def makeChain(samplesPath, singleFile=False):
plotfactory.setpfstyle()
chain = ROOT.TChain('tree')
all_files = glob.glob(samplesPath + '*/HNLTreeProducer*/tree.root')
nfiles = len(all_files)
if singleFile == False:
for file in all_files:
chain.Add(file)
elif singleFile == True:
chain.Add(
'/Users/dehuazhu/SynologyDrive/PhD/5_Projects/analysis/200114_AcceptancePlots/signals_2018/HN3L_M_2_V_0p0307896086367_mu_Dirac_cc_massiveAndCKM_LO/HNLTreeProducer_mmm/tree.root'
)
return chain, nfiles
#################
# Configuration #
#################
import ROOT
import numpy as np
import plotfactory as pf
from glob import glob
pf.setpfstyle()
bewl = input("Makechain: True or False\n")
tt = pf.makechain(bewl)
#file = ROOT.TFile('tree.root')
#tt = file.Get('tree')
ntries = tt.GetEntries()
print('Number of entries: ' + str(ntries))
#################
# Define x-axes #
#################
pTbins = np.arange(5., 73, 5)
###################
# Create canvases #
###################
print('Preparing canvas')
t = ROOT.TCanvas('t', 'bar_d2')
#####################
# Create histograms #
plotfactory.showlumi('N#rightarrow%s; 59.7 fb^{-1} (13 TeV)' % (ch))
can.Update()
if not os.path.isdir(output_dir + 'pdf'): os.mkdir(output_dir + 'pdf')
if not os.path.isdir(output_dir + 'png'): os.mkdir(output_dir + 'png')
if not os.path.isdir(output_dir + 'root'): os.mkdir(output_dir + 'root')
can.SaveAs(output_dir + 'pdf/%s.pdf' % (ch))
can.SaveAs(output_dir + 'png/%s.png' % (ch))
can.SaveAs(output_dir + 'root/%s.root' % (ch))
if __name__ == '__main__':
print('starting draw_limits.py...')
plotfactory.setpfstyle()
channel = 'mmm'
# channel = 'mem_OS'
# channel = 'mem_SS'
# channel = 'eee'
# channel = 'eem_OS'
# channel = 'eem_SS'
#2017
# base_dir = '/work/dezhu/3_figures/2_Limits/2017/mmm/20191119_limits'
#2018
# base_dir = '/work/dezhu/3_figures/2_Limits/2018/%s/20191120_Aachen'%channel
# base_dir = '/work/dezhu/3_figures/2_Limits/2018/%s/20191121_RiccardoDatacards'%channel
base_dir = '/work/dezhu/3_figures/2_Limits/2018/%s/20191125_SignalReweight' % channel
def makeFakeRatePlot(varName):
tt = makechain()
dataframe = ROOT.RDataFrame(tt)
plotfactory.setpfstyle()
can = ROOT.TCanvas('c1', 'c1')
print('running variable %s' % varName)
if varName == 'l1_pt':
binsx = np.array([0., 5., 10., 15., 20., 30., 50., 70.]) # for pt
if varName == 'abs_l1_eta': binsx = np.arange(0., 2.5, 0.4)
if varName == 'abs_l1_dxy': binsx = np.logspace(-1.9, -1.4, 5)
if varName == 'abs_l1_dz': binsx = np.logspace(-1.8, -0.5, 10)
if varName == 'hnl_2d_disp': binsx = np.logspace(-0.7, 0.4, 5)
if varName == 'hnl_dr_12': binsx = np.logspace(-1.3, -0.4, 9)
if varName == 'hnl_m_12': binsx = np.arange(1., 4., 0.5)
if varName == 'sv_prob': binsx = np.arange(0., 1.0, 0.1)
if varName == 'hnl_w_vis_m': binsx = np.arange(0., 200.0, 30)
# if varName == 'sv_prob': binsx = np.logspace(-1.6, 0., 5)
# binsx = np.logspace(-7,2,40)
# binsx = np.arange(0.,200.,10.)
MeasurementRegion = Selections.Region('cr', 'mmm', 'CustomRegion')
ht = dataframe\
.Define('abs_l1_eta','abs(l1_eta)')\
.Define('abs_l1_dxy','abs(l1_dxy)')\
.Define('abs_l1_dz','abs(l1_dz)')\
.Filter(MeasurementRegion.data)\
.Histo1D(('','',len(binsx)-1,binsx),'%s'%varName)
hl = dataframe\
.Define('abs_l1_eta','abs(l1_eta)')\
.Define('abs_l1_dxy','abs(l1_dxy)')\
.Define('abs_l1_dz','abs(l1_dz)')\
.Filter(MeasurementRegion.baseline)\
.Histo1D(('','',len(binsx)-1,binsx),'%s'%varName)
hist_tight = ht.Clone()
hist_loose = hl.Clone()
if varName == 'l1_pt': hist_tight.SetTitle(';lepton p_{T} (GeV); fakerate')
if varName == 'abs_l1_eta': hist_tight.SetTitle(';lepton #eta; fakerate')
if varName == 'abs_l1_dxy':
hist_tight.SetTitle(';lepton impact parameter d_{xy} (cm); fakerate')
if varName == 'abs_l1_dz':
hist_tight.SetTitle(';lepton impact parameter d_{z} (cm); fakerate')
if varName == 'hnl_2d_disp':
hist_tight.SetTitle('; 2D displacement (cm); fakerate')
if varName == 'hnl_dr_12': hist_tight.SetTitle('; #Delta R_{12}; fakerate')
if varName == 'hnl_m_12':
hist_tight.SetTitle('; dilepton invariant mass m_{12} (GeV); fakerate')
if varName == 'sv_prob':
hist_tight.SetTitle('; dilepton vertex quality; fakerate')
if varName == 'hnl_w_vis_m':
hist_tight.SetTitle('; Tri-Lepton Mass (GeV); fakerate')
hist_tight.Divide(hist_loose)
hist_tight.SetMarkerColor(ROOT.kBlue + 2)
hist_tight.SetLineColor(ROOT.kBlue + 2)
hist_tight.GetYaxis().SetRangeUser(0., 0.40)
hist_tight.Draw()
if varName == 'hnl_2d_disp': can.SetLogx()
if varName == 'hnl_dr_12': can.SetLogx()
if varName == 'abs_l1_dz': can.SetLogx()
# if varName == 'sv_prob': can.SetLogx()
# can.SetLogy()
plotfactory.showlumi('59.7 fb^{-1} (13 TeV)')
plotfactory.showlogopreliminary()
can.Update()
plotName = 'fr_%s' % varName
for datatype in ['pdf', 'png', 'tex', 'root']:
can.SaveAs('fakerateplots/%s/%s.%s' % (datatype, plotName, datatype))
for histogram in [ht, hl, hist_tight, hist_loose]:
histogram.Reset()