def type_ordering(VW,L):
N = VW.shape[1]*8
order = []
count = 0
for k in range(VW.shape[0]):
print k
print count
count = 0
for i in range(VW.shape[1]):
NN=count_neighbors_index(VW[k],i,L[k],count=8)[0]
NN.sort()
if i < VW.shape[1]/2:
for j in NN:
if j >= VW.shape[1]/2:
count+=1
if i >= VW.shape[1]/2:
for j in NN:
if j < VW.shape[1]/2:
count+=1
order.append(count/float(N))
x = range(VW.shape[0])
fid = open('typeorder.txt','w')
for i in range(len(order)):
fid.write(('%i %.2f\n'%(i,order[i])))
fid.close()
pyplot.plot(x,order,
xlabel='time',ylabel='ordering',save='typeorder')
def steinhardt_order(M,L,l=6,rcut=17.5,count=14,verbose=False):
print 'l',l
#Find nearest neighbors for all particles and convert to spherical
#######
#neighbors[i][0]=index of neighbors
#neighbors[i][1]=array[i][x,y,z] of distance between neighbors
########
neighbors=[]
for i in range(M.shape[0]):
N = nn.count_neighbors_index(M,i,L,count=count,rcut=rcut,vectors=True)
if i == 0:
print i, N[1]
neighbors.append([N[0],xyz_to_spherical(N[1])])
if verbose:
print 'neighbors',len(N[0])
#find qlm of each particle
#######
#store as values m=-l-l in nparray
#Qlm[i]=np.array(ql-m:ql+m) for particle i
#Qlm[i]=qlm(i)
######
Qlm=np.zeros((M.shape[0],2*l+1),dtype=complex)
qi=np.zeros((1,2*l+1),dtype=complex)
for i in range(M.shape[0]):
for m in range(-l,l+1):
qi[0][m+l]=qlm(neighbors[i][1],l,m)
Qlm[i][:]=qi
Ql=[]
for i in range(M.shape[0]):
Ql.append(ql(Qlm[i],l))
print sum(Ql)/len(Ql)
return Ql
def Qmapq6q4(M,L,count=14,crystal=6,verbose=False,rcut=30):
#Find nearest neighbors for all particles and convert to spherical
#######
#neighbors[i][0]=index of neighbors
#neighbors[i][1]=array[i][x,y,z] of distance between neighbors
########
neighbors=[]
print 'getting neighbors'
for i in range(M.shape[0]):
N = nn.count_neighbors_index(M,i,L,count=count, rcut=rcut,vectors=True)
if len(N[0]) != count:
print 'lenght of N', len(N[0])
neighbors.append([N[0],xyz_to_spherical(N[1])])
#include itself
neighbors[-1][0].append(i)
if verbose:
print 'neighbors',len(N[0])
#find qlm of each particle
#######
#store as values m=-l-l in nparray
#Qlm[i]=np.array(ql-m:ql+m) for particle i
#Qlm[i]=qlm(i)
######
print 'generating qlm matrix'
l=4
qlm_matrix=np.zeros((M.shape[0],2*l+1),dtype=complex)
for i in range(M.shape[0]):
for m in range(-l,l+1):
qlm_matrix[i][m+l]=qlm(neighbors[i][1],l,m)
Ql4=np.zeros((M.shape[0]),dtype=complex)
print 'generating Ql_avg'
for i in range(M.shape[0]):
qlm_avg=np.zeros((2*l+1),dtype=complex)
for m in range(-l,l+1):
for k in neighbors[i][0]:
qlm_avg[m+l] += qlm_matrix[k,m+l]
qlm_avg[m+l]/=len(neighbors[i][0])
Ql4[i]=ql(qlm_avg,l)
print 'Ql4_avg=',l,Ql4.sum()/Ql4.shape[0]
Ql6=np.zeros((M.shape[0]),dtype=complex)
l=6
qlm_matrix=np.zeros((M.shape[0],2*l+1),dtype=complex)
for i in range(M.shape[0]):
for m in range(-l,l+1):
qlm_matrix[i][m+l]=qlm(neighbors[i][1],l,m)
for i in range(M.shape[0]):
