def test_csd_save():
"""Test saving and loading a CrossSpectralDensity."""
csd = _make_csd()
tempdir = _TempDir()
fname = op.join(tempdir, 'csd.h5')
csd.save(fname)
csd2 = read_csd(fname)
assert_array_equal(csd._data, csd2._data)
assert csd.tmin == csd2.tmin
assert csd.tmax == csd2.tmax
assert csd.ch_names == csd2.ch_names
assert csd.frequencies == csd2.frequencies
assert csd._is_sum == csd2._is_sum
def test_csd_save():
"""Test saving and loading a CrossSpectralDensity."""
csd = _make_csd()
tempdir = _TempDir()
fname = op.join(tempdir, 'csd.h5')
csd.save(fname)
csd2 = read_csd(fname)
assert_array_equal(csd._data, csd2._data)
assert csd.tmin == csd2.tmin
assert csd.tmax == csd2.tmax
assert csd.ch_names == csd2.ch_names
assert csd.frequencies == csd2.frequencies
assert csd._is_sum == csd2._is_sum
"{dir}nc_{meg}_from-fs_ico4_exp-r-fwd.fif".format(dir=meg_dir,
meg=meg))
# convert the forward model to one that defines two orthogonal dipoles at each source, that are tangential to a sphere
fwd_tan = conpy.forward_to_tangential(fwd_r)
# get pairs for connectivity calculation
pairs = np.load("{}NEMO_ico4_connectivity_pairs.npy".format(meg_dir))
# pairs are defined in fsaverage space, map them to the source space of the current subject
fsaverage_to_subj = conpy.utils.get_morph_src_mapping(
fs_src, fwd_tan['src'], indices=True, subjects_dir=mri_dir)[0]
pairs = [[fsaverage_to_subj[v] for v in pairs[0]],
[fsaverage_to_subj[v] for v in pairs[1]]]
for cond in exp_conds:
print("Calculations for Condition: ", cond)
for freq, vals in freqs.items():
print("Calculations for Frequency: ", freq)
csd = read_csd("{dir}nc_{meg}-csd_{cond}_{freq}.h5".format(
dir=meg_dir, meg=meg, cond=cond, freq=freq))
csd = csd.mean()
csd = pick_channels_csd(csd, fwd_tan['info']['ch_names'])
con = conpy.dics_connectivity(vertex_pairs=pairs,
fwd=fwd_tan,
data_csd=csd,
reg=0.05,
n_jobs=8)
con.save("{dir}nc_{meg}_{cond}_{freq}-connectivity.h5".format(
dir=meg_dir, meg=meg, cond=cond, freq=freq))
# for the baseline conditions:
print("Running Baseline Conditions")
fwd_r = mne.read_forward_solution(
"{dir}nc_{meg}_from-fs_ico4_bas-r-fwd.fif".format(dir=meg_dir,
meg=meg))
from matplotlib import pyplot as plt
import mne
from mne.time_frequency import read_csd, pick_channels_csd
from config import fname, subjects, freq_bands
info = mne.io.read_info(fname.epo(subject=subjects[0]))
grads = [info['ch_names'][ch] for ch in mne.pick_types(info, meg='grad')]
csd = read_csd(fname.csd(subject=subjects[0], condition='face'))
csd = pick_channels_csd(csd, grads)
csd = csd.mean([f[0] for f in freq_bands], [f[1] for f in freq_bands])
# Plot theta, alpha, low beta
csd[:3].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd1.pdf', bbox_inches='tight')
# Plot high beta 1, high beta 2 and low gamma
csd[3:].plot(info, n_cols=3, show=False)
plt.savefig('../paper/figures/csd2.pdf', bbox_inches='tight')
# Read the forward model
fwd = mne.read_forward_solution(fname.fwd(subject=subject))
# Read the info structure
info = mne.io.read_info(fname.epo(subject=subject))
# Compute source power for all frequency bands and all conditions
fmin = [f[0] for f in freq_bands]
fmax = [f[1] for f in freq_bands]
csds = dict()
for condition in conditions + ['baseline']:
print('Reading CSD matrix for condition:', condition)
# Read the CSD matrix
csds[condition] = read_csd(fname.csd(condition=condition, subject=subject))
# Average the CSDs of both conditions
csd = csds['face'].copy()
csd._data += csds['scrambled']._data
csd._data /= 2
# Compute the DICS beamformer using the CSDs from both conditions, for all
# frequency bands.
filters = make_dics(info=info, forward=fwd, csd=csd.mean(fmin, fmax), reg=reg,
pick_ori='max-power')
stcs = dict()
for condition in conditions + ['baseline']:
print('Computing power for condition:', condition)
stcs[condition], _ = apply_dics_csd(csds[condition].mean(fmin, fmax),
