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brainhops.io.images.minc

Readers for images stored in MINC files (.mnc), MINC1 and MINC2.

MINC is the volume format of the Montreal Neurological Institute and of the MINC toolkit (MNI, CIVET, BigBrain and many rodent pipelines). It comes in two containers, both named .mnc, which are recognised from their content whatever the file name:

Class Container Hints
Minc1Image NetCDF classic (CDF\x01, CDF\x02) minc, minc.1
Minc2Image HDF5, with a /minc-2.0 group minc, minc.2

Both are read with nibabel (the minc extra, pip install brainhops[minc]); MINC2 also needs h5py. A MINC1 file may be gzipped (.mnc.gz). MincImage reads either and returns the matching class.

import brainhops.io as io

image = io.images.load("t1.mnc")   # a Minc1Image or a Minc2Image
image.data.shape                   # F order: (x, y, z) for z,y,x files
image.transformation               # voxel -> MINC world (RAS, mm)
image.transformations[0]           # voxel -> physical (|step|, mm)
image.dimensions                   # the raw MINC dimensions, file order
image.vox2world                    # the (4, 4) matrix of the world affine

Coordinate systems. The voxels are presented in F order (the file's dimension order reversed), and the voxel axes are named after the MINC dimensions (xspace -> x, yspace -> y, zspace -> z, time -> t), whatever order the file stores them in. The world space is MINC's: right, anterior and superior along x, y and z, with each dimension running along its direction cosines from its start by its (possibly negative) step. See brainhops.io.base.minc for the file layout and the limitations.

Writing. nibabel cannot write MINC, so these classes are read-only: save a MINC image to another format (e.g. NIfTI) instead. Writing MINC would need another backend (pyminc, or the MINC toolkit).

Metadata. The dimensions (start, step, direction cosines, units) are kept on the object as [dimensions][brainhops.io.base.minc.MincParser.dimensions]. The other MINC attributes (patient, acquisition, history) are not read.

Classes

Minc1Image 

Minc1Image(
    dimensions: tuple[MincDimension, ...] = (),
    _source: _Source | None = None,
)

Bases: MincImage

An image that is encoded by a MINC1 file: a NetCDF classic file, possibly gzipped (.mnc.gz).

It answers to the hints "minc1" and "minc.1". See MincImage.

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, ...] | None

The shape of the volume in F order (fastest dimension first).

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.

data property writable 
data: ArrayProtocol | None

The voxels in F order, scaled to real values, read on first access and cached, unless set explicitly.

transformations property writable 
transformations: list[Transformation]

The voxel-to-physical and voxel-to-world transformations recorded by the dimensions, decoded on first access unless set explicitly.

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.

VARIANTS class-attribute 
VARIANTS: tuple[type, ...] = ()

The classes that a class with no VERSION hands files to.

version property 
version: int | None

The MINC version of the file (1 or 2).

vox2world property 
vox2world: ndarray | None

The (4, 4) voxel-to-world (RAS) matrix of the F-ordered array.

Its columns follow the spatial axes of system (the non-spatial ones, such as time, are skipped). It is None unless the volume has the three spatial dimensions.

system property writable 
system: CoordinateSystem | None

The voxel coordinate system, in F order, derived from the dimensions unless set explicitly.

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 a file by its content (a MINC2 file is opened by name, so that only its header is read).

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

Score how confident the class is that an open file object holds a MINC file of its version, from its content.

A MINC1 file is a NetCDF file (gzipped or not) whose header names a MINC spatial dimension and an image variable; a NetCDF file that does not is only weakly accepted. A MINC2 file is an HDF5 file with a /minc-2.0 group.

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 a MINC file of its version.

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

On a dispatcher, pick the best-matching registered format and build an instance of it from the file (path or file-like object). On a concrete format, build an instance of this class from the file.

from_filename classmethod 
from_filename(filename: FilenameLike, **kwargs) -> Self

Build the object from a MINC file.

Only the header is read: the voxels are read from the file, by name, on first access. A file that is not local, or that is gzipped, is read into memory first.

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

Build the object from an open MINC file object (gzipped or not), which is read into memory.

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

Build the object from the bytes of a MINC file (gzipped or not).

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.

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.

Minc2Image 

Minc2Image(
    dimensions: tuple[MincDimension, ...] = (),
    _source: _Source | None = None,
)

Bases: MincImage

An image that is encoded by a MINC2 file: an HDF5 file with a /minc-2.0 group. Reading it needs h5py.

It answers to the hints "minc2" and "minc.2". See MincImage.

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, ...] | None

The shape of the volume in F order (fastest dimension first).

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.

data property writable 
data: ArrayProtocol | None

The voxels in F order, scaled to real values, read on first access and cached, unless set explicitly.

transformations property writable 
transformations: list[Transformation]

The voxel-to-physical and voxel-to-world transformations recorded by the dimensions, decoded on first access unless set explicitly.

