def setup_raw_data_analysis(self):
self.interpreter = PyDataInterpreter()
self.histogram = PyDataHistograming()
self.interpreter.set_warning_output(False)
self.histogram.set_no_scan_parameter()
self.histogram.create_occupancy_hist(True)
self.histogram.create_rel_bcid_hist(True)
self.histogram.create_tot_hist(True)
self.histogram.create_tdc_hist(True)
try:
self.histogram.create_tdc_distance_hist(True)
self.interpreter.use_tdc_trigger_time_stamp(True)
except AttributeError:
self.has_tdc_distance = False
else:
self.has_tdc_distance = True
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
self.abs_occ_limit = int(self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)
if self.abs_occ_limit <= 0:
logging.info('Any noise hit will lead to an increased pixel threshold.')
else:
logging.info('The pixel threshold of any pixel with an occpancy >%d will be increased' % self.abs_occ_limit)
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.histogram = PyDataHistograming()
self.interpreter.set_trig_count(self.trig_count)
self.interpreter.set_warning_output(False)
self.histogram.set_no_scan_parameter()
self.histogram.create_occupancy_hist(True)
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 setup_interpretation(self):
self.histograming = PyDataHistograming()
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)
# Variables
self.n_readouts = 0
self.readout = 0
self.fps = 0 # data frames per second
self.hps = 0 # hits per second
self.eps = 0 # events per second
self.plot_delay = 0
self.total_hits = 0
self.total_events = 0
self.updateTime = time.time()
# Histogrammes from interpretation stored for summing
self.tdc_counters = None
self.error_counters = None
self.service_records_counters = None
self.trigger_error_counters = None
def setup_interpretation(self):
self.histograming = PyDataHistograming()
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)
# Variables
self.n_readouts = 0
self.readout = 0
self.fps = 0 # data frames per second
self.hps = 0 # hits per second
self.eps = 0 # events per second
self.plot_delay = 0
self.total_hits = 0
self.total_events = 0
self.updateTime = time.time()
# Histogrammes from interpretation stored for summing
self.tdc_counters = None
self.error_counters = None
self.service_records_counters = None
self.trigger_error_counters = None
def test_trigger_data_format(self):
raw_data = np.array([82411778, 82793472, 82411779, 82794496, 82411780, 82795520, 82379013, 82379014,
82379015, 82379016, 67240383, 82379017, 82379018, 82379019, 82379020, 82379021,
82379022, 82379023, 82379024, 82379025, 3611295745, 82380701, 82380702, 82380703,
82380704, 82380705, 82380706, 82380707, 67240383, 82380708, 82380709, 82380710,
82380711, 82380712, 82380713, 82380714, 82380715, 82380716, 3611361282, 82381368,
82381369, 82381370, 82381371, 82381372, 82381373, 82381374, 67240367, 82381375,
82381376, 82381377, 82381378, 82381379, 82381380, 82381381, 82381382, 82381383,
3611426819, 82382035, 82382036, 82382037, 82382038, 82382039, 82382040, 82382041,
67240383, 82382042, 82382043, 82382044, 82382045, 82382046, 82382047, 82382048,
82382049, 82382050, 3611492356, 82383726, 82383727, 82383728, 82383729, 82383730,
82383731, 82383732, 67240367, 82383733, 82383734, 82383735, 82383736, 82383737,
82383738, 82383739, 82383740, 82383741, 3611557893], np.uint32)
raw_data_tlu = np.array([3611295745, 3611361282, 3611426819, 3611492356, 3611557893], np.uint32)
interpreter = PyDataInterpreter()
histograming = PyDataHistograming()
for i in (0, 1, 2): # 0: trigger data contains trigger number, 1: trigger data contains time stamp, 2: trigger data contains 15bit time stamp and 16bit trigger number
interpreter.set_trigger_data_format(i)
interpreter.set_trig_count(16)
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())
hits = interpreter.get_hits()
if i == 0:
