def __init__(self, tree, tree2=None, datatrees=None):
if datatrees:
for d in datatrees.values(): d.GetEntry(0)
self.datatrees = datatrees
meanbook = autoBook("means")
pullbook = autoBook("pulls")
sigmbook = autoBook("sigma")
nllsbook = autoBook("nlls")
diffbook = autoBook("diff")
for e,m in izip(tree, tree2 if tree2 else tree):
fit, sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)]) if tree2 else (fit, sigma)
alpha = fit / e.fit * e.alpha
gen_fit = e.gen_alpha * e.fit / e.alpha
label = "%+d"%(100*e.gen_alpha)
permil = '#circ#kern[-0.2]{#/}#kern[-0.6]{#lower[0.4]{#circ#circ}}'
meanbook.fill(alpha,label, 30, e.gen_alpha - 1.5, e.gen_alpha+1.5, title=';#alpha')
pullbook.fill( (fit-gen_fit)/sigma, label, 30, -5, 5, title = ';#Delta/#sigma')
sigmbook.fill( sigma, label, 60, 2.54/1000, 2.62/1000, title = ';#sigma')
nlle = -3365000
nllm = -3525000
nlldelta = 30000
nllsbook.fill( (e.NLL, m.NLL), label, (30, 30), (nlle - nlldelta,nllm - nlldelta), (nlle+nlldelta,nllm+nlldelta), title = ';NLL (e);NLL (#mu)' )
diffbook.fill( e.alpha-m.alpha, label, 30, -3, 3, title=';#alpha_{e}-#alpha_{#mu}')
for item in ['mean','pull','sigm','nlls','diff']:
setattr(self,item+'book',eval(item+'book'))
def __init__(self, fname):
self.cal = fitresult(fname)
self.book = autoBook(fname.split('/')[-1])
for e in self.cal.tree:
Ac_y = self.Ac_y_tt(e)
Ac_y_gen = e.gen_Ac_y_ttalt
Ac_y_sig = self.sigmas_Ac_y_tt(e)
self.book.fill( Ac_y - Ac_y_gen, 'delta_Ac_y', 100, -0.05, 0.05)
self.book.fill( Ac_y_sig, 'Ac_y_sig', 100, 0, 0.006)
self.book.fill( (Ac_y - Ac_y_gen) / Ac_y_sig, 'Ac_y_pull', 100, -5, 5)
print Ac_y_gen
self.Draw()
def __init__(self, tree, tree2=None, datatrees=None):
if datatrees:
for d in datatrees.values(): d.GetEntry(0)
self.datatrees = datatrees
meanbook = autoBook("means")
pullbook = autoBook("pulls")
sigmbook = autoBook("sigma")
nllsbook = autoBook("nlls")
for e,m in izip(tree, tree2 if tree2 else tree):
fit, sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
alpha = fit / e.fit * e.alpha
gen_fit = e.gen_alpha * e.fit / e.alpha
label = "%d"%(100*e.lumi_factor)
permil = '#circ#kern[-0.2]{#/}#kern[-0.6]{#lower[0.4]{#circ#circ}}'
meanbook.fill(alpha,label, 30, e.gen_alpha - 1.5, e.gen_alpha+1.5, title=';#alpha')
pullbook.fill( (fit-gen_fit)/sigma, label, 30, -5, 5, title = ';#Delta/#sigma')
sigmbook.fill( sigma, label, 200, 0, 6./1000, title = ';#sigma')
for item in ['mean','pull','sigm']:
setattr(self,item+'book',eval(item+'book'))
def __init__(self, trees, treesA, treesC):
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.ticker import MultipleLocator
xoff = 0.08 if presentation else 0
just = -0.8 + xoff
fsinc = 4 if presentation else 0
fs = 14
lw = 1.3
cs = 4
ct = 0.9*lw
fig = plt.figure(figsize=(6.5,6.5))
ax = fig.add_subplot(111)
ax.set_ylim(-4.5,0.5 +[0,1][unfold])
ax.set_xlim(-2,2)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fs+0.5*fsinc)
ax.set_xlabel(r'$A_c^y$ $(\%)$', fontsize=fs+4+0.5*fsinc)
