def draw_nse_mean(self):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_mean = TGraphErrors()
gr_width = TGraphErrors()
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
f_gaus = self.draw_nse(cent_bin1[1], cent_bin2[1], p_bin1[i],
p_bin2[i], -1, 2)
mean = f_gaus.GetParameter(1)
mean_err = f_gaus.GetParError(1)
width = f_gaus.GetParameter(2)
width_err = f_gaus.GetParError(2)
gr_mean.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, mean)
gr_mean.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, mean_err)
gr_width.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, width)
gr_width.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, width_err)
p = 0.5 * (p_bin1[i] + p_bin2[i])
if p < 1:
nse_low = 0.5 * p - 1.5
else:
nse_low = -1
m = f_gaus.Integral(nse_low, 2.0)
N = f_gaus.Integral(-10.0, 10.0)
gr_eff.SetPoint(i, p, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_mean.SetMarkerStyle(20)
gr_mean.SetMarkerSize(1.5)
gr_mean.SetMarkerColor(632)
gr_width.SetMarkerStyle(20)
gr_width.SetMarkerSize(1.5)
gr_width.SetMarkerColor(600)
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 10, -0.2, 1.2, "p_{T}(GeV/c)", "Mean/Width")
canvas_file = self.__canvas__
canvas_file.cd()
c1 = TCanvas("c1", "c1", 1000, 800)
hx.Draw()
gr_width.Draw("psame")
gr_mean.Draw("psame")
c1.Write("nse")
c1.SaveAs("nse.png")
c2 = TCanvas("c2", "c2", 1000, 800)
hx.GetYaxis().SetTitle("n#sigma_{e} cut efficiency")
hx.Draw()
gr_eff.Draw("psame")
c2.Write("nse_eff")
c2.SaveAs("nse_eff.png")
def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
def draw_tof_cut_eff(self, cent_low, cent_high):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_mean = TGraphErrors()
gr_width = TGraphErrors()
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
f_gaus = self.draw_tof_cut(cent_bin1[1], cent_bin2[1], p_bin1[i],
p_bin2[i])
mean = f_gaus.GetParameter(1)
mean_err = f_gaus.GetParError(1)
width = f_gaus.GetParameter(2)
width_err = f_gaus.GetParError(2)
gr_mean.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, mean)
gr_mean.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, mean_err)
gr_width.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, width)
gr_width.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5, width_err)
m = f_gaus.Integral(0.97, 1.03)
N = f_gaus.GetParameter(0) * math.sqrt(
2 * math.pi) * f_gaus.GetParameter(2)
print m, N
gr_eff.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_mean.SetMarkerStyle(20)
gr_mean.SetMarkerSize(1.5)
gr_mean.SetMarkerColor(632)
gr_width.SetMarkerStyle(20)
gr_width.SetMarkerSize(1.5)
gr_width.SetMarkerColor(600)
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 10, -0.2, 1.2, "p_{T}(GeV/c)", "Mean/Width")
canvas_file = self.__canvas__
canvas_file.cd()
c1 = TCanvas("c1", "c1", 1000, 800)
hx.GetYaxis().SetTitle("1/#beta cut efficency")
hx.Draw()
gr_width.Draw("psame")
gr_mean.Draw("psame")
c1.Write("tof")
c1.SaveAs("tof.png")
c2 = TCanvas("c2", "c2", 1000, 800)
hx.Draw()
gr_eff.Draw("psame")
c2.Write("tof_cut_eff")
c2.SaveAs("tof_cut.png")
def Sigma_Calc(g,i_,x,y,xe,ye,percentage):
#print'Creating TGraph with new uncertainty band'
# sigma factor. What to multiply uncertainties by, assuming current uncertainties are 1 sigma.
#print'percentage/100. = ',percentage/100.
sf = TMath.ErfInverse(percentage/100.)*TMath.Sqrt(2) # sigma value for desired percentage events contained in distribution
#print"sf = ",sf
#for i in range(i_ - 1):
#print'i_ = ',i_
for i in range(i_):
ye[i] = ye[i]*sf # multiply 1 sigma uncertainties by new sigma value.
#print sf,'sig errors = ',ye
#ng = TGraphErrors(i_ - 1, x, y, xe, ye)
ng = TGraphErrors(i_, x, y, xe, ye)
if percentage == 90: j = 2
if percentage == 99.5: j = 4
ng.SetMarkerStyle(g.GetMarkerStyle())
ng.SetLineStyle(g.GetLineStyle())
#ng.SetMarkerColor(g.GetMarkerColor() + j)
#ng.SetLineColor(g.GetLineColor() + j)
ng.SetFillColor(g.GetFillColor() + j)
#ng.SetName(g.GetName())
ng.SetFillStyle(g.GetFillStyle())
return ng
def TGraph(x, y, yError=None, xError=None, **kwargs):
__parseDrawOptions(kwargs)
import sys
#TODO: Need to check the types
try:
assert (len(x) == len(y))
except AssertionError:
print "Oops! Data points x and y aren't of the same size!"
