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,