ready for testing

backend/src/constants.py

@@ -2,6 +2,7 @@
 
 import os
 from pathlib import Path
+from typing import List, Literal
 
 
 LOCATION_PREFIX = Path('src')
@@ -14,6 +15,7 @@ OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
 cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
 OSM_CACHE_DIR = Path(cache_dir_string)
 
+OSM_TYPES = List[Literal['way', 'node', 'relation']]
 
 MEMCACHED_HOST_PATH = os.getenv('MEMCACHED_HOST_PATH', None)
 if MEMCACHED_HOST_PATH == "none":

backend/src/main.py

@@ -3,7 +3,7 @@
 import logging
 import time
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, HTTPException, Query
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Query
 
 from .logging_config import configure_logging
 from .structs.landmark import Landmark, Toilets
@@ -14,8 +14,10 @@ from .utils.landmarks_manager import LandmarkManager
 from .utils.toilets_manager import ToiletsManager
 from .optimization.optimizer import Optimizer
 from .optimization.refiner import Refiner
+from .overpass.overpass import fill_cache
 from .cache import client as cache_client
 
+
 logger = logging.getLogger(__name__)
 
 manager = LandmarkManager()
@@ -35,7 +37,6 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 
 
-
 @app.post("/trip/new")
 def new_trip(preferences: Preferences,
              start: tuple[float, float],
@@ -127,6 +128,9 @@ def new_trip(preferences: Preferences,
     # upon creation of the trip, persistence of both the trip and its landmarks is ensured.
     trip = Trip.from_linked_landmarks(linked_tour, cache_client)
     logger.info(f'Generated a trip of {trip.total_time} minutes with {len(refined_tour)} landmarks in {round(t_generate_landmarks + t_first_stage + t_second_stage,3)} seconds.')
+    
+    background_tasks = BackgroundTasks(fill_cache())
+
     return trip
 
 

backend/src/overpass/caching_strategy.py

@@ -1,9 +1,9 @@
-"""Module defining the caching strategy for overpass requests."""
 import os
 import xml.etree.ElementTree as ET
 import hashlib
+import time
 
-from ..constants import OSM_CACHE_DIR
+from ..constants import OSM_CACHE_DIR, OSM_TYPES
 
 
 def get_cache_key(query: str) -> str:
@@ -13,14 +13,9 @@ def get_cache_key(query: str) -> str:
     """
     return hashlib.md5(query.encode('utf-8')).hexdigest()
 
-
 class CachingStrategyBase:
     """
     Base class for implementing caching strategies.
-
-    This class defines the structure for a caching strategy with basic methods
-    that must be implemented by subclasses. Subclasses should define how to
-    retrieve, store, and close the cache.
     """
     def get(self, key):
         """Retrieve the cached data associated with the provided key."""
@@ -30,6 +25,15 @@ class CachingStrategyBase:
         """Store data in the cache with the specified key."""
         raise NotImplementedError('Subclass should implement set')
 
+    def set_hollow(self, key, cell: tuple, osm_types: OSM_TYPES,
+                    selector: str, conditions=[], out='center'):
+        """Create a hollow (empty) cache entry with a specific key."""
+        raise NotImplementedError('Subclass should implement set_hollow')
+
+    def fill_hollow(self, key, value):
+        """Fill in the cache for an existing hollow entry."""
+        raise NotImplementedError('Subclass should implement fill_hollow')
+
     def close(self):
         """Clean up or close any resources used by the caching strategy."""
 
@@ -37,22 +41,10 @@ class CachingStrategyBase:
 class XMLCache(CachingStrategyBase):
     """
     A caching strategy that stores and retrieves data in XML format.
-
-    This class provides methods to cache data as XML files in a specified directory.
-    The directory is automatically suffixed with '_XML' to distinguish it from other
-    caching strategies. The data is stored and retrieved using XML serialization.
-
-    Args:
-        cache_dir (str): The base directory where XML cache files will be stored.
-                         Defaults to 'OSM_CACHE_DIR' with a '_XML' suffix.
-
-    Methods:
-        get(key): Retrieve cached data from a XML file associated with the given key.
-        set(key, value): Store data in a XML file with the specified key.
     """
     def __init__(self, cache_dir=OSM_CACHE_DIR):
         # Add the class name as a suffix to the directory
-        self._cache_dir = f'{cache_dir}_XML'
+        self._cache_dir = f'{cache_dir}'
         if not os.path.exists(self._cache_dir):
             os.makedirs(self._cache_dir)
 
