massive numpy optimization and more tests

backend/src/utils/landmarks_manager.py

@@ -53,6 +53,8 @@ class LandmarkManager:
         self.overpass = Overpass()
         CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
 
+        self.logger.info('LandmakManager successfully initialized.')
+
 
     def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) -> tuple[list[Landmark], list[Landmark]]:
         """
@@ -71,7 +73,7 @@ class LandmarkManager:
         - A list of all existing landmarks.
         - A list of the most important landmarks based on the user's preferences.
         """
-
+        self.logger.debug('Starting to fetch landmarks...')
         max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
         reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
 
@@ -83,25 +85,32 @@ class LandmarkManager:
 
         # list for sightseeing
         if preferences.sightseeing.score != 0:
+            self.logger.debug('Fetching sightseeing landmarks...')
             score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
             all_landmarks.update(current_landmarks)
+            self.logger.debug('Fetching sightseeing clusters...')
 
             # special pipeline for historic neighborhoods
             neighborhood_manager = ClusterManager(bbox, 'sightseeing')
             historic_clusters = neighborhood_manager.generate_clusters()
             all_landmarks.update(historic_clusters)
+            self.logger.debug('Sightseeing clusters done')
 
         # list for nature
         if preferences.nature.score != 0:
+            self.logger.debug('Fetching nature landmarks...')
             score_function = lambda score: score * 10 * self.nature_coeff * preferences.nature.score / 5
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
             all_landmarks.update(current_landmarks)
 
+
         # list for shopping
         if preferences.shopping.score != 0:
+            self.logger.debug('Fetching shopping landmarks...')
             score_function = lambda score: score * 10 * preferences.shopping.score / 5
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
+            self.logger.debug('Fetching shopping clusters...')
 
             # set time for all shopping activites :
             for landmark in current_landmarks : landmark.duration = 30
@@ -111,18 +120,19 @@ class LandmarkManager:
             shopping_manager = ClusterManager(bbox, 'shopping')
             shopping_clusters = shopping_manager.generate_clusters()
             all_landmarks.update(shopping_clusters)
+            self.logger.debug('Shopping clusters done')
 
 
 
         landmarks_constrained = take_most_important(all_landmarks, self.N_important)
-        self.logger.info(f'Generated {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
+        self.logger.info(f'All landmarks generated : {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
 
         return all_landmarks, landmarks_constrained
 
 
-
+    """
     def count_elements_close_to(self, coordinates: tuple[float, float]) -> int:
-        """
+        
         Count the number of OpenStreetMap elements (nodes, ways, relations) within a specified radius of the given location.
 
         This function constructs a bounding box around the specified coordinates based on the radius. It then queries
@@ -134,7 +144,7 @@ class LandmarkManager:
         Returns:
             int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
                 are found or if an error occurs during the query.
-        """
+        
         
         lat = coordinates[0]
         lon = coordinates[1]
@@ -162,6 +172,7 @@ class LandmarkManager:
             return N_elem
         except:
             return 0
+    """
 
 
     # def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
@@ -211,7 +222,7 @@ class LandmarkManager:
         # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
         # we need to split the selectors into separate queries and merge the results
         for sel in dict_to_selector_list(amenity_selector):
-            self.logger.debug(f"Current selector: {sel}")
+            # self.logger.debug(f"Current selector: {sel}")
 
             element_types = ['way', 'relation']
 
@@ -230,7 +241,7 @@ class LandmarkManager:
                 includeCenter = True,
                 out = 'center'
                 )
-            self.logger.debug(f"Query: {query}")
+            # self.logger.debug(f"Query: {query}")
 
             try:
                 result = self.overpass.query(query)