docs/changes/latest.inc

@@ -67,6 +67,8 @@ Enhancements
 
 - Add more documentation on registering parcellations, coordinates, and masks (:gh:`166` by `Leonard Sasse`_)
 
+- Expose a :func:`junifer.data.parcellations.merge_parcellations` function to merge a list of parcellations (:gh:`146` by Leonard Sasse`_).
+
 Bugs
 ~~~~
 

junifer/data/__init__.py

@@ -13,6 +13,7 @@ from .parcellations import (
     list_parcellations,
     load_parcellation,
     register_parcellation,
+    merge_parcellations,
 )
 
 from .masks import (

junifer/data/parcellations.py

@@ -16,9 +16,9 @@ import nibabel as nib
 import numpy as np
 import pandas as pd
 import requests
-from nilearn import datasets
+from nilearn import datasets, image
 
-from ..utils.logging import logger, raise_error
+from ..utils.logging import logger, raise_error, warn_with_log
 from .utils import closest_resolution
 
 
@@ -700,3 +700,84 @@ def _retrieve_suit(
     ].to_list()
 
     return parcellation_fname, labels
+
+
+def merge_parcellations(
+    parcellations_list: List["Nifti1Image"],
+    parcellations_names: List[str],
+    labels_lists: List[List[str]],
+) -> Tuple["Nifti1Image", List[str]]:
+    """Merge all parcellations from a list into one parcellation.
+
+    Parameters
+    ----------
+    parcellations_list : list of niimg-like object
+        List of parcellations to merge.
+    parcellations_names: list of str
+        List of names for parcellations at the corresponding indices.
+    labels_lists : list of list of str
+        A list of lists. Each list in the list contains the labels for the
+        parcellation at the corresponding index.
+
+    Returns
+    -------
+    parcellation : niimg-like object
+        The parcellation that results from merging the list of input
+        parcellations.
+    labels : list of str
+        List of labels for the resultant parcellation.
+
+    """
+    # Check for duplicated labels
+    labels_lists_flat = [item for sublist in labels_lists for item in sublist]
+    if len(labels_lists_flat) != len(set(labels_lists_flat)):
+        warn_with_log(
+            "The parcellations have duplicated labels. "
+            "Each label will be prefixed with the parcellation name."
+        )
+        for i_parcellation, t_labels in enumerate(labels_lists):
+            labels_lists[i_parcellation] = [
+                f"{parcellations_names[i_parcellation]}_{t_label}"
+                for t_label in t_labels
+            ]
+    overlapping_voxels = False
+    ref_parc = parcellations_list[0]
+    parc_data = ref_parc.get_fdata()
+
+    labels = labels_lists[0]
+
+    for t_parc, t_labels in zip(parcellations_list[1:], labels_lists[1:]):
+        if t_parc.shape != ref_parc.shape:
+            warn_with_log(
+                "The parcellations have different resolutions!"
+                "Resampling all parcellations to the first one in the list."
+            )
+            t_parc = image.resample_to_img(
+                t_parc, ref_parc, interpolation="nearest", copy=True
+            )
+
+        # Get the data from this parcellation
+        t_parc_data = t_parc.get_fdata().copy()  # must be copied
+        # Increase the values of each ROI to match the labels
+        t_parc_data[t_parc_data != 0] += len(labels)
+
+        # Only set new values for the voxels that are 0
+        # This makes sure that the voxels that are in multiple
+        # parcellations are assigned to the parcellation that was
+        # first in the list.
+        if np.any(parc_data[t_parc_data != 0] != 0):
+            overlapping_voxels = True
+
+        parc_data[parc_data == 0] += t_parc_data[parc_data == 0]
+        labels.extend(t_labels)
+
+    if overlapping_voxels:
+        warn_with_log(
+            "The parcellations have overlapping voxels. "
+            "The overlapping voxels will be assigned to the "
+            "parcellation that was first in the list."
