filter_pattern = "{}".format(cutname_to_plot)
else:
filter_pattern = "{}__".format(cutname_to_plot)
if "*" in filter_pattern:
dogrep = True
# cutflow
p.dump_plot(fnames=bkgs,
sig_fnames=sigs,
data_fname=data_fname,
legend_labels=bkg_labels,
signal_labels=signal_labels,
dirname="plots/{}".format(study_name),
filter_pattern=filter_pattern,
dogrep=dogrep,
usercolors=colors,
extraoptions={
"print_yield": True,
"nbins": 20,
"signal_scale": 1,
"legend_ncolumns": 3,
"legend_scalex": 2.0,
"lumi_value": lumi,
"ratio_range": [0., 2.],
},
histxaxislabeloptions=histxaxislabeloptions,
skip2d=True,
)
signal_labels = ["WWZ", "WZZ", "ZZZ"]
usercolors = [2011, 920, 2005, 2007, 2003, 2001]
dirname = "plots/"
p.dump_plot(
filter_pattern="ChannelOnZ__",
dogrep=
True, # Set it to True to match the pattern provided above (i.e. if filter_pattern="fatjetMass_lead" and dogrep=True, then any histogram object with name "*fatjetMass_lead*" will be plotted)
extraoptions={
"signal_scale": 1,
"print_yield": True,
"nbins": 45,
"legend_ncolumns": 3,
"legend_scalex": 1.8,
"legend_scaley": 1.2,
"yield_prec": 4, # yield precision
"ratio_range": [0., 2.],
}, # look at rooutil/plottery/plottery.py for more available option. Also there are some special options in rooutil/plottery_wrapper.py but .. it might be a bit harder to decipher
# _plotter=p.plot_cut_scan # <--- uncomment this line if you want to perform a cut scan optimization
fnames=fnames,
sig_fnames=sig_fnames,
data_fname=data_fname,
legend_labels=legend_labels,
signal_labels=signal_labels,
usercolors=usercolors,
dirname=dirname,
)
p.dump_plot(
filter_pattern="ChannelBTagEMu__",
dogrep=
"LooseLLSR_cutflow": {
"print_yield_bin_indices": range(13, 21)
},
# "TightETSR_cutflow" : {"print_yield_bin_indices" : range(13,21)},
# "TightMTSR_cutflow" : {"print_yield_bin_indices" : range(13,21)},
}
# cutflow
p.dump_plot(
fnames=bkgs,
sig_fnames=[
"{}/vbshww_c2v_6.root".format(hadd_dir),
"{}/vbshww_c2v_4p5.root".format(hadd_dir),
"{}/vbshww_c2v_m2.root".format(hadd_dir),
"{}/vbshww_c2v_3.root".format(hadd_dir),
],
data_fname="{}/data.root".format(hadd_dir),
legend_labels=bkg_labels,
signal_labels=signal_labels,
usercolors=colors,
dirname="cutflow/{}".format(study_name),
filter_pattern="{}_cutflow".format(cutname_to_plot),
dogrep=True,
histxaxislabeloptions=histxaxislabeloptions,
extraoptions={
"print_yield": True,
"yield_prec": 3,
"human_format": False,
},
)
#!/bin/env python
import plottery_wrapper as p
p.dump_plot(
fnames=["hadds/genConvolution_2017/vbshww.root"],
dirname="plots/genConvolution/",
filter_pattern="TagVBSJets",
dogrep=True,
)
def main_analysis_make_plot_userfilter():
import plottery_wrapper as p
import argparse
import sys
parser = argparse.ArgumentParser(description="Plotter for the WVZ analysis")
parser.add_argument('-s' , '--sample_set_name' , dest='sample_set_name' , help='Sample set name (e.g. WVZ2016_v0.0.9 or even the combined ones like WVZ2016_v0.0.9_WVZ2017_v0.0.9_WVZ2018_v0.0.9' , required=True)
parser.add_argument('-t' , '--tag' , dest='tag' , help='Tag of the looper output' , required=True)
parser.add_argument('-d' , '--dirname' , dest='dirname' , help='plots/<sample_set_name>/<tag>/<dirname> for plot output' , required=True)
parser.add_argument('-p' , '--filter_pattern' , dest='filter_pattern' , help='To filter out plot' , required=True)
parser.add_argument('-u' , '--unblind' , dest='unblind' , help='To unblind data' , default=False, action='store_true' )
