def find_neighbors(grid, open_list=[], close_list=[], generated_map=[]):
grid_list = []
if is_valid_grid(grid.x, grid.y - 1, open_list, close_list, generated_map):
grid_list.append(Grid(grid.x, grid.y - 1))
if is_valid_grid(grid.x, grid.y + 1, open_list, close_list, generated_map):
grid_list.append(Grid(grid.x, grid.y + 1))
if is_valid_grid(grid.x - 1, grid.y, open_list, close_list, generated_map):
grid_list.append(Grid(grid.x - 1, grid.y))
if is_valid_grid(grid.x + 1, grid.y, open_list, close_list, generated_map):
grid_list.append(Grid(grid.x + 1, grid.y))
return grid_list
def test_setitem_slice_y(grid):
new_grid = Grid(1, 3, 99)
grid[0, :] = new_grid
assert grid[0, :] == [99] * 3
grid[0, :] = 20
assert grid[0, :] == [20, 20, 20]
grid[1, 1:3] = 30
assert grid[1, 1:3] == [30, 30]
new_grid = Grid(1, 3, 99)
grid[0, :] = new_grid
assert grid[0, :] == [99] * 3
def test_instanciate_with_values():
w, h = 4, 3
values = list(range(w * h))
mygrid = Grid(w, h, values)
assert mygrid[0] == [values, values, values, values]
assert mygrid[1, :] == [values] * 3
def test_instanciate_with_value_copy():
w, h = 4, 3
class Foo():
pass
mygrid = Grid(w, h, value=Foo(), copy=True)
assert mygrid[0, 0] is not mygrid[0, 1]
def test_setitem_slice_x(grid):
value = [10, 11, 12, 13]
grid[:, 0] = value
assert grid[:, 0] == [value] * 4
grid[:, 0] = 20
assert grid[:, 0] == [20, 20, 20, 20]
grid[1:5:2, 2] = 30
assert grid[:, 2] == [8, 30, 10, 30]
new_grid = Grid(4, 1, 99)
grid[:, 0] = new_grid
assert grid[:, 0] == [99] * 4
with pytest.raises(ValueError):
new_grid = Grid(4, 2, 99)
grid[:, 0] = new_grid
def test_eq(grid):
w, h = grid.width, grid.height
ngrid = Grid(w, h)
assert ngrid != grid
for j, line in enumerate(ngrid):
for i, elt in enumerate(line):
ngrid[j][i] = w * j + i
assert ngrid == grid
assert not ngrid is grid
def grid():
# 0 1 2 3
# 4 5 6 7
# 8 9 10 11
w, h = 4, 3
grid = Grid(w, h)
for j, line in enumerate(grid):
for i, elt in enumerate(line):
grid[j][i] = w * j + i
return grid
def single_turn(unittype, generated_map, CarSetting):
walkables = all_walkable(generated_map)
print("-" * 10, "run new simulation turn", "-" * 10, "\n")
# 设置起点和终点
start_grid = Grid(2, 1)
end_grid = Grid(2, 5)
if CarSetting.behavior_type == 0:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
if CarSetting.behavior_type == 1:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
if random.randint(0, 4) > 2:
end_grid = Grid(parkings[unittype][0], parkings[unittype][1])
else:
end_grid = Grid(e[0], e[1])
if CarSetting.behavior_type == 2:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
if random.randint(0, 4) > 2:
start_grid = Grid(inners[unittype][0], inners[unittype][1])
else:
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
if CarSetting.behavior_type == 3:
s = walkables[random.randint(0, len(walkables) // 3)]
e = walkables[random.randint(len(walkables) // 3, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
# 搜索街区终点
result_grid = mycar_walking(start_grid, end_grid, generated_map)
# 回溯街区路径
path = []
while result_grid is not None:
path.append(Grid(result_grid.x, result_grid.y))
result_grid = result_grid.parent
abel_score = 0
# 输出街区和路径，路径用星号表示
for i in range(0, len(generated_map)):
for j in range(0, len(generated_map[0])):
if contain_grid(path, i, j):
