`siibra`

Subpackages

Submodules

Package Contents

Classes

`MapIndex`	Identifies a unique region in a ParcellationMap, combining its labelindex (the "color") and mapindex (the number of the 3Dd map, in case multiple are provided).
`MapType`	Generic enumeration.
`Point`	A single 3D point in reference space.
`PointSet`	A set of 3D points in the same reference space,
`Warmup`
`WarmupLevel`	int([x]) -> integer

Functions

`__dir__`()
`__getattr__`(attr)
`get_map`(parcellation, space[, maptype])
`get_region`(parcellation, region)
`get_template`(space_spec, **kwargs)
`set_cache_size`(maxsize_gbyte)
`set_feasible_download_size`(maxsize_gbyte)
`set_log_level`(level)
`warm_cache`([level])	Preload preconfigured siibra concepts.

Attributes

`QUIET`
`VERBOSE`
`__version__`
`cache`
`fetch_ebrains_token`
`find_regions`
`from_json`
`logger`
`set_ebrains_token`

class siibra.MapIndex(volume: int = None, label: int = None, fragment: str = None)

Identifies a unique region in a ParcellationMap, combining its labelindex (the “color”) and mapindex (the number of the 3Dd map, in case multiple are provided).

__eq__(other): Return self==value.

__hash__(): Return hash(self).

__repr__(): Return repr(self).

__str__(): Return str(self).

classmethod from_dict(spec: dict)

class siibra.MapType

Generic enumeration.

Derive from this class to define new enumerations.

LABELLED = 1

STATISTICAL = 2

class siibra.Point(coordinatespec, space=None, sigma_mm: float = 0.0, label: int | float | tuple = None)

A single 3D point in reference space.

property boundingbox

property homogeneous: The homogenous coordinate of this point as a 4-tuple, obtained by appending ‘1’ to the original 3-tuple.

property id: str

property volume: The volume of a point can be nonzero if it has a location uncertainty.

__add__(other): Add the coordinates of two points to get a new point representing.

__eq__(other: Point): Required to provide comparison and making the object hashable

__ge__(other): Return self>=value.

__getitem__(index): Index access to the coefficients of this point.

__gt__(other): Return self>value.

__hash__(): Return hash(self).

__iter__(): Return an iterator over the location, so the Point can be easily cast to list or tuple.

__le__(other): Return self<=value.

__len__()

__lt__(other): Return self<value.

__mul__(number: float): Return a new point with multiplied coordinates in the same space.

__repr__(): Return repr(self).

__setitem__(index, value): Write access to the coefficients of this point.

__sub__(other): Substract the coordinates of two points to get a new point representing the offset vector. Alternatively, subtract an integer from the all coordinates of this point to create a new one. TODO this needs to maintain sigma

__truediv__(number: float): Return a new point with divided coordinates in the same space.

bigbrain_section(): Estimate the histological section number of BigBrain which corresponds to this point. If the point is given in another space, a warping to BigBrain space will be tried.

get_enclosing_cube(width_mm): Create a bounding box centered around this point with the given width. TODO this should respect sigma (in addition or instead of the offset)

intersection(other: siibra.locations.location.Location) → Point: Return the intersection of two BrainStructures, ie. the other BrainStructure filtered by this BrainStructure.

static parse(spec, unit='mm') → Tuple[float, float, float]

Converts a 3D coordinate specification into a 3D tuple of floats.

Parameters:

spec (Any of str, tuple(float,float,float)) – For string specifications, comma separation with decimal points are expected.
unit (str) – specification of the unit (only ‘mm’ supported so far)

Return type:

tuple(float, float, float)

transform(affine: numpy.ndarray, space=None)

Returns a new Point obtained by transforming the coordinate of this one with the given affine matrix. TODO this needs to maintain sigma

Parameters:

affine (numpy 4x4 ndarray) – affine matrix
space (str, Space, or None) –
Target reference space which is reached after applying the transform

Note

The consistency of this cannot be checked and is up to the user.

warp(space): Creates a new point by warping this point to another space TODO this needs to maintain the sigma parameter!

A set of 3D points in the same reference space, defined by a list of coordinates.

property boundingbox: Return the bounding box of these points. TODO revisit the numerical margin of 1e-6, should not be necessary.

property centroid

property coordinates: numpy.ndarray

property has_constant_sigma: bool

property homogeneous: Access the list of 3D point as an Nx4 array of homogeneous coordinates.

property label_colors: return a color for the given label.

property sigma: List[int | float]: The list of sigmas corresponding to the points.

property volume

__eq__(other: PointSet): Required to provide comparison and making the object hashable

__getitem__(index: int)

__hash__(): Return hash(self).

__iter__(): Return an iterator over the coordinate locations.

__len__(): The number of points in this PointSet.

__str__(): Return str(self).

as_list(): Return the point set as a list of 3D tuples.

find_clusters(min_fraction=1 / 200, max_fraction=1 / 8)

intersection(other: siibra.locations.location.Location)

Return the subset of points that are inside the given mask.

NOTE: The affine matrix of the image must be set to warp voxels coordinates into the reference space of this Bounding Box.

transform(affine: numpy.ndarray, space=None)

Returns a new PointSet obtained by transforming the coordinates of this one with the given affine matrix.

Parameters:

affine (numpy 4x4 ndarray) – affine matrix
space (reference space (id, name, or Space)) – Target reference space which is reached after applying the transform. Note that the consistency of this cannot be checked and is up to the user.

warp(space, chunksize=1000): Creates a new point set by warping its points to another space

class siibra.Warmup

classmethod deregister_warmup_fn(original_fn)

static fn_eql(wrapped_fn, original_fn)

classmethod is_registered(fn)

classmethod register_warmup_fn(warmup_level: WarmupLevel = WarmupLevel.INSTANCE, *, is_factory=False)

classmethod warmup(warmup_level: WarmupLevel = WarmupLevel.INSTANCE, *, max_workers=4)

class siibra.WarmupLevel

Inheritance diagram of siibra.WarmupLevel

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by ‘+’ or ‘-’ and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal. >>> int(‘0b100’, base=0) 4

DATA = 5

INSTANCE = 1

TEST

siibra.__dir__()

siibra.__getattr__(attr: str)

siibra.get_map(parcellation: str, space: str, maptype: commons.MapType = MapType.LABELLED, **kwargs)

siibra.get_region(parcellation: str, region: str)

siibra.get_template(space_spec: str, **kwargs)

siibra.set_cache_size(maxsize_gbyte: int)

siibra.set_feasible_download_size(maxsize_gbyte)

siibra.set_log_level(level)

siibra.warm_cache(level=WarmupLevel.INSTANCE)

Preload preconfigured siibra concepts.

Siibra relies on preconfigurations that simplify integrating various concepts such as parcellations, refernce spaces, and multimodal data features. By preloading the instances, siibra commits all preconfigurations to the memory at once instead of commiting them when required.

siibra.QUIET

siibra.VERBOSE

siibra.__version__

siibra.cache

siibra.fetch_ebrains_token

siibra.find_regions

siibra.from_json

siibra.logger

siibra.set_ebrains_token