Update wizard: populate category from activity type, add cycling mode

work_trace/wizards/wt_import_timeline_wizard.py

@@ -7,10 +7,26 @@ from datetime import datetime, timedelta
 from math import radians, sin, cos, sqrt, atan2
 from collections import Counter
 
-VEHICLE_ACTIVITIES = {'IN_VEHICLE', 'IN_ROAD_VEHICLE', 'IN_RAIL_VEHICLE', 'IN_TWO_WHEELER_VEHICLE'}
-WALKING_ACTIVITIES = {'WALKING', 'ON_FOOT', 'RUNNING', 'ON_BICYCLE'}
+VEHICLE_ACTIVITIES = {'IN_VEHICLE', 'IN_ROAD_VEHICLE', 'IN_RAIL_VEHICLE', 'IN_TWO_WHEELER_VEHICLE', 'IN_PASSENGER_VEHICLE'}
+WALKING_ACTIVITIES = {'WALKING', 'ON_FOOT', 'RUNNING'}
+CYCLING_ACTIVITIES = {'ON_BICYCLE'}
+
+CATEGORY_LABELS = {
+    'IN_PASSENGER_VEHICLE': 'In Vehicle',
+    'IN_VEHICLE': 'In Vehicle',
+    'IN_ROAD_VEHICLE': 'In Vehicle',
+    'IN_RAIL_VEHICLE': 'Rail / Transit',
+    'IN_TWO_WHEELER_VEHICLE': 'Motorcycle / Scooter',
+    'WALKING': 'Walking',
+    'ON_FOOT': 'Walking',
+    'RUNNING': 'Running',
+    'ON_BICYCLE': 'Cycling',
+    'STILL': 'Stationary',
+    'UNKNOWN': 'Unknown',
+    'EXITING_VEHICLE': 'In Vehicle',
+    'TILTING': 'Unknown',
+}
 
-# Positions within this distance (meters) are considered the same location
 PROXIMITY_METERS = 200
 
 
@@ -31,16 +47,18 @@ def _get_travel_mode(activity_type):
         return 'driving'
     if activity_type in WALKING_ACTIVITIES:
         return 'walking'
+    if activity_type in CYCLING_ACTIVITIES:
+        return 'cycling'
     return 'unknown'
 
 
-def _dominant_travel_mode(activities, start_ts, end_ts):
-    """Get dominant travel mode from activity records between two timestamps."""
+def _dominant_activity(activities, start_ts, end_ts):
+    """Get dominant activity type between two timestamps."""
     window = [a for a in activities if start_ts <= a['ts'] <= end_ts]
     if not window:
-        return 'unknown'
+        return 'UNKNOWN'
     counts = Counter(a['type'] for a in window)
-    return _get_travel_mode(counts.most_common(1)[0][0])
+    return counts.most_common(1)[0][0]
 
 
 class WtImportTimelineWizard(models.TransientModel):
@@ -99,7 +117,7 @@ class WtImportTimelineWizard(models.TransientModel):
                 stop['distance_from_previous'] = 0.0
                 stop['travel_time_from_previous'] = 0.0
 
-        # Get existing arrived_at timestamps to avoid duplicates
+        # Skip duplicates
         LocationLog = self.env['wt.location.log']
         existing = set(
             r.arrived_at.strftime('%Y-%m-%d %H:%M:%S')
@@ -125,6 +143,7 @@ class WtImportTimelineWizard(models.TransientModel):
                 'latitude': stop['lat'],
                 'longitude': stop['lng'],
                 'travel_mode': stop.get('travel_mode', 'unknown'),
+                'category': stop.get('category', ''),
                 'distance_from_previous': stop['distance_from_previous'],
                 'travel_time_from_previous': stop['travel_time_from_previous'],
                 'source': 'google_timeline',
@@ -141,7 +160,7 @@ class WtImportTimelineWizard(models.TransientModel):
 
