def rdfExtend(rdfX,rdfY,ndens,rmax=50.0,Niter=5,T=300.0,rm=2.5,epsilon=-1E-10,damped=True,PY=True):
#rm and eps are parameters for the LJ (used as the PY closure)
#T is the temperature for the energy, effects smoothing near the transition point
#PY selects the Percus Yevick Closure for C(r), if false, the HNC criterion are used.
rdfX=list(rdfX)[:-10]
rdfY=list(rdfY)[:-10]
drx = rdfX[1]-rdfX[0]
n = int(np.ceil(rmax/drx))
hrx = np.array([i*drx for i in range(n)])
hry = np.array([(j-1.0) for d,j in enumerate(rdfY + [1.0 for i in hrx[len(rdfY):]])])
phi=[ epsilon*((rm/r)**12-2*(rm/r)**6) if r>0 else 0.0 for r in hrx] #else eps*((rm/1E-10)**12-2*(rm/1E-10)**6) for r in hrx]
#import pylab as pl
#pl.plot(hrx,phi)
#pl.ylim(-10,100)
#pl.show()
#exit(0)
beta = 1.0/8.6173324e-05/T
icut = len(rdfX)
cr = np.array([0.0 for i in hry])
qr = np.array([0.0 for i in hry])
qrp = np.array([0.0 for i in hry])
hrlast = np.array(hry)
qrplast = np.zeros(len(hry))
Lmax=10.0
qbins=1024
minq,maxq,dq=0,Lmax,Lmax/qbins
qs=[i*dq+minq for i in range(qbins)]
qs[0]=1E-10
PY=True
eta = 0.2
qs=[i/100.0 for i in range(1,1000)]
import pylab as pl
kbreak=20
for k in range(Niter):
if k==kbreak: break
qrp = calc_qpr_rltcut(icut,ndens,hrx,hry,cr,qr,qrp)
qrp = calc_qpr_rgtcut(icut,ndens,hrx,hry,cr,qr,qrp)
qrp = eta*qrp + (1-eta)*qrplast
qr = calc_qr (icut,ndens,hrx,hry,cr,qr,qrp)
hry = calc_hr_rgtcut (icut,ndens,hrx,hry,cr,qr,qrp)
cr = calc_cr (icut,ndens,hrx,hry,cr,qr,qrp,phi,beta,PY)
#hry2 = calc_hr (icut,ndens,hrx,hry,cr,qr,qrp)
sq = calcSq(ndens,hrx,hry,qs)
#pl.plot(hrx,hry,label=str(k),c=vizSpec(float(k)/kbreak))
pl.plot(qs,sq,label=str(k),c=vizSpec(float(k)/kbreak))
err = np.power((hrlast-hry),2).sum()
print "step %d, error %f"%(k,err)
if err>100: break
hrlast = np.array(hry)
qrplast = np.array(qrp)
pl.legend(loc=0)
pl.show()
exit(0)
qrp = superSmooth(hrx,qrp,sigma=0.1)
calc_hr(len(hrx),ndens,hrx,hry,cr,qr,qrp)
pl.plot(hrx,hry,c="black",lw=2)
grExtended = np.array([i+1.0 for i in hry])
return cr,hrx,grExtended
if xCol != -1:
pl.xlabel(label[xCol])
pl.ylabel(label[yCol])
if switches["-hist"]:
pl.xlabel(label[yCol])
pl.ylabel("count")
if switches["-3d"]:
if colors == None:
ax.plot(xdata, ydata, zs=i, lw=1.5)
else:
ax.plot(xdata,
ydata,
zs=i,
lw=2,
c=vizSpec(float(i) / len(fnames)))
elif switches["-hist"]:
mn = min(ydata)
mx = max(ydata)
dely = (mx - mn) / nbins
ybins = [i * dely + mn for i in range(0, nbins + 1)]
yvals = np.bincount([(y - mn) / (mx - mn) * nbins
for y in ydata]).tolist()
if switches["-avg"]:
avgy += np.array(yvals)
count += 1
else:
pl.plot(ybins, yvals)
elif switches["-avg"]:
mn=min(ydata)
mx=max(ydata)
if i==0 and len(label)==len(fdata):
if xCol!=-1:
pl.xlabel( label[xCol] )
pl.ylabel( label[yCol] )
if switches["-hist"]:
pl.xlabel( label[yCol] )
pl.ylabel("count")
if switches["-3d"]:
if colors==None:
ax.plot(xdata,ydata,zs=i,lw=1.5)
else:
ax.plot(xdata,ydata,zs=i,lw=2,c=vizSpec(float(i)/len(fnames)))
elif switches["-hist"]:
mn=min(ydata)
mx=max(ydata)
dely=(mx-mn)/nbins
ybins=[i*dely+mn for i in range(0,nbins+1)]
yvals=np.bincount([(y-mn)/(mx-mn)*nbins for y in ydata]).tolist()
if switches["-avg"]:
avgy+=np.array(yvals)
count+=1
else:
pl.plot(ybins,yvals)
elif switches["-avg"]:
if len(avgy)!=len(ydata):
print "Not all data is the same length, unable to average lists of different lengths."
