fix doc warnings on docstring

README.md

@@ -58,7 +58,7 @@ To check if installation is successful, you can run a test
 
 ## Tutorial
 
-We give here the tutorial like introduction to the DeepRank machinery. More informatoin can be found in the documentation <http://deeprank.readthedocs.io/en/latest/>.  We quickly illsutrate here the two main steps of Deeprank :
+We give here the tutorial like introduction to the DeepRank machinery. More informatoin can be found in the documentation <http://deeprank.readthedocs.io/en/latest/>.  We quickly illsutrate here the two main steps of Deeprank:
 
 -   the generation of the data
 -   running deep leaning experiments.
@@ -118,8 +118,8 @@ database.create_database(prog_bar=True)
 
 # define the 3D grid
  grid_info = {
-   'number_of_points' : [30,30,30],
-   'resolution' : [1.,1.,1.],
+   'number_of_points': [30,30,30],
+   'resolution': [1.,1.,1.],
    'atomic_densities': {'C': 1.7, 'N': 1.55, 'O': 1.52, 'S': 1.8},
  }
 
@@ -139,17 +139,17 @@ In  the first part of the script we define the path where to find the PDBs of th
 
 We then initialize the `DataGenerator` object. This object (defined in `deeprank/generate/DataGenerator.py`) needs a few input parameters:
 
--   pdb_source : where to find the pdb to include in the dataset
--   pdb_native : where to find the corresponding native conformations
--   compute_targets : list of modules used to compute the targets
--   compute_features : list of modules used to compute the features
--   hdf5 : Name of the HDF5 file to store the data set
+-   pdb_source: where to find the pdb to include in the dataset
+-   pdb_native: where to find the corresponding native conformations
+-   compute_targets: list of modules used to compute the targets
+-   compute_features: list of modules used to compute the features
+-   hdf5: Name of the HDF5 file to store the data set
 
 We then create the data base with the command `database.create_database()`. This function autmatically create an HDF5 files where each pdb has its own group. In each group we can find the pdb of the complex and its native form, the calculated features and the calculated targets. We can now mapped the features to a grid. This is done via the command `database.map_features()`. As you can see this method requires a dictionary as input. The dictionary contains the instruction to map the data.
 
 -   number_of_points: the number of points in each direction
--   resolution : the resolution in Angs
--   atomic_densities : {'atom_name' : vvdw_radius} the atomic densities required
+-   resolution: the resolution in Angs
+-   atomic_densities: {'atom_name': vvdw_radius} the atomic densities required
 
 The atomic densities are mapped following the [protein-ligand paper](https://arxiv.org/abs/1612.02751). The other features are mapped to the grid points using a Gaussian function (other modes are possible but somehow hard coded)
 

deeprank/features/AtomicFeature.py

@@ -24,14 +24,14 @@ def __init__(self, pdbfile, chain1='A', chain2='B', param_charge=None,
             param_charge (str): file name of the force field file
                 containing the charges e.g. protein-allhdg5.4_new.top.
                 Must be of the format:
-                    * CYM  atom O   type=O      charge=-0.500 end
-                    * ALA    atom N   type=NH1     charge=-0.570 end
+                * CYM  atom O   type=O      charge=-0.500 end
+                * ALA    atom N   type=NH1     charge=-0.570 end
 
             param_vdw (str): file name of the force field containing
                 vdw parameters e.g. protein-allhdg5.4_new.param.
                 Must be of the format:
-                    * NONBonded  CYAA    0.105   3.750       0.013    3.750
-                    * NONBonded  CCIS    0.105   3.750       0.013    3.750
+                * NONBonded  CYAA    0.105   3.750       0.013    3.750
+                * NONBonded  CCIS    0.105   3.750       0.013    3.750
 
             patch_file (str): file name of a valid patch file for
                 the parameters e.g. patch.top.
@@ -112,11 +112,11 @@ def __init__(self, pdbfile, chain1='A', chain2='B', param_charge=None,
     def read_charge_file(self):
         """Read the .top file given in entry.
 
-        This function creates :
+        This function creates:
 
-        - self.charge : dictionary  {(resname,atname):charge}
-        - self.valid_resnames : list ['VAL','ALP', .....]
-        - self.at_name_type_convertor : dict {(resname,atname):attype}
+        - self.charge: dictionary  {(resname,atname):charge}
+        - self.valid_resnames: list ['VAL','ALP', .....]
+        - self.at_name_type_convertor: dict {(resname,atname):attype}
         """
 
         with open(self.param_charge) as f:
@@ -159,8 +159,8 @@ def read_patch(self):
 
         This function creates
 
-            - self.patch_charge : Dict {(resName,atName) : charge}
-            - self.patch_type   : Dict {(resName,atName) : type}
+            - self.patch_charge: Dict {(resName,atName): charge}
+            - self.patch_type : Dict {(resName,atName): type}
         """
 
         with open(self.patch_file) as f:
@@ -197,7 +197,7 @@ def read_vdw_file(self):
 
         This function creates
 
-            - self.vdw : dictionary {attype:[E1,S1]}
+            - self.vdw: dictionary {attype:[E1,S1]}
         """
 
         with open(self.param_vdw) as f:

deeprank/features/BSA.py

@@ -116,7 +116,7 @@ def get_contact_residue_sasa(self, cutoff=5.5):
             # define the bsa
             bsa = asa_unbound - asa_complex
 
-            # define the xyz key : (chain,x,y,z)
+            # define the xyz key: (chain,x,y,z)
             chain = {self.chain1: 0, self.chain2: 1}[res[0]]
 
