def test_rotate_about_axis_consistency(input_atoms, axis, random_seed):
"""
Compare the outcome of :func:`rotate_about_axis()` with
:func:`rotate()`.
"""
np.random.seed(random_seed)
angle = np.random.rand() * 2 * np.pi
angles = np.zeros(3)
angles[axis] = angle
ref_rotated = struc.rotate(input_atoms, angles)
rot_axis = np.zeros(3)
# Length of axis should be irrelevant
rot_axis[axis] = np.random.rand()
test_rotated = struc.rotate_about_axis(
input_atoms,
rot_axis,
angle,
)
assert type(test_rotated) == type(ref_rotated)
assert struc.coord(test_rotated).shape == struc.coord(ref_rotated).shape
assert np.allclose(struc.coord(test_rotated),
struc.coord(ref_rotated),
atol=1e-5)
def test_superimposition_array(path):
pdbx_file = pdbx.PDBxFile()
pdbx_file.read(path)
fixed = pdbx.get_structure(pdbx_file, model=1)
mobile = fixed.copy()
mobile = struc.rotate(mobile, (1, 2, 3))
mobile = struc.translate(mobile, (1, 2, 3))
fitted, transformation = struc.superimpose(fixed, mobile,
(mobile.atom_name == "CA"))
assert struc.rmsd(fixed, fitted) == pytest.approx(0)
fitted = struc.superimpose_apply(mobile, transformation)
assert struc.rmsd(fixed, fitted) == pytest.approx(0)
def test_rotate_known(ndim):
"""
Rotate a vector at the Y-axis about the X-axis by 90 degrees and
expect a rotated vector at the Z-axis.
"""
shape = (1,) * (ndim-1) + (3,)
vector = np.zeros(shape)
vector[...] = [0, 1, 0]
exp_rotated = np.zeros(shape)
exp_rotated[...] = [0, 0, 1]
# Rotation by 90 degrees
test_rotated = struc.rotate(vector, [0.5 * np.pi, 0, 0])
assert test_rotated.shape == exp_rotated.shape
assert np.allclose(test_rotated, exp_rotated, atol=1e-5)
def test_superimposition_array(path):
"""
Take a structure and rotate and translate a copy of it, so that they
are not superimposed anymore.
Then superimpose these structure onto each other and expect an
almost perfect match.
"""
fixed = strucio.load_structure(path, model=1)
mobile = fixed.copy()
mobile = struc.rotate(mobile, (1, 2, 3))
mobile = struc.translate(mobile, (1, 2, 3))
fitted, transformation = struc.superimpose(fixed, mobile)
assert struc.rmsd(fixed, fitted) == pytest.approx(0, abs=6e-4)
fitted = struc.superimpose_apply(mobile, transformation)
assert struc.rmsd(fixed, fitted) == pytest.approx(0, abs=6e-4)
def test_rotate_measure(axis, random_seed):
"""
Rotate and measure resulting angle that should be equal to the input
angle.
"""
np.random.seed(random_seed)
# Maximum rotation is only 180 degrees,
# as with higher angles the measured angle would decrease again
ref_angle = np.random.rand() * np.pi
angles = np.zeros(3)
angles[axis] = ref_angle
# The measured angle is only equal to the input angle,
# if the input coordinates have no component on the rotation axis
input_coord = np.ones(3)
input_coord[axis] = 0
rotated = struc.rotate(input_coord, angles)
test_angle = struc.angle(rotated, 0, input_coord)
# Vector length should be unchanged
assert np.linalg.norm(rotated) \
== pytest.approx(np.linalg.norm(input_coord))
assert test_angle == pytest.approx(ref_angle)
print(structure[structure.res_id == 1])
print()
structure.coord += 100
print("After:")
print(structure[structure.res_id == 1])
########################################################################
# *Biotite* provides also some transformation functions, for example
# :func:`rotate()` for rotations about the *x*-, *y*- or *z*-axis.
structure = mmtf.get_structure(mmtf_file, model=1)
print("Before:")
print(structure[structure.res_id == 1])
print()
# Rotation about z-axis by 90 degrees
structure = struc.rotate(structure, [0, 0, np.deg2rad(90)])
print("After:")
print(structure[structure.res_id == 1])
########################################################################
# For a complete list of transformation functions have a look in the
# :doc:`API reference </apidoc/biotite.structure>`.
#
# Structure analysis
# ------------------
#
# This package would be almost useless, if there wasn't some means to
# analyze your structures.
# Therefore, *Biotite* offers a bunch of functions for this purpose,
# reaching from simple bond angle and length measurements to more
# complex characteristics, like accessible surface area and
