The analyses include:

• Mean Squared Displacement (MSD)
• Diffusion coefficent estimators (from MSD curves) - includes Einstein relation, linear fit, and anomalous diffusion fit.
• Area per lipid estimators
• Bilayer thickness
• Displacement Vector (flow) maps and correlations
• Deuterium order parameter
• Orientation parameters
• Mass and Electron Density Estimators
• and more!

PyBILT has the following major dependencies:

• MDAnalysis - https://www.mdanalysis.org/
• NumPy - http://www.numpy.org/
• SciPy - https://www.scipy.org/
• Matplotlib - https://matplotlib.org/
• Seaborn - https://seaborn.pydata.org/
• six - https://pypi.org/project/six/
• future - http://python-future.org/

To run the pybilt test suite:

• pytest - https://docs.pytest.org/en/latest/

To use the PyBILT Jupyter Notebooks:

• Jupyter - http://jupyter.org/

To build the docs locally requires the following additional packages:

• Sphinx - http://www.sphinx-doc.org/en/master/
• recommonmark - https://recommonmark.readthedocs.io/en/latest/
• sphinx_rtd_theme - https://sphinx-rtd-theme.readthedocs.io/en/latest/

The pybilt package has been tested using Anaconda Python 2.7 and Python 3.6;

The following section describes the process for setting up the dependencies and installing the 'pybilt' package using a conda environment and the setup.py script.

First, clone or download the GitHub repo

git clone https://github.com/blakeaw/PyBILT.git

Then create a new conda environment for pybilt and activate it:

conda create --name pybilt
conda activate pybilt

The install the preferred python version:

• for Python 2.7

conda install python=2.7

• for Python 3.6

conda install python=3.6

Then install all the pybilt run dependencies:

conda install numpy scipy matplotlib seaborn six future
conda install -c conda-forge MDAnalysis

Then install pybilt:

python PyBILT/setup.py install

If you want to run the pybilt tests you can install pytest:

conda install pytest

If you want to run the pybilt Jupyter notebooks (PyBILT/jupyter_notebooks), then install Jupyter:

conda install jupyter

Note that the notebooks have not been updated for Python 3 yet.

If you want to build local versions of doc pages install the following packages:

conda install sphinx
pip install sphinx_rtd_theme
pip install recommonmark

The files environment_py27.yml and environment_py36.yml have been provided to allow for easy setup of a new conda environment with all of the most recently tested versions of dependencies. Run:

• For Python 2.7:

conda env create -f environment_py27.yml

• For Python 3.6

conda env create -f environment_py36.yml

which will create a new conda environment named pybilt with the appropriate dependencies. Then activate the environment

conda activate pybilt

Next, run the setup.py script with install,

python PyBILT/setup.py install

to install the 'pybilt' package into the pybilt environment.

If you want to run the pybilt Jupyter notebooks (PyBILT/jupyter_notebooks), then install Jupyter:

conda install jupyter

Note that the notebooks have not been updated for Python 3 yet.

PyBILT is composed of 2 primary analysis packages:

• bilayer_analyzer -- The bilayer_analyzer is an analysis package that is designed to analyze (quasi) planar lipid bilayer systems. It is accessed through the BilayerAnalyzer object, which can be imported via: from pybilt.bilayer_analyzer import BilayerAnalyzer. The BilayerAnalyzer features automatic dynamic unwrapping of coordinates and leaflet detection. The bilayer_analyzer works on a multiple-representation model, whereby the various analyses are conducted using different representations of the bilayer lipids. Bilayer lipids can be represented using the following four representations:
  • All atom
  • Centers-of-mass -- Each lipid (or selection of atoms from each lipid) is reduced to a center-of-mass.
  • Grid (or lipid grid) -- The lipids are mapped to two-dimensional grids (one for each leaflet) in the style of the GridMAT-MD method
  • Vectors - Each lipid is converted to a vector representation using select reference atoms (or sets of reference atoms) that are used to compute the head and tail of the vector; e.g., a lipid tail atom to lipid head atom, or P-N vectors.

The bilayer_analyzer features various types of analyses and the use of different representations is handled internally based the requirements and design of each analysis type. See the documentation for list of analyses that can be added to intances of the BilayerAnalyzer.

• mda_tools -- This package includes various modules and functions for directly analyzing and operating on MDAnalysis trajectories and objects. e.g. functions to compute density profiles.

Additional packages include:

• lipid_grid -- The lipid grid module can be used construct "lipid grid" grid representations of lipid bilayers, which can be used to accurately estimate quantities such as area per lipid.

• com_trajectory -- This module can be used to construct a center of mass trajectory (COMTraj) out of an MDAnalysis trajectory, which is useful for computing quantities like mean squared displacement. The COMTraj is designed to work with bilayers.

• plot_generation -- This module has several pre-written plotting functions (using matplotlib and seaborn) for some of the properties that can be computed from functions in the other modules. e.g. mean squared displacement and area per lipid.

Visit the PyBILT docs on Read the Docs. Docs can also be viewed offline/locally by opening the PyBILT/docs/build/html/index.html file from the repo in a web browser; however, this build of the docs is not updated often. In addition to the doc pages, there are currently a few Jupyter IPython notebooks that provide some examples and show some basic usage (these have not been updated/tested for/with python 3 yet); updates and more of these are in the pipeline.

To report problems or bugs please open a GitHub Issue. Additionally, any comments, suggestions, or feature requests for PyBILT can also be submitted as a GitHub Issue.

For any other inquiries, including questions about PyBILT use or implementation, you can contact Blake directly via e-mail at either blake.a.wilson@vanderbilt.edu or blakeaw1102@gmail.com; please include "PyBILT inquiry" in the e-mail subject line.

If you would like to contribute directly to PyBILT's development please

1. Fork the repo (https://github.com/blakeaw/PyBILT/fork)
2. Create a new branch for your feature (git checkout -b feature/foo_bar)
3. Create test code for your feature
4. Once your feature passes its own test, run all the tests using pytest (python -m pytest)
5. Once your feature passes all the tests, commit your changes (git commit -am 'Add the foo_bar feature.')
6. Push to the branch (git push origin feature/foo_bar)
7. Create a new Pull Request

This project is licensed under the MIT License - see the LICENSE file for details

• A special thanks to James Pino (https://github.com/JamesPino) for his inciteful comments and suggestions that have helped improve the quality of this code, and thanks to him for pointing out some very useful coding tools.
• Thanks to my advisors, Carlos F. Lopez and Arvind Ramanathan, for catalyzing this project and for providing me with the space and means to pursue it.

• ANACONDA - ANACONDA Python distribution and CONDA package and environment manager
• PyCharm - Text Editor/IDE
• ATOM - Text Editor/IDE
• Sublime Text - Text Editor used in earlier work
• Landscape - Code quality analysis and tracking
• Git - Version control
• GitHub - Development Platform and repository storage
• Sphinx - Python documentation generator
• recommonmark - A docutils-compatibility bridge to CommonMark
• Read the Docs - Documentation hosting
• docstring-coverage - A simple audit tool for examining python source files for missing docstrings.
• Python-Modernize - Automatic modernization of Python 2 code for dual Python 2 and 3 support.

• Blake A Wilson - Currently a Postdoctoral Fellow at Vanderbilt University
  • Vandy e-mail: blake.a.wilson@vanderbilt.edu
  • Gmail: blakeaw1102@gmail.com
  • Blake's VU Website
  • Also find me on LinkedIn and Research Gate