Issues with implementing new dataset

[ecole41]

Hello,

I am trying to implement a new dataset into Simunet but I am getting this error when running n3fit:

  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6131, in _raise_if_missing
    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['ADD'], dtype='object')] are in the [columns]"

It looks as though n3fit is looking for 'ADD' and 'MULT' systematics, but this dataset only has 'stat' uncertainties.

I have added these files into my conda environment file:

(simunet) ellacole ...envs/simunet/share  $ pwd
/Users/ellacole/miniconda3/envs/simunet/share
(simunet) ellacole ...envs/simunet/share  $ find . -name "*BETA*"
./NNPDF/data/theory_270/simu_factors/SIMU_BETA_DECAYS_Beta_Decay_Obs.yaml
./NNPDF/data/commondata/PLOTTING_BETA_DECAYS_Beta_Decay_Obs.yaml
./NNPDF/data/commondata/systypes/SYSTYPE_BETA_DECAYS_Beta_Decay_Obs_DEFAULT.dat
./NNPDF/data/commondata/DATA_BETA_DECAYS_Beta_Decay_Obs.dat
(simunet) ellacole ...envs/simunet/share  $ 

and these into the simunet code directory:

(simunet) ellacole ...simunet/simunet_git/SIMUnet [low_energy] $ pwd
/Users/ellacole/codes/simunet/simunet_git/SIMUnet
(simunet) ellacole ...simunet/simunet_git/SIMUnet [low_energy] $ find . -name "*BETA*"                                              
./buildmaster/meta/BETA_DECAY_Beta_Decay_Obs.yaml
./nnpdfcpp/data/commondata/PLOTTING_BETA_DECAYS_Beta_Decay_Obs.yaml
./nnpdfcpp/data/commondata/systypes/SYSTYPE_BETA_DECAYS_Beta_Decay_Obs_DEFAULT.dat
./nnpdfcpp/data/commondata/DATA_BETA_DECAYS_Beta_Decay_Obs.dat
(simunet) ellacole ...simunet/simunet_git/SIMUnet [low_energy] $ 

I have nsys set to zero in the DATA and metadata files and I cannot see why this error is coming up.

[ElieHammou]

Hi @ecole41 , can you please share your fit runcard?

[ecole41]

# Configuration file for NNPDF++
#
############################################################
description: "Runcard template. This one performs a simultaenous fit with some top operators. The fit was performed as a check to verify that
 the renaming to simu_fac and simu_parameters has been successfull. NNPDF4.0 methodology. Used to produce Figure 4.7 of https://arxiv.org/pdf/2402.03308"

############################################################
# frac: training fraction
# ewk: apply ewk k-factors
# sys: systematics treatment (see systypes)

