def test_scale_from_matrix():
factor = random.random() * 10 - 5
origin = np.random.random(3) - 0.5
direct = np.random.random(3) - 0.5
S0 = t.scale_matrix(factor, origin)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
S0 = t.scale_matrix(factor, origin, direct)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
def test_scale_from_matrix():
factor = random.random() * 10 - 5
origin = np.random.random(3) - 0.5
direct = np.random.random(3) - 0.5
S0 = t.scale_matrix(factor, origin)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
S0 = t.scale_matrix(factor, origin, direct)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
def test_scale_from_matrix():
factor = 7
origin = np.array([0.2, 0.2, 0.2]) # arbitrary values
direct = np.array([0.4, 0.4, 0.4])
S0 = t.scale_matrix(factor, origin)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
S0 = t.scale_matrix(factor, origin, direct)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
def test_scale_from_matrix():
factor = 7
origin = np.array([0.2, 0.2, 0.2]) # arbitrary values
direct = np.array([0.4, 0.4, 0.4])
S0 = t.scale_matrix(factor, origin)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
S0 = t.scale_matrix(factor, origin, direct)
factor, origin, direction = t.scale_from_matrix(S0)
S1 = t.scale_matrix(factor, origin, direction)
assert_equal(t.is_same_transform(S0, S1), True)
def test_superimposition_matrix(self):
v0 = np.random.rand(3, 10)
M = self.f(v0, v0)
assert_allclose(M, np.identity(4), atol=_ATOL)
R = t.random_rotation_matrix(np.random.random(3))
v0 = ((1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 1))
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v0 = (np.random.rand(4, 100) - 0.5) * 20.0
v0[3] = 1.0
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
S = t.scale_matrix(random.random())
T = t.translation_matrix(np.random.random(3) - 0.5)
M = t.concatenate_matrices(T, R, S)
v1 = np.dot(M, v0)
v0[:3] += np.random.normal(0.0, 1e-9, 300).reshape(3, -1)
M = self.f(v0, v1, scaling=True)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v = np.empty((4, 100, 3), dtype=np.float64)
v[:, :, 0] = v0
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v[:, :, 0]), atol=_ATOL)
def test_superimposition_matrix(self):
v0 = np.random.rand(3, 10)
M = self.f(v0, v0)
assert_allclose(M, np.identity(4), atol=_ATOL)
R = t.random_rotation_matrix(np.random.random(3))
v0 = ((1,0,0), (0,1,0), (0,0,1), (1,1,1))
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v0 = (np.random.rand(4, 100) - 0.5) * 20.0
v0[3] = 1.0
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
S = t.scale_matrix(random.random())
T = t.translation_matrix(np.random.random(3)-0.5)
M = t.concatenate_matrices(T, R, S)
v1 = np.dot(M, v0)
v0[:3] += np.random.normal(0.0, 1e-9, 300).reshape(3, -1)
M = self.f(v0, v1, scaling=True)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v = np.empty((4, 100, 3), dtype=np.float64)
v[:, :, 0] = v0
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v[:, :, 0]), atol=_ATOL)
def test_superimposition_matrix(self):
v0 = np.sin(np.linspace(0, 0.99, 30)).reshape(3,
10) # arbitrary values
M = self.f(v0, v0)
assert_allclose(M, np.identity(4), atol=_ATOL)
R = t.random_rotation_matrix(np.array([0.3, 0.4, 0.5]))
v0 = ((1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 1))
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v0 = np.sin(np.linspace(-1, 1, 400)).reshape(4, 100)
v0[3] = 1.0
v1 = np.dot(R, v0)
M = self.f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
S = t.scale_matrix(0.45)
T = t.translation_matrix(np.array([0.2, 0.2, 0.2]) - 0.5)
M = t.concatenate_matrices(T, R, S)
v1 = np.dot(M, v0)
v0[:3] += np.sin(np.linspace(0.0, 1e-9, 300)).reshape(3, -1)
M = self.f(v0, v1, scaling=True)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v = np.empty((4, 100, 3), dtype=np.float64)
v[:, :, 0] = v0
M = self.f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v[:, :, 0]), atol=_ATOL)
def test_superimposition_matrix(f):
v0 = np.sin(np.linspace(0, 0.99, 30)).reshape(3, 10) # arbitrary values
M = f(v0, v0)
assert_allclose(M, np.identity(4), atol=_ATOL)
R = t.random_rotation_matrix(np.array([0.3, 0.4, 0.5]))
v0 = ((1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 1))
v1 = np.dot(R, v0)
M = f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v0 = np.sin(np.linspace(-1, 1, 400)).reshape(4, 100)
v0[3] = 1.0
v1 = np.dot(R, v0)
M = f(v0, v1)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
S = t.scale_matrix(0.45)
T = t.translation_matrix(np.array([0.2, 0.2, 0.2]) - 0.5)
M = t.concatenate_matrices(T, R, S)
v1 = np.dot(M, v0)
v0[:3] += np.sin(np.linspace(0.0, 1e-9, 300)).reshape(3, -1)
M = f(v0, v1, scaling=True)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
M = f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v0), atol=_ATOL)
v = np.empty((4, 100, 3), dtype=np.float64)
v[:, :, 0] = v0
M = f(v0, v1, scaling=True, usesvd=False)
assert_allclose(v1, np.dot(M, v[:, :, 0]), atol=_ATOL)
def test_decompose_matrix_2(self):
S = t.scale_matrix(0.123)
scale, shear, angles, trans, persp = t.decompose_matrix(S)
assert_equal(scale[0], 0.123)
def test_decompose_matrix_2(self):
S = t.scale_matrix(0.123)
scale, shear, angles, trans, persp = t.decompose_matrix(S)
assert_equal(scale[0], 0.123)
