FAQ#

  • I have been using kinisi in my research and would like to cite the package, how should I do this?

    Thanks for using kinisi. kinisi supports the duecredit framework for generating the relevant citations to go with your analysis. If you cannot use this framework, please cite the methodological Journal of Chemical Theory and Computation paper, the JOSS software paper, and specifically refer to the version of kinisi that has been used. If you have used the centre of mass functionality, please also cite our publication on that.

  • How does kinisi work?

    Please have a look at our methodology paper to learn about how kinisi works.

  • Running the documentation locally gave me different numbers, how come?

    kinisi aims to be reproducible on a per-environment basis. Therefore, we do not pin versions in the pyproject.toml hence, when you run pip install '.[docs]' you might get different package versions and due to the stochastic nature of the sampling in kinisi, this leads to slightly different values in the results. kinisi allows a random_state to be passed to many methods, however, this will only ensure reproducibility when the same enviroment is present. Consider using pinned versions in a conda/mamba environment if you want to enable true reproducibility.

  • How are trajectories unwrapped?

    When calculating displacements, kinisi aims to find the minimum displacement between trajectory steps. This can be done for orthorhombic cells with a simple heuristic: if the diplacement is greater than one half the simulation cell length, kinisi wraps that displacement. For the case of non-orthorhombic simulation cells, the displacements to all periodic images are calculated and the minimum used. This scheme assumes that no particle moves more than one cell between steps. Therefore, it requires that enough simulation data is provided to kinisi, in other words, that there is a small enough time skip between trajectory steps. A small enchancement is made to these methods, in order to account for changes in cell volume during NPT simulations. This enhancement is the TOR scheme, developed by Bullerjahn and co-workers. If you use kinisi for an NPT simulation, please cite their work.

  • I don’t want to sample the MSD at all possible time intervals, how can I achieve this?

    This can be achieved through the use of a custom time interval input. You can see how to do this in the MDAnalysis comparison notebook.

  • My analysis is giving very weird numbers for the diffusion coefficient, and my trendline appears very wrong, what’s happening?

    You may be encountering the joys of a covariance matrix with a high condition number. This leads to issues with numerical precision in some of the operations kinisi performs. We have a short documentation page that describes this issue and provides a potential mitigation strategy.