def main():
pass
ybys_bins = ['yb0ys0', 'yb0ys1', 'yb0ys2', 'yb1ys0', 'yb1ys1', 'yb2ys0']
def get_coltype(s):
if s in ['yb_low', 'yb_high', 'ys_low', 'ys_high', 'pt_low', 'pt_high', 'NPCorr', 'nbin']:
return 'Bin'
elif s in ['sigma']:
return 'Sigma'
else:
return 'Error'
for i, ybys_bin in enumerate(ybys_bins):
# unf_data_root = get_root_object('~/dust/dijetana/ana/CMSSW_7_2_3/unf_DATA_NLO.root?{0}/h_ptavg'.format(ybys_bin))
unf_data_root = get_root_object('~/dust/dijetana/ana/CMSSW_7_2_3/unf_DATA_NLO_WITHFAKES.root?{0}/h_ptavg'.format(ybys_bin))
unf_data_root.Scale(1.0, 'width')
unf_data = R2npObject1D(unf_data_root)
# np_factor = get_np_object('~/dust/dijetana/plot/plots/np_factors_calc_{0}.root?res_np_factor'.format(ybys_bin))
# hack for full range
np_factor = get_np_object('~/dust/dijetana/plot/plots/np_factors_nlo_final_{0}.root?res_np_factor'.format(ybys_bin))
np_factor.x = np.concatenate((unf_data.x[0:9],np_factor.x))
np_factor.x = np.concatenate((np_factor.x,unf_data.x[np_factor.x.size:]))
np_factor.y = np.concatenate((np.zeros((9,)),np_factor.y))
np_factor.y = np.concatenate((np_factor.y,np.zeros((unf_data.y.size - np_factor.y.size,))))
np_factor.yerrl = np.concatenate((np.zeros((9,)),np_factor.yerrl))
np_factor.yerrl = np.concatenate((np_factor.yerrl,np.zeros((unf_data.y.size - np_factor.yerrl.size,))))
np_factor.yerru = np.concatenate((np.zeros((9,)),np_factor.yerru))
np_factor.yerru = np.concatenate((np_factor.yerru,np.zeros((unf_data.y.size - np_factor.yerru.size,))))
# hack for full range ewk
ewk_factor = get_np_object('~/dust/dijetana/ewk/ewk_dijet.root?{0}/ewk_corr'.format(ybys_bin))
ewk_factor.x = np.concatenate((unf_data.x[0:9],ewk_factor.x))
ewk_factor.x = np.concatenate((ewk_factor.x,unf_data.x[ewk_factor.x.size:]))
ewk_factor.y = np.concatenate((np.zeros((9,)),ewk_factor.y))
ewk_factor.y = np.concatenate((ewk_factor.y,np.zeros((unf_data.y.size - ewk_factor.y.size,))))
ewk_factor.yerrl = np.concatenate((np.zeros((9,)),ewk_factor.yerrl))
ewk_factor.yerrl = np.concatenate((ewk_factor.yerrl,np.zeros((unf_data.y.size - ewk_factor.yerrl.size,))))
ewk_factor.yerru = np.concatenate((np.zeros((9,)),ewk_factor.yerru))
ewk_factor.yerru = np.concatenate((ewk_factor.yerru,np.zeros((unf_data.y.size - ewk_factor.yerru.size,))))
# just multiply with np _factors for now
np_factor.y = np_factor.y * ewk_factor.y
jer_data = get_np_object('~/dust/dijetana/plot/plots/jer_uncert_{0}.root?jer_uncert'.format(ybys_bin))
data = collections.OrderedDict()
data['nbin'] = np.array([i] * len(unf_data.xl))
data['yb_low'] = np.array([float(ybys_bin[2])] * len(unf_data.xl))
data['yb_high'] = np.array([float(ybys_bin[2]) + 1.0] * len(unf_data.xl))
data['ys_low'] = np.array([float(ybys_bin[5])] * len(unf_data.xl))
data['ys_high'] = np.array([float(ybys_bin[5]) + 1.0] * len(unf_data.xl))
data['pt_low'] = unf_data.xl
data['pt_high'] = unf_data.xu
data['sigma'] = unf_data.y
data['NPCorr'] = np_factor.y
data['nperr'] = 0.5 * (np_factor.yu - np_factor.yl)/np_factor.y * 100.
data['uncor'] = np.array([1.0] * len(unf_data.xl))
data['stat'] = unf_data.yerr/unf_data.y * 100.
data['jererr'] = (jer_data.yu - jer_data.yl)/jer_data.y * 100.
