period_cut=period_cut, drift_order=drift_order)
#fmri = fmri / fmri.mean() * 100
niimgs = nb.Nifti1Image(fmri, affine=np.eye(4))
for iest, hrf_peak in enumerate(peak_range):
# GLM using HRF with a different peak
hrf_est = _gamma_difference_hrf(1., oversampling=1./dt, time_length=hrf_length + dt,
onset=0., delay=hrf_peak, undershoot=hrf_ushoot,
dispersion=1., u_dispersion=1., ratio=0.167)
f_hrf_est = interp1d(x_0, hrf_est)
_, design, _, _ = generate_spikes_time_series(
n_events=n_events, n_blank_events=n_blank_events,
event_spacing=event_spacing, t_r=t_r, event_types=event_types,
return_jitter=True, jitter_min=jitter_min, jitter_max=jitter_max,
period_cut=period_cut, drift_order=drift_order, time_offset=10,
modulation=None, seed=seed, f_hrf=f_hrf_est, hrf_length=hrf_length)
# Testing with a GLM
glm = FirstLevelGLM(mask=mask_img, t_r=t_r, standardize=True,
noise_model='ols')
glm.fit(niimgs, design)
#print 'n_timepoints, n_voxels: ', glm.results_[0][0].norm_resid.shape
#print glm.results_[0][0].resid
#print glm.results_[0][0].logL
snr = np.linalg.norm(fmri, axis=3) / sigma_noise
snr_db = 20 * (np.log10(np.linalg.norm(fmri, axis=3) / sigma_noise))
print 'sigma_noise = ', sigma_noise
print 'SNR = ', snr.mean()
print 'SNR = ', snr_db.mean(), ' dB'
plt.figure(figsize=(12, 8))
i = 0
for hrf_peak in range_peak:
# Simulate with different hrf peaks
hrf_sim = _gamma_difference_hrf(1., oversampling=1./dt, time_length=hrf_length+dt,
onset=0., delay=hrf_peak, undershoot=hrf_ushoot,
dispersion=1., u_dispersion=1., ratio=0.167)
f_hrf_sim = interp1d(x_0, hrf_sim)
paradigm, design, modulation, measurement_time = \
generate_spikes_time_series(n_events=n_events, n_blank_events=n_blank_events,
event_spacing=event_spacing, t_r=t_r, return_jitter=True,
jitter_min=jitter_min, jitter_max=jitter_max, f_hrf=f_hrf_sim,
hrf_length=hrf_length, event_types=event_types, period_cut=64,
time_offset=10, modulation=None, seed=seed)
design = design[event_types].values # forget about drifts for the moment
beta = rng.randn(len(event_types))
ys = design.dot(beta)
noise = rng.randn(design.shape[0])
scale_factor = np.linalg.norm(ys) / np.linalg.norm(noise)
ys_acquired = ys + noise * scale_factor * sigma_noise
snr = 20 * (np.log10(np.linalg.norm(ys_acquired) / np.linalg.norm(ys - ys_acquired)))
print 'SNR = ', snr, ' dB'
# Estimation with 1 hrf. Uses glover as mean GP
hrf_model = 'glover'
