Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources.

Code ↔ Paper

25 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 25 matches

[1] § Results › Multimodal comparison of brain areas ↔ examples/tutorials/2025-paper-fig5.py, lines 17–38 · score 0.98 · high resolution scans, underlying cloud resource, chosen position, oriented cortical, motor area, full resolution image
[2] § Results › Anatomical evaluation of subcortical maps ↔ examples/tutorials/2025-paper-fig6.py, lines 16–49 · score 0.84 · ventral intermediate nucleus, subcortical maps, structural connectivity, thalamus, diffusion, Precentral
[3] § Results › Multimodal comparison of brain areas ↔ siibra/livequeries/bigbrain.py, lines 253–394 · score 0.79 · intersected layer IV, cortical image patch, cortical layer surface, BigBrain, closest, oriented
[4] § Methods › Memory-efficient representation of sparse regional maps ↔ siibra/volumes/parcellationmap.py, lines 1142–1283 · score 0.77 · Statistical maps, region maps, image volume, parcellation maps, coordinate space, numerical
[5] § Results › Anatomically guided reproducible extraction of microscopy data ↔ examples/tutorials/2025-paper-fig5.py, lines 17–38 · score 0.74 · high resolution image, siibra allows, full resolution, BigBrain, precomputed, microscopy
[6] § Results › Design and architecture of siibra ↔ backend/app/bkwdcompat.py, lines 104–173 · score 0.74 · DiFuMo, MNI Colin, cytoarchitectonic maps, cortical layer, Julich Brain, bundle
[7] § Methods › Support of FAIR principles ↔ siibra/volumes/sparsemap.py, lines 489–545 · score 0.72 · NumPy, Nifti1Image, nibabel, nilearn, fetching, siibra
[8] § Methods › Modeling spatial entities in different reference coordinate systems ↔ siibra/livequeries/bigbrain.py, lines 253–394 · score 0.69 · cortical image patch, point clouds, BigBrain, vertices, probability, locations
[9] § Methods › Memory-efficient representation of sparse regional maps ↔ siibra/volumes/sparsemap.py, lines 338–468 · score 0.67 · bounding boxes, Statistical maps, image volume, sparse, weights, probabilistic
[10] § Methods › Separation of content from code › Live queries ↔ siibra/livequeries/ebrains.py, lines 34–145 · score 0.64 · EBRAINS Knowledge Graph, anatomically anchored, live queries, siibra, Atlas
[11] § Results › Anatomical characterization and multimodal profiling for regions of interest ↔ siibra/volumes/parcellationmap.py, lines 1142–1283 · score 0.63 · statistical maps, image volume, brain regions, reference space, correlation, assignment
[12] § Methods › Modular software architecture ↔ siibra/core/space.py, lines 26–143 · score 0.61 · inflated surface, FreeSurfer, pial, volumetric, space, siibra
[13] § Methods › Modeling spatial entities in different reference coordinate systems ↔ hbp_spatial_backend/__init__.py, lines 150–224 · score 0.61 · folding patterns, transform coordinates, alignment, diffeomorphisms, Brain
[14] § Results › Visually guided exploration from full brain networks to cells ↔ backend/app/bkwdcompat.py, lines 104–173 · score 0.61 · MNI Colin, cytoarchitectonic maps, cortical layer, Julich Brain, parcellation, space
[15] § Results › Expansion with new contents ↔ backend/app/bkwdcompat.py, lines 69–102 · score 0.61 · Allen Mouse Brain, Waxholm Space, Rat Brain, Brain Atlas, parcellation
[16] § Results › Anatomically guided reproducible extraction of microscopy data ↔ siibra/features/image/sections.py, lines 38–107 · score 0.59 · cortical patch, bounding box, BigBrain, fetch, resolutions, volume
[17] § Results › Anatomical characterization and multimodal profiling for regions of interest ↔ siibra/volumes/parcellationmap.py, lines 1285–1383 · score 0.59 · Gaussian blob, uncertain coordinates, kernel, split, assignment, volume
[18] § Results › Anatomical characterization and multimodal profiling for regions of interest ↔ siibra/livequeries/allen.py, lines 69–120 · score 0.59 · Allen Human Brain, gene expression, Human Brain Atlas, microarray, tissue, connectivity
[19] § Results › Anatomical characterization and multimodal profiling for regions of interest ↔ siibra/features/tabular/gene_expression.py, lines 30–170 · score 0.58 · gene expression, molecular, Human Brain Atlas, tabular, microarray, Allen
[20] § Methods › Linking data features to atlas elements ↔ siibra/features/tabular/tabular.py, lines 29–157 · score 0.57 · neurotransmitter receptor, tissue samples, properties, locations, anatomical, brain
[21] § Methods › Separation of content from code › Live queries ↔ siibra/livequeries/allen.py, lines 69–120 · score 0.56 · Allen Human Brain, live queries, microarray, API, interfaces, connecting
[22] § Methods › Linking data features to atlas elements ↔ examples/tutorials/2025-paper-fig6.py, lines 16–49 · score 0.55 · inter subject variability, reliable, histological, anatomical, maps
[23] § Results › Anatomical characterization and multimodal profiling for regions of interest ↔ siibra/features/tabular/gene_expression.py, lines 30–170 · score 0.53 · gene expressions, Human Brain Atlas, tabular, Allen, co, retrieved
[24] § Results › Multimodal comparison of brain areas ↔ siibra/livequeries/allen.py, lines 142–200 · score 0.52 · microarray probe, tissue samples, donors, Allen, genes, MNI
[25] § Methods › Modular software architecture ↔ src/viewerModule/nehuba/nehubaViewerGlue/nehubaViewerGlue.component.spec.ts, lines 1–66 · score 0.51 · user interactions, user interface, touch, reactive, Angular, viewer

