labs.spatial_models.mroi

Module: labs.spatial_models.mroi

Inheritance diagram for nipy.labs.spatial_models.mroi:

Class

SubDomains

class nipy.labs.spatial_models.mroi.SubDomains(domain, label, id=None)

Bases: object

This is a class to represent multiple ROI objects, where the reference to a given domain is explicit.

A multiple ROI object is a set of ROI defined on a given domain, each having its own ‘region-level’ characteristics (ROI features).

Every voxel of the domain can have its own characteristics yet, defined at the ‘voxel-level’, but those features can only be accessed familywise (i.e. the values are grouped by ROI).

Parameters:

kint

Number of ROI in the SubDomains object

labelarray of shape (domain.size), dtype=np.int_

An array use to define which voxel belongs to which ROI. The label values greater than -1 correspond to subregions labelling. The labels are recomputed so as to be consecutive integers. The labels should not be accessed outside this class. One has to use the API mapping methods instead.

featuresdict {str: list of object, length=self.k}

Describe the voxels features, grouped by ROI

roi_featuresdict {str: array-like, shape=(self.k, roi_feature_dim)

Describe the ROI features. A special feature, id, is read-only and is used to give an unique identifier for region, which is persistent through the MROI objects manipulations. On should access the different ROI’s features using ids.

__init__(domain, label, id=None)

Initialize subdomains instance

Parameters:

domain: ROI instance

defines the spatial context of the SubDomains

label: array of shape (domain.size), dtype=np.int_,

An array use to define which voxel belongs to which ROI. The label values greater than -1 correspond to subregions labelling. The labels are recomputed so as to be consecutive integers. The labels should not be accessed outside this class. One has to use the select_id() mapping method instead.

id: array of shape (n_roi)

Define the ROI identifiers. Once an id has been associated to a ROI it becomes impossible to change it using the API. Hence, one should access ROI through their id to avoid hazardous manipulations.

copy()

Returns a copy of self.

Note that self.domain is not copied.

feature_to_voxel_map(fid, roi=False, method='mean')

Convert a feature to a flat voxel-mapping array.

Parameters:

fid: str

Identifier of the feature to be mapped.

roi: bool, optional

If True, compute the map from a ROI feature.

method: str, optional

Representative feature computation method if fid is a feature and roi is True.

Returns:

res: array-like, shape=(domain.size, feature_dim)

A flat array, giving the correspondence between voxels and the feature.

get_coord(id=None)

Get coordinates of ROI’s voxels

Parameters:

id: any hashable type

Id of the ROI from which we want the voxels’ coordinates. Can be None (default) if we want all ROIs’s voxels coordinates.

Returns:

coords: array-like, shape=(roi_size, domain_dimension)

if an id is provided,

or list of arrays of shape(roi_size, domain_dimension)

if no id provided (default)

get_feature(fid, id=None)

Return a voxel-wise feature, grouped by ROI.

Parameters:

fid: str,

Feature to be returned

id: any hashable type

Id of the ROI from which we want to get the feature. Can be None (default) if we want all ROIs’s features.

Returns:

feature: array-like, shape=(roi_size, feature_dim)

if an id is provided,

or list of arrays, shape=(roi_size, feature_dim)

if no id provided (default)

get_id()

Return ROI’s id list.

Users must access ROIs with the use of the identifiers of this list and the methods that give access to their properties/features.

get_local_volume(id=None)

Get volume of ROI’s voxels

Parameters:

id: any hashable type

Id of the ROI from which we want the voxels’ volumes. Can be None (default) if we want all ROIs’s voxels volumes.

Returns:

loc_volume: array-like, shape=(roi_size, ),

if an id is provided,

or list of arrays of shape(roi_size, )

if no id provided (default)

get_roi_feature(fid, id=None)

get_size(id=None)

Get ROI size (counted in terms of voxels)

Parameters:

id: any hashable type

Id of the ROI from which we want to get the size. Can be None (default) if we want all ROIs’s sizes.

Returns:

size: int

if an id is provided,

or list of int

if no id provided (default)

get_volume(id=None)

Get ROI volume

Parameters:

id: any hashable type

Id of the ROI from which we want to get the volume. Can be None (default) if we want all ROIs’s volumes.

Returns:

volumefloat

if an id is provided,

or list of float

if no id provided (default)

integrate(fid=None, id=None)

Integrate certain feature on each ROI and return the k results

Parameters:

fidstr

Feature identifier. By default, the 1 function is integrated, yielding ROI volumes.

id: any hashable type

The ROI on which we want to integrate. Can be None if we want the results for every region.

Returns:

lsum = array of shape (self.k, self.feature[fid].shape[1]),

The results

plot_feature(fid, ax=None)

Boxplot the distribution of features within ROIs. Note that this assumes 1-d features.

Parameters:

fid: string

the feature identifier

ax: axis handle, optional

recompute_labels()

Redefine labels so that they are consecutive integers.

