| | import pandas as pd |
| | import numpy as np |
| |
|
| | |
| | saaint_db = pd.read_csv('saaintdb_20260226_all.csv') |
| |
|
| |
|
| | |
| | |
| |
|
| | def chain_ok(x): |
| | |
| | if pd.isna(x): |
| | return False |
| | x = str(x).strip() |
| | return (x != "") and (x.upper() not in {"N.A."}) |
| |
|
| | def hl_label_from_row(row): |
| | |
| | h_ok = chain_ok(row["H_chain_ID"]) |
| | l_ok = chain_ok(row["L_chain_ID"]) |
| |
|
| | if h_ok and l_ok: |
| | return "HL" |
| | elif h_ok and (not l_ok): |
| | return "H_only" |
| | elif (not h_ok) and l_ok: |
| | return "L_only" |
| | else: |
| | return "none" |
| |
|
| | def make_unique_id(row): |
| | |
| | pdb_id = str(row["PDB_ID"]).strip() |
| | heavy = str(row["H_chain_ID"]).strip() if not pd.isna(row["H_chain_ID"]) else "" |
| | light = str(row["L_chain_ID"]).strip() if not pd.isna(row["L_chain_ID"]) else "" |
| |
|
| | h_ok = chain_ok(heavy) |
| | l_ok = chain_ok(light) |
| |
|
| | if h_ok and l_ok: |
| | return f"{pdb_id}_{heavy}_{light}" |
| | elif h_ok: |
| | return f"{pdb_id}_{heavy}" |
| | elif l_ok: |
| | return f"{pdb_id}_{light}" |
| | else: |
| | return pdb_id |
| |
|
| |
|
| | |
| | saaint_db["PDB_ID_chain"] = saaint_db.apply(make_unique_id, axis=1) |
| |
|
| | |
| | saaint_db["hl_label"] = saaint_db.apply(hl_label_from_row, axis=1) |
| |
|
| | |
| | pdb_summary = ( |
| | saaint_db[["PDB_ID", "hl_label"]] |
| | .drop_duplicates(subset=["PDB_ID"]) |
| | .copy() |
| | ) |
| |
|
| | |
| | print("=== Number of unique PDBs per hl_label ===") |
| | print(pdb_summary["hl_label"].value_counts(dropna=False).to_string()) |
| |
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