def analyze_raw_data(input_files, output_file_hits, interpreter_plots, overwrite_output_files, align_at_trigger=True, align_at_tdc=False, use_tdc_trigger_time_stamp=False, max_tdc_delay=80, interpreter_warnings=False):
logging.info('Analyze the raw FE data given in ' + str(len(input_files)) + ' files and store the needed data')
if os.path.isfile(output_file_hits) and not overwrite_output_files: # skip analysis if already done
logging.info('Analyzed data file ' + output_file_hits + ' already exists. Skip analysis for this file.')
else:
with AnalyzeRawData(raw_data_file=input_files, analyzed_data_file=output_file_hits) as analyze_raw_data:
analyze_raw_data.max_tdc_delay = max_tdc_delay # max TDC delay to consider a valid in-time TDC word
analyze_raw_data.use_tdc_trigger_time_stamp = use_tdc_trigger_time_stamp # if you want to also measure the delay between trigger / hit-bus
analyze_raw_data.align_at_trigger = align_at_trigger # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.align_at_tdc = align_at_tdc # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.create_tdc_counter_hist = True # create a histogram for all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.create_tdc_pixel_hist = True
analyze_raw_data.create_tot_pixel_hist = True
analyze_raw_data.create_cluster_hit_table = True # enables the creation of a table with all cluster hits, std. setting is false
analyze_raw_data.create_source_scan_hist = True # create source scan hists
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.interpreter.set_warning_output(interpreter_warnings) # std. setting is True
analyze_raw_data.interpret_word_table() # the actual start conversion command
analyze_raw_data.interpreter.print_summary() # prints the interpreter summary
# Store the enables pixels for good pixel selection in TDC analysis step
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r+') as out_file_h5:
with tb.open_file(analyze_raw_data.files_dict.items()[0][0]) as in_file_h5: # Use first raw data file to extract enable mask
out_file_h5.root.ClusterHits.attrs.enabled_pixels = in_file_h5.root.configuration.Enable[:]
if interpreter_plots:
analyze_raw_data.plot_histograms() # plots all activated histograms into one pdf
def analyze_raw_data(input_file): # FE-I4 raw data analysis
'''Std. raw data analysis of FE-I4 data. A hit table is created for further analysis.
Parameters
----------
input_file : pytables file
output_file_hits : pytables file
'''
with AnalyzeRawData(raw_data_file=input_file,
create_pdf=True) as analyze_raw_data:
#analyze_raw_data.align_at_trigger_number = True # if trigger number is at the beginning of each event activate this for event alignment
analyze_raw_data.use_trigger_time_stamp = False # the trigger number is a time stamp
analyze_raw_data.use_tdc_word = False
analyze_raw_data.create_hit_table = True
analyze_raw_data.create_meta_event_index = True
analyze_raw_data.create_trigger_error_hist = True
analyze_raw_data.create_rel_bcid_hist = True
analyze_raw_data.create_error_hist = True
analyze_raw_data.create_service_record_hist = True
analyze_raw_data.create_occupancy_hist = True
analyze_raw_data.create_tot_hist = False
# analyze_raw_data.n_bcid = 16
# analyze_raw_data.max_tot_value = 13
analyze_raw_data.interpreter.create_empty_event_hits(False)
# analyze_raw_data.interpreter.set_debug_output(False)
# analyze_raw_data.interpreter.set_info_output(False)
analyze_raw_data.interpreter.set_warning_output(False)
# analyze_raw_data.interpreter.debug_events(0, 1, True)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.last_good_threshold[self.increase_threshold])
self.register.set_pixel_register_value('TDAC', self.last_good_tdac[self.increase_threshold])
self.register.set_pixel_register_value('Enable', self.last_good_enable_mask[0]) # use enable mask from the lowest point to mask bad pixels
# write configuration to avaoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
self.register_utils.send_commands(commands)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = 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()
plot_occupancy(self.last_occupancy_hist[self.increase_threshold].T, title='Noisy Pixels at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask[self.increase_threshold].T, title='Occupancy Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.last_good_tdac[self.increase_threshold].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.last_good_tdac[self.increase_threshold].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_good_enable_mask[self.increase_threshold].T, title='Intermediate Enable Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_good_enable_mask[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_good_enable_mask[0].T, title='Final Enable Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[0], self.last_step[0]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_good_enable_mask[0].T, filename=analyze_raw_data.output_pdf)
def analyze_raw_data_per_scan_parameter(input_file, output_file_hits, scan_data_filename, scan_parameters=['PlsrDAC']):
with AnalyzeRawData(raw_data_file=input_file, analyzed_data_file=output_file_hits) as analyze_raw_data:
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_tot_hist = True # creates a ToT histogram
for data_one_step, one_step_parameter in analyze_hits_per_scan_parameter(analyze_data=analyze_raw_data, scan_parameters=scan_parameters):
data_one_step.plot_histograms(scan_data_filename + '_' + one_step_parameter, create_hit_hists_only=True)
def analyze_raw_data(input_files, output_file_hits, scan_parameter):
logging.info('Analyze the raw FE data given in ' + str(len(input_files)) +
' files and store the needed data')
if os.path.isfile(output_file_hits) and not analysis_configuration[
'overwrite_output_files']: # skip analysis if already done
logging.warning('Analyzed data file ' + output_file_hits +
' already exists. Skip analysis for this file.')
