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))
Exemple #2
0
    def CreatCanvas(self):
        H_ref = 600
        W_ref = 800
        W = W_ref
        H = H_ref
        T = 0.08 * H_ref
        B = 0.12 * H_ref
        L = 0.12 * W_ref
        R = 0.04 * W_ref
        # Set the tdr style

        canvas = Canvas(width=W, height=H)
        canvas.SetFillColor(0)
        canvas.SetBorderMode(0)
        canvas.SetFrameFillStyle(0)
        canvas.SetFrameBorderMode(0)
        canvas.SetLeftMargin(L / W)
        canvas.SetRightMargin(R / W)
        canvas.SetTopMargin(T / H)
        canvas.SetBottomMargin(B / H)
        canvas.SetTickx(0)
        canvas.SetTicky(0)
        return canvas
Exemple #3
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# easily set visual attributes
h_simple.linecolor = 'blue'
h_simple.fillcolor = 'green'
h_simple.fillstyle = '/'

# attributes may be accessed in the same way
print(h_simple.name)
print(h_simple.title)
print(h_simple.markersize)

# plot
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_simple.Draw()

# create the legend
legend = Legend([h_simple],
                pad=canvas,
                header='Header',
                leftmargin=0.05,
                rightmargin=0.5)
legend.Draw()

# 2D and 3D histograms are handled in the same way
# the constructor arguments are repetitions of #bins, left bound, right bound.
h2d = Hist2D(10, 0, 1, 50, -40, 10, name='2d hist')
h3d = Hist3D(3, -1, 4, 10, -1000, -200, 2, 0, 1, name='3d hist')
Exemple #4
0
                print variable + " " + min + " " + max + " " + nbins + " " + title + " " + ytitle
                for (xvariable, xmin, xmax, xnbins, xtitle, xytitle,
                     xplot) in zip(xvariable_list, xmin_list, xmax_list,
                                   xnbins_list, xtitle_list, xytitle_list,
                                   xplot_list):
                    if xplot == "Plot":
                        if xvariable == variable:
                            continue
                        # Canvas Variables and declaration

                        print xvariable + " " + xmin + " " + xmax + " " + xnbins + " " + xtitle + " " + xytitle
                        canvas = Canvas(width=700, height=500)
                        #canvas.SetLeftMargin(0.1)
                        canvas.SetRightMargin(0.15)
                        canvas.SetTopMargin(0.05)
                        canvas.Draw()

                        #canvas.SetLogy()
                        canvas.SetGrid()

                        histname = "hist" + xvariable + "_" + variable + background_name
                        print histname
                        histogram = histname + "(" + xnbins + "," + xmin + "," + xmax + "," + nbins + "," + min + "," + max + ")"
                        print histogram
                        histname = compressed.Draw(xvariable + ":" + variable +
                                                   ">>" + histogram,
                                                   selection=weight * total,
                                                   drawstyle='COLZ')
                        histname.set_title(signalprocess + " " +
                                           background_name)
def plot_central_and_systematics(channel):
    global variable, translate_options, k_value, b_tag_bin, maximum, categories
    ROOT.TH1.SetDefaultSumw2(False)
    ROOT.gROOT.SetBatch(True)
    ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
    plotting.setStyle()
    gStyle.SetTitleYOffset(1.4)
    ROOT.gROOT.ForceStyle()
    canvas = Canvas(width=700, height=500)
    canvas.SetLeftMargin(0.15)
    canvas.SetBottomMargin(0.15)
    canvas.SetTopMargin(0.05)
    canvas.SetRightMargin(0.05)
    legend = plotting.create_legend(x0=0.6, y1=0.5)

    hist_data_central = read_xsection_measurement_results(
        'central', channel)[0]['unfolded']

