Data for mne-python testing.

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

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

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)"