真菌元胞自动机Python实现
作者：互联网
2021年美赛A题真菌元胞自动机Python实现
import matplotlib.pyplot as plt
import random
import numpy as np
import matplotlib.animation as animation
def save_fungi_ca_gif(): # save the gif file to the path
target_gif_path = "E:/engineering space/figure/gif format/"
target_gif_name = "aggressive_ca.gif"
target_gif_full = target_gif_path + target_gif_name
anim_1.save(target_gif_full, writer='pillow') # save the animation
print('Saved')
def calculate_random_neighbour(stay, left, up, right, down, no_reproduce): # get the relative location of the newly
total_rate = stay + left + up + right + down + no_reproduce # reproduced fungi cell
if total_rate == 0.0:
total_rate = 1
cap1 = stay / total_rate # cap 1 to 5 is the boundary of probability area
cap2 = cap1 + left / total_rate
cap3 = cap2 + up / total_rate
cap4 = cap3 + right / total_rate
cap5 = cap4 + down / total_rate
rnd = random.random()
if 0 <= rnd < cap1:
return 0
elif cap1 <= rnd < cap2: # divide the range into 6 parts
return 1
elif cap2 <= rnd < cap3: # randomly select one part
return 2
elif cap3 <= rnd < cap4:
return 3
elif cap4 <= rnd < cap5:
return 4
else:
return 5
def get_extension_parameters(i_get_extension_parameters): # get the extension parameters
extension_parameters = extension_rate_list[fungi_list[i_get_extension_parameters][3]]
return extension_parameters
def get_random_neighbour(i_get_random_neighbour): # calculate the density of neighbour mesh as well as self mesh
extension_rate_get_random_neighbour = get_extension_parameters(i_get_random_neighbour)
stay_density = my_board[fungi_list[i_get_random_neighbour][0], fungi_list[i_get_random_neighbour][1]] / one_mesh_max
stay_rate = extension_rate_get_random_neighbour - stay_density
if fungi_list[i_get_random_neighbour][1] != 0:
left_density = my_board[fungi_list[i_get_random_neighbour][0], fungi_list[i_get_random_neighbour][1] - 1] \
/ one_mesh_max
left_rate = extension_rate_get_random_neighbour * (
1 - left_density + 0.1) # further calculate the transferring rate of
# the new fungi
else:
left_density = 0.5
left_rate = 0
if fungi_list[i_get_random_neighbour][0] != 0:
up_density = my_board[fungi_list[i_get_random_neighbour][0] - 1, fungi_list[i_get_random_neighbour][1]] \
/ one_mesh_max
up_rate = extension_rate_get_random_neighbour * (1 - up_density + 0.1)
else:
up_density = 0.5
up_rate = 0
if fungi_list[i_get_random_neighbour][1] != patch_size - 1:
right_density = my_board[
fungi_list[i_get_random_neighbour][0], fungi_list[i_get_random_neighbour][1] + 1] \
/ one_mesh_max
right_rate = extension_rate_get_random_neighbour * (1 - right_density + 0.1)
else:
right_density = 0.5
right_rate = 0
if fungi_list[i_get_random_neighbour][0] != patch_size - 1:
down_density = my_board[fungi_list[i_get_random_neighbour][0] + 1, fungi_list[i_get_random_neighbour][1]] \
/ one_mesh_max
down_rate = extension_rate_get_random_neighbour * (1 - down_density + 0.1)
else:
down_density = 0.5
down_rate = 0
total_density = stay_density + left_density + up_density + right_density + down_density
no_reproduce_rate = total_density / 8
neighbour_location = calculate_random_neighbour(stay_rate, left_rate, up_rate, right_rate, down_rate,
no_reproduce_rate)
return neighbour_location
def reproduce_one_cell(i_reproduce_one_cell): # reproduce a single fungi cell
iterations = 0
while True: # loop until the random neighbour meets the boundary conditions
iterations += 1
# print('iteration:', iterations)
reproduce_location = get_random_neighbour(i_reproduce_one_cell)
# the followings are boundary conditions
if iterations < 3:
if reproduce_location == 0: # stay in one mesh
current_mesh_num = my_board[fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1]]
if current_mesh_num < one_mesh_max:
# haven't arrived the maximum of one mesh
break # this is the location wanted
else:
# print(iterations) # how many loops have been taken
continue # random again
if reproduce_location == 1: # go to left mesh
if fungi_list[i_reproduce_one_cell][1] == 0: # reaches the left boundary
# print(iterations)
continue # random again
elif my_board[fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1] - 1] \
< one_mesh_max:
break # this is the location wanted
else:
continue
if reproduce_location == 2: # go to up mesh