else:
with AnalyzeRawData(
raw_data_file=input_files,
analyzed_data_file=output_file_hits,
scan_parameter_name=scan_parameter) as analyze_raw_data:
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_table = True # enables the creation of a table with all clusters, std. setting is false
analyze_raw_data.create_source_scan_hist = True # create source scan hists
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.interpreter.set_warning_output(
analysis_configuration['interpreter_warnings']
) # std. setting is True
analyze_raw_data.clusterizer.set_warning_output(
analysis_configuration['interpreter_warnings']
) # std. setting is True
analyze_raw_data.interpreter.debug_events(
0, 10, False
) # events to be printed onto the console for debugging, usually deactivated
analyze_raw_data.interpret_word_table(
) # the actual start conversion command
analyze_raw_data.interpreter.print_summary(
) # prints the interpreter summary
analyze_raw_data.plot_histograms(
) # plots all activated histograms into one pdf
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.interpreter.set_warning_output(True)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = False
analyze_raw_data.create_threshold_hists = True
analyze_raw_data.create_fitted_threshold_hists = True
analyze_raw_data.create_threshold_mask = True
analyze_raw_data.n_injections = 100
analyze_raw_data.interpreter.set_warning_output(False) # so far the data structure in a threshold scan was always bad, too many warnings given
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r') as out_file_h5:
thr = out_file_h5.root.HistThresholdFitted[:]
thr_masked = np.ma.masked_where(np.isclose(thr, 0), thr)
corr = [thr_masked[:, i * 2 + 1:i * 2 + 3].mean() for i in range(0, 38)]
corr = np.array(corr)
corr -= corr.min()
# corr = np.around(corr).astype(int)
if "C_High".lower() in map(lambda x: x.lower(), self.enable_shift_masks) and "C_Low".lower() in map(lambda x: x.lower(), self.enable_shift_masks):
self.register.calibration_parameters['Pulser_Corr_C_Inj_High'] = list(corr)
elif "C_High".lower() in map(lambda x: x.lower(), self.enable_shift_masks):
self.register.calibration_parameters['Pulser_Corr_C_Inj_Med'] = list(corr)
elif "C_Low".lower() in map(lambda x: x.lower(), self.enable_shift_masks):
self.register.calibration_parameters['Pulser_Corr_C_Inj_Low'] = list(corr)
else:
raise ValueError('Unknown C_Inj')
def analyze_raw_data(input_files, output_file_hits, interpreter_plots, overwrite_output_files, pdf_filename):
logging.info('Analyze the raw FE data given in ' + str(len(input_files)) + ' files and store the needed data')
if os.path.isfile(output_file_hits) and not overwrite_output_files: # skip analysis if already done
logging.info('Analyzed data file ' + output_file_hits + ' already exists. Skip analysis for this file.')
