backend/src/main.py

@@ -63,7 +63,7 @@ def new_trip(preferences: Preferences, start: tuple[float, float], end: tuple[fl
     refined_tour = refiner.refine_optimization(landmarks, base_tour, preferences.max_time_minute, preferences.detour_tolerance_minute)
 
     linked_tour = LinkedLandmarks(refined_tour)
-    # upon creation of the trip, persistence of both the trip and its landmarks is ensured. Ca
+    # upon creation of the trip, persistence of both the trip and its landmarks is ensured
     trip = Trip.from_linked_landmarks(linked_tour, cache_client)
     return trip
 
@@ -84,4 +84,4 @@ def get_landmark(landmark_uuid: str) -> Landmark:
         landmark = cache_client.get(f"landmark_{landmark_uuid}")
         return landmark
     except KeyError:
-        raise HTTPException(status_code=404, detail="Landmark not found")
+        raise HTTPException(status_code=404, detail="Landmark not found")

backend/src/parameters/landmark_parameters.yaml

@@ -1,6 +1,6 @@
 city_bbox_side: 7500 #m
 radius_close_to: 50
-church_coeff: 0.75
+church_coeff: 0.5
 nature_coeff: 1.25
 overall_coeff: 10
 tag_exponent: 1.15

backend/src/structs/landmark.py

@@ -14,26 +14,22 @@ class Landmark(BaseModel) :
     osm_id : int
     attractiveness : int
     n_tags : int
-    image_url : Optional[str] = None                            # TODO future
+    image_url : Optional[str] = None
     website_url : Optional[str] = None
-    wikipedia_url : Optional[str] = None
     description : Optional[str] = None                          # TODO future
-    duration : Optional[int] = 0                                # TODO future
+    duration : Optional[int] = 0
     name_en : Optional[str] = None
 
     # Unique ID of a given landmark
-    uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
+    uuid: str = Field(default_factory=uuid4)
     
     # Additional properties depending on specific tour
     must_do : Optional[bool] = False
     must_avoid : Optional[bool] = False
     is_secondary : Optional[bool] = False                       # TODO future   
     
-    time_to_reach_next : Optional[int] = 0                      # TODO fix this in existing code
+    time_to_reach_next : Optional[int] = 0
-    next_uuid : Optional[str] = None                            # TODO implement this ASAP
+    next_uuid : Optional[str] = None
-
-    def __hash__(self) -> int:
-        return self.uuid.int
     
     def __str__(self) -> str:
         time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
@@ -42,3 +38,15 @@ class Landmark(BaseModel) :
         if self.type in ["start", "finish", "nature", "shopping"] : type_str += '\t '
         return f'Landmark{type_str}: [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}{is_secondary_str}]'
     
+    def distance(self, value: 'Landmark') -> float:
+        return (self.location[0] - value.location[0])**2 + (self.location[1] - value.location[1])**2
+
+    def __hash__(self) -> int:
+        return hash(self.name)
+
+    def __eq__(self, value: 'Landmark') -> bool:
+        # eq and hash must be consistent
+        # in particular, if two objects are equal, their hash must be equal
+        # uuid and osm_id are just shortcuts to avoid comparing all the properties
+        # if they are equal, we know that the name is also equal and in turn the hash is equal
+        return self.uuid == value.uuid or self.osm_id == value.osm_id or (self.name == value.name and self.distance(value) < 0.001)

backend/src/structs/trip.py

@@ -27,4 +27,4 @@ class Trip(BaseModel):
         # for landmark in landmarks:
         #     cache_client.set(f"landmark_{landmark.uuid}", landmark, expire=3600)
 
-        return trip
+        return trip

backend/src/utils/get_time_separation.py

@@ -16,10 +16,8 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     Args:
         p1 (Tuple[float, float]): Coordinates of the starting location.
         p2 (Tuple[float, float]): Coordinates of the destination.
-        detour (float): Detour factor affecting the distance.
-        speed (float): Walking speed in kilometers per hour.
 
-    Returns:
+        Returns:
         int: Time to travel from p1 to p2 in minutes.
     """
 

backend/src/utils/landmarks_manager.py

@@ -1,15 +1,11 @@
-import math as m
+import math
 import yaml
 import logging
 
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
-from pywikibot import ItemPage, Site
-from pywikibot import config
-config.put_throttle = 0
-config.maxlag = 0
 
-from structs.preferences import Preferences, Preference
+from structs.preferences import Preferences
 from structs.landmark import Landmark
 from .take_most_important import take_most_important
 import constants
@@ -46,7 +42,7 @@ class LandmarkManager:
             self.viewpoint_bonus = parameters['viewpoint_bonus']
             self.pay_bonus = parameters['pay_bonus']
             self.N_important = parameters['N_important']
-            
+
         with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
             self.walking_speed = parameters['average_walking_speed']
@@ -69,87 +65,42 @@ class LandmarkManager:
         preferences (Preferences): The user's preference settings that influence the landmark selection.
 
