conda-env.yml

@@ -13,7 +13,7 @@ dependencies:
   - ruamel.yaml>=0.17,<0.19
   - h5py>=3.10
   - tqdm>=4.66.1,<4.67.0
-  - templateflow>=23.0.0
+  - templateflow>=23.0.0,<25.0.0
   - lapy>=1.0.0,<2.0.0
   - lazy_loader==0.4
   - importlib_metadata

docs/builtin.rst

@@ -743,5 +743,39 @@ Available
    Planned
    ~~~~~~~
 
+
+Maps
+----
+
+..
+    Provide a list of the maps that are implemented or planned.
+
+    Version added: The junifer version in which the maps was added.
+
+Available
+~~~~~~~~~
+
+.. list-table::
+   :widths: auto
+   :header-rows: 1
+
+   * - Name
+     - Options
+     - Keys
+     - Template Spaces
+     - Version Added
+     - Publication
+   * - Smith
+     - ``components``, ``dimension``
+     - | ``Smith_rsn_10``, ``Smith_rsn_20``, ``Smith_rsn_70``,
+       | ``Smith_bm_10``, ``Smith_bm_20``, ``Smith_bm_70``
+     - ``MNI152NLin6Asym``
+     - 0.0.7
+     - | S.M. Smith, P.T. Fox, K.L. Miller et al.
+       | Correspondence of the brain's functional architecture during activation and rest
+       | Proc. Natl. Acad. Sci. U.S.A. 106 (31) 13040-13045 (2009)
+       | https://doi.org/10.1073/pnas.0905267106
+
+
 ..
   helpful site for creating tables: https://rest-sphinx-memo.readthedocs.io/en/latest/ReST.html#tables

docs/changes/newsfragments/458.change

@@ -0,0 +1 @@
+:func:`.get_data`, :func:`.load_data`, :func:`.list_data`, :func:`.register_data` and :func:`.deregister_data` now support ``"maps"`` as a valid argument for ``kind`` by `Synchon Mandal`_

docs/changes/newsfragments/458.feature

@@ -0,0 +1 @@
+Introduce :class:`.MapsRegistry` to centralise probabilistic atlas (maps) data management by `Synchon Mandal`_

docs/changes/newsfragments/458.misc

@@ -0,0 +1 @@
+Constrain ``templateflow`` version to ``>=23.0.0,<25.0.0`` by `Synchon Mandal`_

docs/extending/index.rst

@@ -30,6 +30,7 @@ DataGrabbers, Preprocessors, Markers, etc.,  following the *junifer* way.
    parcellations
    coordinates
    masks
+   maps
    plugins
    data_registries
    data_types

docs/extending/maps.rst

@@ -0,0 +1,140 @@
+.. include:: ../links.inc
+
+.. _adding_maps:
+
+Adding Maps
+===========
+
+Maps in ``junifer`` are basically probabilistic atlases. They are differentiated
+from parcellations for ease of computation and to handle edge cases in a sane manner.
+Before you start adding your own maps, check whether ``junifer`` has
+the map(s) :ref:`in-built already <builtin>`. Perhaps, what is available
+there will suffice to achieve your goals. However, of course ``junifer`` will not
+have every map(s) available that you may want to use, and if so, it will
+be nice to be able to add it yourself using a format that ``junifer`` understands.
+
+Similarly, you may even be interested in creating your own custom maps
+and then adding them to ``junifer``, so you can use ``junifer`` to obtain
+different Markers to assess and validate your own maps. So, how can you do
+this?
+
+Since both of these use-cases are quite common, and not being able to use your
+favourite map(s) is of course quite a buzzkill, ``junifer`` actually
+provides the easy-to-use :func:`.register_data` function to do just that.
+Let's try to understand the API reference and then use this function to register our
+own map(s).
+
+From the API reference, we can see that it has 3 positional arguments:
+
+* ``kind``
+* ``name``
+* ``space``
+
+as well as one optional keyword argument: ``overwrite``.
+As the ``kind`` needs to be ``"maps"``, we can check
+``MapsRegistry.register`` for keyword arguments to be passed:
+
+* ``maps_path``
+* ``maps_labels``
+
+The ``name`` of the map(s) is up to you and will be the name that
+``junifer`` will use to refer to this particular maps. You can think of
+this as being similar to a key in a Python dictionary, i.e. a key that is used to
+obtain and operate on the actual maps data. This ``name`` must always
+be a string. For example, we could call our map(s)
+``"my_custom_maps"`` (Note, that in a real-world use case this is
+likely not a good name, and you should try to choose a meaningful name that
+conveys as much relevant information about your maps as necessary).
+
+The ``maps_path`` must be a ``str`` or ``Path`` object indicating a
+path to a valid 4D NIfTI image, which contains floating-point labels indicating the
+individual regions-of-interest (ROIs) of your map(s).
+
+We also want to make sure that we can associate each label with
+a human readable name (i.e. the name for each ROI). This serves naming the
+features that maps-based markers produce in an unambiguous way, such
+that a user can easily identify which ROIs were used to produce a specific
+feature (multiple ROIs, because some features consist of information from two
+or more ROIs, as for example in functional connectivity). Therefore, we provide
+junifer with a list of strings, that contains the names for each ROI. In this
+list, the label at the i-th position indicates the i-th index in the 4th dimension
+of the NIfTI image.
+
+Lastly, we specify the ``space`` that the map(s) is in, for example,
+``"MNI152NLin6Asym"`` or ``"native"`` (scanner-native space).
+
+Step 1: Prepare code to register a maps
+---------------------------------------
+
+Now we know everything that we need to know to make sure ``junifer`` can use our
+own map(s) to compute any maps-based Marker. A simple example could
+look like this:
+
+.. code-block:: python
+
+  from pathlib import Path
+
+  import numpy as np
+  from junifer.data import register_data
+
+
+  # these are of course just example paths, replace it with your own:
+  path_to_maps = (
+      Path.cwd() / "my_custom_maps.nii.gz"
+  )
+  path_to_labels = (
+      Path.cwd() / "my_custom_maps_labels.txt"
+  )
+
+  my_labels = list(np.loadtxt(path_to_labels, dtype=str))
+
+  register_data(
+      kind="maps",
+      name="my_custom_maps",
+      maps_path=path_to_maps,
+      maps_labels=my_labels,
+      space="MNI152NLin6Asym",
+  )
+
+We can run this code and it seems to work, however, how can we actually
+include the custom map(s) in a ``junifer`` pipeline using a
+:ref:`code-less YAML configuration <codeless>`?
+
+Step 2: Add maps registration to the YAML file
+----------------------------------------------
+
+In order to use the maps in a ``junifer`` pipeline configured by a YAML
+file, we can save the above code in a Python file, say
+``registering_my_maps.py``. We can then simply add this file using the
+``with`` keyword provided by ``junifer``:
+
+.. code-block:: yaml
+
+  with:
+    - registering_my_maps.py
+
+Afterwards continue configuring the rest of the pipeline in this YAML file, and
+you will be able to use this maps using the name you gave the
+maps when registering it. For example, we can add a
+``MapsAggregation`` Marker to demonstrate how this can be done:
+
+.. code-block:: yaml
+
+  markers:
+    - name: CustomMaps_mean
+      kind: MapsAggregation
+      maps: my_custom_maps
+      method: mean
+
+Now, you can simply use this YAML file to run your pipeline.
+
+.. important::
+
+   It's important to keep in mind that if the paths given in
+   ``registering_my_maps.py`` are relative paths, they will be interpreted
+   by ``junifer`` as relative to the jobs directory (i.e. where ``junifer`` will
+   create submit files, logs directory and so on). For simplicity, you may just
+   want to use absolute paths to avoid confusion, yet using relative paths is
+   likely a better way to make your pipeline directory / repository more portable
+   and therefore more reproducible for others. Really, once you understand how
+   these paths are interpreted by ``junifer``, it is quite easy.

