refactor(data): update type annotations for damage files index and related classes. Need better implementation

data/QUGS/convert.py

@@ -7,19 +7,31 @@ from typing import TypedDict, Dict, List
 from joblib import load
 from pprint import pprint
 
-# class DamageFilesIndex(TypedDict):
+# class DamageFilesIndices(TypedDict):
 #     damage_index: int
 #     files: list[int]
-DamageFilesIndex = Dict[int, List[str]]
+OriginalSingleDamageScenarioFilePath = str
+DamageScenarioGroupIndex = int
+OriginalSingleDamageScenario = pd.DataFrame
+SensorIndex = int
+VectorColumnIndex = List[SensorIndex]
+VectorColumnIndices = List[VectorColumnIndex]
+DamageScenarioGroup = List[OriginalSingleDamageScenario]
+GroupDataset = List[DamageScenarioGroup]
 
 
-def generate_damage_files_index(**kwargs) -> DamageFilesIndex:
+class DamageFilesIndices(TypedDict):
-    prefix = kwargs.get("prefix", "zzzAD")
+    damage_index: int
-    extension = kwargs.get("extension", ".TXT")
+    files: List[str]
-    num_damage = kwargs.get("num_damage")
+
-    file_index_start = kwargs.get("file_index_start")
+
-    col = kwargs.get("col")
+def generate_damage_files_index(**kwargs) -> DamageFilesIndices:
-    base_path = kwargs.get("base_path")
+    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
@@ -53,7 +65,7 @@ def generate_damage_files_index(**kwargs) -> DamageFilesIndex:
 
 
 class DataProcessor:
-    def __init__(self, file_index: Dict[int, List[str]], cache_path: str = None):
+    def __init__(self, file_index: DamageFilesIndices, cache_path: str = None):
         self.file_index = file_index
         if cache_path:
             self.data = load(cache_path)
@@ -80,7 +92,7 @@ class DataProcessor:
 
         return tokens  # Prepend 'Time' column if applicable
 
-    def _load_dataframe(self, file_path: str) -> pd.DataFrame:
+    def _load_dataframe(self, file_path: str) -> OriginalSingleDamageScenario:
         """
         Loads a single data file into a pandas DataFrame.
 
@@ -94,7 +106,7 @@ class DataProcessor:
         df.columns = col_names
         return df
 
-    def _load_all_data(self) -> List[List[pd.DataFrame]]:
+    def _load_all_data(self) -> GroupDataset:
         """
         Loads all data files based on the grouping dictionary and returns a nested list.
 
@@ -164,12 +176,12 @@ class DataProcessor:
                 else type(self.data).__name__
             )
 
-    def _create_vector_column_index(self):
+    def _create_vector_column_index(self) -> VectorColumnIndices:
-        vector_col_idx = []
+        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 = []  # column vector c_{j}
+                c: VectorColumnIndex = []  # column vector c_{j}
                 x = 0
                 for _ in range(6):  # TODO: range(6) should be dynamic and parameterized
                     c.append(x + y)
@@ -178,7 +190,7 @@ class DataProcessor:
                 y += 1
             return vector_col_idx
 
-    def create_vector_column(self, overwrite=True):
+    def create_vector_column(self, overwrite=True) -> List[List[List[pd.DataFrame]]]:
         """
         Create a vector column from the loaded data.
 

data/QUGS/test.py

@@ -1,7 +1,8 @@
 from convert import *
-from joblib import dump
+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")
+# dump(DataProcessor(file_index=a), "D:/cache.joblib")
+a = load("D:/cache.joblib")