Neuroimaging in Python — NiBabel 5.4.0.dev1+g3b1c7b37 documentation (original) (raw)

cifti2¶

CIFTI-2 format IO

Module: cifti2.cifti2¶

Read / write access to CIFTI-2 image format

Format of the NIFTI2 container format described here:

Definition of the CIFTI-2 header format and file extensions can be found at:

Module: cifti2.cifti2_axes¶

Defines Axis objects to create, read, and manipulate CIFTI-2 files

These axes provide an alternative interface to the information in the CIFTI-2 header. Each type of CIFTI-2 axes describing the rows/columns in a CIFTI-2 matrix is given a unique class:

• BrainModelAxis: each row/column is a voxel or vertex
• ParcelsAxis: each row/column is a group of voxels and/or vertices
• ScalarAxis: each row/column has a unique name (with optional meta-data)
• LabelAxis: each row/column has a unique name and label table (with optional meta-data)
• SeriesAxis: each row/column is a timepoint, which increases monotonically

All of these classes are derived from the Axis class.

After loading a CIFTI-2 file a tuple of axes describing the rows and columns can be obtained from the cifti2.Cifti2Header.get_axis() method on the header object (e.g. nibabel.load(<filename>).header.get_axis()). Inversely, a newcifti2.Cifti2Header object can be created from existing Axis objects using the cifti2.Cifti2Header.from_axes() factory method.

CIFTI-2 Axis objects of the same type can be concatenated using the ‘+’-operator. Numpy indexing also works on axes (except for SeriesAxis objects, which have to remain monotonically increasing or decreasing).

Creating new CIFTI-2 axes¶

New Axis objects can be constructed by providing a description for what is contained in each row/column of the described tensor. For each Axis sub-class this descriptor is:

• BrainModelAxis: a CIFTI-2 structure name and a voxel or vertex index
• ParcelsAxis: a name and a sequence of voxel and vertex indices
• ScalarAxis: a name and optionally a dict of meta-data
• LabelAxis: a name, dict of label index to name and colour, and optionally a dict of meta-data
• SeriesAxis: the time-point of each row/column is set by setting the start, stop, size, and unit of the time-series

Several helper functions exist to create new BrainModelAxis axes:

• BrainModelAxis.from_mask() creates a new BrainModelAxis volume covering the non-zero values of a mask
• BrainModelAxis.from_surface() creates a new BrainModelAxis surface covering the provided indices of a surface

A ParcelsAxis axis can be created from a sequence of BrainModelAxis axes usingParcelsAxis.from_brain_models().

Examples¶

We can create brain models covering the left cortex and left thalamus using:

from nibabel import cifti2 import numpy as np bm_cortex = cifti2.BrainModelAxis.from_mask([True, False, True, True], ... name='cortex_left') bm_thal = cifti2.BrainModelAxis.from_mask(np.ones((2, 2, 2)), affine=np.eye(4), ... name='thalamus_left')

In this very simple case bm_cortex describes a left cortical surface skipping the second out of four vertices. bm_thal contains all voxels in a 2x2x2 volume.

Brain structure names automatically get converted to valid CIFTI-2 identifiers usingBrainModelAxis.to_cifti_brain_structure_name(). A 1-dimensional mask will be automatically interpreted as a surface element and a 3-dimensional mask as a volume element.

These can be concatenated in a single brain model covering the left cortex and thalamus by simply adding them together

bm_full = bm_cortex + bm_thal

Brain models covering the full HCP grayordinate space can be constructed by adding all the volumetric and surface brain models together like this (or by reading one from an already existing HCP file).

Getting a specific brain region from the full brain model is as simple as:

assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_CORTEX_LEFT'] == bm_cortex assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_THALAMUS_LEFT'] == bm_thal

You can also iterate over all brain structures in a brain model:

for idx, (name, slc, bm) in enumerate(bm_full.iter_structures()): ... print((str(name), slc)) ... assert bm == bm_full[slc] ... assert bm == bm_cortex if idx == 0 else bm_thal ('CIFTI_STRUCTURE_CORTEX_LEFT', slice(0, 3, None)) ('CIFTI_STRUCTURE_THALAMUS_LEFT', slice(3, None, None))

In this case there will be two iterations, namely: (‘CIFTI_STRUCTURE_CORTEX_LEFT’, slice(0, ), bm_cortex) and (‘CIFTI_STRUCTURE_THALAMUS_LEFT’, slice(, None), bm_thal)

ParcelsAxis can be constructed from selections of these brain models:

parcel = cifti2.ParcelsAxis.from_brain_models([ ... ('surface_parcel', bm_cortex[:2]), # contains first 2 cortical vertices ... ('volume_parcel', bm_thal), # contains thalamus ... ('combined_parcel', bm_full[[1, 8, 10]]), # contains selected voxels/vertices ... ])

Time series are represented by their starting time (typically 0), step size (i.e. sampling time or TR), and number of elements:

series = cifti2.SeriesAxis(start=0, step=100, size=5000)

So a header for fMRI data with a TR of 100 ms covering the left cortex and thalamus with 5000 timepoints could be created with

type(cifti2.Cifti2Header.from_axes((series, bm_cortex + bm_thal))) <class 'nibabel.cifti2.cifti2.Cifti2Header'>

Similarly the curvature and cortical thickness on the left cortex could be stored using a header like:

type(cifti2.Cifti2Header.from_axes((cifti2.ScalarAxis(['curvature', 'thickness']), ... bm_cortex))) <class 'nibabel.cifti2.cifti2.Cifti2Header'>

Module: cifti2.parse_cifti2¶

Cifti2BrainModel¶

class nibabel.cifti2.cifti2.Cifti2BrainModel(index_offset=None, index_count=None, model_type=None, brain_structure=None, n_surface_vertices=None, voxel_indices_ijk=None, vertex_indices=None)¶

Bases: XmlSerializable

Element representing a mapping of the dimension to vertex or voxels.

