def initial_placement(intersection_position, intersection_matrix):
x0 = 99990
x1 = 0
y0 = 99990
y1 = 0
for i in intersection_position:
if i[0] < x0:
x0 = i[0]
if i[1] < y0:
y0 = i[1]
if i[0] > x1:
x1 = i[0]
if i[1] > y1:
y1 = i[1]
## 多控制器初始化
draw_list.clear()
rect_list.clear()
position_list = []
no = 0
## 初始布置算法
chushu = 0.00019 * Gp.con_dis * Gp.con_dis
## 在所有路口出放置控制器 (=ﾟωﾟ)=b
for key, i in enumerate(intersection_position):
a = [i[0], i[1]]
position_list.append(a)
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
num = 0
for x, y in zip(a[:, 0], a[:, 1]):
plt.text(x, y + 0.3, str(num), ha='center', va='bottom', fontsize=10.5)
num += 1
plt.show()
## 对于路口间路段长度过长的（>= 2*通信范围），补充控制器以实现全覆盖。(｀･ω･)
c_n = intersection_position.__len__()
for i in range(c_n):
for j in range(c_n):
if intersection_matrix[i][j] > Gp.con_dis * 2:
##（>= 4*通信范围）以圆相切的方式补充控制器
if intersection_matrix[i][j] > Gp.con_dis * 4:
len = intersection_matrix[i][j]
k = (intersection_position[j][1] - intersection_position[i][1]) / \
(intersection_position[j][0] - intersection_position[i][0])
x = math.sqrt(pow(Gp.con_dis * 2, 2) / (k * k + 1))
## 两路口谁长谁高 作比较，做标记
if intersection_position[j][0] > intersection_position[i][0]:
x_flag = 1
else:
x_flag = -1
if intersection_position[j][1] > intersection_position[i][1]:
y_flag = 1
else:
y_flag = -1
c_x = intersection_position[i][0]
c_y = intersection_position[i][1]
## 剩余路段长度放得下时，循环添加控制器
while len >= 0:
c_x += x_flag * x
c_y += y_flag * k * x
a = [c_x, c_y]
position_list.append(a)
len -= Gp.con_dis * 2
c_x += x_flag * x
c_y += y_flag * k * x
a = [c_x, c_y]
position_list.append(a)
## 在最后一个坐标与j中间放一个
##（<= 4*通信范围 and >= 2*通信范围）时，两路口中点放置控制器，即可全覆盖
else:
a = [(intersection_position[i][0] + intersection_position[j][0]) / 2, \
(intersection_position[i][1] + intersection_position[j][1]) / 2]
position_list.append(a)
n_best = 0
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
plt.show()
## 过于拥挤时，减少多余控制器，循环：判断密集控制器，删除密集控制器，鱼鳞布置新控制器 (*´∀`)
for time in range(100):
n = position_list.__len__()
edge_c = 0
adj = [[0 for i in range(n)] for i in range(n)]
dis = 0
## 两路口间距离< 通信距离时，即两路口过于接近，在adj邻接矩阵中做标记
for key_i, i in enumerate(position_list):
for key_j, j in enumerate(position_list):
if key_i != key_j:
dis = pow(i[0] - j[0], 2) + pow(i[1] - j[1], 2)
if dis < pow(Gp.con_dis, 2):
adj[key_i][key_j] = 1
adj[key_j][key_i] = 1
delete_list = []
add_list = []
m = []
ress = []
s = Stack()
count = 0
n = position_list.__len__()
## 根据adj邻接矩阵，划分其路口图的联通子集，并将其转换为最小的长方形，以覆盖子集中所有成员 (σ`∀´)
for i in range(n):
if m.count(i) >= 1:
continue
else:
m.append(i)
result = getstack(adj, i, n, s)
if result == 0:
ress.append([i])
count += 1
else:
relay = [i]
while s.size() != 0:
temp = s.peek()
s.pop()
if m.count(temp) == 0:
relay.append(temp)
m.append(temp)
getstack(adj, temp, n, s)
## new!! 子集中节点为2个，进行判断，如果两点过于接近，就删除两点，取其中点，否则保留两点。 (*ﾟ∇ﾟ)
if relay.__len__() == 2:
d = pow((position_list[relay[0]][0] - position_list[relay[1]][0]), 2) + pow(
(position_list[relay[0]][1] - position_list[relay[1]][1]), 2)
if d <= pow(Gp.con_dis / 2, 2):
add_list.append([(position_list[relay[0]][0] + position_list[relay[1]][0]) / 2,
(position_list[relay[0]][1] + position_list[relay[1]][1]) / 2])
delete_list.append(relay[0])
delete_list.append(relay[1])
continue
for node in relay:
draw_list.append(node)
