Installation of ML-LPA is rather straightforward. A detailed guide is given below.


ML-LPA requires a Python 3 version to be installed on the computer, as well as the following libraries to work:

  • NumPy
  • h5py
  • MDAnalysis
  • pandas
  • scikit-learn (version >= 0.22.0)
  • tess
  • tqdm

During the installation, ML-LPA should automatically install all missing libraries from the list.

From PyPi

ML-LPA can be installed by simply using the pip command. Just open a terminal and type the following line

> pip install mllpa

The page for ML-LPA on PyPi can be visited using this link.

From the GitHub repo

ML-LPA can be installed directly from the source files available on our GitHub repo. The detailed process to install from these files is described below.

Get the files

You will need first to get the source files from our GitHub repo. Use any of the following link to access the file.

See on GitHub DOWNLOAD

Install the package

Important: We recommend to setup a virtualenv first, to avoid any version conflict with pre-existing packages. Instruction on how to install a virtualenv can be found in this link.

  1. Open the MLLPA folder in the Terminal and go to the source folder.

     > cd mllpa-master/
     > cd source/
  2. Install the library using the file.

    The file has been designed to install automatically all the libraries and packages required that cannot be found in the environment.

    Warning: Because of a problem with the MDAnalysis library, MDAnalysis should be installed manually first. This can be done by using the command > pip3 install MDAnalysis

    When you are ready to install, just run the following command in the Terminal.

     > python3 install

Check the installation

To verify that the installation went well, you can simply open a Python shell and try to import ML-LPA.

import mllpa

If the shell does not return any error, ML-LPA is correctly installed in your environment.

What is next?

Now that ML-LPA has been correctly installed on your computer, you can start using it to analyse your lipid bilayers.

If you need to get started in how to use ML-LPA, please visit the Tutorials section of the website.