def get_metadata(input_dir, tree, debug=0):
json_name = 'metadata.json'
file_metadata = {}
json_files = listFiles(input_dir, match=json_name)
if len(json_files) == 0:
print 'no metadata file {} in input dir: {}'.format(
json_name, input_dir)
print 'Will now index files...'
files = listFiles(input_dir)
print '# of files: {}'.format(len(files))
for idx, file_name in enumerate(files):
ntuple = HGCalNtuple([file_name], tree)
nevents = ntuple.nevents()
file_metadata[file_name] = nevents
if debug > 2:
print ' [{}] file: {} # events: {}'.format(
idx, file_name, nevents)
with open(json_name, 'w') as fp:
json.dump(file_metadata, fp)
copy_to_eos(file_name=json_name,
target_dir=input_dir,
target_file_name=json_name)
else:
print 'dir already indexed, will read metadata...'
unique_filename = '{}.json'.format(uuid.uuid4())
copy_from_eos(input_dir=input_dir,
file_name=json_name,
target_file_name=unique_filename)
with open(unique_filename, 'r') as fp:
file_metadata = json.load(fp)
os.remove(unique_filename)
return file_metadata
def main():
ntuple = HGCalNtuple(
"/Users/clange/CERNBox/partGun_PDGid211_x120_E80.0To80.0_NTUP_9.root")
tot_nevents = 0
tot_genpart = 0
tot_rechit = 0
tot_rechit_raw = 0
tot_cluster2d = 0
tot_multiclus = 0
tot_simcluster = 0
tot_pfcluster = 0
tot_calopart = 0
tot_track = 0
for event in ntuple:
# print "Event", event.entry()
tot_nevents += 1
genParts = event.genParticles()
tot_genpart += len(genParts)
recHits = event.recHits()
tot_rechit += len(recHits)
if (ntuple.hasRawRecHits()):
recHitsRaw = event.recHits("rechit_raw")
tot_rechit_raw += len(recHitsRaw)
layerClusters = event.layerClusters()
tot_cluster2d += len(layerClusters)
multiClusters = event.multiClusters()
tot_multiclus += len(multiClusters)
simClusters = event.simClusters()
tot_simcluster += len(simClusters)
pfClusters = event.pfClusters()
tot_pfcluster += len(pfClusters)
pfClusters = event.pfClusters()
tot_pfcluster += len(pfClusters)
caloParts = event.caloParticles()
tot_calopart += len(caloParts)
tracks = event.tracks()
tot_track += len(tracks)
# for genPart in genParts:
# print tot_nevents, "genPart pt:", genPart.pt()
print "Processed %d events" % tot_nevents
print "On average %f generator particles" % (float(tot_genpart) /
tot_nevents)
print "On average %f reconstructed hits" % (float(tot_rechit) /
tot_nevents)
print "On average %f raw reconstructed hits" % (float(tot_rechit_raw) /
tot_nevents)
print "On average %f layer clusters" % (float(tot_cluster2d) / tot_nevents)
print "On average %f multi-clusters" % (float(tot_multiclus) / tot_nevents)
print "On average %f sim-clusters" % (float(tot_simcluster) / tot_nevents)
print "On average %f PF clusters" % (float(tot_pfcluster) / tot_nevents)
print "On average %f calo particles" % (float(tot_calopart) / tot_nevents)
print "On average %f tracks" % (float(tot_track) / tot_nevents)
def main():
relFractionE = 0.001
# set sample/tree - for pions
pidSelected = 211
# GEN_eng = 5.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E5_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E5.0To5.0_NTUP.root")
# GEN_eng = 20.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E20_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E20.0To20.0_NTUP.root")
# GEN_eng = 50.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E50_cmssw93X_withPRs_20170809/NTUP/partGun_PDGid211_x100_E50.0To50.0_NTUP.root")
# GEN_eng = 100.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E100_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E100.0To100.0_NTUP.root")
# GEN_eng = 300.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E300_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E300.0To300.0_NTUP.root")
# set sample/tree - for photons
pidSelected = 22
GEN_eng = 5.
ntuple = HGCalNtuple(
"/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E5_cmssw93X_withPRs_20170809/NTUP/partGun_PDGid22_x100_E5.0To5.0_NTUP.root"
)
# GEN_eng = 20.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E20_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E20.0To20.0_NTUP.root")
# GEN_eng = 50.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E50_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E50.0To50.0_NTUP.root")
# GEN_eng = 100.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E100_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E100.0To100.0_NTUP.root")
# GEN_eng = 300.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E300_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E300.0To300.0_NTUP.root")
# ntuple = HGCalNtuple("../data/_SingleGammaPt100Eta1p6_2p8_PhaseIITDRFall17DR-noPUFEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO/NTUP/_SingleGammaPt100Eta1p6_2p8_PhaseIITDRFall17DR-noPUFEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO_NTUP_1.root")
runCalibrationScaleResolution(pidSelected, GEN_eng, ntuple, relFractionE)
def main():
relFractionE = 0.001
# set sample/tree - for pions
pidSelected = 211
# GEN_eng = 5.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E5_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E5.0To5.0_NTUP.root")
# GEN_eng = 20.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E20_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E20.0To20.0_NTUP.root")
# GEN_eng = 50.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E50_cmssw93X_withPRs_20170809/NTUP/partGun_PDGid211_x100_E50.0To50.0_NTUP.root")
# GEN_eng = 100.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E100_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E100.0To100.0_NTUP.root")
# GEN_eng = 300.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E300_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E300.0To300.0_NTUP.root")
# set sample/tree - for photons
pidSelected = 22
GEN_eng = 5.
ntuple = HGCalNtuple(
"/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E5_cmssw93X_withPRs_20170809/NTUP/partGun_PDGid22_x100_E5.0To5.0_NTUP.root"
)
# GEN_eng = 20.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E20_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E20.0To20.0_NTUP.root")
# GEN_eng = 50.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E50_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E50.0To50.0_NTUP.root")
# GEN_eng = 100.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E100_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E100.0To100.0_NTUP.root")
# GEN_eng = 300.
# ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid22_E300_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid22_x100_E300.0To300.0_NTUP.root")
runCalibrationScaleResolution(pidSelected, GEN_eng, ntuple, relFractionE)
def main():
global opt, args
usage = ('usage: %prog [options]\n' + '%prog -h for help')
parser = optparse.OptionParser(usage)
# input options
parser.add_option(
'',
'--files',
dest='fileString',
type='string',
default=
'/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomPtGunProducer_SinglePiPt2Eta1p6_2p8_Fall17DR-NoPUFEVT_clange_20180129/NTUP/partGun_PDGid211_x60_Pt2.0To2.0_NTUP_6.root',
help='comma-separated file list')
parser.add_option('',
'--gunType',
dest='gunType',
type='string',
default='pt',
help='pt or e')
parser.add_option('',
'--pid',
dest='pid',
type='int',
default=211,
help='pdgId int')
parser.add_option('',
'--genValue',
dest='genValue',
type='float',
default=25,
help='generated pT or energy')
# store options and arguments as global variables
global opt, args
(opt, args) = parser.parse_args()
print "files:", opt.fileString
print "gunType:", opt.gunType
print "pid:", opt.pid
print "GEN_engpt:", opt.genValue
# set sample/tree - for photons
gun_type = opt.gunType
pidSelected = opt.pid
GEN_engpt = opt.genValue
fileList = opt.fileString.split(",")
for fileName in fileList:
ntuple = HGCalNtuple(opt.fileString)
