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skim_cscdt_lazy_cuts.py
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import sys
import pathlib
import numpy as np
import pickle
import ROOT as rt
from ROOT import RDataFrame, TFile
from src.histo_utilities import std_color_list as SCL
# **************************** #
LOCAL_DIR = "/home/psimmerl/mds_analysis"
OUT_DIR = f"{LOCAL_DIR}/reports/weekly/2024-04-15"
STAT = "pedro"
LUMI = 1.1328524540090597e-06 * 23.02 * 1000 # ???
RUN2_BR = 2.16e-03 # Adjust weights to Run 2 BR limit
FN_MC = f"{LOCAL_DIR}/data/raw/mc_pedro.root"
# FN_R3 = f'{LOCAL_DIR}/data/raw/data_pedro.root'
# FN_MC = f'{LOCAL_DIR}/data/processed/mc_pedro_hlt569.root'
FN_R3 = f"{LOCAL_DIR}/data/processed/r3_pedro_hlt569.root"
# **** #
LOW_MET_CUTOFF = 75
HIGH_MET_CUTOFF = 150
# **** #
CUTS_L1 = [
"acceptance",
"HLT",
"L1",
"MET",
#! reset cutflow indices here
"CSC IT",
"DT IT",
"MB1",
"1 CSC-DT",
# 'dPhi',
]
CUTS = [
"acceptance",
"HLT",
"L1",
"MET",
#! reset cutflow indices here
"CSC IT",
"DT IT",
# 'ME1',
"MB1",
# 'n jets',
# 'n leptons',
"CSC jet veto",
"DT jet veto",
"CSC muon veto",
"DT muon veto",
"halo veto",
# 'DT N Station',
# 'DT Avg Station',
# 'DT stn',
# 'BDT',
"DNN",
"1 CSC-DT",
# 'dR',
# 'dEta',
"dPhi",
# 'ABCD'
# 'DT size' # for ABCD?
]
# **** #
CUT_VALUES = {
"l1_lt200": {
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 200,
"MAX_DT_JET": 200,
"MAX_CSC_MUON": -200,
"MAX_DT_MUON": -200,
"MAX_ME1": 0,
"MAX_MB1": 10,
"HALO_CUTOFF": 0,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
},
"scs_lt200": {
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 30,
"MAX_DT_JET": 50,
"MAX_CSC_MUON": 30,
"MAX_DT_MUON": 10,
"MAX_ME1": 0,
"MAX_MB1": 0,
"HALO_CUTOFF": 0.4,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
},
"scs_low": {
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 30,
"MAX_DT_JET": 50,
"MAX_CSC_MUON": 30,
"MAX_DT_MUON": 10,
"MAX_ME1": 0,
"MAX_MB1": 0,
"HALO_CUTOFF": 0.4,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
},
"scs_high": {
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 30,
"MAX_DT_JET": 50,
"MAX_CSC_MUON": 30,
"MAX_DT_MUON": 10,
"MAX_ME1": 0,
"MAX_MB1": 5,
"HALO_CUTOFF": 0.4,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
},
"ropt_lt200": {
# Default guess
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
# 'MAX_N_JETS' : 15,
# 'MAX_N_LEPS' : 5,
"MAX_CSC_JET": 200,
"MAX_DT_JET": 200,
"MAX_CSC_MUON": 200,
"MAX_DT_MUON": 200,
"MAX_ME1": 0,
"MAX_MB1": 10,
"HALO_CUTOFF": 0.2,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
#
},
"ropt_low": {
# # Default guess
# "MIN_CSC_TIME": -5.0,
# "MAX_CSC_TIME": 12.5,
# "MAX_CSC_TSPREAD": 20.0,
# "MAX_RPC_BX": 0,
# "MIN_RPC_HITS": 1,
# # 'MAX_N_JETS' : 15,
# # 'MAX_N_LEPS' : 5,
# "MAX_CSC_JET": 200,
# "MAX_DT_JET": 200,
# "MAX_CSC_MUON": 200,
# "MAX_DT_MUON": 200,
# "MAX_ME1": 0,
# "MAX_MB1": 10,
# "HALO_CUTOFF": 0.2,
# "MIN_DPHI": 0.4,
# "MIN_DETA": 0,
# "MAX_DETA": 4,
# "MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
# Optimized with s2b12 and IT data, USE THIS. s=1371 b=251
"MIN_CSC_TIME": -5.00,
"MAX_CSC_TIME": 12.50,
"MAX_CSC_TSPREAD": 20.00,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 180,
