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root2binary_m.py
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root2binary_m.py
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#!/usr/bin/env python
import os,sys,optparse,logging,numpy,ROOT,json,glob
ROOT.gStyle.SetOptStat(0)
#import tensorflow as tf
logger = logging.getLogger(__name__)
# mapping for tile
'''
* element range meaning
* ------- ----- -------
*
* ros 1 to 4 ReadOutSystem number ( 1,2 = pos/neg Barrel (side A/C)
* 3,4 = pos/neg Ext.Barrel (side A/C) )
* drawer 0 to 63 64 drawers (modules) in one cylinder (phi-slices)
* channel 0 to 47 channel number in the drawer
* adc 0 to 1 ADC number for the channel (0 = low gain, 1 = high gain)
'''
# lar mapping
'''
* Definition and range of values for the elements of the identifier are: <p>
* <pre>
* Connected channels :
* ------------------
* element range meaning
* ------- ----- -------
*
* barrel_ec +/-1 positive/negative barrel - A/C side or P/M half barrel
* " +/-2 positive/negative endcap outer wheel - A/C side
* " +/-3 positive/negative endcap inner wheel - A/C side
*
* sampling 0 both presamplers
* " [1,3] barrel and endcap outer wheel
* " [1,2] endcap inner wheel
*
* region 0 both presamplers
* " [0,1] barrel sampling 1 and 2
* " 0 barrel sampling 3
* "
* " [0,5] endcap outer wheel sampling 1
* " 0 endcap inner wheel sampling 1
* " [0,1] endcap outer wheel sampling 2
* " 0 endcap inner wheel sampling 2
* " 0 endcap outer wheel sampling 3
*
*
* eta for barrel [0,60] presampler - 0< eta <1.52 - deta is approximately equal to 0.025
* " [0,447] sampling 1 region 0 0 < eta < 1.4 - deta = 0.025/8
* " [0,2] sampling 1 region 1 1.4 < eta < 1.475 - deta = 0.025
* " [0,55] sampling 2 region 0 0 < eta < 1.4 - deta = 0.025
* " 0 sampling 2 region 1 1.4 < eta < 1.475 - deta = 0.075
* " [0,26] sampling 3 region 0 0 < eta < 1.35 - deta = 0.050
* "
* phi for barrel [0,63] barrel presampler - dphi = 0.1
* " [0,63] sampling 1 region 0 - dphi = 0.1
* " [0,255] sampling 1 region 1 - dphi = 0.025
* " [0,255] sampling 2 region 0 - dphi = 0.025
* " [0,255] sampling 2 region 1 - dphi = 0.025
* " [0,255] sampling 3 region 0 - dphi = 0.025
*
* number of cells in barrel :
* presampler : 7808
* sampling 1 : 58752
* sampling 2 : 29184
* sampling 3 : 13824
* total :109568
*
* eta for endcap [0,11] presampler sampling 0 region 0 1.5 < eta < 1.8 - deta = 0.025
* " 0 outer wheel sampling 1 region 0 1.375 < eta < 1.425 - deta = 0.05
* " [0,2] outer wheel sampling 1 region 1 1.425 < eta < 1.5 - deta = 0.025
* " [0,95] outer wheel sampling 1 region 2 1.5 < eta < 1.8 - deta = 0.025/8
* " [0,47] outer wheel sampling 1 region 3 1.8 < eta < 2.0 - deta = 0.025/6
* " [0,63] outer wheel sampling 1 region 4 2.0 < eta < 2.4 - deta = 0.025/4
* " [0,3] outer wheel sampling 1 region 5 2.4 < eta < 2.5 - deta = 0.025
* " [0,6] inner wheel sampling 1 region 0 2.5 < eta < 3.2 - deta = 0.1
* " 0 outer wheel sampling 2 region 0 1.375 < eta < 1.425 - deta = 0.05
* " [0,42] outer wheel sampling 2 region 1 1.425 < eta < 2.5 - deta = 0.025
