sarkas.tools.observables.DynamicStructureFactor

class sarkas.tools.observables.DynamicStructureFactor

Dynamic Structure factor.

The species dependent DSF \(S_{AB}(k,\omega)\) is calculated from

\[S_{AB}(k,\omega) = \int_0^\infty dt \, \left \langle | n_{A}( \mathbf k, t)n_{B}( -\mathbf k, t) \right \rangle e^{i \omega t},\]

where the microscopic density of species \(A\) with number of particles \(N_{A}\) is given by

\[n_{A}(\mathbf k,t) = \sum^{N_{A}}_{j} e^{-i \mathbf k \cdot \mathbf r_j(t)} .\]

Methods

DynamicStructureFactor.__init__()
DynamicStructureFactor.average_slices_data()	Calculate the average and standard deviation of the observable from the slices dataframe.
DynamicStructureFactor.calc_k_data()	Calculate and save Fourier space data.
DynamicStructureFactor.calc_nkt_slices_data()	Calculate n(k,t) for each slice.
DynamicStructureFactor.calc_slices_data()	Calculate the observable for each slice.
DynamicStructureFactor.calc_vkt_slices_data()	Calculate v(k,t) for each slice.
DynamicStructureFactor.calculate_corr_times([...])
DynamicStructureFactor.compute()	Routine for computing the observable.
DynamicStructureFactor.compute_kt_data([...])	Calculate Time dependent Fourier space quantities.
DynamicStructureFactor.copy_params(params)
DynamicStructureFactor.create_dirs_filenames()	Create the directories and filenames where to save dataframes.
DynamicStructureFactor.from_dict(input_dict)	Update attributes from input dictionary.
DynamicStructureFactor.from_pickle()	Read the observable's info from the pickle file.
DynamicStructureFactor.grab_sim_data([pva])	Read in particles data into one large array.
DynamicStructureFactor.initialize_hdf()
DynamicStructureFactor.integrate_normalized_acf_squared(...)	Calculate the normalized correlation time as given by
DynamicStructureFactor.parse([acf_data])	Grab the pandas dataframe from the saved csv file.
DynamicStructureFactor.parse_acf()
DynamicStructureFactor.parse_k_data()	Read in the precomputed Fourier space data.
DynamicStructureFactor.parse_kt_data([...])	Read in the precomputed time dependent Fourier space data.
DynamicStructureFactor.plot([scaling, acf, ...])	Plot the observable by calling the pandas.DataFrame.plot() function and save the figure.
DynamicStructureFactor.pretty_print_msg()	Create the message with the basic information of every observable
DynamicStructureFactor.save_acf_hdf()
DynamicStructureFactor.save_hdf()
DynamicStructureFactor.save_kt_hdf([...])	Save the \(n(\mathbf{k},t)\) and/or \(\mathbf{v}(\mathbf{k},t)\) data of each slice to disk.
DynamicStructureFactor.save_pickle()	Save the observable's info into a pickle file.
DynamicStructureFactor.setup(params[, ...])	Assign attributes from simulation's parameters.
DynamicStructureFactor.setup_init(params[, ...])	Assign Observables attributes and copy the simulation's parameters.
DynamicStructureFactor.setup_multirun_dirs()	Set the attributes postprocessing_dir and dump_dirs_list.
DynamicStructureFactor.update_args(**kwargs)	Update observable specific attributes and call update_finish() to save info.
DynamicStructureFactor.update_finish()	Update the slice_steps, CCF's and DSF's attributes, and save pickle file with observable's info.