def test_high_level_glm_with_paths():
# New API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 14)), 3
with InTemporaryDirectory():
mask_file, fmri_files, design_files = write_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(
fmri_files, design_files)
z_image, = multi_session_model.transform([np.eye(rk)[1]] * 2)
assert_array_equal(z_image.get_affine(), load(mask_file).get_affine())
assert_true(z_image.get_data().std() < 3.)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory
del z_image, fmri_files, multi_session_model
def test_high_level_glm_with_paths():
# New API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 14)), 3
with InTemporaryDirectory():
mask_file, fmri_files, design_files = write_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(
fmri_files, design_files)
z_image, = multi_session_model.transform([np.eye(rk)[1]] * 2)
assert_array_equal(z_image.get_affine(), load(mask_file).get_affine())
assert_true(z_image.get_data().std() < 3.)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory
del z_image, fmri_files, multi_session_model
def test_high_level_glm_one_session():
# New API
shapes, rk = [(7, 8, 9, 15)], 3
mask, fmri_data, design_matrices = generate_fake_fmri_data(shapes, rk)
single_session_model = FirstLevelGLM(mask=None).fit(
fmri_data[0], design_matrices[0])
assert_true(isinstance(single_session_model.masker_.mask_img_,
Nifti1Image))
single_session_model = FirstLevelGLM(mask=mask).fit(
fmri_data[0], design_matrices[0])
z1, = single_session_model.transform(np.eye(rk)[:1])
assert_true(isinstance(z1, Nifti1Image))
def test_high_level_glm_one_session():
# New API
shapes, rk = [(7, 8, 9, 15)], 3
mask, fmri_data, design_matrices = generate_fake_fmri_data(shapes, rk)
single_session_model = FirstLevelGLM(mask=None).fit(
fmri_data[0], design_matrices[0])
assert_true(isinstance(single_session_model.masker_.mask_img_,
Nifti1Image))
single_session_model = FirstLevelGLM(mask=mask).fit(
fmri_data[0], design_matrices[0])
z1, = single_session_model.transform(np.eye(rk)[:1])
assert_true(isinstance(z1, Nifti1Image))
def test_high_level_glm_null_contrasts():
# test that contrast computation is resilient to 0 values.
# new API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 19)), 3
mask, fmri_data, design_matrices = generate_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(
fmri_data, design_matrices)
single_session_model = FirstLevelGLM(mask=None).fit(
fmri_data[0], design_matrices[0])
z1, = multi_session_model.transform([np.eye(rk)[:1], np.zeros((1, rk))],
output_z=False, output_stat=True)
z2, = single_session_model.transform([np.eye(rk)[:1]],
output_z=False, output_stat=True)
np.testing.assert_almost_equal(z1.get_data(), z2.get_data())
def test_high_level_glm_with_data():
# New API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 16)), 3
mask, fmri_data, design_matrices = write_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(
fmri_data, design_matrices)
n_voxels = multi_session_model.masker_.mask_img_.get_data().sum()
z_image, = multi_session_model.transform([np.eye(rk)[1]] * 2)
assert_equal(np.sum(z_image.get_data() != 0), n_voxels)
assert_true(z_image.get_data().std() < 3. )
# with mask
multi_session_model = FirstLevelGLM(mask=mask).fit(
fmri_data, design_matrices)
z_image, effect_image, variance_image = multi_session_model.transform(
[np.eye(rk)[:2]] * 2, output_effects=True, output_variance=True)
assert_array_equal(z_image.get_data() == 0., load(mask).get_data() == 0.)