else:
with AnalyzeRawData(raw_data_file=input_files, analyzed_data_file=output_file_hits) as analyze_raw_data:
# analyze_raw_data.interpreter.debug_events(3645978, 3645978, True)
# analyze_raw_data.interpreter.debug_events(100, 110, True)
# analyze_raw_data.use_tdc_trigger_time_stamp = True # if you want to also measure the delay between trigger / hit-bus
# analyze_raw_data.max_tdc_delay = 80
analyze_raw_data.align_at_trigger = False # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.align_at_tdc = True # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.create_tdc_counter_hist = True # create a histogram for all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.create_tdc_pixel_hist = True
analyze_raw_data.create_tot_pixel_hist = True
analyze_raw_data.create_cluster_hit_table = True # enables the creation of a table with all cluster hits, std. setting is false
analyze_raw_data.create_source_scan_hist = True # create source scan hists
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.interpreter.set_warning_output(analysis_configuration['interpreter_warnings']) # std. setting is True
analyze_raw_data.clusterizer.set_warning_output(analysis_configuration['interpreter_warnings']) # std. setting is True
analyze_raw_data.interpreter.print_status()
analyze_raw_data.interpret_word_table() # the actual start conversion command
analyze_raw_data.interpreter.print_summary() # prints the interpreter summary
if interpreter_plots:
analyze_raw_data.plot_histograms() # plots all activated histograms into one pdf
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_hit_table = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist > self.abs_occ_limit] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(
mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
self.inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
self.occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T,
title='Noisy Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
# occ_hist = make_occupancy_hist(*convert_data_array(data_array_from_data_dict_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)).T
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist < self.n_injections] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
self.disable_for_mask = self.disable_for_mask
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(
mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
self.inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
self.enable_for_mask = self.enable_for_mask
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
self.occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T,
title='Stuck Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze_hits(input_file, output_file_hits, scan_data_filename, output_file_hits_analyzed=None):
with AnalyzeRawData(raw_data_file=input_file, analyzed_data_file=output_file_hits) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
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=output_file_hits_analyzed)
analyze_raw_data.plot_histograms(scan_data_filename=scan_data_filename, analyzed_data_file=output_file_hits_analyzed)
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 analyze_raw_data_file(file_name):
with AnalyzeRawData(raw_data_file=file_name,
create_pdf=False) as analyze_raw_data:
analyze_raw_data.create_tot_hist = False
analyze_raw_data.create_fitted_threshold_hists = True
analyze_raw_data.create_threshold_mask = True
analyze_raw_data.interpreter.set_warning_output(
True
) # so far the data structure in a threshold scan was always bad, too many warnings given
analyze_raw_data.interpret_word_table()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpreter.use_tdc_word(True) # align events at the TDC word
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_cluster_size_hist = True # can be set to false to omit cluster hit creation, can save some time, standard setting is false
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_cluster_tot_hist = 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()
def analyze_raw_data(input_file, output_file_hits):
with AnalyzeRawData(raw_data_file=input_file,
analyzed_data_file=output_file_hits,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_hit_table = False # can be set to false to omit hit table creation, std. setting is false
analyze_raw_data.create_cluster_hit_table = False # adds the cluster id and seed info to each hit, std. setting is false
analyze_raw_data.create_cluster_table = False # enables the creation of a table with all clusters, std. setting is false
analyze_raw_data.create_occupancy_hist = True # creates a colxrow histogram with accumulated hits for each scan parameter
analyze_raw_data.create_tot_hist = True # creates a ToT histogram
analyze_raw_data.create_rel_bcid_hist = True # creates a histogram with the relative BCID of the hits
analyze_raw_data.create_service_record_hist = True # creates a histogram with all SR send out from the FE
analyze_raw_data.create_error_hist = True # creates a histogram summing up the event errors that occurred
analyze_raw_data.create_trigger_error_hist = True # creates a histogram summing up the trigger errors
analyze_raw_data.create_source_scan_hist = False # create source scan hists
analyze_raw_data.create_cluster_size_hist = False # enables cluster size histogramming, can save some time, std. setting is false
analyze_raw_data.create_cluster_tot_hist = False # enables cluster ToT histogramming per cluster size, std. setting is false
analyze_raw_data.create_threshold_hists = False # makes only sense if threshold scan data is analyzed, std. setting is false
analyze_raw_data.create_threshold_mask = False # masking of noisy or black pixels during histogramming, only affecting fast-algorithm
analyze_raw_data.create_fitted_threshold_hists = False # makes only sense if threshold scan data is analyzed, std. setting is false
analyze_raw_data.create_fitted_threshold_mask = False # masking of noisy or black pixels during histogramming, only affecting S-curve fitting
analyze_raw_data.create_meta_word_index = False # 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.n_bcid = 16 # set the number of BCIDs per event, needed to judge the event structure, only active if settings are not taken from raw data file
analyze_raw_data.n_injections = 100 # set the numbers of injections, needed for fast threshold/noise determination
analyze_raw_data.max_tot_value = 13 # set the maximum ToT value considered to be a hit, 14 is a late hit
analyze_raw_data.use_trigger_number = False
analyze_raw_data.set_stop_mode = False # special analysis if data was taken in stop mode
analyze_raw_data.interpreter.use_tdc_word(
False
) # use the TDC word to align the events, assume that they are first words in the event
analyze_raw_data.interpreter.use_trigger_time_stamp(
False) # use the trigger number as a time stamp
analyze_raw_data.interpreter.set_debug_output(
False) # std. setting is False
analyze_raw_data.interpreter.set_info_output(
False) # std. setting is False
analyze_raw_data.interpreter.set_warning_output(
True) # std. setting is True
analyze_raw_data.clusterizer.set_warning_output(
True) # std. setting is True
analyze_raw_data.interpreter.debug_events(
3832, 3850, False
) # events to be printed onto the console for debugging, usually deactivated
analyze_raw_data.interpret_word_table(
) # the actual start conversion command
analyze_raw_data.interpreter.print_summary(
) # prints the interpreter summary
analyze_raw_data.plot_histograms(
pdf_filename=input_file
) # plots all activated histograms into one pdf
def analyze_raw_data_file(file_name):
if os.path.isfile(os.path.splitext(file_name)[0] + '_interpreted.h5'): # skip analysis if already done
logging.warning('Analyzed data file ' + file_name + ' already exists. Skip analysis for this file.')
else:
with AnalyzeRawData(raw_data_file=file_name, create_pdf=False) as analyze_raw_data:
analyze_raw_data.create_tot_hist = False
analyze_raw_data.create_tot_pixel_hist = False
analyze_raw_data.create_fitted_threshold_hists = True
analyze_raw_data.create_threshold_mask = True
analyze_raw_data.interpreter.set_warning_output(False) # RX errors would fill the console
analyze_raw_data.interpret_word_table()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > 1] = 1
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(occ_mask.T,
title='Merged Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(occ_mask.T,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = False
analyze_raw_data.create_fitted_threshold_hists = True
analyze_raw_data.create_threshold_mask = True
analyze_raw_data.n_injections = 100
analyze_raw_data.interpreter.set_warning_output(
False
) # so far the data structure in a threshold scan was always bad, too many warnings given
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_hit_table = True
analyze_raw_data.trig_count = self.trig_count # set number of BCID to overwrite the number deduced from the raw data file
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.use_trigger_time_stamp = True
analyze_raw_data.set_stop_mode = True
analyze_raw_data.align_at_trigger = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table(use_settings_from_file=False)
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_hit_table = True
analyze_raw_data.trigger_data_format = self.dut['TLU'][
'DATA_FORMAT']
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.set_stop_mode = True
analyze_raw_data.align_at_trigger = 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()
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.threshold[0])
self.register.set_pixel_register_value('TDAC', self.new_tdac[0])
self.register.set_pixel_register_value('Enable', self.new_enable_mask[0]) # use enable mask from the lowest point to mask bad pixels
# write configuration to avaoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
self.register_utils.send_commands(commands)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = 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()
last_step = None
for step in range(self.plot_n_steps, -1, -1):
if self.threshold[step] is not None:
plot_occupancy(self.occupancy_hist[step].T, title='Occupancy at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occupancy_hist[step].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.occupancy_mask[step].T, title='Noisy pixels at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occupancy_mask[step].T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.tdac[step].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.tdac[step].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.enable_mask[step].T, title='Enable mask at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=1, filename=analyze_raw_data.output_pdf)
# adding Poisson statistics plots
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.set_title("Hit statistics")
hist, bin_edges = np.histogram(self.occupancy_hist[step], bins=np.arange(0.0, np.max(self.occupancy_hist[step]) + 2, 1.0))
try:
_, idx = hist_quantiles(hist, [0.0, 0.9], return_indices=True)
except IndexError:
idx = [0, 1]
bins = np.arange(0, np.maximum(bin_edges[idx[1]], stats.poisson.ppf(0.9999, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)) + 2, 1)
ax.hist(self.occupancy_hist[step].flatten(), bins=bins, align='left', alpha=0.5, label="Measured occupancy")
ax.bar(x=bins[:-1], height=stats.poisson.pmf(k=bins[:-1], mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1) * self.enable_mask[step].sum(), alpha=0.5, width=1.0, color="r", label="Expected occupancy (Poisson statistics)")
# ax.hist(stats.poisson.rvs(mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1, size=self.enable_mask[step].sum()), bins=bins, align='left', alpha=0.5, label="Expected occupancy (Poisson statistics)")
ax.set_xlabel('#Hits')
ax.set_ylabel('#Pixels')
ax.legend()
analyze_raw_data.output_pdf.savefig(fig)
last_step = step
if last_step is not None:
plot_three_way(self.new_tdac[last_step].T, title='Final TDAC after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.new_tdac[last_step].T, title='Final TDAC after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.new_enable_mask[last_step].T, title='Final Enable mask after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), z_max=1, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# noisy pixels are set to 1
occ_mask[occ_hist < self.n_injections] = 1
# make inverse
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(occ_mask.T,
title='Stuck Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
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
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.align_at_tdc = False # align events at the TDC word
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()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_hit_table = True
