def run(da, di, mf):
print(('Processing : %4d nAm (dip %d) : SSS=%s' % (da, di, mf)).ljust(42),
end='')
epochs, evoked, cov, sphere = get_data(base_path, di, da, mf, bads=bads)
# Hack to only use gradiometers
epochs.pick_types(meg='grad')
evoked.pick_types(meg='grad')
# Do LCMV
data_cov = mne.compute_covariance(epochs, tmin=0.)
stc = mne.beamformer.lcmv(evoked,
fwd,
cov,
data_cov,
reg=0.01,
pick_ori='max-power',
reduce_rank=True,
max_ori_out='signed')
stc = abs(stc)
idx_max = np.argmax(np.mean(stc.data, axis=1))
vertno_max = stc.vertices[idx_max]
pos = src[0]['rr'][vertno_max]
ori = None
amp = None
gof = None
actual_params = dict(actual_pos=actual_pos[di - 1],
actual_ori=actual_ori[di - 1],
actual_amp=da / 2.)
error = compute_error(di, pos, ori, amp, **actual_params)
error['gof'] = gof
error['maxfilter'] = mf
return pd.DataFrame(error, index=[0])
def run(da, di, mf):
print(('Processing : %4d nAm (dip %d) : SSS=%s' % (da, di, mf)).ljust(43),
end='')
epochs, evoked, cov, sphere = get_data(base_path, di, da, mf, bads=bads)
# # Hack to only use gradiometers
# epochs.pick_types(meg='grad')
# evoked.pick_types(meg='grad')
dip = mne.beamformer.rap_music(evoked,
fwd,
cov,
n_dipoles=1,
return_residual=False)[0]
t_idx = np.argmax(dip.gof)
pos = dip.pos[t_idx]
ori = dip.ori[t_idx]
gof = dip.gof[t_idx]
amp = dip.amplitude[t_idx]
actual_params = dict(actual_pos=actual_pos[di - 1],
actual_ori=actual_ori[di - 1],
actual_amp=da / 2.)
error = compute_error(di, pos, ori, 1e9 * amp, **actual_params)
error['gof'] = gof
error['maxfilter'] = mf
return pd.DataFrame(error, index=[0])
def run(da, di, mf):
print(('Processing : %4d nAm (dip %d) : SSS=%s' % (da, di, mf)).ljust(43),
end='')
epochs, evoked, cov, sphere = get_data(base_path, di, da, mf, bads=bads)
# Do LCMV
reduce_rank = True # as we use a sphere model
pick_ori = 'max-power'
rank = None
# # Expose MNE bug...
# reduce_rank = False
# pick_ori = None
# rank = None
data_cov = mne.compute_covariance(epochs, tmin=0.05)
filters = mne.beamformer.make_lcmv(epochs.info,
fwd,
data_cov,
reg=0.05,
noise_cov=cov,
pick_ori=pick_ori,
rank=rank,
weight_norm='nai',
reduce_rank=reduce_rank)
stc = mne.beamformer.apply_lcmv(evoked, filters, max_ori_out='signed')
stc = abs(stc)
idx_max = np.argmax(np.mean(stc.data, axis=1))
vertno_max = stc.vertices[idx_max]
pos = src[0]['rr'][vertno_max]
ori = None
amp = None
gof = None
actual_params = dict(actual_pos=actual_pos[di - 1],
actual_ori=actual_ori[di - 1],
actual_amp=da / 2.)
error = compute_error(di, pos, ori, amp, **actual_params)
error['gof'] = gof
error['maxfilter'] = mf
return pd.DataFrame(error, index=[0])
def run(da, di, mf):
print(('Processing : %4d nAm (dip %d) : SSS=%s' % (da, di, mf)).ljust(42),
end="")
epochs, evoked, cov, sphere = get_data(base_path, di, da, mf, bads=bads)
# Hack to only use gradiometers
epochs.pick_types(meg='grad')
evoked.pick_types(meg='grad')
# Do the dipole fit
t_peak = 66e-3 # only fit the largest peak
evoked.crop(t_peak, t_peak)
dip = mne.fit_dipole(evoked, cov, sphere)[0]
pos = dip.pos[0]
ori = dip.ori[0]
gof = dip.gof[0]
actual_params = dict(actual_pos=actual_pos[di - 1],
actual_ori=actual_ori[di - 1],
actual_amp=da / 2.)
error = compute_error(di, pos, ori, 1e9 * dip.amplitude, **actual_params)
error['gof'] = gof
error['maxfilter'] = mf
return pd.DataFrame(error)