###############################################################################
# data simulation
shape = (60, 60)
pos = np.array([[12, 14],
[20, 20],
[30, 20]])
ampli = np.array([3, 4, 4])
x = simul.surrogate_2d_dataset(n_subj=1, shape=shape, pos=pos, ampli=ampli,
width=10.0).squeeze()
th = 2.36
# compute the field structure and perform the watershed
domain = grid_domain_from_shape(shape)
nroi = HROI_from_watershed(domain, np.ravel(x), threshold=th)
label = nroi.label
#compute the region-based signal average
bfm = np.array([np.mean(x.ravel()[label == k]) for k in range(label.max() + 1)])
bmap = np.zeros(x.size)
if label.max() > - 1:
bmap[label > - 1] = bfm[label[label > - 1]]
label = np.reshape(label, shape)
bmap = np.reshape(bmap, shape)
###############################################################################
# plot the input image
# step 1: generate some synthetic data
n_subj = 10
shape = (60, 60)
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')
[20, 20],
[30, 20]])
ampli = np.array([5, 7, 6])
sjitter = 1.0
stats = simul.surrogate_2d_dataset(n_subj=n_subjects, shape=shape, pos=pos,
ampli=ampli, width=5.0)
# set various parameters
threshold = float(st.t.isf(0.01, 100))
sigma = 4. / 1.5
prevalence_threshold = n_subjects * .25
prevalence_pval = 0.9
smin = 5
algorithm = 'co-occurrence' # 'density'
domain = grid_domain_from_shape(shape)
# get the functional information
stats_ = np.array([np.ravel(stats[k]) for k in range(n_subjects)]).T
# run the algo
landmarks, hrois = compute_landmarks(
domain, stats_, sigma, prevalence_pval, prevalence_threshold,
threshold, smin, method='prior', algorithm=algorithm)
display_landmarks_2d(landmarks, hrois, stats)
if landmarks is not None:
landmarks.show()
plt.show()
# step 1: generate some synthetic data
n_subj = 10
shape = (60, 60)
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')
