.gitmodules

@@ -1,3 +1,6 @@
 [submodule "junifer/external/h5io"]
 	path = junifer/external/h5io
 	url = https://github.com/juaml/h5io
+[submodule "junifer/external/BrainPrint"]
+	path = junifer/external/BrainPrint
+	url = https://github.com/juaml/BrainPrint.git

docs/changes/newsfragments/344.feature

@@ -0,0 +1 @@
+Add support for `BrainPrint <https://github.com/Deep-MI/BrainPrint?tab=readme-ov-file>`_ marker by `Synchon Mandal`_

ignore_words.txt

@@ -4,3 +4,4 @@ chang
 sepulcre
 arange
 sinc
+whit

junifer/api/tests/test_api_utils.py

@@ -45,6 +45,7 @@ def test_get_dependency_information_short() -> None:
         "httpx",
         "tqdm",
         "templateflow",
+        "lapy",
         "looseversion",
     ]
 
@@ -74,6 +75,7 @@ def test_get_dependency_information_long() -> None:
         "httpx",
         "tqdm",
         "templateflow",
+        "lapy",
     ]
     for key in dependency_list:
         assert key in dependency_information_keys

junifer/data/tests/test_masks.py

@@ -350,7 +350,7 @@ def test_get_mask_errors() -> None:
         with pytest.raises(ValueError, match=r"callable params"):
             get_mask(masks={"GM_prob0.2": {"param": 1}}, target_data=vbm_gm)
 
-        # Pass only parametesr to the intersection function
+        # Pass only parameters to the intersection function
         with pytest.raises(
             ValueError, match=r" At least one mask is required."
         ):

junifer/datagrabber/pattern.py

@@ -6,6 +6,7 @@
 # License: AGPL
 
 import re
+from copy import deepcopy
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple, Union
 
@@ -364,7 +365,7 @@ class PatternDataGrabber(BaseDataGrabber):
             # Data type dictionary
             t_pattern = self.patterns[t_type]
             # Copy data type dictionary in output
-            out[t_type] = t_pattern.copy()
+            out[t_type] = deepcopy(t_pattern)
             # Iterate to check for nested "types" like mask
             for k, v in t_pattern.items():
                 # Resolve pattern for base data type

junifer/external/BrainPrint

@@ -0,0 +1 @@
+Subproject commit adf2b5b5e3289126e3d66fcbd6cc8575ce5a277c

junifer/markers/__init__.py

@@ -23,3 +23,4 @@ from .temporal_snr import (
     TemporalSNRParcels,
     TemporalSNRSpheres,
 )
+from .brainprint import BrainPrint