@@ -68,7 +60,6 @@ class XMLCache(CachingStrategyBase):
                 tree = ET.parse(filename)
                 return tree.getroot()  # Return the root element of the parsed XML
             except ET.ParseError:
-                # print(f"Error parsing cached XML file: {filename}")
                 return None
         return None
 
@@ -77,25 +68,41 @@ class XMLCache(CachingStrategyBase):
         filename = self._filename(key)
         tree = ET.ElementTree(value)  # value is expected to be an ElementTree root element
         try:
-            # Write the XML data to a file
             with open(filename, 'wb') as file:
                 tree.write(file, encoding='utf-8', xml_declaration=True)
         except IOError as e:
             raise IOError(f"Error writing to cache file: {filename} - {e}") from e
 
+    def set_hollow(self, key, cell: tuple, osm_types: OSM_TYPES,
+                    selector: str, conditions=[], out='center'):
+        """Create an empty placeholder cache entry for a future fill."""
+        hollow_key = f'hollow_{key}'
+        filename = self._filename(hollow_key)
+    
+        # Create the root element <cache>
+        root = ET.Element("params")
+        # Add sub-elements with provided values
+        ET.SubElement(root, "key").text = key
+        ET.SubElement(root, "cell").text = f"({cell[0]}, {cell[1]})"
+        ET.SubElement(root, "osm_types").text = ','.join(osm_types)
+        ET.SubElement(root, "selector").text = selector
+        ET.SubElement(root, "conditions").text = ','.join(conditions) if conditions else "none"
+        ET.SubElement(root, "out").text = out
+
+        # Create an ElementTree object from the root
+        tree = ET.ElementTree(root)
+
+        # Write the XML to the file
+        with open(filename, 'wb') as file:
+            tree.write(file, encoding='utf-8', xml_declaration=True)
+
+    def close(self):
+        """Cleanup method, if needed."""
+        pass
 
 class CachingStrategy:
     """
     A class to manage different caching strategies.
-
-    This class provides an interface to switch between different caching strategies 
-    (e.g., XMLCache, JSONCache) dynamically. It allows caching data in different formats, 
-    depending on the strategy being used. By default, it uses the XMLCache strategy.
-
-    Attributes:
-    __strategy (CachingStrategyBase): The currently active caching strategy.
-    __strategies (dict): A mapping between strategy names (as strings) and their corresponding
-                         classes, allowing dynamic selection of caching strategies.
     """
     __strategy = XMLCache()  # Default caching strategy
     __strategies = {
@@ -104,37 +111,31 @@ class CachingStrategy:
 
     @classmethod
     def use(cls, strategy_name='XML', **kwargs):
-        """
-        Set the caching strategy based on the strategy_name provided.
-
-        Args:
-            strategy_name (str): The name of the caching strategy (e.g., 'XML').
-            **kwargs: Additional keyword arguments to pass when initializing the strategy.
-        """
-        # If a previous strategy exists, close it
         if cls.__strategy:
             cls.__strategy.close()
 
-        # Retrieve the strategy class based on the strategy name
         strategy_class = cls.__strategies.get(strategy_name)
-
         if not strategy_class:
             raise ValueError(f"Unknown caching strategy: {strategy_name}")
 
-        # Instantiate the new strategy with the provided arguments
         cls.__strategy = strategy_class(**kwargs)
         return cls.__strategy
 
     @classmethod
     def get(cls, key):
-        """Get data from the current strategy's cache."""
-        if not cls.__strategy:
-            raise RuntimeError("Caching strategy has not been set.")
         return cls.__strategy.get(key)
 
     @classmethod
     def set(cls, key, value):
-        """Set data in the current strategy's cache."""
-        if not cls.__strategy:
-            raise RuntimeError("Caching strategy has not been set.")
         cls.__strategy.set(key, value)
+
+    @classmethod
+    def set_hollow(cls, key, cell: tuple, osm_types: OSM_TYPES,
+                    selector: str, conditions=[], out='center'):
+        """Create a hollow cache entry."""
+        cls.__strategy.set_hollow(key, cell, osm_types, selector, conditions, out)
+
+    @classmethod
+    def fill_hollow(cls, key, value):
+        """Fill in the hollow cache entry with actual data."""
+        cls.__strategy.fill_hollow(key, value)

backend/src/overpass/overpass.py

@@ -1,14 +1,15 @@
 """Module allowing connexion to overpass api and fectch data from OSM."""
-from typing import Literal, List
+import os
 import urllib
+import math
 import logging
 import xml.etree.ElementTree as ET
 
 from .caching_strategy import get_cache_key, CachingStrategy
-from ..constants import OSM_CACHE_DIR
+from ..constants import OSM_CACHE_DIR, OSM_TYPES
 
