sarkas.tools.observables.VelocityAutoCorrelationFunction#

class sarkas.tools.observables.VelocityAutoCorrelationFunction[source]#

Velocity Auto-correlation function.

Methods

`VelocityAutoCorrelationFunction.__init__`()
`VelocityAutoCorrelationFunction.average_acf_slices_data`()	Calculate the average and standard deviation of the observable autocorrelation function from the slices dataframe.
`VelocityAutoCorrelationFunction.calc_acf_slices_data`()	Calculate the observable acf for each slice.
`VelocityAutoCorrelationFunction.calc_k_data`()	Calculate and save Fourier space data.
`VelocityAutoCorrelationFunction.calc_nkt_slices_data`()	Calculate n(k,t) for each slice.
`VelocityAutoCorrelationFunction.calc_vkt_slices_data`()	Calculate v(k,t) for each slice.
`VelocityAutoCorrelationFunction.calculate_corr_times`([...])
`VelocityAutoCorrelationFunction.calculate_vacf`(vel)	Calculate the velocity autocorrelation function of each species and in each direction.
`VelocityAutoCorrelationFunction.compute`([...])	Routine for computing the observable.
`VelocityAutoCorrelationFunction.compute_acf`([...])	Routine for computing the observable's autocorrelation function.
`VelocityAutoCorrelationFunction.compute_kt_data`([...])	Calculate Time dependent Fourier space quantities.
`VelocityAutoCorrelationFunction.copy_params`(params)
`VelocityAutoCorrelationFunction.create_dirs_filenames`()	Create the directories and filenames where to save dataframes.
`VelocityAutoCorrelationFunction.from_dict`(...)	Update attributes from input dictionary.
`VelocityAutoCorrelationFunction.from_pickle`()	Read the observable's info from the pickle file.
`VelocityAutoCorrelationFunction.grab_sim_data`(...)	Grab the data from simulation dumps.
`VelocityAutoCorrelationFunction.initialize_hdf`()
`VelocityAutoCorrelationFunction.integrate_normalized_acf_squared`(...)	Calculate the normalized correlation time as given by
`VelocityAutoCorrelationFunction.parse`([acf_data])	Grab the pandas dataframe from the saved csv file.
`VelocityAutoCorrelationFunction.parse_acf`()
`VelocityAutoCorrelationFunction.parse_k_data`()	Read in the precomputed Fourier space data.
`VelocityAutoCorrelationFunction.parse_kt_data`([...])	Read in the precomputed time dependent Fourier space data.
`VelocityAutoCorrelationFunction.plot`([...])	Plot the observable by calling the pandas.DataFrame.plot() function and save the figure.
`VelocityAutoCorrelationFunction.pretty_print_msg`()	Create the message with the basic information of every observable
`VelocityAutoCorrelationFunction.save_acf_hdf`()
`VelocityAutoCorrelationFunction.save_acf_slices_data_to_hdf`(...)	Store ACF data of slice isl into a hierarchical dataframe.
`VelocityAutoCorrelationFunction.save_hdf`()
`VelocityAutoCorrelationFunction.save_kt_hdf`([...])	Save the \(n(\mathbf{k},t)\) and/or \(\mathbf{v}(\mathbf{k},t)\) data of each slice to disk.
`VelocityAutoCorrelationFunction.save_pickle`()	Save the observable's info into a pickle file.
`VelocityAutoCorrelationFunction.select_random_indices`([...])	Randomly select a given number of indices that indicate the particles to be used to average the VACF.
`VelocityAutoCorrelationFunction.setup`(params)	Assign attributes from simulation's parameters.
`VelocityAutoCorrelationFunction.setup_init`(params)	Assign Observables attributes and copy the simulation's parameters.
`VelocityAutoCorrelationFunction.setup_multirun_dirs`()	Set the attributes postprocessing_dir and dump_dirs_list.
`VelocityAutoCorrelationFunction.update_args`(...)	Update observable specific attributes and call `update_finish()` to save info.
`VelocityAutoCorrelationFunction.update_finish`()	Update the `slice_steps`, CCF's and DSF's attributes, and save pickle file with observable's info.