sarkas.tools.observables.PressureTensor#

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

Pressure Tensor.

Methods

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