dataset_inputs:
# - {dataset: NMCPD_dw_ite, frac: 0.75}
# - {dataset: NMC, frac: 0.75}
# - {dataset: SLACP_dwsh, frac: 0.75}
# - {dataset: SLACD_dw_ite, frac: 0.75}
# - {dataset: BCDMSP_dwsh, frac: 0.75}
# - {dataset: BCDMSD_dw_ite, frac: 0.75}
# - {dataset: CHORUSNUPb_dw_ite, frac: 0.75}
# - {dataset: CHORUSNBPb_dw_ite, frac: 0.75}
# - {dataset: NTVNUDMNFe_dw_ite, frac: 0.75, cfac: [MAS]}
# - {dataset: NTVNBDMNFe_dw_ite, frac: 0.75, cfac: [MAS]}
# - {dataset: HERACOMBNCEM, frac: 0.75}
# - {dataset: HERACOMBNCEP460, frac: 0.75}
# - {dataset: HERACOMBNCEP575, frac: 0.75}
# - {dataset: HERACOMBNCEP820, frac: 0.75}
# - {dataset: HERACOMBNCEP920, frac: 0.75}
# - {dataset: HERACOMBCCEM, frac: 0.75}
# - {dataset: HERACOMBCCEP, frac: 0.75}
# - {dataset: HERACOMB_SIGMARED_C, frac: 0.75} 
# - {dataset: HERACOMB_SIGMARED_B, frac: 0.75}   
# - {dataset: DYE886R_dw_ite, frac: 0.75, cfac: [QCD]}
# - {dataset: DYE886P, frac: 0.75, cfac: [QCD]}
# - {dataset: DYE605_dw_ite, frac: 0.75, cfac: [QCD]}
# - {dataset: DYE906R_dw_ite, frac: 0.75, cfac: [ACC,QCD]}
# - {dataset: CDFZRAP_NEW, frac: 0.75, cfac: [QCD]}
# - {dataset: D0ZRAP_40, frac: 0.75, cfac: [QCD]}
# - {dataset: D0WMASY, frac: 0.75, cfac: [QCD]}
# - {dataset: ATLASWZRAP36PB, frac: 0.75, cfac: [QCD]}
# - {dataset: ATLASLOMASSDY11EXT, frac: 0.75, cfac: [QCD]}
# - {dataset: ATLASWZRAP11CC, frac: 0.75, cfac: [QCD]}               
# - {dataset: ATLASWZRAP11CF, frac: 0.75, cfac: [QCD]}
# - {dataset: ATLAS_DY_2D_8TEV_LOWMASS, frac: 0.75, cfac: [QCD]} 
# - {dataset: ATLAS_WZ_TOT_13TEV, frac: 0.75, cfac: [NRM, QCD]}       
# - {dataset: ATLAS_WP_JET_8TEV_PT, frac: 0.75, cfac: [QCD]}         
# - {dataset: ATLAS_WM_JET_8TEV_PT, frac: 0.75, cfac: [QCD]}         
# - {dataset: ATLASZPT8TEVMDIST, frac: 0.75, cfac: [QCD], sys: 10}
# - {dataset: ATLASZPT8TEVYDIST, frac: 0.75, cfac: [QCD], sys: 10}
# - {dataset: ATLAS_1JET_8TEV_R06_DEC, frac: 0.75, cfac: [QCD]}
# - {dataset: ATLAS_2JET_7TEV_R06, frac: 0.75, cfac: [QCD]}     
# - {dataset: ATLASPHT15_SF, frac: 0.75, cfac: [QCD, EWK]}         
# - {dataset: CMSWEASY840PB, frac: 0.75, cfac: [QCD]}
# - {dataset: CMSWMASY47FB, frac: 0.75, cfac: [QCD]}
# - {dataset: CMSWMU8TEV, frac: 0.75, cfac: [QCD]}
# - {dataset: CMSZDIFF12, frac: 0.75, cfac: [QCD, NRM], sys: 10}
# - {dataset: CMS_2JET_7TEV, frac: 0.75, cfac: [QCD]}       
# - {dataset: CMS_1JET_8TEV, frac: 0.75, cfac: [QCD]}
# - {dataset: LHCBZ940PB, frac: 0.75, cfac: [QCD]}
# - {dataset: LHCBZEE2FB_40, frac: 0.75, cfac: [QCD]}
# - {dataset: LHCBWZMU7TEV, frac: 0.75, cfac: [NRM, QCD]}
# - {dataset: LHCBWZMU8TEV, frac: 0.75, cfac: [NRM, QCD]}
# - {dataset: LHCB_Z_13TEV_DIMUON, frac: 0.75, cfac: [QCD]}
# - {dataset: LHCB_Z_13TEV_DIELECTRON, frac: 0.75, cfac: [QCD]}
# # Drell - Yan
# - {dataset: ATLASDY2D8TEV, cfac: [QCDEWK]}
# - {dataset: CMSDY2D11, cfac: [QCD]}
# - {dataset: ATLASZHIGHMASS49FB, cfac: [QCD]}
# - {dataset: CMSDY1D12, cfac: ['QCD', 'EWK']}
# - {dataset: CMS_HMDY_13TEV, cfac: ['QCD', 'EWK']}
 #ttbar
- {dataset: ATLASTTBARTOT7TEV, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLASTTBARTOT8TEV, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TOPDIFF_DILEPT_8TEV_TTMNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_8TEV_LJETS_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTB_DIFF_8TEV_LJ_TRAPNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTB_DIFF_8TEV_LJ_TTRAPNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_13TEV_DILEPTON_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_13TEV_HADRONIC_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_13TEV_HADRONIC_2D_TTM_ABSYTTNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_13TEV_LJETS_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: ATLAS_TTBAR_13TEV_TTMNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: CMSTTBARTOT5TEV, cfac: [QCD], simu_fac: "EFT_NLO"}  
- {dataset: CMSTTBARTOT7TEV, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMSTTBARTOT8TEV, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: CMS_TTBAR_2D_DIFF_MTT_TTRAP_NORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: CMSTOPDIFF8TEVTTRAPNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
- {dataset: CMSTTBARTOT13TEV, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_TTB_DIFF_13TEV_2016_2L_TTMNORM, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_TTBAR_13TEV_LJETS_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_TTBAR_13TEV_TTMNORM, cfac: [QCD], simu_fac: "EFT_NLO"} 
# # ttbar AC
- {dataset: ATLAS_TTBAR_8TEV_ASY, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_TTBAR_13TEV_ASY_2022, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_TTBAR_8TEV_ASY, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_TTBAR_13TEV_ASY, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_CMS_TTBAR_8TEV_ASY, cfac: [QCD], simu_fac: "EFT_NLO"}
# # # TTZ
- {dataset: ATLAS_TTBARZ_8TEV_TOTAL, simu_fac: "EFT_LO"}
- {dataset: ATLAS_TTBARZ_13TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_LO"}
