Refactor CANveyor dashboard architecture

- Replace `stats.utils.extractor` with dedicated `loader` and
  `converter`
  modules to improve code organization.
- Implement explicit pipeline stages for ingestion, decoding, and
  precomputation with caching.
- Standardize data loading and J1939 parsing logic across sub-modules.
- Enhance dashboard responsiveness by pre-calculating figures and
  downsampling ID-grouped data.
- Enforce strict typing and add docstrings to public components.

stats/entropy.py

@@ -1,23 +1,21 @@
-# File: entropy.py
+# File: stats/entropy.py
 # Copyright (C) 2026 Erick Ahmed
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+"""CAN bus byte-level entropy analyzer and plotter."""
+
 import argparse
 from pathlib import Path
 from concurrent.futures import ThreadPoolExecutor
+from typing import List
 
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 
-from stats.utils.extractor import load_data
-from stats.utils.extractor import to_int
+from stats.utils.converter import format_can_id_vec as _format_can_id_vec, to_int
+from stats.utils.loader import load_data
 
-def _format_can_id_vec(s: pd.Series) -> pd.Series:
-    s = s.astype('string').str.strip()
-    s = s.str.replace(r'^0x', '', case=False, regex=True)
-    s = s.str.upper()
-    return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
 
 def _entropy_col(a: np.ndarray) -> float:
     a = a[~np.isnan(a)]
@@ -36,6 +34,7 @@ def _entropy_col(a: np.ndarray) -> float:
     p = counts / counts.sum()
     return float(-np.sum(p * np.log2(p)))
 
+
 def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
     available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
     if not available_cols:
@@ -64,7 +63,7 @@ def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
     else:
         groups = []
 
-    def _process_group(sub):
+    def _process_group(sub: np.ndarray) -> np.ndarray:
         res = np.zeros(n_cols, dtype=np.float64)
         for ci in range(n_cols):
             res[ci] = _entropy_col(sub[:, ci])