Paper

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The authors' code

Python · 1,859 lines · 73 KB · Apache-2.0 · 3 matches

# Copyright 2018-2026
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Provides spatial representations for parcellations and regions."""
from collections import defaultdict
from dataclasses import dataclass, asdict
from typing import Union, Dict, List, TYPE_CHECKING, Iterable, Tuple, Literal, NamedTuple
import numpy as np
import pandas as pd
from scipy.ndimage import distance_transform_edt
from . import volume as _volume
from .providers import provider
from .. import exceptions
from ..commons import (
MapIndex,
MapType,
compare_arrays,
resample_img_to_img,
connected_components,
clear_name,
create_key,
create_gaussian_kernel,
siibra_tqdm,
Species,
CompareMapsResult,
generate_uuid,
logger,
QUIET,
)
from ..core import concept, space, parcellation, region as _region
from ..locations import location, point, pointcloud
if TYPE_CHECKING:
from ..core.region import Region
from nilearn.maskers import NiftiLabelsMasker, SurfaceLabelsMasker
import json
from itertools import groupby
from os import path, rename
from ..retrieval.cache import CACHE
@dataclass
class MapAssignment:
input_structure: int
centroid: Union[Tuple[np.ndarray], point.Point]
volume: int
fragment: str
map_value: np.ndarray
time: Union[int, float, None]
@dataclass
class AssignImageResult(CompareMapsResult, MapAssignment):
pass
class _CompressedMapSpec(NamedTuple):
"""What a compression produces, before `compress()` wraps it as a Map."""
volumes: List[_volume.Volume]
indices: Dict[str, List[Dict]]
class Map(concept.AtlasConcept, configuration_folder="maps"):
def __init__(
self,
identifier: str,
name: str,
space_spec: dict,
parcellation_spec: dict,
indices: Dict[str, List[Dict]],
volumes: list = [],
shortname: str = "",
description: str = "",
modality: str = None,
publications: list = [],
datasets: list = [],
prerelease: bool = False,
):
"""
Constructs a new parcellation object.
Parameters
----------
identifier: str
Unique identifier of the parcellation
name: str
Human-readable name of the parcellation
space_spec: dict
Specification of the space (use @id or name fields)
parcellation_spec: str
Specification of the parcellation (use @id or name fields)
indices: dict
Dictionary of indices for the brain regions.
Keys are exact region names.
Per region name, a list of dictionaries with fields "volume" and "label" is expected,
where "volume" points to the index of the Volume object where this region is mapped,
and optional "label" is the voxel label for that region.
For continuous / probability maps, the "label" can be null or omitted.
For single-volume labelled maps, the "volume" can be null or omitted.
volumes: list[Volume]
parcellation volumes
shortname: str, optional
Shortform of human-readable name
description: str, optional
Textual description of the parcellation
modality: str, default: None
Specification of the modality used for creating the parcellation
publications: list
List of associated publications, each a dictionary with "doi" and/or "citation" fields
datasets : list
datasets associated with this concept
"""
concept.AtlasConcept.__init__(
self,
identifier=identifier,
name=name,
species=None, # inherits species from space
shortname=shortname,
description=description,
publications=publications,
datasets=datasets,
modality=modality,
prerelease=prerelease,
)
self._space_spec = space_spec
self._parcellation_spec = parcellation_spec
# Since the volumes might include 4D arrays, where the actual
# volume index points to a z coordinate, we create subvolume
# indexers from the given volume provider if 'z' is specified.
self._indices: Dict[str, List[MapIndex]] = {}
self.volumes: List[_volume.Volume] = []
remap_volumes = {}
# TODO: This assumes knowledge of the preconfigruation specs wrt. z.
# z to subvolume conversion should probably go to the factory.
for regionname, indexlist in indices.items():
k = clear_name(regionname)
self._indices[k] = []
for index in indexlist:
vol = index.get('volume', 0)
assert vol in range(len(volumes))
z = index.get('z')
if (vol, z) not in remap_volumes:
if z is None:
self.volumes.append(volumes[vol])
else:
self.volumes.append(_volume.Subvolume(volumes[vol], z))
remap_volumes[vol, z] = len(self.volumes) - 1
self._indices[k].append(
MapIndex(volume=remap_volumes[vol, z], label=index.get('label'), fragment=index.get('fragment'))
)
# make sure the indices are unique - each map/label pair should appear at most once
all_indices = sum(self._indices.values(), [])
seen = set()
duplicates = {x for x in all_indices if x in seen or seen.add(x)}
self._nonunique_indices = duplicates
self._affine_cached = None
self._compressed_cached: Dict[tuple, "Map"] = {}
@property
def key(self):
_id = self.id
return create_key(_id[len("siibra-map-v0.0.1"):])
@property
def species(self) -> Species:
# lazy implementation
if self._species_cached is None:
self._species_cached = self.space.species
return self.space._species_cached
def get_index(self, region: Union[str, "Region"]):
"""
Returns the unique index corresponding to the specified region.
Tip
----
Use find_indices() method for a less strict search returning all matches.
Parameters
----------
region: str or Region
Returns
-------
MapIndex
Raises
------
NonUniqueIndexError
If not unique or not defined in this parcellation map.
"""
matches = self.find_indices(region)
if len(matches) > 1:
# if there is an exact match, we still use it. If not, we cannot proceed.
regionname = region.name if isinstance(region, _region.Region) \
else region
for index, matched_name in matches.items():
if matched_name == regionname:
return index
raise exceptions.NonUniqueIndexError(
f"The specification '{region}' matches multiple mapped "
f"structures in {str(self)}: {list(matches.values())}"
)
elif len(matches) == 0:
raise exceptions.NonUniqueIndexError(
f"The specification '{region}' does not match to any structure mapped in {self}"
)
else:
return next(iter(matches))
def find_indices(self, region: Union[str, "Region"]):
"""
Returns the volume/label indices in this map which match the given
region specification.
Parameters
----------
region: str or Region
Returns
-------
dict
- keys: MapIndex
- values: region name
"""
if region in self._indices:
return {
idx: region
for idx in self._indices[region]
}
regionname = region.name if isinstance(region, _region.Region) else region
matched_region_names = set(_.name for _ in (self.parcellation.find(regionname)))
matches = matched_region_names & self._indices.keys()
if len(matches) == 0:
logger.warning(f"Region {regionname} not defined in {self}")
return {
idx: regionname
for regionname in matches
for idx in self._indices[regionname]
}
def get_region(self, label: int = None, volume: int = 0, index: MapIndex = None):
"""
Returns the region mapped by the given index, if any.
Tip
----
Use get_index() or find_indices() methods to obtain the MapIndex.
Parameters
----------
label: int, default: None
volume: int, default: 0
index: MapIndex, default: None
Returns
-------
Region
A region object defined in the parcellation map.
"""
if isinstance(label, MapIndex) and index is None:
raise TypeError("Specify MapIndex with 'index' keyword.")
if index is None:
index = MapIndex(volume, label)
matches = [
regionname
for regionname, indexlist in self._indices.items()
if index in indexlist
]
if len(matches) == 0:
logger.warning(f"Index {index} not defined in {self}")
return None
elif len(matches) == 1:
return self.parcellation.get_region(matches[0])
else:
# this should not happen, already tested in constructor
raise RuntimeError(f"Index {index} is not unique in {self}")
@property
def space(self):
for key in ["@id", "name"]:
if key in self._space_spec:
return space.Space.get_instance(self._space_spec[key])
return space.Space(None, "Unspecified space", species=Species.UNSPECIFIED_SPECIES)
@property
def parcellation(self):
for key in ["@id", "name"]:
if key in self._parcellation_spec:
return parcellation.Parcellation.get_instance(self._parcellation_spec[key])
logger.warning(
f"Cannot determine parcellation of {self.__class__.__name__} "
f"{self.name} from {self._parcellation_spec}"
)
return None
@property
def labels(self):
"""
The set of all label indices defined in this map, including "None" if
not defined for one or more regions.
"""
return {d.label for v in self._indices.values() for d in v}
@property
def has_unique_labels(self) -> bool:
"""
True if every mapped region carries a distinct label across all volumes and
fragments. Surface and fragmented volumetric maps are commonly labelled per
hemisphere, so the same label may denote two different regions.
"""
labels = [ix.label for ixs in self._indices.values() for ix in ixs]
return None not in labels and len(labels) == len(set(labels))
@property
def maptype(self) -> MapType:
if all(isinstance(_, int) for _ in self.labels):
return MapType.LABELLED
elif self.labels == {None}:
return MapType.STATISTICAL
else:
raise RuntimeError(
f"Inconsistent label indices encountered in {self}"
)
def __len__(self):
return len(self.volumes)
@property
def regions(self):
return list(self._indices)
def get_volume(
self,
region: Union[str, "Region"] = None,
*,
index: MapIndex = None,
**kwargs,
) -> Union[_volume.Volume, _volume.FilteredVolume, _volume.Subvolume]:
try:
length = len([arg for arg in [region, index] if arg is not None])
assert length == 1
except AssertionError:
if length > 1:
raise exceptions.ExcessiveArgumentException(
"One and only one of region or index can be defined for `get_volume`."
)
mapindex = None
if region is not None:
try:
assert isinstance(region, (str, _region.Region))
except AssertionError:
raise TypeError(f"Please provide a region name or region instance, not a {type(region)}")
mapindex = self.get_index(region)
if index is not None:
assert isinstance(index, MapIndex)
mapindex = index
if mapindex is None:
if len(self) == 1:
mapindex = MapIndex(volume=0, label=None)
elif len(self) > 1:
assert self.maptype == MapType.LABELLED, f"Cannot merge multiple volumes of map type {self.maptype}. Please specify a region or index."
logger.info(
"Map provides multiple volumes and no region specification is"
" provided. Reducing them to a single volume resampled to space template."
)
labels = list(range(1, len(self.volumes) + 1)) if self.labels == {1} else None
merged_volume = _volume.ReducedVolume(self.volumes, labels)
return merged_volume
else:
raise exceptions.NoVolumeFound("Map provides no volumes.")
kwargs_fragment = kwargs.pop("fragment", None)
if kwargs_fragment is not None:
if (mapindex.fragment is not None) and (kwargs_fragment != mapindex.fragment):
raise exceptions.ConflictingArgumentException(
f"Conflicting specifications for fetching volume fragment{f' for region {region}'}: "
f"supplied: {kwargs_fragment}, preconfigured: {mapindex.fragment}"
)
if mapindex.volume is None:
mapindex.volume = 0
if mapindex.volume >= len(self.volumes):
raise IndexError(
f"{self} provides {len(self)} mapped volumes, but #{mapindex.volume} was requested."
)
if mapindex.label is None and mapindex.fragment is None:
return self.volumes[mapindex.volume]
return _volume.FilteredVolume(
parent_volume=self.volumes[mapindex.volume],
label=mapindex.label,
fragment=kwargs_fragment or mapindex.fragment,
)
def fetch(
self,
region: Union[str, "Region"] = None,
*,
index: MapIndex = None,
**fetch_kwargs
):
"""
Fetches one particular volume of this parcellation map.
If there's only one volume, this is the default, otherwise further
specification is requested:
- the volume index,
- the MapIndex (which results in a regional map being returned)
You might also consider fetch_iter() to iterate the volumes, or
compress() to produce a single-volume parcellation map.
Parameters
----------
region: str, Region
Specification of a region name, resulting in a regional map
(mask or statistical map) to be returned.
index: MapIndex
Explicit specification of the map index, typically resulting
in a regional map (mask or statistical map) to be returned.
Note that supplying 'region' will result in retrieving the map index of that region
automatically.
**fetch_kwargs
- resolution_mm: resolution in millimeters as float or a tuple of floats
- format: the format of the volume, like "mesh" or "nii"
- voi: a BoundingBox of interest
Note
----
Not all keyword arguments are supported for volume formats. Format
is restricted by available formats (check formats property).
Returns
-------
An image or mesh
"""
vol = self.get_volume(region=region, index=index, **fetch_kwargs)
return vol.fetch(**fetch_kwargs)
def fetch_iter(self, **kwargs):
"""
Returns an iterator to fetch all mapped volumes sequentially.
All arguments are passed on to function Map.fetch(). By default, it
will go through all fragments as well.
"""
fragments = {kwargs.pop('fragment', None)} or self.fragments or {None}
return (
self.fetch(
index=MapIndex(volume=i, label=None, fragment=frag), **kwargs
)
for frag in fragments
for i in range(len(self))
)
@property
def provides_image(self):
return any(v.provides_image for v in self.volumes)
@property
def fragments(self):
return sorted({
index.fragment
for indices in self._indices.values()
for index in indices
if index.fragment is not None
})
@property
def provides_mesh(self):
return any(v.provides_mesh for v in self.volumes)
@property
def formats(self):
return {f for v in self.volumes for f in v.formats}
@property
def is_labelled(self):
return self.maptype == MapType.LABELLED
@property
def affine(self):
if self._affine_cached is None:
# we compute the affine from a volumetric volume provider
for fmt in _volume.Volume.SUPPORTED_FORMATS:
if fmt not in _volume.Volume.MESH_FORMATS:
if fmt not in self.formats:
continue
try:
self._affine_cached = self.fetch(index=MapIndex(volume=0), format=fmt).affine
break
except Exception:
logger.debug("Caught exceptions:\n", exc_info=1)
continue
else:
raise RuntimeError(f"No volumetric provider in {self} to derive the affine matrix.")
if not isinstance(self._affine_cached, np.ndarray):
logger.error("invalid affine:", self._affine_cached)
return self._affine_cached
def __iter__(self):
return self.fetch_iter()
def compress(self, **kwargs) -> "Map":
"""
Convert this map into an equivalent map whose labels are unique across the
whole image or surface, re-labelling regions sequentially from 1.
Volumetric maps are merged into a single labelled volume on the grid of the
space template. Surface maps keep their fragments, since a vertex belongs to
exactly one of them, and are relabelled so that a label identifies one region
rather than one region per hemisphere.
The compressed image is built and kept as a memory-mapped array on disk, so
neither building nor using it holds the full volume in RAM. Results are also
cached per instance, keyed by the fetch arguments.
Note
----
Labels that no region claims are dropped to background. Surface maps
sometimes carry such labels for technical reasons (e.g. a brainstem label
so the map loads in freesurfer) and they have no name to report.
Parameters
----------
**kwargs
Fetch arguments applied both to the space template, which defines the
output grid, and to the mapped volumes, so that sources are read at the
same resolution and resampling is usually unnecessary. `variant` is
passed to the template only.
Returns
-------
parcellationmap.Map
Raises
------
ValueError
If this map is not labelled.
RuntimeError
If there is nothing to merge.
"""
if not self.is_labelled:
raise ValueError(f"Compression is not possible for {self.maptype} maps.")
if len(self.volumes) == 1 and (not self.fragments or self.has_unique_labels):
raise RuntimeError(
"The map cannot be compressed: it is already a single, uniquely labelled volume."
)
key = tuple(sorted(kwargs.items()))
try:
hash(key)
except TypeError: # an unhashable fetch argument, e.g. target_affine
logger.debug(f"Cannot cache compression of {self} for {kwargs}.")
key = None
if key is not None and key in self._compressed_cached:
return self._compressed_cached[key]
entries = sorted(
(index.volume, index.fragment or "", index.label, regionname)
for regionname, indices in self._indices.items()
for index in indices
)
relabelling_plan = [
(MapIndex(volume=vol, fragment=frag or None, label=label), regionname, newlabel)
for newlabel, (vol, frag, label, regionname) in enumerate(entries, start=1)
]
# everything that shapes the result, so a changed configuration or changed
# fetch arguments never reuse a stale artifact
signature = json.dumps(
{
"map": self.id,
"space": self.space.id,
"kwargs": {k: str(v) for k, v in sorted(kwargs.items())},
"indices": [
[index.volume, index.fragment, index.label, regionname]
for index, regionname, _ in relabelling_plan
],
},
sort_keys=True, # ensure_ascii=True by default: CACHE encodes as ascii
)
if self.provides_image:
spec = self._compress_image_map(relabelling_plan, signature, **kwargs)
elif self.provides_mesh:
if kwargs:
logger.info(f"Fetch arguments {list(kwargs)} are ignored when compressing a surface map.")
spec = self._compress_surface_map(relabelling_plan, signature)
else:
raise NotImplementedError(f"{self} provides neither image nor mesh data to compress.")
compressed = Map(
identifier=f"{create_key(self.name)}_compressed",
name=f"{self.name} compressed",
space_spec=self._space_spec,
parcellation_spec=self._parcellation_spec,
indices=spec.indices,
volumes=spec.volumes,
)
if key is not None:
self._compressed_cached[key] = compressed
return compressed
def _compress_image_map(
self, plan: List[Tuple[MapIndex, str, int]], signature: str, **kwargs
) -> "_CompressedMapSpec":
"""
Merge the volumes and fragments of a volumetric map into a single labelled
volume on the template grid. See `compress()`.
Source volumes are streamed one at a time into a memory-mapped output array,
so peak memory is one source volume rather than the whole map. Where regions
overlap, the later entry of the relabelling plan wins; the number of
contested voxels is reported.
"""
cachefile = CACHE.build_filename(signature, suffix=".npy")
metafile = f"{cachefile}.json"
if path.isfile(cachefile) and path.isfile(metafile):
try:
with open(metafile) as f:
meta = json.load(f)
data = np.lib.format.open_memmap(cachefile, mode="r")
logger.debug(f"Reusing the compressed {self} from {cachefile}")
return _CompressedMapSpec(
volumes=[_volume.from_array(
data, np.array(meta["affine"]), self.space.id,
name=self.name + " compressed", cache=False,
)],
indices=meta["indices"],
)
except (ValueError, OSError, KeyError):
logger.debug(f"Discarding unreadable compression cache {cachefile}")
