TEST: Integration test with gifti_surfaces and anat_ribbon workflows

smriprep/workflows/tests/test_surfaces.py

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+import os
+from shutil import which
+
+import nibabel as nb
+import numpy as np
+import pytest
+from nipype.pipeline import engine as pe
+
+from ..surfaces import init_anat_ribbon_wf, init_gifti_surfaces_wf
+from ...data import load_resource
+
+
+def test_ribbon_workflow(tmp_path):
+    """Create ribbon mask for fsaverage subject"""
+
+    for command in ("mris_convert", "wb_command", "fslmaths"):
+        if not which(command):
+            pytest.skip(f"Could not find {command} in PATH")
+
+    if not os.path.exists(os.getenv('SUBJECTS_DIR')):
+        pytest.skip("Could not find $SUBJECTS_DIR")
+
+    # Low-res file that includes the fsaverage surfaces in its bounding box
+    # We will use it both as a template and a comparison.
+    test_ribbon = load_resource(
+        "../interfaces/tests/data/sub-fsaverage_res-4_desc-cropped_ribbon.nii.gz"
+    )
+
+    gifti_surfaces_wf = init_gifti_surfaces_wf(surfaces=['white', 'pial'])
+    anat_ribbon_wf = init_anat_ribbon_wf()
+    anat_ribbon_wf.inputs.inputnode.t1w_mask = test_ribbon
+
+    gifti_surfaces_wf.inputs.inputnode.subjects_dir = os.getenv('SUBJECTS_DIR')
+    gifti_surfaces_wf.inputs.inputnode.subject_id = 'fsaverage'
+
+    wf = pe.Workflow(name='test_ribbon_wf', base_dir=tmp_path)
+    wf.connect(
+        [
+            (
+                gifti_surfaces_wf,
+                anat_ribbon_wf,
+                [
+                    ('outputnode.white', 'inputnode.white'),
+                    ('outputnode.pial', 'inputnode.pial'),
+                ],
+            ),
+        ]
+    )
+    result = wf.run()
+
+    combine_ribbon_vol_hemis = next(
+        node for node in result.nodes() if node.name == 'combine_ribbon_vol_hemis'
+    )
+
+    expected = nb.load(test_ribbon)
+    ribbon = nb.load(combine_ribbon_vol_hemis.result.outputs.out_file)
+
+    assert ribbon.shape == expected.shape
+    assert np.allclose(ribbon.affine, expected.affine)
+    # Mask data is binary, so we can use np.array_equal
+    assert np.array_equal(ribbon.dataobj, expected.dataobj)