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.

.gitignore

@@ -2,4 +2,17 @@
 data/**/*.csv
 .venv/
 *.pyc
-*.egg-info/
+*.egg-info/
+
+# Latex
+*.aux
+*.log
+*.out
+*.toc
+*.bbl
+*.blg
+*.fdb_latexmk
+*.fls
+*.synctex.gz
+*.dvi
+

.vscode/settings.json

@@ -1,4 +1,7 @@
 {
-  "python.analysis.extraPaths": ["./code/src/features"],
+  "python.analysis.extraPaths": [
+    "./code/src/features",
+    "${workspaceFolder}/code/src"
+  ],
   "jupyter.notebookFileRoot": "${workspaceFolder}/code"
 }

code/src/data_preprocessing.py

@@ -0,0 +1,357 @@
+import pandas as pd
+import os
+import re
+import sys
+import numpy as np
+from colorama import Fore, Style, init
+from typing import TypedDict, Dict, List
+from joblib import load
+from pprint import pprint
+
+# class DamageFilesIndices(TypedDict):
+#     damage_index: int
+#     files: list[int]
+OriginalSingleDamageScenarioFilePath = str
+DamageScenarioGroupIndex = int
+OriginalSingleDamageScenario = pd.DataFrame
+SensorIndex = int
+VectorColumnIndex = List[SensorIndex]
+VectorColumnIndices = List[VectorColumnIndex]
+DamageScenarioGroup = List[OriginalSingleDamageScenario]
+GroupDataset = List[DamageScenarioGroup]
+
+
+class DamageFilesIndices(TypedDict):
+    damage_index: int
+    files: List[str]
+
+def complement_pairs(n, prefix, extension):
+    """
+    Return the four complement tuples for zzzBD<n>.TXT
+    """
+    filename = f"{prefix}{n}.{extension}" # TODO: shouldnt be hardcoded
+    orig_a   = (n - 1) % 5 + 1                # 1 … 5
+    for a in range(1, 6):              # a = 1 … 5
+        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(prefix: str, total_dfs: int=30, extension: str="TXT", first_col_start: int=1, last_col_offset: int=25, 
+                      group_size: int=5, special_groups: list=None, group: bool=True):
+    """
+    Generate a structured list of tuples containing DataFrame references and column indices.
+    
+    Parameters:
+    -----------
+    total_dfs : int, default 30
+        Total number of DataFrames to include in the tuples
+    group_size : int, default 5
+        Number of DataFrames in each group (determines the pattern repeat)
+    prefix : str
+        Prefix for DataFrame variable names
+    first_col_start : int, default 1
+        Starting value for the first column index (1-indexed)
+    last_col_offset : int, default 25
+        Offset to add to first_col_start to get the last column index
+    special_groups : list of dict, optional
+        List of special groups to insert, each dict should contain:
+        - 'df_name': The DataFrame name to use for all tuples in this group
+        - 'position': Where to insert this group (0 for beginning)
+        - 'size': Size of this group (default: same as group_size)
+    
+    Returns:
+    --------
+    list
+        List of tuples, where each tuple contains (df_name, [first_col, last_col])
+    """
+    result = []
+    if group:
+        # Group tuples into sublists of group_size
+        for g in range(6):                # TODO: shouldnt be hardcoded
+            group = []
+            for i in range(1, 6):         # TODO: shouldnt be hardcoded
+                n = g * 5 + i
+                bottom_end = i                           # 1, 2, 3, 4, 5
+                top_end = bottom_end + 25                # 26, 27, 28, 29, 30 # TODO: shouldnt be hardcoded
+                group.append((f"{prefix}{n}.{extension}", [bottom_end, top_end]))
+            result.append(group)
+
+    # Add special groups at specified positions (other than beginning)
+    if special_groups:
+        result.insert(0, special_groups)
+    
+    
+    return result
+
+
+    # file_path = os.path.join(base_path, f"zzz{prefix}D{file_index}.TXT")
+    # df = pd.read_csv(file_path, sep="\t", skiprows=10)  # Read with explicit column names
+
+
+class DataProcessor:
+    def __init__(self, file_index, cache_path: str = None, base_path: str = None, include_time: bool = False):
+        self.file_index = file_index
+        self.base_path = base_path
+        self.include_time = include_time
+        if cache_path:
+            self.data = load(cache_path)
+        else:
+            self.data = self.load_data()
+
+    def load_data(self):
+        for idxs, group in enumerate(self.file_index):
+            for idx, tuple in enumerate(group):
+                file_path = os.path.join(self.base_path, tuple[0]) # ('zzzAD1.TXT')
+                if self.include_time:
+                    col_indices = [0] + tuple[1]  # [1, 26] + [0] -> [0, 1, 26]
+                else:
+                    col_indices = tuple[1] # [1, 26]
+                try:
+                    # Read the CSV file
+                    df = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True)
+                    self.file_index[idxs][idx] = df.iloc[:, col_indices].copy()  # Extract the specified columns
+                    
+                    print(f"Processed {file_path}, extracted columns: {col_indices}")
+                    
+                except Exception as e:
+                    print(f"Error processing {file_path}: {str(e)}")
+    def _load_dataframe(self, file_path: str) -> OriginalSingleDamageScenario:
+        """
+        Loads a single data file into a pandas DataFrame.
+
+        :param file_path: Path to the data file.
+        :return: DataFrame containing the numerical data.
+        """
+        df = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True, nrows=1)
+        return df
+
+    def _load_all_data(self) -> GroupDataset:
+        """
+        Loads all data files based on the grouping dictionary and returns a nested list.
+
+        :return: A nested list of DataFrames where the outer index corresponds to group_idx - 1.
+        """
+        data = []
+        # Find the maximum group index to determine the list size
+        max_group_idx = len(self.file_index) if self.file_index else 0
+
+        # Handle case when file_index is empty
+        if max_group_idx == 0:
+            raise ValueError("No file index provided; file_index is empty.")
+
+        # Initialize empty lists
+        for _ in range(max_group_idx):
+            data.append([])
+
+        # Fill the list with data
+        for group_idx, file_list in self.file_index.items():
+            group_idx -= 1 # adjust due to undamage file
+            data[group_idx] = [self._load_dataframe(file) for file in file_list]
+        return data
+
+    def get_group_data(self, group_idx: int) -> List[pd.DataFrame]:
+        """
+        Returns the list of DataFrames for the given group index.
+
+        :param group_idx: Index of the group.
+        :return: List of DataFrames.
+        """
+        return self.data.get([group_idx, []])
+
+    def get_column_names(self, group_idx: int, file_idx: int = 0) -> List[str]:
+        """
