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())

0.1.8.9+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)

skipskipskipskipskipskipskipskipskipRunning: 9 jobs
                        LO                      NLO NLO_PLUS_NLL  \
0        0.20208+/-0.00029          0.2679+/-0.0004      0.0+/-0
1      0.013713+/-0.000018      0.017043+/-0.000020      0.0+/-0
2    0.0026091+/-0.0000031    0.0031281+/-0.0000035      0.0+/-0
3    0.0007347+/-0.0000008    0.0008593+/-0.0000009      0.0+/-0
4  0.00025475+/-0.00000028  0.00029242+/-0.00000029      0.0+/-0
5  0.00010047+/-0.00000011  0.00011369+/-0.00000011      0.0+/-0
6      (4.322+/-0.004)e-05      (4.841+/-0.005)e-05      0.0+/-0
7    (1.9790+/-0.0020)e-05    (2.2010+/-0.0020)e-05      0.0+/-0
8      (9.489+/-0.009)e-06    (1.0515+/-0.0009)e-05      0.0+/-0

  aNNLO_PLUS_NNLL                                             K_LO  \
0            None  1.00000000000000000000+/-0.00000000000000000026
1            None                                          1.0+/-0
2            None                                          1.0+/-0
3            None                                          1.0+/-0
4            None  1.00000000000000000000+/-0.00000000000000000013
5            None                                          1.0+/-0
6            None                                          1.0+/-0
7            None                                          1.0+/-0
8            None                                          1.0+/-0

             K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL  \
0  1.3255+/-0.0026        0.0+/-0               0.0+/-0              None
1  1.2429+/-0.0022        0.0+/-0               0.0+/-0              None
2  1.1989+/-0.0020        0.0+/-0               0.0+/-0              None
3  1.1697+/-0.0018        0.0+/-0               0.0+/-0              None
4  1.1479+/-0.0017        0.0+/-0               0.0+/-0              None
5  1.1316+/-0.0016        0.0+/-0               0.0+/-0              None
6  1.1199+/-0.0016        0.0+/-0               0.0+/-0              None
7  1.1122+/-0.0015        0.0+/-0               0.0+/-0              None
8  1.1081+/-0.0015        0.0+/-0               0.0+/-0              None

  aNNLO_PLUS_NNLL_OVER_NLO  ... precision max_iters invariant_mass    pt  \
0                     None  ...      0.01        50           auto  auto
1                     None  ...      0.01        50           auto  auto
2                     None  ...      0.01        50           auto  auto
3                     None  ...      0.01        50           auto  auto
4                     None  ...      0.01        50           auto  auto
5                     None  ...      0.01        50           auto  auto
6                     None  ...      0.01        50           auto  auto
7                     None  ...      0.01        50           auto  auto
8                     None  ...      0.01        50           auto  auto

  result id model      mu  mass_1000011                           runner
0  total  5         100.0         100.0  ResumminoRunner-resummino 3.1.1
1  total  5         212.5         212.5  ResumminoRunner-resummino 3.1.1
2  total  5         325.0         325.0  ResumminoRunner-resummino 3.1.1
3  total  5         437.5         437.5  ResumminoRunner-resummino 3.1.1
4  total  5         550.0         550.0  ResumminoRunner-resummino 3.1.1
5  total  5         662.5         662.5  ResumminoRunner-resummino 3.1.1
6  total  5         775.0         775.0  ResumminoRunner-resummino 3.1.1
7  total  5         887.5         887.5  ResumminoRunner-resummino 3.1.1
8  total  5        1000.0        1000.0  ResumminoRunner-resummino 3.1.1

[9 rows x 41 columns]

Pandas

[4]:

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	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	100.0	100.0	ResumminoRunner-resummino 3.1.1
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	212.5	212.5	ResumminoRunner-resummino 3.1.1
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	325.0	325.0	ResumminoRunner-resummino 3.1.1
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	437.5	437.5	ResumminoRunner-resummino 3.1.1
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	550.0	550.0	ResumminoRunner-resummino 3.1.1
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	662.5	662.5	ResumminoRunner-resummino 3.1.1
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	775.0	775.0	ResumminoRunner-resummino 3.1.1
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	887.5	887.5	ResumminoRunner-resummino 3.1.1
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	1000.0	1000.0	ResumminoRunner-resummino 3.1.1

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,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,,100.0,100.0,ResumminoRunner-resummino 3.1.1
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,,212.5,212.5,ResumminoRunner-resummino 3.1.1
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,,325.0,325.0,ResumminoRunner-resummino 3.1.1
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,,437.5,437.5,ResumminoRunner-resummino 3.1.1
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,,550.0,550.0,ResumminoRunner-resummino 3.1.1
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,,662.5,662.5,ResumminoRunner-resummino 3.1.1
(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,,775.0,775.0,ResumminoRunner-resummino 3.1.1
(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,,887.5,887.5,ResumminoRunner-resummino 3.1.1
(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,,1000.0,1000.0,ResumminoRunner-resummino 3.1.1

JSON

[6]:

hepi.write_json(rs_dl,hepi.Order.NLO,"mass_1000011","out.json",error_sym=True)
print(open('out.json','r').read())

{"initial state": "pp", "order": "NLO", "source": "hepi-0.1.8.9+dirty", "contact": "?", "tool": "Resummino-resummino 3.1.1", "process_latex": "$\\tilde{e}_{L}^{-}\\tilde{e}_{L}^{+}$", "comment": "5", "reference": "?", "Ecom [GeV]": "13000", "process_id": "pp_13000_1000011_-1000011", "PDF set": "cteq66", "data": {"100.0": {"xsec_pb": 0.26786916, "unc_pb": 0.00036328325}, "212.5": {"xsec_pb": 0.017042798, "unc_pb": 1.9927369e-05}, "325.0": {"xsec_pb": 0.0031280684, "unc_pb": 3.4502997e-06}, "437.5": {"xsec_pb": 0.00085929957, "unc_pb": 8.9960357e-07}, "550.0": {"xsec_pb": 0.00029242159, "unc_pb": 2.9376357e-07}, "662.5": {"xsec_pb": 0.00011369143, "unc_pb": 1.1027916e-07}, "775.0": {"xsec_pb": 4.8409699e-05, "unc_pb": 4.5616627e-08}, "887.5": {"xsec_pb": 2.2010282e-05, "unc_pb": 2.0217058e-08}, "1000.0": {"xsec_pb": 1.0515472e-05, "unc_pb": 9.4331156e-09}}, "parameters": [["mass_1000011"]]}

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