openeuler/mne repository overview

⁠Quick reference

• The official MNE-Python docker image.

• Maintained by: openEuler CloudNative SIG⁠.

• Where to get help: openEuler CloudNative SIG⁠, openEuler⁠.

⁠MNE-Python | openEuler

Current MNE-Python docker images are built on the openEuler⁠. This repository is free to use and exempted from per-user rate limits.

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

Learn more on MNE-Python Documentation⁠.

⁠Supported tags and respective Dockerfile links

The tag of each mne docker image is consist of the version of mne and the version of basic image. The details are as follows

⁠Usage

Here, users can select the corresponding {Tag} and container startup options based on their requirements.

• Pull the openeuler/mne image from hub.docker.com

  docker pull openeuler/mne:{Tag}
  

  Copy

• Run with an interactive shell

  You can also start the container with an interactive shell to use mne.

  docker run -it --rm openeuler/mne:{Tag} bash
  

  Copy

• A minimal example showing how to load MNE's built-in sample MEG dataset

  The test_mne.py script will load the raw MEG file sample_audvis_raw.fif.

  import mne
  
  # Set the sample data path (downloads it if not present)
  data_path = mne.datasets.sample.data_path()
  raw_fname = str(data_path) + '/MEG/sample/sample_audvis_raw.fif'
  
  # Load the raw MEG data
  raw = mne.io.read_raw_fif(raw_fname, preload=True)
  

  Copy

• Run the script

  python3 test_mne.py
  

  Copy

  You will see printed information about the MEG recording: number of channels, sampling rate, duration, etc.

⁠Question and answering

If you have any questions or want to use some special features, please submit an issue or a pull request on openeuler-docker-images⁠.