def read(scan, param, files, chop, remove_near_min, rezero,
remove_delta=None, improve=False, remove_dups=True):
# print files
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
graph = plot.TGraphFromTree(
limit, param, '2*%s' % DELTANLL, 'quantileExpected > -1.5')
# print 'INPUT'
# graph.Print()
graph.SetName(scan)
graph.Sort()
if remove_dups:
plot.RemoveGraphXDuplicates(graph)
if remove_delta is not None:
plot.RemoveSmallDelta(graph, remove_delta)
plot.RemoveGraphYAbove(graph, chop)
plot.ReZeroTGraph(graph, rezero)
if remove_near_min is not None:
plot.RemoveNearMin(graph, remove_near_min)
if improve:
global NAMECOUNTER
spline = ROOT.TSpline3("spline3", graph)
func = ROOT.TF1('splinefn' + str(NAMECOUNTER), partial(Eval, spline),
graph.GetX()[0], graph.GetX()[graph.GetN() - 1], 1)
func.SetNpx(NPX)
NAMECOUNTER += 1
plot.ImproveMinimum(graph, func, True)
# graph.Print()
if FILTER is not None:
plot.FilterGraph(graph, FILTER)
if REMOVE_X_RANGES is not None:
for remove_x in REMOVE_X_RANGES:
plot.RemoveInXRange(graph, remove_x[0], remove_x[1])
return graph
def read(scan,
param,
files,
chop,
remove_near_min,
rezero,
remove_delta=None,
improve=False):
# print files
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
# require quantileExpected > -0.5 to avoid the final point which is always committed twice
# (even if the fit fails)
graph = plot.TGraphFromTree(limit, param, '2*deltaNLL',
'quantileExpected > -0.5')
graph.SetName(scan)
graph.Sort()
plot.RemoveGraphXDuplicates(graph)
if remove_delta is not None: plot.RemoveSmallDelta(graph, remove_delta)
plot.RemoveGraphYAbove(graph, chop)
plot.ReZeroTGraph(graph, rezero)
if remove_near_min is not None: plot.RemoveNearMin(graph, remove_near_min)
if improve:
global NAMECOUNTER
spline = ROOT.TSpline3("spline3", graph)
func = ROOT.TF1('splinefn' + str(NAMECOUNTER), partial(Eval, spline),
graph.GetX()[0],
graph.GetX()[graph.GetN() - 1], 1)
NAMECOUNTER += 1
plot.ImproveMinimum(graph, func, True)
# graph.Print()
return graph
args.POI, [splitargs[0]],
int(splitargs[2]),
yvals,
args.chop,
args.remove_near_min if args.envelope is False else None,
args.rezero,
args.envelope and i < n_env,
remove_delta=args.remove_delta,
improve=args.improve,
pregraph=tmp_gr))
if args.envelope and args.breakdown:
for i in xrange(n_env):
print '>> Correcting breakdown offsets for envelope index %i' % i
gr = other_scans[i]['graph'].Clone()
plot.RemoveSmallDelta(gr, 1E-6)
bf = other_scans[i + n_env]['val'][0]
y_off = other_scans[i]['func'].Eval(bf)
print '>> Evaluating for offset at best-fit of %f gives %f' % (bf,
y_off)
for j in xrange(1, n_brk):
oth = other_scans[i + j * n_env]
print '>> Applying shift of %f to graph %s' % (
y_off, other_scans_opts[i + j * n_env][0])
plot.ApplyGraphYOffset(oth['graph'], y_off)
color = oth['func'].GetLineColor()
oth['spline'] = ROOT.TSpline3("spline3", oth['graph'])
oth['func'] = ROOT.TF1(
'splinefn' + str(NAMECOUNTER), partial(Eval, oth['spline']),
oth['graph'].GetX()[0],
oth['graph'].GetX()[oth['graph'].GetN() - 1], 1)