diff --git a/README.md b/README.md index ae221c47..2d9afc41 100644 --- a/README.md +++ b/README.md @@ -697,6 +697,7 @@ python runner.py --workflow emctag_ttdilep_sf --json metadata/test_bta_run3.json - `ci:skip syst` : remove `--isSyst all` option - `ci:JERC_split` : change systematic option to split JERC uncertainty sources `--isSyst JERC_split` - `ci:weight_only` : change systematic option to weight only variations `--isSyst weight_only` + ### Running jupyter remotely 1. 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histoutdir = f"hists_{args.workflow}_{sample_json.rstrip('.json')}" - outdir = f"arrays_{args.workflow}_{sample_json.rstrip('.json')}" + histoutdir = f"hists_{args.workflow}" + outdir = f"arrays_{args.workflow}" coffeaoutput = ( f'{histoutdir}/hists_{args.workflow}_{(sample_json).rstrip(".json")}.coffea' ) @@ -267,7 +267,7 @@ def get_main_parser(): isamp = int(args.only) nsamp = len(sample_dict.keys()) if isamp >= nsamp: - print( + raise RuntimeError( f"There are {nsamp} datasets, please use --only n with n<{nsamp}." ) key = list(sample_dict.keys())[isamp] @@ -350,10 +350,7 @@ def get_main_parser(): raise Exception(f"{coffeaoutput} exists") if args.isArray: - if path.exists(outdir) and args.overwrite == False and args.only is None: - raise Exception("Directory exists") - else: - os.system(f"mkdir -p {outdir}") + os.system(f"mkdir -p {outdir}") if args.executor not in ["futures", "iterative", "dask/lpc", "dask/casa"]: """ diff --git a/scripts/dohadd.py b/scripts/dohadd.py new file mode 100644 index 00000000..628104ce --- /dev/null +++ b/scripts/dohadd.py @@ -0,0 +1,23 @@ +import os, sys +from glob import glob + +indir = sys.argv[1] + +systs = os.listdir(indir) + +outfile = open("hadd.sh", "w") + +for syst in systs: + roots = glob(f"{indir}/{syst}/*/*.root") + if len(roots) == 0: + print(f"Skipping {indir}/{syst}. Not the right directory structure.") + continue + samps = os.listdir(f"{indir}/{syst}") + for samp in samps: + if len(glob(f"{indir}/{syst}/{samp}/*.root")) == 0: + continue + outfile.write( + f"hadd -v 0 {indir}/{syst}/{samp}.root {indir}/{syst}/{samp}/*.root\n" + ) + +print("Now run `parallel :::: hadd.sh` from an environment with ROOT installed. E.g. \nconda activate rootenv\nparallel :::: hadd.sh\nconda activate btv_coffea") diff --git a/scripts/dump_processed.py b/scripts/dump_processed.py index cb41cfc9..14169bc8 100644 --- a/scripts/dump_processed.py +++ b/scripts/dump_processed.py @@ -30,14 +30,17 @@ def dump_dataset(output, fname, alljson): original_list, list_from_coffea = {}, {} for j in jsonlist: old = json.load(open(j)) - original_list[j] = [] for o in old.keys(): + if o not in original_list.keys(): + original_list[o] = [] original_list[o].append(old[o]) for m in output.keys(): - list_from_coffea[m] = [] for f in output[m].keys(): - list_from_coffea[f].append(list(set(output[m][f]["fname"]))) + if f not in list_from_coffea.keys(): + list_from_coffea[f] = [] + else: + list_from_coffea[f] += list(set(output[m][f]["fname"])) failed = {} for t in original_list.keys(): failed[t] = [] diff --git a/scripts/fetch.py b/scripts/fetch.py index 09d8d6bc..13157b98 100644 --- a/scripts/fetch.py +++ b/scripts/fetch.py @@ -62,6 +62,24 @@ action="store_true", default=False, ) +parser.add_argument( + '-r', "--redirector", + help="xrootd ridirector in case sites are not found", + choices=["infn","fnal","cern"], + default="infn" +) +parser.add_argument( + '-j', "--ncpus", + help="Number of CPUs to use for validation", + default="4" +) +parser.add_argument( + "--skipvalidation", + action="store_true", + help="If true, the readability of files will not be validated.", + default=False, +) + parser.add_argument("--campaign", help="campaign info", default=None, type=str) @@ -210,9 +228,14 @@ def getFilesFromDas(args): if xrd is None: print( - f"No SITE available in the whitelist for file {dsname}, change to global redirector" + f"No SITE available in the whitelist for file {dsname}, change to global redirector: {args.redirector}" ) - xrd = "root://xrootd-cms.infn.it//" + redirector = { + "infn": "root://xrootd-cms.infn.it//", + "fnal": "root://cmsxrootd.fnal.gov/", + "cern": "root://cms-xrd-global.cern.ch/" + } + xrd = redirector[args.redirector] if args.limit is not None: flist = flist[: args.limit] if dsname not in fdict: @@ -335,7 +358,7 @@ def remove_bad_files(sample_dict, outname, remove_bad=True): _rmap = p_map( validate, sample_dict[sample], - num_cpus=4, + num_cpus=int(args.ncpus), desc=f"Validating {sample[:20]}...", ) @@ -373,6 +396,7 @@ def main(args): outf = open(args.input + "_DAS_" + args.campaign, "w") short_campaign = args.campaign for l in f.readlines(): + print(l) l = l.replace("\n", "") dataset = ( os.popen( @@ -418,7 +442,8 @@ def main(args): empty = False assert empty, "you have empty lists" output_file = "./%s" % (args.output) - # fdict = remove_bad_files(fdict, args.output, True) # remove bad files + if not args.skipvalidation: + fdict = remove_bad_files(fdict, args.output, True) # remove bad files with open(output_file, "w") as fp: json.dump(fdict, fp, indent=4) print("The file is saved at: ", output_file) diff --git a/src/BTVNanoCommissioning/data/JME/Summer22/jec_compiled.pkl.gz b/src/BTVNanoCommissioning/data/JME/Summer22/jec_compiled.pkl.gz index 4d76e409..844d7da5 100644 Binary files a/src/BTVNanoCommissioning/data/JME/Summer22/jec_compiled.pkl.gz and b/src/BTVNanoCommissioning/data/JME/Summer22/jec_compiled.pkl.gz differ diff --git a/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt new file mode 100644 index 00000000..ae7f1dd0 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt @@ -0,0 +1,305 @@ +{2 JetEta Rho 1 JetPt sqrt([0]*abs([0])/(x*x)+[1]*[1]*pow(x,[3])+[2]*[2]) Resolution} +-5.191 -3.839 0 8.24 6 15 3000 5.128 8.486e-07 0.08655 -1 +-5.191 -3.839 8.24 14.52 6 15 3000 5.663 2.909e-06 0.0874 -1 +-5.191 -3.839 14.52 20.8 6 15 3000 6.056 4.597e-06 0.08686 -1 +-5.191 -3.839 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--git a/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv1234_JRV1_DATA_PtResolution_AK4PFPuppi.jr.txt b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv1234_JRV1_DATA_PtResolution_AK4PFPuppi.jr.txt new file mode 100644 index 00000000..db4a25dd --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv1234_JRV1_DATA_PtResolution_AK4PFPuppi.jr.txt @@ -0,0 +1,12321 @@ +{3 JetEta JetPt Rho 1 JetPt (sqrt([0]*abs([0])/(x*x)+[1]*[1]*pow(x,[3])+[2]*[2]))*([4]) Resolution} + -5.191 -4.583 10 15 0 8.24 7 15 3000 5.128 8.486e-07 0.08655 -1 1.03 + -5.191 -4.583 10 15 8.24 14.52 7 15 3000 5.663 2.909e-06 0.0874 -1 1.03 + -5.191 -4.583 10 15 14.52 20.8 7 15 3000 6.056 4.597e-06 0.08686 -1 1.03 + -5.191 -4.583 10 15 20.8 27.08 7 15 3000 6.325 2.337e-06 0.08895 -1 1.03 + -5.191 -4.583 10 15 27.08 33.36 7 15 3000 6.193 0.4097 0.08431 -1 1.03 + -5.191 -4.583 10 15 33.36 39.64 7 15 3000 6.189 0.5206 0.0788 -1 1.03 + -5.191 -4.583 10 15 39.64 45.92 7 15 3000 4.698 0.9193 0.05413 -1 1.03 + -5.191 -4.583 10 15 45.92 52.2 7 15 3000 2.263 1.146 2.814e-05 -1 1.03 + -5.191 -4.583 15 21 0 8.24 7 15 3000 5.128 8.486e-07 0.08655 -1 1.033 + -5.191 -4.583 15 21 8.24 14.52 7 15 3000 5.663 2.909e-06 0.0874 -1 1.033 + -5.191 -4.583 15 21 14.52 20.8 7 15 3000 6.056 4.597e-06 0.08686 -1 1.033 + -5.191 -4.583 15 21 20.8 27.08 7 15 3000 6.325 2.337e-06 0.08895 -1 1.033 + -5.191 -4.583 15 21 27.08 33.36 7 15 3000 6.193 0.4097 0.08431 -1 1.033 + -5.191 -4.583 15 21 33.36 39.64 7 15 3000 6.189 0.5206 0.0788 -1 1.033 + -5.191 -4.583 15 21 39.64 45.92 7 15 3000 4.698 0.9193 0.05413 -1 1.033 + -5.191 -4.583 15 21 45.92 52.2 7 15 3000 2.263 1.146 2.814e-05 -1 1.033 + -5.191 -4.583 21 28 0 8.24 7 15 3000 5.128 8.486e-07 0.08655 -1 1.036 + -5.191 -4.583 21 28 8.24 14.52 7 15 3000 5.663 2.909e-06 0.0874 -1 1.036 + -5.191 -4.583 21 28 14.52 20.8 7 15 3000 6.056 4.597e-06 0.08686 -1 1.036 + -5.191 -4.583 21 28 20.8 27.08 7 15 3000 6.325 2.337e-06 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2.172 373 460 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 373 460 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 373 460 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 373 460 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 460 575 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 460 575 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 460 575 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 460 575 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 460 575 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 460 575 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 460 575 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 460 575 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 575 638 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 575 638 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 575 638 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 575 638 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 575 638 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 575 638 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 575 638 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 575 638 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 638 737 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 638 737 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 638 737 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 638 737 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 638 737 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 638 737 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 638 737 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 638 737 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 737 846 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 737 846 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 737 846 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 737 846 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 737 846 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 737 846 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 737 846 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 737 846 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 846 967 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 846 967 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 846 967 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 846 967 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 846 967 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 846 967 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 846 967 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 846 967 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 967 1101 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 967 1101 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 967 1101 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 967 1101 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 967 1101 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 967 1101 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 967 1101 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 967 1101 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 1101 1248 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 1101 1248 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 1101 1248 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 1101 1248 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 1101 1248 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 1101 1248 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 1101 1248 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 1101 1248 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 1248 1410 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 1248 1410 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 1248 1410 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 1248 1410 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 1248 1410 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 1248 1410 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 1248 1410 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 1248 1410 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 1410 1588 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 1410 1588 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 1410 1588 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 1410 1588 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 1410 1588 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 1410 1588 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 1410 1588 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 1410 1588 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 1588 1784 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.083 + 2.043 2.172 1588 1784 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.083 + 2.043 2.172 1588 1784 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.083 + 2.043 2.172 1588 1784 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.083 + 2.043 2.172 1588 1784 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.083 + 2.043 2.172 1588 1784 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.083 + 2.043 2.172 1588 1784 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.083 + 2.043 2.172 1588 1784 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.083 + 2.043 2.172 1784 2000 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 1784 2000 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 1784 2000 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 1784 2000 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 1784 2000 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 1784 2000 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 1784 2000 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 1784 2000 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 2000 2238 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 2000 2238 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 2000 2238 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 2000 2238 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 2000 2238 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 2000 2238 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 2000 2238 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 2000 2238 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 2238 2500 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 2238 2500 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 2238 2500 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 2238 2500 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 2238 2500 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 2238 2500 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 2238 2500 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 2238 2500 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 2500 2787 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 2500 2787 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 2500 2787 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 2500 2787 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 2500 2787 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 2500 2787 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 2500 2787 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 2500 2787 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 2787 3103 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 2787 3103 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 2787 3103 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 2787 3103 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 2787 3103 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 2787 3103 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 2787 3103 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 2787 3103 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 3103 3450 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 3103 3450 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 3103 3450 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 3103 3450 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 3103 3450 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 3103 3450 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 3103 3450 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 3103 3450 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 3450 4037 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 3450 4037 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 3450 4037 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 3450 4037 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 3450 4037 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 3450 4037 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 3450 4037 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 3450 4037 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 4037 5220 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 4037 5220 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 4037 5220 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 4037 5220 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 4037 5220 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 4037 5220 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 4037 5220 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 4037 5220 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.043 2.172 5220 5373 0 8.24 7 15 3000 1.203 1.119 0.03308 -1 1.082 + 2.043 2.172 5220 5373 8.24 14.52 7 15 3000 2.168 1.164 0.02971 -1 1.082 + 2.043 2.172 5220 5373 14.52 20.8 7 15 3000 3.037 1.185 0.02974 -1 1.082 + 2.043 2.172 5220 5373 20.8 27.08 7 15 3000 3.611 1.194 0.02934 -1 1.082 + 2.043 2.172 5220 5373 27.08 33.36 7 15 3000 4.038 1.222 0.02687 -1 1.082 + 2.043 2.172 5220 5373 33.36 39.64 7 15 3000 4.184 1.253 0.0262 -1 1.082 + 2.043 2.172 5220 5373 39.64 45.92 7 15 3000 4.669 1.275 0.02574 -1 1.082 + 2.043 2.172 5220 5373 45.92 52.2 7 15 3000 4.524 1.356 0.01051 -1 1.082 + 2.172 2.322 10 15 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.032 + 2.172 2.322 10 15 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.032 + 2.172 2.322 10 15 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.032 + 2.172 2.322 10 15 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.032 + 2.172 2.322 10 15 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.032 + 2.172 2.322 10 15 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.032 + 2.172 2.322 10 15 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.032 + 2.172 2.322 10 15 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.032 + 2.172 2.322 15 21 0 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1.321 0.01237 -1 1.051 + 2.172 2.322 21 28 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.051 + 2.172 2.322 28 37 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.059 + 2.172 2.322 28 37 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.059 + 2.172 2.322 28 37 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.059 + 2.172 2.322 28 37 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.059 + 2.172 2.322 28 37 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.059 + 2.172 2.322 28 37 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.059 + 2.172 2.322 28 37 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.059 + 2.172 2.322 28 37 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.059 + 2.172 2.322 37 49 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.069 + 2.172 2.322 37 49 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.069 + 2.172 2.322 37 49 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.069 + 2.172 2.322 37 49 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.069 + 2.172 2.322 37 49 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.069 + 2.172 2.322 37 49 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.069 + 2.172 2.322 37 49 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.069 + 2.172 2.322 37 49 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.069 + 2.172 2.322 49 59 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 49 59 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 49 59 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 49 59 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 49 59 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 49 59 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 49 59 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 49 59 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.075 + 2.172 2.322 59 86 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.084 + 2.172 2.322 59 86 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.084 + 2.172 2.322 59 86 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.084 + 2.172 2.322 59 86 20.8 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7 15 3000 5.108 1.372 4.36e-06 -1 1.099 + 2.172 2.322 170 204 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.102 + 2.172 2.322 170 204 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.102 + 2.172 2.322 170 204 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.102 + 2.172 2.322 170 204 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.102 + 2.172 2.322 170 204 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.102 + 2.172 2.322 170 204 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.102 + 2.172 2.322 170 204 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.102 + 2.172 2.322 170 204 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.102 + 2.172 2.322 204 236 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.103 + 2.172 2.322 204 236 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.103 + 2.172 2.322 204 236 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.103 + 2.172 2.322 204 236 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.103 + 2.172 2.322 204 236 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.103 + 2.172 2.322 204 236 33.36 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638 737 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.094 + 2.172 2.322 638 737 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.094 + 2.172 2.322 638 737 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.094 + 2.172 2.322 737 846 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.091 + 2.172 2.322 737 846 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.091 + 2.172 2.322 737 846 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.091 + 2.172 2.322 737 846 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.091 + 2.172 2.322 737 846 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.091 + 2.172 2.322 737 846 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.091 + 2.172 2.322 737 846 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.091 + 2.172 2.322 737 846 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.091 + 2.172 2.322 846 967 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.088 + 2.172 2.322 846 967 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.088 + 2.172 2.322 846 967 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.088 + 2.172 2.322 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2.172 2.322 1101 1248 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.081 + 2.172 2.322 1101 1248 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.081 + 2.172 2.322 1101 1248 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.081 + 2.172 2.322 1101 1248 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.081 + 2.172 2.322 1101 1248 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.081 + 2.172 2.322 1101 1248 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.081 + 2.172 2.322 1101 1248 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.081 + 2.172 2.322 1248 1410 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.078 + 2.172 2.322 1248 1410 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.078 + 2.172 2.322 1248 1410 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.078 + 2.172 2.322 1248 1410 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.078 + 2.172 2.322 1248 1410 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.078 + 2.172 2.322 1248 1410 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.078 + 2.172 2.322 1248 1410 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.078 + 2.172 2.322 1248 1410 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.078 + 2.172 2.322 1410 1588 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 1410 1588 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 1410 1588 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 1410 1588 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 1410 1588 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 1410 1588 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 1410 1588 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 1410 1588 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.075 + 2.172 2.322 1588 1784 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 1588 1784 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 1588 1784 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 1588 1784 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 1588 1784 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 1588 1784 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 1588 1784 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 1588 1784 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.075 + 2.172 2.322 1784 2000 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 1784 2000 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 1784 2000 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 1784 2000 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 1784 2000 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 1784 2000 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 1784 2000 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 1784 2000 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.075 + 2.172 2.322 2000 2238 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 2000 2238 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 2000 2238 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 2000 2238 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 2000 2238 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 2000 2238 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 