{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"},{"label":"Astronomy","url":"https://skillfed.io/packages/category/scientific-engineering-astronomy"}],"enrichment":{"capability":"Computes dark matter halo concentration parameters from simulated particle data using Voronoi tessellation, a non-parametric technique that does not assume spherical symmetry.","skillfed_tags":["cosmology","n-body-simulation","abandoned"],"use_cases":["Analyze dark matter halo concentration in cosmological N-body simulations using Voronoi tessellation methods.","Compare concentration estimates from non-parametric techniques against traditional NFW profile fitting.","Process particle data from Gadget-2, Gasoline, or other simulation formats to compute halo properties.","Reproduce or verify results from Lang et al. (2015) using the included test halos and benchmarks."],"what_it_does":"TesseRACt is a specialized astrophysics package for computing concentration parameters of dark matter halos from N-body simulation particle data. It implements a Voronoi tessellation-based method that is non-parametric and does not assume spherical symmetry, allowing it to handle substructure in halos. The package includes a C program (vorovol) that computes Voronoi diagrams from particle data in multiple formats (Gadget-2, Gasoline, binary, ASCII) and halo catalogues, plus Python routines to compile, run, and parse its output.\n\nThe package also provides routines for computing concentrations using particle volumes, traditional NFW profile fitting, and non-parametric techniques assuming spherical symmetry. It was published alongside a peer-reviewed paper (Lang et al. 2015) and includes test halos and performance benchmarks from that work. However, the package has been abandoned since 2015-07-13 with no subsequent maintenance or updates.","worth_installing":"No. The package is abandoned (last release 2015-07-13), has high install friction due to a C compilation requirement, and is unmaintained. While it implements a valid scientific technique, the lack of support for modern environments makes it unsuitable for new projects. Use only if reproducing historical results and you can manage C build issues independently."},"id":"tesseract","links":{"html":"https://skillfed.io/packages/tesseract","md":"https://skillfed.io/packages/tesseract.md","pypi":"https://pypi.org/project/tesseract/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2015-07-13","license_spdx":null,"license_treatment":"copyleft","name":"tesseract","python_support":"unspecified","summary":"Tesselation based Recovery of Amorphous halo Concentrations"},"popularity":{"monthly_downloads":82157,"position":14179,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.3"}