def setup_raw_data_analysis(self):
self.interpreter = PyDataInterpreter()
self.histograming = PyDataHistograming()
self.interpreter.set_warning_output(False)
self.histograming.set_no_scan_parameter()
self.histograming.create_occupancy_hist(True)
self.histograming.create_rel_bcid_hist(True)
self.histograming.create_tot_hist(True)
self.histograming.create_tdc_hist(True)
def setUpClass(cls):
cls.interpreter = PyDataInterpreter()
cls.histogram = PyDataHistograming()
cls.clusterizer = PyDataClusterizer()
with AnalyzeRawData(raw_data_file=tests_data_folder + 'unit_test_data_1.h5', analyzed_data_file=tests_data_folder + 'unit_test_data_1_interpreted.h5', create_pdf=False) as analyze_raw_data: # analyze the digital scan raw data, do not show any feedback (no prints to console, no plots)
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_hit_table = True # can be set to false to omit hit table creation, std. setting is false
analyze_raw_data.create_cluster_hit_table = True # adds the cluster id and seed info to each hit, std. setting is false
analyze_raw_data.create_cluster_table = True # enables the creation of a table with all clusters, std. setting is false
analyze_raw_data.create_trigger_error_hist = True # creates a histogram summing up the trigger errors
analyze_raw_data.create_cluster_size_hist = True # enables cluster size histogramming, can save some time, std. setting is false
analyze_raw_data.create_cluster_tot_hist = True # enables cluster ToT histogramming per cluster size, std. setting is false
analyze_raw_data.create_meta_word_index = True # stores the start and stop raw data word index for every event, std. setting is false
analyze_raw_data.create_meta_event_index = True # stores the event number for each readout in an additional meta data array, default: False
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
with AnalyzeRawData(raw_data_file=tests_data_folder + 'unit_test_data_2.h5', analyzed_data_file=tests_data_folder + 'unit_test_data_2_interpreted.h5', create_pdf=False) as analyze_raw_data: # analyze the fast threshold scan raw data, do not show any feedback (no prints to console, no plots)
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_threshold_hists = True # makes only sense if threshold scan data is analyzed, std. setting is false
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
with AnalyzeRawData(raw_data_file=None, analyzed_data_file=tests_data_folder + 'unit_test_data_1_interpreted.h5', create_pdf=False) as analyze_raw_data: # analyze the digital scan hit data, do not show any feedback (no prints to console, no plots)
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_cluster_hit_table = True
analyze_raw_data.create_cluster_table = True
analyze_raw_data.create_cluster_size_hist = True
analyze_raw_data.create_cluster_tot_hist = True
analyze_raw_data.analyze_hit_table(analyzed_data_out_file=tests_data_folder + 'unit_test_data_1_analyzed.h5')
with AnalyzeRawData(raw_data_file=tests_data_folder + 'unit_test_data_3.h5', analyzed_data_file=tests_data_folder + 'unit_test_data_3_interpreted.h5', create_pdf=False) as analyze_raw_data: # analyze the digital scan raw data per scan parameter, do not show any feedback (no prints to console, no plots)
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_hit_table = True # can be set to false to omit hit table creation, std. setting is false
analyze_raw_data.create_cluster_hit_table = True # adds the cluster id and seed info to each hit, std. setting is false
analyze_raw_data.create_cluster_table = True # enables the creation of a table with all clusters, std. setting is false
analyze_raw_data.create_trigger_error_hist = True # creates a histogram summing up the trigger errors
analyze_raw_data.create_cluster_size_hist = True # enables cluster size histogramming, can save some time, std. setting is false
analyze_raw_data.create_cluster_tot_hist = True # enables cluster ToT histogramming per cluster size, std. setting is false
analyze_raw_data.create_meta_word_index = True # stores the start and stop raw data word index for every event, std. setting is false
