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CleanMerged.py
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import logging
from bamboo.analysisutils import loadPlotIt
import os.path
import copy
from bamboo.analysismodules import AnalysisModule, HistogramsModule
class CMSPhase2SimRTBModule(AnalysisModule):
""" Base module for processing Phase2 flat trees """
def __init__(self, args):
super(CMSPhase2SimRTBModule, self).__init__(args)
self._h_genwcount = {}
def prepareTree(self, tree, sample=None, sampleCfg=None):
from bamboo.treedecorators import decorateCMSPhase2SimTree
from bamboo.dataframebackend import DataframeBackend
t = decorateCMSPhase2SimTree(tree, isMC=True)
be, noSel = DataframeBackend.create(t)
from bamboo.root import gbl
self._h_genwcount[sample] = be.rootDF.Histo1D(
gbl.ROOT.RDF.TH1DModel("h_count_genweight",
"genweight sum", 1, 0., 1.),
"_zero_for_stats",
"genweight"
)
return t, noSel, be, tuple()
def readCounters(self, resultsFile):
counters = super(CMSPhase2SimRTBModule, self).readCounters(resultsFile)
# Corrections to the generated sum #
sample = os.path.basename(resultsFile.GetName()).replace('.root','')
if resultsFile.GetListOfKeys().FindObject('generated_sum_corrected'):
counters["sumgenweight"] = resultsFile.Get('generated_sum_corrected').GetBinContent(1)
print (f"Sum event weight for {sample} [Florian method] = {resultsFile.Get('generated_sum_corrected').GetBinContent(1)}")
else:
raise RuntimeError(f'The histogram `generated_sum_corrected` is not found in {sample}, something must have gone wrong')
if sample in self._h_genwcount.keys():
print (f"Sum event weight for {sample} [Pieter method] = {self._h_genwcount[sample].GetBinContent(1)}")
return counters
def mergeCounters(self, outF, infileNames, sample=None):
outF.cd()
super(CMSPhase2SimRTBModule, self).mergeCounters(outF, infileNames, sample)
if outF.GetListOfKeys().FindObject('generated_sum_corrected'): # Main file
self.generated_sum_corrected = copy.deepcopy(outF.Get('generated_sum_corrected'))
else: # All the additional files ("datadriven")
if hasattr(self,'generated_sum_corrected'):
self.generated_sum_corrected.Write()
self._h_genwcount[sample].Write("h_count_genweight")
# BEGIN cutflow reports, adapted from bamboo.analysisutils
logger = logging.getLogger(__name__)
_yieldsTexPreface = "\n".join(f"{ln}" for ln in
r"""\documentclass{report}
\usepackage{graphicx}
\usepackage[a4paper,bindingoffset=0.5cm,left=0cm,right=1cm,top=2cm,bottom=2cm,footskip=0.25cm]{geometry}
\begin{document}
""".split("\n"))
def _makeYieldsTexTable(MCevents, report, samples, entryPlots, stretch=1.5, orientation="v", align="c", yieldPrecision=1, ratioPrecision=2):
if orientation not in ("v", "h"):
raise RuntimeError(
f"Unsupported table orientation: {orientation} (valid: 'h' and 'v')")
import plotit.plotit
from plotit.plotit import Stack
import numpy as np
from itertools import repeat, count
def getHist(smp, plot):
try:
h = smp.getHist(plot)
h.contents # check
return h
except KeyError:
return None
def colEntriesFromCFREntryHists(report, entryHists, precision=1, showUncert=True):
stacks_t = []
colEntries = []
for entries in report.titles.values():
s_entries = []
for eName in entries:
eh = entryHists[eName]
if eh is not None:
if (not isinstance(eh, Stack)) or eh.entries:
s_entries.append(eh)
st_t = Stack(entries=s_entries)
if s_entries:
uncert = " \pm {{:.{}f}}".format(precision).format(
np.sqrt(st_t.sumw2+st_t.syst2)[1]) if showUncert else ""
colEntries.append("${{0:.2e}}$".format(
precision).format(st_t.contents[1]))
stacks_t.append(st_t)
else:
colEntries.append(0.)
stacks_t.append(None)
return stacks_t, colEntries
def colEntriesFromCFREntryHists_forEff(report, entryHists, precision=1, showUncert=True):
stacks_t = []
colEntries = []
for entries in report.titles.values(): # selection names
s_entries = []
for eName in entries:
eh = entryHists[eName]
if eh is not None:
if (not isinstance(eh, Stack)) or eh.entries:
s_entries.append(eh)
st_t = Stack(entries=s_entries)
if s_entries:
# uncert = " \pm {{:.{}f}}".format(precision).format(
# np.sqrt(st_t.sumw2+st_t.syst2)[1]) if showUncert else ""
colEntries.append("{{0}}".format(precision).format(st_t.contents[1]))
stacks_t.append(st_t)
else:
colEntries.append(0.)
stacks_t.append(None)
return stacks_t, colEntries
smp_signal = [smp for smp in samples if smp.cfg.type == "SIGNAL"]
smp_mc = [smp for smp in samples if smp.cfg.type == "MC"]
smp_data = [smp for smp in samples if smp.cfg.type == "DATA"]
sepStr = "|l|"
smpHdrs = []
titles = list(report.titles.keys()) # titles are selections
entries_smp = []
stTotSig, stTotMC, stTotData = None, None, None
if smp_signal:
sepStr += "|"
sel_list = []
for sigSmp in smp_signal:
_, colEntries = colEntriesFromCFREntryHists(report,
{eName: getHist(sigSmp, p) for eName, p in entryPlots.items()}, precision=yieldPrecision)
sepStr += f"{align}|"
smpHdrs.append(
f"${sigSmp.cfg.yields_group}$") # sigSmp.cfg.yields_group is the name in the legend
_, colEntries_forEff = colEntriesFromCFREntryHists_forEff(report, {eName: sigSmp.getHist(
p) for eName, p in entryPlots.items()}, precision=yieldPrecision)
colEntries_matrix = np.array(colEntries_forEff)
sel_eff = np.array([100])
for i in range(1, len(report.titles)):
if float(colEntries_matrix[0]) == 0:
sel_eff = np.append(sel_eff, [0]).tolist()
else:
sel_eff = np.append(sel_eff, [float(
colEntries_matrix[i]) / float(colEntries_matrix[0]) * 100]).tolist()
for i in range(len(report.titles)):
sel_eff[i] = str(f"({sel_eff[i]:.3f}\%)")
colEntries_withEff = []
for i, entry in enumerate(colEntries):
colEntries_withEff.append("{0} {1}".format(
entry, sel_eff[i]))
entries_smp.append(colEntries_withEff)
if len(smp_signal) > 1:
sepStr += f"|{align}|"
smpHdrs.append("\\textbf{Signal}")
stTotSig, colEntries = colEntriesFromCFREntryHists(report, {eName: Stack(entries=[h for h in (getHist(
smp, p) for smp in smp_signal) if h]) for eName, p in entryPlots.items()}, precision=yieldPrecision)
stTotSig, colEntries_forEff = colEntriesFromCFREntryHists_forEff(report, {eName: Stack(entries=[h for h in (getHist(
