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extract_tof_sys.py
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extract_tof_sys.py
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import libxml2
import sys, os, subprocess, math, ROOT
from ROOT import TH1D, TCanvas, TFile, TGraphErrors, TMarker, TF1, TLegend, TText
def plotFiles(meas172, measfile, meas240, syslist):
hists = []
graphs = []
m172 = ExtractPars(meas172)
mval = ExtractPars(measfile)
m240 = ExtractPars(meas240)
m172G4 = ExtractParsG4(meas172)
m200G4 = ExtractParsG4(measfile)
m240G4 = ExtractParsG4(meas240)
print mval, m172, m240
pars = [["TOF_ll",-0.8,0.8], ["TOF_ul",0.5,2.1],
["integralX",0,2*mval[2][0]], ["amplitudeX", 0, 2*mval[3][0]],
["rawmeanX",mval[4][0]-3*mval[4][1], mval[4][0]+3*mval[4][1]],
["rawsigmaX",mval[5][0]-3*mval[5][1], mval[5][0]+3*mval[5][1]],
["meanX",mval[6][0]-3*mval[6][1], mval[6][0]+3*mval[6][1]],
["sigmaX",mval[7][0]-3*mval[7][1], mval[7][0]+3*mval[7][1]],
["integralY",0,2*mval[8][0]], ["amplitudeY", 0, 2*mval[9][0]],
["rawmeanY",mval[10][0]-3*mval[10][1], mval[10][0]+3*mval[10][1]],
["rawsigmaY",mval[11][0]-3*mval[11][1], mval[11][0]+3*mval[11][1]],
["meanY",mval[12][0]-3*mval[12][1], mval[12][0]+3*mval[12][1]],
["sigmaY",mval[13][0]-3*mval[13][1], mval[13][0]+3*mval[13][1]],
["rawmeantheta2",mval[14][0]-3*mval[12][1], mval[14][0]+3*mval[12][1]],
["meantheta2",mval[15][0]-3*mval[12][1], mval[15][0]+3*mval[12][1]],
["momentum",mval[16][0]-3*mval[14][1], mval[16][0]+3*mval[14][1]]
]
# for par in pars:
# hists.append(TH1D(par[0], ";"+par[0],100,par[1],par[2]))
pointlist = []
scale = 1. # 1.18 # 1.13 # 0.894
offset = 0. # -11.28 # -5.95 # 46.4
for sysfile in syslist:
sysval = ExtractPars(sysfile)
pcomp = 200
if sysfile.find("LiHMu_3172") >= 0:
pcomp = 174
'''
p0 = 1.05205 +/- 0.273774
p1 = 1.10656 +/- 0.0016539
'''
sysval[16][0] *= 1.107
sysval[16][0] += 1.05
sysval[16][1] *= 1.107
elif sysfile.find("LiHMu_3200") >= 0:
pcomp = 200
'''
p0 = 1.13876 +/- 0.671571
p1 = 1.10475 +/- 0.00407707
'''
sysval[16][0] *= 1.104
sysval[16][1] *= 1.104
sysval[16][0] += 1.139
elif sysfile.find("LiHMu_3240") >= 0:
pcomp = 240
'''
p0 = -9.41424 +/- 0.293949
p1 = 1.17466 +/- 0.00142602
'''
sysval[16][0] *= 1.17
sysval[16][1] *= 1.17
sysval[16][0] -= 9.41
# sysval[16][1] *= 0.780
if sysval[2][0] > 2000.: # and pcomp < sysval[-1][0] + 10:
pointlist.append(sysval)
print sysval
for par in pars:
graphs.append(TGraphErrors(len(pointlist)))
graphs[-1].SetName(par[0])
# graphs[-1].SetTitle("; #Delta t_{12}(ns);"
if par[0] == "rawmeantheta2" or par[0] == "meantheta2":
graphs[-1].SetTitle("; Momentum (MeV/c); #sqrt{<#theta^{2}>/2} (milliradians)")
elif par[0] == "rawsigmaX" or par[0] == "sigmaX":
graphs[-1].SetTitle("; Momentum (MeV/c); #Theta_{X} (milliradians)")
elif par[0] == "rawsigmaY" or par[0] == "sigmaY":
graphs[-1].SetTitle("; Momentum (MeV/c); #Theta_{Y} (milliradians)")
elif par[0] == "integralX":
graphs[-1].SetTitle("; Momentum (MeV/c); Integral of Events in Bin")
else:
graphs[-1].SetTitle("; Momentum (MeV/c);#Delta t_{01} (ns)")
j = 0
FillGraphs(graphs, pointlist, offset, scale)
# func = TF1("func",'[0] + [1]*13.6*sqrt(x*x + 105.65 * 105.65)/x/x + [2]*13.6*13.6*(x*x + 105.65 * 105.65)/x/x/x/x',150,280)
# func = TF1("func",'[0] + [1]*13.6*sqrt(1 + 105.65 * 105.65/x/x)/x',150,280)
# func = TF1("func",'[0] + [1]*13.6*sqrt(1 + 105.65 * 105.65/(1.21*x - 14.9)/(1.21*x - 14.9))/(1.21*x - 14.9)',150,280)
func = TF1("func",'[0]*13.6*sqrt(1 + 105.65 * 105.65/x/x)/(x)',150,280)
# func = TF1("func",'[0]*13.6*105.65*105.65/sqrt(x*x + 105.65 * 105.65)/x/x',150,280)
func0 = TF1("func0",'[0]*13.6*sqrt(1 + 105.65 * 105.65/x/x)/(x)',150,280)
ftof = TF1("ftof",'[0]/x + [1]', 26.8, 29.4)
i = 0
for graph in graphs:
c = TCanvas()
c.SetLeftMargin(0.125)
c.SetBottomMargin(0.125)
graph.SetMarkerStyle(21)
graph.Draw('ap')
graph.GetHistogram().GetXaxis().SetLabelSize(0.05)
graph.GetHistogram().GetYaxis().SetLabelSize(0.05)
graph.GetHistogram().GetXaxis().SetTitleSize(0.05)
graph.GetHistogram().GetYaxis().SetTitleSize(0.05)
graph.GetHistogram().GetYaxis().SetTitleOffset(1.25)
if graph.GetName() == "integralX":
leg = TLegend(0.15,0.65,0.45,0.89)
else:
leg = TLegend(0.5,0.5,0.89,0.89)
leg.SetLineColor(10)
leg.AddEntry(graph,"Data","p")
if graph.GetName() == "rawmeantheta2" or graph.GetName() == "meantheta2" \
or graph.GetName() == "rawsigmaX" or graph.GetName() == "sigmaX" \
or graph.GetName() == "rawsigmaY" or graph.GetName() == "sigmaY":
graph.Fit('func')
x = ROOT.Double()
y = ROOT.Double()
for k in range(len(pointlist)):
graph.GetPoint(k, x, y)
ex = graph.GetErrorX(k)
ey = graph.GetErrorY(k)
fup = graph.GetFunction('func').Eval(x + 4)
fdn = graph.GetFunction('func').Eval(x - 4)
time = (float(pointlist[k][1][0]) + float(pointlist[k][0][0]))/2.
dpdt = (1.04e3/(time - 23.2)**2)
dsdp = (fup - fdn)/8.
errsys = dpdt * 0.129
print k, j, pointlist[k][1][0], pointlist[k][0][0], x, y, fup, fdn, errsys, graph.GetErrorY(k), math.sqrt(pow(float(pointlist[k][i][1]),2)), \
math.sqrt(pow(float(pointlist[k][i][1]),2) + errsys*errsys)
graph.SetPointError(k, math.sqrt(ex*ex + errsys*errsys), math.sqrt(ey*ey + 0.0*errsys*errsys))
graph.Fit('func')
leg.AddEntry(func, "Fit to #frac{13.6 a}{p#beta}", "l")
fup = graph.GetFunction('func').Clone()
fup.SetName('fup')
fup.SetParameter(0, graph.GetFunction('func').GetParameter('p0') + \
graph.GetFunction('func').GetParError(0))
# fup.SetParameter(1, graph.GetFunction('func').GetParameter('p1') + \
# graph.GetFunction('func').GetParError(1))
fup.SetLineColor(4)
leg.AddEntry(fup, "Fit plus/minus error", "l")
fdn = graph.GetFunction('func').Clone()
fdn.SetName('fdn')
fdn.SetParameter(0, graph.GetFunction('func').GetParameter('p0') - \
graph.GetFunction('func').GetParError(0))
# fdn.SetParameter(1, graph.GetFunction('func').GetParameter('p1') - \
# graph.GetFunction('func').GetParError(1))
fdn.SetLineColor(4)
# leg.AddEntry(fdn, "Fit minus stat. error", "l")
fup.Draw('lsame')
fdn.Draw('lsame')
func0.SetParameter(0, 253*(1 + 0.036*math.log(0.253*0.253)))
func0.SetLineColor(8)
func0.Draw("lsame")
leg.AddEntry(func0,"PDG prediction","l")
if graph.GetName() == "momentum":
graph.Fit('ftof')
print "TOF for 172 MeV/c is ",graph.GetFunction('ftof').Eval(172)
print "TOF for 200 MeV/c is ",graph.GetFunction('ftof').Eval(200)
print "TOF for 240 MeV/c is ",graph.GetFunction('ftof').Eval(240)
leg.AddEntry(ftof, "Fit to #frac{a}{p} + b", "l")
graph.GetHistogram().GetXaxis().SetRangeUser(25,30)
if j!=2 and j!=3 and j!=4 and j!=6 and j!=8 and j!=9 \
and j!=10 and j!=12 and j!=len(graphs)-1:
graph.GetHistogram().GetYaxis().SetRangeUser(10,28)
elif j==4 or j==6 or j==10 or j==12:
graph.GetHistogram().GetYaxis().SetRangeUser(-5,10)
# graph.GetHistogram().GetYaxis().SetRangeUser(float(mval[j][0]) - 30*float(mval[j][1]),
# float(mval[j][0]) + 30*float(mval[j][1]))
# m = TMarker((float(mval[1][0]) + float(mval[0][0]), float(mval[j][0]), 25)
elif j==2:
graph.GetHistogram().GetYaxis().SetRangeUser(0,10000)
if graph.GetName() == "momentum":
m = TMarker(1.104 * float(mval[j][0]) + 1.139, (float(mval[1][0]) + float(mval[0][0]))/2., 20)
m_172 = TMarker(1.107 * float(m172[j][0]) + 1.105, (float(m172[1][0]) + float(m172[0][0]))/2., 20)
m_240 = TMarker(1.175 * float(m240[j][0]) - 9.41, (float(m240[1][0]) + float(m240[0][0]))/2., 20)
# mG4_172 = TMarker(scale * float(m172G4[j][0]) + offset, (float(m172G4[1][0]) + float(m172G4[0][0]))/2., 20)
# mG4_200 = TMarker(scale * float(m200G4[j][0]) + offset, (float(m200G4[1][0]) + float(m200G4[0][0]))/2., 20)
# mG4_240 = TMarker(scale * float(m240G4[j][0]) + offset, (float(m240G4[1][0]) + float(m240G4[0][0]))/2., 20)
else:
m = TMarker((1.104 * float(mval[-1][0]) + 1.139), float(mval[j][0]), 20)
m_172 = TMarker((1.107 * float(m172[-1][0]) + 1.105), float(m172[j][0]), 20)
m_240 = TMarker((1.175 * float(m240[-1][0]) - 9.41), float(m240[j][0]), 20)
mG4_172 = TMarker((1.107 * float(m172G4[-1][0]) + 1.105), float(m172G4[j][0]), 21)
mG4_200 = TMarker((1.104 * float(m200G4[-1][0]) + 1.139), float(m200G4[j][0]), 21)
mG4_240 = TMarker((1.175 * float(m240G4[-1][0]) - 9.41), float(m240G4[j][0]), 21)
m.SetMarkerColor(2)
m.SetMarkerSize(2)
m.Draw("psame")
m_172.SetMarkerColor(2)
m_172.SetMarkerSize(2)
# m_172.Draw("psame")
m_240.SetMarkerColor(2)
m_240.SetMarkerSize(2)
# m_240.Draw("psame")
leg.AddEntry(m,"Nominal Momenta","p")
if graph.GetName() != "momentum" and graph.GetName() != "integralX":
mG4_172.SetMarkerColor(4)
mG4_172.SetMarkerSize(2)
# mG4_172.Draw("psame")
mG4_200.SetMarkerColor(4)
mG4_200.SetMarkerSize(2)
mG4_200.Draw("psame")
mG4_240.SetMarkerColor(4)
mG4_240.SetMarkerSize(2)
# mG4_240.Draw("psame")
leg.AddEntry(mG4_200,"Moliere Predictions","p")
leg.Draw("same")
j+=1
i+=1
t1 = TText(0.18,0.215,"MICE isis cycle 2015/04")
t2 = TText(0.18,0.185,"LiH, MAUS v2.5")
# t1 = TText(0.15,0.875,"MICE preliminary")
# t2 = TText(0.15,0.825,self.desc[0] + ", " + self.desc[1][2:5]+" MeV/c, March 2016, MAUS v2.5")
t1.SetNDC(1)
t1.SetTextSize(0.04)
t1.SetTextFont(42)
t2.SetNDC(1)
t2.SetTextSize(0.03)
t2.SetTextFont(42)
t1.Draw()
t2.Draw()
c.Print(graph.GetName()+".eps")
# print h.GetName(), "Mean Value = ", hist.GetMean(), "+/-", hist.GetMeanError(), \
# "RMS Value = ", hist.GetRMS(),"+/-",hist.GetRMSError()
def FillGraphs(graphs, pointlist, offset, scale):
j = 0
for point in pointlist:
for i in range(2,len(point)):
if graphs[i].GetName() == "momentum":
graphs[i].SetPoint(j,
scale * float(point[i][0]) + offset,
(float(point[1][0]) + float(point[0][0]))/2.)
graphs[i].SetPointError(j,
scale * float(point[i][1]),
(float(point[1][0]) - float(point[0][0]))/2.)
else:
graphs[i].SetPoint(j, (scale * float(point[16][0]) + offset),
float(point[i][0]))
#
graphs[i].SetPointError(j, math.sqrt(pow(scale * float(point[16][1]),2)*(1 + 0.2*0.2)),
math.sqrt(pow(float(point[i][1]),2)))
j+=1
def ExtractPars(xmlfile):
print xmlfile
doc = libxml2.parseFile(xmlfile)
vals = []
rootfile = next(x.prop("name") for x in doc.xpathEval("spec/file") \
if x.prop("id").find("outfile") >= 0)
print rootfile
rf = TFile(rootfile)
if not rf:
return vals
if rf.IsZombie():
return vals
print rootfile
vals.append([next(x.prop("value") for x in doc.xpathEval("spec/cuts") \
if x.prop("name").find("TOF_ll") >= 0), 0.1])
vals.append([next(x.prop("value") for x in doc.xpathEval("spec/cuts") \
if x.prop("name").find("TOF_ul") >= 0), 0.1])
# try:
thX_md = rf.Get("thetaX_measdataMoliere")
# except Exception as inst:
# print inst
# return vals
# if thX_md.ClassName() != 'TH1D':
# return vals
thX_md.Rebin(2)
thY_md = rf.Get("thetaY_measdataMoliere")
thY_md.Rebin(2)
th2_md = rf.Get("theta2Scatt_measdataMoliere")
thX_rc = rf.Get("thetaX_recoMoliere")
thX_rc.Rebin(2)
thY_rc = rf.Get("thetaY_recoMoliere")
thY_md.Rebin(2)
th2_rc = rf.Get("theta2Scatt_recoMoliere")
pcalc = rf.Get("thetaScatt_measdata_vpMoliere")
vals.append([thX_md.Integral(), math.sqrt(thX_md.Integral())])
if thX_md.Integral() < 0:
return vals
else:
limits = math.sqrt(th2_md.GetMean())
thX_md.Fit("gaus","Q","",-limits,limits)
vals.append([thX_md.GetFunction("gaus").GetParameter("Constant"), \
thX_md.GetFunction("gaus").GetParError(0)])
vals.append([1000*thX_md.GetFunction("gaus").GetParameter("Mean"), \
1000*thX_md.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thX_md.GetFunction("gaus").GetParameter("Sigma"), \
1000*thX_md.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thX_rc.Fit("gaus","Q","",-limits,limits)
vals.append([1000*thX_rc.GetFunction("gaus").GetParameter("Mean"), \
1000*thX_rc.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thX_rc.GetFunction("gaus").GetParameter("Sigma"), \
1000*thX_rc.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thY_md.Fit("gaus","Q","",-limits,limits)
vals.append([thY_md.Integral(), math.sqrt(thY_md.Integral())])
vals.append([thY_md.GetFunction("gaus").GetParameter("Constant"), \
thY_md.GetFunction("gaus").GetParError(0)])
vals.append([1000*thY_md.GetFunction("gaus").GetParameter("Mean"), \
1000*thY_md.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thY_md.GetFunction("gaus").GetParameter("Sigma"), \
1000*thY_md.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thY_rc.Fit("gaus","Q","",-limits,limits)
vals.append([1000*thY_rc.GetFunction("gaus").GetParameter("Mean"), \
1000*thY_rc.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thY_rc.GetFunction("gaus").GetParameter("Sigma"), \
1000*thY_rc.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
# th2_md.GetXaxis.SetRangeUser
vals.append([1000 * math.sqrt(th2_md.GetMean()/2.), \
1000 * th2_md.GetMeanError()/math.sqrt(th2_md.GetMean()/2.)])
vals.append([1000 * math.sqrt(th2_rc.GetMean()/2.), \
1000 * th2_rc.GetMeanError()/math.sqrt(th2_rc.GetMean()/2.)])
vals.append([pcalc.GetMean(), pcalc.GetMeanError()])
return vals
def ExtractParsG4(xmlfile):
# print xmlfile
doc = libxml2.parseFile(xmlfile)
vals = []
rootfile = next(x.prop("name") for x in doc.xpathEval("spec/file") \
if x.prop("id").find("outfile") >= 0)
print "Check MC in ",rootfile
vals.append([next(x.prop("value") for x in doc.xpathEval("spec/cuts") \
if x.prop("name").find("TOF_ll") >= 0), 0.1])
vals.append([next(x.prop("value") for x in doc.xpathEval("spec/cuts") \
if x.prop("name").find("TOF_ul") >= 0), 0.1])
rf = TFile(rootfile)
thX_md = rf.Get("thetaX_measuredMoliere")
thX_md.Rebin(2)
thY_md = rf.Get("thetaY_measuredMoliere")
thY_md.Rebin(2)
th2_md = rf.Get("theta2Scatt_measuredMoliere")
thX_rc = rf.Get("thetaX_Moliere")
thX_rc.Rebin(2)
thY_rc = rf.Get("thetaY_Moliere")
thY_md.Rebin(2)
th2_rc = rf.Get("theta2Scatt_Moliere")
# try:
pcalc = rf.Get("thetaScatt_measdata_vpMoliere")
# except:
# pcalc = rf.Get("thetaScatt_measdata_vpMoliere")
vals.append([thX_md.Integral(), math.sqrt(thX_md.Integral())])
if thX_md.Integral() < 2000:
return vals
else:
limits = math.sqrt(th2_md.GetMean())
thX_md.Fit("gaus","Q","",-limits,limits)
vals.append([thX_md.GetFunction("gaus").GetParameter("Constant"), \
thX_md.GetFunction("gaus").GetParError(0)])
vals.append([1000*thX_md.GetFunction("gaus").GetParameter("Mean"), \
1000*thX_md.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thX_md.GetFunction("gaus").GetParameter("Sigma"), \
1000*thX_md.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thX_rc.Fit("gaus","Q","",-limits,limits)
vals.append([1000*thX_rc.GetFunction("gaus").GetParameter("Mean"), \
1000*thX_rc.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thX_rc.GetFunction("gaus").GetParameter("Sigma"), \
1000*thX_rc.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thY_md.Fit("gaus","Q","",-limits,limits)
vals.append([thY_md.Integral(), math.sqrt(thY_md.Integral())])
vals.append([thY_md.GetFunction("gaus").GetParameter("Constant"), \
thY_md.GetFunction("gaus").GetParError(0)])
vals.append([1000*thY_md.GetFunction("gaus").GetParameter("Mean"), \
1000*thY_md.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thY_md.GetFunction("gaus").GetParameter("Sigma"), \
1000*thY_md.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
thY_rc.Fit("gaus","Q","",-limits,limits)
vals.append([1000*thY_rc.GetFunction("gaus").GetParameter("Mean"), \
1000*thY_rc.GetFunction("gaus").GetParameter("Mean")/math.sqrt(thX_md.Integral())])
vals.append([1000*thY_rc.GetFunction("gaus").GetParameter("Sigma"), \
1000*thY_rc.GetFunction("gaus").GetParameter("Sigma")/math.sqrt(thX_md.Integral())])
# th2_md.GetXaxis.SetRangeUser
vals.append([1000 * math.sqrt(th2_md.GetMean()/2.), \
1000 * th2_md.GetMeanError()/math.sqrt(th2_md.GetMean()/2.)])
vals.append([1000 * math.sqrt(th2_rc.GetMean()/2.), \
1000 * th2_rc.GetMeanError()/math.sqrt(th2_rc.GetMean()/2.)])
vals.append([pcalc.GetMean(), pcalc.GetMeanError()])
return vals
if __name__=="__main__":
plotFiles(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4:])