+        Returns the column names for the given group and file indices.
+
+        :param group_idx: Index of the group.
+        :param file_idx: Index of the file in the group.
+        :return: List of column names.
+        """
+        if group_idx in self.data and len(self.data[group_idx]) > file_idx:
+            return self.data[group_idx][file_idx].columns.tolist()
+        return []
+
+    def get_data_info(self):
+        """
+        Print information about the loaded data structure.
+        Adapted for when self.data is a List instead of a Dictionary.
+        """
+        if isinstance(self.data, list):
+            # For each sublist in self.data, get the type names of all elements
+            pprint(
+                [
+                    (
+                        [type(item).__name__ for item in sublist]
+                        if isinstance(sublist, list)
+                        else type(sublist).__name__
+                    )
+                    for sublist in self.data
+                ]
+            )
+        else:
+            pprint(
+                {
+                    key: [type(df).__name__ for df in value]
+                    for key, value in self.data.items()
+                }
+                if isinstance(self.data, dict)
+                else type(self.data).__name__
+            )
+
+    def _create_vector_column_index(self) -> VectorColumnIndices:
+        vector_col_idx: VectorColumnIndices = []
+        y = 0
+        for data_group in self.data:  # len(data_group[i]) = 5
+            for j in data_group:  # len(j[i]) =
+                c: VectorColumnIndex = []
+                x = 0
+                for _ in range(6):  # TODO: range(6) should be dynamic and parameterized
+                    c.append(x + y)
+                    x += 5
+                vector_col_idx.append(c)
+                y += 1
+            return vector_col_idx # TODO: refactor this so that it returns just from first data_group without using for loops through the self.data that seems unnecessary
+
+    def create_vector_column(self, overwrite=True) -> List[List[List[pd.DataFrame]]]:
+        """
+        Create a vector column from the loaded data.
+
+        :param overwrite: Overwrite the original data with vector column-based data.
+        """
+        idxs = self._create_vector_column_index()
+        for i, group in enumerate(self.data):
+            # add 1 to all indices to account for 'Time' being at position 0
+            for j, df in enumerate(group):
+                idx = [_ + 1 for _ in idxs[j]]
+                # slice out the desired columns, copy into a fresh DataFrame,
+                # then overwrite self.data[i][j] with it
+                self.data[i][j] = df.iloc[:, idx].copy()
+
+            # TODO: if !overwrite:
+
+    def create_limited_sensor_vector_column(self, overwrite=True):
+        """
+        Create a vector column from the loaded data.
+
+        :param overwrite: Overwrite the original data with vector column-based data.
+        """
+        idx = self._create_vector_column_index()
+        # if overwrite:
+        for i in range(len(self.data)):  # damage(s)
+            for j in range(len(self.data[i])):  # col(s)
+                # Get the appropriate indices for slicing from idx
+                indices = idx[j]
+
+                # Get the current DataFrame
+                df = self.data[i][j]
+
+                # Keep the 'Time' column and select only specifid 'Real' colmns
+                # First, we add 1 to all indices to acount for 'Time' being at positiion 0
+                real_indices = [index + 1 for index in indices]
+
+                # Create list with Time column index (0) and the adjustedd Real indices
+                all_indices = [0] + [real_indices[0]] + [real_indices[-1]]
+
+                # Apply the slicing
+                self.data[i][j] = df.iloc[:, all_indices]
+        # TODO: if !overwrite:
+
+    def export_to_csv(self, output_dir: str, file_prefix: str = "DAMAGE"):
+        """
+        Export the processed data to CSV files in the required folder structure.
+
+        :param output_dir: Directory to save the CSV files.
+        :param file_prefix: Prefix for the output filenames.
+        """
+        for group_idx, group in enumerate(self.file_index, start=0):
+            group_folder = os.path.join(output_dir, f"{file_prefix}_{group_idx}")
+            os.makedirs(group_folder, exist_ok=True)
+
+            for test_idx, df in enumerate(group, start=1):
+                out1 = os.path.join(group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_01.csv")
+                cols_to_export = [0, 1] if self.include_time else [1]
+                df.iloc[:, cols_to_export].to_csv(out1, index=False)
+
+                out2 = os.path.join(group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_02.csv")
+                cols_to_export = [0, 2] if self.include_time else [2]
+                df.iloc[:, cols_to_export].to_csv(out2, index=False)
+
+# def create_damage_files(base_path, output_base, prefix):
+#     # 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(1, 6),  # Damage 1 files from zzzAD1.csv to zzzAD5.csv
+#         2: range(6, 11),  # Damage 2 files from zzzAD6.csv to zzzAD10.csv
+#         3: range(11, 16),  # Damage 3 files from zzzAD11.csv to zzzAD15.csvs
+#         4: range(16, 21),  # Damage 4 files from zzzAD16.csv to zzzAD20.csv
+#         5: range(21, 26),  # Damage 5 files from zzzAD21.csv to zzzAD25.csv
+#         6: range(26, 31),  # Damage 6 files from zzzAD26.csv to zzzAD30.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"zzz{prefix}D{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"DAMAGE{damage}_TEST{i}_01.csv"
+#             )
+#             print(f"Creating {output_file_1} from taking zzz{prefix}D{file_index}.TXT")
+#             print("Taking datetime column on index 0...")
+#             print(f"Taking `{top_sensor}`...")
+#             os.makedirs(os.path.dirname(output_file_1), exist_ok=True)
+#             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"DAMAGE{damage}_TEST{i}_02.csv"
+#             )
+#             print(f"Creating {output_file_2} from taking zzz{prefix}D{file_index}.TXT")
+#             print("Taking datetime column on index 0...")
+#             print(f"Taking `{bottom_sensor}`...")
+#             os.makedirs(os.path.dirname(output_file_2), exist_ok=True)
+#             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]
+    prefix = sys.argv[3]  # Define output directory
+
+    # Create output folders if they don't exist
+    # for i in range(1, 7):
+    #     os.makedirs(os.path.join(output_base, f'DAMAGE_{i}'), exist_ok=True)
+
+    create_damage_files(base_path, output_base, prefix)
+    print(Fore.YELLOW + Style.BRIGHT + "All files have been created successfully.")
+
+
+if __name__ == "__main__":
+    main()

