updated tests

fury/actors/tests/test_uncertainty.py

@@ -1,42 +1,73 @@
 """
 This spript includes the implementation of dti_uncertainty actor for the
-visualization of the cones of uncertainty which uses matrix perturbation
-analysis for its calculation
+visualization of the cones of uncertainty along with the diffusion tensors for
+comparison
 """
+from dipy.reconst import dti
+from dipy.segment.mask import median_otsu
+
 from fury import actor, window
 
 from dipy.io.image import load_nifti
 from dipy.io.gradients import read_bvals_bvecs
 
-from dipy.data import get_fnames
+from dipy.data import get_fnames, read_stanford_hardi
+
+from fury.primitive import prim_sphere
 
 
 def test_uncertainty():
     hardi_fname, hardi_bval_fname, hardi_bvec_fname =\
         get_fnames('stanford_hardi')
 
     data, affine = load_nifti(hardi_fname)
-    data_ = data[20:24, 68:72, 28:29]
-    print(data_)
-    print(data_.shape)
 
+    # load the b-values and b-vectors
     bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
-    print(bvals, bvecs)
 
     from dipy.segment.mask import median_otsu
 
     maskdata, mask = median_otsu(data, vol_idx=range(10, 50), median_radius=3,
                                  numpass=1, autocrop=True, dilate=2)
 
     uncertainty_cones = actor.dti_uncertainty(
-        data=maskdata[20:24, 68:72, 28:29], bvals=bvals, bvecs=bvecs)
+        data=maskdata[13:43, 44:74, 28:29], bvals=bvals, bvecs=bvecs)
 
     scene = window.Scene()
     scene.background([255, 255, 255])
 
+    scene.add(diffusion_tensors())
+    window.show(scene, reset_camera=False)
+    scene.clear()
+
     scene.add(uncertainty_cones)
+    window.show(scene, reset_camera=False)
 
-    scene.reset_camera()
-    scene.reset_clipping_range()
 
-    window.show(scene, reset_camera=False)
+class Sphere:
+
+    vertices = None
+    faces = None
+
+def diffusion_tensors():
+    # https://dipy.org/documentation/1.0.0./examples_built/reconst_dti/
+    img, gtab = read_stanford_hardi()
+    data = img.get_data()
+
+    maskdata, mask = median_otsu(data, vol_idx=range(10, 50), median_radius=3,
+                                 numpass=1, autocrop=True, dilate=2)
+    tenmodel = dti.TensorModel(gtab)
+    tenfit = tenmodel.fit(maskdata)
+
+    evals = tenfit.evals[13:43, 44:74, 28:29]
+    evecs = tenfit.evecs[13:43, 44:74, 28:29]
+
+    vertices, faces = prim_sphere('symmetric724', True)
+    sphere = Sphere()
+    sphere.vertices = vertices
+    sphere.faces = faces
+
+    from dipy.data import get_sphere
+    sphere = get_sphere('symmetric724')
+
+    return actor.tensor_slicer(evals, evecs, sphere=sphere, scale=0.3)

fury/tests/test_actors.py

@@ -1736,21 +1736,40 @@ def test_marker_actor(interactive=False):
 
 def test_uncertainty_actor(interactive=False):
     scene = window.Scene()
-    scene.background((0, 0, 0))
 
-    data = np.array([0, 0, 0])
-    bvals = np.array([0, 0, 0])
-    bvecs = np.array([0, 0, 0])
+    from dipy.data import get_fnames
+    from dipy.io import read_bvals_bvecs
+    _, fbvals, fbvecs = get_fnames('small_101D')
+    bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
 
-    uncert_cones = actor.dti_uncertainty(data=data, bvals=bvals, bvecs=bvecs,
-                                         scales=1.0, opacity=1.0)
+    n = bvals.shape[0]
+    data = np.ones((10, 10, 1, n)) * 0.1
+
+    uncert_cones = actor.dti_uncertainty(data=data, bvals=bvals, bvecs=bvecs)
+    scene.add(uncert_cones)
+
+    if interactive:
+        window.show(scene)
+
+    report = window.analyze_scene(scene)
+    npt.assert_equal(report.actors, 1)
+    arr = window.snapshot(scene, offscreen=True)
+    report = window.analyze_snapshot(arr)
+    npt.assert_equal(report.objects, 100)
+    scene.clear()
+
+    n = bvals.shape[0]
+    data = np.ones((5, 5, 5, n)) * 0.5
+    uncert_cones = actor.dti_uncertainty(data=data, bvals=bvals, bvecs=bvecs)
     scene.add(uncert_cones)
 
     if interactive:
         window.show(scene)
 
     report = window.analyze_scene(scene)
     npt.assert_equal(report.actors, 1)
+    scene.clear()
+
 
 def test_actors_primitives_count():
     centers = np.array([[1, 1, 1], [2, 2, 2]])