#!/usr/bin/python
import os
import sys
from array import array
from ROOT import gROOT, gDirectory, gStyle, TH1D, TFile, TCanvas, TColor, TLegend, TLatex
gROOT.Reset()
gROOT.LoadMacro("$HOME/RootUtils/AtlasStyle.C")
from ROOT import SetAtlasStyle
SetAtlasStyle()
gROOT.SetBatch(True)
def plot():
region = "ElEl"
flavour = "El"
CR = "RealCR"
tightness = "T"
variable = "Pt"
subdir = "".join((region, CR, flavour, tightness))
path = "../OutputPlots_MMRates_v028/" + subdir + "/"
list_plots = [
"".join((flavour, "Probe", variable, ".root")), "".join(
(flavour, "0", variable, ".root")), "".join(
def useAtlasStyle():
gROOT.LoadMacro("AtlasStyle.C")
from ROOT import SetAtlasStyle
SetAtlasStyle()
gStyle.SetNdivisions(506, 'XYZ')
def useAtlasStyle():
gROOT.LoadMacro("AtlasStyle.C")
from ROOT import SetAtlasStyle
SetAtlasStyle()
def main(argv):
gROOT.SetBatch(True)
#parse command line arguments
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument("-r", "--runnumber", type=str, default=0, dest="runnumber", help="unique identifier for current run")
parser.add_argument("-d", "--data_dir", type=str, required=True, dest="data_dir", help="name of directory where data is stored")
parser.add_argument("-o", "--output_dir", type=str, required=True, dest="output_dir", help="name of directory where GNN output is stored")
parser.add_argument("-s", "--dataset", type=str, required=True, dest="infile_name", help="name of dataset to train on (without hdf5 extension)")
args = parser.parse_args()
runnumber = args.runnumber
infile_name = args.infile_name
infile_path = args.data_dir
outfile_path = args.output_dir
#import options from option file
batch_size = options.batch_size
atlasstyle = options.use_atlas_style
track_pt_bound = options.track_pt_bound
track_d0_bound = options.track_d0_bound
track_z0_bound = options.track_z0_bound
jet_pt_bound = options.jet_pt_bound
jet_eta_bound = options.jet_eta_bound
ntrk_bound = options.ntrk_bound
bin_threshold = options.bin_threshold
mult_threshold = options.mult_threshold
if atlasstyle: SetAtlasStyle()
graphfile_name = outfile_path+runnumber+"/"+infile_name+"_"+runnumber+"_results.bin"
paramfile_name = infile_path+infile_name+"_params"
outfile_name = outfile_path+runnumber+"/"+infile_name+"_"+runnumber
normfile_name = infile_path+infile_name+"_norm"
#calculate number of features in graphs
sample_graph = (dgl.load_graphs(graphfile_name, [0]))[0][0]
incl_errors = incl_corr = incl_hits = incl_vweight = False
nnfeatures_base = sample_graph.ndata['features_base'].size()[1]
nnfeatures = nnfeatures_base
if 'features_vweight' in sample_graph.ndata.keys():
nnfeatures_vweight = sample_graph.ndata['features_vweight'].size()[1]
incl_vweight = True
nnfeatures += nnfeatures_vweight
if 'features_errors' in sample_graph.ndata.keys():
nnfeatures_errors = sample_graph.ndata['features_errors'].size()[1]
incl_errors = True
nnfeatures += nnfeatures_errors
if 'features_hits' in sample_graph.ndata.keys():
nnfeatures_hits = sample_graph.ndata['features_hits'].size()[1]
incl_hits = True
nnfeatures += nnfeatures_hits
if 'features_corr' in sample_graph.ndata.keys():
nnfeatures_corr = sample_graph.ndata['features_corr'].size()[1]
incl_corr = True
nnfeatures += nnfeatures_corr
#check if multi-class classification was performed
if sample_graph.edata['pred'][0,:].numpy().size > 1:
multi_class = True
else:
multi_class = False
hist_trk_pt_b_tot = TH1D("trk_pt_b_tot", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_pt_c_tot = TH1D("trk_pt_c_tot", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_pt_btoc_tot = TH1D("trk_pt_btoc_tot", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_pt_b_bad = TH1D("trk_pt_b_bad", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_pt_c_bad = TH1D("trk_pt_c_bad", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_pt_btoc_bad = TH1D("trk_pt_btoc_bad", "Normalized track pT distribution in test data (total - bad jets);pT [GeV];Difference in normalized entries", 20, track_pt_bound[0], track_pt_bound[1])
hist_trk_theta_b_tot = TH1D("trk_theta_b_tot", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_theta_c_tot = TH1D("trk_theta_c_tot", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_theta_btoc_tot = TH1D("trk_theta_btoc_tot", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_theta_b_bad = TH1D("trk_theta_b_bad", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_theta_c_bad = TH1D("trk_theta_c_bad", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_theta_btoc_bad = TH1D("trk_theta_btoc_bad", "Normalized track #theta distribution in test data (total - bad jets);#theta;Difference in normalized entries", 20, 0, math.pi)
hist_trk_phi_b_tot = TH1D("trk_phi_b_tot", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_phi_c_tot = TH1D("trk_phi_c_tot", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_phi_btoc_tot = TH1D("trk_phi_btoc_tot", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_phi_b_bad = TH1D("trk_phi_b_bad", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_phi_c_bad = TH1D("trk_phi_c_bad", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_phi_btoc_bad = TH1D("trk_phi_btoc_bad", "Normalized track #phi distribution in test data (total - bad jets);#phi;Difference in normalized entries", 20, -math.pi, math.pi)
hist_trk_z0_b_tot = TH1D("trk_z0_b_tot", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_z0_c_tot = TH1D("trk_z0_c_tot", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_z0_btoc_tot = TH1D("trk_z0_btoc_tot", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_z0_b_bad = TH1D("trk_z0_b_bad", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_z0_c_bad = TH1D("trk_z0_c_bad", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_z0_btoc_bad = TH1D("trk_z0_btoc_bad", "Normalized track z0 distribution in test data (total - bad jets);z0 [mm];Difference in normalized entries", 20, -track_z0_bound, track_z0_bound)
hist_trk_d0_b_tot = TH1D("trk_d0_b_tot", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
hist_trk_d0_c_tot = TH1D("trk_d0_c_tot", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
hist_trk_d0_btoc_tot = TH1D("trk_d0_btoc_tot", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
hist_trk_d0_b_bad = TH1D("trk_d0_b_bad", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
hist_trk_d0_c_bad = TH1D("trk_d0_c_bad", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
hist_trk_d0_btoc_bad = TH1D("trk_d0_btoc_bad", "Normalized track d0 distribution in test data (total - bad jets);d0 [mm];Difference in normalized entries", 20, -track_d0_bound, track_d0_bound)
bin_edges = np.linspace(-0.5,ntrk_bound+0.5,ntrk_bound+2)
hist_no_trk_jet_b_tot = TH1D("no_trk_jet_b_tot", "Number of associated tracks per jet in test data;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_c_tot = TH1D("no_trk_jet_c_tot", "Number of associated tracks per jet in test data;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_btoc_tot = TH1D("no_trk_jet_btoc_tot", "Number of associated tracks per jet in test data;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_nohf_tot = TH1D("no_trk_jet_nohf_tot", "Number of associated tracks per jet in test data;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_nm_tot = TH1D("no_trk_jet_nm_tot", "Number of associated tracks per jet in test data;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_b_bad = TH1D("no_trk_jet_b_bad", "Number of associated tracks per jet among badly reconstructed jets;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_c_bad = TH1D("no_trk_jet_c_bad", "Number of associated tracks per jet among badly reconstructed jets;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_btoc_bad = TH1D("no_trk_jet_btoc_bad", "Number of associated tracks per jet among badly reconstructed jets;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_nohf_bad = TH1D("no_trk_jet_nohf_bad", "Number of associated tracks per jet among badly reconstructed jets;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_no_trk_jet_nm_bad = TH1D("no_trk_jet_nm_bad", "Number of associated tracks per jet among badly reconstructed jets;Number of tracks;Normalized entries", ntrk_bound+1, bin_edges)
hist_frac_trk_b_tot = TH1D("frac_trk_b_tot", "Fraction of tracks per jet in test data;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_c_tot = TH1D("frac_trk_c_tot", "Fraction of tracks per jet in test data;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_nohf_tot = TH1D("frac_trk_nohf_tot", "Fraction of tracks per jet; in test dataTrack fraction;Entries", 10, 0, 1)
hist_frac_trk_nm_tot = TH1D("frac_trk_nm_tot", "Fraction of tracks per jet in test data;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_btoc_tot = TH1D("frac_trk_btoc_tot", "Fraction of tracks per jet in test data;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_b_bad = TH1D("frac_trk_b_bad", "Fraction of tracks per jet among badly reconstructed jets;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_c_bad = TH1D("frac_trk_c_bad", "Fraction of tracks per jet among badly reconstructed jets;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_nohf_bad = TH1D("frac_trk_nohf_bad", "Fraction of tracks per jet among badly reconstructed jets;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_nm_bad = TH1D("frac_trk_nm_bad", "Fraction of tracks per jet among badly reconstructed jets;Track fraction;Entries", 10, 0, 1)
hist_frac_trk_btoc_bad = TH1D("frac_trk_btoc_bad", "Fraction of tracks per jet among badly reconstructed jets;Track fraction;Entries", 10, 0, 1)
#edge score and recall histograms
bin_edges = np.linspace(-0.05,1.05,12)
if not multi_class:
pos_r_hist = TH1D("TPR", "Results for each jet;Rate;Fraction of jets",11,bin_edges) #1.001 is the upper bound so this is inclusive of 1
neg_r_hist = TH1D("TNR", "Results for each jet;Rate;Fraction of jets",11,bin_edges)
edge_score_hist = TH1D("", "Edges scores;Score;Fraction of jets",11,bin_edges)
hist_r_list = [neg_r_hist, pos_r_hist]
hist_s_list = [edge_score_hist]
else:
neg_r_hist = TH1D("Class 0 recall", "Results for each jet;Rate;Fraction of jets",11,bin_edges)
b_r_hist = TH1D("Class 1 recall", "Results for each jet;Rate;Fraction of jets",11,bin_edges)
c_r_hist = TH1D("Class 2 recall", "Results for each jet;Rate;Fraction of jets",11,bin_edges)
neg_score_hist = TH1D("Class 0 scores", "Class scores;Score;Fraction of jets",11,bin_edges)
b_score_hist = TH1D("Class 1 scores", "Class scores;Score;Fraction of jets",11,bin_edges)
c_score_hist = TH1D("Class 2 scores", "Class scores;Score;Fraction of jets",11,bin_edges)
hist_r_list = [neg_r_hist, b_r_hist, c_r_hist]
hist_s_list = [neg_score_hist, b_score_hist, c_score_hist]
#read in normalization constants for features
if os.path.isfile(normfile_name):
normfile = open(normfile_name, "r")
mean_features = np.zeros(nnfeatures)
std_features = np.zeros(nnfeatures)
counter = 0
for line in normfile:
if int(counter%2) == 0:
mean_features[int(counter/2)] = float(line)
else:
std_features[int((counter-1)/2)] = float(line)
counter += 1
#initialize overall bad events matrix
bad_events = np.empty((0,3), dtype=np.int)
#read in length of test file
if os.path.isfile(paramfile_name):
paramfile = open(paramfile_name, "r")
train_len = int(float(paramfile.readline()))
val_len = int(float(paramfile.readline()))
test_len = int(float(paramfile.readline()))
else:
print("ERROR: Specified parameter file not found")
return 1
batches = int(math.ceil(test_len/batch_size))
for ibatch in range(batches):
#calculate batch indices
istart = ibatch*batch_size
if ibatch == (batches-1) and test_len%batch_size != 0:
iend = istart + (test_len%batch_size)
else:
iend = (ibatch+1)*batch_size
#load batch from file
batch = dgl.batch(dgl.load_graphs(graphfile_name, list(range(istart, iend)))[0])
g_list = dgl.unbatch(batch)
for g in g_list:
features = g.ndata['features_base'].numpy()
pred = g.edata['pred'].numpy()
track_labels = g.ndata['track_info'].numpy()[:,0]
#fill edge score histograms
if not multi_class:
for i in range(pred.shape[0]):
edge_score_hist.Fill(pred[i,0])
else:
for i in range(pred.shape[0]):
neg_score_hist.Fill(pred[i,0])
b_score_hist.Fill(pred[i,1])
c_score_hist.Fill(pred[i,2])
#evaluate bad jets
if is_bad_jet(g, hist_r_list, multi_class, bin_threshold, mult_threshold):
bad_jet = True
else:
bad_jet = False
ntrk = len(features[:,0])
b_trk = c_trk = btoc_trk = nm_trk = nohf_trk = 0
for i in range(ntrk):
if track_labels[i] == 0:
nm_trk += 1
elif track_labels[i] == 1:
b_trk += 1
hist_trk_pt_b_tot.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_b_tot.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_b_tot.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_b_tot.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_b_tot.Fill(features[i,4]*std_features[4]+mean_features[4])
if bad_jet:
hist_trk_pt_b_bad.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_b_bad.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_b_bad.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_b_bad.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_b_bad.Fill(features[i,4]*std_features[4]+mean_features[4])
elif track_labels[i] == 2:
c_trk += 1
hist_trk_pt_c_tot.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_c_tot.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_c_tot.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_c_tot.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_c_tot.Fill(features[i,4]*std_features[4]+mean_features[4])
if bad_jet:
hist_trk_pt_c_bad.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_c_bad.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_c_bad.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_c_bad.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_c_bad.Fill(features[i,4]*std_features[4]+mean_features[4])
elif track_labels[i] == 3:
btoc_trk += 1
hist_trk_pt_btoc_tot.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_btoc_tot.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_btoc_tot.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_btoc_tot.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_btoc_tot.Fill(features[i,4]*std_features[4]+mean_features[4])
if bad_jet:
hist_trk_pt_btoc_bad.Fill(abs(1/(features[i,0]*std_features[0]+mean_features[0])))
hist_trk_theta_btoc_bad.Fill(features[i,1]*std_features[1]+mean_features[1])
hist_trk_phi_btoc_bad.Fill(features[i,2]*std_features[2]+mean_features[2])
hist_trk_d0_btoc_bad.Fill(features[i,3]*std_features[3]+mean_features[3])
hist_trk_z0_btoc_bad.Fill(features[i,4]*std_features[4]+mean_features[4])
else:
nohf_trk += 1
hist_no_trk_jet_b_tot.Fill(b_trk)
hist_no_trk_jet_c_tot.Fill(c_trk)
hist_no_trk_jet_btoc_tot.Fill(btoc_trk)
hist_no_trk_jet_nm_tot.Fill(nm_trk)
hist_no_trk_jet_nohf_tot.Fill(nohf_trk)
hist_frac_trk_b_tot.Fill(b_trk/ntrk)
hist_frac_trk_c_tot.Fill(c_trk/ntrk)
hist_frac_trk_btoc_tot.Fill(btoc_trk/ntrk)
hist_frac_trk_nm_tot.Fill(nm_trk/ntrk)
hist_frac_trk_nohf_tot.Fill(nohf_trk/ntrk)
if bad_jet:
hist_no_trk_jet_b_bad.Fill(b_trk)
hist_no_trk_jet_c_bad.Fill(c_trk)
hist_no_trk_jet_btoc_bad.Fill(btoc_trk)
hist_no_trk_jet_nm_bad.Fill(nm_trk)
hist_no_trk_jet_nohf_bad.Fill(nohf_trk)
hist_frac_trk_b_bad.Fill(b_trk/ntrk)
hist_frac_trk_c_bad.Fill(c_trk/ntrk)
hist_frac_trk_btoc_bad.Fill(btoc_trk/ntrk)
hist_frac_trk_nm_bad.Fill(nm_trk/ntrk)
hist_frac_trk_nohf_bad.Fill(nohf_trk/ntrk)
base_filename = outfile_path+runnumber+"/"+infile_name+"_"+runnumber
ext = ["_recall.png", "_score.png"]
hist_list_list = [hist_r_list, hist_s_list]
for i in range(len(hist_list_list)):
plot_metric_hist(hist_list_list[i], [0.0,1.4], base_filename+ext[i])
plot_hist_diff([hist_trk_pt_b_tot, hist_trk_pt_c_tot, hist_trk_pt_btoc_tot], [hist_trk_pt_b_bad, hist_trk_pt_c_bad, hist_trk_pt_btoc_bad], ["bH", "prompt cH", "bH->cH"], False, True, base_filename+"_trk_pt_comp.png", "HIST")
plot_hist_diff([hist_trk_theta_b_tot, hist_trk_theta_c_tot, hist_trk_theta_btoc_tot], [hist_trk_theta_b_bad, hist_trk_theta_c_bad, hist_trk_theta_btoc_bad], ["bH", "prompt cH", "bH->cH"], False, True, base_filename+"_trk_theta_comp.png", "HIST")
plot_hist_diff([hist_trk_phi_b_tot, hist_trk_phi_c_tot, hist_trk_phi_btoc_tot], [hist_trk_phi_b_bad, hist_trk_phi_c_bad, hist_trk_phi_btoc_bad], ["bH", "prompt cH", "bH->cH"], False, True, base_filename+"_trk_phi_comp.png", "HIST")
plot_hist_diff([hist_trk_d0_b_tot, hist_trk_d0_c_tot, hist_trk_d0_btoc_tot], [hist_trk_d0_b_bad, hist_trk_d0_c_bad, hist_trk_d0_btoc_bad], ["bH", "prompt cH", "bH->cH"], False, True, base_filename+"_trk_d0_comp.png", "HIST")
plot_hist_diff([hist_trk_z0_b_tot, hist_trk_z0_c_tot, hist_trk_z0_btoc_tot], [hist_trk_z0_b_bad, hist_trk_z0_c_bad, hist_trk_z0_btoc_bad], ["bH", "prompt cH", "bH->cH"], False, True, base_filename+"_trk_z0_comp.png", "HIST")
plot_hist([hist_no_trk_jet_b_tot, hist_no_trk_jet_c_tot, hist_no_trk_jet_btoc_tot, hist_no_trk_jet_nohf_tot, hist_no_trk_jet_nm_tot], ["bH", "prompt cH", "bH->cH", "no HF", "no match"], True, False, True, base_filename+"_no_trk_tot.png", "HIST")
plot_hist([hist_frac_trk_b_tot, hist_frac_trk_c_tot, hist_frac_trk_btoc_tot, hist_frac_trk_nohf_tot, hist_frac_trk_nm_tot], ["bH", "prompt cH", "bH->cH", "no HF", "no match"], True, False, True, base_filename+"_frac_trk_tot.png", "HIST")
plot_hist([hist_no_trk_jet_b_bad, hist_no_trk_jet_c_bad, hist_no_trk_jet_btoc_bad, hist_no_trk_jet_nohf_bad, hist_no_trk_jet_nm_bad], ["bH", "prompt cH", "bH->cH", "no HF", "no match"], True, False, True, base_filename+"_no_trk_bad.png", "HIST")
plot_hist([hist_frac_trk_b_bad, hist_frac_trk_c_bad, hist_frac_trk_btoc_bad, hist_frac_trk_nohf_bad, hist_frac_trk_nm_bad], ["bH", "prompt cH", "bH->cH", "no HF", "no match"], True, False, True, base_filename+"_frac_trk_bad.png", "HIST")
