black update

pynapple/process/decoding.py

@@ -51,34 +51,34 @@ def decode_1d(tuning_curves, group, ep, bin_size, time_units="s", feature=None):
     """
     if isinstance(group, nap.TsdFrame):
         newgroup = group.restrict(ep)
-        
+
         if tuning_curves.shape[1] != newgroup.shape[1]:
             raise RuntimeError("Different shapes for tuning_curves and group")
-    
+
         if not np.all(tuning_curves.columns.values == np.array(newgroup.columns)):
             raise RuntimeError("Different indices for tuning curves and group keys")
-            
+
         count = group
-        
+
     elif isinstance(group, nap.TsGroup):
         newgroup = group.restrict(ep)
-        
+
         if tuning_curves.shape[1] != len(newgroup):
             raise RuntimeError("Different shapes for tuning_curves and group")
-    
+
         if not np.all(tuning_curves.columns.values == np.array(newgroup.keys())):
             raise RuntimeError("Different indices for tuning curves and group keys")
 
         # Bin spikes
         count = newgroup.count(bin_size, ep, time_units)
-              
+
     elif isinstance(group, dict):
         newgroup = nap.TsGroup(group, time_support=ep)
         count = newgroup.count(bin_size, ep, time_units)
-        
+
     else:
         raise RuntimeError("Unknown format for group")
-   
+
     # Occupancy
     if feature is None:
         occupancy = np.ones(tuning_curves.shape[0])
@@ -172,34 +172,38 @@ def decode_2d(tuning_curves, group, ep, bin_size, xy, time_units="s", features=N
     if type(group) is nap.TsdFrame:
         newgroup = group.restrict(ep)
         numcells = newgroup.shape[1]
-        
+
         if len(tuning_curves) != numcells:
             raise RuntimeError("Different shapes for tuning_curves and group")
-
-        if not np.all(np.array(list(tuning_curves.keys())) == np.array(newgroup.columns)):
+
+        if not np.all(
+            np.array(list(tuning_curves.keys())) == np.array(newgroup.columns)
+        ):
             raise RuntimeError("Different indices for tuning curves and group keys")
-        
+
         count = group
-      
+
     elif type(group) is nap.TsGroup:
         newgroup = group.restrict(ep)
         numcells = len(newgroup)
-        
+
         if len(tuning_curves) != numcells:
             raise RuntimeError("Different shapes for tuning_curves and group")
 
-        if not np.all(np.array(list(tuning_curves.keys())) == np.array(newgroup.keys())):
+        if not np.all(
+            np.array(list(tuning_curves.keys())) == np.array(newgroup.keys())
+        ):
             raise RuntimeError("Different indices for tuning curves and group keys")
-        
+
         count = newgroup.count(bin_size, ep, time_units)
-                
+
     elif type(group) is dict:
         newgroup = nap.TsGroup(group, time_support=ep)
         count = newgroup.count(bin_size, ep, time_units)
-            
+
     else:
         raise RuntimeError("Unknown format for group")
-    
+
     indexes = list(tuning_curves.keys())
 
     # Occupancy