[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

capsul/pipeline/pipeline.py

@@ -958,15 +958,11 @@ def add_link(self, link, weak_link=False, allow_export=False):
         if not source_plug.output and source_node is not self:
             raise ValueError(f"Cannot link from an input plug: {link}")
         if source_plug.output and source_node is self:
-            raise ValueError(
-                f"Cannot link from a pipeline output plug: {link}"
-            )
+            raise ValueError(f"Cannot link from a pipeline output plug: {link}")
         if dest_plug.output and dest_node is not self:
             raise ValueError(f"Cannot link to an output plug: {link}")
         if not dest_plug.output and dest_node is self:
-            raise ValueError(
-                f"Cannot link to a pipeline input plug: {link}"
-            )
+            raise ValueError(f"Cannot link to a pipeline input plug: {link}")
 
         # Propagate the plug value from source to destination
         value = getattr(source_node, source_plug_name, None)

capsul/pipeline/pipeline_nodes.py

@@ -146,10 +146,7 @@ def __init__(
         flat_inputs = []
         for switch_name in inputs:
             flat_inputs.extend(
-                [
-                    f"{switch_name}_switch_{plug_name}"
-                    for plug_name in outputs
-                ]
+                [f"{switch_name}_switch_{plug_name}" for plug_name in outputs]
             )
         node_inputs = [
             dict(name="switch"),

capsul/pipeline/python_export.py

@@ -182,9 +182,7 @@ def _write_custom_node(node, pyf, name, enabled):
                 file=pyf,
             )
         else:
-            print(
-                f'        self.add_custom_node("{name}", "{nodename}")', file=pyf
-            )
+            print(f'        self.add_custom_node("{name}", "{nodename}")', file=pyf)
         # optional plugs
         for plug_name, plug in node.plugs.items():
             if plug.optional:

capsul/pipeline/topological_sort.py

@@ -141,9 +141,7 @@ def add_node(self, node):
         if not isinstance(node, GraphNode):
             raise Exception(f"Expect a GraphNode, got {node}")
         if node.name in self._nodes:
-            raise Exception(
-                f"Expect a GraphNode with a unique name, got {node}"
-            )
+            raise Exception(f"Expect a GraphNode with a unique name, got {node}")
         self._nodes[node.name] = node
 
     def find_node(self, node_name):
@@ -170,13 +168,11 @@ def add_link(self, from_node, to_node):
         """
         if from_node not in self._nodes:
             raise Exception(
-                f"Node {from_node} is not defined in the Graph."
-                "Use add_node() method"
+                f"Node {from_node} is not defined in the Graph." "Use add_node() method"
             )
         if to_node not in self._nodes:
             raise Exception(
-                f"Node {to_node} is not defined in the Graph."
-                "Use add_node() method"
+                f"Node {to_node} is not defined in the Graph." "Use add_node() method"
             )
         if (from_node, to_node) not in self._links:
             self._nodes[to_node].add_link_from(self._nodes[from_node])

capsul/qt_apps/utils/application.py

@@ -115,9 +115,7 @@ def __init__(self, extra_options=None):
 
             # If a no valid logging level is found raise an Exception
             if level is None:
-                raise Exception(
-                    f"Warning : unknown logging level {self.options.debug}"
-                )
+                raise Exception(f"Warning : unknown logging level {self.options.debug}")
 
             # Configure the logging module
             logging.basicConfig(level=level, format=logging_format, datefmt=date_format)

capsul/qt_gui/widgets/activation_inspector.py

@@ -248,9 +248,7 @@ def refresh_activation_from_record(self):
                 self.activations.append(current_activations.copy())
 
                 # > Add a line to the activation display
-                self.ui.events.addItem(
-                    f"{iteration}{activation} {node}:{plug}"
-                )
+                self.ui.events.addItem(f"{iteration}{activation} {node}:{plug}")
 
             # Select the last activation step so the pipeline will be
             # in his final configuration
@@ -270,9 +268,7 @@ def update_pipeline_activation(self, index):
             # Restore the plugs and nodes activations
             node_name = node.full_name
             for plug_name, plug in node.plugs.items():
-                plug.activated = activations.get(
-                    f"{node_name}:{plug_name}", False
-                )
+                plug.activated = activations.get(f"{node_name}:{plug_name}", False)
             node.activated = activations.get(f"{node_name}:", False)
 
         # Refresh views relying on plugs and nodes selection

capsul/qt_gui/widgets/pipeline_developer_view.py

@@ -5246,9 +5246,7 @@ def hinted_tuple_hook(obj):
             dic = json.loads(dic, object_hook=hinted_tuple_hook)
 
             if "pipeline_parameters" not in list(dic.keys()):
-                raise KeyError(
-                    f'No "pipeline_parameters" key found in {filename}.'
-                )
+                raise KeyError(f'No "pipeline_parameters" key found in {filename}.')
 
             for field_name, field_value in dic["pipeline_parameters"].items():
                 if field_name not in [

capsul/schemas/brainvisa.py

@@ -190,29 +190,21 @@ def declare_morpho_schemas(morpho_module):
     normalization_fsl_reinit = importlib.import_module(
         f"{axon_module}.normalization_fsl_reinit"
     )
-    t1biascorrection = importlib.import_module(
-        f"{axon_module}.t1biascorrection"
-    )
+    t1biascorrection = importlib.import_module(f"{axon_module}.t1biascorrection")
     histoanalysis = importlib.import_module(f"{axon_module}.histoanalysis")
-    brainsegmentation = importlib.import_module(
-        f"{axon_module}.brainsegmentation"
-    )
+    brainsegmentation = importlib.import_module(f"{axon_module}.brainsegmentation")
     skullstripping = importlib.import_module(f"{axon_module}.skullstripping")
     scalpmesh = importlib.import_module(f"{axon_module}.scalpmesh")
     splitbrain = importlib.import_module(f"{axon_module}.splitbrain")
     greywhiteclassificationhemi = importlib.import_module(
         f"{axon_module}.greywhiteclassificationhemi"
     )
-    greywhitetopology = importlib.import_module(
-        f"{axon_module}.greywhitetopology"
-    )
+    greywhitetopology = importlib.import_module(f"{axon_module}.greywhitetopology")
     greywhitemesh = importlib.import_module(f"{axon_module}.greywhitemesh")
     pialmesh = importlib.import_module(f"{axon_module}.pialmesh")
     sulciskeleton = importlib.import_module(f"{axon_module}.sulciskeleton")
     sulcigraph = importlib.import_module(f"{axon_module}.sulcigraph")
-    sulcilabellingann = importlib.import_module(
-        f"{axon_module}.sulcilabellingann"
-    )
+    sulcilabellingann = importlib.import_module(f"{axon_module}.sulcilabellingann")
     sulcilabellingspamglobal = importlib.import_module(
         f"{axon_module}.sulcilabellingspamglobal"
     )