# parser.add_argument('-f' , '--fake_rate_study' , dest='fake_rate_study' , help='Use MC bkg grouping intended for fake rate study ', default=False, action='store_true' )
parser.add_argument('-n' , '--nbins' , dest='nbins' , help='# of bins' , default=15 )
parser.add_argument('-y' , '--yaxis_log' , dest='yaxis_log' , help='yaxis log' , default=False, action='store_true' )
parser.add_argument('-c' , '--one_signal' , dest='one_signal' , help='one signal hist' , default=False, action='store_true' )
parser.add_argument('-x' , '--xaxis_label' , dest='xaxis_label' , help='xaxis title' , default="" )
parser.add_argument('-r' , '--yaxis_range' , dest='yaxis_range' , help='yaxis range' , default=[] )
parser.add_argument('-i' , '--stack_signal' , dest='stack_signal' , help='stack signal' , default=False, action='store_true' )
args = parser.parse_args()
# Make plot function of the main analysis
# The main thing is that this plotting script runs over the output files
# that was ran with "WVZ201*_v*" ntuples
# This means that it assumes specific histogram files to exists
ntuple_version = args.sample_set_name
tag = args.tag
dirname = args.dirname
filter_pattern = args.filter_pattern
unblind = args.unblind
bkgfiles = [
"outputs/{}/{}/zz.root".format(ntuple_version, tag),
"outputs/{}/{}/ttz.root".format(ntuple_version, tag),
"outputs/{}/{}/twz.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
# "outputs/{}/{}/rare.root".format(ntuple_version, tag),
# "outputs/{}/{}/dyttbar.root".format(ntuple_version, tag),
"outputs/{}/{}/higgs.root".format(ntuple_version, tag),
# "outputs/{}/{}/other.root".format(ntuple_version, tag),
"outputs/{}/{}/othernoh.root".format(ntuple_version, tag),
]
if args.stack_signal:
bkgfiles = [
"outputs/{}/{}/zz.root".format(ntuple_version, tag),
"outputs/{}/{}/ttz.root".format(ntuple_version, tag),
"outputs/{}/{}/twz.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
# "outputs/{}/{}/rare.root".format(ntuple_version, tag),
# "outputs/{}/{}/dyttbar.root".format(ntuple_version, tag),
"outputs/{}/{}/higgs.root".format(ntuple_version, tag),
# "outputs/{}/{}/other.root".format(ntuple_version, tag),
"outputs/{}/{}/othernoh.root".format(ntuple_version, tag),
"outputs/{}/{}/wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/wzz.root".format(ntuple_version, tag),
"outputs/{}/{}/zzz.root".format(ntuple_version, tag),
]
if args.one_signal:
bkgfiles = [
"outputs/{}/{}/zz.root".format(ntuple_version, tag),
"outputs/{}/{}/ttz.root".format(ntuple_version, tag),
"outputs/{}/{}/twz.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
# "outputs/{}/{}/rare.root".format(ntuple_version, tag),
# "outputs/{}/{}/dyttbar.root".format(ntuple_version, tag),
"outputs/{}/{}/higgs.root".format(ntuple_version, tag),
# "outputs/{}/{}/other.root".format(ntuple_version, tag),
"outputs/{}/{}/othernoh.root".format(ntuple_version, tag),
"outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
# bkgnames = ["ZZ", "t#bar{t}Z", "tWZ", "WZ", "Other"]
bkgnames = ["ZZ", "t#bar{t}Z", "tWZ", "WZ", "Higgs", "Other"]
if args.stack_signal:
bkgnames = ["ZZ", "t#bar{t}Z", "tWZ", "WZ", "Higgs", "Other", "WWZ", "WZZ", "ZZZ"]
if args.one_signal:
bkgnames = ["ZZ", "t#bar{t}Z", "tWZ", "WZ", "Higgs", "Other", "VVV"]
# bkgnames = ["t#bar{t}Z", "ZZ", "WZ", "tWZ", "Other"]
# bkgnames = ["t#bar{t}Z", "ZZ", "WZ", "tWZ", "Other", "Z/Z#gamma/t#bar{t}", "Higgs"]
sigfiles = [
# "outputs/{}/{}/zh_wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/wzz.root".format(ntuple_version, tag),
#"outputs/{}/{}/www.root".format(ntuple_version, tag),