#ax.set_xlabel(r'$A_c^y (\%)$ : Calculated', fontsize=fs)
#ax.set_aspect('equal')
ax.get_yaxis().set_visible(False)
ax.xaxis.set_minor_locator(MultipleLocator(0.2))
ax.xaxis.set_major_locator(MultipleLocator(1.0))
ax.tick_params('both', length=10, width=1, which='major')
ax.tick_params('both', length=5, width=1, which='minor')
bookC = autoBook('C')
for e,m in izip(*treesC):
fit,sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
label = e.label[4:6]
if label in ['R.','L.','A.','ZP','A2','R2','L2','mg']: continue
bookC.fill( e.gen_Ac*100, 'gen_'+label, 1000, -2, 2)
bookC.fill( fit*100, "mean_"+label, 100, (e.gen_Ac-e.scale)*100, (e.gen_Ac+e.scale)*100)
lw = 2
lc = (0.9,0.1,0.1)
ax.text( -1.96, 0.55 + [0,1][unfold], "CMS", fontsize=19, weight='heavy')
ax.text( 0.75, 0.55 + [0,1][unfold], "19.6$\,\mathsf{fb^{-1}}$ (8 TeV)", fontsize=16)
if unfold:
unfold_mean = 0.10
unfold_stat = 0.68
unfold_syst = 0.37
ax.errorbar( unfold_mean, 0, xerr=math.sqrt(unfold_stat**2+unfold_syst**2), marker='.', markersize=15, mfc='k', mec='k', color=lc, linewidth=lw, capsize=cs, capthick=ct )
ax.errorbar( unfold_mean, 0, xerr=unfold_stat, color=lc, marker='.', markersize=15, mfc='k', mec='k', linewidth=lw)
ax.text(just, 0, r'$\mathsf{CMS,\ unfold}$', ha='right', fontsize=fs+fsinc)
ax.text(just, 0 - 0.2, ('($\mathsf{%.2fpercent\pm %.2fpercent \pm %.2fpercent})ppp$'%(unfold_mean,unfold_stat,unfold_syst)).replace('percent','').replace('ppp', r'\%'), ha='right', fontsize=11+0.75*fsinc)
fit,sigma = lib.combined_result([(tree.fit,tree.sigma) for tree in trees])
print fit,sigma
sigmaboth = 0.0042
ax.axvspan( 100*(fit-sigmaboth), 100*(fit+sigmaboth), alpha=0.5, fc=(0.7,0.7,0.7), lw=0.1, hatch='', label=r'$68\%$',zorder=-9)
ax.axvspan( 100*(fit-2*sigmaboth), 100*(fit+2*sigmaboth), alpha=1.0, fc='w', ec='k', lw=0.1, hatch='....', label=r'$95\%$', zorder=-10)
ax.errorbar( 100*fit, [0,1][unfold], xerr=100*sigmaboth, color=lc, marker='.', markersize=15, mfc='k', mec='k', linewidth=lw, capsize=cs, capthick=ct)
ax.errorbar( 100*fit, [0,1][unfold], xerr=100*sigma, color=lc, marker='.', markersize=15, mfc='k', mec='k', linewidth=lw)
ax.text(just, [0,1][unfold], r'$\mathsf{CMS,\ template}$', ha='right',fontsize=fs+fsinc)
ax.text(just, [0,1][unfold] - 0.2, r'$(\mathsf{0.33percent\pm 0.26percent \pm 0.33percent})ppp$'.replace('percent','').replace('ppp',r'\%'), ha='right', fontsize=11+0.75*fsinc)
PHerr = 0.0009*100
PH = (tree.scale*100, PHerr)
KR = (0.0102*100, 0.0005*100)
BS = (0.0111*100, 0.0004*100)
andstr = "&" if presentation else "and"
predictions = zip([KR, BS, PH],[(0.75,0,0),(0.5,0,0),(0.2,0.8,0)],[r'$\mathsf{K\"{u}hn}$ $\mathsf{%s}$ $\mathsf{Rodrigo}$'%andstr,r'$\mathsf{Bernreuther}$ $\mathsf{%s}$ $\mathsf{Si}$'%andstr,r'$\mathsf{POWHEG}$'])
for i,((f,s),c,L) in enumerate(predictions):
ax.errorbar( f, -1-i, xerr=s, color=lc, linewidth=lw, capsize=cs, capthick=ct)
ax.text(just, -1-i, L, ha='right',fontsize=fs+fsinc)
names = {'mn':r'$\mathsf{MC@NLO}$'}
order = [0]
