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)
leg2 = TLegend(0.7, 0.15, 1, 0.25)
leg2.SetHeader("Fits:") ##NEEDS TO BE CHANGED
leg2.AddEntry(pol1, "deltar: y = " + a0 + " + " + a1 + "x", "ll")
leg2.AddEntry(pol2, "minmass: y = " + b0 + " + " + b1 + "x", "ll")
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]))
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)
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()
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 showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
graph1_4ntr.GetYaxis().SetRangeUser(59., 69.)
graph1_4ntr.GetYaxis().SetTitle("Mean value")
graph1_4ntr.GetXaxis().SetTitle("Top M(GeV)")
graph1_4ntr.Draw("AP")
graph1_4ntr.SetTitle("Mean value of the inv.mass vs top mass")
graph2_4ntr.SetLineColor(2)
graph2_4ntr.SetMarkerColor(2)
graph2_4ntr.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))
c2.cd()
leg2 = TLegend(0.7, 0.15, 1, 0.25)
leg2.SetHeader("Fits:") ##NEEDS TO BE CHANGED
leg2.AddEntry(pol1, "deltar: y = " + a0 + " + " + a1 + "x", "ll")
leg2.AddEntry(pol2, "minmass: y = " + b0 + " + " + b1 + "x", "ll")
leg2.Draw()
def collectPerDirectionBx(options):
"""Fit data in both directions of a scan"""
nSteps = len(O['nominalPos'][options['scan']])
for i in range(nSteps - 1):
if O['nominalPos'][options['scan']][i+1] == \
O['nominalPos'][options['scan']][i]:
break
else:
for i in range(nSteps - 1):
if abs(O['nominalPos'][options['scan']][i+1] - \
O['nominalPos'][options['scan']][i]) < 10.0:
break
else:
raise RuntimeError('Could not find change of direction.')
if 'nominalPos' in options:
nominalPos = options['nominalPos']
else:
nominalPos = O['nominalPos'][options['scan']]
oldname = options['scan'] + '_' + options['name'] + options['fitted']
if options.get('newname', False):
newname = options['scan'] + '_' + options['newname'] + \
options['fitted']
else:
newname = oldname
if options.get('method', False):
oldname += '_' + options['method']
newname += '_' + options['method']
newname += '_collected'
f = openRootFileR(oldname)
g = openRootFileW(newname)
for bx in options['crossings']:
average = [0 for j in range(nSteps)]
averror = [0 for j in range(nSteps)]
for step in range(nSteps):
print '<<< Access data from:', options['scan'], bx, 'step', step
histname = plotName(oldname+'_bx'+str(bx)+'_step'+str(step), \
timestamp=False)
hist = f.Get(histname)
if options['custom']:
average[step], averror[step] = options['custom'](hist, step)
else:
average[step] = hist.GetMean()
averror[step] = hist.GetMeanError()
plotname = plotName(newname + '_bx' + str(bx), timestamp=False)
plottitl = plotTitle(options['scan'] + ' BX ' + str(bx))
print '<<< Create plot:', plotname
graphs = TMultiGraph(plotname, plottitl)
residuals = TMultiGraph(plotname + '_residuals', '')
for n, rnge in zip([i+1, nSteps-i-1], [lambda l: l[:i+1], \
lambda l: l[i+1:]]):
graph = TGraphErrors(n, \
array('d', [options['x'](a) for a in rnge(nominalPos)]), \
array('d', [options['y'](a) for a in rnge(average)]), \
array('d', [0]*n), \
array('d', [options['e'](a) for a in rnge(averror)]))
graph.Fit(options['fit'])
residual = TGraphErrors(n, \
array('d', [options['x'](a) for a in rnge(nominalPos)]), \
array('d', [options['y'](a) - graph.GetFunction( \
options['fit']).Eval(options['x'](b)) for a, b \
in zip(rnge(average), rnge(nominalPos))]),
array('d', [0]*n), \
array('d', [options['e'](a) for a in rnge(averror)]))
graphs.Add(graph)
residuals.Add(residual)
graphs.Write('', TObject.kOverwrite)
residuals.Write('', TObject.kOverwrite)
closeRootFile(g, newname)
closeRootFile(f, oldname)
def create_fastgrapherror(Fastscinenergy, Fastcherenergy, rmsScin, rmsCher, fem, rmsfem, PrimaryParticleEnergy):
"""Function to perform ROOT graphs and compute he values"""
