def generate_map(self,cutoff,modelfile):
""" cutoff is for vor.f90 """
vor_instance = Vor()
vor_instance.runall(modelfile,cutoff)
index = vor_instance.get_index()
icofrac = []
index = [index[i].strip().split() for i in range(0,len(index))]
for i in range(0,len(index)):
for j in range(0,len(index[i])):
try:
index[i][j] = int(index[i][j])
except:
try:
index[i][j] = float(index[i][j])
except:
pass
index[i] = index[i][6:11]
icofrac.append(int( float(index[i][2])/float(sum(index[i]))*100 ))
model = Model(modelfile)
atoms = model.get_atoms()
atoms = [atom.set_znum(icofrac[i]) for i,atom in enumerate(atoms)]
#for atom in atoms:
# if atom.get_znum() == 0:
# atom.set_znum(1)
nbins = 6
del_bin = 100.0/nbins
for atom in atoms:
for i in range(1,nbins+1):
if( atom.z <= i*del_bin):
atom.z = i
break
atoms = [atom.convert_to_sym() for i,atom in enumerate(atoms)]
print(model.natoms)
print('{0} {1} {2}'.format(model.lx,model.ly,model.lz))
for atom in atoms:
print atom.vesta()
def main():
# sys.argv[1] should be the modelfile in the xyz format
# sys.argv[2] should be the cutoff desired
modelfile = sys.argv[1]
cutoff = float(sys.argv[2])
ag = AtomGraph(modelfile,cutoff)
model = Model(modelfile)
model.generate_neighbors(cutoff)
#submodelfile = sys.argv[3]
#mixedmodel = Model('Mixed atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Mixed'])
#icolikemodel = Model('Ico-like atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if(atom.vp.type == 'Icosahedra-like' or atom.vp.type == 'Full-icosahedra')])
#fullicomodel = Model('Full-icosahedra atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Full-icosahedra'])
#xtalmodel = Model('Xtal-like atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Crystal-like'])
#undefmodel = Model('Undef atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Undef'])
##mixedmodel.write_cif('mixed.cif')
##mixedmodel.write_our_xyz('mixed.xyz')
##icolikemodel.write_cif('icolike.cif')
##icolikemodel.write_our_xyz('icolike.xyz')
##fullicomodel.write_cif('fullico.cif')
##fullicomodel.write_our_xyz('fullico.xyz')
##xtalmodel.write_cif('xtal.cif')
##xtalmodel.write_our_xyz('xtal.xyz')
##undefmodel.write_cif('undef.cif')
##undefmodel.write_our_xyz('undef.xyz')
#icomixedmodel = Model('ico+mix atoms',model.lx,model.ly,model.lz, mixedmodel.atoms + icolikemodel.atoms)
##mixedmodel.write_cif('icomixed.cif')
##mixedmodel.write_our_xyz('icomixed.xyz')
#vpcoloredmodel = Model('vp colored atoms',model.lx,model.ly,model.lz, ag.model.atoms)
#for atom in vpcoloredmodel.atoms:
# if(atom.vp.type == 'Full-icosahedra'):
# atom.z = 1
# elif(atom.vp.type == 'Icosahedra-like'):
# atom.z = 2
# elif(atom.vp.type == 'Mixed'):
# atom.z = 3
# elif(atom.vp.type == 'Crystal-like'):
# atom.z = 4
# elif(atom.vp.type == 'Undef'):
# atom.z = 5
##vpcoloredmodel.write_cif('vpcolored.cif')
##vpcoloredmodel.write_our_xyz('vpcolored.xyz')
#subvpcoloredmodel = Model(submodelfile)
#for atom in subvpcoloredmodel.atoms:
# atom.z = vpcoloredmodel.atoms[ag.model.atoms.index(atom)].z
#subvpcoloredmodel.write_cif('subvpcolored.cif')
#subvpcoloredmodel.write_our_xyz('subvpcolored.xyz')
#return
golden = False
#cluster_prefix = 'ico.t3.'
#cluster_types = 'Icosahedra-like', 'Full-icosahedra' # Need this for final/further analysis
#cluster_prefix = 'fi.t3.'
#cluster_types = ['Full-icosahedra'] # Need this for final/further analysis
cluster_prefix = 'xtal.t3.'
