neurospin.spatial_models.hierarchical_parcellation

Module: neurospin.spatial_models.hierarchical_parcellation

Functions

nipy.neurospin.spatial_models.hierarchical_parcellation.hparcel(Pa, ldata, anat_coord, nbperm=0, niter=5, mu=10.0, dmax=10.0, lamb=100.0, chunksize=100000.0, verbose=0)

Function that performs the parcellation by optimizing the sinter-subject similarity while retaining the connectedness within subject and some consistency across subjects.

Parameters:

Pa: a Parcel structure that essentially contains :

the grid position information and the individual masks anat_coord: array of shape(nbvox,3) which defines the position

of the grid points in some space

nbperm=0: the number of times the parcellation and prfx

computation is performed on sign-swaped data

niter=10: number of iterations to obtain the convergence of the method

information in the clustering algorithm

mu=10., float, relative weight of anatomical information :

nipy.neurospin.spatial_models.hierarchical_parcellation.optim_hparcel(Ranat, RFeature, Feature, Pa, Gs, anat_coord, lamb=1.0, dmax=10.0, chunksize=100000.0, niter=5, verbose=0)

Core function of the heirrachical parcellation procedure.

Parameters:

Ranat: array of shape (n,3): set of positions sampled form the data :

RFeature: array of shape (n,f): assocaited feature Feature: list of subject-related feature arrays Pa : parcellation instance that is updated Gs: graph that represents the topology of the parcellation anat_coord: arrao of shape (nvox,3) space defining set of coordinates lamb=1.0: parameter to weight position

and feature impact on the algorithm

dmax = 10: locality parameter (in the space of anat_coord)

to limit surch volume (CPU save)

chunksize=1.e5 not used here (to be removed) niter = 5: number of iterations in teh algorithm verbose=0: verbosity level

Returns:

U: list of arrays of length nsubj :

subject-dependent parcellations

Proto_anat: array of shape (nvox) labelling of the common space

(template parcellation)

nipy.neurospin.spatial_models.hierarchical_parcellation.perm_prfx(Pa, Gs, F0, ldata, anat_coord, nbperm=100, niter=5, dmax=10.0, lamb=100.0, chunksize=100000.0)

caveat: assumes that the functional dimension is 1