Mapping to vertices of voxels must be specified.

• Description - Maps a range of indices to surface vertices or voxels when IndicesMapToDataType is “CIFTI_INDEX_TYPE_BRAIN_MODELS.”
• Attributes

  • IndexOffset - The matrix index of the first brainordinate of this BrainModel. Note that matrix indices are zero-based.
  • IndexCount - Number of surface vertices or voxels in this brain model, must be positive.
  • ModelType - Type of model representing the brain structure (surface or voxels). Valid values are listed in the table below.
  • BrainStructure - Identifies the brain structure. Valid values for BrainStructure are listed in the table below. However, if the needed structure is not listed in the table, a message should be posted to the CIFTI Forum so that a standardized name can be created for the structure and added to the table.
  • SurfaceNumberOfVertices - When ModelType is CIFTI_MODEL_TYPE_SURFACE this attribute contains the actual (or true) number of vertices in the surface that is associated with this BrainModel. When this BrainModel represents all vertices in the surface, this value is the same as IndexCount. When this BrainModel represents only a subset of the surface’s vertices, IndexCount will be less than this value.

• Child Elements

  • VertexIndices (0…1)
  • VoxelIndicesIJK (0…1)

• Text Content: [NA]
• Parent Element - MatrixIndicesMap

For ModelType values, see CIFTI_MODEL_TYPES module attribute.

For BrainStructure values, see CIFTI_BRAIN_STRUCTURES model attribute.

Attributes:

index_offsetint

Start of the mapping

index_countint

Number of elements in the array to be mapped

model_typestr

One of CIFTI_MODEL_TYPES

brain_structurestr

One of CIFTI_BRAIN_STRUCTURES

surface_number_of_verticesint

Number of vertices in the surface. Use only for surface-type structure

voxel_indices_ijkCifti2VoxelIndicesIJK, optional

Indices on the image towards where the array indices are mapped

vertex_indicesCifti2VertexIndices, optional

Indices of the vertices towards where the array indices are mapped

__init__(index_offset=None, index_count=None, model_type=None, brain_structure=None, n_surface_vertices=None, voxel_indices_ijk=None, vertex_indices=None)¶

property vertex_indices¶

property voxel_indices_ijk¶

Cifti2Image¶

class nibabel.cifti2.cifti2.Cifti2Image(dataobj=None, header=None, nifti_header=None, extra=None, file_map=None, dtype=None)¶

Bases: DataobjImage, SerializableImage

Class for single file CIFTI-2 format image

Initialize image

The image is a combination of (dataobj, header), with optional metadata in nifti_header (a NIfTI2 header). There may be more metadata in the mapping extra. Filename / file-like objects can also go in thefile_map mapping.

Parameters:

dataobjobject

Object containing image data. It should be some object that returns an array from np.asanyarray. It should have ashape attribute or property.

headerCifti2Header instance or sequence of cifti2_axes.Axis

Header with data for / from XML part of CIFTI-2 format. Alternatively a sequence of cifti2_axes.Axis objects can be provided describing each dimension of the array.

nifti_headerNone or mapping or NIfTI2 header instance, optional

Metadata for NIfTI2 component of this format.

extraNone or mapping

Extra metadata not captured by header or nifti_header.

file_mapmapping, optional

Mapping giving file information for this image format.

__init__(dataobj=None, header=None, nifti_header=None, extra=None, file_map=None, dtype=None)¶

Initialize image

The image is a combination of (dataobj, header), with optional metadata in nifti_header (a NIfTI2 header). There may be more metadata in the mapping extra. Filename / file-like objects can also go in thefile_map mapping.

Parameters:

dataobjobject

Object containing image data. It should be some object that returns an array from np.asanyarray. It should have ashape attribute or property.

headerCifti2Header instance or sequence of cifti2_axes.Axis

Header with data for / from XML part of CIFTI-2 format. Alternatively a sequence of cifti2_axes.Axis objects can be provided describing each dimension of the array.

nifti_headerNone or mapping or NIfTI2 header instance, optional

Metadata for NIfTI2 component of this format.

extraNone or mapping

Extra metadata not captured by header or nifti_header.

file_mapmapping, optional

Mapping giving file information for this image format.

files_types_: tuple[ExtensionSpec, ...]_ = (('image', '.nii'),)¶

classmethod from_file_map(file_map, *, mmap=True, keep_file_open=None)¶

Load a CIFTI-2 image from a file_map

Parameters:

file_mapfile_map

Returns:

imgCifti2Image

Returns a Cifti2Image

classmethod from_image(img)¶

Class method to create new instance of own class from img

Parameters:

imginstance

In fact, an object with the API of DataobjImage.

Returns:

cimginstance

Image, of our own class

get_data_dtype()¶

alias of Cifti2Header

makeable_: bool_ = False¶

rw_: bool_ = True¶

set_data_dtype(dtype)¶

to_file_map(file_map=None, dtype=None)¶

Write image to file_map or contained self.file_map

Parameters:

file_mapNone or mapping, optional

files mapping. If None (default) use object’s file_mapattribute instead.

