def process_mc(self):
self.start_time = time.time()
# ------------------------------------------------------------------------
# Use IO helper class to convert detector-level ROOT TTree into
# a SeriesGroupBy object of fastjet particles per event
print('--- {} seconds ---'.format(time.time() - self.start_time))
if self.fast_simulation:
tree_dir = ''
else:
tree_dir = 'PWGHF_TreeCreator'
io_det = process_io.ProcessIO(input_file=self.input_file,
tree_dir=tree_dir,
track_tree_name='tree_Particle',
use_ev_id_ext=False,
is_jetscape=self.jetscape,
event_plane_range=self.event_plane_range)
df_fjparticles_det = io_det.load_data(
m=self.m, reject_tracks_fraction=self.reject_tracks_fraction)
self.nEvents_det = len(df_fjparticles_det.index)
self.nTracks_det = len(io_det.track_df.index)
print('--- {} seconds ---'.format(time.time() - self.start_time))
# If jetscape, store also the negative status particles (holes)
if self.jetscape:
io_det_holes = process_io.ProcessIO(
input_file=self.input_file,
tree_dir=tree_dir,
track_tree_name='tree_Particle',
use_ev_id_ext=False,
is_jetscape=self.jetscape,
holes=True,
event_plane_range=self.event_plane_range)
df_fjparticles_det_holes = io_det_holes.load_data(
m=self.m, reject_tracks_fraction=self.reject_tracks_fraction)
self.nEvents_det_holes = len(df_fjparticles_det_holes.index)
self.nTracks_det_holes = len(io_det_holes.track_df.index)
print('--- {} seconds ---'.format(time.time() - self.start_time))
# ------------------------------------------------------------------------
# Use IO helper class to convert truth-level ROOT TTree into
# a SeriesGroupBy object of fastjet particles per event
io_truth = process_io.ProcessIO(
input_file=self.input_file,
tree_dir=tree_dir,
track_tree_name='tree_Particle_gen',
use_ev_id_ext=False,
is_jetscape=self.jetscape,
event_plane_range=self.event_plane_range)
df_fjparticles_truth = io_truth.load_data(m=self.m)
self.nEvents_truth = len(df_fjparticles_truth.index)
self.nTracks_truth = len(io_truth.track_df.index)
print('--- {} seconds ---'.format(time.time() - self.start_time))
# If jetscape, store also the negative status particles (holes)
if self.jetscape:
io_truth_holes = process_io.ProcessIO(
input_file=self.input_file,
tree_dir=tree_dir,
track_tree_name='tree_Particle_gen',
use_ev_id_ext=False,
is_jetscape=self.jetscape,
holes=True,
event_plane_range=self.event_plane_range)
df_fjparticles_truth_holes = io_truth_holes.load_data(
m=self.m, reject_tracks_fraction=self.reject_tracks_fraction)
self.nEvents_truth_holes = len(df_fjparticles_truth_holes.index)
self.nTracks_truth_holes = len(io_truth_holes.track_df.index)
print('--- {} seconds ---'.format(time.time() - self.start_time))
# ------------------------------------------------------------------------
# Now merge the two SeriesGroupBy to create a groupby df with [ev_id, run_number, fj_1, fj_2]
# (Need a structure such that we can iterate event-by-event through both fj_1, fj_2 simultaneously)
# In the case of jetscape, we merge also the hole collections fj_3, fj_4
print(
'Merge det-level and truth-level into a single dataframe grouped by event...'
)
if self.jetscape:
self.df_fjparticles = pandas.concat([
df_fjparticles_det, df_fjparticles_truth,
df_fjparticles_det_holes, df_fjparticles_truth_holes
],
axis=1)
self.df_fjparticles.columns = [
'fj_particles_det', 'fj_particles_truth',
'fj_particles_det_holes', 'fj_particles_truth_holes'
]
else:
self.df_fjparticles = pandas.concat(
[df_fjparticles_det, df_fjparticles_truth], axis=1)
self.df_fjparticles.columns = [
'fj_particles_det', 'fj_particles_truth'
]
print('--- {} seconds ---'.format(time.time() - self.start_time))
# ------------------------------------------------------------------------
# Set up the Pb-Pb embedding object
if not self.is_pp and not self.thermal_model:
self.process_io_emb = process_io_emb.ProcessIO_Emb(
self.emb_file_list, track_tree_name='tree_Particle', m=self.m)
# ------------------------------------------------------------------------
# Initialize histograms
if not self.dry_run:
self.initialize_output_objects()
# Create constituent subtractor, if configured
if self.do_constituent_subtraction:
self.constituent_subtractor = [
CEventSubtractor(
max_distance=R_max,
alpha=self.alpha,
max_eta=self.max_eta,
bge_rho_grid_size=self.bge_rho_grid_size,
max_pt_correct=self.max_pt_correct,
ghost_area=self.ghost_area,
distance_type=fjcontrib.ConstituentSubtractor.deltaR)
for R_max in self.max_distance
]
print(self)
# Find jets and fill histograms
print('Find jets...')
self.analyze_events()
# Plot histograms
print('Save histograms...')
process_base.ProcessBase.save_output_objects(self)
print('--- {} seconds ---'.format(time.time() - self.start_time))
def process_demo(self):
# Use IO helper class to convert detector-level ROOT TTree into
# a SeriesGroupBy object of fastjet particles per event
tree_dir = 'PWGHF_TreeCreator'
event_tree_name = 'tree_event_char'
io_det = process_io.ProcessIO(input_file=self.input_file_Pythia,
tree_dir=tree_dir,
track_tree_name='tree_Particle',
event_tree_name=event_tree_name,
is_pp=True,
min_cent=0.,
max_cent=10.,
use_ev_id_ext=False)
df_fjparticles_det = io_det.load_data(reject_tracks_fraction=0.)
self.nEvents_det = len(df_fjparticles_det.index)
self.nTracks_det = len(io_det.track_df.index)
print('nEvents in detector-level PYTHIA: {}'.format(self.nEvents_det))
print('nTracks in detector-level PYTHIA: {}'.format(self.nTracks_det))
# ------------------------------------------------------------------------
# Use IO helper class to convert truth-level ROOT TTree into
# a SeriesGroupBy object of fastjet particles per event
io_truth = process_io.ProcessIO(input_file=self.input_file_Pythia,
tree_dir=tree_dir,
track_tree_name='tree_Particle_gen',
event_tree_name=event_tree_name,
is_pp=True,
use_ev_id_ext=False)
df_fjparticles_truth = io_truth.load_data()
self.nEvents_truth = len(df_fjparticles_truth.index)
self.nTracks_truth = len(io_truth.track_df.index)
print('nEvents in truth-level PYTHIA: {}'.format(self.nEvents_truth))
print('nTracks in truth-level PYTHIA: {}'.format(self.nTracks_truth))
# ------------------------------------------------------------------------
# Now merge the two SeriesGroupBy to create a groupby df with [ev_id, run_number, fj_1, fj_2]
# (Need a structure such that we can iterate event-by-event through both fj_1, fj_2 simultaneously)
print(
'Merge det-level and truth-level into a single dataframe grouped by event...'
)
self.df_fjparticles = pandas.concat(
[df_fjparticles_det, df_fjparticles_truth], axis=1)
self.df_fjparticles.columns = [
'fj_particles_det', 'fj_particles_truth'
]
print(self.df_fjparticles)
# ------------------------------------------------------------------------
# Set up the Pb-Pb embedding object
self.process_io_emb = process_io_emb.ProcessIO_Emb(
self.emb_file_list,
track_tree_name='tree_Particle',
min_cent=0.,
max_cent=10.)
# ------------------------------------------------------------------------
self.analyze_events()