docs: remove types from docstrings

deeprank2/domain/aminoacidlist.py

@@ -386,9 +386,8 @@ def convert_aa_nomenclature(aa: str, output_format: Literal[0, 1, 3] = 0) -> str
     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:
+        aa: 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: Nomenclature style to return:
             0 (default) returns the full name,
             1 returns the 1-letter code,
             3 returns the 3-letter code.
@@ -397,7 +396,7 @@ def convert_aa_nomenclature(aa: str, output_format: Literal[0, 1, 3] = 0) -> str
         ValueError: If aa is not recognized or an invalid output format was given
 
     Returns:
-        str: amino acid in the selected nomenclature system.
+        Amino acid identifier in the selected nomenclature system.
     """
     try:
         if len(aa) == 1:

deeprank2/features/contact.py

@@ -32,8 +32,8 @@ def _get_nonbonded_energy(
     However, the potential tends to 0 at large distance.
 
     Args:
-        atoms (list[Atom]): list of all atoms in the structure
-        distances (NDArray[np.float64]): matrix of pairwise distances between all atoms in the structure
+        atoms: list of all atoms in the structure
+        distances: matrix of pairwise distances between all atoms in the structure
             in the format that is the output of scipy.spatial's distance_matrix (i.e. a diagonally symmetric matrix)
 
     Returns:

deeprank2/features/irc.py

@@ -18,7 +18,7 @@ def _id_from_residue(residue: tuple[str, int, str]) -> str:
     """Create and id from pdb2sql rendered residues that is similar to the id of residue nodes.
 
     Args:
-        residue (tuple): Input residue as rendered by pdb2sql: ( str(<chain>), int(<residue_number>), str(<three_letter_code> )
+        residue: Input residue as rendered by pdb2sql: ( str(<chain>), int(<residue_number>), str(<three_letter_code> )
             For example: ('A', 27, 'GLU').
 
     Returns:
@@ -44,9 +44,9 @@ def get_IRCs(pdb_path: str, chains: list[str], cutoff: float = 5.5) -> dict[str,
     """Get all close contact residues from the opposite chain.
 
     Args:
-        pdb_path (str): Path to pdb file to read molecular information from.
-        chains (Sequence[str]): list (or list-like object) containing strings of the chains to be considered.
-        cutoff (float, optional): Cutoff distance (in Ångström) to be considered a close contact. Defaults to 10.
+        pdb_path: Path to pdb file to read molecular information from.
+        chains: list (or list-like object) containing strings of the chains to be considered.
+        cutoff: Cutoff distance (in Ångström) to be considered a close contact. Defaults to 10.
 
     Returns:
         Dict[str, _ContactDensity]:

deeprank2/features/secondary_structure.py

@@ -91,7 +91,7 @@ def _get_secstructure(pdb_path: str) -> dict:
     """Process the DSSP output to extract secondary structure information.
 
     Args:
-        pdb_path (str): The file path of the PDB file to be processed.
+        pdb_path: The file path of the PDB file to be processed.
 
     Returns:
         dict: A dictionary containing secondary structure information for each chain and residue.

deeprank2/molstruct/aminoacid.py

@@ -24,17 +24,17 @@ 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.
+        name: Full name of the amino acid.
+        three_letter_code: Three-letter code of the amino acid (as in PDB).
+        one_letter_code: One-letter of the amino acid (as in fasta).
+        charge: Charge of the amino acid.
+        polarity: The polarity of the amino acid.
+        size: The number of non-hydrogen atoms in the side chain.
+        mass: Average residue mass (i.e. mass of amino acid - H20) in Daltons.
+        pI: Isolectric point; pH at which the molecule has no net electric charge.
+        hydrogen_bond_donors: Number of hydrogen bond donors.
+        hydrogen_bond_acceptors: Number of hydrogen bond acceptors.
+        index: The rank of the amino acid, used for computing one-hot encoding.
     """
 
     def __init__(

deeprank2/molstruct/atom.py

@@ -33,11 +33,11 @@ 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.
+        residue: The residue that this atom belongs to.
+        name: Pdb atom name.
+        element: The chemical element.
+        position: Pdb position xyz of this atom.
+        occupancy: 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).

deeprank2/molstruct/pair.py

@@ -11,8 +11,8 @@ class Pair:
     """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.
+        item1: The pair's first object, must be convertable to string.
+        item2: The pair's second object, must be convertable to string.
     """
 
     def __init__(self, item1: Any, item2: Any):  # noqa: ANN401

deeprank2/molstruct/residue.py

@@ -30,11 +30,10 @@ def __init__(
         Each `Residue` is of a certain `AminoAcid` type and consists of multiple `Atom`s.
 