junifer/data/__init__.pyi

@@ -3,6 +3,7 @@ __all__ = [
     "CoordinatesRegistry",
     "DataDispatcher",
     "ParcellationRegistry",
+    "MapsRegistry",
     "MaskRegistry",
     "get_data",
     "list_data",
@@ -17,6 +18,7 @@ __all__ = [
 from .pipeline_data_registry_base import BasePipelineDataRegistry
 from .coordinates import CoordinatesRegistry
 from .parcellations import ParcellationRegistry
+from .maps import MapsRegistry
 from .masks import MaskRegistry
 
 from ._dispatch import (

junifer/data/_dispatch.py

@@ -17,6 +17,7 @@ from numpy.typing import ArrayLike
 
 from ..utils import raise_error
 from .coordinates import CoordinatesRegistry
+from .maps import MapsRegistry
 from .masks import MaskRegistry
 from .parcellations import ParcellationRegistry
 from .pipeline_data_registry_base import BasePipelineDataRegistry
@@ -52,6 +53,7 @@ class DataDispatcher(MutableMapping):
                 {
                     "coordinates": CoordinatesRegistry,
                     "parcellation": ParcellationRegistry,
+                    "maps": MapsRegistry,
                     "mask": MaskRegistry,
                 }
             )
@@ -118,14 +120,14 @@ def get_data(
     extra_input: Optional[dict[str, Any]] = None,
 ) -> Union[
     tuple[ArrayLike, list[str]],  # coordinates
-    tuple["Nifti1Image", list[str]],  # parcellation
+    tuple["Nifti1Image", list[str]],  # parcellation / maps
     "Nifti1Image",  # mask
 ]:
     """Get tailored ``kind`` for ``target_data``.
 
     Parameters
     ----------
-    kind : {"coordinates", "parcellation", "mask"}
+    kind : {"coordinates", "parcellation", "mask", "maps"}
         Kind of data to fetch and apply.
     names : str or dict or list of str / dict
         The registered name(s) of the data.
@@ -166,7 +168,7 @@ def list_data(kind: str) -> list[str]:
 
     Parameters
     ----------
-    kind : {"coordinates", "parcellation", "mask"}
+    kind : {"coordinates", "parcellation", "mask", "maps"}
         Kind of data registry to list.
 
