FIGARO
FIGARO: hierarchical non-parametric inference for population studies - Published in JOSS (2024)
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 9 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
4 of 8 committers (50.0%) from academic institutions -
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
Fast Inference for GW Astronomy, Research and Observations
Basic Info
Statistics
- Stars: 20
- Watchers: 7
- Forks: 9
- Open Issues: 0
- Releases: 4
Metadata Files
README.md
FIGARO - Fast Inference for GW Astronomy, Research & Observations
FIGARO is an inference code designed to estimate multivariate probability densities given samples from an unknown distribution using a Dirichlet Process Gaussian Mixture Model (DPGMM) as nonparameteric model. It is also possible to perform hierarchical inferences: in this case, the model used is (H)DPGMM, described in Rinaldi & Del Pozzo (2022a). Differently from other DPGMM implementations relying on variational algorithms, FIGARO does not require the user to specify a priori the maximum allowed number of mixture components. The required number of Gaussian distributions to be included in the mixture is inferred from the data. The documentation and user guide for FIGARO is available at the documentation page.
Getting started
You can install FIGARO either via pip (stable release, recommended)
pip install figaro
or from the repository (potentially unstable)
git clone git@github.com:sterinaldi/FIGARO.git
cd FIGARO
pip install .
FIGARO comes with two plug-and-play CLI:
figaro-densityreconstructs a probability density given a set of samples;figaro-hierarchicalreconstructs a probability density given a set of single-event samples, each of them drawn around a sample from the initial probability density.
If you only want to reconstruct some probability density or run a vanilla hierarchical analysis, we strongly recommend using these CLI, which are already tested and optimised. A (hopefully gentle) introduction to them can be found at this page, and a guide on how to use the FIGARO reconstructions is available here. If you want to include FIGARO in your own scripts, an introductive guide can be found here: there we show how to to reconstruct a probability density with FIGARO and how to use its products.
Acknowledgments
If you use FIGARO in your research, please cite Rinaldi & Del Pozzo (2024):
text
@ARTICLE{Rinaldi2024,
author = {{Rinaldi}, Stefano and {Del Pozzo}, Walter},
title = "{FIGARO: hierarchical non-parametric inference for population studies}",
journal = {Journal of Open Source Software},
publisher = {The Open Journal},
year = 2024,
month = may,
volume = {9},
number = {97},
pages = {6589},
doi = {10.21105/joss.06589},
url = {https://doi.org/10.21105/joss.06589}
}
If you make use of the hierarchical analysis, you should mention (H)DPGMM as the model used and cite Rinaldi & Del Pozzo (2022):
text
@ARTICLE{2022MNRAS.509.5454R,
author = {{Rinaldi}, Stefano and {Del Pozzo}, Walter},
title = "{(H)DPGMM: a hierarchy of Dirichlet process Gaussian mixture models for the inference of the black hole mass function}",
journal = {\mnras},
keywords = {gravitational waves, methods: data analysis, methods: statistical, stars: black holes, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology},
year = 2022,
month = feb,
volume = {509},
number = {4},
pages = {5454-5466},
doi = {10.1093/mnras/stab3224},
archivePrefix = {arXiv},
eprint = {2109.05960},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.5454R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Owner
- Name: Stefano Rinaldi
- Login: sterinaldi
- Kind: user
- Location: Pisa
- Company: University of Pisa
- Website: sterinaldi.github.io
- Twitter: SteRinaldi_
- Repositories: 11
- Profile: https://github.com/sterinaldi
PhD candidate in Gravitational Wave Physics
JOSS Publication
FIGARO: hierarchical non-parametric inference for population studies
Authors
Institut für Theoretische Astrophysik, ZAH, Universität Heidelberg, Albert-Ueberle-Str. 2, 69120 Heidelberg, Germany, Dipartimento di Fisica e Astronomia "G. Galilei", Università di Padova, Via F. Marzolo 8, 35121 Padova, Italy
Dipartimento di Fisica "E. Fermi", Università di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy, INFN, Sezione di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
Editor
Dan Foreman-Mackey
Tags
astronomy astrophysics nonparametric methods black holes gravitational wavesGitHub Events
Total
- Issues event: 1
- Watch event: 4
- Delete event: 4
- Issue comment event: 12
- Push event: 74
- Pull request event: 10
- Fork event: 1
- Create event: 3
Last Year
- Issues event: 1
- Watch event: 4
- Delete event: 4
- Issue comment event: 12
- Push event: 74
- Pull request event: 10
- Fork event: 1
- Create event: 3
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Stefano Rinaldi | s****i@l****g | 1,425 |
| Walter Del Pozzo | w****o@l****g | 6 |
| Leo Singer | l****r@l****g | 4 |
| Thomas Ng | t****g@l****k | 3 |
| Riccardo Buscicchio | r****o@u****t | 2 |
| gabriele Demasi | g****i@u****t | 1 |
| d-sanfratello | d****o@s****t | 1 |
| Vera Eris Del Favero | 7****a | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 1
- Total pull requests: 12
- Average time to close issues: 5 days
- Average time to close pull requests: 6 days
- Total issue authors: 1
- Total pull request authors: 3
- Average comments per issue: 3.0
- Average comments per pull request: 1.25
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 10
- Average time to close issues: 5 days
- Average time to close pull requests: 2 days
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 3.0
- Average comments per pull request: 1.5
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- thomasckng (1)
- KailibRyanDoney (1)
Pull Request Authors
- lpsinger (7)
- thomasckng (5)
- xevra (1)
- dfm (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 1,964 last-month
- Total docker downloads: 58
- Total dependent packages: 0
- Total dependent repositories: 4
- Total versions: 59
- Total maintainers: 1
pypi.org: figaro
FIGARO: Fast Inference for GW Astronomy, Research & Observations
- Documentation: https://figaro.readthedocs.io
- License: MIT License Copyright (c) 2022 Stefano Rinaldi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
Latest release: 1.9.1
published 6 months ago
Rankings
Maintainers (1)
Dependencies
- astropy *
- corner *
- cython *
- dill *
- h5py *
- imageio *
- matplotlib *
- numba *
- numpy *
- pyvo *
- scipy *
- tqdm *