Untrack nilearn, set 10.2 as minimum version

fmralign/_utils.py

@@ -4,9 +4,9 @@
 
 import nibabel as nib
 import numpy as np
-from nilearn._utils.niimg_conversions import check_same_fov
+from nilearn._utils.niimg_conversions import _check_same_fov
 from nilearn.image import index_img, new_img_like, smooth_img
-from nilearn.masking import apply_mask_fmri, intersect_masks
+from nilearn.masking import _apply_mask_fmri, intersect_masks
 from nilearn.regions.parcellations import Parcellations
 
 
@@ -107,8 +107,8 @@ def _make_parcellation(
     """
     # check if clustering is provided
     if isinstance(clustering, nib.nifti1.Nifti1Image) or os.path.isfile(clustering):
-        check_same_fov(masker.mask_img_, clustering)
-        labels = apply_mask_fmri(clustering, masker.mask_img_).astype(int)
+        _check_same_fov(masker.mask_img_, clustering)
+        labels = _apply_mask_fmri(clustering, masker.mask_img_).astype(int)
 
     # otherwise check it's needed, if not return 1 everywhere
     elif n_pieces == 1:
@@ -140,7 +140,9 @@ def _make_parcellation(
             )
             err.args += (errmsg,)
             raise err
-        labels = apply_mask_fmri(parcellation.labels_img_, masker.mask_img_).astype(int)
+        labels = _apply_mask_fmri(parcellation.labels_img_, masker.mask_img_).astype(
+            int
+        )
 
     if verbose > 0:
         unique_labels, counts = np.unique(labels, return_counts=True)

fmralign/pairwise_alignment.py

@@ -8,7 +8,7 @@
 import numpy as np
 from joblib import Memory, Parallel, delayed
 from nilearn.image import concat_imgs, load_img
-from nilearn._utils.masker_validation import check_embedded_masker
+from nilearn.maskers._masker_validation import _check_embedded_nifti_masker
 from sklearn.base import BaseEstimator, TransformerMixin, clone
 from sklearn.model_selection import ShuffleSplit
 
@@ -313,7 +313,7 @@ def fit(self, X, Y):
         -------
         self
         """
-        self.masker_ = check_embedded_masker(self)
+        self.masker_ = _check_embedded_nifti_masker(self)
         self.masker_.n_jobs = self.n_jobs
 
         if self.masker_.mask_img is None:
@@ -330,7 +330,7 @@ def fit(self, X, Y):
                     self.clustering, self.masker_.mask_img
                 )
                 self.mask = reduced_mask
-                self.masker_ = check_embedded_masker(self)
+                self.masker_ = _check_embedded_nifti_masker(self)
                 self.masker_.n_jobs = self.n_jobs
                 self.masker_.fit()
                 warnings.warn(

fmralign/template_alignment.py

@@ -9,7 +9,7 @@
 import numpy as np
 from joblib import Memory, Parallel, delayed
 from nilearn.image import concat_imgs, index_img, load_img
-from nilearn._utils.masker_validation import check_embedded_masker
+from nilearn.maskers._masker_validation import _check_embedded_nifti_masker
 from sklearn.base import BaseEstimator, TransformerMixin
 
 from fmralign.pairwise_alignment import PairwiseAlignment
@@ -388,7 +388,7 @@ def fit(self, imgs):
             if isinstance(imgs[0], (list, np.ndarray)):
                 imgs = [concat_imgs(img) for img in imgs]
 
-        self.masker_ = check_embedded_masker(self)
+        self.masker_ = _check_embedded_nifti_masker(self)
         self.masker_.n_jobs = self.n_jobs  # self.n_jobs
 
         # if masker_ has been provided a mask_img

pyproject.toml

@@ -24,7 +24,7 @@ dependencies = [
     "joblib",
     "scipy",
     "nibabel",
-    "nilearn@git+https://github.com/nilearn/nilearn#egg=1b11edf5789813d20995e7744bc58f3a2b6bc4ea",
+    "nilearn>=0.10.2",
     "POT"
 ]
 dynamic = ["version"]