From 5963ac0f25f941e3cf2485120e9af3794c8864f8 Mon Sep 17 00:00:00 2001 From: Thierry Harte Date: Tue, 28 Apr 2026 09:48:41 +0200 Subject: [PATCH 1/4] Added mixed dataset to datasets --- configs/HH4b_common/datasets/mixeddata.json | 82 +++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 configs/HH4b_common/datasets/mixeddata.json diff --git a/configs/HH4b_common/datasets/mixeddata.json b/configs/HH4b_common/datasets/mixeddata.json new file mode 100644 index 00000000..8ac47bb2 --- /dev/null +++ b/configs/HH4b_common/datasets/mixeddata.json @@ -0,0 +1,82 @@ +{ + "MixedData_Pico_2022_postEE": { + "metadata": { + "das_names": "['/Custom/MixedDAta_2022_postEE/PICOAOD']", + "sample": "MixedData_2022_postEE", + "year": "2022_postEE", + "isMC": "False", + "era": "MIX", + "nano_version": 11, + "primaryDataset": "MixedData", + "nevents": "169084201", + "size": "431455409838" + }, + "files": [ + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk0.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk1.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk2.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk3.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk4.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk5.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_EE/picoAOD_mixed_v0.chunk6.root" + ] + }, + "MixedData_Pico_2022_preEE": { + "metadata": { + "das_names": "['/Custom/MixedDAta_2022_preEE/PICOAOD']", + "sample": "MixedData_2022_preEE", + "year": "2022_preEE", + "isMC": "False", + "era": "MIX", + "nano_version": 11, + "primaryDataset": "MixedData", + "nevents": "169084201", + "size": "431455409838" + }, + "files": [ + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_preEE/picoAOD_mixed_v0.chunk0.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2022_preEE/picoAOD_mixed_v0.chunk1.root" + ] + }, + "MixedData_Pico_2023_postBPix": { + "metadata": { + "das_names": "['/Custom/MixedDAta_2023_postBPix/PICOAOD']", + "sample": "MixedData_2023_postBPix", + "year": "2023_postBPix", + "isMC": "False", + "era": "MIX", + "nano_version": 11, + "primaryDataset": "MixedData", + "nevents": "169084201", + "size": "431455409838" + }, + "files": [ + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_BPix/picoAOD_mixed_v0.chunk0.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_BPix/picoAOD_mixed_v0.chunk1.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_BPix/picoAOD_mixed_v0.chunk2.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_BPix/picoAOD_mixed_v0.chunk3.root" + ] + }, + "MixedData_Pico_2023_preBPix": { + "metadata": { + "das_names": "['/Custom/MixedDAta_2023_preBPix/PICOAOD']", + "sample": "MixedData_2023_preBPix", + "year": "2023_preBPix", + "isMC": "False", + "era": "MIX", + "nano_version": 11, + "primaryDataset": "MixedData", + "nevents": "169084201", + "size": "431455409838" + }, + "files": [ + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk0.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk1.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk2.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk3.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk4.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk5.root", + "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/bevila_t/PostDoc/HH4b/ntuples/mixeddata_noTT_pz/mixeddata_all_2023_preBPix/picoAOD_mixed_v0.chunk6.root" + ] + } +} From 58716a99c4b92df1f996a7beed8b7f8833dc9d10 Mon Sep 17 00:00:00 2001 From: Thierry Harte Date: Tue, 28 Apr 2026 16:01:04 +0200 Subject: [PATCH 2/4] Added mixed dataset and added editable cms text to plots --- configs/HH4b/HH4b_parton_matching_config.py | 58 ++--- .../config_files/configurator_tools.py | 112 ++++++---- .../config_files/default_config.py | 3 +- configs/HH4b_common/custom_cuts_common.py | 26 ++- configs/HH4b_common/dnn_input_variables.py | 202 ++++++++++++++++++ configs/HH4b_common/workflow_common.py | 60 ++++-- scripts/plot_2bMorphedvs4b.py | 70 +++++- scripts/plot_DNN_score.py | 154 +++++++------ utils_configs/custom_cut_functions.py | 13 +- utils_configs/plot/args_plot.py | 21 ++ utils_configs/plot/get_columns_from_files.py | 15 +- utils_configs/plot/get_era_lumi.py | 11 +- 12 files changed, 577 insertions(+), 168 deletions(-) diff --git a/configs/HH4b/HH4b_parton_matching_config.py b/configs/HH4b/HH4b_parton_matching_config.py index 737bb61b..8a214eaa 100644 --- a/configs/HH4b/HH4b_parton_matching_config.py +++ b/configs/HH4b/HH4b_parton_matching_config.py @@ -87,25 +87,31 @@ # Defining the used samples sample_ggF_list = [ - # "GluGlutoHHto4B_spanet_kl-1p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-5p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-2p45_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-m2p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-m1p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-0p00_kt-0p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-3p50_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-4p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-3p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-2p00_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-1p50_kt-1p00_c2-0p00_skimmed", - # "GluGlutoHHto4B_spanet_kl-0p50_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-1p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-5p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-2p45_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-m2p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-m1p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-0p00_kt-0p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-3p50_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-4p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-3p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-2p00_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-1p50_kt-1p00_c2-0p00_skimmed", + "GluGlutoHHto4B_spanet_kl-0p50_kt-1p00_c2-0p00_skimmed", ] +sample_mixed_list = [ + # "MixedData_2022_preEE", + # "MixedData_2022_postEE", + # "MixedData_2023_preBPix", + # "MixedData_2023_postBPix" + ] sample_list = [ # "DATA_JetMET_JMENano_C_skimmed", # "DATA_JetMET_JMENano_D_skimmed", - # "DATA_JetMET_JMENano_E_skimmed", + "DATA_JetMET_JMENano_E_skimmed", "DATA_JetMET_JMENano_F_skimmed", - # "DATA_JetMET_JMENano_G_skimmed", + "DATA_JetMET_JMENano_G_skimmed", # "GluGlutoHHto4B_spanet_skimmed", # "GluGlutoHHto4B_spanet_skimmed_SM", # "GluGlutoHHto4B_spanet_skimmed", @@ -116,7 +122,7 @@ # "DATA_ParkingHH_2023_Cv4", # "DATA_ParkingHH_2023_Dv1", # "DATA_ParkingHH_2023_Dv2", -] + sample_ggF_list +] + sample_ggF_list + sample_mixed_list # Define the categories to save categories_dict = define_categories( @@ -125,6 +131,7 @@ spanet=config_options_dict["spanet"], run2=config_options_dict["run2"], vr1=config_options_dict["vr1"], + mixed=config_options_dict["mixeddata"], ) # AKA if no model is applied # print(onnx_model_dict) @@ -169,6 +176,7 @@ "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "add_jet1pt_btag_wp": ["add_jet1pt", "btagPNetB_5wp"], } ) if config_options_dict["spanet"]: @@ -210,7 +218,7 @@ column_listRun2 += get_columns_list({"events": ["sig_bkg_dnn_scoreRun2"]}) if config_options_dict["spanet"] and not any( ["DATA" in sample for sample in sample_list] -): +) and not any(["Mixed" in sample for sample in sample_list]): column_list += get_columns_list( { "events": [ @@ -259,8 +267,8 @@ column_list + ( get_columns_list({"events": ["bkg_morphing_spread_dnn_weights"]}) - if "DATA" in sample - and config_options_dict["bkg_morphing_spread_dnn"] + # if "DATA" in sample + if config_options_dict["bkg_morphing_spread_dnn"] and "postW" in category else [] ) @@ -295,6 +303,7 @@ f"{localdir}/../HH4b_common/datasets/GluGlutoHHto4B_spanet_skimmed_SM.json", f"{localdir}/../HH4b_common/datasets/GluGlutoHHto4B_spanet_skimmed_separateSamples.json", f"{localdir}/../HH4b_common/datasets/DATA_JetMET_skimmed.json", + f"{localdir}/../HH4b_common/datasets/mixeddata.json", # f"{localdir}/../HH4b_common/datasets/QCD.json", # f"{localdir}/../HH4b_common/datasets/SPANet_classification.json", # f"{localdir}/../HH4b_common/datasets/signal_ggF_HH4b_local.json", @@ -314,14 +323,15 @@ skim=cuts.skimming_cut_list(config_options_dict), preselections=preselection, categories=categories_dict, - weights_classes=common_weights - + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2, SF_btag_fixed_multiple_wp], + # weights_classes=[bkg_morphing_dnn_weight],# common_weights + weights_classes=common_weights + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2, SF_btag_fixed_multiple_wp], # calibrators=[legacy_cal.JetsCalibrator, legacy_cal.JetsPtRegressionCalibrator], + # calibrators=[],# [JetsCalibrator], calibrators=[JetsCalibrator], weights={ "common": { - "inclusive": ["genWeight", "lumi", "XS", "pileup", "sf_btag_fixed_multiple_wp"], - # "inclusive": ["genWeight", "lumi", "XS", "pileup"], + # "inclusive": ["genWeight", "lumi", "XS", "pileup", "sf_btag_fixed_multiple_wp"], + "inclusive": ["genWeight", "lumi", "XS", "pileup"], # "inclusive": ["genWeight", "lumi", "XS"], # "inclusive": [], "bycategory": { @@ -332,8 +342,8 @@ variations={ "weights": { "common": { - "inclusive": ["XS", "lumi", "pileup", "sf_btag_fixed_multiple_wp"], - # "inclusive": [], + # "inclusive": ["XS", "lumi", "pileup", "sf_btag_fixed_multiple_wp"], + "inclusive": [], "bycategory": {}, }, "bysample": {}, diff --git a/configs/HH4b_common/config_files/configurator_tools.py b/configs/HH4b_common/config_files/configurator_tools.py index d02854c4..c3f80c6d 100644 --- a/configs/HH4b_common/config_files/configurator_tools.py +++ b/configs/HH4b_common/config_files/configurator_tools.py @@ -1273,31 +1273,32 @@ def create_DNN_columns_list(run2, flatten, columns_dict, btag=True): ) column_dict = {x: list(y) for x, y in column_dict.items()} if btag: - column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( - "btagPNetB" - ) - column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( - "btagPNetB_5wp" - ) - column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( - "provenance" - ) - if f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}" in column_dict: - column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( - "btagPNetB" - ) - column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( - "btagPNetB_5wp" - ) - column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( - "provenance" - ) + if f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}" in column_dict: + column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( + "btagPNetB" + ) + column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( + "btagPNetB_5wp" + ) + column_dict[f"JetGoodFromHiggsOrdered{'Run2' if run2 else ''}"].append( + "provenance" + ) + if f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}" in column_dict: + column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( + "btagPNetB" + ) + column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( + "btagPNetB_5wp" + ) + column_dict[f"JetGoodFromHiggsOrdered5Jets{'Run2' if run2 else ''}"].append( + "provenance" + ) column_list = get_columns_list(column_dict, flatten) return column_list -def define_single_category(category_name): +def define_single_category(category_name, wide_cr=False): """ Define a single category for the analysis. """ @@ -1312,9 +1313,15 @@ def define_single_category(category_name): if "VR1" not in category_name: if "control" in category_name: if "Run2" not in category_name: - cut_list.append(cuts.hh4b_control_region) + if not wide_cr: + cut_list.append(cuts.hh4b_control_region) + else: + cut_list.append(cuts.hh4b_control_region_wide) else: - cut_list.append(cuts.hh4b_control_region_run2) + if not wide_cr: + cut_list.append(cuts.hh4b_control_region_run2) + else: + cut_list.append(cuts.hh4b_control_region_wide_run2) if "signal" in category_name: if "Run2" not in category_name: cut_list.append(cuts.hh4b_signal_region) @@ -1386,6 +1393,7 @@ def define_categories( spanet=False, run2=False, vr1=False, + mixed=False, btag_sf_comp=False, vbf_analysis=False, vbf_discriminator=False, @@ -1404,33 +1412,55 @@ def define_categories( for suffix in suffixes: if not vr1: categories_dict |= define_single_category(f"4b_region{suffix}") - categories_dict |= define_single_category(f"4b_control_region{suffix}") - categories_dict |= define_single_category(f"2b_control_region_preW{suffix}") + categories_dict |= define_single_category(f"4b_control_region{suffix}", mixed) + if not mixed: + categories_dict |= define_single_category(f"2b_control_region_preW{suffix}", mixed) + categories_dict |= define_single_category(f"2b_signal_region_preW{suffix}", mixed) + categories_dict |= ( + define_single_category(f"2b_signal_region_preW_blind{suffix}") + if blind + else {} + ) + else: + categories_dict |= define_single_category(f"4b_control_region_preW{suffix}", mixed) + categories_dict |= define_single_category(f"4b_signal_region_preW{suffix}", mixed) + categories_dict |= ( + define_single_category(f"4b_signal_region_preW_blind{suffix}") + if blind + else {} + ) categories_dict |= ( define_single_category(f"4b_signal_region_blind{suffix}") if blind else {} ) categories_dict |= define_single_category(f"4b_signal_region{suffix}") - categories_dict |= ( - define_single_category(f"2b_signal_region_preW_blind{suffix}") - if blind - else {} - ) - categories_dict |= define_single_category(f"2b_signal_region_preW{suffix}") if bkg_morphing_dnn: - categories_dict |= define_single_category( - f"2b_control_region_postW{suffix}" - ) - categories_dict |= ( - define_single_category(f"2b_signal_region_postW_blind{suffix}") - if blind - else {} - ) - categories_dict |= define_single_category( - f"2b_signal_region_postW{suffix}" - ) + if not mixed: + categories_dict |= define_single_category( + f"2b_control_region_postW{suffix}", mixed + ) + categories_dict |= ( + define_single_category(f"2b_signal_region_postW_blind{suffix}") + if blind + else {} + ) + categories_dict |= define_single_category( + f"2b_signal_region_postW{suffix}" + ) + else: + categories_dict |= define_single_category( + f"4b_control_region_postW{suffix}", mixed + ) + categories_dict |= ( + define_single_category(f"4b_signal_region_postW_blind{suffix}") + if blind + else {} + ) + categories_dict |= define_single_category( + f"4b_signal_region_postW{suffix}" + ) if vbf_analysis: # NOTE: this region requires at least 6 jets diff --git a/configs/HH4b_common/config_files/default_config.py b/configs/HH4b_common/config_files/default_config.py index 97033b8c..9017c712 100644 --- a/configs/HH4b_common/config_files/default_config.py +++ b/configs/HH4b_common/config_files/default_config.py @@ -46,5 +46,6 @@ "qt_preEE": None, "only5jetsbSF": False, "noL1": False, - "approach": "first" + "approach": "first", + "mixeddata": False } | default_onnx_model_dict diff --git a/configs/HH4b_common/custom_cuts_common.py b/configs/HH4b_common/custom_cuts_common.py index 733e2897..d165d136 100644 --- a/configs/HH4b_common/custom_cuts_common.py +++ b/configs/HH4b_common/custom_cuts_common.py @@ -110,6 +110,17 @@ }, function=cuts_f.hh4b_Rhh_cuts, ) +hh4b_control_region_wide = Cut( + name="hh4b_control_region", + params={ + "Run2": False, + "radius_min": 30, + "radius_max": 80, + "higgs_lead_center": 125, + "higgs_sublead_center": 120, + }, + function=cuts_f.hh4b_Rhh_cuts, +) hh4b_signal_region_run2 = Cut( name="hh4b_signal_region_run2", @@ -134,6 +145,17 @@ }, function=cuts_f.hh4b_Rhh_cuts, ) +hh4b_control_region_wide_run2 = Cut( + name="hh4b_control_region", + params={ + "Run2": True, + "radius_min": 30, + "radius_max": 80, + "higgs_lead_center": 125, + "higgs_sublead_center": 120, + }, + function=cuts_f.hh4b_Rhh_cuts, +) hh4b_VR1_signal_region = Cut( name="hh4b_VR1_signal_region", @@ -300,8 +322,10 @@ def skimming_cut_list(configs): eventFlags, goldenJson, get_nPVgood(1), - get_HLTsel(primaryDatasets=["JetMET"]), + get_HLTsel(primaryDatasets=["JetMET"]) ] + if not configs["mixeddata"]: + skimlist = skimlist + [] if not configs["noL1"]: skimlist.append(get_L1sel(primaryDatasets=["JetMET"])) return skimlist diff --git a/configs/HH4b_common/dnn_input_variables.py b/configs/HH4b_common/dnn_input_variables.py index 4cb4c731..f28ff836 100644 --- a/configs/HH4b_common/dnn_input_variables.py +++ b/configs/HH4b_common/dnn_input_variables.py @@ -1059,6 +1059,208 @@ "sigma_over_higgs2_reco_mass": ["events", "sigma_over_higgs2_reco_mass"], } ) +############# +bkg_morphing_dnn_input_variables_mixeddata = OrderedDict( + { + # "era": ["events", "era"], + "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + # "Delta_pairing_probabilities_best_worst": ["events", "Delta_pairing_probabilities_best_worst"], + "higgs1_reco_pt": ["HiggsLeading", "pt"], + "higgs1_reco_eta": ["HiggsLeading", "eta"], + "higgs1_reco_phi": ["HiggsLeading", "phi"], + "higgs1_reco_mass": ["HiggsLeading", "mass"], + "higgs2_reco_pt": ["HiggsSubLeading", "pt"], + "higgs2_reco_eta": ["HiggsSubLeading", "eta"], + "higgs2_reco_phi": ["HiggsSubLeading", "phi"], + "higgs2_reco_mass": ["HiggsSubLeading", "mass"], + "HT": ["events", "HT"], + "higgs1_DeltaRjj": ["HiggsLeading", "dR"], + "higgs2_DeltaRjj": ["HiggsSubLeading", "dR"], + "minDeltaR_Higgjj": ["events", "dR_min"], + "maxDeltaR_Higgjj": ["events", "dR_max"], + "higgs1_helicityCosTheta": ["HiggsLeading", "helicityCosTheta"], + "higgs2_helicityCosTheta": ["HiggsSubLeading", "helicityCosTheta"], + "hh_CosThetaStar_CS": ["HH", "Costhetastar_CS"], + "hh_vec_mass": ["HH", "mass"], + "hh_vec_pt": ["HH", "pt"], + "hh_vec_eta": ["HH", "eta"], + "hh_vec_DeltaR": ["HH", "dR"], + "hh_vec_DeltaPhi": ["HH", "dPhi"], + "hh_vec_DeltaEta": ["HH", "dEta"], + "higgs1_reco_jet1_pt": ["JetGoodFromHiggsOrdered5Jets:0", "pt"], + "higgs1_reco_jet1_eta": ["JetGoodFromHiggsOrdered5Jets:0", "eta"], + "higgs1_reco_jet1_phi": ["JetGoodFromHiggsOrdered5Jets:0", "phi"], + "higgs1_reco_jet1_mass": ["JetGoodFromHiggsOrdered5Jets:0", "mass"], + "higgs1_reco_jet1_btag": ["JetGoodFromHiggsOrdered5Jets:0", "btagPNetB_5wp"], + "higgs1_reco_jet2_pt": ["JetGoodFromHiggsOrdered5Jets:1", "pt"], + "higgs1_reco_jet2_eta": ["JetGoodFromHiggsOrdered5Jets:1", "eta"], + "higgs1_reco_jet2_phi": ["JetGoodFromHiggsOrdered5Jets:1", "phi"], + "higgs1_reco_jet2_mass": ["JetGoodFromHiggsOrdered5Jets:1", "mass"], + "higgs1_reco_jet2_btag": ["JetGoodFromHiggsOrdered5Jets:1", "btagPNetB_5wp"], + "higgs2_reco_jet1_pt": ["JetGoodFromHiggsOrdered5Jets:2", "pt"], + "higgs2_reco_jet1_eta": ["JetGoodFromHiggsOrdered5Jets:2", "eta"], + "higgs2_reco_jet1_phi": ["JetGoodFromHiggsOrdered5Jets:2", "phi"], + "higgs2_reco_jet1_mass": ["JetGoodFromHiggsOrdered5Jets:2", "mass"], + "higgs2_reco_jet1_btag": ["JetGoodFromHiggsOrdered5Jets:2", "btagPNetB_5wp"], + "higgs2_reco_jet2_pt": ["JetGoodFromHiggsOrdered5Jets:3", "pt"], + "higgs2_reco_jet2_eta": ["JetGoodFromHiggsOrdered5Jets:3", "eta"], + "higgs2_reco_jet2_phi": ["JetGoodFromHiggsOrdered5Jets:3", "phi"], + "higgs2_reco_jet2_mass": ["JetGoodFromHiggsOrdered5Jets:3", "mass"], + "higgs2_reco_jet2_btag": ["JetGoodFromHiggsOrdered5Jets:3", "btagPNetB_5wp"], + "add_jet1pt_pt": ["JetGoodFromHiggsOrdered5Jets:4", "pt"], + "add_jet1pt_eta": ["JetGoodFromHiggsOrdered5Jets:4", "eta"], + "add_jet1pt_phi": ["JetGoodFromHiggsOrdered5Jets:4", "phi"], + "add_jet1pt_mass": ["JetGoodFromHiggsOrdered5Jets:4", "mass"], + "add_jet1pt_btag": ["JetGoodFromHiggsOrdered5Jets:4", "btagPNetB_5wp"], + # "sigma_over_higgs1_reco_mass": ["events", "sigma_over_higgs1_reco_mass"], + # "sigma_over_higgs2_reco_mass": ["events", "sigma_over_higgs2_reco_mass"], + } +) +sig_bkg_dnn_input_variables_spanet_mixeddata = OrderedDict( + { + "sequential": { + # "jet_btag": ["JetGoodFromHiggsOrdered5Jets", "btagPNetB_5wp"], + "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], + "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], + "jet_log_pt": ["JetGoodFromHiggsOrdered5Jets", "pt", "log_norm"], + }, + "global": { + # "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + "HT": ["events", "HT", "log_norm"], + "maxDeltaR_Higgjj": ["events", "dR_max"], + "minDeltaR_Higgjj": ["events", "dR_min"], + "higgs1_reco_dR": ["HiggsLeading", "dR"], + "higgs1_reco_eta": ["HiggsLeading", "eta"], + "higgs1_helicityCosTheta": ["HiggsLeading", "helicityCosTheta"], + "higgs1_reco_phi": ["HiggsLeading", "phi"], + "higgs1_reco_mass": ["HiggsLeading", "mass", "log_norm"], + "higgs1_reco_pt": ["HiggsLeading", "pt", "log_norm"], + "higgs2_reco_dR": ["HiggsSubLeading", "dR"], + "higgs2_reco_eta": ["HiggsSubLeading", "eta"], + "higgs2_helicityCosTheta": ["HiggsSubLeading", "helicityCosTheta"], + "higgs2_reco_phi": ["HiggsSubLeading", "phi"], + "higgs2_reco_mass": ["HiggsSubLeading", "mass", "log_norm"], + "higgs2_reco_pt": ["HiggsSubLeading", "pt", "log_norm"], + "hh_CosThetaStar_CS": ["HH", "Costhetastar_CS"], + "hh_vec_DeltaEta": ["HH", "dEta"], + "hh_vec_DeltaR": ["HH", "dR"], + "hh_vec_eta": ["HH", "eta", "norm"], + "hh_vec_mass": ["HH", "mass", "log_norm"], + "hh_vec_phi": ["HH", "phi"], + "hh_vec_pt": ["HH", "pt", "log_norm"], + } + } +) +sig_bkg_dnn_input_variables_spanet_mixeddata_btag = OrderedDict( + { + "sequential": { + "jet_btag": ["JetGoodFromHiggsOrdered5Jets", "btagPNetB_5wp"], + "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], + "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], + "jet_log_pt": ["JetGoodFromHiggsOrdered5Jets", "pt", "log_norm"], + }, + "global": { + # "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + "HT": ["events", "HT", "log_norm"], + "maxDeltaR_Higgjj": ["events", "dR_max"], + "minDeltaR_Higgjj": ["events", "dR_min"], + "higgs1_reco_dR": ["HiggsLeading", "dR"], + "higgs1_reco_eta": ["HiggsLeading", "eta"], + "higgs1_helicityCosTheta": ["HiggsLeading", "helicityCosTheta"], + "higgs1_reco_phi": ["HiggsLeading", "phi"], + "higgs1_reco_mass": ["HiggsLeading", "mass", "log_norm"], + "higgs1_reco_pt": ["HiggsLeading", "pt", "log_norm"], + "higgs2_reco_dR": ["HiggsSubLeading", "dR"], + "higgs2_reco_eta": ["HiggsSubLeading", "eta"], + "higgs2_helicityCosTheta": ["HiggsSubLeading", "helicityCosTheta"], + "higgs2_reco_phi": ["HiggsSubLeading", "phi"], + "higgs2_reco_mass": ["HiggsSubLeading", "mass", "log_norm"], + "higgs2_reco_pt": ["HiggsSubLeading", "pt", "log_norm"], + "hh_CosThetaStar_CS": ["HH", "Costhetastar_CS"], + "hh_vec_DeltaEta": ["HH", "dEta"], + "hh_vec_DeltaR": ["HH", "dR"], + "hh_vec_eta": ["HH", "eta", "norm"], + "hh_vec_mass": ["HH", "mass", "log_norm"], + "hh_vec_phi": ["HH", "phi"], + "hh_vec_pt": ["HH", "pt", "log_norm"], + } + } +) +sig_bkg_dnn_input_variables_spanet_mixeddata_pd = OrderedDict( + { + "sequential": { + # "jet_btag": ["JetGoodFromHiggsOrdered5Jets", "btagPNetB_5wp"], + "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], + "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], + "jet_log_pt": ["JetGoodFromHiggsOrdered5Jets", "pt", "log_norm"], + }, + "global": { + "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + "HT": ["events", "HT", "log_norm"], + "maxDeltaR_Higgjj": ["events", "dR_max"], + "minDeltaR_Higgjj": ["events", "dR_min"], + "higgs1_reco_dR": ["HiggsLeading", "dR"], + "higgs1_reco_eta": ["HiggsLeading", "eta"], + "higgs1_helicityCosTheta": ["HiggsLeading", "helicityCosTheta"], + "higgs1_reco_phi": ["HiggsLeading", "phi"], + "higgs1_reco_mass": ["HiggsLeading", "mass", "log_norm"], + "higgs1_reco_pt": ["HiggsLeading", "pt", "log_norm"], + "higgs2_reco_dR": ["HiggsSubLeading", "dR"], + "higgs2_reco_eta": ["HiggsSubLeading", "eta"], + "higgs2_helicityCosTheta": ["HiggsSubLeading", "helicityCosTheta"], + "higgs2_reco_phi": ["HiggsSubLeading", "phi"], + "higgs2_reco_mass": ["HiggsSubLeading", "mass", "log_norm"], + "higgs2_reco_pt": ["HiggsSubLeading", "pt", "log_norm"], + "hh_CosThetaStar_CS": ["HH", "Costhetastar_CS"], + "hh_vec_DeltaEta": ["HH", "dEta"], + "hh_vec_DeltaR": ["HH", "dR"], + "hh_vec_eta": ["HH", "eta", "norm"], + "hh_vec_mass": ["HH", "mass", "log_norm"], + "hh_vec_phi": ["HH", "phi"], + "hh_vec_pt": ["HH", "pt", "log_norm"], + } + } +) +sig_bkg_dnn_input_variables_spanet_mixeddata_btag_pd = OrderedDict( + { + "sequential": { + "jet_btag": ["JetGoodFromHiggsOrdered5Jets", "btagPNetB_5wp"], + "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], + "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], + "jet_log_pt": ["JetGoodFromHiggsOrdered5Jets", "pt", "log_norm"], + }, + "global": { + "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + "HT": ["events", "HT", "log_norm"], + "maxDeltaR_Higgjj": ["events", "dR_max"], + "minDeltaR_Higgjj": ["events", "dR_min"], + "higgs1_reco_dR": ["HiggsLeading", "dR"], + "higgs1_reco_eta": ["HiggsLeading", "eta"], + "higgs1_helicityCosTheta": ["HiggsLeading", "helicityCosTheta"], + "higgs1_reco_phi": ["HiggsLeading", "phi"], + "higgs1_reco_mass": ["HiggsLeading", "mass", "log_norm"], + "higgs1_reco_pt": ["HiggsLeading", "pt", "log_norm"], + "higgs2_reco_dR": ["HiggsSubLeading", "dR"], + "higgs2_reco_eta": ["HiggsSubLeading", "eta"], + "higgs2_helicityCosTheta": ["HiggsSubLeading", "helicityCosTheta"], + "higgs2_reco_phi": ["HiggsSubLeading", "phi"], + "higgs2_reco_mass": ["HiggsSubLeading", "mass", "log_norm"], + "higgs2_reco_pt": ["HiggsSubLeading", "pt", "log_norm"], + "hh_CosThetaStar_CS": ["HH", "Costhetastar_CS"], + "hh_vec_DeltaEta": ["HH", "dEta"], + "hh_vec_DeltaR": ["HH", "dR"], + "hh_vec_eta": ["HH", "eta", "norm"], + "hh_vec_mass": ["HH", "mass", "log_norm"], + "hh_vec_phi": ["HH", "phi"], + "hh_vec_pt": ["HH", "pt", "log_norm"], + } + } +) + if __name__ == "__main__": diff --git a/configs/HH4b_common/workflow_common.py b/configs/HH4b_common/workflow_common.py index 91339ea9..3cfa5141 100644 --- a/configs/HH4b_common/workflow_common.py +++ b/configs/HH4b_common/workflow_common.py @@ -53,6 +53,10 @@ "2022_postEE_MC": -2, "2023_preBPix_MC": -3, "2023_postBPix_MC": -4, + "2022_preEE_MIX": -5, + "2022_postEE_MIX": -6, + "2023_preBPix_MIX": -7, + "2023_postBPix_MIX": -8, } year_dict = { @@ -765,20 +769,24 @@ def define_dnn_variables( self.events["dR_min"] = ak.min(dR, axis=1) self.events["dR_max"] = ak.max(dR, axis=1) - sigma_over_higgs1_reco_mass = ( - self.get_sigma_mbb( - jets_from_higgs[:, 0], - jets_from_higgs[:, 1], + if not self.mixeddata: + sigma_over_higgs1_reco_mass = ( + self.get_sigma_mbb( + jets_from_higgs[:, 0], + jets_from_higgs[:, 1], + ) + / higgs1.mass ) - / higgs1.mass - ) - sigma_over_higgs2_reco_mass = ( - self.get_sigma_mbb( - jets_from_higgs[:, 2], - jets_from_higgs[:, 3], + sigma_over_higgs2_reco_mass = ( + self.get_sigma_mbb( + jets_from_higgs[:, 2], + jets_from_higgs[:, 3], + ) + / higgs2.mass ) - / higgs2.mass - ) + else: + sigma_over_higgs1_reco_mass = ak.zeros_like(higgs1.mass) + sigma_over_higgs2_reco_mass = ak.zeros_like(higgs1.mass) # Leading-pT H candidate pT , η, φ, and mass # Subleading-pT H candidate pT , η, φ, and mass @@ -976,6 +984,20 @@ def get_true_pairing_and_compare( return matched_jet_higgs_idx_not_none def process_extra_after_presel(self, variation): # -> ak.Array: + # Extract the single weight values for each event: + extract_single_weights = False + if extract_single_weights: + for name, weight in self.weights_manager._weightsObj.items(): + weight_vals = weight.compute(self.events, self.nEvents_after_presel, "nominal").nominal + logger.debug(f"Loading weight {name}") + logger.debug(weight_vals) + self.events = ak.with_field( + self.events, + weight_vals, + f"weight_single_{name}", + ) + logger.debug(f"Saving weight component: weight_single_{name}") + # Define the Delta WP self.events["JetGood"] = self.generate_btag_delta_workingpoints( self.events["JetGood"], 5 @@ -1202,11 +1224,11 @@ def process_extra_after_presel(self, variation): # -> ak.Array: sb_variables=True, # if self.SIG_BKG_DNN else False, ) # Create collection with 5 jets, where the first 4 are the Higgs candidates and the 5th one is the remaining jet from the original collection fed into SPANet - add_jet1pt_list = ak.singletons(self.events.add_jet1pt) - self.events["JetGoodFromHiggsOrdered5Jets"] = ak.concatenate( + add_jet1pt_list = ak.pad_none(ak.singletons(self.events.add_jet1pt), 1, clip=True) + self.events["JetGoodFromHiggsOrdered5Jets"] = ak.pad_none(ak.concatenate( [self.events.JetGoodFromHiggsOrdered, add_jet1pt_list], axis=1, - ) + ), 1) if self.dnn_variables and self.run2: ( self.events["HiggsLeadingRun2"], @@ -1223,11 +1245,11 @@ def process_extra_after_presel(self, variation): # -> ak.Array: sb_variables=True, # if self.sig_bkg_dnn else False, ) # Create collection with 5 jets, where the first 4 are the Higgs candidates and the 5th one is the remaining jet from the original collection fed into SPANet - add_jet1pt_list = ak.singletons(self.events.add_jet1ptRun2) - self.events["JetGoodFromHiggsOrdered5Jets"] = ak.concatenate( - [self.events.JetGoodFromHiggsOrdered, add_jet1pt_list], + add_jet1pt_list = ak.pad_none(ak.singletons(self.events.add_jet1ptRun2), 1, clip=True) + self.events["JetGoodFromHiggsOrdered5JetsRun2"] = ak.pad_none(ak.concatenate( + [self.events.JetGoodFromHiggsOrderedRun2, add_jet1pt_list], axis=1, - ) + ), 1) if self.bkg_morphing_dnn and not self._isMC: ( model_session_bkg_morphing_dnn, diff --git a/scripts/plot_2bMorphedvs4b.py b/scripts/plot_2bMorphedvs4b.py index c6eebb87..659f9d5f 100644 --- a/scripts/plot_2bMorphedvs4b.py +++ b/scripts/plot_2bMorphedvs4b.py @@ -71,6 +71,41 @@ ] ], } +elif args.mixed: + cat_dict |= { + f"CR{args.region_suffix}": [ + [ + f"4b{args.region_suffix}_control_region", + f"4b{args.region_suffix}_control_region_postW", + f"4b{args.region_suffix}_control_region_preW", + ] + ], + f"SR{args.region_suffix}": [ + [ + f"4b{args.region_suffix}_signal_region", + f"4b{args.region_suffix}_signal_region_postW", + f"4b{args.region_suffix}_signal_region_preW", + ] + ], + # f"SR{args.region_suffix}_blind": [ + # f"4b{args.region_suffix}_signal_region_blind", + # f"2b{args.region_suffix}_signal_region_postW_blind", + # f"2b{args.region_suffix}_signal_region_preW_blind", + # ], + # + # Special case for the 2b morphed with the spread of the morphing weights + # Keyword: "SPREAD" + # + # f"SR{args.region_suffix}_SPREAD": [ + # [ + # f"4b{args.region_suffix}_signal_region_postW", + # f"4b{args.region_suffix}_signal_region_postW_SPREAD", + # ] + # ], + # f"CR{args.region_suffix}_2b_Run2SPANet": [f"2b{args.region_suffix}_control_region_preWRun2", f"2b{args.region_suffix}_control_region_preW"], + # f"CR{args.region_suffix}_4b_Run2SPANet": [f"4b{args.region_suffix}_control_regionRun2", f"4b{args.region_suffix}_control_region"], + } + else: cat_dict |= { f"CR{args.region_suffix}": [ @@ -180,11 +215,11 @@ inputfiles = [ os.path.join(input_dir, file) for file in os.listdir(input_dir) - if file.endswith(".coffea") and "DATA" in file + if file.endswith(".coffea") and ("DATA" in file or "Mixed" in file) ] -filter_lambda = (lambda x: ("weight" in x or "score" in x)) if args.spread else None -cat_col_data, total_datasets_list = get_columns_from_files(inputfiles, "nominal", filter_lambda, debug=False, novars=args.novars) +filter_lambda = (lambda x: ("weight" in x or "score" in x)) if args.spread else (lambda x: "prov" not in x and "add_jet1pt_mass" not in x and "events_sigma" not in x and "era" not in x) +cat_col_data, total_datasets_list = get_columns_from_files(inputfiles, "nominal", filter_lambda, debug=False, novars=args.novars, filter_mixed=args.mixed) cat_col_mc = None if args.input_mc: @@ -198,7 +233,7 @@ if file.endswith(".coffea") and "DATA" not in file ] - cat_col_mc, _ = get_columns_from_files(inputfiles_mc, "nominal", filter_lambda, debug=False, novars=args.novars) + cat_col_mc, _ = get_columns_from_files(inputfiles_mc, "nominal", lambda x: "prov" not in x and "era" not in x, debug=False, novars=args.novars, filter_mixed=args.mixed) if args.run2: cols_sig_mc = cat_col_mc[f"4b{args.region_suffix}_signal_regionRun2"] @@ -241,8 +276,19 @@ def plot_weights(weights_list, suffix, lumi, era_string): mean_std_list = [] for i, weights in enumerate(weights_list): var_name = f"Morphing weights" + (f" {i}" if len(weights_list) > 1 else "") + # Remove corrupted weights + nan_events = np.isnan(weights) + if nan_events.any(): + print(f"Found invalid weights in position: {np.where(nan_events)}") + print("These weights are removed for plotting") + weights = weights[~nan_events] + # Calculating limits of the weights: + print(f"minimal weight in the collection is: {np.min(weights)} at index {np.where(weights == np.min(weights))}") + print(f"maximal weight in the collection is: {np.max(weights)} at index {np.where(weights == np.max(weights))}") + log_min = np.floor(np.log10(np.min(weights[weights > 0]))) # negative weights not possible to show in logspace + log_max = np.ceil(np.log10(np.max(weights))) # negative weights not possible to show in logspace hist_w = Hist.new.Var( - np.logspace(-3, 2, 100), + np.logspace(log_min, log_max, 100), name=var_name, flow=False, ).Double() @@ -259,7 +305,7 @@ def plot_weights(weights_list, suffix, lumi, era_string): p = ( HEPPlotter() .set_plot_config( - lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", + lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", cmstext=args.cmstext, figsize=[13, 13], ) .set_output(output_base) @@ -392,6 +438,12 @@ def plot_single_var_from_columns( col_den = col_den[col_den != PAD_VALUE] col_num = col_num[col_num != PAD_VALUE] + ### This is a bad workaround for the case where a few weights are Nan or very high. + if args.mask_large_weights: + bad_weights = np.isnan(weights_den) | (weights_den > 100) + weights_den = weights_den[~bad_weights] + col_den = col_den[~bad_weights] + # WARNING: the weights are different for the additional jets because # the number of events is different since it's computed after the masking of the PAD_VALUE if args.normalisation == "num_events": @@ -518,7 +570,7 @@ def plot_single_var_from_columns( p = ( HEPPlotter(debug=DEBUG) .set_plot_config( - lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", + lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", cmstext=args.cmstext, figsize=[13, 13], ) .set_output(os.path.join(dir_cat, f"{var}")) @@ -541,7 +593,7 @@ def plot_single_var_from_columns( p = ( HEPPlotter(debug=DEBUG) .set_plot_config( - lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", + lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", cmstext=args.cmstext, figsize=[13, 13], ) .set_output(os.path.join(dir_cat, f"{var}")) @@ -602,7 +654,7 @@ def main(cat_cols, lumi, era_string): ) continue - vars_tot = list(cat_col_DATA[cat_list[0]].keys()) + vars_tot = list(cat_col_DATA[cat_list[1]].keys()) vars_tot = [v for v in vars_tot if "year" not in v] if "SPREAD" in cats_name: vars_tot = [v for v in vars_tot if "weight" in v or "score" in v] diff --git a/scripts/plot_DNN_score.py b/scripts/plot_DNN_score.py index 0a13443c..f12b9861 100644 --- a/scripts/plot_DNN_score.py +++ b/scripts/plot_DNN_score.py @@ -38,7 +38,7 @@ # because first the name of the variables is try with the Run2 string # and after without it # First region: data 4b (blinded) -# Second region: data 2b reweighted (unblinded) +# Second region: data signal depleted reweighted (unblinded) # Third region: mc 4b (unblinded) cat_dict = {} if args.run2: @@ -53,6 +53,18 @@ f"2b{args.region_suffix}_signal_region_postWRun2", f"4b{args.region_suffix}_signal_regionRun2", ] +elif args.mixed: + cat_dict[f"CR{args.region_suffix}"] = [ + f"4b{args.region_suffix}_control_region", + f"4b{args.region_suffix}_control_region_postW", + f"4b{args.region_suffix}_control_region", + ] + # cat_dict[f"SR{args.region_suffix}_blindRun2"] = [f"4b{args.region_suffix}_signal_region_blindRun2", f"4b{args.region_suffix}_signal_regionRun2", f"2b{args.region_suffix}_signal_region_postWRun2"] + cat_dict[f"SR{args.region_suffix}"] = [ + f"4b{args.region_suffix}_signal_region", + f"4b{args.region_suffix}_signal_region_postW", + f"4b{args.region_suffix}_signal_region", + ] else: cat_dict[f"CR{args.region_suffix}"] = [ f"4b{args.region_suffix}_control_region", @@ -125,6 +137,10 @@ ] inputfiles_mc = args.input_mc +if args.mixed: + inputfiles_rew = args.input_rew +else: + inputfiles_rew = inputfiles_data filter_lambda = ( ( @@ -155,6 +171,15 @@ novars=args.novars, ) +## Collecting BKG dataset +cat_col_rew, total_datasets_list_rew = get_columns_from_files( + inputfiles_rew, + sel_var="nominal", + filter_lambda=filter_lambda, + debug=False, + novars=args.novars, +) + yields_dict = {} @@ -190,7 +215,7 @@ def compute_sob(hist_1d_dict): # s/np.sqrt(bg-s) with s being the MC_signal and bg being the reweighted data # Assuming the mc did already go through for idx in range(len(hist_1d_dict["Sig+Bkg"]["data"])): - if "DATA" in hist_1d_dict["Sig+Bkg"]["style"]["legend_name"][idx]: + if "BKG" in hist_1d_dict["Sig+Bkg"]["style"]["legend_name"][idx]: b_hist = hist_1d_dict["Sig+Bkg"]["data"][idx] else: s_hist = hist_1d_dict["Sig+Bkg"]["data"][idx] @@ -272,7 +297,7 @@ def plot_single_var_from_columns( weights_den = weights_den[col_den != PAD_VALUE] if args.normalisation == "density": weights_den = weights_den / np.sum(weights_den) - if "DATA" in cat and "2b" in cat: + if "postW" in cat: weights_den = weights_den * ratio2b4b bins_center = (bin_edges[1:] + bin_edges[:-1]) / 2 @@ -396,7 +421,7 @@ def plot_single_var_from_columns( p_sob = ( HEPPlotter(debug=True) .set_plot_config( - lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", + lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", cmstext=args.cmstext, figsize=[13, 13], ) .set_output(os.path.join(dir_cat, f"{var}_{plot_name}")) @@ -416,7 +441,7 @@ def plot_single_var_from_columns( p = ( HEPPlotter(debug=True) .set_plot_config( - lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", + lumitext=f"{era_string}, {lumi}" + r" $fb^{-1}$, (13.6 TeV)", cmstext=args.cmstext, figsize=[13, 13], ) .set_output(os.path.join(dir_cat, f"{var}")) @@ -457,21 +482,32 @@ def main(cat_cols, lumi, era_string): ## Calculating a ratio between the 2b-reweighted and the 4b region # Needs to be done here, as afterwards, we don't have access to all the regions anymore... # This will be calculated in the CR and also applied to SR. But in order to make sure that it makes sense, I also calculate both weights. - print("Calculating ratios as weight from 2b-reweighted to 4b region") + print("Calculating ratios as weight from signal depleted reweighted to 4b region") # HARDCODED: # - First region is CR # - In this region, 1st element is 4b, 3rd element is 2b-reweighted + # PEP-8 compliance: replace lambda expression with function if args.normalisation == "sum_weights": - op_norm = lambda x, y: sum(x) / sum(y) + def op_norm(x, y): return sum(x) / sum(y) elif args.normalisation == "num_events": - op_norm = lambda x, y: len(x) / len(y) + def op_norm(x, y): return len(x) / len(y) elif args.normalisation == "density": - op_norm = lambda x, y: 1 + def op_norm(x, y): return 1 else: raise ValueError( f"Unknown normalisation type {args.normalisation}. Use num_events, sum_weights or density" ) + # In background, filter out all events with a weight > 100 for the reweighted dataset + CR_region_rew = cat_dict[f"CR{args.region_suffix}"][1] + SR_region_rew = cat_dict[f"SR{args.region_suffix}"][1] + CR_weights = cat_cols[1][CR_region_rew]["weight"] + SR_weights = cat_cols[1][SR_region_rew]["weight"] + if args.mask_large_weights: + for key in cat_cols[1][CR_region_rew].keys(): + cat_cols[1][CR_region_rew][key] = cat_cols[1][CR_region_rew][key][CR_weights < 100] + cat_cols[1][SR_region_rew][key] = cat_cols[1][SR_region_rew][key][SR_weights < 100] + try: if args.run2: if args.test: @@ -480,13 +516,13 @@ def main(cat_cols, lumi, era_string): CR_region_keys = cat_dict[f"CR{args.region_suffix}Run2"] CRratio_4b_2bpostW_Run2 = op_norm( cat_cols[0][CR_region_keys[0]]["weight"], - cat_cols[0][CR_region_keys[1]]["weight"], + cat_cols[1][CR_region_keys[1]]["weight"], ) SR_region_keys = cat_dict[f"SR{args.region_suffix}Run2"] SRratio_4b_2bpostW_Run2 = op_norm( cat_cols[0][SR_region_keys[0]]["weight"], - cat_cols[0][SR_region_keys[1]]["weight"], + cat_cols[1][SR_region_keys[1]]["weight"], ) print(f"CR ratio Run2: {CRratio_4b_2bpostW_Run2}") @@ -499,13 +535,13 @@ def main(cat_cols, lumi, era_string): CRratio_4b_2bpostW = op_norm( cat_cols[0][CR_region_keys[0]]["weight"], - cat_cols[0][CR_region_keys[1]]["weight"], + cat_cols[1][CR_region_keys[1]]["weight"], ) SR_region_keys = cat_dict[f"SR{args.region_suffix}"] SRratio_4b_2bpostW = op_norm( cat_cols[0][SR_region_keys[0]]["weight"], - cat_cols[0][SR_region_keys[1]]["weight"], + cat_cols[1][SR_region_keys[1]]["weight"], ) print(f"CR ratio: {CRratio_4b_2bpostW}") @@ -519,7 +555,7 @@ def main(cat_cols, lumi, era_string): sys.exit(1) # cat_dict defined on top (global variable) - for cats_name, cat_list in cat_dict.items(): + for cats_name, cat_list in cat_dict.items(): # Signal and background regions if "Run2SPANet" in cats_name: chi_squared = False color_list = color_list_alt @@ -533,12 +569,10 @@ def main(cat_cols, lumi, era_string): # :param: data_mc means that we make the dictionary one longer such that for each category we save the data and the MC values. col_dict = {} col_list_data_mc = [] - for data_mc, cat_col in zip(["DATA", "MC"], cat_cols): + for data_mc, cat_col, cat in zip(["DATA", "BKG", "MC"], cat_cols, cat_list): print(data_mc) print(cat_col.keys()) - vars_tot = list(cat_col[cat_list[0]].keys()) - if args.test: - vars_tot = vars_tot[:3] + vars_tot = list(cat_col[cat_list[0]].keys()) # We only consider observables present in data # print("vars_tot", vars_tot) vars_to_plot = [] # vars_tot = [v for v in vars_tot if "add" in v or "weight" in v] @@ -558,55 +592,55 @@ def main(cat_cols, lumi, era_string): continue for idx in range(N): - if not f"{v}_{idx}" in col_dict.keys(): + if f"{v}_{idx}" not in col_dict.keys(): col_dict[f"{v}_{idx}"] = {} vars_to_plot.append(f"{v}_{idx}") - for cat in cat_list: - cat_data_mc = f"{cat}_{data_mc}" - if "2b" in cat and data_mc == "MC": - print(f"Skipping {v} for {cat} in {data_mc}") - continue # skip 2b MC - # if not cat in col_dict[f"{v}_{idx}"].keys(): - # col_dict[f"{v}_{idx}"][cat] = {} - try: - col_dict[f"{v}_{idx}"][cat_data_mc] = cat_col[cat][v][ - np.arange(len(cat_col[cat][v])) % N == idx - ] - except KeyError: - col_dict[f"{v}_{idx}"][cat_data_mc] = cat_col[cat][ - v.replace("Run2", "") - ][ - np.arange(len(cat_col[cat][v.replace("Run2", "")])) - % N - == idx - ] - else: - if not v in col_dict.keys(): - col_dict[v] = {} - if v != "weight": - vars_to_plot.append(v) - for cat in cat_list: + # for cat in cat_list: # This is a remnant to remember, that initially we iterated over all combinations. + # Now it is only a one-to-one matching. + # I think this should not have an impact, but in case there is an issue, start with this. cat_data_mc = f"{cat}_{data_mc}" - if "2b" in cat and data_mc == "MC": + if ("post" in cat and data_mc != "BKG") or ("post" not in cat and data_mc == "BKG"): print(f"Skipping {v} for {cat} in {data_mc}") continue # skip 2b MC - if not cat_data_mc in col_list_data_mc: - col_list_data_mc.append(cat_data_mc) - # if not cat in col_dict[v].keys(): - # col_dict[v][cat] = {} try: - col_dict[v][cat_data_mc] = cat_col[cat][v] + col_dict[f"{v}_{idx}"][cat_data_mc] = cat_col[cat][v][ + np.arange(len(cat_col[cat][v])) % N == idx + ] except KeyError: - col_dict[v][cat_data_mc] = cat_col[cat][ + col_dict[f"{v}_{idx}"][cat_data_mc] = cat_col[cat][ v.replace("Run2", "") + ][ + np.arange(len(cat_col[cat][v.replace("Run2", "")])) + % N + == idx ] - if v == "weight": - # Note that the total luminosity is hardcoded here for 2022postEE - col_dict[v][cat_data_mc] = col_dict[v][cat_data_mc] * ( - float(lumi) / (5.79 + 17.6 + 2.88) - if data_mc == "MC" - else 1 - ) + else: + if not v in col_dict.keys(): + col_dict[v] = {} + if v != "weight": + vars_to_plot.append(v) + # for cat in cat_list: + cat_data_mc = f"{cat}_{data_mc}" + if ("post" in cat and data_mc != "BKG") or ("post" not in cat and data_mc == "BKG"): + print(f"Skipping {v} for {cat} in {data_mc}") + continue # skip 2b MC + if not cat_data_mc in col_list_data_mc: + col_list_data_mc.append(cat_data_mc) + # if not cat in col_dict[v].keys(): + # col_dict[v][cat] = {} + try: + col_dict[v][cat_data_mc] = cat_col[cat][v] + except KeyError: + col_dict[v][cat_data_mc] = cat_col[cat][ + v.replace("Run2", "") + ] + if v == "weight": + # Note that the total luminosity is hardcoded here for 2022postEE + col_dict[v][cat_data_mc] = col_dict[v][cat_data_mc] * ( + float(lumi) / (5.79 + 17.6 + 2.88) + if data_mc == "MC" + else 1 + ) # compute the DNN score if onnx model is given if args.onnx_model: @@ -625,7 +659,7 @@ def main(cat_cols, lumi, era_string): for cat in cat_list: cat_data_mc = f"{cat}_{data_mc}" - if "2b" in cat and data_mc == "MC": + if ("post" in cat and data_mc != "BKG") or ("post" not in cat and data_mc == "BKG"): print(f"Skipping {v} for {cat} in {data_mc}") continue # skip 2b MC # if not cat in col_dict[v].keys(): @@ -731,7 +765,7 @@ def main(cat_cols, lumi, era_string): ############# Actual plotting command. Now a list with [datastuff, mcstuff] ###################### main( - [cat_col_data, cat_col_mc], + [cat_col_data, cat_col_rew, cat_col_mc], lumi, era_string, ) diff --git a/utils_configs/custom_cut_functions.py b/utils_configs/custom_cut_functions.py index ff2e7187..3481bf09 100644 --- a/utils_configs/custom_cut_functions.py +++ b/utils_configs/custom_cut_functions.py @@ -1,5 +1,6 @@ import copy import awkward as ak +import numpy as np from pocket_coffea.lib.cut_functions import get_JetVetoMap_Mask from pocket_coffea.lib.jets import jet_selection @@ -34,10 +35,14 @@ def get_custom_JetVetoMap_Mask(events, params, year, processor_params, **kwargs) processor_params_copy.jets_calibration.collection[year] = { "AK4PFPuppi": jet_type_default } - - mask = get_JetVetoMap_Mask( - events_copy, params, year, processor_params_copy, **kwargs - ) + + try: + mask = get_JetVetoMap_Mask( + events_copy, params, year, processor_params_copy, **kwargs + ) + except: + print("Could not load JetVetoMap. Build passthrough instead") + mask = ak.ones_like(events_copy["event"], dtype=np.bool) # remove copies del processor_params_copy diff --git a/utils_configs/plot/args_plot.py b/utils_configs/plot/args_plot.py index 51bbc357..0b41be9e 100644 --- a/utils_configs/plot/args_plot.py +++ b/utils_configs/plot/args_plot.py @@ -18,6 +18,14 @@ help="Input coffea files monte carlo", default=None, ) +parser.add_argument( + "-ir", + "--input-rew", + type=str, + nargs="+", + help="Input coffea files reweighted", + default=None, +) parser.add_argument("-o", "--output", type=str, help="Output directory", default="") parser.add_argument( "-n", @@ -88,6 +96,19 @@ help="If true, old save format without saved variations is expected", default=False, ) +parser.add_argument( + "--mixed", + action="store_true", + help="mixed datasets", + default=False, +) +parser.add_argument( + "-cms", + "--cmstext", + type=str, + help="Text shown on CMS plot", + default="Private", +) args = parser.parse_args() diff --git a/utils_configs/plot/get_columns_from_files.py b/utils_configs/plot/get_columns_from_files.py index dfa67d2f..1c604fe3 100644 --- a/utils_configs/plot/get_columns_from_files.py +++ b/utils_configs/plot/get_columns_from_files.py @@ -17,7 +17,7 @@ def get_columns_from_files( - inputfiles, sel_var="nominal", filter_lambda=None, debug=False, novars=False, max_num_parquet_files=None + inputfiles, sel_var="nominal", filter_lambda=None, debug=False, novars=False, max_num_parquet_files=None, filter_mixed=False, ): if not debug: logger.setLevel(level=logging.INFO) @@ -48,6 +48,12 @@ def get_columns_from_files( for category in categories: if debug: logger.debug(f"category {category}") + if filter_mixed and "Mixed" in dataset and category not in ["4b_control_region_preW", "4b_control_region_postW", "4b_signal_region_preW", "4b_signal_region_postW"]: + logger.debug(f"skipping category {category} for dataset {dataset} due to MixedData skipping") + continue + if filter_mixed and "Mixed" not in dataset and category in ["4b_control_region_preW", "4b_control_region_postW", "4b_signal_region_preW", "4b_signal_region_postW"]: + logger.debug(f"skipping category {category} for dataset {dataset} due to MixedData skipping") + continue if category not in cat_col: cat_col[category] = {} variations = list( @@ -221,14 +227,13 @@ def get_columns_from_parquet( # ------------------------------------------------------------ for input_file in input_files: rootdir, dset = get_parquet_save_directory(input_file) - dirs_datasets[rootdir] = dset + dirs_datasets[dset] = rootdir # ------------------------------------------------------------ # Main loop # ------------------------------------------------------------ - for rootdir, sel_dataset in dirs_datasets.items(): - if debug: - logger.debug(f"Scanning {rootdir}") + for sel_dataset, rootdir in dirs_datasets.items(): + logger.debug(f"Scanning {rootdir}") datasets = ( os.listdir(rootdir) if sel_dataset == "all" else [sel_dataset] diff --git a/utils_configs/plot/get_era_lumi.py b/utils_configs/plot/get_era_lumi.py index 467d2624..9bdf468a 100644 --- a/utils_configs/plot/get_era_lumi.py +++ b/utils_configs/plot/get_era_lumi.py @@ -56,10 +56,7 @@ def get_era_lumi(dataset_data): print("Found eras in datasets") print(era_list) assert len(era_list) > 0 - lumi = sum([era_lumi_dict[era] for era in era_list]) - #convert lumi to string with 2 digits - lumi = "{:.2f}".format(lumi) - + # If nothing else will be satisfied: era_string = ", ".join(era_list) # If only one Era @@ -103,4 +100,10 @@ def get_era_lumi(dataset_data): era_string = "2023" elif all([era in era_list for era in era_lumi_dict]): era_string = "2022, 2023" + if era_string in era_lumi_dict.keys(): + lumi = era_lumi_dict[era_string] + else: + lumi = sum([era_lumi_dict[era] for era in era_list]) + #convert lumi to string with 2 digits + lumi = "{:.2f}".format(lumi) return lumi, era_string From f1603a4b406b7eb0484a927f69d04d2efe69693e Mon Sep 17 00:00:00 2001 From: Thierry Harte Date: Tue, 28 Apr 2026 16:07:12 +0200 Subject: [PATCH 3/4] New configs, allow separate weight save, update b-tag SF efficiencies --- configs/HH4b/HH4b_parton_matching_config.py | 12 + .../HH4b/HH4b_parton_matching_config_mixed.py | 387 + configs/HH4b/params/t3_run_options.yaml | 8 +- .../config_compute_befficiency_HH4b.py | 18 +- ...ewLeptonVeto_OrOfL1Cut_UpdateJetVetoMap.py | 6 +- .../bWPeff_studies_no_model_alljets.py | 2 +- .../bWPeff_studies_spanet_alljets.py | 10 +- ...e_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py | 7 +- ...wLeptonVeto_3L1Cut_UpdateJetVetoMap.py.bkp | 29 + ..._3L1Cut_UpdateJetVetoMap_mixeddata_DATA.py | 31 + ...pdateJetVetoMap_mixeddata_control_30_80.py | 35 + ...ta_control_30_80_var000064_standard_set.py | 35 + ...rol_30_80_var000064_standard_set_withL1.py | 34 + ..._mixeddata_control_30_80_var000065_btag.py | 35 + ...ata_control_30_80_var000065_btag_withL1.py | 34 + ...ap_mixeddata_control_30_80_var000067_pd.py | 35 + ...ddata_control_30_80_var000067_pd_withL1.py | 34 + ...xeddata_control_30_80_var000068_btag_pd.py | 36 + ..._control_30_80_var000068_btag_pd_withL1.py | 35 + ...t_classifier_var000063_2bregion_reduced.py | 30 + configs/HH4b_common/datasets/DATA_JetMET.json | 2 +- ...fficiencies_btagDeepFlavB_2022_postEE.json | 6470 ++++++++-------- ...ag_efficiencies_btagPNetB_2022_postEE.json | 6478 ++++++++--------- ...encies_btagRobustParTAK4B_2022_postEE.json | 6472 ++++++++-------- .../jets_calibration_regression_json.yaml | 8 +- scripts/extract_quantile_transformer.py | 12 +- utils_configs/plot/args_plot.py | 7 + 27 files changed, 10559 insertions(+), 9743 deletions(-) create mode 100644 configs/HH4b/HH4b_parton_matching_config_mixed.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py.bkp create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_DATA.