def create2dHist(varname, params, title):
if "to_pt" in varname and "tagRate" in varname:
h = TProfile(varname, title, 50, params["plotPtRange"][0],
params["plotPtRange"][1])
h.GetXaxis().SetTitle("#tau_{vis} p_{T} [GeV]")
h.GetYaxis().SetTitle("tagging efficiency")
if "to_eta" in varname and "tagRate" in varname:
h = TProfile(varname, title, 50, params["plotEtaRange"][0],
params["plotEtaRange"][1])
h.GetXaxis().SetTitle("#tau_{vis} #eta")
h.GetYaxis().SetTitle("tagging efficiency")
h.Sumw2()
return h
def createHistogram( self, title=None, profile=False ):
## Create an empty histogram for this variable with the given title
# @param title the title of the new histogram
# @param profile set if the histogram should be a TProfile instead
# @return the new histogram
title = title if title is not None else self.title
name = 'h%s_%s' % ( self.name.replace(' ', '').replace('(', '').replace(')',''), uuid.uuid1() )
from ROOT import TH1D, TProfile
if profile:
h = TProfile( name, title, self.binning.nBins, 0., 1. )
else:
h = TH1D( name, title, self.binning.nBins, 0., 1. )
self.binning.setupAxis( h.GetXaxis() )
self.applyToAxis( h.GetXaxis() )
return h
def plotting_init(data, trainvar, histo_dict, masses, weights='totalWeight'):
""" Initializes the plotting
Parameters:
-----------
data : pandas DataFrame
Data to be used for creating the TProfiles
trainvar : str
Name of the training variable.
histo_dict : dict
Dictionary containing the info for plotting for a given trainvar
masses : list
List of masses to be used
[weights='totalWeight'] : str
What column to be used for weight in the data.
Returns:
--------
canvas : ROOT.TCanvas instance
canvas to be plotted on
profile : ROOT.TProfile instance
profile for the fitting
"""
canvas = TCanvas('canvas', 'TProfile plot', 200, 10, 700, 500)
canvas.GetFrame().SetBorderSize(6)
canvas.GetFrame().SetBorderMode(-1)
signal_data = data.loc[data['target'] == 1]
gen_mHH_values = np.array(signal_data['gen_mHH'].values, dtype=np.float)
trainvar_values = np.array(signal_data[trainvar].values, dtype=np.float)
weights = np.array(signal_data[weights].values, dtype=np.float)
sanity_check = len(gen_mHH_values) == len(trainvar_values) == len(weights)
assert sanity_check
title = 'Profile of ' + str(trainvar) + ' vs gen_mHH'
num_bins = (len(masses) - 1)
xlow = masses[0]
xhigh = (masses[(len(masses) - 1)] + 100.0)
ylow = histo_dict["min"]
yhigh = histo_dict["max"]
profile = TProfile('profile', title, num_bins, xlow, xhigh, ylow, yhigh)
mass_bins = np.array(masses, dtype=float)
profile.SetBins((len(mass_bins) - 1), mass_bins)
profile.GetXaxis().SetTitle("gen_mHH (GeV)")
profile.GetYaxis().SetTitle(str(trainvar))
for x, y, w in zip(gen_mHH_values, trainvar_values, weights):
profile.Fill(x, y, w)
profile.Draw()
canvas.Modified()
canvas.Update()
return canvas, profile
def generator(nuslice_tree, rootfile, pset):
n_bins = pset.n_bins
drift_distance = pset.DriftDistance
bin_width = drift_distance/n_bins
half_bin_width = bin_width/2.
xvals = np.arange(half_bin_width, drift_distance, bin_width)
xerrs = np.array([half_bin_width] * len(xvals))
dist_to_anode_bins = n_bins
dist_to_anode_low = 0.