junifer/markers/brainprint.py

@@ -0,0 +1,662 @@
+"""Provide class for BrainPrint."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+try:
+    from importlib.metadata import packages_distributions
+except ImportError:  # pragma: no cover
+    from importlib_metadata import packages_distributions
+
+import uuid
+from copy import deepcopy
+from importlib.util import find_spec
+from itertools import chain
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    ClassVar,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Union,
+)
+
+import numpy as np
+import numpy.typing as npt
+
+from ..api.decorators import register_marker
+from ..external.BrainPrint.brainprint.brainprint import (
+    compute_asymmetry,
+    compute_brainprint,
+)
+from ..external.BrainPrint.brainprint.surfaces import surf_to_vtk
+from ..pipeline import WorkDirManager
+from ..pipeline.utils import check_ext_dependencies
+from ..utils import logger, raise_error, run_ext_cmd
+from .base import BaseMarker
+
+
+if TYPE_CHECKING:
+    from junifer.storage import BaseFeatureStorage
+
+
+@register_marker
+class BrainPrint(BaseMarker):
+    """Class for BrainPrint.
+
+    Parameters
+    ----------
+    num : positive int, optional
+        Number of eigenvalues to compute (default 50).
+    skip_cortex : bool, optional
+        Whether to skip cortical surface or not (default False).
+    keep_eigenvectors : bool, optional
+        Whether to also return eigenvectors or not (default False).
+    norm : str, optional
+        Eigenvalues normalization method (default "none").
+    reweight : bool, optional
+        Whether to reweight eigenvalues or not (default False).
+    asymmetry : bool, optional
+        Whether to calculate asymmetry between lateral structures
+        (default False).
+    asymmetry_distance : {"euc"}, optional
+        Distance measurement to use if ``asymmetry=True``:
+
+        * ``"euc"`` : Euclidean
+
+        (default "euc").
+    use_cholmod : bool, optional
+        If True, attempts to use the Cholesky decomposition for improved
+        execution speed. Requires the ``scikit-sparse`` library. If it cannot
+        be found, an error will be thrown. If False, will use slower LU
+        decomposition (default False).
+    name : str, optional
+        The name of the marker. If None, will use the class name (default
+        None).
+
+    """
+
+    _EXT_DEPENDENCIES: ClassVar[List[Dict[str, Union[str, List[str]]]]] = [
+        {
+            "name": "freesurfer",
+            "commands": [
+                "mri_binarize",
+                "mri_pretess",
+                "mri_mc",
+                "mris_convert",
+            ],
+        },
+    ]
+
+    _DEPENDENCIES: ClassVar[Set[str]] = {"lapy", "numpy"}
+
+    def __init__(
+        self,
+        num: int = 50,
+        skip_cortex=False,
+        keep_eigenvectors: bool = False,
+        norm: str = "none",
+        reweight: bool = False,
+        asymmetry: bool = False,
+        asymmetry_distance: str = "euc",
+        use_cholmod: bool = False,
+        name: Optional[str] = None,
+    ) -> None:
+        self.num = num
+        self.skip_cortex = skip_cortex
+        self.keep_eigenvectors = keep_eigenvectors
+        self.norm = norm
+        self.reweight = reweight
+        self.asymmetry = asymmetry
+        self.asymmetry_distance = asymmetry_distance
+        self.use_cholmod = use_cholmod
+        super().__init__(name=name, on="FreeSurfer")
+
+    def get_valid_inputs(self) -> List[str]:
+        """Get valid data types for input.
+
+        Returns
+        -------
+        list of str
+            The list of data types that can be used as input for this marker.
+
+        """
+        return ["FreeSurfer"]
+
+    # TODO: kept for making this class concrete; should be removed later
+    def get_output_type(self, input_type: str) -> str:
+        """Get output type.
+
+        Parameters
+        ----------
+        input_type : str
+            The data type input to the marker.
+
+        Returns
+        -------
+        str
+            The storage type output by the marker.
+
+        """
+        return "vector"
+
+    # TODO: overridden to allow multiple outputs from single data type; should
+    # be removed later
+    def validate(self, input: List[str]) -> List[str]:
+        """Validate the the pipeline step.
+
+        Parameters
+        ----------
+        input : list of str
+            The input to the pipeline step.
+
+        Returns
+        -------
+        list of str
+            The output of the pipeline step.
+
+        """
+
+        def _check_dependencies(obj) -> None:
+            """Check obj._DEPENDENCIES.
+
+            Parameters
+            ----------
+            obj : object
+                Object to check _DEPENDENCIES of.
+
+            Raises
+            ------
+            ImportError
+                If the pipeline step object is missing dependencies required
+                for its working.
+
+            """
+            # Check if _DEPENDENCIES attribute is found;
+            # (markers and preprocessors will have them but not datareaders
+            # as of now)
+            dependencies_not_found = []
+            if hasattr(obj, "_DEPENDENCIES"):
+                # Check if dependencies are importable
+                for dependency in obj._DEPENDENCIES:
+                    # First perform an easy check
+                    if find_spec(dependency) is None:
+                        # Then check mapped names
+                        if dependency not in list(
