Python surrogate_4d_dataset Examples

Example #1

########################################

positions = np.array([[60, -30, 5], [50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8, 6])

domain = grid_domain_from_image(mask)
my_roi = mroi.subdomain_from_balls(domain, positions, radii)

# to save an image of the ROIs
save(my_roi.to_image(), path.join(write_dir, "roi.nii"))

#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X)[0]
Y = fmri_data.get_data()[mask_array]

# artificially added signal in ROIs to make the example more meaningful
activation = 30 * (X.T[1] + .5 * X.T[0])
for (position, radius) in zip(positions, radii):
    Y[((domain.coord - position)**2).sum(1) < radius**2 + 1] += activation

########################################
# Perform a GLM analysis
########################################

# GLM fit
glm = GeneralLinearModel(X)
glm.fit(Y.T)

Example #2

File: example_glm.py Project: bergtholdt/nipy

########################################
# Design matrix
########################################

paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes, paradigm, drift_model='Cosine', hfcut=128,
               hrf_model=hrf_model, add_regs=motion,
               add_reg_names=add_reg_names)


#######################################
# Get the FMRI data
#######################################

fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0]

# if you want to save it as an image
data_file = op.join(swd, 'fmri_data.nii')
save(fmri_data, data_file)

########################################
# Perform a GLM analysis
########################################

# GLM fit
Y = fmri_data.get_data()
model = "ar1"
method = "kalman"
glm = GLM.glm()
glm.fit(Y.T, X, method=method, model=model)

Example #3

File: example_roi_and_glm.py Project: ofenlab/nipy

paradigm = np.vstack(([conditions, onsets])).T
paradigm = EventRelatedParadigm(conditions, onsets)
X, names = dmtx_light(frametimes,
                      paradigm,
                      drift_model='cosine',
                      hfcut=128,
                      hrf_model=hrf_model,
                      add_regs=motion,
                      add_reg_names=add_reg_names)

#######################################
# Get the FMRI data
#######################################

fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X, seed=1)[0]

########################################
# Perform a GLM analysis
########################################

# GLM fit
Y = fmri_data.get_data()[mask_array]
glm = GeneralLinearModel(X)
glm.fit(Y.T)

# specifiy the contrast [1 -1 0 ..]
contrast = np.zeros(X.shape[1])
contrast[:2] = np.array([1, -1])

# compute the constrast image related to it

Example #4

File: example_roi_and_glm.py Project: fabianp/nipy

########################################
# Design matrix
########################################

paradigm = np.vstack(([conditions, onsets])).T
paradigm = EventRelatedParadigm(conditions, onsets)
X, names = dmtx_light(frametimes, paradigm, drift_model='cosine', hfcut=128,
                      hrf_model=hrf_model, add_regs=motion,
                      add_reg_names=add_reg_names)


#######################################
# Get the FMRI data
#######################################

fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X, seed=1)[0]

# if you want to save it as an image
# data_file = op.join(write_dir,'fmri_data.nii')
# save(fmri_data, data_file)

########################################
# Perform a GLM analysis
########################################

# GLM fit
Y = fmri_data.get_data()[mask_array]
glm = GeneralLinearModel(X)
glm.fit(Y.T)

# specifiy the contrast [1 -1 0 ..]

Example #5

File: example_glm.py Project: zddzxxsmile/nipy

########################################

paradigm = EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes,
                         paradigm,
                         drift_model='cosine',
                         hfcut=128,
                         hrf_model=hrf_model,
                         add_regs=motion,
                         add_reg_names=add_reg_names)

#######################################
# Get the FMRI data
#######################################

fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0]

# if you want to save it as an image
data_file = 'fmri_data.nii'
save(fmri_data, data_file)

########################################
# Perform a GLM analysis
########################################

# GLM fit
Y = fmri_data.get_data().reshape(np.prod(shape), n_scans)
glm = GeneralLinearModel(X)
glm.fit(Y.T)

# specify the contrast [1 -1 0 ..]

Example #6

File: example_roi_and_glm.py Project: GaelVaroquaux/nipy

########################################

positions = np.array([[60, -30, 5], [50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8, 6])

domain = grid_domain_from_image(mask)
my_roi = mroi.subdomain_from_balls(domain, positions, radii)

# to save an image of the ROIs
save(my_roi.to_image(), path.join(write_dir, "roi.nii"))

#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X)[0]
Y = fmri_data.get_data()[mask_array]

# artificially added signal in ROIs to make the example more meaningful
activation = 30 * (X.T[1] + .5 * X.T[0])
for (position, radius) in zip(positions, radii):
    Y[((domain.coord - position) ** 2).sum(1) < radius ** 2 + 1] += activation

########################################
# Perform a GLM analysis
########################################

# GLM fit
glm = GeneralLinearModel(X)
glm.fit(Y.T)