for icat in cats:
frp.sources = zip(filenames, workspaces,
[snapshot % (icat.name, lo, hi) for lo, hi in binedges])
frp.getters = var_vs_pt('chi2Prob')
frp.title = ', '.join(icat.labels)
frp.getdata()
frp.makegraph()
canvases.next('strue_pvalues_vs_phoEt')
frp.plotall(title = 'MC Truth Fits')
plotters.append(frp)
## Make the distribution of the p-values
hist = frp.histogramall(
name = 'h_strue_pvalues',
title = 's_{true} = E^{#gamma}_{reco}/E^{#gamma}_{gen};p-value;Fits',
nbins = 5, xlow = 0, xhigh = 1
)
canvases.next('strue_pvalues_distro')
hist.Draw('e0')
plotters.append(hist)
################################################################################
## Plot the p-values of the reco s-Fits fits
filenames = [filename2] * n
workspaces = ['ws1'] * n
snapshot = 'chi2_sreco_mc_cbShape_mmMass90_%s_PhoEt%d-%d_iter0'
frp = FitResultPlotter(
sources = zip([filename2] * n,
['ws1'] * n,
frp.sources = zip(
filenames, workspaces,
[snapshot % (name, icat.name, lo, hi) for lo, hi in binedges])
frp.getters = var_vs_pt('chi2Prob')
frp.title = ', '.join(icat.labels)
frp.getdata()
frp.makegraph()
canvases.next('strue_pvalues_vs_phoEt_%s' % name)
frp.plotall(title=title)
plotters.append(frp)
## Make the distribution of the p-values
hist = frp.histogramall(name='h_strue_pvalues_%s' % name,
title='%s;p-value;Fits' % title,
nbins=5,
xlow=0,
xhigh=1)
canvases.next('strue_pvalues_distro_%s' % name)
hist.Draw('e0')
hists.append(hist)
canvases.canvases[-1].Update()
################################################################################
## Plot the p-values for 71% range
name = 'FitRange71'
title = '+3% Fit Range'
filenames = [os.path.join(path, 'strue_%s.root' % name)] * n
workspaces = ['ws1'] * n
snapshot = 'chi2_strue_mc_Nominal%s_mmMass80_%s_PhoEt%d-%d_bifurGauss'
frp.sources = zip(filenames, workspaces,
[snapshot % (name, icat.name, lo, hi)
for lo, hi in binedges])
frp.getters = var_vs_pt('chi2Prob')
frp.title = ', '.join(icat.labels)
frp.getdata()
frp.makegraph()
canvases.next('strue_pvalues_vs_phoEt_%s' % name)
frp.plotall(title = title)
plotters.append(frp)
## Make the distribution of the p-values
hist = frp.histogramall(
name = 'h_strue_pvalues_%s' % name,
title = '%s;p-value;Fits' % title,
nbins = 5, xlow = 0, xhigh = 1
)
canvases.next('strue_pvalues_distro_%s' % name)
hist.Draw('e0')
hists.append(hist)
canvases.canvases[-1].Update()
################################################################################
## Plot the p-values for 71% range
name = 'FitRange71'
title = '+3% Fit Range'
filenames = [os.path.join(path, 'strue_%s.root' % name)] * n
workspaces = ['ws1'] * n
