Plotting of mne.combine_evoked output based on AverageTFR input (mutiple channels) only shows TFR output of the first channel

  • MNE-Python version: 0.22.0
  • operating system: Windows 10

Hi everyone,

Please refer to the following figures:

I’d like to draw attention to the middle plots (red-blue freq plot) in each of these two figures, which were created using the following codes extract:

ch_name = ['MEG2013','MEG2022','MEG2023'] #For top plot
ch_name = ['MEG2013'] #For bottom plot


tfr_epochs_1 = tfr_morlet(epochsFIF_condtion1, freqs,
                                          n_cycles=n_cycles, decim=decim, use_fft=False,
                                          return_itc=False, average=False)
tfr_epochs_2 = tfr_morlet(epochsFIF_condtion2, freqs,
                                          n_cycles=n_cycles, decim=decim, use_fft=False,
                                          return_itc=False, average=False)

this_tfr1 = tfr_epochs_1.average()
this_tfr2 = tfr_epochs_2.average()

## Appended as this is a loop function that creates tfr_morlet data out of multiple participants.
## all_power_sets are a collection of data of all participants

## Loop ends here after all participants' data have been extracted. Codes below this are only reached
## after the all_power_sets have been filled with all participants' data.

## Create averaged data across all participants
tfr_condition_1 = mne.grand_average(all_power_set, interpolate_bads=True, drop_bads=True)
tfr_condition_2 = mne.grand_average(all_power_set2, interpolate_bads=True, drop_bads=True)

tfr_contrast = mne.combine_evoked([tfr_condition_1, tfr_condition_2],
                                      weights=[1, -1])
tfr_contrast.plot(axes=ax2, vmin=vmin, vmax=vmax)

The top middle plot was created based on information from 3 sensors whereas the bottom middle plot was created based on information from 1 sensor. However, both plots look exactly the same. Changing the position of sensors in ch_name makes no difference as it always plot the sensor with the smallest number (ie. 2013). I was expecting the top plot to show the averaged time-freq activity across the three sensors but this is not happening.

Am I missing a step somewhere to get the plotting done right? Or is this some kind of bug?

you can compare numerical values with numpy and do the numerics looking at etc.
Basically the data attributes of the TFR objects.

but looking at the plots they look similar but not exactly the same.


Hi @agramfort,

Thanks for the reply! Still a little puzzled. I have now tried a different comparison using 1 vs. 20 gradiometers but the TFR output is looking the same. I believe that is unlikely that the TFR plot for 20 grads would be anywhere near the single grad.

Here above I have the TFR output from 1 grad and 20 grad, which looks too similar to me. The two tfr_contrast objects look correct in terms of the length of data corresponding to the number of sensors, as reflected in the console output. The data is right, but tfr_contrast.plot() for the 20 grads seem to be plotting the TFR of only the first array in the list of tfr_contrast object. Shouldn’t it be attempting to plot the average TFR of all 20 grads instead?

can you replicate using one dataset from mne? maybe the mne somato dataset?

so we can investigate.


There are several things that don’t work as expected; could the behavior you’re seeing be the issue described here?

(Or the related issues BUG: TFR.plot misc issues · Issue #8013 · mne-tools/mne-python · GitHub and

Hey @cbrnr,

Yes, it seems like the same exact issue.

My tfr_contrast is an AverageTFR as well. So I guess the fix is still underway?

@agramfort I can try to replicate using the somato data but on a brief glance of the somato data file, it seems like there is only one event in the entire data file. Gonna go grab dinner for now. I can try to play around later in the evening by adding an additional baseline event to maybe make a simple working example closest to my combine_evoked setup, if required?

@AaronAng can you check out PR #9150 and see if this fixes the problem for you?

@cbrnr Tried replacing the file with the following commit mne-python/ at tfr_misc_issues · eort/mne-python · GitHub but I’m getting an error. This was generated using the codes with the somato data example at the bottom of this reply.

@agramfort the issue can be replicated using the somato example. Note that plotting picks='all', is no different from plotting the first channel in picks=[0].

import os.path as op

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.time_frequency import tfr_morlet, psd_multitaper, psd_welch
from mne.datasets import somato

from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'qt')

data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
                    'sub-{}_task-{}_meg.fif'.format(subject, task))

# Setup for reading the raw data
raw =
events = mne.find_events(raw, stim_channel='STI 014')

# picks MEG gradiometers
picks = mne.pick_types(, meg='grad', eeg=False, eog=True, stim=False)

# Construct Epochs
event_id, tmin, tmax = 1, -1., 3.
baseline = (None, 0)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=baseline, reject=dict(grad=4000e-13, eog=350e-6),

epochs.resample(200., npad='auto')  # resample to reduce computation time

# define frequencies of interest (log-spaced)
freqs = np.logspace(*np.log10([6, 35]), num=8)
n_cycles = freqs / 2.  # different number of cycle per frequency
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, use_fft=True,
                        return_itc=True, decim=3, n_jobs=1)

## Able to plot specific sensor data
power.plot(picks=[82], baseline=(-0.5, 0), vmin=-0.6, vmax=0.6, mode='logratio', title=power.ch_names[82])
## But once you set picks='all', .plot() only plots the first channel in the list of channels.
power.plot(picks='all', baseline=(-0.5, 0), vmin=-0.6, vmax=0.6, mode='logratio', title='all')
## Notice that the first channel's activity is a replica of the 'all' plot.
power.plot(picks=[0], baseline=(-0.5, 0), vmin=-0.6, vmax=0.6, mode='logratio', title=power.ch_names[0])
## Even selecting a reduced list of sensors lead to the same error of plotting only the first sensor
power.plot(picks=['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132'], baseline=(-0.5, 0), vmin=-0.6, vmax=0.6, mode='logratio', title='sub-selected sensors')


you’re answer is in

@larsoner wrote this apparently.

can you open an issue on github maybe?


1 Like