Better landmark finding #27

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remoll merged 6 commits from fix/backend/moore-tags into main 2024-10-22 09:20:54 +00:00
5 changed files with 30 additions and 19 deletions
Showing only changes of commit 40edd923c3 - Show all commits

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@ -9,7 +9,6 @@ name = "pypi"
numpy = "*"
fastapi = "*"
pydantic = "*"
geopy = "*"
shapely = "*"
scipy = "*"
osmpythontools = "*"

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@ -1,5 +1,5 @@
import yaml
from geopy.distance import geodesic
from math import sin, cos, sqrt, atan2, radians
import constants
@ -8,6 +8,7 @@ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
DETOUR_FACTOR = parameters['detour_factor']
AVERAGE_WALKING_SPEED = parameters['average_walking_speed']
EARTH_RADIUS_KM = 6373
def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
"""
@ -22,16 +23,28 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
"""
# Compute the straight-line distance in km
if p1 == p2:
return 0
else:
dist = geodesic(p1, p2).kilometers
# Compute the distance in km along the surface of the Earth
# (assume spherical Earth)
# this is the haversine formula, stolen from stackoverflow
# in order to not use any external libraries
lat1, lon1 = radians(p1[0]), radians(p1[1])
lat2, lon2 = radians(p2[0]), radians(p2[1])
# Consider the detour factor for average cityto deterline walking distance (in km)
walk_dist = dist*DETOUR_FACTOR
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = EARTH_RADIUS_KM * c
# Consider the detour factor for average an average city
walk_distance = distance * DETOUR_FACTOR
# Time to walk this distance (in minutes)
walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
walk_time = walk_distance / AVERAGE_WALKING_SPEED * 60
return round(walk_time)

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@ -3,7 +3,6 @@ import numpy as np
from scipy.optimize import linprog
from collections import defaultdict, deque
from geopy.distance import geodesic
from structs.landmark import Landmark
from .get_time_separation import get_time