docs/api/index.rst

@@ -36,3 +36,13 @@ Configs
    :caption: Contents:
 
    configs
+
+
+External
+--------
+
+.. toctree:: 
+   :maxdepth: 2
+   :caption: Contents:
+
+   nilearn.rst

docs/api/nilearn.rst

@@ -0,0 +1,8 @@
+Nilearn
+=======
+
+This package provides re-implementations of some of the functions in `nilearn`_.
+
+.. automodule:: junifer.external.nilearn
+   :members:
+   :imported-members:

docs/changes/latest.inc

@@ -51,6 +51,12 @@ Enhancements
 
 - Add ``pre_run`` parameter to ``_queue_condor`` (:gh:`188` by `Fede Raimondo`_).
 
+- Allow for empty spheres in :class:`junifer.external.nilearn.JuniferNiftiSpheresMasker`, that will result in NaNs (:gh:`190` by `Fede Raimondo`_).
+
+- Expose ``allow_overlap`` parameter in  :class:`junifer.markers.SphereAggregation` and related markers (:gh:`190` by `Fede Raimondo`_).
+
+- Add aggregation function :func:`junifer.stats.count` that returns the number of elements in a given axis. This allows to count the number of voxels per sphere/parcel when used as ``method`` in markers (:gh:`190` by `Fede Raimondo`_).
+
 Bugs
 ~~~~
 

junifer/external/nilearn/junifer_nifti_spheres_masker.py

@@ -6,14 +6,19 @@
 from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
 
 import numpy as np
+from nilearn import image, masking
 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._utils.niimg_conversions import (
+    _safe_get_data,
+    check_niimg_3d,
+    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 sklearn import neighbors
 
-from ...utils import raise_error
+from ...utils import raise_error, warn_with_log
 
 
 if TYPE_CHECKING:
@@ -59,6 +64,155 @@ DAMAGE.
 """
 
 
+def _apply_mask_and_get_affinity(
+    seeds, niimg, radius, allow_overlap, mask_img=None
+):
+    """Apply mask and get affinity matrix for given seeds.
+
+    Utility function to get only the rows which are occupied by sphere at
+    given seed locations and the provided radius. Rows are in target_affine and
+    target_shape space.
+
+    Parameters
+    ----------
+    seeds : List of triplets of coordinates in native space
+        Seed definitions. List of coordinates of the seeds in the same space
+        as target_affine.
+    niimg : 3D/4D Niimg-like object
+        See :ref:`extracting_data`.
+        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.
+    radius : float
+        Indicates, in millimeters, the radius for the sphere around the seed.
+    allow_overlap : boolean
+        If False, a ValueError is raised if VOIs overlap
+    mask_img : Niimg-like object, optional
+        Mask to apply to regions before extracting signals. If niimg is None,
+        mask_img is used as a reference space in which the spheres 'indices are
+        placed.
+    Returns
+    -------
+    X : 2D numpy.ndarray
+        Signal for each brain voxel in the (masked) niimgs.
+        shape: (number of scans, number of voxels)
+    A : scipy.sparse.lil_matrix
+        Contains the boolean indices for each sphere.
+        shape: (number of seeds, number of voxels)
+    """
+    seeds = list(seeds)
+
+    # Compute world coordinates of all in-mask voxels.
+    if niimg is None:
+        mask, affine = masking._load_mask_img(mask_img)
+        # Get coordinate for all voxels inside of mask
+        mask_coords = np.asarray(np.nonzero(mask)).T.tolist()
+        X = None
+
+    elif mask_img is not None:
+        affine = niimg.affine
+        mask_img = check_niimg_3d(mask_img)
+        mask_img = image.resample_img(
+            mask_img,
+            target_affine=affine,
+            target_shape=niimg.shape[:3],
+            interpolation="nearest",
+        )
+        mask, _ = masking._load_mask_img(mask_img)
+        mask_coords = list(zip(*np.where(mask != 0)))
+
+        X = masking._apply_mask_fmri(niimg, mask_img)
+
+    elif niimg is not None:
+        affine = niimg.affine
+        if np.isnan(np.sum(_safe_get_data(niimg))):
+            warn_with_log(
+                "The imgs you have fed into fit_transform() contains NaN "
+                "values which will be converted to zeroes."
+            )
+            X = _safe_get_data(niimg, True).reshape([-1, niimg.shape[3]]).T
+        else:
+            X = _safe_get_data(niimg).reshape([-1, niimg.shape[3]]).T
+
+        mask_coords = list(np.ndindex(niimg.shape[:3]))
+
+    else:
+        raise_error("Either a niimg or a mask_img must be provided.")
+
+    # For each seed, get coordinates of nearest voxel
+    nearests = []
+    for sx, sy, sz in seeds:
+        nearest = np.round(
+            image.resampling.coord_transform(sx, sy, sz, np.linalg.inv(affine))
+        )