# star = colored('*', 'magenta', attrs=['reverse', 'blink'])
# print(star +", ", end="")
cprint("*" + ", ", "green", attrs=["reverse", "blink"], end="")
abel_score += 1
else:
if generated_map[i][j] == 1:
cprint("1" + ", ", "grey", attrs=["reverse", "blink"], end="")
else:
print(str(generated_map[i][j]) + ", ", end="")
print()
print("abel_score=", abel_score)
return abel_score
def single_turn(unittype, generated_map, CarSetting):
walkables = all_walkable(generated_map)
# print('walkables=', walkables, '#-# '*10)
# 设置随机数种子seed
np.random.seed(456789)
# 生成5个均值为len(walkables)//20，标准差为5的正态分布样本,模拟电量
r = np.random.normal(loc=len(walkables) // 20, scale=5, size=5)
simulate_battery_init = int(r[random.randint(0, 4)])
print("正态分布样 simulate_battery_init:", simulate_battery_init)
s, e = None, None
# 模拟早上和晚上
if random.randint(0, 1) == 0:
# 模拟早上
s = walkables[random.randint(0, len(walkables) - 1)]
while s[2] != 2:
s = walkables[random.randint(0, len(walkables) - 1)]
e = walkables[random.randint(0, len(walkables) - 1)]
else:
# 模拟晚上,正好相反
e = walkables[random.randint(0, len(walkables) - 1)]
while e[2] != 2:
e = walkables[random.randint(0, len(walkables) - 1)]
s = walkables[random.randint(0, len(walkables) - 1)]
# 更多场景 可以修改上面的代码
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
print("-" * 10, "run new simulation turn", "-" * 10, "\n")
# 设置起点和终点
# start_grid = Grid(2, 1)
# end_grid = Grid(2, 5)
# if CarSetting.behavior_type == 0 :
# s = walkables[random.randint(0, len(walkables)//2)]
# e = walkables[random.randint(len(walkables)//2, len(walkables)-1)]
# start_grid = Grid(s[0], s[1])
# end_grid = Grid(e[0], e[1])
# if CarSetting.behavior_type == 1:
# s = walkables[random.randint(0, len(walkables)//2)]
# e = walkables[random.randint(len(walkables)//2, len(walkables)-1)]
# start_grid = Grid(s[0], s[1])
# if random.randint(0, 4) > 2:
# end_grid = Grid(parkings[unittype][0], parkings[unittype][1])
# else:
# end_grid = Grid(e[0], e[1])
# if CarSetting.behavior_type == 2:
# s = walkables[random.randint(0, len(walkables)//2)]
# e = walkables[random.randint(len(walkables)//2, len(walkables)-1)]
# if random.randint(0, 4) > 2:
# start_grid = Grid(inners[unittype][0], inners[unittype][1])
# else:
# start_grid = Grid(s[0], s[1])
# end_grid = Grid(e[0], e[1])
# if CarSetting.behavior_type == 3 :
# s = walkables[random.randint(0, len(walkables)//3)]
# e = walkables[random.randint(len(walkables)//3, len(walkables)-1)]
# start_grid = Grid(s[0], s[1])
# end_grid = Grid(e[0], e[1])
# 搜索街区终点
result_grid = mycar_walking(start_grid, end_grid, generated_map)
# 回溯街区路径
path = []
while result_grid is not None:
path.append(Grid(result_grid.x, result_grid.y))
result_grid = result_grid.parent
ALL_PATHS.append(path)
abel_score = 0
# 输出街区和路径，路径用星号表示
for i in range(0, len(generated_map)):
for j in range(0, len(generated_map[0])):
if contain_grid(path, i, j):
# star = colored('*', 'magenta', attrs=['reverse', 'blink'])
# print(star +", ", end="")
cprint("*" + ", ", "green", attrs=["reverse", "blink"], end="")
abel_score += 1
else:
if generated_map[i][j] == 1:
cprint("1" + ", ",
"grey",
attrs=["reverse", "blink"],
end="")
else:
print(str(generated_map[i][j]) + ", ", end="")
print()
print("abel_score=", abel_score)
return abel_score
def test_instanciate_with_gen():
w, h = 4, 3
values = range(w * h)
