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Major update: support Timeline.json semanticSegments with auto-detection
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tocmo0nlord
2026-03-14 02:11:21 +00:00
parent b57b2e75ab
commit 37b3540eb8
1 changed files with 113 additions and 84 deletions
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197
work_trace/wizards/wt_import_timeline_wizard.py
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@@ -2,33 +2,23 @@ from odoo import models, fields, api, _
from odoo.exceptions import UserError from odoo.exceptions import UserError
import json import json
import base64 import base64
import bisect import re
from datetime import datetime, timedelta from datetime import datetime, timedelta
from math import radians, sin, cos, sqrt, atan2 from math import radians, sin, cos, sqrt, atan2
from collections import Counter from collections import Counter
VEHICLE_ACTIVITIES = {'IN_VEHICLE', 'IN_ROAD_VEHICLE', 'IN_RAIL_VEHICLE', 'IN_TWO_WHEELER_VEHICLE', 'IN_PASSENGER_VEHICLE'} VEHICLE_ACTIVITIES = {'IN_VEHICLE', 'IN_ROAD_VEHICLE', 'IN_RAIL_VEHICLE', 'IN_TWO_WHEELER_VEHICLE'}
WALKING_ACTIVITIES = {'WALKING', 'ON_FOOT', 'RUNNING'} WALKING_ACTIVITIES = {'WALKING', 'ON_FOOT', 'RUNNING', 'ON_BICYCLE'}
CYCLING_ACTIVITIES = {'ON_BICYCLE'}
CATEGORY_LABELS = { SEMANTIC_TYPE_CATEGORY = {
'IN_PASSENGER_VEHICLE': 'In Vehicle', 'HOME': 'Home',
'IN_VEHICLE': 'In Vehicle', 'INFERRED_HOME': 'Home',
'IN_ROAD_VEHICLE': 'In Vehicle', 'WORK': 'Work',
'IN_RAIL_VEHICLE': 'Rail / Transit', 'INFERRED_WORK': 'Work',
'IN_TWO_WHEELER_VEHICLE': 'Motorcycle / Scooter', 'SEARCHED_ADDRESS': 'Searched Address',
'WALKING': 'Walking', 'UNKNOWN': '',
'ON_FOOT': 'Walking',
'RUNNING': 'Running',
'ON_BICYCLE': 'Cycling',
'STILL': 'Stationary',
'UNKNOWN': 'Unknown',
'EXITING_VEHICLE': 'In Vehicle',
'TILTING': 'Unknown',
} }
PROXIMITY_METERS = 200
def _haversine_miles(lat1, lon1, lat2, lon2): def _haversine_miles(lat1, lon1, lat2, lon2):
R = 3958.8 R = 3958.8
@@ -47,18 +37,25 @@ def _get_travel_mode(activity_type):
return 'driving' return 'driving'
if activity_type in WALKING_ACTIVITIES: if activity_type in WALKING_ACTIVITIES:
return 'walking' return 'walking'
if activity_type in CYCLING_ACTIVITIES:
return 'cycling'
return 'unknown' return 'unknown'
def _dominant_activity(activities, start_ts, end_ts): def _parse_latlng(latlng_str):
"""Get dominant activity type between two timestamps.""" """Parse coordinate string like '30.0381046 deg, -95.5899101 deg' handling encoding issues."""
window = [a for a in activities if start_ts <= a['ts'] <= end_ts] nums = re.findall(r'-?\d+\.\d+', latlng_str)
if not window: if len(nums) >= 2:
return 'UNKNOWN' return float(nums[0]), float(nums[1])
counts = Counter(a['type'] for a in window) return None, None
return counts.most_common(1)[0][0]
def _parse_ts(ts_str):
"""Parse ISO 8601 timestamp to naive datetime."""
if not ts_str:
return None
try:
return datetime.fromisoformat(ts_str).replace(tzinfo=None)
except Exception:
return None
class WtImportTimelineWizard(models.TransientModel): class WtImportTimelineWizard(models.TransientModel):
@@ -75,7 +72,7 @@ class WtImportTimelineWizard(models.TransientModel):
proximity_meters = fields.Integer( proximity_meters = fields.Integer(
string='Location Proximity (meters)', string='Location Proximity (meters)',
default=200, default=200,
help='GPS positions within this distance are grouped as the same location' help='For raw signal files only: GPS positions within this distance are grouped as one location'
) )
geocode = fields.Boolean( geocode = fields.Boolean(
string='Resolve Addresses via OpenStreetMap', string='Resolve Addresses via OpenStreetMap',
@@ -90,34 +87,45 @@ class WtImportTimelineWizard(models.TransientModel):
except Exception as e: except Exception as e:
raise UserError(_('Invalid JSON file: %s') % str(e)) raise UserError(_('Invalid JSON file: %s') % str(e))
stops = self._parse_timeline(data, self.proximity_meters) # Auto-detect format
if 'semanticSegments' in data:
stops = self._parse_semantic_timeline(data)
elif 'timelineEdits' in data:
stops = self._parse_raw_timeline(data, self.proximity_meters)
else:
raise UserError(_('Unrecognized format. Expected Timeline.json (semanticSegments) or Timeline Edits.json (timelineEdits).'))
if not stops: if not stops:
raise UserError(_('No location stops found in the uploaded file.')) raise UserError(_('No location stops found in the uploaded file.'))