+        nearest = nearest.astype(int)
+        nearest = (nearest[0], nearest[1], nearest[2])
+        try:
+            nearests.append(mask_coords.index(nearest))
+        except ValueError:
+            nearests.append(None)
+
+    mask_coords = np.asarray(list(zip(*mask_coords)))
+    mask_coords = image.resampling.coord_transform(
+        mask_coords[0], mask_coords[1], mask_coords[2], affine
+    )
+    mask_coords = np.asarray(mask_coords).T
+
+    clf = neighbors.NearestNeighbors(radius=radius)
+    A = clf.fit(mask_coords).radius_neighbors_graph(seeds)
+    A = A.tolil()
+    for i, nearest in enumerate(nearests):
+        if nearest is None:
+            continue
+
+        A[i, nearest] = True
+
+    # Include the voxel containing the seed itself if not masked
+    mask_coords = mask_coords.astype(int).tolist()
+    for i, seed in enumerate(seeds):
+        try:
+            A[i, mask_coords.index(list(map(int, seed)))] = True
+        except ValueError:
+            # seed is not in the mask
+            pass
+
+    if (not allow_overlap) and np.any(A.sum(axis=0) >= 2):
+        raise_error("Overlap detected between spheres")
+
+    return X, A
+
+
+def _iter_signals_from_spheres(
+    seeds, niimg, radius, allow_overlap, mask_img=None
+):
+    """Iterate over spheres.
+
+    Parameters
+    ----------
+    seeds : :obj:`list` of triplets of coordinates in native space
+        Seed definitions. List of coordinates of the seeds in the same space
+        as the images (typically MNI or TAL).
+    niimg : 3D/4D Niimg-like object
+        See :ref:`extracting_data`.
+        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.
+    radius: float
+        Indicates, in millimeters, the radius for the sphere around the seed.
+    allow_overlap: boolean
+        If False, an error is raised if the maps overlaps (ie at least two
+        maps have a non-zero value for the same voxel).
+    mask_img : Niimg-like object, optional
+        See :ref:`extracting_data`.
+        Mask to apply to regions before extracting signals.
+    """
+    X, A = _apply_mask_and_get_affinity(
+        seeds, niimg, radius, allow_overlap, mask_img=mask_img
+    )
+    for _, row in enumerate(A.rows):
+        yield X[:, row]
+
+
 class _JuniferExtractionFunctor:
     """Functor to extract signals from spheres.
 
@@ -149,9 +303,16 @@ class _JuniferExtractionFunctor:
 class JuniferNiftiSpheresMasker(NiftiSpheresMasker):
     """Class for custom NiftiSpheresMasker.
 
+    Differs from :class:`nilearn.maskers.NiftiSpheresMasker` in the following
+    ways:
+
+    * it allows to pass any callable as the ``agg_func`` parameter.
+    * empty spheres do not create an error. Instead, ``agg_func`` is applied to
+      an empty array and the result is passed.
+
     Parameters
     ----------
-    seeds : list of triplet of coordinates in native space
+    seeds : list of float
         Seed definitions. List of coordinates of the seeds in the same space
         as the images (typically MNI or TAL).
     radius : float, optional
@@ -163,13 +324,13 @@ class JuniferNiftiSpheresMasker(NiftiSpheresMasker):
         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
+    dtype : 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`.
+        :class:`nilearn.maskers.NiftiSpheresMasker`.
 
     """
 
@@ -208,11 +369,11 @@ class JuniferNiftiSpheresMasker(NiftiSpheresMasker):
             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
+        confounds : 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
+        sample_mask : np.ndarray, list or tuple, 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`.

junifer/external/nilearn/tests/test_junifer_nifti_spheres_masker.py

@@ -216,8 +216,9 @@ def test_small_radius() -> None:
         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))
+
+    out = masker.fit_transform(nibabel.Nifti1Image(data, affine))
+    assert np.isnan(out).all()
 
     masker = JuniferNiftiSpheresMasker(
         seeds=[seed],

junifer/markers/falff/falff_spheres.py

@@ -27,6 +27,9 @@ class AmplitudeLowFrequencyFluctuationSpheres(
         The radius of the sphere in mm. If None, the signal will be extracted
         from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
         for more information (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     fractional : bool
         Whether to compute fractional ALFF.
     highpass : positive float, optional
@@ -72,6 +75,7 @@ class AmplitudeLowFrequencyFluctuationSpheres(
         coords: str,
         fractional: bool,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         highpass: float = 0.01,
