def compare(fn1, fn2, outbase):
f1, f2 = fs = ROOT.TFile.Open(fn1), ROOT.TFile.Open(fn2)
samples_1 = dict((name, isample) for isample, name in isamplename_iterator(f1))
samples_2 = dict((name, isample) for isample, name in isamplename_iterator(f2))
names_1 = set(samples_1)
names_2 = set(samples_2)
names = sorted(names_1 & names_2)
nnames = len(names)
if not names:
print 'no samples in common between %s and %s' % (fn1, fn2)
return
else:
print '%i samples in common' % nnames
if names_1 != names_2:
print '# samples only in %s: %i' % (fn1, len(names_1 - names_2))
#pprint(sorted(names_1 - names_2))
print '# samples only in %s: %i' % (fn2, len(names_2 - names_1))
#pprint(sorted(names_2 - names_1))
h_fcns = []
def make_h_fcn(nm, fcn):
h = ROOT.TH1D(nm, '%s - %s' % (fn1, fn2), nnames, 0, nnames)
h_fcns.append((h, fcn))
return h
h_eff1v = make_h_fcn('eff1v', eff1v)
h_eff2v = make_h_fcn('eff2v', eff2v)
h_dbvmean = make_h_fcn('dbvmean', lambda f,isample: (h_dbv(f,isample).GetMean(), h_dbv(f,isample).GetMeanError()))
h_dbvrms = make_h_fcn('dbvrms', lambda f,isample: (h_dbv(f,isample).GetRMS(), h_dbv(f,isample).GetRMSError() ))
h_dvvmean = make_h_fcn('dvvmean', lambda f,isample: (h_dvv(f,isample).GetMean(), h_dvv(f,isample).GetMeanError()))
h_dvvrms = make_h_fcn('dvvrms', lambda f,isample: (h_dvv(f,isample).GetRMS(), h_dvv(f,isample).GetRMSError() ))
all_stat_same, all_same = True, True
for iname, name in enumerate(names):
isamples = samples_1[name], samples_2[name]
for h, fcn in h_fcns:
(v1,e1), (v2, e2) = [fcn(f, isample) for f,isample in zip(fs, isamples)]
h.GetXaxis().SetBinLabel(iname+1, name)
d = v1 - v2
e = (e1**2 + e2**2)**0.5
h.SetBinContent(iname+1, d)
h.SetBinError (iname+1, e)
if abs(d) > 1e-5:
all_same = False
if abs(d) > e:
all_stat_same = False
c = ROOT.TCanvas('c', '', 1000, 800)
line = ROOT.TLine(0, 0, nnames, 0)
line.SetLineStyle(2)
line.SetLineWidth(2)
for i,(h,_) in enumerate(h_fcns):
h.SetStats(0)
h.SetLineWidth(2)
h.Draw('e')
line.Draw()
for ext in 'root', 'png':
c.SaveAs(outbase + '/%i_%s.%s' % (i, h.GetName(), ext))
print 'all same? %r statistically? %r' % (all_same, all_stat_same)
print '\t', i
print
print 'samples with gen eff NOT within %s%% of reco eff:' % gen_rec_cut
for i in not_matched:
print '\t', i
c = ROOT.TCanvas()
c.SetTickx()
c.SetTicky()
c.SetRightMargin(0.3)
g_all = ROOT.TGraphErrors(len(x), to_array(x), to_array(y), to_array(ex), to_array(ey))
g_all.SetTitle(';reconstructed-level efficiency;generator-level efficiency')
g_all.GetXaxis().SetLimits(0,1)
g_all.GetHistogram().GetYaxis().SetRangeUser(0,1)
g_all.Draw('AP')
for g in gs:
g.Draw('P')
l1.SetFillColor(0)
l1.Draw()
l2.SetFillColor(0)
l2.Draw()
line0 = ROOT.TLine(0,0,1,1)
line0.SetLineStyle(7)
line1 = ROOT.TLine(0,0,1,1-0.01*gen_rec_cut)
line2 = ROOT.TLine(0,0,1-0.01*gen_rec_cut,1)
line0.Draw()
line1.Draw()
line2.Draw()
if save_plot:
c.SaveAs(os.path.join(plot_path, 'gen_vs_reco_eff_%s_divide_%s%s%s.pdf' % (gen_num, gen_den, '' if ctau == '' else '_%s'%ctau, match)))