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. """ parameters = iom.load_parameters() timegrid = iom.load_inhomogwavepacket_timegrid(blockid=blockid) time = timegrid * parameters["dt"] # The potential used Potential = BlockFactory().create_potential(parameters) # Basis transformator BT = BasisTransformationHAWP(Potential) # Basis shapes BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid) BS = {} for ahash, descr in BS_descr.iteritems(): BS[ahash] = BlockFactory().create_basis_shape(descr) # Initialize a Hagedorn wavepacket with the data descr = iom.load_inhomogwavepacket_description(blockid=blockid) HAWP = BlockFactory().create_wavepacket(descr) BT.set_matrix_builder(HAWP.get_quadrature()) # Store the resulting coefficients here CI = [ [] for i in xrange(HAWP.get_number_components()) ] # Iterate over all timesteps, this is an *expensive* transformation for i, step in enumerate(timegrid): print(" Computing eigentransformation at timestep "+str(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 HAWP.set_parameters(params) HAWP.set_basis_shapes([ BS[int(ha)] for ha in hashes ]) HAWP.set_coefficients(coeffs) # Transform to the eigenbasis. BT.transform_to_eigen(HAWP) for index, item in enumerate(HAWP.get_coefficients()): CI[index].append(item) CI = [ transpose(hstack(item)) for item in CI ] return time, CI
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() # 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 = BlockFactory().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 = BlockFactory().create_wavepacket(descr) HAWPt = BlockFactory().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.iteritems(): BS[ahash] = BlockFactory().create_basis_shape(descr) # Comfigure the original wavepacket # Retrieve simulation data params = iom.load_wavepacket_parameters(timestep=0, blockid=blockid) hashes, coeffs = iom.load_wavepacket_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 = BlockFactory().create_inner_product(obsconfig["innerproduct"]) # Iterate over all timesteps for i, step in enumerate(timesteps): print(" Computing autocorrelations of timestep "+str(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_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 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() # Number of time steps we saved timesteps = iom.load_inhomogwavepacket_timegrid(blockid=blockid) nrtimesteps = timesteps.shape[0] # The potential used Potential = BlockFactory().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 = BlockFactory().create_wavepacket(descr) 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.iteritems(): BS[ahash] = BlockFactory().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 "+str(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)
def compute_norm_hawp(iom, blockid=0, eigentrafo=True): """Compute the norm of a wavepacket timeseries. :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``. """ parameters = iom.load_parameters() # 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 = BlockFactory().create_potential(parameters) BT = BasisTransformationHAWP(Potential) # We want to save norms, thus add a data slot to the data file iom.add_norm(parameters, timeslots=nrtimesteps, blockid=blockid) # Initialize a Hagedorn wavepacket with the data descr = iom.load_wavepacket_description(blockid=blockid) HAWP = BlockFactory().create_wavepacket(descr) if eigentrafo is True: BT.set_matrix_builder(HAWP.get_innerproduct()) # Basis shapes BS_descr = iom.load_wavepacket_basisshapes(blockid=blockid) BS = {} for ahash, descr in BS_descr.iteritems(): BS[ahash] = BlockFactory().create_basis_shape(descr) KEY = ("q", "p", "Q", "P", "S", "adQ") # Iterate over all timesteps for i, step in enumerate(timesteps): print(" Computing norms of timestep " + str(step)) # Retrieve simulation data params = 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 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) # Measure norms in the eigenbasis norm = HAWP.norm() # Save the norms iom.save_norm(norm, timestep=step, blockid=blockid)
