From ef887cc8930c459cd42f46c314e161b65277a15a Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 02:01:12 -0700 Subject: [PATCH 01/10] Samples: the valid 2024 W->qq PTQQ-600 production, W -> cs flags of the dibosons The indexed Wto2Q-2Jets_Bin-PTQQ-600 v2-v2 production is INVALID in DAS (generated with the LHE V pT filter at 400 GeV), so it was left out of the V+jets stitch. The index and make_filelists_v15.py now point to the v2-v3 production (filter at 600 GeV, 72 files on T1_US_FNAL_Disk), with its GenXSecAnalyzer cross section (1.217 pb, 18 MiniAOD files), and the stitch selectors include the sample again. The static guard that raised for W-600 is replaced by a check of the skim: load_samples uses a W-600 skim only if its per-LHE-V-pT-bin totals show no generated event below 600 GeV, so skims of the old production (or without those totals) are left out with a warning, as before. The PTQQ stitch comments in utils.py are shortened. GenV1CS and GenV2CS tested the (c, s) daughter order twice. In the 2024 WW sample every hard-process W -> cs has its daughters as (s, c) (32,008 of 32,008 in the first 50k events), so both flags were always False. They now accept (c, s) and (s, c). --- data/make_filelists_v15.py | 3 +- data/nanoindex_v15.json | 461 +++--------------- src/HH4b/hh_vars.py | 11 +- src/HH4b/processors/GenSelection.py | 4 +- src/HH4b/utils.py | 64 ++- src/HH4b/xsecs.py | 2 +- .../skimmer_signal_2022to2025.yaml | 2 +- 7 files changed, 121 insertions(+), 426 deletions(-) diff --git a/data/make_filelists_v15.py b/data/make_filelists_v15.py index 50ac639b..6da51197 100644 --- a/data/make_filelists_v15.py +++ b/data/make_filelists_v15.py @@ -420,7 +420,8 @@ "Wto2Q-2Jets_Bin-PTQQ-100": "/Wto2Q-2Jets_Bin-PTQQ-100_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v3/NANOAODSIM", "Wto2Q-2Jets_Bin-PTQQ-200": "/Wto2Q-2Jets_Bin-PTQQ-200_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v2/NANOAODSIM", "Wto2Q-2Jets_Bin-PTQQ-400": "/Wto2Q-2Jets_Bin-PTQQ-400_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v3/NANOAODSIM", - "Wto2Q-2Jets_Bin-PTQQ-600": "/Wto2Q-2Jets_Bin-PTQQ-600_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v2/NANOAODSIM", + # v2-v3: LHE V pT filter at 600 GeV (v2-v2 was generated with 400 GeV, INVALID in DAS) + "Wto2Q-2Jets_Bin-PTQQ-600": "/Wto2Q-2Jets_Bin-PTQQ-600_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v3/NANOAODSIM", "Zto2Q-2Jets_Bin-PTQQ-100": "/Zto2Q-2Jets_Bin-PTQQ-100_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v2/NANOAODSIM", "Zto2Q-2Jets_Bin-PTQQ-200": "/Zto2Q-2Jets_Bin-PTQQ-200_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/RunIII2024Summer24NanoAODv15-150X_mcRun3_2024_realistic_v2-v2/NANOAODSIM", "Zto2Q-2Jets_Bin-PTQQ-400": 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"Zto2Q-2Jets_Bin-PTQQ-100": [ "root://cmsdcadisk.fnal.gov//dcache/uscmsdisk///store/mc/RunIII2024Summer24NanoAODv15/Zto2Q-2Jets_Bin-PTQQ-100_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/NANOAODSIM/150X_mcRun3_2024_realistic_v2-v2/130000/012a82ae-a617-4af6-b964-0278db94d159.root", diff --git a/src/HH4b/hh_vars.py b/src/HH4b/hh_vars.py index ff32cfee..960c1851 100644 --- a/src/HH4b/hh_vars.py +++ b/src/HH4b/hh_vars.py @@ -82,14 +82,11 @@ # open-ended 2024 samples, loaded with utils.load_samples(ptqq_stitch=...) so that the four # Bin-PTQQ samples are stitched in GenVPt (utils._stitch_open_ptqq), or with --vjets-stitch-mode # range in LHE V pT ranges (utils.ptqq_range_norm). No 2022-2023 name matches them. -# Wto2Q-2Jets_Bin-PTQQ-600 (2024, v2-v2) is left out: the dataset is INVALID in DAS (generated with -# an LHE filter at 400 GeV instead of 600); W above 600 GeV comes from W-100/200/400 (range mode: -# W-400 alone, whole sample). +# Wto2Q-2Jets_Bin-PTQQ-600 is the v2-v3 production; skims of the INVALID v2-v2 one (LHE filter at +# 400 GeV instead of 600) are left out by utils.load_samples (PTQQ_OPEN_FILTER_CHECK), and W above +# 600 GeV then comes from W-100/200/400 (range mode: W-400 alone, whole sample). vjets_stitch_selectors = common_samples_bg["vjets"] + [ - f"{v}to2Q-2Jets_Bin-PTQQ-{x}?" - for v in ["W", "Z"] - for x in [200, 400, 600] - if not (v == "W" and x == 600) + f"{v}to2Q-2Jets_Bin-PTQQ-{x}?" for v in ["W", "Z"] for x in [200, 400, 600] ] # Opt-in (PostProcess --zz4b-tag): the dedicated ZZto4B-1Jets_TuneCP5_13p6TeV_amcatnloFXFX-pythia8 diff --git a/src/HH4b/processors/GenSelection.py b/src/HH4b/processors/GenSelection.py index 1b065f04..48959202 100644 --- a/src/HH4b/processors/GenSelection.py +++ b/src/HH4b/processors/GenSelection.py @@ -412,7 +412,7 @@ def gen_selection_VV( ).to_numpy() GenVVars["GenV1CS"] = ( ((v0_daughter0_pdgId == c_PDGID) & (v0_daughter1_pdgId == s_PDGID)) - | ((v0_daughter0_pdgId == c_PDGID) & (v0_daughter1_pdgId == s_PDGID)) + | ((v0_daughter0_pdgId == s_PDGID) & (v0_daughter1_pdgId == c_PDGID)) ).to_numpy() GenVVars["GenV2BB"] = ( (v1_daughter0_pdgId == b_PDGID) & (v1_daughter1_pdgId == b_PDGID) @@ -422,7 +422,7 @@ def gen_selection_VV( ).to_numpy() GenVVars["GenV2CS"] = ( ((v1_daughter0_pdgId == c_PDGID) & (v1_daughter1_pdgId == s_PDGID)) - | ((v1_daughter0_pdgId == c_PDGID) & (v1_daughter1_pdgId == s_PDGID)) + | ((v1_daughter0_pdgId == s_PDGID) & (v1_daughter1_pdgId == c_PDGID)) ).to_numpy() # match V to fatjet diff --git a/src/HH4b/utils.py b/src/HH4b/utils.py index d49d22a4..ee14141e 100644 --- a/src/HH4b/utils.py +++ b/src/HH4b/utils.py @@ -452,30 +452,20 @@ def _parquet_has_rows(parquet_file: Path) -> bool: return len(schema.names) > sum(isinstance(col, str) for col in index_columns) -# 2024 (Summer24) V+jets: {W,Z}to2Q-2Jets_Bin-PTQQ-X is generated with PTQQ > X and NO upper edge, -# so the four samples overlap. By default only Bin-PTQQ-100 is loaded (hh_vars vjets selectors). -# With ``load_samples(..., ptqq_stitch=PtqqStitch(...))`` (PostProcess --vjets-stitch) all four are -# loaded and stitched below. The 2022-2023 *_PTQQ-XtoY_{1J,2J} samples are exclusive bins and do not -# match. The generator-level PTQQ (LHE V pT) is not in the skim, so GenVPt (last-copy V, -# GenSelection.py) is used, and a higher-threshold sample is trusted only from PTQQ_STITCH_MARGIN -# above its threshold, where its GenVPt turn-on has reached the plateau. -# ``PtqqStitch(mode="range")`` is the alternative for a skim with the LHE V pT (``GenVLHEPt``) and -# the per-LHE-V-pT-bin totals (skimmer 7332ed8+): each sample covers only [X_i, X_next) in LHE V pT, -# normalised to sigma_i x (its generator-weight fraction there); see ``ptqq_range_norm``. +# 2024 V+jets: {W,Z}to2Q-2Jets_Bin-PTQQ-X has PTQQ > X with no upper edge, so the four samples +# overlap. By default only Bin-PTQQ-100 is loaded; load_samples(ptqq_stitch=...) stitches all four, +# in GenVPt above a margin ("effective-lumi") or in LHE V pT ranges ("range", ptqq_range_norm). _PTQQ_OPEN_RE = re.compile(r"^([wz])to2q-2jets_bin-ptqq-(\d+)$") PTQQ_STITCH_MARGIN = 50.0 # GeV PTQQ_OPEN_INCLUSIVE = 100.0 # the lowest threshold, whose sample covers the whole phase space PTQQ_STITCH_MODES = ("effective-lumi", "range") -# open-ended samples that must not be used (range mode raises if one is selected) -PTQQ_OPEN_INVALID = { - ("w", 600.0): "Wto2Q-2Jets_Bin-PTQQ-600 (2024) is INVALID in DAS: it was generated with the " - "LHE V pT filter at 400 GeV, not 600 GeV", +# open-ended samples with an invalid production, used only if the skim passes _ptqq_lhe_filter_ok +PTQQ_OPEN_FILTER_CHECK = { + ("w", 600.0): "the INVALID v2-v2 production has its LHE filter at 400 GeV", } -# Bin edges (GeV) of the skimmer's per-LHE-V-pT-bin totals (bbbbSkimmer.LHEVPT_EDGES, copied here -# so that postprocessing does not import the processors): arrays of len(edges) + 1 = 202 entries, -# [0] underflow (< 0: no status-2 LHE W/Z), [k] = [edges[k-1], edges[k]), [-1] >= 2000 GeV. The -# thresholds 100/200/400/600 are edges; LHEPart pT is stored with a reduced mantissa, which rounds a -# value just above a threshold to exactly the threshold, never below, so a lower edge is inclusive. +# Edges (GeV) of the skimmer's per-LHE-V-pT-bin totals, copied from bbbbSkimmer.LHEVPT_EDGES: 202 +# bins, [0] = no LHE W/Z, [k] = [edges[k-1], edges[k]), [-1] >= 2000 GeV. A lower edge is inclusive +# (the stored LHE pT rounds to a threshold, never below it). LHEVPT_EDGES = np.arange(0.0, 2001.0, 10.0) LHEVPT_COLUMN = "GenVLHEPt" @@ -596,6 +586,18 @@ def ptqq_range_norm( return out +def _ptqq_lhe_filter_ok(pickles_path: Path, year: str, sample: str, threshold: float) -> bool: + """Whether the skim has LHE V pT totals and no generated event below ``threshold``.""" + pickles_path = Path(pickles_path) + if not pickles_path.is_dir() or not any(f.name != ".DS_Store" for f in pickles_path.iterdir()): + return False + totals = get_pickles(pickles_path, year, sample).get("totals", {}) + if "nevents_lhevpt" not in totals: + return False + nraw = _lhevpt_array(totals, "nevents_lhevpt", sample) + return nraw[: _lhevpt_range_bins(threshold, np.inf).start].sum() == 0 + + def _own_total_variations( events: pd.DataFrame, variations: bool, weight_shifts: dict[str, Syst] | None ) -> list[str]: @@ -624,15 +626,13 @@ def _ptqq_range_ranges(samples: list[str], selector) -> dict[str, tuple[float, f """{sample: (lo, hi)} LHE V pT range of every open-ended sample the selector matches. Per boson the thresholds X_1 < ... < X_n of the matched samples split the axis: sample k keeps - [X_k, X_k+1), the highest one [X_n, inf). Raises for an invalid sample (``PTQQ_OPEN_INVALID``). + [X_k, X_k+1), the highest one [X_n, inf). """ by_boson: dict[str, list[tuple[float, str]]] = {} for sample in samples: thr = ptqq_open_threshold(sample) if check_selector(sample, selector) else None if thr is None: continue - if thr in PTQQ_OPEN_INVALID: - raise ValueError(f"PTQQ range stitch: {sample} selected, but {PTQQ_OPEN_INVALID[thr]}") by_boson.setdefault(thr[0], []).append((thr[1], sample)) ranges = {} for boson, members in by_boson.items(): @@ -1084,16 +1084,30 @@ def load_samples( if label != "data" and load_weight_noxsec: load_columns = columns + format_columns([("weight_noxsec", 1)]) + # leave out skims of invalid open-ended productions (PTQQ_OPEN_FILTER_CHECK) + label_samples = full_samples_list + if ptqq_stitch is not None: + label_samples = [] + for sample in full_samples_list: + thr = ptqq_open_threshold(sample) if check_selector(sample, selector) else None + sample_path = sample_dirs.get(sample, data_dir / sample) + if thr in PTQQ_OPEN_FILTER_CHECK and not _ptqq_lhe_filter_ok( + sample_path / "pickles", year, sample, thr[1] + ): + logger.warning(f"PTQQ stitch: {sample} left out: {PTQQ_OPEN_FILTER_CHECK[thr]}") + continue + label_samples.append(sample) + # lowest open-ended Bin-PTQQ threshold per boson among this label's samples (for the stitch) stitch_lowest = {} if ptqq_stitch is not None: - for sample in full_samples_list: + for sample in label_samples: thr = ptqq_open_threshold(sample) if check_selector(sample, selector) else None if thr is not None: stitch_lowest[thr[0]] = min(thr[1], stitch_lowest.get(thr[0], np.inf)) range_mode = ptqq_stitch is not None and ptqq_stitch.mode == "range" # range stitch: the LHE V pT range [lo, hi) of every matched open-ended sample - ranges = _ptqq_range_ranges(full_samples_list, selector) if range_mode else {} + ranges = _ptqq_range_ranges(label_samples, selector) if range_mode else {} # columns read only for the stitch (dropped after it unless requested) stitch_columns = format_columns( [("GenVLHEPt" if range_mode else "GenVPt", 1), (txbb_str, 2)] @@ -1117,7 +1131,7 @@ def load_samples( events_dict[label] = [] # list of directories we load in for this sample loaded_names = [] # sample (directory) name of each entry in events_dict[label] - for sample in full_samples_list: + for sample in label_samples: # check if this directory passes our selector string if not check_selector(sample, selector): continue diff --git a/src/HH4b/xsecs.py b/src/HH4b/xsecs.py index ae33b916..5b13924c 100644 --- a/src/HH4b/xsecs.py +++ b/src/HH4b/xsecs.py @@ -288,7 +288,7 @@ xsecs["Wto2Q-2Jets_Bin-PTQQ-100"] = 1751.0 xsecs["Wto2Q-2Jets_Bin-PTQQ-200"] = 164.3 xsecs["Wto2Q-2Jets_Bin-PTQQ-400"] = 9.205 -xsecs["Wto2Q-2Jets_Bin-PTQQ-600"] = 2.23 +xsecs["Wto2Q-2Jets_Bin-PTQQ-600"] = 1.217 xsecs["Zto2Q-2Jets_Bin-PTQQ-100"] = 695.0 xsecs["Zto2Q-2Jets_Bin-PTQQ-200"] = 71.56 xsecs["Zto2Q-2Jets_Bin-PTQQ-400"] = 3.811 diff --git a/src/condor/submit_configs/skimmer_signal_2022to2025.yaml b/src/condor/submit_configs/skimmer_signal_2022to2025.yaml index ff71a835..0fd4537e 100644 --- a/src/condor/submit_configs/skimmer_signal_2022to2025.yaml +++ b/src/condor/submit_configs/skimmer_signal_2022to2025.yaml @@ -257,7 +257,7 @@ "Wto2Q-2Jets_Bin-PTQQ-100", "Wto2Q-2Jets_Bin-PTQQ-200", "Wto2Q-2Jets_Bin-PTQQ-400", - # INVALID in DAS (generated with ptMin 400); skimmed but not used by PostProcess + # v2-v3 (LHE filter at 600 GeV); the earlier v2-v2 (ptMin 400) is INVALID in DAS "Wto2Q-2Jets_Bin-PTQQ-600", # Zto2Q NLO samples "Zto2Q-2Jets_Bin-PTQQ-100", From c23768cd56722c571a28604e3f73262c51188a4b Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sat, 24 