# only the grid of the template is needed - never load its data, which for
# large templates (e.g. BigBrain) dominates both time and memory
variant = kwargs.pop("variant", None)
template_img = self.space.get_template(variant=variant).fetch(**kwargs)
shape, affine = tuple(template_img.shape[:3]), template_img.affine
dtype = np.min_scalar_type(len(plan)) # sized by region count, not by the template
nbytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
gib = nbytes / 1024**3
logger.info(
f"Compressing {self} into a {gib:.2f} GiB labelled volume "
f"(shape {shape}, {np.dtype(dtype).name}) from {len(self.volumes)} volume(s)."
)
units = [(unit, list(entries)) for unit, entries in groupby(
plan, key=lambda entry: (entry[0].volume, entry[0].fragment)
)]
tempfile = f"{cachefile}_temp"
result = np.lib.format.open_memmap(tempfile, mode="w+", dtype=dtype, shape=shape)
region_indices = defaultdict(list)
contested, unmapped = 0, []
try:
with provider.SubvolumeProvider.UseCaching():
for (volume, fragment), entries in siibra_tqdm(
units, total=len(units), unit=" maps",
desc=f"Compressing {len(self.volumes)} volume(s) and "
f"{len(self.fragments) or 1} fragment(s) of {self.name}",
disable=len(units) == 1,
):
img = self.fetch(index=MapIndex(volume=volume, fragment=fragment), **kwargs)
if tuple(img.shape[:3]) == shape and np.allclose(img.affine, affine):
img_data = np.asanyarray(img.dataobj)
else:
logger.debug(f"Compression requires resampling volume {volume} (nearest)")
img_data = np.asanyarray(resample_img_to_img(img, template_img).dataobj)
observed = set(np.unique(img_data)) - {0}
for index, regionname, newlabel in entries:
update_voxels = img_data == index.label
if not update_voxels.any():
unmapped.append(regionname)
contested += int(np.count_nonzero(result[update_voxels]))
result[update_voxels] = newlabel
region_indices[regionname].append({"volume": 0, "label": newlabel})
observed.discard(index.label)
if observed:
logger.warning(
f"Labels {sorted(observed)} are observed in volume {volume} "
f"(fragment {fragment}) of {self}, but no region is defined for them."
)
del img_data
result.flush()
finally:
del result # close the memmap before renaming, required on Windows
rename(tempfile, cachefile)
with open(metafile, "w") as f:
json.dump({"affine": affine.tolist(), "indices": region_indices}, f)
if contested:
logger.info(
f"{contested} voxel(s) are mapped by more than one region in {self}; "
"the last entry of the relabelling order was kept for each."
)
if unmapped:
logger.warning(f"{len(unmapped)} region(s) have no voxels after compression:\n{unmapped}")
return _CompressedMapSpec(
volumes=[_volume.from_array(
np.lib.format.open_memmap(cachefile, mode="r"), affine, self.space.id,
name=self.name + " compressed", cache=False,
)],
indices=region_indices,
)
def _compress_surface_map(
self, plan: List[Tuple[MapIndex, str, int]], signature: str
) -> "_CompressedMapSpec":
"""
Relabel the fragments of a surface map so that every region has a globally
unique label. Fragments are preserved, since a vertex belongs to exactly one
of them. See `compress()`.
The label arrays are one per-vertex value per fragment, well under a megabyte
even for the densest fsaverage mesh, so they are held in memory rather than
memory-mapped, and written as GIFTI label files.
Results are cached per fragment as GIFTI label files; a cache hit skips the
relabelling and therefore also its warnings about unnamed labels and empty
regions.
"""
from nibabel import gifti
from os import replace
if len(self.volumes) > 1:
raise NotImplementedError(
f"{self} provides {len(self.volumes)} surface volumes; compression expects one."
)
prov = self.volumes[0]._providers["gii-label"]
filemap, region_indices = {}, defaultdict(list)
units = [(unit, list(entries)) for unit, entries in groupby(
plan, key=lambda entry: (entry[0].volume, entry[0].fragment)
)]
for (_, fragment), entries in siibra_tqdm(
units, total=len(units), unit=" fragments",
desc=f"Relabelling {len(units)} surface fragment(s) of {self.name}",
):
filename = CACHE.build_filename(f"{signature}-{fragment}", suffix=".label.gii")
if not path.isfile(filename):
# read from the provider: Volume.fetch() would also pull the template mesh
data = prov.fetch(fragment=fragment)["labels"]
# GIFTI data arrays support uint8, int32 and float32 only
relabelled = np.zeros_like(data, dtype="int32")
observed = set(np.unique(data)) - {0}
unmapped = []
for index, regionname, newlabel in entries:
selection = data == index.label
if not selection.any():
unmapped.append(regionname)
relabelled[selection] = newlabel
observed.discard(index.label)
if observed:
logger.warning(
f"Labels {sorted(observed)} are observed in fragment '{fragment}' of "
f"{self}, but no region is defined for them."
)
if unmapped:
logger.warning(
f"{len(unmapped)} region(s) have no vertices in fragment "
f"'{fragment}' of {self}:\n{unmapped}"
)
tempfile = CACHE.build_filename(f"{signature}-{fragment}-temp", suffix=".label.gii")
gifti.GiftiImage(darrays=[
gifti.GiftiDataArray(relabelled, intent="NIFTI_INTENT_LABEL")
]).to_filename(tempfile)
replace(tempfile, filename)
for index, regionname, newlabel in entries:
region_indices[regionname].append(
{"volume": 0, "fragment": fragment, "label": newlabel}
)
filemap[fragment] = filename
return _CompressedMapSpec(
volumes=[_volume.from_file(
filemap, space=self.space.id, name=self.name + " compressed", format="gii-label",
)],
indices=region_indices,
)
def compute_centroids(self, split_components: bool = True, **fetch_kwargs) -> Dict[str, pointcloud.PointCloud]:
"""
Compute a dictionary of all regions in this map to their centroids.
By default, the regional masks will be split to connected components
and each point in the PointCloud corresponds to a region component.
Parameters
----------
split_components: bool, default: True
If True, finds the spatial properties for each connected component
found by skimage.measure.label.
Returns
-------
Dict[str, point.Point]
Region names as keys and computed centroids as items.
"""
assert self.provides_image, "Centroid computation for meshes is not supported yet."
centroids = dict()
for regionname, indexlist in siibra_tqdm(
self._indices.items(), unit="regions", desc="Computing centroids"
):
assert regionname not in centroids
# get the mask of the region in this map
with QUIET:
if len(indexlist) >= 1:
merged_volume = _volume.merge(
[
_volume.from_nifti(
self.fetch(index=index, **fetch_kwargs),
self.space,
f"{self.name} - {index}"
)
for index in indexlist
],
labels=[1] * len(indexlist)
)
mapimg = merged_volume.fetch()
elif len(indexlist) == 1:
index = indexlist[0]
mapimg = self.fetch(index=index, **fetch_kwargs) # returns a mask of the region
props = _volume.ComponentSpatialProperties.compute_from_image(
img=mapimg,
space=self.space,
split_components=split_components,
)
try:
centroids[regionname] = pointcloud.from_points([c.centroid for c in props])
except exceptions.EmptyPointCloudError:
centroids[regionname] = None
return centroids
def get_resampled_template(self, **fetch_kwargs) -> _volume.Volume:
"""
Resample the reference space template to fetched map image. Uses
nilearn.image.resample_to_img to resample the template.
Parameters
----------
**fetch_kwargs: takes the arguments of Map.fetch()
Returns
-------
Volume
"""
from nilearn.image import resample_to_img
source_template = self.space.get_template().fetch()
map_image = self.fetch(**fetch_kwargs)
img = resample_to_img(source_template, map_image, interpolation='continuous')
return _volume.from_array(
data=img.dataobj,
affine=img.affine,
space=self.space,
name=f"{source_template} resampled to coordinate system of {self}"
)
def colorize(
self,
values: Union[dict, "pd.Series", "pd.DataFrame"],
background_label: Union[int, float] = 0,
**masker_kwargs
):
"""Colorize the map with the provided regional values.
Parameters
----------
values : dict
Dictionary mapping regions to values
Return
------
Nifti1Image
"""
if not self.is_labelled:
raise NotImplementedError("Since statistical maps can overlap, this is not yet implemented.")
if isinstance(values, dict):
resolved = {}
for spec, value in values.items():
matched = set(self.find_indices(spec).values()) # {MapIndex: regionname}
if not matched:
logger.warning(f"'{spec}' is not mapped in {self} - skipped in colorization.")
continue
for regionname in matched:
resolved[regionname] = value
if not resolved:
raise ValueError(f"None of the {len(values)} provided keys are mapped in {self}.")
values = pd.Series({r: resolved.get(r, background_label) for r in self.regions})
masker_kwargs.setdefault("background_label", background_label)
masker = self.as_nilearn_masker(**masker_kwargs)
masker.fit()
# ensure the order of columns follow the bids table used for masker
values = values[masker.lut["name"]]
return masker.inverse_transform(values)
def get_colormap(self, region_specs: Iterable = None, *, fill_uncolored: bool = False):
"""
Generate a matplotlib colormap from known rgb values of label indices.
Parameters
----------
region_specs: iterable(regions), optional
Optional parameter to only color the desired regions.
fill_uncolored: bool , optional
If a region has no preconfigured color, a color will be randomly (reproducible) created.
Returns
-------
ListedColormap
"""
try:
from matplotlib.colors import ListedColormap
except ImportError as e:
logger.error(
"matplotlib not available. Please install matplotlib to create a matplotlib colormap."
)
raise e
if fill_uncolored:
seed = len(self.regions)
np.random.seed(seed)
logger.info(f"Random colors are allowed for regions without preconfgirued colors. Random seed: {seed}.")
colors = {}
if region_specs is not None:
include_region_names = {
self.parcellation.get_region(region_spec).name for region_spec in region_specs
}
else:
include_region_names = None
use_volindx = False
if len(self.volumes) > 1 and self.labels == {1}:
use_volindx = True
logger.info("Using sequential relabling determined by the order of volumes.")
no_predefined_color = []
for regionname, indices in self._indices.items():
for index in indices:
if index.label is None:
continue
if (include_region_names is not None) and (regionname not in include_region_names):
continue
else:
region = self.get_region(index=index)
if region.rgb is not None:
if use_volindx:
colors[index.volume + 1] = region.rgb
else:
colors[index.label] = region.rgb
elif fill_uncolored:
random_clr = [np.random.randint(0, 255) for r in range(3)]
while random_clr in list(colors.values()):
random_clr = [np.random.randint(0, 255) for r in range(3)]
colors[index.label] = random_clr
else:
no_predefined_color.append(region.name)
if len(colors) == 0:
raise exceptions.NoPredifinedColormapException(
f"There is no predefined/preconfigured colormap for '{self}'."
"Set `fill_uncolored=True` to get a reproducible colormap."
)
if no_predefined_color:
logger.info(
f"No preconfigured color found for the following regions."
"Use `fill_uncolored=True` to display with a non-background color.\n"
f"{no_predefined_color}"
)
max_label_index = max(colors.keys())
palette = np.array(
[
list(colors[i]) + [1] if i in colors else [0, 0, 0, 0]
for i in range(max_label_index + 1)
]
) / [255, 255, 255, 1]
return ListedColormap(palette)
def sample_locations(self, regionspec, numpoints: int):
""" Sample 3D locations inside a given region.
The probability distribution is approximated from the region mask based
on the squared distance transform.
Parameters
----------
regionspec: Region or str
Region to be used
numpoints: int
Number of samples to draw
Returns
-------
PointCloud
Sample points in physical coordinates corresponding to this
parcellationmap
"""
index = self.get_index(regionspec)
mask = self.fetch(index=index)
arr = np.asanyarray(mask.dataobj)
if arr.dtype.char in np.typecodes['AllInteger']:
# a binary mask - use distance transform to get sampling weights
W = distance_transform_edt(np.asanyarray(mask.dataobj))**2
else:
# a statistical map - interpret directly as weights
W = arr
p = (W / W.sum()).ravel()
XYZ_ = np.array(
np.unravel_index(np.random.choice(len(p), numpoints, p=p), W.shape)
).T
XYZ = np.dot(mask.affine, np.c_[XYZ_, np.ones(numpoints)].T)[:3, :].T
return pointcloud.PointCloud(XYZ, space=self.space)
def to_sparse(self):
"""
Creates a SparseMap object from this parcellation map object.
Returns
-------
SparseMap
"""
from .sparsemap import SparseMap
indices = {
regionname: [
{'volume': idx.volume, 'label': idx.label, 'fragment': idx.fragment}
for idx in indexlist
]
for regionname, indexlist in self._indices.items()
}
return SparseMap(
identifier=self.id,
name=self.name,
space_spec={'@id': self.space.id},
parcellation_spec={'@id': self.parcellation.id},
indices=indices,
volumes=self.volumes,
shortname=self.shortname,
description=self.description,
modality=self.modality,
publications=self.publications,
datasets=self.datasets
)
def _read_voxel(
self,
x: Union[int, np.ndarray, List],
y: Union[int, np.ndarray, List],
z: Union[int, np.ndarray, List]
):
def _read_voxels_from_volume(xyz, volimg):
xyz = np.stack(xyz, axis=1)
valid_points_mask = np.all([(0 <= di) & (di < vol_size) for vol_size, di in zip(volimg.shape, xyz.T)], axis=0)
x, y, z = xyz[valid_points_mask].T
valid_points_indices, *_ = np.where(valid_points_mask)
valid_data_points = np.asanyarray(volimg.dataobj)[x, y, z]
return zip(valid_points_indices, valid_data_points)
# integers are just single-element arrays, cast to avoid an extra code branch for integers
x, y, z = [np.array(di) for di in (x, y, z)]
fragments = self.fragments or {None}
return [
(pointindex, volume, fragment, data_point)
for fragment in fragments
for volume, volimg in enumerate(self.fetch_iter(fragment=fragment))
# transformations or user input might produce points outside the volume, filter these out.
for (pointindex, data_point) in _read_voxels_from_volume((x, y, z), volimg)
]
def _assign(
self,
item: location.Location,
minsize_voxel=1,
lower_threshold=0.0,
**kwargs
) -> List[Union[MapAssignment, AssignImageResult]]:
"""
For internal use only. Returns a dataclass, which provides better static type checking.
"""
if isinstance(item, point.Point):
return self._assign_points(
pointcloud.PointCloud([item], item.space, sigma_mm=item.sigma),
lower_threshold
)
if isinstance(item, pointcloud.PointCloud):
return self._assign_points(item, lower_threshold)
if isinstance(item, _volume.Volume):
if isinstance(item, _volume.TimeSeriesVolume):
return self._assign_timeseries_volume(
queryvolume=item,
lower_threshold=lower_threshold,
minsize_voxel=minsize_voxel,
**kwargs
)
else:
return self._assign_volume(
queryvolume=item,
lower_threshold=lower_threshold,
minsize_voxel=minsize_voxel,
**kwargs
)
raise RuntimeError(
f"Items of type {item.__class__.__name__} cannot be used for region assignment."
)
def assign(
self,
item: location.Location,
minsize_voxel=1,
lower_threshold=0.0,
**kwargs
) -> "pd.DataFrame":
"""Assign an input Location to brain regions.
The input is assumed to be defined in the same coordinate space
as this parcellation map.
Parameters
----------
item: Location
A spatial object defined in the same physical reference space as
this parcellation map, which could be a point, set of points, or
image volume. If it is an image, it will be resampled to the same voxel
space if its affine transformation differs from that of the
parcellation map. Resampling will use linear interpolation for float
image types, otherwise nearest neighbor.
minsize_voxel: int, default: 1
Minimum voxel size of image components to be taken into account.
lower_threshold: float, default: 0
Lower threshold on values in the statistical map. Values smaller
than this threshold will be excluded from the assignment computation.
Returns
-------
pandas.DataFrame
A table of associated regions and their scores per component found
in the input image, or per coordinate provided. The scores are:
- Value: Maximum value of the voxels in the map covered by an
input coordinate or input image signal component.
- Pearson correlation coefficient between the brain region map
and an input image signal component (NaN for exact coordinates)
- Contains: Percentage of the brain region map contained in an
input image signal component, measured from their binarized
masks as the ratio between the volume of their intersection
and the volume of the brain region (NaN for exact coordinates)
- Contained: Percentage of an input image signal component
contained in the brain region map, measured from their binary
masks as the ratio between the volume of their intersection and
the volume of the input image signal component (NaN for exact
coordinates)
"""
assignments = self._assign(item, minsize_voxel, lower_threshold, **kwargs)
# format assignments as pandas dataframe
columns = [
"input structure",
"time",
"centroid",
"volume",
"fragment",
"region",
"correlation",
"intersection over union",
"map value",
"map weighted mean",
"map containedness",
"input weighted mean",
"input containedness"
]
if len(assignments) == 0:
return pd.DataFrame(columns=columns).dropna(axis='columns', how='all')
# determine the unique set of observed indices in order to do region lookups
# only once for each map index occurring in the point list
labelled = self.is_labelled # avoid calling this in a loop
observed_indices = { # unique set of observed map indices. NOTE: len(observed_indices) << len(assignments)
(
a.volume,
a.fragment,
a.map_value if labelled else None
)
for a in assignments
}
region_lut = { # lookup table of observed region objects
(v, f, l): self.get_region(
index=MapIndex(
volume=int(v),
label=l if l is None else int(l),
fragment=f
)
)
for v, f, l in observed_indices
}
dataframe_list = []
for a in assignments:
item_to_append = {
"input structure": a.input_structure,
"time": a.time,
"centroid": a.centroid,
"volume": a.volume,
"fragment": a.fragment,
"region": region_lut[
a.volume,
a.fragment,
a.map_value if labelled else None
],
}
# because AssignImageResult is a subclass of Assignment
# need to check for isinstance AssignImageResult first
if isinstance(a, AssignImageResult):
item_to_append = {
**item_to_append,
**{
"correlation": a.correlation,
"intersection over union": a.intersection_over_union,
"map value": a.map_value,
"map weighted mean": a.weighted_mean_of_first,
"map containedness": a.intersection_over_first,
"input weighted mean": a.weighted_mean_of_second,
"input containedness": a.intersection_over_second,
}
}
elif isinstance(a, MapAssignment):
item_to_append = {
**item_to_append,
**{
"correlation": None,
"intersection over union": None,
