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hhbbgg_Plotter.py
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# %%
import os
#os.environ['MPLCONFIGDIR'] = '/uscms_data/d1/sraj/matplotlib_tmp' #Disk quota error fix for shivam on lpc
import matplotlib
matplotlib.use("Agg")
import uproot
from hist import Hist, Stack
import numpy as np
import matplotlib.pyplot as plt
import mplhep as hep
from normalisation import getLumi
from cycler import cycler
# Load CMS style including color-scheme
hep.style.use("CMS")
plt.rcParams["axes.prop_cycle"] = cycler(
color=[
"#3f90da",
"#ffa90e",
"#bd1f01",
"#94a4a2",
"#832db6",
"#a96b59",
"#e76300",
"#b9ac70",
"#717581",
"#92dadd",
]
)
# Function to read histograms
def get_histogram(file_name, hist_name, hist_label=None):
histogram = Hist(file_name[hist_name])
if hist_label is not None:
histogram.name = hist_label
return histogram
# Function to read and sum histograms
def sum_histograms(histograms):
return sum(histograms)
# Function to blind data
def blind_data(hist, blind, start_blind=122, stop_blind=128):
if blind:
blinded_hist = hist.copy()
for bin in range(blinded_hist.axes[0].size):
bin_center = blinded_hist.axes[0].centers[bin]
if bin_center >= start_blind and bin_center <= stop_blind:
blinded_hist[bin] = 0.0
return blinded_hist
else:
return hist
# Function to return histogram ratio
def get_ratio(hist_a, hist_b):
# Extract the bin edges and contents
edges_a = hist_a.axes.edges[0]
edges_b = hist_b.axes.edges[0]
if not np.array_equal(edges_a, edges_b):
raise ValueError("Histograms have different binning")
# Extract the counts
counts_a = np.where(hist_a.values() < 0.0, 0.0, hist_a.values())
counts_b = np.where(hist_b.values() < 0.0, 0.0, hist_b.values())
# Compute the errors for each histogram
errors_a = np.sqrt(counts_a) # Assuming Poisson statistics
errors_b = np.sqrt(counts_b) # Assuming Poisson statistics
# Compute the ratio with handling of division by zero
ratio_counts = np.divide(
counts_a,
counts_b,
out=np.zeros_like(counts_a, dtype=float),
where=counts_b != 0,
)
ratio_hist = Hist(hist_a.axes[0])
ratio_hist[...] = ratio_counts
# Compute the relative errors
with np.errstate(divide="ignore", invalid="ignore"):
relative_errors_a = np.divide(
errors_a,
counts_a,
out=np.zeros_like(errors_a, dtype=float),
where=counts_a != 0,
)
relative_errors_b = np.divide(
errors_b,
counts_b,
out=np.zeros_like(errors_b, dtype=float),
where=counts_b != 0,
)
# Propagate the errors for the ratio
ratio_errors = ratio_counts * np.sqrt(relative_errors_a**2 + relative_errors_b**2)
return ratio_hist, ratio_errors
# List of new variables to blind
blind_vars = [
"dibjet_mass",
"diphoton_mass",
]
def stack1d_histograms(
uproot_loaded_filename,
data_samples,
mc_samples,
signal_samples,
histogram_names,
legend_dict,
xtitle_dict,
output_directory,
blind=True,
):
