Closes #24

feat(stft): Implement STFT processing for vibration data with multiprocessing support to include all the data for training process instead of just using `TEST1` only

code/src/process_stft.py

@@ -0,0 +1,115 @@
+import os
+import pandas as pd
+import numpy as np
+from scipy.signal import stft, hann
+import glob
+import multiprocessing  # Added import for multiprocessing
+
+# Define the base directory where DAMAGE_X folders are located
+damage_base_path = 'D:/thesis/data/converted/raw'
+
+# Define output directories for each sensor
+output_dirs = {
+    'sensor1': os.path.join(damage_base_path, 'sensor1'),
+    'sensor2': os.path.join(damage_base_path, 'sensor2')
+}
+
+# Create output directories if they don't exist
+for dir_path in output_dirs.values():
+    os.makedirs(dir_path, exist_ok=True)
+
+# Define STFT parameters
+window_size = 1024
+hop_size = 512
+window = hann(window_size)
+Fs = 1024
+
+# Number of damage cases (adjust as needed)
+num_damage_cases = 6  # Change to 30 if you have 30 damage cases
+
+# Number of test runs per damage case
+num_test_runs = 5
+
+# Function to perform STFT and return magnitude
+def compute_stft(vibration_data):
+    frequencies, times, Zxx = stft(
+        vibration_data, 
+        fs=Fs, 
+        window=window, 
+        nperseg=window_size, 
+        noverlap=window_size - hop_size
+    )
+    stft_magnitude = np.abs(Zxx)
+    return stft_magnitude.T  # Transpose to have frequencies as columns
+
+def process_damage_case(damage_num):
+    damage_folder = os.path.join(damage_base_path, f'DAMAGE_{damage_num}')
+    
+    # Check if the damage folder exists
+    if not os.path.isdir(damage_folder):
+        print(f"Folder {damage_folder} does not exist. Skipping...")
+        return
+    
+    # Process Sensor 1 and Sensor 2 separately
+    for sensor_num in [1, 2]:
+        aggregated_stft = []  # List to hold STFTs from all test runs
+        
+        # Iterate over all test runs
+        for test_num in range(1, num_test_runs + 1):
+            # Construct the filename based on sensor number
+            # Sensor 1 corresponds to '_01', Sensor 2 corresponds to '_02'
+            sensor_suffix = f'_0{sensor_num}'
+            file_name = f'DAMAGE_{damage_num}_TEST{test_num}{sensor_suffix}.csv'
+            file_path = os.path.join(damage_folder, file_name)
+            
+            # Check if the file exists
+            if not os.path.isfile(file_path):
+                print(f"File {file_path} does not exist. Skipping...")
+                continue
+            
+            # Read the CSV file
+            try:
+                df = pd.read_csv(file_path)
+            except Exception as e:
+                print(f"Error reading {file_path}: {e}. Skipping...")
+                continue
+            
+            # Ensure the CSV has exactly two columns: 'Timestamp (s)' and 'Sensor X'
+            if df.shape[1] != 2:
+                print(f"Unexpected number of columns in {file_path}. Expected 2, got {df.shape[1]}. Skipping...")
+                continue
+            
+            # Extract vibration data (assuming the second column is sensor data)
+            vibration_data = df.iloc[:, 1].values
+            
+            # Perform STFT
+            stft_magnitude = compute_stft(vibration_data)
+            
+            # Convert STFT result to DataFrame
+            df_stft = pd.DataFrame(
+                stft_magnitude, 
+                columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, Fs/2, stft_magnitude.shape[1])]
+            )
+            
+            # Append to the aggregated list
+            aggregated_stft.append(df_stft)
+        
+        # Concatenate all STFT DataFrames vertically
+        if aggregated_stft:
+            df_aggregated = pd.concat(aggregated_stft, ignore_index=True)
+            
+            # Define output filename
+            output_file = os.path.join(
+                output_dirs[f'sensor{sensor_num}'], 
+                f'stft_data{sensor_num}_{damage_num}.csv'
+            )
+            
+            # Save the aggregated STFT to CSV
+            df_aggregated.to_csv(output_file, index=False)
+            print(f"Saved aggregated STFT for Sensor {sensor_num}, Damage {damage_num} to {output_file}")
+        else:
+            print(f"No STFT data aggregated for Sensor {sensor_num}, Damage {damage_num}.")
+
+if __name__ == "__main__":  # Added main guard for multiprocessing
+    with multiprocessing.Pool() as pool:
+        pool.map(process_damage_case, range(1, num_damage_cases + 1))