* " [0,6] inner wheel sampling 2 region 0 2.5 < eta < 3.2 - deta = 0.1
* " [0,19] outer wheel sampling 3 region 0 1.5 < eta < 2.5 - deta = 0.05
*
* phi for endcap [0,63] presampler sampling 0 region 0 - dphi = 0.1
* " [0,63] outer wheel sampling 1 regions [0,5] - dphi = 0.1
* " [0,63] inner wheel sampling 1 region 0 - dphi = 0.1
* " [0,255] outer wheel sampling 2 regions [0,1] - dphi = 0.025
* " [0,63] inner wheel sampling 2 region 0 - dphi = 0.1
* " [0,255] outer wheel sampling 3 region 0 - dphi = 0.025
*
* number of cells in endcap :
* presampler : 1536
* Outer wheel:
* sampling 1 : 27648
* sampling 2 : 22528
* sampling 3 : 10240
* total : 60416
* Inner wheel:
* sampling 1 : 896
* sampling 2 : 896
* total : 1792
*
* Grand Total : 63744
'''
PIDS = {
11:'electron',
12:'electronneutrino',
13:'muon',
14:'muonneutrino',
15:'tau',
16:'tauneutrino',
21:'gluon',
22:'photon',
1:'up',
2:'down',
3:'strange',
4:'charm',
5:'bottom',
6:'top',
}
LEP_JET={
11:'lepton',
12:'leptonneutrino',
13:'lepton',
14:'leptonneutrino',
15:'lepton',
16:'leptonneutrino',
21:'jet',
22:'photon',
1:'jet',
2:'jet',
3:'jet',
4:'jet',
5:'jet',
6:'jet',
}
def main():
''' convert root to hdf5 '''
logging.basicConfig(level=logging.INFO,format='%(asctime)s %(levelname)s:%(name)s:%(message)s')
parser = optparse.OptionParser(description='')
parser.add_option('-i','--input',dest='input',help='glob input for files,use quotes')
parser.add_option('-m','--maps',dest='maps',action='store_true',default=False,help='dump calo maps')
parser.add_option('-p','--plot',dest='plot',action='store_true',default=False,help='plot calorimeter data')
parser.add_option('--plot3d',dest='plot3d',action='store_true',default=False,help='plot the 3D layout of the calos. This will only run once, present the 3D plot, then exit after a button is pressed.')
parser.add_option('-o','--output-path',dest='output_path',default='.',help='path where to output data')
parser.add_option('-n','--njets',dest='njets',default=2,help='set this value to the number of jets in the sample being processed')
parser.add_option('--img-deta',dest='image_deta',default=0.3,help='the width of the cropped image of 1 particle measured in eta.')
parser.add_option('--img-dphi',dest='image_dphi',default=0.3,help='the height of the cropped image of 1 particle measured in phi.')
options,args = parser.parse_args()
file_counters_per_pid = {}
for pid,name in LEP_JET.iteritems():
file_counters_per_pid[name] = 0
manditory_args = [
'input',
]
for man in manditory_args:
if options.__dict__[man] is None:
logger.error('Must specify option: ' + man)
parser.print_help()
sys.exit(-1)
filelist = sorted(glob.glob(options.input))
if not options.plot and not options.plot3d:
ROOT.gROOT.SetBatch()
can = ROOT.TCanvas('can','can',0,0,800,600)
cand = ROOT.TCanvas('cand','cand',0,0,800,600)
cand.Divide(2,1)
can.cd()
tree = ROOT.TChain('calocells')
for file in filelist:
tree.AddFile(file)
#rootfile = ROOT.TFile(options.input)
#tree = rootfile.Get('calocells')
num_events = tree.GetEntries()
logger.info('number of events: %i',num_events)
max_eta = 1.5
deta = 0.05
netabins = int(max_eta/deta*2.)
logger.info(' max eta: %5.2f delta eta: %5.2f eta bins: %5i',max_eta,deta,netabins)
max_phi = 2.*numpy.pi
dphi = 2.*numpy.pi/64.
nphibins = 64
logger.info(' max phi: %5.2f delta phi: %5.2f phi bins: %5i',max_phi,dphi,nphibins)
image_eta_width = 1.0
image_phi_height = dphi * 10.
image_eta_bins = int(image_eta_width / deta)
image_phi_bins = int(image_phi_height / dphi)
logger.info('image size = %d x %d',image_eta_bins,image_phi_bins)
logger.info('image eta width: %f image phi height: %f',image_eta_width,image_phi_height)
drlj = 0.3
min_energy = 0.1
# data array [event number, eta bin, phi bin, em(0) / had(1) calo layer ]
#data_chunk_size = 10
#h5ds = outfile.create_dataset('calocells',(0,2,netabins,nphibins),
#chunks=(data_chunk_size,2,netabins,nphibins),
# dtype=numpy.float16,
# maxshape=(None,2,netabins,nphibins))
#print 'dataset shape:',h5ds.shape
dump_ecal = False
dump_hcal = False
#evtnum = 0
output_events = []
output_truth = []
event_number = 0
for event in tree:
event_number += 1
#if event_number % 100 == 0:
logger.info('particle %d of %d',event_number,num_events)
muon = None
antimuon = None
logger.debug('n particles: %10i',event.n_truthparticles)
particles = []
can.cd()
# find the electron(muon or taon)/positron(antimuon or antitaon)
# keep a list of all the status == 3 particles
for pid,peta,pphi,ppt,pstat in zip(event.particle_id,
event.particle_eta,
event.particle_phi,
event.particle_pt,
event.particle_status):
# change electron into muon and taon and also change their particle id accordingly for different channels
if pid == 13 and muon is None:
logger.debug('e>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
muon = {'id':pid,'eta':peta,'phi':pphi,'pt':ppt,'r':numpy.sqrt(peta*peta+pphi*pphi)}
elif pid == -13 and antimuon is None:
logger.debug('p>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
antimuon = {'id':pid,'eta':peta,'phi':pphi,'pt':ppt,'r':numpy.sqrt(peta*peta+pphi*pphi)}
elif pstat == 3 and numpy.fabs(pid) in (range(1,9) + [21]):
logger.debug('o>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
particles.append({'eta':peta,'phi':pphi,'pt':ppt,'id':pid,'r':numpy.sqrt(peta*peta+pphi*pphi)})
else:
logger.debug('x>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
# list all the jets, eliminate them if they overlap with electron(muon)/positron(antimuon)
jets = []
for jeta,jphi,jpt,jm in zip(event.tjet_eta,
event.tjet_phi,
event.tjet_pt,
event.tjet_m):
# eliminate jets that over lap with elections
jr = numpy.sqrt( jeta*jeta + jphi*jphi )
if ( numpy.fabs( jr - muon['r']) > drlj and numpy.fabs( jr - muon['r']) ):
logger.debug('j>> %5s %6.4f %6.4f %5.1f ',' ',jeta,jphi,jpt)
jets.append({'eta':jeta,'phi':jphi,'pt':jpt,'r':numpy.sqrt(jeta*jeta+jphi*jphi)})
#logger.debug('muon: %s',muon)
#logger.debug('antimuon:%s',antimuon)
# remove all but the last njets worth of partons:
nparticles = len(particles)
for i in range(nparticles-options.njets):
del particles[0]
# loop to match jet to parton
dr_max = 0.4
dpt = 5
jet_particle_match = []
for particle in particles:
logger.debug('p>> %5s %6.4f %6.4f %5.1f ',particle['id'],particle['eta'],particle['phi'],particle['pt'])
candidates = []
for jet in jets:
etadiff = jet['eta'] - particle['eta']
phidiff = jet['phi'] - particle['phi']
dr = numpy.sqrt( etadiff*etadiff + phidiff*phidiff )
if dr < dr_max:
logger.debug('j>> %5s %6.4f %6.4f %5.1f ',' ',jet['eta'],jet['phi'],jet['pt'])
candidates.append(jet)
if len(candidates) == 1:
jet_particle_match.append({'jet':candidates[0],'particle':particle})
elif len(candidates) == 0:
logger.warning('there are no matching jets')
else:
logger.warning('there are more than one jets that match this particle')
truth_data = [muon,antimuon]
logger.debug(' jet_particle_match n = %i',len(jet_particle_match))
for match in jet_particle_match:
p = match['particle']
j = match['jet']
logger.debug('>>><<<')
logger.debug('particle: %6.3f %6.3f %5.1f %3d',p['eta'],p['phi'],p['pt'],p['id'])
logger.debug('jet: %6.3f %6.3f %5.1f ',j['eta'],j['phi'],j['pt'])
j['id'] = p['id']
j['truth_pt'] = p['pt']
truth_data.append(j)
if len(jet_particle_match) != options.njets:
logger.warning(' found %d matches but there are %d jets ',len(jet_particle_match),options.njets)
#continue
# remove objects not inside the eta window defined by max_eta
logger.debug('truth objects before eta range cut: %i',len(truth_data))
new_truth_data = []
for truthobj in truth_data:
if numpy.fabs(truthobj['eta']) < max_eta:
new_truth_data.append(truthobj)
truth_data = new_truth_data
logger.debug('truth objects after eta range cut: %i',len(truth_data))
if len(truth_data) == 0: continue
# now I have all my truth data
output_truth.append(truth_data)
# now I need to create cropped images of these truth objects
output_event = numpy.zeros((2,netabins,nphibins))
logger.debug('n lar cells: %10i',event.lar_n_cells)
# create histogram for human eyes
if options.plot:
lar2dhist = ROOT.TH2D('lar2dhist',';#eta,#phi',netabins,-max_eta,max_eta,nphibins,-3.14159,3.14159)
lar2dmap = ROOT.TGraph()
lar2dmap.SetName('lar2dmap')
lar2dmap.SetTitle(';#eta,#phi')
tile2dhist = ROOT.TH2D('tile2dhist',';#eta,#phi',netabins,-max_eta,max_eta,nphibins,-3.14159,3.14159)
tile2dmap = ROOT.TGraph()
tile2dmap.SetName('tile2dmap')
tile2dmap.SetTitle(';#eta,#phi')
tile2dmap_section = ROOT.TH2D('tile2dmap_section',';#eta,#phi',1000,-5,5, 64,-numpy.pi,numpy.pi)
tile2dmap_module = ROOT.TH2D('tile2dmap_module',';#eta,#phi',1000,-5,5, 64,-numpy.pi,numpy.pi)
if options.plot3d:
lar3d = ROOT.TGraph2D()
lar3d.SetName('lar3d')
lar3d.SetTitle(';x;y;z')
lar3d.SetMarkerSize(0.3)
lar3d.SetMarkerStyle(20)
lar3d.SetMarkerColor(ROOT.kGreen)
tile3d = ROOT.TGraph2D()
tile3d.SetName('tile3d')
tile3d.SetTitle(';x;y;z')
tile3d.SetMarkerStyle(20)
tile3d.SetMarkerSize(0.5)
tile3d.SetMarkerColor(ROOT.kRed)
if options.maps and not dump_ecal:
f = open('lar_map.txt','w')
f.write('%10s %10s %10s %10s %10s %5s %5s %5s %5s %5s\n' % ('eta','phi','x','y','z','brl_ec','sampl','regn','hweta','hwphi'))
