def BeamPlotDemo(PositionDict,MCdf):
Sdf = PositionDict["Beam"][0]
Sdf_trig = PositionDict["Beam"][1]
Sdf_mrd = PositionDict["Beam"][2]
Sdf = Sdf.loc[Sdf["eventTimeTank"]>-9].reset_index(drop=True)
Sdf_trig = Sdf_trig.loc[Sdf_trig["eventTimeTank"]>-9].reset_index(drop=True)
Sdf_mrd = Sdf_mrd.loc[Sdf_mrd["eventTimeTank"]>-9].reset_index(drop=True)
Sdf_TankVeto = es.HasVetoHit_TankClusters(Sdf,Sdf_trig)
print("NUM TANK CLUSTERS WITH VETO HIT: " + str(len(Sdf_TankVeto)))
print("NUM TRIGS: " + str(len(Sdf_trig)))
HasVetoHit = np.where(Sdf_mrd["vetoHit"].values==1)[0]
print("NUM MRD CLUSTERS WITH VETO HIT: " + str(len(HasVetoHit)))
#Let's list some plots we want to make here:
# - Get delayed clusters that have eventTimeTanks matching the prompt/delayed
# cluster times. Apply
# - clusterPE>10 and clusterChargeBalance<0.4 and plot the time distribution
Sdf_prompt_noCB = Sdf.loc[Sdf['clusterTime']<2000].reset_index(drop=True)
Sdf_prompt = Sdf_prompt_noCB.loc[Sdf_prompt_noCB['clusterChargeBalance']<0.9].reset_index(drop=True)
plt.hist(Sdf_prompt['clusterTime'],bins=100,range=(0,2000))
plt.title("Prompt window Tank cluster times")
plt.xlabel("Cluster time [ns]")
plt.show()
print("TOTAL PROMPT TANK CLUSTERS, NO CB: " + str(len(Sdf_prompt_noCB)))
print("TOTAL PROMPT TANK CLUSTERS: " + str(len(Sdf_prompt)))
print("TOTAL PROMPT MRD CLUSTERS: " + str(len(Sdf_mrd)))
labels = {'title': 'Charge balance parameters in time window \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster time (ns)', 'ylabel': 'Charge balance'}
ranges = {'xbins': 40, 'ybins':40, 'xrange':[0,2000],'yrange':[0,1]}
#abp.MakeHexJointPlot(Sdf,'clusterPE','clusterChargeBalance',labels,ranges)
abp.Make2DHist(Sdf_prompt_noCB,'clusterTime','clusterChargeBalance',labels,ranges)
plt.show()
labels = {'title': 'Tank PMT hit cluster count as a function of time \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster time (ns)', 'ylabel': 'Cluster PE'}
ranges = {'xbins': 40, 'ybins':250, 'xrange':[0,2000],'yrange':[0,500]}
#abp.MakeHexJointPlot(Sdf,'clusterPE','clusterChargeBalance',labels,ranges)
abp.Make2DHist(Sdf_prompt_noCB,'clusterTime','clusterPE',labels,ranges)
plt.show()
labels = {'title': 'Tank PMT hit cluster count as a function of time \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster time (ns)', 'ylabel': 'Cluster PE'}
ranges = {'xbins': 40, 'ybins':250, 'xrange':[0,2000],'yrange':[500,5000]}
#abp.MakeHexJointPlot(Sdf,'clusterPE','clusterChargeBalance',labels,ranges)
abp.Make2DHist(Sdf_prompt_noCB,'clusterTime','clusterPE',labels,ranges)
plt.show()
labels = {'title': 'Tank PMT hit cluster count as a function of time \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster time (ns)', 'ylabel': 'Cluster PE'}
ranges = {'xbins': 40, 'ybins':25, 'xrange':[0,2000],'yrange':[0,5000]}
#abp.MakeHexJointPlot(Sdf,'clusterPE','clusterChargeBalance',labels,ranges)
abp.Make2DHist(Sdf_prompt_noCB,'clusterTime','clusterPE',labels,ranges)
plt.show()
plt.hist(Sdf_mrd['clusterTime'].values,bins=80,range=(0,4000),label="All MRD clusters")
plt.title("Prompt window MRD cluster times")
plt.xlabel("Cluster time [ns]")
plt.show()
#Get largest cluster in each acquisition in prompt window
Sdf_maxPE = es.MaxPEClusters(Sdf_prompt)
print("TOTAL HIGHEST PE PROMPT CLUSTERS: " + str(len(Sdf_maxPE)))
Sdf_mrd_maxhit = es.MaxHitClusters(Sdf_mrd)
print("TOTAL MOST PADDLE MRD CLUSTERS: " + str(len(Sdf_mrd_maxhit)))
