我只是从python开始，如果我的问题看起来很简单，很抱歉。我想用细胞自闭症来模拟流行病的传播。这是我的代码：
import matplotlib.colors
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib.animation import FuncAnimation
import matplotlib.animation as ani
import random as rd
import copy
from matplotlib.colors import ListedColormap
def init_graph():
plt.hlines(y=np.arange(n)+0.5, xmin=-0.5, xmax=n-0.5, linewidth=0.25, color="grey")
plt.vlines(x=np.arange(n)+0.5, ymin=-0.5, ymax=n-0.5, linewidth=0.25, color="grey")
def init_matrix_array(n):
m = np.ones((n, n))
m[n//2][n//2]=2
return m.tolist()
def next_to_ill_cell(current_state_matrix, i, j):
for x in [i-1,i,i+1]:
for y in [j-1,j,j+1]:
if not((x==i and y==j) or x==-1 or y==-1 or x==n or y==n):
if current_state_matrix[x][y]==ill:
return True
return False
#Rules
def process_next_state (current_state_matrix):
previous_state_matrix = copy.deepcopy(current_state_matrix)
for i in range (n) :
for j in range (n) :
if previous_state_matrix[i][j] == untouched:
if next_to_ill_cell(previous_state_matrix, i, j)== True:
k = rd.random()#random
if k >= 0.5:
current_state_matrix[i][j] = ill
else:
current_state_matrix[i][j] = untouched
if previous_state_matrix[i][j]==ill:
s = rd.random()
if s >= 0.02875:
current_state_matrix[i][j] = recovered
else:
current_state_matrix[i][j] = dead
return current_state_matrix
def number_of_death(current_state_matrix):
n_death = 0
for i in range(n):
for j in range(n):
if current_state_matrix[i][j] == dead:
n_death += 1
return n_death
def number_of_recovery(previous_state_matrix, current_state_matrix):
"""Calculate the number of recovery"""
if __name__ == '__main__':
cmap = ListedColormap(['k','w','r','b'])
dead = 0
untouched = 1
ill = 2
recovered = 3
n = 50 #number of array (table of 50*50 : 2500 cells)
init_graph()
current_state_matrix = init_matrix_array(n)
day = 1
while day < 10:
previous_state_matrix = current_state_matrix
# Number of death
n_death = number_of_death(current_state_matrix)
plt.imshow(current_state_matrix, cmap=cmap, vmin=0, vmax=3)
plt.text(25, 5, f'day = {day}', horizontalalignment='center')
plt.text(25, 45, f'number of death = {n_death}', horizontalalignment='center')
current_state_matrix = process_next_state(current_state_matrix)
day += 1
plt.pause(1)
plt.show()
我想它可能会有很大的改进，但正如我所说，我是一个初学者。
我希望一个受感染的细胞能在4到8天内保持感染。我该怎么做？
解决方案是使用概率规则，例如：
- 每个细胞都有一个感染天数的计数器，或者有某种时间戳来与当前CA步骤进行比较
- 受感染的细胞在第二天开始时有100%的机会保持感染，直到4天过去，这种可能性一直保持在100%
- 从第五天开始，将这种可能性降低一些。在细胞仍然被感染的情况下，每隔一天重复一次
- 如果它在第八天仍然被感染，那么它保持感染的概率为零