def main():
indir = "input/2019-09-06_hem/"
acc = acc_from_dir(indir)
# for year in [2018]:
# data = re.compile(f'EGamma_{year}')
# mc = re.compile(f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*|WJet.*HT.*){year}')
# for region in ['cr_1e_j','cr_1e_j_nohem', 'cr_2e_j','cr_1e_j_nohem']:
# for distribution in ['recoil','ak4_phi','electron_phi','ak4_pt0']:
# make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, outdir=f'./output/{os.path.basename(indir)}')
for year in [2018]:
data = re.compile(f'MET_{year}')
mc = re.compile(
f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*|WJet.*HT.*){year}'
)
for region in ['cr_1m_j', 'cr_1m_j_nohem']:
for distribution in ['ak4_phi', 'recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
outdir=f'./output/{os.path.basename(indir)}')
def data_mc_after_kfac():
indir = "input/21Aug_newsf"
acc = acc_from_dir(indir)
outdir = f'./output/{os.path.basename(indir)}'
for year in [2017, 2018]:
data = re.compile(f'MET_{year}')
mc = re.compile(
f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*){year}'
)
region = 'cr_2m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e3),
outdir=outdir)
data = re.compile(f'MET_{year}')
mc = re.compile(
f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*|W.*HT.*).*{year}'
)
region = 'cr_1m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e5),
outdir=outdir)
def main():
args = parse_commandline()
klepto = True
if klepto:
acc = dir_archive(args.inpath, serialized=True, compression=0, memsize=1e3)
acc.load('recoil')
acc.load('mjj')
acc.load('sumw')
acc.load('sumw_pileup')
acc.load('nevents')
else:
acc = acc_from_dir(args.inpath)
args.outdir = pjoin('./output/',list(filter(lambda x:x,args.inpath.split('/')))[-1])
for channel in args.channel.split(','):
print(channel)
if channel == 'monojet':
from legacy_monojet import legacy_limit_input_monojet
legacy_limit_input_monojet(acc, args)
elif channel == 'monov':
from legacy_monov import legacy_limit_input_monov
legacy_limit_input_monov(acc, args)
elif channel == 'vbfhinv':
from legacy_vbf import legacy_limit_input_vbf
legacy_limit_input_vbf(acc, outdir=args.outdir, unblind=args.unblind)
def main():
# indir = "/home/albert/repos/bucoffea/bucoffea/plot/input/eff/test"
tag = '120pfht_hltmu'
indir = f"/home/albert/repos/bucoffea/bucoffea/plot/input/eff/{tag}"
acc = acc_from_dir(indir)
for year in [2017, 2018]:
region = '1m'
for dataset in ["WJetsToLNu-MLM", "SingleMuon"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag)
region = '2m'
for dataset in ["DYNJetsToLL_M-50-MLM", "SingleMuon"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag)
region = '1m_hlt'
for dataset in ["WJetsToLNu-MLM", "SingleMuon"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag)
region = '2m_hlt'
for dataset in ["DYNJetsToLL_M-50-MLM", "SingleMuon"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag)
region = '1e'
for dataset in ["WJetsToLNu-MLM", "EGamma"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag, distribution='met')
region = '2e'
for dataset in ["DYNJetsToLL_M-50-MLM", "EGamma"]:
plot_recoil(acc,region,dataset=dataset,year=year, tag=tag, distribution='met')
region_comparison_plot(tag)
sf_comparison_plot(tag)
def main():
args = parse_commandline()
klepto = True
if klepto:
acc = dir_archive(args.inpath,
serialized=True,
compression=0,
memsize=1e3)
acc.load('recoil')
acc.load('mjj')
acc.load('sumw')
acc.load('sumw_pileup')
acc.load('nevents')
else:
acc = acc_from_dir(args.inpath)
outdir = pjoin('./output/', os.path.basename(args.inpath))
if args.channel == 'monojet':
from legacy_monojet import legacy_limit_input_monojet
