""
"""
parameters = iom.load_parameters()
if not parameters["dimension"] == 2:
print("No wavefunction 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 = BlockFactory().create_grid(PP)
V = BlockFactory().create_potential(parameters)
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()
for level in xrange(N):
z = Psi[level]
z = z.reshape(G.get_number_nodes())
# Plot the probability densities projected to the eigenbasis
fig = mlab.figure(size=(800,700))
surfcf(u, v, angle(z), abs(z))
#mlab.contour_surf(u, v, abs(z))
#mlab.outline()
#mlab.axes()
mlab.savefig("wavefunction_level_"+str(level)+"_timestep_"+(5-len(str(step)))*"0"+str(step)+".png")
mlab.close(fig)
print(" Plotting frames finished")
def plot_frames(PP,
iom,
blockid=0,
eigentransform=False,
timerange=None,
view=None,
plotphase=True,
plotcomponents=False,
plotabssqr=False,
load=False,
gridblockid=None,
imgsize=(12, 9),
path='.'):
"""Plot the wavepacket for a series of timesteps.
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
parameters = iom.load_parameters()
BF = BlockFactory()
if not parameters["dimension"] == 1:
print("No one-dimensional wavepacket, silent return!")
return
if PP is None:
PP = parameters
if load is True:
if gridblockid is None:
gridblockid = blockid
print("Loading grid data from datablock '{}'".format(gridblockid))
G = iom.load_grid(blockid=gridblockid)
grid = real(G.reshape(-1))
else:
print("Creating new grid")
G = BlockFactory().create_grid(PP)
grid = real(G.get_nodes(flat=True).reshape(-1))
if eigentransform:
V = BF.create_potential(parameters)
BT = BasisTransformationHAWP(V)
timegrid = iom.load_wavepacket_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!")
# View
if view is not None:
if view[0] is None:
view[0] = grid.min()
if view[1] is None:
view[1] = grid.max()
for step in timegrid:
print(" Plotting frame of timestep # {}".format(step))
HAWP = iom.load_wavepacket(step, blockid=blockid)
# Transform the values to the eigenbasis
if eigentransform:
BT.transform_to_eigen(HAWP)
values = HAWP.evaluate_at(G.get_nodes(), prefactor=True, component=0)
# Plot
fig = figure(figsize=imgsize)
for index, component in enumerate(values):
ax = fig.add_subplot(parameters["ncomponents"], 1, index + 1)
ax.ticklabel_format(style="sci", scilimits=(0, 0), axis="y")
if plotcomponents is True:
ax.plot(grid, real(component))
ax.plot(grid, imag(component))
ax.set_ylabel(r"$\Re \varphi_{%d}, \Im \varphi_{%d}$" %
(index, index))
if plotabssqr is True:
ax.plot(grid, real(component * conj(component)))
ax.set_ylabel(
r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" %
(index, index))
if plotphase is True:
plotcf(grid, angle(component),
real(component * conj(component)))
ax.set_ylabel(
r"$\langle \varphi_{%d} | \varphi_{%d} \rangle$" %
(index, index))
ax.set_xlabel(r"$x$")
# Set the aspect window
ax.set_xlim(view[:2])
ax.set_ylim(view[2:])
if "dt" in parameters:
fig.suptitle(r"$\Psi$ at time $%f$" % (step * parameters["dt"]))
else:
fig.suptitle(r"$\Psi$")
fig.savefig(
os.path.join(
path,
"wavepacket_block_%s_timestep_%07d.png" % (blockid, step)))
close(fig)
