refactor(notebooks): clean up imports, adjust damage case processing, and improve model training structure

- Removed unnecessary imports (os, pandas, numpy) from the STFT notebook.
- Adjusted the number of damage cases in the multiprocessing pool to correctly reflect the range.
- Updated model training code for Sensor B to ensure consistent naming and structure.
- Cleaned up commented-out code for clarity and maintainability.

code/notebooks/stft.ipynb

@@ -217,9 +217,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "from scipy.signal import hann\n",
     "import multiprocessing"
    ]
@@ -244,16 +241,6 @@
     "Fs = 1024"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the base directory where DAMAGE_X folders are located\n",
-    "damage_base_path = 'D:/thesis/data/converted/raw'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -304,11 +291,11 @@
     "# Import custom in-house functions\n",
     "from src.process_stft import process_damage_case\n",
     "\n",
-    "num_damage_cases = 7  # DAMAGE_0 to DAMAGE_6\n",
+    "num_damage_cases = 6  # DAMAGE_0 to DAMAGE_6\n",
     "\n",
     "with multiprocessing.Pool() as pool:\n",
     "    # Process each DAMAGE_X case in parallel\n",
-    "    pool.map(process_damage_case, range(num_damage_cases), Fs, window_size, hop_size, output_dirs)"
+    "    pool.map(process_damage_case, range(num_damage_cases + 1))"
    ]
   },
   {
@@ -665,24 +652,33 @@
     "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
     "    # \"KNN\": KNeighborsClassifier(),\n",
     "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
-    "    # \"SVM\": make_pipeline(\n",
+    "    \"SVM\": make_pipeline(\n",
-    "    #     StandardScaler(),\n",
+    "        StandardScaler(),\n",
-    "    #     SVC(kernel='rbf', probability=True)\n",
+    "        SVC(kernel='rbf')\n",
-    "    # ),\n",
+    "    ),\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    #                                     StandardScaler(),\n",
+    "                                        StandardScaler(),\n",
-    "    #                                     PCA(n_components=10),\n",
+    "                                        PCA(n_components=10),\n",
-    "    #                                     SVC(kernel='rbf')\n",
+    "                                        SVC(kernel='rbf')\n",
-    "    #                                     ),\n",
+    "                                        ),\n",
     "\n",
     "    # \"XGBoost\": XGBClassifier()\n",
-    "    \"MLPClassifier\": make_pipeline(\n",
+    "    # \"MLPClassifier\": make_pipeline(\n",
-    "        StandardScaler(),\n",
+    "    #     StandardScaler(),\n",
-    "        MLPClassifier(hidden_layer_sizes=(1, 10), max_iter=500, random_state=42)\n",
+    "    #     MLPClassifier(hidden_layer_sizes=(1, 10), max_iter=500, random_state=42)\n",
-    "    )\n",
+    "    # )\n",
     "}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -712,9 +708,15 @@
     "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
     "    # \"KNN\": KNeighborsClassifier(),\n",
     "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
-    "    \"SVM\": SVC(),\n",
+    "    \"SVM\": make_pipeline(\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "        StandardScaler(),\n",
-    "    # StandardScaler(),\n",
+    "        SVC(kernel='rbf')\n",
+    "    ),\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "                                        StandardScaler(),\n",
+    "                                        PCA(n_components=10),\n",
+    "                                        SVC(kernel='rbf')\n",
+    "                                        ),\n",
     "    # PCA(n_components=10),\n",
     "    # SVC(kernel='rbf')\n",
     "    # ),\n",
@@ -730,8 +732,8 @@
    "source": [
     "results_sensor2 = []\n",
     "for name, model in models_sensor2.items():\n",
-    "    res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train2, x_test2, y_test2, \n",
+    "    res = train_and_evaluate_model(model, name, \"Sensor B\", x_train2, y_train2, x_test2, y_test2, \n",
-    "                                   export='D:/thesis/models/sensor2')\n",
+    "                                   export='D:/thesis/models/Sensor B')\n",
     "    results_sensor2.append(res)\n",
     "    print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n",
     "\n",
@@ -938,6 +940,194 @@
     "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
     "print(classification_report(y, y_pred_svm))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross Dataset Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# sensor A\n",
+    "x_train1, x_test1, y_train1, y_test1 = train_test_split(X1b, y, test_size=0.2, random_state=2)\n",
+    "# sensor B\n",
+    "x_train2, x_test2, y_train2, y_test2 = train_test_split(X2b, y, test_size=0.2, random_state=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_sensor1 = []\n",
+    "for name, model in models_sensor1.items():\n",
+    "    res = train_and_evaluate_model(model, name, \"Sensor A\", x_train1, y_train1, x_test1, y_test1, \n",
+    "                                   export='D:/thesis/datasetB/models/Sensor A')\n",
+    "    results_sensor1.append(res)\n",
+    "    print(f\"{name} on sensor1: Accuracy = {res['accuracy']:.2f}%\")\n",
+    "\n",
+    "# Display result\n",
+    "results_sensor1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_sensor2 = []\n",
+    "for name, model in models_sensor2.items():\n",
+    "    res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train2, x_test2, y_test2, \n",
+    "                                   export='D:/thesis/datasetB/models/sensor2')\n",
+    "    results_sensor2.append(res)\n",
+    "    print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n",
+    "\n",
+    "# Display result\n",
+    "results_sensor2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sensor A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4. Sensor A Validate on Dataset A\n",
+    "from joblib import load\n",
+    "svm_model = load('D:/thesis/datasetB/models/sensor1/SVM.joblib')\n",
+    "y_pred_svm_1 = svm_model.predict(X1a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n",
+    "# 5. Evaluate\n",
+    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_1))\n",
+    "print(classification_report(y, y_pred_svm_1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Confusion Matrix Sensor A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "\n",
+    "cm = confusion_matrix(y, y_pred_svm_1) # -> ndarray\n",
+    "\n",
+    "# get the class labels\n",
+    "labels = svm_model.classes_\n",
+    "\n",
+    "# Plot\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
+    "disp.plot(cmap=plt.cm.Blues)  # You can change colormap\n",
+    "plt.title(\"Confusion Matrix of Sensor A Test on Dataset B\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sensor B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svm_model = load('D:/thesis/datasetB/models/sensor2/SVM.joblib')\n",
+    "# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
+    "y_pred_svm_2 = svm_model.predict(X2a)\n",
+    "\n",
+    "# 5. Evaluate\n",
+    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
+    "print(classification_report(y, y_pred_svm_2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Confusion Matrix Sensor B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "\n",
+    "cm = confusion_matrix(y, y_pred_svm_2) # -> ndarray\n",
+    "\n",
+    "# get the class labels\n",
+    "labels = svm_model.classes_\n",
+    "\n",
+    "# Plot\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
+    "disp.plot(cmap=plt.cm.Blues)  # You can change colormap\n",
+    "plt.title(\"Confusion Matrix of Sensor B Test on Dataset B\")\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

