If you have a question or issue with MNE-Python, please include the following info:
- MNE-Python version: 0.22.0
- operating system: win10
I have an error when I try to simulate the mixed source space:
raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
Setting up raw simulation: 1 position, "cos2" interpolation
Traceback (most recent call last):
File "<ipython-input-6-b77397278474>", line 1, in <module>
raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
File "<decorator-gen-465>", line 24, in simulate_raw
File "F:\anzhuangfreesurfer\envs\mne\lib\site-packages\mne\simulation\raw.py", line 279, in simulate_raw
forward = restrict_forward_to_stc(forward, verts)
File "F:\anzhuangfreesurfer\envs\mne\lib\site-packages\mne\forward\forward.py", line 1524, in restrict_forward_to_stc
src_sel, _, vertices = _stc_src_sel(fwd['src'], stc, on_missing=on_missing)
File "F:\anzhuangfreesurfer\envs\mne\lib\site-packages\mne\forward\forward.py", line 1273, in _stc_src_sel
raise RuntimeError('Mismatch between number of source spaces (%s) and '
RuntimeError: Mismatch between number of source spaces (9) and STC vertices (2)"
there is my code:
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne import setup_volume_source_space, setup_source_space
from mne import make_forward_solution
from mne.io import read_raw_fif
from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs
from mne.connectivity import spectral_connectivity
from mne.viz import circular_layout, plot_connectivity_circle
print(__doc__)
# For this example, we will be using the information of the sample subject.
# This will download the data if it not already on your machine. We also set
# the subjects directory so we don't need to give it to functions.
data_path = sample.data_path()
subject = 'sample'
data_dir = op.join(data_path, 'MEG', subject)
subjects_dir = op.join(data_path, 'subjects')
bem_dir = op.join(subjects_dir, subject, 'bem')
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'
fname_trans = data_dir + '/sample_audvis_raw-trans.fif'
meg_path = op.join(data_path, 'MEG', subject)
fname_aseg = op.join(subjects_dir, subject, 'mri', 'aseg.mgz')
fname_raw = data_dir + '/sample_audvis_filt-0-40_raw.fif'
fname_trans = data_dir + '/sample_audvis_raw-trans.fif'
fname_cov = data_dir + '/ernoise-cov.fif'
fname_model = op.join(bem_dir, '%s-5120-bem.fif' % subject)
# First, we get an info structure from the test subject.
evoked_fname = op.join(data_path, 'MEG', subject, 'sample_audvis-ave.fif')
info = mne.io.read_info(evoked_fname)
fname_bem = op.join(bem_dir, '%s-5120-bem-sol.fif' % subject)
tstep = 1. / info['sfreq']
fname_cov = op.join(meg_path, 'sample_audvis-cov.fif')
# To simulate sources, we also need a source space. It can be obtained from the
# forward solution of the sample subject.
noise_cov = mne.read_cov(fname_cov)
# To select a region to activate, we use the caudal middle frontal to grow
# a region of interest.
parc = 'aparc'
selected_label = mne.read_labels_from_annot(
subject, parc=parc, subjects_dir=subjects_dir)[0]
location = 'center' # Use the center of the region as a seed.
extent = 10. # Extent in mm of the region.
labels_vol = ['Left-Amygdala',
'Left-Thalamus-Proper',
'Left-Cerebellum-Cortex',
'Brain-Stem',
'Right-Amygdala',
'Right-Thalamus-Proper',
'Right-Cerebellum-Cortex']
src = setup_source_space(subject, subjects_dir=subjects_dir,
spacing='oct5', add_dist=False)
vol_src = setup_volume_source_space(
subject, mri=fname_aseg, pos=10.0, bem=fname_model,
add_interpolator=False, # just for speed, usually use True
volume_label=labels_vol, subjects_dir=subjects_dir)
# Generate the mixed source space
src += vol_src
raw = read_raw_fif(fname_raw)
raw.pick_types(meg=True, eeg=False, eog=True, stim=True).load_data()
events = mne.find_events(raw)
noise_cov = mne.read_cov(fname_cov)
# compute the fwd matrix
fwd = make_forward_solution(raw.info, fname_trans, src, fname_bem,
mindist=5.0) # ignore sources<=5mm from innerskull
label = mne.label.select_sources(
subject, selected_label, location=location, extent=extent,
subjects_dir=subjects_dir)
# Define the time course of the activity for each source of the region to
# activate. Here we use a sine wave at 18 Hz with a peak amplitude
# of 10 nAm.
source_time_series = np.sin(2. * np.pi * 18. * np.arange(100) * tstep) * 10e-9
# Define when the activity occurs using events. The first column is the sample
# of the event, the second is not used, and the third is the event id. Here the
# events occur every 200 samples.
n_events = 50
events = np.zeros((n_events, 3))
events[:, 0] = 100 + 200 * np.arange(n_events) # Events sample.
events[:, 2] = 1 # All events have the sample id.
# Create simulated source activity. Here we use a SourceSimulator whose
# add_data method is key. It specified where (label), what
# (source_time_series), and when (events) an event type will occur.
source_simulator = mne.simulation.SourceSimulator(src, tstep=tstep)
source_simulator.add_data(label, source_time_series, events)
#!!!
# Project the source time series to sensor space and add some noise. The source
# simulator can be given directly to the simulate_raw function.
raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
cov = mne.make_ad_hoc_cov(raw.info)
mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04])
raw.plot()
# Plot evoked data to get another view of the simulated raw data.
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, 1, tmin=-0.05, tmax=0.2)
evoked = epochs.average()
evoked.plot()
