示例#1
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###############################################################################
# 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')
示例#3
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                [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')