add documentation to extra.storage

qsprpred/extra/data/storage/protein/interfaces/protein_storage.py

@@ -10,38 +10,49 @@
 
 
 class ProteinStorage(PropertyStorage, ABC):
+    """Storage for proteins.
+    
+    Attributes:
+        sequenceProp (str): name of the property that contains all protein sequences
+        proteins (Iterable[StoredProtein]): all proteins in the store
+    """
 
     @property
     @abstractmethod
     def sequenceProp(self) -> str:
         """Get the name of the property that contains all protein sequences."""
 
     @abstractmethod
-    def add_protein(self, protein: StoredProtein, raise_on_existing=True):
-        """
-        Add a protein to the store.
+    def add_protein(self, protein: StoredProtein, raise_on_existing=True) -> StoredProtein:
+        """Add a protein to the store.
+        
+        Args:
+            protein (StoredProtein): protein sequence
+            raise_on_existing (bool): 
+                raise an exception if the protein already exists in the store
 
-        :param protein: protein sequence
-        :param raise_on_existing: raise an exception if the protein already exists in the store
-        :return: `StoredProtein` instance of the added protein
+        Returns:
+            StoredProtein: instance of the added protein
         """
 
     @property
     @abstractmethod
     def proteins(self) -> Iterable[StoredProtein]:
-        """
-        Get all proteins in the store.
-
-        :return: iterable of `Protein` instances
+        """Get all proteins in the store.
+        
+        Returns:
+            Iterable[StoredProtein]: iterable of `Protein` instances
         """
 
     @abstractmethod
     def getProtein(self, protein_id: str) -> StoredProtein:
-        """
-        Get a protein from the store using its name.
+        """Get a protein from the store using its name.
 
-        :param protein_id: name of the protein to search
-        :return: instance of `Protein`
+        Args:
+            protein_id (str): name of the protein to search
+            
+        Returns:
+            StoredProtein: instance of `Protein`
         """
 
     @abstractmethod
@@ -57,56 +68,72 @@ def getPCMInfo(self) -> tuple[dict[str, str], dict]:
 
 
 class DockableStore(ChemStore, ABC):
+    """Storage for dockable molecules.
+    
+    Attributes:
+        proteins (Iterable[StoredProtein]): all proteins in the store
+    """
 
     @abstractmethod
     def add_target(self, target: StoredProtein,
                    raise_on_existing=True) -> StoredProtein:
-        """
-        Add a target to the store.
+        """Add a target to the store.
+        
+        Args:
+            target (StoredProtein): target protein
+            raise_on_existing (bool): 
+                raise an exception if the target already exists in the store
 
-        :param target: target protein
-        :param raise_on_existing: raise an exception if the target already exists in the store
-        :return: `Protein` instance of the added target
+        Returns:
+            (StoredProtein): instance of the added target
         """
 
     @abstractmethod
     def add_poses(self, mol_id: str, poses: Chem.Mol, target: StoredProtein,
                   metadata: list[dict[str, Any]] | None = None) -> list[StoredMol]:
-        """
-        Add poses to the store.
-
-        :param mol_id: identifier of the molecule to add poses for
-        :param poses: dictionary of target identifiers and poses
-        :param target: target protein
-        :param metadata: additional metadata to store with the poses
-        :return: Added poses represented as `StoredMol`
+        """Add poses to the store.
+        
+        Args:
+            mol_id (str): identifier of the molecule to add poses for
+            poses (Chem.Mol): dictionary of target identifiers and poses
+            target (StoredProtein): target protein
+            metadata (list[dict[str, Any]]): additional metadata to store with the poses
+            
+        Returns:
+            list[StoredMol]: Added poses represented as `StoredMol`
         """
 
     @abstractmethod
     def get_poses(self, mol_id: str, target_id: str) -> list[StoredMol]:
-        """
-        Get poses from the store.
-
-        :param mol_id: identifier of the molecule to get poses for
-        :param target_id: identifier of the target to get poses for
-        :return:
+        """Get poses from the store.
+        
+        Args:
+            mol_id (str): identifier of the molecule to get poses for
+            target_id (str): identifier of the target to get poses for
+        
+        Returns:
+            list[StoredMol]: poses for the molecule and target
         """
 