## 绘制长方形
min_x = 99999
min_y = 99999
max_x = 0
max_y = 0
for i in relay:
# delete_list.append(position_list[i])
if position_list[i][0] > max_x:
max_x = position_list[i][0]
if position_list[i][1] > max_y:
max_y = position_list[i][1]
if position_list[i][0] < min_x:
min_x = position_list[i][0]
if position_list[i][1] < min_y:
min_y = position_list[i][1]
## new!!适当扩大长方形面积，边缘更好地与其他长方形适应 (*ﾟ∇ﾟ)
if min_x - (Gp.con_dis / chushu) >= x0:
min_x -= (Gp.con_dis / chushu)
if min_y - (Gp.con_dis / chushu) >= y0:
min_y -= (Gp.con_dis / chushu)
if max_x + (Gp.con_dis / chushu) <= x1:
max_x += (Gp.con_dis / chushu)
if max_y + (Gp.con_dis / chushu) <= y1:
max_y += (Gp.con_dis / chushu)
# min_x -= (Gp.con_dis / chushu)
# min_y -= (Gp.con_dis / chushu)
# max_x += (Gp.con_dis / chushu)
# max_y += (Gp.con_dis / chushu)
# # min_x -= (Gp.con_dis / 12)
# # min_y -= (Gp.con_dis / 12)
# # max_x += (Gp.con_dis / 12)
# # max_y += (Gp.con_dis / 12)
rect_list.append([min_x, min_y, max_x, max_y, 0])
## 对长方形内面积实现鱼鳞布置
## new!! 所有子集所属长方形进行判断，如互相重叠，即融合两个长方形 (*ﾟ∇ﾟ)
rett = []
for tt in range(10):
for ri in rect_list:
if ri[4] != 0:
continue
for rj in rect_list:
if ri == rj:
continue
if rj[4] != 0:
continue
if (isOverlap(ri[0], ri[1], ri[2], ri[3], rj[0], rj[1], rj[2], rj[3]) == 1):
ri[4] = 1
rj[4] = 1
## 融合
rett.append([min(ri[0], rj[0]), min(ri[1], rj[1]), max(ri[2], rj[2]), max(ri[3], rj[3]), 0])
for r in rect_list[::-1]:
if r[4] == 1:
rect_list.remove(r)
for r in rett:
rect_list.append(r)
rett.clear()
## new!! 根据新的子集长方形，进行鱼鳞分布 (*ﾟ∇ﾟ)
for rr in rect_list:
add = fish_placement(rr[0], rr[1], rr[2], rr[3], 0)
for i in add:
add_list.append(i)
## new!! 所有长方形范围中节点全部删除 (*ﾟ∇ﾟ)
for r in rect_list:
for node in position_list:
if node[0] >= r[0] and node[0] <= r[2] and node[1] >= r[1] and node[1] <= r[3]:
delete_list.append(node)
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
for r in rect_list:
plt.gca().add_patch(plt.Rectangle(xy=(r[0], r[1]), width=r[2] - r[0], height=r[3] - r[1],
fill=False, linewidth=2))
plt.show()
rect_list.clear()
## 删除子集内所有原控制器
for i in position_list[::-1]:
time1 = 0
if delete_list.count(i) >= 1:
# for node in position_list:
# if time1 >= 2:
# break
# d = pow(i[0] - node[0], 2) + pow(i[1] - node[1], 2)
# if d <= pow(Gp.con_dis,2):
# time1 += 1
# if time1 >= 2:
position_list.remove(i)
## 将鱼鳞布置控制器加入
for iii in add_list:
position_list.append(iii)
# a = np.array(position_list)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
# plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis*Gp.con_dis*0.024, edgecolors='blue')
# plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
# plt.show()
number = position_list.__len__()
## 如果结果稳定，即视为最优解，不然继续循环：判断密集控制器，删除密集控制器，鱼鳞布置新控制器
if number == n_best:
time += 1
if time >= 10:
break
else:
n_best = number
edge_list = []
## 统计所有路口间路段，起始点坐标 (〃∀〃)
for i in range(c_n):
for j in range(i):
if intersection_matrix[i][j] != 0:
edge_list.append([intersection_position[i], intersection_position[j], 0])
flag = 0
## 对每个控制器判断，自身是否覆盖到任一路段，是否自身为无用控制器，对路段自身也做判断，无控制器覆盖则做标记 (*´ω`*)
for con in position_list[::-1]:
for edge in edge_list:
if lc.Judis(edge[0][0], edge[0][1], edge[1][0], edge[1][1], con[0], con[1], Gp.con_dis) == 1:
flag += 1
edge[2] = 1
## 删除所有无效控制器
if flag == 0:
position_list.remove(con)
flag = 0
