docs/changes/latest.inc

@@ -85,6 +85,8 @@ Enhancements
 
 - Refactor the :class:`junifer.datagrabber.BaseDataGrabber` class to allow for easier subclassing (:gh:`123` by `Fede Raimondo`_).
 
+- Allow custom aggregation method for :class:`junifer.markers.SphereAggregation` (:gh:`102` by `Synchon Mandal`_).
+
 Bugs
 ~~~~
 
@@ -97,7 +99,3 @@ Bugs
 
 API changes
 ~~~~~~~~~~~
-
-- Add new argument ``check_file_existence`` for PatternDataGrabber which allows skipping actual file existence check.
-  This is needed for datagrabbers which extend from PatternDataGrabber and DataladDataGrabber and have one or more
-  Datalad subdatasets inside the dataset, for example, DataladHCP1200.

junifer/__init__.py

@@ -16,5 +16,6 @@ from . import (
     stats,
     storage,
     utils,
+    external,
 )
 from ._version import __version__

junifer/external/__init__.py

@@ -0,0 +1,4 @@
+"""Provide imports for external sub-package."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL

junifer/external/nilearn/__init__.py

@@ -0,0 +1,6 @@
+"""Provide imports for custom nilearn objects sub-package."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+from .junifer_nifti_spheres_masker import JuniferNiftiSpheresMasker

