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| title: TDA Explorer | |
| emoji: "\U0001F52E" | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.29.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Interactive Persistent Homology & Betti Numbers | |
| tags: | |
| - topological-data-analysis | |
| - persistent-homology | |
| - mathematics | |
| - data-science | |
| - visualization | |
| # TDA Explorer | |
| **Interactive Topological Data Analysis** - Explore persistent homology, Betti numbers, and simplicial complexes on point cloud datasets. | |
| ## Features | |
| - **8 preset datasets**: Circle, Figure-8, Concentric Circles, Clusters, Moons, Sphere (3D), Torus (3D), Random Noise | |
| - **Persistent homology**: Compute H0 (components), H1 (loops), H2 (voids) using Ripser | |
| - **Persistence diagrams & barcodes**: Visualize feature birth/death | |
| - **Interactive filtration**: Drag the epsilon slider to watch the Vietoris-Rips complex form in real time | |
| - **Betti numbers**: Live count of topological features at any scale | |
| - **Custom data**: Upload your own CSV point cloud | |
| - **Educational**: Learn TDA concepts with the built-in guide | |
| ## How to Use | |
| 1. Select a dataset and click **Generate & Analyze** | |
| 2. Explore the persistence diagram and barcode to understand the topology | |
| 3. Drag the **epsilon slider** to see how the simplicial complex evolves | |
| 4. Watch Betti numbers change as you adjust the filtration | |
| 5. Upload your own data (CSV with x,y or x,y,z columns) | |
| ## Built By | |
| **Dr. Milan Amrutkumar Joshi** - Senior Faculty, AI & Data Science | |
| Research interests: Persistent Homology, Topological Data Analysis, Machine Learning | |
| - [Hugging Face](https://huggingface.co/mlnjsh) | |
| - [Google Scholar](https://scholar.google.com/citations?user=_ML2_woAAAAJ) | |
| - [GitHub](https://github.com/mlnjsh) | |