code/src/data_preprocessing.py

@@ -35,8 +35,8 @@ def complement_pairs(n, prefix, extension):
         if a != orig_a:                # skip original a
             yield (filename, [a, a + 25]) # use yield instead of return to return a generator of tuples
 
-def generate_df_tuples(total_dfs, prefix, extension, first_col_start, last_col_offset, 
+def generate_df_tuples(prefix: str, total_dfs: int=30, extension: str="TXT", first_col_start: int=1, last_col_offset: int=25, 
-                      group_size=5, special_groups=None, group=True):
+                      group_size: int=5, special_groups: list=None, group: bool=True):
     """
     Generate a structured list of tuples containing DataFrame references and column indices.
     

code/src/ml/inference.py

@@ -1,16 +1,190 @@
-from src.ml.model_selection import inference_model
 from joblib import load
+import pandas as pd
+from src.data_preprocessing import *
+from src.process_stft import compute_stft
+from typing import List, Tuple
+from sklearn.base import BaseEstimator
+import json
 
-x = 30
+def probability_damage(pred: Tuple[np.ndarray, np.ndarray], model_classes: BaseEstimator, percentage=False) -> Dict[str, int]:
-file = f"D:/thesis/data/dataset_B/zzzBD{x}.TXT"
+    """
-sensor = 1
+    Process the prediction output to return unique labels and their counts.
-model = {"SVM": f"D:/thesis/models/sensor{sensor}/SVM.joblib", 
+    """
-        "SVM with PCA": f"D:/thesis/models/sensor{sensor}/SVM with StandardScaler and PCA.joblib",
+    labels, counts = np.unique(pred, return_counts=True)
-        "XGBoost": f"D:/thesis/models/sensor{sensor}/XGBoost.joblib"}
+    label_counts = dict(zip(labels, counts))
 
