[Mne_analysis] autoreject

Nathan Weisz nathanweisz at me.com
Tue Jul 10 05:47:53 EDT 2018
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Hi,

i am trying to explore a little the "autoreject" tools. specifically i am trying to apply following example:
https://autoreject.github.io/auto_examples/plot_auto_repair.html#sphx-glr-auto-examples-plot-auto-repair-py <https://autoreject.github.io/auto_examples/plot_auto_repair.html#sphx-glr-auto-examples-plot-auto-repair-py>

to a dataset recorded in salzburg.

i adapted the code to chop out 2s epochs from the fif file. the rest should be the same as in the tutorial example (which works great btw). code below.

however i am getting an error message that is over the top of my head.
>>> (executing lines 34 to 36 of "<tmp 1>")
Running autoreject on ch_type=grad
Traceback (most recent call last):
File "<tmp 1>", line 36, in <module>
ar.fit(epochs)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/autoreject.py", line 878, in fit
self.consensus, self.verbose)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/autoreject.py", line 683, in _run_local_reject_cv
local_reject.fit(epochs)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/autoreject.py", line 600, in fit
epochs.copy(), picks=self.picks_, verbose=self.verbose)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/autoreject.py", line 367, in compute_thresholds
verbose=verbose)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/utils.py", line 231, in clean_by_interp
interpolate_bads(inst_clean, picks=picks, reset_bads=True, mode='fast')
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/utils.py", line 279, in interpolate_bads
_interpolate_bads_meg_fast(inst, picks=meg_picks_interp, mode=mode)
File "/Users/b1019548/anaconda3/lib/python3.6/site-packages/autoreject/utils.py", line 392, in _interpolate_bads_meg_fast
assert ch_names_a == ch_names_b
AssertionError

this is likely due to my ignorance in the proper use of autoreject. but the error message makes it difficult for me to infer what the problem might be. i would appreciate any pointers.

best,
nathan

> import numpy as np
> 
> n_interpolates = np.array([1, 4, 32])
> consensus_percs = np.linspace(0, 1.0, 11)
> 
> ##
> 
> import mne # noqa
> from mne.utils import check_random_state # noqa
> 
> from autoreject import (AutoReject, set_matplotlib_defaults) # noqa
> 
> check_random_state(42)
> 
> data_path = '/Users/b1019548/Desktop/Data_Sternberg/'
> raw_fname = data_path + 'jens_H.fif'
> raw = mne.io.read_raw_fif(raw_fname, preload=True)
> 
> 
> 
> events = mne.make_fixed_length_events(raw, id=1, duration=2)
> 
> raw.info['bads'] = []
> picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=False, include=[], exclude=[])
> 
> raw.info['projs'] = list()
> 
> epochs = mne.Epochs(raw, events, tmin=0, tmax=2,
> baseline=(None, 0), reject=None,
> verbose=False, detrend=0, preload=True)
> 
> 
> ##
> ar = AutoReject(n_interpolates, consensus_percs, picks=picks,
> thresh_method='random_search', random_state=42)
> ar.fit(epochs)
> epochs_clean = ar.transform(epochs)







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