Add custom plotting support for vehicle signal

- Plot pie chart for engine load state

main.py

@@ -410,12 +410,10 @@ def update_corr(method, target, vehicle, bus, tab):
     Input('vehicles-vehicle-selector', 'value')
 )
 def render_vehicles(vehicle):
-    """Render small graph boxes for every decoded signal, combining both buses."""
     if not vehicle or vehicle not in DATA:
         return html.Div("No data available", className="text-muted")
 
     brand = VEHICLE_META.get(vehicle, {}).get("brand", "")
-
     vehicle_module = get_vehicle_module(brand)
 
     dfs = []
@@ -431,13 +429,22 @@ def render_vehicles(vehicle):
         df = df.sort_values('Timestamp', kind='stable').reset_index(drop=True)
 
     cards = []
+
     for nid, frame_def in vehicle_module.DECODER_RULES.items():
         decoded = vehicle_module.decode_dataframe(df, frame_def.can_id)
 
-        for sig in frame_def.signals:
+        for item in frame_def.signals:
-            unit_str = f" ({sig.unit})" if sig.unit else ""
+            if hasattr(item, 'plot_func') and callable(item.plot_func):
-            title = f"{frame_def.can_id} - {sig.name}{unit_str}"
+                title = f"{frame_def.can_id} - {item.name}"
-            fig = vehicle_module.plot_signal(decoded, sig.name, title=title, color=frame_def.color)
+                fig = item.plot_func(decoded, frame_def.color)
+            else:
+                sig = item
+                if getattr(sig, 'skip_plot', False):
+                    continue
+
+                unit_str = f" ({sig.unit})" if sig.unit else ""
+                title = f"{frame_def.can_id} - {sig.name}{unit_str}"
+                fig = vehicle_module.plot_signal(decoded, sig.name, title=title, color=frame_def.color)
 
             card = dbc.Card([
                 dbc.CardBody([

vehicle/komatsu.py

@@ -2,10 +2,32 @@
 # Copyright (C) 2026 Erick Ahmed
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
+import plotly.express as px
+import pandas as pd
+from dataclasses import dataclass
+from typing import Callable
+from copy import deepcopy
 from vehicle.base import (
     SignalDef, FrameDef, normalize_id, decode_dataframe as _decode_dataframe, plot_signal
 )
 
+@dataclass
+class CustomPlotDef:
+    name: str
+    plot_func: Callable
+
+load_state_sig = SignalDef(
+    name="Engine Load State",
+    bit_start=24,
+    bit_length=8,
+    factor=1,
+    offset=0.0,
+    is_signed=False,
+    byte_order="big",
+    unit="",
+)
+load_state_sig.skip_plot = True
+
 DECODER_RULES = {
     normalize_id("0x011F"): FrameDef(
         can_id="0x011F",
@@ -36,7 +58,7 @@ DECODER_RULES = {
     ),
     normalize_id("0x0CFF3300"): FrameDef(
         can_id="0x0CFF3300",
-        description="Engine temperature block",
+        description="Engine temperature and load state block",
         color="#0080fe",
         signals=[
             SignalDef(
@@ -59,9 +81,66 @@ DECODER_RULES = {
                 byte_order="big",
                 unit="℃",
             ),
+            load_state_sig,
+            CustomPlotDef(
+                name="Engine Load State",
+                plot_func=lambda decoded, color: plot_load_state_pie(decoded, color)
+            ),
         ],
     ),
 }
 
+LOAD_STATE_MAP = {
+    0: "Boot up",
+    16: "Normal load",
+    32: "High load"
+}
+
+def plot_load_state_pie(decoded_df, color):
+    if decoded_df is None or decoded_df.empty or "Engine Load State" not in decoded_df.columns:
+        return px.pie(title="No data for Engine Load State")
+
+    states = pd.to_numeric(decoded_df["Engine Load State"], errors='coerce').dropna().astype(int)
+
+    labels = states.map(LOAD_STATE_MAP).fillna("Unknown")
+    counts = labels.value_counts().reset_index()
+    counts.columns = ['State', 'Count']
+
+    total = counts['Count'].sum()
+    counts['Percentage'] = (counts['Count'] / total * 100).round(1)
+    counts['Legend'] = counts['State'] + " (" + counts['Percentage'].astype(str) + "%)"
+
+    fig = px.pie(
+        counts,
+        values='Count',
+        names='Legend',
+        color='State',
+        title='Engine Load State Distribution',
+        color_discrete_map={
+            "Boot up": "#ff9900",
+            "Normal load": "#00cc00",
+            "High load": "#cc0000",
+            "Unknown": "#808080"
+        }
+    )
+
+    fig.update_traces(
+        textinfo='none',
+        hoverinfo='label+percent+value',
+        domain={'x': [0.05, 0.55], 'y': [0.05, 0.95]}
+    )
+    fig.update_layout(
+        margin=dict(l=0, r=10, t=40, b=0),
+        legend=dict(x=0.6, y=0.5)
+    )
+    return fig
+
 def decode_dataframe(df, can_id):
-    return _decode_dataframe(df, can_id, DECODER_RULES)
+    filtered_rules = {}
+    for nid, frame in DECODER_RULES.items():
+        filtered_signals = [sig for sig in frame.signals if isinstance(sig, SignalDef)]
+        new_frame = deepcopy(frame)
+        new_frame.signals = filtered_signals
+        filtered_rules[nid] = new_frame
+
+    return _decode_dataframe(df, can_id, filtered_rules)