def test_cpd_prior():
from coherent_point_drift.align import driftRigid
rng = np.random.RandomState(4)
ndim = 3
R = rotationMatrix(*next(randomRotations(ndim, rng)))
t = rng.normal(size=ndim)
s = rng.normal(size=1)[0]
X = rng.normal(size=(10, ndim))
Y = RigidXform(R, t, s) @ X
_, cpd = last(islice(driftRigid(X, Y, w=np.eye(len(X))), 200))
ls = align(X, Y)
np.testing.assert_almost_equal(cpd.R, ls.R)
np.testing.assert_almost_equal(cpd.t, ls.t)
np.testing.assert_almost_equal(cpd.s, ls.s)
def test_cpd_prior():
from coherent_point_drift.align import driftRigid
rng = np.random.RandomState(4)
ndim = 3
R = rotationMatrix(*next(randomRotations(ndim, rng)))
t = rng.normal(size=ndim)
s = rng.normal(size=1)[0]
X = rng.normal(size=(10, ndim))
Y = RigidXform(R, t, s) @ X
_, cpd = last(islice(driftRigid(X, Y, w=np.eye(len(X))), 200))
ls = align(X, Y)
np.testing.assert_almost_equal(cpd.R, ls.R)
np.testing.assert_almost_equal(cpd.t, ls.t)
np.testing.assert_almost_equal(cpd.s, ls.s)
def test_least_squares():
rng = np.random.RandomState(4)
ndim = 3
R = rotationMatrix(*next(randomRotations(ndim, rng)))
t = rng.normal(size=ndim)
s = rng.lognormal(size=1)[0]
xform = RigidXform(R, t, s)
X = rng.normal(size=(10, ndim))
Y = xform @ X
alignment = align(X, Y)
expected = xform.inverse
np.testing.assert_almost_equal(alignment.R, expected.R)
np.testing.assert_almost_equal(alignment.t, expected.t)
np.testing.assert_almost_equal(alignment.s, expected.s)
np.testing.assert_almost_equal(alignment @ Y, X)
def test_least_squares():
rng = np.random.RandomState(4)
ndim = 3
R = rotationMatrix(*next(randomRotations(ndim, rng)))
t = rng.normal(size=ndim)
s = rng.lognormal(size=1)[0]
xform = RigidXform(R, t, s)
X = rng.normal(size=(10, ndim))
Y = xform @ X
alignment = align(X, Y)
expected = xform.inverse
np.testing.assert_almost_equal(alignment.R, expected.R)
np.testing.assert_almost_equal(alignment.t, expected.t)
np.testing.assert_almost_equal(alignment.s, expected.s)
np.testing.assert_almost_equal(alignment @ Y, X)
from itertools import islice, product as cartesian, starmap
from coherent_point_drift.geometry import rigidXform, rotationMatrix, RMSD, randomRotations
from coherent_point_drift.align import globalAlignment
import matplotlib.pyplot as plt
from collections import defaultdict
import numpy as np
fig, axs = plt.subplots(2, 1)
for ndim, ax in zip((2, 3), axs):
ax.set_title("{}D".format(ndim))
nstepss = range(1, 8)
pointss = np.random.random((10, 12, ndim))
rotations = starmap(rotationMatrix, islice(randomRotations(ndim), 10))
rmsds = defaultdict(list)
for nsteps, points, rotation in cartesian(nstepss, pointss, rotations):
degraded = rigidXform(points, R=rotation)
xform = globalAlignment(points, degraded, w=0.1, nsteps=nsteps)
rmsds[nsteps].append(RMSD(points, rigidXform(degraded, *xform)))
labels = sorted(rmsds.keys())
ax.violinplot([rmsds[i] for i in labels], labels)
fig.tight_layout()
plt.show()