"MAX_DT_JET": 10,
"MAX_CSC_MUON": -158,
"MAX_DT_MUON": 196,
"MAX_ME1": 0,
"MAX_MB1": 0,
"HALO_CUTOFF": 0.00,
"MIN_DPHI": 0.40,
"MIN_DETA": 0.0,
"MAX_DETA": 4.0,
"MIN_CSC_DNN": 0.00,
# "MIN_DT_DNN": 0.00,
# Optimized with s1 and IT data
# "MIN_CSC_TIME": -5.00,
# "MAX_CSC_TIME": 12.50,
# "MAX_CSC_TSPREAD": 20.00,
# "MAX_RPC_BX": 0,
# "MIN_RPC_HITS": 1,
# "MAX_CSC_JET": 10,
# "MAX_DT_JET": 10,
# "MAX_CSC_MUON": 167,
# "MAX_DT_MUON": 199,
# "MAX_ME1": 0,
# "MAX_MB1": 0,
# "HALO_CUTOFF": 0.00,
# "MIN_DPHI": 0.40,
# "MIN_DETA": 0.0,
# "MAX_DETA": 4.0,
# "MIN_CSC_DNN": 0.00,
# "MIN_DT_DNN": 0.00,
},
"ropt_high": {
# # Default guess
# "MIN_CSC_TIME": -5.0,
# "MAX_CSC_TIME": 12.5,
# "MAX_CSC_TSPREAD": 20.0,
# "MAX_RPC_BX": 0,
# "MIN_RPC_HITS": 1,
# # 'MAX_N_JETS' : 15,
# # 'MAX_N_LEPS' : 5,
# "MAX_CSC_JET": 200,
# "MAX_DT_JET": 200,
# "MAX_CSC_MUON": 200,
# "MAX_DT_MUON": 200,
# "MAX_ME1": 0,
# "MAX_MB1": 10,
# "HALO_CUTOFF": 0.2,
# "MIN_DPHI": 0.4,
# "MIN_DETA": 0,
# "MAX_DETA": 4,
# "MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
# Found manually, USE THIS.
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
# 'MAX_N_JETS' : 15,
# 'MAX_N_LEPS' : 5,
"MAX_CSC_JET": 200,
"MAX_DT_JET": 200,
"MAX_CSC_MUON": -200,
"MAX_DT_MUON": -200,
"MAX_ME1": 0,
"MAX_MB1": 10,
"HALO_CUTOFF": 0.0,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
},
"roptDNN_lt200": {
# Default guess
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
# 'MAX_N_JETS' : 15,
# 'MAX_N_LEPS' : 5,
"MAX_CSC_JET": 200,
"MAX_DT_JET": 200,
"MAX_CSC_MUON": 200,
"MAX_DT_MUON": 200,
"MAX_ME1": 0,
"MAX_MB1": 10,
"HALO_CUTOFF": 0.2,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
#
},
"roptDNN_low": {
# # Default guess
# "MIN_CSC_TIME": -5.0,
# "MAX_CSC_TIME": 12.5,
# "MAX_CSC_TSPREAD": 20.0,
# "MAX_RPC_BX": 0,
# "MIN_RPC_HITS": 1,
# # 'MAX_N_JETS' : 15,
# # 'MAX_N_LEPS' : 5,
# "MAX_CSC_JET": 200,
# "MAX_DT_JET": 200,
# "MAX_CSC_MUON": 200,
# "MAX_DT_MUON": 200,
# "MAX_ME1": 0,
# "MAX_MB1": 10,
# "HALO_CUTOFF": 0.2,
# "MIN_DPHI": 0.4,
# "MIN_DETA": 0,
# "MAX_DETA": 4,
# "MIN_CSC_DNN": 0,
# # "MIN_DT_DNN": 0,
# From ropt_low with s1 opt
"MIN_CSC_TIME": -5.00,
"MAX_CSC_TIME": 12.50,
"MAX_CSC_TSPREAD": 20.00,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
"MAX_CSC_JET": 10,
"MAX_DT_JET": 10,
"MAX_CSC_MUON": 167,
"MAX_DT_MUON": 199,
"MAX_ME1": 0,
"MAX_MB1": 0,
"HALO_CUTOFF": 0.00,
"MIN_DPHI": 0.40,
"MIN_DETA": 0.0,
"MAX_DETA": 4.0,
"MIN_CSC_DNN": 0.00,
# "MIN_CSC_DNN": 0.917, # bkgMC
# "MIN_CSC_DNN": 0.97, # bkgMC_plusBeamHalo
# "MIN_CSC_DNN": 0.97, # bkgOOTData # or 0.89 or 0.94 or 0.96
# "MIN_DT_DNN": 0.00,
},
"roptDNN_high": {
# Default guess
"MIN_CSC_TIME": -5.0,
"MAX_CSC_TIME": 12.5,
"MAX_CSC_TSPREAD": 20.0,
"MAX_RPC_BX": 0,
"MIN_RPC_HITS": 1,
# 'MAX_N_JETS' : 15,
# 'MAX_N_LEPS' : 5,
"MAX_CSC_JET": 200,
"MAX_DT_JET": 200,
"MAX_CSC_MUON": 200,
"MAX_DT_MUON": 200,
"MAX_ME1": 0,
"MAX_MB1": 10,
"HALO_CUTOFF": 0.2,
"MIN_DPHI": 0.4,
"MIN_DETA": 0,
"MAX_DETA": 4,
"MIN_CSC_DNN": 0,
# "MIN_DT_DNN": 0,
#
},
}
# OPT_CUTS = ['MB1','CSC DNN','DT DNN']
OPT_CUTS = [
"MB1",
"CSC jet veto",
"DT jet veto",
"CSC muon veto",
"DT muon veto",
"halo veto",
"CSC DNN",
# "DT DNN",
] # ,'DT size','min dPhi','min dEta','max dEta','ABCD phi', 'ABCD size',]#,'ME1']
CUT_OPT_PARS = {
"ME1": {
"col": "cscNHitME1",
"cut": "MAX_ME1",
"values": np.arange(0, 11, 1)[::-1],
"func": lambda v, vd: vd <= v,
},
"MB1": {
"col": "dtNHitStation1",
"cut": "MAX_MB1",
"values": np.arange(0, 11, 1)[::-1],
"func": lambda v, vd: vd <= v,
},
"CSC jet veto": {
"col": "cscJetVetoPt",
"cut": "MAX_CSC_JET",
"values": np.arange(10, 201, 1)[::-1],
# 'func' : lambda v, vd: (10 < vd) & (vd < v),
"func": lambda v, vd: vd < v,
},
"DT jet veto": {
"col": "dtJetVetoPt",
"cut": "MAX_DT_JET",
"values": np.arange(10, 201, 1)[::-1],
# 'func' : lambda v, vd: (10 < vd) & (vd < v),
"func": lambda v, vd: vd < v,
},
"CSC muon veto": {
"col": "cscMuonVetoPt",
"cut": "MAX_CSC_MUON",
"values": np.arange(-200, 201, 1)[::-1],
# 'func' : lambda v, vd: (0 < vd) & (vd < v),
"func": lambda v, vd: vd < v,
},
"DT muon veto": {
"col": "dtMuonVetoPt",
"cut": "MAX_DT_MUON",
"values": np.arange(-200, 201, 1)[::-1],
# 'func' : lambda v, vd: (0 < vd) & (vd < v),
"func": lambda v, vd: vd < v,
},
"halo veto": {
"col": "dtPhi",
"cut": "HALO_CUTOFF",
"values": np.arange(0, 0.52, 0.02),
"func": lambda v, vd: (v < np.abs(vd)) & (np.abs(vd) < PI - v),
},
"min dPhi": {
"col": "tag_dPhi",
"cut": "MIN_DPHI",
"values": np.arange(0, 0.62, 0.02),
"func": lambda v, vd: v < vd,
},
"min dEta": {
"col": "tag_dEta",
"cut": "MIN_DETA",
"values": np.arange(0, 4.1, 0.1),
"func": lambda v, vd: v < vd,
},
"max dEta": {
"col": "tag_dEta",
"cut": "MAX_DETA",
"values": np.arange(0, 4.1, 0.1)[::-1],
"func": lambda v, vd: vd < v,
},
"CSC DNN": {
"col": "cscDNN",
"cut": "MIN_CSC_DNN",
"values": np.arange(0, 1.01, 0.01),
"func": lambda v, vd: v < vd,
},
# "DT DNN": {
# "col": "dtDNN",
# "cut": "MIN_DT_DNN",
# "values": np.arange(0, 1.01, 0.01),
# "func": lambda v, vd: v < vd,
# },
"ABCD phi": {
"col": "tag_dPhi",
"cut": "ABCD_PHI",
"values": np.arange(0, 3.5, 0.01),
"func": lambda v, vd: v <= vd,
},
"ABCD size": {
"col": "dtSize",
"cut": "ABCD_SIZE",
"values": np.arange(50, 150, 1),
"func": lambda v, vd: v <= vd,
},
"DT size": {
"col": "dtSize",
"cut": "MIN_DT_SIZE",
"values": np.arange(50, 150, 1),
"func": lambda v, vd: v <= vd,
},
}
# **** #
C, D = "cscRechitCluster", "dtRechitCluster"
COLUMNS_OUT = [
# 'Flag2_BadChargedCandidateFilter',
# 'Flag2_BadPFMuonFilter',
# 'Flag2_EcalDeadCellTriggerPrimitiveFilter',
# 'Flag2_HBHEIsoNoiseFilter',
# 'Flag2_HBHENoiseFilter',
# 'Flag2_all',
# 'Flag2_ecalBadCalibFilter',
# 'Flag2_eeBadScFilter',
# 'Flag2_globalSuperTightHalo2016Filter',
# 'Flag2_globalTightHalo2016Filter',
# 'Flag_BadChargedCandidateFilter',
# 'Flag_BadPFMuonFilter',
# 'Flag_CSCTightHaloFilter',
# 'Flag_HBHEIsoNoiseFilter',
# 'Flag_HBHENoiseFilter',
# 'Flag_all',
# 'Flag_ecalBadCalibFilter',
# 'Flag_eeBadScFilter',
# 'Flag_globalSuperTightHalo2016Filter',
# 'Flag_goodVertices',
# 'HLTDecision',
# 'MC_condition',
#
"cscRechitClusterAvgStation10",
"cscRechitClusterEta",
"cscRechitClusterJetVetoE",
"cscRechitClusterJetVetoLooseId",
"cscRechitClusterJetVetoPt",
"cscRechitClusterJetVetoTightId",
"cscRechitClusterMaxChamber",
"cscRechitClusterMaxChamberRatio",
"cscRechitClusterMaxStation",
"cscRechitClusterMaxStationRatio",
"cscRechitClusterMet_dPhi",
"cscRechitClusterMuonVetoE",
"cscRechitClusterMuonVetoGlobal",
"cscRechitClusterMuonVetoLooseId",
"cscRechitClusterMuonVetoPt",
"cscRechitClusterNChamber",
"cscRechitClusterNRechitChamberMinus11",
"cscRechitClusterNRechitChamberMinus12",
"cscRechitClusterNRechitChamberMinus13",
"cscRechitClusterNRechitChamberMinus21",
"cscRechitClusterNRechitChamberMinus22",
"cscRechitClusterNRechitChamberMinus31",
"cscRechitClusterNRechitChamberMinus32",
"cscRechitClusterNRechitChamberMinus41",
"cscRechitClusterNRechitChamberMinus42",
"cscRechitClusterNRechitChamberPlus11",
"cscRechitClusterNRechitChamberPlus12",
"cscRechitClusterNRechitChamberPlus13",
"cscRechitClusterNRechitChamberPlus21",
"cscRechitClusterNRechitChamberPlus22",
"cscRechitClusterNRechitChamberPlus31",
"cscRechitClusterNRechitChamberPlus32",
"cscRechitClusterNRechitChamberPlus41",
"cscRechitClusterNRechitChamberPlus42",
