first fixes
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@ -1,11 +1,12 @@
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city_bbox_side: 7500 #m
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radius_close_to: 50
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church_coeff: 0.5
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church_coeff: 0.75
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nature_coeff: 1.25
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overall_coeff: 10
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tag_exponent: 1.15
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image_bonus: 10
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viewpoint_bonus: 15
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wikipedia_bonus: 6
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wikipedia_bonus: 4
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name_bonus: 3
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N_important: 40
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pay_bonus: -1
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@ -3,4 +3,4 @@ detour_corridor_width: 300
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average_walking_speed: 4.8
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max_landmarks: 10
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max_landmarks_refiner: 30
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overshoot: 1.8
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overshoot: 1.2
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@ -35,6 +35,7 @@ class LinkedLandmarks:
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time_to_next = get_time(landmark.location, self._landmarks[i + 1].location)
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landmark.time_to_reach_next = time_to_next
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self.total_time += time_to_next
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self.total_time += landmark.duration
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self._landmarks[-1].next_uuid = None
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self._landmarks[-1].time_to_reach_next = 0
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@ -22,8 +22,8 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
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preferences = Preferences(
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sightseeing=Preference(type='sightseeing', score = 5),
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nature=Preference(type='nature', score = 5),
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shopping=Preference(type='shopping', score = 5),
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max_time_minute=15,
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shopping=Preference(type='shopping', score = 0),
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max_time_minute=30,
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detour_tolerance_minute=0
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)
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@ -74,7 +74,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
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# test(tuple((48.8344400, 2.3220540))) # Café Chez César
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# test(tuple((48.8375946, 2.2949904))) # Point random
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# test(tuple((47.377859, 8.540585))) # Zurich HB
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# test(tuple((45.758217, 4.831814))) # Lyon Bellecour
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test(tuple((45.758217, 4.831814))) # Lyon Bellecour
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# test(tuple((48.5848435, 7.7332974))) # Strasbourg Gare
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# test(tuple((48.2067858, 16.3692340))) # Vienne
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test(tuple((48.084588, 7.280405))) # Turckheim
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# test(tuple((48.2432090, 7.3892691))) # Orschwiller
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@ -39,6 +39,7 @@ class LandmarkManager:
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self.overall_coeff = parameters['overall_coeff']
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self.tag_exponent = parameters['tag_exponent']
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self.image_bonus = parameters['image_bonus']
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self.name_bonus = parameters['name_bonus']
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self.wikipedia_bonus = parameters['wikipedia_bonus']
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self.viewpoint_bonus = parameters['viewpoint_bonus']
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self.pay_bonus = parameters['pay_bonus']
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@ -202,18 +203,29 @@ class LandmarkManager:
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"""
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return_list = []
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if landmarktype == 'nature' : query_conditions = []
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else : query_conditions = ['count_tags()>5']
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# caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
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# we need to split the selectors into separate queries and merge the results
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for sel in dict_to_selector_list(amenity_selector):
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self.logger.debug(f"Current selector: {sel}")
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query_conditions = ['count_tags()>5']
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element_types = ['way', 'relation']
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if 'viewpoint' in sel :
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query_conditions = []
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element_types.append('node')
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query = overpassQueryBuilder(
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bbox = bbox,
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elementType = ['way', 'relation'],
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elementType = element_types,
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# selector can in principle be a list already,
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# but it generates the intersection of the queries
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# we want the union
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selector = sel,
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conditions = ['count_tags()>5'],
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conditions = query_conditions, # except for nature....
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includeCenter = True,
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out = 'body'
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)
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@ -229,18 +241,23 @@ class LandmarkManager:
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name = elem.tag('name')
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location = (elem.centerLat(), elem.centerLon())
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osm_type = elem.type() # Add type: 'way' or 'relation'
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osm_id = elem.id() # Add OSM id
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# TODO: exclude these from the get go
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# skip if unprecise location
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# handle unprecise and no-name locations
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if name is None or location[0] is None:
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continue
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if osm_type == 'node' and 'viewpoint' in elem.tags().values():
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name = 'Viewpoint'
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name_en = 'Viewpoint'
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location = (elem.lat(), elem.lon())
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else :
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continue
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# skip if part of another building
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if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
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continue
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osm_type = elem.type() # Add type: 'way' or 'relation'
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osm_id = elem.id() # Add OSM id
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elem_type = landmarktype # Add the landmark type as 'sightseeing,
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n_tags = len(elem.tags().keys()) # Add number of tags
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score = n_tags**self.tag_exponent # Add score
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@ -248,59 +265,66 @@ class LandmarkManager:
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image_url = None
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name_en = None
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# Adjust scoring
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# Adjust scoring, browse through tag keys
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skip = False
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for tag in elem.tags().keys():
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if "pay" in tag:
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for tag_key in elem.tags().keys():
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if "pay" in tag_key:
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# payment options are misleading and should not count for the scoring.
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score += self.pay_bonus
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if "disused" in tag:
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if "disused" in tag_key:
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# skip disused amenities
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skip = True
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break
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if "wiki" in tag:
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if "name" in tag_key :
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score += self.name_bonus
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if "wiki" in tag_key:
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# wikipedia entries count more
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score += self.wikipedia_bonus
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if "viewpoint" in tag:
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# viewpoints must count more
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score += self.viewpoint_bonus
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duration = 10
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if "image" in tag:
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if "image" in tag_key:
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# images must count more
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score += self.image_bonus
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if elem_type != "nature":
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if "leisure" in tag and elem.tag('leisure') == "park":
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if "leisure" in tag_key and elem.tag('leisure') == "park":
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elem_type = "nature"
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if landmarktype != "shopping":
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if "shop" in tag:
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if "shop" in tag_key:
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skip = True
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break
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if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
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if tag_key == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
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skip = True
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break
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# Extract image, website and english name
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if tag in ['website', 'contact:website']:
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website_url = elem.tag(tag)
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if tag == 'image':
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if tag_key in ['website', 'contact:website']:
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website_url = elem.tag(tag_key)
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if tag_key == 'image':
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image_url = elem.tag('image')
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if tag =='name:en':
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if tag_key =='name:en':
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name_en = elem.tag('name:en')
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if skip:
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continue
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# Don't visit random apartments
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if 'apartments' in elem.tags().values():
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continue
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score = score_function(score)
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if "place_of_worship" in elem.tags().values():
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score = score * self.church_coeff
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duration = 10
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if 'viewpoint' in elem.tags().values() :
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# viewpoints must count more
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score += self.viewpoint_bonus
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duration = 10
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elif "museum" in elem.tags().values() or "aquarium" in elem.tags().values() or "planetarium" in elem.tags().values():
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duration = 60
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