parser.add_argument("-y", "--yrange", type=float, help="The plot range on the y-axis", nargs=2, default=[-2, 5])
args = parser.parse_args()
# File with the simulation data
resultspath = os.path.abspath(args.resultspath)
if not os.path.exists(resultspath):
raise IOError("The results path does not exist: {}".format(args.resultspath))
datafile = os.path.abspath(os.path.join(args.resultspath, args.datafile))
parametersfile = os.path.abspath(os.path.join(args.resultspath, args.parametersfile))
# Read file with simulation data
iom = IOManager()
iom.open_file(filename=datafile)
# Read file with parameter data for grid
parameters = iom.load_parameters()
if args.parametersfile:
PL = ParameterLoader()
gridparams = PL.load_from_file(parametersfile)
else:
gridparams = parameters
# The axes rectangle that is plotted
view = args.xrange + args.yrange
if parameters["dimension"] == 1:
Potential = BlockFactory().create_potential(parameters)
type = str,
help = "The simulation data file",
nargs = "?",
default = GD.file_resultdatafile)
parser.add_argument("-b", "--blockid",
type = str,
help = "The data block to handle",
nargs = "*",
default = ["all"])
args = parser.parse_args()
# Read file with simulation data
iom = IOManager()
iom.open_file(filename=args.datafile)
# Which blocks to handle
blockids = iom.get_block_ids()
if "all" not in args.blockid:
blockids = [bid for bid in args.blockid if bid in blockids]
# Iterate over all blocks
for blockid in blockids:
if iom.has_wavefunction(blockid=blockid):
print("Deleting grid and wavefunction data in block '{}'".format(blockid))
iom.delete_wavefunction(blockid=blockid)
if iom.has_grid(blockid=blockid):
iom.delete_grid(blockid=blockid)
iom.finalize()
ax.set_ylabel("t")
ax.set_title(r"Coefficients $c_k^" + str(jndex) + "$")
fig.savefig("wavepacket_coefficients_map_eigen_cm_component" +
str(jndex) + "_block" + str(index) + GD.output_format)
close(fig)
fig = figure(figsize=imgsize)
ax = gca()
plotcm(coeff, angle(coeff), abs(coeff), darken=True, axes=ax)
ax.set_xlabel("k")
ax.set_ylabel("t")
ax.set_title(r"Coefficients $c_k^" + str(jndex) + "$")
fig.savefig("wavepacket_coefficients_map_eigen_cm_darken_component" +
str(jndex) + "_block" + str(index) + GD.output_format)
close(fig)
if __name__ == "__main__":
iom = IOManager()
# Read file with simulation data
try:
iom.open_file(filename=sys.argv[1])
except IndexError:
iom.open_file()
# Read the data and plot it, one plot for each data block.
read_all_datablocks(iom)
iom.finalize()
close(fig)
fig = figure()
ax = fig.gca()
ax.semilogy(timegridk, data, label=r"$|E_O^0 - \left( E_k^0 + E_p^0 \right) |$")
ax.grid(True)
ax.set_xlabel(r"Time $t$")
ax.set_ylabel(r"$|E_O^0 - \left( E_k^0 + E_p^0 \right) |$")
ax.set_title(r"Energy drift of the wavepacket $\Psi$")
fig.savefig("energy_drift_block"+str(index)+"_log"+GD.output_format)
close(fig)
if __name__ == "__main__":
iom = IOManager()
# Read file with simulation data
try:
iom.open_file(filename=sys.argv[1])
except IndexError:
iom.open_file()
read_all_datablocks(iom)
iom.finalize()
from WaveBlocksND import IOManager
from WaveBlocksND import GlobalDefaults as GD
if __name__ == "__main__":
iomc = IOManager()
iome = IOManager()
# Read file with simulation data
try:
filename = sys.argv[1]
except IndexError:
filename = GD.file_resultdatafile
iomc.open_file(filename=filename)
# New file for eigen transformed data
P = iomc.load_parameters()
iome.create_file(P, filename=filename[:-5]+"_eigen.hdf5")
# Iterate over all groups
for groupid in iomc.get_group_ids():
# Create the group if necessary
if not groupid in iome.get_group_ids():
iome.create_group(groupid=groupid)
for blockid in iomc.get_block_ids(groupid=groupid):
print("Computing eigentransformation of data in block '"+str(blockid)+"'")
default=[None, None])
args = parser.parse_args()
# File with the simulation data
resultspath = os.path.abspath(args.resultspath)
if not os.path.exists(resultspath):
raise IOError("The results path does not exist: {}".format(
args.resultspath))
datafile = os.path.abspath(os.path.join(args.resultspath, args.datafile))
# Read file with simulation data
iom = IOManager()
iom.open_file(filename=datafile)
# Which blocks to handle
blockids = iom.get_block_ids()
if "all" not in args.blockid:
blockids = [bid for bid in args.blockid if bid in blockids]
# The axes rectangle that is plotted
view = args.trange + args.vrange
# Iterate over all blocks
for blockid in blockids:
print("Plotting autocorrelations in data block '{}'".format(blockid))
if iom.has_autocorrelation(blockid=blockid):
plot_autocorrelations(read_data(iom, 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
type = str,
help = "The data file to write the transformed data.")
args = parser.parse_args()
if not args.outputfile:
outputfile = args.inputfile.replace(GD.ext_resultdatafile, "") + "_eigen" + GD.ext_resultdatafile
else:
outputfile = args.outputfile
print("Reading simulation data from the file: "+str(args.inputfile))
print("Writing transformed data to the file: "+str(outputfile))
# Read file with simulation data
iomc = IOManager()
iomc.open_file(args.inputfile)
iome = IOManager()
iome.create_file(outputfile)
# New file for eigen transformed data
P = iomc.load_parameters()
# Save the simulation parameters
iome.add_parameters()
iome.save_parameters(P)
# Iterate over all groups
for groupid in iomc.get_group_ids():
# Create the group if necessary
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
nargs = "?",
default = GD.file_resultdatafile)
parser.add_argument("-b", "--blockid",
help = "The data block to handle",
nargs = "*",
default = [0])
parser.add_argument("-p", "--params",
help = "An additional configuration parameters file")
args = parser.parse_args()
# Read file with simulation data
iom = IOManager()
iom.open_file(filename=args.simfile)
# Read the additional grid parameters
if args.params:
parametersfile = args.params
PA = ParameterLoader().load_from_file(parametersfile)
else:
PA = None
# Which blocks to handle
if "all" in args.blockid:
blocks_to_handle = iom.get_block_ids()
else:
blocks_to_handle = map(int, args.blockid)
# Iterate over all blocks
inputfile = os.path.abspath(os.path.join(args.resultspath, args.inputfile))
# No output file name given
if not args.outputfile:
outputfile = inputfile.replace(GD.ext_resultdatafile, "") + "_eigen" + GD.ext_resultdatafile
else:
outputfile = args.outputfile
outputfile = os.path.abspath(os.path.join(args.resultspath, outputfile))
print("Reading simulation data from the file: {}".format(inputfile))
print("Writing transformed data to the file: {}".format(outputfile))
# Read file with simulation data
iomc = IOManager()
iomc.open_file(inputfile)
iome = IOManager()
iome.create_file(outputfile)
# New file for eigen transformed data
P = iomc.load_parameters()
# Save the simulation parameters
iome.add_parameters()
iome.save_parameters(P)
# Iterate over all groups
for groupid in iomc.get_group_ids():
# Create the group if necessary
