# parameter for the intersection of the mask
ths = .5
# number of parcels
nbparcel = 200
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# prepare the parcel structure
domain, ldata = parcel_input(mask_images, learn_images, ths)
# run the algorithm
fpa = hparcel(domain, ldata, nbparcel, verbose=1)
# produce some output images
write_parcellation_images(fpa, subject_id=subj_id, swd=write_dir)
# do some parcellation-based analysis:
# take some test images whose parcel-based signal needs to be assessed
test_images = [
path.join(data_dir, 'spmT_%s_subj_%02d.nii' % (nbeta, n))
for n in range(nb_subj)
]
# compute and write the parcel-based statistics
rfx_path = path.join(write_dir, 'prfx_%s.nii' % nbeta)
parcellation_based_analysis(fpa, test_images, 'one_sample', rfx_path=rfx_path)
print("Wrote everything in %s" % write_dir)
pos = 3 * np.array([[6, 7],
[10, 10],
[15, 10]])
ampli = np.array([5, 7, 6])
sjitter = 6.0
dataset = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos,
ampli=ampli, width=10.0)
# dataset represents 2D activation images from n_subj subjects,
# step 2 : prepare all the information for the parcellation
nbparcel = 10
ldata = np.reshape(dataset, (n_subj, np.prod(shape), 1))
domain = dom.grid_domain_from_shape(shape)
# step 3 : run the algorithm
Pa = hp.hparcel(domain, ldata, nbparcel, mu=3.0)
# note: play with mu to change the 'stiffness of the parcellation'
# step 4: look at the results
Label = np.array([np.reshape(Pa.individual_labels[:, s], shape)
for s in range(n_subj)])
plt.figure(figsize=(8, 4))
plt.title('Input data')
for s in range(n_subj):
plt.subplot(2, 5, s + 1)
plt.imshow(dataset[s], interpolation='nearest')
plt.axis('off')
plt.figure(figsize=(8, 4))
plt.title('Resulting parcels')
pos = 3 * np.array([[6, 7],
[10, 10],
[15, 10]])
ampli = np.array([5, 7, 6])
sjitter = 6.0
dataset = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos,
ampli=ampli, width=10.0)
# dataset represents 2D activation images from n_subj subjects,
# step 2 : prepare all the information for the parcellation
nbparcel = 10
ldata = np.reshape(dataset, (n_subj, np.prod(shape), 1))
domain = dom.grid_domain_from_shape(shape)
# step 3 : run the algorithm
Pa = hp.hparcel(domain, ldata, nbparcel, mu=3.0)
# note: play with mu to change the 'stiffness of the parcellation'
# step 4: look at the results
Label = np.array([np.reshape(Pa.individual_labels[:, s], shape)
for s in range(n_subj)])
import matplotlib.pylab as mp
mp.figure(figsize=(8, 4))
mp.title('Input data')
for s in range(n_subj):
mp.subplot(2, 5, s + 1)
mp.imshow(dataset[s], interpolation='nearest')
mp.axis('off')
mp.figure(figsize=(8, 4))
# parameter for the intersection of the mask
ths = .5
# number of parcels
nbparcel = 200
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# prepare the parcel structure
domain, ldata = parcel_input(mask_images, learn_images, ths)
# run the algorithm
fpa = hparcel(domain, ldata, nbparcel, verbose=1)
# produce some output images
write_parcellation_images(fpa, subject_id=subj_id, swd=write_dir)
# do some parcellation-based analysis:
# take some test images whose parcel-based signal needs to be assessed
test_images = [path.join(data_dir, 'spmT_%s_subj_%02d.nii' % (nbeta, n))
for n in range(nb_subj)]
# compute and write the parcel-based statistics
rfx_path = path.join(write_dir, 'prfx_%s.nii' % nbeta)
parcellation_based_analysis(fpa, test_images, 'one_sample', rfx_path=rfx_path)
print("Wrote everything in %s" % write_dir)