def voronoi(self, points, points_center):
vor = Voronoi(points)
vor.filtered_points = points_center
vor.filtered_regions = [
vor.regions[vor.point_region[i]] for i in range(len(points_center))
]
return vor
def voronoi(cells, bounding_box):
"""
Solution based on
https://stackoverflow.com/a/33602171
with only slight modification
"""
def in_box(cells, bounding_box):
return np.logical_and(
np.logical_and(bounding_box[0] <= cells[:, 0],
cells[:, 0] <= bounding_box[1]),
np.logical_and(bounding_box[2] <= cells[:, 1],
cells[:, 1] <= bounding_box[3]))
eps = sys.float_info.epsilon
# Select cells inside the bounding box
i = in_box(cells, bounding_box)
# Mirror points
points_center = cells[i, :]
points_left = np.copy(points_center)
points_left[:, 0] = bounding_box[0] - \
(points_left[:, 0] - bounding_box[0])
points_right = np.copy(points_center)
points_right[:, 0] = bounding_box[1] + \
(bounding_box[1] - points_right[:, 0])
points_down = np.copy(points_center)
points_down[:, 1] = bounding_box[2] - \
(points_down[:, 1] - bounding_box[2])
points_up = np.copy(points_center)
points_up[:, 1] = bounding_box[3] + (bounding_box[3] - points_up[:, 1])
points = np.append(points_center,
np.append(np.append(points_left, points_right, axis=0),
np.append(points_down, points_up, axis=0),
axis=0),
axis=0)
# Compute Voronoi
vor = Voronoi(points)
# Filter regions
regions = []
for region in vor.regions:
flag = True
for index in region:
if index == -1:
flag = False
break
else:
x = vor.vertices[index, 0]
y = vor.vertices[index, 1]
if not (bounding_box[0] - eps <= x
and x <= bounding_box[1] + eps
and bounding_box[2] - eps <= y
and y <= bounding_box[3] + eps):
flag = False
break
if region != [] and flag:
regions.append(region)
vor.filtered_points = points_center
vor.filtered_regions = regions
return vor
def voronoi(coords, factor):
"""
Calculate diagram voronoi for a set of datapoints (coords) with a bounding factor
:param coords: Voronoi diagram will be applied to coords points
:param factor: Factor by which we want to limit our diagram
:return: An object of Voronoi type with properties attributes like regions,ets.
"""
bounding_box = np.array([
min(coords[:, 0]) - factor,
max(coords[:, 0]) + factor,
min(coords[:, 1]) - factor,
max(coords[:, 1]) + factor
]) # [x_min, x_max, y_min, y_max] in the map
# Select coords inside the bounding box
i = _in_box(coords, bounding_box)
# Mirror points
points_center = coords[i, :]
points_left = np.copy(points_center)
points_left[:, 0] = bounding_box[0] - (points_left[:, 0] - bounding_box[0])
points_right = np.copy(points_center)
points_right[:,
0] = bounding_box[1] + (bounding_box[1] - points_right[:, 0])
points_down = np.copy(points_center)
points_down[:, 1] = bounding_box[2] - (points_down[:, 1] - bounding_box[2])
points_up = np.copy(points_center)
points_up[:, 1] = bounding_box[3] + (bounding_box[3] - points_up[:, 1])
points = np.append(points_center,
np.append(np.append(points_left, points_right, axis=0),
np.append(points_down, points_up, axis=0),
axis=0),
axis=0)
plt.show()
# Compute Voronoi
vor = Voronoi(points)
# Filter regions
regions = []
for region in vor.regions:
flag = True
for index in region:
if index == -1:
flag = False
break
x = vor.vertices[index, 0]
y = vor.vertices[index, 1]
if not (bounding_box[0] - eps <= x <= bounding_box[1] + eps
and bounding_box[2] - eps <= y <= bounding_box[3] + eps):
flag = False
break
if region != [] and flag:
regions.append(region)
vor.filtered_points = points_center
vor.filtered_regions = regions
return vor
def voronoi(self, towers, bounding_box=None):
if bounding_box is None:
bounding_box = self.bounding_box
self.points = towers
# Select towers inside the bounding box
i = self.in_box(towers, bounding_box)
# Mirror points
points_center = towers[i, :]
points_left = np.copy(points_center)
points_left[:, 0] = bounding_box[0] - (points_left[:, 0] -
bounding_box[0])
points_right = np.copy(points_center)
points_right[:, 0] = bounding_box[1] + (bounding_box[1] -
points_right[:, 0])
points_down = np.copy(points_center)
points_down[:, 1] = bounding_box[2] - (points_down[:, 1] -
bounding_box[2])
points_up = np.copy(points_center)
points_up[:, 1] = bounding_box[3] + (bounding_box[3] - points_up[:, 1])
points = np.append(points_center,
np.append(np.append(points_left,
points_right,
axis=0),
np.append(points_down, points_up, axis=0),
axis=0),
axis=0)
# Compute Voronoi
vor = Voronoi(points)
# Filter regions
regions = []
for region in vor.regions:
flag = True
for index in region:
if index == -1:
flag = False
break
else:
x = vor.vertices[index, 0]
y = vor.vertices[index, 1]
if not (bounding_box[0] - self.eps <= x
and x <= bounding_box[1] + self.eps
and bounding_box[2] - self.eps <= y
and y <= bounding_box[3] + self.eps):
flag = False
break
if region != [] and flag:
regions.append(region)
vor.filtered_points = points_center
vor.filtered_regions = regions
return vor
