Merge pull request #146 from poldracklab/fix/73-fsl-lta-conversions

FIX: Add FSL-LTA-FSL regression tests

Author: oesteban

nitransforms/tests/data/regressions/from-fsnative_to-bold_mode-image.fsl

@@ -0,0 +1,4 @@
+   0.99971074   -0.00958054   -0.02206109   33.80017471 
+  -0.02333562   -0.60849756   -0.79321212  240.58090210 
+  -0.00582473    0.79349738   -0.60854524   70.82102203 
+   0.00000000    0.00000000    0.00000000    1.00000012 

nitransforms/tests/data/regressions/from-fsnative_to-scanner_mode-image.fsl

@@ -0,0 +1,4 @@
+   1.00000024   -0.00016984    0.00015428   49.00255203 
+   0.00015430    0.00009513   -1.00000024  255.96905518 
+   0.00016983    1.00000024    0.00009516   -0.01236603 
+   0.00000000    0.00000000    0.00000000    1.00000012 

nitransforms/tests/data/regressions/from-scanner_to-fsnative_mode-image.fsl

@@ -0,0 +1,4 @@
+   1.00000012    0.00015430    0.00016983  -49.04202271 
+  -0.00016984    0.00009513    1.00000012   -0.00367374 
+   0.00015428   -1.00000012    0.00009516  255.96148682 
+   0.00000000    0.00000000    0.00000000    1.00000012 

nitransforms/tests/test_conversions.py

@@ -7,6 +7,22 @@
 from ..io.lta import FSLinearTransformArray as LTA
 
 
+def test_concatenation(data_path):
+    """Check replacement to lta_concat."""
+    lta0 = _l.load(
+        data_path / "regressions" / "from-scanner_to-fsnative_mode-image.lta", fmt="lta"
+    )
+    lta1 = _l.load(
+        data_path / "regressions" / "from-fsnative_to-bold_mode-image.lta", fmt="lta"
+    )
+
+    lta_combined = _l.load(
+        data_path / "regressions" / "from-scanner_to-bold_mode-image.lta", fmt="lta"
+    )
+
+    assert np.allclose(lta1.matrix.dot(lta0.matrix), lta_combined.matrix)
+
+
 @pytest.mark.parametrize(
     "filename",
     [
@@ -18,8 +34,8 @@
 )
 def test_lta2itk_conversions(data_path, filename):
     """Check conversions between formats."""
-    lta = _l.load(data_path / "regressions" / ".".join((filename, "lta")), fmt="lta")
-    itk = _l.load(data_path / "regressions" / ".".join((filename, "tfm")), fmt="itk")
+    lta = _l.load(data_path / "regressions" / f"{filename}.lta", fmt="lta")
+    itk = _l.load(data_path / "regressions" / f"{filename}.tfm", fmt="itk")
     assert np.allclose(lta.matrix, itk.matrix)
 
 
@@ -59,17 +75,63 @@ def test_itk2lta_conversions(
     assert np.allclose(converted_lta["xforms"][0]["m_L"], exp_lta["xforms"][0]["m_L"])
 
 
-def test_concatenation(data_path):
-    """Check replacement to lta_concat."""
-    lta0 = _l.load(
-        data_path / "regressions" / "from-scanner_to-fsnative_mode-image.lta", fmt="lta"
+@pytest.mark.parametrize(
+    "fromto",
+    [
+        ("fsnative", "bold"),
+        ("fsnative", "scanner"),
+        ("scanner", "bold"),
+        ("scanner", "fsnative"),
+    ],
+)
+def test_lta2fsl_conversions(data_path, fromto, testdata_path):
+    """Check conversions between formats."""
+    filename = f"from-{fromto[0]}_to-{fromto[1]}_mode-image"
+    movname = "bold.nii.gz" if fromto[1] == "bold" else f"T1w_{fromto[1]}.nii.gz"
+
+    lta = _l.load(data_path / "regressions" / f"{filename}.lta", fmt="lta")
+    fsl = _l.load(
+        data_path / "regressions" / f"{filename}.fsl",
+        moving=testdata_path / movname,
+        reference=testdata_path / f"T1w_{fromto[0]}.nii.gz",
+        fmt="fsl",
     )
-    lta1 = _l.load(
-        data_path / "regressions" / "from-fsnative_to-bold_mode-image.lta", fmt="lta"
+    assert np.allclose(lta.matrix, fsl.matrix, atol=1e-4)
+
+
+@pytest.mark.parametrize(
+    "fromto",
+    [
+        ("fsnative", "bold"),
+        ("fsnative", "scanner"),
+        ("scanner", "bold"),
+        ("scanner", "fsnative"),
+    ],
+)
+def test_fsl2lta_conversions(
+    data_path, testdata_path, tmp_path, fromto,
+):
+    """Check conversions between formats."""
+    filename = f"from-{fromto[0]}_to-{fromto[1]}_mode-image"
+    refname = "bold.nii.gz" if fromto[1] == "bold" else f"T1w_{fromto[1]}.nii.gz"
+
+    fsl = _l.load(
+        data_path / "regressions" / f"{filename}.fsl",
+        reference=testdata_path / f"T1w_{fromto[0]}.nii.gz",
+        moving=testdata_path / refname,
+        fmt="fsl"
+    )
+    fsl.to_filename(
+        tmp_path / "test.lta",
+        fmt="fs",
     )
 
-    lta_combined = _l.load(
-        data_path / "regressions" / "from-scanner_to-bold_mode-image.lta", fmt="lta"
+    converted_lta = LTA.from_filename(tmp_path / "test.lta")
+    expected_fname = (
+        data_path / "regressions" / "".join((filename, "_type-ras2ras.lta"))
     )
+    if not expected_fname.exists():
+        expected_fname = data_path / "regressions" / "".join((filename, ".lta"))
 
-    assert np.allclose(lta1.matrix.dot(lta0.matrix), lta_combined.matrix)
+    exp_lta = LTA.from_filename(expected_fname)
+    assert np.allclose(converted_lta["xforms"][0]["m_L"], exp_lta["xforms"][0]["m_L"], atol=1e-4)