def write(self,fname):
R = np.array([[1,1],[-1,1]]) # rotate pi/4, except also scale by sqrt(2)
d_Aqq,d_Aqg = np.sqrt(np.diag(self.sigmas_totl))
d_Ac = math.sqrt(R.dot(self.sigmas_totl.dot(R.T))[0,0])
sim_d_Aqq,sim_d_Aqg = np.sqrt(np.diag(self.sigmas_simu))
sim_d_Ac = math.sqrt(R.dot(self.sigmas_simu.dot(R.T))[0,0])
syst_d_Ac = math.sqrt(R.dot((self.sigmas_syst+self.sigmas_pois).dot(R.T))[0,0])
stat_d_Ac = math.sqrt(R.dot(self.sigmas_stat).dot(R.T)[0,0])
print 100*stat_d_Ac, 100*syst_d_Ac
stat = ellipse(mean=list(self.mean), sigmas2=list(oneSigmaN2LL * self.sigmas_stat))
syst = ellipse(mean=list(self.mean), sigmas2=list(oneSigmaN2LL * self.sigmas_syst))
pois = ellipse(mean=list(self.mean), sigmas2=list(oneSigmaN2LL * self.sigmas_pois))
totl = ellipse(mean=list(self.mean), sigmas2=list(oneSigmaN2LL * self.sigmas_totl))
simu = ellipse(mean=list(self.simmean), sigmas2=list(oneSigmaN2LL * self.sigmas_simu))
poissyst = ellipse(mean=list(self.mean), sigmas2=list(oneSigmaN2LL * (self.sigmas_syst+self.sigmas_pois)))
with open(fname.replace('_points',''), 'w') as wFile:
print >> wFile, 'central', self.mean[0], self.mean[1]
print >> wFile, 'thr30', self.thr30_mean[0], self.thr30_mean[1]
print >> wFile, 'sim', self.simmean[0], self.simmean[1]
print >> wFile, 'sim30', self.simmean30[0], self.simmean30[1]
with open(fname, 'w') as wFile:
N = 100
for t in range(N + 1):
T = t * 2 * math.pi / N
print >> wFile, '\t'.join(str(f) for f in
sum((item.eval(T)
for item in filter(None, [stat,syst,totl,
simu,
lineUD(self.mean[0],d_Aqq),
lineUD(self.mean[1],d_Aqg),
lineUD(sum(self.mean),d_Ac),
lineUD(self.simmean[0], sim_d_Aqq),
lineUD(self.simmean[1], sim_d_Aqg),
lineUD(sum(self.simmean), sim_d_Ac),
pois,
poissyst
])), ()))
temp = r'''
\begin{tabular}{c|r@{.}lr@{.}lr@{.}l}
\hline
&\multicolumn{6}{c}{(%s)}\\
& \multicolumn{2}{c}{$A_c^{y(\QQ)}$} & \multicolumn{2}{c}{$A_c^{y(\QG)}$} & \multicolumn{2}{c}{$A_c^{y}$} \\
\hline
\hline
\POWHEG \sc{ct10} & %s & %s & %s \\
%s & %s & %s & %s \\
\hline
\end{tabular}'''
def form(n,e):
n*=100
e*=100
err_digit = int(math.floor(math.log(abs(e))/math.log(10)))-1 if e else 0
scale = float(10**(-err_digit)) if e else 1
p,d = divmod(round(abs(n*scale))/scale,1)
return (("%s%d&%0"+str(-err_digit)+"d(%d)")%('-' if n<0 else ' ',p,d*scale,round(e*scale))).ljust(8)
simcorrection = self.central.tree.f_gg_hat * self.central.tree.Ac_y_ttgg
print temp%(r'\%',
form(self.simmean[0], sim_d_Aqq),
form(self.simmean[1], sim_d_Aqg),
form(sum(self.simmean)+simcorrection, sim_d_Ac),
self.label,
form(self.mean[0], d_Aqq),
form(self.mean[1], d_Aqg),
form(sum(self.mean)+self.central.tree.correction, d_Ac),
)
'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
sigmas2 = [[e.fitXX, e.fitXY], [e.fitXY, e.fitYY]]
el = ellipse(mean=mean, sigmas2=sigmas2)
if np.dot(truth, el.matrix).dot(truth) < 0: within += 1
tot += 1
nbins = 30
book.fill(meanNLL, 'meanNLL', nbins, -6.2e6, -5.8e6)
book.fill(e.NLL - meanNLL, 'dNLL', nbins, -wNLL, wNLL)
try:
R = np.array([[1, 1],
[-1,
1]]) # rotate pi/4, except also scale by sqrt(2)
sigmas = np.array([[e.fitXX, e.fitXY], [e.fitXY, e.fitYY]
]) / oneSigmaN2LL
sigma = math.sqrt(R.dot(sigmas).dot(R.T)[0, 0])
values = 100 * np.array([
e.fitX - e.gen_fitX, e.fitY - e.gen_fitY,
def write(self, fname):
R = np.array([[1, 1],
[-1, 1]]) # rotate pi/4, except also scale by sqrt(2)
d_Aqq, d_Aqg = np.sqrt(np.diag(self.sigmas_totl))
d_Ac = math.sqrt(R.dot(self.sigmas_totl.dot(R.T))[0, 0])
sim_d_Aqq, sim_d_Aqg = np.sqrt(np.diag(self.sigmas_simu))
