# 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)
else:
_plot_n_print(distributions[transform])
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)
canvas.Update()
if doprint: canvas.Print('overtraining.pdf')
for classifier in classifiers:
# TODO: KS test b/w train & test
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]')