-index = ((x-1) % 5) + 1
+    # init all models classes probability of damage with 0 in dictionary
-inference_model(model["SVM"], file, column_question=index)
+    pod: Dict[np.ndarray, int] = dict.fromkeys(model_classes.classes_, 0)
-print("---")
+
-inference_model(model["SVM with PCA"], file, column_question=index)
+    # update corresponding data
-print("---")
+    pod.update(label_counts)
-inference_model(model["XGBoost"], file, column_question=index)
+    
+    # turn the value into ratio instead of prediction counts
+    for label, count in pod.items():
+
+        ratio: float = count/np.sum(counts)
+
+        if percentage:
+            pod[label] = ratio * 100
+        else:
+            pod[label] = ratio
+    return pod
+
+def convert_keys_to_strings(obj):
+    """
+    Recursively convert all dictionary keys to strings.
+    """
+    if isinstance(obj, dict):
+        return {str(key): convert_keys_to_strings(value) for key, value in obj["data"].items()}
+    elif isinstance(obj, list):
+        return [convert_keys_to_strings(item) for item in obj["data"]]
+    else:
+        return obj
+
+def inference(model_sensor_A_path: str, model_sensor_B_path: str, file_path: str):
+
+    # Generate column indices
+    column_index: List[Tuple[int, int]] = [
+                                            (i + 1, i + 26) 
+                                            for i in range(5)
+                                          ]
+    # Load a single case data
+    df: pd.DataFrame = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True)
+    # Take case name
+    case_name: str = file_path.split("/")[-1].split(".")[0]
+    # Extract relevant columns for each sensor
+    column_data: List[Tuple[pd.Series[float], pd.Series[float]]] = [
+                                                                        (df.iloc[:, i[0]], df.iloc[:, i[1]]) 
+                                                                        for i in column_index
+                                                                   ]
+
+    column_data_stft: List[Tuple[pd.DataFrame, pd.DataFrame]] = [
+                                                                    (compute_stft(sensor_A), compute_stft(sensor_B)) 
+                                                                    for (sensor_A, sensor_B) in column_data
+                                                                ]
+    
+    # Load the model
+    model_sensor_A = load(model_sensor_A_path)
+    model_sensor_B = load(model_sensor_B_path)
+
+    res = {}
+
+    for i, (stft_A, stft_B) in enumerate(column_data_stft):
+        # Make predictions using the model
+        pred_A: list[int] = model_sensor_A.predict(stft_A)
+        pred_B: list[int] = model_sensor_B.predict(stft_B)
+
+        
+        percentage_A = probability_damage(pred_A, model_sensor_A)
+        percentage_B = probability_damage(pred_B, model_sensor_B)
+        
+
+        res[f"Column_{i+1}"] = {
+            "Sensor_A": {
+                # "Predictions": pred_A,
+                "PoD": percentage_A
+            },
+            "Sensor_B": {
+                # "Predictions": pred_B,
+                "PoD": percentage_B
+            }
+        }
+    final_res = {"data": res, "case": case_name}
+    return final_res
+
+def heatmap(result, damage_classes: list[int] = [1, 2, 3, 4, 5, 6]):
+    from scipy.interpolate import RectBivariateSpline
+    resolution = 300
+    y = list(range(1, len(damage_classes)+1))
+
+    # length of column
+    x = list(range(len(result["data"])))
+
+    # X, Y = np.meshgrid(x, y)
+    Z = []
+    for _, column_data in result["data"].items():
+        sensor_a_pod = column_data['Sensor_A']['PoD']
+        Z.append([sensor_a_pod.get(cls, 0) for cls in damage_classes])
+    Z = np.array(Z).T
+
+    y2 = np.linspace(1, len(damage_classes), resolution)
+    x2 = np.linspace(0,4,resolution)
+    f = RectBivariateSpline(x, y, Z.T, kx=2, ky=2) # 2nd degree quadratic spline interpolation
+
+    Z2 = f(x2, y2).T.clip(0, 1) # clip to ignores negative values from cubic interpolation
+
+    X2, Y2 = np.meshgrid(x2, y2)
+    # breakpoint()
+    c = plt.pcolormesh(X2, Y2, Z2, cmap='jet', shading='auto')
+
+    # Add a colorbar
+    plt.colorbar(c, label='Probability of Damage (PoD)')
+    plt.gca().invert_xaxis()
+    plt.grid(True, linestyle='-', alpha=0.7)
+    plt.xticks(np.arange(int(X2.min()), int(X2.max())+1, 1))
+    plt.xlabel("Column Index")
+    plt.ylabel("Damage Index")
+    plt.title(result["case"])
+    # plt.xticks(ticks=x2, labels=[f'Col_{i+1}' for i in range(len(result))])
+    # plt.gca().xaxis.set_major_locator(MultipleLocator(65/4))
+    plt.show()
+    
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    import json
+    from scipy.interpolate import UnivariateSpline
+    
+
+    result = inference(
+        "D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib",
+        "D:/thesis/models/Sensor B/SVM with StandardScaler and PCA.joblib",
+        "D:/thesis/data/dataset_B/zzzBD19.TXT"
+    )
+
+    # heatmap(result)
+    # Convert all keys to strings before dumping to JSON
+    # result_with_string_keys = convert_keys_to_strings(result)
+    # print(json.dumps(result_with_string_keys, indent=4))
+
+    # Create a 5x2 subplot grid (5 rows for each column, 2 columns for sensors)
+    fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(5, 50))
+    
+    # # Define damage class labels for x-axis
+    damage_classes = [1, 2, 3, 4, 5, 6]
+
+    # # Loop through each column in the data
+    for row_idx, (column_name, column_data) in enumerate(result['data'].items()):
+        # Plot Sensor A in the first column of subplots
+        sensor_a_pod = column_data['Sensor_A']['PoD']
+        x_values = list(range(len(damage_classes)))
+        y_values = [sensor_a_pod.get(cls, 0) for cls in damage_classes]
+
+        # x2 = np.linspace(1, 6, 100)
+        # interp = UnivariateSpline(x_values, y_values, s=0)
+        axes[row_idx, 0].plot(x_values, y_values, '-', linewidth=2, markersize=8)
+        axes[row_idx, 0].set_title(f"{column_name} - Sensor A", fontsize=10)
+        axes[row_idx, 0].set_xticks(x_values)
+        axes[row_idx, 0].set_xticklabels(damage_classes)
+        axes[row_idx, 0].set_ylim(0, 1.05)
+        axes[row_idx, 0].set_ylabel('Probability')
+        axes[row_idx, 0].set_xlabel('Damage Class')
+        axes[row_idx, 0].grid(True, linestyle='-', alpha=0.5)
+        
+        # Plot Sensor B in the second column of subplots
+        sensor_b_pod = column_data['Sensor_B']['PoD']
+        y_values = [sensor_b_pod.get(cls, 0) for cls in damage_classes]
+        axes[row_idx, 1].plot(x_values, y_values, '-', linewidth=2, markersize=8)
+        axes[row_idx, 1].set_title(f"{column_name} - Sensor B", fontsize=10)
+        axes[row_idx, 1].set_xticks(x_values)
+        axes[row_idx, 1].set_xticklabels(damage_classes)
+        axes[row_idx, 1].set_ylim(0, 1.05)
+        axes[row_idx, 1].set_ylabel('Probability')
+        axes[row_idx, 1].set_xlabel('Damage Class')
+        axes[row_idx, 1].grid(True, linestyle='-', alpha=0.5)
+    
+    # Adjust layout to prevent overlap
+    fig.tight_layout(rect=[0, 0, 1, 0.96])  # Leave space for suptitle
+    plt.subplots_adjust(hspace=1, wspace=0.3)  # Adjust spacing between subplots
+    plt.suptitle(f"Case {result['case']}", fontsize=16, y=0.98)  # Adjust suptitle position
+    plt.show()
+    

