def test_scaler():
N = 10
u = np.random.rand(N) + 1j * np.random.rand(N)
s = Scaler(u)
print('s:', s)
v = s.predict(u)
w = s.inverse(v)
assert close(np.min(np.real(v)), 0.0)
assert close(np.min(np.imag(v)), 0.0)
assert close(np.max(np.abs(v)), 1.0)
assert close_np(u, w)
def test_point():
x1, x2 = 0.0, 1.0
y1, y2 = 0.0, 0.5
x1_, y1_ = x1 + EPS / 2, y1 - EPS / 2
p1 = Point(x1 + y1 * 1j)
p1_ = Point(x1_ + y1_ * 1j)
p2 = Point(x2 + y1 * 1j)
p3 = Point(x2 + y2 * 1j)
assert close(p2.x, x2)
assert close(p2.y, y1)
assert p1 < p2 and p2 < p3 and p1 < p3
assert p1 == p1_
def test_sym_contour():
u = [0. + 5.j, 1. - 1.j, 2. + 5.j, 1. + 6.j]
sc = SymContour(u, n_max_pixels=1)
print('sc:', sc)
ch = sc.Hull_based
assert len(ch) == len(u) * 2
ap = sc.Approximate
assert len(ap) > 0
sa = sc.Axis_list
assert len(sa) == 1
a = sa[0]
print('a:', a)
assert close(a.Vec.angle, np.pi / 2, MIN_THETA_DIFF)
p1, p2 = a.vertexes(u)
assert close(p1.z, u[1]) and close(p2.z, u[3])
def test_contour():
u = [0. + 5.j, 1. + 1.j, 3. + 6.j, 2. + 7.j]
area = 8.5
per = 3 * 2**0.5 + 17**0.5 + 29**0.5
mult_coef = 2
c = Contour(u, mult_coef)
print('c:', c)
assert close(c.Area, area)
assert close(c.Perimeter, per)
p = c.Pixels
p_next = np.roll(p, 1)
assert np.all(np.abs(p - p_next) <= 2**0.5 + EPS)
ch = c.Convex_hull
assert close_np(c.origin, ch.origin)
m = c.Edge_middles
assert len(m) == len(u)
assert (u[0] + u[1]) / 2 in m
def test_axis_basic():
z1 = 0 + 0j
z2 = 1 + 0j
z3 = 0 + 1j
z_vec = Vector(Point(z2 - z1))
print('z_vec:', z_vec)
line = Axis(Point(z1), Point(z2))
print('line:', line)
line2 = Axis(Point(z1), Point(z3))
print('line2:', line2)
u = [z3]
vec = line.Vec
print('vec', vec)
assert close(line.z1, z1)
assert close(line.z2, z2)
assert vec.collinear(z_vec)
assert close(abs(vec.z), 1.0)
assert line.intersection(line) is None
assert line.intersection(line2) == Point(z1)
assert close_np(line.distance(u), [1.0])
assert line.nearest_ind(u) == 0
def __eq__(self, other):
return close(self.z, other.z, 2 * EPS)
def collinear(self, other):
if close(abs(self.z), 0.0) or close(abs(other.z), 0.0):
return True
theta1 = np.angle(self.z) % np.pi
theta2 = np.angle(other.z) % np.pi
return close(theta1, theta2, MIN_THETA_DIFF)