SaVi - morphology identification using line graph components

Featuring morphology identification with Savi algorithm that uses lines and graph components as described in https://arxiv.org/abs/2009.14147

Other updates include:

• No need for an input file now. Run directly giving path of the XYZ files, and the input will be taken from the command line.
• Process data with multiple time frames.
• Minor bug fixes.

The additional archive contains data used in the paper for collision cascades at 50, 100, 150, and 200 keV in W using DND-BN potential, and 100 and 200 keV using JW and M-S potentials. Launch the web-app by running "python web_server.py" or directly open index.html. The JSON data can also be directly read from "CSaransh_files/cascades-data.js" for analysis and reproduction of the results.

p.s. Savi is an acronym for "Sutra-Sanrachnen Vikar-Vivechanam" which is Sanskrit for "Defect description using the relative layout of lines".

Archiving for JOSS submission

v0.3.5

Updated paper.md as per final reviewer.

v0.3.3

• Added test suite with code coverage (Codecov) and continuous integration (Travis-CI)
• Refactored code and added clang-format
• Updates to documentation, JOSS paper, conda environment.
• Added Zenodo JSON metadata.

v0.3.1

Lammps Support, more optimised react-js single page application, minor fixes.

html-application-v0.3

This is the single page web-app (gh-pages) with cascades view, comparison, statistics and cluster classifications based on UMAP, t-SNE, (H)DBSCAN (as described in this paper). The new version is faster, has one single page for all the analysis ( unlike before when stats and classification had separate pages) and have many small user interface enhancements.

Open index.html and find classification in last pane and open stats.html for general stats. You can also fork the project, go to this commit and open the webapp in github itself. The results are based on the dataset for IAEA challenge for fusion material link.

Classification of clusters web-app

This is the web-app (gh-pages) with cluster classifications based on UMAP, t-SNE, (H)DBSCAN as described in this paper. Also has statistics page separately as submitted in IAEA challenge for fusion material. Open index.html and find classification in last pane and open stats.html for general stats. You can also fork the project, go to this commit and open the webapp in github itself. The results are based on the dataset for IAEA challenge for fusion material link.

iaea-submission

This is as submitted to IAEA Challenge on Materials for Fusion-2018. The results are shown for the data included in the challenge (database link).
To view the web-app directly in the browser open the index.htm file in the attached zip of the gh-pages branch.