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py create mode 100644 configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_spanet_classifier_var000063_2bregion_reduced.py diff --git a/configs/HH4b/HH4b_parton_matching_config.py b/configs/HH4b/HH4b_parton_matching_config.py index 8a214eaa..c49d332d 100644 --- a/configs/HH4b/HH4b_parton_matching_config.py +++ b/configs/HH4b/HH4b_parton_matching_config.py @@ -241,6 +241,18 @@ } ) +save_separate_weights = False +if save_separate_weights: + column_list += get_columns_list( + { + "events": [ + "weight_single_lumi", + "weight_single_XS", + "weight_single_pileup", + "weight_single_sf_btag_fixed_multiple_wp", + ] + } + ) bysample_bycategory_column_dict = {} for sample in sample_list: diff --git a/configs/HH4b/HH4b_parton_matching_config_mixed.py b/configs/HH4b/HH4b_parton_matching_config_mixed.py new file mode 100644 index 00000000..be3c26fc --- /dev/null +++ b/configs/HH4b/HH4b_parton_matching_config_mixed.py @@ -0,0 +1,387 @@ +import os +import cloudpickle +import utils.quantile_transformer as quantile_transformer + +from configs.HH4b_common.config_files.__config_file__ import ( + config_options_dict, + onnx_model_dict, +) +import pocket_coffea.lib.calibrators.legacy.legacy_calibrators as legacy_cal +from pocket_coffea.lib.calibrators.common.common import JetsCalibrator + + +from pocket_coffea.lib.weights.common.common import common_weights + +# from pocket_coffea.parameters.cuts import passthrough +# rom pocket_coffea.lib.columns_manager import ColOut +from pocket_coffea.parameters import defaults +from pocket_coffea.parameters.histograms import * + +# from collections import defaultdict +from pocket_coffea.utils.configurator import Configurator +from workflow import HH4bbQuarkMatchingProcessor + +import configs.HH4b_common.custom_cuts_common as cuts +from configs.HH4b_common.config_files.configurator_tools import ( + SPANET_TRAINING_DEFAULT_COLUMNS_BTWP, + create_DNN_columns_list, + define_categories, + define_preselection, + get_columns_list, + get_variables_dict, + define_single_category, +) +from configs.HH4b_common.custom_weights import ( + bkg_morphing_dnn_weight, + bkg_morphing_dnn_weightRun2, +) +from configs.HH4b_common.params.CustomWeights import SF_btag_fixed_multiple_wp + +localdir = os.path.dirname(os.path.abspath(__file__)) + + +# Loading default parameters + +default_parameters = defaults.get_default_parameters() +defaults.register_configuration_dir("config_dir", localdir) + +# adding object preselection +year = ["2022_postEE", "2022_preEE"] # , "2023_preBPix", "2023_postBPix"] +parameters = defaults.merge_parameters_from_files( + default_parameters, + f"{localdir}/../HH4b_common/params/object_preselection_{config_options_dict['approach']}_approach.yaml", + f"{localdir}/../HH4b_common/params/triggers.yaml", + f"{localdir}/../HH4b_common/params/variations.yaml", + f"{localdir}/../HH4b_common/params/btagging_multipleWP.yaml", + f"{localdir}/../HH4b_common/params/btagging_sampleGroups.yaml", + # f"{localdir}/../HH4b_common/params/jets_calibration_legacy_Calibrator_withoutVariations_withJERC.yaml", + # f"{localdir}/../HH4b_common/params/jets_calibration_legacy_Calibrator_withVariations.yaml", + f"{localdir}/../HH4b_common/params/jets_calibration_regression_json.yaml", + update=True, +) + + +if config_options_dict["save_chunk"]: + config_options_dict["dump_columns_as_arrays_per_chunk"] = config_options_dict["save_chunk"] + + +# score transform still in testing. So far hardcoded to be 2022_postEE... +variables_dict = {} +# Define the variables to save +variables_dict = get_variables_dict( + year, + config_options_dict, + CLASSIFICATION=False, + RANDOM_PT=False, + VBF_VARIABLES=False, + BKG_MORPHING=False, # bool(onnx_model_dict["bkg_morphing_dnn"]), + SCORE=bool(config_options_dict["sig_bkg_dnn"]), + RUN2=config_options_dict["run2"], + SPANET=bool(config_options_dict["spanet"]), +) +# print(variables_dict) + +## Define the preselection to apply +preselection = define_preselection(config_options_dict) + + +# Defining the used samples +sample_ggF_list = [ + # "GluGlutoHHto4B_spanet_kl-1p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-5p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-2p45_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-m2p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-m1p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-0p00_kt-0p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-3p50_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-4p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-3p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-2p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-1p50_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-0p50_kt-1p00_c2-0p00_skimmed", +] +sample_mixed_list = [ + # "MixedData_2022_preEE", + "MixedData_2022_postEE", + # "MixedData_2023_preBPix", + # "MixedData_2023_postBPix" + ] +sample_list = [ + # "DATA_JetMET_JMENano_C_skimmed", + # "DATA_JetMET_JMENano_D_skimmed", + # "DATA_JetMET_JMENano_E_skimmed", + # "DATA_JetMET_JMENano_F_skimmed", + # "DATA_JetMET_JMENano_G_skimmed", + # "GluGlutoHHto4B_spanet_skimmed", + # "GluGlutoHHto4B_spanet_skimmed_SM", + # "GluGlutoHHto4B_spanet_skimmed", + # "GluGlutoHHto4B", + # "DATA_JetMET_JMENano_2023_Cv1_skimmed", + # "DATA_JetMET_JMENano_2023_Cv2_skimmed", + # "DATA_ParkingHH_2023_Cv3", + # "DATA_ParkingHH_2023_Cv4", + # "DATA_ParkingHH_2023_Dv1", + # "DATA_ParkingHH_2023_Dv2", +] + sample_ggF_list + sample_mixed_list + +# Define the categories to save +categories_dict = define_categories( + bkg_morphing_dnn=config_options_dict["bkg_morphing_dnn"], + blind=config_options_dict["blind"], + spanet=config_options_dict["spanet"], + run2=config_options_dict["run2"], + vr1=config_options_dict["vr1"], + mixed=config_options_dict["mixeddata"], +) +# AKA if no model is applied +# print(onnx_model_dict) +if all([model == "" for model in onnx_model_dict.values()]): + print("Didn't find any onnx model. Will choose region for SPANet training") + categories_dict = define_single_category("4b_region") + +# print("categories_dict", categories_dict) + +# VBF SPECIFIC REGIONS +# **{f"4b_semiTight_LeadingPt_region": [hh4b_4b_region, semiTight_leadingPt]}, +# **{f"4b_semiTight_LeadingMjj_region": [hh4b_4b_region, semiTight_leadingMjj]}, +# **{f"4b_semiTight_LeadingMjj_region": [hh4b_4b_region, semiTight_leadingMjj]} +# **{"4b_VBFtight_region": [hh4b_4b_region, vbf_wrapper()]}, +# +# **{ +# f"4b_VBFtight_{list(ab[0].keys())[i]}_region": [ +# hh4b_4b_region, +# vbf_wrapper(ab[i]), +# ] +# for i in range(0, 6) +# }, +# +# **{"4b_VBF_generalSelection_region": [hh4b_4b_region, VBF_generalSelection_region]}, +# **{"4b_VBF_region": [hh4b_4b_region, VBF_region]}, +# **{f"4b_VBF_0{i}qvg_region": [hh4b_4b_region, VBF_region, qvg_regions[f"qvg_0{i}_region"]] for i in range(5, 10)}, +# **{f"4b_VBF_0{i}qvg_generalSelection_region": [hh4b_4b_region, VBF_generalSelection_region, qvg_regions[f"qvg_0{i}_region"]] for i in range(5, 10)}, + +# Define the columns to save +total_input_variables = {} +column_list = [] +column_listRun2 = [] + +assert not (config_options_dict["random_pt"] and config_options_dict["run2"]) +if config_options_dict["dnn_variables"]: + total_input_variables = ( + config_options_dict["sig_bkg_dnn_input_variables"] + | config_options_dict["bkg_morphing_dnn_input_variables"] + | {"year": ["events", "year"]} | + { + "jet_log_pt": ["JetGoodFromHiggsOrdered5Jets", "pt", "log_norm"], + "jet_eta": ["JetGoodFromHiggsOrdered5Jets", "eta", "norm"], + "jet_phi": ["JetGoodFromHiggsOrdered5Jets", "phi", "norm"], + "jet_log_mass": ["JetGoodFromHiggsOrdered5Jets", "mass", "log_norm"], + "add_jet1pt_btag_wp": ["add_jet1pt", "btagPNetB_5wp"], + } + ) + if config_options_dict["spanet"]: + total_input_variables |= { + "Delta_pairing_probabilities": ["events", "Delta_pairing_probabilities"], + "Arctanh_Delta_pairing_probabilities": [ + "events", + "Arctanh_Delta_pairing_probabilities", + ], + # "Delta_pairing_probabilities_best_worst": ["events", "Delta_pairing_probabilities_best_worst"], + # "Arctanh_Delta_pairing_probabilities_best_worst": [ + # "events", + # "Arctanh_Delta_pairing_probabilities_best_worst", + # ], + "Binned_Arctanh_Delta_pairing_probabilities": [ + "events", + "Binned_Arctanh_Delta_pairing_probabilities", + ], + "Padded_Arctanh_Delta_pairing_probabilities": [ + "events", + "Padded_Arctanh_Delta_pairing_probabilities", + ], + } + # print(total_input_variables) + + column_list = create_DNN_columns_list( + False, not config_options_dict["save_chunk"], total_input_variables, btag=True + ) + column_listRun2 = create_DNN_columns_list( + True, not config_options_dict["save_chunk"], total_input_variables, btag=True + ) +elif all([model == "" for model in onnx_model_dict.values()]): + column_list = get_columns_list(SPANET_TRAINING_DEFAULT_COLUMNS_BTWP, not config_options_dict["save_chunk"]) + if config_options_dict["random_pt"]: + column_list += get_columns_list({"events": ["random_pt_weights"]}) +else: + column_list = get_columns_list(flatten=not config_options_dict["save_chunk"]) + column_listRun2 = get_columns_list(flatten=not config_options_dict["save_chunk"]) + +# Add special columns +if config_options_dict["sig_bkg_dnn"] and config_options_dict["spanet"]: + column_list += get_columns_list({"events": ["sig_bkg_dnn_score"]}) +if config_options_dict["sig_bkg_dnn"] and config_options_dict["run2"]: + column_listRun2 += get_columns_list({"events": ["sig_bkg_dnn_scoreRun2"]}) +if config_options_dict["spanet"] and not any( + ["DATA" in sample for sample in sample_list] +) and not config_options_dict["mixeddata"]: + column_list += get_columns_list( + { + "events": [ + "correct_prediction", + "correct_prediction_fully_matched", + "mask_fully_matched", + ] + } + ) +if config_options_dict["run2"] and not any( + ["DATA" in sample for sample in sample_list] +): + column_listRun2 += get_columns_list( + { + "events": [ + "correct_predictionRun2", + "correct_prediction_fully_matchedRun2", + "mask_fully_matched", + ] + } + ) + +save_separate_weights = False +if save_separate_weights: + column_list += get_columns_list( + { + "events": [ + "weight_single_lumi", + "weight_single_XS", + "weight_single_pileup", + "weight_single_sf_btag_fixed_multiple_wp", + ] + } + ) + +bysample_bycategory_column_dict = {} +for sample in sample_list: + bysample_bycategory_column_dict[sample] = { + "inclusive": [], + "bycategory": {}, + } + for category in categories_dict.keys(): + if "Run2" in category: + bysample_bycategory_column_dict[sample]["bycategory"][category] = ( + column_listRun2 + + ( + get_columns_list( + {"events": ["bkg_morphing_spread_dnn_weightsRun2"]} + ) + # if "DATA" in sample + if config_options_dict["bkg_morphing_spread_dnn"] + and "postW" in category + else [] + ) + ) + else: + bysample_bycategory_column_dict[sample]["bycategory"][category] = ( + column_list + + ( + get_columns_list({"events": ["bkg_morphing_spread_dnn_weights"]}) + # if "DATA" in sample + if config_options_dict["bkg_morphing_spread_dnn"] + and "postW" in category + else [] + ) + ) +# print("bysample_bycategory_column_dict", bysample_bycategory_column_dict) + +# Define the weights to apply +bysample_bycategory_weight_dict = {} +for sample in sample_list: + # if "DATA" in sample: + bysample_bycategory_weight_dict[sample] = {"inclusive": [], "bycategory": {}} + for category in categories_dict.keys(): + if "postW" in category: + if "Run2" in category: + bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ + "bkg_morphing_dnn_weightRun2" + ] + else: + bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ + "bkg_morphing_dnn_weight" + ] + +# print("bysample_bycategory_weight_dict", bysample_bycategory_weight_dict) + +cfg = Configurator( + parameters=parameters, + datasets={ + "jsons": [ + f"{localdir}/../HH4b_common/datasets/signal_ggF_HH4b_spanet_redirector.json", + f"{localdir}/../HH4b_common/datasets/signal_ggF_HH4b.json", + f"{localdir}/../HH4b_common/datasets/GluGlutoHHto4B_spanet_skimmed.json", + f"{localdir}/../HH4b_common/datasets/GluGlutoHHto4B_spanet_skimmed_SM.json", + f"{localdir}/../HH4b_common/datasets/GluGlutoHHto4B_spanet_skimmed_separateSamples.json", + f"{localdir}/../HH4b_common/datasets/DATA_JetMET_skimmed.json", + f"{localdir}/../HH4b_common/datasets/mixeddata.json", + # f"{localdir}/../HH4b_common/datasets/QCD.json", + # f"{localdir}/../HH4b_common/datasets/SPANet_classification.json", + # f"{localdir}/../HH4b_common/datasets/signal_ggF_HH4b_local.json", + # f"{localdir}/../HH4b_common/datasets/signal_VBF_HH4b_local.json", + f"{localdir}/../HH4b_common/datasets/DATA_ParkingHH.json", + f"{localdir}/../HH4b_common/datasets/DATA_JetMET.json", + ], + "filter": { + "samples": sample_list, + "samples_exclude": [], + "year": year, + }, + "subsamples": {}, + }, + workflow=HH4bbQuarkMatchingProcessor, + workflow_options=config_options_dict, + skim=cuts.skimming_cut_list(config_options_dict), + preselections=preselection, + categories=categories_dict, + weights_classes=[bkg_morphing_dnn_weight],# common_weights + # weights_classes=common_weights + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2, SF_btag_fixed_multiple_wp], + # calibrators=[legacy_cal.JetsCalibrator, legacy_cal.JetsPtRegressionCalibrator], + calibrators=[],# [JetsCalibrator], + # calibrators=[JetsCalibrator], + weights={ + "common": { + # "inclusive": ["genWeight", "lumi", "XS", "pileup", "sf_btag_fixed_multiple_wp"], + # "inclusive": ["genWeight", "lumi", "XS", "pileup"], + # "inclusive": ["genWeight", "lumi", "XS"], + "inclusive": [], + "bycategory": { + }, + }, + "bysample": bysample_bycategory_weight_dict, + }, + variations={ + "weights": { + "common": { + # "inclusive": ["XS", "lumi", "pileup", "sf_btag_fixed_multiple_wp"], + "inclusive": [], + "bycategory": {}, + }, + "bysample": {}, + }, + "shape": { + "common": { + # "inclusive": ["jet_calibration"], + "inclusive": [], + }, + } + }, + variables=variables_dict, + columns={ + "common": { + "inclusive": [], + "bycategory": {}, + }, + "bysample": bysample_bycategory_column_dict, + # "bysample": {}, + }, +) + + +cloudpickle.register_pickle_by_value(quantile_transformer) diff --git a/configs/HH4b/params/t3_run_options.yaml b/configs/HH4b/params/t3_run_options.yaml index 686beba4..a32dc859 100644 --- a/configs/HH4b/params/t3_run_options.yaml +++ b/configs/HH4b/params/t3_run_options.yaml @@ -1,9 +1,9 @@ -scaleout: 80 +scaleout: 30 cores-per-worker: 1 -mem-per-worker: "25GB" +mem-per-worker: "30GB" disk-per-worker: "1GB" queue: "standard" -walltime: "0:10:00" -chunksize: 50000 +walltime: "03:00:00" +chunksize: 25000 retries: 0 #tree-reduction: 5 diff --git a/configs/HH4b_btagging/config_compute_befficiency_HH4b.py b/configs/HH4b_btagging/config_compute_befficiency_HH4b.py index ca05a0a4..96117009 100644 --- a/configs/HH4b_btagging/config_compute_befficiency_HH4b.py +++ b/configs/HH4b_btagging/config_compute_befficiency_HH4b.py @@ -1,6 +1,6 @@ import os -from configs.HH4b_common.config_files.bWPeff_studies_no_model_alljets import ( +from configs.HH4b_common.config_files.bWPeff_studies_spanet_alljets import ( config_options_dict, onnx_model_dict, ) @@ -17,6 +17,7 @@ from configs.HH4b_common.config_files.configurator_tools import ( DEFAULT_JET_COLUMNS, define_single_category, + define_preselection, get_columns_list, ) from configs.HH4b_common.custom_weights import ( @@ -39,10 +40,10 @@ parameters = defaults.merge_parameters_from_files( default_parameters, f"{localdir}/../HH4b_common/params/object_preselection_first_approach.yaml", - # f"{localdir}/../HH4b_common/params/object_preselection.yaml", f"{localdir}/../HH4b_common/params/triggers.yaml", f"{localdir}/../HH4b_common/params/variations.yaml", f"{localdir}/../HH4b_common/params/btagging_multipleWP.yaml", + f"{localdir}/../HH4b_common/params/btagging_sampleGroups.yaml", # f"{localdir}/../HH4b_common/params/jets_calibration_legacy_Calibrator_withoutVariations_withJERC.yaml", # f"{localdir}/../HH4b_common/params/jets_calibration_legacy_Calibrator_withVariations.yaml", f"{localdir}/../HH4b_common/params/jets_calibration_regression_json.yaml", @@ -50,13 +51,8 @@ ) parameters["run_period"] = "Run3" config_options_dict["num_bins"] = 20 -preselection = [ - ( - cuts.hh4b_presel_nobtag - if config_options_dict["tight_cuts"] is False - else cuts.hh4b_presel_tight - ) -] +preselection = define_preselection({"no_btag": True}) + # Defining the used samples sample_ggF_list = [ "GluGlutoHHto4B_spanet_kl-1p00_kt-1p00_c2-0p00_skimmed", @@ -93,6 +89,7 @@ categories_dict = define_single_category("inclusive") column_list = get_columns_list(DEFAULT_JET_COLUMNS, not config_options_dict["save_chunk"]) +column_listRun2 = get_columns_list(DEFAULT_JET_COLUMNS, not config_options_dict["save_chunk"]) bysample_bycategory_column_dict = {} @@ -144,8 +141,7 @@ + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2], weights={ "common": { - "inclusive": ["genWeight", "lumi", "XS", - # "pileup" + "inclusive": ["genWeight", "lumi", "XS", # "pileup" ], "bycategory": {} }, diff --git a/configs/HH4b_common/config_files/DHH_method_Update_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap.py b/configs/HH4b_common/config_files/DHH_method_Update_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap.py index 86286fcd..329c8c0a 100644 --- a/configs/HH4b_common/config_files/DHH_method_Update_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap.py +++ b/configs/HH4b_common/config_files/DHH_method_Update_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap.py @@ -20,6 +20,8 @@ "fifth_jet": "pt", "pad_value": -999.0, "add_jet_spanet": True, - "save_chunk":"/work/mmalucch/out_hh4b/full_analysis/out_MC_DATA_DHH_newUpdates_JECRegression_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap/sig_vs_bkg/parquet_files/", - "qt_postEE": "/work/mmalucch/out_hh4b/full_analysis/out_MC_DATA_DHH_newUpdates_JECRegression_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap/bkg_morphing/quantile_transformer/SRRun2_qt/qt_events_sig_bkg_dnn_score_DHH_kl_1.00.pkl", + # "save_chunk":"/work/mmalucch/out_hh4b/full_analysis/out_MC_DATA_DHH_newUpdates_JECRegression_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap/sig_vs_bkg/parquet_files/", + # "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/DHH_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap", + # "qt_postEE": "/work/mmalucch/out_hh4b/full_analysis/out_MC_DATA_DHH_newUpdates_JECRegression_newLeptonVeto_OrOfL1Cut_UpdateJetVetoMap/bkg_morphing/quantile_transformer/SRRun2_qt/qt_events_sig_bkg_dnn_score_DHH_kl_1.00.pkl", + "qt_postEE": "/work/tharte/datasets/sig_bkg_classifier/1_19_3_DHH_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_forPhDseminar_with_systs/quantile_transformer/SRRun2_qt/qt_events_sig_bkg_dnn_score_DHH_kl_1.00.pkl", }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/bWPeff_studies_no_model_alljets.py b/configs/HH4b_common/config_files/bWPeff_studies_no_model_alljets.py index 6a58d82d..d1a7e284 100644 --- a/configs/HH4b_common/config_files/bWPeff_studies_no_model_alljets.py +++ b/configs/HH4b_common/config_files/bWPeff_studies_no_model_alljets.py @@ -1,8 +1,8 @@ import os localdir = os.path.dirname(os.path.abspath(__file__)) + import configs.HH4b_common.dnn_input_variables as dnn_vars -from configs.HH4b_common.dnn_input_variables import pairing_spanet_btag from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict diff --git a/configs/HH4b_common/config_files/bWPeff_studies_spanet_alljets.py b/configs/HH4b_common/config_files/bWPeff_studies_spanet_alljets.py index ca539c48..37b4bc1d 100644 --- a/configs/HH4b_common/config_files/bWPeff_studies_spanet_alljets.py +++ b/configs/HH4b_common/config_files/bWPeff_studies_spanet_alljets.py @@ -2,7 +2,7 @@ localdir = os.path.dirname(os.path.abspath(__file__)) -from configs.HH4b_common.dnn_input_variables import pairing_spanet_btagWP5 +import configs.HH4b_common.dnn_input_variables as dnn_vars from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict @@ -20,17 +20,17 @@ "vr1": False, "random_pt": False, "rand_type": 0.3, - "blind": True if onnx_model_dict["sig_bkg_dnn"] else False, - "sig_bkg_dnn_input_variables": None, - "bkg_morphing_dnn_input_variables": None, + "blind": False, "parton_jet_min_dR": 0.4, "max_num_jets_good": 5, + "max_num_jets_spanet": 5, + "max_num_jets_spanet_class": 4, "which_bquark": "last", "fifth_jet": "pt", "donotscale_sumgenweights": False, "pad_value": -999.0, + "pad_value_spanet": 9999.0, "arctanh_delta_prob_bin_edge": 2.44, - "arctanh_delta_prob_pad_limit": 2., "add_jet_spanet": True, "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, "only5jetsbSF": False, diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py index d1da7769..8426d19f 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py @@ -23,6 +23,9 @@ "pad_value": -999.0, "add_jet_spanet": True, "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, - "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap", - "qt_postEE": "/work/tharte/datasets/quantile_transformer/1_19_1_3L1_trigger_cuts/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", + "max_num_jets_spanet_class": 5, + # "mixeddata": True, + # "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap", + # "qt_postEE": "/work/tharte/datasets/quantile_transformer/1_19_1_3L1_trigger_cuts/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", + "qt_postEE": "/work/tharte/datasets/sig_bkg_classifier/1_19_4_spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_forPhDseminar_with_systs_qt_transform_fixed/quantile_transformer_directfromscore/SRSpanet_qt/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py.bkp b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py.bkp new file mode 100644 index 00000000..12f6184f --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap.py.bkp @@ -0,0 +1,29 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop75_minDelta1em5_SPANet_newUpdates_newLeptonVeto_3L1Cut_UpdateJetVetoMap_postEE/best_models/ratio/average_model_from_onnx.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/ML_pytorch/out/sig_bkg_classifier/DNN_AN_1e-3_e20drop75_minDelta1em5_SPANet_newUpdates_newLeptonVeto_3L1Cut_UpdateJetVetoMap_postEE/run100/state_dict/model_best_epoch_31.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + # "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap", + "qt_postEE": "/work/tharte/datasets/quantile_transformer/1_19_1_3L1_trigger_cuts/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", + # "qt_postEE": "/work/tharte/datasets/sig_bkg_classifier/1_19_3_spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_forPhDseminar_with_systs/quantile_transformer/SRSpanet_qt/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_DATA.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_DATA.py new file mode 100644 index 00000000..cae7059d --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_DATA.py @@ -0,0 +1,31 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + # "max_num_jets_spanet_class": 5, + "mixeddata": True, + "qt_postEE": "/work/tharte/datasets/sig_bkg_classifier/1_19_4_spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_forPhDseminar_with_systs_qt_transform_fixed/quantile_transformer_directfromscore/SRSpanet_qt/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", + # "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap", + # "qt_postEE": "/work/tharte/datasets/quantile_transformer/1_19_1_3L1_trigger_cuts/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", + # "qt_postEE": "/work/tharte/datasets/sig_bkg_classifier/1_19_4_spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_forPhDseminar_with_systs_qt_transform_fixed/quantile_transformer_directfromscore/SRSpanet_qt/qt_events_sig_bkg_dnn_score_kl_1.00.pkl", +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py new file mode 100644 index 00000000..3821ab8e --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py @@ -0,0 +1,35 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_1_from_1_14_5b_spanet_hh4b_classifier_test_validation_accuracy_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "noL1": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py new file mode 100644 index 00000000..3119e450 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py @@ -0,0 +1,35 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000064_mixed_model_standard.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "noL1": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py new file mode 100644 index 00000000..44863ead --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py @@ -0,0 +1,34 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000064_mixed_model_standard.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py new file mode 100644 index 00000000..58623e23 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py @@ -0,0 +1,35 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000065_mixed_model_withBtagWP.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_btag, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "noL1": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py new file mode 100644 index 00000000..5edd8473 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py @@ -0,0 +1,34 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000065_mixed_model_withBtagWP.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_btag, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py new file mode 100644 index 00000000..e155eac8 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py @@ -0,0 +1,35 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000067_mixed_model_withPD.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_pd, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "noL1": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py new file mode 100644 index 00000000..0b5f175b --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py @@ -0,0 +1,34 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000067_mixed_model_withPD.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_pd, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py new file mode 100644 index 00000000..ac3aee10 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py @@ -0,0 +1,36 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000068_mixed_model_withBtag_withPD.