junifer/external/nilearn/junifer_nifti_spheres_masker.py

@@ -0,0 +1,290 @@
+"""Provide JuniferNiftiSpheresMasker class."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
+
+import numpy as np
+from nilearn._utils.class_inspect import get_params
+from nilearn._utils.niimg import img_data_dtype
+from nilearn._utils.niimg_conversions import check_niimg_4d
+from nilearn.maskers import NiftiSpheresMasker
+from nilearn.maskers.base_masker import _filter_and_extract
+from nilearn.maskers.nifti_spheres_masker import _iter_signals_from_spheres
+
+from ...utils import raise_error
+
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+    from nibabel import Nifti1Image, Nifti2Image
+    from numpy.typing import ArrayLike, DTypeLike
+    from pandas import DataFrame
+
+
+"""
+New BSD License
+
+Copyright (c) 2007 - 2022 The nilearn developers.
+All rights reserved.
+
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+  c. Neither the name of the nilearn developers nor the names of
+     its contributors may be used to endorse or promote products
+     derived from this software without specific prior written
+     permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.
+"""
+
+
+class _JuniferExtractionFunctor:
+    """Functor to extract signals from spheres.
+
+    Parameters
+    ----------
+    seeds_ : list of triple of coordinates in native space
+        Seed definitions. List of coordinates of the seeds in the same space
+        as the images (typically MNI or TAL).
+    radius : float
+        Indicates, in millimeters, the radius for the sphere around the seed.
+    mask_img : Niimg-like object
+        Mask to apply to regions before extracting signals.
+    agg_func : callable
+        The function to aggregate signals using.
+    allow_overlap : bool
+        If False, an error is raised if the maps overlap.
+    dtype : any type that can be coerced into a numpy dtype or "auto"
+        The dtype for the extraction. If "auto", the data will be converted to
+        int32 if dtype is discrete and float32 if it is continuous.
+
+    """
+
+    func_name = "junifer_nifti_spheres_masker_extractor"
+
+    def __init__(
+        self,
+        seeds_: "ArrayLike",
+        radius: Optional[float],
+        mask_img: Union["Nifti1Image", "Nifti2Image", None],
+        agg_func: Callable,
+        allow_overlap: bool,
+        dtype: Union["DTypeLike", str, None],
+    ) -> None:
+        self.seeds_ = seeds_
+        self.radius = radius
+        self.mask_img = mask_img
+        self.agg_func = agg_func
+        self.allow_overlap = allow_overlap
+        self.dtype = dtype
+
+    def __call__(
+        self,
+        imgs: Union["Nifti1Image", "Nifti2Image"],
+    ) -> Tuple["ArrayLike", None]:
+        """Implement function call overloading.
+
+        Parameters
+        ----------
+         imgs : 4D Niimg-like object
+            If ``imgs`` is an iterable, checks if data is really 4D. Then,
+            considering that it is a list of ``img``, load them one by one.
+            If ``img`` is a string, consider it as a path to Nifti image and
+            call :func:`nibabel.load` on it.
+            If it is an object, check if the affine attribute is present and
+            that :func:`nilearn.image.get_data` returns a result, eval raise
+            TypeError.
+
+        Raises
+        ------
+        TypeError
+            If ``img`` in ``imgs`` is an object without the affine attribute.
+
+        Returns
+        -------
+        tuple of numpy.ndarray and None
+            The numpy.ndarray returned is the array of extracted signals.
+
+        """
+        n_seeds = len(self.seeds_)
+        imgs = check_niimg_4d(imgs, dtype=self.dtype)
+
+        signals = np.empty(
+            (imgs.shape[3], n_seeds), dtype=img_data_dtype(imgs)
+        )
+        for i, sphere in enumerate(
+            _iter_signals_from_spheres(
+                seeds=self.seeds_,
+                niimg=imgs,
+                radius=self.radius,
+                allow_overlap=self.allow_overlap,
+                mask_img=self.mask_img,
+            )
+        ):
+            signals[:, i] = self.agg_func(sphere, axis=1)
+        return signals, None
+
+
+class JuniferNiftiSpheresMasker(NiftiSpheresMasker):
+    """Class for custom NiftiSpheresMasker.
+
+    Parameters
+    ----------
+    seeds : list of triplet of coordinates in native space
+        Seed definitions. List of coordinates of the seeds in the same space
+        as the images (typically MNI or TAL).
+    radius : float, optional
+        Indicates, in millimeters, the radius for the sphere around the seed.
+        If None, signal is extracted on a single voxel (default None).
+    mask_img : Niimg-like object, optional
+        Mask to apply to regions before extracting signals (default None).
