Sphere -> SphereRandom, Cube -> CubeRandom, SphereIcosahedron -> Sphe…

…reIcosahedral

pygsp/graphs/__init__.py

@@ -165,7 +165,7 @@
 
     NNGraph
     Bunny
-    Cube
+    CubeRandom
     ImgPatches
     Grid2dImgPatches
     Sensor
@@ -178,9 +178,9 @@
 
     SphereEquiangular
     SphereGaussLegendre
-    SphereIcosahedron
+    SphereIcosahedral
     SphereHealpix
-    Sphere
+    SphereRandom
 
 """
 
@@ -211,14 +211,14 @@
 _NNGRAPHS = [
     'NNGraph',
     'Bunny',
-    'Cube',
+    'CubeRandom',
     'ImgPatches',
     'Grid2dImgPatches',
     'Sensor',
-    'Sphere',
+    'SphereRandom',
     'SphereGaussLegendre',
     'SphereHealpix',
-    'SphereIcosahedron',
+    'SphereIcosahedral',
     'TwoMoons'
 ]
 

pygsp/graphs/nngraphs/cube.py → pygsp/graphs/nngraphs/cuberandom.py

@@ -5,8 +5,8 @@
 from pygsp.graphs import NNGraph  # prevent circular import in Python < 3.5
 
 
-class Cube(NNGraph):
-    r"""Randomly sampled cube.
+class CubeRandom(NNGraph):
+    r"""Random uniform sampling of a cube.
 
     Parameters
     ----------
@@ -19,11 +19,12 @@ class Cube(NNGraph):
     See Also
     --------
     Sensor : randomly sampled square
+    SphereRandom : randomly sampled hypersphere
 
     Examples
     --------
     >>> import matplotlib.pyplot as plt
-    >>> G = graphs.Cube(seed=42)
+    >>> G = graphs.CubeRandom(seed=42)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(121)
     >>> ax2 = fig.add_subplot(122, projection='3d')
@@ -54,9 +55,9 @@ def __init__(self, N=300, seed=None, **kwargs):
             'distance': 9,
         }
 
-        super(Cube, self).__init__(coords, plotting=plotting, **kwargs)
+        super(CubeRandom, self).__init__(coords, plotting=plotting, **kwargs)
 
     def _get_extra_repr(self):
         attrs = {'seed': self.seed}
-        attrs.update(super(Cube, self)._get_extra_repr())
+        attrs.update(super(CubeRandom, self)._get_extra_repr())
         return attrs

pygsp/graphs/nngraphs/spheregausslegendre.py

@@ -32,7 +32,7 @@ class SphereGaussLegendre(NNGraph):
     See Also
     --------
     SphereEquiangular : based on quadrature theorems
-    SphereIcosahedron, SphereHealpix : based on subdivided polyhedra
+    SphereIcosahedral, SphereHealpix : based on subdivided polyhedra
     SphereRandom : random uniform sampling
 
     Notes

pygsp/graphs/nngraphs/spherehealpix.py

@@ -43,7 +43,7 @@ class SphereHealpix(NNGraph):
     See Also
     --------
     SphereEquiangular, SphereGaussLegendre : based on quadrature theorems
-    SphereIcosahedron : based on subdivided polyhedra
+    SphereIcosahedral : based on subdivided polyhedra
     SphereRandom : random uniform sampling
 
     Notes

pygsp/graphs/nngraphs/sphereicosahedron.py → pygsp/graphs/nngraphs/sphereicosahedral.py

@@ -18,7 +18,7 @@ def _import_trimesh():
     return trimesh
 
 
-class SphereIcosahedron(NNGraph):
+class SphereIcosahedral(NNGraph):
     r"""Sphere sampled as a subdivided icosahedron.
 
     Background information is found at :doc:`/background/spherical_samplings`.
@@ -61,7 +61,7 @@ class SphereIcosahedron(NNGraph):
     Examples
     --------
     >>> import matplotlib.pyplot as plt
-    >>> G = graphs.SphereIcosahedron()
+    >>> G = graphs.SphereIcosahedral()
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(131)
     >>> ax2 = fig.add_subplot(132, projection='3d')
@@ -75,10 +75,10 @@ class SphereIcosahedron(NNGraph):
 
     >>> import matplotlib.pyplot as plt
     >>> fig, axes = plt.subplots(1, 2)
-    >>> graph = graphs.SphereIcosahedron(0, dual=False, k=5)
+    >>> graph = graphs.SphereIcosahedral(0, dual=False, k=5)
     >>> graph.set_coordinates('sphere', dim=2)
     >>> _ = graph.plot(indices=True, ax=axes[0], title='Icosahedron')
-    >>> graph = graphs.SphereIcosahedron(0, dual=True, k=3)
+    >>> graph = graphs.SphereIcosahedral(0, dual=True, k=3)
     >>> graph.set_coordinates('sphere', dim=2)
     >>> _ = graph.plot(indices=True, ax=axes[1], title='Dodecahedron')
 
@@ -127,7 +127,7 @@ def normalize(vertices):
             vertices = mesh.vertices[mesh.faces].mean(axis=1)
             normalize(vertices)
 
-        super(SphereIcosahedron, self).__init__(vertices, **kwargs)
+        super(SphereIcosahedral, self).__init__(vertices, **kwargs)
 
         lat, lon = utils.xyz2latlon(*vertices.T)
         self.signals['lat'] = lat
@@ -138,5 +138,5 @@ def _get_extra_repr(self):
             'subdivisions': self.subdivisions,
             'dual': self.dual,
         }
-        attrs.update(super(SphereIcosahedron, self)._get_extra_repr())
+        attrs.update(super(SphereIcosahedral, self)._get_extra_repr())
         return attrs

pygsp/graphs/nngraphs/sphere.py → pygsp/graphs/nngraphs/sphererandom.py

@@ -6,7 +6,7 @@
 from pygsp import utils
 
 
-class Sphere(NNGraph):
+class SphereRandom(NNGraph):
     r"""Random uniform sampling of an hypersphere.
 
