SIM_PATH = '/Users/Javi/Development/fanalIC_NB/data/sim'
iFileNames = get_sim_file_names(SIM_PATH, EVENT_TYPE)
#iFileNames = get_sim_file_names(SIM_PATH, EVENT_TYPE, file_range=[0,2])
if (VERBOSITY_LEVEL >= 1):
print('\n* {0} {1} input files:'.format(len(iFileNames), EVENT_TYPE))
for iFileName in iFileNames:
print(' ', iFileName)
#--- Ouput files and group
# All thr reconstruction data is stored in a single file ...
RECO_PATH = '/Users/Javi/Development/fanalIC_NB/data/reco/'
oFileName = get_reco_file_name(RECO_PATH, EVENT_TYPE)
reco_group_name = get_reco_group_name(FWHM_Qbb_perc / units.perCent,
SPATIAL_DEFINITION)
if (VERBOSITY_LEVEL >= 1):
print('\n* Output file name:', oFileName)
print(' Reco group name:', reco_group_name)
# Creating the output files and its groups
oFile_filters = tb.Filters(complib='zlib', complevel=4)
oFile = tb.open_file(oFileName, 'w', filters=oFile_filters)
oFile.create_group('/', 'FANALIC')
oFile.create_group('/FANALIC', reco_group_name[9:])
# Storing all the parameters of current reconstruction
# as attributes of the reco_group
oFile.set_node_attr(reco_group_name, 'input_files', iFileNames)
oFile.set_node_attr(reco_group_name, 'event_type', EVENT_TYPE)
def test_raise_value_error_if_unknown_SpatialDef():
with pytest.raises(AttributeError):
x = get_reco_group_name(0.5, SpatialDef.does_not_exist)
def test_reco_group_name():
fwhm = 0.5
spd = SpatialDef.low
reco_group_name = f'/FANALIC/RECO_05fmhm_lowDef'
assert reco_group_name == get_reco_group_name(fwhm, spd)
def fanal_ana(
det_name, # Detector name: 'new', 'next100', 'next500'
event_type, # Event type: 'bb0nu', 'Tl208', 'Bi214'
fwhm, # FWHM at Qbb
voxel_size, # Voxel size (x, y, z)
track_Eth, # Track energy threshold
max_num_tracks, # Maximum number of tracks
blob_radius, # Blob radius
blob_Eth, # Blob energy threshold
roi_Emin, # ROI minimum energy
roi_Emax, # ROI maximum energy
files_in, # Input files
event_range, # Range of events to analyze: all, ... ??
file_out, # Output file
compression, # Compression of output file: 'ZLIB1', 'ZLIB4',
# 'ZLIB5', 'ZLIB9', 'BLOSC5', 'BLZ4HC5'
verbosity_level):
### LOGGER
logger = get_logger('FanalAna', verbosity_level)
### DETECTOR NAME
det_name = getattr(DetName, det_name)
### PRINTING GENERAL INFO
print(
'\n***********************************************************************************'
)
print('***** Detector: {}'.format(det_name.name))
print('***** Analizing {} events'.format(event_type))
print('***** Energy Resolution: {:.2f}% FWFM at Qbb'.format(fwhm /
units.perCent))
print('***** Voxel Size: ({}, {}, {}) mm'.format(voxel_size[0] / units.mm,
voxel_size[1] / units.mm,
voxel_size[2] / units.mm))
print(
'***********************************************************************************\n'
)
print('* Track Eth: {:4.1f} keV Max Num Tracks: {}\n'.format(
track_Eth / units.keV, max_num_tracks))
print('* Blob radius: {:.1f} mm Blob Eth: {:4.1f} keV\n'.format(
blob_radius, blob_Eth / units.keV))
print('* ROI limits: [{:4.1f}, {:4.1f}] keV\n'.format(
roi_Emin / units.keV, roi_Emax / units.keV))
### INPUT RECONSTRUCTION FILE AND GROUP
reco_group_name = get_reco_group_name(fwhm / units.perCent, voxel_size)
print('* {} {} input reco file names:'.format(len(files_in), event_type))
for iFileName in files_in:
print(' ', iFileName)
print(' Reco group name: {}\n'.format(reco_group_name))
### OUTPUT FILE, ITS GROUPS & ATTRIBUTES
# Output analysis file
oFile = tb.open_file(file_out, 'w', filters=tbl_filters(compression))
# Analysis group Name
ana_group_name = get_ana_group_name(fwhm / units.perCent, voxel_size)
oFile.create_group('/', 'FANALIC')
oFile.create_group('/FANALIC', ana_group_name[9:])
print('* Output analysis file name:', file_out)
print(' Ana group name: {}\n'.format(ana_group_name))
# Attributes
oFile.set_node_attr(ana_group_name, 'input_reco_files', files_in)
oFile.set_node_attr(ana_group_name, 'input_reco_group', reco_group_name)
oFile.set_node_attr(ana_group_name, 'event_type', event_type)
oFile.set_node_attr(ana_group_name, 'energy_resolution',
fwhm / units.perCent)
oFile.set_node_attr(ana_group_name, 'track_Eth', track_Eth)
oFile.set_node_attr(ana_group_name, 'max_num_tracks', max_num_tracks)
oFile.set_node_attr(ana_group_name, 'blob_radius', blob_radius)
oFile.set_node_attr(ana_group_name, 'blob_Eth', blob_Eth)
oFile.set_node_attr(ana_group_name, 'roi_Emin', roi_Emin)
oFile.set_node_attr(ana_group_name, 'roi_Emax', roi_Emax)
### DATA TO STORE
# Event counters
simulated_events = 0
stored_events = 0
smE_filter_events = 0
fid_filter_events = 0
tracks_filter_events = 0
blobs_filter_events = 0
roi_filter_events = 0
analyzed_events = 0
toUpdate_events = 1
# Dictionaries for events & voxels data
events_dict = get_events_ana_dict()
voxels_dict = get_voxels_ana_dict()
events_reco_df = pd.DataFrame()
voxels_reco_df = pd.DataFrame()
### ANALYSIS PROCEDURE
print('* Analyzing events ...\n')
# Looping through all the input files
for iFileName in files_in:
# Updating reconstruction counters
with tb.open_file(iFileName, mode='r') as reco_file:
simulated_events += reco_file.get_node_attr(
reco_group_name, 'simulated_events')
stored_events += reco_file.get_node_attr(reco_group_name,
'stored_events')
smE_filter_events += reco_file.get_node_attr(
reco_group_name, 'smE_filter_events')
fid_filter_events += reco_file.get_node_attr(
reco_group_name, 'fid_filter_events')
# Getting the events & voxels data from the reconstruction phase
file_events = pd.read_hdf(iFileName, reco_group_name + '/events')