"map value": a.map_value,
"map weighted mean": None,
"map containedness": None,
"input weighted mean": None,
"input containedness": None,
}
}
else:
raise RuntimeError("assignments must be of type Assignment or AssignImageResult!")
dataframe_list.append(item_to_append)
return (
pd.DataFrame(dataframe_list)
.convert_dtypes() # convert will guess numeric column types
.reindex(columns=columns)
.dropna(axis='columns', how='all')
)
def _assign_points(self, points: pointcloud.PointCloud, lower_threshold: float) -> List[MapAssignment]:
"""
assign a PointCloud to this parcellation map.
Parameters
-----------
lower_threshold: float, default: 0
Lower threshold on values in the statistical map. Values smaller than
this threshold will be excluded from the assignment computation.
"""
assignments = []
if points.space != self.space:
logger.info(
f"Coordinates will be converted from {points.space.name} "
f"to {self.space.name} space for assignment."
)
# convert sigma to voxel coordinates
scaling = np.array(
[np.linalg.norm(self.affine[:, i]) for i in range(3)]
).mean()
phys2vox = np.linalg.inv(self.affine)
# if all points have the same sigma, and lead to a standard deviation
# below 3 voxels, we are much faster with a multi-coordinate readout.
if points.has_constant_sigma:
sigma_vox = points.sigma[0] / scaling
if sigma_vox < 3:
pts_warped = points.warp(self.space.id)
X, Y, Z = (np.dot(phys2vox, pts_warped.homogeneous.T) + 0.5).astype("int")[:3]
for pointindex, vol, frag, value in self._read_voxel(X, Y, Z):
if value > lower_threshold:
position = pts_warped[pointindex].coordinate
assignments.append(
MapAssignment(
input_structure=pointindex,
centroid=tuple(position),
volume=vol,
fragment=frag,
map_value=value,
time=None,
)
)
return assignments
# if we get here, we need to handle each point independently.
# This is much slower but more precise in dealing with the uncertainties
# of the coordinates.
for pointindex, pt in siibra_tqdm(
enumerate(points.warp(self.space.id)),
total=len(points), desc="Assigning points",
):
sigma_vox = pt.sigma / scaling
if sigma_vox < 3:
# voxel-precise - just read out the value in the maps
N = len(self)
logger.debug(f"Assigning coordinate {tuple(pt)} to {N} maps")
x, y, z = (np.dot(phys2vox, pt.homogeneous) + 0.5).astype("int")[:3]
values = self._read_voxel(x, y, z)
for _, vol, frag, value in values:
if value > lower_threshold:
assignments.append(
MapAssignment(
input_structure=pointindex,
centroid=tuple(pt),
volume=vol,
fragment=frag,
map_value=value,
time=None,
)
)
else:
logger.debug(
f"Assigning uncertain coordinate {tuple(pt)} to {len(self)} maps."
)
kernel = create_gaussian_kernel(sigma_vox, 3)
r = int(kernel.shape[0] / 2) # effective radius
assert pt.homogeneous.shape[0] == 1
xyz_vox = (np.dot(phys2vox, pt.homogeneous.T) + 0.5).astype("int")
shift = np.identity(4)
shift[:3, -1] = xyz_vox[:3, 0] - r
# build niftiimage with the Gaussian blob,
# then recurse into this method with the image input
gaussian_kernel = _volume.from_array(
data=kernel,
affine=np.dot(self.affine, shift),
space=self.space,
name=f"Gaussian kernel of {pt}",
cache=False,
)
for entry in self._assign(
item=gaussian_kernel,
lower_threshold=lower_threshold,
split_components=False
):
entry.input_structure = pointindex
entry.centroid = tuple(pt)
assignments.append(entry)
return assignments
def _assign_volume(
self,
queryvolume: "_volume.Volume",
lower_threshold: float,
split_components: bool = True,
time: int = None,
**kwargs,
) -> List[AssignImageResult]:
"""
Assign an image volume to this parcellation map.
Parameters
-----------
queryvolume: Volume
the volume to be compared with maps
minsize_voxel: int, default: 1
Minimum voxel size of image components to be taken into account.
lower_threshold: float, default: 0
Lower threshold on values in the statistical map. Values smaller than
this threshold will be excluded from the assignment computation.
split_components: bool, default: True
Whether to split the query volume into disjoint components.
"""
# TODO: split_components is not known to `assign`
# TODO: `minsize_voxel` is not used here. Consider the implementation of `assign` again.
if kwargs:
logger.info(f"The keywords {[k for k in kwargs]} are not passed on during volume assignment.")
if queryvolume.space != self.space:
raise ValueError("Assigned volume must be in the same space as the map.")
if split_components:
iter_components = lambda arr: connected_components(arr)
else:
iter_components = lambda arr: [(0, arr)]
queryimg = queryvolume.fetch()
assignments = []
all_indices = [
index
for regionindices in self._indices.values()
for index in regionindices
]
with QUIET and provider.SubvolumeProvider.UseCaching():
for index in siibra_tqdm(
all_indices,
desc=f"Assigning {queryvolume} to {self}",
disable=len(all_indices) < 5,
unit="map",
leave=False
):
region_map = self.fetch(index=index)
region_map_arr = np.asanyarray(region_map.dataobj)
# the shape and affine are checked by `nilearn.image.resample_to_img()`
# and returns the original data if resampling is not necessary.
queryimgarr_res = np.asanyarray(
resample_img_to_img(queryimg, region_map).dataobj
)
for compmode, voxelmask in iter_components(queryimgarr_res):
scores = compare_arrays(
voxelmask,
region_map.affine, # after resampling, both should have the same affine
region_map_arr,
region_map.affine
)
component_position = np.array(np.where(voxelmask)).T.mean(0)
if scores.intersection_over_union > lower_threshold:
assignments.append(
AssignImageResult(
input_structure=compmode,
centroid=tuple(component_position.round(2)),
volume=index.volume,
fragment=index.fragment,
map_value=index.label,
time=time,
**asdict(scores)
)
)
return assignments
def _assign_timeseries_volume(
self,
queryvolume: "_volume.TimeSeriesVolume",
lower_threshold: float,
split_components: bool = True,
**kwargs
) -> List[AssignImageResult]:
assignments = []
for v_t in siibra_tqdm(queryvolume, unit='time point'):
assignments_t = self._assign_volume(
v_t,
lower_threshold=lower_threshold,
split_components=split_components,
time=v_t.timepoint,
**kwargs
)
assignments.extend(assignments_t)
return assignments
def to_BIDS_lookup_table(self) -> pd.DataFrame:
"""
Generate a BIDS-compatible lookup table for the labelled map.
The lookup table associates voxel labels with region names and optional
RGB colors derived from the corresponding parcellation regions.
If the map consists of multiple fragments, the fragments are first
compressed into a single labelled image and relabelled to ensure BIDS
compatibility.
Parameters
----------
filepath : str, optional
Path to a ``.tsv`` file where the lookup table should be written.
If provided, the table is saved in tab-separated format.
Returns
-------
pandas.DataFrame
A lookup table with the following columns:
- ``index``: Integer label value in the image.
- ``name``: Name of the corresponding brain region.
- ``color``: Hexadecimal RGB color code associated with the region,
or ``None`` if no color is defined.
Raises
------
Exception
If the map contains more than one volume.
AssertionError
If ``filepath`` does not end with ``.tsv``.
Notes
-----
BIDS lookup tables require a single labelled volume. Maps distributed
across multiple fragments are therefore compressed and relabelled before
generating the table.
"""
if not self.is_labelled:
raise NotImplementedError("Currently, there is not LUT standard defined by BIDS for statistical maps.")
if len(self.volumes) > 1 or not self.has_unique_labels:
logger.info(
f"{self} has {len(self.volumes)} volume(s)/{len(self.fragments)} fragment(s); "
"siibra will compress and reindex it for BIDS compatibility."
)
mp = self.compress()
else:
mp = self
def to_record(regionname: str, index: MapIndex) -> List[Dict]:
rgb = mp.parcellation.get_region(regionname).rgb
color = "#{:02x}{:02x}{:02x}".format(*rgb) if rgb else None
return {
"index": index.label,
"name": regionname,
"color": color,
}
table = pd.DataFrame(
[
to_record(r, indices[0])
for r, indices in mp._indices.items()
]
)
if "gii-label" in mp.formats:
# read from the provider: mp.fetch() would also pull the template mesh
prov = mp.volumes[0]._providers["gii-label"]
observed = set()
for fragment in (mp.fragments or [None]):
observed |= set(np.unique(prov.fetch(fragment=fragment)["labels"]))
for xl in sorted(observed - set(table["index"]) - {0}): # 0 is background
table.loc[len(table)] = {"name": f"{xl} (unnamed)", "index": xl, "color": None}
if not table["index"].is_unique:
duplicated = sorted(table.loc[table["index"].duplicated(), "index"])
raise RuntimeError(
f"Labels {duplicated} are assigned to more than one region in {mp}. "
"A BIDS lookup table requires unique indices."
)
return table
def _as_surfaceimage(self, variant: str = None):
from nilearn.surface import SurfaceImage, PolyData
if "gii-label" not in self.formats:
raise ValueError("`SurfaceImage` representation is only possible for 'gii-label' maps.")
if len(self.volumes) > 1:
raise ValueError("`SurfaceImage` representation is only possible for maps with single and hemisphere fragemented maps.")
giilabel_filemap = {}
for frag in self.fragments:
loader = self.volumes[0]._providers["gii-label"]._loaders[frag]
loader._retrieve()
giilabel_filemap[frag.replace(' hemisphere', "")] = loader.cachefile
return SurfaceImage(mesh=self.space._as_polymesh(variant=variant), data=PolyData(**giilabel_filemap))
def as_nilearn_masker(
self,
strategy: Literal[
"mean",
"median",
"sum",
"minimum",
"maximum",
"standard_deviation",
"variance",
] = "mean",
surface_variant: str = None,
**masker_kwargs,
) -> Union["NiftiLabelsMasker", "SurfaceLabelsMasker"]:
from nilearn import maskers
try:
from ..retrieval.cache import jobmemory_path
masker_kwargs.setdefault("memory", jobmemory_path)
except ImportError:
...
if not self.is_labelled:
raise NotImplementedError(
f"Nilearn maskers for {self.maptype} maps are provided by SparseMap, "
"which projects the data onto the maps instead of summarizing labelled "
"regions. Convert this map with `to_sparse()` first."
)
mp = self.compress() if (len(self.volumes) > 1 or self.fragments) else self
if "lut" in masker_kwargs:
raise ValueError("siibra handles `lut` parameter based on the map.")
masker_kwargs.setdefault("verbose", 1)
masker_kwargs.setdefault("strategy", strategy)
masker_kwargs["lut"] = mp.to_BIDS_lookup_table()
if self.provides_image:
masker = maskers.NiftiLabelsMasker(mp.fetch(), **masker_kwargs)
else:
masker = maskers.SurfaceLabelsMasker(
mp._as_surfaceimage(variant=surface_variant),
**masker_kwargs,
)
return masker
def extract_signals_with_nilearn(
self,
volume: _volume.Volume,
strategy: Literal[
"mean",
"median",
"sum",
"minimum",
"maximum",
"standard_deviation",
"variance",
] = "mean",
confounds: np.ndarray = None,
sample_mask: np.ndarray = None,
surface_variant: str = None,
**masker_kwargs,
) -> pd.DataFrame:
"""
Extract region-wise signals from a 3D/4D volume using `nilearn`.
The regions defined in this map are used to summarize the input volume
with :class:`nilearn.maskers.NiftiLabelsMasker` (labelled volumetric maps),
:class:`nilearn.maskers.SurfaceLabelsMasker` (labelled surface maps), or
:class:`nilearn.maskers.NiftiMapsMasker` (statistical maps). Maps with
several volumes or fragments are compressed into a single labelled map first.
Parameters
----------
volume: Volume
Input 3D or 4D volume from which signals should be extracted, typically
an fMRI image. Both NIfTI and timeseries GIFTI sources are supported.
strategy: str, default: "mean"
How voxels or vertices are summarized within each region. Only applies
to labelled maps; it is ignored for statistical maps, since
`NiftiMapsMasker` projects the data onto the (overlapping, continuous)
maps by least squares instead of summarizing discrete regions.
confounds: array-like, optional
Confounds to regress out during extraction.
See `nilearn.maskers.BaseMasker.transform`.
sample_mask: array-like, optional
Mask of samples to include when extracting signals.
See `nilearn.maskers.BaseMasker.transform`.
surface_variant: str, optional
Template surface variant to use for surface maps, e.g. "inflated".
**masker_kwargs
Passed on to the nilearn masker constructor.
Returns
-------
pandas.DataFrame
One column per region mapped in this map, one row per sample. The index
is the time axis of `volume` when it has one and no `sample_mask` was
given, otherwise a range index.
Notes
-----
Region names are taken from the BIDS lookup table generated by
`to_BIDS_lookup_table()`. Regions that disappear when the map is resampled
onto the input volume are reported and filled with zeros; nilearn's
`keep_masked_labels` does not prevent them from being dropped, and it is
deprecated since nilearn 0.14.
"""
masker = self.as_nilearn_masker(
strategy=strategy if self.is_labelled else None,
surface_variant=surface_variant,
**masker_kwargs
)
if self.provides_image and volume.provides_image:
source = volume.fetch()
elif "gii-label" in self.formats and "gii-timeseries" in volume.formats:
source = volume._as_surfaceimage(variant=surface_variant)
else:
raise ValueError(
f"Cannot extract signals from {volume} with {self}: no common representation. "
f"The map provides {sorted(self.formats)}, the input provides {sorted(volume.formats)}."
)
# np.asarray normalizes plain and pandas output alike. (set_output(transform="pandas")
# raises NotImplementedError before nilearn 0.13, and the column names it
# produces are the ones we assign below anyway.)
signals = np.atleast_2d(np.asarray(
masker.fit_transform(source, confounds=confounds, sample_mask=sample_mask)
))
if self.is_labelled:
# region_names_ maps output column index -> region name. It is available on
# both Nifti and Surface labels maskers since nilearn 0.10.4.
# (masker.labels_ cannot be used here: it includes the background label.
# get_feature_names_out() is equivalent but only exists from nilearn 0.13.)
extracted = [name for _, name in sorted(masker.region_names_.items())]
all_regions = masker.lut["name"].tolist()
else:
# NiftiMapsMasker names its columns positionally, so column i corresponds
# to volume i of the array stacked by _stack_maps().
extracted = all_regions = [
regionname for regionname, indices
in sorted(self._indices.items(), key=lambda kv: kv[1][0].volume)
]
if signals.shape[1] != len(extracted):
raise RuntimeError(
f"nilearn returned {signals.shape[1]} signals but {len(extracted)} "
f"regions were expected for {self}."
)
result = pd.DataFrame(signals, columns=extracted)
missing = [r for r in all_regions if r not in set(extracted)]
if missing:
logger.info(
f"{len(missing)} region(s) were removed when resampling {self} to the "
f"input volume and are filled with zeros:\n{missing}"
)
result[missing] = 0
result = result[all_regions]
result.columns.name = "region"
# sample_mask drops samples, so the time axis would no longer align
time = getattr(volume, "time", None)
if time is not None and sample_mask is None and len(time) == len(result):
result.index = pd.Index(time, name="time")
return result
def from_volume(
name: str,
volume: Union[_volume.Volume, List[_volume.Volume]],
regionnames: List[str],
regionlabels: List[int],
parcellation_spec: Union[str, "parcellation.Parcellation"] = None
) -> 'Map':
"""
Add a custom labelled parcellation map to siibra from a labelled NIfTI file.
Parameters
------------
name: str
Human-readable name of the parcellation.
volume: Volume, or a list of Volumes.
space_spec: str, Space
Specification of the reference space (space object, name, keyword, or id - e.g. 'mni152').
regionnames: list[str]
List of human-readable names of the mapped regions.
regionlabels: list[int]
List of integer labels in the nifti file corresponding to the list of regions.
parcellation: str or Parcellation. Optional.
If the related parcellation already defined or preconfigured in siibra.
"""
# providers and map indices
providers = []
volumes = volume if isinstance(volume, list) else [volume]
map_space = volumes[0].space
assert all(v.space == map_space for v in volumes), "Volumes have to be in the same space"
for vol_idx, vol in enumerate(volumes):
image = vol.fetch()
arr = np.asanyarray(image.dataobj)
labels_in_volume = np.unique(arr)[1:].astype('int')
# populate region indices from given name/label lists
indices = dict()
for label, regionname in zip(regionlabels, regionnames):
if label not in labels_in_volume:
logger.warning(
f"Label {label} not mapped in the provided NIfTI volume -> "
f"region '{regionname} will not be in the map."
)
elif label in [v[0]['label'] for v in indices.values() if v[0]['volume'] == vol_idx]:
logger.warning(f"Label {label} already defined in the same volume; will not map it to '{regionname}'.")
else:
assert regionname not in indices, f"'{regionname}' must be unique in `regionnames`."
indices[regionname] = [{'volume': vol_idx, 'label': label}]
# check for any remaining labels in the NIfTI volume
unnamed_labels = list(set(labels_in_volume) - set(regionlabels))
if unnamed_labels:
logger.warning(
f"The following labels appear in the NIfTI volume {vol_idx}, but not in "
f"the specified regions: {', '.join(str(lb) for lb in unnamed_labels)}. "
"They will be removed from the nifti volume."
)
for label in unnamed_labels:
arr[arr == label] = 0
providers.extend(vol._providers.values())
# parcellation
if parcellation_spec is None:
parcellation_spec = name
try:
parcobj = parcellation.Parcellation.registry().get(parcellation_spec)
logger.info(f"Using '{parcellation_spec}', siibra decoded the parcellation as '{parcobj}'")
except Exception:
logger.info(
f"Using '{parcellation_spec}', siibra could not decode the "
" parcellation. Building a new parcellation."
)
# build a new parcellation
parcobj = parcellation.Parcellation(
identifier=generate_uuid(','.join(regionnames)),
name=name,
species=vol.space.species,
regions=list(map(_region.Region, regionnames)),
)
if parcobj.key not in list(parcellation.Parcellation.registry()):
parcellation.Parcellation.registry().add(parcobj.key, parcobj)
for region in siibra_tqdm(
indices.keys(),
desc="Checking if provided regions are defined in the parcellation."
):
try:
_ = parcobj.get_region(region)
except Exception:
logger.warning(f"'{region}' is missing in the parcellation.")
# build the parcellation map object
parcmap = Map(
identifier=generate_uuid(name),
name=f"{name} map in {map_space.name}",
space_spec={"@id": map_space.id},
parcellation_spec={'name': parcobj.name},
indices=indices,
volumes=volumes
)
# add it to siibra's registry
Map.registry().add(parcmap.key, parcmap)
# return the map - note that it has a pointer to the parcellation
return parcmap

