def test_sd_from_ball():
dom = dd.domain_from_array(np.ones((10, 10)))
radii = np.array([2, 2, 2])
positions = np.array([[3, 3], [3, 7], [7, 7]])
subdomain = subdomain_from_balls(dom, positions, radii)
assert subdomain.k == 3
assert (subdomain.size == np.array([9, 9, 9])).all()
def make_bsa_2d(betas, theta=3., dmax=5., ths=0, thq=0.5, smin=0,
nbeta=[0], method='simple'):
"""
Function for performing bayesian structural analysis on a set of images.
Fixme: 'quick' is not tested
"""
ref_dim = np.shape(betas[0])
nbsubj = betas.shape[0]
xyz = np.array(np.where(betas[:1])).T
nvox = np.size(xyz, 0)
# create the field strcture that encodes image topology
Fbeta = ff.Field(nvox)
Fbeta.from_3d_grid(xyz.astype(np.int), 18)
# Get coordinates in mm
xy = xyz[:, 1:]
coord = xy.astype(np.float)
# get the functional information
lbeta = np.array([np.ravel(betas[k]) for k in range(nbsubj)]).T
# the voxel volume is 1.0
g0 = 1.0/(1.0*nvox)
bdensity = 1
affine = np.eye(3)
shape = (ref_dim[0], ref_dim[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, g0, bdensity)
if method=='ipmi':
group_map, AF, BF, likelihood = \
bsa.compute_BSA_ipmi(dom, lbeta, dmax, thq, smin, ths,
theta, g0, bdensity)
if method=='sbf':
pval = 0.2
group_map, AF, BF = sbf.Compute_Amers (
dom, lbeta, dmax, theta, ths, pval)
return AF, BF
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)
nvox = np.size(xyz, 0)
# create the field strcture that encodes image topology
Fbeta = ff.Field(nvox)
Fbeta.from_3d_grid(xyz, 18)
# 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
# the voxel volume is 1.0
g0 = 1.0/(1.0*nvox)
affine = np.eye(3)
shape = (ref_dim[0], ref_dim[1])
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=='ipmi':
group_map, AF, BF, likelihood = \
bsa.compute_BSA_ipmi(dom, lbeta, dmax, thq, smin, ths,
theta, bdensity)
if method=='loo':
mll, ll0 = bsa.compute_BSA_loo(dom, lbeta, dmax, thq, smin, ths,
theta, bdensity)
return mll, ll0
if method=='dev':
group_map, AF, BF, likelihood = \
bsa.compute_BSA_ipmi(dom, lbeta, dmax, thq, smin, ths,
theta, bdensity, 'gauss_mixture')
if method=='sbf':
likelihood = np.zeros(ref_dim)
group_map, AF, BF = sbf.Compute_Amers (
dom, lbeta, dmax=dmax, thr=theta, ths=ths, pval=thq)
if method not in['loo', 'simple', 'ipmi', 'quick', 'sbf']:
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()
mp.subplot(1,3,1)
mp.imshow(group_map, interpolation='nearest', vmin=-1, vmax=lmax)
mp.title('Blob separation map')
mp.colorbar()
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')
mp.colorbar()
mp.subplot(1,3,3)
likelihood.shape = ref_dim
mp.imshow(likelihood, interpolation='nearest')
mp.title('Spatial density under h1')
mp.colorbar()
mp.figure()
if nsubj==10:
for s in range(nsubj):
mp.subplot(2, 5, s+1)
lw = -np.ones(ref_dim)
if BF[s]!=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')
mp.figure()
if nsubj==10:
for s in range(nsubj):
mp.subplot(2, 5, s+1)
mp.imshow(betas[s], interpolation='nearest', vmin=betas.min(),
vmax=betas.max())
mp.axis('off')
return AF, BF
def make_domain():
"""Create a mulmtiple ROI instance
"""
labels = np.ones(shape)
dom = domain_from_array(labels, affine=None)
return dom
import nipy.neurospin.spatial_models.hroi as hroi
import nipy.neurospin.utils.simul_multisubject_fmri_dataset as simul
from nipy.neurospin.spatial_models.discrete_domain import domain_from_array
##############################################################################
# simulate the data
dimx = 60
dimy = 60
pos = np.array([[12, 14], [20, 20], [30, 20]])
ampli = np.array([3,4,4])
dataset = simul.surrogate_2d_dataset(nbsubj=1, dimx=dimx, dimy=dimy, pos=pos,
ampli=ampli, width=10.0).squeeze()
# 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)
n1 = nroi.copy()
n2 = nroi.reduce_to_leaves()
td = n1.make_forest().depth_from_leaves()
root = np.argmax(td)
lv = n1.make_forest().rooted_subtree(root)
u = nroi.make_graph().cc()
nroi.make_feature('activation', dataset.ravel())
nroi.representative_feature('activation')
