Merge pull request #84 from nipreps/fix/revise-tests

FIX: Miscelaneous revisions to make tests execute properly

src/eddymotion/model.py

@@ -240,7 +240,7 @@ def predict(self, gradient, **kwargs):
 class AverageDWModel:
     """A trivial model that returns an average map."""
 
-    __slots__ = ("_data", "_gtab", "_th_low", "_th_high", "_bias", "_stat")
+    __slots__ = ("_data", "_th_low", "_th_high", "_bias", "_stat")
 
     def __init__(self, gtab, **kwargs):
         r"""
@@ -264,18 +264,19 @@ def __init__(self, gtab, **kwargs):
             Whether the summary statistic to apply is ``"mean"`` or ``"median"``.
 
         """
-        self._gtab = gtab
         self._th_low = kwargs.get("th_low", 50)
-        self._th_high = kwargs.get("th_high", self._gtab[3, ...].max())
+        self._th_high = kwargs.get("th_high", 10000)
         self._bias = kwargs.get("bias", True)
         self._stat = kwargs.get("stat", "median")
 
     def fit(self, data, **kwargs):
         """Calculate the average."""
+        gtab = kwargs.pop("gtab", None)
         # Select the interval of b-values for which DWIs will be averaged
-        b_mask = (self._gtab[3, ...] >= self._th_low) & (
-            self._gtab[3, ...] <= self._th_high
-        )
+        b_mask = (
+            (gtab[3] >= self._th_low)
+            & (gtab[3] <= self._th_high)
+        ) if gtab is not None else np.ones((data.shape[-1], ), dtype=bool)
         shells = data[..., b_mask]
 
         # Regress out global signal differences

test/conftest.py

@@ -28,15 +28,14 @@
 import numpy
 import pytest
 
-test_data_env = os.getenv("TEST_DATA_HOME", str(Path.home() / "eddy-tests"))
+test_data_env = os.getenv("TEST_DATA_HOME", str(Path.home() / "eddymotion-tests"))
 test_output_dir = os.getenv("TEST_OUTPUT_DIR")
 test_workdir = os.getenv("TEST_WORK_DIR")
-data_dir = Path(__file__).parent / "tests" / "data"
 
 
 def pytest_report_header(config):
     return f"""\
-TEST_DATA_HOME={test_data_env}s
+TEST_DATA_HOME={test_data_env}.
 TEST_OUTPUT_DIR={test_output_dir or '<unset> (output files will be discarded)'}.
 TEST_WORK_DIR={test_workdir or '<unset> (intermediate files will be discarded)'}.
 """
@@ -61,9 +60,3 @@ def outdir():
 def datadir():
     """Return a data path outside the package's structure (i.e., large datasets)."""
     return Path(test_data_env)
-
-
-@pytest.fixture
-def pkg_datadir():
-    """Return a data path inside the package's structure (i.e., small, empty files)."""
-    return data_dir

test/test_estimator.py

@@ -39,14 +39,14 @@
 @pytest.mark.parametrize("t_x", [0.0, 1.0])
 @pytest.mark.parametrize("t_y", [0.0, 1.0])
 @pytest.mark.parametrize("t_z", [0.0, 1.0])
-def test_ANTs_config_b0(pkg_datadir, tmp_path, r_x, r_y, r_z, t_x, t_y, t_z):
+def test_ANTs_config_b0(datadir, tmp_path, r_x, r_y, r_z, t_x, t_y, t_z):
     """Check that the registration parameters for b=0
     gives a good estimate of known affine"""
 
     fixed = tmp_path / "b0.nii.gz"
     moving = tmp_path / "moving.nii.gz"
 
-    dwdata = DWI.from_filename(pkg_datadir / "dwi.h5")
+    dwdata = DWI.from_filename(datadir / "dwi.h5")
     b0nii = nb.Nifti1Image(dwdata.bzero, dwdata.affine, None)
     b0nii.to_filename(fixed)
 

test/test_integration.py

@@ -30,14 +30,14 @@
 from eddymotion.estimator import EddyMotionEstimator
 
 
-def test_proximity_estimator_trivial_model(pkg_datadir, tmp_path):
+def test_proximity_estimator_trivial_model(datadir, tmp_path):
     """Check the proximity of transforms estimated by the estimator with a trivial B0 model."""
 
-    dwdata = DWI.from_filename(pkg_datadir / "dwi.h5")
+    dwdata = DWI.from_filename(datadir / "dwi.h5")
     b0nii = nb.Nifti1Image(dwdata.bzero, dwdata.affine, None)
 
     xfms = nt.linear.load(
-        pkg_datadir / "head-motion-parameters.aff12.1D",
+        datadir / "head-motion-parameters.aff12.1D",
         fmt="afni",
     )
     xfms.reference = b0nii

test/test_model.py

@@ -47,50 +47,51 @@ def test_trivial_model():
 def test_average_model():
     """Check the implementation of the average DW model."""
 