+                            chain.from_iterable(
+                                packages_distributions().values()
+                            )
+                        ):
+                            dependencies_not_found.append(dependency)
+            # Raise error if any dependency is not found
+            if dependencies_not_found:
+                raise_error(
+                    msg=(
+                        f"{dependencies_not_found} are not installed but are "
+                        f"required for using {obj.__class__.__name__}."
+                    ),
+                    klass=ImportError,
+                )
+
+        def _check_ext_dependencies(obj) -> None:
+            """Check obj._EXT_DEPENDENCIES.
+
+            Parameters
+            ----------
+            obj : object
+                Object to check _EXT_DEPENDENCIES of.
+
+            """
+            # Check if _EXT_DEPENDENCIES attribute is found;
+            # (some markers and preprocessors might have them)
+            if hasattr(obj, "_EXT_DEPENDENCIES"):
+                for dependency in obj._EXT_DEPENDENCIES:
+                    check_ext_dependencies(**dependency)
+
+        # Check dependencies
+        _check_dependencies(self)
+        # Check external dependencies
+        # _check_ext_dependencies(self)
+        # Validate input
+        _ = self.validate_input(input=input)
+        # Validate output type
+        outputs = ["scalar_table", "vector"]
+        return outputs
+
+    def _create_aseg_surface(
+        self,
+        aseg_path: Path,
+        norm_path: Path,
+        indices: List,
+    ) -> Path:
+        """Generate a surface from the aseg and label files.
+
+        Parameters
+        ----------
+        aseg_path : pathlib.Path
+            The FreeSurfer aseg path.
+        norm_path : pathlib.Path
+            The FreeSurfer norm path.
+        indices : list
+            List of label indices to include in the surface generation.
+
+        Returns
+        -------
+        pathlib.Path
+            Path to the generated surface in VTK format.
+
+        """
+        tempfile_prefix = f"aseg.{uuid.uuid4()}"
+
+        # Set mri_binarize command
+        mri_binarize_output_path = self._tempdir / f"{tempfile_prefix}.mgz"
+        mri_binarize_cmd = [
+            "mri_binarize",
+            f"--i {aseg_path.resolve()}",
+            f"--match {''.join(indices)}",
+            f"--o {mri_binarize_output_path.resolve()}",
+        ]
+        # Call mri_binarize command
+        run_ext_cmd(name="mri_binarize", cmd=mri_binarize_cmd)
+
+        label_value = "1"
+        # Fix label (pretess)
+        # Set mri_pretess command
+        mri_pretess_cmd = [
+            "mri_pretess",
+            f"{mri_binarize_output_path.resolve()}",
+            f"{label_value}",
+            f"{norm_path.resolve()}",
+            f"{mri_binarize_output_path.resolve()}",
+        ]
+        # Call mri_pretess command
+        run_ext_cmd(name="mri_pretess", cmd=mri_pretess_cmd)
+
+        # Run marching cube to extract surface
+        # Set mri_mc command
+        mri_mc_output_path = self._tempdir / f"{tempfile_prefix}.surf"
+        mri_mc_cmd = [
+            "mri_mc",
+            f"{mri_binarize_output_path.resolve()}",
+            f"{label_value}",
+            f"{mri_mc_output_path.resolve()}",
+        ]
+        # Run mri_mc command
+        run_ext_cmd(name="mri_mc", cmd=mri_mc_cmd)
+
+        # Convert to vtk
+        # Set mris_convert command
+        surface_path = (
+            self._element_tempdir / f"aseg.final.{'_'.join(indices)}.vtk"
+        )
+        mris_convert_cmd = [
+            "mris_convert",
+            f"{mri_mc_output_path.resolve()}",
+            f"{surface_path.resolve()}",
+        ]
+        # Run mris_convert command
+        run_ext_cmd(name="mris_convert", cmd=mris_convert_cmd)
+
+        return surface_path
+
+    def _create_aseg_surfaces(
+        self,
+        aseg_path: Path,
+        norm_path: Path,
+    ) -> Dict[str, Path]:
+        """Create surfaces from FreeSurfer aseg labels.
+
+        Parameters
+        ----------
+        aseg_path : pathlib.Path
+            The FreeSurfer aseg path.
+        norm_path : pathlib.Path
+            The FreeSurfer norm path.
+
+        Returns
+        -------
+        dict
+            Dictionary of label names mapped to corresponding surface paths.
+
+        """
+        # Define aseg labels
+
+        # combined and individual aseg labels:
+        # - Left  Striatum: left  Caudate + Putamen + Accumbens
+        # - Right Striatum: right Caudate + Putamen + Accumbens
+        # - CorpusCallosum: 5 subregions combined
+        # - Cerebellum: brainstem + (left+right) cerebellum WM and GM
+        # - Ventricles: (left+right) lat.vent + inf.lat.vent + choroidplexus +
+        #               3rdVent + CSF
+        # - Lateral-Ventricle: lat.vent + inf.lat.vent + choroidplexus
+        # - 3rd-Ventricle: 3rd-Ventricle + CSF
+
+        aseg_labels = {
+            "CorpusCallosum": ["251", "252", "253", "254", "255"],
+            "Cerebellum": ["7", "8", "16", "46", "47"],
+            "Ventricles": ["4", "5", "14", "24", "31", "43", "44", "63"],
+            "3rd-Ventricle": ["14", "24"],
+            "4th-Ventricle": ["15"],
+            "Brain-Stem": ["16"],
+            "Left-Striatum": ["11", "12", "26"],
+            "Left-Lateral-Ventricle": ["4", "5", "31"],
+            "Left-Cerebellum-White-Matter": ["7"],
+            "Left-Cerebellum-Cortex": ["8"],
+            "Left-Thalamus-Proper": ["10"],
