FIX: revert confounds changes

Author: mnoergaard

petprep/workflows/pet/confounds.py

@@ -28,6 +28,7 @@
 
 """
 
+from nipype.algorithms import confounds as nac
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
 from templateflow.api import get as get_template
@@ -242,11 +243,7 @@ def init_pet_confs_wf(
 
     # DVARS
     dvars = pe.Node(
-        niu.Function(
-            function=_compute_dvars,
-            input_names=['pet', 'mask'],
-            output_names=['out_nstd', 'out_std'],
-        ),
+        nac.ComputeDVARS(save_nstd=True, save_std=True, remove_zerovariance=True),
         name='dvars',
         mem_gb=mem_gb,
     )
@@ -354,8 +351,8 @@ def init_pet_confs_wf(
 
     workflow.connect([
         # connect inputnode to each non-anatomical confound node
-        (inputnode, dvars, [('pet', 'pet'),
-                            ('pet_mask', 'mask')]),
+        (inputnode, dvars, [('pet', 'in_file'),
+                            ('pet_mask', 'in_mask')]),
         (inputnode, motion_params, [('motion_xfm', 'xfm_file'),
                                     ('petref', 'petref_file')]),
         (inputnode, rmsd, [('motion_xfm', 'xfm_file'),
@@ -562,30 +559,6 @@ def init_carpetplot_wf(
     return workflow
 
 
-def _compute_dvars(pet, mask):
-    """Compute DVARS only when the timeseries has at least three frames."""
-    from pathlib import Path
-
-    import nibabel as nb
-    import numpy as np
-    from nipype.algorithms import confounds as nac
-
-    nvols = nb.load(pet).shape[-1]
-    if nvols < 3:
-        data = np.zeros((nvols,), dtype=float)
-        out_nstd = Path('dvars.nstd.tsv').absolute()
-        out_std = Path('dvars.std.tsv').absolute()
-        np.savetxt(out_nstd, data)
-        np.savetxt(out_std, data)
-        return str(out_nstd), str(out_std)
-
-    dvars = nac.ComputeDVARS(save_nstd=True, save_std=True, remove_zerovariance=True)
-    dvars.inputs.in_file = pet
-    dvars.inputs.in_mask = mask
-    res = dvars.run()
-    return res.outputs.out_nstd, res.outputs.out_std
-
-
 def _binary_union(mask1, mask2):
     """Generate the union of two masks."""
     from pathlib import Path

petprep/workflows/pet/tests/test_confounds.py

@@ -15,7 +15,7 @@ def test_dvars_connects_pet_mask(tmp_path):
     )
 
     edge = wf._graph.get_edge_data(wf.get_node('inputnode'), wf.get_node('dvars'))
-    assert ('pet_mask', 'mask') in edge['connect']
+    assert ('pet_mask', 'in_mask') in edge['connect']
 
     img = nb.Nifti1Image(np.random.rand(2, 2, 2, 5), np.eye(4))
     mask = nb.Nifti1Image(np.ones((2, 2, 2), dtype=np.uint8), np.eye(4))
@@ -26,36 +26,9 @@ def test_dvars_connects_pet_mask(tmp_path):
 
     node = wf.get_node('dvars')
     node.base_dir = tmp_path
-    node.inputs.pet = str(pet_file)
-    node.inputs.mask = str(mask_file)
+    node.inputs.in_file = str(pet_file)
+    node.inputs.in_mask = str(mask_file)
     result = node.run()
 
     assert result.outputs.out_nstd
     assert result.outputs.out_std
-
-
-def test_dvars_short_series(tmp_path):
-    """DVARS returns empty outputs when fewer than three frames."""
-    wf = init_pet_confs_wf(
-        mem_gb=0.01,
-        metadata={},
-        regressors_all_comps=False,
-        regressors_dvars_th=1.5,
-        regressors_fd_th=0.5,
-    )
-
-    img = nb.Nifti1Image(np.random.rand(2, 2, 2, 2), np.eye(4))
-    mask = nb.Nifti1Image(np.ones((2, 2, 2), dtype=np.uint8), np.eye(4))
-    pet_file = tmp_path / 'pet.nii.gz'
-    mask_file = tmp_path / 'mask.nii.gz'
-    img.to_filename(pet_file)
-    mask.to_filename(mask_file)
-
-    node = wf.get_node('dvars')
-    node.base_dir = tmp_path
-    node.inputs.pet = str(pet_file)
-    node.inputs.mask = str(mask_file)
-    result = node.run()
-
-    assert np.loadtxt(result.outputs.out_nstd).sum() == 0
-    assert np.loadtxt(result.outputs.out_std).sum() == 0