Science Score: 77.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 6 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
4 of 11 committers (36.4%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (20.6%) to scientific vocabulary
Keywords from Contributors
Repository
design and steer profile likelihood fits
Basic Info
- Host: GitHub
- Owner: scikit-hep
- License: bsd-3-clause
- Language: Python
- Default Branch: master
- Homepage: https://cabinetry.readthedocs.io/
- Size: 1.65 MB
Statistics
- Stars: 30
- Watchers: 4
- Forks: 23
- Open Issues: 82
- Releases: 21
Metadata Files
README.md
cabinetry is a Python library for building and steering binned template fits.
It is written with applications in High Energy Physics in mind.
cabinetry interfaces many other powerful libraries to make it easy for an analyzer to run their statistical inference pipeline.
Statistical models in HistFactory format can be built by cabinetry from instructions in a declarative configuration.
cabinetry makes heavy use of pyhf for statistical inference, and provides additional utilities to help study and disseminate fit results.
This includes commonly used visualizations.
Due to its modular approach, analyzers are free to use all of cabinetry's functionality or only some pieces.
cabinetry can be used for inference and visualization with any pyhf-compatible model, whether it was built with cabinetry or not.
Installation
cabinetry can be installed with pip:
bash
python -m pip install cabinetry
This will only install the minimum requirements for the core part of cabinetry.
The following will install additional optional dependencies needed for ROOT file reading:
bash
python -m pip install cabinetry[contrib]
Hello world
To run the following example, first generate the input files via the script utils/create_ntuples.py.
```python import cabinetry
config = cabinetry.configuration.load("config_example.yml")
create template histograms
cabinetry.templates.build(config)
perform histogram post-processing
cabinetry.templates.postprocess(config)
build a workspace
ws = cabinetry.workspace.build(config)
run a fit
model, data = cabinetry.modelutils.modelanddata(ws) fitresults = cabinetry.fit.fit(model, data)
visualize the post-fit model prediction and data
predictionpostfit = cabinetry.modelutils.prediction(model, fitresults=fitresults) cabinetry.visualize.datamc(predictionpostfit, data, config=config) ```
The above is an abbreviated version of an example included in example.py, which shows how to use cabinetry.
It requires additional dependencies obtained with pip install cabinetry[contrib].
Documentation
Find more information in the documentation and tutorial material in the cabinetry-tutorials repository.
cabinetry is also described in a paper submitted to vCHEP 2021: 10.1051/epjconf/202125103067.
Acknowledgements
This work was supported by the U.S. National Science Foundation (NSF) cooperative agreements OAC-1836650 and PHY-2323298 (IRIS-HEP).
Owner
- Name: Scikit-HEP Project
- Login: scikit-hep
- Kind: organization
- Email: scikit-hep-forum@googlegroups.com
- Website: https://scikit-hep.org
- Repositories: 46
- Profile: https://github.com/scikit-hep
A community project for High Energy Physics data analysis in Python
Citation (CITATION.cff)
cff-version: 1.2.0
message: "Please cite the following works when using this software."
type: software
authors:
- family-names: "Held"
given-names: "Alexander"
orcid: "https://orcid.org/0000-0002-8924-5885"
affiliation: "University of Wisconsin-Madison"
title: "cabinetry: v0.6.0"
version: 0.6.0
doi: 10.5281/zenodo.4742752
repository-code: "https://github.com/scikit-hep/cabinetry/releases/tag/v0.6.0"
url: "https://cabinetry.readthedocs.io/"
keywords:
- python
- fitting
- physics
- profile likelihood
license: "BSD-3-Clause"
abstract: |
Statistical models in [HistFactory](https://cds.cern.ch/record/1456844)
format can be built by `cabinetry` from instructions in a declarative
configuration.
`cabinetry` makes heavy use of [`pyhf`](https://pyhf.readthedocs.io/) for
statistical inference, and provides additional utilities to help study and
disseminate fit results.
This includes commonly used visualizations.
Due to its modular approach, analyzers are free to use all of `cabinetry`'s
functionality or only some pieces.