+            "Left-Caudate": ["11"],
+            "Left-Putamen": ["12"],
+            "Left-Pallidum": ["13"],
+            "Left-Hippocampus": ["17"],
+            "Left-Amygdala": ["18"],
+            "Left-Accumbens-area": ["26"],
+            "Left-VentralDC": ["28"],
+            "Right-Striatum": ["50", "51", "58"],
+            "Right-Lateral-Ventricle": ["43", "44", "63"],
+            "Right-Cerebellum-White-Matter": ["46"],
+            "Right-Cerebellum-Cortex": ["47"],
+            "Right-Thalamus-Proper": ["49"],
+            "Right-Caudate": ["50"],
+            "Right-Putamen": ["51"],
+            "Right-Pallidum": ["52"],
+            "Right-Hippocampus": ["53"],
+            "Right-Amygdala": ["54"],
+            "Right-Accumbens-area": ["58"],
+            "Right-VentralDC": ["60"],
+        }
+        return {
+            label: self._create_aseg_surface(
+                aseg_path=aseg_path,
+                norm_path=norm_path,
+                indices=indices,
+            )
+            for label, indices in aseg_labels.items()
+        }
+
+    def _create_cortical_surfaces(
+        self,
+        lh_white_path: Path,
+        rh_white_path: Path,
+        lh_pial_path: Path,
+        rh_pial_path: Path,
+    ) -> Dict[str, Path]:
+        """Create cortical surfaces from FreeSurfer labels.
+
+        Parameters
+        ----------
+        lh_white_path : pathlib.Path
+            The FreeSurfer lh.white path.
+        rh_white_path : pathlib.Path
+            The FreeSurfer rh.white path.
+        lh_pial_path : pathlib.Path
+            The FreeSurfer lh.pial path.
+        rh_pial_path : pathlib.Path
+            The FreeSurfer rh.pial path.
+
+        Returns
+        -------
+        dict
+            Cortical surface label names with their paths as dictionary.
+
+        """
+        return {
+            "lh-white-2d": surf_to_vtk(
+                lh_white_path.resolve(),
+                (self._element_tempdir / "lh.white.vtk").resolve(),
+            ),
+            "rh-white-2d": surf_to_vtk(
+                rh_white_path.resolve(),
+                (self._element_tempdir / "rh.white.vtk").resolve(),
+            ),
+            "lh-pial-2d": surf_to_vtk(
+                lh_pial_path.resolve(),
+                (self._element_tempdir / "lh.pial.vtk").resolve(),
+            ),
+            "rh-pial-2d": surf_to_vtk(
+                rh_pial_path.resolve(),
+                (self._element_tempdir / "rh.pial.vtk").resolve(),
+            ),
+        }
+
+    def compute(
+        self,
+        input: Dict[str, Any],
+        extra_input: Optional[Dict] = None,
+    ) -> Dict:
+        """Compute.
+
+        Parameters
+        ----------
+        input : dict
+            The FreeSurfer data as dictionary.
+        extra_input : dict, optional
+            The other fields in the pipeline data object (default None).
+
+        Returns
+        -------
+        dict
+            The computed result as dictionary. The dictionary has the following
+            keys:
+
+            * ``eigenvalues`` : dict of surface labels (str) and eigenvalues
+                                (``np.ndarray``)
+            * ``eigenvectors`` : dict of surface labels (str) and eigenvectors
+                                 (``np.ndarray``) if ``keep_eigenvectors=True``
+                                 else None
+            * ``distances`` : dict of ``{left_label}_{right_label}`` (str) and
+                              distance (float) if ``asymmetry=True`` else None
+
+        References
+        ----------
+        .. [1] Wachinger, C., Golland, P., Kremen, W. et al. (2015)
+               BrainPrint: A discriminative characterization of brain
+               morphology.
+               NeuroImage, Volume 109, Pages 232-248.
+               https://doi.org/10.1016/j.neuroimage.2015.01.032.
+        .. [2] Reuter, M., Wolter, F.E., Peinecke, N. (2006)
+               Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids.
+               Computer-Aided Design, Volume 38, Issue 4, Pages 342-366.
+               https://doi.org/10.1016/j.cad.2005.10.011.
+
+        """
+        logger.debug("Computing BrainPrint")
+
+        # Create component-scoped tempdir
+        self._tempdir = WorkDirManager().get_tempdir(prefix="brainprint")
+        # Create element-scoped tempdir so that the files are
+        # available later as nibabel stores file path reference for
+        # loading on computation
+        self._element_tempdir = WorkDirManager().get_element_tempdir(
+            prefix="brainprint"
+        )
+        # Generate surfaces
+        surfaces = self._create_aseg_surfaces(
+            aseg_path=input["aseg"]["path"],
+            norm_path=input["norm"]["path"],
+        )
+        if not self.skip_cortex:
+            cortical_surfaces = self._create_cortical_surfaces(
+                lh_white_path=input["lh_white"]["path"],
+                rh_white_path=input["rh_white"]["path"],
+                lh_pial_path=input["lh_pial"]["path"],
+                rh_pial_path=input["rh_pial"]["path"],
+            )
+            surfaces.update(cortical_surfaces)
+        # Compute brainprint
+        eigenvalues, _ = compute_brainprint(
+            surfaces=surfaces,
+            keep_eigenvectors=self.keep_eigenvectors,
+            num=self.num,
+            norm=self.norm,
+            reweight=self.reweight,
+            use_cholmod=self.use_cholmod,
+        )
+        # Calculate distances (if required)
+        distances = None
+        if self.asymmetry:
+            distances = compute_asymmetry(