legacy_limit_input_monojet(acc, outdir=outdir)
elif args.channel == 'monov':
from legacy_monov import legacy_limit_input_monov
legacy_limit_input_monov(acc, outdir=outdir)
elif args.channel == 'vbfhinv':
from legacy_vbf import legacy_limit_input_vbf
legacy_limit_input_vbf(acc, outdir=outdir)
def main():
acc = acc_from_dir("./input/das_lhevpt_v3")
outputrootfile = uproot.recreate(f'2017_gen_v_pt_stat1_qcd_sf.root')
plot_lhe_v_pt(acc,
tag='wjet_dilep',
regex='W.*',
pttype='dilepton',
outputrootfile=outputrootfile)
plot_lhe_v_pt(acc,
tag='dy_dilep',
regex='.*DY.*',
pttype='dilepton',
outputrootfile=outputrootfile)
outputrootfile = uproot.recreate(f'2017_gen_v_pt_lhe_qcd_sf.root')
plot_lhe_v_pt(acc,
tag='wjet_nano',
regex='W.*',
pttype='nano',
outputrootfile=outputrootfile)
plot_lhe_v_pt(acc,
tag='dy_nano',
regex='.*DY.*',
pttype='nano',
outputrootfile=outputrootfile)
def ttbarcheck():
indir = "input/21Aug_newsf"
acc = acc_from_dir(indir)
outdir = f'./output/{os.path.basename(indir)}/ttbar/fxfx_vs_mlm'
for year in [2017, 2018]:
data = re.compile(f'TTJets.*FXFX.*{year}')
mc = re.compile(f'TTJets.*MLM.*{year}')
region = 'cr_2m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e3),
outdir=outdir)
outdir = f'./output/{os.path.basename(indir)}/ttbar/fxfx_vs_pow'
for year in [2017, 2018]:
data = re.compile(f'TTJets.*FXFX.*{year}')
mc = re.compile(f'TT.*pow.*{year}')
region = 'cr_2m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e3),
outdir=outdir)
def main():
infile = os.path.abspath('input/2019-09-09_gen_dilep_sf/')
acc = acc_from_dir(infile)
for year in [2017, 2018]:
mc = {
'cr_1m_j':
re.compile(
f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*|W.*HT.*).*{year}'
),
'cr_1e_j':
re.compile(
f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*|W.*HT.*).*{year}'
),
'cr_2m_j':
re.compile(
f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*).*{year}'
),
'cr_2e_j':
re.compile(
f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*).*{year}'
),
'cr_g_j':
re.compile(f'(GJets.*|QCD_HT.*|W.*HT.*).*{year}'),
}
cr_ratio_plot(acc,
year=year,
tag='losf',
outdir=f'./output/{os.path.basename(infile)}',
mc=mc)
for year in [2017, 2018]:
mc = {
'cr_1m_j':
re.compile(
f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*|W.*FXFX.*).*{year}'
),
'cr_1e_j':
re.compile(
f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*|W.*FXFX.*).*{year}'
),
'cr_2m_j':
re.compile(
f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*).*{year}'
),
'cr_2e_j':
re.compile(
f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*).*{year}'
),
'cr_g_j':
re.compile(f'(GJets.*|QCD_HT.*|W.*FXFX.*).*{year}'),
}
cr_ratio_plot(acc,
year=year,
tag='nlo',
outdir=f'./output/{os.path.basename(infile)}',
mc=mc)
def main():
acc = acc_from_dir("./input/")
# plot_ht_stitching(acc, tag='dy', regex='DY.*HT(?!(.*new.*))')
# acc = acc_from_dir("./input/wjet")
plot_ht_stitching(acc, tag='wjet', regex='W.*HT(?!(.*new.*))')
# acc = acc_from_dir("./input/znunu")
plot_ht_stitching(acc, tag='znunu', regex='ZJets.*NuNu.*HT(?!(.*new.*))')
# acc = acc_from_dir("./input/gjets")
plot_ht_stitching(acc, tag='gjets', regex='GJets.*HT(?!(.*new.*))')
plot_ht_stitching(acc, tag='qcd', regex='QCD.*HT(?!(.*new.*))')
def cr_ratio():
indir=os.path.abspath('input/2019-09-09_gen_dilep_sf/')
acc = acc_from_dir(indir)
for year in [2017,2018]:
data = {
'cr_1m_j' : f'MET_{year}',
'cr_2m_j' : f'MET_{year}',
'cr_1e_j' : f'EGamma_{year}',
'cr_2e_j' : f'EGamma_{year}',
'cr_g_j' : f'EGamma_{year}',
}
mc_lo = {
'cr_1m_j' : re.compile(f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*|W.*HT.*).*{year}'),