Returns:

None

Harmonize NIfTI headers with image data

Ensures that the NIfTI-2 header records the data shape in the last threedim fields. Per the spec:

Because the first four dimensions in NIfTI are reserved for space and time, the CIFTI dimensions are stored in the NIfTI header in dim[5] and up, where dim[5] is the length of the first CIFTI dimension (number of values in a row), dim[6] is the length of the second CIFTI dimension, and dim[7] is the length of the third CIFTI dimension, if applicable. The fields dim[1] through dim[4] will be 1; dim[0] will be 6 or 7, depending on whether a third matrix dimension exists.

import numpy as np data = np.zeros((2,3,4)) img = Cifti2Image(data)
img.shape == (2, 3, 4) True img.update_headers() img.nifti_header.get_data_shape() == (1, 1, 1, 1, 2, 3, 4) True img.shape == (2, 3, 4) True

valid_exts_: tuple[str, ...]_ = ('.nii',)¶

Cifti2Label¶

class nibabel.cifti2.cifti2.Cifti2Label(key=0, label='', red=0.0, green=0.0, blue=0.0, alpha=0.0)¶

Bases: XmlSerializable

CIFTI-2 label: association of integer key with a name and RGBA values

For all color components, value is floating point with range 0.0 to 1.0.

• Description - Associates a label key value with a name and a display color.
• Attributes

  • Key - Integer, data value which is assigned this name and color.
  • Red - Red color component for label. Value is floating point with range 0.0 to 1.0.
  • Green - Green color component for label. Value is floating point with range 0.0 to 1.0.
  • Blue - Blue color component for label. Value is floating point with range 0.0 to 1.0.
  • Alpha - Alpha color component for label. Value is floating point with range 0.0 to 1.0.

• Child Elements: [NA]
• Text Content - Name of the label.
• Parent Element - LabelTable

Attributes:

keyint, optional

Integer, data value which is assigned this name and color.

labelstr, optional

Name of the label.

redfloat, optional

Red color component for label (between 0 and 1).

greenfloat, optional

Green color component for label (between 0 and 1).

bluefloat, optional

Blue color component for label (between 0 and 1).

alphafloat, optional

Alpha color component for label (between 0 and 1).

__init__(key=0, label='', red=0.0, green=0.0, blue=0.0, alpha=0.0)¶

property rgba¶

Returns RGBA as tuple

Cifti2LabelTable¶

class nibabel.cifti2.cifti2.Cifti2LabelTable¶

Bases: XmlSerializable, MutableMapping

CIFTI-2 label table: a sequence of Cifti2Labels

• Description - Used by NamedMap when IndicesMapToDataType is “CIFTI_INDEX_TYPE_LABELS” in order to associate names and display colors with label keys. Note that LABELS is the only mapping type that uses a LabelTable. Display coloring of continuous-valued data is not specified by CIFTI-2.
• Attributes: [NA]
• Child Elements

  • Label (0…N)

• Text Content: [NA]
• Parent Element - NamedMap

__init__()¶

append(label)¶

Cifti2Matrix¶

class nibabel.cifti2.cifti2.Cifti2Matrix¶

Bases: XmlSerializable, MutableSequence

CIFTI-2 Matrix object

This is a list-like container where the elements are instances ofCifti2MatrixIndicesMap.

• Description: contains child elements that describe the meaning of the values in the matrix.
• Attributes: [NA]
• Child Elements

  • MetaData (0 .. 1)
  • MatrixIndicesMap (1 .. N)

• Text Content: [NA]
• Parent Element: CIFTI

For each matrix (data) dimension, exactly one MatrixIndicesMap element must list it in the AppliesToMatrixDimension attribute.

__init__()¶

get_axis(index)¶

Generates the Cifti2 axis for a given dimension

Parameters:

indexint

Dimension for which we want to obtain the mapping.

Returns:

axiscifti2_axes.Axis

get_data_shape()¶

Returns data shape expected based on the CIFTI-2 header

Any dimensions omitted in the CIFTI-2 header will be given a default size of None.

get_index_map(index)¶

Cifti2 Mapping class for a given index

Parameters:

indexint

Index for which we want to obtain the mapping. Must be in the mapped_indices sequence.

Returns:

cifti2_mapCifti2MatrixIndicesMap

Returns the Cifti2MatrixIndicesMap corresponding to the given index.

insert(index, value)¶

S.insert(index, value) – insert value before index

property mapped_indices¶

List of matrix indices that are mapped

property metadata¶

Cifti2MatrixIndicesMap¶

class nibabel.cifti2.cifti2.Cifti2MatrixIndicesMap(applies_to_matrix_dimension, indices_map_to_data_type, number_of_series_points=None, series_exponent=None, series_start=None, series_step=None, series_unit=None, maps=[])¶

Bases: XmlSerializable, MutableSequence

Class for Matrix Indices Map

• Description - Provides a mapping between matrix indices and their interpretation.
• Attributes

  • AppliesToMatrixDimension - Lists the dimension(s) of the matrix to which this MatrixIndicesMap applies. The dimensions of the matrix start at zero (dimension 0 describes the indices along the first dimension, dimension 1 describes the indices along the second dimension, etc.). If this MatrixIndicesMap applies to more than one matrix dimension, the values are separated by a comma.
  • IndicesMapToDataType - Type of data to which the MatrixIndicesMap applies.
  • NumberOfSeriesPoints - Indicates how many samples there are in a series mapping type. For example, this could be the number of timepoints in a timeseries.
  • SeriesExponent - Integer, SeriesStart and SeriesStep must be multiplied by 10 raised to the power of the value of this attribute to give the actual values assigned to indices (e.g., if SeriesStart is “5” and SeriesExponent is “-3”, the value of the first series point is 0.005).
  • SeriesStart - Indicates what quantity should be assigned to the first series point.
  • SeriesStep - Indicates amount of change between each series point.
  • SeriesUnit - Indicates the unit of the result of multiplying SeriesStart and SeriesStep by 10 to the power of SeriesExponent.

