Metadata-Version: 2.1
Name: pints
Version: 0.2.2
Summary: Probabilistic Inference in Noisy Time-Series
Home-page: https://github.com/pints-team/pints
Maintainer: Michael Clerx
Maintainer-email: michael.clerx@cs.ox.ac.uk
License: BSD 3-clause license
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        # What is Pints?
        
        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](http://doi.org/10.5334/jors.252), and can be cited using the information given in our [CITATION file](./CITATION).
        More information about PINTS papers can be found in the [papers directory](./papers/).
        
        
        ## Using PINTS
        
        PINTS can work with any model that implements the [pints.ForwardModel](http://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model) 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)](examples/writing-a-model.ipynb) implements the two methods above, then you are ready to start using PINTS to infer parameter values using [optimisation](examples/optimisation-first-example.ipynb) or [sampling](examples/sampling-first-example.ipynb).
        
        A brief example is shown below:  
        ![An example of using PINTS in an optimisation](example.svg)  
        _(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](examples/readme-example.ipynb) but a friendlier, more elaborate, introduction can be found on the [examples page](examples/README.md).
        
        A graphical overview of the methods included in PINTS can be [viewed here](https://pints-team.github.io/pints-methods-overview/).
        
        ### Examples and documentation
        
        PINTS comes with a number of [detailed examples](examples/README.md), hosted here on github.
        In addition, there is a [full API documentation](http://pints.readthedocs.io/en/latest/), hosted on readthedocs.io.
        
        
        ## Installing PINTS
        
        You'll need the following requirements:
        
        - Python 2.7 or Python 3.5+
        - Python libraries: `cma matplotlib numpy scipy tabulate`
        
        These can easily be installed using `pip`. To do this, first make sure you have the latest version of pip installed:
        
        ```
        $ pip install --upgrade pip
        ```
        
        Then navigate to the path where you downloaded PINTS to, and install both PINTS and its dependencies by typing:
        
        ```
        $ pip install .
        ```
        
        To install PINTS as a [developer](CONTRIBUTING.md), use
        
        ```
        $ pip install -e .[dev,docs]
        ```
        
        To uninstall again, type
        
        ```
        $ pip uninstall pints
        ```
        
        ## Contributing to PINTS
        
        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](CONTRIBUTING.md) first.
        
        ## License
        
        PINTS is fully open source. For more information about its license, see [LICENSE](LICENSE.md).
        
        ## Get in touch
        
        Questions, suggestions, or bug reports? [Open an issue](https://github.com/pints-team/pints/issues) and let us know.
        
        Alternatively, feel free to email us at `pints at maillist.ox.ac.uk`.
        
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Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: dev
