def create_canvas(self, ratio=True):
if ratio:
self.canvas = Canvas(width=700, height=700)
self.canvas.Draw()
self.canvas.cd()
self.main_pad = Pad(0., 0.25, 1., 1.)
self.main_pad.Draw()
self.canvas.cd()
self.ratio_pad = Pad(0., 0., 1., 0.25)
self.ratio_pad.Draw()
self.main_pad.SetTicks(True)
self.main_pad.SetBottomMargin(0.)
self.main_pad.SetLeftMargin(0.15)
self.main_pad.SetRightMargin(0.15)
self.ratio_pad.SetLeftMargin(0.15)
self.ratio_pad.SetRightMargin(0.15)
self.ratio_pad.SetTopMargin(0.)
self.ratio_pad.SetGridy()
self.ratio_pad.SetBottomMargin(0.3)
else:
self.canvas = Canvas(width=700, height=700)
self.canvas.Draw()
self.canvas.cd()
self.main_pad = Pad(0., 0., 1., 1.)
self.main_pad.Draw()
self.canvas.cd()
self.ratio_pad = Pad(-1., -1., -.9, -.9)
self.ratio_pad.Draw() # put it outside the canvas
self.main_pad.SetTicks(True)
self.main_pad.SetTopMargin(0.15)
self.main_pad.SetBottomMargin(0.15)
self.main_pad.SetLeftMargin(0.15)
self.main_pad.SetRightMargin(0.15)
def extract_MET(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "MissingET.MET"
var_qcd = "MissingET.MET"
var_wjet = "MissingET.MET"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
MET_tt = Hist(MET_NBINS, MET_NLO, MET_NHI, title='MET_tt', legendstyle='L')
MET_qcd = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_qcd',
legendstyle='L')
MET_wjet = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_wjet',
legendstyle='L')
# FILLING THE TREE
fill_MET_tree(tt_n_entries, t_tt, leaf_tt, MET_tt)
fill_MET_tree(qcd_n_entries, t_qcd, leaf_qcd, MET_qcd)
fill_MET_tree(wjet_n_entries, t_wjet, leaf_wjet, MET_wjet)
#set line colors
MET_tt.SetLineColor('blue')
MET_qcd.SetLineColor('green')
MET_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
MET_qcd.SetStats(0)
MET_qcd.Draw('HIST')
MET_tt.Draw('HIST SAME')
MET_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([MET_tt, MET_qcd, MET_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/MET_plots/MET.pdf")
#make the plots wait on screen
wait(True)
def draw_efficiency(eff_s, rej_b, field, category, textsize=22):
xtitle = get_xtitle(field)
c = Canvas()
c.SetGridx()
c.SetGridy()
eff_s.painted_graph.yaxis.SetRangeUser(0, 1.10)
eff_s.painted_graph.yaxis.title = 'Efficiency'
eff_s.painted_graph.xaxis.title = xtitle
eff_s.painted_graph.Draw('AP')
rej_b.color = 'red'
rej_b.markerstyle = 'square'
rej_b.painted_graph.Draw('sameP')
right_axis = ROOT.TGaxis(ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymin(),
ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(), 0,
1.10, 510, "+L")
right_axis.SetLineColor(ROOT.kRed)
right_axis.SetLabelColor(ROOT.kRed)
right_axis.SetTextColor(ROOT.kRed)
right_axis.SetTitle('Rejection = 1 - #epsilon_{B}')
right_axis.Draw('same')
ROOT.gStyle.SetPadTickY(0)
ROOT.gPad.Update()
ROOT.gStyle.SetPadTickY(1)
label = ROOT.TLatex(c.GetLeftMargin() + 0.04, 0.9, category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
leg = Legend([eff_s, rej_b], pad=c)
# textsize=20, leftmargin=0.6, topmargin=0.6)
leg.Draw('same')
return c
def draw_DR(DR_tt, DR_qcd, DR_wjet):
#set line colors
DR_tt.SetLineColor(4)
DR_qcd.SetLineColor(8)
DR_wjet.SetLineColor(2)
DR_tt.legendstyle = 'L'
DR_qcd.legendstyle = 'L'
DR_wjet.legendstyle = 'L'
#begin drawing stuff
c1 = Canvas()
DR_qcd.SetStats(0)
DR_qcd.Draw('HIST')
DR_tt.Draw('HIST SAME')
DR_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([DR_tt, DR_qcd, DR_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("DeltaR.pdf")
#make the plots wait on screen
wait(True)
def drawStack(stack, legends, foundVariables, sampleType, subset = 'TestA', dataHist = {}):
"""Draw the given stack.
Keyword arguments:
stack -- the stack to be drawn
legends -- the accompanying legends
foundVariables -- the variables in the stack that are being drawn
sampleType -- signal or bkg
subset -- acts as extra identifier in the filename (default A)
dataHist -- the data histograms (default [])
"""
c2 = Canvas()
c2.cd()
xcount = 0
# should create a ratio plot too!!! get teh scaling right...
for x in stack:
if dataHist:
x.Draw()
xmax = x.GetHistogram().GetMaximum()
dmax = dataHist['data'][xcount].GetMaximum()
x.SetMaximum(max(xmax,dmax)*1.1)
x.Draw('hist')
dataHist['data'][xcount].Draw('same')
else:
x.Draw('hist')
legends[xcount].Draw('same')
c2.SaveAs(foundVariables[xcount]+str(sampleType)+str(subset)+'Stack.png')
c2.Write()
xcount +=1
def plotResults(variable, data, templates, results):
resCan = Canvas()
leg = Legend(nTemplates + 2)
data.Draw('PE')
leg.AddEntry(data, style='LEP')
nBins = len(inputTemplates[variable]['data'][whichBinFromFile])
h_tSumAfter = Hist(nBins, 0, nBins, title='after_' + variable)
if useT1:
plotTemplateAfter(templates[tNames['t1']], results[tNames['t1']][0],
resCan, leg, h_tSumAfter)
pass
if useT2:
plotTemplateAfter(templates[tNames['t2']], results[tNames['t2']][0],
resCan, leg, h_tSumAfter)
pass
if useT3:
plotTemplateAfter(templates[tNames['t3']], results[tNames['t3']][0],
resCan, leg, h_tSumAfter)
pass
if useT4:
plotTemplateAfter(templates[tNames['t4']], results[tNames['t4']][0],
resCan, leg, h_tSumAfter)
pass
leg.Draw()
h_tSumAfter.SetLineColor(2)
h_tSumAfter.SetLineStyle(7)
h_tSumAfter.SetMarkerSize(0)
h_tSumAfter.Draw('SAME HIST')
resCan.Update()
return resCan, h_tSumAfter
def makePage( histos, fileName, fileDescr, separateFiles, logscale):
"""
Prepares a canvas with one histogram per pad
"""
import rootpy
from rootpy.plotting import Hist, Canvas
from ROOT import kBlue,gPad
log = logging.getLogger('pyroplot')
cans = {}
log.info( "Drawing histograms .." )
for idx, name in enumerate(sorted(histos.keys())):
if separateFiles:
log.debug( "Creating new canvas with index %d."%(idx))
c=Canvas( 600, 800)
cans[name]=c
markCanvas(c, fileName, 0.05, y = 0.009, size = 0.025, color = 2 )
if not separateFiles and (idx)%6 == 0:
log.debug( "Creating new canvas with index %d."%(idx/6))
# start a new canvas
c=Canvas( 600, 800)
cans[fileName+'/'+name]=c
c.Divide(2,3)
markCanvas(c, fileName, 0.05, y = 0.009, size = 0.025, color = 2 )
# draw the histogram
hist = histos[name]
log.debug( "Drawing histogram #" + str(idx%6+1) +": " + hist.GetName() + " (" + hist.__class__.__name__ + ") in canvas #" + str(int(idx/6) ))
hist.color = kBlue
if not separateFiles:
c.cd(idx%6+1)
if logscale:
gPad.SetLogy()
plotHistos(histos=[hist],text=name)
if fileDescr:
markPad(text=fileDescr, x=.14, y=.8, size=0.041, color = 2)
return cans
def plotSummary(name, closure, xmin=0, xmax=1, xTitle='m_{vis} [GeV]'):
histoSmoothRatios = []
for hname, graph in closure.data['True/Est'].items():
if 'Histo' in hname and 'Smooth' in hname and not 'Error' in hname:
histoSmoothRatios.append(graph)
################ Plot ratios
histoDummyRatio = Hist(1, xmin, xmax, type='F')
values = []
for ratio in histoSmoothRatios:
for p in xrange(ratio.GetN()):
values.append(ratio.GetY()[p])
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
for ratio in histoSmoothRatios:
ratio.SetLineColor(ROOT.kGray + 3)
ratio.SetLineWidth(1)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('Ratio_Summary_Canvas')
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
for ratio in histoSmoothRatios:
ratio.Draw('l same')
canvasRatio.Print(
'results/{NAME}_NonClosure_Ratio_Summary.png'.format(NAME=name))
def applyacc(wbin, q2bin, iedges, vedges, weights):
# TODO: clone 2d hists to keep snapshots between steps of acceptance
# corrections and zero-filling
vals = ibins2vals(wbin, q2bin)
(wval, wlo, whi) = (vals[0][0], vals[0][1], vals[0][2])
(q2val, q2lo, q2hi) = (vals[1][0], vals[1][1], vals[1][2])
h2s = (hexpsig, hexpsb, h2thr, h2acc) = h2costphis(wbin, q2bin, iedges)
h2rec_acor = h2thr.Clone('h2r_acor')
h2rec_acor.Multiply(h2acc)
inth2r = h2rec_acor.Integral() # integral of reconstructed simulated events
h2rec_acor.Divide(h2acc) # thrown events with acceptance holes
h2thr.Add(h2rec_acor, -1) # thrown events IN acceptance holes
[h2thr.SetBinError(i, j, sqrt(h2thr.GetBinContent(i, j))) for i in range(1, h2thr.GetNbinsX()) for j in range(1, h2thr.GetNbinsY())]
hexpsb.Scale(weights[0]) # weightsb
hexpsig.Add(hexpsb, -1)
inth2e = hexpsig.Integral()/weights[1] # integral of signal region with 3-sigma cut correction
hexpsig.Divide(h2acc)
h2thr.Scale(inth2e/inth2r)
hexpsig.Add(h2thr)
cmmp = Canvas(name='hcostphi_%d_%d' % (round(1000*wval), round(1000*q2val)),
title='Angular Distributions, W, Q2 = %.3f GeV,%.3f GeV2' % (wval, q2val))
cmmp.Divide(2, 2)
for ipad, h2 in enumerate(h2s):
cmmp.cd(ipad+1)
r.gPad.Update()
h2.Draw('colz')
wait()
return hexpsig
def drawAllTrainStacks(signal, bkg, data, labelCodes, weightsPerSampleA, weightsPerSampleB, corrWeightsA = DEFAULT, corrWeightsB = DEFAULT, subset = 'TrainA'):
"""Draw all train stacks for signal and bkg at once.
Keyword arguments:
signal -- the signal sample
bkg -- the background sample
labelCodes -- the label codes for the type of sample (W, Z for eg.)
weightsPerSample -- the XS weight
subset -- extra identifier for the filename (default TrainA)
"""
if corrWeightsA is DEFAULT:
corrWeightsA = hstack(signal.returnTestCorrectionWeights('A'), bkg.returnTestCorrectionWeights('A'))
if corrWeightsB is DEFAULT:
corrWeightsB = hstack(signal.returnTestCorrectionWeights('B'), bkg.returnTestCorrectionWeights('B'))
# store all histograms in output.root
for x in xrange (0, len(signal.train)):
if x == 0:
subset = 'A'
weightsPerSample = weightsPerSampleA
corrWeights = corrWeightsA
else:
subset = 'B'
weightsPerSample = weightsPerSampleB
corrWeights = corrWeightsB
f = ropen('outputTrain'+str(subset)+'.root','recreate')
c1 = Canvas()
c1.cd()
allStack = []
legendAllStack = []
# get sigA histograms
hist, histDictSigA, testAStack, legendSigStack = createHists(signal.returnTrainSample(subset), labelCodes, 'signal', signal.returnTrainSampleLabels(subset), weightsPerSample[0], signal.returnFoundVariables(), allStack, legendAllStack, corrWeights, str('Train'+subset), True)
# get bkgA histograms
# how to fix legends????
hist2, histDictBkgA, testAStackBkg, legendBkgStack = createHists(bkg.returnTrainSample(subset), labelCodes, 'bkg', bkg.returnTrainSampleLabels(subset), weightsPerSample[1], bkg.returnFoundVariables(), allStack, legendAllStack, corrWeights, str('Train'+subset), True)
for hist2idx in xrange(0,len(hist)):
legend = Legend(3)
legend.AddEntry(hist[hist2idx],'F')
legend.AddEntry(hist2[hist2idx],'F')
hist[hist2idx].draw('hist')
hist[hist2idx].Write()
hist2[hist2idx].draw('histsame')
hist2[hist2idx].Write()
legend.Draw('same')
c1.Write()
#c1.SaveAs(signal.returnFoundVariables()[hist2idx]+".png")
hist2idx+=1
drawStack(testAStack, legendSigStack, signal.returnFoundVariables(), 'Sig', str('Train'+subset)) # draw histograms
drawStack(testAStackBkg, legendBkgStack, bkg.returnFoundVariables(), 'Bkg', str('Train'+subset))
drawStack(allStack, legendAllStack, signal.returnFoundVariables(), 'All', str('Train' + subset))
f.close()
def compare (name1, name2, j) :
c = Canvas()
QCD = ROOT.TFile.Open(name1+".root")
Wjet = ROOT.TFile.Open(name2+".root")
c.Divide(2,1)
c.cd(1)
QCD.Draw("AP")
Wjet.Draw("AP")
c.SaveAs("plots/WeightComparison/WjetProbaDecayChannel_"+str(j)+"_WeightComparison.png")
def __prepare_canvas(canvas_args):
style = gStyle
style.SetOptStat(0)
canvas = Canvas(**canvas_args)
canvas.SetGridx(True)
canvas.SetGridy(True)
canvas.title = ""
return canvas, style
def drawSigBkgDistrib(sample, classif, foundVariables):
idx = 0
c1 = Canvas()
c1.cd()
histidx = 0
histSig = []
histBkg = []
classif, sample = sc.sortMultiple(classif,sample)
sample2 = transpose(sample)
binc = bincount(classif)
numzero = binc[0]
numone = binc[1]
global histLimits
for x in sample2:
x = transpose(x)
variableName = foundVariables[histidx]
histSig.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
histSig[histidx].fill_array(x[:numzero])
histSig[histidx].scale(1.0/histSig[histidx].integral())
histSig[histidx].fillcolor='red'
histSig[histidx].SetFillStyle(3004)
histSig[histidx].linecolor='red'
histSig[histidx].GetXaxis().SetTitle(foundVariables[histidx])
histSig[histidx].GetYaxis().SetTitle('# Events Normalised to 1')
histSig[histidx].SetTitle('Background')
# histSig[histidx].fillstyle='solid'
histSig[histidx].SetStats(0)
histBkg.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
histBkg[histidx].fill_array(x[numzero:])
histBkg[histidx].scale(1.0/histBkg[histidx].integral())
histBkg[histidx].fillcolor='blue'
histBkg[histidx].linecolor='blue'
histBkg[histidx].SetFillStyle(3005)
histBkg[histidx].GetXaxis().SetTitle(foundVariables[histidx])
histBkg[histidx].GetYaxis().SetTitle('# Events Normalised to 1')
histBkg[histidx].SetTitle('Signal')
# histBkg[histidx].fillstyle='solid'
histBkg[histidx].SetStats(0)
leg = Legend(2)
leg.AddEntry(histBkg[histidx],'F')
leg.AddEntry(histSig[histidx],'F')
histBkg[histidx].draw('hist')
histSig[histidx].draw('histsame')
leg.Draw('same')
c1.SaveAs(variableName+"SigBkgComparison.png")
histidx+=1
def plot(params, efficiency):
canvas = Canvas(width=500, height=500)
draw_axes(params)
draw_efficiency(params, efficiency)
draw_lines(params)
HGCAL_label(text='HGCAL Simulation', pad=canvas)
draw_legends(params)
canvas.RedrawAxis()
canvas.Print('%s.png' % params.name)
canvas.Print('%s.pdf' % params.name)
canvas.Print('%s.C' % params.name)
return canvas
def plot_comparisons(input_file, comparison_plots):
with root_open(input_file) as file:
for name,histo_config in comparison_plots.items():
canvas = Canvas(1200,700)
canvas.SetLeftMargin(0.08)
canvas.SetRightMargin(0.12)
histogram = file.Get(histo_config.name)
if len(histo_config.range)==2:
histogram.SetAxisRange(histo_config.range[0], histo_config.range[1], 'X')
histogram.color = histo_config.color
histogram.markerstyle = histo_config.marker
histogram.Draw()
canvas.Print('./plots/{}.png'.format(name))
def draw_curve_7(_func, name, title, ylow, yhigh):
""" Draw 7TeV trigger efficiency curves """
graphs = []
for (trigger, color) in triggers:
tool = ROOT.TauTriggerCorrections(
os.path.join(base, 'triggerSF_%s.root' % trigger))
func = getattr(tool, _func)
eff = map(lambda x: func(x, 0), pt)
eff_low = map(lambda x: func(x, -1), pt)
eff_high = map(lambda x: func(x, 1), pt)
graph = Graph(len(pt), name=trigger)
for i, (p, e, e_low,
e_high) in enumerate(zip(pt, eff, eff_low, eff_high)):
graph.SetPoint(i, p / 1000, e)
graph.SetPointError(i, 0.4, 0.4, e - e_low, e_high - e)
graph.linecolor = color
graph.linewidth = 2
graph.fillstyle = '/'
graph.fillcolor = color
graphs.append(graph)
c = Canvas()
leg = Legend(len(graphs),
pad=c,
topmargin=0.4,
leftmargin=0.3,
textsize=25,
margin=0.2)
for i, g in enumerate(graphs):
if i == 0:
g.Draw('3AC')
g.xaxis.title = '#font[52]{p}_{T} [GeV]'
g.xaxis.SetLimits(20, 100)
g.yaxis.SetLimits(ylow, yhigh)
g.yaxis.SetRangeUser(ylow, yhigh)
g.yaxis.title = title
else:
g.Draw('3C SAME')
leg.AddEntry(g, g.name, 'L')
leg.Draw()
lines = []
for thresh in (25, 35):
line = Line(thresh, ylow, thresh, yhigh)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
lines.append(line)
c.SaveAs('trigger_{0}.png'.format(name))
c.SaveAs('trigger_{0}.eps'.format(name))
def plot_event(name, canvas):
primitives = canvas.GetListOfPrimitives()
histo = None
point = None
for primitive in primitives:
if primitive.InheritsFrom(TH2Poly.Class()):
histo = primitive
if primitive.InheritsFrom(TMarker.Class()):
point = primitive
histo.SetXTitle('x [cm]')
histo.SetYTitle('y [cm]')
canvas_new = Canvas(700, 700)
histo.SetAxisRange(77,100,'X')
histo.Draw('colz')
point.Draw()
canvas_new.SetLogz()
canvas_new.Print('./plots/event_{}.png'.format(name))
def plot_distribution(self, output_file):
f = r.TFile.Open(os.path.join(self.input_directory, output_file),
'read')
intuple = f.Get('pythia8-Geant4')
h = Hist2D(100, 0, 350, 100, 0, 6)
for muon in intuple:
px = muon.px
py = muon.py
pz = muon.pz
p = np.sqrt(px**2 + py**2 + pz**2)
pt = np.sqrt(px**2 + py**2)
h.Fill(p, pt)
c = Canvas()
r.gStyle.SetOptStat(11111111)
h.Draw("colz")
c.Draw()
c.SaveAs(os.path.join(self.input_directory, "p_dist.png"))
def checkOnMC(unfolding, method):
global bins, nbins
RooUnfold.SVD_n_toy = 1000
pulls = []
for sub in range(1,9):
inputFile2 = File('../data/unfolding_merged_sub%d.root' % sub, 'read')
h_data = asrootpy(inputFile2.unfoldingAnalyserElectronChannel.measured.Rebin(nbins, 'measured', bins))
nEvents = inputFile2.EventFilter.EventCounter.GetBinContent(1)
lumiweight = 164.5 * 5050 / nEvents
# print sub, nEvents
h_data.Scale(lumiweight)
doUnfoldingSequence(unfolding, h_data, method, '_sub%d' %sub)
pull = unfolding.pull_inputErrorOnly()
# unfolding.printTable()
pulls.append(pull)
unfolding.Reset()
allpulls = []
for pull in pulls:
allpulls.extend(pull)
h_allpulls = Hist(100,-30,30)
filling = h_allpulls.Fill
for entry in allpulls:
filling(entry)
fit = h_allpulls.Fit('gaus', 'WWS')
h_fit = asrootpy(h_allpulls.GetFunction("gaus").GetHistogram())
canvas = Canvas(width=1600, height=1000)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_allpulls.Draw()
fit.Draw('same')
canvas.SaveAs('plots/Pull_allBins_withFit.png')
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_allpulls, label=r'Pull distribution for all bins', emptybins=False)
rplt.hist(h_fit, label=r'fit')
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for all bins', CMS.title)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.legend(numpoints=1)
plt.savefig('plots/Pull_allBins.png')
#individual bins
for bin_i in range(nbins):
h_pull = Hist(100,-30,30)
for pull in pulls:
h_pull.Fill(pull[bin_i])
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_pull, label=r'Pull distribution for bin %d' % (bin_i + 1), emptybins=False)
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for bin %d' % (bin_i + 1), CMS.title)
plt.savefig('Pull_bin_%d.png' % (bin_i + 1))
def draw_efficiencies(effs, field, category, textsize=22):
xtitle = get_xtitle(field)
c = Canvas()
c.SetGridx()
c.SetGridy()
if not isinstance(effs, (list, tuple)):
effs = [effs]
h = Hist(10, effs[0].painted_graph.xaxis.min,
effs[0].painted_graph.xaxis.max)
h.yaxis.SetRangeUser(0.0, 1.1)
h.Draw('HIST')
h.yaxis.title = 'Efficiency'
h.xaxis.title = xtitle
colors = [
'black',
'red',
'orange',
'blue',
'green',
'purple',
'yellow',
'pink',
]
if len(effs) > len(colors):
colors = len(effs) * colors
for eff, col in zip(effs, colors):
eff.color = col
eff.color = col
eff.painted_graph.legendstyle = 'l'
eff.painted_graph.Draw('SAMEP')
label = ROOT.TLatex(c.GetLeftMargin() + 0.04, 0.9, category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
leg = Legend(effs, pad=c, textsize=20)
# textsize=20, leftmargin=0.6, topmargin=0.6)
leg.Draw('same')
return c
def draw_stuff():
ROOT.TH1.AddDirectory(ROOT.kFALSE)
#TH1.SetDirectory(0)
f = root_open(
"/eos/cms/store/group/dpg_dt/comm_dt/dtRootple2015/DTTree_Run259685.root",
"r")
f2 = root_open("tmp.root", "RECREATE")
for path, dirs, objects in f.walk():
print(objects)
tree = f.DTTree
#for branch in f.DTTree.branchnames:
# print branch
canvas = Canvas()
hist = Hist(100, 0.0, 1E3, name='h' + '_' + '_IPchi2', type='D')
#tree.Draw('Ndigis', '', '', hist)
selection = "digi_wheel==-1 & digi_station==4 & digi_sector==4 & digi_sl==1 & digi_layer==1 & (digi_time < 300 | digi_time > 700)" # & digi_wire=='wire0'"
h = tree.Draw('timestamp', selection=selection, create_hist=True)
#h.SetDirectory(0)
canvas.SaveAs("test.png")
def bkg_fitting(filename):
if filename[-5:] == '.root':
filename = filename[:-5]
s_root_path = './data/%s.root' % filename
f_integral = root_open(s_root_path, 'read')
conversion_to_num_electrons = 1. / (
250e6) / 50. / 10. / 1.6e-19 * 2. / 2**12
# find fit limits
l_percentiles = [10, 90]
lb_fit, ub_fit = -30, 30
# grab histogram
h_bkg = f_integral.bkgd_data_integral_hist
c_fit = Canvas()
h_bkg.Draw()
fit_func = root.TF1('fit_func', 'gaus', lb_fit, ub_fit)
h_bkg.Fit('fit_func', 'MELSR')
fit_func.Draw('same')
c_fit.Update()
d_fit = {}
d_fit['bkg_mean'] = fit_func.GetParameter(1) * conversion_to_num_electrons
d_fit['bkg_std'] = fit_func.GetParameter(2) * conversion_to_num_electrons
d_fit['bkg_mean_unc'] = fit_func.GetParError(
1) * conversion_to_num_electrons
d_fit['bkg_std_unc'] = fit_func.GetParError(
2) * conversion_to_num_electrons
print d_fit
raw_input('Press enter to continue...')
pickle.dump(d_fit, open('./bkg_results/bkg_%s.p' % (file), 'w'))
def draw_hists(hists,
field,
category,
textsize=22,
logy=False,
unit_area=False):
xtitle = get_xtitle(field)
c = Canvas()
c.SetGridx()
c.SetGridy()
c.SetLogy(logy)
if not isinstance(hists, (list, tuple)):
hists = [hists]
if unit_area:
for h in hists:
if h.integral() != 0:
h /= h.integral()
hists[0].xaxis.title = xtitle
hists[0].yaxis.title = 'Arbitrary Unit'
y_max = get_ymax(hists)
hists[0].yaxis.SetRangeUser(0., 1.05 * y_max)
hists[0].Draw('HIST')
colors = [
'black',
'red',
'orange',
'blue',
'green',
'purple',
'yellow',
'pink',
]
if len(hists) > len(colors):
colors = len(hists) * colors
for hist, col in zip(hists, colors):
hist.color = col
hist.color = col
hist.fillstyle = 'hollow'
hist.markersize = 0
hist.linewidth = 2
hist.linestyle = 'solid'
hist.drawstyle = 'hist E0'
hist.legendstyle = 'l'
hist.Draw('SAMEHIST')
label = ROOT.TLatex(c.GetLeftMargin() + 0.04, 0.9, category.label)
label.SetNDC()
label.SetTextFont(43)
label.SetTextSize(textsize)
label.Draw()
leg = Legend(hists, pad=c, textsize=20)
# textsize=20, leftmargin=0.6, topmargin=0.6)
leg.Draw('same')
return c
def draw(event, vects, radii=None, colors=None, etamin=-5, etamax=5, phimin=-math.pi, phimax=math.pi):
"""
This function creates a 2D view of an event over eta and phi
"""
c = Canvas()
display = Hist2D(100, etamin, etamax, 100, phimin, phimax)
display.SetXTitle('#eta')
display.SetYTitle('#phi')
display.Draw()
things = []
for i, vect in enumerate(vects):
radius = .4
if radii:
radius = radii[i]
thing = Ellipse(vect.Eta(), vect.Phi(), radius, radius)
thing.SetLineWidth(3)
if colors:
thing.SetLineColor(colors[i])
thing.SetFillStyle(0)
thing.Draw()
things.append(thing)
c.SaveAs('eventviews/event_%i.png' % event.EventNumber)
def plot_distributions(input_file, distribution_plots):
with root_open(input_file) as file:
for name,histo_configs in distribution_plots.items():
canvas = Canvas(700,700)
legend = Legend(len(histo_configs), leftmargin=0.4, margin=0.3)
histograms = []
for histo_config in histo_configs:
histogram = file.Get(histo_config.name)
if len(histo_config.range)==2:
histogram.SetAxisRange(histo_config.range[0], histo_config.range[1], 'X')
histogram.color = histo_config.color
histogram.markerstyle = histo_config.marker
legend.AddEntry(histogram, label=histo_config.legend, style='ep')
histograms.append(histogram)
xmin, xmax, ymin, ymax = get_limits(histograms, logy=False)
option = ''
for histogram in histograms:
histogram.SetAxisRange(ymin,ymax,'Y')
histogram.Draw(option)
option += ' same'
legend.Draw()
canvas.Print('plots/{}.png'.format(name))
def draw_channel(self, channelNumber):
print '\n\n---------- Draw channel ' + str(
channelNumber) + ' ----------\n\n'
assert channelNumber >= 1 and channelNumber <= 17 and type(
channelNumber) == type(
1), 'Improper channel number given - please check input'
failStatus = -1
# grab required histograms
bkgFile = self.lProcessedRootFiles[0]
signalFile = self.lProcessedRootFiles[self.dLightLevels[channelNumber]]
hSignal = signalFile.Get('spe_' + str(channelNumber))
hSignal.DrawStyle = 'hist'
c1 = Canvas()
hSignal.Draw('')
c1.Update()
raw_input('Please press enter when finished...')