-logger = logging.getLogger('Overpass')
+
-osm_types = List[Literal['way', 'node', 'relation']]
+RESOLUTION = 0.05
 
 
 class Overpass :
@@ -16,6 +17,9 @@ class Overpass :
     Overpass class to manage the query building and sending to overpass api.
     The caching strategy is a part of this class and initialized upon creation of the Overpass object.
     """
+    logger = logging.getLogger(__name__)
+
+
     def __init__(self, caching_strategy: str = 'XML', cache_dir: str = OSM_CACHE_DIR) :
         """
         Initialize the Overpass instance with the url, headers and caching strategy.
@@ -26,16 +30,14 @@ class Overpass :
 
 
     @classmethod
-    def build_query(self, area: tuple, osm_types: osm_types,
+    def build_query(self, bbox: tuple, osm_types: OSM_TYPES,
                     selector: str, conditions=[], out='center') -> str:
         """
         Constructs a query string for the Overpass API to retrieve OpenStreetMap (OSM) data.
 
         Args:
-            area (tuple): A tuple representing the geographical search area, typically in the format 
+            bbox (tuple): A tuple representing the geographical search area, typically in the format 
-                        (radius, latitude, longitude). The first element is a string like "around:2000" 
+                        (lat_min, lon_min, lat_max, lon_max).
-                        specifying the search radius, and the second and third elements represent 
-                        the latitude and longitude as floats or strings.
             osm_types (list[str]): A list of OSM element types to search for. Must be one or more of 
                                     'Way', 'Node', or 'Relation'.
             selector (str): The key or tag to filter the OSM elements (e.g., 'amenity', 'highway', etc.).
@@ -52,7 +54,6 @@ class Overpass :
         Notes:
             - If no conditions are provided, the query will just use the `selector` to filter the OSM 
             elements without additional constraints.
-            - The search area must always formatted as "(radius, lat, lon)".
         """
         if not isinstance(conditions, list) :
             conditions = [conditions]
@@ -61,15 +62,8 @@ class Overpass :
 
         query = '('
 
-        # Round the radius to nearest 50 and coordinates to generate less queries
+        # convert the bbox to string.
-        if area[0] > 500 :
+        bbox_str = f"({','.join(map(str, bbox))})"
-            search_radius = round(area[0] / 50) * 50
-            loc = tuple((round(area[1], 2), round(area[2], 2)))
-        else :
-            search_radius = round(area[0] / 25) * 25
-            loc = tuple((round(area[1], 3), round(area[2], 3)))
-
-        search_area = f"(around:{search_radius}, {str(loc[0])}, {str(loc[1])})"
 
         if conditions :
             conditions = '(if: ' + ' && '.join(conditions) + ')'
@@ -77,14 +71,15 @@ class Overpass :
             conditions = ''
 
         for elem in osm_types :
-            query += elem + '[' + selector + ']' + conditions + search_area + ';'
+            query += elem + '[' + selector + ']' + conditions + bbox_str + ';'
 
         query += ');' + f'out {out};'
 
         return query
 
 
-    def send_query(self, query: str) -> ET:
+    def send_query(self, bbox: tuple, osm_types: OSM_TYPES,
+                    selector: str, conditions=[], out='center') -> ET:
         """
         Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
 
@@ -94,18 +89,42 @@ class Overpass :
         Returns:
             dict: The parsed JSON response from the Overpass API, or None if the request fails.
         """
+        # Determine which grid cells overlap with this bounding box.
+        overlapping_cells = self.get_overlapping_cells(bbox)
 
-        # Generate a cache key for the current query
+        # Check the cache for any data that overlaps with these cells
-        cache_key = get_cache_key(query)
+        cell_key_dict = {}
+        for cell in overlapping_cells :
+            for elem in osm_types :
+                key_str = f"{elem}[{selector}]{conditions}({','.join(map(str, cell))})"
             
