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ENH: Add image_kwargs to report.add_epochs #12443
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ENH: Add image_kwargs to report.add_epochs #12443
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for more information, see https://pre-commit.ci
…python into report_add_image_kwargs cannot push without merging upstream
Co-authored-by: Richard Höchenberger <richard.hoechenberger@gmail.com>
…-python into report_add_image_kwargs asked to merge upstream before pushing
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@SophieHerbst Almost there, I believe … But linting fails: mne/report/report.py:1126:89: E501 Line too long (112 > 88)
Found 1 error. |
for more information, see https://pre-commit.ci
hoechenberger
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LGTM
@larsoner There's no tests so far, maybe we should add one…
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@hoechenberger I don't understand what I have to do about the failed tests. |
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Ah. We need to set image_kwargs to an empty dictionary if it's None. Otherwise the **image_kwargs part doesn't work |
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@SophieHerbst What I mean is doing something like: if image_kwargs is None:
image_kwargs = dict():) |
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
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@larsoner Could you take over here please? I'll be busy until the end of the week |
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Added a tiny test, marking for merge-when-green, thanks @SophieHerbst !
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Last CircleCI run worked, no idea why this one didn't but I think we can merge! |
* upstream/main: [pre-commit.ci] pre-commit autoupdate (mne-tools#12453) MAINT: Fix CIs for PyQt6 (mne-tools#12452) DOC: add missing info to interpolate bads docstring (mne-tools#12448) FIX: na_rep for reports (mne-tools#12447) ENH: Multiple raw instance support to head pos average (mne-tools#12445) ENH: Add image_kwargs to report.add_epochs (mne-tools#12443)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Richard Höchenberger <richard.hoechenberger@gmail.com> Co-authored-by: Eric Larson <larson.eric.d@gmail.com>
Reference issue
Needed to address
mne-tools/mne-bids-pipeline#848
What does this implement/fix?
Allow to pass image parameters when plotting epochs_images in report.add_epochs: vmin and vmax to control the color scale across participants.
@hoechenberger can you have a look?