def test_plain(self):
for dt in self.dtypes:
for d in self.n_dims:
for n in self.n_points:
with self.subTest(n=n, d=d, dt=dt):
for _ in range(self.n_reps):
size = (n, d) if d else (n,)
data = self.rs.normal(0, 1, size)
data = data.astype(dt)
# Compute with and without details.
C_A, r2_A = mb.compute(data, details=False)
C_B, r2_B, info = mb.compute(data, details=True)
np.testing.assert_array_equal(C_A, C_B)
self.assertEqual(r2_A, r2_B)
if info is None:
continue
# Related to compute_max_chord().
self.assertNotIn("ids_max", info)
self.assertNotIn("d_max", info)
mb.compute_max_chord(data, info=info)
self.assertIn("ids_max", info)
self.assertIn("d_max", info)
# Upper bound: d_max<=2*radius
upper = 2*np.sqrt(r2_A)+self.tol
self.assertLessEqual(info["d_max"], upper)
def test_numpy_types_nan(self):
# Points containing np.nan are skipped.
# All values positive because of unsigned int tests.
data_in = [[7., np.nan], [2., 6.], [1., 1.], [4, 0]]
ref = ([3., 3.], 10.0)
for dt, dt_ret in self.valid_ftypes.items():
with self.subTest(dt=dt):
data = self.convert_data(data_in, dt)
ret = mb.compute(data, details=self.detailed)
self.check_ret(ret, ref, dtype=dt_ret)
ret = mb.compute(np.array(data, dtype=dt),
details=self.detailed)
self.check_ret(ret, ref, dtype=dt_ret)
def test_numpy_types(self):
# All values positive because of unsigned int tests.
data_in = [[7., 7.], [2., 6.], [1., 1.], [3., 0.]]
ref = ([4., 4.], 18.0)
dtypes = {**self.valid_ftypes, **self.valid_itypes}
for dt, dt_ret in dtypes.items():
with self.subTest(dt=dt):
data = self.convert_data(data_in, dt)
ret = mb.compute(data, details=self.detailed)
self.check_ret(ret, ref, dtype=dt_ret)
ret = mb.compute(np.array(data, dtype=dt),
details=self.detailed)
self.check_ret(ret, ref, dtype=dt_ret)
def test_slices(self):
n = 21
d = 6
t = np.linspace(0, 1, n)
a = np.array([-2]*d)[:, np.newaxis]
b = np.array([2]*d)[:, np.newaxis]
# d = np.ascontiguousarray(((b-a)*t+a).T)
d = ((b-a)*t+a).T
D1 = d
D2 = d[::4, ::2]
ref_all = (np.zeros(D1.shape[1]), 24)
ref_slice = (np.zeros(D2.shape[1]), 12)
ret_all = mb.compute(D1, details=self.detailed)
ret_slice = mb.compute(D2, details=self.detailed)
self.check_ret(ret_all, ref_all, dtype=float)
self.check_ret(ret_slice, ref_slice, dtype=float)
def test_conversion_from_iterable(self):
data = [(4., 4.), (-1., 3.), (-2., -2.), (1, -3)] + [(2., 1.)]*20
ref = ([1., 1.], 18.0)
for cls in self.iterables:
with self.subTest(cls=cls):
d = cls(data)
ret = mb.compute(d, details=self.detailed)
self.check_ret(ret, ref, dtype=float)
def test_get_bounding_ball(self):
"""
See miniball._compat.get_bounding_ball()
"""
for dt in self.dtypes:
with self.subTest(dt=dt):
data = self.rs.normal(0, 1, (100, 5))
data = data.astype(dt)
C_A, r2_A = mb.compute(data)
C_B, r2_B = mb.get_bounding_ball(data)
np.testing.assert_array_equal(C_A, C_B)
self.assertEqual(r2_A, r2_B)
def update(x):
# Update x coordinate of last point.
points[-1, 0] = x
# Re-compute miniball.
C, r2, info = mb.compute(points, details=True)
# Update artists.
circle.center = C
circle.radius = np.sqrt(r2)
center.set_data(C)
line.set_data(points[info["support"], :].T)
point.set_data([[xrange[0], points[-1, 0]],
[points[-1, 1], points[-1, 1]]])
return circle, center, line, point
def example_basic():
points = generate_data()
points[0] = [3, 0]
points[-1] = [-2, -3]
C, r2, info = mb.compute(points, details=True)
_, _ = mb.compute_max_chord(points=points, info=info)
print("Center: %s" % C)
print("Radius: %.3f" % np.sqrt(r2))
print("Info:")
pprint(info, indent=4)
# Visualize.
_, ax = plt.subplots()
visualize_data(ax, points)
visualize_circle(ax, info, points)
def test_corner_cases(self):
# None; results in an exception.
d = None
self.assertRaises(mb.MiniballValueError, mb.compute, d)
# Empty array; results in an exception.
d = []
self.assertRaises(mb.MiniballValueError, mb.compute,
d, details=self.detailed)
self.assertRaises(mb.MiniballValueError, mb.compute,
np.array(d, dtype=int), details=self.detailed)
self.assertRaises(mb.MiniballValueError, mb.compute,
np.empty([0, 4], dtype=float), details=self.detailed)
# Scalar; is treated as one 1D point [42].
d = 42
ret = ([42], 0, dict(center=42, radius=0, n_support=1, support=[0]))
self.check_ret_cc(mb.compute(d, details=self.detailed), ret)
self.check_ret_cc(mb.compute(np.array(d, dtype=int),
details=self.detailed), ret)
# 1D array; is treated as a list of 1D points.
d = [1, 2, 4, 5]
ret = ([3], 4, dict(center=3, radius=4, n_support=2, support=[0, 3]))
self.check_ret_cc(mb.compute(d, details=self.detailed), ret)
self.check_ret_cc(mb.compute(np.array(d, dtype=int),
details=self.detailed), ret)
# 3D array; results in an exception.
d = [[[1, 2, 3], [4, 5, 6]]]
self.assertRaises(mb.MiniballValueError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# String; results in an exception.
d = "abc"
self.assertRaises(mb.MiniballTypeError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# Complex; results in an exception.
d = [[1+2j, 3+4j], [5+6j, 7+8j]]
self.assertRaises(mb.MiniballValueError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# [None]; results in an exception.
d = [None]
self.assertRaises(mb.MiniballValueError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# Mixed container; results in an exception.
d = [set(), 1, "abc"]
self.assertRaises(mb.MiniballValueError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# Mixed container; results in an exception.
d = [2, 1, None]
self.assertRaises(mb.MiniballValueError, mb.compute, d)
self.assertRaises(mb.MiniballValueError, mb.compute, np.array(d))
# All nans.
d = [[np.nan, np.nan], [1, np.nan], [0, np.nan]]
ret = (None, np.nan, dict(center=None, support=None, is_valid=False))
self.check_ret_cc(mb.compute(d, details=self.detailed), ret)
self.check_ret_cc(mb.compute(np.array(d),
details=self.detailed), ret)
def test_miniballcpp_interface(self):
"""
See miniball._compat.Miniball()
"""
for dt in self.dtypes:
with self.subTest(dt=dt):
data = self.rs.normal(0, 1, (100, 5))
data = data.astype(dt)
C_A, r2_A, info = mb.compute(data, details=True)
M = mb.Miniball(data)
C_B = M.center()
r2_B = M.squared_radius()
np.testing.assert_array_equal(C_A, C_B)
self.assertEqual(r2_A, r2_B)
self.assertEqual(info["relative_error"], M.relative_error())
self.assertEqual(info["is_valid"], M.is_valid())
np.testing.assert_almost_equal(info["elapsed"], M.get_time(), 3)
def example_animated():
# Data.
points = generate_data(100)
points[0] = [3, 0]
points[-1] = [-2, -3]
xrange = np.linspace(-4, 4, 120)
# Set up animation.
fig, ax = plt.subplots()
visualize_data(ax, points[:-1], lim=7)
_, _, info = mb.compute(points, details=True)
center, line, circle = visualize_circle(ax, info, points)
# circle = circle # Circle artist
center = center[0] # Line2D artist
line = line[0] # Line2D artist
point = ax.plot(0, 0, 'gx-')[0] # line2D artist
ax.legend((center, line, point),
("bounding circle", "support", "moving point"))
def init():
return circle, center, line, point
def update(x):
# Update x coordinate of last point.
points[-1, 0] = x
# Re-compute miniball.
C, r2, info = mb.compute(points, details=True)
# Update artists.
circle.center = C
circle.radius = np.sqrt(r2)
center.set_data(C)
line.set_data(points[info["support"], :].T)
point.set_data([[xrange[0], points[-1, 0]],
[points[-1, 1], points[-1, 1]]])
return circle, center, line, point
# Info: Blitting seems not to work with OSX backend.
# (Check the backend that is set in .matplotlibrc)
from matplotlib.animation import FuncAnimation
anim = FuncAnimation(fig,
update,
frames=xrange,
interval=30,
init_func=init,
blit=True)
return anim
def test_small_ball(self):
data = [1.0, 1.0001]
_, _, det = mb.compute(data, details=True)
self.assertFalse(det["is_valid"])
_, _, det = mb.compute(data, details=True, tol=1e-10)
self.assertTrue(det["is_valid"])
