def draw_toyMC(hist,title,xlabel="",ylabel="",exponent=None, box="tl"):
fit = root.TF1("gauss","gaus")
textbox = r"\begin{align*}\mu&=%s\\\sigma&=%s\end{align*}"
fig, a = utils.get_plotaxes((4,3.2))
hist.Fit(fit,"LQ")
r2mpl.plot(hist,axes=a, errors=True, color="k", zorder=1)
r2mpl.plot(fit,axes=a, color="r", zorder=0)
utils.text_box(a, box, textbox % (
utils.format_error(fit.GetParameter(1), fit.GetParError(1), exponent=exponent),
utils.format_error(fit.GetParameter(2), fit.GetParError(2), exponent=exponent)))
a.set_title(title)
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
return a, (fit.GetParameter(1), fit.GetParError(1)), (fit.GetParameter(2), fit.GetParError(2))
def plot_mBCdE(data, label=None, title=None, **argk):
"""Make a 2D Plot of mBC and dE"""
f, a = utils.get_plotaxes()
#f = pl.figure(figsize=(4,4))
#a = f.add_axes((0.20,0.13,0.70,0.80))
p = r2mpl.plot(data, a, **argk)
if label is None:
label = r"Entries / $\num{%.3g} \times \num{%.3g}\text{ GeV}^2$" % \
(data.GetXaxis().GetBinWidth(1), data.GetYaxis().GetBinWidth(1))
if title is not None:
a.set_title(title)
formatter = matplotlib.ticker.ScalarFormatter(False,False)
formatter.set_powerlimits((-3,3))
cb = f.colorbar(p, format=formatter, fraction=0.2)
cb.set_label(label)
a.grid()
a.set_xlabel("$M_{BC}$ / GeV")
a.set_ylabel("$\Delta E$ / GeV")
#Draw rectangle to show signal enhanced area
coords = (5.27,-0.04,5.29,0.04)
a.add_patch(matplotlib.patches.Rectangle(coords[:2], coords[2]-coords[0], coords[3]-coords[1], edgecolor="k", facecolor="none"))
print "From fitter:", signal_yield
print "From cuts: ", s2
ngenerated = np.array([(h_ngenerated.GetBinContent(i+1), h_ngenerated.GetBinContent(i+1)) for i in range(2)])
raw_eff = signal_yield / ngenerated
rec_eff = raw_eff * eff_DDKs
ctl_eff = raw_eff * eff_DsD0Km
rec_eff_c = rec_eff * np.array([utils.correction_mc, utils.correction_mc]).T
ctl_eff_c = ctl_eff * np.array([utils.correction_ctrl, utils.correction_ctrl]).T
h_nB0 = b0.draw("nB0", range=(200,0,200), option="goff")
b_mult = h_nB0.GetMean()
f,a = utils.get_plotaxes()
r2mpl.plot(h_nB0, axes=a, log=True)
a.set_title("\PBz Multiplicity")
a.set_xlabel("Number of reconstructed \PBz")
a.set_ylabel("Number of events")
r2mpl.save_all("bmult", png=False, single_pdf=False)
print """ngenerated = np.array([
%d,
%d,
])
efficiency_mc = np.array([
[%.7e, %.7e],
[%.7e, %.7e],
])""" % (tuple(ngenerated[:,0]) + tuple(rec_eff.flat))
colors = {
"yield_signal_br":"r",
"signal_dt_Jc":"r",
"signal_dt_Js1":"g",
"signal_dt_Js2":"b",
"signal_dt_blifetime":"k",
"ratio_continuum_svd1":"r",
"ratio_continuum_svd2":"r",
}
for par, title in zip(linearity_full.lintest_params, linearity_full.lintest_pnames):
result = rfile.Get("%s" % par)
pull = rfile.Get("%s_pull" % par)
if isinstance(result,root.TH2D):
fig1, a1 = utils.get_plotaxes((4,3.2))
fig2, a2 = utils.get_plotaxes((4,3.2))
fig3, a3 = utils.get_plotaxes((4,3.2))
a1.set_ylabel("fit result")
a2.set_ylabel("mean of pull")
a3.set_ylabel("sigma of pull")
for a in a1,a2,a3:
a.set_title(title)
a.set_xlabel("input " + title)
result.Rebin2D(2,1)
pull.Rebin2D(2,1)
fit = root.TF1("gauss","gaus")
lin_result = root.TH1D(
"lin_%s" % par, "", result.GetNbinsX(),
lin_params = ["Jc", "Js1", "Js2"]
cpv_params = ["signal_dt_Jc", "signal_dt_Js1", "signal_dt_Js2"]#, "signal_dt_blifetime"]
cpv_names = [r"$J_C/J_0$", r"$(2J_{s1}/J_0) \sin(2\phi_1)$", r"$(2J_{s2}/J_0) \cos(2\phi_1)$",r"$\tau$"]
rfile = root.TFile(infile+".root")
colors = {
"signal_dt_Jc":"r",
"signal_dt_Js1":"g",
"signal_dt_Js2":"b",
"signal_dt_blifetime":"k"
}
for lp, lp_name in zip(lin_params,cpv_names):
fig1, a1 = utils.get_plotaxes()
fig2, a2 = utils.get_plotaxes()
fig3, a3 = utils.get_plotaxes()
a1.set_ylabel("fit result")
a2.set_ylabel("mean of pull")
a3.set_ylabel("sigma of pull")
for a in a1,a2,a3:
a.set_title("%s, linearity" % lp_name)
a.set_xlabel("input " + lp_name)
for cpv, cpv_name in zip(cpv_params, cpv_names):
result = rfile.Get("%s_%s" % (lp,cpv))
pull = rfile.Get("%s_%s_pull" % (lp,cpv))
fit = root.TF1("gauss","gaus")