         return {
             'type': 'ir.actions.act_window',
-            'name': _('Imported %d new stops (%d skipped as duplicates)') % (len(created_ids), skipped),
+            'name': _('Imported %d new stops (%d skipped)') % (len(created_ids), skipped),
             'res_model': 'wt.location.log',
             'view_mode': 'list,form',
             'domain': [('id', 'in', created_ids)],
@@ -151,11 +170,8 @@ class WtImportTimelineWizard(models.TransientModel):
     def _parse_timeline(self, data, proximity_meters=200):
         """
         Parse Google Timeline Edits JSON into location stops.
-
-        Google records positions primarily when the device is stationary.
-        We cluster consecutive positions within proximity_meters into a single stop.
-        The gap between clusters = travel time.
-        Activity records between clusters determine the travel mode.
+        Positions represent stationary moments — cluster by proximity.
+        Activity records between clusters give travel category/mode.
         """
         positions = []
         activities = []
@@ -188,7 +204,7 @@ class WtImportTimelineWizard(models.TransientModel):
         positions.sort(key=lambda x: x['ts'])
         activities.sort(key=lambda x: x['ts'])
 
-        # Cluster positions by proximity
+        # Cluster consecutive positions within proximity_meters
         stops = []
         current_cluster = [positions[0]]
 
@@ -197,21 +213,17 @@ class WtImportTimelineWizard(models.TransientModel):
             dist = _distance_meters(prev['lat'], prev['lng'], pos['lat'], pos['lng'])
 
             if dist <= proximity_meters:
-                # Same location — extend current cluster
                 current_cluster.append(pos)
             else:
-                # New location — save current cluster as a stop
                 avg_lat = sum(p['lat'] for p in current_cluster) / len(current_cluster)
                 avg_lng = sum(p['lng'] for p in current_cluster) / len(current_cluster)
 
-                # Departed = last position in cluster
-                # Next arrived = first position in new cluster
-                # Travel mode = dominant activity between the two
-                travel_mode = _dominant_travel_mode(
-                    activities,
-                    current_cluster[-1]['ts'],
-                    pos['ts']
+                # Activity between this stop and next = travel mode
+                act_type = _dominant_activity(
+                    activities, current_cluster[-1]['ts'], pos['ts']
                 )
+                travel_mode = _get_travel_mode(act_type)
+                category = CATEGORY_LABELS.get(act_type, act_type.replace('_', ' ').title())
 
                 stops.append({
                     'arrived_at': current_cluster[0]['ts'],
@@ -219,10 +231,11 @@ class WtImportTimelineWizard(models.TransientModel):
                     'lat': avg_lat,
                     'lng': avg_lng,
                     'travel_mode': travel_mode,
+                    'category': category,
                 })
                 current_cluster = [pos]
 
-        # Handle last cluster
+        # Last cluster
         if current_cluster:
             avg_lat = sum(p['lat'] for p in current_cluster) / len(current_cluster)
             avg_lng = sum(p['lng'] for p in current_cluster) / len(current_cluster)
@@ -232,14 +245,13 @@ class WtImportTimelineWizard(models.TransientModel):
                 'lat': avg_lat,
                 'lng': avg_lng,
                 'travel_mode': 'unknown',
+                'category': '',
             })
 
-        # For single-position stops (arrived == departed), estimate duration
-        # using half the gap to the next stop
+        # Estimate duration for single-position stops
         for i, stop in enumerate(stops):
-            if stop['arrived_at'] == stop['departed_at']:
-                if i + 1 < len(stops):
-                    gap = (stops[i + 1]['arrived_at'] - stop['arrived_at']).total_seconds()
-                    stop['departed_at'] = stop['arrived_at'] + timedelta(seconds=gap / 2)
+            if stop['arrived_at'] == stop['departed_at'] and i + 1 < len(stops):
+                gap = (stops[i + 1]['arrived_at'] - stop['arrived_at']).total_seconds()
+                stop['departed_at'] = stop['arrived_at'] + timedelta(seconds=gap / 2)
 
         return stops