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Installation

First, you need to make sure you have MNE-Python installed and working on your system. See the installation instructions. Once this is done, you should be able to run this in a terminal:

$ python -c "import mne; mne.sys_info()"

You can then install the following additional packages via pip. Note that the URL points to the bleeding edge version of mne_bids:

$ pip install datalad $ pip install https://github.com/mne-tools/mne-bids/zipball/master

To get the test data, you need to install git-annex on your system. If you installed MNE-Python via conda, you can simply call:

conda install -c conda-forge git-annex

Now, get the study template through git:

$ git clone https://github.com/mne-tools/mne-study-template.git

If you do not know how to use git, download the study template as a zip file here.

Finally, for source analysis you'll also need FreeSurfer, follow the instructions on their website.

Testing

Test data

The /tests directory contains a module download_test_data.py.

If called as a script, download_test_data accepts a positional argument dataset which can be any dataset key as specified in the module code. If no dataset argument is given, the complete test data will be downloaded.

The data will then be downloaded to your path specified in the "MNE_DATA" field of your MNE-Python config or to the ~/data directory by defaault.

You can also call the script using make fetch, and you can define an environment variable DATASET to specify which dataset should be downloaded

Config files

Nested in the /tests directory is a /configs directory, which contains config files for specific test datasets. For example, the config_ds001810.py file specifies parameters only for the ds001810 data, which should overwrite the more general parameters in the main config.py file.

Running the tests, and continuous integration

The tests are run with help of the tests/run_tests.py module and the run_tests function therein. You can run them by calling python tests/run_tests.py <arg>, where <arg> can be one of the following:

  • --help, to print the help
  • ALL, to test all datasets
  • any dataset name, similar to the description in the "test data" section above

Instead of specifying an argument via the command line, you can also define an environment variable DATASET to pass your option.

For every pull request or merge into the master branch of the mne-study-template, CircleCI will run tests as defined in ./circleci/config.yml.

Debugging

To run the test in debugging mode, you can use the Python Debugger pdb.

Simply define an environment variable DATASET as described in the section above and then call:

python -m pdb tests/run_tests.py

This will place you in debugging mode. Type continue to start running the pipelines. See the pdb help for more commands.