code/src/ml/inference.py

@@ -0,0 +1,190 @@
+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
+
+def probability_damage(pred: Tuple[np.ndarray, np.ndarray], model_classes: BaseEstimator, percentage=False) -> Dict[str, int]:
+    """
+    Process the prediction output to return unique labels and their counts.
+    """
+    labels, counts = np.unique(pred, return_counts=True)
+    label_counts = dict(zip(labels, counts))
+
+    # init all models classes probability of damage with 0 in dictionary
+    pod: Dict[np.ndarray, int] = dict.fromkeys(model_classes.classes_, 0)
+
+    # update corresponding data
+    pod.update(label_counts)
+    
+    # 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/ml/model_selection.py

@@ -1,13 +1,14 @@
 import numpy as np
 import pandas as pd
 import os
-from sklearn.model_selection import train_test_split as sklearn_split
-
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+from joblib import load
 
 def create_ready_data(
     stft_data_path: str,
     stratify: np.ndarray = None,
-) -> tuple:
+) -> tuple[pd.DataFrame, np.ndarray]:
     """
     Create a stratified train-test split from STFT data.
 
@@ -21,13 +22,13 @@ def create_ready_data(
     Returns:
     --------
     tuple
-        (X_train, X_test, y_train, y_test) - Split datasets
+        (pd.DataFrame, np.ndarray) - Combined data and corresponding labels
     """
     ready_data = []
     for file in os.listdir(stft_data_path):
-        ready_data.append(pd.read_csv(os.path.join(stft_data_path, file)))
+        ready_data.append(pd.read_csv(os.path.join(stft_data_path, file), skiprows=1))
 
-    y_data = [i for i in range(len(ready_data))]
+    y_data = [i for i in range(len(ready_data))] # TODO: Should be replaced with actual desired labels
 
     # Combine all dataframes in ready_data into a single dataframe
     if ready_data:  # Check if the list is not empty
@@ -55,3 +56,207 @@ def create_ready_data(
         y = np.array([])
 
     return X, y
+
+
+def train_and_evaluate_model(
+    model, model_name, sensor_label, x_train, y_train, x_test, y_test, export=None
+):
+    """
+    Train a machine learning model, evaluate its performance, and optionally export it.
+
+    This function trains the provided model on the training data, evaluates its
+    performance on test data using accuracy score, and can save the trained model
+    to disk if an export path is provided.
+
+    Parameters
+    ----------
+    model : estimator object
+        The machine learning model to train.
+    model_name : str
+        Name of the model, used for the export filename and in the returned results.
+    sensor_label : str
+        Label identifying which sensor's data the model is being trained on.
+    x_train : array-like or pandas.DataFrame
+        The training input samples.
+    y_train : array-like
+        The target values for training.
+    x_test : array-like or pandas.DataFrame
+        The test input samples.
+    y_test : array-like
+        The target values for testing.
+    export : str, optional
+        Directory path where the trained model should be saved. If None, model won't be saved.
+
+    Returns
+    -------
+    dict
+        Dictionary containing:
+        - 'model': model_name (str)
+        - 'sensor': sensor_label (str)
+        - 'accuracy': accuracy percentage (float)
+
+    Example
+    -------
+    >>> from sklearn.svm import SVC
+    >>> from sklearn.model_selection import train_test_split
+    >>> X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)
+    >>> result = train_and_evaluate_model(
+    ...     SVC(),
+    ...     "SVM",
+    ...     "sensor1",
+    ...     X_train,
+    ...     y_train,
+    ...     X_test,
+    ...     y_test,
+    ...     export="models/sensor1"
+    ... )
+    >>> print(f"Model accuracy: {result['accuracy']:.2f}%")
+    """
+    from sklearn.metrics import accuracy_score
+
+    result = {"model": model_name, "sensor": sensor_label, "success": False}
+
+    try:
+        # Train the model
+        model.fit(x_train, y_train)
+
+        try:
+            y_pred = model.predict(x_test)
+            result["y_pred"] = y_pred  # Convert to numpy array
+        except Exception as e:
+            result["error"] = f"Prediction error: {str(e)}"
+            return result
+
+        # Calculate accuracy
+        try:
+            accuracy = accuracy_score(y_test, y_pred) * 100
+            result["accuracy"] = accuracy
+        except Exception as e:
+            result["error"] = f"Accuracy calculation error: {str(e)}"
+            return result
+
