Design IIR Bandpass Elliptic Filter using Scipy- Python

IIR stands for Infinite Impulse Response, It is one of the striking characteristics of many linear-time invariant systems that are characterized from having an impulse response h(t)/h(n) that does not reach 0 at any stage but instead persists indefinitely.

What is IIR Bandpass Elliptic Filter ?

Elliptical Filter is a special type of Filter that is used in digital signal processing when there is a need for a fast transition from pass to stop band. 

The specifications are as follows:  

• Pass band frequency: 1400-2100 Hz
• Stop band frequency: 1050-24500 Hz
• Pass band ripple: 0.4dB
• Stop band attenuation: 50 dB
• Sampling frequency: 7 kHz
• We will plot the magnitude & phase response of the filter.

Step-by-step Approach:

Step 1: Importing all the necessary libraries.

Python3

# import required library
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt

Step 2: Defining user-defined functions mfreqz() and impz(). The mfreqz is a function for magnitude and phase plot and the impz is a function for impulse and step response].

Python3

# Function to depict magnitude
# and phase plot
def mfreqz(b, a, Fs):

    # Compute frequency response of the
    # filter using signal.freqz function
    wz, hz = signal.freqz(b, a)

    # Calculate Magnitude from hz in dB
    Mag = 20*np.log10(abs(hz))

    # Calculate phase angle in degree from hz
    Phase = np.unwrap(np.arctan2(np.imag(hz),
                                 np.real(hz)))*(180/np.pi)

    # Calculate frequency in Hz from wz
    Freq = wz*Fs/(2*np.pi)

    # Plot filter magnitude and phase responses using subplot.
    fig = plt.figure(figsize=(10, 6))

    # Plot Magnitude response
    sub1 = plt.subplot(2, 1, 1)
    sub1.plot(Freq, Mag, 'r', linewidth=2)
    sub1.axis([1, Fs/2, -100, 5])
    sub1.set_title('Magnitude Response', fontsize=20)
    sub1.set_xlabel('Frequency [Hz]', fontsize=20)
    sub1.set_ylabel('Magnitude [dB]', fontsize=20)
    sub1.grid()

    # Plot phase angle
    sub2 = plt.subplot(2, 1, 2)
    sub2.plot(Freq, Phase, 'g', linewidth=2)
    sub2.set_ylabel('Phase (degree)', fontsize=20)
    sub2.set_xlabel(r'Frequency (Hz)', fontsize=20)
    sub2.set_title(r'Phase response', fontsize=20)
    sub2.grid()

    plt.subplots_adjust(hspace=0.5)
    fig.tight_layout()
    plt.show()


# Define impz(b,a) to calculate impulse
# response and step response of a system
# input: b= an array containing numerator
# coefficients,a= an array containing
# denominator coefficients
def impz(b, a):

    # Define the impulse sequence of length 60
    impulse = np.repeat(0., 60)
    impulse[0] = 1.
    x = np.arange(0, 60)

    # Compute the impulse response
    response = signal.lfilter(b, a, impulse)

    # Plot filter impulse and step response:
    fig = plt.figure(figsize=(10, 6))
    plt.subplot(211)
    plt.stem(x, response, 'm', use_line_collection=True)
    plt.ylabel('Amplitude', fontsize=15)
    plt.xlabel(r'n (samples)', fontsize=15)
    plt.title(r'Impulse response', fontsize=15)

    plt.subplot(212)
    step = np.cumsum(response)

    # Compute step response of the system
    plt.stem(x, step, 'g', use_line_collection=True)
    plt.ylabel('Amplitude', fontsize=15)
    plt.xlabel(r'n (samples)', fontsize=15)
    plt.title(r'Step response', fontsize=15)
    plt.subplots_adjust(hspace=0.5)

    fig.tight_layout()
    plt.show()

Step 3:Define variables with the given specifications of the filter.

Python3

# Given specification

# Sampling frequency in Hz
Fs = 7000

# Pass band frequency in Hz
fp = np.array([1400, 2100])

# Stop band frequency in Hz
fs = np.array([1050, 2450])

# Pass band ripple in dB
Ap = 0.4

# Stop band attenuation in dB
As = 50

Step 4: Compute the cut-off frequency

Python3

# Compute pass band and stop band edge frequencies

# Normalized passband edge
# frequencies w.r.t. Nyquist rate
wp = fp/(Fs/2)

# Normalized stopband
# edge frequencies
ws = fs/(Fs/2)

Step 5: Compute order of the Elliptic Bandpass digital filter.

Python3

# Compute order of the elliptic filter
# using signal.ellipord
N, wc = signal.ellipord(wp, ws, Ap, As)

# Print the order of the filter and
# cutoff frequencies
print('Order of the filter=', N)
print('Cut-off frequency=', wc)

Step 6: Design digital Elliptical bandpass filter.