sys.exit()
nPoints = len(x)
from array import array
if xError is None: xError = __zeros(nPoints)
if yError is None: yError = __zeros(nPoints)
from ROOT import TGraphErrors
graph = TGraphErrors(nPoints, array('d', x), array('d', y),
array('d', xError), array('d', yError))
graph.SetTitle(__drawOptions['title'])
graph.SetMarkerStyle(__drawOptions['mstyle'])
graph.SetMarkerSize(__drawOptions['msize'])
graph.SetMarkerColor(__drawOptions['mcolour'])
graph.SetFillStyle(0)
graph.SetFillColor(0)
return graph
def create_resolutiongraph(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors, graphname):
"""Function to perform ROOT graphs of resolutions"""
#How many points
n = int(n)
TGraphresolution = TGraphErrors(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors)
#Draw + DrawOptions, Fit + parameter estimation
Style = gStyle
Style.SetOptFit()
XAxis = TGraphresolution.GetXaxis() #TGraphresolution
TGraphresolution.SetMarkerColor(4)
TGraphresolution.SetMarkerStyle(20)
TGraphresolution.SetMarkerSize(2)
XAxis.SetTitle("Energy (GeV)")
YAxis = TGraphresolution.GetYaxis()
YAxis.SetTitle("Sigma/Mean")
resolutionfit = TF1("resolutionfit", '([0]/((x)**0.5))+[1]', 0, max(energies)) #somma non quadratura
TGraphresolution.Fit("resolutionfit")
a = resolutionfit.GetParameter(0)
b = resolutionfit.GetParameter(1)
TGraphresolution.Draw("AP")
gPad.SaveAs(graphname)
gPad.Close()
return a, b
def Ratio(histo1,histo2, recorded, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
ratio = []
ratio_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
ratio.append(histo1[i].GetEntries()/histo2[i].GetEntries())
ratio_err.append(0)
graph1 = TGraphErrors(len(recorded),array('d',lumi),array('d',ratio),array('d',lumi_err),array('d',ratio_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("e^{+}e^{-}/#mu^{+}#mu^{-}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs("ZPlots/Z_ratio_"+title+".pdf")
can1.SaveAs("ZPlots/Z_ratio_"+title+".png")
return;
def makeGraph(self, name='', xtitle='', ytitle=''):
"""This function returns an instance of ROOTs TGraphErrors, made with the points in from this class.
Some of the graph's default settings are changed:
- black points
- every point has the symbol of 'x'
- x- and y-axis are centered
Arguments:
name -- ROOT internal name of graph (default = '')
xtitle -- title of x-axis (default = '')
ytitle -- title of y-axis (default = '')
"""
if self.points:
x = self.getX()
y = self.getY()
ex = self.getEX()
ey = self.getEY()
graph = TGraphErrors(self.getLength(), array.array('f', x),
array.array('f', y), array.array('f', ex),
array.array('f', ey))
graph.SetName(name)
graph.SetMarkerColor(1)
graph.SetMarkerStyle(5)
graph.SetTitle("")
graph.GetXaxis().SetTitle(xtitle)
graph.GetXaxis().CenterTitle()
graph.GetYaxis().SetTitle(ytitle)
graph.GetYaxis().CenterTitle()
return graph
else:
return None
def DivideTGraphErrors(num, denom, name):
nPoints = num.GetN()
nSkipPoints = 0 # count of when we can't divide
newTGraph = TGraphErrors(nPoints)
newTGraph.SetName(name)
for i in range(nPoints):
x = num.GetX()[i]
x_err = num.GetEX()[i]
y1 = num.GetY()[i]
y2 = denom.GetY()[i]
y = 0.0
y_err = 0.0
if ((y1 != 0) and (y2 != 0)):
# technically could make this work for y1 = 0
y = y1 / y2
y1_err = num.GetEY()[i]
y2_err = denom.GetEY()[i]
y_err = y * math.sqrt(
math.pow(y1_err / y1, 2) + math.pow(y2_err / y2, 2))
newTGraph.SetPoint(i - nSkipPoints, x, y)
newTGraph.SetPointError(i - nSkipPoints, x_err, y_err)
else:
newTGraph.RemovePoint(i - nSkipPoints)
nSkipPoints += 1
newTGraph.SetLineColor(num.GetLineColor())
newTGraph.SetMarkerColor(num.GetMarkerColor())
newTGraph.SetMarkerStyle(num.GetMarkerStyle())
return newTGraph
def evaluateResolutionVariation(model, bcid, prefix, suffix, vtxres, scaling, \
legend=False):
from array import array
from ROOT import TFile, TMultiGraph, TGraphErrors, gStyle, TCanvas, \
TLegend, TLatex
names = [pre+'_'+model+'_'+bcid+'_'+suf for (pre, suf) in zip(prefix, \
suffix)]
xVal = {name: array('d', [0.0]*len(vtxres)) for name in names}
xErr = {name: array('d', [0.0]*len(vtxres)) for name in names}
yVal = {name: array('d', [0.0]*len(vtxres)) for name in names}
yErr = {name: array('d', [0.0]*len(vtxres)) for name in names}
for name, scale in zip(names, scaling):
for i, vr in enumerate(vtxres):
f = TFile('results/'+name+'_'+vr+'.root')
yVal[name][i] = f.Get('h_overlapInt').GetMean() * 100.0
yErr[name][i] = f.Get('h_integ').GetMeanError() * 100.0
xVal[name][i] = f.Get('h_vtxRes').GetMean() * scale * 1.0e4
multi = TMultiGraph('overlapIntegralBcid'+bcid, '')
graphs = []
for i, name in enumerate(names):
graph = TGraphErrors(len(vtxres), xVal[name], yVal[name], xErr[name], \
yErr[name])
graph.SetName(name)
graph.SetMarkerStyle(20)
graph.SetMarkerColor(1+i)
multi.Add(graph)
graphs.append(graph)
gStyle.SetOptStat(0)
canvas = TCanvas(model+'_'+bcid, '', 600, 600)
multi.Draw('AP')
canvas.Update()
multi.GetXaxis().SetTitle('vertex resolution [#mum]')
multi.GetXaxis().SetLabelSize(0.025)
multi.GetXaxis().SetRangeUser(11, 69)
multi.GetYaxis().SetTitle('overlap integral [a.u.]')
multi.GetYaxis().SetLabelSize(0.025)
multi.GetYaxis().SetRangeUser(0.77, 1.43)
if legend:
leg = TLegend(0.55, 0.15, 0.88, 0.3)
leg.SetBorderSize(0)
for i, name in enumerate(names):
entry = leg.AddEntry(name, legend[i], 'P')
entry.SetMarkerStyle(20)
entry.SetMarkerColor(1+i)
leg.Draw()
drawCMS(wip=True)
text = TLatex()
text.SetNDC()
text.SetTextFont(62)
text.SetTextSize(0.04)
text.SetTextAlign(21)
text.DrawLatex(0.5, 0.92, 'Vertex Resolution Study: '+model+', BCID '+bcid)
canvas.Modified()
canvas.Update()
canvas.SaveAs('plots/'+canvas.GetName()+'.pdf')
canvas.SaveAs('plots/'+canvas.GetName()+'.C')
class TimeConstant(object):
def __init__(self, file_name):
file = open(file_name)
out = open(output, 'w')
for line in file:
#calculate errors and dump the content to output file
#skip comment lines
if '#' in line:
continue
t, v = [float(x) for x in line.split()]
te = osc_error_t(t, TIME_DIV)
ve = osc_error_v(v, POT_DIV)
new_line = [t, v, te, ve]
new_line = ' '.join([str(x) for x in new_line]) + '\n'
out.write(new_line)
file.close()
out.close()
#create Graph. ROOT automatically reads columns
self.graph = TGraphErrors(output)
self.graph.SetMarkerStyle(7)
self.func = TF1("exp_decay", "[0]*exp(-x/[1])")
self.func.SetParameters(2, 20)
def fit_graph(self):
self.graph.Fit("exp_decay", "Q")
self.v0 = self.func.GetParameter(0), self.func.GetParError(0)
self.tau = self.func.GetParameter(1), self.func.GetParError(1)
# print "%.3f \pm %.3f" %self.v0
# print "%.3f \pm %.3f" %self.tau
def get_capacity(self):
c = (self.tau[0] / INTERNAL_RES, self.tau[1] / INTERNAL_RES)
return c
def CompareToys(MwValuemT, MwValuemTStat):
print(len(MwValuemT), len(MwValuemTStat))
n = len(MwValuemT)
x, y = array('d'), array('d')
ex, ey = array('d'), array('d')
for i in range(0, n):
x.append(i + 1)
ex.append(0)
y.append(MwValuemT[i])
ey.append(MwValuemTStat[i])
gr = TGraphErrors(n, x, y, ex, ey)
gr.Draw("P")
gr.SetLineWidth(0)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1)
xax = gr.GetXaxis()
for i in range(0, n):
binIndex = xax.FindBin(i)
xax.SetBinLabel(binIndex, "toys")
Output = ROOT.TFile.Open("Matrix.root", "RECREATE")
gr.Write("gr")
def make1DSummaryPlot(binned_mw, bins, channel, variable, treeSuffix):
nBins = len(bins)
xValues, yValues = array('d'), array('d')
xErrors, yErrors = array('d'), array('d')
for bin in bins:
mW = binned_mw[bin]
lowBinEdge = bins[bin][0]
highBinEdge = bins[bin][1]
binWidth = (bins[bin][1] - bins[bin][0]) / 2
binCentre = bins[bin][1] - binWidth
if bin.split('_')[-1] == 'inf':
binCentre = lowBinEdge * 1.1
binWidth = lowBinEdge * 0.1
# print binCentre
# print bin,bins[bin],mW.getVal(),mW.getError()
xValues.append(binCentre)
yValues.append(mW.getVal())
xErrors.append(binWidth)
yErrors.append(mW.getError())
c = TCanvas('c1', 'A Simple Graph Example', 200, 10, 700, 500)
gr = TGraphErrors(nBins, xValues, yValues, xErrors, yErrors)
gr.SetMarkerColor(4)
gr.SetMarkerStyle(3)
gr.GetXaxis().SetTitle('X title')
gr.GetYaxis().SetTitle('Y title')
gr.SetMinimum(75)
gr.SetMaximum(85)
gr.Draw('AP')
c.Update()
outputDir = 'plots/WStudies/%s%s/%s' % (channel, treeSuffix, variable)
c.Print('%s/Summary.pdf' % outputDir)
class DiodeCurrent(object):
def __init__(self, file_name):
file = open(file_name)
out = open(output, 'w')
for line in file:
#calculate errors and dump the content to output file
if '#' in line:
continue
t, vin, vout = [float(x) for x in line.split()]
te = osc_error_t(t, TIME_DIV)
vd = vin - vout
voute = osc_error_v(vout, POT_DIV)
vde = osc_error_v(vd, POT_DIV)
id = vout / R[0]
if vout:
ide = id * ((voute / vout) + (R[1] / R[0]))
else:
ide = id * (R[1] / R[0])
new_line = [vd, id, vde, ide]
new_line = ' '.join([str(x) for x in new_line]) + '\n'
out.write(new_line)
file.close()
out.close()
#create Graph. ROOT automatically reads columns
self.graph = TGraphErrors(output)
self.graph.SetMarkerStyle(7)
self.func = TF1("shockley", "[0]*(exp(x/[1])-1)", 0.1, 1)
self.func.SetParameters(5, 0.026)
def fit_graph(self):
self.graph.Fit("shockley", "QRW")
self.v0 = self.func.GetParameter(0), self.func.GetParError(0)
self.tau = self.func.GetParameter(1), self.func.GetParError(1)
print("Is = %.3g \pm %.3g" % self.v0)
print("n*Vt = %.3f \pm %.3f" % self.tau)
def ZMultVsLumi(histo, recorded, outputDir, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
mult = []
mult_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
mult.append(histo[i].GetEntries()/float(recorded[i]))
mult_err.append(math.sqrt(histo[i].GetEntries()/float(recorded[i])))
graph1 = TGraphErrors(len(lumi),array('d',lumi),array('d',mult),array('d',lumi_err),array('d',mult_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("#Z / fb^{-1}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".pdf")
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".png")
return graph1;
def draw_neta_cut_eff(self, cent_low, cent_high):
cent_bin1 = [0, 4, 7]
cent_bin2 = [3, 6, 8]
p_bin1 = [
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5,
3.0, 3.5, 4.0, 4.5
]
p_bin2 = [
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 4.5, 5.0
]
gr_eff = TGraphErrors()
for i in range(0, len(p_bin1)):
mn = self.draw_neta_cut(cent_low, cent_high, p_bin1[i], p_bin2[i])
m = mn[0]
N = mn[1]
gr_eff.SetPoint(i, (p_bin1[i] + p_bin2[i]) * 0.5, m / N)
gr_eff.SetPointError(i, (p_bin2[i] - p_bin1[i]) * 0.5,
eff_err(m, N))
gr_eff.SetMarkerStyle(20)
gr_eff.SetMarkerSize(1.5)
gr_eff.SetMarkerColor(600)
hx = histo(0, 8, -0.2, 1.2, "p_{T}(GeV/c)", "n#eta cut efficiency")
canvas_file = self.__canvas__
canvas_file.cd()
c2 = TCanvas("c2", "c2", 1000, 800)
hx.Draw()
gr_eff.Draw("psame")
c2.Write("neta_cut_eff")
c2.SaveAs("neta_cut.png")
def compareEEC( filename="sjm91_all.root", datafilename="../EECMC/share/OPAL/data.dat" ):
f= TFile( filename )
ao= createAnalysisObservable( f, "EEC" )
tokens= datafilename.split( "/" )
exp= tokens[3]
plotoptions= { "xmin": 0.0, "xmax": 3.14159, "ymin": 0.05, "ymax": 5.0, "markerStyle": 20,
"markerSize": 0.5, "drawas": "3", "fillcolor": 6, "title": "EEC "+exp,
"xlabel": "\chi\ [rad.]", "ylabel": "1/\sigma d\Sigma/d\chi", "logy": 1 }
tgest, tgesy= ao.plot( plotoptions )
lines= [ line.rstrip( '\n' ) for line in open( datafilename ) ]
n= len( lines )
points= TVectorD( n )
values= TVectorD( n )
errors= TVectorD( n )
perrs= TVectorD(n)
grad2rad= 3.14159/180.0
for i in range( n ):
line= (lines[i]).split()
points[i]= float(line[0])*grad2rad
values[i]= float(line[3])
errors[i]= float(line[4])
perrs[i]= 0.0
datatge= TGraphErrors( points, values, perrs, errors )
datatge.SetMarkerStyle( 20 )
datatge.SetMarkerSize( 0.5 )
datatge.Draw( "psame" )
legend= TLegend( 0.2, 0.7, 0.5, 0.85 )
datatge.SetName( "datatge" );
tgesy.SetName( "tgesy" )
legend.AddEntry( "datatge", exp+" data", "pe" )
legend.AddEntry( "tgesy", "OPAL "+filename, "f" )
legend.Draw()
return
def make_tgrapherrors(name,
title,
color=1,
marker=20,
marker_size=1,
width=1,
asym_err=False,
style=1,
x=None,
y=None):
if (x and y) is None:
gr = TGraphErrors() if not asym_err else TGraphAsymmErrors()
else:
gr = TGraphErrors(len(x), array(x, 'd'), array(
y, 'd')) if not asym_err else TGraphAsymmErrors(
len(x), array(x, 'd'), array(y), 'd')
gr.SetTitle(title)
gr.SetName(name)
gr.SetMarkerStyle(marker)
gr.SetMarkerColor(color)
gr.SetLineColor(color)
gr.SetMarkerSize(marker_size)
gr.SetLineWidth(width)
gr.SetLineStyle(style)
return gr
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors(len(self.GAIN_AVG_POINTS))
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors(len(self.GAIN_MAX_POINTS))
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors(len(self.GAIN_MIN_POINTS))
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set and print the points
#print "===============Printing Gain Data==============="
#print "[BEGIN_DATA]"
#print "\tVAR_INDEP,VAR_DEP,VAR_DEP_ERR"
for i in range(0, len(self.GAIN_AVG_POINTS)):
#Average
gDet_AvgEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i, 0, self.GAIN_STDDEV_POINTS[i])
#print "\t%f,%f,%f"%(self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i],self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MIN_POINTS[i])
pass
#print "[END_DATA]"
#print ""
#Draw
canv_AvgEffGain = TCanvas(
"canv_{0}_EffGainAvg".format(strDetName),
"{0} Average Effective Gain".format(strDetName), 600, 600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2, 1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
class GraphicsObject:
def __init__(self, data, name):
self._data = data
self._graphics = None
self._style = Style(kBlack, 20)
self._plotrange = {"Min": None, "Max": None}
self._name = name
def SetPlotRange(self, min, max):
self._plotrange[min] = min
self._plotrange[max] = max
def SetStyle(self, style):
self._style = style
def SetName(self, name):
self._name = name
def GetData(self):
return self._data
def GetGraphics(self):
return self._graphics
def GetStyle(self):
return self._style
def Draw(self):
if not self._graphics:
self._graphics = TGraphErrors()
np = 0
for bin in range(1, self._data.GetXaxis().GetNbins() + 1):
if self._plotrange["Min"] and self._data.GetXaxis(
).GetBinLowEdge(bin) < self._plotrange["Min"]:
continue
if self._plotrange["Max"] and self._data.GetXaxis(
).GetBinUpEdge(bin) > self._plotrange["Max"]:
break
self._graphics.SetPoint(
np,
self._data.GetXaxis().GetBinCenter(bin),
self._data.GetBinContent(bin))
self._graphics.SetPointError(
np,
self._data.GetXaxis().GetBinWidth(bin) / 2.,
self._data.GetBinError(bin))
np = np + 1
self._graphics.SetMarkerColor(self._style.GetColor())
self._graphics.SetLineColor(self._style.GetColor())
self._graphics.SetMarkerStyle(self._style.GetMarker())
self._graphics.Draw("epsame")
def AddToLegend(self, legend, title=None):
if self._graphics:
tit = self._name
if title:
tit = title
legend.AddEntry(self._graphics, tit, "lep")
def MeanRMSVsLumi(histo, recorded, outputDir, title):
can1 = makeCMSCanvas(str(random.random()), "mean vs lumi ", 900, 700)
can2 = makeCMSCanvas(str(random.random()), "RMS vs lumi ", 900, 700)
lumi = []
lumi_err = []
mean = []
mean_err = []
RMS = []
RMS_err = []
sumLumi = 0.
for i in range(0, len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i]) / 2)
lumi_err.append(float(recorded[i]) / 2)
mean.append(histo[i].GetMean())
mean_err.append(0.) #dont put error on ISO and SIP histo[i].GetRMS()
RMS.append(histo[i].GetRMS())
RMS_err.append(0.)
