def _set_voronoi_bond_orders(self):
'''
approximate bond order, from purely geometric voronoi solid angles'
returns
def bond_order(cd):
'''
p = self.positions
bond_orders = []
o_index = p.shape[1] / 2
n_images = p.shape[1]
for i in range(p.shape[2]):
#[atomj * image, axis]
vt = VoronoiTess(p[:, :, i].reshape((-1, 3)))
bo = np.zeros([p.shape[0] * p.shape[1]] * 2)
vts = np.array(vt.vertices)
for (k1, k2), v in vt.ridges.items():
if (k1 % n_images != o_index) and (k2 % n_images != o_index):
continue
if -10.101 in vts[v]:
continue
val = solid_angle(vt.points[k1], vts[v])
bo[k1, k2] = val
bo[k2, k1] = val
nshape = (p.shape[0], p.shape[0], p.shape[1], 1)
bond_orders.append(bo[o_index::n_images].reshape(nshape))
self._voronoi_bond_orders = np.concatenate(bond_orders, -1)
def setup_voronoi_list(self, indices, voronoi_cutoff):
"""
Set up of the voronoi list of neighbours by calling qhull
:param indices: indices of the sites for which the Voronoi is needed
:param voronoi_cutoff: Voronoi cutoff for the search of neighbours
:raise RuntimeError: If an infinite vertex is found in the voronoi construction
"""
self.voronoi_list = [None] * len(self.structure)
logging.info('Getting all neighbors in structure')
struct_neighbors = self.structure.get_all_neighbors(voronoi_cutoff,
include_index=True)
t1 = time.clock()
logging.info('Setting up Voronoi list :')
for jj, isite in enumerate(indices):
logging.info(
' - Voronoi analysis for site #{:d} ({:d}/{:d})'.format(
isite, jj + 1, len(indices)))
site = self.structure[isite]
neighbors1 = [(site, 0.0, isite)]
neighbors1.extend(struct_neighbors[isite])
distances = [i[1] for i in sorted(neighbors1, key=lambda s: s[1])]
neighbors = [i[0] for i in sorted(neighbors1, key=lambda s: s[1])]
qvoronoi_input = [s.coords for s in neighbors]
voro = VoronoiTess(qvoronoi_input)
all_vertices = voro.vertices
results = []
maxangle = 0.0
mindist = 10000.0
for nn, vind in list(voro.ridges.items()):
if 0 in nn:
if 0 in vind:
raise RuntimeError("This structure is pathological,"
" infinite vertex in the voronoi "
"construction")
facets = [all_vertices[i] for i in vind]
try:
sa = solid_angle(site.coords, facets)
except ValueError:
sa = my_solid_angle(site.coords, facets)
maxangle = max([sa, maxangle])
mindist = min([mindist, distances[nn[1]]])
for iii, sss in enumerate(self.structure):
if neighbors[nn[1]].is_periodic_image(sss):
myindex = iii
break
results.append((neighbors[nn[1]], {
'angle': sa,
'distance': distances[nn[1]],
'index': myindex
}))
for (nn, dd) in results:
dd['weighted_angle'] = dd['angle'] / maxangle
dd['weighted_distance'] = dd['distance'] / mindist
self.voronoi_list[isite] = results
t2 = time.clock()
logging.info('Voronoi list set up in {:.2f} seconds'.format(t2 - t1))
def setup_voronoi_list(self, indices, voronoi_cutoff):
"""
Set up of the voronoi list of neighbours by calling qhull
:param indices: indices of the sites for which the Voronoi is needed
:param voronoi_cutoff: Voronoi cutoff for the search of neighbours
:raise RuntimeError: If an infinite vertex is found in the voronoi construction
"""
self.voronoi_list2 = [None] * len(self.structure)
logging.info('Getting all neighbors in structure')
struct_neighbors = self.structure.get_all_neighbors(voronoi_cutoff, include_index=True)
t1 = time.clock()
logging.info('Setting up Voronoi list :')
for jj, isite in enumerate(indices):
logging.info(' - Voronoi analysis for site #{:d} ({:d}/{:d})'.format(isite, jj+1, len(indices)))
site = self.structure[isite]
neighbors1 = [(site, 0.0, isite)]
neighbors1.extend(struct_neighbors[isite])
distances = [i[1] for i in sorted(neighbors1, key=lambda s: s[1])]
neighbors = [i[0] for i in sorted(neighbors1, key=lambda s: s[1])]
qvoronoi_input = [s.coords for s in neighbors]
voro = Voronoi(points=qvoronoi_input, qhull_options="o Fv")
all_vertices = voro.vertices
results2 = []
maxangle = 0.0
mindist = 10000.0
for iridge, ridge_points in enumerate(voro.ridge_points):