2000 2238 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 2000 2238 45.92 52.2 7 15 3000 5.108 1.372 4.36e-06 -1 1.075 + 2.172 2.322 2238 2500 0 8.24 7 15 3000 1.423 1.17 0.02376 -1 1.075 + 2.172 2.322 2238 2500 8.24 14.52 7 15 3000 2.924 1.171 0.02612 -1 1.075 + 2.172 2.322 2238 2500 14.52 20.8 7 15 3000 3.72 1.177 0.02705 -1 1.075 + 2.172 2.322 2238 2500 20.8 27.08 7 15 3000 3.877 1.227 0.02052 -1 1.075 + 2.172 2.322 2238 2500 27.08 33.36 7 15 3000 4.291 1.241 0.02132 -1 1.075 + 2.172 2.322 2238 2500 33.36 39.64 7 15 3000 4.53 1.271 0.01796 -1 1.075 + 2.172 2.322 2238 2500 39.64 45.92 7 15 3000 4.614 1.321 0.01237 -1 1.075 + 2.172 2.322 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b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt @@ -0,0 +1,305 @@ +{2 JetEta Rho 1 JetPt sqrt([0]*abs([0])/(x*x)+[1]*[1]*pow(x,[3])+[2]*[2]) Resolution} +-5.191 -3.839 0 8.24 6 15 3000 5.128 8.486e-07 0.08655 -1 +-5.191 -3.839 8.24 14.52 6 15 3000 5.663 2.909e-06 0.0874 -1 +-5.191 -3.839 14.52 20.8 6 15 3000 6.056 4.597e-06 0.08686 -1 +-5.191 -3.839 20.8 27.08 6 15 3000 6.325 2.337e-06 0.08895 -1 +-5.191 -3.839 27.08 33.36 6 15 3000 6.193 0.4097 0.08431 -1 +-5.191 -3.839 33.36 39.64 6 15 3000 6.189 0.5206 0.0788 -1 +-5.191 -3.839 39.64 45.92 6 15 3000 4.698 0.9193 0.05413 -1 +-5.191 -3.839 45.92 52.2 6 15 3000 2.263 1.146 2.814e-05 -1 +-3.839 -3.489 0 8.24 6 15 3000 5.943 4.745e-06 0.09452 -1 +-3.839 -3.489 8.24 14.52 6 15 3000 6.524 8.673e-07 0.09364 -1 +-3.839 -3.489 14.52 20.8 6 15 3000 6.913 1.232e-05 0.09335 -1 +-3.839 -3.489 20.8 27.08 6 15 3000 7.242 1.446e-05 0.09477 -1 +-3.839 -3.489 27.08 33.36 6 15 3000 7.611 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5.191 1248 1410 3 1.103 1.000 2.304 +4.583 5.191 1410 1588 3 1.103 1.000 2.370 +4.583 5.191 1588 1784 3 1.103 1.000 2.437 +4.583 5.191 1784 2000 3 1.103 1.000 2.503 +4.583 5.191 2000 2238 3 1.103 1.000 2.570 +4.583 5.191 2238 2500 3 1.103 1.000 2.636 +4.583 5.191 2500 2787 3 1.103 1.000 2.703 +4.583 5.191 2787 3103 3 1.103 1.000 2.769 +4.583 5.191 3103 3450 3 1.103 1.000 2.836 +4.583 5.191 3450 4037 3 1.103 1.000 2.902 +4.583 5.191 4037 5220 3 1.103 1.000 2.969 +4.583 5.191 5220 5373 3 1.103 1.000 3.035 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt new file mode 100644 index 00000000..ae7f1dd0 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt @@ -0,0 +1,305 @@ +{2 JetEta Rho 1 JetPt sqrt([0]*abs([0])/(x*x)+[1]*[1]*pow(x,[3])+[2]*[2]) 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+4.583 5.191 2787 3103 3 1.010 1.000 2.601 +4.583 5.191 3103 3450 3 1.010 1.000 2.668 +4.583 5.191 3450 4037 3 1.010 1.000 2.734 +4.583 5.191 4037 5220 3 1.010 1.000 2.801 +4.583 5.191 5220 5373 3 1.010 1.000 2.867 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2L3Residual_AK4PFPuppi.hec.txt b/src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2L3Residual_AK4PFPuppi.jec.txt similarity index 100% rename from src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2L3Residual_AK4PFPuppi.hec.txt rename to src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2L3Residual_AK4PFPuppi.jec.txt diff --git a/src/BTVNanoCommissioning/data/JME/Summer23/jec_compiled.pkl.gz b/src/BTVNanoCommissioning/data/JME/Summer23/jec_compiled.pkl.gz index c971bb98..f8bb231e 100644 Binary files a/src/BTVNanoCommissioning/data/JME/Summer23/jec_compiled.pkl.gz and b/src/BTVNanoCommissioning/data/JME/Summer23/jec_compiled.pkl.gz differ diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt new file mode 100644 index 00000000..f0af5ee2 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt @@ -0,0 +1,305 @@ +{2 JetEta Rho 1 JetPt sqrt([0]*abs([0])/(x*x)+[1]*[1]*pow(x,[3])+[2]*[2]) Resolution} +-5.191 -3.839 0 8.23 6 15 3000 5.097 4.303e-06 0.08654 -1 +-5.191 -3.839 8.23 14.49 6 15 3000 5.89 2.242e-06 0.08141 -1 +-5.191 -3.839 14.49 20.75 6 15 3000 6.182 2.079e-05 0.08555 -1 +-5.191 -3.839 20.75 27.01 6 15 3000 6.487 2.15e-05 0.08563 -1 +-5.191 -3.839 27.01 33.27 6 15 3000 6.704 5.417e-05 0.08974 -1 +-5.191 -3.839 33.27 39.53 6 15 3000 6.685 0.274 0.08625 -1 +-5.191 -3.839 39.53 45.79 6 15 3000 5.876 0.7032 0.06777 -1 +-5.191 -3.839 45.79 52.05 6 15 3000 1.738 1.2 1.039e-06 -1 +-3.839 -3.489 0 8.23 6 15 3000 5.965 1.544e-06 0.09458 -1 +-3.839 -3.489 8.23 14.49 6 15 3000 6.606 1.911e-06 0.09296 -1 +-3.839 -3.489 14.49 20.75 6 15 3000 6.962 1.326e-05 0.09403 -1 +-3.839 -3.489 20.75 27.01 6 15 3000 7.286 1.585e-07 0.0943 -1 +-3.839 -3.489 27.01 33.27 6 15 3000 7.515 1.934e-05 0.09567 -1 +-3.839 -3.489 33.27 39.53 6 15 3000 7.482 0.3366 0.0942 -1 +-3.839 -3.489 39.53 45.79 6 15 3000 6.896 0.6686 0.08563 -1 +-3.839 -3.489 45.79 52.05 6 15 3000 2.835 1.208 0.04843 -1 +-3.489 -3.139 0 8.23 6 15 3000 8.07 9.864e-06 0.109 -1 +-3.489 -3.139 8.23 14.49 6 15 3000 8.085 0.4135 0.1045 -1 +-3.489 -3.139 14.49 20.75 6 15 3000 8.371 0.5472 0.09976 -1 +-3.489 -3.139 20.75 27.01 6 15 3000 8.231 0.6846 0.09492 -1 +-3.489 -3.139 27.01 33.27 6 15 3000 8.05 0.7989 0.09257 -1 +-3.489 -3.139 33.27 39.53 6 15 3000 8.186 0.8295 0.09087 -1 +-3.489 -3.139 39.53 45.79 6 15 3000 6.913 1.122 0.07488 -1 +-3.489 -3.139 45.79 52.05 6 15 3000 4.623 1.351 0.05563 -1 +-3.139 -2.964 0 8.23 6 15 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4.583 1588 1784 3 1.019 1.000 2.620 +4.013 4.583 1784 2000 3 1.019 1.000 2.729 +4.013 4.583 2000 2238 3 1.019 1.000 2.837 +4.013 4.583 2238 2500 3 1.019 1.000 2.945 +4.013 4.583 2500 2787 3 1.019 1.000 3.054 +4.013 4.583 2787 3103 3 1.019 1.000 3.162 +4.013 4.583 3103 3450 3 1.019 1.000 3.270 +4.013 4.583 3450 4037 3 1.019 1.000 3.379 +4.013 4.583 4037 5220 3 1.019 1.000 3.487 +4.013 4.583 5220 5373 3 1.019 1.000 3.595 +4.583 5.191 10 15 3 1.049 1.000 1.486 +4.583 5.191 15 21 3 1.063 1.000 1.436 +4.583 5.191 21 28 3 1.082 1.000 1.414 +4.583 5.191 28 37 3 1.105 1.000 1.417 +4.583 5.191 37 49 3 1.136 1.000 1.447 +4.583 5.191 49 59 3 1.168 1.000 1.494 +4.583 5.191 59 86 3 1.218 1.000 1.583 +4.583 5.191 86 110 3 1.279 1.000 1.701 +4.583 5.191 110 132 3 1.329 1.000 1.797 +4.583 5.191 132 170 3 1.387 1.000 1.909 +4.583 5.191 170 204 3 1.387 1.000 2.017 +4.583 5.191 204 236 3 1.387 1.000 2.126 +4.583 5.191 236 279 3 1.387 1.000 2.234 +4.583 5.191 279 302 3 1.387 1.000 2.342 +4.583 5.191 302 373 3 1.387 1.000 2.451 +4.583 5.191 373 460 3 1.387 1.000 2.559 +4.583 5.191 460 575 3 1.387 1.000 2.667 +4.583 5.191 575 638 3 1.387 1.000 2.776 +4.583 5.191 638 737 3 1.387 1.000 2.884 +4.583 5.191 737 846 3 1.387 1.000 2.992 +4.583 5.191 846 967 3 1.387 1.000 3.100 +4.583 5.191 967 1101 3 1.387 1.000 3.209 +4.583 5.191 1101 1248 3 1.387 1.000 3.317 +4.583 5.191 1248 1410 3 1.387 1.000 3.425 +4.583 5.191 1410 1588 3 1.387 1.000 3.534 +4.583 5.191 1588 1784 3 1.387 1.000 3.642 +4.583 5.191 1784 2000 3 1.387 1.000 3.750 +4.583 5.191 2000 2238 3 1.387 1.000 3.859 +4.583 5.191 2238 2500 3 1.387 1.000 3.967 +4.583 5.191 2500 2787 3 1.387 1.000 4.075 +4.583 5.191 2787 3103 3 1.387 1.000 4.183 +4.583 5.191 3103 3450 3 1.387 1.000 4.292 +4.583 5.191 3450 4037 3 1.387 1.000 4.400 +4.583 5.191 4037 5220 3 1.387 1.000 4.508 +4.583 5.191 5220 5373 3 1.387 1.000 4.617 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt new file mode 100644 index 00000000..7f8b0b8e --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt @@ -0,0 +1,2 @@ +{1 JetEta 3 Rho JetPt JetA 1 Correction L1FastJet} +-5.191 5.191 6 0 200 10 3000 0 10 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2L3Residual_AK4PFPuppi.jec.txt b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2L3Residual_AK4PFPuppi.jec.txt new file mode 100644 index 00000000..2b0e294c --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2L3Residual_AK4PFPuppi.jec.txt @@ -0,0 +1,2 @@ +{1 JetEta 1 JetPt 1 Correction L2Relative} +-5.191 5.191 2 4 5000 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt new file mode 100644 index 00000000..b1863382 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt @@ -0,0 +1,119 @@ +{2 JetEta JetPhi 1 JetPt max(0.0001,((x<[10])*([9]))+((x>=[10])*([0]+([1]/(pow(log10(x),2)+[2]))+([3]*exp(-([4]*((log10(x)-[5])*(log10(x)-[5])))))+([6]*exp(-([7]*((log10(x)-[8])*(log10(x)-[8])))))))) Correction L2Relative} + -5.191 -4.889 -3.1416 3.1416 13 11.083962 46.581605 -0.3989156953 87.55041091 113.1410563 1.044072947 2.382490063 1.48684011 0.1999012161 66.45599418 1.241038832 1.267681098 14 + -4.889 -4.716 -3.1416 3.1416 13 10.872088 62.524359 0.431057985 139.3119298 136.0809051 0.2200023612 0.0647198363 4.585004141 -0.3129736904 10.35981962 1.740499261 1.53929589 14 + -4.716 -4.538 -3.1416 3.1416 13 10.739907 81.784388 4.465333959 -0.01141363585 -3.253829336 -0.01382870155 -4.078821361 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b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L3Absolute_AK4PFPuppi.jec.txt new file mode 100644 index 00000000..d47037b9 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L3Absolute_AK4PFPuppi.jec.txt @@ -0,0 +1,2 @@ +{1 JetEta 1 JetPt 1 Correction L3Absolute} +-5.191 5.191 2 1 10000 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt new file mode 100644 index 00000000..9928f365 --- /dev/null +++ b/src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt @@ -0,0 +1,2185 @@ +#Uncertainty sources copied from Autumn18_V18_DATA_AK4PFchs +[AbsoluteStat] +{1 JetEta 1 JetPt "" Correction JECSource} +-5.4 -5.0 150 9.0 0.0037 0.0037 11.0 0.0035 0.0035 13.5 0.0033 0.0033 16.5 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0.0054 0.0054 79.0 0.0043 0.0043 90.5 0.0035 0.0035 105.5 0.0028 0.0028 123.5 0.0025 0.0025 143.0 0.0024 0.0024 163.5 0.0024 0.0024 185.0 0.0024 0.0024 208.0 0.0024 0.0024 232.5 0.0024 0.0024 258.5 0.0023 0.0023 286.0 0.0022 0.0022 331.0 0.0019 0.0019 396.0 0.0015 0.0015 468.5 0.0010 0.0010 549.5 0.0006 0.0006 639.0 0.0008 0.0008 738.0 0.0014 0.0014 847.5 0.0022 0.0022 968.5 0.0030 0.0030 1102.0 0.0039 0.0039 1249.5 0.0044 0.0044 1412.0 0.0050 0.0050 1590.5 0.0056 0.0056 1787.0 0.0063 0.0063 2003.0 0.0070 0.0070 2241.0 0.0077 0.0077 2503.0 0.0084 0.0084 2790.5 0.0091 0.0091 3107.0 0.0099 0.0099 3455.0 0.0107 0.0107 3837.0 0.0117 0.0117 4257.0 0.0126 0.0126 4719.0 0.0136 0.0136 5226.5 0.0145 0.0145 5784.0 0.0155 0.0155 6538.0 0.0166 0.0166 +-2.4 -2.2 150 9.0 0.0426 0.0426 11.0 0.0358 0.0358 13.5 0.0279 0.0279 16.5 0.0210 0.0210 19.5 0.0159 0.0159 22.5 0.0118 0.0118 26.0 0.0079 0.0079 30.0 0.0062 0.0062 34.5 0.0051 0.0051 40.0 0.0041 0.0041 46.0 0.0034 0.0034 52.5 0.0029 0.0029 60.0 0.0025 0.0025 69.0 0.0022 0.0022 79.0 0.0021 0.0021 90.5 0.0023 0.0023 105.5 0.0026 0.0026 123.5 0.0031 0.0031 143.0 0.0034 0.0034 163.5 0.0037 0.0037 185.0 0.0039 0.0039 208.0 0.0040 0.0040 232.5 0.0040 0.0040 258.5 0.0039 0.0039 286.0 0.0038 0.0038 331.0 0.0035 0.0035 396.0 0.0029 0.0029 468.5 0.0023 0.0023 549.5 0.0021 0.0021 639.0 0.0024 0.0024 738.0 0.0033 0.0033 847.5 0.0044 0.0044 968.5 0.0058 0.0058 1102.0 0.0072 0.0072 1249.5 0.0086 0.0086 1412.0 0.0100 0.0100 1590.5 0.0115 0.0115 1787.0 0.0130 0.0130 2003.0 0.0146 0.0146 2241.0 0.0162 0.0162 2503.0 0.0178 0.0178 2790.5 0.0195 0.0195 3107.0 0.0211 0.0211 3455.0 0.0228 0.0228 3837.0 0.0246 0.0246 4257.0 0.0265 0.0265 4719.0 0.0283 0.0283 5226.5 0.0301 0.0301 5784.0 0.0320 0.0320 6538.0 0.0342 0.0342 +-2.2 -2.0 150 9.0 0.0378 0.0378 11.0 0.0323 0.0323 13.5 0.0256 0.0256 16.5 0.0199 0.0199 19.5 0.0156 0.0156 22.5 0.0119 0.0119 26.0 0.0083 0.0083 30.0 0.0076 0.0076 34.5 0.0071 0.0071 40.0 0.0064 0.0064 46.0 0.0057 0.0057 52.5 0.0050 0.0050 60.0 0.0043 0.0043 69.0 0.0036 0.0036 79.0 0.0030 0.0030 90.5 0.0027 0.0027 105.5 0.0028 0.0028 123.5 0.0031 0.0031 143.0 0.0035 0.0035 163.5 0.0038 0.0038 185.0 0.0041 0.0041 208.0 0.0042 0.0042 232.5 0.0042 0.0042 258.5 0.0041 0.0041 286.0 0.0040 0.0040 331.0 0.0036 0.0036 396.0 0.0030 0.0030 468.5 0.0023 0.0023 549.5 0.0018 0.0018 639.0 0.0021 0.0021 738.0 0.0030 0.0030 847.5 0.0042 0.0042 968.5 0.0056 0.0056 1102.0 0.0071 0.0071 1249.5 0.0085 0.0085 1412.0 0.0100 0.0100 1590.5 0.0114 0.0114 1787.0 0.0130 0.0130 2003.0 0.0146 0.0146 2241.0 0.0162 0.0162 2503.0 0.0178 0.0178 2790.5 0.0194 0.0194 3107.0 0.0211 0.0211 3455.0 0.0227 0.0227 3837.0 0.0245 0.0245 4257.0 0.0263 0.0263 4719.0 0.0281 0.0281 5226.5 0.0299 0.0299 5784.0 0.0317 0.0317 6538.0 0.0339 0.0339 +-2.0 -1.8 150 9.0 0.0397 0.0397 11.0 0.0332 0.0332 13.5 0.0261 0.0261 16.5 0.0203 0.0203 19.5 0.0160 0.0160 22.5 0.0125 0.0125 26.0 0.0090 0.0090 30.0 0.0081 0.0081 34.5 0.0074 0.0074 40.0 0.0066 0.0066 46.0 0.0057 0.0057 52.5 0.0049 0.0049 60.0 0.0040 0.0040 69.0 0.0030 0.0030 79.0 0.0022 0.0022 90.5 0.0017 0.0017 105.5 0.0017 0.0017 123.5 0.0022 0.0022 143.0 0.0027 0.0027 163.5 0.0031 0.0031 185.0 0.0034 0.0034 208.0 0.0035 0.0035 232.5 0.0036 0.0036 258.5 0.0036 0.0036 286.0 0.0034 0.0034 331.0 0.0032 0.0032 396.0 0.0026 0.0026 468.5 0.0019 0.0019 549.5 0.0013 0.0013 639.0 0.0013 0.0013 738.0 0.0020 0.0020 847.5 0.0031 0.0031 968.5 0.0043 0.0043 1102.0 0.0056 0.0056 1249.5 0.0066 0.0066 1412.0 0.0078 0.0078 1590.5 0.0089 0.0089 1787.0 0.0101 0.0101 2003.0 0.0114 0.0114 2241.0 0.0126 0.0126 2503.0 0.0139 0.0139 2790.5 0.0152 0.0152 3107.0 0.0165 0.0165 3455.0 0.0178 0.0178 3837.0 0.0192 0.0192 4257.0 0.0206 0.0206 4719.0 0.0220 0.0220 5226.5 0.0234 0.0234 5784.0 0.0248 0.0248 6538.0 0.0265 0.0265 +-1.8 -1.6 150 9.0 0.0438 0.0438 11.0 0.0372 0.0372 13.5 0.0300 0.0300 16.5 0.0240 0.0240 19.5 0.0193 0.0193 22.5 0.0155 0.0155 26.0 0.0117 0.0117 30.0 0.0103 0.0103 34.5 0.0091 0.0091 40.0 0.0078 0.0078 46.0 0.0066 0.0066 52.5 0.0055 0.0055 60.0 0.0043 0.0043 69.0 0.0031 0.0031 79.0 0.0021 0.0021 90.5 0.0012 0.0012 105.5 0.0009 0.0009 123.5 0.0015 0.0015 143.0 0.0021 0.0021 163.5 0.0026 0.0026 185.0 0.0029 0.0029 208.0 0.0031 0.0031 232.5 0.0032 0.0032 258.5 0.0032 0.0032 286.0 0.0031 0.0031 331.0 0.0029 0.0029 396.0 0.0023 0.0023 468.5 0.0017 0.0017 549.5 0.0011 0.0011 639.0 0.0009 0.0009 738.0 0.0015 0.0015 847.5 0.0024 0.0024 968.5 0.0035 0.0035 1102.0 0.0046 0.0046 1249.5 0.0055 0.0055 1412.0 0.0064 0.0064 1590.5 0.0074 0.0074 1787.0 0.0084 0.0084 2003.0 0.0094 0.0094 2241.0 0.0104 0.0104 2503.0 0.0115 0.0115 2790.5 0.0125 0.0125 3107.0 0.0136 0.0136 3455.0 0.0146 0.0146 3837.0 0.0158 0.0158 4257.0 0.0170 0.0170 4719.0 0.0182 0.0182 5226.5 0.0193 0.0193 5784.0 0.0205 0.0205 6538.0 0.0219 0.0219 +-1.6 -1.4 150 9.0 0.0495 0.0495 11.0 0.0427 0.0427 13.5 0.0349 0.0349 16.5 0.0281 0.0281 19.5 0.0227 0.0227 22.5 0.0182 0.0182 26.0 0.0139 0.0139 30.0 0.0120 0.0120 34.5 0.0104 0.0104 40.0 0.0087 0.0087 46.0 0.0072 0.0072 52.5 0.0058 0.0058 60.0 0.0045 0.0045 69.0 0.0033 0.0033 79.0 0.0022 0.0022 90.5 0.0013 0.0013 105.5 0.0010 0.0010 123.5 0.0014 0.0014 143.0 0.0019 0.0019 163.5 0.0023 0.0023 185.0 0.0026 0.0026 208.0 0.0028 0.0028 232.5 0.0028 0.0028 258.5 0.0028 0.0028 286.0 0.0027 0.0027 331.0 0.0025 0.0025 396.0 0.0020 0.0020 468.5 0.0014 0.0014 549.5 0.0007 0.0007 639.0 0.0006 0.0006 738.0 0.0013 0.0013 847.5 0.0022 0.0022 968.5 0.0032 0.0032 1102.0 0.0042 0.0042 1249.5 0.0049 0.0049 1412.0 0.0056 0.0056 1590.5 0.0064 0.0064 1787.0 0.0072 0.0072 2003.0 0.0080 0.0080 2241.0 0.0089 0.0089 2503.0 0.0097 0.0097 2790.5 0.0106 0.0106 3107.0 0.0115 0.0115 3455.0 0.0123 0.0123 3837.0 0.0133 0.0133 4257.0 0.0143 0.0143 4719.0 0.0153 0.0153 5226.5 0.0162 0.0162 5784.0 0.0172 0.0172 6538.0 0.0183 0.0183 +-1.4 -1.2 150 9.0 0.0534 0.0534 11.0 0.0463 0.0463 13.5 0.0382 0.0382 16.5 0.0308 0.0308 19.5 0.0249 0.0249 22.5 0.0200 0.0200 26.0 0.0153 0.0153 30.0 0.0131 0.0131 34.5 0.0112 0.0112 40.0 0.0093 0.0093 46.0 0.0077 0.0077 52.5 0.0062 0.0062 60.0 0.0047 0.0047 69.0 0.0034 0.0034 79.0 0.0022 0.0022 90.5 0.0013 0.0013 105.5 0.0009 0.0009 123.5 0.0014 0.0014 143.0 0.0020 0.0020 163.5 0.0024 0.0024 185.0 0.0027 0.0027 208.0 0.0028 0.0028 232.5 0.0029 0.0029 258.5 0.0029 0.0029 286.0 0.0028 0.0028 331.0 0.0025 0.0025 396.0 0.0020 0.0020 468.5 0.0014 0.0014 549.5 0.0008 0.0008 639.0 0.0006 0.0006 738.0 0.0013 0.0013 847.5 0.0022 0.0022 968.5 0.0032 0.0032 1102.0 0.0043 0.0043 1249.5 0.0050 0.0050 1412.0 0.0057 0.0057 1590.5 0.0066 0.0066 1787.0 0.0074 0.0074 2003.0 0.0082 0.0082 2241.0 0.0091 0.0091 2503.0 0.0100 0.0100 2790.5 0.0109 0.0109 3107.0 0.0117 0.0117 3455.0 0.0126 0.0126 3837.0 0.0136 0.0136 4257.0 0.0147 0.0147 4719.0 0.0157 0.0157 5226.5 0.0166 0.0166 5784.0 0.0176 0.0176 6538.0 0.0188 0.0188 +-1.2 -1.0 150 9.0 0.0545 0.0545 11.0 0.0463 0.0463 13.5 0.0372 0.0372 16.5 0.0291 0.0291 19.5 0.0229 0.0229 22.5 0.0179 0.0179 26.0 0.0133 0.0133 30.0 0.0111 0.0111 34.5 0.0093 0.0093 40.0 0.0075 0.0075 46.0 0.0060 0.0060 52.5 0.0048 0.0048 60.0 0.0036 0.0036 69.0 0.0026 0.0026 79.0 0.0018 0.0018 90.5 0.0013 0.0013 105.5 0.0013 0.0013 123.5 0.0016 0.0016 143.0 0.0020 0.0020 163.5 0.0023 0.0023 185.0 0.0025 0.0025 208.0 0.0025 0.0025 232.5 0.0026 0.0026 258.5 0.0025 0.0025 286.0 0.0024 0.0024 331.0 0.0021 0.0021 396.0 0.0017 0.0017 468.5 0.0011 0.0011 549.5 0.0006 0.0006 639.0 0.0006 0.0006 738.0 0.0013 0.0013 847.5 0.0021 0.0021 968.5 0.0029 0.0029 1102.0 0.0038 0.0038 1249.5 0.0044 0.0044 1412.0 0.0050 0.0050 1590.5 0.0056 0.0056 1787.0 0.0062 0.0062 2003.0 0.0069 0.0069 2241.0 0.0075 0.0075 2503.0 0.0082 0.0082 2790.5 0.0089 0.0089 3107.0 0.0096 0.0096 3455.0 0.0103 0.0103 3837.0 0.0111 0.0111 4257.0 0.0120 0.0120 4719.0 0.0128 0.0128 5226.5 0.0136 0.0136 5784.0 0.0144 0.0144 6538.0 0.0153 0.0153 +-1.0 -0.8 150 9.0 0.0521 0.0521 11.0 0.0443 0.0443 13.5 0.0355 0.0355 16.5 0.0277 0.0277 19.5 0.0217 0.0217 22.5 0.0169 0.0169 26.0 0.0126 0.0126 30.0 0.0105 0.0105 34.5 0.0087 0.0087 40.0 0.0071 0.0071 46.0 0.0057 0.0057 52.5 0.0045 0.0045 60.0 0.0035 0.0035 69.0 0.0025 0.0025 79.0 0.0018 0.0018 90.5 0.0014 0.0014 105.5 0.0014 0.0014 123.5 0.0017 0.0017 143.0 0.0020 0.0020 163.5 0.0022 0.0022 185.0 0.0024 0.0024 208.0 0.0024 0.0024 232.5 0.0024 0.0024 258.5 0.0024 0.0024 286.0 0.0023 0.0023 331.0 0.0020 0.0020 396.0 0.0016 0.0016 468.5 0.0010 0.0010 549.5 0.0005 0.0005 639.0 0.0006 0.0006 738.0 0.0012 0.0012 847.5 0.0020 0.0020 968.5 0.0028 0.0028 1102.0 0.0037 0.0037 1249.5 0.0041 0.0041 1412.0 0.0046 0.0046 1590.5 0.0052 0.0052 1787.0 0.0058 0.0058 2003.0 0.0063 0.0063 2241.0 0.0069 0.0069 2503.0 0.0075 0.0075 2790.5 0.0082 0.0082 3107.0 0.0088 0.0088 3455.0 0.0094 0.0094 3837.0 0.0101 0.0101 4257.0 0.0109 0.0109 4719.0 0.0116 0.0116 5226.5 0.0123 0.0123 5784.0 0.0130 0.0130 6538.0 0.0138 0.0138 +-0.8 -0.6 150 9.0 0.0506 0.0506 11.0 0.0427 0.0427 13.5 0.0340 0.0340 16.5 0.0263 0.0263 19.5 0.0205 0.0205 22.5 0.0161 0.0161 26.0 0.0122 0.0122 30.0 0.0101 0.0101 34.5 0.0084 0.0084 40.0 0.0069 0.0069 46.0 0.0056 0.0056 52.5 0.0045 0.0045 60.0 0.0035 0.0035 69.0 0.0027 0.0027 79.0 0.0021 0.0021 90.5 0.0018 0.0018 105.5 0.0017 0.0017 123.5 0.0019 0.0019 143.0 0.0020 0.0020 163.5 0.0022 0.0022 185.0 0.0023 0.0023 208.0 0.0023 0.0023 232.5 0.0023 0.0023 258.5 0.0022 0.0022 286.0 0.0021 0.0021 331.0 0.0018 0.0018 396.0 0.0014 0.0014 468.5 0.0009 0.0009 549.5 0.0004 0.0004 639.0 0.0006 0.0006 738.0 0.0012 0.0012 847.5 0.0019 0.0019 968.5 0.0026 0.0026 1102.0 0.0034 0.0034 1249.5 0.0038 0.0038 1412.0 0.0042 0.0042 1590.5 0.0046 0.0046 1787.0 0.0051 0.0051 2003.0 0.0056 0.0056 2241.0 0.0061 0.0061 2503.0 0.0066 0.0066 2790.5 0.0071 0.0071 3107.0 0.0076 0.0076 3455.0 0.0081 0.0081 3837.0 0.0087 0.0087 4257.0 0.0093 0.0093 4719.0 0.0099 0.0099 5226.5 0.0105 0.0105 5784.0 0.0111 0.0111 6538.0 0.0118 0.0118 +-0.6 -0.4 150 9.0 0.0492 0.0492 11.0 0.0416 0.0416 13.5 0.0330 0.0330 16.5 0.0255 0.0255 19.5 0.0200 0.0200 22.5 0.0157 0.0157 26.0 0.0121 0.0121 30.0 0.0100 0.0100 34.5 0.0084 0.0084 40.0 0.0068 0.0068 46.0 0.0056 0.0056 52.5 0.0045 0.0045 60.0 0.0036 0.0036 69.0 0.0028 0.0028 79.0 0.0023 0.0023 90.5 0.0020 0.0020 105.5 0.0019 0.0019 123.5 0.0019 0.0019 143.0 0.0021 0.0021 163.5 0.0022 0.0022 185.0 0.0022 0.0022 208.0 0.0023 0.0023 232.5 0.0022 0.0022 258.5 0.0021 0.0021 286.0 0.0020 0.0020 331.0 0.0018 0.0018 396.0 0.0013 0.0013 468.5 0.0008 0.0008 549.5 0.0004 0.0004 639.0 0.0005 0.0005 738.0 0.0012 0.0012 847.5 0.0018 0.0018 968.5 0.0026 0.0026 1102.0 0.0033 0.0033 1249.5 0.0037 0.0037 1412.0 0.0041 0.0041 1590.5 0.0044 0.0044 1787.0 0.0049 0.0049 2003.0 0.0053 0.0053 2241.0 0.0057 0.0057 2503.0 0.0062 0.0062 2790.5 0.0067 0.0067 3107.0 0.0071 0.0071 3455.0 0.0076 0.0076 3837.0 0.0082 0.0082 4257.0 0.0087 0.0087 4719.0 0.0093 0.0093 5226.5 0.0098 0.0098 5784.0 0.0104 0.0104 6538.0 0.0110 0.0110 +-0.4 -0.2 150 9.0 0.0498 0.0498 11.0 0.0419 0.0419 13.5 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0.0274 0.0274 52.5 0.0267 0.0267 60.0 0.0260 0.0260 69.0 0.0253 0.0253 79.0 0.0246 0.0246 90.5 0.0239 0.0239 105.5 0.0232 0.0232 123.5 0.0225 0.0225 143.0 0.0218 0.0218 163.5 0.0213 0.0213 185.0 0.0208 0.0208 208.0 0.0203 0.0203 232.5 0.0200 0.0200 258.5 0.0196 0.0196 286.0 0.0193 0.0193 331.0 0.0188 0.0188 396.0 0.0184 0.0184 468.5 0.0180 0.0180 549.5 0.0177 0.0177 639.0 0.0174 0.0174 738.0 0.0173 0.0173 847.5 0.0172 0.0172 968.5 0.0172 0.0172 1102.0 0.0172 0.0172 1249.5 0.0172 0.0172 1412.0 0.0172 0.0172 1590.5 0.0171 0.0171 1787.0 0.0171 0.0171 2003.0 0.0171 0.0171 2241.0 0.0170 0.0170 2503.0 0.0169 0.0169 2790.5 0.0168 0.0168 3107.0 0.0168 0.0168 3455.0 0.0168 0.0168 3837.0 0.0168 0.0168 4257.0 0.0168 0.0168 4719.0 0.0168 0.0168 5226.5 0.0168 0.0168 5784.0 0.0168 0.0168 6538.0 0.0168 0.0168 +1.6 1.8 150 9.0 0.0489 0.0489 11.0 0.0483 0.0483 13.5 0.0468 0.0468 16.5 0.0454 0.0454 19.5 0.0443 0.0443 22.5 0.0434 0.0434 26.0 0.0425 0.0425 30.0 0.0416 0.0416 34.5 0.0401 0.0401 40.0 0.0385 0.0385 46.0 0.0369 0.0369 52.5 0.0355 0.0355 60.0 0.0340 0.0340 69.0 0.0325 0.0325 79.0 0.0310 0.0310 90.5 0.0295 0.0295 105.5 0.0279 0.0279 123.5 0.0263 0.0263 143.0 0.0248 0.0248 163.5 0.0234 0.0234 185.0 0.0223 0.0223 208.0 0.0211 0.0211 232.5 0.0201 0.0201 258.5 0.0192 0.0192 286.0 0.0183 0.0183 331.0 0.0171 0.0171 396.0 0.0157 0.0157 468.5 0.0145 0.0145 549.5 0.0136 0.0136 639.0 0.0129 0.0129 738.0 0.0123 0.0123 847.5 0.0120 0.0120 968.5 0.0118 0.0118 1102.0 0.0118 0.0118 1249.5 0.0116 0.0116 1412.0 0.0116 0.0116 1590.5 0.0115 0.0115 1787.0 0.0112 0.0112 2003.0 0.0109 0.0109 2241.0 0.0107 0.0107 2503.0 0.0106 0.0106 2790.5 0.0106 0.0106 3107.0 0.0106 0.0106 3455.0 0.0106 0.0106 3837.0 0.0106 0.0106 4257.0 0.0106 0.0106 4719.0 0.0106 0.0106 5226.5 0.0106 0.0106 5784.0 0.0106 0.0106 6538.0 0.0106 0.0106 +1.8 2.0 150 9.0 0.0489 0.0489 11.0 0.0483 0.0483 13.5 0.0468 0.0468 16.5 0.0454 0.0454 19.5 0.0443 0.0443 22.5 0.0434 0.0434 26.0 0.0425 0.0425 30.0 0.0416 0.0416 34.5 0.0401 0.0401 40.0 0.0385 0.0385 46.0 0.0369 0.0369 52.5 0.0355 0.0355 60.0 0.0340 0.0340 69.0 0.0325 0.0325 79.0 0.0310 0.0310 90.5 0.0295 0.0295 105.5 0.0279 0.0279 123.5 0.0263 0.0263 143.0 0.0248 0.0248 163.5 0.0235 0.0235 185.0 0.0223 0.0223 208.0 0.0211 0.0211 232.5 0.0201 0.0201 258.5 0.0192 0.0192 286.0 0.0183 0.0183 331.0 0.0171 0.0171 396.0 0.0157 0.0157 468.5 0.0145 0.0145 549.5 0.0136 0.0136 639.0 0.0129 0.0129 738.0 0.0123 0.0123 847.5 0.0120 0.0120 968.5 0.0118 0.0118 1102.0 0.0118 0.0118 1249.5 0.0116 0.0116 1412.0 0.0116 0.0116 1590.5 0.0115 0.0115 1787.0 0.0112 0.0112 2003.0 0.0110 0.0110 2241.0 0.0110 0.0110 2503.0 0.0110 0.0110 2790.5 0.0110 0.0110 3107.0 0.0110 0.0110 3455.0 0.0110 0.0110 3837.0 0.0110 0.0110 4257.0 0.0110 0.0110 4719.0 0.0110 0.0110 5226.5 0.0110 0.0110 5784.0 0.0110 0.0110 6538.0 0.0110 0.0110 +2.0 2.2 150 9.0 0.0463 0.0463 11.0 0.0458 0.0458 13.5 0.0444 0.0444 16.5 0.0431 0.0431 19.5 0.0421 0.0421 22.5 0.0413 0.0413 26.0 0.0404 0.0404 30.0 0.0397 0.0397 34.5 0.0383 0.0383 40.0 0.0368 0.0368 46.0 0.0354 0.0354 52.5 0.0341 0.0341 60.0 0.0327 0.0327 69.0 0.0313 0.0313 79.0 0.0299 0.0299 90.5 0.0285 0.0285 105.5 0.0270 0.0270 123.5 0.0255 0.0255 143.0 0.0241 0.0241 163.5 0.0229 0.0229 185.0 0.0217 0.0217 208.0 0.0207 0.0207 232.5 0.0197 0.0197 258.5 0.0189 0.0189 286.0 0.0181 0.0181 331.0 0.0169 0.0169 396.0 0.0156 0.0156 468.5 0.0146 0.0146 549.5 0.0137 0.0137 639.0 0.0130 0.0130 738.0 0.0124 0.0124 847.5 0.0121 0.0121 968.5 0.0120 0.0120 1102.0 0.0119 0.0119 1249.5 0.0119 0.0119 1412.0 0.0118 0.0118 1590.5 0.0116 0.0116 1787.0 0.0116 0.0116 2003.0 0.0116 0.0116 2241.0 0.0116 0.0116 2503.0 0.0116 0.0116 2790.5 0.0116 0.0116 3107.0 0.0116 0.0116 3455.0 0.0116 0.0116 3837.0 0.0116 0.0116 4257.0 0.0116 0.0116 4719.0 0.0116 0.0116 5226.5 0.0116 0.0116 5784.0 0.0116 0.0116 6538.0 0.0116 0.0116 +2.2 2.4 150 9.0 0.0434 0.0434 11.0 0.0431 0.0431 13.5 0.0420 0.0420 16.5 0.0410 0.0410 19.5 0.0402 0.0402 22.5 0.0395 0.0395 26.0 0.0389 0.0389 30.0 0.0382 0.0382 34.5 0.0372 0.0372 40.0 0.0362 0.0362 46.0 0.0351 0.0351 52.5 0.0342 0.0342 60.0 0.0332 0.0332 69.0 0.0322 0.0322 79.0 0.0313 0.0313 90.5 0.0304 0.0304 105.5 0.0294 0.0294 123.5 0.0284 0.0284 143.0 0.0275 0.0275 163.5 0.0267 0.0267 185.0 0.0259 0.0259 208.0 0.0252 0.0252 232.5 0.0246 0.0246 258.5 0.0241 0.0241 286.0 0.0235 0.0235 331.0 0.0228 0.0228 396.0 0.0219 0.0219 468.5 0.0212 0.0212 549.5 0.0205 0.0205 639.0 0.0200 0.0200 738.0 0.0195 0.0195 847.5 0.0192 0.0192 968.5 0.0189 0.0189 1102.0 0.0187 0.0187 1249.5 0.0183 0.0183 1412.0 0.0183 0.0183 1590.5 0.0183 0.0183 1787.0 0.0183 0.0183 2003.0 0.0183 0.0183 2241.0 0.0183 0.0183 2503.0 0.0183 0.0183 2790.5 0.0183 0.0183 3107.0 0.0183 0.0183 3455.0 0.0183 0.0183 3837.0 0.0183 0.0183 4257.0 0.0183 0.0183 4719.0 0.0183 0.0183 5226.5 0.0183 0.0183 5784.0 0.0183 0.0183 6538.0 0.0183 0.0183 +2.4 2.6 150 9.0 0.0700 0.0700 11.0 0.0696 0.0696 13.5 0.0659 0.0659 16.5 0.0625 0.0625 19.5 0.0598 0.0598 22.5 0.0575 0.0575 26.0 0.0553 0.0553 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0.0053 1102.0 0.0055 0.0055 1249.5 0.0058 0.0058 1412.0 0.0061 0.0061 1590.5 0.0064 0.0064 1787.0 0.0067 0.0067 2003.0 0.0070 0.0070 2241.0 0.0072 0.0072 2503.0 0.0074 0.0074 2790.5 0.0076 0.0076 3107.0 0.0078 0.0078 3455.0 0.0079 0.0079 3837.0 0.0080 0.0080 4257.0 0.0081 0.0081 4719.0 0.0082 0.0082 5226.5 0.0083 0.0083 5784.0 0.0085 0.0085 6538.0 0.0292 0.0292 +0.2 0.4 150 9.0 0.0452 0.0452 11.0 0.0387 0.0387 13.5 0.0336 0.0336 16.5 0.0288 0.0288 19.5 0.0260 0.0260 22.5 0.0242 0.0242 26.0 0.0230 0.0230 30.0 0.0215 0.0215 34.5 0.0195 0.0195 40.0 0.0176 0.0176 46.0 0.0162 0.0162 52.5 0.0148 0.0148 60.0 0.0138 0.0138 69.0 0.0128 0.0128 79.0 0.0118 0.0118 90.5 0.0109 0.0109 105.5 0.0101 0.0101 123.5 0.0092 0.0092 143.0 0.0085 0.0085 163.5 0.0079 0.0079 185.0 0.0074 0.0074 208.0 0.0070 0.0070 232.5 0.0066 0.0066 258.5 0.0062 0.0062 286.0 0.0059 0.0059 331.0 0.0056 0.0056 396.0 0.0053 0.0053 468.5 0.0051 0.0051 549.5 0.0050 0.0050 639.0 0.0050 0.0050 738.0 0.0051 0.0051 847.5 0.0052 0.0052 