docs: activate missing docstring in public class and public function

- Moved docstrings from __init__ to the class level instead
- Added missing docstrings or suppressed the call for them
- Did not activate missing docstring in public method because there are too many missing
- Deactivated missing docsstrings in tests

deeprank2/domain/aminoacidlist.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 from deeprank2.molstruct.aminoacid import AminoAcid, Polarity
 
 # All info below sourced from above websites in December 2022 and summarized in deeprank2/domain/aminoacid_summary.xlsx
@@ -377,7 +379,26 @@
 amino_acids_by_name = {amino_acid.name: amino_acid for amino_acid in amino_acids}
 
 
-def convert_aa_nomenclature(aa: str, output_type: int | None = None) -> str:
+def convert_aa_nomenclature(aa: str, output_format: Literal[0, 1, 3] = 0) -> str:
+    """Converts amino acid nomenclatures.
+
+    Conversions are possible between the standard 1-letter codes, 3-letter
+    codes, and full names of amino acids.
+
+    Args:
+        aa (str): The amino acid to be converted in any of its formats. The
+            length of the string is used to determine which format is used.
+        output_format (Literal[0, 1, 3], optional): Nomenclature style to return:
+            0 (default) returns the full name,
+            1 returns the 1-letter code,
+            3 returns the 3-letter code.
+
+    Raises:
+        ValueError: If aa is not recognized or an invalid output format was given
+
+    Returns:
+        str: amino acid in the selected nomenclature system.
+    """
     try:
         if len(aa) == 1:
             aa: AminoAcid = next(entry for entry in amino_acids if entry.one_letter_code.lower() == aa.lower())
@@ -389,11 +410,11 @@ def convert_aa_nomenclature(aa: str, output_type: int | None = None) -> str:
         msg = f"{aa} is not a valid amino acid."
         raise ValueError(msg) from e
 
-    if not output_type:
+    if not output_format:
         return aa.name
-    if output_type == 3:  # noqa:PLR2004
+    if output_format == 3:  # noqa:PLR2004
         return aa.three_letter_code
-    if output_type == 1:
+    if output_format == 1:
         return aa.one_letter_code
-    msg = f"output_type {output_type} not recognized. Must be set to None (amino acid name), 1 (one letter code), or 3 (three letter code)."
+    msg = f"output_format {output_format} not recognized. Must be set to 0 (amino acid name), 1 (one letter code), or 3 (three letter code)."
     raise ValueError(msg)

deeprank2/features/components.py

@@ -11,7 +11,7 @@
 _log = logging.getLogger(__name__)
 
 
-def add_features(
+def add_features(  # noqa:D103
     pdb_path: str,  # noqa: ARG001
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,

deeprank2/features/conservation.py

@@ -7,7 +7,7 @@
 from deeprank2.utils.graph import Graph
 
 
-def add_features(
+def add_features(  # noqa:D103
     pdb_path: str,  # noqa: ARG001
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,

deeprank2/features/contact.py

@@ -72,7 +72,7 @@ def _get_nonbonded_energy(
     return E_elec, E_vdw
 
 
-def add_features(
+def add_features(  # noqa:D103
     pdb_path: str,  # noqa: ARG001
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,  # noqa: ARG001

deeprank2/features/exposure.py

@@ -18,23 +18,23 @@
 _log = logging.getLogger(__name__)
 
 
-def handle_sigint(sig, frame) -> None:  # noqa: ARG001, ANN001
+def handle_sigint(sig, frame) -> None:  # noqa: ARG001, ANN001, D103
     _log.info("SIGINT received, terminating.")
     sys.exit()
 
 
-def handle_timeout(sig, frame) -> NoReturn:  # noqa: ARG001, ANN001
+def handle_timeout(sig, frame) -> NoReturn:  # noqa: ARG001, ANN001, D103
     msg = "Timed out!"
     raise TimeoutError(msg)
 
 
-def space_if_none(value: str) -> str:
+def space_if_none(value: str) -> str:  # noqa:D103
     if value is None:
         return " "
     return value
 