         Args:
-            chain (:class:`Chain`): The chain that this residue belongs to.
-            number (int): the residue number
-            amino_acid (:class:`AminoAcid`, optional): The residue's amino acid (if it's part of a protein).
-                Defaults to None.
-            insertion_code (str, optional): The pdb insertion code, if any. Defaults to None.
+            chain: The chain that this residue belongs to.
+            number: the residue number
+            amino_acid: The residue's amino acid (if it's part of a protein). Defaults to None.
+            insertion_code: The pdb insertion code, if any. Defaults to None.
         """
         self._chain = chain
         self._number = number
@@ -122,8 +121,8 @@ class SingleResidueVariant:
     """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.
+        residue: the `Residue` object from the PDBStructure that is mutated.
+        variant_amino_acid: the amino acid that the `Residue` is mutated into.
     """
 
     def __init__(self, residue: Residue, variant_amino_acid: AminoAcid):

deeprank2/molstruct/structure.py

@@ -20,8 +20,7 @@ def __init__(self, id_: str | None = None):
         One PDBStructure consists of a number of `Residue`s, each of which is of a particular `AminoAcid` type and in turn consists of a number of `Atom`s.
 
         Args:
-            id_ (str, optional): An unique identifier for this structure, can be the pdb accession code.
-                Defaults to None.
+            id_: An unique identifier for this structure, can be the pdb accession code. Defaults to None.
         """
         self._id = id_
         self._chains = {}
@@ -76,8 +75,8 @@ def __init__(self, model: PDBStructure, id_: str | None):
         """One chain of a PDBStructure.
 
         Args:
-        model (:class:`PDBStructure`): The model that this chain is part of.
-        id_ (str): The pdb identifier of this chain.
+        model: The model that this chain is part of.
+        id_: The pdb identifier of this chain.
         """
         self._model = model
         self._id = id_

deeprank2/neuralnets/gnn/foutnet.py

@@ -18,10 +18,9 @@ class FoutLayer(nn.Module):
     by Alex Fout et al. NIPS 2018.
 
     Args:
-        in_channels (int): Size of each input sample.
-        out_channels (int): Size of each output sample.
-        bias (bool, optional): If set to :obj:`False`, the layer will not learn
-            an additive bias. Defaults to True.
+        in_channels: Size of each input sample.
+        out_channels: Size of each output sample.
+        bias: If set to :obj:`False`, the layer will not learn an additive bias. Defaults to True.
     """
 
     def __init__(self, in_channels: int, out_channels: int, bias: bool = True):

deeprank2/neuralnets/gnn/naive_gnn.py

@@ -36,9 +36,9 @@ def __init__(self, input_shape: int, output_shape: int, input_shape_edge: int):
         """NaiveNetwork.
 
         Args:
-            input_shape (int): Number of node input features.
-            output_shape (int): Number of output value per graph.
-            input_shape_edge (int): Number of edge input features.
+            input_shape: Number of node input features.
+            output_shape: Number of output value per graph.
+            input_shape_edge: Number of edge input features.
         """
         super().__init__()
         self._external1 = NaiveConvolutionalLayer(input_shape, input_shape_edge)

deeprank2/neuralnets/gnn/sgat.py

@@ -19,11 +19,11 @@ class SGraphAttentionLayer(nn.Module):
     Ni is the number of neighbor of node i
     \\Sum_j runs over the neighbors of node i
     a_ij is the edge attribute between node i and j
+
     Args:
-        in_channels (int): Size of each input sample.
-        out_channels (int): Size of each output sample.
-        bias (bool, optional): If set to :obj:`False`, the layer will not learn
-            an additive bias. Defaults to True.
+        in_channels: Size of each input sample.
+        out_channels: Size of each output sample.
+        bias: If set to :obj:`False`, the layer will not learn an additive bias. Defaults to True.
     """  # noqa: D301
 
     def __init__(