968.5 0.0054 0.0054 1102.0 0.0056 0.0056 1249.5 0.0059 0.0059 1412.0 0.0062 0.0062 1590.5 0.0065 0.0065 1787.0 0.0067 0.0067 2003.0 0.0070 0.0070 2241.0 0.0072 0.0072 2503.0 0.0075 0.0075 2790.5 0.0077 0.0077 3107.0 0.0078 0.0078 3455.0 0.0078 0.0078 3837.0 0.0079 0.0079 4257.0 0.0080 0.0080 4719.0 0.0080 0.0080 5226.5 0.0081 0.0081 5784.0 0.0081 0.0081 6538.0 0.0230 0.0230 +0.4 0.6 150 9.0 0.0456 0.0456 11.0 0.0396 0.0396 13.5 0.0328 0.0328 16.5 0.0286 0.0286 19.5 0.0259 0.0259 22.5 0.0244 0.0244 26.0 0.0227 0.0227 30.0 0.0216 0.0216 34.5 0.0195 0.0195 40.0 0.0175 0.0175 46.0 0.0160 0.0160 52.5 0.0148 0.0148 60.0 0.0138 0.0138 69.0 0.0127 0.0127 79.0 0.0118 0.0118 90.5 0.0109 0.0109 105.5 0.0100 0.0100 123.5 0.0092 0.0092 143.0 0.0085 0.0085 163.5 0.0079 0.0079 185.0 0.0074 0.0074 208.0 0.0070 0.0070 232.5 0.0066 0.0066 258.5 0.0062 0.0062 286.0 0.0059 0.0059 331.0 0.0056 0.0056 396.0 0.0053 0.0053 468.5 0.0051 0.0051 549.5 0.0050 0.0050 639.0 0.0050 0.0050 738.0 0.0051 0.0051 847.5 0.0052 0.0052 968.5 0.0054 0.0054 1102.0 0.0056 0.0056 1249.5 0.0059 0.0059 1412.0 0.0062 0.0062 1590.5 0.0065 0.0065 1787.0 0.0067 0.0067 2003.0 0.0070 0.0070 2241.0 0.0072 0.0072 2503.0 0.0075 0.0075 2790.5 0.0077 0.0077 3107.0 0.0078 0.0078 3455.0 0.0078 0.0078 3837.0 0.0079 0.0079 4257.0 0.0080 0.0080 4719.0 0.0080 0.0080 5226.5 0.0081 0.0081 5784.0 0.0081 0.0081 6538.0 0.0093 0.0093 +0.6 0.8 150 9.0 0.0452 0.0452 11.0 0.0385 0.0385 13.5 0.0321 0.0321 16.5 0.0284 0.0284 19.5 0.0255 0.0255 22.5 0.0238 0.0238 26.0 0.0223 0.0223 30.0 0.0211 0.0211 34.5 0.0189 0.0189 40.0 0.0171 0.0171 46.0 0.0154 0.0154 52.5 0.0142 0.0142 60.0 0.0131 0.0131 69.0 0.0122 0.0122 79.0 0.0113 0.0113 90.5 0.0105 0.0105 105.5 0.0097 0.0097 123.5 0.0090 0.0090 143.0 0.0083 0.0083 163.5 0.0077 0.0077 185.0 0.0073 0.0073 208.0 0.0069 0.0069 232.5 0.0065 0.0065 258.5 0.0062 0.0062 286.0 0.0060 0.0060 331.0 0.0056 0.0056 396.0 0.0053 0.0053 468.5 0.0050 0.0050 549.5 0.0048 0.0048 639.0 0.0047 0.0047 738.0 0.0047 0.0047 847.5 0.0047 0.0047 968.5 0.0048 0.0048 1102.0 0.0049 0.0049 1249.5 0.0050 0.0050 1412.0 0.0052 0.0052 1590.5 0.0053 0.0053 1787.0 0.0055 0.0055 2003.0 0.0057 0.0057 2241.0 0.0057 0.0057 2503.0 0.0058 0.0058 2790.5 0.0059 0.0059 3107.0 0.0060 0.0060 3455.0 0.0061 0.0061 3837.0 0.0061 0.0061 4257.0 0.0062 0.0062 4719.0 0.0063 0.0063 5226.5 0.0063 0.0063 5784.0 0.0064 0.0064 6538.0 0.0269 0.0269 +0.8 1.0 150 9.0 0.0457 0.0457 11.0 0.0387 0.0387 13.5 0.0331 0.0331 16.5 0.0296 0.0296 19.5 0.0272 0.0272 22.5 0.0251 0.0251 26.0 0.0238 0.0238 30.0 0.0229 0.0229 34.5 0.0210 0.0210 40.0 0.0186 0.0186 46.0 0.0174 0.0174 52.5 0.0161 0.0161 60.0 0.0151 0.0151 69.0 0.0140 0.0140 79.0 0.0131 0.0131 90.5 0.0123 0.0123 105.5 0.0115 0.0115 123.5 0.0106 0.0106 143.0 0.0099 0.0099 163.5 0.0093 0.0093 185.0 0.0088 0.0088 208.0 0.0083 0.0083 232.5 0.0079 0.0079 258.5 0.0076 0.0076 286.0 0.0072 0.0072 331.0 0.0068 0.0068 396.0 0.0063 0.0063 468.5 0.0059 0.0059 549.5 0.0056 0.0056 639.0 0.0054 0.0054 738.0 0.0053 0.0053 847.5 0.0052 0.0052 968.5 0.0052 0.0052 1102.0 0.0052 0.0052 1249.5 0.0053 0.0053 1412.0 0.0053 0.0053 1590.5 0.0054 0.0054 1787.0 0.0055 0.0055 2003.0 0.0057 0.0057 2241.0 0.0057 0.0057 2503.0 0.0058 0.0058 2790.5 0.0058 0.0058 3107.0 0.0059 0.0059 3455.0 0.0059 0.0059 3837.0 0.0060 0.0060 4257.0 0.0060 0.0060 4719.0 0.0061 0.0061 5226.5 0.0062 0.0062 5784.0 0.0062 0.0062 6538.0 0.0265 0.0265 +1.0 1.2 150 9.0 0.0522 0.0522 11.0 0.0463 0.0463 13.5 0.0403 0.0403 16.5 0.0361 0.0361 19.5 0.0332 0.0332 22.5 0.0312 0.0312 26.0 0.0294 0.0294 30.0 0.0280 0.0280 34.5 0.0256 0.0256 40.0 0.0236 0.0236 46.0 0.0219 0.0219 52.5 0.0204 0.0204 60.0 0.0191 0.0191 69.0 0.0178 0.0178 79.0 0.0166 0.0166 90.5 0.0154 0.0154 105.5 0.0143 0.0143 123.5 0.0131 0.0131 143.0 0.0120 0.0120 163.5 0.0111 0.0111 185.0 0.0103 0.0103 208.0 0.0096 0.0096 232.5 0.0090 0.0090 258.5 0.0084 0.0084 286.0 0.0079 0.0079 331.0 0.0072 0.0072 396.0 0.0064 0.0064 468.5 0.0058 0.0058 549.5 0.0053 0.0053 639.0 0.0050 0.0050 738.0 0.0049 0.0049 847.5 0.0049 0.0049 968.5 0.0050 0.0050 1102.0 0.0052 0.0052 1249.5 0.0054 0.0054 1412.0 0.0057 0.0057 1590.5 0.0060 0.0060 1787.0 0.0063 0.0063 2003.0 0.0067 0.0067 2241.0 0.0070 0.0070 2503.0 0.0073 0.0073 2790.5 0.0076 0.0076 3107.0 0.0079 0.0079 3455.0 0.0082 0.0082 3837.0 0.0084 0.0084 4257.0 0.0085 0.0085 4719.0 0.0086 0.0086 5226.5 0.0086 0.0086 5784.0 0.0087 0.0087 6538.0 0.0146 0.0146 +1.2 1.4 150 9.0 0.0525 0.0525 11.0 0.0457 0.0457 13.5 0.0396 0.0396 16.5 0.0356 0.0356 19.5 0.0328 0.0328 22.5 0.0308 0.0308 26.0 0.0291 0.0291 30.0 0.0276 0.0276 34.5 0.0255 0.0255 40.0 0.0234 0.0234 46.0 0.0216 0.0216 52.5 0.0203 0.0203 60.0 0.0190 0.0190 69.0 0.0177 0.0177 79.0 0.0166 0.0166 90.5 0.0155 0.0155 105.5 0.0143 0.0143 123.5 0.0131 0.0131 143.0 0.0121 0.0121 163.5 0.0113 0.0113 185.0 0.0105 0.0105 208.0 0.0098 0.0098 232.5 0.0092 0.0092 258.5 0.0086 0.0086 286.0 0.0081 0.0081 331.0 0.0075 0.0075 396.0 0.0067 0.0067 468.5 0.0062 0.0062 549.5 0.0058 0.0058 639.0 0.0056 0.0056 738.0 0.0055 0.0055 847.5 0.0055 0.0055 968.5 0.0056 0.0056 1102.0 0.0058 0.0058 1249.5 0.0060 0.0060 1412.0 0.0063 0.0063 1590.5 0.0066 0.0066 1787.0 0.0069 0.0069 2003.0 0.0072 0.0072 2241.0 0.0075 0.0075 2503.0 0.0078 0.0078 2790.5 0.0080 0.0080 3107.0 0.0083 0.0083 3455.0 0.0085 0.0085 3837.0 0.0085 0.0085 4257.0 0.0086 0.0086 4719.0 0.0086 0.0086 5226.5 0.0086 0.0086 5784.0 0.0087 0.0087 6538.0 0.0203 0.0203 +1.4 1.6 150 9.0 0.0552 0.0552 11.0 0.0487 0.0487 13.5 0.0438 0.0438 16.5 0.0406 0.0406 19.5 0.0388 0.0388 22.5 0.0376 0.0376 26.0 0.0366 0.0366 30.0 0.0360 0.0360 34.5 0.0342 0.0342 40.0 0.0324 0.0324 46.0 0.0312 0.0312 52.5 0.0299 0.0299 60.0 0.0289 0.0289 69.0 0.0279 0.0279 79.0 0.0269 0.0269 90.5 0.0260 0.0260 105.5 0.0250 0.0250 123.5 0.0241 0.0241 143.0 0.0233 0.0233 163.5 0.0226 0.0226 185.0 0.0219 0.0219 208.0 0.0214 0.0214 232.5 0.0209 0.0209 258.5 0.0205 0.0205 286.0 0.0201 0.0201 331.0 0.0196 0.0196 396.0 0.0190 0.0190 468.5 0.0186 0.0186 549.5 0.0183 0.0183 639.0 0.0180 0.0180 738.0 0.0179 0.0179 847.5 0.0178 0.0178 968.5 0.0178 0.0178 1102.0 0.0179 0.0179 1249.5 0.0180 0.0180 1412.0 0.0180 0.0180 1590.5 0.0180 0.0180 1787.0 0.0180 0.0180 2003.0 0.0180 0.0180 2241.0 0.0180 0.0180 2503.0 0.0179 0.0179 2790.5 0.0179 0.0179 3107.0 0.0179 0.0179 3455.0 0.0179 0.0179 3837.0 0.0180 0.0180 4257.0 0.0180 0.0180 4719.0 0.0180 0.0180 5226.5 0.0181 0.0181 5784.0 0.0181 0.0181 6538.0 0.0216 0.0216 +1.6 1.8 150 9.0 0.0650 0.0650 11.0 0.0599 0.0599 13.5 0.0555 0.0555 16.5 0.0523 0.0523 19.5 0.0501 0.0501 22.5 0.0486 0.0486 26.0 0.0473 0.0473 30.0 0.0461 0.0461 34.5 0.0439 0.0439 40.0 0.0417 0.0417 46.0 0.0397 0.0397 52.5 0.0380 0.0380 60.0 0.0363 0.0363 69.0 0.0345 0.0345 79.0 0.0329 0.0329 90.5 0.0313 0.0313 105.5 0.0295 0.0295 123.5 0.0277 0.0277 143.0 0.0261 0.0261 163.5 0.0247 0.0247 185.0 0.0234 0.0234 208.0 0.0222 0.0222 232.5 0.0211 0.0211 258.5 0.0201 0.0201 286.0 0.0192 0.0192 331.0 0.0179 0.0179 396.0 0.0165 0.0165 468.5 0.0153 0.0153 549.5 0.0144 0.0144 639.0 0.0136 0.0136 738.0 0.0131 0.0131 847.5 0.0128 0.0128 968.5 0.0127 0.0127 1102.0 0.0128 0.0128 1249.5 0.0127 0.0127 1412.0 0.0127 0.0127 1590.5 0.0127 0.0127 1787.0 0.0125 0.0125 2003.0 0.0123 0.0123 2241.0 0.0122 0.0122 2503.0 0.0122 0.0122 2790.5 0.0122 0.0122 3107.0 0.0122 0.0122 3455.0 0.0123 0.0123 3837.0 0.0123 0.0123 4257.0 0.0124 0.0124 4719.0 0.0124 0.0124 5226.5 0.0124 0.0124 5784.0 0.0125 0.0125 6538.0 0.0406 0.0406 +1.8 2.0 150 9.0 0.0628 0.0628 11.0 0.0582 0.0582 13.5 0.0539 0.0539 16.5 0.0513 0.0513 19.5 0.0494 0.0494 22.5 0.0481 0.0481 26.0 0.0469 0.0469 30.0 0.0458 0.0458 34.5 0.0437 0.0437 40.0 0.0416 0.0416 46.0 0.0396 0.0396 52.5 0.0379 0.0379 60.0 0.0363 0.0363 69.0 0.0346 0.0346 79.0 0.0329 0.0329 90.5 0.0313 0.0313 105.5 0.0295 0.0295 123.5 0.0277 0.0277 143.0 0.0261 0.0261 163.5 0.0247 0.0247 185.0 0.0234 0.0234 208.0 0.0222 0.0222 232.5 0.0212 0.0212 258.5 0.0202 0.0202 286.0 0.0192 0.0192 331.0 0.0180 0.0180 396.0 0.0165 0.0165 468.5 0.0154 0.0154 549.5 0.0144 0.0144 639.0 0.0137 0.0137 738.0 0.0132 0.0132 847.5 0.0128 0.0128 968.5 0.0127 0.0127 1102.0 0.0128 0.0128 1249.5 0.0127 0.0127 1412.0 0.0127 0.0127 1590.5 0.0127 0.0127 1787.0 0.0125 0.0125 2003.0 0.0124 0.0124 2241.0 0.0125 0.0125 2503.0 0.0125 0.0125 2790.5 0.0126 0.0126 3107.0 0.0126 0.0126 3455.0 0.0126 0.0126 3837.0 0.0127 0.0127 4257.0 0.0127 0.0127 4719.0 0.0127 0.0127 5226.5 0.0128 0.0128 5784.0 0.0128 0.0128 6538.0 0.0492 0.0492 +2.0 2.2 150 9.0 0.0600 0.0600 11.0 0.0557 0.0557 13.5 0.0520 0.0520 16.5 0.0493 0.0493 19.5 0.0478 0.0478 22.5 0.0465 0.0465 26.0 0.0455 0.0455 30.0 0.0445 0.0445 34.5 0.0426 0.0426 40.0 0.0406 0.0406 46.0 0.0389 0.0389 52.5 0.0371 0.0371 60.0 0.0355 0.0355 69.0 0.0339 0.0339 79.0 0.0324 0.0324 90.5 0.0308 0.0308 105.5 0.0291 0.0291 123.5 0.0273 0.0273 143.0 0.0258 0.0258 163.5 0.0244 0.0244 185.0 0.0232 0.0232 208.0 0.0220 0.0220 232.5 0.0210 0.0210 258.5 0.0200 0.0200 286.0 0.0192 0.0192 331.0 0.0179 0.0179 396.0 0.0166 0.0166 468.5 0.0154 0.0154 549.5 0.0145 0.0145 639.0 0.0138 0.0138 738.0 0.0133 0.0133 847.5 0.0131 0.0131 968.5 0.0131 0.0131 1102.0 0.0131 0.0131 1249.5 0.0131 0.0131 1412.0 0.0131 0.0131 1590.5 0.0130 0.0130 1787.0 0.0130 0.0130 2003.0 0.0131 0.0131 2241.0 0.0131 0.0131 2503.0 0.0132 0.0132 2790.5 0.0132 0.0132 3107.0 0.0133 0.0133 3455.0 0.0133 0.0133 3837.0 0.0133 0.0133 4257.0 0.0134 0.0134 4719.0 0.0134 0.0134 5226.5 0.0135 0.0135 5784.0 0.0135 0.0135 6538.0 0.0389 0.0389 +2.2 2.4 150 9.0 0.0601 0.0601 11.0 0.0551 0.0551 13.5 0.0507 0.0507 16.5 0.0477 0.0477 19.5 0.0459 0.0459 22.5 0.0446 0.0446 26.0 0.0435 0.0435 30.0 0.0426 0.0426 34.5 0.0409 0.0409 40.0 0.0393 0.0393 46.0 0.0378 0.0378 52.5 0.0365 0.0365 60.0 0.0354 0.0354 69.0 0.0342 0.0342 79.0 0.0331 0.0331 90.5 0.0321 0.0321 105.5 0.0309 0.0309 123.5 0.0298 0.0298 143.0 0.0287 0.0287 163.5 0.0278 0.0278 185.0 0.0270 0.0270 208.0 0.0263 0.0263 232.5 0.0256 0.0256 258.5 0.0250 0.0250 286.0 0.0244 0.0244 331.0 0.0236 0.0236 396.0 0.0227 0.0227 468.5 0.0219 0.0219 549.5 0.0212 0.0212 639.0 0.0206 0.0206 738.0 0.0201 0.0201 847.5 0.0198 0.0198 968.5 0.0196 0.0196 1102.0 0.0193 0.0193 1249.5 0.0190 0.0190 1412.0 0.0190 0.0190 1590.5 0.0190 0.0190 1787.0 0.0190 0.0190 2003.0 0.0191 0.0191 2241.0 0.0191 0.0191 2503.0 0.0191 0.0191 2790.5 0.0191 0.0191 3107.0 0.0191 0.0191 3455.0 0.0191 0.0191 3837.0 0.0192 0.0192 4257.0 0.0192 0.0192 4719.0 0.0192 0.0192 5226.5 0.0192 0.0192 5784.0 0.0192 0.0192 6538.0 0.0192 0.0192 +2.4 2.6 150 9.0 0.0952 0.0952 11.0 0.0878 0.0878 13.5 0.0793 0.0793 16.5 0.0726 0.0726 19.5 0.0680 0.0680 22.5 0.0644 0.0644 26.0 0.0615 0.0615 30.0 0.0583 0.0583 34.5 0.0551 0.0551 40.0 0.0521 0.0521 46.0 0.0489 0.0489 52.5 0.0463 0.0463 60.0 0.0438 0.0438 69.0 0.0413 0.0413 79.0 0.0389 0.0389 90.5 0.0366 0.0366 105.5 0.0342 0.0342 123.5 0.0318 0.0318 143.0 0.0297 0.0297 163.5 0.0279 0.0279 185.0 0.0264 0.0264 208.0 0.0250 0.0250 232.5 0.0238 0.0238 258.5 0.0228 0.0228 286.0 0.0219 0.0219 331.0 0.0208 0.0208 396.0 0.0198 0.0198 468.5 0.0194 0.0194 549.5 0.0200 0.0200 639.0 0.0188 0.0188 738.0 0.0187 0.0187 847.5 0.0188 0.0188 968.5 0.0185 0.0185 1102.0 0.0184 0.0184 1249.5 0.0185 0.0185 1412.0 0.0186 0.0186 1590.5 0.0187 0.0187 1787.0 0.0188 0.0188 2003.0 0.0189 0.0189 2241.0 0.0190 0.0190 2503.0 0.0190 0.0190 2790.5 0.0191 0.0191 3107.0 0.0192 0.0192 3455.0 0.0192 0.0192 3837.0 0.0193 0.0193 4257.0 0.0194 0.0194 4719.0 0.0194 0.0194 5226.5 0.0195 0.0195 5784.0 0.0196 0.0196 6538.0 0.0483 0.0483 +2.6 2.8 150 9.0 0.0817 0.0817 11.0 0.0709 0.0709 13.5 0.0617 0.0617 16.5 0.0555 0.0555 19.5 0.0502 0.0502 22.5 0.0470 0.0470 26.0 0.0442 0.0442 30.0 0.0435 0.0435 34.5 0.0392 0.0392 40.0 0.0363 0.0363 46.0 0.0343 0.0343 52.5 0.0324 0.0324 60.0 0.0310 0.0310 69.0 0.0291 0.0291 79.0 0.0280 0.0280 90.5 0.0273 0.0273 105.5 0.0271 0.0271 123.5 0.0269 0.0269 143.0 0.0270 0.0270 163.5 0.0283 0.0283 185.0 0.0295 0.0295 208.0 0.0319 0.0319 232.5 0.0332 0.0332 258.5 0.0348 0.0348 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0.0525 46.0 0.0492 0.0492 52.5 0.0464 0.0464 60.0 0.0439 0.0439 69.0 0.0413 0.0413 79.0 0.0389 0.0389 90.5 0.0367 0.0367 105.5 0.0342 0.0342 123.5 0.0319 0.0319 143.0 0.0298 0.0298 163.5 0.0280 0.0280 185.0 0.0265 0.0265 208.0 0.0251 0.0251 232.5 0.0240 0.0240 258.5 0.0229 0.0229 286.0 0.0220 0.0220 331.0 0.0208 0.0208 396.0 0.0200 0.0200 468.5 0.0193 0.0193 549.5 0.0191 0.0191 639.0 0.0189 0.0189 738.0 0.0189 0.0189 847.5 0.0194 0.0194 968.5 0.0188 0.0188 1102.0 0.0189 0.0189 1249.5 0.0191 0.0191 1412.0 0.0193 0.0193 1590.5 0.0196 0.0196 1787.0 0.0199 0.0199 2003.0 0.0202 0.0202 2241.0 0.0205 0.0205 2503.0 0.0209 0.0209 2790.5 0.0213 0.0213 3107.0 0.0217 0.0217 3455.0 0.0221 0.0221 3837.0 0.0226 0.0226 4257.0 0.0232 0.0232 4719.0 0.0238 0.0238 5226.5 0.0244 0.0244 5784.0 0.0250 0.0250 6538.0 0.0456 0.0456 +-2.4 -2.2 150 9.0 0.0641 0.0641 11.0 0.0594 0.0594 13.5 0.0540 0.0540 16.5 0.0498 0.0498 19.5 0.0471 0.0471 22.5 0.0452 0.0452 26.0 0.0437 0.0437 30.0 0.0428 0.0428 34.5 0.0411 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0.0502 13.5 0.0423 0.0423 16.5 0.0355 0.0355 19.5 0.0311 0.0311 22.5 0.0274 0.0274 26.0 0.0247 0.0247 30.0 0.0234 0.0234 34.5 0.0214 0.0214 40.0 0.0195 0.0195 46.0 0.0175 0.0175 52.5 0.0162 0.0162 60.0 0.0151 0.0151 69.0 0.0141 0.0141 79.0 0.0132 0.0132 90.5 0.0123 0.0123 105.5 0.0115 0.0115 123.5 0.0108 0.0108 143.0 0.0101 0.0101 163.5 0.0096 0.0096 185.0 0.0091 0.0091 208.0 0.0087 0.0087 232.5 0.0083 0.0083 258.5 0.0079 0.0079 286.0 0.0076 0.0076 331.0 0.0071 0.0071 396.0 0.0065 0.0065 468.5 0.0060 0.0060 549.5 0.0056 0.0056 639.0 0.0054 0.0054 738.0 0.0054 0.0054 847.5 0.0056 0.0056 968.5 0.0059 0.0059 1102.0 0.0063 0.0063 1249.5 0.0067 0.0067 1412.0 0.0071 0.0071 1590.5 0.0075 0.0075 1787.0 0.0080 0.0080 2003.0 0.0085 0.0085 2241.0 0.0090 0.0090 2503.0 0.0095 0.0095 2790.5 0.0100 0.0100 3107.0 0.0106 0.0106 3455.0 0.0111 0.0111 3837.0 0.0118 0.0118 4257.0 0.0124 0.0124 4719.0 0.0131 0.0131 5226.5 0.0138 0.0138 5784.0 0.0144 0.0144 6538.0 0.0417 0.0417 +-0.8 -0.6 150 9.0 0.0549 0.0549 11.0 0.0477 0.0477 13.5 0.0399 0.0399 16.5 0.0331 0.0331 19.5 0.0284 0.0284 22.5 0.0252 0.0252 26.0 0.0226 0.0226 30.0 0.0214 0.0214 34.5 0.0193 0.0193 40.0 0.0170 0.0170 46.0 0.0156 0.0156 52.5 0.0142 0.0142 60.0 0.0132 0.0132 69.0 0.0121 0.0121 79.0 0.0113 0.0113 90.5 0.0106 0.0106 105.5 0.0098 0.0098 123.5 0.0091 0.0091 143.0 0.0085 0.0085 163.5 0.0080 0.0080 185.0 0.0076 0.0076 208.0 0.0073 0.0073 232.5 0.0069 0.0069 258.5 0.0066 0.0066 286.0 0.0063 0.0063 331.0 0.0059 0.0059 396.0 0.0054 0.0054 468.5 0.0051 0.0051 549.5 0.0048 0.0048 639.0 0.0048 0.0048 738.0 0.0048 0.0048 847.5 0.0051 0.0051 968.5 0.0055 0.0055 1102.0 0.0060 0.0060 1249.5 0.0063 0.0063 1412.0 0.0067 0.0067 1590.5 0.0071 0.0071 1787.0 0.0075 0.0075 2003.0 0.0079 0.0079 2241.0 0.0083 0.0083 2503.0 0.0088 0.0088 2790.5 0.0092 0.0092 3107.0 0.0096 0.0096 3455.0 0.0101 0.0101 3837.0 0.0106 0.0106 4257.0 0.0112 0.0112 4719.0 0.0118 0.0118 5226.5 0.0123 0.0123 5784.0 0.0128 0.0128 6538.0 0.0147 0.0147 +-0.6 -0.4 150 9.0 0.0537 0.0537 11.0 0.0470 0.0470 13.5 0.0392 0.0392 16.5 0.0326 0.0326 19.5 0.0285 0.0285 22.5 0.0252 0.0252 26.0 0.0230 0.0230 30.0 0.0219 0.0219 34.5 0.0196 0.0196 40.0 0.0177 0.0177 46.0 0.0161 0.0161 52.5 0.0148 0.0148 60.0 0.0137 0.0137 69.0 0.0127 0.0127 79.0 0.0118 0.0118 90.5 0.0109 0.0109 105.5 0.0101 0.0101 123.5 0.0094 0.0094 143.0 0.0087 0.0087 163.5 0.0082 0.0082 185.0 0.0077 0.0077 208.0 0.0073 0.0073 232.5 0.0069 0.0069 258.5 0.0066 0.0066 286.0 0.0063 0.0063 331.0 0.0059 0.0059 396.0 0.0054 0.0054 468.5 0.0051 0.0051 549.5 0.0050 0.0050 639.0 0.0050 0.0050 738.0 0.0052 0.0052 847.5 0.0055 0.0055 968.5 0.0060 0.0060 1102.0 0.0065 0.0065 1249.5 0.0069 0.0069 1412.0 0.0074 0.0074 1590.5 0.0078 0.0078 1787.0 0.0083 0.0083 2003.0 0.0088 0.0088 2241.0 0.0092 0.0092 2503.0 0.0097 0.0097 2790.5 0.0102 0.0102 3107.0 0.0106 0.0106 3455.0 0.0109 0.0109 3837.0 0.0114 0.0114 4257.0 0.0118 0.0118 4719.0 0.0123 0.0123 5226.5 0.0127 0.0127 5784.0 0.0132 0.0132 6538.0 0.0305 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0.0114 46.0 0.0101 0.0101 52.5 0.0091 0.0091 60.0 0.0083 0.0083 69.0 0.0077 0.0077 79.0 0.0072 0.0072 90.5 0.0067 0.0067 105.5 0.0064 0.0064 123.5 0.0062 0.0062 143.0 0.0060 0.0060 163.5 0.0058 0.0058 185.0 0.0057 0.0057 208.0 0.0056 0.0056 232.5 0.0055 0.0055 258.5 0.0054 0.0054 286.0 0.0053 0.0053 331.0 0.0051 0.0051 396.0 0.0049 0.0049 468.5 0.0047 0.0047 549.5 0.0046 0.0046 639.0 0.0047 0.0047 738.0 0.0048 0.0048 847.5 0.0051 0.0051 968.5 0.0054 0.0054 1102.0 0.0059 0.0059 1249.5 0.0062 0.0062 1412.0 0.0065 0.0065 1590.5 0.0069 0.0069 1787.0 0.0073 0.0073 2003.0 0.0077 0.0077 2241.0 0.0081 0.0081 2503.0 0.0085 0.0085 2790.5 0.0089 0.0089 3107.0 0.0094 0.0094 3455.0 0.0098 0.0098 3837.0 0.0104 0.0104 4257.0 0.0110 0.0110 4719.0 0.0115 0.0115 5226.5 0.0121 0.0121 5784.0 0.0126 0.0126 6538.0 0.0292 0.0292 +0.8 1.0 150 9.0 0.0577 0.0577 11.0 0.0495 0.0495 13.5 0.0404 0.0404 16.5 0.0331 0.0331 19.5 0.0276 0.0276 22.5 0.0229 0.0229 26.0 0.0193 0.0193 30.0 0.0176 0.0176 34.5 0.0162 0.0162 40.0 0.0140 0.0140 46.0 0.0132 0.0132 52.5 0.0122 0.0122 60.0 0.0112 0.0112 69.0 0.0103 0.0103 79.0 0.0098 0.0098 90.5 0.0093 0.0093 105.5 0.0088 0.0088 123.5 0.0084 0.0084 143.0 0.0081 0.0081 163.5 0.0079 0.0079 185.0 0.0076 0.0076 208.0 0.0074 0.0074 232.5 0.0072 0.0072 258.5 0.0070 0.0070 286.0 0.0067 0.0067 331.0 0.0064 0.0064 396.0 0.0060 0.0060 468.5 0.0057 0.0057 549.5 0.0055 0.0055 639.0 0.0053 0.0053 738.0 0.0054 0.0054 847.5 0.0055 0.0055 968.5 0.0059 0.0059 1102.0 0.0063 0.0063 1249.5 0.0066 0.0066 1412.0 0.0070 0.0070 1590.5 0.0074 0.0074 1787.0 0.0078 0.0078 2003.0 0.0083 0.0083 2241.0 0.0088 0.0088 2503.0 0.0093 0.0093 2790.5 0.0099 0.0099 3107.0 0.0104 0.0104 3455.0 0.0110 0.0110 3837.0 0.0117 0.0117 4257.0 0.0124 0.0124 4719.0 0.0131 0.0131 5226.5 0.0137 0.0137 5784.0 0.0144 0.0144 6538.0 0.0299 0.0299 +1.0 1.2 150 9.0 0.0668 0.0668 11.0 0.0587 0.0587 13.5 0.0491 0.0491 16.5 0.0412 0.0412 19.5 0.0351 0.0351 22.5 0.0307 0.0307 26.0 0.0267 0.0267 30.0 0.0245 0.0245 34.5 0.0224 0.0224 40.0 0.0206 0.0206 46.0 0.0190 0.0190 52.5 0.0176 0.0176 60.0 0.0163 0.0163 69.0 0.0152 0.0152 79.0 0.0141 0.0141 90.5 0.0132 0.0132 105.5 0.0122 0.0122 123.5 0.0113 0.0113 143.0 0.0106 0.0106 163.5 0.0100 0.0100 185.0 0.0094 0.0094 208.0 0.0089 0.0089 232.5 0.0084 0.0084 258.5 0.0079 0.0079 286.0 0.0075 0.0075 331.0 0.0069 0.0069 396.0 0.0061 0.0061 468.5 0.0056 0.0056 549.5 0.0052 0.0052 639.0 0.0050 0.0050 738.0 0.0050 0.0050 847.5 0.0053 0.0053 968.5 0.0058 0.0058 1102.0 0.0065 0.0065 1249.5 0.0070 0.0070 1412.0 0.0076 0.0076 1590.5 0.0082 0.0082 1787.0 0.0089 0.0089 2003.0 0.0096 0.0096 2241.0 0.0103 0.0103 2503.0 0.0111 0.0111 2790.5 0.0118 0.0118 3107.0 0.0126 0.0126 3455.0 0.0134 0.0134 3837.0 0.0142 0.0142 4257.0 0.0150 0.0150 4719.0 0.0157 0.0157 5226.5 0.0165 0.0165 5784.0 0.0172 0.0172 6538.0 0.0216 0.0216 +1.2 1.4 150 9.0 0.0661 0.0661 11.0 0.0592 0.0592 13.5 0.0509 0.0509 16.5 0.0438 0.0438 19.5 0.0380 0.0380 22.5 0.0333 0.0333 26.0 0.0290 0.0290 30.0 0.0265 0.0265 34.5 0.0243 0.0243 40.0 0.0221 0.0221 46.0 0.0201 0.0201 52.5 0.0185 0.0185 60.0 0.0170 0.0170 69.0 0.0155 0.0155 79.0 0.0143 0.0143 90.5 0.0133 0.0133 105.5 0.0123 0.0123 123.5 0.0114 0.0114 143.0 0.0107 0.0107 163.5 0.0101 0.0101 185.0 0.0096 0.0096 208.0 0.0092 0.0092 232.5 0.0087 0.0087 258.5 0.0083 0.0083 286.0 0.0079 0.0079 331.0 0.0073 0.0073 396.0 0.0067 0.0067 468.5 0.0061 0.0061 549.5 0.0057 0.0057 639.0 0.0055 0.0055 738.0 0.0056 0.0056 847.5 0.0060 0.0060 968.5 0.0065 0.0065 1102.0 0.0073 0.0073 1249.5 0.0079 0.0079 1412.0 0.0086 0.0086 1590.5 0.0094 0.0094 1787.0 0.0103 0.0103 2003.0 0.0111 0.0111 2241.0 0.0120 0.0120 2503.0 0.0130 0.0130 2790.5 0.0139 0.0139 3107.0 0.0148 0.0148 3455.0 0.0157 0.0157 3837.0 0.0167 0.0167 4257.0 0.0176 0.0176 4719.0 0.0186 0.0186 5226.5 0.0196 0.0196 5784.0 0.0205 0.0205 6538.0 0.0284 0.0284 +1.4 1.6 150 9.0 0.0631 0.0631 11.0 0.0570 0.0570 13.5 0.0502 0.0502 16.5 0.0447 0.0447 19.5 0.0407 0.0407 22.5 0.0376 0.0376 26.0 0.0351 0.0351 30.0 0.0340 0.0340 34.5 0.0325 0.0325 40.0 0.0309 0.0309 46.0 0.0297 0.0297 52.5 0.0285 0.0285 60.0 0.0274 0.0274 69.0 0.0264 0.0264 79.0 0.0255 0.0255 90.5 0.0247 0.0247 105.5 0.0239 0.0239 123.5 0.0232 0.0232 143.0 0.0225 0.0225 163.5 0.0220 0.0220 185.0 0.0215 0.0215 208.0 0.0211 0.0211 232.5 0.0207 0.0207 258.5 0.0203 0.0203 286.0 0.0200 0.0200 331.0 0.0195 0.0195 396.0 0.0190 0.0190 468.5 0.0185 0.0185 549.5 0.0182 0.0182 639.0 0.0180 0.0180 738.0 0.0179 0.0179 847.5 0.0179 0.0179 968.5 0.0181 0.0181 1102.0 0.0183 0.0183 1249.5 0.0186 0.0186 1412.0 0.0188 0.0188 1590.5 0.0190 0.0190 1787.0 0.0193 0.0193 2003.0 0.0196 0.0196 2241.0 0.0199 0.0199 2503.0 0.0203 0.0203 2790.5 0.0206 0.0206 3107.0 0.0211 0.0211 3455.0 0.0216 0.0216 3837.0 0.0222 0.0222 4257.0 0.0228 0.0228 4719.0 0.0234 0.0234 5226.5 0.0241 0.0241 5784.0 0.0248 0.0248 6538.0 0.0281 0.0281 +1.6 1.8 150 9.0 0.0675 0.0675 11.0 0.0629 0.0629 13.5 0.0576 0.0576 16.5 0.0531 0.0531 19.5 0.0499 0.0499 22.5 0.0475 0.0475 26.0 0.0454 0.0454 30.0 0.0440 0.0440 34.5 0.0422 0.0422 40.0 0.0403 0.0403 46.0 0.0384 0.0384 52.5 0.0367 0.0367 60.0 0.0351 0.0351 69.0 0.0333 0.0333 79.0 0.0317 0.0317 90.5 0.0303 0.0303 105.5 0.0286 0.0286 123.5 0.0269 0.0269 143.0 0.0255 0.0255 163.5 0.0242 0.0242 185.0 0.0230 0.0230 208.0 0.0219 0.0219 232.5 0.0209 0.0209 258.5 0.0200 0.0200 286.0 0.0191 0.0191 331.0 0.0179 0.0179 396.0 0.0164 0.0164 468.5 0.0153 0.0153 549.5 0.0143 0.0143 639.0 0.0136 0.0136 738.0 0.0132 0.0132 847.5 0.0130 0.0130 968.5 0.0132 0.0132 1102.0 0.0135 0.0135 1249.5 0.0138 0.0138 1412.0 0.0141 0.0141 1590.5 0.0145 0.0145 1787.0 0.0148 0.0148 2003.0 0.0152 0.0152 2241.0 0.0156 0.0156 2503.0 0.0163 0.0163 2790.5 0.0170 0.0170 3107.0 0.0178 0.0178 3455.0 0.0185 0.0185 3837.0 0.0194 0.0194 4257.0 0.0204 0.0204 4719.0 0.0213 0.0213 5226.5 0.0222 0.0222 5784.0 0.0232 0.0232 6538.0 0.0456 0.0456 +1.8 2.0 150 9.0 0.0646 0.0646 11.0 0.0600 0.0600 13.5 0.0546 0.0546 16.5 0.0510 0.0510 19.5 0.0483 0.0483 22.5 0.0463 0.0463 26.0 0.0445 0.0445 30.0 0.0434 0.0434 34.5 0.0418 0.0418 40.0 0.0399 0.0399 46.0 0.0382 0.0382 52.5 0.0366 0.0366 60.0 0.0350 0.0350 69.0 0.0334 0.0334 79.0 0.0318 0.0318 90.5 0.0302 0.0302 105.5 0.0286 0.0286 123.5 0.0270 0.0270 143.0 0.0255 0.0255 163.5 0.0242 0.0242 185.0 0.0231 0.0231 208.0 0.0220 0.0220 232.5 0.0210 0.0210 258.5 0.0200 0.0200 286.0 0.0191 0.0191 331.0 0.0179 0.0179 396.0 0.0165 0.0165 468.5 0.0153 0.0153 549.5 0.0143 0.0143 639.0 0.0136 0.0136 738.0 0.0133 0.0133 847.5 0.0132 0.0132 968.5 0.0134 0.0134 1102.0 0.0139 0.0139 1249.5 0.0142 0.0142 1412.0 0.0147 0.0147 1590.5 0.0153 0.0153 1787.0 0.0158 0.0158 2003.0 0.0165 0.0165 2241.0 0.0174 0.0174 2503.0 0.0183 0.0183 2790.5 0.0193 0.0193 3107.0 0.0203 0.0203 3455.0 0.0213 0.0213 3837.0 0.0225 0.0225 4257.0 0.0237 0.0237 4719.0 0.0249 0.0249 5226.5 0.0261 0.0261 5784.0 0.0273 0.0273 6538.0 0.0555 0.0555 +2.0 2.2 150 9.0 0.0620 0.0620 11.0 0.0580 0.0580 13.5 0.0531 0.0531 16.5 0.0493 0.0493 19.5 0.0469 0.0469 22.5 0.0448 0.0448 26.0 0.0432 0.0432 30.0 0.0420 0.0420 34.5 0.0406 0.0406 40.0 0.0390 0.0390 46.0 0.0374 0.0374 52.5 0.0358 0.0358 60.0 0.0342 0.0342 69.0 0.0327 0.0327 79.0 0.0312 0.0312 90.5 0.0297 0.0297 105.5 0.0281 0.0281 123.5 0.0266 0.0266 143.0 0.0252 0.0252 163.5 0.0239 0.0239 185.0 0.0228 0.0228 208.0 0.0218 0.0218 232.5 0.0208 0.0208 258.5 0.0199 0.0199 286.0 0.0191 0.0191 331.0 0.0179 0.0179 396.0 0.0165 0.0165 468.5 0.0154 0.0154 549.5 0.0145 0.0145 639.0 0.0138 0.0138 738.0 0.0135 0.0135 847.5 0.0136 0.0136 968.5 0.0140 0.0140 1102.0 0.0147 0.0147 1249.5 0.0154 0.0154 1412.0 0.0161 0.0161 1590.5 0.0169 0.0169 1787.0 0.0179 0.0179 2003.0 0.0190 0.0190 2241.0 0.0202 0.0202 2503.0 0.0214 0.0214 2790.5 0.0227 0.0227 3107.0 0.0241 0.0241 3455.0 0.0254 0.0254 3837.0 0.0270 0.0270 4257.0 0.0285 0.0285 4719.0 0.0301 0.0301 5226.5 0.0317 0.0317 5784.0 0.0333 0.0333 6538.0 0.0507 0.0507 +2.2 2.4 150 9.0 0.0626 0.0626 11.0 0.0575 0.0575 13.5 0.0516 0.0516 16.5 0.0472 0.0472 19.5 0.0443 0.0443 22.5 0.0423 0.0423 26.0 0.0407 0.0407 30.0 0.0397 0.0397 34.5 0.0385 0.0385 40.0 0.0372 0.0372 46.0 0.0360 0.0360 52.5 0.0350 0.0350 60.0 0.0339 0.0339 69.0 0.0329 0.0329 79.0 0.0320 0.0320 90.5 0.0311 0.0311 105.5 0.0300 0.0300 123.5 0.0291 0.0291 143.0 0.0282 0.0282 163.5 0.0274 0.0274 185.0 0.0267 0.0267 208.0 0.0260 0.0260 232.5 0.0254 0.0254 258.5 0.0248 0.0248 286.0 0.0243 0.0243 331.0 0.0235 0.0235 396.0 0.0226 0.0226 468.5 0.0218 0.0218 549.5 0.0212 0.0212 639.0 0.0206 0.0206 738.0 0.0203 0.0203 847.5 0.0202 0.0202 968.5 0.0203 0.0203 1102.0 0.0205 0.0205 1249.5 0.0206 0.0206 1412.0 0.0211 0.0211 1590.5 0.0218 0.0218 1787.0 0.0226 0.0226 2003.0 0.0235 0.0235 2241.0 0.0244 0.0244 2503.0 0.0254 0.0254 2790.5 0.0265 0.0265 3107.0 0.0277 0.0277 3455.0 0.0289 0.0289 3837.0 0.0303 0.0303 4257.0 0.0317 0.0317 4719.0 0.0331 0.0331 5226.5 0.0346 0.0346 5784.0 0.0361 0.0361 6538.0 0.0380 0.0380 +2.4 2.6 150 9.0 0.1177 0.1177 11.0 0.1036 0.1036 13.5 0.0892 0.0892 16.5 0.0778 0.0778 19.5 0.0703 0.0703 22.5 0.0648 0.0648 26.0 0.0605 0.0605 30.0 0.0567 0.0567 34.5 0.0536 0.0536 40.0 0.0507 0.0507 46.0 0.0476 0.0476 52.5 0.0451 0.0451 60.0 0.0426 0.0426 69.0 0.0402 0.0402 79.0 0.0379 0.0379 90.5 0.0357 0.0357 105.5 0.0334 0.0334 123.5 0.0312 0.0312 143.0 0.0292 0.0292 163.5 0.0275 0.0275 185.0 0.0260 0.0260 208.0 0.0247 0.0247 232.5 0.0236 0.0236 258.5 0.0226 0.0226 286.0 0.0217 0.0217 331.0 0.0207 0.0207 396.0 0.0197 0.0197 468.5 0.0193 0.0193 549.5 0.0199 0.0199 639.0 0.0188 0.0188 738.0 0.0188 0.0188 847.5 0.0189 0.0189 968.5 0.0188 0.0188 1102.0 0.0189 0.0189 1249.5 0.0192 0.0192 1412.0 0.0194 0.0194 1590.5 0.0198 0.0198 1787.0 0.0201 0.0201 2003.0 0.0205 0.0205 2241.0 0.0209 0.0209 2503.0 0.0214 0.0214 2790.5 0.0219 0.0219 3107.0 0.0224 0.0224 3455.0 0.0229 0.0229 3837.0 0.0236 0.0236 4257.0 0.0244 0.0244 4719.0 0.0251 0.0251 5226.5 0.0259 0.0259 5784.0 0.0267 0.0267 6538.0 0.0520 0.0520 +2.6 2.8 150 9.0 0.0912 0.0912 11.0 0.0779 0.0779 13.5 0.0655 0.0655 16.5 0.0566 0.0566 19.5 0.0495 0.0495 22.5 0.0452 0.0452 26.0 0.0415 0.0415 30.0 0.0406 0.0406 34.5 0.0366 0.0366 40.0 0.0340 0.0340 46.0 0.0322 0.0322 52.5 0.0305 0.0305 60.0 0.0293 0.0293 69.0 0.0275 0.0275 79.0 0.0266 0.0266 90.5 0.0261 0.0261 105.5 0.0261 0.0261 123.5 0.0261 0.0261 143.0 0.0264 0.0264 163.5 0.0278 0.0278 185.0 0.0292 0.0292 208.0 0.0317 0.0317 232.5 0.0330 0.0330 258.5 0.0347 0.0347 286.0 0.0370 0.0370 331.0 0.0393 0.0393 396.0 0.0434 0.0434 468.5 0.0464 0.0464 549.5 0.0500 0.0500 639.0 0.0456 0.0456 738.0 0.0460 0.0460 847.5 0.0463 0.0463 968.5 0.0465 0.0465 1102.0 0.0468 0.0468 1249.5 0.0470 0.0470 1412.0 0.0473 0.0473 1590.5 0.0475 0.0475 1787.0 0.0478 0.0478 2003.0 0.0481 0.0481 2241.0 0.0485 0.0485 2503.0 0.0488 0.0488 2790.5 0.0492 0.0492 3107.0 0.0496 0.0496 3455.0 0.0501 0.0501 3837.0 0.0506 0.0506 4257.0 0.0511 0.0511 4719.0 0.0517 0.0517 5226.5 0.0523 0.0523 5784.0 0.0530 0.0530 6538.0 0.0861 0.0861 +2.8 3.0 150 9.0 0.0935 0.0935 11.0 0.0776 0.0776 13.5 0.0638 0.0638 16.5 0.0542 0.0542 19.5 0.0453 0.0453 22.5 0.0435 0.0435 26.0 0.0399 0.0399 30.0 0.0379 0.0379 34.5 0.0340 0.0340 40.0 0.0374 0.0374 46.0 0.0345 0.0345 52.5 0.0341 0.0341 60.0 0.0344 0.0344 69.0 0.0354 0.0354 79.0 0.0365 0.0365 90.5 0.0385 0.0385 105.5 0.0395 0.0395 123.5 0.0409 0.0409 143.0 0.0419 0.0419 163.5 0.0441 0.0441 185.0 0.0452 0.0452 208.0 0.0470 0.0470 232.5 0.0491 0.0491 258.5 0.0513 0.0513 286.0 0.0537 0.0537 331.0 0.0595 0.0595 396.0 0.0561 0.0561 468.5 0.0558 0.0558 549.5 0.0582 0.0582 639.0 0.0581 0.0581 738.0 0.0584 0.0584 847.5 0.0585 0.0585 968.5 0.0587 0.0587 1102.0 0.0589 0.0589 1249.5 0.0591 0.0591 1412.0 0.0593 0.0593 1590.5 0.0595 0.0595 1787.0 0.0597 0.0597 2003.0 0.0599 0.0599 2241.0 0.0601 0.0601 2503.0 0.0603 0.0603 2790.5 0.0606 0.0606 3107.0 0.0609 0.0609 3455.0 0.0612 0.0612 3837.0 0.0615 0.0615 4257.0 0.0619 0.0619 4719.0 0.0624 0.0624 5226.5 0.0628 0.0628 5784.0 0.0816 0.0816 6538.0 0.1451 0.1451 +3.0 3.5 150 9.0 0.1265 0.1265 11.0 0.1070 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0.0058 1102.0 0.0064 0.0064 1249.5 0.0068 0.0068 1412.0 0.0072 0.0072 1590.5 0.0076 0.0076 1787.0 0.0080 0.0080 2003.0 0.0085 0.0085 2241.0 0.0089 0.0089 2503.0 0.0093 0.0093 2790.5 0.0097 0.0097 3107.0 0.0100 0.0100 3455.0 0.0104 0.0104 3837.0 0.0108 0.0108 4257.0 0.0112 0.0112 4719.0 0.0117 0.0117 5226.5 0.0121 0.0121 5784.0 0.0125 0.0125 6538.0 0.0130 0.0130 +0.0 0.2 150 9.0 0.0546 0.0546 11.0 0.0471 0.0471 13.5 0.0391 0.0391 16.5 0.0325 0.0325 19.5 0.0280 0.0280 22.5 0.0249 0.0249 26.0 0.0226 0.0226 30.0 0.0213 0.0213 34.5 0.0192 0.0192 40.0 0.0172 0.0172 46.0 0.0156 0.0156 52.5 0.0143 0.0143 60.0 0.0132 0.0132 69.0 0.0122 0.0122 79.0 0.0113 0.0113 90.5 0.0104 0.0104 105.5 0.0096 0.0096 123.5 0.0088 0.0088 143.0 0.0082 0.0082 163.5 0.0077 0.0077 185.0 0.0072 0.0072 208.0 0.0068 0.0068 232.5 0.0064 0.0064 258.5 0.0061 0.0061 286.0 0.0058 0.0058 331.0 0.0054 0.0054 396.0 0.0051 0.0051 468.5 0.0048 0.0048 549.5 0.0047 0.0047 639.0 0.0048 0.0048 738.0 0.0050 0.0050 847.5 0.0054 0.0054 968.5 0.0058 0.0058 1102.0 0.0064 0.0064 1249.5 0.0068 0.0068 1412.0 0.0072 0.0072 1590.5 0.0076 0.0076 1787.0 0.0080 0.0080 2003.0 0.0085 0.0085 2241.0 0.0089 0.0089 2503.0 0.0093 0.0093 2790.5 0.0097 0.0097 3107.0 0.0101 0.0101 3455.0 0.0104 0.0104 3837.0 0.0109 0.0109 4257.0 0.0113 0.0113 4719.0 0.0117 0.0117 5226.5 0.0122 0.0122 5784.0 0.0126 0.0126 6538.0 0.0131 0.0131 +0.2 0.4 150 9.0 0.0542 0.0542 11.0 0.0467 0.0467 13.5 0.0386 0.0386 16.5 0.0320 0.0320 19.5 0.0275 0.0275 22.5 0.0244 0.0244 26.0 0.0221 0.0221 30.0 0.0209 0.0209 34.5 0.0187 0.0187 40.0 0.0168 0.0168 46.0 0.0152 0.0152 52.5 0.0139 0.0139 60.0 0.0129 0.0129 69.0 0.0119 0.0119 79.0 0.0110 0.0110 90.5 0.0102 0.0102 105.5 0.0094 0.0094 123.5 0.0087 0.0087 143.0 0.0081 0.0081 163.5 0.0076 0.0076 185.0 0.0072 0.0072 208.0 0.0068 0.0068 232.5 0.0065 0.0065 258.5 0.0062 0.0062 286.0 0.0059 0.0059 331.0 0.0056 0.0056 396.0 0.0052 0.0052 468.5 0.0050 0.0050 549.5 0.0049 0.0049 639.0 0.0049 0.0049 738.0 0.0051 0.0051 847.5 0.0055 0.0055 968.5 0.0059 0.0059 1102.0 0.0065 0.0065 1249.5 0.0068 0.0068 1412.0 0.0072 0.0072 1590.5 0.0077 0.0077 1787.0 0.0081 0.0081 2003.0 0.0085 0.0085 2241.0 0.0089 0.0089 2503.0 0.0093 0.0093 2790.5 0.0098 0.0098 3107.0 0.0101 0.0101 3455.0 0.0104 0.0104 3837.0 0.0108 0.0108 4257.0 0.0112 0.0112 4719.0 0.0116 0.0116 5226.5 0.0120 0.0120 5784.0 0.0124 0.0124 6538.0 0.0129 0.0129 +0.4 0.6 150 9.0 0.0535 0.0535 11.0 0.0463 0.0463 13.5 0.0385 0.0385 16.5 0.0321 0.0321 19.5 0.0276 0.0276 22.5 0.0245 0.0245 26.0 0.0221 0.0221 30.0 0.0209 0.0209 34.5 0.0187 0.0187 40.0 0.0167 0.0167 46.0 0.0151 0.0151 52.5 0.0139 0.0139 60.0 0.0129 0.0129 69.0 0.0118 0.0118 79.0 0.0110 0.0110 90.5 0.0102 0.0102 105.5 0.0094 0.0094 123.5 0.0087 0.0087 143.0 0.0081 0.0081 163.5 0.0076 0.0076 185.0 0.0072 0.0072 208.0 0.0068 0.0068 232.5 0.0065 0.0065 258.5 0.0062 0.0062 286.0 0.0059 0.0059 331.0 0.0056 0.0056 396.0 0.0052 0.0052 468.5 0.0050 0.0050 549.5 0.0049 0.0049 639.0 0.0049 0.0049 738.0 0.0051 0.0051 847.5 0.0055 0.0055 968.5 0.0060 0.0060 1102.0 0.0065 0.0065 1249.5 0.0069 0.0069 1412.0 0.0073 0.0073 1590.5 0.0078 0.0078 1787.0 0.0082 0.0082 2003.0 0.0087 0.0087 2241.0 0.0092 0.0092 2503.0 0.0096 0.0096 2790.5 0.0100 0.0100 3107.0 0.0104 0.0104 3455.0 0.0108 0.0108 3837.0 0.0112 0.0112 4257.0 0.0117 0.0117 4719.0 0.0121 0.0121 5226.5 0.0126 0.0126 5784.0 0.0130 0.0130 6538.0 0.0136 0.0136 +0.6 0.8 150 9.0 0.0546 0.0546 11.0 0.0472 0.0472 13.5 0.0391 0.0391 16.5 0.0324 0.0324 19.5 0.0277 0.0277 22.5 0.0244 0.0244 26.0 0.0219 0.0219 30.0 0.0206 0.0206 34.5 0.0184 0.0184 40.0 0.0164 0.0164 46.0 0.0147 0.0147 52.5 0.0134 0.0134 60.0 0.0124 0.0124 69.0 0.0114 0.0114 79.0 0.0106 0.0106 90.5 0.0099 0.0099 105.5 0.0091 0.0091 123.5 0.0085 0.0085 143.0 0.0080 0.0080 163.5 0.0075 0.0075 185.0 0.0071 0.0071 208.0 0.0068 0.0068 232.5 0.0065 0.0065 258.5 0.0062 0.0062 286.0 0.0060 0.0060 331.0 0.0056 0.0056 396.0 0.0052 0.0052 468.5 0.0049 0.0049 549.5 0.0047 0.0047 639.0 0.0047 0.0047 738.0 0.0048 0.0048 847.5 0.0050 0.0050 968.5 0.0054 0.0054 1102.0 0.0059 0.0059 1249.5 0.0063 0.0063 1412.0 0.0066 0.0066 1590.5 0.0070 0.0070 1787.0 0.0074 0.0074 2003.0 0.0079 0.0079 2241.0 0.0083 0.0083 2503.0 0.0087 0.0087 2790.5 0.0091 0.0091 3107.0 0.0096 0.0096 3455.0 0.0100 0.0100 3837.0 0.0105 0.0105 4257.0 0.0111 0.0111 4719.0 0.0116 0.0116 5226.5 0.0122 0.0122 5784.0 0.0127 0.0127 6538.0 0.0133 0.0133 +0.8 1.0 150 9.0 0.0595 0.0595 11.0 0.0515 0.0515 13.5 0.0430 0.0430 16.5 0.0358 0.0358 19.5 0.0306 0.0306 22.5 0.0268 0.0268 26.0 0.0238 0.0238 30.0 0.0224 0.0224 34.5 0.0201 0.0201 40.0 0.0180 0.0180 46.0 0.0164 0.0164 52.5 0.0150 0.0150 60.0 0.0139 0.0139 69.0 0.0129 0.0129 79.0 0.0121 0.0121 90.5 0.0113 0.0113 105.5 0.0106 0.0106 123.5 0.0099 0.0099 143.0 0.0094 0.0094 163.5 0.0089 0.0089 185.0 0.0085 0.0085 208.0 0.0081 0.0081 232.5 0.0078 0.0078 258.5 0.0075 0.0075 286.0 0.0072 0.0072 331.0 0.0067 0.0067 396.0 0.0063 0.0063 468.5 0.0058 0.0058 549.5 0.0055 0.0055 639.0 0.0054 0.0054 738.0 0.0054 0.0054 847.5 0.0055 0.0055 968.5 0.0059 0.0059 1102.0 0.0063 0.0063 1249.5 0.0067 0.0067 1412.0 0.0070 0.0070 1590.5 0.0075 0.0075 1787.0 0.0080 0.0080 2003.0 0.0085 0.0085 2241.0 0.0090 0.0090 2503.0 0.0095 0.0095 2790.5 0.0100 0.0100 3107.0 0.0105 0.0105 3455.0 0.0111 0.0111 3837.0 0.0118 0.0118 4257.0 0.0124 0.0124 4719.0 0.0131 0.0131 5226.5 0.0138 0.0138 5784.0 0.0144 0.0144 6538.0 0.0153 0.0153 +1.0 1.2 150 9.0 0.0684 0.0684 11.0 0.0603 0.0603 13.5 0.0513 0.0513 16.5 0.0437 0.0437 19.5 0.0381 0.0381 22.5 0.0340 0.0340 26.0 0.0306 0.0306 30.0 0.0287 0.0287 34.5 0.0262 0.0262 40.0 0.0238 0.0238 46.0 0.0219 0.0219 52.5 0.0202 0.0202 60.0 0.0188 0.0188 69.0 0.0174 0.0174 79.0 0.0162 0.0162 90.5 0.0151 0.0151 105.5 0.0139 0.0139 123.5 0.0128 0.0128 143.0 0.0119 0.0119 163.5 0.0111 0.0111 185.0 0.0104 0.0104 208.0 0.0098 0.0098 232.5 0.0092 0.0092 258.5 0.0086 0.0086 286.0 0.0081 0.0081 331.0 0.0073 0.0073 396.0 0.0065 0.0065 468.5 0.0058 0.0058 549.5 0.0053 0.0053 639.0 0.0050 0.0050 738.0 0.0050 0.0050 847.5 0.0053 0.0053 968.5 0.0058 0.0058 1102.0 0.0065 0.0065 1249.5 0.0070 0.0070 1412.0 0.0076 0.0076 1590.5 0.0083 0.0083 1787.0 0.0090 0.0090 2003.0 0.0098 0.0098 2241.0 0.0105 0.0105 2503.0 0.0112 0.0112 2790.5 0.0120 0.0120 3107.0 0.0127 0.0127 3455.0 0.0135 0.0135 3837.0 0.0144 0.0144 4257.0 0.0151 0.0151 4719.0 0.0158 0.0158 5226.5 0.0166 0.0166 5784.0 0.0173 0.0173 6538.0 0.0182 0.0182 +1.2 1.4 150 9.0 0.0678 0.0678 11.0 0.0611 0.0611 13.5 0.0533 0.0533 16.5 0.0463 0.0463 19.5 0.0408 0.0408 22.5 0.0365 0.0365 26.0 0.0326 0.0326 30.0 0.0305 0.0305 34.5 0.0278 0.0278 40.0 0.0252 0.0252 46.0 0.0229 0.0229 52.5 0.0210 0.0210 60.0 0.0194 0.0194 69.0 0.0178 0.0178 79.0 0.0164 0.0164 90.5 0.0152 0.0152 105.5 0.0140 0.0140 123.5 0.0129 0.0129 143.0 0.0120 0.0120 163.5 0.0113 0.0113 185.0 0.0106 0.0106 208.0 0.0100 0.0100 232.5 0.0095 0.0095 258.5 0.0090 0.0090 286.0 0.0085 0.0085 331.0 0.0078 0.0078 396.0 0.0070 0.0070 468.5 0.0063 0.0063 549.5 0.0058 0.0058 639.0 0.0056 0.0056 738.0 0.0056 0.0056 847.5 0.0060 0.0060 968.5 0.0065 0.0065 1102.0 0.0073 0.0073 1249.5 0.0079 0.0079 1412.0 0.0087 0.0087 1590.5 0.0095 0.0095 1787.0 0.0104 0.0104 2003.0 0.0113 0.0113 2241.0 0.0122 0.0122 2503.0 0.0131 0.0131 2790.5 0.0140 0.0140 3107.0 0.0150 0.0150 3455.0 0.0159 0.0159 3837.0 0.0168 0.0168 4257.0 0.0177 0.0177 4719.0 0.0187 0.0187 5226.5 0.0196 0.0196 5784.0 0.0206 0.0206 6538.0 0.0218 0.0218 +1.4 1.6 150 9.0 0.0648 0.0648 11.0 0.0588 0.0588 13.5 0.0524 0.0524 16.5 0.0470 0.0470 19.5 0.0431 0.0431 22.5 0.0403 0.0403 26.0 0.0379 0.0379 30.0 0.0369 0.0369 34.5 0.0349 0.0349 40.0 0.0330 0.0330 46.0 0.0313 0.0313 52.5 0.0300 0.0300 60.0 0.0288 0.0288 69.0 0.0276 0.0276 79.0 0.0266 0.0266 90.5 0.0257 0.0257 105.5 0.0247 0.0247 123.5 0.0238 0.0238 143.0 0.0231 0.0231 163.5 0.0224 0.0224 185.0 0.0219 0.0219 208.0 0.0214 0.0214 232.5 0.0210 0.0210 258.5 0.0205 0.0205 286.0 0.0202 0.0202 331.0 0.0196 0.0196 396.0 0.0191 0.0191 468.5 0.0186 0.0186 549.5 0.0183 0.0183 639.0 0.0180 0.0180 738.0 0.0179 0.0179 847.5 0.0179 0.0179 968.5 0.0181 0.0181 1102.0 0.0183 0.0183 1249.5 0.0185 0.0185 1412.0 0.0187 0.0187 1590.5 0.0190 0.0190 1787.0 0.0193 0.0193 2003.0 0.0196 0.0196 2241.0 0.0199 0.0199 2503.0 0.0202 0.0202 2790.5 0.0206 0.0206 3107.0 0.0211 0.0211 3455.0 0.0216 0.0216 3837.0 0.0221 0.0221 4257.0 0.0228 0.0228 4719.0 0.0234 0.0234 5226.5 0.0240 0.0240 5784.0 0.0247 0.0247 6538.0 0.0255 0.0255 +1.6 1.8 150 9.0 0.0690 0.0690 11.0 0.0646 0.0646 13.5 0.0592 0.0592 16.5 0.0549 0.0549 19.5 0.0518 0.0518 22.5 0.0495 0.0495 26.0 0.0474 0.0474 30.0 0.0462 0.0462 34.5 0.0440 0.0440 40.0 0.0417 0.0417 46.0 0.0397 0.0397 52.5 0.0378 0.0378 60.0 0.0360 0.0360 69.0 0.0342 0.0342 79.0 0.0325 0.0325 90.5 0.0309 0.0309 105.5 0.0292 0.0292 123.5 0.0274 0.0274 143.0 0.0259 0.0259 163.5 0.0245 0.0245 185.0 0.0233 0.0233 208.0 0.0222 0.0222 232.5 0.0211 0.0211 258.5 0.0202 0.0202 286.0 0.0193 0.0193 331.0 0.0180 0.0180 396.0 0.0166 0.0166 468.5 0.0153 0.0153 549.5 0.0144 0.0144 639.0 0.0136 0.0136 738.0 0.0132 0.0132 847.5 0.0130 0.0130 968.5 0.0131 0.0131 1102.0 0.0135 0.0135 1249.5 0.0137 0.0137 1412.0 0.0140 0.0140 1590.5 0.0144 0.0144 1787.0 0.0148 0.0148 2003.0 0.0152 0.0152 2241.0 0.0156 0.0156 2503.0 0.0163 0.0163 2790.5 0.0170 0.0170 3107.0 0.0177 0.0177 3455.0 0.0185 0.0185 3837.0 0.0194 0.0194 4257.0 0.0203 0.0203 4719.0 0.0212 0.0212 5226.5 0.0222 0.0222 5784.0 0.0231 0.0231 6538.0 0.0242 0.0242 +1.8 2.0 150 9.0 0.0662 0.0662 11.0 0.0618 0.0618 13.5 0.0567 0.0567 16.5 0.0529 0.0529 19.5 0.0503 0.0503 22.5 0.0484 0.0484 26.0 0.0467 0.0467 30.0 0.0457 0.0457 34.5 0.0436 0.0436 40.0 0.0414 0.0414 46.0 0.0395 0.0395 52.5 0.0377 0.0377 60.0 0.0360 0.0360 69.0 0.0342 0.0342 79.0 0.0325 0.0325 90.5 0.0309 0.0309 105.5 0.0292 0.0292 123.5 0.0275 0.0275 143.0 0.0259 0.0259 163.5 0.0246 0.0246 185.0 0.0234 0.0234 208.0 0.0223 0.0223 232.5 0.0212 0.0212 258.5 0.0202 0.0202 286.0 0.0193 0.0193 331.0 0.0181 0.0181 396.0 0.0166 0.0166 468.5 0.0154 0.0154 549.5 0.0144 0.0144 639.0 0.0137 0.0137 738.0 0.0133 0.0133 847.5 0.0132 0.0132 968.5 0.0134 0.0134 1102.0 0.0138 0.0138 1249.5 0.0142 0.0142 1412.0 0.0147 0.0147 1590.5 0.0153 0.0153 1787.0 0.0158 0.0158 2003.0 0.0165 0.0165 2241.0 0.0174 0.0174 2503.0 0.0183 0.0183 2790.5 0.0193 0.0193 3107.0 0.0203 0.0203 3455.0 0.0213 0.0213 3837.0 0.0225 0.0225 4257.0 0.0236 0.0236 4719.0 0.0248 0.0248 5226.5 0.0260 0.0260 5784.0 0.0272 0.0272 6538.0 0.0287 0.0287 +2.0 2.2 150 9.0 0.0634 0.0634 11.0 0.0595 0.0595 13.5 0.0547 0.0547 16.5 0.0509 0.0509 19.5 0.0483 0.0483 22.5 0.0464 0.0464 26.0 0.0448 0.0448 30.0 0.0439 0.0439 34.5 0.0419 0.0419 40.0 0.0399 0.0399 46.0 0.0381 0.0381 52.5 0.0364 0.0364 60.0 0.0348 0.0348 69.0 0.0331 0.0331 79.0 0.0316 0.0316 90.5 0.0300 0.0300 105.5 0.0284 0.0284 123.5 0.0268 0.0268 143.0 0.0254 0.0254 163.5 0.0241 0.0241 185.0 0.0230 0.0230 208.0 0.0219 0.0219 232.5 0.0210 0.0210 258.5 0.0200 0.0200 286.0 0.0192 0.0192 331.0 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0.0244 331.0 0.0236 0.0236 396.0 0.0226 0.0226 468.5 0.0218 0.0218 549.5 0.0211 0.0211 639.0 0.0206 0.0206 738.0 0.0202 0.0202 847.5 0.0202 0.0202 968.5 0.0202 0.0202 1102.0 0.0205 0.0205 1249.5 0.0206 0.0206 1412.0 0.0212 0.0212 1590.5 0.0219 0.0219 1787.0 0.0227 0.0227 2003.0 0.0235 0.0235 2241.0 0.0245 0.0245 2503.0 0.0255 0.0255 2790.5 0.0266 0.0266 3107.0 0.0278 0.0278 3455.0 0.0290 0.0290 3837.0 0.0303 0.0303 4257.0 0.0317 0.0317 4719.0 0.0332 0.0332 5226.5 0.0347 0.0347 5784.0 0.0362 0.0362 6538.0 0.0381 0.0381 +2.4 2.6 150 9.0 0.1185 0.1185 11.0 0.1046 0.1046 13.5 0.0906 0.0906 16.5 0.0794 0.0794 19.5 0.0719 0.0719 22.5 0.0665 0.0665 26.0 0.0620 0.0620 30.0 0.0587 0.0587 34.5 0.0552 0.0552 40.0 0.0517 0.0517 46.0 0.0487 0.0487 52.5 0.0461 0.0461 60.0 0.0436 0.0436 69.0 0.0411 0.0411 79.0 0.0388 0.0388 90.5 0.0366 0.0366 105.5 0.0342 0.0342 123.5 0.0319 0.0319 143.0 0.0298 0.0298 163.5 0.0280 0.0280 185.0 0.0264 0.0264 208.0 0.0249 0.0249 232.5 0.0236 0.0236 258.5 0.0224 0.0224 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0.0134 0.0134 3837.0 0.0142 0.0142 4257.0 0.0150 0.0150 4719.0 0.0157 0.0157 5226.5 0.0164 0.0164 5784.0 0.0172 0.0172 6538.0 0.0181 0.0181 +1.2 1.4 150 9.0 0.0659 0.0659 11.0 0.0590 0.0590 13.5 0.0509 0.0509 16.5 0.0434 0.0434 19.5 0.0375 0.0375 22.5 0.0328 0.0328 26.0 0.0284 0.0284 30.0 0.0260 0.0260 34.5 0.0237 0.0237 40.0 0.0216 0.0216 46.0 0.0196 0.0196 52.5 0.0180 0.0180 60.0 0.0165 0.0165 69.0 0.0151 0.0151 79.0 0.0139 0.0139 90.5 0.0129 0.0129 105.5 0.0119 0.0119 123.5 0.0110 0.0110 143.0 0.0104 0.0104 163.5 0.0098 0.0098 185.0 0.0094 0.0094 208.0 0.0089 0.0089 232.5 0.0085 0.0085 258.5 0.0081 0.0081 286.0 0.0078 0.0078 331.0 0.0072 0.0072 396.0 0.0066 0.0066 468.5 0.0060 0.0060 549.5 0.0057 0.0057 639.0 0.0055 0.0055 738.0 0.0056 0.0056 847.5 0.0059 0.0059 968.5 0.0065 0.0065 1102.0 0.0073 0.0073 1249.5 0.0079 0.0079 1412.0 0.0086 0.0086 1590.5 0.0094 0.0094 1787.0 0.0103 0.0103 2003.0 0.0111 0.0111 2241.0 0.0120 0.0120 2503.0 0.0129 0.0129 2790.5 0.0139 0.0139 3107.0 0.0148 0.0148 3455.0 0.0157 0.0157 3837.0 0.0167 0.0167 4257.0 0.0176 0.0176 4719.0 0.0186 0.0186 5226.5 0.0195 0.0195 5784.0 0.0205 0.0205 6538.0 0.0217 0.0217 +1.4 1.6 150 9.0 0.0628 0.0628 11.0 0.0566 0.0566 13.5 0.0499 0.0499 16.5 0.0442 0.0442 19.5 0.0401 0.0401 22.5 0.0370 0.0370 26.0 0.0344 0.0344 30.0 0.0332 0.0332 34.5 0.0318 0.0318 40.0 0.0303 0.0303 46.0 0.0290 0.0290 52.5 0.0279 0.0279 60.0 0.0269 0.0269 69.0 0.0259 0.0259 79.0 0.0251 0.0251 90.5 0.0243 0.0243 105.5 0.0236 0.0236 123.5 0.0229 0.0229 143.0 0.0223 0.0223 163.5 0.0218 0.0218 185.0 0.0213 0.0213 208.0 0.0209 0.0209 232.5 0.0205 0.0205 258.5 0.0202 0.0202 286.0 0.0199 0.0199 331.0 0.0194 0.0194 396.0 0.0189 0.0189 468.5 0.0185 0.0185 549.5 0.0182 0.0182 639.0 0.0180 0.0180 738.0 0.0179 0.0179 847.5 0.0179 0.0179 968.5 0.0181 0.0181 1102.0 0.0183 0.0183 1249.5 0.0185 0.0185 1412.0 0.0187 0.0187 1590.5 0.0189 0.0189 1787.0 0.0192 0.0192 2003.0 0.0195 0.0195 2241.0 0.0198 0.0198 2503.0 0.0202 0.0202 2790.5 0.0205 0.0205 3107.0 0.0210 0.0210 3455.0 0.0215 0.0215 3837.0 0.0221 0.0221 4257.0 0.0227 0.0227 4719.0 0.0233 0.0233 5226.5 0.0239 0.0239 5784.0 0.0246 0.0246 6538.0 0.0254 0.0254 +1.6 1.8 150 9.0 0.0672 0.0672 11.0 0.0626 0.0626 13.5 0.0570 0.0570 16.5 0.0525 0.0525 19.5 0.0493 0.0493 22.5 0.0468 0.0468 26.0 0.0447 0.0447 30.0 0.0434 0.0434 34.5 0.0416 0.0416 40.0 0.0396 0.0396 46.0 0.0378 0.0378 52.5 0.0362 0.0362 60.0 0.0346 0.0346 69.0 0.0329 0.0329 79.0 0.0313 0.0313 90.5 0.0298 0.0298 105.5 0.0282 0.0282 123.5 0.0266 0.0266 143.0 0.0252 0.0252 163.5 0.0239 0.0239 185.0 0.0228 0.0228 208.0 0.0217 0.0217 232.5 0.0207 0.0207 258.5 0.0198 0.0198 286.0 0.0189 0.0189 331.0 0.0178 0.0178 396.0 0.0164 0.0164 468.5 0.0152 0.0152 549.5 0.0143 0.0143 639.0 0.0136 0.0136 738.0 0.0132 0.0132 847.5 0.0130 0.0130 968.5 0.0131 0.0131 1102.0 0.0135 0.0135 1249.5 0.0137 0.0137 1412.0 0.0140 0.0140 1590.5 0.0144 0.0144 1787.0 0.0147 0.0147 2003.0 0.0150 0.0150 2241.0 0.0155 0.0155 2503.0 0.0161 0.0161 2790.5 0.0169 0.0169 3107.0 0.0176 0.0176 3455.0 0.0184 0.0184 3837.0 0.0193 0.0193 4257.0 0.0202 0.0202 4719.0 0.0211 0.0211 5226.5 0.0221 0.0221 5784.0 0.0230 0.0230 6538.0 0.0242 0.0242 +1.8 2.0 150 9.0 0.0642 0.0642 11.0 0.0597 0.0597 13.5 0.0544 0.0544 16.5 0.0504 0.0504 19.5 0.0477 0.0477 22.5 0.0456 0.0456 26.0 0.0439 0.0439 30.0 0.0428 0.0428 34.5 0.0411 0.0411 40.0 0.0393 0.0393 46.0 0.0377 0.0377 52.5 0.0361 0.0361 60.0 0.0345 0.0345 69.0 0.0329 0.0329 79.0 0.0313 0.0313 90.5 0.0299 0.0299 105.5 0.0282 0.0282 123.5 0.0267 0.0267 143.0 0.0252 0.0252 163.5 0.0240 0.0240 185.0 0.0228 0.0228 208.0 0.0218 0.0218 232.5 0.0208 0.0208 258.5 0.0199 0.0199 286.0 0.0190 0.0190 331.0 0.0178 0.0178 396.0 0.0164 0.0164 468.5 0.0153 0.0153 549.5 0.0143 0.0143 639.0 0.0136 0.0136 738.0 0.0133 0.0133 847.5 0.0132 0.0132 968.5 0.0134 0.0134 1102.0 0.0138 0.0138 1249.5 0.0141 0.0141 1412.0 0.0146 0.0146 1590.5 0.0152 0.0152 1787.0 0.0157 0.0157 2003.0 0.0164 0.0164 2241.0 0.0173 0.0173 2503.0 0.0182 0.0182 2790.5 0.0192 0.0192 3107.0 0.0202 0.0202 3455.0 0.0212 0.0212 3837.0 0.0224 0.0224 4257.0 0.0235 0.0235 4719.0 0.0247 0.0247 5226.5 0.0259 0.0259 5784.0 0.0272 0.0272 6538.0 0.0287 0.0287 +2.0 2.2 150 9.0 0.0613 0.0613 11.0 0.0574 0.0574 13.5 0.0523 0.0523 16.5 0.0484 0.0484 19.5 0.0456 0.0456 22.5 0.0436 0.0436 26.0 0.0418 0.0418 30.0 0.0408 0.0408 34.5 0.0394 0.0394 40.0 0.0377 0.0377 46.0 0.0362 0.0362 52.5 0.0347 0.0347 60.0 0.0333 0.0333 69.0 0.0318 0.0318 79.0 0.0303 0.0303 90.5 0.0289 0.0289 105.5 0.0274 0.0274 123.5 0.0260 0.0260 143.0 0.0246 0.0246 163.5 0.0235 0.0235 185.0 0.0224 0.0224 208.0 0.0215 0.0215 232.5 0.0205 0.0205 258.5 0.0197 0.0197 286.0 0.0189 0.0189 331.0 0.0178 0.0178 396.0 0.0164 0.0164 468.5 0.0153 0.0153 549.5 0.0145 0.0145 639.0 0.0138 0.0138 738.0 0.0135 0.0135 847.5 0.0135 0.0135 968.5 0.0139 0.0139 1102.0 0.0145 0.0145 1249.5 0.0152 0.0152 1412.0 0.0159 0.0159 1590.5 0.0167 0.0167 1787.0 0.0177 0.0177 2003.0 0.0188 0.0188 2241.0 0.0200 0.0200 2503.0 0.0212 0.0212 2790.5 0.0225 0.0225 3107.0 0.0239 0.0239 3455.0 0.0253 0.0253 3837.0 0.0268 0.0268 4257.0 0.0283 0.0283 4719.0 0.0299 0.0299 5226.5 0.0315 0.0315 5784.0 0.0331 0.0331 6538.0 0.0350 0.0350 +2.2 2.4 150 9.0 0.0624 0.0624 11.0 0.0572 0.0572 13.5 0.0514 0.0514 16.5 0.0469 0.0469 19.5 0.0439 0.0439 22.5 0.0418 0.0418 26.0 0.0402 0.0402 30.0 0.0392 0.0392 34.5 0.0380 0.0380 40.0 0.0367 0.0367 46.0 0.0356 0.0356 52.5 0.0346 0.0346 60.0 0.0336 0.0336 69.0 0.0326 0.0326 79.0 0.0317 0.0317 90.5 0.0308 0.0308 105.5 0.0298 0.0298 123.5 0.0288 0.0288 143.0 0.0280 0.0280 163.5 0.0272 0.0272 185.0 0.0265 0.0265 208.0 0.0258 0.0258 232.5 0.0253 0.0253 258.5 0.0247 0.0247 286.0 0.0242 0.0242 331.0 0.0234 0.0234 396.0 0.0225 0.0225 468.5 0.0217 0.0217 549.5 0.0211 0.0211 639.0 0.0206 0.0206 738.0 0.0202 0.0202 847.5 0.0202 0.0202 968.5 0.0202 0.0202 1102.0 0.0204 0.0204 1249.5 0.0206 0.0206 1412.0 0.0211 0.0211 1590.5 0.0218 0.0218 1787.0 0.0226 0.0226 2003.0 0.0235 0.0235 2241.0 0.0244 0.0244 2503.0 0.0254 0.0254 2790.5 0.0265 0.0265 3107.0 0.0277 0.0277 3455.0 0.0289 0.0289 3837.0 0.0303 0.0303 4257.0 0.0317 0.0317 4719.0 0.0331 0.0331 5226.5 0.0346 0.0346 5784.0 0.0361 0.0361 6538.0 0.0380 0.0380 +2.4 2.6 150 9.0 0.1175 0.1175 11.0 0.1034 0.1034 13.5 0.0892 0.0892 16.5 0.0778 0.0778 19.5 0.0701 0.0701 22.5 0.0645 0.0645 26.0 0.0599 0.0599 30.0 0.0565 0.0565 34.5 0.0533 0.0533 40.0 0.0501 0.0501 46.0 0.0473 0.0473 52.5 0.0448 0.0448 60.0 0.0424 0.0424 69.0 0.0400 0.0400 79.0 0.0378 0.0378 90.5 0.0357 0.0357 105.5 0.0334 0.0334 123.5 0.0312 0.0312 143.0 0.0292 0.0292 163.5 0.0274 0.0274 185.0 0.0259 0.0259 208.0 0.0245 0.0245 232.5 0.0233 0.0233 258.5 0.0221 0.0221 286.0 0.0211 0.0211 331.0 0.0197 0.0197 396.0 0.0182 0.0182 468.5 0.0171 0.0171 549.5 0.0164 0.0164 639.0 0.0160 0.0160 738.0 0.0159 0.0159 847.5 0.0160 0.0160 968.5 0.0158 0.0158 1102.0 0.0158 0.0158 1249.5 0.0160 0.0160 1412.0 0.0163 0.0163 1590.5 0.0166 0.0166 1787.0 0.0170 0.0170 2003.0 0.0174 0.0174 2241.0 0.0178 0.0178 2503.0 0.0183 0.0183 2790.5 0.0188 0.0188 3107.0 0.0193 0.0193 3455.0 0.0199 0.0199 3837.0 0.0207 0.0207 4257.0 0.0215 0.0215 4719.0 0.0223 0.0223 5226.5 0.0231 0.0231 5784.0 0.0239 0.0239 6538.0 0.0250 0.0250 +2.6 2.8 150 9.0 0.0911 0.0911 11.0 0.0778 0.0778 13.5 0.0654 0.0654 16.5 0.0558 0.0558 19.5 0.0494 0.0494 22.5 0.0450 0.0450 26.0 0.0415 0.0415 30.0 0.0390 0.0390 34.5 0.0365 0.0365 40.0 0.0340 0.0340 46.0 0.0320 0.0320 52.5 0.0302 0.0302 60.0 0.0286 0.0286 69.0 0.0268 0.0268 79.0 0.0251 0.0251 90.5 0.0235 0.0235 105.5 0.0218 0.0218 123.5 0.0204 0.0204 143.0 0.0192 0.0192 163.5 0.0183 0.0183 185.0 0.0176 0.0176 208.0 0.0171 0.0171 232.5 0.0168 0.0168 258.5 0.0166 0.0166 286.0 0.0165 0.0165 331.0 0.0167 0.0167 396.0 0.0172 0.0172 468.5 0.0180 0.0180 549.5 0.0188 0.0188 639.0 0.0198 0.0198 738.0 0.0204 0.0204 847.5 0.0205 0.0205 968.5 0.0207 0.0207 1102.0 0.0209 0.0209 1249.5 0.0211 0.0211 1412.0 0.0214 0.0214 1590.5 0.0217 0.0217 1787.0 0.0220 0.0220 2003.0 0.0225 0.0225 2241.0 0.0229 0.0229 2503.0 0.0235 0.0235 2790.5 0.0240 0.0240 3107.0 0.0247 0.0247 3455.0 0.0254 0.0254 3837.0 0.0262 0.0262 4257.0 0.0271 0.0271 4719.0 0.0280 0.0280 5226.5 0.0290 0.0290 5784.0 0.0300 0.0300 6538.0 0.0313 0.0313 +2.8 3.0 150 9.0 0.0927 0.0927 11.0 0.0766 0.0766 13.5 0.0625 0.0625 16.5 0.0518 0.0518 19.5 0.0450 0.0450 22.5 0.0406 0.0406 26.0 0.0373 0.0373 30.0 0.0351 0.0351 34.5 0.0328 0.0328 40.0 0.0308 0.0308 46.0 0.0293 0.0293 52.5 0.0281 0.0281 60.0 0.0272 0.0272 69.0 0.0265 0.0265 79.0 0.0258 0.0258 90.5 0.0254 0.0254 105.5 0.0252 0.0252 123.5 0.0253 0.0253 143.0 0.0256 0.0256 163.5 0.0262 0.0262 185.0 0.0268 0.0268 208.0 0.0275 0.0275 232.5 0.0284 0.0284 258.5 0.0292 0.0292 286.0 0.0302 0.0302 331.0 0.0317 0.0317 396.0 0.0336 0.0336 468.5 0.0350 0.0350 549.5 0.0362 0.0362 639.0 0.0359 0.0359 738.0 0.0360 0.0360 847.5 0.0360 0.0360 968.5 0.0360 0.0360 1102.0 0.0361 0.0361 1249.5 0.0362 0.0362 1412.0 0.0363 0.0363 1590.5 0.0364 0.0364 1787.0 0.0365 0.0365 2003.0 0.0367 0.0367 2241.0 0.0369 0.0369 2503.0 0.0371 0.0371 2790.5 0.0374 0.0374 3107.0 0.0377 0.0377 3455.0 0.0380 0.0380 3837.0 0.0385 0.0385 4257.0 0.0390 0.0390 4719.0 0.0396 0.0396 5226.5 0.0401 0.0401 5784.0 0.0408 0.0408 6538.0 0.0416 0.0416 +3.0 3.5 150 9.0 0.1265 0.1265 11.0 0.1069 0.1069 13.5 0.0887 0.0887 16.5 0.0735 0.0735 19.5 0.0627 0.0627 22.5 0.0550 0.0550 26.0 0.0486 0.0486 30.0 0.0447 0.0447 34.5 0.0417 0.0417 40.0 0.0393 0.0393 46.0 0.0375 0.0375 52.5 0.0364 0.0364 60.0 0.0354 0.0354 69.0 0.0345 0.0345 79.0 0.0339 0.0339 90.5 0.0334 0.0334 105.5 0.0330 0.0330 123.5 0.0328 0.0328 143.0 0.0327 0.0327 163.5 0.0326 0.0326 185.0 0.0327 0.0327 208.0 0.0328 0.0328 232.5 0.0329 0.0329 258.5 0.0331 0.0331 286.0 0.0333 0.0333 331.0 0.0336 0.0336 396.0 0.0341 0.0341 468.5 0.0347 0.0347 549.5 0.0352 0.0352 639.0 0.0351 0.0351 738.0 0.0352 0.0352 847.5 0.0353 0.0353 968.5 0.0354 0.0354 1102.0 0.0355 0.0355 1249.5 0.0356 0.0356 1412.0 0.0357 0.0357 1590.5 0.0358 0.0358 1787.0 0.0359 0.0359 2003.0 0.0361 0.0361 2241.0 0.0362 0.0362 2503.0 0.0363 0.0363 2790.5 0.0364 0.0364 3107.0 0.0365 0.0365 3455.0 0.0366 0.0366 3837.0 0.0368 0.0368 4257.0 0.0370 0.0370 4719.0 0.0372 0.0372 5226.5 0.0374 0.0374 5784.0 0.0375 0.0375 6538.0 0.0377 0.0377 +3.5 4.0 150 9.0 0.1397 0.1397 11.0 0.1178 0.1178 13.5 0.0985 0.0985 16.5 0.0830 0.0830 19.5 0.0726 0.0726 22.5 0.0652 0.0652 26.0 0.0593 0.0593 30.0 0.0552 0.0552 34.5 0.0509 0.0509 40.0 0.0468 0.0468 46.0 0.0434 0.0434 52.5 0.0406 0.0406 60.0 0.0381 0.0381 69.0 0.0352 0.0352 79.0 0.0327 0.0327 90.5 0.0306 0.0306 105.5 0.0289 0.0289 123.5 0.0278 0.0278 143.0 0.0274 0.0274 163.5 0.0276 0.0276 185.0 0.0284 0.0284 208.0 0.0293 0.0293 232.5 0.0304 0.0304 258.5 0.0316 0.0316 286.0 0.0328 0.0328 331.0 0.0349 0.0349 396.0 0.0357 0.0357 468.5 0.0357 0.0357 549.5 0.0358 0.0358 639.0 0.0358 0.0358 738.0 0.0359 0.0359 847.5 0.0360 0.0360 968.5 0.0361 0.0361 1102.0 0.0362 0.0362 1249.5 0.0363 0.0363 1412.0 0.0364 0.0364 1590.5 0.0365 0.0365 1787.0 0.0367 0.0367 2003.0 0.0368 0.0368 2241.0 0.0370 0.0370 2503.0 0.0371 0.0371 2790.5 0.0373 0.0373 3107.0 0.0375 0.0375 3455.0 0.0377 0.0377 3837.0 0.0378 0.0378 4257.0 0.0379 0.0379 4719.0 0.0380 0.0380 5226.5 0.0381 0.0381 5784.0 0.0383 0.0383 6538.0 0.0385 0.0385 +4.0 4.4 150 9.0 0.1391 0.1391 11.0 0.1208 0.1208 13.5 0.1053 0.1053 16.5 0.0937 0.0937 19.5 0.0862 0.0862 22.5 0.0813 0.0813 26.0 0.0774 0.0774 30.0 0.0749 0.0749 34.5 0.0720 0.0720 40.0 0.0693 0.0693 46.0 0.0672 0.0672 52.5 0.0654 0.0654 60.0 0.0639 0.0639 69.0 0.0626 0.0626 79.0 0.0616 0.0616 90.5 0.0608 0.0608 105.5 0.0602 0.0602 123.5 0.0598 0.0598 143.0 0.0597 0.0597 163.5 0.0600 0.0600 185.0 0.0604 0.0604 208.0 0.0608 0.0608 232.5 0.0613 0.0613 258.5 0.0612 0.0612 286.0 0.0612 0.0612 331.0 0.0613 0.0613 396.0 0.0615 0.0615 468.5 0.0617 0.0617 549.5 0.0619 0.0619 639.0 0.0622 0.0622 738.0 0.0625 0.0625 847.5 0.0628 0.0628 968.5 0.0632 0.0632 1102.0 0.0636 0.0636 1249.5 0.0640 0.0640 1412.0 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0.0279 0.0279 123.5 0.0264 0.0264 143.0 0.0250 0.0250 163.5 0.0238 0.0238 185.0 0.0227 0.0227 208.0 0.0216 0.0216 232.5 0.0206 0.0206 258.5 0.0197 0.0197 286.0 0.0188 0.0188 331.0 0.0176 0.0176 396.0 0.0160 0.0160 468.5 0.0147 0.0147 549.5 0.0135 0.0135 639.0 0.0126 0.0126 738.0 0.0121 0.0121 847.5 0.0119 0.0119 968.5 0.0121 0.0121 1102.0 0.0126 0.0126 1249.5 0.0132 0.0132 1412.0 0.0139 0.0139 1590.5 0.0149 0.0149 1787.0 0.0162 0.0162 2003.0 0.0175 0.0175 2241.0 0.0189 0.0189 2503.0 0.0203 0.0203 2790.5 0.0218 0.0218 3107.0 0.0233 0.0233 3455.0 0.0248 0.0248 3837.0 0.0265 0.0265 4257.0 0.0282 0.0282 4719.0 0.0299 0.0299 5226.5 0.0316 0.0316 5784.0 0.0333 0.0333 