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brainhops.io.images.openslide

Whole-slide images -- Aperio SVS, Hamamatsu NDPI/VMS/VMU, MIRAX, Leica SCN, Philips TIFF, Ventana BIF, Sakura SVSlide, Trestle, Zeiss CZI, DICOM WSI and generic tiled TIFF -- read with OpenSlide (read only).

This reader requires the openslide extra (pip install brainhops[openslide]), which installs openslide-python and openslide-bin, the wheels of the OpenSlide C library (Linux, macOS and Windows). Where openslide-bin has no wheel, install the OpenSlide library with the system's package manager (apt install libopenslide0, brew install openslide, conda install -c conda-forge openslide) and openslide-python alone. Without them, these formats are not registered (see Without OpenSlide).

from brainhops.io.images import load

slide = load("CMU-1.svs")            # AperioMultiScaleImage
slide.images[2].data.shape           # (x, y, c), read region by region
slide.images[2].data[:256, :256]     # reads that region only
slide.properties["openslide.mpp-x"]  # every OpenSlide property
level = load("CMU-1.svs", level=2)   # AperioImage: one level
load("slide.tif", hint="openslide")  # a generic tiled TIFF, by OpenSlide

Formats

OpenSlide reads every format through one library, but each is a class of its own, with its own extensions and hints, so that it can be asked for by name. The vendor of a file is the one OpenSlide.detect_format reports:

Vendor (VENDOR): classes; extensions; hints.

  • aperio: AperioImage, AperioMultiScaleImage; extensions .svs; hints aperio, svs.
  • hamamatsu: HamamatsuImage, HamamatsuMultiScaleImage; extensions .ndpi, .vms, .vmu; hints hamamatsu, ndpi, vms, vmu.
  • mirax: MiraxImage, MiraxMultiScaleImage; extensions .mrxs; hints mirax, mrxs, 3dhistech.
  • leica: LeicaImage, LeicaMultiScaleImage; extensions .scn; hints leica, scn.
  • philips: PhilipsImage, PhilipsMultiScaleImage; extensions .tiff; hints philips.
  • ventana: VentanaImage, VentanaMultiScaleImage; extensions .bif, .tif; hints ventana, bif.
  • sakura: SakuraImage, SakuraMultiScaleImage; extensions .svslide; hints sakura, svslide.
  • trestle: TrestleImage, TrestleMultiScaleImage; extensions .tif; hints trestle.
  • zeiss: ZeissImage, ZeissMultiScaleImage; extensions .czi; hints zeiss, czi.
  • dicom: DicomWsiImage, DicomWsiMultiScaleImage; extensions .dcm; hints dicom-wsi.
  • generic-tiff: GenericTiffImage, GenericTiffMultiScaleImage; extensions .tif, .tiff; hints generic-tiff.

Every hint is also qualified by openslide ("openslide.aperio"), and hint="openslide" alone selects all of them. A file is attributed to a vendor's class only if OpenSlide detects that vendor: a class asked for by hint refuses a slide of another vendor.

OpenSlide opens files by name. A local file (or an open file whose name is a local file) is sniffed and read in place. A remote file, a stream or bytes are copied into a temporary file first (deleted with the image), and are only read when asked for by hint, since nothing is sniffed in memory; a format made of several files (MIRAX, VMS, DICOM, Trestle) cannot be read that way.

Which reader reads a TIFF-based slide

SVS, NDPI, Philips, Leica SCN, Ventana BIF, Trestle and generic slides are TIFF files, which the TIFF reader reads too. Formats are chosen by their sniffing scores first, so:

  1. A slide of a known vendor scores CERTAIN (1.0) with its OpenSlide multiscale class. The TIFF reader scores a vendor whole-slide pyramid (tifffile's is_svs, is_ndpi, is_philips, is_scn or is_bif) 0.9 rather than CERTAIN, and a single-scale TIFF image 0.75 (LIKELY). So OpenSlide reads vendor slides when it is installed, and the TIFF reader reads them otherwise.
  2. With level=, the OpenSlide single-scale class scores CERTAIN, the TIFF multiscale reader declines, and the single-scale TIFF reader scores LIKELY.
  3. A generic tiled TIFF (no vendor) scores only MAYBE (0.5), so the TIFF reader, which also reads its OME-XML, ImageJ and resolution metadata, keeps it; hint="openslide" asks for OpenSlide instead.
  4. Without a level, the single-scale class of a pyramid scores 0.8 times its vendor's score, below its multiscale class; a slide with a single level is read as a single-scale image.

