def load_reweighter():
mode = gcm()
# Hard coded check here: Use the RS mode if WS is supplied. Also get a new
# mode object to remove possible MC flags.
# Just recreate the mode here to get rid of potential MC flags
mode = get_mode(mode.polarity, mode.year, mode.mode_short)
if mode.mode == config.D0ToKpipipi_WS:
mode = get_mode(mode.polarity, mode.year, 'RS')
if mode.mode == config.D0ToKpipipi_2tag_WS:
mode = get_mode(mode.polarity, mode.year, '2tag_RS')
outfile = mode.get_output_path('effs') + 'reweighter.p'
return helpers.load(outfile)
def load_classifiers(comb_bkg=False):
mode = gcm()
if comb_bkg:
bdt_folder = 'bdt_comb_bkg'
else:
bdt_folder = 'bdt_rand_spi'
# Hard coded check here: Use the RS mode if WS is supplied. Also get a new
# mode object to remove possible MC flags.
# Just recreate the mode here to get rid of potential MC flags
mode = get_mode(mode.polarity, mode.year, mode.mode_short)
if mode.mode == config.D0ToKpipipi_WS:
mode = get_mode(mode.polarity, mode.year, 'RS')
if mode.mode == config.D0ToKpipipi_2tag_WS:
mode = get_mode(mode.polarity, mode.year, '2tag_RS')
outfile = mode.get_output_path(bdt_folder) + 'classifiers.p'
return helpers.load(outfile)
def load_workspace(mode):
import ROOT
outfile = mode.get_output_path('sweight_fit') + 'mass_fit.p'
return helpers.load(outfile)
def dependence_study(use_efficiencies=False):
extra_vars = [
gcm().ltime_var
]
all_vars = gcm().phsp_vars + extra_vars
# Current mode stuff
data = gcm().get_data([f.var for f in extra_vars])
add_variables.append_phsp(data)
df_sel = final_selection.get_final_selection()
df_sel &= selection.delta_mass_signal_region()
gen = get_model()
if use_efficiencies:
outfile = gcm().get_output_path('effs') + 'Gen_DATA_Eff_dep_eff.pdf'
gen['weight'] = get_efficiency_gen()
else:
outfile = gcm().get_output_path('effs') + 'Gen_DATA_Eff_dep.pdf'
gen['weight'] = 1.
lim_file = gcm().get_output_path('effs') + 'limits_for_eff.p'
with PdfPages(outfile) as pdf:
for selected, plotted in permutations(all_vars, 2):
log.info('Plotting {} in intervals of {}'.format(
plotted.var, selected.var))
percentiles = np.arange(0, 1.1, 0.2)
boundaries = helpers.weighted_quantile(
data[selected.var][df_sel], percentiles)
fig, ax = plt.subplots(figsize=(10, 10))
for low, high in zip(boundaries[:-1], boundaries[1:]):
num_sel = (data[selected.var] > low) & (data[selected.var] < high) # NOQA
den_sel = (gen[selected.var] > low) & (gen[selected.var] < high)
denominator = gen[plotted.var][den_sel]
numerator = data[plotted.var][df_sel & num_sel]
weight_d = gen['weight'][den_sel]
weight_d /= np.sum(weight_d)
weight_n = np.ones(numerator.index.size)*1./numerator.index.size # NOQA
x, y, x_err, y_err = helpers.make_efficiency(
numerator, denominator, 50, weight_n, weight_d, independent=True) # NOQA
options = dict(
fmt='o', markersize=5, capthick=1, capsize=0, elinewidth=2,
alpha=1)
rlow, prec = helpers.rounder(low, boundaries)
rhigh, _ = helpers.rounder(high, boundaries)
spec = '{{:.{}f}}'.format(prec)
label = r'${} <$ {} $ < {}$'.format(
spec.format(rlow), selected.xlabel, spec.format(rhigh))
ax.errorbar(x, y, y_err, x_err, label=label, **options)
ax.set_xlabel(plotted.xlabel)
ax.set_ylabel('Relative efficiency')
try:
limits = load(lim_file)
except:
log.info('Creating new limits file')
limits = {}
if limits is None:
log.info('Creating new limits file')
limits = {}
if (plotted.var, selected.var) not in limits or use_efficiencies is False: # NOQA
plot_utils.y_margin_scaler(ax, hf=0.4)
limits[(plotted.var, selected.var)] = ax.get_ylim()
else:
log.info('Applying limits')
lim = limits[(plotted.var, selected.var)]
ax.set_ylim(lim)
dump(limits, lim_file)
ax.legend()
pdf.savefig(plt.gcf())
plt.close()
def load_bdt(mode=None):
if mode is None:
mode = gcm()
outfile = mode.get_output_path('bdt') + 'bdt.p'
return helpers.load(outfile)