apply diataxis framework to tutorials #13584
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Bumps [davidslusser/actions_python_bandit](https://github.com/davidslusser/actions_python_bandit) from 1.0.0 to 1.0.1. - [Release notes](https://github.com/davidslusser/actions_python_bandit/releases) - [Commits](davidslusser/actions_python_bandit@v1.0.0...v1.0.1) --- updated-dependencies: - dependency-name: davidslusser/actions_python_bandit dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>
…usser/actions_python_bandit-1.0.1 Bump davidslusser/actions_python_bandit from 1.0.0 to 1.0.1
| raw.plot(events=events) | ||
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| # We can see that the simulated dipoles produce sinusoidal bursts at 20 Hz | ||
| # (can we really see that in the plot?) |
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Here I wonder if we can get rid of one of the screenshots of the dipole events and maybe zoom into one event to show the 20Hz burst
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Yes that sounds good, you should be able to see it clearly at least in the average, and depending on the dipole amplitude you can sometimes see it in the raw / epoched data
| cov = mne.compute_covariance(epochs, tmax=bmax) | ||
| mne.viz.plot_evoked_white(epochs["1"].average(), cov) | ||
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| # Not sure what we see here TBH |
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Here we should explain why we plot the whitened data and what the user is expected to see vs. what we actually see in this plot.
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Or can we just link to the whitening / covariance tutorials? It is explained there I think
| # Now we can compare to the actual locations, taking the difference in mm: | ||
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| # Finally, we compare the estimated to the true dipole locations | ||
| # To do this, we calculate the difference by ..... |
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I think we should explain how we compute the difference and why, this seems to be crucial info for a tutorial on comparing estimated vs. true dipoles.
| ax3.set_ylabel("Amplitude error (nAm)") | ||
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| # We can see that the error magnitude depends on the position of the estimate dipole | ||
| # however, the location error is never greater than 5 mm which is good? |
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again I am not sure if we can interpret the error magnitude (or should). I think it would be very useful for a user to get some guidance on what to do with those results but maybe this goes too far for the current tutorial.
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I think we can, we can say something about the potential localization accuracy of MEG. Like if we can get ~2-5mm error localizing these dipoles we can say MEG can localize point sources with that accuracy (given proper coreg etc.), which helps justify claims of "sub-centimeter" accuracy
larsoner
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larsoner
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Didn't read through all changes but wanted to give input on questions!
| raw.plot(events=events) | ||
|
|
||
| # We can see that the simulated dipoles produce sinusoidal bursts at 20 Hz | ||
| # (can we really see that in the plot?) |
There was a problem hiding this comment.
Yes that sounds good, you should be able to see it clearly at least in the average, and depending on the dipole amplitude you can sometimes see it in the raw / epoched data
| cov = mne.compute_covariance(epochs, tmax=bmax) | ||
| mne.viz.plot_evoked_white(epochs["1"].average(), cov) | ||
|
|
||
| # Not sure what we see here TBH |
There was a problem hiding this comment.
Or can we just link to the whitening / covariance tutorials? It is explained there I think
| # Now we can compare to the actual locations, taking the difference in mm: | ||
|
|
||
| # Finally, we compare the estimated to the true dipole locations | ||
| # To do this, we calculate the difference by ..... |
| ax3.set_ylabel("Amplitude error (nAm)") | ||
|
|
||
| # We can see that the error magnitude depends on the position of the estimate dipole | ||
| # however, the location error is never greater than 5 mm which is good? |
There was a problem hiding this comment.
I think we can, we can say something about the potential localization accuracy of MEG. Like if we can get ~2-5mm error localizing these dipoles we can say MEG can localize point sources with that accuracy (given proper coreg etc.), which helps justify claims of "sub-centimeter" accuracy
2 participants
apply diataxis framework to tutorials/inverse/80_brainstorm_phantom_elekta.py