Data

Each level is F-ordered (x, y, c): RGB uint8 samples, row 0 at the top (y points down; this is not encoded as an orientation). OpenSlide decodes RGBA; the alpha channel only marks pixels outside the scanned area, which are composited onto the slide's background colour (openslide.background-color, white by default), so the image is RGB.

Pixels are never read in full unless asked for. image.data is a lazy array that reads, with read_region, only the region it is indexed with (numpy.asarray(image.data) reads the whole level); with lazy=True, or when dask is the array backend, it is a dask array whose chunks are whole tiles (at least 1024 pixels wide).

Geometry

The index space is 0-based, and an integer is the centre of a pixel. The image carries one transformation, a scaling from its "pixel" system to a "physical" one, whose size is the full-resolution pixel size from openslide.mpp-x / openslide.mpp-y (micrometres), times the level's downsampling factor (level_downsamples). When the slide records no pixel size it is unknown: the identity, in no unit. pixel_size= and unit= override it, as for every raster image.

A level covers the same extent as the full resolution: pixel i of a level downsampled by f is centred on the full-resolution pixel coordinate f * i + (f - 1) / 2 (a translation of (f - 1) / 2 full-resolution pixels), as in TIFF and OME-Zarr pyramids. A pyramid's levels all map onto the same "physical" system, and its own transformation is the identity. The bounds of a sparse slide (openslide.bounds-*) are kept as metadata, not applied: every level covers the whole slide.

Metadata

What the slide records besides the pixels is kept on the image: vendor, properties (every OpenSlide property, standard and vendor-specific), n_levels, level_downsamples, background_color, bounds, level (single-scale), and associated_images, the names of the label, macro, thumbnail, ... images, which associated_image(name) reads as (x, y, c) RGB arrays.

Without OpenSlide

When openslide-python or the OpenSlide library is missing, this module is not registered; asking for it by hint ("openslide", "svs", ...) says what to install, and the TIFF-based slides are read by the TIFF reader, if tifffile is installed.

Classes

OpenSlideFormat

An image read with OpenSlide. Each vendor format derives from it, so that the hint "openslide" selects them all, and "openslide.<vendor>" one of them.

Attributes

VENDOR class-attribute 
VENDOR: str | None = None

The vendor name OpenSlide gives to the format (OpenSlide.detect_format).

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

OpenSlideImage magic 

OpenSlideImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: _OpenSlideMixin, BinaryFileParser, FileBasedImage, SingleScaleImage

One level of a whole-slide image, read with OpenSlide: the base of the single-scale image of every vendor.

The data is F-ordered (x, y, c) RGB uint8, and is read region by region when it is indexed (or as a dask array of whole tiles), never in full unless asked for (numpy.asarray(image.data)). The only transformation is a scaling from the pixel system to a "physical" one, in micrometres when the slide records its pixel size (openslide.mpp-x/y) and the identity, in no unit, otherwise.

Attributes

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

File extensions handled by this parser, e.g. (".nii", ".nii.gz").

Used as a first, cheap dispatch pass. When several parsers match, the longest matching extension wins, so a parser declaring ".nii.gz" takes precedence over one declaring ".gz".

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.

VENDOR class-attribute 
VENDOR: str | None = None

The vendor name OpenSlide gives to the format (OpenSlide.detect_format).

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

OpenSlideMultiScaleImage magic 

OpenSlideMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: _OpenSlideMixin, BinaryFileParser, FileBasedImage, MultiScaleImage

A whole-slide image, read with OpenSlide, as a pyramid: the base of the multiscale image of every vendor.

