def make_resolution_plots():
'''
Makes canvases with resolution plots.
'''
global plotters
#==========================================================================
for cfg in get_resolution_configs()[:]:
## MC, EB, 2011A+B, 1 of 4 statistically independent tests
xtitle = 'E_{T}^{#gamma} (GeV)'
ytitle = 'E^{#gamma} Resolution (%)'
plotter = FitResultPlotter(cfg.sources1, cfg.getters1, xtitle, ytitle,
title = 'MC Truth')
plotter.getdata()
plotter.makegraph()
plotter.sources = cfg.sources2
plotter.getters = cfg.getters2
plotter.title = 'MC Fit'
plotter.getdata()
plotter.makegraph()
plotter.sources = cfg.sources3
plotter.getters = cfg.getters3
plotter.title = 'Data Fit'
plotter.getdata()
plotter.makegraph()
canvases.next('c_' + cfg.name).SetGrid()
plotter.plotall(title = cfg.title,
styles = [20, 25, 26],
colors = [ROOT.kBlack, ROOT.kBlue, ROOT.kRed])
plotters.append(plotter)
def make_plots(configurations):
'''
For each configuration in the given list, overlays the graphs of
scale versus pt for all sets of measurements specified.
These measurements are either from the true or the PHOSPHOR fit.
'''
for cfg in configurations[:]:
## Only check EE 2011AB
#if (not 'EE_lowR9' in cfg.name) or (not 'AB' in cfg.name):
#continue
### Only check 2011AB
#if not 'AB' in cfg.name:
#continue
## MC, EB, 2011A+B, 1 of 4 statistically independent tests
plotter = FitResultPlotter(cfg.sources[0], cfg.getters[0], cfg.xtitle,
cfg.ytitle, title = cfg.titles[0],
name=cfg.name, yasymmerrors=True)
for isources, igetters, ititle in zip(cfg.sources,
cfg.getters,
cfg.titles):
plotter.sources = isources
plotter.getters = igetters
plotter.title = ititle
plotter.getdata()
plotter.makegraph()
plotter.plot()
canvases.next('c_' + cfg.name).SetGrid()
## Check if there is a problem with the ranges
# yrange = 'auto'
yrange = (-10, 10)
for graph in plotter.graphs:
if (graph.GetHistogram().GetMaximum() -
graph.GetHistogram().GetMinimum()) < 0.1:
print cfg.name, graph.GetTitle(), 'min:', graph.GetMaximum(),
print ', max:', graph.GetMaximum
yrange = (-5, 10)
plotter.plotall(title = cfg.title,
xrange = (5, 55),
yrange = yrange,
legend_position = 'topright')
#plotter.graphs[0].Draw('p')
canvases.canvases[-1].Modified()
canvases.canvases[-1].Update()
canvases.update()
plotters.append(plotter)
def make_plots(configurations):
'''
For each configuration in the given list, overlays the graphs of
scale versus pt for all sets of measurements specified.
These measurements are either from the true or the PHOSPHOR fit.
'''
for cfg in configurations[:]:
## Only check EE 2011AB
#if (not 'EE_lowR9' in cfg.name) or (not 'AB' in cfg.name):
#continue
### Only check 2011AB
#if not 'AB' in cfg.name:
#continue
## MC, EB, 2011A+B, 1 of 4 statistically independent tests
plotter = FitResultPlotter(cfg.sources[0], cfg.getters[0], cfg.xtitle,
cfg.ytitle, title = cfg.titles[0],
name=cfg.name,
xasymmerrors=True,
yasymmerrors=True)
for isources, igetters, ititle in zip(cfg.sources,
cfg.getters,
cfg.titles):
plotter.sources = isources
plotter.getters = igetters
plotter.title = ititle
plotter.getdata()
plotter.makegraph()
canvases.next('c_' + cfg.name).SetGrid()
if 'EE_highR9' in cfg.name:
plotter.plotall(title = cfg.title,
xrange = (5, 55),
yrange = (0, 15),
legend_position = 'topright')
else:
plotter.plotall(title = cfg.title,
xrange = (5, 55),
legend_position = 'topright')
#plotter.graphs[0].Draw('p')
canvases.canvases[-1].Modified()
canvases.canvases[-1].Update()
canvases.update()
plotters.append(plotter)
def plot_xy(xname, yname, filemask, xtype='var', ytype='var'):
filename = filemask % ptbinedges[0]
## Mass scale vs photon scale
frp = FitResultPlotter(
sources = sources(filename, wsname),
getters = xygetters(xname, yname, xtype, ytype),
xtitle = axistitles[xname],
ytitle = axistitles[yname],
title = 'Dummy Legend Entry',
)
for ptrange in ptbinedges:
filename = filemask % ptrange
frp.sources = sources(filename, wsname)
frp.title = 'E_{T}^{#gamma} #in [%d, %d] GeV' % ptrange
frp.getdata()
frp.makegraph()
canvases.next(yname + '_vs_' + xname).SetGrid()
frp.plotall(title = ptitle)
frps.append(frp)
def make_scale_plots(configurations):
'''
For each configuration in the given list, overlays the graphs of
scale versus pt for all sets of measurements specified.
