bayesmsd 0.1.0

Bayesian MSD fitting

BayesMSD: properly fitting MSDs

While inspection of MSD curves is one of the most ubiquitous ways of analyzing particle tracking data, it is also well known that extracting model parameters from MSD curves is a statistical minefield[^1]. This problem can be addressed quite nicely in the language of Gaussian processes, allowing statistically rigorous MSD fits. This provides, for example, error bars on estimated model parameters, which are quite noticeably missing from the current literature.

For a Quickstart intro, more extensive Tutorials & Examples and the full API reference refer to the documentation hosted at ReadTheDocs.

To install bayesmsd you can use the latest stable version from PyPI

$ pip install --upgrade bayesmsd

or the very latest updates right from GitHub:

$ pip install git+https://github.com/OpenTrajectoryAnalysis/bayesmsd

[^1]: Vestergaard, Blainey, Flyvbjerg, Optimal estimation of diffusion coefficients from single-particle trajectories, Physical Review E, 2014; DOI

Developers

Note the Makefile, which can be used to build the documentation (using Sphinx); run unit tests and check code coverage; and build an updated package for release with GNU make.

When editing the example notebooks, remember to remove output and empty cells before committing to the git repo. nbstripout allows to do this automatically upon commit.