hierarchical_nu
A Bayesian hierarchical model for source-nu associations
Science Score: 44.0%
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✓CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Keywords
Repository
A Bayesian hierarchical model for source-nu associations
Basic Info
Statistics
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 4
- Releases: 0
Topics
Metadata Files
README.md
hierarchical_nu
A Bayesian hierarchical model for source-nu associations.
Installation
The package can currently be installed from this directory via:
pip install git+https://github.com/cescalara/hierarchical_nu
The above command will go ahead and install any dependencies that you may be missing to run the core code.
Setting up Stan
The hierarchical model is implemented in Stan, using the CmdStan and CmdStanPy interfaces. CmdStanPy will be installed as needed using pip if you follow the above instructions. However if you have not set up and compiled CmdStan before, the extra step detailed below is needed. See the CmdStanPy installation docs for more information.
You can set up CmdStan by running the following python code:
python
import cmdstanpy
cmdstanpy.install_cmdstan()
Or via the command line on MacOS/Linux:
install_cmdstan
This will make and install CmdStan in the ~/.cmdstan directory.
A note on updating existing code
For a clean install, be aware that some calculations are cached in your local working directory when your run the code. Please delete any files in .cache/ and the necessary calculations will be re-run as you go along.
Examples
You can find some example notebooks stored as markdown files in the examples/ directory. To run these notebooks, use the jupytext package to open the markdown files.
The first time that you use hierarchical_nu, some longer calculations will be run and cached locally. This is a one-time cost, so please be patient.
Owner
- Name: Francesca Capel
- Login: cescalara
- Kind: user
- Location: Munich, Germany
- Company: Max Planck Institute for Physics
- Website: https://francescacapel.com
- Repositories: 44
- Profile: https://github.com/cescalara
Astrophysics and statistics
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Francesca"
given-names: "Capel"
orcid: "https://orcid.org/0000-0002-1153-2139"
title: "A hierarchical Bayesian approach to point source analysis in high-energy neutrino telescopes"
url: "https://github.com/cescalara/hierarchical_nu"
preferred-citation:
type: article
authors:
- family-names: "Capel"
given-names: "Francesca"
orcid: "https://orcid.org/0000-0002-1153-2139"
- family-names: "Kuhlmann"
given-names: "Julian"
- family-names: "Haack"
given-names: "Christian"
- family-names: "Ha Minh"
given-names: "Martin"
- family-names: "Niederhausen"
given-names: "Hans"
- family-names: "Schumacher"
given-names: "Lisa"
doi: "10.3847/1538-4357/ad7fe9"
journal: "The Astrophysical Journal"
month: 11
start: 12 # First page number
title: "A hierarchical Bayesian approach to point source analysis in high-energy neutrino telescopes"
issue: 1
volume: 976
year: 2024
GitHub Events
Total
- Create event: 1
- Issues event: 1
- Watch event: 3
- Delete event: 1
- Issue comment event: 1
- Public event: 1
- Push event: 6
- Pull request review comment event: 8
- Pull request review event: 7
- Pull request event: 15
- Fork event: 2
Last Year
- Create event: 1
- Issues event: 1
- Watch event: 3
- Delete event: 1
- Issue comment event: 1
- Public event: 1
- Push event: 6
- Pull request review comment event: 8
- Pull request review event: 7
- Pull request event: 15
- Fork event: 2
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- codecov/codecov-action v2 composite