def test_from_npoints_plane(self):
best_fits = ['least_square_distance', 'maximum_distance']
delta = 0.0001
for best_fit in best_fits:
plane = Plane.from_npoints([self.p1, self.p2, self.p3],
best_fit=best_fit)
self.assertTrue(self.plane.is_same_plane_as(plane))
points = [
np.array([5.1, 0.3, -2.3]),
np.array([-2.0, 4.3, -6.3]),
np.array([3.1, 2.3, -21.3]),
np.array([-2, -0.5, 0.05]),
np.array([11, 12, -13]),
np.array([10, 8.3, -6.32])
]
plane = Plane.from_npoints(points, best_fit=best_fit)
fit_error_plane = plane.fit_error(points, fit=best_fit)
coeffs = [[1.0 + delta, 1, 1, 1], [1, 1.0 + delta, 1, 1],
[1, 1, 1.0 + delta, 1], [1, 1, 1, 1.0 + delta],
[1.0 - delta, 1.0 + delta, 1.0 - delta, 1.0 + delta]]
for coeff in coeffs:
plane_changed = Plane.from_coefficients(
coeff[0] * plane.a, coeff[1] * plane.b, coeff[2] * plane.c,
coeff[3] * plane.d)
fit_error = plane_changed.fit_error(points, fit=best_fit)
self.assertGreater(fit_error, fit_error_plane)
coeff = [-2.1, -2.1, -2.1, -2.1]
plane_not_changed = Plane.from_coefficients(
coeff[0] * plane.a, coeff[1] * plane.b, coeff[2] * plane.c,
coeff[3] * plane.d)
fit_error = plane_not_changed.fit_error(points, fit=best_fit)
self.assertAlmostEqual(fit_error, fit_error_plane)
def test_from_npoints_plane(self):
best_fits = ['least_square_distance', 'maximum_distance']
delta = 0.0001
for best_fit in best_fits:
plane = Plane.from_npoints([self.p1, self.p2, self.p3], best_fit=best_fit)
self.assertTrue(self.plane.is_same_plane_as(plane))
points = [np.array([5.1, 0.3, -2.3]), np.array([-2.0, 4.3, -6.3]), np.array([3.1, 2.3, -21.3]),
np.array([-2, -0.5, 0.05]), np.array([11, 12, -13]), np.array([10, 8.3, -6.32])]
plane = Plane.from_npoints(points, best_fit=best_fit)
fit_error_plane = plane.fit_error(points, fit=best_fit)
coeffs = [[1.0+delta, 1, 1, 1],
[1, 1.0+delta, 1, 1],
[1, 1, 1.0+delta, 1],
[1, 1, 1, 1.0+delta],
[1.0-delta, 1.0+delta, 1.0-delta, 1.0+delta]]
for coeff in coeffs:
plane_changed = Plane.from_coefficients(coeff[0]*plane.a, coeff[1]*plane.b,
coeff[2]*plane.c, coeff[3]*plane.d)
fit_error = plane_changed.fit_error(points, fit=best_fit)
self.assertGreater(fit_error, fit_error_plane)
coeff = [-2.1, -2.1, -2.1, -2.1]
plane_not_changed = Plane.from_coefficients(coeff[0]*plane.a, coeff[1]*plane.b,
coeff[2]*plane.c, coeff[3]*plane.d)
fit_error = plane_not_changed.fit_error(points, fit=best_fit)
self.assertAlmostEqual(fit_error, fit_error_plane)
cg_points = [myfactor * np.array(pp) for pp in cg.points]
cg_central_site = myfactor * np.array(cg.central_site)
if sepplane:
pts = [cg_points[ii] for ii in algo.plane_points]
if algo.minimum_number_of_points == 2:
pts.append(cg_central_site)
centre = cg_central_site
else:
centre = np.sum(pts, axis=0) / len(pts)
factor = 1.5
target_dist = max(
[np.dot(pp - centre, pp - centre) for pp in cg_points])
current_dist = np.dot(pts[0] - centre, pts[0] - centre)
factor = factor * target_dist / current_dist
plane = Plane.from_npoints(points=pts)
p1 = centre + factor * (pts[0] - centre)
perp = factor * np.cross(pts[0] - centre, plane.normal_vector)
p2 = centre + perp
p3 = centre - factor * (pts[0] - centre)
p4 = centre - perp
vis.add_faces([[p1, p2, p3, p4]], [1.0, 0.0, 0.0], opacity=0.5)
target_radius = 0.25
radius = myfactor * (
