Fix codefactor warnings (#273)

* Fix pyanitools docstrings

* Make offending ignite docstring raw

* Make flake8 happy

torchani/data/_pyanitools.py

@@ -6,22 +6,15 @@ import os
 
 class datapacker:
     def __init__(self, store_file, mode='w-', complib='gzip', complevel=6):
-        """Wrapper to store arrays within HFD5 file
-        """
-        # opening file
+        """Wrapper to store arrays within HFD5 file"""
         self.store = h5py.File(store_file, mode=mode)
         self.clib = complib
         self.clev = complevel
 
     def store_data(self, store_loc, **kwargs):
-        """Put arrays to store
-        """
-        # print(store_loc)
+        """Put arrays to store"""
         g = self.store.create_group(store_loc)
         for k, v, in kwargs.items():
-            # print(type(v[0]))
-
-            # print(k)
             if isinstance(v, list):
                 if len(v) != 0:
                     if isinstance(v[0], np.str_) or isinstance(v[0], str):
@@ -31,24 +24,22 @@ class datapacker:
                              compression_opts=self.clev)
 
     def cleanup(self):
-        """Wrapper to close HDF5 file
-        """
+        """Wrapper to close HDF5 file"""
         self.store.close()
 
 
 class anidataloader:
 
-    ''' Contructor '''
-
     def __init__(self, store_file):
         if not os.path.exists(store_file):
             exit('Error: file not found - ' + store_file)
         self.store = h5py.File(store_file, 'r')
 
-    ''' Group recursive iterator (iterate through all groups
-        in all branches and return datasets in dicts) '''
-
     def h5py_dataset_iterator(self, g, prefix=''):
+        """Group recursive iterator
+
+        Iterate through all groups in all branches and return datasets in dicts)
+        """
         for key in g.keys():
             item = g[key]
             path = '{}/{}'.format(prefix, key)
@@ -64,38 +55,31 @@ class anidataloader:
                                 if isinstance(dataset[0], np.bytes_):
                                     dataset = [a.decode('ascii')
                                                for a in dataset]
-
                         data.update({k: dataset})
-
                 yield data
             else:  # test for group (go down)
                 yield from self.h5py_dataset_iterator(item, path)
 
-    ''' Default class iterator (iterate through all data) '''
-
     def __iter__(self):
+        """Default class iterator (iterate through all data)"""
         for data in self.h5py_dataset_iterator(self.store):
             yield data
 
-    ''' Returns a list of all groups in the file '''
-
     def get_group_list(self):
+        """Returns a list of all groups in the file"""
         return [g for g in self.store.values()]
 
-    ''' Allows interation through the data in a given group '''
-
     def iter_group(self, g):
+        """Allows interation through the data in a given group"""
         for data in self.h5py_dataset_iterator(g):
             yield data
 
-    ''' Returns the requested dataset '''
-
     def get_data(self, path, prefix=''):
+        """Returns the requested dataset"""
         item = self.store[path]
         path = '{}/{}'.format(prefix, path)
         keys = [i for i in item.keys()]
         data = {'path': path}
-        # print(path)
         for k in keys:
             if not isinstance(item[k], h5py.Group):
                 dataset = np.array(item[k][()])
@@ -108,9 +92,8 @@ class anidataloader:
                 data.update({k: dataset})
         return data
 
-    ''' Returns the number of groups '''
-
     def group_size(self):
+        """Returns the number of groups"""
         return len(self.get_group_list())
 
     def size(self):
@@ -119,7 +102,6 @@ class anidataloader:
             count = count + len(g.items())
         return count
 
-    ''' Close the HDF5 file '''
-
     def cleanup(self):
+        """Close the HDF5 file"""
         self.store.close()

torchani/ignite.py

@@ -9,7 +9,7 @@ from ignite.contrib.metrics.regression import MaximumAbsoluteError
 
 
 class Container(torch.nn.ModuleDict):
-    """Each minibatch is splitted into chunks, as explained in the docstring of
+    r"""Each minibatch is splitted into chunks, as explained in the docstring of
     :method:`torchani.data.load_ani_dataset`, as a result, it is impossible to
     use :class:`torchani.AEVComputer`, :class:`torchani.ANIModel` directly with
     ignite. This class is designed to solve this issue.