def main():
cutoff = {}
cutoff[(40,40)] = 3.6
cutoff[(13,29)] = 3.6
cutoff[(29,13)] = 3.6
cutoff[(40,13)] = 3.6
cutoff[(13,40)] = 3.6
cutoff[(29,40)] = 3.6
cutoff[(40,29)] = 3.6
cutoff[(13,13)] = 3.6
cutoff[(29,29)] = 3.6
cutoff[(41,41)] = 3.7
cutoff[(28,28)] = 3.7
cutoff[(41,28)] = 3.7
cutoff[(28,41)] = 3.7
cutoff[(46,46)] = 3.45
cutoff[(14,14)] = 3.45
cutoff[(46,14)] = 3.45
cutoff[(14,46)] = 3.45
paramfile = sys.argv[1]
vp_dict = load_param_file(paramfile)
modelfiles = sys.argv[2:]
count = defaultdict(int) # Stores how many VPs have been found of each index type
count = 0
direc = 'ZrCuAl/md_80k/'
for modelfile in modelfiles:
print(modelfile)
m = Model(modelfile)
m.generate_neighbors(cutoff)
#voronoi_3d(m,cutoff)
#set_atom_vp_types(m,vp_dict)
#vor_stats(m)
#cats = index_stats(m)
#for atom in m.atoms:
# #new = Model('0,0,12,0; number of atoms is {0};'.format(count), m.lx, m.ly, m.lz, atom.neighs + [atom])
# new = Model('{0}; number of atoms is {1};'.format(atom.vp.index, count[atom.vp.index]), m.lx, m.ly, m.lz, atom.neighs + [atom])
# fix_cluster_pbcs(new)
# val = normalize_bond_distances(new)
# new.comment = '{0}; number of atoms is {1}; bond length scaling factor is {2}'.format(atom.vp.index, count,val)
# center = find_center_atom(new)
# new.remove(center)
# new.add(center)
# vp_str = ''.join([str(x) for x in atom.vp.index])
# if not os.path.exists(direc+vp_str):
# os.makedirs(direc+vp_str)
# new.write(direc+'{0}/{0}.{1}.xyz'.format(vp_str, count[atom.vp.index]))
# count[atom.vp.index] += 1
#print(count)
cn = 0.0
for atom in m.atoms:
cn += atom.cn
cn = float(cn)/m.natoms
print(cn)
for atom in m.atoms:
new_cut = copy.copy(cutoff)
old_cn = atom.cn
inc = 0.0
while atom.cn < 12:
for key,val in new_cut.items(): new_cut[key] = val + 0.1
inc += 0.1
atom.neighs = m.get_atoms_in_cutoff(atom, new_cut)
if(atom in atom.neighs): atom.neighs.remove(atom)
atom.cn = len(atom.neighs)
new = Model('CN changed from {0} to {1};'.format(old_cn, atom.cn), m.lx, m.ly, m.lz, atom.neighs + [atom])
new.write('temp/{0}.xyz'.format(count))
if inc > 0.0: print("Increased shell by {0} Ang. for atom {1}".format(inc, count))
count += 1
cn = 0.0
for atom in m.atoms:
cn += atom.cn
cn = float(cn)/m.natoms
print(cn)
return 0
modelfile = sys.argv[2]
m = Model(modelfile)
xtal_atoms = sys.argv[3]
xtal_atoms = Model(xtal_atoms).atoms
#x,y,z = (round(x,6) for x in self.coord)
#a,b,c = (round(x,6) for x in other.coord)
#print("HERE")
#print([round(x,7) for x in xtal_atoms[13].coord])
#print([round(x,7) for x in m.atoms[153].coord])
#print(type(xtal_atoms[13]))
#print(type(m.atoms[153]))
#print(xtal_atoms[13] == m.atoms[153])
#return 0
glassy_atoms = []
for atom in m.atoms:
if atom not in xtal_atoms:
glassy_atoms.append(atom)
print(len(glassy_atoms))
print(len(xtal_atoms))
assert len(glassy_atoms) + len(xtal_atoms) == m.natoms
voronoi_3d(m, cutoff)
set_atom_vp_types(m, vp_dict)
m.generate_neighbors(3.45)
head, tail = os.path.split(modelfile)
head = head + '/'
if not os.path.exists(head+'glassy/'):
os.makedirs(head+'glassy/')
if not os.path.exists(head+'xtal/'):
os.makedirs(head+'xtal/')
for count, atom in enumerate(xtal_atoms):
i = m.atoms.index(atom)
atom = m.atoms[i]
new = Model('{0}'.format(atom.vp.index), m.lx, m.ly, m.lz, atom.neighs + [atom])
fix_cluster_pbcs(new)
val = normalize_bond_distances(new)
new.comment = '{0}; bond length scaling factor is {1}'.format(atom.vp.index, val)
center = find_center_atom(new)
new.remove(center)
new.add(center)
vp_str = ''.join([str(x) for x in atom.vp.index])
new.write(head+'xtal/{0}.xyz'.format(count))