    hist_data_central.GetXaxis().SetTitle(translate_options[variable] +
                                          ' [GeV]')
    hist_data_central.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' +
                                          translate_options[variable] +
                                          '} [GeV^{-1}]')
    hist_data_central.GetXaxis().SetTitleSize(0.05)
    hist_data_central.GetYaxis().SetTitleSize(0.05)
    hist_data_central.SetMinimum(0)
    hist_data_central.SetMaximum(maximum[variable])
    hist_data_central.SetMarkerSize(1)
    hist_data_central.SetMarkerStyle(20)
    #    plotAsym = TGraphAsymmErrors(hist_data)
    #    plotStatErr = TGraphAsymmErrors(hist_data)
    gStyle.SetEndErrorSize(20)
    hist_data_central.Draw('P')
    #    plotStatErr.Draw('same P')
    #    plotAsym.Draw('same P Z')
    legend.AddEntry(hist_data_central, 'measured (unfolded)', 'P')

    for systematic in categories:
        if systematic != 'central':
            hist_data_systematic = read_xsection_measurement_results(
                systematic, channel)[0]['unfolded']
            hist_data_systematic.SetMarkerSize(0.5)
            hist_data_systematic.SetMarkerStyle(20)
            colour_number = categories.index(systematic) + 1
            if colour_number == 10:
                colour_number = 42
            hist_data_systematic.SetMarkerColor(colour_number)
            hist_data_systematic.Draw('same P')
            legend.AddEntry(hist_data_systematic, systematic, 'P')


#    for central_generator in ['MADGRAPH', 'POWHEG', 'MCATNLO']:
#        hist_MC = read_xsection_measurement_results('central', channel)[0][central_generator]
#        hist_MC.SetLineStyle(7)
#        hist_MC.SetLineWidth(2)
#        #setting colours
#        if central_generator == 'POWHEG':
#            hist_MC.SetLineColor(kBlue)
#        elif central_generator == 'MADGRAPH':
#            hist_MC.SetLineColor(kRed + 1)
#        elif central_generator == 'MCATNLO':
#            hist_MC.SetLineColor(kMagenta + 3)
#        hist_MC.Draw('hist same')
#legend.AddEntry(hist_MC, translate_options[central_generator], 'l')

    legend.Draw()

    mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
    channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
    if channel == 'electron':
        channelLabel.AddText(
            "e, %s, %s, k_v = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    elif channel == 'muon':
        channelLabel.AddText(
            "#mu, %s, %s, k_v = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    else:
        channelLabel.AddText(
            "combined, %s, %s, k_v = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" %
                   (5.8))

    mytext.SetFillStyle(0)
    mytext.SetBorderSize(0)
    mytext.SetTextFont(42)
    mytext.SetTextAlign(13)

    channelLabel.SetFillStyle(0)
    channelLabel.SetBorderSize(0)
    channelLabel.SetTextFont(42)
    channelLabel.SetTextAlign(13)
    mytext.Draw()
    if not channel == 'combination':
        channelLabel.Draw()

    canvas.Modified()
    canvas.Update()

    path = save_path + '/' + variable
    make_folder_if_not_exists(path)
    canvas.SaveAs(path + '/normalised_xsection_' + channel + '_altogether_kv' +
                  str(k_value) + '.png')
    canvas.SaveAs(path + '/normalised_xsection_' + channel + '_altogether_kv' +
                  str(k_value) + '.pdf')
def make_plots_ROOT(histograms, category, save_path, histname, channel):
    global variable, translateOptions, k_value, b_tag_bin, maximum
    ROOT.TH1.SetDefaultSumw2(False)
    ROOT.gROOT.SetBatch(True)
    ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
    plotting.setStyle()
    gStyle.SetTitleYOffset(2.)
    ROOT.gROOT.ForceStyle()
    canvas = Canvas(width=700, height=500)
    canvas.SetLeftMargin(0.18)
    canvas.SetBottomMargin(0.15)
    canvas.SetTopMargin(0.05)
    canvas.SetRightMargin(0.05)
    legend = plotting.create_legend(x0=0.6, y1=0.5)

