def rho_centrality(rho_hist: Hist,
output_info: analysis_objects.PlottingOutputWrapper,
includes_constituent_cut: bool = True) -> None:
""" Plot rho as a function of centrality vs jet pt.
Args:
rho_hist: Rho centrality dependent hist.
output_info: Output information.
includes_constituent_cut: True if the plot was produced using the constituent cut.
Returns:
None. The figure is plotted.
"""
# Setup
import ROOT
canvas = ROOT.TCanvas("c", "c")
canvas.SetRightMargin(0.15)
# Set labels
rho_hist.SetTitle("")
rho_hist.Draw("colz")
# Keep the range more meaningful.
rho_hist.GetYaxis().SetRangeUser(0, 50)
# Draw a profile of the mean
rho_hist_profile = rho_hist.ProfileX(f"{rho_hist.GetName()}_profile")
rho_hist_profile.SetMarkerStyle(ROOT.kFullCircle)
rho_hist_profile.SetMarkerColor(ROOT.kRed)
rho_hist_profile.SetMarkerSize(1.4)
rho_hist_profile.Draw("same")
# Finally, save and cleanup
output_name = "rho_background"
if includes_constituent_cut:
output_name += "_3GeVConstituents"
plot_base.save_plot(output_info, canvas, output_name)
def plot_particle_level_spectra_agreement(
difference: Hist, absolute_value_of_difference: Hist,
output_info: analysis_objects.PlottingOutputWrapper) -> None:
""" Plot the agreement of the particle level spectra between the inclusive and sum of all EP orientations.
Args:
difference: Hist of the sum of the EP orientations spectra minus the inclusive spectra.
absolute_value_of_difference: Same as the difference hist, but having taken the absolute value.
This allows us to plot the difference on a log scale (which is useful if the differences
are small).
output_info: Output information.
Returns:
None.
"""
# Setup
output_name = "difference_of_sum_EP_orientations_vs_inclusive"
canvas = ROOT.TCanvas("canvas", "canvas")
# Labeling
x_label = labels.use_label_with_root(
fr"{labels.jet_pt_display_label(upper_label = 'part')}\:({labels.momentum_units_label_gev()})"
)
y_label = r"\mathrm{d}N/\mathrm{d}p_{\mathrm{T}}"
# Apply settings to hists
for h in [difference, absolute_value_of_difference]:
# Labeling
h.GetXaxis().SetTitle(x_label)
h.GetYaxis().SetTitle(y_label)
# Center axis title
h.GetXaxis().CenterTitle(True)
h.GetYaxis().CenterTitle(True)
# Draw and save the difference histogram.
difference.Draw()
plot_base.save_plot(output_info, canvas, output_name)
# Draw and save the absolute value of the difference histogram.
absolute_value_of_difference.Draw()
canvas.SetLogy(True)
output_name += "_abs"
plot_base.save_plot(output_info, canvas, output_name)
def _plot_response_matrix_with_ROOT(
name: str, x_label: str, y_label: str, output_name: str, hist: Hist,
plot_errors_hist: bool,
output_info: analysis_objects.PlottingOutputWrapper) -> None:
""" Underlying function to actually plot a response matrix with ROOT.
Args:
name: Name of the histogram.
x_label: X axis label.
y_label: Y axis label.
output_name: Output name of the histogram.
hist: The response matrix related 2D hist.
errors_hist: True if the hist is the response matrix errors hist.
output_info: Output information.
Returns:
None
"""
# Setup
canvas = ROOT.TCanvas("canvas", "canvas")
canvas.SetLogz(True)
# Plot the histogram
hist.SetTitle(name)
hist.GetXaxis().SetTitle(labels.use_label_with_root(x_label))
hist.GetYaxis().SetTitle(labels.use_label_with_root(y_label))
hist.Draw("colz")
# Set the final axis ranges.
# Z axis
min_val = ctypes.c_double(0)
max_val = ctypes.c_double(0)
hist.GetMinimumAndMaximum(min_val, max_val)
# * 1.1 to put it slightly above the max value
# min_val doesn't work here, because there are some entries at 0
hist.GetZaxis().SetRangeUser(10e-7, max_val.value * 1.1)
# Save
output_name += "_ROOT"
plot_base.save_plot(output_info, canvas, output_name)