1.1High_Low_pass_filter.py

import numpy as np
import pandas as pd
from scipy import signal
import matplotlib.pyplot as plt
import serial
import time

ComPort = serial.Serial('COM13') 
ComPort.baudrate = 115200          
ComPort.bytesize = 8            
ComPort.parity   = 'N'           
ComPort.stopbits = 1
random_data = np.arange(50)

def receive_data():
 for i in range(0,50,1):
  random_data[i] = int(ComPort.readline())  
 result = pd.DataFrame({'data': random_data} )
 return result

def butter_highpass(cutoff, fs, order=3):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = signal.butter(order, normal_cutoff, btype='high', analog=False)
    return b, a

def butter_highpass_filter(data, cutoff, fs, order=5):
    b, a = butter_highpass(cutoff, fs, order=order)
    y = signal.filtfilt(b, a, data)
    return y

def butter_lowpass(cutoffs, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = signal.butter(order, normal_cutoff, btype='low', analog=False)
    return b, a

def butter_lowpass_filter(data, cutoffs, fs, order=5):
    b, a = butter_lowpass(cutoffs, fs, order=order)
    y = signal.lfilter(b, a, data)
    return y


#order = 6
fps=fs = 10     # sample rate, Hz
cutoffs = 30
cutoff=2

figure, (ax1, ax2) = plt.subplots(2, 1, sharex=True)

axis_x=0
while 1:
 sine = receive_data()
 filtered_sine = butter_highpass_filter(sine.data, cutoff, fps)
 filtered_sine  =  butter_lowpass_filter(filtered_sine.data, cutoffs, fps)
 
 
 ax1.plot(range(axis_x, axis_x+50,1),sine,color = '#0a0b0c') 
 ax2.plot(range(axis_x, axis_x+50,1),filtered_sine, color = '#0a0b0c')
 axis_x1_move=sine["data"]
 ax1.axis([axis_x-4000, axis_x+2000, axis_x1_move[48]-200000, axis_x1_move[48]+200000])
 ax2.axis([axis_x-4000, axis_x+2000, filtered_sine[48]-50, filtered_sine[48]+50]) 
 axis_x=axis_x+50

 
 plt.pause(0.001)
 plt.draw()

#plt.show()