diff --git a/core/processes/dsignal.py b/core/processes/dsignal.py
index 56ac8f03ba58eedc7be36ee086da2078538b06e5..c596075721934a2517c4a20c648deabce8db1453 100644
--- a/core/processes/dsignal.py
+++ b/core/processes/dsignal.py
@@ -9,8 +9,8 @@ class dsignalParameters(ParametersABC):
     :window: Number of timepoints to consider for signal.
     """
 
-    def __init__(self, window):
-        super().__init__()
+    def __init__(self, window: int):
+        self.window = window
 
     @classmethod
     def default(cls):
diff --git a/core/processor.py b/core/processor.py
index 7fad4e69902a2541eb449c507e7a1db093f48613..381203946725b6f7ba7356dfec021e4fd603d8ca 100644
--- a/core/processor.py
+++ b/core/processor.py
@@ -76,7 +76,7 @@ class PostProcessor:
         }
         self.process_parameters = {
             process: self.get_parameters(process)
-            for process in parameters["process_parameters"].keys()
+            for process in parameters["processes"]["processes"]
         }
         self.processes = parameters["processes"]
 
@@ -106,13 +106,15 @@ class PostProcessor:
             self._writer.write(ids, "/postprocessing/cell_info/" + name)
         picks = self.picker.run(self._signal[self.processes["picker"][0]])
         for process, datasets in self.processes["processes"].items():
-            if process in self.parameters.to_dict():
-                loaded_process = self.process_classfun[process](
+            parameters = (
+                self.process_parameters[process].from_dict(
                     self.process_parameters[process]
                 )
-            else:
-                print(self.process_classfun, process)
-                loaded_process = self.process_classfun[process].default()
+                if process in self.parameters["processes"]["process_parameters"]
+                else self.process_parameters[process].default()
+            )
+            print(parameters.to_dict())
+            loaded_process = self.process_classfun[process](parameters)
             for dataset in datasets:
                 if isinstance(dataset, list):  # multisignal process
                     dataset = [self._signal[d] for d in dataset]