     Returns
@@ -195,7 +197,9 @@ def load_data(
     **kwargs,
 ) -> Union[
     tuple[ArrayLike, list[str], str],  # coordinates
-    tuple[Optional["Nifti1Image"], list[str], Path, str],  # parcellation
+    tuple[
+        Optional["Nifti1Image"], list[str], Path, str
+    ],  # parcellation / maps
     tuple[
         Optional[Union["Nifti1Image", Callable]], Optional[Path], str
     ],  # mask
@@ -204,7 +208,7 @@ def load_data(
 
     Parameters
     ----------
-    kind : {"coordinates", "parcellation", "mask"}
+    kind : {"coordinates", "parcellation", "mask", "maps"}
         Kind of data to load.
     name : str
         The registered name of the data.
@@ -244,7 +248,7 @@ def register_data(
 
     Parameters
     ----------
-    kind : {"coordinates", "parcellation", "mask"}
+    kind : {"coordinates", "parcellation", "mask", "maps"}
         Kind of data to register.
     name : str
         The name to register.
@@ -277,7 +281,7 @@ def deregister_data(kind: str, name: str) -> None:
 
     Parameters
     ----------
-    kind : {"coordinates", "parcellation", "mask"}
+    kind : {"coordinates", "parcellation", "mask", "maps"}
         Kind of data to register.
     name : str
         The name to de-register.

junifer/data/maps/__init__.py

@@ -0,0 +1,9 @@
+"""Maps."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import lazy_loader as lazy
+
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

junifer/data/maps/__init__.pyi

@@ -0,0 +1,5 @@
+__all__ = [
+    "MapsRegistry",
+]
+
+from ._maps import MapsRegistry

junifer/data/maps/_ants_maps_warper.py

@@ -0,0 +1,156 @@
+"""Provide class for maps space warping via ANTs."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import uuid
+from typing import TYPE_CHECKING, Any, Optional
+
+import nibabel as nib
+
+from ...pipeline import WorkDirManager
+from ...utils import logger, raise_error, run_ext_cmd
+from ..template_spaces import get_template, get_xfm
+
+
+if TYPE_CHECKING:
+    from nibabel.nifti1 import Nifti1Image
+
+
+__all__ = ["ANTsMapsWarper"]
+
+
+class ANTsMapsWarper:
+    """Class for maps space warping via ANTs.
+
+    This class uses ANTs ``antsApplyTransforms`` for transformation.
+
+    """
+
+    def warp(
+        self,
+        maps_name: str,
+        maps_img: "Nifti1Image",
+        src: str,
+        dst: str,
+        target_data: dict[str, Any],
+        warp_data: Optional[dict[str, Any]],
+    ) -> "Nifti1Image":
+        """Warp ``maps_img`` to correct space.
+
+        Parameters
+        ----------
+        maps_name : str
+            The name of the maps.
+        maps_img : nibabel.nifti1.Nifti1Image
+            The maps image to transform.
+        src : str
+            The data type or template space to warp from.
+            It should be empty string if ``dst="T1w"``.
+        dst : str
+            The data type or template space to warp to.
+            `"T1w"` is the only allowed data type and it uses the resampled T1w
+            found in ``target_data.reference``. The ``"reference"``
+            key is added if the :class:`.SpaceWarper` is used or if the
+            data is provided in native space.
+        target_data : dict
+            The corresponding item of the data object to which the maps
+            will be applied.
+        warp_data : dict or None
+            The warp data item of the data object. The value is unused if
+            ``dst!="T1w"``.
+
+        Returns
+        -------
+        nibabel.nifti1.Nifti1Image
+            The transformed maps image.
+
+        Raises
+        ------
+        ValueError
+            If ``warp_data`` is None when ``dst="T1w"``.
+
+        """
+        # Create element-scoped tempdir so that warped maps is
+        # available later as nibabel stores file path reference for
+        # loading on computation
+        prefix = (
+            f"ants_maps_warper_{maps_name}"
+            f"{'' if not src else f'_from_{src}'}_to_{dst}_"
+            f"{uuid.uuid1()}"
+        )
+        element_tempdir = WorkDirManager().get_element_tempdir(
+            prefix=prefix,
+        )
+
+        # Native space warping
+        if dst == "native":  # pragma: no cover
+            # Warp data check
+            if warp_data is None:
+                raise_error("No `warp_data` provided")
+            if "reference" not in target_data:
+                raise_error("No `reference` provided")
+            if "path" not in target_data["reference"]:
+                raise_error("No `path` provided in `reference`")
+
+            logger.debug("Using ANTs for maps transformation")
+
+            # Save existing maps image to a tempfile
+            prewarp_maps_path = element_tempdir / "prewarp_maps.nii.gz"
+            nib.save(maps_img, prewarp_maps_path)
+
+            # Create a tempfile for warped output
+            warped_maps_path = element_tempdir / "maps_warped.nii.gz"
+            # Set antsApplyTransforms command
+            apply_transforms_cmd = [
+                "antsApplyTransforms",
+                "-d 3",
+                "-e 3",
+                "-n LanczosWindowedSinc",
+                f"-i {prewarp_maps_path.resolve()}",
+                # use resampled reference
+                f"-r {target_data['reference']['path'].resolve()}",
+                f"-t {warp_data['path'].resolve()}",
+                f"-o {warped_maps_path.resolve()}",
+            ]
+            # Call antsApplyTransforms
+            run_ext_cmd(name="antsApplyTransforms", cmd=apply_transforms_cmd)
+
+        # Template space warping
+        else:
+            logger.debug(f"Using ANTs to warp maps from {src} to {dst}")
+
+            # Get xfm file
+            xfm_file_path = get_xfm(src=src, dst=dst)
+            # Get template space image
+            template_space_img = get_template(
+                space=dst,
+                target_img=maps_img,
+                extra_input=None,
+            )
+            # Save template to a tempfile
+            template_space_img_path = element_tempdir / f"{dst}_T1w.nii.gz"
+            nib.save(template_space_img, template_space_img_path)
+
+            # Save existing maps image to a tempfile
+            prewarp_maps_path = element_tempdir / "prewarp_maps.nii.gz"
+            nib.save(maps_img, prewarp_maps_path)
+
+            # Create a tempfile for warped output
+            warped_maps_path = element_tempdir / "maps_warped.nii.gz"
+            # Set antsApplyTransforms command
+            apply_transforms_cmd = [
+                "antsApplyTransforms",
+                "-d 3",
+                "-e 3",
+                "-n LanczosWindowedSinc",
+                f"-i {prewarp_maps_path.resolve()}",
+                f"-r {template_space_img_path.resolve()}",
+                f"-t {xfm_file_path.resolve()}",
+                f"-o {warped_maps_path.resolve()}",
+            ]
+            # Call antsApplyTransforms
+            run_ext_cmd(name="antsApplyTransforms", cmd=apply_transforms_cmd)
+
+        # Load nifti
+        return nib.load(warped_maps_path)

