code/src/process_stft.py

@@ -5,6 +5,7 @@ from scipy.signal import stft
 from scipy.signal.windows import hann
 import glob
 import multiprocessing  # Added import for multiprocessing
+from typing import Union, Tuple
 
 # Define the base directory where DAMAGE_X folders are located
 damage_base_path = 'D:/thesis/data/converted/raw'
@@ -22,10 +23,31 @@ for dir_path in output_dirs.values():
 # Define STFT parameters
 
 # Number of damage cases (adjust as needed)
-num_damage_cases = 0  # Change to 30 if you have 30 damage cases
+num_damage_cases = 6  # Change to 30 if you have 30 damage cases
 
 # Function to perform STFT and return magnitude
-def compute_stft(vibration_data, return_param=False):
+def compute_stft(vibration_data: np.ndarray, return_param: bool = False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, list[int, int, int]]]:
+    """
+    Computes the Short-Time Fourier Transform (STFT) magnitude of the input vibration data.
+
+    Parameters
+    ----------
+    vibration_data : numpy.ndarray
+        The input vibration data as a 1D NumPy array.
+    return_param : bool, optional
+        If True, the function returns additional STFT parameters (window size, hop size, and sampling frequency).
+        Defaults to False.
+
+    Returns
+    -------
+    pd.DataFrame
+        The transposed STFT magnitude, with frequencies as columns, if `return_param` is False.
+    tuple
+        If `return_param` is True, returns a tuple containing:
+        - pd.DataFrame: The transposed STFT magnitude, with frequencies as columns.
+        - list[int, int, int]: A list of STFT parameters [window_size, hop_size, Fs].
+    """
+        
     window_size = 1024
     hop_size = 512
     window = hann(window_size)
@@ -40,12 +62,18 @@ def compute_stft(vibration_data, return_param=False):
                                 )
     stft_magnitude = np.abs(Zxx)
 
+    # Convert STFT result to DataFrame
+    df_stft = pd.DataFrame(
+        stft_magnitude.T, 
+        columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, Fs/2, stft_magnitude.shape[1])]
+    )
+    # breakpoint()
     if return_param:
-        return stft_magnitude.T, [window_size, hop_size, Fs]  # Transpose to have frequencies as columns
+        return df_stft, [window_size, hop_size, Fs]
     else:
-        return stft_magnitude.T
+        return df_stft
 
-def process_damage_case(damage_num, Fs=Fs,):
+def process_damage_case(damage_num):
     damage_folder = os.path.join(damage_base_path, f'DAMAGE_{damage_num}')
     if damage_num == 0:
         # Number of test runs per damage case
@@ -89,13 +117,8 @@ def process_damage_case(damage_num, Fs=Fs,):
             vibration_data = df.iloc[:, 1].values
             
             # Perform STFT
-            stft_magnitude, (window_size, hop_size, Fs) = compute_stft(vibration_data, return_param=True)
+            df_stft = 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])]
-            )
             # only inlcude 21 samples vector features for first 45 num_test_runs else include 22 samples vector features
             if damage_num == 0:
                 print(f"Processing damage_num = 0, test_num = {test_num}")
@@ -130,4 +153,4 @@ def process_damage_case(damage_num, Fs=Fs,):
 
 if __name__ == "__main__":  # Added main guard for multiprocessing
     with multiprocessing.Pool() as pool:
-        pool.map(process_damage_case, range(0, num_damage_cases + 1))
+        pool.map(process_damage_case, range(num_damage_cases + 1))