feat(backend): Add cable graph service and data collectors
## Changelog ### New Features #### Cable Graph Service - Add cable_graph.py for finding shortest path between landing points - Implement haversine distance calculation for great circle distances - Support for dateline crossing (longitude normalization) - NetworkX-based graph for optimal path finding #### Data Collectors - Add ArcGISCableCollector for fetching submarine cable data from ArcGIS GeoJSON API - Add FAOLandingPointCollector for fetching landing point data from FAO CSV API ### Backend Changes #### API Updates - auth.py: Update authentication logic - datasources.py: Add datasource endpoints and management - visualization.py: Add visualization API endpoints - config.py: Update configuration settings - security.py: Improve security settings #### Models & Schemas - task.py: Update task model with new fields - token.py: Update token schema #### Services - collectors/base.py: Improve base collector with better error handling - collectors/__init__.py: Register new collectors - scheduler.py: Update scheduler logic - tasks/scheduler.py: Add task scheduling ### Frontend Changes - AppLayout.tsx: Improve layout component - index.css: Add global styles - DataSources.tsx: Enhance data sources management page - vite.config.ts: Add Vite configuration for earth module
This commit is contained in:
rayd1o
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backend/app/services/cable_graph.py
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backend/app/services/cable_graph.py
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"""Cable graph service for finding shortest path between landing points"""
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import math
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from typing import List, Dict, Any, Optional, Tuple
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import networkx as nx
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def normalize_longitude(lon: float) -> float:
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"""Normalize longitude to -180 to 180 range"""
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while lon > 180:
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lon -= 360
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while lon < -180:
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lon += 360
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return lon
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def haversine_distance(coord1: Tuple[float, float], coord2: Tuple[float, float]) -> float:
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"""Calculate great circle distance between two points in km, handling dateline crossing"""
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lon1, lat1 = normalize_longitude(coord1[0]), coord1[1]
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lon2, lat2 = normalize_longitude(coord2[0]), coord2[1]
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R = 6371
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lat1_rad = math.radians(lat1)
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lat2_rad = math.radians(lat2)
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delta_lat = math.radians(lat2 - lat1)
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delta_lon = math.radians(lon2 - lon1)
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a = (
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math.sin(delta_lat / 2) ** 2
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+ math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(delta_lon / 2) ** 2
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)
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
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return R * c
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class CableGraph:
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def __init__(self, cables: List[Dict], landing_points: List[Dict]):
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self.graph = nx.Graph()
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self.landing_points = {lp["id"]: lp for lp in landing_points}
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self.point_coords = {lp["id"]: (lp["lon"], lp["lat"]) for lp in landing_points}
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self._build_graph(cables)
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def _build_graph(self, cables: List[Dict]):
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"""Build graph from cables and landing points"""
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for cable in cables:
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coords = cable.get("coordinates", [])
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if len(coords) < 2:
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continue
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# Find nearest landing points for start and end (search more points)
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start_point = self._find_nearest_landing_point_multi(coords[:3]) # First 3 points
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end_point = self._find_nearest_landing_point_multi(coords[-3:]) # Last 3 points
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if start_point and end_point and start_point != end_point:
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# Calculate distance via cable route
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distance = self._calculate_cable_distance(coords)
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# Add edge with cable info
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edge_data = {
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"distance": distance,
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"cable_name": cable.get("name", "Unknown"),
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"cable_id": cable.get("id"),
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"coordinates": coords,
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}
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# If edge exists, keep the shorter one
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if self.graph.has_edge(start_point, end_point):
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existing_dist = self.graph[start_point][end_point]["distance"]
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if distance < existing_dist:
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self.graph[start_point][end_point].update(edge_data)
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else:
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self.graph.add_edge(start_point, end_point, **edge_data)
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def _find_nearest_landing_point_multi(self, coords_subset: List[List[float]]) -> Optional[int]:
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"""Find nearest landing point from multiple coordinates (e.g., first/last N points)"""
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best_point = None
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best_dist = float("inf")
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for coord in coords_subset:
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point = self._find_nearest_landing_point(coord)
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if point:
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dist = haversine_distance(
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(normalize_longitude(coord[0]), coord[1]), self.point_coords[point]
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)
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if dist < best_dist:
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best_dist = dist
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best_point = point
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return best_point
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def _find_nearest_landing_point(self, coord: List[float]) -> Optional[int]:
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"""Find nearest landing point to given coordinate"""
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if not self.point_coords:
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return None
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min_dist = float("inf")
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nearest_id = None
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target_lon = normalize_longitude(coord[0])
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target_lat = coord[1]
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for lp_id, (lon, lat) in self.point_coords.items():
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dist = haversine_distance((target_lon, target_lat), (lon, lat))
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if dist < min_dist:
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min_dist = dist
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nearest_id = lp_id
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return nearest_id if min_dist < 500 else None
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def _find_nearest_connected_landing_point(self, coord: List[float]) -> Optional[int]:
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"""Find nearest landing point that's connected to the graph, handling dateline"""
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if not self.point_coords or not self.graph.nodes():
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return None
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connected_nodes = set(self.graph.nodes())
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min_dist = float("inf")
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nearest_id = None
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target_lon, target_lat = normalize_longitude(coord[0]), coord[1]
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for lp_id in connected_nodes:
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lp_lon, lp_lat = self.point_coords[lp_id]
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# Try both normalized versions (for points near dateline)
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dist = haversine_distance((target_lon, target_lat), (lp_lon, lp_lat))
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if dist < min_dist:
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min_dist = dist
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nearest_id = lp_id
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return nearest_id if min_dist < 500 else None
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def _calculate_cable_distance(self, coordinates: List[List[float]]) -> float:
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"""Calculate total distance along cable route"""
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total = 0
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for i in range(len(coordinates) - 1):
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total += haversine_distance(
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(coordinates[i][0], coordinates[i][1]),
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(coordinates[i + 1][0], coordinates[i + 1][1]),
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)
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return total
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def find_shortest_path(
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self, start_coords: List[float], end_coords: List[float]
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) -> Optional[Dict[str, Any]]:
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"""Find shortest path between two coordinates"""
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start_point = self._find_nearest_connected_landing_point(start_coords)
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end_point = self._find_nearest_connected_landing_point(end_coords)
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if not start_point or not end_point:
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return None
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if not nx.has_path(self.graph, start_point, end_point):
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return None
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try:
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path = nx.shortest_path(self.graph, start_point, end_point, weight="distance")
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except nx.NetworkXNoPath:
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return None
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if not nx.has_path(self.graph, start_point, end_point):
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return None
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try:
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path = nx.shortest_path(self.graph, start_point, end_point, weight="distance")
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except nx.NetworkXNoPath:
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return None
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# Build result
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total_distance = 0
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path_segments = []
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for i in range(len(path) - 1):
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u, v = path[i], path[i + 1]
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edge_data = self.graph[u][v]
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total_distance += edge_data["distance"]
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path_segments.append(
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{
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"from": self.landing_points[u],
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"to": self.landing_points[v],
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"cable_name": edge_data["cable_name"],
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"cable_id": edge_data["cable_id"],
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"distance_km": round(edge_data["distance"], 2),
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"coordinates": edge_data["coordinates"],
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}
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)
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return {
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"start": {
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"id": start_point,
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"name": self.landing_points[start_point].get("name", "Unknown"),
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"coords": list(self.point_coords[start_point]),
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},
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"end": {
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"id": end_point,
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"name": self.landing_points[end_point].get("name", "Unknown"),
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"coords": list(self.point_coords[end_point]),
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},
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"total_distance_km": round(total_distance, 2),
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"segments": path_segments,
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"segment_count": len(path_segments),
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}
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def build_graph_from_data(cables_data: Dict, points_data: Dict) -> CableGraph:
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"""Build cable graph from GeoJSON data"""
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cables = []
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for feature in cables_data.get("features", []):
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props = feature.get("properties", {})
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coords = feature.get("geometry", {}).get("coordinates", [])
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if coords and isinstance(coords[0], list):
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coords = coords[0] # MultiLineString - take first line
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cables.append(
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{
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"id": props.get("id"),
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"name": props.get("name", props.get("Name", "Unknown")),
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"coordinates": coords,
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}
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)
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points = []
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for feature in points_data.get("features", []):
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geom = feature.get("geometry", {})
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props = feature.get("properties", {})
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coords = geom.get("coordinates", [])
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if coords and len(coords) >= 2:
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points.append(
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{
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"id": props.get("id"),
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"name": props.get("name", "Unknown"),
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"lon": coords[0],
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"lat": coords[1],
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}
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)
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return CableGraph(cables, points)
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