def CalibrationENC(calibration_filename): # Read data fron the calibration curve dataset C, e_C, noise_rms, d_noise_rms, fall_time, d_fall_time, amplitude, d_amplitude = LoadCalibrationFile(calibration_filename) noise_rms = noise_rms / 1000. # convert form \muV to mV d_noise_rms = d_noise_rms / 1000. ENC = (noise_rms/amplitude) * (C * 20.0) * pow(10,-15) / (1.602 * pow(10,-19) ) #pF * mV = 10^-12 * 10^-3 = 10^-15 C / 10^-19 C = 10^4 electrons d_ENC = np.zeros([len(ENC)]) for i in range(len(ENC)): d_ENC[i] = sqrt( d_noise_rms[i]*d_noise_rms[i]*(C[i] * 20.0/amplitude[i])*(C[i] * 20.0/amplitude[i]) + d_amplitude[i]*d_amplitude[i]*((noise_rms[i]/(amplitude[i]*amplitude[i])) * (C[i] * 20.0))*((noise_rms[i]/(amplitude[i]*amplitude[i])) * (C[i] * 20.0)) ) d_ENC = d_ENC * pow(10,-15) / (1.602 * pow(10,-19) ) # conversted to # of electrons calib_curve = TGraphErrors(len(ENC), array('d', C[:-1].tolist()), array('d', ENC.tolist()), array('d', e_C.tolist()), array('d', d_ENC) ) # Construct the fit with a 1st and 2nd order polynomial fit_curve = TF1('fit_curve','[1]*x + [0]',0.,110.) fit_curve2 = TF1('fit_curve2','[0]*x*x + [1]*x + [2]',0.,110.) # Fit the calibration curve calib_curve.Fit(fit_curve,'R') calib_curve.Fit(fit_curve2, 'R') lin_par0 = fit_curve.GetParameter(0) lin_e_par0 = fit_curve.GetParError(0) lin_par1 = fit_curve.GetParameter(1) lin_e_par1 = fit_curve.GetParError(1) xx = np.linspace(0., 105, 1000) yy = [fit_curve.Eval(x) for x in xx] yy2 = [fit_curve2.Eval(x) for x in xx] par = [lin_par0, lin_par1] l = 'fit pol1 $p_0$: {:.3g} $p_1$: {:.3g}'.format(*par) # Plot the calibration curve with matplotlib font0 = FontProperties() font = font0.copy() font.set_style('italic') font.set_weight('bold') font.set_size('x-large') fig, ax = plt.subplots() ax.set_xlabel('C [pF]') ax.set_ylabel('ENC [# of electrons]') ax.errorbar(C, ENC, d_ENC, marker='o', color='k', linestyle='None', label='Data') ax.plot(xx, yy, color='r', label=l) ax.plot(xx, yy2, color='g', label='fit pol2') ax.text(0.05,0.65, 'Group 1', verticalalignment='bottom', horizontalalignment='left', fontproperties=font, transform=ax.transAxes) ax.legend(loc='upper left', numpoints=1) plt.grid() #plt.show() fig.savefig('../graphics/calibrationENC_diode.pdf') plt.close(fig) plt.clf() return lin_par0, lin_e_par0, lin_par1, lin_e_par1
class TimeConstant(object): def __init__(self, file_name): file = open(file_name) out = open(output, 'w') for line in file: #calculate errors and dump the content to output file #skip comment lines if '#' in line: continue t, v = [float(x) for x in line.split()] te = osc_error_t(t, TIME_DIV) ve = osc_error_v(v, POT_DIV) new_line = [t, v, te, ve] new_line = ' '.join([str(x) for x in new_line]) + '\n' out.write(new_line) file.close() out.close() #create Graph. ROOT automatically reads columns self.graph = TGraphErrors(output) self.graph.SetMarkerStyle(7) self.func = TF1("exp_decay", "[0]*exp(-x/[1])") self.func.SetParameters(2, 20) def fit_graph(self): self.graph.Fit("exp_decay", "Q") self.v0 = self.func.GetParameter(0), self.func.GetParError(0) self.tau = self.func.GetParameter(1), self.func.GetParError(1) # print "%.3f \pm %.3f" %self.v0 # print "%.3f \pm %.3f" %self.tau def get_capacity(self): c = (self.tau[0] / INTERNAL_RES, self.tau[1] / INTERNAL_RES) return c
class CauchyRelation(object): def __init__(self, ref_indexes, err_ref_ind, wavelengths): self.name = 'Cauchy Relation' self.init_graph(ref_indexes, err_ref_ind, wavelengths) self.fit_graph() def init_graph(self, ref_indexes, err_ref_ind, wavelengths): n = array('d', ref_indexes) errn = array('d', err_ref_ind) wl = array('d', wavelengths) errwl = array('d', [0] * len(wavelengths)) self.pars = array('d', [0] * 3) #fit parameters self.parerrs = array('d', [0] * 3) #fit parameter errors self.graph = TGraphErrors(len(wavelengths), wl, n, errwl, errn) set_graph_style(self.graph) self.graph.SetTitle(self.name) def fit_graph(self): self.func = TF1('cauchy', '[0] + [1]/x**2', 400, 700) self.func.SetParameters(10, 1e4) self.graph.Fit('cauchy', 'QR') self.func.GetParameters(self.pars) self.parerrs[0] = self.func.GetParError(0) self.parerrs[1] = self.func.GetParError(1) self.r = ResGraph(self.graph, self.func) #creates residual graph set_graph_style(self.r.graph) self.r.graph.SetTitle(self.r.name) return zip(self.pars, self.parerrs) def show_res_graph(self): show_graph(self.r.graph, self.r.name) def show_graph(self): show_graph(self.graph, self.name)
def create_resolutiongraph(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors, graphname): """Function to perform ROOT graphs of resolutions""" #How many points n = int(n) TGraphresolution = TGraphErrors(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors) #Draw + DrawOptions, Fit + parameter estimation Style = gStyle Style.SetOptFit() XAxis = TGraphresolution.GetXaxis() #TGraphresolution TGraphresolution.SetMarkerColor(4) TGraphresolution.SetMarkerStyle(20) TGraphresolution.SetMarkerSize(2) XAxis.SetTitle("Energy (GeV)") YAxis = TGraphresolution.GetYaxis() YAxis.SetTitle("Sigma/Mean") resolutionfit = TF1("resolutionfit", '([0]/((x)**0.5))+[1]', 0, max(energies)) #somma non quadratura TGraphresolution.Fit("resolutionfit") a = resolutionfit.GetParameter(0) b = resolutionfit.GetParameter(1) TGraphresolution.Draw("AP") gPad.SaveAs(graphname) gPad.Close() return a, b
class DiodeCurrent(object): def __init__(self, file_name): file = open(file_name) out = open(output, 'w') for line in file: #calculate errors and dump the content to output file if '#' in line: continue t, vin, vout = [float(x) for x in line.split()] te = osc_error_t(t, TIME_DIV) vd = vin - vout voute = osc_error_v(vout, POT_DIV) vde = osc_error_v(vd, POT_DIV) id = vout / R[0] if vout: ide = id * ((voute / vout) + (R[1] / R[0])) else: ide = id * (R[1] / R[0]) new_line = [vd, id, vde, ide] new_line = ' '.join([str(x) for x in new_line]) + '\n' out.write(new_line) file.close() out.close() #create Graph. ROOT automatically reads columns self.graph = TGraphErrors(output) self.graph.SetMarkerStyle(7) self.func = TF1("shockley", "[0]*(exp(x/[1])-1)", 0.1, 1) self.func.SetParameters(5, 0.026) def fit_graph(self): self.graph.Fit("shockley", "QRW") self.v0 = self.func.GetParameter(0), self.func.GetParError(0) self.tau = self.func.GetParameter(1), self.func.GetParError(1) print("Is = %.3g \pm %.3g" % self.v0) print("n*Vt = %.3f \pm %.3f" % self.tau)
def CalibrationRiseTime(calibration_filename): # Read data fron the calibration curve dataset C, e_C, noise_rms, d_noise_rms, fall_time, d_fall_time, amplitude, d_amplitude = LoadCalibrationFile(calibration_filename) gStyle.SetOptStat(1111) # Construct the curve calib_curve = TGraphErrors(len(C), array('d',C.tolist()), array('d', fall_time.tolist()), array('d', e_C.tolist()), array('d',d_fall_time.tolist()) ) # Construct the fit with a 2nd order polynomial fit_curve = TF1('fit_curve','[0]*x*x + [1]*x + [2]',0.,110.) fit_curve.SetParameter(0, 0.0) fit_curve.SetParameter(1, 0.0) fit_curve.SetParameter(2, 0.0) fit_curve.SetLineColor(2) # FIt the calibration curve calib_curve.Fit(fit_curve,'R') # Extract the results fron the fit par0 = fit_curve.GetParameter(0) e_par0 = fit_curve.GetParError(0) par1 = fit_curve.GetParameter(1) e_par1 = fit_curve.GetParError(1) par2 = fit_curve.GetParameter(2) e_par2 = fit_curve.GetParError(2) xx = np.linspace(0., 105, 1000) yy = [fit_curve.Eval(x) for x in xx] par = [par2, par1, par0] l = 'fit pol2 $p_0$: {:.3g} $p_1$: {:.3g} $p_2$: {:.2f}'.format(*par) # Plot the calibration curve with matplotlib font0 = FontProperties() font = font0.copy() font.set_style('italic') font.set_weight('bold') font.set_size('x-large') fig, ax = plt.subplots() ax.set_xlabel('C [pF]') ax.set_xlim(0.0, 120) ax.set_ylabel('Rise time [ns]') ax.errorbar(C, fall_time, d_fall_time, marker='o', color='k', linestyle='None', label='Data') ax.plot(xx, yy, label=l, color='r') #plt.set_title('Diode calibration curve') ax.text(0.05,0.65, 'Group 1', verticalalignment='bottom', horizontalalignment='left', fontproperties=font, transform=ax.transAxes) ax.legend(loc='upper left', numpoints=1) plt.grid() #plt.show() fig.savefig('../graphics/calibration_diode.pdf') plt.close(fig) plt.clf() return par0, e_par0, par1, e_par1, par2, e_par2
def angresplot(energies, angrestheta, angresphi, angresthetaerror, angresphierror): energies_arr = array('d', energies) angrestheta_arr = array('d', angrestheta) angresphi_arr = array('d', angresphi) angresthetaerror_arr = array('d', angresthetaerror) angresphierror_arr = array('d', angresphierror) zeros_arr = array('d',[0.0]*len(energies)) ThetaGraph = TGraphErrors(len(energies), energies_arr, angrestheta_arr, zeros_arr, angresthetaerror_arr) PhiGraph = TGraphErrors(len(energies), energies_arr, angresphi_arr, zeros_arr, angresphierror_arr) resolutionfittheta = TF1("resolutionfit", '[0]/(x**0.5)+[1]', 30., 150.) #resolutionfittheta.SetParLimits(2, 0.5, 0.8) resolutionfitphi = TF1("resolutionfit", '[0]/(x**0.5)+[1]', 30., 150.) #resolutionfitphi.SetParLimits(2, 0.5, 0.8) ThetaGraph.Fit(resolutionfittheta) PhiGraph.Fit(resolutionfitphi) return ThetaGraph, PhiGraph
def fit(self, th2, effref): x_points, y_points, error_points = self.get_points(th2, effref) import array from ROOT import TGraphErrors, TF1 g = TGraphErrors(len(x_points), array.array( 'd', y_points, ), array.array('d', x_points), array.array('d', [0.] * len(x_points)), array.array('d', error_points)) firstBinVal = th2.GetYaxis().GetBinLowEdge(th2.GetYaxis().GetFirst()) lastBinVal = th2.GetYaxis().GetBinLowEdge(th2.GetYaxis().GetLast() + 1) f1 = TF1('f1', 'pol1', firstBinVal, lastBinVal) g.Fit(f1, "FRq") slope = f1.GetParameter(1) offset = f1.GetParameter(0) return slope, offset, x_points, y_points, error_points
class CalibrazionePotenziometro: def __init__(self, tabella): self.file_tabella = tabella self.gr = TGraphErrors(tabella) self.gr.SetTitle("calibrazione potenziometro;potenziometro;R [#Omega]") self.result = self.gr.Fit("pol1", "SQ") def disegna_grafico(self): self.can = TCanvas("can", "can") self.gr.Draw("AP") can.SaveAs("relazione/img/{0}.eps".format(self.file_tabella)) def salva_calibrazione(self): with open("fit.{0}".format(self.file_tabella), "w") as out_file: a = self.result.Parameter(1) sigma_a = self.result.ParError(1) b = self.result.Parameter(0) sigma_b = self.result.ParError(1) line = [a, sigma_a, b, sigma_b] line = [str(x) for x in line] line = " ".join(line) out_file.write(line) def stampa_tabella(self): print("\\begin{tabular}{r@{ $\\pm$ }lr@{ $\\pm$ }l}") print( "\\multicolumn{2}{c}{$R [\\unit[]{\\ohm}]$} &\\multicolumn{2}{c}{potenziometro} \\\\ " ) print("\\hline") with open(name_in) as file: for line in file: if "//" in line: continue line = [float(x) for x in line.split()] print( "{0[1]:.2f} & {0[3]:.2f} & {0[0]:.2f} & {0[2]:.2f} \\\\ ". format(line)) print("\\end{tabular}")
def _FitKuriePlot(self, centralList, kurieList, errorList): logger.debug("Fitter private method") kurieGraph = TGraphErrors() for i, KE in enumerate(centralList): kurieGraph.SetPoint(i, KE, kurieList[i]) kurieGraph.SetPointError(i, 0, errorList[i]) self.fitFunction = TF1("kurieFit", "[0]*([1]-x)*(x<[1]) + [2]", centralList[0], centralList[-1], 3) self.fitFunction.SetParameters(kurieList[0] / (18600 - centralList[0]), 18600, kurieList[-1]) rootResults = kurieGraph.Fit(self.fitFunction, 'MERS') resultsDict = { "amplitude": rootResults.Parameter(0), "err_amplitude": rootResults.ParError(0), "endpoint": rootResults.Parameter(1), "err_endpoint": rootResults.ParError(1), "background": rootResults.Parameter(2), "err_background": rootResults.ParError(2), "chi2": rootResults.Chi2(), "ndf": rootResults.Ndf(), "p-value": rootResults.Prob() } logger.debug("Fit done") return resultsDict
class Thickness: def __init__(self, name): self.name = name self.canvas = TCanvas(name, name) self.canvas.SetFillColor(0) self.name_lin = name + "_lin" self.linearize() self.make_graph() def linearize(self): with open(self.name) as file: with open(self.name_lin, "w") as out_file: for line in file: x, i = [float(_) for _ in line.split()] i_err = 1 / sqrt(i) i = log(i) output_line = [x, i, 0, i_err] output_line = " ".join([str(x) for x in output_line]) output_line += "\n" out_file.write(output_line) def make_graph(self): self.graph = TGraphErrors(self.name_lin) self.graph.SetTitle(self.name) self.graph.GetYaxis().SetTitle("log(n_events)") self.graph.GetXaxis().SetTitle("thickness #[]{#mu m}") self.graph.SetMarkerStyle(8) self.graph.Fit("pol1") def draw(self): self.canvas.cd() self.graph.Draw("ap") def save(self): self.canvas.SaveAs(self.name + ".eps")
graph2.SetPointError(i, 0, zerr[i]) c2.cd() graph1.Draw("AP") graph1.GetYaxis().SetRangeUser(52., 62.) graph1.GetYaxis().SetTitle("Mean value") graph1.GetXaxis().SetTitle("Top M(GeV)") graph1.Draw("AP") graph1.SetTitle("Mean value of the histogram vs top mass") graph2.SetLineColor(2) graph2.SetMarkerColor(2) graph2.Draw("P") # fit graph1.Fit("pol1") graph2.Fit("pol1") pol1 = TF1() pol1 = graph1.GetFunction("pol1") pol2 = TF1() pol2 = graph2.GetFunction("pol1") a0 = str(round(pol1.GetParameter(0), 2)) a1 = str(round(pol1.GetParameter(1), 2)) b0 = str(round(pol2.GetParameter(0), 2)) b1 = str(round(pol2.GetParameter(1), 2)) pol1.SetLineColor(1) pol2.SetLineColor(2)
linFunc.SetParameters(0.0, 1.0) for i in range(rawfitresultList.GetSize()): #tageffValVList[i] = rawfitresultList.At(i).getValV(); #tageffErrorList[i] = rawfitresultList.At(i).getError(); #etaAvgValList[i] = etaAvgValVarList.At(i).getValV(); #etaAvgErrorList[i] = etaAvgValVarList.At(i).getError(); tageffVsEtaGraph.SetPoint(i, etaAvgValVarList.At(i).getValV(), rawfitresultList.At(i).getValV()) tageffVsEtaGraph.SetPointError(i, etaAvgValVarList.At(i).getError(), rawfitresultList.At(i).getError()) tageffVsEtaGraph.SetLineColor(currentColor) linFunc.SetLineColor(currentColor) tageffVsEtaGraph.Fit(linFunc) graphHolder.Add(tageffVsEtaGraph) currentColor += 1 #tageffVsEtaGraph = TGraph(rawfitresultList.GetSize(),etaAvgValList,tageffValVList)#,etaAvgErrorList,tageffErrorList); #tageffVsEtaGraph. #ROOT.gSystem.ProcessEvents(); #img.FromPad(theCanvas); os.chdir("..") theCanvas = TCanvas() if (graphHolder.GetListOfGraphs().GetSize() == 1): graphHolder.SetTitle(
def MeasurePSF_in_Sections(data, fitted_line, nsecs = 3, tgraph_filename = '', DEBUG = False, DEBUG_Filenum = 0): #get new coefficients as distance to line uses straight line of form ax + by + c = 0 a = -1. * fitted_line.a b = 1 c = -1. * fitted_line.b histmin = -1 * max(data.shape[0], data.shape[1]) histmax = max(data.shape[0], data.shape[1]) nbins = (histmax - histmin) * 2 hists = [] for i in range(nsecs): hists.append(TH1F('Track section ' + str(i), 'Track section ' + str(i), nbins, histmin, histmax)) for xcoord in range(data.shape[0]): for ycoord in range(data.shape[1]): x = xcoord + 0.5 #adjust for bin centers - NB This is important! y = ycoord + 0.5 #adjust for bin centers - NB This is important! secnum = GetSecNum(data, x, y, nsecs, None) # TODO! value = float(data[xcoord,ycoord]) # if value < 800: non_abs_distance = (a*x + b*y + c) / (a**2 + b**2)**0.5 hists[secnum].Fill(non_abs_distance, value) sigmas, sigma_errors = [], [] for i, hist in enumerate(hists): fitmin, fitmax = GetLastBinAboveX(hist, 0.1) # viewmin, viewmax = GetLastBinAboveX(hist, 0.1) viewmin, viewmax = -2,2 hist.GetXaxis().SetRangeUser(viewmin,viewmax) fit_func = TF1("gaus", "gaus", fitmin, fitmax) fit_func.SetNpx(1000) hist.Fit(fit_func, "MEQ", "", fitmin, fitmax) legend_text = [] sigma = fit_func.GetParameter(2) sigma_error = fit_func.GetParError(2) sigmas.append(abs(15*sigma)) #15 for the 15um per pixel sigma_errors.append(abs(15*sigma_error)) #15 for the 15um per pixel mean = fit_func.GetParameter(15*1) #15 for the 15um per pixel mean_error = fit_func.GetParError(15*1) #10 for the 15um per pixel chisq = fit_func.GetChisquare() NDF = fit_func.GetNDF() try: chisqr_over_NDF = chisq/NDF except: chisqr_over_NDF = -1 # if chisqr_over_NDF > 500 or chisqr_over_NDF <= 1: # return [], [], [] legend_text.append('mean = ' + str(mean) + ' #pm ' + str(mean_error) + " #mum") legend_text.append('#sigma = ' + str(round(sigma,4)) + ' #pm ' + str(round(sigma_error,4)) + " #mum") if DEBUG: #For showing each of n PSF *Histograms* per track c1 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT) hist.Draw("") if legend_text != '': from ROOT import TPaveText textbox = TPaveText(0.0,1.0,0.2,0.8,"NDC") for line in legend_text: textbox.AddText(line) textbox.SetFillColor(0) textbox.Draw("same") c1.SaveAs(OUTPUT_PATH + str(DEBUG_Filenum) + "psf_section_" + str(i) + FILE_TYPE) del c1 from ROOT import TGraphErrors c2 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT) assert nsecs == len(sigmas) == len(sigma_errors) xpoints = GenXPoints(nsecs, 250.) gr = TGraphErrors(nsecs, np.asarray(xpoints,dtype = float), np.asarray(sigmas,dtype = float), np.asarray([0 for i in range(nsecs)],dtype = float), np.asarray(sigma_errors,dtype = float)) #populate graph with data points gr.SetLineColor(2) gr.SetMarkerColor(2) gr.Draw("AP") fit_func = TF1("line","[1]*x + [0]", -1, nsecs+1) fit_func.SetNpx(1000) gr.Fit(fit_func, "MEQ", "") a = fit_func.GetParameter(1) a_error = fit_func.GetParError(1) if DEBUG: if tgraph_filename == '': tgraph_filename = OUTPUT_PATH + 'psf_graph' + '.png' gr.SetTitle("") gr.GetYaxis().SetTitle('PSF #sigma (#mum)') gr.GetXaxis().SetTitle('Av. Si Depth (#mum)') c2.SaveAs(tgraph_filename) # if a_error >= a: # print "Inconclusive muon directionality - skipped track %s"%tgraph_filename # return [],[],[] del c2, hists, gr import gc gc.collect() for j in range(nsecs): if sigma_errors[j] > sigmas[j]: print "bad fit skipped" return [],[],[] if a < 0: sigmas.reverse() sigma_errors.reverse() return xpoints, sigmas, sigma_errors else: return xpoints, sigmas, sigma_errors
class waveLength: """ ***classe waveLength*** legge un file formattato con tre colonne: ordine nonio A nonio B 5 213.54 33.58 4 212.52 32.48 ... ... ... (significa 213°54', 33°58' per il massimo al quinto ordine etc.) opera automaticamente la conversione e la media, per poi disporre in grafico (self.graph). self.fitGraph() esegue l'interpolazione lineare self.showGraph() mostra il grafico self.saveToEps() salve il grafico in formato .eps la lunghezza d'onda con si calcola con self.getWaveLen(), che restituisce una valore ed errore in una lista """ # a = (12.65e-6, 0.05e-6) #passo del reticolo, con errore # measerr = 5.*2/300 #errore (in gradi) stimato sulla misura col nonio def __init__(self, fileName): self.wavelen = [0, 0] self.name = fileName self.nData = 0 self.deg = array('d') self.degerr = array('d') self.ord = array('d') self.pars = array('d', [0, 0]) #fit parameters self.parerrs = array('d', [0, 0]) #fit parameter errors self.fillDeg() #reads data from file self.convertToSine() #centers mean maximum, calculates sines self.initGraph() #initialize graph, set style def fillDeg(self): file = open(self.name) for line in file: o = float(line.split()[0]) n1 = float(line.split()[1]) - 180 n2 = float(line.split()[2]) self.ord.append(o) int1 = floor(n1) int2 = floor(n2) rem1 = 5. * (n1 - int1) / 3 rem2 = 5. * (n2 - int2) / 3 int1 += rem1 int2 += rem2 self.deg.append((int1 + int2) / 2) self.degerr.append(measerr / sqrt(2)) self.nData += 1 file.close() def convertToSine(self): j = self.ord.index(0) #finds central maximum center = self.deg[j] for i in xrange(self.nData): angle = self.deg[i] angle -= center self.deg[i] = angle self.deg[i] = sin(pi * angle / 180) self.degerr[i] = cos(angle) * argerr def initGraph(self): self.graph = TGraphErrors(self.nData, self.ord, self.deg, array('d', [0] * self.nData), self.degerr) self.setGraphStyle() pass def fitGraph(self): line = TF1('line', 'pol1', -5, 5) #funzione lineare per fit self.graph.Fit('line', 'QR') line.GetParameters(self.pars) self.parerrs[0] = line.GetParError(0) self.parerrs[1] = line.GetParError(1) return zip(self.pars, self.parerrs) def getWaveLen(self): self.wavelen[0] = fabs(self.pars[1] * a[0]) * 1e9 self.wavelen[1] = self.wavelen[0] * sqrt( (self.parerrs[1] / self.pars[1])**2 + (a[1] / a[0])**2) print 'lambda %s = %.1f \pm %.1f nm' % (self.name, self.wavelen[0], self.wavelen[1]) return self.wavelen def showGraph(self): c = TCanvas(self.name, self.name) self.graph.Draw('AEP') raw_input('Press ENTER to continue...') def saveToEps(self): if self.pars[1]: c = TCanvas(self.name, self.name) self.graph.Draw('AEP') c.SaveAs(self.name + '.fit.eps') else: pass def printData(self): """test per verificare la corretta lettura dei dati""" for o, d, e in zip(self.ord, self.deg, self.degerr): print '%i \t %.3f \t %.3f' % (o, d, e) def setGraphStyle(self): self.graph.SetMarkerStyle(8) self.graph.GetXaxis().SetTitle("order") self.graph.GetYaxis().SetTitle("sine") self.graph.GetYaxis().SetTitleOffset(1.2) self.graph.GetXaxis().SetTitleSize(0.03) self.graph.GetYaxis().SetTitleSize(0.03) self.graph.GetXaxis().SetLabelSize(0.03) self.graph.GetYaxis().SetLabelSize(0.03) self.graph.GetXaxis().SetDecimals() self.graph.GetYaxis().SetDecimals() # self.graph.SetStats( kFALSE ); self.graph.SetTitle(self.name)
gr2.SetMarkerColor( 2 ) gr2.SetMarkerStyle( 20 ) gr2.SetMarkerSize( makerSize ) gr2.SetTitle( '' ) gr2.GetXaxis().SetTitle( 'E_{particle} (GeV)' ) gr2.GetYaxis().SetTitle( '#sigma_{reco}/E_{reco}' ) gr2.GetXaxis().SetTitleOffset(1.2) gr2.GetYaxis().SetTitleOffset(1.9) gr2.GetYaxis().SetRangeUser(0, 0.1) gPad.SetLeftMargin(0.15) gr2.Draw( 'AP' ) funReso = TF1('detReso', 'sqrt([0]*[0]/x+[1]*[1])', 0., 100.) funReso.SetLineStyle(9) funReso.SetLineColor(4) gr2.Fit('detReso', 'q') a0 = funReso.GetParameter('p0') * 100. a1 = funReso.GetParameter('p1') * 100. resoFormula = '#frac{#sigma}{E} = #frac{' + str( round(a0, 1) ) + '%}{#sqrt{E}} #oplus ' + str( round(a1, 1) ) + '%' txt = TLatex() txt.SetTextSize(0.035) txt.DrawLatex(35., 0.08, resoFormula) c2.Print('resolution.eps') print('Fitting results:', a0, a1)
gLin.GetYaxis().SetRangeUser(-0.9,0.9) # Prepare canvas if not calo_init.args.noLinearity: cRes, padRes, padLin = prepare_double_canvas("resolution","Energy resolution", factor) padRes.cd() else: cRes = prepare_single_canvas("resolution","Energy resolution") cRes.cd() # Fit energy resolution fRes = TF1("res", "sqrt([0]*[0] + pow([1]/sqrt(x),2))",5,600) fRes.SetParName(0,"const") fRes.SetParName(1,"sqrt") fRes.SetLineColor(colour) fitResult = gRes.Fit(fRes, 'S') # Draw graph and all labels gRes.Draw("ape") if calo_init.args.axisMax: gRes.GetYaxis().SetRangeUser(0, calo_init.args.axisMax) formula = "#frac{#sigma_{E}}{E} = " + str(round(fitResult.Get().Parameter(0)*100,2))+"% #oplus #frac{"+str(round(fitResult.Get().Parameter(1)*100,2))+"%}{#sqrt{E}}" if not calo_init.args.noLinearity: draw_text([formula], [0.55,0.8,0.95,0.95], colour, 0).SetTextSize(0.05) else: draw_text([formula], [0.55,0.7,0.95,0.85], colour, 0).SetTextSize(0.05) if calo_init.args.technical: constString = "const: "+str(round(fitResult.Get().Parameter(0),4))+" #pm "+str(round(fitResult.Get().Error(0),4)) samplingString = "sampl: "+str(round(fitResult.Get().Parameter(1),4))+" #pm "+str(round(fitResult.Get().Error(1),4)) draw_text([constString, samplingString], [0.55,0.68,0.88,0.76], colour+1, 0).SetTextSize(0.05) draw_text(["energy resolution"], [0.2,0.88, 0.45,0.98], 1, 0).SetTextSize(0.05)
legendPhi.SetTextSize(0.05) legendPhi.SetTextFont(42) canvProfile.Update() canvPhi.Update() # save canvases filled for each energy and eta if calo_init.output(ifile): canvPhi.SaveAs( calo_init.output(ifile) + "_previewPhi_eta" + str(eta) + ".png") else: canvPhi.SaveAs("upstremCorrection_previewPhi_eta" + str(eta) + "_" + str(layer * width) + "cm.png") # fit energy-dependent parameters fitP0 = TF1("fitP0", "pol1", 0, energy) par0result = param0.Fit(fitP0, "SR") fitP1 = TF1("fitP1", "[0]+[1]/sqrt(x)", 0, energy) par1result = param1.Fit(fitP1, "SR") cEnergy = prepare_divided_canvas( 'upstreamParams_eta' + str(eta), 'Energy upstream E=p0+p1E for eta=' + str(eta), 2) cEnergy.cd(1) prepare_graph(param0, "param0", 'P0 (E);' + axisName + '; parameter P0') param0.Draw("aep") param0.GetYaxis().SetRangeUser(param0.GetYaxis().GetXmin(), param0.GetYaxis().GetXmax() * 1.2) cEnergy.cd(2) prepare_graph(param1, "param1", 'P1 (E);' + axisName + '; parameter P1') param1.Draw("aep") param1.GetYaxis().SetRangeUser(param1.GetYaxis().GetXmin(), param1.GetYaxis().GetXmax() * 1.2)
in_file = "spettro.preamp.dat" out_file = "preamp.grafici.out" with open(out_file, "w") as output: with open(in_file) as file: for line, tuple in zip(file, means): pot, tens, _ = [float(x) for x in line.split()] media, err_media = tuple new_line = [pot, tens, media, 0, 0.03 * tens, err_media] new_line = [str(x) for x in new_line] new_line = " ".join(new_line) + "\n" output.write(new_line) can_pot = TCanvas(out_file, out_file) gr_pot = TGraphErrors(out_file, "%lg %*lg %lg %lg %*g %lg") gr_pot.SetMarkerStyle(8) gr_pot.Draw("ap") f_pot = TF1("retta", "pol1") gr_pot.Fit("retta") print(gr_pot.GetCorrelationFactor()) print(f_pot.GetChisquare(), "/", f_pot.GetNDF()) can_tens = TCanvas(out_file + "2", out_file) gr_tens = TGraphErrors(out_file, "%*lg %lg %lg %*lg %g %lg") gr_tens.SetMarkerStyle(8) gr_tens.Draw("ap") f_tens = TF1("retta", "pol1") gr_tens.Fit("retta") print(f_tens.GetChisquare(), "/", f_tens.GetNDF()) input()
