Python High Pass Filter

Question

I implemented an high pass filter in python using this code:

from scipy.signal import butter, filtfilt
import numpy as np

def butter_highpass(cutoff, fs, or         


        

Answer 1

I add function to verify above Konstantin Purtov's code, so you could see relationship between order and cut off frequency. The code is mostly from Warren Weckesser.

import scipy
import sys  
from scipy import signal
from scipy import pi
from scipy.io.wavfile import write
import matplotlib.pyplot as plt
import numpy as np    
from scipy.signal import butter, lfilter, freqz   


# ----- ----- ----- -----    
def butter_highpass(cutoff, fs, order=5):
    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


# ----- -----    
# (1)
def foo(sel):
    if (sel == 1):
        # Filter requirements.
        order = 6
        fs = 300.0  # sample rate, Hz
        cutoff = 10  # desired cutoff frequency of the filter, Hz

        # Get the filter coefficients so we can check its frequency response.
        b, a = butter_highpass(cutoff, fs, order)

        # Plot the frequency response.
        w, h = freqz(b, a, worN=8000)
        plt.subplot(2, 1, 1)
        plt.plot(0.5 * fs * w / np.pi, np.abs(h), 'b')
        plt.plot(cutoff, 0.5 * np.sqrt(2), 'ko')
        plt.axvline(cutoff, color='k')
        plt.xlim(0, 0.5 * fs)
        plt.title("High Filter Frequency Response")
        plt.xlabel('Frequency [Hz]')
        plt.grid()

        # Demonstrate the use of the filter.
        # First make some data to be filtered.
        T = 0.5  # seconds
        n = int(T * fs)  # total number of samples
        t = np.linspace(0, T, n, endpoint=False)
        # "Noisy" data.  We want to recover the 20 Hz signal from this.
        data = np.sin(1.2 * 2 * np.pi * t) + 1.5 * np.cos(5 * 2 * np.pi * t) + 0.5 * np.sin(20.0 * 2 * np.pi * t)

        # Filter the data, and plot both the original and filtered signals.
        y = butter_highpass_filter(data, cutoff, fs, order)

        plt.subplot(2, 1, 2)
        plt.plot(t, data, 'b-', label='data')
        plt.plot(t, y, 'g-', linewidth=2, label='filtered data')
        plt.xlabel('Time [sec]')
        plt.grid()
        plt.legend()

        plt.subplots_adjust(hspace=0.35)
        plt.show()
    else:
        print ('Please, choose among choices, thanks.')


# ----- -----
def main():
    sel = int (sys.argv[1])
    foo(sel)


# ----- ----- ----- ----- ----- -----
if __name__ == '__main__':
    main()