cluster_types = 'Crystal-like' # Need this for final/further analysis
#cluster_prefix = 'mix.t3'
#cluster_types = ['Mixed'] # Need this for final/further analysis
#cluster_prefix = 'undef.t3'
#cluster_types = ['Undef'] # Need this for final/further analysis
# Decide what time of clustering you want to do
#clusters = ag.get_clusters_with_n_numneighs(cutoff,5,cluster_types) #Vertex
#clusters = ag.get_vertex_sharing_clusters(cutoff,cluster_types) #Vertex
#clusters = ag.get_edge_sharing_clusters(cutoff,cluster_types) #Edge
#clusters = ag.get_face_sharing_clusters(cutoff,cluster_types) #Face
#clusters = ag.get_interpenetrating_atoms(cutoff,cluster_types) #Interpenetrating
#clusters = ag.get_interpenetrating_clusters_with_neighs(cutoff,cluster_types) #Interpenetrating+neighs
#clusters = ag.get_connected_clusters_with_neighs(cutoff, cluster_types) #Connected (vertex) + neighs
v,e,f,i = ag.vefi_sharing(cluster_types)
print("V: {0} E: {1} F: {2} I: {3}".format(int(v),int(e),int(f),int(i)))
return
orig_clusters = clusters[:]
# Print orig clusters
j = 0
for i,cluster in enumerate(clusters):
print("Orig cluster {0} contains {1} atoms.".format(i,len(cluster)))
if(golden):
for atom in cluster:
if(atom.vp.type in cluster_types):
atom.z = 0
# Save cluster files
cluster_model = Model("Orig cluster {0} contains {1} atoms.".format(i,len(cluster)),model.lx, model.ly, model.lz, cluster)
cluster_model.write_cif('{1}cluster{0}.cif'.format(i,cluster_prefix))
cluster_model.write_our_xyz('{1}cluster{0}.xyz'.format(i,cluster_prefix))
allclusters = []
for cluster in clusters:
for atom in cluster:
if(atom not in allclusters):
allclusters.append(atom)
#if(atom.vp.type in cluster_types): print(' {0}\t{1}'.format(atom,atom.vp.type))
allclusters = Model("All clusters.",model.lx, model.ly, model.lz, allclusters)
allclusters.write_cif('{0}allclusters.cif'.format(cluster_prefix))
allclusters.write_our_xyz('{0}allclusters.xyz'.format(cluster_prefix))
print("{0}allclusters.cif and {0}allclusters.xyz contain {1} atoms.".format(cluster_prefix, allclusters.natoms))
if(not golden):
x_cluster = []
for i,atom in enumerate(model.atoms):
if atom not in allclusters.atoms:
x_cluster.append(atom)
x_cluster = Model("Opposite cluster of {0}".format(cluster_prefix),model.lx, model.ly, model.lz, x_cluster)
x_cluster.write_cif('{0}opposite.cif'.format(cluster_prefix))
x_cluster.write_our_xyz('{0}opposite.xyz'.format(cluster_prefix))
print('{0}opposite.cif and {0}opposite.xyz contain {1} atoms.'.format(cluster_prefix, x_cluster.natoms))
if(False): # Further analysis
cn = 0.0
for atom in model.atoms:
cn += atom.cn
cn /= model.natoms
vpcn = 0.0
count = 0
for atom in ag.model.atoms:
if( atom.vp.type in cluster_types ):
vpcn += atom.cn
count += 1
vpcn /= count
natomsinVPclusters = allclusters.natoms # Number of atoms in VP clusters
nVPatoms = count # Number of VP atoms
numsepVPatoms = nVPatoms * vpcn # Number of atoms in VP clusters if all clusters were separated
maxnumatoms = model.natoms # Max number of atoms in VP clusters if all clusters were separated but still within the model size
print('Average CN is {0}'.format(cn))
print('Average CN of VP atoms is {0}'.format(vpcn))
print('# atoms in all clusters: {0}. # VP atoms * vpcn: {1}. # VP atoms: {2}'.format(natomsinVPclusters,numsepVPatoms,nVPatoms))
print('~ Number of VP that can fit in the model: {0}'.format(maxnumatoms/vpcn))
print('Ratio of: (# atoms involved in VP clusters)/(# atoms involved in VP clusters if all clusters were completely separated): {0}% <--- Therefore {1}% sharing.'.format(round(float(natomsinVPclusters)/(numsepVPatoms)*100.0,3),100.0-round(float(natomsinVPclusters)/(numsepVPatoms)*100.0,3)))
print('Ratio of: (# atoms involved in VP clusters)/(# atoms involved in VP clusters if all clusters were separated as much as possible within the model): {0}% <--- Therefore {1}% sharing.'.format(round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3),100.0-round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3) if numsepVPatoms < maxnumatoms else round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3)))
vor_instance = Vor()
vor_instance.runall(modelfile,cutoff)
index = vor_instance.get_indexes()
vor_instance.set_atom_vp_indexes(model)
vp_dict = categorize_vor.load_param_file('/home/jjmaldonis/model_analysis/scripts/categorize_parameters_iso.txt')
atom_dict = categorize_vor.generate_atom_dict(index,vp_dict)
categorize_vor.set_atom_vp_types(model,vp_dict)
# Count the number of common neighbors in each of the VP
vp_atoms = []
for atom in model.atoms:
if(atom.vp.type in cluster_types):
vp_atoms.append(atom)
common_neighs = 0.0
atom_pairs = []
for atomi in vp_atoms:
for atomj in vp_atoms:
if(atomi != atomj):
if(atomi in atomj.neighs and [atomi,atomj] not in atom_pairs and [atomj,atomi] not in atom_pairs):
common_neighs += 1
atom_pairs.append([atomi,atomj])
#if(atomj in atomi.neighs): common_neighs += 0.5
for n in atomi.neighs:
if(n in atomj.neighs and [n,atomj] not in atom_pairs and [atomj,n] not in atom_pairs):
common_neighs += 1
atom_pairs.append([n,atomj])
#for n in atomj.neighs:
# if(n in atomi.neighs): common_neighs += 0.5
# Now common_neighs is the number of shared atoms
#print(common_neighs)
print('Percent shared based on common neighsbors: {0}'.format(100.0*common_neighs/natomsinVPclusters))
