def photon_sf_plot(tag):
for year in [2017, 2018]:
opts = markers('data')
# opts['markersize'] = 5
# opts['fillstyle'] = 'none'
opts.pop('emarker', '')
fig = plt.gcf()
ax = plt.gca()
x, y, yerr = {}, {}, {}
fnum = f'output/{tag}/table_g_HLT_PFHT1050_photon_pt0_JetHT_{year}.txt'
fden = f'output/{tag}/table_g_HLT_PFHT590_photon_pt0_GJets_HT_MLM_{year}.txt'
xnum, _, ynum, yerrnum = get_xy(fnum)
xden, _, yden, yerrden = get_xy(fden)
x = xnum
y = ynum / yden
dy = ratio_unc(ynum, yden, yerrnum, yerrden)
ax.errorbar(x, y, yerr=dy, label=f'{year}', **opts)
# for f in files: plot(f)
outdir = f"./output/{tag}"
# ax.set_ylim(0.9,1)
ax.legend()
ax.set_ylabel("Data / MC SF")
ax.xaxis.set_major_locator(MultipleLocator(200))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.yaxis.set_major_locator(MultipleLocator(0.05))
ax.yaxis.set_minor_locator(MultipleLocator(0.01))
ax.set_ylim(0.95, 1.02)
ax.grid(1)
# rax.set_ylim(0.95,1.05)
# rax.yaxis.set_major_locator(MultipleLocator(0.05))
# rax.yaxis.set_minor_locator(MultipleLocator(0.01))
# rax.grid(1)
# rax.set_xlabel("Recoil or $p_{T}^{miss}$ (GeV)")
# rax.set_ylabel(r"Ratio to single-$\mu$")
# rax.plot([0,1000],[0.99,0.99],color='blue')
# rax.plot([0,1000],[1.01,1.01],color='blue')
# rax.plot([250,250],[0.95,0.95],color='blue')
ax.plot([215, 215], [0.9, 1.1], color='red', zorder=-1)
plt.text(1.,
1.,
r"%.1f fb$^{-1}$ (13 TeV)" % lumi(year),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
plt.text(0.,
1.,
f'{year}',
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
fig.savefig(pjoin(outdir, f'photon_sf_{year}.pdf'))
fig.clear()
plt.close(fig)
def region_comparison_plot(tag):
for year in [2017,2018]:
regions = ['1m', '2m', '1e','1m_hlt','2m_hlt']
opts = markers('data')
opts['markersize'] = 5.
opts['fillstyle'] = 'none'
emarker = opts.pop('emarker', '')
fig, ax, rax = fig_ratio()
x, y, yerr = {}, {}, {}
for region in regions:
if region.endswith('e'):
file = f'output/{tag}/table_{region}_EGamma_{year}.txt'
else:
file = f'output/{tag}/table_{region}_SingleMuon_{year}.txt'
x[region], y[region], yerr[region] = get_xy(file)
opts['color'] = colors[region]
ax.errorbar(x[region], y[region], yerr=yerr[region],label=f'{region} region', **opts)
# opts.pop('elinewidth')
if region=='1m':
continue
rax.errorbar(x['1m'], y[region]/y['1m'], yerr[region]/y['1m'], **opts)
# for f in files: plot(f)
outdir = f"./output/{tag}"
# ax.set_ylim(0.9,1)
ax.legend()
ax.set_ylabel("Efficiency")
ax.xaxis.set_major_locator(MultipleLocator(200))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.yaxis.set_major_locator(MultipleLocator(0.05))
ax.yaxis.set_minor_locator(MultipleLocator(0.01))
ax.set_ylim(0.9,1.02)
ax.grid(1)
rax.set_ylim(0.9,1.1)
rax.grid(1)
rax.set_xlabel("Recoil or $p_{T}^{miss}$ (GeV)")
rax.set_ylabel(r"Ratio to single-$\mu$")
plt.text(1., 1., r"$\approx$ %.1f fb$^{-1}$ (13 TeV)" % lumi(year),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes
)
plt.text(0., 1., f'{year}',
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes
)
fig.savefig(pjoin(outdir, f'region_comparison_data_{year}.pdf'))
fig.clear()
plt.close(fig)
def lumi_by_region(region, year):
if 'HLT' not in region:
return lumi(year)
else:
if year == 2017:
if 'HLT_PFHT590' in region: return 0.445
if 'HLT_PFHT680' in region: return 0.786
if 'HLT_PFHT780' in region: return 1.462
if 'HLT_PFHT890' in region: return 2.802
if 'HLT_PFHT1050' in region: return 41.527
elif year == 2018:
if 'HLT_PFHT590' in region: return 0.467
if 'HLT_PFHT680' in region: return 0.924
if 'HLT_PFHT780' in region: return 1.838
if 'HLT_PFHT890' in region: return 3.661
if 'HLT_PFHT1050' in region: return 59.736
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 plot_recoil(acc, region_tag="1m", dataset='SingleMuon', year=2018, tag="test", distribution="recoil"):
h = acc[distribution]
h = merge_extensions(h)
# h = scale_to_xs(h, acc)
h = merge_datasets(h)
newbin = hist.Bin(distribution,f"{distribution} (GeV)",np.array(list(range(0,400,20)) + list(range(400,1100,100))))
h = h.rebin(h.axis(distribution), newbin)
ds = f'{dataset}_{year}'
h = h.project(h.axis('dataset'), ds)
# print(h)
hnum = h.project(h.axis('region'),f'tr_{region_tag}_num')
hden = h.project(h.axis('region'),f'tr_{region_tag}_den')
# print(hden, hnum)
# Recoil plot
try:
fig, ax,_ = hist.plot1d(hnum, binwnorm=True)
except KeyError:
print(f'ERROR: {region_tag}, {dataset}, {year}')
return
hist.plot1d(hden, ax=ax, clear=False, binwnorm=True)
plt.yscale('log')
plt.gca().set_ylim(0.1,1e6)
outdir = f"./output/{tag}"
if not os.path.exists(outdir):
os.makedirs(outdir)
fig.savefig(pjoin(outdir, f'{distribution}_{region_tag}_{dataset}_{year}.pdf'))
with open(pjoin(outdir,f'table_{region_tag}_{dataset}_{year}.txt'),"w") as f:
f.write(content_table(hnum, hden) + "\n")
plt.close(fig)
# Efficiency
fig, ax,_ = hist.plotratio(hnum, hden,
guide_opts={},
unc='clopper-pearson',
error_opts=markers('data')
)
ax.set_ylim(0,1.1)
ax.set_xlim(0,xmax)
ax.set_ylabel("Efficiency")
plt.text(1., 1., r"$\approx$ %.1f fb$^{-1}$ (13 TeV)" % lumi(year),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes
)
plt.text(0., 1., f'{region_tag}, {year}',
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes
)
plt.text(1., 0., f'{trgname(year, tag)}',
fontsize=10,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes
)
plt.plot([0,xmax],[0.95,0.95],'r-')
fig.savefig(pjoin(outdir, f'eff_{region_tag}_{dataset}_{year}.pdf'))
plt.close(fig)
def sf_comparison_plot(tag):
for year in [2017,2018]:
regions = ['1m', '2m', '1m_hlt', '2m_hlt']
opts = markers('data')
opts['markersize'] = 5
opts['fillstyle'] = 'none'
emarker = opts.pop('emarker', '')
fig, ax, rax = fig_ratio()
x, y, yerr = {}, {}, {}
for region in regions:
if '1e' in region:
fnum = f'output/{tag}/table_{region}_EGamma_{year}.txt'
fden = f'output/{tag}/table_{region}_WJetsToLNu-MLM_{year}.txt'
elif '1m' in region:
fnum = f'output/{tag}/table_{region}_SingleMuon_{year}.txt'
fden = f'output/{tag}/table_{region}_WJetsToLNu-MLM_{year}.txt'
elif '2m' in region:
fnum = f'output/{tag}/table_{region}_SingleMuon_{year}.txt'
fden = f'output/{tag}/table_{region}_DYNJetsToLL_M-50-MLM_{year}.txt'
xnum, ynum, yerrnum = get_xy(fnum)
xden, yden, yerrden = get_xy(fden)
x[region] = xnum
y[region] = ynum / yden
yerr[region] = ratio_unc(ynum, yden, yerrnum, yerrden)
opts['color'] = colors[region]
opts['marker'] = region_marker[region]
ax.errorbar(x[region], y[region], yerr=yerr[region],label=f'{region} region', **opts)