         Returns:
-            tuple[list[Landmark], list[Landmark]]:
+        tuple[list[Landmark], list[Landmark]]:
-                - A list of all existing landmarks.
+        - A list of all existing landmarks.
-                - A list of the most important landmarks based on the user's preferences.
+        - A list of the most important landmarks based on the user's preferences.
         """
 
         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)
 
-        L = []
+        # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
+        all_landmarks = set()
+
         bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
         # list for sightseeing
         if preferences.sightseeing.score != 0:
-            score_function = lambda score: int(score*10*preferences.sightseeing.score/5)   # self.count_elements_close_to(loc) +
+            score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
-            L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
-            L += L1
+            all_landmarks.update(current_landmarks)
 
         # list for nature
         if preferences.nature.score != 0:
-            score_function = lambda score: int(score*10*self.nature_coeff*preferences.nature.score/5)   # self.count_elements_close_to(loc) +
+            score_function = lambda score: score * 10 * self.nature_coeff * preferences.nature.score / 5
-            L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
-            L += L2
+            all_landmarks.update(current_landmarks)
 
         # list for shopping
         if preferences.shopping.score != 0:
-            score_function = lambda score: int(score*10*preferences.shopping.score/5)   # self.count_elements_close_to(loc) +
+            score_function = lambda score: score * 10 * preferences.shopping.score / 5
-            L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
-            L += L3
+            all_landmarks.update(current_landmarks)
 
 
-        L = self.remove_duplicates(L)
+        landmarks_constrained = take_most_important(all_landmarks, self.N_important)
-        # self.correct_score(L, preferences)
+        self.logger.info(f'Generated {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
 
-        L_constrained = take_most_important(L, self.N_important)
+        return all_landmarks, landmarks_constrained
-        self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
 
-        return L, L_constrained
-
-
-    def remove_duplicates(self, landmarks: list[Landmark]) -> list[Landmark]:
-        """
-        Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
-
-        Parameters:
-        landmarks (list[Landmark]): A list of Landmark objects.
-
-        Returns:
-        list[Landmark]: A list of unique Landmark objects based on their names.
-        """
-
-        L_clean = []
-        names = []
-
-        for landmark in landmarks:
-            if landmark.name in names: 
-                continue  
-            else:
-                names.append(landmark.name)
-                L_clean.append(landmark)
-        
-        return L_clean
-        
-
-    def correct_score(self, landmarks: list[Landmark], preferences: Preferences) -> None:
-        """
-        Adjust the attractiveness score of each landmark in the list based on user preferences.
-
-        This method updates the attractiveness of each landmark by scaling it according to the user's preference score.
-        The score adjustment is computed using a simple linear transformation based on the preference score.
-
-        Args:
-            landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
-            preferences (Preferences): The user's preference settings that influence the attractiveness score adjustment.
-        """
-
-        score_dict = {
-            preferences.sightseeing.type: preferences.sightseeing.score,
-            preferences.nature.type: preferences.nature.score,
-            preferences.shopping.type: preferences.shopping.score
-        }
-        for landmark in landmarks:
-            landmark.attractiveness = int(landmark.attractiveness * score_dict[landmark.type] / 5)        
 
 
     def count_elements_close_to(self, coordinates: tuple[float, float]) -> int:
@@ -172,7 +123,7 @@ class LandmarkManager:
 
         radius = self.radius_close_to
 
-        alpha = (180*radius) / (6371000*m.pi)
+        alpha = (180 * radius) / (6371000 * math.pi)
         bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
 
         # Build the query to find elements within the radius
@@ -216,7 +167,7 @@ class LandmarkManager:
 
         # Convert distance to degrees
         lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
-        lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat)))  # Adjust for longitude based on latitude
+        lon_diff = half_side_length_km / (111 * math.cos(math.radians(lat)))  # Adjust for longitude based on latitude
 
         # Calculate bbox
         min_lat = lat - lat_diff
@@ -265,22 +216,18 @@ class LandmarkManager:
                 result = self.overpass.query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
-                return
+                continue
-            
+
             for elem in result.elements():
 
-                name = elem.tag('name')                             # Add name
+                name = elem.tag('name')
-                location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+                location = (elem.centerLat(), elem.centerLon())
 
                 # TODO: exclude these from the get go
                 # skip if unprecise location
                 if name is None or location[0] is None:
                     continue
 
-                # skip if unused
-                # if 'disused:leisure' in elem.tags().keys():
-                #     continue
-                
                 # skip if part of another building
                 if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
                     continue
@@ -291,7 +238,6 @@ class LandmarkManager:
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 score = n_tags**self.tag_exponent   # Add score
                 website_url = None
-                wikpedia_url = None
                 image_url = None
                 name_en = None
 
