Question

我想用matplotlib更新一个线图，并且想知道，如果对代码进行了很好的修改，那么绘制的线只会更新而不是每次重绘。以下是示例代码：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
matplotlib.style.use('ggplot')

plt.ion() 
fig=plt.figure()

i=0
df = pd.DataFrame({"time": [pd.datetime.now()], "value": 0}).set_index("time")
plt.plot(df);

while True:
    temp_y=np.random.random();
    df2 = pd.DataFrame({"time": [pd.datetime.now()], "value": temp_y}).set_index("time")
    df = df.append(df2)
    plt.plot(df)
    i+=1
    plt.show()
    plt.pause(0.000001)

如您所见，一段时间后绘图变得越来越慢，我认为每次迭代都会重新绘制折线图，因为它会改变颜色。

Answer 1

是

x = np.arange(10)
y = np.random.rand(10)

line, = plt.plot(x,y)
line.set_data(x,np.random.rand(10))
plt.draw()

但是，您的绘图变慢了，因为您正在扩展数据框，并且每个追加操作可能会将内存中的帧复制到新位置。随着数据框大小的增加，复制操作需要更长时间。我会循环索引并绘制（for ii in range(len(data)): line.set_data(x[:ii], y[:ii])）

编辑：

import numpy as np
import matplotlib.pyplot as plt; plt.ion()
import pandas as pd

n = 100
x = np.arange(n)
y = np.random.rand(n)
# I don't get the obsession with pandas...
df = pd.DataFrame(dict(time=x, value=y))

# initialise plot and line
line, = plt.plot(df['time'], df['value'])

# simulate line drawing
for ii in range(len(df)):
    line.set_data(df['time'][:ii], df['value'][:ii]) # couldn't be bothered to look up the proper way to index in pd
    plt.draw()
    plt.pause(0.001)

Answer 2

您可以将以下代码用于matplotlib中的实时绘图

import random 
from itertools import count
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

plt.style.use('fivethirtyeight')

x_vals = []
y_vals = []

index = count()


def analyse(i):

    data = pd.read_csv(<your file which is updated or live data>)#u can use the pandas or any other data frame work
    x = data['index']
    y1 = data['readings']

    plt.cla()

    plt.plot(x, y1, label='Temperature') #in my case data is temperature
    
    plt.legend(loc='upper left')
    plt.tight_layout()

ani = FuncAnimation(plt.gcf(), analyse, interval=1000) #adjust intervals for call

plt.tight_layout()
plt.show()

Matplotlib更新实时情节

2 个答案: