def plotting(rbins, rdist):
pl.plot(rbins, rdist)
pl.ylabel(r"$P \left (Q_%d \right )" % l)
pl.xlabel(r"$Q_%d$" % l)
pl.title("Bond Orientation | File:%s | Step:%s" %
(sys.argv[1], head.split()[-1]))
pr.prshow("bondOrientation.png")
avgy=[i/count for i in avgy]
pl.plot(avgx,avgy)
""" #Still figuring this one out...
if switches["-saveData"]:
data=label[xCol] + " " + label[yCol] + "\n"
for x,y in zip(avgx,avgy):
data+=str(x)+" "+str(y)+"\n"
open("lazy.data","w").write(data)
"""
if not switches["-noLeg"]:
pl.legend(loc=0)
pl.gca().autoscale_view(True,True,True)
if switches["-title"]:
pl.title(switches["-title"])
if switches["-xlabel"]:
pl.xlabel(switches["-xlabel"])
if switches["-ylabel"]:
pl.ylabel(switches["-ylabel"])
if switches["-saveFig"]:
pl.savefig("lazy.png")
print "Wrote file lazy.png"
else:
pr.prshow("lazy.png")
print orderVals
for i,(ov,r) in enumerate(orderVals):
print poscarNames[i],"\t\t",sum(ov)/len(ov)
elif op=="TN":
print "Average Translational Order <tao> ="
for i,ov in enumerate(orderVals):
print poscarNames[i],"\t\t",sum(ov)
elif op=="CN":
hd,rcuts=zip(*orderVals)
pl.hist(hd,bins=range(0,16),normed=True,histtype='bar',align='left',rwidth=0.8)
for i,ov in enumerate(orderVals):
cn=float(sum(ov[0]))/len(ov[0])
print "Average CN (%s):"%poscarNames[i],cn
labels.append("CN%3.3f rcut%3.3f %s"%(cn,rcuts[i],poscarNames[i]))
pl.xlabel(xylabels["CN"][0])
pl.ylabel(xylabels["CN"][1])
if args.averageFlag:
pl.legend(["Average "+" ".join(poscarNames)],loc=0,fontsize=10)
else:
if len(labels)>0:
pl.legend(labels,loc=0,fontsize=10)
else:
pl.legend(poscarNames,loc=0,fontsize=10)
#Don't plot for some values
if op not in ["TN","TET"]:
pr.prshow("%s_chart_%s.png"%(sys.argv[1],op))
for z in zip(enGrid, ocGrid[i]):
print "%f %f\n" % (z[0], z[1])
if orb != porb:
if orb == "t":
temp = "total"
else:
temp = orb
pl.plot(enGrid, ocGrid[i], label=temp, color=c, lw=2)
else:
pl.plot(enGrid, ocGrid[i], color=c, lw=2)
porb = orb
pl.xlabel("Energy (in eV)")
pl.ylabel("DOS")
pl.legend()
pr.prshow("DOS_from%s" % procarfile)
elif yvals == "kp":
for kp in range(len(kpoints)):
pl.figure()
for i in range(Norbs):
orb = olabels[i][0]
if orb == "s":
c = "blue"
elif orb == "p":
c = "green"
elif orb == "d":
c = "purple"
elif orb == "f":
c = "red"
elif orb == "t":
mxy = 0
mny = 0
for lmpx, lmpy, lmpname in zip(lmpxs, lmpys, lmpnames):
pl.plot(lmpx[i], lmpy[i], label=lmpname)
if i > 0:
g = [k for k in range(len(lmpx[i])) if lmpx[i][k] < 1.5][-1]
mxy = max([mxy, max(lmpy[i][g:])])
mny = min([mny, min(lmpy[i][g:])])
pl.xlim([1.4, 6])
pl.ylim([mny * 1.2, mxy * 1.2])
if pf:
pl.xlim(xlims)
pl.ylim(ylims)
if i == 2:
pl.ylim([0, 2])
pl.ylabel(var)
pl.legend(loc=0, fontsize='small')
#pl.subplot(326)
#pl.plot(lmpx[0],[i-j for i,j in zip(lmpys[0][4],lmpys[1][4])])
#pl.legend()
#pl.savefig("/home/acadien/Dropbox/stuff%s.png"%var)
pl.tight_layout()
pr.prshow("lammpsPotential.png")
#pl.show()
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")
logTime = list()
isfs = list()
for isfSline in open(isfsFile,"r").readlines():
try:
a = map(float,isfSline.split())
t = a.pop(0)
i = sum(a)/len(a)
except ValueError:
continue
logTime.append(t)
isfs.append(i)
import pylab as pl
pl.semilogx(logTime,isfs)
pr.prshow()
exit(0)
N = len(logTime)
m = max(logTime)
linTime = logTime#[float(i)*m/N for i in range(N)]
linISFs = isfs#interp1d(logTime,isfs,linTime)
T = m/N
xf = np.linspace(0,0.5/T, N/2)
yf = fftpack.fft(linISFs)
pl.semilogx(xf, yf[:N/2])
#pl.semilogx(xf, np.imag(yf[:len(isfs)/2])*xf/len(isfs))
pr.prshow()
if xSmoothEnable or ySmoothEnable:
if not switches["-noGhost"]:
if colors==None:
cp, = pl.plot(xdata,ydata,alpha=0.4,zorder=1)
cc = cp.get_color()
else:
pl.plot(xdata,ydata,alpha=0.4,zorder=1,c=cc)
if xSmoothEnable and ySmoothEnable:
pl.plot(xdataSmooth,ydataSmooth,c=cc,lw=2,alpha=1.0,label=fnames[i])
elif ySmoothEnable:
pl.plot(xdata,ydataSmooth,c=cc,lw=2,alpha=1.0,label=fnames[i])
elif xSmoothEnable:
pl.plot(xdataSmooth,ydata,c=cc,lw=2,alpha=1.0,label=fnames[i])
else:
pl.plot(xdata,ydata,lw=1.5,c=cc,label=fnames[i])
if switches["-avg"]:
avgy=[i/count for i in avgy]
pl.plot(avgx,avgy)
if switches["-saveData"]:
data=label[xCol] + " " + label[yCol] + "\n"
for x,y in zip(avgx,avgy):
data+=str(x)+" "+str(y)+"\n"
open("plot2.data","w").write(data)
if not switches["-noleg"]:
pl.legend(loc=0)
pr.prshow("plot2.png")
print "Average Translational Order <tao> ="
for i, ov in enumerate(orderVals):
print poscarNames[i], "\t\t", sum(ov)
elif op == "CN":
hd, rcuts = zip(*orderVals)
pl.hist(hd,
bins=range(0, 16),
normed=True,
histtype='bar',
align='left',
rwidth=0.8)
for i, ov in enumerate(orderVals):
cn = float(sum(ov[0])) / len(ov[0])
print "Average CN (%s):" % poscarNames[i], cn
labels.append("CN%3.3f rcut%3.3f %s" % (cn, rcuts[i], poscarNames[i]))
pl.xlabel(xylabels["CN"][0])
pl.ylabel(xylabels["CN"][1])
if args.averageFlag:
pl.legend(["Average " + " ".join(poscarNames)], loc=0, fontsize=10)
else:
if len(labels) > 0:
pl.legend(labels, loc=0, fontsize=10)
else:
pl.legend(poscarNames, loc=0, fontsize=10)
#Don't plot for some values
if op not in ["TN", "TET"]:
pr.prshow("%s_chart_%s.png" % (sys.argv[1], op))
print "Defects: ",len(defectDelE)
print "Melt/Quench: ",len(meltDelE)
print "Feedback: ",len(feedDelE)
print "Elastic: ",len(strainDelE)
print "SlowQ: ",len(slowDelE)
labels=["EOS","Melt","Feedback","Defects","Elastic","SlowQ"]
toHist = [i for i in [eosDelE,meltDelE,feedDelE,defectDelE,strainDelE,slowDelE] if len(i)>0]
toLabel = [labels[j] for j,i in enumerate([eosDelE,meltDelE,feedDelE,defectDelE,strainDelE,slowDelE]) if len(i)>0]
pl.hist(toHist,20,label=toLabel,histtype='barstacked',fill=True)
else:
pl.hist(absdelE,20)
pl.xlabel("$|\Delta E|$")
pl.ylabel("count")
pl.legend(loc=0)
print "Worst fits: \ncnt %-48.48s |delE|"%"config#"
cnt=0
for i,val in enumerate(reversed(sorted(absdelE))):
if val<mxerr:
break
if fdb!=-1:
print "%4d, %-50.50s, %f"%(absdelE.index(val),fdb[absdelE.index(val)],val)
else:
print i," ",absdelE.index(val)," \t",val
cnt=i
print "\n%d configurations with error above %f"%(cnt,mxerr)
pr.prshow("energyErrorHist.png")
#pl.savefig("/home/acadien/Dropbox/GeData/hist_energy_error.png")
toHistL = [
j for i, j in
zip([eosDelS, meltDelS, feedDelS, defectDelS, strainDelS, slowDelS],
["EOS", "Melt", "Feedback", "Defects", "Elastic", "SlowQ"])
if len(i) > 0
]
pl.hist(toHist, 20, label=toHistL, histtype='barstacked', fill=True)
else:
pl.hist(absdelS, 20)
pl.xlabel("$|\Delta S|$")
pl.ylabel("count")
pl.legend(loc=0)
print "Worst fits: \ncnt %-38.38s dirN |delS|" % "config#"
cnt = 0
for i, val in enumerate(reversed(sorted(absdelS))):
if val < mxerr:
break
if fdb != -1:
ix = absdelS.index(val)
print "%4d, %-40.40s %-3.3s | %f" % (ix, fdb[int(
ix / 6)], stressDirn[ix % 6], absdelS[ix])
else:
print i, " ", absdelS.index(val) / 6, " \t", val
cnt = i
print "\n%d configurations with error above %f" % (cnt, mxerr)
pr.prshow("stressErrorHist.png")
#pl.savefig("/home/acadien/Dropbox/GeData/hist_energy_error.png")
lydata[2]=map(float,lmpdata[nrho+nr:nrho+2*nr])
lydata[2]=[lydata[2][i]/(lxdata[2][i]+1E-20) for i in range(nr)]
lxdata[3]=map(lambda x:x*dr,range(nr)) #u
lydata[3]=map(float,lmpdata[nrho+2*nr:nrho+3*nr])
lxdata[4]=map(lambda x:x*dr,range(nr)) #w
lydata[4]=map(float,lmpdata[nrho+3*nr:nrho+4*nr])
print lxdata[2]
#print "Variable : minval - maxval"
pl.figure(figsize=(9,6))
for i,var in enumerate(varnames):
pl.subplot(321+i)
pl.scatter(datax[i],datay[i])
xlims=pl.xlim()
ylims=pl.ylim()
pl.plot(lxdata[i],lydata[i],label=var)
pl.xlim(xlims)
pl.ylim(ylims)
#pl.ylim([ylims[0]/20,ylims[1]/50])
# prit "%3.3s : %4.4e - %4.4e"%(var,min(datay[i]),max(datay[i]))
pl.legend(loc=0)
#pl.legend()
#pl.savefig("/home/acadien/Dropbox/stuff%s.png"%var)
pl.legend(loc=0)
pr.prshow("SampledPotential.eps")
#pl.show()
print "Feedback: ",len(feedDelS)
print "Defects: ",len(defectDelS)
print "Elastic: ",len(strainDelS)
print "SlowQ: ",len(slowDelS)
toHist = [i for i in [eosDelS,meltDelS,feedDelS,defectDelS,strainDelS,slowDelS] if len(i)>0]
toHistL = [j for i,j in zip([eosDelS,meltDelS,feedDelS,defectDelS,strainDelS,slowDelS],["EOS","Melt","Feedback","Defects","Elastic","SlowQ"]) if len(i)>0]
pl.hist(toHist,20,label=toHistL,histtype='barstacked',fill=True)
else:
pl.hist(absdelS,20)
pl.xlabel("$|\Delta S|$")
pl.ylabel("count")
pl.legend(loc=0)
print "Worst fits: \ncnt %-38.38s dirN |delS|"%"config#"
cnt=0
for i,val in enumerate(reversed(sorted(absdelS))):
if val<mxerr:
break
if fdb!=-1:
ix=absdelS.index(val)
print "%4d, %-40.40s %-3.3s | %f"%(ix,fdb[int(ix/6)],stressDirn[ix%6],absdelS[ix])
else:
print i," ",absdelS.index(val)/6," \t",val
cnt=i
print "\n%d configurations with error above %f"%(cnt,mxerr)
pr.prshow("stressErrorHist.png")
#pl.savefig("/home/acadien/Dropbox/GeData/hist_energy_error.png")
elif orb == 'f':
c = 'red'
elif orb == 't':
c = 'black'
if orb != porb:
if orb == 't': temp = 'total'
else: temp = orb
pl.plot(enGrid, ocGrid[i], label=temp, color=c)
else:
pl.plot(enGrid, ocGrid[i], color=c)
porb = orb
pl.xlabel("Energy (in eV)")
