add regression tests

Author: oesteban

nipype/algorithms/metrics.py

@@ -446,18 +446,19 @@ def _run_interface(self, runtime):
                 'Size of "in_tst" %s must match that of "in_ref" %s.' %
                 (tstdata.shape, refdata.shape))
 
+        ncomp = refdata.shape[-1]
+
         # Load mask
-        mask = np.ones_like(refdata[..., 0], dtype=bool)
+        mask = np.ones_like(refdata, dtype=bool)
         if isdefined(self.inputs.in_mask):
             mask = nb.load(self.inputs.in_mask).get_data()
             mask = mask > 0
-            assert mask.shape == refdata.shape[:-1]
-
-        ncomp = refdata.shape[-1]
+            mask = np.repeat(mask[..., np.newaxis], ncomp, -1)
+            assert mask.shape == refdata.shape
 
         # Drop data outside mask
-        refdata = refdata[mask[..., np.newaxis]]
-        tstdata = tstdata[mask[..., np.newaxis]]
+        refdata = refdata[mask]
+        tstdata = tstdata[mask]
 
         if np.any(refdata < 0.0):
             iflogger.warning('Negative values encountered in "in_ref" input, '

nipype/algorithms/tests/test_metrics.py

@@ -0,0 +1,58 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+import numpy as np
+import nibabel as nb
+from nipype.testing import example_data
+from ..metrics import FuzzyOverlap
+
+
+def test_fuzzy_overlap(tmpdir):
+    tmpdir.chdir()
+
+    # Tests with tissue probability maps
+    in_mask = example_data('tpms_msk.nii.gz')
+    tpms = [example_data('tpm_%02d.nii.gz' % i) for i in range(3)]
+    out = FuzzyOverlap(in_ref=tpms[0], in_tst=tpms[0]).run().outputs
+    assert out.dice == 1
+
+    out = FuzzyOverlap(
+        in_mask=in_mask, in_ref=tpms[0], in_tst=tpms[0]).run().outputs
+    assert out.dice == 1
+
+    out = FuzzyOverlap(
+        in_mask=in_mask, in_ref=tpms[0], in_tst=tpms[1]).run().outputs
+    assert 0 < out.dice < 1
+
+    out = FuzzyOverlap(in_ref=tpms, in_tst=tpms).run().outputs
+    assert out.dice == 1.0
+
+    out = FuzzyOverlap(
+        in_mask=in_mask, in_ref=tpms, in_tst=tpms).run().outputs
+    assert out.dice == 1.0
+
+    # Tests with synthetic 3x3x3 images
+    data = np.zeros((3, 3, 3), dtype=float)
+    data[0, 0, 0] = 0.5
+    data[2, 2, 2] = 0.25
+    data[1, 1, 1] = 0.3
+    nb.Nifti1Image(data, np.eye(4)).to_filename('test1.nii.gz')
+
+    data = np.zeros((3, 3, 3), dtype=float)
+    data[0, 0, 0] = 0.6
+    data[1, 1, 1] = 0.3
+    nb.Nifti1Image(data, np.eye(4)).to_filename('test2.nii.gz')
+
+    out = FuzzyOverlap(in_ref='test1.nii.gz', in_tst='test2.nii.gz').run().outputs
+    assert np.allclose(out.dice, 0.82051)
+
+    # Just considering the mask, the central pixel
+    # that raised the index now is left aside.
+    data = np.zeros((3, 3, 3), dtype=int)
+    data[0, 0, 0] = 1
+    data[2, 2, 2] = 1
+    nb.Nifti1Image(data, np.eye(4)).to_filename('mask.nii.gz')
+
+    out = FuzzyOverlap(in_ref='test1.nii.gz', in_tst='test2.nii.gz',
+                       in_mask='mask.nii.gz').run().outputs
+    assert np.allclose(out.dice, 0.74074)