def test_fg_fit_par():
"""Test FOOOFGroup fit, running in parallel."""
n_spectra = 2
xs, ys = gen_group_power_spectra(n_spectra, *default_group_params())
tfg = FOOOFGroup()
tfg.fit(xs, ys, n_jobs=2)
out = tfg.get_results()
assert out
assert len(out) == n_spectra
assert isinstance(out[0], FOOOFResult)
assert np.all(out[1].background_params)
else:
freqs = np.squeeze(dat['fxx'])
aper = np.empty([2, dat['pxx'].shape[1], dat['pxx'].shape[2]])
g = np.zeros([dat['pxx'].shape[1], 75, dat['pxx'].shape[2]])
for iseg in range(0, dat['pxx'].shape[2]):
print('%d / %d' % (iseg, dat['pxx'].shape[2]))
fm = FOOOFGroup(peak_width_limits=[1, 8],
min_peak_height=0.05,
max_n_peaks=6)
fm._maxfev = 30000
freq_range = [3, 40]
fm.fit(freqs, np.transpose(dat['pxx'][:, :, iseg]), freq_range)
tmp = fm.get_results()
for isens in range(0, dat['pxx'].shape[1]):
aper[:, isens, iseg] = tmp[isens].aperiodic_params
F = fm.freqs
for isens in range(0, dat['pxx'].shape[1]):
for i in range(0, len(tmp[isens].gaussian_params)):
c = tmp[isens].gaussian_params[i][0]
w = tmp[isens].gaussian_params[i][2]
a = tmp[isens].gaussian_params[i][1]
g[isens, :, iseg] = g[isens, :, iseg] + a * np.exp(
(-(F - c)**2) / (2 * pow(w, 2)))
b = tmp[isens].aperiodic_params[0]
e = tmp[isens].aperiodic_params[1]
v = 2
all_subj = [
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, 30, 31, 32, 33, 34
]
for isubj in all_subj:
for iblock in range(1, 3):
try:
fm = FOOOFGroup()
fm.load(
'/home/tpfeffer/pp/proc/src/pp_hh_fooof_result_lo_s%d_b%d_v%d.json'
% (isubj, iblock, v))
res_lo = fm.get_results()
fm.load(
'/home/tpfeffer/pp/proc/src/pp_hh_fooof_result_me_s%d_b%d_v%d.json'
% (isubj, iblock, v))
res_me = fm.get_results()
fm.load(
'/home/tpfeffer/pp/proc/src/pp_hh_fooof_result_hi_s%d_b%d_v%d.json'
% (isubj, iblock, v))
res_hi = fm.get_results()
except:
print('Could not find data...')
continue
# Frequencies: 0 - 40 Hz in steps of 0.25 Hz
F = fm.freqs
# g_lo will contain the complete PS fit, i.e., aperiodic + periodic components
average='mean',
nperseg=200,
nfft=400,
return_onesided=True)
theta_params_clean = np.zeros(shape=[64, len(eps), 3]) - 1
for elec_n in np.arange(n_elec):
if np.logical_not(elec_n % 10):
print('\telec : %s' % eps.info['ch_names'][elec_n])
fg = FOOOFGroup(verbose=False,
max_n_peaks=4,
peak_width_limits=[.5, 2])
fg.fit(freqs_welch, amp_welch[:, elec_n, :], [2, 20], n_jobs=-1)
theta_params =\
np.array([fg_res_n.peak_params[(fg_res_n.peak_params[:,0]>3.9)\
& (fg_res_n.peak_params[:,0]<8)] for fg_res_n in fg.get_results()])
for trial_n in np.arange(len(theta_params)):
if len(theta_params[trial_n]) == 1:
theta_params_clean[
elec_n, trial_n, :] = theta_params[trial_n].squeeze()
elif len(theta_params[trial_n]) > 1:
max_amp_ind = np.argmax(theta_params[trial_n][:, 1]).squeeze()
theta_params_clean[elec_n, trial_n, :] = theta_params[trial_n][
max_amp_ind, :].squeeze()
np.savez(obs_eegpath +\
'obs_%i_fooof_params_theta.npz' % obs_i,
{'theta_params_clean':theta_params_clean, 'instr_type':eps.metadata['instr_type'].values,
'respcorrect':eps.metadata['respcorrect'].values, 'resptime':eps.metadata['resptime'].values})