smp, p) for smp in smp_signal) if h]) for eName, p in entryPlots.items()}, precision=yieldPrecision)
colEntries_matrix = np.array(colEntries_forEff)
sel_eff = np.array([100])
for i in range(1, len(report.titles)):
if float(colEntries_matrix[0]) == 0:
sel_eff = np.append(sel_eff, [0]).tolist()
else:
sel_eff = np.append(sel_eff, [float(
colEntries_matrix[i]) / float(colEntries_matrix[0]) * 100]).tolist()
for i in range(len(report.titles)):
sel_eff[i] = str(f"({sel_eff[i]:.3f}\%)")
colEntries_withEff = []
for i, entry in enumerate(colEntries):
colEntries_withEff.append("{0} {1}".format(
entry, sel_eff[i]))
entries_smp.append(colEntries_withEff)
if smp_mc:
sepStr += "|"
for mcSmp in smp_mc:
stTotMC, colEntries = colEntriesFromCFREntryHists(report,
{eName: getHist(mcSmp, p) for eName, p in entryPlots.items()}, precision=yieldPrecision)
sepStr += f"{align}|"
if isinstance(mcSmp, plotit.plotit.Group):
smpHdrs.append(f"${mcSmp.name}$")
else:
smpHdrs.append(f"${mcSmp.cfg.yields_group}$")
_, colEntries_forEff = colEntriesFromCFREntryHists_forEff(report, {eName: mcSmp.getHist(
p) for eName, p in entryPlots.items()}, precision=yieldPrecision)
colEntries_matrix = np.array(colEntries_forEff)
sel_eff = np.array([100])
for i in range(1, len(report.titles)):
if float(colEntries_matrix[0]) == 0:
sel_eff = np.append(sel_eff, [0]).tolist()
else:
sel_eff = np.append(sel_eff, [float(
colEntries_matrix[i]) / float(colEntries_matrix[0]) * 100]).tolist()
for i in range(len(report.titles)):
sel_eff[i] = str(f"({sel_eff[i]:.3f}\%)")
colEntries_withEff = []
for i, entry in enumerate(colEntries):
colEntries_withEff.append("{0} {1}".format(
entry, sel_eff[i]))
entries_smp.append(colEntries_withEff)
if len(smp_mc) > 1:
sepStr += f"|{align}|"
smpHdrs.append("\\textbf{Background}")
stTotMC, colEntries = colEntriesFromCFREntryHists(report, {eName: Stack(entries=[h for h in (getHist(
smp, p) for smp in smp_mc) if h]) for eName, p in entryPlots.items()}, precision=yieldPrecision)
stTotMC, colEntries_forEff = colEntriesFromCFREntryHists_forEff(report, {eName: Stack(entries=[h for h in (getHist(
smp, p) for smp in smp_mc) if h]) for eName, p in entryPlots.items()}, precision=yieldPrecision)
colEntries_matrix = np.array(colEntries_forEff)
sel_eff = np.array([100])
for i in range(1, len(report.titles)):
if float(colEntries_matrix[0]) == 0:
sel_eff = np.append(sel_eff, [0]).tolist()
else:
sel_eff = np.append(sel_eff, [float(
colEntries_matrix[i]) / float(colEntries_matrix[0]) * 100]).tolist()
for i in range(len(report.titles)):
sel_eff[i] = str(f"({sel_eff[i]:.3f}\%)")
colEntries_withEff = []
for i, entry in enumerate(colEntries):
colEntries_withEff.append("{0} {1}".format(
entry, sel_eff[i]))
entries_smp.append(colEntries_withEff)
if smp_data:
sepStr += f"|{align}|"
smpHdrs.append("Data")
stTotData, colEntries = colEntriesFromCFREntryHists(report, {eName: Stack(entries=[h for h in (getHist(
smp, p) for smp in smp_data) if h]) for eName, p in entryPlots.items()}, precision=0, showUncert=False)
entries_smp.append(colEntries)
if smp_data and smp_mc:
sepStr += f"|{align}|"
smpHdrs.append("Data/MC")
colEntries = []
import numpy.ma as ma
for stData, stMC in zip(stTotData, stTotMC):
if stData is not None and stMC is not None:
dtCont = stData.contents
mcCont = ma.array(stMC.contents)
ratio = dtCont/mcCont
ratioErr = np.sqrt(mcCont**2*stData.sumw2 +
dtCont**2*(stMC.sumw2+stMC.syst2))/mcCont**2
if mcCont[1] != 0.:
colEntries.append("${{0:.{0}f}}$".format(
ratioPrecision).format(ratio[1]))
else:
colEntries.append("---")
else:
colEntries.append("---")
entries_smp.append(colEntries)
c_bySmp = entries_smp
c_byHdr = [[smpEntries[i] for smpEntries in entries_smp]
for i in range(len(titles))]
if orientation == "v":
rowHdrs = titles # selections
colHdrs = ["Selections"]+smpHdrs # samples
c_byRow = c_byHdr
c_byCol = c_bySmp
else: # horizontal
sepStr = "|l|{0}|".format("|".join(repeat(align, len(titles))))
rowHdrs = smpHdrs # samples
colHdrs = ["Samples"]+titles # selections
c_byRow = c_bySmp
c_byCol = c_byHdr
if entries_smp:
colWidths = [max(len(rh) for rh in rowHdrs)+1]+[max(len(hdr), max(len(c)
for c in col))+1 for hdr, col in zip(colHdrs[1:], c_byCol)]
return "\n".join([
f"\\resizebox{{\\textwidth}}{{!}}{{",
f"\\begin{{tabular}}{{ {sepStr} }}",
" \\hline",
" {0} \\\\".format(" & ".join(h.ljust(cw)
for cw, h in zip(colWidths, colHdrs))),
" \\hline"]+[
" {0} \\\\".format(" & ".join(en.rjust(cw)
for cw, en in zip(colWidths, [rh]+rowEntries)))
for rh, rowEntries in zip(rowHdrs, c_byRow)
]+[
" \\hline",
"\\end{tabular}"
"}"
"\\end{document}"
])
def printCutFlowReports(config, reportList, workdir=".", resultsdir=".", suffix=None, readCounters=lambda f: -1., eras=("all", None), verbose=False):
"""
Print yields to the log file, and write a LaTeX yields table for each
Samples can be grouped (only for the LaTeX table) by specifying the
``yields-group`` key (overriding the regular ``groups`` used for plots).
The sample (or group) name to use in this table should be specified
through the ``yields-title`` sample key.
In addition, the following options in the ``plotIt`` section of
the YAML configuration file influence the layout of the LaTeX yields table:
- ``yields-table-stretch``: ``\\arraystretch`` value, 1.15 by default
- ``yields-table-align``: orientation, ``h`` (default), samples in rows, or ``v``, samples in columns
- ``yields-table-text-align``: alignment of text in table cells (default: ``c``)
- ``yields-table-numerical-precision-yields``: number of digits after the decimal point for yields (default: 1)
- ``yields-table-numerical-precision-ratio``: number of digits after the decimal point for ratios (default: 2)
"""
eraMode, eras = eras
if not eras: # from config if not specified
eras = list(config["eras"].keys())
# helper: print one bamboo.plots.CutFlowReport.Entry
def printEntry(entry, printFun=logger.info, recursive=True, genEvents=None):
if entry.nominal is not None:
effMsg = ""
if entry.parent:
sumPass = entry.nominal.GetBinContent(1)
sumTotal = (entry.parent.nominal.GetBinContent(
1) if entry.parent.nominal is not None else 0.)