+    agg_func : callable, optional
+        The function to aggregate signals using (default numpy.mean).
+    allow_overlap : bool, optional
+        If False, an error is raised if the maps overlap (default None).
+    dtype : any type that can be coerced into a numpy dtype or "auto", optional
+        The dtype for the extraction. If "auto", the data will be converted to
+        int32 if dtype is discrete and float32 if it is continuous
+        (default None).
+    **kwargs
+        Keyword arguments are passed to the
+        :func:`nilearn.maskers.NiftiSpheresMasker`.
+
+    """
+
+    def __init__(
+        self,
+        seeds: "ArrayLike",
+        radius: Optional[float] = None,
+        mask_img: Union["Nifti1Image", "Nifti2Image", None] = None,
+        agg_func: Callable = np.mean,
+        allow_overlap: bool = False,
+        dtype: Union["DTypeLike", str, None] = None,
+        **kwargs,  # TODO: to keep or not?
+    ) -> None:
+        self.agg_func = agg_func
+        super().__init__(
+            seeds=seeds,
+            radius=radius,
+            mask_img=mask_img,
+            allow_overlap=allow_overlap,
+            **kwargs,
+        )
+
+    def transform_single_imgs(
+        self,
+        imgs: Union["Nifti1Image", "Nifti2Image"],
+        confounds: Union[
+            str, "Path", "ArrayLike", "DataFrame", List, None
+        ] = None,
+        sample_mask: Union["ArrayLike", List, Tuple, None] = None,
+    ) -> "ArrayLike":
+        """Extract signals from a single 4D niimg.
+
+        Parameters
+        ----------
+        imgs : 3D/4D Niimg-like object
+            Images to process.
+            If a 3D niimg is provided, a singleton dimension will be added to
+            the output to represent the single scan in the niimg.
+        confounds : CSV file or array-like or pandas.DataFrame, optional
+            This parameter is passed to :func:`nilearn.signal.clean`.
+            Please see the related documentation for details.
+            shape: (number of scans, number of confounds)
+        sample_mask : Any type compatible with numpy-array indexing, optional
+            Masks the niimgs along time/fourth dimension to perform scrubbing
+            (remove volumes with high motion) and/or non-steady-state volumes.
+            This parameter is passed to :func:`nilearn.signal.clean`.
+            shape: (number of scans - number of volumes removed, )
+
+        Returns
+        -------
+        region_signals : 2D numpy.ndarray
+            Signal for each sphere.
+            shape: (number of scans, number of spheres)
+
+        Warns
+        -----
+        DeprecationWarning
+            If a 3D niimg input is provided, the current behavior
+            (adding a singleton dimension to produce a 2D array) is deprecated.
+            Starting in version 0.12, a 1D array will be returned for 3D
+            inputs.
+
+        """
+        self._check_fitted()
+
+        params = get_params(NiftiSpheresMasker, self)
+
+        signals, _ = self._cache(
+            _filter_and_extract, ignore=["verbose", "memory", "memory_level"]
+        )(
+            imgs,
+            _JuniferExtractionFunctor(
+                seeds_=self.seeds_,
+                radius=self.radius,
+                mask_img=self.mask_img,
+                agg_func=self.agg_func,
+                allow_overlap=self.allow_overlap,
+                dtype=self.dtype,
+            ),
+            # Pre-processing
+            params,
+            confounds=confounds,
+            sample_mask=sample_mask,
+            dtype=self.dtype,
+            # Caching
+            memory=self.memory,
+            memory_level=self.memory_level,
+            # kwargs
+            verbose=self.verbose,
+        )
+        return signals
+
+    def inverse_transform(self, region_signals: "ArrayLike") -> "Nifti1Image":
+        """Compute voxel signals from spheres signals.
+
+        Parameters
+        ----------
+        region_signals : 1D/2D numpy.ndarray
+            Signal for each region.
+            If a 1D array is provided, then the shape should be
+            (number of elements,), and a 3D img will be returned.
+            If a 2D array is provided, then the shape should be
+            (number of scans, number of elements), and a 4D img will be
+            returned.
+
+        Returns
+        -------
+        voxel_signals : nibabel.nifti1.Nifti1Image
+            Signal for each sphere.
+            shape: (mask_img, number of scans).
+
+        """
+        raise_error(
+            msg="As this class allows multiple methods of aggregation while "
+            "transforming, some of which are non-reversible, "
+            "inverse_transform() should not be implemented.",
+            klass=NotImplementedError,
+        )

