uf3.data.io.DataCoordinator

class DataCoordinator(atoms_key='geometry', energy_key='energy', force_key='force', size_key='size', overwrite=False)

Bases: object

Handler class for reading data from atomistic codes and organizing data into DataFrames using Pandas.

Parameters

• atoms_key (str) – column name for geometries, default “geometry”. Modify when parsed geometries are part of a larger pipeline.

• energy_key (str) – column name for energies, default “energy”.

• force_key (str) – identifier for forces, default “force”.

• size_key (str) – column name for number of atoms per geometry, default “size”.

• overwrite (bool) – Allow overwriting of existing DataFrame with matching key when loading.

Methods

consolidate	Wrapper for io.concat_dataframes()
dataframe_from_lammps_run	Wrapper for io.parse_lammps_outputs()
dataframe_from_lists	Wrapper for io.prepare_dataframe_from_lists()
dataframe_from_trajectory	Wrapper for io.parse_trajectory()
dataframe_from_vasprun	Wrapper for io.parse_trajectory()
dataframe_from_xyz	Wrapper for io.parse_trajectory()
from_config	Instantiate from configuration dictionary
load_dataframe	Load existing pd.DataFrame

consolidate(remove_duplicates=True, keep='first')

Wrapper for io.concat_dataframes()

dataframe_from_lammps_run(path, lammps_aliases, prefix=None, column_subs={'PotEng': 'energy'}, log_fname='log.lammps', dump_fname='dump.lammpstrj', load=True, **kwargs)

Wrapper for io.parse_lammps_outputs()

dataframe_from_lists(geometries, prefix=None, energies=None, forces=None, load=True, **kwargs)

Wrapper for io.prepare_dataframe_from_lists()

dataframe_from_trajectory(filename, prefix=None, load=True, energy_key=None, force_key=None, **kwargs)

Wrapper for io.parse_trajectory()

dataframe_from_vasprun(filename, prefix=None, load=True, energy_key=None, force_key=None, **kwargs)

Wrapper for io.parse_trajectory()

dataframe_from_xyz(filename, prefix=None, load=True, energy_key=None, force_key=None, **kwargs)

Wrapper for io.parse_trajectory()

static from_config(config)

Instantiate from configuration dictionary

load_dataframe(dataframe, prefix=None)

Load existing pd.DataFrame