StructurePreprocessor.build_cat

StructurePreprocessor.build_cat(features, dim_names_override=None)[source]

Build the df_cat metadata frame for features.

Pure registry lookup — corpus-free. df_cat[category] is always 'Structure' for every StructurePreprocessor feature; the per-key semantics live in df_cat[subcategory] (see registry).

Added in version 1.1.0.

Parameters:

features (list of str) – Feature keys from the StructurePreprocessor registry, in the order they appear along the D axis of the encoder outputs.
dim_names_override (list of str, optional) – Replacement names for the D columns; length must equal the total dimensionality across features.

Returns:

df_cat – One row per dimension: scale_id, category, subcategory, scale_name, scale_description. category is the top-level color/redundancy-bucket bucket; subcategory carries the fine-grained semantic split ('DSSP_SS_3state', 'Flexibility_bfactor', etc.).

Return type:

pd.DataFrame, shape (D_total, 5)

See also

StructurePreprocessor.build_scales(): corpus-derived pseudo-scale companion to df_cat.

Examples

build_cat returns the corpus-free df_cat metadata that names each structure dimension with its category (always Structure, the locked redundancy / color bucket) and a descriptive subcategory for the CPPPlot.feature_map y-axis — the drop-in df_cat for CPP.run_num.

import warnings
from pathlib import Path
import numpy as np
import pandas as pd
import aaanalysis as aa
import aaanalysis.utils as ut
aa.options['verbose'] = False
warnings.filterwarnings('ignore')

PDB_FIXTURES = Path(aa.__file__).resolve().parent / '_data' / 'pdb_test'
strp = aa.StructurePreprocessor(verbose=False)
df_seq = pd.DataFrame({'entry': ['AF_TINY'],
                       'sequence': ['ACDEFGHIKLMNPQRSTVWYACDEFGHIKL']})

df_cat = strp.build_cat(features=['plddt', 'contact_count_8A', 'bfactor'])
print('categories:', df_cat[ut.COL_CAT].unique().tolist())
df_cat

categories: ['Structure']

	scale_id	category	subcategory	scale_name	scale_description
0	plddt	Structure	AlphaFold pLDDT (raw)	plddt	Structure/AlphaFold pLDDT (raw)
1	contacts_8A	Structure	CA-CA contacts (8 A)	contacts_8A	Structure/CA-CA contacts (8 A)
2	bfactor	Structure	B-factor (CA mean)	bfactor	Structure/B-factor (CA mean)

Further parameters. StructurePreprocessor.build_cat also accepts: dim_names_override — Replacement names for the D columns; length must equal the total dimensionality across features.

# Further parameter: rename the D structure dimensions via ``dim_names_override``
# (length must equal the total dimensionality across ``features``; here D = 3).
df_cat_named = strp.build_cat(
    features=['plddt', 'contact_count_8A', 'bfactor'],
    dim_names_override=['plddt_d', 'contacts_d', 'bfactor_d'])
aa.display_df(df_cat_named, n_rows=10, show_shape=True)

DataFrame shape: (3, 5)

	scale_id	category	subcategory	scale_name	scale_description
1	plddt_d	Structure	AlphaFold pLDDT (raw)	plddt_d	Structure/Alpha...old pLDDT (raw)
2	contacts_d	Structure	CA-CA contacts (8 A)	contacts_d	Structure/CA-CA contacts (8 A)
3	bfactor_d	Structure	B-factor (CA mean)	bfactor_d	Structure/B-factor (CA mean)