Signal-to-Noise Simulator

A visual educational tool for understanding signal-to-noise ratio in chromatographic peak detection.

Overview

The Signal-to-Noise Simulator is an interactive application designed to help students and researchers understand signal-to-noise ratio (SNR) in peak detection. This tool visualizes how varying levels of noise affect peak detection and measurement, and demonstrates different signal processing techniques used in analytical chemistry.

Features

• Interactive Peak Visualization: Adjust SNR values and see real-time changes in peak detectability
• Customizable Parameters:
  • Control the number of peaks displayed (1-10)
  • Adjust sampling rate with "Points Across Peak" setting
  • Toggle between display modes (Normal/High-Res with Noise)
• Educational Annotations: Display peak heights, SNR values, and noise regions
• Intelligent Background Detection: Automatic detection of noise-free regions for accurate measurements
• Multiple Analysis Modes: Compare different signal processing techniques on the same chromatogram
• Signal Processing Visualization: Toggle first and second derivative displays
• Educational Resources: Comprehensive information about SNR in analytical chemistry

Installation

System Requirements

• Python 3.6 or higher
• Tkinter support for your Python installation

Dependencies

The simulator requires the following Python libraries:

• numpy
• matplotlib
• scipy
• tkinter (usually included with Python)
• PIL with ImageTk support (for plotting)

Installation Steps

1. Clone the repository or download the source code

git clone https://github.com/maccoss/Signal-to-Noise-Simulator.git
cd Signal-to-Noise-Simulator

2. Install the required dependencies:

On Debian/Ubuntu Linux:

sudo apt-get install python3-tk python3-pil.imagetk
pip install numpy matplotlib scipy

On macOS:

pip install numpy matplotlib scipy pillow

On Windows:

pip install numpy matplotlib scipy pillow

Usage

Run the simulator with:

python3 Chromatography.py

SNR Impact Visualization Tab

• Enter an SNR Value to see how different noise levels affect peak visibility
• Set the Number of Peaks to simulate simple or complex chromatograms
• Adjust Points Across Peak to simulate different sampling rates
• Select a Display Mode to visualize high-resolution signal with noise
• Toggle Show Peak Heights to display measurements and SNR values for each peak
• Enable Show Noise Region to highlight the background areas used for noise calculation
• Click Update Visualization to generate a new random chromatogram with current settings

Detection Methods Comparison Tab

This tab demonstrates different signal processing techniques applied to the same chromatogram:

• Raw noisy signal
• Savitzky-Golay filtered signal (polynomial smoothing)
• Toggle Show 1st Derivative for peak detection visualization
• Toggle Show 2nd Derivative for peak inflection point detection
• Optional Show Peak Boundaries for visualizing peak integration regions

Educational Resources Tab

Contains detailed information about SNR in chromatography:

• Definition and calculation methods
• Importance in analytical method validation
• Factors affecting SNR in instrumental analysis
• Techniques for improving SNR
• Practical implications for data analysis

Interpreting the Visualization

• Green Line: The ideal signal without noise
• Blue Line: The signal with noise at current sampling rate
• Red Line (optional): The sampled signal points when viewing high-res mode
• Red Dots: Peak positions
• Yellow Regions: Areas used for background noise calculation
• Orange Dashed Lines: ±1 standard deviation of the noise
• Black Dashed Lines: Peak boundaries (when enabled)
• Red Text: Measured noise standard deviation
• Blue Text: Points sampled across the peak width

Troubleshooting

If you encounter the error ModuleNotFoundError: No module named 'tkinter':

• Install the tkinter package for your system as described in the installation steps

If you encounter the error ImportError: cannot import name 'ImageTk' from 'PIL':

• Install the Python Imaging Library Tkinter integration with sudo apt-get install python3-pil.imagetk on Debian/Ubuntu
• For other operating systems, try pip install pillow

Educational Use

This simulator is ideal for:

• Teaching instrumental analysis concepts
• Laboratory method development training
• Signal processing demonstrations
• Method validation education
• Understanding the importance of proper sampling rates in chromatography

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Developed for educational purposes and for evaluation of different peak detection algorithms.