def test_sym_hemisphere():
assert_raises(ValueError, sym_hemisphere, VERTICES, 'k')
assert_raises(ValueError, sym_hemisphere, VERTICES, '%z')
for hem in ('x', 'y', 'z', '-x', '-y', '-z'):
vert_inds = sym_hemisphere(VERTICES, hem)
assert_equal(vert_inds.shape, (3, ))
verts = VERTICES[vert_inds]
# there are no symmetrical points remanining in vertices
for vert in verts:
assert_false(np.any(np.all(vert * -1 == verts)))
# Test the sphere mesh data
vertices, _ = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
assert_array_equal(vert_inds, np.arange(n_vertices / 2))
def test_neighbor_max():
# test ability to find maxima on sphere using neighbors
vert_inds = sym_hemisphere(VERTICES)
adj_inds = vertinds_to_neighbors(vert_inds, FACES)
# test slow and fast routine
for func in (argmax_from_adj, dcr.argmax_from_adj):
# all equal, no maxima
vert_vals = np.zeros((N_VERTICES,))
inds = func(vert_vals,
vert_inds,
adj_inds)
assert_equal(inds.size, 0)
# just ome max
for max_pos in range(3):
vert_vals = np.zeros((N_VERTICES,))
vert_vals[max_pos] = 1
inds = func(vert_vals,
vert_inds,
adj_inds)
assert_array_equal(inds, [max_pos])
# maxima outside hemisphere don't appear
for max_pos in range(3,6):
vert_vals = np.zeros((N_VERTICES,))
vert_vals[max_pos] = 1
inds = func(vert_vals,
vert_inds,
adj_inds)
assert_equal(inds.size, 0)
# use whole mesh, with two maxima
w_vert_inds = np.arange(6)
w_adj_inds = vertinds_to_neighbors(w_vert_inds, FACES)
vert_vals = np.array([1.0, 0, 0, 0, 0, 2])
inds = func(vert_vals, w_vert_inds, w_adj_inds)
assert_array_equal(inds, [0, 5])
def test_neighbor_max():
# test ability to find maxima on sphere using neighbors
vert_inds = sym_hemisphere(VERTICES)
adj_inds = vertinds_to_neighbors(vert_inds, FACES)
# test slow and fast routine
for func in (argmax_from_adj, dcr.argmax_from_adj):
# all equal, no maxima
vert_vals = np.zeros((N_VERTICES, ))
inds = func(vert_vals, vert_inds, adj_inds)
assert_equal(inds.size, 0)
# just ome max
for max_pos in range(3):
vert_vals = np.zeros((N_VERTICES, ))
vert_vals[max_pos] = 1
inds = func(vert_vals, vert_inds, adj_inds)
assert_array_equal(inds, [max_pos])
# maxima outside hemisphere don't appear
for max_pos in range(3, 6):
vert_vals = np.zeros((N_VERTICES, ))
vert_vals[max_pos] = 1
inds = func(vert_vals, vert_inds, adj_inds)
assert_equal(inds.size, 0)
# use whole mesh, with two maxima
w_vert_inds = np.arange(6)
w_adj_inds = vertinds_to_neighbors(w_vert_inds, FACES)
vert_vals = np.array([1.0, 0, 0, 0, 0, 2])
inds = func(vert_vals, w_vert_inds, w_adj_inds)
assert_array_equal(inds, [0, 5])
def test_sym_hemisphere():
assert_raises(ValueError, sym_hemisphere,
VERTICES, 'k')
assert_raises(ValueError, sym_hemisphere,
VERTICES, '%z')
for hem in ('x', 'y', 'z', '-x', '-y', '-z'):
vert_inds = sym_hemisphere(VERTICES, hem)
assert_equal(vert_inds.shape, (3,))
verts = VERTICES[vert_inds]
# there are no symmetrical points remanining in vertices
for vert in verts:
assert_false(np.any(np.all(
vert * -1 == verts)))
# Test the sphere mesh data
vertices, _ = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
assert_array_equal(vert_inds,
np.arange(n_vertices / 2))
def test_performance():
# test this implementation against Frank Yeh implementation
vertices, faces = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
adj = vertinds_to_neighbors(vert_inds, faces)
np.random.seed(42)
vert_vals = np.random.uniform(size=(n_vertices,))
maxinds = argmax_from_adj(vert_vals, vert_inds, adj)
maxes, pfmaxinds = dcr.peak_finding(vert_vals, faces)
assert_array_equal(maxinds, pfmaxinds[::-1])
def test_performance():
# test this implementation against Frank Yeh implementation
vertices, faces = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
adj = vertinds_to_neighbors(vert_inds, faces)
np.random.seed(42)
vert_vals = np.random.uniform(size=(n_vertices, ))
maxinds = argmax_from_adj(vert_vals, vert_inds, adj)
maxes, pfmaxinds = dcr.peak_finding(vert_vals, faces)
assert_array_equal(maxinds, pfmaxinds[::-1])