def transform_inhawp_to_eigen(iomin, iomout, blockidin=0, blockidout=0): """Compute the transformation to the eigenbasis for a wavepacket. Save the result back to a file. :param iomin: An :py:class:`IOManager: instance providing the simulation data. :param iomout: An :py:class:`IOManager: instance for saving the transformed data. :param blockidin: The data block from which the values are read. Default is `0`. :param blockidout: The data block to which the values are written. Default is `0`. """ parameters = iomin.load_parameters() KEY = ("q","p","Q","P","S","adQ") # Number of time steps we saved timesteps = iomin.load_inhomogwavepacket_timegrid(blockid=blockidin) nrtimesteps = timesteps.shape[0] # The potential used Potential = BlockFactory().create_potential(parameters) # Basis transformator BT = BasisTransformationHAWP(Potential) # Initialize a Hagedorn wavepacket with the data descr = iomin.load_inhomogwavepacket_description(blockid=blockidin) HAWP = BlockFactory().create_wavepacket(descr) iomout.add_inhomogwavepacket(descr, timeslots=nrtimesteps, blockid=blockidout, key=KEY) iomout.save_inhomogwavepacket_description(HAWP.get_description(), blockid=blockidout) BT.set_matrix_builder(HAWP.get_innerproduct()) # Basis shapes BS_descr = iomin.load_inhomogwavepacket_basisshapes(blockid=blockidin) BS = {} for ahash, descr in BS_descr.iteritems(): BS[ahash] = BlockFactory().create_basis_shape(descr) # Iterate over all timesteps for i, step in enumerate(timesteps): print(" Compute eigentransform at timestep # " + str(step)) # Retrieve simulation data params = iomin.load_inhomogwavepacket_parameters(timestep=step, blockid=blockidin, key=KEY) hashes, coeffs = iomin.load_inhomogwavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockidin) # 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. BT.transform_to_eigen(HAWP) # Save the transformed packet # Pi iomout.save_inhomogwavepacket_parameters(HAWP.get_parameters(key=KEY), timestep=step, blockid=blockidout, key=KEY) # Basis shapes (in case they changed!) for shape in HAWP.get_basis_shapes(): iomout.save_inhomogwavepacket_basisshapes(shape, blockid=blockidout) # Coefficients iomout.save_inhomogwavepacket_coefficients(HAWP.get_coefficients(), HAWP.get_basis_shapes(), timestep=step, blockid=blockidout)
def transform_inhawp_to_eigen(iomin, iomout, blockidin=0, blockidout=0): """Compute the transformation to the eigenbasis for a wavepacket. Save the result back to a file. :param iomin: An :py:class:`IOManager: instance providing the simulation data. :param iomout: An :py:class:`IOManager: instance for saving the transformed data. :param blockidin: The data block from which the values are read. Default is `0`. :param blockidout: The data block to which the values are written. Default is `0`. """ parameters = iomin.load_parameters() KEY = ("q", "p", "Q", "P", "S", "adQ") # Number of time steps we saved timesteps = iomin.load_inhomogwavepacket_timegrid(blockid=blockidin) nrtimesteps = timesteps.shape[0] # The potential used Potential = BlockFactory().create_potential(parameters) # Basis transformator BT = BasisTransformationHAWP(Potential) # Initialize a Hagedorn wavepacket with the data descr = iomin.load_inhomogwavepacket_description(blockid=blockidin) HAWP = BlockFactory().create_wavepacket(descr) iomout.add_inhomogwavepacket(descr, timeslots=nrtimesteps, blockid=blockidout, key=KEY) iomout.save_inhomogwavepacket_description(HAWP.get_description(), blockid=blockidout) BT.set_matrix_builder(HAWP.get_innerproduct()) # Basis shapes BS_descr = iomin.load_inhomogwavepacket_basisshapes(blockid=blockidin) BS = {} for ahash, descr in BS_descr.items(): BS[ahash] = BlockFactory().create_basis_shape(descr) # Iterate over all timesteps for i, step in enumerate(timesteps): print(" Compute eigentransform at timestep %d" % step) # Retrieve simulation data params = iomin.load_inhomogwavepacket_parameters(timestep=step, blockid=blockidin, key=KEY) hashes, coeffs = iomin.load_inhomogwavepacket_coefficients(timestep=step, get_hashes=True, blockid=blockidin) # 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. BT.transform_to_eigen(HAWP) # Save the transformed packet # Pi iomout.save_inhomogwavepacket_parameters(HAWP.get_parameters(key=KEY), timestep=step, blockid=blockidout, key=KEY) # Basis shapes (in case they changed!) for shape in HAWP.get_basis_shapes(): iomout.save_inhomogwavepacket_basisshapes(shape, blockid=blockidout) # Coefficients iomout.save_inhomogwavepacket_coefficients(HAWP.get_coefficients(), HAWP.get_basis_shapes(), timestep=step, blockid=blockidout)