-
Notifications
You must be signed in to change notification settings - Fork 0
/
root2binary_t.py
executable file
·574 lines (476 loc) · 22.2 KB
/
root2binary_t.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
#!/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)
tauon = None
antitauon = None
logger.debug('n particles: %10i',event.n_truthparticles)
particles = []
can.cd()
# find the tauon/antitauon
# 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 electrin into muon and tauon and also change their particle id accordingly for different channels
if pid == 15 and tauon is None:
logger.debug('e>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
tauon = {'id':pid,'eta':peta,'phi':pphi,'pt':ppt,'r':numpy.sqrt(peta*peta+pphi*pphi)}
elif pid == -15 and antitauon is None:
logger.debug('p>> %5i %12.4f %12.4f %5.1f %7i ',pid,peta,pphi,ppt,pstat)
antitauon = {'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 tauon/antitauon
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 - tauon['r']) > drlj and numpy.fabs( jr - antitauon['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('tauon: %s',tauon)
#logger.debug('antitauon:%s',antitauon)
# 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 = [tauon,antitauon]
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()