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/correlation.py

@@ -1,25 +1,23 @@
-# File: correlation.py
+# File: stats/correlation.py
 # Copyright (C) 2026 Erick Ahmed
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+"""CAN bus inter-byte correlation 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 _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
+def _ensure_int_bytes(df: pd.DataFrame, cols: List[str]) -> pd.DataFrame:
     needs = [c for c in cols if not pd.api.types.is_numeric_dtype(df[c])]
     if needs:
         df = df.copy()
@@ -27,6 +25,7 @@ def _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
             df[c] = df[c].apply(to_int)
     return df
 
+
 def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None = None) -> pd.DataFrame:
     available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
 
@@ -70,7 +69,7 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None =
     else:
         groups = []
 
-    def _process_group(sub):
+    def _process_group(sub: np.ndarray) -> np.ndarray:
         mask = ~np.isnan(sub).any(axis=1)
         sub = sub[mask]
         if len(sub) > 1: