mne-tools / mne-tools/mne-python
Caching channel interpolation matrix
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- Dominant language
- Python
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Description
Interpolating bad channels on multiple evoked objects can be quite slow, and the bottleneck is computation of the interpolation matrix. When the evoked objects are from the same raw file they ought to have the same bad channels, so the interpolation matrix is redundantly computed multiple times. How about exposing the function to compute the interpolation matrix and adding a parameter to interpolate_channels() that allows providing a precomputed interpolation matrix?
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 from the interpolate_channels() entry point and trace how its interpolation matrix is computed for evoked objects. Done means the matrix computation is exposed and interpolate_channels() accepts a precomputed matrix so repeated interpolation avoids redundant computation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100