6538.0 0.0509 0.0509 +-2.0 -1.8 150 9.0 0.0640 0.0640 11.0 0.0596 0.0596 13.5 0.0544 0.0544 16.5 0.0505 0.0505 19.5 0.0481 0.0481 22.5 0.0460 0.0460 26.0 0.0443 0.0443 30.0 0.0432 0.0432 34.5 0.0415 0.0415 40.0 0.0396 0.0396 46.0 0.0379 0.0379 52.5 0.0363 0.0363 60.0 0.0347 0.0347 69.0 0.0331 0.0331 79.0 0.0315 0.0315 90.5 0.0300 0.0300 105.5 0.0284 0.0284 123.5 0.0268 0.0268 143.0 0.0254 0.0254 163.5 0.0241 0.0241 185.0 0.0229 0.0229 208.0 0.0218 0.0218 232.5 0.0208 0.0208 258.5 0.0198 0.0198 286.0 0.0189 0.0189 331.0 0.0176 0.0176 396.0 0.0160 0.0160 468.5 0.0146 0.0146 549.5 0.0135 0.0135 639.0 0.0126 0.0126 738.0 0.0119 0.0119 847.5 0.0116 0.0116 968.5 0.0116 0.0116 1102.0 0.0120 0.0120 1249.5 0.0121 0.0121 1412.0 0.0125 0.0125 1590.5 0.0130 0.0130 1787.0 0.0137 0.0137 2003.0 0.0145 0.0145 2241.0 0.0155 0.0155 2503.0 0.0166 0.0166 2790.5 0.0177 0.0177 3107.0 0.0189 0.0189 3455.0 0.0201 0.0201 3837.0 0.0213 0.0213 4257.0 0.0226 0.0226 4719.0 0.0239 0.0239 5226.5 0.0252 0.0252 5784.0 0.0266 0.0266 6538.0 0.0416 0.0416 +-1.8 -1.6 150 9.0 0.0667 0.0667 11.0 0.0619 0.0619 13.5 0.0571 0.0571 16.5 0.0523 0.0523 19.5 0.0492 0.0492 22.5 0.0469 0.0469 26.0 0.0450 0.0450 30.0 0.0437 0.0437 34.5 0.0419 0.0419 40.0 0.0399 0.0399 46.0 0.0380 0.0380 52.5 0.0364 0.0364 60.0 0.0347 0.0347 69.0 0.0331 0.0331 79.0 0.0316 0.0316 90.5 0.0300 0.0300 105.5 0.0284 0.0284 123.5 0.0268 0.0268 143.0 0.0253 0.0253 163.5 0.0240 0.0240 185.0 0.0228 0.0228 208.0 0.0217 0.0217 232.5 0.0207 0.0207 258.5 0.0197 0.0197 286.0 0.0188 0.0188 331.0 0.0175 0.0175 396.0 0.0160 0.0160 468.5 0.0146 0.0146 549.5 0.0134 0.0134 639.0 0.0125 0.0125 738.0 0.0118 0.0118 847.5 0.0114 0.0114 968.5 0.0113 0.0113 1102.0 0.0116 0.0116 1249.5 0.0116 0.0116 1412.0 0.0117 0.0117 1590.5 0.0120 0.0120 1787.0 0.0124 0.0124 2003.0 0.0129 0.0129 2241.0 0.0135 0.0135 2503.0 0.0144 0.0144 2790.5 0.0152 0.0152 3107.0 0.0161 0.0161 3455.0 0.0171 0.0171 3837.0 0.0181 0.0181 4257.0 0.0192 0.0192 4719.0 0.0203 0.0203 5226.5 0.0213 0.0213 5784.0 0.0224 0.0224 6538.0 0.0442 0.0442 +-1.6 -1.4 150 9.0 0.0596 0.0596 11.0 0.0537 0.0537 13.5 0.0472 0.0472 16.5 0.0427 0.0427 19.5 0.0389 0.0389 22.5 0.0363 0.0363 26.0 0.0340 0.0340 30.0 0.0331 0.0331 34.5 0.0319 0.0319 40.0 0.0303 0.0303 46.0 0.0292 0.0292 52.5 0.0281 0.0281 60.0 0.0271 0.0271 69.0 0.0262 0.0262 79.0 0.0253 0.0253 90.5 0.0245 0.0245 105.5 0.0237 0.0237 123.5 0.0230 0.0230 143.0 0.0224 0.0224 163.5 0.0218 0.0218 185.0 0.0213 0.0213 208.0 0.0209 0.0209 232.5 0.0205 0.0205 258.5 0.0201 0.0201 286.0 0.0197 0.0197 331.0 0.0192 0.0192 396.0 0.0186 0.0186 468.5 0.0182 0.0182 549.5 0.0178 0.0178 639.0 0.0175 0.0175 738.0 0.0174 0.0174 847.5 0.0174 0.0174 968.5 0.0175 0.0175 1102.0 0.0177 0.0177 1249.5 0.0179 0.0179 1412.0 0.0181 0.0181 1590.5 0.0183 0.0183 1787.0 0.0185 0.0185 2003.0 0.0188 0.0188 2241.0 0.0191 0.0191 2503.0 0.0195 0.0195 2790.5 0.0199 0.0199 3107.0 0.0204 0.0204 3455.0 0.0210 0.0210 3837.0 0.0216 0.0216 4257.0 0.0222 0.0222 4719.0 0.0229 0.0229 5226.5 0.0236 0.0236 5784.0 0.0242 0.0242 6538.0 0.0285 0.0285 +-1.4 -1.2 150 9.0 0.0614 0.0614 11.0 0.0548 0.0548 13.5 0.0469 0.0469 16.5 0.0401 0.0401 19.5 0.0348 0.0348 22.5 0.0307 0.0307 26.0 0.0268 0.0268 30.0 0.0247 0.0247 34.5 0.0227 0.0227 40.0 0.0209 0.0209 46.0 0.0192 0.0192 52.5 0.0176 0.0176 60.0 0.0163 0.0163 69.0 0.0150 0.0150 79.0 0.0139 0.0139 90.5 0.0129 0.0129 105.5 0.0119 0.0119 123.5 0.0110 0.0110 143.0 0.0103 0.0103 163.5 0.0097 0.0097 185.0 0.0092 0.0092 208.0 0.0087 0.0087 232.5 0.0082 0.0082 258.5 0.0077 0.0077 286.0 0.0073 0.0073 331.0 0.0067 0.0067 396.0 0.0060 0.0060 468.5 0.0054 0.0054 549.5 0.0049 0.0049 639.0 0.0048 0.0048 738.0 0.0048 0.0048 847.5 0.0052 0.0052 968.5 0.0058 0.0058 1102.0 0.0066 0.0066 1249.5 0.0072 0.0072 1412.0 0.0079 0.0079 1590.5 0.0087 0.0087 1787.0 0.0095 0.0095 2003.0 0.0104 0.0104 2241.0 0.0113 0.0113 2503.0 0.0122 0.0122 2790.5 0.0131 0.0131 3107.0 0.0140 0.0140 3455.0 0.0148 0.0148 3837.0 0.0157 0.0157 4257.0 0.0166 0.0166 4719.0 0.0175 0.0175 5226.5 0.0184 0.0184 5784.0 0.0193 0.0193 6538.0 0.0274 0.0274 +-1.2 -1.0 150 9.0 0.0624 0.0624 11.0 0.0551 0.0551 13.5 0.0461 0.0461 16.5 0.0390 0.0390 19.5 0.0335 0.0335 22.5 0.0293 0.0293 26.0 0.0257 0.0257 30.0 0.0239 0.0239 34.5 0.0218 0.0218 40.0 0.0201 0.0201 46.0 0.0186 0.0186 52.5 0.0173 0.0173 60.0 0.0160 0.0160 69.0 0.0148 0.0148 79.0 0.0138 0.0138 90.5 0.0129 0.0129 105.5 0.0119 0.0119 123.5 0.0110 0.0110 143.0 0.0102 0.0102 163.5 0.0095 0.0095 185.0 0.0089 0.0089 208.0 0.0084 0.0084 232.5 0.0079 0.0079 258.5 0.0074 0.0074 286.0 0.0069 0.0069 331.0 0.0062 0.0062 396.0 0.0054 0.0054 468.5 0.0048 0.0048 549.5 0.0043 0.0043 639.0 0.0041 0.0041 738.0 0.0041 0.0041 847.5 0.0045 0.0045 968.5 0.0050 0.0050 1102.0 0.0057 0.0057 1249.5 0.0063 0.0063 1412.0 0.0069 0.0069 1590.5 0.0075 0.0075 1787.0 0.0082 0.0082 2003.0 0.0089 0.0089 2241.0 0.0097 0.0097 2503.0 0.0104 0.0104 2790.5 0.0112 0.0112 3107.0 0.0119 0.0119 3455.0 0.0127 0.0127 3837.0 0.0135 0.0135 4257.0 0.0143 0.0143 4719.0 0.0150 0.0150 5226.5 0.0157 0.0157 5784.0 0.0164 0.0164 6538.0 0.0203 0.0203 +-1.0 -0.8 150 9.0 0.0550 0.0550 11.0 0.0475 0.0475 13.5 0.0391 0.0391 16.5 0.0316 0.0316 19.5 0.0265 0.0265 22.5 0.0221 0.0221 26.0 0.0186 0.0186 30.0 0.0168 0.0168 34.5 0.0154 0.0154 40.0 0.0142 0.0142 46.0 0.0125 0.0125 52.5 0.0115 0.0115 60.0 0.0107 0.0107 69.0 0.0100 0.0100 79.0 0.0093 0.0093 90.5 0.0088 0.0088 105.5 0.0083 0.0083 123.5 0.0079 0.0079 143.0 0.0076 0.0076 163.5 0.0073 0.0073 185.0 0.0070 0.0070 208.0 0.0068 0.0068 232.5 0.0065 0.0065 258.5 0.0063 0.0063 286.0 0.0061 0.0061 331.0 0.0057 0.0057 396.0 0.0053 0.0053 468.5 0.0049 0.0049 549.5 0.0047 0.0047 639.0 0.0045 0.0045 738.0 0.0046 0.0046 847.5 0.0048 0.0048 968.5 0.0051 0.0051 1102.0 0.0056 0.0056 1249.5 0.0060 0.0060 1412.0 0.0064 0.0064 1590.5 0.0068 0.0068 1787.0 0.0073 0.0073 2003.0 0.0078 0.0078 2241.0 0.0083 0.0083 2503.0 0.0088 0.0088 2790.5 0.0094 0.0094 3107.0 0.0100 0.0100 3455.0 0.0106 0.0106 3837.0 0.0112 0.0112 4257.0 0.0119 0.0119 4719.0 0.0126 0.0126 5226.5 0.0133 0.0133 5784.0 0.0140 0.0140 6538.0 0.0415 0.0415 +-0.8 -0.6 150 9.0 0.0524 0.0524 11.0 0.0449 0.0449 13.5 0.0365 0.0365 16.5 0.0289 0.0289 19.5 0.0233 0.0233 22.5 0.0193 0.0193 26.0 0.0157 0.0157 30.0 0.0140 0.0140 34.5 0.0125 0.0125 40.0 0.0106 0.0106 46.0 0.0097 0.0097 52.5 0.0086 0.0086 60.0 0.0078 0.0078 69.0 0.0071 0.0071 79.0 0.0065 0.0065 90.5 0.0061 0.0061 105.5 0.0057 0.0057 123.5 0.0054 0.0054 143.0 0.0053 0.0053 163.5 0.0051 0.0051 185.0 0.0050 0.0050 208.0 0.0048 0.0048 232.5 0.0047 0.0047 258.5 0.0046 0.0046 286.0 0.0044 0.0044 331.0 0.0042 0.0042 396.0 0.0039 0.0039 468.5 0.0037 0.0037 549.5 0.0036 0.0036 639.0 0.0036 0.0036 738.0 0.0038 0.0038 847.5 0.0041 0.0041 968.5 0.0045 0.0045 1102.0 0.0051 0.0051 1249.5 0.0054 0.0054 1412.0 0.0058 0.0058 1590.5 0.0062 0.0062 1787.0 0.0066 0.0066 2003.0 0.0070 0.0070 2241.0 0.0074 0.0074 2503.0 0.0079 0.0079 2790.5 0.0084 0.0084 3107.0 0.0089 0.0089 3455.0 0.0093 0.0093 3837.0 0.0099 0.0099 4257.0 0.0105 0.0105 4719.0 0.0111 0.0111 5226.5 0.0117 0.0117 5784.0 0.0123 0.0123 6538.0 0.0142 0.0142 +-0.6 -0.4 150 9.0 0.0511 0.0511 11.0 0.0440 0.0440 13.5 0.0354 0.0354 16.5 0.0281 0.0281 19.5 0.0231 0.0231 22.5 0.0189 0.0189 26.0 0.0159 0.0159 30.0 0.0142 0.0142 34.5 0.0123 0.0123 40.0 0.0110 0.0110 46.0 0.0099 0.0099 52.5 0.0089 0.0089 60.0 0.0080 0.0080 69.0 0.0073 0.0073 79.0 0.0067 0.0067 90.5 0.0062 0.0062 105.5 0.0059 0.0059 123.5 0.0056 0.0056 143.0 0.0054 0.0054 163.5 0.0052 0.0052 185.0 0.0050 0.0050 208.0 0.0049 0.0049 232.5 0.0047 0.0047 258.5 0.0046 0.0046 286.0 0.0044 0.0044 331.0 0.0042 0.0042 396.0 0.0039 0.0039 468.5 0.0037 0.0037 549.5 0.0036 0.0036 639.0 0.0036 0.0036 738.0 0.0038 0.0038 847.5 0.0041 0.0041 968.5 0.0045 0.0045 1102.0 0.0050 0.0050 1249.5 0.0053 0.0053 1412.0 0.0057 0.0057 1590.5 0.0061 0.0061 1787.0 0.0064 0.0064 2003.0 0.0068 0.0068 2241.0 0.0072 0.0072 2503.0 0.0077 0.0077 2790.5 0.0081 0.0081 3107.0 0.0085 0.0085 3455.0 0.0090 0.0090 3837.0 0.0095 0.0095 4257.0 0.0100 0.0100 4719.0 0.0106 0.0106 5226.5 0.0111 0.0111 5784.0 0.0117 0.0117 6538.0 0.0298 0.0298 +-0.4 -0.2 150 9.0 0.0517 0.0517 11.0 0.0442 0.0442 13.5 0.0364 0.0364 16.5 0.0281 0.0281 19.5 0.0230 0.0230 22.5 0.0190 0.0190 26.0 0.0158 0.0158 30.0 0.0143 0.0143 34.5 0.0124 0.0124 40.0 0.0113 0.0113 46.0 0.0099 0.0099 52.5 0.0089 0.0089 60.0 0.0081 0.0081 69.0 0.0073 0.0073 79.0 0.0068 0.0068 90.5 0.0063 0.0063 105.5 0.0059 0.0059 123.5 0.0056 0.0056 143.0 0.0054 0.0054 163.5 0.0052 0.0052 185.0 0.0050 0.0050 208.0 0.0048 0.0048 232.5 0.0047 0.0047 258.5 0.0045 0.0045 286.0 0.0044 0.0044 331.0 0.0041 0.0041 396.0 0.0039 0.0039 468.5 0.0037 0.0037 549.5 0.0036 0.0036 639.0 0.0036 0.0036 738.0 0.0038 0.0038 847.5 0.0041 0.0041 968.5 0.0045 0.0045 1102.0 0.0050 0.0050 1249.5 0.0053 0.0053 1412.0 0.0056 0.0056 1590.5 0.0060 0.0060 1787.0 0.0063 0.0063 2003.0 0.0067 0.0067 2241.0 0.0071 0.0071 2503.0 0.0074 0.0074 2790.5 0.0078 0.0078 3107.0 0.0083 0.0083 3455.0 0.0087 0.0087 3837.0 0.0092 0.0092 4257.0 0.0097 0.0097 4719.0 0.0102 0.0102 5226.5 0.0107 0.0107 5784.0 0.0112 0.0112 6538.0 0.0125 0.0125 +-0.2 0.0 150 9.0 0.0522 0.0522 11.0 0.0442 0.0442 13.5 0.0360 0.0360 16.5 0.0284 0.0284 19.5 0.0233 0.0233 22.5 0.0193 0.0193 26.0 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0.0194 26.0 0.0162 0.0162 30.0 0.0144 0.0144 34.5 0.0126 0.0126 40.0 0.0110 0.0110 46.0 0.0099 0.0099 52.5 0.0088 0.0088 60.0 0.0079 0.0079 69.0 0.0072 0.0072 79.0 0.0066 0.0066 90.5 0.0060 0.0060 105.5 0.0056 0.0056 123.5 0.0052 0.0052 143.0 0.0050 0.0050 163.5 0.0048 0.0048 185.0 0.0046 0.0046 208.0 0.0045 0.0045 232.5 0.0044 0.0044 258.5 0.0042 0.0042 286.0 0.0041 0.0041 331.0 0.0040 0.0040 396.0 0.0038 0.0038 468.5 0.0037 0.0037 549.5 0.0037 0.0037 639.0 0.0039 0.0039 738.0 0.0041 0.0041 847.5 0.0045 0.0045 968.5 0.0049 0.0049 1102.0 0.0055 0.0055 1249.5 0.0058 0.0058 1412.0 0.0061 0.0061 1590.5 0.0065 0.0065 1787.0 0.0068 0.0068 2003.0 0.0072 0.0072 2241.0 0.0076 0.0076 2503.0 0.0080 0.0080 2790.5 0.0084 0.0084 3107.0 0.0088 0.0088 3455.0 0.0092 0.0092 3837.0 0.0097 0.0097 4257.0 0.0102 0.0102 4719.0 0.0107 0.0107 5226.5 0.0112 0.0112 5784.0 0.0116 0.0116 6538.0 0.0305 0.0305 +0.2 0.4 150 9.0 0.0520 0.0520 11.0 0.0444 0.0444 13.5 0.0363 0.0363 16.5 0.0285 0.0285 19.5 0.0230 0.0230 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0.0592 13.5 0.0509 0.0509 16.5 0.0437 0.0437 19.5 0.0379 0.0379 22.5 0.0332 0.0332 26.0 0.0289 0.0289 30.0 0.0264 0.0264 34.5 0.0242 0.0242 40.0 0.0219 0.0219 46.0 0.0199 0.0199 52.5 0.0183 0.0183 60.0 0.0167 0.0167 69.0 0.0153 0.0153 79.0 0.0140 0.0140 90.5 0.0130 0.0130 105.5 0.0119 0.0119 123.5 0.0110 0.0110 143.0 0.0103 0.0103 163.5 0.0097 0.0097 185.0 0.0092 0.0092 208.0 0.0087 0.0087 232.5 0.0083 0.0083 258.5 0.0078 0.0078 286.0 0.0074 0.0074 331.0 0.0068 0.0068 396.0 0.0060 0.0060 468.5 0.0054 0.0054 549.5 0.0049 0.0049 639.0 0.0047 0.0047 738.0 0.0048 0.0048 847.5 0.0052 0.0052 968.5 0.0059 0.0059 1102.0 0.0067 0.0067 1249.5 0.0074 0.0074 1412.0 0.0082 0.0082 1590.5 0.0090 0.0090 1787.0 0.0099 0.0099 2003.0 0.0108 0.0108 2241.0 0.0117 0.0117 2503.0 0.0126 0.0126 2790.5 0.0136 0.0136 3107.0 0.0146 0.0146 3455.0 0.0155 0.0155 3837.0 0.0164 0.0164 4257.0 0.0174 0.0174 4719.0 0.0184 0.0184 5226.5 0.0194 0.0194 5784.0 0.0203 0.0203 6538.0 0.0282 0.0282 +1.4 1.6 150 9.0 0.0630 0.0630 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0.0673 0.0673 11.0 0.0627 0.0627 13.5 0.0573 0.0573 16.5 0.0529 0.0529 19.5 0.0496 0.0496 22.5 0.0472 0.0472 26.0 0.0451 0.0451 30.0 0.0437 0.0437 34.5 0.0419 0.0419 40.0 0.0399 0.0399 46.0 0.0381 0.0381 52.5 0.0364 0.0364 60.0 0.0348 0.0348 69.0 0.0330 0.0330 79.0 0.0315 0.0315 90.5 0.0300 0.0300 105.5 0.0284 0.0284 123.5 0.0267 0.0267 143.0 0.0253 0.0253 163.5 0.0240 0.0240 185.0 0.0228 0.0228 208.0 0.0217 0.0217 232.5 0.0207 0.0207 258.5 0.0197 0.0197 286.0 0.0188 0.0188 331.0 0.0175 0.0175 396.0 0.0160 0.0160 468.5 0.0146 0.0146 549.5 0.0134 0.0134 639.0 0.0125 0.0125 738.0 0.0118 0.0118 847.5 0.0114 0.0114 968.5 0.0113 0.0113 1102.0 0.0115 0.0115 1249.5 0.0115 0.0115 1412.0 0.0116 0.0116 1590.5 0.0118 0.0118 1787.0 0.0122 0.0122 2003.0 0.0127 0.0127 2241.0 0.0132 0.0132 2503.0 0.0140 0.0140 2790.5 0.0148 0.0148 3107.0 0.0157 0.0157 3455.0 0.0166 0.0166 3837.0 0.0176 0.0176 4257.0 0.0186 0.0186 4719.0 0.0196 0.0196 5226.5 0.0206 0.0206 5784.0 0.0216 0.0216 6538.0 0.0449 0.0449 +1.8 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0.0295 79.0 0.0282 0.0282 90.5 0.0273 0.0273 105.5 0.0269 0.0269 123.5 0.0267 0.0267 143.0 0.0272 0.0272 163.5 0.0289 0.0289 185.0 0.0301 0.0301 208.0 0.0321 0.0321 232.5 0.0331 0.0331 258.5 0.0345 0.0345 286.0 0.0365 0.0365 331.0 0.0396 0.0396 396.0 0.0450 0.0450 468.5 0.0478 0.0478 549.5 0.0456 0.0456 639.0 0.0451 0.0451 738.0 0.0455 0.0455 847.5 0.0457 0.0457 968.5 0.0460 0.0460 1102.0 0.0462 0.0462 1249.5 0.0464 0.0464 1412.0 0.0466 0.0466 1590.5 0.0469 0.0469 1787.0 0.0471 0.0471 2003.0 0.0474 0.0474 2241.0 0.0477 0.0477 2503.0 0.0480 0.0480 2790.5 0.0483 0.0483 3107.0 0.0486 0.0486 3455.0 0.0490 0.0490 3837.0 0.0494 0.0494 4257.0 0.0499 0.0499 4719.0 0.0504 0.0504 5226.5 0.0509 0.0509 5784.0 0.0311 0.0311 6538.0 0.0320 0.0320 +-2.6 -2.4 150 9.0 0.1197 0.1197 11.0 0.1055 0.1055 13.5 0.0913 0.0913 16.5 0.0798 0.0798 19.5 0.0724 0.0724 22.5 0.0669 0.0669 26.0 0.0624 0.0624 30.0 0.0595 0.0595 34.5 0.0554 0.0554 40.0 0.0525 0.0525 46.0 0.0492 0.0492 52.5 0.0464 0.0464 60.0 0.0439 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0.0159 0.0159 5226.5 0.0166 0.0166 5784.0 0.0173 0.0173 6538.0 0.0217 0.0217 +1.2 1.4 150 9.0 0.0680 0.0680 11.0 0.0614 0.0614 13.5 0.0534 0.0534 16.5 0.0466 0.0466 19.5 0.0412 0.0412 22.5 0.0369 0.0369 26.0 0.0331 0.0331 30.0 0.0310 0.0310 34.5 0.0283 0.0283 40.0 0.0256 0.0256 46.0 0.0233 0.0233 52.5 0.0215 0.0215 60.0 0.0198 0.0198 69.0 0.0182 0.0182 79.0 0.0168 0.0168 90.5 0.0155 0.0155 105.5 0.0143 0.0143 123.5 0.0132 0.0132 143.0 0.0123 0.0123 163.5 0.0115 0.0115 185.0 0.0108 0.0108 208.0 0.0102 0.0102 232.5 0.0097 0.0097 258.5 0.0091 0.0091 286.0 0.0086 0.0086 331.0 0.0079 0.0079 396.0 0.0071 0.0071 468.5 0.0064 0.0064 549.5 0.0059 0.0059 639.0 0.0056 0.0056 738.0 0.0056 0.0056 847.5 0.0060 0.0060 968.5 0.0065 0.0065 1102.0 0.0073 0.0073 1249.5 0.0079 0.0079 1412.0 0.0087 0.0087 1590.5 0.0095 0.0095 1787.0 0.0104 0.0104 2003.0 0.0113 0.0113 2241.0 0.0122 0.0122 2503.0 0.0131 0.0131 2790.5 0.0140 0.0140 3107.0 0.0150 0.0150 3455.0 0.0159 0.0159 3837.0 0.0168 0.0168 4257.0 0.0178 0.0178 4719.0 0.0187 0.0187 5226.5 0.0197 0.0197 5784.0 0.0206 0.0206 6538.0 0.0284 0.0284 +1.4 1.6 150 9.0 0.0651 0.0651 11.0 0.0592 0.0592 13.5 0.0527 0.0527 16.5 0.0474 0.0474 19.5 0.0437 0.0437 22.5 0.0409 0.0409 26.0 0.0385 0.0385 30.0 0.0376 0.0376 34.5 0.0356 0.0356 40.0 0.0335 0.0335 46.0 0.0319 0.0319 52.5 0.0305 0.0305 60.0 0.0292 0.0292 69.0 0.0280 0.0280 79.0 0.0270 0.0270 90.5 0.0260 0.0260 105.5 0.0250 0.0250 123.5 0.0241 0.0241 143.0 0.0233 0.0233 163.5 0.0227 0.0227 185.0 0.0221 0.0221 208.0 0.0216 0.0216 232.5 0.0211 0.0211 258.5 0.0207 0.0207 286.0 0.0203 0.0203 331.0 0.0197 0.0197 396.0 0.0191 0.0191 468.5 0.0186 0.0186 549.5 0.0183 0.0183 639.0 0.0180 0.0180 738.0 0.0179 0.0179 847.5 0.0179 0.0179 968.5 0.0181 0.0181 1102.0 0.0183 0.0183 1249.5 0.0186 0.0186 1412.0 0.0188 0.0188 1590.5 0.0191 0.0191 1787.0 0.0194 0.0194 2003.0 0.0197 0.0197 2241.0 0.0200 0.0200 2503.0 0.0204 0.0204 2790.5 0.0207 0.0207 3107.0 0.0212 0.0212 3455.0 0.0217 0.0217 3837.0 0.0223 0.0223 4257.0 0.0229 0.0229 4719.0 0.0235 0.0235 5226.5 0.0242 0.0242 5784.0 0.0248 0.0248 6538.0 0.0282 0.0282 +1.6 1.8 150 9.0 0.0694 0.0694 11.0 0.0649 0.0649 13.5 0.0597 0.0597 16.5 0.0555 0.0555 19.5 0.0524 0.0524 22.5 0.0501 0.0501 26.0 0.0481 0.0481 30.0 0.0468 0.0468 34.5 0.0446 0.0446 40.0 0.0423 0.0423 46.0 0.0402 0.0402 52.5 0.0383 0.0383 60.0 0.0365 0.0365 69.0 0.0347 0.0347 79.0 0.0329 0.0329 90.5 0.0313 0.0313 105.5 0.0295 0.0295 123.5 0.0277 0.0277 143.0 0.0262 0.0262 163.5 0.0248 0.0248 185.0 0.0235 0.0235 208.0 0.0224 0.0224 232.5 0.0213 0.0213 258.5 0.0203 0.0203 286.0 0.0194 0.0194 331.0 0.0181 0.0181 396.0 0.0166 0.0166 468.5 0.0154 0.0154 549.5 0.0144 0.0144 639.0 0.0136 0.0136 738.0 0.0132 0.0132 847.5 0.0130 0.0130 968.5 0.0132 0.0132 1102.0 0.0136 0.0136 1249.5 0.0138 0.0138 1412.0 0.0141 0.0141 1590.5 0.0145 0.0145 1787.0 0.0149 0.0149 2003.0 0.0153 0.0153 2241.0 0.0157 0.0157 2503.0 0.0164 0.0164 2790.5 0.0171 0.0171 3107.0 0.0179 0.0179 3455.0 0.0186 0.0186 3837.0 0.0195 0.0195 4257.0 0.0204 0.0204 4719.0 0.0214 0.0214 5226.5 0.0223 0.0223 5784.0 0.0233 0.0233 6538.0 0.0457 0.0457 +1.8 2.0 150 9.0 0.0665 0.0665 11.0 0.0621 0.0621 13.5 0.0569 0.0569 16.5 0.0534 0.0534 19.5 0.0509 0.0509 22.5 0.0490 0.0490 26.0 0.0473 0.0473 30.0 0.0462 0.0462 34.5 0.0442 0.0442 40.0 0.0420 0.0420 46.0 0.0400 0.0400 52.5 0.0382 0.0382 60.0 0.0365 0.0365 69.0 0.0347 0.0347 79.0 0.0330 0.0330 90.5 0.0313 0.0313 105.5 0.0295 0.0295 123.5 0.0278 0.0278 143.0 0.0262 0.0262 163.5 0.0248 0.0248 185.0 0.0236 0.0236 208.0 0.0225 0.0225 232.5 0.0214 0.0214 258.5 0.0204 0.0204 286.0 0.0195 0.0195 331.0 0.0182 0.0182 396.0 0.0167 0.0167 468.5 0.0154 0.0154 549.5 0.0144 0.0144 639.0 0.0137 0.0137 738.0 0.0133 0.0133 847.5 0.0132 0.0132 968.5 0.0134 0.0134 1102.0 0.0139 0.0139 1249.5 0.0143 0.0143 1412.0 0.0148 0.0148 1590.5 0.0154 0.0154 1787.0 0.0159 0.0159 2003.0 0.0167 0.0167 2241.0 0.0175 0.0175 2503.0 0.0184 0.0184 2790.5 0.0194 0.0194 3107.0 0.0204 0.0204 3455.0 0.0214 0.0214 3837.0 0.0226 0.0226 4257.0 0.0238 0.0238 4719.0 0.0249 0.0249 5226.5 0.0262 0.0262 5784.0 0.0274 0.0274 6538.0 0.0555 0.0555 +2.0 2.2 150 9.0 0.0640 0.0640 11.0 0.0602 0.0602 13.5 0.0555 0.0555 16.5 0.0518 0.0518 19.5 0.0495 0.0495 22.5 0.0475 0.0475 26.0 0.0460 0.0460 30.0 0.0450 0.0450 34.5 0.0431 0.0431 40.0 0.0411 0.0411 46.0 0.0392 0.0392 52.5 0.0374 0.0374 60.0 0.0357 0.0357 69.0 0.0341 0.0341 79.0 0.0324 0.0324 90.5 0.0308 0.0308 105.5 0.0291 0.0291 123.5 0.0274 0.0274 143.0 0.0259 0.0259 163.5 0.0246 0.0246 185.0 0.0234 0.0234 208.0 0.0223 0.0223 232.5 0.0212 0.0212 258.5 0.0203 0.0203 286.0 0.0194 0.0194 331.0 0.0181 0.0181 396.0 0.0167 0.0167 468.5 0.0155 0.0155 549.5 0.0145 0.0145 639.0 0.0139 0.0139 738.0 0.0135 0.0135 847.5 0.0136 0.0136 968.5 0.0140 0.0140 1102.0 0.0147 0.0147 1249.5 0.0154 0.0154 1412.0 0.0162 0.0162 1590.5 0.0169 0.0169 1787.0 0.0180 0.0180 2003.0 0.0191 0.0191 2241.0 0.0203 0.0203 2503.0 0.0215 0.0215 2790.5 0.0228 0.0228 3107.0 0.0241 0.0241 3455.0 0.0255 0.0255 3837.0 0.0270 0.0270 4257.0 0.0286 0.0286 4719.0 0.0302 0.0302 5226.5 0.0317 0.0317 5784.0 0.0333 0.0333 6538.0 0.0507 0.0507 +2.2 2.4 150 9.0 0.0646 0.0646 11.0 0.0597 0.0597 13.5 0.0540 0.0540 16.5 0.0498 0.0498 19.5 0.0471 0.0471 22.5 0.0452 0.0452 26.0 0.0437 0.0437 30.0 0.0428 0.0428 34.5 0.0411 0.0411 40.0 0.0394 0.0394 46.0 0.0379 0.0379 52.5 0.0366 0.0366 60.0 0.0355 0.0355 69.0 0.0343 0.0343 79.0 0.0332 0.0332 90.5 0.0321 0.0321 105.5 0.0309 0.0309 123.5 0.0298 0.0298 143.0 0.0288 0.0288 163.5 0.0279 0.0279 185.0 0.0272 0.0272 208.0 0.0264 0.0264 232.5 0.0258 0.0258 258.5 0.0251 0.0251 286.0 0.0246 0.0246 331.0 0.0237 0.0237 396.0 0.0227 0.0227 468.5 0.0219 0.0219 549.5 0.0212 0.0212 639.0 0.0207 0.0207 738.0 0.0203 0.0203 847.5 0.0202 0.0202 968.5 0.0203 0.0203 1102.0 0.0205 0.0205 1249.5 0.0206 0.0206 1412.0 0.0212 0.0212 1590.5 0.0219 0.0219 1787.0 0.0227 0.0227 2003.0 0.0236 0.0236 2241.0 0.0245 0.0245 2503.0 0.0255 0.0255 2790.5 0.0266 0.0266 3107.0 0.0278 0.0278 3455.0 0.0290 0.0290 3837.0 0.0303 0.0303 4257.0 0.0317 0.0317 4719.0 0.0332 0.0332 5226.5 0.0347 0.0347 5784.0 0.0362 0.0362 6538.0 0.0381 0.0381 +2.4 2.6 150 9.0 0.1187 0.1187 11.0 0.1049 0.1049 13.5 0.0906 0.0906 16.5 0.0795 0.0795 19.5 0.0721 0.0721 22.5 0.0667 0.0667 26.0 0.0626 0.0626 30.0 0.0589 0.0589 34.5 0.0555 0.0555 40.0 0.0524 0.0524 46.0 0.0490 0.0490 52.5 0.0464 0.0464 60.0 0.0439 0.0439 69.0 0.0413 0.0413 79.0 0.0389 0.0389 90.5 0.0366 0.0366 105.5 0.0342 0.0342 123.5 0.0319 0.0319 143.0 0.0298 0.0298 163.5 0.0280 0.0280 185.0 0.0265 0.0265 208.0 0.0251 0.0251 232.5 0.0240 0.0240 258.5 0.0229 0.0229 286.0 0.0220 0.0220 331.0 0.0209 0.0209 396.0 0.0199 0.0199 468.5 0.0194 0.0194 549.5 0.0200 0.0200 639.0 0.0188 0.0188 738.0 0.0188 0.0188 847.5 0.0189 0.0189 968.5 0.0188 0.0188 1102.0 0.0189 0.0189 1249.5 0.0192 0.0192 1412.0 0.0195 0.0195 1590.5 0.0198 0.0198 1787.0 0.0202 0.0202 2003.0 0.0206 0.0206 2241.0 0.0210 0.0210 2503.0 0.0215 0.0215 2790.5 0.0220 0.0220 3107.0 0.0225 0.0225 3455.0 0.0230 0.0230 3837.0 0.0237 0.0237 4257.0 0.0244 0.0244 4719.0 0.0252 0.0252 5226.5 0.0259 0.0259 5784.0 0.0267 0.0267 6538.0 0.0521 0.0521 +2.6 2.8 150 9.0 0.0926 0.0926 11.0 0.0795 0.0795 13.5 0.0674 0.0674 16.5 0.0588 0.0588 19.5 0.0520 0.0520 22.5 0.0479 0.0479 26.0 0.0444 0.0444 30.0 0.0436 0.0436 34.5 0.0394 0.0394 40.0 0.0364 0.0364 46.0 0.0343 0.0343 52.5 0.0324 0.0324 60.0 0.0310 0.0310 69.0 0.0291 0.0291 79.0 0.0280 0.0280 90.5 0.0273 0.0273 105.5 0.0271 0.0271 123.5 0.0270 0.0270 143.0 0.0271 0.0271 163.5 0.0284 0.0284 185.0 0.0296 0.0296 208.0 0.0320 0.0320 232.5 0.0333 0.0333 258.5 0.0349 0.0349 286.0 0.0372 0.0372 331.0 0.0395 0.0395 396.0 0.0435 0.0435 468.5 0.0464 0.0464 549.5 0.0500 0.0500 639.0 0.0456 0.0456 738.0 0.0460 0.0460 847.5 0.0463 0.0463 968.5 0.0465 0.0465 1102.0 0.0468 0.0468 1249.5 0.0470 0.0470 1412.0 0.0473 0.0473 1590.5 0.0476 0.0476 1787.0 0.0479 0.0479 2003.0 0.0482 0.0482 2241.0 0.0485 0.0485 2503.0 0.0489 0.0489 2790.5 0.0493 0.0493 3107.0 0.0496 0.0496 3455.0 0.0501 0.0501 3837.0 0.0506 0.0506 4257.0 0.0512 0.0512 4719.0 0.0518 0.0518 5226.5 0.0524 0.0524 5784.0 0.0530 0.0530 6538.0 0.0861 0.0861 +2.8 3.0 150 9.0 0.0949 0.0949 11.0 0.0792 0.0792 13.5 0.0657 0.0657 16.5 0.0565 0.0565 19.5 0.0480 0.0480 22.5 0.0464 0.0464 26.0 0.0430 0.0430 30.0 0.0411 0.0411 34.5 0.0369 0.0369 40.0 0.0396 0.0396 46.0 0.0365 0.0365 52.5 0.0358 0.0358 60.0 0.0359 0.0359 69.0 0.0367 0.0367 79.0 0.0375 0.0375 90.5 0.0393 0.0393 105.5 0.0402 0.0402 123.5 0.0414 0.0414 143.0 0.0424 0.0424 163.5 0.0445 0.0445 185.0 0.0454 0.0454 208.0 0.0472 0.0472 232.5 0.0492 0.0492 258.5 0.0515 0.0515 286.0 0.0539 0.0539 331.0 0.0596 0.0596 396.0 0.0561 0.0561 468.5 0.0559 0.0559 549.5 0.0582 0.0582 639.0 0.0582 0.0582 738.0 0.0584 0.0584 847.5 0.0585 0.0585 968.5 0.0587 0.0587 1102.0 0.0589 0.0589 1249.5 0.0591 0.0591 1412.0 0.0593 0.0593 1590.5 0.0595 0.0595 1787.0 0.0597 0.0597 2003.0 0.0599 0.0599 2241.0 0.0601 0.0601 2503.0 0.0604 0.0604 2790.5 0.0606 0.0606 3107.0 0.0609 0.0609 3455.0 0.0612 0.0612 3837.0 0.0616 0.0616 4257.0 0.0620 0.0620 4719.0 0.0624 0.0624 5226.5 0.0628 0.0628 5784.0 0.0817 0.0817 6538.0 0.1452 0.1452 +3.0 3.5 150 9.0 0.1275 0.1275 11.0 0.1081 0.1081 13.5 0.0902 0.0902 16.5 0.0753 0.0753 19.5 0.0648 0.0648 22.5 0.0574 0.0574 26.0 0.0513 0.0513 30.0 0.0476 0.0476 34.5 0.0442 0.0442 40.0 0.0415 0.0415 46.0 0.0395 0.0395 52.5 0.0381 0.0381 60.0 0.0370 0.0370 69.0 0.0359 0.0359 79.0 0.0351 0.0351 90.5 0.0345 0.0345 105.5 0.0339 0.0339 123.5 0.0336 0.0336 143.0 0.0333 0.0333 163.5 0.0332 0.0332 185.0 0.0331 0.0331 208.0 0.0332 0.0332 232.5 0.0332 0.0332 258.5 0.0334 0.0334 286.0 0.0335 0.0335 331.0 0.0338 0.0338 396.0 0.0342 0.0342 468.5 0.0348 0.0348 549.5 0.0352 0.0352 639.0 0.0351 0.0351 738.0 0.0352 0.0352 847.5 0.0353 0.0353 968.5 0.0354 0.0354 1102.0 0.0355 0.0355 1249.5 0.0356 0.0356 1412.0 0.0357 0.0357 1590.5 0.0359 0.0359 1787.0 0.0360 0.0360 2003.0 0.0361 0.0361 2241.0 0.0362 0.0362 2503.0 0.0364 0.0364 2790.5 0.0365 0.0365 3107.0 0.0366 0.0366 3455.0 0.0367 0.0367 3837.0 0.0369 0.0369 4257.0 0.0371 0.0371 4719.0 0.0373 0.0373 5226.5 0.0375 0.0375 5784.0 0.0376 0.0376 6538.0 0.0378 0.0378 +3.5 4.0 150 9.0 0.1407 0.1407 11.0 0.1189 0.1189 13.5 0.0999 0.0999 16.5 0.0846 0.0846 19.5 0.0744 0.0744 22.5 0.0673 0.0673 26.0 0.0615 0.0615 30.0 0.0576 0.0576 34.5 0.0530 0.0530 40.0 0.0487 0.0487 46.0 0.0451 0.0451 52.5 0.0422 0.0422 60.0 0.0396 0.0396 69.0 0.0365 0.0365 79.0 0.0339 0.0339 90.5 0.0318 0.0318 105.5 0.0299 0.0299 123.5 0.0287 0.0287 143.0 0.0282 0.0282 163.5 0.0283 0.0283 185.0 0.0289 0.0289 208.0 0.0297 0.0297 232.5 0.0307 0.0307 258.5 0.0318 0.0318 286.0 0.0330 0.0330 331.0 0.0350 0.0350 396.0 0.0358 0.0358 468.5 0.0358 0.0358 549.5 0.0358 0.0358 639.0 0.0358 0.0358 738.0 0.0359 0.0359 847.5 0.0360 0.0360 968.5 0.0361 0.0361 1102.0 0.0362 0.0362 1249.5 0.0363 0.0363 1412.0 0.0365 0.0365 1590.5 0.0366 0.0366 1787.0 0.0367 0.0367 2003.0 0.0369 0.0369 2241.0 0.0371 0.0371 2503.0 0.0372 0.0372 2790.5 0.0374 0.0374 3107.0 0.0376 0.0376 3455.0 0.0378 0.0378 3837.0 0.0379 0.0379 4257.0 0.0380 0.0380 4719.0 0.0381 0.0381 5226.5 0.0383 0.0383 5784.0 0.0384 0.0384 6538.0 0.0386 0.0386 +4.0 4.4 150 9.0 0.1401 0.1401 11.0 0.1220 0.1220 13.5 0.1067 0.1067 16.5 0.0950 0.0950 19.5 0.0878 0.0878 22.5 0.0830 0.0830 26.0 0.0792 0.0792 30.0 0.0767 0.0767 34.5 0.0736 0.0736 40.0 0.0707 0.0707 46.0 0.0684 0.0684 52.5 0.0664 0.0664 60.0 0.0648 0.0648 69.0 0.0635 0.0635 79.0 0.0624 0.0624 90.5 0.0615 0.0615 105.5 0.0608 0.0608 123.5 0.0603 0.0603 143.0 0.0601 0.0601 163.5 0.0603 0.0603 185.0 0.0607 0.0607 208.0 0.0611 0.0611 232.5 0.0615 0.0615 258.5 0.0613 0.0613 286.0 0.0614 0.0614 331.0 0.0614 0.0614 396.0 0.0615 0.0615 468.5 0.0617 0.0617 549.5 0.0619 0.0619 639.0 0.0622 0.0622 738.0 0.0625 0.0625 847.5 0.0628 0.0628 968.5 0.0632 0.0632 1102.0 0.0636 0.0636 1249.5 0.0640 0.0640 1412.0 0.0645 0.0645 1590.5 0.0649 0.0649 1787.0 0.0654 0.0654 2003.0 0.0659 0.0659 2241.0 0.0664 0.0664 2503.0 0.0669 0.0669 2790.5 0.0675 0.0675 3107.0 0.0681 0.0681 3455.0 0.0686 0.0686 3837.0 0.0691 0.0691 4257.0 0.0696 0.0696 4719.0 0.0701 0.0701 5226.5 0.0707 0.0707 5784.0 0.0712 0.0712 6538.0 0.0719 0.0719 +4.4 5.0 150 9.0 0.1052 0.1052 11.0 0.0966 0.0966 13.5 0.0896 0.0896 16.5 0.0847 0.0847 19.5 0.0813 0.0813 22.5 0.0792 0.0792 26.0 0.0774 0.0774 30.0 0.0755 0.0755 34.5 0.0726 0.0726 40.0 0.0698 0.0698 46.0 0.0674 0.0674 52.5 0.0655 0.0655 60.0 0.0640 0.0640 69.0 0.0628 0.0628 79.0 0.0619 0.0619 90.5 0.0615 0.0615 105.5 0.0614 0.0614 123.5 0.0617 0.0617 143.0 0.0623 0.0623 163.5 0.0633 0.0633 185.0 0.0644 0.0644 208.0 0.0656 0.0656 232.5 0.0667 0.0667 258.5 0.0673 0.0673 286.0 0.0680 0.0680 331.0 0.0691 0.0691 396.0 0.0704 0.0704 468.5 0.0716 0.0716 549.5 0.0728 0.0728 639.0 0.0739 0.0739 738.0 0.0750 0.0750 847.5 0.0761 0.0761 968.5 0.0771 0.0771 1102.0 0.0781 0.0781 1249.5 0.0791 0.0791 1412.0 0.0800 0.0800 1590.5 