BUG: Fix tests

Fix GP model testing: `GaussianProcessModel` is now hosted in the
`model.dipy` module.

Fix bvals array dimensionality and make bvecs be unit vectors in angle
computation test.

Fix the pairwise angle computation doctests.

Test the case where the second argument to the pairwaise angle
computation function is `None`. Simplify the test parameterization as
the former corresponds to providing the same value for both gtab
arguments. Adapt the test so that we avoid making computations if the
second argument is `None`.

src/eddymotion/model/dipy.py

@@ -380,20 +380,20 @@ def compute_pairwise_angles(
 
     Examples
     --------
-    >>> compute_pairwise_angles(
-    ...     ((1.0, -1.0), (0.0, 0.0), (0.0, 0.0)),
-    ...     closest_polarity=False,
-    ... )[0, 1]  # doctest: +ELLIPSIS
+    >>> from dipy.core.gradients import gradient_table
+    >>> bvecs = np.asarray([(1.0, -1.0), (0.0, 0.0), (0.0, 0.0)])
+    >>> gtab = gradient_table([1000] * bvecs.shape[-1], bvecs)
+    >>> compute_pairwise_angles(gtab, closest_polarity=False)[0, 1]  # doctest: +ELLIPSIS
     3.1415...
-    >>> compute_pairwise_angles(
-    ...     ((1.0, -1.0), (0.0, 0.0), (0.0, 0.0)),
-    ...     ((1.0, -1.0), (0.0, 0.0), (0.0, 0.0)),
-    ...     closest_polarity=False,
-    ... )[0, 1]  # doctest: +ELLIPSIS
+    >>> bvecs1 = np.asarray([(1.0, -1.0), (0.0, 0.0), (0.0, 0.0)])
+    >>> bvecs2 = np.asarray([(1.0, -1.0), (0.0, 0.0), (0.0, 0.0)])
+    >>> gtab1 = gradient_table([1000] * bvecs1.shape[-1], bvecs1)
+    >>> gtab2 = gradient_table([1000] * bvecs2.shape[-1], bvecs2)
+    >>> compute_pairwise_angles(gtab1, gtab2, closest_polarity=False)[0, 1]  # doctest: +ELLIPSIS
     3.1415...
-    >>> compute_pairwise_angles(
-    ...     ((1.0, -1.0), (0.0, 0.0), (0.0, 0.0)),
-    ... )[0, 1]
+    >>> bvecs = np.asarray([(1.0, -1.0), (0.0, 0.0), (0.0, 0.0)])
+    >>> gtab = gradient_table([1000] * bvecs.shape[-1], bvecs)
+    >>> compute_pairwise_angles(gtab)[0, 1]
     0.0
 
     References

test/test_dipy.py

@@ -46,23 +46,47 @@
                     [0, 0, 1, 0, 1, 1, 1],
                 ]
             ),
+            None,
+            True,
+            np.array(
+                [
+                    [0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, np.pi / 4],
+                    [np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 2],
+                    [np.pi / 2, np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 4],
+                    [np.pi / 4, np.pi / 4, np.pi / 2, 0.0, np.pi / 3, np.pi / 3, np.pi / 3],
+                    [np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, 0.0, np.pi / 3, np.pi / 2],
+                    [np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 3, np.pi / 3, 0.0, np.pi / 3],
+                    [np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, np.pi / 2, np.pi / 3, 0.0],
+                ]
+            ),
+        ),
+        (
             np.array(
                 [
                     [1, 0, 0, 1, 1, 0, -1],
                     [0, 1, 0, 1, 0, 1, 0],
                     [0, 0, 1, 0, 1, 1, 1],
                 ]
             ),
-            True,
+            None,
+            False,
             np.array(
                 [
-                    [0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, np.pi / 4],
+                    [0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
                     [np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 2],
                     [np.pi / 2, np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 4],
-                    [np.pi / 4, np.pi / 4, np.pi / 2, 0.0, np.pi / 3, np.pi / 3, np.pi / 3],
+                    [np.pi / 4, np.pi / 4, np.pi / 2, 0.0, np.pi / 3, np.pi / 3, 2 * np.pi / 3],
                     [np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, 0.0, np.pi / 3, np.pi / 2],
                     [np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 3, np.pi / 3, 0.0, np.pi / 3],
-                    [np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, np.pi / 2, np.pi / 3, 0.0],
+                    [
+                        3 * np.pi / 4,
+                        np.pi / 2,
+                        np.pi / 4,
+                        2 * np.pi / 3,
+                        np.pi / 2,
+                        np.pi / 3,
+                        0.0,
+                    ],
                 ]
             ),
         ),
@@ -125,11 +149,20 @@
     ],
 )
 def test_compute_pairwise_angles(bvecs1, bvecs2, closest_polarity, expected):
-    gtab1 = gradient_table([1000] * len(bvecs1), bvecs1)
-    gtab2 = gradient_table([1000] * len(bvecs2), bvecs2)
+    # DIPY requires the vectors to be normalized
+    _bvecs1 = bvecs1 / np.linalg.norm(bvecs1, axis=0)
+    gtab1 = gradient_table([1000] * _bvecs1.shape[-1], _bvecs1)
+
+    _bvecs2 = None
+    gtab2 = None
+    if bvecs2 is not None:
+        _bvecs2 = bvecs2 / np.linalg.norm(bvecs2, axis=0)
+        gtab2 = gradient_table([1000] * _bvecs2.shape[-1], _bvecs2)
+
     obtained = compute_pairwise_angles(gtab1, gtab2, closest_polarity)
 
-    assert (bvecs1.shape[-1], bvecs2.shape[-1]) == obtained.shape
+    if _bvecs2 is not None:
+        assert (_bvecs1.shape[-1], _bvecs2.shape[-1]) == obtained.shape
     assert obtained.shape == expected.shape
     np.testing.assert_array_almost_equal(obtained, expected, decimal=2)
 

test/test_model.py

@@ -110,7 +110,7 @@ def test_average_model():
 def test_gp_model():
     gp = GaussianProcessModel(kernel="default")
 
-    assert isinstance(gp, model.GaussianProcessModel)
+    assert isinstance(gp, model.dipy.GaussianProcessModel)
 
     X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
     gp.fit(X, y)