parcellationmap.py at commit 0a6a1e8, under Apache-2.0 · at the source

Overview

Authors: Timo Dickscheid^1,2,3, Xiaoyun Gui¹, Ahmet N Simsek¹, Christian Schiffer^1,3, Jean-Francois Mangin⁴, Yann Leprince⁴, Viktor Jirsa⁵, Jan G Bjaalie⁶, Trygve B Leergaard⁶, Sebastian Bludau¹, Katrin Amunts^1,7

ORCID iDs: Timo Dickscheid, Ahmet N Simsek, Yann Leprince, Viktor Jirsa, Jan G Bjaalie, Trygve B Leergaard, Sebastian Bludau, Katrin Amunts

Institutions: Forschungszentrum Jülich (Germany); University of Koblenz and Landau (Germany); Universität Koblenz (Germany); Heinrich Heine University Düsseldorf (Germany); Centre National de la Recherche Scientifique (France); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); CEA Paris-Saclay - Etablissement de Saclay (France); CEA Paris-Saclay (France); Inserm (France); Aix-Marseille Université (France); Institut de Neurosciences des Systèmes (France); University of Oslo (Norway); Düsseldorf University Hospital (Germany)

Journal: Nature methods, volume 23, issue 8, pages 1647-1658

Dates: received 18 March 2025; accepted 15 June 2026; published online 20 July 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41592-026-03159-x · PMID 42477446 · PMCID PMC13441889 · OpenAlex W4410759675

Open access: hybrid, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.05.20.655042

Status: code verified

Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)

Methods: Spectral & time-frequency, fMRI & imaging

Keywords: Computational neuroscience, Data integration, Software, Neuroscience

MeSH: Atlases as Topic*, Brain*, Brain Mapping*, Software*, Humans, Image Processing, Computer-Assisted, Internet, Magnetic Resonance Imaging, User-Computer Interface (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (945539, 101058516); Helmholtz Association (InterLabs-0015, Portfolio Theme SMHB, ZT-I-PF-4-061); Deutsche Forschungsgemeinschaft (German Research Foundation) (501864659, SPP2041)

Citations: cited by 1 paper (Europe PMC); 76 references in the paper

Research resources: RRID:SCR_001362, RRID:SCR_002498, RRID:SCR_002577, Matplotlib RRID:SCR_008624, as NumPy RRID:SCR_008633, stored in the EBRAINS Knowledge Graph RRID:SCR_017612, fetched tabular data as pandas RRID:SCR_018214, napari RRID:SCR_022765, RRID:SCR_023173, RRID:SCR_023498

Abstract

Computational technology opens new possibilities toward understanding the complexity of the human brain, but it requires integrating measurements from different modalities and scales in an anatomical context and exposing them in an interoperable, actionable form. Especially with growing big data resources, accessing information from different scales and modalities coherently for visual exploration, reproducible analysis and application development remains challenging. Here we present siibra, a tool suite that connects diverse data from cloud resources to reference atlases and coordinate spaces. It supports different use cases by making contents accessible through a web viewer, Python library and HTTP application programming interface. Using siibra we implemented a Multilevel Human Brain Atlas linking macro-anatomical concepts and their inter-subject variability with measurements of the microstructural composition and intrinsic variance of brain regions, building on cytoarchitecture as a reference and supporting MRI-based and microscopic templates. The atlas is integrated with the EBRAINS research infrastructure. All software and content are openly accessible.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 25 matches between paragraphs and lines of code.

siibra-python.readthedocs.io

License: none: the authors keep all their rights

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Found in: “Code availability”

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Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

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At the source: siibra-python.readthedocs.io/en/v1

HumanBrainProject/hbp-spatial-backend

License: Apache-2.0

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Evidence: files inventoried

Commit: 0acac3bc230fb8a65e6cf1a3f9196648606da65f, 1 April 2025

Languages: Python (13), Shell (2)

Size: 54 files, 15 scripts

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Found in: the text, “Modeling spatial entities in different reference”

Holds: README, license file, environment (Dockerfile, Dockerfile.server, setup.cfg, setup.py, docker-aims/Dockerfile), tests, continuous integration

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docker-aims/script.sh, Shell, 69 lines
get_local_image_transform_command.py, Python, 177 lines
hbp_spatial_backend/__init__.py, Python, 224 lines, 1 match
hbp_spatial_backend/api_v1.py, Python, 401 lines
hbp_spatial_backend/apply_transform.py, Python, 111 lines
hbp_spatial_backend/transform_graph.py, Python, 118 lines
hbp_spatial_backend/wsgi.py, Python, 21 lines
remote_tests/test_graph_consistency.py, Python, 101 lines
run-flask-dev.sh, Shell, 11 lines
setup.py, Python, 92 lines
tests/conftest.py, Python, 32 lines
tests/test_api_v1.py, Python, 202 lines
tests/test_app.py, Python, 136 lines
tests/test_apply_transform.py, Python, 152 lines
tests/test_transform_graph.py, Python, 97 lines
LICENCE.txt, License, 202 lines
README.rst, Text, 243 lines

At the source: github.com/HumanBrainProject/hbp-spatial-backend

FZJ-INM1-BDA/siibra-tutorials

License: Apache-2.0

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Tools: Nilearn (12 files), Matplotlib (10 files), NumPy (2 files), pandas (2 files)

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01-BasicConcepts.ipynb, Jupyter, 222 lines
02-DataFeatures.ipynb, Jupyter, 143 lines
03-ProbabilisticAssignment.ipynb, Jupyter, 94 lines
04-DifferentialGeneExpressions.ipynb, Jupyter, 73 lines
siibra_overview.ipynb, Jupyter, 272 lines
using_your_data_with_siibra.ipynb, Jupyter, 206 lines
workshops/BigBrainWorkshop2023.ipynb, Jupyter, 190 lines
workshops/HIBALL-winterschool-2023.ipynb, Jupyter, 522 lines
workshops/imb9345-2024.ipynb, Jupyter, 187 lines
workshops/ohbm-2023-example.ipynb, Jupyter, 168 lines
workshops/siibra_basics_ebrains_student_conf_2026.ipynb, Jupyter, 254 lines
workshops/using_your_data_with_siibra_ebrains_student_conf_2026.ipynb, Jupyter, 208 lines
LICENSE, License, 201 lines
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At the source: github.com/FZJ-INM1-BDA/siibra-tutorials