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_btag_pd, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "noL1": True, + "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd", +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py new file mode 100644 index 00000000..4b1daf87 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py @@ -0,0 +1,35 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_JetVetoMap_fix/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_best_worst/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop95_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_different_model/best_models/average_model_from_onnx.onnx", + # "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e10drop75_minDelta1em5_SPANet_3L1Cut_postEE_mixeddata_control_30_80_tryimprovement/best_models/average_model_from_onnx.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_2_from_1_14_5b_spanet_hh4b_classifier_test_classifier_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH_new_conversion_script_with_vbf_variables.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000068_mixed_model_withBtag_withPD.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata_btag_pd, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables_mixeddata, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, + "mixeddata": True, + "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd", +}| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_spanet_classifier_var000063_2bregion_reduced.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_spanet_classifier_var000063_2bregion_reduced.py new file mode 100644 index 00000000..7a312ab7 --- /dev/null +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_spanet_classifier_var000063_2bregion_reduced.py @@ -0,0 +1,30 @@ +import configs.HH4b_common.dnn_input_variables as dnn_vars + + +from configs.HH4b_common.config_files.default_config import default_onnx_model_dict as onnx_model_dict + +from configs.HH4b_common.config_files.default_config import default_config_options_dict as config_options_dict + + +onnx_model_dict |= { + "spanet": "/work/tharte/datasets/onnx_spanet_models_for_pairing_and_mass_sculpting_studies/spanet_1_14_5_h4b_5jets_ptvary_loose_300_btag_wp_newLeptonVeto_3L1Cut_UpdateJetVetoMap.onnx", + "bkg_morphing_dnn": "/work/tharte/datasets/ML_pytorch/out/bkg_reweighting/DNN_AN_1e-3_e20drop75_minDelta1em5_SPANet_newUpdates_newLeptonVeto_3L1Cut_UpdateJetVetoMap_postEE/best_models/ratio/average_model_from_onnx.onnx", + "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/spanet_hh4b_classifier_var000063_2bregion_reduced_model.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_8_spanet_classifier_fromKevin_classification_5jetinput_initial_inputset.onnx", + # "sig_bkg_dnn": "/work/tharte/datasets/onnx_spanet_models_for_classification_sig_bkg/1_15_1_from_1_14_5b_spanet_hh4b_classifier_test_validation_accuracy_metric_jet_ptetaphimass_glob_arcanh_dr_ht_higgsleadsublead_HH.onnx", +} + + +config_options_dict |= { + "dnn_variables": True, + "run2": False, + "max_num_jets_good": 5, + "sig_bkg_dnn_input_variables": dnn_vars.sig_bkg_dnn_input_variables_spanet_mixeddata, + "bkg_morphing_dnn_input_variables": dnn_vars.bkg_morphing_dnn_input_variables, + "fifth_jet": "pt", + "pad_value": -999.0, + "add_jet_spanet": True, + "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, + "qt_postEE": "", + "max_num_jets_spanet_class": 5, +}| onnx_model_dict diff --git a/configs/HH4b_common/datasets/DATA_JetMET.json b/configs/HH4b_common/datasets/DATA_JetMET.json index 97b8721b..97fed431 100644 --- a/configs/HH4b_common/datasets/DATA_JetMET.json +++ b/configs/HH4b_common/datasets/DATA_JetMET.json @@ -1491,4 +1491,4 @@ "root://cmsdcadisk.fnal.gov//dcache/uscmsdisk/store/data/Run2023C/JetMET1/NANOAOD/JMENano12p5_v2-v1/2550000/f5bb107e-a44d-4202-bc68-1e68fc1cae7a.root" ] } -} \ No newline at end of file +} diff --git a/configs/HH4b_common/params/btag_efficiencies_multipleWP/btag_efficiencies_btagDeepFlavB_2022_postEE.json b/configs/HH4b_common/params/btag_efficiencies_multipleWP/btag_efficiencies_btagDeepFlavB_2022_postEE.json index c9f26bc2..30ce3c7b 100644 --- a/configs/HH4b_common/params/btag_efficiencies_multipleWP/btag_efficiencies_btagDeepFlavB_2022_postEE.json +++ b/configs/HH4b_common/params/btag_efficiencies_multipleWP/btag_efficiencies_btagDeepFlavB_2022_postEE.json @@ -60,60 +60,60 @@ ] ], "content": [ - 0.12825042160079977, - 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0.0007634093000424929, + 0.6359297526617416, + 0.0, + 0.0, + 0.45433907360307657, + 0.0, + 0.0, + 0.6115325517422803, + 0.0, + 0.0, + 0.3936651376962283 ], "flow": "error" } diff --git a/configs/HH4b_common/params/jets_calibration_regression_json.yaml b/configs/HH4b_common/params/jets_calibration_regression_json.yaml index ff41a70e..83de00fa 100644 --- a/configs/HH4b_common/params/jets_calibration_regression_json.yaml +++ b/configs/HH4b_common/params/jets_calibration_regression_json.yaml @@ -56,9 +56,9 @@ default_jets_calibration: json_path: ${cvmfs:Run3-22EFGSep23-Summer22EE-NanoAODv12,JME,jet_jerc.json.gz} jec_mc: Summer22EE_22Sep2023_V3_MC jec_data: - E: Summer22EE_22Sep2023_RunE_V3_DATA - F: Summer22EE_22Sep2023_RunF_V3_DATA - G: Summer22EE_22Sep2023_RunG_V3_DATA + E: Summer22EE_22Sep2023_V3_DATA + F: Summer22EE_22Sep2023_V3_DATA + G: Summer22EE_22Sep2023_V3_DATA jer: Summer22EE_22Sep2023_JRV1_MC level: L1L2L3Res @@ -796,4 +796,4 @@ jets_calibration: # variations: "${default_jets_calibration.variations.full_variations}" variations: "${default_jets_calibration.variations.total_variation}" -path_to_JERC_regression_files: "/pnfs/psi.ch/cms/trivcat/store/user/mmalucch/JERC_json/JERC_json_forPNetUParT_WithJER/" \ No newline at end of file +path_to_JERC_regression_files: "/pnfs/psi.ch/cms/trivcat/store/user/mmalucch/JERC_json/JERC_json_forPNetUParT_WithJER/" diff --git a/scripts/extract_quantile_transformer.py b/scripts/extract_quantile_transformer.py index 7a9f1ba9..eaa549a9 100644 --- a/scripts/extract_quantile_transformer.py +++ b/scripts/extract_quantile_transformer.py @@ -1,6 +1,7 @@ import logging import matplotlib.pyplot as plt import os +import re import numpy as np from hist import Hist import awkward as ak @@ -118,9 +119,14 @@ def extract_quantile_transformer(cat_col, outputdir): cat_mc = f"{category}_MC" input_variables_array = [] for input_var in dnn_input_list: - input_variables_array.append( - np.array(col_dict[input_var][cat_mc], dtype=np.float32) - ) + if re.search(r'_\d+$', input_var) and input_var not in col_dict.keys(): + index = re.search(r'\d+$', input_var).group(0) + arr = np.vstack(col_dict[input_var[:-2]][cat_mc]) + input_variables_array.append(np.array(arr[:,int(index)], dtype=np.float32)) + else: + input_variables_array.append( + np.array(col_dict[input_var][cat_mc], dtype=np.float32) + ) input_variables_array = np.stack(input_variables_array, axis=-1) inputs_complete = {input_name_SIG_BKG_DNN[0]: input_variables_array} outputs = model_session_SIG_BKG_DNN.run( diff --git a/utils_configs/plot/args_plot.py b/utils_configs/plot/args_plot.py index 0b41be9e..13188276 100644 --- a/utils_configs/plot/args_plot.py +++ b/utils_configs/plot/args_plot.py @@ -109,6 +109,13 @@ help="Text shown on CMS plot", default="Private", ) +parser.add_argument( + "-mw", + "--mask-large-weights", + action="store_true", + help="Removes events with large weights from the plots (w>100)", + default=True, +) args = parser.parse_args() From b3ddd9c4a6ede5eaec3822e8668e3d91d4e347e9 Mon Sep 17 00:00:00 2001 From: Thierry Harte Date: Wed, 29 Apr 2026 14:08:38 +0200 Subject: [PATCH 4/4] Fixed different smaller issues, separated mixeddata and larger control region parameters --- configs/HH4b/HH4b_parton_matching_config.py | 75 ++++++++++--------- .../HH4b/HH4b_parton_matching_config_mixed.py | 34 ++++----- .../config_files/configurator_tools.py | 23 +++--- ...pdateJetVetoMap_mixeddata_control_30_80.py | 2 +- ...ta_control_30_80_var000064_standard_set.py | 2 +- ..._30_80_var000064_standard_set_DATAorMC.py} | 2 +- ..._mixeddata_control_30_80_var000065_btag.py | 2 +- ..._control_30_80_var000065_btag_DATAorMC.py} | 2 +- ...ap_mixeddata_control_30_80_var000067_pd.py | 2 +- ...ta_control_30_80_var000067_pd_DATAorMC.py} | 2 +- ...xeddata_control_30_80_var000068_btag_pd.py | 2 +- ...ntrol_30_80_var000068_btag_pd_DATAorMC.py} | 2 +- configs/HH4b_common/custom_cuts_common.py | 5 +- scripts/plot_2bMorphedvs4b.py | 11 ++- scripts/plot_DNN_score.py | 2 +- utils_configs/custom_cut_functions.py | 10 +-- 16 files changed, 90 insertions(+), 88 deletions(-) rename configs/HH4b_common/config_files/{spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py => spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_DATAorMC.py} (99%) rename configs/HH4b_common/config_files/{spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py => spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_DATAorMC.py} (99%) rename configs/HH4b_common/config_files/{spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py => spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_DATAorMC.py} (99%) rename configs/HH4b_common/config_files/{spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py => spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_DATAorMC.py} (99%) diff --git a/configs/HH4b/HH4b_parton_matching_config.py b/configs/HH4b/HH4b_parton_matching_config.py index c49d332d..fa1c01f8 100644 --- a/configs/HH4b/HH4b_parton_matching_config.py +++ b/configs/HH4b/HH4b_parton_matching_config.py @@ -88,41 +88,46 @@ # Defining the used samples sample_ggF_list = [ "GluGlutoHHto4B_spanet_kl-1p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-5p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-2p45_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-m2p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-m1p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-0p00_kt-0p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-3p50_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-4p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-3p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-2p00_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-1p50_kt-1p00_c2-0p00_skimmed", - "GluGlutoHHto4B_spanet_kl-0p50_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-5p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-2p45_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-m2p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-m1p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-0p00_kt-0p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-3p50_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-4p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-3p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-2p00_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-1p50_kt-1p00_c2-0p00_skimmed", + # "GluGlutoHHto4B_spanet_kl-0p50_kt-1p00_c2-0p00_skimmed", ] sample_mixed_list = [ # "MixedData_2022_preEE", - # "MixedData_2022_postEE", + "MixedData_2022_postEE", # "MixedData_2023_preBPix", # "MixedData_2023_postBPix" ] -sample_list = [ - # "DATA_JetMET_JMENano_C_skimmed", - # "DATA_JetMET_JMENano_D_skimmed", - "DATA_JetMET_JMENano_E_skimmed", - "DATA_JetMET_JMENano_F_skimmed", - "DATA_JetMET_JMENano_G_skimmed", - # "GluGlutoHHto4B_spanet_skimmed", - # "GluGlutoHHto4B_spanet_skimmed_SM", - # "GluGlutoHHto4B_spanet_skimmed", - # "GluGlutoHHto4B", - # "DATA_JetMET_JMENano_2023_Cv1_skimmed", - # "DATA_JetMET_JMENano_2023_Cv2_skimmed", - # "DATA_ParkingHH_2023_Cv3", - # "DATA_ParkingHH_2023_Cv4", - # "DATA_ParkingHH_2023_Dv1", - # "DATA_ParkingHH_2023_Dv2", -] + sample_ggF_list + sample_mixed_list + +if config_options_dict["mixeddata"]: + sample_list = sample_mixed_list +else: + sample_list = [ + # "DATA_JetMET_JMENano_C_skimmed", + # "DATA_JetMET_JMENano_D_skimmed", + # "DATA_JetMET_JMENano_E_skimmed", + # "DATA_JetMET_JMENano_F_skimmed", + "DATA_JetMET_JMENano_G_skimmed", + # "GluGlutoHHto4B_spanet_skimmed", + # "GluGlutoHHto4B_spanet_skimmed_SM", + # "GluGlutoHHto4B_spanet_skimmed", + # "GluGlutoHHto4B", + # "DATA_JetMET_JMENano_2023_Cv1_skimmed", + # "DATA_JetMET_JMENano_2023_Cv2_skimmed", + # "DATA_ParkingHH_2023_Cv3", + # "DATA_ParkingHH_2023_Cv4", + # "DATA_ParkingHH_2023_Dv1", + # "DATA_ParkingHH_2023_Dv2", + ] + sample_ggF_list + # Define the categories to save categories_dict = define_categories( @@ -131,7 +136,7 @@ spanet=config_options_dict["spanet"], run2=config_options_dict["run2"], vr1=config_options_dict["vr1"], - mixed=config_options_dict["mixeddata"], + expandCR=config_options_dict["expandCR"], ) # AKA if no model is applied # print(onnx_model_dict) @@ -268,7 +273,7 @@ get_columns_list( {"events": ["bkg_morphing_spread_dnn_weightsRun2"]} ) - if "DATA" in sample + if "DATA" in sample.upper() and config_options_dict["bkg_morphing_spread_dnn"] and "postW" in category else [] @@ -290,7 +295,7 @@ # Define the weights to apply bysample_bycategory_weight_dict = {} for sample in sample_list: - if "DATA" in sample: + if "DATA" in sample.upper(): bysample_bycategory_weight_dict[sample] = {"inclusive": [], "bycategory": {}} for category in categories_dict.keys(): if "postW" in category: @@ -336,10 +341,8 @@ preselections=preselection, categories=categories_dict, # weights_classes=[bkg_morphing_dnn_weight],# common_weights - weights_classes=common_weights + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2, SF_btag_fixed_multiple_wp], - # calibrators=[legacy_cal.JetsCalibrator, legacy_cal.JetsPtRegressionCalibrator], - # calibrators=[],# [JetsCalibrator], - calibrators=[JetsCalibrator], + weights_classes=common_weights + [bkg_morphing_dnn_weight, bkg_morphing_dnn_weightRun2, SF_btag_fixed_multiple_wp] , + calibrators=[JetsCalibrator] if not config_options_dict['mixeddata'] else [], weights={ "common": { # "inclusive": ["genWeight", "lumi", "XS", "pileup", "sf_btag_fixed_multiple_wp"], diff --git a/configs/HH4b/HH4b_parton_matching_config_mixed.py b/configs/HH4b/HH4b_parton_matching_config_mixed.py index be3c26fc..39140451 100644 --- a/configs/HH4b/HH4b_parton_matching_config_mixed.py +++ b/configs/HH4b/HH4b_parton_matching_config_mixed.py @@ -1,6 +1,6 @@ import os import cloudpickle -import utils.quantile_transformer as quantile_transformer +import utils_configs.quantile_transformer as quantile_transformer from configs.HH4b_common.config_files.