add support for epochs #3
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meegpowreg/power_features.py
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| @@ -67,8 +67,7 @@ def compute_features( | |||
| fbands={'alpha': (8.0, 12.0)}, | |||
| clean_func=lambda x: x, | |||
| n_jobs=1): | |||
| #doc str | |||
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| """Compute features from raw data or clean epochs.""" | |||
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you can do better. See an example in
https://github.com/mne-tools/mne-python/blob/main/mne/event.py#L24
you could also read this page
| covs = list() | ||
| for _, fb in fbands.items(): | ||
| ec = epochs.copy().load_data().filter(fb[0], fb[1]) | ||
| cov = mne.compute_covariance(ec, method='oas', rank=None) |
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OK for now to have this hard-coded. Later these parameters should be exposed.
| def test_compute_features_epochs(): | ||
| raw = mne.io.read_raw_fif(raw_fname, verbose=False) | ||
| raw = raw.copy().crop(0, 200).pick( | ||
| list(range(2)) + list(range(330, 333)) # take some MEG and EEG |
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wouldn't it be more readable to just type the random picks: [0, 1, 330, 331, 332] ?
| n_fb = len(fbands) | ||
| assert ( | ||
| set(features.keys()) == | ||
| set(['psds', 'freqs', 'covs', 'xfreqcovs', 'xfreqcorrs', 'cospcovs']) |
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you could use set literals here {'psds', 'freqs', ...}
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@apmellot thank you for the PR! I have only minor comments. It looks as if this code would allow us to reproduce the pipeline from our papers. If you address the 2 mini issues I have raised OK to merge from my side. |
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Thank you for the PR Apolline. I have comments I will share with you tomorrow morning. |
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@dengemann you merged this PR but it seems that my comments have not been taken into account. Most of them were cosmetic but my first 2 comments were important I think |
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I could not find your comments for some reason. |
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@DavidSabbagh you commented but I am sure you forgot to submit the review so we don't see them |
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you're right Alex, sorry |
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