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 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 coords_to_zeobuilder(org_coords, opt_coords, atoms, parent, graph=None):
if graph == None:
atomgroups = [numpy.arange(len(atoms))]
else:
atomgroups = graph.independent_nodes
for group in atomgroups:
group_org = org_coords[group]
group_opt = opt_coords[group]
# Transform the guessed geometry as to overlap with the original geometry
transf = superpose(group_org, group_opt)
group_opt = numpy.dot(group_opt, transf.r.transpose()) + transf.t
# Put coordinates of guessed geometry back into Zeobuilder model
for i,atomindex in enumerate(group):
translation = Translation()
atom = graph.molecule.atoms[atomindex]
# Make sure atoms in subframes are treated properly
transf = atom.parent.get_frame_relative_to(parent)
org_pos = atom.transformation.t
opt_pos = transf.vector_apply_inverse(group_opt[i])
translation = Translation()
translation.t = opt_pos - org_pos
primitive.Transform(atom, translation)
def coords_to_zeobuilder(org_coords, opt_coords, atoms, parent):
# Transform the guessed geometry as to overlap with the original geometry
transf = superpose(org_coords, opt_coords)
opt_coords = numpy.dot(opt_coords, transf.r.transpose()) + transf.t
# Put coordinates of guess geometry back into Zeobuilder model
for i in xrange(len(atoms)):
translation = Translation()
atom = atoms[i]
# Make sure atoms in subframes are treated properly
transf = atom.parent.get_frame_relative_to(parent)
org_pos = atom.transformation.t
opt_pos = transf.vector_apply_inverse(opt_coords[i])
translation.t = opt_pos - org_pos
primitive.Transform(atom, translation)
def fit_geometry(ref_coordinates, mtr, mtw, do_geom=False, do_transform=False, weights=None):
"""Fits a trajectorie of cartesian coordinates to a reference geometry
This is the well-known rmsd fit typically performed on a trajectory after
a long solute/solvent simulation (on the solute). It is based on the
Kabsch algorithm:
http://en.wikipedia.org/wiki/Kabsch_algorithm
http://dx.doi.org/10.1107/S0567739476001873
This implementation has a few features that are sorely missing in
comparable software tools. One can optionally use the atomic masses as
weights, which is a better approximation when one is interested in
removing globale linear and angular momentum.
Arguments:
ref_coordinates -- the reference coordinates to fit to (Nx3 array)
mtr -- A MultiTracksReader that iterates of the frames of the
trajectory.
mtw -- A MultiTracksWriter that writes out the rmsd, and optionally
the rotated and translated geometry (do_geom=True), and the
rotation matrix and translation vector. (do_transform=True)
do_geom -- When True, the rotated and translated geometry is also
written out
do_transform -- When True, the rotation matrix and translation vector
are also written
weights -- The weights to be used in the Kabsch algorithm
"""
for frame in mtr:
transform = superpose(ref_coordinates, frame[0], weights)
new_coordinates = numpy.dot(frame[0], transform.r.transpose()) + transform.t
rmsd = ((new_coordinates - ref_coordinates)**2).mean()
row = [rmsd]
if do_geom:
row.append(new_coordinates)
if do_transform:
row.append(transform.t)
row.append(transform.r)
mtw.dump_row(tuple(row))
mtw.finish()
