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_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)
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,
colors=[ROOT.kBlack],
)
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.graph.Fit("pol1")
canvases.next("c_" + cfg.name).SetGrid()
plotter.graph.Draw("ap")
plotter.graph.GetXaxis().SetTitle(cfg.xtitle)
plotter.graph.GetYaxis().SetTitle(cfg.ytitle)
# if 'EE_highR9' in cfg.name:
# plotter.plotall(title = cfg.title,
##xrange = (0, 10),
##yrange = (0, 10),
# 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 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 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)
plotter = FitResultPlotter(None, None)
for etar9 in cats:
frp = FitResultPlotter(
sources='dummy',
getters=var_vs_pt('#Deltas'),
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))
for cfg in cfgs:
#------------------------------------------------------------------------------
## Scale Comparison
## Baseline v2
frp = FitResultPlotter(
sources = zip(cfg.filenames1, cfg.wsnames, cfg.sreco_snapshots1),
getters = var_vs_pt('#Deltas'),
xtitle = 'E_{T}^{#gamma} (GeV)',
ytitle = 's_{reco} = E^{#gamma}_{reco}/E^{kin}_{reco} - 1 (%)',
title = 'Baseline v2',
)
frp.getdata()
frp.makegraph()
## Proposal 2
frp.sources = zip(cfg.filenames2, cfg.wsnames, cfg.sreco_snapshots2)
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,
lambda ws, i=iter(ktransmc[cfg.name]['sreco']): (
ws.var('#Deltas').getVal() + i.next() # y
),
lambda ws, i=iter(binhalfwidths): i.next(), # ex
lambda ws, i=iter(ktransmc[cfg.name]['esreco']):
(oplus(ws.var('#Deltas').getError(), i.next())), # ey
),
xtitle='E_{T}^{#gamma} (GeV)',
ytitle='s_{reco} = E^{#gamma}_{reco}/E^{kin}_{reco} - 1 (%)',
title='PDF Morph',
)
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,
)
resolution_configurations.append(cfg)
## End of loop categories
plotters = []
#==============================================================================
for cfg in scale_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'
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])
ws.var('#Deltas').getVal() + i.next() # y
),
lambda ws, i = iter(binhalfwidths): i.next(), # ex
lambda ws, i = iter(ktransmc[cfg.name]['esreco']): (
oplus(ws.var('#Deltas').getError(), i.next())
), # ey
),
xtitle = 'E_{T}^{#gamma} (GeV)',
ytitle = 's_{reco} = E^{#gamma}_{reco}/E^{kin}_{reco} - 1 (%)',
title = 'PDF Morph',
)
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,
workspaces = ['ws1'] * n
snapshot = 'sFit_strue_mc_Nominal{f}_mmMass80_{c}_PhoEt{l}-{h}_bifurGauss'
plotter = FitResultPlotter(None, None)
for etar9 in cats:
frp = FitResultPlotter(
sources = 'dummy',
getters = var_vs_pt('#Deltas'),
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))
