tail_end_to_end | MDFactory

End-to-end tail distance analysis.

func_tail_end_to_end_frame(atomgroup, species_map) → list[tuple[str, int, np.ndarray]]

paramatomgroup

paramspecies_map

Returns

list[tuple[str, int, numpy.numpy.ndarray]]

func_tail_end_to_end_frame_for_index(args) → list[tuple[str, int, np.ndarray]]

paramargstuple[int, str, str, dict[str, dict[str, list[int]]]]

Returns

list[tuple[str, int, numpy.numpy.ndarray]]

functail_end_to_end(simulation, *, start_ns=0.0, stride=1, n_bins=50, backend='multiprocessing', n_workers=4) → pd.DataFrame

Compute head-to-tail end-to-end distance distributions.

paramsimulationSimulation

Simulation instance with universe and build input.

paramstart_nsfloat | None

= 0.0

Start time for analysis in ns.

paramstrideint

= 1

Frame stride.

paramn_binsint

= 50

Number of histogram bins.

parambackendstr

= 'multiprocessing'

MDAnalysis backend for parallel execution.

paramn_workersint

= 4

Number of workers for parallel execution.

Returns

pandas.DataFrame

Columns: resname, tail_index, distance, probability_density.