if 0 in ridge_points:
ridge_vertices_indices = voro.ridge_vertices[iridge]
if -1 in ridge_vertices_indices:
raise RuntimeError("This structure is pathological,"
" infinite vertex in the voronoi "
"construction")
ridge_point2 = max(ridge_points)
facets = [all_vertices[i] for i in ridge_vertices_indices]
try:
sa = solid_angle(site.coords, facets)
except ValueError:
sa = my_solid_angle(site.coords, facets)
maxangle = max([sa, maxangle])
mindist = min([mindist, distances[ridge_point2]])
for iii, sss in enumerate(self.structure):
if neighbors[ridge_point2].is_periodic_image(sss):
myindex = iii
break
results2.append({'site': neighbors[ridge_point2],
'angle': sa,
'distance': distances[ridge_point2],
'index': myindex})
for dd in results2:
dd['normalized_angle'] = dd['angle'] / maxangle
dd['normalized_distance'] = dd['distance'] / mindist
self.voronoi_list2[isite] = results2
t2 = time.clock()
logging.info('Voronoi list set up in {:.2f} seconds'.format(t2-t1))
def test_solid_angle(self):
center = [2.294508207929496, 4.4078057081404, 2.299997773791287]
coords = [[1.627286218099362, 3.081185538926995, 3.278749383217061],
[1.776793751092763, 2.93741167455471, 3.058701096568852],
[3.318412187495734, 2.997331084033472, 2.022167590167672],
[3.874524708023352, 4.425301459451914, 2.771990305592935],
[2.055778446743566, 4.437449313863041, 4.061046832034642]]
self.assertAlmostEqual(solid_angle(center, coords), 1.83570965938, 7,
"Wrong result returned by solid_angle")
def test_solid_angle(self):
center = [2.294508207929496, 4.4078057081404, 2.299997773791287]
coords = [[1.627286218099362, 3.081185538926995, 3.278749383217061],
[1.776793751092763, 2.93741167455471, 3.058701096568852],
[3.318412187495734, 2.997331084033472, 2.022167590167672],
[3.874524708023352, 4.425301459451914, 2.771990305592935],
[2.055778446743566, 4.437449313863041, 4.061046832034642]]
self.assertAlmostEqual(solid_angle(center, coords), 1.83570965938, 7,
"Wrong result returned by solid_angle")
def _delaunay_midpoints(self, structure):
"""
returns the midpoints of the delaunay triangulation for a structure.
returns in the format of a list of lists, where the first index
corresponds to the index of the site in the original structure that
neighbors the point. I.e. dms[0] is the list of delaunay midpoints
around the site structure[0]
We can't simply use the ridges from the voronoi tesselation because
the ridges don't include nearest neighbors with a solid angle of 0
(whose voronoi volumes are edge sharing)
"""
logging.debug("Starting calculating Delaunay points")
all_coords = self._fake_periodic(structure)
vt = VoronoiTess(all_coords)
dt = DelaunayTri(all_coords)
v_array = np.array(vt.vertices)
p_array = np.array(vt.points)
dms = [[] for i in range(len(structure))]
for simplex in dt.vertices:
if min(simplex) >= len(structure):
continue
for pt1, pt2 in itertools.permutations(simplex, 2):
if pt1 >= len(structure):
continue
center = structure[pt1].coords
# get points that make up the shared plane
if pt2 > pt1:
vertices = vt.ridges.get((pt1, pt2), None)
else:
vertices = vt.ridges.get((pt2, pt1), None)
# get the solid angle, if pt1 and pt2 share a plane
if vertices is not None:
sa = solid_angle(center, v_array[vertices])
else:
sa = 0
new_coord = (p_array[pt1] + p_array[pt2]) / 2
if sa >= self._msa:
dms[pt1].append(new_coord)
logging.debug("Finished calculating Delaunay points")
return dms
def get_voronoi_dicts(structure,
cutoff_radius=VORONOI_CUTOFF_RADIUS,
cutoff_weight_fraction=VORONOI_CUTOFF_WEIGHT_FRACTION):
voronoi_finder = VoronoiCoordFinder(structure)
voronoi_finder.cutoff = cutoff_radius
voronoi_finder_2 = VoronoiNN()
voronoi_finder_2.cutoff = cutoff_radius
all_neighbor_sites = {}
all_neighbor_pairs = {}
for center_site_index in range(0, len(structure.sites)):
neighbor_sites_dict = {}
neighbor_pairs_list = []
# print "center_site_index: ", center_site_index
# Construct initial voronoi polyhedra using cutoff_radius
neighborhood_sites = [structure[center_site_index]