 
-def add_features(
+def add_features(  # noqa:D103
     pdb_path: str,
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,  # noqa: ARG001

deeprank2/features/irc.py

@@ -101,7 +101,7 @@ def get_IRCs(pdb_path: str, chains: list[str], cutoff: float = 5.5) -> dict[str,
     return residue_contacts
 
 
-def add_features(  # noqa: C901
+def add_features(  # noqa: C901, D103
     pdb_path: str,
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,

deeprank2/features/secondary_structure.py

@@ -124,7 +124,7 @@ def _get_secstructure(pdb_path: str) -> dict:
     return sec_structure_dict
 
 
-def add_features(
+def add_features(  # noqa:D103
     pdb_path: str,
     graph: Graph,
     single_amino_acid_variant: SingleResidueVariant | None = None,  # noqa: ARG001

deeprank2/features/surfacearea.py

@@ -12,7 +12,7 @@
 logging.getLogger(__name__)
 
 
-def add_sasa(pdb_path: str, graph: Graph) -> None:
+def add_sasa(pdb_path: str, graph: Graph) -> None:  # noqa:D103
     structure = freesasa.Structure(pdb_path)
     result = freesasa.calc(structure)
 
@@ -38,7 +38,7 @@ def add_sasa(pdb_path: str, graph: Graph) -> None:
         node.features[Nfeat.SASA] = area
 
 
-def add_bsa(graph: Graph) -> None:
+def add_bsa(graph: Graph) -> None:  # noqa:D103
     sasa_complete_structure = freesasa.Structure()
     sasa_chain_structures = {}
 

deeprank2/molstruct/aminoacid.py

@@ -21,6 +21,22 @@ def onehot(self) -> NDArray:
 
 
 class AminoAcid:
+    """An amino acid represents the type of `Residue` in a `PDBStructure`.
+
+    Args:
+        name (str): Full name of the amino acid.
+        three_letter_code (str): Three-letter code of the amino acid (as in PDB).
+        one_letter_code (str): One-letter of the amino acid (as in fasta).
+        charge (int): Charge of the amino acid.
+        polarity (:class:`Polarity`): The polarity of the amino acid.
+        size (int): The number of non-hydrogen atoms in the side chain.
+        mass (float): Average residue mass (i.e. mass of amino acid - H20) in Daltons.
+        pI (float): Isolectric point; pH at which the molecule has no net electric charge.
+        hydrogen_bond_donors (int): Number of hydrogen bond donors.
+        hydrogen_bond_acceptors (int): Number of hydrogen bond acceptors.
+        index (int): The rank of the amino acid, used for computing one-hot encoding.
+    """
+
     def __init__(
         self,
         name: str,
@@ -35,21 +51,6 @@ def __init__(
         hydrogen_bond_acceptors: int,
         index: int,
     ):
-        """An amino acid represents the type of `Residue` in a `PDBStructure`.
-
-        Args:
-            name (str): Full name of the amino acid.
-            three_letter_code (str): Three-letter code of the amino acid (as in PDB).
-            one_letter_code (str): One-letter of the amino acid (as in fasta).
-            charge (int): Charge of the amino acid.
-            polarity (:class:`Polarity`): The polarity of the amino acid.
-            size (int): The number of non-hydrogen atoms in the side chain.
-            mass (float): Average residue mass (i.e. mass of amino acid - H20) in Daltons.
-            pI (float): Isolectric point; pH at which the molecule has no net electric charge.
-            hydrogen_bond_donors (int): Number of hydrogen bond donors.
-            hydrogen_bond_acceptors (int): Number of hydrogen bond acceptors.
-            index (int): The rank of the amino acid, used for computing one-hot encoding.
-        """
         # amino acid nomenclature
         self._name = name
         self._three_letter_code = three_letter_code

deeprank2/molstruct/atom.py

@@ -30,6 +30,19 @@ def onehot(self) -> np.array:
 
