mne-tools / mne-tools/mne-python
Enhancements to modified Beer Lambert Law
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3.5k
- Forks
- 1.6k
- Avg merge
- 1d 6h
- Merged PRs (30d)
- 100
Description
Describe the new feature or enhancement
In the discussion at https://github.com/mne-tools/mne-python/pull/8711 the following enhancements for the BLL were mentioned. I am listing them here so these suggestions don't get lost.
- Add support for alternative extinction coefficients.
- Refactor code to have private function where source-detector distance can be specified.
- Add some references to literature.
- Add age dependent partial path length factor (reference)
Additional comments
The code works fine as is. This is just optional improvements for when anyone has time. Please list additional feature requests below too.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the discussion in pull request #8711 and locating the current modified Beer-Lambert Law implementation. Before coding, scope one enhancement from the list, confirm its expected behavior and literature reference, and identify relevant tests; done means the selected improvement is implemented and validated without changing existing behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100