for event in ntuple:
if (event.entry() > 11):
break
SACEvt = SACevent(event, 60)
SACEvt.Print()
def main():
inFile = sys.argv[1]
ntuple = HGCalNtuple(inFile)
maxEvents = 10
tot_nevents = 0
tot_genpart = 0
tot_rechit = 0
tot_cluster2d = 0
tot_multiclus = 0
tot_simcluster = 0
tot_pfcluster = 0
tot_calopart = 0
tot_track = 0
for event in ntuple:
if event.entry() >= maxEvents:
break
print("Event", event.entry() + 1)
tot_nevents += 1
genParts = event.genParticles()
tot_genpart += len(genParts)
recHits = event.recHits()
tot_rechit += len(recHits)
layerClusters = event.layerClusters()
tot_cluster2d += len(layerClusters)
multiClusters = event.multiClusters()
tot_multiclus += len(multiClusters)
simClusters = event.simClusters()
tot_simcluster += len(simClusters)
pfClusters = event.pfClusters()
tot_pfcluster += len(pfClusters)
pfClusters = event.pfClusters()
tot_pfcluster += len(pfClusters)
caloParts = event.caloParticles()
tot_calopart += len(caloParts)
tracks = event.tracks()
tot_track += len(tracks)
# for genPart in genParts:
# print(tot_nevents, "genPart pt:", genPart.pt()
print("Processed %d events" % tot_nevents)
print("On average %f generator particles" %
(float(tot_genpart) / tot_nevents))
print("On average %f reconstructed hits" %
(float(tot_rechit) / tot_nevents))
print("On average %f layer clusters" %
(float(tot_cluster2d) / tot_nevents))
print("On average %f multi-clusters" %
(float(tot_multiclus) / tot_nevents))
print("On average %f sim-clusters" % (float(tot_simcluster) / tot_nevents))
print("On average %f PF clusters" % (float(tot_pfcluster) / tot_nevents))
print("On average %f calo particles" % (float(tot_calopart) / tot_nevents))
print("On average %f tracks" % (float(tot_track) / tot_nevents))
def main():
# ============================================
# configuration bit
maxEvents = 100
debug = 1
input_base_dir = '/Users/cerminar/cernbox/hgcal/CMSSW932/'
#input_sample_dir = 'FlatRandomEGunProducer_EleGunE50_1p7_2p8_PU0_20171005/NTUP/'
#output_filename = 'histos_EleE50_PU0.root'
# input_sample_dir = 'FlatRandomEGunProducer_EleGunE50_1p7_2p8_PU50_20171005/NTUP/'
# output_filename = 'histos_EleE50_PU50.root'
input_sample_dir = 'FlatRandomEGunProducer_EleGunE50_1p7_2p8_PU200_20171005/NTUP/'
output_filename = 'histos_EleE50_PU200.root'
# ============================================
input_files = listFiles(os.path.join(input_base_dir, input_sample_dir))
print('- dir {} contains {} files.'.format(input_sample_dir,
len(input_files)))
chain = getChain('hgcalTriggerNtuplizer/HGCalTriggerNtuple', input_files)
print('- created TChain containing {} events'.format(chain.GetEntries()))
ntuple = HGCalNtuple(input_files,
tree='hgcalTriggerNtuplizer/HGCalTriggerNtuple')
output = ROOT.TFile(output_filename, "RECREATE")
output.cd()
hgen = histos.GenPartHistos('h_genAll')
htc = histos.TCHistos('h_tcAll')
h2dcl = histos.ClusterHistos('h_clAll')
h3dcl = histos.Cluster3DHistos('h_cl3dAll')
import time
import numpy as np
def loopNP(narray, buff):
for e in range(0, len(narray)):
buff += narray[e]
return buff
def loopROOT(narray, buff):
for e in range(0, len(narray)):
buff += narray[e]
return buff
def loopCMG(narray, buff):
for row in narray['energy']:
buff += row
buff = np.sum(narray['energy'])
# # print
# for e in range(0, len(narray._dataframe)):
# buff += narray._dataframe.loc[e].tc_energy
return buff
for ientry, entry in enumerate(chain):
print(ientry)
if ientry == 2:
break
start = time.clock()
narray_id = np.array(entry.tc_id)
narray_subdet = np.array(entry.tc_subdet)
narray_zside = np.array(entry.tc_zside)
narray_wafer = np.array(entry.tc_wafer)
narray_wafertype = np.array(entry.tc_wafertype)
narray_energy = np.array(entry.tc_energy)
narray_eta = np.array(entry.tc_eta)
narray_phi = np.array(entry.tc_phi)
narray_z = np.array(entry.tc_z)
narray_cell = np.array(entry.tc_cell)
narray_data = np.array(entry.tc_data)
narray_layer = np.array(entry.tc_layer)
buff = 0
buff = loopNP(narray_energy, buff)
end = time.clock()
print("LEN NP: {}".format(len(narray_energy)))
print("PERF Numpy: {}".format(end - start))
print("SUM: {}".format(buff))
buff = 0
print("LEN PY: {}".format(len(entry.tc_energy)))
start = time.clock()
buff = loopROOT(entry.tc_energy, buff)
end = time.clock()
print("PERF py: {}".format(end - start))
print("SUM: {}".format(buff))
buff = 0
start = time.clock()
event = Event(chain, ientry)
triggerCells = event.getDataFrame(prefix='tc')
buff = loopCMG(triggerCells, buff)
end = time.clock()
print("LEN CMG: {}".format(len(triggerCells.energy)))
print("PERF CMG: {}".format(end - start))
print("SUM: {}".format(buff))
sys.exit(0)
def analyze(params, batch_idx=0):
print(params)
doAlternative = False
debug = int(params.debug)
pool = Pool(5)
tc_geom_df = pd.DataFrame()
cell_geom_df = pd.DataFrame()
geom_file = params.input_base_dir + '/geom/test_triggergeom_v1.root'
print 'Loading the geometry...'
tc_geom_tree = HGCalNtuple([geom_file],
tree='hgcaltriggergeomtester/TreeTriggerCells')
tc_geom_df = convertGeomTreeToDF(tc_geom_tree._tree)
tc_geom_df['radius'] = np.sqrt(tc_geom_df['x']**2 + tc_geom_df['y']**2)
tc_geom_df['eta'] = np.arcsinh(tc_geom_df.z / tc_geom_df.radius)
cell_geom_tree = HGCalNtuple([geom_file],
tree='hgcaltriggergeomtester/TreeCells')
cell_geom_df = convertGeomTreeToDF(cell_geom_tree._tree)
debugPrintOut(debug,
'Cell geometry',
toCount=cell_geom_df,
toPrint=cell_geom_df.iloc[:3])
debugPrintOut(debug,
'TC geometry',
toCount=tc_geom_df,
toPrint=tc_geom_df.iloc[:3])
display = EventDisplayManager(cell_geom=cell_geom_df,
trigger_cell_geom=tc_geom_tree)
# for index, tc_geom in tc_geom_df.iterrows():
# tc_geom.max_dist_neigh = np.max(tc_geom.neighbor_distance)
algos = ['DEF', 'DBS']
particles = [
Particle('ele', PID.electron),
Particle('photon', PID.photon),
Particle('pion', PID.pion),
Particle('pizero', PID.pizero)
]
tc_layer = tc_geom_df[(tc_geom_df.eta < 0) & (tc_geom_df.layer == 1)]
tc_layer['energy'] = tc_layer.id * 1000
gridM = Grid(x_nbins=68,
x_min=-170.,
x_max=170.,
y_nbins=68,
y_min=-170.,
y_max=170.,
z=-320.755005)
gridP = Grid(x_nbins=68,
x_min=-170.,
x_max=170.,
y_nbins=68,
y_min=-170.,
y_max=170.,
z=320.755005)
towerMap = TowerMaps(refGridPlus=gridP, refGridMinus=gridM)
#display.displayTriggerCells(1, tc_layer)
#display.displayTowers(1, -1, 3, 1, gridM)
tc_layer_1_EE = tc_geom_df[(tc_geom_df.eta < 0) & (tc_geom_df.layer == 1) &
(tc_geom_df.subdet == 3)]
print tc_layer_1_EE.iloc[1]
gridEE_m_l1 = towerMap.extrapolateXY(tc_layer_1_EE.iloc[1].z)
print gridEE_m_l1.getCorners(34, 34)
gridEE_m_l1.getBinCenter(65, 34)
gridEE_m_l1.getBinCenter(64, 34)
gridEE_m_l1.getBinCenter(42, 34)
gridEE_m_l1.getBinCenter(43, 34)
tc_layer_1_FH = tc_geom_df[(tc_geom_df.eta < 0) & (tc_geom_df.layer == 1) &
(tc_geom_df.subdet == 4)]
print tc_layer_1_FH.iloc[1]
gridFH_m_l1 = towerMap.extrapolateXY(tc_layer_1_FH.iloc[1].z)
gridFH_m_l1.getBinCenter(65, 34)
gridFH_m_l1.getBinCenter(64, 34)
gridFH_m_l1.getBinCenter(42, 34)
gridFH_m_l1.getBinCenter(43, 34)
print gridFH_m_l1.getCorners(34, 34)
tc_layer_1_BH = tc_geom_df[(tc_geom_df.eta < 0) & (tc_geom_df.layer == 1) &
(tc_geom_df.subdet == 5)]
print tc_layer_1_BH.iloc[1]
gridBH_m_l1 = towerMap.extrapolateXY(tc_layer_1_BH.iloc[1].z)
print gridBH_m_l1.getCorners(34, 34)
gridBH_m_l1.getBinCenter(65, 34)
gridBH_m_l1.getBinCenter(64, 34)
gridBH_m_l1.getBinCenter(42, 34)
gridBH_m_l1.getBinCenter(43, 34)
tc_layer_12_BH = tc_geom_df[(tc_geom_df.eta < 0) & (tc_geom_df.layer == 12)
& (tc_geom_df.subdet == 5)]
print tc_layer_12_BH.iloc[1]
gridBH_m_l12 = towerMap.extrapolateXY(tc_layer_12_BH.iloc[1].z)
print gridBH_m_l12.getCorners(34, 34)
#display.show(1)
sys.exit(0)
input_files = listFiles(
os.path.join(params.input_base_dir, params.input_sample_dir))
print('- dir {} contains {} files.'.format(params.input_sample_dir,
len(input_files)))
ntuple = HGCalNtuple(input_files,
tree='hgcalTriggerNtuplizer/HGCalTriggerNtuple')
print('- created TChain containing {} events'.format(ntuple.nevents()))
event_n = 0
while (False):
event_n = input('Enter event number (-1 to quit): ')
print 'Processing event: {}'.format(event_n)
if event_n == -1:
break
event = ntuple.getEvent(event_n)
print("--- Event {}, @ {}".format(event.entry(),
datetime.datetime.now()))
print(' run: {}, lumi: {}, event: {}'.format(
event.run(), event.lumi(), event.event()))
genParts = event.getDataFrame(prefix='gen')
if len(genParts[(genParts.eta > 1.7) & (genParts.eta < 2.5)]) == 0:
print "No particles in interesting era range"
continue
genParticles = event.getDataFrame(prefix='genpart')
genParticles['pdgid'] = genParticles.pid
hgcDigis = event.getDataFrame(prefix='hgcdigi')
triggerCells = event.getDataFrame(prefix='tc')
# this is not needed anymore in recent versions of the ntuples
# tcsWithPos = pd.merge(triggerCells, tc_geom_df[['id', 'x', 'y']], on='id')
triggerClusters = event.getDataFrame(prefix='cl')
triggerClusters['ncells'] = [len(x) for x in triggerClusters.cells]
if 'x' not in triggerClusters.columns:
triggerClusters = pd.merge(triggerClusters,
tc_geom_df[['z', 'id']],
on='id')
triggerClusters['R'] = triggerClusters.z / np.sinh(
triggerClusters.eta)
triggerClusters['x'] = triggerClusters.R * np.cos(
triggerClusters.phi)
triggerClusters['y'] = triggerClusters.R * np.sin(
triggerClusters.phi)
trigger3DClusters = event.getDataFrame(prefix='cl3d')
trigger3DClusters['nclu'] = [
len(x) for x in trigger3DClusters.clusters
]
triggerClustersGEO = pd.DataFrame()
trigger3DClustersGEO = pd.DataFrame()
triggerClustersDBS = pd.DataFrame()
trigger3DClustersDBS = pd.DataFrame()
trigger3DClustersDBSp = pd.DataFrame()
debugPrintOut(debug, 'gen parts', toCount=genParts, toPrint=genParts)
debugPrintOut(debug,
'gen particles',
toCount=genParticles,
toPrint=genParticles)
# [['eta', 'phi', 'pt', 'energy', 'mother', 'gen', 'pid', 'pdgid', 'reachedEE']]
debugPrintOut(debug,
'digis',
toCount=hgcDigis,
toPrint=hgcDigis.iloc[:3])
debugPrintOut(debug,
'Trigger Cells',
toCount=triggerCells,
toPrint=triggerCells.iloc[:3])
debugPrintOut(debug,
'2D clusters',
toCount=triggerClusters,
toPrint=triggerClusters.iloc[:3])
debugPrintOut(debug,
'3D clusters',
toCount=trigger3DClusters,
toPrint=trigger3DClusters.iloc[:3])
if params.clusterize and False:
# Now build DBSCAN 2D clusters
for zside in [-1, 1]:
arg = [(layer, zside, triggerCells) for layer in range(0, 29)]
results = pool.map(clAlgo.buildDBSCANClustersUnpack, arg)
for clres in results:
triggerClustersDBS = triggerClustersDBS.append(
clres, ignore_index=True)
debugPrintOut(debug,
'DBS 2D clusters',
toCount=triggerClustersDBS,
toPrint=triggerClustersDBS.iloc[:3])
trigger3DClustersDBS = build3DClusters(
'DBS', clAlgo.build3DClustersEtaPhi, triggerClustersDBS, pool,
debug)
trigger3DClustersDBSp = build3DClusters('DBSp',
clAlgo.build3DClustersProj,
triggerClustersDBS, pool,
debug)
display.displayTriggerCells(event_n, triggerCells)
display.displayClusters(event_n, triggerClusters, triggerCells)
display.displayGenParticle(
event_n, genParticles[(genParticles.gen > 0)
& (genParticles.pid == PID.photon) &
(genParticles.reachedEE == 2) &
(np.abs(genParticles.eta) < 2.8) &
(np.abs(genParticles.eta) > 1.7)])
display.show(event_n)
def analyze(params, batch_idx=0):
print(params)
debug = int(params.debug)
tc_geom_df = pd.DataFrame()
tc_rod_bins = pd.DataFrame()
if False:
# read the geometry dump
geom_file = os.path.join(params.input_base_dir,
'geom/test_triggergeom.root')
tc_geom_tree = HGCalNtuple(
[geom_file], tree='hgcaltriggergeomtester/TreeTriggerCells')
tc_geom_tree.setCache(learn_events=100)
print('read TC GEOM tree with # events: {}'.format(
tc_geom_tree.nevents()))
tc_geom_df = convertGeomTreeToDF(tc_geom_tree._tree)
tc_geom_df['radius'] = np.sqrt(tc_geom_df['x']**2 + tc_geom_df['y']**2)
tc_geom_df['eta'] = np.arcsinh(tc_geom_df.z / tc_geom_df.radius)
if False:
tc_rod_bins = pd.read_csv(
filepath_or_buffer='data/TCmapping_v2.txt',
sep=' ',
names=['id', 'rod_x', 'rod_y'],
index_col=False)
tc_rod_bins['rod_bin'] = tc_rod_bins.apply(
func=lambda cell: (int(cell.rod_x), int(cell.rod_y)), axis=1)
tc_geom_df = pd.merge(tc_geom_df, tc_rod_bins, on='id')
if debug == -4:
tc_geom_tree.PrintCacheStats()
print('...done')
tree_name = 'hgcalTriggerNtuplizer/HGCalTriggerNtuple'
input_files = []
range_ev = (0, params.maxEvents)
if params.events_per_job == -1:
print 'This is interactive processing...'
input_files = fm.get_files_for_processing(
input_dir=os.path.join(params.input_base_dir,
params.input_sample_dir),
tree=tree_name,
nev_toprocess=params.maxEvents,
debug=debug)
else:
print 'This is batch processing...'
input_files, range_ev = fm.get_files_and_events_for_batchprocessing(
input_dir=os.path.join(params.input_base_dir,
params.input_sample_dir),
tree=tree_name,
nev_toprocess=params.maxEvents,
nev_perjob=params.events_per_job,
batch_id=batch_idx,
debug=debug)
# print ('- dir {} contains {} files.'.format(params.input_sample_dir, len(input_files)))
print '- will read {} files from dir {}:'.format(len(input_files),
params.input_sample_dir)
for file_name in input_files:
print ' - {}'.format(file_name)
ntuple = HGCalNtuple(input_files, tree=tree_name)
if params.events_per_job == -1:
if params.maxEvents == -1:
range_ev = (0, ntuple.nevents())
print('- created TChain containing {} events'.format(ntuple.nevents()))
print('- reading from event: {} to event {}'.format(
range_ev[0], range_ev[1]))
ntuple.setCache(learn_events=1, entry_range=range_ev)
output = ROOT.TFile(params.output_filename, "RECREATE")
output.cd()
if False:
hTCGeom = histos.GeomHistos('hTCGeom')
hTCGeom.fill(tc_geom_df[(np.abs(tc_geom_df.eta) > 1.65)
& (np.abs(tc_geom_df.eta) < 2.85)])
# instantiate all the plotters
plotter_collection = []
plotter_collection.extend(params.plotters)
print plotter_collection
# -------------------------------------------------------
# book histos
for plotter in plotter_collection:
plotter.book_histos()
# -------------------------------------------------------
# event loop
ev_manager = collections.EventManager()
if params.weight_file is not None:
ev_manager.read_weight_file(params.weight_file)
nev = 0
for evt_idx in range(range_ev[0], range_ev[1] + 1):
# print(evt_idx)
event = ntuple.getEvent(evt_idx)
if (params.maxEvents != -1 and nev >= params.maxEvents):
break
if debug >= 2 or event.entry() % 100 == 0:
print("--- Event {}, @ {}".format(event.entry(),
datetime.datetime.now()))
print(' run: {}, lumi: {}, event: {}'.format(
event.run(), event.lumi(), event.event()))
nev += 1
try:
ev_manager.read(event, debug)
puInfo = event.getPUInfo()
debugPrintOut(debug, 'PU', toCount=puInfo, toPrint=puInfo)
for plotter in plotter_collection:
#print plotter
plotter.fill_histos(debug=debug)
except Exception as inst:
print("[EXCEPTION OCCURRED:] --- Event {}, @ {}".format(
event.entry(), datetime.datetime.now()))
print(' run: {}, lumi: {}, event: {}'.format(
event.run(), event.lumi(), event.event()))
print(str(inst))
print("Unexpected error:", sys.exc_info()[0])
traceback.print_exc()
sys.exit(200)
print("Processed {} events/{} TOT events".format(nev, ntuple.nevents()))
print("Writing histos to file {}".format(params.output_filename))
lastfile = ntuple.tree().GetFile()
print 'Read bytes: {}, # of transaction: {}'.format(
lastfile.GetBytesRead(), lastfile.GetReadCalls())
if debug == -4:
ntuple.PrintCacheStats()
output.cd()
hm = histos.HistoManager()
hm.writeHistos()
output.Close()
return
def main():
# init output stuff
outDir = "testReClusteringExample"
if not os.path.exists(outDir): os.makedirs(outDir)
histDict = {}
# get sample/tree
# please give an CMSSW930 NTUP root file.
#########################################
ntuple = HGCalNtuple(
"root://eoscms.cern.ch//eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomEGunProducer_pdgid211_E20_cmssw93X_withPRs_20170817/NTUP/partGun_PDGid211_x100_E20.0To20.0_NTUP_1.root"
) # CMSSW_9_3_0_pre3 with some pre4 PRs on top
#ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomPtGunProducer_predragm_PDGid22_nPart1_Pt20to100_Eta2p3to2p5_cmssw921_20170605/NTUP/partGun_PDGid22_x400_Pt20.0To100.0_NTUP_1.root") # cmssw921 with all recent fixes as of June 12
#ntuple = HGCalNtuple("/eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/FlatRandomPtGunProducer_predragm_PDGid22_id211_id11_id15_id130_nPart1_Pt20to100_Eta2p3to2p5_cmssw921_20170606/NTUP/partGun_PDGid22_id211_id11_id15_id130_x400_Pt20.0To100.0_NTUP_1.root")# cmssw921 with all recent fixes as of June 12
# prepare some lists for comparions
multiClusters_nClust2DDiff = []
tot_nClust2D_reco = []
clusters2D_eng_reco = []
tot_nClust2D_rerun = []
clusters2D_eng_rerun = []
clusters2DMultiSelected_eng_rerun = []
# start event loop
for event in ntuple:
if (not event.entry() in allowedRangeEvents):
continue # checking external condition
if (verbosityLevel >= 1): print("\nCurrent event: ", event.entry())
# get collections of raw rechits, sim clusters, 2D clusters, multi clusters, etc.
recHitsRaw = event.recHits()
simClusters = event.simClusters()
layerClusters = event.layerClusters()
multiClusters = event.multiClusters()
# get flat list of rechist associated to sim-cluster hits
rHitsSimAssoc = getRecHitsSimAssoc(recHitsRaw, simClusters)
# get flat list of raw rechits which satisfy treshold condition
rHitsCleaned = [
rechit for rechit in recHitsRaw
if recHitAboveTreshold(rechit, ecut, dependSensor)[1]
]
### Imaging algo run at RECO step (CMSSW)
# get flat list of all clusters 2D produced with algo at RECO step (CMSSW)
clusters2DList_reco = [cls2D for cls2D in layerClusters]
# get flat list of all multi-clusters produced with algo at RECO step (CMSSW)
multiClustersList_reco = [
multiCluster for multiCluster in multiClusters
]
### Imaging algo run as stand-alone (python)
# instantiate the stand-alone clustering implemented in HGCalImagingAlgo
HGCalAlgo = HGCalImagingAlgo(ecut=ecut,
deltac=deltac,
multiclusterRadii=multiclusterRadii,
minClusters=minClusters,
dependSensor=dependSensor,
verbosityLevel=0)
# produce 2D clusters with stand-alone algo, out of all raw rechits
clusters2D_rerun = HGCalAlgo.makeClusters(
recHitsRaw) # nested list of "hexels", per layer, per 2D cluster
# produce multi-clusters with stand-alone algo, out of all 2D clusters
multiClustersList_rerun = HGCalAlgo.make3DClusters(
clusters2D_rerun
) # flat list of multi-clusters (as basic clusters)
# get for testing: flat list of 2D clustered, and flat list of clustered non-halo "hexeles" (from stand-alone algo)
clusters2DList_rerun = HGCalAlgo.getClusters(
clusters2D_rerun,
verbosityLevel=0) # flat list of 2D clusters (as basic clusters)
hexelsClustered_rerun = [
iNode for bClust in clusters2DList_rerun
for iNode in bClust.thisCluster if not iNode.isHalo
] # flat list of clustered "hexeles", without the "halo" hexels
### Produce some basic histograms for each event (2D/3D view of associated sim-clusters, selected rec-hits, etc.)
if (verbosityLevel >= 2):
# histograming of rechist associated to sim-cluster hits
histDict = histRecHitsSimAssoc(rHitsSimAssoc,
event.entry(),
histDict,
tag="rHitsSimAssoc_",
zoomed=False)
# histograming of raw rechist (with ecut cleaning)
histDict = histRecHits(rHitsCleaned,
event.entry(),
histDict,
tag="rHitsCleaned_",
zoomed=True)
# histograming of clustered hexels
histDict = histHexelsClustered(hexelsClustered_rerun,
event.entry(),
histDict,
tag="clustHex_",
zoomed=False)
### Compare stand-alone clustering and sim-clusters
rHitsSimAssocDID = [
rechit.detid() for simClus in rHitsSimAssoc for rechit in simClus
] # list of detids for sim-associated rehits (with ecut cleaning)
rHitsClustdDID = [iNode.detid for iNode in hexelsClustered_rerun
] # list of detids for clustered hexels
# print some info if requested
if (verbosityLevel >= 1):
print("num of rechits associated with sim-clusters : ",
len(rHitsSimAssocDID))
print("num of rechits clustered with imaging algo. : ",
len(rHitsClustdDID))
print("num of clustered not found in sim-associated:",
len(list(set(rHitsClustdDID) - set(rHitsSimAssocDID))))
print("num of sim-associated not found in clustered:",
len(list(set(rHitsSimAssocDID) - set(rHitsClustdDID))))
### Compare stand-alone and reco-level clustering
clusters2DListMultiSelected_rerun = [
cls for multiCluster in multiClustersList_rerun
for cls in multiCluster.thisCluster
]
# print more details if requested
if (verbosityLevel >= 1):
ls = sorted(
range(len(clusters2DListMultiSelected_rerun)),
key=lambda k: clusters2DListMultiSelected_rerun[k].thisCluster[
0].layer,
reverse=False) # indices sorted by increasing layer number
for index in range(len(multiClustersList_rerun)):
print("Multi-cluster (RE-RUN) index: ", index,
", No. of 2D-clusters = ",
len(multiClustersList_rerun[index].thisCluster),
", Energy = ", multiClustersList_rerun[index].energy,
", Phi = ", multiClustersList_rerun[index].phi,
", Eta = ", multiClustersList_rerun[index].eta, ", z = ",
multiClustersList_rerun[index].z)
ls = sorted(
range(len(clusters2DList_reco)),
key=lambda k: clusters2DList_reco[k].layer(),
reverse=False) # indices sorted by increasing layer number
for index in range(len(multiClustersList_reco)):
print("Multi-cluster (RECO) index: ", index,
", No. of 2D-clusters = ",
len(multiClustersList_reco[index].cluster2d()),
", Energy = ", multiClustersList_reco[index].energy(),
", Phi = ", multiClustersList_reco[index].phi(),
", Eta = ", multiClustersList_reco[index].eta(),
", z = ", multiClustersList_reco[index].z())
print("num of clusters2D @reco : ", len(clusters2DList_reco))
print("num of clusters2D re-run: ",
len(clusters2DListMultiSelected_rerun))
print("num of multi-cluster @reco : ", len(multiClustersList_reco))
print("num of multi-cluster re-run: ",
len(multiClustersList_rerun))
### Produce some basic histograms with general info (one per sample)
if (verbosityLevel >= 2):
# relative diff. in number of 2D clusters (re-run vs. reco)
multiClusters_nClust2DDiff.append(100 * float(
len(clusters2DListMultiSelected_rerun) -
len(clusters2DList_reco)) / float(len(clusters2DList_reco)))
# number of 2D clusters from algo at re-run step
tot_nClust2D_rerun.append(len(clusters2DListMultiSelected_rerun))
clusters2D_eng_rerun.extend([
clusters2DList_rerun[k].energy
for k in range(0, len(clusters2DList_rerun))
]) # eng re-run
# number of 2D clusters from algo at RECO step
tot_nClust2D_reco.append(len(clusters2DList_reco))
clusters2D_eng_reco.extend([
clusters2DList_reco[k].energy()
for k in range(0, len(clusters2DList_reco))
]) # eng reco
# histograms - re-run vs. reco, 2D clusters counting
histDict = histValue1D(multiClusters_nClust2DDiff,
histDict,
tag="MultClust_nCl2DRelDiff_RerunReco",
title="Rerun vs. Reco: rel. diff. Num(2D clusters)",
axunit="#deltaN_{cl.2D}[%]",
binsRangeList=[200, -10, 10],
ayunit="N(events)")
histDict = histValue1D(tot_nClust2D_rerun,
histDict,
tag="tot_nClust2D_rerun",
title="Rerun: total Num(2D clusters)",
axunit="N_{cl.2D}",
binsRangeList=[100, 0, 1000],
ayunit="total occurences")
histDict = histValue1D(tot_nClust2D_reco,
histDict,
tag="tot_nClust2D_reco",
title="Reco: total Num(2D clusters)",
axunit="N_{cl.2D}",
binsRangeList=[100, 0, 1000],
ayunit="total occurences")
# histograms - 2D clusters energy spectra
histDict = histValue1D(clusters2D_eng_rerun,
histDict,
tag="Clust2D_Eng_Rerun",
title="Rerun E(all 2D clusters)",
axunit="#E_{cl.2D}[GeV]",
binsRangeList=[1000, 0, 5],
ayunit="N(2D clusters)")
histDict = histValue1D(clusters2D_eng_reco,
histDict,
tag="Clust2D_Eng_Reco",
title="Reco E(all 2D clusters)",
axunit="#E_{cl.2D}[GeV]",
binsRangeList=[1000, 0, 5],
ayunit="N(2D clusters)")
# print/save histograms
histPrintSaveAll(histDict, outDir)
def main():
global opt, args
usage = ('usage: %prog [options]\n' + '%prog -h for help')
parser = optparse.OptionParser(usage)
# input options
# parser.add_option('', '--files', dest='fileString', type='string', default='root://eoscms.cern.ch//eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/_SinglePiPt50Eta1p6_2p8_PhaseIITDRFall17DR-noPUFEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO/NTUP/_SinglePiPt50Eta1p6_2p8_PhaseIITDRFall17DR-noPUFEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO_NTUP_1_0.root', help='comma-separated file list')
parser.add_option(
'',
'--files',
dest='fileString',
type='string',
default=
'root://eoscms.cern.ch//eos/cms/store/cmst3/group/hgcal/CMG_studies/Production/_SinglePiPt50Eta1p6_2p8_PhaseIITDRFall17DR-PU200FEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO/NTUP/_SinglePiPt50Eta1p6_2p8_PhaseIITDRFall17DR-PU200FEVT_93X_upgrade2023_realistic_v2-v1_GEN-SIM-RECO_NTUP_2.root',
help='comma-separated file list')
parser.add_option('',
'--gunType',
dest='gunType',
type='string',
default='pt',
help='pt or e')
parser.add_option('',
'--pid',
dest='pid',
type='int',
default=211,
help='pdgId int')
parser.add_option('',
'--genValue',
dest='genValue',
type='int',
default=50,
help='generated pT or energy')
parser.add_option('',
'--tag',
dest='tag',
type='string',
default='noPU',
help='some tag, best used for PU and other info')
parser.add_option('',
'--ref',
dest='refName',
type='string',
default='genpart',
help='reference collection')
parser.add_option('',
'--obj',
dest='objName',
type='string',
default='pfcluster',
help='object of interest collection')
# store options and arguments as global variables
global opt, args
(opt, args) = parser.parse_args()
print "files:", opt.fileString
print "gunType:", opt.gunType
print "pid:", opt.pid
print "GEN_engpt:", opt.genValue
print "refName:", opt.refName
print "objName:", opt.objName
# set sample/tree - for photons
gun_type = opt.gunType
pidSelected = opt.pid
GEN_engpt = opt.genValue
tag = opt.tag
refName = opt.refName
objName = opt.objName
histDict = {}
fileList = opt.fileString.split(",")
start_time = timeit.default_timer()
for fileName in fileList:
ntuple = HGCalNtuple(opt.fileString)
eventLoop(ntuple, refName, objName, gun_type, pidSelected, GEN_engpt,
histDict)
f = ROOT.TFile(
"{}_{}_{}GeV_{}_{}_{}.root".format(gun_type, pidSelected, GEN_engpt,
refName, objName, tag), "recreate")
for etaBinName in etaBins:
for phiBinName in phiBins:
if "ref_Energy_eta" + etaBinName + "_phi" + phiBinName in histDict[
etaBinName][phiBinName]:
histDict[etaBinName][phiBinName]["ref_Energy_eta" +
etaBinName + "_phi" +
phiBinName].Write()
histDict[etaBinName][phiBinName]["ref_Pt_eta" + etaBinName +
"_phi" + phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_Energy_eta" +
etaBinName + "_phi" +
phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_Pt_eta" + etaBinName +
"_phi" + phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_dEoverE_eta" +
etaBinName + "_phi" +
phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_dPtoverPt_eta" +
etaBinName + "_phi" +
phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_dE_eta" + etaBinName +
"_phi" + phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_dPt_eta" + etaBinName +
"_phi" + phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_EoverERef_eta" +
etaBinName + "_phi" +
phiBinName].Write()
histDict[etaBinName][phiBinName]["obj_PtoverPtRef_eta" +
etaBinName + "_phi" +
phiBinName].Write()
f.Write()
f.Close()
elapsed = timeit.default_timer() - start_time
print "Time:", elapsed
def main():
if not os.path.exists(outDir): os.makedirs(outDir)
for ntupleNumber in range(minNtuple, maxNtuple + 1):
print("\nCurrent ntup: ", ntupleNumber)
ntuple = HGCalNtuple(inputPath + "{}.root".format(ntupleNumber))
# start event loop
for event in ntuple:
startEvent = time.time()
eventID = event.entry()
startEvent = time.time()
print("\nCurrent event: ", eventID)
# check if particles reached EE
genParticles = event.genParticles()
skipEvent = False
for particle in genParticles:
if not particle.reachedEE():
# print("particle didn't reach EE -- skipping the event!!")
skipEvent = True
break
if skipEvent: continue
eventDir = outDir + "/ntup{}/event{}".format(ntupleNumber, eventID)
if not os.path.exists(eventDir): os.makedirs(eventDir)
# get raw rec hits
print("\n\npreparing raw recHits...", end='')
start = time.time()
recHitsRaw = event.getDataFrame("rechit")
end = time.time()
print(" done (", end - start, " s)")
# get simulated hits associated with a cluster
print("preparing simulated hits and clusters...", end='')
start = time.time()
simClusters = event.getDataFrame("simcluster")
simHitsPerClusterArray = getHitsPerCluster(recHitsRaw, simClusters)
end = time.time()
print(" done (", end - start, " s)")
# re-run clustering with HGCalAlgo, save to file
print("running clustering algorithm...", end='')
start = time.time()
recClusters, rec3Dclusters = getRecClustersFromImagingAlgo(
recHitsRaw)
end = time.time()
print(" done (", end - start, " s)")
# recClusters -> array of hexel objects
print("looking for hits associated with hexels...", end='')
start = time.time()
recHitsPerClusterArray = getRecHitsPerHexel(
recHitsRaw, recClusters)
end = time.time()
print(" done (", end - start, " s)")
# perform final analysis, fill in histograms and save to files
print("\nGenerating final hists...")
start = time.time()
energyComparisonHist = ROOT.TH2D("energy comparison",
"energy comparison", 100, 0, 100,
100, 0, 100)
energyComparisonOverlapHist = ROOT.TH2D(
"energy comparison overlap.", "energy comparison overlap.",
100, 0, 100, 100, 0, 100)
for layer in range(minLayer, maxLayer):
# print("layer:",layer)
for recClusterIndex, recCluster in enumerate(
recHitsPerClusterArray):
# print("rec cluster:",recCluster)
recHitsInLayerInCluster = recCluster[getLayerMask(
recCluster, layer)]
recEnergy = recHitsInLayerInCluster["energy"].sum()
xMaxRec = recHitsInLayerInCluster["x"].max()
xMinRec = recHitsInLayerInCluster["x"].min()
yMaxRec = recHitsInLayerInCluster["y"].max()
yMinRec = recHitsInLayerInCluster["y"].min()
recClusterX = xMinRec + (xMaxRec - xMinRec) / 2.
recClusterY = yMinRec + (yMaxRec - yMinRec) / 2.
recClusterR = max((xMaxRec - xMinRec) / 2.,
(yMaxRec - yMinRec) / 2.)
assocSimEnergy = 0
for simClusterIndex, simCluster in enumerate(
simHitsPerClusterArray):
# print("sim cluster:",simCluster)
simHitsInLayerInCluster = simCluster[getLayerMask(
simCluster, layer)]
simEnergy = simHitsInLayerInCluster["energy"].sum()
xMaxSim = simHitsInLayerInCluster["x"].max()
xMinSim = simHitsInLayerInCluster["x"].min()
yMaxSim = simHitsInLayerInCluster["y"].max()
yMinSim = simHitsInLayerInCluster["y"].min()
simClusterX = xMinSim + (xMaxSim - xMinSim) / 2.
simClusterY = yMinSim + (yMaxSim - yMinSim) / 2.
simClusterR = max((xMaxSim - xMinSim) / 2.,
(yMaxSim - yMinSim) / 2.)
if recEnergy * simEnergy != 0:
energyComparisonHist.Fill(recEnergy, simEnergy)
# if circlesOverlap(recClusterX,recClusterY,recClusterR,simClusterX,simClusterY,simClusterR):
# energyComparisonOverlapHist.Fill(recEnergy,simEnergy)
if pointWithinCircle(simClusterX, simClusterY,
recClusterX, recClusterY,
recClusterR, clusterAcceptScale):
# if circlesOverlap(recClusterX,recClusterY,recClusterR,simClusterX,simClusterY,simClusterR,clusterAcceptScale):
assocSimEnergy += simEnergy
if recEnergy * assocSimEnergy != 0:
energyComparisonOverlapHist.Fill(
recEnergy, assocSimEnergy)
energyComparisonHist.SaveAs(
"{}/energyComparisonHist.root".format(eventDir))
energyComparisonOverlapHist.SaveAs(
"{}/energyComparisonOverlapHist.root".format(eventDir))
end = time.time()
print(" done (", end - start, " s)")
for index in range(len(rec3Dclusters)):
print(
f"Multi-cluster (RE-RUN) index: {index}",
f", No. of 2D-clusters = {len(rec3Dclusters[index].thisCluster)}",
f", Energy = {rec3Dclusters[index].energy:.2f}",
f", Phi = {rec3Dclusters[index].phi:.2f}",
f", Eta = {rec3Dclusters[index].eta:.2f}",
f", z = {rec3Dclusters[index].z:.2f}")
endEvent = time.time()
print("Total event processing time: ", endEvent - startEvent, " s")
def analyze(params, batch_idx=0):
print str(params)
debug = int(params.debug)
# pool = Pool(5)
n_phi_bins = 72
n_eta_bins = 18
phi_bins = np.linspace(-1 * math.pi, math.pi, n_phi_bins + 1)
eta_bins = np.linspace(1.479, 3.0, n_eta_bins + 1)
# eta_bins = np.linspace(1.41, 3.1, n_eta_bins+1)
eta_bin_size = eta_bins[1] - eta_bins[0]
eta_bin_first = eta_bins[0] + eta_bin_size / 2
phi_bin_size = phi_bins[1] - phi_bins[0]
phi_bin_first = phi_bins[0] + phi_bin_size / 2
print '-- Eta bin size: {}, first bin center: {}, # bins: {}'.format(
eta_bin_size, eta_bin_first,
len(eta_bins) - 1)
print ' {}'.format(eta_bins)
print '-- Phi bin size: {}, first bin center: {}, # bins: {}'.format(
phi_bin_size, phi_bin_first,
len(phi_bins) - 1)
print ' {}'.format(phi_bins)
tc_geom_df = pd.DataFrame()
cell_geom_df = pd.DataFrame()
geom_file = os.path.join(params.input_base_dir,
'geom/test_triggergeom.root')
# geom_file = params.input_base_dir+'/geom/test_triggergeom_v1.root'
print 'Loading the geometry...'
tc_geom_tree = HGCalNtuple([geom_file],
tree='hgcaltriggergeomtester/TreeTriggerCells')
tc_geom_tree.setCache(learn_events=100)
tc_geom_df = convertGeomTreeToDF(tc_geom_tree._tree)
tc_geom_df['radius'] = np.sqrt(tc_geom_df['x']**2 + tc_geom_df['y']**2)
tc_geom_df['eta'] = np.arcsinh(tc_geom_df.z / tc_geom_df.radius)
tc_geom_df['phi'] = np.arctan2(tc_geom_df.y, tc_geom_df.x)
# cell_geom_tree = HGCalNtuple([geom_file], tree='hgcaltriggergeomtester/TreeCells')
# cell_geom_tree.setCache(learn_events=100)
# cell_geom_df = convertGeomTreeToDF(cell_geom_tree._tree)
#
# bhcell_geom_tree = HGCalNtuple([geom_file], tree='hgcaltriggergeomtester/TreeCellsBH')
# bhcell_geom_tree.setCache(learn_events=100)
# bhcell_geom_df = convertGeomTreeToDF(bhcell_geom_tree._tree)
print '...done'
# display_mgr = display.EventDisplayManager(cell_geom=cell_geom_df,
# trigger_cell_geom=tc_geom_tree)
# tc_geom_df['cell'] = hgcdetid.v_cell(tc_geom_df.id)
sel_cells = tc_geom_df[tc_geom_df.subdet.isin([3, 4]) & ~(
(tc_geom_df.subdet == 3) & (tc_geom_df.layer % 2 == 0))].copy()
# some checks on the selection:
#
# print '@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'
# print sel_cells[(sel_cells.subdet == 3) & (sel_cells.layer % 2 != 0)]
# print '#################################'
# print sel_cells[(sel_cells.subdet == 3) & (sel_cells.layer % 2 == 0)]
#
print sel_cells.subdet.unique()
print sel_cells[sel_cells.subdet == 3].layer.unique()
sel_cells['wafertype'] = 1
# FIXME: in the current implementation of the TriggerGeometry all TCs have wafertype = 1
# here we need to know which modules have 120um thinckness to map them to the 6 HGCROC layouts
# we crudely assume that this is true for EE and FH modules with radius < 70cm
sel_cells['wafertype'] = hgcdetid.v_settype_on_radius(sel_cells['radius'])
def map_wafertype_majority(data):
# print data
# counts = data['wafertype'].value_counts()
# # if len(counts) > 1:
# wafertype = counts.index[0]
# data.loc[data.index, 'wafertype'] = wafertype
data['wafertype'] = data['wafertype'].value_counts().index[0]
return data
# now we actually correct the wafertype for the rest of the module based on majority logic
print 'Starting wafertype mapping on majority logic: {}'.format(
datetime.datetime.now())
sel_cells = sel_cells.groupby(['subdet', 'zside', 'layer',
'wafer']).apply(map_wafertype_majority)
print '...done: {}'.format(datetime.datetime.now())
# we now assign the hgcroc value
# tc_geom_df['sector'] = hgcdetid.v_module_sector(tc_geom_df.id)
# tc_geom_df['subcell'] = tc_geom_df.cell - tc_geom_df.sector*16
sel_cells['hgcroc'] = hgcdetid.v_hgcroc_big(sel_cells.id)
sel_cells.loc[sel_cells.wafertype == -1,
('hgcroc')] = hgcdetid.v_hgcroc_small(
sel_cells.loc[sel_cells.wafertype == -1, ('id')])
sel_cells['eta_bin'] = np.digitize(np.fabs(sel_cells.eta), eta_bins) - 1
# tc_geom_df['eta_bin_c'] = np.digitize(np.fabs(tc_geom_df.eta), eta_bins)-1
sel_cells['phi_bin'] = np.digitize(sel_cells.phi, phi_bins) - 1
# deal with rounding effects on pi
sel_cells.loc[sel_cells.phi_bin == n_phi_bins,
('phi_bin')] = n_phi_bins - 1
sel_cells.loc[sel_cells.phi_bin == -1, ('phi_bin')] = 0
# deal with the fact that some of the cells hactually have eta outside the bin range
sel_cells.loc[sel_cells.eta_bin == n_eta_bins,
('eta_bin')] = n_eta_bins - 1
sel_cells.loc[sel_cells.eta_bin == -1, ('eta_bin')] = 0
tc_overflow = sel_cells[(sel_cells.eta_bin < 0) |
(sel_cells.eta_bin > n_eta_bins - 1) |
(sel_cells.phi_bin < 0) |
(sel_cells.phi_bin > n_phi_bins - 1)][[
'id', 'eta', 'phi', 'eta_bin', 'phi_bin'
]]
if not tc_overflow.empty:
print 'ERROR: some of the TCs have a bin outside the allowed range'
print tc_overflow
# This needs to be fixed after all the rounding has been take care of
sel_cells['tt_bin'] = sel_cells.apply(
func=lambda cell: (int(cell.eta_bin), int(cell.phi_bin)), axis=1)
sel_cells['hgcroc_tt_bin'] = sel_cells['tt_bin']
sel_cells['wafer_tt_bin'] = sel_cells['tt_bin']
# temp_bins = pd.Series()
# now we assign all hgcrocs or modules to the same tower on a majority logic on the TCs belonging to them
def map_hgcroctt_majority(data):
tt_bin = data['tt_bin'].value_counts().index[0]
data['hgcroc_tt_bin'] = pd.Series([tt_bin for x in data.index],
index=data.index)
return data
# counts = data['tt_bin'].value_counts()
# if len(counts) > 1:
# tt_bin = counts.index[0]
# data.loc[data.index, 'hgcroc_tt_bin'] = pd.Series([tt_bin for x in data.index],
# index=data.index)
print 'Starting hgcroc mapping to TT on majority logic: {}'.format(
datetime.datetime.now())
sel_cells = sel_cells.groupby(
['subdet', 'zside', 'layer', 'wafer',
'hgcroc']).apply(map_hgcroctt_majority)
print '...done: {}'.format(datetime.datetime.now())
def map_wafertt_majority(data):
tt_bin = data['tt_bin'].value_counts().index[0]
data['wafer_tt_bin'] = pd.Series([tt_bin for x in data.index],
index=data.index)
return data
# counts = data['tt_bin'].value_counts()
# if len(counts) > 1:
# tt_bin = counts.index[0]
# data.loc[data.index, 'hgcroc_tt_bin'] = pd.Series([tt_bin for x in data.index],
# index=data.index)
print 'Starting wafer mapping to TT on majority logic: {}'.format(
datetime.datetime.now())
sel_cells = sel_cells.groupby(['subdet', 'zside', 'layer',
'wafer']).apply(map_wafertt_majority)
print '...done: {}'.format(datetime.datetime.now())
def dump_mapping(tc_map, field, file_name):
tower_tc_mapping = pd.DataFrame(
columns=['id', 'towerbin_x', 'towerbin_y'], dtype=np.int64)
tower_tc_mapping.id = tc_map.id
tower_tc_mapping.towerbin_x = tc_map.apply(
func=(lambda x: x[field][0]), axis=1)
tower_tc_mapping.towerbin_y = tc_map.apply(
func=(lambda x: x[field][1]), axis=1)
tower_tc_mapping.to_csv(file_name,
sep=' ',
float_format='%.0f',
header=False,
index=False)
return tower_tc_mapping
hgcroc_sel_cells = dump_mapping(
sel_cells,
field='hgcroc_tt_bin',
file_name='TCmapping_hgcroc_eta-phi_v2.txt')
wafer_sel_cells = dump_mapping(sel_cells,
field='wafer_tt_bin',
file_name='TCmapping_wafer_eta-phi_v2.txt')
missing = np.array([
'1780744193', '1780744194', '1780744195', '1780744196', '1780744197',
'1780744198', '1780744199', '1780744200', '1780744201', '1780744202',
'1780744203', '1780744204', '1780744205', '1780744206', '1780744207',
'1780744208', '1780744209', '1780744210', '1780744211', '1780744212',
'1780744213', '1780744214', '1780744215', '1780744216', '1780744217',
'1780744218', '1780744219', '1780744220', '1780744221', '1780744222',
'1780744223', '1780744224', '1780744225', '1780744226', '1780744227',
'1780744228', '1780744229', '1780744230', '1780744231', '1780744232',
'1780744233', '1780744234', '1780744235', '1780744236', '1780744237',
'1780744238', '1780744239', '1780744240', '1780744241', '1780744242',
'1780744243', '1780744244', '1780744245', '1780744246', '1780744247',
'1780744248', '1780744249', '1780744250', '1780744251', '1780744252',
'1780744253', '1780744254', '1780744255', '1780744256', '1780744257',
'1780744258', '1780744259', '1780744260', '1780744261', '1780744262',
'1780744263', '1780744264', '1797521409', '1797521410', '1797521411',
'1797521412', '1797521413', '1797521414', '1797521415', '1797521416',
'1797521417', '1797521418', '1797521419', '1797521420', '1797521421',
'1797521422', '1797521423', '1797521424', '1797521425', '1797521426',
'1797521427', '1797521428', '1797521429', '1797521430', '1797521431',
'1797521432', '1797521433', '1797521434', '1797521435', '1797521436',
'1797521437', '1797521438', '1797521439', '1797521440', '1797521441',
'1797521442', '1797521443', '1797521444', '1797521445', '1797521446',
'1797521447', '1797521448', '1797521449', '1797521450', '1797521451',
'1797521452', '1797521453', '1797521454', '1797521455', '1797521456',
'1797521457', '1797521458', '1797521459', '1797521460', '1797521461',
'1797521462', '1797521463', '1797521464', '1797521465', '1797521466',
'1797521467', '1797521468', '1797521469', '1797521470', '1797521471',
'1797521472', '1797521473', '1797521474', '1797521475', '1797521476',
'1797521477', '1797521478', '1797521479', '1797521480'
],
dtype='|S10')
print sel_cells[sel_cells.id.isin(missing)]
print "# of TCs = {}".format(len(tc_geom_df.id.unique()))
print "# of TCs mapped to TT (hgcroc) = {}".format(
len(hgcroc_sel_cells.id.unique()))
print "# of bins (hgcroc) = {}".format(
len(sel_cells.hgcroc_tt_bin.unique()))
print "# of TCs mapped to TT (wafer) = {}".format(
len(wafer_sel_cells.id.unique()))
print "# of bins (wafer) = {}".format(len(sel_cells.wafer_tt_bin.unique()))
sys.exit(0)
# tc_ids_all = pd.DataFrame(columns=['wf', 'wtf', 'hgcroc'])
# results = []
#
#
#
#
# for index, tc in tc_geom_df.iterrows():
# if index % 1000 == 0:
# print 'TC: {}'.format(index)
# detid = HGCalDetId(tc.id)
#
# tc_ids = pd.DataFrame(columns=['wf', 'wtf', 'hgcroc'])
# tc_ids['wf'] = detid.wafer()
# tc_ids['wft'] = detid.waferType()
# tc_ids['hgcroc'] = detid.hgcroc()
# results.append(tc_ids)
# tc_ids_all = pd.concatenate(results)
#
# for index, cell in cell_geom_df.iterrows():
# if index % 1000 == 0:
# print 'Cell: {}'.format(index)
# detid = HGCalDetId(cell.tc_id)
# cell['wf'] = detid.wafer()
# cell['wft'] = detid.waferType()
# cell['hgcroc'] = detid.hgcroc()
cell_sel_type_p1 = cell_geom_df[(cell_geom_df.wafertype == 1)
& (cell_geom_df.layer == 1) &
(cell_geom_df.wafer == 180) &
(cell_geom_df.zside == -1)]
cell_sel_type_m1 = cell_geom_df[(cell_geom_df.wafertype == -1)
& (cell_geom_df.layer == 1) &
(cell_geom_df.wafer == 101) &
(cell_geom_df.zside == -1)]
tc_sel_p1 = tc_geom_df[(tc_geom_df.subdet == 3) & (tc_geom_df.layer == 1) &
# (tc_geom_df.wafer == 101) &
(tc_geom_df.wafertype == -1) &
(tc_geom_df.zside == -1)]
print '--------------------------------------------------------------------'
print cell_sel_type_p1[['id', 'layer', 'subdet', 'zside', 'wafer', 'cell']]
print '---------------------------------------------------------------------'
print cell_sel_type_m1
#
#
# tc_geom_df['wf'] = tc_geom_df.apply(compute_wafer, axis=1)
# cell_geom_df['wf'] = cell_geom_df.apply(compute_wafer, axis=1)
# tc_geom_df['wft'] = tc_geom_df.apply(compute_waferType, axis=1)
# cell_geom_df['wft'] = cell_geom_df.apply(compute_waferType, axis=1)
#
print '---------------------------------------------------------------------'
debugPrintOut(debug,
'Cell geometry',
toCount=cell_geom_df,
toPrint=cell_geom_df.iloc[:3])
print '---------------------------------------------------------------------'
debugPrintOut(debug,
'BH geometry',
toCount=bhcell_geom_df,
toPrint=bhcell_geom_df.iloc[:3])
print '---------------------------------------------------------------------'
debugPrintOut(debug,
'TC geometry',
toCount=tc_geom_df,
toPrint=tc_geom_df.iloc[:3])
cell_sel_type_p1['color'] = cell_sel_type_p1.cell
cell_sel_type_m1['color'] = cell_sel_type_m1.cell
tc_sel_p1['energy'] = tc_sel_p1.hgcroc
print '---------------------------------------------------------------------'
print tc_sel_p1
# display_mgr.displayCells(event=1, cells=cell_sel_type_p1)
# display_mgr.displayCells(event=1, cells=cell_sel_type_m1)
# display_mgr.displayTriggerCells(event=1, tcs=tc_sel_p1)
# display_mgr.show(event=1)
sys.exit(0)
def analyze(params, batch_idx=0):
print(params)
debug = int(params.debug)
pool = Pool(5)
tc_geom_df = pd.DataFrame()
tc_rod_bins = pd.DataFrame()
if False:
# read the geometry dump
geom_file = os.path.join(params.input_base_dir,
'geom/test_triggergeom.root')
tc_geom_tree = HGCalNtuple(
[geom_file], tree='hgcaltriggergeomtester/TreeTriggerCells')
tc_geom_tree.setCache(learn_events=100)
print('read TC GEOM tree with # events: {}'.format(
tc_geom_tree.nevents()))
tc_geom_df = convertGeomTreeToDF(tc_geom_tree._tree)
tc_geom_df['radius'] = np.sqrt(tc_geom_df['x']**2 + tc_geom_df['y']**2)
tc_geom_df['eta'] = np.arcsinh(tc_geom_df.z / tc_geom_df.radius)
if False:
tc_rod_bins = pd.read_csv(
filepath_or_buffer='data/TCmapping_v2.txt',
sep=' ',
names=['id', 'rod_x', 'rod_y'],
index_col=False)
tc_rod_bins['rod_bin'] = tc_rod_bins.apply(
func=lambda cell: (int(cell.rod_x), int(cell.rod_y)), axis=1)
tc_geom_df = pd.merge(tc_geom_df, tc_rod_bins, on='id')
if debug == -4:
tc_geom_tree.PrintCacheStats()
print('...done')
tree_name = 'hgcalTriggerNtuplizer/HGCalTriggerNtuple'
input_files = []
range_ev = (0, params.maxEvents)
if params.events_per_job == -1:
print 'This is interactive processing...'
input_files = fm.get_files_for_processing(
input_dir=os.path.join(params.input_base_dir,
params.input_sample_dir),
tree=tree_name,
nev_toprocess=params.maxEvents,
debug=debug)
else:
print 'This is batch processing...'
input_files, range_ev = fm.get_files_and_events_for_batchprocessing(
input_dir=os.path.join(params.input_base_dir,
params.input_sample_dir),
tree=tree_name,
nev_toprocess=params.maxEvents,
nev_perjob=params.events_per_job,
batch_id=batch_idx,
debug=debug)
# print ('- dir {} contains {} files.'.format(params.input_sample_dir, len(input_files)))
print '- will read {} files from dir {}:'.format(len(input_files),
params.input_sample_dir)
for file_name in input_files:
print ' - {}'.format(file_name)
ntuple = HGCalNtuple(input_files, tree=tree_name)
if params.events_per_job == -1:
if params.maxEvents == -1:
range_ev = (0, ntuple.nevents())
print('- created TChain containing {} events'.format(ntuple.nevents()))
print('- reading from event: {} to event {}'.format(
range_ev[0], range_ev[1]))
ntuple.setCache(learn_events=1, entry_range=range_ev)
output = ROOT.TFile(params.output_filename, "RECREATE")
output.cd()
if False:
hTCGeom = histos.GeomHistos('hTCGeom')
hTCGeom.fill(tc_geom_df[(np.abs(tc_geom_df.eta) > 1.65)
& (np.abs(tc_geom_df.eta) < 2.85)])
# instantiate all the plotters
nev = 0
for evt_idx in range(range_ev[0], range_ev[1] + 1):
# print(evt_idx)
event = ntuple.getEvent(evt_idx)
if (params.maxEvents != -1 and nev >= params.maxEvents):
break
if debug >= 2 or event.entry() % 100 == 0:
print("--- Event {}, @ {}".format(event.entry(),
datetime.datetime.now()))
print(' run: {}, lumi: {}, event: {}'.format(
event.run(), event.lumi(), event.event()))
nev += 1
# get the interesting data-frames
triggerCells = event.getDataFrame(prefix='tc')
clusters = event.getDataFrame(prefix='hmVRcl3d')
clusters_truth = event.getDataFrame(prefix='cl3dtruth')
gen_info = event.getDataFrame(prefix='gen')
gen_particles = event.getDataFrame(prefix='genpart')
puInfo = event.getPUInfo()
debugPrintOut(debug, 'PU', toCount=puInfo, toPrint=puInfo)
debugPrintOut(debug,
'Trigger Cells',
toCount=triggerCells,
toPrint=triggerCells.iloc[:3])
# print gen_particles.columns
print gen_particles[['pid', 'eta', 'phi', 'pt', 'mother', 'gen']]
def find_gen_particle(cluster, triggerCells):
return triggerCells[triggerCells.id.isin(
cluster.clusters)].genparticle.unique()[0]
def find_cluster_components(cluster, triggerCells):
return triggerCells[triggerCells.id.isin(cluster.clusters)]
# for index, cluster in clusters_truth.iterrows():
# print cluster
# print 'corresponding gen particle: {}'.format(find_gen_particle(cluster, triggerCells))
if not clusters_truth.empty:
clusters_truth['genparticle'] = clusters_truth.apply(
func=lambda cl: find_gen_particle(cl, triggerCells), axis=1)
print clusters_truth
best_match_indexes = {}
best_match_indexes_truth = {}
if not clusters.empty:
best_match_indexes, allmatches = utils.match_etaphi(
gen_particles[['eta', 'phi']],
