# cifti2¶

CIFTI-2 format IO

 cifti2 Read / write access to CIFTI-2 image format cifti2_axes Defines Axis objects to create, read, and manipulate CIFTI-2 files

## Module: cifti2.cifti2¶

Format of the NIFTI2 container format described here:

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

 Cifti2BrainModel([index_offset, …]) Element representing a mapping of the dimension to vertex or voxels. Cifti2Header([matrix, version]) Class for CIFTI-2 header extension Cifti2HeaderError Error in CIFTI-2 header Cifti2Image([dataobj, header, nifti_header, …]) Class for single file CIFTI-2 format image Cifti2Label([key, label, red, green, blue, …]) CIFTI-2 label: association of integer key with a name and RGBA values CIFTI-2 label table: a sequence of Cifti2Labels CIFTI-2 Matrix object Cifti2MatrixIndicesMap(…[, …]) Class for Matrix Indices Map Cifti2MetaData([metadata]) A list of name-value pairs Cifti2NamedMap([map_name, metadata, label_table]) CIFTI-2 named map: association of name and optional data with a map index Cifti2Parcel([name, voxel_indices_ijk, vertices]) CIFTI-2 parcel: association of a name with vertices and/or voxels Cifti2Surface([brain_structure, …]) Cifti surface: association of brain structure and number of vertices Matrix that translates voxel indices to spatial coordinates Cifti2VertexIndices([indices]) CIFTI-2 vertex indices: vertex indices for an associated brain model Cifti2Vertices([brain_structure, vertices]) CIFTI-2 vertices - association of brain structure and a list of vertices Cifti2Volume([volume_dimensions, …]) CIFTI-2 volume: information about a volume for mappings that use voxels Cifti2VoxelIndicesIJK([indices]) CIFTI-2 VoxelIndicesIJK: Set of voxel indices contained in a structure

## 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:

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 new cifti2.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:

Several helper functions exist to create new BrainModelAxis axes:

A ParcelsAxis axis can be created from a sequence of BrainModelAxis axes using ParcelsAxis.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 indentifiers using BrainModelAxis.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, <size of cortex mask>), bm_cortex) and (‘CIFTI_STRUCTURE_THALAMUS_LEFT’, slice(<size of cortex mask>, 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)))


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)))

 Axis Abstract class for any object describing the rows or columns of a CIFTI-2 vector/matrix BrainModelAxis(name[, voxel, vertex, …]) Each row/column in the CIFTI-2 vector/matrix represents a single vertex or voxel LabelAxis(name, label[, meta]) Defines CIFTI-2 axis for label array. ParcelsAxis(name, voxels, vertices[, …]) Each row/column in the CIFTI-2 vector/matrix represents a parcel of voxels/vertices ScalarAxis(name[, meta]) Along this axis of the CIFTI-2 vector/matrix each row/column has been given a unique name and optionally metadata SeriesAxis(start, step, size[, unit]) Along this axis of the CIFTI-2 vector/matrix the rows/columns increase monotonously in time Parses the MatrixIndicesMap to find the appropriate CIFTI-2 axis describing the rows or columns to_header(axes) Converts the axes describing the rows/columns of a CIFTI-2 vector/matrix to a Cifti2Header

## Module: cifti2.parse_cifti2¶

 Cifti2Extension([code, content]) Cifti2Parser([encoding, buffer_size, verbose]) Class to parse an XML string into a CIFTI-2 header object

### 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)

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)

Initialize self. See help(type(self)) for accurate signature.

vertex_indices
voxel_indices_ijk

### Cifti2Header¶

class nibabel.cifti2.cifti2.Cifti2Header(matrix=None, version='2.0')

__init__(matrix=None, version='2.0')

Initialize self. See help(type(self)) for accurate signature.

classmethod from_axes(axes)

Creates a new Cifti2 header based on the Cifti2 axes

Parameters
axestuple of :class.cifti2_axes.Axis

sequence of Cifti2 axes describing each row/column of the matrix to be stored

Returns

new header describing the rows/columns in a format consistent with Cifti2

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_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.

