def test_random():
# Just test that these triangulate without exception.
# TODO: later on, we can turn this same test into an image comparison
# with Polygon.
N = 10
np.random.seed(0)
for i in range(4):
pts = np.random.normal(size=(N, 2))
edges = np.zeros((N, 2), dtype=int)
edges[:, 0] = np.arange(N)
edges[:, 1] = np.arange(1, N + 1) % N
t = T(pts, edges)
t.triangulate()
theta = np.linspace(0, 2 * np.pi, 11)[:-1]
pts = np.hstack(
[np.cos(theta)[:, np.newaxis],
np.sin(theta)[:, np.newaxis]])
pts[::2] *= 0.4
edges = np.empty((pts.shape[0], 2), dtype=np.uint)
edges[:, 0] = np.arange(pts.shape[0])
edges[:, 1] = edges[:, 0] + 1
edges[-1, 1] = 0
t = T(pts, edges)
t.triangulate()
def test_intersect_edge_arrays():
global t
pts = np.array([
[0., 0.],
[0., 10.],
[5., 0.],
[-5., 0.],
[-1., 11.],
[1., 9.],
])
edges = np.array([
[0, 1],
[2, 3],
[0, 3],
[4, 5],
[4, 1],
[0, 1],
])
lines = pts[edges]
t = T(pts, edges)
# intersect array of one edge with a array of many edges
intercepts = t._intersect_edge_arrays(lines[0:1], lines[1:])
expect = np.array([0.5, 0.0, 0.5, 1.0, np.nan])
assert_array_eq(intercepts, expect)
# intersect every line with every line
intercepts = t._intersect_edge_arrays(lines[:, np.newaxis, ...],
lines[np.newaxis, ...])
for i in range(lines.shape[0]):
int2 = t._intersect_edge_arrays(lines[i], lines)
assert_array_eq(intercepts[i], int2)
def test_utility_methods():
global t
pts = np.array([
[0, 0],
[1, 0],
[2, 0],
[3, 0],
[1.5, 2],
[1.5, -2],
])
edges = np.array([
[4, 5], # edge cuts through triangle (1, 2, 4)
])
t = T(pts, edges)
# skip initialization and just simulate being part-way through
# triangulation
for tri in [[0, 1, 4], [1, 2, 4], [2, 3, 4]]:
t._add_tri(*tri)
# find_cut_triangle
assert t._find_cut_triangle((4, 5)) == (4, 1, 2)
# orientation
assert t._orientation((4, 5), 0) == 1
assert t._orientation((4, 5), 1) == 1
assert t._orientation((4, 5), 2) == -1
assert t._orientation((4, 5), 3) == -1
assert t._orientation((4, 5), 4) == 0
assert t._orientation((4, 5), 5) == 0
# distance
dist = ((t.pts[0] - t.pts[1])**2).sum()**0.5
assert t._distance(t.pts[0], t.pts[1]) == dist
# adjacent_tri
assert t._adjacent_tri((1, 4), 0) == (4, 1, 2)
assert t._adjacent_tri((0, 4), 1) is None
assert t._adjacent_tri((1, 4), (1, 4, 0)) == (4, 1, 2)
assert t._adjacent_tri((0, 4), (1, 4, 0)) is None
try:
t._adjacent_tri((1, 4), 5)
except RuntimeError:
pass
else:
raise Exception("Expected RuntimeError.")
# edges_intersect
assert not t._edges_intersect((0, 1), (1, 2))
assert not t._edges_intersect((0, 2), (1, 2))
assert t._edges_intersect((4, 5), (1, 2))
# is_constraining_edge
assert t._is_constraining_edge((4, 5))
assert t._is_constraining_edge((5, 4))
assert not t._is_constraining_edge((3, 5))
assert not t._is_constraining_edge((3, 2))
def test_projection():
pts = np.array([[0, 0], [5, 0], [1, 2], [3, 4]])
t = T(pts, np.zeros((0, 2)))
a, b, c, d = pts
assert np.allclose(t._projection(a, c, b), [1, 0])
assert np.allclose(t._projection(b, c, a), [1, 0])
assert np.allclose(t._projection(a, d, b), [3, 0])
assert np.allclose(t._projection(b, d, a), [3, 0])
assert np.allclose(t._projection(a, b, c), [1, 2])
assert np.allclose(t._projection(c, b, a), [1, 2])
def test_orthogonal():
# make lines that are entirely vertical / horizontal
np.random.seed(1)
N = 100
pts = [[0, 0]]
for i in range(N - 1):
p = pts[-1][:]
p[i % 2] += np.random.normal()
pts.append(p)
pts = np.array(pts)
edges = np.zeros((N, 2), dtype=int)
edges[:, 0] = np.arange(N)
edges[:, 1] = np.arange(1, N + 1) % N
t = T(pts, edges)
t.triangulate()
def test_merge_duplicate_points():
