def test_simulate_evoked():
""" Test simulation of evoked data """
raw = mne.fiff.Raw(raw_fname)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = mne.read_cov(cov_fname)
label_names = ['Aud-lh', 'Aud-rh']
labels = [
read_label(
op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label))
for label in label_names
]
evoked_template = mne.fiff.read_evoked(ave_fname, setno=0, baseline=None)
evoked_template = pick_types_evoked(evoked_template,
meg=True,
eeg=True,
exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series from 2 Morlet wavelets
stc_data = np.zeros((len(labels), len(times)))
Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5])
stc_data[0][:len(Ws[0])] = np.real(Ws[0])
stc_data[1][:len(Ws[1])] = np.real(Ws[1])
stc_data *= 100 * 1e-9 # use nAm as unit
# time translation
stc_data[1] = np.roll(stc_data[1], 80)
stc = generate_sparse_stc(fwd['src'],
labels,
stc_data,
tmin,
tstep,
random_state=0)
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = generate_evoked(fwd,
stc,
evoked_template,
cov,
snr,
tmin=0.0,
tmax=0.2,
iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
def test_simulate_evoked():
""" Test simulation of evoked data """
raw = Raw(raw_fname)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
label_names = ['Aud-lh', 'Aud-rh']
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template = pick_types_evoked(evoked_template, meg=True, eeg=True,
exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series from 2 Morlet wavelets
stc_data = np.zeros((len(labels), len(times)))
Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5])
stc_data[0][:len(Ws[0])] = np.real(Ws[0])
stc_data[1][:len(Ws[1])] = np.real(Ws[1])
stc_data *= 100 * 1e-9 # use nAm as unit
# time translation
stc_data[1] = np.roll(stc_data[1], 80)
stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep,
random_state=0)
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = generate_evoked(fwd, stc, evoked_template, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertno[0][0] = mv + 1
assert_raises(RuntimeError, generate_evoked, fwd, stc_bad,
evoked_template, cov, snr, tmin=0.0, tmax=0.2)
###############################################################################
# Load real data as templates
data_path = sample.data_path()
raw = mne.fiff.Raw(data_path + '/MEG/sample/sample_audvis_raw.fif')
proj = mne.read_proj(data_path + '/MEG/sample/sample_audvis_ecg_proj.fif')
raw.info['projs'] += proj
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = mne.read_cov(cov_fname)
evoked_template = mne.fiff.read_evoked(ave_fname, setno=0, baseline=None)
evoked_template = pick_types_evoked(evoked_template, meg=True, eeg=True,
exclude=raw.info['bads'])
label_names = ['Aud-lh', 'Aud-rh']
labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names]
###############################################################################
# Generate source time courses and the correspond evoked data
snr = 6 # dB
tmin = -0.1
from mne.viz import plot_evoked, plot_sparse_source_estimates
from mne.simulation import generate_sparse_stc, generate_evoked
import virtual_electrode as ve
###############################################################################
# Load real data as templates
data_path = sample.data_path("/Users/sudregp/mne-python/examples/")
raw = mne.fiff.Raw(data_path + "/MEG/sample/sample_audvis_raw.fif", preload=True)
fwd_fname = data_path + "/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif"
ave_fname = data_path + "/MEG/sample/sample_audvis-no-filter-ave.fif"
cov_fname = data_path + "/MEG/sample/sample_audvis-cov.fif"
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = pick_types_forward(fwd, meg=True)
cov = mne.read_cov(cov_fname)
evoked_template = mne.fiff.read_evoked(ave_fname, setno=0, baseline=None)
label_names = ["Aud-lh", "Aud-lh", "Aud-lh", "Aud-lh", "Aud-lh"]
labels = [mne.read_label(data_path + "/MEG/sample/labels/%s.label" % ln) for ln in label_names]
###############################################################################
# Generate source time courses and the correspond evoked data
snr = 1 # dB
tmin = 0
tstep = 1.0 / raw.info["sfreq"]
# ntimes = 2000
from mne.viz import plot_evoked, plot_sparse_source_estimates
from mne.simulation import generate_sparse_stc, generate_evoked
import virtual_electrode as ve
###############################################################################
# Load real data as templates
data_path = sample.data_path('/Users/sudregp/mne-python/examples/')
raw = mne.fiff.Raw(data_path + '/MEG/sample/sample_audvis_raw.fif', preload=True)
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = pick_types_forward(fwd, meg=True)
cov = mne.read_cov(cov_fname)
evoked_template = mne.fiff.read_evoked(ave_fname, setno=0, baseline=None)
label_names = ['Aud-lh', 'Aud-lh', 'Aud-lh', 'Aud-lh', 'Aud-lh']
labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names]
###############################################################################
# Generate source time courses and the correspond evoked data
snr = 1 # dB
tmin = 0
tstep = 1. / raw.info['sfreq']
# ntimes = 2000