junifer/data/maps/_fsl_maps_warper.py

@@ -0,0 +1,85 @@
+"""Provide class for maps space warping via FSL FLIRT."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import uuid
+from typing import TYPE_CHECKING, Any
+
+import nibabel as nib
+
+from ...pipeline import WorkDirManager
+from ...utils import logger, run_ext_cmd
+
+
+if TYPE_CHECKING:
+    from nibabel.nifti1 import Nifti1Image
+
+
+__all__ = ["FSLMapsWarper"]
+
+
+class FSLMapsWarper:
+    """Class for maps space warping via FSL FLIRT.
+
+    This class uses FSL FLIRT's ``applywarp`` for transformation.
+
+    """
+
+    def warp(
+        self,
+        maps_name: str,
+        maps_img: "Nifti1Image",
+        target_data: dict[str, Any],
+        warp_data: dict[str, Any],
+    ) -> "Nifti1Image":  # pragma: no cover
+        """Warp ``maps_img`` to correct space.
+
+        Parameters
+        ----------
+        maps_name : str
+            The name of the maps.
+        maps_img : nibabel.nifti1.Nifti1Image
+            The maps image to transform.
+        target_data : dict
+            The corresponding item of the data object to which the maps
+            will be applied.
+        warp_data : dict
+            The warp data item of the data object.
+
+        Returns
+        -------
+        nibabel.nifti1.Nifti1Image
+            The transformed maps image.
+
+        """
+        logger.debug("Using FSL for maps transformation")
+
+        # Create element-scoped tempdir so that warped maps is
+        # available later as nibabel stores file path reference for
+        # loading on computation
+        element_tempdir = WorkDirManager().get_element_tempdir(
+            prefix=f"fsl_maps_warper_{maps_name}_{uuid.uuid1()}"
+        )
+
+        # Save existing maps image to a tempfile
+        prewarp_maps_path = element_tempdir / "prewarp_maps.nii.gz"
+        nib.save(maps_img, prewarp_maps_path)
+
+        # Create a tempfile for warped output
+        warped_maps_path = element_tempdir / "maps_warped.nii.gz"
+        # Set applywarp command
+        applywarp_cmd = [
+            "applywarp",
+            "--interp=spline",
+            f"-i {prewarp_maps_path.resolve()}",
+            # use resampled reference
+            f"-r {target_data['reference']['path'].resolve()}",
+            f"-w {warp_data['path'].resolve()}",
+            f"-o {warped_maps_path.resolve()}",
+        ]
+        # Call applywarp
+        run_ext_cmd(name="applywarp", cmd=applywarp_cmd)
+
+        # Load nifti
+        return nib.load(warped_maps_path)

