misc["tasks"] = Conditions
misc["mask_url"] = paths['mask']
misc.write()
# step 2. Create one design matrix for each session
design_matrices = {}
for sess in Sessions:
design_matrices[sess] = glm_tools.design_matrix(
paths['misc'], paths['dmtx'][sess], sess, paths['paradigm'],
frametimes, hrf_model=hrf_model, drift_model=drift_model,
hfcut=hfcut, model=model_id)
# step 3. Compute the Mask
# fixme : it should be possible to provide a pre-computed mask
print "Computing the Mask"
mask_array = compute_mask_files( paths['fmri'].values()[0][0],
paths['mask'], True, infTh, supTh)
# step 4. Creating functional contrasts
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=ConfigObj(paths['misc']),
model=model_id)
generate_localizer_contrasts(clist)
contrast = clist.save_dic(paths['contrast_file'])
CompletePaths = glm_tools.generate_brainvisa_ouput_paths(
paths["contrasts"], contrast)
# step 5. Fit the glm for each session
glms = {}
for sess in Sessions:
print "Fitting GLM for session : %s" % sess
glms[sess] = glm_tools.glm_fit(
paradigm = dm.load_protocol_from_csv_file(paradigm_file, session=0)
design_matrix = dm.DesignMatrix( frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut)
design_matrix.show()
pylab.savefig(op.join(swd, 'design_matrix.png'))
# design_matrix.save(...)
########################################
# Mask the data
########################################
print 'Computing a brain mask...'
mask_path = op.join(swd, 'mask.nii')
mask_array = compute_mask_files( data_path, mask_path, False, 0.4, 0.9)
########################################
# Perform a GLM analysis
########################################
print 'Fitting a GLM (this takes time)...'
fmri_image = load(data_path)
Y = fmri_image.get_data()[mask_array]
model = "ar1"
method = "kalman"
my_glm = nipy.neurospin.glm.glm()
glm = my_glm.fit(Y.T, design_matrix.matrix,
method="kalman", model="ar1")
#########################################
