PyFilterLab

A simple Python toolkit for designing, visualizing, and applying digital FIR/IIR filters.

Features

• FIR/IIR filter design
• Signal filtering
• Frequency response visualization
• Time-domain signal plotting

Install

pip install pyfilterlab

Usage

from pyfilterlab import design_fir, apply_filter, plot_response, plot_signal

Ouput:-

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📘 Use Reference

Function	Description	Parameters	Returns
design_fir(num_taps, cutoff, fs, window='hamming')	Designs a FIR low-pass filter using the window method.	num_taps (int): Number of filter coefficients cutoff (float): Cutoff frequency in Hz fs (float): Sampling frequency in Hz window (str): Type of window (e.g., 'hamming', 'hann', 'blackman')	numpy.ndarray: FIR filter coefficients
design_iir(order, cutoff, fs, ftype='butter')	Designs an IIR low-pass filter.	order (int): Filter order cutoff (float): Cutoff frequency in Hz fs (float): Sampling frequency in Hz ftype (str): IIR type ('butter', 'cheby1', 'cheby2', 'ellip')	Tuple (b, a): Numerator and denominator coefficients
apply_filter(b, a, signal)	Applies a filter to a signal using the filter coefficients.	b (array): Numerator coefficients a (array or 1): Denominator coefficients signal (array): Input signal	numpy.ndarray: Filtered signal
plot_response(b, a=1, fs=1.0, title='Frequency Response')	Plots the frequency response (gain vs frequency) of the filter.	b, a: Filter coefficients fs: Sampling frequency title: Plot title	Displays a Matplotlib plot
plot_signal(signal, fs=1.0, title='Signal')	Plots a time-domain signal.	signal: Signal data fs: Sampling frequency title: Plot title	Displays a Matplotlib plot