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brainhops.io.images.afni

Readers and writers for images stored as AFNI datasets.

AfniImage reads and writes AFNI's native datasets, with no dependency beyond numpy: a text header, prefix+view.HEAD, and the voxel values, prefix+view.BRIK, which may be compressed (.BRIK.gz, .BRIK.bz2). Either file, or the dataset's name without extension, can be given.

The header format, and every convention checked against the AFNI sources, is described in brainhops.io.base.afni, which the image reader shares with the AFNI transformation formats.

import brainhops.io as io

image = io.load("epi+orig.HEAD")       # an AfniImage
image.data                             # [x, y, z, sub-brick], scaled
image.transformation                   # voxel -> "orig", LPS mm (Affine)
image.header["HISTORY_NOTE"]           # any attribute of the header
image.header.labels                    # sub-brick labels (BRICK_LABS)
image.save("copy+orig.BRIK.gz")        # .HEAD + gzipped .BRIK
io.save(image, "epi.nii.gz")           # or any other image format

Data. The sub-bricks are stored one after the other, x fastest: the array is indexed [x, y, z], or [x, y, z, sub-brick] when there are several sub-bricks, in F order. The fourth axis is t (of type time) for a time series -- a dataset with a TAXIS_NUMS attribute -- and brick otherwise. The data of an uncompressed local BRIK stay memory-mapped until they are indexed; a compressed BRIK, or one whose sub-bricks have different types, is read into memory. The scaling factor of each sub-brick (BRICK_FLOAT_FACS, zero meaning none) is applied when data is first accessed; dataobj holds the stored values.

Coordinate systems. AFNI's world is "DICOM order": LPS millimetres (x to the left, y to the back, z up), which AFNI calls RAI. The world spaces are named after the dataset's view: orig, acpc or tlrc. The transformations are, in order:

  1. voxel -> physical: a Scaling by the voxel sizes (|DELTA|), and by the repetition time of a time series;
  2. voxel -> <view>-cardinal: the cardinal grid AFNI programs compute on, from ORIENT_SPECIFIC, ORIGIN and DELTA;
  3. voxel -> <view>: the true, possibly oblique, geometry of IJK_TO_DICOM_REAL (the cardinal grid when the header has none). It is the preferred transformation, and the matrix AFNI itself exports to NIfTI (3dAFNItoNIFTI) and nibabel reads.

The two affines are the same unless the dataset is oblique.

Writing. The preferred transformation is converted to voxel-to-DICOM (an RAS world is flipped) and written as IJK_TO_DICOM_REAL; its closest cardinal grid becomes ORIENT_SPECIFIC, ORIGIN, DELTA and IJK_TO_DICOM, as AFNI computes it when it reads a NIfTI file. A 3D array is one sub-brick, and a 4D array has one sub-brick per volume. Writer options set the view (default: from the file name, out+tlrc.HEAD, else from the name of the world space, else the view read, else orig), the stored datatype (default: the data's own type, or the closest AFNI has) and extra or removed attributes. The attributes read from the source header (HISTORY_NOTE, BRICK_LABS, ...) are written back, except those that no longer describe the data. The data are written unscaled, in little-endian order, and a .BRIK.gz or .BRIK.bz2 name compresses them.

One class for every view

The view (+orig, +acpc, +tlrc) is a property of the dataset -- the world space its coordinates are in, recorded in SCENE_DATA -- not a different file format: the three views are read and written the same way, so a single class reads them all, and names its world space after the view.

Classes

AfniImage 

AfniImage(
    _header: AfniHeader | None = None,
    dataobj: Any | None = None,
)

Bases: AfniParser, WritableFileBasedImage, SingleScaleImage

An image that is encoded by an AFNI dataset (.HEAD + .BRIK).

The data are indexed [x, y, z], or [x, y, z, sub-brick] for a dataset with several sub-bricks, in F order. The data of an uncompressed local BRIK stay memory-mapped until they are indexed. The scaling factors of the sub-bricks (BRICK_FLOAT_FACS) are applied on access; dataobj holds the stored values.

The voxel-to-world transformations are, in order:

  1. voxel -> physical: a Scaling by the voxel sizes (|DELTA|, in mm), and the repetition time of a time series;
  2. voxel -> <view>-cardinal (orig-cardinal, tlrc-cardinal, ...): the Affine from ORIENT_SPECIFIC, ORIGIN and DELTA, the grid AFNI programs compute on;
  3. voxel -> <view> (orig, acpc or tlrc): the Affine of IJK_TO_DICOM_REAL, the true (possibly oblique) geometry, which AFNI exports to NIfTI. Without that attribute, it is the cardinal one.

The world spaces are LPS millimetres (AFNI's "DICOM order"). The last transformation is the preferred one. The attributes the data model has no slot for are kept in header and written back.

Why the bases are in this order

As for NiftiImage: SingleScaleImage comes last so that its data field follows the defaulted fields of the parser, and the lazy properties of this class take precedence over the plain fields.

Attributes

PREFIXES class-attribute 
PREFIXES: tuple[str, ...] = ()

Filename prefixes required by this parser, e.g. ("y_", "iy_").

An empty tuple means "no constraint". A parser that constrains the prefix is more specific than one that does not, and wins ties.

Declaring EXTENSIONS and PREFIXES separately states the cross-product implicitly, which is how these conventions actually work: SPM's four names are {y_, iy_} x {.nii, .nii.gz}.

PRIORITY class-attribute 
PRIORITY: int = 10

Kind precedence, used only to break ties that confidence could not.

A NIfTI file is legitimately both an image and a set of affines, so when nothing else separates them the image wins. Scoring sniffers (e.g. NIfTI intent codes) normally decide well before this matters.

shape property 
shape: tuple[int, ...]

The shape of the image data.

ndim property 
ndim: int

The number of dimensions of the image data.

dtype property 
dtype: dtype

The data type of the image data.

grid property 
grid: CartesianField

The Cartesian field that defines the sampling grid of the image.

This is the grid of the image's geometry.

transformation property writable 
transformation: Transformation

The preferred transformation.

It is always the last transformation in the list.

Assigning a transformation appends it as the new preferred transformation. Assigning an integer or a string selects an existing transformation by position or by output-space name and moves it to the end. Assigning a transformation that is already in the list moves it to the end instead of adding a copy.

A transformation is recognized as already present by identity (transformations compare by identity): a distinct transformation with the same parameters is appended as a new preferred transformation.

geometry property 
geometry: Geometry

A transformation that is the composition of the preferred voxel-to-world transformation and the cartesian field corresponding to the image's shape.

This transformation can be used to reslice any image onto the same grid as this image.

header property writable 
header: AfniHeader | None

The AFNI header this object was read from, if any.

data property writable 
data: Any | None

The image data, scaled, unless set explicitly.

system property writable 
system: CoordinateSystem | None

The voxel coordinate system, derived from the header, unless set explicitly. None when there is no header.

transformations property writable 
transformations: list[Transformation]

The voxel-to-world transformations recorded by the header, decoded on access unless set explicitly.

An image built from data alone has no header, so it records no transformation and the list is empty.

Methods:

sniff classmethod 
sniff(
    file: FileOrContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read file. On a concrete format, score how confident it is that file, in any supported form, is its own.

sniff_file classmethod 
sniff_file(
    file: FileLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read the file (path or file-like object). On a concrete format, score how confident it is that the file is its own.

sniff_filename classmethod 
sniff_filename(
    filename: FilenameLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Score how confident the class is that a path names an AFNI dataset: its .HEAD is read, whichever of its files is named.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Score how confident the class is that a stream holds an AFNI header.

sniff_content classmethod 
sniff_content(
    content: ContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read the content (text or bytes). On a concrete format, score how confident it is that the content is its own.

sniff_bytes classmethod 
sniff_bytes(
    content: bytes,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Score how confident the class is that bytes hold an AFNI header.

sniff_text classmethod 
sniff_text(
    text: str,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read the text. On a concrete format, score how confident it is that the text is its own.

sniff_lines classmethod 
sniff_lines(
    lines: Iterable[str],
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read the lines. On a concrete format, score how confident it is that the lines are its own.

sniff_line classmethod 
sniff_line(
    line: str,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> type | None