assert_true(
(variance_image.get_data()[load(mask).get_data() > 0, 0] > .001).all())
def test_high_level_glm_null_contrasts():
# test that contrast computation is resilient to 0 values.
# new API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 19)), 3
mask, fmri_data, design_matrices = generate_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(fmri_data,
design_matrices)
single_session_model = FirstLevelGLM(mask=None).fit(
fmri_data[0], design_matrices[0])
z1, = multi_session_model.transform(
[np.eye(rk)[:1], np.zeros((1, rk))], output_z=False, output_stat=True)
z2, = single_session_model.transform([np.eye(rk)[:1]],
output_z=False,
output_stat=True)
np.testing.assert_almost_equal(z1.get_data(), z2.get_data())
def test_fmri_inputs():
# Test processing of FMRI inputs
with InTemporaryDirectory():
shapes = ((7, 8, 9, 10), )
mask, FUNCFILE, _ = write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=[''])
des_fname = 'design.csv'
des.to_csv(des_fname)
for fi in func_img, FUNCFILE:
for d in des, des_fname:
FirstLevelGLM().fit(fi, d)
FirstLevelGLM(mask=None).fit([fi], d)
FirstLevelGLM(mask=mask).fit(fi, [d])
FirstLevelGLM(mask=mask).fit([fi], [d])
FirstLevelGLM(mask=mask).fit([fi, fi], [d, d])
FirstLevelGLM(mask=None).fit((fi, fi), (d, d))
assert_raises(ValueError,
FirstLevelGLM(mask=None).fit, [fi, fi], d)
assert_raises(ValueError,
FirstLevelGLM(mask=None).fit, fi, [d, d])
def test_high_level_glm_with_data():
# New API
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 16)), 3
mask, fmri_data, design_matrices = write_fake_fmri_data(shapes, rk)
multi_session_model = FirstLevelGLM(mask=None).fit(fmri_data,
design_matrices)
n_voxels = multi_session_model.masker_.mask_img_.get_data().sum()
z_image, = multi_session_model.transform([np.eye(rk)[1]] * 2)
assert_equal(np.sum(z_image.get_data() != 0), n_voxels)
assert_true(z_image.get_data().std() < 3.)
# with mask
multi_session_model = FirstLevelGLM(mask=mask).fit(fmri_data,
design_matrices)
z_image, effect_image, variance_image = multi_session_model.transform(
[np.eye(rk)[:2]] * 2, output_effects=True, output_variance=True)
assert_array_equal(z_image.get_data() == 0., load(mask).get_data() == 0.)
assert_true(
(variance_image.get_data()[load(mask).get_data() > 0, 0] > .001).all())
# 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_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),
from nistats.glm import FirstLevelGLM
from nistats import datasets
# 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)
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
# 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
threshold = 0.7
seed = 42
mask_img = nb.Nifti1Image(np.ones((n_x, n_y, n_z)), affine=np.eye(4))
masker = NiftiMasker(mask_img=mask_img)
masker.fit()
fmri, paradigm, design, masks = generate_fmri(
n_x=n_x, n_y=n_y, n_z=n_y, modulation=None, n_events=n_events,
event_types=event_types, n_blank_events=n_blank_events,
event_spacing=event_spacing, t_r=t_r, smoothing_fwhm=smoothing_fwhm,
sigma=sigma, sigma_noise=sigma_noise, threshold=threshold, seed=seed)
niimgs = nb.Nifti1Image(fmri, affine=np.eye(4))
# Testing with a GLM
glm = FirstLevelGLM(mask=mask_img, t_r=t_r, standardize=True,
noise_model='ols')
glm.fit(niimgs, design)
contrast_matrix = np.eye(design.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design.columns)])
z_maps = {}
for condition_id in event_types:
z_maps[condition_id] = glm.transform(contrasts[condition_id],
contrast_name=condition_id,
output_z=True, output_stat=False,
output_effects=False,
output_variance=False)
fig, axx = plt.subplots(nrows=len(event_types), ncols=2, figsize=(8, 8))
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=period_cut)
# specify contrasts
contrast_matrix = np.eye(design_matrix.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
# Specify one interesting contrast
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FirstLevelGLM(noise_model='ar1',
standardize=False).fit([fmri_img], design_matrix)
print("Computing contrasts ..")
