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