def generateRCut(atoms,basis,debug=False):
#Set rcut to be the first minimum of g(r)
rvals,gr = rdf_periodic(atoms,basis,cutoff=6.0)
#Smoothed G(r)
sgr=windowAvg(gr,n=25).tolist()
#derivative of smoothed-G(r)
dsgr = windowAvg(windowAvg([(sgr[i+1]-sgr[i])/(rvals[1]-rvals[0]) for i in range(len(sgr)-1)],n=50),n=20).tolist()#[47:]+[0]*47
#Find the first minima by searching for the first 2 maxima and finding the minima between the two
#More robust version uses first point at which the first derivative becomes positive after the first peak
first_neg = [i for i,v in enumerate(dsgr) if v<0][0]
first_peak = sgr.index(max(sgr[:first_neg]))
rindex = first_neg + [i for i,v in enumerate(dsgr[first_neg:]) if v>=0][0]
rcut = rvals[rindex]
#Should probably check out this plot before continuing
if debug:
print rcut
pl.plot(rvals,[i for i in sgr],lw=3,c="green",label="Smooth G(r)")
pl.plot(rvals[1:],dsgr,c="red",label="Smooth G'(r)")
pl.plot([rcut,rcut],[min(gr),max(gr)],lw=3,c="black",label="rcut")
pl.plot(rvals,gr,c="blue",label="G(r)")
pl.legend(loc=0)
pl.show()
return rcut
else:
xdata = fdata[xCol]
#Smoothing:
xSmoothEnable = xSmoothEnables[i]
ySmoothEnable = ySmoothEnables[i]
xWAN = xWANs[i]
yWAN = yWANs[i]
xdataSmooth = []
ydataSmooth = []
if xSmoothEnable:
if switches["-gauss"]:
xdataSmooth = superSmooth(xdata, ydata, xWAN / 100.0)
else:
xdataSmooth = windowAvg(xdata, xWAN)
if ySmoothEnable:
if switches["-gauss"]:
ydataSmooth = superSmooth(xdata, ydata, yWAN / 100.0)
else:
ydataSmooth = windowAvg(ydata, yWAN)
#Correct for window offset, average introduces extra points that need to be chopped off
if not switches["-gauss"] and xSmoothEnable or ySmoothEnable:
WAN = max(xWAN, yWAN)
xdataSmooth = xdataSmooth[WAN / 2 + 1:WAN / -2]
ydataSmooth = ydataSmooth[WAN / 2 + 1:WAN / -2]
xdata = xdata[WAN / 2 + 1:WAN / -2]
ydata = ydata[WAN / 2 + 1:WAN / -2]
#Scaling - multiply by constant
strsy = list()
strsz = list()
count = 0
for line in outcar:
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 = windowAvg(strsavg, n=3)
#Plotting
pl.figure()
pl.plot(strsx, ls='--')
pl.plot(strsy, ls='--')
pl.plot(strsz, ls='--')
pl.plot(strsavg)
pl.plot(winavg, c="black", lw=2)
pl.legend(["StressX", "StressY", "StressZ", "Avg", "Windowed Avg"], loc=0)
pl.xlabel("Timestep")
pl.ylabel("Stress (kB)")
pl.title(sys.argv[1])
pr.prshow("outcarStressTime.png")
zBinLow = [i*delz+zmin for i in range(nSlices-1)]
zVals = [0.]*nSlices
zCount = [0]*nSlices
#Loop over atoms and bin orderParameter based on z-coordinate of atom
az=[i%zmax for i in az]
for a,atomZ in enumerate(az):
i = sum([1 for zLow in zBinLow if atomZ > zLow])
zCount[i] += 1
zVals[i] += ops[a]
#window average and eliminate 0's
zBins,zVals = map(list,zip(*[[b,z/c] for z,c,b in zip(zVals,zCount,zBins) if c!=0]))
if zBins[0]!=0:
zBins=[0]+zBins
zVals=[zVals[-1]]+zVals
zValsAvg=windowAvg(zVals[-3:]+zVals+zVals[:3],5)[3:-3]
#plot
pl.figure()
pl.plot(zBins,zVals)
pl.plot(zBins,zValsAvg)
pl.xlabel("Z")
pl.ylabel(op)
pl.xlim(zmin,zmax)
pl.draw()
pl.show()
prm.prmshow(fname="POSCAR.png")
else:
xdata=fdata[xCol]
#Smoothing:
xSmoothEnable=xSmoothEnables[i]
ySmoothEnable=ySmoothEnables[i]
xWAN=xWANs[i]
yWAN=yWANs[i]
xdataSmooth=[]
ydataSmooth=[]
if xSmoothEnable:
if switches["-gauss"]:
xdataSmooth=superSmooth(xdata,ydata,xWAN/100.0)
else:
xdataSmooth=windowAvg(xdata,xWAN)
if ySmoothEnable:
if switches["-gauss"]:
ydataSmooth=superSmooth(xdata,ydata,yWAN/100.0)
else:
ydataSmooth=windowAvg(ydata,yWAN)
#Correct for window offset, average introduces extra points that need to be chopped off
if not switches["-gauss"] and xSmoothEnable or ySmoothEnable:
WAN=max(xWAN,yWAN)
xdataSmooth=xdataSmooth[WAN/2+1:WAN/-2]
ydataSmooth=ydataSmooth[WAN/2+1:WAN/-2]
xdata=xdata[WAN/2+1:WAN/-2]
ydata=ydata[WAN/2+1:WAN/-2]
#Scaling - multiply by constant
strsy=list()
strsz=list()
count=0
for line in outcar:
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=windowAvg(strsavg,n=3)
#Plotting
pl.figure()
pl.plot(strsx,ls='--')
pl.plot(strsy,ls='--')
pl.plot(strsz,ls='--')
pl.plot(strsavg)
pl.plot(winavg,c="black",lw=2)
pl.legend(["StressX","StressY","StressZ","Avg","Windowed Avg"],loc=0)
pl.xlabel("Timestep")
pl.ylabel("Stress (kB)")
pl.title(sys.argv[1])
pr.prshow("outcarStressTime.png")
zBinLow = [i*delz+zmin for i in range(nSlices-1)]
zVals = [0.]*nSlices
zCount = [0]*nSlices
#Loop over atoms and bin orderParameter based on z-coordinate of atom
az=[i%zmax for i in az]
for a,atomZ in enumerate(az):
i = sum([1 for zLow in zBinLow if atomZ > zLow])
zCount[i] += 1
zVals[i] += ops[a]
#window average and eliminate 0's
zBins,zVals = map(list,zip(*[[b,z/c] for z,c,b in zip(zVals,zCount,zBins) if c!=0]))
if zBins[0]!=0:
zBins=[0]+zBins
zVals=[zVals[-1]]+zVals
zValsAvg=datatools.windowAvg(zVals[-3:]+zVals+zVals[:3],5)[3:-3]
#plot
pl.figure()
pl.plot(zBins,zVals)
pl.plot(zBins,zValsAvg)
pl.xlabel("Z")
pl.ylabel(op)
pl.xlim(zmin,zmax)
pl.draw()
pl.show()
prm.prmshow(fname="plot3.png")
