Inheritance diagram for nipy.labs.spatial_models.structural_bfls:
The main routine of this module implement the LandmarkRegions class, that is used to represent Regions of interest at the population level (in a template space).
This has been used in Thirion et al. Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies. IEEE TMI 2007
Author : Bertrand Thirion, 2006-2010
Bases: object
This class is intended to represent a set of inter-subject regions It should inherit from some abstract multiple ROI class, not implemented yet.
Methods
centers() | returns the average of the coordinates for each region |
density(k[, coord, dmax, dof]) | Posterior density of component k |
get_feature(fid) | |
homogeneity() | returns the mean distance between points within each LR |
hpd(k[, coord, pval, dmax]) | Sample the posterior probability of being in k |
map_label([coord, pval, dmax]) | Sample the set of landmark regions |
prevalence_density() | Returns a weighted map of self.prevalence |
roi_confidence([ths, fid]) | assuming that a certain feature fid field has been set |
roi_prevalence([fid]) | assuming that fid=’confidence’ field has been set |
set_feature(fid, data) | |
show() | function to print basic information on self |
weighted_feature_density(feature) | Given a set of feature values, produce a weighted feature map, |
Building the landmark_region
Parameters: | domain: ROI instance :
k: int, the number of regions considered : indiv_coord: k-length list of arrays, optional, :
subj: k-length list of integers :
id: string, optional, identifier : |
---|
returns the average of the coordinates for each region
Posterior density of component k
Parameters: | k: int, less or equal to self.k :
coord: array of shape(n, self.dom.em_dim), optional :
dmax: float, optional :
dof: float, optional, :
|
---|---|
Returns: | pd: array of shape(n) :
delta: array of shape(n) :
|
returns the mean distance between points within each LR
Sample the posterior probability of being in k on a grid defined by cs, assuming that the roi is an ellipsoid
Parameters: | k: int, less or equal to self.k :
coord: array of shape(n,dim), optional :
pval: float<1, optional, :
dmax=1.0: an upper bound for the spatial variance :
|
---|---|
Returns: | hpd array of shape(n) that yields the value : |
Sample the set of landmark regions on the proposed coordiante set cs, assuming a Gaussian shape
Parameters: | coord: array of shape(n,dim), optional, :
pval: float in [0,1]), optional :
dmax: an upper bound for the spatial variance :
|
---|---|
Returns: | label: array of shape (n): the posterior labelling : |
Returns a weighted map of self.prevalence
Returns: | wp: array of shape(n_samples) : |
---|
assuming that a certain feature fid field has been set as a discrete feature, this creates an approximate p-value that states how confident one might that the LR is defined in at least ths individuals if conficence is not defined as a discrete_feature, it is assumed to be 1.
Parameters: | ths: integer that yields the representativity threshold : |
---|---|
Returns: | pvals: array of shape self.k :
|
assuming that fid=’confidence’ field has been set as a discrete feature, this creates the expectancy of the confidence measure i.e. expected numberof detection of the roi in the observed group
Returns: | confid: array of shape self.k :
|
---|
function to print basic information on self
Given a set of feature values, produce a weighted feature map, where roi-levle features are mapped smoothly based on the density of the components
Parameters: | feature: array of shape (self.k), :
|
---|---|
Returns: | wsm: array of shape(self.shape) : |
Given a list of hierarchical ROIs, and an associated labelling, this creates an Amer structure wuch groups ROIs with the same label.
Parameters: | bf : list of nipy.labs.spatial_models.hroi.Nroi instances
thq=0.95, ths=0 defines the condition (c): :
dmax: float optional, :
|
---|---|
Returns: | LR : None or structural_bfls.LR instance
newlabel: a relabelling of the individual ROIs, similar to u, :
|