FeaturizeDoscar

FeaturizeDoscar#

class lobsterpy.featurize.core.FeaturizeDoscar(path_to_structure, path_to_doscar, e_range=[-10.0, 0.0], add_element_dos_moments=False)[source]#

Bases: object

Class to compute DOS moments and fingerprints from DOSCAR.lobster / DOSCAR.LSO.lobster.

Parameters:

  • path_to_structure (str | Path) – path to POSCAR

  • path_to_doscar (str | Path) – path to DOSCAR.lobster or DOSCAR.LSO.lobster

  • e_range (list[float] | None) – range of energy relative to fermi for which moment features and features needs to be computed

  • add_element_dos_moments (bool) – add element dos moment features alongside orbital dos

get_df(ids=None)[source]#

Return a pandas dataframe with computed DOS moment features as columns.

Parameters:

ids (str | None) – set index name in the pandas dataframe. Default is None. When None, LOBSTER calc directory name is used as index name.

Return type:

DataFrame

Moment features are PDOS center, width, skewness, kurtosis and upper band edge.

Returns:

A pandas dataframe object

Parameters:

ids (str | None)

Return type:

DataFrame

get_fingerprint_df(ids=None, fp_type='summed_pdos', binning=True, n_bins=256, normalize=True)[source]#

Generate a dataframe consisting of DOS fingerprint (fp).

Parameters:

  • ids (str | None) – set index name in the pandas dataframe. Default is None. When None, LOBSTER calc directory name is used as index name.

  • fp_type (str) – Specify fingerprint type to compute, can accept s/p/d/f/summed_pdos (default is summed_pdos)

  • binning (bool) – If true, the DOS fingerprint is binned using np.linspace and n_bins. Default is True.

  • n_bins (int) – Number of bins to be used in the fingerprint (default is 256)

  • normalize (bool) – If true, normalizes the area under fp to equal to 1. Default is True.

Returns:

A pandas dataframe object with DOS fingerprints

Return type:

DataFrame