## 将无控制器负责的路段 在路段中间架设控制器 (ゝ∀･)☆
for edge in edge_list:
if edge[2] == 0:
position_list.append([(edge[0][0] + edge[1][0]) / 2, (edge[0][1] + edge[1][1]) / 2])
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
plt.show()
## 避免过于密集，再次鱼鳞 (＾o＾)ﾉ
n = position_list.__len__()
edge_c = 0
adj = [[0 for i in range(n)] for i in range(n)]
dis = 0
for con in position_list:
con.append(0)
con.append(0)
time = 0
ve = 0
flag = 0
movement_matrix, init_position_matrix = Gm.get_position('tiexi1.tcl')
time_topo = [[] for i in range(10000)]
for i in movement_matrix:
time_topo[int(i[0, 0])].append([i[0, 2], i[0, 3]])
## 统计sumo每一时刻数据中车辆位置数据，对于孤岛节点用controller中第四个属性做标记 (*´∀`)
for graph in time_topo:
for node in graph:
for nodei in graph:
if node != nodei:
if pow(nodei[1] - node[1], 2) + pow(nodei[0] - node[0], 2) <= pow(Gp.com_dis, 2):
flag = 1
break
if flag == 0:
for con in position_list:
if pow(con[1] - node[1], 2) + pow(con[0] - node[0], 2) <= pow(Gp.con_dis, 2):
con[3] += 1
flag = 0
## 对车辆拓扑进行长时间统计，对控制范围下车辆过于稀疏的控制器进行删除处理 (*´∀`)
with open("tiexi1.tcl", 'r') as f:
item_list = []
for line in f:
line_list = re.split('[\s]', line)
if line_list[0] != '':
if (float(line_list[2]) > time):
time = float(line_list[2])
if (float(line_list[3][8:-1]) > ve):
ve = float(line_list[3][8:-1])
item_list.append(float(line_list[2]))
item_list.append(float(line_list[3][8:-1]))
item_list.append(float(line_list[5]))
item_list.append(float(line_list[6]))
item_list.append(float(line_list[7][0:-1]))
## 统计sumo每一时刻数据中车辆位置数据，对于车流量用controller中第三个属性做标记 *･ﾟ(*´ω`*)･
for con in position_list:
d = pow(con[0] - item_list[2], 2) + pow(con[1] - item_list[3], 2)
if d < pow(Gp.con_dis, 2):
con[2] += 1
item_list.clear()
flag = 0
## 可能删去的偏僻控制器 *･ﾟ(*´ω`*)･
position_list_can = []
## 车流量大需要固定的控制器 (*ﾟ∇ﾟ)♡
position_list_fixed = []
for con in position_list[::-1]:
# 阈值应根据多个因素做调整，包括通信半径，数据包含时长，数据总车辆数等，车流量小且孤岛节点多的控制器删去 ( ﾟ 3ﾟ)
if con[2] <= (pow(Gp.con_dis, 2) * math.pi) / ((x1 - x0) * (y1 - y0)) * ve * time / 6 and con[3] > (
(pow(Gp.con_dis, 2)) / ((x1 - x0) * (y1 - y0)) * ve * time / 14.3):
position_list_can.append(con)
position_list.remove(con)
# 车流量大的固定
if con[2] >= (pow(Gp.con_dis, 2) * math.pi) / ((x1 - x0) * (y1 - y0)) * ve * time / 3:
position_list_fixed.append(con)
position_list.remove(con)
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024)
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_fixed)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='y', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_can)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='m', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
plt.show()
## 对车流量大的控制器进行标记，相互之间距离近的连接 (＾o＾)ﾉ
n = position_list_fixed.__len__()
fixed_position_adj = [[0 for i in range(n)] for i in range(n)]
for numi, i in enumerate(position_list_fixed):
for numj, j in enumerate(position_list_fixed):
if pow(j[1] - i[1], 2) + pow(j[0] - i[0], 2) <= pow(Gp.con_dis * 2, 2):
fixed_position_adj[numi][numj] = 1
delete_list = []
add_list = []
m = []
ress = []
s = Stack()
rect_list.clear()
count = 0
## 与上文同样的步骤，联通子集，画长方形 (`ε´ )
for i in range(n):
if m.count(i) >= 1:
continue
else:
m.append(i)
result = getstack(fixed_position_adj, i, n, s)
if result == 0:
ress.append([i])
count += 1
else:
relay = [i]
while s.size() != 0:
temp = s.peek()
s.pop()
