]
for fit_name, nuis_x in [('b', nuis_b), ('s', nuis_s)]:
if nuis_x == None:
row += [" n/a "]
else:
nuisIsSymm = abs(
abs(nuis_x.getErrorLo()) -
abs(nuis_x.getErrorHi())) < 0.01 or nuis_x.getErrorLo() == 0
if nuisIsSymm: nuis_x.setError(nuis_x.getErrorHi())
nuiselo = abs(nuis_x.getErrorLo()
) if nuis_x.getErrorLo() > 0 else nuis_x.getError()
nuisehi = nuis_x.getErrorHi()
if options.pullDef and nuis_p != None:
nx, ned, neu = CP.returnPullAsym(options.pullDef,
nuis_x.getVal(),
mean_p, nuisehi, sigma_pu,
abs(nuiselo), abs(sigma_pd))
else:
nx, ned, neu = nuis_x.getVal(), nuiselo, nuisehi
if nuisIsSymm:
row += ["%+.2f +/- %.2f" % (nx, (abs(ned) + abs(neu)) / 2)]
else:
row += ["%+.2f +%.2f %.2f" % (nx, neu, ned)]
if nuis_p != None:
if options.plotfile:
if fit_name == 'b':
nuis_p_i += 1
if options.pullDef and nuis_p != None:
#nx,ned,neu = CP.returnPullAsym(options.pullDef,nuis_x.getVal(),mean_p,nuis_x.getErrorHi(),sigma_pu,abs(nuis_x.getErrorLo()),abs(sigma_pd))
text_entries = []
redo_boxes = []
for p in xrange(n_params):
i = n_params - (p + 1)
pre = pdata[p]['prefit']
fit = pdata[p]['fit']
tp = pdata[p]['type']
seen_types.add(tp)
if pdata[p]['type'] != 'Unconstrained':
pre_err_hi = (pre[2] - pre[1])
pre_err_lo = (pre[1] - pre[0])
if externalPullDef:
fit_err_hi = (fit[2] - fit[1])
fit_err_lo = (fit[1] - fit[0])
pull, pull_hi, pull_lo = CP.returnPullAsym(args.pullDef,fit[1],pre[1],fit_err_hi,pre_err_hi,fit_err_lo,pre_err_lo)
else:
pull = fit[1] - pre[1]
pull = (pull/pre_err_hi) if pull >= 0 else (pull/pre_err_lo)
pull_hi = fit[2] - pre[1]
pull_hi = (pull_hi/pre_err_hi) if pull_hi >= 0 else (pull_hi/pre_err_lo)
pull_hi = pull_hi - pull
pull_lo = fit[0] - pre[1]
pull_lo = (pull_lo/pre_err_hi) if pull_lo >= 0 else (pull_lo/pre_err_lo)
pull_lo = pull - pull_lo
g_pulls.SetPoint(i, pull, float(i) + 0.5)
g_pulls.SetPointError(
i, pull_lo, pull_hi, 0., 0.)
else:
# Hide this point
else: row += [ "%.6f +%.6f %.6f" % (nuis_p.getVal(), nuis_p.getErrorHi(), nuis_p.getErrorLo()) ]
for fit_name, nuis_x in [('b', nuis_b), ('s',nuis_s)]:
if nuis_x == None:
row += [ " n/a " ]
else:
nuisIsSymm = abs(abs(nuis_x.getErrorLo())-abs(nuis_x.getErrorHi()))<0.01 or nuis_x.getErrorLo() == 0
if nuisIsSymm : row += [ "%+.2f +/- %.2f" % (nuis_x.getVal(), nuis_x.getError()) ]
else: row += [ "%+.2f +%.2f %.2f" % (nuis_x.getVal(), nuis_x.getErrorHi(), nuis_x.getErrorLo()) ]
if nuis_x.getErrorLo()==0 : nuis_x.setErrorLo(nuis_x.getErrorHi())
if nuis_p != None:
if options.plotfile:
if fit_name=='b':
nuis_p_i+=1
if options.pullDef and nuis_p!=None:
nx,ned,neu = CP.returnPullAsym(options.pullDef,nuis_x.getVal(),mean_p,nuis_x.getErrorHi(),sigma_pu,abs(nuis_x.getErrorLo()),abs(sigma_pd))
gr_fit_b.SetPoint(nuis_p_i-1,nuis_p_i-0.5+0.1,nx)
gr_fit_b.SetPointError(nuis_p_i-1,0,0,ned,neu)
else:
gr_fit_b.SetPoint(nuis_p_i-1,nuis_p_i-0.5+0.1,nuis_x.getVal())
gr_fit_b.SetPointError(nuis_p_i-1,0,0,abs(nuis_x.getErrorLo()),nuis_x.getErrorHi())
hist_fit_b.SetBinContent(nuis_p_i,nuis_x.getVal())
hist_fit_b.SetBinError(nuis_p_i,nuis_x.getError())
hist_fit_b.GetXaxis().SetBinLabel(nuis_p_i,name)
gr_fit_b.GetXaxis().SetBinLabel(nuis_p_i,name)
if fit_name=='s':
if options.pullDef and nuis_p!=None:
nx,ned,neu = CP.returnPullAsym(options.pullDef,nuis_x.getVal(),mean_p,nuis_x.getErrorHi(),sigma_pu,abs(nuis_x.getErrorLo()),abs(sigma_pd))
gr_fit_s.SetPoint(nuis_p_i-1,nuis_p_i-0.5-0.1,nx)
gr_fit_s.SetPointError(nuis_p_i-1,0,0,ned,neu)
else:
row += [
"%+.2f +/- %.2f" % (nuis_x.getVal(), nuis_x.getError())
]
else:
row += [
"%+.2f +%.2f %.2f" %
(nuis_x.getVal(), nuis_x.getErrorHi(), nuis_x.getErrorLo())
]
if nuis_x.getErrorLo() == 0: nuis_x.setErrorLo(nuis_x.getErrorHi())
if nuis_p != None:
if options.plotfile:
if fit_name == 'b':
nuis_p_i += 1
if options.pullDef and nuis_p != None:
nx, ned, neu = CP.returnPullAsym(
options.pullDef, nuis_x.getVal(), mean_p,
nuis_x.getErrorHi(), sigma_pu,
abs(nuis_x.getErrorLo()), abs(sigma_pd))
gr_fit_b.SetPoint(nuis_p_i - 1,
nuis_p_i - 0.5 + 0.1, nx)
gr_fit_b.SetPointError(nuis_p_i - 1, 0, 0, ned,
neu)
else:
gr_fit_b.SetPoint(nuis_p_i - 1,
nuis_p_i - 0.5 + 0.1,
nuis_x.getVal())
gr_fit_b.SetPointError(nuis_p_i - 1, 0, 0,
abs(nuis_x.getErrorLo()),
nuis_x.getErrorHi())
hist_fit_b.SetBinContent(nuis_p_i, nuis_x.getVal())
hist_fit_b.SetBinError(nuis_p_i, nuis_x.getError())
hist_fit_b.GetXaxis().SetBinLabel(nuis_p_i, name)
