PINTS (Probabilistic Inference on Noisy Time-Series) is a framework for optimisation and Bayesian inference on ODE 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.
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.
A graphical overview of the methods included in PINTS can be viewed here.
PINTS comes with a number of detailed examples, hosted here on github. In addition, there is a full API documentation, hosted on readthedocs.io.
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
To see what's changed in the latest release, see the CHANGELOG.
If you'd like to help us develop PINTS by adding new methods, writing documentation, or fixing embarassing bugs, please have a look at these guidelines first.
PINTS is fully open source. For more information about its license, see LICENSE.
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.
FAQs
Probabilistic Inference in Noisy Time-Series
We found that pints demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.