linting

backend/src/utils/landmarks_manager.py

@@ -1,5 +1,6 @@
 """Module used to import data from OSM and arrange them in categories."""
-import math, yaml, logging
+import logging
+import yaml
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
@@ -15,14 +16,17 @@ logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
 
 
 class LandmarkManager:
-
+    """
+    Use this to manage landmarks.
+    Uses the overpass api to fetch landmarks and classify them.
+    """
     logger = logging.getLogger(__name__)
 
     radius_close_to: int    # radius in meters
     church_coeff: float     # coeff to adjsut score of churches
     nature_coeff: float       # coeff to adjust score of parks
     overall_coeff: float        # coeff to adjust weight of tags
-    N_important: int        # number of important landmarks to consider
+    n_important: int        # number of important landmarks to consider
 
 
     def __init__(self) -> None:
@@ -43,7 +47,7 @@ class LandmarkManager:
             self.wikipedia_bonus = parameters['wikipedia_bonus']
             self.viewpoint_bonus = parameters['viewpoint_bonus']
             self.pay_bonus = parameters['pay_bonus']
-            self.N_important = parameters['N_important']
+            self.n_important = parameters['N_important']
 
         with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
@@ -113,7 +117,8 @@ class LandmarkManager:
             self.logger.debug('Fetching shopping clusters...')
 
             # set time for all shopping activites :
-            for landmark in current_landmarks : landmark.duration = 30
+            for landmark in current_landmarks :
+                landmark.duration = 30
             all_landmarks.update(current_landmarks)
 
             # special pipeline for shopping malls
@@ -124,77 +129,12 @@ class LandmarkManager:
 
 
 
-        landmarks_constrained = take_most_important(all_landmarks, self.N_important)
+        landmarks_constrained = take_most_important(all_landmarks, self.n_important)
         # 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
-        OpenStreetMap data to count the number of elements within that bounding box.
-
-        Args:
-            coordinates (tuple[float, float]): The latitude and longitude of the location to search around.
-
-        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]
-
-        radius = self.radius_close_to
-
-        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
-        radius_query = overpassQueryBuilder(
-            bbox=[bbox['latLower'],
-            bbox['lonLower'],
-            bbox['latHigher'],
-            bbox['lonHigher']],
-            elementType=['node', 'way', 'relation']
-        )
-
-        try:
-            radius_result = self.overpass.query(radius_query)
-            N_elem = radius_result.countWays() + radius_result.countRelations()
-            self.logger.debug(f"There are {N_elem} ways/relations within 50m")
-            if N_elem is None:
-                return 0
-            return N_elem
-        except:
-            return 0
-    """
-
-
-    # def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
-    #     """
-    #     Create a bounding box around the given coordinates.
-
-    #     Args:
-    #         coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
-    #         reachable_bbox_side (int): The side length of the bounding box in meters.
-
-    #     Returns:
-    #         tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
-    #                                             defining the bounding box.
-    #     """
-
-    #     # Half the side length in m (since it's a square bbox)
-    #     half_side_length_m = reachable_bbox_side / 2
-
-    #     return tuple((f"around:{half_side_length_m}", str(coordinates[0]), str(coordinates[1])))
-
-
-
     def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
         """
         Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
@@ -241,7 +181,7 @@ class LandmarkManager:
                 includeCenter = True,
                 out = 'center'
                 )
-            # self.logger.debug(f"Query: {query}")
+            self.logger.debug(f"Query: {query}")
 
             try:
                 result = self.overpass.query(query)
@@ -274,7 +214,7 @@ class LandmarkManager:
                 n_tags = len(elem.tags().keys())        # Add number of tags
                 score = n_tags**self.tag_exponent       # Add score
                 duration = 5                            # Set base duration to 5 minutes
-                skip = False                            # Set skipping parameter to false
+                # skip = False                            # Set skipping parameter to false
                 tag_values = set(elem.tags().values())  # Store tag values
 
 
@@ -369,10 +309,10 @@ def dict_to_selector_list(d: dict) -> list:
     """
     return_list = []
     for key, value in d.items():
-        if type(value) == list:
+        if isinstance(value, list):
             val = '|'.join(value)
             return_list.append(f'{key}~"^({val})$"')
-        elif type(value) == str and len(value) == 0:
+        elif isinstance(value, str) and len(value) == 0:
             return_list.append(f'{key}')
         else:
             return_list.append(f'{key}={value}')