LiPyphilic: A love of lipids and python!¶
A Python toolkit for the analysis of lipid membrane simulations
lipyphilic is free software licensed under the GNU General Public License v2 or later (GPLv2+)
Overview¶
lipyphilic is a set of tools for analysing MD simulations of lipid bilayers. It is an object-oriented Python package built directly on top of MDAnalysis, and makes use of NumPy and SciPy for efficient computation. The analysis classes are designed with the same interface as those of MDAnalysis - so if you know how to use analysis modules in MDAnalysis then learning lipyphilic will be a breeze.
Analysis tools in lipyphilic include: identifying sterol flip-flop events, calculating domain registration over time, and calculating local lipid compositions. lipyphilic also has three on-the-fly trajectory transformations to i) fix membranes split across periodic boundaries and ii) perform nojump coordinate unwrapping and iii) convert triclinic coordinates to their orthorhombic representation.
These tools position lipyphilic as complementary to, rather than competing against, existing membrane analysis software such as MemSurfer and FatSlim.
Interactive tutorials¶
We recommend new users take a look out our interactive tutorials. These will show you how to get the most out of lipyphilic
Basic Usage¶
Alternatively, check out the Basic Usage example to see how to use lipyphilic, and see the Analysis tools section for detailed information and examples on each tool.
Installation¶
The easiest way to install lipyphilic along with its dependencies is through Conda:
conda config --add channels conda-forge
conda install lipyphilic
See the installation guide for futher information.
Citing¶
If you use lipyphilic in your project, please cite MDAnalysis and if you use the Area Per Lipid tool please also cite Freud.
There is currently no paper describing lipyphilic, but we’re working on it. In the meantime, if you like what we do, please tell everyone you know to check out lipyphilic! And if there are things you think we could improve, features you would like to see added, or pesky bugs that need to be fixed, please raise an issue on github.