-        # Try to fetch the result from the cache
+            cell_key_dict[cell] = get_cache_key(key_str)
-        cached_response = self.caching_strategy.get(cache_key)
+
-        if cached_response is not None :
+        cached_responses = []
-            logger.debug("Cache hit.")
+        hollow_cache_keys = []
-            return cached_response
+
+        # Retrieve the cached data and mark the missing entries as hollow
+        for cell, key in cell_key_dict.items():
+            cached_data = self.caching_strategy.get(key)
+            if cached_data is not None :
+                cached_responses.append(cached_data)
+            else:
+                # Cache miss: Mark the cache key as hollow
+                self.caching_strategy.set_hollow(key, cell, osm_types, selector, conditions, out)
+                hollow_cache_keys.append(key)
+
+        # If there is no missing data, return the cached responses
+        if not hollow_cache_keys :
+            self.logger.debug(f'Cache hit.')
+            return self.combine_cached_data(cached_responses)
+        
+        # TODO If there is SOME missing data : hybrid stuff with partial cache
+        
+        # Build the query string in case of needed overpass query
+        query_str = self.build_query(bbox, osm_types, selector, conditions, out)
 
         # Prepare the data to be sent as POST request, encoded as bytes
-        data = urllib.parse.urlencode({'data': query}).encode('utf-8')
+        data = urllib.parse.urlencode({'data': query_str}).encode('utf-8')
 
         try:
             # Create a Request object with the specified URL, data, and headers
@@ -117,9 +136,7 @@ class Overpass :
                 response_data = response.read().decode('utf-8')
                 root = ET.fromstring(response_data)
 
-                # Cache the response data as an ElementTree root
+                self.logger.debug(f'Cache miss. Fetching data through Overpass\nQuery = {query_str}')
-                self.caching_strategy.set(cache_key, root)
-                logger.debug("Response data added to cache.")
 
                 return root
 
@@ -127,7 +144,108 @@ class Overpass :
             raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
 
 
-def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
+    def build_query_from_hollow(self, xml_string):
+        """Extract variables from an XML string."""
+        
+        # Parse the XML string into an ElementTree object
+        root = ET.fromstring(xml_string)
+        
+        # Extract values from the XML tree
+        key = root.find('key').text
+        cell = tuple(map(float, root.find('cell').text.strip('()').split(',')))
+        bbox = self.get_bbox_from_grid_cell(cell[0], cell[1])
+        osm_types = root.find('osm_types').text.split(',')
+        selector = root.find('selector').text
+        conditions = root.find('conditions').text.split(',') if root.find('conditions').text != "none" else []
+        out = root.find('out').text
+
+        query_str = self.build_query(bbox, osm_types, selector, conditions, out)
+        
+        return query_str, key
+
+    def get_grid_cell(self, lat: float, lon: float):
+        """
+        Returns the grid cell coordinates for a given latitude and longitude.
+        Each grid cell is 0.05°lat x 0.05°lon resolution in size.
+        """
+        lat_index = math.floor(lat / RESOLUTION)
+        lon_index = math.floor(lon / RESOLUTION)
+        return (lat_index, lon_index)
+
+
+    def get_bbox_from_grid_cell(self, lat_index: int, lon_index: int):
+        """
+        Returns the bounding box for a given grid cell index.
+        Each grid cell is resolution x resolution in size.
+
+        The bounding box is returned as (min_lat, min_lon, max_lat, max_lon).
+        """
+        # Calculate the southwest (min_lat, min_lon) corner of the bounding box
+        min_lat = round(lat_index * RESOLUTION, 2)
+        min_lon = round(lon_index * RESOLUTION, 2)
+
+        # Calculate the northeast (max_lat, max_lon) corner of the bounding box
+        max_lat = round((lat_index + 1) * RESOLUTION, 2)
+        max_lon = round((lon_index + 1) * RESOLUTION, 2)
+
+        return (min_lat, min_lon, max_lat, max_lon)
+
+
+
+    def get_overlapping_cells(self, query_bbox: tuple):
+        """
+        Returns a set of all grid cells that overlap with the given bounding box.
+        """
+        # Extract location from the query bbox
+        lat_min, lon_min, lat_max, lon_max = query_bbox
+
+        min_lat_cell, min_lon_cell = self.get_grid_cell(lat_min, lon_min)
+        max_lat_cell, max_lon_cell = self.get_grid_cell(lat_max, lon_max)
+
+        overlapping_cells = set()
+        for lat_idx in range(min_lat_cell, max_lat_cell + 1):
+            for lon_idx in range(min_lon_cell, max_lon_cell + 1):
+                overlapping_cells.add((lat_idx, lon_idx))
+        
+        return overlapping_cells
+
+
+    def combine_cached_data(self, cached_data_list):
+        """
+        Combines data from multiple cached responses into a single result.
+        """
+        combined_data = ET.Element("osm")
+        for cached_data in cached_data_list:
+            for element in cached_data:
+                combined_data.append(element)
+        return combined_data
+    
+
+    def fill_cache(self, query_str: str, cache_key) :
+        
+        # Prepare the data to be sent as POST request, encoded as bytes
+        data = urllib.parse.urlencode({'data': query_str}).encode('utf-8')
+
+        try:
+            # Create a Request object with the specified URL, data, and headers
+            request = urllib.request.Request(self.overpass_url, data=data, headers=self.headers)
+
+            # Send the request and read the response
+            with urllib.request.urlopen(request) as response:
+                # Read and decode the response
+                response_data = response.read().decode('utf-8')
+                root = ET.fromstring(response_data)
+
+                self.caching_strategy.set(cache_key, root)
+                self.logger.debug(f'Cache set')
+
+        except urllib.error.URLError as e:
+            raise ConnectionError(f"Error connecting to Overpass API: {e}") from e
+
+
+
+
+def get_base_info(elem: ET.Element, osm_type: OSM_TYPES, with_name=False) :
     """
     Extracts base information (coordinates, OSM ID, and optionally a name) from an OSM element.
 