cgen = []
cfit = []
cerr = []
clab = []
for k,v in bookC.items():
if 'gen' in k: continue
cgen.append(bookC[k.replace('mean','gen')].GetMean())
cfit.append(v.GetMean())
cerr.append(v.GetMeanError())
clab.append(k)
for i,(g,f,e,l) in enumerate(sorted(zip(cgen,cfit,cerr,clab))):
ax.errorbar([g], [-4-order[i]], xerr=PHerr, color=lc, linewidth=lw, capsize=cs, capthick=ct )
#ax.plot([g], [-4-order[i]], 'ob', color=(0,0,0.85), )
#ax.arrow(f, -4-order[i], g-f, 0, color=(0,0,0.85), head_length=0.1, head_width=0.2)
ax.text(just, -4-order[i], names[l[-2:]], ha='right', fontsize=fs+fsinc)
for k,g,f,e in sorted(zip(clab,cgen,cfit,cerr), key=lambda x: x[1]):
print k, g, f, e
ax.legend(loc='upper right', prop={'size':fs+0.4*fsinc}, title='Confidence').draw_frame(False)
plt.subplots_adjust(top=0.95,right=0.95,left=0.05)
outName = 'output/result_plot%s.pdf'%("_presentation" if presentation else "")
pp = PdfPages(outName)
pp.savefig(fig)
pp.close()
print "Wrote:", outName
r.tdrStyle.SetErrorX(r.TStyle().GetErrorX())
r.tdrStyle.SetPadTopMargin(0.065)
r.TGaxis.SetMaxDigits(3)
r.tdrStyle.SetEndErrorSize(6)
#r.tdrStyle.SetPadRightMargin(0.06)
def unqueue(h):
return lib.unQueuedBins(h,5,[-1,1],[-1,1])
threeD = False
comps = ['ttag','ttqg','ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
projections = {}
book = autoBook("stuff")
for extra in [True,False,'only']:
for template in [None]+range(1000):
print template
#sys.stdout.flush()
channels = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=threeD, extra=extra, templateID=template,getTT=True)) for lep in ['el', 'mu']])
for lep,ch in channels.items():
for comp,color in zip(comps,colors):
name = '_'.join(['extra' if extra=='only' else 'total' if extra else 'orig' ,lep, comp])
if template==None: name += '_actual'
tot = unqueue(ch.samples[comp].datas[0].ProjectionX())
v = (100*lib.asymmetry(tot.ProjectionX())[0],
100*lib.asymmetry(tot.ProjectionY())[0])
book.fill( v, name, (100,100), (-3,-3), (3,3))
def __init__(self, trees, treesA, treesC):
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
fs = 14
lw = 1.3
fig = plt.figure(figsize=(6.5,6.5))
ax = fig.add_subplot(111)
ax.set_ylim(-2,2)
ax.set_xlim(-2,2)
ax.set_ylabel(r'$A_c^y (\%)$ : Measured', fontsize=fs)
ax.set_xlabel(r'$A_c^y (\%)$ : Calculated', fontsize=fs)
ax.set_aspect('equal')
t = np.arange(-2,2,0.01)
ax.plot(t,t, lw=0.5, color='k')[0].set_dashes([1,1])
bookA = autoBook('A')
for e,m in izip(*treesA):
fit,sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
label = e.label[1:-9]
#print float(label)*e.scale, fit
bookA.fill( fit*100, "mean_"+label, 100, (float(label)-1)*e.scale*100, (float(label)+1)*e.scale*100)
gen = []
fit = []
err = []
for k,v in sorted(bookA.items()):
gen.append(float(k[5:])*e.scale*100)
fit.append(v.GetMean())
err.append(v.GetMeanError())
ax.errorbar(gen,fit,yerr=err,fmt='o', color='k', mec=(0,0.9,0), mfc='none', label='Extended POWHEG', mew=1)
bookC = autoBook('C')
for e,m in izip(*treesC):
