def test_analysis_utils_get_events_in_both_arrays(self): # check compiled get_events_in_both_arrays function
event_numbers = np.array(
[[0, 0, 2, 2, 2, 4, 5, 5, 6, 7, 7, 7, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.int64
)
event_numbers_2 = np.array([1, 1, 1, 2, 2, 2, 4, 4, 4, 7], dtype=np.int64)
result = analysis_utils.get_events_in_both_arrays(event_numbers[0], event_numbers_2)
self.assertListEqual([2, 4, 7], result.tolist())
def test_analysis_utils_get_events_in_both_arrays(
self): # check compiled get_events_in_both_arrays function
event_numbers = np.array([[0, 0, 2, 2, 2, 4, 5, 5, 6, 7, 7, 7, 8],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.int64)
event_numbers_2 = np.array([1, 1, 1, 2, 2, 2, 4, 4, 4, 7],
dtype=np.int64)
result = analysis_utils.get_events_in_both_arrays(
event_numbers[0], event_numbers_2)
self.assertListEqual([2, 4, 7], result.tolist())
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