junifer/external/nilearn/tests/test_junifer_nifti_spheres_masker.py

@@ -0,0 +1,334 @@
+"""Provide tests for JuniferNiftiSpheresMasker class."""
+
+# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
+# License: AGPL
+
+import warnings
+
+import nibabel
+import numpy as np
+import pytest
+from nilearn._utils import data_gen
+from nilearn.image import get_data
+from numpy.testing import assert_array_equal
+
+from junifer.external.nilearn import JuniferNiftiSpheresMasker
+
+
+"""
+New BSD License
+
+Copyright (c) 2007 - 2022 The nilearn developers.
+All rights reserved.
+
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+  c. Neither the name of the nilearn developers nor the names of
+     its contributors may be used to endorse or promote products
+     derived from this software without specific prior written
+     permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.
+"""
+
+
+def test_seed_extraction() -> None:
+    """Test seed extraction."""
+    data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
+    img = nibabel.Nifti1Image(data, np.eye(4))
+    masker = JuniferNiftiSpheresMasker(seeds=[(1, 1, 1)])
+    # Test the fit
+    masker.fit()
+    # Test the transform
+    s = masker.transform(img)
+    assert_array_equal(s[:, 0], data[1, 1, 1])
+
+
+def test_sphere_extraction() -> None:
+    """Test sphere extraction."""
+    data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
+    img = nibabel.Nifti1Image(data, np.eye(4))
+    masker = JuniferNiftiSpheresMasker(seeds=[(1, 1, 1)], radius=1)
+
+    # Check attributes defined at fit
+    assert not hasattr(masker, "seeds_")
+    assert not hasattr(masker, "n_elements_")
+
+    masker.fit()
+
+    # Check attributes defined at fit
+    assert hasattr(masker, "seeds_")
+    assert hasattr(masker, "n_elements_")
+    assert masker.n_elements_ == 1
+
+    # Test the fit
+    masker.fit()
+
+    # Test the transform
+    s = masker.transform(img)
+    mask = np.zeros((3, 3, 3), dtype=bool)
+    mask[:, 1, 1] = True
+    mask[1, :, 1] = True
+    mask[1, 1, :] = True
+    assert_array_equal(s[:, 0], np.mean(data[mask], axis=0))
+
+    # Now with a mask
+    mask_img = np.zeros((3, 3, 3))
+    mask_img[1, :, :] = 1
+    mask_img = nibabel.Nifti1Image(mask_img, np.eye(4))
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[(1, 1, 1)],
+        radius=1,
+        mask_img=mask_img,
+    )
+    masker.fit()
+    s = masker.transform(img)
+    assert_array_equal(
+        s[:, 0],
+        np.mean(
+            data[np.logical_and(mask, get_data(mask_img))],
+            axis=0,
+        ),
+    )
+
+
+def test_anisotropic_sphere_extraction() -> None:
+    """Test anisotropic sphere extraction."""
+    data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
+    affine = np.eye(4)
+    affine[0, 0] = 2
+    affine[2, 2] = 2
+    img = nibabel.Nifti1Image(data, affine)
+    masker = JuniferNiftiSpheresMasker(seeds=[(2, 1, 2)], radius=1)
+    # Test the fit
+    masker.fit()
+    # Test the transform
+    s = masker.transform(img)
+    mask = np.zeros((3, 3, 3), dtype=bool)
+    mask[1, :, 1] = True
+    assert_array_equal(s[:, 0], np.mean(data[mask], axis=0))
+    # Now with a mask
+    mask_img = np.zeros((3, 2, 3))
+    mask_img[1, 0, 1] = 1
+    affine_2 = affine.copy()
+    affine_2[0, 0] = 4
+    mask_img = nibabel.Nifti1Image(mask_img, affine=affine_2)
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[(2, 1, 2)],
+        radius=1,
+        mask_img=mask_img,
+    )
+
+    masker.fit()
+    s = masker.transform(img)
+    assert_array_equal(s[:, 0], data[1, 0, 1])
+
+
+def test_errors() -> None:
+    """Test errors."""
+    masker = JuniferNiftiSpheresMasker(seeds=([1, 2]), radius=0.2)
+    with pytest.raises(ValueError, match="Seeds must be a list .+"):
+        masker.fit()
+
+
+def test_nifti_spheres_masker_overlap() -> None:
+    """Test overlapping sphere extraction."""
+    # Test resampling in NiftiMapsMasker
+    affine = np.eye(4)
+    shape = (5, 5, 5)
+
+    data = np.random.RandomState(42).random_sample(shape + (5,))
+    fmri_img = nibabel.Nifti1Image(data, affine)
+
+    seeds = [(0, 0, 0), (2, 2, 2)]
+
+    overlapping_masker = JuniferNiftiSpheresMasker(
+        seeds,
+        radius=1,
+        allow_overlap=True,
+    )
+    overlapping_masker.fit_transform(fmri_img)
+    overlapping_masker = JuniferNiftiSpheresMasker(
+        seeds,
+        radius=2,
+        allow_overlap=True,
+    )
+    overlapping_masker.fit_transform(fmri_img)
+
+    noverlapping_masker = JuniferNiftiSpheresMasker(
+        seeds,
+        radius=1,
+        allow_overlap=False,
+    )
+    noverlapping_masker.fit_transform(fmri_img)
+    noverlapping_masker = JuniferNiftiSpheresMasker(
+        seeds,