@@ -299,22 +245,17 @@ class LandmarkManager:
                 skip = False
                 for tag in elem.tags().keys():
                     if "pay" in tag:
-                        score += self.pay_bonus             # discard payment options for tags
+                        # payment options are a good sign
+                        score += self.pay_bonus
 
                     if "disused" in tag:
-                        skip = True             # skip disused amenities
+                        # skip disused amenities
+                        skip = True
                         break
 
                     if "wiki" in tag:
-                        score += self.wikipedia_bonus             # wikipedia entries count more
+                        # wikipedia entries count more
-                        
+                        score += self.wikipedia_bonus
-                    # if tag == "wikidata":
-                    #     Q = elem.tag('wikidata')
-                    #     site = Site("wikidata", "wikidata")
-                    #     item = ItemPage(site, Q)
-                    #     item.get()
-                    #     n_languages = len(item.labels)
-                    #     n_tags += n_languages/10
 
                     if "viewpoint" in tag:
                         score += self.viewpoint_bonus
@@ -335,47 +276,43 @@ class LandmarkManager:
                         if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
                             skip = True
                             break
-                    
+
-                    # Get additional information
+                    if tag in ['website', 'contact:website']:
-                    # if tag == 'wikipedia' :
-                    #     wikpedia_url = elem.tag('wikipedia')
-                    if tag in ['website', 'contact:website'] :
                         website_url = elem.tag(tag)
-                    if tag == 'image' :
+                    if tag == 'image':
                         image_url = elem.tag('image')
-                    if tag =='name:en' :
+                    if tag =='name:en':
                         name_en = elem.tag('name:en')
 
                 if skip:
                     continue
 
                 score = score_function(score)
-                if "place_of_worship" in elem.tags().values() :
+                if "place_of_worship" in elem.tags().values():
-                    score = int(score*self.church_coeff)
+                    score = score * self.church_coeff
                     duration = 15
                 
-                elif "museum" in elem.tags().values() :
+                elif "museum" in elem.tags().values():
-                    score = int(score*self.church_coeff)
+                    score = score * self.church_coeff
                     duration = 60
                 
-                else : 
+                else:
                     duration = 5
 
-                # Generate the landmark and append it to the list
+                # finally create our own landmark object
                 landmark = Landmark(
-                    name=name,
+                    name = name,
-                    type=elem_type,
+                    type = elem_type,
-                    location=location,
+                    location = location,
-                    osm_type=osm_type,
+                    osm_type = osm_type,
-                    osm_id=osm_id,
+                    osm_id = osm_id,
-                    attractiveness=score,
+                    attractiveness = int(score),
-                    must_do=False,
+                    must_do = False,
-                    n_tags=int(n_tags),
+                    n_tags = int(n_tags),
-                    duration = duration, 
+                    duration = int(duration),
-                    name_en=name_en,
+                    name_en = name_en,
-                    image_url=image_url,
+                    image_url = image_url,
-                    # wikipedia_url=wikpedia_url,
+                    website_url = website_url
-                    website_url=website_url
                 )
                 return_list.append(landmark)
         

backend/src/utils/take_most_important.py

@@ -1,38 +1,16 @@
 from structs.landmark import Landmark
 
-def take_most_important(landmarks: list[Landmark], N_important) -> list[Landmark] :
+def take_most_important(landmarks: list[Landmark], n_important) -> list[Landmark]:
-    L = len(landmarks)
+    """
-    L_copy = []
+    Given a list of landmarks, return the n_important most important landmarks
-    L_clean = []
+    Parameters:
-    scores = [0]*len(landmarks)
+    landmarks: list[Landmark] - list of landmarks
-    names = []
+    n_important: int - number of most important landmarks to return
-    name_id = {}
+    Returns:
+    list[Landmark] - list of the n_important most important landmarks
+    """
 
-    for i, elem in enumerate(landmarks) :
+    # Sort landmarks by attractiveness (descending)
-        if elem.name not in names :
+    landmarks.sort(key=lambda x: x.attractiveness, reverse=True)
-            names.append(elem.name)
-            name_id[elem.name] = [i]
-            L_copy.append(elem)
-        else :
-            name_id[elem.name] += [i]
-            scores = []
-            for j in name_id[elem.name] :
-                scores.append(L[j].attractiveness)
-            best_id = max(range(len(scores)), key=scores.__getitem__)
-            t = name_id[elem.name][best_id]
-            if t == i :
-                for old in L_copy :
-                    if old.name == elem.name :
-                        old.attractiveness = L[t].attractiveness
-    
-    scores = [0]*len(L_copy)
-    for i, elem in enumerate(L_copy) :
-        scores[i] = elem.attractiveness
 
-    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-L):]
+    return landmarks[:n_important]
-
-    for i, elem in enumerate(L_copy) :
-        if i in res :
-            L_clean.append(elem)
-
-    return L_clean