+        # Export model if requested
+        if export:
+            try:
+                import joblib
+
+                full_path = os.path.join(export, f"{model_name}.joblib")
+                os.makedirs(os.path.dirname(full_path), exist_ok=True)
+                joblib.dump(model, full_path)
+                print(f"Model saved to {full_path}")
+            except Exception as e:
+                print(f"Warning: Failed to export model to {export}: {str(e)}")
+                result["export_error"] = str(e)
+                # Continue despite export error
+
+        result["success"] = True
+        return result
+
+    except Exception as e:
+        result["error"] = f"Training error: {str(e)}"
+        return result
+def plot_confusion_matrix(results_sensor, y_test, title):
+    """
+    Plot confusion matrices for each model in results_sensor1.
+
+    Parameters:
+    -----------
+    results_sensor1 : list
+        List of dictionaries containing model results.
+    x_test1 : array-like
+        Test input samples.
+    y_test : array-like
+        True labels for the test samples.
+
+    Returns:
+    --------
+    None
+    This function will display confusion matrices for each model in results_sensor1.
+
+    Example
+    -------
+    >>> results_sensor1 = [
+    ...     {'model': 'model1', 'accuracy': 95.0},
+    ...     {'model': 'model2', 'accuracy': 90.0}
+    ... ]
+    >>> x_test1 = np.random.rand(100, 10)  # Example test data
+    >>> y_test = np.random.randint(0, 2, size=100)  # Example true labels
+    >>> plot_confusion_matrix(results_sensor1, x_test1, y_test)
+    """
+    # Iterate through each model result and plot confusion matrix
+    for i in results_sensor:
+        model = load(f"D:/thesis/models/{i['sensor']}/{i['model']}.joblib")
+        cm = confusion_matrix(y_test, i['y_pred']) # -> ndarray
+
+        # get the class labels
+        labels = model.classes_
+        # Plot
+        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
+        disp.plot(cmap=plt.cm.Blues)  # You can change colormap
+        plt.title(f"{title}")
+
+def calculate_label_percentages(labels):
+    """
+    Calculate and print the percentage distribution of unique labels in a numpy array.
+
+    Parameters:
+        labels (np.array): Input array of labels.
+
+    Returns:
+        None
+    """
+    # Count occurrences of each unique label
+    unique, counts = np.unique(labels, return_counts=True)
+
+    # Calculate percentages
+    percentages = (counts / len(labels)) * 100
+
+    # Build and print the result string
+    result = "\n".join([f"Label {label}: {percentage:.2f}%" for label, percentage in zip(unique, percentages)])
+    return print(result)
+
+def inference_model(
+    models, raw_file, column_question: int = None
+):
+    """
+    Perform inference using a trained machine learning model on a raw vibration data file with questioned column grid.
+
+    Parameters
+    ----------
+    model : dict with some exported model path
+        The trained machine learning model to use for inference.
+    x_test : array-like or pandas.DataFrame
+        The input samples for which predictions are to be made.
+    export : str, optional
+        Directory path where the predictions should be saved. If None, predictions won't be saved.
+
+    Returns
+    -------
+    np.ndarray
+        Array of predicted values.
+
+    Example
+    -------
+    >>> from sklearn.svm import SVC
+    >>> model = {"SVM": "models/sensor1/SVM.joblib", "SVM with PCA": "models/sensor1/SVM_with_PCA.joblib"}
+    >>> inference_model(model["SVM"], "zzzAD1.TXT", column_question=1)
+    """
+    df = pd.read_csv(raw_file, delim_whitespace=True, skiprows=10, header=0, memory_map=True)
+    col_idx = []
+    for i in range(1,6):
+        idx = [i, i+5, i+10, i+15, i+20, i+25]
+        col_idx.append(idx)
+    vibration_data = df.iloc[:, column_question].values
+    # Perform STFT
+    from scipy.signal import stft, hann
+    freq, times, Zxx = stft(
+                            vibration_data, 
+                            fs=1024, 
+                            window=hann(1024), 
+                            nperseg=1024, 
+                            noverlap=1024-512
+                            )
+    data = pd.DataFrame(np.abs(Zxx).T, columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, 1024/2, Zxx.shape[1])])
+    data = data.rename(columns={"Freq_0.00": "00"}) # To match the model input format
+    model = load(models)  # Load the model from the provided path
+    return calculate_label_percentages(model.predict(data.iloc[:21,:]))