if m.count(temp) == 0:
relay.append(temp)
m.append(temp)
getstack(fixed_position_adj, temp, n, s)
for node in relay:
draw_list.append(node)
## 绘制长方形
min_x = 99999
min_y = 99999
max_x = 0
max_y = 0
for i in relay:
# delete_list.append(position_list_fixed[i])
if position_list_fixed[i][0] > max_x:
max_x = position_list_fixed[i][0]
if position_list_fixed[i][1] > max_y:
max_y = position_list_fixed[i][1]
if position_list_fixed[i][0] < min_x:
min_x = position_list_fixed[i][0]
if position_list_fixed[i][1] < min_y:
min_y = position_list_fixed[i][1]
# min_x -= (Gp.con_dis / chushu)
# min_y -= (Gp.con_dis / chushu)
# max_x += (Gp.con_dis / chushu)
# max_y += (Gp.con_dis / chushu)
# # min_x -= (Gp.con_dis / 12)
# # min_y -= (Gp.con_dis / 12)
# # max_x += (Gp.con_dis / 12)
# # max_y += (Gp.con_dis / 12)
rect_list.append([min_x, min_y, max_x, max_y, 0])
## 对长方形内面积实现鱼鳞布置
fig = plt.figure(figsize=(8, 7.82), dpi=80)
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024)
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_fixed)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='y', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_can)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='m', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
for r in rect_list:
plt.gca().add_patch(plt.Rectangle(xy=(r[0], r[1]), width=r[2] - r[0], height=r[3] - r[1],
fill=False, linewidth=2))
plt.show()
# ## new!! 所有子集所属长方形进行判断，如互相重叠，即融合两个长方形 (*ﾟ∇ﾟ)
# rett = []
# for tt in range(10):
# for ri in rect_list:
# if ri[4] != 0:
# continue
# for rj in rect_list:
# if ri == rj:
# continue
# if rj[4] != 0:
# continue
# if (isOverlap(ri[0], ri[1], ri[2], ri[3], rj[0], rj[1], rj[2], rj[3]) == 1):
# ri[4] = 1
# rj[4] = 1
# ## 融合
# rett.append([min(ri[0], rj[0]), min(ri[1], rj[1]), max(ri[2], rj[2]), max(ri[3], rj[3]), 0])
# for r in rect_list[::-1]:
# if r[4] == 1:
# rect_list.remove(r)
# for r in rett:
# rect_list.append(r)
# rett.clear()
## new!! 根据新的子集长方形，进行鱼鳞分布 (*ﾟ∇ﾟ)
for rr in rect_list:
xx = range(int(rr[0]), int(rr[2]), int(4 * Gp.con_dis))
yy = range(int(rr[1]), int(rr[3]), int(4 * Gp.con_dis))
## 将长方形分割成同样大小的长方形，如果长方形内固定控制器超过一定数量,删去之中所有节点，稀疏布置 σ`∀´)
for i in xx:
for j in yy:
count = 0
for con in position_list_fixed:
if i + 4 * Gp.con_dis >= con[0] >= i and j <= con[1] <= j + 4 * Gp.con_dis:
count += 1
if count >= 4:
for con in position_list_fixed[::-1]:
if i + 4 * Gp.con_dis >= con[0] >= i and j <= con[1] <= j + 4 * Gp.con_dis:
position_list_fixed.remove(con)
for con in position_list[::-1]:
if i + 4 * Gp.con_dis >= con[0] >= i and j <= con[1] <= j + 4 * Gp.con_dis:
position_list.remove(con)
position_list_fixed.append([i + Gp.con_dis, j + Gp.con_dis, 0, 0])
position_list_fixed.append([i + 3 * Gp.con_dis, j + Gp.con_dis, 0, 0])
position_list_fixed.append([i + 3 * Gp.con_dis, j + 3 * Gp.con_dis, 0, 0])
position_list_fixed.append([i + Gp.con_dis, j + 3 * Gp.con_dis, 0, 0])
a = np.array(position_list)
fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024)
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_fixed)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='y', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
a = np.array(position_list_can)
# fig = plt.figure(figsize=(8, 7.82), dpi=80)
plt.scatter(a[:, 0], a[:, 1], alpha=0.5, s=Gp.con_dis * Gp.con_dis * 0.024, c='m', edgecolors='blue')