         lowpass: float = 0.1,
         tr: Optional[float] = None,
@@ -83,6 +87,7 @@ class AmplitudeLowFrequencyFluctuationSpheres(
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         self.masks = masks
         self.method = method
         self.method_params = method_params
@@ -124,6 +129,7 @@ class AmplitudeLowFrequencyFluctuationSpheres(
         pa = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             method=self.method,
             method_params=self.method_params,
             masks=self.masks,

junifer/markers/functional_connectivity/edge_functional_connectivity_spheres.py

@@ -25,6 +25,9 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
         The radius of the sphere in mm. If None, the signal will be extracted
         from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
         for more information (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     agg_method : str, optional
         The aggregation method to use.
         See :func:`junifer.stats.get_aggfunc_by_name` for more information
@@ -59,6 +62,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
         self,
         coords: str,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
@@ -68,6 +72,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         if radius is None or radius <= 0:
             raise_error(f"radius should be > 0: provided {radius}")
         super().__init__(
@@ -84,6 +89,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             method=self.agg_method,
             method_params=self.agg_method_params,
             masks=self.masks,

junifer/markers/functional_connectivity/functional_connectivity_spheres.py

@@ -26,6 +26,9 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
         The radius of the sphere in mm. If None, the signal will be extracted
         from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
         for more information (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     agg_method : str, optional
         The aggregation method to use.
         See :func:`junifer.stats.get_aggfunc_by_name` for more information
@@ -53,6 +56,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
         self,
         coords: str,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
@@ -62,6 +66,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         if radius is None or radius <= 0:
             raise_error(f"radius should be > 0: provided {radius}")
         super().__init__(
@@ -78,6 +83,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             method=self.agg_method,
             method_params=self.agg_method_params,
             masks=self.masks,

junifer/markers/reho/reho_spheres.py

@@ -28,6 +28,9 @@ class ReHoSpheres(ReHoBase):
         extracted from a single voxel. See
         :class:`nilearn.maskers.NiftiSpheresMasker` for more information
         (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     use_afni : bool, optional
         Whether to use AFNI for computing. If None, will use AFNI only
         if available (default None).
@@ -93,6 +96,7 @@ class ReHoSpheres(ReHoBase):
         self,
         coords: str,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         use_afni: Optional[bool] = None,
         reho_params: Optional[Dict] = None,
         agg_method: str = "mean",
@@ -102,6 +106,7 @@ class ReHoSpheres(ReHoBase):
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         self.reho_params = reho_params
         self.agg_method = agg_method
         self.agg_method_params = agg_method_params
@@ -142,6 +147,7 @@ class ReHoSpheres(ReHoBase):
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             method=self.agg_method,
             method_params=self.agg_method_params,
             masks=self.masks,

junifer/markers/sphere_aggregation.py

@@ -28,6 +28,9 @@ class SphereAggregation(BaseMarker):
         extracted from a single voxel. See
         :class:`nilearn.maskers.NiftiSpheresMasker` for more information
         (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     method : str, optional
         The aggregation method to use.
         See :func:`junifer.stats.get_aggfunc_by_name` for more information
@@ -54,6 +57,7 @@ class SphereAggregation(BaseMarker):
         self,
         coords: str,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         method: str = "mean",
         method_params: Optional[Dict[str, Any]] = None,
         masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
@@ -62,6 +66,7 @@ class SphereAggregation(BaseMarker):
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         self.method = method
