pvalues = array('f', [])
n_iterations = array('f', [])
y_error = array('f', [])
x_error = array('f', [])
c_refoldeds = []
refolded_hists = []
c_sanitys = []
sanity_hists = []
for i in range(1, 20, 1):
print '--------- i = ', i
unfoldreco = RooUnfoldBayes(response, reco, i)
reco_unfolded = unfoldreco.Vreco()
reco_asvector = unfoldreco.Vmeasured()
m = response.Mresponse()
print 'response : ', m.GetNrows(), ' x ', m.GetNcols()
refolded = ROOT.TVectorD(reco_unfolded)
refolded *= m #TMatrixD(m,TMatrixD.kMult,reco_unfolded)
print 'refolded : ', refolded.GetNrows()
refolded_hist = ROOT.TH1D("refolded_hist_" + str(i),
"refolded_hist_" + str(i),
reco.GetNbinsX() * reco.GetNbinsY(), 0,
reco.GetNbinsX() * reco.GetNbinsY())
sanity_hist = ROOT.TH1D("sanity_hist_" + str(i), "sanity_hist_" + str(i),
reco.GetNbinsX() * reco.GetNbinsY(), 0,
reco.GetNbinsX() * reco.GetNbinsY())
for ibin in xrange(0, refolded.GetNrows()):
refolded_hist.SetBinContent(ibin + 1, refolded[ibin])
for ibin in xrange(0, reco_asvector.GetNrows()):
if rel_err_reco > 0:
err_ratio = rel_err_unfold / rel_err_reco
if rel_err_recoUp > 0:
err_ratioUp = rel_err_unfoldUp / rel_err_recoUp
if rel_err_recoDn > 0:
err_ratioDn = rel_err_unfoldDn / rel_err_recoDn
hErrorRatio[i_iter-1].SetBinContent(ibin+1, err_ratio)
hErrorRatioUp[i_iter-1].SetBinContent(ibin+1, err_ratioUp)
hErrorRatioDn[i_iter-1].SetBinContent(ibin+1, err_ratioDn)
# unfolded & measured (vectors)
vReco = unfold.Vreco()
vMeas = unfold.Vmeasured()
vRecoUp = unfoldUp.Vreco()
vRecoDn = unfoldDn.Vreco()
vMeasUp = unfoldUp.Vmeasured()
vMeasDn = unfoldDn.Vmeasured()
# response matrix as matrix instead of 2D histogram: (row,column)=(measured,truth)
mResponse = response.Mresponse()
# refolded (vector)
vRefold = TVectorD(vReco)
vRefold *= mResponse
vRefoldUp = TVectorD(vRecoUp)
vRefoldDn = TVectorD(vRecoDn)
