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- #!/usr/bin/env python
- from sklearn.metrics.pairwise import haversine_distances
- import numpy as np
- class Waypoint:
- def dms2dd(coordinate : str):
- split = coordinate.split('.')
- if 4 != len(split):
- return 0.0
- direction = split[0][1]
- degrees = float(split[0][1:])
- minutes = float(split[1])
- seconds = float(split[2]) * (float(split[3]) / 1000.0)
- dd = degrees + minutes / 60.0 + seconds / (60 * 60)
- if 'E' == direction or 'S' == direction:
- dd *= -1.0
- return dd
- def __init__(self, name : str, latitude : float, longitude : float):
- self.name = name
- self.coordinate = np.array([ latitude, longitude ])
- def __str__(self):
- return 'Name: ' + self.name + ', Lat: ' + str(self.coordinate[0]) + ', Lon: ' + str(self.coordinate[1])
- def haversine(self, other):
- self_radians = [np.radians(_) for _ in self.coordinate]
- other_radians = [np.radians(_) for _ in other.coordinate]
- return 6371.0 * haversine_distances([self_radians, other_radians])[0][1]
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