data/QUGS/convert.py

@@ -25,6 +25,15 @@ class DamageFilesIndices(TypedDict):
     damage_index: int
     files: List[str]
 
+def complement_pairs(n):
+    """
+    Return the four complement tuples for zzzBD<n>.TXT
+    """
+    filename = f"zzzAD{n}.TXT" # 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(total_dfs=30, group_size=5, prefix="zzzAD", extension="TXT", first_col_start=1, last_col_offset=25, 
                       special_groups=None, group=True):
@@ -68,22 +77,10 @@ def generate_df_tuples(total_dfs=30, group_size=5, prefix="zzzAD", extension="TX
 
     # Add special groups at specified positions (other than beginning)
     if special_groups:
-        for group in special_groups:
-            position = group.get('position', 0) # default value is 0 if not specified
-            df_name = group['df_name']
-            size = group.get('size', group_size)
-            
-            # Create the special group tuples
-            special_tuples = []
-            for i in range(size):
-                first_col = first_col_start + i
-                last_col = first_col + last_col_offset
-                special_tuples.append((df_name, [first_col, last_col]))
-                
-        tuples.insert(position, special_tuples)
+        result.insert(0, special_groups)
     
     
-    return tuples
+    return result
 
 
     # file_path = os.path.join(base_path, f"zzz{prefix}D{file_index}.TXT")

data/QUGS/test.py

@@ -19,8 +19,15 @@ special_groups_B = [
 ]
 
 # Generate the tuples with the special group
-# a = generate_df_tuples(special_groups=special_groups_A)
-b = generate_df_tuples(special_groups=special_groups_B, prefix="zzzBD")
+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(
@@ -32,14 +39,14 @@ b = generate_df_tuples(special_groups=special_groups_B, prefix="zzzBD")
 #     # undamage_file="zzzBU.TXT"
 # )
 
-# data_A = DataProcessor(file_index=a, base_path="D:/thesis/data/dataset_A", include_time=True)
+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_A.export_to_csv("D:/thesis/data/converted/raw")
 
-data_B = DataProcessor(file_index=b, base_path="D:/thesis/data/dataset_B", include_time=True)
+# 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")
+# data_B.export_to_csv("D:/thesis/data/converted/raw_B")
 # a = load("D:/cache.joblib")
 # breakpoint()