0.0809 0.0809 1787.0 0.0818 0.0818 2003.0 0.0827 0.0827 2241.0 0.0836 0.0836 2503.0 0.0844 0.0844 2790.5 0.0853 0.0853 3107.0 0.0861 0.0861 3455.0 0.0869 0.0869 3837.0 0.0879 0.0879 4257.0 0.0889 0.0889 4719.0 0.0899 0.0899 5226.5 0.0908 0.0908 5784.0 0.0917 0.0917 6538.0 0.0928 0.0928 +5.0 5.4 150 9.0 0.1131 0.1131 11.0 0.1022 0.1022 13.5 0.0934 0.0934 16.5 0.0871 0.0871 19.5 0.0832 0.0832 22.5 0.0806 0.0806 26.0 0.0784 0.0784 30.0 0.0762 0.0762 34.5 0.0731 0.0731 40.0 0.0701 0.0701 46.0 0.0681 0.0681 52.5 0.0657 0.0657 60.0 0.0644 0.0644 69.0 0.0628 0.0628 79.0 0.0620 0.0620 90.5 0.0615 0.0615 105.5 0.0614 0.0614 123.5 0.0616 0.0616 143.0 0.0623 0.0623 163.5 0.0633 0.0633 185.0 0.0644 0.0644 208.0 0.0656 0.0656 232.5 0.0667 0.0667 258.5 0.0673 0.0673 286.0 0.0680 0.0680 331.0 0.0690 0.0690 396.0 0.0703 0.0703 468.5 0.0716 0.0716 549.5 0.0728 0.0728 639.0 0.0739 0.0739 738.0 0.0750 0.0750 847.5 0.0761 0.0761 968.5 0.0771 0.0771 1102.0 0.0781 0.0781 1249.5 0.0791 0.0791 1412.0 0.0800 0.0800 1590.5 0.0809 0.0809 1787.0 0.0818 0.0818 2003.0 0.0827 0.0827 2241.0 0.0836 0.0836 2503.0 0.0844 0.0844 2790.5 0.0853 0.0853 3107.0 0.0861 0.0861 3455.0 0.0869 0.0869 3837.0 0.0879 0.0879 4257.0 0.0889 0.0889 4719.0 0.0899 0.0899 5226.5 0.0908 0.0908 5784.0 0.0917 0.0917 6538.0 0.0928 0.0928 diff --git a/src/BTVNanoCommissioning/data/JME/Summer23BPix/jec_compiled.pkl.gz b/src/BTVNanoCommissioning/data/JME/Summer23BPix/jec_compiled.pkl.gz new file mode 100644 index 00000000..f9233ee7 Binary files /dev/null and b/src/BTVNanoCommissioning/data/JME/Summer23BPix/jec_compiled.pkl.gz differ diff --git a/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraCD_355862_357900_Golden.json b/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraCD_355862_357900_Golden.json new file mode 100644 index 00000000..f7e30033 --- /dev/null +++ b/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraCD_355862_357900_Golden.json @@ -0,0 +1,178 @@ +{ + "355862": [[121, 133]], + "355863": [[1, 14]], + "355870": [[31, 67]], + "355871": [[1, 5]], + "355872": [ + [1, 738], + [758, 995], + [997, 1217] + ], + "355892": [[14, 197]], + "355912": [[43, 200]], + "355913": [[1, 106]], + "355921": [[38, 442]], + "355933": [[75, 448]], + "355942": [[24, 189], [193, 213]], + "355988": [[43, 80], [85, 90]], + "355989": [[1, 24]], + "355998": [[1, 35]], + "355999": [[1, 9]], + "356004": [[1, 19]], + "356005": [[1, 187]], + "356043": [[1, 65]], + "356071": [[37, 191]], + "356074": [[1, 26]], + "356075": [[1, 125]], + "356076": [[1, 153]], + "356077": [[1, 472]], + "356135": [[46, 71]], + "356309": [[61, 184]], + "356316": [[45, 185]], + "356322": [[1, 19]], + "356323": [[1, 67], [69, 650]], + "356371": [[41, 50], [67, 72]], + "356375": [[35, 77], [101, 125]], + "356378": [ + [8, 208], + [210, 219], + [221, 304] + ], + "356381": [[1, 1193]], + "356383": [[1, 33]], + "356385": [[1, 30]], + "356386": [[1, 122]], + "356426": [[39, 60]], + "356428": [[1, 300]], + "356433": [[1, 310]], + "356434": [[1, 13]], + "356435": [[1, 3], [8, 8]], + "356446": [[10, 623]], + "356523": [[32, 410], [412, 898]], + "356531": [[1, 56]], + "356563": [ + [36, 113], + [117, 164], + [168, 177], + [181, 191], + [193, 194], + [199, 343] + ], + "356568": [[42, 64]], + "356569": [[1, 251]], + "356570": [[1, 98]], + "356576": [[58, 240]], + "356578": [[1, 865]], + "356580": [[1, 51]], + "356582": [[7, 104]], + "356614": [[1, 10], [16, 19], [27, 62]], + "356615": [[1, 1297]], + "356619": [[1, 173]], + "356810": [[44, 163]], + "356811": [[1, 44]], + "356812": [[1, 107]], + "356813": [[1, 54]], + "356814": [ + [1, 305], + [307, 309], + [311, 366], + [368, 672] + ], + "356815": [[1, 54], [176, 219]], + "356824": [[1, 66]], + "356908": [[1, 26]], + "356919": [[29, 116]], + "356937": [[20, 138]], + "356946": [[1, 129]], + "356947": [[1, 350]], + "356948": [[1, 88]], + "356949": [[1, 94]], + "356951": [[1, 274]], + "356954": [[1, 364]], + "356955": [[1, 380]], + "356956": [[1, 109]], + "356968": [[81, 252]], + "356969": [[1, 236]], + "356970": [[1, 366]], + "356998": [[1, 5]], + "356999": [[1, 58]], + "357000": [[1, 50]], + "357001": [[1, 183]], + "357079": [[1, 22]], + "357080": [[1, 616]], + "357081": [[1, 759]], + "357101": [[54, 103]], + "357102": [[1, 13], [43, 134]], + "357104": [[1, 4]], + "357106": [[1, 60]], + "357112": [[1, 519]], + "357268": [[70, 143]], + "357271": [[1, 20], [22, 1570]], + "357328": [[44, 105]], + "357329": [[1, 668]], + "357330": [[1, 157]], + "357331": [[1, 23]], + "357332": [[1, 430]], + "357333": [[1, 207]], + "357401": [[48, 664]], + "357406": [[50, 174]], + "357438": [[35, 230]], + "357440": [[1, 354]], + "357441": [[1, 83]], + "357442": [[1, 1373]], + "357447": [[40, 50]], + "357472": [[34, 60]], + "357478": [[43, 50]], + "357479": [[1, 1046]], + "357482": [[1, 5], [21, 220]], + "357538": [[39, 63]], + "357542": [[1, 11], [13, 252]], + "357550": [[1, 36]], + "357610": [[63, 253]], + "357611": [[1, 412]], + "357612": [[1, 736]], + "357613": [[1, 256]], + "357688": [[1, 380]], + "357696": [[31, 319], [341, 410]], + "357697": [[1, 39]], + "357698": [[1, 63]], + "357699": [[1, 30]], + "357700": [[1, 757]], + "357701": [[1, 310]], + "357705": [[1, 202]], + "357706": [[1, 161]], + "357720": [[32, 92]], + "357732": [[30, 157]], + "357734": [[1, 300]], + "357735": [ + [1, 387], + [389, 821], + [823, 947], + [949, 1126] + ], + "357754": [[29, 145]], + "357756": [[1, 425]], + "357757": [[1, 9]], + "357758": [[1, 85]], + "357759": [[1, 70]], + "357766": [[10, 124]], + "357777": [[1, 85]], + "357778": [[1, 359]], + "357779": [[1, 74]], + "357781": [[1, 10]], + "357802": [[43, 206]], + "357803": [[1, 153]], + "357804": [[1, 23]], + "357805": [[1, 88]], + "357806": [[1, 56]], + "357807": [[1, 244]], + "357808": [[1, 17]], + "357809": [[1, 41]], + "357812": [[1, 50]], + "357813": [[1, 293]], + "357814": [[1, 212]], + "357815": [[1, 944]], + "357898": [[1, 313]], + "357899": [[1, 637]], + "357900": [[1, 516]] +} \ No newline at end of file diff --git a/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraEFG_359022_362760_Golden.json b/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraEFG_359022_362760_Golden.json new file mode 100644 index 00000000..6a14fd64 --- /dev/null +++ b/src/BTVNanoCommissioning/data/lumiMasks/Cert_Collisions2022_eraEFG_359022_362760_Golden.json @@ -0,0 +1,258 @@ +{ + "359569": [[1, 83]], + "359571": [ + [1, 4], + [7, 28], + [30, 39], + [41, 50], + [52, 61], + [63, 72], + [74, 83], + [85, 94], + [96, 105], + [107, 116], + [118, 127], + [129, 138], + [140, 149], + [151, 160], + [162, 169] + ], + "359575": [ + [1, 13], + [15, 24], + [26, 35], + [37, 46], + [48, 57], + [59, 68], + [70, 89] + ], + "359595": [[17, 289]], + "359597": [[1, 94]], + "359602": [[1, 74]], + "359609": [[1, 25]], + "359612": [[1, 12]], + "359661": [[42, 233]], + "359685": [[1, 132]], + "359686": [[1, 934]], + "359691": [[19, 139]], + "359693": [[1, 604]], + "359694": [[1, 1080]], + "359699": [[28, 1883]], + "359718": [[1, 199], [202, 368]], + "359750": [[52, 186]], + "359751": [[15, 1007]], + "359762": [[44, 266]], + "359763": [[1, 410], [412, 459]], + "359764": [[28, 1111]], + "359776": [[104, 432]], + "359806": [[42, 410]], + "359808": [[1, 46]], + "359809": [[1, 43]], + "359810": [[1, 183]], + "359812": [[1, 227]], + "359814": [[1, 366]], + "359870": [[2, 7]], + "359871": [[1, 228]], + "359887": [[30, 502]], + "359899": [[19, 305]], + "359903": [[1, 12]], + "359998": [ + [24, 757], + [759, 759], + [761, 785], + [787, 1918] + ], + "360017": [[42, 81]], + "360019": [[1, 153], [155, 1489]], + "360075": [[20, 429], [462, 809]], + "360090": [[1, 794]], + "360116": [[77, 432]], + "360125": [ + [132, 195], + [200, 262], + [264, 324] + ], + "360126": [[1, 35], [37, 370]], + "360127": [[1, 201]], + "360128": [[1, 163]], + "360130": [[87, 91]], + "360131": [[1, 378]], + "360141": [[25, 775]], + "360224": [[43, 259]], + "360225": [[1, 169], [171, 356]], + "360295": [[35, 771], [774, 1475]], + "360296": [[1, 519]], + "360327": [[32, 383]], + "360390": [[5, 36], [38, 76]], + "360391": [[1, 4]], + "360392": [[1, 61]], + "360393": [[1, 124]], + "360400": [[40, 119]], + "360413": [[37, 126]], + "360428": [[50, 123]], + "360432": [[1, 28]], + "360435": [[1, 37], [43, 53]], + "360437": [[1, 40]], + "360438": [[1, 6]], + "360458": [[45, 215], [234, 246]], + "360459": [[1, 1332]], + "360460": [[1, 1077]], + "360486": [[45, 322]], + "360490": [[8, 936], [940, 1187]], + "360491": [[3, 157], [159, 179]], + "360737": [[10, 27]], + "360761": [[34, 190]], + "360794": [[37, 713]], + "360795": [[1, 395]], + "360797": [[1, 5]], + "360820": [[1, 885]], + "360825": [[1, 358]], + "360826": [[1, 91], [93, 119]], + "360856": [[42, 97]], + "360874": [[31, 82]], + "360876": [[1, 341]], + "360887": [[12, 134]], + "360888": [[1, 289]], + "360889": [[1, 36]], + "360890": [[1, 216], [219, 506]], + "360892": [[1, 296]], + "360895": [[1, 700]], + "360919": [[39, 836]], + "360920": [[1, 4]], + "360921": [[1, 218]], + "360927": [ + [37, 331], + [333, 581], + [583, 1930] + ], + "360941": [[1, 72]], + "360942": [[1, 344]], + "360945": [[1, 91]], + "360946": [[1, 364]], + "360948": [[1, 119]], + "360950": [ + [1, 179], + [181, 468], + [470, 718] + ], + "360951": [[1, 190]], + "360991": [[41, 270]], + "360992": [[1, 99]], + "361020": [[41, 439]], + "361044": [[42, 166]], + "361045": [[1, 322], [325, 947]], + "361050": [[1, 10]], + "361052": [[1, 65]], + "361054": [[1, 461]], + "361083": [[6, 59]], + "361091": [[38, 418]], + "361105": [[41, 448]], + "361106": [[1, 31]], + "361107": [[1, 156]], + "361108": [[1, 7]], + "361110": [[1, 197]], + "361188": [[46, 186]], + "361193": [[1, 29]], + "361195": [[1, 20]], + "361197": [[1, 960], [991, 2177]], + "361223": [[36, 730]], + "361239": [[35, 1090]], + "361240": [[1, 1015]], + "361272": [[1, 125]], + "361280": [[1, 267]], + "361283": [[1, 26]], + "361284": [[1, 36]], + "361297": [[38, 970]], + "361303": [[36, 2275]], + "361318": [[34, 490]], + "361319": [[1, 162]], + "361320": [[1, 382]], + "361333": [[36, 337]], + "361361": [[60, 105]], + "361362": [[1, 31]], + "361363": [[1, 27]], + "361365": [[1, 489]], + "361366": [[1, 201]], + "361400": [[41, 496]], + "361417": [[38, 1659]], + "361443": [[40, 2313]], + "361468": [[3, 1883]], + "361475": [ + [37, 614], + [617, 836], + [838, 838], + [840, 1121], + [1124, 1273] + ], + "361512": [[36, 849], [851, 1573]], + "361569": [[35, 598]], + "361573": [[1, 305]], + "361579": [[43, 905]], + "361580": [[1, 422]], + "361957": [[1, 1163]], + "361971": [[38, 731], [733, 2381]], + "361989": [[37, 112], [114, 151]], + "361990": [[1, 204]], + "361994": [[1, 116]], + "362058": [[39, 209]], + "362059": [[1, 63]], + "362060": [[1, 83], [85, 85]], + "362061": [[1, 314]], + "362062": [[1, 207]], + "362063": [[1, 70], [77, 92]], + "362064": [ + [93, 218], + [220, 508], + [510, 1067] + ], + "362087": [[1, 13], [15, 146]], + "362091": [[1, 1418]], + "362104": [[33, 147]], + "362105": [[1, 179]], + "362106": [[1, 78]], + "362107": [[1, 785]], + "362148": [[39, 758]], + "362153": [[1, 140]], + "362154": [[1, 784]], + "362159": [[29, 35]], + "362161": [[1, 120]], + "362163": [[1, 65]], + "362166": [[1, 331]], + "362167": [[1, 646]], + "362437": [[1, 11], [73, 92]], + "362439": [[8, 34], [249, 1595]], + "362597": [[1, 139]], + "362614": [[1, 153]], + "362615": [[1, 149]], + "362616": [[1, 351]], + "362617": [[1, 244]], + "362618": [[1, 274]], + "362653": [[40, 305]], + "362654": [[1, 26]], + "362655": [ + [1, 140], + [143, 144], + [148, 338] + ], + "362657": [[1, 198]], + "362695": [[39, 372]], + "362696": [ + [1, 1112], + [1114, 1120], + [1122, 1124], + [1126, 1128], + [1130, 1132], + [1134, 1140], + [1142, 1144], + [1146, 1148], + [1150, 1152], + [1154, 1720] + ], + "362698": [[1, 374]], + "362720": [[52, 949]], + "362727": [[4, 7]], + "362728": [[1, 388]], + "362757": [[32, 93]], + "362758": [[1, 205]], + "362760": [[1, 728]] +} diff --git a/src/BTVNanoCommissioning/helpers/func.py b/src/BTVNanoCommissioning/helpers/func.py index 509abdb6..dd7666ce 100644 --- a/src/BTVNanoCommissioning/helpers/func.py +++ b/src/BTVNanoCommissioning/helpers/func.py @@ -2,6 +2,7 @@ import numpy as np from coffea import processor import psutil, os +import uproot def memory_usage_psutil(): @@ -103,9 +104,12 @@ def uproot_writeable(events, include=["events", "run", "luminosityBlock"]): and "Flavor" not in n ): continue - if not _is_rootcompat(events[bname][n]) and ak.num( - events[bname][n], axis=0 - ) != len(flatten(events[bname][n])): + evnums = ak.num(events[bname][n], axis=0) + if not isinstance(evnums, int): + continue + if not _is_rootcompat(events[bname][n]) and evnums != len( + flatten(events[bname][n]) + ): continue # skip IdxG if "IdxG" in n: @@ -115,3 +119,92 @@ def uproot_writeable(events, include=["events", "run", "luminosityBlock"]): ) ev[bname] = ak.zip(b_nest) return ev + + +def array_writer( + processor_class, # the NanoProcessor class ("self") + pruned_event, # the event with specific calculated variables stored + nano_event, # entire NanoAOD/PFNano event with many variables + systname, # name of systematic shift + dataset, # dataset name + isRealData, # boolean + remove=[ + "SoftMuon", + "MuonJet", + "dilep", + "OtherJets", + "Jet", + ], # remove from variable list + kinOnly=[ + "Muon", + "Jet", + "SoftMuon", + "dilep", + "charge", + "MET", + ], # variables for which only kinematic properties are kept + kins=[ + "pt", + "eta", + "phi", + "mass", + "pfRelIso04_all", + "dxy", + "dz", + ], # kinematic propoerties for the above variables + othersData=[ + "PFCands_*", + "MuonJet_*", + "SV_*", + "PV_npvs", + "PV_npvsGood", + "Rho_*", + "SoftMuon_dxySig", + "Muon_sip3d", + ], # other fields, for Data and MC + othersMC=["Pileup_nTrueInt", "Pileup_nPU"], # other fields, for MC only + empty=False +): + if empty: + print("WARNING: No events selected. Writing blank file.") + out_branch = [] + else: + # Get only the variables that were added newly + out_branch = np.setdiff1d( + np.array(pruned_event.fields), np.array(nano_event.fields) + ) + + # Handle kinOnly vars + for v in remove: + out_branch = np.delete(out_branch, np.where((out_branch == v))) + + for kin in kins: + for obj in kinOnly: + if "MET" in obj and ("pt" != kin or "phi" != kin): + continue + if (obj != "Muon" and obj != "SoftMuon") and ( + "pfRelIso04_all" == kin or "d" in kin + ): + continue + out_branch = np.append(out_branch, [f"{obj}_{kin}"]) + + # Handle data vars + out_branch = np.append(out_branch, othersData) + + if not isRealData: + out_branch = np.append(out_branch, othersMC) + + # Write to root files + print("Branches to write:", out_branch) + outdir = f"{processor_class.name}/{systname}/{dataset}/" + os.system(f"mkdir -p {outdir}") + + with uproot.recreate( + f"{outdir}/{nano_event.metadata['filename'].split('/')[-1].replace('.root','')}_{int(nano_event.metadata['entrystop']/processor_class.chunksize)}.root" + ) as fout: + if not empty: fout["Events"] = uproot_writeable(pruned_event, include=out_branch) + fout["TotalEventCount"] = ak.Array( + [nano_event.metadata["entrystop"] - nano_event.metadata["entrystart"]] + ) + if not isRealData: + fout["TotalEventWeight"] = ak.Array([ak.sum(nano_event.genWeight)]) diff --git a/src/BTVNanoCommissioning/helpers/update_branch.py b/src/BTVNanoCommissioning/helpers/update_branch.py index 83013045..5dbeb9ef 100644 --- a/src/BTVNanoCommissioning/helpers/update_branch.py +++ b/src/BTVNanoCommissioning/helpers/update_branch.py @@ -9,6 +9,8 @@ def missing_branch(events): if hasattr(events, "fixedGridRhoFastjetAll") else events.Rho.fixedGridRhoFastjetAll ) + ## calculate missing nodes + if not hasattr(events.Jet, "btagDeepFlavB"): jets = events.Jet jets["btagDeepFlavB"] = ( @@ -75,9 +77,51 @@ def missing_branch(events): "btagDeepFlavCvB": jets.btagDeepFlavCvB, }, ) + if hasattr(events.Jet, "btagDeepB_b") and not hasattr(events.Jet, "btagDeepB"): + jets = events.Jet + jets["btagDeepB"] = events.Jet.btagDeepB_b + events.Jet.btagDeepB_bb + events.Jet = update( + events.Jet, + {"btagDeepB": jets.btagDeepB}, + ) + if hasattr(events.Jet, "btagDeepC") and not hasattr(events.Jet, "btagDeepCvL"): + jets["btagDeepCvL"] = np.maximum( + np.minimum( + np.where( + (events.Jet.btagDeepC > 0) & (events.Jet.pt > 15), + (events.Jet.btagDeepC / (1.0 - events.Jet.btagDeepB)), + -1, + ), + 0.999999, + ), + -1, + ) + jets["btagDeepCvB"] = np.maximum( + np.minimum( + np.where( + (events.Jet.btagDeepC > 0) & (events.Jet.pt > 15), + ( + events.Jet.btagDeepC + / (events.Jet.btagDeepC + events.Jet.btagDeepB) + ), + -1, + ), + 0.999999, + ), + -1, + ) + events.Jet = update( + events.Jet, + { + "btagDeepCvL": jets.btagDeepCvL, + "btagDeepCvB": jets.btagDeepCvB, + }, + ) if hasattr(events, "METFixEE2017"): events.MET = events.METFixEE2017 - if not hasattr(events.PuppiMET, "MetUnclustEnUpDeltaX"): + if hasattr(events.PuppiMET, "ptUnclusteredUp") and not hasattr( + events.PuppiMET, "MetUnclustEnUpDeltaX" + ): met = events.PuppiMET met["MetUnclustEnUpDeltaX"] = met.ptUnclusteredUp * np.cos(met.phiUnclusteredUp) met["MetUnclustEnUpDeltaY"] = met.ptUnclusteredUp * np.sin(met.phiUnclusteredUp) @@ -89,45 +133,3 @@ def missing_branch(events): }, ) return events - - -### Not used anymore -def add_jec(events, campaign, jmestuff): - dataset = events.metadata["dataset"] - jet_factory = jmestuff["jet_factory"] - met_factory = jmestuff["met_factory"] - isRealData = not hasattr(events, "genWeight") - if isRealData: - if "un" in dataset: - jecname = dataset[dataset.find("un") + 6] - else: - print("No valid jec name") - raise NameError - if "2016_" in campaign: - if "B" == jecname or "C" == jecname or "D" == jecname: - jecname = "BCD" - elif "E" == jecname or "F" == jecname: - jecname = "EF" - elif "F" == jecname or "G" == jecname or "H" == jecname: - jecname = "FGH" - else: - raise NameError - elif campaign == "Rereco17_94X": - jecname = "" - jets = jet_factory[f"data{jecname}"].build( - add_jec_variables(events.Jet, events.fixedGridRhoFastjetAll), - lazy_cache=events.caches[0], - ) - else: - jets = jet_factory["mc"].build( - add_jec_variables(events.Jet, events.fixedGridRhoFastjetAll), - lazy_cache=events.caches[0], - ) - - if "Run3" not in campaign: - met = met_factory.build(events.MET, jets, {}) - events = update(events, {"Jet": jets, "MET": met}) - else: - met = met_factory.build(events.PuppiMET, jets, {}) - events = update(events, {"Jet": jets, "PuppiMET": met}) - return events diff --git a/src/BTVNanoCommissioning/helpers/xsection.py b/src/BTVNanoCommissioning/helpers/xsection.py index f580886d..442c60de 100644 --- a/src/BTVNanoCommissioning/helpers/xsection.py +++ b/src/BTVNanoCommissioning/helpers/xsection.py @@ -1,4 +1,1195 @@ xsection = [ + { + "id": "61641f28294617432cff1379", + "process_name": "QCD_Pt-30to50_EMEnriched_TuneCP5_13TeV_pythia8", + "cross_section": "6447000.0", + "total_uncertainty": "19870.0", + "equivalent_lumi": "0.0001551", + "matrix_generator": "none", + "energy": "13", + }, + { + "id": "61641f29294617432cff137a", + "process_name": "QCD_Pt-50to80_EMEnriched_TuneCP5_13TeV_pythia8", + "cross_section": "1988000.0", + "total_uncertainty": "5961.0", + "equivalent_lumi": "0.0005029", + "matrix_generator": "none", + "energy": "13", + }, + { + "id": "61641f2a294617432cff137b", + "process_name": "QCD_Pt-80to120_EMEnriched_TuneCP5_13TeV_pythia8", + "cross_section": "367500.0", + "total_uncertainty": "1091.0", + "equivalent_lumi": "0.002721", + "matrix_generator": "none", + "energy": "13", + }, + { + "id": "61641f25294617432cff1374", + "process_name": "QCD_Pt-120to170_EMEnriched_TuneCP5_13TeV_pythia8", + "cross_section": "66590.0", + "total_uncertainty": "196.6", + "equivalent_lumi": "0.01502", + "matrix_generator": 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"5f6c2a01f368571e6ae3c791", + "process_name": "ST_s-channel_4f_hadronicDecays_TuneCP5_13TeV-amcatnlo-pythia8", + "cross_section": "11.24", + "total_uncertainty": "0.0108", + "DAS": "/ST_s-channel_4f_hadronicDecays_TuneCP5_13TeV-amcatnlo-pythia8/RunIIFall17MiniAODv2-PU2017_12Apr2018_94X_mc2017_realistic_v14-v1/MINIAODSIM", + "fraction_negative_weight": "0.1867", + "matrix_generator": "none", + "energy": "13", + }, { "process_name": "DYJetsToLL_M-10to50_TuneCP5_13p6TeV-madgraphMLM-pythia8", "cross_section": "19317.5", diff --git a/src/BTVNanoCommissioning/utils/AK4_parameters.py b/src/BTVNanoCommissioning/utils/AK4_parameters.py index a73ad57f..301c3973 100644 --- a/src/BTVNanoCommissioning/utils/AK4_parameters.py +++ b/src/BTVNanoCommissioning/utils/AK4_parameters.py @@ -91,15 +91,15 @@ "Summer23": { "lumiMask": "Cert_Collisions2023_366442_370790_Golden.json", "PU": "puwei_Summer23.histo.root", - "JME": { - "MC": "Summer23Prompt23_V1", - "Run2023C-22Sep2023_v1": "Summer23Prompt23_RunCv123_V1", - "Run2023C-22Sep2023_v2": "Summer23Prompt23_RunCv123_V1", - "Run2023C-22Sep2023_v3": "Summer23Prompt23_RunCv123_V1", - "Run2023C-22Sep2023_v4": "Summer23Prompt23_RunCv4_V1", - }, - # "JME": "jec_compiled.pkl.gz", - "jetveto": {"Run2023BC jetvetomap_all": "Summer23Prompt23_RunC_v1.histo.root"}, + # "JME": { + # "MC": "Summer23Prompt23_V1", + # "Run2023C-22Sep2023_v1": "Summer23Prompt23_RunCv123_V1", + # "Run2023C-22Sep2023_v2": "Summer23Prompt23_RunCv123_V1", + # "Run2023C-22Sep2023_v3": "Summer23Prompt23_RunCv123_V1", + # "Run2023C-22Sep2023_v4": "Summer23Prompt23_RunCv4_V1", + # }, + "JME": "jec_compiled.pkl.gz", + # "jetveto": {"Run2023BC jetvetomap_all": "Summer23Prompt23_RunC_v1.histo.root"}, "JPCalib": { "Run2023C-22Sep2023_v1": "calibeHistoWrite_Data2023C-22Sep2023_v1.root", "Run2023C-22Sep2023_v2": "calibeHistoWrite_Data2023C-22Sep2023_v2.root", @@ -111,18 +111,19 @@ "Summer23BPix": { "lumiMask": "Cert_Collisions2023_366442_370790_Golden.json", "PU": "puwei_Summer23BPix.histo.root", - "JME": { - "MC": "Summer23BPixPrompt23_V1", - "Run2023D": "Summer23BPixPrompt23_RunD_V1", - }, - "jetveto": { - "Run2023D jetvetomap_all": "Summer23BPixPrompt23_RunD_v1.histo.root" - }, # this is from Mikko https://indico.cern.ch/event/1315421/contributions/5532963/attachments/2697975/4683826/2023_08_16_jetvetomaps_v3.pdf + # "JME": { + # "MC": "Summer23BPixPrompt23_V1", + # "Run2023D": "Summer23BPixPrompt23_RunD_V1", + # }, + "JME": "jec_compiled.pkl.gz", + # "jetveto": { + # "Run2023D jetvetomap_all": "Summer23BPixPrompt23_RunD_v1.histo.root" + # }, # this is from Mikko https://indico.cern.ch/event/1315421/contributions/5532963/attachments/2697975/4683826/2023_08_16_jetvetomaps_v3.pdf "JPCalib": { "Run2023D-22Sep2023_v1": "calibeHistoWrite_Data2023D-22Sep2023_v1.root", "Run2023D-22Sep2023_v2": "calibeHistoWrite_Data2023D-22Sep2023_v2.root", "MC": "calibeHistoWrite_MC2023_Summer23BPix.root", }, }, - "prompt_dataMC": {"lumiMask": "$LUMIMASK_DATAMC"}, + "CAMPAIGN_prompt_dataMC": {"lumiMask": "$LUMIMASK_DATAMC"}, } diff --git a/src/BTVNanoCommissioning/utils/compile_jec.py b/src/BTVNanoCommissioning/utils/compile_jec.py index 26fe3cdf..662a4654 100644 --- a/src/BTVNanoCommissioning/utils/compile_jec.py +++ b/src/BTVNanoCommissioning/utils/compile_jec.py @@ -31,336 +31,294 @@ def jet_factory_factory(files): return CorrectedJetsFactory(jec_name_map, jec_stack) +jet_factory = { + "Rereco17_94X": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_V3b_MC_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_V3b_MC_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L2L3Residual_AK4PFchs.jec.txt", + ], + "data": [ + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L3Absolute_AK4PFchs.jec.txt", + ], + }, + "2016preVFP_UL": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_MC_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/RegroupedV2_Summer19UL16APV_V7_MC_UncertaintySources_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_Uncertainty_AK4PFchs.junc.txt", + ], + "dataBCD": [ + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_Uncertainty_AK4PFchs.junc.txt", + ], + "dataEF": [ + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + }, + "2016postVFP_UL": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/RegroupedV2_Summer19UL16_V7_MC_UncertaintySources_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_MC_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_MC_SF_AK4PFchs.jersf.txt", + ], + "dataFGH": [ + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_DATA_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + }, + "2017_UL": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/RegroupedV2_Summer19UL17_V5_MC_UncertaintySources_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_JRV2_MC_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_JRV2_MC_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_Uncertainty_AK4PFchs.junc.txt", + ], + "dataB": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataC": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataD": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataE": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataF": [ + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + }, + "2018_UL": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/2018_UL/RegroupedV2_Summer19UL18_V5_MC_UncertaintySources_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_JRV2_MC_PtResolution_AK4PFchs.jr.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_JRV2_MC_SF_AK4PFchs.jersf.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_Uncertainty_AK4PFchs.junc.txt", + ], + "dataA": [ + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataB": [ + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataC": [ + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + "dataD": [ + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L1FastJet_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L2Relative_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L3Absolute_AK4PFchs.jec.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_Uncertainty_AK4PFchs.junc.txt", + "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", + ], + }, + "Winter22Run3": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V1_MC_PtResolution_AK4PFPuppi.jr.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V1_MC_SF_AK4PFPuppi.jersf.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L2Residual_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_UncertaintySources_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_Uncertainty_AK4PFPuppi.junc.txt", + ], + "dataC": [ + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + "dataD": [ + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + }, + "Summer22": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_L2Residual_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22Run3_V1_MC_SF_AK4PFPuppi.jersf.