print 'SNR = ', snr, ' dB'
# Estimation with 1 hrf. Uses glover as mean GP
hrf_model = 'glover'
hrf_0 = _get_hrf_model(hrf_model, hrf_length=hrf_length + dt,
dt=dt, normalize=True)
f_hrf = interp1d(x_0, hrf_0)
gp = SuperDuperGP(hrf_length=hrf_length, t_r=t_r, oversampling=1./dt, gamma=gamma,
modulation=modulation, fmin_max_iter=fmin_max_iter, sigma_noise=1.,
time_offset=time_offset, n_iter=n_iter, normalize_y=normalize_y, verbose=True,
optimize=optimize, n_restarts_optimizer=n_restarts_optimizer,
zeros_extremes=zeros_extremes, f_mean=f_hrf)
(hx, hy, hrf_var,
resid_norm_sq,
sigma_sq_resid) = gp.fit(ys_acquired, paradigm)
print 'residual norm square = ', resid_norm_sq
hy *= np.sign(hy[np.argmax(np.abs(hy))]) / np.abs(hy).max()
hrf_0 /= hrf_0.max()
hrf_sim /= hrf_sim.max()
# Plotting each HRF simulated vs estimated
if len(range_peak)==5 or len(range_peak)==6:
plt.subplot(2, 3, i + 1)
plt.tight_layout()
elif len(range_peak)==3 or len(range_peak)==4:
plt.subplot(2, 2, i + 1)
plt.tight_layout()
elif len(range_peak)==2:
ax = plt.subplot(1, 2, i + 1)
n_iter = 3
normalize_y = False
optimize = True
zeros_extremes = True
# Estimation
gp = SuperDuperGP(hrf_length=hrf_length, t_r=t_r, oversampling=1./dt,
gamma=gamma, modulation=modulation,
fmin_max_iter=fmin_max_iter, sigma_noise=1.0,
time_offset=time_offset, n_iter=n_iter,
normalize_y=normalize_y, verbose=True,
optimize=optimize,
n_restarts_optimizer=n_restarts_optimizer,
zeros_extremes=zeros_extremes, f_mean=f_hrf)
(hx, hy, hrf_var, resid_norm_sq, sigma_sq_resid) = gp.fit(ys, paradigm)
print 'residual norm square = ', resid_norm_sq
# Testing with a GLM
mask_img = nb.Nifti1Image(np.ones((2, 2, 2)), affine=np.eye(4))
masker = NiftiMasker(mask_img=mask_img)
masker.fit()
ys2 = np.ones((2, 2, 2, ys.shape[0])) * ys[np.newaxis, np.newaxis, np.newaxis, :]
niimgs = nb.Nifti1Image(ys2, affine=np.eye(4))
glm = FirstLevelGLM(mask=mask_img, t_r=t_r, standardize=True, noise_model='ols')
glm.fit(niimgs, dm)
norm_resid = (np.linalg.norm(glm.results_[0][0].resid, axis=0)**2).mean()
ys_pred_glm = glm.results_[0][0].predicted[:, 0]
# Predict GP