FZJ-INM1-BDA/siibra-python

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170 files

.ebrains/spack/siibra/package.py, Python, 30 lines
config_schema/check_schema.py, Python, 134 lines
docs/conf.py, Python, 192 lines
e2e/core/test_parcellation.py, Python, 69 lines
e2e/core/test_region.py, Python, 125 lines
e2e/core/test_space.py, Python, 42 lines
e2e/examples/run_example.py, Python, 19 lines
e2e/examples/test_examples.py, Python, 82 lines
e2e/features/activity_timeseries/test_activity_timeseries.py, Python, 49 lines
e2e/features/connectivity/test_connectivity.py, Python, 94 lines
e2e/features/external/test_ebrains.py, Python, 28 lines
e2e/features/image/test_image.py, Python, 65 lines
e2e/features/molecular/test_genes.py, Python, 77 lines
e2e/features/molecular/test_receptor_density_fingerprint.py, Python, 25 lines
e2e/features/test_generic.py, Python, 133 lines
e2e/features/test_get.py, Python, 125 lines
e2e/features/test_plot.py, Python, 25 lines
e2e/locations/test_locations.py, Python, 12 lines
e2e/retrieval/test_datasets.py, Python, 9 lines
e2e/retrieval/test_repositories.py, Python, 43 lines
e2e/util.py, Python, 59 lines
e2e/volumes/test_compute_centroids.py, Python, 51 lines
e2e/volumes/test_extract_signals_from_map.py, Python, 49 lines
e2e/volumes/test_extracting_bbox_from_template.py, Python, 23 lines
e2e/volumes/test_mapshape.py, Python, 60 lines
e2e/volumes/test_parcellationmap.py, Python, 184 lines
e2e/volumes/test_preconfigured_boundingbox.py, Python, 58 lines
e2e/volumes/test_sparsemap_cache_uniqueness.py, Python, 17 lines
e2e/volumes/test_sparsemap_volume_assignment.py, Python, 23 lines
e2e/volumes/test_subfragmented_ngmesh.py, Python, 29 lines
e2e/volumes/test_surface_maps.py, Python, 54 lines
e2e/volumes/test_time_series_volume.py, Python, 62 lines
e2e/volumes/test_volume.py, Python, 35 lines
examples/01_atlases_and_parcellations/000_accessing_atlases.py, Python, 98 lines
examples/01_atlases_and_parcellations/001_accessing_parcellations.py, Python, 94 lines
examples/01_atlases_and_parcellations/002_explore_region_hierarchy.py, Python, 55 lines
examples/01_atlases_and_parcellations/003_find_regions.py, Python, 89 lines
examples/01_atlases_and_parcellations/004_brain_region_metadata.py, Python, 65 lines
examples/01_atlases_and_parcellations/005_brain_region_spatialprops.py, Python, 73 lines
examples/02_maps_and_templates/001_selecting_reference_spaces.py, Python, 62 lines
examples/02_maps_and_templates/002_accessing_templates.py, Python, 114 lines
examples/02_maps_and_templates/003_accessing_maps.py, Python, 142 lines
examples/02_maps_and_templates/004_access_bigbrain.py, Python, 137 lines
examples/02_maps_and_templates/005_access_surface_maps.py, Python, 76 lines
examples/02_maps_and_templates/006_bigbrain_cortical_layers.py, Python, 82 lines
examples/02_maps_and_templates/007_adding_custom_parcellation.py, Python, 111 lines
examples/03_data_features/000_matchings.py, Python, 155 lines
examples/03_data_features/001_receptor_densities.py, Python, 76 lines
examples/03_data_features/002_colorize_map.py, Python, 69 lines
examples/03_data_features/003_cell_distributions.py, Python, 100 lines
examples/03_data_features/004_gene_expressions.py, Python, 72 lines
examples/03_data_features/005_ebrains_datasets.py, Python, 50 lines
examples/03_data_features/006_connectivity_matrices.py, Python, 121 lines
examples/03_data_features/007_comparative_assessment.py, Python, 85 lines
examples/03_data_features/008_functional_timeseries.py, Python, 80 lines
examples/03_data_features/009_compound_features.py, Python, 74 lines
examples/03_data_features/010_lfp_query.py, Python, 118 lines
examples/04_locations/000_employing_locations_of_interest.py, Python, 94 lines
examples/05_anatomical_assignment/001_coordinates.py, Python, 82 lines
examples/05_anatomical_assignment/002_activation_maps.py, Python, 77 lines
examples/tutorials/2025-paper-fig3.py, Python, 231 lines
examples/tutorials/2025-paper-fig4.py, Python, 177 lines
examples/tutorials/2025-paper-fig5.py, Python, 171 lines, 2 matches
examples/tutorials/2025-paper-fig6.py, Python, 461 lines, 2 matches
examples/tutorials/fmri_analysis_workflow.py, Python, 364 lines
setup.py, Python, 58 lines
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siibra/commons.py, Python, 836 lines
siibra/configuration/__init__.py, Python, 17 lines
siibra/configuration/configuration.py, Python, 189 lines
siibra/configuration/factory.py, Python, 613 lines
siibra/core/__init__.py, Python, 16 lines
siibra/core/assignment.py, Python, 122 lines
siibra/core/atlas.py, Python, 239 lines
siibra/core/concept.py, Python, 313 lines
siibra/core/parcellation.py, Python, 389 lines
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test/retrieval/test_requests.py, Python, 143 lines
test/retrieval/test_retrieval_download_file.py, Python, 14 lines
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LICENSE, License, 201 lines
README.rst, Text, 155 lines

At the source: github.com/FZJ-INM1-BDA/siibra-python

FZJ-INM1-BDA/siibra-explorer

License: Apache-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: a1b93e091f2457ff8d683dab27001b89332e29f9, 26 July 2026

Languages: TypeScript (574), JavaScript (23), Python (22), Shell (2)

Size: 1,304 files, 621 scripts

Software Heritage: archived

Found in: “Code availability”

Holds: README, license file, environment (Dockerfile, backend/requirements.txt, docs/Dockerfile, docs/requirements.txt, docs/develop/docker-compose.yml), tests, continuous integration, documentation

Not found: CITATION.cff

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

623 files

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cypress/e2e/screenshot.cy.js, JavaScript, 77 lines
features/_convert.py, Python, 125 lines
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src/assets/images/persp-view/processing_raw/rename.py, Python, 35 lines
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typings/index.d.ts, TypeScript, 12 lines
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LICENSE, License, 201 lines
README.md, Text, 107 lines

At the source: github.com/FZJ-INM1-BDA/siibra-explorer

FZJ-INM1-BDA/siibra-api

License: Apache-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 3af86fe2c1f9b7532203d05f47eb4f682b8108b7, 23 September 2026

Languages: Python (489), Shell (2)

Size: 549 files, 491 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, environment (docker-compose.yml, Dockerfile, server.dockerfile, worker-v4.dockerfile, worker.dockerfile, .docker/docker-compose.yaml, requirements/all.txt, requirements/docs.txt, requirements/partial-all.txt, requirements/partial-worker.txt, requirements/server.txt, requirements/siibra.txt), tests, continuous integration, documentation

Not found: CITATION.cff

Tools: NumPy (7 files), NiBabel (2 files), pandas (2 files), Plotly (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