trigger_number_ref = raw_data_tlu & 0x7FFFFFFF
trigger_time_stamp_ref = np.zeros_like(raw_data_tlu)
elif i == 1:
trigger_number_ref = np.zeros_like(raw_data_tlu)
trigger_time_stamp_ref = raw_data_tlu & 0x7FFFFFFF
elif i == 2:
trigger_number_ref = raw_data_tlu & 0x0000FFFF
trigger_time_stamp_ref = (raw_data_tlu & 0x7FFF0000) >> 16
self.assertTrue(np.all(hits["trigger_number"] == trigger_number_ref))
self.assertTrue(np.all(hits["trigger_time_stamp"] == trigger_time_stamp_ref))
class DataWorker(QtCore.QObject):
run_start = QtCore.pyqtSignal()
run_config_data = QtCore.pyqtSignal(dict)
global_config_data = QtCore.pyqtSignal(dict)
filename = QtCore.pyqtSignal(dict)
interpreted_data = QtCore.pyqtSignal(dict)
meta_data = QtCore.pyqtSignal(dict)
finished = QtCore.pyqtSignal()
def __init__(self):
QtCore.QObject.__init__(self)
self.integrate_readouts = 1
self.n_readout = 0
self._stop_readout = Event()
self.setup_raw_data_analysis()
self.reset_lock = Lock()
def setup_raw_data_analysis(self):
self.interpreter = PyDataInterpreter()
self.histogram = PyDataHistograming()
self.interpreter.set_warning_output(False)
self.histogram.set_no_scan_parameter()
self.histogram.create_occupancy_hist(True)
self.histogram.create_rel_bcid_hist(True)
self.histogram.create_tot_hist(True)
self.histogram.create_tdc_hist(True)
try:
self.histogram.create_tdc_distance_hist(True)
self.interpreter.use_tdc_trigger_time_stamp(True)
except AttributeError:
self.has_tdc_distance = False
else:
self.has_tdc_distance = True
def connect(self, socket_addr):
self.socket_addr = socket_addr
self.context = zmq.Context()
self.socket_pull = self.context.socket(zmq.SUB) # subscriber
self.socket_pull.setsockopt(zmq.SUBSCRIBE,
'') # do not filter any data
self.socket_pull.connect(self.socket_addr)
def on_set_integrate_readouts(self, value):
self.integrate_readouts = value
def reset(self):
with self.reset_lock:
self.histogram.reset()
self.interpreter.reset()
self.n_readout = 0
def analyze_raw_data(self, raw_data):
self.interpreter.interpret_raw_data(raw_data)
self.histogram.add_hits(self.interpreter.get_hits())
def process_data(
self
): # infinite loop via QObject.moveToThread(), does not block event loop
while (not self._stop_readout.wait(0.01)
): # use wait(), do not block here
with self.reset_lock:
try:
meta_data = self.socket_pull.recv_json(flags=zmq.NOBLOCK)
except zmq.Again:
pass
else:
name = meta_data.pop('name')
if name == 'ReadoutData':
data = self.socket_pull.recv()
# reconstruct numpy array
buf = buffer(data)
dtype = meta_data.pop('dtype')
shape = meta_data.pop('shape')
data_array = np.frombuffer(buf,
dtype=dtype).reshape(shape)
# count readouts and reset
self.n_readout += 1
if self.integrate_readouts != 0 and self.n_readout % self.integrate_readouts == 0:
self.histogram.reset()
# we do not want to reset interpreter to keep the error counters
# self.interpreter.reset()
# interpreted data
self.analyze_raw_data(data_array)
if self.integrate_readouts == 0 or self.n_readout % self.integrate_readouts == self.integrate_readouts - 1:
interpreted_data = {
'occupancy':
self.histogram.get_occupancy(),
'tot_hist':
self.histogram.get_tot_hist(),
'tdc_counters':
self.interpreter.get_tdc_counters(),
'tdc_distance':
self.interpreter.get_tdc_distance()
if self.has_tdc_distance else np.zeros(
(256, ), dtype=np.uint8),
'error_counters':
self.interpreter.get_error_counters(),
'service_records_counters':
self.interpreter.get_service_records_counters(
),
'trigger_error_counters':
self.interpreter.get_trigger_error_counters(),
'rel_bcid_hist':
self.histogram.get_rel_bcid_hist()
}
self.interpreted_data.emit(interpreted_data)
# meta data
meta_data.update({
'n_hits':
self.interpreter.get_n_hits(),
'n_events':
self.interpreter.get_n_events()
})
self.meta_data.emit(meta_data)
elif name == 'RunConf':
self.run_config_data.emit(meta_data)
elif name == 'GlobalRegisterConf':
trig_count = int(meta_data['conf']['Trig_Count'])
self.interpreter.set_trig_count(trig_count)
self.global_config_data.emit(meta_data)
elif name == 'Reset':
self.histogram.reset()
self.interpreter.reset()
self.run_start.emit()
elif name == 'Filename':
self.filename.emit(meta_data)
self.finished.emit()
def stop(self):
self._stop_readout.set()
class PybarFEI4Histogrammer(Transceiver):
def setup_transceiver(self):
self.set_bidirectional_communication() # We want to be able to change the histogrammmer settings
def setup_interpretation(self):
self.histograming = PyDataHistograming()
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)
# Variables
self.n_readouts = 0
self.readout = 0
self.fps = 0 # data frames per second
self.hps = 0 # hits per second
self.eps = 0 # events per second
self.plot_delay = 0
self.total_hits = 0
self.total_events = 0
self.updateTime = time.time()
# Histogrammes from interpretation stored for summing
self.tdc_counters = None
self.error_counters = None
self.service_records_counters = None
self.trigger_error_counters = None
def deserialze_data(self, data):
return jsonapi.loads(data, object_hook=utils.json_numpy_obj_hook)
def interpret_data(self, data):
if 'meta_data' in data[0][1]: # Meta data is directly forwarded to the receiver, only hit data, event counters are histogramed; 0 from frontend index, 1 for data dict
meta_data = data[0][1]['meta_data']
now = time.time()
recent_total_hits = meta_data['n_hits']
recent_total_events = meta_data['n_events']
recent_fps = 1.0 / (now - self.updateTime) # calculate FPS
recent_hps = (recent_total_hits - self.total_hits) / (now - self.updateTime)
recent_eps = (recent_total_events - self.total_events) / (now - self.updateTime)
self.updateTime = now
self.total_hits = recent_total_hits
self.total_events = recent_total_events
self.fps = self.fps * 0.7 + recent_fps * 0.3
self.hps = self.hps + (recent_hps - self.hps) * 0.3 / self.fps
self.eps = self.eps + (recent_eps - self.eps) * 0.3 / self.fps
meta_data.update({'fps': self.fps, 'hps': self.hps, 'total_hits': self.total_hits, 'eps': self.eps, 'total_events': self.total_events})
return [data[0][1]]
self.readout += 1
if self.n_readouts != 0: # = 0 for infinite integration
if self.readout % self.n_readouts == 0:
self.histograming.reset()
self.tdc_counters = np.zeros_like(self.tdc_counters)
self.error_counters = np.zeros_like(self.error_counters)
self.service_records_counters = np.zeros_like(self.service_records_counters)
self.trigger_error_counters = np.zeros_like(self.trigger_error_counters)
self.readouts = 0
interpreted_data = data[0][1]
self.histograming.add_hits(interpreted_data['hits'])
# Sum up interpreter histograms
if self.tdc_counters is not None:
self.tdc_counters += interpreted_data['tdc_counters']
else:
self.tdc_counters = interpreted_data['tdc_counters'].copy() # Copy needed to give ownage to histogrammer
if self.error_counters is not None:
self.error_counters += interpreted_data['error_counters']
else:
self.error_counters = interpreted_data['error_counters'].copy() # Copy needed to give ownage to histogrammer
if self.service_records_counters is not None:
self.service_records_counters += interpreted_data['service_records_counters']
else:
self.service_records_counters = interpreted_data['service_records_counters'].copy() # Copy needed to give ownage to histogrammer
if self.trigger_error_counters is not None:
self.trigger_error_counters += interpreted_data['trigger_error_counters']
else:
self.trigger_error_counters = interpreted_data['trigger_error_counters'].copy() # Copy needed to give ownage to histogrammer
histogrammed_data = {
'occupancy': self.histograming.get_occupancy(),
'tot_hist': self.histograming.get_tot_hist(),
'tdc_counters': self.tdc_counters,
'error_counters': self.error_counters,
'service_records_counters': self.service_records_counters,
'trigger_error_counters': self.trigger_error_counters,
'rel_bcid_hist': self.histograming.get_rel_bcid_hist()
}
return [histogrammed_data]
def serialze_data(self, data):
return jsonapi.dumps(data, cls=utils.NumpyEncoder)
def handle_command(self, command):
if command[0] == 'RESET':
self.histograming.reset()
self.tdc_counters = np.zeros_like(self.tdc_counters)
self.error_counters = np.zeros_like(self.error_counters)
self.service_records_counters = np.zeros_like(self.service_records_counters)
self.trigger_error_counters = np.zeros_like(self.trigger_error_counters)
else:
self.n_readouts = int(command[0])
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 : string
HDF5 filename of the file containing the cluster table. If a scan parameter is given in the meta data, the occupancy histogramming is done per scan parameter step.
Returns
-------
occupancy_array: numpy.array with dimensions (col, row, #scan_parameter)
'''
with tb.open_file(analyzed_data_file, mode="r") as in_file_h5:
with tb.open_file(output_file, mode="w") as out_file_h5:
histogram = PyDataHistograming()
histogram.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)
histogram.add_meta_event_index(
event_number_indices,
array_length=len(scan_parameters['event_number']))
histogram.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")
histogram.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided, use no scan parameter")
histogram.set_no_scan_parameter()
logging.info('Histogram cluster seeds...')
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
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)
histogram.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 = histogram.get_occupancy().T
occupancy_array_table = out_file_h5.create_carray(
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
def test_libraries_stability(self): # calls 50 times the constructor and destructor to check the libraries
for _ in range(50):
interpreter = PyDataInterpreter()
histogram = PyDataHistograming()
del interpreter
del histogram
def setUpClass(self):
self.interpreter = PyDataInterpreter()
self.histogram = PyDataHistograming()
class PybarFEI4Histogrammer(Transceiver):
def setup_transceiver(self):
self.set_bidirectional_communication(
) # We want to be able to change the histogrammmer settings
def setup_interpretation(self):
self.histograming = PyDataHistograming()
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)
# Variables
self.n_readouts = 0
self.readout = 0
self.fps = 0 # data frames per second
self.hps = 0 # hits per second
self.eps = 0 # events per second
self.plot_delay = 0
self.total_hits = 0
self.total_events = 0
self.updateTime = time.time()
# Histogrammes from interpretation stored for summing
self.tdc_counters = None
self.error_counters = None
self.service_records_counters = None
self.trigger_error_counters = None
def deserialze_data(self, data):
return jsonapi.loads(data, object_hook=utils.json_numpy_obj_hook)
def interpret_data(self, data):
# Meta data is directly forwarded to the receiver, only hit data and event counters are histogramed
if 'meta_data' in data[0][1]: # 0 for frontend index, 1 for data dict
meta_data = data[0][1]['meta_data']
now = time.time()
recent_total_hits = meta_data['n_hits']
recent_total_events = meta_data['n_events']
recent_fps = 1.0 / (now - self.updateTime) # calculate FPS
recent_hps = (recent_total_hits -
self.total_hits) / (now - self.updateTime)
recent_eps = (recent_total_events -
self.total_events) / (now - self.updateTime)
self.updateTime = now
self.total_hits = recent_total_hits
self.total_events = recent_total_events
self.fps = self.fps * 0.7 + recent_fps * 0.3
self.hps = self.hps + (recent_hps - self.hps) * 0.3 / self.fps
self.eps = self.eps + (recent_eps - self.eps) * 0.3 / self.fps
meta_data.update({
'fps': self.fps,
'hps': self.hps,
'total_hits': self.total_hits,
'eps': self.eps,
'total_events': self.total_events
})
return [data[0][1]]
self.readout += 1
if self.n_readouts != 0: # 0 for infinite integration
if self.readout % self.n_readouts == 0:
self.histograming.reset()
self.tdc_counters = np.zeros_like(self.tdc_counters)
self.error_counters = np.zeros_like(self.error_counters)
self.service_records_counters = np.zeros_like(
self.service_records_counters)
self.trigger_error_counters = np.zeros_like(
self.trigger_error_counters)
self.readouts = 0
interpreted_data = data[0][1]
self.histograming.add_hits(interpreted_data['hits'])
# Sum up interpreter histograms
if self.tdc_counters is not None:
self.tdc_counters += interpreted_data['tdc_counters']
else:
self.tdc_counters = interpreted_data['tdc_counters'].copy(
) # Copy needed to give ownage to histogrammer
if self.error_counters is not None:
self.error_counters += interpreted_data['error_counters']
else:
self.error_counters = interpreted_data['error_counters'].copy(
) # Copy needed to give ownage to histogrammer
if self.service_records_counters is not None:
self.service_records_counters += interpreted_data[
'service_records_counters']
else:
self.service_records_counters = interpreted_data[
'service_records_counters'].copy(
) # Copy needed to give ownage to histogrammer
if self.trigger_error_counters is not None:
self.trigger_error_counters += interpreted_data[
'trigger_error_counters']
else:
self.trigger_error_counters = interpreted_data[
'trigger_error_counters'].copy(
) # Copy needed to give ownage to histogrammer
histogrammed_data = {
'occupancy': self.histograming.get_occupancy(),
'tot_hist': self.histograming.get_tot_hist(),
'tdc_counters': self.tdc_counters,
'error_counters': self.error_counters,
'service_records_counters': self.service_records_counters,
'trigger_error_counters': self.trigger_error_counters,
'rel_bcid_hist': self.histograming.get_rel_bcid_hist()
}
return [histogrammed_data]
def serialze_data(self, data):
return jsonapi.dumps(data, cls=utils.NumpyEncoder)
def handle_command(self, command):
if command[0] == 'RESET': # Reset command to reset the histograms
self.histograming.reset()
self.tdc_counters = np.zeros_like(self.tdc_counters)
self.error_counters = np.zeros_like(self.error_counters)
self.service_records_counters = np.zeros_like(
self.service_records_counters)
self.trigger_error_counters = np.zeros_like(
self.trigger_error_counters)
else:
self.n_readouts = int(command[0])
''' Example how to interpret raw data and how to histogram the hits. ''' import numpy as np from pybar_fei4_interpreter.data_interpreter import PyDataInterpreter from pybar_fei4_interpreter.data_histograming import PyDataHistograming # Initialize interpretation modules interpreter = PyDataInterpreter() histograming = PyDataHistograming() # Create raw data 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) # Set settings histograming.set_no_scan_parameter() # The data has no scan parameter, thus should not be histogrammed per scan parameter histograming.create_occupancy_hist(True) # Tell the histogrammer to create a occupancy hist # Interpret the raw data (builds hits) interpreter.interpret_raw_data(raw_data) # Hits are buffered per event, since the interpret_raw_data call does not have to be called event aligned; # to tell the interpreter that the last event is finished this has to be called interpreter.store_event() # Histogram the htis hits = interpreter.get_hits() histograming.add_hits(hits) # Get and show the occupancy hist occ_hist = histograming.get_occupancy()[:, :, 0] # 0 because there is no scan parameter, otherwise histogramming is done per scan parameter
class ThresholdBaselineTuning(Fei4RunBase):
'''Threshold Baseline Tuning
Tuning the FEI4 to the lowest possible threshold (GDAC and TDAC). Feedback current will not be tuned.
NOTE: In case of RX errors decrease the trigger frequency (= increase trigger_rate_limit)
NOTE: To increase the TDAC range, decrease TdacVbp.
'''
_default_run_conf = {
"occupancy_limit": 0, # occupancy limit, when reached the TDAC will be decreased (increasing threshold). 0 will mask any pixel with occupancy greater than zero
"scan_parameters": [('Vthin_AltFine', (120, None)), ('Step', 60)], # the Vthin_AltFine range, number of steps (repetition at constant Vthin_AltFine)
"increase_threshold": 5, # increasing the global threshold (Vthin_AltFine) after tuning
"disabled_pixels_limit": 0.01, # limit of disabled pixels, fraction of all pixels
"use_enable_mask": False, # if True, enable mask from config file anded with mask (from col_span and row_span), if False use mask only for enable mask
"n_triggers": 10000, # total number of trigger sent to FE
"trigger_rate_limit": 500, # artificially limiting the trigger rate, in BCs (25ns)
"trig_count": 0, # FE-I4 trigger count, number of consecutive BCs, 0 means 16, from 0 to 15
"col_span": [1, 80], # column range (from minimum to maximum value). From 1 to 80.
"row_span": [1, 336], # row range (from minimum to maximum value). From 1 to 336.
}
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
self.abs_occ_limit = int(self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)
if self.abs_occ_limit <= 0:
logging.info('Any noise hit will lead to an increased pixel threshold.')
else:
logging.info('The pixel threshold of any pixel with an occpancy >%d will be increased' % self.abs_occ_limit)
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.histogram = PyDataHistograming()
self.interpreter.set_trig_count(self.trig_count)
self.interpreter.set_warning_output(False)
self.histogram.set_no_scan_parameter()
self.histogram.create_occupancy_hist(True)
def scan(self):
scan_parameter_range = [self.register.get_global_register_value("Vthin_AltFine"), 0]
if self.scan_parameters.Vthin_AltFine[0]:
scan_parameter_range[0] = self.scan_parameters.Vthin_AltFine[0]
if self.scan_parameters.Vthin_AltFine[1]:
scan_parameter_range[1] = self.scan_parameters.Vthin_AltFine[1]
steps = 1
if self.scan_parameters.Step:
steps = self.scan_parameters.Step
lvl1_command = self.register.get_commands("LV1")[0] + self.register.get_commands("zeros", length=self.trigger_rate_limit)[0]
self.total_scan_time = int(lvl1_command.length() * 25 * (10 ** -9) * self.n_triggers)
preselected_pixels = invert_pixel_mask(self.register.get_pixel_register_value('Enable')).sum()
disabled_pixels_limit_cnt = int(self.disabled_pixels_limit * self.register.get_pixel_register_value('Enable').sum())
disabled_pixels = 0
self.last_reg_val = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_step = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_good_threshold = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_good_tdac = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_good_enable_mask = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_occupancy_hist = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
self.last_occupancy_mask = deque([None] * self.increase_threshold, maxlen=self.increase_threshold + 1)
for reg_val in range(scan_parameter_range[0], scan_parameter_range[1] - 1, -1):
if self.stop_run.is_set():
break
logging.info('Scanning Vthin_AltFine %d', reg_val)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register.set_global_register_value("Vthin_AltFine", reg_val) # set number of consecutive triggers
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
# setting FE into RunMode
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
step = 0
while True:
if self.stop_run.is_set():
break
self.histogram.reset()
step += 1
logging.info('Step %d / %d at Vthin_AltFine %d', step, steps, reg_val)
logging.info('Estimated scan time: %ds', self.total_scan_time)
with self.readout(Vthin_AltFine=reg_val, Step=step, reset_sram_fifo=True, fill_buffer=True, clear_buffer=True, callback=self.handle_data):
got_data = False
start = time()
self.register_utils.send_command(lvl1_command, repeat=self.n_triggers, wait_for_finish=False, set_length=True, clear_memory=False)
while not self.stop_run.wait(0.1):
if self.register_utils.is_ready:
if got_data:
self.progressbar.finish()
logging.info('Finished sending %d triggers', self.n_triggers)
break
if not got_data:
if self.fifo_readout.data_words_per_second() > 0:
got_data = True
logging.info('Taking data...')
self.progressbar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.Timer()], maxval=self.total_scan_time, poll=10, term_width=80).start()
else:
try:
self.progressbar.update(time() - start)
except ValueError:
pass
# Use fast C++ hit histogramming to save time
raw_data = np.ascontiguousarray(data_array_from_data_iterable(self.fifo_readout.data), dtype=np.uint32)
self.interpreter.interpret_raw_data(raw_data)
self.interpreter.store_event() # force to create latest event
self.histogram.add_hits(self.interpreter.get_hits())
occ_hist = self.histogram.get_occupancy()[:, :, 0]
# noisy pixels are set to 1
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > self.abs_occ_limit] = 1
tdac_reg = self.register.get_pixel_register_value('TDAC')
decrease_pixel_mask = np.logical_and(occ_mask > 0, tdac_reg > 0)
disable_pixel_mask = np.logical_and(occ_mask > 0, tdac_reg == 0)
enable_reg = self.register.get_pixel_register_value('Enable')
enable_mask = np.logical_and(enable_reg, invert_pixel_mask(disable_pixel_mask))
if np.logical_and(occ_mask > 0, enable_reg == 0).sum():
logging.warning('Received data from disabled pixels')
# disabled_pixels += disable_pixel_mask.sum() # can lead to wrong values if the enable reg is corrupted
disabled_pixels = invert_pixel_mask(enable_mask).sum() - preselected_pixels
if disabled_pixels > disabled_pixels_limit_cnt:
logging.info('Limit of disabled pixels reached: %d (limit %d)... stopping scan' % (disabled_pixels, disabled_pixels_limit_cnt))
break
else:
logging.info('Increasing threshold of %d pixel(s)', decrease_pixel_mask.sum())
logging.info('Disabling %d pixel(s), total number of disabled pixel(s): %d', disable_pixel_mask.sum(), disabled_pixels)
tdac_reg[decrease_pixel_mask] -= 1
self.register.set_pixel_register_value('TDAC', tdac_reg)
self.register.set_pixel_register_value('Enable', enable_mask)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
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'))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if occ_mask.sum() == 0 or step == steps or decrease_pixel_mask.sum() < disabled_pixels_limit_cnt:
self.last_reg_val.appendleft(reg_val)
self.last_step.appendleft(step)
self.last_good_threshold.appendleft(self.register.get_global_register_value("Vthin_AltFine"))
self.last_good_tdac.appendleft(self.register.get_pixel_register_value("TDAC"))
self.last_good_enable_mask.appendleft(self.register.get_pixel_register_value("Enable"))
self.last_occupancy_hist.appendleft(occ_hist.copy())
self.last_occupancy_mask.appendleft(occ_mask.copy())
break
else:
logging.info('Found %d noisy pixels... repeat tuning step for Vthin_AltFine %d', occ_mask.sum(), reg_val)
if disabled_pixels > disabled_pixels_limit_cnt or scan_parameter_range[1] == reg_val:
break
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)