Welcome to the Photonics Repository! This repository houses a collection of powerful tools and algorithms designed for SEM image processing, curve smoothing, curve approximation, and the creation of predictive models for analyzing depression profiles post-ablation in nanophotonics applications. This repository is essential part of paper "Image-Driven Laser Ablation Optimization".
- Image Processing: Explore our image processing tools to preprocess and segment images efficiently (file pipeline.py).
- Curve Smoothing: Utilize advanced algorithms for smoothing curves to enhance data quality and accuracy (file process_curve.py).
- Curve Approximation: Discover methods for approximating curves to extract meaningful insights from data(file approximation.py).
- Predictive Modeling: Dive into our predictive modeling techniques tailored for analyzing depression profiles post-ablation, empowering accurate predictions and valuable insights (file model.py).
- Clone the Repository: Clone or download the repository to your local machine.
- Explore the Files: Navigate through the folders to access the scripts and algorithms tailored for your photonics research needs.
- Run the Scripts: Execute the scripts using your preferred environment or IDE to apply various processing techniques to your data.
- Customize and Experiment: Feel free to customize the scripts and experiment with different parameters to achieve optimal results for your specific applications.
We welcome contributions from the photonics community to enhance and expand the capabilities of this repository. Whether you have new algorithms, optimizations, or bug fixes to share, we encourage you to contribute and collaborate with us to advance the field of photonics research.
This repository is licensed under the MIT License, allowing for open collaboration and widespread use in both academic and commercial settings.
Happy exploring and analyzing in the fascinating world of photonics! 🌟✨