109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
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import statistics
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import overpy
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import utils
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import time
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CorrectSlope = bool
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SlopeConfidence = float
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class RiverTooShort(Exception):
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pass
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class River:
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POOR_CONFIDENCE = 0.1
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MEDIUM_CONFIDENCE = 0.5
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HIGH_CONFIDENCE = 0.9
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id: int
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way: overpy.Way
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is_correct_slope: bool
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correct_slope_confidence: float
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start_ele: float
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end_ele: float
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def __init__(self, wayid, way):
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self.wayid = wayid
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self.way = way
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@property
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def name(self):
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return self.way.tags.get('name', None)
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def osm_link(self):
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return f'OSM LINK: https://www.openstreetmap.org/way/{self.wayid}'
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def __repr__(self):
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return f"River {self.name} {self.wayid}"
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@property
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def length(self) -> float:
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return utils.distance(self.way.nodes[0], self.way.nodes[-1])
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def to_dict(self):
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return {
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'name': self.name,
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'wayid': self.wayid,
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'nodes': [(float(pt.lat), float(pt.lon)) for pt in self.way.nodes],
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'length': self.length,
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'start_ele': utils.get_elevation(self.way.nodes[0].lat, self.way.nodes[0].lon),
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'end_ele': utils.get_elevation(self.way.nodes[1].lat, self.way.nodes[1].lon),
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'is_correct_slope': self.is_correct_slope,
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'correct_slope_confidence': self.correct_slope_confidence,
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'centroid': [float(self.centroid[0]), float(self.centroid[1])],
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'timestap': int(time.time()),
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}
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@property
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def centroid(self) -> tuple[float, float]:
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lats = [pt.lat for pt in self.way.nodes]
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lons = [pt.lon for pt in self.way.nodes]
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return (statistics.mean(lats), statistics.mean(lons))
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def check_slope(self):
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self.is_correct_slope, self.correct_slope_confidence = self._calculate_slope()
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def _calculate_slope(self) -> tuple[CorrectSlope, SlopeConfidence]:
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start_pts = self.way.nodes[0:5]
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end_pts = self.way.nodes[-5:]
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start_eles = [utils.get_elevation(pt.lat, pt.lon) for pt in start_pts]
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end_eles = [utils.get_elevation(pt.lat, pt.lon) for pt in end_pts]
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start_median = statistics.median(start_eles)
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self.start_ele = start_median
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end_median = statistics.median(end_eles)
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self.end_ele = end_median
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length = self.length
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if length < 1:
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raise RiverTooShort()
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slope = (end_median - start_median) / length
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absele = abs(end_median - start_median)
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if absele < 10:
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# 10 meters difference between start and end... too risky
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return (start_median >= end_median, self.POOR_CONFIDENCE)
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if absele > 300:
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# 300 meters difference. High confidence
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return (start_median > end_median, self.HIGH_CONFIDENCE)
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if slope < -0.02:
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return (True, self.MEDIUM_CONFIDENCE)
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if slope < 0:
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return (True, self.POOR_CONFIDENCE)
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if slope > 0.03:
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return (False, self.MEDIUM_CONFIDENCE)
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if slope >= 0:
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return (False, self.POOR_CONFIDENCE)
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# we should never reach this point
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1/0
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return (False, self.POOR_CONFIDENCE)
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