""" number of nearest neighbors in cluster

@param atom is the atom around which we want to find its neighbors

@param model is the model that the cluster belongs to

@param cluster is the cluster of atoms, in list form """

n = 0

for atomi in atom.neighs:

if atomi in cluster:

n += 1

# 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))

# Debugging stuff, mainly for get_connected_clusters_with_neighs function

#print(clusters[0][57], clusters[0][57].vesta())

#for atom in ag.model.atoms:

#cm = Model('temp',model.lx,model.ly,model.lz,clusters[0])

#cm.generate_neighbors(3.5)

#for i,atom in enumerate(cm.atoms):

# found = False

# for n in atom.neighs:

# if(n.vp.type in cluster_types):

# found = True

# if(not found and atom.vp.type not in cluster_types):

# #print('Found an atom that isnt connected to a VP type! {0} {1}'.format(allclusters.atoms.index(atom),atom.neighs))

# print('Found an atom that isnt connected to a VP type! {0} {1} {2} {3}'.format(i+1,atom,atom.neighs,atom.vp.type))

# #if(atom.neighs == []):

# # for atom2 in ag.model.atoms:

# # if(atom in atom2.neighs):

# # print('Found an atom with empty neighbors as a neighbor of another! {0} {1} {2}'.format(atom,atom2,atom2.neighs))

# #if(clusters[0][57] in atom.neighs):