         self.method_params = method_params or {}
         self.masks = masks
@@ -147,6 +152,7 @@ class SphereAggregation(BaseMarker):
         masker = JuniferNiftiSpheresMasker(
             seeds=coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             mask_img=mask_img,
             agg_func=agg_func,
         )

junifer/markers/temporal_snr/temporal_snr_spheres.py

@@ -24,6 +24,9 @@ class TemporalSNRSpheres(TemporalSNRBase):
         The radius of the sphere in mm. If None, the signal will be extracted
         from a single voxel. See :class:`nilearn.maskers.NiftiSpheresMasker`
         for more information (default None).
+    allow_overlap : bool, optional
+        Whether to allow overlapping spheres. If False, an error is raised if
+        the spheres overlap (default is False).
     agg_method : str, optional
         The aggregation method to use.
         See :func:`junifer.stats.get_aggfunc_by_name` for more information
@@ -45,6 +48,7 @@ class TemporalSNRSpheres(TemporalSNRBase):
         self,
         coords: str,
         radius: Optional[float] = None,
+        allow_overlap: bool = False,
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
         masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
@@ -52,6 +56,7 @@ class TemporalSNRSpheres(TemporalSNRBase):
     ) -> None:
         self.coords = coords
         self.radius = radius
+        self.allow_overlap = allow_overlap
         if radius is None or radius <= 0:
             raise_error(f"radius should be > 0: provided {radius}")
         super().__init__(
@@ -91,6 +96,7 @@ class TemporalSNRSpheres(TemporalSNRBase):
         sphere_aggregation = SphereAggregation(
             coords=self.coords,
             radius=self.radius,
+            allow_overlap=self.allow_overlap,
             method=self.agg_method,
             method_params=self.agg_method_params,
             masks=self.masks,

junifer/stats.py

@@ -28,6 +28,7 @@ def get_aggfunc_by_name(
         * ``mean`` -> :func:`numpy.mean`
         * ``std`` -> :func:`numpy.std`
         * ``trim_mean`` -> :func:`scipy.stats.trim_mean`
+        * ``count`` -> :func:`junifer.stats.count`
 
     func_params : dict, optional
         Parameters to pass to the function.
@@ -42,7 +43,13 @@ def get_aggfunc_by_name(
     from functools import partial  # local import to avoid sphinx error
 
     # check validity of names
-    _valid_func_names = {"winsorized_mean", "mean", "std", "trim_mean"}
+    _valid_func_names = {
+        "winsorized_mean",
+        "mean",
+        "std",
+        "trim_mean",
+        "count",
+    }
     if func_params is None:
         func_params = {}
     # apply functions
@@ -74,6 +81,8 @@ def get_aggfunc_by_name(
         func = np.std
     elif name == "trim_mean":
         func = partial(trim_mean, **func_params)
+    elif name == "count":
+        func = count
     else:
         raise_error(
             f"Function {name} unknown. Please provide any of "
@@ -82,6 +91,24 @@ def get_aggfunc_by_name(
     return func
 
 
+def count(data: np.ndarray, axis: int = 0) -> int:
+    """Count the number elements along the given axis.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        Data to count elements on.
+    axis : int, optional
+        The axis to count elements on (default 0).
+
+    Returns
+    -------
+    int
+        Number of elements along the given axis.
+    """
+    return data.shape[axis]
+
+
 def winsorized_mean(
     data: np.ndarray, axis: Optional[int] = None, **win_params
 ) -> np.ndarray:

junifer/tests/test_stats.py

@@ -8,7 +8,7 @@ from typing import Dict, Optional
 import numpy as np
 import pytest
 
-from junifer.stats import get_aggfunc_by_name, winsorized_mean
+from junifer.stats import count, get_aggfunc_by_name, winsorized_mean
 
 
 @pytest.mark.parametrize(
@@ -17,6 +17,7 @@ from junifer.stats import get_aggfunc_by_name, winsorized_mean
         ("winsorized_mean", {"limits": [0.2, 0.7]}),
         ("mean", None),
         ("std", None),
+        ("count", None),
         ("trim_mean", None),
         ("trim_mean", {"proportiontocut": 0.1}),
     ],
@@ -74,3 +75,16 @@ def test_winsorized_mean() -> None:
     input = np.array([22, 4, 9, 8, 5, 3, 7, 2, 1, 6])
     output = winsorized_mean(input, limits=[0.1, 0.1])
     assert output == 5.5
+
+
+def test_count() -> None:
+    """Test count."""
+    input = np.zeros((10, 3))
+    assert count(input, axis=-1) == 3
+    assert count(input, axis=1) == 3
+    assert count(input, axis=0) == 10
+
+    input = np.zeros((10, 0))
+    assert count(input, axis=-1) == 0
+    assert count(input, axis=1) == 0
+    assert count(input, axis=0) == 10