Oct 2026 16:37:11 -0700 Subject: [PATCH 02/10] Index the missing 2024I and 2025C/F JetMET data The data index lacked part of three eras: 2024I MINIv6NANOv15_v2 (runs 386694-386951) and 2025C/F PromptReco-v1 (runs 392174-393087 and 396629-397596). Each holds the runs of its era that the indexed dataset does not (the prompt processing changed version mid-era), so the skims missed 49% of the 2024I and 61% and 72% of the 2025C and 2025F golden luminosity. They are added to make_filelists_v15.py and to those three entries of nanoindex_v15.json; JetMET0 2025C-v1 and JetMET1 2025F-v1 are not on FNAL disk and are read through the global redirector. --- data/make_filelists_v15.py | 12 + data/nanoindex_v15.json | 2789 +++++++++++++++++++++++++++++++++++- 2 files changed, 2800 insertions(+), 1 deletion(-) diff --git a/data/make_filelists_v15.py b/data/make_filelists_v15.py index 6da51197..58262f02 100644 --- a/data/make_filelists_v15.py +++ b/data/make_filelists_v15.py @@ -747,8 +747,12 @@ "/JetMET1/Run2024H-MINIv6NANOv15-v2/NANOAOD", ], "JetMET_Run2024I": [ + # MINIv6NANOv15: runs 386478-386693 "/JetMET0/Run2024I-MINIv6NANOv15-v2/NANOAOD", "/JetMET1/Run2024I-MINIv6NANOv15-v1/NANOAOD", + # MINIv6NANOv15_v2: runs 386694-386951 + "/JetMET0/Run2024I-MINIv6NANOv15_v2-v1/NANOAOD", + "/JetMET1/Run2024I-MINIv6NANOv15_v2-v2/NANOAOD", ], }, "EGamma": { @@ -815,6 +819,10 @@ "2025": { "JetMET": { "JetMET_Run2025C": [ + # v1: runs 392174-393087 + 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"root://cmsdcadisk.fnal.gov//dcache/uscmsdisk///store/data/Run2025F/JetMET0/NANOAOD/PromptReco-v2/000/397/619/00000/029631f9-d917-4470-afc9-9fe38e9781aa.root", "root://cmsdcadisk.fnal.gov//dcache/uscmsdisk///store/data/Run2025F/JetMET0/NANOAOD/PromptReco-v2/000/397/619/00000/14dcc70e-2214-4425-81ac-703e7468867a.root", "root://cmsdcadisk.fnal.gov//dcache/uscmsdisk///store/data/Run2025F/JetMET0/NANOAOD/PromptReco-v2/000/397/619/00000/5402f5e1-181e-4fd2-ad40-8f30a683229e.root", From 174f1d5dd93569b80b9429b7b0c57be7bf9277ab Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 02:30:25 -0700 Subject: [PATCH 03/10] Luminosity: golden JSON mask, LUMI, unreadable data files and lost lumisections The Run 3 golden JSONs are pinned in corrections/data/golden_json/ (fetch_golden_json.py updates them from the CMS CAT area). utils.load_samples now drops the data events outside the year's golden JSON (hh_vars.GOLDEN_JSON); run and luminosityBlock are read for it and dropped again unless requested. No lumi mask was applied before: about 2% of the 2024/2025 data events (up to 5.6% per era) and up to 0.5% in 2022-2023 were in uncertified lumisections. LUMI is the recorded luminosity of these golden JSONs in the analyzed eras, now covered by the data index, from brilcalc (corrections/golden_lumi.py, which also reports how much of it the data skims of a tag cover; install_brilcalc.sh installs brilcalc for offsite use): normtag_PHYSICS, and for 2025, which normtag_PHYSICS does not cover yet, normtag_BRIL (preliminary). 2025 now includes era G. 2022-2025: 282484.9 pb^-1 instead of 261808.6. MC skims keep the luminosity they were made with until the next re-skim; the 1x/2x check of the 2022-2023 W cross sections now tolerates a LUMI update since the skim. The FOM slim cache key gets "__lumi" (content of the golden JSON, LUMI of the year and of its MC source year), so caches built before are not read. coffea skips a data file that raises FileNotFoundError at once, and a chunk whose reads keep timing out after its retries, with only a warning in the job log, so lost data went unnoticed (run.py meant to stop skipping for data, but compared the fileset keys with the sample names, which never match). Data jobs now open every file first, three times one minute apart (an authentication error still fails the job), leave out the files that never open, and save in the job pickle these files with their error, the entries of the others, and the entries the skimmer read from each file. A job whose processor fails exits non-zero without copying any output. src/condor/check_data_files.py (logic in HH4b/data_files.py) lists per data sample the lost files (not opened, read incompletely, in no job's output) and the files read twice, with their jobs, and resubmits those jobs with --submit-missing. The first load of a year now writes /lumi_.json (HH4b/data_lumi.py): {} if the data skims hold every golden lumisection of LUMI[year], else the lumisections they lost and lumi_pb, the luminosity they hold. The lost lumisections come from the job records (those of the lost files from DAS, or from the file), or, for skims made before the records, are the golden lumisections without any data event in the skims. lumi_pb = LUMI x (1 - f), f the lost fraction of the golden luminosity from the per-lumisection luminosity of the DC pileup JSON. utils.load_samples drops the lost lumisections from the data (whole lumisections: they are split between JetMET0 and JetMET1 and over several files) and scales the MC used for the year by lumi_pb / LUMI; the FOM cache key includes the file and golden_lumi.py --tag leaves the lost lumisections out. The era run ranges move to hh_vars.DATA_ERAS. On the current skims, which have no records, this matches the brilcalc coverage within 0.2%; their 2024 and 2025 MC are scaled by 0.9475 and 0.7046. --- ...t_Collisions2022_355100_362760_Golden.json | 2574 +++++++++++ ...t_Collisions2023_366442_370790_Golden.json | 1560 +++++++ ...t_Collisions2024_378981_386951_Golden.json | 3660 +++++++++++++++ ...t_Collisions2025_391658_398903_Golden.json | 3960 +++++++++++++++++ src/HH4b/corrections/fetch_golden_json.py | 74 + src/HH4b/corrections/golden_lumi.py | 166 + src/HH4b/corrections/install_brilcalc.sh | 18 + src/HH4b/data_files.py | 61 + src/HH4b/data_lumi.py | 178 + src/HH4b/hh_vars.py | 76 +- src/HH4b/postprocessing/PostProcess.py | 16 +- src/HH4b/processors/bbbbSkimmer.py | 8 +- src/HH4b/utils.py | 66 +- src/condor/check_data_files.py | 71 + src/condor/submit.templ.sh | 3 +- src/run.py | 50 +- 16 files changed, 12503 insertions(+), 38 deletions(-) create mode 100644 src/HH4b/corrections/data/golden_json/Cert_Collisions2022_355100_362760_Golden.json create mode 100644 src/HH4b/corrections/data/golden_json/Cert_Collisions2023_366442_370790_Golden.json create mode 100644 src/HH4b/corrections/data/golden_json/Cert_Collisions2024_378981_386951_Golden.json create mode 100644 src/HH4b/corrections/data/golden_json/Cert_Collisions2025_391658_398903_Golden.json create mode 100644 src/HH4b/corrections/fetch_golden_json.py create mode 100644 src/HH4b/corrections/golden_lumi.py create mode 100755 src/HH4b/corrections/install_brilcalc.sh create mode 100644 src/HH4b/data_files.py create mode 100644 src/HH4b/data_lumi.py create mode 100644 src/condor/check_data_files.py diff --git a/src/HH4b/corrections/data/golden_json/Cert_Collisions2022_355100_362760_Golden.json b/src/HH4b/corrections/data/golden_json/Cert_Collisions2022_355100_362760_Golden.json new file mode 100644 index 00000000..17e89218 --- /dev/null +++ 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a/src/HH4b/corrections/fetch_golden_json.py b/src/HH4b/corrections/fetch_golden_json.py new file mode 100644 index 00000000..296ec02d --- /dev/null +++ b/src/HH4b/corrections/fetch_golden_json.py @@ -0,0 +1,74 @@ +""" +Copy the newest full-year golden JSONs of Run 3 from the CMS CAT area on CVMFS into +``data/golden_json/``, one file per year (``Cert_Collisions___Golden.json``). + +The newest file is the one CAT links as ``Collisions/latest/golden_json_latest.json``; its +certified name is the ``Cert_Collisions_*_Golden.json`` with the same content. An older file of +the same year in ``data/golden_json/`` is replaced. Files are compared by content and written with +a final newline (as the pre-commit hooks want). Without ``--update`` nothing is written. + +Usage: python fetch_golden_json.py [--years 2022 2023 2024 2025] [--update] + +Author(s): Zichun Hao +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + +CAT_DC = Path("/cvmfs/cms-griddata.cern.ch/cat/metadata/DC") +OUT_DIR = Path(__file__).resolve().parent / "data" / "golden_json" + + +def md5(path: Path) -> str: + return hashlib.md5(path.read_bytes()).hexdigest() + + +def newest_golden(year: str) -> tuple[Path, Path]: + """(certified file, dated CAT folder holding it) of the newest full-year golden JSON.""" + folder = CAT_DC / f"Collisions{year[2:]}" + pointer = md5(folder / "latest" / "golden_json_latest.json") + named = [ + f + for f in sorted((folder / "latest").glob(f"Cert_Collisions{year}_*_Golden.json")) + if "_era" not in f.name and md5(f) == pointer + ] + if len(named) != 1: + raise RuntimeError(f"{year}: {len(named)} certified files match golden_json_latest.json") + dated = sorted( + d for d in folder.iterdir() if d.name[:1].isdigit() and (d / named[0].name).is_file() + ) + source = next(d for d in reversed(dated) if md5(d / named[0].name) == pointer) + return source / named[0].name, source + + +def main(): + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--years", nargs="+", default=["2022", "2023", "2024", "2025"]) + parser.add_argument("--update", action="store_true", help="write the new files") + args = parser.parse_args() + + for year in args.years: + source, folder = newest_golden(year) + target = OUT_DIR / source.name + old = [f for f in OUT_DIR.glob(f"Cert_Collisions{year}_*_Golden.json") if f != target] + same = target.is_file() and json.loads(target.read_text()) == json.loads(source.read_text()) + if same and not old: + print(f"{year}: {source.name} is up to date (CAT {folder.parent.name}/{folder.name})") + continue + replaced = [f.name for f in old] + print( + f"{year}: {source.name} from CAT {folder.parent.name}/{folder.name}, replaces {replaced}" + ) + if args.update: + OUT_DIR.mkdir(parents=True, exist_ok=True) + target.write_text(source.read_text().rstrip("\n") + "\n") + for f in old: + f.unlink() + + +if __name__ == "__main__": + main() diff --git a/src/HH4b/corrections/golden_lumi.py b/src/HH4b/corrections/golden_lumi.py new file mode 100644 index 00000000..10b87912 --- /dev/null +++ b/src/HH4b/corrections/golden_lumi.py @@ -0,0 +1,166 @@ +""" +Luminosity of the analyzed Run 3 data: per era, the recorded luminosity (brilcalc) of the +lumisections of the year's golden JSON (``hh_vars.GOLDEN_JSON``, in ``data/golden_json/``) in the +era's runs. These are the ``hh_vars.LUMI`` values. With ``--tag``, the lumisections the data skims +of that skimmer tag lost (``/lumi_.json``, ``HH4b.data_lumi``) are left out, and the +golden lumisections that hold events in the skims, with their luminosity, show how much of it the +skims cover. + +Writes, per era, ``/golden_.json`` (with ``--tag`` also +``processed_golden_.json``) and ``/lumi.json``. brilcalc runs offsite with +``-c web``; ``install_brilcalc.sh`` installs it. + +Usage: python golden_lumi.py --brilcalc [--data-dir --tag ] [--years ...] + +Author(s): Zichun Hao +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import subprocess +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from HH4b import data_lumi, hh_vars +from HH4b.utils import GOLDEN_JSON_DIR, check_selector + +NORMTAG_DIR = Path("/cvmfs/cms-bril.cern.ch/cms-lumi-pog/Normtags") +LS_SCALE = 1_000_000 # key of a lumisection: run x LS_SCALE + LS + +# normtag_PHYSICS (the LUM POG recommendation) ends with 2024; 2025: BRIL's best luminometer, +# preliminary (4.6% below normtag_PHYSICS in 2024G) +NORMTAGS = dict.fromkeys(hh_vars.DATA_ERAS, NORMTAG_DIR / "normtag_PHYSICS.json") +NORMTAGS["2025"] = NORMTAG_DIR / "normtag_BRIL.json" + + +def md5(path: Path | str) -> str: + return hashlib.md5(Path(path).read_bytes()).hexdigest() + + +def golden_lumisections(path: Path) -> np.ndarray: + """Sorted keys of the lumisections of a CMS JSON.""" + keys = [ + int(run) * LS_SCALE + ls + for run, ranges in json.loads(path.read_text()).items() + for first, last in ranges + for ls in range(first, last + 1) + ] + return np.unique(np.array(keys, dtype=np.int64)) + + +def skim_lumisections(parquet: Path) -> np.ndarray: + """Keys of the lumisections with events in one skim file.""" + if not pq.read_metadata(parquet).num_rows: # empty skim files have no columns + return np.empty(0, np.int64) + table = pq.read_table(parquet, columns=["('run', '0')", "('luminosityBlock', '0')"]) + run, ls = (table.column(i).to_numpy().astype(np.int64) for i in range(2)) + return np.unique(run * LS_SCALE + ls) + + +def cms_json(keys: np.ndarray) -> dict[str, list[list[int]]]: + """{run: [[first LS, last LS], ...]