junifer/data/maps/_maps.py

@@ -0,0 +1,446 @@
+"""Provide a class for centralized maps data registry."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import nibabel as nib
+import nilearn.image as nimg
+import numpy as np
+from junifer_data import get
+
+from ...utils import logger, raise_error
+from ..pipeline_data_registry_base import BasePipelineDataRegistry
+from ..utils import (
+    JUNIFER_DATA_PARAMS,
+    closest_resolution,
+    get_dataset_path,
+    get_native_warper,
+)
+from ._ants_maps_warper import ANTsMapsWarper
+from ._fsl_maps_warper import FSLMapsWarper
+
+
+if TYPE_CHECKING:
+    from nibabel.nifti1 import Nifti1Image
+
+
+__all__ = ["MapsRegistry"]
+
+
+class MapsRegistry(BasePipelineDataRegistry):
+    """Class for maps data registry.
+
+    This class is a singleton and is used for managing available maps
+    data in a centralized manner.
+
+    """
+
+    def __init__(self) -> None:
+        """Initialize the class."""
+        super().__init__()
+        # Each entry in registry is a dictionary that must contain at least
+        # the following keys:
+        # * 'family': the maps' family name (e.g., 'Smith')
+        # * 'space': the maps' space (e.g., 'MNI')
+        # and can also have optional key(s):
+        # * 'valid_resolutions': a list of valid resolutions for the
+        # maps (e.g., [1, 2])
+        # The built-in maps are files that are shipped with the
+        # junifer-data dataset.
+        # Make built-in and external dictionaries for validation later
+        self._builtin = {}
+        self._external = {}
+
+        # Add Smith
+        for comp in ["rsn", "bm"]:
+            for dim in [10, 20, 70]:
+                self._builtin.update(
+                    {
+                        f"Smith_{comp}_{dim}": {
+                            "family": "Smith2009",
+                            "components": comp,
+                            "dimension": dim,
+                            "space": "MNI152NLin6Asym",
+                        }
+                    }
+                )
+
+        # Update registry with built-in ones
+        self._registry.update(self._builtin)
+
+    def register(
+        self,
+        name: str,
+        maps_path: Union[str, Path],
+        maps_labels: list[str],
+        space: str,
+        overwrite: bool = False,
+    ) -> None:
+        """Register a custom user map(s).
+
+        Parameters
+        ----------
+        name : str
+            The name of the map(s).
+        maps_path : str or pathlib.Path
+            The path to the map(s) file.
+        maps_labels : list of str
+            The list of labels for the map(s).
+        space : str
+            The template space of the map(s), e.g., "MNI152NLin6Asym".
+        overwrite : bool, optional
+            If True, overwrite an existing maps with the same name.
+            Does not apply to built-in maps (default False).
+
+        Raises
+        ------
+        ValueError
+            If the map(s) ``name`` is a built-in map(s) or
+            if the map(s) ``name`` is already registered and
+            ``overwrite=False``.
+
+        """
+        # Check for attempt of overwriting built-in maps
+        if name in self._builtin:
+            raise_error(
+                f"Map(s): {name} already registered as built-in map(s)."
+            )
+        # Check for attempt of overwriting external maps
+        if name in self._external:
+            if overwrite:
+                logger.info(f"Overwriting map(s): {name}")
+            else:
+                raise_error(
+                    f"Map(s): {name} already registered. Set "
+                    "`overwrite=True` to update its value."
+                )
+        # Convert str to Path
+        if not isinstance(maps_path, Path):
+            maps_path = Path(maps_path)
+        # Registration
+        logger.info(f"Registering map(s): {name}")
+        # Add user maps info
+        self._external[name] = {
+            "path": maps_path,
+            "labels": maps_labels,
+            "family": "CustomUserMaps",
+            "space": space,
+        }
+        # Update registry
+        self._registry[name] = {
+            "path": maps_path,
+            "labels": maps_labels,
+            "family": "CustomUserMaps",
+            "space": space,
+        }
+
+    def deregister(self, name: str) -> None:
+        """De-register a custom user map(s).
+
+        Parameters
+        ----------
+        name : str
+            The name of the map(s).
+
+        """
+        logger.info(f"De-registering map(s): {name}")
+        # Remove maps info
+        _ = self._external.pop(name)
+        # Update registry
+        _ = self._registry.pop(name)
+
+    def load(
+        self,
+        name: str,
+        target_space: str,
+        resolution: Optional[float] = None,
+        path_only: bool = False,
+    ) -> tuple[Optional["Nifti1Image"], list[str], Path, str]:
+        """Load map(s) and labels.
+
+        Parameters
+        ----------
+        name : str
+            The name of the map(s).
+        target_space : str
+            The desired space of the map(s).
+        resolution : float, optional
+            The desired resolution of the map(s) to load. If it is not
+            available, the closest resolution will be loaded. Preferably, use a
+            resolution higher than the desired one. By default, will load the
+            highest one (default None).
+        path_only : bool, optional
+            If True, the map(s) image will not be loaded (default False).
+
+        Returns
+        -------
+        Nifti1Image or None
+            Loaded map(s) image.
+        list of str
+            Map(s) labels.
+        pathlib.Path
+            File path to the map(s) image.
+        str
+            The space of the map(s).
+
+        Raises
+        ------
+        ValueError
+            If ``name`` is invalid or
+            if the map(s) family is invalid or
+            if the map(s) values and labels
+            don't have equal dimension or if the value range is invalid.
+
+        """
+        # Check for valid maps name
+        if name not in self._registry:
+            raise_error(
+                f"Map(s): {name} not found. Valid options are: {self.list}"
+            )
+
+        # Copy maps definition to avoid edits in original object
+        maps_def = self._registry[name].copy()
+        t_family = maps_def.pop("family")
+        space = maps_def.pop("space")
+
+        # Check and get highest resolution
+        if space != target_space:
+            logger.info(
+                f"Map(s) will be warped from {space} to {target_space} "
+                "using highest resolution"
+            )
+            resolution = None
+
+        # Check if the maps family is custom or built-in
+        if t_family == "CustomUserMaps":
+            maps_fname = maps_def["path"]
+            maps_labels = maps_def["labels"]
+        elif t_family in [
+            "Smith2009",
+        ]:
+            # Load maps and labels
+            if t_family == "Smith2009":
+                maps_fname, maps_labels = _retrieve_smith(
+                    resolution=resolution,
+                    **maps_def,
+                )
+        else:  # pragma: no cover
+            raise_error(f"Unknown map(s) family: {t_family}")
+
+        # Load maps image and values
+        logger.info(f"Loading map(s): {maps_fname.absolute()!s}")
+        maps_img = None
+        if not path_only:
+            # Load image via nibabel
+            maps_img = nib.load(maps_fname)
+            # Get regions
+            maps_regions = maps_img.get_fdata().shape[-1]
+            # Check for dimension
+            if maps_regions != len(maps_labels):
+                raise_error(
+                    f"Map(s) {name} has {maps_regions} "
+                    f"regions but {len(maps_labels)} labels."
+                )
+
+        return maps_img, maps_labels, maps_fname, space
+
+    def get(
+        self,
+        maps: str,
+        target_data: dict[str, Any],
+        extra_input: Optional[dict[str, Any]] = None,
+    ) -> tuple["Nifti1Image", list[str]]:
+        """Get map(s), tailored for the target image.
+
+        Parameters
+        ----------
+        maps : str
+            The name of the map(s).
+        target_data : dict
+            The corresponding item of the data object to which the map(s)
+            will be applied.
+        extra_input : dict, optional
+            The other fields in the data object. Useful for accessing other
+            data kinds that needs to be used in the computation of
+            map(s) (default None).
+
+        Returns
+        -------
+        Nifti1Image
+            The map(s) image.
+        list of str
+            Map(s) labels.
+
+        Raises
+        ------
+        ValueError
+            If ``extra_input`` is None when ``target_data``'s space is native.
+
+        """
+        # Check pre-requirements for space manipulation
+        target_space = target_data["space"]
+        logger.debug(f"Getting {maps} in {target_space} space.")
+        # Extra data type requirement check if target space is native
+        if target_space == "native":  # pragma: no cover
+            # Check for extra inputs
+            if extra_input is None:
+                raise_error(
+                    "No extra input provided, requires `Warp` and `T1w` "
+                    "data types in particular for transformation to "
+                    f"{target_data['space']} space for further computation."
+                )
+            # Get native space warper spec
+            warper_spec = get_native_warper(
+                target_data=target_data,
+                other_data=extra_input,
+            )
+            # Set target standard space to warp file space source
+            target_std_space = warper_spec["src"]
+            logger.debug(
+                f"Target space is native. Will warp from {target_std_space}"
+            )
+        else:
+            # Set target standard space to target space
+            target_std_space = target_space
+
+        # Get the min of the voxels sizes and use it as the resolution
+        target_img = target_data["data"]
+        resolution = np.min(target_img.header.get_zooms()[:3])
+
+        # Load maps
+        logger.debug(f"Loading map(s) {maps}")
+        img, labels, _, space = self.load(
+            name=maps,
+            resolution=resolution,
+            target_space=target_space,
+        )
+
+        # Convert maps spaces if required;
+        # cannot be "native" due to earlier check
+        if space != target_std_space:
+            logger.debug(
+                f"Warping {maps} to {target_std_space} space using ANTs."
+            )
+            raw_img = ANTsMapsWarper().warp(
+                maps_name=maps,
+                maps_img=img,
+                src=space,
+                dst=target_std_space,
+                target_data=target_data,
+                warp_data=None,
+            )
+            # Remove extra dimension added by ANTs
+            img = nimg.math_img("np.squeeze(img)", img=raw_img)
+
+        if target_space != "native":
+            # No warping is going to happen, just resampling, because
+            # we are in the correct space
+            logger.debug(f"Resampling {maps} to target image.")
+            # Resample maps to target image
+            img = nimg.resample_to_img(
+                source_img=img,
+                target_img=target_img,
+                interpolation="continuous",
+                copy=True,
+            )
+        else:  # pragma: no cover
+            # Warp maps if target space is native as either
+            # the image is in the right non-native space or it's
+            # warped from one non-native space to another non-native space
+            logger.debug(
+                "Warping map(s) to native space using "
+                f"{warper_spec['warper']}."
+            )
+            # extra_input check done earlier and warper_spec exists
+            if warper_spec["warper"] == "fsl":
+                img = FSLMapsWarper().warp(
+                    maps_name="native",
+                    maps_img=img,
+                    target_data=target_data,
+                    warp_data=warper_spec,
+                )
+            elif warper_spec["warper"] == "ants":
+                img = ANTsMapsWarper().warp(
+                    maps_name="native",
+                    maps_img=img,
+                    src="",
+                    dst="native",
+                    target_data=target_data,
+                    warp_data=warper_spec,
+                )
+
+        return img, labels
+
+
+def _retrieve_smith(
+    resolution: Optional[float] = None,
+    components: Optional[str] = None,
+    dimension: Optional[int] = None,
+) -> tuple[Path, list[str]]:
+    """Retrieve Smith maps.
+
+    Parameters
+    ----------
+    resolution : 2.0, optional
+        The desired resolution of the maps to load. If it is not
+        available, the closest resolution will be loaded. Preferably, use a
+        resolution higher than the desired one. By default, will load the
+        highest one (default None). Available resolution for these
+        maps are 2mm.
+    components : {"rsn", "bm"}, optional
+        The components to load. "rsn" loads the resting-fMRI components and
+        "bm" loads the BrainMap components (default None).
+    dimension : {10, 20, 70}, optional
+        The number of dimensions to load (default None).
+
+    Returns
+    -------
+    pathlib.Path
+        File path to the maps image.
+    list of str
+        Maps labels.
+
+    Raises
+    ------
+    ValueError
+        If invalid value is provided for ``components`` or ``dimension``.
+
+    """
+    logger.info("Maps parameters:")
+    logger.info(f"\tresolution: {resolution}")
+    logger.info(f"\tcomponents: {components}")
+    logger.info(f"\tdimension: {dimension}")
+
+    # Check resolution
+    _valid_resolutions = [2.0]
+    resolution = closest_resolution(resolution, _valid_resolutions)
+
+    # Check components value
+    _valid_components = ["rsn", "bm"]
+    if components not in _valid_components:
+        raise_error(
+            f"The parameter `components` ({components}) needs to be one of "
+            f"the following: {_valid_components}"
+        )
+
+    # Check dimension value
+    _valid_dimension = [10, 20, 70]
+    if dimension not in _valid_dimension:
+        raise_error(
+            f"The parameter `dimension` ({dimension}) needs to be one of the "
+            f"following: {_valid_dimension}"
+        )
+
+    # Fetch file path
+    maps_img_path = get(
+        file_path=Path(
+            f"parcellations/Smith2009/{components}{dimension}.nii.gz"
+        ),
+        dataset_path=get_dataset_path(),
+        **JUNIFER_DATA_PARAMS,
+    )
+
+    return maps_img_path, [f"Map_{i}" for i in range(dimension)]