class _Efficiency(object): def __init__(self, num_pars=0, pars=None, norm=True): pars = pars or [] self._numPars = num_pars self.parameter = pars self.fCov = [[None for j in range(self._numPars)] for i in range(self._numPars) ] # Simple matrix replacement self._dEff_dP = list() self.TGraph = TGraphErrors() # Normalization factors self._doNorm = norm self.norm = 1.0 self.TF1.FixParameter(0, self.norm) # Normalization self.TF1.SetRange(0, 10000) # Default range for efficiency function self._fitInput = Pairs(lambda x: ufloat(x, 0)) # if self.parameter: # Parameters were given # map(lambda i: self.TF1.SetParameter(i + 1, self.parameter[i]), range(1, len(pars))) # Set initial parameters # else: # self.parameter = [None for i in range(1, self._numPars + 1)] # self.TF1.SetParName(0, "N") # Normalization for i in range(0, num_pars): self._dEff_dP.append(None) if num_pars <= len(string.ascii_lowercase): self.TF1.SetParName(i + 1, string.ascii_lowercase[i]) def _getParameter(self): """ Get parameter of efficiency function """ pars = list() for i in range(self._numPars): pars.append(self.TF1.GetParameter(i)) return pars def _setParameter(self, pars): """ Set parameter for efficiency function """ for i in range(self._numPars): try: self.TF1.SetParameter(i, pars[i]) except IndexError: self.TF1.SetParameter(i, 0) parameter = property(_getParameter, _setParameter) def __call__(self, E): value = self.value(E) try: error = self.error(E) except TypeError: error = None return ufloat(value, error) def _set_fitInput(self, fitPairs): self._fitInput = fitPairs for i in range(len(self._fitInput)): p = self._fitInput[i] try: e_nominal_value = p[0].nominal_value e_std_dev = p[0].std_dev except AttributeError: e_nominal_value = float(p[0]) e_std_dev = 0. try: eff_nominal_value = p[1].nominal_value eff_std_dev = p[1].std_dev except AttributeError: eff_nominal_value = float(p[1]) eff_std_dev = 0. self.TGraph.SetPoint(i, e_nominal_value, eff_nominal_value) self.TGraph.SetPointError(i, e_std_dev, eff_std_dev) def _get_fitInput(self): return self._fitInput fitInput = property(_get_fitInput, _set_fitInput) def fit(self, fitPairs=None, quiet=True): """ Fit efficiency curve to values given by 'fitPairs' which should be a list of energy<->efficiency pairs. (See hdtv.util.Pairs()) 'energies' and 'efficiencies' may be a list of ufloats """ # TODO: Unify this with the energy calibration fitter if fitPairs is not None: #self.fitInput = fitPairs self._fitInput = fitPairs E = array.array("d") delta_E = array.array("d") eff = array.array("d") delta_eff = array.array("d") EN = array.array("d") effN = array.array("d") # map(energies.append(self.fitInput[0]), self.fitInput) # map(efficiencies.append(self.fitInput[1]), self.fitInput) hasXerrors = False # Convert energies to array needed by ROOT try: list(map(lambda x: E.append(x[0].nominal_value), self._fitInput)) list(map(lambda x: delta_E.append(x[0].std_dev), self._fitInput)) list(map(lambda x: EN.append(0.0), self._fitInput)) hasXerrors = True except AttributeError: # energies does not seem to be ufloat list list(map(lambda x: E.append(x[0]), self._fitInput)) list(map(lambda x: delta_E.append(0.0), self._fitInput)) # Convert efficiencies to array needed by ROOT try: list(map(lambda x: eff.append(x[1].nominal_value), self._fitInput)) list(map(lambda x: delta_eff.append(x[1].std_dev), self._fitInput)) list(map(lambda x: effN.append(0.0), self._fitInput)) except AttributeError: # energies does not seem to be ufloat list list(map(lambda x: eff.append(x[1]), self._fitInput)) list(map(lambda x: delta_eff.append(0.0), self._fitInput)) # if fit has errors we first fit without errors to get good initial values # if hasXerrors == True: # print "Fit parameter without errors included:" #self.TGraphWithoutErrors = TGraphErrors(len(E), E, eff, EN, effN) #fitWithoutErrors = self.TGraphWithoutErrors.Fit(self.id, "SF") hdtv.ui.msg("Fit parameter with errors included:") # Preliminary normalization # if self._doNorm: # self.norm = 1 / max(efficiencies) # for i in range(len(eff)): # eff[i] *= self.norm # delta_eff[i] *= self.norm #self.TF1.SetRange(0, max(E) * 1.1) # self.TF1.SetParameter(0, 1) # Unset normalization for fitting self.TGraph = TGraphErrors(len(E), E, eff, delta_E, delta_eff) # Do the fit fitopts = "0" # Do not plot if hasXerrors: # We must use the iterative fitter (minuit) to take x errors # into account. fitopts += "F" hdtv.ui.info( "switching to non-linear fitter (minuit) for x error weighting" ) if quiet: fitopts += "Q" fitopts += "S" # Additional fitinfo returned needed for ROOT5.26 workaround below fitreturn = self.TGraph.Fit(self.id, fitopts) try: # Workaround for checking the fitstatus in ROOT 5.26 (TFitResultPtr # does not cast properly to int) fitstatus = fitreturn.Get().Status() except AttributeError: # This is for ROOT <= 5.24, where fit returns an int fitstatus = int(fitreturn) if fitstatus != 0: # raise RuntimeError, "Fit failed" hdtv.ui.msg("Fit failed") # # Final normalization # if self._doNorm: # self.normalize() # Get parameter for i in range(self._numPars): self.parameter[i] = self.TF1.GetParameter(i) # Get covariance matrix tvf = TVirtualFitter.GetFitter() ## cov = tvf.GetCovarianceMatrix() for i in range(0, self._numPars): for j in range(0, self._numPars): self.fCov[i][j] = tvf.GetCovarianceMatrixElement(i, j) ## self.fCov[i][j] = cov[i * self._numPars + j] return self.parameter def normalize(self): # Normalize the efficiency funtion try: self.norm = 1.0 / self.TF1.GetMaximum(0.0, 0.0) except ZeroDivisionError: self.norm = 1.0 self.TF1.SetParameter(0, self.norm) normfunc = TF2("norm_" + hex(id(self)), "[0]*y") normfunc.SetParameter(0, self.norm) self.TGraph.Apply(normfunc) def value(self, E): try: value = E.nominal_value except AttributeError: value = E return self.TF1.Eval(value) def error(self, E): """ Calculate error using the covariance matrix via: delta_Eff = sqrt((dEff_dP[0], dEff_dP[1], ... dEff_dP[num_pars]) x cov x (dEff_dP[0], dEff_dP[1], ... dEff_dP[num_pars])) """ try: value = E.nominal_value except AttributeError: value = E if not self.fCov or (len(self.fCov) != self._numPars): raise ValueError("Incorrect size of covariance matrix") res = 0.0 # Do matrix multiplication for i in range(0, self._numPars): tmp = 0.0 for j in range(0, self._numPars): tmp += (self._dEff_dP[j](value, self.parameter) * self.fCov[i][j]) res += (self._dEff_dP[i](value, self.parameter) * tmp) return sqrt(res) def loadPar(self, parfile): """ Read parameter from file """ vals = [] file = TxtFile(parfile) file.read() for line in file.lines: vals.append(float(line)) if len(vals) != self._numPars: raise RuntimeError("Incorrect number of parameters found in file") self.parameter = vals if self._doNorm: self.normalize() def loadCov(self, covfile): """ Load covariance matrix from file """ vals = [] file = TxtFile(covfile) file.read() for line in file.lines: val_row = [float(s) for s in line.split()] if len(val_row) != self._numPars: raise RuntimeError("Incorrect format of parameter error file") vals.append(val_row) if len(vals) != self._numPars: raise RuntimeError("Incorrect format of parameter error file") self.fCov = vals def load(self, parfile, covfile=None): """ Read parameter and covariance matrix from file """ self.loadPar(parfile) if covfile: self.loadCov(covfile) def savePar(self, parfile): """ Save parameter to file """ file = TxtFile(parfile, "w") for p in self.parameter: file.lines.append(str(p)) file.write() def saveCov(self, covfile): """ Save covariance matrix to file """ file = TxtFile(covfile, "w") for i in range(0, self._numPars): line = "" for j in range(0, self._numPars): line += str(self.fCov[i][j]) + " " file.lines.append(line) file.write() def save(self, parfile, covfile=None): """ Save parameter and covariance matrix to files """ # Write paramter self.savePar(parfile) # Write covariance matrix if covfile is not None: self.saveCov(covfile)
#define some data points ... ax = array('f',( -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0) ) ay = array('f', (5.0935, 2.1777, 0.2089, -2.3949, -2.4457, -3.0430, -2.2731, -2.0706, -1.6231, -2.5605, -0.7703, -0.3055, 1.6817, 1.8728, 3.6586, 3.2353, 4.2520, 5.2550, 3.8766, 4.2890 ) ) ey=np.array(len(data_x)*[0.5],dtype=np.float) ex=np.array(len(data_y)*[0],dtype=np.float) # ... and pass to TGraphErros object gr=TGraphErrors(nPoints,data_x,data_y,ex,ey) gr.SetTitle("TGraphErrors mit Fit") gr.Draw("AP"); Pol=["pol1","pol2","pol3","pol4","pol5","pol6","pol7"] # Polifit for i in range(len(polynom)): gr.Fit(polynom[i],"V") c1.Update() gr.Draw('AP') fitrp = TVirtualFitter.GetFitter() nPar = fitrp.GetNumberTotalParameters() covmat = TMatrixD(nPar, nPar,fitrp.GetCovarianceMatrix()) print('The Covariance Matrix is: ') covmat.Print() cormat = TMatrixD(covmat) for i in range(nPar): for j in range(nPar): cormat[i][j] = cormat[i][j] / (np.sqrt(covmat[i][i]) * np.sqrt(covmat[j][j])) print('The Correlation Matrix is: ') cormat.Print() raw_input('Press <ret> to continue -> ')
def signal(category): interPar = True n = len(genPoints) cColor = color[category] if category in color else 4 nBtag = category.count('b') isAH = False #relict from using Alberto's more complex script if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR): os.makedirs(PLOTDIR + "MC_signal_" + YEAR) #*******************************************************# # # # Variables and selections # # # #*******************************************************# X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV") j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV") jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.) jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.) jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.) fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.) fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.) jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.) jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.) jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.) jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.) jnmuons_loose_1 = RooRealVar("jnmuons_loose_1", "jnmuons_loose_1", -1., 8.) jnmuons_loose_2 = RooRealVar("jnmuons_loose_2", "jnmuons_loose_2", -1., 8.) nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.) nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.) HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.) HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.) HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.) HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.) HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.) HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.) HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.) HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.) #HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. ) #HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. ) #HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. ) #HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. ) #HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. ) #HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. ) weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9) # Define the RooArgSet which will include all the variables defined before # there is a maximum of 9 variables in the declaration, so the others need to be added with 'add' variables = RooArgSet(X_mass) variables.add( RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1, fatjetmass_2, jnmuons_1, jnmuons_2, weight)) variables.add( RooArgSet(nmuons, nelectrons, jid_1, jid_2, jnmuons_loose_1, jnmuons_loose_2)) variables.add( RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID, HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550, HLT_CaloJet550_NoJetID, HLT_PFHT1050)) #variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71)) X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax()) X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax()) if VARBINS: binsXmass = RooBinning(len(abins) - 1, abins) X_mass.setBinning(binsXmass) plot_binning = RooBinning( int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(), X_mass.getMax()) else: X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10)) binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(), X_mass.getMax()) plot_binning = binsXmass X_mass.setBinning(plot_binning, "PLOT") #X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10)) #binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax()) #X_mass.setBinning(binsXmass, "PLOT") massArg = RooArgSet(X_mass) # Cuts if BTAGGING == 'semimedium': SRcut = aliasSM[category] #SRcut = aliasSM[category+"_vetoAK8"] else: SRcut = alias[category].format(WP=working_points[BTAGGING]) #SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING]) if ADDSELECTION: SRcut += SELECTIONS[options.selection] print " Cut:\t", SRcut #*******************************************************# # # # Signal fits # # # #*******************************************************# treeSign = {} setSignal = {} vmean = {} vsigma = {} valpha1 = {} vslope1 = {} valpha2 = {} vslope2 = {} smean = {} ssigma = {} salpha1 = {} sslope1 = {} salpha2 = {} sslope2 = {} sbrwig = {} signal = {} signalExt = {} signalYield = {} signalIntegral = {} signalNorm = {} signalXS = {} frSignal = {} frSignal1 = {} frSignal2 = {} frSignal3 = {} # Signal shape uncertainties (common amongst all mass points) xmean_jes = RooRealVar( "CMS" + YEAR + "_sig_" + category + "_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.02, -1., 1.) #0.001 smean_jes = RooRealVar( "CMS" + YEAR + "_sig_" + category + "_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10) xsigma_jer = RooRealVar( "CMS" + YEAR + "_sig_" + category + "_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.10, -1., 1.) ssigma_jer = RooRealVar( "CMS" + YEAR + "_sig_" + category + "_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10) xmean_jes.setConstant(True) smean_jes.setConstant(True) xsigma_jer.setConstant(True) ssigma_jer.setConstant(True) for m in massPoints: signalMass = "%s_M%d" % (stype, m) signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m) sampleName = "ZpBB_M{}".format(m) signalColor = sample[sampleName][ 'linecolor'] if signalName in sample else 1 # define the signal PDF vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m * 0.96, m * 1.05) smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)", RooArgList(vmean[m], xmean_jes, smean_jes)) vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m * 0.0233, m * 0.019, m * 0.025) ssigma[m] = RooFormulaVar( signalName + "_sigma", "@0*(1+@1*@2)", RooArgList(vsigma[m], xsigma_jer, ssigma_jer)) valpha1[m] = RooRealVar( signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28 ) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0", RooArgList(valpha1[m])) #vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 13., 10., 20.) # slope of the power tail sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0", RooArgList(vslope1[m])) valpha2[m] = RooRealVar(signalName + "_valpha2", "Crystal Ball alpha 2", 1.) valpha2[m].setConstant(True) salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0", RooArgList(valpha2[m])) #vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail ## FIXME test FIXME vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * ( -1.65059568762e-06) + m * m * m * (1.25128996309e-10) vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", vslope2_estimation, vslope2_estimation * 0.9, vslope2_estimation * 1.1) # slope of the higher power tail ## FIXME end FIXME sslope2[m] = RooFormulaVar( signalName + "_slope2", "@0", RooArgList(vslope2[m])) # slope of the higher power tail signal[m] = RooDoubleCrystalBall(signalName, "m_{%s'} = %d GeV" % ('X', m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m], salpha2[m], sslope2[m]) # extend the PDF with the yield to perform an extended likelihood fit signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50, 0., 1.e15) signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0., 1.e15) signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15) signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f", signal[m], signalYield[m]) # ---------- if there is no simulated signal, skip this mass point ---------- if m in genPoints: if VERBOSE: print " - Mass point", m # define the dataset for the signal applying the SR cuts treeSign[m] = TChain("tree") if YEAR == 'run2': pd = sample[sampleName]['files'] if len(pd) > 3: print "multiple files given than years for a single masspoint:", pd sys.exit() for ss in pd: if not '2016' in ss and not '2017' in ss and not '2018' in ss: print "unknown year given in:", ss sys.exit() else: pd = [x for x in sample[sampleName]['files'] if YEAR in x] if len(pd) > 1: print "multiple files given for a single masspoint/year:", pd sys.exit() for ss in pd: if options.unskimmed: j = 0 while True: if os.path.exists(NTUPLEDIR + ss + "/" + ss + "_flatTuple_{}.root".format(j)): treeSign[m].Add(NTUPLEDIR + ss + "/" + ss + "_flatTuple_{}.root".format(j)) j += 1 else: print "found {} files for sample:".format(j), ss break else: if os.path.exists(NTUPLEDIR + ss + ".root"): treeSign[m].Add(NTUPLEDIR + ss + ".root") else: print "found no file for sample:", ss if treeSign[m].GetEntries() <= 0.: if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..." signalNorm[m].setVal(-1) vmean[m].setConstant(True) vsigma[m].setConstant(True) salpha1[m].setConstant(True) sslope1[m].setConstant(True) salpha2[m].setConstant(True) sslope2[m].setConstant(True) signalNorm[m].setConstant(True) signalXS[m].setConstant(True) continue #setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m])) setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m])) if VERBOSE: print " - Dataset with", setSignal[m].sumEntries( ), "events loaded" # FIT entries = setSignal[m].sumEntries() if entries < 0. or entries != entries: entries = 0 signalYield[m].setVal(entries) # Instead of eventWeightLumi #signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) ) if treeSign[m].GetEntries(SRcut) > 5: if VERBOSE: print " - Running fit" frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1)) if VERBOSE: print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[ m].Print(), "\n", "*" * 80 if VERBOSE: frSignal[m].correlationMatrix().Print() drawPlot(signalMass + "_" + category, stype + category, X_mass, signal[m], setSignal[m], frSignal[m]) else: print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist" # Remove HVT cross sections #xs = getCrossSection(stype, channel, m) xs = 1. signalXS[m].setVal(xs * 1000.) signalIntegral[m] = signalExt[m].createIntegral( massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range")) boundaryFactor = signalIntegral[m].getVal() if boundaryFactor < 0. or boundaryFactor != boundaryFactor: boundaryFactor = 0 if VERBOSE: print " - Fit normalization vs integral:", signalYield[ m].getVal(), "/", boundaryFactor, "events" signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal() ) # here normalize to sigma(X) x Br = 1 [fb] vmean[m].setConstant(True) vsigma[m].setConstant(True) valpha1[m].setConstant(True) vslope1[m].setConstant(True) valpha2[m].setConstant(True) vslope2[m].setConstant(True) signalNorm[m].setConstant(True) signalXS[m].setConstant(True) #*******************************************************# # # # Signal interpolation # # # #*******************************************************# ### FIXME FIXME just for a test FIXME FIXME #print #print #print "slope2 fit results:" #print #y_vals = [] #for m in genPoints: # y_vals.append(vslope2[m].getVal()) #print "m =", genPoints #print "y =", y_vals #sys.exit() ### FIXME FIXME test end FIXME FIXME # ====== CONTROL PLOT ====== color_scheme = [ 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633 ] c_signal = TCanvas("c_signal", "c_signal", 800, 600) c_signal.cd() frame_signal = X_mass.frame() for j, m in enumerate(genPoints): if m in signalExt.keys(): #print "color:",(j%9)+1 #print "signalNorm[m].getVal() =", signalNorm[m].getVal() #print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent signal[m].plotOn( frame_signal, RooFit.LineColor(color_scheme[j]), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range")) frame_signal.GetXaxis().SetRangeUser(0, 10000) frame_signal.Draw() drawCMS(-1, "Simulation Preliminary", year=YEAR) #drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True) #drawCMS(-1, "", year=YEAR, suppressCMS=True) drawAnalysis(category) drawRegion(category) c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_Signal.pdf") c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_Signal.png") #if VERBOSE: raw_input("Press Enter to continue...") # ====== CONTROL PLOT ====== # Normalization gnorm = TGraphErrors() gnorm.SetTitle(";m_{X} (GeV);integral (GeV)") gnorm.SetMarkerStyle(20) gnorm.SetMarkerColor(1) gnorm.SetMaximum(0) inorm = TGraphErrors() inorm.SetMarkerStyle(24) fnorm = TF1("fnorm", "pol9", 700, 3000) fnorm.SetLineColor(920) fnorm.SetLineStyle(7) fnorm.SetFillColor(2) fnorm.SetLineColor(cColor) # Mean gmean = TGraphErrors() gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)") gmean.SetMarkerStyle(20) gmean.SetMarkerColor(cColor) gmean.SetLineColor(cColor) imean = TGraphErrors() imean.SetMarkerStyle(24) fmean = TF1("fmean", "pol1", 0, 10000) fmean.SetLineColor(2) fmean.SetFillColor(2) # Width gsigma = TGraphErrors() gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)") gsigma.SetMarkerStyle(20) gsigma.SetMarkerColor(cColor) gsigma.SetLineColor(cColor) isigma = TGraphErrors() isigma.SetMarkerStyle(24) fsigma = TF1("fsigma", "pol1", 0, 10000) fsigma.SetLineColor(2) fsigma.SetFillColor(2) # Alpha1 galpha1 = TGraphErrors() galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha") galpha1.SetMarkerStyle(20) galpha1.SetMarkerColor(cColor) galpha1.SetLineColor(cColor) ialpha1 = TGraphErrors() ialpha1.SetMarkerStyle(24) falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0 falpha1.SetLineColor(2) falpha1.SetFillColor(2) # Slope1 gslope1 = TGraphErrors() gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)") gslope1.SetMarkerStyle(20) gslope1.SetMarkerColor(cColor) gslope1.SetLineColor(cColor) islope1 = TGraphErrors() islope1.SetMarkerStyle(24) fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0 fslope1.SetLineColor(2) fslope1.SetFillColor(2) # Alpha2 galpha2 = TGraphErrors() galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha") galpha2.SetMarkerStyle(20) galpha2.SetMarkerColor(cColor) galpha2.SetLineColor(cColor) ialpha2 = TGraphErrors() ialpha2.SetMarkerStyle(24) falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0 falpha2.SetLineColor(2) falpha2.SetFillColor(2) # Slope2 gslope2 = TGraphErrors() gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)") gslope2.SetMarkerStyle(20) gslope2.SetMarkerColor(cColor) gslope2.SetLineColor(cColor) islope2 = TGraphErrors() islope2.SetMarkerStyle(24) fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0 fslope2.SetLineColor(2) fslope2.SetFillColor(2) n = 0 for i, m in enumerate(genPoints): if not m in signalNorm.keys(): continue if signalNorm[m].getVal() < 1.e-6: continue if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal()) gnorm.SetPoint(n, m, signalNorm[m].getVal()) #gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast())) gmean.SetPoint(n, m, vmean[m].getVal()) gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal() * 0.02)) gsigma.SetPoint(n, m, vsigma[m].getVal()) gsigma.SetPointError( n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05)) galpha1.SetPoint(n, m, valpha1[m].getVal()) galpha1.SetPointError( n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10)) gslope1.SetPoint(n, m, vslope1[m].getVal()) gslope1.SetPointError( n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10)) galpha2.SetPoint(n, m, salpha2[m].getVal()) galpha2.SetPointError( n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10)) gslope2.SetPoint(n, m, sslope2[m].getVal()) gslope2.SetPointError( n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10)) #tmpVar = w.var(var+"_"+signalString) #print m, tmpVar.getVal(), tmpVar.getError() n = n + 1 gmean.Fit(fmean, "Q0", "SAME") gsigma.Fit(fsigma, "Q0", "SAME") galpha1.Fit(falpha1, "Q0", "SAME") gslope1.Fit(fslope1, "Q0", "SAME") galpha2.Fit(falpha2, "Q0", "SAME") gslope2.Fit(fslope2, "Q0", "SAME") # gnorm.Fit(fnorm, "Q0", "", 700, 5000) #for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S")) #gnorm.Fit(fnorm, "Q", "SAME", 700, 6000) gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently for m in massPoints: if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.) # Interpolation method syield = gnorm.Eval(m) spline = gnorm.Eval(m, 0, "S") sfunct = fnorm.Eval(m) #delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield)) delta = abs(1. - spline / sfunct) if sfunct > 0 else 0 syield = spline if interPar: #jmean = gmean.Eval(m) #jsigma = gsigma.Eval(m) #jalpha1 = galpha1.Eval(m) #jslope1 = gslope1.Eval(m) #jalpha2 = galpha2.Eval(m) #jslope2 = gslope2.Eval(m) jmean = gmean.Eval(m, 0, "S") jsigma = gsigma.Eval(m, 0, "S") jalpha1 = galpha1.Eval(m, 0, "S") jslope1 = gslope1.Eval(m, 0, "S") jalpha2 = galpha2.Eval(m, 0, "S") jslope2 = gslope2.Eval(m, 0, "S") else: jmean = fmean.GetParameter( 0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m jsigma = fsigma.GetParameter(0) + fsigma.GetParameter( 1) * m + fsigma.GetParameter(2) * m * m jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter( 1) * m + falpha1.GetParameter(2) * m * m jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter( 1) * m + fslope1.GetParameter(2) * m * m jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter( 1) * m + falpha2.GetParameter(2) * m * m jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter( 1) * m + fslope2.GetParameter(2) * m * m inorm.SetPoint(inorm.GetN(), m, syield) signalNorm[m].setVal(max(0., syield)) imean.SetPoint(imean.GetN(), m, jmean) if jmean > 0: vmean[m].setVal(jmean) isigma.SetPoint(isigma.GetN(), m, jsigma) if jsigma > 0: vsigma[m].setVal(jsigma) ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1) if not jalpha1 == 0: valpha1[m].setVal(jalpha1) islope1.SetPoint(islope1.GetN(), m, jslope1) if jslope1 > 0: vslope1[m].setVal(jslope1) ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2) if not jalpha2 == 0: valpha2[m].setVal(jalpha2) islope2.SetPoint(islope2.GetN(), m, jslope2) if jslope2 > 0: vslope2[m].setVal(jslope2) #### newly introduced, not yet sure if helpful: vmean[m].removeError() vsigma[m].removeError() valpha1[m].removeError() valpha2[m].removeError() vslope1[m].removeError() vslope2[m].removeError() #signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit #c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200 #c1.Divide(2, 3) c1 = TCanvas("c1", "Crystal Ball", 1800, 800) c1.Divide(3, 2) c1.cd(1) gmean.SetMinimum(0.) gmean.Draw("APL") imean.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME c1.cd(2) gsigma.SetMinimum(0.) gsigma.Draw("APL") isigma.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME c1.cd(3) galpha1.Draw("APL") ialpha1.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2 c1.cd(4) gslope1.Draw("APL") islope1.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME gslope1.GetYaxis().SetRangeUser(0., 150.) #adjusted upper limit from 125 to 60 if True: #isAH: c1.cd(5) galpha2.Draw("APL") ialpha2.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME galpha2.GetYaxis().SetRangeUser(0., 2.) c1.cd(6) gslope2.Draw("APL") islope2.Draw("P, SAME") drawRegion(category) drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME gslope2.GetYaxis().SetRangeUser(0., 20.) c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_SignalShape.pdf") c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_SignalShape.png") c2 = TCanvas("c2", "Signal Efficiency", 800, 600) c2.cd(1) gnorm.SetMarkerColor(cColor) gnorm.SetMarkerStyle(20) gnorm.SetLineColor(cColor) gnorm.SetLineWidth(2) gnorm.Draw("APL") inorm.Draw("P, SAME") gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100) gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25) drawCMS(-1, "Simulation Preliminary", year=YEAR) #drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True) #drawCMS(-1, "", year=YEAR, suppressCMS=True) drawAnalysis(category) drawRegion(category) c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_SignalNorm.pdf") c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category + "_SignalNorm.png") #*******************************************************# # # # Generate workspace # # # #*******************************************************# # create workspace w = RooWorkspace("Zprime_" + YEAR, "workspace") for m in massPoints: getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName())) getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName())) getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName())) w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True) print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % ( YEAR, category), "saved successfully"
def signal(channel, stype): if 'VBF' in channel: stype = 'XZHVBF' else: stype = 'XZH' # HVT model if stype.startswith('X'): signalType = 'HVT' genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000] massPoints = [x for x in range(800, 5000+1, 100)] interPar = True else: print "Signal type", stype, "not recognized" return n = len(genPoints) category = channel cColor = color[category] if category in color else 1 nElec = channel.count('e') nMuon = channel.count('m') nLept = nElec + nMuon nBtag = channel.count('b') if '0b' in channel: nBtag = 0 X_name = "VH_mass" if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category) #*******************************************************# # # # Variables and selections # # # #*******************************************************# X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV") J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV") V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV") CSV1 = RooRealVar( "H_csv1", "", -999., 2. ) CSV2 = RooRealVar( "H_csv2", "", -999., 2. ) DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. ) DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. ) H_ntag = RooRealVar( "H_ntag", "", -9., 9. ) H_dbt = RooRealVar( "H_dbt", "", -2., 2. ) H_tau21 = RooRealVar( "H_tau21", "", -9., 2. ) H_eta = RooRealVar( "H_eta", "", -9., 9. ) H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. ) MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. ) H_chf = RooRealVar( "H_chf", "", -1., 2. ) MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. ) DPhi = RooRealVar( "DPhi", "", -1., 99. ) DEta = RooRealVar( "DEta", "", -1., 99. ) Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. ) Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. ) nTaus = RooRealVar( "nTaus", "", -1., 99. ) Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 ) V_pt = RooRealVar( "V_pt", "", -1., 1.e6 ) H_pt = RooRealVar( "H_pt", "", -1., 1.e6 ) VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. ) isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. ) isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. ) isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. ) isHtobb = RooRealVar( "isHtobb", "", 0., 2. ) isVBF = RooRealVar( "isVBF", "", 0., 2. ) isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. ) weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 ) Xmin = XBINMIN Xmax = XBINMAX # Define the RooArgSet which will include all the variables defined before # there is a maximum of 9 variables in the declaration, so the others need to be added with 'add' variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21) variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt)) variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt)) variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight)) variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta)) #X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax()) X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax()) X_mass.setRange("X_integration_range", Xmin, Xmax) X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100)) binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax()) X_mass.setBinning(binsXmass, "PLOT") massArg = RooArgSet(X_mass) # Cuts SRcut = selection[category]+selection['SR'] print " Cut:\t", SRcut #*******************************************************# # # # Signal fits # # # #*******************************************************# treeSign = {} setSignal = {} vmean = {} vsigma = {} valpha1 = {} vslope1 = {} smean = {} ssigma = {} salpha1 = {} sslope1 = {} salpha2 = {} sslope2 = {} a1 = {} a2 = {} sbrwig = {} signal = {} signalExt = {} signalYield = {} signalIntegral = {} signalNorm = {} signalXS = {} frSignal = {} frSignal1 = {} frSignal2 = {} frSignal3 = {} # Signal shape uncertainties (common amongst all mass points) xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.) smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10) xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001 smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10) xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.) smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10) xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.) smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10) xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.) ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10) xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001 ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10) xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.) ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10) xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.) ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10) xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.) ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10) xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.) salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10) xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.) sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10) xmean_fit.setConstant(True) smean_fit.setConstant(True) xmean_jes.setConstant(True) smean_jes.setConstant(True) xmean_e.setConstant(True) smean_e.setConstant(True) xmean_m.setConstant(True) smean_m.setConstant(True) xsigma_fit.setConstant(True) ssigma_fit.setConstant(True) xsigma_jes.setConstant(True) ssigma_jes.setConstant(True) xsigma_jer.setConstant(True) ssigma_jer.setConstant(True) xsigma_e.setConstant(True) ssigma_e.setConstant(True) xsigma_m.setConstant(True) ssigma_m.setConstant(True) xalpha1_fit.setConstant(True) salpha1_fit.setConstant(True) xslope1_fit.setConstant(True) sslope1_fit.setConstant(True) # the alpha method is now done. for m in massPoints: signalString = "M%d" % m signalMass = "%s_M%d" % (stype, m) signalName = "%s%s_M%d" % (stype, category, m) signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1 # define the signal PDF vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25) smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit)) vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4) sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer) sigmaList.add(RooArgList(xsigma_fit, ssigma_fit)) ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList) valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit)) vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60 sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit)) salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail #define polynomial #a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m) a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m) a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.) #if channel=='nnbbVBF' or channel=='nn0bVBF': # signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m])) #else: # signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel # extend the PDF with the yield to perform an extended likelihood fit signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6) signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6) signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6) signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m]) vslope1[m].setMax(50.) vslope1[m].setVal(20.) #valpha1[m].setVal(1.0) #valpha1[m].setConstant(True) if 'bb' in channel and 'VBF' not in channel: if 'nn' in channel: valpha1[m].setVal(0.5) elif '0b' in channel and 'VBF' not in channel: if 'nn' in channel: if m==800: valpha1[m].setVal(2.) vsigma[m].setVal(m*0.04) elif 'ee' in channel: valpha1[m].setVal(0.8) if m==800: #valpha1[m].setVal(1.2) valpha1[m].setVal(2.5) vslope1[m].setVal(50.) elif 'mm' in channel: if m==800: valpha1[m].setVal(2.) vsigma[m].setVal(m*0.03) else: vmean[m].setVal(m*0.9) vsigma[m].setVal(m*0.08) elif 'bb' in channel and 'VBF' in channel: if 'nn' in channel: if m!=1800: vmean[m].setVal(m*0.8) vsigma[m].setVal(m*0.08) valpha1[m].setMin(1.) elif 'ee' in channel: valpha1[m].setVal(0.7) elif 'mm' in channel: if m==800: vslope1[m].setVal(50.) valpha1[m].setVal(0.7) elif '0b' in channel and 'VBF' in channel: if 'nn' in channel: valpha1[m].setVal(3.) vmean[m].setVal(m*0.8) vsigma[m].setVal(m*0.08) valpha1[m].setMin(1.) elif 'ee' in channel: if m<2500: valpha1[m].setVal(2.) if m==800: vsigma[m].setVal(m*0.05) elif m==1000: vsigma[m].setVal(m*0.03) elif m>1000 and m<1800: vsigma[m].setVal(m*0.04) elif 'mm' in channel: if m<2000: valpha1[m].setVal(2.) if m==1000 or m==1800: vsigma[m].setVal(m*0.03) elif m==1200 or m==1600: vsigma[m].setVal(m*0.04) #if m < 1000: vsigma[m].setVal(m*0.06) # If it's not the proper channel, make it a gaussian #if nLept==0 and 'VBF' in channel: # valpha1[m].setVal(5) # valpha1[m].setConstant(True) # vslope1[m].setConstant(True) # salpha2[m].setConstant(True) # sslope2[m].setConstant(True) # ---------- if there is no simulated signal, skip this mass point ---------- if m in genPoints: if VERBOSE: print " - Mass point", m # define the dataset for the signal applying the SR cuts treeSign[m] = TChain("tree") for j, ss in enumerate(sample[signalMass]['files']): treeSign[m].Add(NTUPLEDIR + ss + ".root") if treeSign[m].GetEntries() <= 0.: if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..." signalNorm[m].setVal(-1) vmean[m].setConstant(True) vsigma[m].setConstant(True) salpha1[m].setConstant(True) sslope1[m].setConstant(True) salpha2[m].setConstant(True) sslope2[m].setConstant(True) signalNorm[m].setConstant(True) signalXS[m].setConstant(True) continue setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m])) if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded" # FIT signalYield[m].setVal(setSignal[m].sumEntries()) if treeSign[m].GetEntries(SRcut) > 5: if VERBOSE: print " - Running fit" frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1)) if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80 if VERBOSE: frSignal[m].correlationMatrix().Print() drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m]) else: print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist" # Remove HVT cross section (which is the same for Zlep and Zinv) if stype == "XZHVBF": sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m else: sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m xs = xsection[sample_name]['xsec'] signalXS[m].setVal(xs * 1000.) signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range")) boundaryFactor = signalIntegral[m].getVal() if VERBOSE: print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events" if channel=='nnbb' and m==5000: signalNorm[m].setVal(2.5) elif channel=='nn0b' and m==5000: signalNorm[m].setVal(6.7) else: signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb] a1[m].setConstant(True) a2[m].setConstant(True) vmean[m].setConstant(True) vsigma[m].setConstant(True) valpha1[m].setConstant(True) vslope1[m].setConstant(True) salpha2[m].setConstant(True) sslope2[m].setConstant(True) signalNorm[m].setConstant(True) signalXS[m].setConstant(True) #*******************************************************# # # # Signal interpolation # # # #*******************************************************# # ====== CONTROL PLOT ====== c_signal = TCanvas("c_signal", "c_signal", 800, 600) c_signal.cd() frame_signal = X_mass.frame() for m in genPoints[:-2]: if m in signalExt.keys(): signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range")) frame_signal.GetXaxis().SetRangeUser(0, 6500) frame_signal.Draw() drawCMS(-1, YEAR, "Simulation") drawAnalysis(channel) drawRegion(channel) c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf") c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png") #if VERBOSE: raw_input("Press Enter to continue...") # ====== CONTROL PLOT ====== # Normalization gnorm = TGraphErrors() gnorm.SetTitle(";m_{X} (GeV);integral (GeV)") gnorm.SetMarkerStyle(20) gnorm.SetMarkerColor(1) gnorm.SetMaximum(0) inorm = TGraphErrors() inorm.SetMarkerStyle(24) fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800)) fnorm.SetLineColor(920) fnorm.SetLineStyle(7) fnorm.SetFillColor(2) fnorm.SetLineColor(cColor) # Mean gmean = TGraphErrors() gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)") gmean.SetMarkerStyle(20) gmean.SetMarkerColor(cColor) gmean.SetLineColor(cColor) imean = TGraphErrors() imean.SetMarkerStyle(24) fmean = TF1("fmean", "pol1", 0, 5000) fmean.SetLineColor(2) fmean.SetFillColor(2) # Width gsigma = TGraphErrors() gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)") gsigma.SetMarkerStyle(20) gsigma.SetMarkerColor(cColor) gsigma.SetLineColor(cColor) isigma = TGraphErrors() isigma.SetMarkerStyle(24) fsigma = TF1("fsigma", "pol1", 0, 5000) fsigma.SetLineColor(2) fsigma.SetFillColor(2) # Alpha1 galpha1 = TGraphErrors() galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha") galpha1.SetMarkerStyle(20) galpha1.SetMarkerColor(cColor) galpha1.SetLineColor(cColor) ialpha1 = TGraphErrors() ialpha1.SetMarkerStyle(24) falpha1 = TF1("falpha", "pol0", 0, 5000) falpha1.SetLineColor(2) falpha1.SetFillColor(2) # Slope1 gslope1 = TGraphErrors() gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)") gslope1.SetMarkerStyle(20) gslope1.SetMarkerColor(cColor) gslope1.SetLineColor(cColor) islope1 = TGraphErrors() islope1.SetMarkerStyle(24) fslope1 = TF1("fslope", "pol0", 0, 5000) fslope1.SetLineColor(2) fslope1.SetFillColor(2) # Alpha2 galpha2 = TGraphErrors() galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha") galpha2.SetMarkerStyle(20) galpha2.SetMarkerColor(cColor) galpha2.SetLineColor(cColor) ialpha2 = TGraphErrors() ialpha2.SetMarkerStyle(24) falpha2 = TF1("falpha", "pol0", 0, 5000) falpha2.SetLineColor(2) falpha2.SetFillColor(2) # Slope2 gslope2 = TGraphErrors() gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)") gslope2.SetMarkerStyle(20) gslope2.SetMarkerColor(cColor) gslope2.SetLineColor(cColor) islope2 = TGraphErrors() islope2.SetMarkerStyle(24) fslope2 = TF1("fslope", "pol0", 0, 5000) fslope2.SetLineColor(2) fslope2.SetFillColor(2) n = 0 for i, m in enumerate(genPoints): if not m in signalNorm.keys(): continue if signalNorm[m].getVal() < 1.e-6: continue signalString = "M%d" % m signalName = "%s_M%d" % (stype, m) if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal()) gnorm.SetPoint(n, m, signalNorm[m].getVal()) gmean.SetPoint(n, m, vmean[m].getVal()) gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02)) gsigma.SetPoint(n, m, vsigma[m].getVal()) gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05)) galpha1.SetPoint(n, m, valpha1[m].getVal()) galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10)) gslope1.SetPoint(n, m, vslope1[m].getVal()) gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10)) galpha2.SetPoint(n, m, salpha2[m].getVal()) galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10)) gslope2.SetPoint(n, m, sslope2[m].getVal()) gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10)) n = n + 1 print "fit on gmean:" gmean.Fit(fmean, "Q0", "SAME") print "fit on gsigma:" gsigma.Fit(fsigma, "Q0", "SAME") print "fit on galpha:" galpha1.Fit(falpha1, "Q0", "SAME") print "fit on gslope:" gslope1.Fit(fslope1, "Q0", "SAME") galpha2.Fit(falpha2, "Q0", "SAME") gslope2.Fit(fslope2, "Q0", "SAME") #for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S")) gnorm.Fit(fnorm, "Q", "SAME", 700, 5000) for m in massPoints: signalName = "%s_M%d" % (stype, m) if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.) # Interpolation method syield = gnorm.Eval(m) spline = gnorm.Eval(m, 0, "S") sfunct = fnorm.Eval(m) #delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield)) delta = abs(1.-spline/sfunct) if sfunct > 0 else 0 syield = spline if interPar: jmean = gmean.Eval(m) jsigma = gsigma.Eval(m) jalpha1 = galpha1.Eval(m) jslope1 = gslope1.Eval(m) else: jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m inorm.SetPoint(inorm.GetN(), m, syield) signalNorm[m].setVal(syield) imean.SetPoint(imean.GetN(), m, jmean) if jmean > 0: vmean[m].setVal(jmean) isigma.SetPoint(isigma.GetN(), m, jsigma) if jsigma > 0: vsigma[m].setVal(jsigma) ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1) if not jalpha1==0: valpha1[m].setVal(jalpha1) islope1.SetPoint(islope1.GetN(), m, jslope1) if jslope1 > 0: vslope1[m].setVal(jslope1) c1 = TCanvas("c1", "Crystal Ball", 1200, 800) c1.Divide(2, 2) c1.cd(1) gmean.SetMinimum(0.) gmean.Draw("APL") imean.Draw("P, SAME") drawRegion(channel) c1.cd(2) gsigma.SetMinimum(0.) gsigma.Draw("APL") isigma.Draw("P, SAME") drawRegion(channel) c1.cd(3) galpha1.Draw("APL") ialpha1.Draw("P, SAME") drawRegion(channel) galpha1.GetYaxis().SetRangeUser(0., 5.) c1.cd(4) gslope1.Draw("APL") islope1.Draw("P, SAME") drawRegion(channel) gslope1.GetYaxis().SetRangeUser(0., 125.) if False: c1.cd(5) galpha2.Draw("APL") ialpha2.Draw("P, SAME") drawRegion(channel) c1.cd(6) gslope2.Draw("APL") islope2.Draw("P, SAME") drawRegion(channel) gslope2.GetYaxis().SetRangeUser(0., 10.) c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf") c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png") c2 = TCanvas("c2", "Signal Efficiency", 800, 600) c2.cd(1) gnorm.SetMarkerColor(cColor) gnorm.SetMarkerStyle(20) gnorm.SetLineColor(cColor) gnorm.SetLineWidth(2) gnorm.Draw("APL") inorm.Draw("P, SAME") gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100) gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25) drawCMS(-1,YEAR , "Simulation") drawAnalysis(channel) drawRegion(channel) c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf") c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png") #*******************************************************# # # # Generate workspace # # # #*******************************************************# # create workspace w = RooWorkspace("ZH_RunII", "workspace") for m in massPoints: getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName())) getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName())) getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName())) w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True) print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully" sys.exit()
def Model_Comparisons(Comb_Dict): label_size=22 axis_size=34 plot_power = False Colors = ["red","blue"] Markers = ["s","o"] fig = plt.figure(figsize=(8,8)) fig.add_axes((0.1,0.3,0.88,0.6)) for SYS,sys_col,marker in zip(reversed(Systems),reversed(Colors),reversed(Markers)): #Systematics Efficiency_Uncertainty = 0.056*Comb_Dict["%s_Combined_FF"%(SYS)] Eta_Cor = Eta_Correction #see default_value.py for value Eta_Cor_Uncertainty = Eta_Correction_Uncertainty*Comb_Dict["%s_Combined_FF"%(SYS)] if not(Apply_Eta_Correction and SYS=="p-Pb"): Eta_Cor_Uncertainty = 0 #2% otherwise FF_Central = Comb_Dict["%s_Combined_FF"%(SYS)] #Eta Correction is applied when creating Dictionary! Sys_Uncertainty = np.sqrt(Efficiency_Uncertainty**2 + Comb_Dict["%s_purity_Uncertainty"%(SYS)]**2 + Eta_Cor_Uncertainty**2) if (SYS=="pp"): pp_sys_Error = Sys_Uncertainty elif (SYS=="p-Pb"): p_Pb_sys_Error=Sys_Uncertainty #Plots if (SYS=="pp"): leg_string = SYS if (SYS=="p-Pb"): leg_string = "p$-$Pb" plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, yerr=Comb_Dict["%s_Combined_FF_Errors"%(SYS)][:NzT-ZT_OFF_PLOT],linewidth=1, fmt=marker,color=sys_col,capsize=0)#for lines plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],marker,linewidth=0,color=sys_col, label=leg_string)#for legend without lines if (SYS == "pp"): Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color=sys_col,alpha=0.3,edgecolor=sys_col) else: Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2,align='center',color=sys_col,fill=False,edgecolor="blue") if (plot_power): model,p,chi2dof = Fit_FF_PowerLaw(Comb_Dict,SYS) plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], model, sys_col,label=r"%s $\alpha = %1.2f\pm 0.1 \chi^2 = %1.2f$"%(SYS,p,chi2dof)) if (Use_MC): plt.plot(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,'--',color="forestgreen",label="PYTHIA 8.2 Monash") plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,yerr=pythia_FF_Errors,fmt='--',color="forestgreen",capsize=0) plt.yscale('log') plt.ylabel(r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}|\Delta\varphi|\mathrm{d}\Delta\eta}$",fontsize=axis_size,y=0.76) plt.ylim(0.037,15) plt.yticks(fontsize=20) plt.xticks(fontsize=0) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,top=True,bottom=False,length=10) plt.tick_params(which='minor',length=5) #pp_sys_Error = (Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["pp"]**2+0.056**2) #p_Pb_sys_Error = (Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["p-Pb"]**2+0.056**2+Eta_Cor**2) Chi2,NDF,Pval = Get_pp_pPb_List_Chi2(Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["pp_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], pp_sys_Error, Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["p-Pb_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], p_Pb_sys_Error) leg = plt.legend(numpoints=1,frameon=True,edgecolor='white', framealpha=0.0, fontsize=label_size,handlelength=1,labelspacing=0.2,loc='lower left',bbox_to_anchor=(0.001, 0.05)) plt.annotate(r"ALICE, $\sqrt{s_{\mathrm{_{NN}}}}=5.02$ TeV",xy=(0.115,0.008),xycoords='axes fraction', ha='left',va='bottom',fontsize=label_size) plt.annotate(r"%1.0f < $p_\mathrm{T}^{\gamma}$ < %1.0f GeV/$c$"%(pTbins[0],pTbins[N_pT_Bins]),xy=(0.97, 0.81), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate(r"%1.1f < $p_\mathrm{T}^\mathrm{h}$ < %1.1f GeV/$c$"%(Min_Hadron_pT,Max_Hadron_pT),xy=(0.97, 0.89), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate("$\chi^2/\mathrm{ndf}$ = %1.1f/%i, $p$ = %1.2f"%(Chi2*NDF,NDF,Pval), xy=(0.97, 0.97), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) #RATIO SECOND Y_AXIS fig.add_axes((0.1,0.1,0.88,0.2)) pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] Ratio = pPb_Combined/pp_Combined Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, fmt='ko',capsize=0, ms=6,lw=1) Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pp_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio Eta_Cor_Uncertainty = Eta_Correction_Uncertainty/Comb_Dict["p-Pb_Combined_FF"]*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2 + Eta_Cor_Uncertainty**2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color='black',alpha=0.25) ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "grey" Show_Fits = True if (Show_Fits): sys_const = 0.19 #23% relative from purity + tracking plt.annotate("$c = {0:.2f} \pm {1:.2f} \pm {2:.2f}$".format(p0,p0e,sys_const), xy=(0.98, 0.9), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) plt.annotate(r"$p = %1.2f$"%(pval), xy=(0.98, 0.75), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) c_error = math.sqrt(p0e**2 + sys_const**2) plt.fill_between(np.arange(0,1.1,0.1), p0+c_error, p0-c_error,color=p0col,alpha=.3) ###LABELS/AXES### plt.axhline(y=1, color='k', linestyle='--') #plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=axis_size-8,x=0.9) plt.xlabel(r"varphi = $\varphi$, phi = $\phi$",fontsize = 30) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=axis_size,y=0.5) plt.ylim((-0.0, 2.8)) plt.xticks(fontsize=20) plt.yticks([0.5,1.0,1.5,2.0,2.5],fontsize=20) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) plt.tick_params(which='both',direction='in',top=True,length=5) plt.fill_between(QGP_zt,QGP_IpPb_LowAll,QGP_IpPb_HighAll,label="QGP Droplet",color='orange') plt.plot(CNM_zt,CNM_IpPb,color='red',label="CNM Model") plt.legend(loc='upper left') plt.savefig("pics/%s/%s/Model_Comparisons.pdf"%(Shower,description_string), bbox_inches = "tight") plt.show()
if s.Get() and s.Get().IsValid() and s.Get().CovMatrixStatus() == 3: massArr.append(cleanhist.GetYaxis().GetBinCenter(iy)) zeroArr.append(0) xintArr.append(s.Parameter(1)) yintArr.append(s.Parameter(0)) xintErrArr.append(s.ParError(1)) yintErrArr.append(s.ParError(0)) xintgraph = TGraphErrors(len(massArr), massArr, xintArr, zeroArr, xintErrArr) xintgraph.SetTitle("X-intercept;mass [GeV];D1") xintgraph.SetName("xintgraph") xintgraph.Write() xintgraph.Draw("A*") xintgraph.GetYaxis().SetRangeUser(0, 1000) xintgraph.Fit("pol3") c.Print(outfilename + ".pdf") yintgraph = TGraphErrors(len(massArr), massArr, yintArr, zeroArr, yintErrArr) yintgraph.Draw("A*") yintgraph.GetYaxis().SetRangeUser(0.5, 1.5) c.Print(outfilename + ".pdf") #clean.Draw("D1>>cleanslice(10,0,500)",xfcut+" && mass>{0} && mass<{1}".format(5.0,5.2)) #cleanslice = gDirectory.Get("cleanslice") #messyclean.Draw("D1>>messyslice(10,0,500)",xfcut+" && mass>{0} && mass<{1}".format(5.0,5.2)) #messyslice = gDirectory.Get("messyslice") c.Print(outfilename + ".pdf]") outfile.Close()
def Model_Ratio_Comparisons(Comb_Dict): label_size=24 axis_size=34 plot_power = False Colors = ["red","blue"] Markers = ["s","o"] fig = plt.figure(figsize=(8,8)) fig.add_axes((0.1,0.3,0.88,0.6)) # fig.add_axes((0.1,0.1,0.88,0.2)) pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] Ratio = pPb_Combined/pp_Combined Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, fmt='ko',capsize=0, ms=6,lw=1) Ratio_for_Legend = plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT],'ko',label='Data') Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pp_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio Eta_Cor_Uncertainty = Eta_Correction_Uncertainty/Comb_Dict["p-Pb_Combined_FF"]*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2 + Eta_Cor_Uncertainty**2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color='black',alpha=0.25) ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "grey" Show_Fits = True if (Show_Fits): sys_const = 0.19 #23% relative from purity + tracking plt.annotate("$c = {0:.2f} \pm {1:.2f} \pm {2:.2f}$".format(p0,p0e,sys_const), xy=(0.02, 0.15), xycoords='axes fraction', ha='left', va='top', color="black",fontsize=label_size,alpha=.9) plt.annotate(r"$p = %1.2f$"%(pval), xy=(0.02, 0.09), xycoords='axes fraction', ha='left', va='top', color="black",fontsize=label_size,alpha=.9) c_error = math.sqrt(p0e**2 + sys_const**2) plt.fill_between(np.arange(0,1.1,0.1), p0+c_error, p0-c_error,color=p0col,alpha=.3) ###LABELS/AXES### plt.axhline(y=1, color='k', linestyle='--') plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=axis_size-8,x=0.9) #plt.xlabel(r"varphi = $\varphi$, phi = $\phi$",fontsize = 30) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=axis_size,y=0.88) plt.ylim((-0.0, 2.8)) plt.xticks(fontsize=20) plt.yticks([0.5,1.0,1.5,2.0,2.5],fontsize=20) plt.yticks(np.arange(0,3,0.25),["","","0.5","","1.0","","1.5","","2.0","","2.5"]) #Ratio_Plot.yaxis.set_minor_locator(AutoMinorLocator(1)) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) plt.tick_params(which='both',direction='in',top=True,length=5) plt.fill_between(QGP_zt,QGP_IpPb_LowAll,QGP_IpPb_HighAll,label="QGP Droplet",color='orange') plt.plot(CNM_zt,CNM_IpPb,color='red',label="CNM Model",linewidth=2) leg = plt.legend(loc='upper right',fontsize=20,frameon=False) leg.set_title("ALICE, $\sqrt{s_{\mathrm{_{NN}}}} = $ 5.02 TeV") plt.setp(leg.get_title(),fontsize=24) plt.savefig("pics/%s/%s/Model_Comparisons_Ratio.pdf"%(Shower,description_string), bbox_inches = "tight") plt.show()
def draw(): # N = 33 N = 8 ecms = array('f', N * [0]) ecms_err = array('f', N * [0]) factor = array('f', N * [0]) factor_err = array('f', N * [0]) path = './txts/sys_err_width_raw.txt' mbc = TCanvas('mbc', 'mbc', 800, 600) set_canvas_style(mbc) f = open(path, 'r') lines = f.readlines() count = 0 sum_mean = 0 sum_err = 0 for line in lines: fargs = map(float, line.strip('\n').strip().split()) ecms[count] = fargs[0] ecms_err[count] = 0.0022 factor[count] = fargs[1] factor_err[count] = fargs[2] sum_mean += fargs[1] sum_err += fargs[2] count += 1 grerr = TGraphErrors(N, ecms, factor, ecms_err, factor_err) xtitle = 'E_{cms} (GeV)' ytitle = 'f^{M(K^{-}#pi^{+}#pi^{+})}' set_graph_style(grerr, xtitle, ytitle) f = TF1('f', '[0]', ecms[0], ecms[1]) grerr.Fit(f) chi2 = f.GetChisquare() ndf = f.GetNDF() F = f.GetParameter(0) F_err = f.GetParError(0) grerr.Draw('ap') pt = TPaveText(0.25, 0.65, 0.45, 0.85, "BRNDC") set_pavetext(pt) pt.Draw() line = 'f#pm#sigma_{f^{M(K^{-}#pi^{+}#pi^{+})}} = ' + str(round( F, 3)) + '#pm' + str(round(F_err, 3)) pt.AddText(line) line = '#chi^{2}/ndf = ' + str(round(chi2, 3)) + '/' + str(round( ndf, 3)) + ' = ' + str(round(chi2 / ndf, 3)) pt.AddText(line) line = '#Delta_{f^{M(K^{-}#pi^{+}#pi^{+})}}/#sigma_{f^{M(K^{-}#pi^{+}#pi^{+})}}=' + str( round((1 - F) / F_err, 3)) pt.AddText(line) mbc.Update() if not os.path.exists('./figs/'): os.makedirs('./figs/') mbc.SaveAs('./figs/sys_err_width.pdf') if not os.path.exists('./txts/'): os.makedirs('./txts/') with open('./txts/f_m_Kpipi.txt', 'w') as f_out: f_out.write(str(F) + '\n') ecms = [ 4190, 4200, 4210, 4220, 4230, 4237, 4245, 4246, 4260, 4270, 4280, 4290, 4310, 4315, 4340, 4360, 4380, 4390, 4400, 4420, 4440, 4470, 4530, 4575, 4600, 4610, 4620, 4640, 4660, 4680, 4700, 4740, 4750, 4780, 4840, 4914, 4946 ] with open('./txts/sys_err_width.txt', 'w') as f_out: for ecm in ecms: out = str(ecm / 1000.) + '\t' + str(round(F_err * 100, 1)) + '\n' f_out.write(out) raw_input('Enter anything to end...')
def pp_pPB_Avg_Ratio(Comb_Dict,pT_Start): pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] #Ratio Ratio = pPb_Combined/pp_Combined #Stat. Error in ratio Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio #Save np.save("npy_files/%s_Averaged_FF_Ratio_%s.npy"%(Shower,description_string),Ratio) np.save("npy_files/%s_Averaged_FF_Ratio_Errors_%s.npy"%(Shower,description_string),Ratio_Error) #Sys. Error in ratio Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pPb_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2) plt.figure(figsize=(10,7)) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) #Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], # bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zt_box[:NzT-ZT_OFF_PLOT], align='center',edgecolor="k",color='w') #bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zt_box[:NzT-ZT_OFF_PLOT], align='center',color='black',alpha=0.2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], 2*Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=1.0-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=2*zT_widths[:NzT-ZT_OFF_PLOT], align='center',color='black',alpha = 0.2) empt4, = plt.plot([], [],' ') Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT], fmt='ko',capsize=3, ms=6,lw=1) plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=20) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=20) plt.ylim((-0.49, 2.9)) #plt.yticks(np.arange(-0, 2, step=0.2)) if(NzT == 6): plt.xlim(xmin = 0.0,xmax=0.7) elif(NzT==7): plt.xlim(xmin = 0.0,xmax=1.0) #plt.xlim(xmin = 0.0,xmax=zTbins[NzT-ZT_OFF_PLOT]) plt.xlim(xmin = 0.0,xmax=0.67) plt.axhline(y=1, color='k', linestyle='--') ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "blue" if (Show_Fits): plt.annotate("Constant Fit", xy=(0.05, 0.99), xycoords='axes fraction', ha='left', va='top', color=p0col,fontsize=20,alpha=.7) plt.annotate(r"$p0 = {0:.2f} \pm {1:.2f}$".format(p0,p0e), xy=(0.05, 0.94), xycoords='axes fraction', ha='left', va='top', color=p0col,fontsize=18,alpha=.7) plt.annotate(r"$\chi^2_{red} = %1.2f$"%(chi2_red), xy=(0.05, 0.89), xycoords='axes fraction', ha='left', va='top', color=p0col,fontsize=18,alpha=.7) plt.annotate(r"$p_{val} = %1.2f$"%(pval), xy=(0.05, 0.84), xycoords='axes fraction', ha='left', va='top', color=p0col,fontsize=18,alpha=.7) plt.fill_between(np.arange(0,1.1,0.1), p0+p0e, p0-p0e,color=p0col,alpha=.2) Ratio_TGraph.Fit("pol1","S") #zT_Points = np.linspace(0.05,zTbins[NzT-1],20) zT_Points = np.linspace(0.0,1,20) Fit_Band = ROOT.TGraphErrors(len(zT_Points)); for i in range(len(zT_Points)-ZT_OFF_PLOT): Fit_Band.SetPoint(i, zT_Points[i], 0) (ROOT.TVirtualFitter.GetFitter()).GetConfidenceIntervals(Fit_Band,0.68) band_errors = np.zeros(len(zT_Points)) for i in range (len(zT_Points)): band_errors[i] = Fit_Band.GetErrorY(i) print(band_errors) f2 = Ratio_TGraph.GetFunction("pol1") chi2_red = f2.GetChisquare()/f2.GetNDF() pval = f2.GetProb() p0 = f2.GetParameter(0) p0e = f2.GetParError(0) p1 = f2.GetParameter(1) p1e = f2.GetParError(1) print(p1e) p1col = "Green" if (Show_Fits): plt.annotate("Linear Fit", xy=(0.4, 0.99), xycoords='axes fraction', ha='left', va='top', color=p1col,fontsize=20,alpha=.7) plt.annotate(r"$p0 = %1.2f \pm %1.2f$"%(p0,p0e), xy=(0.4, 0.94), xycoords='axes fraction', ha='left', va='top', color=p1col,fontsize=18,alpha=.7) plt.annotate(r"$p1 = %1.2f \pm %1.2f$"%(p1,p1e), xy=(0.4, 0.89), xycoords='axes fraction', ha='left', va='top', color=p1col,fontsize=18,alpha=.7) plt.annotate(r"$\chi^2_{red} = %1.2f$"%(chi2_red), xy=(0.4, 0.84), xycoords='axes fraction', ha='left', va='top', color=p1col,fontsize=18,alpha=.7) plt.annotate(r"$p_{val} = %1.2f$"%(pval), xy=(0.4, 0.79), xycoords='axes fraction', ha='left', va='top', color=p1col,fontsize=18,alpha=.7) axes = plt.gca() x_vals = np.array(zT_Points) y_vals = p0 + p1 * zT_Points plt.plot(zT_Points, y_vals, '--',color=p1col,linewidth=2,alpha=0.5) plt.fill_between(zT_Points,y_vals+band_errors,y_vals-band_errors,color=p1col,alpha=0.2) ### ROOT DONE ### leg = plt.legend([Ratio_Plot,empt4],["Statistical Error",r'%1.0f < $p_\mathrm{T}^{\mathrm{trig}}$ < %1.0f GeV/$c$'%(pTbins[pT_Start],pTbins[N_pT_Bins])],frameon=False,numpoints=1,loc="lower left",title=' ',prop={'size':18}) leg.set_title("ALICE Work in Progress\n $\sqrt{s_{\mathrm{_{NN}}}} = $ 5 TeV") plt.setp(leg.get_title(),fontsize=20) plt.gcf() plt.savefig("pics/%s/%s/Ratio_Fits.pdf"%(Shower,description_string), bbox='tight') plt.show() print(" Central Values:") print(Ratio[:NzT-ZT_OFF_PLOT]) print("\n Satistical Uncertainty Absolute:") print(Ratio_Error[:NzT-ZT_OFF_PLOT]) print("\n Relative Satistical Uncertainty:") print(Ratio_Error[:NzT-ZT_OFF_PLOT]/Ratio[:NzT-ZT_OFF_PLOT]) print("\n Ratio Uncertainty from Purity:") print(Purity_Uncertainty[:NzT-ZT_OFF_PLOT]) print("\n Ratio Uncertainty from Single Track Efficiency:") print(Efficiency_Uncertainty[:NzT-ZT_OFF_PLOT]) print("\n Full Systematic Uncertainty:") print(Ratio_Systematic[:NzT-ZT_OFF_PLOT]) print("\n Relative Full Systematic:") print(Ratio_Systematic[:NzT-ZT_OFF_PLOT]/Ratio[:NzT-ZT_OFF_PLOT]) print("\n LaTeX Table:") print("$\zt$ range & pp & p--Pb & p--Pb/pp \\\\") for izt in range (NzT): print("%1.2f - %1.2f & %1.3f $\pm$ %1.3f & %1.3f $\pm$ %1.3f & %1.3f $\pm$ %1.3f \\\\" %(zTbins[izt], zTbins[izt+1], pp_Combined[izt], pp_Combined_Errors[izt], pPb_Combined[izt], pPb_Combined_Errors[izt], Ratio[izt], Ratio_Error[izt]))