+        )
+
+    parcellation_img_res = image.new_img_like(parcellations_list[0], parc_data)
+
+    return parcellation_img_res, labels

junifer/data/tests/test_parcellations.py

@@ -9,6 +9,7 @@ from pathlib import Path
 from typing import List
 
 import nibabel as nib
+import numpy as np
 import pytest
 from nilearn.image import new_img_like
 from numpy.testing import assert_array_almost_equal, assert_array_equal
@@ -20,6 +21,7 @@ from junifer.data.parcellations import (
     _retrieve_tian,
     list_parcellations,
     load_parcellation,
+    merge_parcellations,
     register_parcellation,
 )
 
@@ -236,9 +238,7 @@ def test_schaefer_parcellation(tmp_path: Path) -> None:
     )
     # Load parcellation
     img2, lbl, fname = load_parcellation(
-        name="Schaefer100x7",
+        name="Schaefer100x7", parcellations_dir=tmp_path, resolution=3
-        parcellations_dir=tmp_path,
-        resolution=3,
     )
     # Check parcellation values
     assert fname.name == fname2
@@ -247,9 +247,7 @@ def test_schaefer_parcellation(tmp_path: Path) -> None:
     assert_array_equal(img2.header["pixdim"][1:4], [2, 2, 2])  # type: ignore
     # Load parcellation
     img2, lbl, fname = load_parcellation(
-        "Schaefer100x7",
+        "Schaefer100x7", parcellations_dir=tmp_path, resolution=2.1
-        parcellations_dir=tmp_path,
-        resolution=2.1,
     )
     # Check parcellation values
     assert fname.name == fname2
@@ -258,9 +256,7 @@ def test_schaefer_parcellation(tmp_path: Path) -> None:
     assert_array_equal(img2.header["pixdim"][1:4], [2, 2, 2])  # type: ignore
     # Load parcellation
     img2, lbl, fname = load_parcellation(
-        "Schaefer100x7",
+        "Schaefer100x7", parcellations_dir=tmp_path, resolution=1.99
-        parcellations_dir=tmp_path,
-        resolution=1.99,
     )
     # Check parcellation values
     assert fname.name == fname1
@@ -269,9 +265,7 @@ def test_schaefer_parcellation(tmp_path: Path) -> None:
     assert_array_equal(img2.header["pixdim"][1:4], [1, 1, 1])  # type: ignore
     # Load parcellation
     img2, lbl, fname = load_parcellation(
-        "Schaefer100x7",
+        "Schaefer100x7", parcellations_dir=tmp_path, resolution=0.5
-        parcellations_dir=tmp_path,
-        resolution=0.5,
     )
     # Check parcellation values
     assert fname.name == fname1
@@ -383,18 +377,10 @@ def test_retrieve_suit_incorrect_space(tmp_path: Path) -> None:
 
 
 @pytest.mark.parametrize(
-    "scale, n_label",
+    "scale, n_label", [(1, 16), (2, 32), (3, 50), (4, 54)]
-    [
-        (1, 16),
-        (2, 32),
-        (3, 50),
-        (4, 54),
-    ],
 )
 def test_tian_3T_6thgeneration(
-    tmp_path: Path,
+    tmp_path: Path, scale: int, n_label: int
-    scale: int,
-    n_label: int,
 ) -> None:
     """Test Tian parcellation.
 
@@ -415,8 +401,7 @@ def test_tian_3T_6thgeneration(
     assert "TianxS4x3TxMNI6thgeneration" in parcellations
     # Load parcellation
     img, lbl, fname = load_parcellation(
-        name=f"TianxS{scale}x3TxMNI6thgeneration",
+        name=f"TianxS{scale}x3TxMNI6thgeneration", parcellations_dir=tmp_path
-        parcellations_dir=tmp_path,
     )
     fname1 = f"Tian_Subcortex_S{scale}_3T_1mm.nii.gz"
     assert img is not None
@@ -437,18 +422,10 @@ def test_tian_3T_6thgeneration(
 
 
 @pytest.mark.parametrize(
-    "scale, n_label",
+    "scale, n_label", [(1, 16), (2, 32), (3, 50), (4, 54)]
-    [
-        (1, 16),
-        (2, 32),
-        (3, 50),
-        (4, 54),
-    ],
 )
 def test_tian_3T_nonlinear2009cAsym(
-    tmp_path: Path,
+    tmp_path: Path, scale: int, n_label: int
-    scale: int,
-    n_label: int,
 ) -> None:
     """Test Tian parcellation.