+                eigenvalues=eigenvalues,
+                distance=self.asymmetry_distance,
+                skip_cortex=self.skip_cortex,
+            )
+
+        # Delete tempdir
+        WorkDirManager().delete_tempdir(self._tempdir)
+
+        output = {
+            "eigenvalues": {
+                "data": self._fix_nan(
+                    [val[2:] for val in eigenvalues.values()]
+                ).T,
+                "col_names": list(eigenvalues.keys()),
+                "row_names": [f"ev{i}" for i in range(self.num)],
+                "row_header_col_name": "eigenvalue",
+            },
+            "areas": {
+                "data": self._fix_nan(
+                    [val[0] for val in eigenvalues.values()]
+                ),
+                "col_names": list(eigenvalues.keys()),
+            },
+            "volumes": {
+                "data": self._fix_nan(
+                    [val[1] for val in eigenvalues.values()]
+                ),
+                "col_names": list(eigenvalues.keys()),
+            },
+        }
+        if self.asymmetry:
+            output["distances"] = {
+                "data": self._fix_nan(list(distances.values())),
+                "col_names": list(distances.keys()),
+            }
+        return output
+
+    def _fix_nan(
+        self,
+        input_data: List[Union[float, str, npt.ArrayLike]],
+    ) -> np.ndarray:
+        """Convert BrainPrint output with string NaN to ``numpy.nan``.
+
+        Parameters
+        ----------
+        input_data : list of str, float or numpy.ndarray-like
+            The data to convert.
+
+        Returns
+        -------
+        np.ndarray
+            The converted data as ``numpy.ndarray``.
+
+        """
+        arr = np.asarray(input_data)
+        arr[arr == "NaN"] = np.nan
+        return arr.astype(np.float64)
+
+    # TODO: overridden to allow storing multiple outputs from single input;
+    # should be removed later
+    def store(
+        self,
+        type_: str,
+        feature: str,
+        out: Dict[str, Any],
+        storage: "BaseFeatureStorage",
+    ) -> None:
+        """Store.
+
+        Parameters
+        ----------
+        type_ : str
+            The data type to store.
+        feature : {"eigenvalues", "distances", "areas", "volumes"}
+            The feature name to store.
+        out : dict
+            The computed result as a dictionary to store.
+        storage : storage-like
+            The storage class, for example, SQLiteFeatureStorage.
+
+        Raises
+        ------
+        ValueError
+            If ``feature`` is invalid.
+
+        """
+        if feature == "eigenvalues":
+            output_type = "scalar_table"
+        elif feature in ["distances", "areas", "volumes"]:
+            output_type = "vector"
+        else:
+            raise_error(f"Unknown feature: {feature}")
+
+        logger.debug(f"Storing {output_type} in {storage}")
+        storage.store(kind=output_type, **out)
+
+    # TODO: overridden to allow storing multiple outputs from single input;
+    # should be removed later
+    def _fit_transform(
+        self,
+        input: Dict[str, Dict],
+        storage: Optional["BaseFeatureStorage"] = None,
+    ) -> Dict:
+        """Fit and transform.
+
+        Parameters
+        ----------
+        input : dict
+            The Junifer Data object.
+        storage : storage-like, optional
+            The storage class, for example, SQLiteFeatureStorage.
+
+        Returns
+        -------
+        dict
+            The processed output as a dictionary. If `storage` is provided,
+            empty dictionary is returned.
+
+        """
+        out = {}
+        for type_ in self._on:
+            if type_ in input.keys():
+                logger.info(f"Computing {type_}")
+                t_input = input[type_]
+                extra_input = input.copy()
+                extra_input.pop(type_)
+                t_meta = t_input["meta"].copy()
+                t_meta["type"] = type_
+
+                # Returns multiple features
+                t_out = self.compute(input=t_input, extra_input=extra_input)
+
+                if storage is None:
+                    out[type_] = {}
+
+                for feature_name, feature_data in t_out.items():
+                    # Make deep copy of the feature data for manipulation
+                    feature_data_copy = deepcopy(feature_data)
+                    # Make deep copy of metadata and add to feature data
+                    feature_data_copy["meta"] = deepcopy(t_meta)
+                    # Update metadata for the feature,
+                    # feature data is not manipulated, only meta
+                    self.update_meta(feature_data_copy, "marker")
+                    # Update marker feature's metadata name
+                    feature_data_copy["meta"]["marker"][
+                        "name"
+                    ] += f"_{feature_name}"
+
+                    if storage is not None:
+                        logger.info(f"Storing in {storage}")
+                        self.store(
+                            type_=type_,
+                            feature=feature_name,
+                            out=feature_data_copy,
+                            storage=storage,
+                        )
+                    else:
+                        logger.info(
+                            "No storage specified, returning dictionary"
+                        )
+                        out[type_][feature_name] = feature_data_copy
+
+        return out