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22Run3_V1_MC_PtResolution_AK4PFPuppi.jr.txt", + ], + "dataC": [ + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + "dataD": [ + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22/Summer22_RunCD_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + }, + "Summer22EE": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EEPrompt22_JRV1_MC_SF_AK4PFPuppi.jersf.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EEPrompt22_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_L2Residual_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", + ], + "dataE": [ + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunE_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunE_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunE_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunE_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + "dataF": [ + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunF_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunF_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunF_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunF_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + "dataG": [ + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunG_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunG_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunG_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer22EE/Summer22EE_RunG_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + }, + "Summer23": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2Residual_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_JRV1_MC_SF_AK4PFPuppi.jersf.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt", + + ], + "dataCv123": [ + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + "dataCv4": [ + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", + ], + }, + "Summer23BPix": { + "mc": [ + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L2Residual_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt", + "src/BTVNanoCommissioning/data/JME/Summer23BPix/Summer23BPixPrompt23_JRV1_MC_SF_AK4PFPuppi.jersf.txt", + ], + }, +} + + def jet_factories(campaign): - jet_factory = { - "Rereco17_94X": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_V3b_MC_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_V3b_MC_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/MC/Fall17_17Nov2017_V32_MC_L2L3Residual_AK4PFchs.jec.txt", - ] - ), - "data": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/Rereco17_94X/RunDE/Fall17_17Nov2017DE_V32_DATA_L3Absolute_AK4PFchs.jec.txt", - ] - ), - }, - "2016preVFP_UL": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_MC_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/RegroupedV2_Summer19UL16APV_V7_MC_UncertaintySources_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_V7_MC_Uncertainty_AK4PFchs.junc.txt", - ] - ), - "dataBCD": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunBCD_V7_DATA_Uncertainty_AK4PFchs.junc.txt", - ] - ), - "dataEF": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer20UL16APV_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016preVFP_UL/Summer19UL16APV_RunEF_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - }, - "2016postVFP_UL": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/RegroupedV2_Summer19UL16_V7_MC_UncertaintySources_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_V7_MC_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_MC_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_MC_SF_AK4PFchs.jersf.txt", - ] - ), - "dataFGH": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_DATA_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer20UL16_JRV3_DATA_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2016postVFP_UL/Summer19UL16_RunFGH_V7_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - }, - "2017_UL": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/RegroupedV2_Summer19UL17_V5_MC_UncertaintySources_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_JRV2_MC_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_JRV2_MC_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_V5_MC_Uncertainty_AK4PFchs.junc.txt", - ] - ), - "dataB": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunB_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataC": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunC_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataD": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunD_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataE": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunE_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataF": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2017_UL/Summer19UL17_RunF_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - }, - "2018_UL": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2018_UL/RegroupedV2_Summer19UL18_V5_MC_UncertaintySources_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_JRV2_MC_PtResolution_AK4PFchs.jr.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_JRV2_MC_SF_AK4PFchs.jersf.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_V5_MC_Uncertainty_AK4PFchs.junc.txt", - ] - ), - "dataA": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunA_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataB": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunB_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataC": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunC_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - "dataD": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L1FastJet_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L2L3Residual_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L2Relative_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_L3Absolute_AK4PFchs.jec.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_Uncertainty_AK4PFchs.junc.txt", - "src/BTVNanoCommissioning/data/JME/2018_UL/Summer19UL18_RunD_V5_DATA_UncertaintySources_AK4PFchs.junc.txt", - ] - ), - }, - "Winter22Run3": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V1_MC_PtResolution_AK4PFPuppi.jr.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V1_MC_SF_AK4PFPuppi.jersf.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L2Residual_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_UncertaintySources_AK4PFPuppi.junc.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_V2_MC_Uncertainty_AK4PFPuppi.junc.txt", - ] - ), - "dataC": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunC_V2_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - "dataD": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Winter22Run3/Winter22Run3_RunD_V2_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - }, - "Summer22Run3": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_L2Residual_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22Run3_V1_MC_SF_AK4PFPuppi.jersf.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22Run3_V1_MC_PtResolution_AK4PFPuppi.jr.txt", - ] - ), - "dataC": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - "dataD": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22_RunCD_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - }, - "Summer22EERun3": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EEPrompt22_JRV1_MC_SF_AK4PFPuppi.jersf.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EEPrompt22_JRV1_MC_PtResolution_AK4PFPuppi.jr.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_L2Residual_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", - ] - ), - "dataE": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunE_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunE_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunE_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunE_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - "dataF": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunF_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunF_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunF_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunF_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - "dataG": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunG_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunG_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunG_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer22EERun3/Summer22EE_RunG_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - }, - "Summer23": { - "mc": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L2Residual_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_UncertaintySources_AK4PFPuppi.junc.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_V1_MC_Uncertainty_AK4PFPuppi.junc.txt", - # "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22Run3_V1_MC_SF_AK4PFPuppi.jersf.txt", - # "src/BTVNanoCommissioning/data/JME/Summer22Run3/Summer22Run3_V1_MC_PtResolution_AK4PFPuppi.jr.txt", - ] - ), - "dataCv123": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv123_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - "dataCv4": jet_factory_factory( - files=[ - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L1FastJet_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L2Relative_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L3Absolute_AK4PFPuppi.jec.txt", - "src/BTVNanoCommissioning/data/JME/Summer23/Summer23Prompt23_RunCv4_V1_DATA_L2L3Residual_AK4PFPuppi.jec.txt", - ] - ), - }, + factory_info = { + j: jet_factory_factory(files=jet_factory[campaign][j]) + for j in jet_factory[campaign].keys() } - return jet_factory[campaign] + + return factory_info if __name__ == "__main__": diff --git a/src/BTVNanoCommissioning/utils/correction.py b/src/BTVNanoCommissioning/utils/correction.py index 495cc377..707c845c 100644 --- a/src/BTVNanoCommissioning/utils/correction.py +++ b/src/BTVNanoCommissioning/utils/correction.py @@ -314,113 +314,14 @@ def load_lumi(campaign): return LumiMask(filename) -## MET filters -met_filters = { - "2016preVFP_UL": { - "data": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "eeBadScFilter", - ], - "mc": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "eeBadScFilter", - ], - }, - "2016postVFP_UL": { - "data": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "eeBadScFilter", - ], - "mc": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "eeBadScFilter", - ], - }, - "2017_UL": { - "data": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "hfNoisyHitsFilter", - "eeBadScFilter", - "ecalBadCalibFilter", - ], - "mc": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "hfNoisyHitsFilter", - "eeBadScFilter", - "ecalBadCalibFilter", - ], - }, - "2018_UL": { - "data": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "hfNoisyHitsFilter", - "eeBadScFilter", - "ecalBadCalibFilter", - ], - "mc": [ - "goodVertices", - "globalSuperTightHalo2016Filter", - "HBHENoiseFilter", - "HBHENoiseIsoFilter", - "EcalDeadCellTriggerPrimitiveFilter", - "BadPFMuonFilter", - "BadPFMuonDzFilter", - "hfNoisyHitsFilter", - "eeBadScFilter", - "ecalBadCalibFilter", - ], - }, -} - - -def jetveto(events, correct_map): +def jetveto(jets, correct_map): return ak.where( correct_map["jetveto"][list(correct_map["jetveto"].keys())[0]]( - events.Jet.phi, events.Jet.eta + jets.phi, jets.eta ) > 0, - ak.ones_like(events.Jet.eta), - ak.zeros_like(events.Jet.eta), + ak.ones_like(jets.eta), + ak.zeros_like(jets.eta), ) @@ -458,6 +359,7 @@ def JME_shifts( systematic=False, exclude_jetveto=False, ): + dataset = events.metadata["dataset"] jecname = "" # https://cms-jerc.web.cern.ch/JECUncertaintySources/, currently no recommendation of reduced/ full split sources @@ -689,9 +591,11 @@ def JME_shifts( jets = events.Jet # perform jet veto if "jetveto" in correct_map.keys(): - events.Jet = update(events.Jet, {"veto": jetveto(events, correct_map)}) + jets = update(jets, {"veto": jetveto(jets, correct_map)}) + + if "Summer22" in campaign: + jets = jets[jets.veto != 1] shifts += [({"Jet": jets, "MET": met}, None)] - print("in shift", met.pt) return shifts @@ -816,7 +720,7 @@ def puwei(nPU, correct_map, weights, syst=False): def btagSFs(jet, correct_map, weights, SFtype, syst=False): - if SFtype == "DeepJetC" or SFtype == "DeepCSVC": + if SFtype.endswith("C"): systlist = [ "Extrap", "Interp", @@ -832,7 +736,7 @@ def btagSFs(jet, correct_map, weights, SFtype, syst=False): "jer", "jesTotal", ] - elif SFtype == "DeepJetB" or SFtype == "DeepCSVB": + elif SFtype.endswith("B"): systlist = [ "hf", "lf", diff --git a/src/BTVNanoCommissioning/utils/histogrammer.py b/src/BTVNanoCommissioning/utils/histogrammer.py index 29d08b28..88fa9813 100644 --- a/src/BTVNanoCommissioning/utils/histogrammer.py +++ b/src/BTVNanoCommissioning/utils/histogrammer.py @@ -7,7 +7,7 @@ def histogrammer(events, workflow): ## Common variables flav_axis = Hist.axis.IntCategory([0, 1, 4, 5, 6], name="flav", label="Genflavour") syst_axis = Hist.axis.StrCategory([], name="syst", growth=True) - pt_axis = Hist.axis.Regular(300, 0, 300, name="pt", label=" $p_{T}$ [GeV]") + pt_axis = Hist.axis.Regular(60, 0, 300, name="pt", label=" $p_{T}$ [GeV]") jpt_axis = Hist.axis.Regular(300, 0, 3000, name="pt", label=" $p_{T}$ [GeV]") softlpt_axis = Hist.axis.Regular(25, 0, 25, name="pt", label=" $p_{T}$ [GeV]") mass_axis = Hist.axis.Regular(50, 0, 300, name="mass", label=" $p_{T}$ [GeV]") @@ -18,7 +18,7 @@ def histogrammer(events, workflow): softliso_axis = Hist.axis.Regular( 20, 0.2, 6.2, name="pfRelIso03_all", label="Rel. Iso" ) - npvs_axis = Hist.axis.Regular(100, 0, 100, name="npv", label="N PVs") + npvs_axis = Hist.axis.Integer(0, 100, name="npv", label="N PVs") dr_axis = Hist.axis.Regular(20, 0, 8, name="dr", label="$\Delta$R") dr_s_axis = Hist.axis.Regular(20, 0, 0.5, name="dr", label="$\Delta$R") dr_SV_axis = Hist.axis.Regular(20, 0, 1.0, name="dr", label="$\Delta$R") @@ -194,52 +194,6 @@ def histogrammer(events, workflow): _hist_dict[f"{i}_ptratio"] = Hist.Hist( syst_axis, flav_axis, ptratio_axis, Hist.storage.Weight() ) - elif "ctag_ttsemilep_sf" in workflow: - obj_list = ["hl", "soft_l", "MET", "z", "w", "mujet"] - _hist_dict["z_mass"] = Hist.Hist( - syst_axis, - Hist.axis.Regular(50, 50, 100, name="mass", label="$m_{\\ell\\ell}$ [GeV]"), - Hist.storage.Weight(), - ) - _hist_dict["w_mass"] = Hist.Hist( - syst_axis, - Hist.axis.Regular(50, 50, 100, name="mass", label="$m_{\\ell\\nu}$ [GeV]"), - Hist.storage.Weight(), - ) - # delta R between soft muon and mu-jet - _hist_dict["dr_lmujetsmu"] = Hist.Hist( - syst_axis, flav_axis, dr_s_axis, Hist.storage.Weight() - ) - # delta R between hard muon and mu-jet - _hist_dict["dr_lmujethmu"] = Hist.Hist( - syst_axis, flav_axis, dr_axis, Hist.storage.Weight() - ) - # delta R between hard muon and soft-muon - _hist_dict["dr_lmusmu"] = Hist.Hist(syst_axis, dr_axis, Hist.storage.Weight()) - for i in ["hl", "soft_l"]: - if i == "soft_l": - _hist_dict[f"soft_l_pfRelIso04_all"] = Hist.Hist( - syst_axis, flav_axis, softliso_axis, Hist.storage.Weight() - ) - _hist_dict[f"{i}_dxy"] = Hist.Hist( - syst_axis, flav_axis, dxy_axis, Hist.storage.Weight() - ) - _hist_dict[f"{i}_dz"] = Hist.Hist( - syst_axis, flav_axis, dz_axis, Hist.storage.Weight() - ) - else: - _hist_dict[f"{i}_pfRelIso04_all"] = Hist.Hist( - syst_axis, iso_axis, Hist.storage.Weight() - ) - _hist_dict[f"{i}_dxy"] = Hist.Hist( - syst_axis, dxy_axis, Hist.storage.Weight() - ) - _hist_dict[f"{i}_dz"] = Hist.Hist( - syst_axis, dz_axis, Hist.storage.Weight() - ) - _hist_dict[f"{i}_ptratio"] = Hist.Hist( - syst_axis, flav_axis, ptratio_axis, Hist.storage.Weight() - ) elif "Wc_sf" in workflow: obj_list = ["hl", "soft_l", "MET", "z", "w", "mujet"] _hist_dict["SV_charge"] = Hist.Hist( @@ -524,6 +478,7 @@ def histogrammer(events, workflow): "btagNegDeepFlavQG", "btagNegDeepFlavUDS", "btagNegPNetB", + "btagNegPNetC", "btagNegPNetCvB", "btagNegPNetCvL", "btagNegPNetProbB", diff --git a/src/BTVNanoCommissioning/utils/selection.py b/src/BTVNanoCommissioning/utils/selection.py index 25347ec7..dc4e36c5 100644 --- a/src/BTVNanoCommissioning/utils/selection.py +++ b/src/BTVNanoCommissioning/utils/selection.py @@ -37,12 +37,13 @@ def ele_mvatightid(events, campaign): return elemask -def softmu_mask(events, campaign): +def softmu_mask(events, campaign, dxySigCut=0): softmumask = ( (events.Muon.pt < 25) & (abs(events.Muon.eta) < 2.4) & (events.Muon.tightId > 0.5) & (events.Muon.pfRelIso04_all > 0.2) + & (abs(events.Muon.dxy / events.Muon.dxyErr) > dxySigCut) & (events.Muon.jetIdx != -1) ) @@ -74,6 +75,44 @@ def jet_cut(events, campaign): return multijetmask +def MET_filters(events, campaign): + # apply MET filter + metfilter = ak.ones_like(events.run, dtype=bool) + print(metfilter) + for flag in met_filters[campaign]["data" if "Run" else "mc"]: + metfilter = events.Flag[flag] & metfilter + ## Flag_ecalBadCalibFilter + badjet = ( + (events.Jet.pt > 50) + & (events.Jet.eta >= -0.5) + & (events.Jet.eta <= -0.1) + & (events.Jet.phi >= -2.1) + & (events.Jet.phi <= -1.8) + & ((events.Jet.neEmEF > 0.9) | (events.Jet.chEmEF > 0.9)) + & (events.Jet.delta_phi(events.PuppiMET) > 2.9) + ) + ecalBadCalibFilter = ( + (ak.sum(badjet, axis=-1) >= 1) + & (events.PuppiMET.pt > 100) + & (events.run >= 362433) + & (events.run <= 367144) + ) + + metfilter = metfilter & ~ecalBadCalibFilter + return metfilter + + +def btag_wp(jets, campaign, tagger, borc, wp): + WP = btag_wp_dict[campaign] + if borc == "b": + jet_mask = jets[f"btag{tagger}B"] > WP[tagger]["b"][wp] + else: + jet_mask = (jets[f"btag{tagger}CvB"] > WP[tagger]["c"][wp][1]) & ( + jets[f"btag{tagger}CvL"] > WP[tagger]["c"][wp][0] + ) + return jet_mask + + btag_wp_dict = { "Summer22": { "DeepFlav": { @@ -176,14 +215,179 @@ def jet_cut(events, campaign): }, }, } - - -def btag_wp(jets, campaign, tagger, borc, wp): - WP = btag_wp_dict[campaign] - if borc == "b": - jet_mask = jets[f"btag{tagger}B"] > WP[tagger]["b"][wp] - else: - jet_mask = (jets[f"btag{tagger}CvB"] > WP[tagger]["c"][wp][1]) & ( - jets[f"btag{tagger}CvL"] > WP[tagger]["c"][wp][0] - ) - return jet_mask +met_filters = { + "2016preVFP_UL": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "eeBadScFilter", + ], + }, + "2016postVFP_UL": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "eeBadScFilter", + ], + }, + "2017_UL": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + "ecalBadCalibFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + "ecalBadCalibFilter", + ], + }, + "2018_UL": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + "ecalBadCalibFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "HBHENoiseFilter", + "HBHENoiseIsoFilter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + "ecalBadCalibFilter", + ], + }, + "Summer22": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + }, + "Summer22EE": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + }, + "Summer23": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + }, + "Summer23BPix": { + "data": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + "mc": [ + "goodVertices", + "globalSuperTightHalo2016Filter", + "EcalDeadCellTriggerPrimitiveFilter", + "BadPFMuonFilter", + "BadPFMuonDzFilter", + "hfNoisyHitsFilter", + "eeBadScFilter", + ], + }, +} diff --git a/src/BTVNanoCommissioning/workflows/BTA_ttbar_producer.py b/src/BTVNanoCommissioning/workflows/BTA_ttbar_producer.py index e4fecbd5..e99681e9 100644 --- a/src/BTVNanoCommissioning/workflows/BTA_ttbar_producer.py +++ b/src/BTVNanoCommissioning/workflows/BTA_ttbar_producer.py @@ -60,6 +60,7 @@ def process(self, events): return processor.accumulate( self.process_shift(update(events, collections), name) for collections, name in shifts + if name != None ) def process_shift(self, events, shift_name): diff --git a/src/BTVNanoCommissioning/workflows/QCD_validation.py b/src/BTVNanoCommissioning/workflows/QCD_validation.py index 63d947d0..d55e1d80 100644 --- a/src/BTVNanoCommissioning/workflows/QCD_validation.py +++ b/src/BTVNanoCommissioning/workflows/QCD_validation.py @@ -2,9 +2,9 @@ from coffea import processor from coffea.analysis_tools import Weights from BTVNanoCommissioning.utils.selection import jet_cut -from BTVNanoCommissioning.helpers.func import flatten, update +from BTVNanoCommissioning.helpers.func import flatten, update, dump_lumi from BTVNanoCommissioning.utils.histogrammer import histogrammer -from BTVNanoCommissioning.helpers.update_branch import missing_branch, add_jec +from BTVNanoCommissioning.helpers.update_branch import missing_branch from BTVNanoCommissioning.utils.correction import ( load_SF, load_lumi, @@ -94,8 +94,6 @@ def process_shift(self, events, shift_name): "sumw": processor.defaultdict_accumulator(float), **_hist_event_dict, } - if "JME" in self.SF_map.keys() or "jetveto" in self.SF_map.keys(): - events.Jet = update(events.Jet, {"veto": jetveto(events, self.SF_map)}) if isRealData: output["sumw"] = len(events) @@ -120,14 +118,18 @@ def process_shift(self, events, shift_name): req_trig = np.zeros(len(events), dtype="bool") for t in trig_arrs: req_trig = req_trig | t - + req_lumi = np.ones(len(events), dtype="bool") + if isRealData: + req_lumi = self.lumiMask(events.run, events.luminosityBlock) + if shift_name is None: + output = dump_lumi(events[req_lumi], output) ## Jet cuts events.Jet = events.Jet[ jet_cut(events, self._campaign) & (events.Jet.veto != 1) ] req_jets = ak.count(events.Jet.pt, axis=1) >= 1 - event_level = ak.fill_none(req_trig & req_jets, False) + event_level = ak.fill_none(req_lumi & req_trig & req_jets, False) if len(events[event_level]) == 0: return {dataset: output} @@ -140,10 +142,11 @@ def process_shift(self, events, shift_name): # Selected SV # ############### selev = events[event_level] - matched_JetSVs = selev.Jet[selev.JetSVs.jetIdx] - lj_matched_JetSVs = matched_JetSVs[selev.JetSVs.jetIdx == 0] - lj_SVs = selev.JetSVs[selev.JetSVs.jetIdx == 0] - nJetSVs = ak.count(lj_SVs.pt, axis=1) + if "JetSVs" in events.Jet.fields: + matched_JetSVs = selev.Jet[selev.JetSVs.jetIdx] + lj_matched_JetSVs = matched_JetSVs[selev.JetSVs.jetIdx == 0] + lj_SVs = selev.JetSVs[selev.JetSVs.jetIdx == 0] + nJetSVs = ak.count(lj_SVs.pt, axis=1) #################### # Weight & Geninfo # @@ -156,12 +159,13 @@ def process_shift(self, events, shift_name): # genweiev = ak.flatten( # ak.broadcast_arrays(weights.weight()[event_level], sjets["pt"])[0] # ) - lj_matched_JetSVs_par_flav = (lj_matched_JetSVs.partonFlavour == 0) & ( - lj_matched_JetSVs.hadronFlavour == 0 - ) - lj_matched_JetSVs_genflav = ( - lj_matched_JetSVs.hadronFlavour + 1 * lj_matched_JetSVs_par_flav - ) + if "JetSVs" in events.Jet.fields: + lj_matched_JetSVs_par_flav = (lj_matched_JetSVs.partonFlavour == 0) & ( + lj_matched_JetSVs.hadronFlavour == 0 + ) + lj_matched_JetSVs_genflav = ( + lj_matched_JetSVs.hadronFlavour + 1 * lj_matched_JetSVs_par_flav + ) syst_wei = True if self.isSyst != None else False if "PU" in self.SF_map.keys(): puwei( @@ -185,6 +189,7 @@ def process_shift(self, events, shift_name): pseval = correctionlib.CorrectionSet.from_file( f"src/BTVNanoCommissioning/data/Prescales/ps_weight_{triggers[0]}_run{run_num}.json" ) + # if 369869 in selev.run: continue psweight = pseval["prescaleWeight"].evaluate( selev.run, f"HLT_{triggers[0]}", @@ -192,7 +197,10 @@ def process_shift(self, events, shift_name): ) weights.add("psweight", psweight) genflavor = ak.zeros_like(sjets.pt, dtype=int) - lj_matched_JetSVs_genflav = ak.zeros_like(lj_matched_JetSVs.pt, dtype=int) + if "JetSVs" in events.Jet.fields: + lj_matched_JetSVs_genflav = ak.zeros_like( + lj_matched_JetSVs.pt, dtype=int + ) # Systematics information if shift_name is None: @@ -204,7 +212,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### # Fill histogram # @@ -241,7 +249,7 @@ def process_shift(self, events, shift_name): flatten(sjets[:, 0][histname.replace(f"jet0_", "")]), weight=weight, ) - elif "JetSVs_" in histname: + elif "JetSVs_" in histname and "JetSVs" in events.Jet.fields: h.fill( syst, flatten(lj_matched_JetSVs_genflav), @@ -280,14 +288,17 @@ def process_shift(self, events, shift_name): ) output["njet"].fill(syst, njet, weight=weight) - output["nJetSVs"].fill(syst, nJetSVs, weight=weight) + if "JetSVs" in events.Jet.fields: + output["nJetSVs"].fill(syst, nJetSVs, weight=weight) - output["dr_SVjet0"].fill( - syst, - flatten(lj_matched_JetSVs_genflav), - flatten(abs(lj_SVs.deltaR) - 0.1), - weight=flatten(ak.broadcast_arrays(weight, lj_matched_JetSVs["pt"])[0]), - ) + output["dr_SVjet0"].fill( + syst, + flatten(lj_matched_JetSVs_genflav), + flatten(abs(lj_SVs.deltaR) - 0.1), + weight=flatten( + ak.broadcast_arrays(weight, lj_matched_JetSVs["pt"])[0] + ), + ) output["npvs"].fill(syst, flatten(selev.PV.npvsGood), weight=weight) if not isRealData: if syst == "nominal": diff --git a/src/BTVNanoCommissioning/workflows/__init__.py b/src/BTVNanoCommissioning/workflows/__init__.py index 33ccaab8..ad2c85b4 100644 --- a/src/BTVNanoCommissioning/workflows/__init__.py +++ b/src/BTVNanoCommissioning/workflows/__init__.py @@ -66,19 +66,25 @@ workflows["emctag_ttdilep_sf"] = CTAGEMDilepttValidSFProcessor workflows["ctag_ttdilep_sf"] = CTAGDilepttValidSFProcessor workflows["ectag_ttdilep_sf"] = CTAGEDilepttValidSFProcessor -workflows["ctag_ttsemilep_sf"] = CTAGSemilepttValidSFProcessor -workflows["ectag_ttsemilep_sf"] = CTAGETTSemilepValidSFProcessor +# workflows["ctag_ttsemilep_sf"] = CTAGSemilepttValidSFProcessor +workflows["ctag_ttsemilep_sf"] = partial( + CTAGWcValidSFProcessor, selectionModifier="semittM" +) +# workflows["ectag_ttsemilep_sf"] = CTAGETTSemilepValidSFProcessor +workflows["ectag_ttsemilep_sf"] = partial( + CTAGWcValidSFProcessor, selectionModifier="semittE" +) ##QCD workflows["QCD_sf"] = QCDValidProcessor # W+c -workflows["ctag_Wc_sf"] = CTAGWcValidSFProcessor -workflows["ectag_Wc_sf"] = CTAGeWcValidSFProcessor +workflows["ctag_Wc_sf"] = partial(CTAGWcValidSFProcessor, selectionModifier="WcM") +workflows["ectag_Wc_sf"] = partial(CTAGWcValidSFProcessor, selectionModifier="WcE") # DY -workflows["ctag_DY_sf"] = CTAGDYValidSFProcessor -workflows["ectag_DY_sf"] = CTAGeDYValidSFProcessor +workflows["ctag_DY_sf"] = partial(CTAGDYValidSFProcessor, selectionModifier="DYM") +workflows["ectag_DY_sf"] = partial(CTAGDYValidSFProcessor, selectionModifier="DYE") # Tutorial # workflows["example"] = ExampleProcessor diff --git a/src/BTVNanoCommissioning/workflows/ctag_DY_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_DY_valid_sf.py index 4d650dad..a6726763 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_DY_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_DY_valid_sf.py @@ -8,6 +8,7 @@ load_lumi, load_SF, muSFs, + eleSFs, puwei, btagSFs, JME_shifts, @@ -17,13 +18,18 @@ from BTVNanoCommissioning.helpers.func import ( flatten, update, - uproot_writeable, + array_writer, dump_lumi, ) from BTVNanoCommissioning.helpers.update_branch import missing_branch from BTVNanoCommissioning.utils.histogrammer import histogrammer -from BTVNanoCommissioning.utils.selection import jet_id, mu_idiso, ele_mvatightid +from BTVNanoCommissioning.utils.selection import ( + jet_id, + mu_idiso, + ele_mvatightid, + MET_filters, +) class NanoProcessor(processor.ProcessorABC): @@ -36,6 +42,7 @@ def __init__( isArray=False, noHist=False, chunksize=75000, + selectionModifier="DYM", ): self._year = year self._campaign = campaign @@ -45,6 +52,7 @@ def __init__( self.noHist = noHist self.lumiMask = load_lumi(self._campaign) self.chunksize = chunksize + self.selMod = selectionModifier ## Load corrections self.SF_map = load_SF(self._campaign) @@ -93,20 +101,31 @@ def process(self, events): def process_shift(self, events, shift_name): dataset = events.metadata["dataset"] isRealData = not hasattr(events, "genWeight") + + isMu = False + isEle = False + if "DYM" in self.selMod: + triggers = ["Mu17_TrkIsoVVL_Mu8_TrkIsoVVL_DZ_Mass8"] + isMu = True + elif "DYE" in self.selMod: + triggers = ["Ele23_Ele12_CaloIdL_TrackIdL_IsoVL"] + isEle = True + else: + raise ValueError(self.selMod, "is not a valid selection modifier.") + + histname = {"DYM": "ctag_DY_sf", "DYE": "ectag_DY_sf"} _hist_event_dict = ( - {"": None} if self.noHist else histogrammer(events, "ctag_DY_sf") + {"": None} if self.noHist else histogrammer(events, histname[self.selMod]) ) - output = { "sumw": processor.defaultdict_accumulator(float), **_hist_event_dict, } - if shift_name is None: - if isRealData: - output["sumw"] = len(events) - else: - output["sumw"] = ak.sum(events.genWeight) + if isRealData: + output["sumw"] = len(events) + else: + output["sumw"] = ak.sum(events.genWeight) #################### # Selections # @@ -120,7 +139,6 @@ def process_shift(self, events, shift_name): output = dump_lumi(events[req_lumi], output) ## HLT - triggers = ["Mu17_TrkIsoVVL_Mu8_TrkIsoVVL_DZ_Mass8"] checkHLT = ak.Array([hasattr(events.HLT, _trig) for _trig in triggers]) if ak.all(checkHLT == False): raise ValueError("HLT paths:", triggers, " are all invalid in", dataset) @@ -133,25 +151,54 @@ def process_shift(self, events, shift_name): for t in trig_arrs: req_trig = req_trig | t + req_metfilter = MET_filters(events, self._campaign) + ## Muon cuts dilep_mu = events.Muon[(events.Muon.pt > 12) & mu_idiso(events, self._campaign)] ## Electron cuts dilep_ele = events.Electron[ (events.Electron.pt > 15) & ele_mvatightid(events, self._campaign) ] + if isMu: + thisdilep = dilep_mu + otherdilep = dilep_ele + else: + thisdilep = dilep_ele + otherdilep = dilep_mu ## dilepton - pos_dilep = dilep_mu[dilep_mu.charge > 0] - neg_dilep = dilep_mu[dilep_mu.charge < 0] + pos_dilep = thisdilep[thisdilep.charge > 0] + neg_dilep = thisdilep[thisdilep.charge < 0] req_dilep = ak.fill_none( ( (ak.num(pos_dilep.pt) >= 1) & (ak.num(neg_dilep.pt) >= 1) - & (ak.num(dilep_mu.charge) >= 2) - & (ak.num(dilep_ele.charge) == 0) + & (ak.num(thisdilep.charge) >= 2) + & (ak.num(otherdilep.charge) == 0) ), False, axis=-1, ) + + jet_sel = ak.fill_none( + jet_id(events, self._campaign) + & ( + ak.all( + events.Jet.metric_table(pos_dilep) > 0.4, + axis=2, + mask_identity=True, + ) + ) + & ( + ak.all( + events.Jet.metric_table(neg_dilep) > 0.4, + axis=2, + mask_identity=True, + ) + ), + False, + axis=-1, + ) + pos_dilep = ak.pad_none(pos_dilep, 1, axis=1) neg_dilep = ak.pad_none(neg_dilep, 1, axis=1) @@ -162,7 +209,8 @@ def process_shift(self, events, shift_name): ## Jet cuts event_jet = events.Jet[ - ak.fill_none( + (events.Jet.veto != 1) + & ak.fill_none( jet_id(events, self._campaign) & ( ak.all( @@ -182,38 +230,23 @@ def process_shift(self, events, shift_name): axis=-1, ) ] - req_jets = ak.num(event_jet.pt) >= 1 - event_jet = ak.pad_none(event_jet, 1, axis=1) + req_jets = ak.count(event_jet.pt, axis=1) >= 1 + # event_jet = ak.pad_none(event_jet, 1, axis=1) ## store jet index for PFCands, create mask on the jet index jetindx = ak.mask( ak.local_index(events.Jet.pt), - ( - jet_id(events, self._campaign) - & ( - ak.all( - events.Jet.metric_table(pos_dilep[:, 0]) > 0.4, - axis=2, - mask_identity=True, - ) - ) - & ( - ak.all( - events.Jet.metric_table(neg_dilep[:, 0]) > 0.4, - axis=2, - mask_identity=True, - ) - ) - ) - == 1, + jet_sel == 1, ) jetindx = ak.pad_none(jetindx, 1) jetindx = jetindx[:, 0] event_level = ak.fill_none( - req_lumi & req_trig & req_dilep & req_dilepmass & req_jets, False + req_lumi & req_trig & req_dilep & req_dilepmass & req_jets & req_metfilter, + False, ) if len(events[event_level]) == 0: + array_writer(self, events[event_level], events, "nominal", dataset, isRealData, empty=True) return {dataset: output} #################### # Selected objects # @@ -261,8 +294,10 @@ def process_shift(self, events, shift_name): weights, syst_wei, ) - if "MUO" in self.SF_map.keys(): + if isMu and "MUO" in self.SF_map.keys(): muSFs(smu, self.SF_map, weights, syst_wei, False) + if isEle and "EGM" in self.SF_map.keys(): + eleSFs(smu, self.SF_map, weights, syst_wei, False) if "BTV" in self.SF_map.keys(): btagSFs(sel_jet, self.SF_map, weights, "DeepJetC", syst_wei) btagSFs(sel_jet, self.SF_map, weights, "DeepJetB", syst_wei) @@ -280,7 +315,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### @@ -399,13 +434,22 @@ def process_shift(self, events, shift_name): if self.isArray: # Keep the structure of events and pruned the object size pruned_ev = events[event_level] - pruned_ev.Jet = sel_jet - pruned_ev.Muon = smu + pruned_ev["SelJet"] = sjets + pruned_ev["LeadJet"] = sel_jet + if isMu: + pruned_ev["MuonPlus"] = sposmu + pruned_ev["MuonMinus"] = snegmu + kinOnly = ["Muon", "MuonPlus", "MuonMinus", "Jet"] + else: + pruned_ev["ElectronPlus"] = sposmu + pruned_ev["ElectronMinus"] = snegmu + kinOnly = ["Electron", "ElectronPlus", "ElectronMinus", "Jet"] pruned_ev["dilep"] = sposmu + snegmu pruned_ev["dilep", "pt"] = pruned_ev.dilep.pt pruned_ev["dilep", "eta"] = pruned_ev.dilep.eta pruned_ev["dilep", "phi"] = pruned_ev.dilep.phi pruned_ev["dilep", "mass"] = pruned_ev.dilep.mass + pruned_ev["njet"] = njet if "PFCands" in events.fields: pruned_ev.PFCands = spfcands # Add custom variables @@ -419,29 +463,17 @@ def process_shift(self, events, shift_name): pruned_ev["dr_mu1jet"] = sposmu.delta_r(sel_jet) pruned_ev["dr_mu2jet"] = snegmu.delta_r(sel_jet) - # Create a list of variables want to store. For objects from the PFNano file, specify as {object}_{variable}, wildcard option only accepted at the end of the string - out_branch = np.setdiff1d( - np.array(pruned_ev.fields), np.array(events.fields) - ) - out_branch = np.delete( - out_branch, - np.where((out_branch == "dilep")), + array_writer( + self, + pruned_ev, + events, + systematics[0], + dataset, + isRealData, + kinOnly=kinOnly, + remove=kinOnly, ) - for kin in ["pt", "eta", "phi", "mass", "pfRelIso04_all", "dxy", "dz"]: - for obj in ["Muon", "Jet", "dilep"]: - if (obj != "Muon") and ("pfRelIso04_all" == kin or "d" in kin): - continue - out_branch = np.append(out_branch, [f"{obj}_{kin}"]) - out_branch = np.append( - out_branch, ["Jet_btagDeep*", "Jet_DeepJet*", "PFCands_*"] - ) - # write to root files - os.system(f"mkdir -p {self.name}/{dataset}") - with uproot.recreate( - f"{self.name}/{dataset}/f{events.metadata['filename'].split('_')[-1].replace('.root','')}_{systematics[0]}_{int(events.metadata['entrystop']/self.chunksize)}.root" - ) as fout: - fout["Events"] = uproot_writeable(pruned_ev, include=out_branch) return {dataset: output} def postprocess(self, accumulator): diff --git a/src/BTVNanoCommissioning/workflows/ctag_Wc_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_Wc_valid_sf.py index 4cb05e86..77627a1b 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_Wc_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_Wc_valid_sf.py @@ -1,6 +1,5 @@ import os import collections, awkward as ak, numpy as np -import os import uproot from coffea import processor from coffea.analysis_tools import Weights @@ -9,6 +8,7 @@ load_lumi, load_SF, muSFs, + eleSFs, puwei, btagSFs, JME_shifts, @@ -17,7 +17,7 @@ from BTVNanoCommissioning.helpers.func import ( flatten, update, - uproot_writeable, + array_writer, dump_lumi, ) from BTVNanoCommissioning.helpers.update_branch import missing_branch @@ -40,6 +40,7 @@ def __init__( isArray=False, noHist=False, chunksize=75000, + selectionModifier="WcM", ): self._year = year self._campaign = campaign @@ -51,6 +52,7 @@ def __init__( self.chunksize = chunksize ## Load corrections self.SF_map = load_SF(self._campaign) + self.selMod = selectionModifier @property def accumulator(self): @@ -97,8 +99,34 @@ def process(self, events): def process_shift(self, events, shift_name): dataset = events.metadata["dataset"] isRealData = not hasattr(events, "genWeight") + + isMu = False + isEle = False + if "WcM" in self.selMod or "semittM" in self.selMod: + triggers = ["IsoMu27","IsoMu24"] + isMu = True + dxySigcut = 1 + muNeEmSum = 0.7 + muonpTratioCut = 0.4 + isolepdz, isolepdxy, isolepsip3d = 0.01, 0.002, 2 + elif "WcE" in self.selMod or "semittE" in self.selMod: + triggers = ["Ele32_WPTight_Gsf_L1DoubleEG"] + isEle = True + dxySigcut = 0 + muNeEmSum = 1 + muonpTratioCut = 0.6 + isolepdz, isolepdxy, isolepsip3d = 0.02, 0.01, 2.5 + else: + raise ValueError(self.selMod, "is not a valid selection modifier.") + + histoname = { + "WcM": "ctag_Wc_sf", + "WcE": "ectag_Wc_sf", + "semittM": "ctag_Wc_sf", # same histogram representation as W+c + "semittE": "ectag_Wc_sf", # same histogram representation as W+c + } _hist_event_dict = ( - {"": None} if self.noHist else histogrammer(events, "ctag_Wc_sf") + {"": None} if self.noHist else histogrammer(events, histoname[self.selMod]) ) output = { @@ -106,11 +134,10 @@ def process_shift(self, events, shift_name): **_hist_event_dict, } - if shift_name is None: - if isRealData: - output["sumw"] = len(events) - else: - output["sumw"] = ak.sum(events.genWeight) + if isRealData: + output["sumw"] = len(events) + else: + output["sumw"] = ak.sum(events.genWeight) #################### # Selections # #################### @@ -123,7 +150,6 @@ def process_shift(self, events, shift_name): output = dump_lumi(events[req_lumi], output) ## HLT - triggers = ["IsoMu24"] checkHLT = ak.Array([hasattr(events.HLT, _trig) for _trig in triggers]) if ak.all(checkHLT == False): raise ValueError("HLT paths:", triggers, " are all invalid in", dataset) @@ -136,49 +162,69 @@ def process_shift(self, events, shift_name): for t in trig_arrs: req_trig = req_trig | t - ## Muon cuts - # muon twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2 - iso_muon = events.Muon[(events.Muon.pt > 30) & mu_idiso(events, self._campaign)] - req_muon = ak.count(iso_muon.pt, axis=1) == 1 + ## Lepton cuts + if isMu: + # muon twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2 + iso_lep = events.Muon[ + (events.Muon.pt > 30) & mu_idiso(events, self._campaign) + ] + elif isEle: + iso_lep = events.Electron[ + (events.Electron.pt > 34) & ele_mvatightid(events, self._campaign) + ] + req_lep = ak.count(iso_lep.pt, axis=1) == 1 jet_sel = ak.fill_none( jet_id(events, self._campaign) - & (ak.all(events.Jet.metric_table(iso_muon) > 0.5, axis=2)), + & (ak.all(events.Jet.metric_table(iso_lep) > 0.5, axis=2)), False, axis=-1, ) - iso_muon = ak.pad_none(iso_muon, 1, axis=1) - iso_muon = iso_muon[:, 0] - iso_muindx = ak.mask( - ak.local_index(events.Muon.pt), - ((events.Muon.pt > 30) & mu_idiso(events, self._campaign)) == 1, - ) - iso_muindx = ak.pad_none(iso_muindx, 1) - iso_muindx = iso_muindx[:, 0] + iso_lep = ak.pad_none(iso_lep, 1, axis=1) + iso_lep = iso_lep[:, 0] + if isMu: + iso_lepindx = ak.mask( + ak.local_index(events.Muon.pt), + ((events.Muon.pt > 30) & mu_idiso(events, self._campaign)) == 1, + ) + elif isEle: + iso_lepindx = ak.mask( + ak.local_index(events.Electron.pt), + ((events.Electron.pt > 34) & ele_mvatightid(events, self._campaign)) + == 1, + ) + iso_lepindx = ak.pad_none(iso_lepindx, 1) + iso_lepindx = iso_lepindx[:, 0] ## Jet cuts if "DeepJet_nsv" in events.Jet.fields: jet_sel = jet_sel & (events.Jet.DeepJet_nsv > 0) event_jet = events.Jet[jet_sel] - req_jets = (ak.num(event_jet.pt) >= 1) & (ak.num(event_jet.pt) <= 3) + nseljet = ak.count(event_jet.pt, axis=1) + if "Wc" in self.selMod: + req_jets = (nseljet >= 1) & (nseljet <= 3) + else: + req_jets = nseljet >= 4 ## Soft Muon cuts soft_muon = events.Muon[ softmu_mask(events, self._campaign) - & (abs(events.Muon.dxy / events.Muon.dxyErr) > 1.0) + & (abs(events.Muon.dxy / events.Muon.dxyErr) > dxySigcut) ] req_softmu = ak.count(soft_muon.pt, axis=1) >= 1 mujetsel = ak.fill_none( ( (ak.all(event_jet.metric_table(soft_muon) <= 0.4, axis=2)) & ((event_jet.muonIdx1 != -1) | (event_jet.muonIdx2 != -1)) - & ((event_jet.muEF + event_jet.neEmEF) < 0.7) + & ((event_jet.muEF + event_jet.neEmEF) < muNeEmSum) + & (event_jet.pt > 20) + & ((event_jet.pt / event_jet.E) > 0.03) ), False, axis=-1, ) mujetsel2 = ak.fill_none( ( - ((events.Jet.muEF + events.Jet.neEmEF) < 0.7) + ((events.Jet.muEF + events.Jet.neEmEF) < muNeEmSum) & ( ak.all( events.Jet.metric_table(soft_muon) <= 0.4, @@ -192,8 +238,10 @@ def process_shift(self, events, shift_name): axis=-1, ) soft_muon = ak.pad_none(soft_muon, 1, axis=1) + soft_muon["dxySig"] = soft_muon.dxy / soft_muon.dxyErr ## Muon-jet cuts + event_jet["isMuonJet"] = mujetsel mu_jet = event_jet[mujetsel] otherjets = event_jet[~mujetsel] req_mujet = ak.num(mu_jet.pt, axis=1) >= 1 @@ -208,24 +256,24 @@ def process_shift(self, events, shift_name): jetindx = jetindx[:, 0] # Other cuts - req_pTratio = (soft_muon[:, 0].pt / mu_jet[:, 0].pt) < 0.4 - idx = np.where(iso_muon.jetIdx == -1, 0, iso_muon.jetIdx) - req_QCDveto = ( - (iso_muon.pfRelIso04_all < 0.05) - & (abs(iso_muon.dz) < 0.01) - & (abs(iso_muon.dxy) < 0.002) - & (iso_muon.sip3d < 2) - & ( - iso_muon.pt - / ak.firsts( - events.Jet[ - (events.Jet.muonIdx1 == iso_muindx) - | ((events.Jet.muonIdx2 == iso_muindx)) - ].pt - ) - > 0.75 - ) - ) + req_pTratio = (soft_muon[:, 0].pt / mu_jet[:, 0].pt) < muonpTratioCut + idx = np.where(iso_lep.jetIdx == -1, 0, iso_lep.jetIdx) + # req_QCDveto = ( + # (iso_lep.pfRelIso04_all < 0.05) + # & (abs(iso_lep.dz) < isolepdz) + # & (abs(iso_lep.dxy) < isolepdxy) + # & (iso_lep.sip3d < isolepsip3d) + # & ( + # iso_lep.pt + # / ak.firsts( + # events.Jet[ + # (events.Jet.muonIdx1 == iso_lepindx) + # | ((events.Jet.muonIdx2 == iso_lepindx)) + # ].pt + # ) + # > 0.75 + # ) + # ) dilep_mu = events.Muon[(events.Muon.pt > 12) & mu_idiso(events, self._campaign)] dilep_ele = events.Electron[ @@ -235,31 +283,62 @@ def process_shift(self, events, shift_name): ak.count(dilep_mu.pt, axis=1) + ak.count(dilep_ele.pt, axis=1) != 2 ) - dilep_mass = iso_muon + soft_muon[:, 0] - req_dilepmass = (dilep_mass.mass > 12.0) & ( - (dilep_mass.mass < 80) | (dilep_mass.mass > 100) - ) + if isMu: + dilep_mass = iso_lep + soft_muon[:, 0] + req_dilepmass = (dilep_mass.mass > 12.0) & ( + (dilep_mass.mass < 80) | (dilep_mass.mass > 100) + ) + elif isEle: + req_dilepmass = iso_lep.pt > 0 - MET = ak.zip( + iso_lep_trans = ak.zip( { - "pt": events.MET.pt, - "eta": ak.zeros_like(events.MET.pt), - "phi": events.MET.phi, - "mass": ak.zeros_like(events.MET.pt), + "pt": iso_lep.pt, + "eta": ak.zeros_like(iso_lep.pt), + "phi": iso_lep.phi, + "mass": iso_lep.mass, }, with_name="PtEtaPhiMLorentzVector", ) - Wmass = MET + iso_muon - # modified to transverse mass - req_mtw = ( - np.sqrt(2 * iso_muon.pt * MET.pt * (1 - np.cos(iso_muon.delta_phi(MET)))) - > 55 - ) + if "Run3" not in self._campaign: + MET = ak.zip( + { + "pt": events.MET.pt, + "eta": ak.zeros_like(events.MET.pt), + "phi": events.MET.phi, + "mass": ak.zeros_like(events.MET.pt), + }, + with_name="PtEtaPhiMLorentzVector", + ) + else: + MET = ak.zip( + { + "pt": events.PuppiMET.pt, + "eta": ak.zeros_like(events.PuppiMET.pt), + "phi": events.PuppiMET.phi, + "mass": ak.zeros_like(events.PuppiMET.pt), + }, + with_name="PtEtaPhiMLorentzVector", + ) + + wmasscut = 55 + if "semitt" in self.selMod: + wmasscut = 0 + Wcand = MET + iso_lep_trans # transverse mass + Wmass = Wcand.mass + Wpt = Wcand.pt + req_mtw = Wmass > wmasscut + + # ==This is the manual calculation for transverse mass== + # dphi = iso_lep.phi-events.PuppiMET.phi + # dphi = np.where(dphinp.pi,dphi-2*np.pi,dphi) + # trans = np.sqrt(2*iso_lep.pt*events.PuppiMET.pt*(1-np.cos(dphi))) event_level = ( req_trig & req_lumi - & req_muon + & req_lep & req_jets & req_softmu & req_dilepmass @@ -270,12 +349,15 @@ def process_shift(self, events, shift_name): ) event_level = ak.fill_none(event_level, False) if len(events[event_level]) == 0: + array_writer(self, events[event_level], events, "nominal", dataset, isRealData, empty=True) return {dataset: output} #################### # Selected objects # #################### - shmu = iso_muon[event_level] + shmu = iso_lep[event_level] + wm = Wmass[event_level] + wp = Wpt[event_level] sjets = event_jet[event_level] ssmu = soft_muon[event_level] smet = MET[event_level] @@ -287,7 +369,12 @@ def process_shift(self, events, shift_name): ssmu = ssmu[:, 0] sz = shmu + ssmu sw = shmu + smet - osss = ak.values_astype(shmu.charge * ssmu.charge * -1, int) + + osss = 1 + ossswrite = shmu.charge * ssmu.charge * -1 + if "Wc" in self.selMod: + osss = shmu.charge * ssmu.charge * -1 + njet = ak.count(sjets.pt, axis=1) # Find the PFCands associate with selected jets. Search from jetindex->JetPFCands->PFCand if "PFCands" in events.fields: @@ -320,8 +407,10 @@ def process_shift(self, events, shift_name): weights, syst_wei, ) - if "MUO" in self.SF_map.keys(): + if isMu and "MUO" in self.SF_map.keys(): muSFs(shmu, self.SF_map, weights, syst_wei, False) + if isEle and "EGM" in self.SF_map.keys(): + eleSFs(shmu, self.SF_map, weights, syst_wei, False) if "BTV" in self.SF_map.keys(): btagSFs(smuon_jet, self.SF_map, weights, "DeepJetC", syst_wei) btagSFs(smuon_jet, self.SF_map, weights, "DeepJetB", syst_wei) @@ -341,7 +430,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### @@ -436,7 +525,11 @@ def process_shift(self, events, shift_name): syst="noSF", flav=smflav, osss=osss, - discr=smuon_jet[histname.replace(f"_{i}", "")], + discr=np.where( + smuon_jet[histname.replace(f"_{i}", "")] < 0, + -0.2, + smuon_jet[histname.replace(f"_{i}", "")], + ), weight=weights.partial_weight(exclude=exclude_btv), ) if not isRealData and "btag" in self.SF_map.keys(): @@ -444,10 +537,16 @@ def process_shift(self, events, shift_name): syst=syst, flav=smflav, osss=osss, - discr=smuon_jet[histname.replace(f"_{i}", "")], + discr=np.where( + smuon_jet[histname.replace(f"_{i}", "")] < 0, + -0.2, + smuon_jet[histname.replace(f"_{i}", "")], + ), weight=weight, ) elif "btag" in histname and "Trans" in histname: + if histname not in smuon_jet: + continue for i in range(2): histname = histname.replace("Trans", "").replace(f"_{i}", "") h.fill( @@ -522,12 +621,18 @@ def process_shift(self, events, shift_name): if self.isArray: # Keep the structure of events and pruned the object size pruned_ev = events[event_level] - pruned_ev["Jet"] = sjets + pruned_ev["SelJet"] = sjets pruned_ev["Muon"] = shmu pruned_ev["MuonJet"] = smuon_jet pruned_ev["SoftMuon"] = ssmu pruned_ev["OtherJets"] = sotherjets - pruned_ev["osss"] = osss + if "Wc" in self.selMod: + pruned_ev["osss"] = osss + else: + pruned_ev["osss"] = ossswrite + pruned_ev["njet"] = njet + pruned_ev["W_transmass"] = wm + pruned_ev["W_pt"] = wp if "PFCands" in events.fields: pruned_ev.PFCands = spfcands # Add custom variables @@ -543,47 +648,10 @@ def process_shift(self, events, shift_name): pruned_ev["dr_lep1_softmu"] = shmu.delta_r(ssmu) pruned_ev["soft_l_ptratio"] = ssmu.pt / smuon_jet.pt pruned_ev["l1_ptratio"] = shmu.pt / smuon_jet.pt + pruned_ev["MuonJet_beta"] = smuon_jet.pt / smuon_jet.E + pruned_ev["MuonJet_muneuEF"] = smuon_jet.muEF + smuon_jet.neEmEF - # Create a list of variables want to store. For objects from the PFNano file, specify as {object}_{variable}, wildcard option only accepted at the end of the string - out_branch = np.setdiff1d( - np.array(pruned_ev.fields), np.array(events.fields) - ) - out_branch = np.delete( - out_branch, - np.where( - (out_branch == "SoftMuon") - | (out_branch == "MuonJet") - | (out_branch == "dilep") - | (out_branch == "OtherJets") - ), - ) - - for kin in ["pt", "eta", "phi", "mass", "pfRelIso04_all", "dxy", "dz"]: - for obj in [ - "Muon", - "Jet", - "SoftMuon", - "MuonJet", - "dilep", - "charge", - "MET", - ]: - if "MET" in obj and ("pt" != kin or "phi" != kin): - continue - if (obj != "Muon" and obj != "SoftMuon") and ( - "pfRelIso04_all" == kin or "d" in kin - ): - continue - out_branch = np.append(out_branch, [f"{obj}_{kin}"]) - out_branch = np.append( - out_branch, ["Jet_btagDeep*", "Jet_DeepJet*", "PFCands_*", "SV_*"] - ) - # write to root files - os.system(f"mkdir -p {self.name}/{dataset}") - with uproot.recreate( - f"{self.name}/{dataset}/{systematics[0]}_{events.metadata['filename'].split('/')[-1].replace('.root','')}_{int(events.metadata['entrystop']/self.chunksize)}.root" - ) as fout: - fout["Events"] = uproot_writeable(pruned_ev, include=out_branch) + array_writer(self, pruned_ev, events, systematics[0], dataset, isRealData) return {dataset: output} diff --git a/src/BTVNanoCommissioning/workflows/ctag_eDY_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_eDY_valid_sf.py index e4732b41..d834cab8 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_eDY_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_eDY_valid_sf.py @@ -280,7 +280,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ctag_eWc_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_eWc_valid_sf.py index 482c3f8a..966dfef8 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_eWc_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_eWc_valid_sf.py @@ -164,7 +164,7 @@ def process_shift(self, events, shift_name): req_jets = (ak.num(event_jet.pt) >= 1) & (ak.num(event_jet.pt) <= 3) ## Soft Muon cuts - soft_muon = events.Muon[softmu_mask(events, self._campaign)] + soft_muon = events.Muon[softmu_Ď€mask(events, self._campaign)] req_softmu = ak.count(soft_muon.pt, axis=1) >= 1 mujetsel = ak.fill_none( (ak.all(event_jet.metric_table(soft_muon) <= 0.4, axis=2)) @@ -326,7 +326,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ctag_edileptt_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_edileptt_valid_sf.py index 8596f2fe..43ae5801 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_edileptt_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_edileptt_valid_sf.py @@ -299,7 +299,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ctag_emdileptt_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_emdileptt_valid_sf.py index b7d97b7a..dfebeee0 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_emdileptt_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_emdileptt_valid_sf.py @@ -346,7 +346,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ctag_ettsemilep_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_ettsemilep_valid_sf.py index c869959b..a22a8f24 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_ettsemilep_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_ettsemilep_valid_sf.py @@ -341,7 +341,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ctag_semileptt_valid_sf.py b/src/BTVNanoCommissioning/workflows/ctag_semileptt_valid_sf.py index 931d0153..62ca5d84 100644 --- a/src/BTVNanoCommissioning/workflows/ctag_semileptt_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ctag_semileptt_valid_sf.py @@ -356,7 +356,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/example.py b/src/BTVNanoCommissioning/workflows/example.py index bbd1b62e..e140f1a7 100644 --- a/src/BTVNanoCommissioning/workflows/example.py +++ b/src/BTVNanoCommissioning/workflows/example.py @@ -227,7 +227,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ttdilep_valid_sf.py b/src/BTVNanoCommissioning/workflows/ttdilep_valid_sf.py index 1bb37bd2..b03ed6ff 100644 --- a/src/BTVNanoCommissioning/workflows/ttdilep_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ttdilep_valid_sf.py @@ -292,7 +292,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py b/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py index 1c6a4348..f543daca 100644 --- a/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py +++ b/src/BTVNanoCommissioning/workflows/ttsemilep_valid_sf.py @@ -23,7 +23,8 @@ ) from BTVNanoCommissioning.helpers.update_branch import missing_branch from BTVNanoCommissioning.utils.histogrammer import histogrammer -from BTVNanoCommissioning.utils.selection import jet_id, btag_mu_idiso +from BTVNanoCommissioning.utils.selection import jet_id, btag_mu_idiso, MET_filters +import hist class NanoProcessor(processor.ProcessorABC): @@ -143,7 +144,8 @@ def process_shift(self, events, shift_name): ## Jet cuts event_jet = events.Jet[ - ak.fill_none( + (events.Jet.veto != 1) + & ak.fill_none( jet_id(events, self._campaign) & ( ak.all( @@ -163,6 +165,7 @@ def process_shift(self, events, shift_name): ak.local_index(events.Jet.pt), ( jet_id(events, self._campaign) + & (events.Jet.veto != 1) & ( ak.all( events.Jet.metric_table(events.Muon) > 0.4, @@ -190,8 +193,9 @@ def process_shift(self, events, shift_name): req_MET = MET.pt > 50 + req_metfilter = MET_filters(events, self._campaign) event_level = ak.fill_none( - req_trig & req_jets & req_muon & req_MET & req_lumi, False + req_trig & req_jets & req_muon & req_MET & req_lumi & req_metfilter, False ) if len(events[event_level]) == 0: return {dataset: output} @@ -255,7 +259,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs #################### diff --git a/src/BTVNanoCommissioning/workflows/validation.py b/src/BTVNanoCommissioning/workflows/validation.py index d719928d..7887b007 100644 --- a/src/BTVNanoCommissioning/workflows/validation.py +++ b/src/BTVNanoCommissioning/workflows/validation.py @@ -231,7 +231,7 @@ def process_shift(self, events, shift_name): "DeepCSVC", "DeepCSVB", "DeepJetB", - "DeepJetB", + "DeepJetC", ] # exclude b-tag SFs for btag inputs ####################