#How many graph points
n = len(Fastscinenergy)
TGraphfasthescin = TGraphErrors(n, fem, Fastscinenergy, rmsfem, rmsScin)
TGraphfasthecher = TGraphErrors(n, fem, Fastcherenergy, rmsfem, rmsCher)
#Draw + DrawOptions, Fit + parameter estimation
Style = gStyle
Style.SetOptFit(1)
Style.SetOptStat(0) #Do not show statistics
XAxis = TGraphfasthescin.GetXaxis() #TGraphfasthescin
TGraphfasthescin.SetMarkerColor(4)
TGraphfasthescin.SetMarkerStyle(1)
TGraphfasthescin.SetMarkerSize(1)
XAxis.SetTitle("fem")
XAxis.SetLimits(0.0,1.0)
YAxis = TGraphfasthescin.GetYaxis()
YAxis.SetTitle("Energy scin (MeV)")
YAxis.SetRange(int(0.0),int(float(PrimaryParticleEnergy)+10000.0))
TGraphfasthescin.Fit("pol1")
myfit = TGraphfasthescin.GetFunction("pol1")
Fasthescin = myfit.GetParameter(0)/PrimaryParticleEnergy
Fasthescinerror = myfit.GetParError(0)/PrimaryParticleEnergy
Fasthescin2 = (PrimaryParticleEnergy - myfit.GetParameter(1))/PrimaryParticleEnergy
TGraphfasthescin.SetTitle("")
TGraphfasthescin.Draw("AP")
gPad.SaveAs("Fasthescin-pol1fit.pdf")
gPad.Close()
combinedfit = TF1("combinedfit", '(x)*([1]-[0]*[1])+[0]*[1]', 0, max(fem))
combinedfit.FixParameter(1, PrimaryParticleEnergy)
TGraphfasthescin.Fit("combinedfit")
hes = combinedfit.GetParameter(0)
heserror = combinedfit.GetParError(0)
TGraphfasthescin.Draw("AP")
gPad.SaveAs("Fasthescin.pdf")
gPad.Close()
XAxis = TGraphfasthecher.GetXaxis() #TGraphfasthecher
TGraphfasthecher.SetMarkerColor(4)
TGraphfasthecher.SetMarkerStyle(1)
TGraphfasthecher.SetMarkerSize(1)
XAxis.SetTitle("fem")
XAxis.SetLimits(0.0,1.0)
YAxis = TGraphfasthecher.GetYaxis()
YAxis.SetTitle("Energy Cher (MeV)")
YAxis.SetRange(int(0.0),int(float(PrimaryParticleEnergy)+10000.0))
TGraphfasthecher.Fit("pol1")
myfit = TGraphfasthecher.GetFunction("pol1")
Fasthecher = myfit.GetParameter(0)/PrimaryParticleEnergy
Fasthechererror = myfit.GetParError(0)/PrimaryParticleEnergy
Fasthecher2 = (PrimaryParticleEnergy - myfit.GetParameter(1))/PrimaryParticleEnergy
TGraphfasthecher.SetTitle("")
TGraphfasthecher.Draw("AP")
gPad.SaveAs("Fasthecher-pol1fit.pdf")
gPad.Close()
TGraphfasthecher.Fit("combinedfit")
hec = combinedfit.GetParameter(0)
hecerror = combinedfit.GetParError(0)
TGraphfasthecher.Draw("AP")
gPad.SaveAs("Fasthecher.pdf")
gPad.Close()
return Fasthescin, Fasthecher, Fasthescinerror, Fasthechererror, Fasthescin2, Fasthecher2, hes, hec, heserror, hecerror
c2.Divide(2,1,0.01,0.01)
# fit
graph1.Fit("pol1")
graph2.Fit("pol1")
graph1_t.Fit("pol1")
graph2_t.Fit("pol1")
graph1_tup.Fit("pol1")
graph2_tup.Fit("pol1")
line1.SetLineStyle(7)
line2.SetLineStyle(7)
pol1=graph1.GetFunction("pol1")
pol2=graph2.GetFunction("pol1")
pol1.SetLineColor(4)
pol2.SetLineColor(4)
pol1.SetLineStyle(2)
pol2.SetLineStyle(2)
graph1_scaledown.SetLineColor(6)
graph2_scaledown.SetLineColor(6)
graph1_scaledown.SetMarkerColor(6)
graph2_scaledown.SetMarkerColor(6)
graph1_scaledown.SetMarkerStyle(21)
graph2_scaledown.SetMarkerStyle(21)
graph1_scaledown.SetMarkerSize(1.0)
graph2_scaledown.SetMarkerSize(1.0)
graph1_scaleup.SetLineColor(2)
yerr[i] = ts_correct[i].GetMeanError()
graph1.SetPoint(i, x[i], y[i])
graph1.SetPointError(i, 0, yerr[i])
c2.cd()
graph1.Draw("AP")
graph1.GetYaxis().SetTitle("Mean value")
graph1.GetXaxis().SetTitle("Top M(GeV)")
graph1.Draw("AP")
graph1.Fit("pol1")
pol1=TF1()
pol1=graph1.GetFunction("pol1")
a0=str(round(pol1.GetParameter(0),2))
a1=str(round(pol1.GetParameter(1),2))
pol1.SetLineColor(1)
graph1.SetMarkerColor(1)
graph1.SetLineColor(1)
leg2 = TLegend(0.7,0.15,1,0.25)
leg2.SetHeader("Fits:") ##NEEDS TO BE CHANGED
leg2.AddEntry(pol1, "deltar: y = " + a0 + " + " + a1 + "x", "ll")
leg2.Draw()
# part for relative ratios
# part for relative ratios
# part for relative ratios