• Child Elements

  • BrainModel (0…N)
  • NamedMap (0…N)
  • Parcel (0…N)
  • Surface (0…N)
  • Volume (0…1)

• Text Content: [NA]
• Parent Element - Matrix

Attributes:

applies_to_matrix_dimensionlist of ints

Dimensions of this matrix that follow this mapping

indices_map_to_data_typestr one of CIFTI_MAP_TYPES

Type of mapping to the matrix indices

number_of_series_pointsint, optional

If it is a series, number of points in the series

series_exponentint, optional

If it is a series the exponent of the increment

series_startfloat, optional

If it is a series, starting time

series_stepfloat, optional

If it is a series, step per element

series_unitstr, optional

If it is a series, units

__init__(applies_to_matrix_dimension, indices_map_to_data_type, number_of_series_points=None, series_exponent=None, series_start=None, series_step=None, series_unit=None, maps=[])¶

property brain_models¶

insert(index, value)¶

S.insert(index, value) – insert value before index

property named_maps¶

property parcels¶

property surfaces¶

property volume¶

Cifti2MetaData¶

class nibabel.cifti2.cifti2.Cifti2MetaData(*args, **kwargs)¶

Bases: CaretMetaData

A list of name-value pairs

• Description - Provides a simple method for user-supplied metadata that associates names with values.
• Attributes: [NA]
• Child Elements

  • MD (0…N)

• Text Content: [NA]
• Parent Elements - Matrix, NamedMap

MD elements are a single metadata entry consisting of a name and a value.

Attributes:

datalist of (name, value) tuples

__init__(*args, **kwargs)¶

property data¶

difference_update(metadata)¶

Remove metadata key-value pairs

Parameters:

metadatadict-like datatype

Returns:

None

Cifti2NamedMap¶

class nibabel.cifti2.cifti2.Cifti2NamedMap(map_name=None, metadata=None, label_table=None)¶

Bases: XmlSerializable

CIFTI-2 named map: association of name and optional data with a map index

Associates a name, optional metadata, and possibly a LabelTable with an index in a map.

• Description - Associates a name, optional metadata, and possibly a LabelTable with an index in a map.
• Attributes: [NA]
• Child Elements

  • MapName (1)
  • LabelTable (0…1)
  • MetaData (0…1)

• Text Content: [NA]
• Parent Element - MatrixIndicesMap

Attributes:

map_namestr

Name of map

metadataNone or Cifti2MetaData

Metadata associated with named map

label_tableNone or Cifti2LabelTable

Label table associated with named map

__init__(map_name=None, metadata=None, label_table=None)¶

property label_table¶

property metadata¶

Cifti2Parcel¶

class nibabel.cifti2.cifti2.Cifti2Parcel(name=None, voxel_indices_ijk=None, vertices=None)¶

Bases: XmlSerializable

CIFTI-2 parcel: association of a name with vertices and/or voxels

• Description - Associates a name, plus vertices and/or voxels, with an index.
• Attributes

  • Name - The name of the parcel

• Child Elements

  • Vertices (0…N)
  • VoxelIndicesIJK (0…1)

• Text Content: [NA]
• Parent Element - MatrixIndicesMap

Attributes:

namestr

Name of parcel

voxel_indices_ijkNone or Cifti2VoxelIndicesIJK

Voxel indices associated with parcel

verticeslist of Cifti2Vertices

Vertices associated with parcel

__init__(name=None, voxel_indices_ijk=None, vertices=None)¶

append_cifti_vertices(vertices)¶

Appends a Cifti2Vertices element to the Cifti2Parcel

Parameters:

verticesCifti2Vertices

pop_cifti2_vertices(ith)¶

Pops the ith vertices element from the Cifti2Parcel

property voxel_indices_ijk¶

Cifti2Surface¶

class nibabel.cifti2.cifti2.Cifti2Surface(brain_structure=None, surface_number_of_vertices=None)¶

Bases: XmlSerializable

Cifti surface: association of brain structure and number of vertices

• Description - Specifies the number of vertices for a surface, when IndicesMapToDataType is “CIFTI_INDEX_TYPE_PARCELS.” This is separate from the Parcel element because there can be multiple parcels on one surface, and one parcel may involve multiple surfaces.
• Attributes

  • BrainStructure - A string from the BrainStructure list to identify what surface structure this element refers to (usually left cortex, right cortex, or cerebellum).
  • SurfaceNumberOfVertices - The number of vertices that this structure’s surface contains.