pl.ylabel("DOS")
pl.legend()
pr.prshow("DOS_from%s" % procarfile)
elif yvals == 'kp':
for kp in range(len(kpoints)):
pl.figure()
for i in range(Norbs):
orb = olabels[i][0]
if orb == 's':
c = 'blue'
elif orb == 'p':
c = 'green'
elif orb == 'd':
c = 'purple'
elif orb == 'f':
c = 'red'
elif orb == 't':
lxdata[2] = map(lambda x: x * dr, range(nr)) #phi
lydata[2] = map(float, lmpdata[nrho + nr:nrho + 2 * nr])
lydata[2] = [lydata[2][i] / (lxdata[2][i] + 1E-20) for i in range(nr)]
lxdata[3] = map(lambda x: x * dr, range(nr)) #u
lydata[3] = map(float, lmpdata[nrho + 2 * nr:nrho + 3 * nr])
lxdata[4] = map(lambda x: x * dr, range(nr)) #w
lydata[4] = map(float, lmpdata[nrho + 3 * nr:nrho + 4 * nr])
print lxdata[2]
#print "Variable : minval - maxval"
pl.figure(figsize=(9, 6))
for i, var in enumerate(varnames):
pl.subplot(321 + i)
pl.scatter(datax[i], datay[i])
xlims = pl.xlim()
ylims = pl.ylim()
pl.plot(lxdata[i], lydata[i], label=var)
pl.xlim(xlims)
pl.ylim(ylims)
#pl.ylim([ylims[0]/20,ylims[1]/50])
# prit "%3.3s : %4.4e - %4.4e"%(var,min(datay[i]),max(datay[i]))
pl.legend(loc=0)
#pl.legend()
#pl.savefig("/home/acadien/Dropbox/stuff%s.png"%var)
pl.legend(loc=0)
pr.prshow("SampledPotential.eps")
#pl.show()
mxy=0
mny=0
for lmpx,lmpy,lmpname in zip(lmpxs,lmpys,lmpnames):
pl.plot(lmpx[i],lmpy[i],label=lmpname)
if i>0:
g=[k for k in range(len(lmpx[i])) if lmpx[i][k]<1.5][-1]
mxy=max([mxy,max(lmpy[i][g:])])
mny=min([mny,min(lmpy[i][g:])])
pl.xlim([1.4,6])
pl.ylim([mny*1.2,mxy*1.2])
if pf:
pl.xlim(xlims)
pl.ylim(ylims)
if i==2:
pl.ylim([0,2])
pl.ylabel(var)
pl.legend(loc=0,fontsize='small')
#pl.subplot(326)
#pl.plot(lmpx[0],[i-j for i,j in zip(lmpys[0][4],lmpys[1][4])])
#pl.legend()
#pl.savefig("/home/acadien/Dropbox/stuff%s.png"%var)
pl.tight_layout()
pr.prshow("lammpsPotential.png")
#pl.show()
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")
def plotting(rbins, rdist):
pl.plot(rbins, rdist)
pl.xlabel("radius ($\AA$)")
pl.ylabel("g(r)")
pl.title("RDF | File:%s | Step:%s" % (sys.argv[1], head.split()[-1]))
pr.prshow("RDF_lammps.png")
def plotting(rbins,rdist):
pl.plot(rbins,rdist)
pl.xlabel("radius ($\AA$)")
pl.ylabel("g(r)")
pl.title("RDF | File:%s | Step:%s"%(sys.argv[1],head.split()[-1]))
pr.prshow("RDF_lammps.png")
"""
pl.subplot(312)
pl.plot(rmsdAvg,label="AvgRMSD")
pl.plot(slowRMSDAvg,label="SlowAvgRMSD")
pl.plot(fastRMSDAvg,label="FastAvgRMSD")
for i in fastAtoms:
pl.plot(i)
pl.legend(loc=0)
pl.subplot(313)
pl.scatter(range(nIon),jumpedFlag10)
pl.xlim([0,nIon])
"""
nJumped = sum([1 for i in jumpedFlag50 if i > 0])
print "%d: %d / %d" % (50, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag100 if i > 0])
print "%d: %d / %d" % (100, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag200 if i > 0])
print "%d: %d / %d" % (200, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag300 if i > 0])
print "%d: %d / %d" % (300, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag400 if i > 0])
print "%d: %d / %d" % (400, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag500 if i > 0])
