def content_table(hnum, hden):
table = []
axis = 'recoil'
try:
hnum.axis(axis)
except KeyError:
axis = 'met'
for x,ynum, yden in zip(hnum.axis(axis).identifiers(),hnum.values()[()],hden.values()[()]):
eff = ynum/ yden if yden != 0 else 0
unc = clopper_pearson_interval(ynum, yden, 0.68)
line = [(x.lo + x.hi)/2, ynum, yden, eff,unc[0], unc[1]]
table.append(line)
return tabulate(table, headers=['Recoil', 'Numerator', 'Denominator',"Efficiency", "Eff-sigma","Eff+sigma"])
def test_clopper_pearson_interval():
from coffea.hist.plot import clopper_pearson_interval
# Reference values for CL=0.6800 calculated with ROOT's TEfficiency
num = np.array([1., 5., 10., 10.])
denom = np.array([10., 10., 10., 437.])
ref_hi = np.array([0.293313782248242, 0.6944224231766912, 1.0, 0.032438865381336446])
ref_lo = np.array([0.01728422272382846, 0.3055775768233088, 0.8325532074018731, 0.015839046981153772])
interval = clopper_pearson_interval(num, denom, coverage=0.68)
threshold = 1e-6
assert(all((interval[1, :] / ref_hi) - 1 < threshold))
assert(all((interval[0, :] / ref_lo) - 1 < threshold))
pileup_corr_puDown = lookup_tools.dense_lookup.dense_lookup(
corr_puDown, fin_pileup["pileup"].edges)
corrections['2018_pileupweight'] = pileup_corr
corrections['2018_pileupweight_puUp'] = pileup_corr_puUp
corrections['2018_pileupweight_puDown'] = pileup_corr_puDown
with uproot.open(
"correction_files/RUNTriggerEfficiencies_SingleMuon_Run2016_V2p1_v03.root"
) as fin:
denom = fin[
"DijetTriggerEfficiencySeveralTriggers/jet1SoftDropMassjet1PtDenom_cutJet"]
num = fin[
"DijetTriggerEfficiencySeveralTriggers/jet1SoftDropMassjet1PtPassing_cutJet"]
eff = num.values / np.maximum(denom.values, 1)
efferr = plot.clopper_pearson_interval(num.values, denom.values)
msd_bins, pt_bins = num.edges
# Cut pt < 200
cutpt = pt_bins >= 200
pt_bins = pt_bins[cutpt]
cutpt = cutpt[:-1]
eff = eff[:, cutpt]
eff_trigweightDown = efferr[0, :, cutpt]
eff_trigweightUp = efferr[1, :, cutpt]
corrections['2016_trigweight_msd_pt'] = lookup_tools.dense_lookup.dense_lookup(
eff, (msd_bins, pt_bins))
corrections[
'2016_trigweight_msd_pt_trigweightDown'] = lookup_tools.dense_lookup.dense_lookup(
eff_trigweightDown, (msd_bins, pt_bins))
corrections[
def plotratio(
num,
denom,
ax=None,
clear=True,
overflow=False,
error_opts=None,
denom_fill_opts=None,
guide_opts=None,
unc="clopper-pearson",
label=None,
ratio_yticks=[], # [start, stop, step] -> [0.5, 1.5, 0.1]
):
if ax is None:
fig, ax = plt.subplots(1, 1)
else:
if not isinstance(ax, plt.Axes):
raise ValueError("ax must be a matplotlib Axes object")
if clear:
ax.clear()
if error_opts is None and denom_fill_opts is None and guide_opts is None:
error_opts = {}
denom_fill_opts = {}
(naxis,) = num.axes
(daxis,) = denom.axes
assert isinstance(naxis, (hist.axis.Regular, hist.axis.Variable))
assert isinstance(daxis, (hist.axis.Regular, hist.axis.Variable))
assert all(naxis.edges == daxis.edges)
ax.set_xlabel(naxis.label)
ax.set_ylabel("Ratio")
edges = naxis.edges
centers = naxis.centers
sumw_num = num.view(flow=overflow)["value"]
sumw2_num = num.view(flow=overflow)["variance"]
sumw_denom = denom.view(flow=overflow)["value"]
sumw2_denom = denom.view(flow=overflow)["variance"]
rsumw = sumw_num / sumw_denom
if unc == "clopper-pearson":
rsumw_err = np.abs(clopper_pearson_interval(sumw_num, sumw_denom) - rsumw)
elif unc == "poisson-ratio":
# poisson ratio n/m is equivalent to binomial n/(n+m)
rsumw_err = np.abs(clopper_pearson_interval(sumw_num, sumw_num + sumw_denom) - rsumw)
elif unc == "num":
rsumw_err = np.abs(poisson_interval(rsumw, sumw2_num / sumw_denom ** 2) - rsumw)
elif unc == "normal":
rsumw_err = np.abs(normal_interval(sumw_num, sumw_denom, sumw2_num, sumw2_denom))
else:
raise ValueError("Unrecognized uncertainty option: %r" % unc)
if error_opts is not None:
opts = {"label": label, "linestyle": "none"}
opts.update(error_opts)
emarker = opts.pop("emarker", "")
errbar = ax.errorbar(x=centers, y=rsumw, yerr=rsumw_err, **opts)
plt.setp(errbar[1], "marker", emarker)
if denom_fill_opts is not None:
unity = np.ones_like(sumw_denom)
denom_unc = poisson_interval(unity, sumw2_denom / sumw_denom ** 2)
opts = {"step": "post", "facecolor": (0, 0, 0, 0.3), "linewidth": 0}
opts.update(denom_fill_opts)
ax.fill_between(
edges,
np.r_[denom_unc[0], denom_unc[0, -1]],
np.r_[denom_unc[1], denom_unc[1, -1]],
**opts,
)
if guide_opts is not None:
opts = {"linestyle": "--", "color": (0, 0, 0, 0.5), "linewidth": 1}
opts.update(guide_opts)
ax.axhline(1.0, **opts)
if ratio_yticks:
start, stop, step = ratio_yticks
ax.set_yticks(np.arange(start, stop + step, step).tolist())
for label in ax.yaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax.set_ylim(start, stop)
if clear:
ax.autoscale(axis="x", tight=True)
ax.set_ylim(0, None)
return ax