def test_shear_from_matrix():
# This seems to fail sometimes if the random numbers
# roll certain values....
# angle = (random.random() - 0.5) * 4*math.pi
# direct = np.random.random(3) - 0.5
# point = np.random.random(3) - 0.5
# normal = np.cross(direct, np.random.random(3))
# So here are some of my random numbers
angle = 2.8965075413405783
direct = np.array([-0.31117458, -0.41769518, -0.01188556])
point = np.array([-0.0035982, -0.40997482, 0.42241425])
normal = np.cross(direct, np.array([0.08122421, 0.4747914, 0.19851859]))
S0 = t.shear_matrix(angle, direct, point, normal)
angle, direct, point, normal = t.shear_from_matrix(S0)
S1 = t.shear_matrix(angle, direct, point, normal)
assert_equal(t.is_same_transform(S0, S1), True)
def test_shear_from_matrix():
# This seems to fail sometimes if the random numbers
# roll certain values....
# angle = (random.random() - 0.5) * 4*math.pi
# direct = np.random.random(3) - 0.5
# point = np.random.random(3) - 0.5
# normal = np.cross(direct, np.random.random(3))
# So here are some of my random numbers
angle = 2.8965075413405783
direct = np.array([-0.31117458, -0.41769518, -0.01188556])
point = np.array([-0.0035982, -0.40997482, 0.42241425])
normal = np.cross(direct, np.array([ 0.08122421, 0.4747914 , 0.19851859]))
S0 = t.shear_matrix(angle, direct, point, normal)
angle, direct, point, normal = t.shear_from_matrix(S0)
S1 = t.shear_matrix(angle, direct, point, normal)
assert_equal(t.is_same_transform(S0, S1), True)
def test_shear_from_matrix():
# This seems to fail sometimes if the random numbers
# roll certain values....
# angle = (random.random() - 0.5) * 4*np.pi
# direct = np.random.random(3) - 0.5
# point = np.random.random(3) - 0.5
# normal = np.cross(direct, np.random.random(3))
# In this random configuration the test will fail about 0.05% of all times.
# Then we hit some edge-cases of the algorithm. The edge cases for these
# values are slightly different for the linalg library used (MKL/LAPACK).
# So here are some of my random numbers
angle = 2.8969075413405783 # arbitrary values
direct = np.array([-0.31117458, -0.41769518, -0.01188556])
point = np.array([-0.0035982, -0.40997482, 0.42241425])
normal = np.cross(direct, np.array([0.08122421, 0.4747914, 0.19851859]))
S0 = t.shear_matrix(angle, direct, point, normal)
angle, direct, point, normal = t.shear_from_matrix(S0)
S1 = t.shear_matrix(angle, direct, point, normal)
assert_equal(t.is_same_transform(S0, S1), True)
def test_shear_from_matrix():
# This seems to fail sometimes if the random numbers
# roll certain values....
# angle = (random.random() - 0.5) * 4*np.pi
# direct = np.random.random(3) - 0.5
# point = np.random.random(3) - 0.5
# normal = np.cross(direct, np.random.random(3))
# In this random configuration the test will fail about 0.05% of all times.
# Then we hit some edge-cases of the algorithm. The edge cases for these
# values are slightly different for the linalg library used (MKL/LAPACK).
# So here are some of my random numbers
angle = 2.8969075413405783 # arbitrary values
direct = np.array([-0.31117458, -0.41769518, -0.01188556])
point = np.array([-0.0035982, -0.40997482, 0.42241425])
normal = np.cross(direct, np.array([0.08122421, 0.4747914, 0.19851859]))
S0 = t.shear_matrix(angle, direct, point, normal)
angle, direct, point, normal = t.shear_from_matrix(S0)
S1 = t.shear_matrix(angle, direct, point, normal)
assert_equal(t.is_same_transform(S0, S1), True)