These measurements are either from the true or the PHOSPHOR fit.
'''
for cfg in configurations[:2]:
## Only check EE 2011AB
#if (not 'EE_lowR9' in cfg.name) or (not 'AB' in cfg.name):
#continue
### Only check 2011AB
#if not 'AB' in cfg.name:
#continue
## MC, EB, 2011A+B, 1 of 4 statistically independent tests
plotter = FitResultPlotter(cfg.sources[1], cfg.getters_true[1], cfg.xtitle,
cfg.ytitle, title = 'MC Truth 1', name=cfg.name)
for i in range(1,5):
plotter.sources = cfg.sources[i]
plotter.getters = cfg.getters_true[i]
plotter.title = 'MC Truth %d' % i
plotter.getdata()
plotter.makegraph()
for i in range(1,5):
plotter.sources = cfg.sources[i]
plotter.getters = cfg.getters_fit[i]
plotter.title = 'MC Fit %d' % i
plotter.getdata()
plotter.makegraph()
canvases.next('c_' + cfg.name).SetGrid()
plotter.plotall(title = cfg.title,
xrange = (0, 80),
legend_position = 'topright')
plotter.graphs[0].Draw('p')
canvases.canvases[-1].Modified()
canvases.canvases[-1].Update()
canvases.update()
plotters.append(plotter)
)
for fitrange, title in zip(['FitRange' + x for x in '65 68 71'.split()],
'-3% Nominal +3%'.split()):
filenames = [os.path.join(path, 'strue_%s.root' % fitrange)] * n
snapshots = [
snapshot.format(f=fitrange, c=etar9.name, l=lo, h=hi)
for lo, hi in binedges
]
frp.sources = zip(filenames, workspaces, snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = title
frp.getdata()
frp.makegraph()
canvases.next('strue_FitRangeSystematics' + etar9.name)
frp.plotall(title=etar9.title)
plotters.append(frp)
graph = frp.graphs[0].Clone('g_' + etar9.name)
for i in range(graph.GetN()):
x = graph.GetX()[i]
ylo = min([g.GetY()[i] for g in frp.graphs])
yhi = max([g.GetY()[i] for g in frp.graphs])
graph.SetPoint(i, x, 0.5 * (yhi - ylo))
graph.SetPointError(i, graph.GetEX()[i], 0)
plotter.graphs.append(graph)
plotter.titles.append(etar9.title)
canvases.next('strue_FitRangeSystematics')
plotter.xtitle = 'E_{T}^{#gamma} (GeV)'
frp.getters = var_vs_pt('#Deltas')
frp.title = 'm_{#mu#mu} < 90 GeV'
frp.getdata()
frp.makegraph()
## True
frp.sources = zip(cfg.filenames2, cfg.wsnames, cfg.strue_snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = 'MC Truth'
frp.getdata()
frp.makegraph()
## Compare Proposal 1, Baseline and MC truth scale
canvases.next('s_' + cfg.name).SetGrid()
frp.plotall(title = cfg.title,
styles = [20, 25, 22],
colors = [kBlue, kRed, kBlack])
plotters.append(frp)
#------------------------------------------------------------------------------
## S width Comparison
## Baseline
## frp = FitResultPlotter(
## sources = zip(cfg.filenames, cfg.wsnames, cfg.sreco_snapshots),
## getters = (
## lambda ws, i = iter(bincenters): i.next(), # x
## lambda ws, i = iter(lyonmc[cfg.name]['sigma']): i.next(), # y
## lambda ws, i = iter(binhalfwidths): i.next(), # ex
## lambda ws, i = iter(lyonmc[cfg.name]['esigma']): i.next(), # ey
## ),
xtitle = 'E_{T}^{#gamma} (GeV)',
ytitle = 's_{reco} = E^{#gamma}_{reco}/E^{kin}_{reco} - 1 (%)',
title = 'Lyon',
)
frp.getdata()
frp.makegraph()
## Caltech
frp.getters = var_vs_pt('#Deltas')
frp.title = 'Caltech'
frp.getdata()
frp.makegraph()
## Compare Caltech and Lyon scale
canvases.next()
frp.plotall(title = 'Barrel, R_{9} < 0.94, Baseline',
styles = [20, 25])
plotters.append(frp)
#------------------------------------------------------------------------------
## S width Comparison
## Lyon
frp = FitResultPlotter(
sources = zip(filenames, wsnames, sreco_snapshots),
getters = (
lambda ws, i = iter(bincenters): i.next(), # x
lambda ws, i = iter(sigma_lyon): i.next(), # y
lambda ws, i = iter(binhalfwidths): i.next(), # ex
lambda ws, i = iter(esigma_lyon): i.next(), # ey
),
xtitle = 'E_{T}^{#gamma} (GeV)',
)
frp.getdata()
frp.makegraph()
## New Baseline
frp.sources = zip(cfg.filenames, cfg.wsnames, cfg.sreco_snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = 'Baseline'