'cr_1e_j' : re.compile(f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*|W.*HT.*).*{year}'),
'cr_2m_j' : re.compile(f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*).*{year}'),
'cr_2e_j' : re.compile(f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DYJetsToLL_M-50_HT_MLM.*).*{year}'),
'cr_g_j' : re.compile(f'(GJets.*|QCD_HT.*|W.*HT.*).*{year}'),
}
mc_nlo = {
'cr_1m_j' : re.compile(f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*|W.*FXFX.*).*{year}'),
'cr_1e_j' : re.compile(f'(TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*|W.*FXFX.*).*{year}'),
'cr_2m_j' : re.compile(f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*).*{year}'),
'cr_2e_j' : re.compile(f'(EW.*|TTJets.*FXFX.*|Diboson.*|ST.*|QCD_HT.*|.*DY.*FXFX.*).*{year}'),
'cr_g_j' : re.compile(f'(GJets.*|QCD_HT.*|W.*FXFX.*).*{year}'),
}
outdir = f'./output/{os.path.basename(indir)}/ratios'
cr_ratio_plot(acc, year=year,tag='losf',outdir=outdir, mc=mc_lo)
cr_ratio_plot(acc, year=year,tag='nlo',outdir=outdir, mc=mc_nlo)
for region in mc_lo.keys():
outdir = f'./output/{os.path.basename(indir)}/{region}'
plotset = plot_settings[region]
for distribution in plotset.keys():
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data[region],
mc=mc_lo[region],
ylim=plot_settings['ylim'],
tag = 'losf',
outdir=f'./output/{os.path.basename(indir)}/{region}')
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data[region],
mc=mc_nlo[region],
ylim=plot_settings['ylim'],
tag = 'nlo',
outdir=f'./output/{os.path.basename(indir)}/{region}')
def main():
# indir = "../input/21Aug19_v4_pdfwgt"
# acc = acc_from_dir(indir)
# for year in [2017]:
# data = re.compile(f'MET_{year}')
# mc = re.compile(f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*){year}')
# region='cr_2m_j'
# for distribution in ['recoil']:
# make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e3), outdir=f'./output/{os.path.basename(indir)}')
indir = "../../input/21Aug19_v2_newpu"
acc = acc_from_dir(indir)
for year in [2017]:
data = re.compile(f'MET_{year}')
mc = re.compile(
f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*){year}'
)
region = 'cr_2m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e3),
outdir=f'./output/{os.path.basename(indir)}')
for year in [2017]:
data = re.compile(f'MET_{year}')
mc = re.compile(
f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*){year}'
)
region = 'cr_2m_j'
for distribution in ['recoil', 'ak4_pt0']:
make_plot(acc,
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc,
ylim=(1e-3, 1e3),
outdir=f'./output/{os.path.basename(indir)}/ttbarcheck')
def photon_triggers():
tag = 'gamma'
indir = f"/home/albert/repos/bucoffea/bucoffea/plot/input/gamma_alltrig"
acc = acc_from_dir(indir)
# All regions
regions = []
for k in acc['photon_pt0'].axis('region').identifiers():
k = str(k)
if not k.startswith('tr_g'):
continue
if 'pt_trig_cut' in k:
continue
regions.append(
k.replace("tr_", "").replace("_num", "").replace("_den", ""))
for year in [2017, 2018]:
# for distribution in ['photon_pt0', 'recoil']:
for region in set(regions):
for dataset in ["GJets_HT_MLM", "JetHT"]:
if 'photon_pt_' in region:
distribution = 'recoil_noweight'
axis_name = 'recoil'
else:
distribution = 'photon_pt0'
axis_name = 'pt'
for noscale in [False]:
if ('JetHT' in dataset) and noscale:
continue
plot_recoil(copy.deepcopy(acc),
region,
dataset=dataset,
year=year,
tag=tag,
distribution=distribution,
axis_name=axis_name,
noscale=noscale)
# plot_recoil(acc,region,dataset=dataset,year=year, tag=tag, distribution='recoil',axis_name='recoil')
data_mc_comparison_plot(tag)
def main():
# The input is saved in individual *.coffea files
# in the directory given here.
indir = "./input/eff/gamma"
# 'acc' is short for 'accumulator', which is the output
# produced by a coffea processor. It behaves like a python dict,
# so you can import it also in an interactive python shell to play with it
# Note that 'acc_from_indir' uses caching, so subsequent readings will be much faster!
acc = acc_from_dir(indir)
# The make_plot function currently just makes a dimuon
# mass plot for the dimuon control region.
# TODO:
# * Make more flexible: More regions, more plots, etc
# * Selection of input processes is currently just hardcoded -> handle better!
for year in [2017]:
data = re.compile(f'SingleMuon_{year}')
mc = re.compile(f'DY.*HT.*{year}')
region = 'cr_2m_j'
for distribution in ['recoil', 'dimuon_mass']:
make_plot(copy.deepcopy(acc),
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc)
for year in [2017, 2018]:
data = re.compile(f'SingleMuon_{year}')
mc = re.compile(f'W.*HT.*{year}')
region = 'cr_1m_j'
for distribution in ['recoil']:
make_plot(copy.deepcopy(acc),
region=region,
distribution=distribution,
year=year,
data=data,
mc=mc)
def met_triggers_ht():
tag = '120pfht_hltmu'
indir = f"/home/albert/repos/bucoffea/bucoffea/plot/input/16Jul19_incomplete_v7"
acc = acc_from_dir(indir)
# for noscale in [False]:
# distribution = 'recoil_noweight' if noscale else 'recoil'
# for year in [2017, 2018]:
# region = '1m'
# for dataset in ["WJetsToLNu_HT_MLM", "SingleMuon"]:
# if dataset=='SingleMuon' and noscale:
# continue
# plot_recoil(acc,region,distribution=distribution,axis_name='recoil',dataset=dataset,year=year, tag=tag, noscale=noscale)
# region = '2m'
# for dataset in ["DYJetsToLL_M-50_HT_MLM", "SingleMuon"]:
# if dataset=='SingleMuon' and noscale:
# continue
# plot_recoil(acc,region,distribution=distribution,axis_name='recoil',dataset=dataset,year=year, tag=tag, noscale=noscale)
# # region = '1m_hlt'
# # for dataset in ["WJetsToLNu_HT_MLM", "SingleMuon"]:
# # plot_recoil(acc,region,dataset=dataset,year=year, tag=tag)
# region = '2m_hlt'
# for dataset in ["DYJetsToLL_M-50_HT_MLM", "SingleMuon"]:
# if dataset=='SingleMuon' and noscale:
# continue
# plot_recoil(acc,region,distribution=distribution,axis_name='recoil',dataset=dataset,year=year, tag=tag, noscale=noscale)
# region = '1e'
# for dataset in ["WJetsToLNu_HT_MLM", "EGamma"]:
# plot_recoil(acc,region,dataset=dataset,year=year, tag=tag, distribution='met')
# # region = '2e'
# # for dataset in ["DYJetsToLL_M-50_HT_MLM", "EGamma"]:
# # plot_recoil(acc,region,dataset=dataset,year=year, tag=tag, distribution='met')
# region_comparison_plot(tag)
# sf_comparison_plot(tag)
data_mc_comparison_plot(tag)
def main():
acc = acc_from_dir('input/photon_pt_cut/')
pdf_plot(acc)
def eta_phi_plot(inpath):
indir = os.path.abspath(inpath)
acc = acc_from_dir(indir)
outdir = pjoin('./output/', os.path.basename(indir))
if not os.path.exists(outdir):
os.makedirs(outdir)
for year in [2017, 2018]:
data = {
'cr_1m_j': f'MET_{year}',
'cr_2m_j': f'MET_{year}',
'cr_1e_j': f'EGamma_{year}',
'cr_2e_j': f'EGamma_{year}',
# 'cr_g_j' : f'EGamma_{year}',
}
for region, datare in data.items():
distributions = ['ak4_eta_phi']
if 'e_' in region:
distributions.append('electron_eta_phi')
elif 'm_' in region:
distributions.append('muon_eta_phi')
for distribution in distributions:
h = copy.deepcopy(acc[distribution])
h = merge_extensions(h,
acc,
reweight_pu=('nopu' in distribution))
scale_xs_lumi(h)
h = merge_datasets(h)
h = h.integrate('dataset', datare)
h = h.integrate(h.axis('region'), region)
fig, ax, _ = plot2d(h, xaxis='eta')
ax.text(0.,
1.,
region,
fontsize=10,
horizontalalignment='left',
verticalalignment='top',
color='white',
transform=ax.transAxes)
ax.text(1.,
0.,
distribution,
fontsize=10,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
fig.text(1.,
1.,
f'{lumi(year)} fb$^{{-1}}$ ({year})',
fontsize=14,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
fig.text(0.,
1.,
'$\\bf{CMS}$ internal',
fontsize=14,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
outname = pjoin(outdir, f'{region}_{distribution}_{year}.pdf')
fig.savefig(outname)
print(f'Created file {outname}')
def main():
indir = "../lo_vs_nlo/input/2019-09-23_photon_overlap"
acc = acc_from_dir(indir)
for region in ['cr_2m_j', 'cr_2e_j', 'cr_1m_j', 'cr_1e_j', 'cr_g_j']:
if '_1m_' in region or '_2m_' in region:
sel_2017 = re.compile(f'MET_2017')
sel_2018 = re.compile(f'MET_2018')
else:
sel_2017 = re.compile(f'EGamma_2017')
sel_2018 = re.compile(f'EGamma_2018')
for distribution in ['recoil']:
# fig, ax, rax = make_plot(acc, region=region,distribution=distribution, year=2016, data=data, mc=mc, ylim=(1e-3,1e3), rylim=(0,2),outdir=f'./output/{os.path.basename(indir)}')
fig, (ax,
rax) = plt.subplots(2,
1,
figsize=(7, 7),
gridspec_kw={"height_ratios": (3, 1)},
sharex=True)
h = acc['recoil']
h = merge_extensions(h, acc, reweight_pu=('nopu' in distribution))
# scale_xs_lumi(h)
h = merge_datasets(h)
h = h.integrate(h.axis('region'), region)
s = Style()
try:
newax = s.get_binning(distribution, region)
h = h.rebin(h.axis(newax.name), newax)
except KeyError:
pass
f = uproot.open('fitDiagnostics.root')
h2016 = f['shapes_prefit'][regions[region]]['data']
x, y = {}, {}
# x[2016] = np.r_[0.5*(h2016.bins[:,0] + h2016.bins[:,1]),1500., 1700., 1900.]
pprint(dir(h2016))
x[2016] = np.r_[h2016.xvalues, 1500., 1700., 1900.]