 
@@ -480,18 +457,10 @@ def test_tian_3T_nonlinear2009cAsym(
 
 
 @pytest.mark.parametrize(
-    "scale, n_label",
+    "scale, n_label", [(1, 16), (2, 34), (3, 54), (4, 62)]
-    [
-        (1, 16),
-        (2, 34),
-        (3, 54),
-        (4, 62),
-    ],
 )
 def test_tian_7T_6thgeneration(
-    tmp_path: Path,
+    tmp_path: Path, scale: int, n_label: int
-    scale: int,
-    n_label: int,
 ) -> None:
     """Test Tian parcellation.
 
@@ -534,10 +503,7 @@ def test_retrieve_tian_incorrect_space(tmp_path: Path) -> None:
     """
     with pytest.raises(ValueError, match=r"The parameter `space`"):
         _retrieve_tian(
-            parcellations_dir=tmp_path,
+            parcellations_dir=tmp_path, resolution=1, scale=1, space="wrong"
-            resolution=1,
-            scale=1,
-            space="wrong",
         )
 
     with pytest.raises(ValueError, match=r"MNI6thgeneration"):
@@ -584,3 +550,162 @@ def test_retrieve_tian_incorrect_scale(tmp_path: Path) -> None:
             scale=5,
             space="MNI6thgeneration",
         )
+
+
+def test_merge_parcellations() -> None:
+    """Test merging parcellations."""
+    # load some parcellations for testing
+    schaefer_parcellation, schaefer_labels, _ = load_parcellation(
+        "Schaefer100x17"
+    )
+    tian_parcellation, tian_labels, _ = load_parcellation(
+        "TianxS2x3TxMNInonlinear2009cAsym"
+    )
+    # prepare the list of the actual parcellations
+    parcellation_list = [schaefer_parcellation, tian_parcellation]
+    # prepare a list of names
+    names = ["Schaefer100x17", "TianxS2x3TxMNInonlinear2009cAsym"]
+    # prepare a list of label lists
+    labels_lists = [schaefer_labels, tian_labels]
+    # merge the parcellations
+    merged_parc, labels = merge_parcellations(
+        parcellation_list, names, labels_lists
+    )
+
+    # we should have 132 integer labels plus 1 for background
+    parc_data = merged_parc.get_fdata()
+    assert len(np.unique(parc_data)) == 133
+    # no background label, so labels is one less
+    assert len(labels) == 132
+
+
+def test_merge_parcellations_3D_multiple_non_overlapping(
+    tmp_path: Path,
+) -> None:
+    """Test merge_parcellations with multiple non-overlapping parcellations.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    # Get the testing parcellation
+    parcellation, labels, _ = load_parcellation("Schaefer100x7")
+
+    assert parcellation is not None
+
+    # Create two parcellations from it
+    parcellation_data = parcellation.get_fdata()
+    parcellation1_data = parcellation_data.copy()
+    parcellation1_data[parcellation1_data > 50] = 0
+    parcellation2_data = parcellation_data.copy()
+    parcellation2_data[parcellation2_data <= 50] = 0
+    parcellation2_data[parcellation2_data > 0] -= 50
+    labels1 = labels[:50]
+    labels2 = labels[50:]
+
+    parcellation1_img = new_img_like(parcellation, parcellation1_data)
+    parcellation2_img = new_img_like(parcellation, parcellation2_data)
+
+    parcellation_list = [parcellation1_img, parcellation2_img]
+    names = ["high", "low"]
+    labels_lists = [labels1, labels2]
+
+    merged_parc, merged_labels = merge_parcellations(
+        parcellation_list, names, labels_lists
+    )
+
+    parc_data = parcellation.get_fdata()
+    assert_array_equal(parc_data, merged_parc.get_fdata())
+    assert len(labels) == 100
+    assert len(np.unique(parc_data)) == 101  # 100 + 1 because background 0
+
+
+def test_merge_parcellations_3D_multiple_overlapping(tmp_path: Path) -> None:
+    """Test merge_parcellations with multiple overlapping parcellations.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+
+    # Get the testing parcellation
+    parcellation, labels, _ = load_parcellation("Schaefer100x7")
+
+    assert parcellation is not None
+
+    # Create two parcellations from it
+    parcellation_data = parcellation.get_fdata()
+    parcellation1_data = parcellation_data.copy()
+    parcellation1_data[parcellation1_data > 50] = 0