if fungi_list[i_reproduce_one_cell][0] == 0: # reaches the up boundary
# print(iterations)
continue # random again
elif my_board[fungi_list[i_reproduce_one_cell][0] - 1, fungi_list[i_reproduce_one_cell][1]] \
< one_mesh_max:
break # this is the location wanted
else:
continue
if reproduce_location == 3: # go to right mesh
if fungi_list[i_reproduce_one_cell][1] == patch_size - 1: # reaches the right boundary
# print(iterations)
continue # random again
elif my_board[fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1] + 1] \
< one_mesh_max:
break # this is the location wanted
else:
continue
if reproduce_location == 4: # go to down mesh
if fungi_list[i_reproduce_one_cell][0] == patch_size - 1: # reaches the down boundary
continue
# print(iterations)
elif my_board[fungi_list[i_reproduce_one_cell][0] + 1, fungi_list[i_reproduce_one_cell][1]] \
< one_mesh_max:
break # this is the location wanted
else:
continue
else:
reproduce_location = 5
break
global fungi_num
# take reproducing activity
if reproduce_location != 5:
if reproduce_location == 0:
fungi_list.append([fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1], 0, fungi_list[
i_reproduce_one_cell][3]])
# print('Stayed')
if reproduce_location == 1:
fungi_list.append([fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1] - 1, 0,
fungi_list[i_reproduce_one_cell][3]])
if reproduce_location == 2:
fungi_list.append([fungi_list[i_reproduce_one_cell][0] - 1, fungi_list[i_reproduce_one_cell][1], 0,
fungi_list[i_reproduce_one_cell][3]])
if reproduce_location == 3:
fungi_list.append([fungi_list[i_reproduce_one_cell][0], fungi_list[i_reproduce_one_cell][1] + 1, 0,
fungi_list[i_reproduce_one_cell][3]])
if reproduce_location == 4:
fungi_list.append([fungi_list[i_reproduce_one_cell][0] + 1, fungi_list[i_reproduce_one_cell][1], 0,
fungi_list[i_reproduce_one_cell][3]])
fungi_list[i_reproduce_one_cell][2] += 1 # the reproduced time of a fungi
if fungi_list[i_reproduce_one_cell][2] == reproduce_time_to_die_list[fungi_list[i_reproduce_one_cell][3]]:
# when reproduced an exact times to die
my_board[fungi_list[i_reproduce_one_cell][0]][fungi_list[i_reproduce_one_cell][1]] -= 1 # remove fungi
# in number board
del fungi_list[i_reproduce_one_cell] # the death of a fungi
fungi_num = len(fungi_list)
update_board(fungi_num) # get a board according to the new fungi reproduced
else:
fungi_list[i_reproduce_one_cell][2] += 1 # the life span of a fungi
def reproduce_round(): # reproduce all the current fungi cells
for i_reproduce_round in range(fungi_num):
reproduce_one_cell(i_reproduce_round) # reproduce a single fungi cell
def update_board(fungi_num_update_board): # get a number board according to the new fungi
my_board[fungi_list[fungi_num_update_board - 1][0]][fungi_list[fungi_num_update_board - 1][1]] += 1
def get_rgb_board():
paint_rgb_board = [[[1.0 for k in range(3)] for j in range(patch_size)] for i in range(patch_size)] # initialize
global red_fungi_num
global green_fungi_num
global blue_fungi_num
red_fungi_num = 0
green_fungi_num = 0
blue_fungi_num = 0
# color board
for i_paint_rgb_board in range(fungi_num):
if fungi_list[i_paint_rgb_board][3] == 0:
red_fungi_num += 1
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][1] -= (0.99
/ one_mesh_max)
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][2] -= (0.99
/ one_mesh_max)
elif fungi_list[i_paint_rgb_board][3] == 1:
green_fungi_num += 1
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][0] -= (0.99
/ one_mesh_max)
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][2] -= (0.99
/ one_mesh_max)
else:
blue_fungi_num += 1
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][0] -= (0.99
/ one_mesh_max)
paint_rgb_board[fungi_list[i_paint_rgb_board][0]][fungi_list[i_paint_rgb_board][1]][1] -= (0.99
/ one_mesh_max)
return paint_rgb_board
def attack_round():
global fungi_num
for i_attack_round in range(fungi_num):
if i_attack_round == fungi_num - 1:
break
if fungi_list[i_attack_round][2] >= reproduce_time_to_die_list[fungi_list[i_attack_round][3]]:
# when reproduced an exact times to die
my_board[fungi_list[i_attack_round][0]][fungi_list[i_attack_round][1]] -= 1 # remove fungi in number board
del fungi_list[i_attack_round] # the death of a fungi