# opts.pop('elinewidth')
if region=='1m':
continue
rax.errorbar(x['1m'], y[region]/y['1m'], ratio_unc(y[region],y['1m'],yerr[region],yerr['1m']), **opts)
# for f in files: plot(f)
outdir = f"./output/{tag}"
# ax.set_ylim(0.9,1)
ax.legend()
ax.set_ylabel("Data / MC SF")
ax.xaxis.set_major_locator(MultipleLocator(200))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.yaxis.set_major_locator(MultipleLocator(0.05))
ax.yaxis.set_minor_locator(MultipleLocator(0.01))
ax.set_ylim(0.9,1.1)
ax.grid(1)
rax.set_ylim(0.95,1.05)
rax.yaxis.set_major_locator(MultipleLocator(0.05))
rax.yaxis.set_minor_locator(MultipleLocator(0.01))
rax.grid(1)
rax.set_xlabel("Recoil or $p_{T}^{miss}$ (GeV)")
rax.set_ylabel(r"Ratio to single-$\mu$")
plt.text(1., 1., r"$\approx$ %.1f fb$^{-1}$ (13 TeV)" % lumi(year),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes
)
plt.text(0., 1., f'{year}',
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes
)
fig.savefig(pjoin(outdir, f'sf_comparison_{year}.pdf'))
fig.clear()
plt.close(fig)
def fit(tag, year):
outdir = './output/gamma/fit/'
try:
os.makedirs(outdir)
except FileExistsError:
pass
x, eff, eff_up, eff_down = {}, {}, {}, {}
x['data'], eff['data'], eff_up['data'], eff_down['data'] = load(tag, 'JetHT', year)
x['mc'], eff['mc'], eff_up['mc'], eff_down['mc'] = load(tag, 'GJets_HT_MLM', year)
pars = {}
cross = {}
for key in ['data','mc']:
pars[key], _ = curve_fit(sigmoid, x[key], eff[key], sigma=0.5*(eff_up[key]-eff_down[key]), p0=[1e-3,200,0.1,1])
cross[key] = minimize( lambda x: np.abs(sigmoid(x, *pars[key]) - 0.95),x0=230)
fig, ax, rax = fig_ratio()
xinterp = np.linspace(min(x['data']), max(x['data']), 1000)
handles = []
ax.errorbar(x['data'], eff['data'], 0.5*(eff_up['data']-eff_down['data']),fmt='o',label='Data',color=colors["data"])
ax.errorbar(x['mc'], eff['mc'], 0.5*(eff_up['mc']-eff_down['mc']),fmt='s',label='MC',fillstyle='none',color=colors["mc"])
ax.plot(xinterp, sigmoid(xinterp, *pars['data']), label='Data fit',color=colors["data"],zorder=-1)
ax.plot(xinterp, sigmoid(xinterp, *pars['mc']), label='MC fit',color=colors["mc"],zorder=-1,linestyle='--')
ax.set_ylim(0.,1.1)
ax.set_xlim(100,1100)
ax.legend()
ax.text(350,.4,'f(x) = c + (d-c) / (1 + exp(-a * (x-b)))')
ax.text(
300,
0.1,
'\n'.join([
f"a = {pars['data'][0]:.3f} / GeV",
f"b = {pars['data'][1]:.2f} GeV",
f"c = {pars['data'][2]:.3f}",
f"d = {pars['data'][3]:.3f}"
]),
color=colors['data']
)
ax.text(
600,
0.1,
'\n'.join([
f"a = {pars['mc'][0]:.3f} / GeV",
f"b = {pars['mc'][1]:.2f} GeV",
f"c = {pars['mc'][2]:.3f}",
f"d = {pars['mc'][3]:.3f}"
]),
color=colors['mc']
)
ax.text(700,0.8,
"\n".join([
f"Data > 95% @ {cross['data'].x[0]:.0f} GeV",
f"MC > 95% @ {cross['mc'].x[0]:.0f} GeV",
])
)
ax.text(1., 1., r"%.1f fb$^{-1}$ (13 TeV)" % lumi(year),
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes
)
rax.set_ylim(0.95,1.1)
rax.grid()
# rax.plot([cross['data'].x[0],cross['data'].x[0]], [0.8,1.05],color='k',)
# ax.plot([cross['data'].x[0],cross['data'].x[0]], [0.,1.05],color='k',linestyle='--')
# ax.plot([cross['mc'].x[0],cross['mc'].x[0]], [0.9,1.05],color='r')
# ax.plot([215,215],[0.9,1.05])
rax.errorbar(x["data"], eff["data"] / eff["mc"], 0.5*(eff_up["data"] - eff_down["data"]) / eff["mc"],fmt='o',label='Data / MC',color=colors['data'])
