[pre-commit.ci] auto fixes from pre-commit.com hooks

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graphein/protein/graphs.py

@@ -96,12 +96,18 @@ def read_pdb_to_dataframe(
     :rtype: pd.DataFrame
     """
     if pdb_code is None and path is None and uniprot_id is None:
-        raise NameError("One of pdb_code, path or uniprot_id must be specified!")
+        raise NameError(
+            "One of pdb_code, path or uniprot_id must be specified!"
+        )
 
     if path is not None:
         if isinstance(path, Path):
             path = os.fsdecode(path)
-        if path.endswith(".pdb") or path.endswith(".pdb.gz") or path.endswith(".ent"):
+        if (
+            path.endswith(".pdb")
+            or path.endswith(".pdb.gz")
+            or path.endswith(".ent")
+        ):
             atomic_df = PandasPdb().read_pdb(path)
         elif path.endswith(".mmtf") or path.endswith(".mmtf.gz"):
             atomic_df = PandasMmtf().read_mmtf(path)
@@ -110,7 +116,9 @@ def read_pdb_to_dataframe(
                 f"File {path} must be either .pdb(.gz), .mmtf(.gz) or .ent, not {path.split('.')[-1]}"
             )
     elif uniprot_id is not None:
-        atomic_df = PandasPdb().fetch_pdb(uniprot_id=uniprot_id, source="alphafold2-v3")
+        atomic_df = PandasPdb().fetch_pdb(
+            uniprot_id=uniprot_id, source="alphafold2-v3"
+        )
     else:
         atomic_df = PandasPdb().fetch_pdb(pdb_code)
 
@@ -164,7 +172,11 @@ def label_node_id(
         df["node_id"] = df["node_id"] + ":" + df["atom_name"]
     elif granularity in {"rna_atom", "rna_centroid"}:
         df["node_id"] = (
-            df["node_id"] + ":" + df["atom_number"].apply(str) + ":" + df["atom_name"]
+            df["node_id"]
+            + ":"
+            + df["atom_number"].apply(str)
+            + ":"
+            + df["atom_name"]
         )
     return df
 
@@ -177,7 +189,9 @@ def deprotonate_structure(df: pd.DataFrame) -> pd.DataFrame:
     :returns: Atomic dataframe with all ``element_symbol == "H" or "D" or "T"`` removed.
     :rtype: pd.DataFrame
     """
-    log.debug("Deprotonating protein. This removes H atoms from the pdb_df dataframe")
+    log.debug(
+        "Deprotonating protein. This removes H atoms from the pdb_df dataframe"
+    )
     return filter_dataframe(
         df,
         by_column="element_symbol",
@@ -211,7 +225,9 @@ def convert_structure_to_centroids(df: pd.DataFrame) -> pd.DataFrame:
     return df
 
 
-def subset_structure_to_atom_type(df: pd.DataFrame, granularity: str) -> pd.DataFrame:
+def subset_structure_to_atom_type(
+    df: pd.DataFrame, granularity: str
+) -> pd.DataFrame:
     """
     Return a subset of atomic dataframe that contains only certain atom names.
 
@@ -225,7 +241,9 @@ def subset_structure_to_atom_type(df: pd.DataFrame, granularity: str) -> pd.Data
     )
 
 
-def remove_alt_locs(df: pd.DataFrame, keep: str = "max_occupancy") -> pd.DataFrame:
+def remove_alt_locs(
+    df: pd.DataFrame, keep: str = "max_occupancy"
+) -> pd.DataFrame:
     """
     This function removes alternatively located atoms from PDB DataFrames
     (see https://proteopedia.org/wiki/index.php/Alternate_locations). Among the
@@ -289,7 +307,9 @@ def remove_insertions(
     )
 
 
-def filter_hetatms(df: pd.DataFrame, keep_hets: List[str]) -> List[pd.DataFrame]:
+def filter_hetatms(
+    df: pd.DataFrame, keep_hets: List[str]
+) -> List[pd.DataFrame]:
     """Return hetatms of interest.
 
     :param df: Protein Structure dataframe to filter hetatoms from.
@@ -434,7 +454,9 @@ def sort_dataframe(df: pd.DataFrame) -> pd.DataFrame:
     :return: Sorted protein dataframe.
     :rtype: pd.DataFrame
     """
-    return df.sort_values(by=["chain_id", "residue_number", "atom_number", "insertion"])
+    return df.sort_values(
+        by=["chain_id", "residue_number", "atom_number", "insertion"]
+    )
 
