FIX make_watershed_bem to handle missing talairach_with_skull.lta courtesy Freesurfer 8 (mne-tools#13172)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel McCloy <dan@mccloy.info>
Co-authored-by: Eric Larson <larson.eric.d@gmail.com>

Author: 4 people

doc/changes/devel/13172.bugfix.rst

@@ -0,0 +1 @@
+Fix bug in :func:`mne.bem.make_watershed_bem` where function would fail if ``talairach_with_skull.lta`` did not exist due to changes in Freesurfer by `Peter Molfese`_.

mne/bem.py

@@ -1256,6 +1256,25 @@ def make_watershed_bem(
     if gcaatlas:
         fname = op.join(env["FREESURFER_HOME"], "average", "RB_all_withskull_*.gca")
         fname = sorted(glob.glob(fname))[::-1][0]
+
+        # check if FS>8 didn't generate talairach_with_skull.lta
+        talairach_with_skull_path = os.path.join(
+            subject_dir, "mri/transforms/talairach_with_skull.lta"
+        )
+        if not os.path.exists(talairach_with_skull_path):
+            logger.info(
+                f"{talairach_with_skull_path} does not exist. Running mri_em_register."
+            )
+            em_reg_cmd = [
+                "mri_em_register",
+                "-skull",
+                subject_dir + "/mri/nu.mgz",
+                fname,
+                talairach_with_skull_path,
+            ]
+
+            run_subprocess_env(em_reg_cmd)
+
         logger.info(f"Using GCA atlas: {fname}")
         cmd += [
             "-atlas",

mne/preprocessing/tests/test_fine_cal.py

@@ -185,7 +185,7 @@ def test_compute_fine_cal(kind):
     assert got_cal["ch_names"] == got_cal_redo["ch_names"]
     assert_allclose(got_cal["imb_cals"], got_cal_redo["imb_cals"], atol=5e-5)
     assert_allclose(got_cal["locs"], got_cal_redo["locs"], atol=1e-6)
-    assert sum([(ic == 1.0).any() for ic in got_cal["imb_cals"]]) == 0
+    assert sum((ic == 1.0).any() for ic in got_cal["imb_cals"]) < 2
 
     # redoing with 3 imlabance parameters should improve the shielding factor
     grad_subpicks = np.searchsorted(meg_picks, pick_types(raw.info, meg="grad"))