BatchCoxxFingerprint

BatchCoxxFingerprint#

class lobsterpy.featurize.batch.BatchCoxxFingerprint(path_to_lobster_calcs, feature_type='overall', label_list=None, tanimoto=True, normalize=True, spin_type='summed', n_bins=56, e_range=[-15.0, 0.0], n_jobs=4, fingerprint_for='cohp')[source]#

Bases: object

BatchFeaturizer to generate COHP/COOP/COBI fingerprints and Tanimoto index similarity matrix.

Parameters:

  • path_to_lobster_calcs (str | Path) – path to root directory consisting of all lobster calc

  • feature_type (Literal['bonding', 'antibonding', 'overall']) – set the feature type for moment features. Possible options are bonding, antibonding or overall

  • label_list (list[str] | None) – bond labels list for which fingerprints needs to be generated.

  • tanimoto (bool) – bool to state to compute tanimoto index between fingerprint objects

  • normalize (bool) – bool to state to normalize the fingerprint data

  • spin_type (Literal['summed', 'up', 'down']) – can be summed or up or down.

  • n_bins (int) – sets number for bins for fingerprint objects

  • e_range (list[float]) – range of energy relative to fermi for which moment features needs to be computed

  • n_jobs – number of parallel processes to run

  • fingerprint_for (Literal['cohp', 'cobi', 'coop']) – Possible options are cohp or cobi or coop. Based on this fingerprints will be computed for COHPCAR/COOBICAR/COOPCAR.lobster files

Variables:

fingerprint_df – A pandas dataframe with fingerprint objects

get_similarity_matrix_df()[source]#

Compute pairwise similarity index for each fingerprint object in input dataframe.

Returns:

A Pandas dataframe

Return type:

DataFrame