Python multithreading realizes drawing dynamic graphs

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1. Background

• In some cases, we are faced with real-time updated data, and hope to visualize it in a window and update it in real time, so that we can observe the changes in the data, so as to perform data analysis, such as: drawing audio waveforms, drawing dynamic curves, etc., The following introduces the use of matplotlib combined with multi-threading to draw dynamic graphs, hoping to help friends in need.
• The scene I encountered: I was just learning the genetic algorithm in artificial intelligence recently, and I used this algorithm to solve TSP. Friends who know this algorithm know that this algorithm is to find the optimal solution with large fitness through continuous iteration, in order to understand the iterative process. Changes in fitness, I want to be able to update the fitness in the iterative process in real time and visualize it (the amount of data is increasing)
• Directly above:

2. Steps

1. Use matplotlib to draw dynamic graphs

• tool: matplotlib.animation

2. Create a thread for updating data

• threading

3. Code framework

# Author: 浅若清风cyf
# Date: 2020/12/11

import threading
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.lines as line
import numpy as np

CHUNK = 2048  # 初始数据量
data=np.random.normal(0,1,CHUNK)  # 存放数据，用于绘制图像，数据类型可为列表

# 定义画布
fig = plt.figure()
ax = plt.subplot(111,ylim=(0,5))
line = line.Line2D([], [])  # 绘制直线

# 初始化图像
def plot_init():
    ax.add_line(line)
    return line, # 必须加逗号,否则会报错（TypeError: 'Line2D' object is not iterable）

# 更新图像(animation会不断调用此函数刷新图像，实现动态图的效果)
def plot_update(i):
    global data  # data为全局变量
    data_copy = data.copy()  # 为避免线程不同步导致获取到的data在绘制图像时被更新，这里复制数据的副本，否则绘制图像的时候可能会出现x和y的数据维度不相等的情况
    x_data=np.arange(0,data_copy.shape[0],1)  # x轴根据y轴数据自动生成（可根据需要修改）
    ax.set_xlim(0,data_copy.shape[0])  # 横坐标范围（横坐标的范围和刻度可根据数据长度更新）
    ax.set_title("title",fontsize=8)  # 设置title
    line.set_xdata(x_data)  # 更新直线的数据
    line.set_ydata(data_copy)  # 更新直线的数据
	# 大标题（若有多个子图，可为其设置大标题）
    plt.suptitle('Suptitle',fontsize=8)
    # 重新渲染子图
    ax.figure.canvas.draw()  # 必须加入这一行代码，才能更新title和坐标!!!
    return line,  # 必须加逗号,否则会报错（TypeError: 'Line2D' object is not iterable）

# 绘制动态图
ani = animation.FuncAnimation(fig,   # 画布
							  plot_update,  # 图像更新
                              init_func=plot_init,  # 图像初始化
                              frames=1,
                              interval=30,  # 图像更新间隔
                              blit=True)

# 数据更新函数
def dataUpdate_thead():
    global data
    # 为了方便理解代码，这里生成正态分布的随机数据
    while True:  # 为了方便测试，让数据不停的更新
	    data=np.random.normal(0,1,CHUNK)

# 为数据更新函数单独创建一个线程，与图像绘制的线程并发执行
ad_rdy_ev = threading.Event()
ad_rdy_ev.set()  # 设置线程运行
t = threading.Thread(target=dataUpdate_thead, args=()) # 更新数据，参数说明：target是线程需要执行的函数，args是传递给函数的参数）
t.daemon = True
t.start()  # 线程执行

plt.show() # 显示图像(0,1,CHUNK)

# 为数据更新函数单独创建一个线程，与图像绘制的线程并发执行
ad_rdy_ev = threading.Event()
ad_rdy_ev.set()  # 设置线程运行
t = threading.Thread(target=dataUpdate_thead, args=()) # 更新数据，参数说明：target是线程需要执行的函数，args是传递给函数的参数）
t.daemon = True
t.start()  # 线程执行

plt.show() # 显示图像
复制代码

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