-
Notifications
You must be signed in to change notification settings - Fork 1.4k
ENH: Add polyphase resampling #12268
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.
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
Changes from 1 commit
Commits
File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -5,28 +5,15 @@ | |
From raw data to dSPM on SPM Faces dataset | ||
========================================== | ||
|
||
Runs a full pipeline using MNE-Python: | ||
|
||
- artifact removal | ||
- averaging Epochs | ||
- forward model computation | ||
- source reconstruction using dSPM on the contrast : "faces - scrambled" | ||
|
||
.. note:: This example does quite a bit of processing, so even on a | ||
fast machine it can take several minutes to complete. | ||
Runs a full pipeline using MNE-Python. This example does quite a bit of processing, so | ||
even on a fast machine it can take several minutes to complete. | ||
""" | ||
# Authors: Alexandre Gramfort <[email protected]> | ||
# Denis Engemann <[email protected]> | ||
# | ||
# License: BSD-3-Clause | ||
# Copyright the MNE-Python contributors. | ||
|
||
# %% | ||
|
||
# sphinx_gallery_thumbnail_number = 10 | ||
|
||
import matplotlib.pyplot as plt | ||
|
||
import mne | ||
from mne import combine_evoked, io | ||
from mne.datasets import spm_face | ||
|
@@ -40,114 +27,77 @@ | |
spm_path = data_path / "MEG" / "spm" | ||
|
||
# %% | ||
# Load and filter data, set up epochs | ||
# Load data, filter it, and fit ICA. | ||
|
||
raw_fname = spm_path / "SPM_CTF_MEG_example_faces1_3D.ds" | ||
|
||
raw = io.read_raw_ctf(raw_fname, preload=True) # Take first run | ||
# Here to save memory and time we'll downsample heavily -- this is not | ||
# advised for real data as it can effectively jitter events! | ||
raw.resample(120.0, npad="auto") | ||
|
||
picks = mne.pick_types(raw.info, meg=True, exclude="bads") | ||
raw.filter(1, 30, method="fir", fir_design="firwin") | ||
raw.resample(100) | ||
raw.filter(1.0, None) # high-pass | ||
reject = dict(mag=5e-12) | ||
ica = ICA(n_components=0.95, max_iter="auto", random_state=0) | ||
ica.fit(raw, reject=reject) | ||
# compute correlation scores, get bad indices sorted by score | ||
eog_epochs = create_eog_epochs(raw, ch_name="MRT31-2908", reject=reject) | ||
eog_inds, eog_scores = ica.find_bads_eog(eog_epochs, ch_name="MRT31-2908") | ||
ica.plot_scores(eog_scores, eog_inds) # see scores the selection is based on | ||
ica.plot_components(eog_inds) # view topographic sensitivity of components | ||
ica.exclude += eog_inds[:1] # we saw the 2nd ECG component looked too dipolar | ||
ica.plot_overlay(eog_epochs.average()) # inspect artifact removal | ||
|
||
# %% | ||
# Epoch data and apply ICA. | ||
events = mne.find_events(raw, stim_channel="UPPT001") | ||
|
||
# plot the events to get an idea of the paradigm | ||
mne.viz.plot_events(events, raw.info["sfreq"]) | ||
|
||
event_ids = {"faces": 1, "scrambled": 2} | ||
|
||
tmin, tmax = -0.2, 0.6 | ||
baseline = None # no baseline as high-pass is applied | ||
larsoner marked this conversation as resolved.