     @abstractmethod
     def get_complex_for_pose(self, mol_id: str, target_id: str) -> Chem.Mol:
-        """
-        Get the complex for a pose.
-
-        :param mol_id: identifier of the molecule to get the complex for
-        :param target_id: identifier of the target to get the complex for
-        :return: tuple of the complex and the target
+        """Get the complex for a pose.
+        
+        Args:
+            mol_id (str): identifier of the molecule to get the complex for
+            target_id (str): identifier of the target to get the complex for
+            
+        Returns:
+            tuple: complex and the target
         """
 
     @abstractmethod
     def get_target(self, target_id: str) -> StoredProtein:
-        """
-        Get a target from the store using its ID.
-
-        :param target_id: identifier of the target to search
-        :return: instance of `Protein`
+        """Get a target from the store using its ID.
+        
+        Args:
+            target_id (str): identifier of the target to search
+        
+        Returns:
+            StoredProtein: instance of `Protein`
         """

qsprpred/extra/data/storage/protein/interfaces/storedprotein.py

@@ -5,39 +5,51 @@
 
 
 class StoredProtein(ABC):
-    """
-    A protein object
+    """A protein object.
+    
+    Attributes:
+        id (str): id of the protein
+        sequence (str): sequence of the protein
+        props (dict[str, Any]): properties of the protein
+        representations (Iterable[StoredProtein]): representations of the protein
     """
 
     @property
     @abstractmethod
     def id(self) -> str:
+        """Get the id of the protein."""
         pass
 
     @property
     @abstractmethod
     def sequence(self) -> str | None:
+        """Get the sequence of the protein."""
         pass
 
     @property
     @abstractmethod
     def props(self) -> dict[str, Any] | None:
+        """Get the properties of the protein."""
         pass
 
     @abstractmethod
     def as_pdb(self) -> str | None:
+        """Return the protein as a PDB file."""
         pass
 
     @abstractmethod
     def as_fasta(self) -> str | None:
+        """Return the protein as a FASTA file."""
         pass
 
     def as_rd_mol(self) -> Chem.Mol | None:
+        """Return the protein as an RDKit molecule."""
         pdb = self.as_pdb()
         if pdb is not None:
             return Chem.MolFromPDBBlock(self.as_pdb())
 
     @property
     @abstractmethod
     def representations(self) -> Iterable["StoredProtein"]:
+        """Get all representations of the protein."""
         pass

qsprpred/extra/data/storage/protein/tabular_pcm.py

@@ -12,6 +12,14 @@
 
 
 class TabularProtein(StoredProtein):
+    """A protein object that is stored in a tabular format.
+    
+    Attributes:
+        id (str): id of the protein
+        sequence (str): sequence of the protein
+        props (dict[str, Any]): properties of the protein
+        representations (Iterable[TabularProtein]): representations of the protein
+    """
 
     def __init__(
             self,
@@ -21,12 +29,14 @@ def __init__(
             props: dict[str, Any] | None = None,
             representations: Iterable["TabularProtein"] | None = None,
     ) -> None:
-        """
-        Create a new protein instance.
-
-        :param parent: parent protein
-        :param protein_id: identifier of the protein
-        :param sequence: sequence of the protein
+        """Create a new protein instance.
+        
+        Args:
+            protein_id (str): identifier of the protein
+            sequence (str): sequence of the protein
+            parent (TabularProtein): parent protein
+            props (dict[str, Any]): properties of the protein
+            representations (Iterable[TabularProtein]): representations of the protein
         """
         self._parent = parent
         self._id = protein_id
@@ -36,28 +46,42 @@ def __init__(
 
     @property
     def id(self) -> str:
+        """Get the id of the protein."""
         return self._id
 
     @property
     def sequence(self) -> str | None:
+        """Get the sequence of the protein."""
         return self._sequence
 
