【Fix】 format code

stereo/algorithm/community_detection.py

@@ -235,7 +235,8 @@ def _main(self, slices, annotation="annotation", **kwargs):
                 f'tissue_{algo.method_key}']
             if np.nan in self.slices[slice_id]._ann_data.obs['cell_communities'].values:
                 if 'unknown' not in self.slices[slice_id]._ann_data.obs['cell_communities'].cat.categories:
-                    self.slices[slice_id]._ann_data.obs['cell_communities'] = self.slices[slice_id]._ann_data.obs['cell_communities'].cat.add_categories('unknown')
+                    self.slices[slice_id]._ann_data.obs['cell_communities'] = self.slices[slice_id]._ann_data.obs[
+                        'cell_communities'].cat.add_categories('unknown')
                 self.slices[slice_id]._ann_data.obs['cell_communities'].fillna('unknown', inplace=True)
             # self.slices[slice_id]._ann_data.obs['cell_communities'].fillna('unknown', inplace=True)
 

stereo/algorithm/regulatory_network_inference/main.py

@@ -92,7 +92,7 @@ def main(self,
         self.use_raw = use_raw
         if use_raw and self.stereo_exp_data.raw is None:
             raise Exception("The raw data is not found, you need to run 'raw_checkpoint()' first.")
-        
+
         if use_raw:
             logger.info('the raw expression matrix will be used.')
             matrix = self.stereo_exp_data.raw.to_df()
@@ -523,12 +523,12 @@ def regulons_to_csv(self, regulon_list: list, fn: str = 'regulon_list.csv', fn_p
             w.writerows(regulon_dict.items())
 
     def to_loom(
-        self,
-        matrix: pd.DataFrame,
-        auc_matrix: pd.DataFrame,
-        regulons: list,
-        loom_fn: str = 'grn_output.loom',
-        fn_prefix: str = None
+            self,
+            matrix: pd.DataFrame,
+            auc_matrix: pd.DataFrame,
+            regulons: list,
+            loom_fn: str = 'grn_output.loom',
+            fn_prefix: str = None
     ):
         """
         Save GRN results in one loom file

stereo/core/ms_pipeline.py

@@ -34,15 +34,15 @@ def result(self):
     @result.setter
     def result(self, new_result):
         self._result = new_result
-    
+
     @property
     def key_record(self):
         return self._key_record
-    
+
     @key_record.setter
     def key_record(self, key_record):
         self._key_record = key_record
-    
+
     # @property
     # def scope_data(self):
     #     return self._scope_data
@@ -65,7 +65,7 @@ def _use_integrate_method(self, item, *args, **kwargs):
             ms_data_view = self.ms_data[scope]
         if not ms_data_view.merged_data:
             ms_data_view.integrate(result=self.ms_data.tl.result)
-        
+
         # key_name = "scope_[" + ",".join(
         #     [str(self.ms_data._names.index(name)) for name in ms_data_view._names]) + "]"
         # self._scope_data[key_name] = self.ms_data._merged_data
@@ -105,10 +105,10 @@ def contain_method(item):
         def reset_key_record(key, res_key):
             key_name = "scope_[" + ",".join(
                 [str(self.ms_data._names.index(name)) for name in ms_data_view._names]) + "]"
-            
+
             ms_data_view._merged_data.tl._reset_key_record(key, res_key)
             self._key_record[key_name] = ms_data_view._merged_data.tl.key_record
-        
+
         ms_data_view._merged_data.tl.reset_key_record = reset_key_record
 
         new_attr = self.__class__.BASE_CLASS.__dict__.get(item, None)

stereo/core/st_pipeline.py

@@ -1322,10 +1322,12 @@ def annotation(
 
         cluster_res: pd.DataFrame = self.result[cluster_res_key]
 
-        if isinstance(annotation_information, (list, np.ndarray)) and len(annotation_information) != cluster_res['group'].cat.categories.size:
+        if isinstance(annotation_information, (list, np.ndarray)) and \
+                len(annotation_information) != cluster_res['group'].cat.categories.size:
             raise Exception(f"The length of annotation information is {len(annotation_information)}, \
-                            not equal to the categories of cluster result whoes lenght is {cluster_res['group'].cat.categories.size}.")
-
+                            not equal to the categories of cluster result whoes"
+                            f" lenght is {cluster_res['group'].cat.categories.size}.")
+
         if isinstance(annotation_information, (list, np.ndarray)):
             new_categories = np.array(annotation_information, dtype='U')
         elif isinstance(annotation_information, dict):
@@ -1334,8 +1336,8 @@ def annotation(
                 new_categories_list.append(annotation_information[i])
             new_categories = np.array(new_categories_list, dtype='U')
         else:
-            raise TypeError(f"The type of 'annotation_information' only supports list, ndarray or dict.")
-        
+            raise TypeError("The type of 'annotation_information' only supports list, ndarray or dict.")
+
         new_categories_values = new_categories[cluster_res['group'].cat.codes]
 
         self.result[res_key] = pd.DataFrame(data={

stereo/core/stereo_exp_data.py

@@ -601,7 +601,7 @@ def reset_position(self):
                 self.position[idx] += self.position_min[bno]
         self.position_offset = None
         self.position_min = None
-    
+
     def __add__(self, other):
         from stereo.core.ms_data import MSData
         if isinstance(other, StereoExpData):
@@ -767,7 +767,7 @@ def position_z(self, position_z: np.ndarray):
     @property
     def bin_type(self):
         return self._ann_data.uns.get('bin_type', 'bins')
-    
+
     @bin_type.setter
     def bin_type(self, bin_type):
         self.bin_type_check(bin_type)
@@ -776,7 +776,7 @@ def bin_type(self, bin_type):
     @property
     def bin_size(self):
         return self._ann_data.uns.get('bin_size', 1)
-    
+
     @bin_size.setter
     def bin_size(self, bin_size):
         self._ann_data.uns['bin_size'] = bin_size
@@ -793,7 +793,7 @@ def sn(self):
                 for _, row in sn_data.iterrows():
                     sn[row['batch']] = row['sn']
         return sn
-    
+
     @sn.setter
     def sn(self, sn):
         if isinstance(sn, str):