Science Score: 62.0%
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
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
8 of 25 committers (32.0%) from academic institutions -
✓Institutional organization owner
Organization pints-team has institutional domain (www.cs.ox.ac.uk) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.9%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Probabilistic Inference on Noisy Time Series
Basic Info
- Host: GitHub
- Owner: pints-team
- License: other
- Language: Python
- Default Branch: main
- Homepage: http://pints.readthedocs.io
- Size: 192 MB
Statistics
- Stars: 239
- Watchers: 11
- Forks: 34
- Open Issues: 146
- Releases: 6
Topics
Metadata Files
README.md
What is Pints?
PINTS (Probabilistic Inference on Noisy Time-Series) is a framework for optimisation and Bayesian inference. Although it can be used as a general-purpose inference tool, it was developed specifically for models of noisy time-series, such as arise in electrochemistry and cardiac electrophysiology.
PINTS is described in this publication in JORS, and can be cited using the information given in our CITATION file. More information about PINTS papers can be found in the papers directory.
Using PINTS
PINTS can work with any model that implements the pints.ForwardModel interface. This has just two methods:
``` n_parameters() --> Returns the dimension of the parameter space.
simulate(parameters, times) --> Returns a vector of model evaluations at the given times, using the given parameters ```
Experimental data sets in PINTS are defined simply as lists (or arrays) of times and corresponding experimental values.
If you have this kind of data, and if your model (or model wrapper) implements the two methods above, then you are ready to start using PINTS to infer parameter values using optimisation or sampling.
A brief example is shown below:
(Left) A noisy experimental time series and a computational forward model.
(Right) Example code for an optimisation problem.
The full code can be viewed here but a friendlier, more elaborate, introduction can be found on the examples page.
Beyond time-series models, PINTS can be used on any error function or log-likelihood that takes real-valued, continuous parameters.
A graphical overview of the methods included in PINTS can be viewed here.
Examples and documentation
PINTS comes with a number of detailed examples, hosted here on github. In addition, there is a full API documentation, hosted on readthedocs.io.
Installing PINTS
The latest release of PINTS can be installed without downloading (cloning) the git repository, by opening a console and typing
$ pip install --upgrade pip
$ pip install pints
Note that you'll need Python 3.6 or newer.
If you prefer to have the latest cutting-edge version, you can instead install from the repository, by typing
$ git clone https://github.com/pints-team/pints.git
$ cd pints
$ pip install -e .[dev,docs]
To uninstall again, type:
$ pip uninstall pints
What's new in this version of PINTS?
To see what's changed in the latest release, see the CHANGELOG.
Contributing to PINTS
There are lots of ways to contribute to PINTS development, and anyone is free to join in! For example, you can report problems or make feature requests on the issues pages.
Similarly, if you want to contribute documentation or code you can tell us your idea on this page, and then provide a pull request for review. Because PINTS is a big project, we've written extensive contribution guidelines to help standardise the code — but don't worry, this will become clear during review.
License
PINTS is fully open source. For more information about its license, see LICENSE.
Get in touch
Questions, suggestions, or bug reports? Open an issue and let us know.
Alternatively, feel free to email us at pints at maillist.ox.ac.uk.
Owner
- Name: PINTS - Probabilistic Inference for Noisy Time Series
- Login: pints-team
- Kind: organization
- Website: https://www.cs.ox.ac.uk/projects/PINTS/
- Repositories: 18
- Profile: https://github.com/pints-team
Citation (CITATION)
To cite PINTS in publications, please use:
Clerx, M., Robinson, M., Lambert, B., Lei, C. L., Ghosh, S., Mirams, G. R., & Gavaghan, D. J. (2019).
Probabilistic Inference on Noisy Time Series (PINTS).