__config_file__ import ( config_options_dict, @@ -131,7 +131,7 @@ spanet=config_options_dict["spanet"], run2=config_options_dict["run2"], vr1=config_options_dict["vr1"], - mixed=config_options_dict["mixeddata"], + expandCR=config_options_dict["expandCR"], ) # AKA if no model is applied # print(onnx_model_dict) @@ -223,7 +223,7 @@ column_listRun2 += get_columns_list({"events": ["sig_bkg_dnn_scoreRun2"]}) if config_options_dict["spanet"] and not any( ["DATA" in sample for sample in sample_list] -) and not config_options_dict["mixeddata"]: +) and not any(["Mixed" in sample for sample in sample_list]): column_list += get_columns_list( { "events": [ @@ -273,8 +273,8 @@ get_columns_list( {"events": ["bkg_morphing_spread_dnn_weightsRun2"]} ) - # if "DATA" in sample - if config_options_dict["bkg_morphing_spread_dnn"] + if "DATA" in sample.upper() + and config_options_dict["bkg_morphing_spread_dnn"] and "postW" in category else [] ) @@ -295,18 +295,18 @@ # Define the weights to apply bysample_bycategory_weight_dict = {} for sample in sample_list: - # if "DATA" in sample: - bysample_bycategory_weight_dict[sample] = {"inclusive": [], "bycategory": {}} - for category in categories_dict.keys(): - if "postW" in category: - if "Run2" in category: - bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ - "bkg_morphing_dnn_weightRun2" - ] - else: - bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ - "bkg_morphing_dnn_weight" - ] + if "DATA" in sample.upper(): + bysample_bycategory_weight_dict[sample] = {"inclusive": [], "bycategory": {}} + for category in categories_dict.keys(): + if "postW" in category: + if "Run2" in category: + bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ + "bkg_morphing_dnn_weightRun2" + ] + else: + bysample_bycategory_weight_dict[sample]["bycategory"][category] = [ + "bkg_morphing_dnn_weight" + ] # print("bysample_bycategory_weight_dict", bysample_bycategory_weight_dict) diff --git a/configs/HH4b_common/config_files/configurator_tools.py b/configs/HH4b_common/config_files/configurator_tools.py index c3f80c6d..7bba5c94 100644 --- a/configs/HH4b_common/config_files/configurator_tools.py +++ b/configs/HH4b_common/config_files/configurator_tools.py @@ -1393,7 +1393,7 @@ def define_categories( spanet=False, run2=False, vr1=False, - mixed=False, + expandCR=False, btag_sf_comp=False, vbf_analysis=False, vbf_discriminator=False, @@ -1412,18 +1412,18 @@ def define_categories( for suffix in suffixes: if not vr1: categories_dict |= define_single_category(f"4b_region{suffix}") - categories_dict |= define_single_category(f"4b_control_region{suffix}", mixed) - if not mixed: - categories_dict |= define_single_category(f"2b_control_region_preW{suffix}", mixed) - categories_dict |= define_single_category(f"2b_signal_region_preW{suffix}", mixed) + categories_dict |= define_single_category(f"4b_control_region{suffix}", expandCR) + if not expandCR: + categories_dict |= define_single_category(f"2b_control_region_preW{suffix}", expandCR) + categories_dict |= define_single_category(f"2b_signal_region_preW{suffix}", expandCR) categories_dict |= ( define_single_category(f"2b_signal_region_preW_blind{suffix}") if blind else {} ) else: - categories_dict |= define_single_category(f"4b_control_region_preW{suffix}", mixed) - categories_dict |= define_single_category(f"4b_signal_region_preW{suffix}", mixed) + categories_dict |= define_single_category(f"4b_control_region_preW{suffix}", expandCR) + categories_dict |= define_single_category(f"4b_signal_region_preW{suffix}", expandCR) categories_dict |= ( define_single_category(f"4b_signal_region_preW_blind{suffix}") if blind @@ -1437,9 +1437,9 @@ def define_categories( categories_dict |= define_single_category(f"4b_signal_region{suffix}") if bkg_morphing_dnn: - if not mixed: + if not expandCR: categories_dict |= define_single_category( - f"2b_control_region_postW{suffix}", mixed + f"2b_control_region_postW{suffix}", expandCR ) categories_dict |= ( define_single_category(f"2b_signal_region_postW_blind{suffix}") @@ -1451,7 +1451,7 @@ def define_categories( ) else: categories_dict |= define_single_category( - f"4b_control_region_postW{suffix}", mixed + f"4b_control_region_postW{suffix}", expandCR ) categories_dict |= ( define_single_category(f"4b_signal_region_postW_blind{suffix}") @@ -1531,6 +1531,7 @@ def define_preselection(options): # Add the Jet Veto Map # Do this in the preselection to select jets based on # corrected pT after the Calibrators have run - preselection.append(cuts.hh4b_JetVetoMap) + if not options["mixeddata"]: + preselection.append(cuts.hh4b_JetVetoMap) return preselection diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py index 3821ab8e..3c3c0cd3 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80.py @@ -31,5 +31,5 @@ "qt_postEE": "", "max_num_jets_spanet_class": 5, "mixeddata": True, - "noL1": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py index 3119e450..d271fb17 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set.py @@ -31,5 +31,5 @@ "qt_postEE": "", "max_num_jets_spanet_class": 5, "mixeddata": True, - "noL1": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_DATAorMC.py similarity index 99% rename from configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py rename to configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_DATAorMC.py index 44863ead..2f2e75ac 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_withL1.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000064_standard_set_DATAorMC.py @@ -30,5 +30,5 @@ "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, "qt_postEE": "", "max_num_jets_spanet_class": 5, - "mixeddata": True, + "noL1": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py index 58623e23..5dc228b8 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag.py @@ -31,5 +31,5 @@ "qt_postEE": "", "max_num_jets_spanet_class": 5, "mixeddata": True, - "noL1": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_DATAorMC.py similarity index 99% rename from configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py rename to configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_DATAorMC.py index 5edd8473..1335f7a4 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_withL1.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000065_btag_DATAorMC.py @@ -30,5 +30,5 @@ "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, "qt_postEE": "", "max_num_jets_spanet_class": 5, - "mixeddata": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py index e155eac8..944628cd 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd.py @@ -31,5 +31,5 @@ "qt_postEE": "", "max_num_jets_spanet_class": 5, "mixeddata": True, - "noL1": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_DATAorMC.py similarity index 99% rename from configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py rename to configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_DATAorMC.py index 0b5f175b..f5f8c74a 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_withL1.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000067_pd_DATAorMC.py @@ -30,5 +30,5 @@ "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, "qt_postEE": "", "max_num_jets_spanet_class": 5, - "mixeddata": True, + "expandCR": True, }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py index ac3aee10..9548438a 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd.py @@ -31,6 +31,6 @@ "qt_postEE": "", "max_num_jets_spanet_class": 5, "mixeddata": True, - "noL1": True, + "expandCR": True, "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd", }| onnx_model_dict diff --git a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_DATAorMC.py similarity index 99% rename from configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py rename to configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_DATAorMC.py index 4b1daf87..b77f60ec 100644 --- a/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_withL1.py +++ b/configs/HH4b_common/config_files/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd_DATAorMC.py @@ -30,6 +30,6 @@ "spanet_input_name": dnn_vars.pairing_spanet_btagWP5, "qt_postEE": "", "max_num_jets_spanet_class": 5, - "mixeddata": True, + "expandCR": True, "save_chunk": "root://t3dcachedb03.psi.ch:1094//pnfs/psi.ch/cms/trivcat/store/user/tharte/HH4b/spanet_ptflat_Update_newLeptonVeto_3L1Cut_UpdateJetVetoMap_mixeddata_control_30_80_var000068_btag_pd", }| onnx_model_dict diff --git a/configs/HH4b_common/custom_cuts_common.py b/configs/HH4b_common/custom_cuts_common.py index d165d136..407fef1c 100644 --- a/configs/HH4b_common/custom_cuts_common.py +++ b/configs/HH4b_common/custom_cuts_common.py @@ -322,10 +322,9 @@ def skimming_cut_list(configs): eventFlags, goldenJson, get_nPVgood(1), - get_HLTsel(primaryDatasets=["JetMET"]) ] if not configs["mixeddata"]: - skimlist = skimlist + [] - if not configs["noL1"]: + skimlist = skimlist + [get_HLTsel(primaryDatasets=["JetMET"])] + if not configs["noL1"] and not configs["mixeddata"]: skimlist.append(get_L1sel(primaryDatasets=["JetMET"])) return skimlist diff --git a/scripts/plot_2bMorphedvs4b.py b/scripts/plot_2bMorphedvs4b.py index 659f9d5f..8a60ccd5 100644 --- a/scripts/plot_2bMorphedvs4b.py +++ b/scripts/plot_2bMorphedvs4b.py @@ -23,7 +23,7 @@ args.output = "plots_2bVS4b" else: args.output = "test_2bVS4b" - + NUMBER_OF_BINS = 20 PAD_VALUE = -999 BLIND_VALUE = 0.9 @@ -218,7 +218,7 @@ if file.endswith(".coffea") and ("DATA" in file or "Mixed" in file) ] -filter_lambda = (lambda x: ("weight" in x or "score" in x)) if args.spread else (lambda x: "prov" not in x and "add_jet1pt_mass" not in x and "events_sigma" not in x and "era" not in x) +filter_lambda = (lambda x: ("weight" in x or "score" in x)) if args.spread else (lambda x: "prov" not in x and "events_sigma" not in x and "era" not in x) cat_col_data, total_datasets_list = get_columns_from_files(inputfiles, "nominal", filter_lambda, debug=False, novars=args.novars, filter_mixed=args.mixed) cat_col_mc = None @@ -654,10 +654,13 @@ def main(cat_cols, lumi, era_string): ) continue - vars_tot = list(cat_col_DATA[cat_list[1]].keys()) - vars_tot = [v for v in vars_tot if "year" not in v] if "SPREAD" in cats_name: + vars_tot = list(cat_col_DATA[cat_list[0]].keys()) vars_tot = [v for v in vars_tot if "weight" in v or "score" in v] + else: + vars_tot = list(cat_col_DATA[cat_list[1]].keys()) + vars_tot = [v for v in vars_tot if "year" not in v] + if args.test: # vars_tot = vars_tot[:3] diff --git a/scripts/plot_DNN_score.py b/scripts/plot_DNN_score.py index f12b9861..8cf797ae 100644 --- a/scripts/plot_DNN_score.py +++ b/scripts/plot_DNN_score.py @@ -385,7 +385,7 @@ def plot_single_var_from_columns( hist_1d_dict["Sig+Bkg"]["style"]["edgecolor"].append(color_list[i][0]) hist_1d_dict["Sig+Bkg"]["style"]["facecolor"].append(color_list[i][1]) hist_1d_dict["Sig+Bkg"]["style"]["alpha"].append( - 0.5 if "DATA" in cat else 1 + 0.5 if "BKG" in cat_plot_name else 1 ) hist_1d_dict["Sig+Bkg"]["style"]["legend_name"].append(cat_plot_name) hist_1d_dict["Sig+Bkg"]["style"]["bin_edges_plotting"].append( diff --git a/utils_configs/custom_cut_functions.py b/utils_configs/custom_cut_functions.py index 3481bf09..ffd92b48 100644 --- a/utils_configs/custom_cut_functions.py +++ b/utils_configs/custom_cut_functions.py @@ -36,13 +36,9 @@ def get_custom_JetVetoMap_Mask(events, params, year, processor_params, **kwargs) "AK4PFPuppi": jet_type_default } - try: - mask = get_JetVetoMap_Mask( - events_copy, params, year, processor_params_copy, **kwargs - ) - except: - print("Could not load JetVetoMap. Build passthrough instead") - mask = ak.ones_like(events_copy["event"], dtype=np.bool) + mask = get_JetVetoMap_Mask( + events_copy, params, year, processor_params_copy, **kwargs + ) # remove copies del processor_params_copy