#!/usr/bin/env python # -*- coding:utf-8 _*- __author__ = 'LJjia' # ******************************************************************* # Filename @ draw_scatter.py # Author @ Jia Liangjun # Create date @ 2020/02/06 11:38 # Email @ LJjiahf@163.com # Description @ python画动态散点图 # ******************************************************************** import numpy as np # 数组相关的库 import matplotlib.pyplot as plt # 绘图库 import time from pylab import mpl # 设置字体 mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 # 总共的人数统计 sum_of_human = 2000 # 潜伏期 表示患者感染上病毒后多少天才患病 incubation_period = 40 # 诊疗意愿，患者发病后多少天去医院 treat_willingness=40 isolation_all_day=incubation_period+treat_willingness # 可容纳的隔离数 isolation_pos = 400 # 感染范围 infection_area = 0.02 # 运动范围参数 0-1 move_area_param =0.3 # 初始感染者 first_infected_cnt = 10 # 生成画布 plt.figure(figsize=(18, 14), dpi=80) plt.ion() def get_normal_list(mu=0.0, sigma=0.7, sampleNo=sum_of_human, seed=0): ''' :param mu: 对称轴 :param sigma: 方差 :param sampleNo: 样点个数 :return: ''' if seed: np.random.seed(seed) else: np.random.seed(round(time.time() * 100000) % 10000) if sigma < 0: sigma = 0 s = np.random.normal(mu, sigma, sampleNo) return s x1 = get_normal_list(seed=1, sampleNo=int(sum_of_human / 2)) x2 = get_normal_list(seed=2, sampleNo=int(sum_of_human / 2)) x = get_normal_list(seed=3, sampleNo=sum_of_human) y1 = get_normal_list(seed=5, sampleNo=int(sum_of_human / 2)) y2 = get_normal_list(seed=6, sampleNo=int(sum_of_human / 2)) np.random.seed(10) first_infected_cnt_index = np.random.randint(0, sum_of_human, size=first_infected_cnt) infected_index_set = set(first_infected_cnt_index) infected_index_history = [] infected_index_history.append(infected_index_set) print('初始感染者', infected_index_set) # 初始化相关变量 isolation_set=set() plt_x_isolation=np.array([]) plt_y_isolation = np.array([]) plt_x_sick=np.array([]) plt_y_sick=np.array([]) move_mu = 0 move_sigma = 0.01*move_area_param for day in range(1, 1000): plt.cla() plt.grid(False) # 人员运动 move_x1 = get_normal_list(mu=move_mu , sigma=move_sigma, sampleNo=int(sum_of_human / 2)) move_x2 = get_normal_list(mu=-move_mu , sigma=move_sigma, sampleNo=int(sum_of_human / 2)) move_y1 = get_normal_list(mu=move_mu , sigma=move_sigma, sampleNo=int(sum_of_human / 2)) move_y2 = get_normal_list(mu=-move_mu , sigma=move_sigma, sampleNo=int(sum_of_human / 2)) x1 = x1 + move_x1 x2 = x2 + move_x2 y1 = y1 + move_y1 y2 = y2 + move_y2 plt_x = np.concatenate((x1, x2), axis=0) plt_y = np.concatenate((y1, y2), axis=0) set_infected_index_today=set() for infected_index in infected_index_set: infected_point = (plt_x[infected_index], plt_y[infected_index]) infected_array_index_today = np.where( (plt_x > infected_point[0] - infection_area) & (plt_x < infected_point[0] + infection_area) \ & (plt_y > infected_point[1] - infection_area) & (plt_y < infected_point[1] + infection_area)) infected_array_index_today=infected_array_index_today[0] set_infected_index_today=set_infected_index_today|set(infected_array_index_today) # 记录当天之后感染的所有人 set 和历史感染set的统计集合list infected_index_set = infected_index_set | set_infected_index_today infected_index_history.append(infected_index_set) # 已经在潜伏期的人数 plt_x_infection=plt_x[list(infected_index_set)] plt_y_infection=plt_y[list(infected_index_set)] # 已出现病症的人数 if day>=incubation_period: plt_x_sick=plt_x[list(infected_index_history[day-incubation_period])] plt_y_sick = plt_y[list(infected_index_history[day - incubation_period])] # 隔离人数 if day>=isolation_all_day: if len(isolation_set)= incubation_period: plt.scatter(plt_x_sick, plt_y_sick, c='red', alpha='0.8', marker='.',label='发病') # 隔离人 if day >= isolation_all_day: plt.scatter(plt_x_isolation, plt_y_isolation, c='white', marker='.') plt.legend(loc='upper right') plt.pause(0.0001)