     @property
     def props(self) -> dict[str, Any] | None:
+        """Get the properties of the protein."""
         return self._props
 
     def as_pdb(self) -> str | None:
+        """Return the protein as a PDB file."""
         return self._props["pdb"] if "pdb" in self._props else None
 
     def as_fasta(self) -> str | None:
+        """Return the protein as a FASTA file."""
         return self._props["fasta"] if "fasta" in self._props else None
 
     @property
     def representations(self) -> Iterable["TabularProtein"]:
+        """Get all representations of the protein."""
         return self._representations
 
 
 class TabularProteinStorage(ProteinStorage, PandasDataTable):
+    """A storage class for proteins stored in a tabular format.
+    
+    Attributes:
+        sequenceCol (str): name of the column that contains all protein sequences
+        proteinSeqProvider (Callable): function that provides protein
+        sequenceProp (str): name of the property that contains all protein sequences
+        proteins (Iterable[TabularProtein]): all proteins in the store
+    """
 
     def __init__(
             self,
@@ -75,6 +99,23 @@ def __init__(
             store_format: str = "pkl",
             parallel_generator: ParallelGenerator | None = None,
     ):
+        """Create a new protein storage instance.	
+        
+        Args:
+            name (str): name of the storage
+            df (pd.DataFrame): data frame containing the proteins
+            sequence_col (str): name of the column that contains all protein sequences
+            sequence_provider (Callable): function that provides protein
+            store_dir (str): directory to store the data
+            overwrite (bool): overwrite the existing data
+            index_cols (list[str]): columns to use as index
+            n_jobs (int): number of parallel jobs
+            chunk_size (int): size of the chunks
+            protein_col (str): name of the column that contains the protein ids
+            random_state (int): random state
+            store_format (str): format to store the data
+            parallel_generator (ParallelGenerator): parallel generator
+        """
         super().__init__(
             name,
             df if df is not None else pd.DataFrame(columns=[sequence_col,
@@ -158,16 +199,32 @@ def getPCMInfo(self) -> tuple[dict[str, str], dict]:
 
     @property
     def sequenceProp(self) -> str:
+        """Get the name of the property that contains all protein sequences."""	
         return self._sequenceCol
 
     def add_protein(self, protein: TabularProtein, raise_on_existing=True):
+        """Add a protein to the store.
+        
+        Args:
+            protein (TabularProtein): protein sequence
+            raise_on_existing (bool): 
+                raise an exception if the protein already exists in the store
+        """
         self.addEntries(
             [protein.id],
             {prop: [val] for prop, val in protein.props},
             raise_on_existing
         )
 
     def _make_proteins_from_chunk(self, df: pd.DataFrame) -> list[TabularProtein]:
+        """Create a list of proteins from a chunk of the data frame.
+        
+        Args:
+            df (pd.DataFrame): chunk of the data frame
+            
+        Returns:
+            list[TabularProtein]: list of proteins
+        """
         ids = df[self.idProp].values
         sequences = df[self.sequenceProp].values
         props = df.columns.difference([self.idProp, self.sequenceProp])
@@ -182,12 +239,28 @@ def _make_proteins_from_chunk(self, df: pd.DataFrame) -> list[TabularProtein]:
 
     @property
     def proteins(self) -> list[TabularProtein]:
+        """Get all proteins in the store.
+        
+        Returns:
+            list[TabularProtein]: list of proteins
+        """
         ret = []
         for chunk in self.iterChunks(len(self)):
             ret.extend(self._make_proteins_from_chunk(chunk))
         return ret
 
     def getProtein(self, protein_id: str) -> TabularProtein:
+        """Get a protein from the store using its name.	
+        
+        Args:
+            protein_id (str): name of the protein to search
+        
+        Returns:
+            TabularProtein: instance of `Protein`
+            
+        Raises:
+            ValueError: if the protein is not found
+        """
         df = self.getDF()
         protein = df[df[self.idProp] == protein_id]
         if protein.empty: