Digital Low Pass Butterworth Filter in Python
In this article, we are going to discuss how to design a Digital Low Pass Butterworth Filter using Python. The Butterworth filter is a type of signal processing filter designed to have a frequency response as flat as possible in the pass band. Let us take the below specifications to design the filter and observe the Magnitude, Phase & Impulse Response of the Digital Butterworth Filter.
The specifications are as follows:
- Sampling rate of 40 kHz
- Pass band edge frequency of 4 kHz
- Stop band edge frequency of 8kHz
- Pass band ripple of 0.5 dB
- Minimum stop band attenuation of40 dB
We will plot the magnitude, phase, and impulse response of the filter.
Step-by-step Approach:
Step 1: Importing all the necessary libraries.
Python3
# import required modulesimport numpy as npimport matplotlib.pyplot as pltfrom scipy import signalimport math |
Step 2: Define variables with the given specifications of the filter.
Python3
# Specifications of Filter # sampling frequencyf_sample = 40000 # pass band frequencyf_pass = 4000 # stop band frequencyf_stop = 8000 # pass band ripplefs = 0.5 # pass band freq in radianwp = f_pass/(f_sample/2) # stop band freq in radianws = f_stop/(f_sample/2) # Sampling TimeTd = 1 # pass band rippleg_pass = 0.5 # stop band attenuationg_stop = 40 |
Step3: Building the filter using signal.buttord function.
Python3
# Conversion to prewrapped analog frequencyomega_p = (2/Td)*np.tan(wp/2)omega_s = (2/Td)*np.tan(ws/2) # Design of Filter using signal.buttord functionN, Wn = signal.buttord(omega_p, omega_s, g_pass, g_stop, analog=True) # Printing the values of order & cut-off frequency!print("Order of the Filter=", N) # N is the order# Wn is the cut-off freq of the filterprint("Cut-off frequency= {:.3f} rad/s ".format(Wn)) # Conversion in Z-domain # b is the numerator of the filter & a is the denominatorb, a = signal.butter(N, Wn, 'low', True)z, p = signal.bilinear(b, a, fs)# w is the freq in z-domain & h is the magnitude in z-domainw, h = signal.freqz(z, p, 512) |
Output:
Step 4: Plotting the Magnitude Response.
Python3
# Magnitude Responseplt.semilogx(w, 20*np.log10(abs(h)))plt.xscale('log')plt.title('Butterworth filter frequency response')plt.xlabel('Frequency [Hz]')plt.ylabel('Amplitude [dB]')plt.margins(0, 0.1)plt.grid(which='both', axis='both')plt.axvline(100, color='green')plt.show() |
Output:
Step 5: Plotting the Impulse Response.
Python3
# Impulse Responseimp = signal.unit_impulse(40)c, d = signal.butter(N, 0.5)response = signal.lfilter(c, d, imp) plt.stem(np.arange(0, 40), imp, use_line_collection=True)plt.stem(np.arange(0, 40), response, use_line_collection=True)plt.margins(0, 0.1) plt.xlabel('Time [samples]')plt.ylabel('Amplitude')plt.grid(True)plt.show() |
Output:
Step 6: Plotting the Phase Response.
Python3
# Phase Responsefig, ax1 = plt.subplots() ax1.set_title('Digital filter frequency response')ax1.set_ylabel('Angle(radians)', color='g')ax1.set_xlabel('Frequency [Hz]') angles = np.unwrap(np.angle(h)) ax1.plot(w/2*np.pi, angles, 'g')ax1.grid()ax1.axis('tight')plt.show() |
Output:
Below is the complete program based on the above approach:
Python
# import required modulesimport numpy as npimport matplotlib.pyplot as pltfrom scipy import signalimport math # Specifications of Filter # sampling frequencyf_sample = 40000 # pass band frequencyf_pass = 4000 # stop band frequencyf_stop = 8000 # pass band ripplefs = 0.5 # pass band freq in radianwp = f_pass/(f_sample/2) # stop band freq in radianws = f_stop/(f_sample/2) # Sampling TimeTd = 1 # pass band rippleg_pass = 0.5 # stop band attenuationg_stop = 40 # Conversion to prewrapped analog frequencyomega_p = (2/Td)*np.tan(wp/2)omega_s = (2/Td)*np.tan(ws/2) # Design of Filter using signal.buttord functionN, Wn = signal.buttord(omega_p, omega_s, g_pass, g_stop, analog=True) # Printing the values of order & cut-off frequency!print("Order of the Filter=", N) # N is the order# Wn is the cut-off freq of the filterprint("Cut-off frequency= {:.3f} rad/s ".format(Wn)) # Conversion in Z-domain # b is the numerator of the filter & a is the denominatorb, a = signal.butter(N, Wn, 'low', True)z, p = signal.bilinear(b, a, fs)# w is the freq in z-domain & h is the magnitude in z-domainw, h = signal.freqz(z, p, 512) # Magnitude Responseplt.semilogx(w, 20*np.log10(abs(h)))plt.xscale('log')plt.title('Butterworth filter frequency response')plt.xlabel('Frequency [Hz]')plt.ylabel('Amplitude [dB]')plt.margins(0, 0.1)plt.grid(which='both', axis='both')plt.axvline(100, color='green')plt.show() # Impulse Responseimp = signal.unit_impulse(40)c, d = signal.butter(N, 0.5)response = signal.lfilter(c, d, imp)plt.stem(np.arange(0, 40), imp, use_line_collection=True)plt.stem(np.arange(0, 40), response, use_line_collection=True)plt.margins(0, 0.1)plt.xlabel('Time [samples]')plt.ylabel('Amplitude')plt.grid(True)plt.show() # Phase Responsefig, ax1 = plt.subplots()ax1.set_title('Digital filter frequency response')ax1.set_ylabel('Angle(radians)', color='g')ax1.set_xlabel('Frequency [Hz]')angles = np.unwrap(np.angle(h))ax1.plot(w/2*np.pi, angles, 'g')ax1.grid()ax1.axis('tight')plt.show() |
Output:
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