sim_d_Ac = math.sqrt(R.dot(self.sigmas_simu.dot(R.T))[0, 0])
syst_d_Ac = math.sqrt(
R.dot((self.sigmas_syst + self.sigmas_pois).dot(R.T))[0, 0])
stat_d_Ac = math.sqrt(R.dot(self.sigmas_stat).dot(R.T)[0, 0])
print 100 * stat_d_Ac, 100 * syst_d_Ac
stat = ellipse(mean=list(self.mean),
sigmas2=list(oneSigmaN2LL * self.sigmas_stat))
syst = ellipse(mean=list(self.mean),
sigmas2=list(oneSigmaN2LL * self.sigmas_syst))
pois = ellipse(mean=list(self.mean),
sigmas2=list(oneSigmaN2LL * self.sigmas_pois))
totl = ellipse(mean=list(self.mean),
sigmas2=list(oneSigmaN2LL * self.sigmas_totl))
simu = ellipse(mean=list(self.simmean),
sigmas2=list(oneSigmaN2LL * self.sigmas_simu))
poissyst = ellipse(mean=list(self.mean),
sigmas2=list(oneSigmaN2LL *
(self.sigmas_syst + self.sigmas_pois)))
with open(fname.replace('_points', ''), 'w') as wFile:
print >> wFile, 'central', self.mean[0], self.mean[1]
print >> wFile, 'thr30', self.thr30_mean[0], self.thr30_mean[1]
print >> wFile, 'sim', self.simmean[0], self.simmean[1]
print >> wFile, 'sim30', self.simmean30[0], self.simmean30[1]
with open(fname, 'w') as wFile:
N = 100
for t in range(N + 1):
T = t * 2 * math.pi / N
print >> wFile, '\t'.join(
str(f) for f in sum((
item.eval(T)
for item in filter(None, [
stat, syst, totl, simu,
lineUD(self.mean[0], d_Aqq),
lineUD(self.mean[1], d_Aqg),
lineUD(sum(self.mean), d_Ac),
lineUD(self.simmean[0], sim_d_Aqq),
lineUD(self.simmean[1], sim_d_Aqg),
lineUD(sum(self.simmean), sim_d_Ac), pois, poissyst
])), ()))
temp = r'''
\begin{tabular}{c|r@{.}lr@{.}lr@{.}l}
\hline
&\multicolumn{6}{c}{(%s)}\\
& \multicolumn{2}{c}{$A_c^{y(\QQ)}$} & \multicolumn{2}{c}{$A_c^{y(\QG)}$} & \multicolumn{2}{c}{$A_c^{y}$} \\
\hline
\hline
\POWHEG \sc{ct10} & %s & %s & %s \\
%s & %s & %s & %s \\
\hline
\end{tabular}'''
def form(n, e):
n *= 100
e *= 100
err_digit = int(math.floor(math.log(abs(e)) /
math.log(10))) - 1 if e else 0
scale = float(10**(-err_digit)) if e else 1
p, d = divmod(round(abs(n * scale)) / scale, 1)
return (("%s%d&%0" + str(-err_digit) + "d(%d)") %
('-' if n < 0 else ' ', p, d * scale, round(
e * scale))).ljust(8)
simcorrection = self.central.tree.f_gg_hat * self.central.tree.Ac_y_ttgg
print temp % (
r'\%',
form(self.simmean[0], sim_d_Aqq),
form(self.simmean[1], sim_d_Aqg),
form(sum(self.simmean) + simcorrection, sim_d_Ac),
self.label,
form(self.mean[0], d_Aqq),
form(self.mean[1], d_Aqg),
form(sum(self.mean) + self.central.tree.correction, d_Ac),
)
import numpy as np
tfile = r.TFile.Open("jiggled_asymmetries.root")
ells = {}
for comp in ['ttqq','ttqg','ttag']:
for lep in ['el','mu']:
for epoch in ['orig','extra','total']:
h = tfile.Get('_'.join([epoch,lep,comp]))
stats = array.array('d',7*[0.])
h.GetStats(stats)
dx2 = stats[3]/stats[0] - stats[2]**2/stats[0]**2
dy2 = stats[5]/stats[0] - stats[4]**2/stats[0]**2
dxy = stats[6]/stats[0] - stats[2]*stats[4]/stats[0]**2
sigmas2 = [[dx2, dxy], [dxy, dy2]]
mean = ( h.GetMean(1), h.GetMean(2) )
el = ellipse(mean=mean, sigmas2=sigmas2)
ells[h.GetName()] = el
order = ['_'.join([epoch,lep,'']) for lep in ['el','mu'] for epoch in ['orig','extra','total']]
for comp in ['ttqq','ttqg','ttag']:
with open(comp+'_points.txt','w') as out:
meanel = ells['total_el_'+comp].mean
meanmu = ells['total_mu_'+comp].mean
print comp, meanel[0], meanel[1], meanmu[0], meanmu[1]
print>> out, '# ', '\t'.join(order)
N = 100
for t in range(N + 1):
T = t * 2 * math.pi / N
print >> out, '\t'.join("%f\t%f\t\t"%ells[name].eval(T) for name in [o+comp for o in order])