def test_normal_interval():
from coffea.hist.plot import normal_interval
# Reference weighted efficiency and error from ROOTs TEfficiency
denom = np.array([
89.01457591590004,
2177.066076428943,
6122.5256890981855,
0.0,
100.27757990710668,
])
num = np.array([
75.14287743709515,
2177.066076428943,
5193.454723043864,
0.0,
84.97723540536361,
])
denom_sumw2 = np.array([
94.37919737476827, 10000.0, 6463.46795877633, 0.0, 105.90898005417333
])
num_sumw2 = np.array(
[67.2202147680005, 10000.0, 4647.983931785646, 0.0, 76.01275761253757])
ref_hi = np.array([
0.0514643476600107, 0.0, 0.0061403263960343, np.nan, 0.0480731185500146
])
ref_lo = np.array([
0.0514643476600107, 0.0, 0.0061403263960343, np.nan, 0.0480731185500146
])
interval = normal_interval(num, denom, num_sumw2, denom_sumw2)
threshold = 1e-6
lo, hi = interval
assert len(ref_hi) == len(hi)
assert len(ref_lo) == len(lo)
for i in range(len(ref_hi)):
if np.isnan(ref_hi[i]):
assert np.isnan(ref_hi[i])
elif ref_hi[i] == 0.0:
assert hi[i] == 0.0
else:
assert np.abs(hi[i] / ref_hi[i] - 1) < threshold
if np.isnan(ref_lo[i]):
assert np.isnan(ref_lo[i])
elif ref_lo[i] == 0.0:
assert lo[i] == 0.0
else:
assert np.abs(lo[i] / ref_lo[i] - 1) < threshold
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