} of sorted unique lumisection keys.""" + out = {} + for run in np.unique(keys // LS_SCALE): + ls = keys[keys // LS_SCALE == run] % LS_SCALE + breaks = np.flatnonzero(np.diff(ls) != 1) + starts, ends = np.r_[ls[0], ls[breaks + 1]], np.r_[ls[breaks], ls[-1]] + out[str(run)] = [[int(a), int(b)] for a, b in zip(starts, ends)] + return out + + +def brilcalc_recorded(brilcalc: str, lumi_json: Path, normtag: str) -> tuple[float, str]: + """Recorded luminosity (pb^-1) of the lumisections of ``lumi_json``, and the list brilcalc + prints of those it has no luminosity for.""" + cmd = [brilcalc, "lumi", "-c", "web", "-b", "STABLE BEAMS", "--normtag", normtag, "-u", "/pb"] + cmd += ["-i", str(lumi_json), "--output-style", "csv"] + env = {**os.environ, "PYTHONNOUSERSITE": "1"} # brilconda's SQLAlchemy, not a user-site one + out = subprocess.run(cmd, capture_output=True, text=True, check=True, env=env).stdout + lines = out.splitlines() + summary = lines[lines.index("#nfill,nrun,nls,ncms,totdelivered(/pb),totrecorded(/pb)") + 1] + missing = next(line for line in lines if line.startswith("#(run,ls) in json but not in")) + return float(summary.split(",")[-1]), missing.split(":", 1)[1].strip() + + +def write_lumi(outdir: Path, name: str, keys: np.ndarray, brilcalc: str, normtag) -> float | None: + """Write the CMS JSON of ``keys`` and return its luminosity (pb^-1) if there is any.""" + path = outdir / f"{name}.json" + path.write_text(json.dumps(cms_json(keys)) + "\n") + if not len(keys): + return None + lumi, missing = brilcalc_recorded(brilcalc, path, normtag) + print(f" {name}: {len(keys)} lumisections, {lumi:.1f} pb-1, without luminosity: {missing}") + return lumi + + +def main(): + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--brilcalc", required=True, help="brilcalc executable") + parser.add_argument("--normtag", help="for every year, instead of NORMTAGS") + parser.add_argument("--years", nargs="+", default=list(hh_vars.DATA_ERAS)) + parser.add_argument("--data-dir", help="skimmer output directory (with --tag)") + parser.add_argument("--tag", help="skimmer tag whose data skims to check") + parser.add_argument("--outdir", type=Path, default=Path("golden_lumi")) + parser.add_argument("--workers", type=int, default=16, help="threads reading the skims") + args = parser.parse_args() + if args.tag and not args.data_dir: + parser.error("--tag needs --data-dir") + + args.outdir.mkdir(parents=True, exist_ok=True) + result = {"tag": args.tag, "years": {}} + for year in args.years: + normtag = args.normtag or NORMTAGS[year] + golden_path = GOLDEN_JSON_DIR / hh_vars.GOLDEN_JSON[year] + golden = golden_lumisections(golden_path) + skim = None + if args.tag: + year_dir = Path(args.data_dir) / args.tag / year + selector = hh_vars.samples_run3[year][hh_vars.data_key] + files = [ + f + for p in sorted(year_dir.iterdir()) + if check_selector(p.name, selector) + for f in sorted((p / "parquet").glob("*.parquet")) + ] + with ThreadPoolExecutor(args.workers) as pool: + per_file = list(pool.map(skim_lumisections, files)) + skim = np.unique(np.concatenate([np.empty(0, np.int64), *per_file])) + print(f"{year} ({golden_path.name}, {Path(normtag).name})") + res = {"golden_json": {golden_path.name: md5(golden_path)}} + res |= {"normtag": {str(normtag): md5(normtag)}, "eras": {}} + lost = data_lumi.lumi_corrections(Path(args.data_dir) / args.tag, year) if args.tag else {} + if "lost_lumis" in lost: + golden = np.setdiff1d(golden, data_lumi.keys_of(lost["lost_lumis"])) + res["lost_fraction"] = lost["lost_fraction"] + print(f" lumisections lost by the skims left out ({lost['method']})") + for era, (first, last) in hh_vars.DATA_ERAS[year].items(): + in_era = golden[(golden // LS_SCALE >= first) & (golden // LS_SCALE <= last)] + name = f"golden_{year}{era}" + era_res = {"lumi_pb": write_lumi(args.outdir, name, in_era, args.brilcalc, normtag)} + if skim is not None: + processed = np.intersect1d(in_era, skim) + name = f"processed_golden_{year}{era}" + lumi = write_lumi(args.outdir, name, processed, args.brilcalc, normtag) + era_res["skim_lumi_pb"] = lumi or 0.0 + res["eras"][era] = era_res + res["lumi_pb"] = sum(r["lumi_pb"] for r in res["eras"].values()) + if skim is not None: + res["skim_lumi_pb"] = sum(r["skim_lumi_pb"] for r in res["eras"].values()) + print(f" skims cover {res['skim_lumi_pb']:.1f} of {res['lumi_pb']:.1f} pb-1") + result["years"][year] = res + (args.outdir / "lumi.json").write_text(json.dumps(result, indent=1) + "\n") + print("LUMI (pb^-1):") + for year, res in result["years"].items(): + print(f' "{year}": {res["lumi_pb"]:.1f},') + for era, r in res["eras"].items(): + print(f' "{year}{era}": {r["lumi_pb"]:.1f},') + + +if __name__ == "__main__": + main() diff --git a/src/HH4b/corrections/install_brilcalc.sh b/src/HH4b/corrections/install_brilcalc.sh new file mode 100755 index 00000000..7c394dc6 --- /dev/null +++ b/src/HH4b/corrections/install_brilcalc.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# Install brilcalc (brilws) for use outside CERN, e.g. on the UAF: a venv on top of the CVMFS +# brilconda, whose SQLAlchemy has the Frontier dialect that brilcalc -c web needs (the PyPI one +# does not). Run brilcalc with PYTHONNOUSERSITE=1 so that a user-site SQLAlchemy does not shadow +# brilconda's (golden_lumi.py sets it). +# +# Usage: ./install_brilcalc.sh [venv directory, default brilenv] +# PYTHONNOUSERSITE=1 brilenv/bin/brilcalc lumi -c web -b "STABLE BEAMS" -u /pb \ +# --normtag /cvmfs/cms-bril.cern.ch/cms-lumi-pog/Normtags/normtag_PHYSICS.json -i +# +# Author(s): Zichun Hao + +set -euo pipefail + +venv=${1:-brilenv} +/cvmfs/cms-bril.cern.ch/brilconda310/bin/python3 -m venv --system-site-packages "$venv" +PYTHONNOUSERSITE=1 "$venv/bin/python" -m pip install --quiet --no-deps brilws==3.9.4 +PYTHONNOUSERSITE=1 "$venv/bin/brilcalc" --version diff --git a/src/HH4b/data_files.py b/src/HH4b/data_files.py new file mode 100644 index 00000000..eb977860 --- /dev/null +++ b/src/HH4b/data_files.py @@ -0,0 +1,61 @@ +""" +Bookkeeping of the input files of the data skims, saved in the job pickles by run.py and the +skimmer: per file, the entries run.py found when it opened it, the entries the skimmer read, and, +for the files that did not open after retries, the error. + +Author(s): Zichun Hao +""" + +from __future__ import annotations + +import pickle +from pathlib import Path + + +def lfn(fname: str) -> str: + return "/store/" + fname.split("/store/", 1)[1] + + +def index_files(index: dict, year: str, sample: str) -> list[str]: + """Files of a data sample in the nano index, in the order the jobs were split.""" + for category in index[year].values(): + if sample in category: + return category[sample] + raise KeyError(f"{sample} ({year}) is not in the nano index") + + +def read_records(sample_dir: Path, year: str, sample: str) -> dict[int, dict] | None: + """{job: {"file_entries": .., "processed_entries": .., "bad_files": ..}} of the job pickles + (``pickles/out_.pkl``) of a data sample; None if they hold no file records (skims made + before they were saved).""" + records = {} + for pkl in sorted((sample_dir / "pickles").glob("out_*.pkl")): + with pkl.open("rb") as f: + out = pickle.load(f).get(year, {}).get(sample, {}) + if "file_entries" not in out and "bad_files" not in out: + return None + records[int(pkl.stem.rsplit("_", 1)[1])] = out + return records + + +def file_problems(records: dict[int, dict], files: list[str]) -> dict[str, tuple[str, list[int]]]: + """{file: (problem, jobs)} of the ``files`` of a sample (its nano index files) that were lost + (not opened, read incompletely, in no job's records) or read twice.""" + problems, read_by = {}, {} + for job, out in sorted(records.items()): + entries, bad = out.get("file_entries", {}), out.get("bad_files", {}) + processed = out.get("processed_entries", {}) + for fname, error in bad.items(): + problems[fname] = (f"not opened: {error}", [job]) + read_by.setdefault(fname, []).append(job) + for fname, n in entries.items(): + read_by.setdefault(fname, []).append(job) + if processed.get(fname, 0) != n: + problems[fname] = (f"read {processed.get(fname, 0)} of {n} entries", [job]) + for fname, jobs in read_by.items(): + if len(jobs) > 1: + problems[fname] = ("read twice", jobs) + for fname in files: + if fname not in read_by: + problems[fname] = ("in no job's output", []) + return problems diff --git a/src/HH4b/data_lumi.py b/src/HH4b/data_lumi.py new file mode 100644 index 00000000..07e6f8a2 --- /dev/null +++ b/src/HH4b/data_lumi.py @@ -0,0 +1,178 @@ +""" +Luminosity the data skims of a tag hold: ``/lumi_.json``, one per analysis year, written +by ``lumi_corrections`` the first time the year is loaded. It exists only once the year's data +skims have been checked: ``{}`` if they hold every golden lumisection counted in ``LUMI[year]``; +else the golden lumisections they lost (``lost_lumis``) and the luminosity they hold (``lumi_pb``). +``utils.load_samples`` drops the lost lumisections from the data and scales the MC used for the year +to ``lumi_pb``. Delete the file when the data skims change; the next load writes it again. + +Lost lumisections: those of the files the job records (``HH4b.data_files``) show as lost, from DAS +for /store/data files, else from the file itself; for skims made before those records, the golden +lumisections without any data event in the skims. A lumisection is split between JetMET0 and +JetMET1 and over several files, so it is dropped as a whole. ``lumi_pb`` = ``LUMI[year]`` x (1 - f), +f the fraction of the golden luminosity in the lost lumisections, from the per-lumisection recorded +luminosity of the DC pileup JSONs. + +Author(s): Zichun Hao +""" + +from __future__ import annotations + +import json +import logging +import os +import subprocess +from concurrent.futures import ThreadPoolExecutor +from functools import lru_cache +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import uproot + +from HH4b.data_files import file_problems, index_files, lfn, read_records +from HH4b.hh_vars import DATA_ERAS, DATA_SAMPLES, GOLDEN_JSON, LUMI + +logger = logging.getLogger(__name__) + +GOLDEN_JSON_DIR = Path(__file__).resolve().parent / "corrections" / "data" / "golden_json" +NANO_INDEX = Path(__file__).resolve().parents[2] / "data" / "nanoindex_v15.json" +_DC = Path("/cvmfs/cms-griddata.cern.ch/cat/metadata/DC") +PILEUP_JSON = { + "2022": _DC / "Collisions22/latest/PileUp/BCDEFG/pileup_JSON.txt", + "2022EE": _DC / "Collisions22/latest/PileUp/BCDEFG/pileup_JSON.txt", + "2023": _DC / "Collisions23/latest/PileUp/BCD/pileup_JSON.txt", + "2023BPix": _DC / "Collisions23/latest/PileUp/BCD/pileup_JSON.txt", + "2024": _DC / "Collisions24/latest/PileUp/pileup_JSON-2024BCDEFGHI_Golden.txt", + "2025": _DC / "Collisions25/latest/PileUp/pileup_JSON-2025_Golden.txt", +} +DASGOCLIENT = "/cvmfs/cms.cern.ch/common/dasgoclient" +LS_SCALE = 1_000_000 # key of a lumisection: run x LS_SCALE + LS + + +def keys_of(lumis: dict) -> np.ndarray: + """Sorted keys of the lumisections of a CMS JSON ({run: [[first, last], ...]}).""" + keys = [ + int(run) * LS_SCALE + ls + for run, ranges in lumis.items() + for first, last in ranges + for ls in range(first, last + 1) + ] + return np.unique(np.array(keys, dtype=np.int64)) + + +def cms_json(keys: np.ndarray) -> dict[str, list[list[int]]]: + out = {} + for key in np.unique(keys): + run, ls = str(key // LS_SCALE), int(key % LS_SCALE) + ranges = out.setdefault(run, []) + if ranges and ranges[-1][1] == ls - 1: + ranges[-1][1] = ls + else: + ranges.append([ls, ls]) + return out + + +def golden_keys(year: str) -> np.ndarray: + """The golden lumisections of the analyzed eras of ``year`` (those ``LUMI[year]`` counts).""" + keys = keys_of(json.loads((GOLDEN_JSON_DIR / GOLDEN_JSON[year]).read_text())) + runs = keys // LS_SCALE + in_eras = np.zeros(len(keys), dtype=bool) + for first, last in DATA_ERAS[year].values(): + in_eras |= (runs >= first) & (runs <= last) + return keys[in_eras] + + +def file_lumisections(fname: str) -> np.ndarray: + """Lumisection keys of a file: from DAS for /store/data files, else from the file.""" + if lfn(fname).startswith("/store/data/"): + das = Path(DASGOCLIENT) if Path(DASGOCLIENT).is_file() else Path("dasgoclient") + query = [str(das), f"-query=lumi file={lfn(fname)}", "-json"] + out = subprocess.run(query, capture_output=True, text=True, check=True).stdout + lumis = [ls for record in json.loads(out) for ls in record["lumi"]] + keys = [int(ls["run_number"]) * LS_SCALE + int(ls["lumi_section_num"]) for ls in lumis] + else: + with uproot.open(fname) as f: + arrays = f["Events"].arrays(["run", "luminosityBlock"], library="np") + keys = arrays["run"].astype(np.int64) * LS_SCALE + arrays["luminosityBlock"] + if not len(keys): + raise RuntimeError(f"no lumisections found for {lfn(fname)}") + return np.unique(np.asarray(keys, dtype=np.int64)) + + +def parquet_keys(parquet: Path) -> np.ndarray: + """Lumisection keys with events in one skim file.""" + if not pq.read_metadata(parquet).num_rows: # empty skim files have no columns + return np.empty(0, np.int64) + table = pq.read_table(parquet, columns=["('run', '0')", "('luminosityBlock', '0')"]) + run, ls = (table.column(i).to_numpy().astype(np.int64) for i in range(2)) + return np.unique(run * LS_SCALE + ls) + + +def skim_keys(sample_dirs: list[Path]) -> np.ndarray: + """Lumisection keys with events in the parquet files of data samples.""" + files = [f for d in sample_dirs for f in sorted((d / "parquet").glob("*.parquet"))] + with ThreadPoolExecutor(16) as pool: + per_file = list(pool.map(parquet_keys, files)) + return np.unique(np.concatenate([np.empty(0, np.int64), *per_file])) + + +def