"outputs/{}/{}/zzz.root".format(ntuple_version, tag),
# "outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
onesigfiles = [
# "outputs/{}/{}/zh_wwz.root".format(ntuple_version, tag),
# "outputs/{}/{}/wwz.root".format(ntuple_version, tag),
# "outputs/{}/{}/wzz.root".format(ntuple_version, tag),
#"outputs/{}/{}/www.root".format(ntuple_version, tag),
# "outputs/{}/{}/zzz.root".format(ntuple_version, tag),
"outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
sigfiles_detail = [
"outputs/{}/{}/nonh_wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/zh_wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/nonh_wzz.root".format(ntuple_version, tag),
"outputs/{}/{}/wh_wzz.root".format(ntuple_version, tag),
#"outputs/{}/{}/www.root".format(ntuple_version, tag),
"outputs/{}/{}/nonh_zzz.root".format(ntuple_version, tag),
"outputs/{}/{}/zh_zzz.root".format(ntuple_version, tag),
# "outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
bkgfilesfake = [
"outputs/{}/{}/triother.root".format(ntuple_version, tag),
"outputs/{}/{}/dy.root".format(ntuple_version, tag),
"outputs/{}/{}/ttbar.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
]
bkgnamesddfake = ["Other", "DY", "t#bar{t}", "WZ"]
if args.stack_signal:
sigfiles = []
onesigfiles = []
# sigfiles = sigfiles_detail
sig_labels = ["WWZ", "WZZ", "ZZZ", "VVV"]
# sig_labels = ["WWZ", "ZH#rightarrowWW", "WZZ", "WH#rightarrowZZ", "ZZZ", "ZH#rightarrowZZ"]
colors = [2001, 2005, 2007, 2003, 2011, 920, 2012, 2011, 2002]
if args.stack_signal:
colors = [2001, 2005, 2007, 2003, 2011, 920, 2, 4, 1]
fakeVRcolors = [920, 2012, 2011, 2003]
if "2016" in ntuple_version: lumi = 35.9
if "2017" in ntuple_version: lumi = 41.3
if "2018" in ntuple_version: lumi = 59.74
if "2016" in ntuple_version and "2017" in ntuple_version and "2018" in ntuple_version: lumi = 137
p.dump_plot(fnames=bkgfilesfake if "PlusX" in filter_pattern else bkgfiles,
sig_fnames=[] if "PlusX" in filter_pattern else (onesigfiles if args.one_signal else sigfiles),
data_fname="outputs/{}/{}/data.root".format(ntuple_version, tag) if unblind else None,
usercolors=fakeVRcolors if "PlusX" in filter_pattern else colors,
legend_labels=bkgnamesddfake if "PlusX" in filter_pattern else bkgnames,
signal_labels=["VVV"] if args.one_signal else sig_labels,
dirname="plots/{}/{}/{}".format(ntuple_version, tag, dirname),
filter_pattern=filter_pattern,
dogrep=True,
extraoptions={
"print_yield":True,
"nbins":int(args.nbins),
"signal_scale": 1,
# "signal_scale": 20,
# "signal_scale": 10,
# "signal_scale": "auto",
"legend_scalex":2.0 if "PlusX" in filter_pattern else 1.3,
"legend_scaley":0.7 if "PlusX" in filter_pattern else 1.2,
"legend_ncolumns": 2 if "PlusX" in filter_pattern else 3,
# "legend_smart": False if args.yaxis_log else True,
"legend_smart": True,
"yaxis_log":args.yaxis_log,
"ymax_scale": 1.5 if "PlusX" in filter_pattern else 1.2,
"lumi_value":lumi,
# "no_overflow": True,
"remove_underflow": True,
"xaxis_ndivisions":505,
"ratio_range":[0.,2.],
"xaxis_label":args.xaxis_label,
"ratio_xaxis_title":args.xaxis_label,
"yaxis_range":[float(x) for x in args.yaxis_range.split(",")] if isinstance(args.yaxis_range, six.string_types) and len(args.yaxis_range) > 0 else [],
"no_ratio": False if unblind else True,
"yield_prec":2,
},
# _plotter=p.plot_cut_scan,
)
p.dump_plot(
fnames=bkg_fnames,
sig_fnames=sig_fnames,
data_fname="{}/data.root".format(input_dir),
dirname=output_dir+"/log" if args.yaxis_log else output_dir+"/lin",
legend_labels=legend_labels,
signal_labels=["WWW", "VH"],
donorm=False,
filter_pattern=hist_filters,
signal_scale=sig_scale,
extraoptions={
"nbins":int(args.nbins),
"print_yield":True,
"yaxis_log":args.yaxis_log,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_smart":False if args.yaxis_log else True,
"yaxis_range":args.yaxis_range.split(',') if args.yaxis_range else [],
"remove_underflow":args.rm_udflow,
"bkg_sort_method": "unsorted",
"lumi_value": lumi,
"blind": not args.draw_data,
"ratio_range": [0., 2.],
"xaxis_label": args.xaxis_title if args.xaxis_title and not args.draw_data else "",
"xaxis_title_size": 0.05 if args.xaxis_title and not args.draw_data else None,
"xaxis_title_offset": 1.5 if args.xaxis_title and not args.draw_data else None,
"ratio_xaxis_title": args.xaxis_title if args.xaxis_title and args.draw_data else "",
"ratio_xaxis_title_size": 0.135 if args.xaxis_title and args.draw_data else None,
"ratio_xaxis_title_offset": 1.25 if args.xaxis_title and args.draw_data else None,
},
do_sum=args.sum_hists,
dogrep=args.do_grep,
output_name=args.output_name if args.sum_hists else None,
usercolors=histcolors,
)
#!/bin/env python
from __future__ import absolute_import
import plottery_wrapper as p
p.dump_plot(
fnames=["4l2v.root"],
data_fname="incl.root",
dirname="plots/lin",
# donorm=True,
extraoptions={
"nbins": 20,
"ratio_range": [0., 2.],
"print_yield": True,
"legend_datalabel": "incl",
"lumi_value": 1
},
)
p.dump_plot(
fnames=["4l2v.root"],
data_fname="incl.root",
dirname="plots/log",
# donorm=True,
extraoptions={
"nbins": 20,
"ratio_range": [0., 2.],
"print_yield": True,
"yield_prec": 5,
"yaxis_log": True,
"legend_smart": False,
import glob
import sys
if len(sys.argv) > 1:
histfiles = [sys.argv[1]]
else:
histfiles = glob.glob("*_hist.root")
for histfile in histfiles:
suffix = histfile.replace("_hist.root", "")
p.dump_plot(
fnames=[histfile],
filter_pattern="Wgt__",
dogrep=True,
dirname="plots_{}".format(suffix),
extraoptions={
"nbins": 60,
"print_yield": True,
"lumi_value": 1,
},
)
p.dump_plot(
fnames=[histfile],
dirname="plots_{}_log".format(suffix),
filter_pattern="Wgt__",
dogrep=True,
extraoptions={
"yaxis_log": True,
"lumi_value": 1,
},
)
# "{}/merged/GluGluHToWWToLNuQQ_merged.root".format(input_path_dir),
# "outputs/HWW2016_v5.0.0/test15/merged/VHToNonbb_merged.root",
]
legend_labels = ["t#bar{t}", "W#rightarrowlv", "WW", "QCD"]
p.dump_plot(
dirname=output_dir+"/log" if args.yaxis_log else output_dir+"/lin",
fnames=bkg_fnames,
sig_fnames=sig_fnames,
filter_pattern=filter_pattern,
signal_scale=sig_scale,
legend_labels=legend_labels,
extraoptions={
"nbins":int(args.nbins),
"print_yield":True,
"yaxis_log":args.yaxis_log,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_smart":False if args.yaxis_log else True,
"yaxis_range":args.yaxis_range.split(',') if args.yaxis_range else [],
"remove_underflow":args.rm_udflow,
"bkg_sort_method": "unsorted",
},
# _plotter=p.plot_cut_scan,
)
if args.do_scan:
p.dump_plot(
dirname=output_dir + "/scan",
fnames=bkg_fnames,
sig_fnames=sig_fnames,
def main_analysis_make_plot_userfilter():
# Make plot function of the main analysis
# The main thing is that this plotting script runs over the output files
# that was ran with "WVZ201*_v*" ntuples
# This means that it assumes specific histogram files to exists
try:
ntuple_version = sys.argv[1]
tag = sys.argv[2]
dirname = sys.argv[3]
filterpattern = sys.argv[4]
except:
usage()
bkgfiles = [
"outputs/{}/{}/ttz.root".format(ntuple_version, tag),
"outputs/{}/{}/zz.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
"outputs/{}/{}/twz.root".format(ntuple_version, tag),
"outputs/{}/{}/rare.root".format(ntuple_version, tag),
"outputs/{}/{}/dyttbar.root".format(ntuple_version, tag),
"outputs/{}/{}/higgs.root".format(ntuple_version, tag),
]
sigfiles = [
"outputs/{}/{}/wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/wzz.root".format(ntuple_version, tag),
#"outputs/{}/{}/www.root".format(ntuple_version, tag),
"outputs/{}/{}/zzz.root".format(ntuple_version, tag),
# "outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
sigfiles_detail = [
"outputs/{}/{}/nonh_wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/zh_wwz.root".format(ntuple_version, tag),
"outputs/{}/{}/nonh_wzz.root".format(ntuple_version, tag),
"outputs/{}/{}/wh_wzz.root".format(ntuple_version, tag),
#"outputs/{}/{}/www.root".format(ntuple_version, tag),
"outputs/{}/{}/nonh_zzz.root".format(ntuple_version, tag),
"outputs/{}/{}/zh_zzz.root".format(ntuple_version, tag),
# "outputs/{}/{}/sig.root".format(ntuple_version, tag),
]
bkgfilesfake = [
"outputs/{}/{}/ttz.root".format(ntuple_version, tag),
"outputs/{}/{}/zz.root".format(ntuple_version, tag),
"outputs/{}/{}/wz.root".format(ntuple_version, tag),
"outputs/{}/{}/twz.root".format(ntuple_version, tag),
"outputs/{}/{}/rare.root".format(ntuple_version, tag),
"outputs/{}/{}/dy.root".format(ntuple_version, tag),
"outputs/{}/{}/ttbar.root".format(ntuple_version, tag),
]
colors = [2005, 2001, 2003, 2007, 920, 2012, 2011]
if "2016" in ntuple_version: lumi = 35.9
if "2017" in ntuple_version: lumi = 41.3
if "2018" in ntuple_version: lumi = 59.74
p.dump_plot(fnames=bkgfiles,
sig_fnames=sigfiles,
data_fname="outputs/{}/{}/data.root".format(
ntuple_version, tag),
usercolors=colors,
legend_labels=[
"t#bar{t}Z", "ZZ", "WZ", "tWZ", "Other",
"Z/Z#gamma/t#bar{t}", "Higgs"
],
signal_labels=["WWZ", "WZZ", "ZZZ", "VVV"],
dirname="plots/{}/{}/{}".format(ntuple_version, tag, dirname),
filter_pattern=filter_pattern,
dogrep=True,
extraoptions={
"print_yield": True,
"nbins": 15,
"signal_scale": 1,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_ncolumns": 3,
"ymax_scale": 1.2,
"lumi_value": lumi,
})
def dilep_analysis_make_plot():
# Make plot function of the dilepton region
# This is going to run over looper output from "Dilep201*_v*" ntuple sets
# The "Dilep201*_v*" ntuple sets have different MC and data list
# As of May 19, 2019 it had only DY and dimuon data events
try:
ntuple_version = sys.argv[1]
tag = sys.argv[2]
except:
usage()
bkgfiles = [
"outputs/{}/{}/top.root".format(ntuple_version, tag),
"outputs/{}/{}/dy.root".format(ntuple_version, tag),
"outputs/{}/{}/wj.root".format(ntuple_version, tag),
"outputs/{}/{}/ww.root".format(ntuple_version, tag),
]
sigfiles = []
sigfiles_detail = []
data_fname = "outputs/{}/{}/data.root".format(ntuple_version, tag)
colors = [2005, 2001, 2003, 2007, 920, 2012, 2011]
p.dump_plot(fnames=bkgfiles,
sig_fnames=sigfiles,
data_fname=data_fname,
usercolors=colors,
legend_labels=["t#bar{t}", "DY", "W", "WW"],
signal_labels=[],
dirname="plots/{}/{}/lin".format(ntuple_version, tag),
filter_pattern="__",
dogrep=True,
extraoptions={
"print_yield": True,
"nbins": 180,
"signal_scale": 1,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_ncolumns": 3,
"ymax_scale": 1.2,
"lumi_value": 59.74,
"remove_underflow": True,
})
p.dump_plot(fnames=bkgfiles,
sig_fnames=sigfiles,
data_fname=data_fname,
usercolors=colors,
legend_labels=["t#bar{t}", "DY", "W", "WW"],
signal_labels=[],
dirname="plots/{}/{}/log".format(ntuple_version, tag),
filter_pattern="__",
dogrep=True,
extraoptions={
"print_yield": True,
"nbins": 180,
"signal_scale": 1,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_ncolumns": 3,
"ymax_scale": 1.2,
"lumi_value": 59.74,
"yaxis_log": True,
"legend_smart": False,
"remove_underflow": True,
})
sigfnames = ["outputs/sig_skim.root"]
colors = [2001, 2005, 2007, 2003, 920]
bkgfnames.reverse()
colors.reverse()
p.dump_plot(
fnames=bkgfnames,
sig_fnames=sigfnames,
dirname="plots/",
usercolors=colors,
signal_scale=1,
# If one wants to plot just a few
# filter_pattern="HighBDT__lep3MT,HighBDT__lep4MT",
# dogrep=True,
filter_pattern="Weight__BDTCombine",
dogrep=True,
extraoptions={
"nbins": 5,
"signal_scale": 1,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"legend_ncolumns": 3,
"lumi_value": 137,
"xaxis_ndivisions": 505,
"bkg_sort_method": "unsorted",
"print_yield": True,
},
# _plotter=p.plot_cut_scan,
)
hadd_dir = "hadds/{}".format(study_name)
# cutflow
p.dump_plot(
fnames=[
"{}/wz.root".format(hadd_dir),
"{}/tt1l.root".format(hadd_dir),
"{}/tt2l.root".format(hadd_dir),
"{}/ttw.root".format(hadd_dir),
"{}/ttz.root".format(hadd_dir),
"{}/tth.root".format(hadd_dir),
"{}/ssww.root".format(hadd_dir),
],
sig_fnames=[
"{}/vbshww.root".format(hadd_dir),
# "{}/lambda20_vbshww.root".format(hadd_dir),
# "{}/lambdam20_vbshww.root".format(hadd_dir),
],
# data_fname="{}/vbshww.root".format(hadd_dir),
dirname="plots/cutflow/{}".format(study_name),
filter_pattern="{}_cutflow".format(
cutname_to_plot), # TODO this is not generalized yet
extraoptions={
"print_yield": True,
"yield_prec": 3,
},
)
# cutflow
p.dump_plot(
fnames=[
p.dump_plot(
fnames=[
"{}/wz.root".format(hadd_dir),
"{}/tt1l.root".format(hadd_dir),
"{}/tt2l.root".format(hadd_dir),
"{}/ttw.root".format(hadd_dir),
"{}/ttz.root".format(hadd_dir),
"{}/tth.root".format(hadd_dir),
"{}/ssww.root".format(hadd_dir),
],
sig_fnames=[
"{}/vbshww.root".format(hadd_dir),
# "{}/lambda20_vbshww.root".format(hadd_dir),
# "{}/lambdam20_vbshww.root".format(hadd_dir),
],
legend_labels=bkg_labels,
signal_labels=signal_labels,
dirname="plots/sig_150x/{}".format(study_name),
filter_pattern="{}__".format(cutname_to_plot),
dogrep=True,
usercolors=colors,
extraoptions={
"print_yield": True,
"nbins": 20,
"signal_scale": 150,
# "signal_scale": "auto",
"legend_ncolumns": 3,
"legend_scalex": 1.8,
"lumi_value": lumi,
},
)
from plottery import plottery as plt
import ROOT as r
import sys
filename = "debug.root"
if len(sys.argv) > 1:
filename = sys.argv[1]
p.dump_plot(
fnames=[filename],
dirname="plots/md",
dogrep=True,
filter_pattern="Root__dz_md",
extraoptions={
"yaxis_log": True,
"legend_smart": False,
"print_yield": True,
"remove_overflow": False,
"remove_underflow": False,
"print_mean": True
},
)
p.dump_plot(
fnames=[filename],
dirname="plots/md",
dogrep=True,
filter_pattern="Root__dz_true_md",
extraoptions={
"yaxis_log": True,
"legend_smart": False,
from plottery import plottery as plt
import ROOT as r
import sys
filename = "debug.root"
if len(sys.argv) > 1:
filename = sys.argv[1]
p.dump_plot(
fnames=[filename],
dirname="plots/hit",
dogrep=True,
filter_pattern="Root__nhits_",
extraoptions={
"yaxis_log": False,
"legend_smart": False,
"print_yield": True,
"remove_overflow": False,
"remove_underflow": False,
"print_mean": True
},
)
p.dump_plot(
fnames=[filename],
dirname="plots/mdoccu",
dogrep=True,
filter_pattern="Root__n_md_",
extraoptions={
"yaxis_log": False,
"legend_smart": False,