`cabinetry` can be used for inference and visualization with any
`pyhf`-compatible model, whether it was built with `cabinetry` or not.
references:
- type: article
authors:
- family-names: "Held"
given-names: "Alexander"
orcid: "https://orcid.org/0000-0002-8924-5885"
affiliation: "New York University"
- family-names: "Cranmer"
given-names: "Kyle"
orcid: "https://orcid.org/0000-0002-5769-7094"
affiliation: "New York University"
title: "Building and steering binned template fits with cabinetry"
doi: 10.1051/epjconf/202125103067
url: "https://doi.org/10.1051/epjconf/202125103067"
year: 2021
publisher:
name: "EDP Sciences"
volume: 251
pages: 03067
journal: EPJ Web of Web of Conferences
GitHub Events
Total
- Create event: 15
- Issues event: 25
- Watch event: 2
- Delete event: 12
- Member event: 1
- Issue comment event: 71
- Push event: 47
- Pull request review comment event: 42
- Pull request review event: 67
- Pull request event: 43
- Fork event: 2
Last Year
- Create event: 15
- Issues event: 25
- Watch event: 2
- Delete event: 12
- Member event: 1
- Issue comment event: 71
- Push event: 47
- Pull request review comment event: 42
- Pull request review event: 67
- Pull request event: 43
- Fork event: 2
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Alexander Held | 4****d | 341 |
| Matthew Feickert | m****t@c****h | 16 |
| Mohamed Aly | 8****8 | 5 |
| Henry Schreiner | H****I@g****m | 3 |
| lhenkelm | l****n@c****h | 2 |
| Lorenz Gaertner | l****r@p****e | 2 |
| muellerr | 7****r | 1 |
| Vangelis Kourlitis | e****s@c****h | 1 |
| Nathan Simpson | e****n@g****m | 1 |
| Elliott Kauffman | 6****a | 1 |
| Angus Hollands | g****5@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 82
- Total pull requests: 153
- Average time to close issues: 4 months
- Average time to close pull requests: 19 days
- Total issue authors: 16
- Total pull request authors: 12
- Average comments per issue: 1.95
- Average comments per pull request: 1.46
- Merged pull requests: 117
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 21
- Pull requests: 52
- Average time to close issues: about 1 month
- Average time to close pull requests: 24 days
- Issue authors: 5
- Pull request authors: 3
- Average comments per issue: 0.38
- Average comments per pull request: 1.5
- Merged pull requests: 27
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- alexander-held (45)
- MoAly98 (11)
- rmnmllr (3)
- lhenkelm (3)
- lorenzennio (2)
- Tomoya-Iizawa (2)
- matthewfeickert (2)
- eduardo-rodrigues (1)
- nkang9 (1)
- miholzbo (1)
- ekourlit (1)
- andrzejnovak (1)
- kratsg (1)
- pariRieck (1)
- MarcelHoh (1)
Pull Request Authors
- alexander-held (119)
- MoAly98 (26)
- matthewfeickert (10)
- ekourlit (3)
- henryiii (3)
- lorenzennio (2)
- lhenkelm (2)
- agoose77 (1)
- andrzejnovak (1)
- ekauffma (1)
- rmnmllr (1)
- MarcelHoh (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 546 last-month
- Total docker downloads: 43
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 6
(may contain duplicates) - Total versions: 28
- Total maintainers: 2
pypi.org: cabinetry
design and steer profile likelihood fits
- Homepage: https://github.com/scikit-hep/cabinetry
- Documentation: https://cabinetry.readthedocs.io/
- License: BSD 3-Clause
-
Latest release: 0.6.0
published over 2 years ago
Rankings
Maintainers (2)
conda-forge.org: cabinetry
cabinetry is a Python library for building and steering binned template fits. It is written with applications in High Energy Physics in mind. cabinetry interfaces many other powerful libraries to make it easy for an analyzer to run their statistical inference pipeline. Statistical models in HistFactory format can be built by cabinetry from instructions in a declarative configuration. cabinetry makes heavy use of pyhf for statistical inference, and provides additional utilities to help study and disseminate fit results. This includes commonly used visualizations. Due to its modular approach, analyzers are free to use all of cabinetry's functionality or only some pieces. cabinetry can be used for inference and visualization with any pyhf-compatible model, whether it was built with cabinetry or not.
- Homepage: https://github.com/scikit-hep/cabinetry
- License: BSD-3-Clause
-
Latest release: 0.5.1
published over 3 years ago
Rankings
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v3 composite
- pre-commit/action v3.0.0 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite