def get_hlt_prescales(runs):
fn = 'prescales_temp/hltprescales.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
prescales = defaultdict(lambda: defaultdict(lambda: defaultdict(set)))
l = len(runs)
for i,run in enumerate(runs):
print 'get_hlt_prescales run %i (%i/%i)' % (run, i+1, l)
res = lumi_context('hltbyls', {run:None})[run]
for line in res:
ls = line[0]
for hlt_name, prescale, duh, duh2 in line[-1]:
prescales[hlt_name][prescale][run].add(ls)
for hlt_name, duh in prescales.iteritems():
for prescale, duh2 in duh.iteritems():
for run in duh2:
duh2[run] = collapse(duh2[run])
duh[prescale] = dict(duh2)
prescales[hlt_name] = dict(duh)
prescales = dict(prescales)
to_pickle(prescales, fn)
return prescales
def strip(cls, fn, new_fn):
lls = from_pickle(fn, comp=True)
for ll in lls:
del ll.time
del ll.beam_status
del ll.energy
to_pickle(lls, new_fn, comp=True)
def get_hlt_prescales(runs):
fn = 'prescales_temp/hltprescales.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
prescales = defaultdict(lambda: defaultdict(lambda: defaultdict(set)))
l = len(runs)
for i, run in enumerate(runs):
print 'get_hlt_prescales run %i (%i/%i)' % (run, i + 1, l)
res = lumi_context('hltbyls', {run: None})[run]
for line in res:
ls = line[0]
for hlt_name, prescale, duh, duh2 in line[-1]:
prescales[hlt_name][prescale][run].add(ls)
for hlt_name, duh in prescales.iteritems():
for prescale, duh2 in duh.iteritems():
for run in duh2:
duh2[run] = collapse(duh2[run])
duh[prescale] = dict(duh2)
prescales[hlt_name] = dict(duh)
prescales = dict(prescales)
to_pickle(prescales, fn)
return prescales
def strip(cls, fn, new_fn):
lls = from_pickle(fn, comp=True)
for ll in lls:
del ll.time
del ll.beam_status
del ll.energy
to_pickle(lls, new_fn, comp=True)
def one_from_r(ex, name, csvs=True):
if csvs:
def read_csv(fn):
lines = [x.strip() for x in open(fn).read().replace('"', '').split('\n') if x.strip()]
lines.pop(0)
vs = []
for line in lines:
ws = [float(x) for x in line.split(',')]
ws.pop(0)
if len(ws) == 1:
ws = ws[0]
vs.append(ws)
return vs
x = read_csv('%s_x.csv' % ex)
y = read_csv('%s_y.csv' % ex)
z = read_csv('%s_z.csv' % ex)
else:
x,y,z = from_pickle('/uscms/home/tucker/afshome/%sxyz.gzpickle' % ex)
assert sorted(x) == x
assert sorted(y) == y
nx = len(x)
ny = len(y)
x.append(x[-1] + (x[-1] - x[-2]))
y.append(y[-1] + (y[-1] - y[-2]))
#print ex, nx, ny
x = array('d', x)
y = array('d', y)
h = ROOT.TH2F(name, '', len(x)-1, x, len(y)-1, y)
h.SetStats(0)
for ix in xrange(1, nx+1):
for iy in xrange(1, ny+1):
h.SetBinContent(ix, iy, z[ix-1][iy-1])
return h
def get_lumibylses(runs):
fn = 'prescales_temp/lumibylses.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
d = {}
for run in runs:
d.update(parse_lumibyls(run))
to_pickle(d, fn)
return d
def get_hlt_prescales_collapsed(runs, hlt_prescales=None):
fn = 'prescales_temp/hltprescales_collapsed.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
if hlt_prescales is None:
hlt_prescales = get_hlt_prescales(runs)
hlt_prescales_collapsed = collapse_versions(hlt_prescales)
to_pickle(hlt_prescales_collapsed, fn)
return hlt_prescales_collapsed
def get_lumibylses(runs):
fn = 'prescales_temp/lumibylses.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
d = {}
for run in runs:
d.update(parse_lumibyls(run))
to_pickle(d, fn)
return d
def get_hlt_prescales_collapsed(runs, hlt_prescales=None):
fn = 'prescales_temp/hltprescales_collapsed.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
if hlt_prescales is None:
hlt_prescales = get_hlt_prescales(runs)
hlt_prescales_collapsed = collapse_versions(hlt_prescales)
to_pickle(hlt_prescales_collapsed, fn)
return hlt_prescales_collapsed
def get_menus(runs):
fn = 'prescales_temp/menus.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
menus = defaultdict(set)
for run in runs:
menu = lumi_context('hltmenu', {run:None})[run]
for hlt_path, l1_seed, l1_bit in menu:
assert l1_bit == 'n/a' or l1_seed == l1_bit
menu = tuple([(hlt_path, l1_seed) for hlt_path, l1_seed, l1_bit in menu])
menus[menu].add(run)
to_pickle(menus, fn)
return menus
def get_menus(runs):
fn = 'prescales_temp/menus.gzpickle'
if os.path.isfile(fn):
return from_pickle(fn)
menus = defaultdict(set)
for run in runs:
menu = lumi_context('hltmenu', {run: None})[run]
for hlt_path, l1_seed, l1_bit in menu:
assert l1_bit == 'n/a' or l1_seed == l1_bit
menu = tuple([(hlt_path, l1_seed)
for hlt_path, l1_seed, l1_bit in menu])
menus[menu].add(run)
to_pickle(menus, fn)
return menus
def load(cls, fn, is_csv=False):
if is_csv or fn.endswith('.csv'):
return cls.load_csv(fn)
return from_pickle(fn, comp=True)
def plot():
set_style()
ps = plot_saver(plot_dir('pdfuncerts'), size=(600, 600), log=False)
ps.c.SetLeftMargin(0.11)
parabs = {
300: ((300, 0.06), (585, 0.02), (877, 0.009)),
1000: ((300, 0.03), (550, 0.01), (873, 0.006)),
10000: ((300, 0.04), (550, 0.015), (877, 0.005)),
}
lines = {
300: ((877, 0.009), (3000, 0.075)),
1000: ((873, 0.006), (3000, 0.05)),
10000: ((877, 0.005), (3000, 0.035)),
}
def fparab(points, _n=[0]):
(x1, y1), (x2, y2), (x3, y3) = sorted(points)
c1 = y1 / (x1 - x2) / (x1 - x3)
c2 = y2 / (x2 - x1) / (x2 - x3)
c3 = y3 / (x3 - x1) / (x3 - x2)
a = c1 + c2 + c3
b = -((c2 + c3) * x1 + (c1 + c3) * x2 + (c1 + c2) * x3)
c = c1 * x2 * x3 + c2 * x1 * x3 + c3 * x1 * x2
fm = '%.4g*x**2 + %.4g*x + %.4g' % (a, b, c)
fcn = ROOT.TF1('parab%i' % _n[0], fm, x1, x3)
_n[0] += 1
return fcn, fm
def fline(points, _n=[0]):
(x1, y1), (x2, y2) = sorted(points)
m = (y2 - y1) / (x2 - x1)
b = y1 - m * x1
fm = '%.4g*x + %.4g' % (m, b)
fcn = ROOT.TF1('line%i' % _n[0], fm, x1, x2)
_n[0] += 1
return fcn, fm
samples = defaultdict(list)
for s in from_pickle('pdfuncerts.gzpickle'):
tau = int(s.name.split('tau')[1].split('um')[0])
samples[tau].append(s)
for tau in 300, 1000, 10000:
gneu, gstop = [], []
for s in sorted(samples[tau], key=lambda s: s.name):
mass = float(s.name.split('_M')[1].split('_')[0])
num, den = s.nominal / s.w, s.norm
e, ee = wilson_score_vpme(num, den)
st = num**-0.5
print s.name, tau, mass, s.ctlrelmd, st, s.nominal, e, ee, num, den, clopper_pearson(
num, den)
(gneu if 'neu' in s.name else gstop).append((mass, s.ctlrelmd, ee))
gneu, gstop = tgraph(gneu) if gneu else None, tgraph(gstop)
for g, c in (gneu, 4), (gstop, 2):
if not g: continue
g.SetTitle(';mass (GeV);relative acc. unc.')
