Plot a noise covariance matrix.
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_evo = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
cov = mne.read_cov(fname_cov)
print(cov)
evoked = mne.read_evokeds(fname_evo)[0]
Script output:
<Covariance | size : 366 x 366, n_samples : 15972, data : [[ 2.27235589e-23 4.79818505e-24 7.12852747e-25 ..., 4.85348042e-18
2.02846360e-18 8.26727393e-18]
[ 4.79818505e-24 5.33468523e-24 1.80261790e-25 ..., 2.33583009e-19
-6.93161055e-19 2.35199238e-18]
[ 7.12852747e-25 1.80261790e-25 5.79073915e-26 ..., 1.09498615e-19
6.16924072e-21 2.93873875e-19]
...,
[ 4.85348042e-18 2.33583009e-19 1.09498615e-19 ..., 1.40677185e-11
1.27444183e-11 1.08634620e-11]
[ 2.02846360e-18 -6.93161055e-19 6.16924072e-21 ..., 1.27444183e-11
1.59818134e-11 8.51070563e-12]
[ 8.26727393e-18 2.35199238e-18 2.93873875e-19 ..., 1.08634620e-11
8.51070563e-12 1.53708918e-11]]>
Show covariance
cov.plot(evoked.info, exclude='bads', show_svd=False)
Total running time of the script: (0 minutes 0.510 seconds)
Download Python source code: plot_read_noise_covariance_matrix.py