    hist_data = histograms['unfolded']
    hist_data.GetXaxis().SetTitle(translate_options[variable] + ' [GeV]')
    hist_data.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' +
                                  translate_options[variable] + '} [GeV^{-1}]')
    hist_data.GetXaxis().SetTitleSize(0.05)
    hist_data.GetYaxis().SetTitleSize(0.05)
    hist_data.SetMinimum(0)
    hist_data.SetMaximum(maximum[variable])
    hist_data.SetMarkerSize(1)
    hist_data.SetMarkerStyle(8)
    plotAsym = TGraphAsymmErrors(hist_data)
    plotStatErr = TGraphAsymmErrors(hist_data)

    xsections = read_unfolded_xsections(channel)
    bins = variable_bins_ROOT[variable]
    assert (len(bins) == len(xsections['central']))

    for bin_i in range(len(bins)):
        scale = 1  # / width
        centralresult = xsections['central'][bin_i]
        fit_error = centralresult[1]
        uncertainty = calculateTotalUncertainty(xsections, bin_i)
        uncertainty_total_plus = uncertainty['Total+'][0]
        uncertainty_total_minus = uncertainty['Total-'][0]
        uncertainty_total_plus, uncertainty_total_minus = symmetriseErrors(
            uncertainty_total_plus, uncertainty_total_minus)
        error_up = sqrt(fit_error**2 + uncertainty_total_plus**2) * scale
        error_down = sqrt(fit_error**2 + uncertainty_total_minus**2) * scale
        plotStatErr.SetPointEYhigh(bin_i, fit_error * scale)
        plotStatErr.SetPointEYlow(bin_i, fit_error * scale)
        plotAsym.SetPointEYhigh(bin_i, error_up)
        plotAsym.SetPointEYlow(bin_i, error_down)

    gStyle.SetEndErrorSize(20)
    plotAsym.SetLineWidth(2)
    plotStatErr.SetLineWidth(2)
    hist_data.Draw('P')
    plotStatErr.Draw('same P')
    plotAsym.Draw('same P Z')
    legend.AddEntry(hist_data, 'unfolded', 'P')

    hist_measured = histograms['measured']
    hist_measured.SetMarkerSize(1)
    hist_measured.SetMarkerStyle(20)
    hist_measured.SetMarkerColor(2)
    #hist_measured.Draw('same P')
    #legend.AddEntry(hist_measured, 'measured', 'P')

    for key, hist in sorted(histograms.iteritems()):
        if not 'unfolded' in key and not 'measured' in key:
            hist.SetLineStyle(7)
            hist.SetLineWidth(2)
            # setting colours
            if 'POWHEG' in key or 'matchingdown' in key:
                hist.SetLineColor(kBlue)
            elif 'MADGRAPH' in key or 'matchingup' in key:
                hist.SetLineColor(kRed + 1)
            elif 'MCATNLO' in key or 'scaleup' in key:
                hist.SetLineColor(kGreen - 3)
            elif 'scaledown' in key:
                hist.SetLineColor(kMagenta + 3)
            hist.Draw('hist same')
            legend.AddEntry(hist, translate_options[key], 'l')

    legend.Draw()

    mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
    channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
    if 'electron' in histname:
        channelLabel.AddText(
            "e, %s, %s, k = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    elif 'muon' in histname:
        channelLabel.AddText(
            "#mu, %s, %s, k = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    else:
        channelLabel.AddText(
            "combined, %s, %s, k = %s" %
            ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
    mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" %
                   (5.8))

    mytext.SetFillStyle(0)
    mytext.SetBorderSize(0)
    mytext.SetTextFont(42)
    mytext.SetTextAlign(13)

    channelLabel.SetFillStyle(0)
    channelLabel.SetBorderSize(0)
    channelLabel.SetTextFont(42)
    channelLabel.SetTextAlign(13)
    mytext.Draw()
    channelLabel.Draw()