"cscRechitClusterNStation10",
"cscRechitClusterPhi",
"cscRechitClusterSize",
"cscRechitClusterTime",
"cscRechitClusterTimeSpread",
"cscRechitClusterTimeSpreadWeightedAll",
"cscRechitClusterTimeWeighted",
"cscRechitClusterX",
"cscRechitClusterY",
"cscRechitClusterZ",
"cscRechitCluster_match_MB1Seg_0p4",
"cscRechitCluster_match_RB1_0p4",
"cscRechitCluster_match_RE12_0p4",
"cscRechitCluster_match_dtSeg_0p4",
# 'cscRechitCluster_match_gLLP',
# 'cscRechitCluster_match_gLLP_csc',
# 'cscRechitCluster_match_gLLP_decay_r',
# 'cscRechitCluster_match_gLLP_decay_z',
# 'cscRechitCluster_match_gLLP_dt',
# 'cscRechitCluster_match_gLLP_e',
# 'cscRechitCluster_match_gLLP_eta',
# 'cscRechitCluster_match_gLLP_index',
# 'cscRechitCluster_match_gLLP_minDeltaR',
# 'cscRechitCluster_match_gLLP_phi',
#
# 'ctau',
#
"dtRechitClusterAvgStation10",
"dtRechitClusterEta",
"dtRechitClusterJetVetoE",
"dtRechitClusterJetVetoLooseId",
"dtRechitClusterJetVetoPt",
"dtRechitClusterJetVetoTightId",
"dtRechitClusterMaxChamber",
"dtRechitClusterMaxChamberRatio",
"dtRechitClusterMaxStation",
"dtRechitClusterMaxStationRatio",
"dtRechitClusterMet_dPhi",
"dtRechitClusterMuonVetoE",
"dtRechitClusterMuonVetoGlobal",
"dtRechitClusterMuonVetoLooseId",
"dtRechitClusterMuonVetoPt",
"dtRechitClusterMuonVetoTightId",
"dtRechitClusterNChamber",
"dtRechitClusterNHitStation1",
"dtRechitClusterNHitStation2",
"dtRechitClusterNHitStation3",
"dtRechitClusterNHitStation4",
"dtRechitClusterNOppositeSegStation1",
"dtRechitClusterNOppositeSegStation2",
"dtRechitClusterNOppositeSegStation3",
"dtRechitClusterNOppositeSegStation4",
"dtRechitClusterNSegStation1",
"dtRechitClusterNSegStation2",
"dtRechitClusterNSegStation3",
"dtRechitClusterNSegStation4",
"dtRechitClusterNStation10",
"dtRechitClusterNoiseHit",
"dtRechitClusterNoiseHitStation1",
"dtRechitClusterNoiseHitStation2",
"dtRechitClusterNoiseHitStation3",
"dtRechitClusterNoiseHitStation4",
"dtRechitClusterOverlap",
"dtRechitClusterPhi",
"dtRechitClusterSize",
"dtRechitClusterWheel",
"dtRechitClusterX",
"dtRechitClusterY",
"dtRechitClusterZ",
"dtRechitCluster_match_MB1Seg_0p4",
"dtRechitCluster_match_MB1Seg_0p5",
"dtRechitCluster_match_MB1hits_0p4",
"dtRechitCluster_match_MB1hits_0p5",
"dtRechitCluster_match_MB1hits_cosmics_minus",
"dtRechitCluster_match_MB1hits_cosmics_plus",
"dtRechitCluster_match_RB1_0p4",
"dtRechitCluster_match_RB1_dPhi0p5",
"dtRechitCluster_match_RPCBx_dPhi0p5",
"dtRechitCluster_match_RPChits_dPhi0p5",
# 'dtRechitCluster_match_gLLP',
# 'dtRechitCluster_match_gLLP_csc',
# 'dtRechitCluster_match_gLLP_decay_r',
# 'dtRechitCluster_match_gLLP_decay_z',
# 'dtRechitCluster_match_gLLP_dt',
# 'dtRechitCluster_match_gLLP_e',
# 'dtRechitCluster_match_gLLP_eta',
# 'dtRechitCluster_match_gLLP_index',
# 'dtRechitCluster_match_gLLP_minDeltaR',
# 'dtRechitCluster_match_gLLP_phi',
#
"evtNum",
# 'gHiggsE',
# 'gHiggsEta',
# 'gHiggsPhi',
# 'gHiggsPt',
# 'gLLP_beta',
# 'gLLP_csc',
# 'gLLP_ctau',
# 'gLLP_decay_vertex_r',
# 'gLLP_decay_vertex_x',
# 'gLLP_decay_vertex_y',
# 'gLLP_decay_vertex_z',
# 'gLLP_dt',
# 'gLLP_e',
# 'gLLP_eta',
# 'gLLP_phi',
# 'gLLP_pt',
"jetE",
"jetEta",
"jetPhi",
"jetPt",
"jetTightPassId",
"lepDZ",
"lepE",
"lepEta",
"lepPassLooseIso",
"lepPassTightIso",
"lepPassVTightIso",
"lepPassVVTightIso",
"lepPdgId",
"lepPhi",
"lepPt",
"lepTightId",
# 'lumiSec',
# 'mH',
# 'mX',
"met",
"metPhi",
# 'nCscRechitClusters',
# 'nCscRings',
# 'nDtRechitClusters',
# 'nDtRings',
# 'nGLLP',
"nJets",
"nLeptons",
# 'npu',
# 'npv',
# 'pileupWeight',
# 'rho',
# 'runNum',
"weight",
]
# **** #
pathlib.Path(OUT_DIR).mkdir(parents=True, exist_ok=True)
rng = np.random.default_rng()
rt.gErrorIgnoreLevel = 1001 # rt.kInfo + 1
rt.gROOT.SetBatch(True)
PI = rt.TMath.Pi()
gc = [] # ROOT garbage collector
# **** #
HISTO_FORMS_1D = {
"cscAvgStation10": ("", ";cscAvgStation10;count", 6, -0.5, 5.5),
"cscEta": ("", ";cscEta;count", 20, -5, 5),
"cscJetVetoE": ("", ";cscJetVetoE;count", 20, 0, 200),
"cscJetVetoLooseId": ("", ";cscJetVetoLooseId;count", 3, -0.5, 2.5),
"cscJetVetoPt": ("", ";cscJetVetoPt;count", 20, 0, 200),
"cscJetVetoTightId": ("", ";cscJetVetoTightId;count", 3, -0.5, 2.5),
"cscMaxChamber": ("", ";cscMaxChamber;count", 21, -0.5, 20.5),
"cscMaxChamberRatio": ("", ";cscMaxChamberRatio;count", 20, 0, 1),
"cscMaxStation": ("", ";cscMaxStation;count", 6, -0.5, 5.5),
"cscMaxStationRatio": ("", ";cscMaxStationRatio;count", 20, 0, 1),
"cscMet_dPhi": ("", ";cscMet_dPhi;count", 20, -PI, PI),
"cscMuonVetoE": ("", ";cscMuonVetoE;count", 20, 0, 200),
"cscMuonVetoGlobal": ("", ";cscMuonVetoGlobal;count", 3, -0.5, 2.5),
"cscMuonVetoLooseId": ("", ";cscMuonVetoLooseId;count", 3, -0.5, 2.5),
"cscMuonVetoPt": ("", ";cscMuonVetoPt;count", 20, 0, 200),
"cscNChamber": ("", ";cscNChamber;count", 21, -0.5, 20.5),
"cscNRechitChamberMinus11": ("", ";cscNRechitChamberMinus11;count", 20, 0, 200),
"cscNRechitChamberMinus12": ("", ";cscNRechitChamberMinus12;count", 20, 0, 200),
"cscNRechitChamberMinus13": ("", ";cscNRechitChamberMinus13;count", 20, 0, 200),
"cscNRechitChamberMinus21": ("", ";cscNRechitChamberMinus21;count", 20, 0, 200),
"cscNRechitChamberMinus22": ("", ";cscNRechitChamberMinus22;count", 20, 0, 200),
"cscNRechitChamberMinus31": ("", ";cscNRechitChamberMinus31;count", 20, 0, 200),
"cscNRechitChamberMinus32": ("", ";cscNRechitChamberMinus32;count", 20, 0, 200),
"cscNRechitChamberMinus41": ("", ";cscNRechitChamberMinus41;count", 20, 0, 200),
"cscNRechitChamberMinus42": ("", ";cscNRechitChamberMinus42;count", 20, 0, 200),
"cscNRechitChamberPlus11": ("", ";cscNRechitChamberPlus11;count", 20, 0, 200),
"cscNRechitChamberPlus12": ("", ";cscNRechitChamberPlus12;count", 20, 0, 200),
"cscNRechitChamberPlus13": ("", ";cscNRechitChamberPlus13;count", 20, 0, 200),
"cscNRechitChamberPlus21": ("", ";cscNRechitChamberPlus21;count", 20, 0, 200),
"cscNRechitChamberPlus22": ("", ";cscNRechitChamberPlus22;count", 20, 0, 200),
"cscNRechitChamberPlus31": ("", ";cscNRechitChamberPlus31;count", 20, 0, 200),
"cscNRechitChamberPlus32": ("", ";cscNRechitChamberPlus32;count", 20, 0, 200),
"cscNRechitChamberPlus41": ("", ";cscNRechitChamberPlus41;count", 20, 0, 200),
"cscNRechitChamberPlus42": ("", ";cscNRechitChamberPlus42;count", 20, 0, 200),
"cscNStation10": ("", ";cscNStation10;count", 6, -0.5, 5.5),
"cscPhi": ("", ";cscPhi;count", 20, -PI, PI),
"cscSize": ("", ";cscSize;count", 20, 0, 200),
"cscTime": ("", ";cscTime;count", 20, -10, 20),
"cscTimeSpread": ("", ";cscTimeSpread;count", 20, 0, 50),
"cscTimeSpreadWeightedAll": ("", ";cscTimeSpreadWeightedAll;count", 20, 0, 50),
"cscTimeWeighted": ("", ";cscTimeWeighted;count", 20, -20, 20),
"cscX": ("", ";cscX;count", 20, -1500, 1500),
"cscY": ("", ";cscY;count", 20, -1500, 1500),
"cscZ": ("", ";cscZ;count", 20, -1500, 1500),
"csc_match_MB1Seg_0p4": ("", ";csc_match_MB1Seg_0p4;count", 20, 0, 200),
"csc_match_RB1_0p4": ("", ";csc_match_RB1_0p4;count", 20, 0, 200),
"csc_match_RE12_0p4": ("", ";csc_match_RE12_0p4;count", 20, 0, 200),
"csc_match_dtSeg_0p4": ("", ";csc_match_dtSeg_0p4;count", 20, 0, 200),
#
"cscDNN_bkgMC": ("", ";cscDNN_bkgMC;count", 20, 0, 1),
"cscDNN_bkgMC_plusBeamHalo": ("", ";cscDNN_bkgMC_plusBeamHalo;count", 20, 0, 1),
"cscDNN_bkgOOTData": ("", ";cscDNN_bkgOOTData;count", 20, 0, 1),
#
"dtAvgStation10": ("", ";dtAvgStation10:count", 6, -0.5, 5.5),