] # begin list with center site
polyhedra_sites = voronoi_finder.get_voronoi_polyhedra(
center_site_index)
polyhedra_sites_2 = voronoi_finder_2.get_voronoi_polyhedra(
structure, center_site_index)
cutoff_weight = cutoff_weight_fraction * sum(polyhedra_sites.values())
for neighbor_site, neighbor_weight in polyhedra_sites.items():
if neighbor_weight > cutoff_weight:
neighborhood_sites.append(neighbor_site)
# Re-tesselate only with sites that also meet cutoff_weight_fraction criteria
voronoi_input_coords = [site.coords for site in neighborhood_sites]
try:
voronoi_tess = VoronoiTess(voronoi_input_coords)
except IndexError:
raise RuntimeError('VoronoiTess: details unknown')
local_solid_angles = []
center_coords = structure[center_site_index].coords
n_neighborhood_sites = len(neighborhood_sites)
n_voronoi_vertices = len(voronoi_tess.vertices)
neighbor_lookup = np.zeros(shape=(n_voronoi_vertices,
n_voronoi_vertices, 2),
dtype=np.int8)
# Loop over neighbor sites (which surrond center_site) to:
# - calculate solid angles
# - construct neighbor_lookup array (which maps each voronoi edge to two adjacent neighbors)
for neighbor_index in range(1, n_neighborhood_sites):
facet_vertex_list = voronoi_tess.ridges[(
0, neighbor_index)] # 0 = center site
if 0 in facet_vertex_list:
raise RuntimeError(
"Pathological structure: voronoi facet includes vertex at infinity"
)
# Calculate solid angle of facet between center site and this neighbor site
facet_coords = [
voronoi_tess.vertices[i] for i in facet_vertex_list
]
local_solid_angles.append(solid_angle(center_coords, facet_coords))
# Add this voronoi ridge to neighbor_lookup array, to identify adjacent voronoi facets
n_vertices = len(facet_vertex_list)
for vertex1_index in range(0, n_vertices):
vertex2_index = vertex1_index + 1
if vertex2_index == n_vertices:
vertex2_index = 0 # wrap vertex2_index
# Properly order vertex indicies to only use upper triangle of neighbor_lookup array
(low_vert_index,
high_vert_index) = (facet_vertex_list[vertex1_index],
facet_vertex_list[vertex2_index])
if low_vert_index > high_vert_index:
(low_vert_index, high_vert_index) = (high_vert_index,
low_vert_index)
# Store adjacent voronoi neighbors on different levels of neighbor_lookup, at location defined
# by common voronoi edge running between low_vert_index (row) and high_vert_index (column).
if neighbor_lookup[low_vert_index, high_vert_index, 0] == 0:
neighbor_lookup[low_vert_index, high_vert_index,
0] = neighbor_index # first neighbor
else:
neighbor_lookup[low_vert_index, high_vert_index,
1] = neighbor_index # second neighbor
# Loop over neighbor sites again to construct neighbor_sites_dict with solid angle weights
max_local_solid_angle = max(local_solid_angles)
for neighbor_index in range(1, n_neighborhood_sites):
# Note: neighborhood_sites (which starts with center) is one longer than local_solid_angles
neighbor_sites_dict[neighborhood_sites[neighbor_index]] = \
local_solid_angles[neighbor_index - 1] / max_local_solid_angle
# Loop through upper triangle of neighbor_lookup and build a list of tuples of neighbor pair sites
for low_vert_index in range(1, n_voronoi_vertices):
for high_vert_index in range(low_vert_index + 1,
n_voronoi_vertices):
neighbor1_index = neighbor_lookup[low_vert_index,
high_vert_index, 0]
neighbor2_index = neighbor_lookup[low_vert_index,
high_vert_index, 1]
if neighbor1_index > 0 and neighbor2_index > 0: # Both are non-zero
neighbor_pairs_list.append(
(neighborhood_sites[neighbor1_index],
neighborhood_sites[neighbor2_index]))
elif neighbor1_index != 0 or neighbor2_index != 0: # Only one is non-zero
raise RuntimeError(
"Unexpected neighbor_lookup matrix: non-paired edge")
# Append per site values
all_neighbor_sites[
structure.sites[center_site_index]] = neighbor_sites_dict
all_neighbor_pairs[
structure.sites[center_site_index]] = neighbor_pairs_list
return (all_neighbor_sites, all_neighbor_pairs)