lumi_fraction(lost: np.ndarray, golden: np.ndarray, year: str) -> float: + """Fraction of the recorded luminosity of ``golden`` in ``lost``, from the DC pileup JSON.""" + pileup = json.loads(PILEUP_JSON[year].read_text()) + pu_keys = np.array([int(r) * LS_SCALE + int(x[0]) for r, rows in pileup.items() for x in rows]) + pu_lumi = np.array([float(x[1]) for rows in pileup.values() for x in rows]) + return float(pu_lumi[np.isin(pu_keys, lost)].sum() / pu_lumi[np.isin(pu_keys, golden)].sum()) + + +def check_year(tag_dir: Path, year: str) -> dict | None: + """Content of ``/lumi_.json``; None if the tag has no data skims of ``year``.""" + year_dir = tag_dir / year + samples = [p for p in sorted(year_dir.glob("*")) if p.name.startswith(tuple(DATA_SAMPLES))] + if not samples: + return None + golden = golden_keys(year) + records = {p.name: read_records(p, year, p.name) for p in samples} + if all(r is not None for r in records.values()): + method = "file records" + index = json.loads(NANO_INDEX.read_text()) + lost = [np.empty(0, np.int64)] + for sample, sample_records in records.items(): + problems = file_problems(sample_records, index_files(index, year, sample)) + for fname, (problem, jobs) in sorted(problems.items()): + logger.warning(f"{year} {sample}: {lfn(fname)} (jobs {jobs}): {problem}") + if problem != "read twice": + lost.append(file_lumisections(fname)) + lost = np.intersect1d(golden, np.concatenate(lost)) + else: + method = "coverage of the skims (no file records)" + lost = np.setdiff1d(golden, skim_keys(samples)) + if not len(lost): + return {} + fraction = lumi_fraction(lost, golden, year) + logger.warning(f"{year}: {100 * fraction:.3g}% of the golden luminosity lost ({method})") + return { + "lumi_pb": LUMI[year] * (1 - fraction), + "lost_fraction": fraction, + "method": method, + "lost_lumis": cms_json(lost), + } + + +@lru_cache(maxsize=None) +def lumi_corrections(tag_dir: Path, year: str) -> dict: + """``/lumi_.json``, written first if it is missing ({} without data skims).""" + path = Path(tag_dir) / f"lumi_{year}.json" + if path.is_file(): + return json.loads(path.read_text()) + result = check_year(Path(tag_dir), year) + if result is None: + logger.warning(f"No data skims of {year} in {tag_dir}: luminosity not checked") + return {} + try: + tmp = path.with_name(f"{path.name}.{os.getpid()}.tmp") + tmp.write_text(json.dumps(result) + "\n") + tmp.replace(path) + except OSError as e: + logger.warning(f"Could not write {path} ({e}); using the result of this check only") + return result diff --git a/src/HH4b/hh_vars.py b/src/HH4b/hh_vars.py index 960c1851..83436ee4 100644 --- a/src/HH4b/hh_vars.py +++ b/src/HH4b/hh_vars.py @@ -10,26 +10,64 @@ years = ["2022", "2022EE", "2023", "2023BPix", "2024", "2025"] -# in pb^-1 +# Golden JSON of each year's data (corrections/data/golden_json/, corrections/fetch_golden_json.py); +# utils.load_samples drops the data events outside it. +GOLDEN_JSON = { + "2022": "Cert_Collisions2022_355100_362760_Golden.json", + "2022EE": "Cert_Collisions2022_355100_362760_Golden.json", + "2023": "Cert_Collisions2023_366442_370790_Golden.json", + "2023BPix": "Cert_Collisions2023_366442_370790_Golden.json", + "2024": "Cert_Collisions2024_378981_386951_Golden.json", + "2025": "Cert_Collisions2025_391658_398903_Golden.json", +} + +# analyzed eras: first and last run of their golden lumisections, from the CAT per-era golden JSONs +# (2022-2024) and the runs of the JetMET0 PromptReco datasets on DAS (2025); the B eras are not used +DATA_ERAS = { + "2022": {"C": (355862, 357482), "D": (357538, 357900)}, + "2022EE": {"E": (359022, 360331), "F": (360390, 362167), "G": (362433, 362760)}, + "2023": {"C": (367095, 368823)}, + "2023BPix": {"D": (369803, 370790)}, + "2024": { + "C": (379416, 380236), + "D": (380306, 380947), + "E": (380963, 381544), + "F": (382229, 383779), + "G": (383811, 385801), + "H": (385836, 386319), + "I": (386478, 386951), + }, + "2025": { + "C": (392174, 393516), + "D": (394393, 395948), + "E": (395982, 396422), + "F": (396629, 397853), + "G": (397867, 398903), + }, +} + +# in pb^-1; Run 3: brilcalc of the golden JSONs above in the analyzed eras +# (corrections/golden_lumi.py), normtag_PHYSICS; 2025 normtag_BRIL, preliminary (normtag_PHYSICS has +# no 2025 runs yet) LUMI = { - "2022": 7980.5, - "2022EE": 26671.6, - "2023": 18084.4, - "2023BPix": 9692.1, - "2024": 108960.0, - "2024C": 7240.0, - "2024D": 7960.0, - "2024E": 11320.0, - "2024F": 27760.0, - "2024G": 37770.0, - "2024H": 5440.0, - "2024I": 11470.0, - "2025C": 20780.0, - "2025E": 14000.0, - "2025D": 25290.0, - "2025F": 30350.0, - # TODO: 2025G ongoing - "2025": 90420.0, + "2022": 7989.5, + "2022EE": 26674.9, + "2023": 17964.2, + "2023BPix": 9676.7, + "2024": 109804.9, + "2024C": 7250.7, + "2024D": 7976.1, + "2024E": 11423.2, + "2024F": 28037.8, + "2024G": 38069.1, + "2024H": 5489.7, + "2024I": 11558.2, + "2025": 110374.7, + "2025C": 21563.6, + "2025D": 25823.9, + "2025E": 14048.2, + "2025F": 26693.6, + "2025G": 22245.3, "2018": 59830.0, "2017": 41480.0, "2016": 36330.0, diff --git a/src/HH4b/postprocessing/PostProcess.py b/src/HH4b/postprocessing/PostProcess.py index 1950cdfd..4709269d 100644 --- a/src/HH4b/postprocessing/PostProcess.py +++ b/src/HH4b/postprocessing/PostProcess.py @@ -23,7 +23,7 @@ import uproot import xgboost as xgb -from HH4b import hh_vars, plotting, postprocessing, run_utils +from HH4b import data_lumi, hh_vars, plotting, postprocessing, run_utils from HH4b.eventlist_manifest import write_eventlist_manifest from HH4b.hh_vars import ( bg_keys, @@ -47,6 +47,7 @@ ) from HH4b.postprocessing.bdt_inference import _add_year_features, model_feature_names from HH4b.utils import ( + GOLDEN_JSON_DIR, W_XSEC_CORRECTION_ERAS, PtqqStitch, ShapeVar, @@ -76,6 +77,8 @@ # (hh_vars "ZZ?", "WW?", "WZ?") load only the inclusive samples, where the prefix selectors summed both FOM_CACHE_DIBOSON_SUFFIX = "__dibosonexact" DIBOSON_EXACT_ERAS = ("2022", "2022EE", "2023", "2023BPix") +# ... and of every year: its data in the golden JSON, and LUMI (a hash of both; fom_cache_tag) +FOM_CACHE_LUMI_PREFIX = "__lumi" # ... and of every year: its ttbar frame stores the ttbar BDT-shape SF of its weights # (TTBAR_BDTSF_COL), which the --fom-cache path re-evaluates with the scanned model FOM_CACHE_TTSF_SUFFIX = "__ttbdtsf" @@ -696,6 +699,12 @@ def fom_cache_tag(args, year: str) -> str: diboson (key without the suffix) is never read again. 2024 and 2025 (2024 MC) have only the inclusive samples and keep their keys. + Plus "__lumi" (after "__hbb1prod" and "__dibosonexact") for every year (since + 2026-10-24): its data are the golden events the skims hold and its MC is scaled to their + luminosity (``utils._apply_golden_json``, ``HH4b.data_lumi``), and the MC split factor depends + on LUMI; the hash covers the year's golden JSON, ``/lumi_.json`` and the LUMI of the + year and of its MC source year, so a cache of other data or luminosities is never read again. + Plus "__ttbdtsf" at the end of every key (since 2026-09-29): the ttbar frame stores the ttbar BDT-shape SF of its weights (``TTBAR_BDTSF_COL``), which ``fom_cache_rescore`` re-evaluates with the scanned model. Caches without it (ttbar weights with the SF of the model that built @@ -725,6 +734,11 @@ def fom_cache_tag(args, year: str) -> str: xsec_suffix += FOM_CACHE_TTH_SUFFIX if source_year in DIBOSON_EXACT_ERAS: xsec_suffix += FOM_CACHE_DIBOSON_SUFFIX + golden = hh_vars.GOLDEN_JSON.get(year) + golden_md5 = golden and hashlib.md5((GOLDEN_JSON_DIR / golden).read_bytes()).hexdigest() + skim_lumi = data_lumi.lumi_corrections(Path(args.data_dir) / args.tag, year) + lumi = json.dumps([golden_md5, skim_lumi, hh_vars.LUMI[year], hh_vars.LUMI[source_year]]) + xsec_suffix += FOM_CACHE_LUMI_PREFIX + hashlib.sha1(lumi.encode()).hexdigest()[:8] stitch = get_vjets_stitch(args) override_tag = getattr(args, "override_tag", None) if stitch is None and not override_tag: diff --git a/src/HH4b/processors/bbbbSkimmer.py b/src/HH4b/processors/bbbbSkimmer.py index 427f00c4..fe7d747f 100644 --- a/src/HH4b/processors/bbbbSkimmer.py +++ b/src/HH4b/processors/bbbbSkimmer.py @@ -1291,8 +1291,14 @@ def process(self, events: ak.Array): fname = events.behavior["__events_factory__"]._partition_key.replace("/", "_") + ".parquet" self.dump_table(dataframe, fname) + output = {"totals": totals_dict, "cutflow": cutflow} + if isData: + # entries read from each input file (check_data_files.py) + meta = events.metadata + output["processed_entries"] = {meta["filename"]: meta["entrystop"] - meta["entrystart"]} + print("Return ", f"{time.time() - start:.2f}") - return {year: {dataset: {"totals": totals_dict, "cutflow": cutflow}}} + return {year: {dataset: output}} def postprocess(self, accumulator): return accumulator diff --git a/src/HH4b/utils.py b/src/HH4b/utils.py index ee14141e..c4230b85 100644 --- a/src/HH4b/utils.py +++ b/src/HH4b/utils.py @@ -17,6 +17,7 @@ import warnings from copy import deepcopy from dataclasses import dataclass, field +from functools import lru_cache from os import listdir from pathlib import Path @@ -26,12 +27,15 @@ import pyarrow.parquet as pq import vector from coffea.analysis_tools import PackedSelection +from coffea.lumi_tools import LumiMask from coffea.processor.accumulator import accumulate from hist import Hist +from HH4b.data_lumi import GOLDEN_JSON_DIR, LS_SCALE, keys_of, lumi_corrections from HH4b.xsecs import xsecs from .hh_vars import ( + GOLDEN_JSON, LUMI, ZZ4B_MZ_WINDOW, ZZ4B_SAMPLE, @@ -244,6 +248,8 @@ def format_columns(columns: list): for njets in ["1J", "2J"] ) _W_XSEC_CHECK_RTOL = 1e-9 +# of the 1x / 2x check: admits a LUMI update since the skim (the 2x case is not rescaled for it) +_W_XSEC_LUMI_RTOL = 0.02 def _skim_norm_weight_columns(events: pd.DataFrame) -> list[str]: @@ -296,9 +302,9 @@ def _apply_w_xsec_correction(events: pd.DataFrame, year: str, sample: str) -> No ) norm = xsecs[sample] * LUMI[year] rel = norm / skim_norm - if abs(rel - 1.0) <= _W_XSEC_CHECK_RTOL: + if abs(rel - 1.0) <= _W_XSEC_LUMI_RTOL: return # skim made with the xsecs.py value - if abs(rel - 0.5) > 0.5 * _W_XSEC_CHECK_RTOL: + if abs(rel - 0.5) > 0.5 * _W_XSEC_LUMI_RTOL: raise ValueError( f"{sample} ({year}): skim xsec x lumi {skim_norm:.8g} is neither 1x nor 2x the " f"xsecs.py x LUMI {norm:.8g} (ratio {rel:.8g}); re-skim or check xsecs.py" @@ -922,6 +928,27 @@ def _replace_zz4b( ] +@lru_cache(maxsize=None) +def _golden_lumi_mask(year: str) -> LumiMask: + return LumiMask(GOLDEN_JSON_DIR / GOLDEN_JSON[year]) + + +def _apply_golden_json( + events: pd.DataFrame, year: str, sample: str, lost: np.ndarray | None = None +) -> pd.DataFrame: + """The data events in the golden JSON of ``year`` (``hh_vars.GOLDEN_JSON``) and not in the + lumisections ``lost`` by the skims (keys run x LS_SCALE + LS, ``HH4b.data_lumi``).""" + if year not in GOLDEN_JSON: + logger.warning(f"{sample}: no golden JSON for {year}, all data events kept") + return events + run, ls = _col1d(events, "run"), _col1d(events, "luminosityBlock") + keep = _golden_lumi_mask(year)(run, ls) + if lost is not None: + keep &= ~np.isin(run.astype(np.int64) * LS_SCALE + ls, lost) + logger.info(f"{sample}: {np.sum(~keep)} of {len(events)} events not golden or lost") + return events.loc[keep].reset_index(drop=True) + + def _mc_sharing_veto(events: pd.DataFrame, mc_year: str) -> pd.DataFrame: """Keep the events that pass the jet veto map of ``mc_year`` (2024 MC skims, see ``MC_SHARING_COLUMNS``).""" @@ -1053,6 +1080,13 @@ def load_samples( """ events_dict = {} + # data the skims of this tag lost (HH4b.data_lumi): their lumisections are dropped from the + # data, and the MC used for the year is scaled to the luminosity the data hold + lumi_year = mc_year or year + lumi = lumi_corrections(Path(data_dir), lumi_year) if lumi_year in GOLDEN_JSON else {} + lost = keys_of(lumi["lost_lumis"]) if "lost_lumis" in lumi else None + mc_lumi_scale = lumi["lumi_pb"] / LUMI[lumi_year] if "lumi_pb" in lumi else 1.0 + data_dir = Path(data_dir) / year full_samples_list = listdir(data_dir) # get all directories in data_dir sample_dirs = {} # samples read from an override_dir instead of data_dir @@ -1083,6 +1117,12 @@ def load_samples( load_columns = columns if label != "data" and load_weight_noxsec: load_columns = columns + format_columns([("weight_noxsec", 1)]) + # data: the run and lumisection for the golden JSON (dropped after it unless requested) + golden_columns = [] + if label == data_key and columns is not None: + run_ls = format_columns([("run", 1), ("luminosityBlock", 1)]) + golden_columns = [col for col in run_ls if col not in columns] + load_columns = columns + golden_columns # leave out skims of invalid open-ended productions (PTQQ_OPEN_FILTER_CHECK) label_samples = full_samples_list @@ -1224,6 +1264,9 @@ def load_samples( warnings.warn(f"No events for {sample}!", stacklevel=1) continue + if label == data_key: + events = _apply_golden_json(events, year, sample, lost) + if reorder_txbb: _reorder_txbb(events, txbb_str) @@ -1261,6 +1304,12 @@ def load_samples( events["weight_nonorm"] = events["weight"] events["finalWeight"] = events["weight"] / n_events + if label != data_key and mc_lumi_scale != 1.0: + for col in [*_skim_norm_weight_columns(events), "finalWeight", "weight_nonorm"]: + if col in events: + events[col] = events[col].to_numpy() * mc_lumi_scale + logger.info(f"{sample}: weights x {mc_lumi_scale:.6f} (data lost by the skims)") + if range_mode and sample in ranges: # keep only this sample's LHE V pT range, normalised to sigma x f (after # _normalize_weights, whose np_* totals the factors correct) @@ -1276,7 +1325,9 @@ def load_samples( loaded_names.append(sample) logger.info(f"Loaded {sample: <50}: {len(events)} entries") - stitch_only_columns = set() # parquet names read only for the stitch, dropped after it + # parquet names read only for the golden JSON, the stitch or the ZZto4B replacement, + # dropped after them + load_only_columns = set(golden_columns) if ptqq_stitch is not None and len(events_dict[label]): if not range_mode: # remove the overlap between the open-ended Bin-PTQQ V+jets samples (2024 MC) @@ -1284,26 +1335,25 @@ def load_samples( list(zip(loaded_names, events_dict[label])), txbb_str, ptqq_stitch ) if load_columns is not None: - stitch_only_columns = {col for col in stitch_columns if col not in load_columns} + load_only_columns |= {col for col in stitch_columns if col not in load_columns} if zz4b_on and len(events_dict[label]): # ZZ->bbbb in the ZZto4B window from ZZto4B-1Jets alone, before the concat events_dict[label] = _replace_zz4b( list(zip(loaded_names, events_dict[label])), zz4b_replace ) if load_columns is not None: - stitch_only_columns |= {col for col in zz4b_columns if col not in load_columns} + load_only_columns |= {col for col in zz4b_columns if col not in load_columns} if len(events_dict[label]): events_dict[label] = pd.concat(events_dict[label]) # Deduplicate columns that can arise when concatenating multiple sub-samples keep = ~events_dict[label].columns.duplicated() - if stitch_only_columns: + if load_only_columns: # match by the parquet name "('name', 'i')": pandas restores the second column # level from the parquet metadata (int64 in the skims), so tuples of str miss it keep &= ~np.array( [ - isinstance(col, tuple) - and f"('{col[0]}', '{col[1]}')" in stitch_only_columns + isinstance(col, tuple) and f"('{col[0]}', '{col[1]}')" in load_only_columns for col in events_dict[label].columns ] ) diff --git a/src/condor/check_data_files.py b/src/condor/check_data_files.py new file mode 100644 index 00000000..7bcc15af --- /dev/null +++ b/src/condor/check_data_files.py @@ -0,0 +1,71 @@ +""" +Checks that the data skims of a tag read every entry of their input files (``HH4b.data_files``): +lists per data sample the files that were lost (not opened, read incompletely, in no job's output) +or read twice, with their jobs, and resubmits those jobs with ``--submit-missing``. + +Run it from the directory the jobs were submitted from (job scripts in condor/skimmer//): + python src/condor/check_data_files.py --data-dir --tag --year + [--submit-missing] + +Author(s): Zichun Hao +""" + +from __future__ import annotations + +import argparse +import json +import re +import subprocess +from pathlib import Path + +from HH4b.data_files import file_problems, index_files, lfn, read_records +from HH4b.hh_vars import DATA_SAMPLES + + +def job_of_files(condor_dir: Path, year: str, sample: str, files: list[str]) -> dict[str, int]: + """{file: job}, from --starti/--endi of the job scripts.""" + jobs = {} + for script in condor_dir.glob(f"{year}_{sample}_*.sh"): + job = int(script.stem.rsplit("_", 1)[1]) + match = re.search(r"--starti (\d+) --endi (-?\d+)", script.read_text()) + starti, endi = int(match[1]), int(match[2]) + jobs |= dict.fromkeys(files[starti:] if endi < 0 else files[starti:endi], job) + return jobs + + +def main(): + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--data-dir", required=True, help="skimmer output directory") + parser.add_argument("--tag", required=True) + parser.add_argument("--year", required=True) + parser.add_argument("--nano-version", default="v15") + parser.add_argument("--submit-missing", action="store_true", help="resubmit the jobs") + args = parser.parse_args() + + year_dir = Path(args.data_dir) / args.tag / args.year + condor_dir = Path("condor") / "skimmer" / args.tag + index = json.loads(Path(f"data/nanoindex_{args.nano_version}.json").read_text()) + samples = sorted(p.name for p in year_dir.iterdir() if p.name.startswith(tuple(DATA_SAMPLES))) + jobs_to_rerun = set() + for sample in samples: + files = index_files(index, args.year, sample) + records = read_records(year_dir / sample, args.year, sample) + if records is None: + print(f"{sample}: no file records (skimmed before they were saved)") + continue + job_of = job_of_files(condor_dir, args.year, sample, files) + problems = file_problems(records, files) + print(f"{sample}: {len(files)} files, {len(problems)} with problems") + for fname, (problem, read_by) in sorted(problems.items()): + jobs = read_by or ([job_of[fname]] if fname in job_of else []) + print(f" jobs {jobs}: {lfn(fname)}: {problem}") + jobs_to_rerun.update((sample, job) for job in jobs) + + if args.submit_missing: + for sample, job in sorted(jobs_to_rerun): + jdl = condor_dir / f"{args.year}_{sample}_{job}.jdl" + subprocess.run(["condor_submit", str(jdl)], check=True) + + +if __name__ == "__main__": + main() diff --git a/src/condor/submit.templ.sh b/src/condor/submit.templ.sh index c1ff8a50..22a8cf76 100644 --- a/src/condor/submit.templ.sh +++ b/src/condor/submit.templ.sh @@ -60,7 +60,8 @@ pip install -r requirements.txt # run code # pip install --user onnxruntime -python -u -W ignore $script --year $year --starti $starti --endi $endi --samples $sample --subsamples $subsample --processor $processor --maxchunks $maxchunks --chunksize $chunksize ${save_systematics} --nano-version ${nano_version} --txbb ${txbb} ${region} ${save_root} +# a failed job copies no output and exits non-zero +python -u -W ignore $script --year $year --starti $starti --endi $endi --samples $sample --subsamples $subsample --processor $processor --maxchunks $maxchunks --chunksize $chunksize ${save_systematics} --nano-version ${nano_version} --txbb ${txbb} ${region} ${save_root} || exit 1 #move output to t2s for t2_prefix in ${t2_prefixes} diff --git a/src/run.py b/src/run.py index 3729e577..5dd52547 100644 --- a/src/run.py +++ b/src/run.py @@ -88,8 +88,35 @@ def run_dask(p: processor, fileset: dict, args): pickle.dump(out["pkl"], f) -def run(p: processor, fileset: dict, skipbadfiles: bool, args): - """Run processor without fancy dask (outputs then need to be accumulated manually)""" +def open_data_files(fileset: dict, attempts: int = 3, wait: int = 60) -> tuple[dict, dict, dict]: + """Open every file of a data fileset, up to ``attempts`` times ``wait`` s apart. Returns the + fileset without the files that never opened, {key: {file: entries}} of the others and + {key: {file: last error}} of the left-out ones. Authentication errors are raised.""" + good, entries, bad = {}, {}, {} + for key, files in fileset.items(): + for fname in files: + for attempt in range(attempts): + try: + with uproot.open(fname) as rfile: + entries.setdefault(key, {})[fname] = rfile["Events"].num_entries + good.setdefault(key, []).append(fname) + break + except Exception as e: + if "Auth failed" in str(e): + raise + if attempt == attempts - 1: + error = f"{type(e).__name__}: {str(e).strip().splitlines()[0]}" + bad.setdefault(key, {})[fname] = error + print(f"Leaving out {fname}: {error}") + else: + time.sleep(wait) + return good, entries, bad + + +def run(p: processor, fileset: dict, skipbadfiles: bool, args, data_files: tuple | None = None): + """Run processor without fancy dask (outputs then need to be accumulated manually). + ``data_files``: the entries and the left-out files of ``open_data_files``, saved in the + pickle next to the entries the processor read (check_data_files.py compares them).""" run_utils.add_mixins(nanoevents) # update nanoevents schema # outputs are saved here as pickles @@ -132,7 +159,8 @@ def run(p: processor, fileset: dict, skipbadfiles: bool, args): ) # try file opening 3 times if it fails - for i in range(3): + out = {} + for i in range(3 if fileset else 0): try: out, _metrics = run(fileset, "Events", processor_instance=p) break @@ -145,6 +173,11 @@ def run(p: processor, fileset: dict, skipbadfiles: bool, args): else: raise e + for label, per_key in zip(["file_entries", "bad_files"], data_files or ({}, {})): + for key, files in per_key.items(): + year, dataset = key.split("_", 1) + out.setdefault(year, {}).setdefault(dataset, {})[label] = files + print(out) with Path(f"{outdir}/{args.starti}-{args.endi}.pkl").open("wb") as f: @@ -199,6 +232,7 @@ def main(args): ) skipbadfiles = True + data_files = None if len(args.files): fileset = {f"{args.year}_{args.files_name}": args.files} @@ -230,16 +264,16 @@ def main(args): args.endi, ) - # don't skip "bad" files for data - we want it throw an error in that case - for key in fileset: - if key in DATA_SAMPLES: - skipbadfiles = False + # data: files that do not open after retries are left out and recorded in the pickle + if any(sample in DATA_SAMPLES for sample in samples): + fileset, file_entries, bad_files = open_data_files(fileset) + data_files = (file_entries, bad_files) print(f"Running on fileset {fileset}") if args.executor == "dask": run_dask(p, fileset, args) else: - run(p, fileset, skipbadfiles, args) + run(p, fileset, skipbadfiles, args, data_files) if __name__ == "__main__": From 89e842dae0cba4570a30b83f3d3dc59d5e6db65f Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 02:03:42 -0700 Subject: [PATCH 04/10] MET filters: era-dependent lists, 2022-2023 bad-crystal veto, 2025 ecalBadCalibFilter at load time bbbbSkimmer listed eeBadScFilter twice. The base list now has each Run 3 flag once, and Flag_ecalBadCalibFilter is added for 2024 only: in 2022-2023 NanoAOD it is still the Run 2 filter of 35 EE crystals and misses the bad EB crystal of those years, for which the JME recipe is a data-only jet veto instead. A listed flag that is missing from the input is now logged instead of skipped silently. ttSkimmer listed "hfNoisyHitsFilt", which the silent check dropped, so hfNoisyHitsFilter was never applied there. In 2022-2023 NanoAOD Flag_ecalBadCalibFilter is still the Run 2 filter of 35 EE crystals and misses the bad EB crystal of runs 362433-367144, so data get the JME jet-based veto: an event of those runs is rejected if PuppiMET pT > 100 GeV and a jet has pT > 50 GeV, -0.5 < eta < -0.1, -2.1 < phi < -1.8, a neutral or charged EM fraction > 0.9 and |dphi(jet, PuppiMET)| > 2.9. It removes a few ppm of the 2022G JetMET events. The skims save Flag_ecalBadCalibFilter for data, and utils.load_samples requires it for the 2025 data (MC_SHARING_DATA_FILTERS, TODO(mc-sharing)): their MC is the 2024 MC, whose MET filter list has it, while the 2025 recommendation omits it, so the skimmer keeps the per-year lists. Data skims without the column are loaded as before, with a warning. The flag passes 99.93% of the 2025C JetMET events. --- src/HH4b/processors/bbbbSkimmer.py | 33 ++++++++++++++++++++++++++++-- src/HH4b/processors/ttSkimmer.py | 2 +- src/HH4b/utils.py | 23 +++++++++++++++++++++ 3 files changed, 55 insertions(+), 3 deletions(-) diff --git a/src/HH4b/processors/bbbbSkimmer.py b/src/HH4b/processors/bbbbSkimmer.py index fe7d747f..920db01a 100644 --- a/src/HH4b/processors/bbbbSkimmer.py +++ b/src/HH4b/processors/bbbbSkimmer.py @@ -137,6 +137,24 @@ def lhevpt_sum(idx: np.ndarray, w: np.ndarray) -> np.ndarray: return np.bincount(idx, weights=w, minlength=len(LHEVPT_EDGES) + 1).astype(np.float64) +def ecal_bad_crystal_veto(events) -> np.ndarray: + """Data events hit by the bad EB crystal of runs 362433-367144 (JME recipe, data only): + PuppiMET pT > 100 GeV and a jet with pT > 50 GeV, -0.5 < eta < -0.1, -2.1 < phi < -1.8, a + neutral or charged EM fraction > 0.9 and |dphi(jet, PuppiMET)| > 2.9.""" + jets, met = events.Jet, events.PuppiMET + bad_jet = ( + (jets.pt > 50) + & (jets.eta > -0.5) + & (jets.eta < -0.1) + & (jets.phi > -2.1) + & (jets.phi < -1.8) + & ((jets.neEmEF > 0.9) | (jets.chEmEF > 0.9)) + & (np.abs(jets.delta_phi(met)) > 2.9) + ) + in_runs = (events.run >= 362433) & (events.run <= 367144) + return ak.to_numpy(in_runs & (met.pt > 100) & ak.any(bad_jet, axis=1)) + + class bbbbSkimmer(SkimmerABC): """ Skims nanoaod files, saving selected branches and events passing preselection cuts @@ -425,8 +443,11 @@ def __init__( "BadPFMuonDzFilter", "eeBadScFilter", "hfNoisyHitsFilter", - "eeBadScFilter", ] + # Flag_ecalBadCalibFilter is recommended for 2024. In 2022-2023 NanoAOD it is still the + # Run 2 filter of 35 EE crystals and misses the bad EB crystal of those years, which the + # twiki covers with a jet-based recipe (ecal_bad_crystal_veto). + self.year_met_filters = {"2024": ["ecalBadCalibFilter"]} """ signal region: @@ -944,6 +965,10 @@ def process(self, events: ak.Array): eventVars["ht"] = ht.to_numpy() eventVars["nJets"] = ak.sum(jets_sel, axis=1).to_numpy() eventVars["nFatJets"] = ak.num(fatjets).to_numpy() + # for