#Now, get the index number for clusterTime pairs in the same triggers
TankIndices, MRDIndices = es.MatchingEventTimes(Sdf_maxPE,Sdf_mrd_maxhit)
TankTimes = Sdf_maxPE["clusterTime"].values[TankIndices]
MRDTimes = Sdf_mrd_maxhit["clusterTime"].values[MRDIndices]
Pairs_HaveVeto = Sdf_mrd_maxhit.loc[(Sdf_mrd_maxhit["vetoHit"].values[MRDIndices]==1)]
print("NUM OF MRD CLUSTERS IN TRIG WITH A TANK CLUSTER: " + str(len(MRDTimes)))
print("NUM OF MRD CLUSTERS WITH VETO IN SUBSET: " + str(len(Pairs_HaveVeto)))
plt.scatter(TankTimes,MRDTimes,marker='o',s=15,color='blue',alpha=0.7)
plt.title("Tank and MRD cluster times in prompt window \n (Largest PE tank clusters, largest paddle count MRD clusters)")
plt.xlabel("Tank Cluster time [ns]")
plt.ylabel("MRD Cluster time [ns]")
plt.show()
plt.hist(MRDTimes - TankTimes, bins = 160, color='blue', alpha=0.7)
plt.axvline(x=700,color='black',linewidth=6)
plt.axvline(x=800,color='black',linewidth=6)
plt.title("Difference in MRD and Tank cluster times in acquisitions \n (Largest PE tank clusters, largest paddle count MRD clusters)")
plt.xlabel("MRD cluster time - Tank cluster time [ns]")
plt.show()
#Get indices for MRD/Tank cluster times within the coincident window
clusterIndices_match = np.where(((MRDTimes - TankTimes)<800) & ((MRDTimes - TankTimes) > 600))[0]
MRDIndices_match = MRDIndices[clusterIndices_match]
TankIndices_match = TankIndices[clusterIndices_match]
MatchedMRDTimes = Sdf_mrd_maxhit['clusterTime'].values[MRDIndices_match]
MatchedTankTimes = Sdf_maxPE['clusterTime'].values[TankIndices_match]
print("NUMBER OF MATCHED TANKS: " + str(len(MatchedTankTimes)))
print("NUMBER OF MATCHED MRDS: " + str(len(MatchedMRDTimes)))
plt.hist(MatchedMRDTimes - MatchedTankTimes, bins = 80,color='blue')
plt.axvline(x=700,color='black',linewidth=6)
plt.axvline(x=800,color='black',linewidth=6)
plt.title("Time distribution for matched MRD and Tank times")
plt.xlabel("MRD cluster time - Tank cluster time [ns]")
plt.show()
plt.hist(Sdf_mrd['clusterTime'].values,bins=80,range=(0,4000),label="All MRD clusters")
plt.hist(Sdf_mrd_maxhit['clusterTime'].values,bins=80,range=(0,4000),label="MRD clusters with most hits")
#plt.hist(Sdf_mrd_maxhit['clusterTime'].values[MRDIndices],bins=80,range=(0,4000),label="+ Tank Cluster pair")
plt.hist(Sdf_mrd_maxhit['clusterTime'].values[MRDIndices_match],bins=80,range=(0,4000),label="+ Tank cluster match")
plt.title("Prompt window MRD cluster times \n event selection impact")
plt.xlabel("Cluster time [ns]")
plt.ylabel("Number of clusters")
leg = plt.legend(loc=1,fontsize=24)
leg.set_frame_on(True)
leg.draw_frame(True)
plt.show()
plt.hist(Sdf_prompt_noCB['clusterTime'].values,bins=80,range=(0,2000),label="All Tank clusters")
plt.hist(Sdf_prompt['clusterTime'].values,bins=80,range=(0,2000),label="+ CB<0.9")
plt.hist(Sdf_maxPE['clusterTime'].values,bins=80,range=(0,2000),label="+ cluster with highest PE")
#plt.hist(Sdf_maxPE['clusterTime'].values[TankIndices],bins=80,range=(0,2000),label="+ MRD Cluster Match")
plt.hist(Sdf_maxPE['clusterTime'].values[TankIndices_match],bins=80,range=(0,2000),label="+ MRD cluster match")
plt.title("Prompt window Tank cluster times \n event selection impact")
plt.ylabel("Number of clusters")
plt.xlabel("Cluster time [ns]")
leg = plt.legend(loc=1,fontsize=24)
leg.set_frame_on(True)
leg.draw_frame(True)
plt.show()
#Now, Get all clusters past 12 us with the Event Times from TankIndices
TankEventTimes_match = Sdf_maxPE["eventTimeTank"].values[TankIndices_match]
Sdf_ClustersInPromptCandidates = es.FilterByEventTime(Sdf,TankEventTimes_match) #All clusters in events with a PMT/MRD match
print("ALL CLUSTER COUNT IN EVENTS WITH PMT/MRD ACTIVITY: " + str(len(Sdf_ClustersInPromptCandidates)))
plt.hist(Sdf_ClustersInPromptCandidates['clusterTime'].values,bins=40,range=(0,67000),label="All clusters")
plt.title("All tank cluster times \n (Acquisitions with valid prompt event selection)")
plt.ylabel("Number of clusters")
plt.xlabel("Cluster time [ns]")
plt.show()
print("CLUSTER COUNT IN EVENTS BEFORE 2 US: " + str(len(Sdf_ClustersInPromptCandidates.loc[Sdf_ClustersInPromptCandidates["clusterTime"]<2000].values)))
Sdf_ValidDelayedClusters = Sdf_ClustersInPromptCandidates.loc[Sdf_ClustersInPromptCandidates['clusterTime']>12000].reset_index(drop=True)
Sdf_ValidDelayedClustersCB = Sdf_ClustersInPromptCandidates.loc[Sdf_ClustersInPromptCandidates['clusterChargeBalance']<0.4].reset_index(drop=True)
print("CLUSTER COUNT IN EVENTS WITH PMT/MRD ACTIVITY PAST 12 US: " + str(len(Sdf_ValidDelayedClusters)))
plt.hist(Sdf.loc[Sdf["clusterTime"]>12000,"clusterTime"],bins=20,range=(12000,65000),label='No PMT/MRD pairing in prompt',alpha=0.8)
plt.hist(Sdf_ValidDelayedClusters["clusterTime"], bins=20, range=(12000,65000),label='PMT/MRD pair required in prompt',alpha=0.8)
plt.hist(Sdf_ValidDelayedClustersCB["clusterTime"], bins=20, range=(12000,65000),label=' + CB < 0.4',alpha=0.8)
plt.title("Delayed cluster times in beam runs")
plt.ylabel("Number of clusters")
plt.xlabel("Cluster time [ns]")
leg = plt.legend(loc=1,fontsize=24)
leg.set_frame_on(True)
leg.draw_frame(True)
plt.show()
#Let's try to make the energy calibration plot
#plt.hist(Sdf_maxPE["clusterPE"], bins=40, range=(0,5000))
#plt.title("Prompt cluster PE \n (Highest PE cluster in prompt window)")
#plt.xlabel("Cluster PE")
#leg = plt.legend(loc=1,fontsize=24)
#leg.set_frame_on(True)
#leg.draw_frame(True)
#plt.show()
Sdf_MatchingPrompts = Sdf_maxPE.loc[TankIndices_match].reset_index(drop=True)
Sdf_MatchingPromptsMRD = Sdf_mrd_maxhit.loc[MRDIndices_match].reset_index(drop=True)
#print("LEN OF TANK MATCHES: " + str(len(Sdf_MatchingPrompts)))
#print("LEN OF MRD MATCHES: " + str(len(Sdf_MatchingPromptsMRD)))
HasVetoHit = np.where(Sdf_MatchingPromptsMRD["vetoHit"].values==1)[0]
print("NUMBER OF INTERACTIONS WITH A VETO HIT: " + str(len(HasVetoHit)))
OneTrack = np.where(Sdf_MatchingPromptsMRD["numClusterTracks"].values==1)[0]
print("NUMBER OF INTERACTIONS WITH ONE TRACK: " + str(len(OneTrack)))
ThroughGoingCandidates = np.intersect1d(HasVetoHit,OneTrack)
Sdf_ThroughGoingCandidates = Sdf_MatchingPrompts.loc[ThroughGoingCandidates].reset_index(drop=True)
#plt.hist(Sdf_ThroughGoingCandidates["clusterPE"], bins=40, range=(0,5000))
#plt.title("Prompt cluster PE \n (Matching MRD cluster + one track + veto hit)")
#plt.xlabel("Cluster PE")
#leg = plt.legend(loc=1,fontsize=24)
#leg.set_frame_on(True)
#leg.draw_frame(True)
#plt.show()
##Now, apply track event selection
#TGValidTrackInds = es.SingleTrackSelection(Sdf_MatchingPromptsMRD.loc[ThroughGoingCandidates].reset_index(drop=True),100,1.0,10)
#print("VALID TRACK INDICES: " + str(TGValidTrackInds))
#Sdf_TGValidTracks = Sdf_ThroughGoingCandidates.loc[TGValidTrackInds].reset_index(drop=True)
#plt.hist(Sdf_TGValidTracks["clusterPE"], bins=40, range=(0,5000))
#plt.title("Prompt cluster PE \n (Matching MRD cluster + one track + veto hit + track cuts)")
#plt.xlabel("Cluster PE")
#leg = plt.legend(loc=1,fontsize=24)
#leg.set_frame_on(True)
#leg.draw_frame(True)
#plt.show()
##Now, apply aggressive track event selection
#TGValidTrackInds = es.SingleTrackSelection(Sdf_MatchingPromptsMRD.loc[ThroughGoingCandidates].reset_index(drop=True),60,0.4,60)
#Sdf_TGValidTracks = Sdf_ThroughGoingCandidates.loc[TGValidTrackInds].reset_index(drop=True)
#Sdf_TGValidTracks = Sdf_TGValidTracks.loc[Sdf_TGValidTracks['clusterHits']>70].reset_index(drop=True)
#plt.hist(Sdf_TGValidTracks["clusterPE"], bins=40, range=(0,5000))
#plt.title("Prompt cluster PE, aggressive cuts \n (Matching MRD cluster + one track + veto hit + track cuts)")
#plt.xlabel("Cluster PE")
#leg = plt.legend(loc=1,fontsize=24)
#leg.set_frame_on(True)
#leg.draw_frame(True)
#plt.show()
#Let's estimate the visible energy for coincident Tank/Cluster events
NoVetoHit = np.where(Sdf_MatchingPromptsMRD["vetoHit"].values==0)[0]
print("NUMBER OF INTERACTIONS WITH NO VETO HIT: " + str(len(NoVetoHit)))
OneTrack = np.where(Sdf_MatchingPromptsMRD["numClusterTracks"].values==1)[0]
NuCandidates = np.intersect1d(NoVetoHit,OneTrack)
Sdf_NuCandidates = Sdf_MatchingPrompts.loc[NuCandidates].reset_index(drop=True)
print("NUMBER OF NU INTERACTION CANDIDATES: " + str(len(Sdf_NuCandidates)))
Sdf_NuCandidatesMRD = Sdf_MatchingPromptsMRD.loc[NuCandidates].reset_index(drop=True)
NUC_PE = Sdf_NuCandidates['clusterPE'].values
NUC_EVENTTIMETANKS = Sdf_NuCandidates['eventTimeTank'].values
NUC_TANKMEV = NUC_PE / PEPERMEV
NUC_MRDENERGY = es.SingleTrackEnergies(Sdf_NuCandidatesMRD)
VISIBLE_ENERGY = (NUC_TANKMEV + NUC_MRDENERGY)/1000
plt.hist(VISIBLE_ENERGY,bins=40,range=(0,4))
plt.xlabel("Visible energy estimate [GeV]")
plt.ylabel("Normalized count rate (A.U.)")
plt.title("Visible energy for single track event in tank and MRD")
plt.show()
#Try to do a normalized comparison.
mc_energy = MCdf["trueMuonEnergy"].values/1000.
mc_energy = MCdf.loc[(MCdf["Pi0Count"]==0) & (MCdf["PiPlusCount"]==0) &
(MCdf["PiMinusCount"]==0),"trueMuonEnergy"].values/1000.
mc_bins, mc_binedges = np.histogram(mc_energy,bins=15,range=(0,2))
mc_binlefts =mc_binedges[0:len(mc_binedges)-1]
binwidth = (mc_binlefts[1]-mc_binlefts[0])
mc_binrights = mc_binlefts + binwidth
mc_bincenters = mc_binlefts + (binwidth/2.)
mc_bins_unc = np.sqrt(mc_bins)
mc_bins_normed = mc_bins/np.sum(mc_bins)
mc_bins_unc_normed = mc_bins_unc/np.sum(mc_bins)
data_bins, data_binedges = np.histogram(VISIBLE_ENERGY,bins=15,range=(0,2))
data_binlefts =data_binedges[0:len(data_binedges)-1]
binwidth = (data_binlefts[1]-data_binlefts[0])
data_bincenters = data_binlefts + (binwidth/2.)
data_bins_unc = np.sqrt(data_bins)
data_bins_normed = data_bins/np.sum(data_bins)
data_bins_unc_normed = data_bins_unc/np.sum(data_bins)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax = abp.NiceBins(ax,mc_binlefts,mc_binrights,mc_bins_normed,'dark blue',"$E_{\mu}$ MC truth")
ax.errorbar(data_bincenters,data_bins_normed,xerr=binwidth/2., yerr=data_bins_unc_normed,
color='black',linestyle='None',markersize=6,label='ANNIE beam data')
#plt.hist(mc_energy,density=True,bins=20,range=(0,2),label='$E_{\mu}$ MC Truth')
#plt.hist(VISIBLE_ENERGY,normed=True,bins=20,range=(0,2), label='Beam data')
plt.xlabel("Visible energy estimate [GeV]")
plt.ylabel("Normalized count rate (A.U.)")
#plt.title("Visible energy of neutrino interaction candidates \n compared to MC truth information")
plt.title("Visible energy of neutrino interaction candidates")
leg = plt.legend(loc=1,fontsize=24)
leg.set_frame_on(True)
leg.draw_frame(True)
plt.show()
zerobins = np.where(data_bins_unc==0)[0]
data_bins_unc_normed[zerobins] = 1.15/np.sum(data_bins)
print("CHISQUARE: " + str(np.sum(((data_bins_normed-mc_bins_normed)/np.sqrt(data_bins_unc_normed**2 + mc_bins_unc_normed**2))**2)))
print("NDOF: " + str(len(data_bins_normed)))
plt.hist(Sdf_maxPE['clusterTime'].values[TankIndices_match],bins=80,range=(0,2000),label="PMT clusters w/ matched MRD")
plt.hist(Sdf_NuCandidates['clusterTime'],bins=80,range=(0,2000),label="+ single track and no veto")
plt.title("Prompt window Tank cluster times \n (Highest PE cluster in event)")
plt.xlabel("Cluster time [ns]")
plt.ylabel("Number of clusters")
leg = plt.legend(loc=1,fontsize=24)
leg.set_frame_on(True)
leg.draw_frame(True)
plt.show()
#Last plots... Average delayed cluster multiplicity as a function of visible energy
#Delayed cluster multiplicity for these candidates in a histogram
NuEventClusters = es.FilterByEventTime(Sdf,Sdf_NuCandidates['eventTimeTank'].values)
NuEventClusters_trig = es.FilterByEventTime(Sdf_trig,Sdf_NuCandidates['eventTimeTank'].values)
NuEventDelayedClusters = NuEventClusters.loc[NuEventClusters['clusterTime']>12000].reset_index(drop=True)
NuEventDelayedClusters = NuEventDelayedClusters.loc[NuEventDelayedClusters['clusterChargeBalance']<0.4].reset_index(drop=True)
MData = abp.MakeClusterMultiplicityPlot(NuEventDelayedClusters,NuEventClusters_trig)
plt.hist(MData,bins=6, range=(0,6), label="Beam data", alpha=0.5,histtype='stepfilled',linewidth=6,color='green')
plt.hist(MData,bins=6, range=(0,6), label="Beam data", alpha=0.9,histtype='step',linewidth=6,color='green')
Michael_NM = [64200, 26663, 7045, 4229, 3121, 2288, 1609, 1024, 258, 4]
Michael_NMCounts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Michael_NMX = np.array(Michael_NM)+0.5
#plt.errorbar(Michael_NMX, Michael_NM, xerr=0.5,color='black',label='GENIE MC')
plt.xlabel("Neutron candidate multiplicity")
plt.ylabel("Number of neutrino candidates")
plt.title("Neutron candidate multiplicity for neutrino candidate events \n (CB<0.4, [12,67] $\mu s$ interval)")
#leg = plt.legend(loc=1,fontsize=24)
#leg.set_frame_on(True)
#leg.draw_frame(True)
plt.show()
energy_min = 0
energy_max = 1.4
EnergyBins, ClusterMultiplicity,ClusterMultiplicity_unc = bp.EstimateEnergyPerClusterRelation(VISIBLE_ENERGY,
NUC_EVENTTIMETANKS, NuEventDelayedClusters,energy_min,energy_max,8)
errwidth = (EnergyBins[1] - EnergyBins[0])/2
plt.errorbar(EnergyBins+errwidth, ClusterMultiplicity,xerr=errwidth,yerr=ClusterMultiplicity_unc,linestyle='None',marker='o',markersize=10,color='green',alpha=0.8,linewidth=5)
plt.xlabel("Visible energy [GeV]")
plt.ylabel("Neutron candidates per event")
plt.title("Mean number of neutron candidates per neutrino candidate")
plt.show()
Sdf_NuCandidates_Aggressive = Sdf_NuCandidates.loc[(Sdf_NuCandidates['clusterPE']>500)&(Sdf_NuCandidates['clusterPE']<3500)]
theinds = Sdf_NuCandidates_Aggressive.index.values
Sdf_NuCandidatesMRD_Aggressive = Sdf_NuCandidatesMRD.loc[theinds]
NUC_PE_AGG = Sdf_NuCandidates_Aggressive['clusterPE']
NUC_EVENTTIMETANKS_AGG = Sdf_NuCandidates_Aggressive['eventTimeTank'].values
NUC_TANKMEV_AGG = NUC_PE_AGG / PEPERMEV
NUC_MRDENERGY_AGG = es.SingleTrackEnergies(Sdf_NuCandidatesMRD_Aggressive)
VISIBLE_ENERGY_AGG = (NUC_TANKMEV_AGG + NUC_MRDENERGY_AGG)/1000
NuEventClusters_Agg = es.FilterByEventTime(Sdf,Sdf_NuCandidates_Aggressive['eventTimeTank'].values)
NuEventDelayedClusters_Agg = NuEventClusters_Agg.loc[NuEventClusters_Agg['clusterTime']>12000].reset_index(drop=True)
NuEventDelayedClusters_Agg = NuEventDelayedClusters_Agg.loc[NuEventDelayedClusters_Agg['clusterChargeBalance']<0.4].reset_index(drop=True)
energy_min = 0
energy_max = 1.4
EnergyBins, ClusterMultiplicity,ClusterMultiplicity_unc = bp.EstimateEnergyPerClusterRelation(VISIBLE_ENERGY_AGG,
NUC_EVENTTIMETANKS_AGG, NuEventDelayedClusters_Agg,energy_min,energy_max,8)
errwidth = (EnergyBins[1] - EnergyBins[0])/2
plt.errorbar(EnergyBins+errwidth, ClusterMultiplicity,xerr=errwidth,yerr=ClusterMultiplicity_unc,linestyle='None',marker='o',markersize=10,color='green',alpha=0.8,linewidth=5)
plt.xlabel("Visible energy [GeV]")
plt.ylabel("Neutron candidates per event")
plt.title("Mean number of neutron candidates per neutrino candidate \n (500 < Tank cluster PE < 3500)")
plt.show()
pe_min = 0
pe_max = 6000
PEBins, ClusterMultiplicity,ClusterMultiplicity_unc = bp.EstimatePEPerClusterRelation(NUC_PE,
NUC_EVENTTIMETANKS, NuEventDelayedClusters,pe_min,pe_max,8)
errwidth = (PEBins[1] - PEBins[0])/2
plt.errorbar(PEBins+errwidth, ClusterMultiplicity,xerr=errwidth,yerr=ClusterMultiplicity_unc,linestyle='None',marker='o',markersize=10,color='green',alpha=0.8,linewidth=5)
plt.xlabel("Visible tank energy [p.e.]")
plt.ylabel("Neutron candidates per event")
plt.title("Mean number of neutron candidates per neutrino candidate")
plt.show()
def BeamPlotDemo(PositionDict, MCdf):
Sdf = PositionDict["Beam"][0]
Sdf_trig = PositionDict["Beam"][1]
Sdf_mrd = PositionDict["Beam"][2]
print("Sdf ", Sdf.head())
print("All columns are: ", Sdf.columns.values.tolist())
Sdf = Sdf.loc[Sdf["eventTimeTank"] > -9].reset_index(drop=True)
Sdf_trig = Sdf_trig.loc[Sdf_trig["eventTimeTank"] > -9].reset_index(
drop=True)
Sdf_mrd = Sdf_mrd.loc[Sdf_mrd["eventTimeTank"] > -9].reset_index(drop=True)
Sdf_TankVeto = es.HasVetoHit_TankClusters(Sdf, Sdf_trig)
print("NUM TANK CLUSTERS WITH VETO HIT: " + str(len(Sdf_TankVeto)))
print("NUM TRIGS: " + str(len(Sdf_trig)))
HasVetoHit = np.where(Sdf_mrd["vetoHit"].values == 1)[0]
print("NUM MRD CLUSTERS WITH VETO HIT: " + str(len(HasVetoHit)))
#---- My Plots:
Sdf_prompt = Sdf.loc[Sdf['clusterTime'] < 2000].reset_index(
drop=True) #prompt events
plt.hist(Sdf_prompt['clusterTime'], bins=100, range=(0, 2000))
plt.title("Prompt window Tank cluster times - no cuts")
plt.xlabel("Cluster time [ns]")
plt.show()
# plt.savefig("plots/time_prompt.png")
Sdf_del = Sdf.loc[Sdf['clusterTime'] >= 2000].reset_index(
drop=True) #delayed events
plt.hist(Sdf_del['clusterTime']) #,bins=100,range=(10000,70000))
plt.title("Delayed window Tank cluster times - no cuts")
plt.xlabel("Cluster time [ns]")
plt.show()
# plt.savefig("plots/time_del.png")
#--- CB to cluster Time:
labels = {
'title':
'Charge balance parameters in time window \n (Beam data, $t_{c}>=2 \, \mu s$)',
'xlabel': 'Cluster time (ns)',
'ylabel': 'Charge balance'
}
ranges = {
'xbins': 58,
'ybins': 50,
'xrange': [2000, 60000],
'yrange': [0, 1]
}
abp.Make2DHist(Sdf_del, 'clusterTime', 'clusterChargeBalance', labels,
ranges)
plt.show()
# plt.savefig("plots/CB_time_del.png")
labels = {
'title':
'Charge balance parameters in time window \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster time (ns)',
'ylabel': 'Charge balance'
}
ranges = {'xbins': 20, 'ybins': 50, 'xrange': [0, 2000], 'yrange': [0, 1]}
abp.Make2DHist(Sdf_prompt, 'clusterTime', 'clusterChargeBalance', labels,
ranges)
plt.show()
# plt.savefig("plots/CB_time_prompt.png")
#--- CB to clusterPE:
labels = {
'title':
'Charge balance parameters in time window \n (Beam data, $t_{c}>=2 \, \mu s$)',
'xlabel': 'Cluster PE',
'ylabel': 'Charge balance'
}
ranges = {'xbins': 58, 'ybins': 50, 'xrange': [0, 500], 'yrange': [0, 1]}
abp.Make2DHist(Sdf_del, 'clusterPE', 'clusterChargeBalance', labels,
ranges)
plt.show()
# plt.savefig("plots/CB_PE_del.png")
labels = {
'title':
'Charge balance parameters in time window \n (Beam data, $t_{c}<2 \, \mu s$)',
'xlabel': 'Cluster PE',
'ylabel': 'Charge balance'
}
ranges = {'xbins': 20, 'ybins': 50, 'xrange': [0, 500], 'yrange': [0, 1]}
abp.Make2DHist(Sdf_prompt, 'clusterPE', 'clusterChargeBalance', labels,
ranges)
plt.show()
# plt.savefig("plots/CB_PE_prompt.png")
#splitting to CB categories:
#--- CB>=0.9
Sdf_prompt_highCB = Sdf_prompt.loc[
Sdf_prompt['clusterChargeBalance'] >= 0.9].reset_index(drop=True)
Sdf_del_highCB = Sdf_del.loc[
Sdf_del['clusterChargeBalance'] >= 0.9].reset_index(drop=True)
labels = {
'title':
'Total PE vs Maximum PE in Cluster for \n (Beam data, $t_{c}<2 \, \mu s$) \n CB>=0.9 ',
'xlabel': 'Cluster PE',
'ylabel': 'Maximum PE in Cluster'
}
ranges = {
'xbins': 200,
'ybins': 200,
'xrange': [0, 200],
'yrange': [0, 200]
}
#abp.Make2DHist(Sdf_prompt_highCB,'clusterPE','clusterMaxPE',labels,ranges)
abp.Make2DHist(Sdf_prompt_highCB, 'clusterPE', 'clusterMaxPE', labels,
ranges)
plt.show()
# plt.savefig("plots/PE_maxPE_prompt_highCB.png")
#PE = np.hstack(Sdf_del_highCB['hitPE'])
#ID = np.hstack(Sdf_del_highCB['hitDetID'])
#T = np.hstack(Sdf_del_highCB['hitT'])
#maxPE_highCB = max(np.hstack(Sdf_prompt_highCB.hitPE))
#print("maxPE_highCB ",maxPE_highCB," clusterMaxPE ",Sdf_prompt_highCB.clusterMaxPE)
highCB_PE = np.hstack(Sdf_prompt_highCB.hitPE)
highCB_DetID = np.hstack(Sdf_prompt_highCB.hitDetID)
# highCB_PE = np.hstack(Sdf_del_highCB.hitPE)
# highCB_DetID = np.hstack(Sdf_del_highCB.hitDetID)
plt.hist2d(highCB_DetID, highCB_PE)
plt.title("PE distribution for all hits in clusters, CB>=0.9)")
plt.xlabel("Tube ID")
plt.ylabel("PE")
plt.show()
# plt.savefig("plots/TubeID_PE_prompt_highCB.png")
#--- 0.6<CB<0.9
Sdf_prompt_upperCB = Sdf_prompt.loc[
(Sdf_prompt['clusterChargeBalance'] < 0.9)
& (Sdf_prompt['clusterChargeBalance'] >= 0.6)].reset_index(drop=True)
Sdf_del_upperCB = Sdf_del.loc[(Sdf_del['clusterChargeBalance'] < 0.9) & (
Sdf_prompt['clusterChargeBalance'] >= 0.6)].reset_index(drop=True)
labels = {
'title':
'Total PE vs Maximum PE in Cluster for \n (Beam data, $t_{c}<2 \, \mu s$) \n 0.6<=CB<0.9',
'xlabel': 'Cluster PE',
'ylabel': 'Maximum PE in Cluster'
}
ranges = {
'xbins': 200,
'ybins': 200,
'xrange': [0, 200],
'yrange': [0, 200]
}
abp.Make2DHist(Sdf_prompt_upperCB, 'clusterPE', 'clusterMaxPE', labels,
ranges)
plt.show()
# plt.savefig("plots/PE_maxPE_prompt_upperCB.png")
upperCB_PE = np.hstack(Sdf_prompt_upperCB.hitPE)
upperCB_DetID = np.hstack(Sdf_prompt_upperCB.hitDetID)
plt.hist2d(upperCB_DetID, upperCB_PE)
plt.title("PE distribution for all hits in clusters, 0.6=<CB<0.9)")
plt.xlabel("Tube ID")
plt.ylabel("PE")
plt.show()
# plt.savefig("plots/TubeID_PE_prompt_upperCB.png")
#--- 0.4<CB<0.6
Sdf_prompt_midCB = Sdf_prompt.loc[
(Sdf_prompt['clusterChargeBalance'] < 0.6)
& (Sdf_prompt['clusterChargeBalance'] >= 0.4)].reset_index(drop=True)
Sdf_del_midCB = Sdf_del.loc[(Sdf_del['clusterChargeBalance'] < 0.6) & (
Sdf_prompt['clusterChargeBalance'] >= 0.4)].reset_index(drop=True)
labels = {
'title':
'Total PE vs Maximum PE in Cluster for \n (Beam data, $t_{c}<2 \, \mu s$)\n 0.4<=CB<0.6',
'xlabel': 'Cluster PE',
'ylabel': 'Maximum PE in Cluster'
}
ranges = {
'xbins': 200,
'ybins': 200,
'xrange': [0, 200],
'yrange': [0, 200]
}
abp.Make2DHist(Sdf_prompt_midCB, 'clusterPE', 'clusterMaxPE', labels,
ranges)
plt.show()
# plt.savefig("plots/PE_maxPE_prompt_midCB.png")
midCB_PE = np.hstack(Sdf_prompt_midCB.hitPE)
midCB_DetID = np.hstack(Sdf_prompt_midCB.hitDetID)
plt.hist2d(midCB_DetID, midCB_PE)
plt.title("PE distribution for all hits in clusters, 0.4=<CB<0.6)")
plt.xlabel("Tube ID")
plt.ylabel("PE")
plt.show()
# plt.savefig("plots/TubeID_PE_prompt_midCB.png")
#--- CB<0.4
Sdf_prompt_lowCB = Sdf_prompt.loc[
Sdf_prompt['clusterChargeBalance'] < 0.4].reset_index(drop=True)
Sdf_del_lowCB = Sdf_del.loc[
Sdf_del['clusterChargeBalance'] < 0.4].reset_index(drop=True)
labels = {
'title':
'Total PE vs Maximum PE in Cluster for \n (Beam data, $t_{c}<2 \, \mu s$) \n CB<0.4',
'xlabel': 'Cluster PE',
'ylabel': 'Maximum PE in Cluster'
}
ranges = {
'xbins': 200,
'ybins': 200,
'xrange': [0, 200],
'yrange': [0, 200]
}
abp.Make2DHist(Sdf_prompt_lowCB, 'clusterPE', 'clusterMaxPE', labels,
ranges)
plt.show()
# plt.savefig("plots/PE_maxPE_prompt_lowCB.png")
lowCB_PE = np.hstack(Sdf_prompt_lowCB.hitPE)
lowCB_DetID = np.hstack(Sdf_prompt_lowCB.hitDetID)
plt.hist2d(lowCB_DetID, lowCB_PE)
plt.title("PE distribution for all hits in clusters, CB<=0.4)")
plt.xlabel("Tube ID")
plt.ylabel("PE")
plt.show()