Here is a checklist of things to complete before submitting a formal review. An abridged version from info on this site:
Test coverage for new functionality (test output of boxy.py and compare to another source, such as p_pod)
Document new functionality (thorough docstring explanations and tutorials explaining functionality)
Avoid public API changes if possible (don't think we've made any such changes... Just added our own functions)
Add deprecation warnings if removing/changing functionality (again, don't think this applies to us)
Describes changes in the changelog, "doc/whats_new.rst" (sounds like this should be done AFTER the PR is finalised)
Test locally before opening PR (kind of did both, tested locally and using CI tests)
Make tests fast and thorough (currently tests compare raw, epoch, evoke to p_pod. Anything else? Need to make smaller data files for such tests)
adhere to pep8 (currently seems fine, but can check by running "make flake")
Use consistent variable names (should make sure variable names make sense too)
follow NumPy style for docstrings (again, should be fine now but can be checked by running "make docstyle")
Docstrings and documentation pages should cross-reference mentioned classes, functions, etc. (can probably use the "plot_70" tutorial as an example)
Use single quotes where possible
Prefer generators or comprehensives over filter() and map() (need to check into this...)
Use explicit functional constructors (eg. list(), dict(), set())
Avoid nested functions or class methods if possible
Avoid *args and **kwargs in function/method signatures
Import in this order (python built-in > standard scientific (eg. numpy, scipy) > others > MNE imports)
Methods should modify in-place and return self
Functions should return copies, where applicable
Docstrings should contain informative names for return values, even if that particular name isn't used
Visualisation capabilities should be available in method and function form
Here is a checklist of things to complete before submitting a formal review. An abridged version from info on this site:
https://mne.tools/stable/install/contributing.html#mne-python-coding-conventions
Test coverage for new functionality (test output of boxy.py and compare to another source, such as p_pod)
Document new functionality (thorough docstring explanations and tutorials explaining functionality)
Avoid public API changes if possible (don't think we've made any such changes... Just added our own functions)
Add deprecation warnings if removing/changing functionality (again, don't think this applies to us)
Describes changes in the changelog, "doc/whats_new.rst" (sounds like this should be done AFTER the PR is finalised)
Test locally before opening PR (kind of did both, tested locally and using CI tests)
Make tests fast and thorough (currently tests compare raw, epoch, evoke to p_pod. Anything else? Need to make smaller data files for such tests)
adhere to pep8 (currently seems fine, but can check by running "make flake")
Use consistent variable names (should make sure variable names make sense too)
follow NumPy style for docstrings (again, should be fine now but can be checked by running "make docstyle")
Docstrings and documentation pages should cross-reference mentioned classes, functions, etc. (can probably use the "plot_70" tutorial as an example)
Use single quotes where possible
Prefer generators or comprehensives over filter() and map() (need to check into this...)
Use explicit functional constructors (eg. list(), dict(), set())
Avoid nested functions or class methods if possible
Avoid *args and **kwargs in function/method signatures
Import in this order (python built-in > standard scientific (eg. numpy, scipy) > others > MNE imports)
Methods should modify in-place and return self
Functions should return copies, where applicable
Docstrings should contain informative names for return values, even if that particular name isn't used
Visualisation capabilities should be available in method and function form