selection_tight=selection_tight,
pandas_selection=pandas_selection,
lumi=59700.,
model='/'.join([
env['BASE_DIR'], 'nn', 'trainings', training, 'net_model_weighted.h5'
]),
transformation='/'.join([
env['BASE_DIR'], 'nn', 'trainings', training,
'input_tranformation_weighted.pck'
]),
features='/'.join(
[env['BASE_DIR'], 'nn', 'trainings', training, 'input_features.pck']),
process_signals=True, # switch off for control regions
mini_signals=False, # process only the signals that you'll plot
plot_signals=True,
blinded=False,
datacards=[
'hnl_m_12_lxy_lt_0p5', 'hnl_m_12_lxy_0p5_to_1p5',
'hnl_m_12_lxy_1p5_to_4p0', 'hnl_m_12_lxy_mt_4p0'
], # FIXME! improve this to accept wildcards / regex
mc_subtraction=True,
data_driven=True,
)
if __name__ == '__main__':
plotter.plot()
# save the plotter and all
save_plotter_and_selections(plotter, selection, selection_mc,
selection_tight)
pass
model='/'.join([
env['BASE_DIR'], 'nn', 'trainings', training, 'net_model_weighted.h5'
]),
transformation='/'.join([
env['BASE_DIR'], 'nn', 'trainings', training,
'input_tranformation_weighted.pck'
]),
features='/'.join(
[env['BASE_DIR'], 'nn', 'trainings', training, 'input_features.pck']),
process_signals=True, # switch off for control regions
mini_signals=False, # process only the signals that you'll plot
plot_signals=True,
blinded=True,
datacards=[
'log_hnl_2d_disp', 'hnl_m_12', 'hnl_m_12_lxy_lt_0p5',
'hnl_m_12_lxy_0p5_to_1p5', 'hnl_m_12_lxy_1p5_to_4p0',
'hnl_m_12_lxy_mt_4p0', 'hnl_m_12_widebins_lxy_lt_0p5',
'hnl_m_12_widebins_lxy_0p5_to_1p5_lin',
'hnl_m_12_widebins_lxy_1p5_to_4p0', 'hnl_m_12_widebins_lxy_mt_4p0'
], # FIXME! improve this to accept wildcards / regex
mc_subtraction=True,
data_driven=True,
)
if __name__ == '__main__':
plotter.plot()
# save the plotter and all
save_plotter_and_selections(plotter, selection, selection_mc,
selection_tight, training)
pass