'r_{fit} - r_{shape}: %.4f #pm %.4f' % (
t1width.getVal() - t1pdf.shapewidth,
t1width.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' % (
t1width.getVal() / t1pdf.shapewidth,
t1width.getError() / t1pdf.shapewidth),
], position=(0.2, 0.8)).draw()
## Plot fT2(t2|s,r) fitted to training data.
canvases.next('t2pdf').SetGrid()
t.setRange(-1, 1)
t.setVal(0)
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
phoScale.setVal(t2pdf.s0val)
phoRes.setVal(t2pdf.r0val)
t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(2.0)))
xarange = (t2pdf.s0val - 5 * t2pdf.r0val, t2pdf.s0val + 5 * t2pdf.r0val)
xarange = [math.log(1 + 0.01 * x) for x in xarange]
plot = t.frame(roo.Range(*xarange))
t2data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (
phoScale.getVal() - t2pdf.s0val,
phoScale.getError()
),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
phoEResPdf.paramOn(plot)
plot.Draw()
t = w.factory('t[0,-1,1]')
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
tfunc = w.factory('expr::tfunc("log(0.01 * phoERes + 1)", {phoERes})')
tfunc.SetName('t')
data.addColumn(tfunc)
## Build the model for log(Ereco/Egen) ft2(t2|r,s)
t2pdf = LogPhoeresKeysPdf('t2pdf', 't2pdf', phoERes, t, phoScale, phoRes, data,
rho=1.5)
## Fit nominal data with ft2(t2|r,s)
canvases.next('t2pdf').SetGrid()
t2pdf.fitTo(data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(2.0)))
plot = t.frame(roo.Range(-0.15, 0.15))
data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{sfit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (
phoScale.getVal() - t2pdf.s0val,
phoScale.getError()
),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' % (
phoRes.getVal() / t2pdf.r0val,
canvases.next('xt1_proj_t1')
t.setRange(*t1range)
t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})')
plot = t.frame()
xt1data.plotOn(plot)
xt1pdf.plotOn(plot)
plot.Draw()
## Plot fT2(t2|s,r) fitted to training data.
canvases.next('t2pdf').SetGrid()
t.setRange(*t2range)
t.setVal(0)
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
phoScale.setVal(t2pdf.s0val)
phoRes.setVal(t2pdf.r0val)
t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(1.5)))
plot = t.frame(roo.Range(-0.3, 0.3))
t2data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (
phoScale.getVal() - t2pdf.s0val,
phoScale.getError()
),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' % (
phoRes.getVal() / t2pdf.r0val,
tfunc.SetName('t')
data.addColumn(tfunc)
## Build the model for log(Ereco/Egen) ft2(t2|r,s)
t2pdf = LogPhoeresKeysPdf('t2pdf',
't2pdf',
phoERes,
t,
phoScale,
phoRes,
data,
rho=1.5)
## Fit nominal data with ft2(t2|r,s)
canvases.next('t2pdf').SetGrid()
t2pdf.fitTo(data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(2.0)))
plot = t.frame(roo.Range(-0.15, 0.15))
data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{sfit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(phoScale.getVal() - t2pdf.s0val, phoScale.getError()),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / t2pdf.r0val, phoRes.getError() / t2pdf.r0val),
],
position=(0.2, 0.75)).draw()
