def getPlots(args):
tag = args.tag
savename = args.savetag
odir = 'plots/%s/'%tag
os.system('mkdir -p %s'%odir)
pwd = os.getcwd()
# open hists
hists_unmapped = load('%s.coffea'%args.hists)
os.chdir(odir)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
# properties
hist_name = args.hist
vars_cut = {}
if (len(args.sel)%3==0):
for vi in range(int(len(args.sel)/3)):
vars_cut[args.sel[vi*3]] = [float(args.sel[vi*3+1]), float(args.sel[vi*3+2])]
h = hists_mapped[hist_name]
drawSolo(h,args.hist,vars_cut,savename)
os.chdir(pwd)
def getPlots(args):
lumifb = float(args.lumi)
tag = args.tag
savename = args.savetag
odir = 'plots/%s/' % tag
os.system('mkdir -p %s' % odir)
pwd = os.getcwd()
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
os.chdir(odir)
# map to hists
hists_mapped = {}
#print(hists_unmapped,hists_mapped)
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
print(key, val)
hists_mapped[key] = processmap.apply(val)
# normalize to lumi
print('normalizing mc to %.1f fb^-1 lumi')
#print(
for h in hists_mapped.values():
h.scale(
{
p: lumifb
for p in h.identifiers('process')
if 'JetHT' not in str(p) and 'SingleMuon' not in str(p)
},
axis="process")
h.scale({'tt': 0.8}, axis="process")
h.scale({'tttoleptonic': 0.8}, axis="process")
h.scale({'tttosemileptonic': 0.8}, axis="process")
h.scale({'tttohadronic': 0.8}, axis="process")
# properties
hist_name = args.hist
var_name = args.var
var_label = r"$%s$" % args.varlabel
vars_cut = {}
if (len(args.sel) % 3 == 0):
for vi in range(int(len(args.sel) / 3)):
vars_cut[args.sel[vi * 3]] = [
float(args.sel[vi * 3 + 1]),
float(args.sel[vi * 3 + 2])
]
h = hists_mapped[hist_name]
save(h, 'test.coffea')
drawSolo(h, args.hist, var_name, var_label, args.title, lumifb, vars_cut,
args.regions, savename, args.plotData, args.plotDensity)
os.chdir(pwd)
def getPlots(args):
print(args.lumi)
lumifb = float(args.lumi)
tag = args.tag
savename = args.savetag
odir = 'plots/%s/' % tag
os.system('mkdir -p %s' % odir)
pwd = os.getcwd()
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
os.chdir(odir)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
# normalize to lumi
for h in hists_mapped.values():
h.scale({p: lumifb for p in h.identifiers('process')}, axis="process")
print(hists_mapped)
# properties
hist_name = args.hist
var_name = args.var
var_label = r"$%s$" % args.varlabel
vars_cut = {}
#print(args.sel)
if (len(args.sel) % 3 == 0):
for vi in range(int(len(args.sel) / 3)):
vars_cut[args.sel[vi * 3]] = [
float(args.sel[vi * 3 + 1]),
float(args.sel[vi * 3 + 2])
]
print(vars_cut)
h = hists_mapped[hist_name]
print(h)
drawSolo(h, args.hist, var_name, var_label, args.title, lumifb, vars_cut,
args.regions, savename)
os.chdir(pwd)
def getPlots(args):
lumifb = args.lumi
tag = args.tag
odir = 'plots/%s/' % tag
os.system('mkdir -p %s' % odir)
pwd = os.getcwd()
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
os.chdir(odir)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
print(hists_mapped)
# normalize to lumi
for h in hists_mapped.values():
h.scale({p: lumifb for p in h.identifiers('process')}, axis="process")
# properties
#hist_name = 'jet_museljet'
hist_name = 'jet_museljet'
var_name = 'jetMu_pt'
var_label = r'Jet p$_{T}$ (GeV)'
vars_cut = {
'jetMu_lsf3': [0.78, 1],
'jetMu_msd': [20, 200],
}
h = hists_mapped[hist_name]
drawStack(h, var_name, var_label, lumifb, vars_cut)
os.chdir(pwd)
import os
import uproot
import numpy as np
from coffea import hist
from coffea.util import load, save
import processmap
hists_unmapped = load('hists_Hbb_create_2017.coffea')
hists = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists[key] = processmap.apply(val)
if os.path.exists("templates.root"):
os.remove("templates.root")
fout = uproot.create("templates.root")
nodata = re.compile("(?!data_obs)")
h = hists['templates_signalregion'][nodata]
lumi = 41.1
h.scale({p: lumi for p in h[nodata].identifiers('process')}, axis="process")
for proc in h.identifiers('process'):
for i, ptbin in enumerate(h.identifiers('AK8Puppijet0_pt')):
for syst in h.identifiers('systematic'):
mproj = (slice(None), 'all')
def drawCutflow(h, plottitle, lumifb, regionsel):
cutlist = []
for c in h[list(h.keys())[0]]:
cutlist.append(c)
cutnames = []
newlinei = 8
for c in cutlist:
if (len(c) < newlinei):
cutnames.append(c)
else:
isCut = False
cuti = getindices(c)
if not cuti:
cutnames.append(c)
isCut = True
else:
for ic in cuti:
if (ic >= newlinei):
if (c[ic] in special_char_list):
cutnames.append(c[:ic] + "\n" + c[ic + 1:])
else:
cutnames.append(c[:ic] + "\n" + c[ic:])
isCut = True
break
if not isCut:
cutnames.append(c)
print(cutlist)
print(cutnames)
cuthist_raw = hist.Hist(
'Events',
hist.Cat('dataset', 'Dataset'),
hist.Bin('cut', 'Cut', len(cutlist), -0.5,
float(len(cutlist)) - 0.5),
)
for s in h:
for ic, c in enumerate(cutlist):
cuthist_raw.fill(
dataset=s,
cut=ic,
weight=h[s][c],
)
x = processmap.apply(cuthist_raw)
for ih, hkey in enumerate(x.identifiers('process')):
x.identifiers('process')[ih].label = process_latex[hkey.name]
mc_processes = [
'zqq',
'wqq',
'qcd',
'tt',
'st',
'wlnu',
]
data_processes = [
'SingleMuon',
'JetHT',
]
mc = x.remove(data_processes, 'process')
data = x.remove(
mc_processes +
['SingleMuon'] if 'signal' in regionsel else mc_processes + ['JetHT'],
'process')
mc.scale({p: lumifb for p in mc.identifiers('process')}, axis="process")
mc.axis('process').sorting = 'integral'
fig, ax = plt.subplots()
hist.plot1d(
mc,
overlay='process',
ax=ax,
clear=False,
stack=True,
fill_opts=fill_opts,
error_opts=err_opts,
)
hist.plot1d(data, ax=ax, clear=False, line_opts=line_opts)
all_bkg = 0
#for key,val in x[nosig].values().items():
# all_bkg+=val.sum()
#x_nobkg = x[nobkg]
#x_nobkg.scale(50)
#all_sig = 0
#for key,val in x_nobkg.values().items():
# all_sig +=val.sum()
#print('%.4f %.4f %.4f'%(all_bkg,all_sig,all_sig/math.sqrt(all_bkg)))
#hist.plot1d(x_nobkg,ax=ax,overlay='process',clear=False,line_opts=line_opts)
ax.autoscale(axis='x', tight=True)
#ax.set_xlim(20, 200)
ax.set_ylim(0, None)
plt.xticks(range(len(cutnames)), cutnames, rotation=60)
#ax.ticklabel_format(axis='x', style='sci')
old_handles, old_labels = ax.get_legend_handles_labels()
new_labels = []
for xl in old_labels:
if ('ggH' in xl): xl = xl + " (x 50)"
new_labels.append(xl)
leg = ax.legend(handles=old_handles,
labels=new_labels,
title=r'$%s$' % plottitle,
frameon=True,
framealpha=1.0,
facecolor='white',
loc='lower left')
lumi = plt.text(1.,
1.,
r"%.1f fb$^{-1}$ (13 TeV)" % lumifb,
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
cmstext = plt.text(0.,
1.,
"CMS",
fontsize=19,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes,
fontweight='bold')
addtext = plt.text(0.085,
1.,
"Simulation Preliminary",
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes,
style='italic')
#hep.cms.cmslabel(ax, data=False, paper=False, year='2017')
#ax.semilogy()
ax.set_yscale("log")
minvals = []
for xd in x.values():
minvals.append(min(np.trim_zeros(x.values()[xd])))
decsplit = str(min(minvals)).split('.')