file_voxels = pd.read_hdf(iFileName, reco_group_name + '/voxels')
# Updating reconstruction dataframes
events_reco_df = pd.concat([events_reco_df, file_events], axis=0)
voxels_reco_df = pd.concat([voxels_reco_df, file_voxels], axis=0)
print('* Processing {} ...'.format(iFileName))
### Looping through all the events that passed the fiducial filter
for event_number, event_df in file_events[
file_events.fid_filter].iterrows():
# Updating counter of analyzed events
analyzed_events += 1
logger.info('Analyzing event Id: {0} ...'.format(event_number))
# Getting event data
event_data = get_event_ana_data()
event_data['event_id'] = event_number
event_voxels = file_voxels.loc[event_number]
num_event_voxels = len(event_voxels)
num_event_voxels_negli = len(event_voxels[event_voxels.negli])
voxel_dimensions = (event_df.voxel_sizeX, event_df.voxel_sizeY,
event_df.voxel_sizeZ)
logger.info(' Total Voxels: {} Negli. Voxels: {} Voxels Size: ({:3.1f}, {:3.1f}, {:3.1f}) mm'\
.format(num_event_voxels, num_event_voxels_negli, voxel_dimensions[0],
voxel_dimensions[1], voxel_dimensions[2]))
# If there is any negligible Voxel, distribute its energy between its neighbours,
# if not, all voxels maintain their previous energies
if num_event_voxels_negli:
event_voxels_newE = get_new_energies(event_voxels)
else:
event_voxels_newE = event_voxels.E.tolist()
# Translate fanalIC voxels info to IC voxels to make tracks
#ic_voxels = [Voxel(event_voxels.iloc[i].X, event_voxels.iloc[i].Y, event_voxels.iloc[i].Z,
# event_voxels_newE[i], voxel_dimensions) for i in range(num_event_voxels)]
ic_voxels = []
for i, voxel in event_voxels.iterrows():
ic_voxel = Voxel(voxel.X, voxel.Y, voxel.Z,
event_voxels_newE[i], voxel_dimensions)
ic_voxels.append(ic_voxel)
# Make tracks
event_tracks = make_track_graphs(ic_voxels)
num_ini_tracks = len(event_tracks)
logger.info(' Num initial tracks: {:2}'.format(num_ini_tracks))
# Appending to every voxel, the track it belongs to
event_voxels_tracks = get_voxel_track_relations(
event_voxels, event_tracks)
# Appending ana-info to this event voxels
extend_voxels_ana_dict(voxels_dict, event_number,
event_voxels.index.tolist(),
event_voxels_newE, event_voxels_tracks)
# Processing tracks: Getting energies, sorting and filtering ...
event_sorted_tracks = process_tracks(event_tracks, track_Eth)
event_data['num_tracks'] = len(event_sorted_tracks)
# Getting 3 hottest tracks info
if event_data['num_tracks'] >= 1:
event_data['track0_E'] = event_sorted_tracks[0][0]
event_data['track0_length'] = event_sorted_tracks[0][1]
event_data['track0_voxels'] = len(
event_sorted_tracks[0][2].nodes())
if event_data['num_tracks'] >= 2:
event_data['track1_E'] = event_sorted_tracks[1][0]
event_data['track1_length'] = event_sorted_tracks[1][1]
event_data['track1_voxels'] = len(
event_sorted_tracks[1][2].nodes())
if event_data['num_tracks'] >= 3:
event_data['track2_E'] = event_sorted_tracks[2][0]
event_data['track2_length'] = event_sorted_tracks[2][1]
event_data['track2_voxels'] = len(
event_sorted_tracks[2][2].nodes())
# Applying the tracks filter consisting on:
# 0 < num tracks < max_num_tracks
# the track length must be longer than 2 times the blob_radius
event_data['tracks_filter'] = (
(event_data['num_tracks'] > 0) &
(event_data['num_tracks'] <= max_num_tracks) &
(event_data['track0_length'] >= 2. * blob_radius))
# Verbosing
logger.info(' Num final tracks: {:2} --> tracks_filter: {}' \
.format(event_data['num_tracks'], event_data['tracks_filter']))
### For those events passing the tracks filter:
if event_data['tracks_filter']:
# Getting the blob energies of the track with highest energy
event_data['blob1_E'], event_data['blob2_E'] = \
blob_energies(event_sorted_tracks[0][2], blob_radius)
# Applying the blobs filter
event_data['blobs_filter'] = (event_data['blob2_E'] > blob_Eth)
# Verbosing
logger.info(' Blob 1 energy: {:4.1f} keV Blob 2 energy: {:4.1f} keV --> Blobs filter: {}'\
.format(event_data['blob1_E']/units.keV, event_data['blob2_E']/units.keV,
event_data['blobs_filter']))
### For those events passing the blobs filter:
if event_data['blobs_filter']:
# Getting the total event smeared energy
event_smE = file_events.loc[event_number].smE
# Applying the ROI filter
event_data['roi_filter'] = ((event_smE >= roi_Emin) &
(event_smE <= roi_Emax))
# Verbosing
logger.info(' Event energy: {:6.1f} keV --> ROI filter: {}'\
.format(event_smE / units.keV, event_data['roi_filter']))
# Storing event_data
extend_events_ana_dict(events_dict, event_data)
# Verbosing
if (not (analyzed_events % toUpdate_events)):
print('* Num analyzed events: {}'.format(analyzed_events))
if (analyzed_events == (10 * toUpdate_events)):
toUpdate_events *= 10
### STORING ANALYSIS DATA
print('* Total analyzed events: {}'.format(analyzed_events))
# Storing events and voxels dataframes
print('\n* Storing data in the output file ...\n {}\n'.format(file_out))
store_events_ana_dict(file_out, ana_group_name, events_reco_df,
events_dict)
store_voxels_ana_dict(file_out, ana_group_name, voxels_reco_df,
voxels_dict)
# Storing event counters as attributes
tracks_filter_events = sum(events_dict['tracks_filter'])
blobs_filter_events = sum(events_dict['blobs_filter'])
roi_filter_events = sum(events_dict['roi_filter'])
store_events_ana_counters(oFile, ana_group_name, simulated_events,
stored_events, smE_filter_events,
fid_filter_events, tracks_filter_events,
blobs_filter_events, roi_filter_events)
### Ending ...
oFile.close()
print('* Analysis done !!\n')
print('''* Event counters:
Simulated events: {0:9}
Stored events: {1:9}
smE_filter events: {2:9}
fid_filter events: {3:9}
tracks_filter events: {4:9}
blobs_filter events: {5:9}
roi_filter events: {6:9}'''.format(simulated_events, stored_events,
smE_filter_events, fid_filter_events,
tracks_filter_events,
blobs_filter_events,
roi_filter_events))
def fanal_ana(det_name, # Detector name: 'new', 'next100', 'next500'
event_type, # Event type: 'bb0nu', 'Tl208', 'Bi214'
fwhm, # FWHM at Qbb
spatial_def, # Spatial definition: 'low', 'high'
voxel_Eth, # Voxel energy threshold
track_Eth, # Track energy threshold
max_num_tracks, # Maximum number of tracks
blob_radius, # Blob radius
blob_Eth, # Blob energy threshold
roi_Emin, # ROI minimum energy
roi_Emax, # ROI maximum energy
files_in, # Input files
event_range, # Range of events to analyze: all, ... ??
file_out, # Output file
compression, # Compression of output file: 'ZLIB1', 'ZLIB4',
# 'ZLIB5', 'ZLIB9', 'BLOSC5', 'BLZ4HC5'
verbosity_level):
### LOGGER
logger = get_logger('FanalAna', verbosity_level)
### DETECTOR NAME
det_name = getattr(DetName, det_name)
### SPATIAL DEFINITION
spatial_def = getattr(SpatialDef, spatial_def)
### PRINTING GENERAL INFO
print('\n***********************************************************************************')
print('***** Detector: {}'.format(det_name.name))
print('***** Analizing {} events'.format(event_type))
print('***** Energy Resolution: {:.2f}% FWFM at Qbb'.format(fwhm / units.perCent))
print('***** Spatial definition: {}'.format(spatial_def.name))
print('***********************************************************************************\n')
print('* Voxel Eth: {:4.1f} keV Track Eth: {:4.1f} keV Max Num Tracks: {}\n'
.format(voxel_Eth/units.keV, track_Eth/units.keV, max_num_tracks))
print('* Blob radius: {:.1f} mm Blob Eth: {:4.1f} keV\n'
.format(blob_radius, blob_Eth / units.keV))
print('* ROI limits: [{:4.1f}, {:4.1f}] keV\n'
.format(roi_Emin/units.keV, roi_Emax/units.keV))
### INPUT RECONSTRUCTION FILE AND GROUP
reco_group_name = get_reco_group_name(fwhm/units.perCent, spatial_def)
print('* Input reco file name:', files_in)
print(' Reco group name: {}\n'.format(reco_group_name))
### OUTPUT FILE, ITS GROUPS & ATTRIBUTES
# Output analysis file
oFile = tb.open_file(file_out, 'w', filters=tbl_filters(compression))
# Analysis group Name
ana_group_name = get_ana_group_name(fwhm/units.perCent, spatial_def)
oFile.create_group('/', 'FANALIC')
oFile.create_group('/FANALIC', ana_group_name[9:])
print('* Output analysis file name:', file_out)
print(' Ana group name: {}\n'.format(ana_group_name))
# Attributes
oFile.set_node_attr(ana_group_name, 'input_reco_file', files_in[0])
oFile.set_node_attr(ana_group_name, 'input_reco_group', reco_group_name)
oFile.set_node_attr(ana_group_name, 'event_type', event_type)
oFile.set_node_attr(ana_group_name, 'energy_resolution', fwhm / units.perCent)
oFile.set_node_attr(ana_group_name, 'voxel_Eth', voxel_Eth)
oFile.set_node_attr(ana_group_name, 'track_Eth', track_Eth)
oFile.set_node_attr(ana_group_name, 'max_num_tracks', max_num_tracks)
oFile.set_node_attr(ana_group_name, 'blob_radius', blob_radius)
oFile.set_node_attr(ana_group_name, 'blob_Eth', blob_Eth)
oFile.set_node_attr(ana_group_name, 'roi_Emin', roi_Emin)
oFile.set_node_attr(ana_group_name, 'roi_Emax', roi_Emax)
### DATA TO STORE
# Dictionaries for events & voxels data
events_dict = get_events_ana_dict()
voxels_dict = get_voxels_ana_dict()
### ANALYSIS PROCEDURE
# Getting the events & voxels data from the reconstruction phase
# This is the option a little bit slower that requires less memory ... (TO BE CHECKED!!)
# event_numbers_toAnalize = pd.read_hdf(files_in[0], reco_group_name + '/events',
# where=['fid_filter = True']).index
# And this is the fastest option requiring more memory
events_df = pd.read_hdf(files_in[0], reco_group_name + '/events')
voxels_df = pd.read_hdf(files_in[0], reco_group_name + '/voxels')
# Identifying as negligible all the voxels with energy lower than threshold
voxels_df['negli'] = voxels_df.E < voxel_Eth
print('* Total Voxels in File: {0} Negligible Voxels (below {1:3.1f} keV): {2}\n'
.format(len(voxels_df), voxel_Eth / units.keV,
len(voxels_df[voxels_df.negli == True])))
# Analyzing only the fiducial events ...
print('* Analyzing events ...\n')
# Counter of analyzed events for verbosing pourpouses
num_analyzed_events = 0
# Looping through all the events that passed the fiducial filter
for event_id in events_df[events_df.fid_filter].index:
# Updating counter of analyzed events
num_analyzed_events += 1
if not int(str(num_analyzed_events)[-int(math.log10(num_analyzed_events)):]):
print('* Num analyzed events: {}'.format(num_analyzed_events))
# Verbosing
logger.info('Analyzing event Id: {0} ...'.format(event_id))
# Getting the voxels of current event and their sizes
event_voxels = voxels_df[voxels_df.event_id == event_id]
num_event_voxels = len(event_voxels)
num_event_voxels_negli = len(event_voxels[event_voxels.negli == True])
event_data = events_df.loc[event_id]
voxel_dimensions = (event_data.voxel_sizeX, event_data.voxel_sizeY,
event_data.voxel_sizeZ)
logger.info(' Total Voxels: {} Negli. Voxels: {} Voxels Size: ({:3.1f}, {:3.1f}, {:3.1f}) mm'
.format(num_event_voxels, num_event_voxels_negli,
voxel_dimensions[0], voxel_dimensions[1],
voxel_dimensions[2]))
# If there is any negligible Voxel,
# distribute its energy between its neighbours,
# if not, all voxels maintain their previous energies
if num_event_voxels_negli:
event_voxels_newE = get_new_energies(event_voxels)
else:
event_voxels_newE = event_voxels.E.tolist()
# Translate fanalIC voxels info to IC voxels to make tracks
ic_voxels = [Voxel(event_voxels.iloc[i].X, event_voxels.iloc[i].Y,
event_voxels.iloc[i].Z, event_voxels_newE[i],
voxel_dimensions) for i in range(num_event_voxels)]
# Make tracks
event_tracks = make_track_graphs(ic_voxels)
num_event_tracks = len(event_tracks)
logger.info(' Num initial tracks: {:2}'.format(num_event_tracks))
# Appending to every voxel, the track it belongs to
event_voxels_tracks = get_voxel_track_relations(event_voxels, event_tracks)
# Appending info of this event voxels
extend_voxels_ana_data(voxels_dict, event_voxels.index,
event_voxels_newE, event_voxels_tracks)
# Processing tracks: Getting energies, sorting and filtering ...
event_sorted_tracks = process_tracks(event_tracks, track_Eth)
num_event_tracks = len(event_sorted_tracks)
# Storing 3 hottest track info
if num_event_tracks >= 1:
track0_E = event_sorted_tracks[0][0]
track0_voxels = len(event_sorted_tracks[0][1].nodes())
track0_length = track_length(event_sorted_tracks[0][1])
else:
track0_E = track0_voxels = track0_length = np.nan
if num_event_tracks >= 2:
track1_E = event_sorted_tracks[1][0]
track1_voxels = len(event_sorted_tracks[1][1].nodes())
track1_length = track_length(event_sorted_tracks[1][1])
else:
track1_E = track1_voxels = track1_length = np.nan
if num_event_tracks >= 3:
track2_E = event_sorted_tracks[2][0]
track2_voxels = len(event_sorted_tracks[2][1].nodes())
track2_length = track_length(event_sorted_tracks[2][1])
else:
track2_E = track2_voxels = track2_length = np.nan
# Applying the tracks filter
tracks_filter = ((num_event_tracks > 0) &
(num_event_tracks <= max_num_tracks))
# Verbosing
logger.info(' Num final tracks: {:2} --> tracks_filter: {}'
.format(num_event_tracks, tracks_filter))
# For those events NOT passing the tracks filter:
# Storing data of NON tracks_filter vents
if not tracks_filter:
extend_events_ana_data(events_dict, event_id, num_event_tracks,
track0_E, track0_voxels, track0_length,
track1_E, track1_voxels, track1_length,
track2_E, track2_voxels, track2_length,
tracks_filter)
# Only for those events passing the tracks filter:
else:
# Getting the blob energies of the track with highest energy
blobs_E = blob_energies(event_sorted_tracks[0][1], blob_radius)
blob1_E = blobs_E[1]
blob2_E = blobs_E[0]
# Applying the blobs filter
blobs_filter = (blob2_E > blob_Eth)
# Verbosing
logger.info(' Blob 1 energy: {:4.1f} keV Blob 2 energy: {:4.1f} keV --> Blobs filter: {}'
.format(blob1_E/units.keV, blob2_E/units.keV, blobs_filter))
# For those events NOT passing the blobs filter:
# Storing data of NON blobs_filter vents
if not blobs_filter:
extend_events_ana_data(events_dict, event_id, num_event_tracks,
track0_E, track0_voxels, track0_length,
track1_E, track1_voxels, track1_length,
track2_E, track2_voxels, track2_length,
tracks_filter, blob1_E = blob1_E,
blob2_E = blob2_E, blobs_filter = blobs_filter)
# Only for those events passing the blobs filter:
else:
# Getting the total event smeared energy
event_smE = events_df.loc[event_id].smE
# Applying the ROI filter
roi_filter = ((event_smE >= roi_Emin) & (event_smE <= roi_Emax))
# Verbosing
logger.info(' Event energy: {:6.1f} keV --> ROI filter: {}'
.format(event_smE / units.keV, roi_filter))
# Storing all the events (as this is the last filter)
extend_events_ana_data(events_dict, event_id, num_event_tracks,
track0_E, track0_voxels, track0_length,
track1_E, track1_voxels, track1_length,
track2_E, track2_voxels, track2_length,
tracks_filter, blob1_E = blob1_E,
blob2_E = blob2_E, blobs_filter = blobs_filter,
roi_filter = roi_filter)
### STORING DATA
# Storing events and voxels dataframes
print('\n* Storing data in the output file ...\n {}\n'.format(file_out))
store_events_ana_data(file_out, ana_group_name, events_df, events_dict)
store_voxels_ana_data(file_out, ana_group_name, voxels_df, voxels_dict)
# Storing event counters as attributes
tracks_filter_events, blobs_filter_events, roi_filter_events = \
store_events_ana_counters(oFile, ana_group_name, events_df)
### Ending ...
oFile.close()
print('* Analysis done !!\n')
# Printing analysis numbers
with tb.open_file(files_in[0], mode='r') as iFile:
simulated_events = iFile.get_node_attr(reco_group_name, 'simulated_events')
stored_events = iFile.get_node_attr(reco_group_name, 'stored_events')
smE_filter_events = iFile.get_node_attr(reco_group_name, 'smE_filter_events')
fid_filter_events = iFile.get_node_attr(reco_group_name, 'fid_filter_events')
print('''* Event counters:
Simulated events: {0:9}
Stored events: {1:9}
smE_filter events: {2:9}
fid_filter events: {3:9}
tracks_filter events: {4:9}
blobs_filter events: {5:9}
roi_filter events: {6:9}\n'''
.format(simulated_events, stored_events, smE_filter_events,
fid_filter_events, tracks_filter_events, blobs_filter_events,
roi_filter_events))
def fanal_reco(det_name, # Detector name: 'new', 'next100', 'next500'
event_type, # Event type: 'bb0nu', 'Tl208', 'Bi214'
fwhm, # FWHM at Qbb
e_min, # Minimum smeared energy for energy filtering
e_max, # Maximum smeared energy for energy filtering
voxel_size, # Voxel size (x, y, z)
voxel_Eth, # Voxel energy threshold
veto_width, # Veto width for fiducial filtering
min_veto_e, # Minimum energy in veto for fiducial filtering
files_in, # Input files
event_range, # Range of events to analyze: all, ... ??
file_out, # Output file
compression, # Compression of output file: 'ZLIB1', 'ZLIB4',
# 'ZLIB5', 'ZLIB9', 'BLOSC5', 'BLZ4HC5'
verbosity_level):
### LOGGER
logger = get_logger('FanalReco', verbosity_level)
### DETECTOR NAME & its ACTIVE dimensions
det_name = getattr(DetName, det_name)
ACTIVE_dimensions = get_active_size(det_name)
fid_dimensions = get_fiducial_size(det_name, veto_width)
### RECONSTRUCTION DATA
# Smearing energy settings
fwhm_Qbb = fwhm * Qbb
sigma_Qbb = fwhm_Qbb / 2.355
assert e_max > e_min, 'SmE_filter settings not valid. e_max must be higher than e_min.'
### PRINTING GENERAL INFO
print('\n***********************************************************************************')
print('***** Detector: {}'.format(det_name.name))
print('***** Reconstructing {} events'.format(event_type))
print('***** Energy Resolution: {:.2f}% fwhm at Qbb'.format(fwhm / units.perCent))
print('***** Voxel Size: ({}, {}, {}) mm'.format(voxel_size[0] / units.mm,
voxel_size[1] / units.mm,
voxel_size[2] / units.mm))
print('***********************************************************************************\n')
print('* Sigma at Qbb: {:.3f} keV.\n'.format(sigma_Qbb / units.keV))
print('* Voxel_size: ({}, {}, {}) mm'.format(voxel_size[0] / units.mm,
voxel_size[1] / units.mm,
voxel_size[2] / units.mm))
print(' Voxel Eth: {:4.1f} keV\n'.format(voxel_Eth/units.keV))
print('* Detector-Active dimensions [mm]: Zmin: {:7.1f} Zmax: {:7.1f} Rmax: {:7.1f}'
.format(ACTIVE_dimensions.z_min, ACTIVE_dimensions.z_max,
ACTIVE_dimensions.rad))
print(' ... fiducial limits [mm]: Zmin: {:7.1f} Zmax: {:7.1f} Rmax: {:7.1f}\n'
.format(fid_dimensions.z_min, fid_dimensions.z_max, fid_dimensions.rad))
print('* {0} {1} input files:'.format(len(files_in), event_type))
for iFileName in files_in:
print(' ', iFileName)
### OUTPUT FILE, ITS GROUPS & ATTRIBUTES
# Output file
oFile = tb.open_file(file_out, 'w', filters = tbl_filters(compression))
# Reco group Name
reco_group_name = get_reco_group_name(fwhm/units.perCent, voxel_size)
oFile.create_group('/', 'FANALIC')
oFile.create_group('/FANALIC', reco_group_name[9:])
print('\n* Output file name:', file_out)
print(' Reco group name: {}\n'.format(reco_group_name))
# Attributes
oFile.set_node_attr(reco_group_name, 'input_sim_files', files_in)
oFile.set_node_attr(reco_group_name, 'event_type', event_type)
oFile.set_node_attr(reco_group_name, 'energy_resolution', fwhm/units.perCent)
oFile.set_node_attr(reco_group_name, 'voxel_sizeX', voxel_size[0])
oFile.set_node_attr(reco_group_name, 'voxel_sizeY', voxel_size[1])
oFile.set_node_attr(reco_group_name, 'voxel_sizeZ', voxel_size[2])
oFile.set_node_attr(reco_group_name, 'voxel_Eth', voxel_Eth)
oFile.set_node_attr(reco_group_name, 'smE_filter_Emin', e_min)
oFile.set_node_attr(reco_group_name, 'smE_filter_Emax', e_max)
oFile.set_node_attr(reco_group_name, 'fiducial_filter_VetoWidth', veto_width)
oFile.set_node_attr(reco_group_name, 'fiducial_filter_MinVetoE', min_veto_e)
### DATA TO STORE
# Event counters
simulated_events = 0
stored_events = 0
analyzed_events = 0
toUpdate_events = 1
# Dictionaries for events & voxels data
events_dict = get_events_reco_dict()
voxels_dict = get_voxels_reco_dict()
### RECONSTRUCTION PROCEDURE
# Looping through all the input files
for iFileName in files_in:
# Updating simulated and stored event counters
configuration_df = pd.read_hdf(iFileName, '/MC/configuration', mode='r')
simulated_events += int(configuration_df[configuration_df.param_key == 'num_events'].param_value)
stored_events += int(configuration_df[configuration_df.param_key == 'saved_events'].param_value)
# Getting event numbers
file_extents = pd.read_hdf(iFileName, '/MC/extents', mode='r')
file_event_numbers = file_extents.evt_number
print('* Processing {0} ({1} events) ...'.format(iFileName, len(file_event_numbers)))
# Getting mc hits
file_mcHits = load_mc_hits(iFileName)
# Looping through all the events in iFile
for event_number in file_event_numbers:
# Updating counter of analyzed events
analyzed_events += 1
logger.info('Reconstructing event Id: {0} ...'.format(event_number))
# Getting event data
event_data = get_event_reco_data()
event_data['event_id'] = event_number
event_mcHits = file_mcHits.loc[event_number, :]
active_mcHits = event_mcHits[event_mcHits.label == 'ACTIVE'].copy()
event_data['num_MCparts'] = get_num_mc_particles(file_extents, event_number)
event_data['num_MChits'] = len(active_mcHits)
# The event mc energy is the sum of the energy of all the hits except
# for Bi214 events, in which the number of S1 in the event is considered
if (event_type == 'Bi214'):
event_data['mcE'] = get_mc_energy(active_mcHits)
else:
event_data['mcE'] = active_mcHits.E.sum()
# Smearing the event energy
event_data['smE'] = smear_evt_energy(event_data['mcE'], sigma_Qbb, Qbb)
# Applying the smE filter
event_data['smE_filter'] = (e_min <= event_data['smE'] <= e_max)
# Verbosing
logger.info(' Num mcHits: {0:3} mcE: {1:.1f} keV smE: {2:.1f} keV smE_filter: {3}' \
.format(event_data['num_MChits'], event_data['mcE']/units.keV,
event_data['smE']/units.keV, event_data['smE_filter']))
# For those events passing the smE filter:
if event_data['smE_filter']:
# Smearing hit energies
smearing_factor = event_data['smE'] / event_data['mcE']
active_mcHits['smE'] = active_mcHits['E'] * smearing_factor
# Translating hit Z positions from delayed hits
translate_hit_positions(det_name, active_mcHits, DRIFT_VELOCITY)
# Creating the IChits with the smeared energies and translated Z positions
# to be passed to paolina functions
#IChits = []
#for i, hit in active_mcHits[active_mcHits.shifted_z < ACTIVE_dimensions.z_max].iterrows():
# IChit = MCHit((hit.x, hit.y, hit.shifted_z), hit.time, hit.smE, 'ACTIVE')
# IChits.append(IChit)
IChits = active_mcHits[(active_mcHits.shifted_z < ACTIVE_dimensions.z_max) &
(active_mcHits.shifted_z > ACTIVE_dimensions.z_min)] \
.apply(lambda hit: MCHit((hit.x, hit.y, hit.shifted_z),
hit.time, hit.smE, 'ACTIVE'), axis=1).tolist()
# Voxelizing using the IChits ...
event_voxels = voxelize_hits(IChits, voxel_size, strict_voxel_size=False)
event_data['num_voxels'] = len(event_voxels)
eff_voxel_size = event_voxels[0].size
event_data['voxel_sizeX'] = eff_voxel_size[0]
event_data['voxel_sizeY'] = eff_voxel_size[1]
event_data['voxel_sizeZ'] = eff_voxel_size[2]
# Storing voxels info
for voxel_id in range(len(event_voxels)):
extend_voxels_reco_dict(voxels_dict, event_number, voxel_id,
event_voxels[voxel_id], voxel_Eth)
# Check fiduciality
event_data['voxels_minZ'], event_data['voxels_maxZ'], \
event_data['voxels_maxRad'], event_data['veto_energy'], \
event_data['fid_filter'] = \
check_event_fiduciality(det_name, veto_width, min_veto_e, event_voxels)
# Verbosing
logger.info(' NumVoxels: {:3} minZ: {:.1f} mm maxZ: {:.1f} mm maxR: {:.1f} mm veto_E: {:.1f} keV fid_filter: {}' \
.format(event_data['num_voxels'], event_data['voxels_minZ'],
event_data['voxels_maxZ'], event_data['voxels_maxRad'],
event_data['veto_energy'] / units.keV,
event_data['fid_filter']))
for voxel in event_voxels:
logger.debug(' Voxel pos: ({:5.1f}, {:5.1f}, {:5.1f}) mm E: {:5.1f} keV'\
.format(voxel.X/units.mm, voxel.Y/units.mm,
voxel.Z/units.mm, voxel.E/units.keV))
# Storing event_data
extend_events_reco_dict(events_dict, event_data)
# Verbosing
if (not(analyzed_events % toUpdate_events)):
print('* Num analyzed events: {}'.format(analyzed_events))
if (analyzed_events == (10 * toUpdate_events)): toUpdate_events *= 10
### STORING RECONSTRUCTION DATA
# Storing events and voxels dataframes
print('\n* Storing data in the output file: {}'.format(file_out))
store_events_reco_dict(file_out, reco_group_name, events_dict)
store_voxels_reco_dict(file_out, reco_group_name, voxels_dict)
# Storing event counters as attributes
smE_filter_events = sum(events_dict['smE_filter'])
fid_filter_events = sum(events_dict['fid_filter'])
store_events_reco_counters(oFile, reco_group_name, simulated_events,
stored_events, smE_filter_events, fid_filter_events)
oFile.close()
print('* Reconstruction done !!\n')
# Printing reconstruction numbers
print('* Event counters ...')
print(''' Simulated events: {0:9}
Stored events: {1:9}
smE_filter events: {2:9}
fid_filter events: {3:9}\n'''
.format(simulated_events, stored_events, smE_filter_events, fid_filter_events))
def fanal_reco(
det_name, # Detector name: 'new', 'next100', 'next500'
event_type, # Event type: 'bb0nu', 'Tl208', 'Bi214'
fwhm, # FWHM at Qbb
e_min, # Minimum smeared energy for energy filtering
e_max, # Maximum smeared energy for energy filtering
spatial_def, # Spatial definition: 'low', 'high'
veto_width, # Veto width for fiducial filtering
min_veto_e, # Minimum energy in veto for fiducial filtering
files_in, # Input files
event_range, # Range of events to analyze: all, ... ??
file_out, # Output file
compression, # Compression of output file: 'ZLIB1', 'ZLIB4',
# 'ZLIB5', 'ZLIB9', 'BLOSC5', 'BLZ4HC5'
verbosity_level):
### LOGGER
logger = get_logger('FanalReco', verbosity_level)
### DETECTOR NAME & its ACTIVE dimensions
det_name = getattr(DetName, det_name)
ACTIVE_dimensions = get_active_size(det_name)
### RECONSTRUCTION DATA
# Smearing energy settings
fwhm_Qbb = fwhm * Qbb
sigma_Qbb = fwhm_Qbb / 2.355
assert e_max > e_min, 'SmE_filter settings not valid. e_max must be higher than e_min.'
# Spatial definition
spatial_def = getattr(SpatialDef, spatial_def)
# Voxel size
voxel_size = get_voxel_size(spatial_def)
# Fiducial limits
fid_dimensions = get_fiducial_size(ACTIVE_dimensions, veto_width)
### PRINTING GENERAL INFO
print(
'\n***********************************************************************************'
)
print('***** Detector: {}'.format(det_name.name))
print('***** Reconstructing {} events'.format(event_type))
print('***** Energy Resolution: {:.2f}% FWFM at Qbb'.format(fwhm /
units.perCent))
print('***** Spatial definition: {}'.format(spatial_def.name))
print(
'***********************************************************************************\n'
)
print(
'* Detector-Active dimensions [mm]: Zmin: {:7.1f} Zmax: {:7.1f} Rmax: {:7.1f}'
.format(ACTIVE_dimensions.z_min, ACTIVE_dimensions.z_max,
ACTIVE_dimensions.rad))
print(
' ... fiducial limits [mm]: Zmin: {:7.1f} Zmax: {:7.1f} Rmax: {:7.1f}\n'
.format(fid_dimensions.z_min, fid_dimensions.z_max,
fid_dimensions.rad))
print('* Sigma at Qbb: {:.3f} keV.\n'.format(sigma_Qbb / units.keV))
print('* Voxel_size: {} mm.\n'.format(voxel_size))
print('* {0} {1} input files:'.format(len(files_in), event_type))
for iFileName in files_in:
print(' ', iFileName)
### OUTPUT FILE, ITS GROUPS & ATTRIBUTES
# Output file
oFile = tb.open_file(file_out, 'w', filters=tbl_filters(compression))
# Reco group Name
reco_group_name = get_reco_group_name(fwhm / units.perCent, spatial_def)
oFile.create_group('/', 'FANALIC')
oFile.create_group('/FANALIC', reco_group_name[9:])
print('\n* Output file name:', file_out)
print(' Reco group name: {}\n'.format(reco_group_name))
# Attributes
oFile.set_node_attr(reco_group_name, 'input_sim_files', files_in)
oFile.set_node_attr(reco_group_name, 'event_type', event_type)
oFile.set_node_attr(reco_group_name, 'energy_resolution',
fwhm / units.perCent)
oFile.set_node_attr(reco_group_name, 'smE_filter_Emin', e_min)
oFile.set_node_attr(reco_group_name, 'smE_filter_Emax', e_max)
oFile.set_node_attr(reco_group_name, 'fiducial_filter_VetoWidth',
veto_width)
oFile.set_node_attr(reco_group_name, 'fiducial_filter_MinVetoE',
min_veto_e)
### DATA TO STORE
# Event counters
simulated_events = 0
stored_events = 0
analyzed_events = 0
# Dictionaries for events & voxels data
events_dict = get_events_reco_dict()
voxels_dict = get_voxels_reco_dict()
### RECONSTRUCTION PROCEDURE
# Looping through all the input files
for iFileName in files_in:
# Updating simulated and stored event counters
configuration_df = pd.read_hdf(iFileName,
'/MC/configuration',
mode='r')
simulated_events += int(configuration_df[configuration_df.param_key ==
'num_events'].param_value)
stored_events += int(configuration_df[configuration_df.param_key ==
'saved_events'].param_value)
with tb.open_file(iFileName, mode='r') as iFile:
file_event_numbers = iFile.root.MC.extents.cols.evt_number
print('* Processing {0} ({1} events) ...'.format(
iFileName, len(file_event_numbers)))
# Loading into memory all the particles & hits in the file
file_mcParts = load_mcparticles(iFileName)
file_mcHits = load_mchits(iFileName)
# Looping through all the events in the file
for event_number in file_event_numbers:
# Updating counter of analyzed events
analyzed_events += 1
#if not int(str(analyzed_events)[-int(math.log10(analyzed_events)):]):
# print('* Num analyzed events: {}'.format(analyzed_events))
# Verbosing
logger.info(
'Reconstructing event Id: {0} ...'.format(event_number))
# Getting mcParts of the event, using the event_number as the key
event_mcParts = file_mcParts[event_number]
num_parts = len(event_mcParts)
# Getting mcHits of the event, using the event_number as the key
event_mcHits = file_mcHits[event_number]
active_mcHits = [
hit for hit in event_mcHits if hit.label == 'ACTIVE'
]
num_hits = len(active_mcHits)
# The event mc energy is the sum of the energy of all the hits
event_mcE = sum([hit.E for hit in active_mcHits])
# Smearing the event energy
event_smE = smear_evt_energy(event_mcE, sigma_Qbb, Qbb)
# Applying the smE filter
event_smE_filter = (e_min <= event_smE <= e_max)
# Verbosing
logger.info(
' Num mcHits: {0:3} mcE: {1:.1f} keV smE: {2:.1f} keV smE_filter: {3}'
.format(num_hits, event_mcE / units.keV,
event_smE / units.keV, event_smE_filter))
# For those events NOT passing the smE filter:
# Storing data of NON smE_filter vents
if not event_smE_filter:
extend_events_reco_data(events_dict,
event_number,
evt_num_MCparts=num_parts,
evt_num_MChits=num_hits,
evt_mcE=event_mcE,
evt_smE=event_smE,
evt_smE_filter=event_smE_filter)
# Only for those events passing the smE filter:
else:
# Smearing hit energies
hits_smE = smear_hit_energies(active_mcHits,
event_smE / event_mcE)
# Translating hit positions
hits_transPositions = translate_hit_positions(
active_mcHits, DRIFT_VELOCITY)
# Creating the smHits with the smeared energies and translated positions
active_smHits = []
for i in range(num_hits):
smHit = MCHit(hits_transPositions[i],
active_mcHits[i].time, hits_smE[i],
'ACTIVE')
active_smHits.append(smHit)
# Filtering hits outside the ACTIVE region (due to translation)
active_smHits = [hit for hit in active_smHits \
if hit.Z < ACTIVE_dimensions.z_max]
# Voxelizing using the active_smHits ...
event_voxels = voxelize_hits(active_smHits,
voxel_size,
strict_voxel_size=True)
eff_voxel_size = event_voxels[0].size
# Storing voxels info
for voxel in event_voxels:
extend_voxels_reco_data(voxels_dict, event_number,
voxel)
# Check fiduciality
voxels_minZ, voxels_maxZ, voxels_maxRad, veto_energy, fiducial_filter = \
check_event_fiduciality(event_voxels, fid_dimensions, min_veto_e)
# Storing data of NON smE_filter vents
extend_events_reco_data(events_dict,
event_number,
evt_num_MCparts=num_parts,
evt_num_MChits=num_hits,
evt_mcE=event_mcE,
evt_smE=event_smE,
evt_smE_filter=event_smE_filter,
evt_num_voxels=len(event_voxels),
evt_voxel_sizeX=eff_voxel_size[0],
evt_voxel_sizeY=eff_voxel_size[1],
evt_voxel_sizeZ=eff_voxel_size[2],
evt_voxels_minZ=voxels_minZ,
evt_voxels_maxZ=voxels_maxZ,
evt_voxels_maxRad=voxels_maxRad,
evt_veto_energy=veto_energy,
evt_fid_filter=fiducial_filter)
# Verbosing
logger.info(
' NumVoxels: {:3} minZ: {:.1f} mm maxZ: {:.1f} mm maxR: {:.1f} mm veto_E: {:.1f} keV fid_filter: {}'
.format(len(event_voxels), voxels_minZ, voxels_maxZ,
voxels_maxRad, veto_energy / units.keV,
fiducial_filter))
for voxel in event_voxels:
logger.debug(
' Voxel pos: ({:5.1f}, {:5.1f}, {:5.1f}) mm E: {:5.1f} keV'
.format(voxel.X / units.mm, voxel.Y / units.mm,
voxel.Z / units.mm, voxel.E / units.keV))
### STORING DATA
# Storing events and voxels dataframes
print('\n* Storing data in the output file ...\n {}\n'.format(file_out))
store_events_reco_data(file_out, reco_group_name, events_dict)
store_voxels_reco_data(file_out, reco_group_name, voxels_dict)
# Storing event counters as attributes
smE_filter_events = sum(events_dict['smE_filter'])
fid_filter_events = sum(events_dict['fid_filter'])
store_events_reco_counters(oFile, reco_group_name, simulated_events,
stored_events, smE_filter_events,
fid_filter_events)
oFile.close()
print('* Reconstruction done !!\n')
# Printing reconstruction numbers
print('* Event counters ...')
print(''' Simulated events: {0:9}
Stored events: {1:9}
smE_filter events: {2:9}
fid_filter events: {3:9}\n'''.format(simulated_events, stored_events,
smE_filter_events, fid_filter_events))
def test_reco_group_name():
fwhm = 0.51
voxel_size = (12. * units.mm, .5 * units.cm, 6.)
reco_group_name = '/FANALIC/RECO_fwhm_051_voxel_12x5x6'
assert reco_group_name == get_reco_group_name(fwhm, voxel_size)