Result writing example
[1]:
import hepi
print(hepi.__version__)
import smpl
import numpy as np
import hepi.resummino as rs
import hepi.util as util
import matplotlib.pyplot as plt
rs.set_path("~/git/resummino_release")
print (rs.get_path())
0.1.6.19+dirty
~/git/resummino_release/
[2]:
params = [
"mastercode_with_gm2.in",
]
pss = [
(1000011,-1000011),
]
Print
[3]:
for pa,pb in pss:
for param in params:
i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,"cteq6l1","cteq66",1., 1.,id="5")
#i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,"CT14lo","CT14lo",1., 1.,model_path=model_path,id="5")
li = [i]
li = hepi.mass_scan([i],pa, np.linspace(100,1000,9))
rs_dl = rs.run(li,noskip=False)
print(rs_dl)
Running: 9 jobs
{'LO': array([0.20208405+/-0.00029302563, 0.013712558+/-1.7676046e-05,
0.0026090769+/-3.1243321e-06, 0.00073465133+/-8.3509755e-07,
0.00025475179+/-2.7854162e-07, 0.00010047166+/-1.0658448e-07,
4.3224927e-05+/-4.4747701e-08, 1.9789732e-05+/-2.0079547e-08,
9.4894125e-06+/-9.4681338e-09], dtype=object), 'NLO': array([0.26786916+/-0.00036328325, 0.017042798+/-1.9927369e-05,
0.0031280684+/-3.4502997e-06, 0.00085929957+/-8.9960357e-07,
0.00029242159+/-2.9376357e-07, 0.00011369143+/-1.1027916e-07,
4.8409699e-05+/-4.5616627e-08, 2.2010282e-05+/-2.0217058e-08,
1.0515472e-05+/-9.4331156e-09], dtype=object), 'NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,
0.0+/-0, 0.0+/-0], dtype=object), 'aNNLO_PLUS_NNLL': array([None, None, None, None, None, None, None, None, None], dtype=object), 'K_LO': array([1.0+/-2.602590409850336e-19, 1.0+/-0, 1.0+/-0, 1.0+/-0,
1.0+/-1.2666606380662415e-19, 1.0+/-0, 1.0+/-0, 1.0+/-0, 1.0+/-0],
dtype=object), 'K_NLO': array([1.3255334104794516+/-0.002631717384869068,
1.2428605953754217+/-0.0021629996223859554,
1.1989176708436613+/-0.0019519208991142045,
1.1696699303600253+/-0.0018075671400151847,
1.1478686371546203+/-0.0017043776737595882,
1.1315771034339435+/-0.001626583762921709,
1.1199486583285612+/-0.0015677821392965133,
1.112207178955228+/-0.0015222201496370444,
1.108126767594938+/-0.0014868134066253849], dtype=object), 'K_NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,
0.0+/-0, 0.0+/-0], dtype=object), 'NLO_PLUS_NLL_OVER_NLO': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,
0.0+/-0, 0.0+/-0], dtype=object), 'K_aNNLO_PLUS_NNLL': array([None, None, None, None, None, None, None, None, None], dtype=object), 'aNNLO_PLUS_NNLL_OVER_NLO': array([None, None, None, None, None, None, None, None, None], dtype=object), 'VNLO': array([0.010615536+/-8.4512336e-05, 0.00063926004+/-4.632389e-06,
0.00011283865+/-7.642409e-07, 3.0095916e-05+/-1.9609956e-07,
9.9948463e-06+/-6.2984029e-08, 3.8048342e-06+/-2.3308244e-08,
1.5903164e-06+/-9.5087621e-09, 7.1148582e-07+/-4.1666038e-09,
3.3509692e-07+/-1.926412e-09], dtype=object), 'P_PLUS_K': array([0.029977367+/-0.00016932363, 0.0015237026+/-6.0107947e-06,
0.00026283529+/-1.0235514e-06, 7.2806828e-05+/-2.5010226e-07,
2.5616614e-05+/-7.9953718e-08, 1.0433286e-05+/-3.0123817e-08,
4.6784194e-06+/-1.2700738e-08, 2.2422485e-06+/-5.7793878e-09,
1.1286971e-06+/-2.7714589e-09], dtype=object), 'RNLOG': array([-0.0021761091+/-4.7592963e-05, -0.00010655742+/-1.9718797e-06,
-1.5536608e-05+/-2.907792e-07, -3.5006405e-06+/-6.3650574e-08,
-1.0050803e-06+/-1.7184012e-08, -3.4380552e-07+/-3.2594707e-09,
-1.2446762e-07+/-1.5589143e-09, -4.8978122e-08+/-8.7732056e-10,
-2.0648145e-08+/-3.8538584e-10], dtype=object), 'RNLOQ': array([0.0099487103+/-2.4886658e-05, 0.00037843043+/-8.8163305e-07,
4.905887e-05+/-1.0890402e-07, 1.0307804e-05+/-2.1819124e-08,
2.8204412e-06+/-5.7653472e-09, 9.122583e-07+/-1.8026799e-09,
3.3116334e-07+/-6.3405971e-10, 1.307273e-07+/-2.4517185e-10,
5.4966422e-08+/-1.0015655e-10], dtype=object), 'VNLO_PLUS_P_PLUS_K': array([0.040592903+/-0.00018924277162563912,
0.00216296264+/-7.588720628200059e-06,
