# 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]')