graph1 = TGraphErrors(len(recorded), array('d', lumi), array('d', mean),
array('d', lumi_err), array('d', mean_err))
graph2 = TGraphErrors(len(recorded), array('d', lumi), array('d', RMS),
array('d', lumi_err), array('d', RMS_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle(" ")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/" + title + "_mean.pdf")
can1.SaveAs(str(outputDir) + "/" + title + "_mean.png")
can2.cd()
graph2.SetTitle("")
graph2.GetXaxis().SetTitle(" ")
graph2.GetYaxis().SetTitle("Width [GeV]")
graph2.SetMarkerStyle(20)
graph2.SetMarkerSize(1)
graph2.Draw("AP")
printLumiPrelOut(can2)
can2.SaveAs(str(outputDir) + "/" + title + "_width.pdf")
can2.SaveAs(str(outputDir) + "/" + title + "_width.png")
return graph1, graph2
def GraphVsLumi(result, outputDir, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()), "mean vs lumi ", 900, 700)
can2 = makeCMSCanvas(str(random.random()), "width vs lumi ", 900, 700)
lumi = []
lumi_err = []
mean = []
mean_err = []
width = []
width_err = []
sumLumi = 0.
for i in range(0, len(result)):
sumLumi += float(result[i].lumi)
lumi.append(sumLumi - float(result[i].lumi) / 2)
lumi_err.append(float(result[i].lumi) / 2)
mean.append(result[i].mean)
mean_err.append(result[i].mean_err)
width.append(result[i].width)
width_err.append(result[i].width_err)
graph1 = TGraphErrors(len(result), array('d', lumi), array('d', mean),
array('d', lumi_err), array('d', mean_err))
graph2 = TGraphErrors(len(result), array('d', lumi), array('d', width),
array('d', lumi_err), array('d', width_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("Mass [GeV]")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/" + title + "_mean.pdf")
can1.SaveAs(str(outputDir) + "/" + title + "_mean.png")
can2.cd()
graph2.SetTitle("")
graph2.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph2.GetYaxis().SetTitle("Width [GeV]")
graph2.SetMarkerStyle(20)
graph2.SetMarkerSize(1)
graph2.Draw("AP")
printLumiPrelOut(can2)
can2.SaveAs(str(outputDir) + "/" + title + "_width.pdf")
can2.SaveAs(str(outputDir) + "/" + title + "_width.png")
return graph1, graph2
def plotGraph(self, x='vv', y='acc'):
'''
Plot a graph with specified quantities on x and y axes , and saves the graph
'''
if (x not in self.quantities.keys()) or (y not in self.quantities.keys()):
raise RuntimeError('selected quantities not available, available quantities are: \n{}'.format(self.quantities.keys()))
xq = self.quantities[x]
yq = self.quantities[y]
#graph = TGraphAsymmErrors()
#graph = TGraph()
graph = TGraphErrors()
for i,s in enumerate(self.samples):
graph.SetPoint(i,getattr(s, xq.name), getattr(s, yq.name) )
#if xq.err:
# graph.SetPointEXhigh(i, getattr(s, xq.name+'_errup')) # errup errdn
# graph.SetPointEXlow (i, getattr(s, xq.name+'_errdn'))
if yq.err:
graph.SetPointError(i, 0, getattr(s, yq.name+'_errup'))
# graph.SetPointEYhigh(i, getattr(s, yq.name+'_errup'))
# graph.SetPointEYlow (i, getattr(s, yq.name+'_errdn'))
c = TCanvas()
graph.SetLineWidth(2)
graph.SetMarkerStyle(22)
graph.SetTitle(';{x};{y}'.format(y=yq.title,x=xq.title))
graph.Draw('APLE')
if yq.forceRange:
graph.SetMinimum(yq.Range[0])
graph.SetMaximum(yq.Range[1])
gPad.Modified()
gPad.Update()
if yq.name=='expNevts':
line = TLine(gPad.GetUxmin(),3,gPad.GetUxmax(),3)
line.SetLineColor(ROOT.kBlue)
line.Draw('same')
#graph.SetMinimum(0.01)
#graph.SetMaximum(1E06)
if xq.log: c.SetLogx()
if yq.log: c.SetLogy()
c.SetGridx()
c.SetGridy()
c.SaveAs('./plots/{}{}/{}_{}VS{}.pdf'.format(self.label,suffix,self.name,yq.name,xq.name))
c.SaveAs('./plots/{}{}/{}_{}VS{}.C'.format(self.label,suffix,self.name,yq.name,xq.name))
c.SaveAs('./plots/{}{}/{}_{}VS{}.png'.format(self.label,suffix,self.name,yq.name,xq.name))
self.graphs['{}VS{}'.format(yq.name,xq.name)] = graph
# add the graph container for memory?
graph_saver.append(graph)
def plotPDSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgPD = TGraphErrors(len(self.PD_AVG_POINTS))
gDet_AvgPD.SetName("g_{0}_PDAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxPD = TGraphErrors(len(self.PD_MAX_POINTS))
gDet_MaxPD.SetName("g_{0}_PDMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinPD = TGraphErrors(len(self.PD_MIN_POINTS))
gDet_MinPD.SetName("g_" + strDetName + "_PDMin")
gDet_MinPD.SetName("g_{0}_PDMin".format(strDetName))
#Set the points
for i in range(0, len(self.PD_AVG_POINTS)):
#Average
gDet_AvgPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_AVG_POINTS[i])
gDet_AvgPD.SetPointError(i, self.GAIN_STDDEV_POINTS[i],
self.PD_STDDEV_POINTS[i])
#Max
gDet_MaxPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MAX_POINTS[i])
#Min
gDet_MinPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MIN_POINTS[i])
#Draw
canv_AvgPD = TCanvas("canv_{0}_PDAvg".format(strDetName),
"{0} Discharge Probability".format(strDetName),
600, 600)
canv_AvgPD.cd()
canv_AvgPD.cd().SetLogx()
canv_AvgPD.cd().SetLogy()
gDet_AvgPD.GetXaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgPD.GetYaxis().SetTitle("Discharge Probability P_{D}")
gDet_AvgPD.GetYaxis().SetRangeUser(1e-11, 1e-6)
gDet_AvgPD.SetMarkerStyle(21)
gDet_AvgPD.Draw("AP")
gDet_MaxPD.Draw("sameL")
gDet_MinPD.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.GetDirectory("Summary")
dir_Summary.cd()
canv_AvgPD.Write()
gDet_AvgPD.Write()
gDet_MaxPD.Write()
gDet_MinPD.Write()
return
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[3. * nchannels - 1.5 - 3. * i for i in range(0, nchannels)])
ey = np.array([0.8 for i in range(0, nchannels)])
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
x1 = gStyle.GetPadLeftMargin() + 0.01
y2 = 1 - gStyle.GetPadTopMargin() - 0.01
leg = TLegend(x1, y2 - 0.17, x1 + 0.25, y2)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
leg.AddEntry(gobs, "Observed", "pl")
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def correctedCrossSectionsPlot(crossings, shapes, overDiff):
uncorrected = {41: (3.2575816286, 0.00514858611944), \
281: (3.26316215713, 0.00468789412223), \
872: (3.27340775031, 0.00484925398906), \
1783: (3.24986926821, 0.00460908436455), \
2063: (3.26363843728, 0.0044071069983)}
multi = TMultiGraph('sigmavis', '')
graphs = []
n = len(shapes) + 1
for i, shape in enumerate([''] + list(shapes)):
xval = array('d',
[a + 0.08 * (i - 0.5 * n) for a in range(len(crossings))])
xerr = array('d', len(crossings) * [0])
yval = array('d', [uncorrected[int(bx)][0] for bx in crossings])
yerr = array('d', [uncorrected[int(bx)][1] for bx in crossings])
if shape:
for j, bx in enumerate(crossings):
yval[j] *= 1 + overDiff[shape][bx]
yerr[j] *= 1 + overDiff[shape][bx]
graph = TGraphErrors(len(crossings), xval, yval, xerr, yerr)
graph.SetName('ge' + shape)
graph.SetMarkerStyle(20)
graph.SetMarkerColor(1 + i)
multi.Add(graph)
graphs.append(graph)
gStyle.SetOptStat(0)
hist = TH2F('hist', '', len(crossings), -0.5,
len(crossings) - 0.5, 100, 3.23, 3.33)
for i, bx in enumerate(crossings):
hist.GetXaxis().SetBinLabel(i + 1, bx)
canvas = TCanvas('c_' + multi.GetName(), '', 600, 600)
hist.Draw('AXIS')
multi.Draw('P')
canvas.Update()
hist.GetXaxis().SetLabelSize(0.035)
hist.GetXaxis().SetNdivisions(len(crossings), False)
hist.GetYaxis().SetTitle('#sigma_{vis} [b]')
hist.GetYaxis().SetLabelSize(0.025)
hist.GetYaxis().SetTitleOffset(1.3)
leg = TLegend(0.15, 0.82, 0.85, 0.85)
leg.SetNColumns(len(shapes) + 1)
leg.SetBorderSize(0)
for i, shape in enumerate([''] + list(shapes)):
entry = leg.AddEntry('ge' + shape, shapeNames[shape], 'P')
entry.SetMarkerStyle(20)
entry.SetMarkerColor(1 + i)
leg.Draw()
drawCMS()
canvas.Modified()
canvas.Update()
canvas.SaveAs('summaryPlots/' + canvas.GetName() + '.pdf')
canvas.SaveAs('summaryPlots/' + canvas.GetName() + '.C')
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[nchannels - 1.5 - float(i) for i in range(0, nchannels)])
ey = np.full(nchannels, 0.494)
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.75, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def plot_correction( h2, slope, offset, x_points, y_points, error_points, outname,
xlabel='',
etBinIdx=None,
etaBinIdx=None,
etBins=None,
etaBins=None,
label='Internal',
ref_value=None,
pd_value=None,
palette=kBlackBody):
def toStrBin(etlist = None, etalist = None, etidx = None, etaidx = None):
if etlist and etidx is not None:
etlist=copy(etlist)
if etlist[-1]>9999: etlist[-1]='#infty'
binEt = (str(etlist[etidx]) + ' < E_{T} [GeV] < ' + str(etlist[etidx+1]) if etidx+1 < len(etlist) else
'E_{T} > ' + str(etlist[etidx]) + ' GeV')
return binEt
if etalist and etaidx is not None:
binEta = (str(etalist[etaidx]) + ' < |#eta| < ' + str(etalist[etaidx+1]) if etaidx+1 < len(etalist) else
str(etalist[etaidx]) + ' <|#eta| < 2.47')
return binEta
canvas = TCanvas("canvas","canvas",500,500)
rpl.set_figure(canvas)
gStyle.SetPalette(palette)
canvas.SetRightMargin(0.15)
canvas.SetTopMargin(0.15)
canvas.SetLogz()
h2.GetXaxis().SetTitle('Neural Network output (Discriminant)')
h2.GetYaxis().SetTitle(xlabel)
h2.GetZaxis().SetTitle('Count')
h2.Draw('colz')
g = TGraphErrors(len(x_points), array.array('d',x_points), array.array('d',y_points), array.array('d',error_points), array.array('d',[0]*len(x_points)))
g.SetMarkerColor(kBlue)
g.SetMarkerStyle(8)
g.SetMarkerSize(1)
g.Draw("P same")
line = TLine(slope*h2.GetYaxis().GetXmin()+offset,h2.GetYaxis().GetXmin(), slope*h2.GetYaxis().GetXmax()+offset, h2.GetYaxis().GetXmax())
line.SetLineColor(kBlack)
line.SetLineWidth(2)
line.Draw()
# Add text labels into the canvas
text = toStrBin(etlist=etBins, etidx=etBinIdx)
text+= ', '+toStrBin(etalist=etaBins, etaidx=etaBinIdx)
if ref_value and pd_value:
text+=', P_{D} = %1.2f (%1.2f) [%%]'%(pd_value*100, ref_value*100)
rpl.add_text(0.15, 0.885, text, textsize=.03)
rpl.set_atlas_label(0.15, 0.94, label)
rpl.format_canvas_axes(XLabelSize=16, YLabelSize=16, XTitleOffset=0.87, ZLabelSize=16,ZTitleSize=16, YTitleOffset=0.87, ZTitleOffset=1.1)
canvas.SaveAs(outname)
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors( len(self.GAIN_AVG_POINTS) )
#gDet_AvgEffGain.SetName("g_" + strDetName + "_EffGainAvg")
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors( len(self.GAIN_MAX_POINTS) )
#gDet_MaxEffGain.SetName("g_" + strDetName + "_EffGainMax")
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors( len(self.GAIN_MIN_POINTS) )
#gDet_MinEffGain.SetName("g_" + strDetName + "_EffGainMin")
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set the points
for i in range(0, len(self.GAIN_AVG_POINTS) ):
#Average
gDet_AvgEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i,0,self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MIN_POINTS[i])
#Draw
#canv_AvgEffGain = TCanvas("canv_" + strDetName + "_EffGainAvg",strDetName + " Average Effective Gain",600,600)
canv_AvgEffGain = TCanvas("canv_{0}_EffGainAvg".format(strDetName),"{0} Average Effective Gain".format(strDetName),600,600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2,1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
def fit_slices(histo, x_range, x_bin_step, y_fit_range):
x_start = histo.GetXaxis().FindBin(x_range[0])
x_stop = histo.GetXaxis().FindBin(x_range[1])
x_values = array('d')
slices = []
fits = []
for i, x_bin in enumerate(range(x_start, x_stop + 1, x_bin_step)):
projec = histo.ProjectionY(histo.GetTitle() + '_proj{}'.format(i),
x_bin, x_bin + x_bin_step)
fit = TF1(histo.GetTitle() + '_fit{}'.format(i), 'gaus')
fit.SetTitle(histo.GetTitle() + '_fit{}'.format(i))
fit.SetName(histo.GetTitle() + '_fit{}'.format(i))
if y_fit_range:
fit.SetParameter(1, 0.5 * (y_fit_range[0] + y_fit_range[1]))
projec.Fit(fit, 'R', '', y_fit_range[0], y_fit_range[1])
slices.append(projec)
fits.append(fit)
x_low = histo.GetXaxis().GetBinCenter(x_bin)
x_high = histo.GetXaxis().GetBinCenter(x_bin + x_bin_step)
x_values.append(0.5 * (x_high + x_low))
means = array('d')
means_err = array('d')
stds = array('d')
stds_err = array('d')
zeros = array('d')
for f in fits:
means.append(f.GetParameter(1))
means_err.append(f.GetParError(1))
stds.append(f.GetParameter(2))
stds_err.append(
f.GetParError(1)) ### change if want the sigma of dist.
zeros.append(0.0)
graph = TGraphErrors(len(x_values), x_values, means, zeros, stds_err)
graph.SetName('g_' + histo.GetName())
graph.SetMarkerStyle(21)
graph.SetMarkerColor(kRed)
return graph, slices, fits
