## variable distributions
if options.distribs:
# histogram order: signal, background (repeat for diff transforms)
distributions = get_hists(transforms, rfileconf, pathtool, robj_t=ROOT.TH1)
ROOT.gStyle.SetHatchesLineWidth(1)
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(3, 3, 2000, 1200)
plotter.alpha = 0.2
canvas = plotter.prep_canvas()
if options.doprint:
canvas.Print('{}_transforms.pdf['.format(prefix))
def _plot_n_print(hlist, plotter=plotter, canvas=canvas):
plotter.draw_hist(hlist, 'hist')
canvas.Update()
if options.doprint:
canvas.Print('{}_transforms.pdf'.format(prefix))
for transform in transforms:
if len(distributions[transform]) > plotter.nplots:
for hists in partition(distributions[transform], plotter.nplots):
_plot_n_print(hists)
matches = ['MVA_{}{}'.format(cl, string) for cl in classifiers]
return lambda k: k.GetName() in matches
_filter1 = lambda string: lambda k: _filter(string+'_S')(k) or _filter(string+'_B')(k)
_filter2 = lambda str1, str2: lambda k: _filter1(str1)(k) or _filter1(str2)(k)
distribs = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter2('', '_Train'))
if rarity:
rarity = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter1('_Rarity'))
if probab:
probab = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter1('_Proba'))
plotter = Rplot(1, 1, 800, 600)
plotter.alpha = 0.2
plotter.fill_colours = (ROOT.kAzure, ROOT.kRed, ROOT.kAzure, ROOT.kRed)
plotter.line_colours = (ROOT.kAzure-6, ROOT.kRed+2, ROOT.kAzure-6, ROOT.kRed+2)
plotter.markers = (ROOT.kPlus, ROOT.kPlus, ROOT.kPlus, ROOT.kPlus)
canvas = plotter.prep_canvas()
if doprint: canvas.Print('overtraining.pdf[')
ROOT.gStyle.SetHatchesLineWidth(1)
ROOT.gStyle.SetHatchesSpacing(1)
def _style(l):
l[0].SetFillStyle(3345)
l[1].SetFillStyle(3354)
def _plot(plots, opts):
plotter.draw_hist(plots, opts)
# 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]')
else:
print 'Unknown permutation of cuts, weird things will happen.'
sel = trees[mode].Draw('{}>>{}'.format(var, hist.GetName()), cut, 'goff')
hpair[mode] = hist
# determine max Y for normalised histograms
if max_y_n < hpair[mode].GetMaximum()/hpair[mode].Integral():
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)
print 'Unknown permutation of cuts, weird things will happen.'
sel = trees[mode].Draw('{}>>{}'.format(var, hist.GetName()), cut,
'goff')
hpair[mode] = hist
# determine max Y for normalised histograms
if max_y_n < hpair[mode].GetMaximum() / hpair[mode].Integral():
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)
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
robj_p=_filter1('_Rarity'))
else:
rarity = None
if options.probab:
probab = get_hists(classifiers,
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
robj_p=_filter1('_Proba'))
else:
probab = None
plotter = Rplot(1, 1, 800, 600)
plotter.alpha = 0.2
plotter.fill_colours = (ROOT.kAzure, ROOT.kRed, ROOT.kAzure, ROOT.kRed)
plotter.line_colours = (ROOT.kAzure - 6, ROOT.kRed + 2, ROOT.kAzure - 6,
ROOT.kRed + 2)
plotter.markers = (ROOT.kPlus, ROOT.kPlus, ROOT.kPlus, ROOT.kPlus)
canvas = plotter.prep_canvas()
if options.doprint:
canvas.Print('{}_overtraining.pdf['.format(prefix))
ROOT.gStyle.SetHatchesLineWidth(1)
ROOT.gStyle.SetHatchesSpacing(1)
def _style(l):
l[0].SetFillStyle(3345)