linting

backend/src/utils/cluster_manager.py

@@ -1,3 +1,4 @@
+"""Find clusters of interest to add more general areas of visit to the tour."""
 import logging
 from typing import Literal
 
@@ -38,11 +39,24 @@ class Cluster(BaseModel):
 
 
 class ClusterManager:
+    """
+    A manager responsible for clustering points of interest, such as shops or historic sites, 
+    to identify areas worth visiting. It uses the DBSCAN algorithm to detect clusters 
+    based on a set of points retrieved from OpenStreetMap (OSM).
 
+    Attributes:
+        logger (logging.Logger): Logger for capturing relevant events and errors.
+        valid (bool): Indicates whether clusters were successfully identified.
+        all_points (list): All points retrieved from OSM, representing locations of interest.
+        cluster_points (list): Points identified as part of a cluster.
+        cluster_labels (list): Labels corresponding to the clusters each point belongs to.
+        cluster_type (Literal['sightseeing', 'shopping']): Type of clustering, either for sightseeing 
+            landmarks or shopping areas.
+    """
     logger = logging.getLogger(__name__)
 
     # NOTE: all points are in (lat, lon) format
-    valid: bool             # Ensure the manager is valid (ie there are some clusters to be found) 
+    valid: bool             # Ensure the manager is valid (ie there are some clusters to be found)
     all_points: list
     cluster_points: list
     cluster_labels: list
@@ -65,8 +79,6 @@ class ClusterManager:
         Args: 
             bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
         """
-
-        # Initialize overpass and cache
         self.overpass = Overpass()
         CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
 
@@ -96,7 +108,7 @@ class ClusterManager:
 
         if len(result.elements()) == 0 :
             self.valid = False
-        
+
         else :
             points = []
             for elem in result.elements() :
@@ -126,8 +138,8 @@ class ClusterManager:
                 self.filter_clusters()      # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
                 self.valid = True
 
-            else : 
-                self.valid = False      
+            else :
+                self.valid = False
 
 
     def generate_clusters(self) -> list[Landmark]:
@@ -155,7 +167,7 @@ class ClusterManager:
 
             # Extract points belonging to the current cluster
             current_cluster = self.cluster_points[self.cluster_labels == label]
-            
+
             # Calculate the centroid as the mean of the points
             centroid = np.mean(current_cluster, axis=0)
 
@@ -205,7 +217,7 @@ class ClusterManager:
             selectors.append('"shop"="mall"')
             new_name = 'Shopping Area'
             t = 40
-        else : 
+        else :
             new_name = 'Neighborhood'
             t = 15
 
@@ -214,7 +226,7 @@ class ClusterManager:
         osm_id = 0
         osm_type = 'node'
 
-        for sel in selectors : 
+        for sel in selectors :
             query = overpassQueryBuilder(
                 bbox = bbox,
                 elementType = ['node', 'way', 'relation'],
@@ -233,11 +245,11 @@ class ClusterManager:
                 location = (elem.centerLat(), elem.centerLon())
 
                 # Skip if element has neither name or location
-                if elem.tag('name') is None : 
+                if elem.tag('name') is None :
                     continue
-                if location[0] is None : 
+                if location[0] is None :
                     location = (elem.lat(), elem.lon())
-                    if location[0] is None : 
+                    if location[0] is None :
                         continue
 
                 d = get_distance(cluster.centroid, location)
@@ -245,14 +257,14 @@ class ClusterManager:
                     min_dist = d
                     new_name = elem.tag('name')
                     osm_type = elem.type()      # Add type: 'way' or 'relation'
-                    osm_id = elem.id()          # Add OSM id 
+                    osm_id = elem.id()          # Add OSM id
 
                     # Add english name if it exists
                     try :
                         new_name_en = elem.tag('name:en')
-                    except:
-                        pass 
-        
+                    except Exception:
+                        pass
+
         return Landmark(
             name=new_name,
             type=self.cluster_type,
@@ -290,4 +302,3 @@ class ClusterManager:
         # update the cluster points and labels with the filtered data
         self.cluster_points = np.vstack(filtered_cluster_points)        # ValueError here
         self.cluster_labels = np.concatenate(filtered_cluster_labels)
-