fit,sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
label = e.label[4:6]
bookC.fill( e.gen_Ac*100, 'gen_'+label, 1000, -2, 2)
bookC.fill( fit*100, "mean_"+label, 100, (e.gen_Ac-e.scale)*100, (e.gen_Ac+e.scale)*100)
cgen = []
cfit = []
cerr = []
clab = []
for k,v in bookC.items():
if 'gen' in k: continue
cgen.append(bookC[k.replace('mean','gen')].GetMean())
cfit.append(v.GetMean())
cerr.append(v.GetMeanError())
clab.append(k)
ax.errorbar(cgen,cfit,yerr=cerr,fmt='.', color=(0,0,0.85), mec='k', label=r'Alternative $\mathrm{t\bar{t}}$ models')
for k,g,f,e in sorted(zip(clab,cgen,cfit,cerr), key=lambda x: x[1]):
print k, g, f, e
fit,sigma = lib.combined_result([(tree.fit,tree.sigma) for tree in trees])
ax.axhspan( -100, -99, alpha=0.3, fc='k', hatch='', label=r'$(e\oplus\mu)\pm\sigma_{stat}$')
ax.axhspan( 100*(fit-sigma), 100*(fit+sigma), alpha=0.2, fc='k', hatch='')
ax.axhspan( 100*(fit-0.0039), 100*(fit+0.0039), alpha=0.15, fc='k', hatch='', label=r'$(e\oplus\mu)\pm\sigma_{stat}\pm\sigma_{sys}$')
PH = (tree.scale*100, 0.0009*100)
KR = (0.0102*100, 0.0005*100)
BS = (0.0111*100, 0.0004*100)
predictions = zip([PH, KR, BS],[(0.2,0.8,0),(0.75,0,0),(0.5,0,0)],['POWHEG','K&R','B&S'])
for (f,s),c,L in predictions:
ax.axvspan( f-s, f+s, alpha=0.6, fc=c, ec=c, label=L)
ax.legend(loc='upper left', prop={'size':10}).draw_frame(False)
labelsfonts = {'fontsize':8}
ax.text(-0.4, -0.15, 'madgraph', labelsfonts, ha='right')
ax.text(0.15, 0.5, r"$Z'$", labelsfonts, ha='right')
ax.annotate('right', xy=(0.479041039944,0.418250670293), xytext=(0.4,0.1), arrowprops={'fc':'k', 'width':0.05, 'shrink':0.2, 'headwidth':2}, fontsize=8)
ax.text(0.55, 0.3, 'mc@nlo', labelsfonts)
ax.text(0.65, 0.45, 'RIGHT', labelsfonts)
ax.text(0.5, 0.7, 'left', labelsfonts, ha='right')
ax.text(1.15, 0.9, 'AXIAL', labelsfonts)
ax.text(1.65, 1.3, 'axial', labelsfonts)
output = 'output/bias_plot.pdf'
pp = PdfPages(output)
print 'Wrote:', output
pp.savefig(fig)
pp.close()
infile = r.TFile.Open(sys.argv[1])
tree = infile.Get('fitresult')
outname = sys.argv[1].replace('asymmetry_',
'ensemble').replace('.root', '')
if outname in sys.argv[1]:
print "Please rename input file."
exit()
mark = len(sys.argv) > 2
if mark:
mfile = r.TFile.Open(sys.argv[2])
mtree = infile.Get('fitresult')
mtree.GetEntry(0)
tfile = r.TFile.Open(outname + '.root', 'RECREATE')
book = autoBook(tfile)
names = [
'delta_Aqq', 'delta_Aqg', 'delta_A', 'error_Aqq', 'error_Aqg',
'error_A', 'pullqq', 'pullqg', 'pull'
]
fixedLimits = [(-1.5, 1.5), (-1.5, 1.5), (-1.5, 1.5), (0.05, 1.05),
(0.05, 1.05), (0.05, 1.05), (-5, 5), (-5, 5), (-5, 5)]
meanNLL = sum(e.NLL for e in tree) / tree.GetEntries()
limits = fixedLimits
wNLL = 40000
within = 0
tot = 0
for e in tree:
truth = e.gen_fitX, e.gen_fitY, 1.
mean = e.fitX, e.fitY
infile = r.TFile.Open(sys.argv[1])
tree = infile.Get('fitresult')
outname = sys.argv[1].replace('asymmetry_','ensemble').replace('.root', '')
if outname in sys.argv[1]:
print "Please rename input file."