Each OpenSlide level is a single-scale image of the same vendor, finest first, read region by region. A level's pixel size is the full-resolution pixel size times its downsampling factor (level_downsamples), and it covers the same extent as the full resolution: pixel i of a level downsampled by f is centred on the full-resolution pixel coordinate f * i + (f - 1) / 2, as in a TIFF or OME-Zarr pyramid. Every level maps onto the same "physical" system, so the pyramid's own transformations are empty.

Attributes

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

File extensions handled by this parser, e.g. (".nii", ".nii.gz").

Used as a first, cheap dispatch pass. When several parsers match, the longest matching extension wins, so a parser declaring ".nii.gz" takes precedence over one declaring ".gz".

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}.

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

VENDOR class-attribute 
VENDOR: str | None = None

The vendor name OpenSlide gives to the format (OpenSlide.detect_format).

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

PRIORITY class-attribute 
PRIORITY: int = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

AperioImage 

AperioImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: AperioFormat, OpenSlideImage

One level of a Aperio SVS slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

AperioMultiScaleImage 

AperioMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: AperioFormat, OpenSlideMultiScaleImage

A Aperio SVS slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

DicomWsiImage 

DicomWsiImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: DicomWsiFormat, OpenSlideImage

One level of a DICOM WSI slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

DicomWsiMultiScaleImage 

DicomWsiMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: DicomWsiFormat, OpenSlideMultiScaleImage

A DICOM WSI slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

GenericTiffImage 

GenericTiffImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: GenericTiffFormat, OpenSlideImage

One level of a generic tiled TIFF slide (see OpenSlideImage).

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.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

GenericTiffMultiScaleImage 

GenericTiffMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: GenericTiffFormat, OpenSlideMultiScaleImage

A generic tiled TIFF slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

HamamatsuImage 

HamamatsuImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: HamamatsuFormat, OpenSlideImage

One level of a Hamamatsu slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

HamamatsuMultiScaleImage 

HamamatsuMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: HamamatsuFormat, OpenSlideMultiScaleImage

A Hamamatsu slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

LeicaImage 

LeicaImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: LeicaFormat, OpenSlideImage

One level of a Leica SCN slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

LeicaMultiScaleImage 

LeicaMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: LeicaFormat, OpenSlideMultiScaleImage

A Leica SCN slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

MiraxImage 

MiraxImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: MiraxFormat, OpenSlideImage

One level of a MIRAX slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

MiraxMultiScaleImage 

MiraxMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: MiraxFormat, OpenSlideMultiScaleImage

A MIRAX slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

PhilipsImage 

PhilipsImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: PhilipsFormat, OpenSlideImage

One level of a Philips TIFF slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

PhilipsMultiScaleImage 

PhilipsMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: PhilipsFormat, OpenSlideMultiScaleImage

A Philips TIFF slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

SakuraImage 

SakuraImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: SakuraFormat, OpenSlideImage

One level of a Sakura SVSlide slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

SakuraMultiScaleImage 

SakuraMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: SakuraFormat, OpenSlideMultiScaleImage

A Sakura SVSlide slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

TrestleImage 

TrestleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: TrestleFormat, OpenSlideImage

One level of a Trestle TIFF slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

TrestleMultiScaleImage 

TrestleMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: TrestleFormat, OpenSlideMultiScaleImage

A Trestle TIFF slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

VentanaImage 

VentanaImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: VentanaFormat, OpenSlideImage

One level of a Ventana BIF slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

VentanaMultiScaleImage 

VentanaMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: VentanaFormat, OpenSlideMultiScaleImage

A Ventana BIF slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.

ZeissImage 

ZeissImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    level: int | None = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: ZeissFormat, OpenSlideImage

One level of a Zeiss CZI slide (see OpenSlideImage).

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    level: int | None = None,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read one level of a slide.

Parameters:

Name Type Description Default
slide _Slide

The slide.

 required
level int

The level to read: 0 (the default) is the full resolution; negative values count from the end. A level covers the same extent as the full resolution (see OpenSlideMultiScaleImage).

 None
pixel_size float | Sequence[float] | Mapping[str, float]

The full-resolution pixel size, which overrides the slide's.

 None
unit str | Unit

The unit of pixel_size, or the unit to convert the slide's (micrometres) to.

 None
lazy bool

Read the pixels as a dask array of whole tiles. By default, only when dask is the array backend; otherwise data reads the region it is indexed with.

 None

Raises:

Type Description
ParserContentError

If OpenSlide cannot read the file, or it is a slide of another vendor.
IndexError

If the slide has no such level.

ZeissMultiScaleImage 

ZeissMultiScaleImage(
    vendor: _Vendor = None,
    properties: _Properties = None,
    n_levels: _NLevels = None,
    level_downsamples: _Downsamples = None,
    background_color: _Background = None,
    bounds: _Bounds = None,
    associated_images: _Associated = None,
)

Bases: ZeissFormat, OpenSlideMultiScaleImage

A Zeiss CZI slide, as a pyramid (see OpenSlideMultiScaleImage).

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 = FileBasedImage.PRIORITY + 1

Content held in memory is not sniffed, so when it is read by hint the pyramid and its single-scale class would tie: the pyramid is tried first (and declines a level).

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.

data property 
data: ArrayProtocol

The data of the highest-resolution level of the pyramid.

nscales property 
nscales: int

Return the number of scales in the multi-resolution pyramid.

scales property 
scales: Iterator[SingleScaleImage]

Yield all levels as single-resolution images.

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

The geometry of the highest-resolution image in the pyramid.

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.

SCORE class-attribute 
SCORE: float = Confidence.CERTAIN

How confident the format is in a file OpenSlide attributes to its vendor.

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,
    level: int | None = None,
    **kwargs,
) -> float

Score how confident the class is that a file is a slide of its vendor, as OpenSlide detects it. Only a local file is sniffed.

sniff_fileobj classmethod 
sniff_fileobj(
    file: IO,
    error: bool | Type[Exception] = False,
    level: int | None = None,
    **kwargs,
) -> float

Score an open file, by the name of the local file it was opened from. A stream with no such name is not sniffed (NO): OpenSlide reads files by name, so it is read only when asked for by hint.

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: BinaryContentLike,
    error: bool | Type[Exception] = False,
    **kwargs,
) -> float

Content in memory is not sniffed (NO); it is read only when asked for by hint.

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

Read the slide from a file. A local file is opened by OpenSlide directly; a remote one is copied into a temporary file first (which only works for the formats held in a single file). See from_source for the options.

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

Read the slide from an open file: by its name if it is a local file, or else from a temporary copy. See from_source for the options.

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: BinaryContentLike, **kwargs) -> Self

Read the slide from the bytes of a file, through a temporary copy. See from_source for the options.

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.

to_singlescale 
to_singlescale(index: int = 0) -> SingleScaleImage

Return one of the levels as a single-resolution image.

reslice 
reslice(
    geometry: Image
    | 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 highest-resolution level 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
intrinsic Geometry | Transformation | None

An optional transformation that defines the intrinsic geometry of the highest-resolution image in the output pyramid. If provided, it is used to compute the geometry of each level in the output pyramid. If not provided, this function returns a single-scale image instead.

 required
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
SingleScaleImage

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

Apply a transformation to the multi-scale image.

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 "intrinsic-to-world" transformation is defined as self.transformation @ transform.inverse().

 required

Returns:

Type Description
MultiScaleImage

The transformed image.
associated_image 
associated_image(name: str) -> ndarray

An associated image of the slide ("label", "macro", "thumbnail", ... as listed in associated_images), as an F-ordered (x, y, c) RGB array, composited onto white.

from_source classmethod 
from_source(
    slide: _Slide,
    pixel_size: Any = None,
    unit: Any = None,
    lazy: bool | None = None,
    **kwargs,
) -> Self

Read every level of a slide. The options are those of the single-scale image's from_source, but for level.