+    parcellation2_data = parcellation_data.copy()
+
+    # Make the second parcellation overlap with the first
+    parcellation2_data[parcellation2_data <= 45] = 0
+    parcellation2_data[parcellation2_data > 0] -= 45
+    labels1 = [f"low_{x}" for x in labels[:50]]  # Change the labels
+    labels2 = [f"high_{x}" for x in labels[45:]]  # Change the labels
+
+    parcellation1_img = new_img_like(parcellation, parcellation1_data)
+    parcellation2_img = new_img_like(parcellation, parcellation2_data)
+
+    parcellation_list = [parcellation1_img, parcellation2_img]
+    names = ["high", "low"]
+    labels_lists = [labels1, labels2]
+
+    with pytest.warns(RuntimeWarning, match="overlapping voxels"):
+        merged_parc, merged_labels = merge_parcellations(
+            parcellation_list, names, labels_lists
+        )
+
+    parc_data = parcellation.get_fdata()
+    assert len(labels) == 100
+    assert len(np.unique(parc_data)) == 101  # 100 + 1 because background 0
+
+
+def test_merge_parcellations_3D_multiple_duplicated_labels(
+    tmp_path: Path,
+) -> None:
+    """Test merge_parcellations with two parcellations with duplicated labels.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+
+    # Get the testing parcellation
+    parcellation, labels, _ = load_parcellation("Schaefer100x7")
+
+    assert parcellation is not None
+
+    # Create two parcellations from it
+    parcellation_data = parcellation.get_fdata()
+    parcellation1_data = parcellation_data.copy()
+    parcellation1_data[parcellation1_data > 50] = 0
+    parcellation2_data = parcellation_data.copy()
+    parcellation2_data[parcellation2_data <= 50] = 0
+    parcellation2_data[parcellation2_data > 0] -= 50
+    labels1 = labels[:50]
+    labels2 = labels[49:-1]  # One label is duplicated
+
+    parcellation1_img = new_img_like(parcellation, parcellation1_data)
+    parcellation2_img = new_img_like(parcellation, parcellation2_data)
+
+    parcellation_list = [parcellation1_img, parcellation2_img]
+    names = ["high", "low"]
+    labels_lists = [labels1, labels2]
+
+    with pytest.warns(RuntimeWarning, match="duplicated labels."):
+        merged_parc, merged_labels = merge_parcellations(
+            parcellation_list, names, labels_lists
+        )
+
+    parc_data = parcellation.get_fdata()
+    assert_array_equal(parc_data, merged_parc.get_fdata())
+    assert len(labels) == 100
+    assert len(np.unique(parc_data)) == 101  # 100 + 1 because background 0

junifer/datagrabber/datalad_base.py

@@ -178,9 +178,7 @@ class DataladDataGrabber(BaseDataGrabber):
             try:
                 dl_out = self._dataset.get(to_get, result_renderer="disabled")
             except IncompleteResultsError as e:
-                raise_error(
+                raise_error(f"Failed to get from dataset: {e.failed}")
-                    f"Failed to get from dataset: {e.failed}"
-                )
             if not self._was_cloned:
                 # If the dataset was already installed, check that the
                 # file was actually downloaded to avoid removing a

junifer/markers/parcel_aggregation.py

@@ -7,13 +7,13 @@
 from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
-from nilearn.image import math_img, new_img_like, resample_to_img
+from nilearn.image import math_img, resample_to_img
 from nilearn.maskers import NiftiMasker
 
 from ..api.decorators import register_marker
-from ..data import get_mask, load_parcellation
+from ..data import get_mask, load_parcellation, merge_parcellations
 from ..stats import get_aggfunc_by_name
-from ..utils import logger, warn_with_log
+from ..utils import logger
 from .base import BaseMarker
 
 
@@ -98,9 +98,7 @@ class ParcelAggregation(BaseMarker):
             raise ValueError(f"Unknown input kind for {input_type}")
 
     def compute(
-        self,
+        self, input: Dict[str, Any], extra_input: Optional[Dict] = None
-        input: Dict[str, Any],
-        extra_input: Optional[Dict] = None,
     ) -> Dict:
         """Compute.
