mne-tools / mne-tools/mne-python
Autoscale individual channels in raw.plot
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- Dominant language
- Python
- Stars
- 3.5k
- Forks
- 1.6k
- Avg merge
- 1d 6h
- Merged PRs (30d)
- 100
Description
Sometimes it would be useful if individual channels instead of all channels of a specific type could be autoscaled. For example, channels might get assigned to a wrong type, or some channels containing outliers currently affect the scaling of all other channels of the same type.
Would anyone be interested in such a feature? Technically, this could be tackled by assigning special meaning to a certain value for the scalings parameter, e.g. auto-individual (better naming suggestions more than welcome).
See e.g. this EDF file for an example, where autoscaling currently makes most EEG signals a flat line because some channels contain very high values.
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 at the raw.plot entry point and review how the scalings parameter currently handles autoscaling by channel type. Define the behavior and naming for an individual-channel autoscaling option, then verify it with the linked EDF example so outlier channels no longer flatten the remaining EEG signals.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100