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, 2006-2013
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.
Building the landmark_region
Parameters: | domain: ROI instance :
k: int, :
indiv_coord: k-length list of arrays, :
subjects: k-length list of integers :
confidence: k-length list of arrays, :
|
---|
returns the average of the coordinates for each region
Compute the density of a component as a kde
Parameters: | k: int (<= self.k) or None :
coord: array of shape(n, self.dom.em_dim), optional :
sigma: float, optional :
|
---|---|
Returns: | kde: array of shape(n) :
|
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 :
sigma: float, positive, optional :
|
---|---|
Returns: | label: array of shape (n): the posterior labelling : |
Return a confidence index over the different rois
Returns: | confid: array of shape self.k :
|
---|
function to print basic information on self
Given a list of hierarchical ROIs, and an associated labelling, this creates an Amer structure wuch groups ROIs with the same label.
Parameters: | domain: discrete_domain.DiscreteDomain instance, :
coords: array of shape(n, 3) :
subjects: array of shape (n), dtype = np.int :
labels: array of shape (n), dtype = np.int :
confidence: array of shape (n), :
prevalence_pval: float, optional : prevalence_threshold: float, optional, :
sigma: float optional, :
|
---|---|
Returns: | LR : None or structural_bfls.LR instance
newlabel: array of shape (n) :
|