     Parameters
@@ -29,7 +29,7 @@ class Sphere(NNGraph):
     See Also
     --------
     SphereEquiangular, SphereGaussLegendre : based on quadrature theorems
-    SphereIcosahedron, SphereHealpix : based on subdivided polyhedra
+    SphereIcosahedral, SphereHealpix : based on subdivided polyhedra
     CubeRandom : randomly sampled cube
 
     References
@@ -42,7 +42,7 @@ class Sphere(NNGraph):
     Examples
     --------
     >>> import matplotlib.pyplot as plt
-    >>> G = graphs.Sphere(100, seed=42)
+    >>> G = graphs.SphereRandom(100, seed=42)
     >>> fig = plt.figure()
     >>> ax1 = fig.add_subplot(131)
     >>> ax2 = fig.add_subplot(132, projection='3d')
@@ -67,7 +67,7 @@ def __init__(self, N=300, dim=3, seed=None, **kwargs):
             'vertex_size': 80,
         }
 
-        super(Sphere, self).__init__(coords, plotting=plotting, **kwargs)
+        super(SphereRandom, self).__init__(coords, plotting=plotting, **kwargs)
 
         if dim == 3:
             lat, lon = utils.xyz2latlon(*coords.T)
@@ -79,5 +79,5 @@ def _get_extra_repr(self):
             'dim': self.dim,
             'seed': self.seed,
         }
-        attrs.update(super(Sphere, self)._get_extra_repr())
+        attrs.update(super(SphereRandom, self)._get_extra_repr())
         return attrs

pygsp/graphs/sphereequiangular.py

@@ -35,7 +35,7 @@ class SphereEquiangular(Graph):
     See Also
     --------
     SphereGaussLegendre : based on quadrature theorems
-    SphereIcosahedron, SphereHealpix : based on subdivided polyhedra
+    SphereIcosahedral, SphereHealpix : based on subdivided polyhedra
     SphereRandom : random uniform sampling
 
     Notes

pygsp/tests/test_graphs.py

@@ -481,10 +481,10 @@ def test_nngraph(self, n_vertices=30):
     def test_bunny(self):
         graphs.Bunny()
 
-    def test_cube(self):
-        self.assertEqual(graphs.Cube(60).n_vertices, 60)
-        self.assertEqual(graphs.Cube(65).n_vertices, 60)
-        graph = graphs.Cube(30)
+    def test_cube_random(self):
+        self.assertEqual(graphs.CubeRandom(60).n_vertices, 60)
+        self.assertEqual(graphs.CubeRandom(65).n_vertices, 60)
+        graph = graphs.CubeRandom(30)
         self.assertTrue(np.all(graph.coords >= 0))
         self.assertTrue(np.all(graph.coords <= 1))
 
@@ -503,11 +503,11 @@ def _test_sphere(self, graph):
         graph.set_coordinates('sphere', dim=3)
         np.testing.assert_allclose(graph.coords, coords, atol=1e-7)
 
-    def test_sphere(self):
-        graph = graphs.Sphere(20, dim=4)
+    def test_sphere_random(self):
+        graph = graphs.SphereRandom(20, dim=4)
         self.assertTupleEqual(graph.coords.shape, (20, 4))
         np.testing.assert_allclose(np.linalg.norm(graph.coords, axis=1), 1)
-        self._test_sphere(graphs.Sphere())
+        self._test_sphere(graphs.SphereRandom())
 
     def test_sphere_equiangular(self, size=7):
         for poles in [0, 1, 2]:
@@ -555,17 +555,17 @@ def test_sphere_gausslegendre(self, nrings=11):
         self.assertRaises(NotImplementedError, graphs.SphereGaussLegendre,
                           reduced='glesp-equal-area')
 
-    def test_sphere_icosahedron(self, subdivisions=3):
+    def test_sphere_icosahedral(self, subdivisions=3):
         n_faces = 20 * 4**subdivisions
         n_edges = 30 * 4**subdivisions
         n_vertices = n_edges - n_faces + 2
-        graph = graphs.SphereIcosahedron(subdivisions, kind='radius',
+        graph = graphs.SphereIcosahedral(subdivisions, kind='radius',
                                          radius=1.6/2**subdivisions)
         self.assertEqual(graph.n_vertices, n_vertices)
         self.assertEqual(graph.n_edges, n_edges)
         self._test_sphere(graph)
         self.assertEqual(np.sum(graph.signals['lat'] == 0), 4*2**subdivisions)
-        graph = graphs.SphereIcosahedron(subdivisions, dual=True, k=3)
+        graph = graphs.SphereIcosahedral(subdivisions, dual=True, k=3)
         self.assertEqual(graph.n_vertices, n_faces)
         self.assertEqual(graph.n_edges, n_edges)
         self._test_sphere(graph)