 
 class Atom:
+    """One atom in a PDBStructure.
+
+    Args:
+        residue (:class:`Residue`): The residue that this atom belongs to.
+        name (str): Pdb atom name.
+        element (:class:`AtomicElement`): The chemical element.
+        position (np.array): Pdb position xyz of this atom.
+        occupancy (float): Pdb occupancy value.
+            This represents the proportion of structures where the atom is detected at a given position.
+            Sometimes a single atom can be detected at multiple positions. In that case separate structures exist where sum(occupancy) == 1.
+            Note that only the highest occupancy atom is used by deeprank2 (see tools.pdb._add_atom_to_residue).
+    """
+
     def __init__(
         self,
         residue: Residue,
@@ -38,18 +51,6 @@ def __init__(
         position: NDArray,
         occupancy: float,
     ):
-        """One atom in a PDBStructure.
-
-        Args:
-            residue (:class:`Residue`): The residue that this atom belongs to.
-            name (str): Pdb atom name.
-            element (:class:`AtomicElement`): The chemical element.
-            position (np.array): Pdb position xyz of this atom.
-            occupancy (float): Pdb occupancy value.
-                This represents the proportion of structures where the atom is detected at a given position.
-                Sometimes a single atom can be detected at multiple positions. In that case separate structures exist where sum(occupancy) == 1.
-                Note that only the highest occupancy atom is used by deeprank2 (see tools.pdb._add_atom_to_residue).
-        """
         self._residue = residue
         self._name = name
         self._element = element

deeprank2/molstruct/pair.py

@@ -8,13 +8,14 @@
 
 
 class Pair:
-    def __init__(self, item1: Any, item2: Any):  # noqa: ANN401
-        """A hashable, comparable object for any set of two inputs where order doesn't matter.
+    """A hashable, comparable object for any set of two inputs where order doesn't matter.
+
+    Args:
+        item1 (Any object): The pair's first object, must be convertable to string.
+        item2 (Any object): The pair's second object, must be convertable to string.
+    """
 
-        Args:
-            item1 (Any object): The pair's first object, must be convertable to string.
-            item2 (Any object): The pair's second object, must be convertable to string.
-        """
+    def __init__(self, item1: Any, item2: Any):  # noqa: ANN401
         self.item1 = item1
         self.item2 = item2
 

deeprank2/molstruct/residue.py

@@ -119,13 +119,14 @@ def get_center(self) -> NDArray:
 
 
 class SingleResidueVariant:
-    def __init__(self, residue: Residue, variant_amino_acid: AminoAcid):
-        """A single residue mutation of a PDBStrcture.
+    """A single residue mutation of a PDBStrcture.
 
-        Args:
-            residue (Residue): the `Residue` object from the PDBStructure that is mutated.
-            variant_amino_acid (AminoAcid): the amino acid that the `Residue` is mutated into.
-        """
+    Args:
+        residue (Residue): the `Residue` object from the PDBStructure that is mutated.
+        variant_amino_acid (AminoAcid): the amino acid that the `Residue` is mutated into.
+    """
+
+    def __init__(self, residue: Residue, variant_amino_acid: AminoAcid):
         self._residue = residue
         self._variant_amino_acid = variant_amino_acid
 

deeprank2/neuralnets/cnn/model3d.py

@@ -23,7 +23,7 @@
 # ----------------------------------------------------------------------
 
 
-class CnnRegression(torch.nn.Module):
+class CnnRegression(torch.nn.Module):  # noqa: D101
     def __init__(self, num_features: int, box_shape: tuple[int]):
         super().__init__()
 
@@ -76,7 +76,7 @@ def forward(self, data):
 # ----------------------------------------------------------------------
 
 
-class CnnClassification(torch.nn.Module):
+class CnnClassification(torch.nn.Module):  # noqa: D101
     def __init__(self, num_features, box_shape):
         super().__init__()
 

deeprank2/neuralnets/gnn/alignmentnet.py

@@ -6,7 +6,7 @@
 __author__ = "Daniel-Tobias Rademaker"
 
