def make_fixed_range_log_plot(self):
c1 = canvases.next(self.name + '_log_fixedrange')
c1.SetGrid()
c1.SetLogy()
self.canvases.append(c1)
## Use the ModalInterval class to display the shortest range containing
## 100% of all the entries.
mi = self.modal_interval
mi.setFraction(1)
fullrange = (mi.lowerBound(), mi.upperBound())
plot = self.deltaE.frame(roo.Range(*self.fixed_range_log))
plot.SetTitle(', '.join(self.labels))
self.fit_data.plotOn(plot, roo.MarkerColor(self.color_data),
roo.MarkerStyle(self.marker_style),
roo.LineColor(self.color_data))
self.model.plotOn(plot, roo.LineColor(self.color_model))
if fullrange[0] + fullrange[1] > 0:
layout = (0.6, 0.9, 0.87)
else:
layout = (0.2, 0.5, 0.87)
self.model.paramOn(plot, roo.Format("NEU", roo.AutoPrecision(2)),
roo.Layout(*layout))
## Fine tune the y-axis range so that we see all the events.
plot.SetMinimum(0.5)
## Add a larger top margin to the y-axis range
plot.SetMaximum(pow(plot.GetMaximum(), 1.1))
plot.Draw()
self.plots.append(plot)
def make_uuplot(ux, uy):
plot = ux.uniform_var.frame()
ux.data.plotOn(plot, roo.LineColor(ux.color), roo.MarkerColor(ux.color))
ux.uniform_pdf.plotOn(plot, roo.LineColor(ux.color), roo.Range(0, 1))
uy.uniform_pdf.plotOn(plot, roo.LineColor(uy.color), roo.Range(0, 1),
roo.LineStyle(ROOT.kDashed))
uy.data.plotOn(plot, roo.LineColor(uy.color), roo.MarkerColor(uy.color),
roo.MarkerStyle(4))
canvases.next('uu')
plot.Draw()
return plot
def get_validation_plot(self):
plot = self.variable.frame()
plot.SetTitle(self.GetTitle() + ' Validation')
scale = self.target_data.sumEntries() / self.raw_data.sumEntries()
self.corrected_data = self.get_corrected_data()
self.target_data.plotOn(plot, roo.Name('target'),
*color_args(self.target_color))
self.raw_data.plotOn(plot, roo.Name('raw'), roo.Rescale(scale),
roo.MarkerStyle(24), *color_args(self.raw_color))
self.corrected_data.plotOn(plot, roo.Name('corrected'),
roo.Rescale(scale), roo.MarkerStyle(25),
*color_args(self.qq_color))
names = 'target raw corrected'.split()
histos = [plot.findObject(name) for name in names]
titles = [name.capitalize() for name in names]
plot.legend = Legend(histos, titles)
## Some syntactic sugar to get automatic legend drawing
plot.DrawBase = plot.Draw
plot.Draw = lambda: plot.DrawBase() or plot.legend.draw()
return plot
def make_fixed_range_zoom_plot(self):
c1 = canvases.next(self.name + '_lin_fixedrange')
c1.SetGrid()
self.canvases.append(c1)
plot = self.deltaE.frame(roo.Range(*self.fixed_range_zoom))
plot.SetTitle(', '.join(self.labels))
self.fit_data.plotOn(plot, roo.MarkerColor(self.color_data),
roo.MarkerStyle(self.marker_style),
roo.LineColor(self.color_data))
self.model.plotOn(plot, roo.LineColor(self.color_model))
self.model.paramOn(plot, roo.Format("NEU", roo.AutoPrecision(2)),
roo.Layout(0.2, 0.52, 0.87))
plot.Draw()
self.plots.append(plot)
def test_downsampling():
'''
Tests the downsampling.
'''
import FWLite.Tools.canvases as canvases
print '== Downsampling Test =='
big_data = get_toy_data(1000)
resampler = Resampler(big_data)
small_data = resampler.downsample(50)
keep(big_data, small_data)
big_data.Print()
small_data.Print()
xvar = big_data.get()['x']
xvar.setBins(20)
plot = xvar.frame()
big_data.plotOn(plot, roo.MarkerColor(ROOT.kRed), roo.LineColor(ROOT.kRed))
small_data.plotOn(plot, roo.MarkerStyle(24))
canvases.next('downsampling_test')
plot.Draw()
keep(plot)