mne-tools / mne-tools/mne-python
Raw browser issues (and ideas)
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BUG
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- Python
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Description
I think that a couple of features might be missing from our raw browser (not specific to any backend):
- There is no indication of the absolute values of a channel, because the scale bar only shows a range. It would be very helpful if e.g. the zero value (or min/max) was shown as well.
- The browser always shows "0.0 AU" in the scale bar for "stim" and "misc" channels (and possibly other types).
- There is only one scale bar even if there are two different channels types (e.g. "misc" and "stim").
- Could we automatically show the best unit prefix (e.g. instead of 20,000µV -> 20mV)? This would make the scale bar easier to read for more exotic scalings.
Here's an example which demonstrates these issues:
import mne
from numpy.random import default_rng
fs, nchans = 250, 3
rng = default_rng(42)
data = rng.standard_normal(size=(nchans, 50 * fs)) * 5e-6
data[0] += 10
info = mne.create_info(nchans, fs, ["eeg", "misc", "stim"])
raw = mne.io.RawArray(data, info)
raw.plot()
- The first channel has an offset of 10V, but this is not apparent from the plot.
- The second channel has a scale bar with "0.0 AU".
- The third channel does not have a separate scale bar, although it has a different type than the second channel.
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 running the provided Python example with mne.io.RawArray and raw.plot() to reproduce the scale-bar behavior. Trace the raw browser entry point and assess the four requested behaviors: absolute values, nonzero units, separate bars for channel types, and automatic unit prefixes. Done means the requested scale information is displayed correctly for the example without backend-specific assumptions.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100