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'
@@ -19,32 +21,65 @@ 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):
+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)
+    Fs = 1024
+
     frequencies, times, Zxx = stft(
-        vibration_data, 
-        fs=Fs, 
-        window=window, 
-        nperseg=window_size, 
-        noverlap=window_size - hop_size
-    )
+                                    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
+
+    # 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
+        num_test_runs = 120
+    else:
+        num_test_runs = 5
     # Check if the damage folder exists
     if not os.path.isdir(damage_folder):
         print(f"Folder {damage_folder} does not exist. Skipping...")
@@ -79,20 +114,24 @@ def process_damage_case(damage_num):
                 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])]
-            )
+            df_stft = compute_stft(vibration_data)
+
+            # 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}")
+                if test_num <= 45:
+                    df_stft = df_stft.iloc[:22, :]
+                    print(f"Reduced df_stft shape (21 samples): {df_stft.shape}")
+                else:
+                    df_stft = df_stft.iloc[:21, :]
+                    print(f"Reduced df_stft shape (22 samples): {df_stft.shape}")
             
             # Append to the aggregated list
             aggregated_stft.append(df_stft)
+            print(sum(df.shape[0] for df in aggregated_stft))
         
         # Concatenate all STFT DataFrames vertically
         if aggregated_stft:
@@ -105,11 +144,13 @@ def process_damage_case(damage_num):
             )
             