analyze_raw_data.create_meta_event_index = True # stores the event number for each readout in an additional meta data array, default: False
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
with AnalyzeRawData(raw_data_file=tests_data_folder + 'unit_test_data_2.h5', analyzed_data_file=tests_data_folder + 'unit_test_data_2_hits.h5', create_pdf=False) as analyze_raw_data: # analyze the fast threshold scan raw data, do not show any feedback (no prints to console, no plots)
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_hit_table = True
analyze_raw_data.create_threshold_hists = True # makes only sense if threshold scan data is analyzed, std. setting is false
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
with AnalyzeRawData(raw_data_file=None, analyzed_data_file=tests_data_folder + 'unit_test_data_2_hits.h5', create_pdf=False) as analyze_raw_data:
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_threshold_hists = True
analyze_raw_data.analyze_hit_table(analyzed_data_out_file=tests_data_folder + 'unit_test_data_2_analyzed.h5')
with AnalyzeRawData(raw_data_file=tests_data_folder + 'unit_test_data_4.h5', analyzed_data_file=tests_data_folder + 'unit_test_data_4_interpreted.h5', create_pdf=False) as analyze_raw_data:
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_hit_table = True
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
with AnalyzeRawData(raw_data_file=[tests_data_folder + 'unit_test_data_4_parameter_128.h5', tests_data_folder + 'unit_test_data_4_parameter_256.h5'], analyzed_data_file=tests_data_folder + 'unit_test_data_4_interpreted_2.h5', scan_parameter_name='parameter', create_pdf=False) as analyze_raw_data:
analyze_raw_data.chunk_size = 2999999
analyze_raw_data.create_hit_table = True
analyze_raw_data.interpret_word_table(use_settings_from_file=False, fei4b=False) # the actual start conversion command
def test_libraries_stability(self): # calls 50 times the constructor and destructor to check the libraries
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.ETA()], maxval=50, term_width=80)
progress_bar.start()
for i in range(50):
interpreter = PyDataInterpreter()
histogram = PyDataHistograming()
clusterizer = PyDataClusterizer()
del interpreter
del histogram
del clusterizer
progress_bar.update(i)
progress_bar.finish()
def test_hit_histograming(self):
raw_data = np.array([67307647, 67645759, 67660079, 67541711, 67718111, 67913663, 67914223, 67847647, 67978655, 68081199, 68219119, 68219487, 68425615, 68311343, 68490719, 68373295, 68553519, 68693039, 68573503, 68709951, 68717058, 68734735, 68604719, 68753999, 68761151, 68847327, 69014799, 69079791, 69211359, 69221055, 69279567, 69499247, 69773183, 69788527, 69998559, 69868559, 69872655, 70003599, 69902527, 70274575, 70321471, 70429983, 70563295, 70574959, 70447631, 70584591, 70783023, 71091999, 70972687, 70985087, 71214815, 71382623, 71609135, 71643519, 71720527, 71897695, 72167199, 72040047, 72264927, 72423983, 77471983, 77602863, 77604383, 77485295, 77616415, 77618927, 77619231, 77639983, 77655871, 77544159, 77548303, 77338399, 77345567, 77346287, 77360399, 77255407, 77386211, 77268287, 77279215, 77409599, 77075983, 76951903, 76980527, 77117023, 76991055, 77011007, 77148127, 77148815, 76827167, 76700031, 76868895, 76758575, 76889567, 76558303, 76429599, 76584783, 76468191, 76610943, 76613743, 76620879, 76629375, 76285999, 76321908, 76194319, 76205599, 76233759, 76065391, 76075839, 76093759, 75801311, 75826319, 75829215, 75699231, 75403887, 75565039, 75439135, 75111711, 75115151, 75251487, 75258399, 75138015, 75303471, 74974111, 74868559, 75030047, 75050079, 74714591, 74722847, 74595103, 74649935, 74656815, 74796511, 74455519, 74391519, 74402607, 74534383, 74189695, 74064911, 74246271, 74116063, 74248719, 74133119, 73935183, 73941087, 73811295, 73663583, 73743423, 73449647, 73453391, 73323743, 73343471, 73474159, 73345087, 73206751, 72899295, 72958559, 72828447, 72542623, 82383232, 67374687, 67503967, 67766575, 68179999, 68052847, 68198239, 68104495, 68235759, 68238223, 68472415, 68490463, 68501279, 68621071, 68623903, 68821791, 68988639, 68864047, 69003183, 68876015, 69007423, 68891407, 69267743, 69272367, 69159567, 69666911, 69684447, 70003247, 