def draw_MT(MT_tt, MT_qcd, MT_wjet):
#set line colors
MT_tt.SetLineColor(4)
MT_qcd.SetLineColor(8)
MT_wjet.SetLineColor(2)
MT_tt.legendstyle = 'L'
MT_qcd.legendstyle = 'L'
MT_wjet.legendstyle = 'L'
#begin drawing stuff
c1 = Canvas()
MT_qcd.SetStats(0)
MT_qcd.Draw('HIST')
MT_tt.Draw('HIST SAME')
MT_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([MT_tt, MT_qcd, MT_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("MT.pdf")
def draw_HT(HT_tt, HT_qcd, HT_wjet):
#set line colors
HT_tt.SetLineColor(4)
HT_qcd.SetLineColor(8)
HT_wjet.SetLineColor(2)
HT_tt.legendstyle = 'L'
HT_qcd.legendstyle = 'L'
HT_wjet.legendstyle = 'L'
#begin drawing stuff
c1 = Canvas()
HT_wjet.SetStats(0)
HT_wjet.Draw('HIST')
HT_tt.Draw('HIST SAME')
HT_qcd.Draw('HIST SAME')
#make legend
l1 = Legend([HT_tt, HT_qcd, HT_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("HT.pdf")
def draw_DPHImj(DPHI_tt, DPHI_qcd, DPHI_wjet):
#set line colors
DPHI_tt.SetLineColor(4)
DPHI_qcd.SetLineColor(8)
DPHI_wjet.SetLineColor(2)
DPHI_tt.legendstyle = 'L'
DPHI_qcd.legendstyle = 'L'
DPHI_wjet.legendstyle = 'L'
#begin drawing stuff
c1 = Canvas()
DPHI_qcd.SetStats(0)
DPHI_qcd.Draw('HIST')
DPHI_tt.Draw('HIST SAME')
DPHI_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([DPHI_tt, DPHI_qcd, DPHI_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("DPHI_metjet.pdf")
def plot(params, rates):
canvas = Canvas(width=500, height=500)
draw_axes(params)
draw_rates(params, rates)
HGCAL_label(text='HGCAL Simulation', pad=canvas)
canvas.SetLogy()
canvas.RedrawAxis()
canvas.Print('%s.png' % params.name)
canvas.Print('%s.pdf' % params.name)
canvas.Print('%s.C' % params.name)
def test_cmstdr():
style = get_style('CMSTDR')
with style:
canvas = Canvas()
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
ROOT.gRandom.SetSeed()
for i in xrange(1000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(100.)
hpx.Draw()
CMS_label("Testing 2050", sqrts=100)
if INTERACTIVE:
wait()
def test_lhcb():
style = get_style('LHCb')
with style:
canvas = Canvas()
hpx = Hist(100, -4, 4, name="hpx", title="This is the px distribution")
ROOT.gRandom.SetSeed()
for i in xrange(1000):
hpx.Fill(ROOT.gRandom.Gaus())
hpx.GetXaxis().SetTitle("random variable [unit]")
hpx.GetYaxis().SetTitle("#frac{dN}{dr} [unit^{-1}]")
hpx.SetMaximum(80.)
hpx.Draw()
LHCb_label("R", "preliminary")
if INTERACTIVE:
wait()
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonCandMatchChecker('Check PF cand matching', [fileName])
checker.processSample()
h = checker.hists['pfCandPt']
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
c = Canvas(1000, 1000)
(xax, yax), lims = draw(
h,
c,
ytitle='PF Cands',
xtitle='PF Cand p_T',
)
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPt.png')
h2 = checker.hists['pfCandPtVsGenPt']
c2 = Canvas(1000, 1000)
h2.xaxis.SetTitleOffset(1.8)
h2.yaxis.SetTitleOffset(1.7)
h2.xaxis.SetTitleSize(0.5 * h2.xaxis.GetTitleSize())
h2.yaxis.SetTitleSize(0.5 * h2.yaxis.GetTitleSize())
h2.xaxis.SetLabelSize(0.6 * h2.xaxis.GetLabelSize())
h2.yaxis.SetLabelSize(0.6 * h2.yaxis.GetLabelSize())
h2.xaxis.SetTitle("Gen #mu p_{T}")
h2.yaxis.SetTitle("PF cand p_{T}")
h2.Draw("COLZ")
c2.Print(
'/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPtVsGenPt.png')
print "Out of {} good muons, {} failed PF ID ({}%).".format(
checker.nTot, checker.nFailPF,
100. * float(checker.nFailPF) / checker.nTot)
print(
"Out of those failing PF, {} shared a track with a PF charged hadron "
"({}%, {}% of the total)").format(
checker.nPFMatched, 100. * checker.nPFMatched / checker.nFailPF,
100. * checker.nPFMatched / checker.nTot)
def makePage(histos, fileName, fileDescr, separateFiles, logscale):
"""
Prepares a canvas with one histogram per pad
"""
import rootpy
from rootpy.plotting import Hist, Canvas
from ROOT import kBlue, gPad
log = logging.getLogger('pyroplot')
cans = {}
log.info("Drawing histograms ..")
for idx, name in enumerate(sorted(histos.keys())):
if separateFiles:
log.debug("Creating new canvas with index %d." % (idx))
c = Canvas(600, 800)
cans[name] = c
markCanvas(c, fileName, 0.05, y=0.009, size=0.025, color=2)
if not separateFiles and (idx) % 6 == 0:
log.debug("Creating new canvas with index %d." % (idx / 6))
# start a new canvas
c = Canvas(600, 800)
cans[fileName + '/' + name] = c
c.Divide(2, 3)
markCanvas(c, fileName, 0.05, y=0.009, size=0.025, color=2)
# draw the histogram
hist = histos[name]
log.debug("Drawing histogram #" + str(idx % 6 + 1) + ": " +
hist.GetName() + " (" + hist.__class__.__name__ +
") in canvas #" + str(int(idx / 6)))
hist.color = kBlue
if not separateFiles:
c.cd(idx % 6 + 1)
if logscale:
gPad.SetLogy()
plotHistos(histos=[hist], text=name)
if fileDescr:
markPad(text=fileDescr, x=.14, y=.8, size=0.041, color=2)
return cans
def pvalue_plot(poi, pvalues, pad=None,
xtitle='X', ytitle='P_{0}',
linestyle=None,
linecolor=None,
yrange=None,
verbose=False):
"""
Draw a pvalue plot
Parameters
----------
poi : list
List of POI values tested
pvalues : list
List of p-values or list of lists of p-values to overlay
multiple p-value curves
pad : Canvas or Pad, optional (default=None)
Pad to draw onto. Create new pad if None.
xtitle : str, optional (default='X')
The x-axis label (POI name)
ytitle : str, optional (default='P_{0}')
The y-axis label
linestyle : str or list, optional (default=None)
Line style for the p-value graph or a list of linestyles for
multiple p-value graphs.
linecolor : str or list, optional (default=None)
Line color for the p-value graph or a list of linestyles for
multiple p-value graphs.
Returns
-------
pad : Canvas
The pad.