Python3

# Design digital elliptic bandpass filter
# using signal.ellip function
z, p = signal.ellip(N, Ap, As, wc, 'bandpass')


# Print numerator and denomerator
# coefficients of the filter
print('Numerator Coefficients:', z)
print('Denominator Coefficients:', p)

Step 7: Plot magnitude and phase response.

Python3

# Depicting visualizations

# Call mfreqz to plot the magnitude and phase response
mfreqz(z, p, Fs)

Step 8: Plot impulse and step response of the filter.

Python3

# Call impz function to plot impulse
# and step response of the filter
impz(z, p)

Below is the complete implementation of the above stepwise approach:

Python3

# Import required library
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt


# Function to depict magnitude
# and phase plot
def mfreqz(b, a, Fs):

    # Compute frequency response of the
    # filter using signal.freqz function
    wz, hz = signal.freqz(b, a)

    # Calculate Magnitude from hz in dB
    Mag = 20*np.log10(abs(hz))

    # Calculate phase angle in degree from hz
    Phase = np.unwrap(np.arctan2(np.imag(hz),
                                 np.real(hz)))*(180/np.pi)

    # Calculate frequency in Hz from wz
    Freq = wz*Fs/(2*np.pi)

    # Plot filter magnitude and phase responses using subplot.
    fig = plt.figure(figsize=(10, 6))

    # Plot Magnitude response
    sub1 = plt.subplot(2, 1, 1)
    sub1.plot(Freq, Mag, 'r', linewidth=2)
    sub1.axis([1, Fs/2, -100, 5])
    sub1.set_title('Magnitude Response', fontsize=20)
    sub1.set_xlabel('Frequency [Hz]', fontsize=20)
    sub1.set_ylabel('Magnitude [dB]', fontsize=20)
    sub1.grid()

    # Plot phase angle
    sub2 = plt.subplot(2, 1, 2)
    sub2.plot(Freq, Phase, 'g', linewidth=2)
    sub2.set_ylabel('Phase (degree)', fontsize=20)
    sub2.set_xlabel(r'Frequency (Hz)', fontsize=20)
    sub2.set_title(r'Phase response', fontsize=20)
    sub2.grid()

    plt.subplots_adjust(hspace=0.5)
    fig.tight_layout()
    plt.show()


# Define impz(b,a) to calculate impulse
# response and step response of a system
# input: b= an array containing numerator
# coefficients,a= an array containing
# denominator coefficients
def impz(b, a):

    # Define the impulse sequence of length 60
    impulse = np.repeat(0., 60)
    impulse[0] = 1.
    x = np.arange(0, 60)

    # Compute the impulse response
    response = signal.lfilter(b, a, impulse)

    # Plot filter impulse and step response:
    fig = plt.figure(figsize=(10, 6))
    plt.subplot(211)
    plt.stem(x, response, 'm', use_line_collection=True)
    plt.ylabel('Amplitude', fontsize=15)
    plt.xlabel(r'n (samples)', fontsize=15)
    plt.title(r'Impulse response', fontsize=15)

    plt.subplot(212)
    step = np.cumsum(response)

    # Compute step response of the system
    plt.stem(x, step, 'g', use_line_collection=True)
    plt.ylabel('Amplitude', fontsize=15)
    plt.xlabel(r'n (samples)', fontsize=15)
    plt.title(r'Step response', fontsize=15)
    plt.subplots_adjust(hspace=0.5)

    fig.tight_layout()
    plt.show()


# Given specification

# Sampling frequency in Hz
Fs = 7000

# Pass band frequency in Hz
fp = np.array([1400, 2100])

# Stop band frequency in Hz
fs = np.array([1050, 2450])

# Pass band ripple in dB
Ap = 0.4

# Stop band attenuation in dB
As = 50

# Compute pass band and
# stop band edge frequencies
# Normalized passband edge frequencies
# w.r.t. Nyquist rate
wp = fp/(Fs/2)

# Normalized stopband edge frequencies
ws = fs/(Fs/2)

# Compute order of the elliptic filter
# using signal.ellipord
N, wc = signal.ellipord(wp, ws, Ap, As)

# Print the order of the filter and cutoff frequencies
print('Order of the filter=', N)
print('Cut-off frequency=', wc)

# Design digital elliptic bandpass filter
# using signal.ellip() function
z, p = signal.ellip(N, Ap, As, wc, 'bandpass')


# Print numerator and denomerator coefficients of the filter
print('Numerator Coefficients:', z)
print('Denominator Coefficients:', p)


# Depicting visualizations

# Call mfreqz to plot the magnitude and
# phase response
mfreqz(z, p, Fs)
# Call impz function to plot impulse and
# step response of the filter
impz(z, p)

Output: