FIR IIR python

FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) are two common types of digital filters used in signal processing. In Python, there are various libraries available for designing and implementing FIR and IIR filters, such as scipy.signal.

To design an FIR filter in Python, you can use the `scipy.signal.firwin` function. This function allows you to design different types of FIR filters, including low-pass, high-pass, bandpass, and bandstop filters. Here's an example of designing a low-pass FIR filter:

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

# Filter specifications
order = 50  # Filter order
cutoff_freq = 0.2  # Cutoff frequency

# Design the FIR filter coefficients
coeffs = firwin(order+1, cutoff_freq)

# Generate and filter a random input signal
input_signal = np.random.rand(1000)
output_signal = filtfilt(coeffs, 1, input_signal)

To design an IIR filter in Python, you can use the `scipy.signal.iirfilter` function. This function allows you to design IIR filters using various methods, such as Butterworth, Chebyshev, and elliptic filters. Here's an example of designing a Butterworth low-pass IIR filter:

from scipy.signal import iirfilter, lfilter

# Filter specifications
order = 4  # Filter order
cutoff_freq = 0.2  # Cutoff frequency

# Design the IIR filter coefficients
b, a = iirfilter(order, cutoff_freq)

# Generate and filter a random input signal
input_signal = np.random.rand(1000)
output_signal = lfilter(b, a, input_signal)

These are just basic examples to get you started with FIR and IIR filters in Python using the scipy library. There are many more options and techniques available depending on your specific requirements.