exit(0)
if ySmoothEnable:
avgy+=np.array(ydataSmooth)
else:
avgy+=np.array(ydata)
count+=1
elif switches["-scatter"]:
if xSmoothEnable:
xdata=xdataSmooth
if ySmoothEnable:
ydata=ydataSmooth
nPoints = int(switches["-scatter"])
pl.scatter(xdata[::nPoints],ydata[::nPoints],lw=0.1,label=fileNames[i],facecolor=vizSpec(float(i)/max((nFileNames-1),1) ))
else: #Regular plot, multiple lines
cc=vizSpec(float(i)/max(nFileNames-1,1) )
if xSmoothEnable:
xdata=xdataSmooth
if ySmoothEnable:
ydata=ydataSmooth
pl.plot(xdata,ydata,lw=1.5,c=cc,label=fileNames[i],alpha=alpha)
if switches["-avg"]:
avgy=[i/count for i in avgy]
pl.plot(avgx,avgy)
if nEvolve != None:
[xs,ys]=zip(*orderVals)
xs=xs[0]
nSamples=len(ys)
nDel=nSamples/nEvolve
yEvs=list()
lablel=list()
for i in range(nEvolve):
start, finish = i*nDel, (i+1)*nDel
labels.append(str(start)+" - "+str(finish))
yEvs.append(map(lambda x: sum(x)/len(x),zip(*ys[start:finish])))
if not(stagger):
for i in range(nEvolve):
pl.plot(xs,yEvs[i],color=vizSpec(i/float(nEvolve)))
else:
yDel=( max(map(max,yEvs)) - min(map(min,yEvs)) )/2.
for i in range(nEvolve):
yvals=[y+yDel*i for y in yEvs[i]]
pl.plot(xs,yvals,color=vizSpec(i/float(nEvolve)))
pl.text((max(xs)-min(xs))*0.8,yDel*(i+0.2),labels[i])
pl.yticks([])
pl.xlabel(xylabels[op][0])
pl.ylabel(xylabels[op][1])
pr.prshow("%s_chart_evolve.png"%op)
exit(0)
if args.averageFlag:
if op not in ["BO","CN","TN","TET"]:
t = zip(*[orderVals[i][1] for i in range(len(orderVals))])
if nEvolve != None:
[xs, ys] = zip(*orderVals)
xs = xs[0]
nSamples = len(ys)
nDel = nSamples / nEvolve
yEvs = list()
lablel = list()
for i in range(nEvolve):
start, finish = i * nDel, (i + 1) * nDel
labels.append(str(start) + " - " + str(finish))
yEvs.append(map(lambda x: sum(x) / len(x), zip(*ys[start:finish])))
if not (stagger):
for i in range(nEvolve):
pl.plot(xs, yEvs[i], color=vizSpec(i / float(nEvolve)))
else:
yDel = (max(map(max, yEvs)) - min(map(min, yEvs))) / 2.
for i in range(nEvolve):
yvals = [y + yDel * i for y in yEvs[i]]
pl.plot(xs, yvals, color=vizSpec(i / float(nEvolve)))
pl.text((max(xs) - min(xs)) * 0.8, yDel * (i + 0.2), labels[i])
pl.yticks([])
pl.xlabel(xylabels[op][0])
pl.ylabel(xylabels[op][1])
pr.prshow("%s_chart_evolve.png" % op)
exit(0)
if args.averageFlag:
if op not in ["BO", "CN", "TN", "TET"]:
t = zip(*[orderVals[i][1] for i in range(len(orderVals))])
