cnt=0
while True:
try:
dump,bounds,types,atoms,head = dumpReadNext(dump)
cnt+=1
except Exception as inst:
#last of the configurations
if avg==0:
print "No more configs in dump file."
else:
print "Found %d configurations."%cnt
break
[rbins,rdist]=rdf_periodic(array(atoms),array(bounds),cutoff=cutoff,nbins=nbins)
if avg==0:
if smooth==1:
smdist=rdist[:]
smdist = wsmooth(smdist,nbins/10)
plotting(rbins,smdist)
else:
plotting(rbins,rdist)
else:
rdistavg+=rdist
if avg>=1 and cnt%avg==0 and cnt>0:
plotting(rbins,rdistavg/avg)
rdistavg=zeros(nbins)
if avg==1:
cntavg=zeros(nbins)
cnt=0
while True:
try:
dump,bounds,types,atoms,head = dumpReadNext(dump)
cnt+=1
except Exception as inst:
#last of the configurations
if avg==0:
print "No more configs in dump file."
else:
print "Found %d configurations."%cnt
break
bounds=[[0,bounds[0][0]],[0,bounds[1][1]],[0,bounds[2][2]]]
Qis=orderParam.bondOrientation(array(atoms),array(bounds),l,neighbCut=cutoff,ortho=True)
(cnts,bins) = histogram(Qis,bins=nbins,range=(0,cutoff))
if avg==0:
if smooth==1:
smCnts = wsmooth(cnts,nbins/10)
plotting(bins,smCnts)
else:
plotting(bins,cnts)
else:
cntavg+=cnts
if avg==1:
plotting(bins,cntavg/cnt)
def residual_qhr(coefs, y, x):
Q = interp(dx, coefs, x)
hr = y
return x * hr - hrq_calc(dr, Q, hr, x)
basis, atypes, atoms, head, poscar = poscarIO.read(
open(sys.argv[1]).readlines())
atoms = array(atoms)
basis = array(basis)
rads, gr = radialDistribution(atoms, basis)
rads = array(rads)
hr = wsmooth(gr)[:-1] - 1 #superSmooth(rads,gr)[:-20] - 1
#hr = gr - 1
dr = rads[1] - rads[0]
density = atoms.shape[0] / dot(cross(basis[0], basis[1]), basis[2])
rcut = rads[-1]
radsCut = [i for i in rads if i <= rcut]
hrCut = [hr[i] for i in range(len(rads)) if rads[i] <= rcut]
Nknots = 70
xknots = array([i * max(radsCut) / Nknots for i in range(Nknots)])
dx = xknots[1] - xknots[0]
yknots = list()
for i in range(Nknots):
if i == 0:
yknots.append(0)
cq = array([ 4*pi*simps(rads*cr*np.sin(q*rads)/q,dx=dx) for q in qs])
return 1./(1. - density*cq)
def residual_qhr(coefs,y,x):
Q = interp(dx,coefs,x)
hr = y
return x*hr - hrq_calc(dr,Q,hr,x)
basis,atypes,atoms,head,poscar = poscarIO.read(open(sys.argv[1]).readlines())
atoms=array(atoms)
basis=array(basis)
rads,gr = radialDistribution(atoms,basis)
rads = array(rads)
hr = wsmooth(gr)[:-1] - 1#superSmooth(rads,gr)[:-20] - 1
#hr = gr - 1
dr=rads[1]-rads[0]
density = atoms.shape[0]/dot(cross(basis[0],basis[1]),basis[2])
rcut = rads[-1]
radsCut = [i for i in rads if i <= rcut]
hrCut = [hr[i] for i in range(len(rads)) if rads[i] <= rcut]
Nknots=70
xknots=array([i*max(radsCut)/Nknots for i in range(Nknots)])
dx=xknots[1]-xknots[0]
yknots=list()
for i in range(Nknots):
if i==0:
yknots.append(0)
count = 0
for line in outcar:
if "(temperature" in line:
tmpt.append(float(line.split("(temperature")[1].split()[0]))
if "in kB" in line:
# print line.split("in kB")[1].strip()
[sx, sy, sz] = [float(i) for i in line.split("in kB")[1].split()[:3]]
strsx.append(sx)
strsy.append(sy)
strsz.append(sz)
# The average Stress
strsavg = [(strsx[i] + strsy[i] + strsz[i]) / 3 for i in range(len(strsx))]
# A windowed average
winavg = wsmooth(strsavg, 10)
print len(winavg), len(strsavg), len(strsz), len(tmpt)
# Plotting
pl.figure()
pl.plot(tmpt, strsx, ls="--")
pl.plot(tmpt, strsy, ls="--")
pl.plot(tmpt, strsz, ls="--")
pl.plot(tmpt, strsavg, ls="--")
pl.plot(tmpt, winavg, c="black", lw=2)
pl.legend(["StressX", "StressY", "StressZ", "Avg", "Running Avg"], loc=0)
pl.xlabel("Temperature (K)")
pl.ylabel("Stress (kB)")
pl.title(sys.argv[1])
pr.prshow("outcarStressT.png")
pl.show()
exit(0)
#Special Case for FRDF histogrammic RDF
if op == "FRDF":
from numpy import bincount,array,zeros
import numpy as np
from datatools import wsmooth
t = zip(*[[j*100 for j in orderVals[i][1]] for i in range(len(orderVals))])
avgx = orderVals[0][0]
a=zeros([len(t),2000])
mx=0
for i,v in enumerate(t):
a[i]=bincount(v,minlength=2000)
a[i]=wsmooth(a[i],window_len=75)[0:2000]
m=max(a[i])
a[i]=np.log([j/m for j in a[i]])
pl.imshow(a.T,origin='lower',cmap=pl.get_cmap("Blues"))
pl.yticks(range(0,1000,100),range(10))
pl.xticks(range(0,1001,100),range(11))
pl.show()
exit(0)
def savetxtWrapper(defaultFileName,data,delimiter=" ",header=" ",comments=""):
try:
if saveFileName is "None":
savetxt(defaultFileName,data,delimiter=delimiter,header=header,comments=comments)
else:
while True:
try:
dump, bounds, types, atoms, head = dumpReadNext(dump)
cnt += 1
except Exception as inst:
#last of the configurations
if avg == 0:
print "No more configs in dump file."
