def test_superpose(self):
# create a few test sets with random data points, including degenerate
# situations. (e.g. one point, two points, linear points)
references = [ # a list of 2-tuples: (points, degenerate)
(numpy.random.normal(0, 5, (n, 3)), False) for n in xrange(4, 40)
] + [
#(numpy.array([[0,0,1]], float), True),
#(numpy.array([[0,0,0],[0,0,1]], float), True),
#(numpy.array([[0,0,0],[0,0,1],[0,0,2]], float), True),
#(numpy.array([[0,0,0],[0,0,1],[0,0,2],[0,0,4]], float), True),
#(numpy.random.normal(0, 5, (3, 3)), True)
]
# Do a random transformation on the points
randomized = []
for points, degenerate in references:
#points[:] -= points.mean(axis=0)
axis = random_unit(3)
angle = numpy.random.uniform(0, numpy.pi * 2)
transformation = Complete()
transformation.set_rotation_properties(angle, axis, False)
transformation.t[:] = numpy.random.normal(0, 5, 3)
randomized.append(
(numpy.array([transformation.vector_apply(p)
for p in points]), transformation))
for (ref_points, degenerate), (tr_points,
transf) in zip(references, randomized):
check_transf = superpose(ref_points, tr_points)
# check whether the rotation matrix is orthogonal
self.assertArraysAlmostEqual(
numpy.dot(check_transf.r, check_transf.r.transpose()),
numpy.identity(3, float), 1e-5)
# first check whether check_transf brings the tr_points back to the ref_points
check_points = numpy.dot(
tr_points, check_transf.r.transpose()) + check_transf.t
self.assertArraysAlmostEqual(ref_points,
check_points,
1e-5,
doabs=True)
if not degenerate:
# if the set of points is not degenerate, check whether transf and check_transf
# are each other inverses
tmp = Complete()
tmp.apply_before(transf)
tmp.apply_before(check_transf)
self.assertArraysAlmostEqual(
numpy.dot(transf.r, check_transf.r),
numpy.identity(3, float), 1e-5)
self.assertArrayAlmostZero(tmp.t, 1e-5)
# Add some distortion to the transformed points
randomized = []
for tr_points, transf in randomized:
tr_points[:] += numpy.random.normal(0, 1.0, len(tr_points))
# Do a blind test
for (ref_points, degenerate), (tr_points,
transf) in zip(references, randomized):
superpose(ref_points, tr_points)
def setUp(self):
self.test_transformations = []
for i in xrange(20):
test_transformation = Complete()
test_transformation.set_rotation_properties(
random.random() * math.pi * 2, numpy.random.uniform(-3, 3, 3),
random.sample([True, False], 1)[0])
test_transformation.t = numpy.random.uniform(-3, 3, 3)
self.test_transformations.append(test_transformation)
def __init__(self, affected_atoms, transformation, molecule=None):
self.affected_atoms = affected_atoms
self.transformation = Complete()
if isinstance(transformation, Rotation):
self.transformation.r[:] = transformation.r
if isinstance(transformation, Translation):
self.transformation.t[:] = transformation.t
if molecule is not None:
for i in affected_atoms:
molecule.coordinates[i] = transformation.vector_apply(
molecule.coordinates[i])
def read_from_file(cls, filename):
"""Construct a MolecularDistortion object from a file"""
with open(filename) as f:
lines = list(line for line in f if line[0] != '#')
r = []
t = []
for line in lines[:3]:
values = list(float(word) for word in line.split())
r.append(values[:3])
t.append(values[3])
transformation = Complete(r, t)
affected_atoms = set(int(word) for word in lines[3].split())
return cls(affected_atoms, transformation)
def read_from_file(cls, filename):
f = file(filename)
lines = list(line for line in f if line[0] != '#')
f.close()
r = []
t = []
for line in lines[:3]:
values = list(float(word) for word in line.split())
r.append(values[:3])
t.append(values[3])
transformation = Complete()
transformation.r[:] = r
transformation.t[:] = t
affected_atoms = set(int(word) for word in lines[3].split())
return cls(affected_atoms, transformation)
def do_test(self, coordinates, masses, expected_is):
reference = MolecularDescriptorTV1(coordinates, masses)
self.assert_(reference.inversion_symmetric == expected_is)
for counter in xrange(3):
transformation = Complete()
transformation.set_rotation_properties(
random.uniform(0, 2 * math.pi),
numpy.random.uniform(-1, 1, (3, )),
False,
)
transformation.t = numpy.random.uniform(-5, 5, (3, ))
new_coordinates = numpy.array([
transformation.vector_apply(coordinate)
for coordinate in coordinates
], float)
new_descriptor = MolecularDescriptorTV1(new_coordinates, masses)
self.assert_(reference.compare_structure(new_descriptor))
self.assert_(not reference.compare_global_rotation(new_descriptor))
self.assert_(
not reference.compare_global_translation(new_descriptor))
def compute_transformation(self, connection):
transformation = Complete(self.rotation2.r, connection.t)
del connection.t
return transformation