On a dispatcher, identify which registered format would read the line. On a concrete format, score how confident it is that the line is its own.

load classmethod 
load(other: FileOrContentLike, **kwargs) -> Self

On a dispatcher, pick the best-matching registered format and build an instance of it from other. On a concrete format, build an instance of this class from other, in any supported form.

from_spec classmethod 
from_spec(spec: SourceSpec, **kwargs) -> Self

Load a structured source specification through this dispatcher.

from_file classmethod 
from_file(file: FileLike, **kwargs) -> Self

Build the object from an AFNI dataset (path or file object).

from_filename classmethod 
from_filename(
    filename: FilenameLike, mmap: bool = True, **kwargs
) -> Self

Build the object from the path of a .HEAD, of a .BRIK (.BRIK.gz, .BRIK.bz2), or of the dataset without extension.

An uncompressed local BRIK is memory-mapped unless mmap is false.

from_fileobj classmethod 
from_fileobj(file: BinaryIO, **kwargs) -> Self

Build the object from an open .HEAD (or .BRIK) file object.

The other file of the dataset is found from the stream's name, which it must therefore have.

from_content classmethod 
from_content(content: ContentLike, **kwargs) -> Self

On a dispatcher, pick the best-matching registered format and build an instance of it from the content (text or bytes). On a concrete format, build an instance of this class from the content.

from_bytes classmethod 
from_bytes(content: bytes, **kwargs) -> Self

An AFNI dataset is two files, so bytes alone cannot hold one.

from_text classmethod 
from_text(text: str, **kwargs) -> Self

On a dispatcher, pick the best-matching registered format and build an instance of it from the text. On a concrete format, build an instance of this class from the text.

from_lines classmethod 
from_lines(lines: Iterable[str], **kwargs) -> Self

On a dispatcher, pick the best-matching registered format and build an instance of it from the lines. On a concrete format, build an instance of this class from the lines.

from_line classmethod 
from_line(line: str, **kwargs) -> Self

On a dispatcher, pick the best-matching registered format and build an instance of it from the line. On a concrete format, build an instance of this class from the line.

save 
save(file: FileLike, **kwargs) -> None

Write the object to a file (path or file-like object).

This is the generic front door to the to_* family. It is named save rather than to because to already means something else on the data models these parsers are mixed into: Transformation.to converts an object to another type. A writer's to was shadowed by it on every writable transformation.

Parameters:

Name Type Description Default
file FileLike

The file to write to.

 required
**kwargs

Parser-specific options.

 {}
to_file 
to_file(file: FileLike, **kwargs) -> None

Write to a path; an AFNI dataset cannot be written to a stream.

to_filename 
to_filename(filename: FilenameLike, **kwargs) -> None

Write the dataset: its .HEAD and its .BRIK.

The path may name the .HEAD, the .BRIK (.BRIK.gz or .BRIK.bz2 to compress it), or the dataset without extension (out+orig). Another BRIK of the same dataset, compressed differently, is removed (as AFNI does), so that it cannot be read in place of the new one.

to_fileobj 
to_fileobj(file: IO, **kwargs) -> None

An AFNI dataset is two files, which a stream cannot hold.

to_bytes 
to_bytes(**kwargs) -> bytes

An AFNI dataset is two files, which bytes cannot hold.

to_text 
to_text(**kwargs) -> str

Return a text version of the file.

Parameters:

Name Type Description Default
**kwargs

Parser-specific options.

 {}

Returns:

Type Description
str

A text version of the file.
to_lines 
to_lines(**kwargs) -> Iterator[str]

Return a text version of the file as an iterable of lines.

Parameters:

Name Type Description Default
**kwargs

Parser-specific options.

 {}

Returns:

Type Description
Iterator[str]

An iterable of lines representing the object.
to_line 
to_line(**kwargs) -> str

Return a line representing the object.

Parameters:

Name Type Description Default
**kwargs

Parser-specific options.

 {}

Returns:

Type Description
str

A line representing the object.
from_dict classmethod 
from_dict(other: Mapping, *args, **kwargs) -> Self

Create an instance of the class from a dictionary-like object.