+    data = np.ones((100, 100, 100, 6), dtype=float)
+
     gtab = np.array(
         [
+            [0, 0, 0, 0],
             [-0.31, 0.933, 0.785, 25],
             [0.25, 0.565, 0.21, 500],
             [-0.861, -0.464, 0.564, 1000],
             [0.307, -0.766, 0.677, 1000],
-            [0.736, 0.013, 0.774, 2000],
+            [0.736, 0.013, 0.774, 1300],
         ]
     )
 
-    gtab_w25 = gtab[1:, :]
-    gtab_1000 = gtab[2:3, :]
-    gtab_2000 = gtab[2:, :]
+    data *= gtab[:, -1]
 
     tmodel_mean = model.AverageDWModel(gtab=gtab, bias=False, stat="mean")
     tmodel_median = model.AverageDWModel(gtab=gtab, bias=False, stat="median")
     tmodel_1000 = model.AverageDWModel(
-        gtab=gtab, bias=False, th_high=1000, th_low=1000
+        gtab=gtab, bias=False, th_high=1000, th_low=900
     )
     tmodel_2000 = model.AverageDWModel(
-        gtab=gtab, bias=False, th_high=2000, th_low=1000
+        gtab=gtab, bias=False, th_high=2000, th_low=900, stat="mean",
     )
 
     # Verify that fit function returns nothing
-    assert tmodel_mean.fit() is None
+    assert tmodel_mean.fit(data[..., 1:], gtab=gtab[1:].T) is None
 
-    tmodel_median.fit()
-    tmodel_1000.fit()
-    tmodel_2000.fit()
+    tmodel_median.fit(data[..., 1:], gtab=gtab[1:].T)
+    tmodel_1000.fit(data[..., 1:], gtab=gtab[1:].T)
+    tmodel_2000.fit(data[..., 1:], gtab=gtab[1:].T)
 
     # Verify that the right statistics is applied and that the model discard b-values < 50
-    assert np.all(tmodel_mean.predict() == np.mean(gtab_w25[:, :2], axis=0))
-    assert np.all(tmodel_median.predict() == np.median(gtab_w25[:, :2], axis=0))
+    assert np.all(tmodel_mean.predict([0, 0, 0]) == 950)
+    assert np.all(tmodel_median.predict([0, 0, 0]) == 1000)
 
     # Verify that the threshold for b-value selection works as expected
-    assert np.all(tmodel_1000.predict() == np.median(gtab_1000[:, :2], axis=0))
-    assert np.all(tmodel_2000.predict() == np.median(gtab_2000[:, :2], axis=0))
+    assert np.all(tmodel_1000.predict([0, 0, 0]) == 1000)
+    assert np.all(tmodel_2000.predict([0, 0, 0]) == 1100)
 
 
-def test_two_initialisations(pkg_datadir):
+def test_two_initialisations(datadir):
     """Check that the two different initialisations result in the same models"""
 
     # Load test data
-    dmri_dataset = DWI.from_filename(pkg_datadir / "dwi.h5")
+    dmri_dataset = DWI.from_filename(datadir / "dwi.h5")
 
     # Split data into test and train set
     data_train, data_test = dmri_dataset.logo_split(10)
@@ -101,10 +102,10 @@ def test_two_initialisations(pkg_datadir):
         S0=dmri_dataset.bzero,
         th_low=100,
         th_high=1000,
-        bias=True,
+        bias=False,
         stat="mean",
     )
-    model1.fit(data_train[0])
+    model1.fit(data_train[0], gtab=data_train[1])
     predicted1 = model1.predict(data_test[1])
 
     # Initialisation via ModelFactory
@@ -114,10 +115,10 @@ def test_two_initialisations(pkg_datadir):
         S0=dmri_dataset.bzero,
         th_low=100,
         th_high=1000,
-        bias=True,
+        bias=False,
         stat="mean",
     )
-    model2.fit(data_train[0])
+    model2.fit(data_train[0], gtab=data_train[1])
     predicted2 = model2.predict(data_test[1])
 
-    assert predicted1 == predicted2
+    assert np.all(predicted1 == predicted2)

test/test_version.py

@@ -23,7 +23,6 @@
 """Test _version.py."""
 import sys
 from importlib import reload
-from pkg_resources import get_distribution
 
 import eddymotion
 
@@ -37,8 +36,3 @@ class _version:
     monkeypatch.setitem(sys.modules, "eddymotion._version", _version)
     reload(eddymotion)
     assert eddymotion.__version__ == "10.0.0"
-
-
-def test_version_scm1():
-    """Retrieve the version via pkg_resources."""
-    assert get_distribution("eddymotion").version != "0.0"