junifer/data/maps/tests/test_maps.py

@@ -0,0 +1,255 @@
+"""Provide tests for maps."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import pytest
+from numpy.testing import assert_array_equal
+
+from junifer.data import (
+    deregister_data,
+    get_data,
+    list_data,
+    load_data,
+    register_data,
+)
+from junifer.data.maps._maps import _retrieve_smith
+from junifer.datagrabber import PatternDataladDataGrabber
+from junifer.datareader import DefaultDataReader
+from junifer.pipeline.utils import _check_ants
+from junifer.testing.datagrabbers import PartlyCloudyTestingDataGrabber
+
+
+def test_register_built_in_check() -> None:
+    """Test maps registration check for built-in maps."""
+    with pytest.raises(ValueError, match=r"built-in"):
+        register_data(
+            kind="maps",
+            name="Smith_rsn_10",
+            maps_path="testmaps.nii.gz",
+            maps_labels=["1", "2"],
+            space="MNI",
+        )
+
+
+def test_list_incorrect() -> None:
+    """Test incorrect information check for list mapss."""
+    assert "testmaps" not in list_data(kind="maps")
+
+
+def test_register_overwrite() -> None:
+    """Test maps registration check for overwriting."""
+    register_data(
+        kind="maps",
+        name="testmaps",
+        maps_path="testmaps.nii.gz",
+        maps_labels=["1", "2"],
+        space="MNI152NLin6Sym",
+    )
+    with pytest.raises(ValueError, match=r"already registered"):
+        register_data(
+            kind="maps",
+            name="testmaps",
+            maps_path="testmaps.nii.gz",
+            maps_labels=["1", "2"],
+            space="MNI152NLin6Sym",
+            overwrite=False,
+        )
+
+    register_data(
+        kind="maps",
+        name="testmaps",
+        maps_path="testmaps.nii.gz",
+        maps_labels=["1", "2"],
+        space="MNI152NLin6Sym",
+        overwrite=True,
+    )
+
+    assert (
+        load_data(
+            kind="maps",
+            name="testmaps",
+            target_space="MNI152NLin6Sym",
+            path_only=True,
+        )[2].name
+        == "testmaps.nii.gz"
+    )
+
+
+def test_register_valid_input() -> None:
+    """Test maps registration check for valid input."""
+    maps, labels, maps_path, _ = load_data(
+        kind="maps",
+        name="Smith_rsn_10",
+        target_space="MNI152NLin6Asym",
+    )
+    assert maps is not None
+
+    # Test wrong number of labels
+    register_data(
+        kind="maps",
+        name="WrongLabels",
+        maps_path=maps_path,
+        maps_labels=labels[:5],
+        space="MNI152NLin6Asym",
+    )
+    with pytest.raises(ValueError, match=r"has 10 regions but 5"):
+        load_data(
+            kind="maps",
+            name="WrongLabels",
+            target_space="MNI152NLin6Asym",
+        )
+
+
+def test_list() -> None:
+    """Test listing of available coordinates."""
+    assert {"Smith_rsn_10", "Smith_bm_70"}.issubset(
+        set(list_data(kind="maps"))
+    )
+
+
+def test_load_nonexisting() -> None:
+    """Test loading maps that not exist."""
+    with pytest.raises(ValueError, match=r"not found"):
+        load_data(kind="maps", name="nomaps", target_space="MNI152NLin6Sym")
+
+
+def test_get() -> None:
+    """Test tailored maps fetch."""
+    with PatternDataladDataGrabber(
+        uri="https://github.com/OpenNeuroDatasets/ds005226.git",
+        types=["BOLD"],
+        patterns={
+            "BOLD": {
+                "pattern": (
+                    "derivatives/pre-processed_data/space-MNI/{subject}/"
+                    "{subject-padded}_task-{task}_run-{run}_space-MNI152NLin6Asym"
+                    "_res-2_desc-preproc_bold.nii.gz"
+                ),
+                "space": "MNI152NLin6Asym",
+            },
+        },
+        replacements=["subject", "subject-padded", "task", "run"],
+    ) as dg:
+        element = dg[("sub-01", "sub-001", "rest", "1")]
+        element_data = DefaultDataReader().fit_transform(element)
+        bold = element_data["BOLD"]
+        bold_img = bold["data"]
+        # Get tailored coordinates
+        tailored_maps, tailored_labels = get_data(
+            kind="maps", names="Smith_rsn_10", target_data=bold
+        )
+
+        # Check shape with original element data
+        assert tailored_maps.shape[:3] == bold_img.shape[:3]
+
+        # Get raw maps
+        raw_maps, raw_labels, _, _ = load_data(
+            kind="maps", name="Smith_rsn_10", target_space="MNI152NLin6Asym"
+        )
+        # Tailored and raw shape should be same
+        assert tailored_maps.shape[:3] == raw_maps.shape[:3]
+        assert tailored_labels == raw_labels
+
+
+@pytest.mark.skipif(
+    _check_ants() is False, reason="requires ANTs to be in PATH"
+)
+def test_get_different_space() -> None:
+    """Test tailored maps fetch in different space."""
+    with PartlyCloudyTestingDataGrabber() as dg:
+        element = dg["sub-01"]
+        element_data = DefaultDataReader().fit_transform(element)
+        bold = element_data["BOLD"]
+        bold_img = bold["data"]
+        # Get tailored coordinates
+        tailored_maps, tailored_labels = get_data(
+            kind="maps", names="Smith_rsn_10", target_data=bold
+        )
+
+        # Check shape with original element data
+        assert tailored_maps.shape[:3] == bold_img.shape[:3]
+
+        # Get raw maps
+        raw_maps, raw_labels, _, _ = load_data(
+            kind="maps",
+            name="Smith_rsn_10",
+            target_space="MNI152NLin2009cAsym",
+        )
+        # Tailored and raw should not be same
+        assert tailored_maps.shape[:3] != raw_maps.shape[:3]
+        assert tailored_labels == raw_labels
+
+
+def test_deregister() -> None:
+    """Test maps deregistration."""
+    deregister_data(kind="maps", name="testmaps")
+    assert "testmaps" not in list_data(kind="maps")
+
+
+@pytest.mark.parametrize(
+    "resolution, components, dimension",
+    [
+        (2.0, "rsn", 10),
+        (2.0, "rsn", 20),
+        (2.0, "rsn", 70),
+        (2.0, "bm", 10),
+        (2.0, "bm", 20),
+        (2.0, "bm", 70),
+    ],
+)
+def test_smith(
+    resolution: float,
+    components: str,
+    dimension: int,
+) -> None:
+    """Test Smith maps.
+
+    Parameters
+    ----------
+    resolution : float
+        The parametrized resolution values.
+    components : str
+        The parametrized components values.
+    dimension : int
+        The parametrized dimension values.
+
+    """
+    maps = list_data(kind="maps")
+    maps_name = f"Smith_{components}_{dimension}"
+    assert maps_name in maps
+
+    maps_file = f"{components}{dimension}.nii.gz"
+    # Load maps
+    img, label, img_path, space = load_data(
+        kind="maps",
+        name=maps_name,
+        target_space="MNI152NLin6Asym",
+        resolution=resolution,
+    )
+    assert img is not None
+    assert img_path.name == maps_file
+    assert space == "MNI152NLin6Asym"
+    assert len(label) == dimension
+    assert_array_equal(
+        img.header["pixdim"][1:4],
+        3 * [2.0],
+    )
+
+
+def test_retrieve_smith_incorrect_components() -> None:
+    """Test retrieve Smith with incorrect components."""
+    with pytest.raises(ValueError, match="The parameter `components`"):
+        _retrieve_smith(
+            components="abc",
+            dimension=10,
+        )
+
+
+def test_retrieve_smith_incorrect_dimension() -> None:
+    """Test retrieve Smith with incorrect dimension."""
+    with pytest.raises(ValueError, match="The parameter `dimension`"):
+        _retrieve_smith(
+            components="rsn",
+            dimension=100,
+        )