mapped_indices

List of matrix indices that are mapped

classmethod may_contain_header(binaryblock)
number_of_mapped_indices

Number of mapped indices

### Cifti2HeaderError¶

class nibabel.cifti2.cifti2.Cifti2HeaderError

Bases: Exception

__init__($self, /, *args, **kwargs) Initialize self. See help(type(self)) for accurate signature. ### Cifti2Image¶ class nibabel.cifti2.cifti2.Cifti2Image(dataobj=None, header=None, nifti_header=None, extra=None, file_map=None) 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 the file_map mapping. Parameters dataobjobject Object containing image data. It should be some object that returns an array from np.asanyarray. It should have a shape 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) 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 the file_map mapping. Parameters dataobjobject Object containing image data. It should be some object that returns an array from np.asanyarray. It should have a shape 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 = (('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() header_class alias of Cifti2Header makeable = False nifti_header rw = True set_data_dtype(dtype) to_file_map(file_map=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_map attribute instead. Returns None update_headers() Harmonize NIfTI headers with image data >>> 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() == (2, 3, 4) True  valid_exts = ('.nii',) ### Cifti2Label¶ class nibabel.cifti2.cifti2.Cifti2Label(key=0, label='', red=0.0, green=0.0, blue=0.0, alpha=0.0) 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) Initialize self. See help(type(self)) for accurate signature. rgba Returns RGBA as tuple ### Cifti2LabelTable¶ class nibabel.cifti2.cifti2.Cifti2LabelTable Bases: nibabel.xmlutils.XmlSerializable, collections.abc.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__() Initialize self. See help(type(self)) for accurate signature. append(label) ### Cifti2Matrix¶ class nibabel.cifti2.cifti2.Cifti2Matrix Bases: nibabel.xmlutils.XmlSerializable, collections.abc.MutableSequence CIFTI-2 Matrix object This is a list-like container where the elements are instances of Cifti2MatrixIndicesMap. • 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__() Initialize self. See help(type(self)) for accurate signature. 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 mapped_indices List of matrix indices that are mapped 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: nibabel.xmlutils.XmlSerializable, collections.abc.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 __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=[]) Initialize self. See help(type(self)) for accurate signature. brain_models insert(index, value) S.insert(index, value) – insert value before index named_maps parcels surfaces volume ### Cifti2MetaData¶ class nibabel.cifti2.cifti2.Cifti2MetaData(metadata=None) Bases: nibabel.xmlutils.XmlSerializable, collections.abc.MutableMapping 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__(metadata=None) Initialize self. See help(type(self)) for accurate signature. 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) 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) Initialize self. See help(type(self)) for accurate signature. label_table metadata ### Cifti2Parcel¶ class nibabel.cifti2.cifti2.Cifti2Parcel(name=None, voxel_indices_ijk=None, vertices=None) 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) Initialize self. See help(type(self)) for accurate signature. 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 voxel_indices_ijk ### Cifti2Surface¶ class nibabel.cifti2.cifti2.Cifti2Surface(brain_structure=None, surface_number_of_vertices=None) 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) Initialize self. See help(type(self)) for accurate signature. ### Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ¶ class nibabel.cifti2.cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(meter_exponent=None, matrix=None) 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) Initialize self. See help(type(self)) for accurate signature. ### Cifti2VertexIndices¶ class nibabel.cifti2.cifti2.Cifti2VertexIndices(indices=None) Bases: nibabel.xmlutils.XmlSerializable, collections.abc.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) Initialize self. See help(type(self)) for accurate signature. insert(index, value) S.insert(index, value) – insert value before index ### Cifti2Vertices¶ class nibabel.cifti2.cifti2.Cifti2Vertices(brain_structure=None, vertices=None) Bases: nibabel.xmlutils.XmlSerializable, collections.abc.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) Initialize self. See help(type(self)) for accurate signature. insert(index, value) S.insert(index, value) – insert value before index ### Cifti2Volume¶ class nibabel.cifti2.cifti2.Cifti2Volume(volume_dimensions=None, transform_matrix=None) 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) Initialize self. See help(type(self)) for accurate signature. ### Cifti2VoxelIndicesIJK¶ class nibabel.cifti2.cifti2.Cifti2VoxelIndicesIJK(indices=None) Bases: nibabel.xmlutils.XmlSerializable, collections.abc.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) Initialize self. See help(type(self)) for accurate signature. insert(index, value) S.insert(index, value) – insert value before index ### Axis¶ class nibabel.cifti2.cifti2_axes.Axis Bases: object 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: __init__($self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

size

### BrainModelAxis¶

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

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 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 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)

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

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
name

The brain structure to which the voxel/vertices of belong

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
:class:.cifti2.Cifti2MatrixIndicesMap
volume_mask

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

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)

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
:class:.cifti2.Cifti2MatrixIndicesMap

### ParcelsAxis¶

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

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, using parcel = parcel_axis[name]

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

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
:class:cifti2.Cifti2MatrixIndicesMap
volume_shape

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

### ScalarAxis¶

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

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
:class:.cifti2.Cifti2MatrixIndicesMap

### SeriesAxis¶

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

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
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
:class:cifti2.Cifti2MatrixIndicesMap
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

nibabel.cifti2.cifti2_axes.to_header(axes)

Converts the axes describing the rows/columns of a CIFTI-2 vector/matrix to a Cifti2Header

Parameters
axesiterable of Axis objects

one or more axes describing each dimension in turn

Returns
headercifti2.Cifti2Header

### Cifti2Extension¶

class nibabel.cifti2.parse_cifti2.Cifti2Extension(code=None, content=None)
__init__(code=None, content=None)
Parameters
codeint or str

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

contentstr

Extension content as read from the NIfTI file header. This content is converted into a runtime representation.

code = 32

### Cifti2Parser¶

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

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

pending_data

True if there is character data pending for processing