0.00037567393999999996+/-1.2773885946237228e-06,
0.00010290274400000001+/-3.178146911162245e-07,
3.56114603e-05+/-1.0178204621197378e-07,
1.42381202e-05+/-3.808829989659587e-08,
6.2687358e-06+/-1.5865853346701538e-08,
2.95373432e-06+/-7.124739333404646e-09,
1.46379402e-06+/-3.3752107531431587e-09], dtype=object), 'RNLO': array([0.0077726012+/-5.370694436968401e-05,
0.00027187300999999996+/-2.1599968486376067e-06,
3.352226199999999e-05+/-3.105038304510919e-07,
6.807163500000001e-06+/-6.728647518377562e-08,
1.8153609e-06+/-1.8125382665000258e-08,
5.6845278e-07+/-3.724755571312365e-09,
2.0669572e-07+/-1.6829276605338016e-09,
8.174917800000001e-08+/-9.109339169397176e-10,
3.4318277e-08+/-3.9818787045866686e-10], dtype=object), 'RNLO_PLUS_VNLO_PLUS_P_PLUS_K': array([0.0483655042+/-0.00019671619782336716,
0.00243483565+/-7.890137334609168e-06,
0.00040919620199999997+/-1.3145852008902164e-06,
0.00010970990750000001+/-3.248594274943519e-07,
3.74268212e-05+/-1.0338333728337975e-07,
1.480657298e-05+/-3.8269993377044725e-08,
6.47543152e-06+/-1.5954859696331715e-08,
3.035483498e-06+/-7.18273702497832e-09,
1.498112297e-06+/-3.3986175436953212e-09], dtype=object), 'order': array([1, 1, 1, 1, 1, 1, 1, 1, 1]), 'energy': array([13000, 13000, 13000, 13000, 13000, 13000, 13000, 13000, 13000]), 'energyhalf': array([6500., 6500., 6500., 6500., 6500., 6500., 6500., 6500., 6500.]), 'particle1': array([1000011, 1000011, 1000011, 1000011, 1000011, 1000011, 1000011,
1000011, 1000011]), 'particle2': array([-1000011, -1000011, -1000011, -1000011, -1000011, -1000011,
-1000011, -1000011, -1000011]), 'slha': array(['mastercode_with_gm2.in_mass_1000011_100.0',
'mastercode_with_gm2.in_mass_1000011_212.5',
'mastercode_with_gm2.in_mass_1000011_325.0',
'mastercode_with_gm2.in_mass_1000011_437.5',
'mastercode_with_gm2.in_mass_1000011_550.0',
'mastercode_with_gm2.in_mass_1000011_662.5',
'mastercode_with_gm2.in_mass_1000011_775.0',
'mastercode_with_gm2.in_mass_1000011_887.5',
'mastercode_with_gm2.in_mass_1000011_1000.0'], dtype='<U42'), 'pdf_lo': array(['cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1',
'cteq6l1', 'cteq6l1', 'cteq6l1'], dtype='<U7'), 'pdfset_lo': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_nlo': array(['cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66',
'cteq66', 'cteq66', 'cteq66'], dtype='<U6'), 'pdfset_nlo': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_lo_id': array([10042, 10042, 10042, 10042, 10042, 10042, 10042, 10042, 10042]), 'pdf_nlo_id': array([10550, 10550, 10550, 10550, 10550, 10550, 10550, 10550, 10550]), 'mu_f': array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'mu_r': array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'precision': array([0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]), 'max_iters': array([50, 50, 50, 50, 50, 50, 50, 50, 50]), 'invariant_mass': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',
'auto'], dtype='<U4'), 'pt': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',
'auto'], dtype='<U4'), 'result': array(['total', 'total', 'total', 'total', 'total', 'total', 'total',
'total', 'total'], dtype='<U5'), 'id': array(['5', '5', '5', '5', '5', '5', '5', '5', '5'], dtype='<U1'), 'model_path': array(['/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',
'/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO'], dtype='<U40'), 'mu': array([ 100. , 212.5, 325. , 437.5, 550. , 662.5, 775. , 887.5,
1000. ]), 'mass_1000011': array([ 100. , 212.5, 325. , 437.5, 550. , 662.5, 775. , 887.5,
1000. ]), 'runner': array(['ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner',
'ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner',
'ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner'],
dtype='<U15')}
Pandas
[4]:
hepi.LD2DF(rs_dl)
[4]:
