def uniform_random_sampling_with_distance_constraint(
self, sample_count, min_distance):
u"""
Perform sampling by substituting the source polygon with uniformly
distributed and randomized points that are separated from each other by the
given minimum distance.
"""
self.samples = list()
self.triangulate()
self.constrain_triangulation()
buffered_zone = Polygon()
while len(self.samples) < sample_count:
wr = weighted_random_sub(map(attrgetter('area'), self.triangles))
triangle = self.triangles[wr]
sp = self.create_point_in_triangle(triangle)
if buffered_zone.contains(sp):
continue
sp_buffer = sp.buffer(min_distance)
buffered_zone = buffered_zone.union(sp_buffer)
self.samples.append(sp)
else:
open(r"d:\tmp\buf.txt", 'wb').write(buffered_zone.__str__())
def uniform_random_sampling_with_distance_constraint(self, sample_count, min_distance):
u"""
Perform sampling by substituting the source polygon with uniformly
distributed and randomized points that are separated from each other by the
given minimum distance.
"""
self.samples = list()
self.triangulate()
self.constrain_triangulation()
buffered_zone = Polygon()
while len(self.samples) < sample_count:
wr = weighted_random_sub(map(attrgetter('area'), self.triangles))
triangle = self.triangles[wr]
sp = self.create_point_in_triangle(triangle)
if buffered_zone.contains(sp):
continue
sp_buffer = sp.buffer(min_distance)
buffered_zone = buffered_zone.union(sp_buffer)
self.samples.append(sp)
else:
open(r"d:\tmp\buf.txt", 'wb').write(buffered_zone.__str__())
def __str__(self):
return '{}: {}'.format(self._name, Polygon.__str__(self))
