def __init__(self, configurations, sampleDict, signalsampleIds=[6], datasampleIds=[9], scaleSignal="integral", drawRatios=False, pullRange=0.8, fileName="BDT_training_mva.root", sysName="", endings=["pdf"], directory="plots", overFlowLastBin=True, underFlowFirstBin=False, calculateSeparation=True, verbose=False, treeName='Tree'):
self._sampleDict = sampleDict
# List of signal sample names; if set, will be drawn as separate line
self._signalsampleIds = signalsampleIds
# That's fixed for now
self._datasampleIds = datasampleIds
# Scale signal to "integral" or "luminosity" (i.e. cross section)
self._scaleSignal = scaleSignal
""" a configuration is a list of dicts that contain the following information
"var": variable name (string)
"legend": "none", "top left", "top right"
"logy": True/False
"""
self._configurations = configurations
# In which formats the plots are given out
self._endings = endings
self._directory = directory
# Whether the ratio below the plot shall be drawn
self._drawRatios = drawRatios
self._pullRange = 0.8
self._errorBandFillColor = 14
self._errorBandStyle = 3354
# Pattern for file name: {0}: sample name, {1}: lepton name, {2}: sys addendum
self._fileName = fileName
self._treeName = treeName
self._tree = 0
# For a shape systematic based on up/down files:
# Legend entry - if this is present, the systematic uncertainty will be drawn
self._sysName = sysName
# Whether to calculate and print separation measures
self._calculateSeparation = calculateSeparation
# Be verbose or not
self._verbose = verbose
# Whether to put the overflow into the last bin
self._overFlowLastBin = overFlowLastBin
# Whether to put the underflow into the first bin
self._underFlowFirstBin = underFlowFirstBin
# List of open root files
self._rootFiles = {}
PlotStyle.initStyle()
# remove duplicates, just in case
infiles = list(set(infiles))
if args.outdir == 'ZZA_NORMAL':
outdir = os.path.join(os.environ["zza"], '/plots/cutSummary')
else:
# remove trailing slash
if args.outdir.endswith('/'):
outdir = args.outdir[:-1]
else:
outdir = args.outdir
makeDirectory(outdir)
# Look decent
style = PlotStyle()
histos = {}
numbers = {}
cutNames = {}
cutCountMax = -1
# Loop over inputs, making one histogram for each channel in each input
for infile in infiles:
sample = infile.replace('_cutflow','').replace('.txt','').split('/')[-1]
with open(infile, 'r') as f:
channel = ''
cutCount = 0
# loop over lines
for line in f:
rlog["/ROOT.TCanvas.Print"].setLevel(rlog.WARNING)
rlog["/ROOT.TUnixSystem.SetDisplay"].setLevel(rlog.ERROR)
from rootpy.io import root_open
from rootpy.plotting import Hist, Canvas, Legend, Pad, Graph
from rootpy.ROOT import TLine
from rootpy.plotting.utils import draw
from rootpy import asrootpy
from glob import glob
from os import path
from PlotStyle import PlotStyle
style = PlotStyle()
masses = [125, 300, 750, 1000, 1500, 2500]
outdir = '/afs/cern.ch/user/n/nawoods/www/muonHighMassResults/'
fileList = glob('/data/nawoods/ntuples/zzNtuples_mc_76HighPtID_0/results/*.root')
fileListLeg = glob('/data/nawoods/ntuples/zzNtuples_mc_76HighPtID_0/results_legacy/*.root')
files = {}
filesLeg = {}
for m in masses:
for f in fileList:
if str(m) in f:
files[m] = root_open(f)
