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'
print glm.results_[0][0].norm_resid.mean()
norm_resid[isim, iest] = (np.linalg.norm(glm.results_[0][0].resid, axis=0)**2).mean()
if not op.exists(fig_folder): os.makedirs(fig_folder)
fig_name = op.join(fig_folder, 'glm_residual_norm_sigma' + str(sigma_noise))
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# Data and analysis parameters
data = datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
# GLM specification
fmri_glm = FirstLevelGLM(data['mask'], standardize=False, noise_model='ar1')
# GLM fitting
fmri_glm.fit(fmri_img, design_matrices)
# compute fixed effects of the two runs and compute related images
n_columns = design_matrices[0].shape[1]
def pad_vector(contrast_, n_columns):
return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))
contrasts = {'SStSSp_minus_DStDSp': pad_vector([1, 0, 0, -1], n_columns),
'DStDSp_minus_SStSSp': pad_vector([-1, 0, 0, 1], n_columns),
'DSt_minus_SSt': pad_vector([-1, -1, 1, 1], n_columns),
'DSp_minus_SSp': pad_vector([-1, 1, -1, 1], n_columns),
'DSt_minus_SSt_for_DSp': pad_vector([0, -1, 0, 1], n_columns),
'DSp_minus_SSp_for_DSt': pad_vector([0, 0, -1, 1], n_columns),
'Deactivation': pad_vector([-1, -1, -1, -1, 4], n_columns),
'Effects_of_interest': np.eye(n_columns)[:5]}
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
# XXX: Do we need to predict for GLM???
ys_pred, matrix, betas, resid = gp.predict(ys, paradigm)
corr_gp = np.corrcoef(ys_pred, ys)[1, 0]
corr_glm = np.corrcoef(ys_pred_glm, ys)[1, 0]
print "corr glm: %s, corr gp: %s" % (corr_glm, corr_gp)
data = {}
data['ys'] = ys
data['study'] = study
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# Data and analysis parameters
data = datasets.fetch_fiac_first_level()
fmri_files = [data['func1'], data['func2']]
design_files = [data['design_matrix1'], data['design_matrix2']]
# Load all the data into a common GLM
multi_session_model = FirstLevelGLM(data['mask'], standardize=False,
noise_model='ar1')
# GLM fitting
multi_session_model.fit(fmri_files, design_files)
def make_fiac_contrasts(n_columns):
""" Specify some contrasts for the FIAC experiment"""
contrast = {}
# the design matrices of both runs comprise 13 columns
# the first 5 columns of the design matrices correspond to the following
# conditions: ['SSt-SSp', 'SSt-DSp', 'DSt-SSp', 'DSt-DSp', 'FirstSt']
def _pad_vector(contrast_, n_columns):
return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))
contrast['SStSSp_minus_DStDSp'] = _pad_vector([1, 0, 0, -1], n_columns)
contrast['DStDSp_minus_SStSSp'] = - contrast['SStSSp_minus_DStDSp']
contrast['DSt_minus_SSt'] = _pad_vector([- 1, - 1, 1, 1], n_columns)
contrast['DSp_minus_SSp'] = _pad_vector([- 1, 1, - 1, 1], n_columns)
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# Data and analysis parameters
data = datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
# GLM specification
fmri_glm = FirstLevelGLM(data['mask'], standardize=False, noise_model='ar1')
# GLM fitting
fmri_glm.fit(fmri_img, design_matrices)
# compute fixed effects of the two runs and compute related images
n_columns = design_matrices[0].shape[1]
def pad_vector(contrast_, n_columns):
return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))
contrasts = {
'SStSSp_minus_DStDSp': pad_vector([1, 0, 0, -1], n_columns),
'DStDSp_minus_SStSSp': pad_vector([-1, 0, 0, 1], n_columns),
'DSt_minus_SSt': pad_vector([-1, -1, 1, 1], n_columns),
'DSp_minus_SSp': pad_vector([-1, 1, -1, 1], n_columns),
'DSt_minus_SSt_for_DSp': pad_vector([0, -1, 0, 1], n_columns),