70018895, 69898927, 69938543, 69942031, 70198863, 70339919, 70587455, 70462783, 70597679, 70796399, 70800015, 70703887, 71121183, 71323151, 71243535, 71578703, 71467695, 71622879, 71629359, 71831264, 71836511, 71710319, 71992943, 72353855, 72355039, 77606628, 77608287, 77622047, 77510223, 77653263, 77664319, 77546223, 77677471, 77549375, 77213519, 77219551, 77232207, 77234991, 77366511, 77373791, 77389647, 77404383, 77070655, 77087199, 76956975, 76996431, 77009183, 77015327, 76683567, 76840351, 76862255, 76888804, 76548975, 76554767, 76427087, 76560159, 76451967, 76456847, 76468015, 76627295, 76352831, 76354863, 76365887, 75923999, 76074175, 75955439, 76086063, 75774239, 75781535, 75792671, 75662111, 75793647, 75797167, 75827023, 75696543, 75390527, 75522031, 75533663, 75541775, 75432255, 75571535, 75115535, 75247999, 75145197, 75151391, 75160799, 74974991, 74852831, 74871839, 74882783, 75023199, 74896943, 75028767, 75046431, 74922463, 74725711, 74621199, 74658623, 74663183, 74336383, 74484559, 74364526, 74370287, 74370639, 74517983, 74393615, 74205471, 74217359, 74227263, 74231727, 74102559, 74237999, 74248735, 73953599, 73868591, 74000703, 74002975, 73877295, 73664910, 73695967, 73704751, 73579583, 73582639, 73719055, 73405998, 73448207, 73481951, 73008831, 73175087, 73044495, 73058863, 73194895, 73197919, 73093151, 72895567, 72918543, 72947039, 72957919, 82383481, 67392015, 67303135, 67312799, 67318303, 67453727, 67454767, 67634719, 67645887, 67717391, 67914111, 67947919, 67818463, 68052959, 68097215, 68500543, 68711909, 68584735, 68726975, 68741679, 68615471, 68750559, 68755487, 68629311, 68764687, 68765648, 68990175, 69022959, 69023727, 69217327, 69547327, 69665839, 69809983, 69814815, 70006831, 70037807, 70055951, 70068511, 70184031, 70323999, 70334687, 70566095, 70588751, 70723935, 71049695, 70952031, 71084831, 71376863, 71256287, 71611039, 71487727, 71618591, 71623999, 71514239, 71891231, 71897327, 71897663, 72036783, 72391487, 77604975, 77608163, 77621327, 77501983, 77635039, 77646559, 77654671, 77655695, 77546543, 77678383, 77345471, 77224735, 77375519, 77385519, 77393967, 76944399, 76975663, 77114628, 77115231, 77127525, 77142959, 76677423, 76699967, 76722287, 76857647, 76739039, 76883567, 76891615, 76453343, 76584335, 76590623, 76594607, 76600031, 76611167, 76617743, 76622303, 76285999, 76329231, 76335839, 76348175, 76350351, 76356783, 75910383, 75639343, 75787615, 75660079, 75796895, 75797615, 75692559, 75827999, 75833487, 75836479, 75518943, 75568143, 75278943, 75290271, 75297903, 75309391, 75312479, 75315119, 74852223, 74987055, 74858047, 74992943, 74875439, 75008031, 74885407, 75027743, 75055583, 74927839, 74738719, 74629087, 74767391, 74779295, 74789343, 74791247, 74323183, 74454239, 74349455, 74364751, 74516047, 74528559, 74192207, 74201535, 74084367, 74220511, 74109039, 74263263, 74133215, 73807119, 73945313, 73868148, 74001631, 73536815, 73684815, 73711439, 73275407, 73408799, 73052767, 73190975, 73209823, 72788271, 72960607, 72487647, 82383730, 67407151, 67415583, 67322127, 67523871, 67700959, 67583039, 67905375, 67793199, 68159583, 68237791, 68306479, 68492399], np.uint32)
interpreter = PyDataInterpreter()
histograming = PyDataHistograming()
interpreter.set_trig_count(1)
interpreter.set_warning_output(False)
histograming.set_no_scan_parameter()
histograming.create_occupancy_hist(True)
interpreter.interpret_raw_data(raw_data)
interpreter.store_event()
histograming.add_hits(interpreter.get_hits())
occ_hist_cpp = histograming.get_occupancy()[:, :, 0]
col_arr, row_arr = convert_data_array(raw_data, filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)
occ_hist_python, _, _ = np.histogram2d(col_arr, row_arr, bins=(80, 336), range=[[1, 80], [1, 336]])
self.assertTrue(np.all(occ_hist_cpp == occ_hist_python))
def configure(self):
if self.trig_count == 0:
self.consecutive_lvl1 = (2 ** self.register.global_registers['Trig_Count']['bitlength'])
else:
self.consecutive_lvl1 = self.trig_count
if self.occupancy_limit * self.n_triggers * self.consecutive_lvl1 < 1.0:
logging.warning('Number of triggers too low for given occupancy limit. Any noise hit will lead to a masked pixel.')
