DOC: metadata must have the same number of rows as events

I am trying to create epochs with metadata from raw, but run into an error with a piece of code that worked fine about 2 months ago (some previous version, likely 0.20.dev). I updated to the latest master.

I want to use a subset of events of interest, while my raw data contains more events. Creating epochs without metadata works fine.

(I tried to comment out the size check in _check_metadata, but it breaks later then.)

File “/home/sh254795/anaconda3/lib/python3.7/site-packages/mne/utils/mixin.py”, line 358, in _check_metadata % (len(metadata), len(self.events)))

ValueError: metadata must have the same number of rows (38) as events (86)

Have there been any changes to this code that could explain the error?

Example:

import os
import numpy as np
import mne
import pandas as pd

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                                    'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
raw.crop(tmax=60).load_data()

# find all events
events = mne.find_events(raw, stim_channel='STI 014')

# define events of interest 
event_id = dict(A=4, B=3)

tmin = -0.1
tmax = 0.5

#%% create metadata for the events of interest

# count events of interest 
n_events = sum(events[:,2] == 3) + sum(events[:,2] == 4)

# create arbitrary metadata
METADATA = np.arange(0,n_events)
 
META_DF = pd.DataFrame(data=METADATA, columns = ['trial'])
   
# Epoch the data without metadata - FINE
print('creating epochs...')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax)
      

# Epoch the data with metadata - BREAKS
print('creating epochs...')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, metadata=META_DF)

[SophieHerbst]

Yes, thanks! My point is more that metadata is a super useful feature, which we use more and more, but it is quite difficult to implement for people less proficient with python. Hence, one nice complete tutorial where the above points are made explicit would be great.

[larsoner]

So if the metadata has to match the length of selected events, this would require to create metadata for events for which it is not needed. Especially tricky if metadata are loaded from a separate file which contains behavioral responses. I am afraid that this would increase the difficulty of working with metadata.

I would suggest you trim your events to the ones you plan on using. This could just be a quick mask line like:

events = events[np.in1d(events[:, 2], list(event_id.values())]