[FIX] fix positions markers on images on sphere masker reports

doc/changes/latest.rst

@@ -10,6 +10,7 @@ Fixes
 
 - :bdg-dark:`Code` Fix errant warning when using ``stat_type`` in :func:`nilearn.glm.compute_contrast` (:gh:`4257` by `Eric Larson`_).
 - :bdg-dark:`Code` Fix when thresholding is applied to images by GLM reports (:gh:`4258` by `Rémi Gau`_).
+- :bdg-dark:`Code` Fix positions of the markers on the images on the sphere masker reports (:gh:`4285` by `Rémi Gau`_).
 - :bdg-dark:`Code` Make sure that :class:`nilearn.maskers.NiftiSpheresMasker` reports displays properly when it contains only 1 sphere (:gh:`4269` by `Rémi Gau`_).
 
 Enhancements

examples/03_connectivity/plot_sphere_based_connectome.py

@@ -8,8 +8,8 @@
 and the Dosenbach-160 atlas (:footcite:t:`Dosenbach2010`).
 
 We estimate connectomes using two different methods: **sparse inverse
-covariance** and **partial_correlation**, to recover the functional brain
-**networks structure**.
+covariance** and **partial_correlation**,
+to recover the functional brain **networks structure**.
 
 We'll start by extracting signals from Default Mode Network regions and
 computing a connectome from them.
@@ -83,9 +83,9 @@
 # %%
 # Display spheres summary report
 # ------------------------------
-# By default all spheres are displayed. This can be tweaked by passing an
-# integer or list/array of indices to the ``displayed_spheres`` argument of
-# ``generate_report``.
+# By default all spheres are displayed.
+# This can be tweaked by passing an integer or list/array of indices
+# to the ``displayed_spheres`` argument of ``generate_report``.
 report = masker.generate_report()
 report
 
@@ -106,9 +106,9 @@
 # %%
 # Compute partial correlation matrix
 # -----------------------------------
-# Using object :class:`nilearn.connectome.ConnectivityMeasure`: Its
-# default covariance estimator is Ledoit-Wolf, allowing to obtain accurate
-# partial correlations.
+# Using object :class:`nilearn.connectome.ConnectivityMeasure`: 
+# its default covariance estimator is Ledoit-Wolf,
+# allowing to obtain accurate partial correlations.
 
 from nilearn.connectome import ConnectivityMeasure
 

nilearn/maskers/nifti_spheres_masker.py

@@ -507,10 +507,10 @@ def _reporting(self):
             regions_summary["relative size (in %)"].append("not implemented")
             if idx in spheres_to_be_displayed:
                 display = plotting.plot_img(
-                    img, cut_coords=positions[idx], cmap="gray"
+                    img, cut_coords=seeds[idx], cmap="gray"
                 )
                 display.add_markers(
-                    marker_coords=[positions[idx]],
+                    marker_coords=[seeds[idx]],
                     marker_color="g",
                     marker_size=20 * radius,
                 )