def main(args):
"""Main"""
set_TDR_style()
cntfile = r.TFile.Open(args.centralfile)
# Open correction maps here, to keep histos alive
chi1_cf = r.TFile.Open(args.chi1corrfile)
chi2_cf = r.TFile.Open(args.chi2corrfile)
var = args.direction
corrmap = get_correction_map(var, chi1_cf, chi2_cf)
graph = get_graph(cntfile, corrmap, not args.uncorr, args.direction)
can = make_plot_comp_ana_shapes(graph, var, args.mc)
if args.compgraph is not None:
cmpfile = r.TFile.Open(args.compgraph)
cmpgraph = get_graph(cmpfile, corrmap, not args.uncorr, args.direction)
mkplot(cmpgraph, can=can, drawOpt='samePE', attr=RATIO_ATTR[1:])
can.SaveAs(args.outfile)
if args.saveto is not None:
outfile = r.TFile(args.saveto, 'recreate')
outfile.cd()
graph.SetName(RATIO_NAME.format(args.direction))
graph.Write()
outfile.Close()
def make_plot(hists, savename, reference, doleg=False):
"""
Make plot
"""
set_TDR_style()
can = r.TCanvas(create_random_str(16), '', 50, 50, 600, 600)
can.cd()
# preliminary collect them and delete entries if necessary
legentries = []
with_ref = False
if reference is not None:
ref_hist = hists[reference]
with_ref = True
rest_hists = [hists[k] for k in hists if k != reference]
pull_hists = [do_ratio(ref_hist, h) for h in rest_hists]
legentries = [reference] + [n for n in hists if n != reference]
else:
rest_hists = hists.values()
legentries = hists.keys()
if doleg:
for i, entry in enumerate(legentries):
legentries[i] = entry.replace('jpsi_kin_sel', '').replace('__', '_')
else:
legentries = []
y_max = get_y_max(hists.values()) * 1.1
leg=setup_legend()
if with_ref:
pad = r.TPad('ratio_pad', 'ratio_pad', 0, 0.3, 1, 1)
r.SetOwnership(pad, False)
pad.Draw()
pad.cd()
# passing all legentries here, since indexing doesn't work with an empty
# list and mkplot picks only the first in this case
make_ratio_plot(pad, [ref_hist], leg, legentries,
yRange=[0, y_max], attr=gen_attributes, drawOpt='E2',
legOpt='F')
make_ratio_plot(pad, rest_hists, leg, legentries[1:],
yRange=[0, y_max], attr=comp_attributes, drawOpt='sameE1',
legOpt='PLE')
can.cd()
pull_pad = r.TPad('pull_pad', 'pull_pad', 0, 0, 1, 0.3)
r.SetOwnership(pull_pad, False)
pull_pad.Draw()
pull_pad.cd()
make_ratio_plot(pull_pad, pull_hists, None, [], yRange=[None, None],
attr=comp_attributes)
else:
make_ratio_plot(can, rest_hists, leg, legentries,
yRange=[0, y_max], attr=comp_attributes)
can.SaveAs(savename)
def main():
"""Main"""
set_TDR_style()
r.gStyle.SetPadRightMargin(r.gStyle.GetPadRightMargin() + 0.01)
data = apply_selections(get_dataframe(INFILE,
columns=collect_requirements(SELECTIONS)),
SELECTIONS)
can = make_photon_pt_dist_plot(data)
can.SaveAs(os.path.join(OUTDIR, 'photon_pt_dist.pdf'))
def main(args):
"""Main"""
mcdata = get_dataframe(args.mcfile, columns=MC_VARIABLES)
chi1data = get_dataframe(args.chic1file, 'tr', columns=TOY_VARIABLES)
chi2data = get_dataframe(args.chic2file, 'tr', columns=TOY_VARIABLES)
mc_ratios = OrderedDict()
toy_ratios = OrderedDict()
for name in SELECTIONS_TOY:
toy_ratios[name] = OrderedDict()
mc_ratios[name] = OrderedDict()
toy_sel = SELECTIONS_TOY[name]
mc_sel = SELECTIONS_MC[name]
for var in VARIABLES:
hist_sett = VARIABLES[var]['sett']
toy_ratios[name][var] = get_ratio(chi1data, chi2data,
VARIABLES[var]['var'], toy_sel,
hist_sett, get_weight_func())
mc_ratios[name][var] = get_ratio_mc(mcdata, VARIABLES[var]['var'],
mc_sel, hist_sett)
set_TDR_style()
for name in SELECTIONS_TOY:
for var in VARIABLES:
leg = setup_legend(*LEGPOS[var])
can = mkplot(mc_ratios[name][var],
attr=[PLOT_ATTRIBUTES[name]['mc']],
drawOpt='E2',
legOpt='F',
xLabel=LABELS[var],
yLabel='#chi_{c2} / #chi_{c1}',
leg=leg,
legEntries=['real mc'])
can = mkplot(toy_ratios[name][var],
attr=[PLOT_ATTRIBUTES[name]['toy']],
drawOpt='E1same',
legOpt='PLE',
can=can,
xLabel=LABELS[var],
yLabel='#chi_{c2} / #chi_{c1}',
leg=leg,
legEntries=['toy mc'])
latex = setup_latex()
can.add_tobject(latex)
put_on_latex(latex, [(0.18, 0.96, name)])
savename = '_'.join(['comp_real_toy', name,
var]).replace(' + ', '_')
can.SaveAs(savename + '.pdf')
def main(args):
"""Main"""
set_TDR_style()
ucfile = r.TFile.Open(args.uncorrfile)
uncorr_g = get_graph(ucfile, 'r_chic2_chic1', args.variable)
cfile = r.TFile.Open(args.corrfile)
corr_g = get_graph(cfile, 'r_chic2_chic1', args.variable)
can = make_ratio_plot(uncorr_g, corr_g, args.variable)
cond_mkdir(args.outdir)
can.SaveAs('{}/r_chic2_chic1_v_{}_comp_corr_uncorr.pdf'.format(
args.outdir, args.variable))
def make_plot(data_graph, mc_graphs, pdfname, canvas_sett):
"""
Make plot and save
"""
mc_attributes = [{
'color': default_colors()[0],
'marker': 25,
'size': 1.5,
'fillalpha': (default_colors()[0], 0.5)
}, {
'color': default_colors()[1],
'marker': 26,
'size': 1.5,
'fillalpha': (default_colors()[1], 0.5)
}, {
'color': default_colors()[2],
'marker': 27,
'size': 1.5,
'fillalpha': (default_colors()[2], 0.5)
}]
data_attributes = [{'color': 1, 'marker': 20, 'size': 1.5}]
set_TDR_style()
can = r.TCanvas('rcan', 'rcan', 50, 50, 600, 600)
frame = can.DrawFrame(*canvas_sett['range'])
frame.SetXTitle(canvas_sett['xtitle'])
frame.SetYTitle(canvas_sett['ytitle'])
leg = setup_legend()
mc_rescaled = rescale_MC_graphs(data_graph, mc_graphs.values(), SCALE_FUNC)
plot_on_canvas(can,
mc_rescaled,
drawOpt='P2',
leg=leg,
legOpt='PF',
legEntries=mc_graphs.keys(),
attr=mc_attributes)
plot_on_canvas(can, [data_graph],
drawOpt='samePE',
leg=leg,
legEntries=['data'],
attr=data_attributes)
can.SaveAs(pdfname)
def main(args):
"""Main"""
set_TDR_style()
files = parse_inputs(args.input, enforce_keys=True)
var_x = 'lambda_1'
var_y = 'lambda_2'
contours = get_all_contours(files, args.conf_levels, var_x, var_y)
best_fits = get_best_fits(files, var_x, var_y)
can = make_plot(contours, best_fits, files.keys(),
xRange=[-2, 2], yRange=[-2, 2],
xLabel='#lambda_{#vartheta}^{#chi_{c1}}',
yLabel='#lambda_{#vartheta}^{#chi_{c2}}')
can.SaveAs(args.outfile)
def main(args):
"""Main"""
set_TDR_style()
graphfile = r.TFile.Open(args.graphfile)
g_lth = get_graph(graphfile, 'lth')
g_dlth = get_graph(graphfile, 'dlth')
g_lth2 = get_graph(graphfile, 'lth2')
if args.systematics is not None:
syst_file = r.TFile.Open(args.systematics)
syst_graph = syst_file.Get('dlth_v_pt_syst')
g_dlth = get_combined_graph(g_dlth, syst_graph)
g_lth2 = get_combined_graph(g_lth2, syst_graph)
can = make_plot(g_lth, g_dlth, g_lth2)
can.SaveAs(args.output)
def main():
"""Main"""
set_TDR_style()
cond_mkdir(OUTDIR)
data = apply_selections(get_dataframe(INFILE), SELECTIONS)
r.gStyle.SetPadRightMargin(0.129)
r.gStyle.SetPadLeftMargin(r.gStyle.GetPadLeftMargin() - 0.007)
can = make_costh_phi_plot(data, 'HX')
can.SaveAs(os.path.join(OUTDIR, 'costh_phi_fold_HX_pt_12_18_all.pdf'))
can = make_costh_phi_plot(data, 'CS')
can.SaveAs(os.path.join(OUTDIR, 'costh_phi_fold_CS_pt_12_18_all.pdf'))
set_TDR_style()
can = make_costh_mu_pt_plot(data)
can.SaveAs(os.path.join(OUTDIR, 'costh_HX_comp_mu_pt_jpsipt_12_18_all.pdf'))
def main():
"""Main"""
set_TDR_style()
r.gStyle.SetPadTopMargin(r.gStyle.GetPadTopMargin() + 0.005)
ppd_files = open_ppd_files('costh')
can = make_lth_plot(ppd_files)
can.SaveAs(os.path.join(OUTDIR, 'ppd_lth_w_lower_lim_90_combined.pdf'))
can = make_dlth_plot(ppd_files)
can.SaveAs(os.path.join(OUTDIR, 'ppd_dlth_combined.pdf'))
ppd_files = open_ppd_files('phi')
can = make_lph_plot(ppd_files)
can.SaveAs(os.path.join(OUTDIR, 'ppd_lph_combined.pdf'))
can = make_dlph_plot(ppd_files)
can.SaveAs(os.path.join(OUTDIR, 'ppd_dlph_combined.pdf'))
def main(args):
"""Main"""
set_TDR_style()
ppd_file = r.TFile.Open(args.ppdfile)
# Only now do we know the shift, so put it here
PLOT_FUNCTIONS['dlth'] = lambda f: _make_dlth_plot(f, args.shift_by)
plot_vars = []
if 'costh' in args.variables:
plot_vars.extend(['lth', 'dlth', 'norm_costh'])
if 'phi' in args.variables:
plot_vars.extend(['lph', 'dlph', 'norm_phi'])
if '_' in args.variables:
plot_vars.extend(['ltilde', 'dltilde'])
for var in plot_vars:
func = PLOT_FUNCTIONS[var]
can = func(ppd_file)
can.SaveAs('ppd_{}.pdf'.format(var))
def main(args):
"""Main"""
with open(args.configfile, 'r') as configfile:
config = json.load(configfile)
model = BinnedFitModel(config)
ffile = r.TFile.Open(args.fitfile)
wsp = ffile.Get('ws_mass_fit')
if args.outdir is None:
outdir = dirname(args.fitfile)
else:
outdir = args.outdir
cond_mkdir(outdir)
if args.publication:
set_TDR_style()
# Saving the plots
if not args.no_plots:
cans = model.plot(wsp, verbose=args.verbose, publication=args.publication,
preliminary=args.preliminary)
canp = model.plot_fit_params(wsp)
for bin_name in model.bins:
plotname = '/'.join([outdir, bin_name+'_massfit.pdf'])
if args.publication:
add_auxiliary_info(cans[bin_name], 2012, prelim=True)
cans[bin_name].SaveAs(plotname)
parname = '/'.join([outdir, bin_name+'_massfit_res.pdf'])
canp[bin_name].SaveAs(parname)
if args.graphs:
outfile = '/'.join([outdir, 'proto_param_graphs.root'])
store_proto_pars(wsp, model, outfile, args.symmetric)
def main(args):
"""Main"""
set_TDR_style()
graphfile = r.TFile.Open(args.graphfile)
var, graphs = get_graphs(graphfile)
if args.systematics is None:
can = make_plot(graphs, var, args.sigmas.split(','))
else:
syst_file = r.TFile.Open(args.systematics)
can = make_plot_with_syst(graphs, var, syst_file, args.fit,
args.pt_over_m)
comb_gr = can.pltables[1]
comb_gr.SetName(graphs.values()[0].GetName())
outfile = r.TFile(
'{}/comb_graph_{}_v_pt.root'.format(args.outdir, var), 'recreate')
outfile.cd()
comb_gr.Write()
outfile.Close()
cond_mkdir(args.outdir)
can.SaveAs('{}/{}_v_pt.pdf'.format(args.outdir, var))
def main(args):
"""Main"""
set_TDR_style()
rfile = r.TFile.Open(args.ratiofile)
ratio = get_graph(rfile, 'r_chic2_chic1', args.variable)
if args.variable == 'costh':
analytic_scens = get_analytic_scenarios_costh(args.delta_lambda,
args.lambda1,
args.lambda2,
args.no_extremes)
else:
# not implemented currently for phi direction
analytic_scens = OrderedDict()
if args.pull_plot:
can = make_ratio_pull_plot(ratio, analytic_scens, args.variable)
else:
can = make_ratio_plot(ratio, analytic_scens, args.variable)
cond_mkdir_file(args.outfile)
can.SaveAs(args.outfile)
def main(args):
"""Main"""
set_TDR_style()