Labels are used as a map to associate voxels to a given ROI. It is an inner object that should not be accessed outside this class. The number of nodes is updated appropriately.

Notes

This method must be called every time the MROI structure is modified.

remove_feature(fid)

Remove a certain feature

Parameters:

fid: str

Feature id

Returns:

fobject

The removed feature.

remove_roi_feature(fid)

Remove a certain ROI feature.

The id ROI feature cannot be removed.

Returns:

fobject

The removed Roi feature.

representative_feature(fid, method='mean', id=None, assess_quality=False)

Compute a ROI representative of a given feature.

Parameters:

fidstr

Feature id

methodstr, optional

Method used to compute a representative. Chosen among ‘mean’ (default), ‘max’, ‘median’, ‘min’, ‘weighted mean’.

idany hashable type, optional

Id of the ROI from which we want to extract a representative feature. Can be None (default) if we want to get all ROIs’s representatives.

assess_quality: bool, optional

If True, a new roi feature is created, which represent the quality of the feature representative (the number of non-nan value for the feature over the ROI size). Default is False.

Returns:

summary_feature: np.ndarray, shape=(self.k, feature_dim)

Representative feature computed according to method.

select_id(id, roi=True)

Convert a ROI id into an index to be used to index features safely.

Parameters:

idany hashable type, must be in self.get_id()

The id of the region one wants to access.

roibool

If True (default), return the ROI index in the ROI list. If False, return the indices of the voxels of the ROI with the given id. That way, internal access to self.label can be made.

Returns:

indexint or np.array of shape (roi.size, )

Either the position of the ROI in the ROI list (if roi == True), or the positions of the voxels of the ROI with id id with respect to the self.label array.

select_roi(id_list)

Returns an instance of MROI with only the subset of chosen ROIs.

Parameters:

id_list: list of id (any hashable type)

The id of the ROI to be kept in the structure.

set_feature(fid, data, id=None, override=False)

Append or modify a feature

Parameters:

fidstr

feature identifier

data: list or array

The feature data. Can be a list of self.k arrays of shape(self.size[k], p) or array of shape(self.size[k])

id: any hashable type, optional

Id of the ROI from which we want to set the feature. Can be None (default) if we want to set all ROIs’s features.

override: bool, optional

Allow feature overriding

Note that we cannot create a feature having the same name than

a ROI feature.

set_roi_feature(fid, data, id=None, override=False)

Append or modify a ROI feature

Parameters:

fid: str,

feature identifier

data: list of self.k features or a single feature

The ROI feature data

id: any hashable type

Id of the ROI of which we want to set the ROI feature. Can be None (default) if we want to set all ROIs’s ROI features.

override: bool, optional,

Allow feature overriding

Note that we cannot create a ROI feature having the same name than

a feature.

Note that the `id` feature cannot be modified as an internal

component.

to_image(fid=None, roi=False, method='mean', descrip=None)

Generates a label image that represents self.

Parameters:

fid: str,

Feature to be represented. If None, a binary image of the MROI domain will be we created.

roi: bool,

Whether or not to write the desired feature as a ROI one. (i.e. a ROI feature corresponding to fid will be looked upon, and if not found, a representative feature will be computed from the fid feature).

method: str,

If a feature is written as a ROI feature, this keyword tweaks the way the representative feature is computed.

descrip: str,

Description of the image, to be written in its header.

Returns:

nimnibabel nifti image

Nifti image corresponding to the ROI feature to be written.

Notes

Requires that self.dom is an ddom.NDGridDomain

Functions

nipy.labs.spatial_models.mroi.subdomain_from_array(labels, affine=None, nn=0)

Return a SubDomain from an n-d int array

Parameters:

label: np.array instance

A supposedly boolean array that yields the regions.

affine: np.array, optional

Affine transform that maps the array coordinates to some embedding space by default, this is np.eye(dim+1, dim+1).

nn: int,

Neighboring system considered. Unused at the moment.

Notes

Only labels > -1 are considered.

nipy.labs.spatial_models.mroi.subdomain_from_balls(domain, positions, radii)

Create discrete ROIs as a set of balls within a certain coordinate systems.

Parameters:

domain: StructuredDomain instance,

the description of a discrete domain

positions: array of shape(k, dim):

the positions of the balls

radii: array of shape(k):

the sphere radii

nipy.labs.spatial_models.mroi.subdomain_from_image(mim, nn=18)

Return a SubDomain instance from the input mask image.

Parameters:

mim: NiftiIImage instance, or string path toward such an image

supposedly a label image

nn: int, optional

Neighboring system considered from the image can be 6, 18 or 26.

Returns:

The MultipleROI instance

Notes

Only labels > -1 are considered

nipy.labs.spatial_models.mroi.subdomain_from_position_and_image(nim, pos)

Keep the set of labels of the image corresponding to a certain index so that their position is closest to the prescribed one.

Parameters:

mim: NiftiIImage instance, or string path toward such an image

supposedly a label image

pos: array of shape(3) or list of length 3,

the prescribed position