def basic_sel(df, jpsiPt, photonPt=0):
"""
Do a basic selection that mimics analysis and acceptance cuts
"""
return (df.muP_pt > 3) & (df.muN_pt > 3) \
& (df.muP_eta.abs() < 1.6) & (df.muN_eta.abs() < 1.6) \
& (df.jpsiRap.abs() < 1.2) \
& get_bin_cut_df(df, 'jpsiPt', *jpsiPt) \
& (df.photonPt > photonPt)
def jpsi_kin_sel(df, jpsiPt, noPt=False, noRap=False):
ptname = 'JpsiPt'
rapname = 'JpsiRap'
selection = np.ones(df.shape[0], dtype=bool)
if not noPt:
selection &= get_bin_cut_df(df, ptname, *jpsiPt)
if not noRap:
selection &= df[rapname].abs() < 1.2
return selection
def basic_sel(df, mc=False, gen=False):
selection = np.ones(df.shape[0], dtype=bool)
if not gen:
selection &= get_bin_cut_df(df, 'chicMass', 3.325, 3.725)
if not mc and not gen:
selection &= df.vtxProb > 0.01
# selection &= (df.costh_HX.abs() < 0.5)
# selection &= (df.wChic1 == 1)
return selection
def create_mass_hist(dfr, costh_bin=None):
"""
Create a mass histogram in a given costh_bin (or integrated if None)
"""
if costh_bin is not None:
plot_data = dfr[get_bin_cut_df(dfr, lambda d: d.costh_HX.abs(),
*costh_bin)]
else:
plot_data = dfr
return create_histogram(plot_data.chicMass, (75, 3.325, 3.725))
def do_chic_ratio(args):
"""
Make the chic ratio plot
"""
ffile = r.TFile.Open(args.fitfile)
ws = ffile.Get('ws_mass_fit')
outdir = get_outdir(args.outdir, args.fitfile)
bin_sel_info = get_bin_sel_info(args.pklfile, args.fitfile)
costh_bins = bin_sel_info['costh_bins']
costh_means = bin_sel_info['costh_means']
graph = get_ratio_graph(ws, 'Nchic2', ws, 'Nchic1', costh_bins,
costh_means)
ptmin, ptmax = get_pt_range(bin_sel_info['basic_sel'])
mcdf = get_dataframe(args.mcfile, 'chic_mc_tuple')
mc_sel = (np.abs(mcdf.JpsiRap) < 1.2) & ((mcdf.trigger & 2) == 2) & \
(mcdf.vtxProb > 0.01) & \
(get_bin_cut_df(mcdf, 'chicPt', 0, 990)) & \
(get_bin_cut_df(mcdf, 'JpsiPt', ptmin, ptmax))
unpol = get_mc_ratio_graph(mcdf[mc_sel], mcdf[mc_sel], 'wChic2', 'wChic1',
costh_bins, costh_means, None, None)
nrqcd = get_mc_ratio_graph(mcdf[mc_sel], mcdf[mc_sel], 'wChic2', 'wChic1',
costh_bins, costh_means, -0.3, 0.5)
extreme = get_mc_ratio_graph(mcdf[mc_sel], mcdf[mc_sel], 'wChic2',
'wChic1', costh_bins, costh_means, -0.6, 1)
mc_graphs = {'unpol': unpol, 'nrqcd': nrqcd, 'extreme': extreme}
plot_sett = {
'range': [0, 0, 1, 0.75],
'xtitle': '|cos#theta^{HX}|',
'ytitle': '#chi_{c2} / #chi_{c1}'
}
plot_name = '{}/chic2_chic1_pt{}_{}_nbins{}_costh_HX.pdf'.format(
outdir, ptmin, ptmax, len(costh_bins))
make_plot(graph, mc_graphs, plot_name, plot_sett)
def get_mc_vals(dfr, state, costh_bins, pol=None):
"""
Get the number of events in each costh_bin for a given pol scenario
"""
if pol is None:
weight = state
else:
weight = get_storable_name('wPol_HX_lth_{:.2f}'.format(pol))
state_sel = dfr[state] == 1 # select the events for a given state
vals = []
for ctbin in costh_bins:
costh_sel = get_bin_cut_df(dfr, lambda d: np.abs(d.costh_HX), *ctbin)
bin_events = dfr[costh_sel & state_sel][weight].sum()
vals.append((
bin_events,
np.sqrt(bin_events),
))
return vals
def create_costh_binned_hists(dfr, hist_func, selection_weights, binning):
"""
Create the histograms in costh bins
"""
hists = OrderedDict()
for name in selection_weights:
sel_f, sel_w = selection_weights[name]
hists[name] = OrderedDict()
# Apply the selections as soon as possible and don't repeat them
sel_dfr = apply_selections(dfr, sel_f)
