def do_jet_pt_rel_error_with_var_cuts(histname, cuts, input_filename, output_filename):
ROOT.gStyle.SetPalette(palette_1D)
tf = cu.open_root_file(input_filename)
h3d = cu.get_from_tfile(tf, histname)
if h3d.GetEntries() == 0:
return
pt_hists = []
for cut in cuts:
max_bin = h3d.GetZaxis().FindFixBin(cut)
# print("cut:", cut, "bin:", max_bin)
h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e")
h2 = h.Clone()
h2.Rebin(2)
if h.GetEntries() > 0:
h3 = qgp.hist_divide_bin_width(h2)
# convert bin contents to bin error/bin contents
for ibin in range(1, h2.GetNbinsX()+1):
if h3.GetBinContent(ibin) == 0:
continue
h3.SetBinContent(ibin, h3.GetBinError(ibin) / h3.GetBinContent(ibin))
h3.SetBinError(ibin, 0)
pt_hists.append(h3)
line_styles = [1, 2, 3]
n_line_styles = len(line_styles)
conts = [Contribution(h, label=" < %g" % cut,
line_color=cu.get_colour_seq(ind, len(cuts)),
line_style=line_styles[ind % n_line_styles],
line_width=2,
marker_color=cu.get_colour_seq(ind, len(cuts)),
subplot=pt_hists[-1])
for ind, (h, cut) in enumerate(zip(pt_hists, cuts))]
jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str
weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)"
ratio_lims = (0.98, 1.02) if "unweighted" in histname else None
plot = Plot(conts, what='hist',
title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str),
xtitle=None,
ytitle='Relative error',
# xlim=None, ylim=None,
legend=True,
subplot_type='ratio',
subplot_title='* / var < %g' % cuts[-1],
subplot_limits=ratio_lims,
has_data=False)
plot.y_padding_max_log = 200
plot.subplot_maximum_ceil = 2
plot.subplot_maximum_floor = 1.02
plot.subplot_minimum_ceil = 0.98
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.89)
plot.legend.SetX1(0.78)
plot.legend.SetX2(0.88)
plot.plot("NOSTACK HISTE", "NOSTACK HIST")
plot.set_logx(True, do_more_labels=True)
plot.set_logy(True, do_more_labels=False)
plot.save(output_filename)
def do_jet_pt_with_var_cuts(histname, cuts, input_filename, output_filename):
ROOT.gStyle.SetPalette(palette_1D)
total = len(cuts) - 1 + .1 # slight offset to not hit the maximum or minimum
# if len(cuts) <= 3:
# ROOT.gStyle.SetPalette(ROOT.kCool)
# num_colours = ROOT.TColor.GetPalette().fN - 1
# print('num_colours:', num_colours)
# for index in range(len(cuts)):
# print(num_colours, index, len(cuts), index / len(cuts), num_colours * index / total)
# print(index, ROOT.TColor.GetColorPalette(int(num_colours * 1. * index / total)))
tf = cu.open_root_file(input_filename)
h3d = cu.get_from_tfile(tf, histname)
if h3d.GetEntries() == 0:
return
pt_hists = []
for cut in cuts:
max_bin = h3d.GetZaxis().FindFixBin(cut)
# print("cut:", cut, "bin:", max_bin)
h = h3d.ProjectionY("pt_var_lt_%g" % cut, 0, -1, 0, max_bin, "e")
h2 = h.Clone()
h2.Rebin(2)
if h.GetEntries() > 0:
h3 = qgp.hist_divide_bin_width(h2)
pt_hists.append(h3)
line_styles = [1, 2, 3]
if len(cuts) <= 3:
line_styles = [1]
n_line_styles = len(line_styles)
ref_ind = 0
conts = [Contribution(h, label=" < %g" % cut,
line_color=cu.get_colour_seq(ind, total),
line_style=line_styles[ind % n_line_styles],
line_width=2,
marker_color=cu.get_colour_seq(ind, total),
subplot=pt_hists[ref_ind] if ind != ref_ind else None)
for ind, (h, cut) in enumerate(zip(pt_hists, cuts))]
jet_str = pt_genjet_str if "_vs_pt_genjet_vs_" in histname else pt_str
weight_str = "(unweighted)" if "unweighted" in histname else "(weighted)"
ratio_lims = (0.5, 2.5)
ratio_lims = (0.5, 1.1)
plot = Plot(conts, what='hist',
title='%s for cuts on %s %s' % (jet_str, get_var_str(histname), weight_str),
xtitle=None,
ytitle='N',
# xlim=None, ylim=None,
legend=True,
subplot_type='ratio',
subplot_title='* / var < %g' % cuts[ref_ind],
subplot_limits=ratio_lims,
has_data=False)
plot.y_padding_max_log = 200
plot.subplot_maximum_ceil = 4
plot.subplot_maximum_floor = 1.02
plot.subplot_minimum_ceil = 0.98
plot.legend.SetY1(0.7)
plot.legend.SetY2(0.89)
plot.legend.SetX1(0.78)
plot.legend.SetX2(0.88)
plot.plot("NOSTACK HISTE", "NOSTACK HIST")
plot.set_logx(True, do_more_labels=True)
plot.set_logy(True, do_more_labels=False)
plot.save(output_filename)