residuals = TCanvas('malus_res', "Residuals", 1200, 800) residuals.Divide(2, 2) for i, r in enumerate(res): residuals.cd(i + 1) r.res_graph.Draw('AEP') r.res_graph.SetMarkerStyle(6) residuals.Update() n = 1 current = [0] * n + [1] * n + [2] * n + [3] * n current = array('d', current) for tuple in zip(current, phi, phi_err): print "%i %.1f \pm %.1f" % tuple zeros = array('d', [0] * n * 4) graph = TGraphErrors(n * 4, current, phi, zeros, phi_err) canv = TCanvas('shift', 'shift') graph.SetMarkerStyle(8) func = TF1('pol1', 'pol1', 0, 4) graph.Fit('pol1', 'V') graph.Draw('AEP') slope = func.GetParameter(1) slope_err = func.GetParError(1) verdet = -(slope * ls) / (mu0 * N * lv) verdet_err = error(verdet, (slope, slope_err)) print 'Verdet = ', verdet, verdet_err print 'Chi2 / NDF = ', func.GetChisquare(), func.GetNDF() raw_input()
def Plot_pp_pPb_Avg_FF_and_Ratio(Comb_Dict): label_size=22 axis_size=34 plot_power = False Colors = ["red","blue"] Markers = ["s","o"] fig = plt.figure(figsize=(8,8)) pp_sys_Error = 0 p_Pb_sys_Error = 0 fig.add_axes((0.1,0.3,0.88,0.6)) for SYS,sys_col,marker in zip(reversed(Systems),reversed(Colors),reversed(Markers)): #Systematics Efficiency_Uncertainty = 0.056*Comb_Dict["%s_Combined_FF"%(SYS)] Eta_Cor = Eta_Correction #see default_value.py for value Eta_Cor_Uncertainty = Eta_Correction_Uncertainty*Comb_Dict["%s_Combined_FF"%(SYS)] if not(Apply_Eta_Correction and SYS=="p-Pb"): Eta_Cor_Uncertainty = 0 #2% otherwise FF_Central = Comb_Dict["%s_Combined_FF"%(SYS)] #Eta Correction is applied when creating Dictionary! Sys_Uncertainty = np.sqrt(Efficiency_Uncertainty**2 + Comb_Dict["%s_purity_Uncertainty"%(SYS)]**2 + Eta_Cor_Uncertainty**2) if (SYS=="pp"): pp_sys_Error = Sys_Uncertainty elif (SYS=="p-Pb"): p_Pb_sys_Error=Sys_Uncertainty #Plots if (SYS=="pp"): leg_string = SYS if (SYS=="p-Pb"): leg_string = "p$-$Pb" plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, yerr=Comb_Dict["%s_Combined_FF_Errors"%(SYS)][:NzT-ZT_OFF_PLOT],linewidth=1, fmt=marker,color=sys_col,capsize=0)#for lines plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT],marker,linewidth=0,color=sys_col, label=leg_string)#for legend without lines if (SYS == "pp"): Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color=sys_col,alpha=0.3,edgecolor=sys_col) else: Sys_Plot_pp = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Sys_Uncertainty[:NzT-ZT_OFF_PLOT]+Sys_Uncertainty[:NzT-ZT_OFF_PLOT], bottom=Comb_Dict["%s_Combined_FF"%(SYS)][:NzT-ZT_OFF_PLOT]-Sys_Uncertainty[:NzT-ZT_OFF_PLOT],width=zT_widths[:NzT-ZT_OFF_PLOT]*2,align='center',color=sys_col,fill=False,edgecolor="blue") if (plot_power): model,p,chi2dof = Fit_FF_PowerLaw(Comb_Dict,SYS) plt.plot(zT_centers[:NzT-ZT_OFF_PLOT], model, sys_col,label=r"%s $\alpha = %1.2f\pm 0.1 \chi^2 = %1.2f$"%(SYS,p,chi2dof)) if (Use_MC): plt.plot(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,'--',color="forestgreen",label="PYTHIA 8.2 Monash") plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT],pythia_FF,yerr=pythia_FF_Errors,fmt='--',color="forestgreen",capsize=0) plt.yscale('log') plt.ylabel(r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}|\Delta\varphi|\mathrm{d}\Delta\eta}$",fontsize=axis_size,y=0.76) plt.ylim(0.037,15) plt.yticks(fontsize=20) plt.xticks(fontsize=0) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,top=True,bottom=False,length=10) plt.tick_params(which='minor',length=5) #pp_sys_Error = (Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["pp"]**2+0.056**2) #p_Pb_sys_Error = (Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT])*math.sqrt(Rel_pUncert["p-Pb"]**2+0.056**2+Eta_Cor**2) Chi2,NDF,Pval = Get_pp_pPb_List_Chi2(Comb_Dict["pp_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["pp_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], pp_sys_Error, Comb_Dict["p-Pb_Combined_FF"][:NzT-ZT_OFF_PLOT], Comb_Dict["p-Pb_Combined_FF_Errors"][:NzT-ZT_OFF_PLOT], p_Pb_sys_Error) leg = plt.legend(numpoints=1,frameon=True,edgecolor='white', framealpha=0.0, fontsize=label_size,handlelength=1,labelspacing=0.2,loc='lower left',bbox_to_anchor=(0.001, 0.05)) plt.annotate(r"ALICE, $\sqrt{s_{\mathrm{_{NN}}}}=5.02$ TeV",xy=(0.115,0.008),xycoords='axes fraction', ha='left',va='bottom',fontsize=label_size) plt.annotate(r"%1.0f < $p_\mathrm{T}^{\gamma}$ < %1.0f GeV/$c$"%(pTbins[0],pTbins[N_pT_Bins]),xy=(0.97, 0.81), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate(r"%1.1f < $p_\mathrm{T}^\mathrm{h}$ < %1.1f GeV/$c$"%(Min_Hadron_pT,Max_Hadron_pT),xy=(0.97, 0.89), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) plt.annotate("$\chi^2/\mathrm{ndf}$ = %1.1f/%i, $p$ = %1.2f"%(Chi2*NDF,NDF,Pval), xy=(0.97, 0.97), xycoords='axes fraction', ha='right', va='top', fontsize=label_size) #HEP FF Fig5 = Table("Figure 5 Top Panel") Fig5.description = "$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." Fig5.location = "Data from Figure 5 Top Panel, Page 15" Fig5.keywords["observables"] = ["$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] Fig5.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt.values = zT_edges Fig5.add_variable(zt) # y-axis: p-Pb Yields pPb_data = Variable("p$-$Pb conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pPb_data.values = Comb_Dict["p-Pb_Combined_FF"] pPb_sys = Uncertainty("p-Pb Systematic", is_symmetric=True) pPb_sys.values = p_Pb_sys_Error pPb_stat = Uncertainty("p-Pb Statistical", is_symmetric=True) pPb_stat.values = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_data.add_uncertainty(pPb_sys) pPb_data.add_uncertainty(pPb_stat) # y-axis: pp Yields pp_data = Variable("pp conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pp_data.values = Comb_Dict["pp_Combined_FF"] pp_sys = Uncertainty("pp Systematic", is_symmetric=True) pp_sys.values = pp_sys_Error pp_stat = Uncertainty("pp Statistical", is_symmetric=True) pp_stat.values = Comb_Dict["pp_Combined_FF_Errors"] pp_data.add_uncertainty(pp_sys) pp_data.add_uncertainty(pp_stat) # y-axis: PYTHIA Yields pythia_data = Variable("PYTHIA conditional yield of associated hadrons", is_independent=False, is_binned=False, units="") pythia_data.values = pythia_FF pythia_stat = Uncertainty("PYTHIA Statistical", is_symmetric=True) pythia_stat.values = pythia_FF_Errors pythia_data.add_uncertainty(pythia_stat) #Add everything to the HEP Table Fig5.add_variable(pPb_data) Fig5.add_variable(pp_data) Fig5.add_variable(pythia_data) submission.add_table(Fig5) #RATIO SECOND Y_AXIS fig.add_axes((0.1,0.1,0.88,0.2)) pPb_Combined = Comb_Dict["p-Pb_Combined_FF"] pPb_Combined_Errors = Comb_Dict["p-Pb_Combined_FF_Errors"] pPb_purity_Uncertainty = Comb_Dict["p-Pb_purity_Uncertainty"] pp_Combined = Comb_Dict["pp_Combined_FF"] pp_Combined_Errors = Comb_Dict["pp_Combined_FF_Errors"] pp_purity_Uncertainty = Comb_Dict["pp_purity_Uncertainty"] Ratio = pPb_Combined/pp_Combined Ratio_Error = np.sqrt((pPb_Combined_Errors/pPb_Combined)**2 + (pp_Combined_Errors/pp_Combined)**2)*Ratio Ratio_Plot = plt.errorbar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio[:NzT-ZT_OFF_PLOT], yerr=Ratio_Error[:NzT-ZT_OFF_PLOT],xerr=zT_widths[:NzT-ZT_OFF_PLOT]*0, fmt='ko',capsize=0, ms=6,lw=1) #Save np.save("npy_files/%s_Averaged_FF_Ratio_%s.npy"%(Shower,description_string),Ratio) np.save("npy_files/%s_Averaged_FF_Ratio_Errors_%s.npy"%(Shower,description_string),Ratio_Error) Purity_Uncertainty = np.sqrt((pp_purity_Uncertainty/pp_Combined)**2 + (pPb_purity_Uncertainty/pPb_Combined)**2)*Ratio Efficiency_Uncertainty = np.ones(len(pPb_Combined))*0.056*math.sqrt(2)*Ratio Eta_Cor_Uncertainty = Eta_Correction_Uncertainty/Comb_Dict["p-Pb_Combined_FF"]*Ratio if (CorrectedP): Ratio_Systematic = np.sqrt(Purity_Uncertainty**2 + Efficiency_Uncertainty**2 + Eta_Cor_Uncertainty**2) Sys_Plot = plt.bar(zT_centers[:NzT-ZT_OFF_PLOT], Ratio_Systematic[:NzT-ZT_OFF_PLOT]+Ratio_Systematic[:NzT-ZT_OFF_PLOT], bottom=Ratio[:NzT-ZT_OFF_PLOT]-Ratio_Systematic[:NzT-ZT_OFF_PLOT], width=zT_widths[:NzT-ZT_OFF_PLOT]*2, align='center',color='black',alpha=0.25) ### ROOT LINEAR and CONSTANT FITS ### Ratio_TGraph = TGraphErrors() for izt in range (len(Ratio)-ZT_OFF_PLOT): Ratio_TGraph.SetPoint(izt,zT_centers[izt],Ratio[izt]) Ratio_TGraph.SetPointError(izt,0,Ratio_Error[izt]) Ratio_TGraph.Fit("pol0","S") f = Ratio_TGraph.GetFunction("pol0") chi2_red = f.GetChisquare()/f.GetNDF() pval = f.GetProb() p0 = f.GetParameter(0) p0e = f.GetParError(0) p0col = "grey" Show_Fits = True if (Show_Fits): sys_const = 0.19 #23% relative from purity + tracking #sys_const = 0.504245 #IRC plt.annotate("$c = {0:.2f} \pm {1:.2f} \pm {2:.2f}$".format(p0,p0e,sys_const), xy=(0.98, 0.9), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) plt.annotate(r"$p = %1.2f$"%(pval), xy=(0.98, 0.75), xycoords='axes fraction', ha='right', va='top', color="black",fontsize=label_size,alpha=.9) c_error = math.sqrt(p0e**2 + sys_const**2) plt.fill_between(np.arange(0,1.1,0.1), p0+c_error, p0-c_error,color=p0col,alpha=.3) ###LABELS/AXES### plt.axhline(y=1, color='k', linestyle='--') plt.xlabel("${z_\mathrm{T}} = p_\mathrm{T}^{\mathrm{h}}/p_\mathrm{T}^\gamma$",fontsize=axis_size-8,x=0.9) plt.ylabel(r"$\frac{\mathrm{p-Pb}}{\mathrm{pp}}$",fontsize=axis_size,y=0.5) plt.ylim((-0.0, 2.8)) plt.xticks(fontsize=20) plt.yticks([0.5,1.0,1.5,2.0,2.5],fontsize=20) plt.xlim(0,0.65) plt.tick_params(which='both',direction='in',right=True,bottom=True,top=True,length=10) plt.tick_params(which='both',direction='in',top=True,length=5) plt.savefig("pics/%s/%s/Final_FFunction_and_Ratio.pdf"%(Shower,description_string), bbox_inches = "tight") plt.show() #RATIO HEP FigRatio = Table("Figure 5 Bottom Panel") FigRatio.description = r"$\gamma^\mathrm{iso}$-tagged fragmentation function for pp (red) and p$-$Pb data (blue) at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV as measured by the ALICE detector. The boxes represent the systematic uncertainties while the vertical bars indicate the statistical uncertainties. The dashed green line corresponds to PYTHIA 8.2 Monash Tune. The $\chi^2$ test for the comparison of pp and p$-$Pb data incorporates correlations among different $z_\mathrm{T}$ intervals. A constant that was fit to the ratio is shown as grey band, with the width indicating the uncertainty on the fit." FigRatio.location = "Data from Figure 5, Bottom Panel, Page 15" FigRatio.keywords["observables"] = [r"$\frac{1}{N_{\mathrm{\gamma}}}\frac{\mathrm{d}^3N}{\mathrm{d}z_{\mathrm{T}}\mathrm{d}\Delta\varphi\mathrm{d}\Delta\eta}$"] FigRatio.add_image("./pics/LO/zT_Rebin_8_006zT06zT13fnew/Final_FFunction_and_Ratio.pdf") # x-axis: zT zt_ratio = Variable(r"$z_\mathrm{T}$", is_independent=True, is_binned=True, units="") zt_ratio.values = zT_edges FigRatio.add_variable(zt_ratio) # y-axis: p-Pb Yields Ratio_HEP = Variable("Ratio conditional yield of associated hadrons in pp and p$-$Pb", is_independent=False, is_binned=False, units="") Ratio_HEP.values = Ratio Ratio_sys = Uncertainty("Ratio Systematic", is_symmetric=True) Ratio_sys.values = Ratio_Systematic Ratio_stat = Uncertainty("Ratio Statistical", is_symmetric=True) Ratio_stat.values = Ratio_Error Ratio_HEP.add_uncertainty(Ratio_stat) Ratio_HEP.add_uncertainty(Ratio_sys) FigRatio.add_variable(Ratio_HEP) submission.add_table(FigRatio)