analyze_raw_data.n_bcid = self.bcid_window
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.use_trigger_time_stamp = True
analyze_raw_data.set_stop_mode = True
analyze_raw_data.align_at_trigger = True
analyze_raw_data.create_cluster_size_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.clusterizer.set_warning_output(False)
analyze_raw_data.interpret_word_table(use_settings_from_file=False)
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
def analyze_hits(input_file_hits):
with AnalyzeRawData(
raw_data_file=None,
analyzed_data_file=input_file_hits) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
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.create_tdc_hist = True
analyze_raw_data.analyze_hit_table(
analyzed_data_out_file=input_file_hits[:-3] + '_analyzed.h5')
analyze_raw_data.plot_histograms(
pdf_filename=input_file_hits[:-3],
analyzed_data_file=input_file_hits[:-3] + '_analyzed.h5')
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_hit_table = True
analyze_raw_data.n_bcid = self.bcid_window
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.use_trigger_time_stamp = True
analyze_raw_data.set_stop_mode = True
analyze_raw_data.interpreter.use_trigger_number(True)
analyze_raw_data.create_cluster_size_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.clusterizer.set_warning_output(False)
# analyze_raw_data.interpreter.debug_events(0, 10, True) # events to be printed onto the console for debugging, usually deactivated
analyze_raw_data.interpret_word_table(use_settings_from_file=False)
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
def analyse_selected_hits(input_file_hits,
output_file_hits,
output_file_hits_analyzed,
scan_data_filenames,
cluster_size_condition='cluster_size==1',
n_cluster_condition='n_cluster==1'):
logging.info('Analyze selected hits with ' + cluster_size_condition +
' and ' + n_cluster_condition + ' in ' + input_file_hits)
if os.path.isfile(output_file_hits) and not analysis_configuration[
"overwrite_output_files"]: # skip analysis if already done
logging.warning('Selected hit data file ' + output_file_hits +
' already exists. Skip analysis for this file.')
else:
analysis.select_hits_from_cluster_info(
input_file_hits=input_file_hits,
output_file_hits=output_file_hits,
cluster_size_condition=cluster_size_condition,
n_cluster_condition=n_cluster_condition
) # select hits and copy the mto new file
if os.path.isfile(
output_file_hits_analyzed) and not analysis_configuration[
"overwrite_output_files"]: # skip analysis if already done
logging.warning('Analyzed selected hit data file ' +
output_file_hits_analyzed +
' already exists. Skip analysis for this file.')
else:
logging.info('Analyze selected hits in ' + output_file_hits)
with AnalyzeRawData(
raw_data_file=None,
analyzed_data_file=output_file_hits) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_tot_hist = False
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=output_file_hits_analyzed)
analyze_raw_data.plot_histograms(
scan_data_filename=output_file_hits_analyzed,
analyzed_data_file=output_file_hits_analyzed)
with tb.openFile(
input_file_hits, mode="r"
) as in_hit_file_h5: # copy meta data to the new analyzed file
with tb.openFile(output_file_hits_analyzed,
mode="r+") as output_hit_file_h5:
in_hit_file_h5.root.meta_data.copy(
output_hit_file_h5.root) # copy meta_data note to new file
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine",
self.last_good_threshold)
self.register.set_pixel_register_value('TDAC', self.last_good_tdac)
self.register.set_pixel_register_value('Enable',
self.last_good_enable_mask)
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = 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()
plot_occupancy(self.last_occupancy_hist.T,
title='Noisy Pixels at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist.T,
filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask.T,
title='Occupancy Mask at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask.T,
filename=analyze_raw_data.output_pdf)
plotThreeWay(self.last_tdac_distribution.T,
title='TDAC at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
x_axis_title="TDAC",
filename=analyze_raw_data.output_pdf,
maximum=31,
bins=32)
plot_occupancy(self.last_tdac_distribution.T,
title='TDAC at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
z_max=31,
filename=analyze_raw_data.output_pdf)
plot_occupancy(self.register.get_pixel_register_value('Enable').T,
title='Enable Mask',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(
self.register.get_pixel_register_value('Enable').T,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpreter.use_tdc_word(
True) # align events at the TDC word
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > 0] = 1
plot_occupancy(occ_mask.T,
title='Merged Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
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
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_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
