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| Auteurs principaux: | , , , , |
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| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2025
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| Accès en ligne: | https://doi.org/10.5281/zenodo.15476935 |
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Table des matières:
- <h1>Phase Diagram Clustering Analysis</h1> <p>This project performs a series of computational experiments to evaluate different clustering methods and affinity measures on synthetic phase diagram data. It systematically sweeps through various parameters, records the performance (Fowlkes-Mallows score), and stores the results in a SQLite database.</p> <h2>Project Structure</h2> <p>- run.py: The main executable script that orchestrates the parameter sweeps and data processing.<br>- lib.py: A utility library containing helper functions for data manipulation (e.g., label conversion, coordinate transformation, Fowlkes-Mallows score calculation) and plotting.<br>- DataDaemon.py: Implements a daemon process for asynchronous, multiprocessing-safe writing of results to a SQLite database. This is crucial for efficiently handling the large number of results generated by the parameter sweeps.<br>- phase_data/: This directory contains the input phase diagram data used by `run.py`. See `phase_data/README.md` for more details.<br>- db/: This directory will contain the SQLite database (`results.db` by default) where the outcomes of the experiments are stored.</p>