myc = ROOT.TCanvas('myc', '', 800, 500)
myc.cd()
obj.Draw()
else:
obj.Draw('same')
if myc:
myc.Print(fname)
del myc
# def _write(rfile, hists):
# for pair in hists:
# map(lambda h: rfile.WriteTObject(h) if h else None, pair)
if options.dump:
rfile = ROOT.TFile.Open('correlation_hists.root', 'recreate')
for transform in transforms:
plotter.draw_hist(hists[transform+'_corr'], opts)
canvas.Update()
if options.dump:
# _write(rfile, hists[transform+'_corr'])
_draw_match('{}_matched_corrln_{}.pdf'.format(prefix, transform),
hists[transform+'_corr'])
if options.doprint:
canvas.Print('{}_corrln_grid_{}.png'.format(prefix, transform))
# canvas.Print('{}_correlation_grid.pdf'.format(prefix))
# if options.doprint:
# canvas.Print('{}_correlation_grid.pdf]'.format(prefix))
del plotter, canvas
# axis titles
blabel, swlabel, ylabel = "B mass [MeV]", "#it{s}-weights", "Candidates"
titles = {hist_m4: (blabel, ylabel), hist_m5: (blabel, ylabel), hist_m6: (blabel, ylabel)}
from rplot.tselect import Tselect
selector = Tselect(tree)
selector.exprs = [
("lab0_MM>>hist_m4", "5310<lab0_MM && lab0_MM<5430"),
("lab0_MM>>hist_m5", "sw*(5310<lab0_MM && lab0_MM<5430)"),
("lab0_MM>>hist_m6", "(sw-0.113)*(5310<lab0_MM && lab0_MM<5430)"),
]
hists = selector.fill_hists()
map(lambda h: (h.GetXaxis().SetTitle(titles[h][0]), h.GetYaxis().SetTitle(titles[h][1])), hists)
from rplot.rplot import Rplot
plotter = Rplot(1, 1, 800, 500)
legend = ROOT.TLegend(0.65, 0.5, 0.9, 0.9)
legend.SetFillStyle(0)
legend.SetLineWidth(0)
plotter.add_legend(legend, "lep")
canvas = plotter.prep_canvas()
canvas.Print("sw-B-mass.pdf[")
plotter.draw_hist([hists], "e1")
canvas.Print("sw-B-mass.pdf")
canvas.Print("sw-B-mass.pdf]")
hist_m5: (blabel, ylabel),
hist_m6: (blabel, ylabel),
}
from rplot.tselect import Tselect
selector = Tselect(tree)
selector.exprs = [
('lab0_MM>>hist_m4', '5310<lab0_MM && lab0_MM<5430'),
('lab0_MM>>hist_m5', 'sw*(5310<lab0_MM && lab0_MM<5430)'),
('lab0_MM>>hist_m6', '(sw-0.113)*(5310<lab0_MM && lab0_MM<5430)'),
]
hists = selector.fill_hists()
map(
lambda h:
(h.GetXaxis().SetTitle(titles[h][0]), h.GetYaxis().SetTitle(titles[h][1])),
hists)
from rplot.rplot import Rplot
plotter = Rplot(1, 1, 800, 500)
legend = ROOT.TLegend(0.65, 0.5, 0.9, 0.9)
legend.SetFillStyle(0)
legend.SetLineWidth(0)
plotter.add_legend(legend, 'lep')
canvas = plotter.prep_canvas()
canvas.Print('sw-B-mass.pdf[')
plotter.draw_hist([hists], 'e1')
canvas.Print('sw-B-mass.pdf')
canvas.Print('sw-B-mass.pdf]')
max_y_n = hpair[mode].GetMaximum() / hpair[mode].Integral()
# set max Y so that histograms fit in pad
for mode in modes:
hpair[mode].SetMaximum(1.1 * max_y_n * hpair[mode].Integral())
histograms.append(hpair)
hists = map(lambda hs: (hs['dsk'], hs['dspi']), histograms)
from rplot.rplot import Rplot
plotter = Rplot(1, 3, 1024, 1920)
canvas = plotter.prep_canvas()
plotfile = 'plots/timelt2ps_%s.pdf' % sanitise_str_src('_'.join(variables))
if doPrint:
canvas.Print(plotfile + '[')
# create legend
legend = TLegend(0.6, 0.6, 0.95, 0.75)
legend.SetLineWidth(0)
legend.SetFillStyle(0)
legend.SetTextSize(0.035)
legend.SetHeader('%s ps'.format(sanitise_str(str(cuts['timelt2ps']))))
plotter.add_legend(legend, 'lep')
for i in xrange(0, len(hists), 3):
plotter.draw_hist(hists[i:i+3], 'e1', normalised=True)
if doPrint:
canvas.Print(plotfile)
if doPrint:
canvas.Print(plotfile + ']')
del plotter
ROOT.gStyle.SetHatchesSpacing(2.5)
for transform in transforms:
def _style(l):
# l.reverse()
l[0].SetFillStyle(3345)
l[1].SetFillStyle(3354)
distributions[transform] = arrange(distributions[transform], 2,
predicate=_style)
plotter = Rplot(5, 3, 2000, 1200)
plotter.alpha = 0.2
canvas = plotter.prep_canvas()
if doprint: canvas.Print('transforms.pdf[')
for transform in transforms:
plotter.draw_hist(distributions[transform], 'hist')
canvas.Update()
if doprint: canvas.Print('transforms.pdf')
if doprint: canvas.Print('transforms.pdf]')
del plotter, canvas
## correlation plots
if lcorrns or scatter:
_filter = lambda string: lambda k: k.GetName().find(string) > 0
sig_hists = get_hists(['{}_corr'.format(k) for k in transforms],
rfileconf, rpath_tool, robj_t = ROOT.TH1,
robj_p = _filter('Signal'))
bkg_hists = get_hists(['{}_corr'.format(k) for k in transforms],
rfileconf, rpath_tool, robj_t = ROOT.TH1,
max_y_n = hpair[mode].GetMaximum() / hpair[mode].Integral()
# set max Y so that histograms fit in pad
for mode in modes:
hpair[mode].SetMaximum(1.1 * max_y_n * hpair[mode].Integral())
histograms.append(hpair)
hists = map(lambda hs: (hs['dsk'], hs['dspi']), histograms)
from rplot.rplot import Rplot
plotter = Rplot(1, 3, 1024, 1920)
canvas = plotter.prep_canvas()
plotfile = 'plots/timelt2ps_%s.pdf' % sanitise_str_src('_'.join(variables))
if doPrint:
canvas.Print(plotfile + '[')
# create legend
legend = TLegend(0.6, 0.6, 0.95, 0.75)
legend.SetLineWidth(0)
legend.SetFillStyle(0)
legend.SetTextSize(0.035)
legend.SetHeader('%s ps'.format(sanitise_str(str(cuts['timelt2ps']))))
plotter.add_legend(legend, 'lep')
for i in xrange(0, len(hists), 3):
plotter.draw_hist(hists[i:i + 3], 'e1', normalised=True)
if doPrint:
canvas.Print(plotfile)
if doPrint:
canvas.Print(plotfile + ']')
del plotter