             # get the center

deeprank/features/FeatureClass.py

@@ -8,47 +8,41 @@ def __init__(self, feature_type):
         Arguments
             feature_type(str): 'Atomic' or 'Residue'
 
-        Notes:
+        Note:
             Each subclass must compute:
 
-            - self.feature_data: dictionary of features in
-                human readable format, e.g.
-
-                for atomic features:
-                {'coulomb': data_dict_clb, 'vdwaals': data_dict_vdw}
-                    data_dict_clb = {atom_info: [values]}
-                        atom_info = (chainID, resSeq, resName, name)
-
-                for residue features:
-                {'PSSM_ALA': data_dict_pssmALA, ...}
-                    data_dict_pssmALA = {residue_info: [values]}
-                        residue_info = (chainID, resSeq, resName, name)
-
-            - self.feature_data_xyz: dictionary of features in
-                xyz-val format, e.g.
-
-                {'coulomb': data_dict_clb, 'vdwaals': data_dict_vdw}
-                    data_dict_clb = {xyz_info: [values]}
-                        xyz_info = (chainNum, x, y, z)
+            - self.feature_data: dictionary of features in human readable format, e.g.
+                - for atomic features:
+                    - {'coulomb': data_dict_clb, 'vdwaals': data_dict_vdw}
+                    - data_dict_clb = {atom_info: [values]}
+                    - atom_info = (chainID, resSeq, resName, name)
+                - for residue features:
+                    - {'PSSM_ALA': data_dict_pssmALA, ...}
+                    - data_dict_pssmALA = {residue_info: [values]}
+                    - residue_info = (chainID, resSeq, resName, name)
+            - self.feature_data_xyz: dictionary of features in xyz-val format, e.g.
+                - {'coulomb': data_dict_clb, 'vdwaals': data_dict_vdw}
+                - data_dict_clb = {xyz_info: [values]}
+                - xyz_info = (chainNum, x, y, z)
         """
-        
+
         self.type = feature_type
         self.feature_data = {}
         self.feature_data_xyz = {}
 
     def export_data_hdf5(self, featgrp):
         """Export the data in xyz-val format in an HDF5 file group.
-        
+
         Arguments:
             featgrp {[hdf5_group]} -- The hdf5 group of the feature
-        
-        Notes:
+
+        Note:
             - For atomic features, the format of the data must be:
                 {(chainID, resSeq, resName, name): [values]}
             - For residue features, the format must be:
                 {(chainID, resSeq, resName): [values]}
         """
-    
+
         # loop through the datadict and name
         for name, data in self.feature_data.items():
 
@@ -89,14 +83,14 @@ def export_data_hdf5(self, featgrp):
             else:
                 featgrp.create_dataset(name + '_raw', data=ds)
 
-    
+
     def export_dataxyz_hdf5(self, featgrp):
         """Export the data in xyz-val format in an HDF5 file group.
-        
+
         Arguments:
             featgrp {[hdf5_group]} -- The hdf5 group of the feature
         """
-        
+
         # loop through the datadict and name
         for name, data in self.feature_data_xyz.items():
 
@@ -113,17 +107,17 @@ def export_dataxyz_hdf5(self, featgrp):
     @staticmethod
     def get_residue_center(sql, centers=['CB','CA','mean'], res=None):
         """Computes the center of each residue by trying different options
-        
+
         Arguments:
             sql {pdb2sql} -- The pdb2sql instance
-        
+
         Keyword Arguments:
             centers {list} -- list of strings (default: {['CB','CA','mean']})
             res {list} -- list of residue to be considered ([[chainID, resSeq, resName]])
-        
+
         Raises:
             ValueError: [description]
-        
+
         Returns:
             [type] -- list(res), list(xyz)
         """
@@ -133,27 +127,27 @@ def get_residue_center(sql, centers=['CB','CA','mean'], res=None):
         if res is None:
             res = [tuple(x) for x in sql.get('chainID,resSeq,resName')]
             res = sorted(set(res), key=res.index)
-            
-            
+
+
         # make sure that we have a list of res
         # even if ony 1 res was provided
         # res=[chainID, resSeq, resName] -> res=[[chainID, resSeq, resName]]
         elif not isinstance(res[0],list):
-            res = [res]            
-            
+            res = [res]
+
         # make sure that we have a list of possible centers
         if not isinstance(centers,list):
             centers = list(centers)
-        
+
         xyz = []
-        
+
         for r in res:
-            
+
             for ctr in centers:
-                
+
                 if ctr in ['CB','CA']:
-                    
-                    xyz_res = sql.get('x,y,z', 
+
+                    xyz_res = sql.get('x,y,z',
                                       chainID=r[0],
                                       resSeq=r[1],
                                       resName=r[2],
@@ -177,9 +171,8 @@ def get_residue_center(sql, centers=['CB','CA','mean'], res=None):
 
                 else:
                     raise ValueError('Residue center not found')
-        
+
         if len(xyz) == 0:
             raise ValueError('Center not found')
 
         return res, xyz
-

deeprank/features/ResidueDensity.py

@@ -11,7 +11,7 @@ class ResidueDensity(FeatureClass):
     def __init__(self, pdb_data, chain1='A', chain2='B'):
         """Compute the residue contacts between polar/apolar/charged residues.
 
-        Args :
+        Args:
             pdb_data (list(byte) or str): pdb data or pdb filename
             chain1 (str): First chain ID. Defaults to 'A'
             chain2 (str): Second chain ID. Defaults to 'B'