plt.scatter(a[:, 0], a[:, 1], alpha=1, s=1, edgecolors='black')
for r in rect_list:
plt.gca().add_patch(plt.Rectangle(xy=(r[0], r[1]), width=r[2] - r[0], height=r[3] - r[1],
fill=False, linewidth=2))
plt.show()
## 对固定控制器之中较为靠近控制器对进行稀疏分布 (*ﾟ∇ﾟ)
for numi,i in enumerate(position_list_fixed):
for numj,j in enumerate(position_list_fixed):
if numi < numj:
d = pow(i[0] - j[0], 2) + pow(i[1] - j[1], 2)
if d <= pow(Gp.con_dis, 2):
add_list.append([(i[0] + j[0]) / 2, (i[1] + j[1]) / 2, 0,0])
delete_list.append(i)
delete_list.append(j)
continue
for i in add_list:
position_list_fixed.append(i)
for j in delete_list:
position_list_fixed.remove(j)
## 返回所有控制器位置，算法完成 (＾o＾)ﾉ
return position_list, position_list_fixed
import Get_Move as Gm
import Init
import numpy as np
import Global_Par as Gp
node_list = []
com_node_list = []
node_num, sim_time = Gm.get_sim_parameter('grid.config.tcl')
movement_matrix, init_position_matrix = Gm.get_position('grid.mobility.tcl')
controller = Init.init_controller(0, node_num)
init_position_arranged = init_position_matrix[np.lexsort(
init_position_matrix[:, ::-1].T)]
node_position = init_position_arranged[0]
node_position = np.insert(node_position, 0, values=np.zeros(node_num), axis=1)
node_position = np.column_stack((node_position, node_position[:, 2:4]))
node_position = np.insert(node_position, 6, values=np.zeros(node_num), axis=1)
node_list = (Init.init_node(node_position, controller))
com_node_list.extend(Init.get_communication_node(node_num))
# for time in range(sim_time):
time = 0
print('Time: %d' % time)
current_move = movement_matrix[np.nonzero(movement_matrix[:,
0].A == time)[0], :]
for value in current_move:
for i in range(2, 4):
node_position[int(value[0, 1]), i] = value[0, i]
node_id_position = node_position[:, [1, 2, 3]]
# print(node_id_position[44])
for node in node_list:
import time as t
import jhmmtg as jh
import junction_init as ji
# import big_junction_init as bji
import big_jhmmtg as bjh
# import big_HRLB as bhr
import HRLB as hr
import re
import tibar_prediction as tp
import tgeaa as tg
node_list = []
com_node_list = []
sim_time = 309 # int(input("sim_time:"))
# 位置文件读取
movement_matrix, init_position_matrix = Gm.get_position('tiexi1.tcl')
node_num = init_position_matrix.shape[0]
# 控制器初始化
controller = Init.init_controller(node_num)
# 位置数据处理
init_position_arranged = init_position_matrix[np.lexsort(
init_position_matrix[:, ::-1].T)]
node_position = init_position_arranged[0]
# node_position = np.insert(node_position, 0, values=np.zeros(node_num), axis=1)
# node_position = np.column_stack((node_position, node_position[:, 2:4]))
# node_position = np.insert(node_position, 6, values=np.zeros(node_num), axis=1)
# ji.inti()
import sys
import re
import Get_Move as Gm
# command example: 'aodv --animation=true'
if __name__ == '__main__':
configfile_path = r'grid.config.tcl'
commandstr = input('Please enter a command：')
cmdlist = re.split('[\s=-]', commandstr)
try:
animation = cmdlist[cmdlist.index('animation') + 1]
except:
print("Invalid Input")
sys.exit(0)
if animation != 'true' and animation != 'false':
print('Animation option error\n')
sys.exit(0)
flag = (animation == 'true')
node_num, sim_time = Gm.get_sim_parameter(configfile_path)
Gm.control_movement(flag)