junifer/markers/reho/_afni_reho.py

@@ -72,7 +72,7 @@ class AFNIReHo:
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             * 7 : for facewise neighbours only
-            * 19 : for face- and edge-wise nieghbours
+            * 19 : for face- and edge-wise neighbours
             * 27 : for face-, edge-, and node-wise neighbors
 
             (default 27).

junifer/markers/reho/_junifer_reho.py

@@ -60,7 +60,7 @@ class JuniferReHo:
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             * 7 : for facewise neighbours only
-            * 19 : for face- and edge-wise nieghbours
+            * 19 : for face- and edge-wise neighbours
             * 27 : for face-, edge-, and node-wise neighbors
             * 125 : for 5x5 cuboidal volume
 

junifer/markers/reho/reho_parcels.py

@@ -37,7 +37,7 @@ class ReHoParcels(ReHoBase):
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             - 7 : for facewise neighbours only
-            - 19 : for face- and edge-wise nieghbours
+            - 19 : for face- and edge-wise neighbours
             - 27 : for face-, edge-, and node-wise neighbors
 
         * ``neigh_rad`` : positive float, optional
@@ -67,7 +67,7 @@ class ReHoParcels(ReHoBase):
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             * 7 : for facewise neighbours only
-            * 19 : for face- and edge-wise nieghbours
+            * 19 : for face- and edge-wise neighbours
             * 27 : for face-, edge-, and node-wise neighbors
             * 125 : for 5x5 cuboidal volume
 

junifer/markers/reho/reho_spheres.py

@@ -48,7 +48,7 @@ class ReHoSpheres(ReHoBase):
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             - 7 : for facewise neighbours only
-            - 19 : for face- and edge-wise nieghbours
+            - 19 : for face- and edge-wise neighbours
             - 27 : for face-, edge-, and node-wise neighbors
 
         * ``neigh_rad`` : positive float, optional
@@ -78,7 +78,7 @@ class ReHoSpheres(ReHoBase):
             Number of voxels in the neighbourhood, inclusive. Can be:
 