junifer/data/masks/_masks.py

@@ -231,8 +231,9 @@ class MaskRegistry(BasePipelineDataRegistry):
         # * 'family': the mask's family name
         #             (e.g., 'Vickery-Patil', 'Callable')
         # * 'space': the mask's space (e.g., 'MNI', 'inherit')
-        # The built-in masks are files that are shipped with the package in the
-        # data/masks directory. The user can also register their own masks.
+        # The built-in masks are files that are shipped either with the
+        # junifer-data dataset or computed on-demand. The user can also
+        # register their own masks.
         # Callable masks should be functions that take at least one parameter:
         # * `target_img`: the image to which the mask will be applied.
         # and should be included in the registry as a value to a key: `func`.

junifer/data/parcellations/_parcellations.py

@@ -55,7 +55,7 @@ class ParcellationRegistry(BasePipelineDataRegistry):
         # and can also have optional key(s):
         # * 'valid_resolutions': a list of valid resolutions for the
         # parcellation (e.g., [1, 2])
-        # The built-in coordinates are files that are shipped with the
+        # The built-in parcellations are files that are shipped with the
         # junifer-data dataset.
         # Make built-in and external dictionaries for validation later
         self._builtin = {}

pyproject.toml

@@ -47,7 +47,7 @@ dependencies = [
     "ruamel.yaml>=0.17,<0.19",
     "h5py>=3.10",
     "tqdm>=4.66.1,<4.67.0",
-    "templateflow>=23.0.0",
+    "templateflow>=23.0.0,<25.0.0",
     "lapy>=1.0.0,<2.0.0",
     "lazy_loader==0.4",
     "importlib_metadata; python_version<'3.9'",