def PlotCompare(dataType, outputDir, mcsets, histname, leg, cutoff = -1, precise = False): # # plot a set of histograms defined by histname and comparing mc samples # NOTE: ALL SAMPLES MUST CONTAIN THE EXACT SAME LIST OF HISTOGRAMS, OTHERWISE THIS FUNCTION WILL NOT WORK # # this function takes the following parameters: # dataType....type of the lepton ('mu', 'el') # outputDir...basic output directory # mcsets......list of mc samples [mcsample1, mcsample2, ..] # leg.........legend to be plotted # cutoff......cutoff for the histogram name (see function getSaveName() in lib.py) # precise.....True if only the histogram with the name matching histname exactly shall be plotted # define canvas and pads canv = helper.makeCanvas(900, 675) pad_plot = helper.makePad('plot') pad_ratio = helper.makePad('ratio') pad_ratio.cd() # iterate over all histograms in root files # it does not matter which sample we iterate on, as all samples contain the same list of histograms for hist in mcsets[0].hists: # get index of histogram i = mcsets[0].hists.index(hist) pad_plot.cd() # if precise is True, we only plot the histogram with the name matching histname exactly # else, we plot all histograms which have histname in their name (useful for several versions or bins of a histogram) if precise and not histname == hist.GetName(): continue if not precise and not histname in hist.GetName(): continue # pre- and postpends prepend = '' postpend = '_compare' if '_Loose_' in hist.GetName(): prepend = 'Loose_' if '_Tight_' in hist.GetName(): prepend = 'Tight_' # draw first histogram hist.Draw('hist') hist.SetFillStyle(0) hist.Scale(1.0/hist.Integral()) max = hist.GetMaximum() for j in range(1,len(mcsets)): mcsets[j].hists[i].Draw('hist same') mcsets[j].hists[i].SetFillStyle(0) mcsets[j].hists[i].SetLineStyle(2) mcsets[j].hists[i].Scale(1.0/mcsets[j].hists[i].Integral()) if max < mcsets[j].hists[i].GetMaximum(): max = mcsets[j].hists[i].GetMaximum() # do some cosmetics hist.SetMinimum(0.0001) hist.SetMaximum(1.5 * max) hist = helper.set1dPlotStyle(dataType, hist, helper.getColor(mcsets[0].GetName()), '', hist, '1/Integral') # draw legend leg.Draw() # draw ratio plot pad_ratio.cd() hist_ratio = copy.deepcopy(hist) hist_ratio.Divide(copy.deepcopy(mcsets[1].hists[i])) hist_ratio.Draw("p e1") hist_ratio = helper.setRatioStyle(dataType, hist_ratio, hist) line = helper.makeLine(hist_ratio.GetXaxis().GetXmin(), 1.00, hist_ratio.GetXaxis().GetXmax(), 1.00) line.Draw() # make plot helper.saveCanvas(canv, pad_plot, outputDir + "compare/", prepend + helper.getSaveName(hist, cutoff) + postpend)
def PlotJPtZooms(dataType, outputDir, dataset, mcsets, leg): # # plotting JetPt and JetRawPt distributions in bins of lepton eta and lepton pt (binning according to fake rate maps) # NOTE: ALL SAMPLES MUST CONTAIN THE EXACT SAME LIST OF HISTOGRAMS, OTHERWISE THIS FUNCTION WILL NOT WORK # # this function takes the following parameters: # dataType....type of the lepton ('mu', 'el') # outputDir...basic output directory # datasets....list of data samples [datasample1, datasample2, ..] # mcsets......list of mc samples [mcsample1, mcsample2, ..] # leg.........legend to be plotted # define canvas and pads canv = helper.makeCanvas(900, 675, 'c1dZ') pad_plot = helper.makePad('tot') pad_plot.SetTicks(1,1) pad_plot.cd() # text for eta binning t_eta = ROOT.TLatex() t_eta.SetNDC() t_eta.SetTextSize(0.05) t_eta.SetTextAlign(11) t_eta.SetTextColor(ROOT.kBlack) # text for pt binning t_pt = ROOT.TLatex() t_pt.SetNDC() t_pt.SetTextSize(0.05) t_pt.SetTextAlign(11) t_pt.SetTextColor(ROOT.kBlack) # bins in eta and pt, with total number of bins bins_eta = [0.0, 1.0, 2.4] #bins_pt = [10.0, 20.0, 30.0, 35.0, 37.5, 40.0, 42.5, 45.0, 47.5, 50.0, 55.0, 60.0, 70.0] # old bins_pt = [10.0, 20.0, 22.5, 25.0, 27.5, 30.0, 32.5, 35.0, 40.0, 50.0, 60.0, 