Merge pull request #18 from nuluh/feature/15-normalize-dataset-by-preprocess-relatives-value-between-two-acceloremeter-sensors

Feature/15 normalize dataset by preprocess relatives value between two acceloremeter sensors

.gitignore

@@ -1,4 +1,4 @@
 # Ignore CSV files in the data directory and all its subdirectories
 data/**/*.csv
-
+.venv/
 *.pyc

generate_dummy_data.py

@@ -16,8 +16,10 @@ os.makedirs(processed_path, exist_ok=True)
 # Define the number of zeros to pad
 num_damages = 5
 num_tests = 10
+num_sensors = 2
 damage_pad = len(str(num_damages))
 test_pad = len(str(num_tests))
+sensor_pad = len(str(num_sensors))
 
 for damage in range(1, num_damages + 1):  # 5 Damage levels starts from 1
     damage_folder = f"DAMAGE_{damage:0{damage_pad}}"
@@ -25,23 +27,24 @@ for damage in range(1, num_damages + 1):  # 5 Damage levels starts from 1
     os.makedirs(damage_path, exist_ok=True)
 
     for test in range(1, 11):  # 10 Tests per damage level
-        # Filename for the CSV
+        for sensor in range(1, 3):  # 2 Sensors per test
-        csv_filename = f"D{damage:0{damage_pad}}_TEST{test:0{test_pad}}.csv"
+            # Filename for the CSV
-        csv_path = os.path.join(damage_path, csv_filename)
+            csv_filename = f"D{damage:0{damage_pad}}_TEST{test:0{test_pad}}_{sensor:0{sensor_pad}}.csv"
+            csv_path = os.path.join(damage_path, csv_filename)
+            
+            # Generate dummy data
+            num_rows = 10
+            start_time = datetime.now()
+            timestamps = [start_time + timedelta(seconds=i*0.0078125) for i in range(num_rows)]
+            values = np.random.randn(num_rows)  # Random float values
 
-        # Generate dummy data
+            # Create DataFrame
-        num_rows = 10
+            df = pd.DataFrame({
-        start_time = datetime.now()
+                "Time": timestamps,
-        timestamps = [start_time + timedelta(seconds=i*0.0078125) for i in range(num_rows)]
+                "Value": values
-        values = np.random.randn(num_rows)  # Random float values
+            })
 
-        # Create DataFrame
+            # Save the CSV file with a custom header
-        df = pd.DataFrame({
+            with open(csv_path, 'w') as file:
-            "Time": timestamps,
+                file.write('sep=,\n')  # Writing the separator hint
-            "Value": values
+                df.to_csv(file, index=False)
-        })
-
-        # Save the CSV file with a custom header
-        with open(csv_path, 'w') as file:
-            file.write('sep=,\n')  # Writing the separator hint
-            df.to_csv(file, index=False)