55Parameterizing neural power spectra with MNE, doing a topographical analysis.
66
77This tutorial requires that you have `MNE <https://mne-tools.github.io/>`_
8- installed.
8+ installed. This tutorial needs mne >= 1.2.
99
1010If you don't already have MNE, you can follow instructions to get it
1111`here <https://mne-tools.github.io/stable/getting_started.html>`_.
1616
1717###################################################################################################
1818
19- import os .path
20-
2119# General imports
2220import numpy as np
2321import matplotlib .pyplot as plt
2422from matplotlib import cm , colors , colorbar
2523
2624# Import MNE, as well as the MNE sample dataset
2725import mne
28- from mne import io
2926from mne .datasets import sample
30- from mne .viz import plot_topomap
3127
3228# FOOOF imports
3329from fooof import FOOOFGroup
5349###################################################################################################
5450
5551# Get the data path for the MNE example data
56- raw_fname = os .path .join (sample .data_path (), 'MEG' , 'sample' , 'sample_audvis_filt-0-40_raw.fif' )
57- event_fname = os .path .join (sample .data_path (), 'MEG' , 'sample' , 'sample_audvis_filt-0-40_raw-eve.fif' )
52+ raw_fname = sample .data_path () / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif'
5853
5954# Load the example MNE data
6055raw = mne .io .read_raw_fif (raw_fname , preload = True , verbose = False )
6156
6257###################################################################################################
6358
6459# Select EEG channels from the dataset
65- raw = raw .pick_types ( meg = False , eeg = True , eog = False , exclude = 'bads' )
60+ raw = raw .pick ([ ' eeg' ] , exclude = 'bads' )
6661
6762###################################################################################################
6863
@@ -195,7 +190,7 @@ def check_nans(data, nan_policy='zero'):
195190###################################################################################################
196191
197192# Plot the topography of alpha power
198- plot_topomap (alpha_pw , raw .info , cmap = cm .viridis , contours = 0 );
193+ mne . viz . plot_topomap (alpha_pw , raw .info , cmap = cm .viridis , contours = 0 , size = 4 )
199194
200195###################################################################################################
201196#
@@ -216,8 +211,7 @@ def check_nans(data, nan_policy='zero'):
216211 band_power = check_nans (get_band_peak_fg (fg , band_def )[:, 1 ])
217212
218213 # Create a topomap for the current oscillation band
219- mne .viz .plot_topomap (band_power , raw .info , cmap = cm .viridis , contours = 0 ,
220- axes = axes [ind ], show = False );
214+ mne .viz .plot_topomap (band_power , raw .info , cmap = cm .viridis , contours = 0 , axes = axes [ind ])
221215
222216 # Set the plot title
223217 axes [ind ].set_title (label + ' power' , {'fontsize' : 20 })
@@ -270,7 +264,7 @@ def check_nans(data, nan_policy='zero'):
270264###################################################################################################
271265
272266# Plot the topography of aperiodic exponents
273- plot_topomap (exps , raw .info , cmap = cm .viridis , contours = 0 )
267+ mne . viz . plot_topomap (exps , raw .info , cmap = cm .viridis , contours = 0 , size = 4 )
274268
275269###################################################################################################
276270#
@@ -299,6 +293,3 @@ def check_nans(data, nan_policy='zero'):
299293# In this example, we have seen how to apply power spectrum models to data that is
300294# managed and processed with MNE.
301295#
302-
303- ###################################################################################################
304- #
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