g.SetMarkerColor(c)
g.SetLineColor(c)
g.SetMarkerStyle(20)
gstop.Draw('AP')
if gneu: gneu.Draw('P')
gstop.GetYaxis().SetRangeUser(0, 0.08)
leg = ROOT.TLegend(
0.249, 0.742, 0.599, 0.852, '#tau = %s' % {
300: '300 #mum',
1000: '1 mm',
10000: '10 mm'
}[tau])
leg.SetBorderSize(0)
leg.AddEntry(gstop, 'dijet', 'LP')
if gneu: leg.AddEntry(gneu, 'multijet', 'LP')
leg.Draw()
prb, pf = fparab(parabs[tau])
prb.Draw('same')
lin, lf = fline(lines[tau])
lin.Draw('same')
print tau, pf, lf
ps.save('tau%06i' % tau)
def load(cls, fn, is_csv=False):
if is_csv or fn.endswith('.csv') or fn.endswith('.csv.gz'):
return cls.load_csv(fn)
return from_pickle(fn, comp=True)
def plot():
set_style()
ps = plot_saver(plot_dir('ren_fac_%s' % year), size=(600, 600), log=False)
ps.c.SetLeftMargin(0.11)
samples = defaultdict(list)
for s in from_pickle('ren_fac.gzpickle'):
tau = int(s.name.split('tau')[1].split('um')[0])
samples[tau].append(s)
for tau in 300, 1000, 10000:
gneu, gstop = [], []
for s in sorted(samples[tau], key=lambda s: s.name):
mass = float(s.name.split('_M')[1].split('_')[0])
num, den = s.nominal / s.w, s.norm
e, ee = wilson_score_vpme(num, den)
st = num**-0.5
(gneu if 'neu' in s.name else gstop).append((mass, s.ctlrelmd, ee))
gneu, gstop = tgraph(gneu) if gneu else None, tgraph(gstop)
for g, c in (gneu, 4), (gstop, 2):
if not g: continue
g.SetTitle(';mass (GeV);relative acc. unc.')
g.SetMarkerColor(c)
g.SetLineColor(c)
g.SetMarkerStyle(20)
gstop.Draw('AP')
if gneu: gneu.Draw('P')
gstop.GetYaxis().SetRangeUser(0, 0.1)
leg = ROOT.TLegend(
0.249, 0.742, 0.599, 0.852, '#tau = %s' % {
300: '300 #mum',
1000: '1 mm',
10000: '10 mm'
}[tau])
leg.SetBorderSize(0)
leg.AddEntry(gstop, 'dijet', 'LP')
if gneu: leg.AddEntry(gneu, 'multijet', 'LP')
leg.Draw()
fitopt = "RSQ0"
fitdict = {
(300, 500): "pol1",
(500, 1000): "pol1",
(1000, 3000): "pol0"
}
for key in fitdict:
fitfunc = fitdict[key]
fitrange = key
gstop.Fit(fitfunc, fitopt, "", *fitrange)
for kind in ["mfv_neu", "mfv_stopdbardbar"]:
if kind == "mfv_neu": g = gneu
elif kind == "mfv_stopdbardbar": g = gstop
else: os.abort("invalid kind!")
outstr = "('%s', %s, '%s') : lambda x: \n" % (kind, tau, year)
fit_M_300_to_500 = g.Fit("pol1", fitopt, "", 300, 500)
fit_M_500_to_1000 = g.Fit("pol1", fitopt, "", 500, 1000)
fit_M_1000_to_3000 = g.Fit("pol0", fitopt, "", 1000, 3000)
outstr += "%E + %E*x if x < 500 " % (fit_M_300_to_500.Parameter(0),
fit_M_300_to_500.Parameter(1))
outstr += "else %E + %E*x if x < 1000 " % (
fit_M_500_to_1000.Parameter(0), fit_M_500_to_1000.Parameter(1))
outstr += "else %E," % (fit_M_1000_to_3000.Parameter(0))
print outstr
ps.save('tau%06i' % tau)
