def analyze_hits_per_scan_parameter(analyze_data,
scan_parameters=None,
chunk_size=50000):
'''Takes the hit table and analyzes the hits per scan parameter
Parameters
----------
analyze_data : analysis.analyze_raw_data.AnalyzeRawData object with an opened hit file (AnalyzeRawData.out_file_h5) or a
file name with the hit data given (AnalyzeRawData._analyzed_data_file)
scan_parameters : list of strings:
The names of the scan parameters to use
chunk_size : int:
The chunk size of one hit table read. The bigger the faster. Too big causes memory errors.
Returns
-------
yields the analysis.analyze_raw_data.AnalyzeRawData for each scan parameter
'''
if analyze_data.out_file_h5 is None or analyze_data.out_file_h5.isopen == 0:
in_hit_file_h5 = tb.open_file(analyze_data._analyzed_data_file, 'r+')
close_file = True
else:
in_hit_file_h5 = analyze_data.out_file_h5
close_file = False
meta_data = in_hit_file_h5.root.meta_data[:] # get the meta data table
try:
hit_table = in_hit_file_h5.root.Hits # get the hit table
except tb.NoSuchNodeError:
logging.error(
'analyze_hits_per_scan_parameter needs a hit table, but no hit table found.'
)
return
meta_data_table_at_scan_parameter = analysis_utils.get_unique_scan_parameter_combinations(
meta_data, scan_parameters=scan_parameters)
parameter_values = analysis_utils.get_scan_parameters_table_from_meta_data(
meta_data_table_at_scan_parameter, scan_parameters)
event_number_ranges = analysis_utils.get_ranges_from_array(
meta_data_table_at_scan_parameter['event_number']
) # get the event number ranges for the different scan parameter settings
analysis_utils.index_event_number(
hit_table
) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = chunk_size # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number,
stop_event_number) in enumerate(event_number_ranges):
logging.info('Analyze hits for ' + str(scan_parameters) + ' = ' +
str(parameter_values[parameter_index]))
analyze_data.reset(
) # resets the front end data of the last analysis step but not the options
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=start_event_number,
stop_event_number=stop_event_number,
start_index=index,
chunk_size=best_chunk_size):
analyze_data.analyze_hits(
hits,
scan_parameter=False) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
best_chunk_size = int(
1.5 * readout_hit_len
) if int(1.05 * readout_hit_len) < chunk_size and int(
1.05 * readout_hit_len
) > 1e3 else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
file_name = " ".join(re.findall(
"[a-zA-Z0-9]+", str(scan_parameters))) + '_' + " ".join(
re.findall("[a-zA-Z0-9]+",
str(parameter_values[parameter_index])))
analyze_data._create_additional_hit_data(safe_to_file=False)
analyze_data._create_additional_cluster_data(safe_to_file=False)
yield analyze_data, file_name
if close_file:
in_hit_file_h5.close()
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 analyze_cluster_size_per_scan_parameter(input_file_hits,
output_file_cluster_size,
parameter='GDAC',
max_chunk_size=10000000,
overwrite_output_files=False,
output_pdf=None):
''' This method takes multiple hit files and determines the cluster size for different scan parameter values of
Parameters
----------
input_files_hits: string
output_file_cluster_size: string
The data file with the results
parameter: string
The name of the parameter to separate the data into (e.g.: PlsrDAC)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
overwrite_output_files: bool
Set to true to overwrite the output file if it already exists
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen, if False nothing is printed
'''
logging.info('Analyze the cluster sizes for different ' + parameter +
' settings for ' + input_file_hits)
if os.path.isfile(
output_file_cluster_size
) and not overwrite_output_files: # skip analysis if already done
logging.info('Analyzed cluster size file ' + output_file_cluster_size +
' already exists. Skip cluster size analysis.')
else:
with tb.open_file(
output_file_cluster_size,
mode="w") as out_file_h5: # file to write the data into
filter_table = tb.Filters(
complib='blosc', complevel=5,
fletcher32=False) # compression of the written data
parameter_goup = out_file_h5.create_group(
out_file_h5.root, parameter,
title=parameter) # note to store the data
cluster_size_total = None # final array for the cluster size per GDAC
with tb.open_file(
input_file_hits,
mode="r+") as in_hit_file_h5: # open the actual hit file
meta_data_array = in_hit_file_h5.root.meta_data[:]
scan_parameter = analysis_utils.get_scan_parameter(
meta_data_array) # get the scan parameters
if scan_parameter: # if a GDAC scan parameter was used analyze the cluster size per GDAC setting
scan_parameter_values = scan_parameter[
parameter] # scan parameter settings used
if len(
scan_parameter_values
) == 1: # only analyze per scan step if there are more than one scan step
logging.warning('The file ' + str(input_file_hits) +
' has no different ' + str(parameter) +
' parameter values. Omit analysis.')
else:
logging.info('Analyze ' + input_file_hits +
' per scan parameter ' + parameter +
' for ' +
str(len(scan_parameter_values)) +
' values from ' +
str(np.amin(scan_parameter_values)) +
' to ' +
str(np.amax(scan_parameter_values)))
event_numbers = analysis_utils.get_meta_data_at_scan_parameter(
meta_data_array, parameter
)['event_number'] # get the event numbers in meta_data where the scan parameter changes
parameter_ranges = np.column_stack(
(scan_parameter_values,
analysis_utils.get_ranges_from_array(
event_numbers)))
hit_table = in_hit_file_h5.root.Hits
analysis_utils.index_event_number(hit_table)
total_hits, total_hits_2, index = 0, 0, 0
chunk_size = max_chunk_size
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_cluster_size_hist = True
analyze_data.create_cluster_tot_hist = True
analyze_data.histogram.set_no_scan_parameter(
) # one has to tell histogram the # of scan parameters for correct occupancy hist allocation
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*',
left='|',
right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=hit_table.shape[0],
term_width=80)
progress_bar.start()
for parameter_index, parameter_range in enumerate(
parameter_ranges
): # loop over the selected events
analyze_data.reset(
) # resets the data of the last analysis
logging.debug(
'Analyze GDAC = ' + str(parameter_range[0]) +
' ' + str(
int(
float(
float(parameter_index) /
float(len(parameter_ranges)) *
100.0))) + '%')
start_event_number = parameter_range[1]
stop_event_number = parameter_range[2]
logging.debug('Data from events = [' +
str(start_event_number) + ',' +
str(stop_event_number) + '[')
actual_parameter_group = out_file_h5.create_group(
parameter_goup,
name=parameter + '_' + str(parameter_range[0]),
title=parameter + '_' +
str(parameter_range[0]))
# loop over the hits in the actual selected events with optimizations: variable chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=start_event_number,
stop_event_number=stop_event_number,
start_index=index,
chunk_size=chunk_size):
total_hits += hits.shape[0]
analyze_data.analyze_hits(
hits
) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
chunk_size = int(1.05 * readout_hit_len) if int(
1.05 * readout_hit_len
) < max_chunk_size else max_chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if chunk_size < 50: # limit the lower chunk size, there can always be a crazy event with more than 20 hits
chunk_size = 50
# get occupancy hist
occupancy = analyze_data.histogram.get_occupancy(
) # just check here if histogram is consistent
# store and plot cluster size hist
cluster_size_hist = analyze_data.clusterizer.get_cluster_size_hist(
)
cluster_size_hist_table = out_file_h5.create_carray(
actual_parameter_group,
name='HistClusterSize',
title='Cluster Size Histogram',
atom=tb.Atom.from_dtype(
cluster_size_hist.dtype),
shape=cluster_size_hist.shape,
filters=filter_table)
cluster_size_hist_table[:] = cluster_size_hist
if output_pdf is not False:
plotting.plot_cluster_size(
hist=cluster_size_hist,
title='Cluster size (' +
str(np.sum(cluster_size_hist)) +
' entries) for ' + parameter + ' = ' +
str(scan_parameter_values[parameter_index]
),
filename=output_pdf)
if cluster_size_total is None: # true if no data was appended to the array yet
cluster_size_total = cluster_size_hist
else:
cluster_size_total = np.vstack(
[cluster_size_total, cluster_size_hist])
total_hits_2 += np.sum(occupancy)
progress_bar.finish()
if total_hits != total_hits_2:
logging.warning(
'Analysis shows inconsistent number of hits. Check needed!'
)
logging.info('Analyzed %d hits!', total_hits)
cluster_size_total_out = out_file_h5.create_carray(
out_file_h5.root,
name='AllHistClusterSize',
title='All Cluster Size Histograms',
atom=tb.Atom.from_dtype(cluster_size_total.dtype),
shape=cluster_size_total.shape,
filters=filter_table)
cluster_size_total_out[:] = cluster_size_total
def analyse_n_cluster_per_event(scan_base,
include_no_cluster=False,
time_line_absolute=True,
combine_n_readouts=1000,
chunk_size=10000000,
plot_n_cluster_hists=False,
output_pdf=None,
output_file=None):
''' Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5',
mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(analysis_utils.get_ranges_from_array(
meta_data_array['timestamp_start'][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(
meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=total_cluster,
term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(
parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) +
' and data from events = [' +
str(parameter_range[2]) + ',' +
str(parameter_range[3]) + '[ ' + str(
int(
float(
float(parameter_index) /
float(len(parameter_ranges)) *
100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(
cluster_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start_index=index,
chunk_size=best_chunk_size):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(
clusters['event_number']
)[:,
1] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event,
bins=10,
range=(0, 10))[0]
else:
hist = np.add(
hist,
np.histogram(n_cluster_per_event,
bins=10,
range=(0, 10))[0])
if include_no_cluster and parameter_range[
3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] -
parameter_range[2]) - len(
n_cluster_per_event
) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(
1.05 * readout_cluster_len
) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(
hist,
title='Number of cluster per event at ' +
str(parameter_range[0]),
x_axis_title='Number of cluster',
y_axis_title='#',
log_y=True,
filename=output_pdf)
hist = hist.astype('f4') / np.sum(
hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning(
'Not all clusters were selected during analysis. Analysis is therefore not exact'
)
if time_line_absolute:
plotting.plot_scatter_time(
time_stamp,
n_cluster,
title='Number of cluster per event as a function of time',
marker_style='o',
filename=output_pdf,
legend=('0 cluster', '1 cluster', '2 cluster',
'3 cluster') if include_no_cluster else
('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
else:
plotting.plot_scatter(
time_stamp,
n_cluster,
title='Number of cluster per event as a function of time',
x_label='time [min.]',
marker_style='o',
filename=output_pdf,
legend=('0 cluster', '1 cluster', '2 cluster',
'3 cluster') if include_no_cluster else
('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(zip(time_stamp, cluster_array[:, 0],
cluster_array[:, 1], cluster_array[:, 2],
cluster_array[:, 3], cluster_array[:, 4],
cluster_array[:, 5]),
dtype=[('time_stamp', float),
('cluster_0', float),
('cluster_1', float),
('cluster_2', float),
('cluster_3', float),
('cluster_4', float),
('cluster_5', float)
]).view(np.recarray)
try:
n_cluster_table = out_file_h5.create_table(
out_file_h5.root,
name='n_cluster',
description=rec_array,
title='Cluster per event',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(
output_file +
' has already a Beamspot note, do not overwrite existing.')
return time_stamp, n_cluster
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 analyze_hits_per_scan_parameter(analyze_data, scan_parameters=None, chunk_size=50000):
"""Takes the hit table and analyzes the hits per scan parameter
Parameters
----------
analyze_data : analysis.analyze_raw_data.AnalyzeRawData object with an opened hit file (AnalyzeRawData.out_file_h5) or a
file name with the hit data given (AnalyzeRawData._analyzed_data_file)
scan_parameters : list of strings:
The names of the scan parameters to use
chunk_size : int:
The chunk size of one hit table read. The bigger the faster. Too big causes memory errors.
Returns
-------
yields the analysis.analyze_raw_data.AnalyzeRawData for each scan parameter
"""
if analyze_data.out_file_h5 is None or analyze_data.out_file_h5.isopen == 0:
in_hit_file_h5 = tb.open_file(analyze_data._analyzed_data_file, "r+")
opened_file = True
else:
in_hit_file_h5 = analyze_data.out_file_h5
opened_file = False
meta_data = in_hit_file_h5.root.meta_data[:] # get the meta data table
try:
hit_table = in_hit_file_h5.root.Hits # get the hit table
except tb.NoSuchNodeError:
logging.error("analyze_hits_per_scan_parameter needs a hit table, but no hit table found.")
return
meta_data_table_at_scan_parameter = analysis_utils.get_unique_scan_parameter_combinations(
meta_data, scan_parameters=scan_parameters
)
parameter_values = analysis_utils.get_scan_parameters_table_from_meta_data(
meta_data_table_at_scan_parameter, scan_parameters
)
event_number_ranges = analysis_utils.get_ranges_from_array(
meta_data_table_at_scan_parameter["event_number"]
) # get the event number ranges for the different scan parameter settings
analysis_utils.index_event_number(
hit_table
) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = (
chunk_size
) # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number, stop_event_number) in enumerate(event_number_ranges):
logging.info("Analyze hits for " + str(scan_parameters) + " = " + str(parameter_values[parameter_index]))
analyze_data.reset() # resets the front end data of the last analysis step but not the options
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=start_event_number,
stop_event_number=stop_event_number,
start=index,
chunk_size=best_chunk_size,
):
analyze_data.analyze_hits(hits, scan_parameter=False) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
best_chunk_size = (
int(1.5 * readout_hit_len)
if int(1.05 * readout_hit_len) < chunk_size and int(1.05 * readout_hit_len) > 1e3
else chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
file_name = (
" ".join(re.findall("[a-zA-Z0-9]+", str(scan_parameters)))
+ "_"
+ " ".join(re.findall("[a-zA-Z0-9]+", str(parameter_values[parameter_index])))
)
analyze_data._create_additional_hit_data(safe_to_file=False)
analyze_data._create_additional_cluster_data(safe_to_file=False)
yield analyze_data, file_name
if opened_file:
in_hit_file_h5.close()
def analyze_beam_spot(scan_base,
combine_n_readouts=1000,
chunk_size=10000000,
plot_occupancy_hists=False,
output_pdf=None,
output_file=None):
''' Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5',
mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(analysis_utils.get_ranges_from_array(
meta_data_array['timestamp_start'][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(
meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histogram.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=hit_table.shape[0],
term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(
parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) +
' and data from events = [' +
str(parameter_range[2]) + ',' +
str(parameter_range[3]) + '[ ' + str(
int(
float(
float(parameter_index) /
float(len(parameter_ranges)) *
100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start_index=index,
chunk_size=best_chunk_size):
analyze_data.analyze_hits(
hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_hit_len) if int(
1.05 * readout_hit_len
) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histogram.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(
analysis_utils.get_mean_from_histogram(projection_x,
bin_positions=range(
0, 80)))
y.append(
analysis_utils.get_mean_from_histogram(projection_y,
bin_positions=range(
0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(
occupancy_array[:, :, 0],
title='Occupancy for events between ' + time.strftime(
'%H:%M:%S', time.localtime(parameter_range[0])) +
' and ' + time.strftime(
'%H:%M:%S', time.localtime(parameter_range[1])),
filename=output_pdf)
progress_bar.finish()
plotting.plot_scatter([i * 250 for i in x], [i * 50 for i in y],
title='Mean beam position',
x_label='x [um]',
y_label='y [um]',
marker_style='-o',
filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y),
dtype=[('time_stamp', float), ('x', float),
('y', float)])
try:
beam_spot_table = out_file_h5.create_table(
out_file_h5.root,
name='Beamspot',
description=rec_array,
title='Beam spot position',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(
output_file +
' has already a Beamspot note, do not overwrite existing.')
return time_stamp, x, y
def analyze_cluster_size_per_scan_parameter(
input_file_hits,
output_file_cluster_size,
parameter="GDAC",
max_chunk_size=10000000,
overwrite_output_files=False,
output_pdf=None,
):
""" This method takes multiple hit files and determines the cluster size for different scan parameter values of
Parameters
----------
input_files_hits: string
output_file_cluster_size: string
The data file with the results
parameter: string
The name of the parameter to separate the data into (e.g.: PlsrDAC)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
overwrite_output_files: bool
Set to true to overwrite the output file if it already exists
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen, if False nothing is printed
"""
logging.info("Analyze the cluster sizes for different " + parameter + " settings for " + input_file_hits)
if os.path.isfile(output_file_cluster_size) and not overwrite_output_files: # skip analysis if already done
logging.info(
"Analyzed cluster size file " + output_file_cluster_size + " already exists. Skip cluster size analysis."
)
else:
with tb.openFile(output_file_cluster_size, mode="w") as out_file_h5: # file to write the data into
filter_table = tb.Filters(complib="blosc", complevel=5, fletcher32=False) # compression of the written data
parameter_goup = out_file_h5.createGroup(
out_file_h5.root, parameter, title=parameter
) # note to store the data
cluster_size_total = None # final array for the cluster size per GDAC
with tb.openFile(input_file_hits, mode="r+") as in_hit_file_h5: # open the actual hit file
meta_data_array = in_hit_file_h5.root.meta_data[:]
scan_parameter = analysis_utils.get_scan_parameter(meta_data_array) # get the scan parameters
if scan_parameter: # if a GDAC scan parameter was used analyze the cluster size per GDAC setting
scan_parameter_values = scan_parameter[parameter] # scan parameter settings used
if (
len(scan_parameter_values) == 1
): # only analyze per scan step if there are more than one scan step
logging.warning(
"The file "
+ str(input_file_hits)
+ " has no different "
+ str(parameter)
+ " parameter values. Omit analysis."
)
else:
logging.info(
"Analyze "
+ input_file_hits
+ " per scan parameter "
+ parameter
+ " for "
+ str(len(scan_parameter_values))
+ " values from "
+ str(np.amin(scan_parameter_values))
+ " to "
+ str(np.amax(scan_parameter_values))
)
event_numbers = analysis_utils.get_meta_data_at_scan_parameter(meta_data_array, parameter)[
"event_number"
] # get the event numbers in meta_data where the scan parameter changes
parameter_ranges = np.column_stack(
(scan_parameter_values, analysis_utils.get_ranges_from_array(event_numbers))
)
hit_table = in_hit_file_h5.root.Hits
analysis_utils.index_event_number(hit_table)
total_hits, total_hits_2, index = 0, 0, 0
chunk_size = max_chunk_size
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_cluster_size_hist = True
analyze_data.create_cluster_tot_hist = True
analyze_data.histograming.set_no_scan_parameter() # one has to tell the histogramer the # of scan parameters for correct occupancy hist allocation
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=hit_table.shape[0],
term_width=80,
)
progress_bar.start()
for parameter_index, parameter_range in enumerate(
parameter_ranges
): # loop over the selected events
analyze_data.reset() # resets the data of the last analysis
logging.debug(
"Analyze GDAC = "
+ str(parameter_range[0])
+ " "
+ str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0)))
+ "%"
)
start_event_number = parameter_range[1]
stop_event_number = parameter_range[2]
logging.debug(
"Data from events = [" + str(start_event_number) + "," + str(stop_event_number) + "["
)
actual_parameter_group = out_file_h5.createGroup(
parameter_goup,
name=parameter + "_" + str(parameter_range[0]),
title=parameter + "_" + str(parameter_range[0]),
)
# loop over the hits in the actual selected events with optimizations: variable chunk size, start word index given
readout_hit_len = (
0
) # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=start_event_number,
stop_event_number=stop_event_number,
start=index,
chunk_size=chunk_size,
):
total_hits += hits.shape[0]
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
chunk_size = (
int(1.05 * readout_hit_len)
if int(1.05 * readout_hit_len) < max_chunk_size
else max_chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
if (
chunk_size < 50
): # limit the lower chunk size, there can always be a crazy event with more than 20 hits
chunk_size = 50
# get occupancy hist
occupancy = (
analyze_data.histograming.get_occupancy()
) # just here to check histograming is consistend
# store and plot cluster size hist
cluster_size_hist = analyze_data.clusterizer.get_cluster_size_hist()
cluster_size_hist_table = out_file_h5.createCArray(
actual_parameter_group,
name="HistClusterSize",
title="Cluster Size Histogram",
atom=tb.Atom.from_dtype(cluster_size_hist.dtype),
shape=cluster_size_hist.shape,
filters=filter_table,
)
cluster_size_hist_table[:] = cluster_size_hist
if output_pdf is not False:
plotting.plot_cluster_size(
hist=cluster_size_hist,
title="Cluster size ("
+ str(np.sum(cluster_size_hist))
+ " entries) for "
+ parameter
+ " = "
+ str(scan_parameter_values[parameter_index]),
filename=output_pdf,
)
if cluster_size_total is None: # true if no data was appended to the array yet
cluster_size_total = cluster_size_hist
else:
cluster_size_total = np.vstack([cluster_size_total, cluster_size_hist])
total_hits_2 += np.sum(occupancy)
progress_bar.finish()
if total_hits != total_hits_2:
logging.warning("Analysis shows inconsistent number of hits. Check needed!")
logging.info("Analyzed %d hits!", total_hits)
cluster_size_total_out = out_file_h5.createCArray(
out_file_h5.root,
name="AllHistClusterSize",
title="All Cluster Size Histograms",
atom=tb.Atom.from_dtype(cluster_size_total.dtype),
shape=cluster_size_total.shape,
filters=filter_table,
)
cluster_size_total_out[:] = cluster_size_total
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.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
def analyse_n_cluster_per_event(
scan_base,
include_no_cluster=False,
time_line_absolute=True,
combine_n_readouts=1000,
chunk_size=10000000,
plot_n_cluster_hists=False,
output_pdf=None,
output_file=None,
):
""" Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
"""
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.openFile(data_file + "_interpreted.h5", mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(
analysis_utils.get_ranges_from_array(meta_data_array["timestamp_start"][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(meta_data_array["event_number"][::combine_n_readouts]),
)
)
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=total_cluster,
term_width=80,
)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug(
"Analyze time stamp "
+ str(parameter_range[0])
+ " and data from events = ["
+ str(parameter_range[2])
+ ","
+ str(parameter_range[3])
+ "[ "
+ str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0)))
+ "%"
)
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = (
0
) # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(
cluster_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start=index,
chunk_size=best_chunk_size,
):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters["event_number"])[
:, 1
] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0]
else:
hist = np.add(hist, np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0])
if include_no_cluster and parameter_range[3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] - parameter_range[2]) - len(
n_cluster_per_event
) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = (
int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(
hist,
title="Number of cluster per event at " + str(parameter_range[0]),
x_axis_title="Number of cluster",
y_axis_title="#",
log_y=True,
filename=output_pdf,
)
hist = hist.astype("f4") / np.sum(hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning("Not all clusters were selected during analysis. Analysis is therefore not exact")
if time_line_absolute:
plotting.plot_scatter_time(
time_stamp,
n_cluster,
title="Number of cluster per event as a function of time",
marker_style="o",
filename=output_pdf,
legend=("0 cluster", "1 cluster", "2 cluster", "3 cluster")
if include_no_cluster
else ("0 cluster not plotted", "1 cluster", "2 cluster", "3 cluster"),
)
else:
plotting.plot_scatter(
time_stamp,
n_cluster,
title="Number of cluster per event as a function of time",
x_label="time [min.]",
marker_style="o",
filename=output_pdf,
legend=("0 cluster", "1 cluster", "2 cluster", "3 cluster")
if include_no_cluster
else ("0 cluster not plotted", "1 cluster", "2 cluster", "3 cluster"),
)
if output_file:
with tb.openFile(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(
zip(
time_stamp,
cluster_array[:, 0],
cluster_array[:, 1],
cluster_array[:, 2],
cluster_array[:, 3],
cluster_array[:, 4],
cluster_array[:, 5],
),
dtype=[
("time_stamp", float),
("cluster_0", float),
("cluster_1", float),
("cluster_2", float),
("cluster_3", float),
("cluster_4", float),
("cluster_5", float),
],
).view(np.recarray)
try:
n_cluster_table = out_file_h5.createTable(
out_file_h5.root,
name="n_cluster",
description=rec_array,
title="Cluster per event",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + " has already a Beamspot note, do not overwrite existing.")
return time_stamp, n_cluster
def analyze_beam_spot(
scan_base,
combine_n_readouts=1000,
chunk_size=10000000,
plot_occupancy_hists=False,
output_pdf=None,
output_file=None,
):
""" Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
"""
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.openFile(data_file + "_interpreted.h5", mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(
analysis_utils.get_ranges_from_array(meta_data_array["timestamp_start"][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(meta_data_array["event_number"][::combine_n_readouts]),
)
)
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histograming.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=hit_table.shape[0],
term_width=80,
)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug(
"Analyze time stamp "
+ str(parameter_range[0])
+ " and data from events = ["
+ str(parameter_range[2])
+ ","
+ str(parameter_range[3])
+ "[ "
+ str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0)))
+ "%"
)
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = (
0
) # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start=index,
chunk_size=best_chunk_size,
):
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = (
int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size else chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histograming.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(analysis_utils.get_mean_from_histogram(projection_x, bin_positions=range(0, 80)))
y.append(analysis_utils.get_mean_from_histogram(projection_y, bin_positions=range(0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(
occupancy_array[:, :, 0],
title="Occupancy for events between "
+ time.strftime("%H:%M:%S", time.localtime(parameter_range[0]))
+ " and "
+ time.strftime("%H:%M:%S", time.localtime(parameter_range[1])),
filename=output_pdf,
)
progress_bar.finish()
plotting.plot_scatter(
[i * 250 for i in x],
[i * 50 for i in y],
title="Mean beam position",
x_label="x [um]",
y_label="y [um]",
marker_style="-o",
filename=output_pdf,
)
if output_file:
with tb.openFile(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y), dtype=[("time_stamp", float), ("x", float), ("y", float)])
try:
beam_spot_table = out_file_h5.createTable(
out_file_h5.root,
name="Beamspot",
description=rec_array,
title="Beam spot position",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + " has already a Beamspot note, do not overwrite existing.")
return time_stamp, x, y