for hist_name in histogram_names:
fig, (ax, ax_ratio) = plt.subplots(
2, 1, gridspec_kw={"height_ratios": [3, 1]}, sharex=True
)
fig.subplots_adjust(
hspace=0.05
) # Adjust space between main plot and ratio plot
# Plot data histogram
data_histogram = sum_histograms(
[
get_histogram(uproot_loaded_filename, f"{data_sample}/{hist_name}")
for data_sample in data_samples
]
)
if any(keyword in hist_name for keyword in blind_vars):
blinded_data_histogram = blind_data(
data_histogram, blind, start_blind=110, stop_blind=130
)
yerrors = np.sqrt(blinded_data_histogram.to_numpy()[0])
blinded_data_histogram.plot(
ax=ax,
stack=False,
histtype="errorbar",
yerr=yerrors,
xerr=True,
color="black",
label="Data",
flow="sum",
)
else:
data_histogram.plot(
ax=ax,
stack=False,
histtype="errorbar",
yerr=True,
xerr=True,
color="black",
label="Data",
flow="sum",
)
# Plot MC histograms as stack
mc_stack = Stack(
*[
get_histogram(
uproot_loaded_filename,
f"{mc_sample}/{hist_name}",
legend_dict[mc_sample],
)
for mc_sample in mc_samples
]
)
mc_stack.plot(ax=ax, stack=True, histtype="fill", flow="sum", sort="yield")
# Plot signal histograms
# if "srbbgg" in hist_name or "srbbggMET" in hist_name:
# for signal_sample in signal_samples:
# signal_histogram = get_histogram(
# uproot_loaded_filename, f"{signal_sample}/{hist_name}"
# )
# signal_histogram.plot(
# ax=ax,
# histtype="step",
# yerr=True,
# xerr=True,
# label=legend_dict[signal_sample],
# color="red",
# )
#
# region_name = r'sr: $b\bar{b}\gamma\gamma$' if "srbbgg" in hist_name else r'sr: $b\bar{b}\gamma\gamma$(MET)'
#
# ax.text(
# x=ax.get_xlim()[0] + 0.05 * (ax.get_xlim()[1] - ax.get_xlim()[0]),
# # y=ax.get_ylim()[1] - 0.05 * (ax.get_ylim()[1] - ax.get_ylim()[0]),
# y = 1e8*0.20,
# s=region_name,
# fontsize=25,
# ha='left',
# va='top',
# bbox=dict(facecolor='white', alpha=1)
# )
#
# # for control regions
# if "crantibbgg" in hist_name or "crbbantigg" in hist_name:
# region_name = r'cr: anti$b\bar{b}\gamma\gamma$' if "crantibbgg" in hist_name else r'cr: $b\bar{b}anti\gamma\gamma$'
#
# ax.text(
# x=ax.get_xlim()[0] + 0.05 * (ax.get_xlim()[1] - ax.get_xlim()[0]),
# # y=ax.get_ylim()[1] - 0.05 * (ax.get_ylim()[1] - ax.get_ylim()[0]),
# y = 1e8*0.20,
# s=region_name,
# fontsize=20,
# ha='left',
# va='top',
# bbox=dict(facecolor='white', alpha=1)
# )
#
text_srbbgg = "Pass medium Btag\nPass tight photonID "
text_srbbggMET = "Pass medium Btag\nPass tight photonID"
text_crantibbgg = "Fail Medium Btag\nPass tight photonID"
text_crbbantigg = "Pass Medium Btag\nPass loose photonID\nfail tight photonID "
if "srbbgg" in hist_name:
region_name = r"sr: $b\bar{b}\gamma\gamma$"
additional_text = text_srbbgg
elif "srbbggMET" in hist_name:
region_name = r"sr: $b\bar{b}\gamma\gamma$+MET"
additional_text = text_srbbggMET
elif "crantibbgg" in hist_name:
region_name = r"cr: anti$b\bar{b}\gamma\gamma$"