exit()
mark = len(sys.argv) > 2
if mark:
mfile = r.TFile.Open(sys.argv[2])
mtree = infile.Get('fitresult')
mtree.GetEntry(0)
tfile = r.TFile.Open(outname+'.root', 'RECREATE')
book = autoBook(tfile)
names = ['delta_Aqq','delta_Aqg','delta_A',
'error_Aqq','error_Aqg','error_A',
'pullqq', 'pullqg', 'pull']
fixedLimits = [(-1.5,1.5),(-1.5,1.5),(-1.5,1.5),
(0.05,1.05),(0.05,1.05),(0.05,1.05),
(-5,5),(-5,5),(-5,5)]
meanNLL = sum(e.NLL for e in tree) / tree.GetEntries()
limits = fixedLimits
wNLL = 40000
within=0
tot = 0
for e in tree:
truth = e.gen_fitX,e.gen_fitY,1.
mean = e.fitX,e.fitY
r.TGaxis.SetMaxDigits(3)
r.tdrStyle.SetEndErrorSize(6)
#r.tdrStyle.SetPadRightMargin(0.06)
def unqueue(h):
return lib.unQueuedBins(h, 5, [-1, 1], [-1, 1])
threeD = False
comps = ['ttag', 'ttqg', 'ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
projections = {}
book = autoBook("stuff")
for extra in [True, False, 'only']:
for template in [None] + range(1000):
print template
#sys.stdout.flush()
channels = dict([
(lep,
channel_data(lep,
'top',
signal='fitTopQueuedBin5_TridiscriminantWTopQCD',
threeD=threeD,
extra=extra,
templateID=template,
getTT=True)) for lep in ['el', 'mu']
])
def __init__(self, trees, treesA, treesC):
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.ticker import MultipleLocator
fs = 16
lw = 1.3
fig = plt.figure(figsize=(6.5,6.5))
ax = fig.add_subplot(111)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fs)
ylimabs = 0.8
ax.set_ylim(-ylimabs,ylimabs)
ax.set_xlim(-2,2)
ax.set_ylabel(r'Bias $(\%)$', fontsize=fs+2)
ax.set_xlabel(r'$A_c^y$ $(\%)$', fontsize=fs+2)
#ax.set_aspect('equal')
ax.xaxis.set_minor_locator(MultipleLocator(0.2))
ax.xaxis.set_major_locator(MultipleLocator(1.0))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
ax.tick_params('both', length=10, width=1, which='major')
ax.tick_params('both', length=5, width=1, which='minor')
t = np.arange(-2,2,0.01)
ax.plot(t,np.zeros(len(t)), lw=0.5, color='k')[0].set_dashes([1,1])
bookA = autoBook('A')
for e,m in izip(*treesA):
fit,sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
label = e.label[1:-9]
#print float(label)*e.scale, fit
bookA.fill( fit*100, "mean_"+label, 100, (float(label)-1)*e.scale*100, (float(label)+1)*e.scale*100)
gen = []
fit = []
err = []
for k,v in sorted(bookA.items()):
gen.append(float(k[5:])*e.scale*100)
fit.append(v.GetMean())
err.append(v.GetMeanError())
ax.errorbar(gen,fit-np.array(gen),yerr=err,fmt='o', color='k', mec=(0,0.9,0), mfc='none', label='Extended POWHEG', mew=1, capsize=0, ms=7)
bookC = autoBook('C')
for e,m in izip(*treesC):
fit,sigma = lib.combined_result([(e.fit,e.sigma),(m.fit,m.sigma)])
label = e.label[4:6]
bookC.fill( e.gen_Ac*100, 'gen_'+label, 1000, -2, 2)
bookC.fill( fit*100, "mean_"+label, 100, (e.gen_Ac-2*e.scale)*100, (e.gen_Ac+2*e.scale)*100)
cgen = []
cfit = []
cerr = []
clab = []
iLow = []
iHi = []
iOther = []
iSM = []
fitfile = "output/bias_plot2-fits.pdf"
canvas = r.TCanvas()
canvas.Print(fitfile+'[')
for i, (k,v) in enumerate(x for x in bookC.items() if 'gen' not in x[0]):
if 'gen' in k: continue
cgen.append(bookC[k.replace('mean','gen')].GetMean())
#cfit.append(v.GetMean())