• Child Elements: [NA]
• Text Content: [NA]
• Parent Element - MatrixIndicesMap

Attributes:

brain_structurestr

Name of brain structure

surface_number_of_verticesint

Number of vertices on surface

__init__(brain_structure=None, surface_number_of_vertices=None)¶

Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ¶

class nibabel.cifti2.cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(meter_exponent=None, matrix=None)¶

Bases: XmlSerializable

Matrix that translates voxel indices to spatial coordinates

• Description - Contains a matrix that translates Voxel IJK Indices to spatial XYZ coordinates (+X=>right, +Y=>anterior, +Z=> superior). The resulting coordinate is the center of the voxel.
• Attributes

  • MeterExponent - Integer, specifies that the coordinate result from the transformation matrix should be multiplied by 10 to this power to get the spatial coordinates in meters (e.g., if this is “-3”, then the transformation matrix is in millimeters).

• Child Elements: [NA]
• Text Content - Sixteen floating-point values, in row-major order, that form a 4x4 homogeneous transformation matrix.
• Parent Element - Volume

Attributes:

meter_exponentint

See attribute description above.

matrixarray-like shape (4, 4)

Affine transformation matrix from voxel indices to RAS space.

__init__(meter_exponent=None, matrix=None)¶

Cifti2VertexIndices¶

class nibabel.cifti2.cifti2.Cifti2VertexIndices(indices=None)¶

Bases: XmlSerializable, MutableSequence

CIFTI-2 vertex indices: vertex indices for an associated brain model

The vertex indices (which are independent for each surface, and zero-based) that are used in this brain model[.] The parent BrainModel’s index_count indicates the number of indices.

• Description - Contains a list of vertex indices for a BrainModel with ModelType equal to CIFTI_MODEL_TYPE_SURFACE.
• Attributes: [NA]
• Child Elements: [NA]
• Text Content - The vertex indices (which are independent for each surface, and zero-based) that are used in this brain model, with each index separated by a whitespace character. The parent BrainModel’s IndexCount attribute indicates the number of indices in this element’s content.
• Parent Element - BrainModel

__init__(indices=None)¶

insert(index, value)¶

S.insert(index, value) – insert value before index

Cifti2Vertices¶

class nibabel.cifti2.cifti2.Cifti2Vertices(brain_structure=None, vertices=None)¶

Bases: XmlSerializable, MutableSequence

CIFTI-2 vertices - association of brain structure and a list of vertices

• Description - Contains a BrainStructure type and a list of vertex indices within a Parcel.
• Attributes

  • BrainStructure - A string from the BrainStructure list to identify what surface this vertex list is from (usually left cortex, right cortex, or cerebellum).

• Child Elements: [NA]
• Text Content - Vertex indices (which are independent for each surface, and zero-based) separated by whitespace characters.
• Parent Element - Parcel

The class behaves like a list of Vertex indices (which are independent for each surface, and zero-based)

Attributes:

brain_structurestr

A string from the BrainStructure list to identify what surface this vertex list is from (usually left cortex, right cortex, or cerebellum).

__init__(brain_structure=None, vertices=None)¶

insert(index, value)¶

S.insert(index, value) – insert value before index

Cifti2Volume¶

class nibabel.cifti2.cifti2.Cifti2Volume(volume_dimensions=None, transform_matrix=None)¶

Bases: XmlSerializable

CIFTI-2 volume: information about a volume for mappings that use voxels

• Description - Provides information about the volume for any mappings that use voxels.
• Attributes

  • VolumeDimensions - Three integer values separated by commas, the lengths of the three volume file dimensions that are related to spatial coordinates, in number of voxels. Voxel indices (which are zero-based) that are used in the mapping that this element applies to must be within these dimensions.

• Child Elements

  • TransformationMatrixVoxelIndicesIJKtoXYZ (1)

• Text Content: [NA]
• Parent Element - MatrixIndicesMap

Attributes:

volume_dimensionsarray-like shape (3,)

See attribute description above.

transformation_matrix_voxel_indices_ijk_to_xyzCifti2TransformationMatrixVoxelIndicesIJKtoXYZ

Matrix that translates voxel indices to spatial coordinates

__init__(volume_dimensions=None, transform_matrix=None)¶

Cifti2VoxelIndicesIJK¶

class nibabel.cifti2.cifti2.Cifti2VoxelIndicesIJK(indices=None)¶

Bases: XmlSerializable, MutableSequence

CIFTI-2 VoxelIndicesIJK: Set of voxel indices contained in a structure

• Description - Identifies the voxels that model a brain structure, or participate in a parcel. Note that when this is a child of BrainModel, the IndexCount attribute of the BrainModel indicates the number of voxels contained in this element.
• Attributes: [NA]
• Child Elements: [NA]
• Text Content - IJK indices (which are zero-based) of each voxel in this brain model or parcel, with each index separated by a whitespace character. There are three indices per voxel. If the parent element is BrainModel, then the BrainModel element’s IndexCount attribute indicates the number of triplets (IJK indices) in this element’s content.
• Parent Elements - BrainModel, Parcel

Each element of this sequence is a triple of integers.

__init__(indices=None)¶

insert(index, value)¶

S.insert(index, value) – insert value before index

Axis¶

class nibabel.cifti2.cifti2_axes.Axis¶

Bases: ABC

Abstract class for any object describing the rows or columns of a CIFTI-2 vector/matrix

Mainly used for type checking.

