Fix bug with IdentityFilter

doc_octopy/datasets/defaults.py

@@ -52,11 +52,13 @@ def __init__(
         ground_truth_data_path: Path,
         save_path: Path,
         tasks_to_use: Sequence[str] = EXPERIMENTS_TO_USE,
+        debug: bool = False,
     ):
         self.emg_data_path = emg_data_path
         self.ground_truth_data_path = ground_truth_data_path
         self.save_path = save_path
         self.tasks_to_use = tasks_to_use
+        self.debug = debug
 
     def create_dataset(self):
         EMGDataset(
@@ -79,13 +81,13 @@ def create_dataset(self):
             emg_representations_to_filter_after_chunking=["Last"],
             ground_truth_filter_pipeline_before_chunking=[
                 [
-                    ApplyFunctionFilter(function=np.reshape, newshape=(63, -1)),
+                    ApplyFunctionFilter(function=np.reshape, name="Reshape", newshape=(63, -1)),
                     IndexDataFilter(indices=(slice(3, 63),)),
                 ]
             ],
             ground_truth_representations_to_filter_before_chunking=["Input"],
             ground_truth_filter_after_pipeline_chunking=[
-                [ApplyFunctionFilter(function=np.mean, axis=-1, is_output=True)]
+                [ApplyFunctionFilter(function=np.mean, name="Mean", axis=-1, is_output=True)]
             ],
             ground_truth_representations_to_filter_after_pipeline_chunking=["Last"],
             augmentation_pipelines=[
@@ -94,6 +96,7 @@ def create_dataset(self):
                 [WaveletDecomposition(level=3, is_output=True, nr_of_grids=5)],
             ],
             amount_of_chunks_to_augment_at_once=500,
+            debug=self.debug,
         ).create_dataset()
 
 

doc_octopy/datasets/filters/generic.py

@@ -186,21 +186,12 @@ class IdentityFilter(FilterBaseClass):
 
     This filter is useful for debugging and testing purposes.
 
-    .. important:: If the input array is provided at initialization, the filter will always return that array.
-
     Parameters
     ----------
     input_is_chunked : bool
         Whether the input is chunked or not.
     is_output : bool
         Whether the filter is an output filter. If True, the resulting signal will be outputted by and dataset pipeline.
-    input_array : np.ndarray
-        The input array that will be returned by the filter. If provided, the filter will always return this array.
-
-    Attributes
-    ----------
-    input_array : np.ndarray
-        The input array that was filtered. This is stored after the filter is called.
 
     Methods
     -------
@@ -212,7 +203,6 @@ def __init__(
         self,
         input_is_chunked: bool = None,
         is_output: bool = False,
-        input_array: np.ndarray = None,
         name: str = None,
     ):
         super().__init__(
@@ -222,9 +212,5 @@ def __init__(
             name=name,
         )
 
-        self.input_array = input_array
-
     def _filter(self, input_array: np.ndarray) -> np.ndarray:
-        if self.input_array is None:
-            self.input_array = input_array
-        return self.input_array
+        return input_array

doc_octopy/datasets/supervised.py

@@ -227,7 +227,12 @@ def create_dataset(self):
                 )
 
             if self.debug:
+                print("After loading:")
+
+                print(emg_data_from_task)
                 emg_data_from_task.plot_graph()
+
+                print(ground_truth_data_from_task)
                 ground_truth_data_from_task.plot_graph()
 
             if not emg_data_from_task.is_chunked["Input"]:
@@ -262,7 +267,12 @@ def create_dataset(self):
                 chunked_ground_truth_data_from_task = ground_truth_data_from_task
 
                 if self.debug:
+                    print("After chunking:")
+
+                    print(chunked_emg_data_from_task)
                     chunked_emg_data_from_task.plot_graph()
+
+                    print(chunked_ground_truth_data_from_task)
                     chunked_ground_truth_data_from_task.plot_graph()
             else:
                 chunked_emg_data_from_task = emg_data_from_task  # [:min_length]
@@ -298,7 +308,12 @@ def create_dataset(self):
             )
 
             if self.debug:
+                print("After filtering the chunked data:")
+
+                print(emg_data_from_task)
                 chunked_emg_data_from_task.plot_graph()
+
+                print(ground_truth_data_from_task)
                 chunked_ground_truth_data_from_task.plot_graph()
 
             for group_name, chunked_data_from_task in zip(
@@ -321,6 +336,16 @@ def create_dataset(self):
                 chunked_emg_data_from_task.output_representations.values()
             )[-1].shape[0]
 
+            data_length_ground_truth = list(
+                chunked_ground_truth_data_from_task.output_representations.values()
+            )[-1].shape[0]
+
+            assert (
+                data_length == data_length_ground_truth
+            ), "The data lengths of the EMG and ground truth data should be the same. For task {}, the EMG data has length {} and the ground truth data has length {}.".format(
+                task, data_length, data_length_ground_truth
+            )
+
             for g in (training_group, testing_group, validation_group):
                 _add_to_dataset(
                     g,