+        radius=2,
+        allow_overlap=False,
+    )
+    with pytest.raises(ValueError, match="Overlap detected"):
+        noverlapping_masker.fit_transform(fmri_img)
+
+
+def test_small_radius() -> None:
+    """Test sphere extraction with small radius."""
+    affine = np.eye(4)
+    shape = (3, 3, 3)
+
+    data = np.random.RandomState(42).random_sample(shape)
+    mask = np.zeros(shape)
+    mask[1, 1, 1] = 1
+    mask[2, 2, 2] = 1
+    affine = np.eye(4) * 1.2
+    seed = (1.4, 1.4, 1.4)
+
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[seed],
+        radius=0.1,
+        mask_img=nibabel.Nifti1Image(mask, affine),
+    )
+    masker.fit_transform(nibabel.Nifti1Image(data, affine))
+
+    # Test if masking is taken into account
+    mask[1, 1, 1] = 0
+    mask[1, 1, 0] = 1
+
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[seed],
+        radius=0.1,
+        mask_img=nibabel.Nifti1Image(mask, affine),
+    )
+    with pytest.raises(ValueError, match="These spheres are empty"):
+        masker.fit_transform(nibabel.Nifti1Image(data, affine))
+
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[seed],
+        radius=1.6,
+        mask_img=nibabel.Nifti1Image(mask, affine),
+    )
+    masker.fit_transform(nibabel.Nifti1Image(data, affine))
+
+
+def test_is_nifti_spheres_masker_give_nans() -> None:
+    """Test NaN interaction with masker."""
+    affine = np.eye(4)
+
+    data_with_nans = np.zeros((10, 10, 10), dtype=np.float32)
+    data_with_nans[:, :, :] = np.nan
+
+    data_without_nans = np.random.RandomState(42).random_sample((9, 9, 9))
+    indices = np.nonzero(data_without_nans)
+
+    # Leaving nans outside of some data
+    data_with_nans[indices] = data_without_nans[indices]
+    img = nibabel.Nifti1Image(data_with_nans, affine)
+    seed = [(7, 7, 7)]
+
+    # Interaction of seed with nans
+    masker = JuniferNiftiSpheresMasker(seeds=seed, radius=2.0)
+    assert not np.isnan(np.sum(masker.fit_transform(img)))
+
+    mask = np.ones((9, 9, 9))
+    mask_img = nibabel.Nifti1Image(mask, affine)
+    # When mask_img is provided, the seed interacts within the brain, so no nan
+    masker = JuniferNiftiSpheresMasker(
+        seeds=seed,
+        radius=2.0,
+        mask_img=mask_img,
+    )
+    assert not np.isnan(np.sum(masker.fit_transform(img)))
+
+
+def test_standardization() -> None:
+    """Test standardization."""
+    data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
+    img = nibabel.Nifti1Image(data, np.eye(4))
+
+    # test zscore
+    masker = JuniferNiftiSpheresMasker(seeds=[(1, 1, 1)], standardize="zscore")
+    # Test the fit
+    s = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(s.mean(), 0)
+    np.testing.assert_almost_equal(s.std(), 1)
+
+    # test psc
+    masker = JuniferNiftiSpheresMasker(seeds=[(1, 1, 1)], standardize="psc")
+    # Test the fit
+    s = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(s.mean(), 0)
+    np.testing.assert_almost_equal(
+        s.ravel(),
+        data[1, 1, 1] / data[1, 1, 1].mean() * 100 - 100,
+    )
+
+
+def test_nifti_spheres_masker_inverse_transform() -> None:
+    """Test inverse transform."""
+    data = np.random.RandomState(42).random_sample((3, 3, 3, 5))
+    masker = JuniferNiftiSpheresMasker(seeds=[(1, 1, 1)], radius=1)
+    with pytest.raises(
+        NotImplementedError,
+        match="some of which are non-reversible",
+    ):
+        masker.inverse_transform(data[0, 0, 0, :])
+
+
+def test_nifti_spheres_masker_io_shapes() -> None:
+    """Ensure that masker handles 1D/2D/3D/4D data appropriately.
+
+    transform(4D image) --> 2D output, no warning
+    transform(3D image) --> 2D output, DeprecationWarning
+
+    """
+    n_regions, n_volumes = 2, 5
+    shape_3d = (10, 11, 12)
+    shape_4d = (10, 11, 12, n_volumes)
+    affine = np.eye(4)
+
+    img_4d, mask_img = data_gen.generate_random_img(
+        shape_4d,
+        affine=affine,
+    )
+    img_3d, _ = data_gen.generate_random_img(shape_3d, affine=affine)
+
+    masker = JuniferNiftiSpheresMasker(
+        seeds=[(1, 1, 1), (4, 4, 4)],  # number of tuples equal to n_regions
+        radius=1,
+        mask_img=mask_img,
+    )
+    masker.fit()
+
+    # DeprecationWarning *should* be raised for 3D inputs
+    with pytest.warns(DeprecationWarning, match="Starting in version 0.12"):
+        test_data = masker.transform(img_3d)
+        assert test_data.shape == (1, n_regions)
+
+    # DeprecationWarning should *not* be raised for 4D inputs
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "error",
+            message="Starting in version 0.12",
+            category=DeprecationWarning,
+        )
+        test_data = masker.transform(img_4d)
+        assert test_data.shape == (n_volumes, n_regions)

