This repository includes two subprojects: a library Tracking and a visual analysis system Tracking-graph-visual-demo.
git clone https://github.com/tdavislab/GWMT.git
cd GWMT/Tracking
Example.ipynb describes the pipeline to use the pFGW library to apply feature tracking using merge trees from time-varying scalar fields.
please refer to Example.ipynb for the usage of this library.
This library requires POT, matplotlib, networkx, and pandas to run.
If you do not have these packages installed, please use the following command to intall them.
pip install POT
pip install matplotlib
pip install networkx
pip install pandas
Tracking-graph-visual-demo is a web-based framework for interactive analysis and visualization of probabilistic traking graphs and corresponding scalar fields.
The main features of our system:
- Select a specific timestamp to highlight it and its neighboring timestamps both in the tracking graph and scalar fields.
- Slide the range bar to filter edges with low probabilities.
- Select a specific feature to highlight edges related to it.
git clone https://github.com/tdavislab/GWMT.git
cd GWMT/Tracking-graph-visual-demo
python app.py
After running the above commands, you can run Tracking-graph-visual-demo by visiting http://127.0.0.1:5000/ on the local machine (If possible, please use Chrome).
This software requires flask and numpy to run.
If you do not have these packages installed, please use the following command to intall them.
pip install flask
pip install numpy
Flexible and Probabilistic Topology Tracking with Partial Optimal Transport.
Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, Bei Wang.
IEEE Transactions on Visualization and Computer Graphics, 2025.