if sumTotal != 0.:
effMsg = f", Eff={sumPass/sumTotal:.2%}"
if genEvents:
effMsg += f", TotalEff={sumPass/genEvents:.2%}"
printFun(
f"Selection {entry.name}: N={entry.nominal.GetEntries()}, SumW={entry.nominal.GetBinContent(1)}{effMsg}")
printFun(f"Selection {entry.name}: N={entry.nominal.GetEntries()}")
if recursive:
for c in entry.children:
printEntry(c, printFun=printFun,
recursive=recursive, genEvents=genEvents)
def unwMCevents(entry, smp, mcevents, genEvents=None):
if entry.nominal is not None:
mcevents.append(entry.nominal.GetEntries())
for c in entry.children:
unwMCevents(c, smp, mcevents, genEvents=genEvents)
return mcevents
# retrieve results files, get generated events for each sample
from bamboo.root import gbl
resultsFiles = dict()
generated_events = dict()
for smp, smpCfg in config["samples"].items():
if "era" not in smpCfg or smpCfg["era"] in eras:
resF = gbl.TFile.Open(os.path.join(resultsdir, f"{smp}.root"))
resultsFiles[smp] = resF
genEvts = None
if "generated-events" in smpCfg:
if isinstance(smpCfg["generated-events"], str):
genEvts = readCounters(resF)[smpCfg["generated-events"]]
else:
genEvts = smpCfg["generated-events"]
generated_events[smp] = genEvts
has_plotit = None
try:
import plotit.plotit
has_plotit = True
except ImportError:
has_plotit = False
from bamboo.plots import EquidistantBinning as EqB
class YieldPlot:
def __init__(self, name):
self.name = name
self.plotopts = dict()
self.axisTitles = ("Yield",)
self.binnings = [EqB(1, 0., 1.)]
for report in reportList:
smpReports = {smp: report.readFromResults(
resF) for smp, resF in resultsFiles.items()}
# debug print
MCevents = {}
for smp, smpRep in smpReports.items():
# if smpRep.printInLog:
logger.info(f"Cutflow report {report.name} for sample {smp}")
MCevents[smp] = []
for root in smpRep.rootEntries():
printEntry(root, genEvents=generated_events[smp])
mcevents = []
MCevents[smp].append(unwMCevents(
root, smp, mcevents, genEvents=generated_events[smp]))
# save yields.tex (if needed)
if any(len(cb) > 1 or tt != cb[0] for tt, cb in report.titles.items()):
if not has_plotit:
logger.error(
f"Could not load plotit python library, no TeX yields tables for {report.name}")
else:
yield_plots = [YieldPlot(f"{report.name}_{eName}")
for tEntries in report.titles.values() for eName in tEntries]
out_eras = []
if len(eras) > 1 and eraMode in ("all", "combined"):
nParts = [report.name]
if suffix:
nParts.append(suffix)
out_eras.append(("{0}.tex".format("_".join(nParts)), eras))
if len(eras) == 1 or eraMode in ("split", "all"):
for era in eras:
nParts = [report.name]
if suffix:
nParts.append(suffix)
nParts.append(era)
out_eras.append(
("{0}.tex".format("_".join(nParts)), [era]))
for outName, iEras in out_eras:
pConfig, samples, plots, _, _ = loadPlotIt(
config, yield_plots, eras=iEras, workdir=workdir, resultsdir=resultsdir, readCounters=readCounters)
tabBlock = _makeYieldsTexTable(MCevents, report, samples,
{p.name[len(
report.name)+1:]: p for p in plots},
stretch=pConfig.yields_table_stretch,
orientation=pConfig.yields_table_align,
align=pConfig.yields_table_text_align,
yieldPrecision=pConfig.yields_table_numerical_precision_yields,
ratioPrecision=pConfig.yields_table_numerical_precision_ratio)
if tabBlock:
with open(os.path.join(workdir, outName), "w") as ytf:
ytf.write("\n".join((_yieldsTexPreface, tabBlock)))
logger.info("Yields table for era(s) {0} was written to {1}".format(
",".join(iEras), os.path.join(workdir, outName)))
else:
logger.warning(
f"No samples for era(s) {','.join(iEras)}, so no yields.tex")
# END cutflow reports, adapted from bamboo.analysisutils
class CMSPhase2SimRTBHistoModule(CMSPhase2SimRTBModule, HistogramsModule):
""" Base module for producing plots from Phase2 flat trees """
def __init__(self, args):
super(CMSPhase2SimRTBHistoModule, self).__init__(args)
def postProcess(self, taskList, config=None, workdir=None, resultsdir=None):
super(CMSPhase2SimRTBHistoModule, self).postProcess(taskList, config=config, workdir=workdir, resultsdir=resultsdir)
""" Customised cutflow reports and plots """
if not self.plotList:
self.plotList = self.getPlotList(resultsdir=resultsdir)
from bamboo.plots import Plot, DerivedPlot, CutFlowReport
plotList_cutflowreport = [
ap for ap in self.plotList if isinstance(ap, CutFlowReport)]
plotList_plotIt = [ap for ap in self.plotList if (isinstance(
ap, Plot) or isinstance(ap, DerivedPlot)) and len(ap.binnings) == 1]
eraMode, eras = self.args.eras
if eras is None:
eras = list(config["eras"].keys())
if plotList_cutflowreport:
printCutFlowReports(config, plotList_cutflowreport, workdir=workdir, resultsdir=resultsdir,
readCounters=self.readCounters, eras=(eraMode, eras), verbose=self.args.verbose)
if plotList_plotIt:
from bamboo.analysisutils import writePlotIt, runPlotIt
import os.path
cfgName = os.path.join(workdir, "plots.yml")
writePlotIt(config, plotList_plotIt, cfgName, eras=eras, workdir=workdir, resultsdir=resultsdir,
readCounters=self.readCounters, vetoFileAttributes=self.__class__.CustomSampleAttributes, plotDefaults=self.plotDefaults)
runPlotIt(cfgName, workdir=workdir, plotIt=self.args.plotIt,
eras=(eraMode, eras), verbose=self.args.verbose)
#mvaSkim
#import os.path
from bamboo.plots import Skim
skims = [ap for ap in self.plotList if isinstance(ap, Skim)]
if self.args.mvaSkim and skims:
from bamboo.analysisutils import loadPlotIt
p_config, samples, _, systematics, legend = loadPlotIt(config, [], eras=self.args.eras[1], workdir=workdir, resultsdir=resultsdir, readCounters=self.readCounters, vetoFileAttributes=self.__class__.CustomSampleAttributes)
#try:
from bamboo.root import gbl
import pandas as pd
import os.path
#except ImportError as ex:
#logger.error("Could not import pandas, no dataframes will be saved")
for skim in skims:
frames = []
for smp in samples:
for cb in (smp.files if hasattr(smp, "files") else [smp]): # could be a helper in plotit
# Take specific columns
tree = cb.tFile.Get(skim.treeName)
if not tree:
