Add h_bb folder to FCCAnalyzer/analyses

analyses/examples/tag2.py

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+
+import ROOT
+#import functions
+ROOT.TH1.SetDefaultSumw2(ROOT.kTRUE)
+
+
+
+# list of all processes
+fraction = 0.05
+processList = {
+    'wzp6_ee_nunuH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hss_ecm240':          {'fraction':fraction},
+}
+
+
+
+
+inputDir = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/"
+procDict = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/samplesDict.json"
+
+# additional/custom C++ functions
+includePaths = ["tagfuncs.h", "tagfuncs_gen.h"]
+
+
+# output directory
+outputDir   = "output/clustering_tagging/histmaker/"
+
+
+# optional: ncpus, default is 4, -1 uses all cores available
+nCPUS       = 24
+
+# scale the histograms with the cross-section and integrated luminosity
+doScale = True
+intLumi = 10800000
+
+# define histograms
+bins_m = (250, 0, 250) # 100 MeV bins
+bins_score = (100, 0, 1)
+
+
+################################################################################
+## load modules and files for jet clustering and flavor tagging
+################################################################################
+## latest particle transformer model, trainied on 9M jets in winter2023 samples
+model_name = "fccee_flavtagging_edm4hep_wc_v1"
+
+# model files needed for unit testing in CI
+url_model_dir = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+url_preproc = "{}/{}.json".format(url_model_dir, model_name)
+url_model = "{}/{}.onnx".format(url_model_dir, model_name)
+
+# model files locally stored on /eos
+model_dir = "/eos/experiment/fcc/ee/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+local_preproc = "{}/{}.json".format(model_dir, model_name)
+local_model = "{}/{}.onnx".format(model_dir, model_name)
+
+# get local file, else download from url
+def get_file_path(url, filename):
+    import os
+    if os.path.exists(filename):
+        return os.path.abspath(filename)
+    else:
+        import urllib.request
+        urllib.request.urlretrieve(url, os.path.basename(url))
+        return os.path.basename(url)
+
+weaver_preproc = get_file_path(url_preproc, local_preproc)
+weaver_model = get_file_path(url_model, local_model)
+
+from addons.ONNXRuntime.jetFlavourHelper import JetFlavourHelper
+from addons.FastJet.jetClusteringHelper import ExclusiveJetClusteringHelper
+from examples.FCCee.weaver.config import collections, njets
+
+################################################################################
+
+
+def build_graph(df, dataset):
+
+    hists, cols = [], []
+
+    df = df.Define("weight", "1.0")
+    weightsum = df.Sum("weight")
+
+
+    # define collections
+    df = df.Alias("Particle0", "Particle#0.index")
+    df = df.Alias("Particle1", "Particle#1.index")
+    df = df.Alias("MCRecoAssociations0", "MCRecoAssociations#0.index")
+    df = df.Alias("MCRecoAssociations1", "MCRecoAssociations#1.index")
+
+
+    # run jet clustering
+    njets = 2
+    jetClusteringHelper = ExclusiveJetClusteringHelper("ReconstructedParticles", njets)
+    df = jetClusteringHelper.define(df)
+
+
+    # run flavor tagging
+    jetFlavourHelper = JetFlavourHelper(collections, jetClusteringHelper.jets, jetClusteringHelper.constituents, "")
+    df = jetFlavourHelper.define(df) # define variables
+    df = jetFlavourHelper.inference(weaver_preproc, weaver_model, df) # run inference
+
+    # jetFlavourHelper adds new columns for each jet flavor (recojet_isB/C/S/...)
+    # each column is a vector of probabilities per jet to be that specific flavor
+    hists.append(df.Histo1D(("recojet_isB", "", *bins_score), "recojet_isB"))
+    hists.append(df.Histo1D(("recojet_isC", "", *bins_score), "recojet_isC"))
+    hists.append(df.Histo1D(("recojet_isS", "", *bins_score), "recojet_isS"))
+
+    # compute the invariant mass of the jets
+    df = df.Define("jet_p4", f"JetConstituentsUtils::compute_tlv_jets({jetClusteringHelper.jets})")
+    df = df.Define("event_invariant_mass", "JetConstituentsUtils::InvariantMass(jet_p4[0], jet_p4[1])")
+    hists.append(df.Histo1D(("event_invariant_mass", "", *bins_m), "event_invariant_mass"))
+    return hists, weightsum
+

analyses/examples/tag2plotter.py

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+import uproot
+import matplotlib.pyplot as plt
+import mplhep as hep
+
+hep.style.use("ROOT")
+plt.style.use(hep.style.ROOT)
+
+def plot(fIn, fOut):
+    file = uproot.open(fIn)
+    score_B = file["recojet_isB"].to_hist()
+    score_C = file["recojet_isC"].to_hist()
+    score_S = file["recojet_isS"].to_hist()
+
+    fig = plt.figure()
+    ax = fig.subplots()
+
+    hep.histplot(score_B, label="b-score", ax=ax)
+    hep.histplot(score_C, label="c-score", ax=ax)
+    hep.histplot(score_S, label="s-score", ax=ax)
+
+    ax.legend(fontsize='x-small')
+    ax.set_xlabel("Tagger score")
+    ax.set_ylabel("Events")
+    ax.set_xlim(0, 1)
+    ax.set_yscale('log')
+
+    plt.savefig(f"{fOut}.png", bbox_inches="tight")
+    plt.close()  # Close the figure to avoid display in some environments
+
+
+
+if __name__ == "__main__":
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hbb_ecm240.root", "nunuH_Hbb")
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hcc_ecm240.root", "nunuH_Hcc")
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hss_ecm240.root", "nunuH_Hss")