@@ -169,3 +287,26 @@ def get_base_info(elem: ET.Element, osm_type: osm_types, with_name=False) :
         return osm_id, coords, name
     else :
         return osm_id, coords
+
+
+def fill_cache():
+
+    overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
+
+    with os.scandir(OSM_CACHE_DIR) as it:
+        for entry in it:
+            if entry.is_file() and entry.name.startswith('hollow_'):
+                
+                # Read the whole file content as a string
+                with open(entry.path, 'r') as f:
+                    xml_string = f.read()  
+
+                # Build the query and cache key from the hollow XML string
+                query_str, key = overpass.build_query_from_hollow(xml_string)
+                
+                # Fill the cache with the query and key
+                overpass.fill_cache(query_str, key)
+
+                # Now delete the file as the cache is filled
+                os.remove(entry.path)
+                

backend/src/parameters/landmark_parameters.yaml

@@ -1,4 +1,4 @@
-city_bbox_side: 7500 #m
+max_bbox_side: 4000   #m
 radius_close_to: 50
 church_coeff: 0.55
 nature_coeff: 1.4

backend/src/structs/landmark.py

@@ -2,7 +2,7 @@
 
 from typing import Optional, Literal
 from uuid import uuid4, UUID
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 # Output to frontend
@@ -144,8 +144,4 @@ class Toilets(BaseModel) :
         """
         return f'Toilets @{self.location}'
 
-    class Config:
+    model_config = ConfigDict(from_attributes=True)
-        """
-        This allows us to easily convert the model to and from dictionaries
-        """
-        from_attributes = True

backend/src/tests/test_main.py

@@ -27,8 +27,8 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
         "/trip/new",
         json={
             "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
-            "nature": {"type": "nature", "score": 5},
+            "nature": {"type": "nature", "score": 0},
-            "shopping": {"type": "shopping", "score": 5},
+            "shopping": {"type": "shopping", "score": 0},
             "max_time_minute": duration_minutes,
             "detour_tolerance_minute": 0},
             "start": [48.084588, 7.280405]
@@ -51,11 +51,11 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     assert response.status_code == 200  # check for successful planning
     assert isinstance(landmarks, list)  # check that the return type is a list
     assert len(landmarks) > 2           # check that there is something to visit
+    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     assert duration_minutes*0.8 < result['total_time'], f"Trip too short: {result['total_time']} instead of {duration_minutes}"
     assert duration_minutes*1.2 > result['total_time'], f"Trip too long: {result['total_time']} instead of {duration_minutes}"
     # assert 2!= 3
 
-
 def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     """
     Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.

backend/src/utils/cluster_manager.py

@@ -10,6 +10,8 @@ from ..overpass.overpass import Overpass, get_base_info
 from ..structs.landmark import Landmark
 from .get_time_distance import get_distance
 from ..constants import OSM_CACHE_DIR
+from .utils import create_bbox
+
 
 
 # silence the overpass logger
@@ -81,7 +83,6 @@ class ClusterManager:
         # Setup the caching in the Overpass class.
         self.overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
 