dist_to_anode_up = drift_distance
profile_bins = n_bins
profile_option = 's' # errors are the standard deviation
dy_spreads = [None] * n_bins
dy_means = [None] * n_bins
dy_hist = TH2D("dy_hist", "#Delta y",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_bins, pset.dy_low, pset.dy_up)
dy_hist.GetXaxis().SetTitle("distance from anode (cm)")
dy_hist.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_prof = TProfile("dy_prof", "Profile of dy_spreads in #Delta y",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dy_low*2, pset.dy_up*2, profile_option)
dy_prof.GetXaxis().SetTitle("distance from anode (cm)")
dy_prof.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dy_h1 = TH1D("dy_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dy_h1.GetXaxis().SetTitle("distance from anode (cm)")
dy_h1.GetYaxis().SetTitle("y_flash - y_TPC (cm)")
dz_spreads = [None] * n_bins
dz_means = [None] * n_bins
dz_hist = TH2D("dz_hist", "#Delta z",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_bins, pset.dz_low, pset.dz_up)
dz_hist.GetXaxis().SetTitle("distance from anode (cm)")
dz_hist.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_prof = TProfile("dz_prof", "Profile of dz_spreads in #Delta z",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.dz_low*2.5, pset.dz_up*2.5, profile_option)
dz_prof.GetXaxis().SetTitle("distance from anode (cm)")
dz_prof.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
dz_h1 = TH1D("dz_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
dz_h1.GetXaxis().SetTitle("distance from anode (cm)")
dz_h1.GetYaxis().SetTitle("z_flash - z_TPC (cm)")
rr_spreads = [None] * n_bins
rr_means = [None] * n_bins
rr_hist = TH2D("rr_hist", "PE Spread",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_bins, pset.rr_low, pset.rr_up)
rr_hist.GetXaxis().SetTitle("distance from anode (cm)")
rr_hist.GetYaxis().SetTitle("RMS flash (cm)")
rr_prof = TProfile("rr_prof", "Profile of PE Spread",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.rr_low, pset.rr_up, profile_option)
rr_prof.GetXaxis().SetTitle("distance from anode (cm)")
rr_prof.GetYaxis().SetTitle("RMS flash (cm)")
rr_h1 = TH1D("rr_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
rr_h1.GetXaxis().SetTitle("distance from anode (cm)")
rr_h1.GetYaxis().SetTitle("RMS flash (cm)")
if detector == "sbnd":
pe_spreads = [None] * n_bins
pe_means = [None] * n_bins
pe_hist = TH2D("pe_hist", "Uncoated/Coated Ratio",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_bins, pset.pe_low, pset.pe_up)
pe_hist.GetXaxis().SetTitle("distance from anode (cm)")
pe_hist.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_prof = TProfile("pe_prof", "Profile of Uncoated/Coated Ratio",
profile_bins, dist_to_anode_low, dist_to_anode_up,
pset.pe_low, pset.pe_up, profile_option)
pe_prof.GetXaxis().SetTitle("distance from anode (cm)")
pe_prof.GetYaxis().SetTitle("ratio_{uncoated/coated}")
pe_h1 = TH1D("pe_h1", "",
profile_bins, dist_to_anode_low, dist_to_anode_up)
pe_h1.GetXaxis().SetTitle("distance from anode (cm)")
pe_h1.GetYaxis().SetTitle("ratio_{uncoated/coated}")
match_score_scatter = TH2D("match_score_scatter", "Scatter plot of match scores",
dist_to_anode_bins, dist_to_anode_low, dist_to_anode_up,
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up*(3./5.))
match_score_scatter.GetXaxis().SetTitle("distance from anode (cm)")
match_score_scatter.GetYaxis().SetTitle("match score (arbitrary)")
match_score_hist = TH1D("match_score", "Match Score",
pset.score_hist_bins, pset.score_hist_low, pset.score_hist_up)
match_score_hist.GetXaxis().SetTitle("match score (arbitrary)")
for e in nuslice_tree:
slice = e.charge_x
dy_hist.Fill(slice, e.flash_y - e.charge_y)
dy_prof.Fill(slice, e.flash_y - e.charge_y)
dz_hist.Fill(slice, e.flash_z - e.charge_z)
dz_prof.Fill(slice, e.flash_z - e.charge_z)
rr_hist.Fill(slice, e.flash_r)
rr_prof.Fill(slice, e.flash_r)
if detector == "sbnd":
pe_hist.Fill(slice, e.flash_ratio)
pe_prof.Fill(slice, e.flash_ratio)
# fill histograms for match score calculation from profile histograms
for ib in list(range(0, profile_bins)):
ibp = ib + 1
dy_h1.SetBinContent(ibp, dy_prof.GetBinContent(ibp))
dy_h1.SetBinError(ibp, dy_prof.GetBinError(ibp))
dy_means[int(ib)] = dy_prof.GetBinContent(ibp)
dy_spreads[int(ib)] = dy_prof.GetBinError(ibp)
dz_h1.SetBinContent(ibp, dz_prof.GetBinContent(ibp))
dz_h1.SetBinError(ibp, dz_prof.GetBinError(ibp))
dz_means[int(ib)] = dz_prof.GetBinContent(ibp)
dz_spreads[int(ib)] = dz_prof.GetBinError(ibp)
rr_h1.SetBinContent(ibp, rr_prof.GetBinContent(ibp))
rr_h1.SetBinError(ibp, rr_prof.GetBinError(ibp))
rr_means[int(ib)] = rr_prof.GetBinContent(ibp)
rr_spreads[int(ib)] = rr_prof.GetBinError(ibp)
if detector == "sbnd":
pe_h1.SetBinContent(ibp, pe_prof.GetBinContent(ibp))
pe_h1.SetBinError(ibp, pe_prof.GetBinError(ibp))
pe_means[int(ib)] = pe_prof.GetBinContent(ibp)
pe_spreads[int(ib)] = pe_prof.GetBinError(ibp)
for e in nuslice_tree:
slice = e.charge_x
# calculate match score
isl = int(slice/bin_width)
score = 0.
