autopep8 ran

Author: mdesco

src/scilpy/cli/scil_connectivity_functional_graph_measures.py

@@ -58,9 +58,12 @@ def _build_arg_parser():
                    help='Input fonctional connectivity matrix (.npy).')
     p.add_argument('out_json',
                    help='Path of the output json.')
-    p.add_argument('--conn_threshold', type=float, default=0.25,
-                   help='Threshold for the functional connectivity values. All values \n'
-                   'lower or equal to will be set to zero. (default: 0.25)')
+    p.add_argument(
+        '--conn_threshold',
+        type=float,
+        default=0.25,
+        help='Threshold for the functional connectivity values. All values \n'
+        'lower or equal to will be set to zero. (default: 0.25)')
     p.add_argument('--filtering_mask',
                    help='Binary filtering mask to apply before computing the '
                         'measures.')
@@ -89,7 +92,7 @@ def main():
     else:
         logging.info('Using --append_json, make sure to delete {} '
                      'before re-launching a group analysis.'.format(
-                            args.out_json))
+                         args.out_json))
 
     if args.append_json and args.overwrite:
         parser.error('Cannot use the append option at the same time as '
@@ -99,11 +102,12 @@ def main():
     conn_matrix = load_matrix_in_any_format(args.in_conn_matrix)
 
     if args.filtering_mask:
-        mask_matrix = load_matrix_in_any_format(args.filtering_mask).astype(bool)
+        mask_matrix = load_matrix_in_any_format(
+            args.filtering_mask).astype(bool)
         conn_matrix *= mask_matrix
 
-    gtm_dict = evaluate_functional_graph_measures(conn_matrix, args.conn_threshold,
-                                                  args.avg_node_wise)
+    gtm_dict = evaluate_functional_graph_measures(
+        conn_matrix, args.conn_threshold, args.avg_node_wise)
 
     if os.path.isfile(args.out_json) and args.append_json:
         with open(args.out_json) as json_data:

src/scilpy/cli/scil_connectivity_structural_graph_measures.py

@@ -60,7 +60,7 @@ def _build_arg_parser():
     p.add_argument('out_json',
                    help='Path of the output json.')
 
-    p.add_argument('--length_matrix', 
+    p.add_argument('--length_matrix',
                    help='Input length-weighted matrix (.npy).')
     p.add_argument('--filtering_mask',
                    help='Binary filtering mask to apply before computing the '
@@ -93,7 +93,7 @@ def main():
     else:
         logging.info('Using --append_json, make sure to delete {} '
                      'before re-launching a group analysis.'.format(
-                            args.out_json))
+                         args.out_json))
 
     if args.append_json and args.overwrite:
         parser.error('Cannot use the append option at the same time as '
@@ -102,20 +102,21 @@ def main():
 
     conn_matrix = load_matrix_in_any_format(args.in_conn_matrix)
     len_matrix = None
-    
+
     if args.length_matrix:
         len_matrix = load_matrix_in_any_format(args.length_matrix)
 
     if args.filtering_mask:
-        mask_matrix = load_matrix_in_any_format(args.filtering_mask).astype(bool)
+        mask_matrix = load_matrix_in_any_format(
+            args.filtering_mask).astype(bool)
         conn_matrix *= mask_matrix
-        
+
         if args.length_matrix:
             len_matrix *= mask_matrix
 
     if len_matrix is None:
         print("Warning: No length-weighted matrix provided. ")
-        
+
     gtm_dict = evaluate_graph_measures(conn_matrix, len_matrix,
                                        args.avg_node_wise, args.small_world)
 

src/scilpy/connectivity/matrix_tools.py

@@ -83,7 +83,7 @@ def evaluate_functional_graph_measures(conn_matrix, conn_threshold,
     Parameters
     ----------
     conn_matrix: np.ndarray
-        2D matrix of functional connectivity weights 
+        2D matrix of functional connectivity weights
     conn_threshold: float
         2D matrix of bundle lengths.
     avg_node_wise: bool
@@ -114,11 +114,12 @@ def list_cast(_input):
     Wp = np.copy(conn_matrix)
     Wp = np.abs(conn_matrix)
     Wp[Wp <= conn_threshold] = 0
-    
+
     gtm_dict = {}
     ci, gtm_dict['modularity'] = bct.modularity_louvain_und(Wp, seed=0)
     gtm_dict['assortativity'] = bct.assortativity_wei(Wp, flag=0)
-    gtm_dict['participation'] = func_cast(bct.participation_coef_sign(Wp, ci)[0])
+    gtm_dict['participation'] = func_cast(
+        bct.participation_coef_sign(Wp, ci)[0])
     gtm_dict['clustering'] = func_cast(bct.clustering_coef_wu(Wp))
 
     gtm_dict['nodal_strength'] = func_cast(bct.strengths_und(Wp))
@@ -127,7 +128,7 @@ def list_cast(_input):
     # Rich club always gives an error for the matrix rank and gives NaN
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        tmp_rich_club = bct.rich_club_wu(Wp)        
+        tmp_rich_club = bct.rich_club_wu(Wp)
     gtm_dict['rich_club'] = func_cast(tmp_rich_club[~np.isnan(tmp_rich_club)])
 
     return gtm_dict
@@ -170,12 +171,14 @@ def list_cast(_input):
 
     gtm_dict = {}
 
-    if len_matrix is not None :
-        betweenness_centrality = bct.betweenness_wei(len_matrix) / ((N-1)*(N-2))
+    if len_matrix is not None:
+        betweenness_centrality = bct.betweenness_wei(
+            len_matrix) / ((N - 1) * (N - 2))
         gtm_dict['betweenness_centrality'] = func_cast(betweenness_centrality)
-        gtm_dict['local_efficiency'] = func_cast(bct.efficiency_wei(len_matrix,
-                                                                    local=True))
-        gtm_dict['global_efficiency'] = func_cast(bct.efficiency_wei(len_matrix))
+        gtm_dict['local_efficiency'] = func_cast(
+            bct.efficiency_wei(len_matrix, local=True))
+        gtm_dict['global_efficiency'] = func_cast(
+            bct.efficiency_wei(len_matrix))
 
         # Path length gives an infinite distance for unconnected nodes
         # All of this is simply to fix that
@@ -193,12 +196,9 @@ def list_cast(_input):
                     gtm_dict['path_length'].insert(i, -1)
                     gtm_dict['edge_count'].insert(i, -1)
 
-        
         if small_world:
             gtm_dict['omega'], gtm_dict['sigma'] = omega_sigma(len_matrix)
 
-
-            
     ci, gtm_dict['modularity'] = bct.modularity_louvain_und(conn_matrix,
                                                             seed=0)
     gtm_dict['assortativity'] = bct.assortativity_wei(conn_matrix,