postprocessing, which applies it to the data of a year whose MC has it + # (utils.MC_SHARING_DATA_FILTERS) + if isData and "ecalBadCalibFilter" in events.Flag.fields: + eventVars["Flag_ecalBadCalibFilter"] = events.Flag.ecalBadCalibFilter.to_numpy() if isData: pileupVars = {key: np.ones(len(events)) * PAD_VAL for key in self.skim_vars["Pileup"]} else: @@ -1127,9 +1152,13 @@ def process(self, events: ak.Array): # metfilters cut_metfilters = np.ones(len(events), dtype="bool") - for mf in self.met_filters: + for mf in self.met_filters + self.year_met_filters.get(year, []): if mf in events.Flag.fields: cut_metfilters = cut_metfilters & events.Flag[mf] + else: + logger.warning(f"Missing MET filter {mf}!") + if isData: + cut_metfilters = cut_metfilters & ~ecal_bad_crystal_veto(events) add_selection("met_filters", cut_metfilters, *selection_args) # jet veto maps diff --git a/src/HH4b/processors/ttSkimmer.py b/src/HH4b/processors/ttSkimmer.py index 6ed9f710..4820eda1 100644 --- a/src/HH4b/processors/ttSkimmer.py +++ b/src/HH4b/processors/ttSkimmer.py @@ -167,7 +167,7 @@ def __init__( "BadPFMuonFilter", "BadPFMuonDzFilter", "eeBadScFilter", - "hfNoisyHitsFilt", + "hfNoisyHitsFilter", ] self._accumulator = processor.dict_accumulator({}) diff --git a/src/HH4b/utils.py b/src/HH4b/utils.py index c4230b85..9cd34ac3 100644 --- a/src/HH4b/utils.py +++ b/src/HH4b/utils.py @@ -927,6 +927,11 @@ def _replace_zz4b( ("pileup_2025_over_2024", 3), ] +# TODO(mc-sharing): remove once 2025 has MC of its own. Its MC is the 2024 MC, so its data also pass +# the MET filter that only the 2024 list has (bbbbSkimmer.year_met_filters; the skims save it for +# the data). Data skims without the column are loaded as before. +MC_SHARING_DATA_FILTERS = {"2025": "Flag_ecalBadCalibFilter"} + @lru_cache(maxsize=None) def _golden_lumi_mask(year: str) -> LumiMask: @@ -949,6 +954,13 @@ def _apply_golden_json( return events.loc[keep].reset_index(drop=True) +def _mc_sharing_data_filter(events: pd.DataFrame, flag: str, sample: str) -> pd.DataFrame: + """The data events that pass ``flag`` (``MC_SHARING_DATA_FILTERS``).""" + keep = _col1d(events, flag).astype(bool) + logger.info(f"{sample}: {np.sum(~keep)} of {len(events)} events fail {flag}") + return events.loc[keep].reset_index(drop=True) + + def _mc_sharing_veto(events: pd.DataFrame, mc_year: str) -> pd.DataFrame: """Keep the events that pass the jet veto map of ``mc_year`` (2024 MC skims, see ``MC_SHARING_COLUMNS``).""" @@ -1211,6 +1223,15 @@ def load_samples( sample_columns = sample_columns + [ col for col in format_columns(MC_SHARING_COLUMNS) if col not in sample_columns ] + # TODO(mc-sharing): the MET filter of the shared MC's year for the data + data_filter = MC_SHARING_DATA_FILTERS.get(year) if label == data_key else None + if data_filter is not None and parquet_path.exists(): + first = next((f for f in sorted(parquet_path.glob("*.parquet")) if _parquet_has_rows(f)), None) # fmt: skip + if first is None or format_columns([(data_filter, 1)])[0] not in pq.read_schema(first).names: # fmt: skip + logger.warning(f"{sample} has no {data_filter}: loaded without it") + data_filter = None + elif sample_columns is not None: + sample_columns = sample_columns + format_columns([(data_filter, 1)]) if mc_year not in (None, year) and not mc_sharing and label != data_key: logger.warning( f"{sample} ({year} skim used for {mc_year}) has no {MC_SHARING_COLUMNS[0][0]}: " @@ -1266,6 +1287,8 @@ def load_samples( if label == data_key: events = _apply_golden_json(events, year, sample, lost) + if data_filter is not None: + events = _mc_sharing_data_filter(events, data_filter, sample) if reorder_txbb: _reorder_txbb(events, txbb_str) From 27e6c6aa05055409833e5265e8f3bf158046e7ae Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Mon, 26 Oct 2026 19:23:44 -0700 Subject: [PATCH 05/10] Corrections: 2022-2023 pileup at nTrueInt and AK4 jet ID from the JSON The 2022-2023 inputs are the PFNano 25v2, NanoAODv14 files skimmed through the v15 path, where their jets went through jetid_v12, the fix for the wrong NanoAODv12 jetId bits: beyond |eta| = 3 it kept 46-57% of the jets instead of the 89-93% the JetID criteria keep, so 2022-2023 had fewer VBF jets (SM VBF HH events with two VBF jets: 49.9% in 2022EE, 57.6% in 2024). Their Tight and TightLeptonVeto now come from the JME JetID JSON, corrections/data/jetid.json.gz (the file of the 2022-2025 JME folders of the CAT area), with its int inputs read as real, since correctionlib 2.2.2 cannot bin on int inputs; a TODO falls back to jetid_v15 once the 2022-2023 NanoAODv15 MCs are ready. 2024-2025 keep jetid_v15, which the JSON reproduces except at |eta| = 3.0 exactly. Below |eta| = 3 the 2022-2023 ID changes for 0.2% of the jets; a 2022EE SM VBF HH chunk now has two VBF jets in 56.5% of the events. The 2022-2023 pileup weights are a function of the true number of interactions but were evaluated at Pileup_nPU, kept there so that new skims of those years matched the existing ones, which the re-skim of every year no longer needs; they are now evaluated at Pileup_nTrueInt (2018 keeps nPU). The 2018, 2024 and 2025 weights, the JECs and the jet veto decisions are unchanged; in 50k TTto4Q events of each 2022-2023 era the mean pileup weight is now 1.000 within 0.2% (it was 0.955-1.058) and its RMS drops from 0.53-0.78 to 0.48-0.62. The sample normalization, divided by the sum of these weights, does not change. --- src/HH4b/corrections/data/jetid.json.gz | Bin 0 -> 942 bytes src/HH4b/processors/corrections.py | 57 ++++++++++++++++++++---- src/HH4b/processors/objects.py | 7 ++- 3 files changed, 53 insertions(+), 11 deletions(-) create mode 100644 src/HH4b/corrections/data/jetid.json.gz diff --git a/src/HH4b/corrections/data/jetid.json.gz b/src/HH4b/corrections/data/jetid.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..7c093b8fe667dbc30408e77de85324056ab139a3 GIT binary patch literal 942 zcmV;f15x}RiwFpT(Hv+3|4LwV{bArGBQI%MPGI@E^2dcZUF3> zT~FIE6o&tbm8(tbT3R+%kfv!WMX(JC0pkW$$RwwYx5SAYr+`rZ`<#^0PuirRwAEfN zBH}u)k3Wv}oTKw-MYc;mzv+^SL?EpLRx2^fp~zMu)cqFg2WfWVkkq0rY@W2Th7y{T z;H(`eb{@-Jc20`ypte)1H4dAstpcC9T8Gg=rK0b|Us1M6$+GSC-zv94R=R@jDvi^N ziW%VKu(})WUDDMzMcuE}x@F9;17$Rm@gif!^`j-ZPl-X_e||dqeRg(ob16Em{zah< zWidv?4`fceW)mUa$}3%M(^(5C+#6u5vio@@@Uw*%gv2&>rv|F=@6}6 zdN)aJY@Hg%+-p+E&Mdpg>GV~0$~z@iEAJ(bFD15{ynZg%T4S=;i*=uGhiB!F)kMqa zS4+(NJE544yIA)5f|!;~9{c+Rw1*ePZ1H*_yXR{cv3qbyyuQ`bx5mU6>0>|nTi*DW z2B`H<;`{-grq=d?yYw_G6{x+z(%`eP-9<=XqO?^E%G+pw|j_d9_#6 z+zAxlluv~=e8EkyoTKLOrrsDUj8pb9Yz?9YQG=*K)FA5B5%nsm_djOS&1201Jh&9K zfxTs$ud{_A+b@q6Sfes6o^qYGjje_sMpwldX4!t$#v8uBVIwgf_Me-3bM$^BBLf5HOXMy&1c+J?;ijF Q0RR630H-o3qt#ge0QEE2lmGw# literal 0 HcmV?d00001 diff --git a/src/HH4b/processors/corrections.py b/src/HH4b/processors/corrections.py index 45ce6575..6c56c951 100644 --- a/src/HH4b/processors/corrections.py +++ b/src/HH4b/processors/corrections.py @@ -12,8 +12,10 @@ from __future__ import annotations import gzip +import json import pathlib import pickle +from functools import lru_cache import awkward as ak import cachetools @@ -118,6 +120,46 @@ }, } + +@lru_cache(maxsize=None) +def _jetid_correction_set(path: str) -> correctionlib.CorrectionSet: + """A JME JetID JSON with its int inputs declared real: correctionlib 2.2.2 (requirements.txt, + for the Python 3.8 job image) cannot bin on int inputs; the IDs are identical to those of + correctionlib 2.9 on the unchanged file.""" + with gzip.open(path) as f: + data = json.load(f) + for correction in data["corrections"]: + for inp in correction["inputs"]: + if inp["type"] == "int": + inp["type"] = "real" + return correctionlib.CorrectionSet.from_string(json.dumps(data)) + + +def jetid_json(jets: JetArray) -> tuple[ak.Array, ak.Array]: + """AK4 PUPPI Tight and TightLeptonVeto IDs of the JME JetID JSON, i.e. + https://twiki.cern.ch/twiki/bin/view/CMS/JetID13p6TeV as the file cites: data/jetid.json.gz, + copied from JME/Run3-22CDSep23-Summer22-NanoAODv12/2026-06-05 of the CAT area; the JME folders + of all Run 3 years in cat_corrections have the same AK4 PUPPI criteria.""" + cset = _jetid_correction_set(f"{package_path}/corrections/data/jetid.json.gz") + flat = ak.flatten(jets) + names = [ + "eta", + "chHEF", + "neHEF", + "chEmEF", + "neEmEF", + "muEF", + "chMultiplicity", + "neMultiplicity", + ] + inputs = [np.asarray(flat[name], dtype=np.float64) for name in names] + inputs.append(inputs[-2] + inputs[-1]) # multiplicity: charged + neutral + return tuple( + ak.unflatten(cset[f"AK4PUPPI_{wp}"].evaluate(*inputs) > 0.5, ak.num(jets)) + for wp in ["Tight", "TightLeptonVeto"] + ) + + # input names of CorrectedMETFactory -> MET and jet fields (as corrections/archive/build_jec.py) met_name_map = { "JetPt": "pt", @@ -159,7 +201,9 @@ def get_pog_json(obj: str, year: str) -> str: def get_pileup_weights(year: str, n_int: np.ndarray) -> dict[str, np.ndarray]: """Official pileup weights (nominal, up, down) of ``year`` at ``n_int``, clipped to [0, 10] to - avoid large weights. https://twiki.cern.ch/twiki/bin/view/CMS/LumiRecommendationsRun3""" + avoid large weights. The weights are a function of the true number of interactions + (Pileup_nTrueInt); 2018 passes nPU, as before. + https://twiki.cern.ch/twiki/bin/view/CMS/LumiRecommendationsRun3""" cset = correctionlib.CorrectionSet.from_file(get_pog_json("pileup", year)) corr = { "2018": "Collisions18_UltraLegacy_goldenJSON", @@ -206,14 +250,9 @@ def add_pileup_weight( else: # official weights, a function of the true number of interactions (Pileup_nTrueInt) - # NOTE: 2018 and 2022-2023 evaluate them at nPU, kept as is so that new skims of those - # years match the existing ones - if year in ["2024", "2025"]: - if n_true_int is None: - raise ValueError(f"The {year} pileup weights need Pileup_nTrueInt") - values = get_pileup_weights(year, n_true_int) - else: - values = get_pileup_weights(year, nPU) + if year != "2018" and n_true_int is None: + raise ValueError(f"The {year} pileup weights need Pileup_nTrueInt") + values = get_pileup_weights(year, nPU if year == "2018" else n_true_int) weights.add("pileup", values["nominal"], values["up"], values["down"]) diff --git a/src/HH4b/processors/objects.py b/src/HH4b/processors/objects.py index 6e9aef42..5a876800 100644 --- a/src/HH4b/processors/objects.py +++ b/src/HH4b/processors/objects.py @@ -10,6 +10,8 @@ TauArray, ) +from .corrections import jetid_json + # https://twiki.cern.ch/twiki/bin/view/CMS/MuonRun32022 @@ -194,8 +196,9 @@ def good_ak4jets(jets: JetArray, year: str, nano_version: str): jetidtightlepveto = jets.isTightLeptonVeto elif nano_version.startswith("v15"): if year in ["2022", "2022EE", "2023", "2023BPix"]: - # still using v12 MCs (2022, 2022EE, 2023, 2023BPix) - jetidtight, jetidtightlepveto = jetid_v12(jets) + # the 2022-2023 PFNano are NanoAODv14: the JME JetID JSON + # TODO: fall back to jetid_v15 when the 2022-2023 NanoAODv15 MCs are ready + jetidtight, jetidtightlepveto = jetid_json(jets) else: jetidtight, jetidtightlepveto = jetid_v15(jets) From 747b6e1cc60b6b3da66bc30434a6be8246a3e1c7 Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sat, 24 Oct 2026 18:27:34 -0700 Subject: [PATCH 06/10] Skimmer: VBF jets are the two highest-pT candidates objects.vbf_jets returned the first two jets passing its selection, and the jets keep the NanoAOD order, the pT order with the production's JECs; with the re-applied JECs and JER the second VBF jet had the higher pT in 2-3.5% of the 2024 events. It now takes the two candidates with the highest corrected pT; the jet collections and the other selections are unchanged. In a 2024 TTto4Q chunk no VBF pair is out of order. --- src/HH4b/processors/objects.