junifer/markers/sphere_aggregation.py

@@ -6,11 +6,11 @@
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
-from nilearn.maskers import NiftiSpheresMasker
-
 from ..api.decorators import register_marker
 from ..data import load_coordinates
-from ..utils import logger, raise_error
+from ..external.nilearn import JuniferNiftiSpheresMasker
+from ..stats import get_aggfunc_by_name
+from ..utils import logger
 from .base import BaseMarker
 
 if TYPE_CHECKING:
@@ -27,21 +27,24 @@ class SphereAggregation(BaseMarker):
         The name of the coordinates list to use. See
         :func:`junifer.data.coordinates.list_coordinates` for options.
     radius : float, optional
-        The radius of the sphere in mm. If None, the signal will be extracted
+        The radius of the sphere in millimeters. If None, the signal will be
-        from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
+        extracted from a single voxel. See
-        for more information (default None).
+        :class:`nilearn.maskers.NiftiSpheresMasker` for more information
+        (default None).
     method : str, optional
         The aggregation method to use.
         See :func:`junifer.stats.get_aggfunc_by_name` for more information
         (default "mean").
     method_params : dict, optional
         The parameters to pass to the aggregation method (default None).
-    on : list of str, optional
+    on : {"T1w", "BOLD", "VBM_GM", "VBM_WM", "fALFF", "GCOR", "LCOR"} or \
-        The kind of data to apply the marker to. By default, will work on all
+         list of the options, optional
+        The data types to apply the marker to. If None, will work on all
         available data (default None).
     name : str, optional
         The name of the marker. By default, it will use KIND_SphereAggregation
         where KIND is the kind of data it was applied to (default None).
+
     """
 
     def __init__(
@@ -49,21 +52,12 @@ class SphereAggregation(BaseMarker):
         coords: str,
         radius: Optional[float] = None,
         method: str = "mean",
-        method_params: Optional[Dict] = None,
+        method_params: Optional[Dict[str, Any]] = None,
-        on: Optional[Union[List[str], str]] = None,
+        on: Union[List[str], str, None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.coords = coords
         self.radius = radius
-
-        if method != "mean":
-            raise_error(
-                "Only mean aggregation is supported for sphere aggregation. "
-                "If you need other aggregation methods, please open an issue "
-                "on `junifer github`_.",
-                NotImplementedError,
-            )
-
         self.method = method
         self.method_params = {} if method_params is None else method_params
         super().__init__(on=on, name=name)
@@ -90,7 +84,7 @@ class SphereAggregation(BaseMarker):
         Returns
         -------
         list of str
-            The kind of output.
+            The list of storage kinds.
 
         """
         outputs = []
@@ -159,17 +153,21 @@ class SphereAggregation(BaseMarker):
         """
         t_input = input["data"]
         logger.debug(f"Sphere aggregation using {self.method}")
-        # agg_func = get_aggfunc_by_name(
+        # Get aggregation function
-        #     self.method, func_params=self.method_params
+        agg_func = get_aggfunc_by_name(
-        # )
+            self.method, func_params=self.method_params
-        coords, out_labels = load_coordinates(self.coords)
+        )
-        masker = NiftiSpheresMasker(
+        # Get seeds and labels
+        coords, out_labels = load_coordinates(name=self.coords)
+        masker = JuniferNiftiSpheresMasker(
             seeds=coords,
             radius=self.radius,
             mask_img=None,  # TODO: support this (needs #79)
+            agg_func=agg_func,
         )
-
+        # Fit and transform the marker on the data
         out_values = masker.fit_transform(t_input)
+        # Format the output
         out = {"data": out_values, "columns": out_labels}
         if out_values.shape[0] > 1:
             out["row_names"] = "scan"

junifer/markers/tests/test_sphere_aggregation.py

@@ -16,11 +16,15 @@ from junifer.data import load_coordinates
 from junifer.markers.sphere_aggregation import SphereAggregation
 from junifer.storage import SQLiteFeatureStorage
 
+# Define common variables
+COORDS = "DMNBuckner"
+RADIUS = 8
+
 
 def test_SphereAggregation_input_output() -> None:
     """Test SphereAggregation input and output types."""
     marker = SphereAggregation(
-        coords="DMNBuckner", method="mean", radius=8, on="VBM_GM"
+        coords=COORDS, method="mean", radius=RADIUS, on="VBM_GM"
     )
 
     output = marker.get_output_kind(["VBM_GM", "BOLD"])
@@ -33,9 +37,7 @@ def test_SphereAggregation_input_output() -> None:
 def test_SphereAggregation_3D() -> None:
     """Test SphereAggregation object on 3D images."""
     # Get the testing coordinates (for nilearn)
-    coord_names = "DMNBuckner"
+    coordinates, labels = load_coordinates(COORDS)
-    radius = 8
-    coordinates, labels = load_coordinates(coord_names)
 
     # Get the oasis VBM data
     oasis_dataset = datasets.fetch_oasis_vbm(n_subjects=1)
@@ -43,12 +45,12 @@ def test_SphereAggregation_3D() -> None:
     img = nib.load(vbm)
 