 
 def select_chains(
@@ -536,7 +558,8 @@ def initialise_graph_with_metadata(
         elif granularity == "atom":
             sequence = (
                 protein_df.loc[
-                    (protein_df["chain_id"] == c) & (protein_df["atom_name"] == "CA")
+                    (protein_df["chain_id"] == c)
+                    & (protein_df["atom_name"] == "CA")
                 ]["residue_name"]
                 .apply(three_to_one_with_mods)
                 .str.cat()
@@ -587,9 +610,13 @@ def add_nodes_to_graph(
     # Set intrinsic node attributes
     nx.set_node_attributes(G, dict(zip(nodes, chain_id)), "chain_id")
     nx.set_node_attributes(G, dict(zip(nodes, residue_name)), "residue_name")
-    nx.set_node_attributes(G, dict(zip(nodes, residue_number)), "residue_number")
+    nx.set_node_attributes(
+        G, dict(zip(nodes, residue_number)), "residue_number"
+    )
     nx.set_node_attributes(G, dict(zip(nodes, atom_type)), "atom_type")
-    nx.set_node_attributes(G, dict(zip(nodes, element_symbol)), "element_symbol")
+    nx.set_node_attributes(
+        G, dict(zip(nodes, element_symbol)), "element_symbol"
+    )
     nx.set_node_attributes(G, dict(zip(nodes, coords)), "coords")
     nx.set_node_attributes(G, dict(zip(nodes, b_factor)), "b_factor")
 
@@ -615,7 +642,9 @@ def calculate_centroid_positions(
     :rtype: pd.DataFrame
     """
     centroids = (
-        atoms.groupby(["residue_number", "chain_id", "residue_name", "insertion"])
+        atoms.groupby(
+            ["residue_number", "chain_id", "residue_name", "insertion"]
+        )
         .mean(numeric_only=True)[["x_coord", "y_coord", "z_coord"]]
         .reset_index()
     )
@@ -873,9 +902,13 @@ def _mp_graph_constructor(
     func = partial(construct_graph, config=config)
     try:
         if source == "pdb_code":
-            return func(pdb_code=args[0], chain_selection=args[1], model_index=args[2])
+            return func(
+                pdb_code=args[0], chain_selection=args[1], model_index=args[2]
+            )
         elif source == "path":
-            return func(path=args[0], chain_selection=args[1], model_index=args[2])
+            return func(
+                path=args[0], chain_selection=args[1], model_index=args[2]
+            )
         elif source == "uniprot_id":
             return func(
                 uniprot_id=args[0],
@@ -971,7 +1004,9 @@ def construct_graphs_mp(
     )
     if out_path is not None:
         [
-            nx.write_gpickle(g, str(f"{out_path}/" + f"{g.graph['name']}.pickle"))
+            nx.write_gpickle(
+                g, str(f"{out_path}/" + f"{g.graph['name']}.pickle")
+            )
             for g in graphs
         ]
 
@@ -1035,11 +1070,15 @@ def compute_chain_graph(
 
     # Add edges
     for u, v, d in g.edges(data=True):
-        h.add_edge(g.nodes[u]["chain_id"], g.nodes[v]["chain_id"], kind=d["kind"])
+        h.add_edge(
+            g.nodes[u]["chain_id"], g.nodes[v]["chain_id"], kind=d["kind"]
+        )
     # Remove self-loops if necessary. Checks for equality between nodes in a
     # given edge.
     if remove_self_loops:
-        edges_to_remove: List[Tuple[str]] = [(u, v) for u, v in h.edges() if u == v]
+        edges_to_remove: List[Tuple[str]] = [
+            (u, v) for u, v in h.edges() if u == v
+        ]
         h.remove_edges_from(edges_to_remove)
 
     # Compute a weighted graph if required.
@@ -1142,10 +1181,16 @@ def compute_secondary_structure_graph(
         ss_list = ss_list[~ss_list.str.contains("-")]
     # Subset to only allowable SS elements if necessary
     if allowable_ss_elements:
-        ss_list = ss_list[ss_list.str.contains("|".join(allowable_ss_elements))]
+        ss_list = ss_list[
+            ss_list.str.contains("|".join(allowable_ss_elements))
+        ]
 
-    constituent_residues: Dict[str, List[str]] = ss_list.index.groupby(ss_list.values)
-    constituent_residues = {k: list(v) for k, v in constituent_residues.items()}
+    constituent_residues: Dict[str, List[str]] = ss_list.index.groupby(
+        ss_list.values
+    )
+    constituent_residues = {
+        k: list(v) for k, v in constituent_residues.items()
+    }
     residue_counts: Dict[str, int] = ss_list.groupby(ss_list).count().to_dict()
 
     # Add Nodes from secondary structure list
@@ -1164,7 +1209,9 @@ def compute_secondary_structure_graph(
     # Iterate over edges in source graph and add SS-SS edges to new graph.
     for u, v, d in g.edges(data=True):
         try:
-            h.add_edge(ss_list[u], ss_list[v], kind=d["kind"], source=f"{u}_{v}")
+            h.add_edge(
+                ss_list[u], ss_list[v], kind=d["kind"], source=f"{u}_{v}"
+            )
         except KeyError as e:
             log.debug(
                 f"Edge {u}-{v} not added to secondary structure graph. \
@@ -1174,7 +1221,9 @@ def compute_secondary_structure_graph(
     # Remove self-loops if necessary.
     # Checks for equality between nodes in a given edge.
     if remove_self_loops:
-        edges_to_remove: List[Tuple[str]] = [(u, v) for u, v in h.edges() if u == v]
+        edges_to_remove: List[Tuple[str]] = [
+            (u, v) for u, v in h.edges() if u == v
+        ]
         h.remove_edges_from(edges_to_remove)
 
     # Create weighted graph from h