feat(data): add export_to_csv method for saving processed data into individuals sensor end and update test script

Closes #40

data/QUGS/convert.py

@@ -2,6 +2,7 @@ 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
@@ -225,25 +226,56 @@ class DataProcessor:
         """
         idx = self._create_vector_column_index()
         # if overwrite:
-        for i in range(len(self.data)):
+        for i in range(len(self.data)):  # damage(s)
-            for j in range(len(self.data[i])):
+            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 specified 'Real' columns
+                # Keep the 'Time' column and select only specifid 'Real' colmns
-                # First, we add 1 to all indices to account for 'Time' being at position 0
+                # 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 adjusted Real 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

data/QUGS/test.py

@@ -1,8 +1,12 @@
 from convert import *
 from joblib import dump, load
 
-# a = generate_damage_files_index(
+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"
-# )
+)
-# dump(DataProcessor(file_index=a), "D:/cache.joblib")
+data = DataProcessor(file_index=a)
-a = load("D:/cache.joblib")
+# data.create_vector_column(overwrite=True)
+data.create_limited_sensor_vector_column(overwrite=True)
+data.export_to_csv("D:/thesis/data/")
+# a = load("D:/cache.joblib")
+breakpoint()