-
         self.cluster_type = cluster_type
         if cluster_type == 'shopping' :
             osm_types = ['node']
@@ -95,16 +96,13 @@ class ClusterManager:
             raise NotImplementedError("Please choose only an available option for cluster detection")
 
         # Initialize the points for cluster detection
-        query = self.overpass.build_query(
+        try:
-            area = bbox,
+            result = self.overpass.send_query(
+            bbox = bbox,
             osm_types = osm_types,
             selector = sel,
             out = out
         )
-        self.logger.debug(f"Cluster query: {query}")
-
-        try:
-            result = self.overpass.send_query(query)
         except Exception as e:
             self.logger.error(f"Error fetching landmarks: {e}")
 
@@ -218,8 +216,7 @@ class ClusterManager:
         """
 
         # Define the bounding box for a given radius around the coordinates
-        lat, lon = cluster.centroid
+        bbox = create_bbox(cluster.centroid, 1000)
-        bbox = (1000, lat, lon)
         
         # Query neighborhoods and shopping malls
         selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
@@ -238,15 +235,12 @@ class ClusterManager:
         osm_types = ['node', 'way', 'relation']
 
         for sel in selectors :
-            query = self.overpass.build_query(
+            try:
-                area = bbox,
+                result = self.overpass.send_query(bbox = bbox,
                                                   osm_types = osm_types,
                                                   selector = sel,
                                                   out = 'ids center'
                                                   )
-
-            try:
-                result = self.overpass.send_query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue

backend/src/utils/landmarks_manager.py

@@ -1,5 +1,6 @@
 """Module used to import data from OSM and arrange them in categories."""
 import logging
+import math as m
 import xml.etree.ElementTree as ET
 import yaml
 
@@ -9,12 +10,10 @@ from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
 from ..overpass.overpass import Overpass, get_base_info
+from .utils import create_bbox
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
-# silence the overpass logger
-logging.getLogger('Overpass').setLevel(level=logging.CRITICAL)
-
 
 class LandmarkManager:
     """
@@ -37,8 +36,7 @@ class LandmarkManager:
 
         with LANDMARK_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
-            self.max_bbox_side = parameters['city_bbox_side']
+            self.max_bbox_side = parameters['max_bbox_side']
-            self.radius_close_to = parameters['radius_close_to']
             self.church_coeff = parameters['church_coeff']
             self.nature_coeff = parameters['nature_coeff']
             self.overall_coeff = parameters['overall_coeff']
@@ -80,13 +78,13 @@ class LandmarkManager:
         """
         self.logger.debug('Starting to fetch landmarks...')
         max_walk_dist = int((preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor)
-        reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
+        radius = min(max_walk_dist, int(self.max_bbox_side/2))
 
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
         # Create a bbox using the around technique, tuple of strings
-        bbox = tuple((min(2000, reachable_bbox_side/2), center_coordinates[0], center_coordinates[1]))
+        bbox = create_bbox(center_coordinates, radius)
 
         # list for sightseeing
         if preferences.sightseeing.score != 0:
@@ -193,17 +191,15 @@ class LandmarkManager:
                 query_conditions = []
                 osm_types.append('node')
 
-            query = self.overpass.build_query(
+            # Send the overpass query
-                area = bbox,
+            try:
+                result = self.overpass.send_query(
+                bbox = bbox,
                 osm_types = osm_types,
                 selector = sel,
                 conditions = query_conditions,        # except for nature....
                 out = 'center'
                 )
-            self.logger.debug(f"Query: {query}")
-
-            try:
-                result = self.overpass.send_query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue

backend/src/utils/utils.py

@@ -0,0 +1,27 @@
+"""Various helper functions"""
+import math as m
+
+def create_bbox(coords: tuple[float, float], radius: int):
+    """
+    Create a bounding box around the given coordinates.
+
+    Args:
+        coords (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+        radius (int): The half-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.
+    """
+    # Earth's radius in meters
+    R = 6378137
+    lat, lon = coords
+    d_lat = radius / R
+    d_lon = radius / (R * m.cos(m.pi * lat / 180))
+
+    lat_min = lat - d_lat * 180 / m.pi
+    lat_max = lat + d_lat * 180 / m.pi
+    lon_min = lon - d_lon * 180 / m.pi
+    lon_max = lon + d_lon * 180 / m.pi
+
+    return (lat_min, lon_min, lat_max, lon_max)