# Filter by minimum stop duration # Filter by minimum stop duration
min_secs = self.min_stop_minutes * 60 min_secs = self.min_stop_minutes * 60
stops = [s for s in stops stops = [s for s in stops
if (s['departed_at'] - s['arrived_at']).total_seconds() >= min_secs] if s.get('arrived_at') and s.get('departed_at')
and (s['departed_at'] - s['arrived_at']).total_seconds() >= min_secs]
if not stops: if not stops:
raise UserError(_('No stops found matching the minimum duration filter.')) raise UserError(_('No stops found matching the minimum duration filter.'))
stops.sort(key=lambda s: s['arrived_at'])
# Compute distances and travel times between consecutive stops # Compute distances and travel times between consecutive stops
for i, stop in enumerate(stops): for i, stop in enumerate(stops):
if i > 0: if i > 0:
prev = stops[i - 1] prev = stops[i - 1]
stop['distance_from_previous'] = _haversine_miles( if prev.get('lat') and stop.get('lat'):
prev['lat'], prev['lng'], stop['lat'], stop['lng'] stop['distance_from_previous'] = _haversine_miles(
) prev['lat'], prev['lng'], stop['lat'], stop['lng']
)
else:
stop['distance_from_previous'] = 0.0
travel_delta = stop['arrived_at'] - prev['departed_at'] travel_delta = stop['arrived_at'] - prev['departed_at']
stop['travel_time_from_previous'] = max( stop['travel_time_from_previous'] = max(travel_delta.total_seconds() / 3600, 0.0)
travel_delta.total_seconds() / 3600, 0.0
)
else: else:
stop['distance_from_previous'] = 0.0 stop['distance_from_previous'] = 0.0
stop['travel_time_from_previous'] = 0.0 stop['travel_time_from_previous'] = 0.0
# Skip duplicates # Skip existing records
LocationLog = self.env['wt.location.log'] LocationLog = self.env['wt.location.log']
existing = set( existing = set(
r.arrived_at.strftime('%Y-%m-%d %H:%M:%S') r.arrived_at.strftime('%Y-%m-%d %H:%M:%S')
@@ -128,24 +136,22 @@ class WtImportTimelineWizard(models.TransientModel):
created_ids = [] created_ids = []
skipped = 0 skipped = 0
for stop in stops: for stop in stops:
arrived = stop['arrived_at'].replace(tzinfo=None) arrived_str = stop['arrived_at'].strftime('%Y-%m-%d %H:%M:%S')
departed = stop['departed_at'].replace(tzinfo=None)
arrived_str = arrived.strftime('%Y-%m-%d %H:%M:%S')
if arrived_str in existing: if arrived_str in existing:
skipped += 1 skipped += 1
continue continue
log = LocationLog.create({ log = LocationLog.create({
'date': arrived.date(), 'date': stop['arrived_at'].date(),
'arrived_at': arrived, 'arrived_at': stop['arrived_at'],
'departed_at': departed, 'departed_at': stop['departed_at'],
'latitude': stop['lat'], 'latitude': stop.get('lat') or 0.0,
'longitude': stop['lng'], 'longitude': stop.get('lng') or 0.0,
'travel_mode': stop.get('travel_mode', 'unknown'), 'place_name': stop.get('place_name', ''),
'category': stop.get('category', ''), 'category': stop.get('category', ''),
'distance_from_previous': stop['distance_from_previous'], 'travel_mode': stop.get('travel_mode', 'unknown'),
'travel_time_from_previous': stop['travel_time_from_previous'], 'distance_from_previous': stop.get('distance_from_previous', 0.0),
'travel_time_from_previous': stop.get('travel_time_from_previous', 0.0),
'source': 'google_timeline', 'source': 'google_timeline',
}) })
created_ids.append(log.id) created_ids.append(log.id)
@@ -155,7 +161,7 @@ class WtImportTimelineWizard(models.TransientModel):
raise UserError(_('All %d stops already exist. Nothing new to import.') % skipped) raise UserError(_('All %d stops already exist. Nothing new to import.') % skipped)
created = LocationLog.browse(created_ids) created = LocationLog.browse(created_ids)
if self.geocode: if self.geocode and created:
created.action_geocode() created.action_geocode()
return { return {
@@ -167,18 +173,51 @@ class WtImportTimelineWizard(models.TransientModel):
'target': 'current', 'target': 'current',
} }
def _parse_timeline(self, data, proximity_meters=200): def _parse_semantic_timeline(self, data):
""" """
Parse Google Timeline Edits JSON into location stops. Parse Timeline.json semanticSegments format.
Positions represent stationary moments — cluster by proximity. Only 'visit' segments are location stops.
Activity records between clusters give travel category/mode. 'timelinePath' segments are travel (ignored — distance calculated from stop coords).
