enh: add test checking stacked models

Related: #12.

Author: oesteban

src/nifreeze/registration/ants.py

@@ -97,7 +97,7 @@ def _prepare_registration_data(
     affine: np.ndarray,
     vol_idx: int,
     dirname: Path | str,
-    clip: str | None = None,
+    clip: str | bool | None = None,
     init_affine: np.ndarray | None = None,
 ) -> tuple[Path, Path, Path | None]:
     """
@@ -129,20 +129,20 @@ def _prepare_registration_data(
         An initialization affine (for second and further estimators).
 
     """
-    clip = clip or "none"
+
     predicted_path = Path(dirname) / f"predicted_{vol_idx:05d}.nii.gz"
     sample_path = Path(dirname) / f"sample_{vol_idx:05d}.nii.gz"
     _to_nifti(
         sample,
         affine,
         sample_path,
-        clip=clip.lower() in ("sample", "both"),
+        clip=str(clip).lower() in ("sample", "both", "true"),
     )
     _to_nifti(
         predicted,
         affine,
         predicted_path,
-        clip=clip.lower() in ("predicted", "both"),
+        clip=str(clip).lower() in ("predicted", "both", "true"),
     )
 
     init_path = None
@@ -463,10 +463,11 @@ def _run_registration(
     registration = generate_command(
         fixed_path,
         moving_path,
-        terminal_output="file",
         environ=environ,
         **align_kwargs,
     )
+    registration.terminal_output = "file"
+
     if num_threads:
         registration.inputs.num_threads = num_threads
 

test/test_integration.py

@@ -95,3 +95,23 @@ def test_proximity_estimator_trivial_model(datadir, tmp_path):
             ).max()
             < 0.25
         )
+
+
+def test_stacked_estimators(datadir):
+    """Check that models can be stacked."""
+
+    # Load test data
+    dmri_dataset = DWI.from_filename(datadir / "dwi.h5")
+
+    estimator1 = Estimator(
+        TrivialModel(dmri_dataset),
+        ants_config="dwi-to-dwi_level0.json",
+        clip=False,
+    )
+    estimator2 = Estimator(
+        TrivialModel(dmri_dataset),
+        prev=estimator1,
+        clip=False,
+    )
+
+    estimator2.run(dmri_dataset)