y[2016] = np.r_[h2016.yvalues / lumi(2016), 0, 0, 0]
h2017 = h[sel_2017].integrate('dataset')
h2018 = h[sel_2018].integrate('dataset')
x[2017] = h2017.axis('recoil').centers()
y[2017] = h2017.values()[()] / (np.diff(
h2017.axis('recoil').edges())) / lumi(2017)
x[2018] = h2018.axis('recoil').centers()
y[2018] = h2018.values()[()] / (np.diff(
h2018.axis('recoil').edges())) / lumi(2018)
for year in x.keys():
ax.plot(x[year], y[year], 'o-', label=f'{year}')
rax.plot(x[year], y[year] / y[2016], '-o')
ax.legend(title=region)
ax.set_yscale('log')
rax.set_xlabel(distribution)
ax.set_xlabel(distribution)
rax.set_ylabel('Ratio to 2016')
ax.set_ylabel('Data cross section / bin')
loc1 = matplotlib.ticker.MultipleLocator(base=0.2)
loc2 = matplotlib.ticker.MultipleLocator(base=0.1)
rax.yaxis.set_major_locator(loc1)
rax.yaxis.set_minor_locator(loc2)
rax.grid(axis='y', which='minor', linestyle='--')
rax.grid(axis='y', which='major', linestyle='--')
rax.set_ylim(0.5, 1.5)
fig.savefig(f'output/{region}.pdf')
def photon_triggers_merged():
tag = 'gamma'
indir = f"/home/albert/repos/bucoffea/bucoffea/plot/input/eff/{tag}/sel"
acc = acc_from_dir(indir)
# All regions
regions = []
for k in acc['photon_pt0'].axis('region').identifiers():
k = str(k)
if not k.startswith('tr_g'):
continue
regions.append(k)
lumi_by_trig = {
2017: {
"HLT_PFHT1050": 41.527192272,
"HLT_PFHT590": 0.444896852,
"HLT_PFHT680": 0.785538800,
"HLT_PFHT780": 1.461679046,
"HLT_PFHT890": 2.802018098,
},
2018: {
"HLT_PFHT1050": 59.735969368,
"HLT_PFHT590": 0.467058719,
"HLT_PFHT680": 0.924441113,
"HLT_PFHT780": 1.837625206,
"HLT_PFHT890": 3.661338636,
}
}
overall_lumi = {2017: 41.527192272, 2018: 59.735969368}
for year in [2017, 2018]:
acc_cp = copy.deepcopy(acc)
# Region combination mapping
# 1. Scale each region according to effective lumi
# 2. Combine regions with different triggers
mapping = defaultdict(set)
lumi_mapping = {}
for rname in regions:
base = re.sub(r'_HLT_PFHT(\d+)', '', rname)
mapping[base].add(rname)
for hlt, lumi in lumi_by_trig[year].items():
if hlt in rname:
lumi_mapping[rname] = lumi / overall_lumi[year]
pprint(lumi_mapping)
pprint(mapping)
region_ax = hist.Cat("region", "Selection region")
year_regex = re.compile(f'.*{year}')
for distribution in ['photon_pt0', 'recoil']:
h = copy.deepcopy(acc[distribution])
h = h[year_regex]
h.scale(lumi_mapping, 'region')
acc_cp[distribution] = h.group(
region_ax, "region", {k: list(v)
for k, v in mapping.items()})
for region in mapping.keys():
print(f"Region {region}")
region = region.replace("tr_",
"").replace("_num",
"").replace("_den", "")
for dataset in ["GJets_HT_MLM", "JetHT"]:
if 'photon_pt_' in region:
distribution = 'recoil'
axis_name = 'recoil'
else:
distribution = 'photon_pt0'
axis_name = 'pt'
plot_recoil(acc_cp,
region,
dataset=dataset,
year=year,
tag=tag,
distribution=distribution,
axis_name=axis_name)
def main():
acc = acc_from_dir('input/pdfweight/')
pdf_plot(acc)
def main():
# indir = "../input/21Aug19_v4_pdfwgt"
# acc = acc_from_dir(indir)
# for year in [2017]:
# data = re.compile(f'MET_{year}')
# mc = re.compile(f'(EW.*|TTJets.*|ZZ.*|ST.*|QCD_HT.*|WW.*|WZ.*|.*DYJetsToLL_M-50_HT_MLM.*){year}')
# region='cr_2m_j'
# for distribution in ['recoil']:
# make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e3), outdir=f'./output/{os.path.basename(indir)}')
indir = "./input/2019-09-05_all_new_sf_neweletrig"
acc = acc_from_dir(indir)
def dimuon_plots():
for year in [2017,2018]:
data = re.compile(f'MET_{year}')
mc = re.compile(f'(EW.*|TTJets.*FXFX.*|ST.*|QCD_HT.*|Diboson.*|.*DYJetsToLL_M-50_HT_MLM.*){year}')