- {dataset: ATLAS_TTBARZ_13TEV_PTZNORM, simu_fac: "EFT_LO"}
- {dataset: CMS_TTBARZ_8TEV_TOTAL, simu_fac: "EFT_LO"}
- {dataset: CMS_TTBARZ_13TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_LO"}
- {dataset: CMS_TTBARZ_13TEV_PTZNORM, simu_fac: "EFT_LO"}
# # # TTW
- {dataset: ATLAS_TTBARW_8TEV_TOTAL, simu_fac: "EFT_LO"}
- {dataset: ATLAS_TTBARW_13TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_LO"}
- {dataset: CMS_TTBARW_8TEV_TOTAL, simu_fac: "EFT_LO"}
- {dataset: CMS_TTBARW_13TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_LO"}
# # # singletop
- {dataset: ATLAS_SINGLETOP_TCH_7TEV_T, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_7TEV_TB, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_7TEV_T_RAP_NORM, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_7TEV_TBAR_RAP_NORM, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_8TEV_T, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_8TEV_TB, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_8TEV_T_RAP_NORM, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_8TEV_TBAR_RAP_NORM, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_SCH_8TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_13TEV_T, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_TCH_13TEV_TB, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOP_SCH_13TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_TOT_7TEV, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_8TEV_T, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_8TEV_TB, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_SCH_8TEV_TOTAL, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_13TEV_T, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_13TEV_TB, cfac: [QCD], simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOP_TCH_13TEV_YTNORM, cfac: [QCD], simu_fac: "EFT_NLO"}
# # # tW
- {dataset: ATLAS_SINGLETOPW_8TEV_TOTAL, simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOPW_8TEV_SLEP_TOTAL, simu_fac: "EFT_NLO"}
- {dataset: ATLAS_SINGLETOPW_13TEV_TOTAL, simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOPW_8TEV_TOTAL, simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOPW_13TEV_TOTAL, simu_fac: "EFT_NLO"}
- {dataset: CMS_SINGLETOPW_13TEV_SLEP_TOTAL, simu_fac: "EFT_NLO"}
# Whel
- {dataset: ATLAS_WHEL_13TEV, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: ATLAS_CMS_WHEL_8TEV, simu_fac: "EFT_NLO", use_fixed_predictions: True}
# # ttgamma
- {dataset: ATLAS_TTBARGAMMA_8TEV_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: CMS_TTBARGAMMA_8TEV_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# 4Heavy
# - {dataset: ATLAS_4TOP_13TEV_MULTILEP_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: ATLAS_4TOP_13TEV_SLEP_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: CMS_4TOP_13TEV_MULTILEP_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: CMS_4TOP_13TEV_SLEP_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: CMS_TTBB_13TEV_ALLJET_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: CMS_TTBB_13TEV_DILEPTON_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: CMS_TTBB_13TEV_LJETS_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# - {dataset: ATLAS_TTBB_13TEV_LJETS_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
# # tz
- {dataset: ATLAS_SINGLETOPZ_13TEV_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: CMS_SINGLETOPZ_13TEV_TOTAL, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: CMS_SINGLETOPZ_13TEV_PTT, simu_fac: "EFT_LO", use_fixed_predictions: True}
# EWPO
- {dataset: LEP_ZDATA, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: LEP_BRW, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: LEP_BHABHA, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: ALPHAEW_WITHTHUNC, simu_fac: "EFT_LO", use_fixed_predictions: True}
# Higgs
- {dataset: ATLAS_CMS_SSINC_RUNI, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: CMS_SSINC_RUNII, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: ATLAS_STXS_RUNII, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: ATLAS_SSINC_RUNII_ZGAM, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: ATLAS_SSINC_RUNII_MUMU, simu_fac: "EFT_LO", use_fixed_predictions: True}
# Diboson
- {dataset: LEP_EEWW_182GEV, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: LEP_EEWW_189GEV, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: LEP_EEWW_198GEV, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: LEP_EEWW_206GEV, simu_fac: "EFT_LO", use_fixed_predictions: True}
- {dataset: ATLAS_WW_13TEV_2016_MEMU, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: ATLAS_WZ_13TEV_2016_MTWZ, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: CMS_WZ_13TEV_2016_PTZ, simu_fac: "EFT_NLO", use_fixed_predictions: True}
- {dataset: ATLAS_ZJJ_13TEV_2016, simu_fac: "EFT_LO", use_fixed_predictions: True}
# Beta Decay
- {dataset: BETA_DECAYS_Beta_Decay_Obs, simu_fac: "EFT_LO",use_fixed_predictions: True} #Fixed Predictions: no PDFs

