scream-effect-propagation-simulation/main5.py at main · Zysishuiyears/scream-effect-propagation-simulation · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Sat May 27 12:59:01 2023

@author: 23783
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import statistics
import random
from random_function import random_mi
from parameter_calculate import Pre_calculation,in_time_calculation1,in_time_calculation2
from Pulse_function import total_pulse
from spread import spread

##################################################################################################
#read in the data
node_data = pd.read_csv('nodes.csv')
edge_data = pd.read_csv('edges.csv')

G = nx.DiGraph()

for i in range(len(node_data)):
    G.add_node(node_data.iloc[i,0], label=node_data.iloc[i,1], types = 'S')


n = len(edge_data)
weights = random_mi(2.0,n)
for i in range(n):
    G.add_edge(edge_data.iloc[i,0], edge_data.iloc[i,1], weight = weights[i])


###############################################################################################
#信息传播过程计算
starting_point = 26
time_limit=30
prob=0.37
starting = -1

times = np.linspace(0, time_limit,time_limit+1)
time_line = total_pulse(times,starting)

S = list(G.nodes)
S.remove(starting_point)
I = [starting_point]
E = []
R = []

G.add_node(starting_point, types = 'I')
#######################################
total =float(len(G.nodes))
S_Ratio = [1]
E_Ratio = [0]
I_Ratio = [0]
R_Ratio = [0]

Pre_calculation(G)
in_time_calculation1(G,0,time_line,1.5)
S,E,I,R = spread(G, 0, S, E, I, R, prob)

S_Ratio.append(len(S)/total)
E_Ratio.append(len(E)/total)
I_Ratio.append(len(I)/total)
R_Ratio.append(len(R)/total)

for node in list(G.nodes):
    G.add_node(node, sit0 = G.nodes[node]['types'])

for t in range(1,time_limit):


    in_time_calculation2(G,t,time_line)
    S,E,I,R = spread(G, t, S, E, I, R, prob)

    for node in list(G.nodes):
        name = 'sit'+str(t)
        G.add_node(node, **{name: G.nodes[node]['types']})


    S_Ratio.append(len(S)/total)
    E_Ratio.append(len(E)/total)
    I_Ratio.append(len(I)/total)
    R_Ratio.append(len(R)/total)


# #########################################################绘图
# # 设置ggplot风格
# plt.style.use('seaborn-whitegrid')
# plt.rcParams['font.family'] = ['sans-serif']
# plt.rcParams['font.sans-serif'] = ['SimHei']

# # 绘制图像
# # plt.plot(times, S_Ratio, linestyle='-', label='S')
# # plt.plot(times, E_Ratio, linestyle=':', label='E')
# # plt.plot(times, I_Ratio, linestyle='-.',label='I')
# # plt.plot(times, R_Ratio, linestyle='–',label='R',color='gold')

# plt.plot(times, S_Ratio, marker='s',markersize=6, label='S')
# plt.plot(times, E_Ratio, marker='o',markersize=6, label='E')
# plt.plot(times, I_Ratio, marker='^',markersize=6, label='I')
# plt.plot(times, R_Ratio, marker='D',markersize=6, label='R',color='gold')

# # 添加图例
# plt.legend()

# # 添加标签和标题
# plt.xlabel('时间')
# plt.ylabel('比例')
# plt.title('各类人群占比随时间变化图')

# # 显示图像
# plt.show()

# ################################################差分图
# # 设置ggplot风格
# plt.style.use('seaborn-whitegrid')
# plt.rcParams['font.family'] = ['sans-serif']
# plt.rcParams['font.sans-serif'] = ['SimHei']

# # 绘制图像
# S_diff= np.abs(np.diff(S_Ratio))
# E_diff= np.diff(E_Ratio)
# I_diff= np.diff(I_Ratio)
# R_diff= np.diff(R_Ratio)
# totoal_diff = E_diff+I_diff+R_diff

# # a = S_diff[0]
# # S_diff[0] = S_diff[1]
# # S_diff[1] = a

# plt.plot(times[1:], S_diff,color='gold')

# # 添加图例
# plt.legend()

# # 添加标签和标题
# plt.xlabel('时间')
# plt.ylabel('比例')
# plt.title('各时间消息传播人群占总人数比例')

# # 显示图像
# plt.show()