rxinterp = np.linspace(cross['data'].x[0], max(xinterp),1000)
rax.plot(rxinterp, sigmoid(rxinterp, *pars['data']) / sigmoid(rxinterp, *pars['mc']),label=f"Data / MC fit ratio, plateau at {100*(sigmoid(rxinterp, *pars['data']) / sigmoid(rxinterp, *pars['mc']))[-1]:.1f} %",color='k')
rax.plot(rxinterp, 0.99*sigmoid(rxinterp, *pars['data']) / sigmoid(rxinterp, *pars['mc']), label='1% uncertainty on fit',linestyle='--',color='gray')
rax.plot(rxinterp, 1.01*sigmoid(rxinterp, *pars['data']) / sigmoid(rxinterp, *pars['mc']),linestyle='--',color='gray')
# rxinterp2 = np.linspace(min(xinterp),cross['data'].x[0], 1000)
# rax.plot(rxinterp2, sigmoid(rxinterp2, *pars['data']) / sigmoid(rxinterp2, *pars['mc']),color='k',linestyle=':')
rax.legend()
rax.set_ylabel("Ratio")
ax.set_ylabel("Trigger efficiency")
ax.set_xlabel("Photon $p_{T}$ (GeV)")
rax.set_xlabel("Photon $p_{T}$ (GeV)")
ax.figure.savefig(pjoin(outdir, f'fit_{tag}_{year}.pdf'))
ax.figure.clf()
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)
def main():
for mode in ['monojet', 'zpt']:
if mode == 'monojet':
regions = {
# 'ch1' : 'sr_j',
'ch2': 'cr_2m_j',
'ch3': 'cr_1m_j',
# # 'ch4' : 'cr_g_j',
# # 'ch5' : 'cr_2e_j',
'ch6': 'cr_1e_j'
}
fitfile = 'fitDiagnostics.root'
bins = [
250, 280, 310, 340, 370, 400, 430, 470, 510, 550, 590, 640,
690, 740, 790, 840, 900, 960, 1020, 1090, 1160, 1250, 1400
]
elif mode == 'zpt':
regions = {
# 'ch1' : 'sr_j',
'monojet_singlemu': 'cr_1m_j',
# 'ch4' : 'cr_g_j',
# 'ch5' : 'cr_2e_j',
'monojet_singleel': 'cr_1e_j'
}
fitfile = 'fitDiagnostics_zpt.root'
bins = [250, 275, 300, 350, 400, 450, 500, 650, 800, 1150, 1400]
tmp = {}
for k, v in regions.items():
tmp[v] = k
regions.update(tmp)
inpath = "input/2020-01-07_fine_recoil_bins_v4"
acc = dir_archive(
inpath,
serialized=True,
compression=0,
memsize=1e3,
)
acc.load('recoil')
acc.load('sumw')
acc.load('sumw_pileup')
acc.load('nevents')
for region in [x for x in regions.keys() if 'r_' in x]:
if '_1m_' in region or '_2m_' in region:
sel_2016 = re.compile(f'MET_2016')
sel_2017 = re.compile(f'MET_2017')
sel_2018 = re.compile(f'MET_2018')
else:
sel_2016 = re.compile(f'EGamma_2016')
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()
# Rebin
newax = hist.Bin('recoil', 'Recoil (GeV)', bins)
h = h.rebin(h.axis(newax.name), newax)
# Retrieve input from fit diagnostics
f = uproot.open(fitfile)
tg2016 = f['shapes_prefit'][regions[region]]['data']
# Prepare x and y values for easy plotting
x, y = {}, {}
# x[2016] = np.r_[0.5*(h2016.bins[:,0] + h2016.bins[:,1]),1500., 1700., 1900.]
x['2016orig'] = np.r_[tg2016.xvalues]
y['2016orig'] = np.r_[tg2016.yvalues / lumi(2016)]
h2016 = h[sel_2016].integrate('dataset')
h2017 = h[sel_2017].integrate('dataset')
h2018 = h[sel_2018].integrate('dataset')
x[2016] = h2016.axis('recoil').centers()
y[2016] = h2016.values()[()] / (np.diff(
h2016.axis('recoil').edges())) / lumi(2016)
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)
# Actual plotting
for year in x.keys():
print(x[year])
ax.plot(x[year],
y[year],
'o-',
label=f'{year} ({sum(y[year]*np.diff(bins)):.2e})')
rax.plot(x[year], y[year] / y['2016orig'], '-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/{mode}_{region}.pdf')