target_radius - 0.2
) / 0.002 # due to how the radius is obtained in add_partial_sphere
# of pymatgen
if algo.minimum_number_of_points == 2:
continue
local_plane = Plane.from_3points(
points_combination[0], points_combination[1], lgf.local_geometry.central_site
)
found = True
break
elif npoints == 3:
if collinear(points_combination[0], points_combination[1], points_combination[2], tolerance=0.25):
continue
local_plane = Plane.from_3points(
points_combination[0], points_combination[1], points_combination[2]
)
found = True
break
elif npoints > 3:
local_plane = Plane.from_npoints(points_combination, best_fit="least_square_distance")
found = True
break
else:
raise ValueError("Wrong number of points to initialize separation plane")
points_perfect = lgf.perfect_geometry.points_wocs_ctwocc()
# Actual test of the permutations
cgsm = lgf._cg_csm_separation_plane(
coordination_geometry=cg,
sepplane=sepplanealgo,
local_plane=local_plane,
plane_separations=[],
dist_tolerances=[0.05, 0.1, 0.2, 0.3],
testing=True,
points_perfect=points_perfect,
algo._permutations = permutations
print('Safe permutations found ({:d})'.format(len(permutations)))
# Definition of the planes
all_planes_point_indices = [algo.plane_points]
if algo.other_plane_points is not None:
all_planes_point_indices.extend(algo.other_plane_points)
# Loop on the planes
explicit_permutations_per_plane = []
for iplane, plane_point_indices in enumerate(all_planes_point_indices):
prt1(string='In plane {:d} ({})'.format(iplane, '-'.join(str(pp) for pp in plane_point_indices)),
printing_volume=printing_volume)
points_combination = [lgf.local_geometry._coords[ii] for ii in plane_point_indices]
local_plane = Plane.from_npoints(points_combination, best_fit='least_square_distance')
# Actual test of the permutations
csms, perms, algos, sep_perms = lgf._cg_csm_separation_plane(coordination_geometry=cg,
sepplane=algo,
local_plane=local_plane,
plane_separations=[],
dist_tolerances=[0.05, 0.1,
0.2, 0.3],
testing=True,
points_perfect=points_perfect)
prt1(string='Continuous symmetry measures', printing_volume=printing_volume)
prt1(string=csms, printing_volume=printing_volume)
csms_with_recorded_permutation = []
explicit_permutations = []
vis = visualize(cg=cg, vis=vis, myfactor=myfactor)
cg_points = [myfactor*np.array(pp) for pp in cg.points]
cg_central_site = myfactor*np.array(cg.central_site)
if sepplane:
pts = [cg_points[ii] for ii in algo.plane_points]
if algo.minimum_number_of_points == 2:
pts.append(cg_central_site)
centre = cg_central_site
else:
centre = np.sum(pts, axis=0) / len(pts)
factor = 1.5
target_dist = max([np.dot(pp-centre, pp-centre) for pp in cg_points])
current_dist = np.dot(pts[0] - centre, pts[0] - centre)
factor = factor * target_dist / current_dist
plane = Plane.from_npoints(points=pts)
p1 = centre + factor * (pts[0] - centre)
perp = factor * np.cross(pts[0] - centre, plane.normal_vector)
p2 = centre + perp
p3 = centre - factor * (pts[0] - centre)
p4 = centre - perp
vis.add_faces([[p1, p2, p3, p4]], [1.0, 0.0, 0.0], opacity=0.5)
target_radius = 0.25
radius = 1.5 * target_radius
if algo.minimum_number_of_points == 2:
vis.add_partial_sphere(coords=cg_central_site, radius=radius,
color=[1.0, 0.0, 0.0], start=0, end=360,
opacity=0.5)
