feat: Add absolute value option to time feature extraction

Introduce a Python script for transforming QUGS 2D grid structure data into a simplified 1D beam format suitable for SVM-based damage detection. The script efficiently slices original CSV files into smaller, manageable sets, correlating specific damage scenarios with their corresponding sensor data. This change addresses the challenge of retaining critical damage localization information during the data conversion process, ensuring high-quality, relevant data for 1D analysis.

Closes #20

.gitignore

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

code/src/features/time_domain_features.py

@@ -36,9 +36,12 @@ class FeatureExtractor:
             result += f"{feature}: {value:.4f}\n"
         return result
 
-def ExtractTimeFeatures(object):
+def ExtractTimeFeatures(object, absolute):
     data = pd.read_csv(object, skiprows=1) # Skip the header row separator char info
-    extractor = FeatureExtractor(data.iloc[:, 1].values) # Assuming the data is in the second column
+    if absolute:
+        extractor = FeatureExtractor(np.abs(data.iloc[:, 1].values)) # Assuming the data is in the second column
+    else:
+        extractor = FeatureExtractor(data.iloc[:, 1].values)
     features = extractor.features
     return features
     # Save features to a file

data/QUGS/convert.py

@@ -0,0 +1,65 @@
+import pandas as pd
+import os
+import sys
+from colorama import Fore, Style, init
+
+def create_damage_files(base_path, output_base):
+    # Initialize colorama
+    init(autoreset=True)
+    
+    # Generate column labels based on expected duplication in input files
+    columns = ['Real'] + [f'Real.{i}' for i in range(1, 30)]  # Explicitly setting column names
+
+    sensor_end_map = {1: 'Real.25', 2: 'Real.26', 3: 'Real.27', 4: 'Real.28', 5: 'Real.29'}
+
+    # Define the damage scenarios and the corresponding original file indices
+    damage_scenarios = {
+        1: range(6, 11),  # Damage 1 files from zzzAD6.csv to zzzAD10.csv
+        2: range(11, 16), # Damage 2 files from zzzAD11.csv to zzzAD15.csvs
+        3: range(16, 21), # Damage 3 files from zzzAD16.csv to zzzAD20.csv
+        4: range(21, 26)  # Damage 4 files from zzzAD21.csv to zzzAD25.csv
+    }
+    damage_pad = len(str(len(damage_scenarios)))
+    test_pad = len(str(30))
+
+    for damage, files in damage_scenarios.items():
+        for i, file_index in enumerate(files, start=1):
+            # Load original data file
+            file_path = os.path.join(base_path, f'zzzAD{file_index}.TXT')
+            df = pd.read_csv(file_path, sep='\t', skiprows=10)  # Read with explicit column names
+
+            top_sensor = columns[i-1]
+            print(top_sensor, type(top_sensor))
+            output_file_1 = os.path.join(output_base, f'DAMAGE_{damage}', f'D{damage:0{damage_pad}}_TEST{i:0{test_pad}}_01.csv')
+            print(f"Creating {output_file_1} from taking zzzAD{file_index}.TXT")
+            print("Taking datetime column on index 0...")
+            print(f"Taking `{top_sensor}`...")
+            df[['Time', top_sensor]].to_csv(output_file_1, index=False)
+            print(Fore.GREEN + "Done")
+
+            bottom_sensor = sensor_end_map[i]
+            output_file_2 = os.path.join(output_base, f'DAMAGE_{damage}', f'D{damage}_TEST{i}_02.csv')
+            print(f"Creating {output_file_2} from taking zzzAD{file_index}.TXT")
+            print("Taking datetime column on index 0...")
+            print(f"Taking `{bottom_sensor}`...")
+            df[['Time', bottom_sensor]].to_csv(output_file_2, index=False)
+            print(Fore.GREEN + "Done")
+            print("---")
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python convert.py <path_to_csv_files>")
+        sys.exit(1)
+
+    base_path = sys.argv[1]
+    output_base = sys.argv[2]  # Define output directory
+
+    # Create output folders if they don't exist
+    for i in range(1, 5):
+        os.makedirs(os.path.join(output_base, f'DAMAGE_{i}'), exist_ok=True)
+
+    create_damage_files(base_path, output_base)
+    print(Fore.YELLOW + Style.BRIGHT + "All files have been created successfully.")
+
+if __name__ == "__main__":
+    main()

data/raw/README.md

@@ -1,8 +1,8 @@
-# Processed Data Directory
+# Raw Data Directory
 
 ## Overview
 
-This `data/processed` directory contains structured data that has been processed and formatted for analysis. Each subdirectory within `processed` represents a different level of simulated damage, and each contains multiple test files from experiments conducted under that specific damage scenario.
+This `data/raw` directory contains structured data that has been processed and formatted for analysis. Each subdirectory within `raw` represents a different level of simulated damage, and each contains multiple test files from experiments conducted under that specific damage scenario.
 
 ## Directory Structure
 
@@ -12,12 +12,12 @@ The directory is organized as follows:
 data
 └── processed
 ├── DAMAGE_1
-│ ├── D1_TEST1.csv
-│ ├── D1_TEST2.csv
+│   ├── D1_TEST1.csv
+│   ├── D1_TEST2.csv
 │ ...
-│ └── D1_TEST10.csv
+│   └── D1_TEST10.csv
 ├── DAMAGE_2
-│ ├── D2_TEST1.csv
+│   ├── D2_TEST1.csv
 │ ...
 ├── DAMAGE_3
 │ ...

generate_dummy_data.py

@@ -13,29 +13,38 @@ processed_path = os.path.join(base_path, "processed")
 os.makedirs(raw_path, exist_ok=True)
 os.makedirs(processed_path, exist_ok=True)
 
-for damage in range(1, 6):  # 5 Damage levels
-    damage_folder = f"DAMAGE_{damage}"
-    damage_path = os.path.join(processed_path, damage_folder)
+# 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}}"
+    damage_path = os.path.join(raw_path, damage_folder)
     os.makedirs(damage_path, exist_ok=True)
 
     for test in range(1, 11):  # 10 Tests per damage level
-        # Filename for the CSV
-        csv_filename = f"D{damage}_TEST{test}.csv"
-        csv_path = os.path.join(damage_path, csv_filename)
+        for sensor in range(1, 3):  # 2 Sensors per test
+            # Filename for the CSV
+            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
-        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
+            # Create DataFrame
+            df = pd.DataFrame({
+                "Time": timestamps,
+                "Value": values
+            })
 
-        # Create DataFrame
-        df = pd.DataFrame({
-            "Time": timestamps,
-            "Value": values
-        })
-
-        # 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)
+            # 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)