dimy = 60
pos = np.array([[12, 14], [20, 20], [30, 20]])
ampli = np.array([3, 4, 4])
nbvox = dimx * dimy
dataset = simul.surrogate_2d_dataset(nbsubj=1, dimx=dimx, dimy=dimy, pos=pos,
ampli=ampli, width=10.0).squeeze()
values = dataset.ravel()
#-------------------------------------------------------
# Computations
#-------------------------------------------------------
# create a domain descriptor associated with this
domain = domain_from_array(dataset ** 2 > 0)
nroi = hroi.HROI_as_discrete_domain_blobs(domain, dataset.ravel(),
threshold=2.0, smin=3)
label = np.reshape(nroi.label, ((dimx, dimy)))
# create an average activaion image
nroi.make_feature('activation', dataset.ravel())
mean_activation = nroi.representative_feature('activation')
bmap = - np.ones((dimx, dimy))
for k in range(nroi.k):
bmap[label == k] = mean_activation[k]
#--------------------------------------------------------
# Result display
#--------------------------------------------------------
def make_bsa_2d(betas, theta=3., dmax=5., ths=0, thq=0.5, smin=0,
method='simple', verbose=0):
"""
Function for performing bayesian structural analysis
on a set of images.
Parameters
----------
betas, array of shape (nsubj, dimx, dimy) the data used
Note that it is assumed to be a t- or z-variate
theta=3., float,
first level threshold of betas
dmax=5., float, expected between subject variability
ths=0, float,
null hypothesis for the prevalence statistic
thq=0.5, float,
p-value of the null rejection
smin=0, int,
threshold on the nu_mber of contiguous voxels
to make regions meaningful structures
method= 'simple', string,
estimation method used ; to be chosen among
'simple', 'quick', 'loo', 'ipmi'
verbose=0, verbosity mode
Returns
-------
AF the landmark_regions instance describing the result
BF: list of hroi instances describing the individual data
"""
ref_dim = np.shape(betas[0])
nsubj = betas.shape[0]
xyz = np.array(np.where(betas[:1])).T.astype(np.int)
# Get coordinates in mm
xyz = xyz[:, 1:] # switch to dimension 2
coord = xyz.astype(np.float)
# get the functional information
lbeta = np.array([np.ravel(betas[k]) for k in range(nsubj)]).T
lmax = 0
bdensity = 1
dom = domain_from_array(np.ones(ref_dim))
if method == 'simple':
group_map, AF, BF, likelihood = \
bsa.compute_BSA_simple(dom, lbeta, dmax, thq, smin, ths,
theta)
if method == 'quick':
likelihood = np.zeros(ref_dim)
group_map, AF, BF, coclustering = \
bsa.compute_BSA_quick(dom, lbeta, dmax, thq, smin, ths,
theta)
if method == 'loo':
mll, ll0 = bsa.compute_BSA_loo(dom, lbeta, dmax, thq, smin, ths,
theta, bdensity)
return mll, ll0
if method not in['loo', 'simple', 'quick']:
raise ValueError('method is not correctly defined')
if verbose == 0:
return AF, BF
if AF != None:
lmax = AF.k + 2
AF.show()
group_map.shape = ref_dim
mp.figure(figsize=(8, 3))
ax = mp.subplot(1, 3, 1)
mp.imshow(group_map, interpolation='nearest', vmin=-1, vmax=lmax)
mp.title('Blob separation map', fontsize=10)
mp.axis('off')
plop = mp.colorbar(shrink=.8)
if AF != None:
group_map = AF.map_label(coord, 0.95, dmax)
group_map.shape = ref_dim
mp.subplot(1, 3, 2)
mp.imshow(group_map, interpolation='nearest', vmin=-1, vmax=lmax)
mp.title('group-level position 95% \n confidence regions', fontsize=10)
mp.axis('off')
mp.colorbar(shrink=.8)
mp.subplot(1, 3, 3)
likelihood.shape = ref_dim
mp.imshow(likelihood, interpolation='nearest')
mp.title('Spatial density under h1', fontsize=10)
mp.axis('off')
mp.colorbar(shrink=.8)
fig_output = mp.figure(figsize=(8, 3.5))
fig_output.text(.5, .9, "Individual landmark regions", ha="center")
for s in range(nsubj):
ax = mp.subplot(nsubj / 5, 5, s + 1)
#ax.set_position([.02, .02, .96, .96])
lw = - np.ones(ref_dim)
if BF[s] is not None:
nls = BF[s].get_roi_feature('label')
nls[nls == - 1] = np.size(AF) + 2
for k in range(BF[s].k):
np.ravel(lw)[BF[s].label == k] = nls[k]
mp.imshow(lw, interpolation='nearest', vmin=-1, vmax=lmax)
mp.axis('off')
fig_input = mp.figure(figsize=(8, 3.5))
fig_input.text(.5,.9, "Input activation maps", ha='center')
for s in range(nsubj):
ax = mp.subplot(nsubj / 5, 5, s + 1)
#ax.set_position([.02, .02, .96, .96])
mp.imshow(betas[s], interpolation='nearest', vmin=betas.min(),
vmax=betas.max())
mp.axis('off')
return AF, BF
