From 82158fea68c94d0847ed932e3bf8afa34b4f74e9 Mon Sep 17 00:00:00 2001 From: ViktorSemericov Date: Wed, 29 Oct 2025 15:54:17 +0300 Subject: [PATCH] =?UTF-8?q?=D0=9E=D0=B1=D0=BD=D0=BE=D0=B2=D0=B8=D1=82?= =?UTF-8?q?=D1=8C=20route.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- route.py | 913 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 460 insertions(+), 453 deletions(-) diff --git a/route.py b/route.py index 4d4ce69..7571f2a 100644 --- a/route.py +++ b/route.py @@ -1,454 +1,461 @@ -import math -import itertools -import requests -from typing import List, Tuple, Dict, Optional, Set - -class Point: - def __init__(self, coord: List[float], tag: str, visit_time: int): - 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) - -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')) - 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] - 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] - 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 points_list in grouped_points.values(): - all_available_points.extend(points_list) - - # 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 entry in data: - point = Point(list(map(float,entry['coordinate'].split(', '))),entry['type'],entry['time_to_visit']) - point_list.append(point) - return point_list - -def build_route(data, mapping,start_coord,total_time,n_nodes): - # 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) - 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 - - 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!") - -#if __name__ == "__main__": +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): + 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) + +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 entry in data: + point = Point(list(map(float,entry['coordinate'].split(', '))),entry['type'],entry['time_to_visit']) + 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 + + 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!") + return route_coords +#if __name__ == "__main__": # build_route() \ No newline at end of file