else:
print "Found %d configurations." % cnt
break
[rbins, rdist] = rdf_periodic(array(atoms),
array(bounds),
cutoff=cutoff,
nbins=nbins)
if avg == 0:
if smooth == 1:
smdist = rdist[:]
smdist = wsmooth(smdist, nbins / 10)
plotting(rbins, smdist)
else:
plotting(rbins, rdist)
else:
rdistavg += rdist
if avg >= 1 and cnt % avg == 0 and cnt > 0:
plotting(rbins, rdistavg / avg)
rdistavg = zeros(nbins)
exit(0)
#Special Case for FRDF histogrammic RDF
if op == "FRDF":
from numpy import bincount, array, zeros
import numpy as np
from datatools import wsmooth
t = zip(*[[j * 100 for j in orderVals[i][1]]
for i in range(len(orderVals))])
avgx = orderVals[0][0]
a = zeros([len(t), 2000])
mx = 0
for i, v in enumerate(t):
a[i] = bincount(v, minlength=2000)
a[i] = wsmooth(a[i], window_len=75)[0:2000]
m = max(a[i])
a[i] = np.log([j / m for j in a[i]])
pl.imshow(a.T, origin='lower', cmap=pl.get_cmap("Blues"))
pl.yticks(range(0, 1000, 100), range(10))
pl.xticks(range(0, 1001, 100), range(11))
pl.show()
exit(0)
def savetxtWrapper(defaultFileName,
data,
delimiter=" ",
header=" ",
utils.usage(["<.ensembleFile>","<window size (int or all)>"],2,2)
inputFile = sys.argv[1]
possibleSuffixes = [".tetra",".rmsd",".cn"]
if not any([suffix for suffix in possibleSuffixes if suffix in inputFile]):
print "wrong input file dummy."
exit(0)
windowSize = sys.argv[2]
outputFile = inputFile+".avg"+windowSize
#assume first line is a header
head = open(inputFile,"r").readline()
configIterator = parserGens.parseEnsemble(inputFile,keepAvg=True)
ops = zip(*[a for a in configIterator])
if windowSize == "all":
opAvg = [sum(op)/len(op) for op in ops]
opLines = [" ".join(map(str,opAvg))+"\n"]
else:
windowSize = int(windowSize)
opAvg = zip(*[datatools.wsmooth(atet,windowSize) for atet in ops])
opLines = [" ".join(map(str,line))+"\n" for line in opAvg]
out = open(outputFile,"w")
out.write(head)
out.writelines(opLines)
print "wrong input file dummy."
exit(0)
windowSize = sys.argv[2]
outputFile = inputFile + ".avg" + windowSize
configIterator = parserGens.parseEnsemble(inputFile, keepAvg=True)
tetras = zip(*[a for a in configIterator])
if windowSize == "all":
tetraAvg = [sum(atet) / len(atet) for atet in tetras]
tetraLines = [" ".join(map(str, tetraAvg)) + "\n"]
else:
windowSize = int(windowSize)
tetraAvg = zip(*[datatools.wsmooth(atet, windowSize) for atet in tetras])
tetraLines = [" ".join(map(str, line)) + "\n" for line in tetraAvg]
"""
tetraAvg = zip(*[datatools.windowAvg(atet,n=windowSize,option='valid') for atet in tetras])
tetraPre = list()
tetraPost = list()
def avg(x):
return sum(x)/len(x)
for i in range(windowSize/2):
print i
tetraPre.append(map(avg,tetras[:i]))
tetraPost.append(map(avg,tetras[-windowSize+i:]))
tetraLines = [" ".join(map(str,line))+"\n" for line in tetraPre] +\
[" ".join(map(str,line))+"\n" for line in tetraAvg] +\
[" ".join(map(str,line))+"\n" for line in tetraPost]
while True:
try:
dump, bounds, types, atoms, head = dumpReadNext(dump)
cnt += 1
except Exception as inst:
#last of the configurations
if avg == 0:
print "No more configs in dump file."
else:
print "Found %d configurations." % cnt
break
bounds = [[0, bounds[0][0]], [0, bounds[1][1]], [0, bounds[2][2]]]
Qis = orderParam.bondOrientation(array(atoms),
array(bounds),
l,
neighbCut=cutoff,
ortho=True)
(cnts, bins) = histogram(Qis, bins=nbins, range=(0, cutoff))
if avg == 0:
if smooth == 1:
smCnts = wsmooth(cnts, nbins / 10)
plotting(bins, smCnts)
else:
plotting(bins, cnts)
else:
cntavg += cnts
if avg == 1:
plotting(bins, cntavg / cnt)