fungi_num = len(fungi_list)
# update_board(fungi_num) # get a board according to the new fungi reproduced
else:
continue
def update(): # update the board information and fungi list in a round
reproduce_round() # reproduce all the current fungi cells
rgb_board_update = get_rgb_board() # get a color board after a round of reproduction
attack_round()
return rgb_board_update
def calculate_initial_position(): # calculate the initial position of cells for fair competition
radius_calculate_initial_position = patch_size / 6 # distance between initial cells
middle_point_initial_position = patch_size / 2
for i_calculate_initial_position in range(initial_fungi_num):
rotation_degree = i_calculate_initial_position / initial_fungi_num * 2 * np.pi
x_initial_position = middle_point_initial_position + np.cos(rotation_degree) * radius_calculate_initial_position
y_initial_position = middle_point_initial_position + np.sin(rotation_degree) * radius_calculate_initial_position
x_initial_list.append(int(round(x_initial_position)))
y_initial_list.append(int(round(y_initial_position)))
def animate_func(frame_sequence): # depict the animation
ax_1.imshow(update())
update_extension_rate()
# ax_1.text(patch_size / 12, patch_size / 12, frame_sequence, color='white', size=20)
print('total:', fungi_num, 'red:', red_fungi_num, 'green:', green_fungi_num, 'blue:', blue_fungi_num)
print('frame number:', frame_sequence + 1)
def update_extension_rate():
global extension_rate_list
red_fungi_rate = red_fungi_num / fungi_num
green_fungi_rate = green_fungi_num / fungi_num
blue_fungi_rate = blue_fungi_num / fungi_num
if red_fungi_rate <= rate_limit:
extension_rate_list[0] = fungi_num / red_fungi_num
# total_extension_rate = sum(extension_rate_list)
# extension_rate_list = extension_rate_list / total_extension_rate
elif green_fungi_rate <= rate_limit:
extension_rate_list[1] = fungi_num / green_fungi_num
# total_extension_rate = sum(extension_rate_list)
# extension_rate_list = extension_rate_list / total_extension_rate
elif blue_fungi_rate <= rate_limit:
extension_rate_list[2] = fungi_num / blue_fungi_num
# total_extension_rate = sum(extension_rate_list)
# extension_rate_list = extension_rate_list / total_extension_rate
if __name__ == '__main__':
patch_size = 25 # size of our patch
# frame_number = 30 # number of frames
one_mesh_max = 7 # the maximum number of fungi cells in a single mesh
initial_fungi_num = 3 # the initial number of fungi cells as well as species
fungi_list = [] # list of cells
my_board = np.zeros((patch_size, patch_size)) # initialize number board
x_initial_list = [] # the x, y position of initial cells
y_initial_list = []
# calculate_initial_position() # calculate the initial position of cells for fair competition
red_fungi_num = 1 # number of each species of fungi
green_fungi_num = 1
blue_fungi_num = 1
rate_limit = 0.01
for i_initial in range(initial_fungi_num): # initialize fungi list
x_initial = np.random.randint(patch_size) # int(patch_size / 10) + i_initial * int(8 * patch_size / 10) #
y_initial = np.random.randint(patch_size) # x_initial #
# fungi_list.append([x_initial_list[i_initial], y_initial_list[i_initial], 0, i_initial]) # the indexes are
# x, y,
fungi_list.append([x_initial, y_initial, 0, i_initial])
# reproduce time and species num
update_board(i_initial + 1)
fungi_num = initial_fungi_num
extension_rate_list = [0.6, 0.2, 0.2] #
extension_rate_list = np.array(extension_rate_list)
# different species of fungi have different extension rate
attack_parameter_list = [0.3, 0.1, 0.5] # density parameter of different species of fungi
reproduce_time_to_die_list = [2, 2, 2] # each cell reproduce two times until death
rgb_board = [[[1.0 for k in range(3)] for j in range(patch_size)] for i in range(patch_size)] # initialize color
# board
fig_1 = plt.figure(1) # create a plot
ax_1 = fig_1.add_subplot(1, 1, 1) # create an axes
# ims = [] # prepared for the frames to put in
# anim_1 = animation.ArtistAnimation(fig_1, ims, repeat=False) # create an animation
# save_fungi_ca_gif() # save the gif file
anim_2 = animation.FuncAnimation(fig_1, animate_func)
plt.show()
# print(my_board)
标签：Python,initial,list,fungi,rate,num,元胞,position,自动机 来源： https://blog.csdn.net/joshua_shi_t/article/details/121132699