if dy_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dy_spreads[isl]: {dy_spreads[isl]} ")
dy_spreads[isl] = dy_spreads[isl+1]
if dz_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. dz_spreads[isl]: {dz_spreads[isl]} ")
dz_spreads[isl] = dz_spreads[isl+1]
if rr_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. rr_spreads[isl]: {rr_spreads[isl]} ")
rr_spreads[isl] = rr_spreads[isl+1]
if detector == "sbnd" and pe_spreads[isl] <= 1.e-8:
print("Warning zero spread.\n",
f"slice: {slice}. isl: {isl}. pe_spreads[isl]: {pe_spreads[isl]} ")
pe_spreads[isl] = pe_spreads[isl+1]
score += abs(abs(e.flash_y-e.charge_y) - dy_means[isl])/dy_spreads[isl]
score += abs(abs(e.flash_z-e.charge_z) - dz_means[isl])/dz_spreads[isl]
score += abs(e.flash_r-rr_means[isl])/rr_spreads[isl]
if detector == "sbnd" and pset.UseUncoatedPMT:
score += abs(e.flash_ratio-pe_means[isl])/pe_spreads[isl]
match_score_scatter.Fill(slice, score)
match_score_hist.Fill(score)
metrics_filename = 'fm_metrics_' + detector + '.root'
hfile = gROOT.FindObject(metrics_filename)
if hfile:
hfile.Close()
hfile = TFile(metrics_filename, 'RECREATE',
'Simple flash matching metrics for ' + detector.upper())
dy_hist.Write()
dy_prof.Write()
dy_h1.Write()
dz_hist.Write()
dz_prof.Write()
dz_h1.Write()
rr_hist.Write()
rr_prof.Write()
rr_h1.Write()
if detector == "sbnd":
pe_hist.Write()
pe_prof.Write()
pe_h1.Write()
match_score_scatter.Write()
match_score_hist.Write()
hfile.Close()
canv = TCanvas("canv")
dy_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dy_means),
array('f', xerrs), array('f', dy_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dy.pdf")
canv.Update()
dz_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', dz_means),
array('f', xerrs), array('f', dz_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("dz.pdf")
canv.Update()
rr_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', rr_means),
array('f', xerrs), array('f', rr_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("rr.pdf")
canv.Update()
if detector == "sbnd":
pe_hist.Draw()
crosses = TGraphErrors(n_bins,
array('f', xvals), array('f', pe_means),
array('f', xerrs), array('f', pe_spreads))
crosses.SetLineColor(9)
crosses.SetLineWidth(3)
crosses.Draw("Psame")
canv.Print("pe.pdf")
canv.Update()
match_score_scatter.Draw()
canv.Print("match_score_scatter.pdf")
canv.Update()
match_score_hist.Draw()
canv.Print("match_score.pdf")
canv.Update()
sleep(20)
def main():
parser = ArgumentParser(description='Scan over ROOT files to find the '+ \
'lumisections belonging to a specific run.')