additional_text = text_crantibbgg
elif "crbbantigg" in hist_name:
region_name = r"cr: $b\bar{b}anti\gamma\gamma$"
additional_text = text_crbbantigg
else:
region_name = None
additional_text = None
if region_name and additional_text:
combined_text = f"{region_name}\n{additional_text}"
ax.text(
x=ax.get_xlim()[0] + 0.02 * (ax.get_xlim()[1] - ax.get_xlim()[0]),
y=1e8 * 0.20,
s=combined_text,
fontsize=15,
ha="left",
va="top",
bbox=dict(facecolor="white", alpha=1, edgecolor="none"),
)
# plot signal histogram
if "srbbgg" in hist_name or "srbbggMET" in hist_name:
for signal_sample in signal_samples:
signal_histogram = get_histogram(
uproot_loaded_filename, f"{signal_sample}/{hist_name}"
)
signal_histogram.plot(
ax=ax,
histtype="step",
yerr=True,
xerr=True,
label=legend_dict[signal_sample],
color="red",
)
# Plot ratio plot
ratio, error = get_ratio(data_histogram, sum_histograms(mc_stack))
if "dibjet_mass" in hist_name or "diphoton_mass" in hist_name:
blinded_ratio = blind_data(ratio, blind, start_blind=110, stop_blind=130)
blinded_ratio.plot(
ax=ax_ratio,
histtype="errorbar",
yerr=error,
xerr=True,
color="black",
flow="sum",
)
else:
ratio.plot(
ax=ax_ratio,
histtype="errorbar",
yerr=error,
xerr=True,
color="black",
flow="sum",
)
ax_ratio.axhline(1, linestyle="--", color="gray")
ax_ratio.set_ylim(0, 3)
ax_ratio.set_ylabel("Data / MC")
# Style
# x_axis_bining = data_histogram.axes.edges[0]
# ax.set_xlim(min(x_axis_bining), max(x_axis_bining))
ax.set_yscale("log")
ax.set_ylim(0.1, 1e8)
ax.set_xlabel("")
ax.set_ylabel("Events")
hep.cms.label(
"",
ax=ax,
lumi="{0:.2f}".format(getLumi()),
loc=0,
llabel="Work in progress",
com=13.6,
)
ax.legend(ncol=2, loc="upper right", fontsize=18)
ax_ratio.set_xlabel(xtitle_dict[hist_name.split("-")[-1]])
ax_ratio.set_ylabel("Data/MC")
# Save plots
plot_reg_name = hist_name.split("-")[0]
# check and create plot directory
if not os.path.exists(f"{output_directory}/{plot_reg_name}"):
os.makedirs(f"{output_directory}/{plot_reg_name}")
plot_var_name = hist_name.split("-")[-1]
plt.tight_layout() # Adding to ensure everything is well-spaced
plt.savefig(
f"{output_directory}/{plot_reg_name}/{plot_var_name}.pdf",
bbox_inches="tight",
)
plt.savefig(
f"{output_directory}/{plot_reg_name}/{plot_var_name}.png",
bbox_inches="tight",
)
plt.close()
print(f"{hist_name} has been plotted")
def main():
# Open the ROOT file
file_path = "outputfiles/hhbbgg_analyzer-histograms.root"
uproot_loaded_filename = uproot.open(file_path)
# List of data histograms
data_samples = ["Data_EraE", "Data_EraF", "Data_EraG"]
# List of MC processes
mc_samples = [
"GGJets",
"GJetPt20To40",
"GJetPt40",
"GluGluHToGG",
"VBFHToGG",
"VHToGG",
"ttHToGG",
]
# List of signal processes
signal_samples = [
# "GluGluToHH",
"NMSSM_X300_Y60", # updating signal sample
]
#signal_samples = [
# "GluGluToHH",
# "NMSSM_X300_Y60",
# "NMSSM_X300_Y70",
# "NMSSM_X300_Y80",
# "NMSSM_X300_Y90",
# "NMSSM_X300_Y95",
# "NMSSM_X300_Y100",
# "NMSSM_X300_Y125",
# "NMSSM_X400_Y60",
# "NMSSM_X400_Y70",
# "NMSSM_X400_Y80",
# "NMSSM_X400_Y90",
# "NMSSM_X400_Y95",
# "NMSSM_X400_Y100",
# "NMSSM_X400_Y125",
# "NMSSM_X500_Y60",
# "NMSSM_X500_Y70",
# "NMSSM_X500_Y80",
# "NMSSM_X500_Y90",
# "NMSSM_X500_Y95",
# "NMSSM_X500_Y100",
# "NMSSM_X500_Y125",
# "NMSSM_X550_Y60",
# "NMSSM_X550_Y70",
# "NMSSM_X550_Y80",
# "NMSSM_X550_Y90",
# "NMSSM_X550_Y95",
# "NMSSM_X550_Y100",
# "NMSSM_X550_Y125",
# "NMSSM_X600_Y60",
# "NMSSM_X600_Y70",
# "NMSSM_X600_Y80",
# "NMSSM_X600_Y90",
# "NMSSM_X600_Y95",
# "NMSSM_X600_Y100",
# "NMSSM_X600_Y125",
# "NMSSM_X650_Y60",
# "NMSSM_X650_Y70",
# "NMSSM_X650_Y80",
# "NMSSM_X650_Y90",
# "NMSSM_X650_Y95",
# "NMSSM_X650_Y100",
# "NMSSM_X650_Y125",
# "NMSSM_X700_Y60",
# "NMSSM_X700_Y70",
# "NMSSM_X700_Y80",
# "NMSSM_X700_Y90",
# "NMSSM_X700_Y95",
# "NMSSM_X700_Y100",
# "NMSSM_X700_Y125"
#]
# Dictionary for legends
legend_dict = {
"GGJets": r"$\gamma\gamma$+jets",
"GJetPt20To40": r"$\gamma$+jets ($20< p_T < 40$)",
"GJetPt40": r"$\gamma$+jets ($p_T > 40$)",
"GluGluHToGG": r"$gg\rightarrow\,H\rightarrow\gamma\gamma$",
"VBFHToGG": r"$VBF\:H\rightarrow\gamma\gamma$",
"VHToGG": r"$V\:H\rightarrow\gamma\gamma$",
"ttHToGG": r"$t\bar{t}H\rightarrow\gamma\gamma$",
#"GluGluToHH": r"$gg\rightarrow\,HH$ \times 10",
"NMSSM_X300_Y60": r"$NMSSM\_X_{300}\_Y_{60} \times 10$",
# NMSSM samples legends
#"NMSSM_X300_Y60": r"$NMSSM\_X300\_Y60$",
#"NMSSM_X300_Y70": r"$NMSSM\_X300\_Y70$",
#"NMSSM_X300_Y80": r"$NMSSM\_X300\_Y80$",
#"NMSSM_X300_Y90": r"$NMSSM\_X300\_Y90$",
#"NMSSM_X300_Y95": r"$NMSSM\_X300\_Y95$",
#"NMSSM_X300_Y100": r"$NMSSM\_X300\_Y100$",
#"NMSSM_X300_Y125": r"$NMSSM\_X300\_Y125$",
#"NMSSM_X400_Y60": r"$NMSSM\_X400\_Y60$",
#"NMSSM_X400_Y70": r"$NMSSM\_X400\_Y70$",
#"NMSSM_X400_Y80": r"$NMSSM\_X400\_Y80$",
#"NMSSM_X400_Y90": r"$NMSSM\_X400\_Y90$",
#"NMSSM_X400_Y95": r"$NMSSM\_X400\_Y95$",
#"NMSSM_X400_Y100": r"$NMSSM\_X400\_Y100$",
#"NMSSM_X400_Y125": r"$NMSSM\_X400\_Y125$",
#"NMSSM_X500_Y60": r"$NMSSM\_X500\_Y60$",
#"NMSSM_X500_Y70": r"$NMSSM\_X500\_Y70$",
#"NMSSM_X500_Y80": r"$NMSSM\_X500\_Y80$",
#"NMSSM_X500_Y90": r"$NMSSM\_X500\_Y90$",
#"NMSSM_X500_Y95": r"$NMSSM\_X500\_Y95$",
#"NMSSM_X500_Y100": r"$NMSSM\_X500\_Y100$",
#"NMSSM_X500_Y125": r"$NMSSM\_X500\_Y125$",
#"NMSSM_X550_Y60": r"$NMSSM\_X550\_Y60$",
#"NMSSM_X550_Y70": r"$NMSSM\_X550\_Y70$",
#"NMSSM_X550_Y80": r"$NMSSM\_X550\_Y80$",
#"NMSSM_X550_Y90": r"$NMSSM\_X550\_Y90$",
#"NMSSM_X550_Y95": r"$NMSSM\_X550\_Y95$",
#"NMSSM_X550_Y100": r"$NMSSM\_X550\_Y100$",
#"NMSSM_X550_Y125": r"$NMSSM\_X550\_Y125$",
#"NMSSM_X600_Y60": r"$NMSSM\_X600\_Y60$",
#"NMSSM_X600_Y70": r"$NMSSM\_X600\_Y70$",
#"NMSSM_X600_Y80": r"$NMSSM\_X600\_Y80$",
#"NMSSM_X600_Y90": r"$NMSSM\_X600\_Y90$",
#"NMSSM_X600_Y95": r"$NMSSM\_X600\_Y95$",