Show resolved
Hide resolved
|
||
reject = dict(mag=5e-12) | ||
|
||
epochs = mne.Epochs( | ||
raw, | ||
events, | ||
event_ids, | ||
tmin, | ||
tmax, | ||
picks=picks, | ||
baseline=baseline, | ||
picks="meg", | ||
baseline=None, | ||
preload=True, | ||
reject=reject, | ||
) | ||
|
||
# Fit ICA, find and remove major artifacts | ||
ica = ICA(n_components=0.95, max_iter="auto", random_state=0) | ||
ica.fit(raw, decim=1, reject=reject) | ||
|
||
# compute correlation scores, get bad indices sorted by score | ||
eog_epochs = create_eog_epochs(raw, ch_name="MRT31-2908", reject=reject) | ||
eog_inds, eog_scores = ica.find_bads_eog(eog_epochs, ch_name="MRT31-2908") | ||
ica.plot_scores(eog_scores, eog_inds) # see scores the selection is based on | ||
ica.plot_components(eog_inds) # view topographic sensitivity of components | ||
ica.exclude += eog_inds[:1] # we saw the 2nd ECG component looked too dipolar | ||
ica.plot_overlay(eog_epochs.average()) # inspect artifact removal | ||
del raw | ||
ica.apply(epochs) # clean data, default in place | ||
|
||
evoked = [epochs[k].average() for k in event_ids] | ||
|
||
contrast = combine_evoked(evoked, weights=[-1, 1]) # Faces - scrambled | ||
|
||
evoked.append(contrast) | ||
|
||
for e in evoked: | ||
e.plot(ylim=dict(mag=[-400, 400])) | ||
|
||
plt.show() | ||
|
||
# estimate noise covarariance | ||
noise_cov = mne.compute_covariance(epochs, tmax=0, method="shrunk", rank=None) | ||
|
||
# %% | ||
# Visualize fields on MEG helmet | ||
|
||
# The transformation here was aligned using the dig-montage. It's included in | ||
# the spm_faces dataset and is named SPM_dig_montage.fif. | ||
trans_fname = spm_path / "SPM_CTF_MEG_example_faces1_3D_raw-trans.fif" | ||
|
||
maps = mne.make_field_map( | ||
evoked[0], trans_fname, subject="spm", subjects_dir=subjects_dir, n_jobs=None | ||
) | ||
|
||
evoked[0].plot_field(maps, time=0.170, time_viewer=False) | ||
|
||
# %% | ||
# Look at the whitened evoked daat | ||
# Estimate noise covariance and look at the whitened evoked data | ||
|
||
noise_cov = mne.compute_covariance(epochs, tmax=0, method="shrunk", rank=None) | ||
evoked[0].plot_white(noise_cov) | ||
|
||
# %% | ||
# Compute forward model | ||
|
||
trans_fname = spm_path / "SPM_CTF_MEG_example_faces1_3D_raw-trans.fif" | ||
src = subjects_dir / "spm" / "bem" / "spm-oct-6-src.fif" | ||
bem = subjects_dir / "spm" / "bem" / "spm-5120-5120-5120-bem-sol.fif" | ||
forward = mne.make_forward_solution(contrast.info, trans_fname, src, bem) | ||
|
||
# %% | ||
# Compute inverse solution | ||
# Compute inverse solution and plot | ||
|
||
# sphinx_gallery_thumbnail_number = 8 | ||
|
||
snr = 3.0 | ||
lambda2 = 1.0 / snr**2 | ||
method = "dSPM" | ||
|
||
inverse_operator = make_inverse_operator( | ||
contrast.info, forward, noise_cov, loose=0.2, depth=0.8 | ||
) | ||
|
||
# Compute inverse solution on contrast | ||
stc = apply_inverse(contrast, inverse_operator, lambda2, method, pick_ori=None) | ||
# stc.save('spm_%s_dSPM_inverse' % contrast.comment) | ||
|
||
# Plot contrast in 3D with mne.viz.Brain if available | ||
inverse_operator = make_inverse_operator(contrast.info, forward, noise_cov) | ||
stc = apply_inverse(contrast, inverse_operator, lambda2, method="dSPM", pick_ori=None) | ||
brain = stc.plot( | ||
hemi="both", | ||
subjects_dir=subjects_dir, | ||
initial_time=0.170, | ||
views=["ven"], | ||
clim={"kind": "value", "lims": [3.0, 6.0, 9.0]}, | ||
) | ||
# brain.save_image('dSPM_map.png') |
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
|
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.