     # Create NiftiLabelsMasker
-    nifti_masker = NiftiSpheresMasker(seeds=coordinates, radius=radius)
+    nifti_masker = NiftiSpheresMasker(seeds=coordinates, radius=RADIUS)
     auto4d = nifti_masker.fit_transform(img)
 
     # Create SphereAggregation object
     marker = SphereAggregation(
-        coords=coord_names, method="mean", radius=radius, on="VBM_GM"
+        coords=COORDS, method="mean", radius=RADIUS, on="VBM_GM"
     )
     input = {"VBM_GM": {"data": img}}
     jun_values4d = marker.fit_transform(input)["VBM_GM"]["data"]
@@ -59,37 +61,30 @@ def test_SphereAggregation_3D() -> None:
 
     meta = marker.get_meta("VBM_GM")["marker"]
     assert meta["method"] == "mean"
-    assert meta["coords"] == coord_names
+    assert meta["coords"] == COORDS
-    assert meta["radius"] == radius
+    assert meta["radius"] == RADIUS
     assert meta["name"] == "VBM_GM_SphereAggregation"
     assert meta["class"] == "SphereAggregation"
     assert meta["kind"] == "VBM_GM"
     assert meta["method_params"] == {}
 
-    with pytest.raises(NotImplementedError, match="mean aggregation"):
-        marker = SphereAggregation(
-            coords=coord_names, method="std", radius=radius, on="BOLD"
-        )
-
 
 def test_SphereAggregation_4D() -> None:
     """Test SphereAggregation object on 4D images."""
     # Get the testing coordinates (for nilearn)
-    coord_names = "DMNBuckner"
+    coordinates, labels = load_coordinates(COORDS)
-    radius = 8
-    coordinates, labels = load_coordinates(coord_names)
 
-    # Get the SPM auditory data:
+    # Get the SPM auditory data
     subject_data = datasets.fetch_spm_auditory()
     fmri_img = concat_imgs(subject_data.func)  # type: ignore
 
     # Create NiftiLabelsMasker
-    nifti_masker = NiftiSpheresMasker(seeds=coordinates, radius=radius)
+    nifti_masker = NiftiSpheresMasker(seeds=coordinates, radius=RADIUS)
     auto4d = nifti_masker.fit_transform(fmri_img)
 
     # Create SphereAggregation object
     marker = SphereAggregation(
-        coords=coord_names, method="mean", radius=radius
+        coords=COORDS, method="mean", radius=RADIUS
     )
     input = {"BOLD": {"data": fmri_img}}
     jun_values4d = marker.fit_transform(input)["BOLD"]["data"]
@@ -100,8 +95,8 @@ def test_SphereAggregation_4D() -> None:
 
     meta = marker.get_meta("BOLD")["marker"]
     assert meta["method"] == "mean"
-    assert meta["coords"] == coord_names
+    assert meta["coords"] == COORDS
-    assert meta["radius"] == radius
+    assert meta["radius"] == RADIUS
     assert meta["name"] == "BOLD_SphereAggregation"
     assert meta["class"] == "SphereAggregation"
     assert meta["kind"] == "BOLD"
@@ -133,7 +128,7 @@ def test_SphereAggregation_storage(tmp_path: Path) -> None:
     }
     input = {"VBM_GM": {"data": img}, "meta": meta}
     marker = SphereAggregation(
-        coords="DMNBuckner", method="mean", radius=8, on="VBM_GM"
+        coords=COORDS, method="mean", radius=RADIUS, on="VBM_GM"
     )
 
     marker.fit_transform(input, storage=storage)
@@ -143,12 +138,12 @@ def test_SphereAggregation_storage(tmp_path: Path) -> None:
         "version": "0.0.1",
         "marker": {"name": "BOLD_fcname"},
     }
-    # Get the SPM auditory data:
+    # Get the SPM auditory data
     subject_data = datasets.fetch_spm_auditory()
     fmri_img = concat_imgs(subject_data.func)  # type: ignore
     input = {"BOLD": {"data": fmri_img}, "meta": meta}
     marker = SphereAggregation(
-        coords="DMNBuckner", method="mean", radius=8, on="BOLD"
+        coords=COORDS, method="mean", radius=RADIUS, on="BOLD"
     )
 
     marker.fit_transform(input, storage=storage)