 
-class GNNLayer(nn.Module):
+class GNNLayer(nn.Module):  # noqa: D101
     def __init__(
         self,
         nmb_edge_projection,
@@ -104,7 +104,7 @@ def output(self, hidden_features, get_attention=True):
         return output
 
 
-class SuperGNN(nn.Module):
+class SuperGNN(nn.Module):  # noqa: D101
     def __init__(
         self,
         nmb_edge_attr,
@@ -172,7 +172,7 @@ def run_through_network(self, edges, edge_attr, node_attr, with_output_attention
         return self.modlist[-1].output(node_attr, True)  # (boolean-positional-value-in-call)
 
 
-class AlignmentGNN(SuperGNN):
+class AlignmentGNN(SuperGNN):  # noqa: D101
     def __init__(
         self,
         nmb_edge_attr,

deeprank2/neuralnets/gnn/foutnet.py

@@ -72,7 +72,7 @@ def __repr__(self):
         return f"{self.__class__.__name__}({self.in_channels}, {self.out_channels})"
 
 
-class FoutNet(torch.nn.Module):
+class FoutNet(torch.nn.Module):  # noqa: D101
     def __init__(
         self,
         input_shape,

deeprank2/neuralnets/gnn/ginet.py

@@ -10,7 +10,7 @@
 # ruff: noqa: ANN001, ANN201
 
 
-class GINetConvLayer(torch.nn.Module):
+class GINetConvLayer(torch.nn.Module):  # noqa: D101
     def __init__(self, in_channels, out_channels, number_edge_features=1, bias=False):
         super().__init__()
 
@@ -54,7 +54,7 @@ def __repr__(self):
         return f"{self.__class__.__name__}({self.in_channels}, {self.out_channels})"
 
 
-class GINet(torch.nn.Module):
+class GINet(torch.nn.Module):  # noqa: D101
     # input_shape -> number of node input features
     # output_shape -> number of output value per graph
     # input_shape_edge -> number of edge input features

deeprank2/neuralnets/gnn/ginet_nocluster.py

@@ -7,7 +7,7 @@
 # ruff: noqa: ANN001, ANN201
 
 
-class GINetConvLayer(torch.nn.Module):
+class GINetConvLayer(torch.nn.Module):  # noqa: D101
     def __init__(self, in_channels, out_channels, number_edge_features=1, bias=False):
         super().__init__()
 
@@ -51,7 +51,7 @@ def __repr__(self):
         return f"{self.__class__.__name__}({self.in_channels}, {self.out_channels})"
 
 
-class GINet(torch.nn.Module):
+class GINet(torch.nn.Module):  # noqa: D101
     # input_shape -> number of node input features
     # output_shape -> number of output value per graph
     # input_shape_edge -> number of edge input features

deeprank2/neuralnets/gnn/naive_gnn.py

@@ -7,7 +7,7 @@
 # ruff: noqa: ANN001, ANN201
 
 
-class NaiveConvolutionalLayer(Module):
+class NaiveConvolutionalLayer(Module):  # noqa: D101
     def __init__(self, count_node_features, count_edge_features):
         super().__init__()
         message_size = 32
@@ -31,7 +31,7 @@ def forward(self, node_features, edge_node_indices, edge_features):
         return self._node_mlp(node_input)
 
 
-class NaiveNetwork(Module):
+class NaiveNetwork(Module):  # noqa: D101
     def __init__(self, input_shape: int, output_shape: int, input_shape_edge: int):
         """NaiveNetwork.
 

deeprank2/neuralnets/gnn/sgat.py

@@ -88,7 +88,7 @@ def __repr__(self):
         return f"{self.__class__.__name__}({self.in_channels}, {self.out_channels})"
 
 
-class SGAT(torch.nn.Module):
+class SGAT(torch.nn.Module):  # noqa:D101
     def __init__(
         self,
         input_shape,