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257 lines
7.2 KiB
257 lines
7.2 KiB
"""
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LPA_star 2D
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@author: huiming zhou
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"""
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import os
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import sys
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import math
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import matplotlib.pyplot as plt
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
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"/../../Search_based_Planning/")
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from Search_2D import plotting, env
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class LPAStar:
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def __init__(self, s_start, s_goal, heuristic_type):
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self.s_start, self.s_goal = s_start, s_goal
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self.heuristic_type = heuristic_type
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self.Env = env.Env()
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self.Plot = plotting.Plotting(self.s_start, self.s_goal)
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self.u_set = self.Env.motions
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self.obs = self.Env.obs
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self.x = self.Env.x_range
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self.y = self.Env.y_range
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self.g, self.rhs, self.U = {}, {}, {}
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for i in range(self.Env.x_range):
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for j in range(self.Env.y_range):
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self.rhs[(i, j)] = float("inf")
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self.g[(i, j)] = float("inf")
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self.rhs[self.s_start] = 0
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self.U[self.s_start] = self.CalculateKey(self.s_start)
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self.visited = set()
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self.count = 0
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self.fig = plt.figure()
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def run(self):
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self.Plot.plot_grid("Lifelong Planning A*")
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self.ComputeShortestPath()
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self.plot_path(self.extract_path())
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self.fig.canvas.mpl_connect('button_press_event', self.on_press)
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plt.show()
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def on_press(self, event):
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x, y = event.xdata, event.ydata
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if x < 0 or x > self.x - 1 or y < 0 or y > self.y - 1:
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print("Please choose right area!")
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else:
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x, y = int(x), int(y)
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print("Change position: s =", x, ",", "y =", y)
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self.visited = set()
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self.count += 1
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if (x, y) not in self.obs:
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self.obs.add((x, y))
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else:
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self.obs.remove((x, y))
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self.UpdateVertex((x, y))
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self.Plot.update_obs(self.obs)
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for s_n in self.get_neighbor((x, y)):
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self.UpdateVertex(s_n)
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self.ComputeShortestPath()
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plt.cla()
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self.Plot.plot_grid("Lifelong Planning A*")
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self.plot_visited(self.visited)
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self.plot_path(self.extract_path())
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self.fig.canvas.draw_idle()
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def ComputeShortestPath(self):
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while True:
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s, v = self.TopKey()
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if v >= self.CalculateKey(self.s_goal) and \
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self.rhs[self.s_goal] == self.g[self.s_goal]:
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break
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self.U.pop(s)
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self.visited.add(s)
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if self.g[s] > self.rhs[s]:
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# Condition: over-consistent (eg: deleted obstacles)
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# So, rhs[s] decreased -- > rhs[s] < g[s]
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self.g[s] = self.rhs[s]
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else:
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# Condition: # under-consistent (eg: added obstacles)
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# So, rhs[s] increased --> rhs[s] > g[s]
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self.g[s] = float("inf")
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self.UpdateVertex(s)
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for s_n in self.get_neighbor(s):
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self.UpdateVertex(s_n)
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def UpdateVertex(self, s):
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"""
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update the status and the current cost to come of state s.
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:param s: state s
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"""
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if s != self.s_start:
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# Condition: cost of parent of s changed
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# Since we do not record the children of a state, we need to enumerate its neighbors
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self.rhs[s] = min(self.g[s_n] + self.cost(s_n, s)
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for s_n in self.get_neighbor(s))
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if s in self.U:
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self.U.pop(s)
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if self.g[s] != self.rhs[s]:
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# Condition: current cost to come is different to that of last time
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# state s should be added into OPEN set (set U)
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self.U[s] = self.CalculateKey(s)
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def TopKey(self):
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"""
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:return: return the min key and its value.
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"""
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s = min(self.U, key=self.U.get)
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return s, self.U[s]
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def CalculateKey(self, s):
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return [min(self.g[s], self.rhs[s]) + self.h(s),
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min(self.g[s], self.rhs[s])]
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def get_neighbor(self, s):
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"""
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find neighbors of state s that not in obstacles.
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:param s: state
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:return: neighbors
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"""
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s_list = set()
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for u in self.u_set:
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s_next = tuple([s[i] + u[i] for i in range(2)])
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if s_next not in self.obs:
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s_list.add(s_next)
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return s_list
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def h(self, s):
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"""
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Calculate heuristic.
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:param s: current node (state)
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:return: heuristic function value
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"""
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heuristic_type = self.heuristic_type # heuristic type
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goal = self.s_goal # goal node
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if heuristic_type == "manhattan":
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return abs(goal[0] - s[0]) + abs(goal[1] - s[1])
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else:
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return math.hypot(goal[0] - s[0], goal[1] - s[1])
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def cost(self, s_start, s_goal):
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"""
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Calculate Cost for this motion
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:param s_start: starting node
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:param s_goal: end node
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:return: Cost for this motion
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:note: Cost function could be more complicate!
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"""
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if self.is_collision(s_start, s_goal):
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return float("inf")
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return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
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def is_collision(self, s_start, s_end):
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if s_start in self.obs or s_end in self.obs:
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return True
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if s_start[0] != s_end[0] and s_start[1] != s_end[1]:
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if s_end[0] - s_start[0] == s_start[1] - s_end[1]:
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s1 = (min(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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else:
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s1 = (min(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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if s1 in self.obs or s2 in self.obs:
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return True
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return False
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def extract_path(self):
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"""
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Extract the path based on the PARENT set.
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:return: The planning path
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"""
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path = [self.s_goal]
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s = self.s_goal
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for k in range(100):
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g_list = {}
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for x in self.get_neighbor(s):
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if not self.is_collision(s, x):
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g_list[x] = self.g[x]
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s = min(g_list, key=g_list.get)
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path.append(s)
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if s == self.s_start:
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break
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return list(reversed(path))
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def plot_path(self, path):
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px = [x[0] for x in path]
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py = [x[1] for x in path]
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plt.plot(px, py, linewidth=2)
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plt.plot(self.s_start[0], self.s_start[1], "bs")
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plt.plot(self.s_goal[0], self.s_goal[1], "gs")
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def plot_visited(self, visited):
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color = ['gainsboro', 'lightgray', 'silver', 'darkgray',
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'bisque', 'navajowhite', 'moccasin', 'wheat',
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'powderblue', 'skyblue', 'lightskyblue', 'cornflowerblue']
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if self.count >= len(color) - 1:
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self.count = 0
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for x in visited:
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plt.plot(x[0], x[1], marker='s', color=color[self.count])
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def main():
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x_start = (5, 5)
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x_goal = (45, 25)
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lpastar = LPAStar(x_start, x_goal, "Euclidean")
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lpastar.run()
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if __name__ == '__main__':
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main()
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