print "%d: %d / %d" % (500, nJumped, nIon)
nJumped = sum([1 for i in jumpedFlag1000 if i > 0])
print "%d: %d / %d" % (1000, nJumped, nIon)
pr.prshow()
def plotting(rbins,rdist):
pl.plot(rbins,rdist)
pl.ylabel(r"$P \left (Q_%d \right )"%l)
pl.xlabel(r"$Q_%d$"%l)
pl.title("Bond Orientation | File:%s | Step:%s"%(sys.argv[1],head.split()[-1]))
pr.prshow("bondOrientation.png")
pl.legend(loc=0, fontsize=12)
# pl.subplot(subs+2)
pl.figure(figsize=(20, 12))
dictScatter(Vpressures, Venergies, phases)
if lmppot != -1:
dictPlot(Lpressures, Lenergies, phases, "-", 1.5)
pl.xlabel("Pressure ($GPa$)", size=17)
pl.ylabel("Energy ($eV / atom$)", size=17)
pl.ylim([-4.55, -3])
pl.xlim([-15, 50])
pl.legend(loc=0, fontsize=12)
# dictScatterInset(Vpressures,Venergies,phases,xlim=[-20,20],ylim=[-8,-7.5], xticks=[-20,0,20], yticks=[-8,-7.5])
# pr.prshow("EOS_PE.png")
pr.prshow("EOS_PVol.png")
exit(0)
# pl.subplot(subs+3)
pl.figure()
dictScatter(Vpressures, Venthalpies, phases)
# pl.xlim([0,500])
# pl.ylim([-8.5,-2])
if lmppot != -1:
dictPlot(Lpressures, Lenthalpies, phases, "-", 1.5)
pl.xlabel("Pressure ($GPa$)", size=17)
pl.ylabel("Enthalpy ($eV / atom$)", size=17)
pl.legend(loc=0, fontsize=12)
# dictScatterInset(Vpressures,Venergies,phases,xlim=[0,100],ylim=[-8,-7], xticks=[0,20,40,60,80,100], yticks=[-8,-7])
# pr.prshow("EOS_PH.png")
# pl.subplot(subs+4)
elif ySmoothEnable:
pl.plot(xdata,
ydataSmooth,
c=cc,
lw=2,
alpha=1.0,
label=fnames[i])
elif xSmoothEnable:
pl.plot(xdataSmooth,
ydata,
c=cc,
lw=2,
alpha=1.0,
label=fnames[i])
else:
pl.plot(xdata, ydata, lw=1.5, c=cc, label=fnames[i])
if switches["-avg"]:
avgy = [i / count for i in avgy]
pl.plot(avgx, avgy)
if switches["-saveData"]:
data = label[xCol] + " " + label[yCol] + "\n"
for x, y in zip(avgx, avgy):
data += str(x) + " " + str(y) + "\n"
open("plot2.data", "w").write(data)
if not switches["-noleg"]:
pl.legend(loc=0)
pr.prshow("plot2.png")
pl.xlabel("Volume ($\AA^3 / atom$)", size=17)
pl.ylabel("Energy ($eV / atom$)", size=17)
pl.legend(loc=0, fontsize=12)
#pl.subplot(subs+2)
pl.figure(figsize=(20, 12))
dictScatter(Vpressures, Venergies, phases)
if lmppot != -1: dictPlot(Lpressures, Lenergies, phases, "-", 1.5)
pl.xlabel("Pressure ($GPa$)", size=17)
pl.ylabel("Energy ($eV / atom$)", size=17)
pl.ylim([-4.55, -3])
pl.xlim([-15, 50])
pl.legend(loc=0, fontsize=12)
#dictScatterInset(Vpressures,Venergies,phases,xlim=[-20,20],ylim=[-8,-7.5], xticks=[-20,0,20], yticks=[-8,-7.5])
#pr.prshow("EOS_PE.png")
pr.prshow("EOS_PVol.png")
exit(0)
#pl.subplot(subs+3)
pl.figure()
dictScatter(Vpressures, Venthalpies, phases)
#pl.xlim([0,500])
#pl.ylim([-8.5,-2])
if lmppot != -1: dictPlot(Lpressures, Lenthalpies, phases, "-", 1.5)
pl.xlabel("Pressure ($GPa$)", size=17)
pl.ylabel("Enthalpy ($eV / atom$)", size=17)
pl.legend(loc=0, fontsize=12)
#dictScatterInset(Vpressures,Venergies,phases,xlim=[0,100],ylim=[-8,-7], xticks=[0,20,40,60,80,100], yticks=[-8,-7])
#pr.prshow("EOS_PH.png")
#pl.subplot(subs+4)
pl.figure()