Merge pull request #16 from nuluh/revert-8-feature/csv-padding-naming

Revert "Add Zero-Padding to CSV Filenames"

.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

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

@@ -1,65 +0,0 @@
-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/processed/README.md

@@ -1,8 +1,8 @@
-# Raw Data Directory
+# Processed Data Directory
 
 ## Overview
 
-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.
+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.
 
 ## Directory Structure
 

generate_dummy_data.py

@@ -13,23 +13,14 @@ processed_path = os.path.join(base_path, "processed")
 os.makedirs(raw_path, exist_ok=True)
 os.makedirs(processed_path, exist_ok=True)
 
-# Define the number of zeros to pad
+for damage in range(1, 6):  # 5 Damage levels
-num_damages = 5
+    damage_folder = f"DAMAGE_{damage}"
-num_tests = 10
+    damage_path = os.path.join(processed_path, damage_folder)
-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
-        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_filename = f"D{damage}_TEST{test}.csv"
         csv_path = os.path.join(damage_path, csv_filename)
 
         # Generate dummy data