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Low pass filter is a term in signal processing basics and is often used to filter signals to obtain more accurate results.

This tutorial will discuss the low-pass filter and how to create and implement it in Python.

A low-pass filter is used to pass signals with frequencies below a cutoff frequency, which is held at a certain value specified by the user. All signals with frequencies above the cutoff frequency are attenuated.

ScipyCreating a low-pass Butterworth filter in Python using

In Python, we can SciPycreate a low pass filter using functions from the library. SciPyScientific Python is an abbreviation for Scientific Python, which is a library that provides functions for performing signal processing, optimization, and statistics. This library also uses the following NumPylibrary.

There are several low-pass filters that exist in the real world. However, we will create a Butterworth low-pass filter in Python because it has a maximum frequency of flatness, which means there are no ripples in the passband. This makes it one of the most popular and commonly used low-pass filters.

To successfully implement this method in Python, we first need to import the NumPy, SciPy, and Matplotlibmodules into our Python code.

SciPyThe following code creates a low-pass Butterworth filter in Python using the module.

import numpy as np
from scipy.signal import butter, lfilter, freqz
import matplotlib.pyplot as plt


def butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype="low", analog=False)
    return b, a


def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


# Setting standard filter requirements.
order = 6
fs = 30.0
cutoff = 3.667

b, a = butter_lowpass(cutoff, fs, order)

# Plotting 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("Lowpass Filter Frequency Response")
plt.xlabel("Frequency [Hz]")
plt.grid()


# Creating the data for filteration
T = 5.0  # value taken in seconds
n = int(T * fs)  # indicates total samples
t = np.linspace(0, T, n, endpoint=False)

data = (
    np.sin(1.2 * 2 * np.pi * t)
    + 1.5 * np.cos(9 * 2 * np.pi * t)
    + 0.5 * np.sin(12.0 * 2 * np.pi * t)
)

# Filtering and plotting
y = butter_lowpass_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()