tk.update()
time.sleep(0.01)
if common.is_all_in_target(all_sheep) or step > 2000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
break
step += 1
return step
if __name__ == '__main__':
"""
选择不同的角度：10, 20, 30, 40, 50, 60, 70
羊群规模设定为：60只
"""
tk, canvas = gui.init_tkinter()
steps = []
n = 60
theta = math.pi / 4.5
for k in range(1, n):
all_sheep, sheep_dict, shepherd_a = init_sheep(canvas, n)
step = run_animation(all_sheep, sheep_dict, shepherd_a, theta, k)
steps.append(step)
print("current theta = {}, and step {}".format(k, step))
print("max double distance animation over!")
common.print_list(steps)
tk.mainloop()
def run_animation(all_sheep, sheep_dict, herd):
step = 0
target = np.array([600, 600])
r_dist = 250
r_rep = 14
speed = 2
n = len(all_sheep)
app_dist = n + 50
theta = math.pi / 4.5
fn = math.sqrt(n) * r_rep
last_vector = np.zeros((n, 2), dtype=np.float32)
# 处理轨迹
UU = []
VV = []
XX = []
YY = []
Px = {}
Py = {}
for i in range(n):
Px['coor' + str(i)] = []
Py['coor' + str(i)] = []
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
dist_center = 0
dist_shepherd = 0
pre_mean = np.array([np.mean(all_sheep[:, 0]), np.mean(all_sheep[:, 1])])
pre_herd = herd.position2point()
while True:
herd_point = herd.position2point().copy()
if common.check_sector(all_sheep, theta, target) and common.check_dist(
all_sheep, target, fn):
shepherdR.driving(herd, all_sheep, speed, target, app_dist)
else:
shepherdR.collecting(herd, all_sheep, speed, app_dist, target)
sheepR.sheep_move(herd_point, all_sheep, r_dist, r_rep, speed,
sheep_dict, last_vector)
tk.update()
time.sleep(0.01)
if common.is_all_in_target(all_sheep) or step > 4000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
print("dispersion: ", common.calculate_dispersion(all_sheep))
break
# 处理轨迹
global_mean = np.array(
[np.mean(all_sheep[:, 0]),
np.mean(all_sheep[:, 1])])
dist_center += la.norm(pre_mean - global_mean)
pre_mean = global_mean
dist_shepherd += la.norm(pre_herd - herd.position2point())
pre_herd = herd.position2point()
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
XX.append(herd.position2point()[0])
YY.append(herd.position2point()[1])
UU.append(global_mean[0])
VV.append(global_mean[1])
step += 1
xx = np.array(XX)
yy = np.array(YY)
common.print_list(xx)
common.print_list(yy)
return step
