Implement a modular vehicle decoding system

- Add Komatsu specific rules
- Possibility to expand to any brand

main.py

@@ -11,6 +11,7 @@ import dash
 from dash import dcc, html, Input, Output, State
 import dash_bootstrap_components as dbc
 import numpy as np
+import pandas as pd
 
 from parser import parse_log, parse_csv
 from decoder import decode_j1939_frames
@@ -20,6 +21,7 @@ from stats.frequency import calculate_frequency, plot_frequency
 from stats.correlation import calculate_correlation, plot_correlation_heatmap
 from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
 from logs.view import get_logs_table_component, prepare_logs_data
+from vehicle import get_vehicle_module
 
 RAW_LOG_DIR = "data/logs"
 PAGE_SIZE = 25000
@@ -131,6 +133,18 @@ app.layout = dbc.Container([
         dbc.Tab(label="Overview", tab_id="overview", children=[
             html.Div(id="overview-content")
         ]),
+        dbc.Tab(label="Vehicles", tab_id="vehicles", children=[
+            dbc.Row([
+                dbc.Col(html.Label("Select Vehicle:", className="mt-2"), width="auto"),
+                dbc.Col(dcc.Dropdown(
+                    id='vehicles-vehicle-selector',
+                    options=[{'label': v, 'value': v} for v in DATA.keys()],
+                    value=list(DATA.keys())[0] if DATA else None,
+                    clearable=False
+                ), width=3, className="me-4"),
+            ], className="mb-3 mt-3", align="end"),
+            html.Div(id='vehicles-content', className="mt-3")
+        ]),
         dbc.Tab(label="Logs", tab_id="logs", children=[
             dbc.Row([
                 dbc.Col(html.Label("Select Vehicle:", className="mt-2"), width="auto"),
@@ -391,5 +405,58 @@ def update_corr(method, target, vehicle, bus, tab):
 
     return plot_correlation_heatmap(corr_df, target_id=target_id, title=title)
 
+@app.callback(
+    Output('vehicles-content', 'children'),
+    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 = []
+    for bus_df in DATA[vehicle].values():
+        dfs.append(bus_df)
+
+    if not dfs:
+        return html.Div("No data available", className="text-muted")
+
+    df = pd.concat(dfs, ignore_index=True)
+
+    if 'Timestamp' in df.columns:
+        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:
+            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)
+
+            card = dbc.Card([
+                dbc.CardBody([
+                    dcc.Graph(figure=fig, config={'displayModeBar': False},
+                              style={'height': '280px'})
+                ], className="p-2"),
+            ], className="shadow-sm border-0 h-100")
+
+            cards.append(
+                dbc.Col(card, xs=12, sm=6, md=4, lg=3, className="mb-3")
+            )
+
+    if not cards:
+        return html.Div(
+            "No decoded signals available. Add rules in the vehicle module.",
+            className="text-muted"
+        )
+
+    return dbc.Row(cards)
+
 if __name__ == '__main__':
     app.run(debug=False)