def fitRatioSlope(self,fitFuncRatio,fitRange):
self.plots.phiPropRatio.FitSlicesY()
histRatio=ROOT.gDirectory.Get(self.plots.phiPropRatio.GetName()+'_1')
histRatio.SetName(self.name+'_RatioMean')
if self.type==0:
self.plots.phiBPropRatioUnSc.FitSlicesY()
histBRatioUnSc=ROOT.gDirectory.Get(self.plots.phiBPropRatioUnSc.GetName()+'_1')
histBRatioUnSc.SetName(self.name+'_BRatioMean_unSc')
self.plots.phiBPropRatioSc.FitSlicesY()
histBRatioSc=ROOT.gDirectory.Get(self.plots.phiBPropRatioSc.GetName()+'_1')
histBRatioSc.SetName(self.name+'_BRatioMean_sc')
# Set up fitting functions
fitRatio=ROOT.TF1(histRatio.GetName(),fitFuncRatio,fitRange[0],fitRange[1])
if self.type==0:
fitBRatioUnSc=ROOT.TF1(histBRatioUnSc.GetName(),fitFuncRatio,fitRange[0],fitRange[1])
fitBRatioSc=ROOT.TF1(histBRatioSc.GetName(),fitFuncRatio,fitRange[0],fitRange[1])
# Draw ratio fit function
canvRatio=ROOT.TCanvas(histRatio.GetName()+'_canv')
canvRatio.cd()
canvRatio.SetGrid()
histRatio.Draw()
histRatio.GetXaxis().SetRangeUser(-1000,1000)
histRatio.GetXaxis().SetTitle('#phi_{stub}')
histRatio.GetYaxis().SetRangeUser(-1000,1000)
histRatio.GetYaxis().SetTitle('#phi_{prop}')
gStyle.SetOptFit()
if self.type==0:
canvBRatioUnSc=ROOT.TCanvas(histBRatioUnSc.GetName()+'_canv')
canvBRatioUnSc.cd()
canvBRatioUnSc.SetGrid()
histBRatioUnSc.Draw()
histBRatioUnSc.GetXaxis().SetRangeUser(-1000,1000)
histBRatioUnSc.GetXaxis().SetTitle('#phi_{stub,b}')
histBRatioUnSc.GetYaxis().SetRangeUser(-1000,1000)
histBRatioUnSc.GetYaxis().SetTitle('#phi_{prop,b}')
gStyle.SetOptFit()
canvBRatioSc=ROOT.TCanvas(histBRatioSc.GetName()+'_canv')
canvBRatioSc.cd()
canvBRatioSc.SetGrid()
histBRatioSc.Draw()
histBRatioSc.GetXaxis().SetRangeUser(-1000,1000)
histBRatioSc.GetXaxis().SetTitle('#phi_{stub,b}')
histBRatioSc.GetYaxis().SetRangeUser(-1000,1000)
histBRatioSc.GetYaxis().SetTitle('#phi_{prop,b}')
gStyle.SetOptFit()
histRatio.Fit(fitRatio,'R')
if self.type==0:
histBRatioUnSc.Fit(fitBRatioUnSc,'R')
histBRatioSc.Fit(fitBRatioSc,'R')
self.plots.PhiPropRatioMean=copy.deepcopy(histRatio)
if self.type==0:
self.plots.PhiBPropRatioMeanUnSc=copy.deepcopy(histBRatioUnSc)
self.plots.PhiBPropRatioMeanSc=copy.deepcopy(histBRatioSc)
def plot(obj, plot_name, plot_notes=[]):
canvas = TCanvas('canvas' + plot_name, "Canvas", 450, 450)
gPad.SetLeftMargin(.13)
gPad.SetTopMargin(.05)
gStyle.SetOptStat(11)
gStyle.SetOptFit(1111)
if 'TH2' in obj.ClassName():
gPad.SetRightMargin(.13)
draw_options = 'COLZ'
if 'profile' in obj.GetName():
gPad.SetRightMargin(.05)
draw_options = ''
if 'graph' in obj.GetName():
gPad.SetRightMargin(.05)
draw_options = 'APE'
obj.SetMarkerSize(.7)
obj.SetMarkerStyle(21)
note = TPaveText(.2, .7, .5, .9, "brNDC")
note.SetFillStyle(0)
note.SetFillColor(0)
note.SetBorderSize(0)
note.SetTextColor(1)
note.SetTextFont(42)
note.SetTextAlign(11)
for note_text in plot_notes:
note.AddText(note_text)
obj.Draw(draw_options)
note.Draw()
canvas.Print('output/' + plot_name + '.pdf')
def drawFit(self,h,figdir='',SetLogy=False,SetLogx=False,extraName=None):
'''
draw histogram with fit parameters
'''
name = h.GetName()
if extraName is not None: name += '_' + extraName
title = h.GetTitle()
if SetLogx: name += '_logx'
if SetLogy: name += '_logy'
pdf = os.path.join( figdir , name + '.pdf' )
ps = os.path.join( figdir , name + '.ps' )
xsize,ysize = 1100,850 # landscape style
noPopUp = True
if noPopUp : gROOT.ProcessLine("gROOT->SetBatch()")
canvas = TCanvas(pdf,title,xsize,ysize)
gStyle.SetOptFit(1111)
h.Draw()
if SetLogy: canvas.SetLogy(1)
if SetLogx: canvas.SetLogx(1)
canvas.Draw()
canvas.SetGrid(1)
canvas.SetTicks(1)
canvas.cd()
canvas.Modified()
canvas.Update()
canvas.Print(ps,'Landscape')
os.system('ps2pdf ' + ps + ' ' + pdf)
if os.path.exists(pdf): os.system('rm ' + ps)
return
def DoCBFit(histo, ZZtree, title, p):
canv = makeCMSCanvas(str(random.random()),"Fit result ",900,700)
Z1_fitFunction = TF1('Z1_fitFunction',DoubleCB(),60. ,120., 7)
Z1_fitFunction.SetParameters(p[0],p[1],p[2],p[3],p[4],p[5],p[6])
Z1_fitFunction.SetParLimits(0,85.,95.)#mean
Z1_fitFunction.SetParLimits(1,0.,5.)#width
Z1_fitFunction.SetParLimits(2,0.,2.)#alpha1
Z1_fitFunction.SetParLimits(3,0.,100.)#n1
Z1_fitFunction.SetParLimits(4,0.,2.)#alpha2
Z1_fitFunction.SetParLimits(5,0.,100.)#n2
Z1_fitFunction.SetParLimits(6,0.,1000000.)#const
Z1_fitFunction.SetParName(0,"Mean")#mean
Z1_fitFunction.SetParName(1,"Sigma")#width
Z1_fitFunction.SetParName(2,"#alpha_{1}")#alpha1
Z1_fitFunction.SetParName(3,"n_{1}")#n1
Z1_fitFunction.SetParName(4,"#alpha_{2}")#alpha2
Z1_fitFunction.SetParName(5,"n_{2}")#n2
Z1_fitFunction.SetParName(6,"C")#const
canv.cd()
histo.Fit(Z1_fitFunction)
gStyle.SetOptFit()
histo.SetTitle("")
histo.Draw()
print ("Mass: " +str(Z1_fitFunction.GetParameters()[0]) + " Width: " + str(Z1_fitFunction.GetParameters()[1]))
printLumiPrelOut(canv)
canv.SaveAs("DataVsMC/FitResults/"+title+"_fit.pdf")
canv.SaveAs("DataVsMC/FitResults/"+title+"_fit.png")
return Z1_fitFunction.GetParameters()[0], Z1_fitFunction.GetParError(0);
def main():
fwhm = np.array([], dtype="float")
da.save_Tree(filepath, win_percent=0.1)
f = TFile(filepath + "/" + "focus.root", "recreate")
f.mkdir("distribution_of_amp")
f.distribution.cd()
da.hist2(filepath, 1, 0, 1)
h2 = f.distribution.hist2
gDirectory.cd("..")
f.mkfir("projection")
f.projection.cd()
z = h2.GetNbinsY()
for i in range(0, z):
da.projection(h2, 'x', name="_pz", i, i + 1)
fwhm_value = calculate_FWHM(f.projection.hist2_pz)
np.append(fwhm, fwhm_value)
da.projection(h2, 'x', "_z" + str(i), i, i + 1)
f2 = TFile(filepath + "/" + "position_and_amp.root")
z_min = f2.t1.GetMinimum("z")
z_max = f2.t1.GetMaximum("z")
z = np.arange(z_min, z_max + 1)
z = z.astype("float")
tgr = TGraph(z.size, z, fwhm)
t1 = TCanva("t1", "focus")
tgr.GetXaxis().SetTitle("z[um]")
tgr.GetYaxis().SetTitle("FWHM[um]")
tgr.Draw("AC*")
tgr.Fit("pol2")
gStyle.SetOptFit(1)
c1.Update()
c1.SaveAs("./result/focus.pdf")
def TopMassStyle():
"""Set the style plot in order to create fancier plots!"""
gStyle.SetPadTickX(1);
gStyle.SetPadTickY(1);
gStyle.SetHistLineWidth(3);
gStyle.SetMarkerStyle(1);
gStyle.SetTextSize(0.065);
gStyle.SetOptFit(1111);
gStyle.SetTitleSize(.05,"X");#.055
gStyle.SetTitleOffset(1.1,"X");#1.2,0.9
gStyle.SetLabelSize(.05,"X");
gStyle.SetTitleSize(.05,"Y");#.055
gStyle.SetTitleOffset(1.1,"Y");
gStyle.SetLabelSize(.05,"Y");
gStyle.SetPadLeftMargin(.16);
gStyle.SetPadBottomMargin(.12);
gStyle.SetTitleSize(.05,"Z");
gStyle.SetTitleOffset(1.8,"Z");
gStyle.SetLabelSize(0.06,"Z");
gStyle.SetLegendTextSize(0.04);
gStyle.SetOptStat(112210);
gStyle.SetPadLeftMargin(.12);
gStyle.SetPadRightMargin(.02);
gStyle.SetPadBottomMargin(.12);
gStyle.SetPadTopMargin(.07);
gStyle.SetPadGridX(1);
gStyle.SetPadGridY(1);
def DoLandauFit(histo, lumi, ZZtree, title):
#gSystem.Exec("mkdir -p FitResults")
can = []
result = []
for i in range(0,len(histo)):
canv = makeCMSCanvas(str(random.random())+str(i),"Fit result "+str(i),900,700)
can.append(canv)
Z1_fitFunction = TF1('Z1_fitFunction',"[0]*TMath::Landau(x,[1],[2])",0,4)
Z1_fitFunction.SetParameters(100,0.1,1.0)
#Z1_fitFunction.SetParLimits(0,85.,95.)
#Z1_fitFunction.SetParLimits(1,0.,5.)
#Z1_fitFunction.SetParLimits(2,0.,2.)
Z1_fitFunction.SetParName(0,"Const")#constant
Z1_fitFunction.SetParName(1,"Position")#position
Z1_fitFunction.SetParName(2,"Scale")#scale
for i in range(0,len(histo)):
can[i].cd()
histo[i].Fit(Z1_fitFunction,"","",0.,2.)
histo[i].Draw()
gStyle.SetOptFit()
position = Z1_fitFunction.GetParameters()[1]
scale = Z1_fitFunction.GetParameters()[2]
position_err = Z1_fitFunction.GetParError(1)
scale_err = Z1_fitFunction.GetParError(2)
result.append(Result(position,scale,lumi[i],position_err,scale_err))
print ("Position: " +str(position) + " Scale: " + str(scale))
printLumiPrelOut(can[i])
can[i].SaveAs("FitResults/"+title+"_Landfit_"+str(i)+".pdf")
can[i].SaveAs("FitResults/"+title+"_Landfit_"+str(i)+".png")
return result;
def setStyle():
NRGBs = 5
NCont = 255
#gErrorIgnoreLevel
#Double_t stops[NRGBs] = { 0.00, 0.34, 0.61, 0.84, 1.00 };
#Double_t red[NRGBs] = { 0.00, 0.00, 0.87, 1.00, 0.51 };
#Double_t green[NRGBs] = { 0.00, 0.81, 1.00, 0.20, 0.00 };
#Double_t blue[NRGBs] = { 0.51, 1.00, 0.12, 0.00, 0.00 };
#TColor::CreateGradientColorTable(NRGBs, stops, red, green, blue, NCont);
#gStyle.SetNumberContours(NCont);
# gStyle.SetPadBorderMode(0)
# gStyle.SetFrameBorderMode(0)
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
# gStyle.SetLabelSize(0.05,"X")
# gStyle.SetLabelSize(0.05,"Y")
# 0.035
# gStyle.SetLabelSize(0.035,"XY")
gStyle.SetTitleSize(AxisLabelSizeX, "X")
gStyle.SetTitleSize(AxisLabelSizeY, "Y")
gStyle.SetTitleOffset(kDefaultTitleOffsetX, "X")
gStyle.SetTitleOffset(kDefaultTitleOffsetY, "Y")
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gStyle.SetTitleSize(0.035, "xy")
def set2DStyle(self, opt="BasicRainBow"):
gStyle.SetPadRightMargin(
0.2
) # Leave more space to the right side of the current Pad to show the histogram scale
if opt == "FancyRainBow":
icol = 0
gStyle.SetFrameBorderMode(icol)
gStyle.SetFrameFillColor(icol)
gStyle.SetCanvasBorderMode(icol)
gStyle.SetCanvasColor(icol)
gStyle.SetPadBorderMode(icol)
gStyle.SetPadColor(icol)
gStyle.SetStatColor(icol)
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
ncontours = 999
s = array.array('d', [0.00, 0.34, 0.61, 0.84, 1.00])
r = array.array('d', [0.00, 0.00, 0.87, 1.00, 0.51])
g = array.array('d', [0.00, 0.81, 1.00, 0.20, 0.00])
b = array.array('d', [0.51, 1.00, 0.12, 0.00, 0.00])
npoints = len(s)
TColor.CreateGradientColorTable(npoints, s, r, g, b, ncontours)
gStyle.SetNumberContours(ncontours)
if opt == "BasicRainBow":
gStyle.SetPalette(
1
) # This resets the color palette to a simple Rainbow Color Map w/ 50 colors. See https://root.cern.ch/doc/master/classTColor.html
def calceff(run,tdc,strip=71):
f82=TFile("./histo%d_0.root" % run);
f82.cd("/run%d/TDC%d/LmAnalysis" % (run,tdc));
c1=TCanvas();
gStyle.SetOptFit();
heff=f82.Get("/run%d/TDC%d/LmAnalysis/Efficiency" % (run,tdc));
hstrip=f82.Get("/run%d/TDC%d/LmAnalysis/hdts%d" % (run,tdc,strip));
hstrip.Rebin(2)
scfit=TF1("scfit","gaus",hstrip.GetMean()-6.*hstrip.GetRMS(),hstrip.GetMean()+6.*hstrip.GetRMS())
hstrip.GetXaxis().SetRangeUser(hstrip.GetMean()-6.*hstrip.GetRMS(),hstrip.GetMean()+6.*hstrip.GetRMS())
hstrip.Fit("scfit","Q");
dtmean=scfit.GetParameter(1)
dtres=scfit.GetParameter(2)
ntrg=heff.GetBinContent(2)
n1=heff.GetBinContent(3)
n2=heff.GetBinContent(4)
eff=n1/ntrg
deff=math.sqrt(eff*(1-eff)/ntrg)
effp=n2/ntrg
deffp=math.sqrt(effp*(1-effp)/ntrg)
print("|%d|150|%d|%d|%d|%5.2f|%5.2f|%5.2f|%5.2f|%5.2f|%5.3f|" % (run,int(ntrg),int(n1),int(n2),eff*100,deff*100,effp*100,deffp*100,dtmean,dtres))
hstrip.Draw()
c1.Update()
c1.SaveAs("Run%d_Strip%d_pos.png" % (run,strip));
val = raw_input()
def SetStyle():
gROOT.SetStyle('Plain')
gROOT.ForceStyle()
gStyle.SetTextFont(42)
gStyle.SetOptTitle(0)
gStyle.SetOptFit(1112)
gStyle.SetOptStat(1110)
gStyle.SetPadRightMargin(0.08)
gStyle.SetPadTopMargin(0.11)
gStyle.SetPadBottomMargin(0.12)
gStyle.SetTitleFont(42, 'x')
gStyle.SetTitleFont(42, 'y')
gStyle.SetTitleFont(42, 'z')
gStyle.SetTitleOffset(1.05, 'x')
gStyle.SetTitleOffset(1.00, 'y')
gStyle.SetTitleSize(0.05, 'x')
gStyle.SetTitleSize(0.05, 'y')
gStyle.SetTitleSize(0.05, 'z')
gStyle.SetLabelFont(42, 'x')
gStyle.SetLabelFont(42, 'y')
gStyle.SetLabelFont(42, 'z')
gStyle.SetLabelSize(0.05, 'x')
gStyle.SetLabelSize(0.05, 'y')
gStyle.SetLabelSize(0.05, 'z')
TGaxis.SetMaxDigits(3)
gStyle.SetStatY(0.9)
def fitProfile(run,sel=92):
f82=TFile("./histo%d_0.root" % run);
#f82.cd("/run%d/TDC%d/LmAnalysis/Timing" % (run,tdc));
f82.cd("/run%d/ChamberAll" % (run));
c1=TCanvas();
gStyle.SetOptFit();
pos0=[]
pmean0=[]
pos1=[]
pmean1=[]
hall=f82.Get("/run%d/ChamberAll/XY" % (run));
for i in range(sel,sel+1):
pos0.append(0)
pmean0.append(0)
#hstrip=f82.Get("/run%d/TDC%d/LmAnalysis/Timing/hdtpos%d" % (run,tdc,i+71));
hstrip=hall.ProjectionY("strip%d" % (i),(i),i+1)
if (hstrip.GetEntries()<100):
continue
scfit=TF1("scfit","gaus",hstrip.GetMean()-2.*hstrip.GetRMS(),hstrip.GetMean()+2.*hstrip.GetRMS())
hstrip.Fit("scfit","Q","",hstrip.GetMean()-2.*hstrip.GetRMS(),hstrip.GetMean()+2.*hstrip.GetRMS())
dtmean=scfit.GetParameter(1)
dtres=scfit.GetParameter(2)
print(run,i,dtmean,dtres,dtmean*80./8.3,dtres*80/8.3)
c1.Update()
#val = raw_input()
time.sleep(2)
def fit_distribution(run_num,
attr1='FitResult_Energy',
attr2='FitResult_XtalNum==0',
draw=False):
"""give an output file number from fitter,
fit peak to find mean and variance with their errors"""
#in_file = TFile("/home/newg2/Workspace/L1Tests/crunchedFiles/gm2uw_%05i_analysis.root" % run_num)
in_file = TFile(
"/Users/kimsiang/Work/UWCENPA/gm2/SLAC2016/Data/gm2uw_run%05i_analysis.root"
% run_num)
tree = in_file.Get("slacAnalyzer/eventTree")
tree.Draw("%s>>h1(200)" % attr1, attr2, "goff")
hist = gROOT.FindObject("h1")
mean = hist.GetMean()
std = hist.GetRMS()
low = mean - 3 * std
high = mean + 3 * std
tree.Draw("%s>>h2(100,%s,%s)" % (attr1, str(low), str(high)), attr2,
"goff")
hist = gROOT.FindObject("h2")
hist.Fit("gaus", "0q")
func = hist.GetFunction("gaus")
out = (func.GetParameter(1), func.GetParameter(2), func.GetParError(1),
func.GetParError(2))
if draw:
gStyle.SetOptFit(1)
hist.Draw()
hist.GetFunction("gaus").Draw("same")
raw_input('any key to continue')
in_file.Close()
return out
def doSuperTGraph( data, canvas, obj):
xmin=-1.8
xmax=1.8
gStyle.SetOptFit(0)
gStyle.SetOptStat(0)
gROOT.SetBatch(1)
color=[601,417,920,633,910,433,400,619,1,922]
mg = TMultiGraph();
leg = TLegend(0.58,0.75,0.89,0.89);
i=0
for ivar in data['var']:
if not os.path.exists(data['fold']+"/"+ivar+"_ll.root"):
continue
print '>>> Opening File :' , data['fold']+"/"+ivar+"_ll.root"
inFile = TFile.Open ( data['fold']+"/"+ivar+"_ll.root" ," READ ")
c1 = inFile.Get(canvas);
gr = c1.GetListOfPrimitives().FindObject(obj)
xaxis = gr.GetXaxis().GetTitle()
yaxis = gr.GetYaxis().GetTitle()
gr.SetLineColor(color[i])
mg.Add(gr)
leg.AddEntry(gr,ivar,"l")
i=i+1
print i
c = TCanvas()
c.cd()
c.SetGrid()
mg.Draw("al")
line1 = TF1("line1","pol0",-100,100)
line1.FixParameter(0,1.)
line1.SetLineWidth(2)
line1.SetLineStyle(2)
line1.SetLineColor(2)
line1.Draw("same")
line2 = TF1("line2","pol0",-100,100)
line2.FixParameter(0,3.84)
line2.SetLineWidth(2)
line2.SetLineStyle(2)
line2.SetLineColor(2)
line2.Draw("same")
mg.SetTitle(";" +xaxis+";" +yaxis)
mg.GetYaxis().SetRangeUser(0,10)
leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.Draw("same")
c.SaveAs(data['fold']+"combined10.png")
c.SaveAs(data['fold']+"combined10.pdf")
c.SaveAs(data['fold']+"combined10.root")
def DefaultPlotStyle(self):
gStyle.SetPalette(55) # 55 is kRainBow. 53 is kDarkBodyRadiator
gStyle.SetOptStat(self.optStat1D)
gStyle.SetOptFit(11111)
gStyle.SetStatH(0.12)
gStyle.SetStatW(0.15)
gStyle.SetPadBottomMargin(0.15)
gStyle.SetPadTopMargin(0.15)
def doSuperTGraphAll( file_list, canvas, obj, output):
gStyle.SetOptFit(0)
gStyle.SetOptStat(0)
gROOT.SetBatch(1)
mg = TMultiGraph();
leg = TLegend(0.58,0.75,0.89,0.89);
for iFile in file_list:
if not os.path.exists(iFile['path']):
print '>>> This File does not exist --> '+iFile['path']+'... check path ... '
continue
print '>>> Opening File :' , iFile['path']
inFile = TFile.Open ( iFile['path'] ," READ ")
c1 = inFile.Get(canvas);
gr = c1.GetListOfPrimitives().FindObject(obj)
xaxis = gr.GetXaxis().GetTitle()
yaxis = gr.GetYaxis().GetTitle()
gr.SetLineColor(iFile['color'])
mg.Add(gr)
leg.AddEntry(gr,iFile['legend'],"l")
c = TCanvas()
c.cd()
c.SetGrid()
mg.Draw("al")
line1 = TF1("line1","pol0",-100,100)
line1.FixParameter(0,1.)
line1.SetLineWidth(2)
line1.SetLineStyle(2)
line1.SetLineColor(2)
line1.Draw("same")
line2 = TF1("line2","pol0",-100,100)
line2.FixParameter(0,3.84)
line2.SetLineWidth(2)
line2.SetLineStyle(2)
line2.SetLineColor(2)
line2.Draw("same")
mg.SetTitle(";" +xaxis+";" +yaxis)
mg.GetYaxis().SetRangeUser(0,10)
leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.Draw("same")
c.SaveAs(output+"10.png")
c.SaveAs(output+"10.pdf")
c.SaveAs(output+"10.root")
def _initDefaultStyle(self):
gROOT.Reset()
gROOT.SetStyle("Plain")
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetPadLeftMargin(0.12)
gStyle.SetPadBottomMargin(0.12)
gStyle.SetMarkerSize(1.)
gStyle.SetHistLineWidth(1)
def set_meeting_style():
from ROOT import gStyle
gStyle.SetOptDate(0)
gStyle.SetOptTitle(0)
gStyle.SetOptStat(1111)
gStyle.SetStatBorderSize(1)
gStyle.SetStatColor(10)
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetOptFit(1111)
def GaussianFit(self, histo):
'''
Fit Histograms for Exercise 3.
### FALTA Fit Breit Wigner
'''
self.gHisto = self.Gfile.Get('g_' + histo)
self.gHisto.Fit("gaus")
#self.fit1 = self.gHisto.GetFunction("gaus")
gStyle.SetOptFit()
self.createCanvas(self.gHisto, 'fit_' + histo)
def fit(self, distribution, s_graphs):
""" Fit Gaussian distribution over TGraphs. """
graphs = self._get_graphs(s_graphs)
for idx, graph in enumerate(graphs):
try:
numbering = self._numbering[idx]
except IndexError:
numbering = -1
graph.Fit(distribution, 'Q')
graph.GetFunction(distribution).SetLineColor(4)
self._fits.append(
ToolboxFit(graph.GetFunction(distribution), numbering))
# If there is only one fit, show stats
if len(graphs) == 1:
gStyle.SetOptFit(1111111)
else:
gStyle.SetOptFit(0000000)
def fun():
c1 = TCanvas("c1", "c1", 600, 500)
t.Draw()
u.Draw("SAMES")
v.Draw("SAMES")
w.Draw("SAMES")
o.Draw("SAMES")
v.GetXaxis().SetTitle("Photons in Evt")
v.GetYaxis().SetTitle("Events")
v.GetYaxis().SetTitleOffset(1.3)
u.GetXaxis().SetTitle("Photons in Evt")
u.GetYaxis().SetTitle("Events")
u.GetYaxis().SetTitleOffset(1.3)
t.GetXaxis().SetTitle("Photons in Evt")
t.GetYaxis().SetTitle("Events")
t.GetYaxis().SetTitleOffset(1.3)
w.GetXaxis().SetTitle("Photons in Evt")
w.GetYaxis().SetTitle("Events")
w.GetYaxis().SetTitleOffset(1.3)
o.GetXaxis().SetTitle("Photons in Evt")
o.GetYaxis().SetTitle("Events")
o.GetYaxis().SetTitleOffset(1.3)
#Stats/Fit Options, stat box placement (top/bottom)
gPad.Update()
# su = u.FindObject("stats")
# su.SetY1NDC(.1)
# su.SetY2NDC(.25)
# su.SetTextColor(ROOT.kRed)
u.Fit(fit_u)
# sv = v.FindObject("stats")
# sv.SetY1NDC(.25)
# sv.SetY2NDC(.4)
# sv.SetTextColor(ROOT.kGreen)
v.Fit(fit_v)
# sw = w.FindObject("stats")
# sw.SetY1NDC(.4)
# sw.SetY2NDC(.55)
# sw.SetTextColor(ROOT.kViolet)
w.Fit(fit_w)
gStyle.SetOptFit(0)
gStyle.SetOptStat(0)
# st = t.FindObject("stats")
# st.SetY1NDC(.55)
# st.SetY2NDC(.7)
# st.SetTextColor(ROOT.kBlue)
t.Fit(fit_t)
#so = o.FindObject("stats")
#so.SetY1NDC(.7)
#so.SetY2NDC(.85)
#so.SetTextColor(ROOT.kCyan)
o.Fit(fit_o)
return c1
def export(info, args, npull=15):
from ROOT import TFile, gROOT, gStyle, TPad, TCanvas
from math import ceil
gROOT.SetStyle("Plain")
gStyle.SetOptFit(1)
gStyle.SetOptStat(0)
gROOT.SetBatch(1)
# outdir = outdir_base % info.year
# outvar = '%s/%s/' % (outdir,info.variable)
# if not os.path.isdir(outvar): os.mkdir(outvar)
# outsys = '%s/%s' % (outvar,info.sysdir)
# if not os.path.isdir(outsys): os.mkdir(outsys)
# outname = 'pulls_%s.pdf' % info.variable
# output = '%s/%s' % (outsys,outname)
output = "pulls.pdf"
pulls = TFile.Open(args.g)
canvas = pulls.Get("nuisances")
objlist = canvas.GetListOfPrimitives()
prefit = objlist.At(0).Clone()
fit_bg = objlist.At(2).Clone()
fit_bs = objlist.At(3).Clone()
legend = objlist.At(6).Clone()
prefit.GetYaxis().SetRangeUser(-4, 4)
legend.SetX1(0.76)
legend.SetX2(0.99)
npages = int(ceil(float(prefit.GetNbinsX()) / npull))
for page in range(npages):
canvas = TCanvas()
canvas.SetBottomMargin(0.15)
canvas.SetRightMargin(0.25)
canvas.SetGridx()
canvas.Draw()
xlo = page * npull + 1
xhi = page * npull + npull
if xhi > prefit.GetNbinsX(): xhi = prefit.GetNbinsX()
prefit.GetXaxis().SetRange(xlo, xhi)
prefit.Draw("E2")
prefit.Draw("histsame")
fit_bg.Draw("EPsame")
fit_bs.Draw("EPsame")
legend.Draw()
if page == 0: canvas.SaveAs(output + "(")
elif page + 1 == npages: canvas.SaveAs(output + ")")
else: canvas.SaveAs(output)
def makeMassRes(inputDATA,inputMC,output,weights,weights2,trackType,funct,mcIsData,dataIsMC): style = setTDRStyle() gStyle.SetTitleYOffset(1.45) gStyle.SetTitleXOffset(1.45) gStyle.SetOptFit(0) gStyle.SetStatX(.9) gStyle.SetStatY(.9) (data_B,mc_B,ptdaB,ptmcB) = loadHistos(inputDATA,inputMC,"BB",weights,weights2,trackType,mcIsData,dataIsMC) (data_E,mc_E,ptdaE,ptmcE) = loadHistos(inputDATA,inputMC,"BE",weights,weights2,trackType,mcIsData,dataIsMC) # ~ (data_E2,mc_E2,ptdaE2,ptmcE2) = loadHistos(inputDATA,inputMC,"BE_neweta",weights,weights2,trackType,mcIsData,dataIsMC) drawMassRes(data_B,mc_B,output,"BB",ptdaB,ptmcB,trackType,funct,mcIsData,dataIsMC,inputDATA) drawMassRes(data_E,mc_E,output,"BE",ptdaE,ptmcE,trackType,funct,mcIsData,dataIsMC,inputDATA)
def fitpos(run,tdc):
f82=TFile("./histo%d_0.root" % run);
#f82.cd("/run%d/TDC%d/LmAnalysis/Timing" % (run,tdc));
f82.cd("/run%d/Chamber%d/Timing" % (run,tdc));
c1=TCanvas();
gStyle.SetOptFit();
pos=[]
pmean=[]
for i in range(48):
pos.append(0)
pmean.append(0)
#hstrip=f82.Get("/run%d/TDC%d/LmAnalysis/Timing/hdtpos%d" % (run,tdc,i+71));
hstrip=f82.Get("/run%d/Chamber%d/Timing/All/hdtpos%d" % (run,tdc,i+71));
if (hstrip == None):
continue
hstrip.Rebin(2)
hstrip.Draw()
c1.Update()
print("Enter min max")
#hmin = float(raw_input())
#hmax = float(raw_input())
hmin=-20.
hmax=20.
print(hmin,hmax)
#scfit=TF1("scfit","gaus",hstrip.GetMean()-3.*hstrip.GetRMS(),hstrip.GetMean()+3.*hstrip.GetRMS())
#hstrip.GetXaxis().SetRangeUser(hstrip.GetMean()-3.*hstrip.GetRMS(),hstrip.GetMean()+3.*hstrip.GetRMS())
scfit=TF1("scfit","gaus",hmin,hmax)
hstrip.GetXaxis().SetRangeUser(hmin,hmax)
hstrip.Fit("scfit","Q");
dtmean=scfit.GetParameter(1)
dtres=scfit.GetParameter(2)
hstrip.Draw()
c1.Update()
#c1.SaveAs("Run%d_Strip_pos.png" % (run));
val = raw_input()
pmean[i]=hstrip.GetMean()
if (dtres>1. and dtres<4):
pos[i]=dtmean
else:
pos[i]=hstrip.GetMean()
print(pos)
print(pmean)
for i in range(12):
print("fe1_2tr[%d]=%5.3f;" % (i,pos[i]))
def initGraphics(self, ndet, nlb):
"Initialise graphics and setup histogram for status matrix"
from ROOT import gROOT, TCanvas, TH2F, gStyle
hist = TH2F(
"DetectorStatus", "%s tag %s runs %i to %i (%i LBs)" %
(self.foldername, self.tag, self.runmin, self.runmax, nlb),
ndet + 1, 0.5, ndet + 1.5, nlb, 0.5, nlb + 0.5)
hist.Fill(ndet + 1, 1, 49.)
hist.SetNdivisions(0)
gROOT.SetStyle("Plain")
gStyle.SetPalette(0)
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
canvas = TCanvas(self.foldername, self.foldername, 0, 0, 500, 600)
return (hist, canvas)
def set_statbox(x=.95,
y=.88,
w=.16,
entries=3,
only_fit=False,
opt=None,
form=None):
if only_fit:
gStyle.SetOptStat(0011)
gStyle.SetOptFit(1)
gStyle.SetOptStat(opt) if opt is not None else do_nothing()
gStyle.SetFitFormat(form) if form is not None else do_nothing()
gStyle.SetStatX(x)
gStyle.SetStatY(y)
gStyle.SetStatW(w)
gStyle.SetStatH(.04 * entries)
def _initDefaultStyle(self):
gROOT.Reset()
gROOT.SetStyle("Plain")
#gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetPadLeftMargin(0.1)
gStyle.SetPadBottomMargin(0.1)
gStyle.SetMarkerSize(1.5)
gStyle.SetHistLineWidth(1)
gStyle.SetStatFontSize(0.025)
gStyle.SetTitleFontSize(0.05)
gStyle.SetTitle(
'CMS Preliminary tH, H#to#tau#tau, 19.7 fb^{-1} at 8 TeV')
gStyle.SetTitleSize(0.06, "XYZ")
gStyle.SetLabelSize(0.05, "XYZ")
gStyle.SetNdivisions(510, "XYZ")
def setStyle():
gROOT.Reset()
icol = 0
# WHITE
font = 42
# Helvetica
tsize = 0.05
gStyle.SetFrameBorderMode(icol)
gStyle.SetFrameFillColor(icol)
gStyle.SetCanvasBorderMode(icol)
gStyle.SetCanvasColor(icol)
gStyle.SetPadBorderMode(icol)
gStyle.SetPadColor(icol)
gStyle.SetStatColor(icol)
gStyle.SetPaperSize(20, 26)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadRightMargin(0.08)
gStyle.SetPadBottomMargin(0.15)
gStyle.SetPadLeftMargin(0.12)
gStyle.SetTitleXOffset(1.05)
gStyle.SetTitleYOffset(0.95)
gStyle.SetTextFont(font)
gStyle.SetTextSize(tsize)
gStyle.SetLabelFont(font, "x")
gStyle.SetTitleFont(font, "x")
gStyle.SetLabelFont(font, "y")
gStyle.SetTitleFont(font, "y")
gStyle.SetLabelFont(font, "z")
gStyle.SetTitleFont(font, "z")
gStyle.SetLabelSize(tsize * 0.85, "x")
gStyle.SetTitleSize(tsize * 1.10, "x")
gStyle.SetLabelSize(tsize * 0.85, "y")
gStyle.SetTitleSize(tsize * 1.10, "y")
gStyle.SetLabelSize(tsize * 0.85, "z")
gStyle.SetTitleSize(tsize * 1.10, "z")
gStyle.SetMarkerStyle(20)
gStyle.SetMarkerSize(1.)
gStyle.SetHistLineWidth(2)
gStyle.SetLineStyleString(2, "[12 12]")
# postscript dashes
gStyle.SetEndErrorSize(0.)
# gStyle.SetOptTitle(0);
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
gStyle.SetPadTickX(1)
gStyle.SetPadTickY(1)
gStyle.SetPaintTextFormat("4.1f")
def plotPerBxStep(options):
"""Save histograms (per BX and step) to PDF files"""
name = options['scan'] + '_' + options['name'] + options['extra']
if 'method' in options:
name += '_' + options['method']
f = openRootFileR(name)
for bx in options['crossings']:
for step in range(len(O['nominalPos'][options['scan']])):
histname = plotName(name+'_bx'+str(bx)+'_step'+str(step), \
timestamp=False)
filename = plotName(name+'_bx'+str(bx)+'_step'+str(step), \
timestamp=True)
filepath = plotPath(name+'_bx'+str(bx)+'_step'+str(step), \
timestamp=True)
print '<<< Save plot:', filepath
hist = f.Get(histname)
canvas = TCanvas()
canvas.SetLogx(options['logx'])
canvas.SetLogy(options['logy'])
gStyle.SetOptStat(options['optstat'])
gStyle.SetOptFit(options['optfit'])
hist.Draw()
gPad.Update()
hist.GetXaxis().SetTitle(options['xtitle'])
hist.GetXaxis().SetRangeUser(options['xmin'], options['xmax'])
hist.GetYaxis().SetTitle(options['ytitle'])
hist.GetYaxis().SetTitleOffset(1.2)
for axis in [hist.GetXaxis(), hist.GetYaxis()]:
axis.SetTitleFont(133)
axis.SetTitleSize(16)
axis.SetLabelFont(133)
axis.SetLabelSize(12)
axis.CenterTitle()
stats = hist.FindObject('stats')
stats.SetTextFont(133)
stats.SetTextSize(16)
drawSignature(filename)
gPad.Modified()
gPad.Update()
if 'custom' in options:
extragraphs = options['custom'](hist)
canvas.Print(filepath)
canvas.Close()
closeRootFile(f, name)
def main():
# configure and run
_MGR = sf2r_manager(DEBUG)
_MGR.ff_type_detector()
plots = _MGR.run()
file = None
if not os.path.exists('fluka2root.root'):
file = TFile('fluka2root.root', 'new')
else:
file = TFile('fluka2root.root', 'recreate')
# set for apperance
c = TCanvas('USRTRACK CARTESIAN REFERENCE FRAME 2D - XY',
'USRTRACK CARTESIAN REFERENCE FRAME 2D - XY', 670, 400)
gStyle.SetPalette(1)
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
# make a test plot and store plots in file
for plot in plots:
if plot.get_type() == '1DPLOT':
if PLOT1D:
plot.get_histo().Draw()
plot.get_histo().Write()
if plot.get_type() == '2DPLOT':
if PLOT2D:
#gPad.SetLogz()
#gPad.SetGridx()
#gPad.SetGridy()
#plot.get_histo().Draw('P')
plot.get_histo().Write()
if plot.get_type() == '3DPLOT':
if PLOT3D:
#gPad.SetLogz();
#gPad.SetGridx();
#gPad.SetGridy();
#plot.get_histo().Draw('P')
plot.get_histo().Write()
file.Close()
print ' --> Press enter to finish... '
raw_input()