if (int(decsplit[0]) == 0):
logmin = 0.1**float(
len(decsplit[1]) - len(decsplit[1].lstrip('0')) + 2)
else:
logmin = 10.**float(len(decsplit[0]) - 1)
ax.set_ylim(logmin / 10., None)
plt.minorticks_on()
locmaj = mpltick.LogLocator(base=10.0, subs=(1.0, ), numticks=100)
ax.yaxis.set_major_locator(locmaj)
locmin = mpltick.LogLocator(base=10.0,
subs=np.arange(2, 10) * .1,
numticks=100)
ax.yaxis.set_minor_locator(locmin)
ax.yaxis.set_minor_formatter(mpltick.NullFormatter())
fig.savefig("cutflow_%s_lumi%i_logy.pdf" % (regionsel, lumifb))
#----------------------------
fig, ax = plt.subplots()
x.scale(
{(str(xd).split('\''))[1]: 1. / x.values()[xd][0]
for xd in x.values()}, 'process')
hist.plot1d(
x,
overlay='process',
ax=ax,
clear=False,
stack=False,
#fill_opts=fill_opts,
#error_opts=err_opts,
line_opts=line_opts_nocolor)
ax.autoscale(axis='x', tight=True)
#ax.set_xlim(20, 200)
ax.set_ylim(0, None)
plt.xticks(range(len(cutnames)), cutnames, rotation=60)
#ax.ticklabel_format(axis='x', style='sci')
old_handles, old_labels = ax.get_legend_handles_labels()
new_labels = []
for xl in old_labels:
new_labels.append(xl)
leg = ax.legend(handles=old_handles,
labels=new_labels,
title=r'$%s$' % plottitle,
frameon=True,
framealpha=1.0,
facecolor='white',
loc='lower left')
lumi = plt.text(1.,
1.,
r"%.1f fb$^{-1}$ (13 TeV)" % lumifb,
fontsize=16,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes)
cmstext = plt.text(0.,
1.,
"CMS",
fontsize=20,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes,
fontweight='bold')
addtext = plt.text(0.085,
1.,
"Simulation Preliminary",
fontsize=16,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes,
style='italic')
#hep.cms.cmslabel(ax, data=False, paper=False, year='2017')
ax.semilogy()
minvals = []
for xd in x.values():
minvals.append(min(np.trim_zeros(x.values()[xd])))
ax.set_ylim(10.**float(math.floor(math.log10(min(minvals)))))
ylo, yhi = ax.get_ylim()
nticksy = int(math.floor(math.log10(yhi)) -
math.floor(math.log10(ylo))) + 2
plt.minorticks_on()
locmaj = mpltick.LogLocator(base=10.0, subs=(1.0, ), numticks=100)
ax.yaxis.set_major_locator(locmaj)
locmin = mpltick.LogLocator(base=10.0,
subs=np.arange(2, 10) * .1,
numticks=100)
ax.yaxis.set_minor_locator(locmin)
ax.yaxis.set_minor_formatter(mpltick.NullFormatter())
fig.savefig("cuteff_%s_lumi%i_logy.pdf" % (regionsel, lumifb))
def getRoc(args):
# vars
vars = args.vars.split(',')
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
print(hists_unmapped)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
print('hists mapped ', hists_mapped)
# build roc for all vars
vals = {}
rocs = {}
labels = {}
for lep in ['ele', 'mu']:
for jet in ['jet0', 'jet1']:
vals['%s_%s' % (lep, jet)] = {}
rocs['%s_%s' % (lep, jet)] = {}
for var in vars:
for lep in ['ele', 'mu']:
for jet in ['jet0', 'jet1']:
print('getting roc for ', var, lep, jet)
hist_name = 'roc_%ssel%s' % (lep, jet)
if 'lsf' in var:
var_name = jet + '_' + var
var_cut_dir = -1
else:
var_name = lep + '0_' + var
var_cut_dir = 1
# get hist
h = hists_mapped[hist_name]
print(h)
print([
ax for ax in h.axes()
if ax.name not in {'process', var_name}
])
x = h.sum(*[
ax for ax in h.axes()
if ax.name not in {'process', var_name}
])
bkg = 'qcd'
sig = 'h125'
#vals['%s_%s'%(lep,jet)][var], rocs['%s_%s'%(lep,jet)][var] = roc(x, bkg, sig, direction=var_cut_dir)
labels[var] = var
# plot variable
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
print(x)
hist.plot1d(x,
ax=ax,
overlay='process',
clear=False,
density=True)
fig.savefig("plots/rocs/lsf_%s_%s_%s.png" % (var, lep, jet))
return vals, rocs, labels
def getPlots(args):
odir = 'plots/trigger/'
os.system('mkdir -p %s' % odir)
pwd = os.getcwd()
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
os.chdir(odir)
print(hists_unmapped)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
print(hists_mapped)
# properties: denom and num
trigger_plots = {
'had_hadtrig': {
'vars': ['genH_pt', 'jet0_pt'],
'denom': 'hadseljet0Trignone',
'num': 'hadseljet0Trighad',
'title': 'HLT_PFHT1050 || HLT_AK8*_TrimMass50'
},
'ele_hadtrig': {
'vars': ['genH_pt', 'genEle_pt'],
'denom': 'eleseljet0Trignone',
'num': 'eleseljet0Trighad',
'title': 'HLT_PFHT1050 || HLT_AK8*_TrimMass50'
},
'ele_eletrig': {
'vars': ['genH_pt', 'genEle_pt'],
'denom': 'eleseljet0Trignone',
'num': 'eleseljet0Trigele',
'title': 'HLT_Ele115_CaloIdVT_GsfTrkIdT and others'
},
'ele_vveletrig': {
'vars': ['genH_pt', 'genEle_pt'],
'denom': 'eleseljet0Trignone',
'num': 'eleseljet0Trigvvlele',
'title': 'HLT_Ele15_IsoVVVL_PFHT450',
},
'mu_hadtrig': {
'vars': ['genH_pt', 'genMu_pt'],
'denom': 'museljet0Trignone',
'num': 'museljet0Trighad',
'title': 'HLT_PFHT1050 || HLT_AK8*_TrimMass50',
},
'mu_mutrig': {
'vars': ['genH_pt', 'genMu_pt'],
'denom': 'museljet0Trignone',
'num': 'museljet0Trigmu',
'title': 'HLT_Mu50'
},
'mu_vvmutrig': {
'vars': ['genH_pt', 'genMu_pt'],
'denom': 'museljet0Trignone',
'num': 'museljet0Trigvvlmu',
'title': 'HLT_Mu15_IsoVVVL_PFHT600'
},
'mu_mettrig': {
'vars': ['genH_pt', 'MET_pt'],
'denom': 'museljet0Trignone',
'num': 'museljet0Trigmet',
'title': 'HLT_PFMETNoMu110_PFMHTNoMu110_IDTight'
},
'mu_btagtrig': {
'vars': ['genH_pt', 'genMu_pt'],
'denom': 'museljet0Trignone',
'num': 'museljet0Trigbtagmu',
'title': 'HLT_BTagMu_AK8Jet300_Mu5'
},
}
for key, item in trigger_plots.items():
h_num = hists_mapped[item['num']]
h_denom = hists_mapped[item['denom']]
for var in item['vars']:
drawEfficiency(h_num,
h_denom,
var,
fig_lab=key + '_' + var,
title=item['title'])
os.chdir(pwd)