 
@@ -129,8 +127,7 @@ class ParcelAggregation(BaseMarker):
         t_input_img = input["data"]
         logger.debug(f"Parcel aggregation using {self.method}")
         agg_func = get_aggfunc_by_name(
-            name=self.method,
+            name=self.method, func_params=self.method_params
-            func_params=self.method_params,
         )
         # Get the min of the voxels sizes and use it as the resolution
         resolution = np.min(t_input_img.header.get_zooms()[:3])
@@ -140,15 +137,11 @@ class ParcelAggregation(BaseMarker):
         all_labels = []
         for t_parc_name in self.parcellation:
             t_parcellation, t_labels, _ = load_parcellation(
-                name=t_parc_name,
+                name=t_parc_name, resolution=resolution
-                resolution=resolution,
             )
             # Resample all of them to the image
             t_parcellation_img_res = resample_to_img(
-                t_parcellation,
+                t_parcellation, t_input_img, interpolation="nearest", copy=True
-                t_input_img,
-                interpolation="nearest",
-                copy=True,
             )
             all_parcelations.append(t_parcellation_img_res)
             all_labels.append(t_labels)
@@ -159,56 +152,11 @@ class ParcelAggregation(BaseMarker):
             labels = all_labels[0]
         else:
             # Merge the parcellations
-
+            parcellation_img_res, labels = merge_parcellations(
-            # Check for duplicated labels
+                all_parcelations, self.parcellation, all_labels
-            all_labels_flat = [
-                item for sublist in all_labels for item in sublist
-            ]
-            if len(all_labels_flat) != len(set(all_labels_flat)):
-                warn_with_log(
-                    "The parcellations have duplicated labels. "
-                    "Each label will be prefixed with the parcellation name."
-                )
-                for i_parcellation, t_labels in enumerate(all_labels):
-                    all_labels[i_parcellation] = [
-                        f"{self.parcellation[i_parcellation]}_{t_label}"
-                        for t_label in t_labels
-                    ]
-            overlapping_voxels = False
-            parc_data = all_parcelations[0].get_fdata()
-            labels = all_labels[0]
-            for t_parc, t_labels in zip(all_parcelations[1:], all_labels[1:]):
-                # Get the data from this parcellation
-                t_parc_data = t_parc.get_fdata().copy()  # must be copied
-                # Increase the values of each ROI to match the labels
-                t_parc_data[t_parc_data != 0] += len(labels)
-
-                # Only set new values for the voxels that are 0
-                # This makes sure that the voxels that are in multiple
-                # parcellations are assigned to the parcellation that was
-                # first in the list.
-                if np.any(parc_data[t_parc_data != 0] != 0):
-                    overlapping_voxels = True
-
-                parc_data[parc_data == 0] += t_parc_data[parc_data == 0]
-                labels.extend(t_labels)
-
-            if overlapping_voxels:
-                warn_with_log(
-                    "The parcellations have overlapping voxels. "
-                    "The overlapping voxels will be assigned to the "
-                    "parcellation that was first in the list."
             )
 
-            parcellation_img_res = new_img_like(
+        parcellation_bin = math_img("img != 0", img=parcellation_img_res)
-                all_parcelations[0],
-                parc_data,
-            )
-
-        parcellation_bin = math_img(
-            "img != 0",
-            img=parcellation_img_res,
-        )
 
         if self.masks is not None:
             logger.debug(f"Masking with {self.masks}")