fixed_pdf_fit: True
load_weights_from_fit: 221103-jmm-no_top_1000_iterated # If this is uncommented, training starts here.
analytic_initialisation_pdf: 221103-jmm-no_top_1000_iterated

analytic_check: True

automatic_scale_choice: False

simu_parameters:
# Dipoles
- {name: 'OtZ', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OtW', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OtG', scale: 1.0, initialisation: {type: analytic}}
# Quark Currents
- {name: 'Opt', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O3pQ3', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O3pq', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpQM', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpqMi', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Opui', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Opdi', scale: 1.0, initialisation: {type: analytic}}
# Lepton currents
- {name: 'O3pl', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Opl', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Ope', scale: 1.0, initialisation: {type: analytic}}
# 4 Fermions 4Q
- {name: 'O1qd', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O1qu', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O1dt', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O1qt', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O1ut', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O11qq', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O13qq', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O8qd', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O8qu', scale: 1.0, initialisation: {type: analytic}}
- {name: 'O8dt', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O8qt', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O8ut', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O81qq', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'O83qq', scale: 1.0, initialisation: {type: analytic}}
# 4 Fermions 4HeavyQ
# - {name: 'OQQ1', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'OQQ8', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'OQt1', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'OQt8', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Ott1', scale: 0.01, initialisation: {type: analytic}}
# 4 Fermions 2L2Q
# - {name: 'Oeu', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Olu', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Oed', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Olq3', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Olq1', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Oqe', scale: 0.01, initialisation: {type: analytic}}
# - {name: 'Old', scale: 0.01, initialisation: {type: analytic}}
# 4 Fermions 4L
- {name: 'Oll', scale: 1.0, initialisation: {type: analytic}}
# Yukawa
- {name: 'Omup', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Otap', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Otp', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Obp', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Ocp', scale: 1.0, initialisation: {type: analytic}}
# Bosonic
# - {name: 'OG', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OWWW', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpG', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpW', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpB', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpWB', scale: 1.0, initialisation: {type: analytic}}
- {name: 'Opd', scale: 1.0, initialisation: {type: analytic}}
- {name: 'OpD', scale: 1.0, initialisation: {type: analytic}}

############################################################
datacuts:
  t0pdfset: 221103-jmm-no_top_1000_iterated # PDF set to generate t0 covmat
  q2min: 3.49                        # Q2 minimum
  w2min: 12.5                        # W2 minimum

############################################################
theory:
  theoryid: 270   # database id

############################################################
trvlseed: 475038818
nnseed: 2394641471
mcseed: 1831662593
save: "weights.h5"
genrep: true      # true = generate MC replicas, false = use real data

############################################################


parameters: # This defines the parameter dictionary that is passed to the Model Trainer
  nodes_per_layer: [25, 20, 8]
  activation_per_layer: [tanh, tanh, linear]
  initializer: glorot_normal
  optimizer:
    clipnorm: 6.073e-6
    #learning_rate: 0.0
    learning_rate: 2.621e-3
    optimizer_name: Nadam
  epochs: 30000
  positivity:
    initial: 184.8
    multiplier:
  integrability:
    initial: 184.8
    multiplier:
  stopping_patience: 0.2
  layer_type: dense
  dropout: 0.0
  threshold_chi2: 3.5

fitting:
  # EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7)
  # EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7)
  # FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7)
  fitbasis: EVOL  # EVOL (7), EVOLQED (8), etc.
  basis:
  - {fl: sng, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      1.093, 1.121], largex: [1.486, 3.287]}
  - {fl: g, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.8329, 1.071], largex: [3.084, 6.767]}
  - {fl: v, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.5202, 0.7431], largex: [1.556, 3.639]}
  - {fl: v3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.1205, 0.4839], largex: [1.736, 3.622]}
  - {fl: v8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.5864, 0.7987], largex: [1.559, 3.569]}
  - {fl: t3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      -0.5019, 1.126], largex: [1.754, 3.479]}
  - {fl: t8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.6305, 0.8806], largex: [1.544, 3.481]}
  - {fl: t15, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      1.087, 1.139], largex: [1.48, 3.365]}

############################################################
positivity:
  posdatasets:
  - {dataset: POSF2U, maxlambda: 1e6}        # Positivity Lagrange Multiplier
  - {dataset: POSF2DW, maxlambda: 1e6}
  - {dataset: POSF2S, maxlambda: 1e6}
  - {dataset: POSFLL, maxlambda: 1e6}
  - {dataset: POSDYU, maxlambda: 1e10}
  - {dataset: POSDYD, maxlambda: 1e10}
  - {dataset: POSDYS, maxlambda: 1e10}
  - {dataset: POSF2C, maxlambda: 1e6}
  - {dataset: POSXUQ, maxlambda: 1e6}        # Positivity of MSbar PDFs
  - {dataset: POSXUB, maxlambda: 1e6}
  - {dataset: POSXDQ, maxlambda: 1e6}
  - {dataset: POSXDB, maxlambda: 1e6}
  - {dataset: POSXSQ, maxlambda: 1e6}
  - {dataset: POSXSB, maxlambda: 1e6}
  - {dataset: POSXGL, maxlambda: 1e6}

############################################################
integrability:
  integdatasets:
  - {dataset: INTEGXT8, maxlambda: 1e2}
  - {dataset: INTEGXT3, maxlambda: 1e2}

############################################################
debug: false
maxcores: 4

[ElieHammou]

It looks very reasonable. The issue possibly comes from the use of the fixed_prediction feature which does not work on main. @FrancescoMerlotti recently debugged it on #86
I would personally make a much simpler runcard with only the dataset you are testing and the relevant SMEFT ops and try it on the new branch.

[ecole41]

I have simplified my run card and moved to the fixed_prediction_pdf branch but I am still getting the same error.