print( f"KEY TTree {skim.treeName} does not exist, we are gonna skip this {smp}\n")
else:
N = tree.GetEntries()
cols = gbl.ROOT.RDataFrame(tree).AsNumpy()
cols["weight"] *= cb.scale
cols["process"] = [smp.name]*len(cols["weight"])
frames.append(pd.DataFrame(cols))
df = pd.concat(frames)
df["process"] = pd.Categorical(df["process"], categories=pd.unique(df["process"]), ordered=False)
pqoutname = os.path.join(resultsdir, f"{skim.name}.parquet")
df.to_parquet(pqoutname)
logger.info(f"Dataframe for skim {skim.name} saved to {pqoutname}")
#produce histograms "with datacard conventions"
if self.args.datacards:
datacardPlots = [ap for ap in self.plotList if ap.name == "Empty_histo" or ap.name =="Inv_mass_gg" or ap.name =="Inv_mass_bb" or ap.name =="Inv_mass_HH" or (self.args.mvaEval and ap.name =="dnn_score")]
p_config, samples, plots_dc, systematics, legend = loadPlotIt(
config, datacardPlots, eras=self.args.eras[1], workdir=workdir, resultsdir=resultsdir,
readCounters=self.readCounters, vetoFileAttributes=self.__class__.CustomSampleAttributes)
dcdir = os.path.join(workdir, "datacard_histograms")
import os
import numpy as np
os.makedirs(dcdir, exist_ok=True)
def _saveHist(obj, name, tdir=None):
if tdir:
tdir.cd()
obj.Write(name)
from functools import partial
import plotit.systematics
from bamboo.root import gbl
for era in (self.args.eras[1] or config["eras"].keys()):
f_dch = gbl.TFile.Open(os.path.join(dcdir, f"histo_for_combine_{era}.root"), "RECREATE")
saveHist = partial(_saveHist, tdir=f_dch)
smp = next(smp for smp in samples if smp.cfg.type == "SIGNAL")
plot = next(plot for plot in plots_dc if plot.name == "Empty_histo")
h = smp.getHist(plot, eras=era)
saveHist(h.obj, f"data_obs")
for plot in plots_dc:
if plot.name != "Empty_histo":
for smp in samples:
smpName = smp.name
if smpName.endswith(".root"):
smpName = smpName[:-5]
h = smp.getHist(plot, eras=era)
saveHist(h.obj, f"h_{plot.name}_{smpName}")
f_dch.Close()
################################
## An analysis module example ##
################################
class SnowmassExample(CMSPhase2SimRTBHistoModule):
def addArgs(self, parser):
super().addArgs(parser)
parser.add_argument("--mvaSkim", action="store_true", help="Produce MVA training skims")
parser.add_argument("--datacards", action="store_true", help="Produce histograms for datacards")
parser.add_argument("--mvaEval", action="store_true", help="Import MVA model and evaluate it on the dataframe")
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
plots = []
#count no of events here
plots.append(Plot.make1D("genweight", t.genweight, noSel, EqB(40000, -20000, 20000.)))
#noSel2 = noSel.refine("withgenweight", weight=t.genweight)
#if 'HH' in sample:
noSel = noSel.refine("weightfix", cut=[t.genweight<300])
plots.append(Plot.make1D("generated_sum_corrected",
op.c_float(0.5), # fill the single bin with 1 * weight
noSel, # the latest selection
EqB(1,0.,1.), # a single bin to old the sum
weight=t.genweight, # we want the sum of genWeight
autoSyst=False)) # no need for systematics
noSel = noSel.refine("genweight", weight=t.genweight)
# Plot that will serve as basis to gen the gen weight sum #
#yields
yields_OneL = CutFlowReport("yields_OneL", recursive=True, printInLog=True)
yields_TwoL = CutFlowReport("yields_TwoL", recursive=True, printInLog=True)
yields_ZeroL = CutFlowReport("yields_ZeroL", recursive=True, printInLog=True)
yields_OneTau = CutFlowReport("yields_OneTau", recursive=True, printInLog=True)
yields_TwoTaus = CutFlowReport("yields_TwoTaus", recursive=True, printInLog=True)
yields_OneL.add(noSel, title= 'noSel')
yields_TwoL.add(noSel, title= 'noSel')
yields_ZeroL.add(noSel, title= 'noSel')
yields_OneTau.add(noSel, title= 'noSel')
yields_TwoTaus.add(noSel, title= 'noSel')
plots.append(yields_OneL)
plots.append(yields_TwoL)
plots.append(yields_ZeroL)
plots.append(yields_OneTau)
plots.append(yields_TwoTaus)
#selection of photons with eta in the detector acceptance
photons = op.select(t.gamma, lambda ph : op.AND(op.abs(ph.eta)<2.5, op.OR(op.abs(ph.eta)>1.556, op.abs(ph.eta)<1.442), ph.pt >25.))
#sort photons by pT
sort_ph = op.sort(photons, lambda ph : -ph.pt)
#selection of photons with loose ID
isoPhotons = op.select(sort_ph, lambda ph : ph.isopass & (1<<0))
idPhotons = op.select(isoPhotons, lambda ph : ph.idpass & (1<<0))
electrons = op.select(t.elec, lambda el : op.AND(el.pt > 10., op.OR(op.abs(el.eta)>1.556, op.abs(el.eta)<1.442), op.abs(el.eta) < 2.5))
#electrons = op.select(t.elec, lambda el : op.AND(el.pt > 10., op.abs(el.eta) < 2.5))
jets = op.select(t.jetpuppi, lambda jet : op.AND(jet.pt > 30., op.abs(jet.eta) < 5))
clElectrons = op.select(electrons, lambda el : op.AND(
op.NOT(op.rng_any(idPhotons, lambda ph : op.deltaR(el.p4, ph.p4) < 0.4 )),
#op.NOT(op.rng_any(jets, lambda j : op.deltaR(el.p4, j.p4) < 0.4 ))
))
sort_el = op.sort(clElectrons, lambda el : -el.pt)
isoElectrons = op.select(sort_el, lambda el : el.isopass & (1<<0))
idElectrons = op.select(isoElectrons, lambda el : el.idpass & (1<<0))
#slElectrons = op.select(idElectrons, lambda el : op.NOT(op.in_range(86.187, op.rng_any(idPhotons,lambda ph:op.invariant_mass(el.p4, ph.p4)), 90.187000))) #apply the removal of rmZee peak
muons = op.select(t.muon, lambda mu : op.AND(mu.pt > 10., op.abs(mu.eta) < 2.5))
clMuons = op.select(muons, lambda mu : op.AND(op.NOT(op.rng_any(idPhotons, lambda ph : op.deltaR(mu.p4, ph.p4) < 0.4 )),
op.NOT(op.rng_any(jets, lambda j : op.deltaR(mu.p4, j.p4) < 0.4 ))))
sort_mu = op.sort(clMuons, lambda mu : -mu.pt)
idMuons = op.select(sort_mu, lambda mu : mu.idpass & (1<<2)) #apply tight ID
isoMuons = op.select(idMuons, lambda mu : mu.isopass & (1<<2)) #apply tight isolation
taus = op.sort(op.select(t.tau, lambda tau: op.AND(tau.pt > 20., op.abs(tau.eta) < 2.5)), lambda tau: -tau.pt)
cleanedTaus = op.select(taus, lambda tau: op.AND(op.NOT(op.rng_any(idPhotons, lambda ph: op.deltaR(tau.p4, ph.p4) < 0.2)),
op.NOT(op.rng_any(idElectrons, lambda el: op.deltaR(tau.p4, el.p4) < 0.2)),
