Contributions are welcome in the form of pull requests. We heavily rely on the contribution guides of MNE-Python
Once the implementation of a piece of functionality is considered to be bug
free and properly documented (both API docs and an example script),
it can be incorporated into the main branch.
To help developing mne-connectivity, you will need a few adjustments to your
installation as shown below.
If you want to run the tests with a development version of MNE-Python, you can install it by running
$ pip install -U https://github.com/mne-tools/mne-python/archive/main.zip
First, you should fork the mne-connectivity repository. Then, clone the fork and install it in
"editable" mode.
$ git clone https://github.com/<your-GitHub-username>/mne-connectivity
$ pip install -e ./mne-connectivity
Install the following packages for testing purposes, plus all optonal MNE-connectivity dependencies to ensure you will be able to run all tests.
$ pip install -r requirements_testing.txt
Now you can finally run the tests by running pytest in the
mne-connectivity directory.
$ cd mne-connectivity
$ pytest
The documentation can be built using sphinx. For that, please additionally install the following:
$ pip install -r requirements_doc.text
To build the documentation locally, one can run:
$ cd doc/
$ make html
or
$ make html-noplot
if you don't want to run the examples to build the documentation. This will result in a faster build but produce no plots in the examples.
All documentation examples are built on a CI pipeline that occurs online for free. For example, our docs are built with circleCI perhaps. This limits the ability for us to run large data examples that have a lot of RAM usage. For this reason, many times we crop, downsample, or limit the analysis in some way to reduce RAM usage.
Some good tools for profiling memory are mprof. For example, one can memory profile a specific example, such as:
mprof run examples/connectivity_classes.py
Then one could plot the memory usage:
mprof plot