import math import itertools import requests from typing import List, Tuple, Dict, Optional, Set import random class Point: def __init__(self, coord: List[float], tag: str, visit_time: int, list_id=0): self.coord = coord self.tag = tag self.visit_time = visit_time self.matrix_index = None # Индекс точки в матрице расстояний self.estimated_time = None # Оценочное время (перемещение + посещение) self.list_id = list_id def haversine(coord1: List[float], coord2: List[float]) -> float: """Calculate the great-circle distance between two points in kilometers.""" lat1, lon1 = coord1 lat2, lon2 = coord2 R = 6371 # Earth radius in km dlat = math.radians(lat2 - lat1) dlon = math.radians(lon2 - lon1) a = (math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)) return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) def filter_points_by_time(start_coord: List[float], points: List[Point], total_time: int) -> List[Point]: """Filter points based on straight-line distance and visit time.""" filtered = [] for point in points: distance = haversine(start_coord, point.coord) travel_time = distance * 10 # Assume 6 km/h walking speed (10 min/km) point.estimated_time = travel_time + point.visit_time if point.estimated_time <= total_time: filtered.append(point) return filtered def filter_points_by_tag_proximity(points: List[Point], max_per_tag: int = 10) -> List[Point]: """For each tag, keep only the closest points (by estimated time).""" # Group points by tag tag_to_points = {} for point in points: if point.tag not in tag_to_points: tag_to_points[point.tag] = [] tag_to_points[point.tag].append(point) # For each tag, sort by estimated time and keep top max_per_tag filtered_points = [] for tag, tag_points in tag_to_points.items(): # Sort by estimated time (ascending) sorted_points = sorted(tag_points, key=lambda p: p.estimated_time) # Keep at most max_per_tag points kept_points = sorted_points[:max_per_tag] filtered_points.extend(kept_points) print(f"Tag '{tag}': kept {len(kept_points)} out of {len(tag_points)} points") return filtered_points def get_duration_matrix(points: List[List[float]]) -> Optional[Tuple[List[List[float]], List[List[float]]]]: """Get duration matrix from server.""" url = "https://ha1m-maap-pdmc.gw-1a.dockhost.net/table" payload = {"points": points} headers = {"content-type": "application/json"} try: response = requests.post(url, json=payload, headers=headers, timeout=30) if response.status_code == 200: data = response.json() return data.get("distances"), data.get("durations") else: print(f"Server error: {response.status_code}") return None except Exception as e: print(f"Error requesting duration matrix: {e}") return None def group_points_by_significance(points: List[Point], tag_importance: Dict[str, int]) -> Dict[int, List[Point]]: """Group points by their importance level.""" grouped = {} for point in points: importance = tag_importance.get(point.tag, float('inf'))[0] if importance not in grouped: grouped[importance] = [] grouped[importance].append(point) return grouped def calculate_route_time_with_matrix(route: List[Point], start_coord: List[float], duration_matrix: List[List[float]]) -> float: """Calculate total time for a route using the duration matrix.""" total_time = 0 current_index = 0 # Start point index for point in route: next_index = point.matrix_index travel_time_seconds = duration_matrix[current_index][next_index] travel_time_minutes = travel_time_seconds / 60.0 total_time += travel_time_minutes + point.visit_time current_index = next_index return total_time def check_tags_constraint(points: List[Point]) -> bool: """Check if there are no more than 5 unique tags.""" unique_tags = set(point.tag for point in points) return len(unique_tags) <= 5 def generate_routes_exact_tags(grouped_points: Dict[int, List[Point]], all_tags: Set[str], tag_importance: Dict[str, int]) -> List[List[Point]]: """Generate routes where each tag is visited exactly once using different coordinates.""" # Create a mapping from tag to points tag_to_points = {} for points_list in grouped_points.values(): for point in points_list: if point.tag not in tag_to_points: tag_to_points[point.tag] = [] tag_to_points[point.tag].append(point) # For each tag, we need to select exactly one point tag_selections = [] for tag in all_tags: tag_selections.append(tag_to_points[tag]) # Generate all combinations of points (one per tag) all_routes = [] print(len(list(itertools.product(*tag_selections)))) for point_combination in itertools.product(*tag_selections): # Check if all points have unique coordinates coords = [tuple(point.coord) for point in point_combination] if len(coords) != len(set(coords)): continue # Skip if any coordinates are duplicated # Group points by importance points_by_importance = {} for point in point_combination: imp = tag_importance[point.tag][0] if imp not in points_by_importance: points_by_importance[imp] = [] points_by_importance[imp].append(point) # Sort by importance sorted_importances = sorted(points_by_importance.keys()) # Generate all permutations within each importance group importance_groups = [points_by_importance[imp] for imp in sorted_importances] for ordering in itertools.product(*[itertools.permutations(group) for group in importance_groups]): route = [] for group in ordering: route.extend(group) all_routes.append(route) return all_routes def generate_routes_with_repeats(grouped_points: Dict[int, List[Point]], all_tags: Set[str], tag_importance: Dict[str, int], num_points: int) -> List[List[Point]]: """Generate routes when we need to repeat tags to reach the required number of points, ensuring unique coordinates.""" # Create a mapping from tag to points tag_to_points = {} for points_list in grouped_points.values(): for point in points_list: if point.tag not in tag_to_points: tag_to_points[point.tag] = [] tag_to_points[point.tag].append(point) all_routes = [] # First, select one point for each tag (mandatory points) mandatory_selections = [tag_to_points[tag] for tag in all_tags] # Generate all combinations of mandatory points (one per tag) for mandatory_combo in itertools.product(*mandatory_selections): mandatory_points = list(mandatory_combo) # Check if mandatory points have unique coordinates mandatory_coords = [tuple(point.coord) for point in mandatory_points] if len(mandatory_coords) != len(set(mandatory_coords)): continue # Skip if any coordinates are duplicated in mandatory points # We need to add (num_points - len(mandatory_points)) additional points num_additional = num_points - len(mandatory_points) if num_additional == 0: # We have exactly the right number of points points_by_importance = {} for point in mandatory_points: imp = tag_importance[point.tag][0] if imp not in points_by_importance: points_by_importance[imp] = [] points_by_importance[imp].append(point) sorted_importances = sorted(points_by_importance.keys()) importance_groups = [points_by_importance[imp] for imp in sorted_importances] for ordering in itertools.product(*[itertools.permutations(group) for group in importance_groups]): route = [] for group in ordering: route.extend(group) all_routes.append(route) else: # We need to add additional points (can be from any tag, including repeats) # But we must ensure all coordinates are unique # Get all available points excluding mandatory points all_available_points = [] for key in grouped_points.keys(): if tag_importance[key][1]: all_available_points.extend(grouped_points[key]) # Remove mandatory points from available points available_points = [p for p in all_available_points if p not in mandatory_points] # Generate combinations of additional points for additional_combo in itertools.combinations(available_points, num_additional): # Check if additional points have unique coordinates and don't duplicate with mandatory additional_coords = [tuple(point.coord) for point in additional_combo] if len(additional_coords) != len(set(additional_coords)): continue # Skip if any coordinates are duplicated in additional points # Check if additional points don't duplicate with mandatory points all_coords = mandatory_coords + additional_coords if len(all_coords) != len(set(all_coords)): continue # Skip if any coordinates are duplicated between mandatory and additional full_route_candidate = mandatory_points + list(additional_combo) # Group by importance points_by_importance = {} for point in full_route_candidate: imp = tag_importance[point.tag] if imp not in points_by_importance: points_by_importance[imp] = [] points_by_importance[imp].append(point) sorted_importances = sorted(points_by_importance.keys()) importance_groups = [points_by_importance[imp] for imp in sorted_importances] for ordering in itertools.product(*[itertools.permutations(group) for group in importance_groups]): route = [] for group in ordering: route.extend(group) all_routes.append(route) return all_routes def form_point_list(data): point_list =[] for list_id,entry in enumerate(data): point = Point(list(map(float,entry['coordinate'].split(', '))),entry['type'],entry['time_to_visit'],list_id) point_list.append(point) return point_list def build_route(data, mapping,start_coord,total_time,n_nodes,strategy='best'): # Example input data - теперь с не более чем 5 уникальными тегами start_coord_test = [56.331576, 44.003277] total_time_test = 180 # Увеличим время до 4 часов для большего выбора points = form_point_list(data) tag_importance =mapping # Используем 3 уникальных тега для демонстрации points_test = [ Point([56.32448, 43.983546], "Памятник", 20), Point([56.335607, 43.97481], "Архитектура", 20), Point([56.313472, 43.990747], "Памятник", 20), #Point([56.324157, 44.002696], "Памятник", 20), #Point([56.316436, 43.994177], "Памятник", 20), #Point([56.32377, 44.001879], "Памятник", 20), #Point([56.329867, 43.99687], "Памятник", 20), Point([56.311066, 43.94595], "Памятник", 20), Point([56.333265, 43.972417], "Памятник", 20), # Point([56.332166, 44.012111], "Памятник", 20), #Point([56.326786, 44.006836], "Памятник", 20), Point([56.330232, 44.010941], "Парк", 20), Point([56.282221, 43.979263], "Парк", 20), Point([56.277315, 43.921408], "Мозаика", 20), Point([56.284829, 44.01893], "Парк", 20), Point([56.308973, 43.99821], "Парк", 20), Point([56.321545, 44.001921], "Парк", 20), #Point([56.301798, 44.044003], "Мозаика", 20), Point([56.268282, 43.919475], "Парк", 20), Point([56.239625, 43.854551], "Парк", 20), #Point([56.311214, 43.933981], "Парк", 20), Point([56.314984, 44.007347], "Парк", 20), Point([56.32509, 43.983433], "Парк", 20), Point([56.27449, 43.973357], "Парк", 20), Point([56.278073, 43.940886], "Парк", 20), Point([56.358805, 43.825376], "Парк", 20), Point([56.329995, 44.009444], "Памятник", 20), Point([56.328551, 43.998718], "Памятник", 20), Point([56.330355, 43.993105], "Архитектура", 20), Point([56.321416, 43.973897], "Архитектура", 20), # Point([56.327298, 44.005706], "Архитектура", 20), #Point([56.328757, 43.998183], "Архитектура", 20), # Point([56.328908, 43.995645], "Архитектура", 20), Point([56.317578, 43.995805], "Архитектура", 20), Point([56.329433, 44.012764], "Архитектура", 20), Point([56.3301, 44.008831], "Архитектура", 20), #Point([56.32995, 43.999495], "Архитектура", 20), Point([56.327454, 44.041745], "Архитектура", 20), #Point([56.328576, 44.004872], "Архитектура", 20), Point([56.3275, 44.007658], "Архитектура", 20), Point([56.330679, 44.013874], "Архитектура", 20), # Point([56.331541, 44.001747], "Архитектура", 20), # Point([56.335071, 43.974627], "Архитектура", 20), #Point([56.317707, 43.995847], "Архитектура", 20), #Point([56.323851, 43.985939], "Архитектура", 20), Point([56.325701, 44.001527], "Архитектура", 20), Point([56.328754, 43.998954], "Архитектура", 20), #Point([56.323937, 43.990728], "Музей", 20), #Point([56.2841, 43.84621], "Музей", 20), #Point([56.328646, 44.028973], "Музей", 20), Point([56.327391, 43.857522], "Мозаика", 20), #Point([56.252239, 43.889066], "Мозаика", 20), #Point([56.248436, 43.88106], "Мозаика", 20), #Point([56.321257, 43.94545], "Мозаика", 20), # Point([56.365284, 43.823251], "Мозаика", 20), Point([56.294371, 43.912625], "Мозаика", 20), #Point([56.241768, 43.859687], "Мозаика", 20), #Point([56.300073, 43.938526], "Мозаика", 20), #Point([56.229652, 43.947973], "Мозаика", 20), # Point([56.269486, 43.9238], "Мозаика", 20), Point([56.299251, 43.985146], "Мозаика", 20), Point([56.293297, 44.034095], "Мозаика", 20), Point([56.299251, 43.985146], "Мозаика", 20), Point([56.229652, 43.947973], "Мозаика", 20), Point([56.269486, 43.9238], "Мозаика", 20), #Point([56.293297, 44.034095], "Мозаика", 20), #Point([56.229652, 43.947973], "Мозаика", 20) ] tag_importance_test = { "Памятник": 1, "Парк": 1, "Мозаика": 1, "Архитектура": 1, #"Музей": 1 } # Check tags constraint if not check_tags_constraint(points): print("Error: More than 5 unique tags in the input data") return print("Input data validation: OK (5 or fewer unique tags)") # Step 1: Filter points using straight-line distance and total time filtered_by_time = filter_points_by_time(start_coord, points, total_time) print(f"After initial time filtering: {len(filtered_by_time)} points") if len(filtered_by_time) < 3: print("Not enough points after time filtering") return # Step 2: Filter points by tag proximity (keep max 10 closest points per tag) filtered_points = filter_points_by_tag_proximity(filtered_by_time, max_per_tag=10) print(f"After tag proximity filtering: {len(filtered_points)} points") if len(filtered_points) < 3: print("Not enough points after tag proximity filtering") return # Step 3: Prepare points for server request (start point + filtered points) points_for_matrix = [start_coord] + [point.coord for point in filtered_points] print("Requesting duration matrix from server...") # Step 4: Get duration matrix from server result = get_duration_matrix(points_for_matrix) if result is None: print("Failed to get duration matrix from server") return distances_matrix, durations_matrix = result print("Duration matrix received successfully") # Assign matrix indices to points for i, point in enumerate(filtered_points): point.matrix_index = i + 1 # +1 because index 0 is the start point # Step 5: Group by importance grouped_points = group_points_by_significance(filtered_points, tag_importance) # Get all unique tags all_tags = set(point.tag for point in filtered_points) num_unique_tags = len(all_tags) print(f"Unique tags: {all_tags} ({num_unique_tags} tags)") # Step 6: Generate possible routes print("Generating possible routes...") # Determine the number of points in the route if num_unique_tags >= n_nodes: # Each tag must be visited exactly once print("Each tag will be visited exactly once with unique coordinates") possible_routes = generate_routes_exact_tags(grouped_points, all_tags, tag_importance) else: # We have fewer than 3 unique tags, need to repeat some tags print(f"Only {num_unique_tags} unique tags available, will repeat tags to reach 3 points with unique coordinates") possible_routes = generate_routes_with_repeats(grouped_points, all_tags, tag_importance, n_nodes) if not possible_routes: print("No valid routes found that cover all tags with unique coordinates") return # Step 7: Calculate time for each route and filter by total_time valid_routes = [] for route in possible_routes: route_time = calculate_route_time_with_matrix(route, start_coord, durations_matrix) if route_time <= total_time: valid_routes.append((route, route_time)) if not valid_routes: print("No valid routes found within time constraint") return # Step 8: Find optimal route (minimum time) if strategy=='random': optimal_route, min_time = random.choice(valid_routes) elif strategy=='longest': optimal_route, min_time = max(valid_routes, key=lambda x: x[1]) else: optimal_route, min_time = min(valid_routes, key=lambda x: x[1]) print(f"\nOptimal route (time: {min_time:.2f} min):") for i, point in enumerate(optimal_route, 1): print(f"{i}. {point.tag} at {point.coord} ({point.visit_time} min)") # Print route details with travel times print("\nRoute details:") current_index = 0 total_route_time = 0 for i, point in enumerate(optimal_route): travel_time_seconds = durations_matrix[current_index][point.matrix_index] travel_time_minutes = travel_time_seconds / 60.0 segment_time = travel_time_minutes + point.visit_time total_route_time += segment_time point.time_to_travel = travel_time_minutes print(f"Segment {i+1}: {travel_time_minutes:.2f} min travel + {point.visit_time} min visit = {segment_time:.2f} min") current_index = point.matrix_index print(f"Total route time: {total_route_time:.2f} min") # Display all tags covered by the route route_tags = set(point.tag for point in optimal_route) #print(f"\nTags covered in this route: {', '.join(route_tags)}") if all_tags.issubset(route_tags): print("All tags are covered in this route!") # Verify all coordinates are unique route_coords = [tuple(point.coord) for point in optimal_route] if len(route_coords) == len(set(route_coords)): print("All coordinates in the route are unique!") else: print("ERROR: Duplicate coordinates found in the route!") places = [] for point in optimal_route: place = data[point.list_id].copy() place["time_to_travel"] = round(point.time_to_travel, 2) places.append(place) return route_coords, places #if __name__ == "__main__": # build_route()