op.NOT(op.rng_any(isoMuons,lambda mu: op.deltaR(tau.p4, mu.p4) < 0.2))
))
isolatedTaus = op.select(cleanedTaus, lambda tau: tau.isopass & (1 << 2)) # tight working point Oguz is using loose ISO
# All tau pairs
allTauPairs = op.combine(isolatedTaus, N=2, pred=lambda t1, t2: t1.charge != t2.charge)
# Best tau pair with invariant mass closest to Higgs mass
bestTauPair = op.rng_min_element_by(allTauPairs, lambda tt: op.abs(op.invariant_mass(tt[0].p4, tt[1].p4)-125))
clJets = op.select(jets, lambda j : op.AND(
op.NOT(op.rng_any(idPhotons, lambda ph : op.deltaR(ph.p4, j.p4) < 0.4) ),
op.NOT(op.rng_any(idElectrons, lambda el : op.deltaR(el.p4, j.p4) < 0.4) ),
op.NOT(op.rng_any(isoMuons, lambda mu : op.deltaR(mu.p4, j.p4) < 0.4) ),
op.NOT(op.rng_any(isolatedTaus, lambda tau: op.deltaR(j.p4, tau.p4) < 0.4))
))
sort_jets = op.sort(clJets, lambda jet : -jet.pt)
idJets = op.select(sort_jets, lambda j : j.idpass & (1<<2))
bJets = op.select(idJets, lambda j: j.btag & (1 << 1))
mGG = op.invariant_mass(idPhotons[0].p4, idPhotons[1].p4)
mTauTau = op.invariant_mass(isolatedTaus[0].p4, isolatedTaus[1].p4)
pTGG = (op.sum(idPhotons[0].p4, idPhotons[1].p4)).pt()
mJets = op.invariant_mass(idJets[0].p4, idJets[1].p4)
mJets_SL= op.invariant_mass(idJets[1].p4, idJets[2].p4)
#Fully leptonic FL invmasses
mE = op.invariant_mass(idElectrons[0].p4, idElectrons[1].p4)
mMu = op.invariant_mass(isoMuons[0].p4, isoMuons[1].p4)
mEMu = op.invariant_mass(idElectrons[0].p4, isoMuons[0].p4)
#missing transverse energy
met = op.select(t.metpuppi)
metPt= met[0].pt
#define more variables for ease of use
nElec = op.rng_len(idElectrons)
nMuon = op.rng_len(isoMuons)
nJet = op.rng_len(idJets)
nPhoton = op.rng_len(idPhotons)
nTau = op.rng_len(isolatedTaus)
#defining more DNN variables
pT_mGGL = op.product(idPhotons[0].pt, op.pow(mGG, -1))
pT_mGGSL = op.product(idPhotons[1].pt, op.pow(mGG, -1))
E_mGGL = op.product(idPhotons[0].p4.energy(), op.pow(mGG, -1))
E_mGGSL = op.product(idPhotons[1].p4.energy(), op.pow(mGG, -1))
#FH DNN variables
w1 = op.sum(idJets[0].p4, idJets[1].p4)
w1_invmass = op.invariant_mass(idJets[0].p4, idJets[1].p4)
w2 = op.sum(idJets[2].p4, idJets[3].p4)
w2_invmass = op.invariant_mass(idJets[2].p4, idJets[3].p4)
ww = op.sum(idJets[0].p4, idJets[1].p4,idJets[2].p4, idJets[3].p4)
ww_invmass = op.invariant_mass(idJets[0].p4, idJets[1].p4,idJets[2].p4, idJets[3].p4)
GG_sum = op.sum(idPhotons[0].p4, idPhotons[1].p4)
#selections for efficiency check
sel1_p = noSel.refine("2Photon", cut = op.AND((op.rng_len(sort_ph) >= 2), (sort_ph[0].pt > 35.)))
sel2_p = sel1_p.refine("idPhoton", cut = op.AND((op.rng_len(idPhotons) >= 2), (idPhotons[0].pt > 35.)))
#selections for the event inv mass of photons within the 100-180 window
hasInvM = sel2_p.refine("hasInvM", cut= op.AND(
(op.in_range(100, op.invariant_mass(idPhotons[0].p4, idPhotons[1].p4), 180))
))
sel2_p_80 = sel1_p.refine("LP80", cut = op.AND((op.rng_len(idPhotons) >= 2), (idPhotons[0].pt > 80.)))
hasInvM80 = sel2_p_80.refine("hasInvM80", cut= op.AND(
(op.in_range(115, op.invariant_mass(idPhotons[0].p4, idPhotons[1].p4), 135))
))
sel2_p_100 = sel1_p.refine("LP100", cut = op.AND((op.rng_len(idPhotons) >= 2), (idPhotons[0].pt > 100.)))
hasInvM100 = sel2_p_100.refine("hasInvM100", cut= op.AND(
(op.in_range(115, op.invariant_mass(idPhotons[0].p4, idPhotons[1].p4), 135))
))
sel1_e = noSel.refine("OneE", cut = op.rng_len(sort_el) >= 1)
sel2_e = sel1_e.refine("idElectron", cut = op.rng_len(idElectrons) >= 1)
sel3_e = sel2_e.refine("slElectron", cut = op.AND(op.rng_len(idElectrons) >= 1))
sel1_m = noSel.refine("OneM", cut = op.rng_len(sort_mu) >= 1)
sel2_m = sel1_m.refine("idMuon", cut = op.rng_len(isoMuons) >= 1)
sel3_m = sel2_m.refine("isoMuon", cut = op.AND(op.rng_len(isoMuons) >= 1))
## Categories ##
#selections for semileptonic final state
hasOneL = hasInvM.refine("hasOneL", cut = op.OR(op.AND(nElec == 1, nMuon == 0), op.AND(nElec == 0, nMuon == 1)))
yields_OneL.add(hasOneL, title='hasOneL')
#hasOneL80 = hasInvM80.refine("hasOneL80", cut = op.AND(op.OR(op.AND(nElec == 1, nMuon == 0), op.AND(nElec == 0, nMuon == 1)), met[0].pt > 80))
#yields.add(hasOneL80, title='hasOneL80')
#hasOneL100 = hasInvM100.refine("hasOneL100", cut = op.AND(op.OR(op.AND(nElec == 1, nMuon == 0), op.AND(nElec == 0, nMuon == 1)), met[0].pt > 100))
#yields.add(hasOneL100, title='hasOneL100')
hasOneEl = hasInvM.refine("hasOneEl", cut = op.AND(nElec == 1, nMuon == 0))
hasOneMu = hasInvM.refine("hasOneMu", cut = op.AND(nElec == 0, nMuon == 1))
#adding jets on the semileptonic final state
#hasOneJ = hasOneL.refine("hasOneJ", cut = nJet >= 1)
#hasTwoJ = hasOneJ.refine("hasTwoJ", cut = nJet >= 2)
#hasThreeJ = hasTwoJ.refine("hasThreeJ", cut = nJet >= 3)
##### TwoL variables ###########################
m_eg = op.invariant_mass(idElectrons[0].p4, idPhotons[0].p4)
m_Z = 91.18
thirdEl = op.switch(op.rng_len(idElectrons) < 3, op.c_float(0.), idElectrons[2].pt)
thirdMu = op.switch(op.rng_len(isoMuons) < 3, op.c_float(0.), isoMuons[2].pt)
#############################################
hasTwoL = hasInvM.refine('hasTwoL', cut = op.AND(
op.OR(
op.AND(op.AND(nElec >= 2, nMuon == 0), idElectrons[0].charge != idElectrons[1].charge, op.NOT(op.deltaR(idElectrons[0].p4, idElectrons[1].p4) < 0.4), op.OR(mE < 80, mE >100),op.abs(m_eg - m_Z) > 5),
op.AND(op.AND(nElec >= 1, nMuon == 1), idElectrons[0].charge != isoMuons[0].charge, op.NOT(op.deltaR(idElectrons[0].p4, isoMuons[0].p4) < 0.4), op.OR(mEMu < 80, mEMu >100), op.abs(m_eg - m_Z) > 5),
op.AND(op.AND(nElec == 1, nMuon >= 1), idElectrons[0].charge != isoMuons[0].charge, op.NOT(op.deltaR(idElectrons[0].p4, isoMuons[0].p4) < 0.4), op.OR(mEMu < 80, mEMu >100), op.abs(m_eg - m_Z) > 5),
op.AND(op.AND(nMuon >= 2, nElec == 0), isoMuons[0].charge != isoMuons[1].charge, op.NOT(op.deltaR(isoMuons[0].p4, isoMuons[1].p4) < 0.4), op.OR(mMu < 80, mMu >100))),
pTGG > 91,
#op.AND(thirdEl < 10, thirdMu < 10),
op.rng_len(bJets) < 1 ,
met[0].pt > 20.
))
yields_TwoL.add(hasTwoL, title='hasTwoL')
hasZeroL = hasInvM.refine('hasZeroL', cut = op.AND(nJet >= 4, nElec == 0, nMuon == 0, nTau == 0))
yields_ZeroL.add(hasZeroL, title='hasZeroL')
c3 = hasInvM.refine("hasOneTauNoLept", cut=op.AND( nTau == 1, op.rng_len(idElectrons) == 0, op.rng_len(isoMuons) == 0 ))
yields_OneTau.add(c3, "One Tau No Lept")
c4 = hasInvM.refine("hasTwoTaus", cut=op.AND(nTau >= 2, op.rng_len(idElectrons) == 0, op.rng_len(isoMuons) == 0 ))
yields_TwoTaus.add(c4, "Two Taus")
########## Z veto ##########
#c4_Zveto = c4.refine( "hasTwoTaus_Zveto", cut=op.NOT(op.in_range(80, mTauTau, 100)))
## End of Categories ##
#plots
#hasOneL
plots.append(Plot.make1D("LeadingPhotonPtOneL", idPhotons[0].pt, hasOneL, EqB(30, 0., 300.), title="Leading Photon pT"))
plots.append(Plot.make1D("SubLeadingPhotonPtOneL", idPhotons[1].pt, hasOneL, EqB(30, 0., 300.), title="SubLeading Photon pT"))
plots.append(Plot.make1D("LeadingPhotonEtaOneL", idPhotons[0].eta, hasOneL, EqB(80, -4., 4.), title="Leading Photon eta"))
plots.append(Plot.make1D("SubLeadingPhotonEtaOneL", idPhotons[1].eta, hasOneL, EqB(80, -4., 4.), title="SubLeading Photon eta"))
plots.append(Plot.make1D("LeadingPhotonPhiOneL", idPhotons[0].phi, hasOneL, EqB(100, -3.5, 3.5), title="Leading Photon phi"))
plots.append(Plot.make1D("SubLeadingPhotonPhiOneL", idPhotons[1].phi, hasOneL, EqB(100, -3.5, 3.5), title="SubLeading Photon phi"))
plots.append(Plot.make1D("nElectronsOneL", nElec, hasOneL, EqB(10, 0., 10.), title="Number of electrons"))
plots.append(Plot.make1D("nMuonsOneL", nMuon, hasOneL, EqB(10, 0., 10.), title="Number of Muons"))
plots.append(Plot.make1D("nJetsOneL", nJet, hasOneL, EqB(10, 0., 10.), title="Number of Jets"))
plots.append(Plot.make1D("LeadingPhotonpT_mGGLhasOneL", pT_mGGL, hasOneL,EqB(100, 0., 5.) ,title = "Leading Photon p_{T}/m_{\gamma\gamma}"))
plots.append(Plot.make1D("SubLeadingPhotonpT_mGGLhasOneL", pT_mGGSL, hasOneL,EqB(100, 0., 5.) ,title = "SubLeading Photon p_{T}/m_{\gamma\gamma}"))
plots.append(Plot.make1D("LeadingPhotonE_mGGLhasOneL", E_mGGL, hasOneL,EqB(100, 0., 5.) ,title = "Leading Photon E/m_{\gamma\gamma}"))
plots.append(Plot.make1D("SubLeadingPhotonE_mGGLhasOneL", E_mGGSL, hasOneL,EqB(100, 0., 5.) ,title = "SubLeading Photon E/m_{\gamma\gamma}"))
plots.append(Plot.make1D("MET", metPt, hasOneL,EqB(80, 0., 800.) ,title="MET"))
plots.append(Plot.make1D("Inv_mass_gghasOneL",mGG , hasOneL, EqB(80, 100.,180.), title = "m_{\gamma\gamma}"))
plots.append(Plot.make1D("Inv_mass_gghasOneL_135",mGG , hasOneL, EqB(20, 115.,135.), title = "m_{\gamma\gamma}"))
#plots.append(Plot.make1D("Inv_mass_gghasOneL80",mGG , hasOneL80, EqB(20, 115.,135.), title = "m_{\gamma\gamma}"))
#plots.append(Plot.make1D("Inv_mass_gghasOneL100",mGG , hasOneL100, EqB(20, 115.,135.), title = "m_{\gamma\gamma}"))
#Leading electron Plots
ElectronpT = Plot.make1D("ElectronpT", idElectrons[0].pt, hasOneEl, EqB(30, 0., 300.), title = 'Leading Electron pT')
ElectronE = Plot.make1D("ElectronE", idElectrons[0].p4.E(), hasOneEl, EqB(50, 0., 500.), title = 'Leading Electron E')
ElectronEta = Plot.make1D("ElectronEta", idElectrons[0].eta, hasOneEl, EqB(80, -4., 4.), title = 'Leading Electron eta')
ElectronPhi = Plot.make1D("ElectronPhi", idElectrons[0].phi, hasOneEl, EqB(100, -3.5, 3.5), title = 'Leading Electron phi')
#Leading muon Plots
MuonpT = Plot.make1D("MuonpT", isoMuons[0].pt, hasOneMu, EqB(30, 0., 100.), title = 'Leading Muon pT')
MuonE = Plot.make1D("MuonE", isoMuons[0].p4.E(), hasOneMu, EqB(50, 0., 500.), title = 'Leading Muon E')
MuonEta = Plot.make1D("MuonEta", isoMuons[0].eta, hasOneMu, EqB(80, -4., 4.), title = 'Leading Muon eta')
MuonPhi = Plot.make1D("MuonPhi", isoMuons[0].phi, hasOneMu, EqB(100, -3.5, 3.5), title = 'Leading Muon phi')
#Lepton Plots
LeptonpT = SummedPlot('LeptonpT', [ElectronpT,MuonpT], xTitle = 'Leading Lepton pT')
plots.append(ElectronpT)
plots.append(MuonpT)
plots.append(LeptonpT)
LeptonE = SummedPlot('LeptonE', [ElectronE,MuonE], xTitle = 'Leading Lepton E')
plots.append(ElectronE)
plots.append(MuonE)
plots.append(LeptonE)
LeptonEta = SummedPlot('LeptonEta', [ElectronEta,MuonEta], xTitle = 'Leading Lepton Eta')
plots.append(ElectronEta)
plots.append(MuonEta)
plots.append(LeptonEta)
LeptonPhi = SummedPlot('LeptonPhi', [ElectronPhi,MuonPhi], xTitle = 'Leading Lepton Phi')
plots.append(ElectronPhi)
plots.append(MuonPhi)
plots.append(LeptonPhi)
# #Fully leptonic FL invmasses
# mE = op.invariant_mass(idElectrons[0].p4, idElectrons[1].p4)
# mMu = op.invariant_mass(isoMuons[0].p4, isoMuons[1].p4)
# mEMu = op.invariant_mass(idElectrons[0].p4, isoMuons[0].p4)
# #missing transverse energy
# met = op.select(t.metpuppi)
# metPt= met[0].pt
hasTwoL_E= hasInvM.refine('hasTwoL_E', cut = op.AND(
op.AND(op.AND(nElec >= 2, nMuon == 0), idElectrons[0].charge != idElectrons[1].charge, op.NOT(op.deltaR(idElectrons[0].p4, idElectrons[1].p4) < 0.4), op.OR(mE < 80, mE >100),op.abs(m_eg - m_Z) > 5),
pTGG > 91,
op.AND(thirdEl < 10, thirdMu < 10),
op.rng_len(bJets) < 1 ,
met[0].pt > 20
))
hasTwoL_Mu = hasInvM.refine('hasTwoL_Mu', cut = op.AND(
op.AND(op.AND(nMuon >= 2, nElec == 0), isoMuons[0].charge != isoMuons[1].charge, op.NOT(op.deltaR(isoMuons[0].p4, isoMuons[1].p4) < 0.4), op.OR(mMu < 80, mMu >100)),
pTGG > 91,
op.AND(thirdEl < 10, thirdMu < 10),
op.rng_len(bJets) < 1 ,
met[0].pt > 20
))
hasTwoL_EMu = hasInvM.refine('hasTwoL_EMu', cut = op.AND(
op.OR(
op.AND(op.AND(nElec >= 1, nMuon == 1), idElectrons[0].charge != isoMuons[0].charge, op.NOT(op.deltaR(idElectrons[0].p4, isoMuons[0].p4) < 0.4), op.OR(mEMu < 80, mEMu >100), op.abs(m_eg - m_Z) > 5),
op.AND(op.AND(nElec == 1, nMuon >= 1), idElectrons[0].charge != isoMuons[0].charge, op.NOT(op.deltaR(idElectrons[0].p4, isoMuons[0].p4) < 0.4), op.OR(mEMu < 80, mEMu >100), op.abs(m_eg - m_Z) > 5)
),
pTGG > 91,
op.AND(thirdEl < 10, thirdMu < 10),
op.rng_len(bJets) < 1 ,
met[0].pt > 20
))
mll_e = Plot.make1D("mll_e", mE, hasTwoL_E, EqB(150, 0., 150.), title = 'mll')
mll_mu = Plot.make1D("mll_mu", mMu, hasTwoL_Mu, EqB(150, 0., 150.), title = 'mll')
mll_emu = Plot.make1D("mll_emu", mEMu, hasTwoL_EMu, EqB(150, 0., 150.), title = 'mll')
M_ll = SummedPlot("M_ll", [mll_e, mll_mu,mll_emu], xTitle= 'm_ll')
plots.append(mll_e)
plots.append(mll_mu)
plots.append(mll_emu)
plots.append(M_ll)
#hasTwoL
plots.append(Plot.make1D("Inv_mass_gghasTwoL",mGG , hasTwoL, EqB(80, 100.,180.), title = "m_{\gamma\gamma}"))
plots.append(Plot.make1D("Inv_mass_gghasTwoL_135",mGG , hasTwoL, EqB(20, 115.,135.), title = "m_{\gamma\gamma}"))
plots.append(Plot.make1D("MET600", metPt, hasTwoL,EqB(600, 0., 600.) ,title="MET"))
plots.append(Plot.make1D("MET500", metPt, hasTwoL,EqB(500, 0., 500.) ,title="MET"))
#hasZeroL
plots.append(Plot.make1D("Inv_mass_gghasZeroL",mGG , hasZeroL, EqB(80, 100.,180.), title = "m_{\gamma\gamma}"))
# tau category plots
plots.append(Plot.make1D("mGG_c3", mGG, c3, EqB(80, 100, 180), title="M_{\gamma\gamma}", plotopts={"log-y": True}))
plots.append(Plot.make1D("mGG_c3_135", mGG, c3, EqB(20, 115, 135), title="M_{\gamma\gamma}", plotopts={"log-y": True}))
plots.append(Plot.make1D("mGG_c4", mGG, c4, EqB(80, 100, 180), title="M_{\gamma\gamma}", plotopts={"log-y": True}))
plots.append(Plot.make1D("mGG_c4_135", mGG, c4, EqB(20, 115, 135), title="M_{\gamma\gamma}", plotopts={"log-y": True}))
# #hasOneJ
# plots.append(Plot.make1D("LeadingJetPtOneJ", idJets[0].pt, hasOneJ, EqB(30, 0., 300.), title = 'Leading Jet pT'))
# plots.append(Plot.make1D("LeadingJetEtaOneJ", idJets[0].eta, hasOneJ, EqB(80, -4., 4.), title="Leading Jet eta"))
# plots.append(Plot.make1D("LeadingJetPhiOneJ", idJets[0].phi, hasOneJ, EqB(100, -3.5, 3.5), title="Leading Jet phi"))
# plots.append(Plot.make1D("LeadingJetEOnej", idJets[0].p4.energy(), hasOneJ, EqB(50, 0.,500.), title = 'Leading Jet E'))
# #hasTwoJ
# plots.append(Plot.make1D("SubLeadingJetPtTwoJ", idJets[1].pt, hasTwoJ, EqB(30, 0., 300.), title = 'SubLeading Jet pT'))
# plots.append(Plot.make1D("SubLeadingJetEtaTwoJ", idJets[1].eta, hasTwoJ, EqB(80, -4., 4.), title="SubLeading Jet eta"))
# plots.append(Plot.make1D("SubLeadingJetPhiTwoJ", idJets[1].phi, hasTwoJ, EqB(100, -3.5, 3.5), title="SubLeading Jet phi"))
# plots.append(Plot.make1D("SubLeadingJetETwoJ", idJets[1].p4.energy(), hasTwoJ, EqB(50, 0.,500.), title = 'SubLeading Jet E'))
# plots.append(Plot.make1D("Inv_mass_jjTwoJ", mJets, hasTwoJ, EqB(80, 20.,220.), title = "m_{jets}"))
# #hasThreeJ
# plots.append(Plot.make1D("Inv_mass_jjThreeJ", mJets_SL, hasThreeJ, EqB(80, 100.,180.), title = "m_{jets}"))
mvaVariables = {
"weight": noSel.weight,
"Eta_ph1": idPhotons[0].eta,
"Phi_ph1": idPhotons[0].phi,
"E_mGG_ph1": E_mGGL,
"pT_mGG_ph1": pT_mGGL,
"Eta_ph2": idPhotons[1].eta,
"Phi_ph2": idPhotons[1].phi,
"E_mGG_ph2": E_mGGSL,
"pT_mGG_ph2": pT_mGGSL,
"Electron_E": op.switch(op.rng_len(idElectrons)==0,op.c_float(0.),idElectrons[0].p4.E()),
"Electron_pT": op.switch(op.rng_len(idElectrons)==0,op.c_float(0.),idElectrons[0].pt),
"Electron_Eta": op.switch(op.rng_len(idElectrons)==0,op.c_float(0.),idElectrons[0].eta),