mygrid = Grid.from_iter(w, h, values)
assert mygrid[0] == [0, 1, 2, 3]
assert mygrid[1, :] == [1, 5, 9]
def test_instanciate_with_iter_wrong_len():
w, h = 4, 3
values = list(range(w * h - 2))
with pytest.raises(ValueError):
mygrid = Grid.from_iter(w, h, values)
def test_instanciate_with_iter():
w, h = 4, 3
iterable = list(range(w * h))
mygrid = Grid.from_iter(w, h, iterable)
assert mygrid[0] == [0, 1, 2, 3]
assert mygrid[1, :] == [1, 5, 9]
def test_len():
grid = Grid(10, 10)
assert len(grid) == 100
def test_wrong_size():
with pytest.raises(ValueError):
grid = Grid(-1, 10)
def test_setitem_slice_xy(grid):
new_grid = Grid(2, 2, 99)
grid[1:3, 0:2] = new_grid
assert grid[:, 0] == [0, 99, 99, 3]
assert grid[1, :] == [99, 99, 9]
def transpose(m): new = [] for i in range(len(m[0])): # new.append([]) row = [] for j in range(len(m)): # row[i].append(m[j][i]) row.append(m[j][i]) new.append(row) return new # newmatrix = transpose(multiplication(transfor_matrix, transpose(fmatrix))) newmatrix = transpose(multiplication(transfor_matrix, transpose(amatrix))) g = Grid() for i in range(len(amatrix)): x1, y1 = amatrix[i] x2, y2 = amatrix[(i+1) % len(amatrix)] # print(x1, y1, x2, y2) g.draw_line(x1, y1, x2, y2, 'red') for i in range(len(newmatrix)): x1, y1 = newmatrix[i] x2, y2 = newmatrix[(i+1) % len(newmatrix)] # print(x1, y1, x2, y2) g.draw_line(x1, y1, x2, y2, 'red') for i in amatrix:
def single_turn(unittype, generated_map, oldman):
walkables = all_walkable(generated_map)
# print('$'*20, walkables)
# print(len(walkables))
# welcome = colored('#'*10+' This turn generated_map:'+'#'*10, 'red', attrs=['reverse', 'blink'])
# print(welcome, '\n')
print('-' * 10, 'run new simulation turn', '-' * 10, '\n')
# 设置起点和终点
start_grid = Grid(2, 1)
end_grid = Grid(2, 5)
if oldman.behavior_type == 0:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
if oldman.behavior_type == 1:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
if random.randint(0, 4) > 2:
end_grid = Grid(beds[unittype][0], beds[unittype][1])
else:
end_grid = Grid(e[0], e[1])
if oldman.behavior_type == 2:
s = walkables[random.randint(0, len(walkables) // 2)]
e = walkables[random.randint(len(walkables) // 2, len(walkables) - 1)]
if random.randint(0, 4) > 2:
start_grid = Grid(bathrooms[unittype][0], bathrooms[unittype][1])
else:
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
if oldman.behavior_type == 3:
s = walkables[random.randint(0, len(walkables) // 3)]
e = walkables[random.randint(len(walkables) // 3, len(walkables) - 1)]
start_grid = Grid(s[0], s[1])
end_grid = Grid(e[0], e[1])
# 搜索房间终点
result_grid = elder_walking(start_grid, end_grid, generated_map)
# 回溯房间路径
path = []
while result_grid is not None:
path.append(Grid(result_grid.x, result_grid.y))
result_grid = result_grid.parent
abel_score = 0
# 输出房间和路径，路径用星号表示
for i in range(0, len(generated_map)):
for j in range(0, len(generated_map[0])):
if contain_grid(path, i, j):
# star = colored('*', 'magenta', attrs=['reverse', 'blink'])
# print(star +", ", end="")
cprint('*' + ", ", "green", attrs=['reverse', 'blink'], end="")
abel_score += 1
else:
if generated_map[i][j] == 1:
cprint('1' + ", ",
"grey",
attrs=['reverse', 'blink'],
end="")
else:
print(str(generated_map[i][j]) + ", ", end="")
print()
print('abel_score=', abel_score)
return abel_score