# lumi
lumi_unc = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/lumi_unc_relative.root?{0}/lumi_unc_up'.format(ybys_bin))
data['lumi'] = (lumi_unc.y - 1.) * 100.
# non-gaussian tails
# unf_smeared = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/SMEARED_NEW2_QCDMGP6.root?{0}/h_ptavg'.format(ybys_bin))
# unf_scaled = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/SMEARED_OLD_QCDMGP6.root?{0}/h_ptavg'.format(ybys_bin))
# data['nongaussiantails'] = np.abs(unf_smeared.y - unf_scaled.y)/unf_smeared.y / 2.0 * 100.
data['nongaussiantails'] = nongaussian_unc[ybys_bin]
# data['nongaussiantails'] = smooth(data['nongaussiantails'], 3)
# np.set_printoptions(precision=2)
# np.set_printoptions(suppress=True)
# print ybys_bin, np.array_repr(np.nan_to_num(data['nongaussiantails']))
jec_sources = [
'AbsoluteScale','AbsoluteStat','AbsoluteMPFBias',
'Fragmentation',
'SinglePionECAL',
'SinglePionHCAL',
'FlavorQCD',
'RelativeJEREC1','RelativeJEREC2','RelativeJERHF',
'RelativePtBB','RelativePtEC1','RelativePtEC2','RelativePtHF',
'RelativeFSR',
'RelativeStatEC2', 'RelativeStatHF', 'RelativeStatFSR',
'PileUpDataMC',
'PileUpPtRef',
'PileUpPtBB','PileUpPtEC1','PileUpPtEC2','PileUpPtHF',
]
jec_default = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}/h_genptavg'.format(ybys_bin))
for jec_source in jec_sources:
jec_up = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_{1}_up/h_genptavg'.format(ybys_bin, jec_source))
jec_dn = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_{1}_dn/h_genptavg'.format(ybys_bin, jec_source))
# data['{0}_up'.format(jec_source)] = jec_up.y/jec_default.y -1.
# data['{0}_dn'.format(jec_source)] = 1. - jec_dn.y/jec_default.y
# data['{0}'.format(jec_source)] = np.abs((jec_up.y - jec_dn.y)/2.0)/jec_default.y * 100.
# data['{0}'.format(jec_source)] = np.maximum(np.abs(1. - jec_up.y/jec_default.y), np.abs(1. - jec_dn.y/jec_default.y)) * 100.
data['{0}'.format(jec_source)] = 0.5 *np.abs((jec_up.y -jec_dn.y))/jec_default.y * 100.
data['{0}'.format(jec_source)][np.abs(data['{0}'.format(jec_source)]) < 0.05 ] = 0.0
# data['{0}'.format(jec_source)] = np.abs(1 - jec_up.y/jec_default.y) * 100.
for k,v in data.iteritems():
v[np.isnan(v)] = 0.
v[np.isinf(v)] = 0.
if 'yb0ys0' == ybys_bin:
labels = data.keys()
print ', '.join(['\'{0}\''.format(label) for label in labels])
print ', '.join(['\'{0}\''.format(get_coltype(label)) for label in labels])
print 'NColumn', len(labels)
print_data(data, labels=labels, ybys_bin=ybys_bin)
def main():
pass
ybys_bins = ['yb0ys0', 'yb0ys1', 'yb0ys2', 'yb1ys0', 'yb1ys1', 'yb2ys0']
def get_coltype(s):
if s in ['yb_low', 'yb_high', 'ys_low', 'ys_high', 'pt_low', 'pt_high', 'NPCorr', 'nbin']:
return 'Bin'
elif s in ['sigma']:
return 'Sigma'
else:
return 'Error'
f = ROOT.TFile('data_summary.root', 'RECREATE')
for i, ybys_bin in enumerate(ybys_bins):
# unf_data_root = get_root_object('~/dust/dijetana/ana/CMSSW_7_2_3/unf_DATA_NLO.root?{0}/h_ptavg'.format(ybys_bin))
unf_data_root = get_root_object('~/dust/dijetana/ana/CMSSW_7_2_3/unf_DATA_NLO_WITHFAKES.root?{0}/h_ptavg'.format(ybys_bin))
unf_data_root.Scale(1.0, 'width')
unf_data = R2npObject1D(unf_data_root)
np_factor = get_np_object('~/dust/dijetana/plot/plots/np_factors_calc_{0}.root?res_np_factor'.format(ybys_bin))
jer_data = get_np_object('~/dust/dijetana/plot/plots/jer_uncert_{0}.root?jer_uncert'.format(ybys_bin))
data = collections.OrderedDict()
data['nbin'] = np.array([i] * len(unf_data.xl))
data['yb_low'] = np.array([float(ybys_bin[2])] * len(unf_data.xl))
data['yb_high'] = np.array([float(ybys_bin[2]) + 1.0] * len(unf_data.xl))
data['ys_low'] = np.array([float(ybys_bin[5])] * len(unf_data.xl))
data['ys_high'] = np.array([float(ybys_bin[5]) + 1.0] * len(unf_data.xl))
data['pt_low'] = unf_data.xl
data['pt_high'] = unf_data.xu
data['sigma'] = unf_data.y
data['uncor'] = np.array([1.0] * len(unf_data.xl))
data['stat'] = unf_data.yerr/unf_data.y * 100.