#ecal_data = numpy.zeros((netabins,nphibins),dtype=numpy.float16)
for i in xrange(event.lar_n_cells):
ecal_eta = event.lar_eta[i]
ecal_phi = event.lar_phi[i]
ecal_Et = event.lar_Et[i]
ecal_x = event.lar_x[i]
ecal_y = event.lar_y[i]
ecal_z = event.lar_z[i]
ecal_bad_cell = event.lar_bad_cell[i]
ecal_barrel_ec = event.lar_barrel_ec[i]
ecal_sampling = event.lar_sampling[i]
ecal_region = event.lar_region[i]
ecal_hw_eta = event.lar_hw_eta[i]
ecal_hw_phi = event.lar_hw_phi[i]
if numpy.fabs(ecal_eta) <= max_eta:
etabin = int((ecal_eta + max_eta) / deta)
phibin = int((ecal_phi + numpy.pi + 0.0001) / dphi)
if ecal_Et > min_energy:
output_event[0][etabin][phibin] += ecal_Et
#print ecal_eta,etabin,ecal_phi,phibin
if options.maps and not dump_ecal:
f.write('%10.4f %10.4f %10.2f %10.2f %10.2f %5i %5i %5i %5i %5i\n' % (ecal_eta,ecal_phi,ecal_x,ecal_y,ecal_z,ecal_barrel_ec,ecal_sampling,ecal_region,ecal_hw_eta,ecal_hw_phi))
if options.plot:
if ecal_Et > min_energy:
lar2dhist.Fill(ecal_eta,ecal_phi,ecal_Et)
if ecal_barrel_ec == 1 or ecal_barrel_ec == -1:
lar2dmap.SetPoint(lar2dmap.GetN()+1,ecal_eta,ecal_phi)
if options.plot3d:
if numpy.fabs(ecal_barrel_ec) == 1:
lar3d.SetPoint(lar3d.GetN(),ecal_x,ecal_y,ecal_z)
if options.maps and not dump_ecal:
f.close()
dump_ecal = True
if options.plot:
lar2dhist.Draw('colz')
can.SaveAs('lar2dhist.png')
#raw_input('press enter...')
lar2dmap.Draw('ap')
can.SaveAs('lar2dmap.png')
#raw_input('press enter...')
logger.debug('n tile cells: %10i',event.tile_n_cells)
if options.maps and not dump_hcal:
f = open('tile_map.txt','w')
f.write('%10.4s %10.4s %10.2s %10.2s %10.2s %5s %5s %5s %5s\n' % ('eta','phi','x','y','z','sect','mod','tow','sampl'))
for i in xrange(event.tile_n_cells):
tile_eta = event.tile_eta[i]
tile_phi = event.tile_phi[i]
tile_Et = event.tile_Et[i]
tile_x = event.tile_x[i]
tile_y = event.tile_y[i]
tile_z = event.tile_z[i]
tile_bad_cell = event.tile_bad_cell[i]
tile_section = event.tile_section[i]
tile_module = event.tile_module[i]
tile_tower = event.tile_tower[i]
tile_sample = event.tile_sample[i]
if numpy.fabs(tile_eta) < max_eta:
etabin = int((tile_eta + max_eta) / deta)
phibin = int((tile_phi + numpy.pi + 0.0001) / dphi)
if tile_Et > min_energy:
output_event[1][etabin][phibin] += tile_Et
if options.maps and not dump_hcal:
f.write('%10.4f %10.4f %10.2f %10.2f %10.2f %5i %5i %5i %5i\n' % (tile_eta,tile_phi,tile_x,tile_y,tile_z,tile_section,tile_module,tile_tower,tile_sample))
if options.plot:
if tile_Et > min_energy:
tile2dhist.Fill(tile_eta,tile_phi,tile_Et)
tile2dmap.SetPoint(tile2dmap.GetN()+1,tile_eta,tile_phi)
tile2dmap_section.Fill(tile_eta,tile_phi,tile_section)
tile2dmap_module.Fill(tile_eta,tile_phi,tile_module)
if options.plot3d:
tile3d.SetPoint(tile3d.GetN(),tile_x,tile_y,tile_z)
if options.maps and not dump_hcal:
f.close()
dump_hcal = True
if options.plot:
tile2dhist.Draw('colz')
can.SaveAs('tile2dhist.png')
#raw_input('press enter...')
tile2dmap.Draw('ap')
can.SaveAs('tile2dmap.png')
#raw_input('press enter...')
tile2dmap_section.Draw('colz')
can.SaveAs('tile2dmap_section.png')
#raw_input('press enter...')
tile2dmap_module.Draw('colz')
can.SaveAs('tile2dmap_module.png')
cand.cd(1)
lar2dhist.Draw('colz')
cand.cd(2)
tile2dhist.Draw('colz')
#raw_input('...')
if options.plot3d:
can.cd()
tile3d.Draw('ap')
lar3d.Draw('p same')
raw_input('...')
sys.exit(-1)
output_events.append(output_event)
logger.debug('events written: %8i',len(output_events))
for obj in truth_data:
logger.debug('truth obj: %5s %6.4f %6.4f %5.1f ',obj['id'],obj['eta'],obj['phi'],obj['pt'])
subimg = numpy.zeros((image_eta_bins,image_phi_bins,2),dtype=numpy.float32)
fpid = numpy.fabs(obj['id'])
obj_etabin = int((obj['eta'] + max_eta) / deta)
obj_etabin_min = obj_etabin - int(image_eta_bins/2.)
obj_etabin_max = obj_etabin + int(image_eta_bins/2.)
if obj_etabin_min < 0:
obj_etabin_max = obj_etabin_max + numpy.fabs(obj_etabin_min)
obj_etabin_min = 0
if obj_etabin_max >= netabins:
obj_etabin_min = obj_etabin_min - (obj_etabin_max - netabins - 1)
obj_etabin_max = netabins - 1
obj_phibin = int((obj['phi'] + numpy.pi + 0.0001) / dphi)
obj_phibin_min = obj_phibin - int(image_phi_bins/2.)
obj_phibin_max = obj_phibin + int(image_phi_bins/2.)
phibins = range(obj_phibin_min,obj_phibin_max)
for i in range(len(phibins)):
bin = phibins[i]
if bin < 0:
phibins[i] = nphibins + bin
elif bin >= nphibins:
phibins[i] = bin - nphibins
if obj_phibin_min < 0:
obj_phibin_max = obj_phibin_max + numpy.fabs(obj_phibin_min)
obj_phibin_min = 0
if obj_phibin_max >= nphibins:
obj_phibin_min = obj_phibin_min - (obj_phibin_max - nphibins - 1)
obj_phibin_max = nphibins - 1
logger.debug('obj eta bin: %i %i %i',obj_etabin_min,obj_etabin,obj_etabin_max)
logger.debug('obj phi bin: %i %s',obj_phibin,str(phibins))
# loop over the whole image to fill the smaller image
for eta_bin in xrange(output_event.shape[1]):
if obj_etabin_min < eta_bin and eta_bin <= obj_etabin_max:
subimg_etabin = eta_bin - obj_etabin_min - 1
for phi_bin in xrange(output_event.shape[2]):
if phi_bin in phibins:
for layer in xrange(output_event.shape[0]):
subimg_phibin = phibins.index(phi_bin)
subimg[subimg_etabin][subimg_phibin][layer] = output_event[layer][eta_bin][phi_bin]
combined_event = [obj,subimg]
filename = options.output_path + '/subimg_%s_n%08d.data' % (LEP_JET[fpid],file_counters_per_pid[LEP_JET[fpid]])
logger.debug(' writing file: ' + filename)
f = open(filename,'wb')
f.write(subimg.tobytes())
f.close()
file_counters_per_pid[LEP_JET[fpid]] += 1
f = open(filename.replace('.data','.json'),'w')
f.write(json.dumps(obj))
f.close()
#raw_input('...')
#break
f = open('test.bin','w')
for event in output_events:
logger.debug('event size %i',len(event.tobytes()))
f.write(event.tobytes())
if __name__ == "__main__":
main()