try to give better results by preselecting spaces
@@ -69,12 +69,24 @@ class Locator:
scan_values = LocatorPoint.convert_scan(scan, self.stations, create=False)
station_ids = frozenset(scan_values.keys())
+ if not scan_values:
+ return None
+
+ # get visible spaces
spaces = tuple((pk, space, station_ids & space.stations_set)
for pk, space in self.spaces.items()
if pk not in restrictions.spaces)
- spaces = tuple((pk, space, station_ids) for pk, space, station_ids in spaces if station_ids)
+
+ # get relevant spaces
+ best_station_id = min(scan_values.items(), key=operator.itemgetter(1))[0]
+ spaces = tuple((pk, space, station_ids)
+ for pk, space, station_ids in spaces
+ if station_ids and best_station_id in station_ids)
+
if not spaces:
return None
+
+ # get good spaces
good_spaces = tuple((pk, space, station_ids) for pk, space, station_ids in spaces if len(station_ids) >= 3)
if not good_spaces:
for station_id in station_ids:
@@ -85,7 +97,7 @@ class Locator:
best_score = float('inf')
for pk, space, station_ids in good_spaces:
- point, score = space.get_best_point(scan_values, station_ids)
+ point, score = space.get_best_point(scan_values, station_ids, needed_station_id=best_station_id)
if score < best_score:
location = CustomLocation(router.spaces[pk].level, point.x, point.y,
permissions=permissions, icon='my_location')
@@ -125,11 +137,15 @@ class LocatorSpace:
for station_id, value in point.values.items():
self.levels[i][self.stations_lookup[station_id]] = value**3
- def get_best_point(self, scan_values, station_ids):
+ def get_best_point(self, scan_values, station_ids, needed_station_id=None):
stations = tuple(self.stations_lookup[station_id] for station_id in station_ids)
values = np.array(tuple(scan_values[station_id]**3 for station_id in station_ids), dtype=np.int64)
- scores = np.sum((self.levels[:, stations]-values)**2, axis=1) / len(stations)
- best_point = np.argmin(scores).ravel()[0]
+ acceptable_point_ids = tuple(
+ np.argwhere(self.levels[:, self.stations_lookup[needed_station_id]] > -90).ravel()
+ )
+ scores = np.sum((self.levels[acceptable_point_ids, stations]-values)**2, axis=1) / len(stations)
+
+ best_point = acceptable_point_ids[np.argmin(scores).ravel()[0]]
return self.points[best_point], scores[best_point]
