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)