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.

VARIANTS class-attribute 
VARIANTS: tuple[type, ...] = ()

The classes that a class with no VERSION hands files to.

version property 
version: int | None

The MINC version of the file (1 or 2).

vox2world property 
vox2world: ndarray | None

The (4, 4) voxel-to-world (RAS) matrix of the F-ordered array.

Its columns follow the spatial axes of system (the non-spatial ones, such as time, are skipped). It is None unless the volume has the three spatial dimensions.

system property writable 
system: CoordinateSystem | None

The voxel coordinate system, in F order, derived from the dimensions unless set explicitly.

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 a file by its content (a MINC2 file is opened by name, so that only its header is read).

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

Score how confident the class is that an open file object holds a MINC file of its version, from its content.

A MINC1 file is a NetCDF file (gzipped or not) whose header names a MINC spatial dimension and an image variable; a NetCDF file that does not is only weakly accepted. A MINC2 file is an HDF5 file with a /minc-2.0 group.

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 a MINC file of its version.

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

On a dispatcher, pick the best-matching registered format and build an instance of it from the file (path or file-like object). On a concrete format, build an instance of this class from the file.

from_filename classmethod 
from_filename(filename: FilenameLike, **kwargs) -> Self

Build the object from a MINC file.

Only the header is read: the voxels are read from the file, by name, on first access. A file that is not local, or that is gzipped, is read into memory first.

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

Build the object from an open MINC file object (gzipped or not), which is read into memory.

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

Build the object from the bytes of a MINC file (gzipped or not).

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.

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.

MincImage 

MincImage(
    dimensions: tuple[MincDimension, ...] = (),
    _source: _Source | None = None,
)

Bases: MincParser, FileBasedImage, SingleScaleImage

An image that is encoded by a MINC file (MINC1 or MINC2).

This class reads both versions and hands a file to Minc1Image or Minc2Image, which are the classes registered for dispatch. It answers to the hint "minc".

The voxels are read with nibabel, scaled to real values (MINC's image-min/image-max, per slice or global), and presented in F order: the dimensions are reversed from the order the file lists them in, so that a zspace, yspace, xspace file reads as (x, y, z). The voxel axes are named after the MINC dimensions (xspace -> x, yspace -> y, zspace -> z, time -> t), whatever their order in the file.

The transformations are, in order:

  1. a Scaling from voxels to the scaled voxel space "physical": the absolute step of each dimension, in its units (millimetres by default for the spatial ones);
  2. the voxel-to-world affine, whose output is the RAS space named "world": each spatial dimension runs along its direction cosines, scaled by its (signed) step, from its start. It is the preferred transformation. It is only recorded for a volume with the three spatial dimensions.

MINC cannot be written: nibabel only reads it.

Why the bases are in this order

As for NiftiImage: SingleScaleImage.data has no default, so it comes last, and MincParser leads so that its lazy data/system properties win.

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, ...] | None

The shape of the volume in F order (fastest dimension first).

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.

data property writable 
data: ArrayProtocol | None

The voxels in F order, scaled to real values, read on first access and cached, unless set explicitly.

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.

VERSION class-attribute 
VERSION: int | None = None

The MINC version read by this class, or None for both.

VARIANTS class-attribute 
VARIANTS: tuple[type, ...] = ()

The classes that a class with no VERSION hands files to.

version property 
version: int | None

The MINC version of the file (1 or 2).

vox2world property 
vox2world: ndarray | None

The (4, 4) voxel-to-world (RAS) matrix of the F-ordered array.

Its columns follow the spatial axes of system (the non-spatial ones, such as time, are skipped). It is None unless the volume has the three spatial dimensions.

system property writable 
system: CoordinateSystem | None

The voxel coordinate system, in F order, derived from the dimensions unless set explicitly.

transformations property writable 
transformations: list[Transformation]

The voxel-to-physical and voxel-to-world transformations recorded by the dimensions, decoded on first access unless set explicitly.

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 a file by its content (a MINC2 file is opened by name, so that only its header is read).

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

Score how confident the class is that an open file object holds a MINC file of its version, from its content.

A MINC1 file is a NetCDF file (gzipped or not) whose header names a MINC spatial dimension and an image variable; a NetCDF file that does not is only weakly accepted. A MINC2 file is an HDF5 file with a /minc-2.0 group.

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 a MINC file of its version.

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

On a dispatcher, pick the best-matching registered format and build an instance of it from the file (path or file-like object). On a concrete format, build an instance of this class from the file.

from_filename classmethod 
from_filename(filename: FilenameLike, **kwargs) -> Self

Build the object from a MINC file.

Only the header is read: the voxels are read from the file, by name, on first access. A file that is not local, or that is gzipped, is read into memory first.

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

Build the object from an open MINC file object (gzipped or not), which is read into memory.

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

Build the object from the bytes of a MINC file (gzipped or not).

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.

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.