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Data for mne-python testing

MATLAB 32.00% OpenEdge ABL 10.48% Smarty 17.56% Rich Text Format 2.43% HolyC 37.53%

mne-testing-data's Introduction

mne-testing-data

Data for mne-python testing.

Acquire this data in MNE

Use mne.datasets.testing.data_path(verbose=True).

Update to the latest version in MNE

Use mne.datasets.testing.data_path(force_update=True, verbose=True).

Add or change files in the repo for use with MNE

  1. Ensure your only option is to add files here. Alternatives would be e.g.:

    • See if you can make use of existing files
    • Synthesize the necessary testing files using e.g. RawArray and NumPy directly
  2. If new files are needed, make a PR to this repo to add your files.

    .. warning:: Make files as small as possible while ensuring proper testing! This often means e.g. cropping to a very short segment of data or using a small subset of channels.

  3. Update the version.txt of the repo in your PR to the next increment.

  4. Once your PR is merged, ask a maintainer to cut a new release of the testing data, e.g. 0.53.

  5. In MNE, update mne/datasets/config.py to:

    1. Change the 'testing' value in the RELEASES dict in mne/datasets/config.py to the new version.

    2. Set the new hash. This can be easily done by either:

      1. Downloading and running md5sum on this (with the proper version number):

        https://codeload.github.com/mne-tools/mne-testing-data/tar.gz/0.53

        or

      2. Force-updating the repo and looking at the error message (as it gives the new true hash), e.g.:

        $ python -c "import mne; mne.datasets.testing.data_path(force_update=True)"
        

mne-testing-data's People

Contributors

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