graphs : list of Graph
The p-value graphs
"""
if not pvalues:
raise ValueError("pvalues is empty")
if not poi:
raise ValueError("poi is empty")
# determine if pvalues is list or list of lists
if not isinstance(pvalues[0], (list, tuple)):
pvalues = [pvalues]
if linecolor is not None:
if not isinstance(linecolor, list):
linecolor = [linecolor]
linecolor = cycle(linecolor)
if linestyle is not None:
if not isinstance(linestyle, list):
linestyle = [linestyle]
linestyle = cycle(linestyle)
with preserve_current_canvas():
if pad is None:
pad = Canvas()
pad.cd()
pad.SetLogy()
# create the axis
min_poi, max_poi = min(poi), max(poi)
haxis = Hist(1000, min_poi, max_poi)
xaxis = haxis.xaxis
yaxis = haxis.yaxis
xaxis.SetRangeUser(min_poi, max_poi)
haxis.Draw('AXIS')
min_pvalue = float('inf')
graphs = []
for ipv, pv in enumerate(pvalues):
graph = Graph(len(poi), linestyle='dashed',
drawstyle='L', linewidth=2)
for idx, (point, pvalue) in enumerate(zip(poi, pv)):
graph.SetPoint(idx, point, pvalue)
if linestyle is not None:
graph.linestyle = linestyle.next()
if linecolor is not None:
graph.linecolor = linecolor.next()
graphs.append(graph)
curr_min_pvalue = min(pv)
if curr_min_pvalue < min_pvalue:
min_pvalue = curr_min_pvalue
if verbose:
for graph in graphs:
log.info(['{0:1.1f}'.format(xval) for xval in list(graph.x())])
log.info(['{0:0.3f}'.format(yval) for yval in list(graph.y())])
# automatically handles axis limits
axes, bounds = draw(graphs, pad=pad, same=True, logy=True,
xtitle=xtitle, ytitle=ytitle,
xaxis=xaxis, yaxis=yaxis, ypadding=(0.2, 0.1),
logy_crop_value=1E-300)
if yrange is not None:
xaxis, yaxis = axes
yaxis.SetLimits(*yrange)
yaxis.SetRangeUser(*yrange)
min_pvalue = yrange[0]
# draw sigma levels up to minimum of pvalues
line = Line()
line.SetLineStyle(2)
line.SetLineColor(2)
latex = ROOT.TLatex()
latex.SetNDC(False)
latex.SetTextSize(20)
latex.SetTextColor(2)
sigma = 0
while True:
pvalue = gaussian_cdf_c(sigma)
if pvalue < min_pvalue:
break
keepalive(pad, latex.DrawLatex(max_poi, pvalue, " {0}#sigma".format(sigma)))
keepalive(pad, line.DrawLine(min_poi, pvalue, max_poi, pvalue))
sigma += 1
pad.RedrawAxis()
pad.Update()
return pad, graphs
plots_path = None
topLevelPath = h.path
while True:
# non-empty dict, so break
if plots_paths.get(topLevelPath, {}):
break
topLevelPath = os.path.dirname(topLevelPath)
# top level path empty so break
if not topLevelPath:
break
plots_path = plots_paths.get(topLevelPath, {})
# create new canvas
canvasConfigs = copy.copy(plots_config.get('canvas', {}))
canvasConfigs.update(plots_path.get('canvas', {}))
canvas = Canvas(canvasConfigs.get('width', 500), canvasConfigs.get('height', 500))
canvas.SetRightMargin(canvasConfigs.get('rightmargin', 0.1))
canvas.SetBottomMargin(canvasConfigs.get('bottommargin', 0.2))
canvas.SetLeftMargin(canvasConfigs.get('leftmargin', 0.2))
if canvasConfigs.get('logy', False) == True:
canvas.set_logy()
# create a legend (an entry for each group)
legendConfigs = copy.copy(plots_config.get('legend', {}))
legendConfigs.update(plots_path.get('legend', {}))
legend = Legend(len(h), **legendConfigs)
# scale the histograms before doing anything else
for hgroup in h:
plots_path = None
topLevelPath = h.path
while True:
# non-empty dict, so break
if plots_paths.get(topLevelPath, {}):
break
topLevelPath = os.path.dirname(topLevelPath)
# top level path empty so break
if not topLevelPath:
break
plots_path = plots_paths.get(topLevelPath, {})
# create new canvas
canvasConfigs = copy.copy(plots_config.get('canvas', {}))
canvasConfigs.update(plots_path.get('canvas', {}))
canvas = Canvas(canvasConfigs.get('width', 500), canvasConfigs.get('height', 500))
canvas.SetRightMargin(canvasConfigs.get('rightmargin', 0.1))
canvas.SetBottomMargin(canvasConfigs.get('bottommargin', 0.2))
canvas.SetLeftMargin(canvasConfigs.get('leftmargin', 0.2))
if canvasConfigs.get('logy', False) == True:
canvas.set_logy()
# create a legend (an entry for each group)
legendConfigs = copy.copy(plots_config.get('legend', {}))
legendConfigs.update(plots_path.get('legend', {}))
legend_numColumns = legendConfigs.pop('num columns', 1)
legend = Legend(len(h), **legendConfigs)
legend.SetNColumns(legend_numColumns)
def do_stuff(file_name, plot=True):
tiq_data = TIQData(file_name)
NFRAMES = 100
LFRAMES = 1024
tiq_data.read(nframes=NFRAMES, lframes=LFRAMES, sframes=1)
center = tiq_data.center
# do fft
ff, pp, _ = tiq_data.get_fft()
freq_lower = center + ff[0] / 1e6
freq_upper = center + ff[-1] / 1e6
# create hist
h1 = Hist(NFRAMES * LFRAMES, freq_lower, freq_upper, name='h1', title='Frequency Spectrum',
drawstyle='hist',
legendstyle='F',
fillstyle='/',
linecolor='green')
for i in range(len(pp)):
h1.set_bin_content(i, pp[i])
# set visual attributes
h1.GetXaxis().SetTitle('Freuqency [MHz]')
h1.linecolor = 'green'
# h1.fillcolor = 'green'
# h1.fillstyle = '/'
if plot:
# plot
c = Canvas(name='c1', width=700, height=500)
c.set_left_margin(0.15)
c.set_bottom_margin(0.15)
c.set_top_margin(0.10)
c.set_right_margin(0.05)
c.toggle_editor()
c.toggle_event_status()
c.toggle_tool_bar()
# c.set_crosshair()
h1.Draw()
# create the legend
legend = Legend([h1], pad=c,
header='Header',
leftmargin=0.05,
rightmargin=0.5)
legend.Draw()
# wait for key press
wait()
x1A = vstack((sigTestA, bkgTestA))
y1A = hstack((onesInt(len(sigTestA)), zerosInt(len(bkgTestA))))
y1A = transpose(y1A)
from rootpy.interactive import wait
from rootpy.plotting import Canvas, Hist, Hist2D, Hist3D, Legend
from rootpy.io import root_open as ropen, DoesNotExist
from rootpy.plotting import HistStack
import ROOT
ROOT.gROOT.SetBatch(True)
f = ropen('output.root','recreate')
c1 = Canvas()
c1.cd()
histDictSigA = {'W':[],'Z':[],'WW':[],'ZZ':[],'st':[],'ttbar':[],'WZ':[],'WH125':[]}
histDictBkgA = {'W':[],'Z':[],'WW':[],'ZZ':[],'st':[],'ttbar':[],'WZ':[],'WH125':[]}
coloursForStack = ['blue', 'green', 'red', 'yellow', 'black', 'pink', 'magenta', 'cyan']
colourDict = {'W':0,'Z':1,'WW':2,'ZZ':3,'st':4,'ttbar':5,'WZ':6,'WH125':7}
lblcount = 0
sigTestA = transpose(sigTestA)
bkgTestA = transpose(bkgTestA)
sigtemp1B = cutTree(sig,False,len(sig)/2,'B')
def makeComparisionPage( histodicts , fileNames, fileDescr, separateFiles):
"""
Prepares a canvas comparing multiple histograms: plotting all in one pad and their ratios in the second
"""
import rootpy
from rootpy.plotting import Hist, Canvas, Legend
import ROOT
from ROOT import gPad
log = logging.getLogger('pyroplot')
cans = {}
colors = [ROOT.kBlue, ROOT.kRed+1,ROOT.kViolet-1, ROOT.kOrange+7,ROOT.kGreen-7,ROOT.kOrange-6,
ROOT.kPink-9,ROOT.kTeal-6,ROOT.kBlue+4,ROOT.kAzure+2]
log.info( "Drawing histograms .." )
# prepare set of histograms to compare to the reference on (the first)
# loop over the reference set of histos (sorted by key):
for hidx, refname in enumerate(sorted(histodicts[0].keys())):
# prepare canvas
if separateFiles:
log.debug( "Creating new canvas with index %d."%(hidx))
c=Canvas( 600, 270)
cans[refname] = c
c.Divide(3,1)
c.cd(1)
if not separateFiles and (hidx)%4 == 0:
log.debug( "Creating new canvas with index %d."%(hidx/3))
# start a new canvas
c=Canvas( 600, 800)
cans[refname] = c
c.Divide(3,4)
# prepare histograms for drawing
log.debug( "Drawing histogram #" + str(hidx+1) +" (" + refname + ") on canvas #" + str(len(cans)) )
hists = []
ratiohists = []
hiter = iter (histodicts)
# special treatment for tprofile: prepare the reference projection for the ratio
if histodicts[0][refname].__class__.__name__=="Profile":
refProj = histodicts[0][refname].ProjectionX()
refProj.SetName("reference_proj")
for idx, h in enumerate(hiter):
# make sure we have this histogram loaded:
if not refname in h:
continue
# access the corresponding histogram of the other files at the same hidx as used for ref
h[refname].color = colors[idx]
h[refname].linestyle = idx
hists.append (h[refname])
# prepare ratio is this is not the first (i.e. reference) histogram
if idx:
# special treatment for TProfile:
if h[refname].__class__.__name__=="Profile":
myratio = Hist(h[refname].nbins(), h[refname].lowerbound(), h[refname].upperbound()) #dummy hist
myratioproj = h[refname].ProjectionX()
myratioproj.SetName("cmp_hist_proj"+str(idx))
try:
myratio.divide(myratioproj,refProj)
except rootpy.ROOTError, e:
log.error("Calculation of ratio for histogram %s caused ROOTError exception ('%s')"%(h[refname].GetName(),e.msg))
break
myratio.color = colors[idx]
else:
myratio = h[refname].clone() # make sure that the ratio has the right type
try:
myratio.Divide(h[refname], histodicts[0][refname]) # divide by reference hist
except rootpy.ROOTError, e:
log.error("Calculation of ratio for histogram %s caused ROOTError exception ('%s')"%(h[refname].GetName(),e.msg))
break
myratio.yaxis.SetTitle("(h_{cmp} - h_{ref})/h_{ref}")
myratio.SetTitle("ratio to reference")
myratio.SetMaximum(2)
myratio.SetMinimum(0)
myratio.SetStats(0)
ratiohists.append(myratio)
fakes_myhist = asrootpy(plotter.get_histogram(
'Zjets',
'emt/skimCounter',
))
styling.apply_style(fakes_myhist, **samplestyles.SAMPLE_STYLES['ztt'])
fakes = asrootpy(fakes_myhist.th1)
data = asrootpy(plotter.get_histogram(
'data_DoubleMu',
'emt/skimCounter',
).th1)
canvas = Canvas(800, 800)
canvas.SetRightMargin(0.05)
legend = ROOT.TLegend(0.55, 0.60, 0.9, 0.90, "", "brNDC")
legend.SetFillStyle(0)
legend.SetBorderSize(0)
data.SetMarkerSize(2)
data.SetTitle("data")
data.SetLineWidth(2)
legend.AddEntry(data, "Observed", "lp")
signal.SetLineStyle(1)
signal.SetLineWidth(3)
signal.SetTitle("(5#times) m_{H}=120 GeV")
signal.SetLineColor(ROOT.EColor.kRed)
signal.SetFillStyle(0)
data0 = "normal_small", np.random.normal(0.5, 1, 200)
data1 = "normal", np.random.normal(0.5, 1, 100000)
data2 = "uniform", np.random.random(100000)
data3 = "normal+uniform", np.concatenate((data1[1], 10 * data2[1]))
data4 = "normal+normal", np.concatenate((data1[1], np.random.normal(2.5, 0.1, 100000)))
if ROOT.gROOT.IsBatch():
datas = (data0, data1)
else:
datas = (data0, data1, data2, data3, data4)
recipes = (
"manual1", "sturges", "sturges-doane", "scott", "sqrt",
"doane", "freedman-diaconis", "risk", "knuth")
objs = []
canvas = Canvas()
canvas.Divide(len(recipes), len(datas), 1E-3, 1E-3)
print '-' * 57
print '\t\t{0:<20s}{1:>10s} {2:<6s}'.format('method', 'bins', 'time [s]')
print '-' * 57
for id, (dataname, d) in enumerate(datas):
print dataname
for ir, r in enumerate(recipes):
canvas.cd(id * len(recipes) + ir + 1)
timer = Timer()
if r == "manual1":
with timer:
bins, h = histogram(d, 50, np.min(d), np.max(d),
drawstyle='hist')
else:
with timer:
def Hist_comp_ratios (dec, bkg) :
# weights = pickle.load( open( "weights_train.pck", "rb" ) )
# probas = pickle.load( open( "class_proba.pck", "rb" ) )
# mt_dec = pickle.load( open( "inputs_train.pck", "rb" ) )
# classes = pickle.load( open( "targets_train.pck", "rb" ) )
weights = pickle.load( open( "weights.pck", "rb" ) )
probas = pickle.load( open( "class_probaWholeSample.pck", "rb" ) )
mt_dec = pickle.load( open( "inputs.pck", "rb" ) )
classes = pickle.load( open( "classes.pck", "rb" ) )
#put mt_dec weights and the RELEVANT class probability together
class_num = (GetClassIndex(bkg)-1)
all_data = np.transpose(np.vstack((mt_dec[:,0],mt_dec[:,1], classes, weights, probas[:,class_num])))
all_data = np.array(filter(lambda x: x[2] == class_num, all_data))
#extract the relevant values for the specific decay channel
Filter = np.array(filter(lambda x: x[1] == dec, all_data))
MT = Filter[:,0]
Weight = Filter[:,3]
Prob_bkg = Filter[:,4]
SumProbXweight = np.multiply(Prob_bkg,Weight)
h1 = TH1D("h1","SumProbXweight_"+bkg+str(dec), 25 , 0.0 ,250.)
root_open("plots/ROOTfiles/H1_SumProbXweight_"+bkg+str(dec)+"Samptot.root", 'recreate')
fill_hist(h1,MT, weights=SumProbXweight)
h1.Write()
h2 = TH1D("h2","SumWeight_"+bkg+str(dec), 25 , 0.0 ,250.)
root_open("plots/ROOTfiles/H1_SumWeight_"+bkg+str(dec)+"Samptot.root", 'recreate')
fill_hist(h2,MT,weights=Weight)
h2.Write()
h3 = h1.Clone("h3")
h3.Divide(h2)
if (dec == 0.0) :
Filter2 = np.array(filter(lambda x: x[1] == 1.0, all_data))
MT2 = Filter2[:,0]
Weight2 = Filter2[:,3]
Prob_bkg2 = Filter2[:,4]
SumProbXweight2 = np.multiply(Prob_bkg2,Weight2)
h12 = TH1D("h12","SumProbXweight_"+bkg+str(1.0), 25 , 0.0 ,250.)
root_open("plots/ROOTfiles/H1_SumProbXweight_"+bkg+str(1.0)+"Samptot.root", 'recreate')
fill_hist(h12,MT2, weights=SumProbXweight2)
h12.Write()
h22 = TH1D("h22","SumWeight_"+bkg+str(1.0), 25 , 0.0 ,250.)
root_open("plots/ROOTfiles/H1_SumWeight_"+bkg+str(1.0)+"Samptot.root", 'recreate')
fill_hist(h22,MT2,weights=Weight2)
h22.Write()
h32 = h12.Clone("h32")
h32.Divide(h22)
h12.SetStats(0)
h22.SetStats(0)
h32.SetStats(0)
h12.SetLineColor(2)
h22.SetLineColor(2)
h32.SetLineColor(0)
h32.SetMarkerStyle(23)
h32.SetMarkerColor(2)
h32.SetMarkerSize(1.2)
#create Canvas and save the plot as png
c = Canvas()
c.Divide(2,2)
c.cd(1)
h1.SetStats(0)
if (dec == 0.0) :
if (h1.GetMaximum() > h12.GetMaximum()) :
print "h1"
h12.GetYaxis().SetRangeUser(0.,h1.GetMaximum())
else :
print "h12"
h12.GetYaxis().SetRangeUser(0.,h12.GetMaximum())
h12.Draw("HIST")
h1.Draw("HIST SAME")
c.cd(2)
h2.SetStats(0)
if (dec == 0.0) :
if (h2.GetMaximum() > h22.GetMaximum()) :
h22.GetYaxis().SetRangeUser(0.,h2.GetMaximum())
else :
h22.GetYaxis().SetRangeUser(0.,h22.GetMaximum())
h22.Draw("HIST")
h2.Draw("HIST SAME")
c.cd(3)
f1 = root_open(GetClassProbaPath(bkg))
#get the 2 histograms for the 2 decay channels: 1 track & 3 tracks
H1 = f1.Get("h_w_2d")
if (dec == 10.0) :
#3 tracks
h_data = Hist(list(H1.xedges()))
h_data[:] = H1[:,2]
else :
#1 track
h_data = Hist(list(H1.xedges()))
h_data[:] = H1[:,1]
h_data.GetXaxis().SetRangeUser(0.,250.)
h_data.GetYaxis().SetRangeUser(0.,1.)
h_data.fillstyle = '/'
h_data.fillcolor = (255,255,0) #yellow
h_data.SetStats(0)
h_data.Draw("HIST")
# h3.SetFillColor(4) #blue
# h3.SetFillStyle(3005)
h3.SetLineColor(0)
h3.SetMarkerStyle(21)
h3.SetMarkerColor(4)
h3.SetMarkerSize(1.2)
h3.SetStats(0)
h3.SetTitle(bkg+str(dec))
h3.GetXaxis().SetTitle("m_{T}")
h3.GetYaxis().SetTitle("Class probability")
h3.Draw("HIST P SAME")
if (dec == 0.0) :
h32.Draw("HIST P SAME")
c.Update()
if (dec == 0.0) :
legend = Legend(3, leftmargin=0.45, margin=0.3)
legend.AddEntry(h3, "training, no #pi^{0}", style='P')
legend.AddEntry(h32, "training, with #pi^{0}", style='P')
legend.AddEntry(h_data, "data", style='F')
legend.Draw()
else :
legend = Legend(2, leftmargin=0.45, margin=0.3)
legend.AddEntry(h3, "training", style='P')
legend.AddEntry(h_data, "data", style='F')
legend.Draw()
c.SaveAs("plots/H1_"+bkg+str(dec)+"_RatioCompSamptot.png")
Draw a Quantile-Quantile Plot and Confidence Band ================================================= This is an example of drawing a quantile-quantile plot with a confidential level (CL) band, originally by Zhiyi Liu, [email protected] """ import ROOT from rootpy.interactive import wait from rootpy.plotting import Hist, Canvas, Legend from rootpy.plotting.contrib.quantiles import qqgraph ROOT.gROOT.SetStyle("Plain") ROOT.gStyle.SetOptStat(0) ROOT.gStyle.SetOptTitle(0) c = Canvas(width=1000, height=300) c.Divide(2, 1, 1e-3, 1e-3) rand = ROOT.TRandom3() h1 = Hist(100, -5, 5, name="h1", title="Histogram 1", linecolor='red') h1.Sumw2() h1.SetLineColor(ROOT.kRed) h2 = Hist(100, -5, 5, name="h2", title="Histogram 2", linecolor='blue') h2.SetLineColor(ROOT.kBlue) for ievt in xrange(10000): #some test histograms: #1. let 2 histograms screwed #h1.Fill(rand.Gaus(0.5, 0.8)) #h2.Fill(rand.Gaus(0, 1))
try:
stack.sum.Integral()
except:
print "stack has no integral!"
continue
if plotWithMPL:
gs = mpl.gridspec.GridSpec(2,1,height_ratios=[4,1])
gs.update(wspace=0.00, hspace=0.00)
axes = plt.subplot(gs[0])
axes_ratio = plt.subplot(gs[1], sharex=axes)
plt.setp(axes.get_xticklabels(), visible=False)
if plotWithROOT:
c = Canvas(700,700)
c.cd()
pad1 = Pad( 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0); # Upper and lower plot are joined
pad1.SetGrid(); # Vertical grid
pad1.Draw(); # Draw the upper pad: pad1
c.cd()
pad2 = Pad( 0, 0.05, 1, 0.3);
pad2.SetTopMargin(0); # Upper and lower plot are joined
pad2.SetBottomMargin(0.3); # Upper and lower plot are joined
pad2.SetGrid(); # Vertical grid
pad2.Draw(); # Draw the upper pad: pad1
pad1.cd(); # pad1 becomes the current pad
rootstack = ROOT.THStack(stack)
rootstack.Draw('HIST')
def createHists(sample, labelCodes, nameOfType, labelsForSample, weightsPerSample, foundVariables, allHistStack, allLegendStack, corrWeights, subset = 'TestA', createLog = False):
"""Create all of the histograms for each sample and save them to file.
Keyword arguments:
sample -- the sample from which the histograms are created
labelCodes -- the codes for all of the labels (0 == W, for eg.)
nameOfType -- signal or bkg (default bkg)
labelsForSample -- the labels of all the entries in the sample
weightsPerSample -- the XS weight
foundVariables -- the variables in the order in which they were found
allHistStack -- the histogram stack of all of the samples
allLegendStack -- the legends for all samples and entries to go on the stack
subset -- extra label for the output file (default A)
createLog -- create a log for the output (default False)
"""
global histLimits
#TODO: These should really be read in from a settings file
#histDict = {'W':[],'Z':[],'WW':[],'ZZ':[],'st':[],'ttbar':[],'WZ':[],'WH125':[]}
histDict = {'Wl':[],'Wcc':[],'Wc':[],'Wb':[],'Zb':[],'Z':[],'WW':[],'ZZ':[],'stop':[],'ttbar':[],'WZ':[],'WH125':[]}
#coloursForStack = ['Green', 'Blue', 'Orange', 'Orange', 'Orange-2', 'Yellow', 'Pink', 'Red']
coloursForStack = [3, 4, 800, 800, 795, 5, 6, 2]
#colourDict = {'W':0,'Z':1,'WW':2,'ZZ':3,'st':4,'ttbar':5,'WZ':6,'WH125':7}
colourDict = {'Wl':0,'Wcc':0,'Wc':0,'Wb':0,'Zb':1,'Z':1,'WW':2,'ZZ':3,'stop':4,'ttbar':5,'WZ':6,'WH125':7}
if nameOfType == 'signal':
fillcol = 'blue'
else:
fillcol = 'red'
hist = []
histidx = 0
log = open(nameOfType+subset+'.log','w')
c1 = Canvas()
c1.cd()
log.write('########################### '+ nameOfType +' ###########################\n')
for c in sample:
variableName = foundVariables[histidx]
#hist.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
hist.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
hist[histidx].fill_array(c)
hist[histidx].scale(1.0/hist[histidx].integral())
hist[histidx].fillcolor=fillcol
hist[histidx].linecolor=fillcol
hist[histidx].GetXaxis().SetTitle(foundVariables[histidx])
hist[histidx].GetYaxis().SetTitle('# Events Normalised to 1')
hist[histidx].SetTitle(nameOfType)
hist[histidx].fillstyle='solid'
hist[histidx].SetStats(0)
lblcount = 0
for k in histDict.iterkeys():
#histDict[k].append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
histDict[k].append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
#histDict[k][histidx].fillcolor = coloursForStack[int(colourDict[k])]
histDict[k][histidx].SetFillColor(int(coloursForStack[int(colourDict[k])]))
histDict[k][histidx].fillstyle = 'solid'
histDict[k][histidx].SetOption('hist')
histDict[k][histidx].SetTitle(str(k))# + str(foundVariables[histidx]))
histDict[k][histidx].SetStats(0)
histDict[k][histidx].GetXaxis().SetTitle(foundVariables[histidx])
histDict[k][histidx].GetYaxis().SetTitle('# Events')
for i in c:
lbl = labelCodes[int(labelsForSample[lblcount])]
if lbl in histDict.keys():
histDict[lbl][histidx].fill(i,corrWeights[histidx])
#histDict[lbl][histidx].scale(corrWeights[histidx])
lblcount += 1
histidx+=1
# create stacks and legends
histStack = []
legendStack = []
if not allHistStack:
initStack = True
else:
initStack = False
for st in foundVariables:
if initStack == True:
allHistStack.append(HistStack(st,st))
allLegendStack.append(Legend(7))
histStack.append(HistStack(st,st))
legendStack.append(Legend(7))
rwcount_outer = 0
for rw in histDict.iterkeys():
log.write(rw + ' length: '+str(len(histDict[rw]))+'\n')
for rwcount in xrange(0,len(histDict[rw])):
if histDict[rw][rwcount].GetEntries() > 0:
if rw in weightsPerSample:
histDict[rw][rwcount].scale(weightsPerSample[rw])
histStack[rwcount].Add(histDict[rw][rwcount].Clone())
allHistStack[rwcount].Add(histDict[rw][rwcount].Clone())
histStack[rwcount].Draw()
allHistStack[rwcount].Draw()
histDict[rw][rwcount].draw('hist')
histStack[rwcount].GetXaxis().SetTitle(histDict[rw][rwcount].GetXaxis().GetTitle())
histStack[rwcount].GetYaxis().SetTitle('# Events')
allHistStack[rwcount].GetXaxis().SetTitle(histDict[rw][rwcount].GetXaxis().GetTitle())
allHistStack[rwcount].GetYaxis().SetTitle('# Events')
legendStack[rwcount].AddEntry( histDict[rw][rwcount], 'F')
allLegendStack[rwcount].AddEntry( histDict[rw][rwcount], 'F')
#c1.SaveAs("histDict"+str(nameOfType)+str(subset)+str(rwcount)+".png")
log.write(rw + '['+str(rwcount)+'] entries: ' + str(histDict[rw][rwcount].GetEntries())+'\n')
rwcount_outer += 1
log.close()
return hist,histDict,histStack,legendStack
h_t1.SetLineColor(4)
h_t2.SetLineColor(8)
h_t3.SetLineColor(6)
h_t4.SetLineColor(7)
h_data.SetLineColor(1)
h_data.SetMarkerColor(1)
ymax = getMax( [h_data, h_t1, h_t2, h_t3] )
ymax = ymax*1.1
h_data.GetYaxis().SetRangeUser(0,ymax)
h_t1.GetYaxis().SetRangeUser(0,ymax)
h_t2.GetYaxis().SetRangeUser(0,ymax)
h_t3.GetYaxis().SetRangeUser(0,ymax)
h_t4.GetYaxis().SetRangeUser(0,ymax)
c = Canvas()
c.Divide(2)
c.cd(1)
h_data.Draw('PE')
h_t1.Draw('SAME HIST')
h_t2.Draw('SAME HIST')
h_t3.Draw('SAME HIST')
h_t4.Draw('SAME HIST')
templates = {
}
if useT1: templates['t1'] = h_t1
if useT2: templates['t2'] = h_t2
if useT3: templates['t3'] = h_t3
if useT4: templates['t4'] = h_t4
# aesthetics
heff.linecolor = colours[j]
heff.markercolor = colours[j]
heff.markersize = 0.2
heff.SetMaximum(6)
heff.SetMinimum(0)
heff.SetStats(False)
hists[i].append(heff)
## Plots
nvars = len(variables)
ncuts = len(mva_cuts)
# ROOT
canvas = Canvas(800, 400, 500, 10)
canvas.Divide(2, 1)
for i, var in enumerate(variables):
for j in (0, 1): # sig, bkg
canvas.cd(j+1)
for k, cut in enumerate(mva_cuts):
opts = 'e1' if k == 0 else 'e1 same'
hists[j][i*ncuts + k].Draw(opts)
canvas.Modified()
canvas.Update()
if doprint:
for typ in ['png']: #, 'pdf']:
canvas.Print('{}_bkg_sig_eff_{}.{}'.format(var, n, typ))
del tmp, canvas # clean up
# Matplotlib
from rootpy.plotting.style import set_style
from rootpy.plotting import Hist, Legend, Canvas
from FireROOT.Analysis.Utils import *
fn = os.path.join(
os.getenv('CMSSW_BASE'),
'src/FireROOT/Analysis/python/outputs/rootfiles/modules/leptonjetCosmicVeto.root'
)
outdir = os.path.join(
os.getenv('CMSSW_BASE'),
'src/FireROOT/Analysis/python/etc/plots/leptonjetCosmicVetoCutEfficiency')
if not os.path.isdir(outdir): os.makedirs(outdir)
set_style(MyStyle())
c = Canvas()
f = root_open(fn)
def routine(chan, varname, title):
hs = []
effs = OrderedDict()
chandir = getattr(f, 'ch' + chan)
for it, sigtag in enumerate(sigTAGS):
h = getattr(getattr(chandir.sig, sigtag), varname) # Hist
h_total = h.integral(overflow=True)
h_ = h.clone()
for i in range(1, h.nbins() + 1):
h_[i] = h.integral(xbin1=i, overflow=True) / h_total
def drawAll( m, c, eff = True ) :
can = Canvas( width=600, height=3000 )
pad = 2;
can.Divide( 1, 6 )
if ( eff ) :
can.cd(pad)
pad+=1
yePi = f.tofEff.Get( "eff_Pi_"+m+"_"+c )
yeK = f.tofEff.Get( "eff_K_"+m+"_"+c )
yeP = f.tofEff.Get( "eff_P_"+m+"_"+c )
yePi.Draw()
yePi.SetTitle( "Fit Yields; P [GeV]; dN/(P dP)" )
yePi.GetYaxis().SetRangeUser( 0.0, 1.7 )
yeK.SetMarkerColor( R.kRed )
yeK.Draw( "same")
yeP.SetMarkerColor( R.kBlue )
yeP.Draw("same")
############################################
# zb mu
can.cd(pad)
pad+=1
R.gPad.SetLogy(0)
piMu = f.Pi_zbMu.Get( "mu_Pi_"+ m +"_" + c )
kMu = f.K_zbMu.Get( "mu_K_"+ m +"_" + c )
pMu = f.P_zbMu.Get( "mu_P_"+ m +"_" + c )
pidMu = f.Pi_zbMu.Get( "deltamu_Pi_"+ m +"_" + c ).Clone( "pisdmu" )
kdMu = f.K_zbMu.Get( "deltamu_K_"+ m +"_" + c ).Clone( "ksdmu" )
pdMu = f.P_zbMu.Get( "deltamu_P_"+ m +"_" + c ).Clone( "psdmu" )
piMu.SetTitle( "<zb_{fit}> - <zb_{exp}>; P [GeV]" )
R.gPad.SetLogy(0)
piMu.GetYaxis().SetRangeUser( -0.2, 0.2)
piMu.Draw()
kMu.Draw("same")
kMu.SetMarkerColor( R.kRed )
pMu.Draw("same")
pMu.SetMarkerColor( R.kBlue )
pidMu.Scale(10);
pidMu.SetMarkerStyle( R.kOpenCircle )
pidMu.Draw("same")
kdMu.Scale(10);
kdMu.SetMarkerColor( R.kRed )
kdMu.SetMarkerStyle( R.kOpenCircle )
kdMu.Draw("same")
pdMu.Scale(10);
pdMu.SetMarkerColor( R.kBlue )
pdMu.SetMarkerStyle( R.kOpenCircle )
pdMu.Draw("same")
#######################################################
#zd mu
try :
can.cd(pad)
pad+=1
R.gPad.SetLogy(0)
piMu = f.Pi_zdMu.Get( "mu_Pi_"+ m +"_" + c )
kMu = f.K_zdMu.Get( "mu_K_"+ m +"_" + c )
pMu = f.P_zdMu.Get( "mu_P_"+ m +"_" + c )
pidMu = f.Pi_zbMu.Get( "deltamu_Pi_"+ m +"_" + c ).Clone( "zdpisdmu" )
kdMu = f.K_zbMu.Get( "deltamu_K_"+ m +"_" + c ).Clone( "zdksdmu" )
pdMu = f.P_zbMu.Get( "deltamu_P_"+ m +"_" + c ).Clone( "zdpsdmu" )
piMu.SetTitle( "<zd_{fit}> - <zd_{exp}>" )
piMu.Draw()
zdSig = 0.08
piMu.GetYaxis().SetRangeUser( -zdSig * 5.0, zdSig * 5.0)
kMu.Draw("same")
kMu.SetMarkerColor( R.kRed )
pMu.Draw("same")
pMu.SetMarkerColor( R.kBlue )
pidMu.Scale(10);
pidMu.SetMarkerStyle( R.kOpenCircle )
pidMu.Draw("same")
kdMu.Scale(10);
kdMu.SetMarkerColor( R.kRed )
kdMu.SetMarkerStyle( R.kOpenCircle )
kdMu.Draw("same")
pdMu.Scale(10);
pdMu.SetMarkerColor( R.kBlue )
pdMu.SetMarkerStyle( R.kOpenCircle )
pdMu.Draw("same")
except :
print "no"
##########################################################
#zbSig
try :
can.cd(pad)
pad+=1
R.gPad.SetLogy(0)
piMu = f.Pi_zbSigma.Get( "sigma_Pi_"+ m +"_" + c )
kMu = f.K_zbSigma.Get( "sigma_K_"+ m +"_" + c )
pMu = f.P_zbSigma.Get( "sigma_P_"+ m +"_" + c )
piMu.SetTitle( "#sigma zb_{fit}" )
piMu.Draw()
piMu.GetYaxis().SetRangeUser( 0.000, 0.055)
kMu.Draw("same")
kMu.SetMarkerColor( R.kRed )
pMu.Draw("same")
pMu.SetMarkerColor( R.kBlue )
R.gPad.SetGrid(1, 1)
except :
print "no"
##########################################################
#zdSig
can.cd(pad)
try :
pad+=1
R.gPad.SetLogy(0)
piMu = f.Pi_zdSigma.Get( "sigma_Pi_"+ m +"_" + c )
kMu = f.K_zdSigma.Get( "sigma_K_"+ m +"_" + c )
pMu = f.P_zdSigma.Get( "sigma_P_"+ m +"_" + c )
piMu.SetTitle( "#sigma zd_{fit}" )
piMu.Draw()
piMu.GetYaxis().SetRangeUser( 0.0, 0.09)
kMu.Draw("same")
kMu.SetMarkerColor( R.kRed )
pMu.Draw("same")
pMu.SetMarkerColor( R.kBlue )
R.gPad.SetGrid(1,1)
except :
print "no"
##########################################################
#Yield
can.cd(1)
yPi = f.Pi_yield.Get( "yield_Pi_"+m+"_"+c )
yK = f.K_yield.Get( "yield_K_"+m+"_"+c )
yP = f.P_yield.Get( "yield_P_"+m+"_"+c )
yPi.Draw()
yPi.SetTitle( "Fit Yields; P [GeV]; dN/(P dP)" )
yPi.GetYaxis().SetRangeUser( 1e-6, yPi.GetMaximum() *10 )
yK.SetMarkerColor( R.kRed )
yK.Draw( "same")
yP.SetMarkerColor( R.kBlue )
yP.Draw("same")
R.gPad.SetLogy(1)
return can
fn = os.path.join(
os.getenv('CMSSW_BASE'),
'src/FireROOT/Analysis/python/outputs/rootfiles/centralSig/validategen.root'
)
outdir = os.path.join(os.getenv('CMSSW_BASE'),
'src/FireROOT/Analysis/python/etc/plots/validategen')
if not os.path.isdir(outdir): os.makedirs(outdir)
set_style(MyStyle())
f = root_open(fn)
#################################################
# dark photon lxy
c = Canvas()
masstag = 'mXX-1000_mA-1p2'
mxx, ma = masstag.split('_')
mxx = mxx.split('-')[-1]
ma = ma.split('-')[1].replace('p', '.')
massdir = getattr(f.ch4mu, masstag)
lxytags = [x.name for x in massdir.keys()] # lxy-0p3_ctau-0p0025
hsd = {}
for tag in lxytags:
lxy_, ctau_ = tag.split('_')
lxy = lxy_.split('-')[1].replace('p', '.')
ctau = ctau_.split('-')[1].replace('p', '.')
h = getattr(massdir, tag).dplxy
def createHistsData(sample, foundVariables, allHistStack, allLegendStack, subset = 'TestA', createLog = False):
"""Create histograms for data.
Keyword arguments:
sample -- the input data sample
foundVariables -- the variables in the sample, in order
allHistStack -- the histogram stack for all other samples
subset -- an extra identifier for the filename (default TestA)
createLog -- create a log (default False)
"""
histDict = {'data':[]}
coloursForStack = ['blue', 'green', 'red', 'yellow', 'black', 'pink', 'magenta', 'cyan']
global histLimits
fillcol = 'black'
hist = []
histidx = 0
log = open('data'+str(subset)+'.log','w')
c1 = Canvas()
c1.cd()
log.write('########################### DATA ###########################\n')
for c in sample:
variableName = foundVariables[histidx]
#hist.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
hist.append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
hist[histidx].fill_array(c)
hist[histidx].scale(1.0/hist[histidx].integral())
hist[histidx].fillcolor=fillcol
hist[histidx].linecolor=fillcol
hist[histidx].GetXaxis().SetTitle(foundVariables[histidx])
hist[histidx].GetYaxis().SetTitle('# Events Normalised to 1')
hist[histidx].SetTitle('data')
hist[histidx].fillstyle='solid'
hist[histidx].SetStats(0)
#histDict['data'].append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
histDict['data'].append(Hist(50,int(histLimits[variableName][0]),int(histLimits[variableName][1])))
histDict['data'][histidx].fillcolor=fillcol
histDict['data'][histidx].linecolor=fillcol
histDict['data'][histidx].SetOption('hist')
histDict['data'][histidx].SetTitle('data')
histDict['data'][histidx].GetXaxis().SetTitle(foundVariables[histidx])
histDict['data'][histidx].GetYaxis().SetTitle("# Events")
histDict['data'][histidx].SetStats(0)
for i in c:
histDict['data'][histidx].fill(i)
histidx+=1
# create stacks and legends
histStack = []
legendStack = []
for st in foundVariables:
histStack.append(HistStack(st,st))
legendStack.append(Legend(7))
for rw in histDict.keys():
log.write(rw + ' length: '+str(len(histDict[rw]))+'\n')
for rwcount in xrange(0,len(histDict[rw])):
if histDict[rw][rwcount].GetEntries() > 0:
histStack[rwcount].Add(histDict[rw][rwcount].Clone())
histStack[rwcount].Draw()
histStack[rwcount].GetXaxis().SetTitle(histDict[rw][rwcount].GetXaxis().GetTitle())
histStack[rwcount].GetYaxis().SetTitle('# Events')
histDict[rw][rwcount].draw('hist')
legendStack[rwcount].AddEntry( histDict[rw][rwcount], 'F')
allLegendStack[rwcount].AddEntry(histDict[rw][rwcount], 'F')
#c1.SaveAs("histDictData"+str(subset)+str(rwcount)+".png")
log.write(rw + '['+str(rwcount)+'] entries: ' + str(histDict[rw][rwcount].GetEntries())+'\n')
log.close()
return hist,histDict,histStack,legendStack