Base class for the following concrete CIFTI-2 axes:

• BrainModelAxis: each row/column is a voxel or vertex
• ParcelsAxis: each row/column is a group of voxels and/or vertices
• ScalarAxis: each row/column has a unique name with optional meta-data
• LabelAxis: each row/column has a unique name and label table with optional meta-data
• SeriesAxis: each row/column is a timepoint, which increases monotonically

__init__(*args, **kwargs)¶

property size¶

BrainModelAxis¶

class nibabel.cifti2.cifti2_axes.BrainModelAxis(name, voxel=None, vertex=None, affine=None, volume_shape=None, nvertices=None)¶

Bases: Axis

Each row/column in the CIFTI-2 vector/matrix represents a single vertex or voxel

This Axis describes which vertex/voxel is represented by each row/column.

New BrainModelAxis axes can be constructed by passing on the greyordinate brain-structure names and voxel/vertex indices to the constructor or by one of the factory methods:

• from_mask(): creates surface or volumetric BrainModelAxis axis from respectively 1D or 3D masks
• from_surface(): creates a surface BrainModelAxis axis

The resulting BrainModelAxis axes can be concatenated by adding them together.

Parameters:

namearray_like

brain structure name or (N, ) string array with the brain structure names

voxelarray_like, optional

(N, 3) array with the voxel indices (can be omitted for CIFTI-2 files only covering the surface)

vertexarray_like, optional

(N, ) array with the vertex indices (can be omitted for volumetric CIFTI-2 files)

affinearray_like, optional

(4, 4) array mapping voxel indices to mm space (not needed for CIFTI-2 files only covering the surface)

volume_shapetuple of three integers, optional

shape of the volume in which the voxels were defined (not needed for CIFTI-2 files only covering the surface)

nverticesdict from string to integer, optional

maps names of surface elements to integers (not needed for volumetric CIFTI-2 files)

__init__(name, voxel=None, vertex=None, affine=None, volume_shape=None, nvertices=None)¶

New BrainModelAxis axes can be constructed by passing on the greyordinate brain-structure names and voxel/vertex indices to the constructor or by one of the factory methods:

• from_mask(): creates surface or volumetric BrainModelAxis axis from respectively 1D or 3D masks
• from_surface(): creates a surface BrainModelAxis axis

The resulting BrainModelAxis axes can be concatenated by adding them together.

Parameters:

namearray_like

brain structure name or (N, ) string array with the brain structure names

voxelarray_like, optional

(N, 3) array with the voxel indices (can be omitted for CIFTI-2 files only covering the surface)

vertexarray_like, optional

(N, ) array with the vertex indices (can be omitted for volumetric CIFTI-2 files)

affinearray_like, optional

(4, 4) array mapping voxel indices to mm space (not needed for CIFTI-2 files only covering the surface)

volume_shapetuple of three integers, optional

shape of the volume in which the voxels were defined (not needed for CIFTI-2 files only covering the surface)

nverticesdict from string to integer, optional

maps names of surface elements to integers (not needed for volumetric CIFTI-2 files)

property affine¶

Affine of the volumetric image in which the greyordinate voxels were defined

classmethod from_index_mapping(mim)¶

Creates a new BrainModel axis based on a CIFTI-2 dataset

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

BrainModelAxis

classmethod from_mask(mask, name='other', affine=None)¶

Creates a new BrainModelAxis axis describing the provided mask

Parameters:

maskarray_like

all non-zero voxels will be included in the BrainModelAxis axis should be (Nx, Ny, Nz) array for volume mask or (Nvertex, ) array for surface mask

namestr, optional

Name of the brain structure (e.g. ‘CortexRight’, ‘thalamus_left’ or ‘brain_stem’)

affinearray_like, optional

(4, 4) array with the voxel to mm transformation (defaults to identity matrix) Argument will be ignored for surface masks

Returns:

BrainModelAxis which covers the provided mask

classmethod from_surface(vertices, nvertex, name='Other')¶

Creates a new BrainModelAxis axis describing the vertices on a surface

Parameters:

verticesarray_like

indices of the vertices on the surface

nvertexint

total number of vertices on the surface

namestr

Name of the brain structure (e.g. ‘CortexLeft’ or ‘CortexRight’)

Returns:

BrainModelAxis which covers (part of) the surface

get_element(index)¶

Describes a single element from the axis

Parameters:

indexint

Indexes the row/column of interest

Returns:

tuple with 3 elements

• str, ‘CIFTI_MODEL_TYPE_SURFACE’ for vertex or ‘CIFTI_MODEL_TYPE_VOXELS’ for voxel
• vertex index if it is a surface element, otherwise array with 3 voxel indices
• structure.BrainStructure object describing the brain structure the element was taken from

iter_structures()¶

Iterates over all brain structures in the order that they appear along the axis

Yields:

tuple with 3 elements:

• CIFTI-2 brain structure name
• slice to select the data associated with the brain structure from the tensor
• brain model covering that specific brain structure

property name¶

The brain structure to which the voxel/vertices of belong

property surface_mask¶

(N, ) boolean array which is true for any element on the surface

static to_cifti_brain_structure_name(name)¶

Attempts to convert the name of an anatomical region in a format recognized by CIFTI-2

This function returns:

• the name if it is in the CIFTI-2 format already
• if the name is a tuple the first element is assumed to be the structure name while the second is assumed to be the hemisphere (left, right or both). The latter will default to both.
• names like left_cortex, cortex_left, LeftCortex, or CortexLeft will be converted to CIFTI_STRUCTURE_CORTEX_LEFT

see nibabel.cifti2.tests.test_name() for examples of which conversions are possible

