def structure_factor(VW, L, n_frames=10, n_start=1,
filters=0.05, dh=0.05,save='sf'):
import MD.analysis.sfactor as sf
#Find Structure Factor
stmp,qx = sf.sfactor(VW[n_start:n_start+n_frames],L=L,l=20)
S,Q,primitive_vectors = sf.sort_sfpeaks(stmp,qx)
#Plot the graph
xlabel = '$|\\vec{q}$ $|$'
ylabel = '$S(\\vec{q}$ $)$ '
pyplot.plot(Q, S, linestyle='', marker='x',
xlabel=xlabel, ylabel=ylabel, save=save)
util.pickle_dump(Q,'Vsfx%i.pkl'%n_start)
util.pickle_dump(S,'Vsfy%i.pkl'%n_start)
#if len(QQ)>500:
# bars,height = sf.sf_max(QQ,qpeaks=8)
# db_x,db_y = sf.dbfactor(msdr=5)
# pyplot.plot_bar(bars, height, xlabel=xlabel, ylabel=ylabel,
# save=save)
# pyplot.plot(db_x, db_y, linestyle='--', xlabel=xlabel, ylabel=ylabel,
# save=save)
# #return q, s, bars, height, db_x, db_y
# return Q, S, primitive_vectors
#filter out the noise
Q, S, primitive_vectors = sf.sf_filter(Q, S, primitive_vectors,
filters=filters, save = 'sf%i'%n_start)
return Q, S, primitive_vectors
def self_intermediate_scattering(VW, L, x, n_start=1,
filters=0.05, dh=0.05,save='sf',l=5,delta=5,calc=True):
import MD.analysis.sfactor as sf
#Find Structure Factor
if calc:
import time
from MD.canalysis.int_scattering import int_scatter
F = []
for i in range(delta):
print i
print "caculating Average"
print VW.shape
print "Structure Factor"
A = VW[n_start+i]
stmp,qx = sf.sfactor(np.array([A]),L=L[n_start],l=l)
S,Q,primitive_vectors = sf.sort_sfpeaks(stmp,qx)
S,Q,primitive_vectors = sf.sf_filter_max(S,Q,primitive_vectors,filters=0.5)
index = S.index(max(S))
k = primitive_vectors[S.index(max(S))]
#take the average over different starting configurations
print "Calculating Int Scattering"
start = time.clock()
scat_c = int_scatter(VW[n_start+i:],np.array(L[0]),np.array(k),
np.array(x[:-(n_start+i)]))
end = time.clock()
print 'cython runtime'
print end-start
F.append(scat_c)
F_avg = np.zeros((len(F[-1])))
for i in F:
for j in range(len(F[-1])):
F_avg[j] += i[j]
F_avg /= delta
#reset the zero of x so that the plot starts at the correct time
x = range(len(F[-1]))
util.pickle_dump([x,F_avg],'si_scat.pkl')
xlabel = 't'
ylabel = 'F(q,t)'
save = 'int_scatter'
A = util.pickle_load('si_scat.pkl')
x = A[0]
F_avg = A[1]
#Kohlraush-Williams-Watts function
# A exp[-(t/tau_b)^beta]
beta = 1.4
tau_a = 6
K = []
K_x = []
fid = open('self_scat.dat','w')
for i in range(len(A[0])):
fid.write('%i %.4f\n'%(A[0][i],A[1][i]))
fid.close()
for i in np.arange(1,len(x),0.1):
K.append(F_avg[1] * math.exp(-(i/tau_a)**beta))
K_x.append(i)
def find_lattice(MD,VW,L,n_finish=1,n_start=0,delta=20,filters=0.2):
# Identify the bcc lattice as a grid and find the recipricol lattice
#Find Structure Factor
#print VW.shape
redo = True
while redo == True:
stmp,qx = sf.sfactor(VW[-10:-1],L=L,l=10)
S,Q,primitive_vectors = sf.sort_sfpeaks(stmp,qx)
print np.array(Q)
Q, S, primitive_vectors = sf.sf_filter(Q, S, primitive_vectors,
filters=filters, save = 'sffilter')
a,b = MD.analysis.sfactor.recipricol(Q,primitive_vectors)
#Find the best fit for the qlattice at a particular frame by looking
#for the lowest value of the msdr
x = np.arange(n_start,n_finish,delta)
defects = np.zeros((len(x),15))
lowest=10000
for k in x:
