def plot(limit_type, low_mass, high_mass, logy=True):
'''
Application entry point
'''
supported_limit_types = ("narrow", "wide", "kk")
if limit_type not in supported_limit_types:
raise RuntimeError("supported limit types: {0!r}".format(
supported_limit_types))
data = load_data(low_mass, high_mass)
class Limits(object): pass
# container for low and high limits
limits = Limits
limits.low = {
"visible": None,
"invisible": None
}
limits.high = copy.deepcopy(limits.low)
limits.low_fix_observed = copy.deepcopy(limits.low)
# convert YAML to dictionary
split_point = {
"narrow": 1000,
"wide": 1100,
"kk": 1000
}.get(limit_type)
(limits.low["visible"],
limits.low["invisible"]) = get_limits(data.low, is_low_mass=True,
split_point=split_point,
transform_x=gev_to_tev)
(limits.high["visible"],
limits.high["invisible"]) = get_limits(data.high, is_low_mass=False,
split_point=split_point,
transform_x=gev_to_tev)
(limits.low_fix_observed["visible"],
limits.low_fix_observed["invisible"]) = get_limits(data.low, is_low_mass=True,
split_point=split_point,
low_mass_x=True,
transform_x=gev_to_tev)
smooth.data(limits.high["visible"], n=2)
legend = ROOT.TLegend(0.5, 0.50, 0.80, 0.88)
line_width = 3
combo = ROOT.TMultiGraph()
# 2 sigma band
cur = limits.low["visible"]
graph = ROOT.TGraphAsymmErrors(len(cur["x"]), cur["x"], cur["expected"],
cur["xerr"], cur["xerr"],
cur["two_sigma_down"], cur["two_sigma_up"])
graph.SetFillColor(ROOT.kGray + 1)
graph.SetLineWidth(0)
combo.Add(graph)
g_2s_lm = graph
cur = limits.high["visible"]
graph = ROOT.TGraphAsymmErrors(len(cur["x"]), cur["x"], cur["expected"],
cur["xerr"], cur["xerr"],
cur["two_sigma_down"], cur["two_sigma_up"])
graph.SetFillColor(ROOT.kGray + 1)
graph.SetLineWidth(0)
combo.Add(graph)
g_2s_hm = graph
# 1 sigma band
cur = limits.low["visible"]
graph = ROOT.TGraphAsymmErrors(len(cur["x"]), cur["x"], cur["expected"],
cur["xerr"], cur["xerr"],
cur["one_sigma_down"], cur["one_sigma_up"])
graph.SetFillColor(ROOT.kGray)
graph.SetLineWidth(0)
combo.Add(graph)
g_1s_lm = graph
cur = limits.high["visible"]
graph = ROOT.TGraphAsymmErrors(len(cur["x"]), cur["x"], cur["expected"],
cur["xerr"], cur["xerr"],
cur["one_sigma_down"], cur["one_sigma_up"])
graph.SetFillColor(ROOT.kGray)
graph.SetLineWidth(0)
combo.Add(graph)
g_1s_hm = graph
# expected
cur = limits.low["visible"]
graph = ROOT.TGraph(len(cur["x"]), cur["x"], cur["expected"])
graph.SetLineColor(ROOT.kBlack)
graph.SetLineWidth(line_width)
combo.Add(graph)
legend.AddEntry(graph, "Expected (95% CL)", "l")
cur = limits.high["visible"]
graph = ROOT.TGraph(len(cur["x"]), cur["x"], cur["expected"])
graph.SetLineColor(ROOT.kBlack)
graph.SetLineWidth(line_width)
combo.Add(graph)
# observed
cur = limits.low_fix_observed["visible"]
graph = ROOT.TGraph(len(cur["observed_x"]), cur["observed_x"], cur["observed"])
graph.SetLineColor(ROOT.kRed + 1)
graph.SetLineWidth(line_width)
combo.Add(graph)
legend.AddEntry(graph, "Observed (95% CL)", "l")
cur = limits.high["visible"]
graph = ROOT.TGraph(len(cur["observed_x"]), cur["observed_x"], cur["observed"])
graph.SetLineColor(ROOT.kRed + 1)
graph.SetLineWidth(line_width)
combo.Add(graph)
# Theory
theories = {
"narrow": ([theory.zprime, 1.2, False], ),
"wide": ([theory.zprime, 10.0, False], ),
"kk": ([theory.kk, None, None], )
}.get(limit_type)
for index, (theory_function, theory_width, use_old_theory) in enumerate(theories, 0):
class Theory(object): pass
x, y, label = (theory_function(theory_width, use_old_theory)
if theory_width
else theory_function())
# remove all the points below 500 GeV
x, y = zip(*[(xi, yi) for xi, yi in zip(x, y) if not 500 > xi])
x = gev_to_tev(x)
graph = ROOT.TGraph(len(x), array('d', x), array('d', y))
graph.SetLineColor([ROOT.kBlue + 1, ROOT.kMagenta + 1, ROOT.kGreen + 1][index] if index < 3 else ROOT.kBlue + 1)
graph.SetLineWidth(3)
graph.SetLineStyle(2)
combo.Add(graph)
legend.AddEntry(graph, label, "l")
theory_data = Theory()
theory_data.x = x
theory_data.y = y
if limit_type != 'kk':
print("low mass".capitalize())
expected_exclusion, observed_exclusion = exclude(limits.low["visible"], theory_data)
print("Expected exclusion:", expected_exclusion)
print("Observed exclusion:", observed_exclusion)
print()
print("high mass".capitalize())
expected_exclusion, observed_exclusion = exclude(limits.high["visible"], theory_data)
print("Expected exclusion:", expected_exclusion)
print("Observed exclusion:", observed_exclusion)
legend.AddEntry(g_1s_lm, "#pm 1 #sigma Expected", "f")
legend.AddEntry(g_2s_lm, "#pm 2 #sigma Expected", "f")
# Draw
cv = ROOT.TCanvas()
style.canvas(cv)
combo.Draw("3al")
# Split point
if limit_type != 'kk':
line = ROOT.TGraph(2)
line.SetPoint(0, split_point * 1e-3, 1e1)
line.SetPoint(1, split_point * 1e-3, 3e-2)
line.SetLineColor(ROOT.kGray + 2)
line.SetLineStyle(2)
line.SetLineWidth(3)
line.Draw("L")
legend.Draw()
if logy:
cv.SetLogy(True)
style.combo(combo, maximum=1e2 if logy else None)
combo.GetXaxis().SetRangeUser(0, 4)
style.legend(legend)
legend.SetTextSize(0.04)
plot_labels = labels.create({
#"narrow": "{0:.1f}% Width Assumption".format(theory_width if theory_width else 0),
"narrow": "Z' with 1% Decay Width",
"wide": "Z' with 10% Decay Width",
"kk": "KK Gluon Assumption"}.get(limit_type, None))
map(ROOT.TObject.Draw, plot_labels)
cv.Update()
cv.SaveAs("limits-{0}.pdf".format(limit_type))
def plot(limit_type, filename, logy=True, smooth_data=True):
"""
Application entry point
"""
supported_limit_types = ("narrow", "wide", "kk")
if limit_type not in supported_limit_types:
raise RuntimeError("supported limit types: {0!r}".format(supported_limit_types))
data = load_data(filename, 0.1)
if smooth_data:
smooth.data(data, n=40, log=logy)
# print(sorted(data.keys()))
# for m in sorted(data.keys()):
# print(m, data[m][0])
legend = ROOT.TLegend(0.5, 0.50, 0.80, 0.88)
line_width = 3
combo = ROOT.TMultiGraph()
# 2 sigma band
cur = get_limits(data)
graph = ROOT.TGraphAsymmErrors(
len(cur["x"]), cur["x"], cur["expected"], cur["xerr"], cur["xerr"], cur["two_sigma_down"], cur["two_sigma_up"]
)
graph.SetFillColor(ROOT.kGray + 1)
graph.SetLineWidth(0)
combo.Add(graph)
g_2s = graph
# 1 sigma band
graph = ROOT.TGraphAsymmErrors(
len(cur["x"]), cur["x"], cur["expected"], cur["xerr"], cur["xerr"], cur["one_sigma_down"], cur["one_sigma_up"]
)
graph.SetFillColor(ROOT.kGray)
graph.SetLineWidth(0)
combo.Add(graph)
g_1s = graph
# expected
graph = ROOT.TGraph(len(cur["x"]), cur["x"], cur["expected"])
graph.SetLineColor(ROOT.kBlack)
graph.SetLineWidth(line_width)
combo.Add(graph)
legend.AddEntry(graph, "Expected (95% Bayesian)", "l")
# observed
graph = ROOT.TGraph(len(cur["observed_x"]), cur["observed_x"], cur["observed"])
graph.SetLineColor(ROOT.kRed + 1)
graph.SetLineWidth(line_width)
combo.Add(graph)
legend.AddEntry(graph, "Observed (95% Bayesian)", "l")
# Theory
theories = {
"narrow": ([theory.zprime, 1.2, False],),
"wide": ([theory.zprime, 10.0, False],),
"kk": ([theory.kkgluon, None, None],),
}.get(limit_type)
for index, (theory_function, theory_width, use_old_theory) in enumerate(theories, 0):
class Theory(object):
pass
x, y, label = theory_function(theory_width, use_old_theory) if theory_width else theory_function()
# remove all the points below 500 GeV
x, y = zip(*[(xi, yi) for xi, yi in zip(x, y) if not 750 > xi])
graph = ROOT.TGraph(len(x), array("d", x), array("d", y))
graph.SetLineColor([ROOT.kBlue + 1, ROOT.kMagenta + 1, ROOT.kGreen + 1][index] if index < 3 else ROOT.kBlue + 1)
graph.SetLineWidth(3)
graph.SetLineStyle(2)
combo.Add(graph)
legend.AddEntry(graph, label, "l")
theory_data = Theory()
theory_data.x = x
theory_data.y = y
expected_exclusion, observed_exclusion = exclude(cur, theory_data)
print("Expected exclusion:", expected_exclusion)
print("Observed exclusion:", observed_exclusion)
legend.AddEntry(g_1s, "#pm 1 s.d. Expected", "f")
legend.AddEntry(g_2s, "#pm 2 s.d. Expected", "f")
# Draw
cv = ROOT.TCanvas()
style.canvas(cv)
combo.Draw("3al")
legend.Draw()
if logy:
cv.SetLogy(True)
if limit_type == "kk":
style.combo(
combo,
ytitle="Upper Limit #sigma_{g_{KK}} x B [pb]",
maximum=1e2 if logy else None,
minimum=1e-2 if logy else None,
)
else:
style.combo(combo, maximum=1e2 if logy else None)
style.legend(legend)
legend.SetTextSize(0.04)
plot_labels = labels.create(
{"narrow": "Narrow Width Assumption", "wide": "10% Width Assumption", "kk": "KK Gluon Assumption"}.get(
limit_type, None
)
)
map(ROOT.TObject.Draw, plot_labels)
cv.Update()
cv.SaveAs("limits-{0}.pdf".format(limit_type))