commands = []
commands.extend(self.register.get_commands("ConfMode"))
# TDAC
tdac_max = 2 ** self.register.pixel_registers['TDAC']['bitlength'] - 1
self.register.set_pixel_register_value("TDAC", tdac_max)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
mask = make_box_pixel_mask_from_col_row(column=self.col_span, row=self.row_span)
# Enable
if self.use_enable_mask:
self.register.set_pixel_register_value("Enable", np.logical_and(mask, self.register.get_pixel_register_value("Enable")))
else:
self.register.set_pixel_register_value("Enable", mask)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
# Imon
self.register.set_pixel_register_value('Imon', 1)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name='Imon'))
# C_High
self.register.set_pixel_register_value('C_High', 0)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name='C_High'))
# C_Low
self.register.set_pixel_register_value('C_Low', 0)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name='C_Low'))
# Registers
# self.register.set_global_register_value("Trig_Lat", self.trigger_latency) # set trigger latency
self.register.set_global_register_value("Trig_Count", self.trig_count) # set number of consecutive triggers
commands.extend(self.register.get_commands("WrRegister", name=["Trig_Count"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
self.interpreter = PyDataInterpreter()
self.histograming = PyDataHistograming()
self.interpreter.set_trig_count(self.trig_count)
self.interpreter.set_warning_output(False)
self.histograming.set_no_scan_parameter()
self.histograming.create_occupancy_hist(True)
def histogram_cluster_table(analyzed_data_file,
output_file,
chunk_size=10000000):
'''Reads in the cluster info table in chunks and histograms the seed pixels into one occupancy array.
The 3rd dimension of the occupancy array is the number of different scan parameters used
Parameters
----------
analyzed_data_file : hdf5 file containing the cluster table. If a scan parameter is given in the meta data the occupancy
histograming is done per scan parameter.
Returns
-------
occupancy_array: numpy.array with dimensions (col, row, #scan_parameter)
'''
with tb.openFile(analyzed_data_file, mode="r") as in_file_h5:
with tb.openFile(output_file, mode="w") as out_file_h5:
histograming = PyDataHistograming()
histograming.create_occupancy_hist(True)
scan_parameters = None
event_number_indices = None
scan_parameter_indices = None
try:
meta_data = in_file_h5.root.meta_data[:]
scan_parameters = analysis_utils.get_unique_scan_parameter_combinations(
meta_data)
if scan_parameters is not None:
scan_parameter_indices = np.array(range(
0, len(scan_parameters)),
dtype='u4')
event_number_indices = np.ascontiguousarray(
scan_parameters['event_number']).astype(np.uint64)
histograming.add_meta_event_index(
event_number_indices,
array_length=len(scan_parameters['event_number']))
histograming.add_scan_parameter(scan_parameter_indices)
logging.info(
"Add %d different scan parameter(s) for analysis",
len(scan_parameters))
else:
logging.info("No scan parameter data provided")
histograming.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided, use no scan parameter")
histograming.set_no_scan_parameter()
logging.info('Histogram cluster seeds...')
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
analysis_utils.ETA()
],
maxval=in_file_h5.root.Cluster.shape[0],
term_width=80)
progress_bar.start()
total_cluster = 0 # to check analysis
for cluster, index in analysis_utils.data_aligned_at_events(
in_file_h5.root.Cluster, chunk_size=chunk_size):
total_cluster += len(cluster)
histograming.add_cluster_seed_hits(cluster, len(cluster))
progress_bar.update(index)
progress_bar.finish()
filter_table = tb.Filters(
complib='blosc', complevel=5,
fletcher32=False) # compression of the written data
occupancy_array = histograming.get_occupancy().T
occupancy_array_table = out_file_h5.createCArray(
out_file_h5.root,
name='HistOcc',
title='Occupancy Histogram',
atom=tb.Atom.from_dtype(occupancy_array.dtype),
shape=occupancy_array.shape,
filters=filter_table)
occupancy_array_table[:] = occupancy_array
if total_cluster != np.sum(occupancy_array):
logging.warning(
'Analysis shows inconsistent number of cluster used. Check needed!'
)
in_file_h5.root.meta_data.copy(
out_file_h5.root) # copy meta_data note to new file