Only keys in the dictionary that match keyword-like fields of this class, or the keywords its constructor takes without storing them (its InitVars, such as the matrix= of an Affine), will be used. Other keys are ignored, but see from_other, which refuses them.

Additional positional and/or keyword arguments can be provided, and will take precedence over the values in the dictionary.

A key naming a field that this class fixes (a field that cannot be passed to its constructor) is checked instead of used: a dictionary that sets it to anything other than None or the value of this class is refused with a ValueError.

from_instance classmethod 
from_instance(other: Any, *args, **kwargs) -> Self

Create an instance from an instance of a similar class.

The data model copies the fields both classes share, by name. A field that a file format declares for its own use -- such as the nibabel image and header of the NIfTI and MGH formats -- is only copied from an object of that same format: from any other object, a field of the same name holds something else (a NIfTI image is no MGH image), so this class's default is kept instead. Saving a NIfTI image to MGH, or the converse, therefore converts the data model only, and the format-specific state is rebuilt by the writer.

from_other classmethod 
from_other(other: Any, *args, **kwargs) -> Self

Create an instance from a file, or from anything the data model reads.

A path (str or os.PathLike), an open file, bytes or a structured source (SourceSpec) is read with load: on a dispatcher such as FileBasedImage, the best-matching registered format reads it, and on a concrete format, that format does. Any other value is handed to the data model's own from_other, which reads a mapping field by field, copies an instance of a similar class, and passes anything else to the constructor.

Parameters:

Name Type Description Default
other Any

A file, its content, a mapping, or an instance of a similar class.

 required
*args

Constructor arguments. A file is read with keyword options only.

 ()
**kwargs

Format-specific options when reading a file, and field values otherwise.

 {}

Returns:

Type Description
obj

The object that was built.

Raises:

Type Description
TypeError

If positional arguments come with a file to read.
__array__ 
__array__(dtype: DTypeLike | None = None) -> ndarray

Return the image data as an array.

reslice 
reslice(
    geometry: Self
    | Geometry
    | Transformation
    | None = None,
    degree: int = 1,
    bound: str = "reflect",
    coeff: bool = False,
    copy: bool = False,
) -> Self

Apply transformations to current data and return new image.

Parameters:

Name Type Description Default
geometry Image | Geometry | Transformation

Geometry of the output image.

The geometry is a voxel-to-world transformation that defines the grid onto which the image will be resliced.

If it is a Geometry, then it also defines the shape of the output image. Otherwise, the current shape of the image is used.

If it is None, the image is resampled onto its own grid.

 None
degree 0..5

The spline degree. 0=nearest, 1=linear, 2=quadratic, etc.

 0..5
bound (nearest, reflect, mirror, grid - wrap, wrap)

The boundary condition. If a string, one of: - 'nearest': nearest edge value (a a a a | a b c d | d d d d) - 'reflect': reflect at edge (d c b a | a b c d | d c b a) - 'mirror': mirror at edge (d c b | a b c d | c b a) - 'grid-wrap': wrap around (a b c d | a b c d | a b c d) - 'wrap': wrap around with shift (d b c d | a b c d | b c a b) If a float, the constant value to use beyond the edge.

 'nearest'
coeff bool

If True, the input image is assumed to already contain spline coefficients. If False, the input image is prefiltered before interpolation.

 False
copy bool

Whether the output data must be a fresh array. As with torch.Tensor.to, when False the output data may share memory with the input data: a reslice that only gathers (a flip, a permutation, or a unit-step slice, such as a reslice onto the image's own grid) can return a view of it. When True the output data never shares memory with the input data. A dask array is never copied: it is immutable, and writing into the output rebinds the output's own graph, never the input's, so the lazy output is returned as is.

 False

Returns:

Type Description
Image

The resliced image.
__call__ 
__call__(transform: Transformation) -> SingleScaleImage

Apply a transformation to the image, but do not compute.

Parameters:

Name Type Description Default
transform Transformation

The transformation to apply.

The output space of this transformation should match (or be compatible with) the output space of the preferred transformation. That is, the new "voxel-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
Image

The updated (not-yet-resliced) image.
__getitem__ 
__getitem__(
    index: tuple[int | slice | None, ...],
) -> SingleScaleImage

Index into the image data while preserving the geometry of the image.