Convert outlier_idx to a list.

xcp_d/utils/utils.py

@@ -465,7 +465,7 @@ def denoise_with_nilearn(
     )
     # Replace any high-motion volumes at the beginning or end of the run with the closest
     # low-motion volume's data.
-    outlier_idx = np.where(~sample_mask)[0]
+    outlier_idx = list(np.where(~sample_mask)[0])
     if outlier_idx:
         # Use https://stackoverflow.com/a/48106843/2589328 to group consecutive blocks of outliers.
         gaps = [[s, e] for s, e in zip(outlier_idx, outlier_idx[1:]) if s + 1 < e]