"dtEta": ("", ";dtEta:count", 20, -5, 5),
"dtJetVetoE": ("", ";dtJetVetoE:count", 20, 0, 200),
"dtJetVetoLooseId": ("", ";dtJetVetoLooseId:count", 3, -0.5, 2.5),
"dtJetVetoPt": ("", ";dtJetVetoPt:count", 20, 0, 200),
"dtJetVetoTightId": ("", ";dtJetVetoTightId:count", 3, -0.5, 2.5),
"dtMaxChamber": ("", ";dtMaxChamber:count", 21, -0.5, 20.5),
"dtMaxChamberRatio": ("", ";dtMaxChamberRatio:count", 20, 0, 1),
"dtMaxStation": ("", ";dtMaxStation:count", 6, -0.5, 5.5),
"dtMaxStationRatio": ("", ";dtMaxStationRatio:count", 20, 0, 1),
"dtMet_dPhi": ("", ";dtMet_dPhi:count", 20, -PI, PI),
"dtMuonVetoE": ("", ";dtMuonVetoE:count", 20, 0, 200),
"dtMuonVetoGlobal": ("", ";dtMuonVetoGlobal:count", 3, -0.5, 2.5),
"dtMuonVetoLooseId": ("", ";dtMuonVetoLooseId:count", 3, -0.5, 2.5),
"dtMuonVetoPt": ("", ";dtMuonVetoPt:count", 20, 0, 200),
"dtMuonVetoTightId": ("", ";dtMuonVetoTightId:count", 3, -0.5, 2.5),
"dtNChamber": ("", ";dtNChamber:count", 21, -0.5, 20.5),
"dtNHitStation1": ("", ";dtNHitStation1:count", 20, 0, 200),
"dtNHitStation2": ("", ";dtNHitStation2:count", 20, 0, 200),
"dtNHitStation3": ("", ";dtNHitStation3:count", 20, 0, 200),
"dtNHitStation4": ("", ";dtNHitStation4:count", 20, 0, 200),
"dtNOppositeSegStation1": ("", ";dtNOppositeSegStation1:count", 20, 0, 200),
"dtNOppositeSegStation2": ("", ";dtNOppositeSegStation2:count", 20, 0, 200),
"dtNOppositeSegStation3": ("", ";dtNOppositeSegStation3:count", 20, 0, 200),
"dtNOppositeSegStation4": ("", ";dtNOppositeSegStation4:count", 20, 0, 200),
"dtNSegStation1": ("", ";dtNSegStation1:count", 20, 0, 200),
"dtNSegStation2": ("", ";dtNSegStation2:count", 20, 0, 200),
"dtNSegStation3": ("", ";dtNSegStation3:count", 20, 0, 200),
"dtNSegStation4": ("", ";dtNSegStation4:count", 20, 0, 200),
"dtNStation10": ("", ";dtNStation10:count", 6, -0.5, 5.5),
"dtNoiseHit": ("", ";dtNoiseHit:count", 20, 0, 200),
"dtNoiseHitStation1": ("", ";dtNoiseHitStation1:count", 20, 0, 200),
"dtNoiseHitStation2": ("", ";dtNoiseHitStation2:count", 20, 0, 200),
"dtNoiseHitStation3": ("", ";dtNoiseHitStation3:count", 20, 0, 200),
"dtNoiseHitStation4": ("", ";dtNoiseHitStation4:count", 20, 0, 200),
"dtOverlap": ("", ";dtOverlap:count", 20, 0, 1),
"dtPhi": ("", ";dtPhi:count", 20, -PI, PI),
"dtSize": ("", ";dtSize:count", 20, 0, 200),
"dtWheel": ("", ";dtWheel:count", 21, -0.5, 20.5),
"dtX": ("", ";dtX:count", 20, -1500, 1500),
"dtY": ("", ";dtY:count", 20, -1500, 1500),
"dtZ": ("", ";dtZ:count", 20, -1500, 1500),
"dt_match_MB1Seg_0p4": ("", ";dt_match_MB1Seg_0p4:count", 20, 0, 200),
"dt_match_MB1Seg_0p5": ("", ";dt_match_MB1Seg_0p5:count", 20, 0, 200),
"dt_match_MB1hits_0p4": ("", ";dt_match_MB1hits_0p4:count", 20, 0, 200),
"dt_match_MB1hits_0p5": ("", ";dt_match_MB1hits_0p5:count", 20, 0, 200),
"dt_match_MB1hits_cosmics_minus": ("", ";dt_match_MB1hits_cosmics_minus:count", 20, 0, 200),
"dt_match_MB1hits_cosmics_plus": ("", ";dt_match_MB1hits_cosmics_plus:count", 20, 0, 200),
"dt_match_RB1_0p4": ("", ";dt_match_RB1_0p4:count", 20, 0, 200),
"dt_match_RB1_dPhi0p5": ("", ";dt_match_RB1_dPhi0p5:count", 20, 0, 200),
"dt_match_RPCBx_dPhi0p5": ("", ";dt_match_RPCBx_dPhi0p5:count", 11, -5.5, 5.5),
"dt_match_RPChits_dPhi0p5": ("", ";dt_match_RPChits_dPhi0p5:count", 20, 0, 200),
#
"met": ("", ";met;count", 20, 0, 200),
"metPhi": ("", ";metPhi;count", 20, -PI, PI),
}
# **************************** #
def optimize_cut(_rdfs, col, cut_func, cut_values=None, cut0=None):
vmc = _rdfs["mc"].AsNumpy(["weight", col]) # ,'tag_dPhi','dtSize'
vr3 = _rdfs["r3"].AsNumpy(["weight", col]) # ,'tag_dPhi','dtSize'
wmc, vmc = vmc["weight"], vmc[col]
wr3, vr3 = vr3["weight"], vr3[col]
# wmc_tot, wr3_tot = wmc.sum(), wr3.sum() # may have issues when cut_func has preselection?
# vmin, vmax = min(vmc.min(), vr3.min()), max(vmc.max(), vr3.max())
# **** #
nmc, nr3 = _rdfs["mc"].Count().GetValue(), _rdfs["r3"].Count().GetValue()
if isinstance(OPT_FRAC, float):
idxs_mc = np.random.randint(0, nmc, int(OPT_FRAC * nmc))
idxs_r3 = np.random.randint(0, nr3, int(OPT_FRAC * nr3))
elif OPT_FRAC == "sqrt":
idxs_mc = np.random.randint(0, nmc, int(nmc**0.5))
idxs_r3 = np.random.randint(0, nr3, int(nr3**0.5))
wmc, vmc = wmc[idxs_mc], vmc[idxs_mc]
wr3, vr3 = wr3[idxs_r3], vr3[idxs_r3]
# **** #
# if 'ABCD' in CUTS:
# ABCD_DPHI = CUT_VALUES['ABCD_DPHI']
# ABCD_SIZE = CUT_VALUES['ABCD_SIZE']
# a_mc, b_mc, c_mc, d_mc =
wmc0, wr30 = wmc[cut_func(cut0, vmc)].sum(), wr3[cut_func(cut0, vr3)].sum()
wmc = np.array([wmc[cut_func(v, vmc)].sum() for v in cut_values])
wr3 = np.array([wr3[cut_func(v, vr3)].sum() for v in cut_values])
wmc_tot, wr3_tot = (
wmc.max(),
wr3.max(),
) # relative to itself? sorta fixes preselection bias
# **** #
# Scoring Metrics
# S/rtB, sometimes falls into minimum where B=1 and S=small
# s2b0 = wmc0/(wr30**0.5) if wr30 else 0
# s2b = np.divide(wmc, np.sqrt(wr3), out=np.zeros_like(wmc), where=(wr3>0))
# # F1 to balance precision/recall (avoid small S minimum?)
# f10 = wmc0 /(2*wmc0+wr30+(wmc_tot-wmc0)) if 2*wmc0+wr30+(wmc_tot-wmc0) else 0
# f1 = np.divide(wmc, 2*wmc+wr3+(wmc_tot-wmc),out=np.zeros_like(wmc), where=2*wmc+wr3+(wmc_tot-wmc)>0)
# # Modified F-score where precision = S / (S + rtB)
# f10 = wmc0 /(2*wmc0+(wr30**0.5)+(wmc_tot-wmc0)) if 2*wmc0+(wr30**0.5)+(wmc_tot-wmc0) else 0
# f1 = np.divide(wmc, 2*wmc+np.sqrt(wr3)+(wmc_tot-wmc),out=np.zeros_like(wmc), where=2*wmc+np.sqrt(wr3)+(wmc_tot-wmc)>0)
# # Modified F-score where precision = S / rtB, harmonic mean of S/rtB and S recall
# f10 = wmc0 /(wmc_tot+(wr30**0.5)) if wmc_tot+(wr30**0.5) else 0
# f1 = np.divide(wmc, wmc_tot+np.sqrt(wr3),out=np.zeros_like(wmc), where=wmc_tot+np.sqrt(wr3)>0)
# https://arxiv.org/pdf/hep-ph/0204326.pdf
s2b0 = 2 * (np.sqrt(wmc0 + wr30) - np.sqrt(wr30))
s2b = 2 * (np.sqrt(wmc + wr3) - np.sqrt(wr3))
# **** #
score0, score = s2b0, s2b
# score0, score = s2b0, s2b
# score0, score = f10, f1
# score0, score = s2b120, s2b12
# # Find best at this working point
# idx = np.argmax(score)
# Randomly select a new cut that's better than before
idx = rng.choice(np.arange(len(score))[score >= score0]) if (score >= score0).sum() else 0
# **** #
if cut0 is not None:
return cut_values[idx], s2b[idx], wmc[idx], wr3[idx], s2b0, wmc0, wr30
return cut_values[idx], s2b[idx], wmc[idx], wr3[idx]
# **************************** #
if __name__ == "__main__":
print("+------------------------+")
print("| Starting skim_cscdt.py |")
print("+------------------------+")
rt.EnableImplicitMT()
print(" Enabled ROOT's implicit multithreading (sometimes causes a crash)")
# **************************** #
N_ITERATIONS = 1
OPT_SCORES = []
OPT_FRAC = 1.0 # fraction with replacement, if 1 just bootstrap
LOO = False
RAND = False
PRINT_CUTFLOW = False
MET_CATEGORY = ("lt200", "low", "high")[0]
CUTSET = "scs"
OOT = False
DNN_VERSION = (None, "bkgMC", "bkgMC_plusBeamHalo", "bkgOOTData")[0]
args = " " + " ".join(sys.argv[1:]) if len(sys.argv) > 1 else ""
if "cutflow" in args:
print(" Printing cutflow tables")
PRINT_CUTFLOW = True
if "l1" in args:
print(" Using the reduced cut set (l1)")
CUTS = CUTS_L1
CUTSET = "l1"
if " low" in args: # otherwise cutflow breaks it
print(" Low met category")
CUTS = [c.replace("MET", "low MET") if "MET" == c else c for c in CUTS]
MET_CATEGORY = "low"
elif "high" in args:
print(" High met category")
print(" REMOVING HALO, JET, & MUON VETOS")
CUTS = [c.replace("MET", "high MET") if "MET" == c else c for c in CUTS]
CUTS = [c for c in CUTS if "halo" not in c]
CUTS = [c for c in CUTS if "jet" not in c]
CUTS = [c for c in CUTS if "muon" not in c]
OPT_CUTS = [c for c in OPT_CUTS if "halo" not in c]
OPT_CUTS = [c for c in OPT_CUTS if "jet" not in c]
OPT_CUTS = [c for c in OPT_CUTS if "muon" not in c]
MET_CATEGORY = "high"
else:
print(" No met categorization (only met<200)")
MET_CATEGORY = "lt200"
if "roptDNN" in args:
print(" Using the randomly optimized cut set with DNN (roptDNN)")
CUTSET = "roptDNN"
elif "ropt" in args:
print(" Using the randomly optimized cut set (ropt)")
CUTSET = "ropt"
elif "loptDNN" in args:
print(" Using the leave-one-out optimized cut set with DNN (loptDNN)")
CUTSET = "loptDNN"
elif "lopt" in args:
print(" Using the leave-one-out optimized cut set (lopt)")
CUTSET = "lopt"
else:
print(" Using the standard cut set (scs)")
CUTSET = "scs"
if "oot" in args:
print(" Using out-of-time 2nd cluster")
CUTS = [c.replace("DT IT", "DT OOT") if "DT IT" == c else c for c in CUTS]
OOT = True
else:
print(" Using in-time 2nd cluster")
if "loo" in args:
print(" PERFORMING LOO OPTIMIZATION")
if OOT:
print(" FORCING MC TO IN-TIME")
LOO = True
N_ITERATIONS = 501
if "rand" in args:
print(" PERFORMING RAND OPTIMIZATION")
if OOT:
print(" FORCING MC TO IN-TIME")
RAND = True
N_ITERATIONS = 501
# else:
# print(' REMOVING DT SIZE CUT')
# CUTS = [c for c in CUTS if 'DT size' not in c]
if "DNN" not in CUTSET:
print(" Removing DNN from CUTS")
CUTS = [c for c in CUTS if "DNN" not in c]
OPT_CUTS = [c for c in OPT_CUTS if "DNN" not in c]
else:
if RAND or LOO:
print(" OPTIMIZING JUST DNN")
OPT_CUTS = [c for c in OPT_CUTS if "DNN" in c]
if "bkgMC_plusBeamHalo" in args:
print(" Using DNN version bkgMC_plusBeamHalo")
DNN_VERSION = "bkgMC_plusBeamHalo"
elif "bkgMC" in args:
print(" Using DNN version bkgMC")
DNN_VERSION = "bkgMC"
CUT_OPT_PARS["CSC DNN"]["values"] = np.arange(0.830, 1.001, 0.001)
elif "bkgOOTData" in args:
print(" Using DNN version bkgOOTData")
DNN_VERSION = "bkgOOTData"
if DNN_VERSION is not None:
CUT_OPT_PARS["CSC DNN"]["col"] = f"cscDNN_{DNN_VERSION}"
if f"{CUTSET}_{MET_CATEGORY}" in CUT_VALUES:
CUT_VALUES = CUT_VALUES[f"{CUTSET}_{MET_CATEGORY}"]
elif f"{CUTSET}_lt200" in CUT_VALUES:
CUT_VALUES = CUT_VALUES[f"{CUTSET}_lt200"]
print(f" No cutset for {MET_CATEGORY=} found, defaulting to 'lt200'")
else:
raise ValueError(f"no cutset associated with {CUTSET=} and {MET_CATEGORY=} found")
print("")
# **** #
if "1 CSC-DT" not in CUTS:
raise NotImplementedError("cant handle multiple pairs yet")
# # if PRINT_CUTFLOW:
# name = f'cscdt{"OOT" if OOT else ""}_{CUTSET}'