VAST

VAST: the Void Analysis Software Toolkit - Published in JOSS (2022)

https://github.com/desi-ur/vast

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
    6 of 26 committers (23.1%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 83% confidence
Last synced: 4 months ago · JSON representation

Repository

Void Analysis Software Toolkit

Basic Info
Statistics
  • Stars: 11
  • Watchers: 3
  • Forks: 8
  • Open Issues: 18
  • Releases: 4
Created over 7 years ago · Last pushed 4 months ago
Metadata Files
Readme Changelog License

README.md

VAST: Void Analysis Software Toolkit

DOI tests Documentation Status

The Void Analysis Software Toolkit, or VAST, provides pure Python implementations of two popular classes of void-finding algorithms in galaxy catalogs:

  1. Void identification by growing spherical voids.
  2. Void identification using watershed algorithms.

Our docs can be found here: https://vast.readthedocs.io/en/latest/

VoidFinder

VoidFinder is an algorithm which utilizes a sphere-growing method on a grid search and a unionization of the sufficiently large spheres. The VoidFinder directory contains the package, which includes an efficient Multi-Process Cythonized version of VoidFinder (from vast.voidfinder import find_voids), as well as an OpenGL based visualization for the output of VoidFinder (the vast.voidfinder.viz package).

See here for 3D OpenGL-based visualization of VoidFinder's voids in SDSS DR7!

V2

V2 is a voronoi-tesselation-based algorithm for finding the void regions, based on the ZOBOV algorithm. ZOBOV uses the gradient of the volume of adjacent voronoi cells to flow multiple cells together into large void regions.
The Vsquared directory contains the package, which includes five different
methods for building void regions from voronoi cells, as well as an OpenGL based visualization for the output of V2 (the vast.vsquared.viz package).

Owner

  • Name: DESI-UR
  • Login: DESI-UR
  • Kind: organization

JOSS Publication

VAST: the Void Analysis Software Toolkit
Published
September 29, 2022
Volume 7, Issue 77, Page 4033
Authors
Kelly A. Douglass ORCID
University of Rochester
Dahlia Veyrat ORCID
University of Rochester
Stephen W. O'Neill ORCID
Independent Researcher
Segev BenZvi ORCID
University of Rochester
Fatima Zaidouni ORCID
University of Rochester, Massachusetts Institute of Technology
Michaela Guzzetti ORCID
University of Rochester, Smith College, University of Washington
Editor
Dan Foreman-Mackey ORCID
Tags
astronomy voids large-scale structure redshift survey

GitHub Events

Total
  • Issues event: 12
  • Watch event: 1
  • Delete event: 10
  • Issue comment event: 49
  • Push event: 78
  • Pull request review comment event: 1
  • Pull request event: 18
  • Pull request review event: 12
  • Create event: 8
Last Year
  • Issues event: 12
  • Watch event: 1
  • Delete event: 10
  • Issue comment event: 49
  • Push event: 78
  • Pull request review comment event: 1
  • Pull request event: 18
  • Pull request review event: 12
  • Create event: 8

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 737
  • Total Committers: 26
  • Avg Commits per committer: 28.346
  • Development Distribution Score (DDS): 0.697
Past Year
  • Commits: 61
  • Committers: 15
  • Avg Commits per committer: 4.067
  • Development Distribution Score (DDS): 0.689
Top Committers
Name Email Commits
Kelly k****7@d****u 223
Segev BenZvi s****i@i****u 153
Stephen W. O'Neill Jr s****l@l****m 111
Dylan Veyrat d****t@u****u 66
Hernan Rincon h****2@g****m 44
Micahaela Guzzetti m****i@n****y 39
Segev BenZvi s****i 32
QuiteAFoxtrot s****5@g****m 32
Hernan h****2@g****m 7
Fatima Zaidouni 4****i 5
Hernan Benjamin Rincon h****n@l****v 4
Hernan Benjamin Rincon h****n@l****v 3
ChangHoon Hahn c****n@p****u 2
Hernan Benjamin Rincon h****n@l****v 2
Hernan Benjamin Rincon h****n@l****v 2
Segev BenZvi s****i@r****u 2
Fatima F****a@H****l 1
Hernan Benjamin Rincon h****n@l****v 1
Hernan Benjamin Rincon h****n@l****v 1
Hernan Benjamin Rincon h****n@l****v 1
Hernan Benjamin Rincon h****n@l****v 1
Hernan Benjamin Rincon h****n@l****v 1
Hernan Benjamin Rincon h****n@l****v 1
KELLY DOUGLASS k****7@b****u 1
Michaela Guzzetti 4****i 1
Dan Foreman-Mackey d****m@d****o 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 48
  • Total pull requests: 76
  • Average time to close issues: 7 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 11
  • Total pull request authors: 8
  • Average comments per issue: 1.71
  • Average comments per pull request: 0.7
  • Merged pull requests: 62
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 9
  • Pull requests: 18
  • Average time to close issues: 24 days
  • Average time to close pull requests: 13 days
  • Issue authors: 5
  • Pull request authors: 3
  • Average comments per issue: 2.11
  • Average comments per pull request: 1.78
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • QuiteAFoxtrot (15)
  • kadglass (10)
  • lavaux (6)
  • hbrincon (5)
  • sybenzvi (4)
  • o-curtis (2)
  • gideonkmc (2)
  • changhoonhahn (1)
  • dveyrat (1)
  • ANSalcedo (1)
  • mrhossen38 (1)
Pull Request Authors
  • QuiteAFoxtrot (23)
  • sybenzvi (22)
  • hbrincon (13)
  • kadglass (13)
  • dveyrat (3)
  • changhoonhahn (2)
  • fzaidouni (1)
  • dfm (1)
Top Labels
Issue Labels
enhancement (21) bug (11) help wanted (6) good first issue (2) Docs (1) duplicate (1)
Pull Request Labels
enhancement (2)

Dependencies

requirements.txt pypi
  • Cython >=0.29.21
  • PyOpenGL >=3.1.5
  • PyOpenGL-accelerate >=3.1.5
  • astropy >=4.1
  • h5py >=3.1.0
  • healpy >=1.15.0
  • matplotlib >=3.3.3
  • numpy >=1.19.4
  • psutil >=5.7.3
  • pyopengltk *
  • pytest >=6.1.2
  • scikit-learn >=0.23.2
  • vispy >=0.6.5
.github/workflows/tests.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite