def select_hits(input_file_hits,
output_file_hits,
condition=None,
cluster_size_condition=None,
n_cluster_condition=None,
chunk_size=5000000):
''' Takes a hit table and stores only selected hits into a new table. The selection of hits is done with a numexp string. Only if
this expression evaluates to true the hit is taken. One can also select hits from cluster conditions. This selection is done
on an event basis, meaning events are selected where the cluster condition is true and then hits of these events are taken.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
condition: str
Numexpr string to select hits (e.g.: '(relative_BCID == 6) & (column == row)')
All hit infos can be used (column, row, ...)
cluster_size_condition: int
Hit of events with the given cluster size are selected.
n_cluster_condition: int
Hit of events with the given cluster number are selected.
'''
logging.info('Write hits with ' + condition + ' into ' +
str(output_file_hits))
if cluster_size_condition is None and n_cluster_condition is None: # no cluster cuts are done
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(
in_hit_file_h5.root.Hits
) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(
out_hit_file_h5.root,
name='Hits',
description=data_struct.HitInfoTable,
title='hit_data',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
analysis_utils.write_hits_in_event_range(
hit_table_in=in_hit_file_h5.root.Hits,
hit_table_out=hit_table_out,
condition=condition
) # write the hits of the selected events into a new table
in_hit_file_h5.root.meta_data.copy(
out_hit_file_h5.root) # copy meta_data note to new file
else:
with tb.open_file(
input_file_hits, mode="r+"
) as in_hit_file_h5: # open file with hit/cluster data with r+ to be able to create index
analysis_utils.index_event_number(
in_hit_file_h5.root.Hits
) # create event index for faster selection
analysis_utils.index_event_number(
in_hit_file_h5.root.Cluster
) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(
out_hit_file_h5.root,
name='Hits',
description=data_struct.HitInfoTable,
title='hit_data',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=cluster_table.shape[0],
term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(
cluster_table, chunk_size=chunk_size):
if cluster_size_condition is not None:
selected_events = analysis_utils.get_events_with_cluster_size(
event_number=data['event_number'],
cluster_size=data['size'],
condition='cluster_size == ' +
str(cluster_size_condition)
) # select the events with only 1 hit cluster
if n_cluster_condition is not None:
selected_events_2 = analysis_utils.get_events_with_n_cluster(
event_number=data['event_number'],
condition='n_cluster == ' +
str(n_cluster_condition)
) # select the events with only 1 cluster
selected_events = selected_events[
analysis_utils.in1d_events(
selected_events, selected_events_2
)] # select events with the first two conditions above
elif n_cluster_condition is not None:
selected_events = analysis_utils.get_events_with_n_cluster(
event_number=data['event_number'],
condition='n_cluster == ' +
str(n_cluster_condition))
else:
raise RuntimeError(
'Cannot understand cluster selection criterion')
last_word_number = analysis_utils.write_hits_in_events(
hit_table_in=in_hit_file_h5.root.Hits,
hit_table_out=hit_table_out,
events=selected_events,
start_hit_word=last_word_number,
condition=condition,
chunk_size=chunk_size
) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(
out_hit_file_h5.root) # copy meta_data note to new file
def select_hits(
input_file_hits,
output_file_hits,
condition=None,
cluster_size_condition=None,
n_cluster_condition=None,
chunk_size=5000000,
):
""" Takes a hit table and stores only selected hits into a new table. The selection of hits is done with a numexp string. Only if
this expression evaluates to true the hit is taken. One can also select hits from cluster conditions. This selection is done
on an event basis, meaning events are selected where the cluster condition is true and then hits of these events are taken.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
condition: str
Numexpr string to select hits (e.g.: '(relative_BCID == 6) & (column == row)')
All hit infos can be used (column, row, ...)
cluster_size_condition: int
Hit of events with the given cluster size are selected.
n_cluster_condition: int
Hit of events with the given cluster number are selected.
"""
logging.info("Write hits with " + condition + " into " + str(output_file_hits))
if cluster_size_condition is None and n_cluster_condition is None: # no cluster cuts are done
with tb.openFile(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
with tb.openFile(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.createTable(
out_hit_file_h5.root,
name="Hits",
description=data_struct.HitInfoTable,
title="hit_data",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
analysis_utils.write_hits_in_event_range(
hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, condition=condition
) # write the hits of the selected events into a new table
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root) # copy meta_data note to new file
else:
with tb.openFile(
input_file_hits, mode="r+"
) as in_hit_file_h5: # open file with hit/cluster data with r+ to be able to create index
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster) # create event index for faster selection
with tb.openFile(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.createTable(
out_hit_file_h5.root,
name="Hits",
description=data_struct.HitInfoTable,
title="hit_data",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=cluster_table.shape[0],
term_width=80,
)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
if cluster_size_condition is not None:
selected_events = analysis_utils.get_events_with_cluster_size(
event_number=data["event_number"],
cluster_size=data["size"],
condition="cluster_size == " + str(cluster_size_condition),
) # select the events with only 1 hit cluster
if n_cluster_condition is not None:
selected_events_2 = analysis_utils.get_events_with_n_cluster(
event_number=data["event_number"], condition="n_cluster == " + str(n_cluster_condition)
) # select the events with only 1 cluster
selected_events = selected_events[
analysis_utils.in1d_events(selected_events, selected_events_2)
] # select events with the first two conditions above
elif n_cluster_condition is not None:
selected_events = analysis_utils.get_events_with_n_cluster(
event_number=data["event_number"], condition="n_cluster == " + str(n_cluster_condition)
)
else:
raise RuntimeError("Cannot understand cluster selection criterion")
last_word_number = analysis_utils.write_hits_in_events(
hit_table_in=in_hit_file_h5.root.Hits,
hit_table_out=hit_table_out,
events=selected_events,
start_hit_word=last_word_number,
condition=condition,
chunk_size=chunk_size,
) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root) # copy meta_data note to new file
def select_hits_from_cluster_info(input_file_hits,
output_file_hits,
cluster_size_condition,
n_cluster_condition,
chunk_size=4000000):
''' Takes a hit table and stores only selected hits into a new table. The selection is done on an event base and events are selected if they have a certain number of cluster or cluster size.
To increase the analysis speed a event index for the input hit file is created first. Since a cluster hit table can be created to this way of hit selection is
not needed anymore.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
cluster_size_condition: str
the cluster size condition to select events (e.g.: 'cluster_size_condition <= 2')
n_cluster_condition: str
the number of cluster in a event ((e.g.: 'n_cluster_condition == 1')
'''
logging.info('Write hits of events from ' + str(input_file_hits) +
' with ' + cluster_size_condition + ' and ' +
n_cluster_condition + ' into ' + str(output_file_hits))
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits)
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster)
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(
out_hit_file_h5.root,
name='Hits',
description=data_struct.HitInfoTable,
title='hit_data',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=cluster_table.shape[0],
term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(
cluster_table, chunk_size=chunk_size):
selected_events_1 = analysis_utils.get_events_with_cluster_size(
event_number=data['event_number'],
cluster_size=data['size'],
condition=cluster_size_condition
) # select the events with clusters of a certain size
selected_events_2 = analysis_utils.get_events_with_n_cluster(
event_number=data['event_number'],
condition=n_cluster_condition
) # select the events with a certain cluster number
selected_events = analysis_utils.get_events_in_both_arrays(
selected_events_1, selected_events_2
) # select events with both conditions above
logging.debug('Selected ' + str(len(selected_events)) +
' events with ' + n_cluster_condition + ' and ' +
cluster_size_condition)
last_word_number = analysis_utils.write_hits_in_events(
hit_table_in=in_hit_file_h5.root.Hits,
hit_table_out=hit_table_out,
events=selected_events,
start_hit_word=last_word_number
) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(
out_hit_file_h5.root) # copy meta_data note to new file
def select_hits_from_cluster_info(
input_file_hits, output_file_hits, cluster_size_condition, n_cluster_condition, chunk_size=4000000
):
""" Takes a hit table and stores only selected hits into a new table. The selection is done on an event base and events are selected if they have a certain number of cluster or cluster size.
To increase the analysis speed a event index for the input hit file is created first. Since a cluster hit table can be created to this way of hit selection is
not needed anymore.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
cluster_size_condition: str
the cluster size condition to select events (e.g.: 'cluster_size_condition <= 2')
n_cluster_condition: str
the number of cluster in a event ((e.g.: 'n_cluster_condition == 1')
"""
logging.info(
"Write hits of events from "
+ str(input_file_hits)
+ " with "
+ cluster_size_condition
+ " and "
+ n_cluster_condition
+ " into "
+ str(output_file_hits)
)
with tb.openFile(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits)
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster)
with tb.openFile(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.createTable(
out_hit_file_h5.root,
name="Hits",
description=data_struct.HitInfoTable,
title="hit_data",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=cluster_table.shape[0],
term_width=80,
)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
selected_events_1 = analysis_utils.get_events_with_cluster_size(
event_number=data["event_number"], cluster_size=data["size"], condition=cluster_size_condition
) # select the events with clusters of a certain size
selected_events_2 = analysis_utils.get_events_with_n_cluster(
event_number=data["event_number"], condition=n_cluster_condition
) # select the events with a certain cluster number
selected_events = analysis_utils.get_events_in_both_arrays(
selected_events_1, selected_events_2
) # select events with both conditions above
logging.debug(
"Selected "
+ str(len(selected_events))
+ " events with "
+ n_cluster_condition
+ " and "
+ cluster_size_condition
)
last_word_number = analysis_utils.write_hits_in_events(
hit_table_in=in_hit_file_h5.root.Hits,
hit_table_out=hit_table_out,
events=selected_events,
start_hit_word=last_word_number,
) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root) # copy meta_data note to new file
