def plot_clustersize_per_event(data_files, raw_data_convert=True,logy=False):
for data_file in data_files:
if raw_data_convert:
analyze_raw_data = AnalyzeRawData(raw_data_file=data_file + '.h5', create_pdf=True)
analyze_raw_data.trigger_data_format = 2 # self.dut['TLU']['DATA_FORMAT']
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_cluster_size_hist = True
analyze_raw_data.create_cluster_tot_hist = True
analyze_raw_data.align_at_trigger = True
analyze_raw_data.create_cluster_table = True
analyze_raw_data.create_empty_event_hits = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
timestamp, n_cluster = pyana.analyse_n_cluster_per_event(scan_base=[data_file],
combine_n_readouts=500,
time_line_absolute = False,
percentage = False,
output_pdf = None,
output_file = data_file + '_n_cluster_per_event_histo.h5'
)
n_cluster = np.array(n_cluster)
plot_n_cluster = n_cluster[:,1:5]
cluster_above_3 = n_cluster[:,4:].sum(axis=1)
plot_n_cluster[:,3] = cluster_above_3
title = os.path.split(data_file)[1][:-26]
plt.clf()
plt.title('Run' + title + ' - Number of cluster per event as a function of time')
lineObjects = plt.semilogy(timestamp, plot_n_cluster,linestyle='None', marker='o',markersize=3)
plt.xlabel('time [min.]')
plt.grid()
plt.legend(lineObjects, ('1 cluster, mean = %.3f' % np.mean(plot_n_cluster[:,0], axis=0),
'2 cluster, mean = %.3f' % np.mean(plot_n_cluster[:,1], axis=0) ,
'3 cluster, mean = %.3f' % np.mean(plot_n_cluster[:,2], axis=0),
'4 or more cluster\nmean = %.3f' % np.mean(plot_n_cluster[:,3], axis=0)
)
)
plt.savefig(data_file + '_n_cluster_per_event.pdf')
cluster_sizes = [0,1,2,3,4,5,6,7,8,9]
plt.clf()
plt.title('Run' + title + ' - Number of cluster per event')
if logy:
plt.yscale('log')
lineObjects = plt.plot(cluster_sizes, n_cluster.sum(axis=0),linestyle='None',marker='o',markersize=3) #
else:
lineObjects = plt.plot(timestamp, plot_n_cluster,linestyle='None', marker='o',markersize=1)
plt.xlabel('cluster size')
plt.grid()
plt.savefig(data_file + '_n_cluster_cluster_size_logscale.pdf')
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 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