parser.add_argument('-b', action='store_true', help='enable batch mode')
parser.add_argument('--dataset', required=True, choices=['PromptReco2015', \
'ReRecoOct2015', 'ReRecoDec2015', 'PromptReco2016', \
'2015ReRecoJan2017', '2016ReRecoJan2017'], \
help='specify data-taking period and reconstruction')
parser.add_argument('-n', nargs=1, default=1, type=int, help='Specify '+ \
'the number of ZeroBias datasets to be included')
parser.add_argument('-run', nargs=1, required=True, type=int, \
help='Specify the run number for the selection of '+ \
'the lumisections')
parser.add_argument('-range', nargs=2, type=int, help='Specify a range '+ \
'in lumisections for the histogram')
parser.add_argument('-X', dest='coords', action='append_const', \
const='vtx_x', help='look for vtx_x')
parser.add_argument('-Y', dest='coords', action='append_const', \
const='vtx_y', help='look for vtx_y')
parser.add_argument('-noscan', action='store_const', const=True, \
default=False, help='don\'t repeat the scan of the '+ \
'ROOT files, just do the plotting')
parser.add_argument('-title', nargs=1, help='Specify a title for the '+ \
'histogram')
args = parser.parse_args()
from importlib import import_module
from lsctools import config
from lsctools.config import options as O, EOSPATH as eos
from lsctools.tools import openRootFileU, closeRootFile, writeFiles, \
plotName, plotTitle, loadFiles, plotPath, \
drawSignature
from lsctools.prepare import loopOverRootFiles
from ROOT import TChain, TObject, TProfile, TCanvas, gStyle, gPad
getattr(config, 'PCC' + args.dataset)()
O['fulltrees'] = O['fulltrees'][:args.n]
run = args.run[0]
files = []
if args.noscan:
files = loadFiles('fulltrees_' + str(run))
else:
def action(tree, filename):
condition = 'run == ' + str(run)
if tree.GetEntries(condition) > 0:
files.append(filename)
print '<<< Found file:', filename
loopOverRootFiles(action, 'fulltrees')
writeFiles(files, 'fulltrees_' + str(run))
chain = TChain(O['treename']['fulltrees'])
for filename in files:
chain.Add(eos + filename)
name = 'vtxPos_perLS'
title1 = 'run' + str(run) + '_perLS'
title2 = 'Run ' + str(run)
if (args.title):
title2 = args.title[0] + ' (' + title2 + ')'
f = openRootFileU(name)
if args.range:
mini = args.range[0]
maxi = args.range[1]
title1 += '_from' + str(mini) + 'to' + str(maxi)
else:
print '<<< Get minimum lumisection'
mini = int(chain.GetMinimum('LS'))
print '<<< Get maximum lumisection'
maxi = int(chain.GetMaximum('LS'))
for coord in args.coords:
print '<<< Analyze coordinate', coord
histname = plotName(coord + '_' + title1, timestamp=False)
histtitl = plotTitle('- ' + title2)
histfile = plotName(coord + '_' + title1)
histpath = plotPath(coord + '_' + title1)
hist = TProfile(histname, histtitl, maxi - mini + 1, mini - 0.5,
maxi + 0.5)
chain.Draw(coord + '*1e4:LS>>' + histname, 'run == ' + str(run),
'goff')
hist.Write('', TObject.kOverwrite)
print '<<< Save plot:', histpath
canvas = TCanvas()
gStyle.SetOptStat(0)
hist.Draw()
hist.GetXaxis().SetTitle('LS')
hist.GetYaxis().SetTitle(coord + ' [#mum]')
hist.GetYaxis().SetTitleOffset(1.2)
for axis in [hist.GetXaxis(), hist.GetYaxis()]:
axis.SetTitleFont(133)
axis.SetTitleSize(16)
axis.SetLabelFont(133)
axis.SetLabelSize(12)
axis.CenterTitle()
drawSignature(histfile)
gPad.Modified()
gPad.Update()
canvas.Print(histpath)
canvas.Close()
closeRootFile(f, name)
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
scales = [100, 1, 1, 1, 1]
color = [4, 2, 3, 6, 7, 8]
filename = 'ecal_ratio_multi.pdf'
#Get Actual Data
#d=h5py.File("/eos/project/d/dshep/LCD/V1/EleEscan/EleEscan_1_1.h5")
d = h5py.File("/afs/cern.ch/work/g/gkhattak/public/Ele_v1_1_2.h5", 'r')
X = np.array(d.get('ECAL')[0:num_events], np.float64)
Y = np.array(d.get('target')[0:num_events][:, 1], np.float64)
X[X < 1e-6] = 0
Y = Y
Data = np.sum(X, axis=(1, 2, 3))
for j in np.arange(num_events):
Eprof.Fill(Y[j], Data[j] / Y[j])
Eprof.SetTitle("Ratio of Ecal and Ep")
Eprof.GetXaxis().SetTitle("Ep")
Eprof.GetYaxis().SetTitle("Ecal/Ep")
Eprof.Draw()
Eprof.Fit('pol6')
c.Update()
Eprof.GetFunction("pol6").SetLineColor(color[0])
c.Update()
Eprof.SetStats(0)
Eprof.GetYaxis().SetRangeUser(0, 0.04)
Eprof.SetLineColor(color[0])
legend = TLegend(0.7, 0.7, 0.9, 0.9)
legend.AddEntry(Eprof, "Data", "l")
Gprof = []
for i, gweight in enumerate(gweights):
Gprof.append(TProfile("Gprof" + str(i), "Gprof" + str(i), 100, 0, 500))
#Gprof[i].SetStates(0)