Cleanup

src/hdmf/backends/hdf5/h5tools.py

@@ -1086,7 +1086,7 @@ def write_link(self, **kwargs):
         self.__set_written(builder)
         return link_obj
 
-    @docval({'name': 'parent', 'type': Group, 'doc': 'the parent HDF5 object'},  # noqa: C901
+    @docval({'name': 'parent', 'type': Group, 'doc': 'the parent HDF5 object'},
             {'name': 'builder', 'type': DatasetBuilder, 'doc': 'the DatasetBuilder to write'},
             {'name': 'link_data', 'type': bool,
              'doc': 'If not specified otherwise link (True) or copy (False) HDF5 Datasets', 'default': True},

src/hdmf/build/objectmapper.py

@@ -184,7 +184,7 @@ def no_convert(cls, obj_type):
         """
         cls.__no_convert.add(obj_type)
 
-    @classmethod  # noqa: C901
+    @classmethod
     def convert_dtype(cls, spec, value, spec_dtype=None):  # noqa: C901
         """
         Convert values to the specified dtype. For example, if a literal int
@@ -276,7 +276,7 @@ def __check_convert_numeric(cls, value_type):
                 np.issubdtype(value_dtype, np.integer)):
             raise ValueError("Cannot convert from %s to 'numeric' specification dtype." % value_type)
 
-    @classmethod  # noqa: C901
+    @classmethod
     def __check_edgecases(cls, spec, value, spec_dtype):  # noqa: C901
         """
         Check edge cases in converting data to a dtype

src/hdmf/common/resources.py

@@ -1000,9 +1000,9 @@ def get_zip_directory(cls, path):
         directory = os.path.dirname(os.path.realpath(path))
         return directory
 
-    @classmethod  # noqa: C901
+    @classmethod
     @docval({'name': 'path', 'type': str, 'doc': 'The path to the zip file.'})
-    def from_zip(cls, **kwargs):
+    def from_zip(cls, **kwargs):  # noqa: C901
         """
         Method to read in zipped tsv files to populate HERD.
         """

src/hdmf/common/table.py

@@ -304,7 +304,7 @@ def __gather_columns(cls, name, bases, classdict):
                     except AttributeError:   # raises error when "__columns__" is not an attr of item
                         continue
 
-    @docval({'name': 'name', 'type': str, 'doc': 'the name of this table'},  # noqa: C901
+    @docval({'name': 'name', 'type': str, 'doc': 'the name of this table'},
             {'name': 'description', 'type': str, 'doc': 'a description of what is in this table'},
             {'name': 'id', 'type': ('array_data', 'data', ElementIdentifiers), 'doc': 'the identifiers for this table',
              'default': None},
@@ -749,7 +749,7 @@ def __eq__(self, other):
             return False
         return self.to_dataframe().equals(other.to_dataframe())
 
-    @docval({'name': 'name', 'type': str, 'doc': 'the name of this VectorData'},  # noqa: C901
+    @docval({'name': 'name', 'type': str, 'doc': 'the name of this VectorData'},
             {'name': 'description', 'type': str, 'doc': 'a description for this column'},
             {'name': 'data', 'type': ('array_data', 'data'),
              'doc': 'a dataset where the first dimension is a concatenation of multiple vectors', 'default': list()},

src/hdmf/spec/spec.py

@@ -1062,7 +1062,7 @@ def is_inherited_spec(self, **kwargs):
         return False
 
     @docval({'name': 'spec', 'type': Spec, 'doc': 'the specification to check'})
-    def is_overridden_spec(self, **kwargs):  # noqa: C901
+    def is_overridden_spec(self, **kwargs):
         ''' Returns 'True' if specification overrides a specification from the parent type '''
         spec = getargs('spec', kwargs)
         spec_name = spec.name
@@ -1086,23 +1086,20 @@ def is_overridden_spec(self, **kwargs):  # noqa: C901
             return self.is_overridden_dataset(spec_name)
         elif spec_name in self.__data_types:
             return self.is_overridden_type(spec_name)
+        elif super().is_overridden_spec(spec):  # attribute spec
+            return True
         else:
-            if super().is_overridden_spec(spec):  # check if overridden attribute
-                return True
+            parent_name = spec.parent.name
+            if parent_name is None:
+                parent_name = spec.parent.data_type
+            if isinstance(spec.parent, DatasetSpec):
+                if (parent_name in self.__datasets and self.is_overridden_dataset(parent_name) and
+                    self.__datasets[parent_name].is_overridden_spec(spec)):
+                        return True
             else:
-                parent_name = spec.parent.name
-                if parent_name is None:
-                   parent_name = spec.parent.data_type
-                if isinstance(spec.parent, DatasetSpec):
-                    if parent_name in self.__datasets:
-                        if self.is_overridden_dataset(parent_name):
-                            if self.__datasets[parent_name].is_overridden_spec(spec):
-                                return True
-                else:
-                    if parent_name in self.__groups:
-                        if self.is_overridden_group(parent_name):
-                            if self.__groups[parent_name].is_overridden_spec(spec):
-                                return True
+                if (parent_name in self.__groups and self.is_overridden_group(parent_name) and
+                    self.__groups[parent_name].is_overridden_spec(spec)):
+                        return True
         return False
 
     @docval({'name': 'spec', 'type': (BaseStorageSpec, str), 'doc': 'the specification to check'})

src/hdmf/validate/validator.py

@@ -472,9 +472,9 @@ class GroupValidator(BaseStorageValidator):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
-    @docval({"name": "builder", "type": GroupBuilder, "doc": "the builder to validate"},  # noqa: C901
+    @docval({"name": "builder", "type": GroupBuilder, "doc": "the builder to validate"},
             returns='a list of Errors', rtype=list)
-    def validate(self, **kwargs):  # noqa: C901
+    def validate(self, **kwargs):
         builder = getargs('builder', kwargs)
         errors = super().validate(builder)
         errors.extend(self.__validate_children(builder))