This is the new run card:

# Configuration file for NNPDF++
#
############################################################
description: "Runcard template. This one performs a simultaenous fit with some top operators. The fit was performed as a check to verify that
 the renaming to simu_fac and simu_parameters has been successfull. NNPDF4.0 methodology. Used to produce Figure 4.7 of https://arxiv.org/pdf/2402.03308"

############################################################
# frac: training fraction
# ewk: apply ewk k-factors
# sys: systematics treatment (see systypes)

dataset_inputs:
# Beta Decay
- {dataset: BETA_DECAYS_Beta_Decay_Obs, simu_fac: "EFT_LO",use_fixed_predictions: True} #Fixed Predictions: no PDFs

fixed_pdf_fit: True
load_weights_from_fit: 221103-jmm-no_top_1000_iterated # If this is uncommented, training starts here.
analytic_initialisation_pdf: 221103-jmm-no_top_1000_iterated

analytic_check: True

automatic_scale_choice: False

simu_parameters:
# Quark Currents
- {name: 'O3pq', scale: 1.0, initialisation: {type: analytic}} 
# Lepton currents
- {name: 'O3pl', scale: 1.0, initialisation: {type: analytic}}
# 4 Fermions 4HeavyQ
- {name: 'Olq3', scale: 0.01, initialisation: {type: analytic}} 
# 4 Fermions 4L
- {name: 'Oll', scale: 1.0, initialisation: {type: analytic}} 
# BETA
- {name: 'Opud', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'OulS', scale: 1.0, initialisation: {type: analytic}} 
- {name: 'OulT', scale: 1.0, initialisation: {type: analytic}} 

############################################################
datacuts:
  t0pdfset: 221103-jmm-no_top_1000_iterated # PDF set to generate t0 covmat
  q2min: 3.49                        # Q2 minimum
  w2min: 12.5                        # W2 minimum

############################################################
theory:
  theoryid: 270   # database id

############################################################
trvlseed: 475038818
nnseed: 2394641471
mcseed: 1831662593
save: "weights.h5"
genrep: true      # true = generate MC replicas, false = use real data

############################################################


parameters: # This defines the parameter dictionary that is passed to the Model Trainer
  nodes_per_layer: [25, 20, 8]
  activation_per_layer: [tanh, tanh, linear]
  initializer: glorot_normal
  optimizer:
    clipnorm: 6.073e-6
    #learning_rate: 0.0
    learning_rate: 2.621e-3
    optimizer_name: Nadam
  epochs: 30000
  positivity:
    initial: 184.8
    multiplier:
  integrability:
    initial: 184.8
    multiplier:
  stopping_patience: 0.2
  layer_type: dense
  dropout: 0.0
  threshold_chi2: 3.5

fitting:
  # EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7)
  # EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7)
  # FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7)
  fitbasis: EVOL  # EVOL (7), EVOLQED (8), etc.
  basis:
  - {fl: sng, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      1.093, 1.121], largex: [1.486, 3.287]}
  - {fl: g, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.8329, 1.071], largex: [3.084, 6.767]}
  - {fl: v, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.5202, 0.7431], largex: [1.556, 3.639]}
  - {fl: v3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.1205, 0.4839], largex: [1.736, 3.622]}
  - {fl: v8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.5864, 0.7987], largex: [1.559, 3.569]}
  - {fl: t3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      -0.5019, 1.126], largex: [1.754, 3.479]}
  - {fl: t8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      0.6305, 0.8806], largex: [1.544, 3.481]}
  - {fl: t15, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [
      1.087, 1.139], largex: [1.48, 3.365]}

############################################################
positivity:
  posdatasets:
  - {dataset: POSF2U, maxlambda: 1e6}        # Positivity Lagrange Multiplier
  - {dataset: POSF2DW, maxlambda: 1e6}
  - {dataset: POSF2S, maxlambda: 1e6}
  - {dataset: POSFLL, maxlambda: 1e6}
  - {dataset: POSDYU, maxlambda: 1e10}
  - {dataset: POSDYD, maxlambda: 1e10}
  - {dataset: POSDYS, maxlambda: 1e10}
  - {dataset: POSF2C, maxlambda: 1e6}
  - {dataset: POSXUQ, maxlambda: 1e6}        # Positivity of MSbar PDFs
  - {dataset: POSXUB, maxlambda: 1e6}
  - {dataset: POSXDQ, maxlambda: 1e6}
  - {dataset: POSXDB, maxlambda: 1e6}
  - {dataset: POSXSQ, maxlambda: 1e6}
  - {dataset: POSXSB, maxlambda: 1e6}
  - {dataset: POSXGL, maxlambda: 1e6}