70.0] # new bins_tot = (len(bins_eta)-1)*(len(bins_pt)-1) # iterate over all histograms in root files # it does not matter which sample we iterate on, as all samples contain the same list of histograms for hist in dataset.hists: # get index of the histogram i = dataset.hists.index(hist) # only histograms in the parameter histlist are plotted if not "JPtZoom" in hist.GetName(): continue # pre- and postpends prepend = '' postpend = '' if '_Loose_' in hist.GetName(): prepend = 'Loose_' if '_Tight_' in hist.GetName(): prepend = 'Tight_' # get the id of the plot, i.e. the last number in the name (e.g. '0' in h_Loose_DJPtZoomC_0) id = hist.GetName().split('_')[-1] # draw data hist.Scale(1.0/hist.Integral()) hist.SetFillStyle(0) hist.SetLineStyle(2) hist.Draw("HIST") max = hist.GetMaximum() # draw mc samples for mc in mcsets: mc.hists[i].Scale(1.0/mc.hists[i].Integral()) mc.hists[i].SetFillStyle(0) mc.hists[i].Draw("HIST SAME") if mc.hists[i].GetMaximum()>max: max = mc.hists[i].GetMaximum() # do some cosmetics hist.SetMaximum(1.5*max) hist.GetXaxis().SetTitle(helper.getXTitle(dataType, hist)) hist.GetYaxis().SetTitle("1/Integral") hist.SetTitle("") hist.GetXaxis().SetTitleSize(0.07) hist.GetXaxis().SetLabelSize(0.07) hist.GetYaxis().SetTitleSize(0.07) hist.GetYaxis().SetLabelSize(0.07) hist.GetXaxis().SetNdivisions(505) hist.GetYaxis().SetNdivisions(505) # draw legend leg.Draw() # write bin texts m = int(id)//(len(bins_pt)-1) n = int(id)%(len(bins_pt)-1) if "ZoomC" in hist.GetName(): write = "corr." else: write = "raw" text_eta = str(bins_eta[m]) + " #leq jet-|#eta| < " + str(bins_eta[m+1]) text_pt = str(bins_pt[n]) + " #leq jet-p_{T} (" + write + ") < " + str(bins_pt[n+1]) t_eta.DrawLatex(0.22, 0.8, text_eta) t_pt.DrawLatex(0.22, 0.73, text_pt) # draw plots helper.saveCanvas(canv, pad_plot, outputDir + "zoom_jpt/", prepend + helper.getSaveName(hist, '-2:') + postpend, False)
def Plot2d(dataType, outputDir, datasets, mcsets, histlist): # # this function is basically the 2d analogon to Plot1d: create 2d plots according to the histograms in the list # NOTE: ALL SAMPLES MUST CONTAIN THE EXACT SAME LIST OF HISTOGRAMS, OTHERWISE THIS FUNCTION WILL NOT WORK # # this function takes the following parameters: # dataType....type of the lepton ('mu', 'el') # outputDir...basic output directory # datasets....list of data samples [datasample1, datasample2, ..] # mcsets......list of mc samples [mcsample1, mcsample2, ..] # histlist....list of 1d histogram names ['h_Loose_LepIso', 'h_Loose_LepPhi', ..] # define canvas and pads canv = helper.makeCanvas(900, 675, 'c2d') pad_plot = helper.makePad('tot') pad_plot.cd() pad_plot.SetTicks(1,1) # iterate over all histograms in root files # it does not matter which sample we iterate on, as all samples contain the same list of histograms for hist in datasets[0].hists: # get index of the histogram i = datasets[0].hists.index(hist) # only histograms in the parameter histlist are plotted if not hist.GetName() in histlist: continue # pre- and postpends prepend = '' postpend = '' if '_Loose_' in hist.GetName(): prepend = 'Loose_' if '_Tight_' in hist.GetName(): prepend = 'Tight_' # we stack both data and mc samples (i.e. one may use several data and several mc samples) data = ROOT.THStack() mc = ROOT.THStack() for dataset in datasets: data.Add(dataset.hists[i]) for mcset in mcsets: mc .Add(mcset .hists[i]) # call make2dPlot to make plots for data, total MC, each mc sample make2dPlot(dataType, canv, pad_plot, outputDir, data.GetStack().Last(), 'data', prepend + helper.getSaveName(hist) + postpend) make2dPlot(dataType, canv, pad_plot, outputDir, mc .GetStack().Last(), 'MC' , prepend + helper.getSaveName(hist) + postpend) for mcset in mcsets: make2dPlot(dataType, canv, pad_plot, outputDir, mcset.hists[i], mcset.GetName(), prepend + helper.getSaveName(hist) + postpend)
def PlotMETZooms(dataType, outputDir, datasets, mcsets, leg, grouping = False): # # plotting MET distribution in bins of lepton eta and lepton pt (binning according to fake rate maps) # NOTE: ALL SAMPLES MUST CONTAIN THE EXACT SAME LIST OF HISTOGRAMS, OTHERWISE THIS FUNCTION WILL NOT WORK # # dataType....type of the lepton ('mu', 'el') # outputDir...basic output directory # datasets....list of data samples [datasample1, datasample2, ..] # mcsets......list of mc samples [mcsample1, mcsample2, ..] # leg.........legend to be plotted # grouping....True if mc samples should be grouped before stacking (useful in case of e.g. several QCD files) # define canvas and pads canv = helper.makeCanvas(900, 675, 'c1dM') pad_plot = helper.makePad('plot') pad_ratio = helper.makePad('ratio') pad_plot.SetTicks(1,1) pad_ratio.SetTicks(1,1) # text for eta binning t_eta = ROOT.TLatex() t_eta.SetNDC() t_eta.SetTextSize(0.05) t_eta.SetTextAlign(11) t_eta.SetTextColor(ROOT.kBlack) # text for pt binning t_pt = ROOT.TLatex() t_pt.SetNDC() t_pt.SetTextSize(0.05) t_pt.SetTextAlign(11) t_pt.SetTextColor(ROOT.kBlack) # bins in eta and pt, with total number of bins bins_eta = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5] bins_pt = [20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0] bins_tot = (len(bins_eta)-1)*(len(bins_pt)-1) # iterate over all histograms in root files # it does not matter which sample we iterate on, as all samples contain the same list of histograms for hist in datasets[0].hists: # get index of the histogram i = datasets[0].hists.index(hist) pad_plot.cd() # only histograms in the parameter histlist are plotted if not "METZoom" in hist.GetName(): continue # pre- and postpends prepend = '' postpend = '' if '_Loose_' in hist.GetName(): prepend = 'Loose_' if '_Tight_' in hist.GetName(): prepend = 'Tight_' # get the id of the plot, i.e. the last number in the name (e.g. '0' in h_Loose_FRMETZoom_0) id = hist.GetName().split('_')[-1] # we stack both data and mc samples (i.e. one may use several data and several mc samples) data = ROOT.THStack() mc = ROOT.THStack() for dataset in datasets: data.Add(dataset.hists[i]) # if grouping is enabled, we first sum all 'similar' mc samples before adding them to the stack # similar means, that we sum all samples which have the same name up to a few digits at the end # in this way, e.g., the samples dyjets50 and dyjets10 are stacked together # if grouping is not enabled, we just stack all mc samples in one stack if grouping: mcgroups = [] mcnames = [] for mcset in mcsets: label = ''.join([j for j in mcset.GetName() if not j.isdigit()]) foundat = -1 for j, mcname in enumerate(mcnames): if label == mcname: foundat = j if foundat == -1: mcgroups.append(ROOT.THStack()) mcgroups[len(mcgroups)-1].Add(mcset.hists[i]) mcnames.append(label) else: mcgroups[foundat].Add(mcset.hists[i]) for j, group in enumerate(mcgroups): group.Draw('hist') histogram = group.GetStack().Last() mc.Add(histogram) else: for mcset in mcsets: mc.Add(mcset.hists[i]) # draw histogram mc.Draw('hist') # define the list of histograms which shall be plotted # i.e. one has to take the histogram of the stack, not the stack itself hists = [] hists.append([data.GetStack().Last(), 'data' ]) hists.append([mc , 'totbg']) # plot pad_plot first, then we add the texts and plot ratio afterwards make1dPlot_plot(dataType, pad_plot, hists, hists[0][0], leg) # write bin texts m = int(id)//(len(bins_pt)-1) n = int(id)%(len(bins_pt)-1) if dataType == 'el': lepton = 'e' else : lepton = '#mu' text_eta = str(bins_eta[m]) + " #leq " + lepton + "-|#eta| < " + str(bins_eta[m+1]) text_pt = str(bins_pt[n]) + " #leq " + lepton + "-p_{T} < " + str(bins_pt[n+1]) t_eta.DrawLatex(0.22, 0.8, text_eta) t_pt.DrawLatex(0.22, 0.73, text_pt) # plot ratio #make1dPlot_ratio(dataType, pad_ratio, hists, hists[0][0]) # calling the function to produce the ratio plot results in a malfunction i do not understand properly # so we copy the lines from make1dPlot_ratio() while we still keep that function for other purposes pad_ratio.cd() data_bg_ratio = copy.deepcopy(hists[0][0]) data_bg_ratio.Divide(copy.deepcopy(hists[1][0].GetStack().Last())) data_bg_ratio.Draw("p e") data_bg_ratio = helper.setRatioStyle(dataType, data_bg_ratio, hists[0][0]) line = helper.makeLine(data_bg_ratio.GetXaxis().GetXmin(), 1.00, data_bg_ratio.GetXaxis().GetXmax(), 1.00) line.Draw() # save plot ROOT.gPad.RedrawAxis() helper.saveCanvas(canv, pad_plot, outputDir + "zoom_met/", prepend + helper.getSaveName(hist, '-2:') + postpend)
def Plot1d(dataType, outputDir, datasets, mcsets, histlist, leg, grouping = False): # # given data and mc samples, all histograms in histlist are produced with mc samples stacked # NOTE: ALL SAMPLES MUST CONTAIN THE EXACT SAME LIST OF HISTOGRAMS, OTHERWISE THIS FUNCTION WILL NOT WORK # # this function takes the following parameters: # dataType....type of the lepton ('mu', 'el') # outputDir...basic output directory # datasets....list of data samples [datasample1, datasample2, ..] # mcsets......list of mc samples [mcsample1, mcsample2, ..] # histlist....list of 1d histogram names ['h_Loose_LepIso', 'h_Loose_LepPhi', ..] # leg.........legend to be drawn # grouping....True if mc samples should be grouped before stacking (useful in case of e.g. several QCD files) # define canvas and pads canv = helper.makeCanvas(900, 675, 'c1d') pad_plot = helper.makePad('plot') pad_ratio = helper.makePad('ratio') pad_plot.SetTicks(1,1) pad_ratio.SetTicks(1,1) # iterate over all histograms in root files # it does not matter which sample we iterate on, as all samples contain the same list of histograms for hist in datasets[0].hists: # get index of the histogram i = datasets[0].hists.index(hist) pad_plot.cd() # only histograms in the parameter histlist are plotted if not hist.GetName() in histlist: continue # there are two NVertices plots in the list with different binning # we only plot the NVertices, but not the NVertices1 (this we use for the fake rate plots, see lib_FakeRate.py) if hist.GetName()[-10:] == "NVertices1": continue # pre- and postpends prepend = '' postpend = '' if '_Loose_' in hist.GetName(): prepend = 'Loose_' if '_Tight_' in hist.GetName(): prepend = 'Tight_' # we stack both data and mc samples (i.e. one may use several data and several mc samples) data = ROOT.THStack() mc = ROOT.THStack() for dataset in datasets: data.Add(dataset.hists[i]) # if grouping is enabled, we first sum all 'similar' mc samples before adding them to the stack # similar means, that we sum all samples which have the same name up to a few digits at the end # in this way, e.g., the samples dyjets50 and dyjets10 are stacked together # if grouping is not enabled, we just stack all mc samples in one stack if grouping: mcgroups = [] mcnames = [] for mcset in mcsets: label = ''.join([j for j in mcset.GetName() if not j.isdigit()]) foundat = -1 for j, mcname in enumerate(mcnames): if label == mcname: foundat = j if foundat == -1: mcgroups.append(ROOT.THStack()) mcgroups[len(mcgroups)-1].Add(mcset.hists[i]) mcnames.append(label) else: mcgroups[foundat].Add(mcset.hists[i]) for j, group in enumerate(mcgroups): group.Draw('hist') histogram = group.GetStack().Last() mc.Add(histogram) else: for mc in mcsets: mc.Add(mc.hists[i]) # define the list of histograms which shall be plotted # i.e. one has to take the histogram of the stack, not the stack itself histstoplot = [] histstoplot.append([data.GetStack().Last(), 'data' ]) histstoplot.append([mc , 'totbg']) # call make1dPlot to create the plot make1dPlot(dataType, canv, pad_plot, pad_ratio, outputDir, histstoplot, hist, prepend + helper.getSaveName(hist) + postpend, leg)