1224 change project structure to sub modules

loop_system/Baseline.py

@@ -7,25 +7,26 @@
 
 warnings.filterwarnings("ignore")
 
-
 def select_file(title, filetypes):
     root = tk.Tk()
     root.withdraw()  # hide the main Tkinter window
 
-    file_path = filedialog.askopenfilename(title=title, filetypes=filetypes)
+    file_path = filedialog.askopenfilename(
+        title=title,
+        filetypes=filetypes
+    )
 
     if not file_path:
         print("No file selected. Exiting...")
         sys.exit(1)
 
     return file_path
 
-
 def main():
     start_time = time.time()
 
     # Prompt user to select the XDF file
-    file_path = select_file("Select the Baseline data file", [("XDF files", "*.xdf")])
+    file_path = select_file("Select the Baseline data file",[("XDF files", "*.xdf")])
 
     # Extract folder and condition name from the file path
     folder = os.path.dirname(file_path)
@@ -70,6 +71,5 @@ def main():
     except Exception as e:
         print(f"An error occurred saving the Baseline Dataframe: {e}")
 
-
 if __name__ == "__main__":
     main()

loop_system/Manager.py

@@ -58,9 +58,7 @@ def run(self):
         modelTrainer = ModelTrainer()
 
         """Load and Select Baseline Data file"""
-        baseline_file = select_file(
-            "Select the Baseline data file.", [("CSV files", "*.csv")]
-        )
+        baseline_file = select_file("Select the Baseline data file.", [("CSV files", "*.csv")])
 
         if not baseline_file:
             print("No baseline file selected. Exiting...")
@@ -78,9 +76,7 @@ def run(self):
 
         """Load and Select Training Data file"""
 
-        training_data = select_file(
-            "Select the training data file.", [("CSV files", "*.csv")]
-        )
+        training_data = select_file("Select the training data file.", [("CSV files", "*.csv")])
 
         if not training_data:
             print("No training data file selected. Exiting...")
@@ -118,9 +114,7 @@ def run(self):
         os.makedirs(log_folder, exist_ok=True)  # Ensure directory exists
         log_file_path = os.path.join(log_folder, "output.txt")
 
-        print(
-            "Logging output to {log_file_path}...".format(log_file_path=log_file_path)
-        )
+        print("Logging output to {log_file_path}...".format(log_file_path=log_file_path))
 
         log_file = open(log_file_path, "w")
         sys.stdout = TeeLogger(log_file)
@@ -245,8 +239,11 @@ def run(self):
             pass
         finally:
             try:
-                model_path = os.path.join(base_dir, f"model_v{self.model_version}.pkl")
-                joblib.dump(self.model, model_path)
+                model_path = os.path.join(base_dir, f"model_v{self.model_version}.pkl" )
+                joblib.dump(
+                    self.model,
+                    model_path
+                )
                 print(f"Model saved successfully to {model_path}")
             except Exception as e:
                 print(f"Error saving the model: {e}")
@@ -265,7 +262,6 @@ def run(self):
             data_sender.join()
             print("Closed all processes.")
 
-
 if __name__ == "__main__":
     multiprocessing.freeze_support()
     manager = Manager()

loop_system/Training_Model.py

@@ -9,7 +9,6 @@
 
 warnings.filterwarnings("ignore")
 
-
 def main():
 
     start_time = time.time()
@@ -20,9 +19,7 @@ def main():
 
     """Load Baseline Data file"""
 
-    baseline_file = select_file(
-        "Select the Baseline data file", [("CSV files", "*.csv")]
-    )
+    baseline_file = select_file("Select the Baseline data file", [("CSV files", "*.csv")])
 
     """Baseline Dataframe"""
     try:
@@ -61,6 +58,7 @@ def main():
         print(f"An error occurred loading the N-Back data: {e}")
         sys.exit(1)
 
+
     """Signals Processing"""
     signals = getSignals(
         data, "OpenSignals", "PsychoPy Markers", "PsychoPy Ratings", sensors=sensors
@@ -106,6 +104,7 @@ def main():
     print("Performing GridSearchCV to find the best models...")
     best_models = gridSearchCV(X, Y)
 
+
     print("GridSearch completed.")
     # Sort the models by their best score in descending order
     sorted_models = sorted(
@@ -138,6 +137,5 @@ def main():
 
     print(f"Elapsed time = {(time.time()-start_time)/60:.2f} minutes")
 
-
 if __name__ == "__main__":
     main()

physiological_data/Epochs.py

@@ -7,7 +7,7 @@ def getMarkers(marker, timestamps):
     category, onset, offset = list(), list(), list()
 
     for timestamp, markers in zip(timestamps, marker):
-        if markers[1] == "1":
+        if markers[1] == '1':
             offset.append(timestamp)
         elif markers[0] == "end":
             offset.append(timestamp)

physiological_data/Load.py

@@ -43,12 +43,8 @@ def Load_Ratings(data, stream_name: str):
                     else:
                         arousal.append("High")
 
-        ratings = {
-            "Valence": valence,
-            "Arousal": arousal,
-            "Valence Timestamps": valence_timestamps,
-            "Arousal Timestamps": arousal_timestamps,
-        }
+        ratings = {"Valence": valence, "Arousal": arousal, "Valence Timestamps": valence_timestamps,
+                   "Arousal Timestamps": arousal_timestamps}
 
     return ratings