explicitly disallow multitaper in presence of bad annotations#12535
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drammock merged 2 commits intomne-tools:mainfrom Apr 9, 2024
Merged
explicitly disallow multitaper in presence of bad annotations#12535drammock merged 2 commits intomne-tools:mainfrom
drammock merged 2 commits intomne-tools:mainfrom
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larsoner
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Apr 9, 2024
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Agreed that's a good way to proceed. This doesn't resolve #11413 though, right? |
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* upstream/main: (50 commits) ENH: Improve OPM auditory dataset and example (mne-tools#12539) MAINT: Bump to latest pydata-sphinx-theme (mne-tools#12228) MRG: Simplify manual installation instructions a little by dropping explicit mention of (lib)mamba (mne-tools#12362) fix PSD weights handling when bad annotations present (mne-tools#12538) Fix phase loading (mne-tools#12537) align FFT windows to good data spans in psd_array_welch (mne-tools#12536) explicitly disallow multitaper in presence of bad annotations (mne-tools#12535) MAINT: Clean up PyVista contexts (mne-tools#12533) MAINT: Complete API change of ordered (mne-tools#12534) MAINT: Reinstall statsmodels and improve logging (mne-tools#12532) MAINT: Remove scipy.signal.morlet2 (mne-tools#12531) Update README badge links (mne-tools#12529) BUG: Fix bug with reading his_id from snirf (mne-tools#12526) [pre-commit.ci] pre-commit autoupdate (mne-tools#12524) Fix file format check in _check_eeglab_fname function (mne-tools#12523) MAINT: Reenable picard in pre testing (mne-tools#12525) MAINT: Bump to large resource class (mne-tools#12522) MAINT: Restore 2 jobs on Windows (mne-tools#12520) Add exclude_after_unique option to mne.io.read_raw_edf/read_raw_edf to search for exclude channels after making channel names unique (mne-tools#12518) Improve consistency of sensor types in code and documentation (mne-tools#12509) ...
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closes #12519
For now, with the 1.7 release pending, @larsoner and I agreed that disallowing multitaper on Raws when there are
bad_*annotations present (andreject_by_annotation=True) is the best way to go.In the long term, we could make that combination actually work by adding a new param (
chunk_sizeor so, analogous ton_per_segin Welch) and basically doing a welch-MT combination (using multitaper for each chunk, then combining the estimates from each chunk at the end). It's a bit complicated because (we think) you would need to aggregate across-chunks-within-taper first, and then agg across tapers afterward. (cc @mmagnuski who I think has done that for his research before / maybe even implemented in borsar?)