############################################################
integrability:
  integdatasets:
  - {dataset: INTEGXT8, maxlambda: 1e2}
  - {dataset: INTEGXT3, maxlambda: 1e2}

############################################################
debug: false
maxcores: 4

[ecole41]

This is the full error message:

  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/n3fit/src/n3fit/scripts/n3fit_exec.py", line 251, in run
    super().run()
  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/app.py", line 158, in run
    super().run()
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/reportengine/app.py", line 380, in run
    rb.execute_sequential()
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/reportengine/resourcebuilder.py", line 166, in execute_sequential
    result = self.get_result(callspec.function,
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/reportengine/resourcebuilder.py", line 177, in get_result
    fres =  function(**kwdict)
  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/covmats.py", line 337, in dataset_inputs_t0_covmat_from_systematics
    return dataset_inputs_covmat_from_systematics(
  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/covmats.py", line 206, in dataset_inputs_covmat_from_systematics
    sys_errors = cd.systematic_errors(central_values[cd.commondata_table_indices])
  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/coredata.py", line 297, in systematic_errors
    self.multiplicative_errors * central_values[:, np.newaxis] / 100
  File "/Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/coredata.py", line 242, in multiplicative_errors
    mult_table = self.systematics_table.loc[:, ["MULT"]]
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 1067, in __getitem__
    return self._getitem_tuple(key)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 1256, in _getitem_tuple
    return self._getitem_tuple_same_dim(tup)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 924, in _getitem_tuple_same_dim
    retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 1301, in _getitem_axis
    return self._getitem_iterable(key, axis=axis)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 1239, in _getitem_iterable
    keyarr, indexer = self._get_listlike_indexer(key, axis)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexing.py", line 1432, in _get_listlike_indexer
    keyarr, indexer = ax._get_indexer_strict(key, axis_name)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
    self._raise_if_missing(keyarr, indexer, axis_name)
  File "/Users/ellacole/miniconda3/envs/simunet/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['MULT'], dtype='object')] are in the [columns]"

[ElieHammou]

Ok, it looks again that SIMunet looks for a column in the pandas dataframe systematics_table that does not exist.
I think this is an issue in the commondata systematics implementation. Try to add:

print(self.systematics_table)

in /Users/ellacole/codes/simunet/simunet_git/SIMUnet/validphys2/src/validphys/coredata.py just before

mult_table = self.systematics_table.loc[:, ["MULT"]]

on line 242.

[ElieHammou]

Also please share your DATA_BETA_DECAYS_Beta_Decay_Obs.dat and SYSTYPE_BETA_DECAYS_Beta_Decay_Obs_DEFAULT.dat files

[ecole41]

These are the outputs from:

 print('systematics_table:   ',self.systematics_table)
 print('systype_table:   ',self.systype_table)

OUT:

systematics_table:    Empty DataFrame
Columns: []
Index: [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]
systype_table:    Empty DataFrame
Columns: [type, name]
Index: []

It looks empty, as should be because there are no systematics. I'm wondering if simunet is set up to work for a dataset with no systematics? The only datasets that I can see with nsys=0 are positivity and integrability datasets.

[FrancescoMerlotti]

@ecole41 were you able to vp-setupfit?

[ecole41]

Yes, vp-setupfit was all okay, no errors

[ElieHammou]

It looks empty, as should be because there are no systematics. I'm wondering if simunet is set up to work for a dataset with no systematics? The only datasets that I can see with nsys=0 are positivity and integrability datasets.

I think you may be right. Maybe try to add systematics set to 0