clusters[['eta', 'phi']],
clusters['pt'],
deltaR=0.1)
if not clusters_truth.empty:
best_match_indexes_truth, allmatches_truth = utils.match_etaphi(
gen_particles[['eta', 'phi']],
clusters_truth[['eta', 'phi']],
clusters_truth['pt'],
deltaR=0.1)
for idx, gen_particle in gen_particles[
(abs(gen_particles.eta) > 1.5)
& (abs(gen_particles.eta) < 2.4)].iterrows():
if idx in best_match_indexes.keys():
print '-----------------------'
print gen_particle
matched3DCluster = clusters.loc[[best_match_indexes[idx]]]
print 'RECO cluster:'
print matched3DCluster
print matched3DCluster.clusters
print find_cluster_components(matched3DCluster.iloc[0],
triggerCells)
response = matched3DCluster.pt / gen_particle.pt
if idx in best_match_indexes_truth.keys():
matched3DCluster_truth = clusters_truth.loc[[
best_match_indexes_truth[idx]
]]
print 'True cluster:'
print matched3DCluster_truth
print find_cluster_components(matched3DCluster_truth.iloc[0],
triggerCells)
response_truth = matched3DCluster_truth.pt / gen_particle.pt
# print '# towers eta >0 {}'.format(len(triggerTowers[triggerTowers.eta > 0]))
# print '# towers eta <0 {}'.format(len(triggerTowers[triggerTowers.eta < 0]))
print("Processed {} events/{} TOT events".format(nev, ntuple.nevents()))
print("Writing histos to file {}".format(params.output_filename))
lastfile = ntuple.tree().GetFile()
print 'Read bytes: {}, # of transaction: {}'.format(
lastfile.GetBytesRead(), lastfile.GetReadCalls())
if debug == -4:
ntuple.PrintCacheStats()
return
def main():
samples2Run = [
'FlatRandomPtGunProducer_SinglePion_35GeV_20170523',
'FlatRandomPtGunProducer_SinglePhoton_35GeV_20170523',
'RelValTTbar_14TeV_CMSSW_9_1_0_pre3-PU25ns_91X_upgrade2023_realistic_v1_D13PU200-v2_GEN-SIM-RECO'
]
sampleManager = SampleManager()
for sampleName in samples2Run:
sample = sampleManager.getSample(sampleName)
print "Sample {} has {} files".format(sampleName,
len(sample.getFiles()))
outDir = sampleName
HGCalHelpers.createOutputDir(outDir)
rootOnly = False
imgType = 'png'
canvas = None
if not rootOnly:
canvas = ROOT.TCanvas(outDir, outDir, 600, 600)
outFile = ROOT.TFile.Open("{}.root".format(sampleName), "RECREATE")
histDict = getHists()
currentEvent = 0
for inFile in sample.getFiles():
if maxEvents > 0:
if currentEvent > maxEvents:
break
print inFile
ntuple = HGCalNtuple(inFile)
for event in ntuple:
currentEvent += 1
if (currentEvent % 10 == 0):
print "Event", currentEvent, "of", maxEvents
if maxEvents > 0:
if currentEvent > maxEvents:
break
# # multi clusters
# multiClusters = event.multiClusters()
# multiClusterCounter = {}
# for currentRange in rangeFolders:
# multiClusterCounter[currentRange] = 0
# for multiCluster in multiClusters:
# if (multiCluster.z() < 0):
# multiClusterCounter['minus_z'] += 1
# histDict['{}_{}_eta'.format('MultiClus', currentRange)].Fill(abs(multiCluster.eta()))
# histDict['{}_{}_pt'.format('MultiClus', currentRange)].Fill(multiCluster.pt())
# histDict['{}_{}_nclus'.format('MultiClus', currentRange)].Fill(len(multiCluster.cluster2d()))
# if (1.6 < abs(multiCluster.eta()) < 2.6):
# multiClusterCounter['minus_z_eta'] += 1
# histDict['{}_{}_eta'.format('MultiClus', currentRange)].Fill(abs(multiCluster.eta()))
# histDict['{}_{}_pt'.format('MultiClus', currentRange)].Fill(multiCluster.pt())
# histDict['{}_{}_nclus'.format('MultiClus', currentRange)].Fill(len(multiCluster.cluster2d()))
# # print multiCluster.pt()
# else:
# multiClusterCounter['plus_z'] += 1
# histDict['{}_{}_eta'.format('MultiClus', currentRange)].Fill(abs(multiCluster.eta()))
# histDict['{}_{}_pt'.format('MultiClus', currentRange)].Fill(multiCluster.pt())
# histDict['{}_{}_nclus'.format('MultiClus', currentRange)].Fill(len(multiCluster.cluster2d()))
# if (1.6 < abs(multiCluster.eta()) < 2.6):
# multiClusterCounter['plus_z_eta'] += 1
# histDict['{}_{}_eta'.format('MultiClus', currentRange)].Fill(abs(multiCluster.eta()))
# histDict['{}_{}_pt'.format('MultiClus', currentRange)].Fill(multiCluster.pt())
# histDict['{}_{}_nclus'.format('MultiClus', currentRange)].Fill(len(multiCluster.cluster2d()))
# layer clusters
layerClusters = event.layerClusters()
recHits = event.recHits()
layerClusterCounter = {}
for currentRange in rangeFolders:
layerClusterCounter[currentRange] = []
layerClusterCounter["100_" + currentRange] = []
layerClusterCounter["200_" + currentRange] = []
layerClusterCounter["300_" + currentRange] = []
layerClusterCounter["other_" + currentRange] = []
for layer in range(0, 53):
# use index zero as overall counter
layerClusterCounter[currentRange].append(0)
layerClusterCounter["100_" + currentRange].append(0)
layerClusterCounter["200_" + currentRange].append(0)
layerClusterCounter["300_" + currentRange].append(0)
layerClusterCounter["other_" + currentRange].append(0)
for layerCluster in layerClusters:
# if recHits[layerCluster.rechitSeed()].flags() != 0:
# print recHits[layerCluster.rechitSeed()].flags(), layerCluster.pt(), len(layerCluster.rechits())
histDict["eventDisplay_{}".format(currentEvent)].Fill(
layerCluster.layer(), layerCluster.phi(),
layerCluster.eta(), layerCluster.pt())
# print layerCluster.rechitSeed()
# seedHitThickness = int(recHits[layerCluster.rechitSeed()].thickness())
# # print "seedHitThickness", seedHitThickness
# histStringsForFilling = []
# if (layerCluster.z() < 0):
# histStringsForFilling.append('minus_z')
# histStringsForFilling.append(thicknessDict[seedHitThickness] + 'minus_z')
# if (1.6 < abs(layerCluster.eta()) < 2.6):
# histStringsForFilling.append('minus_z_eta')
# histStringsForFilling.append(thicknessDict[seedHitThickness] + 'minus_z_eta')
# else:
# histStringsForFilling.append('plus_z')
# histStringsForFilling.append(thicknessDict[seedHitThickness] + 'plus_z')
# if (1.6 < abs(layerCluster.eta()) < 2.6):
# histStringsForFilling.append('plus_z_eta')
# histStringsForFilling.append(thicknessDict[seedHitThickness] + 'plus_z_eta')
# for histString in histStringsForFilling:
# layerClusterCounter[histString][0] += 1
# layerClusterCounter[histString][layerCluster.layer()] += 1
# histDict['{}_{}_eta'.format('LayerClus', histString)].Fill(abs(layerCluster.eta()))
# histDict['{0}_{1}_{2:0>2}_eta'.format('LayerClus', histString, layerCluster.layer())].Fill(abs(layerCluster.eta()))
# histDict['{}_{}_pt'.format('LayerClus', histString)].Fill(layerCluster.pt())
# histDict['{0}_{1}_{2:0>2}_pt'.format('LayerClus', histString, layerCluster.layer())].Fill(layerCluster.pt())
# histDict['{}_{}_nhitCore'.format('LayerClus', histString)].Fill(layerCluster.nhitCore())
# histDict['{0}_{1}_{2:0>2}_nhitCore'.format('LayerClus', histString, layerCluster.layer())].Fill(layerCluster.nhitCore())
# histDict['{}_{}_nhitAll'.format('LayerClus', histString)].Fill(layerCluster.nhitAll())
# histDict['{0}_{1}_{2:0>2}_nhitAll'.format('LayerClus', histString, layerCluster.layer())].Fill(layerCluster.nhitAll())
# fill counting histograms
# for currentRange in rangeFolders:
# for layer in range(0, 53):
# # if (currentEvent == 1):
# # print currentRange, layer, layerClusterCounter[currentRange][layer]
# if (layer == 0):
# histDict['{}_{}_mult'.format('LayerClus', currentRange)].Fill(layerClusterCounter[currentRange][layer])
# histDict['{}_{}_mult'.format('MultiClus', currentRange)].Fill(multiClusterCounter[currentRange])
# else:
# histDict['{0}_{1}_{2:0>2}_mult'.format('LayerClus', currentRange, layer)].Fill(layerClusterCounter[currentRange][layer])
outFile.cd()
# for key, value in histDict.items():
# if value.GetEntries() != 0:
# value.Scale(1./maxEvents)
HGCalHelpers.saveHistograms(histDict,
canvas,
outDir,
imgType,
logScale=False,
rootOnly=rootOnly)
outFile.Write()
outFile.Close()