Adjust nilearn calls for bleeding-edge

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:
@@ -140,9 +140,7 @@ 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/fetch_example_data.py

@@ -2,7 +2,7 @@
 import os
 
 import pandas as pd
-from nilearn.datasets.utils import _fetch_files, _get_dataset_dir
+from nilearn.datasets._utils import fetch_files, get_dataset_dir
 
 
 def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
@@ -34,7 +34,7 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
         Path to the mask to be used on the data
     Notes
     ------
-    This function is a caller to nilearn.datasets.utils._fetch_files in order
+    This function is a caller to nilearn.datasets._utils.fetch_files in order
     to simplify examples reading and understanding for fmralign.
     See Also
     ---------
@@ -45,11 +45,11 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
     if subjects == "all":
         subjects = ["sub-{i:02d}" for i in [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15]]
     dataset_name = "ibc"
-    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
+    data_dir = get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
 
     # download or retrieve metadatas, put it in a dataframe,
     # list all condition and specify path to the right directory
-    metadata_path = _fetch_files(
+    metadata_path = fetch_files(
         data_dir,
         [
             (
@@ -67,7 +67,7 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
     metadata_df = metadata_df[metadata_df.subject.isin(subjects)]
 
     # download / retrieve mask niimg and find its path
-    mask = _fetch_files(
+    mask = fetch_files(
         data_dir,
         [("gm_mask_3mm.nii.gz", "https://osf.io/yvju3/download", {"uncompress": True})],
         verbose=verbose,
@@ -106,5 +106,5 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
                 for condition in conditions
             ]
         )
-        files.append(_fetch_files(data_dir, filenames, verbose=verbose))
+        files.append(fetch_files(data_dir, filenames, verbose=verbose))
     return files, metadata_df, mask

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.maskers._masker_validation import _check_embedded_nifti_masker
+from nilearn._utils.masker_validation import check_embedded_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_nifti_masker(self)
+        self.masker_ = check_embedded_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_nifti_masker(self)
+                self.masker_ = check_embedded_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.maskers._masker_validation import _check_embedded_nifti_masker
+from nilearn._utils.masker_validation import check_embedded_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_nifti_masker(self)
+        self.masker_ = check_embedded_masker(self)
         self.masker_.n_jobs = self.n_jobs  # self.n_jobs
 
         # if masker_ has been provided a mask_img