Sampling-Theory-Studio

A desktop application illustrating signal sampling and recovery based on the Nyquist–Shannon theorem. Users can load and compose signals, sample at various frequencies, visualize original, sampled, and reconstructed signals in real-time, and explore different reconstruction methods while adding noise and investigating aliasing effects.

Table of Contents

• Demo
• Prerequisites
• Installation
• Features
• Contributors

Demo

Sampling-Theory-Studio-Demo.mp4

Prerequisites

• Python 3.6 or higher

Installation

1. Clone the repository:

   git clone https://github.com/AhmedAmgadElsharkawy/Sampling-Theory-Studio.git
   

2. Install The Dependincies:

   pip install -r requirements.txt
   

3. Run The App:

   python main.py
   

Features

• Load Signal: Support loading pre-recorded signals from CSV files.
• Signal Mixer: Compose custom signals by summing sinusoids with different frequencies, magnitudes, and phase shifts, with real-time generation.
• Export Signal: Save composed signals to CSV files, allowing you to load and share them at any time.
• Signal Sampling: Sample signals and display the sampling frequency in either actual or normalized form (0×fmax to 4×fmax).
• Signal-to-Noise Ratio (SNR): Control the SNR to introduce noise into signals.
• Signal Reconstruction: Provide five different signal reconstruction options: Whittaker–Shannon, Lanczos, Cubic Spline, Zero-Order Hold, and First-Order Hold.

Contributors

• AhmedAmgadElsharkawy: GitHub Profile
• AbdullahMahmoudHanafy: GitHub Profile
• Mohamed-185: GitHub Profile
• RawanAhmed444: GitHub Profile