frp.getdata()
frp.makegraph()
## True
frp.sources = zip(cfg.filenames, cfg.wsnames, cfg.strue_snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = 'MC Truth'
frp.getdata()
frp.makegraph()
## Compare New Baseline, MC PDF and MC truth scale
canvases.next('s_' + cfg.name).SetGrid()
frp.plotall(title=cfg.title,
styles=[20, 25, 22],
colors=[kBlue, kRed, kBlack])
plotters.append(frp)
## end of loop over cfgs
if __name__ == '__main__':
import user
xtitle='E_{T}^{#gamma} (GeV)',
ytitle='s_{reco} = E^{#gamma}_{reco}/E^{kin}_{reco} - 1 (%)',
title='Lyon',
)
frp.getdata()
frp.makegraph()
## Caltech
frp.getters = var_vs_pt('#Deltas')
frp.title = 'Caltech'
frp.getdata()
frp.makegraph()
## Compare Caltech and Lyon scale
canvases.next()
frp.plotall(title='Barrel, R_{9} < 0.94, Baseline', styles=[20, 25])
plotters.append(frp)
#------------------------------------------------------------------------------
## S width Comparison
## Lyon
frp = FitResultPlotter(
sources=zip(filenames, wsnames, sreco_snapshots),
getters=(
lambda ws, i=iter(bincenters): i.next(), # x
lambda ws, i=iter(sigma_lyon): i.next(), # y
lambda ws, i=iter(binhalfwidths): i.next(), # ex
lambda ws, i=iter(esigma_lyon): i.next(), # ey
),
xtitle='E_{T}^{#gamma} (GeV)',
plotter.sources = cfg.sources2
plotter.getters = cfg.getters2
plotter.title = 'MC Fit'
plotter.getdata()
plotter.makegraph()
plotter.sources = cfg.sources3
plotter.getters = cfg.getters3
plotter.title = 'Data Fit'
plotter.getdata()
plotter.makegraph()
canvases.next('c_' + cfg.name).SetGrid()
plotter.plotall(title = cfg.title,
styles = [20, 25, 26],
colors = [ROOT.kBlack, ROOT.kBlue, ROOT.kRed])
plotters.append(plotter)
## End of loop over configurations.
#==============================================================================
for cfg in resolution_configurations[:]:
## MC, EB, 2011A+B, 1 of 4 statistically independent tests
plotter = FitResultPlotter(cfg.sources1, cfg.getters1, cfg.xtitle,
cfg.ytitle, title = 'MC Truth')
plotter.getdata()
plotter.makegraph()
plotter.sources = cfg.sources2
plotter.getters = cfg.getters2
plotter.title = 'MC Fit'
)
frp.getdata()
frp.makegraph()
## New Baseline
frp.sources = zip(cfg.filenames, cfg.wsnames, cfg.sreco_snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = 'Baseline'
frp.getdata()
frp.makegraph()
## True
frp.sources = zip(cfg.filenames, cfg.wsnames, cfg.strue_snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = 'MC Truth'
frp.getdata()
frp.makegraph()
## Compare New Baseline, MC PDF and MC truth scale
canvases.next('s_' + cfg.name).SetGrid()
frp.plotall(title = cfg.title,
styles = [20, 25, 22],
colors = [kBlue, kRed, kBlack])
plotters.append(frp)
## end of loop over cfgs
if __name__ == '__main__':
import user
xtitle = 'E_{T}^{#gamma} (GeV)',
ytitle = 's_{gen} = E^{#gamma}_{reco}/E^{#gamma}_{gen} - 1 (%)',
)
for fitrange, title in zip(['FitRange' + x for x in '65 68 71'.split()],
'-3% Nominal +3%'.split()):
filenames = [os.path.join(path, 'strue_%s.root' % fitrange)] * n
snapshots = [snapshot.format(f=fitrange, c=etar9.name, l=lo, h=hi)
for lo, hi in binedges]
frp.sources = zip(filenames, workspaces, snapshots)
frp.getters = var_vs_pt('#Deltas')
frp.title = title
frp.getdata()
frp.makegraph()
canvases.next('strue_FitRangeSystematics' + etar9.name)
frp.plotall(title=etar9.title)
plotters.append(frp)
graph = frp.graphs[0].Clone('g_' + etar9.name)
for i in range(graph.GetN()):
x = graph.GetX()[i]
ylo = min([g.GetY()[i] for g in frp.graphs])
yhi = max([g.GetY()[i] for g in frp.graphs])
graph.SetPoint(i, x, 0.5 * (yhi - ylo))
graph.SetPointError(i, graph.GetEX()[i], 0)
plotter.graphs.append(graph)
plotter.titles.append(etar9.title)
canvases.next('strue_FitRangeSystematics')
plotter.xtitle = 'E_{T}^{#gamma} (GeV)'