    canvas.Modified()
    canvas.Update()

    path = save_path + '/' + variable + '/' + category
    make_folder_if_not_exists(path)
    canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.png')
    canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.pdf')
def plot_fit_results(histograms, category, channel):
    global variable, translate_options, b_tag_bin, save_path
    #ROOT.TH1.SetDefaultSumw2(False)
    ROOT.gROOT.SetBatch(True)
    ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
    plotting.setStyle()
    gStyle.SetTitleYOffset(1.4)
    ROOT.gROOT.ForceStyle()

    for variable_bin in variable_bins_ROOT[variable]:
        path = save_path + '/' + variable + '/' + category + '/fit_results/'
        make_folder_if_not_exists(path)
        plotname = path + channel + '_bin_' + variable_bin + '.png'
        # check if template plots exist already
        if os.path.isfile(plotname):
            continue
        canvas = Canvas(width=700, height=500)
        canvas.SetLeftMargin(0.15)
        canvas.SetBottomMargin(0.15)
        canvas.SetTopMargin(0.05)
        canvas.SetRightMargin(0.05)
        legend = plotting.create_legend(x0=0.7, y1=0.8)
        h_data = histograms[variable_bin]['data']
        h_signal = histograms[variable_bin]['signal']
        h_background = histograms[variable_bin]['background']

        h_data.GetXaxis().SetTitle('Lepton #eta')
        h_data.GetYaxis().SetTitle('Number of Events')
        h_data.GetXaxis().SetTitleSize(0.05)
        h_data.GetYaxis().SetTitleSize(0.05)
        h_data.SetMinimum(0)
        h_data.SetMarkerSize(1)
        h_data.SetMarkerStyle(20)
        gStyle.SetEndErrorSize(20)
        h_data.Draw('P')

        h_signal.SetFillColor(kRed + 1)
        h_background.SetFillColor(kGreen - 3)
        h_signal.SetLineWidth(2)
        h_background.SetLineWidth(2)
        h_signal.SetFillStyle(1001)
        h_background.SetFillStyle(1001)

        mcStack = THStack("MC", "MC")
        mcStack.Add(h_background)
        mcStack.Add(h_signal)

        mcStack.Draw('hist same')
        h_data.Draw('error P same')
        legend.AddEntry(h_data, 'data', 'P')
        legend.AddEntry(h_signal, 'signal', 'F')
        legend.AddEntry(h_background, 'background', 'F')
        legend.Draw()

        mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
        channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
        if channel == 'electron':
            channelLabel.AddText("e, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        elif channel == 'muon':
            channelLabel.AddText("#mu, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        else:
            channelLabel.AddText("combined, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        mytext.AddText(
            "CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))

        mytext.SetFillStyle(0)
        mytext.SetBorderSize(0)
        mytext.SetTextFont(42)
        mytext.SetTextAlign(13)

        channelLabel.SetFillStyle(0)
        channelLabel.SetBorderSize(0)
        channelLabel.SetTextFont(42)
        channelLabel.SetTextAlign(13)
        mytext.Draw()
        channelLabel.Draw()

        canvas.Modified()
        canvas.Update()
        canvas.SaveAs(plotname)
        canvas.SaveAs(plotname.replace('png', 'pdf'))
def make_template_plots(histograms, category, channel):
    global variable, translate_options, b_tag_bin, save_path
    ROOT.TH1.SetDefaultSumw2(False)
    ROOT.gROOT.SetBatch(True)
    ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
    plotting.setStyle()
    gStyle.SetTitleYOffset(1.4)
    ROOT.gROOT.ForceStyle()