for bin_low, bin_high in zip(binning[:-1], binning[1:]):
binname = '{:.2f}_{:.2f}'.format(bin_low, bin_high)
bin_f = lambda d: get_bin_cut_df(d, lambda d: d.costh_HX.abs(),
bin_low, bin_high),
hists[name][binname] = hist_func(sel_dfr, bin_f, sel_w)
# Rearrange histograms so that the dictionary is flat
flat_hists = OrderedDict()
for sel in hists:
for cbin in hists[sel]:
flat_hists['_'.join([sel, cbin])] = hists[sel][cbin]
return flat_hists
def do_jpsi_ratios(args):
"""
Make the jpsi ratio plots
"""
chic_ff = r.TFile.Open(args.chic_ff)
chic_ws = chic_ff.Get('ws_mass_fit')
jpsi_ff = r.TFile.Open(args.jpsi_ff)
jpsi_ws = jpsi_ff.Get('ws_mass_fit')
outdir = get_outdir(args.outdir, args.chic_ff)
# Only need the chic information here, since the information in the J/psi
# file that is needed here is redundant
bin_info = get_bin_sel_info(args.pklfile, args.chic_ff)
costh_bins = bin_info['costh_bins']
costh_means = bin_info['costh_means']
chic1_graph = get_ratio_graph(chic_ws, 'Nchic1', jpsi_ws, 'Njpsi',
costh_bins, costh_means)
chic2_graph = get_ratio_graph(chic_ws, 'Nchic2', jpsi_ws, 'Njpsi',
costh_bins, costh_means)
ptmin, ptmax = get_pt_range(bin_info['basic_sel'])
chic_df = get_dataframe(args.mc_chic, 'chic_mc_tuple')
chic_sel_mc = (np.abs(chic_df.JpsiRap) < 1.2) & \
((chic_df.trigger & 2) == 2) & (chic_df.vtxProb > 0.01) & \
(get_bin_cut_df(chic_df, 'chicPt', 0, 990)) & \
(get_bin_cut_df(chic_df, 'JpsiPt', ptmin, ptmax))
jpsi_df = get_dataframe(args.mc_jpsi, 'jpsi_mc_tuple')
jpsi_sel_mc = (np.abs(jpsi_df.JpsiRap) < 1.2) & (jpsi_df.trigger == 1) & \
(jpsi_df.vtxProb > 0.01) & \
(get_bin_cut_df(jpsi_df, 'JpsiPt', ptmin, ptmax))
unpol = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic1', 'wJpsi', costh_bins, costh_means,
None, None)
nrqcd = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic1', 'wJpsi', costh_bins, costh_means, 0.5,
None)
extreme = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic1', 'wJpsi', costh_bins, costh_means, 1,
None)
can = r.TCanvas('rcan', 'rcan', 50, 50, 600, 600)
frame = can.DrawFrame(0, 0, 1, 0.1)
mc_graphs = {'unpol': unpol, 'nrqcd': nrqcd, 'extreme': extreme}
plot_sett = {
'range': [0, 0, 1, 0.125],
'xtitle': '|cos#theta^{HX}|',
'ytitle': '#chi_{c1} / J/#psi'
}
plot_name = '{}/chic1_jpsi_pt{}_{}_nbins{}_costh_HX.pdf'.format(
outdir, ptmin, ptmax, len(costh_bins))
make_plot(chic1_graph, mc_graphs, plot_name, plot_sett)
unpol = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic2', 'wJpsi', costh_bins, costh_means,
None, None)
nrqcd = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic2', 'wJpsi', costh_bins, costh_means,
-0.3, None)
extreme = get_mc_ratio_graph(chic_df[chic_sel_mc], jpsi_df[jpsi_sel_mc],
'wChic2', 'wJpsi', costh_bins, costh_means,
-0.6, None)
mc_graphs = {'unpol': unpol, 'nrqcd': nrqcd, 'extreme': extreme}
plot_sett['range'] = [0, 0, 1, 0.075]
plot_sett['ytitle'] = '#chi_{c2} / J/#psi'
plot_name = plot_name.replace('chic1', 'chic2')
make_plot(chic2_graph, mc_graphs, plot_name, plot_sett)
def jpsi_pt_M_sel(dfr, min_ptM=2.3, max_ptM=5.2, gen=False):
"""Kinematic J/psi selection using a Jpsi pT / chicMass selection"""
jpsi_pt = sf.get_gen_name('JpsiPt', gen)
chic_mass = sf.get_gen_name('chicMass', gen)
pt_M = lambda d: _get_var(d, jpsi_pt) / _get_var(d, chic_mass)
return get_bin_cut_df(dfr, pt_M, min_ptM, max_ptM)