global t
pts = np.array([
[0, 0],
[1, 1],
[0.1, 0.7],
[2, 3],
[0, 0],
[0.1, 0.7],
[5, 6],
])
edges = np.array([
[0, 6],
[1, 5],
[2, 4],
[3, 6],
[4, 5],
])
t = T(pts, edges)
t._merge_duplicate_points()
pts = np.array([
[0, 0],
[1, 1],
[0.1, 0.7],
[2, 3],
[5, 6],
])
edges = np.array([
[0, 4],
[1, 2],
[2, 0],
[3, 4],
[0, 2],
])
assert np.allclose(t.pts, pts)
assert np.all(t.edges == edges)
def test_edge_intersections():
global t
pts = np.array([
[0, 0],
[1, 0],
[1, 1],
[0, 1],
[0, 0.5], # three edges intersect here
[2, 0.5],
[-1, 0.2],
[2, 0.8],
[-1, 1],
[0, 0.5],
])
edges = np.array([
[0, 1],
[1, 2],
[2, 3],
[3, 0],
[4, 5],
[6, 7],
[8, 9],
])
t = T(pts, edges)
# first test find_edge_intersections
cuts = t._find_edge_intersections()
expect = {
0: [],
1: [(0.5, [1., 0.5]), (0.6, [1., 0.6])],
2: [],
3: [(0.5, [0., 0.5]), (0.6, [0., 0.4])],
4: [(0.25, [0.5, 0.5]), (0.5, [1., 0.5])],
5: [(1. / 3., [0., 0.4]), (0.5, [0.5, 0.5]), (2. / 3., [1., 0.6])],
}
assert len(expect) == len(cuts)
for k, v in expect.items():
assert len(v) == len(cuts[k])
for i, ecut in enumerate(v):
vcut = cuts[k][i]
assert len(vcut) == len(ecut)
for j in range(len(vcut)):
assert_array_almost_equal(np.array(ecut[j]), np.array(vcut[j]))
# next test that we can split the edges correctly
t._split_intersecting_edges()
pts = np.array([[0., 0.], [1., 0.], [1., 1.], [0., 1.], [0., 0.5],
[2., 0.5], [-1., 0.2], [2., 0.8], [-1., 1.], [0., 0.5],
[1., 0.5], [1., 0.6], [0., 0.5], [0., 0.4], [0.5, 0.5],
[1., 0.5], [0., 0.4], [0.5, 0.5], [1., 0.6]])
edges = np.array([[0, 1], [1, 10], [2, 3], [3, 12], [4, 14], [6, 16],
[8, 9], [10, 11], [11, 2], [12, 13], [13, 0], [14, 15],
[15, 5], [16, 17], [17, 18], [18, 7]])
if sys.version[0] == '3':
raise SkipTest('Triangulation differences on Py3k')
assert_array_almost_equal(pts, t.pts)
assert np.all(edges == t.edges)
# Test _nearly_ parallel lines.
pts = np.array([[0., 0.], [1.62434542, 0.], [1.62434542, -0.61175638],
[1.09617364, -0.61175638]])
edges = np.array([[0, 1], [1, 2], [2, 3], [3, 0]])
t = T(pts, edges)
for edge, cuts in t._find_edge_intersections().items():
assert len(cuts) == 0
def test_edge_event():
# mode 1
pts = np.array([
[0, 0],
[5, -10],
[10, 0],
[6, -5],
[5, 5],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 2
pts = np.array([
[0, 0],
[10, 0],
[20, 0],
[5, 11],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 1, 2
pts = np.array([
[0, 0],
[10, 0],
[20, 0],
[5, 11],
[9, 10],
[0, 20],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 2, 1
pts = np.array([
[0, 0],
[10, 0],
[20, 0],
[15, 8],
[15, 1],
[-5, 10],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 2, 1 with many triangles
pts = np.array([
[0, 10],
[2, 8],
[4, 6],
[6, 4],
[8, 2],
[10, 0],
[20, 5],
[20, 20],
[2, 13],
[4, 11],
[6, 9],
[8, 7],
[10, 5],
[10, 1],
[0, 15],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 1, 2, 1, 2, 1
pts = np.array([
[0, 10],
[2, 9],
[4, 8],
[6, 7],
[8, 6],
[10, 5],
[20, 5],
[20, 20],
[2, 11],
[19, 19],
[6, 9],
[19, 18],
[10, 7],
[11, 5.1],
[0, 11.1],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# mode 2, 1, 2, 1
pts = np.array([
[0, 10],
[2, 9],
[4, 8],
[6, 7],
[8, 6],
[10, 5],
[20, 5],
[20, 20],
[6, 9],
[19, 18],
[10, 7],
[11, 5.1],
[0, 11.1],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
# 1, 2 upper/lower polygon order check
pts = np.array([
[-5, 0],
[-3, 0],
[10, 0],
[15, 15],
[4, 9],
[6, 8.8],
[9, 10],
])
inds = np.arange(pts.shape[0])[:, np.newaxis]
edges = np.hstack([inds, np.roll(inds, -1)])
t = T(pts, edges)
t.triangulate()
t = T(pts * [-1, 1], edges)
t.triangulate()
