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))