def test_asarray(self):
x = np.linspace(0, 5)
y = x**2
cs = CoordString(np.c_[x, y])
arr = cs.asarray()
self.assertTrue(np.all(arr == np.c_[x, y]))
return
def test_creation_invalid(self):
x = np.linspace(0, 5)
y = x**2
z = x**3
with self.assertRaises(ValueError):
# this one has too few dimensions
cs = CoordString(x)
with self.assertRaises(ValueError):
# this one has too many dimensions
cs = CoordString(np.c_[x, y, z, z])
with self.assertRaises(ValueError):
# this one has the wrong shape
cs = CoordString(np.r_[x, y, z])
return
def test_creation2(self):
x = np.linspace(0, 5)
y = x**2
cs = CoordString(np.c_[x, y])
self.assertEqual(cs.rank, 2)
self.assertEqual(len(cs), len(x))
return
def test_setitem(self):
x = np.linspace(0, 5)
y = x**2
cs = CoordString(np.c_[x, y])
cs[22] = np.array((-1, -3), dtype=np.float64)
self.assertEqual(cs[22], (-1, -3))
return
def test_bbox3(self):
x = np.linspace(0, 5)
y = x**2
z = x**3
cs = CoordString(np.c_[x, y, z])
self.assertEqual(cs.bbox, (0, 0, 5, 25))
return
def test_quadtree_duplicates(self):
# a naive quadtree with enter an infinite loop when there are
# duplicates exceeding leaf node capacity. this test ensures that this
# doesn't happen, and that the duplicate points can be retrieved
vertices = [(3.0, 4.0) for _ in range(11)]
cs = CoordString(vertices)
quadtree = QuadTree(cs, leaf_capacity=10)
indices_within = quadtree.search_within(2, 3, 4, 5)
self.assertEqual(len(indices_within), 11)
return
def test_quadtree_search(self):
vertices = [(x, x**2) for x in np.linspace(-10, 10, 1000)]
cs = CoordString(vertices)
quadtree = QuadTree(cs)
indices_within = quadtree.search_within(-5, 0, 5, 25)
self.assertEqual(len(indices_within), 500)
self.assertEqual(min(indices_within), 250)
self.assertEqual(max(indices_within), 749)
return
def test_quadtree_construction(self):
vertices = [(x, x**2) for x in np.linspace(-10, 10, 1000)]
cs = CoordString(vertices)
quadtree = QuadTree(cs)
self.assertEqual(len(quadtree), 1000)
return
def test_slicing_stepped(self):
x = np.linspace(0, 5)
y = x**2
cs = CoordString(np.c_[x, y])
self.assertTrue(np.all(cs.slice(10, 35, 3) == np.c_[x, y][10:35:3]))
return
def test_slicing(self):
x = np.linspace(0, 5)
y = x**2
cs = CoordString(np.c_[x, y])
self.assertTrue(np.all(cs.slice(10, 25) == np.c_[x, y][10:25]))
return
def test_asarray_empty(self):
cs = CoordString([])
arr = cs.asarray()
self.assertTrue(np.all(arr == np.array([[]], dtype=np.float64)))
def test_nan_raises(self):
coords = [(1, 2), (3, 4), (5, np.nan), (7, 8), (9, 10)]
with self.assertRaises(ValueError):
CoordString(coords)
def test_hash(self):
A = CoordString([(i, i + 1) for i in range(0, 100, 3)])
B = CoordString([(i + 1, i) for i in range(100, 0, -3)])
C = CoordString([(i, i + 1) for i in range(0, 100, 3)])
self.assertEqual(hash(A), hash(C))
self.assertNotEqual(hash(A), hash(B))