Parameters:

name: iterable of 2-element tuples of integer and string

input name of an anatomical region

Returns:

CIFTI-2 compatible name

Raises:

ValueError: raised if the input name does not match a known anatomical structure in CIFTI-2

to_mapping(dim)¶

Converts the brain model axis to a MatrixIndicesMap for storage in CIFTI-2 format

Parameters:

dimint

which dimension of the CIFTI-2 vector/matrix is described by this dataset (zero-based)

Returns:

cifti2.Cifti2MatrixIndicesMap

property volume_mask¶

(N, ) boolean array which is true for any element on the surface

property volume_shape¶

Shape of the volumetric image in which the greyordinate voxels were defined

LabelAxis¶

class nibabel.cifti2.cifti2_axes.LabelAxis(name, label, meta=None)¶

Bases: Axis

Defines CIFTI-2 axis for label array.

Along this axis of the CIFTI-2 vector/matrix each row/column has been given a unique name, label table, and optionally metadata

Parameters:

namearray_like

(N, ) string array with the parcel names

labelarray_like

single dictionary or (N, ) object array with dictionaries mapping from integers to (name, (R, G, B, A)), where name is a string and R, G, B, and A are floats between 0 and 1 giving the colour and alpha (i.e., transparency)

metaarray_like, optional

(N, ) object array with a dictionary of metadata for each row/column

__init__(name, label, meta=None)¶

Parameters:

namearray_like

(N, ) string array with the parcel names

labelarray_like

single dictionary or (N, ) object array with dictionaries mapping from integers to (name, (R, G, B, A)), where name is a string and R, G, B, and A are floats between 0 and 1 giving the colour and alpha (i.e., transparency)

metaarray_like, optional

(N, ) object array with a dictionary of metadata for each row/column

classmethod from_index_mapping(mim)¶

Creates a new Label axis based on a CIFTI-2 dataset

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

LabelAxis

get_element(index)¶

Describes a single element from the axis

Parameters:

indexint

Indexes the row/column of interest

Returns:

tuple with 2 elements

• unicode name of the row/column
• dictionary with the label table
• dictionary with the element metadata

to_mapping(dim)¶

Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI-2 format

Parameters:

dimint

which dimension of the CIFTI-2 vector/matrix is described by this dataset (zero-based)

Returns:

cifti2.Cifti2MatrixIndicesMap

ParcelsAxis¶

class nibabel.cifti2.cifti2_axes.ParcelsAxis(name, voxels, vertices, affine=None, volume_shape=None, nvertices=None)¶

Bases: Axis

Each row/column in the CIFTI-2 vector/matrix represents a parcel of voxels/vertices

This Axis describes which parcel is represented by each row/column.

Individual parcels can be accessed based on their name, usingparcel = parcel_axis[name]

Use of this constructor is not recommended. New ParcelsAxis axes can be constructed more easily from a sequence of BrainModelAxis axes usingfrom_brain_models()

Parameters:

namearray_like

(N, ) string array with the parcel names

voxelsarray_like

(N, ) object array each containing a sequence of voxels. For each parcel the voxels are represented by a (M, 3) index array

verticesarray_like

(N, ) object array each containing a sequence of vertices. For each parcel the vertices are represented by a mapping from brain structure name to (M, ) index array

affinearray_like, optional

(4, 4) array mapping voxel indices to mm space (not needed for CIFTI-2 files only covering the surface)

volume_shapetuple of three integers, optional

shape of the volume in which the voxels were defined (not needed for CIFTI-2 files only covering the surface)

nverticesdict from string to integer, optional

maps names of surface elements to integers (not needed for volumetric CIFTI-2 files)

__init__(name, voxels, vertices, affine=None, volume_shape=None, nvertices=None)¶

Use of this constructor is not recommended. New ParcelsAxis axes can be constructed more easily from a sequence of BrainModelAxis axes usingfrom_brain_models()

Parameters:

namearray_like

(N, ) string array with the parcel names

voxelsarray_like

(N, ) object array each containing a sequence of voxels. For each parcel the voxels are represented by a (M, 3) index array

verticesarray_like

(N, ) object array each containing a sequence of vertices. For each parcel the vertices are represented by a mapping from brain structure name to (M, ) index array

affinearray_like, optional

(4, 4) array mapping voxel indices to mm space (not needed for CIFTI-2 files only covering the surface)

volume_shapetuple of three integers, optional

shape of the volume in which the voxels were defined (not needed for CIFTI-2 files only covering the surface)

nverticesdict from string to integer, optional

maps names of surface elements to integers (not needed for volumetric CIFTI-2 files)

property affine¶

Affine of the volumetric image in which the greyordinate voxels were defined

classmethod from_brain_models(named_brain_models)¶

Creates a Parcel axis from a list of BrainModelAxis axes with names

Parameters:

named_brain_modelsiterable of 2-element tuples of string and BrainModelAxis

list of (parcel name, brain model representation) pairs defining each parcel

Returns:

ParcelsAxis

classmethod from_index_mapping(mim)¶

Creates a new Parcels axis based on a CIFTI-2 dataset

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

ParcelsAxis

get_element(index)¶

Describes a single element from the axis

Parameters:

indexint

Indexes the row/column of interest

Returns:

tuple with 3 elements

• unicode name of the parcel
• (M, 3) int array with voxel indices
• dict from string to (K, ) int array with vertex indices
  for a specific surface brain structure

to_mapping(dim)¶

Converts the Parcel to a MatrixIndicesMap for storage in CIFTI-2 format

Parameters:

dimint

which dimension of the CIFTI-2 vector/matrix is described by this dataset (zero-based)

