def plot_dfs(name,data,fits,label,title=None,log=False, unit="GeV", stack=True):
"""Plot data, fit and normalized residuals"""
fig, data_axes, sigma_axes = utils.get_plotaxes_stacked()
colors = {"signal":"r", "misrecon":"m", "bbar":"g", "continuum":"b", "dummy":"r"}
last = None
for i,(l,f) in enumerate(fits):
if last is not None:
last.Add(f)
else:
last = f.Clone("tmp")
hist = stack and last or f
plot_fit(data_axes, hist, data, colors[l], l, i)
if not stack and len(fits)>1:
plot_fit(data_axes, last, data, "k", "combined", len(fits))
r2mpl.plot(data,errors=True,axes=data_axes, color="k", label="data", linewidth=0.5, capsize=1.0, zorder=10)
sigma = calc_sigmas(name + "_sigma",data, last)
r2mpl.plot(sigma,errors=True,axes=sigma_axes, color="k", linewidth=0.5, capsize=1.0)
data_axes.set_ylim(ymin=0)
sigma_axes.set_ylim(-4,4)
sigma_axes.axhline(-2, color="r")
sigma_axes.axhline( 0, color="k")
sigma_axes.axhline(+2, color="r")
sigma_axes.set_yticks([-4,-3,-2,-1,0,1,2,3,4])
sigma_axes.set_yticklabels(["","","$-2$","","$0$","","$2$","",""])
sigma_axes.set_xlabel(label)
sigma_axes.set_ylabel("normalized\nresiduals")
data_axes.set_ylabel(r"Entries / \num{%.3g} %s" % (data.GetBinWidth(1), unit))
data_axes.set_title(title)
#data_axes.legend(loc="upper left", numpoints=2)
if log:
data_axes.set_yscale("log",nonposy="clip")
ymin=data.GetMinimum(0.5)/2.0
for (l,f) in fits:
ymin = min(ymin,f.GetMaximum()/2.0)
#data_axes.set_ylim(ymin=data.GetMinimum(0.5)/2.0)
data_axes.set_ylim(ymin)
return data_axes
def plot_asymmetry(data1, data2, fits1, fits2, label, val1, val2):
"""Plot 2 dT flavours and the asymmetry between them"""
fig, a1, a2 = utils.get_plotaxes_stacked()
f1 = accumulate(fits1)
f2 = accumulate(fits2)
asym_data = data1.GetAsymmetry(data2)
asym_fit = f1.GetAsymmetry(f2)
plot_fit(a1, f1, data1, "r")
plot_fit(a1, f2, data2, "b")
r2mpl.plot(data1, axes=a1, errors=True, color="r", zorder=2, linewidth=0.5, capsize=1.0, label="$%s=%s$"% (label, val1))#, marker="^")
r2mpl.plot(data2, axes=a1, errors=True, color="b", zorder=2, linewidth=0.5, capsize=1.0, label="$%s=%s$"% (label, val2))#, marker="o")
plot_fit(a2, asym_fit, asym_data, "r")
r2mpl.plot(asym_data, axes=a2, errors=True, color="k", zorder=2, linewidth=0.5, capsize=1.0)
a1.legend(loc="upper left", numpoints=1)
a2.set_ylim(-1.0,1.0)
a1.set_ylabel(r"Entries / \num{%.3g} ps" % data1.GetBinWidth(1))
a2.set_xlabel(r"$\Delta t$ / ps")