for count, atom in enumerate(glassy_atoms):
i = m.atoms.index(atom)
atom = m.atoms[i]
new = Model('{0}'.format(atom.vp.index), m.lx, m.ly, m.lz, atom.neighs + [atom])
fix_cluster_pbcs(new)
val = normalize_bond_distances(new)
new.comment = '{0}; bond length scaling factor is {1}'.format(atom.vp.index, val)
center = find_center_atom(new)
new.remove(center)
new.add(center)
vp_str = ''.join([str(x) for x in atom.vp.index])
new.write(head+'glassy/{0}.xyz'.format(count))
return 0
for atom in volume_atoms.atoms:
for i,atom2 in enumerate(m.atoms):
if(atom.z == atom2.z and [round(x, 5) for x in atom.coord] == [round(x, 5) for x in atom2.coord]): good[i] = True
count = defaultdict(int) # Stores how many VPs have been found of each index type
for modelfile in modelfiles:
print(modelfile)
m = Model(modelfile)
voronoi_3d(m,cutoff)
set_atom_vp_types(m,vp_dict)
#vor_stats(m)
#cats = index_stats(m)
for i,atom in enumerate(m.atoms):
if not good[i]: continue
#new = Model('0,0,12,0; number of atoms is {0};'.format(count), m.lx, m.ly, m.lz, atom.neighs + [atom])
new = Model('{0}; number of atoms is {1};'.format(atom.vp.index, count), m.lx, m.ly, m.lz, atom.neighs + [atom])
fix_cluster_pbcs(new)
val = normalize_bond_distances(new)
new.comment = '{0}; number of atoms is {1}; bond length scaling factor is {2}'.format(atom.vp.index, count,val)
center = find_center_atom(new)
new.remove(center)
new.add(center)
vp_str = ''.join([str(x) for x in atom.vp.index])
if not os.path.exists(vp_str):
os.makedirs(vp_str)
new.write('{0}/{0}.{1}.xyz'.format(vp_str, count[atom.vp.index]))
count[atom.vp.index] += 1
print(count)
for c,v in count.items():
print("{0}: {1}".format(c,v))
print(sum(count.values()))
cn = 0.0
for atom in m.atoms:
cn += atom.cn
cn = float(cn)/m.natoms
print(cn)
def main():
# Load the MD model
modelfile = sys.argv[1]
md = Model(modelfile)
# Make a model to hold all the atoms we pull out to make sure the original model was sampled uniformly.
# Only the center atoms are added to this model.
holding_model = Model(comment='holding box', xsize=md.xsize, ysize=md.ysize, zsize=md.zsize, atoms=[])
# Load the cutoff dictionary so that we can generate neighbors for every atom
from cutoff import cutoff
# Directory name to save the files to
dir = 'all_91200_clusters'
if not os.path.exists(dir):
os.makedirs(dir)
# Set the number of clusters to randomly select
num_clusters = 'all'
if num_clusters == 'all' or num_clusters == md.natoms:
random_selection = False
num_clusters = md.natoms
else:
num_clusters = min(num_clusters, md.natoms)
random_selection = True
if random_selection:
unpicked = range(md.natoms)
start = 0
for n in range(start, num_clusters+start):
if random_selection:
rand = random.choice(unpicked)
unpicked.remove(rand)
atom = md.atoms[rand]
else:
atom = md.atoms[n-start]
rand = n-start
# Generate neighbors for this atom
atom.neighs = md.get_atoms_in_cutoff(atom, cutoff)
if(atom in atom.neighs): atom.neighs.remove(atom)
atom.cn = len(atom.neighs)
holding_model.add(atom)
# Create the cluster, normalize the bond distances, and write to disk
atoms = atom.neighs + [atom]
# c = create_cluster(md, atom, start, n, rand, center_included=True)
c = Cluster(
comment='cluster #{0} from atom {1}'.format(n-start, rand),
xsize=md.xsize,
ysize=md.ysize,
zsize=md.zsize,
atoms=atoms,
center_included = True
)
ratio = c.normalize_bond_distances()
c.comment='cluster #{0} from atom {1}; normalized bond distances by {2}'.format(n-start, rand, ratio)
c.write(os.path.join(dir, '{0}.xyz'.format(n)))
print(n-start, atom.id)
#print(c)
holding_model.write(os.path.join(dir, 'holding_model.xyz'))