new endpoint for toilets

backend/src/utils/get_time_separation.py

@@ -15,8 +15,8 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     Calculate the time in minutes to travel from one location to another.
 
     Args:
-        p1 (Tuple[float, float]): Coordinates of the starting location.
-        p2 (Tuple[float, float]): Coordinates of the destination.
+        p1 (tuple[float, float]): Coordinates of the starting location.
+        p2 (tuple[float, float]): Coordinates of the destination.
 
         Returns:
         int: Time to travel from p1 to p2 in minutes.
@@ -55,8 +55,8 @@ def get_distance(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     Calculate the time in minutes to travel from one location to another.
 
     Args:
-        p1 (Tuple[float, float]): Coordinates of the starting location.
-        p2 (Tuple[float, float]): Coordinates of the destination.
+        p1 (tuple[float, float]): Coordinates of the starting location.
+        p2 (tuple[float, float]): Coordinates of the destination.
 
         Returns:
         int: Time to travel from p1 to p2 in minutes.

backend/src/utils/landmarks_manager.py

@@ -79,6 +79,7 @@ class LandmarkManager:
 
         # Create a bbox using the around technique
         bbox = tuple((f"around:{reachable_bbox_side/2}", str(center_coordinates[0]), str(center_coordinates[1])))
+        
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             score_function = lambda score: score * 10 * preferences.sightseeing.score / 5

backend/src/utils/optimizer.py

@@ -44,7 +44,7 @@ class Optimizer:
             resx (list[float]): List of edge weights.
 
         Returns:
-            Tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+            tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
         """
         
         for i, elem in enumerate(resx):
@@ -79,7 +79,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
+            tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
         """
 
         l1 = [0]*L*L
@@ -107,7 +107,7 @@ class Optimizer:
             resx (list): List of edge weights.
 
         Returns:
-            Tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
+            tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
         """
 
         # first round the results to have only 0-1 values
@@ -180,7 +180,7 @@ class Optimizer:
             max_time (int): Maximum time of visit allowed.
 
         Returns:
-            Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
+            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
         """
         
         # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
@@ -212,7 +212,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         ones = [1]*L
@@ -239,7 +239,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         upper_ind = np.triu_indices(L,0,L)
@@ -270,7 +270,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
+            tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
         """
 
         l = [0]*L*L
@@ -293,7 +293,7 @@ class Optimizer:
             landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         L = len(landmarks)
@@ -319,7 +319,7 @@ class Optimizer:
             landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         L = len(landmarks)
@@ -346,7 +346,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
@@ -374,7 +374,7 @@ class Optimizer:
             L (int): Number of landmarks.
 
         Returns:
-            Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
         """
 
         A = [0]*L*L

backend/src/utils/refiner.py

@@ -2,7 +2,6 @@ import yaml, logging
 
 from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 from math import pi
-from typing import List
 
 from ..structs.landmark import Landmark
 from . import take_most_important, get_time_separation
@@ -135,7 +134,7 @@ class Refiner :
     
         return tour
     
-    def integrate_landmarks(self, sub_list: List[Landmark], main_list: List[Landmark]) :
+    def integrate_landmarks(self, sub_list: list[Landmark], main_list: list[Landmark]) :
         """
         Inserts 'sub_list' of Landmarks inside the 'main_list' by leaving the ends untouched.
         

backend/src/utils/toilets_manager.py

@@ -0,0 +1,78 @@
+import logging, yaml
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
+
+from ..structs.landmark import Toilets
+from ..constants import LANDMARK_PARAMETERS_PATH, OSM_CACHE_DIR
+
+
+# silence the overpass logger
+logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
+
+class ToiletsManager:
+
+    logger = logging.getLogger(__name__)
+
+    location: tuple[float, float]
+    radius: int    # radius in meters
+
+
+    def __init__(self, location: tuple[float, float], radius : int) -> None:
+
+        self.radius = radius
+        self.location = location
+        self.overpass = Overpass()
+        CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+
+
+    def generate_toilet_list(self) -> list[Toilets] :
+
+        
+        # Create a bbox using the around technique
+        bbox = tuple((f"around:{self.radius}", str(self.location[0]), str(self.location[1])))
+        toilets_list = []
+
+        query = overpassQueryBuilder(
+                bbox = bbox,
+                elementType = ['node', 'way', 'relation'],
+                # selector can in principle be a list already,
+                # but it generates the intersection of the queries
+                # we want the union
+                selector = ['"amenity"="toilets"'],
+                includeCenter = True,
+                out = 'center'
+                )
+        self.logger.debug(f"Query: {query}")
+
+        try:
+            result = self.overpass.query(query)
+        except Exception as e:
+            self.logger.error(f"Error fetching landmarks: {e}")
+            return None
+
+        for elem in result.elements():
+            location = (elem.centerLat(), elem.centerLon())
+
+            # handle unprecise and no-name locations
+            if location[0] is None:
+                    location = (elem.lat(), elem.lon())
+            else : 
+                continue
+            
+            toilets = Toilets(location=location)
+            
+            if 'wheelchair' in elem.tags().keys() and elem.tag('wheelchair') == 'yes':
+                toilets.wheelchair = True
+
+            if 'changing_table' in elem.tags().keys() and elem.tag('changing_table') == 'yes':
+                toilets.changing_table = True
+
+            if 'fee' in elem.tags().keys() and elem.tag('fee') == 'yes':
+                toilets.fee = True
+
+            if 'opening_hours' in elem.tags().keys() :
+                toilets.opening_hours = elem.tag('opening_hours')
+
+            toilets_list.append(toilets)
+
+        return toilets_list