Skip to content

Fix MNE example to work with MNE 1.3 #259

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Jun 29, 2023
Merged

Fix MNE example to work with MNE 1.3 #259

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
6bfdd32
Fix MNE example to work with MNE 1.3, which has deprecated psd_welch …
charlesbmi Apr 6, 2023
3bf4a00
add new min version for MNE example
TomDonoghue Jun 29, 2023
d9f3218
update mne example
TomDonoghue Jun 29, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 12 additions & 19 deletions examples/analyses/plot_mne_example.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
Parameterizing neural power spectra with MNE, doing a topographical analysis.

This tutorial requires that you have `MNE <https://mne-tools.github.io/>`_
installed.
installed. This tutorial needs mne >= 1.2.

If you don't already have MNE, you can follow instructions to get it
`here <https://mne-tools.github.io/stable/getting_started.html>`_.
Expand All @@ -23,10 +23,7 @@

# Import MNE, as well as the MNE sample dataset
import mne
from mne import io
from mne.datasets import sample
from mne.viz import plot_topomap
from mne.time_frequency import psd_welch

# FOOOF imports
from fooof import FOOOFGroup
Expand All @@ -52,16 +49,15 @@
###################################################################################################

# Get the data path for the MNE example data
raw_fname = sample.data_path() + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = sample.data_path() + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
raw_fname = sample.data_path() / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif'

# Load the example MNE data
raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose=False)

###################################################################################################

# Select EEG channels from the dataset
raw = raw.pick_types(meg=False, eeg=True, eog=False, exclude='bads')
raw = raw.pick(['eeg'], exclude='bads')

###################################################################################################

Expand Down Expand Up @@ -110,15 +106,16 @@ def check_nans(data, nan_policy='zero'):
# frequency representations - meaning we have to calculate power spectra.
#
# To do so, we will leverage the time frequency tools available with MNE,
# in the `time_frequency` module. In particular, we can use the ``psd_welch``
# function, that takes in MNE data objects and calculates and returns power spectra.
# in the `time_frequency` module. In particular, we can use the ``compute_psd``
# method, that takes in MNE data objects and calculates and returns power spectra.
#

###################################################################################################

# Calculate power spectra across the the continuous data
spectra, freqs = psd_welch(raw, fmin=1, fmax=40, tmin=0, tmax=250,
n_overlap=150, n_fft=300)
# Calculate power spectra across the continuous data
psd = raw.compute_psd(method="welch", fmin=1, fmax=40, tmin=0, tmax=250,
n_overlap=150, n_fft=300)
spectra, freqs = psd.get_data(return_freqs=True)

###################################################################################################
# Fitting Power Spectrum Models
Expand Down Expand Up @@ -193,7 +190,7 @@ def check_nans(data, nan_policy='zero'):
###################################################################################################

# Plot the topography of alpha power
plot_topomap(alpha_pw, raw.info, cmap=cm.viridis, contours=0);
mne.viz.plot_topomap(alpha_pw, raw.info, cmap=cm.viridis, contours=0, size=4)

###################################################################################################
#
Expand All @@ -214,8 +211,7 @@ def check_nans(data, nan_policy='zero'):
band_power = check_nans(get_band_peak_fg(fg, band_def)[:, 1])

# Create a topomap for the current oscillation band
mne.viz.plot_topomap(band_power, raw.info, cmap=cm.viridis, contours=0,
axes=axes[ind], show=False);
mne.viz.plot_topomap(band_power, raw.info, cmap=cm.viridis, contours=0, axes=axes[ind])

# Set the plot title
axes[ind].set_title(label + ' power', {'fontsize' : 20})
Expand Down Expand Up @@ -268,7 +264,7 @@ def check_nans(data, nan_policy='zero'):
###################################################################################################

# Plot the topography of aperiodic exponents
plot_topomap(exps, raw.info, cmap=cm.viridis, contours=0)
mne.viz.plot_topomap(exps, raw.info, cmap=cm.viridis, contours=0, size=4)

###################################################################################################
#
Expand Down Expand Up @@ -297,6 +293,3 @@ def check_nans(data, nan_policy='zero'):
# In this example, we have seen how to apply power spectrum models to data that is
# managed and processed with MNE.
#

###################################################################################################
#
2 changes: 1 addition & 1 deletion requirements-docs.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ matplotlib
tqdm

# Requirements for running the examples
mne
mne > 1.2

# Requirements for running the motivations
neurodsp >= 2.0.0