"Electron_Phi": op.switch(op.rng_len(idElectrons)==0,op.c_float(0.),idElectrons[0].phi),
"Muon_E": op.switch(op.rng_len(isoMuons)==0,op.c_float(0.),isoMuons[0].p4.E()),
"Muon_pT": op.switch(op.rng_len(isoMuons)==0,op.c_float(0.),isoMuons[0].pt),
"Muon_Eta": op.switch(op.rng_len(isoMuons)==0,op.c_float(0.),isoMuons[0].eta),
"Muon_Phi": op.switch(op.rng_len(isoMuons)==0,op.c_float(0.),isoMuons[0].phi),
"nJets": nJet,
"E_jet1": op.switch(op.rng_len(idJets)==0,op.c_float(0.),idJets[0].p4.E()),
"pT_jet1": op.switch(op.rng_len(idJets)==0,op.c_float(0.),idJets[0].pt),
"Eta_jet1": op.switch(op.rng_len(idJets)==0,op.c_float(0.),idJets[0].eta),
"Phi_jet1": op.switch(op.rng_len(idJets)==0,op.c_float(0.),idJets[0].phi),
"E_jet2": op.switch(op.rng_len(idJets)<2,op.c_float(0.),idJets[1].p4.E()),
"pT_jet2": op.switch(op.rng_len(idJets)<2,op.c_float(0.),idJets[1].pt),
"Eta_jet2": op.switch(op.rng_len(idJets)<2,op.c_float(0.),idJets[1].eta),
"Phi_jet2": op.switch(op.rng_len(idJets)<2,op.c_float(0.),idJets[1].phi),
"InvM_jet": op.switch(op.rng_len(idJets)<2,op.c_float(0.),mJets),
"InvM_jet2": op.switch(op.rng_len(idJets)<3,op.c_float(0.),mJets_SL),
"met":metPt
}
mvaVariables_c3 = {
"weight": noSel.weight,
# Event level variables
"nJets": nJet,
"nBJets": op.rng_len(bJets),
"metPt": metPt,
# Photon and di-Photon variables
"L_pt_mGG": pT_mGGL,
"L_photon_eta": idPhotons[0].eta,
"L_photon_phi": idPhotons[0].phi,
"E_mGG_ph1": E_mGGL,
"E_mGG_ph2": E_mGGSL,
"SL_pt_mGG": pT_mGGSL,
"SL_photon_eta": idPhotons[1].eta,
"SL_photon_phi": idPhotons[1].phi,
"LTauE": isolatedTaus[0].p4.E(),
"LtauPt": isolatedTaus[0].pt,
"LtauEta": isolatedTaus[0].eta,
"LtauPhi": isolatedTaus[0].phi,
"Ljet_Pt": op.switch(nJet == 0, op.c_float(0.), idJets[0].pt),
"Ljet_Eta": op.switch(nJet == 0, op.c_float(0.), idJets[0].eta),
"SLjet_Pt": op.switch(nJet < 2, op.c_float(0.), idJets[1].pt),
"SLjet_Eta": op.switch(nJet < 2, op.c_float(0.), idJets[1].eta),
}
mvaVariables_FH = {
"weight": noSel.weight,
"Eta_ph1": idPhotons[0].eta,
"Phi_ph1": idPhotons[0].phi,
"E_mGG_ph1": E_mGGL,
"pT_mGG_ph1": pT_mGGL,
"Eta_ph2": idPhotons[1].eta,
"Phi_ph2": idPhotons[1].phi,
"E_mGG_ph2": E_mGGSL,
"pT_mGG_ph2": pT_mGGSL,
"deltaPhi_DiPh": op.deltaPhi(idPhotons[0].p4, idPhotons[1].p4),
"deltaR_DiPh": op.deltaR(idPhotons[0].p4, idPhotons[1].p4),
"nBJets": op.rng_len(bJets),
"bJet1_pt": op.switch(op.rng_len(bJets) == 0, op.c_float(0.), bJets[0].pt),
"bJet1_eta": op.switch(op.rng_len(bJets) == 0, op.c_float(0.), bJets[0].eta),
"bJet1_phi": op.switch(op.rng_len(bJets) == 0, op.c_float(0.), bJets[0].phi),
"bJet1_E": op.switch(op.rng_len(bJets) == 0, op.c_float(0.), bJets[0].p4.E()),
"bJet2_pt": op.switch(op.rng_len(bJets) < 2, op.c_float(0.), bJets[1].pt),
"bJet2_eta": op.switch(op.rng_len(bJets) < 2, op.c_float(0.), bJets[1].eta),
"bJet2_phi": op.switch(op.rng_len(bJets) < 2, op.c_float(0.), bJets[1].phi),
"bJet4_E": op.switch(op.rng_len(bJets) < 2, op.c_float(0.), bJets[1].p4.E()),
"nJets": nJet,
"E_jet1": idJets[0].p4.E(),
"pT_jet1": idJets[0].pt,
"Eta_jet1": idJets[0].eta,
"Phi_jet1": idJets[0].phi,
"E_jet2": idJets[1].p4.E(),
"pT_jet2": idJets[1].pt,
"Eta_jet2": idJets[1].eta,
"Phi_jet2": idJets[1].phi,
"E_jet3": idJets[2].p4.E(),
"pT_jet3": idJets[2].pt,
"Eta_jet3": idJets[2].eta,
"Phi_jet3": idJets[2].phi,
"E_jet4": idJets[3].p4.E(),
"pT_jet4": idJets[3].pt,
"Eta_jet4": idJets[3].eta,
"Phi_jet4": idJets[3].phi,
"w1_pT": w1.pt(),
"w1_eta": op.sum(idJets[0].p4, idJets[1].p4).eta(),
"w1_mass": w1_invmass,
"w2_pT": op.sum(idJets[2].p4, idJets[3].p4).pt(),
"w2_eta": op.sum(idJets[2].p4, idJets[3].p4).eta(),
"w2_mass": w2_invmass,
"ww_pT": op.sum(idJets[0].p4, idJets[1].p4, idJets[2].p4, idJets[3].p4).pt(),
"ww_eta": op.sum(idJets[0].p4, idJets[1].p4, idJets[2].p4, idJets[3].p4).eta(),
"ww_mass": ww_invmass,
"mindeltaRPJ": op.rng_min(idJets, lambda jet : op.deltaR( idPhotons[0].p4, jet.p4)),
"maxdeltaRPJ": op.rng_max(idJets, lambda jet : op.deltaR( idPhotons[0].p4, jet.p4)),
"deltaRJJ": op.deltaR( idJets[0].p4, idJets[1].p4),
"deltaRJJ2": op.deltaR( idJets[2].p4, idJets[3].p4),
"deltaPhi_HH": op.deltaPhi(ww,GG_sum),
"deltaR_HH": op.deltaR(ww,GG_sum)
}
# save mvaVariables to be retrieved later in the postprocessor and save in a parquet file
#if self.args.mvaSkim or self.args.mvaEval:
if self.args.mvaSkim:
from bamboo.plots import Skim
plots.append(Skim("Skim", mvaVariables,hasOneL))
plots.append(Skim("c3", mvaVariables_c3, c3))
plots.append(Skim("Skim_FH", mvaVariables_FH, hasZeroL))
# evaluate dnn model on data
if self.args.mvaEval:
#from IPython import embed