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/frequency.py

@@ -1,19 +1,19 @@
-# File: frequency.py
+# File: stats/frequency.py
 # Copyright (C) 2026 Erick Ahmed
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+"""CAN bus message frequency analyzer and plotter."""
+
 import argparse
 from pathlib import Path
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
-from stats.utils.extractor 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')
+from stats.utils.converter import format_can_id_vec as _format_can_id_vec
+from stats.utils.loader import load_data
+
 
 def calculate_frequency(df: pd.DataFrame) -> pd.DataFrame:
     can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
@@ -28,6 +28,7 @@ def calculate_frequency(df: pd.DataFrame) -> pd.DataFrame:
     freq_df['Percentage'] = np.round(freq_df['Count'] / total * 100, 2) if total else 0.0
     return freq_df.sort_values('Count', ascending=True).reset_index(drop=True)
 
+
 def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure:
     n = len(stats_df)
     fig = go.Figure(go.Bar(
@@ -106,6 +107,7 @@ def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure:
     )
     return fig
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Analyze CAN bus message frequency")
     parser.add_argument("input", type=Path, help="Path to the input CAN log file")