如何使用 Python 实时优化绘制串行数据

Question

我正在尝试绘制我从串行设备实时收到的制表符分隔值。我对 python 很陌生，但已经设法拼凑出一个管理它的脚本，但它似乎无法处理接收数据的速度，并且在减慢并最终冻结之前使用大量处理能力。我能做的任何事情都可以防止这种情况。我附上了我正在使用的数据和我的脚本的示例

我收到的数据看起来像这样，大约每半秒接收一行。

546     5986637 3598844 +26.0   01A0
547     5986641 3598843 +25.50  0198
548     5986634 3598844 +24.50  0188

from matplotlib import pyplot as plt
from matplotlib import animation
import serial
from pandas import DataFrame
from datetime import datetime
import csv
filename = datetime.now().strftime("%d-%m-%Y_%I-%M-%S_%p")  # Gets time and date in readable format for filenaming.
Data1 = {'Value': [0], 'Frequency 1': [0], 'Frequency2': [0], 'Temperature': [0]}
df = DataFrame(Data1, columns=['Value', 'Frequency1', 'Frequency2', 'Temperature'])
serial_port = 'COM5';  # Different port for linux/mac
baud_rate = 9600;  # In arduino, Serial.begin(baud_rate)
write_to_file_path = "output.txt";
data = []
ft = []
output_file = open(write_to_file_path, "w+");
ser = serial.Serial(serial_port, baud_rate)
plt.ion()
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, sharey=False, )
ax1.set_title('Temp')
ax2.set_title('Freq 1')
ax3.set_title('Freq 2')
ax1.set_ylabel('Temperature')
ax2.set_ylabel('Frequency')
ax3.set_ylabel('Frequency')
ax1.ticklabel_format(useOffset=False)
ax2.ticklabel_format(useOffset=False)
ax3.ticklabel_format(useOffset=False)
ax1.ticklabel_format(style='plain', axis='y', scilimits=(0, 0))
ax2.ticklabel_format(style='sci', axis='y', scilimits=(6, 6))
ax3.ticklabel_format(style='sci', axis='y', scilimits=(6, 6))
while True:
line = ser.readline();
line = line.decode("utf-8")  # ser.readline returns a binary, convert to string
print(line)
line1 = line.split('t')  # Separates values by tabs
output_file.write(line);  # Writes to text file
data.append(line1)  # Adds line to data file
newline = [float(line1[0]), float(line1[1]), float(line1[2]), float(line1[3])]  # Creates line of float values
ft.append(newline)  # Adds to list of floats
f1 = float(line1[0])  # Line number (count)
f2 = float(line1[1])  # Frequency 1
f3 = float(line1[2])  # Frequency 2
f4 = float(line1[3])  # Temperature in C
f5 = str(line1[4])  # Temperature in Hex, treated as a string
#    Data2 = {'Value':[f1],'Frequency 1':[f2],'Frequency2':[f3], 'Temperature':[f4]}
#    df2 = DataFrame(Data2,columns=['Value', 'Frequency1','Frequency2','Temperature'])
#    df.append(df2)
# DataFrame still not working, need to fix so that data is stores as integer or float
plt.pause(0.1)
ax1.plot(f1, f4, marker='.', linestyle='solid')  # subplot of freq 1
ax2.plot(f1, f2, marker='.', linestyle='solid')  # subplot of freq 2
ax3.plot(f1, f3, marker='.', linestyle='solid')  # subplot of Temp in C
plt.subplot
plt.xlabel("Count")
with open(filename + ".csv", "a") as f:  # Writes data to CSV, hex values for temp don't seem to be writing
writer = csv.writer(f, delimiter=",")
writer.writerow([f1, f2, f3, f4, f5])
plt.draw()
plt.savefig(filename + '.png', bbox_inches='tight')  # Saves the plot

Answer 1

您可以考虑使用线程来拆分任务。您可能不需要在每次收到新数据时都保存该数字。例如，您可以通过仅每 30 秒左右更新一次绘图来减少计算负载。您还可以拆分写入 csv，这样您就有三个线程，一个查找数据，一个存储缓冲数据，另一个更新绘图。

这个答案可能是一个很好的参考。

在 foo(( 的末尾，创建一个在 10 秒后调用 foo(( 本身的计时器。 因为，计时器创建一个新线程来调用 foo((。

import time, threading
def foo():
print(time.ctime())
threading.Timer(10, foo).start()
foo()
#output:
#Thu Dec 22 14:46:08 2011
#Thu Dec 22 14:46:18 2011
#Thu Dec 22 14:46:28 2011
#Thu Dec 22 14:46:38 2011