#"NMSSM_X600_Y100": r"$NMSSM\_X600\_Y100$",
#"NMSSM_X600_Y125": r"$NMSSM\_X600\_Y125$",
#"NMSSM_X650_Y60": r"$NMSSM\_X650\_Y60$",
#"NMSSM_X650_Y70": r"$NMSSM\_X650\_Y70$",
#"NMSSM_X650_Y80": r"$NMSSM\_X650\_Y80$",
#"NMSSM_X650_Y90": r"$NMSSM\_X650\_Y90$",
#"NMSSM_X650_Y95": r"$NMSSM\_X650\_Y95$",
#"NMSSM_X650_Y100": r"$NMSSM\_X650\_Y100$",
#"NMSSM_X650_Y125": r"$NMSSM\_X650\_Y125$",
#"NMSSM_X700_Y60": r"$NMSSM\_X700\_Y60$",
#"NMSSM_X700_Y70": r"$NMSSM\_X700\_Y70$",
#"NMSSM_X700_Y80": r"$NMSSM\_X700\_Y80$",
#"NMSSM_X700_Y90": r"$NMSSM\_X700\_Y90$",
#"NMSSM_X700_Y95": r"$NMSSM\_X700\_Y95$",
#"NMSSM_X700_Y100": r"$NMSSM\_X700\_Y100$",
#"NMSSM_X700_Y125": r"$NMSSM\_X700\_Y125$",
}
# List of regions names
regions = [
"preselection",
"selection",
"srbbgg",
"srbbggMET",
"crantibbgg",
"crbbantigg",
]
# List of variable names
variable_names = [
"dibjet_mass",
"diphoton_mass",
"bbgg_mass",
"dibjet_pt",
"diphoton_pt",
"bbgg_pt",
"bbgg_eta",
"bbgg_phi",
"lead_pho_pt",
"sublead_pho_pt",
"dibjet_pt",
"lead_pho_eta",
"lead_pho_phi",
"sublead_pho_eta",
"sublead_pho_phi",
"dibjet_eta",
"dibjet_phi",
"diphoton_eta",
"diphoton_phi",
"lead_bjet_pt",
"sublead_bjet_pt",
"lead_bjet_eta",
"sublead_bjet_eta",
"lead_bjet_phi",
"sublead_bjet_phi",
"sublead_bjet_PNetB",
"lead_bjet_PNetB",
"CosThetaStar_gg",
"CosThetaStar_CS",
"CosThetaStar_jj",
"DeltaR_jg_min",
"pholead_PtOverM",
"phosublead_PtOverM",
"FirstJet_PtOverM",
"SecondJet_PtOverM",
"lead_pt_over_diphoton_mass",
"sublead_pt_over_diphoton_mass",
"lead_pt_over_dibjet_mass",
"sublead_pt_over_dibjet_mass",
"diphoton_bbgg_mass",
"dibjet_bbgg_mass",
"lead_pho_mvaID_WP90",
"lead_pho_mvaID_WP80",
"sublead_pho_mvaID_WP90",
"sublead_pho_mvaID_WP80",
"lead_pho_mvaID",
"sublead_pho_mvaID",
]
specific_variable_names = [
"puppiMET_pt",
"puppiMET_phi",
# "puppiMET_phiJERDown", "puppiMET_phiJERUp", "puppiMET_phiJESDown",
# "puppiMET_phiJESUp", "puppiMET_phiUnclusteredDown",
# "puppiMET_phiUnclusteredUp", "puppiMET_ptJERDown",
# "puppiMET_ptJERUp", "puppiMET_ptJESDown",
# "puppiMET_ptJESUp", "puppiMET_ptUnclusteredDown",
# "puppiMET_ptUnclusteredUp", "puppiMET_sumEt"
]
specific_regions = ["srbbggMET"]
histogram_names = [
f"{region}-{variable_name}"
for region in regions
for variable_name in variable_names
]
specific_histogram_names = [
f"{specific_region}-{specific_variable_name}"
for specific_region in specific_regions
for specific_variable_name in specific_variable_names
]
histogram_names = histogram_names + specific_histogram_names
# Dictionary for x-axis titles
xaxis_titles = {
"dibjet_mass": r"$m_{b\bar{b}}$ [GeV]",
"diphoton_mass": r"$m_{\gamma\gamma}$ [GeV]",
"bbgg_mass": r"$m_{b\bar{b}\gamma\gamma}$ [GeV]",
"lead_pho_pt": r"lead $p_T^{\gamma}$ [GeV]",
"sublead_pho_pt": r"sublead $p_T^{\gamma}$ [GeV]",
"dibjet_pt": r"$p_T^{b\bar{b}}$ [GeV]",