qlm_avg=np.zeros((2*l+1),dtype=complex)
for m in range(-l,l+1):
for k in neighbors[i][0]:
qlm_avg[m+l]+=qlm_matrix[k,m+l]
qlm_avg[m+l]/=len(neighbors[i][0])
Ql6[i]=ql(qlm_avg,l)
print 'Ql6_avg=',l,Ql6.sum()/Ql6.shape[0]
return Ql4,Ql6
def solid_particles(M,L,count=14,l=6,c_cut=0.15,crystal=6,verbose=False,rcut=30):
#Find nearest neighbors for all particles and convert to spherical
#######
#neighbors[i][0]=index of neighbors
#neighbors[i][1]=array[i][x,y,z] of distance between neighbors
########
neighbors=[]
for i in range(M.shape[0]):
N = nn.count_neighbors_index(M,i,L,count=count,rcut=rcut, vectors=True)
neighbors.append([N[0],xyz_to_spherical(N[1])])
if verbose:
print 'neighbors',len(N[0])
#find qlm of each particle
#######
#store as values m=-l-l in nparray
#Qlm[i]=np.array(ql-m:ql+m) for particle i
#Qlm[i]=qlm(i)
######
Qlm=np.zeros((M.shape[0],2*l+1),dtype=complex)
qi=np.zeros((1,2*l+1),dtype=complex)
for i in range(M.shape[0]):
for m in range(-l,l+1):
qi[0][m+l]=qlm(neighbors[i][1],l,m)
Qlm[i][:]=qi
#for nearest neighbors find the Sij vector
S=[]
for i in range(M.shape[0]):
S.append(Sij(Qlm[i],Qlm[neighbors[i][0]]))
#for each particle lets counts its neighbors
#if it has more than ccut we say it is a solid
iscrystal=[0.0 for i in range(M.shape[0])]
counter=0
store_j = []
for i in S:
#if verbose:
# print i
count=0
for j in i:
#this is the cutoff for declaring 2 particles connected
if j.real>c_cut:
store_j.append(j.real)
count+=1
if count >= crystal:
iscrystal[counter]=1
counter+=1
try:
print sum(store_j)/len(store_j)
except:
print 'no bonds'
num=iscrystal.count(1)
print 'crystals',num
return num, iscrystal
def Qmap(M,L,count=14,l=6,crystal=6,verbose=False,rcut=30):
#Find nearest neighbors for all particles and convert to spherical
#######
#neighbors[i][0]=index of neighbors
#neighbors[i][1]=array[i][x,y,z] of distance between neighbors
########
neighbors=[]
#otree = node(L,delta=(M.shape[0]/2)**0.333)
#otree.add_particles(M)
print 'getting neighbors'
for i in range(M.shape[0]):
#N = cn_tree_index(M,i,otree,L,count=count,rcut=rcut)
N = nn.count_neighbors_index(M,i,L,count=count,rcut=rcut,vectors=True)
neighbors.append([N[0],xyz_to_spherical(N[1])])
#include itself
neighbors[-1][0].append(i)
if verbose:
print 'neighbors',len(N[0])
#find qlm of each particle
#######
#store as values m=-l-l in nparray
#Qlm[i]=np.array(ql-m:ql+m) for particle i
#Qlm[i]=qlm(i)
######
print 'generating qlm matrix'
qlm_matrix=np.zeros((M.shape[0],2*l+1),dtype=complex)
for i in range(M.shape[0]):
for m in range(-l,l+1):
qlm_matrix[i][m+l]=qlm(neighbors[i][1],l,m)
Ql=np.zeros((M.shape[0]),dtype=complex)
print 'generating Ql_avg'
for i in range(M.shape[0]):
qlm_avg=np.zeros((2*l+1),dtype=complex)
for m in range(-l,l+1):
for k in neighbors[i][0]:
qlm_avg[m+l]+=qlm_matrix[k,m+l]
qlm_avg[m+l]/=len(neighbors[i][0])
Ql[i]=ql(qlm_avg,l)
print 'Ql=',l,Ql.sum()/Ql.shape[0]
return Ql
