def plot_unfolded_with_yoda_normalised(self,
do_chi2=False,
do_zoomed=True):
data_total_errors_style = dict(
label="Data (total unc.)",
line_color=self.plot_styles['unfolded_total_colour'],
line_width=self.line_width,
line_style=1,
marker_color=self.plot_styles['unfolded_total_colour'],
marker_style=cu.Marker.get('circle'),
marker_size=self.plot_styles['unfolded_marker_size'],
leg_draw_opt="LEP")
data_stat_errors_style = dict(
label="Data (stat. unc.)",
line_color=self.plot_styles['unfolded_stat_colour'],
line_width=self.line_width,
line_style=1,
marker_color=self.plot_styles['unfolded_stat_colour'],
marker_style=cu.Marker.get('circle'),
marker_size=0.0001,
leg_draw_opt="LEP"
) # you need a non-0 marker to get the horizontal bars at the end of errors
mc_style = dict(label=self.region['mc_label'],
line_color=self.plot_styles['gen_colour'],
line_width=self.line_width,
marker_color=self.plot_styles['gen_colour'],
marker_size=self.plot_styles['gen_marker_size'],
marker_style=self.plot_styles['gen_marker'],
leg_draw_opt="LEP"
if self.plot_styles['gen_marker_size'] > 0 else "LE")
rivet_path, rivet_region, rivet_radius, rivet_lambda, rivet_pt_bins = get_matching_rivet_setup(
self.setup)
for ibin, (bin_edge_low, bin_edge_high) in enumerate(
zip(self.bins[:-1], self.bins[1:])):
hbc_args = dict(ind=ibin, binning_scheme='generator')
mc_gen_hist_bin = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'hist_truth', **hbc_args)
unfolded_hist_bin_stat_errors = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'unfolded_stat_err', **hbc_args)
unfolded_hist_bin_total_errors = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
'unfolded', **hbc_args)
# Get RIVET hists, which are absolute counts, so need normalising
rivet_hist_name = '/%s/%s' % (
rivet_path,
rn.get_plot_name(rivet_radius, rivet_region, rivet_lambda,
rivet_pt_bins[ibin]))
rivet_hists = [
qgp.normalise_hist_divide_bin_width(
yoda.root.to_root(ent['yoda_dict'][rivet_hist_name]))
for ent in self.rivet_entries
]
# Create copy of data to go on top of stat unc,
# but remove vertical error bar so we can see the stat unc
# Note that you CAN'T set it to 0, otherwise vertical lines connecting
# bins start being drawn. Instead set it to some super small value.
unfolded_hist_bin_total_errors_marker_noerror = unfolded_hist_bin_total_errors.Clone(
) # clone to avoid restyling the original as well
for i in range(
1,
unfolded_hist_bin_total_errors_marker_noerror.GetNbinsX() +
1):
unfolded_hist_bin_total_errors_marker_noerror.SetBinError(
i, 1E-100)
data_entries = [
Contribution(unfolded_hist_bin_total_errors,
**data_total_errors_style),
Contribution(unfolded_hist_bin_stat_errors,
**data_stat_errors_style),
# do data with black marker to get it on top
Contribution(unfolded_hist_bin_total_errors_marker_noerror,
**data_total_errors_style),
]
# For subplot to ensure only MC errors drawn, not MC+data
data_no_errors = unfolded_hist_bin_total_errors_marker_noerror.Clone(
)
cu.remove_th1_errors(data_no_errors)
this_mc_style = deepcopy(mc_style)
rivet_styles = []
for ind, _ in enumerate(rivet_hists):
s_dict = self.rivet_entries[ind]['style_dict']
rivet_styles.append(
dict(label=s_dict['label'],
line_color=s_dict['color'],
line_width=self.line_width,
marker_color=s_dict['color'],
marker_size=s_dict.get(
'marker_size',
self.plot_styles['gen_marker_size']),
marker_style=s_dict['marker_style'],
leg_draw_opt="LEP"
if self.plot_styles['gen_marker_size'] > 0 else "LE"))
# Calculate chi2 between data and MCs if desired
if do_chi2:
# print("unfolded_alt_truth bin", ibin)
ematrix = self.hist_bin_chopper.get_pt_bin_normed_div_bin_width(
self.unfolder.total_ematrix_name, **hbc_args)
# stats are chi2, ndof, p
mc_stats = calc_chi2_stats(unfolded_hist_bin_total_errors,
mc_gen_hist_bin, ematrix)
# print(mc_stats)
# print(alt_mc_stats)
nbins = sum([
1 for i in range(
1,
unfolded_hist_bin_total_errors.GetNbinsX() + 1)
if unfolded_hist_bin_total_errors.GetBinContent(i) != 0
])
# reduced_chi2 = mc_stats[0] / nbins
# alt_reduced_chi2 = alt_mc_stats[0] / nbins
n_sig_fig = 2
chi2_template = "\n#lower[-0.1]{{(#chi^{{2}} / N_{{bins}} = {chi2:g} / {nbins:d})}}"
this_mc_style['label'] += chi2_template.format(chi2=cu.nsf(
mc_stats[0], n_sig_fig),
nbins=nbins)
for ind, h in enumerate(rivet_hists):
this_stats = calc_chi2_stats(
unfolded_hist_bin_total_errors, h, ematrix)
rivet_styles[ind]['label'] += chi2_template.format(
chi2=cu.nsf(this_stats[0], n_sig_fig), nbins=nbins)
mc_entries = [
Contribution(mc_gen_hist_bin,
subplot=data_no_errors,
**this_mc_style),
]
for h, s_dict in zip(rivet_hists, rivet_styles):
mc_entries.append(
Contribution(h, subplot=data_no_errors, **s_dict))
entries = [
# Draw MC
*mc_entries,
# Draw data after to put on top of MC
*data_entries
]
func_name = cu.get_current_func_name()
if not self.check_entries(entries, "%s bin %d" %
(func_name, ibin)):
return
ymin = 0
if np.any(
cu.th1_to_ndarray(unfolded_hist_bin_total_errors)[0] < 0):
ymin = None # let it do its thing and auto calc ymin
max_rel_err = 0.5 if "multiplicity" in self.setup.angle.var.lower(
) else -1
plot = Plot(
entries,
ytitle=self.setup.pt_bin_normalised_differential_label,
title=self.get_pt_bin_title(bin_edge_low, bin_edge_high),
legend=True,
xlim=qgp.calc_auto_xlim(
entries[2:3],
max_rel_err=0.5), # set x lim to where data is non-0
ylim=[ymin, None],
**self.pt_bin_plot_args)
plot.subplot_title = qgc.SIM_DATA_STR
self._modify_plot_paper(plot)
# disable adding objects to legend & drawing - we'll do it manually
plot.do_legend = False
plot.legend.SetTextSize(0.03)
plot.legend.SetY1(0.6)
plot.legend.SetX1(0.57)
plot.legend.SetX2(0.93)
if len(entries) > 4:
# if lots of entries, try auto-expand
plot.legend.SetY1(0.6 - (0.02 * (len(entries) - 4)))
# plot.legend.SetEntrySeparation(0.005)
subplot_draw_opts = "NOSTACK E1"
plot.plot("NOSTACK E1", subplot_draw_opts)
dummy_graphs = qgp.do_fancy_legend(chain(data_entries[:2],
mc_entries),
plot,
use_splitline=False)
plot.canvas.cd()
plot.legend.Draw()
# Create hists for data with error region for ratio
# Easiest way to get errors right is to do data (with 0 errors)
# and divide by data (with errors), as if you had MC = data with 0 error
data_stat_ratio = data_no_errors.Clone()
data_stat_ratio.Divide(unfolded_hist_bin_stat_errors)
data_stat_ratio.SetFillStyle(3245)
data_stat_ratio.SetFillColor(
self.plot_styles['unfolded_stat_colour'])
data_stat_ratio.SetLineWidth(0)
data_stat_ratio.SetMarkerSize(0)
data_total_ratio = data_no_errors.Clone()
data_total_ratio.Divide(unfolded_hist_bin_total_errors)
data_total_ratio.SetFillStyle(3254)
data_total_ratio.SetFillColor(
self.plot_styles['unfolded_total_colour'])
data_total_ratio.SetLineWidth(0)
data_total_ratio.SetMarkerSize(0)
# now draw the data error shaded area
# this is a bit hacky - basically draw them on the ratio pad,
# then redraw the existing hists & line to get them ontop
# note that we use "same" for all - this is to keep the original axes
# (we may want to rethink this later?)
plot.subplot_pad.cd()
draw_opt = "E2 SAME"
data_stat_ratio.Draw(draw_opt)
data_total_ratio.Draw(draw_opt)
plot.subplot_line.Draw()
plot.subplot_container.Draw("SAME" + subplot_draw_opts)
# Add subplot legend
x_left = 0.25
y_bottom = 0.75
width = 0.67
height = 0.15
plot.subplot_legend = ROOT.TLegend(x_left, y_bottom,
x_left + width,
y_bottom + height)
plot.subplot_legend.AddEntry(data_total_ratio,
qgc.DATA_TOTAL_UNC_STR, "F")
plot.subplot_legend.AddEntry(data_stat_ratio,
qgc.DATA_STAT_UNC_STR, "F")
plot.subplot_legend.SetTextSize(0.085)
plot.subplot_legend.SetFillStyle(0)
plot.subplot_legend.SetNColumns(2)
plot.subplot_legend.Draw()
plot.canvas.cd()
stp = self.setup
fname = f"unfolded_{stp.append}_rivet_bin_{ibin:d}_divBinWidth{stp.paper_str}.{stp.output_fmt}"
self.save_plot(plot, os.path.join(stp.output_dir, fname))
# Do version with small x values only
if do_zoomed:
if self.setup.angle.var in [
"jet_thrust_charged", "jet_width_charged",
"jet_thrust", "jet_width"
]:
# plot.ylim = (1E-5)
plot.y_padding_max_log = 50
plot.y_padding_min_log = 0.5
plot.ylim = None
plot.set_logy(do_exponent=False, do_more_labels=False)
fname = f"unfolded_{stp.append}_alt_truth_bin_{ibin:d}_divBinWidth_logY.{stp.output_fmt}"
self.save_plot(plot, os.path.join(stp.output_dir, fname))
if self.setup.angle.var in [
"jet_LHA_charged", "jet_thrust_charged",
"jet_width_charged", "jet_thrust", "jet_width"
]:
bin_edges = cu.get_bin_edges(mc_gen_hist_bin, 'x')
# get the bin edge thats smallest between 0.2, and 5th bin
bin_lt_lim = [x for x in bin_edges if x < 0.2][-1]
upper_bin = min(bin_edges[5], bin_lt_lim)
plot2 = Plot(
entries,
ytitle=self.setup.pt_bin_normalised_differential_label,
title=self.get_pt_bin_title(bin_edge_low,
bin_edge_high),
xlim=(0, upper_bin),
**self.pt_bin_plot_args)
self._modify_plot(plot2)
plot2.subplot_title = "* / Generator"
plot2.plot("NOSTACK E1")
# plot2.set_logx(do_exponent=False)
fname = f"unfolded_{stp.append}_rivet_bin_{ibin:d}_divBinWidth_lowX.{stp.output_fmt}"
self.save_plot(plot2, os.path.join(stp.output_dir, fname))
