def test_spherical_projection():
"""Compared against tests in orix."""
n = 10
v_arr = np.random.random_sample(n * 3).reshape((n, 3))
v = Vector3d(v_arr)
# Vector3d
polar = SphericalProjection.project(v_arr)
assert np.allclose(polar[..., 0], v.theta.data)
assert np.allclose(polar[..., 1], v.phi.data)
assert np.allclose(polar[..., 2], v.r.data)
assert np.allclose(get_theta(v), v.theta.data)
assert np.allclose(get_phi(v), v.phi.data)
assert np.allclose(get_r(v), v.r.data)
# NumPy array
polar2 = SphericalProjection.project(v)
assert np.allclose(polar2[..., 0], v.theta.data)
assert np.allclose(polar2[..., 1], v.phi.data)
assert np.allclose(polar2[..., 2], v.r.data)
assert np.allclose(get_theta(v_arr), v.theta.data)
assert np.allclose(get_phi(v_arr), v.phi.data)
assert np.allclose(get_r(v_arr), v.r.data)
def project_cartesian(cartesian: np.ndarray,
radius: Optional[float] = 10) -> np.ndarray:
"""Return the Hesse normal form of plane(s) given by cartesian
coordinates.
Parameters
----------
cartesian
Cartesian coordinates x, y, z.
radius
Hesse radius. Default is 10.
Returns
-------
hesse
Hesse normal form coordinates distance and angle.
"""
theta = get_theta(cartesian)
hesse = np.zeros((theta.size, 2))
hesse_distance = np.tan(0.5 * np.pi - theta)
hesse[..., 0] = hesse_distance
hesse[..., 1] = np.arccos(hesse_distance / radius)
return hesse