clean up

src/hdmf/build/objectmapper.py

@@ -828,48 +828,55 @@ def __check_dset_spec(self, orig, ext):
             spec = ext
         return dtype, shape, dims, spec
 
+    def __get_matched_dimension(self, data_shape, spec_shape, spec_dtype=None):
+        # if shape is a list of allowed shapes, find the index of the shape that matches the data
+        if isinstance(spec_shape[0], list):
+            match_shape_inds = list()
+            for i, s in enumerate(spec_shape):
+                # skip this shape if it has a different number of dimensions from the data
+                if len(s) != len(data_shape):
+                    continue
+                # check each dimension. None means any length is allowed
+                match = True
+                for j, d in enumerate(data_shape):
+                    if s[j] is not None and s[j] != d:
+                        match = False
+                        break
+                if match:
+                    match_shape_inds.append(i)
+            return match_shape_inds
+
     def __get_spec_info(self, data, spec_shape, spec_dims, spec_dtype=None):
         """This will return the dimension labels and shape by matching the data shape to a permissible spec shape."""
         if spec_shape is None and spec_dims is None:
             return None, None
-        elif spec_shape is not None and spec_dims is None:
-            return None, tuple(spec_shape)
-        elif spec_shape is None and spec_dims is not None:
-            return spec_dims, None
+        # elif spec_shape is not None and spec_dims is None:
         else:
             if spec_dtype is not None and isinstance(spec_dtype, list):
                 data_shape = (len(data),)
             else:
                 data_shape = get_data_shape(data)
-            # if shape is a list of allowed shapes, find the index of the shape that matches the data
+
             if isinstance(spec_shape[0], list):
-                match_shape_inds = list()
-                for i, s in enumerate(spec_shape):
-                    # skip this shape if it has a different number of dimensions from the data
-                    if len(s) != len(data_shape):
-                        continue
-                    # check each dimension. None means any length is allowed
-                    match = True
-                    for j, d in enumerate(data_shape):
-                        if s[j] is not None and s[j] != d:
-                            match = False
-                            break
-                    if match:
-                        match_shape_inds.append(i)
-                # use the most specific match -- the one with the fewest Nones
-                if match_shape_inds:
-                    if len(match_shape_inds) == 1:
-                        return tuple(spec_dims[match_shape_inds[0]]), tuple(spec_shape[match_shape_inds[0]])
-                    else:
-                        count_nones = [len([x for x in spec_shape[k] if x is None]) for k in match_shape_inds]
-                        index_min_count = count_nones.index(min(count_nones))
-                        best_match_ind = match_shape_inds[index_min_count]
-                        return tuple(spec_dims[best_match_ind]), tuple(spec_shape[best_match_ind])
-                else:
+                match_shape_inds = self.__get_matched_dimension(data_shape, spec_shape, spec_dtype)
+                if len(match_shape_inds) == 0:
                     # no matches found
                     msg = "Shape of data does not match any allowed shapes in spec '%s'" % self.spec.path
                     warnings.warn(msg, IncorrectDatasetShapeBuildWarning)
                     return None, None
+                elif len(match_shape_inds) == 1:
+                    if spec_dims is not None:
+                        return tuple(spec_dims[match_shape_inds[0]]), tuple(spec_shape[match_shape_inds[0]])
+                    else:
+                        return spec_dims, tuple(spec_shape[match_shape_inds[0]])
+                else:
+                    count_nones = [len([x for x in spec_shape[k] if x is None]) for k in match_shape_inds]
+                    index_min_count = count_nones.index(min(count_nones))
+                    best_match_ind = match_shape_inds[index_min_count]
+                    if spec_dims is not None:
+                        return tuple(spec_dims[best_match_ind]), tuple(spec_shape[best_match_ind])
+                    else:
+                        return spec_dims, tuple(spec_shape[best_match_ind])
             else:
                 if len(data_shape) != len(spec_shape):
                     msg = "Shape of data does not match shape in spec '%s'" % self.spec.path
@@ -886,7 +893,10 @@ def __get_spec_info(self, data, spec_shape, spec_dims, spec_dtype=None):
                     warnings.warn(msg, IncorrectDatasetShapeBuildWarning)
                     return None, None
                 # shape is a single list of allowed dimension lengths
-                return tuple(spec_dims), tuple(spec_shape)
+                if spec_dims is not None:
+                    return tuple(spec_dims), tuple(spec_shape)
+                else:
+                    return None, tuple(spec_shape)
 
     def __is_reftype(self, data):
         if (isinstance(data, AbstractDataChunkIterator) or