data['jer_up'] = jer_data.yerru/jer_data.y * 100.
data['jer_dn'] = jer_data.yerrl/jer_data.y * 100.
# lumi
lumi_unc = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/lumi_unc_relative.root?{0}/lumi_unc_up'.format(ybys_bin))
data['lumi'] = (lumi_unc.y - 1.) * 100.
# non-gaussian tails
# unf_smeared = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/SMEARED_NEW2_QCDMGP6.root?{0}/h_ptavg'.format(ybys_bin))
# unf_scaled = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/SMEARED_OLD_QCDMGP6.root?{0}/h_ptavg'.format(ybys_bin))
# data['nongaussiantails'] = np.abs(unf_smeared.y - unf_scaled.y)/unf_smeared.y / 2.0 * 100.
data['nongaussiantails'] = nongaussian_unc[ybys_bin]
# data['nongaussiantails'] = smooth(data['nongaussiantails'], 3)
# np.set_printoptions(precision=2)
# np.set_printoptions(suppress=True)
# print ybys_bin, np.array_repr(np.nan_to_num(data['nongaussiantails']))
jec_sources = [
'AbsoluteScale','AbsoluteStat','AbsoluteMPFBias',
'Fragmentation',
'SinglePionECAL',
'SinglePionHCAL',
'FlavorQCD',
'RelativeJEREC1','RelativeJEREC2','RelativeJERHF',
'RelativePtBB','RelativePtEC1','RelativePtEC2','RelativePtHF',
'RelativeFSR',
'RelativeStatEC2', 'RelativeStatHF', 'RelativeStatFSR',
'PileUpDataMC',
'PileUpPtRef',
'PileUpPtBB','PileUpPtEC1','PileUpPtEC2','PileUpPtHF',
]
jec_default = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}/h_genptavg'.format(ybys_bin))
for jec_source in jec_sources:
jec_up = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_{1}_up/h_genptavg'.format(ybys_bin, jec_source))
jec_dn = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_{1}_dn/h_genptavg'.format(ybys_bin, jec_source))
# data['{0}_up'.format(jec_source)] = jec_up.y/jec_default.y -1.
# data['{0}_dn'.format(jec_source)] = 1. - jec_dn.y/jec_default.y
# data['{0}'.format(jec_source)] = np.abs((jec_up.y - jec_dn.y)/2.0)/jec_default.y * 100.
data['{0}_up'.format(jec_source)] = ((np.maximum(np.maximum(jec_up.y - jec_default.y, jec_dn.y - jec_default.y), 0))/ jec_default.y) * 100.
data['{0}_dn'.format(jec_source)] = ((np.maximum(np.maximum(jec_default.y - jec_up.y, jec_default.y - jec_dn.y), 0))/ jec_default.y) * 100.
# data['{0}_sym'.format(jec_source)] = np.maximum(np.abs(1. - jec_up.y/jec_default.y), np.abs(1. - jec_dn.y/jec_default.y)) * 100.
data['{0}_sym'.format(jec_source)] = 0.5 *( (jec_up.y -jec_dn.y)/jec_default.y) * 100.
# data['{0}'.format(jec_source)] = np.abs(1 - jec_up.y/jec_default.y) * 100.
jec_total_up = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_Total_up/h_genptavg'.format(ybys_bin))
jec_total_dn = get_np_object('~/dust/dijetana/ana/CMSSW_7_2_3/JEC_GEN.root?{0}_Total_dn/h_genptavg'.format(ybys_bin))
data['Total_up'.format(jec_source)] = ((np.maximum(np.maximum(jec_total_up.y - jec_default.y, jec_total_dn.y - jec_default.y), 0))/ jec_default.y) * 100.
data['Total_dn'.format(jec_source)] = ((np.maximum(np.maximum(jec_default.y - jec_total_up.y, jec_default.y - jec_total_dn.y), 0))/ jec_default.y) * 100.
for k,v in data.iteritems():
v[np.isnan(v)] = 0.
v[np.isinf(v)] = 0.