code/src/process_stft.py

@@ -1,9 +1,11 @@
 import os
 import pandas as pd
 import numpy as np
-from scipy.signal import stft, hann
+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'
@@ -19,27 +21,59 @@ 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 = 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, Fs=Fs, window_size=window_size, hop_size=hop_size):
+def compute_stft(vibration_data: np.ndarray, return_param: bool = False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, list[int, int, int]]]:
-    frequencies, times, Zxx = stft(
+    """
-        vibration_data, 
+    Computes the Short-Time Fourier Transform (STFT) magnitude of the input 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, Fs=Fs, window_size=window_size, hop_size=hop_size, output_dirs=output_dirs):
+    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)
+    Fs = 1024
+
+    frequencies, times, Zxx = stft(
+                                    vibration_data, 
+                                    fs=Fs, 
+                                    window=window, 
+                                    nperseg=window_size, 
+                                    noverlap=window_size - hop_size
+                                )
+    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 df_stft, [window_size, hop_size, Fs]
+    else:
+        return df_stft
+
+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
@@ -83,13 +117,8 @@ def process_damage_case(damage_num, Fs=Fs, window_size=window_size, hop_size=hop
             vibration_data = df.iloc[:, 1].values
             
             # Perform STFT
-            stft_magnitude = compute_stft(vibration_data, Fs=Fs, window_size=window_size, hop_size=hop_size)
+            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}")
@@ -117,11 +146,11 @@ def process_damage_case(damage_num, Fs=Fs, window_size=window_size, hop_size=hop
             # Save the aggregated STFT to CSV
             with open(output_file, 'w') as file:
                 file.write('sep=,\n')
-                df_aggregated.to_csv(output_file, index=False)
+                df_aggregated.to_csv(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(0, num_damage_cases + 1))
+        pool.map(process_damage_case, range(num_damage_cases + 1))