#cerr.append(v.GetMeanError())
v.Scale(1./v.Integral())
v.Fit("gaus","QEML")
canvas.Print(fitfile)
cfit.append(v.GetFunction("gaus").GetParameter('Mean'))
cerr.append(v.GetFunction("gaus").GetParameter('Sigma'))
clab.append(k)
print k
if '.' in k:
iLow.append(i)
elif '2' in k:
iHi.append(i)
elif k in ['mean_mg','mean_mn']:
iSM.append(i)
else: iOther.append(i)
canvas.Print(fitfile+']')
#ax.errorbar(cgen,cfit-np.array(cgen),yerr=cerr,fmt='.', color=(0,0,0.85), mec='k', label=r'Alternative $\mathsf{t\bar{t}}$ models')
ax.errorbar(np.array(cgen)[iOther],(cfit-np.array(cgen))[iOther],yerr=np.array(cerr)[iOther],fmt='s', color=(0,0,0.85), mec='k', capsize=0, label=r"$\mathrm{Z}'$ model", ms=5)
ax.errorbar(np.array(cgen)[iLow],(cfit-np.array(cgen))[iLow],yerr=np.array(cerr)[iLow],fmt='^', color=(0,0,0.85), mec='b', mfc='none',label=r'200$\mathsf{\,GeV}$ axigluon models', capsize=0, ms=8)
ax.errorbar(np.array(cgen)[iHi],(cfit-np.array(cgen))[iHi],yerr=np.array(cerr)[iHi],fmt='v', color=(0,0,0.85), mec='k', label=r'2$\mathsf{\,TeV}$ axigluon models', capsize=0, ms=6)
ax.errorbar(np.array(cgen)[iSM],(cfit-np.array(cgen))[iSM],yerr=np.array(cerr)[iSM],fmt='.', color=(0,0,0.85), mec='k', label=r'Standard model simulations ', capsize=0)
for k,g,f,e in sorted(zip(clab,cgen,cfit,cerr), key=lambda x: x[1]):
print k, g, f, e
fit,sigma = lib.combined_result([(tree.fit,tree.sigma) for tree in trees])
#ax.axhspan( -100, -99, alpha=0.3, fc='k', hatch='', label=r'$(e\oplus\mu)\pm\sigma_{stat}$')
#ax.axhspan( 100*(fit-sigma), 100*(fit+sigma), alpha=0.2, fc='k', hatch='')
#ax.axhspan( 100*(fit-0.0039), 100*(fit+0.0039), alpha=0.15, fc='k', hatch='', label=r'$(e\oplus\mu)\pm\sigma_{stat}\pm\sigma_{sys}$')
sys = {'mcstat':0.153, 'modeling': 0.017, 'pdf': 0.018, 'scale': 0.136}
sys_th = math.sqrt(sum(s*s for s in sys.values()))
ax.axhspan( -sys_th, sys_th, alpha=0.4, fc='white', hatch='//', edgecolor='k', label=r'Modeling systematic uncertainties')
handles_, labels_ = [],[]
handles, labels = ax.get_legend_handles_labels()
for frag in ['Ext','Stand','GeV','TeV','Z','Mod']:
i = next(i for i,j in enumerate(labels) if frag in j)
handles_.append(handles[i])
labels_.append(labels[i])
ax.legend(handles_, labels_, loc = 'lower left', prop={'size':13}, numpoints=1).draw_frame(False)
#ax.legend(loc='lower left', prop={'size':13}, numpoints=1).draw_frame(False)
labelsfonts = {'fontsize':13}
ax.text(-0.4, 0.15, 'MadGraph', labelsfonts, ha='right')
#ax.text(0.15, 0.4, r"$Z'$", labelsfonts, ha='right')
#ax.annotate('right', xy=(0.479041039944,0.418250670293), xytext=(0.4,0.1), arrowprops={'fc':'k', 'width':0.05, 'shrink':0.2, 'headwidth':2}, fontsize=8)
ax.text(0.4, -0.19, 'MC@NLO', labelsfonts, ha='right')
#ax.text(0.65, 0.45, 'RIGHT', labelsfonts)
#ax.text(0.5, 0.7, 'left', labelsfonts, ha='right')
#ax.text(1.15, 0.9, 'AXIAL', labelsfonts)
#ax.text(1.65, 1.3, 'axial', labelsfonts)
ax.text( -1.85, 0.7, "CMS", fontsize=20, weight='heavy')
ax.text( 0.6, 0.82, "19.6$\,\mathsf{fb^{-1}}$ (8 TeV)", fontsize=16)
plt.subplots_adjust(top=0.95,right=0.95,left=0.15)
output = 'output/bias_plot2.pdf'
pp = PdfPages(output)
print 'Wrote:', output
pp.savefig(fig)
pp.close()