             # Save the aggregated STFT to CSV
-            df_aggregated.to_csv(output_file, index=False)
+            with open(output_file, 'w') as file:
+                file.write('sep=,\n')
+                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(1, num_damage_cases + 1))
+        pool.map(process_damage_case, range(num_damage_cases + 1))

data/QUGS/convert.py

@@ -1,360 +0,0 @@
-import pandas as pd
-import os
-import re
-import sys
-import numpy as np
-from colorama import Fore, Style, init
-from typing import TypedDict, Dict, List
-from joblib import load
-from pprint import pprint
-
-# class DamageFilesIndices(TypedDict):
-#     damage_index: int
-#     files: list[int]
-OriginalSingleDamageScenarioFilePath = str
-DamageScenarioGroupIndex = int
-OriginalSingleDamageScenario = pd.DataFrame
-SensorIndex = int
-VectorColumnIndex = List[SensorIndex]
-VectorColumnIndices = List[VectorColumnIndex]
-DamageScenarioGroup = List[OriginalSingleDamageScenario]
-GroupDataset = List[DamageScenarioGroup]
-
-
-class DamageFilesIndices(TypedDict):
-    damage_index: int
-    files: List[str]
-
-
-def generate_damage_files_index(**kwargs) -> DamageFilesIndices:
-    prefix: str = kwargs.get("prefix", "zzzAD")
-    extension: str = kwargs.get("extension", ".TXT")
-    num_damage: int = kwargs.get("num_damage")
-    file_index_start: int = kwargs.get("file_index_start")
-    col: int = kwargs.get("col")
-    base_path: str = kwargs.get("base_path")
-
-    damage_scenarios = {}
-    a = file_index_start
-    b = col + 1
-    for i in range(1, num_damage + 1):
-        damage_scenarios[i] = range(a, b)
-        a += col
-        b += col
-
-    # return damage_scenarios
-
-    x = {}
-    for damage, files in damage_scenarios.items():
-        x[damage] = []  # Initialize each key with an empty list
-        for i, file_index in enumerate(files, start=1):
-            if base_path:
-                x[damage].append(
-                    os.path.normpath(
-                        os.path.join(base_path, f"{prefix}{file_index}{extension}")
-                    )
-                )
-                # if not os.path.exists(file_path):
-                #     print(Fore.RED + f"File {file_path} does not exist.")
-                #     continue
-            else:
-                x[damage].append(f"{prefix}{file_index}{extension}")
-    return x
-
-    # file_path = os.path.join(base_path, f"zzz{prefix}D{file_index}.TXT")
-    # df = pd.read_csv( file_path, sep="\t", skiprows=10)  # Read with explicit column names
-
-
-class DataProcessor:
-    def __init__(self, file_index: DamageFilesIndices, cache_path: str = None):
-        self.file_index = file_index
-        if cache_path:
-            self.data = load(cache_path)
-        else:
-            self.data = self._load_all_data()
-
-    def _extract_column_names(self, file_path: str) -> List[str]:
-        """
-        Extracts column names from the header of the given file.
-        Assumes the 6th line contains column names.
-
-        :param file_path: Path to the data file.
-        :return: List of column names.
-        """
-        with open(file_path, "r") as f:
-            header_lines = [next(f) for _ in range(12)]
-
-        # Extract column names from the 6th line
-        channel_line = header_lines[10].strip()
-        tokens = re.findall(r'"([^"]+)"', channel_line)
-        if not channel_line.startswith('"'):
-            first_token = channel_line.split()[0]
-            tokens = [first_token] + tokens
-
-        return tokens  # Prepend 'Time' column if applicable
-
-    def _load_dataframe(self, file_path: str) -> OriginalSingleDamageScenario:
-        """
-        Loads a single data file into a pandas DataFrame.
-
-        :param file_path: Path to the data file.
-        :return: DataFrame containing the numerical data.
-        """
-        col_names = self._extract_column_names(file_path)
-        df = pd.read_csv(
-            file_path, delim_whitespace=True, skiprows=11, header=None, memory_map=True
-        )
-        df.columns = col_names
-        return df
-
-    def _load_all_data(self) -> GroupDataset:
-        """
-        Loads all data files based on the grouping dictionary and returns a nested list.
-
-        :return: A nested list of DataFrames where the outer index corresponds to group_idx - 1.
-        """
-        data = []
-        # Find the maximum group index to determine the list size
-        max_group_idx = max(self.file_index.keys()) if self.file_index else 0
-
-        # Initialize empty lists
-        for _ in range(max_group_idx):
-            data.append([])
-
-        # Fill the list with data
-        for group_idx, file_list in self.file_index.items():
-            # Adjust index to be 0-based
-            list_idx = group_idx - 1
-            data[list_idx] = [self._load_dataframe(file) for file in file_list]
-
-        return data
-
-    def get_group_data(self, group_idx: int) -> List[pd.DataFrame]:
-        """
-        Returns the list of DataFrames for the given group index.
-
-        :param group_idx: Index of the group.
-        :return: List of DataFrames.
-        """
-        return self.data.get([group_idx, []])
-
-    def get_column_names(self, group_idx: int, file_idx: int = 0) -> List[str]:
-        """
-        Returns the column names for the given group and file indices.
-
-        :param group_idx: Index of the group.
-        :param file_idx: Index of the file in the group.
-        :return: List of column names.
-        """
-        if group_idx in self.data and len(self.data[group_idx]) > file_idx:
-            return self.data[group_idx][file_idx].columns.tolist()
-        return []
-
-    def get_data_info(self):
-        """
-        Print information about the loaded data structure.
-        Adapted for when self.data is a List instead of a Dictionary.
-        """
-        if isinstance(self.data, list):
-            # For each sublist in self.data, get the type names of all elements
-            pprint(
-                [
-                    (
-                        [type(item).__name__ for item in sublist]
-                        if isinstance(sublist, list)
-                        else type(sublist).__name__
-                    )
-                    for sublist in self.data
-                ]
-            )
-        else:
-            pprint(
-                {
-                    key: [type(df).__name__ for df in value]
-                    for key, value in self.data.items()
-                }
-                if isinstance(self.data, dict)
-                else type(self.data).__name__
-            )
-
-    def _create_vector_column_index(self) -> VectorColumnIndices:
-        vector_col_idx: VectorColumnIndices = []
-        y = 0
-        for data_group in self.data:  # len(data_group[i]) = 5
-            for j in data_group:  # len(j[i]) =
-                c: VectorColumnIndex = []  # column vector c_{j}
-                x = 0
-                for _ in range(6):  # TODO: range(6) should be dynamic and parameterized
-                    c.append(x + y)
-                    x += 5
-                vector_col_idx.append(c)
-                y += 1
-            return vector_col_idx
-
-    def create_vector_column(self, overwrite=True) -> List[List[List[pd.DataFrame]]]:
-        """
-        Create a vector column from the loaded data.
-
-        :param overwrite: Overwrite the original data with vector column-based data.
-        """
-        idx = self._create_vector_column_index()
-        # if overwrite:
-        for i in range(len(self.data)):
-            for j in range(len(self.data[i])):
-                # Get the appropriate indices for slicing from idx
-                indices = idx[j]
-
-                # Get the current DataFrame
-                df = self.data[i][j]
-
-                # Keep the 'Time' column and select only specified 'Real' columns
-                # First, we add 1 to all indices to account for 'Time' being at position 0
-                real_indices = [index + 1 for index in indices]
-
-                # Create list with Time column index (0) and the adjusted Real indices
-                all_indices = [0] + real_indices
-
-                # Apply the slicing
-                self.data[i][j] = df.iloc[:, all_indices]
-        # TODO: if !overwrite:
-
-    def create_limited_sensor_vector_column(self, overwrite=True):
-        """
-        Create a vector column from the loaded data.
-
-        :param overwrite: Overwrite the original data with vector column-based data.
-        """
-        idx = self._create_vector_column_index()
-        # if overwrite:
-        for i in range(len(self.data)):  # damage(s)
-            for j in range(len(self.data[i])):  # col(s)
-                # Get the appropriate indices for slicing from idx
-                indices = idx[j]
-
-                # Get the current DataFrame
-                df = self.data[i][j]
-
-                # Keep the 'Time' column and select only specifid 'Real' colmns
-                # First, we add 1 to all indices to acount for 'Time' being at positiion 0
-                real_indices = [index + 1 for index in indices]
-
-                # Create list with Time column index (0) and the adjustedd Real indices
-                all_indices = [0] + [real_indices[0]] + [real_indices[-1]]
-
-                # Apply the slicing
-                self.data[i][j] = df.iloc[:, all_indices]
-        # TODO: if !overwrite:
-
-    def export_to_csv(self, output_dir: str, file_prefix: str = "DAMAGE"):
-        """
-        Export the processed data to CSV files in the required folder structure.
-
-        :param output_dir: Directory to save the CSV files.
-        :param file_prefix: Prefix for the output filenames.
-        """
-        for group_idx, group in enumerate(self.data, start=1):
-            group_folder = os.path.join(output_dir, f"{file_prefix}_{group_idx}")
-            os.makedirs(group_folder, exist_ok=True)
-            for test_idx, df in enumerate(group, start=1):
-                # Ensure columns are named uniquely if duplicated
-                df = df.copy()
-                df.columns = ["Time", "Real_0", "Real_1"]  # Rename
-
-                # Export first Real column
-                out1 = os.path.join(
-                    group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_01.csv"
-                )
-                df[["Time", "Real_0"]].rename(columns={"Real_0": "Real"}).to_csv(
-                    out1, index=False
-                )
-
-                # Export last Real column
-                out2 = os.path.join(
-                    group_folder, f"{file_prefix}_{group_idx}_TEST{test_idx}_02.csv"
-                )
-                df[["Time", "Real_1"]].rename(columns={"Real_1": "Real"}).to_csv(
-                    out2, index=False
-                )
-
-
-def create_damage_files(base_path, output_base, prefix):
-    # 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(1, 6),  # Damage 1 files from zzzAD1.csv to zzzAD5.csv
-        2: range(6, 11),  # Damage 2 files from zzzAD6.csv to zzzAD10.csv
-        3: range(11, 16),  # Damage 3 files from zzzAD11.csv to zzzAD15.csvs
-        4: range(16, 21),  # Damage 4 files from zzzAD16.csv to zzzAD20.csv
-        5: range(21, 26),  # Damage 5 files from zzzAD21.csv to zzzAD25.csv
-        6: range(26, 31),  # Damage 6 files from zzzAD26.csv to zzzAD30.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"zzz{prefix}D{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"DAMAGE{damage}_TEST{i}_01.csv"
-            )
-            print(f"Creating {output_file_1} from taking zzz{prefix}D{file_index}.TXT")
-            print("Taking datetime column on index 0...")
-            print(f"Taking `{top_sensor}`...")
-            os.makedirs(os.path.dirname(output_file_1), exist_ok=True)
-            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"DAMAGE{damage}_TEST{i}_02.csv"
-            )
-            print(f"Creating {output_file_2} from taking zzz{prefix}D{file_index}.TXT")
-            print("Taking datetime column on index 0...")
-            print(f"Taking `{bottom_sensor}`...")
-            os.makedirs(os.path.dirname(output_file_2), exist_ok=True)
-            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]
-    prefix = sys.argv[3]  # Define output directory
-
-    # Create output folders if they don't exist
-    # for i in range(1, 7):
-    #     os.makedirs(os.path.join(output_base, f'DAMAGE_{i}'), exist_ok=True)
-
-    create_damage_files(base_path, output_base, prefix)
-    print(Fore.YELLOW + Style.BRIGHT + "All files have been created successfully.")
-
-
-if __name__ == "__main__":
-    main()

