feat(data): Enhance DataProcessor to support dynamic base path and improve data loading with error handling and memory efficiency

data/QUGS/convert.py

@@ -104,13 +104,28 @@ def generate_df_tuples(total_dfs=30, group_size=5, prefix="zzzAD", ext="TXT", fi
 
 
 class DataProcessor:
-    def __init__(self, file_index: DamageFilesIndices, cache_path: str = None):
+    def __init__(self, file_index, cache_path: str = None, base_path: str = None):
         self.file_index = file_index
+        self.base_path = base_path
         if cache_path:
             self.data = load(cache_path)
         else:
-            self.data = self._load_all_data()
+            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')
+                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.
@@ -118,7 +133,7 @@ class DataProcessor:
         :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)
+        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:
@@ -141,6 +156,7 @@ class DataProcessor:
 
         # 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
 
@@ -204,7 +220,7 @@ class DataProcessor:
                     x += 5
                 vector_col_idx.append(c)
                 y += 1
-            return vector_col_idx
+            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]]]:
         """
@@ -212,25 +228,15 @@ class DataProcessor:
 
         :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]
+        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()
 
-                # 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):