| LO | NLO | NLO_PLUS_NLL | aNNLO_PLUS_NNLL | K_LO | K_NLO | K_NLO_PLUS_NLL | NLO_PLUS_NLL_OVER_NLO | K_aNNLO_PLUS_NNLL | aNNLO_PLUS_NNLL_OVER_NLO | ... | precision | max_iters | invariant_mass | pt | result | id | model_path | mu | mass_1000011 | runner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.20208+/-0.00029 | 0.2679+/-0.0004 | 0.0+/-0 | None | 1.00000000000000000000+/-0.00000000000000000026 | 1.3255+/-0.0026 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 100.0 | 100.0 | ResumminoRunner |
| 1 | 0.013713+/-0.000018 | 0.017043+/-0.000020 | 0.0+/-0 | None | 1.0+/-0 | 1.2429+/-0.0022 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 212.5 | 212.5 | ResumminoRunner |
| 2 | 0.0026091+/-0.0000031 | 0.0031281+/-0.0000035 | 0.0+/-0 | None | 1.0+/-0 | 1.1989+/-0.0020 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 325.0 | 325.0 | ResumminoRunner |
| 3 | 0.0007347+/-0.0000008 | 0.0008593+/-0.0000009 | 0.0+/-0 | None | 1.0+/-0 | 1.1697+/-0.0018 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 437.5 | 437.5 | ResumminoRunner |
| 4 | 0.00025475+/-0.00000028 | 0.00029242+/-0.00000029 | 0.0+/-0 | None | 1.00000000000000000000+/-0.00000000000000000013 | 1.1479+/-0.0017 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 550.0 | 550.0 | ResumminoRunner |
| 5 | 0.00010047+/-0.00000011 | 0.00011369+/-0.00000011 | 0.0+/-0 | None | 1.0+/-0 | 1.1316+/-0.0016 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 662.5 | 662.5 | ResumminoRunner |
| 6 | (4.322+/-0.004)e-05 | (4.841+/-0.005)e-05 | 0.0+/-0 | None | 1.0+/-0 | 1.1199+/-0.0016 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 775.0 | 775.0 | ResumminoRunner |
| 7 | (1.9790+/-0.0020)e-05 | (2.2010+/-0.0020)e-05 | 0.0+/-0 | None | 1.0+/-0 | 1.1122+/-0.0015 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 887.5 | 887.5 | ResumminoRunner |
| 8 | (9.489+/-0.009)e-06 | (1.0515+/-0.0009)e-05 | 0.0+/-0 | None | 1.0+/-0 | 1.1081+/-0.0015 | 0.0+/-0 | 0.0+/-0 | None | None | ... | 0.01 | 50 | auto | auto | total | 5 | /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO | 1000.0 | 1000.0 | ResumminoRunner |
9 rows × 41 columns
CSV
[5]:
hepi.write_csv(rs_dl,"out.csv")
print(open('out.csv','r').read())
LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,order,energy,energyhalf,particle1,particle2,slha,pdf_lo,pdfset_lo,pdf_nlo,pdfset_nlo,pdf_lo_id,pdf_nlo_id,mu_f,mu_r,precision,max_iters,invariant_mass,pt,result,id,model_path,mu,mass_1000011,runner
0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,100.0,100.0,ResumminoRunner
0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,212.5,212.5,ResumminoRunner
0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,325.0,325.0,ResumminoRunner
0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,437.5,437.5,ResumminoRunner
0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,550.0,550.0,ResumminoRunner
0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,662.5,662.5,ResumminoRunner
(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,775.0,775.0,ResumminoRunner
(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,887.5,887.5,ResumminoRunner
(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,1000.0,1000.0,ResumminoRunner
JSON
[6]:
hepi.write_json(rs_dl,hepi.Order.NLO,"mass_1000011","out.json",error_sym=True)
print(open('out.json','r').read())
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/tmp/ipykernel_29869/1301378516.py in <module>
----> 1 hepi.write_json(rs_dl,hepi.Order.NLO,"mass_1000011","out.json",error_sym=True)
2 print(open('out.json','r').read())
~/.local/lib/python3.8/site-packages/hepi/output.py in write_json(dict_list, o, parameter, filename, error_sym, error_asym)
89 jd["source"] = "HEPi"
90 jd["contact"] = "?"
---> 91 jd["tool"] = dict_list["code"][0]
92 jd["process_latex"] = "$" + get_name(dict_list["particle1"][0]) + get_name(
93 dict_list["particle2"][0]) + "$"
KeyError: 'code'
[ ]:
[ ]: