print("**************************************************")
# Iterate over all blocks
for blockid in blockids:
print("Computing the autocorrelation in data block '{}'".format(blockid))
if iom.has_autocorrelation(blockid=blockid):
print("Datablock '{}' already contains autocorrelation data, silent skip.".format(blockid))
continue
# No configuration parameters so far
if PA is None:
print("Warning: Using (possibly improper) default values for inner product")
if iom.has_wavepacket(blockid=blockid):
descr = iom.load_wavepacket_description(blockid=blockid)
PA = integrator(descr)
elif iom.has_inhomogwavepacket(blockid=blockid):
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
PA = integrator(descr)
# NOTE: Add new algorithms here
if iom.has_wavepacket(blockid=blockid):
from WaveBlocksND.Interface import AutocorrelationWavepacket
AutocorrelationWavepacket.compute_autocorrelation_hawp(iom, PA, blockid=blockid, eigentrafo=args.noeigentransform)
elif iom.has_wavefunction(blockid=blockid):
from WaveBlocksND.Interface import AutocorrelationWavefunction
AutocorrelationWavefunction.compute_autocorrelation(iom, PA, blockid=blockid, eigentrafo=args.noeigentransform)
elif iom.has_inhomogwavepacket(blockid=blockid):
from WaveBlocksND.Interface import AutocorrelationWavepacket
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
print("Computing the autocorrelation in data block '{}'".format(blockid))
if iom.has_autocorrelation(blockid=blockid):
print(
"Datablock '{}' already contains autocorrelation data, silent skip."
.format(blockid))
continue
# No configuration parameters so far
if PA is None:
print(
"Warning: Using (possibly improper) default values for inner product"
)
if iom.has_wavepacket(blockid=blockid):
descr = iom.load_wavepacket_description(blockid=blockid)
PA = integrator(descr)
elif iom.has_inhomogwavepacket(blockid=blockid):
descr = iom.load_inhomogwavepacket_description(blockid=blockid)
PA = integrator(descr)
# NOTE: Add new algorithms here
if iom.has_wavepacket(blockid=blockid):
from WaveBlocksND.Interface import AutocorrelationWavepacket
AutocorrelationWavepacket.compute_autocorrelation_hawp(
iom, PA, blockid=blockid, eigentrafo=args.noeigentransform)
elif iom.has_wavefunction(blockid=blockid):
from WaveBlocksND.Interface import AutocorrelationWavefunction
AutocorrelationWavefunction.compute_autocorrelation(
iom, PA, blockid=blockid, eigentrafo=args.noeigentransform)