region='cr_2m_j'
for distribution in ['recoil','ak4_pt0','dimuon_mass','muon_pt', 'dimuon_pt','met']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e3), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_phi0','muon_phi','dimuon_mass']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e1,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_eta0', 'muon_eta','dimuon_eta']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(-3,3),ylim=(1e3,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_chf0','ak4_nhf0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,1),ylim=(1e2,1e6), outdir=f'./output/{os.path.basename(indir)}/{region}')
def dielectron_plots():
for year in [2017,2018]:
data = re.compile(f'EGamma_{year}')
mc = re.compile(f'(EW.*|TTJets.*FXFX.*|ST.*|QCD_HT.*|Diboson.*|.*DYJetsToLL_M-50_HT_MLM.*){year}')
region='cr_2e_j'
for distribution in ['recoil','ak4_pt0','dielectron_mass','electron_pt', 'dielectron_pt','met']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e3), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_phi0','electron_phi','dielectron_mass']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e1,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_eta0', 'electron_eta','dielectron_eta']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(-3,3),ylim=(1e3,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_chf0','ak4_nhf0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,1),ylim=(1e2,1e6), outdir=f'./output/{os.path.basename(indir)}/{region}')
def single_muon_plots():
for year in [2017, 2018]:
data = re.compile(f'MET_{year}')
mc = re.compile(f'(TTJets.*FXFX.*|ST.*|QCD_HT.*|Diboson.*|W.*HT.*).*{year}')
region='cr_1m_j'
for distribution in ['recoil','ak4_pt0','muon_pt','met']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_phi0','muon_phi']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e4,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_eta0', 'muon_eta']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(-3,3),ylim=(1e4,1e6), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_chf0','ak4_nhf0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,1),ylim=(1e3,1e8), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['muon_mt']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,180),ylim=(1e1,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
def single_electron_plots():
for year in [2017, 2018]:
data = re.compile(f'EGamma_{year}')
mc = re.compile(f'(TTJets.*FXFX.*|ST.*|QCD_HT.*|Diboson.*|W.*HT.*).*{year}')
region='cr_1e_j'
for distribution in ['recoil','ak4_pt0','electron_pt','met']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_phi0','electron_phi']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e4,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_eta0', 'electron_eta']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(-3,3),ylim=(1e4,1e6), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_chf0','ak4_nhf0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,1),ylim=(1e3,1e8), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['electron_mt']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,180),ylim=(1e1,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
def photon_plots():
for year in [2017, 2018]:
data = re.compile(f'EGamma_{year}')
mc = re.compile(f'(GJets.*|QCD_HT.*|W.*HT.*).*{year}')
region='cr_g_j'
for distribution in ['recoil','ak4_pt0','photon_pt0','met']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(1e-3,1e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_phi0','photon_phi0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, ylim=(4e4,5e5), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_eta0', 'photon_eta0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(-3,3),ylim=(1e4,1e6), outdir=f'./output/{os.path.basename(indir)}/{region}')
for distribution in ['ak4_chf0','ak4_nhf0']:
make_plot(acc, region=region,distribution=distribution, year=year, data=data, mc=mc, xlim=(0,1),ylim=(1e3,1e8), outdir=f'./output/{os.path.basename(indir)}/{region}')
# single_muon_plots()
# dimuon_plots()
# single_electron_plots()
photon_plots()
dielectron_plots()
#!/usr/bin/env python
import re
from coffea.util import load
from coffea import hist
from matplotlib import pyplot as plt
from bucoffea.plot.util import acc_from_dir
import copy
from bucoffea.plot.util import merge_extensions, merge_datasets, scale_xs_lumi
acc = acc_from_dir('input/das_lhevpt_v2')
data_err_opts = {
'linestyle': 'none',
'marker': '.',
'markersize': 10.,
'color': 'k',
'elinewidth': 1,
'emarker': '_'
}
h = copy.deepcopy(acc['gen_vpt'])
h = merge_extensions(h, acc, reweight_pu=False)
scale_xs_lumi(h)
h = merge_datasets(h)
h = h.integrate('weight_type', 'nominal')
h = h.integrate('weight_index', slice(-0.5, 0.5))
h = h[re.compile('.*DY.*HT.*')].integrate('dataset')
new_ax = hist.Bin('vpt', 'Gen V $p_{T}$ (GeV)',
list(range(80, 800, 40)) + list(range(800, 2000, 100)))
def main():
indir = "../lo_vs_nlo/input/2019-09-26_checks/"
acc = acc_from_dir(indir)
for region in [
'cr_2m_j', 'cr_1m_j', 'cr_2m_j_noveto_tau', 'cr_1m_j_noveto_tau',
'cr_2m_j_noveto_photon', 'cr_1m_j_noveto_photon'
]:
sel_2016 = re.compile(f'MET_2016')
sel_2017 = re.compile(f'MET_2017')
sel_2018 = re.compile(f'MET_2018')
for distribution in axes.keys():
# fig, ax, rax = make_plot(acc, region=region,distribution=distribution, year=2016, data=data, mc=mc, ylim=(1e-3,1e3), rylim=(0,2),outdir=f'./output/{os.path.basename(indir)}')
fig, (ax,
rax) = plt.subplots(2,
1,
figsize=(7, 7),
gridspec_kw={"height_ratios": (3, 1)},
sharex=True)
h = acc[distribution]
h = merge_extensions(h, acc, reweight_pu=('nopu' in distribution))
# scale_xs_lumi(h)
h = merge_datasets(h)
h = h.integrate(h.axis('region'), region)
s = Style()
try:
newax = s.get_binning(distribution, region)
h = h.rebin(h.axis(newax.name), newax)
except KeyError:
pass
x, y = {}, {}
h2016 = h[sel_2016].integrate('dataset')
h2017 = h[sel_2017].integrate('dataset')
h2018 = h[sel_2018].integrate('dataset')
try:
x[2016] = h2016.axis(axes[distribution]).centers()
y[2016] = h2016.values()[()] / (np.diff(
h2016.axis(axes[distribution]).edges())) / lumi(2016)
x[2017] = h2017.axis(axes[distribution]).centers()
y[2017] = h2017.values()[()] / (np.diff(
h2017.axis(axes[distribution]).edges())) / lumi(2017)
x[2018] = h2018.axis(axes[distribution]).centers()
y[2018] = h2018.values()[()] / (np.diff(
h2018.axis(axes[distribution]).edges())) / lumi(2018)
except KeyError:
continue
for year in x.keys():
ax.plot(x[year],
y[year],
'o-',
label=f'{year} / {lumi(year)} fb-1')
rax.plot(x[year], y[year] / y[2016], '-o')
ax.legend(title=region)
ax.set_yscale('log')
rax.set_xlabel(distribution)
ax.set_xlabel(distribution)
rax.set_ylabel('Ratio to 2016')
ax.set_ylabel('Data cross section / bin')
loc1 = matplotlib.ticker.MultipleLocator(base=0.2)
loc2 = matplotlib.ticker.MultipleLocator(base=0.1)
rax.yaxis.set_major_locator(loc1)
rax.yaxis.set_minor_locator(loc2)
rax.grid(axis='y', which='minor', linestyle='--')
rax.grid(axis='y', which='major', linestyle='--')
rax.set_ylim(0.5, 1.5)
outname = f'output/bu/{region}_{distribution}.pdf'
fig.savefig(outname)
print(f"Saved {outname}")
plt.close(fig)
