https://github.com/geeksloth/signal-frequency-filtering-example

A simple demonstration of signal frequency filtering using Python
https://github.com/geeksloth/signal-frequency-filtering-example

frequency-analysis signal-processing

Last synced: about 1 year ago
JSON representation

A simple demonstration of signal frequency filtering using Python

• Host: GitHub
• URL: https://github.com/geeksloth/signal-frequency-filtering-example
• Owner: geeksloth
• Created: 2024-02-23T10:59:50.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-23T11:41:15.000Z (over 2 years ago)
• Last Synced: 2025-03-25T16:52:11.290Z (about 1 year ago)
• Topics: frequency-analysis, signal-processing
• Language: Python
• Homepage:
• Size: 260 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Signal Frequency Filtering Example

A simple demonstration of signal frequency filtering using Python.

![Figure_1.png](static/Figure_1.png)

## Step 1. Signal Generation

1. Generate a linear space-time starting value of the sequence from 0, to the stop sequence at 10, and there are 2000 samples. 

2. Generate a signal from the previous space-time with 2 frequencies with different amplitude.

3. Analyze the frequencies by applying the `Fourier Analysis`. If the length of our data is in `second`, now it is in `Hz`.

![code-1.png](static/code-1.png)

## Step 2. Filtering Process

4. Cutting some frequencies from the signal.

5. Transform the result from `Frequency Domain` back to the `Spatial Domain`.

![code-2.png](static/code-2.png)

## Step 3. Visualization

6. Plotting the process as a result from the begining of this readme file :)

7. For the fast way to share, the code is in below:

```python

import numpy as np

from scipy.fftpack import rfft, irfft, fftfreq

import pylab as plt

time   = np.linspace(0,10,2000)

signal = np.cos(5*np.pi*time) + np.cos(7*np.pi*time)

W = fftfreq(signal.size, d=time[1]-time[0])

f_signal = rfft(signal)

   

cut_f_signal = f_signal.copy()

cut_f_signal[(W<6)] = 0

cut_signal = irfft(cut_f_signal)

plt.subplot(221)

plt.plot(time,signal)

plt.subplot(222)

plt.plot(W,f_signal)

plt.xlim(0,10)

plt.subplot(223)

plt.plot(W,cut_f_signal)

plt.xlim(0,10)

plt.subplot(224)

plt.plot(time,cut_signal)

plt.show()

```