for i in range(10):
crystal, c = make.make_bcc(a[0], a[1], a[2], VW[k][i], L[0]+5)
msdr_x = msdr.msd_r(VW[k], crystal, L)
if lowest > msdr_x:
lowest = msdr_x
index = i
frame = k
crystal, cword = make.make_bcc(a[0], a[1], a[2], VW[frame][index], L[0]+5)
print np.array(b)
print np.array(a)
if raw_input('Do you want to retry?[no]/yes ') == 'yes':
redo = True
else:
redo = False
util.pickle_dump(cword,'cword.pkl')
util.pickle_dump(crystal,'ccrystal.pkl')
#filter out the comensurate crystal
C = []
for i in crystal:
#make sure there aren't two that are close due to pbc
if rcut_neighbors_point(np.array(C),i,L,rcut=6) == 0:
C.append(i)
#write out the lattice
fid = open('testlattice.xyz','w')
fid.write(('%i\n')%(len(C)))
fid.write('Atoms\n')
for i in range(len(C)):
fid.write(('%c %f %f %f\n')%('A',C[i][0],C[i][1], C[i][2]))
fid.close()
util.pickle_dump(C,'C.pkl')
return crystal, C
def structure_factor(VW, L, n_frames=10, n_start=1,
filters=0.05, dh=0.05,save='sf',l=10):
import MD.analysis.sfactor as sf
#Find Structure Factor
A = Average(VW,L,n_start,n_frames)
#A = VW[n_start]
stmp,qx = sf.sfactor(np.array([A]),L=L[n_start],l=l)
S,Q,primitive_vectors = sf.sort_sfpeaks(stmp,qx)
fid = open('sftotal.txt','w')
fid.write('S Q\n')
for i in range(len(S)):
fid.write('%.5f %.5f\n'%(S[i],Q[i]))
fid.close()
#Plot the graph
xlabel = '$|\\vec{q}$ $|$'
ylabel = '$S(\\vec{q}$ $)$ '
#sp4 = 0.634698
#sp12 = 0.530493
#sc 1.121997
#pyplot_eps.plot_sf(Q, S,0.552381, linestyle='', marker='x',
#xlabel=xlabel, ylabel=ylabel, save=save,xlen=20)
pyplot.plot_sf(Q, S,0.552381, linestyle='', marker='x',
xlabel=xlabel, ylabel=ylabel, save=save,xlen=20)
util.pickle_dump(Q,'Vsfx%i.pkl'%n_start)
util.pickle_dump(S,'Vsfy%i.pkl'%n_start)
print 'finished reconstruction'
#if len(QQ)>500:
# bars,height = sf.sf_max(QQ,qpeaks=8)
# db_x,db_y = sf.dbfactor(msdr=5)
# pyplot.plot_bar(bars, height, xlabel=xlabel, ylabel=ylabel,
# save=save)
# pyplot.plot(db_x, db_y, linestyle='--', xlabel=xlabel, ylabel=ylabel,
# save=save)
# #return q, s, bars, height, db_x, db_y
# return Q, S, primitive_vectors
#filter out the noise
Q, S, primitive_vectors = sf.sf_filter(Q, S, primitive_vectors,
filters=filters,save=save)
import MD.analysis.reconstruct_lattice as rl
import MD.unit.make_bcc as mb
con = True
while con:
a1,a2,a3 = rl.reconstruct_lattice(Q,primitive_vectors,save+'recipricol.txt')
print a1,a2,a3
mb.make_bcc(a1,a2,a3,A[0],L[n_start])
if raw_input('Reselect? (y)/N') == 'N':
con = False
return Q, S, primitive_vectors
def structure_factor(VW, L, n_frames=10, n_start=1,
filters=0.05, dh=0.05,save='sf',l=10):
import MD.analysis.sfactor as sf
#Find Structure Factor
#Average_position
stmp,qx = sf.sfactor(np.array([VW]),L=L,l=l)
S,Q,primitive_vectors = sf.sort_sfpeaks(stmp,qx)
#Plot the graph
xlabel = '$|\\vec{q}$ $|$'
ylabel = '$S(\\vec{q}$ $)$ '
pyplot.plot(Q, S, linestyle='', marker='x',
xlabel=xlabel, ylabel=ylabel, save=save)
util.pickle_dump(Q,'Vsfx%i.pkl'%n_start)
util.pickle_dump(S,'Vsfy%i.pkl'%n_start)
print 'finished reconstruction'
Q, S, primitive_vectors = sf.sf_filter(Q, S, primitive_vectors,
filters=filters,save=save)
print 'sorting peaks'
return Q, S, primitive_vectors