Returns:

cifti2.Cifti2MatrixIndicesMap

property volume_shape¶

Shape of the volumetric image in which the greyordinate voxels were defined

ScalarAxis¶

class nibabel.cifti2.cifti2_axes.ScalarAxis(name, meta=None)¶

Bases: Axis

Along this axis of the CIFTI-2 vector/matrix each row/column has been given a unique name and optionally metadata

Parameters:

namearray_like

(N, ) string array with the parcel names

metaarray_like

(N, ) object array with a dictionary of metadata for each row/column. Defaults to empty dictionary

__init__(name, meta=None)¶

Parameters:

namearray_like

(N, ) string array with the parcel names

metaarray_like

(N, ) object array with a dictionary of metadata for each row/column. Defaults to empty dictionary

classmethod from_index_mapping(mim)¶

Creates a new Scalar axis based on a CIFTI-2 dataset

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

ScalarAxis

get_element(index)¶

Describes a single element from the axis

Parameters:

indexint

Indexes the row/column of interest

Returns:

tuple with 2 elements

• unicode name of the row/column
• dictionary with the element metadata

to_mapping(dim)¶

Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI-2 format

Parameters:

dimint

which dimension of the CIFTI-2 vector/matrix is described by this dataset (zero-based)

Returns:

cifti2.Cifti2MatrixIndicesMap

SeriesAxis¶

class nibabel.cifti2.cifti2_axes.SeriesAxis(start, step, size, unit='SECOND')¶

Bases: Axis

Along this axis of the CIFTI-2 vector/matrix the rows/columns increase monotonously in time

This Axis describes the time point of each row/column.

Creates a new SeriesAxis axis

Parameters:

startfloat

starting time point

stepfloat

sampling time (TR)

sizeint

number of time points

unitstr

Unit of the step size (one of ‘second’, ‘hertz’, ‘meter’, or ‘radian’)

__init__(start, step, size, unit='SECOND')¶

Creates a new SeriesAxis axis

Parameters:

startfloat

starting time point

stepfloat

sampling time (TR)

sizeint

number of time points

unitstr

Unit of the step size (one of ‘second’, ‘hertz’, ‘meter’, or ‘radian’)

classmethod from_index_mapping(mim)¶

Creates a new SeriesAxis axis based on a CIFTI-2 dataset

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

SeriesAxis

get_element(index)¶

Gives the time point of a specific row/column

Parameters:

indexint

Indexes the row/column of interest

Returns:

float

size = None¶

property time¶

to_mapping(dim)¶

Converts the SeriesAxis to a MatrixIndicesMap for storage in CIFTI-2 format

Parameters:

dimint

which dimension of the CIFTI-2 vector/matrix is described by this dataset (zero-based)

Returns:

cifti2.Cifti2MatrixIndicesMap

property unit¶

from_index_mapping¶

nibabel.cifti2.cifti2_axes.from_index_mapping(mim)¶

Parses the MatrixIndicesMap to find the appropriate CIFTI-2 axis describing the rows or columns

Parameters:

mimcifti2.Cifti2MatrixIndicesMap

Returns:

axissubclass of Axis

Cifti2Extension¶

class nibabel.cifti2.parse_cifti2.Cifti2Extension(code: int | str, content: bytes = b'', object: T | None = None)¶

Bases: Nifti1Extension[Cifti2Header]

Parameters:

codeint or str

Canonical extension code as defined in the NIfTI standard, given either as integer or corresponding label (see extension_codes)

contentbytes, optional

Extension content as read from the NIfTI file header.

objectoptional

Extension content in runtime form.

__init__(code: int | str, content: bytes = b'', object: T | None = None) → None¶

Parameters:

codeint or str

Canonical extension code as defined in the NIfTI standard, given either as integer or corresponding label (see extension_codes)

contentbytes, optional

Extension content as read from the NIfTI file header.

objectoptional

Extension content in runtime form.

code_: int_ = 32¶

Cifti2Parser¶

class nibabel.cifti2.parse_cifti2.Cifti2Parser(encoding=None, buffer_size=3500000, verbose=0)¶

Bases: XmlParser

Class to parse an XML string into a CIFTI-2 header object

Parameters:

encodingstr

string containing xml document

buffer_size: None or int, optional

size of read buffer. None uses default buffer_size from xml.parsers.expat.

verboseint, optional

amount of output during parsing (0=silent, by default).

__init__(encoding=None, buffer_size=3500000, verbose=0)¶

Parameters:

encodingstr

string containing xml document

buffer_size: None or int, optional

size of read buffer. None uses default buffer_size from xml.parsers.expat.

verboseint, optional

amount of output during parsing (0=silent, by default).

CharacterDataHandler(data)¶

Collect character data chunks pending collation

The parser breaks the data up into chunks of size depending on the buffer_size of the parser. A large bit of character data, with standard parser buffer_size (such as 8K) can easily span many calls to this function. We thus collect the chunks and process them when we hit start or end tags.

EndElementHandler(name)¶

StartElementHandler(name, attrs)¶

flush_chardata()¶

Collate and process collected character data

property pending_data¶

True if there is character data pending for processing