#sums
stat_error_u = data['sigma'] * (data['stat']/100.)
stat_error_l = data['sigma'] * (data['stat']/100.)
syst_error_u = np.zeros((len(data['sigma'])))
syst_error_l = np.zeros((len(data['sigma'])))
jec_error_u = np.zeros((len(data['sigma'])))
jec_error_l = np.zeros((len(data['sigma'])))
jec_total_error_u = (data['Total_up']/100.) * data['sigma']
jec_total_error_l = (data['Total_dn']/100.) * data['sigma']
jer_error_u = np.zeros((len(data['sigma'])))
jer_error_l = np.zeros((len(data['sigma'])))
nongaussian_error_u = np.zeros((len(data['sigma'])))
nongaussian_error_l = np.zeros((len(data['sigma'])))
unc_error_u = np.zeros((len(data['sigma'])))
unc_error_l = np.zeros((len(data['sigma'])))
lumi_error_u = np.zeros((len(data['sigma'])))
lumi_error_l = np.zeros((len(data['sigma'])))
lumi_error_u = data['lumi']/100. * data['sigma']
lumi_error_l = data['lumi']/100. * data['sigma']
unc_error_u = data['uncor']/100. * data['sigma']
unc_error_l = data['uncor']/100. * data['sigma']
jer_error_u = data['jer_up']/100. * data['sigma']
jer_error_l = data['jer_dn']/100. * data['sigma']
nongaussian_error_u = data['nongaussiantails']/100. * data['sigma']
nongaussian_error_l = data['nongaussiantails']/100. * data['sigma']
syst_error_u += (data['nongaussiantails']/100. * data['sigma'])**2
syst_error_l += (data['nongaussiantails']/100. * data['sigma'])**2
syst_error_u += (data['uncor']/100. * data['sigma'])**2
syst_error_l += (data['uncor']/100. * data['sigma'])**2
syst_error_u += (data['lumi']/100. * data['sigma'])**2
syst_error_l += (data['lumi']/100. * data['sigma'])**2
for jec_source in jec_sources:
syst_error_u += (data['{0}_up'.format(jec_source)]/100. * data['sigma'])**2
syst_error_l += (data['{0}_dn'.format(jec_source)]/100. * data['sigma'])**2
jec_error_u += (data['{0}_up'.format(jec_source)]/100. * data['sigma'])**2
jec_error_l += (data['{0}_dn'.format(jec_source)]/100. * data['sigma'])**2
jec_error_u = np.sqrt(jec_error_u)
jec_error_l = np.sqrt(jec_error_l)
# jec_error_u = jec_total_error_u
# jec_error_l = jec_total_error_l
syst_error_u = np.sqrt(syst_error_u)
syst_error_l = np.sqrt(syst_error_l)
f.cd()
f.mkdir(ybys_bin)
f.Cd("/" + ybys_bin)
data_tot = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_stat = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_syst = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_lumi = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_unc = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_jec = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_jer = ROOT.TGraphAsymmErrors(len(data['sigma']))
data_nongaussian = ROOT.TGraphAsymmErrors(len(data['sigma']))
for i in range(len(data['sigma'])):
data_tot.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_tot.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], np.sqrt(stat_error_l[i]**2+syst_error_l[i]**2), np.sqrt(stat_error_u[i]**2+syst_error_u[i]**2))
data_stat.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_stat.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], stat_error_l[i], stat_error_u[i])
data_syst.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_syst.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], syst_error_l[i], syst_error_u[i])
data_lumi.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_lumi.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], lumi_error_l[i], lumi_error_u[i])
data_unc.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_unc.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], unc_error_l[i], unc_error_u[i])
data_jec.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_jec.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], jec_error_l[i], jec_error_u[i])
data_jer.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_jer.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], jer_error_l[i], jer_error_u[i])
data_nongaussian.SetPoint(i, unf_data.x[i], data['sigma'][i])
data_nongaussian.SetPointError(i, unf_data.xerrl[i], unf_data.xerru[i], nongaussian_error_l[i], nongaussian_error_u[i])
data_tot.Write('data_tot')
data_stat.Write('data_stat')
data_syst.Write('data_syst')
data_lumi.Write('data_lumi')
data_unc.Write('data_unc')
data_jec.Write('data_jec')
data_jer.Write('data_jer')
data_nongaussian.Write('data_nongaussian')