output_dir = 'results'
if not os.path.exists(output_dir):
os.mkdir(output_dir)
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, eff_map, var_map = fmri_glm.transform(
contrasts[contrast_id],
contrast_name=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True)
paradigm_file = data.paradigm
fmri_img = data.epi_img
### Design matrix ########################################
paradigm = pd.read_csv(paradigm_file, sep=' ', header=None, index_col=None)
paradigm.columns = ['session', 'name', 'onset']
design_matrix = make_design_matrix(frame_times,
paradigm,
hrf_model='canonical with derivative',
drift_model="cosine",
period_cut=128)
### Perform a GLM analysis ########################################
fmri_glm = FirstLevelGLM().fit(fmri_img, design_matrix)
### Estimate contrasts #########################################
# Specify the contrasts
contrast_matrix = np.eye(design_matrix.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrasts["audio"] = contrasts["clicDaudio"] + contrasts["clicGaudio"] +\
contrasts["calculaudio"] + contrasts["phraseaudio"]
contrasts["video"] = contrasts["clicDvideo"] + contrasts["clicGvideo"] + \
contrasts["calculvideo"] + contrasts["phrasevideo"]
contrasts["computation"] = contrasts["calculaudio"] + contrasts["calculvideo"]
contrasts["sentences"] = contrasts["phraseaudio"] + contrasts["phrasevideo"]
# specify contrasts
contrast_matrix = np.eye(design_matrix.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
# more interesting contrasts
contrasts = {
'faces-scrambled': contrasts['faces'] - contrasts['scrambled'],
'scrambled-faces': -contrasts['faces'] + contrasts['scrambled'],
'effects_of_interest': np.vstack((contrasts['faces'],
contrasts['scrambled']))
}
# fit GLM
print('Fitting a GLM')
fmri_glm = FirstLevelGLM(standardize=False).fit(fmri_img, design_matrices)
# compute contrast maps
print('Computing contrasts')
from nilearn import plotting
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, = fmri_glm.transform(
[contrast_val] * 2, contrast_name=contrast_id, output_z=True)
plotting.plot_stat_map(
z_map, bg_img=mean_image, threshold=3.0, display_mode='z',
cut_coords=3, black_bg=True, title=contrast_id)
plotting.show()
hrf_model = 'glover + derivative'
design_matrix = make_design_matrix(
frame_times, paradigm, hrf_model=hrf_model, drift_model=drift_model,
period_cut=period_cut)
# specify contrasts
contrast_matrix = np.eye(design_matrix.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
# Specify one interesting contrast
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FirstLevelGLM(noise_model='ar1', standardize=False).fit(
[fmri_img], design_matrix)
print("Computing contrasts ..")
output_dir = 'results'
if not os.path.exists(output_dir):
os.mkdir(output_dir)
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, eff_map, var_map = fmri_glm.transform(
contrasts[contrast_id], contrast_name=contrast_id, output_z=True,
output_stat=True, output_effects=True, output_variance=True)
# store stat maps to disk
for dtype, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, eff_map, var_map]):
data = datasets.fetch_localizer_first_level()
paradigm_file = data.paradigm
fmri_img = data.epi_img
### Design matrix ########################################
paradigm = pd.read_csv(paradigm_file, sep=' ', header=None, index_col=None)
paradigm.columns = ['session', 'name', 'onset']
n_conditions = len(paradigm.name.unique())
design_matrix = make_design_matrix(
frame_times, paradigm, hrf_model='glover + derivative',
drift_model='cosine', period_cut=128)
### Perform a GLM analysis ########################################
fmri_glm = FirstLevelGLM().fit(fmri_img, design_matrix)
### Estimate contrasts #########################################
# Specify the contrasts
contrast_matrix = np.eye(design_matrix.shape[1])
contrasts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrasts["audio"] = contrasts["clicDaudio"] + contrasts["clicGaudio"] +\
contrasts["calculaudio"] + contrasts["phraseaudio"]
contrasts["video"] = contrasts["clicDvideo"] + contrasts["clicGvideo"] + \
contrasts["calculvideo"] + contrasts["phrasevideo"]
contrasts["computation"] = contrasts["calculaudio"] + contrasts["calculvideo"]
contrasts["sentences"] = contrasts["phraseaudio"] + contrasts["phrasevideo"]
# 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'
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)
from nistats import datasets
# 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_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),