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/id_viewer.py

@@ -1,25 +1,30 @@
-# File: id_viewer.py
+# File: stats/id_viewer.py
 # Copyright (C) 2026 Erick Ahmed
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+"""Interactive CAN bus byte-change visualizer."""
+
 import argparse
 from pathlib import Path
+from typing import List, Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 from plotly_resampler import FigureResampler
-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 prepare_data(df, target_id):
+_BYTE_COLORS = [
+    '#e41a1c', '#377eb8', '#4daf4a', '#984ea3',
+    '#ff7f00', '#ffff33', '#a65628', '#f781bf',
+]
+
+
+def prepare_data(df: pd.DataFrame, target_id: str) -> Tuple[pd.DataFrame, List[str]]:
     can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
-    formatted = _format_can_id_vec(df[can_id_col])
-    df = df.assign(Formatted_ID=formatted)
+    df = df.assign(Formatted_ID=_format_can_id_vec(df[can_id_col]))
     target_id_clean = _format_can_id_vec(pd.Series([target_id])).iloc[0]
     filtered = df[df['Formatted_ID'] == target_id_clean]
 
@@ -29,7 +34,9 @@ def prepare_data(df, target_id):
 
     for col in byte_cols:
         if not pd.api.types.is_numeric_dtype(filtered[col]):
-            filtered = filtered.assign(**{col: pd.to_numeric(filtered[col], errors='coerce').astype('float32')})
+            filtered = filtered.assign(
+                **{col: pd.to_numeric(filtered[col], errors='coerce').astype('float32')}
+            )
 
     filtered = filtered.sort_values('Timestamp', kind='stable')
     arr = filtered[byte_cols].to_numpy(dtype=np.float32, copy=False)
@@ -40,12 +47,12 @@ def prepare_data(df, target_id):
 
     return filtered, byte_cols
 
-def plot_bits(df, byte_cols, can_id, title):
+
+def plot_bits(df: pd.DataFrame, byte_cols: List[str], can_id: str, title: str) -> FigureResampler:
     fig = FigureResampler(
         resampled_trace_prefix_suffix=("", ""),
         show_mean_aggregation_size=False
     )
-    colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33', '#a65628', '#f781bf']
     n = len(byte_cols)
 
     x = df['Timestamp'].to_numpy() if not df.empty else np.array([])
@@ -54,7 +61,7 @@ def plot_bits(df, byte_cols, can_id, title):
 
         fig.add_trace(go.Scatter(
             mode='lines',
-            line=dict(shape='hv', width=2, color=colors[i % len(colors)]),
+            line=dict(shape='hv', width=2, color=_BYTE_COLORS[i % len(_BYTE_COLORS)]),
             name=col.upper(),
             legendgroup=col.upper(),
             hovertemplate=f"<b>{col.upper()}</b><br>Time: %{{x}}<br>Value: %{{y}}<extra></extra>",
@@ -121,6 +128,7 @@ def plot_bits(df, byte_cols, can_id, title):
     )
     return fig
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Visualize CAN bus byte changes over time")
     parser.add_argument("input", type=Path, help="Path to the input CAN log file")