Journal of Open Research Software, 7(1), 23.
https://doi.org/10.5334/jors.252
A BibTeX entry for LaTeX users is
@article{Clerx2019Pints,
title={Probabilistic Inference on Noisy Time Series ({PINTS})},
author={Clerx, Michael and Robinson, Martin and Lambert, Ben and Lei, Chon Lok and Ghosh, Sanmitra and Mirams, Gary R and Gavaghan, David J},
journal={Journal of Open Research Software},
volume={7},
number={1},
pages={23},
year={2019},
doi={10.5334/jors.252}
}
GitHub Events
Total
- Issues event: 13
- Watch event: 12
- Delete event: 7
- Issue comment event: 28
- Push event: 22
- Pull request review event: 4
- Pull request review comment event: 2
- Pull request event: 16
- Fork event: 2
- Create event: 6
Last Year
- Issues event: 13
- Watch event: 12
- Delete event: 7
- Issue comment event: 28
- Push event: 22
- Pull request review event: 4
- Pull request review comment event: 2
- Pull request event: 16
- Fork event: 2
- Create event: 6
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 3,253
- Total Committers: 25
- Avg Commits per committer: 130.12
- Development Distribution Score (DDS): 0.676
Top Committers
| Name | Commits | |
|---|---|---|
| Michael Clerx | m****x@c****k | 1,053 |
| ben18785 | b****t@g****m | 598 |
| Chon Lok Lei | c****i@g****m | 417 |
| DavAug | d****n@g****t | 231 |
| Michael Clerx | m****x@n****k | 174 |
| Martin Robinson | m****s@g****m | 174 |
| Michael Clerx | M****x@u****m | 142 |
| rcw5890 | r****l@h****k | 132 |
| phumtutum | v****0@g****m | 97 |
| Fergus Cooper | f****2@g****m | 92 |
| danielfridman98 | d****n@y****u | 37 |
| Michael Clerx (UM) | M****x@m****l | 28 |
| lorcandelaney | l****6@g****m | 19 |
| Chon Lei | c****i@c****k | 17 |
| Simon Marchant | s****t@l****k | 9 |
| danielfridman98 | 3****8@u****m | 8 |
| Jack Arthur | j****8@y****m | 7 |
| naunauyoh | a****h@g****m | 5 |
| Sanmitra Ghosh | s****5@h****m | 4 |
| rcw5890 | 5****0@u****m | 3 |
| Gary Mirams | g****s@g****m | 2 |
| chonlei | c****i@D****n | 1 |
| Graham Lee | l****g@l****e | 1 |
| Steven Lang | s****z@g****m | 1 |
| alisterde | a****s@d****k | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 238
- Total pull requests: 92
- Average time to close issues: over 3 years
- Average time to close pull requests: 3 months
- Total issue authors: 20
- Total pull request authors: 16
- Average comments per issue: 2.62
- Average comments per pull request: 3.37
- Merged pull requests: 68
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 10
- Pull requests: 8
- Average time to close issues: 5 days
- Average time to close pull requests: 13 days
- Issue authors: 5
- Pull request authors: 2
- Average comments per issue: 0.9
- Average comments per pull request: 1.38
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- MichaelClerx (112)
- ben18785 (78)
- DavAug (12)
- chonlei (10)
- martinjrobins (5)
- rccreswell (4)
- I-Bouros (3)
- k-shep (2)
- HOLL95 (2)
- MarcBerliner (1)
- elizavetasemenova (1)
- abillscmu (1)
- zaikunzhang (1)
- feresro (1)
- jarthu (1)
Pull Request Authors
- MichaelClerx (69)
- I-Bouros (5)
- ben18785 (5)
- phumtutum (4)
- k-shep (3)
- lorcandelaney (3)
- DavAug (3)
- chonlei (3)
- rccreswell (2)
- FelixNoessler (2)
- RomanSyunyaev (2)
- alisterde (2)
- jarthur36 (2)
- Rebecca-Rumney (1)
- abillscmu (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 8,968 last-month
- Total dependent packages: 3
- Total dependent repositories: 11
- Total versions: 4
- Total maintainers: 2
pypi.org: pints
Probabilistic Inference in Noisy Time-Series
- Homepage: https://github.com/pints-team/pints
- Documentation: https://pints.readthedocs.io
- License: BSD 3-clause license
-
Latest release: 0.5.0
published over 2 years ago
Rankings
Maintainers (2)
Dependencies
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- Note *
- cma >=2
- matplotlib >=1.5
- numpy >=1.8
- on *
- outside *
- scipy >=0.14
- tabulate *
- threadpoolctl *
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