ruff fixes

nidmresults/graph.py

@@ -1023,7 +1023,7 @@ def load_contrasts(self, workaround=False):
                         pe_ids = pe_ids + (NIIRI.qname(str(args_pe["pe_id"])),)
 
                 mpe_id = NIIRI.qname(str(args["mpe_id"]))
-                if not (mpe_id, pe_ids) in contrasts:
+                if (mpe_id, pe_ids) not in contrasts:
                     contrasts[(mpe_id, pe_ids)] = [con]
                 else:
                     contrasts[(mpe_id, pe_ids)].append(con)

nidmresults/objects/contrast.py

@@ -62,7 +62,7 @@ def __init__(
     ):
         super().__init__(oid=oid)
         self.contrast_name = contrast_name
-        if not type(contrast_weights) is list:
+        if type(contrast_weights) is not list:
             contrast_weights = json.loads(contrast_weights)
         self.contrast_weights = contrast_weights
         self.contrast_num = contrast_num

nidmresults/objects/generic.py

@@ -32,7 +32,7 @@ def __init__(self, oid=None):
         if oid is None:
             self.id = NIIRI[str(uuid.uuid4())]
         else:
-            if not type(oid) is QualifiedName:
+            if type(oid) is not QualifiedName:
                 oid = NIIRI.qname(Identifier(oid))
             self.id = oid
 
@@ -187,7 +187,7 @@ def __init__(
             units = ["mm", "mm", "mm"]
 
         self.number_of_dimensions = numdim
-        if not type(vox_to_world) is np.ndarray:
+        if type(vox_to_world) is not np.ndarray:
             # This is useful if info was read from a NIDM pack
             vox_to_world = np.array(json.loads(vox_to_world))
             dimensions = np.array(json.loads(dimensions))

nidmresults/objects/inference.py

@@ -1232,13 +1232,13 @@ def __init__(
         if x is not None and y is not None and z is not None:
             self.coord_vector = [x, y, z]
         else:
-            if coord_vector and not type(coord_vector) is list:
+            if coord_vector and type(coord_vector) is not list:
                 coord_vector = json.loads(coord_vector)
             self.coord_vector = coord_vector
         if x_std is not None and y_std is not None and z_std is not None:
             self.coord_vector_std = [x_std, y_std, z_std]
         else:
-            if coord_vector_std and not type(coord_vector_std) is list:
+            if coord_vector_std and type(coord_vector_std) is not list:
                 coord_vector_std = json.loads(coord_vector_std)
             self.coord_vector_std = coord_vector_std
 

nidmresults/objects/modelfitting.py

@@ -265,7 +265,7 @@ def __init__(
             self.image = image_file
         else:
             self.image = Image(image_file, img_filename)
-        if not type(regressors) is list:
+        if type(regressors) is not list:
             regressors = json.loads(regressors)
         self.regressors = regressors