def _create_groups(self, vectors):
groups = []
for center in self._centers:
groups.append([center])
for vector in vectors:
group_number, min_distance = 0, utils._distance(vector, self._centers[0])
for (number, center) in enumerate(self._centers):
cur_distance = utils._distance(vector, center)
if cur_distance < min_distance:
group_number = number
min_distance = cur_distance
groups[group_number].append(vector)
return groups
def ping(self, pinger_id, pingee_id):
pinger_loc = self.location(pinger_id)
pingee_loc = self.location(pingee_id)
distance = utils._distance(pinger_loc, pingee_loc)
return round(distance)
def _calculate_new_group(self, group_center, threshold_distance):
group = []
new_center = []
while not utils._are_equals(group_center, new_center):
global group
group = []
group_center = new_center
for vector in self._vectors:
distance_to_center = utils._distance(vector, group_center)
if distance_to_center < threshold_distance:
group.append(vector)
new_center = self._calculate_group_center(group)
for vector in group:
self._vectors.remove(vector)
self._groups.append(group)
from utils import _distance
# [200~40.59.36 73.22.36 73.21.40[201~
h1 = 73
m1 = 22
s1 = 36
h2 = 73
m2 = 21
s2 = 40
hh = 40
mm = 59
ss = 36
def conv(h, m, s):
return h + m/60.0 + s/3600.0
l1 = (conv(hh, mm, ss), conv(h1, m1, s1))
l2 = (conv(hh, mm, ss), conv(h2, m2, s2))
print _distance(l1, l2)
def process_f(distance_marker_set, i):
for m in distance_marker_set.distance_markers:
key = "%.1f" % m.distance
points.append((key, m.location))
print utils._distance(target.location(), m.location)
kml.points(points, "%s-%d" % (time.time(), i))