fitfile = r.TFile.Open(args.fitfile)
cont_1sigma = get_contour(fitfile.Get('chi2_scan_contour_1sigma'))
cont_2sigma = get_contour(fitfile.Get('chi2_scan_contour_2sigma'))
cont_3sigma = get_contour(fitfile.Get('chi2_scan_contour_3sigma'))
leg = setup_legend(0.5, 0.78, 0.8, 0.94)
leg.SetTextSize(0.035)
leg.SetEntrySeparation(0.005)
leg.SetFillStyle(1001)
can = mkplot([cont_1sigma, cont_2sigma, cont_3sigma],
drawOpt='L',
xRange=[-1, 1.6],
xLabel='#lambda_{#vartheta}^{#chi_{c1}}',
yRange=[-1.2, 1.6],
yLabel='#lambda_{#vartheta}^{#chi_{c2}}',
attr=CONT_ATTR,
leg=leg,
legEntries=['{} % CL'.format(v) for v in [68, 95, 99]],
legOpt='L')
mkplot(get_phys_region(),
can=can,
drawOpt='same',
attr=[{
'color': r.kRed,
'line': 7,
'width': 3
}])
mkplot([r.TLine(0, -1.2, 0, 1.6),
r.TLine(-1, 0, 1.6, 0)],
can=can,
drawOpt='same',
attr=[{
'color': 12,
'line': 7,
'width': 2
}])
mkplot(r.TGraph(1, np.array([0]), np.array([0])),
can=can,
drawOpt='Psame',
attr=[{
'color': 1,
'marker': 24,
'size': 2.75
}])
mkplot(get_jz_graph(),
can=can,
drawOpt='Psame',
attr=[{
'color': 1,
'marker': 30,
'size': 3
}])
ltx = setup_latex()
ltx.SetTextSize(0.04)
put_on_latex(ltx, [(-0.4, 1.3, 'J_{z}'), (-0.4, 1.1, '#pm1'),
(0.975, 1.1, '0'), (-0.54, 0.95, '#pm2'),
(-0.54, -0.38, '#pm1'), (-0.47, -0.65, '0')],
ndc=False)
add_auxiliary_info(can, 2012, 'left')
can.SaveAs(args.outfile)
def main(args):
"""Main"""
# need PlotServer here, to keep the root file open
pserver = PlotServer(args.histfile)
norm_mode = 'at0' if args.normalize_at_zero else 'nsignal'
year_trg_ids, leg_entries = get_trigger_year_info(args.input)
hists = get_plot_hists(pserver,
year_trg_ids,
args.ratio,
args.variable,
args.frame,
args.ptbin,
state=args.state,
norm_mode=norm_mode,
norm_ratio=args.norm_ratio)
# split into data and mc hist due to different plotting styles for the two
data_hists = {k: hists[k] for k in hists if 'data' in k}
mc_hists = {k: hists[k] for k in hists if 'mc' in k}
leg = None
data_leg = []
mc_leg = []
if args.legend:
leg = setup_legend(args.frame, args.ratio)
if args.ratio:
data_leg = [leg_entries[(k[0], k[1]), ] for k in data_hists]
mc_leg = [leg_entries[(k[0], k[1]), ] for k in mc_hists]
else:
data_leg = [
', '.join([leg_entries[(k[0], k[1]), ], k[2]])
for k in data_hists
]
data_leg = nice_leg_entries(data_leg)
mc_leg = [
', '.join([leg_entries[(k[0], k[1]), ], k[2]])
for k in mc_hists
]
mc_leg = nice_leg_entries(mc_leg)
dpl_attr = [get_plot_attributes(*k) for k in data_hists]
mpl_attr = [get_plot_attributes(*k) for k in mc_hists]
y_label = get_ylabel(not args.ratio, args.normalize_at_zero,
args.norm_ratio)
if args.state == 'chib':
y_label = re.sub(r'c(\d)', r'b\1', y_label)
x_label = get_xlabel(args.variable, args.frame)
# to have same y-range for everything, have to do it manually, as
# mkplot can't handle it in two different calls
y_max = get_y_max(data_hists.values() + mc_hists.values()) * 1.1
set_TDR_style()
can = None # declare here to be able to switch the two calls below
can = mkplot(mc_hists.values(),
yRange=[0, y_max],
drawOpt='E2',
attr=mpl_attr,
can=can,
leg=leg,
legEntries=mc_leg,
legOpt='F',
xLabel=x_label,
yLabel=y_label)
can = mkplot(data_hists.values(),
yRange=[0, y_max],
drawOpt='E1',
attr=dpl_attr,
can=can,
leg=leg,
legEntries=data_leg,
legOpt='PLE',
xLabel=x_label,
yLabel=y_label)
lumi_text = get_lumi_text(year_trg_ids)
add_lumi_info(can, lumi_text)
can.SaveAs(args.output)