493 files

.docker/restart.sh, Shell, 10 lines
api/common/__init__.py, Python, 5 lines
api/common/data_handlers/__init__.py, Python, 11 lines
api/common/data_handlers/compounds/__init__.py, Python, 1 line
api/common/data_handlers/compounds/download.py, Python, 242 lines
api/common/data_handlers/core/__init__.py, Python, 5 lines
api/common/data_handlers/core/atlas.py, Python, 28 lines
api/common/data_handlers/core/misc.py, Python, 259 lines
api/common/data_handlers/core/parcellation.py, Python, 28 lines
api/common/data_handlers/core/region.py, Python, 58 lines
api/common/data_handlers/core/space.py, Python, 28 lines
api/common/data_handlers/features/__init__.py, Python, 1 line
api/common/data_handlers/features/misc.py, Python, 24 lines
api/common/data_handlers/features/types.py, Python, 200 lines
api/common/data_handlers/overrides.py, Python, 60 lines
api/common/data_handlers/vocabularies/__init__.py, Python, 1 line
api/common/data_handlers/vocabularies/gene.py, Python, 24 lines
api/common/decorators.py, Python, 154 lines
api/common/exceptions.py, Python, 28 lines
api/common/logger.py, Python, 63 lines
api/common/siibra_api_typing.py, Python, 8 lines
api/common/storage.py, Python, 19 lines
api/common/timer.py, Python, 50 lines
api/models/__init__.py, Python, 22 lines
api/models/_commons.py, Python, 123 lines
api/models/_retrieval/__init__.py, Python, 1 line
api/models/_retrieval/datasets.py, Python, 27 lines
api/models/core/__init__.py, Python, 1 line
api/models/core/_concept.py, Python, 24 lines
api/models/core/atlas.py, Python, 19 lines
api/models/core/parcellation.py, Python, 38 lines
api/models/core/region.py, Python, 36 lines
api/models/core/space.py, Python, 26 lines
api/models/features/__init__.py, Python, 2 lines
api/models/features/_basetypes/__init__.py, Python, 8 lines
api/models/features/_basetypes/cortical_profiles.py, Python, 8 lines
api/models/features/_basetypes/feature.py, Python, 22 lines
api/models/features/_basetypes/regional_connectivity.py, Python, 9 lines
api/models/features/_basetypes/tabular.py, Python, 14 lines
api/models/features/_basetypes/volume_of_interest.py, Python, 8 lines
api/models/features/anchor.py, Python, 32 lines
api/models/features/dataset/ebrains.py, Python, 5 lines
api/models/features/molecular/gene_expression.py, Python, 5 lines
api/models/features/molecular/receptor_density_fingerprint.py, Python, 7 lines
api/models/intents/__init__.py, Python, 1 line
api/models/intents/base.py, Python, 5 lines
api/models/intents/colorization.py, Python, 14 lines
api/models/locations/__init__.py, Python, 1 line
api/models/locations/boundingbox.py, Python, 12 lines
api/models/locations/location.py, Python, 13 lines
api/models/locations/point.py, Python, 9 lines
api/models/openminds/SANDS/__init__.py, Python, 1 line
api/models/openminds/SANDS/v3/__init__.py, Python, 1 line
api/models/openminds/SANDS/v3/atlas/__init__.py, Python, 1 line
api/models/openminds/SANDS/v3/atlas/atlasAnnotation.py, Python, 100 lines
api/models/openminds/SANDS/v3/atlas/brainAtlas.py, Python, 99 lines
api/models/openminds/SANDS/v3/atlas/brainAtlasVersion.py, Python, 245 lines
api/models/openminds/SANDS/v3/atlas/commonCoordinateSpace.py, Python, 111 lines
api/models/openminds/SANDS/v3/atlas/parcellationEntity.py, Python, 51 lines
api/models/openminds/SANDS/v3/atlas/parcellationEntityVersion.py, Python, 223 lines
api/models/openminds/SANDS/v3/atlas/parcellationTerminology.py, Python, 32 lines
api/models/openminds/SANDS/v3/atlas/parcellationTerminologyVersion.py, Python, 32 lines
api/models/openminds/SANDS/v3/miscellaneous/__init__.py, Python, 1 line
api/models/openminds/SANDS/v3/miscellaneous/coordinatePoint.py, Python, 45 lines
api/models/openminds/SANDS/v3/miscellaneous/qualitativeRelationAssessment.py, Python, 30 lines
api/models/openminds/SANDS/v3/miscellaneous/quantitativeRelationAssessment.py, Python, 71 lines
api/models/openminds/SANDS/v3/non-atlas/__init__.py, Python, 1 line
api/models/openminds/SANDS/v3/non-atlas/customAnatomicalEntity.py, Python, 205 lines
api/models/openminds/SANDS/v3/non-atlas/customAnnotation.py, Python, 106 lines
api/models/openminds/SANDS/v3/non-atlas/customCoordinateSpace.py, Python, 66 lines
api/models/openminds/__init__.py, Python, 1 line
api/models/openminds/_populate_static_models.py, Python, 129 lines
api/models/openminds/base.py, Python, 18 lines
api/models/openminds/computation/__init__.py, Python, 1 line
api/models/openminds/computation/v1/__init__.py, Python, 1 line
api/models/openminds/computation/v1/dataAnalysis.py, Python, 100 lines
api/models/openminds/computation/v1/environment.py, Python, 39 lines
api/models/openminds/computation/v1/hardwareSystem.py, Python, 27 lines
api/models/openminds/computation/v1/launchConfiguration.py, Python, 40 lines
api/models/openminds/computation/v1/optimization.py, Python, 100 lines
api/models/openminds/computation/v1/simulation.py, Python, 100 lines
api/models/openminds/computation/v1/softwareAgent.py, Python, 26 lines
api/models/openminds/computation/v1/visualization.py, Python, 100 lines
api/models/openminds/computation/v1/workflowExecution.py, Python, 18 lines
api/models/openminds/controlledTerms/__init__.py, Python, 1 line
api/models/openminds/controlledTerms/v1/UBERONParcellation.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/__init__.py, Python, 1 line
api/models/openminds/controlledTerms/v1/actionStatusType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/ageCategory.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/anatomicalAxesOrientation.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/atlasType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/biologicalOrder.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/biologicalSex.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/breedingType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/cellType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/contributionType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/criteriaQualityType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/dataType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/deviceType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/disease.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/diseaseModel.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/ethicsAssessment.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/experimentalApproach.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/fileBundleGrouping.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/fileRepositoryType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/fileUsageRole.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/geneticStrainType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/handedness.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/language.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/laterality.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/metaDataModelType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/modelAbstractionLevel.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/modelScope.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/molecularEntity.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/operatingDevice.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/operatingSystem.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/organ.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/preparationType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/productAccessibility.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/programmingLanguage.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/qualitativeOverlap.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/semanticDataType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/service.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/softwareApplicationCategory.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/softwareFeature.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/species.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/stimulationApproach.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/stimulusType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/subjectAttribute.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/technique.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/termSuggestion.py, Python, 71 lines
api/models/openminds/controlledTerms/v1/terminology.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/tissueSampleAttribute.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/tissueSampleType.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/typeOfUncertainty.py, Python, 57 lines
api/models/openminds/controlledTerms/v1/unitOfMeasurement.py, Python, 57 lines
api/models/openminds/core/__init__.py, Python, 1 line
api/models/openminds/core/v4/__init__.py, Python, 1 line
api/models/openminds/core/v4/actors/__init__.py, Python, 1 line
api/models/openminds/core/v4/actors/affiliation.py, Python, 31 lines
api/models/openminds/core/v4/actors/contactInformation.py, Python, 16 lines
api/models/openminds/core/v4/actors/contribution.py, Python, 25 lines
api/models/openminds/core/v4/actors/organization.py, Python, 43 lines
api/models/openminds/core/v4/actors/person.py, Python, 62 lines
api/models/openminds/core/v4/data/__init__.py, Python, 1 line
api/models/openminds/core/v4/data/contentType.py, Python, 56 lines
api/models/openminds/core/v4/data/contentTypePattern.py, Python, 23 lines
api/models/openminds/core/v4/data/copyright.py, Python, 25 lines
api/models/openminds/core/v4/data/file.py, Python, 112 lines
api/models/openminds/core/v4/data/fileBundle.py, Python, 94 lines
api/models/openminds/core/v4/data/filePathPattern.py, Python, 20 lines
api/models/openminds/core/v4/data/fileRepository.py, Python, 108 lines
api/models/openminds/core/v4/data/fileRepositoryStructure.py, Python, 25 lines
api/models/openminds/core/v4/data/hash.py, Python, 22 lines
api/models/openminds/core/v4/data/license.py, Python, 37 lines
api/models/openminds/core/v4/data/serviceLink.py, Python, 29 lines
api/models/openminds/core/v4/miscellaneous/DOI.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/GRIDID.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/ISBN.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/ORCID.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/RORID.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/RRID.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/SWHID.py, Python, 18 lines
api/models/openminds/core/v4/miscellaneous/URL.py, Python, 13 lines
api/models/openminds/core/v4/miscellaneous/__init__.py, Python, 1 line
api/models/openminds/core/v4/miscellaneous/funding.py, Python, 36 lines
api/models/openminds/core/v4/miscellaneous/quantitativeValue.py, Python, 40 lines
api/models/openminds/core/v4/miscellaneous/quantitativeValueRange.py, Python, 34 lines
api/models/openminds/core/v4/miscellaneous/stockNumber.py, Python, 21 lines
api/models/openminds/core/v4/products/__init__.py, Python, 1 line
api/models/openminds/core/v4/products/dataset.py, Python, 71 lines
api/models/openminds/core/v4/products/datasetVersion.py, Python, 253 lines
api/models/openminds/core/v4/products/metaDataModel.py, Python, 71 lines
api/models/openminds/core/v4/products/metaDataModelVersion.py, Python, 222 lines
api/models/openminds/core/v4/products/model.py, Python, 90 lines
api/models/openminds/core/v4/products/modelVersion.py, Python, 219 lines
api/models/openminds/core/v4/products/project.py, Python, 50 lines
api/models/openminds/core/v4/products/software.py, Python, 71 lines
api/models/openminds/core/v4/products/softwareVersion.py, Python, 262 lines
api/models/openminds/core/v4/research/__init__.py, Python, 1 line
api/models/openminds/core/v4/research/behavioralProtocol.py, Python, 36 lines
api/models/openminds/core/v4/research/numericalParameter.py, Python, 65 lines
api/models/openminds/core/v4/research/parameterSet.py, Python, 86 lines
api/models/openminds/core/v4/research/protocol.py, Python, 37 lines
api/models/openminds/core/v4/research/protocolExecution.py, Python, 161 lines
api/models/openminds/core/v4/research/stimulation.py, Python, 33 lines
api/models/openminds/core/v4/research/strain.py, Python, 102 lines
api/models/openminds/core/v4/research/stringParameter.py, Python, 22 lines
api/models/openminds/core/v4/research/subject.py, Python, 49 lines
api/models/openminds/core/v4/research/subjectGroup.py, Python, 56 lines
api/models/openminds/core/v4/research/subjectGroupState.py, Python, 148 lines
api/models/openminds/core/v4/research/subjectState.py, Python, 146 lines
api/models/openminds/core/v4/research/tissueSample.py, Python, 75 lines
api/models/openminds/core/v4/research/tissueSampleCollection.py, Python, 84 lines
api/models/openminds/core/v4/research/tissueSampleCollectionState.py, Python, 140 lines
api/models/openminds/core/v4/research/tissueSampleState.py, Python, 140 lines
api/models/util/__init__.py, Python, 1 line
api/models/util/numpyarray.py, Python, 47 lines
api/models/vocabularies/__init__.py, Python, 1 line
api/models/vocabularies/base.py, Python, 7 lines
api/models/vocabularies/genes.py, Python, 5 lines
api/models/volumes/__init__.py, Python, 4 lines
api/models/volumes/parcellationmap.py, Python, 15 lines
api/models/volumes/volume.py, Python, 33 lines
api/serialization/__init__.py, Python, 14 lines
api/serialization/_common.py, Python, 73 lines
api/serialization/_retrieval/__init__.py, Python, 1 line
api/serialization/_retrieval/datasets.py, Python, 41 lines
api/serialization/core/__init__.py, Python, 8 lines
api/serialization/core/_concept.py, Python, 23 lines
api/serialization/core/atlas.py, Python, 75 lines
api/serialization/core/parcellation.py, Python, 104 lines
api/serialization/core/region.py, Python, 199 lines
api/serialization/core/space.py, Python, 37 lines
api/serialization/features/__init__.py, Python, 1 line
api/serialization/features/_basetypes/__init__.py, Python, 7 lines
api/serialization/features/_basetypes/cortical_profiles.py, Python, 27 lines
api/serialization/features/_basetypes/feature.py, Python, 24 lines
api/serialization/features/_basetypes/regional_connectivity.py, Python, 27 lines
api/serialization/features/_basetypes/tabular.py, Python, 26 lines
api/serialization/features/_basetypes/volume_of_interest.py, Python, 31 lines
api/serialization/features/anchor.py, Python, 45 lines
api/serialization/features/dataset/__init__.py, Python, 1 line
api/serialization/features/dataset/ebrains.py, Python, 18 lines
api/serialization/features/molecular/__init__.py, Python, 1 line
api/serialization/features/molecular/gene_expression.py, Python, 9 lines
api/serialization/features/molecular/receptor_density_fingerprint.py, Python, 15 lines
api/serialization/locations/__init__.py, Python, 1 line
api/serialization/locations/boundingbox.py, Python, 23 lines
api/serialization/locations/location.py, Python, 16 lines
api/serialization/locations/point.py, Python, 32 lines
api/serialization/util/__init__.py, Python, 101 lines
api/serialization/util/siibra.py, Python, 43 lines
api/serialization/volumes/__init__.py, Python, 2 lines
api/serialization/volumes/parcellationmap.py, Python, 28 lines
api/serialization/volumes/volume.py, Python, 60 lines
api/server/__init__.py, Python, 14 lines
api/server/api.py, Python, 423 lines
api/server/cache/__init__.py, Python, 26 lines
api/server/cache/redis.py, Python, 160 lines
api/server/code_snippet.py, Python, 119 lines
api/server/compounds/__init__.py, Python, 6 lines
api/server/compounds/download.py, Python, 93 lines
api/server/const.py, Python, 15 lines
api/server/core/__init__.py, Python, 9 lines
api/server/core/atlas.py, Python, 34 lines
api/server/core/parcellation.py, Python, 35 lines
api/server/core/region.py, Python, 51 lines
api/server/core/space.py, Python, 34 lines
api/server/features/__init__.py, Python, 335 lines
api/server/features/util.py, Python, 38 lines
api/server/metrics.py, Python, 238 lines
api/server/util.py, Python, 97 lines
api/server/volcabularies/__init__.py, Python, 26 lines
api/server/volumes/__init__.py, Python, 8 lines
api/server/volumes/maps.py, Python, 43 lines
api/server/volumes/parcellationmap.py, Python, 140 lines
api/siibra_api_config.py, Python, 145 lines
api/worker/__init__.py, Python, 3 lines
api/worker/app.py, Python, 10 lines
e2e_test/__init__.py, Python, 1 line
e2e_test/regions/__init__.py, Python, 1 line
e2e_test/regions/test_related.py, Python, 23 lines
e2e_test/vocabularies/__init__.py, Python, 1 line
e2e_test/vocabularies/test_genes.py, Python, 22 lines
new_api/common/__init__.py, Python, 1 line
new_api/common/decorators.py, Python, 45 lines
new_api/common/exceptions.py, Python, 28 lines
new_api/common/logger.py, Python, 63 lines
new_api/common/storage.py, Python, 19 lines
new_api/data_handlers/__init__.py, Python, 1 line
new_api/data_handlers/data.py, Python, 1,530 lines
new_api/data_handlers/features.py, Python, 32 lines
new_api/data_handlers/maps.py, Python, 96 lines
new_api/data_handlers/overrides.py, Python, 60 lines
new_api/run_warmup.py, Python, 6 lines
new_api/siibra_api_config.py, Python, 139 lines
new_api/v3/__init__.py, Python, 1 line
new_api/v3/models/__init__.py, Python, 22 lines
new_api/v3/models/_commons.py, Python, 124 lines
new_api/v3/models/_retrieval/__init__.py, Python, 1 line
new_api/v3/models/_retrieval/datasets.py, Python, 27 lines
new_api/v3/models/core/__init__.py, Python, 1 line
new_api/v3/models/core/_concept.py, Python, 24 lines
new_api/v3/models/core/atlas.py, Python, 19 lines
new_api/v3/models/core/parcellation.py, Python, 37 lines
new_api/v3/models/core/region.py, Python, 36 lines
new_api/v3/models/core/space.py, Python, 27 lines
new_api/v3/models/features/__init__.py, Python, 2 lines
new_api/v3/models/features/_basetypes/__init__.py, Python, 8 lines
new_api/v3/models/features/_basetypes/cortical_profiles.py, Python, 8 lines
new_api/v3/models/features/_basetypes/feature.py, Python, 22 lines
new_api/v3/models/features/_basetypes/regional_connectivity.py, Python, 9 lines
new_api/v3/models/features/_basetypes/tabular.py, Python, 14 lines
new_api/v3/models/features/_basetypes/volume_of_interest.py, Python, 8 lines
new_api/v3/models/features/anchor.py, Python, 32 lines
new_api/v3/models/features/dataset/ebrains.py, Python, 5 lines
new_api/v3/models/features/molecular/gene_expression.py, Python, 5 lines
new_api/v3/models/features/molecular/receptor_density_fingerprint.py, Python, 7 lines
new_api/v3/models/intents/__init__.py, Python, 1 line
new_api/v3/models/intents/base.py, Python, 5 lines
new_api/v3/models/intents/colorization.py, Python, 14 lines
new_api/v3/models/locations/__init__.py, Python, 1 line
new_api/v3/models/locations/boundingbox.py, Python, 12 lines
new_api/v3/models/locations/location.py, Python, 13 lines
new_api/v3/models/locations/point.py, Python, 9 lines
new_api/v3/models/openminds/SANDS/__init__.py, Python, 1 line
new_api/v3/models/openminds/SANDS/v3/__init__.py, Python, 1 line
new_api/v3/models/openminds/SANDS/v3/atlas/__init__.py, Python, 1 line
new_api/v3/models/openminds/SANDS/v3/atlas/atlasAnnotation.py, Python, 100 lines
new_api/v3/models/openminds/SANDS/v3/atlas/brainAtlas.py, Python, 99 lines
new_api/v3/models/openminds/SANDS/v3/atlas/brainAtlasVersion.py, Python, 245 lines
new_api/v3/models/openminds/SANDS/v3/atlas/commonCoordinateSpace.py, Python, 111 lines
new_api/v3/models/openminds/SANDS/v3/atlas/parcellationEntity.py, Python, 51 lines
new_api/v3/models/openminds/SANDS/v3/atlas/parcellationEntityVersion.py, Python, 223 lines
new_api/v3/models/openminds/SANDS/v3/atlas/parcellationTerminology.py, Python, 32 lines
new_api/v3/models/openminds/SANDS/v3/atlas/parcellationTerminologyVersion.py, Python, 32 lines
new_api/v3/models/openminds/SANDS/v3/miscellaneous/__init__.py, Python, 1 line
new_api/v3/models/openminds/SANDS/v3/miscellaneous/coordinatePoint.py, Python, 45 lines
new_api/v3/models/openminds/SANDS/v3/miscellaneous/qualitativeRelationAssessment.py, Python, 30 lines
new_api/v3/models/openminds/SANDS/v3/miscellaneous/quantitativeRelationAssessment.py, Python, 71 lines
new_api/v3/models/openminds/SANDS/v3/non-atlas/__init__.py, Python, 1 line
new_api/v3/models/openminds/SANDS/v3/non-atlas/customAnatomicalEntity.py, Python, 205 lines
new_api/v3/models/openminds/SANDS/v3/non-atlas/customAnnotation.py, Python, 106 lines
new_api/v3/models/openminds/SANDS/v3/non-atlas/customCoordinateSpace.py, Python, 66 lines
new_api/v3/models/openminds/__init__.py, Python, 1 line
new_api/v3/models/openminds/_populate_static_models.py, Python, 129 lines
new_api/v3/models/openminds/base.py, Python, 18 lines
new_api/v3/models/openminds/computation/__init__.py, Python, 1 line
new_api/v3/models/openminds/computation/v1/__init__.py, Python, 1 line
new_api/v3/models/openminds/computation/v1/dataAnalysis.py, Python, 100 lines
new_api/v3/models/openminds/computation/v1/environment.py, Python, 39 lines
new_api/v3/models/openminds/computation/v1/hardwareSystem.py, Python, 27 lines
new_api/v3/models/openminds/computation/v1/launchConfiguration.py, Python, 40 lines
new_api/v3/models/openminds/computation/v1/optimization.py, Python, 100 lines
new_api/v3/models/openminds/computation/v1/simulation.py, Python, 100 lines
new_api/v3/models/openminds/computation/v1/softwareAgent.py, Python, 26 lines
new_api/v3/models/openminds/computation/v1/visualization.py, Python, 100 lines
new_api/v3/models/openminds/computation/v1/workflowExecution.py, Python, 18 lines
new_api/v3/models/openminds/controlledTerms/__init__.py, Python, 1 line
new_api/v3/models/openminds/controlledTerms/v1/UBERONParcellation.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/__init__.py, Python, 1 line
new_api/v3/models/openminds/controlledTerms/v1/actionStatusType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/ageCategory.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/anatomicalAxesOrientation.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/atlasType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/biologicalOrder.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/biologicalSex.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/breedingType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/cellType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/contributionType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/criteriaQualityType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/dataType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/deviceType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/disease.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/diseaseModel.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/ethicsAssessment.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/experimentalApproach.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/fileBundleGrouping.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/fileRepositoryType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/fileUsageRole.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/geneticStrainType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/handedness.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/language.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/laterality.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/metaDataModelType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/modelAbstractionLevel.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/modelScope.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/molecularEntity.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/operatingDevice.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/operatingSystem.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/organ.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/preparationType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/productAccessibility.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/programmingLanguage.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/qualitativeOverlap.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/semanticDataType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/service.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/softwareApplicationCategory.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/softwareFeature.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/species.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/stimulationApproach.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/stimulusType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/subjectAttribute.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/technique.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/termSuggestion.py, Python, 71 lines
new_api/v3/models/openminds/controlledTerms/v1/terminology.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/tissueSampleAttribute.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/tissueSampleType.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/typeOfUncertainty.py, Python, 57 lines
new_api/v3/models/openminds/controlledTerms/v1/unitOfMeasurement.py, Python, 57 lines
new_api/v3/models/openminds/core/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/actors/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/actors/affiliation.py, Python, 31 lines
new_api/v3/models/openminds/core/v4/actors/contactInformation.py, Python, 16 lines
new_api/v3/models/openminds/core/v4/actors/contribution.py, Python, 25 lines
new_api/v3/models/openminds/core/v4/actors/organization.py, Python, 43 lines
new_api/v3/models/openminds/core/v4/actors/person.py, Python, 62 lines
new_api/v3/models/openminds/core/v4/data/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/data/contentType.py, Python, 56 lines
new_api/v3/models/openminds/core/v4/data/contentTypePattern.py, Python, 23 lines
new_api/v3/models/openminds/core/v4/data/copyright.py, Python, 25 lines
new_api/v3/models/openminds/core/v4/data/file.py, Python, 112 lines
new_api/v3/models/openminds/core/v4/data/fileBundle.py, Python, 94 lines
new_api/v3/models/openminds/core/v4/data/filePathPattern.py, Python, 20 lines
new_api/v3/models/openminds/core/v4/data/fileRepository.py, Python, 108 lines
new_api/v3/models/openminds/core/v4/data/fileRepositoryStructure.py, Python, 25 lines
new_api/v3/models/openminds/core/v4/data/hash.py, Python, 22 lines
new_api/v3/models/openminds/core/v4/data/license.py, Python, 37 lines
new_api/v3/models/openminds/core/v4/data/serviceLink.py, Python, 29 lines
new_api/v3/models/openminds/core/v4/miscellaneous/DOI.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/GRIDID.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/ISBN.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/ORCID.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/RORID.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/RRID.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/SWHID.py, Python, 18 lines
new_api/v3/models/openminds/core/v4/miscellaneous/URL.py, Python, 13 lines
new_api/v3/models/openminds/core/v4/miscellaneous/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/miscellaneous/funding.py, Python, 36 lines
new_api/v3/models/openminds/core/v4/miscellaneous/quantitativeValue.py, Python, 40 lines
new_api/v3/models/openminds/core/v4/miscellaneous/quantitativeValueRange.py, Python, 34 lines
new_api/v3/models/openminds/core/v4/miscellaneous/stockNumber.py, Python, 21 lines
new_api/v3/models/openminds/core/v4/products/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/products/dataset.py, Python, 71 lines
new_api/v3/models/openminds/core/v4/products/datasetVersion.py, Python, 253 lines
new_api/v3/models/openminds/core/v4/products/metaDataModel.py, Python, 71 lines
new_api/v3/models/openminds/core/v4/products/metaDataModelVersion.py, Python, 222 lines
new_api/v3/models/openminds/core/v4/products/model.py, Python, 90 lines
new_api/v3/models/openminds/core/v4/products/modelVersion.py, Python, 219 lines
new_api/v3/models/openminds/core/v4/products/project.py, Python, 50 lines
new_api/v3/models/openminds/core/v4/products/software.py, Python, 71 lines
new_api/v3/models/openminds/core/v4/products/softwareVersion.py, Python, 262 lines
new_api/v3/models/openminds/core/v4/research/__init__.py, Python, 1 line
new_api/v3/models/openminds/core/v4/research/behavioralProtocol.py, Python, 36 lines
new_api/v3/models/openminds/core/v4/research/numericalParameter.py, Python, 65 lines
new_api/v3/models/openminds/core/v4/research/parameterSet.py, Python, 86 lines
new_api/v3/models/openminds/core/v4/research/protocol.py, Python, 37 lines
new_api/v3/models/openminds/core/v4/research/protocolExecution.py, Python, 161 lines
new_api/v3/models/openminds/core/v4/research/stimulation.py, Python, 33 lines
new_api/v3/models/openminds/core/v4/research/strain.py, Python, 102 lines
new_api/v3/models/openminds/core/v4/research/stringParameter.py, Python, 22 lines
new_api/v3/models/openminds/core/v4/research/subject.py, Python, 49 lines
new_api/v3/models/openminds/core/v4/research/subjectGroup.py, Python, 56 lines
new_api/v3/models/openminds/core/v4/research/subjectGroupState.py, Python, 148 lines
new_api/v3/models/openminds/core/v4/research/subjectState.py, Python, 146 lines
new_api/v3/models/openminds/core/v4/research/tissueSample.py, Python, 75 lines
new_api/v3/models/openminds/core/v4/research/tissueSampleCollection.py, Python, 84 lines
new_api/v3/models/openminds/core/v4/research/tissueSampleCollectionState.py, Python, 140 lines
new_api/v3/models/openminds/core/v4/research/tissueSampleState.py, Python, 140 lines
new_api/v3/models/util/__init__.py, Python, 1 line
new_api/v3/models/util/numpyarray.py, Python, 47 lines
new_api/v3/models/vocabularies/__init__.py, Python, 1 line
new_api/v3/models/vocabularies/base.py, Python, 7 lines
new_api/v3/models/vocabularies/genes.py, Python, 5 lines
new_api/v3/models/volumes/__init__.py, Python, 4 lines
new_api/v3/models/volumes/parcellationmap.py, Python, 13 lines
new_api/v3/models/volumes/volume.py, Python, 33 lines
new_api/v3/serialization/__init__.py, Python, 35 lines
new_api/v3/serialization/_common.py, Python, 62 lines
new_api/v3/serialization/feature.py, Python, 129 lines
new_api/warmup/__init__.py, Python, 14 lines
new_api/worker/__init__.py, Python, 9 lines
prepare_release.sh, Shell, 169 lines
server_health.py, Python, 26 lines
test/__init__.py, Python, 1 line
test/core/__init__.py, Python, 1 line
test/core/test_atlas_api.py, Python, 31 lines
test/core/test_parcellation_api.py, Python, 100 lines
test/core/test_regions_api.py, Python, 208 lines
test/core/test_siibra_api.py, Python, 55 lines
test/core/test_space_api.py, Python, 133 lines
test/serialization/features/molecular/test_receptor_density_fingerprint.py, Python, 22 lines
test/serialization/features/test_connectivity.py, Python, 39 lines
test/serialization/features/test_feature.py, Python, 77 lines
test/serialization/features/test_tabular.py, Python, 59 lines
test/serialization/features/test_voi.py, Python, 22 lines
test/serialization/test_atlas.py, Python, 11 lines
test/serialization/test_datasets.py, Python, 1 line
test/serialization/test_parcellation.py, Python, 13 lines
test/serialization/test_region.py, Python, 16 lines
test/serialization/test_space.py, Python, 16 lines
test/serialization/test_util.py, Python, 24 lines
test/serialization/test_volumes.py, Python, 20 lines
test/serialization/volumes/test_volume.py, Python, 22 lines
test/service/__init__.py, Python, 1 line
test/service/test_request_utils.py, Python, 152 lines
test/test_app.py, Python, 232 lines
worker_health.py, Python, 14 lines
worker_health_v4.py, Python, 14 lines
LICENSE, License, 201 lines
README.md, Text, 50 lines

At the source: github.com/FZJ-INM1-BDA/siibra-api

apache.org/licenses

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

At the source: apache.org/licenses/

Zenodo 21127648

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file, 0 scripts

Software Heritage: not checked

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

At the source: zenodo.org/records/21127648/files/20250221-drag-drop-siibra-paper.mp4

Code availability

Source codes for siibra are available on GitHub (https://github.com/FZJ-INM1-BDA/siibra-python; https://github.com/FZJ-INM1-BDA/siibra-explorer and https://github.com/FZJ-INM1-BDA/siibra-api) and provided under the Apache 2.0 license (http://www.apache.org/licenses/). We have integrated all components of siibra with Zenodo, which generates unique DOIs for each new release of the tool suite. An installation of the interactive viewer is publicly accessible at https://atlases.ebrains.eu/viewer as a core component of the brain atlas services in the research infrastructure EBRAINS. The web API can be accessed at https://siibra-api.apps.ebrains.eu/v3_0/docs. Researchers can install siibra-python through the Python Package Index from https://pypi.org/project/siibra. Comprehensive online documentation, including executable codes to reproduce all figures can be accessed via https://siibra-python.readthedocs.io/en/v1. More detailed tutorials notebooks are provided at https://github.com/FZJ-INM1-BDA/siibra-tutorials.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

8 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1,307 scripts, each with its path and the digest of its content;
25 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

doi:10.25493/evpc-mvh, at the source; found in the references
doi:10.25493/jwtf-pab, at the source; found in the references
doi:10.25493/nvs8-xs5, at the source; found in the references
doi:10.25493/the9-vqu, at the source; found in the references
doi:10.25493/v8hw-cvc, at the source; found in the references
github.com/openmetadatainitiative/openminds, at github.com; found in the text, “Results”

Data Availability Statement

All data used in this work are publicly available via the URLs and references provided with the respective figures. Most contents are published as curated datasets with permanent identifiers on the EBRAINS platform (https://ebrains.eu). Gene expression levels were retrieved from the Allen Human Brain microarray data18,19. The current foundational content linked with the atlas is defined and maintained at https://github.com/FZJ-INM1-BDA/siibra-configurations/tree/v1. An overview is provided as Extended Data Table 2.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 4 keywords, 9 MeSH terms, 3 funders, 60 references, 10 RRIDs.

Cite

This paper

Dickscheid, T., Gui, X., Simsek, A. N., Schiffer, C., Mangin, J.-F., Leprince, Y., Jirsa, V., Bjaalie, J. G., Leergaard, T. B., Bludau, S., & Amunts, K. (2026). Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources. Nature methods, 23(8), 1647-1658. https://doi.org/10.1038/s41592-026-03159-x

BibTeX

@article{dickscheid2026siibra, author = {Dickscheid, Timo and Gui, Xiaoyun and Simsek, Ahmet N and Schiffer, Christian and Mangin, Jean-Francois and Leprince, Yann and Jirsa, Viktor and Bjaalie, Jan G and Leergaard, Trygve B and Bludau, Sebastian and Amunts, Katrin}, title = {{Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources}}, journal = {Nature methods}, year = {2026}, month = jul, volume = {23}, number = {8}, pages = {1647--1658}, publisher = {Nature Portfolio}, issn = {1548-7091}, doi = {10.1038/s41592-026-03159-x}, url = {https://doi.org/10.1038/s41592-026-03159-x}, pmid = {42477446}, pmcid = {PMC13441889} }

RIS

TY - JOUR AU - Dickscheid, Timo AU - Gui, Xiaoyun AU - Simsek, Ahmet N AU - Schiffer, Christian AU - Mangin, Jean-Francois AU - Leprince, Yann AU - Jirsa, Viktor AU - Bjaalie, Jan G AU - Leergaard, Trygve B AU - Bludau, Sebastian AU - Amunts, Katrin TI - Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources T2 - Nature methods J2 - Nat Methods PY - 2026 DA - 2026/07/20 VL - 23 IS - 8 SP - 1647 EP - 1658 SN - 1548-7091 PB - Nature Portfolio DO - 10.1038/s41592-026-03159-x UR - https://doi.org/10.1038/s41592-026-03159-x LA - en ER -

CSL-JSON

{ "id": "10.1038/s41592-026-03159-x", "type": "article-journal", "title": "Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources", "container-title": "Nature methods", "author": [ { "family": "Dickscheid", "given": "Timo" }, { "family": "Gui", "given": "Xiaoyun" }, { "family": "Simsek", "given": "Ahmet N" }, { "family": "Schiffer", "given": "Christian" }, { "family": "Mangin", "given": "Jean-Francois" }, { "family": "Leprince", "given": "Yann" }, { "family": "Jirsa", "given": "Viktor" }, { "family": "Bjaalie", "given": "Jan G" }, { "family": "Leergaard", "given": "Trygve B" }, { "family": "Bludau", "given": "Sebastian" }, { "family": "Amunts", "given": "Katrin" } ], "container-title-short": "Nat Methods", "volume": "23", "issue": "8", "page": "1647-1658", "DOI": "10.1038/s41592-026-03159-x", "PMID": "42477446", "PMCID": "PMC13441889", "ISSN": "1548-7091", "publisher": "Nature Portfolio", "URL": "https://doi.org/10.1038/s41592-026-03159-x", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 20 ] ] } }

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Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources.

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