data/QUGS/test.py

@@ -1,25 +1,52 @@
-from convert import *
+from data_preprocessing import *
 from joblib import dump, load
 
+# b = generate_damage_files_index(
+#     num_damage=6,
+#     file_index_start=1,
+#     col=5,
+#     base_path="D:/thesis/data/dataset_B",
+#     prefix="zzzBD",
+#     # undamage_file="zzzBU.TXT"
+# )
+# Example: Generate tuples with a special group of df0 at the beginning
+special_groups_A = [
+    {'df_name': 'zzzAU.TXT', 'position': 0, 'size': 5}  # Add at beginning
+]
+
+special_groups_B = [
+    {'df_name': 'zzzBU.TXT', 'position': 0, 'size': 5}  # Add at beginning
+]
+
+# Generate the tuples with the special group
+a_complement = [(comp)
+                for n in range(1, 31)
+                for comp in complement_pairs(n)]
+a = generate_df_tuples(special_groups=a_complement, prefix="zzzAD")
+
+# b_complement = [(comp)
+#                 for n in range(1, 31)
+#                 for comp in complement_pairs(n)]
+# b = generate_df_tuples(special_groups=b_complement, prefix="zzzBD")
+
+
 # a = generate_damage_files_index(
-#     num_damage=6, file_index_start=1, col=5, base_path="D:/thesis/data/dataset_A"
+#     num_damage=6,
+#     file_index_start=1,
+#     col=5,
+#     base_path="D:/thesis/data/dataset_A",
+#     prefix="zzzAD",
+#     # undamage_file="zzzBU.TXT"
 # )
 
-b = generate_damage_files_index(
-    num_damage=6,
-    file_index_start=1,
-    col=5,
-    base_path="D:/thesis/data/dataset_B",
-    prefix="zzzBD",
-)
-# data_A = DataProcessor(file_index=a)
-# # data.create_vector_column(overwrite=True)
-# data_A.create_limited_sensor_vector_column(overwrite=True)
-# data_A.export_to_csv("D:/thesis/data/converted/raw")
+data_A = DataProcessor(file_index=a, base_path="D:/thesis/data/dataset_A", include_time=True)
+# data_A.create_vector_column(overwrite=True)
+# # data_A.create_limited_sensor_vector_column(overwrite=True)
+data_A.export_to_csv("D:/thesis/data/converted/raw")
 
-data_B = DataProcessor(file_index=b)
-# data.create_vector_column(overwrite=True)
-data_B.create_limited_sensor_vector_column(overwrite=True)
-data_B.export_to_csv("D:/thesis/data/converted/raw_B")
+# data_B = DataProcessor(file_index=b, base_path="D:/thesis/data/dataset_B", include_time=True)
+# data_B.create_vector_column(overwrite=True)
+# # data_B.create_limited_sensor_vector_column(overwrite=True)
+# data_B.export_to_csv("D:/thesis/data/converted/raw_B")
 # a = load("D:/cache.joblib")
-# breakpoint()
+# breakpoint()