"diphoton_pt": r"$p_{T}^{\gamma\gamma}$ [GeV]",
"bbgg_pt": r"$p_T^{b\bar{b}\gamma\gamma}$ [GeV]",
"bbgg_eta": r"$\eta^{b\bar{b}\gamma\gamma}$",
"bbgg_phi": r"$\phi^{b\bar{b}\gamma\gamma}$",
"lead_pho_eta": r"lead $\eta^{\gamma}$",
"lead_pho_phi": r"lead $\phi^{\gamma}$",
"sublead_pho_eta": r"sublead $\eta^{\gamma}$",
"sublead_pho_phi": r"sublead $\phi^{\gamma}$",
"dibjet_eta": r"$\eta^{b\bar{b}}$",
"dibjet_phi": r"$\phi^{b\bar{b}}$",
"diphoton_eta": r"$\eta^{\gamma\gamma}$",
"diphoton_phi": r"$\phi^{\gamma\gamma}$",
"lead_bjet_pt": r"lead $p_T^{b}$ [GeV]",
"sublead_bjet_pt": r"sublead $p_T^{b}$ [GeV]",
"lead_bjet_eta": r"lead $\eta^{b}$",
"lead_bjet_phi": r"lead $\phi^{b}$",
"sublead_bjet_eta": r"sublead $\eta^{b}$",
"sublead_bjet_phi": r"sublead $\phi^{b}$",
"sublead_bjet_PNetB": r"sublead b PNetScore",
"lead_bjet_PNetB": r"lead b PNetScore",
"CosThetaStar_gg": r"$|Cos(\theta^{CS}_{gg})|$",
"CosThetaStar_jj": r"$|Cos(\theta^{CS}_{jj})|$",
"CosThetaStar_CS": r"$|Cos(\theta^{CS}_{HH})|$",
"DeltaR_jg_min": r"min($\Delta$R($\gamma$,jets))",
"pholead_PtOverM": r"lead $p_T^{\gamma1}/M_{\gamma\gamma}$",
"FirstJet_PtOverM": r"$p_T^{j1}/M_{jj}$",
"SecondJet_PtOverM": r"$p_T^{j2}/M_{jj})$",
"phosublead_PtOverM": r"sublead $p_T^{\gamma1}/M_{\gamma\gamma}$",
"lead_pt_over_diphoton_mass": r"lead $p_T^{\gamma}/M_{\gamma\gamma}$",
"sublead_pt_over_diphoton_mass": r"sublead $p_T^{\gamma}/M_{\gamma\gamma}$",
"lead_pt_over_dibjet_mass": r"lead $p_T^{j}/M_{b\bar{b}}$",
"sublead_pt_over_dibjet_mass": r"sublead $p_T(j)/M_{b\bar{b}}$",
"diphoton_bbgg_mass": r"$p_T^{\gamma\gamma}/M_{b\bar{b}\gamma\gamma}$",
"dibjet_bbgg_mass": r"$p_T^{b\bar{b}}/m_{b\bar{b}\gamma\gamma}$",
"puppiMET_pt": r"puppi $p_T^{miss}$ [GeV]",
"puppiMET_phi": r"puppi $\phi^{miss}$",
# "puppiMET_phiJERDown": r"puppi $\phi^{JERDown}$",
# "puppiMET_phiJERUp": r"puppi $\phi^{JERUp}$",
# "puppiMET_phiJESDown": r"puppi $\phi^{JESDown}$",
# "puppiMET_phiUnclusteredDown": r"puppi $\phi^{UnclusterDown}$",
# "puppiMET_phiUnclusteredUp": r"puppi $\phi^{UnclusterUp}$",
# "puppiMET_ptJERDown": r"puppi $p_T^{JERDown}$ [GeV]",
# "puppiMET_ptJERUp": r"puppi $p_T^{JERUp}$ [GeV]",
# "puppiMET_ptJESDown": r"puppi $p_T^{JESDown}$ [GeV]",
# "puppiMET_ptJESUp": r"puppi $p_T^{JESUp}$ [GeV]",
"lead_pho_mvaID_WP90": r"lead_PhoMVAID90",
"sublead_pho_mvaID_WP90": r"sublead_PhoMVAID90",
"lead_pho_mvaID_WP80": r"lead_PhoMVAID80",
"sublead_pho_mvaID_WP80": r"sublead_PhoMVAID80",
"lead_pho_mvaID": r"lead_PhoMVAID",
"sublead_pho_mvaID": r"sublead_PhoMVAID",
}
# create the tdirectory to save plots
output_directory = "stack_plots"
if not os.path.exists(output_directory):
os.makedirs(output_directory)
stack1d_histograms(
uproot_loaded_filename,
data_samples,
mc_samples,
signal_samples,
histogram_names,
legend_dict,
xaxis_titles,
output_directory,
blind=False,
)
if __name__ == "__main__":
main()
# %%