             * 7 : for facewise neighbours only
-            * 19 : for face- and edge-wise nieghbours
+            * 19 : for face- and edge-wise neighbours
             * 27 : for face-, edge-, and node-wise neighbors
             * 125 : for 5x5 cuboidal volume
 

junifer/markers/tests/test_brainprint.py

@@ -0,0 +1,47 @@
+"""Provide tests for BrainPrint."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import socket
+
+import pytest
+
+from junifer.datagrabber import DataladAOMICID1000
+from junifer.datareader import DefaultDataReader
+from junifer.markers import BrainPrint
+from junifer.pipeline.utils import _check_freesurfer
+
+
+def test_get_output_type() -> None:
+    """Test BrainPrint get_output_type()."""
+    marker = BrainPrint()
+    assert marker.get_output_type("FreeSurfer") == "vector"
+
+
+def test_validate() -> None:
+    """Test BrainPrint validate()."""
+    marker = BrainPrint()
+    assert set(marker.validate(["FreeSurfer"])) == {"scalar_table", "vector"}
+
+
+@pytest.mark.skipif(
+    _check_freesurfer() is False, reason="requires FreeSurfer to be in PATH"
+)
+@pytest.mark.skipif(
+    socket.gethostname() != "juseless",
+    reason="only for juseless",
+)
+def test_compute() -> None:
+    """Test BrainPrint compute()."""
+    with DataladAOMICID1000(types="FreeSurfer") as dg:
+        # Fetch element
+        element = dg["sub-0001"]
+        # Fetch element data
+        element_data = DefaultDataReader().fit_transform(element)
+        # Initialize the marker
+        marker = BrainPrint()
+        # Compute the marker
+        feature_map = marker.fit_transform(element_data)
+        # Assert the output keys
+        assert {"eigenvalues", "areas", "volumes"} == set(feature_map.keys())

pyproject.toml

@@ -49,6 +49,7 @@ dependencies = [
     "httpx[http2]==0.26.0",
     "tqdm==4.66.1",
     "templateflow>=23.0.0",
+    "lapy>=1.0.0,<2.0.0",
     "importlib_metadata; python_version<'3.10'",
     "looseversion==1.3.0; python_version>='3.12'",
 ]
@@ -91,7 +92,12 @@ docs = [
 ################
 
 [tool.setuptools]
-packages = ["junifer", "junifer.external.h5io.h5io"]
+packages = [
+    "junifer",
+    "junifer.external.h5io.h5io",
+    "junifer.external.BrainPrint.brainprint",
+    "junifer.external.BrainPrint.brainprint.utils",
+]
 
 [tool.setuptools_scm]
 version_scheme = "guess-next-dev"
@@ -104,11 +110,12 @@ target-version = ["py38", "py39", "py310", "py311", "py312"]
 extend-exclude = """
 (
   junifer/external/h5io
+  | junifer/external/BrainPrint
 )
 """
 
 [tool.codespell]
-skip = "*/auto_examples/*,*.html,.git/,*.pyc,*/_build/*,*/h5io/*"
+skip = "*/auto_examples/*,*.html,.git/,*.pyc,*/_build/*,*/h5io/*,*/BrainPrint/*"
 count = ""
 quiet-level = 3
 ignore-words = "ignore_words.txt"
@@ -152,6 +159,7 @@ select = [
 extend-exclude = [
     "__init__.py",
     "junifer/external/h5io",
+    "junifer/external/BrainPrint",
     "docs",
     "examples",
     "tools",
@@ -197,6 +205,8 @@ known-third-party =[
     "templateflow",
     "bct",
     "neurokit2",
+    "brainprint",
+    "lapy",
     "pytest",
 ]
 
@@ -205,7 +215,7 @@ max-complexity = 20
 
 [tool.pytest.ini_options]
 minversion = "7.0"
-addopts = "--ignore=junifer/external/h5io -vv"
+addopts = "--ignore=junifer/external/h5io --ignore=junifer/external/BrainPrint -vv"
 
 [tool.towncrier]
 directory = "docs/changes/newsfragments"

tox.ini

@@ -78,6 +78,7 @@ omit =
     */tests/*
     */junifer/configs/*
     */junifer/external/h5io/*
+    */junifer/external/BrainPrint/*
 parallel = false
 
 [coverage:report]