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/HH4b/processors/objects.py b/src/HH4b/processors/objects.py index 5a876800..2a7a3532 100644 --- a/src/HH4b/processors/objects.py +++ b/src/HH4b/processors/objects.py @@ -507,7 +507,9 @@ def vbf_jets( & ak.all(jets.metric_table(muons) > dr_leptons, axis=2) ) - return jets[ak4_sel][:, :2] + # the jets keep the NanoAOD order, the pT order with the production's JECs + vbf = jets[ak4_sel] + return vbf[ak.argsort(vbf.pt, ascending=False)][:, :2] def ak4_jets_awayfromak8( From 49d2f0e48be841b64a1a2ff7938928deb91a5eb2 Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 02:47:28 -0700 Subject: [PATCH 07/10] Postprocessing: per-event trigger-SF uncertainty, jets outside the efficiency maps at the edge bins MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The trigger-SF uncertainty multiplied the absolute errors of the (pT, mSD) and TXbb efficiencies of each jet instead of adding the relative errors in quadrature, and the templates then took a flat per-sample factor of 1 ± 1e-5-1e-3 instead of the per-event uncertainty that trigger_SF already computed. Jets outside the maps (in 2022-2023 mostly second jets with mSD below 50 GeV, in 53% of the ggF HH events) got efficiency and error 1.0 from the map flow, i.e. SF = 1; they now take the edge bins. weight_triggerUp/Down use the per-event uncertainty, with the OR of the two jets written without dividing by 1 - efficiency. On the ggF HH skims the uncertainty is now 1.5% (2022EE), 3.7% (2023BPix) and 1.9% (2024) per event, and the nominal SF moves by -0.13%, -0.43% and 0; the FOM cache key gets "__trigedge". The unit tests of calculate_trigger_weights expect the per-event uncertainty. --- src/HH4b/postprocessing/PostProcess.py | 16 +++++++++--- src/HH4b/postprocessing/corrections.py | 36 +++++++++++++++++--------- tests/test_postprocess_weights.py | 15 +++++------ 3 files changed, 42 insertions(+), 25 deletions(-) diff --git a/src/HH4b/postprocessing/PostProcess.py b/src/HH4b/postprocessing/PostProcess.py index 4709269d..04e0a7e8 100644 --- a/src/HH4b/postprocessing/PostProcess.py +++ b/src/HH4b/postprocessing/PostProcess.py @@ -83,6 +83,9 @@ # (TTBAR_BDTSF_COL), which the --fom-cache path re-evaluates with the scanned model FOM_CACHE_TTSF_SUFFIX = "__ttbdtsf" TTBAR_BDTSF_COL = "ttbar_bdtsf_nom" +# ... and of every year: its MC weights carry the trigger SF with the jets outside the efficiency +# maps at the edge bins (corrections.trigger_SF), not at SF = 1 +FOM_CACHE_TRIG_SUFFIX = "__trigedge" plt.style.use(hep.style.CMS) hep.style.use("CMS") @@ -496,11 +499,12 @@ def calculate_trigger_weights( trigger_weight_up = np.ones(n_events) trigger_weight_dn = np.ones(n_events) if key != "data": - trigger_weight, _, total, total_err = corrections.trigger_SF( + # per-event SF and its statistical uncertainty (both jets, data and MC efficiencies) + trigger_weight, trigger_unc, _, _ = corrections.trigger_SF( year, events_dict, txbb_strings[txbb], trigger_region ) - trigger_weight_up = trigger_weight * (1 + total_err / total) - trigger_weight_dn = trigger_weight * (1 - total_err / total) + trigger_weight_up = trigger_weight + trigger_unc + trigger_weight_dn = np.maximum(trigger_weight - trigger_unc, 0) return trigger_weight, trigger_weight_up, trigger_weight_dn @@ -710,11 +714,15 @@ def fom_cache_tag(args, year: str) -> str: with the scanned model. Caches without it (ttbar weights with the SF of the model that built them, key without the suffix) are never read again. + Plus "__trigedge" just before "__ttbdtsf" in every key (since 2026-10-24): the MC weights carry + the trigger SF with the jets outside the efficiency maps at the edge bins + (``corrections.trigger_SF``), where they had SF = 1 before. + Plus "__zz4b-" (before any "__jmsr...") for every year whose MC source year has a ZZto4B-1Jets directory in --zz4b-tag (``zz4b_suffix``): its zz frame holds ZZto4B in place of the inclusive ZZ -> bbbb events. The other years keep their keys. """ - tail = FOM_CACHE_TTSF_SUFFIX + tail = FOM_CACHE_TRIG_SUFFIX + FOM_CACHE_TTSF_SUFFIX if glopartv3_jmsr_on(args): vals = json.dumps(hh_vars.jmsr_values[mreg_strings[args.txbb]], sort_keys=True) tail = "__jmsr" + hashlib.sha1(vals.encode()).hexdigest()[:8] + tail diff --git a/src/HH4b/postprocessing/corrections.py b/src/HH4b/postprocessing/corrections.py index 6829a8a6..15d6c826 100644 --- a/src/HH4b/postprocessing/corrections.py +++ b/src/HH4b/postprocessing/corrections.py @@ -272,11 +272,15 @@ def trigger_SF(year: str, events_dict: dict[str, pd.DataFrame], txbb_str: str, r totals = [] total_errs = [] + def clamp(x, edges): + """Jets outside a map take its edge bin (the maps give 1.0 there, efficiency and error).""" + return np.clip(x, edges[0], np.nextafter(edges[-1], -np.inf)) + # iterate over jets for jet in range(2): - pt = events_dict["bbFatJetPt"][jet] - msd = events_dict["bbFatJetMsd"][jet] - xbb = events_dict[txbb][jet] + pt = clamp(events_dict["bbFatJetPt"][jet], pt_range) + msd = clamp(events_dict["bbFatJetMsd"][jet], msd_range) + xbb = clamp(events_dict[txbb][jet], xbb_range) num_ev = pt.shape[0] @@ -287,6 +291,7 @@ def trigger_SF(year: str, events_dict: dict[str, pd.DataFrame], txbb_str: str, r eff_data_per_jet[jet] = {} eff_mc_per_jet[jet] = {} + vals = {} for var in ["nominal", "stat_up"]: eff_data_val = np.zeros(num_ev) eff_data_btag_val = np.zeros(num_ev) @@ -310,8 +315,18 @@ def trigger_SF(year: str, events_dict: dict[str, pd.DataFrame], txbb_str: str, r eff_mc_val[matched == 1] = eff_mc_all[matched == 1] eff_mc_btag_val[matched == 1] = eff_mc_btag_all[matched == 1] - eff_data_per_jet[jet][var] = eff_data_val * eff_data_btag_val - eff_mc_per_jet[jet][var] = eff_mc_val * eff_mc_btag_val + vals[var] = (eff_data_val, eff_data_btag_val, eff_mc_val, eff_mc_btag_val) + + # efficiency = (pT, mSD) efficiency x TXbb efficiency; stat_up is the absolute error of each + # map, so the errors add in quadrature relative to the efficiencies + (n_data, n_data_btag, n_mc, n_mc_btag), (s_data, s_data_btag, s_mc, s_mc_btag) = ( + vals["nominal"], + vals["stat_up"], + ) + eff_data_per_jet[jet]["nominal"] = n_data * n_data_btag + eff_data_per_jet[jet]["stat_up"] = np.hypot(s_data * n_data_btag, n_data * s_data_btag) + eff_mc_per_jet[jet]["nominal"] = n_mc * n_mc_btag + eff_mc_per_jet[jet]["stat_up"] = np.hypot(s_mc * n_mc_btag, n_mc * s_mc_btag) sf_per_jet = eff_data_per_jet[jet]["nominal"] / eff_mc_per_jet[jet]["nominal"] sf_err_per_jet = sf_per_jet * np.sqrt( @@ -347,23 +362,20 @@ def trigger_SF(year: str, events_dict: dict[str, pd.DataFrame], txbb_str: str, r # unc on eff: (1 - z): dz # z = x * y = (1-eff_1)(1-eff_2) - # dz = z * sqrt( (dx/x)**2 + (dy/y)**2 ) + # dz = sqrt( (y dx)**2 + (x dy)**2 ), i.e. z * sqrt( (dx/x)**2 + (dy/y)**2 ) without dividing by + # x or y, which are 0 for a jet at efficiency 1 for var in ["up"]: dx_data = eff_data_per_jet[0][f"stat_{var}"] dy_data = eff_data_per_jet[1][f"stat_{var}"] x_data = 1 - eff_data_per_jet[0]["nominal"] y_data = 1 - eff_data_per_jet[1]["nominal"] - z = x_data * y_data - dz = z * np.sqrt((dx_data / x_data) ** 2 + (dy_data / y_data) ** 2) - unc_eff_data = dz + unc_eff_data = np.hypot(y_data * dx_data, x_data * dy_data) dx_mc = eff_mc_per_jet[0][f"stat_{var}"] dy_mc = eff_mc_per_jet[1][f"stat_{var}"] x_mc = 1 - eff_mc_per_jet[0]["nominal"] y_mc = 1 - eff_mc_per_jet[1]["nominal"] - z = x_mc * y_mc - dz = z * np.sqrt((dx_mc / x_mc) ** 2 + (dy_mc / y_mc) ** 2) - unc_eff_mc = dz + unc_eff_mc = np.hypot(y_mc * dx_mc, x_mc * dy_mc) unc_sf = sf * np.sqrt((unc_eff_data / tot_eff_data) ** 2 + (unc_eff_mc / tot_eff_mc) ** 2) diff --git a/tests/test_postprocess_weights.py b/tests/test_postprocess_weights.py index b90670d8..5c2d60d6 100644 --- a/tests/test_postprocess_weights.py +++ b/tests/test_postprocess_weights.py @@ -40,12 +40,11 @@ def test_data_returns_ones(self): @patch("HH4b.postprocessing.PostProcess.corrections.trigger_SF") def test_mc_applies_sf(self, mock_trigger_sf): - """MC samples should call corrections.trigger_SF and propagate the uncertainty.""" + """MC samples should call corrections.trigger_SF and propagate its per-event uncertainty.""" n = 3 sf = np.array([1.1, 0.9, 1.0]) - total = 100.0 - total_err = 5.0 - mock_trigger_sf.return_value = (sf, None, total, total_err) + unc = np.array([0.05, 0.02, 1.5]) + mock_trigger_sf.return_value = (sf, unc, 100.0, 5.0) w, w_up, w_dn = calculate_trigger_weights( events_dict=MagicMock(), @@ -57,10 +56,8 @@ def test_mc_applies_sf(self, mock_trigger_sf): ) np.testing.assert_array_equal(w, sf) - expected_up = sf * (1 + total_err / total) - expected_dn = sf * (1 - total_err / total) - np.testing.assert_allclose(w_up, expected_up) - np.testing.assert_allclose(w_dn, expected_dn) + np.testing.assert_allclose(w_up, sf + unc) + np.testing.assert_allclose(w_dn, np.maximum(sf - unc, 0)) mock_trigger_sf.assert_called_once() @patch("HH4b.postprocessing.PostProcess.corrections.trigger_SF") @@ -68,7 +65,7 @@ def test_signal_also_applies_sf(self, mock_trigger_sf): """Signal samples (key != 'data') should also get trigger SF.""" n = 2 sf = np.array([1.05, 0.95]) - mock_trigger_sf.return_value = (sf, None, 50.0, 2.0) + mock_trigger_sf.return_value = (sf, np.array([0.01, 0.02]), 50.0, 2.0) w, _, _ = calculate_trigger_weights( events_dict=MagicMock(), From a29fbc7a5a4465b87b48e52d58d86e653fae2e76 Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 00:54:31 -0700 Subject: [PATCH 08/10] Condor: job image option, zichun/hh4b-docker by default The jobs asked for coffeateam/coffea-dask:latest-py3.8, which is no longer on /cvmfs/unpacked.cern.ch, so every job failed at the container. submit.py --image now chooses the image of the jobs: - zichun/hh4b-docker (default): gitlab-registry.cern.ch/zichun/hh4b-docker, the coffea-base-almalinux8:0.7.22-py3.8 image with a Python 3.11 environment that holds the pinned requirements. The job runs HH4b from the clone with PYTHONPATH=src, without pip install. - coffeateam/coffea-base-almalinux8: coffea-base-almalinux8:0.7.22-py3.8 (Python 3.8 and coffea); the job pip installs HH4b and requirements.txt, as before. requirements.txt keeps its pins, which the pip install on Python 3.8 needs, with a note on the versions Python 3.11 needs, which the image has: correctionlib 2.9.0 (2.2.2 has no Python 3.11 wheel) and numba 0.60.0 (newer numba breaks the numba code of awkward 1.10). pytest ignores the DeprecationWarnings of sre_parse and sre_constants, which the lark parser of awkward 1.10 imports on Python 3.11. One-chunk skims of 2024 TTto4Q and 2025C JetMET in the hh4b-docker environment equal those of the current environment, up to 6e-17 in two MET phi columns; a test of 22 one-file jobs (TTto4Q and one JetMET era per year, 2022-2025) ran with the coffea-base image. --- pyproject.toml | 3 +++ requirements.txt | 2 ++ src/condor/submit.py | 33 ++++++++++++++++++++++++++++++++- src/condor/submit.templ.jdl | 2 +- src/condor/submit.templ.sh | 11 +++++++++-- 5 files changed, 47 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c8e01d69..7d7b389b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,6 +89,9 @@ filterwarnings = [ "ignore:pkg_resources is deprecated as an API:DeprecationWarning", # hepdata_validator triggers jsonschema's deprecation warnings on import "ignore:jsonschema.* is deprecated:DeprecationWarning", + # Python 3.11: the lark parser of awkward 1.10 imports sre_parse and sre_constants + "ignore:module 'sre_parse' is deprecated:DeprecationWarning", + "ignore:module 'sre_constants' is deprecated:DeprecationWarning", ] log_cli_level = "INFO" testpaths = [ diff --git a/requirements.txt b/requirements.txt index 5b26b3ad..87961c61 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,8 @@ cachetools==5.3.1 cloudpickle>=3.0.0 coffea==0.7.21 colorama==0.4.6 +# Python 3.11 (the zichun/hh4b-docker job image) needs correctionlib==2.9.0, since 2.2.2 has no +# Python 3.11 wheel, and numba==0.60.0, since newer numba breaks the numba code of awkward 1.10 correctionlib==2.2.2 hist==2.7.1 lz4 diff --git a/src/condor/submit.py b/src/condor/submit.py index 38db6d66..1fd08b1c 100644 --- a/src/condor/submit.py +++ b/src/condor/submit.py @@ -20,6 +20,21 @@ "ucsd": "root://redir.t2.ucsd.edu:1095//", } +# job images: (CVMFS path, whether the job pip installs HH4b and requirements.txt) +job_images = { + # coffea-base-almalinux8 with a Python 3.11 environment that holds the pinned requirements + # (https://gitlab.cern.ch/zichun/hh4b-docker) + "zichun/hh4b-docker": ( + "/cvmfs/unpacked.cern.ch/gitlab-registry.cern.ch/zichun/hh4b-docker:latest", + False, + ), + # Python 3.8 and coffea, without the pinned requirements + "coffeateam/coffea-base-almalinux8": ( + "/cvmfs/unpacked.cern.ch/registry.hub.docker.com/coffeateam/coffea-base-almalinux8:0.7.22-py3.8", + True, + ), +} + def write_template(templ_file: str, out_file: str, templ_args: dict): """Write to ``out_file`` based on template from ``templ_file`` using ``templ_args``""" @@ -85,6 +100,7 @@ def main(args): jdl_templ = "src/condor/submit.templ.jdl" sh_templ = "src/condor/submit.templ.sh" + image, pip_install = job_images[args.image] # submit jobs nsubmit = 0 @@ -102,7 +118,13 @@ def main(args): prefix = f"{args.year}_{subsample}" localcondor = f"{local_dir}/{prefix}_{j}.jdl" - jdl_args = {"dir": local_dir, "prefix": prefix, "jobid": j, "proxy": proxy} + jdl_args = { + "dir": local_dir, + "prefix": prefix, + "jobid": j, + "proxy": proxy, + "image": image, + } write_template(jdl_templ, localcondor, jdl_args) localsh = f"{local_dir}/{prefix}_{j}.sh" @@ -128,6 +150,7 @@ def main(args): "--save-systematics" if args.save_systematics else "--no-save-systematics" ), "region": f"--region {args.region}" if "skimmer" in args.processor else "", + "pip_install": "true" if pip_install else "false", } write_template(sh_templ, localsh, sh_args) os.system(f"chmod u+x {localsh}") @@ -175,6 +198,14 @@ def parse_args(parser): run_utils.add_bool_arg( parser, "submit", default=False, help="submit files as well as create them" ) + parser.add_argument( + "--image", + default="zichun/hh4b-docker", + help="job image; with coffeateam/coffea-base-almalinux8 the jobs pip install HH4b and " + "requirements.txt", + type=str, + choices=list(job_images), + ) parser.add_argument("--git-branch", required=True, help="git branch to use", type=str) parser.add_argument("--git-user", default="LPC-HH", help="which user's repo to use", type=str) run_utils.add_bool_arg( diff --git a/src/condor/submit.templ.jdl b/src/condor/submit.templ.jdl index 7b4d0305..cfc3f77b 100644 --- a/src/condor/submit.templ.jdl +++ b/src/condor/submit.templ.jdl @@ -11,6 +11,6 @@ output = $dir/logs/${prefix}_$jobid.out error = $dir/logs/${prefix}_$jobid.err log = $dir/logs/${prefix}_$jobid.log -+SingularityImage = "/cvmfs/unpacked.cern.ch/registry.hub.docker.com/coffeateam/coffea-dask:latest-py3.8" ++SingularityImage = "$image" Queue 1 diff --git a/src/condor/submit.templ.sh b/src/condor/submit.templ.sh index 22a8cf76..1c809153 100644 --- a/src/condor/submit.templ.sh +++ b/src/condor/submit.templ.sh @@ -55,8 +55,15 @@ do xrdcp -f commithash.txt $${t2_prefix}/$${modified_outdir}/githashes/commithash_${jobnum}.txt done -pip install -e . -pip install -r requirements.txt +# zichun/hh4b-docker holds the requirements, so HH4b runs from the clone; with an image without +# them (submit.py --image coffeateam/coffea-base-almalinux8), the job installs them +pip_install=${pip_install} +if [[ "$$pip_install" == "true" ]]; then + pip install -e . + pip install -r requirements.txt +else + export PYTHONPATH=$$PWD/src +fi # run code # pip install --user onnxruntime From 0806e410aa89fa3d4212356444e388048b91a771 Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Sun, 25 Oct 2026 02:51:36 -0700 Subject: [PATCH 09/10] GloParT-v3 JMR placeholder: one-sided, up 1.05 The GloParT-v3 JMR placeholder goes from up 1.15 to up 1.05, down 1 (no extra smearing). JMR can only widen the mass, so CreateDatacard --jmr-symmetrize (on by default) ignores the JMR-down templates of the GloParT-v3 placeholder and gives combine the up variation mirrored about the nominal, Down = max(2 x Nominal - Up, 0) per bin, instead of a one-sided morph; rhalphalib's own symmetrization is wrong for shape effects. The measured GloParT-v2 and PNet JMR keep their down templates. --- src/HH4b/hh_vars.py | 16 ++++---- src/HH4b/postprocessing/CreateDatacard.py | 49 +++++++++++++++++++++++ 2 files changed, 58 insertions(+), 7 deletions(-) diff --git a/src/HH4b/hh_vars.py b/src/HH4b/hh_vars.py index 83436ee4..4e05c127 100644 --- a/src/HH4b/hh_vars.py +++ b/src/HH4b/hh_vars.py @@ -609,15 +609,17 @@ "2025": {"nom": 1.0, "down": 1.0, "up": 1.0}, # TODO: placeholder for future } # GloParT-v3 regressed mass (v15 skims). There is no GloParT-v3 jet-mass measurement, so these are -# PLACEHOLDERS with safe uncertainties chosen by the user (2026-09-26), the same in every era: JMS -# nominal 1 (no correction), down/up 0.95/1.05; JMR nominal 1 (no extra smearing), down 1 (one-sided), -# up 1.15. JMR is a RESOLUTION scaling (sigma_new = JMR * sigma_MC): postprocessing.jmsr_correct_mass -# adds a Gaussian smear of width sqrt(JMR^2 - 1) * jmsr_res (below), so the nominal and JMR-down masses -# are the uncorrected MC mass. The v15 skimmer applies JMS/JMR only to ParTmassVis, so PostProcess -# applies these at load time (--no-glopartv3-jmsr = off). +# PLACEHOLDERS with safe uncertainties, the same in every era: JMS nominal 1 (no correction), +# down/up 0.95/1.05; JMR nominal 1 (no extra smearing), up 1.05 (was 1.15), down 1. JMR is a +# RESOLUTION scaling (sigma_new = JMR * sigma_MC): postprocessing.jmsr_correct_mass adds a Gaussian +# smear of width sqrt(JMR^2 - 1) * jmsr_res (below), so the nominal and JMR-down masses are the +# uncorrected MC mass. JMR is one-sided: CreateDatacard does not use the JMR-down templates and +# gives combine the up template mirrored about the nominal (Down = 2 x Nominal - Up, +# --jmr-symmetrize). The v15 skimmer applies JMS/JMR only to ParTmassVis, so PostProcess applies +# these at load time (--no-glopartv3-jmsr = off). jmsr_values["bbFatJetParT3massX2p"] = { "JMR": { - year: {"nom": 1.0, "down": 1.0, "up": 1.15} + year: {"nom": 1.0, "down": 1.0, "up": 1.05} for year in ["2022", "2022EE", "2023", "2023BPix", "2024", "2025"] }, "JMS": { diff --git a/src/HH4b/postprocessing/CreateDatacard.py b/src/HH4b/postprocessing/CreateDatacard.py index 0c14fec7..297e55ac 100644 --- a/src/HH4b/postprocessing/CreateDatacard.py +++ b/src/HH4b/postprocessing/CreateDatacard.py @@ -163,6 +163,13 @@ "lumi-scaled and averaged; separate 2024 and 2025 nuisances kept)", default=True, ) +add_bool_arg( + parser, + "jmr-symmetrize", + "treat the glopart-v3 JMR as one-sided: ignore the JMR-down templates and give combine the " + "JMR-up variation mirrored about the nominal, Down = max(2 x Nominal - Up, 0) per bin", + default=True, +) args = parser.parse_args() @@ -526,6 +533,22 @@ def templates_have_split_shared_mc(templates_dir: str) -> bool: "split halves, lumi-scaled and averaged); separate 2024/2025 nuisances kept" ) +# One-sided JMR (--jmr-symmetrize; meeting of 2026-10-06). The GloParT-v3 JMR placeholder can only +# widen the mass, so its JMR-down templates equal the nominal, and a one-sided morph makes combine's +# interpolation non-monotonic for theta < 0. Combine gets instead the JMR-up variation mirrored +# about the nominal (`mirror_up`), per bin with N the nominal yield and r = Up / N: +# +# Down = N x (2 - r) = 2 N - Up, floored at 0 +# +# rhalphalib's own symmetrization (setParamEffect with effect_down=None) is wrong for shape +# nuisances: it builds Down = N x (N - |N - r|), mixing a yield with a ratio (Down = Up above ~1 +# event, negative below); even the relative form N x (1 - |r - 1|) gives Down = Up where r < 1, i.e. +# in the peak bins of a widening. The measured glopart-v2 and PNet JMR keep their down templates. +symmetrized_shape_systs = set() +if args.jmr_symmetrize and args.jmsr and templates_have_glopartv3_jmsr(args.templates_dir): + symmetrized_shape_systs.add("JMR") + logging.info("jmr-symmetrize: JMR-down templates ignored, Down = max(2 x Nominal - Up, 0)") + # Empty-bin floor (fix of 2026-09-29, default on): see `floor_empty_bins` in fill_regions. if args.shape_empty_bin_floor > 0: logging.info( @@ -849,6 +872,25 @@ def fill_regions( (f"{skey}_{uncorr_label}", skey, syst, values_up, values_down) ) + # one-sided systematics (--jmr-symmetrize): the Down templates are replaced by the Up + # variation mirrored about the all-era nominal + if symmetrized_shape_systs: + nominal_all = np.maximum(sample_template.values(), 0.0) + variations = [ + ( + sdkey, + skey, + syst, + values_up, + ( + mirror_up(nominal_all, values_up) + if skey in symmetrized_shape_systs + else values_down + ), + ) + for sdkey, skey, syst, values_up, values_down in variations + ] + # nominal values, errors values_nominal = np.maximum(sample_template.values(), 0.0) @@ -963,6 +1005,13 @@ def fill_regions( return floor_stats +def mirror_up(values_nominal: np.ndarray, values_up: np.ndarray) -> np.ndarray: + """Down yields of a one-sided shape systematic: the Up yields mirrored about the nominal, + 2 x Nominal - Up per bin, floored at 0 (combine needs a Down template for every shape + nuisance). Where the nominal is 0 the Down yield is 0, as for any shift into an empty bin.""" + return np.maximum(2.0 * values_nominal - values_up, 0.0) + + def floor_empty_bins( values_nominal: np.ndarray, variances_nominal: np.ndarray, From 6ad84f7d86dc8e54f0ef6f39f5bcf930c04b8093 Mon Sep 17 00:00:00 2001 From: Zichun Hao Date: Mon, 26 Oct 2026 23:33:48 -0700 Subject: [PATCH 10/10] Skimmers: softdrop mass from the subjets re-corrected with the AK4 JEC NanoAOD msoftdrop sums the subjets with the JEC of the MiniAOD production, while the jets carry the re-applied JECs. In 2024 that subjet JEC (Winter24 in MC, a pre-Summer24 payload in the 2024C data) puts msoftdrop +0.6% (MC) and -1.6% (data) away from Summer24Prompt24_V5, and in the 2023 PFNano data msoftdrop is 7% below the mass of its own subjets. As in PocketCoffea's msoftdrop_correction, each raw subjet is now corrected with the AK4 PUPPI JEC of the AK4 jets (L1L2L3 in MC, with the residuals in data), without JER, and the softdrop mass is the mass of their sum; jets without subjets or with msoftdrop <= 0, and NaN masses, keep NanoAOD's value, which is saved as MsdNano. The cuts and the JMS/JMR variations use the new mass. On the 2024 ggHH file of the #367 ntuple comparison it equals the V5 softdrop mass of that comparison to 6e-8, which approach 1 and PocketCoffea reproduce. In one-chunk skims only the softdrop columns change: by 0.992 in 2024 MC, 1.000 in 2022 MC, 1.04 in 2025C data and 1.11 in 2023C data (median ratio to NanoAOD), with 1 and 12 more data events passing the msd cuts. --- src/HH4b/processors/bbbbSkimmer.py | 4 ++ src/HH4b/processors/corrections.py | 59 ++++++++++++++++++++++++++++++ src/HH4b/processors/ttSkimmer.py | 3 ++ 3 files changed, 66 insertions(+) diff --git a/src/HH4b/processors/bbbbSkimmer.py b/src/HH4b/processors/bbbbSkimmer.py index 920db01a..23417695 100644 --- a/src/HH4b/processors/bbbbSkimmer.py +++ b/src/HH4b/processors/bbbbSkimmer.py @@ -179,6 +179,7 @@ class bbbbSkimmer(SkimmerABC): "FatJet": { **P4, "msoftdrop": "Msd", + "msoftdrop_nano": "MsdNano", "Txbb": "PNetTXbb", # these are discriminants "Txjj": "PNetTXjj", "Tqcd": "PNetTQCD", @@ -729,6 +730,9 @@ def process(self, events: ak.Array): jets = events.Jet jec_shifted_jetvars = {} + # softdrop mass from the subjets re-corrected with the AK4 JEC above (NanoAOD's: MsdNano) + fatjets = JEC_loader.correct_msoftdrop(events, fatjets, year, isData) + # MET: Type-1 PUPPI MET from RawPuppiMET with the JECs re-applied above (NanoAOD PuppiMET # carries the JECs of its production); PuppiMET and RawPuppiMET are saved too. In MC, also # its JES/JER/unclustered-energy variations, which the shifted BDTs use. diff --git a/src/HH4b/processors/corrections.py b/src/HH4b/processors/corrections.py index 6c56c951..e2546337 100644 --- a/src/HH4b/processors/corrections.py +++ b/src/HH4b/processors/corrections.py @@ -403,6 +403,65 @@ def type1_puppi_met( path = f"{cat['jme']}/jet_jerc.json.gz" return type1_puppi_met(events, path, cat["jec"], isData, jer_smear_factor) + def correct_msoftdrop( + self, events: NanoEventsArray, fatjets: FatJetArray, year: str, isData: bool + ) -> FatJetArray: + """``fatjets`` (in NanoAOD FatJet order) with ``msoftdrop`` from their subjets re-corrected + with the AK4 JEC of the AK4 jets, and NanoAOD's value as ``msoftdrop_nano``: NanoAOD + msoftdrop sums the subjets with the JEC of the MiniAOD production. As PocketCoffea's + ``msoftdrop_correction`` (pocket_coffea/lib/jets.py): each raw subjet (rawFactor undone) is + corrected with the AK4 PUPPI L1L2L3 (MC) or L1L2L3Res (data) JEC, without JER; the + softdrop mass is the mass of the sum of the corrected subjets; jets without subjets or with + NanoAOD msoftdrop <= 0, and NaN masses, keep NanoAOD's value. Years without correctionlib + JECs keep NanoAOD's msoftdrop.""" + fatjets["msoftdrop_nano"] = fatjets.msoftdrop + if year not in cat_corrections: + return fatjets + + corrector = self.jet_factory["ak4"][year if isData else f"{year}mc"].jec_stack.jec + subjets = events.FatJet.subjets + subjets = subjets[~ak.is_none(subjets, axis=2)] + raw_factor = 1 - ak.values_astype(subjets.rawFactor, np.float64) + raw_pt = ak.values_astype(subjets.pt, np.float64) * raw_factor + raw_mass = ak.values_astype(subjets.mass, np.float64) * raw_factor + eta = ak.values_astype(subjets.eta, np.float64) + phi = ak.values_astype(subjets.phi, np.float64) + rho = ( + events.Rho.fixedGridRhoFastjetAll + if "Rho" in events.fields + else events.fixedGridRhoFastjetAll + ) + inputs = { + "JetPt": raw_pt, + "JetEta": eta, + "JetPhi": phi, + # PUPPI jets have no L1 correction (L1FastJet = 1), so the area is a dummy + "JetA": ak.full_like(raw_pt, 0.5), + "Rho": ak.broadcast_arrays(rho, raw_pt)[0], + "run": ak.broadcast_arrays(events.run, raw_pt)[0], + } + corr = corrector.getCorrection( + **{name: ak.flatten(inputs[name], axis=None) for name in corrector.signature} + ) + corr = ak.unflatten( + ak.unflatten(np.asarray(corr, dtype=np.float64), ak.flatten(ak.num(raw_pt, axis=2))), + ak.num(raw_pt, axis=1), + ) + + pt, mass = raw_pt * corr, raw_mass * corr + px, py, pz = pt * np.cos(phi), pt * np.sin(phi), pt * np.sinh(eta) + e = np.sqrt(px**2 + py**2 + pz**2 + mass**2) + m2 = ( + ak.sum(e, axis=2) ** 2 + - ak.sum(px, axis=2) ** 2 + - ak.sum(py, axis=2) ** 2 + - ak.sum(pz, axis=2) ** 2 + ) + msoftdrop = ak.values_astype(np.sqrt(np.maximum(m2, 0)), np.float32) + use = (ak.num(subjets, axis=2) > 0) & (fatjets.msoftdrop_nano > 0) & ~np.isnan(msoftdrop) + fatjets["msoftdrop"] = ak.where(use, msoftdrop, fatjets.msoftdrop_nano) + return fatjets + def _add_jec_variables( self, jets: JetArray, event_rho: ak.Array, isData: bool, run: ak.Array | None = None ) -> JetArray: diff --git a/src/HH4b/processors/ttSkimmer.py b/src/HH4b/processors/ttSkimmer.py index 4820eda1..4f699629 100644 --- a/src/HH4b/processors/ttSkimmer.py +++ b/src/HH4b/processors/ttSkimmer.py @@ -61,6 +61,7 @@ class ttSkimmer(SkimmerABC): "FatJet": { **P4, "msoftdrop": "Msd", + "msoftdrop_nano": "MsdNano", "Txbb": "PNetTXbb", # these are discriminants "Txjj": "PNetTXjj", "Tqcd": "PNetTQCD", @@ -292,6 +293,8 @@ def process(self, events: ak.Array): dataset=dataset, nano_version=self._nano_version, ) + # softdrop mass from the subjets re-corrected with the AK4 JEC (NanoAOD's: MsdNano) + fatjets = JEC_loader.correct_msoftdrop(events, fatjets, year, isData) # save variations with 10\% jmsr_shifted_vars = get_jmsr(