{"id":13699429,"url":"https://github.com/pints-team/pints","last_synced_at":"2026-03-06T14:13:55.982Z","repository":{"id":41827564,"uuid":"91454079","full_name":"pints-team/pints","owner":"pints-team","description":"Probabilistic Inference on Noisy Time 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tests on multiple python versions](https://github.com/pints-team/pints/actions/workflows/unit-test-python-coverage.yml/badge.svg)](https://github.com/pints-team/pints/actions/workflows/unit-test-python-coverage.yml)\n[![Unit tests on multiple operating systems](https://github.com/pints-team/pints/actions/workflows/unit-test-os-coverage.yml/badge.svg)](https://github.com/pints-team/pints/actions/workflows/unit-test-os-coverage.yml)\n[![codecov](https://codecov.io/gh/pints-team/pints/branch/main/graph/badge.svg)](https://codecov.io/gh/pints-team/pints)\n[![Change-point testing code](https://raw.githubusercontent.com/pints-team/change-point-testing/main/badge-code.svg)](https://github.com/pints-team/change-point-testing)\n[![Change-point testing results](https://raw.githubusercontent.com/pints-team/change-point-testing/main/badge-results.svg)](https://www.cs.ox.ac.uk/projects/PINTS/functional-testing)\n[![binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pints-team/pints/main?filepath=examples)\n[![readthedocs](https://readthedocs.org/projects/pints/badge/?version=latest)](http://pints.readthedocs.io/en/latest/?badge=latest)\n\n\n# What is Pints?\n\nPINTS (Probabilistic Inference on Noisy Time-Series) is a framework for optimisation and Bayesian inference.\nAlthough 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.\n\nPINTS is described in [this publication in JORS](http://doi.org/10.5334/jors.252) and can be cited using the information given in our [CITATION file](https://github.com/pints-team/pints/blob/main/CITATION).\nMore information about PINTS papers can be found in the [papers directory](https://github.com/pints-team/pints/tree/main/papers).\n\n\n## Using PINTS\n\nPINTS can work with any model that implements the [pints.ForwardModel](http://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model) interface.\nThis has just two methods:\n\n```\nn_parameters() --\u003e Returns the dimension of the parameter space.\n\nsimulate(parameters, times) --\u003e Returns a vector of model evaluations at\n                                the given times, using the given parameters\n```\n\nExperimental data sets in PINTS are defined simply as lists (or arrays) of `times` and corresponding experimental `values`.\nIf you have this kind of data, and if [your model (or model wrapper)](https://github.com/pints-team/pints/blob/main/examples/stats/custom-model.ipynb) implements the two methods above, then you are ready to start using PINTS to infer parameter values using [optimisation](https://github.com/pints-team/pints/blob/main/examples/optimisation/first-example.ipynb) or [sampling](https://github.com/pints-team/pints/blob/main/examples/sampling/first-example.ipynb).\n\nA brief example is shown below:\n\n![An example of using PINTS in an optimisation](https://raw.githubusercontent.com/pints-team/pints/main/example.svg)\n\n_(Left)_ A noisy experimental time series and a computational forward model.\n_(Right)_ Example code for an optimisation problem.\nThe full code can be [viewed here](https://github.com/pints-team/pints/blob/main/examples/sampling/readme-example.ipynb) but a friendlier, more elaborate, introduction can be found on the [examples page](https://github.com/pints-team/pints/blob/main/examples/README.md).\n\nBeyond time-series models, PINTS can be used on any error function or log-likelihood that takes real-valued, continuous parameters.\n\nAn overview of the methods provided by PINTS can be [viewed here](https://pints.readthedocs.io/en/latest/#provided-methods).\n\n\n### Examples and documentation\n\nPINTS comes with a number of [detailed examples](https://github.com/pints-team/pints/blob/main/examples/README.md), hosted here on github.\nIn addition, there is a [full API documentation](http://pints.readthedocs.io/en/latest/), hosted on readthedocs.io.\n\n\n## Installing PINTS\n\nThe latest release of PINTS can be installed with `pip`, by opening a console and typing\n\n```\n$ pip install --upgrade pip\n$ pip install pints\n```\n\nNote that you'll need Python 3.6 or newer.\n\nIf you prefer to have the latest cutting-edge version, you can instead install from the repository, by typing\n\n```\n$ git clone https://github.com/pints-team/pints.git\n$ cd pints\n$ pip install -e .[dev,docs]\n```\n\nTo uninstall again, type:\n\n```\n$ pip uninstall pints\n```\n\n\n## What's new in this version of PINTS?\n\nTo see what's changed in the latest release, see the [CHANGELOG](https://github.com/pints-team/pints/blob/main/CHANGELOG.md).\n\n\n## Contributing to PINTS\n\nThere are lots of ways to contribute to PINTS development, and anyone is free to join in.\nFor example, you can report problems or make feature requests on the [issues](https://github.com/pints-team/pints/issues) pages.\n\nSimilarly, 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.\nBecause PINTS is a big project, we've written extensive [contribution guidelines](https://github.com/pints-team/pints/blob/master/CONTRIBUTING.md) to help standardise the code — but don't worry, this will become clear during review.\n\n\n## License\n\nPINTS is fully open source. \nFor more information about its license, see [LICENSE](https://github.com/pints-team/pints/blob/main/LICENSE.md).\n\n\n## Get in touch\n\nQuestions, suggestions, or bug reports? \n[Start a discussion](https://github.com/pints-team/pints/discussions) or [open an issue](https://github.com/pints-team/pints/issues) to let us know!\n","funding_links":[],"categories":["\u003cspan id=\"head30\"\u003e3.4. 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