def computeGeoDist(G):
"""Compute Geographical distances with waypoints"""
path = nx.all_pairs_dijkstra_path(G)
outf = open("african-origin-homelands-distances-1.txt", "w")
for src, src_coordl in source_dict.iteritems():
# print src, src_coordl
src_area = src_coordl[0]
src_coord = src_coordl[1]
for hm, hm_coordl in hmcoord_dict.iteritems():
hm_area = hm_coordl[0]
hm_coord = hm_coordl[1]
distance = 0
if src_area != hm_area:
path_list = path[src_area][hm_area]
coord_list = []
coord_list.append(src_coord)
# distance += getDistance(src_coord,G[src_area][path_list[1]]["coord"])
for i in range(len(path_list)-1):
coord_list.append(G[path_list[i]][path_list[i+1]]["coord"])
coord_list.append(hm_coord)
# print coord_list
for i in range(len(coord_list)-1):
distance += getDistance(coord_list[i],coord_list[i+1])
else:
distance = getDistance(src_coord, hm_coord)
outf.write(src+"\t"+hm+"\t"+str(distance)+"\n")
print src, hm, distance
def getclosestpoint (curpos):
for pos in range(len(latlngdata)-1):
pathpos = (latlngdata[pos][0], latlngdata[pos][1])
dist = getDistance(pathpos,curpos)
pathpos = (latlngdata[pos+1][0], latlngdata[pos+1][1])
nextdist = getDistance(pathpos,curpos)
if nextdist >= dist:
break
#~ print dist
#~ print pos
return sum(distancedata[0:pos])
def getclosestpoint (curpos,lastpos): for pos in range(len(latlngdata)-1): #~ savedpos = pos # iterate only over future positions #~ pos = savedpos + pos # iterate only over future positions pathpos = (latlngdata[pos+lastpos][0], latlngdata[pos+lastpos][1]) dist = getDistance(pathpos,curpos) pathpos = (latlngdata[pos+lastpos+1][0], latlngdata[pos+lastpos+1][1]) nextdist = getDistance(pathpos,curpos) if nextdist >= dist: break #~ print dist #~ print pos #~ savedpos = pos # iterate only over future positions return (sum(distancedata[0:pos+lastpos]),pos+lastpos)
def nearestTubeStations(geolocation): r = [] for uuid in tube_stations: geo_station=tube_stations[uuid][4] if (gislib.isWithinDistance(geolocation,geo_station,0.9)): distance_km = gislib.getDistance(geolocation,geo_station) r.append((tube_stations[uuid][2],int(1000*distance_km),calculate_walking_time(distance_km))) return r
def __findNearest_helper(self, db, lat, lon, bearing, speed):
cur = db.cursor()
# SQL to define a circle around the home point with a given radius
circle = 'BuildCircleMbr(%f, %f, %f)' % (lat, lon, self.__radius / 111319.0)
# SQL to calculate distance between geocache and home point
dist = 'greatcirclelength(geomfromtext("linestring(" || x(location) || " " || y(location) || ", %f %f)", 4326))' % (lat, lon)
# Query for caches within circle and order by distance
rs = cur.execute('select code, description, x(location), y(location), URL from gc where MbrWithin(location, %(searchCircle)s)' % {'dist':dist, 'searchCircle':circle})
data = []
for row in rs:
coord = (row[2], row[3])
data.append({
'code': row[0],
'description': row[1],
'URL': row[4],
'distance': int(1000.0 * gislib.getDistance(coord, (lat, lon))),
'bearing': gislib.calculateBearing((lat, lon), coord),
'position': coord
})
if len(data) == 0: return None
# sort by distance again since we aren't using the database distance
# this should be removed if I switch back to using the Spatialite dist.
data.sort(key=lambda x: x['distance'])
# If first cache is within closeRadius return that
if data[0]['distance'] < CLOSE_RADIUS:
print "cache within ", CLOSE_RADIUS, "m"
return data[0]
# only right in front of us.
if (speed < .2778 * SPEED_THRESHOLD):
print "slow speed", (speed / .2778), "km/h"
return data[0]
# sift through caches looking for closest one within our travel path
for row in data:
# look for caches within 200m of path of travel
distanceFromPath = geom.distanceFromPath(bearing, row['bearing'], row['distance'])
if distanceFromPath and distanceFromPath < MAXIMUM_DISTANCE_FROM_PATH:
return row
# look for cache within 30 degree arc. This has dumb behaviour if dist is large
#if gislib.isAngleWithin(bearing, row['bearing'], 15):
# return row
return None
def __findNearest_helper(self, db, lat, lon, bearing, speed):
cur = db.cursor()
# SQL to define a circle around the home point with a given radius
circle = 'BuildCircleMbr(%f, %f, %f)' % (lat, lon,
self.__radius / 111319.0)
# SQL to calculate distance between geocache and home point
dist = 'greatcirclelength(geomfromtext("linestring(" || x(location) || " " || y(location) || ", %f %f)", 4326))' % (
lat, lon)
# Query for caches within circle and order by distance
rs = cur.execute(
'select code, description, x(location), y(location) from gc where MbrWithin(location, %(searchCircle)s)'
% {
'dist': dist,
'searchCircle': circle
})
data = []
for row in rs:
coord = (row[2], row[3])
data.append({
'code':
row[0],
'description':
row[1],
'distance':
int(1000.0 * gislib.getDistance(coord, (lat, lon))),
'bearing':
gislib.calculateBearing((lat, lon), coord),
'position':
coord
})
if len(data) == 0: return None
# sort by distance again since we aren't using the database distance
# this should be removed if I switch back to using the Spatialite dist.
data.sort(key=lambda x: x['distance'])
# If first cache is within closeRadius return that
if data[0]['distance'] < CLOSE_RADIUS:
print "cache within ", CLOSE_RADIUS, "m"
return data[0]
# only right in front of us.
if (speed < .2778 * SPEED_THRESHOLD):
print "slow speed", (speed / .2778), "km/h"
return data[0]
# sift through caches looking for closest one within our travel path
for row in data:
# look for caches within 200m of path of travel
distanceFromPath = geom.distanceFromPath(bearing, row['bearing'],
row['distance'])
if distanceFromPath and distanceFromPath < MAXIMUM_DISTANCE_FROM_PATH:
return row
# look for cache within 30 degree arc. This has dumb behaviour if dist is large
#if gislib.isAngleWithin(bearing, row['bearing'], 15):
# return row
return None