    for variable_bin in variable_bins_ROOT[variable]:
        path = save_path + '/' + variable + '/' + category + '/fit_templates/'
        make_folder_if_not_exists(path)
        plotname = path + channel + '_templates_bin_' + variable_bin + '.png'
        # check if template plots exist already
        if os.path.isfile(plotname):
            continue
        canvas = Canvas(width=700, height=500)
        canvas.SetLeftMargin(0.15)
        canvas.SetBottomMargin(0.15)
        canvas.SetTopMargin(0.05)
        canvas.SetRightMargin(0.05)
        legend = plotting.create_legend(x0=0.7, y1=0.8)
        h_signal = histograms[variable_bin]['signal']
        h_VJets = histograms[variable_bin]['V+Jets']
        h_QCD = histograms[variable_bin]['QCD']

        h_signal.GetXaxis().SetTitle('Lepton #eta')
        h_signal.GetYaxis().SetTitle('Normalised Events')
        h_signal.GetXaxis().SetTitleSize(0.05)
        h_signal.GetYaxis().SetTitleSize(0.05)
        h_signal.SetMinimum(0)
        h_signal.SetMaximum(0.2)
        h_signal.SetLineWidth(2)
        h_VJets.SetLineWidth(2)
        h_QCD.SetLineWidth(2)
        h_signal.SetLineColor(kRed + 1)
        h_VJets.SetLineColor(kBlue)
        h_QCD.SetLineColor(kYellow)
        h_signal.Draw('hist')
        h_VJets.Draw('hist same')
        h_QCD.Draw('hist same')
        legend.AddEntry(h_signal, 'signal', 'l')
        legend.AddEntry(h_VJets, 'V+Jets', 'l')
        legend.AddEntry(h_QCD, 'QCD', 'l')
        legend.Draw()

        mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
        channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
        if channel == 'electron':
            channelLabel.AddText("e, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        elif channel == 'muon':
            channelLabel.AddText("#mu, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        else:
            channelLabel.AddText("combined, %s, %s" %
                                 ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
        mytext.AddText(
            "CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))

        mytext.SetFillStyle(0)
        mytext.SetBorderSize(0)
        mytext.SetTextFont(42)
        mytext.SetTextAlign(13)

        channelLabel.SetFillStyle(0)
        channelLabel.SetBorderSize(0)
        channelLabel.SetTextFont(42)
        channelLabel.SetTextAlign(13)
        mytext.Draw()
        channelLabel.Draw()

        canvas.Modified()
        canvas.Update()
        canvas.SaveAs(plotname)
        canvas.SaveAs(plotname.replace('png', 'pdf'))
Exemple #9
0
                        else:
                            expr = expr.format(
                                var_info['name'], h.name,
                                (var_info['bins'], var_info['range'][0], var_info['range'][1]))
                        print expr
                        tree.Draw(expr, cut, var_info['style'])
                        h = asrootpy(ROOT.gPad.GetPrimitive(h.name))
                        h.xaxis.title = get_label(var_info)
                        h.yaxis.title = 'Number of Events'
                        if h.integral() != 0:
                            h /= h.integral() 
                        h.SetLineWidth(2)
                        h.yaxis.SetRangeUser(0, 0.5)
                        if 'binlabels' in var_info.keys():
                            for ib, lab in enumerate(var_info['binlabels']):
                                h.xaxis.SetBinLabel(ib + 1, lab)

                        c = Canvas()
                        # c.SetLogy()
                        c.SetTopMargin(0.08)
                        h.Draw('HIST')
                        label = ROOT.TLatex(
                            c.GetLeftMargin() + 0.05, 1 - c.GetTopMargin() + 0.02,
                            '{0}H #rightarrow #tau#tau (m_{{H}} = {1} GeV) - Hist Integral = {2}'.format(
                                get_dir_mode(d), get_dir_mass(d), h.Integral()))
                        label.SetNDC()
                        label.SetTextSize(20)
                        label.Draw('same')
                        c.SaveAs('./plots/{0}_mass{1}_mode{2}.png'.format(
                                key, get_dir_mass(d), get_dir_mode(d)))