""" """
stops = []
for seg in data.get('semanticSegments', []):
visit = seg.get('visit')
if not visit:
continue
start_ts = _parse_ts(seg.get('startTime'))
end_ts = _parse_ts(seg.get('endTime'))
if not start_ts or not end_ts:
continue
candidate = visit.get('topCandidate', {})
semantic_type = candidate.get('semanticType', '')
latlng_str = candidate.get('placeLocation', {}).get('latLng', '')
lat, lng = _parse_latlng(latlng_str) if latlng_str else (None, None)
category = SEMANTIC_TYPE_CATEGORY.get(semantic_type, '')
if not category and semantic_type:
category = semantic_type.replace('_', ' ').title()
stops.append({
'arrived_at': start_ts,
'departed_at': end_ts,
'lat': lat,
'lng': lng,
'place_name': '',
'category': category,
'travel_mode': 'unknown',
})
return stops
def _parse_raw_timeline(self, data, proximity_meters=200):
"""Parse Timeline Edits.json raw signal format using proximity clustering."""
positions = [] positions = []
activities = [] activities = []
for entry in data.get('timelineEdits', []): for entry in data.get('timelineEdits', []):
raw = entry.get('rawSignal', {}).get('signal', {}) raw = entry.get('rawSignal', {}).get('signal', {})
if 'position' in raw: if 'position' in raw:
pos = raw['position'] pos = raw['position']
point = pos.get('point', {}) point = pos.get('point', {})
@@ -188,7 +227,6 @@ class WtImportTimelineWizard(models.TransientModel):
if ts_str and lat and lng: if ts_str and lat and lng:
ts = datetime.fromisoformat(ts_str.replace('Z', '+00:00')) ts = datetime.fromisoformat(ts_str.replace('Z', '+00:00'))
positions.append({'ts': ts, 'lat': lat, 'lng': lng}) positions.append({'ts': ts, 'lat': lat, 'lng': lng})
elif 'activityRecord' in raw: elif 'activityRecord' in raw:
ar = raw['activityRecord'] ar = raw['activityRecord']
ts_str = ar.get('timestamp', '') ts_str = ar.get('timestamp', '')
@@ -204,51 +242,42 @@ class WtImportTimelineWizard(models.TransientModel):
positions.sort(key=lambda x: x['ts']) positions.sort(key=lambda x: x['ts'])
activities.sort(key=lambda x: x['ts']) activities.sort(key=lambda x: x['ts'])
# Cluster consecutive positions within proximity_meters def dominant_mode(start_ts, end_ts):
window = [a for a in activities if start_ts <= a['ts'] <= end_ts]
if not window:
return 'unknown'
counts = Counter(a['type'] for a in window)
return _get_travel_mode(counts.most_common(1)[0][0])
stops = [] stops = []
current_cluster = [positions[0]] current_cluster = [positions[0]]
for pos in positions[1:]: for pos in positions[1:]:
prev = current_cluster[-1] prev = current_cluster[-1]
dist = _distance_meters(prev['lat'], prev['lng'], pos['lat'], pos['lng']) if _distance_meters(prev['lat'], prev['lng'], pos['lat'], pos['lng']) <= proximity_meters:
if dist <= proximity_meters:
current_cluster.append(pos) current_cluster.append(pos)
else: else:
avg_lat = sum(p['lat'] for p in current_cluster) / len(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) avg_lng = sum(p['lng'] for p in current_cluster) / len(current_cluster)
# 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({ stops.append({
'arrived_at': current_cluster[0]['ts'], 'arrived_at': current_cluster[0]['ts'].replace(tzinfo=None),
'departed_at': current_cluster[-1]['ts'], 'departed_at': current_cluster[-1]['ts'].replace(tzinfo=None),
'lat': avg_lat, 'lat': avg_lat, 'lng': avg_lng,
'lng': avg_lng, 'travel_mode': dominant_mode(current_cluster[-1]['ts'], pos['ts']),
'travel_mode': travel_mode, 'category': '', 'place_name': '',
'category': category,
}) })
current_cluster = [pos] current_cluster = [pos]
# Last cluster
if current_cluster: if current_cluster:
avg_lat = sum(p['lat'] for p in current_cluster) / len(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) avg_lng = sum(p['lng'] for p in current_cluster) / len(current_cluster)
stops.append({ stops.append({
'arrived_at': current_cluster[0]['ts'], 'arrived_at': current_cluster[0]['ts'].replace(tzinfo=None),
'departed_at': current_cluster[-1]['ts'], 'departed_at': current_cluster[-1]['ts'].replace(tzinfo=None),
'lat': avg_lat, 'lat': avg_lat, 'lng': avg_lng,
'lng': avg_lng, 'travel_mode': 'unknown', 'category': '', 'place_name': '',
'travel_mode': 'unknown',
'category': '',
}) })
# Estimate duration for single-position stops
for i, stop in enumerate(stops): for i, stop in enumerate(stops):
if stop['arrived_at'] == stop['departed_at'] and i + 1 < len(stops): if stop['arrived_at'] == stop['departed_at'] and i + 1 < len(stops):
gap = (stops[i + 1]['arrived_at'] - stop['arrived_at']).total_seconds() gap = (stops[i + 1]['arrived_at'] - stop['arrived_at']).total_seconds()